20221104_101732.log

2022-11-04 10:17:32,205 - mmseg - INFO - Multi-processing start method is `None`
2022-11-04 10:17:32,205 - mmseg - INFO - OpenCV num_threads is `184
2022-11-04 10:17:32,205 - mmseg - INFO - OMP num threads is 1
2022-11-04 10:17:32,250 - mmseg - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.9.5 (default, Jun  4 2021, 12:28:51) [GCC 7.5.0]
CUDA available: True
GPU 0,1,2,3,4,5,6,7: A100-SXM4-40GB
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.0, V11.0.221
GCC: gcc (GCC) 7.3.1 20180303 (Red Hat 7.3.1-5)
PyTorch: 1.10.1+cu113
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.3
  - 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_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
  - CuDNN 8.2
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -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 -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.1, 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, 

TorchVision: 0.11.2+cu113
OpenCV: 4.5.4
MMCV: 1.6.2
MMCV Compiler: GCC 9.3
MMCV CUDA Compiler: 11.3
MMSegmentation: 0.28.0+
------------------------------------------------------------

2022-11-04 10:17:32,250 - mmseg - INFO - Distributed training: True
2022-11-04 10:17:32,566 - mmseg - INFO - Config:
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
    type='EncoderDecoder',
    pretrained=None,
    backbone=dict(
        type='MetaMobile',
        depths=[3, 3, 9, 3],
        stem_dim=24,
        embed_dims=[32, 48, 120, 200],
        exp_ratios=[2.0, 2.5, 3.0, 3.5],
        norm_layers=['bn_2d', 'bn_2d', 'ln_2d', 'ln_2d'],
        act_layers=['silu', 'silu', 'gelu', 'gelu'],
        dw_kss=[3, 3, 5, 5],
        group_size=1,
        se_ratios=[0.0, 0.0, 0.0, 0.0],
        dim_heads=[16, 16, 20, 20],
        window_sizes=[7, 7, 7, 7],
        attn_ss=[False, False, True, True],
        attn_s_skips=[False, False, False, False],
        qkv_bias=True,
        attn_drop=0.0,
        drop=0.0,
        drop_path=0.05,
        v_group=False,
        attn_pre=True,
        conv_l=True,
        skip_l=True,
        pre_dim=0,
        sync_bn=True,
        out_indices=(1, 2, 3, 4),
        pretrained='../../ckpt_models/MetaMobile_2M/net_E.pth',
        frozen_stages=-1,
        norm_eval=False),
    decode_head=dict(
        type='ASPPHead',
        in_channels=200,
        in_index=3,
        channels=256,
        dilations=(1, 12, 24, 36),
        dropout_ratio=0.1,
        num_classes=150,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
    auxiliary_head=dict(
        type='FCNHead',
        in_channels=120,
        in_index=2,
        channels=256,
        num_convs=1,
        concat_input=False,
        dropout_ratio=0.1,
        num_classes=150,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
    train_cfg=dict(),
    test_cfg=dict(mode='whole'))
dataset_type = 'ADE20KDataset'
data_root = 'data/ade/ADEChallengeData2016'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', reduce_zero_label=True),
    dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
    dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_semantic_seg'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(2048, 512),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=4,
    workers_per_gpu=4,
    train=dict(
        type='ADE20KDataset',
        data_root='data/ade/ADEChallengeData2016',
        img_dir='images/training',
        ann_dir='annotations/training',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', reduce_zero_label=True),
            dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
            dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
            dict(type='RandomFlip', prob=0.5),
            dict(type='PhotoMetricDistortion'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_semantic_seg'])
        ]),
    val=dict(
        type='ADE20KDataset',
        data_root='data/ade/ADEChallengeData2016',
        img_dir='images/validation',
        ann_dir='annotations/validation',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(2048, 512),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]),
    test=dict(
        type='ADE20KDataset',
        data_root='data/ade/ADEChallengeData2016',
        img_dir='images/validation',
        ann_dir='annotations/validation',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(2048, 512),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]))
log_config = dict(
    interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
cudnn_benchmark = True
optimizer_config = dict()
runner = dict(type='IterBasedRunner', max_iters=80000)
checkpoint_config = dict(by_epoch=False, interval=8000)
evaluation = dict(interval=8000, metric='mIoU', pre_eval=True)
bs_ratio = 1
optimizer = dict(
    type='AdamW',
    lr=0.00012,
    betas=(0.9, 0.999),
    weight_decay=0.01,
    paramwise_cfg=dict(
        custom_keys=dict(
            absolute_pos_embed=dict(decay_mult=0.0),
            relative_position_bias_table=dict(decay_mult=0.0),
            norm=dict(decay_mult=0.0))))
lr_config = dict(policy='poly', power=0.9, min_lr=0.0, by_epoch=False)
work_dir = './work_dirs/deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256'
gpu_ids = range(0, 8)
auto_resume = False

2022-11-04 10:17:45,281 - mmseg - INFO - Set random seed to 1375167395, deterministic: False
2022-11-04 10:17:45,519 - mmseg - INFO - initialize ASPPHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}
2022-11-04 10:17:45,538 - mmseg - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}
Name of parameter - Initialization information

backbone.stage0.0.convs.0.0.weight - torch.Size([24, 3, 3, 3]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage0.0.convs.0.0.bias - torch.Size([24]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage0.0.convs.0.1.weight - torch.Size([24]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage0.0.convs.0.1.bias - torch.Size([24]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage0.1.conv_local.conv.weight - torch.Size([24, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage0.1.conv_local.norm.weight - torch.Size([24]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage0.1.conv_local.norm.bias - torch.Size([24]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage0.1.se.conv_reduce.weight - torch.Size([24, 24, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage0.1.se.conv_reduce.bias - torch.Size([24]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage0.1.se.conv_expand.weight - torch.Size([24, 24, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage0.1.se.conv_expand.bias - torch.Size([24]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage0.1.proj.conv.weight - torch.Size([24, 24, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.0.norm.weight - torch.Size([24]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.0.norm.bias - torch.Size([24]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.0.v.conv.weight - torch.Size([96, 24, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.0.v.conv.bias - torch.Size([96]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.0.conv_local.conv.weight - torch.Size([96, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.0.conv_local.norm.weight - torch.Size([96]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.0.conv_local.norm.bias - torch.Size([96]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.0.proj.conv.weight - torch.Size([32, 96, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.1.norm.weight - torch.Size([32]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.1.norm.bias - torch.Size([32]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.1.v.conv.weight - torch.Size([64, 32, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.1.v.conv.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.1.conv_local.conv.weight - torch.Size([64, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.1.conv_local.norm.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.1.conv_local.norm.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.1.proj.conv.weight - torch.Size([32, 64, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.2.norm.weight - torch.Size([32]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.2.norm.bias - torch.Size([32]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.2.v.conv.weight - torch.Size([64, 32, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.2.v.conv.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.2.conv_local.conv.weight - torch.Size([64, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.2.conv_local.norm.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.2.conv_local.norm.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage1.2.proj.conv.weight - torch.Size([32, 64, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.0.norm.weight - torch.Size([32]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.0.norm.bias - torch.Size([32]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.0.v.conv.weight - torch.Size([160, 32, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.0.v.conv.bias - torch.Size([160]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.0.conv_local.conv.weight - torch.Size([160, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.0.conv_local.norm.weight - torch.Size([160]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.0.conv_local.norm.bias - torch.Size([160]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.0.proj.conv.weight - torch.Size([48, 160, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.1.norm.weight - torch.Size([48]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.1.norm.bias - torch.Size([48]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.1.v.conv.weight - torch.Size([120, 48, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.1.v.conv.bias - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.1.conv_local.conv.weight - torch.Size([120, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.1.conv_local.norm.weight - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.1.conv_local.norm.bias - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.1.proj.conv.weight - torch.Size([48, 120, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.2.norm.weight - torch.Size([48]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.2.norm.bias - torch.Size([48]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.2.v.conv.weight - torch.Size([120, 48, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.2.v.conv.bias - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.2.conv_local.conv.weight - torch.Size([120, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.2.conv_local.norm.weight - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.2.conv_local.norm.bias - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage2.2.proj.conv.weight - torch.Size([48, 120, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.0.norm.norm.weight - torch.Size([48]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.0.norm.norm.bias - torch.Size([48]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.0.v.conv.weight - torch.Size([288, 48, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.0.v.conv.bias - torch.Size([288]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.0.conv_local.conv.weight - torch.Size([288, 1, 5, 5]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.0.conv_local.norm.weight - torch.Size([288]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.0.conv_local.norm.bias - torch.Size([288]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.0.proj.conv.weight - torch.Size([120, 288, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.1.norm.norm.weight - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.1.norm.norm.bias - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.1.qk.conv.weight - torch.Size([240, 120, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.1.qk.conv.bias - torch.Size([240]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.1.v.conv.weight - torch.Size([360, 120, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.1.v.conv.bias - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.1.conv_local.conv.weight - torch.Size([360, 1, 5, 5]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.1.conv_local.norm.weight - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.1.conv_local.norm.bias - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.1.proj.conv.weight - torch.Size([120, 360, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.2.norm.norm.weight - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.2.norm.norm.bias - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.2.qk.conv.weight - torch.Size([240, 120, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.2.qk.conv.bias - torch.Size([240]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.2.v.conv.weight - torch.Size([360, 120, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.2.v.conv.bias - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.2.conv_local.conv.weight - torch.Size([360, 1, 5, 5]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.2.conv_local.norm.weight - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.2.conv_local.norm.bias - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.2.proj.conv.weight - torch.Size([120, 360, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.3.norm.norm.weight - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.3.norm.norm.bias - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.3.qk.conv.weight - torch.Size([240, 120, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.3.qk.conv.bias - torch.Size([240]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.3.v.conv.weight - torch.Size([360, 120, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.3.v.conv.bias - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.3.conv_local.conv.weight - torch.Size([360, 1, 5, 5]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.3.conv_local.norm.weight - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.3.conv_local.norm.bias - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.3.proj.conv.weight - torch.Size([120, 360, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.4.norm.norm.weight - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.4.norm.norm.bias - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.4.qk.conv.weight - torch.Size([240, 120, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.4.qk.conv.bias - torch.Size([240]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.4.v.conv.weight - torch.Size([360, 120, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.4.v.conv.bias - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.4.conv_local.conv.weight - torch.Size([360, 1, 5, 5]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.4.conv_local.norm.weight - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.4.conv_local.norm.bias - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.4.proj.conv.weight - torch.Size([120, 360, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.5.norm.norm.weight - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.5.norm.norm.bias - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.5.qk.conv.weight - torch.Size([240, 120, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.5.qk.conv.bias - torch.Size([240]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.5.v.conv.weight - torch.Size([360, 120, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.5.v.conv.bias - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.5.conv_local.conv.weight - torch.Size([360, 1, 5, 5]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.5.conv_local.norm.weight - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.5.conv_local.norm.bias - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.5.proj.conv.weight - torch.Size([120, 360, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.6.norm.norm.weight - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.6.norm.norm.bias - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.6.qk.conv.weight - torch.Size([240, 120, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.6.qk.conv.bias - torch.Size([240]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.6.v.conv.weight - torch.Size([360, 120, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.6.v.conv.bias - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.6.conv_local.conv.weight - torch.Size([360, 1, 5, 5]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.6.conv_local.norm.weight - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.6.conv_local.norm.bias - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.6.proj.conv.weight - torch.Size([120, 360, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.7.norm.norm.weight - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.7.norm.norm.bias - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.7.qk.conv.weight - torch.Size([240, 120, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.7.qk.conv.bias - torch.Size([240]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.7.v.conv.weight - torch.Size([360, 120, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.7.v.conv.bias - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.7.conv_local.conv.weight - torch.Size([360, 1, 5, 5]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.7.conv_local.norm.weight - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.7.conv_local.norm.bias - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.7.proj.conv.weight - torch.Size([120, 360, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.8.norm.norm.weight - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.8.norm.norm.bias - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.8.qk.conv.weight - torch.Size([240, 120, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.8.qk.conv.bias - torch.Size([240]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.8.v.conv.weight - torch.Size([360, 120, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.8.v.conv.bias - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.8.conv_local.conv.weight - torch.Size([360, 1, 5, 5]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.8.conv_local.norm.weight - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.8.conv_local.norm.bias - torch.Size([360]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage3.8.proj.conv.weight - torch.Size([120, 360, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.0.norm.norm.weight - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.0.norm.norm.bias - torch.Size([120]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.0.v.conv.weight - torch.Size([840, 120, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.0.v.conv.bias - torch.Size([840]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.0.conv_local.conv.weight - torch.Size([840, 1, 5, 5]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.0.conv_local.norm.weight - torch.Size([840]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.0.conv_local.norm.bias - torch.Size([840]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.0.proj.conv.weight - torch.Size([200, 840, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.1.norm.norm.weight - torch.Size([200]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.1.norm.norm.bias - torch.Size([200]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.1.qk.conv.weight - torch.Size([400, 200, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.1.qk.conv.bias - torch.Size([400]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.1.v.conv.weight - torch.Size([700, 200, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.1.v.conv.bias - torch.Size([700]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.1.conv_local.conv.weight - torch.Size([700, 1, 5, 5]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.1.conv_local.norm.weight - torch.Size([700]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.1.conv_local.norm.bias - torch.Size([700]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.1.proj.conv.weight - torch.Size([200, 700, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.2.norm.norm.weight - torch.Size([200]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.2.norm.norm.bias - torch.Size([200]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.2.qk.conv.weight - torch.Size([400, 200, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.2.qk.conv.bias - torch.Size([400]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.2.v.conv.weight - torch.Size([700, 200, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.2.v.conv.bias - torch.Size([700]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.2.conv_local.conv.weight - torch.Size([700, 1, 5, 5]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.2.conv_local.norm.weight - torch.Size([700]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.2.conv_local.norm.bias - torch.Size([700]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

backbone.stage4.2.proj.conv.weight - torch.Size([200, 700, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

decode_head.conv_seg.weight - torch.Size([150, 256, 1, 1]): 
NormalInit: mean=0, std=0.01, bias=0 

decode_head.conv_seg.bias - torch.Size([150]): 
NormalInit: mean=0, std=0.01, bias=0 

decode_head.image_pool.1.conv.weight - torch.Size([256, 200, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

decode_head.image_pool.1.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

decode_head.image_pool.1.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

decode_head.aspp_modules.0.conv.weight - torch.Size([256, 200, 1, 1]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

decode_head.aspp_modules.0.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

decode_head.aspp_modules.0.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

decode_head.aspp_modules.1.conv.weight - torch.Size([256, 200, 3, 3]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

decode_head.aspp_modules.1.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

decode_head.aspp_modules.1.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

decode_head.aspp_modules.2.conv.weight - torch.Size([256, 200, 3, 3]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

decode_head.aspp_modules.2.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

decode_head.aspp_modules.2.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

decode_head.aspp_modules.3.conv.weight - torch.Size([256, 200, 3, 3]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

decode_head.aspp_modules.3.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

decode_head.aspp_modules.3.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

decode_head.bottleneck.conv.weight - torch.Size([256, 1280, 3, 3]): 
Initialized by user-defined `init_weights` in ConvModule  

decode_head.bottleneck.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

decode_head.bottleneck.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

auxiliary_head.conv_seg.weight - torch.Size([150, 256, 1, 1]): 
NormalInit: mean=0, std=0.01, bias=0 

auxiliary_head.conv_seg.bias - torch.Size([150]): 
NormalInit: mean=0, std=0.01, bias=0 

auxiliary_head.convs.0.conv.weight - torch.Size([256, 120, 3, 3]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

auxiliary_head.convs.0.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  

auxiliary_head.convs.0.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of EncoderDecoder  
2022-11-04 10:17:45,542 - mmseg - INFO - EncoderDecoder(
  (backbone): MetaMobile(
    (stage0): ModuleList(
      (0): MSPatchEmb(
        (convs): ModuleList(
          (0): Sequential(
            (0): Conv2d(3, 24, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
            (1): SyncBatchNorm(24, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
            (2): Identity()
          )
        )
      )
      (1): MetaMobileModule(
        (norm): Identity()
        (v): Identity()
        (conv_local): ConvNormAct(
          (conv): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=24, bias=False)
          (norm): SyncBatchNorm(24, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (se): SqueezeExcite(
          (conv_reduce): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1))
          (act1): SiLU(inplace=True)
          (conv_expand): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1))
          (gate): Sigmoid()
        )
        (proj_drop): Dropout(p=0.0, inplace=False)
        (proj): ConvNormAct(
          (conv): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (ls): Identity()
        (drop_path): Identity()
      )
    )
    (stage1): ModuleList(
      (0): MetaMobileModule(
        (norm): SyncBatchNorm(24, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
        (v): ConvNormAct(
          (conv): Conv2d(24, 96, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (conv_local): ConvNormAct(
          (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)
          (norm): SyncBatchNorm(96, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (se): Identity()
        (proj_drop): Dropout(p=0.0, inplace=False)
        (proj): ConvNormAct(
          (conv): Conv2d(96, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (ls): Identity()
        (drop_path): Identity()
      )
      (1): MetaMobileModule(
        (norm): SyncBatchNorm(32, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
        (v): ConvNormAct(
          (conv): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (conv_local): ConvNormAct(
          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64, bias=False)
          (norm): SyncBatchNorm(64, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (se): Identity()
        (proj_drop): Dropout(p=0.0, inplace=False)
        (proj): ConvNormAct(
          (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (ls): Identity()
        (drop_path): DropPath(drop_prob=0.003)
      )
      (2): MetaMobileModule(
        (norm): SyncBatchNorm(32, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
        (v): ConvNormAct(
          (conv): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (conv_local): ConvNormAct(
          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64, bias=False)
          (norm): SyncBatchNorm(64, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (se): Identity()
        (proj_drop): Dropout(p=0.0, inplace=False)
        (proj): ConvNormAct(
          (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (ls): Identity()
        (drop_path): DropPath(drop_prob=0.006)
      )
    )
    (stage2): ModuleList(
      (0): MetaMobileModule(
        (norm): SyncBatchNorm(32, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
        (v): ConvNormAct(
          (conv): Conv2d(32, 160, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (conv_local): ConvNormAct(
          (conv): Conv2d(160, 160, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=160, bias=False)
          (norm): SyncBatchNorm(160, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (se): Identity()
        (proj_drop): Dropout(p=0.0, inplace=False)
        (proj): ConvNormAct(
          (conv): Conv2d(160, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (ls): Identity()
        (drop_path): DropPath(drop_prob=0.009)
      )
      (1): MetaMobileModule(
        (norm): SyncBatchNorm(48, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
        (v): ConvNormAct(
          (conv): Conv2d(48, 120, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (conv_local): ConvNormAct(
          (conv): Conv2d(120, 120, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=120, bias=False)
          (norm): SyncBatchNorm(120, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (se): Identity()
        (proj_drop): Dropout(p=0.0, inplace=False)
        (proj): ConvNormAct(
          (conv): Conv2d(120, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (ls): Identity()
        (drop_path): DropPath(drop_prob=0.012)
      )
      (2): MetaMobileModule(
        (norm): SyncBatchNorm(48, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
        (v): ConvNormAct(
          (conv): Conv2d(48, 120, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (conv_local): ConvNormAct(
          (conv): Conv2d(120, 120, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=120, bias=False)
          (norm): SyncBatchNorm(120, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (se): Identity()
        (proj_drop): Dropout(p=0.0, inplace=False)
        (proj): ConvNormAct(
          (conv): Conv2d(120, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (ls): Identity()
        (drop_path): DropPath(drop_prob=0.015)
      )
    )
    (stage3): ModuleList(
      (0): MetaMobileModule(
        (norm): LayerNorm2d(
          (norm): LayerNorm((48,), eps=1e-06, elementwise_affine=True)
        )
        (v): ConvNormAct(
          (conv): Conv2d(48, 288, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): GELU()
          (drop_path): Identity()
        )
        (conv_local): ConvNormAct(
          (conv): Conv2d(288, 288, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=288, bias=False)
          (norm): SyncBatchNorm(288, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (se): Identity()
        (proj_drop): Dropout(p=0.0, inplace=False)
        (proj): ConvNormAct(
          (conv): Conv2d(288, 120, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (ls): Identity()
        (drop_path): DropPath(drop_prob=0.018)
      )
      (1): MetaMobileModule(
        (norm): LayerNorm2d(
          (norm): LayerNorm((120,), eps=1e-06, elementwise_affine=True)
        )
        (qk): ConvNormAct(
          (conv): Conv2d(120, 240, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (v): ConvNormAct(
          (conv): Conv2d(120, 360, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): GELU()
          (drop_path): Identity()
        )
        (attn_drop): Dropout(p=0.0, inplace=False)
        (conv_local): ConvNormAct(
          (conv): Conv2d(360, 360, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=360, bias=False)
          (norm): SyncBatchNorm(360, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (se): Identity()
        (proj_drop): Dropout(p=0.0, inplace=False)
        (proj): ConvNormAct(
          (conv): Conv2d(360, 120, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (ls): Identity()
        (drop_path): DropPath(drop_prob=0.021)
      )
      (2): MetaMobileModule(
        (norm): LayerNorm2d(
          (norm): LayerNorm((120,), eps=1e-06, elementwise_affine=True)
        )
        (qk): ConvNormAct(
          (conv): Conv2d(120, 240, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (v): ConvNormAct(
          (conv): Conv2d(120, 360, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): GELU()
          (drop_path): Identity()
        )
        (attn_drop): Dropout(p=0.0, inplace=False)
        (conv_local): ConvNormAct(
          (conv): Conv2d(360, 360, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=360, bias=False)
          (norm): SyncBatchNorm(360, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (se): Identity()
        (proj_drop): Dropout(p=0.0, inplace=False)
        (proj): ConvNormAct(
          (conv): Conv2d(360, 120, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (ls): Identity()
        (drop_path): DropPath(drop_prob=0.024)
      )
      (3): MetaMobileModule(
        (norm): LayerNorm2d(
          (norm): LayerNorm((120,), eps=1e-06, elementwise_affine=True)
        )
        (qk): ConvNormAct(
          (conv): Conv2d(120, 240, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (v): ConvNormAct(
          (conv): Conv2d(120, 360, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): GELU()
          (drop_path): Identity()
        )
        (attn_drop): Dropout(p=0.0, inplace=False)
        (conv_local): ConvNormAct(
          (conv): Conv2d(360, 360, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=360, bias=False)
          (norm): SyncBatchNorm(360, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (se): Identity()
        (proj_drop): Dropout(p=0.0, inplace=False)
        (proj): ConvNormAct(
          (conv): Conv2d(360, 120, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (ls): Identity()
        (drop_path): DropPath(drop_prob=0.026)
      )
      (4): MetaMobileModule(
        (norm): LayerNorm2d(
          (norm): LayerNorm((120,), eps=1e-06, elementwise_affine=True)
        )
        (qk): ConvNormAct(
          (conv): Conv2d(120, 240, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (v): ConvNormAct(
          (conv): Conv2d(120, 360, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): GELU()
          (drop_path): Identity()
        )
        (attn_drop): Dropout(p=0.0, inplace=False)
        (conv_local): ConvNormAct(
          (conv): Conv2d(360, 360, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=360, bias=False)
          (norm): SyncBatchNorm(360, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (se): Identity()
        (proj_drop): Dropout(p=0.0, inplace=False)
        (proj): ConvNormAct(
          (conv): Conv2d(360, 120, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (ls): Identity()
        (drop_path): DropPath(drop_prob=0.029)
      )
      (5): MetaMobileModule(
        (norm): LayerNorm2d(
          (norm): LayerNorm((120,), eps=1e-06, elementwise_affine=True)
        )
        (qk): ConvNormAct(
          (conv): Conv2d(120, 240, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (v): ConvNormAct(
          (conv): Conv2d(120, 360, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): GELU()
          (drop_path): Identity()
        )
        (attn_drop): Dropout(p=0.0, inplace=False)
        (conv_local): ConvNormAct(
          (conv): Conv2d(360, 360, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=360, bias=False)
          (norm): SyncBatchNorm(360, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (se): Identity()
        (proj_drop): Dropout(p=0.0, inplace=False)
        (proj): ConvNormAct(
          (conv): Conv2d(360, 120, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (ls): Identity()
        (drop_path): DropPath(drop_prob=0.032)
      )
      (6): MetaMobileModule(
        (norm): LayerNorm2d(
          (norm): LayerNorm((120,), eps=1e-06, elementwise_affine=True)
        )
        (qk): ConvNormAct(
          (conv): Conv2d(120, 240, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (v): ConvNormAct(
          (conv): Conv2d(120, 360, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): GELU()
          (drop_path): Identity()
        )
        (attn_drop): Dropout(p=0.0, inplace=False)
        (conv_local): ConvNormAct(
          (conv): Conv2d(360, 360, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=360, bias=False)
          (norm): SyncBatchNorm(360, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (se): Identity()
        (proj_drop): Dropout(p=0.0, inplace=False)
        (proj): ConvNormAct(
          (conv): Conv2d(360, 120, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (ls): Identity()
        (drop_path): DropPath(drop_prob=0.035)
      )
      (7): MetaMobileModule(
        (norm): LayerNorm2d(
          (norm): LayerNorm((120,), eps=1e-06, elementwise_affine=True)
        )
        (qk): ConvNormAct(
          (conv): Conv2d(120, 240, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (v): ConvNormAct(
          (conv): Conv2d(120, 360, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): GELU()
          (drop_path): Identity()
        )
        (attn_drop): Dropout(p=0.0, inplace=False)
        (conv_local): ConvNormAct(
          (conv): Conv2d(360, 360, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=360, bias=False)
          (norm): SyncBatchNorm(360, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (se): Identity()
        (proj_drop): Dropout(p=0.0, inplace=False)
        (proj): ConvNormAct(
          (conv): Conv2d(360, 120, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (ls): Identity()
        (drop_path): DropPath(drop_prob=0.038)
      )
      (8): MetaMobileModule(
        (norm): LayerNorm2d(
          (norm): LayerNorm((120,), eps=1e-06, elementwise_affine=True)
        )
        (qk): ConvNormAct(
          (conv): Conv2d(120, 240, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (v): ConvNormAct(
          (conv): Conv2d(120, 360, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): GELU()
          (drop_path): Identity()
        )
        (attn_drop): Dropout(p=0.0, inplace=False)
        (conv_local): ConvNormAct(
          (conv): Conv2d(360, 360, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=360, bias=False)
          (norm): SyncBatchNorm(360, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (se): Identity()
        (proj_drop): Dropout(p=0.0, inplace=False)
        (proj): ConvNormAct(
          (conv): Conv2d(360, 120, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (ls): Identity()
        (drop_path): DropPath(drop_prob=0.041)
      )
    )
    (stage4): ModuleList(
      (0): MetaMobileModule(
        (norm): LayerNorm2d(
          (norm): LayerNorm((120,), eps=1e-06, elementwise_affine=True)
        )
        (v): ConvNormAct(
          (conv): Conv2d(120, 840, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): GELU()
          (drop_path): Identity()
        )
        (conv_local): ConvNormAct(
          (conv): Conv2d(840, 840, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=840, bias=False)
          (norm): SyncBatchNorm(840, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (se): Identity()
        (proj_drop): Dropout(p=0.0, inplace=False)
        (proj): ConvNormAct(
          (conv): Conv2d(840, 200, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (ls): Identity()
        (drop_path): DropPath(drop_prob=0.044)
      )
      (1): MetaMobileModule(
        (norm): LayerNorm2d(
          (norm): LayerNorm((200,), eps=1e-06, elementwise_affine=True)
        )
        (qk): ConvNormAct(
          (conv): Conv2d(200, 400, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (v): ConvNormAct(
          (conv): Conv2d(200, 700, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): GELU()
          (drop_path): Identity()
        )
        (attn_drop): Dropout(p=0.0, inplace=False)
        (conv_local): ConvNormAct(
          (conv): Conv2d(700, 700, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=700, bias=False)
          (norm): SyncBatchNorm(700, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (se): Identity()
        (proj_drop): Dropout(p=0.0, inplace=False)
        (proj): ConvNormAct(
          (conv): Conv2d(700, 200, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (ls): Identity()
        (drop_path): DropPath(drop_prob=0.047)
      )
      (2): MetaMobileModule(
        (norm): LayerNorm2d(
          (norm): LayerNorm((200,), eps=1e-06, elementwise_affine=True)
        )
        (qk): ConvNormAct(
          (conv): Conv2d(200, 400, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (v): ConvNormAct(
          (conv): Conv2d(200, 700, kernel_size=(1, 1), stride=(1, 1))
          (norm): Identity()
          (act): GELU()
          (drop_path): Identity()
        )
        (attn_drop): Dropout(p=0.0, inplace=False)
        (conv_local): ConvNormAct(
          (conv): Conv2d(700, 700, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=700, bias=False)
          (norm): SyncBatchNorm(700, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU(inplace=True)
          (drop_path): Identity()
        )
        (se): Identity()
        (proj_drop): Dropout(p=0.0, inplace=False)
        (proj): ConvNormAct(
          (conv): Conv2d(700, 200, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm): Identity()
          (act): Identity()
          (drop_path): Identity()
        )
        (ls): Identity()
        (drop_path): DropPath(drop_prob=0.050)
      )
    )
  )
  (decode_head): ASPPHead(
    input_transform=None, ignore_index=255, align_corners=False
    (loss_decode): CrossEntropyLoss(avg_non_ignore=False)
    (conv_seg): Conv2d(256, 150, kernel_size=(1, 1), stride=(1, 1))
    (dropout): Dropout2d(p=0.1, inplace=False)
    (image_pool): Sequential(
      (0): AdaptiveAvgPool2d(output_size=1)
      (1): ConvModule(
        (conv): Conv2d(200, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (activate): ReLU(inplace=True)
      )
    )
    (aspp_modules): ASPPModule(
      (0): ConvModule(
        (conv): Conv2d(200, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (activate): ReLU(inplace=True)
      )
      (1): ConvModule(
        (conv): Conv2d(200, 256, kernel_size=(3, 3), stride=(1, 1), padding=(12, 12), dilation=(12, 12), bias=False)
        (bn): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (activate): ReLU(inplace=True)
      )
      (2): ConvModule(
        (conv): Conv2d(200, 256, kernel_size=(3, 3), stride=(1, 1), padding=(24, 24), dilation=(24, 24), bias=False)
        (bn): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (activate): ReLU(inplace=True)
      )
      (3): ConvModule(
        (conv): Conv2d(200, 256, kernel_size=(3, 3), stride=(1, 1), padding=(36, 36), dilation=(36, 36), bias=False)
        (bn): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (activate): ReLU(inplace=True)
      )
    )
    (bottleneck): ConvModule(
      (conv): Conv2d(1280, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (activate): ReLU(inplace=True)
    )
  )
  init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}
  (auxiliary_head): FCNHead(
    input_transform=None, ignore_index=255, align_corners=False
    (loss_decode): CrossEntropyLoss(avg_non_ignore=False)
    (conv_seg): Conv2d(256, 150, kernel_size=(1, 1), stride=(1, 1))
    (dropout): Dropout2d(p=0.1, inplace=False)
    (convs): Sequential(
      (0): ConvModule(
        (conv): Conv2d(120, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (activate): ReLU(inplace=True)
      )
    )
  )
  init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}
)
2022-11-04 10:17:45,982 - mmseg - INFO - Loaded 20210 images
2022-11-04 10:17:46,839 - mmseg - INFO - Loaded 2000 images
2022-11-04 10:17:46,839 - mmseg - INFO - Start running, host: root@ts-814a5279acd146f982df4d88e236c421-launcher, work_dir: /youtu_fuxi_team1_ceph/vtzhang/codes/pts_cls/down-stream-tasks/mmsegmentation/work_dirs/deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256
2022-11-04 10:17:46,840 - mmseg - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH   ) PolyLrUpdaterHook                  
(NORMAL      ) CheckpointHook                     
(LOW         ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_train_epoch:
(VERY_HIGH   ) PolyLrUpdaterHook                  
(LOW         ) IterTimerHook                      
(LOW         ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_train_iter:
(VERY_HIGH   ) PolyLrUpdaterHook                  
(LOW         ) IterTimerHook                      
(LOW         ) DistEvalHook                       
 -------------------- 
after_train_iter:
(ABOVE_NORMAL) OptimizerHook                      
(NORMAL      ) CheckpointHook                     
(LOW         ) IterTimerHook                      
(LOW         ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
after_train_epoch:
(NORMAL      ) CheckpointHook                     
(LOW         ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_val_epoch:
(LOW         ) IterTimerHook                      
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_val_iter:
(LOW         ) IterTimerHook                      
 -------------------- 
after_val_iter:
(LOW         ) IterTimerHook                      
 -------------------- 
after_val_epoch:
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
after_run:
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
2022-11-04 10:17:46,840 - mmseg - INFO - workflow: [('train', 1)], max: 80000 iters
2022-11-04 10:17:46,840 - mmseg - INFO - Checkpoints will be saved to /youtu_fuxi_team1_ceph/vtzhang/codes/pts_cls/down-stream-tasks/mmsegmentation/work_dirs/deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256 by HardDiskBackend.
2022-11-04 10:18:24,674 - mmseg - INFO - Iter [50/80000]	lr: 1.199e-04, eta: 10:18:48, time: 0.464, data_time: 0.015, memory: 5246, decode.loss_ce: 3.5396, decode.acc_seg: 31.6397, aux.loss_ce: 1.4732, aux.acc_seg: 26.1287, loss: 5.0128
2022-11-04 10:18:42,572 - mmseg - INFO - Iter [100/80000]	lr: 1.199e-04, eta: 9:07:31, time: 0.358, data_time: 0.008, memory: 5246, decode.loss_ce: 2.7480, decode.acc_seg: 43.5318, aux.loss_ce: 1.1602, aux.acc_seg: 39.2158, loss: 3.9083
2022-11-04 10:19:01,940 - mmseg - INFO - Iter [150/80000]	lr: 1.198e-04, eta: 8:56:37, time: 0.387, data_time: 0.008, memory: 5246, decode.loss_ce: 2.3513, decode.acc_seg: 44.5411, aux.loss_ce: 0.9986, aux.acc_seg: 41.1305, loss: 3.3499
2022-11-04 10:19:20,109 - mmseg - INFO - Iter [200/80000]	lr: 1.197e-04, eta: 8:43:02, time: 0.363, data_time: 0.009, memory: 5246, decode.loss_ce: 2.0331, decode.acc_seg: 49.5735, aux.loss_ce: 0.8735, aux.acc_seg: 45.0958, loss: 2.9066
2022-11-04 10:19:37,385 - mmseg - INFO - Iter [250/80000]	lr: 1.197e-04, eta: 8:29:59, time: 0.345, data_time: 0.008, memory: 5246, decode.loss_ce: 1.8888, decode.acc_seg: 52.0886, aux.loss_ce: 0.8144, aux.acc_seg: 48.1422, loss: 2.7032
2022-11-04 10:19:54,533 - mmseg - INFO - Iter [300/80000]	lr: 1.196e-04, eta: 8:20:39, time: 0.343, data_time: 0.008, memory: 5246, decode.loss_ce: 1.7895, decode.acc_seg: 52.3378, aux.loss_ce: 0.7726, aux.acc_seg: 49.0614, loss: 2.5620
2022-11-04 10:20:11,503 - mmseg - INFO - Iter [350/80000]	lr: 1.195e-04, eta: 8:13:14, time: 0.339, data_time: 0.009, memory: 5246, decode.loss_ce: 1.7166, decode.acc_seg: 53.8404, aux.loss_ce: 0.7405, aux.acc_seg: 50.6783, loss: 2.4571
2022-11-04 10:20:28,737 - mmseg - INFO - Iter [400/80000]	lr: 1.195e-04, eta: 8:08:28, time: 0.345, data_time: 0.009, memory: 5246, decode.loss_ce: 1.6037, decode.acc_seg: 56.1404, aux.loss_ce: 0.6937, aux.acc_seg: 53.4757, loss: 2.2974
2022-11-04 10:20:46,211 - mmseg - INFO - Iter [450/80000]	lr: 1.194e-04, eta: 8:05:24, time: 0.349, data_time: 0.009, memory: 5246, decode.loss_ce: 1.6033, decode.acc_seg: 55.6051, aux.loss_ce: 0.6873, aux.acc_seg: 53.2230, loss: 2.2905
2022-11-04 10:21:03,275 - mmseg - INFO - Iter [500/80000]	lr: 1.193e-04, eta: 8:01:48, time: 0.341, data_time: 0.008, memory: 5246, decode.loss_ce: 1.5559, decode.acc_seg: 56.6159, aux.loss_ce: 0.6687, aux.acc_seg: 54.1920, loss: 2.2247
2022-11-04 10:21:20,479 - mmseg - INFO - Iter [550/80000]	lr: 1.193e-04, eta: 7:59:09, time: 0.344, data_time: 0.009, memory: 5246, decode.loss_ce: 1.5518, decode.acc_seg: 55.9716, aux.loss_ce: 0.6675, aux.acc_seg: 53.3239, loss: 2.2193
2022-11-04 10:21:37,690 - mmseg - INFO - Iter [600/80000]	lr: 1.192e-04, eta: 7:56:54, time: 0.344, data_time: 0.009, memory: 5246, decode.loss_ce: 1.5050, decode.acc_seg: 57.4158, aux.loss_ce: 0.6449, aux.acc_seg: 54.9817, loss: 2.1499
2022-11-04 10:21:58,200 - mmseg - INFO - Iter [650/80000]	lr: 1.191e-04, eta: 8:01:40, time: 0.410, data_time: 0.064, memory: 5246, decode.loss_ce: 1.5064, decode.acc_seg: 56.9373, aux.loss_ce: 0.6485, aux.acc_seg: 54.4561, loss: 2.1549
2022-11-04 10:22:15,317 - mmseg - INFO - Iter [700/80000]	lr: 1.191e-04, eta: 7:59:18, time: 0.342, data_time: 0.008, memory: 5246, decode.loss_ce: 1.4328, decode.acc_seg: 58.5163, aux.loss_ce: 0.6176, aux.acc_seg: 56.0434, loss: 2.0503
2022-11-04 10:22:32,639 - mmseg - INFO - Iter [750/80000]	lr: 1.190e-04, eta: 7:57:34, time: 0.346, data_time: 0.009, memory: 5246, decode.loss_ce: 1.3839, decode.acc_seg: 59.7637, aux.loss_ce: 0.5998, aux.acc_seg: 57.2732, loss: 1.9836
2022-11-04 10:22:50,074 - mmseg - INFO - Iter [800/80000]	lr: 1.189e-04, eta: 7:56:11, time: 0.349, data_time: 0.009, memory: 5246, decode.loss_ce: 1.3869, decode.acc_seg: 59.5144, aux.loss_ce: 0.5990, aux.acc_seg: 57.1926, loss: 1.9860
2022-11-04 10:23:07,213 - mmseg - INFO - Iter [850/80000]	lr: 1.189e-04, eta: 7:54:30, time: 0.343, data_time: 0.010, memory: 5246, decode.loss_ce: 1.3850, decode.acc_seg: 59.2077, aux.loss_ce: 0.5993, aux.acc_seg: 56.9816, loss: 1.9843
2022-11-04 10:23:24,244 - mmseg - INFO - Iter [900/80000]	lr: 1.188e-04, eta: 7:52:48, time: 0.341, data_time: 0.008, memory: 5246, decode.loss_ce: 1.3211, decode.acc_seg: 60.1543, aux.loss_ce: 0.5717, aux.acc_seg: 57.5701, loss: 1.8928
2022-11-04 10:23:41,336 - mmseg - INFO - Iter [950/80000]	lr: 1.187e-04, eta: 7:51:20, time: 0.342, data_time: 0.008, memory: 5246, decode.loss_ce: 1.2942, decode.acc_seg: 61.1528, aux.loss_ce: 0.5640, aux.acc_seg: 58.6436, loss: 1.8582
2022-11-04 10:23:58,555 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 10:23:58,556 - mmseg - INFO - Iter [1000/80000]	lr: 1.187e-04, eta: 7:50:10, time: 0.344, data_time: 0.009, memory: 5246, decode.loss_ce: 1.3128, decode.acc_seg: 60.7094, aux.loss_ce: 0.5731, aux.acc_seg: 58.3034, loss: 1.8859
2022-11-04 10:24:15,553 - mmseg - INFO - Iter [1050/80000]	lr: 1.186e-04, eta: 7:48:47, time: 0.340, data_time: 0.009, memory: 5246, decode.loss_ce: 1.3034, decode.acc_seg: 60.8980, aux.loss_ce: 0.5670, aux.acc_seg: 58.5786, loss: 1.8704
2022-11-04 10:24:32,569 - mmseg - INFO - Iter [1100/80000]	lr: 1.185e-04, eta: 7:47:32, time: 0.340, data_time: 0.009, memory: 5246, decode.loss_ce: 1.2922, decode.acc_seg: 61.0689, aux.loss_ce: 0.5618, aux.acc_seg: 59.2246, loss: 1.8540
2022-11-04 10:24:49,899 - mmseg - INFO - Iter [1150/80000]	lr: 1.184e-04, eta: 7:46:44, time: 0.347, data_time: 0.009, memory: 5246, decode.loss_ce: 1.3387, decode.acc_seg: 59.8985, aux.loss_ce: 0.5845, aux.acc_seg: 57.5058, loss: 1.9232
2022-11-04 10:25:07,028 - mmseg - INFO - Iter [1200/80000]	lr: 1.184e-04, eta: 7:45:44, time: 0.342, data_time: 0.009, memory: 5246, decode.loss_ce: 1.2169, decode.acc_seg: 62.5325, aux.loss_ce: 0.5325, aux.acc_seg: 60.2961, loss: 1.7494
2022-11-04 10:25:24,203 - mmseg - INFO - Iter [1250/80000]	lr: 1.183e-04, eta: 7:44:52, time: 0.344, data_time: 0.009, memory: 5246, decode.loss_ce: 1.2734, decode.acc_seg: 61.2481, aux.loss_ce: 0.5596, aux.acc_seg: 58.9377, loss: 1.8330
2022-11-04 10:25:44,247 - mmseg - INFO - Iter [1300/80000]	lr: 1.182e-04, eta: 7:46:55, time: 0.401, data_time: 0.068, memory: 5246, decode.loss_ce: 1.2565, decode.acc_seg: 61.4823, aux.loss_ce: 0.5541, aux.acc_seg: 59.0815, loss: 1.8106
2022-11-04 10:26:01,227 - mmseg - INFO - Iter [1350/80000]	lr: 1.182e-04, eta: 7:45:50, time: 0.340, data_time: 0.009, memory: 5246, decode.loss_ce: 1.2167, decode.acc_seg: 62.4883, aux.loss_ce: 0.5360, aux.acc_seg: 60.2461, loss: 1.7527
2022-11-04 10:26:18,349 - mmseg - INFO - Iter [1400/80000]	lr: 1.181e-04, eta: 7:44:56, time: 0.342, data_time: 0.009, memory: 5246, decode.loss_ce: 1.2137, decode.acc_seg: 62.1341, aux.loss_ce: 0.5378, aux.acc_seg: 59.6034, loss: 1.7515
2022-11-04 10:26:35,369 - mmseg - INFO - Iter [1450/80000]	lr: 1.180e-04, eta: 7:43:59, time: 0.340, data_time: 0.009, memory: 5246, decode.loss_ce: 1.1864, decode.acc_seg: 63.4853, aux.loss_ce: 0.5265, aux.acc_seg: 60.9115, loss: 1.7129
2022-11-04 10:26:52,410 - mmseg - INFO - Iter [1500/80000]	lr: 1.180e-04, eta: 7:43:05, time: 0.341, data_time: 0.009, memory: 5246, decode.loss_ce: 1.1945, decode.acc_seg: 62.2345, aux.loss_ce: 0.5313, aux.acc_seg: 59.8859, loss: 1.7258
2022-11-04 10:27:09,630 - mmseg - INFO - Iter [1550/80000]	lr: 1.179e-04, eta: 7:42:23, time: 0.344, data_time: 0.009, memory: 5246, decode.loss_ce: 1.1428, decode.acc_seg: 64.0158, aux.loss_ce: 0.5109, aux.acc_seg: 61.3433, loss: 1.6538
2022-11-04 10:27:26,657 - mmseg - INFO - Iter [1600/80000]	lr: 1.178e-04, eta: 7:41:34, time: 0.341, data_time: 0.009, memory: 5246, decode.loss_ce: 1.1367, decode.acc_seg: 63.9060, aux.loss_ce: 0.5093, aux.acc_seg: 60.8699, loss: 1.6459
2022-11-04 10:27:44,075 - mmseg - INFO - Iter [1650/80000]	lr: 1.178e-04, eta: 7:41:04, time: 0.348, data_time: 0.008, memory: 5246, decode.loss_ce: 1.1387, decode.acc_seg: 64.3431, aux.loss_ce: 0.5057, aux.acc_seg: 61.8116, loss: 1.6444
2022-11-04 10:28:01,223 - mmseg - INFO - Iter [1700/80000]	lr: 1.177e-04, eta: 7:40:23, time: 0.343, data_time: 0.009, memory: 5246, decode.loss_ce: 1.1541, decode.acc_seg: 63.7557, aux.loss_ce: 0.5175, aux.acc_seg: 60.9180, loss: 1.6716
2022-11-04 10:28:18,357 - mmseg - INFO - Iter [1750/80000]	lr: 1.176e-04, eta: 7:39:43, time: 0.343, data_time: 0.009, memory: 5246, decode.loss_ce: 1.1234, decode.acc_seg: 64.5049, aux.loss_ce: 0.5012, aux.acc_seg: 62.0789, loss: 1.6246
2022-11-04 10:28:35,427 - mmseg - INFO - Iter [1800/80000]	lr: 1.176e-04, eta: 7:39:01, time: 0.341, data_time: 0.009, memory: 5246, decode.loss_ce: 1.1446, decode.acc_seg: 62.8995, aux.loss_ce: 0.5151, aux.acc_seg: 60.2259, loss: 1.6597
2022-11-04 10:28:52,759 - mmseg - INFO - Iter [1850/80000]	lr: 1.175e-04, eta: 7:38:32, time: 0.347, data_time: 0.009, memory: 5246, decode.loss_ce: 1.1243, decode.acc_seg: 64.1023, aux.loss_ce: 0.5050, aux.acc_seg: 61.2420, loss: 1.6294
2022-11-04 10:29:12,820 - mmseg - INFO - Iter [1900/80000]	lr: 1.174e-04, eta: 7:39:55, time: 0.401, data_time: 0.061, memory: 5246, decode.loss_ce: 1.1481, decode.acc_seg: 63.1494, aux.loss_ce: 0.5109, aux.acc_seg: 60.9175, loss: 1.6590
2022-11-04 10:29:29,888 - mmseg - INFO - Iter [1950/80000]	lr: 1.174e-04, eta: 7:39:14, time: 0.341, data_time: 0.009, memory: 5246, decode.loss_ce: 1.0940, decode.acc_seg: 64.6336, aux.loss_ce: 0.4962, aux.acc_seg: 61.7169, loss: 1.5901
2022-11-04 10:29:47,351 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 10:29:47,352 - mmseg - INFO - Iter [2000/80000]	lr: 1.173e-04, eta: 7:38:49, time: 0.349, data_time: 0.009, memory: 5246, decode.loss_ce: 1.0700, decode.acc_seg: 65.1275, aux.loss_ce: 0.4871, aux.acc_seg: 62.0911, loss: 1.5571
2022-11-04 10:30:04,759 - mmseg - INFO - Iter [2050/80000]	lr: 1.172e-04, eta: 7:38:22, time: 0.348, data_time: 0.009, memory: 5246, decode.loss_ce: 1.0559, decode.acc_seg: 65.4746, aux.loss_ce: 0.4802, aux.acc_seg: 62.8312, loss: 1.5361
2022-11-04 10:30:21,791 - mmseg - INFO - Iter [2100/80000]	lr: 1.172e-04, eta: 7:37:42, time: 0.341, data_time: 0.009, memory: 5246, decode.loss_ce: 1.0617, decode.acc_seg: 65.5264, aux.loss_ce: 0.4822, aux.acc_seg: 62.9344, loss: 1.5439
2022-11-04 10:30:38,894 - mmseg - INFO - Iter [2150/80000]	lr: 1.171e-04, eta: 7:37:05, time: 0.342, data_time: 0.008, memory: 5246, decode.loss_ce: 1.0687, decode.acc_seg: 65.0228, aux.loss_ce: 0.4849, aux.acc_seg: 61.8025, loss: 1.5535
2022-11-04 10:30:55,983 - mmseg - INFO - Iter [2200/80000]	lr: 1.170e-04, eta: 7:36:29, time: 0.342, data_time: 0.008, memory: 5246, decode.loss_ce: 1.0536, decode.acc_seg: 66.1192, aux.loss_ce: 0.4836, aux.acc_seg: 62.7203, loss: 1.5372
2022-11-04 10:31:13,094 - mmseg - INFO - Iter [2250/80000]	lr: 1.170e-04, eta: 7:35:54, time: 0.342, data_time: 0.009, memory: 5246, decode.loss_ce: 1.0358, decode.acc_seg: 66.1642, aux.loss_ce: 0.4727, aux.acc_seg: 63.1252, loss: 1.5086
2022-11-04 10:31:30,149 - mmseg - INFO - Iter [2300/80000]	lr: 1.169e-04, eta: 7:35:19, time: 0.341, data_time: 0.008, memory: 5246, decode.loss_ce: 1.0689, decode.acc_seg: 65.0451, aux.loss_ce: 0.4858, aux.acc_seg: 61.9436, loss: 1.5547
2022-11-04 10:31:47,357 - mmseg - INFO - Iter [2350/80000]	lr: 1.168e-04, eta: 7:34:49, time: 0.344, data_time: 0.009, memory: 5246, decode.loss_ce: 1.0674, decode.acc_seg: 65.1779, aux.loss_ce: 0.4841, aux.acc_seg: 62.3182, loss: 1.5515
2022-11-04 10:32:04,507 - mmseg - INFO - Iter [2400/80000]	lr: 1.168e-04, eta: 7:34:17, time: 0.343, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9986, decode.acc_seg: 66.6141, aux.loss_ce: 0.4585, aux.acc_seg: 63.5371, loss: 1.4571
2022-11-04 10:32:21,867 - mmseg - INFO - Iter [2450/80000]	lr: 1.167e-04, eta: 7:33:53, time: 0.347, data_time: 0.009, memory: 5246, decode.loss_ce: 1.0453, decode.acc_seg: 65.6010, aux.loss_ce: 0.4816, aux.acc_seg: 62.1875, loss: 1.5269
2022-11-04 10:32:39,022 - mmseg - INFO - Iter [2500/80000]	lr: 1.166e-04, eta: 7:33:23, time: 0.343, data_time: 0.009, memory: 5246, decode.loss_ce: 1.0384, decode.acc_seg: 65.9402, aux.loss_ce: 0.4782, aux.acc_seg: 62.4775, loss: 1.5166
2022-11-04 10:32:59,119 - mmseg - INFO - Iter [2550/80000]	lr: 1.166e-04, eta: 7:34:23, time: 0.402, data_time: 0.063, memory: 5246, decode.loss_ce: 1.0387, decode.acc_seg: 65.6753, aux.loss_ce: 0.4729, aux.acc_seg: 62.6528, loss: 1.5117
2022-11-04 10:33:16,402 - mmseg - INFO - Iter [2600/80000]	lr: 1.165e-04, eta: 7:33:56, time: 0.346, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9879, decode.acc_seg: 66.9167, aux.loss_ce: 0.4558, aux.acc_seg: 63.8154, loss: 1.4436
2022-11-04 10:33:33,416 - mmseg - INFO - Iter [2650/80000]	lr: 1.164e-04, eta: 7:33:21, time: 0.340, data_time: 0.010, memory: 5246, decode.loss_ce: 1.0065, decode.acc_seg: 66.7616, aux.loss_ce: 0.4645, aux.acc_seg: 63.7397, loss: 1.4710
2022-11-04 10:33:50,476 - mmseg - INFO - Iter [2700/80000]	lr: 1.164e-04, eta: 7:32:49, time: 0.341, data_time: 0.008, memory: 5246, decode.loss_ce: 1.0061, decode.acc_seg: 66.3057, aux.loss_ce: 0.4634, aux.acc_seg: 63.0552, loss: 1.4696
2022-11-04 10:34:07,682 - mmseg - INFO - Iter [2750/80000]	lr: 1.163e-04, eta: 7:32:21, time: 0.344, data_time: 0.008, memory: 5246, decode.loss_ce: 0.9774, decode.acc_seg: 67.3434, aux.loss_ce: 0.4548, aux.acc_seg: 63.6539, loss: 1.4322
2022-11-04 10:34:24,898 - mmseg - INFO - Iter [2800/80000]	lr: 1.162e-04, eta: 7:31:54, time: 0.344, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9940, decode.acc_seg: 66.6697, aux.loss_ce: 0.4559, aux.acc_seg: 63.8838, loss: 1.4498
2022-11-04 10:34:42,032 - mmseg - INFO - Iter [2850/80000]	lr: 1.161e-04, eta: 7:31:25, time: 0.343, data_time: 0.008, memory: 5246, decode.loss_ce: 1.0226, decode.acc_seg: 65.4578, aux.loss_ce: 0.4696, aux.acc_seg: 62.3457, loss: 1.4922
2022-11-04 10:34:59,335 - mmseg - INFO - Iter [2900/80000]	lr: 1.161e-04, eta: 7:31:00, time: 0.346, data_time: 0.010, memory: 5246, decode.loss_ce: 0.9946, decode.acc_seg: 66.5279, aux.loss_ce: 0.4582, aux.acc_seg: 63.3901, loss: 1.4529
2022-11-04 10:35:16,620 - mmseg - INFO - Iter [2950/80000]	lr: 1.160e-04, eta: 7:30:36, time: 0.346, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9737, decode.acc_seg: 67.7338, aux.loss_ce: 0.4551, aux.acc_seg: 63.9614, loss: 1.4288
2022-11-04 10:35:33,647 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 10:35:33,647 - mmseg - INFO - Iter [3000/80000]	lr: 1.159e-04, eta: 7:30:05, time: 0.341, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9752, decode.acc_seg: 67.6268, aux.loss_ce: 0.4550, aux.acc_seg: 64.0083, loss: 1.4302
2022-11-04 10:35:50,660 - mmseg - INFO - Iter [3050/80000]	lr: 1.159e-04, eta: 7:29:34, time: 0.340, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9787, decode.acc_seg: 66.7141, aux.loss_ce: 0.4504, aux.acc_seg: 63.6860, loss: 1.4291
2022-11-04 10:36:08,032 - mmseg - INFO - Iter [3100/80000]	lr: 1.158e-04, eta: 7:29:13, time: 0.347, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9931, decode.acc_seg: 66.7334, aux.loss_ce: 0.4609, aux.acc_seg: 63.1216, loss: 1.4540
2022-11-04 10:36:24,930 - mmseg - INFO - Iter [3150/80000]	lr: 1.157e-04, eta: 7:28:40, time: 0.338, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9268, decode.acc_seg: 68.3089, aux.loss_ce: 0.4290, aux.acc_seg: 65.0189, loss: 1.3557
2022-11-04 10:36:45,171 - mmseg - INFO - Iter [3200/80000]	lr: 1.157e-04, eta: 7:29:28, time: 0.405, data_time: 0.055, memory: 5246, decode.loss_ce: 0.9515, decode.acc_seg: 68.1758, aux.loss_ce: 0.4485, aux.acc_seg: 64.2711, loss: 1.4000
2022-11-04 10:37:02,241 - mmseg - INFO - Iter [3250/80000]	lr: 1.156e-04, eta: 7:28:59, time: 0.341, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9235, decode.acc_seg: 68.8191, aux.loss_ce: 0.4321, aux.acc_seg: 65.2383, loss: 1.3556
2022-11-04 10:37:19,388 - mmseg - INFO - Iter [3300/80000]	lr: 1.155e-04, eta: 7:28:32, time: 0.343, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9425, decode.acc_seg: 67.6536, aux.loss_ce: 0.4442, aux.acc_seg: 63.9599, loss: 1.3866
2022-11-04 10:37:36,521 - mmseg - INFO - Iter [3350/80000]	lr: 1.155e-04, eta: 7:28:05, time: 0.343, data_time: 0.008, memory: 5246, decode.loss_ce: 0.9591, decode.acc_seg: 67.7215, aux.loss_ce: 0.4528, aux.acc_seg: 63.4174, loss: 1.4119
2022-11-04 10:37:53,504 - mmseg - INFO - Iter [3400/80000]	lr: 1.154e-04, eta: 7:27:35, time: 0.340, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9089, decode.acc_seg: 68.8375, aux.loss_ce: 0.4301, aux.acc_seg: 65.0569, loss: 1.3389
2022-11-04 10:38:10,783 - mmseg - INFO - Iter [3450/80000]	lr: 1.153e-04, eta: 7:27:12, time: 0.346, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9423, decode.acc_seg: 68.4126, aux.loss_ce: 0.4429, aux.acc_seg: 64.7462, loss: 1.3852
2022-11-04 10:38:27,842 - mmseg - INFO - Iter [3500/80000]	lr: 1.153e-04, eta: 7:26:44, time: 0.341, data_time: 0.010, memory: 5246, decode.loss_ce: 0.9570, decode.acc_seg: 66.8848, aux.loss_ce: 0.4467, aux.acc_seg: 63.4864, loss: 1.4036
2022-11-04 10:38:45,085 - mmseg - INFO - Iter [3550/80000]	lr: 1.152e-04, eta: 7:26:21, time: 0.345, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9435, decode.acc_seg: 67.6166, aux.loss_ce: 0.4357, aux.acc_seg: 64.2237, loss: 1.3792
2022-11-04 10:39:02,092 - mmseg - INFO - Iter [3600/80000]	lr: 1.151e-04, eta: 7:25:52, time: 0.340, data_time: 0.010, memory: 5246, decode.loss_ce: 0.9469, decode.acc_seg: 68.1522, aux.loss_ce: 0.4416, aux.acc_seg: 64.5905, loss: 1.3885
2022-11-04 10:39:19,438 - mmseg - INFO - Iter [3650/80000]	lr: 1.151e-04, eta: 7:25:31, time: 0.347, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9214, decode.acc_seg: 68.2916, aux.loss_ce: 0.4310, aux.acc_seg: 64.9871, loss: 1.3523
2022-11-04 10:39:36,526 - mmseg - INFO - Iter [3700/80000]	lr: 1.150e-04, eta: 7:25:05, time: 0.342, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9138, decode.acc_seg: 69.0704, aux.loss_ce: 0.4286, aux.acc_seg: 65.6252, loss: 1.3424
2022-11-04 10:39:53,700 - mmseg - INFO - Iter [3750/80000]	lr: 1.149e-04, eta: 7:24:41, time: 0.343, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9223, decode.acc_seg: 68.7009, aux.loss_ce: 0.4297, aux.acc_seg: 65.5830, loss: 1.3520
2022-11-04 10:40:13,626 - mmseg - INFO - Iter [3800/80000]	lr: 1.149e-04, eta: 7:25:12, time: 0.399, data_time: 0.067, memory: 5246, decode.loss_ce: 0.9262, decode.acc_seg: 68.0732, aux.loss_ce: 0.4397, aux.acc_seg: 64.1940, loss: 1.3659
2022-11-04 10:40:30,860 - mmseg - INFO - Iter [3850/80000]	lr: 1.148e-04, eta: 7:24:49, time: 0.345, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8996, decode.acc_seg: 68.7870, aux.loss_ce: 0.4318, aux.acc_seg: 64.6112, loss: 1.3314
2022-11-04 10:40:47,988 - mmseg - INFO - Iter [3900/80000]	lr: 1.147e-04, eta: 7:24:24, time: 0.343, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8841, decode.acc_seg: 69.7272, aux.loss_ce: 0.4235, aux.acc_seg: 65.4924, loss: 1.3076
2022-11-04 10:41:05,217 - mmseg - INFO - Iter [3950/80000]	lr: 1.147e-04, eta: 7:24:01, time: 0.345, data_time: 0.010, memory: 5246, decode.loss_ce: 0.9078, decode.acc_seg: 68.7350, aux.loss_ce: 0.4264, aux.acc_seg: 65.1695, loss: 1.3342
2022-11-04 10:41:22,300 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 10:41:22,301 - mmseg - INFO - Iter [4000/80000]	lr: 1.146e-04, eta: 7:23:35, time: 0.342, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9200, decode.acc_seg: 69.0594, aux.loss_ce: 0.4352, aux.acc_seg: 64.7154, loss: 1.3551
2022-11-04 10:41:39,463 - mmseg - INFO - Iter [4050/80000]	lr: 1.145e-04, eta: 7:23:11, time: 0.343, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8897, decode.acc_seg: 68.8042, aux.loss_ce: 0.4280, aux.acc_seg: 64.4752, loss: 1.3177
2022-11-04 10:41:56,626 - mmseg - INFO - Iter [4100/80000]	lr: 1.145e-04, eta: 7:22:47, time: 0.343, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9011, decode.acc_seg: 68.9556, aux.loss_ce: 0.4227, aux.acc_seg: 65.7350, loss: 1.3238
2022-11-04 10:42:13,740 - mmseg - INFO - Iter [4150/80000]	lr: 1.144e-04, eta: 7:22:22, time: 0.342, data_time: 0.008, memory: 5246, decode.loss_ce: 0.8639, decode.acc_seg: 69.9574, aux.loss_ce: 0.4076, aux.acc_seg: 66.3808, loss: 1.2714
2022-11-04 10:42:30,741 - mmseg - INFO - Iter [4200/80000]	lr: 1.143e-04, eta: 7:21:56, time: 0.340, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8784, decode.acc_seg: 69.8158, aux.loss_ce: 0.4145, aux.acc_seg: 66.0956, loss: 1.2929
2022-11-04 10:42:47,873 - mmseg - INFO - Iter [4250/80000]	lr: 1.142e-04, eta: 7:21:32, time: 0.343, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9013, decode.acc_seg: 69.1400, aux.loss_ce: 0.4278, aux.acc_seg: 65.0003, loss: 1.3291
2022-11-04 10:43:04,842 - mmseg - INFO - Iter [4300/80000]	lr: 1.142e-04, eta: 7:21:05, time: 0.339, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8861, decode.acc_seg: 69.3026, aux.loss_ce: 0.4199, aux.acc_seg: 65.2395, loss: 1.3060
2022-11-04 10:43:21,694 - mmseg - INFO - Iter [4350/80000]	lr: 1.141e-04, eta: 7:20:37, time: 0.337, data_time: 0.009, memory: 5246, decode.loss_ce: 0.9108, decode.acc_seg: 68.1527, aux.loss_ce: 0.4289, aux.acc_seg: 64.3605, loss: 1.3397
2022-11-04 10:43:38,875 - mmseg - INFO - Iter [4400/80000]	lr: 1.140e-04, eta: 7:20:14, time: 0.344, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8744, decode.acc_seg: 69.0344, aux.loss_ce: 0.4144, aux.acc_seg: 65.3824, loss: 1.2888
2022-11-04 10:43:58,882 - mmseg - INFO - Iter [4450/80000]	lr: 1.140e-04, eta: 7:20:40, time: 0.400, data_time: 0.060, memory: 5246, decode.loss_ce: 0.8708, decode.acc_seg: 69.7134, aux.loss_ce: 0.4156, aux.acc_seg: 65.6850, loss: 1.2864
2022-11-04 10:44:15,911 - mmseg - INFO - Iter [4500/80000]	lr: 1.139e-04, eta: 7:20:15, time: 0.341, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8552, decode.acc_seg: 69.9902, aux.loss_ce: 0.4125, aux.acc_seg: 65.9535, loss: 1.2677
2022-11-04 10:44:33,210 - mmseg - INFO - Iter [4550/80000]	lr: 1.138e-04, eta: 7:19:54, time: 0.346, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8599, decode.acc_seg: 69.6735, aux.loss_ce: 0.4086, aux.acc_seg: 66.4740, loss: 1.2685
2022-11-04 10:44:50,420 - mmseg - INFO - Iter [4600/80000]	lr: 1.138e-04, eta: 7:19:32, time: 0.344, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8791, decode.acc_seg: 69.0193, aux.loss_ce: 0.4249, aux.acc_seg: 64.8724, loss: 1.3040
2022-11-04 10:45:07,584 - mmseg - INFO - Iter [4650/80000]	lr: 1.137e-04, eta: 7:19:09, time: 0.343, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8832, decode.acc_seg: 69.2150, aux.loss_ce: 0.4214, aux.acc_seg: 65.4124, loss: 1.3046
2022-11-04 10:45:24,658 - mmseg - INFO - Iter [4700/80000]	lr: 1.136e-04, eta: 7:18:45, time: 0.341, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8775, decode.acc_seg: 69.4015, aux.loss_ce: 0.4156, aux.acc_seg: 65.4902, loss: 1.2932
2022-11-04 10:45:41,849 - mmseg - INFO - Iter [4750/80000]	lr: 1.136e-04, eta: 7:18:23, time: 0.344, data_time: 0.010, memory: 5246, decode.loss_ce: 0.8833, decode.acc_seg: 69.3939, aux.loss_ce: 0.4192, aux.acc_seg: 65.5419, loss: 1.3026
2022-11-04 10:45:58,886 - mmseg - INFO - Iter [4800/80000]	lr: 1.135e-04, eta: 7:17:59, time: 0.341, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8522, decode.acc_seg: 70.2303, aux.loss_ce: 0.4057, aux.acc_seg: 65.8221, loss: 1.2579
2022-11-04 10:46:16,147 - mmseg - INFO - Iter [4850/80000]	lr: 1.134e-04, eta: 7:17:38, time: 0.345, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8535, decode.acc_seg: 69.8831, aux.loss_ce: 0.4053, aux.acc_seg: 66.0731, loss: 1.2589
2022-11-04 10:46:33,192 - mmseg - INFO - Iter [4900/80000]	lr: 1.134e-04, eta: 7:17:14, time: 0.341, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8625, decode.acc_seg: 69.4960, aux.loss_ce: 0.4128, aux.acc_seg: 65.5682, loss: 1.2753
2022-11-04 10:46:50,189 - mmseg - INFO - Iter [4950/80000]	lr: 1.133e-04, eta: 7:16:49, time: 0.340, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8884, decode.acc_seg: 69.0678, aux.loss_ce: 0.4248, aux.acc_seg: 65.0467, loss: 1.3132
2022-11-04 10:47:07,491 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 10:47:07,492 - mmseg - INFO - Iter [5000/80000]	lr: 1.132e-04, eta: 7:16:29, time: 0.346, data_time: 0.010, memory: 5246, decode.loss_ce: 0.8820, decode.acc_seg: 69.5295, aux.loss_ce: 0.4123, aux.acc_seg: 66.0712, loss: 1.2943
2022-11-04 10:47:27,659 - mmseg - INFO - Iter [5050/80000]	lr: 1.132e-04, eta: 7:16:52, time: 0.403, data_time: 0.062, memory: 5246, decode.loss_ce: 0.8291, decode.acc_seg: 70.6403, aux.loss_ce: 0.3925, aux.acc_seg: 67.6908, loss: 1.2215
2022-11-04 10:47:44,794 - mmseg - INFO - Iter [5100/80000]	lr: 1.131e-04, eta: 7:16:29, time: 0.343, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8483, decode.acc_seg: 70.3788, aux.loss_ce: 0.4111, aux.acc_seg: 65.9885, loss: 1.2594
2022-11-04 10:48:01,680 - mmseg - INFO - Iter [5150/80000]	lr: 1.130e-04, eta: 7:16:03, time: 0.338, data_time: 0.008, memory: 5246, decode.loss_ce: 0.8553, decode.acc_seg: 70.0892, aux.loss_ce: 0.4108, aux.acc_seg: 66.0825, loss: 1.2662
2022-11-04 10:48:18,735 - mmseg - INFO - Iter [5200/80000]	lr: 1.130e-04, eta: 7:15:40, time: 0.341, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8032, decode.acc_seg: 71.8576, aux.loss_ce: 0.3883, aux.acc_seg: 67.7080, loss: 1.1915
2022-11-04 10:48:35,653 - mmseg - INFO - Iter [5250/80000]	lr: 1.129e-04, eta: 7:15:14, time: 0.338, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8169, decode.acc_seg: 70.4763, aux.loss_ce: 0.3957, aux.acc_seg: 66.3902, loss: 1.2126
2022-11-04 10:48:52,813 - mmseg - INFO - Iter [5300/80000]	lr: 1.128e-04, eta: 7:14:52, time: 0.343, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8535, decode.acc_seg: 69.9511, aux.loss_ce: 0.4079, aux.acc_seg: 66.1758, loss: 1.2614
2022-11-04 10:49:09,745 - mmseg - INFO - Iter [5350/80000]	lr: 1.128e-04, eta: 7:14:28, time: 0.339, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8530, decode.acc_seg: 69.3357, aux.loss_ce: 0.4050, aux.acc_seg: 65.5268, loss: 1.2580
2022-11-04 10:49:26,995 - mmseg - INFO - Iter [5400/80000]	lr: 1.127e-04, eta: 7:14:07, time: 0.345, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8685, decode.acc_seg: 70.1460, aux.loss_ce: 0.4163, aux.acc_seg: 65.9722, loss: 1.2848
2022-11-04 10:49:44,101 - mmseg - INFO - Iter [5450/80000]	lr: 1.126e-04, eta: 7:13:45, time: 0.342, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8507, decode.acc_seg: 70.2141, aux.loss_ce: 0.4022, aux.acc_seg: 66.6334, loss: 1.2529
2022-11-04 10:50:01,114 - mmseg - INFO - Iter [5500/80000]	lr: 1.126e-04, eta: 7:13:21, time: 0.340, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8269, decode.acc_seg: 70.5728, aux.loss_ce: 0.3972, aux.acc_seg: 66.5022, loss: 1.2242
2022-11-04 10:50:18,081 - mmseg - INFO - Iter [5550/80000]	lr: 1.125e-04, eta: 7:12:57, time: 0.339, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8454, decode.acc_seg: 70.5911, aux.loss_ce: 0.4091, aux.acc_seg: 66.3239, loss: 1.2546
2022-11-04 10:50:35,394 - mmseg - INFO - Iter [5600/80000]	lr: 1.124e-04, eta: 7:12:38, time: 0.346, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8300, decode.acc_seg: 70.9642, aux.loss_ce: 0.3931, aux.acc_seg: 67.1699, loss: 1.2231
2022-11-04 10:50:52,484 - mmseg - INFO - Iter [5650/80000]	lr: 1.123e-04, eta: 7:12:16, time: 0.342, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8568, decode.acc_seg: 70.5768, aux.loss_ce: 0.4076, aux.acc_seg: 66.5948, loss: 1.2645
2022-11-04 10:51:12,729 - mmseg - INFO - Iter [5700/80000]	lr: 1.123e-04, eta: 7:12:35, time: 0.405, data_time: 0.059, memory: 5246, decode.loss_ce: 0.8289, decode.acc_seg: 70.1903, aux.loss_ce: 0.3958, aux.acc_seg: 66.4170, loss: 1.2247
2022-11-04 10:51:29,691 - mmseg - INFO - Iter [5750/80000]	lr: 1.122e-04, eta: 7:12:11, time: 0.339, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8103, decode.acc_seg: 71.7397, aux.loss_ce: 0.3951, aux.acc_seg: 67.3046, loss: 1.2055
2022-11-04 10:51:46,824 - mmseg - INFO - Iter [5800/80000]	lr: 1.121e-04, eta: 7:11:50, time: 0.343, data_time: 0.008, memory: 5246, decode.loss_ce: 0.8122, decode.acc_seg: 71.0653, aux.loss_ce: 0.3935, aux.acc_seg: 67.1433, loss: 1.2058
2022-11-04 10:52:03,921 - mmseg - INFO - Iter [5850/80000]	lr: 1.121e-04, eta: 7:11:27, time: 0.342, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8333, decode.acc_seg: 70.4550, aux.loss_ce: 0.4040, aux.acc_seg: 66.2763, loss: 1.2373
2022-11-04 10:52:21,210 - mmseg - INFO - Iter [5900/80000]	lr: 1.120e-04, eta: 7:11:08, time: 0.346, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8335, decode.acc_seg: 70.6823, aux.loss_ce: 0.3981, aux.acc_seg: 66.8819, loss: 1.2316
2022-11-04 10:52:38,407 - mmseg - INFO - Iter [5950/80000]	lr: 1.119e-04, eta: 7:10:47, time: 0.344, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7850, decode.acc_seg: 72.0363, aux.loss_ce: 0.3822, aux.acc_seg: 67.6628, loss: 1.1672
2022-11-04 10:52:55,380 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 10:52:55,381 - mmseg - INFO - Iter [6000/80000]	lr: 1.119e-04, eta: 7:10:24, time: 0.339, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8261, decode.acc_seg: 71.1773, aux.loss_ce: 0.4005, aux.acc_seg: 67.2051, loss: 1.2266
2022-11-04 10:53:12,430 - mmseg - INFO - Iter [6050/80000]	lr: 1.118e-04, eta: 7:10:02, time: 0.341, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7963, decode.acc_seg: 71.7176, aux.loss_ce: 0.3845, aux.acc_seg: 67.5651, loss: 1.1808
2022-11-04 10:53:29,430 - mmseg - INFO - Iter [6100/80000]	lr: 1.117e-04, eta: 7:09:39, time: 0.340, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7959, decode.acc_seg: 71.3912, aux.loss_ce: 0.3848, aux.acc_seg: 67.4936, loss: 1.1807
2022-11-04 10:53:46,343 - mmseg - INFO - Iter [6150/80000]	lr: 1.117e-04, eta: 7:09:15, time: 0.338, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7957, decode.acc_seg: 71.2953, aux.loss_ce: 0.3856, aux.acc_seg: 67.2806, loss: 1.1813
2022-11-04 10:54:03,470 - mmseg - INFO - Iter [6200/80000]	lr: 1.116e-04, eta: 7:08:54, time: 0.343, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7971, decode.acc_seg: 71.3286, aux.loss_ce: 0.3881, aux.acc_seg: 67.2243, loss: 1.1852
2022-11-04 10:54:20,519 - mmseg - INFO - Iter [6250/80000]	lr: 1.115e-04, eta: 7:08:32, time: 0.341, data_time: 0.008, memory: 5246, decode.loss_ce: 0.8244, decode.acc_seg: 71.3421, aux.loss_ce: 0.3970, aux.acc_seg: 66.9351, loss: 1.2214
2022-11-04 10:54:37,618 - mmseg - INFO - Iter [6300/80000]	lr: 1.115e-04, eta: 7:08:10, time: 0.342, data_time: 0.008, memory: 5246, decode.loss_ce: 0.8103, decode.acc_seg: 71.4139, aux.loss_ce: 0.3877, aux.acc_seg: 67.7113, loss: 1.1979
2022-11-04 10:54:57,770 - mmseg - INFO - Iter [6350/80000]	lr: 1.114e-04, eta: 7:08:25, time: 0.403, data_time: 0.067, memory: 5246, decode.loss_ce: 0.8187, decode.acc_seg: 70.9099, aux.loss_ce: 0.4009, aux.acc_seg: 66.7545, loss: 1.2195
2022-11-04 10:55:14,962 - mmseg - INFO - Iter [6400/80000]	lr: 1.113e-04, eta: 7:08:04, time: 0.344, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7904, decode.acc_seg: 71.4493, aux.loss_ce: 0.3871, aux.acc_seg: 66.8631, loss: 1.1775
2022-11-04 10:55:32,244 - mmseg - INFO - Iter [6450/80000]	lr: 1.113e-04, eta: 7:07:45, time: 0.346, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7861, decode.acc_seg: 72.5046, aux.loss_ce: 0.3838, aux.acc_seg: 68.2513, loss: 1.1699
2022-11-04 10:55:49,187 - mmseg - INFO - Iter [6500/80000]	lr: 1.112e-04, eta: 7:07:22, time: 0.339, data_time: 0.008, memory: 5246, decode.loss_ce: 0.7953, decode.acc_seg: 71.9784, aux.loss_ce: 0.3910, aux.acc_seg: 67.5396, loss: 1.1864
2022-11-04 10:56:06,131 - mmseg - INFO - Iter [6550/80000]	lr: 1.111e-04, eta: 7:06:59, time: 0.339, data_time: 0.008, memory: 5246, decode.loss_ce: 0.7687, decode.acc_seg: 72.0959, aux.loss_ce: 0.3725, aux.acc_seg: 67.9741, loss: 1.1413
2022-11-04 10:56:23,429 - mmseg - INFO - Iter [6600/80000]	lr: 1.111e-04, eta: 7:06:40, time: 0.346, data_time: 0.008, memory: 5246, decode.loss_ce: 0.7922, decode.acc_seg: 72.0724, aux.loss_ce: 0.3826, aux.acc_seg: 68.0981, loss: 1.1747
2022-11-04 10:56:40,622 - mmseg - INFO - Iter [6650/80000]	lr: 1.110e-04, eta: 7:06:19, time: 0.344, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7893, decode.acc_seg: 71.5065, aux.loss_ce: 0.3826, aux.acc_seg: 67.6847, loss: 1.1719
2022-11-04 10:56:57,491 - mmseg - INFO - Iter [6700/80000]	lr: 1.109e-04, eta: 7:05:56, time: 0.337, data_time: 0.008, memory: 5246, decode.loss_ce: 0.7890, decode.acc_seg: 71.4801, aux.loss_ce: 0.3800, aux.acc_seg: 67.6624, loss: 1.1690
2022-11-04 10:57:14,908 - mmseg - INFO - Iter [6750/80000]	lr: 1.108e-04, eta: 7:05:38, time: 0.348, data_time: 0.010, memory: 5246, decode.loss_ce: 0.7941, decode.acc_seg: 72.0919, aux.loss_ce: 0.3829, aux.acc_seg: 67.9224, loss: 1.1770
2022-11-04 10:57:32,128 - mmseg - INFO - Iter [6800/80000]	lr: 1.108e-04, eta: 7:05:18, time: 0.344, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7847, decode.acc_seg: 71.2448, aux.loss_ce: 0.3765, aux.acc_seg: 67.4313, loss: 1.1611
2022-11-04 10:57:49,318 - mmseg - INFO - Iter [6850/80000]	lr: 1.107e-04, eta: 7:04:58, time: 0.344, data_time: 0.008, memory: 5246, decode.loss_ce: 0.7960, decode.acc_seg: 71.3059, aux.loss_ce: 0.3843, aux.acc_seg: 67.4434, loss: 1.1803
2022-11-04 10:58:06,281 - mmseg - INFO - Iter [6900/80000]	lr: 1.106e-04, eta: 7:04:36, time: 0.339, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7704, decode.acc_seg: 72.0269, aux.loss_ce: 0.3753, aux.acc_seg: 68.2331, loss: 1.1457
2022-11-04 10:58:26,279 - mmseg - INFO - Iter [6950/80000]	lr: 1.106e-04, eta: 7:04:46, time: 0.400, data_time: 0.066, memory: 5246, decode.loss_ce: 0.7989, decode.acc_seg: 71.6973, aux.loss_ce: 0.3887, aux.acc_seg: 67.4219, loss: 1.1875
2022-11-04 10:58:43,650 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 10:58:43,650 - mmseg - INFO - Iter [7000/80000]	lr: 1.105e-04, eta: 7:04:27, time: 0.347, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7473, decode.acc_seg: 73.1468, aux.loss_ce: 0.3651, aux.acc_seg: 68.8945, loss: 1.1124
2022-11-04 10:59:01,045 - mmseg - INFO - Iter [7050/80000]	lr: 1.104e-04, eta: 7:04:09, time: 0.348, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7626, decode.acc_seg: 72.0862, aux.loss_ce: 0.3694, aux.acc_seg: 68.2255, loss: 1.1320
2022-11-04 10:59:18,019 - mmseg - INFO - Iter [7100/80000]	lr: 1.104e-04, eta: 7:03:47, time: 0.339, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7814, decode.acc_seg: 72.4033, aux.loss_ce: 0.3795, aux.acc_seg: 68.4013, loss: 1.1608
2022-11-04 10:59:35,279 - mmseg - INFO - Iter [7150/80000]	lr: 1.103e-04, eta: 7:03:28, time: 0.345, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7833, decode.acc_seg: 71.7734, aux.loss_ce: 0.3799, aux.acc_seg: 67.9047, loss: 1.1632
2022-11-04 10:59:52,306 - mmseg - INFO - Iter [7200/80000]	lr: 1.102e-04, eta: 7:03:06, time: 0.341, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8013, decode.acc_seg: 71.5512, aux.loss_ce: 0.3848, aux.acc_seg: 67.7541, loss: 1.1862
2022-11-04 11:00:09,356 - mmseg - INFO - Iter [7250/80000]	lr: 1.102e-04, eta: 7:02:45, time: 0.341, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7765, decode.acc_seg: 72.1936, aux.loss_ce: 0.3848, aux.acc_seg: 67.5726, loss: 1.1613
2022-11-04 11:00:26,245 - mmseg - INFO - Iter [7300/80000]	lr: 1.101e-04, eta: 7:02:22, time: 0.338, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7791, decode.acc_seg: 71.8868, aux.loss_ce: 0.3814, aux.acc_seg: 67.6378, loss: 1.1605
2022-11-04 11:00:43,260 - mmseg - INFO - Iter [7350/80000]	lr: 1.100e-04, eta: 7:02:01, time: 0.340, data_time: 0.008, memory: 5246, decode.loss_ce: 0.7969, decode.acc_seg: 71.5976, aux.loss_ce: 0.3838, aux.acc_seg: 67.4069, loss: 1.1807
2022-11-04 11:01:00,327 - mmseg - INFO - Iter [7400/80000]	lr: 1.100e-04, eta: 7:01:40, time: 0.341, data_time: 0.009, memory: 5246, decode.loss_ce: 0.8193, decode.acc_seg: 70.9113, aux.loss_ce: 0.3955, aux.acc_seg: 66.9482, loss: 1.2148
2022-11-04 11:01:17,531 - mmseg - INFO - Iter [7450/80000]	lr: 1.099e-04, eta: 7:01:20, time: 0.344, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7757, decode.acc_seg: 72.7898, aux.loss_ce: 0.3750, aux.acc_seg: 68.8786, loss: 1.1508
2022-11-04 11:01:34,573 - mmseg - INFO - Iter [7500/80000]	lr: 1.098e-04, eta: 7:00:59, time: 0.341, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7972, decode.acc_seg: 71.8732, aux.loss_ce: 0.3864, aux.acc_seg: 67.8964, loss: 1.1836
2022-11-04 11:01:51,688 - mmseg - INFO - Iter [7550/80000]	lr: 1.098e-04, eta: 7:00:39, time: 0.342, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7512, decode.acc_seg: 72.7332, aux.loss_ce: 0.3680, aux.acc_seg: 68.5425, loss: 1.1192
2022-11-04 11:02:11,697 - mmseg - INFO - Iter [7600/80000]	lr: 1.097e-04, eta: 7:00:46, time: 0.400, data_time: 0.072, memory: 5246, decode.loss_ce: 0.7613, decode.acc_seg: 72.2616, aux.loss_ce: 0.3753, aux.acc_seg: 67.7667, loss: 1.1366
2022-11-04 11:02:28,667 - mmseg - INFO - Iter [7650/80000]	lr: 1.096e-04, eta: 7:00:24, time: 0.339, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7461, decode.acc_seg: 72.7214, aux.loss_ce: 0.3678, aux.acc_seg: 67.8132, loss: 1.1140
2022-11-04 11:02:45,742 - mmseg - INFO - Iter [7700/80000]	lr: 1.096e-04, eta: 7:00:03, time: 0.341, data_time: 0.010, memory: 5246, decode.loss_ce: 0.7463, decode.acc_seg: 72.9138, aux.loss_ce: 0.3659, aux.acc_seg: 68.8130, loss: 1.1122
2022-11-04 11:03:02,925 - mmseg - INFO - Iter [7750/80000]	lr: 1.095e-04, eta: 6:59:43, time: 0.344, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7617, decode.acc_seg: 72.1822, aux.loss_ce: 0.3754, aux.acc_seg: 68.2446, loss: 1.1371
2022-11-04 11:03:20,051 - mmseg - INFO - Iter [7800/80000]	lr: 1.094e-04, eta: 6:59:23, time: 0.343, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7705, decode.acc_seg: 72.5012, aux.loss_ce: 0.3761, aux.acc_seg: 68.6832, loss: 1.1466
2022-11-04 11:03:37,403 - mmseg - INFO - Iter [7850/80000]	lr: 1.093e-04, eta: 6:59:05, time: 0.347, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7604, decode.acc_seg: 72.6369, aux.loss_ce: 0.3774, aux.acc_seg: 68.0664, loss: 1.1379
2022-11-04 11:03:54,422 - mmseg - INFO - Iter [7900/80000]	lr: 1.093e-04, eta: 6:58:44, time: 0.340, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7804, decode.acc_seg: 71.4808, aux.loss_ce: 0.3826, aux.acc_seg: 67.2101, loss: 1.1630
2022-11-04 11:04:11,457 - mmseg - INFO - Iter [7950/80000]	lr: 1.092e-04, eta: 6:58:23, time: 0.341, data_time: 0.009, memory: 5246, decode.loss_ce: 0.7423, decode.acc_seg: 73.3901, aux.loss_ce: 0.3609, aux.acc_seg: 69.5296, loss: 1.1031
2022-11-04 11:04:28,330 - mmseg - INFO - Saving checkpoint at 8000 iterations
2022-11-04 11:04:28,567 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 11:04:28,568 - mmseg - INFO - Iter [8000/80000]	lr: 1.091e-04, eta: 6:58:03, time: 0.342, data_time: 0.008, memory: 5246, decode.loss_ce: 0.7841, decode.acc_seg: 72.0019, aux.loss_ce: 0.3796, aux.acc_seg: 67.9728, loss: 1.1637
2022-11-04 11:05:16,479 - mmseg - INFO - per class results:
2022-11-04 11:05:16,486 - mmseg - INFO - 
+---------------------+-------+-------+
|        Class        |  IoU  |  Acc  |
+---------------------+-------+-------+
|         wall        | 62.26 | 81.14 |
|       building      | 73.01 | 90.53 |
|         sky         | 87.81 | 92.51 |
|        floor        | 68.17 | 84.97 |
|         tree        |  64.9 | 80.17 |
|       ceiling       | 71.76 | 81.99 |
|         road        | 73.58 | 84.94 |
|         bed         | 71.02 |  89.2 |
|      windowpane     | 46.45 |  64.8 |
|        grass        | 62.98 | 79.66 |
|       cabinet       | 42.78 | 53.17 |
|       sidewalk      | 47.04 | 63.07 |
|        person       | 62.41 | 78.69 |
|        earth        | 27.71 | 37.65 |
|         door        | 20.76 | 37.05 |
|        table        | 34.55 | 51.08 |
|       mountain      | 53.91 | 72.38 |
|        plant        | 42.96 | 55.13 |
|       curtain       | 51.26 | 73.08 |
|        chair        | 33.82 | 46.63 |
|         car         | 67.26 | 85.25 |
|        water        | 42.49 | 64.29 |
|       painting      |  51.3 | 65.39 |
|         sofa        | 43.86 | 69.69 |
|        shelf        |  31.5 | 49.43 |
|        house        | 21.76 | 26.19 |
|         sea         | 44.38 | 65.52 |
|        mirror       | 27.86 | 32.01 |
|         rug         | 35.57 | 42.42 |
|        field        | 29.94 |  56.6 |
|       armchair      | 12.71 | 15.95 |
|         seat        | 45.13 |  60.3 |
|        fence        | 23.28 | 39.25 |
|         desk        | 23.56 | 37.27 |
|         rock        | 42.94 | 60.83 |
|       wardrobe      | 34.76 | 60.65 |
|         lamp        | 31.54 | 42.72 |
|       bathtub       | 45.15 | 64.14 |
|       railing       | 25.12 | 34.37 |
|       cushion       | 21.61 | 27.56 |
|         base        |  7.42 | 10.13 |
|         box         |  9.88 | 14.68 |
|        column       |  19.2 | 22.78 |
|      signboard      | 19.88 | 27.81 |
|   chest of drawers  | 25.01 | 45.06 |
|       counter       | 21.88 | 31.92 |
|         sand        | 29.79 | 42.94 |
|         sink        | 33.42 | 43.79 |
|      skyscraper     | 62.59 | 84.77 |
|      fireplace      | 42.97 | 61.36 |
|     refrigerator    | 37.63 | 57.23 |
|      grandstand     | 27.16 | 67.04 |
|         path        |  7.4  |  8.8  |
|        stairs       |  8.02 |  9.13 |
|        runway       | 70.91 | 91.33 |
|         case        | 43.31 | 64.82 |
|      pool table     | 76.92 | 91.22 |
|        pillow       | 28.24 | 36.44 |
|     screen door     |  44.3 | 58.51 |
|       stairway      | 20.71 | 36.59 |
|        river        |  8.93 | 13.86 |
|        bridge       | 40.51 | 66.53 |
|       bookcase      | 21.69 | 35.91 |
|        blind        |  4.39 |  4.55 |
|     coffee table    | 32.31 | 53.52 |
|        toilet       | 55.42 | 72.85 |
|        flower       | 22.26 | 32.02 |
|         book        | 37.25 |  58.1 |
|         hill        |  3.65 |  3.85 |
|        bench        |  30.7 | 42.66 |
|      countertop     | 22.14 | 27.42 |
|        stove        | 38.07 | 60.48 |
|         palm        |  32.9 | 42.05 |
|    kitchen island   | 18.63 | 35.82 |
|       computer      | 37.93 |  54.5 |
|     swivel chair    | 24.07 | 31.66 |
|         boat        | 29.47 | 37.17 |
|         bar         |  14.4 | 16.67 |
|    arcade machine   | 20.58 | 23.38 |
|        hovel        | 28.71 | 34.75 |
|         bus         | 65.88 | 76.94 |
|        towel        | 30.22 | 40.45 |
|        light        |  4.18 |  4.29 |
|        truck        |  5.16 |  9.28 |
|        tower        | 19.66 | 19.85 |
|      chandelier     | 39.42 | 57.56 |
|        awning       |  3.26 |  3.46 |
|     streetlight     |  1.21 |  1.27 |
|        booth        | 16.61 | 16.66 |
| television receiver | 42.62 | 58.58 |
|       airplane      | 40.43 | 51.48 |
|      dirt track     |  0.0  |  0.0  |
|       apparel       |  6.75 |  8.92 |
|         pole        |  2.2  |  2.53 |
|         land        |  0.0  |  0.0  |
|      bannister      |  0.24 |  0.25 |
|      escalator      | 30.63 | 33.41 |
|       ottoman       |  0.0  |  0.0  |
|        bottle       |  1.92 |  2.02 |
|        buffet       |  6.22 |  7.3  |
|        poster       |  0.69 |  0.77 |
|        stage        |  4.3  | 11.66 |
|         van         |  4.5  |  5.28 |
|         ship        | 44.81 | 63.85 |
|       fountain      |  1.15 |  1.17 |
|    conveyer belt    | 54.31 |  76.5 |
|        canopy       |  4.71 |  5.02 |
|        washer       | 45.82 | 61.98 |
|      plaything      |  0.86 |  0.98 |
|    swimming pool    | 37.68 | 41.72 |
|        stool        |  0.78 |  0.78 |
|        barrel       | 25.85 | 56.43 |
|        basket       |  0.78 |  0.79 |
|      waterfall      | 50.01 | 60.77 |
|         tent        | 83.65 | 95.79 |
|         bag         |  0.0  |  0.0  |
|       minibike      | 28.03 | 31.35 |
|        cradle       | 56.09 | 77.87 |
|         oven        |  2.0  |  2.17 |
|         ball        |  0.01 |  0.01 |
|         food        | 44.21 |  54.6 |
|         step        |  0.0  |  0.0  |
|         tank        |  0.0  |  0.0  |
|      trade name     | 15.16 | 16.46 |
|      microwave      | 20.63 | 23.92 |
|         pot         |  20.3 | 22.86 |
|        animal       | 38.93 | 40.62 |
|       bicycle       | 34.05 | 58.76 |
|         lake        |  0.0  |  0.0  |
|      dishwasher     |  7.49 |  7.92 |
|        screen       |  40.0 | 54.09 |
|       blanket       |  0.0  |  0.0  |
|      sculpture      |  5.26 |  5.55 |
|         hood        |  4.51 |  4.69 |
|        sconce       |  0.91 |  0.91 |
|         vase        | 11.64 | 15.34 |
|    traffic light    |  1.4  |  1.42 |
|         tray        |  0.38 |  0.38 |
|        ashcan       |  7.38 |  7.71 |
|         fan         |  9.07 |  9.59 |
|         pier        | 31.46 | 46.72 |
|      crt screen     |  0.0  |  0.0  |
|        plate        |  7.53 |  7.71 |
|       monitor       | 23.44 | 58.21 |
|    bulletin board   | 16.71 | 20.41 |
|        shower       |  0.0  |  0.0  |
|       radiator      | 13.88 | 14.16 |
|        glass        |  0.0  |  0.0  |
|        clock        |  0.07 |  0.07 |
|         flag        |  3.81 |  3.81 |
+---------------------+-------+-------+
2022-11-04 11:05:16,487 - mmseg - INFO - Summary:
2022-11-04 11:05:16,487 - mmseg - INFO - 
+-------+-------+-------+
|  aAcc |  mIoU |  mAcc |
+-------+-------+-------+
| 72.58 | 27.22 | 36.78 |
+-------+-------+-------+
2022-11-04 11:05:16,488 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 11:05:16,488 - mmseg - INFO - Iter(val) [250]	aAcc: 0.7258, mIoU: 0.2722, mAcc: 0.3678, IoU.wall: 0.6226, IoU.building: 0.7301, IoU.sky: 0.8781, IoU.floor: 0.6817, IoU.tree: 0.6490, IoU.ceiling: 0.7176, IoU.road: 0.7358, IoU.bed : 0.7102, IoU.windowpane: 0.4645, IoU.grass: 0.6298, IoU.cabinet: 0.4278, IoU.sidewalk: 0.4704, IoU.person: 0.6241, IoU.earth: 0.2771, IoU.door: 0.2076, IoU.table: 0.3455, IoU.mountain: 0.5391, IoU.plant: 0.4296, IoU.curtain: 0.5126, IoU.chair: 0.3382, IoU.car: 0.6726, IoU.water: 0.4249, IoU.painting: 0.5130, IoU.sofa: 0.4386, IoU.shelf: 0.3150, IoU.house: 0.2176, IoU.sea: 0.4438, IoU.mirror: 0.2786, IoU.rug: 0.3557, IoU.field: 0.2994, IoU.armchair: 0.1271, IoU.seat: 0.4513, IoU.fence: 0.2328, IoU.desk: 0.2356, IoU.rock: 0.4294, IoU.wardrobe: 0.3476, IoU.lamp: 0.3154, IoU.bathtub: 0.4515, IoU.railing: 0.2512, IoU.cushion: 0.2161, IoU.base: 0.0742, IoU.box: 0.0988, IoU.column: 0.1920, IoU.signboard: 0.1988, IoU.chest of drawers: 0.2501, IoU.counter: 0.2188, IoU.sand: 0.2979, IoU.sink: 0.3342, IoU.skyscraper: 0.6259, IoU.fireplace: 0.4297, IoU.refrigerator: 0.3763, IoU.grandstand: 0.2716, IoU.path: 0.0740, IoU.stairs: 0.0802, IoU.runway: 0.7091, IoU.case: 0.4331, IoU.pool table: 0.7692, IoU.pillow: 0.2824, IoU.screen door: 0.4430, IoU.stairway: 0.2071, IoU.river: 0.0893, IoU.bridge: 0.4051, IoU.bookcase: 0.2169, IoU.blind: 0.0439, IoU.coffee table: 0.3231, IoU.toilet: 0.5542, IoU.flower: 0.2226, IoU.book: 0.3725, IoU.hill: 0.0365, IoU.bench: 0.3070, IoU.countertop: 0.2214, IoU.stove: 0.3807, IoU.palm: 0.3290, IoU.kitchen island: 0.1863, IoU.computer: 0.3793, IoU.swivel chair: 0.2407, IoU.boat: 0.2947, IoU.bar: 0.1440, IoU.arcade machine: 0.2058, IoU.hovel: 0.2871, IoU.bus: 0.6588, IoU.towel: 0.3022, IoU.light: 0.0418, IoU.truck: 0.0516, IoU.tower: 0.1966, IoU.chandelier: 0.3942, IoU.awning: 0.0326, IoU.streetlight: 0.0121, IoU.booth: 0.1661, IoU.television receiver: 0.4262, IoU.airplane: 0.4043, IoU.dirt track: 0.0000, IoU.apparel: 0.0675, IoU.pole: 0.0220, IoU.land: 0.0000, IoU.bannister: 0.0024, IoU.escalator: 0.3063, IoU.ottoman: 0.0000, IoU.bottle: 0.0192, IoU.buffet: 0.0622, IoU.poster: 0.0069, IoU.stage: 0.0430, IoU.van: 0.0450, IoU.ship: 0.4481, IoU.fountain: 0.0115, IoU.conveyer belt: 0.5431, IoU.canopy: 0.0471, IoU.washer: 0.4582, IoU.plaything: 0.0086, IoU.swimming pool: 0.3768, IoU.stool: 0.0078, IoU.barrel: 0.2585, IoU.basket: 0.0078, IoU.waterfall: 0.5001, IoU.tent: 0.8365, IoU.bag: 0.0000, IoU.minibike: 0.2803, IoU.cradle: 0.5609, IoU.oven: 0.0200, IoU.ball: 0.0001, IoU.food: 0.4421, IoU.step: 0.0000, IoU.tank: 0.0000, IoU.trade name: 0.1516, IoU.microwave: 0.2063, IoU.pot: 0.2030, IoU.animal: 0.3893, IoU.bicycle: 0.3405, IoU.lake: 0.0000, IoU.dishwasher: 0.0749, IoU.screen: 0.4000, IoU.blanket: 0.0000, IoU.sculpture: 0.0526, IoU.hood: 0.0451, IoU.sconce: 0.0091, IoU.vase: 0.1164, IoU.traffic light: 0.0140, IoU.tray: 0.0038, IoU.ashcan: 0.0738, IoU.fan: 0.0907, IoU.pier: 0.3146, IoU.crt screen: 0.0000, IoU.plate: 0.0753, IoU.monitor: 0.2344, IoU.bulletin board: 0.1671, IoU.shower: 0.0000, IoU.radiator: 0.1388, IoU.glass: 0.0000, IoU.clock: 0.0007, IoU.flag: 0.0381, Acc.wall: 0.8114, Acc.building: 0.9053, Acc.sky: 0.9251, Acc.floor: 0.8497, Acc.tree: 0.8017, Acc.ceiling: 0.8199, Acc.road: 0.8494, Acc.bed : 0.8920, Acc.windowpane: 0.6480, Acc.grass: 0.7966, Acc.cabinet: 0.5317, Acc.sidewalk: 0.6307, Acc.person: 0.7869, Acc.earth: 0.3765, Acc.door: 0.3705, Acc.table: 0.5108, Acc.mountain: 0.7238, Acc.plant: 0.5513, Acc.curtain: 0.7308, Acc.chair: 0.4663, Acc.car: 0.8525, Acc.water: 0.6429, Acc.painting: 0.6539, Acc.sofa: 0.6969, Acc.shelf: 0.4943, Acc.house: 0.2619, Acc.sea: 0.6552, Acc.mirror: 0.3201, Acc.rug: 0.4242, Acc.field: 0.5660, Acc.armchair: 0.1595, Acc.seat: 0.6030, Acc.fence: 0.3925, Acc.desk: 0.3727, Acc.rock: 0.6083, Acc.wardrobe: 0.6065, Acc.lamp: 0.4272, Acc.bathtub: 0.6414, Acc.railing: 0.3437, Acc.cushion: 0.2756, Acc.base: 0.1013, Acc.box: 0.1468, Acc.column: 0.2278, Acc.signboard: 0.2781, Acc.chest of drawers: 0.4506, Acc.counter: 0.3192, Acc.sand: 0.4294, Acc.sink: 0.4379, Acc.skyscraper: 0.8477, Acc.fireplace: 0.6136, Acc.refrigerator: 0.5723, Acc.grandstand: 0.6704, Acc.path: 0.0880, Acc.stairs: 0.0913, Acc.runway: 0.9133, Acc.case: 0.6482, Acc.pool table: 0.9122, Acc.pillow: 0.3644, Acc.screen door: 0.5851, Acc.stairway: 0.3659, Acc.river: 0.1386, Acc.bridge: 0.6653, Acc.bookcase: 0.3591, Acc.blind: 0.0455, Acc.coffee table: 0.5352, Acc.toilet: 0.7285, Acc.flower: 0.3202, Acc.book: 0.5810, Acc.hill: 0.0385, Acc.bench: 0.4266, Acc.countertop: 0.2742, Acc.stove: 0.6048, Acc.palm: 0.4205, Acc.kitchen island: 0.3582, Acc.computer: 0.5450, Acc.swivel chair: 0.3166, Acc.boat: 0.3717, Acc.bar: 0.1667, Acc.arcade machine: 0.2338, Acc.hovel: 0.3475, Acc.bus: 0.7694, Acc.towel: 0.4045, Acc.light: 0.0429, Acc.truck: 0.0928, Acc.tower: 0.1985, Acc.chandelier: 0.5756, Acc.awning: 0.0346, Acc.streetlight: 0.0127, Acc.booth: 0.1666, Acc.television receiver: 0.5858, Acc.airplane: 0.5148, Acc.dirt track: 0.0000, Acc.apparel: 0.0892, Acc.pole: 0.0253, Acc.land: 0.0000, Acc.bannister: 0.0025, Acc.escalator: 0.3341, Acc.ottoman: 0.0000, Acc.bottle: 0.0202, Acc.buffet: 0.0730, Acc.poster: 0.0077, Acc.stage: 0.1166, Acc.van: 0.0528, Acc.ship: 0.6385, Acc.fountain: 0.0117, Acc.conveyer belt: 0.7650, Acc.canopy: 0.0502, Acc.washer: 0.6198, Acc.plaything: 0.0098, Acc.swimming pool: 0.4172, Acc.stool: 0.0078, Acc.barrel: 0.5643, Acc.basket: 0.0079, Acc.waterfall: 0.6077, Acc.tent: 0.9579, Acc.bag: 0.0000, Acc.minibike: 0.3135, Acc.cradle: 0.7787, Acc.oven: 0.0217, Acc.ball: 0.0001, Acc.food: 0.5460, Acc.step: 0.0000, Acc.tank: 0.0000, Acc.trade name: 0.1646, Acc.microwave: 0.2392, Acc.pot: 0.2286, Acc.animal: 0.4062, Acc.bicycle: 0.5876, Acc.lake: 0.0000, Acc.dishwasher: 0.0792, Acc.screen: 0.5409, Acc.blanket: 0.0000, Acc.sculpture: 0.0555, Acc.hood: 0.0469, Acc.sconce: 0.0091, Acc.vase: 0.1534, Acc.traffic light: 0.0142, Acc.tray: 0.0038, Acc.ashcan: 0.0771, Acc.fan: 0.0959, Acc.pier: 0.4672, Acc.crt screen: 0.0000, Acc.plate: 0.0771, Acc.monitor: 0.5821, Acc.bulletin board: 0.2041, Acc.shower: 0.0000, Acc.radiator: 0.1416, Acc.glass: 0.0000, Acc.clock: 0.0007, Acc.flag: 0.0381
2022-11-04 11:05:34,065 - mmseg - INFO - Iter [8050/80000]	lr: 1.091e-04, eta: 7:04:55, time: 1.310, data_time: 0.967, memory: 12988, decode.loss_ce: 0.7650, decode.acc_seg: 72.7158, aux.loss_ce: 0.3742, aux.acc_seg: 68.4548, loss: 1.1392
2022-11-04 11:05:51,200 - mmseg - INFO - Iter [8100/80000]	lr: 1.090e-04, eta: 7:04:32, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7321, decode.acc_seg: 73.8152, aux.loss_ce: 0.3608, aux.acc_seg: 69.3025, loss: 1.0929
2022-11-04 11:06:08,276 - mmseg - INFO - Iter [8150/80000]	lr: 1.089e-04, eta: 7:04:09, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7669, decode.acc_seg: 72.0030, aux.loss_ce: 0.3775, aux.acc_seg: 67.7289, loss: 1.1444
2022-11-04 11:06:25,261 - mmseg - INFO - Iter [8200/80000]	lr: 1.089e-04, eta: 7:03:45, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7724, decode.acc_seg: 72.3891, aux.loss_ce: 0.3679, aux.acc_seg: 68.7655, loss: 1.1402
2022-11-04 11:06:45,433 - mmseg - INFO - Iter [8250/80000]	lr: 1.088e-04, eta: 7:03:49, time: 0.403, data_time: 0.065, memory: 12988, decode.loss_ce: 0.7572, decode.acc_seg: 73.3957, aux.loss_ce: 0.3745, aux.acc_seg: 68.9388, loss: 1.1317
2022-11-04 11:07:02,748 - mmseg - INFO - Iter [8300/80000]	lr: 1.087e-04, eta: 7:03:27, time: 0.346, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7028, decode.acc_seg: 73.9816, aux.loss_ce: 0.3440, aux.acc_seg: 70.1119, loss: 1.0468
2022-11-04 11:07:20,242 - mmseg - INFO - Iter [8350/80000]	lr: 1.087e-04, eta: 7:03:08, time: 0.350, data_time: 0.008, memory: 12988, decode.loss_ce: 0.7341, decode.acc_seg: 73.2920, aux.loss_ce: 0.3626, aux.acc_seg: 69.1134, loss: 1.0966
2022-11-04 11:07:37,109 - mmseg - INFO - Iter [8400/80000]	lr: 1.086e-04, eta: 7:02:43, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7814, decode.acc_seg: 71.8804, aux.loss_ce: 0.3833, aux.acc_seg: 67.2980, loss: 1.1647
2022-11-04 11:07:54,162 - mmseg - INFO - Iter [8450/80000]	lr: 1.085e-04, eta: 7:02:19, time: 0.341, data_time: 0.008, memory: 12988, decode.loss_ce: 0.7588, decode.acc_seg: 72.6764, aux.loss_ce: 0.3698, aux.acc_seg: 68.5820, loss: 1.1286
2022-11-04 11:08:11,093 - mmseg - INFO - Iter [8500/80000]	lr: 1.085e-04, eta: 7:01:55, time: 0.339, data_time: 0.008, memory: 12988, decode.loss_ce: 0.7393, decode.acc_seg: 73.4741, aux.loss_ce: 0.3645, aux.acc_seg: 69.1116, loss: 1.1038
2022-11-04 11:08:28,063 - mmseg - INFO - Iter [8550/80000]	lr: 1.084e-04, eta: 7:01:31, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7317, decode.acc_seg: 73.1111, aux.loss_ce: 0.3597, aux.acc_seg: 68.9886, loss: 1.0914
2022-11-04 11:08:45,204 - mmseg - INFO - Iter [8600/80000]	lr: 1.083e-04, eta: 7:01:09, time: 0.343, data_time: 0.008, memory: 12988, decode.loss_ce: 0.7671, decode.acc_seg: 72.4899, aux.loss_ce: 0.3743, aux.acc_seg: 68.2125, loss: 1.1414
2022-11-04 11:09:02,087 - mmseg - INFO - Iter [8650/80000]	lr: 1.083e-04, eta: 7:00:45, time: 0.338, data_time: 0.008, memory: 12988, decode.loss_ce: 0.7700, decode.acc_seg: 72.0810, aux.loss_ce: 0.3801, aux.acc_seg: 67.8030, loss: 1.1500
2022-11-04 11:09:19,222 - mmseg - INFO - Iter [8700/80000]	lr: 1.082e-04, eta: 7:00:22, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7437, decode.acc_seg: 73.6214, aux.loss_ce: 0.3691, aux.acc_seg: 68.7703, loss: 1.1127
2022-11-04 11:09:36,254 - mmseg - INFO - Iter [8750/80000]	lr: 1.081e-04, eta: 6:59:59, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7493, decode.acc_seg: 72.8788, aux.loss_ce: 0.3680, aux.acc_seg: 69.0085, loss: 1.1173
2022-11-04 11:09:53,119 - mmseg - INFO - Iter [8800/80000]	lr: 1.081e-04, eta: 6:59:35, time: 0.337, data_time: 0.008, memory: 12988, decode.loss_ce: 0.7644, decode.acc_seg: 71.9536, aux.loss_ce: 0.3718, aux.acc_seg: 68.3767, loss: 1.1363
2022-11-04 11:10:13,111 - mmseg - INFO - Iter [8850/80000]	lr: 1.080e-04, eta: 6:59:36, time: 0.400, data_time: 0.062, memory: 12988, decode.loss_ce: 0.7495, decode.acc_seg: 73.2491, aux.loss_ce: 0.3682, aux.acc_seg: 68.9203, loss: 1.1177
2022-11-04 11:10:30,039 - mmseg - INFO - Iter [8900/80000]	lr: 1.079e-04, eta: 6:59:12, time: 0.339, data_time: 0.008, memory: 12988, decode.loss_ce: 0.7314, decode.acc_seg: 73.3385, aux.loss_ce: 0.3596, aux.acc_seg: 69.0690, loss: 1.0909
2022-11-04 11:10:47,069 - mmseg - INFO - Iter [8950/80000]	lr: 1.078e-04, eta: 6:58:49, time: 0.341, data_time: 0.008, memory: 12988, decode.loss_ce: 0.7491, decode.acc_seg: 73.2200, aux.loss_ce: 0.3773, aux.acc_seg: 68.2908, loss: 1.1264
2022-11-04 11:11:04,170 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 11:11:04,170 - mmseg - INFO - Iter [9000/80000]	lr: 1.078e-04, eta: 6:58:27, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7084, decode.acc_seg: 73.9704, aux.loss_ce: 0.3514, aux.acc_seg: 69.6012, loss: 1.0598
2022-11-04 11:11:21,223 - mmseg - INFO - Iter [9050/80000]	lr: 1.077e-04, eta: 6:58:04, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7262, decode.acc_seg: 73.6589, aux.loss_ce: 0.3572, aux.acc_seg: 69.5597, loss: 1.0834
2022-11-04 11:11:38,538 - mmseg - INFO - Iter [9100/80000]	lr: 1.076e-04, eta: 6:57:44, time: 0.346, data_time: 0.008, memory: 12988, decode.loss_ce: 0.7327, decode.acc_seg: 73.3256, aux.loss_ce: 0.3623, aux.acc_seg: 68.9046, loss: 1.0951
2022-11-04 11:11:55,559 - mmseg - INFO - Iter [9150/80000]	lr: 1.076e-04, eta: 6:57:21, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7454, decode.acc_seg: 73.4545, aux.loss_ce: 0.3662, aux.acc_seg: 69.2296, loss: 1.1116
2022-11-04 11:12:12,801 - mmseg - INFO - Iter [9200/80000]	lr: 1.075e-04, eta: 6:57:00, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7430, decode.acc_seg: 72.5316, aux.loss_ce: 0.3619, aux.acc_seg: 68.3013, loss: 1.1049
2022-11-04 11:12:30,086 - mmseg - INFO - Iter [9250/80000]	lr: 1.074e-04, eta: 6:56:39, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.7565, decode.acc_seg: 72.7177, aux.loss_ce: 0.3674, aux.acc_seg: 68.9612, loss: 1.1239
2022-11-04 11:12:47,171 - mmseg - INFO - Iter [9300/80000]	lr: 1.074e-04, eta: 6:56:17, time: 0.342, data_time: 0.008, memory: 12988, decode.loss_ce: 0.7522, decode.acc_seg: 72.7055, aux.loss_ce: 0.3647, aux.acc_seg: 68.9306, loss: 1.1169
2022-11-04 11:13:04,398 - mmseg - INFO - Iter [9350/80000]	lr: 1.073e-04, eta: 6:55:56, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7210, decode.acc_seg: 73.4011, aux.loss_ce: 0.3561, aux.acc_seg: 69.1082, loss: 1.0771
2022-11-04 11:13:21,570 - mmseg - INFO - Iter [9400/80000]	lr: 1.072e-04, eta: 6:55:35, time: 0.343, data_time: 0.008, memory: 12988, decode.loss_ce: 0.7136, decode.acc_seg: 73.9934, aux.loss_ce: 0.3532, aux.acc_seg: 69.6155, loss: 1.0668
2022-11-04 11:13:38,781 - mmseg - INFO - Iter [9450/80000]	lr: 1.072e-04, eta: 6:55:14, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7322, decode.acc_seg: 73.3236, aux.loss_ce: 0.3613, aux.acc_seg: 69.0412, loss: 1.0934
2022-11-04 11:13:58,878 - mmseg - INFO - Iter [9500/80000]	lr: 1.071e-04, eta: 6:55:14, time: 0.402, data_time: 0.075, memory: 12988, decode.loss_ce: 0.7584, decode.acc_seg: 72.8514, aux.loss_ce: 0.3746, aux.acc_seg: 68.4046, loss: 1.1330
2022-11-04 11:14:16,138 - mmseg - INFO - Iter [9550/80000]	lr: 1.070e-04, eta: 6:54:54, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7325, decode.acc_seg: 73.3265, aux.loss_ce: 0.3635, aux.acc_seg: 69.1355, loss: 1.0960
2022-11-04 11:14:33,318 - mmseg - INFO - Iter [9600/80000]	lr: 1.070e-04, eta: 6:54:32, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7107, decode.acc_seg: 73.6740, aux.loss_ce: 0.3485, aux.acc_seg: 69.8371, loss: 1.0592
2022-11-04 11:14:50,318 - mmseg - INFO - Iter [9650/80000]	lr: 1.069e-04, eta: 6:54:10, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7315, decode.acc_seg: 73.5394, aux.loss_ce: 0.3636, aux.acc_seg: 69.1733, loss: 1.0951
2022-11-04 11:15:07,259 - mmseg - INFO - Iter [9700/80000]	lr: 1.068e-04, eta: 6:53:47, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.7054, decode.acc_seg: 73.8942, aux.loss_ce: 0.3508, aux.acc_seg: 69.4498, loss: 1.0562
2022-11-04 11:15:24,370 - mmseg - INFO - Iter [9750/80000]	lr: 1.068e-04, eta: 6:53:25, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6986, decode.acc_seg: 74.2789, aux.loss_ce: 0.3463, aux.acc_seg: 70.1523, loss: 1.0449
2022-11-04 11:15:41,445 - mmseg - INFO - Iter [9800/80000]	lr: 1.067e-04, eta: 6:53:04, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7346, decode.acc_seg: 73.5726, aux.loss_ce: 0.3624, aux.acc_seg: 69.3280, loss: 1.0970
2022-11-04 11:15:58,699 - mmseg - INFO - Iter [9850/80000]	lr: 1.066e-04, eta: 6:52:43, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7301, decode.acc_seg: 73.7925, aux.loss_ce: 0.3587, aux.acc_seg: 69.9578, loss: 1.0888
2022-11-04 11:16:15,735 - mmseg - INFO - Iter [9900/80000]	lr: 1.065e-04, eta: 6:52:21, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.7142, decode.acc_seg: 73.6168, aux.loss_ce: 0.3488, aux.acc_seg: 69.4868, loss: 1.0630
2022-11-04 11:16:32,786 - mmseg - INFO - Iter [9950/80000]	lr: 1.065e-04, eta: 6:51:59, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7272, decode.acc_seg: 73.4588, aux.loss_ce: 0.3601, aux.acc_seg: 68.8188, loss: 1.0873
2022-11-04 11:16:50,004 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 11:16:50,004 - mmseg - INFO - Iter [10000/80000]	lr: 1.064e-04, eta: 6:51:39, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7174, decode.acc_seg: 73.4820, aux.loss_ce: 0.3579, aux.acc_seg: 68.8485, loss: 1.0753
2022-11-04 11:17:06,988 - mmseg - INFO - Iter [10050/80000]	lr: 1.063e-04, eta: 6:51:16, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7243, decode.acc_seg: 73.8578, aux.loss_ce: 0.3542, aux.acc_seg: 69.8020, loss: 1.0785
2022-11-04 11:17:27,174 - mmseg - INFO - Iter [10100/80000]	lr: 1.063e-04, eta: 6:51:16, time: 0.404, data_time: 0.067, memory: 12988, decode.loss_ce: 0.7336, decode.acc_seg: 73.3451, aux.loss_ce: 0.3624, aux.acc_seg: 68.8034, loss: 1.0960
2022-11-04 11:17:44,281 - mmseg - INFO - Iter [10150/80000]	lr: 1.062e-04, eta: 6:50:55, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6941, decode.acc_seg: 74.1061, aux.loss_ce: 0.3439, aux.acc_seg: 69.8908, loss: 1.0381
2022-11-04 11:18:01,322 - mmseg - INFO - Iter [10200/80000]	lr: 1.061e-04, eta: 6:50:33, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7053, decode.acc_seg: 74.7228, aux.loss_ce: 0.3589, aux.acc_seg: 69.9105, loss: 1.0641
2022-11-04 11:18:18,303 - mmseg - INFO - Iter [10250/80000]	lr: 1.061e-04, eta: 6:50:11, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.7279, decode.acc_seg: 73.4850, aux.loss_ce: 0.3588, aux.acc_seg: 69.1147, loss: 1.0866
2022-11-04 11:18:35,205 - mmseg - INFO - Iter [10300/80000]	lr: 1.060e-04, eta: 6:49:48, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6987, decode.acc_seg: 74.1557, aux.loss_ce: 0.3433, aux.acc_seg: 70.0567, loss: 1.0421
2022-11-04 11:18:52,254 - mmseg - INFO - Iter [10350/80000]	lr: 1.059e-04, eta: 6:49:27, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7103, decode.acc_seg: 74.1993, aux.loss_ce: 0.3549, aux.acc_seg: 69.7108, loss: 1.0652
2022-11-04 11:19:09,499 - mmseg - INFO - Iter [10400/80000]	lr: 1.059e-04, eta: 6:49:07, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7190, decode.acc_seg: 73.7977, aux.loss_ce: 0.3640, aux.acc_seg: 68.7487, loss: 1.0829
2022-11-04 11:19:26,612 - mmseg - INFO - Iter [10450/80000]	lr: 1.058e-04, eta: 6:48:45, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7069, decode.acc_seg: 74.1753, aux.loss_ce: 0.3505, aux.acc_seg: 70.0605, loss: 1.0574
2022-11-04 11:19:43,748 - mmseg - INFO - Iter [10500/80000]	lr: 1.057e-04, eta: 6:48:24, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7061, decode.acc_seg: 74.1886, aux.loss_ce: 0.3526, aux.acc_seg: 69.6047, loss: 1.0587
2022-11-04 11:20:01,016 - mmseg - INFO - Iter [10550/80000]	lr: 1.057e-04, eta: 6:48:04, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7235, decode.acc_seg: 73.5477, aux.loss_ce: 0.3603, aux.acc_seg: 69.0076, loss: 1.0837
2022-11-04 11:20:18,399 - mmseg - INFO - Iter [10600/80000]	lr: 1.056e-04, eta: 6:47:45, time: 0.348, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7028, decode.acc_seg: 74.3748, aux.loss_ce: 0.3458, aux.acc_seg: 70.1714, loss: 1.0485
2022-11-04 11:20:35,783 - mmseg - INFO - Iter [10650/80000]	lr: 1.055e-04, eta: 6:47:26, time: 0.348, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7229, decode.acc_seg: 73.8106, aux.loss_ce: 0.3585, aux.acc_seg: 69.2061, loss: 1.0814
2022-11-04 11:20:52,697 - mmseg - INFO - Iter [10700/80000]	lr: 1.055e-04, eta: 6:47:04, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7024, decode.acc_seg: 74.1177, aux.loss_ce: 0.3537, aux.acc_seg: 69.6700, loss: 1.0561
2022-11-04 11:21:12,791 - mmseg - INFO - Iter [10750/80000]	lr: 1.054e-04, eta: 6:47:02, time: 0.402, data_time: 0.063, memory: 12988, decode.loss_ce: 0.7056, decode.acc_seg: 74.3059, aux.loss_ce: 0.3506, aux.acc_seg: 69.7490, loss: 1.0561
2022-11-04 11:21:29,753 - mmseg - INFO - Iter [10800/80000]	lr: 1.053e-04, eta: 6:46:40, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6946, decode.acc_seg: 74.8495, aux.loss_ce: 0.3439, aux.acc_seg: 70.6255, loss: 1.0385
2022-11-04 11:21:46,546 - mmseg - INFO - Iter [10850/80000]	lr: 1.052e-04, eta: 6:46:17, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7209, decode.acc_seg: 73.7926, aux.loss_ce: 0.3609, aux.acc_seg: 68.9550, loss: 1.0818
2022-11-04 11:22:03,599 - mmseg - INFO - Iter [10900/80000]	lr: 1.052e-04, eta: 6:45:56, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6965, decode.acc_seg: 74.1644, aux.loss_ce: 0.3493, aux.acc_seg: 69.5937, loss: 1.0458
2022-11-04 11:22:20,490 - mmseg - INFO - Iter [10950/80000]	lr: 1.051e-04, eta: 6:45:34, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7104, decode.acc_seg: 73.8065, aux.loss_ce: 0.3554, aux.acc_seg: 69.2484, loss: 1.0658
2022-11-04 11:22:37,500 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 11:22:37,500 - mmseg - INFO - Iter [11000/80000]	lr: 1.050e-04, eta: 6:45:12, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6918, decode.acc_seg: 74.5860, aux.loss_ce: 0.3435, aux.acc_seg: 70.1899, loss: 1.0353
2022-11-04 11:22:54,544 - mmseg - INFO - Iter [11050/80000]	lr: 1.050e-04, eta: 6:44:51, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6984, decode.acc_seg: 75.0291, aux.loss_ce: 0.3517, aux.acc_seg: 70.1118, loss: 1.0501
2022-11-04 11:23:11,563 - mmseg - INFO - Iter [11100/80000]	lr: 1.049e-04, eta: 6:44:30, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6995, decode.acc_seg: 74.7248, aux.loss_ce: 0.3489, aux.acc_seg: 70.4012, loss: 1.0484
2022-11-04 11:23:28,736 - mmseg - INFO - Iter [11150/80000]	lr: 1.048e-04, eta: 6:44:09, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6972, decode.acc_seg: 74.1834, aux.loss_ce: 0.3459, aux.acc_seg: 69.8671, loss: 1.0431
2022-11-04 11:23:45,739 - mmseg - INFO - Iter [11200/80000]	lr: 1.048e-04, eta: 6:43:48, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6793, decode.acc_seg: 74.7366, aux.loss_ce: 0.3388, aux.acc_seg: 70.4911, loss: 1.0181
2022-11-04 11:24:02,933 - mmseg - INFO - Iter [11250/80000]	lr: 1.047e-04, eta: 6:43:28, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6881, decode.acc_seg: 74.4882, aux.loss_ce: 0.3417, aux.acc_seg: 69.9357, loss: 1.0298
2022-11-04 11:24:20,000 - mmseg - INFO - Iter [11300/80000]	lr: 1.046e-04, eta: 6:43:07, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.7165, decode.acc_seg: 73.9700, aux.loss_ce: 0.3542, aux.acc_seg: 69.3993, loss: 1.0707
2022-11-04 11:24:37,363 - mmseg - INFO - Iter [11350/80000]	lr: 1.046e-04, eta: 6:42:48, time: 0.347, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7092, decode.acc_seg: 73.9481, aux.loss_ce: 0.3533, aux.acc_seg: 69.3696, loss: 1.0625
2022-11-04 11:24:57,417 - mmseg - INFO - Iter [11400/80000]	lr: 1.045e-04, eta: 6:42:45, time: 0.401, data_time: 0.070, memory: 12988, decode.loss_ce: 0.6722, decode.acc_seg: 75.3920, aux.loss_ce: 0.3395, aux.acc_seg: 70.6962, loss: 1.0117
2022-11-04 11:25:14,470 - mmseg - INFO - Iter [11450/80000]	lr: 1.044e-04, eta: 6:42:24, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6880, decode.acc_seg: 74.3425, aux.loss_ce: 0.3487, aux.acc_seg: 69.9913, loss: 1.0367
2022-11-04 11:25:31,502 - mmseg - INFO - Iter [11500/80000]	lr: 1.044e-04, eta: 6:42:03, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6917, decode.acc_seg: 74.6963, aux.loss_ce: 0.3476, aux.acc_seg: 70.0014, loss: 1.0393
2022-11-04 11:25:48,516 - mmseg - INFO - Iter [11550/80000]	lr: 1.043e-04, eta: 6:41:42, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6885, decode.acc_seg: 74.5132, aux.loss_ce: 0.3431, aux.acc_seg: 69.7023, loss: 1.0315
2022-11-04 11:26:05,540 - mmseg - INFO - Iter [11600/80000]	lr: 1.042e-04, eta: 6:41:21, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6733, decode.acc_seg: 75.0654, aux.loss_ce: 0.3355, aux.acc_seg: 70.5432, loss: 1.0089
2022-11-04 11:26:22,500 - mmseg - INFO - Iter [11650/80000]	lr: 1.042e-04, eta: 6:40:59, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6889, decode.acc_seg: 74.9484, aux.loss_ce: 0.3492, aux.acc_seg: 70.0163, loss: 1.0382
2022-11-04 11:26:39,435 - mmseg - INFO - Iter [11700/80000]	lr: 1.041e-04, eta: 6:40:38, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6822, decode.acc_seg: 75.0212, aux.loss_ce: 0.3413, aux.acc_seg: 70.5813, loss: 1.0234
2022-11-04 11:26:56,301 - mmseg - INFO - Iter [11750/80000]	lr: 1.040e-04, eta: 6:40:16, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6942, decode.acc_seg: 74.4051, aux.loss_ce: 0.3463, aux.acc_seg: 69.8394, loss: 1.0405
2022-11-04 11:27:13,489 - mmseg - INFO - Iter [11800/80000]	lr: 1.039e-04, eta: 6:39:56, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6829, decode.acc_seg: 74.7776, aux.loss_ce: 0.3414, aux.acc_seg: 70.6024, loss: 1.0243
2022-11-04 11:27:30,441 - mmseg - INFO - Iter [11850/80000]	lr: 1.039e-04, eta: 6:39:35, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7000, decode.acc_seg: 74.8734, aux.loss_ce: 0.3512, aux.acc_seg: 70.3870, loss: 1.0512
2022-11-04 11:27:47,504 - mmseg - INFO - Iter [11900/80000]	lr: 1.038e-04, eta: 6:39:14, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.7162, decode.acc_seg: 74.1012, aux.loss_ce: 0.3633, aux.acc_seg: 69.3308, loss: 1.0795
2022-11-04 11:28:04,278 - mmseg - INFO - Iter [11950/80000]	lr: 1.037e-04, eta: 6:38:52, time: 0.335, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6852, decode.acc_seg: 74.6861, aux.loss_ce: 0.3394, aux.acc_seg: 70.5696, loss: 1.0246
2022-11-04 11:28:24,319 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 11:28:24,320 - mmseg - INFO - Iter [12000/80000]	lr: 1.037e-04, eta: 6:38:48, time: 0.401, data_time: 0.063, memory: 12988, decode.loss_ce: 0.7050, decode.acc_seg: 73.6740, aux.loss_ce: 0.3462, aux.acc_seg: 69.6565, loss: 1.0512
2022-11-04 11:28:41,304 - mmseg - INFO - Iter [12050/80000]	lr: 1.036e-04, eta: 6:38:27, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6866, decode.acc_seg: 74.8769, aux.loss_ce: 0.3477, aux.acc_seg: 70.1928, loss: 1.0343
2022-11-04 11:28:58,286 - mmseg - INFO - Iter [12100/80000]	lr: 1.035e-04, eta: 6:38:06, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6719, decode.acc_seg: 75.1035, aux.loss_ce: 0.3438, aux.acc_seg: 70.0988, loss: 1.0157
2022-11-04 11:29:15,367 - mmseg - INFO - Iter [12150/80000]	lr: 1.035e-04, eta: 6:37:46, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6665, decode.acc_seg: 75.2920, aux.loss_ce: 0.3384, aux.acc_seg: 70.6222, loss: 1.0049
2022-11-04 11:29:32,227 - mmseg - INFO - Iter [12200/80000]	lr: 1.034e-04, eta: 6:37:24, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6784, decode.acc_seg: 75.0936, aux.loss_ce: 0.3439, aux.acc_seg: 70.7987, loss: 1.0223
2022-11-04 11:29:49,214 - mmseg - INFO - Iter [12250/80000]	lr: 1.033e-04, eta: 6:37:03, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.7036, decode.acc_seg: 73.9573, aux.loss_ce: 0.3498, aux.acc_seg: 69.3925, loss: 1.0534
2022-11-04 11:30:06,406 - mmseg - INFO - Iter [12300/80000]	lr: 1.033e-04, eta: 6:36:44, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6692, decode.acc_seg: 74.7925, aux.loss_ce: 0.3396, aux.acc_seg: 70.3112, loss: 1.0088
2022-11-04 11:30:23,462 - mmseg - INFO - Iter [12350/80000]	lr: 1.032e-04, eta: 6:36:23, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6811, decode.acc_seg: 74.7195, aux.loss_ce: 0.3396, aux.acc_seg: 70.5357, loss: 1.0207
2022-11-04 11:30:40,680 - mmseg - INFO - Iter [12400/80000]	lr: 1.031e-04, eta: 6:36:04, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6726, decode.acc_seg: 74.9504, aux.loss_ce: 0.3401, aux.acc_seg: 69.9887, loss: 1.0127
2022-11-04 11:30:57,914 - mmseg - INFO - Iter [12450/80000]	lr: 1.031e-04, eta: 6:35:44, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6721, decode.acc_seg: 75.2894, aux.loss_ce: 0.3383, aux.acc_seg: 70.5578, loss: 1.0104
2022-11-04 11:31:14,684 - mmseg - INFO - Iter [12500/80000]	lr: 1.030e-04, eta: 6:35:22, time: 0.335, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6694, decode.acc_seg: 74.5668, aux.loss_ce: 0.3278, aux.acc_seg: 70.7841, loss: 0.9972
2022-11-04 11:31:31,888 - mmseg - INFO - Iter [12550/80000]	lr: 1.029e-04, eta: 6:35:03, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6869, decode.acc_seg: 74.7017, aux.loss_ce: 0.3465, aux.acc_seg: 69.9507, loss: 1.0334
2022-11-04 11:31:48,806 - mmseg - INFO - Iter [12600/80000]	lr: 1.028e-04, eta: 6:34:42, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6900, decode.acc_seg: 74.7034, aux.loss_ce: 0.3442, aux.acc_seg: 70.4191, loss: 1.0342
2022-11-04 11:32:09,475 - mmseg - INFO - Iter [12650/80000]	lr: 1.028e-04, eta: 6:34:41, time: 0.413, data_time: 0.076, memory: 12988, decode.loss_ce: 0.6519, decode.acc_seg: 76.2722, aux.loss_ce: 0.3300, aux.acc_seg: 71.5426, loss: 0.9818
2022-11-04 11:32:26,946 - mmseg - INFO - Iter [12700/80000]	lr: 1.027e-04, eta: 6:34:22, time: 0.349, data_time: 0.017, memory: 12988, decode.loss_ce: 0.6804, decode.acc_seg: 74.6607, aux.loss_ce: 0.3437, aux.acc_seg: 69.9632, loss: 1.0241
2022-11-04 11:32:43,927 - mmseg - INFO - Iter [12750/80000]	lr: 1.026e-04, eta: 6:34:02, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6553, decode.acc_seg: 75.7270, aux.loss_ce: 0.3336, aux.acc_seg: 70.9587, loss: 0.9889
2022-11-04 11:33:00,807 - mmseg - INFO - Iter [12800/80000]	lr: 1.026e-04, eta: 6:33:40, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6764, decode.acc_seg: 75.2046, aux.loss_ce: 0.3443, aux.acc_seg: 70.4821, loss: 1.0207
2022-11-04 11:33:17,727 - mmseg - INFO - Iter [12850/80000]	lr: 1.025e-04, eta: 6:33:19, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6636, decode.acc_seg: 75.4296, aux.loss_ce: 0.3363, aux.acc_seg: 71.0208, loss: 0.9999
2022-11-04 11:33:34,677 - mmseg - INFO - Iter [12900/80000]	lr: 1.024e-04, eta: 6:32:59, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6697, decode.acc_seg: 74.9305, aux.loss_ce: 0.3356, aux.acc_seg: 70.6085, loss: 1.0053
2022-11-04 11:33:51,902 - mmseg - INFO - Iter [12950/80000]	lr: 1.024e-04, eta: 6:32:39, time: 0.344, data_time: 0.011, memory: 12988, decode.loss_ce: 0.6748, decode.acc_seg: 75.0295, aux.loss_ce: 0.3427, aux.acc_seg: 70.2041, loss: 1.0174
2022-11-04 11:34:09,012 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 11:34:09,012 - mmseg - INFO - Iter [13000/80000]	lr: 1.023e-04, eta: 6:32:19, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6765, decode.acc_seg: 75.0087, aux.loss_ce: 0.3448, aux.acc_seg: 69.9998, loss: 1.0214
2022-11-04 11:34:26,100 - mmseg - INFO - Iter [13050/80000]	lr: 1.022e-04, eta: 6:31:59, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6564, decode.acc_seg: 75.5746, aux.loss_ce: 0.3306, aux.acc_seg: 70.9345, loss: 0.9870
2022-11-04 11:34:43,133 - mmseg - INFO - Iter [13100/80000]	lr: 1.022e-04, eta: 6:31:39, time: 0.341, data_time: 0.008, memory: 12988, decode.loss_ce: 0.6711, decode.acc_seg: 75.6236, aux.loss_ce: 0.3410, aux.acc_seg: 70.7002, loss: 1.0121
2022-11-04 11:35:00,227 - mmseg - INFO - Iter [13150/80000]	lr: 1.021e-04, eta: 6:31:19, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6772, decode.acc_seg: 75.3637, aux.loss_ce: 0.3404, aux.acc_seg: 71.0297, loss: 1.0176
2022-11-04 11:35:17,197 - mmseg - INFO - Iter [13200/80000]	lr: 1.020e-04, eta: 6:30:59, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6845, decode.acc_seg: 74.7224, aux.loss_ce: 0.3359, aux.acc_seg: 70.6093, loss: 1.0204
2022-11-04 11:35:33,852 - mmseg - INFO - Iter [13250/80000]	lr: 1.020e-04, eta: 6:30:36, time: 0.333, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6976, decode.acc_seg: 74.3234, aux.loss_ce: 0.3488, aux.acc_seg: 69.8944, loss: 1.0463
2022-11-04 11:35:54,106 - mmseg - INFO - Iter [13300/80000]	lr: 1.019e-04, eta: 6:30:32, time: 0.405, data_time: 0.061, memory: 12988, decode.loss_ce: 0.6591, decode.acc_seg: 75.4448, aux.loss_ce: 0.3325, aux.acc_seg: 71.0100, loss: 0.9916
2022-11-04 11:36:11,028 - mmseg - INFO - Iter [13350/80000]	lr: 1.018e-04, eta: 6:30:12, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6520, decode.acc_seg: 75.6183, aux.loss_ce: 0.3347, aux.acc_seg: 70.7088, loss: 0.9867
2022-11-04 11:36:28,058 - mmseg - INFO - Iter [13400/80000]	lr: 1.017e-04, eta: 6:29:51, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6580, decode.acc_seg: 75.4141, aux.loss_ce: 0.3314, aux.acc_seg: 70.7619, loss: 0.9894
2022-11-04 11:36:45,262 - mmseg - INFO - Iter [13450/80000]	lr: 1.017e-04, eta: 6:29:32, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6864, decode.acc_seg: 74.7681, aux.loss_ce: 0.3498, aux.acc_seg: 69.5825, loss: 1.0361
2022-11-04 11:37:02,393 - mmseg - INFO - Iter [13500/80000]	lr: 1.016e-04, eta: 6:29:12, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6677, decode.acc_seg: 75.3025, aux.loss_ce: 0.3388, aux.acc_seg: 70.4820, loss: 1.0064
2022-11-04 11:37:19,652 - mmseg - INFO - Iter [13550/80000]	lr: 1.015e-04, eta: 6:28:53, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6600, decode.acc_seg: 75.6724, aux.loss_ce: 0.3334, aux.acc_seg: 71.3852, loss: 0.9934
2022-11-04 11:37:36,662 - mmseg - INFO - Iter [13600/80000]	lr: 1.015e-04, eta: 6:28:33, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6593, decode.acc_seg: 75.2980, aux.loss_ce: 0.3371, aux.acc_seg: 70.2408, loss: 0.9964
2022-11-04 11:37:53,703 - mmseg - INFO - Iter [13650/80000]	lr: 1.014e-04, eta: 6:28:13, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6675, decode.acc_seg: 75.4788, aux.loss_ce: 0.3320, aux.acc_seg: 71.3870, loss: 0.9995
2022-11-04 11:38:10,667 - mmseg - INFO - Iter [13700/80000]	lr: 1.013e-04, eta: 6:27:53, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6672, decode.acc_seg: 75.3393, aux.loss_ce: 0.3378, aux.acc_seg: 70.5123, loss: 1.0051
2022-11-04 11:38:27,675 - mmseg - INFO - Iter [13750/80000]	lr: 1.013e-04, eta: 6:27:32, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6783, decode.acc_seg: 74.8170, aux.loss_ce: 0.3414, aux.acc_seg: 70.1058, loss: 1.0197
2022-11-04 11:38:44,490 - mmseg - INFO - Iter [13800/80000]	lr: 1.012e-04, eta: 6:27:11, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6566, decode.acc_seg: 75.6332, aux.loss_ce: 0.3317, aux.acc_seg: 71.4438, loss: 0.9883
2022-11-04 11:39:01,749 - mmseg - INFO - Iter [13850/80000]	lr: 1.011e-04, eta: 6:26:52, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6475, decode.acc_seg: 75.8083, aux.loss_ce: 0.3247, aux.acc_seg: 71.4068, loss: 0.9722
2022-11-04 11:39:21,836 - mmseg - INFO - Iter [13900/80000]	lr: 1.011e-04, eta: 6:26:47, time: 0.402, data_time: 0.065, memory: 12988, decode.loss_ce: 0.6595, decode.acc_seg: 75.6491, aux.loss_ce: 0.3339, aux.acc_seg: 70.8330, loss: 0.9934
2022-11-04 11:39:39,208 - mmseg - INFO - Iter [13950/80000]	lr: 1.010e-04, eta: 6:26:29, time: 0.347, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6469, decode.acc_seg: 75.9515, aux.loss_ce: 0.3336, aux.acc_seg: 70.7625, loss: 0.9805
2022-11-04 11:39:56,227 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 11:39:56,228 - mmseg - INFO - Iter [14000/80000]	lr: 1.009e-04, eta: 6:26:09, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6496, decode.acc_seg: 75.8914, aux.loss_ce: 0.3314, aux.acc_seg: 71.0925, loss: 0.9809
2022-11-04 11:40:13,331 - mmseg - INFO - Iter [14050/80000]	lr: 1.009e-04, eta: 6:25:49, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6241, decode.acc_seg: 76.7300, aux.loss_ce: 0.3237, aux.acc_seg: 71.7493, loss: 0.9478
2022-11-04 11:40:30,263 - mmseg - INFO - Iter [14100/80000]	lr: 1.008e-04, eta: 6:25:28, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6814, decode.acc_seg: 75.0510, aux.loss_ce: 0.3405, aux.acc_seg: 70.5106, loss: 1.0219
2022-11-04 11:40:47,204 - mmseg - INFO - Iter [14150/80000]	lr: 1.007e-04, eta: 6:25:08, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6465, decode.acc_seg: 76.1255, aux.loss_ce: 0.3334, aux.acc_seg: 70.7291, loss: 0.9799
2022-11-04 11:41:04,255 - mmseg - INFO - Iter [14200/80000]	lr: 1.006e-04, eta: 6:24:48, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6272, decode.acc_seg: 76.2837, aux.loss_ce: 0.3244, aux.acc_seg: 71.5133, loss: 0.9515
2022-11-04 11:41:21,170 - mmseg - INFO - Iter [14250/80000]	lr: 1.006e-04, eta: 6:24:28, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6590, decode.acc_seg: 75.4622, aux.loss_ce: 0.3337, aux.acc_seg: 70.7473, loss: 0.9927
2022-11-04 11:41:38,528 - mmseg - INFO - Iter [14300/80000]	lr: 1.005e-04, eta: 6:24:09, time: 0.347, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6549, decode.acc_seg: 75.7011, aux.loss_ce: 0.3312, aux.acc_seg: 71.1926, loss: 0.9861
2022-11-04 11:41:55,265 - mmseg - INFO - Iter [14350/80000]	lr: 1.004e-04, eta: 6:23:48, time: 0.335, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6330, decode.acc_seg: 76.2811, aux.loss_ce: 0.3236, aux.acc_seg: 71.4623, loss: 0.9566
2022-11-04 11:42:12,236 - mmseg - INFO - Iter [14400/80000]	lr: 1.004e-04, eta: 6:23:28, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6742, decode.acc_seg: 75.0138, aux.loss_ce: 0.3372, aux.acc_seg: 70.5614, loss: 1.0114
2022-11-04 11:42:29,500 - mmseg - INFO - Iter [14450/80000]	lr: 1.003e-04, eta: 6:23:09, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6449, decode.acc_seg: 76.0459, aux.loss_ce: 0.3228, aux.acc_seg: 72.0796, loss: 0.9676
2022-11-04 11:42:46,491 - mmseg - INFO - Iter [14500/80000]	lr: 1.002e-04, eta: 6:22:49, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6751, decode.acc_seg: 75.0435, aux.loss_ce: 0.3442, aux.acc_seg: 70.2355, loss: 1.0193
2022-11-04 11:43:06,269 - mmseg - INFO - Iter [14550/80000]	lr: 1.002e-04, eta: 6:22:42, time: 0.395, data_time: 0.063, memory: 12988, decode.loss_ce: 0.6343, decode.acc_seg: 76.8276, aux.loss_ce: 0.3219, aux.acc_seg: 72.1012, loss: 0.9562
2022-11-04 11:43:23,186 - mmseg - INFO - Iter [14600/80000]	lr: 1.001e-04, eta: 6:22:21, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6656, decode.acc_seg: 75.4155, aux.loss_ce: 0.3357, aux.acc_seg: 70.7908, loss: 1.0013
2022-11-04 11:43:40,549 - mmseg - INFO - Iter [14650/80000]	lr: 1.000e-04, eta: 6:22:03, time: 0.347, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6561, decode.acc_seg: 75.7652, aux.loss_ce: 0.3343, aux.acc_seg: 70.9701, loss: 0.9904
2022-11-04 11:43:57,478 - mmseg - INFO - Iter [14700/80000]	lr: 9.996e-05, eta: 6:21:43, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6487, decode.acc_seg: 75.6376, aux.loss_ce: 0.3400, aux.acc_seg: 70.6301, loss: 0.9888
2022-11-04 11:44:14,525 - mmseg - INFO - Iter [14750/80000]	lr: 9.989e-05, eta: 6:21:23, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6401, decode.acc_seg: 75.6997, aux.loss_ce: 0.3312, aux.acc_seg: 70.4850, loss: 0.9713
2022-11-04 11:44:31,667 - mmseg - INFO - Iter [14800/80000]	lr: 9.982e-05, eta: 6:21:04, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6624, decode.acc_seg: 75.5929, aux.loss_ce: 0.3370, aux.acc_seg: 70.7994, loss: 0.9994
2022-11-04 11:44:48,669 - mmseg - INFO - Iter [14850/80000]	lr: 9.975e-05, eta: 6:20:44, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6509, decode.acc_seg: 76.0688, aux.loss_ce: 0.3337, aux.acc_seg: 71.0248, loss: 0.9846
2022-11-04 11:45:05,899 - mmseg - INFO - Iter [14900/80000]	lr: 9.968e-05, eta: 6:20:25, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6772, decode.acc_seg: 75.5800, aux.loss_ce: 0.3441, aux.acc_seg: 70.6299, loss: 1.0214
2022-11-04 11:45:22,967 - mmseg - INFO - Iter [14950/80000]	lr: 9.962e-05, eta: 6:20:06, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6384, decode.acc_seg: 76.2424, aux.loss_ce: 0.3239, aux.acc_seg: 71.4031, loss: 0.9623
2022-11-04 11:45:39,723 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 11:45:39,724 - mmseg - INFO - Iter [15000/80000]	lr: 9.955e-05, eta: 6:19:45, time: 0.335, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6480, decode.acc_seg: 75.7314, aux.loss_ce: 0.3331, aux.acc_seg: 71.0180, loss: 0.9811
2022-11-04 11:45:56,818 - mmseg - INFO - Iter [15050/80000]	lr: 9.948e-05, eta: 6:19:25, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6402, decode.acc_seg: 76.1380, aux.loss_ce: 0.3213, aux.acc_seg: 71.3807, loss: 0.9616
2022-11-04 11:46:13,931 - mmseg - INFO - Iter [15100/80000]	lr: 9.941e-05, eta: 6:19:06, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6552, decode.acc_seg: 75.9431, aux.loss_ce: 0.3343, aux.acc_seg: 71.2267, loss: 0.9895
2022-11-04 11:46:33,925 - mmseg - INFO - Iter [15150/80000]	lr: 9.934e-05, eta: 6:18:59, time: 0.400, data_time: 0.066, memory: 12988, decode.loss_ce: 0.6492, decode.acc_seg: 75.7612, aux.loss_ce: 0.3303, aux.acc_seg: 71.1774, loss: 0.9795
2022-11-04 11:46:51,026 - mmseg - INFO - Iter [15200/80000]	lr: 9.927e-05, eta: 6:18:40, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6181, decode.acc_seg: 76.8330, aux.loss_ce: 0.3224, aux.acc_seg: 71.8585, loss: 0.9405
2022-11-04 11:47:08,111 - mmseg - INFO - Iter [15250/80000]	lr: 9.920e-05, eta: 6:18:20, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6207, decode.acc_seg: 76.8727, aux.loss_ce: 0.3187, aux.acc_seg: 72.3009, loss: 0.9393
2022-11-04 11:47:25,131 - mmseg - INFO - Iter [15300/80000]	lr: 9.913e-05, eta: 6:18:01, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6320, decode.acc_seg: 76.4546, aux.loss_ce: 0.3274, aux.acc_seg: 71.3079, loss: 0.9594
2022-11-04 11:47:42,159 - mmseg - INFO - Iter [15350/80000]	lr: 9.906e-05, eta: 6:17:41, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6336, decode.acc_seg: 76.2505, aux.loss_ce: 0.3261, aux.acc_seg: 71.6042, loss: 0.9597
2022-11-04 11:47:59,337 - mmseg - INFO - Iter [15400/80000]	lr: 9.900e-05, eta: 6:17:22, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6479, decode.acc_seg: 75.8788, aux.loss_ce: 0.3308, aux.acc_seg: 70.8993, loss: 0.9787
2022-11-04 11:48:16,250 - mmseg - INFO - Iter [15450/80000]	lr: 9.893e-05, eta: 6:17:02, time: 0.338, data_time: 0.008, memory: 12988, decode.loss_ce: 0.6258, decode.acc_seg: 76.6275, aux.loss_ce: 0.3183, aux.acc_seg: 72.0393, loss: 0.9442
2022-11-04 11:48:33,071 - mmseg - INFO - Iter [15500/80000]	lr: 9.886e-05, eta: 6:16:41, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6429, decode.acc_seg: 75.9141, aux.loss_ce: 0.3261, aux.acc_seg: 71.5828, loss: 0.9689
2022-11-04 11:48:49,942 - mmseg - INFO - Iter [15550/80000]	lr: 9.879e-05, eta: 6:16:21, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6544, decode.acc_seg: 75.4949, aux.loss_ce: 0.3303, aux.acc_seg: 71.2159, loss: 0.9846
2022-11-04 11:49:07,159 - mmseg - INFO - Iter [15600/80000]	lr: 9.872e-05, eta: 6:16:02, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6521, decode.acc_seg: 75.5580, aux.loss_ce: 0.3369, aux.acc_seg: 70.2866, loss: 0.9890
2022-11-04 11:49:24,003 - mmseg - INFO - Iter [15650/80000]	lr: 9.865e-05, eta: 6:15:42, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6693, decode.acc_seg: 75.1748, aux.loss_ce: 0.3434, aux.acc_seg: 70.4421, loss: 1.0127
2022-11-04 11:49:40,925 - mmseg - INFO - Iter [15700/80000]	lr: 9.858e-05, eta: 6:15:22, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6104, decode.acc_seg: 77.2107, aux.loss_ce: 0.3109, aux.acc_seg: 72.9149, loss: 0.9213
2022-11-04 11:49:57,795 - mmseg - INFO - Iter [15750/80000]	lr: 9.851e-05, eta: 6:15:02, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6359, decode.acc_seg: 75.6769, aux.loss_ce: 0.3248, aux.acc_seg: 71.0403, loss: 0.9607
2022-11-04 11:50:18,143 - mmseg - INFO - Iter [15800/80000]	lr: 9.844e-05, eta: 6:14:56, time: 0.407, data_time: 0.066, memory: 12988, decode.loss_ce: 0.6346, decode.acc_seg: 76.6585, aux.loss_ce: 0.3235, aux.acc_seg: 71.7154, loss: 0.9581
2022-11-04 11:50:35,446 - mmseg - INFO - Iter [15850/80000]	lr: 9.837e-05, eta: 6:14:38, time: 0.346, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6207, decode.acc_seg: 76.9618, aux.loss_ce: 0.3185, aux.acc_seg: 72.0884, loss: 0.9392
2022-11-04 11:50:52,324 - mmseg - INFO - Iter [15900/80000]	lr: 9.831e-05, eta: 6:14:18, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6369, decode.acc_seg: 76.3891, aux.loss_ce: 0.3276, aux.acc_seg: 71.2666, loss: 0.9646
2022-11-04 11:51:09,604 - mmseg - INFO - Iter [15950/80000]	lr: 9.824e-05, eta: 6:13:59, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6325, decode.acc_seg: 76.4798, aux.loss_ce: 0.3226, aux.acc_seg: 71.9322, loss: 0.9551
2022-11-04 11:51:26,514 - mmseg - INFO - Saving checkpoint at 16000 iterations
2022-11-04 11:51:26,803 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 11:51:26,803 - mmseg - INFO - Iter [16000/80000]	lr: 9.817e-05, eta: 6:13:40, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6184, decode.acc_seg: 76.7474, aux.loss_ce: 0.3158, aux.acc_seg: 72.0665, loss: 0.9342
2022-11-04 11:51:36,006 - mmseg - INFO - per class results:
2022-11-04 11:51:36,013 - mmseg - INFO - 
+---------------------+-------+-------+
|        Class        |  IoU  |  Acc  |
+---------------------+-------+-------+
|         wall        | 65.13 |  82.7 |
|       building      | 74.89 | 90.12 |
|         sky         | 88.25 | 91.92 |
|        floor        | 70.08 | 83.47 |
|         tree        | 66.67 |  84.2 |
|       ceiling       | 73.63 | 83.08 |
|         road        | 74.61 | 84.73 |
|         bed         | 73.15 | 88.81 |
|      windowpane     | 47.85 |  63.3 |
|        grass        | 64.28 | 84.94 |
|       cabinet       | 44.94 |  62.1 |
|       sidewalk      | 49.42 | 70.62 |
|        person       | 63.31 | 82.32 |
|        earth        | 32.32 | 47.63 |
|         door        | 23.07 | 34.12 |
|        table        | 38.36 | 55.02 |
|       mountain      | 52.72 | 69.12 |
|        plant        |  42.1 |  51.1 |
|       curtain       | 53.56 | 71.85 |
|        chair        | 34.46 | 47.98 |
|         car         | 69.98 | 84.55 |
|        water        | 44.97 | 68.19 |
|       painting      | 52.49 | 70.23 |
|         sofa        | 44.11 | 62.71 |
|        shelf        | 30.25 | 48.66 |
|        house        | 32.27 | 45.48 |
|         sea         | 45.77 | 71.37 |
|        mirror       | 39.64 | 51.32 |
|         rug         | 43.67 | 51.46 |
|        field        | 25.49 | 32.97 |
|       armchair      | 21.81 | 39.76 |
|         seat        | 46.25 | 57.42 |
|        fence        | 21.72 |  34.9 |
|         desk        | 26.73 | 40.63 |
|         rock        | 44.37 |  64.6 |
|       wardrobe      | 35.16 | 60.02 |
|         lamp        | 34.14 | 45.66 |
|       bathtub       | 43.37 |  54.9 |
|       railing       | 22.75 | 35.45 |
|       cushion       | 27.86 | 36.29 |
|         base        | 14.32 | 21.52 |
|         box         |  8.86 | 10.74 |
|        column       | 23.63 | 29.42 |
|      signboard      | 20.76 | 26.75 |
|   chest of drawers  | 23.97 | 37.03 |
|       counter       | 29.62 | 36.01 |
|         sand        | 29.36 | 45.63 |
|         sink        | 39.99 | 54.39 |
|      skyscraper     | 63.88 |  80.9 |
|      fireplace      | 43.53 | 72.39 |
|     refrigerator    | 44.14 | 67.52 |
|      grandstand     | 38.18 | 65.86 |
|         path        |  8.32 | 10.32 |
|        stairs       | 14.29 | 15.21 |
|        runway       |  73.7 | 94.35 |
|         case        | 45.08 | 57.21 |
|      pool table     | 77.64 | 90.05 |
|        pillow       | 35.61 | 43.55 |
|     screen door     | 53.34 | 68.94 |
|       stairway      | 24.04 | 34.48 |
|        river        |  9.38 | 12.23 |
|        bridge       | 25.07 |  40.6 |
|       bookcase      | 27.54 | 52.49 |
|        blind        | 15.37 | 18.43 |
|     coffee table    | 37.82 | 63.99 |
|        toilet       | 60.74 | 75.43 |
|        flower       | 23.89 | 36.01 |
|         book        |  25.2 | 31.78 |
|         hill        |  6.18 |  7.66 |
|        bench        | 31.27 | 45.84 |
|      countertop     | 41.95 | 59.48 |
|        stove        | 43.29 | 60.66 |
|         palm        | 33.83 | 43.08 |
|    kitchen island   | 15.96 | 46.22 |
|       computer      | 41.49 | 54.41 |
|     swivel chair    | 29.27 | 38.27 |
|         boat        | 52.71 | 71.11 |
|         bar         | 12.22 | 13.99 |
|    arcade machine   | 29.92 | 32.58 |
|        hovel        | 30.62 | 38.11 |
|         bus         | 67.68 | 82.28 |
|        towel        | 33.17 | 46.26 |
|        light        | 10.07 | 10.61 |
|        truck        |  2.55 |  2.75 |
|        tower        | 34.03 | 40.32 |
|      chandelier     |  44.4 | 58.46 |
|        awning       | 13.63 | 15.65 |
|     streetlight     |  4.31 |  4.87 |
|        booth        | 27.92 | 31.52 |
| television receiver | 47.06 | 63.65 |
|       airplane      | 42.16 | 52.67 |
|      dirt track     |  7.58 |  7.8  |
|       apparel       |  9.45 | 11.29 |
|         pole        |  5.34 |  8.81 |
|         land        |  0.0  |  0.0  |
|      bannister      |  0.18 |  0.21 |
|      escalator      | 48.21 | 67.53 |
|       ottoman       |  6.99 |  7.29 |
|        bottle       |  2.49 |  2.63 |
|        buffet       | 22.26 |  29.0 |
|        poster       |  4.56 |  5.19 |
|        stage        |  7.18 | 10.72 |
|         van         |  3.63 |  3.86 |
|         ship        | 49.77 | 51.57 |
|       fountain      |  3.65 |  3.7  |
|    conveyer belt    | 50.83 | 85.54 |
|        canopy       |  8.94 | 13.52 |
|        washer       | 48.71 | 52.45 |
|      plaything      |  2.89 |  5.02 |
|    swimming pool    | 23.95 | 26.01 |
|        stool        |  4.97 |  5.63 |
|        barrel       | 38.83 | 58.29 |
|        basket       |  9.59 | 10.48 |
|      waterfall      | 51.12 | 61.69 |
|         tent        | 87.36 | 97.86 |
|         bag         |  0.0  |  0.0  |
|       minibike      | 37.56 | 46.85 |
|        cradle       | 55.16 | 84.75 |
|         oven        |  14.9 | 24.66 |
|         ball        | 14.39 | 19.37 |
|         food        | 47.48 |  60.2 |
|         step        |  0.0  |  0.0  |
|         tank        | 14.65 | 14.81 |
|      trade name     |  17.7 | 20.13 |
|      microwave      |  24.5 | 30.29 |
|         pot         | 20.33 | 24.57 |
|        animal       | 41.72 |  46.2 |
|       bicycle       | 34.54 | 53.15 |
|         lake        |  0.0  |  0.0  |
|      dishwasher     | 19.21 | 27.62 |
|        screen       | 58.76 | 75.76 |
|       blanket       |  0.0  |  0.0  |
|      sculpture      | 16.46 |  22.0 |
|         hood        | 20.08 | 25.39 |
|        sconce       |  6.57 |  6.74 |
|         vase        |  16.3 | 24.07 |
|    traffic light    |  6.81 |  8.38 |
|         tray        |  0.02 |  0.02 |
|        ashcan       | 15.29 | 18.05 |
|         fan         | 21.79 | 25.66 |
|         pier        | 14.63 | 15.68 |
|      crt screen     |  0.0  |  0.0  |
|        plate        | 19.71 | 21.68 |
|       monitor       | 27.65 | 67.24 |
|    bulletin board   | 28.23 | 30.86 |
|        shower       |  0.0  |  0.0  |
|       radiator      | 27.63 | 31.36 |
|        glass        |  0.58 |  0.59 |
|        clock        |  4.19 |  4.52 |
|         flag        |  8.32 |  8.45 |
+---------------------+-------+-------+
2022-11-04 11:51:36,013 - mmseg - INFO - Summary:
2022-11-04 11:51:36,014 - mmseg - INFO - 
+-------+-------+------+
|  aAcc |  mIoU | mAcc |
+-------+-------+------+
| 74.17 | 31.01 | 41.1 |
+-------+-------+------+
2022-11-04 11:51:36,015 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 11:51:36,015 - mmseg - INFO - Iter(val) [250]	aAcc: 0.7417, mIoU: 0.3101, mAcc: 0.4110, IoU.wall: 0.6513, IoU.building: 0.7489, IoU.sky: 0.8825, IoU.floor: 0.7008, IoU.tree: 0.6667, IoU.ceiling: 0.7363, IoU.road: 0.7461, IoU.bed : 0.7315, IoU.windowpane: 0.4785, IoU.grass: 0.6428, IoU.cabinet: 0.4494, IoU.sidewalk: 0.4942, IoU.person: 0.6331, IoU.earth: 0.3232, IoU.door: 0.2307, IoU.table: 0.3836, IoU.mountain: 0.5272, IoU.plant: 0.4210, IoU.curtain: 0.5356, IoU.chair: 0.3446, IoU.car: 0.6998, IoU.water: 0.4497, IoU.painting: 0.5249, IoU.sofa: 0.4411, IoU.shelf: 0.3025, IoU.house: 0.3227, IoU.sea: 0.4577, IoU.mirror: 0.3964, IoU.rug: 0.4367, IoU.field: 0.2549, IoU.armchair: 0.2181, IoU.seat: 0.4625, IoU.fence: 0.2172, IoU.desk: 0.2673, IoU.rock: 0.4437, IoU.wardrobe: 0.3516, IoU.lamp: 0.3414, IoU.bathtub: 0.4337, IoU.railing: 0.2275, IoU.cushion: 0.2786, IoU.base: 0.1432, IoU.box: 0.0886, IoU.column: 0.2363, IoU.signboard: 0.2076, IoU.chest of drawers: 0.2397, IoU.counter: 0.2962, IoU.sand: 0.2936, IoU.sink: 0.3999, IoU.skyscraper: 0.6388, IoU.fireplace: 0.4353, IoU.refrigerator: 0.4414, IoU.grandstand: 0.3818, IoU.path: 0.0832, IoU.stairs: 0.1429, IoU.runway: 0.7370, IoU.case: 0.4508, IoU.pool table: 0.7764, IoU.pillow: 0.3561, IoU.screen door: 0.5334, IoU.stairway: 0.2404, IoU.river: 0.0938, IoU.bridge: 0.2507, IoU.bookcase: 0.2754, IoU.blind: 0.1537, IoU.coffee table: 0.3782, IoU.toilet: 0.6074, IoU.flower: 0.2389, IoU.book: 0.2520, IoU.hill: 0.0618, IoU.bench: 0.3127, IoU.countertop: 0.4195, IoU.stove: 0.4329, IoU.palm: 0.3383, IoU.kitchen island: 0.1596, IoU.computer: 0.4149, IoU.swivel chair: 0.2927, IoU.boat: 0.5271, IoU.bar: 0.1222, IoU.arcade machine: 0.2992, IoU.hovel: 0.3062, IoU.bus: 0.6768, IoU.towel: 0.3317, IoU.light: 0.1007, IoU.truck: 0.0255, IoU.tower: 0.3403, IoU.chandelier: 0.4440, IoU.awning: 0.1363, IoU.streetlight: 0.0431, IoU.booth: 0.2792, IoU.television receiver: 0.4706, IoU.airplane: 0.4216, IoU.dirt track: 0.0758, IoU.apparel: 0.0945, IoU.pole: 0.0534, IoU.land: 0.0000, IoU.bannister: 0.0018, IoU.escalator: 0.4821, IoU.ottoman: 0.0699, IoU.bottle: 0.0249, IoU.buffet: 0.2226, IoU.poster: 0.0456, IoU.stage: 0.0718, IoU.van: 0.0363, IoU.ship: 0.4977, IoU.fountain: 0.0365, IoU.conveyer belt: 0.5083, IoU.canopy: 0.0894, IoU.washer: 0.4871, IoU.plaything: 0.0289, IoU.swimming pool: 0.2395, IoU.stool: 0.0497, IoU.barrel: 0.3883, IoU.basket: 0.0959, IoU.waterfall: 0.5112, IoU.tent: 0.8736, IoU.bag: 0.0000, IoU.minibike: 0.3756, IoU.cradle: 0.5516, IoU.oven: 0.1490, IoU.ball: 0.1439, IoU.food: 0.4748, IoU.step: 0.0000, IoU.tank: 0.1465, IoU.trade name: 0.1770, IoU.microwave: 0.2450, IoU.pot: 0.2033, IoU.animal: 0.4172, IoU.bicycle: 0.3454, IoU.lake: 0.0000, IoU.dishwasher: 0.1921, IoU.screen: 0.5876, IoU.blanket: 0.0000, IoU.sculpture: 0.1646, IoU.hood: 0.2008, IoU.sconce: 0.0657, IoU.vase: 0.1630, IoU.traffic light: 0.0681, IoU.tray: 0.0002, IoU.ashcan: 0.1529, IoU.fan: 0.2179, IoU.pier: 0.1463, IoU.crt screen: 0.0000, IoU.plate: 0.1971, IoU.monitor: 0.2765, IoU.bulletin board: 0.2823, IoU.shower: 0.0000, IoU.radiator: 0.2763, IoU.glass: 0.0058, IoU.clock: 0.0419, IoU.flag: 0.0832, Acc.wall: 0.8270, Acc.building: 0.9012, Acc.sky: 0.9192, Acc.floor: 0.8347, Acc.tree: 0.8420, Acc.ceiling: 0.8308, Acc.road: 0.8473, Acc.bed : 0.8881, Acc.windowpane: 0.6330, Acc.grass: 0.8494, Acc.cabinet: 0.6210, Acc.sidewalk: 0.7062, Acc.person: 0.8232, Acc.earth: 0.4763, Acc.door: 0.3412, Acc.table: 0.5502, Acc.mountain: 0.6912, Acc.plant: 0.5110, Acc.curtain: 0.7185, Acc.chair: 0.4798, Acc.car: 0.8455, Acc.water: 0.6819, Acc.painting: 0.7023, Acc.sofa: 0.6271, Acc.shelf: 0.4866, Acc.house: 0.4548, Acc.sea: 0.7137, Acc.mirror: 0.5132, Acc.rug: 0.5146, Acc.field: 0.3297, Acc.armchair: 0.3976, Acc.seat: 0.5742, Acc.fence: 0.3490, Acc.desk: 0.4063, Acc.rock: 0.6460, Acc.wardrobe: 0.6002, Acc.lamp: 0.4566, Acc.bathtub: 0.5490, Acc.railing: 0.3545, Acc.cushion: 0.3629, Acc.base: 0.2152, Acc.box: 0.1074, Acc.column: 0.2942, Acc.signboard: 0.2675, Acc.chest of drawers: 0.3703, Acc.counter: 0.3601, Acc.sand: 0.4563, Acc.sink: 0.5439, Acc.skyscraper: 0.8090, Acc.fireplace: 0.7239, Acc.refrigerator: 0.6752, Acc.grandstand: 0.6586, Acc.path: 0.1032, Acc.stairs: 0.1521, Acc.runway: 0.9435, Acc.case: 0.5721, Acc.pool table: 0.9005, Acc.pillow: 0.4355, Acc.screen door: 0.6894, Acc.stairway: 0.3448, Acc.river: 0.1223, Acc.bridge: 0.4060, Acc.bookcase: 0.5249, Acc.blind: 0.1843, Acc.coffee table: 0.6399, Acc.toilet: 0.7543, Acc.flower: 0.3601, Acc.book: 0.3178, Acc.hill: 0.0766, Acc.bench: 0.4584, Acc.countertop: 0.5948, Acc.stove: 0.6066, Acc.palm: 0.4308, Acc.kitchen island: 0.4622, Acc.computer: 0.5441, Acc.swivel chair: 0.3827, Acc.boat: 0.7111, Acc.bar: 0.1399, Acc.arcade machine: 0.3258, Acc.hovel: 0.3811, Acc.bus: 0.8228, Acc.towel: 0.4626, Acc.light: 0.1061, Acc.truck: 0.0275, Acc.tower: 0.4032, Acc.chandelier: 0.5846, Acc.awning: 0.1565, Acc.streetlight: 0.0487, Acc.booth: 0.3152, Acc.television receiver: 0.6365, Acc.airplane: 0.5267, Acc.dirt track: 0.0780, Acc.apparel: 0.1129, Acc.pole: 0.0881, Acc.land: 0.0000, Acc.bannister: 0.0021, Acc.escalator: 0.6753, Acc.ottoman: 0.0729, Acc.bottle: 0.0263, Acc.buffet: 0.2900, Acc.poster: 0.0519, Acc.stage: 0.1072, Acc.van: 0.0386, Acc.ship: 0.5157, Acc.fountain: 0.0370, Acc.conveyer belt: 0.8554, Acc.canopy: 0.1352, Acc.washer: 0.5245, Acc.plaything: 0.0502, Acc.swimming pool: 0.2601, Acc.stool: 0.0563, Acc.barrel: 0.5829, Acc.basket: 0.1048, Acc.waterfall: 0.6169, Acc.tent: 0.9786, Acc.bag: 0.0000, Acc.minibike: 0.4685, Acc.cradle: 0.8475, Acc.oven: 0.2466, Acc.ball: 0.1937, Acc.food: 0.6020, Acc.step: 0.0000, Acc.tank: 0.1481, Acc.trade name: 0.2013, Acc.microwave: 0.3029, Acc.pot: 0.2457, Acc.animal: 0.4620, Acc.bicycle: 0.5315, Acc.lake: 0.0000, Acc.dishwasher: 0.2762, Acc.screen: 0.7576, Acc.blanket: 0.0000, Acc.sculpture: 0.2200, Acc.hood: 0.2539, Acc.sconce: 0.0674, Acc.vase: 0.2407, Acc.traffic light: 0.0838, Acc.tray: 0.0002, Acc.ashcan: 0.1805, Acc.fan: 0.2566, Acc.pier: 0.1568, Acc.crt screen: 0.0000, Acc.plate: 0.2168, Acc.monitor: 0.6724, Acc.bulletin board: 0.3086, Acc.shower: 0.0000, Acc.radiator: 0.3136, Acc.glass: 0.0059, Acc.clock: 0.0452, Acc.flag: 0.0845
2022-11-04 11:51:53,175 - mmseg - INFO - Iter [16050/80000]	lr: 9.810e-05, eta: 6:13:58, time: 0.527, data_time: 0.193, memory: 12988, decode.loss_ce: 0.6181, decode.acc_seg: 76.2239, aux.loss_ce: 0.3167, aux.acc_seg: 71.7910, loss: 0.9348
2022-11-04 11:52:10,224 - mmseg - INFO - Iter [16100/80000]	lr: 9.803e-05, eta: 6:13:39, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6450, decode.acc_seg: 75.8265, aux.loss_ce: 0.3259, aux.acc_seg: 71.3867, loss: 0.9709
2022-11-04 11:52:27,423 - mmseg - INFO - Iter [16150/80000]	lr: 9.796e-05, eta: 6:13:20, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6254, decode.acc_seg: 76.5537, aux.loss_ce: 0.3257, aux.acc_seg: 71.3276, loss: 0.9512
2022-11-04 11:52:44,327 - mmseg - INFO - Iter [16200/80000]	lr: 9.789e-05, eta: 6:13:00, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6373, decode.acc_seg: 76.3697, aux.loss_ce: 0.3202, aux.acc_seg: 71.9468, loss: 0.9575
2022-11-04 11:53:01,642 - mmseg - INFO - Iter [16250/80000]	lr: 9.782e-05, eta: 6:12:41, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6444, decode.acc_seg: 75.6273, aux.loss_ce: 0.3329, aux.acc_seg: 70.3786, loss: 0.9773
2022-11-04 11:53:18,681 - mmseg - INFO - Iter [16300/80000]	lr: 9.775e-05, eta: 6:12:22, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6267, decode.acc_seg: 76.9737, aux.loss_ce: 0.3257, aux.acc_seg: 71.7681, loss: 0.9524
2022-11-04 11:53:35,808 - mmseg - INFO - Iter [16350/80000]	lr: 9.768e-05, eta: 6:12:03, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6153, decode.acc_seg: 77.2399, aux.loss_ce: 0.3150, aux.acc_seg: 72.6288, loss: 0.9302
2022-11-04 11:53:52,838 - mmseg - INFO - Iter [16400/80000]	lr: 9.762e-05, eta: 6:11:43, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6090, decode.acc_seg: 77.0678, aux.loss_ce: 0.3131, aux.acc_seg: 72.3570, loss: 0.9220
2022-11-04 11:54:12,704 - mmseg - INFO - Iter [16450/80000]	lr: 9.755e-05, eta: 6:11:34, time: 0.397, data_time: 0.062, memory: 12988, decode.loss_ce: 0.5968, decode.acc_seg: 77.2529, aux.loss_ce: 0.3126, aux.acc_seg: 72.3033, loss: 0.9094
2022-11-04 11:54:29,847 - mmseg - INFO - Iter [16500/80000]	lr: 9.748e-05, eta: 6:11:15, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6097, decode.acc_seg: 77.0926, aux.loss_ce: 0.3147, aux.acc_seg: 72.2299, loss: 0.9244
2022-11-04 11:54:47,025 - mmseg - INFO - Iter [16550/80000]	lr: 9.741e-05, eta: 6:10:56, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6461, decode.acc_seg: 76.0966, aux.loss_ce: 0.3380, aux.acc_seg: 70.4513, loss: 0.9841
2022-11-04 11:55:04,179 - mmseg - INFO - Iter [16600/80000]	lr: 9.734e-05, eta: 6:10:37, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6183, decode.acc_seg: 76.6671, aux.loss_ce: 0.3256, aux.acc_seg: 71.5212, loss: 0.9439
2022-11-04 11:55:21,076 - mmseg - INFO - Iter [16650/80000]	lr: 9.727e-05, eta: 6:10:17, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6431, decode.acc_seg: 75.9402, aux.loss_ce: 0.3222, aux.acc_seg: 71.6378, loss: 0.9653
2022-11-04 11:55:38,039 - mmseg - INFO - Iter [16700/80000]	lr: 9.720e-05, eta: 6:09:58, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6417, decode.acc_seg: 76.1292, aux.loss_ce: 0.3359, aux.acc_seg: 70.5363, loss: 0.9775
2022-11-04 11:55:55,165 - mmseg - INFO - Iter [16750/80000]	lr: 9.713e-05, eta: 6:09:39, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6452, decode.acc_seg: 76.2241, aux.loss_ce: 0.3236, aux.acc_seg: 72.0473, loss: 0.9688
2022-11-04 11:56:12,325 - mmseg - INFO - Iter [16800/80000]	lr: 9.706e-05, eta: 6:09:20, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6062, decode.acc_seg: 76.5089, aux.loss_ce: 0.3101, aux.acc_seg: 71.9501, loss: 0.9163
2022-11-04 11:56:29,426 - mmseg - INFO - Iter [16850/80000]	lr: 9.699e-05, eta: 6:09:01, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6209, decode.acc_seg: 76.7461, aux.loss_ce: 0.3183, aux.acc_seg: 71.7252, loss: 0.9392
2022-11-04 11:56:46,599 - mmseg - INFO - Iter [16900/80000]	lr: 9.692e-05, eta: 6:08:42, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6493, decode.acc_seg: 75.8553, aux.loss_ce: 0.3315, aux.acc_seg: 71.1974, loss: 0.9808
2022-11-04 11:57:03,629 - mmseg - INFO - Iter [16950/80000]	lr: 9.686e-05, eta: 6:08:22, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6237, decode.acc_seg: 76.8344, aux.loss_ce: 0.3170, aux.acc_seg: 72.3296, loss: 0.9407
2022-11-04 11:57:20,717 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 11:57:20,718 - mmseg - INFO - Iter [17000/80000]	lr: 9.679e-05, eta: 6:08:03, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6199, decode.acc_seg: 76.9044, aux.loss_ce: 0.3206, aux.acc_seg: 72.2786, loss: 0.9405
2022-11-04 11:57:40,729 - mmseg - INFO - Iter [17050/80000]	lr: 9.672e-05, eta: 6:07:55, time: 0.400, data_time: 0.065, memory: 12988, decode.loss_ce: 0.6196, decode.acc_seg: 76.9710, aux.loss_ce: 0.3156, aux.acc_seg: 72.3360, loss: 0.9352
2022-11-04 11:57:57,929 - mmseg - INFO - Iter [17100/80000]	lr: 9.665e-05, eta: 6:07:36, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6310, decode.acc_seg: 76.6134, aux.loss_ce: 0.3301, aux.acc_seg: 71.4746, loss: 0.9611
2022-11-04 11:58:14,700 - mmseg - INFO - Iter [17150/80000]	lr: 9.658e-05, eta: 6:07:16, time: 0.335, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5969, decode.acc_seg: 77.1955, aux.loss_ce: 0.3067, aux.acc_seg: 72.7689, loss: 0.9036
2022-11-04 11:58:31,767 - mmseg - INFO - Iter [17200/80000]	lr: 9.651e-05, eta: 6:06:57, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6173, decode.acc_seg: 76.5725, aux.loss_ce: 0.3208, aux.acc_seg: 71.6147, loss: 0.9381
2022-11-04 11:58:49,002 - mmseg - INFO - Iter [17250/80000]	lr: 9.644e-05, eta: 6:06:38, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6057, decode.acc_seg: 77.2456, aux.loss_ce: 0.3123, aux.acc_seg: 72.4887, loss: 0.9180
2022-11-04 11:59:06,412 - mmseg - INFO - Iter [17300/80000]	lr: 9.637e-05, eta: 6:06:20, time: 0.348, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5958, decode.acc_seg: 76.9740, aux.loss_ce: 0.3071, aux.acc_seg: 72.2328, loss: 0.9029
2022-11-04 11:59:23,524 - mmseg - INFO - Iter [17350/80000]	lr: 9.630e-05, eta: 6:06:01, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6232, decode.acc_seg: 76.6524, aux.loss_ce: 0.3286, aux.acc_seg: 71.1314, loss: 0.9519
2022-11-04 11:59:40,860 - mmseg - INFO - Iter [17400/80000]	lr: 9.623e-05, eta: 6:05:43, time: 0.347, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6220, decode.acc_seg: 76.2591, aux.loss_ce: 0.3178, aux.acc_seg: 71.7876, loss: 0.9398
2022-11-04 11:59:57,935 - mmseg - INFO - Iter [17450/80000]	lr: 9.616e-05, eta: 6:05:24, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6400, decode.acc_seg: 76.0836, aux.loss_ce: 0.3299, aux.acc_seg: 70.9432, loss: 0.9699
2022-11-04 12:00:15,150 - mmseg - INFO - Iter [17500/80000]	lr: 9.609e-05, eta: 6:05:05, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6079, decode.acc_seg: 77.2544, aux.loss_ce: 0.3084, aux.acc_seg: 72.9867, loss: 0.9163
2022-11-04 12:00:32,299 - mmseg - INFO - Iter [17550/80000]	lr: 9.603e-05, eta: 6:04:46, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6087, decode.acc_seg: 77.1120, aux.loss_ce: 0.3165, aux.acc_seg: 72.1424, loss: 0.9253
2022-11-04 12:00:49,261 - mmseg - INFO - Iter [17600/80000]	lr: 9.596e-05, eta: 6:04:27, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6350, decode.acc_seg: 76.3169, aux.loss_ce: 0.3272, aux.acc_seg: 71.4022, loss: 0.9621
2022-11-04 12:01:06,538 - mmseg - INFO - Iter [17650/80000]	lr: 9.589e-05, eta: 6:04:08, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6208, decode.acc_seg: 77.4700, aux.loss_ce: 0.3186, aux.acc_seg: 72.6322, loss: 0.9394
2022-11-04 12:01:26,757 - mmseg - INFO - Iter [17700/80000]	lr: 9.582e-05, eta: 6:04:00, time: 0.404, data_time: 0.072, memory: 12988, decode.loss_ce: 0.6140, decode.acc_seg: 77.0046, aux.loss_ce: 0.3211, aux.acc_seg: 71.7980, loss: 0.9351
2022-11-04 12:01:43,786 - mmseg - INFO - Iter [17750/80000]	lr: 9.575e-05, eta: 6:03:41, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6090, decode.acc_seg: 77.0825, aux.loss_ce: 0.3189, aux.acc_seg: 71.4833, loss: 0.9279
2022-11-04 12:02:00,736 - mmseg - INFO - Iter [17800/80000]	lr: 9.568e-05, eta: 6:03:21, time: 0.339, data_time: 0.008, memory: 12988, decode.loss_ce: 0.6065, decode.acc_seg: 77.6454, aux.loss_ce: 0.3160, aux.acc_seg: 72.6535, loss: 0.9225
2022-11-04 12:02:17,867 - mmseg - INFO - Iter [17850/80000]	lr: 9.561e-05, eta: 6:03:02, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6042, decode.acc_seg: 77.2664, aux.loss_ce: 0.3117, aux.acc_seg: 72.1411, loss: 0.9158
2022-11-04 12:02:34,897 - mmseg - INFO - Iter [17900/80000]	lr: 9.554e-05, eta: 6:02:43, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6073, decode.acc_seg: 77.6383, aux.loss_ce: 0.3158, aux.acc_seg: 72.4538, loss: 0.9230
2022-11-04 12:02:51,901 - mmseg - INFO - Iter [17950/80000]	lr: 9.547e-05, eta: 6:02:24, time: 0.340, data_time: 0.008, memory: 12988, decode.loss_ce: 0.6172, decode.acc_seg: 76.8804, aux.loss_ce: 0.3206, aux.acc_seg: 71.6885, loss: 0.9378
2022-11-04 12:03:08,725 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 12:03:08,726 - mmseg - INFO - Iter [18000/80000]	lr: 9.540e-05, eta: 6:02:04, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6296, decode.acc_seg: 76.3148, aux.loss_ce: 0.3211, aux.acc_seg: 71.6972, loss: 0.9507
2022-11-04 12:03:25,794 - mmseg - INFO - Iter [18050/80000]	lr: 9.533e-05, eta: 6:01:45, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6060, decode.acc_seg: 77.1926, aux.loss_ce: 0.3170, aux.acc_seg: 71.8294, loss: 0.9230
2022-11-04 12:03:42,600 - mmseg - INFO - Iter [18100/80000]	lr: 9.526e-05, eta: 6:01:25, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6135, decode.acc_seg: 76.7095, aux.loss_ce: 0.3203, aux.acc_seg: 71.8317, loss: 0.9338
2022-11-04 12:03:59,918 - mmseg - INFO - Iter [18150/80000]	lr: 9.519e-05, eta: 6:01:07, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6338, decode.acc_seg: 76.7115, aux.loss_ce: 0.3289, aux.acc_seg: 71.5497, loss: 0.9627
2022-11-04 12:04:16,729 - mmseg - INFO - Iter [18200/80000]	lr: 9.513e-05, eta: 6:00:47, time: 0.336, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6165, decode.acc_seg: 77.2117, aux.loss_ce: 0.3185, aux.acc_seg: 71.9632, loss: 0.9350
2022-11-04 12:04:33,760 - mmseg - INFO - Iter [18250/80000]	lr: 9.506e-05, eta: 6:00:28, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6083, decode.acc_seg: 77.2343, aux.loss_ce: 0.3080, aux.acc_seg: 72.4785, loss: 0.9164
2022-11-04 12:04:53,605 - mmseg - INFO - Iter [18300/80000]	lr: 9.499e-05, eta: 6:00:18, time: 0.396, data_time: 0.059, memory: 12988, decode.loss_ce: 0.6333, decode.acc_seg: 76.4318, aux.loss_ce: 0.3296, aux.acc_seg: 71.1758, loss: 0.9630
2022-11-04 12:05:10,894 - mmseg - INFO - Iter [18350/80000]	lr: 9.492e-05, eta: 6:00:00, time: 0.347, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5978, decode.acc_seg: 77.6950, aux.loss_ce: 0.3134, aux.acc_seg: 72.4606, loss: 0.9112
2022-11-04 12:05:28,029 - mmseg - INFO - Iter [18400/80000]	lr: 9.485e-05, eta: 5:59:41, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6117, decode.acc_seg: 77.1945, aux.loss_ce: 0.3243, aux.acc_seg: 71.7885, loss: 0.9360
2022-11-04 12:05:44,868 - mmseg - INFO - Iter [18450/80000]	lr: 9.478e-05, eta: 5:59:21, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6065, decode.acc_seg: 77.1306, aux.loss_ce: 0.3160, aux.acc_seg: 71.9675, loss: 0.9225
2022-11-04 12:06:01,963 - mmseg - INFO - Iter [18500/80000]	lr: 9.471e-05, eta: 5:59:02, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6211, decode.acc_seg: 77.0501, aux.loss_ce: 0.3209, aux.acc_seg: 71.8973, loss: 0.9420
2022-11-04 12:06:18,849 - mmseg - INFO - Iter [18550/80000]	lr: 9.464e-05, eta: 5:58:43, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5966, decode.acc_seg: 77.6931, aux.loss_ce: 0.3120, aux.acc_seg: 71.8454, loss: 0.9086
2022-11-04 12:06:35,912 - mmseg - INFO - Iter [18600/80000]	lr: 9.457e-05, eta: 5:58:24, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5907, decode.acc_seg: 77.5114, aux.loss_ce: 0.3074, aux.acc_seg: 72.6601, loss: 0.8981
2022-11-04 12:06:52,954 - mmseg - INFO - Iter [18650/80000]	lr: 9.450e-05, eta: 5:58:05, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6127, decode.acc_seg: 76.9993, aux.loss_ce: 0.3188, aux.acc_seg: 71.9941, loss: 0.9315
2022-11-04 12:07:10,127 - mmseg - INFO - Iter [18700/80000]	lr: 9.443e-05, eta: 5:57:46, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6239, decode.acc_seg: 76.6862, aux.loss_ce: 0.3223, aux.acc_seg: 71.6024, loss: 0.9462
2022-11-04 12:07:26,990 - mmseg - INFO - Iter [18750/80000]	lr: 9.436e-05, eta: 5:57:26, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6186, decode.acc_seg: 77.1302, aux.loss_ce: 0.3233, aux.acc_seg: 72.0990, loss: 0.9420
2022-11-04 12:07:43,898 - mmseg - INFO - Iter [18800/80000]	lr: 9.429e-05, eta: 5:57:07, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6191, decode.acc_seg: 77.2484, aux.loss_ce: 0.3181, aux.acc_seg: 72.6222, loss: 0.9372
2022-11-04 12:08:00,998 - mmseg - INFO - Iter [18850/80000]	lr: 9.422e-05, eta: 5:56:48, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6278, decode.acc_seg: 76.2335, aux.loss_ce: 0.3214, aux.acc_seg: 71.3247, loss: 0.9491
2022-11-04 12:08:18,015 - mmseg - INFO - Iter [18900/80000]	lr: 9.416e-05, eta: 5:56:29, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5997, decode.acc_seg: 77.2555, aux.loss_ce: 0.3085, aux.acc_seg: 73.1367, loss: 0.9082
2022-11-04 12:08:38,268 - mmseg - INFO - Iter [18950/80000]	lr: 9.409e-05, eta: 5:56:20, time: 0.405, data_time: 0.072, memory: 12988, decode.loss_ce: 0.6085, decode.acc_seg: 77.1770, aux.loss_ce: 0.3120, aux.acc_seg: 72.3945, loss: 0.9205
2022-11-04 12:08:55,399 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 12:08:55,399 - mmseg - INFO - Iter [19000/80000]	lr: 9.402e-05, eta: 5:56:02, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6233, decode.acc_seg: 76.6125, aux.loss_ce: 0.3207, aux.acc_seg: 71.9695, loss: 0.9440
2022-11-04 12:09:12,576 - mmseg - INFO - Iter [19050/80000]	lr: 9.395e-05, eta: 5:55:43, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5698, decode.acc_seg: 78.3308, aux.loss_ce: 0.2972, aux.acc_seg: 73.6637, loss: 0.8669
2022-11-04 12:09:29,563 - mmseg - INFO - Iter [19100/80000]	lr: 9.388e-05, eta: 5:55:24, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5819, decode.acc_seg: 77.8650, aux.loss_ce: 0.3067, aux.acc_seg: 72.5782, loss: 0.8886
2022-11-04 12:09:46,677 - mmseg - INFO - Iter [19150/80000]	lr: 9.381e-05, eta: 5:55:05, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6018, decode.acc_seg: 77.5973, aux.loss_ce: 0.3108, aux.acc_seg: 72.5823, loss: 0.9126
2022-11-04 12:10:03,905 - mmseg - INFO - Iter [19200/80000]	lr: 9.374e-05, eta: 5:54:47, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5993, decode.acc_seg: 77.6282, aux.loss_ce: 0.3134, aux.acc_seg: 72.4004, loss: 0.9127
2022-11-04 12:10:20,970 - mmseg - INFO - Iter [19250/80000]	lr: 9.367e-05, eta: 5:54:28, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6082, decode.acc_seg: 77.2249, aux.loss_ce: 0.3179, aux.acc_seg: 72.2000, loss: 0.9261
2022-11-04 12:10:37,931 - mmseg - INFO - Iter [19300/80000]	lr: 9.360e-05, eta: 5:54:09, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5942, decode.acc_seg: 77.6609, aux.loss_ce: 0.3068, aux.acc_seg: 72.8830, loss: 0.9010
2022-11-04 12:10:55,137 - mmseg - INFO - Iter [19350/80000]	lr: 9.353e-05, eta: 5:53:50, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6089, decode.acc_seg: 77.2573, aux.loss_ce: 0.3144, aux.acc_seg: 72.4500, loss: 0.9232
2022-11-04 12:11:12,152 - mmseg - INFO - Iter [19400/80000]	lr: 9.346e-05, eta: 5:53:31, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6126, decode.acc_seg: 76.7781, aux.loss_ce: 0.3210, aux.acc_seg: 71.4659, loss: 0.9336
2022-11-04 12:11:29,155 - mmseg - INFO - Iter [19450/80000]	lr: 9.339e-05, eta: 5:53:12, time: 0.340, data_time: 0.008, memory: 12988, decode.loss_ce: 0.5913, decode.acc_seg: 77.6188, aux.loss_ce: 0.3087, aux.acc_seg: 72.5741, loss: 0.9000
2022-11-04 12:11:46,436 - mmseg - INFO - Iter [19500/80000]	lr: 9.332e-05, eta: 5:52:54, time: 0.346, data_time: 0.019, memory: 12988, decode.loss_ce: 0.6136, decode.acc_seg: 76.6787, aux.loss_ce: 0.3167, aux.acc_seg: 71.9458, loss: 0.9303
2022-11-04 12:12:03,466 - mmseg - INFO - Iter [19550/80000]	lr: 9.325e-05, eta: 5:52:35, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5884, decode.acc_seg: 77.7758, aux.loss_ce: 0.3121, aux.acc_seg: 72.2418, loss: 0.9004
2022-11-04 12:12:23,721 - mmseg - INFO - Iter [19600/80000]	lr: 9.318e-05, eta: 5:52:26, time: 0.405, data_time: 0.070, memory: 12988, decode.loss_ce: 0.5826, decode.acc_seg: 78.1890, aux.loss_ce: 0.3057, aux.acc_seg: 73.1363, loss: 0.8883
2022-11-04 12:12:40,533 - mmseg - INFO - Iter [19650/80000]	lr: 9.311e-05, eta: 5:52:06, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6022, decode.acc_seg: 77.2936, aux.loss_ce: 0.3176, aux.acc_seg: 72.2440, loss: 0.9197
2022-11-04 12:12:57,547 - mmseg - INFO - Iter [19700/80000]	lr: 9.304e-05, eta: 5:51:47, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5952, decode.acc_seg: 77.6179, aux.loss_ce: 0.3089, aux.acc_seg: 72.7201, loss: 0.9041
2022-11-04 12:13:14,616 - mmseg - INFO - Iter [19750/80000]	lr: 9.298e-05, eta: 5:51:28, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6010, decode.acc_seg: 77.2728, aux.loss_ce: 0.3153, aux.acc_seg: 72.3264, loss: 0.9163
2022-11-04 12:13:31,718 - mmseg - INFO - Iter [19800/80000]	lr: 9.291e-05, eta: 5:51:10, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5956, decode.acc_seg: 77.7193, aux.loss_ce: 0.3110, aux.acc_seg: 72.3655, loss: 0.9066
2022-11-04 12:13:48,773 - mmseg - INFO - Iter [19850/80000]	lr: 9.284e-05, eta: 5:50:51, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6200, decode.acc_seg: 76.5931, aux.loss_ce: 0.3173, aux.acc_seg: 71.9652, loss: 0.9373
2022-11-04 12:14:05,786 - mmseg - INFO - Iter [19900/80000]	lr: 9.277e-05, eta: 5:50:32, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5981, decode.acc_seg: 77.4453, aux.loss_ce: 0.3105, aux.acc_seg: 72.6548, loss: 0.9085
2022-11-04 12:14:22,849 - mmseg - INFO - Iter [19950/80000]	lr: 9.270e-05, eta: 5:50:13, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5962, decode.acc_seg: 77.7192, aux.loss_ce: 0.3104, aux.acc_seg: 72.7300, loss: 0.9066
2022-11-04 12:14:39,891 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 12:14:39,892 - mmseg - INFO - Iter [20000/80000]	lr: 9.263e-05, eta: 5:49:54, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6057, decode.acc_seg: 77.4577, aux.loss_ce: 0.3180, aux.acc_seg: 71.9920, loss: 0.9238
2022-11-04 12:14:56,887 - mmseg - INFO - Iter [20050/80000]	lr: 9.256e-05, eta: 5:49:35, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6079, decode.acc_seg: 77.5195, aux.loss_ce: 0.3180, aux.acc_seg: 72.3709, loss: 0.9259
2022-11-04 12:15:14,286 - mmseg - INFO - Iter [20100/80000]	lr: 9.249e-05, eta: 5:49:17, time: 0.348, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5800, decode.acc_seg: 78.1691, aux.loss_ce: 0.3016, aux.acc_seg: 73.2104, loss: 0.8816
2022-11-04 12:15:31,459 - mmseg - INFO - Iter [20150/80000]	lr: 9.242e-05, eta: 5:48:59, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5995, decode.acc_seg: 77.5704, aux.loss_ce: 0.3085, aux.acc_seg: 72.6963, loss: 0.9080
2022-11-04 12:15:51,497 - mmseg - INFO - Iter [20200/80000]	lr: 9.235e-05, eta: 5:48:49, time: 0.401, data_time: 0.065, memory: 12988, decode.loss_ce: 0.5953, decode.acc_seg: 77.8204, aux.loss_ce: 0.3131, aux.acc_seg: 72.5319, loss: 0.9083
2022-11-04 12:16:08,796 - mmseg - INFO - Iter [20250/80000]	lr: 9.228e-05, eta: 5:48:31, time: 0.346, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5958, decode.acc_seg: 77.3936, aux.loss_ce: 0.3122, aux.acc_seg: 72.0252, loss: 0.9080
2022-11-04 12:16:26,757 - mmseg - INFO - Iter [20300/80000]	lr: 9.221e-05, eta: 5:48:15, time: 0.359, data_time: 0.027, memory: 12988, decode.loss_ce: 0.5934, decode.acc_seg: 77.6724, aux.loss_ce: 0.3150, aux.acc_seg: 72.2216, loss: 0.9084
2022-11-04 12:16:43,764 - mmseg - INFO - Iter [20350/80000]	lr: 9.214e-05, eta: 5:47:56, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6015, decode.acc_seg: 77.5423, aux.loss_ce: 0.3115, aux.acc_seg: 72.5249, loss: 0.9130
2022-11-04 12:17:00,671 - mmseg - INFO - Iter [20400/80000]	lr: 9.207e-05, eta: 5:47:37, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5732, decode.acc_seg: 78.5133, aux.loss_ce: 0.2993, aux.acc_seg: 73.3589, loss: 0.8724
2022-11-04 12:17:17,912 - mmseg - INFO - Iter [20450/80000]	lr: 9.200e-05, eta: 5:47:18, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5833, decode.acc_seg: 77.6348, aux.loss_ce: 0.3101, aux.acc_seg: 72.3232, loss: 0.8934
2022-11-04 12:17:34,848 - mmseg - INFO - Iter [20500/80000]	lr: 9.193e-05, eta: 5:46:59, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5881, decode.acc_seg: 78.1431, aux.loss_ce: 0.3087, aux.acc_seg: 72.9401, loss: 0.8968
2022-11-04 12:17:51,851 - mmseg - INFO - Iter [20550/80000]	lr: 9.186e-05, eta: 5:46:40, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5988, decode.acc_seg: 77.7203, aux.loss_ce: 0.3156, aux.acc_seg: 72.3704, loss: 0.9144
2022-11-04 12:18:08,777 - mmseg - INFO - Iter [20600/80000]	lr: 9.179e-05, eta: 5:46:21, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5855, decode.acc_seg: 78.0362, aux.loss_ce: 0.3093, aux.acc_seg: 72.6094, loss: 0.8948
2022-11-04 12:18:25,651 - mmseg - INFO - Iter [20650/80000]	lr: 9.172e-05, eta: 5:46:02, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5934, decode.acc_seg: 77.6599, aux.loss_ce: 0.3097, aux.acc_seg: 72.9168, loss: 0.9031
2022-11-04 12:18:42,809 - mmseg - INFO - Iter [20700/80000]	lr: 9.165e-05, eta: 5:45:43, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5876, decode.acc_seg: 77.8277, aux.loss_ce: 0.3028, aux.acc_seg: 72.7732, loss: 0.8904
2022-11-04 12:18:59,889 - mmseg - INFO - Iter [20750/80000]	lr: 9.159e-05, eta: 5:45:25, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5889, decode.acc_seg: 77.6880, aux.loss_ce: 0.3049, aux.acc_seg: 72.7445, loss: 0.8937
2022-11-04 12:19:16,793 - mmseg - INFO - Iter [20800/80000]	lr: 9.152e-05, eta: 5:45:06, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5924, decode.acc_seg: 78.2922, aux.loss_ce: 0.3158, aux.acc_seg: 73.0936, loss: 0.9082
2022-11-04 12:19:37,065 - mmseg - INFO - Iter [20850/80000]	lr: 9.145e-05, eta: 5:44:56, time: 0.405, data_time: 0.067, memory: 12988, decode.loss_ce: 0.5971, decode.acc_seg: 77.6859, aux.loss_ce: 0.3195, aux.acc_seg: 71.8837, loss: 0.9165
2022-11-04 12:19:54,005 - mmseg - INFO - Iter [20900/80000]	lr: 9.138e-05, eta: 5:44:37, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5772, decode.acc_seg: 78.2670, aux.loss_ce: 0.3053, aux.acc_seg: 72.8254, loss: 0.8826
2022-11-04 12:20:11,122 - mmseg - INFO - Iter [20950/80000]	lr: 9.131e-05, eta: 5:44:18, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5749, decode.acc_seg: 78.3808, aux.loss_ce: 0.2996, aux.acc_seg: 73.2201, loss: 0.8745
2022-11-04 12:20:28,405 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 12:20:28,405 - mmseg - INFO - Iter [21000/80000]	lr: 9.124e-05, eta: 5:44:00, time: 0.346, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5605, decode.acc_seg: 78.7245, aux.loss_ce: 0.2956, aux.acc_seg: 73.7497, loss: 0.8561
2022-11-04 12:20:45,468 - mmseg - INFO - Iter [21050/80000]	lr: 9.117e-05, eta: 5:43:42, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5912, decode.acc_seg: 77.5145, aux.loss_ce: 0.3072, aux.acc_seg: 72.8168, loss: 0.8984
2022-11-04 12:21:02,328 - mmseg - INFO - Iter [21100/80000]	lr: 9.110e-05, eta: 5:43:22, time: 0.337, data_time: 0.008, memory: 12988, decode.loss_ce: 0.5729, decode.acc_seg: 78.3313, aux.loss_ce: 0.3042, aux.acc_seg: 72.9932, loss: 0.8771
2022-11-04 12:21:19,196 - mmseg - INFO - Iter [21150/80000]	lr: 9.103e-05, eta: 5:43:03, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5891, decode.acc_seg: 77.8988, aux.loss_ce: 0.3083, aux.acc_seg: 73.2155, loss: 0.8974
2022-11-04 12:21:36,586 - mmseg - INFO - Iter [21200/80000]	lr: 9.096e-05, eta: 5:42:45, time: 0.348, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5875, decode.acc_seg: 77.8982, aux.loss_ce: 0.3080, aux.acc_seg: 73.1565, loss: 0.8955
2022-11-04 12:21:53,682 - mmseg - INFO - Iter [21250/80000]	lr: 9.089e-05, eta: 5:42:27, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5995, decode.acc_seg: 77.3975, aux.loss_ce: 0.3162, aux.acc_seg: 72.1110, loss: 0.9157
2022-11-04 12:22:10,635 - mmseg - INFO - Iter [21300/80000]	lr: 9.082e-05, eta: 5:42:08, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5849, decode.acc_seg: 77.9688, aux.loss_ce: 0.3062, aux.acc_seg: 72.9743, loss: 0.8911
2022-11-04 12:22:27,883 - mmseg - INFO - Iter [21350/80000]	lr: 9.075e-05, eta: 5:41:50, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6040, decode.acc_seg: 77.3021, aux.loss_ce: 0.3149, aux.acc_seg: 72.7080, loss: 0.9189
2022-11-04 12:22:45,115 - mmseg - INFO - Iter [21400/80000]	lr: 9.068e-05, eta: 5:41:32, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.6011, decode.acc_seg: 77.2432, aux.loss_ce: 0.3127, aux.acc_seg: 72.1633, loss: 0.9139
2022-11-04 12:23:01,792 - mmseg - INFO - Iter [21450/80000]	lr: 9.061e-05, eta: 5:41:12, time: 0.334, data_time: 0.010, memory: 12988, decode.loss_ce: 0.6019, decode.acc_seg: 77.6776, aux.loss_ce: 0.3106, aux.acc_seg: 72.7735, loss: 0.9124
2022-11-04 12:23:22,159 - mmseg - INFO - Iter [21500/80000]	lr: 9.054e-05, eta: 5:41:02, time: 0.407, data_time: 0.062, memory: 12988, decode.loss_ce: 0.5828, decode.acc_seg: 78.2138, aux.loss_ce: 0.3108, aux.acc_seg: 72.5695, loss: 0.8936
2022-11-04 12:23:39,094 - mmseg - INFO - Iter [21550/80000]	lr: 9.047e-05, eta: 5:40:43, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5752, decode.acc_seg: 78.1973, aux.loss_ce: 0.3019, aux.acc_seg: 73.0526, loss: 0.8771
2022-11-04 12:23:56,275 - mmseg - INFO - Iter [21600/80000]	lr: 9.040e-05, eta: 5:40:25, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5814, decode.acc_seg: 78.2006, aux.loss_ce: 0.3113, aux.acc_seg: 72.4323, loss: 0.8927
2022-11-04 12:24:13,479 - mmseg - INFO - Iter [21650/80000]	lr: 9.033e-05, eta: 5:40:07, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5821, decode.acc_seg: 78.0954, aux.loss_ce: 0.3001, aux.acc_seg: 73.1241, loss: 0.8822
2022-11-04 12:24:30,441 - mmseg - INFO - Iter [21700/80000]	lr: 9.026e-05, eta: 5:39:48, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5884, decode.acc_seg: 77.8003, aux.loss_ce: 0.3061, aux.acc_seg: 72.8164, loss: 0.8945
2022-11-04 12:24:47,567 - mmseg - INFO - Iter [21750/80000]	lr: 9.019e-05, eta: 5:39:29, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5822, decode.acc_seg: 78.5400, aux.loss_ce: 0.3138, aux.acc_seg: 72.9533, loss: 0.8960
2022-11-04 12:25:04,514 - mmseg - INFO - Iter [21800/80000]	lr: 9.012e-05, eta: 5:39:10, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5967, decode.acc_seg: 77.4833, aux.loss_ce: 0.3103, aux.acc_seg: 72.4846, loss: 0.9070
2022-11-04 12:25:21,467 - mmseg - INFO - Iter [21850/80000]	lr: 9.005e-05, eta: 5:38:51, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5534, decode.acc_seg: 78.9649, aux.loss_ce: 0.2964, aux.acc_seg: 73.4966, loss: 0.8498
2022-11-04 12:25:38,416 - mmseg - INFO - Iter [21900/80000]	lr: 8.998e-05, eta: 5:38:33, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5635, decode.acc_seg: 78.6488, aux.loss_ce: 0.3008, aux.acc_seg: 73.0292, loss: 0.8643
2022-11-04 12:25:55,354 - mmseg - INFO - Iter [21950/80000]	lr: 8.991e-05, eta: 5:38:14, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5787, decode.acc_seg: 77.8234, aux.loss_ce: 0.3058, aux.acc_seg: 72.7199, loss: 0.8845
2022-11-04 12:26:12,248 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 12:26:12,248 - mmseg - INFO - Iter [22000/80000]	lr: 8.984e-05, eta: 5:37:55, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5751, decode.acc_seg: 78.2072, aux.loss_ce: 0.2985, aux.acc_seg: 73.0676, loss: 0.8736
2022-11-04 12:26:29,100 - mmseg - INFO - Iter [22050/80000]	lr: 8.977e-05, eta: 5:37:36, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5740, decode.acc_seg: 78.3027, aux.loss_ce: 0.2985, aux.acc_seg: 73.5213, loss: 0.8725
2022-11-04 12:26:49,082 - mmseg - INFO - Iter [22100/80000]	lr: 8.971e-05, eta: 5:37:25, time: 0.400, data_time: 0.061, memory: 12988, decode.loss_ce: 0.5961, decode.acc_seg: 77.9541, aux.loss_ce: 0.3155, aux.acc_seg: 72.5276, loss: 0.9116
2022-11-04 12:27:06,052 - mmseg - INFO - Iter [22150/80000]	lr: 8.964e-05, eta: 5:37:06, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5716, decode.acc_seg: 78.4248, aux.loss_ce: 0.3015, aux.acc_seg: 72.8017, loss: 0.8730
2022-11-04 12:27:23,152 - mmseg - INFO - Iter [22200/80000]	lr: 8.957e-05, eta: 5:36:47, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5713, decode.acc_seg: 78.7555, aux.loss_ce: 0.2965, aux.acc_seg: 73.8953, loss: 0.8679
2022-11-04 12:27:40,267 - mmseg - INFO - Iter [22250/80000]	lr: 8.950e-05, eta: 5:36:29, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5767, decode.acc_seg: 78.4080, aux.loss_ce: 0.3068, aux.acc_seg: 72.9412, loss: 0.8835
2022-11-04 12:27:57,658 - mmseg - INFO - Iter [22300/80000]	lr: 8.943e-05, eta: 5:36:11, time: 0.348, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5935, decode.acc_seg: 77.6319, aux.loss_ce: 0.3144, aux.acc_seg: 72.5715, loss: 0.9079
2022-11-04 12:28:14,534 - mmseg - INFO - Iter [22350/80000]	lr: 8.936e-05, eta: 5:35:52, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5741, decode.acc_seg: 78.1213, aux.loss_ce: 0.3047, aux.acc_seg: 72.9335, loss: 0.8788
2022-11-04 12:28:31,400 - mmseg - INFO - Iter [22400/80000]	lr: 8.929e-05, eta: 5:35:33, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5666, decode.acc_seg: 78.6410, aux.loss_ce: 0.2952, aux.acc_seg: 73.9120, loss: 0.8618
2022-11-04 12:28:48,590 - mmseg - INFO - Iter [22450/80000]	lr: 8.922e-05, eta: 5:35:15, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5925, decode.acc_seg: 77.8952, aux.loss_ce: 0.3139, aux.acc_seg: 72.5978, loss: 0.9064
2022-11-04 12:29:05,770 - mmseg - INFO - Iter [22500/80000]	lr: 8.915e-05, eta: 5:34:57, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5850, decode.acc_seg: 77.9297, aux.loss_ce: 0.3093, aux.acc_seg: 72.4258, loss: 0.8943
2022-11-04 12:29:22,627 - mmseg - INFO - Iter [22550/80000]	lr: 8.908e-05, eta: 5:34:38, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5728, decode.acc_seg: 78.0453, aux.loss_ce: 0.2986, aux.acc_seg: 73.3689, loss: 0.8714
2022-11-04 12:29:39,795 - mmseg - INFO - Iter [22600/80000]	lr: 8.901e-05, eta: 5:34:19, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5932, decode.acc_seg: 77.6037, aux.loss_ce: 0.3124, aux.acc_seg: 72.7474, loss: 0.9056
2022-11-04 12:29:56,685 - mmseg - INFO - Iter [22650/80000]	lr: 8.894e-05, eta: 5:34:00, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5940, decode.acc_seg: 77.4421, aux.loss_ce: 0.3069, aux.acc_seg: 72.7862, loss: 0.9009
2022-11-04 12:30:13,801 - mmseg - INFO - Iter [22700/80000]	lr: 8.887e-05, eta: 5:33:42, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5976, decode.acc_seg: 77.6752, aux.loss_ce: 0.3125, aux.acc_seg: 72.6118, loss: 0.9102
2022-11-04 12:30:33,711 - mmseg - INFO - Iter [22750/80000]	lr: 8.880e-05, eta: 5:33:31, time: 0.398, data_time: 0.069, memory: 12988, decode.loss_ce: 0.5834, decode.acc_seg: 78.1953, aux.loss_ce: 0.3097, aux.acc_seg: 72.8977, loss: 0.8932
2022-11-04 12:30:50,654 - mmseg - INFO - Iter [22800/80000]	lr: 8.873e-05, eta: 5:33:12, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5579, decode.acc_seg: 79.3183, aux.loss_ce: 0.2946, aux.acc_seg: 73.8887, loss: 0.8525
2022-11-04 12:31:07,682 - mmseg - INFO - Iter [22850/80000]	lr: 8.866e-05, eta: 5:32:53, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5721, decode.acc_seg: 78.0260, aux.loss_ce: 0.3035, aux.acc_seg: 72.8272, loss: 0.8757
2022-11-04 12:31:24,672 - mmseg - INFO - Iter [22900/80000]	lr: 8.859e-05, eta: 5:32:35, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5957, decode.acc_seg: 77.7696, aux.loss_ce: 0.3157, aux.acc_seg: 72.3736, loss: 0.9114
2022-11-04 12:31:41,788 - mmseg - INFO - Iter [22950/80000]	lr: 8.852e-05, eta: 5:32:16, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5741, decode.acc_seg: 78.3501, aux.loss_ce: 0.3063, aux.acc_seg: 72.9340, loss: 0.8805
2022-11-04 12:31:59,082 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 12:31:59,082 - mmseg - INFO - Iter [23000/80000]	lr: 8.845e-05, eta: 5:31:58, time: 0.346, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5828, decode.acc_seg: 77.7626, aux.loss_ce: 0.3025, aux.acc_seg: 73.1001, loss: 0.8853
2022-11-04 12:32:15,959 - mmseg - INFO - Iter [23050/80000]	lr: 8.838e-05, eta: 5:31:39, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5690, decode.acc_seg: 78.6305, aux.loss_ce: 0.3090, aux.acc_seg: 72.6938, loss: 0.8780
2022-11-04 12:32:33,097 - mmseg - INFO - Iter [23100/80000]	lr: 8.831e-05, eta: 5:31:21, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5696, decode.acc_seg: 78.4616, aux.loss_ce: 0.2999, aux.acc_seg: 73.5094, loss: 0.8695
2022-11-04 12:32:50,087 - mmseg - INFO - Iter [23150/80000]	lr: 8.824e-05, eta: 5:31:02, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5386, decode.acc_seg: 79.4177, aux.loss_ce: 0.2898, aux.acc_seg: 73.9804, loss: 0.8284
2022-11-04 12:33:07,170 - mmseg - INFO - Iter [23200/80000]	lr: 8.817e-05, eta: 5:30:44, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5616, decode.acc_seg: 78.4740, aux.loss_ce: 0.2983, aux.acc_seg: 73.2414, loss: 0.8598
2022-11-04 12:33:24,115 - mmseg - INFO - Iter [23250/80000]	lr: 8.810e-05, eta: 5:30:25, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5710, decode.acc_seg: 78.4519, aux.loss_ce: 0.3030, aux.acc_seg: 73.1705, loss: 0.8740
2022-11-04 12:33:41,129 - mmseg - INFO - Iter [23300/80000]	lr: 8.803e-05, eta: 5:30:07, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5658, decode.acc_seg: 78.7291, aux.loss_ce: 0.3033, aux.acc_seg: 73.5815, loss: 0.8690
2022-11-04 12:34:01,293 - mmseg - INFO - Iter [23350/80000]	lr: 8.796e-05, eta: 5:29:56, time: 0.403, data_time: 0.059, memory: 12988, decode.loss_ce: 0.5950, decode.acc_seg: 78.0748, aux.loss_ce: 0.3156, aux.acc_seg: 72.9518, loss: 0.9107
2022-11-04 12:34:18,550 - mmseg - INFO - Iter [23400/80000]	lr: 8.789e-05, eta: 5:29:38, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5571, decode.acc_seg: 78.7175, aux.loss_ce: 0.2938, aux.acc_seg: 74.0164, loss: 0.8509
2022-11-04 12:34:35,392 - mmseg - INFO - Iter [23450/80000]	lr: 8.782e-05, eta: 5:29:19, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5470, decode.acc_seg: 79.1033, aux.loss_ce: 0.2897, aux.acc_seg: 74.0561, loss: 0.8366
2022-11-04 12:34:52,301 - mmseg - INFO - Iter [23500/80000]	lr: 8.775e-05, eta: 5:29:00, time: 0.338, data_time: 0.011, memory: 12988, decode.loss_ce: 0.5623, decode.acc_seg: 78.8552, aux.loss_ce: 0.3005, aux.acc_seg: 73.4752, loss: 0.8628
2022-11-04 12:35:09,183 - mmseg - INFO - Iter [23550/80000]	lr: 8.768e-05, eta: 5:28:41, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5772, decode.acc_seg: 78.6846, aux.loss_ce: 0.3083, aux.acc_seg: 73.1622, loss: 0.8855
2022-11-04 12:35:26,363 - mmseg - INFO - Iter [23600/80000]	lr: 8.761e-05, eta: 5:28:23, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5781, decode.acc_seg: 78.1932, aux.loss_ce: 0.3091, aux.acc_seg: 72.8320, loss: 0.8872
2022-11-04 12:35:43,261 - mmseg - INFO - Iter [23650/80000]	lr: 8.754e-05, eta: 5:28:04, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5710, decode.acc_seg: 78.5678, aux.loss_ce: 0.3015, aux.acc_seg: 73.4268, loss: 0.8725
2022-11-04 12:36:00,477 - mmseg - INFO - Iter [23700/80000]	lr: 8.747e-05, eta: 5:27:46, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5782, decode.acc_seg: 78.3184, aux.loss_ce: 0.3074, aux.acc_seg: 72.7280, loss: 0.8855
2022-11-04 12:36:17,365 - mmseg - INFO - Iter [23750/80000]	lr: 8.740e-05, eta: 5:27:27, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5499, decode.acc_seg: 79.4345, aux.loss_ce: 0.2958, aux.acc_seg: 73.5953, loss: 0.8457
2022-11-04 12:36:34,294 - mmseg - INFO - Iter [23800/80000]	lr: 8.733e-05, eta: 5:27:08, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5627, decode.acc_seg: 78.6694, aux.loss_ce: 0.3055, aux.acc_seg: 72.8395, loss: 0.8681
2022-11-04 12:36:51,472 - mmseg - INFO - Iter [23850/80000]	lr: 8.726e-05, eta: 5:26:50, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5781, decode.acc_seg: 77.8901, aux.loss_ce: 0.3031, aux.acc_seg: 72.7641, loss: 0.8812
2022-11-04 12:37:08,477 - mmseg - INFO - Iter [23900/80000]	lr: 8.719e-05, eta: 5:26:32, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5825, decode.acc_seg: 77.8413, aux.loss_ce: 0.3097, aux.acc_seg: 72.6607, loss: 0.8922
2022-11-04 12:37:25,663 - mmseg - INFO - Iter [23950/80000]	lr: 8.712e-05, eta: 5:26:14, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5771, decode.acc_seg: 78.1826, aux.loss_ce: 0.3068, aux.acc_seg: 72.9134, loss: 0.8839
2022-11-04 12:37:45,591 - mmseg - INFO - Saving checkpoint at 24000 iterations
2022-11-04 12:37:45,856 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 12:37:45,856 - mmseg - INFO - Iter [24000/80000]	lr: 8.705e-05, eta: 5:26:03, time: 0.404, data_time: 0.068, memory: 12988, decode.loss_ce: 0.5805, decode.acc_seg: 77.8586, aux.loss_ce: 0.3062, aux.acc_seg: 72.5416, loss: 0.8867
2022-11-04 12:37:54,938 - mmseg - INFO - per class results:
2022-11-04 12:37:54,945 - mmseg - INFO - 
+---------------------+-------+-------+
|        Class        |  IoU  |  Acc  |
+---------------------+-------+-------+
|         wall        | 65.44 | 80.51 |
|       building      | 74.21 | 89.56 |
|         sky         | 88.17 | 92.51 |
|        floor        | 70.27 | 84.61 |
|         tree        | 66.73 | 82.91 |
|       ceiling       | 74.64 | 86.64 |
|         road        |  72.3 | 79.13 |
|         bed         | 73.41 | 89.68 |
|      windowpane     | 49.62 | 69.05 |
|        grass        | 60.82 |  79.7 |
|       cabinet       | 46.69 | 60.64 |
|       sidewalk      | 49.34 | 71.23 |
|        person       | 63.33 | 81.98 |
|        earth        | 25.36 | 35.51 |
|         door        | 24.84 | 42.02 |
|        table        | 38.36 | 51.96 |
|       mountain      |  57.0 | 74.49 |
|        plant        | 40.68 | 51.74 |
|       curtain       | 53.56 | 70.61 |
|        chair        | 37.77 | 55.45 |
|         car         | 70.83 | 84.28 |
|        water        | 39.08 | 53.45 |
|       painting      | 54.54 | 70.61 |
|         sofa        | 45.81 |  65.7 |
|        shelf        | 32.55 | 48.19 |
|        house        |  44.4 | 65.99 |
|         sea         | 40.59 | 62.24 |
|        mirror       |  41.0 | 50.03 |
|         rug         | 47.41 | 57.58 |
|        field        | 25.44 | 48.34 |
|       armchair      |  24.3 | 37.49 |
|         seat        | 46.91 | 67.83 |
|        fence        | 23.79 | 46.64 |
|         desk        |  30.1 | 49.48 |
|         rock        |  42.3 |  72.6 |
|       wardrobe      | 39.37 | 67.47 |
|         lamp        | 34.44 | 45.78 |
|       bathtub       | 52.46 |  69.3 |
|       railing       | 20.93 | 32.57 |
|       cushion       | 28.52 |  36.4 |
|         base        | 16.26 | 28.82 |
|         box         | 15.33 | 20.68 |
|        column       | 21.01 | 25.07 |
|      signboard      | 20.85 | 26.46 |
|   chest of drawers  | 26.81 | 42.36 |
|       counter       | 23.77 | 32.36 |
|         sand        | 28.36 | 40.92 |
|         sink        | 43.66 |  54.7 |
|      skyscraper     | 57.03 | 72.29 |
|      fireplace      | 53.45 |  67.8 |
|     refrigerator    | 47.79 | 61.56 |
|      grandstand     | 47.18 | 66.06 |
|         path        | 14.51 | 21.07 |
|        stairs       | 21.29 | 25.41 |
|        runway       | 72.35 | 85.17 |
|         case        |  45.2 | 52.64 |
|      pool table     | 80.58 | 88.21 |
|        pillow       | 34.96 | 42.08 |
|     screen door     | 46.62 | 78.42 |
|       stairway      | 34.06 | 52.64 |
|        river        | 15.34 | 42.46 |
|        bridge       | 25.05 | 36.52 |
|       bookcase      | 25.75 | 40.62 |
|        blind        | 11.85 | 12.89 |
|     coffee table    |  36.3 | 65.82 |
|        toilet       | 60.94 | 73.42 |
|        flower       | 18.68 | 35.82 |
|         book        |  34.7 | 48.75 |
|         hill        |  3.07 |  3.9  |
|        bench        | 28.25 |  41.0 |
|      countertop     | 39.45 |  50.3 |
|        stove        | 43.21 | 58.58 |
|         palm        | 32.14 | 52.31 |
|    kitchen island   | 16.08 | 32.88 |
|       computer      | 40.46 |  61.6 |
|     swivel chair    | 30.64 | 43.18 |
|         boat        | 38.04 | 46.99 |
|         bar         | 24.53 | 31.77 |
|    arcade machine   |  28.4 | 32.36 |
|        hovel        | 34.04 |  42.2 |
|         bus         | 70.88 | 79.81 |
|        towel        | 34.67 | 40.68 |
|        light        | 10.86 | 11.51 |
|        truck        |  2.29 |  2.62 |
|        tower        | 28.18 | 34.94 |
|      chandelier     | 44.81 |  60.6 |
|        awning       | 16.58 | 20.31 |
|     streetlight     |  5.36 |  6.16 |
|        booth        | 24.76 | 36.07 |
| television receiver | 48.69 | 67.55 |
|       airplane      | 43.78 | 56.31 |
|      dirt track     |  0.13 |  0.16 |
|       apparel       | 10.14 |  12.6 |
|         pole        |  4.27 |  6.78 |
|         land        |  0.06 |  0.09 |
|      bannister      |  0.98 |  1.68 |
|      escalator      | 43.22 | 59.22 |
|       ottoman       | 14.32 | 16.13 |
|        bottle       |  6.54 |  7.62 |
|        buffet       | 30.95 | 39.78 |
|        poster       |  4.69 |  5.09 |
|        stage        |  4.76 | 12.83 |
|         van         | 12.32 | 14.02 |
|         ship        | 64.07 |  83.7 |
|       fountain      | 18.69 | 20.25 |
|    conveyer belt    | 57.58 | 70.34 |
|        canopy       |  9.61 | 13.12 |
|        washer       | 58.71 | 64.52 |
|      plaything      |  5.73 | 10.64 |
|    swimming pool    | 38.58 | 67.12 |
|        stool        |  4.5  |  4.95 |
|        barrel       | 15.94 | 22.37 |
|        basket       | 11.09 | 12.41 |
|      waterfall      | 42.14 | 56.31 |
|         tent        | 88.74 | 94.98 |
|         bag         |  0.13 |  0.13 |
|       minibike      | 36.08 | 43.45 |
|        cradle       | 52.42 | 80.97 |
|         oven        |  18.6 | 23.39 |
|         ball        | 25.83 |  33.8 |
|         food        | 37.57 | 45.55 |
|         step        |  3.14 |  3.29 |
|         tank        |  7.01 |  7.26 |
|      trade name     | 15.98 | 17.78 |
|      microwave      | 24.64 | 27.19 |
|         pot         | 18.92 | 22.93 |
|        animal       | 38.28 |  42.4 |
|       bicycle       | 33.48 | 56.41 |
|         lake        |  0.05 |  0.05 |
|      dishwasher     | 23.75 | 29.33 |
|        screen       | 54.02 | 77.11 |
|       blanket       |  0.0  |  0.0  |
|      sculpture      | 24.29 | 36.12 |
|         hood        | 23.76 | 31.19 |
|        sconce       |  9.14 |  9.53 |
|         vase        | 15.19 | 21.77 |
|    traffic light    |  7.73 | 10.72 |
|         tray        |  0.84 |  1.15 |
|        ashcan       | 15.02 | 19.52 |
|         fan         | 25.91 | 31.74 |
|         pier        | 40.41 | 67.18 |
|      crt screen     |  0.0  |  0.0  |
|        plate        |  25.5 | 30.82 |
|       monitor       | 32.53 | 55.09 |
|    bulletin board   | 29.85 |  38.1 |
|        shower       |  0.0  |  0.0  |
|       radiator      | 35.59 | 40.49 |
|        glass        |  0.47 |  0.48 |
|        clock        |  6.26 |  6.92 |
|         flag        | 13.45 |  14.8 |
+---------------------+-------+-------+
2022-11-04 12:37:54,945 - mmseg - INFO - Summary:
2022-11-04 12:37:54,946 - mmseg - INFO - 
+-------+------+-------+
|  aAcc | mIoU |  mAcc |
+-------+------+-------+
| 74.09 | 32.2 | 42.93 |
+-------+------+-------+
2022-11-04 12:37:54,947 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 12:37:54,947 - mmseg - INFO - Iter(val) [250]	aAcc: 0.7409, mIoU: 0.3220, mAcc: 0.4293, IoU.wall: 0.6544, IoU.building: 0.7421, IoU.sky: 0.8817, IoU.floor: 0.7027, IoU.tree: 0.6673, IoU.ceiling: 0.7464, IoU.road: 0.7230, IoU.bed : 0.7341, IoU.windowpane: 0.4962, IoU.grass: 0.6082, IoU.cabinet: 0.4669, IoU.sidewalk: 0.4934, IoU.person: 0.6333, IoU.earth: 0.2536, IoU.door: 0.2484, IoU.table: 0.3836, IoU.mountain: 0.5700, IoU.plant: 0.4068, IoU.curtain: 0.5356, IoU.chair: 0.3777, IoU.car: 0.7083, IoU.water: 0.3908, IoU.painting: 0.5454, IoU.sofa: 0.4581, IoU.shelf: 0.3255, IoU.house: 0.4440, IoU.sea: 0.4059, IoU.mirror: 0.4100, IoU.rug: 0.4741, IoU.field: 0.2544, IoU.armchair: 0.2430, IoU.seat: 0.4691, IoU.fence: 0.2379, IoU.desk: 0.3010, IoU.rock: 0.4230, IoU.wardrobe: 0.3937, IoU.lamp: 0.3444, IoU.bathtub: 0.5246, IoU.railing: 0.2093, IoU.cushion: 0.2852, IoU.base: 0.1626, IoU.box: 0.1533, IoU.column: 0.2101, IoU.signboard: 0.2085, IoU.chest of drawers: 0.2681, IoU.counter: 0.2377, IoU.sand: 0.2836, IoU.sink: 0.4366, IoU.skyscraper: 0.5703, IoU.fireplace: 0.5345, IoU.refrigerator: 0.4779, IoU.grandstand: 0.4718, IoU.path: 0.1451, IoU.stairs: 0.2129, IoU.runway: 0.7235, IoU.case: 0.4520, IoU.pool table: 0.8058, IoU.pillow: 0.3496, IoU.screen door: 0.4662, IoU.stairway: 0.3406, IoU.river: 0.1534, IoU.bridge: 0.2505, IoU.bookcase: 0.2575, IoU.blind: 0.1185, IoU.coffee table: 0.3630, IoU.toilet: 0.6094, IoU.flower: 0.1868, IoU.book: 0.3470, IoU.hill: 0.0307, IoU.bench: 0.2825, IoU.countertop: 0.3945, IoU.stove: 0.4321, IoU.palm: 0.3214, IoU.kitchen island: 0.1608, IoU.computer: 0.4046, IoU.swivel chair: 0.3064, IoU.boat: 0.3804, IoU.bar: 0.2453, IoU.arcade machine: 0.2840, IoU.hovel: 0.3404, IoU.bus: 0.7088, IoU.towel: 0.3467, IoU.light: 0.1086, IoU.truck: 0.0229, IoU.tower: 0.2818, IoU.chandelier: 0.4481, IoU.awning: 0.1658, IoU.streetlight: 0.0536, IoU.booth: 0.2476, IoU.television receiver: 0.4869, IoU.airplane: 0.4378, IoU.dirt track: 0.0013, IoU.apparel: 0.1014, IoU.pole: 0.0427, IoU.land: 0.0006, IoU.bannister: 0.0098, IoU.escalator: 0.4322, IoU.ottoman: 0.1432, IoU.bottle: 0.0654, IoU.buffet: 0.3095, IoU.poster: 0.0469, IoU.stage: 0.0476, IoU.van: 0.1232, IoU.ship: 0.6407, IoU.fountain: 0.1869, IoU.conveyer belt: 0.5758, IoU.canopy: 0.0961, IoU.washer: 0.5871, IoU.plaything: 0.0573, IoU.swimming pool: 0.3858, IoU.stool: 0.0450, IoU.barrel: 0.1594, IoU.basket: 0.1109, IoU.waterfall: 0.4214, IoU.tent: 0.8874, IoU.bag: 0.0013, IoU.minibike: 0.3608, IoU.cradle: 0.5242, IoU.oven: 0.1860, IoU.ball: 0.2583, IoU.food: 0.3757, IoU.step: 0.0314, IoU.tank: 0.0701, IoU.trade name: 0.1598, IoU.microwave: 0.2464, IoU.pot: 0.1892, IoU.animal: 0.3828, IoU.bicycle: 0.3348, IoU.lake: 0.0005, IoU.dishwasher: 0.2375, IoU.screen: 0.5402, IoU.blanket: 0.0000, IoU.sculpture: 0.2429, IoU.hood: 0.2376, IoU.sconce: 0.0914, IoU.vase: 0.1519, IoU.traffic light: 0.0773, IoU.tray: 0.0084, IoU.ashcan: 0.1502, IoU.fan: 0.2591, IoU.pier: 0.4041, IoU.crt screen: 0.0000, IoU.plate: 0.2550, IoU.monitor: 0.3253, IoU.bulletin board: 0.2985, IoU.shower: 0.0000, IoU.radiator: 0.3559, IoU.glass: 0.0047, IoU.clock: 0.0626, IoU.flag: 0.1345, Acc.wall: 0.8051, Acc.building: 0.8956, Acc.sky: 0.9251, Acc.floor: 0.8461, Acc.tree: 0.8291, Acc.ceiling: 0.8664, Acc.road: 0.7913, Acc.bed : 0.8968, Acc.windowpane: 0.6905, Acc.grass: 0.7970, Acc.cabinet: 0.6064, Acc.sidewalk: 0.7123, Acc.person: 0.8198, Acc.earth: 0.3551, Acc.door: 0.4202, Acc.table: 0.5196, Acc.mountain: 0.7449, Acc.plant: 0.5174, Acc.curtain: 0.7061, Acc.chair: 0.5545, Acc.car: 0.8428, Acc.water: 0.5345, Acc.painting: 0.7061, Acc.sofa: 0.6570, Acc.shelf: 0.4819, Acc.house: 0.6599, Acc.sea: 0.6224, Acc.mirror: 0.5003, Acc.rug: 0.5758, Acc.field: 0.4834, Acc.armchair: 0.3749, Acc.seat: 0.6783, Acc.fence: 0.4664, Acc.desk: 0.4948, Acc.rock: 0.7260, Acc.wardrobe: 0.6747, Acc.lamp: 0.4578, Acc.bathtub: 0.6930, Acc.railing: 0.3257, Acc.cushion: 0.3640, Acc.base: 0.2882, Acc.box: 0.2068, Acc.column: 0.2507, Acc.signboard: 0.2646, Acc.chest of drawers: 0.4236, Acc.counter: 0.3236, Acc.sand: 0.4092, Acc.sink: 0.5470, Acc.skyscraper: 0.7229, Acc.fireplace: 0.6780, Acc.refrigerator: 0.6156, Acc.grandstand: 0.6606, Acc.path: 0.2107, Acc.stairs: 0.2541, Acc.runway: 0.8517, Acc.case: 0.5264, Acc.pool table: 0.8821, Acc.pillow: 0.4208, Acc.screen door: 0.7842, Acc.stairway: 0.5264, Acc.river: 0.4246, Acc.bridge: 0.3652, Acc.bookcase: 0.4062, Acc.blind: 0.1289, Acc.coffee table: 0.6582, Acc.toilet: 0.7342, Acc.flower: 0.3582, Acc.book: 0.4875, Acc.hill: 0.0390, Acc.bench: 0.4100, Acc.countertop: 0.5030, Acc.stove: 0.5858, Acc.palm: 0.5231, Acc.kitchen island: 0.3288, Acc.computer: 0.6160, Acc.swivel chair: 0.4318, Acc.boat: 0.4699, Acc.bar: 0.3177, Acc.arcade machine: 0.3236, Acc.hovel: 0.4220, Acc.bus: 0.7981, Acc.towel: 0.4068, Acc.light: 0.1151, Acc.truck: 0.0262, Acc.tower: 0.3494, Acc.chandelier: 0.6060, Acc.awning: 0.2031, Acc.streetlight: 0.0616, Acc.booth: 0.3607, Acc.television receiver: 0.6755, Acc.airplane: 0.5631, Acc.dirt track: 0.0016, Acc.apparel: 0.1260, Acc.pole: 0.0678, Acc.land: 0.0009, Acc.bannister: 0.0168, Acc.escalator: 0.5922, Acc.ottoman: 0.1613, Acc.bottle: 0.0762, Acc.buffet: 0.3978, Acc.poster: 0.0509, Acc.stage: 0.1283, Acc.van: 0.1402, Acc.ship: 0.8370, Acc.fountain: 0.2025, Acc.conveyer belt: 0.7034, Acc.canopy: 0.1312, Acc.washer: 0.6452, Acc.plaything: 0.1064, Acc.swimming pool: 0.6712, Acc.stool: 0.0495, Acc.barrel: 0.2237, Acc.basket: 0.1241, Acc.waterfall: 0.5631, Acc.tent: 0.9498, Acc.bag: 0.0013, Acc.minibike: 0.4345, Acc.cradle: 0.8097, Acc.oven: 0.2339, Acc.ball: 0.3380, Acc.food: 0.4555, Acc.step: 0.0329, Acc.tank: 0.0726, Acc.trade name: 0.1778, Acc.microwave: 0.2719, Acc.pot: 0.2293, Acc.animal: 0.4240, Acc.bicycle: 0.5641, Acc.lake: 0.0005, Acc.dishwasher: 0.2933, Acc.screen: 0.7711, Acc.blanket: 0.0000, Acc.sculpture: 0.3612, Acc.hood: 0.3119, Acc.sconce: 0.0953, Acc.vase: 0.2177, Acc.traffic light: 0.1072, Acc.tray: 0.0115, Acc.ashcan: 0.1952, Acc.fan: 0.3174, Acc.pier: 0.6718, Acc.crt screen: 0.0000, Acc.plate: 0.3082, Acc.monitor: 0.5509, Acc.bulletin board: 0.3810, Acc.shower: 0.0000, Acc.radiator: 0.4049, Acc.glass: 0.0048, Acc.clock: 0.0692, Acc.flag: 0.1480
2022-11-04 12:38:12,184 - mmseg - INFO - Iter [24050/80000]	lr: 8.698e-05, eta: 5:26:06, time: 0.527, data_time: 0.191, memory: 12988, decode.loss_ce: 0.5550, decode.acc_seg: 78.7290, aux.loss_ce: 0.2966, aux.acc_seg: 73.7908, loss: 0.8516
2022-11-04 12:38:29,076 - mmseg - INFO - Iter [24100/80000]	lr: 8.691e-05, eta: 5:25:47, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5320, decode.acc_seg: 79.4469, aux.loss_ce: 0.2835, aux.acc_seg: 74.4496, loss: 0.8155
2022-11-04 12:38:45,918 - mmseg - INFO - Iter [24150/80000]	lr: 8.684e-05, eta: 5:25:28, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5519, decode.acc_seg: 78.9850, aux.loss_ce: 0.2976, aux.acc_seg: 73.1922, loss: 0.8495
2022-11-04 12:39:03,161 - mmseg - INFO - Iter [24200/80000]	lr: 8.677e-05, eta: 5:25:10, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5662, decode.acc_seg: 78.7688, aux.loss_ce: 0.2986, aux.acc_seg: 73.6207, loss: 0.8648
2022-11-04 12:39:20,099 - mmseg - INFO - Iter [24250/80000]	lr: 8.670e-05, eta: 5:24:51, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5626, decode.acc_seg: 78.7455, aux.loss_ce: 0.3059, aux.acc_seg: 73.0521, loss: 0.8685
2022-11-04 12:39:37,235 - mmseg - INFO - Iter [24300/80000]	lr: 8.663e-05, eta: 5:24:33, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5672, decode.acc_seg: 78.6973, aux.loss_ce: 0.2947, aux.acc_seg: 73.7704, loss: 0.8619
2022-11-04 12:39:54,395 - mmseg - INFO - Iter [24350/80000]	lr: 8.656e-05, eta: 5:24:15, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5655, decode.acc_seg: 78.4367, aux.loss_ce: 0.2998, aux.acc_seg: 72.7034, loss: 0.8653
2022-11-04 12:40:11,773 - mmseg - INFO - Iter [24400/80000]	lr: 8.649e-05, eta: 5:23:57, time: 0.348, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5551, decode.acc_seg: 79.2156, aux.loss_ce: 0.2986, aux.acc_seg: 73.5163, loss: 0.8538
2022-11-04 12:40:28,860 - mmseg - INFO - Iter [24450/80000]	lr: 8.642e-05, eta: 5:23:38, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5547, decode.acc_seg: 78.7088, aux.loss_ce: 0.2969, aux.acc_seg: 73.6582, loss: 0.8516
2022-11-04 12:40:45,904 - mmseg - INFO - Iter [24500/80000]	lr: 8.635e-05, eta: 5:23:20, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5668, decode.acc_seg: 78.6507, aux.loss_ce: 0.3052, aux.acc_seg: 73.3492, loss: 0.8720
2022-11-04 12:41:02,963 - mmseg - INFO - Iter [24550/80000]	lr: 8.628e-05, eta: 5:23:02, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5681, decode.acc_seg: 78.5734, aux.loss_ce: 0.3040, aux.acc_seg: 73.7304, loss: 0.8720
2022-11-04 12:41:20,036 - mmseg - INFO - Iter [24600/80000]	lr: 8.621e-05, eta: 5:22:43, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5650, decode.acc_seg: 78.3995, aux.loss_ce: 0.2960, aux.acc_seg: 73.4067, loss: 0.8609
2022-11-04 12:41:40,229 - mmseg - INFO - Iter [24650/80000]	lr: 8.614e-05, eta: 5:22:32, time: 0.404, data_time: 0.067, memory: 12988, decode.loss_ce: 0.5538, decode.acc_seg: 78.8664, aux.loss_ce: 0.2957, aux.acc_seg: 73.3567, loss: 0.8494
2022-11-04 12:41:57,598 - mmseg - INFO - Iter [24700/80000]	lr: 8.607e-05, eta: 5:22:14, time: 0.347, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5762, decode.acc_seg: 78.4090, aux.loss_ce: 0.3103, aux.acc_seg: 72.6695, loss: 0.8865
2022-11-04 12:42:14,593 - mmseg - INFO - Iter [24750/80000]	lr: 8.600e-05, eta: 5:21:56, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5656, decode.acc_seg: 78.2872, aux.loss_ce: 0.3028, aux.acc_seg: 73.2664, loss: 0.8684
2022-11-04 12:42:31,536 - mmseg - INFO - Iter [24800/80000]	lr: 8.593e-05, eta: 5:21:37, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5597, decode.acc_seg: 78.3435, aux.loss_ce: 0.2969, aux.acc_seg: 73.3000, loss: 0.8566
2022-11-04 12:42:48,419 - mmseg - INFO - Iter [24850/80000]	lr: 8.586e-05, eta: 5:21:18, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5694, decode.acc_seg: 78.5488, aux.loss_ce: 0.3046, aux.acc_seg: 72.8547, loss: 0.8740
2022-11-04 12:43:05,431 - mmseg - INFO - Iter [24900/80000]	lr: 8.579e-05, eta: 5:21:00, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5616, decode.acc_seg: 78.1918, aux.loss_ce: 0.2994, aux.acc_seg: 73.1343, loss: 0.8609
2022-11-04 12:43:22,448 - mmseg - INFO - Iter [24950/80000]	lr: 8.572e-05, eta: 5:20:41, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5416, decode.acc_seg: 79.4729, aux.loss_ce: 0.2915, aux.acc_seg: 73.8786, loss: 0.8331
2022-11-04 12:43:39,383 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 12:43:39,383 - mmseg - INFO - Iter [25000/80000]	lr: 8.565e-05, eta: 5:20:22, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5598, decode.acc_seg: 78.6667, aux.loss_ce: 0.3034, aux.acc_seg: 73.3767, loss: 0.8632
2022-11-04 12:43:56,722 - mmseg - INFO - Iter [25050/80000]	lr: 8.558e-05, eta: 5:20:05, time: 0.347, data_time: 0.012, memory: 12988, decode.loss_ce: 0.5508, decode.acc_seg: 79.0165, aux.loss_ce: 0.2935, aux.acc_seg: 74.0393, loss: 0.8443
2022-11-04 12:44:13,498 - mmseg - INFO - Iter [25100/80000]	lr: 8.551e-05, eta: 5:19:46, time: 0.336, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5675, decode.acc_seg: 78.3859, aux.loss_ce: 0.3021, aux.acc_seg: 72.9794, loss: 0.8696
2022-11-04 12:44:30,622 - mmseg - INFO - Iter [25150/80000]	lr: 8.544e-05, eta: 5:19:27, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5537, decode.acc_seg: 79.1404, aux.loss_ce: 0.2961, aux.acc_seg: 73.7611, loss: 0.8499
2022-11-04 12:44:47,440 - mmseg - INFO - Iter [25200/80000]	lr: 8.537e-05, eta: 5:19:08, time: 0.336, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5657, decode.acc_seg: 78.5831, aux.loss_ce: 0.2925, aux.acc_seg: 73.8329, loss: 0.8582
2022-11-04 12:45:07,298 - mmseg - INFO - Iter [25250/80000]	lr: 8.530e-05, eta: 5:18:56, time: 0.397, data_time: 0.063, memory: 12988, decode.loss_ce: 0.5643, decode.acc_seg: 78.8366, aux.loss_ce: 0.3067, aux.acc_seg: 72.9812, loss: 0.8709
2022-11-04 12:45:24,229 - mmseg - INFO - Iter [25300/80000]	lr: 8.523e-05, eta: 5:18:37, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5595, decode.acc_seg: 78.9023, aux.loss_ce: 0.2932, aux.acc_seg: 73.8207, loss: 0.8527
2022-11-04 12:45:41,334 - mmseg - INFO - Iter [25350/80000]	lr: 8.516e-05, eta: 5:18:19, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5650, decode.acc_seg: 78.7493, aux.loss_ce: 0.2975, aux.acc_seg: 73.8881, loss: 0.8625
2022-11-04 12:45:58,449 - mmseg - INFO - Iter [25400/80000]	lr: 8.509e-05, eta: 5:18:01, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5412, decode.acc_seg: 79.3412, aux.loss_ce: 0.2911, aux.acc_seg: 73.8468, loss: 0.8323
2022-11-04 12:46:15,421 - mmseg - INFO - Iter [25450/80000]	lr: 8.502e-05, eta: 5:17:42, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5458, decode.acc_seg: 78.9422, aux.loss_ce: 0.2874, aux.acc_seg: 74.0434, loss: 0.8332
2022-11-04 12:46:32,543 - mmseg - INFO - Iter [25500/80000]	lr: 8.495e-05, eta: 5:17:24, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5552, decode.acc_seg: 78.7646, aux.loss_ce: 0.2970, aux.acc_seg: 73.5231, loss: 0.8523
2022-11-04 12:46:49,481 - mmseg - INFO - Iter [25550/80000]	lr: 8.488e-05, eta: 5:17:06, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5752, decode.acc_seg: 78.2377, aux.loss_ce: 0.3079, aux.acc_seg: 72.8735, loss: 0.8831
2022-11-04 12:47:06,650 - mmseg - INFO - Iter [25600/80000]	lr: 8.481e-05, eta: 5:16:47, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5618, decode.acc_seg: 78.3347, aux.loss_ce: 0.2968, aux.acc_seg: 73.3729, loss: 0.8586
2022-11-04 12:47:23,733 - mmseg - INFO - Iter [25650/80000]	lr: 8.474e-05, eta: 5:16:29, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5562, decode.acc_seg: 78.5229, aux.loss_ce: 0.2933, aux.acc_seg: 73.5258, loss: 0.8496
2022-11-04 12:47:40,786 - mmseg - INFO - Iter [25700/80000]	lr: 8.467e-05, eta: 5:16:11, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5526, decode.acc_seg: 79.2471, aux.loss_ce: 0.2942, aux.acc_seg: 74.0536, loss: 0.8468
2022-11-04 12:47:57,897 - mmseg - INFO - Iter [25750/80000]	lr: 8.460e-05, eta: 5:15:53, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5626, decode.acc_seg: 79.0525, aux.loss_ce: 0.3006, aux.acc_seg: 73.0830, loss: 0.8632
2022-11-04 12:48:14,881 - mmseg - INFO - Iter [25800/80000]	lr: 8.453e-05, eta: 5:15:34, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5529, decode.acc_seg: 78.9213, aux.loss_ce: 0.2915, aux.acc_seg: 74.1974, loss: 0.8443
2022-11-04 12:48:31,899 - mmseg - INFO - Iter [25850/80000]	lr: 8.446e-05, eta: 5:15:16, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5533, decode.acc_seg: 78.8355, aux.loss_ce: 0.2968, aux.acc_seg: 73.1630, loss: 0.8501
2022-11-04 12:48:51,947 - mmseg - INFO - Iter [25900/80000]	lr: 8.439e-05, eta: 5:15:04, time: 0.401, data_time: 0.057, memory: 12988, decode.loss_ce: 0.5807, decode.acc_seg: 78.1540, aux.loss_ce: 0.3076, aux.acc_seg: 72.8359, loss: 0.8884
2022-11-04 12:49:08,992 - mmseg - INFO - Iter [25950/80000]	lr: 8.432e-05, eta: 5:14:45, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5641, decode.acc_seg: 78.5517, aux.loss_ce: 0.3050, aux.acc_seg: 73.0083, loss: 0.8692
2022-11-04 12:49:25,961 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 12:49:25,961 - mmseg - INFO - Iter [26000/80000]	lr: 8.425e-05, eta: 5:14:27, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5314, decode.acc_seg: 79.4782, aux.loss_ce: 0.2916, aux.acc_seg: 73.4115, loss: 0.8230
2022-11-04 12:49:43,074 - mmseg - INFO - Iter [26050/80000]	lr: 8.418e-05, eta: 5:14:09, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5488, decode.acc_seg: 78.8754, aux.loss_ce: 0.2934, aux.acc_seg: 73.5638, loss: 0.8423
2022-11-04 12:50:00,195 - mmseg - INFO - Iter [26100/80000]	lr: 8.411e-05, eta: 5:13:50, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5426, decode.acc_seg: 78.9532, aux.loss_ce: 0.2927, aux.acc_seg: 73.4132, loss: 0.8353
2022-11-04 12:50:17,350 - mmseg - INFO - Iter [26150/80000]	lr: 8.404e-05, eta: 5:13:32, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5555, decode.acc_seg: 79.0969, aux.loss_ce: 0.2980, aux.acc_seg: 73.5156, loss: 0.8535
2022-11-04 12:50:34,301 - mmseg - INFO - Iter [26200/80000]	lr: 8.397e-05, eta: 5:13:14, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5416, decode.acc_seg: 79.4811, aux.loss_ce: 0.2932, aux.acc_seg: 74.1759, loss: 0.8348
2022-11-04 12:50:51,356 - mmseg - INFO - Iter [26250/80000]	lr: 8.390e-05, eta: 5:12:55, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5658, decode.acc_seg: 78.7638, aux.loss_ce: 0.2964, aux.acc_seg: 73.7875, loss: 0.8621
2022-11-04 12:51:08,217 - mmseg - INFO - Iter [26300/80000]	lr: 8.383e-05, eta: 5:12:37, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5463, decode.acc_seg: 79.2508, aux.loss_ce: 0.2960, aux.acc_seg: 73.5547, loss: 0.8422
2022-11-04 12:51:25,244 - mmseg - INFO - Iter [26350/80000]	lr: 8.376e-05, eta: 5:12:18, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5713, decode.acc_seg: 78.6523, aux.loss_ce: 0.3049, aux.acc_seg: 73.4755, loss: 0.8762
2022-11-04 12:51:42,065 - mmseg - INFO - Iter [26400/80000]	lr: 8.369e-05, eta: 5:12:00, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5253, decode.acc_seg: 79.7346, aux.loss_ce: 0.2777, aux.acc_seg: 74.8924, loss: 0.8031
2022-11-04 12:51:58,985 - mmseg - INFO - Iter [26450/80000]	lr: 8.362e-05, eta: 5:11:41, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5436, decode.acc_seg: 79.1759, aux.loss_ce: 0.2916, aux.acc_seg: 74.0026, loss: 0.8352
2022-11-04 12:52:15,818 - mmseg - INFO - Iter [26500/80000]	lr: 8.355e-05, eta: 5:11:22, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5519, decode.acc_seg: 79.2198, aux.loss_ce: 0.2912, aux.acc_seg: 74.2612, loss: 0.8431
2022-11-04 12:52:35,916 - mmseg - INFO - Iter [26550/80000]	lr: 8.348e-05, eta: 5:11:10, time: 0.402, data_time: 0.068, memory: 12988, decode.loss_ce: 0.5385, decode.acc_seg: 79.1304, aux.loss_ce: 0.2911, aux.acc_seg: 73.8136, loss: 0.8296
2022-11-04 12:52:53,037 - mmseg - INFO - Iter [26600/80000]	lr: 8.341e-05, eta: 5:10:52, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5232, decode.acc_seg: 79.6997, aux.loss_ce: 0.2816, aux.acc_seg: 74.2661, loss: 0.8048
2022-11-04 12:53:09,989 - mmseg - INFO - Iter [26650/80000]	lr: 8.334e-05, eta: 5:10:33, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5383, decode.acc_seg: 79.7800, aux.loss_ce: 0.2951, aux.acc_seg: 73.9962, loss: 0.8334
2022-11-04 12:53:27,213 - mmseg - INFO - Iter [26700/80000]	lr: 8.326e-05, eta: 5:10:16, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5361, decode.acc_seg: 79.5760, aux.loss_ce: 0.2860, aux.acc_seg: 74.4315, loss: 0.8221
2022-11-04 12:53:44,200 - mmseg - INFO - Iter [26750/80000]	lr: 8.319e-05, eta: 5:09:57, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5469, decode.acc_seg: 78.9213, aux.loss_ce: 0.2965, aux.acc_seg: 73.3997, loss: 0.8434
2022-11-04 12:54:01,369 - mmseg - INFO - Iter [26800/80000]	lr: 8.312e-05, eta: 5:09:39, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5515, decode.acc_seg: 79.0688, aux.loss_ce: 0.2924, aux.acc_seg: 73.7146, loss: 0.8439
2022-11-04 12:54:18,498 - mmseg - INFO - Iter [26850/80000]	lr: 8.305e-05, eta: 5:09:21, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5634, decode.acc_seg: 78.3745, aux.loss_ce: 0.3008, aux.acc_seg: 73.1883, loss: 0.8642
2022-11-04 12:54:35,645 - mmseg - INFO - Iter [26900/80000]	lr: 8.298e-05, eta: 5:09:03, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5612, decode.acc_seg: 78.7826, aux.loss_ce: 0.2998, aux.acc_seg: 73.4997, loss: 0.8611
2022-11-04 12:54:52,596 - mmseg - INFO - Iter [26950/80000]	lr: 8.291e-05, eta: 5:08:44, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5431, decode.acc_seg: 79.4086, aux.loss_ce: 0.2880, aux.acc_seg: 74.1882, loss: 0.8310
2022-11-04 12:55:09,492 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 12:55:09,493 - mmseg - INFO - Iter [27000/80000]	lr: 8.284e-05, eta: 5:08:26, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5656, decode.acc_seg: 78.7650, aux.loss_ce: 0.2982, aux.acc_seg: 73.8392, loss: 0.8638
2022-11-04 12:55:26,705 - mmseg - INFO - Iter [27050/80000]	lr: 8.277e-05, eta: 5:08:08, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5472, decode.acc_seg: 78.9828, aux.loss_ce: 0.2965, aux.acc_seg: 73.3658, loss: 0.8437
2022-11-04 12:55:43,743 - mmseg - INFO - Iter [27100/80000]	lr: 8.270e-05, eta: 5:07:50, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5495, decode.acc_seg: 79.3070, aux.loss_ce: 0.3016, aux.acc_seg: 73.5007, loss: 0.8511
2022-11-04 12:56:03,698 - mmseg - INFO - Iter [27150/80000]	lr: 8.263e-05, eta: 5:07:37, time: 0.399, data_time: 0.071, memory: 12988, decode.loss_ce: 0.5612, decode.acc_seg: 78.5745, aux.loss_ce: 0.2984, aux.acc_seg: 73.3481, loss: 0.8596
2022-11-04 12:56:20,517 - mmseg - INFO - Iter [27200/80000]	lr: 8.256e-05, eta: 5:07:18, time: 0.336, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5373, decode.acc_seg: 79.8423, aux.loss_ce: 0.2845, aux.acc_seg: 74.8721, loss: 0.8218
2022-11-04 12:56:37,483 - mmseg - INFO - Iter [27250/80000]	lr: 8.249e-05, eta: 5:07:00, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5459, decode.acc_seg: 79.0911, aux.loss_ce: 0.2950, aux.acc_seg: 73.6665, loss: 0.8409
2022-11-04 12:56:54,508 - mmseg - INFO - Iter [27300/80000]	lr: 8.242e-05, eta: 5:06:42, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5384, decode.acc_seg: 79.4849, aux.loss_ce: 0.2876, aux.acc_seg: 74.5596, loss: 0.8261
2022-11-04 12:57:11,719 - mmseg - INFO - Iter [27350/80000]	lr: 8.235e-05, eta: 5:06:24, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5272, decode.acc_seg: 79.8842, aux.loss_ce: 0.2867, aux.acc_seg: 74.7086, loss: 0.8139
2022-11-04 12:57:28,948 - mmseg - INFO - Iter [27400/80000]	lr: 8.228e-05, eta: 5:06:06, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5326, decode.acc_seg: 79.8204, aux.loss_ce: 0.2878, aux.acc_seg: 74.9553, loss: 0.8204
2022-11-04 12:57:45,888 - mmseg - INFO - Iter [27450/80000]	lr: 8.221e-05, eta: 5:05:47, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5411, decode.acc_seg: 79.3331, aux.loss_ce: 0.2912, aux.acc_seg: 74.2141, loss: 0.8324
2022-11-04 12:58:02,967 - mmseg - INFO - Iter [27500/80000]	lr: 8.214e-05, eta: 5:05:29, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5367, decode.acc_seg: 79.3278, aux.loss_ce: 0.2918, aux.acc_seg: 73.8234, loss: 0.8285
2022-11-04 12:58:20,208 - mmseg - INFO - Iter [27550/80000]	lr: 8.207e-05, eta: 5:05:11, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5599, decode.acc_seg: 78.6958, aux.loss_ce: 0.2996, aux.acc_seg: 73.4153, loss: 0.8596
2022-11-04 12:58:37,602 - mmseg - INFO - Iter [27600/80000]	lr: 8.200e-05, eta: 5:04:54, time: 0.348, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5437, decode.acc_seg: 79.0962, aux.loss_ce: 0.2880, aux.acc_seg: 73.8751, loss: 0.8317
2022-11-04 12:58:54,844 - mmseg - INFO - Iter [27650/80000]	lr: 8.193e-05, eta: 5:04:36, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5578, decode.acc_seg: 79.0263, aux.loss_ce: 0.3010, aux.acc_seg: 73.4110, loss: 0.8589
2022-11-04 12:59:11,905 - mmseg - INFO - Iter [27700/80000]	lr: 8.186e-05, eta: 5:04:18, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5695, decode.acc_seg: 78.5963, aux.loss_ce: 0.3107, aux.acc_seg: 72.6780, loss: 0.8802
2022-11-04 12:59:28,856 - mmseg - INFO - Iter [27750/80000]	lr: 8.179e-05, eta: 5:03:59, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5295, decode.acc_seg: 79.5779, aux.loss_ce: 0.2864, aux.acc_seg: 74.1697, loss: 0.8159
2022-11-04 12:59:48,762 - mmseg - INFO - Iter [27800/80000]	lr: 8.172e-05, eta: 5:03:46, time: 0.398, data_time: 0.068, memory: 12988, decode.loss_ce: 0.5284, decode.acc_seg: 79.5163, aux.loss_ce: 0.2859, aux.acc_seg: 74.1402, loss: 0.8143
2022-11-04 13:00:06,074 - mmseg - INFO - Iter [27850/80000]	lr: 8.165e-05, eta: 5:03:29, time: 0.346, data_time: 0.008, memory: 12988, decode.loss_ce: 0.5187, decode.acc_seg: 79.6194, aux.loss_ce: 0.2797, aux.acc_seg: 74.0517, loss: 0.7985
2022-11-04 13:00:23,311 - mmseg - INFO - Iter [27900/80000]	lr: 8.158e-05, eta: 5:03:11, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5415, decode.acc_seg: 79.4579, aux.loss_ce: 0.2939, aux.acc_seg: 73.5809, loss: 0.8354
2022-11-04 13:00:40,200 - mmseg - INFO - Iter [27950/80000]	lr: 8.151e-05, eta: 5:02:52, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5622, decode.acc_seg: 78.8770, aux.loss_ce: 0.3026, aux.acc_seg: 73.3022, loss: 0.8648
2022-11-04 13:00:57,261 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 13:00:57,262 - mmseg - INFO - Iter [28000/80000]	lr: 8.143e-05, eta: 5:02:34, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5311, decode.acc_seg: 79.3676, aux.loss_ce: 0.2852, aux.acc_seg: 74.6006, loss: 0.8163
2022-11-04 13:01:14,583 - mmseg - INFO - Iter [28050/80000]	lr: 8.136e-05, eta: 5:02:16, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5504, decode.acc_seg: 79.1717, aux.loss_ce: 0.2964, aux.acc_seg: 73.7325, loss: 0.8468
2022-11-04 13:01:31,710 - mmseg - INFO - Iter [28100/80000]	lr: 8.129e-05, eta: 5:01:58, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5464, decode.acc_seg: 79.5660, aux.loss_ce: 0.2952, aux.acc_seg: 74.0256, loss: 0.8416
2022-11-04 13:01:48,737 - mmseg - INFO - Iter [28150/80000]	lr: 8.122e-05, eta: 5:01:40, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5275, decode.acc_seg: 79.9397, aux.loss_ce: 0.2880, aux.acc_seg: 74.1156, loss: 0.8156
2022-11-04 13:02:05,609 - mmseg - INFO - Iter [28200/80000]	lr: 8.115e-05, eta: 5:01:21, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5303, decode.acc_seg: 79.9189, aux.loss_ce: 0.2905, aux.acc_seg: 74.3023, loss: 0.8208
2022-11-04 13:02:22,519 - mmseg - INFO - Iter [28250/80000]	lr: 8.108e-05, eta: 5:01:03, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5244, decode.acc_seg: 79.9884, aux.loss_ce: 0.2839, aux.acc_seg: 74.6166, loss: 0.8083
2022-11-04 13:02:39,482 - mmseg - INFO - Iter [28300/80000]	lr: 8.101e-05, eta: 5:00:45, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5623, decode.acc_seg: 78.7032, aux.loss_ce: 0.2992, aux.acc_seg: 73.3802, loss: 0.8615
2022-11-04 13:02:56,426 - mmseg - INFO - Iter [28350/80000]	lr: 8.094e-05, eta: 5:00:26, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5422, decode.acc_seg: 78.7450, aux.loss_ce: 0.2945, aux.acc_seg: 73.3345, loss: 0.8367
2022-11-04 13:03:16,702 - mmseg - INFO - Iter [28400/80000]	lr: 8.087e-05, eta: 5:00:14, time: 0.406, data_time: 0.064, memory: 12988, decode.loss_ce: 0.5354, decode.acc_seg: 79.6166, aux.loss_ce: 0.2877, aux.acc_seg: 74.3515, loss: 0.8231
2022-11-04 13:03:33,746 - mmseg - INFO - Iter [28450/80000]	lr: 8.080e-05, eta: 4:59:56, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5368, decode.acc_seg: 79.7702, aux.loss_ce: 0.2947, aux.acc_seg: 74.0264, loss: 0.8315
2022-11-04 13:03:50,741 - mmseg - INFO - Iter [28500/80000]	lr: 8.073e-05, eta: 4:59:37, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5128, decode.acc_seg: 80.2418, aux.loss_ce: 0.2821, aux.acc_seg: 74.4641, loss: 0.7949
2022-11-04 13:04:07,650 - mmseg - INFO - Iter [28550/80000]	lr: 8.066e-05, eta: 4:59:19, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5317, decode.acc_seg: 79.6889, aux.loss_ce: 0.2890, aux.acc_seg: 73.9399, loss: 0.8207
2022-11-04 13:04:24,710 - mmseg - INFO - Iter [28600/80000]	lr: 8.059e-05, eta: 4:59:01, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5269, decode.acc_seg: 79.8482, aux.loss_ce: 0.2804, aux.acc_seg: 74.8258, loss: 0.8072
2022-11-04 13:04:41,876 - mmseg - INFO - Iter [28650/80000]	lr: 8.052e-05, eta: 4:58:43, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5402, decode.acc_seg: 79.2148, aux.loss_ce: 0.2949, aux.acc_seg: 73.9000, loss: 0.8351
2022-11-04 13:04:58,785 - mmseg - INFO - Iter [28700/80000]	lr: 8.045e-05, eta: 4:58:24, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5236, decode.acc_seg: 80.0348, aux.loss_ce: 0.2870, aux.acc_seg: 74.6125, loss: 0.8106
2022-11-04 13:05:15,957 - mmseg - INFO - Iter [28750/80000]	lr: 8.038e-05, eta: 4:58:07, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5575, decode.acc_seg: 78.8156, aux.loss_ce: 0.3051, aux.acc_seg: 73.0317, loss: 0.8626
2022-11-04 13:05:33,319 - mmseg - INFO - Iter [28800/80000]	lr: 8.031e-05, eta: 4:57:49, time: 0.347, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5378, decode.acc_seg: 79.4219, aux.loss_ce: 0.2886, aux.acc_seg: 73.7650, loss: 0.8264
2022-11-04 13:05:50,348 - mmseg - INFO - Iter [28850/80000]	lr: 8.024e-05, eta: 4:57:31, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5441, decode.acc_seg: 79.4710, aux.loss_ce: 0.2971, aux.acc_seg: 73.7963, loss: 0.8412
2022-11-04 13:06:07,276 - mmseg - INFO - Iter [28900/80000]	lr: 8.017e-05, eta: 4:57:12, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5583, decode.acc_seg: 79.4956, aux.loss_ce: 0.2985, aux.acc_seg: 74.0530, loss: 0.8568
2022-11-04 13:06:24,350 - mmseg - INFO - Iter [28950/80000]	lr: 8.009e-05, eta: 4:56:54, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5443, decode.acc_seg: 79.1884, aux.loss_ce: 0.2937, aux.acc_seg: 73.6820, loss: 0.8380
2022-11-04 13:06:41,360 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 13:06:41,360 - mmseg - INFO - Iter [29000/80000]	lr: 8.002e-05, eta: 4:56:36, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5375, decode.acc_seg: 79.4197, aux.loss_ce: 0.2845, aux.acc_seg: 74.5278, loss: 0.8220
2022-11-04 13:07:01,257 - mmseg - INFO - Iter [29050/80000]	lr: 7.995e-05, eta: 4:56:23, time: 0.398, data_time: 0.065, memory: 12988, decode.loss_ce: 0.5233, decode.acc_seg: 80.0291, aux.loss_ce: 0.2790, aux.acc_seg: 74.9883, loss: 0.8022
2022-11-04 13:07:18,708 - mmseg - INFO - Iter [29100/80000]	lr: 7.988e-05, eta: 4:56:05, time: 0.349, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5330, decode.acc_seg: 79.5761, aux.loss_ce: 0.2925, aux.acc_seg: 74.2879, loss: 0.8255
2022-11-04 13:07:35,894 - mmseg - INFO - Iter [29150/80000]	lr: 7.981e-05, eta: 4:55:48, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5178, decode.acc_seg: 80.1322, aux.loss_ce: 0.2896, aux.acc_seg: 73.9433, loss: 0.8074
2022-11-04 13:07:53,112 - mmseg - INFO - Iter [29200/80000]	lr: 7.974e-05, eta: 4:55:30, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5322, decode.acc_seg: 79.5786, aux.loss_ce: 0.2883, aux.acc_seg: 74.2959, loss: 0.8205
2022-11-04 13:08:10,286 - mmseg - INFO - Iter [29250/80000]	lr: 7.967e-05, eta: 4:55:12, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5328, decode.acc_seg: 79.9707, aux.loss_ce: 0.2885, aux.acc_seg: 74.1577, loss: 0.8214
2022-11-04 13:08:27,400 - mmseg - INFO - Iter [29300/80000]	lr: 7.960e-05, eta: 4:54:54, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5374, decode.acc_seg: 79.2407, aux.loss_ce: 0.2922, aux.acc_seg: 73.5721, loss: 0.8296
2022-11-04 13:08:44,460 - mmseg - INFO - Iter [29350/80000]	lr: 7.953e-05, eta: 4:54:36, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5116, decode.acc_seg: 80.4907, aux.loss_ce: 0.2775, aux.acc_seg: 75.0963, loss: 0.7891
2022-11-04 13:09:01,658 - mmseg - INFO - Iter [29400/80000]	lr: 7.946e-05, eta: 4:54:18, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5209, decode.acc_seg: 80.2454, aux.loss_ce: 0.2815, aux.acc_seg: 75.1474, loss: 0.8024
2022-11-04 13:09:18,669 - mmseg - INFO - Iter [29450/80000]	lr: 7.939e-05, eta: 4:53:59, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5395, decode.acc_seg: 79.3704, aux.loss_ce: 0.2878, aux.acc_seg: 74.1167, loss: 0.8273
2022-11-04 13:09:35,743 - mmseg - INFO - Iter [29500/80000]	lr: 7.932e-05, eta: 4:53:41, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5359, decode.acc_seg: 79.8699, aux.loss_ce: 0.2862, aux.acc_seg: 74.7584, loss: 0.8221
2022-11-04 13:09:53,095 - mmseg - INFO - Iter [29550/80000]	lr: 7.925e-05, eta: 4:53:24, time: 0.347, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5506, decode.acc_seg: 79.3018, aux.loss_ce: 0.2952, aux.acc_seg: 74.2495, loss: 0.8458
2022-11-04 13:10:09,781 - mmseg - INFO - Iter [29600/80000]	lr: 7.918e-05, eta: 4:53:05, time: 0.334, data_time: 0.008, memory: 12988, decode.loss_ce: 0.5276, decode.acc_seg: 79.5761, aux.loss_ce: 0.2830, aux.acc_seg: 74.4828, loss: 0.8106
2022-11-04 13:10:26,676 - mmseg - INFO - Iter [29650/80000]	lr: 7.911e-05, eta: 4:52:47, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5496, decode.acc_seg: 79.2237, aux.loss_ce: 0.3024, aux.acc_seg: 73.3958, loss: 0.8520
2022-11-04 13:10:46,664 - mmseg - INFO - Iter [29700/80000]	lr: 7.903e-05, eta: 4:52:34, time: 0.400, data_time: 0.061, memory: 12988, decode.loss_ce: 0.5435, decode.acc_seg: 79.6049, aux.loss_ce: 0.2951, aux.acc_seg: 74.1520, loss: 0.8386
2022-11-04 13:11:03,836 - mmseg - INFO - Iter [29750/80000]	lr: 7.896e-05, eta: 4:52:16, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5148, decode.acc_seg: 80.0308, aux.loss_ce: 0.2852, aux.acc_seg: 74.3626, loss: 0.8000
2022-11-04 13:11:20,912 - mmseg - INFO - Iter [29800/80000]	lr: 7.889e-05, eta: 4:51:58, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5397, decode.acc_seg: 79.0497, aux.loss_ce: 0.2914, aux.acc_seg: 74.0169, loss: 0.8311
2022-11-04 13:11:38,206 - mmseg - INFO - Iter [29850/80000]	lr: 7.882e-05, eta: 4:51:40, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5190, decode.acc_seg: 79.8284, aux.loss_ce: 0.2804, aux.acc_seg: 74.4209, loss: 0.7994
2022-11-04 13:11:55,104 - mmseg - INFO - Iter [29900/80000]	lr: 7.875e-05, eta: 4:51:21, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5247, decode.acc_seg: 79.9839, aux.loss_ce: 0.2802, aux.acc_seg: 74.8084, loss: 0.8048
2022-11-04 13:12:12,260 - mmseg - INFO - Iter [29950/80000]	lr: 7.868e-05, eta: 4:51:04, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5225, decode.acc_seg: 80.3303, aux.loss_ce: 0.2854, aux.acc_seg: 74.5934, loss: 0.8079
2022-11-04 13:12:29,095 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 13:12:29,096 - mmseg - INFO - Iter [30000/80000]	lr: 7.861e-05, eta: 4:50:45, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5203, decode.acc_seg: 80.1383, aux.loss_ce: 0.2828, aux.acc_seg: 74.7102, loss: 0.8030
2022-11-04 13:12:45,863 - mmseg - INFO - Iter [30050/80000]	lr: 7.854e-05, eta: 4:50:26, time: 0.335, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5294, decode.acc_seg: 79.6663, aux.loss_ce: 0.2869, aux.acc_seg: 74.1569, loss: 0.8163
2022-11-04 13:13:02,999 - mmseg - INFO - Iter [30100/80000]	lr: 7.847e-05, eta: 4:50:09, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5137, decode.acc_seg: 80.1785, aux.loss_ce: 0.2850, aux.acc_seg: 74.7506, loss: 0.7987
2022-11-04 13:13:20,261 - mmseg - INFO - Iter [30150/80000]	lr: 7.840e-05, eta: 4:49:51, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5267, decode.acc_seg: 79.9796, aux.loss_ce: 0.2901, aux.acc_seg: 74.2310, loss: 0.8167
2022-11-04 13:13:37,506 - mmseg - INFO - Iter [30200/80000]	lr: 7.833e-05, eta: 4:49:33, time: 0.345, data_time: 0.008, memory: 12988, decode.loss_ce: 0.5408, decode.acc_seg: 79.5261, aux.loss_ce: 0.2939, aux.acc_seg: 74.0628, loss: 0.8346
2022-11-04 13:13:54,377 - mmseg - INFO - Iter [30250/80000]	lr: 7.826e-05, eta: 4:49:15, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5356, decode.acc_seg: 79.4495, aux.loss_ce: 0.2906, aux.acc_seg: 73.9802, loss: 0.8261
2022-11-04 13:14:14,340 - mmseg - INFO - Iter [30300/80000]	lr: 7.819e-05, eta: 4:49:01, time: 0.399, data_time: 0.067, memory: 12988, decode.loss_ce: 0.5152, decode.acc_seg: 80.1751, aux.loss_ce: 0.2851, aux.acc_seg: 74.7015, loss: 0.8004
2022-11-04 13:14:31,293 - mmseg - INFO - Iter [30350/80000]	lr: 7.812e-05, eta: 4:48:43, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5186, decode.acc_seg: 80.2634, aux.loss_ce: 0.2826, aux.acc_seg: 74.9767, loss: 0.8011
2022-11-04 13:14:48,637 - mmseg - INFO - Iter [30400/80000]	lr: 7.804e-05, eta: 4:48:25, time: 0.347, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5212, decode.acc_seg: 79.8726, aux.loss_ce: 0.2835, aux.acc_seg: 74.4395, loss: 0.8047
2022-11-04 13:15:06,026 - mmseg - INFO - Iter [30450/80000]	lr: 7.797e-05, eta: 4:48:08, time: 0.348, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5212, decode.acc_seg: 80.0648, aux.loss_ce: 0.2849, aux.acc_seg: 74.5886, loss: 0.8061
2022-11-04 13:15:23,081 - mmseg - INFO - Iter [30500/80000]	lr: 7.790e-05, eta: 4:47:50, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5133, decode.acc_seg: 80.3545, aux.loss_ce: 0.2878, aux.acc_seg: 74.3347, loss: 0.8011
2022-11-04 13:15:40,115 - mmseg - INFO - Iter [30550/80000]	lr: 7.783e-05, eta: 4:47:32, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5006, decode.acc_seg: 80.8339, aux.loss_ce: 0.2738, aux.acc_seg: 75.6980, loss: 0.7744
2022-11-04 13:15:56,978 - mmseg - INFO - Iter [30600/80000]	lr: 7.776e-05, eta: 4:47:13, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5223, decode.acc_seg: 79.7045, aux.loss_ce: 0.2857, aux.acc_seg: 74.3050, loss: 0.8080
2022-11-04 13:16:13,924 - mmseg - INFO - Iter [30650/80000]	lr: 7.769e-05, eta: 4:46:55, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5112, decode.acc_seg: 80.1653, aux.loss_ce: 0.2798, aux.acc_seg: 74.4928, loss: 0.7910
2022-11-04 13:16:30,962 - mmseg - INFO - Iter [30700/80000]	lr: 7.762e-05, eta: 4:46:37, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5417, decode.acc_seg: 79.3604, aux.loss_ce: 0.2950, aux.acc_seg: 73.3590, loss: 0.8367
2022-11-04 13:16:48,208 - mmseg - INFO - Iter [30750/80000]	lr: 7.755e-05, eta: 4:46:19, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5246, decode.acc_seg: 79.9743, aux.loss_ce: 0.2830, aux.acc_seg: 74.6693, loss: 0.8076
2022-11-04 13:17:05,337 - mmseg - INFO - Iter [30800/80000]	lr: 7.748e-05, eta: 4:46:01, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5247, decode.acc_seg: 79.7047, aux.loss_ce: 0.2819, aux.acc_seg: 74.6352, loss: 0.8066
2022-11-04 13:17:22,271 - mmseg - INFO - Iter [30850/80000]	lr: 7.741e-05, eta: 4:45:43, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5414, decode.acc_seg: 79.6963, aux.loss_ce: 0.2923, aux.acc_seg: 74.3500, loss: 0.8337
2022-11-04 13:17:39,341 - mmseg - INFO - Iter [30900/80000]	lr: 7.734e-05, eta: 4:45:25, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5329, decode.acc_seg: 79.8449, aux.loss_ce: 0.2860, aux.acc_seg: 74.7458, loss: 0.8189
2022-11-04 13:17:59,212 - mmseg - INFO - Iter [30950/80000]	lr: 7.727e-05, eta: 4:45:11, time: 0.397, data_time: 0.061, memory: 12988, decode.loss_ce: 0.5193, decode.acc_seg: 80.2863, aux.loss_ce: 0.2862, aux.acc_seg: 74.4844, loss: 0.8055
2022-11-04 13:18:16,411 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 13:18:16,411 - mmseg - INFO - Iter [31000/80000]	lr: 7.719e-05, eta: 4:44:54, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5145, decode.acc_seg: 80.2749, aux.loss_ce: 0.2806, aux.acc_seg: 74.9044, loss: 0.7950
2022-11-04 13:18:33,506 - mmseg - INFO - Iter [31050/80000]	lr: 7.712e-05, eta: 4:44:36, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5111, decode.acc_seg: 80.6131, aux.loss_ce: 0.2804, aux.acc_seg: 75.4606, loss: 0.7915
2022-11-04 13:18:50,721 - mmseg - INFO - Iter [31100/80000]	lr: 7.705e-05, eta: 4:44:18, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5194, decode.acc_seg: 80.0898, aux.loss_ce: 0.2854, aux.acc_seg: 74.4675, loss: 0.8048
2022-11-04 13:19:07,824 - mmseg - INFO - Iter [31150/80000]	lr: 7.698e-05, eta: 4:44:00, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5292, decode.acc_seg: 79.9597, aux.loss_ce: 0.2887, aux.acc_seg: 74.5068, loss: 0.8178
2022-11-04 13:19:24,784 - mmseg - INFO - Iter [31200/80000]	lr: 7.691e-05, eta: 4:43:42, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5228, decode.acc_seg: 79.8284, aux.loss_ce: 0.2865, aux.acc_seg: 74.1313, loss: 0.8093
2022-11-04 13:19:42,079 - mmseg - INFO - Iter [31250/80000]	lr: 7.684e-05, eta: 4:43:24, time: 0.346, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5149, decode.acc_seg: 80.2978, aux.loss_ce: 0.2814, aux.acc_seg: 74.8424, loss: 0.7963
2022-11-04 13:19:58,979 - mmseg - INFO - Iter [31300/80000]	lr: 7.677e-05, eta: 4:43:06, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5162, decode.acc_seg: 80.2753, aux.loss_ce: 0.2859, aux.acc_seg: 74.2656, loss: 0.8021
2022-11-04 13:20:15,792 - mmseg - INFO - Iter [31350/80000]	lr: 7.670e-05, eta: 4:42:47, time: 0.336, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5139, decode.acc_seg: 80.2259, aux.loss_ce: 0.2870, aux.acc_seg: 74.3975, loss: 0.8009
2022-11-04 13:20:32,908 - mmseg - INFO - Iter [31400/80000]	lr: 7.663e-05, eta: 4:42:29, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5210, decode.acc_seg: 79.8620, aux.loss_ce: 0.2848, aux.acc_seg: 74.2484, loss: 0.8058
2022-11-04 13:20:49,768 - mmseg - INFO - Iter [31450/80000]	lr: 7.656e-05, eta: 4:42:11, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5286, decode.acc_seg: 79.4280, aux.loss_ce: 0.2879, aux.acc_seg: 74.1334, loss: 0.8165
2022-11-04 13:21:06,773 - mmseg - INFO - Iter [31500/80000]	lr: 7.648e-05, eta: 4:41:53, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5191, decode.acc_seg: 80.1701, aux.loss_ce: 0.2833, aux.acc_seg: 74.4654, loss: 0.8024
2022-11-04 13:21:23,482 - mmseg - INFO - Iter [31550/80000]	lr: 7.641e-05, eta: 4:41:34, time: 0.334, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5312, decode.acc_seg: 79.9058, aux.loss_ce: 0.2886, aux.acc_seg: 74.6025, loss: 0.8198
2022-11-04 13:21:43,819 - mmseg - INFO - Iter [31600/80000]	lr: 7.634e-05, eta: 4:41:21, time: 0.407, data_time: 0.064, memory: 12988, decode.loss_ce: 0.5099, decode.acc_seg: 80.6229, aux.loss_ce: 0.2793, aux.acc_seg: 75.1279, loss: 0.7892
2022-11-04 13:22:00,990 - mmseg - INFO - Iter [31650/80000]	lr: 7.627e-05, eta: 4:41:03, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5227, decode.acc_seg: 79.6099, aux.loss_ce: 0.2855, aux.acc_seg: 74.0986, loss: 0.8082
2022-11-04 13:22:17,835 - mmseg - INFO - Iter [31700/80000]	lr: 7.620e-05, eta: 4:40:45, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5007, decode.acc_seg: 80.7264, aux.loss_ce: 0.2789, aux.acc_seg: 75.1564, loss: 0.7796
2022-11-04 13:22:34,898 - mmseg - INFO - Iter [31750/80000]	lr: 7.613e-05, eta: 4:40:27, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5126, decode.acc_seg: 80.4208, aux.loss_ce: 0.2798, aux.acc_seg: 74.9755, loss: 0.7924
2022-11-04 13:22:51,786 - mmseg - INFO - Iter [31800/80000]	lr: 7.606e-05, eta: 4:40:09, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5166, decode.acc_seg: 80.3287, aux.loss_ce: 0.2834, aux.acc_seg: 74.5259, loss: 0.8001
2022-11-04 13:23:08,632 - mmseg - INFO - Iter [31850/80000]	lr: 7.599e-05, eta: 4:39:51, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5274, decode.acc_seg: 79.6592, aux.loss_ce: 0.2848, aux.acc_seg: 74.3412, loss: 0.8122
2022-11-04 13:23:25,663 - mmseg - INFO - Iter [31900/80000]	lr: 7.592e-05, eta: 4:39:32, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4987, decode.acc_seg: 80.8045, aux.loss_ce: 0.2747, aux.acc_seg: 75.2973, loss: 0.7734
2022-11-04 13:23:42,653 - mmseg - INFO - Iter [31950/80000]	lr: 7.585e-05, eta: 4:39:14, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5167, decode.acc_seg: 80.2443, aux.loss_ce: 0.2885, aux.acc_seg: 73.7496, loss: 0.8052
2022-11-04 13:23:59,774 - mmseg - INFO - Saving checkpoint at 32000 iterations
2022-11-04 13:24:00,272 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 13:24:00,273 - mmseg - INFO - Iter [32000/80000]	lr: 7.577e-05, eta: 4:38:57, time: 0.352, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5218, decode.acc_seg: 79.9395, aux.loss_ce: 0.2885, aux.acc_seg: 74.1765, loss: 0.8103
2022-11-04 13:24:09,635 - mmseg - INFO - per class results:
2022-11-04 13:24:09,642 - mmseg - INFO - 
+---------------------+-------+-------+
|        Class        |  IoU  |  Acc  |
+---------------------+-------+-------+
|         wall        | 65.91 | 82.85 |
|       building      | 73.89 | 87.27 |
|         sky         | 88.42 | 92.58 |
|        floor        | 70.16 | 86.05 |
|         tree        | 67.45 | 86.22 |
|       ceiling       | 75.08 |  85.5 |
|         road        | 75.49 | 85.13 |
|         bed         |  74.0 | 89.93 |
|      windowpane     | 49.64 | 64.32 |
|        grass        | 63.09 | 81.62 |
|       cabinet       | 47.64 | 58.92 |
|       sidewalk      | 52.06 |  73.0 |
|        person       | 62.89 |  83.4 |
|        earth        | 27.62 | 35.99 |
|         door        | 24.92 | 41.19 |
|        table        | 39.81 | 55.82 |
|       mountain      | 55.59 | 75.89 |
|        plant        | 43.11 | 54.57 |
|       curtain       |  54.6 | 70.75 |
|        chair        | 35.22 | 46.54 |
|         car         | 71.39 |  86.3 |
|        water        | 43.98 | 61.52 |
|       painting      | 55.32 | 70.53 |
|         sofa        | 46.47 | 68.24 |
|        shelf        | 32.27 | 48.01 |
|        house        | 35.88 | 55.92 |
|         sea         | 42.36 | 68.68 |
|        mirror       |  41.6 | 48.34 |
|         rug         | 40.48 | 46.09 |
|        field        |  27.4 | 45.24 |
|       armchair      | 23.31 | 34.41 |
|         seat        | 45.54 | 66.32 |
|        fence        | 26.63 | 39.48 |
|         desk        | 31.07 | 51.69 |
|         rock        | 42.26 |  62.7 |
|       wardrobe      | 38.04 | 57.59 |
|         lamp        | 35.11 | 45.66 |
|       bathtub       | 50.55 |  66.6 |
|       railing       | 25.29 | 37.73 |
|       cushion       | 28.27 | 36.23 |
|         base        | 16.99 | 25.62 |
|         box         | 16.23 |  24.6 |
|        column       | 26.16 | 33.24 |
|      signboard      | 21.53 | 30.61 |
|   chest of drawers  | 29.54 | 49.09 |
|       counter       | 20.05 | 26.41 |
|         sand        | 28.43 | 46.45 |
|         sink        |  43.1 | 54.38 |
|      skyscraper     | 60.39 | 87.85 |
|      fireplace      | 52.78 | 69.41 |
|     refrigerator    | 50.19 |  69.0 |
|      grandstand     | 42.95 |  61.2 |
|         path        | 12.21 | 16.89 |
|        stairs       | 25.57 |  32.0 |
|        runway       | 73.49 |  88.7 |
|         case        | 51.49 | 56.84 |
|      pool table     | 79.51 | 87.38 |
|        pillow       | 31.13 | 36.79 |
|     screen door     | 50.77 | 73.94 |
|       stairway      | 32.44 | 40.74 |
|        river        |  9.72 | 17.27 |
|        bridge       | 27.81 | 38.32 |
|       bookcase      | 29.62 | 47.13 |
|        blind        | 18.48 | 21.33 |
|     coffee table    | 43.45 | 63.82 |
|        toilet       | 61.95 | 77.57 |
|        flower       | 26.88 | 36.48 |
|         book        | 35.67 | 52.63 |
|         hill        |  4.15 |  5.51 |
|        bench        | 29.03 | 35.66 |
|      countertop     | 43.28 | 55.68 |
|        stove        | 49.13 | 66.54 |
|         palm        | 34.81 | 46.38 |
|    kitchen island   | 19.93 | 46.55 |
|       computer      | 42.68 | 56.02 |
|     swivel chair    | 28.92 | 36.82 |
|         boat        | 56.92 | 66.15 |
|         bar         |  20.8 | 29.51 |
|    arcade machine   | 30.25 | 33.57 |
|        hovel        | 29.62 | 39.83 |
|         bus         | 69.77 |  81.9 |
|        towel        | 32.67 | 39.72 |
|        light        | 14.44 | 15.75 |
|        truck        |  5.02 |  6.23 |
|        tower        | 33.18 | 44.72 |
|      chandelier     | 46.95 | 63.75 |
|        awning       | 14.51 | 17.01 |
|     streetlight     |  6.17 |  7.13 |
|        booth        | 27.09 | 37.41 |
| television receiver | 48.96 | 62.26 |
|       airplane      | 44.58 | 54.42 |
|      dirt track     | 11.77 | 26.75 |
|       apparel       | 12.48 | 16.83 |
|         pole        |  4.34 |  7.31 |
|         land        |  0.15 |  0.19 |
|      bannister      |  0.79 |  1.26 |
|      escalator      | 38.99 | 41.36 |
|       ottoman       | 21.25 | 26.12 |
|        bottle       | 15.33 | 22.22 |
|        buffet       | 33.79 | 44.06 |
|        poster       |  9.14 | 10.76 |
|        stage        |  7.71 | 15.29 |
|         van         | 19.43 | 23.25 |
|         ship        | 36.43 | 39.53 |
|       fountain      | 21.14 | 22.39 |
|    conveyer belt    | 55.63 | 81.71 |
|        canopy       | 21.78 | 36.44 |
|        washer       | 54.07 | 68.63 |
|      plaything      |  8.07 | 14.12 |
|    swimming pool    | 33.36 | 45.25 |
|        stool        |  9.91 | 11.44 |
|        barrel       | 15.86 | 42.71 |
|        basket       |  12.8 | 15.39 |
|      waterfall      | 48.69 | 62.45 |
|         tent        | 87.16 | 97.47 |
|         bag         |  2.06 |  2.3  |
|       minibike      | 38.47 | 49.22 |
|        cradle       | 59.05 | 81.84 |
|         oven        | 22.64 | 28.32 |
|         ball        | 29.54 | 37.08 |
|         food        | 41.86 | 52.35 |
|         step        |  4.93 |  5.82 |
|         tank        | 25.86 | 28.49 |
|      trade name     | 19.88 | 23.87 |
|      microwave      | 25.59 | 28.86 |
|         pot         | 23.14 | 29.24 |
|        animal       | 39.92 |  43.0 |
|       bicycle       | 31.88 | 54.19 |
|         lake        |  1.19 |  1.38 |
|      dishwasher     | 25.27 | 34.08 |
|        screen       |  57.9 | 78.88 |
|       blanket       |  0.67 |  0.68 |
|      sculpture      | 24.95 | 32.08 |
|         hood        | 27.73 | 34.06 |
|        sconce       | 15.09 | 16.48 |
|         vase        | 14.87 | 20.03 |
|    traffic light    |  8.76 | 13.47 |
|         tray        |  0.47 |  0.93 |
|        ashcan       | 16.26 | 25.08 |
|         fan         | 24.46 | 28.61 |
|         pier        | 26.73 | 28.28 |
|      crt screen     |  0.0  |  0.0  |
|        plate        | 14.74 | 16.06 |
|       monitor       |  40.3 | 49.02 |
|    bulletin board   | 34.35 | 38.69 |
|        shower       |  0.0  |  0.0  |
|       radiator      | 33.51 | 39.16 |
|        glass        |  0.39 |  0.4  |
|        clock        |  6.98 |  7.94 |
|         flag        | 16.19 |  18.1 |
+---------------------+-------+-------+
2022-11-04 13:24:09,642 - mmseg - INFO - Summary:
2022-11-04 13:24:09,643 - mmseg - INFO - 
+-------+-------+------+
|  aAcc |  mIoU | mAcc |
+-------+-------+------+
| 74.74 | 33.45 | 43.8 |
+-------+-------+------+
2022-11-04 13:24:09,644 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 13:24:09,644 - mmseg - INFO - Iter(val) [250]	aAcc: 0.7474, mIoU: 0.3345, mAcc: 0.4380, IoU.wall: 0.6591, IoU.building: 0.7389, IoU.sky: 0.8842, IoU.floor: 0.7016, IoU.tree: 0.6745, IoU.ceiling: 0.7508, IoU.road: 0.7549, IoU.bed : 0.7400, IoU.windowpane: 0.4964, IoU.grass: 0.6309, IoU.cabinet: 0.4764, IoU.sidewalk: 0.5206, IoU.person: 0.6289, IoU.earth: 0.2762, IoU.door: 0.2492, IoU.table: 0.3981, IoU.mountain: 0.5559, IoU.plant: 0.4311, IoU.curtain: 0.5460, IoU.chair: 0.3522, IoU.car: 0.7139, IoU.water: 0.4398, IoU.painting: 0.5532, IoU.sofa: 0.4647, IoU.shelf: 0.3227, IoU.house: 0.3588, IoU.sea: 0.4236, IoU.mirror: 0.4160, IoU.rug: 0.4048, IoU.field: 0.2740, IoU.armchair: 0.2331, IoU.seat: 0.4554, IoU.fence: 0.2663, IoU.desk: 0.3107, IoU.rock: 0.4226, IoU.wardrobe: 0.3804, IoU.lamp: 0.3511, IoU.bathtub: 0.5055, IoU.railing: 0.2529, IoU.cushion: 0.2827, IoU.base: 0.1699, IoU.box: 0.1623, IoU.column: 0.2616, IoU.signboard: 0.2153, IoU.chest of drawers: 0.2954, IoU.counter: 0.2005, IoU.sand: 0.2843, IoU.sink: 0.4310, IoU.skyscraper: 0.6039, IoU.fireplace: 0.5278, IoU.refrigerator: 0.5019, IoU.grandstand: 0.4295, IoU.path: 0.1221, IoU.stairs: 0.2557, IoU.runway: 0.7349, IoU.case: 0.5149, IoU.pool table: 0.7951, IoU.pillow: 0.3113, IoU.screen door: 0.5077, IoU.stairway: 0.3244, IoU.river: 0.0972, IoU.bridge: 0.2781, IoU.bookcase: 0.2962, IoU.blind: 0.1848, IoU.coffee table: 0.4345, IoU.toilet: 0.6195, IoU.flower: 0.2688, IoU.book: 0.3567, IoU.hill: 0.0415, IoU.bench: 0.2903, IoU.countertop: 0.4328, IoU.stove: 0.4913, IoU.palm: 0.3481, IoU.kitchen island: 0.1993, IoU.computer: 0.4268, IoU.swivel chair: 0.2892, IoU.boat: 0.5692, IoU.bar: 0.2080, IoU.arcade machine: 0.3025, IoU.hovel: 0.2962, IoU.bus: 0.6977, IoU.towel: 0.3267, IoU.light: 0.1444, IoU.truck: 0.0502, IoU.tower: 0.3318, IoU.chandelier: 0.4695, IoU.awning: 0.1451, IoU.streetlight: 0.0617, IoU.booth: 0.2709, IoU.television receiver: 0.4896, IoU.airplane: 0.4458, IoU.dirt track: 0.1177, IoU.apparel: 0.1248, IoU.pole: 0.0434, IoU.land: 0.0015, IoU.bannister: 0.0079, IoU.escalator: 0.3899, IoU.ottoman: 0.2125, IoU.bottle: 0.1533, IoU.buffet: 0.3379, IoU.poster: 0.0914, IoU.stage: 0.0771, IoU.van: 0.1943, IoU.ship: 0.3643, IoU.fountain: 0.2114, IoU.conveyer belt: 0.5563, IoU.canopy: 0.2178, IoU.washer: 0.5407, IoU.plaything: 0.0807, IoU.swimming pool: 0.3336, IoU.stool: 0.0991, IoU.barrel: 0.1586, IoU.basket: 0.1280, IoU.waterfall: 0.4869, IoU.tent: 0.8716, IoU.bag: 0.0206, IoU.minibike: 0.3847, IoU.cradle: 0.5905, IoU.oven: 0.2264, IoU.ball: 0.2954, IoU.food: 0.4186, IoU.step: 0.0493, IoU.tank: 0.2586, IoU.trade name: 0.1988, IoU.microwave: 0.2559, IoU.pot: 0.2314, IoU.animal: 0.3992, IoU.bicycle: 0.3188, IoU.lake: 0.0119, IoU.dishwasher: 0.2527, IoU.screen: 0.5790, IoU.blanket: 0.0067, IoU.sculpture: 0.2495, IoU.hood: 0.2773, IoU.sconce: 0.1509, IoU.vase: 0.1487, IoU.traffic light: 0.0876, IoU.tray: 0.0047, IoU.ashcan: 0.1626, IoU.fan: 0.2446, IoU.pier: 0.2673, IoU.crt screen: 0.0000, IoU.plate: 0.1474, IoU.monitor: 0.4030, IoU.bulletin board: 0.3435, IoU.shower: 0.0000, IoU.radiator: 0.3351, IoU.glass: 0.0039, IoU.clock: 0.0698, IoU.flag: 0.1619, Acc.wall: 0.8285, Acc.building: 0.8727, Acc.sky: 0.9258, Acc.floor: 0.8605, Acc.tree: 0.8622, Acc.ceiling: 0.8550, Acc.road: 0.8513, Acc.bed : 0.8993, Acc.windowpane: 0.6432, Acc.grass: 0.8162, Acc.cabinet: 0.5892, Acc.sidewalk: 0.7300, Acc.person: 0.8340, Acc.earth: 0.3599, Acc.door: 0.4119, Acc.table: 0.5582, Acc.mountain: 0.7589, Acc.plant: 0.5457, Acc.curtain: 0.7075, Acc.chair: 0.4654, Acc.car: 0.8630, Acc.water: 0.6152, Acc.painting: 0.7053, Acc.sofa: 0.6824, Acc.shelf: 0.4801, Acc.house: 0.5592, Acc.sea: 0.6868, Acc.mirror: 0.4834, Acc.rug: 0.4609, Acc.field: 0.4524, Acc.armchair: 0.3441, Acc.seat: 0.6632, Acc.fence: 0.3948, Acc.desk: 0.5169, Acc.rock: 0.6270, Acc.wardrobe: 0.5759, Acc.lamp: 0.4566, Acc.bathtub: 0.6660, Acc.railing: 0.3773, Acc.cushion: 0.3623, Acc.base: 0.2562, Acc.box: 0.2460, Acc.column: 0.3324, Acc.signboard: 0.3061, Acc.chest of drawers: 0.4909, Acc.counter: 0.2641, Acc.sand: 0.4645, Acc.sink: 0.5438, Acc.skyscraper: 0.8785, Acc.fireplace: 0.6941, Acc.refrigerator: 0.6900, Acc.grandstand: 0.6120, Acc.path: 0.1689, Acc.stairs: 0.3200, Acc.runway: 0.8870, Acc.case: 0.5684, Acc.pool table: 0.8738, Acc.pillow: 0.3679, Acc.screen door: 0.7394, Acc.stairway: 0.4074, Acc.river: 0.1727, Acc.bridge: 0.3832, Acc.bookcase: 0.4713, Acc.blind: 0.2133, Acc.coffee table: 0.6382, Acc.toilet: 0.7757, Acc.flower: 0.3648, Acc.book: 0.5263, Acc.hill: 0.0551, Acc.bench: 0.3566, Acc.countertop: 0.5568, Acc.stove: 0.6654, Acc.palm: 0.4638, Acc.kitchen island: 0.4655, Acc.computer: 0.5602, Acc.swivel chair: 0.3682, Acc.boat: 0.6615, Acc.bar: 0.2951, Acc.arcade machine: 0.3357, Acc.hovel: 0.3983, Acc.bus: 0.8190, Acc.towel: 0.3972, Acc.light: 0.1575, Acc.truck: 0.0623, Acc.tower: 0.4472, Acc.chandelier: 0.6375, Acc.awning: 0.1701, Acc.streetlight: 0.0713, Acc.booth: 0.3741, Acc.television receiver: 0.6226, Acc.airplane: 0.5442, Acc.dirt track: 0.2675, Acc.apparel: 0.1683, Acc.pole: 0.0731, Acc.land: 0.0019, Acc.bannister: 0.0126, Acc.escalator: 0.4136, Acc.ottoman: 0.2612, Acc.bottle: 0.2222, Acc.buffet: 0.4406, Acc.poster: 0.1076, Acc.stage: 0.1529, Acc.van: 0.2325, Acc.ship: 0.3953, Acc.fountain: 0.2239, Acc.conveyer belt: 0.8171, Acc.canopy: 0.3644, Acc.washer: 0.6863, Acc.plaything: 0.1412, Acc.swimming pool: 0.4525, Acc.stool: 0.1144, Acc.barrel: 0.4271, Acc.basket: 0.1539, Acc.waterfall: 0.6245, Acc.tent: 0.9747, Acc.bag: 0.0230, Acc.minibike: 0.4922, Acc.cradle: 0.8184, Acc.oven: 0.2832, Acc.ball: 0.3708, Acc.food: 0.5235, Acc.step: 0.0582, Acc.tank: 0.2849, Acc.trade name: 0.2387, Acc.microwave: 0.2886, Acc.pot: 0.2924, Acc.animal: 0.4300, Acc.bicycle: 0.5419, Acc.lake: 0.0138, Acc.dishwasher: 0.3408, Acc.screen: 0.7888, Acc.blanket: 0.0068, Acc.sculpture: 0.3208, Acc.hood: 0.3406, Acc.sconce: 0.1648, Acc.vase: 0.2003, Acc.traffic light: 0.1347, Acc.tray: 0.0093, Acc.ashcan: 0.2508, Acc.fan: 0.2861, Acc.pier: 0.2828, Acc.crt screen: 0.0000, Acc.plate: 0.1606, Acc.monitor: 0.4902, Acc.bulletin board: 0.3869, Acc.shower: 0.0000, Acc.radiator: 0.3916, Acc.glass: 0.0040, Acc.clock: 0.0794, Acc.flag: 0.1810
2022-11-04 13:24:26,506 - mmseg - INFO - Iter [32050/80000]	lr: 7.570e-05, eta: 4:38:53, time: 0.525, data_time: 0.197, memory: 12988, decode.loss_ce: 0.5268, decode.acc_seg: 80.1947, aux.loss_ce: 0.2887, aux.acc_seg: 74.3013, loss: 0.8155
2022-11-04 13:24:43,519 - mmseg - INFO - Iter [32100/80000]	lr: 7.563e-05, eta: 4:38:35, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5174, decode.acc_seg: 80.2212, aux.loss_ce: 0.2803, aux.acc_seg: 74.8581, loss: 0.7977
2022-11-04 13:25:00,520 - mmseg - INFO - Iter [32150/80000]	lr: 7.556e-05, eta: 4:38:17, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5051, decode.acc_seg: 80.6263, aux.loss_ce: 0.2793, aux.acc_seg: 74.7559, loss: 0.7845
2022-11-04 13:25:20,521 - mmseg - INFO - Iter [32200/80000]	lr: 7.549e-05, eta: 4:38:03, time: 0.400, data_time: 0.064, memory: 12988, decode.loss_ce: 0.5184, decode.acc_seg: 80.1235, aux.loss_ce: 0.2851, aux.acc_seg: 74.8876, loss: 0.8036
2022-11-04 13:25:37,566 - mmseg - INFO - Iter [32250/80000]	lr: 7.542e-05, eta: 4:37:45, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5164, decode.acc_seg: 80.3486, aux.loss_ce: 0.2883, aux.acc_seg: 74.5145, loss: 0.8046
2022-11-04 13:25:54,653 - mmseg - INFO - Iter [32300/80000]	lr: 7.535e-05, eta: 4:37:27, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5207, decode.acc_seg: 80.0544, aux.loss_ce: 0.2813, aux.acc_seg: 74.6058, loss: 0.8020
2022-11-04 13:26:11,780 - mmseg - INFO - Iter [32350/80000]	lr: 7.528e-05, eta: 4:37:09, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5205, decode.acc_seg: 79.7965, aux.loss_ce: 0.2894, aux.acc_seg: 74.1348, loss: 0.8099
2022-11-04 13:26:28,689 - mmseg - INFO - Iter [32400/80000]	lr: 7.521e-05, eta: 4:36:51, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5021, decode.acc_seg: 80.9502, aux.loss_ce: 0.2757, aux.acc_seg: 75.5192, loss: 0.7777
2022-11-04 13:26:45,767 - mmseg - INFO - Iter [32450/80000]	lr: 7.514e-05, eta: 4:36:33, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5057, decode.acc_seg: 80.3317, aux.loss_ce: 0.2804, aux.acc_seg: 74.7012, loss: 0.7860
2022-11-04 13:27:02,812 - mmseg - INFO - Iter [32500/80000]	lr: 7.506e-05, eta: 4:36:15, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5221, decode.acc_seg: 79.9231, aux.loss_ce: 0.2851, aux.acc_seg: 74.2875, loss: 0.8071
2022-11-04 13:27:19,870 - mmseg - INFO - Iter [32550/80000]	lr: 7.499e-05, eta: 4:35:57, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5222, decode.acc_seg: 80.0937, aux.loss_ce: 0.2826, aux.acc_seg: 74.8998, loss: 0.8048
2022-11-04 13:27:36,992 - mmseg - INFO - Iter [32600/80000]	lr: 7.492e-05, eta: 4:35:39, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5174, decode.acc_seg: 80.3264, aux.loss_ce: 0.2831, aux.acc_seg: 74.9965, loss: 0.8005
2022-11-04 13:27:54,109 - mmseg - INFO - Iter [32650/80000]	lr: 7.485e-05, eta: 4:35:21, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5262, decode.acc_seg: 79.6009, aux.loss_ce: 0.2852, aux.acc_seg: 74.4176, loss: 0.8114
2022-11-04 13:28:11,521 - mmseg - INFO - Iter [32700/80000]	lr: 7.478e-05, eta: 4:35:03, time: 0.348, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5374, decode.acc_seg: 79.4382, aux.loss_ce: 0.2954, aux.acc_seg: 73.7294, loss: 0.8329
2022-11-04 13:28:28,477 - mmseg - INFO - Iter [32750/80000]	lr: 7.471e-05, eta: 4:34:45, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5199, decode.acc_seg: 80.1688, aux.loss_ce: 0.2816, aux.acc_seg: 74.9182, loss: 0.8015
2022-11-04 13:28:45,473 - mmseg - INFO - Iter [32800/80000]	lr: 7.464e-05, eta: 4:34:27, time: 0.340, data_time: 0.008, memory: 12988, decode.loss_ce: 0.4929, decode.acc_seg: 80.8172, aux.loss_ce: 0.2689, aux.acc_seg: 75.6024, loss: 0.7618
2022-11-04 13:29:05,543 - mmseg - INFO - Iter [32850/80000]	lr: 7.457e-05, eta: 4:34:14, time: 0.401, data_time: 0.068, memory: 12988, decode.loss_ce: 0.5081, decode.acc_seg: 80.6222, aux.loss_ce: 0.2813, aux.acc_seg: 75.0546, loss: 0.7894
2022-11-04 13:29:22,482 - mmseg - INFO - Iter [32900/80000]	lr: 7.450e-05, eta: 4:33:55, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4886, decode.acc_seg: 81.0634, aux.loss_ce: 0.2685, aux.acc_seg: 75.6148, loss: 0.7570
2022-11-04 13:29:39,442 - mmseg - INFO - Iter [32950/80000]	lr: 7.442e-05, eta: 4:33:37, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5052, decode.acc_seg: 80.3966, aux.loss_ce: 0.2773, aux.acc_seg: 75.0776, loss: 0.7825
2022-11-04 13:29:56,661 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 13:29:56,661 - mmseg - INFO - Iter [33000/80000]	lr: 7.435e-05, eta: 4:33:19, time: 0.344, data_time: 0.013, memory: 12988, decode.loss_ce: 0.5044, decode.acc_seg: 80.5974, aux.loss_ce: 0.2712, aux.acc_seg: 75.5884, loss: 0.7757
2022-11-04 13:30:13,659 - mmseg - INFO - Iter [33050/80000]	lr: 7.428e-05, eta: 4:33:01, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5098, decode.acc_seg: 80.1845, aux.loss_ce: 0.2839, aux.acc_seg: 74.4007, loss: 0.7938
2022-11-04 13:30:30,648 - mmseg - INFO - Iter [33100/80000]	lr: 7.421e-05, eta: 4:32:43, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5271, decode.acc_seg: 79.9172, aux.loss_ce: 0.2945, aux.acc_seg: 73.9694, loss: 0.8216
2022-11-04 13:30:47,746 - mmseg - INFO - Iter [33150/80000]	lr: 7.414e-05, eta: 4:32:25, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5129, decode.acc_seg: 80.1855, aux.loss_ce: 0.2784, aux.acc_seg: 75.0860, loss: 0.7913
2022-11-04 13:31:04,927 - mmseg - INFO - Iter [33200/80000]	lr: 7.407e-05, eta: 4:32:07, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5211, decode.acc_seg: 80.4146, aux.loss_ce: 0.2880, aux.acc_seg: 74.3619, loss: 0.8091
2022-11-04 13:31:21,955 - mmseg - INFO - Iter [33250/80000]	lr: 7.400e-05, eta: 4:31:49, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5186, decode.acc_seg: 80.3406, aux.loss_ce: 0.2783, aux.acc_seg: 75.1763, loss: 0.7970
2022-11-04 13:31:38,993 - mmseg - INFO - Iter [33300/80000]	lr: 7.393e-05, eta: 4:31:31, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5047, decode.acc_seg: 80.9712, aux.loss_ce: 0.2799, aux.acc_seg: 75.1047, loss: 0.7845
2022-11-04 13:31:55,858 - mmseg - INFO - Iter [33350/80000]	lr: 7.385e-05, eta: 4:31:13, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5142, decode.acc_seg: 80.5592, aux.loss_ce: 0.2811, aux.acc_seg: 75.0284, loss: 0.7953
2022-11-04 13:32:12,704 - mmseg - INFO - Iter [33400/80000]	lr: 7.378e-05, eta: 4:30:55, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5219, decode.acc_seg: 79.9185, aux.loss_ce: 0.2851, aux.acc_seg: 74.5773, loss: 0.8071
2022-11-04 13:32:32,511 - mmseg - INFO - Iter [33450/80000]	lr: 7.371e-05, eta: 4:30:41, time: 0.396, data_time: 0.066, memory: 12988, decode.loss_ce: 0.5107, decode.acc_seg: 80.3519, aux.loss_ce: 0.2796, aux.acc_seg: 74.8610, loss: 0.7904
2022-11-04 13:32:49,463 - mmseg - INFO - Iter [33500/80000]	lr: 7.364e-05, eta: 4:30:23, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4920, decode.acc_seg: 80.8385, aux.loss_ce: 0.2683, aux.acc_seg: 75.4727, loss: 0.7604
2022-11-04 13:33:06,195 - mmseg - INFO - Iter [33550/80000]	lr: 7.357e-05, eta: 4:30:04, time: 0.335, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5012, decode.acc_seg: 80.5459, aux.loss_ce: 0.2709, aux.acc_seg: 75.2570, loss: 0.7721
2022-11-04 13:33:23,475 - mmseg - INFO - Iter [33600/80000]	lr: 7.350e-05, eta: 4:29:47, time: 0.346, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4991, decode.acc_seg: 80.4724, aux.loss_ce: 0.2803, aux.acc_seg: 74.6509, loss: 0.7794
2022-11-04 13:33:40,299 - mmseg - INFO - Iter [33650/80000]	lr: 7.343e-05, eta: 4:29:28, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4957, decode.acc_seg: 80.8564, aux.loss_ce: 0.2729, aux.acc_seg: 75.4525, loss: 0.7686
2022-11-04 13:33:57,591 - mmseg - INFO - Iter [33700/80000]	lr: 7.336e-05, eta: 4:29:11, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5129, decode.acc_seg: 80.7102, aux.loss_ce: 0.2807, aux.acc_seg: 74.6647, loss: 0.7936
2022-11-04 13:34:14,640 - mmseg - INFO - Iter [33750/80000]	lr: 7.328e-05, eta: 4:28:53, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5181, decode.acc_seg: 80.2835, aux.loss_ce: 0.2813, aux.acc_seg: 74.8528, loss: 0.7994
2022-11-04 13:34:31,444 - mmseg - INFO - Iter [33800/80000]	lr: 7.321e-05, eta: 4:28:34, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4981, decode.acc_seg: 80.5289, aux.loss_ce: 0.2773, aux.acc_seg: 75.0440, loss: 0.7754
2022-11-04 13:34:48,265 - mmseg - INFO - Iter [33850/80000]	lr: 7.314e-05, eta: 4:28:16, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5046, decode.acc_seg: 80.7754, aux.loss_ce: 0.2781, aux.acc_seg: 75.2409, loss: 0.7826
2022-11-04 13:35:05,300 - mmseg - INFO - Iter [33900/80000]	lr: 7.307e-05, eta: 4:27:58, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5189, decode.acc_seg: 80.2057, aux.loss_ce: 0.2856, aux.acc_seg: 74.3809, loss: 0.8045
2022-11-04 13:35:22,223 - mmseg - INFO - Iter [33950/80000]	lr: 7.300e-05, eta: 4:27:40, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5205, decode.acc_seg: 79.9525, aux.loss_ce: 0.2817, aux.acc_seg: 75.0345, loss: 0.8022
2022-11-04 13:35:39,254 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 13:35:39,255 - mmseg - INFO - Iter [34000/80000]	lr: 7.293e-05, eta: 4:27:22, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5299, decode.acc_seg: 79.2431, aux.loss_ce: 0.2865, aux.acc_seg: 74.1246, loss: 0.8164
2022-11-04 13:35:56,260 - mmseg - INFO - Iter [34050/80000]	lr: 7.286e-05, eta: 4:27:04, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5028, decode.acc_seg: 80.6046, aux.loss_ce: 0.2745, aux.acc_seg: 74.8877, loss: 0.7772
2022-11-04 13:36:15,917 - mmseg - INFO - Iter [34100/80000]	lr: 7.278e-05, eta: 4:26:49, time: 0.393, data_time: 0.063, memory: 12988, decode.loss_ce: 0.5012, decode.acc_seg: 80.6591, aux.loss_ce: 0.2746, aux.acc_seg: 75.2272, loss: 0.7758
2022-11-04 13:36:32,884 - mmseg - INFO - Iter [34150/80000]	lr: 7.271e-05, eta: 4:26:31, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4998, decode.acc_seg: 80.9149, aux.loss_ce: 0.2782, aux.acc_seg: 75.2243, loss: 0.7780
2022-11-04 13:36:49,873 - mmseg - INFO - Iter [34200/80000]	lr: 7.264e-05, eta: 4:26:13, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5140, decode.acc_seg: 80.5107, aux.loss_ce: 0.2841, aux.acc_seg: 74.9627, loss: 0.7981
2022-11-04 13:37:06,871 - mmseg - INFO - Iter [34250/80000]	lr: 7.257e-05, eta: 4:25:55, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5034, decode.acc_seg: 80.8406, aux.loss_ce: 0.2774, aux.acc_seg: 75.2388, loss: 0.7808
2022-11-04 13:37:24,020 - mmseg - INFO - Iter [34300/80000]	lr: 7.250e-05, eta: 4:25:37, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5116, decode.acc_seg: 80.4077, aux.loss_ce: 0.2849, aux.acc_seg: 74.3135, loss: 0.7965
2022-11-04 13:37:40,879 - mmseg - INFO - Iter [34350/80000]	lr: 7.243e-05, eta: 4:25:19, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4871, decode.acc_seg: 81.1443, aux.loss_ce: 0.2712, aux.acc_seg: 75.5610, loss: 0.7583
2022-11-04 13:37:57,854 - mmseg - INFO - Iter [34400/80000]	lr: 7.236e-05, eta: 4:25:01, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4908, decode.acc_seg: 81.1409, aux.loss_ce: 0.2691, aux.acc_seg: 75.6966, loss: 0.7600
2022-11-04 13:38:14,874 - mmseg - INFO - Iter [34450/80000]	lr: 7.229e-05, eta: 4:24:43, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4989, decode.acc_seg: 80.7416, aux.loss_ce: 0.2693, aux.acc_seg: 75.7284, loss: 0.7682
2022-11-04 13:38:31,792 - mmseg - INFO - Iter [34500/80000]	lr: 7.221e-05, eta: 4:24:25, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4964, decode.acc_seg: 80.4141, aux.loss_ce: 0.2730, aux.acc_seg: 74.8713, loss: 0.7694
2022-11-04 13:38:49,040 - mmseg - INFO - Iter [34550/80000]	lr: 7.214e-05, eta: 4:24:07, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5096, decode.acc_seg: 80.4037, aux.loss_ce: 0.2848, aux.acc_seg: 74.5681, loss: 0.7944
2022-11-04 13:39:05,939 - mmseg - INFO - Iter [34600/80000]	lr: 7.207e-05, eta: 4:23:49, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5073, decode.acc_seg: 80.4000, aux.loss_ce: 0.2789, aux.acc_seg: 74.7284, loss: 0.7863
2022-11-04 13:39:22,861 - mmseg - INFO - Iter [34650/80000]	lr: 7.200e-05, eta: 4:23:31, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5181, decode.acc_seg: 80.2055, aux.loss_ce: 0.2779, aux.acc_seg: 75.0633, loss: 0.7960
2022-11-04 13:39:39,738 - mmseg - INFO - Iter [34700/80000]	lr: 7.193e-05, eta: 4:23:13, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5202, decode.acc_seg: 80.0175, aux.loss_ce: 0.2807, aux.acc_seg: 74.8271, loss: 0.8009
2022-11-04 13:39:59,796 - mmseg - INFO - Iter [34750/80000]	lr: 7.186e-05, eta: 4:22:59, time: 0.401, data_time: 0.062, memory: 12988, decode.loss_ce: 0.5115, decode.acc_seg: 80.7623, aux.loss_ce: 0.2889, aux.acc_seg: 74.6687, loss: 0.8004
2022-11-04 13:40:17,178 - mmseg - INFO - Iter [34800/80000]	lr: 7.178e-05, eta: 4:22:42, time: 0.348, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5095, decode.acc_seg: 80.7054, aux.loss_ce: 0.2822, aux.acc_seg: 74.6038, loss: 0.7917
2022-11-04 13:40:34,204 - mmseg - INFO - Iter [34850/80000]	lr: 7.171e-05, eta: 4:22:24, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5018, decode.acc_seg: 80.7566, aux.loss_ce: 0.2760, aux.acc_seg: 75.1172, loss: 0.7778
2022-11-04 13:40:51,255 - mmseg - INFO - Iter [34900/80000]	lr: 7.164e-05, eta: 4:22:06, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5027, decode.acc_seg: 80.8882, aux.loss_ce: 0.2747, aux.acc_seg: 75.3352, loss: 0.7774
2022-11-04 13:41:08,138 - mmseg - INFO - Iter [34950/80000]	lr: 7.157e-05, eta: 4:21:47, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4927, decode.acc_seg: 80.7622, aux.loss_ce: 0.2700, aux.acc_seg: 75.2158, loss: 0.7627
2022-11-04 13:41:25,565 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 13:41:25,566 - mmseg - INFO - Iter [35000/80000]	lr: 7.150e-05, eta: 4:21:30, time: 0.349, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5203, decode.acc_seg: 80.0321, aux.loss_ce: 0.2860, aux.acc_seg: 74.6447, loss: 0.8063
2022-11-04 13:41:42,379 - mmseg - INFO - Iter [35050/80000]	lr: 7.143e-05, eta: 4:21:12, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5015, decode.acc_seg: 80.9065, aux.loss_ce: 0.2739, aux.acc_seg: 75.4778, loss: 0.7753
2022-11-04 13:41:59,515 - mmseg - INFO - Iter [35100/80000]	lr: 7.136e-05, eta: 4:20:54, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5128, decode.acc_seg: 80.2778, aux.loss_ce: 0.2814, aux.acc_seg: 75.0984, loss: 0.7942
2022-11-04 13:42:16,572 - mmseg - INFO - Iter [35150/80000]	lr: 7.128e-05, eta: 4:20:36, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5066, decode.acc_seg: 80.7264, aux.loss_ce: 0.2736, aux.acc_seg: 75.6163, loss: 0.7802
2022-11-04 13:42:33,696 - mmseg - INFO - Iter [35200/80000]	lr: 7.121e-05, eta: 4:20:18, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5097, decode.acc_seg: 80.2854, aux.loss_ce: 0.2839, aux.acc_seg: 74.8617, loss: 0.7936
2022-11-04 13:42:50,917 - mmseg - INFO - Iter [35250/80000]	lr: 7.114e-05, eta: 4:20:01, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4880, decode.acc_seg: 81.0092, aux.loss_ce: 0.2699, aux.acc_seg: 75.4364, loss: 0.7579
2022-11-04 13:43:08,023 - mmseg - INFO - Iter [35300/80000]	lr: 7.107e-05, eta: 4:19:43, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5227, decode.acc_seg: 80.2315, aux.loss_ce: 0.2890, aux.acc_seg: 74.4790, loss: 0.8117
2022-11-04 13:43:28,170 - mmseg - INFO - Iter [35350/80000]	lr: 7.100e-05, eta: 4:19:29, time: 0.403, data_time: 0.059, memory: 12988, decode.loss_ce: 0.4861, decode.acc_seg: 81.3820, aux.loss_ce: 0.2677, aux.acc_seg: 75.8372, loss: 0.7538
2022-11-04 13:43:45,371 - mmseg - INFO - Iter [35400/80000]	lr: 7.093e-05, eta: 4:19:11, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4980, decode.acc_seg: 81.0100, aux.loss_ce: 0.2794, aux.acc_seg: 75.1988, loss: 0.7774
2022-11-04 13:44:02,586 - mmseg - INFO - Iter [35450/80000]	lr: 7.086e-05, eta: 4:18:53, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5016, decode.acc_seg: 80.4414, aux.loss_ce: 0.2709, aux.acc_seg: 75.3508, loss: 0.7725
2022-11-04 13:44:19,508 - mmseg - INFO - Iter [35500/80000]	lr: 7.078e-05, eta: 4:18:35, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5024, decode.acc_seg: 80.8188, aux.loss_ce: 0.2777, aux.acc_seg: 75.1706, loss: 0.7801
2022-11-04 13:44:36,718 - mmseg - INFO - Iter [35550/80000]	lr: 7.071e-05, eta: 4:18:17, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4830, decode.acc_seg: 81.2015, aux.loss_ce: 0.2670, aux.acc_seg: 75.5955, loss: 0.7500
2022-11-04 13:44:54,036 - mmseg - INFO - Iter [35600/80000]	lr: 7.064e-05, eta: 4:18:00, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4942, decode.acc_seg: 81.3634, aux.loss_ce: 0.2742, aux.acc_seg: 75.5387, loss: 0.7684
2022-11-04 13:45:11,162 - mmseg - INFO - Iter [35650/80000]	lr: 7.057e-05, eta: 4:17:42, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4928, decode.acc_seg: 80.8675, aux.loss_ce: 0.2749, aux.acc_seg: 74.9351, loss: 0.7677
2022-11-04 13:45:28,330 - mmseg - INFO - Iter [35700/80000]	lr: 7.050e-05, eta: 4:17:24, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5118, decode.acc_seg: 80.4360, aux.loss_ce: 0.2837, aux.acc_seg: 74.3512, loss: 0.7955
2022-11-04 13:45:45,117 - mmseg - INFO - Iter [35750/80000]	lr: 7.043e-05, eta: 4:17:06, time: 0.336, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4953, decode.acc_seg: 80.4222, aux.loss_ce: 0.2701, aux.acc_seg: 75.1752, loss: 0.7653
2022-11-04 13:46:02,315 - mmseg - INFO - Iter [35800/80000]	lr: 7.035e-05, eta: 4:16:48, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5020, decode.acc_seg: 80.5567, aux.loss_ce: 0.2798, aux.acc_seg: 74.8279, loss: 0.7818
2022-11-04 13:46:19,329 - mmseg - INFO - Iter [35850/80000]	lr: 7.028e-05, eta: 4:16:30, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5034, decode.acc_seg: 80.7802, aux.loss_ce: 0.2835, aux.acc_seg: 74.8187, loss: 0.7870
2022-11-04 13:46:36,486 - mmseg - INFO - Iter [35900/80000]	lr: 7.021e-05, eta: 4:16:13, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5100, decode.acc_seg: 80.2714, aux.loss_ce: 0.2800, aux.acc_seg: 74.9087, loss: 0.7900
2022-11-04 13:46:53,317 - mmseg - INFO - Iter [35950/80000]	lr: 7.014e-05, eta: 4:15:54, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4974, decode.acc_seg: 81.1549, aux.loss_ce: 0.2787, aux.acc_seg: 74.9806, loss: 0.7760
2022-11-04 13:47:13,371 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 13:47:13,372 - mmseg - INFO - Iter [36000/80000]	lr: 7.007e-05, eta: 4:15:40, time: 0.401, data_time: 0.062, memory: 12988, decode.loss_ce: 0.4810, decode.acc_seg: 81.5372, aux.loss_ce: 0.2764, aux.acc_seg: 74.8770, loss: 0.7574
2022-11-04 13:47:30,290 - mmseg - INFO - Iter [36050/80000]	lr: 7.000e-05, eta: 4:15:22, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4896, decode.acc_seg: 81.2733, aux.loss_ce: 0.2720, aux.acc_seg: 75.9932, loss: 0.7617
2022-11-04 13:47:47,491 - mmseg - INFO - Iter [36100/80000]	lr: 6.992e-05, eta: 4:15:04, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4899, decode.acc_seg: 81.0451, aux.loss_ce: 0.2698, aux.acc_seg: 75.2822, loss: 0.7596
2022-11-04 13:48:04,483 - mmseg - INFO - Iter [36150/80000]	lr: 6.985e-05, eta: 4:14:47, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4998, decode.acc_seg: 80.8158, aux.loss_ce: 0.2802, aux.acc_seg: 75.1163, loss: 0.7800
2022-11-04 13:48:21,541 - mmseg - INFO - Iter [36200/80000]	lr: 6.978e-05, eta: 4:14:29, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4950, decode.acc_seg: 80.8313, aux.loss_ce: 0.2742, aux.acc_seg: 75.0611, loss: 0.7692
2022-11-04 13:48:38,663 - mmseg - INFO - Iter [36250/80000]	lr: 6.971e-05, eta: 4:14:11, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5066, decode.acc_seg: 80.2823, aux.loss_ce: 0.2806, aux.acc_seg: 74.6450, loss: 0.7872
2022-11-04 13:48:55,823 - mmseg - INFO - Iter [36300/80000]	lr: 6.964e-05, eta: 4:13:53, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5031, decode.acc_seg: 80.4428, aux.loss_ce: 0.2774, aux.acc_seg: 75.0473, loss: 0.7806
2022-11-04 13:49:12,821 - mmseg - INFO - Iter [36350/80000]	lr: 6.957e-05, eta: 4:13:35, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4972, decode.acc_seg: 81.0611, aux.loss_ce: 0.2724, aux.acc_seg: 75.7954, loss: 0.7697
2022-11-04 13:49:29,947 - mmseg - INFO - Iter [36400/80000]	lr: 6.949e-05, eta: 4:13:17, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5045, decode.acc_seg: 80.3546, aux.loss_ce: 0.2747, aux.acc_seg: 75.3286, loss: 0.7792
2022-11-04 13:49:47,107 - mmseg - INFO - Iter [36450/80000]	lr: 6.942e-05, eta: 4:13:00, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4889, decode.acc_seg: 81.3912, aux.loss_ce: 0.2733, aux.acc_seg: 75.7492, loss: 0.7622
2022-11-04 13:50:04,189 - mmseg - INFO - Iter [36500/80000]	lr: 6.935e-05, eta: 4:12:42, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5085, decode.acc_seg: 80.8824, aux.loss_ce: 0.2759, aux.acc_seg: 75.4593, loss: 0.7844
2022-11-04 13:50:21,217 - mmseg - INFO - Iter [36550/80000]	lr: 6.928e-05, eta: 4:12:24, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5019, decode.acc_seg: 80.5366, aux.loss_ce: 0.2758, aux.acc_seg: 75.3505, loss: 0.7776
2022-11-04 13:50:41,053 - mmseg - INFO - Iter [36600/80000]	lr: 6.921e-05, eta: 4:12:09, time: 0.397, data_time: 0.065, memory: 12988, decode.loss_ce: 0.4777, decode.acc_seg: 81.0693, aux.loss_ce: 0.2696, aux.acc_seg: 75.1667, loss: 0.7473
2022-11-04 13:50:57,848 - mmseg - INFO - Iter [36650/80000]	lr: 6.914e-05, eta: 4:11:51, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4857, decode.acc_seg: 81.2780, aux.loss_ce: 0.2689, aux.acc_seg: 75.7019, loss: 0.7546
2022-11-04 13:51:15,080 - mmseg - INFO - Iter [36700/80000]	lr: 6.906e-05, eta: 4:11:33, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4844, decode.acc_seg: 81.1651, aux.loss_ce: 0.2693, aux.acc_seg: 75.4289, loss: 0.7536
2022-11-04 13:51:32,229 - mmseg - INFO - Iter [36750/80000]	lr: 6.899e-05, eta: 4:11:16, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4914, decode.acc_seg: 81.7308, aux.loss_ce: 0.2746, aux.acc_seg: 76.1354, loss: 0.7660
2022-11-04 13:51:49,316 - mmseg - INFO - Iter [36800/80000]	lr: 6.892e-05, eta: 4:10:58, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4988, decode.acc_seg: 80.9154, aux.loss_ce: 0.2766, aux.acc_seg: 75.3229, loss: 0.7754
2022-11-04 13:52:06,444 - mmseg - INFO - Iter [36850/80000]	lr: 6.885e-05, eta: 4:10:40, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4748, decode.acc_seg: 81.3180, aux.loss_ce: 0.2587, aux.acc_seg: 76.4055, loss: 0.7336
2022-11-04 13:52:23,427 - mmseg - INFO - Iter [36900/80000]	lr: 6.878e-05, eta: 4:10:22, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5116, decode.acc_seg: 80.3375, aux.loss_ce: 0.2843, aux.acc_seg: 74.4604, loss: 0.7959
2022-11-04 13:52:40,457 - mmseg - INFO - Iter [36950/80000]	lr: 6.870e-05, eta: 4:10:04, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4987, decode.acc_seg: 80.7218, aux.loss_ce: 0.2740, aux.acc_seg: 75.4292, loss: 0.7726
2022-11-04 13:52:57,637 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 13:52:57,638 - mmseg - INFO - Iter [37000/80000]	lr: 6.863e-05, eta: 4:09:46, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4899, decode.acc_seg: 81.1358, aux.loss_ce: 0.2771, aux.acc_seg: 74.9492, loss: 0.7669
2022-11-04 13:53:14,512 - mmseg - INFO - Iter [37050/80000]	lr: 6.856e-05, eta: 4:09:28, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5077, decode.acc_seg: 81.0186, aux.loss_ce: 0.2799, aux.acc_seg: 75.1057, loss: 0.7876
2022-11-04 13:53:31,514 - mmseg - INFO - Iter [37100/80000]	lr: 6.849e-05, eta: 4:09:11, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5161, decode.acc_seg: 80.1928, aux.loss_ce: 0.2847, aux.acc_seg: 74.2084, loss: 0.8008
2022-11-04 13:53:48,422 - mmseg - INFO - Iter [37150/80000]	lr: 6.842e-05, eta: 4:08:52, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4934, decode.acc_seg: 80.9490, aux.loss_ce: 0.2756, aux.acc_seg: 75.0055, loss: 0.7690
2022-11-04 13:54:05,516 - mmseg - INFO - Iter [37200/80000]	lr: 6.835e-05, eta: 4:08:35, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5004, decode.acc_seg: 80.5128, aux.loss_ce: 0.2768, aux.acc_seg: 74.8475, loss: 0.7772
2022-11-04 13:54:25,579 - mmseg - INFO - Iter [37250/80000]	lr: 6.827e-05, eta: 4:08:20, time: 0.401, data_time: 0.061, memory: 12988, decode.loss_ce: 0.4926, decode.acc_seg: 80.8980, aux.loss_ce: 0.2784, aux.acc_seg: 74.9227, loss: 0.7710
2022-11-04 13:54:42,539 - mmseg - INFO - Iter [37300/80000]	lr: 6.820e-05, eta: 4:08:02, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4901, decode.acc_seg: 80.8215, aux.loss_ce: 0.2796, aux.acc_seg: 74.8192, loss: 0.7697
2022-11-04 13:54:59,421 - mmseg - INFO - Iter [37350/80000]	lr: 6.813e-05, eta: 4:07:44, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4738, decode.acc_seg: 81.5414, aux.loss_ce: 0.2666, aux.acc_seg: 75.8897, loss: 0.7404
2022-11-04 13:55:16,568 - mmseg - INFO - Iter [37400/80000]	lr: 6.806e-05, eta: 4:07:27, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4874, decode.acc_seg: 81.1068, aux.loss_ce: 0.2734, aux.acc_seg: 75.1394, loss: 0.7608
2022-11-04 13:55:33,524 - mmseg - INFO - Iter [37450/80000]	lr: 6.799e-05, eta: 4:07:09, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4966, decode.acc_seg: 80.6365, aux.loss_ce: 0.2728, aux.acc_seg: 75.1256, loss: 0.7694
2022-11-04 13:55:50,412 - mmseg - INFO - Iter [37500/80000]	lr: 6.791e-05, eta: 4:06:51, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4944, decode.acc_seg: 80.9493, aux.loss_ce: 0.2759, aux.acc_seg: 74.9934, loss: 0.7703
2022-11-04 13:56:07,350 - mmseg - INFO - Iter [37550/80000]	lr: 6.784e-05, eta: 4:06:33, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5024, decode.acc_seg: 80.6428, aux.loss_ce: 0.2792, aux.acc_seg: 74.9513, loss: 0.7817
2022-11-04 13:56:24,407 - mmseg - INFO - Iter [37600/80000]	lr: 6.777e-05, eta: 4:06:15, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4929, decode.acc_seg: 81.1747, aux.loss_ce: 0.2764, aux.acc_seg: 75.2717, loss: 0.7693
2022-11-04 13:56:41,458 - mmseg - INFO - Iter [37650/80000]	lr: 6.770e-05, eta: 4:05:57, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4696, decode.acc_seg: 81.8644, aux.loss_ce: 0.2597, aux.acc_seg: 76.5357, loss: 0.7293
2022-11-04 13:56:58,557 - mmseg - INFO - Iter [37700/80000]	lr: 6.763e-05, eta: 4:05:39, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4983, decode.acc_seg: 80.6006, aux.loss_ce: 0.2739, aux.acc_seg: 75.1374, loss: 0.7722
2022-11-04 13:57:15,532 - mmseg - INFO - Iter [37750/80000]	lr: 6.755e-05, eta: 4:05:21, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5085, decode.acc_seg: 80.3706, aux.loss_ce: 0.2730, aux.acc_seg: 75.2703, loss: 0.7815
2022-11-04 13:57:32,462 - mmseg - INFO - Iter [37800/80000]	lr: 6.748e-05, eta: 4:05:03, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5118, decode.acc_seg: 80.3679, aux.loss_ce: 0.2812, aux.acc_seg: 74.8002, loss: 0.7931
2022-11-04 13:57:49,479 - mmseg - INFO - Iter [37850/80000]	lr: 6.741e-05, eta: 4:04:45, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4794, decode.acc_seg: 81.5961, aux.loss_ce: 0.2641, aux.acc_seg: 76.2309, loss: 0.7435
2022-11-04 13:58:09,716 - mmseg - INFO - Iter [37900/80000]	lr: 6.734e-05, eta: 4:04:31, time: 0.405, data_time: 0.066, memory: 12988, decode.loss_ce: 0.4756, decode.acc_seg: 81.5310, aux.loss_ce: 0.2674, aux.acc_seg: 75.7419, loss: 0.7430
2022-11-04 13:58:26,717 - mmseg - INFO - Iter [37950/80000]	lr: 6.727e-05, eta: 4:04:13, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4953, decode.acc_seg: 80.6933, aux.loss_ce: 0.2780, aux.acc_seg: 74.7854, loss: 0.7734
2022-11-04 13:58:43,538 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 13:58:43,539 - mmseg - INFO - Iter [38000/80000]	lr: 6.719e-05, eta: 4:03:55, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4856, decode.acc_seg: 81.3813, aux.loss_ce: 0.2673, aux.acc_seg: 75.8575, loss: 0.7528
2022-11-04 13:59:00,540 - mmseg - INFO - Iter [38050/80000]	lr: 6.712e-05, eta: 4:03:37, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4905, decode.acc_seg: 81.2026, aux.loss_ce: 0.2691, aux.acc_seg: 75.6532, loss: 0.7596
2022-11-04 13:59:17,456 - mmseg - INFO - Iter [38100/80000]	lr: 6.705e-05, eta: 4:03:19, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4964, decode.acc_seg: 81.1340, aux.loss_ce: 0.2792, aux.acc_seg: 75.0449, loss: 0.7755
2022-11-04 13:59:34,630 - mmseg - INFO - Iter [38150/80000]	lr: 6.698e-05, eta: 4:03:01, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4719, decode.acc_seg: 81.8376, aux.loss_ce: 0.2625, aux.acc_seg: 76.3585, loss: 0.7344
2022-11-04 13:59:51,528 - mmseg - INFO - Iter [38200/80000]	lr: 6.691e-05, eta: 4:02:43, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4867, decode.acc_seg: 81.0655, aux.loss_ce: 0.2663, aux.acc_seg: 75.9032, loss: 0.7530
2022-11-04 14:00:08,406 - mmseg - INFO - Iter [38250/80000]	lr: 6.683e-05, eta: 4:02:25, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4938, decode.acc_seg: 80.9233, aux.loss_ce: 0.2756, aux.acc_seg: 75.0845, loss: 0.7694
2022-11-04 14:00:25,620 - mmseg - INFO - Iter [38300/80000]	lr: 6.676e-05, eta: 4:02:08, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5091, decode.acc_seg: 80.4937, aux.loss_ce: 0.2808, aux.acc_seg: 74.6048, loss: 0.7898
2022-11-04 14:00:42,798 - mmseg - INFO - Iter [38350/80000]	lr: 6.669e-05, eta: 4:01:50, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4959, decode.acc_seg: 80.7295, aux.loss_ce: 0.2767, aux.acc_seg: 75.1589, loss: 0.7726
2022-11-04 14:00:59,995 - mmseg - INFO - Iter [38400/80000]	lr: 6.662e-05, eta: 4:01:32, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4905, decode.acc_seg: 81.0803, aux.loss_ce: 0.2706, aux.acc_seg: 75.5072, loss: 0.7612
2022-11-04 14:01:16,929 - mmseg - INFO - Iter [38450/80000]	lr: 6.655e-05, eta: 4:01:15, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4965, decode.acc_seg: 80.8343, aux.loss_ce: 0.2744, aux.acc_seg: 75.0582, loss: 0.7709
2022-11-04 14:01:36,846 - mmseg - INFO - Iter [38500/80000]	lr: 6.647e-05, eta: 4:01:00, time: 0.398, data_time: 0.067, memory: 12988, decode.loss_ce: 0.5038, decode.acc_seg: 80.7944, aux.loss_ce: 0.2762, aux.acc_seg: 75.4739, loss: 0.7801
2022-11-04 14:01:53,968 - mmseg - INFO - Iter [38550/80000]	lr: 6.640e-05, eta: 4:00:42, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4830, decode.acc_seg: 81.6655, aux.loss_ce: 0.2693, aux.acc_seg: 75.9173, loss: 0.7523
2022-11-04 14:02:10,891 - mmseg - INFO - Iter [38600/80000]	lr: 6.633e-05, eta: 4:00:24, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4859, decode.acc_seg: 81.6125, aux.loss_ce: 0.2759, aux.acc_seg: 75.9329, loss: 0.7619
2022-11-04 14:02:27,992 - mmseg - INFO - Iter [38650/80000]	lr: 6.626e-05, eta: 4:00:06, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4926, decode.acc_seg: 81.2026, aux.loss_ce: 0.2771, aux.acc_seg: 75.4066, loss: 0.7697
2022-11-04 14:02:45,285 - mmseg - INFO - Iter [38700/80000]	lr: 6.619e-05, eta: 3:59:49, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4779, decode.acc_seg: 81.4147, aux.loss_ce: 0.2638, aux.acc_seg: 76.1043, loss: 0.7417
2022-11-04 14:03:02,296 - mmseg - INFO - Iter [38750/80000]	lr: 6.611e-05, eta: 3:59:31, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4922, decode.acc_seg: 80.9872, aux.loss_ce: 0.2684, aux.acc_seg: 75.8406, loss: 0.7605
2022-11-04 14:03:19,376 - mmseg - INFO - Iter [38800/80000]	lr: 6.604e-05, eta: 3:59:13, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4748, decode.acc_seg: 81.3963, aux.loss_ce: 0.2656, aux.acc_seg: 75.8581, loss: 0.7403
2022-11-04 14:03:36,836 - mmseg - INFO - Iter [38850/80000]	lr: 6.597e-05, eta: 3:58:56, time: 0.349, data_time: 0.017, memory: 12988, decode.loss_ce: 0.4759, decode.acc_seg: 81.4830, aux.loss_ce: 0.2626, aux.acc_seg: 76.1818, loss: 0.7385
2022-11-04 14:03:53,866 - mmseg - INFO - Iter [38900/80000]	lr: 6.590e-05, eta: 3:58:38, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5020, decode.acc_seg: 80.3972, aux.loss_ce: 0.2786, aux.acc_seg: 74.4158, loss: 0.7806
2022-11-04 14:04:10,839 - mmseg - INFO - Iter [38950/80000]	lr: 6.583e-05, eta: 3:58:20, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5081, decode.acc_seg: 80.2903, aux.loss_ce: 0.2799, aux.acc_seg: 74.7264, loss: 0.7880
2022-11-04 14:04:27,888 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 14:04:27,889 - mmseg - INFO - Iter [39000/80000]	lr: 6.575e-05, eta: 3:58:02, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4933, decode.acc_seg: 81.0485, aux.loss_ce: 0.2709, aux.acc_seg: 75.6136, loss: 0.7643
2022-11-04 14:04:45,230 - mmseg - INFO - Iter [39050/80000]	lr: 6.568e-05, eta: 3:57:45, time: 0.347, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5031, decode.acc_seg: 80.7131, aux.loss_ce: 0.2758, aux.acc_seg: 75.1982, loss: 0.7789
2022-11-04 14:05:02,622 - mmseg - INFO - Iter [39100/80000]	lr: 6.561e-05, eta: 3:57:27, time: 0.348, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4910, decode.acc_seg: 81.3591, aux.loss_ce: 0.2725, aux.acc_seg: 75.7204, loss: 0.7636
2022-11-04 14:05:22,698 - mmseg - INFO - Iter [39150/80000]	lr: 6.554e-05, eta: 3:57:13, time: 0.402, data_time: 0.058, memory: 12988, decode.loss_ce: 0.4649, decode.acc_seg: 82.0265, aux.loss_ce: 0.2618, aux.acc_seg: 76.3560, loss: 0.7267
2022-11-04 14:05:39,875 - mmseg - INFO - Iter [39200/80000]	lr: 6.546e-05, eta: 3:56:55, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4823, decode.acc_seg: 81.4991, aux.loss_ce: 0.2716, aux.acc_seg: 75.5599, loss: 0.7539
2022-11-04 14:05:57,109 - mmseg - INFO - Iter [39250/80000]	lr: 6.539e-05, eta: 3:56:37, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4890, decode.acc_seg: 81.3269, aux.loss_ce: 0.2723, aux.acc_seg: 75.4168, loss: 0.7614
2022-11-04 14:06:14,281 - mmseg - INFO - Iter [39300/80000]	lr: 6.532e-05, eta: 3:56:20, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4749, decode.acc_seg: 81.8410, aux.loss_ce: 0.2718, aux.acc_seg: 76.1991, loss: 0.7467
2022-11-04 14:06:31,329 - mmseg - INFO - Iter [39350/80000]	lr: 6.525e-05, eta: 3:56:02, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4710, decode.acc_seg: 81.7185, aux.loss_ce: 0.2642, aux.acc_seg: 75.7602, loss: 0.7352
2022-11-04 14:06:48,167 - mmseg - INFO - Iter [39400/80000]	lr: 6.518e-05, eta: 3:55:44, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4869, decode.acc_seg: 81.0800, aux.loss_ce: 0.2726, aux.acc_seg: 75.3277, loss: 0.7595
2022-11-04 14:07:05,305 - mmseg - INFO - Iter [39450/80000]	lr: 6.510e-05, eta: 3:55:26, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4683, decode.acc_seg: 81.8110, aux.loss_ce: 0.2614, aux.acc_seg: 76.2213, loss: 0.7297
2022-11-04 14:07:22,383 - mmseg - INFO - Iter [39500/80000]	lr: 6.503e-05, eta: 3:55:08, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5019, decode.acc_seg: 80.8509, aux.loss_ce: 0.2770, aux.acc_seg: 75.0824, loss: 0.7789
2022-11-04 14:07:39,437 - mmseg - INFO - Iter [39550/80000]	lr: 6.496e-05, eta: 3:54:51, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4902, decode.acc_seg: 81.1687, aux.loss_ce: 0.2722, aux.acc_seg: 75.5273, loss: 0.7624
2022-11-04 14:07:56,646 - mmseg - INFO - Iter [39600/80000]	lr: 6.489e-05, eta: 3:54:33, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.5034, decode.acc_seg: 80.8567, aux.loss_ce: 0.2800, aux.acc_seg: 75.4151, loss: 0.7834
2022-11-04 14:08:13,649 - mmseg - INFO - Iter [39650/80000]	lr: 6.481e-05, eta: 3:54:15, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4791, decode.acc_seg: 81.4447, aux.loss_ce: 0.2702, aux.acc_seg: 75.5214, loss: 0.7493
2022-11-04 14:08:30,646 - mmseg - INFO - Iter [39700/80000]	lr: 6.474e-05, eta: 3:53:57, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4985, decode.acc_seg: 80.9906, aux.loss_ce: 0.2803, aux.acc_seg: 74.8768, loss: 0.7788
2022-11-04 14:08:47,741 - mmseg - INFO - Iter [39750/80000]	lr: 6.467e-05, eta: 3:53:40, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4839, decode.acc_seg: 81.0686, aux.loss_ce: 0.2669, aux.acc_seg: 75.5812, loss: 0.7508
2022-11-04 14:09:07,581 - mmseg - INFO - Iter [39800/80000]	lr: 6.460e-05, eta: 3:53:25, time: 0.397, data_time: 0.058, memory: 12988, decode.loss_ce: 0.4866, decode.acc_seg: 81.1750, aux.loss_ce: 0.2714, aux.acc_seg: 75.4215, loss: 0.7581
2022-11-04 14:09:24,456 - mmseg - INFO - Iter [39850/80000]	lr: 6.452e-05, eta: 3:53:07, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4748, decode.acc_seg: 81.6819, aux.loss_ce: 0.2637, aux.acc_seg: 76.2385, loss: 0.7385
2022-11-04 14:09:41,344 - mmseg - INFO - Iter [39900/80000]	lr: 6.445e-05, eta: 3:52:49, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4797, decode.acc_seg: 81.6091, aux.loss_ce: 0.2672, aux.acc_seg: 75.6765, loss: 0.7469
2022-11-04 14:09:58,302 - mmseg - INFO - Iter [39950/80000]	lr: 6.438e-05, eta: 3:52:31, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4771, decode.acc_seg: 81.3694, aux.loss_ce: 0.2688, aux.acc_seg: 75.5927, loss: 0.7459
2022-11-04 14:10:15,420 - mmseg - INFO - Saving checkpoint at 40000 iterations
2022-11-04 14:10:15,717 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 14:10:15,718 - mmseg - INFO - Iter [40000/80000]	lr: 6.431e-05, eta: 3:52:13, time: 0.348, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4833, decode.acc_seg: 81.3545, aux.loss_ce: 0.2695, aux.acc_seg: 75.7564, loss: 0.7528
2022-11-04 14:10:24,996 - mmseg - INFO - per class results:
2022-11-04 14:10:25,003 - mmseg - INFO - 
+---------------------+-------+-------+
|        Class        |  IoU  |  Acc  |
+---------------------+-------+-------+
|         wall        | 66.42 | 82.62 |
|       building      | 75.07 | 89.52 |
|         sky         | 88.51 | 93.35 |
|        floor        | 71.14 | 84.27 |
|         tree        | 67.48 | 84.62 |
|       ceiling       | 75.07 | 85.61 |
|         road        | 76.37 | 87.49 |
|         bed         | 75.05 | 89.76 |
|      windowpane     | 50.61 | 66.89 |
|        grass        |  61.8 | 86.73 |
|       cabinet       | 47.69 | 59.64 |
|       sidewalk      | 50.48 | 63.01 |
|        person       | 63.75 | 82.73 |
|        earth        | 28.37 | 36.61 |
|         door        | 26.18 | 42.37 |
|        table        | 40.98 | 57.26 |
|       mountain      | 54.57 | 67.61 |
|        plant        | 42.89 | 53.25 |
|       curtain       | 55.86 | 74.15 |
|        chair        | 38.78 | 55.26 |
|         car         | 71.52 | 84.93 |
|        water        | 42.58 | 61.98 |
|       painting      | 54.69 | 68.51 |
|         sofa        |  45.8 | 63.21 |
|        shelf        | 30.13 | 46.01 |
|        house        | 33.96 | 45.29 |
|         sea         | 38.66 |  60.0 |
|        mirror       | 43.82 | 52.79 |
|         rug         |  48.2 |  56.9 |
|        field        | 24.24 | 38.32 |
|       armchair      | 27.02 | 43.76 |
|         seat        | 47.73 | 65.76 |
|        fence        |  24.7 | 46.76 |
|         desk        | 31.94 | 45.76 |
|         rock        | 43.12 | 68.86 |
|       wardrobe      | 38.56 | 62.61 |
|         lamp        | 36.03 | 48.17 |
|       bathtub       | 51.31 | 66.75 |
|       railing       | 26.29 | 37.44 |
|       cushion       | 31.22 | 40.83 |
|         base        |  16.1 | 26.52 |
|         box         | 14.93 | 21.61 |
|        column       | 30.71 | 38.82 |
|      signboard      | 20.62 | 29.51 |
|   chest of drawers  | 27.71 | 46.47 |
|       counter       |  19.2 | 24.71 |
|         sand        | 29.79 |  43.8 |
|         sink        | 44.66 | 54.54 |
|      skyscraper     | 65.82 |  78.3 |
|      fireplace      | 51.67 | 74.86 |
|     refrigerator    | 48.84 | 63.49 |
|      grandstand     | 32.09 | 61.93 |
|         path        | 11.77 | 15.34 |
|        stairs       | 23.41 | 28.08 |
|        runway       | 74.35 | 89.99 |
|         case        | 45.53 | 62.15 |
|      pool table     | 80.12 | 87.77 |
|        pillow       | 36.17 | 44.89 |
|     screen door     | 52.57 | 75.38 |
|       stairway      | 30.25 | 43.49 |
|        river        | 19.49 | 38.15 |
|        bridge       | 50.08 | 65.12 |
|       bookcase      | 24.76 | 39.03 |
|        blind        | 20.69 | 24.01 |
|     coffee table    | 41.84 | 68.84 |
|        toilet       |  63.4 | 78.79 |
|        flower       | 26.37 | 42.45 |
|         book        | 34.26 | 49.61 |
|         hill        |  4.31 |  7.8  |
|        bench        | 30.07 | 39.91 |
|      countertop     | 46.51 | 62.17 |
|        stove        |  48.3 | 60.42 |
|         palm        | 35.24 | 51.39 |
|    kitchen island   | 17.33 | 32.45 |
|       computer      | 43.48 | 60.15 |
|     swivel chair    |  35.4 | 47.86 |
|         boat        | 47.86 | 55.58 |
|         bar         | 22.27 | 28.02 |
|    arcade machine   | 25.07 |  27.3 |
|        hovel        | 21.43 |  37.7 |
|         bus         | 69.64 | 82.72 |
|        towel        | 35.31 | 45.08 |
|        light        | 15.35 | 16.97 |
|        truck        |  4.03 |  5.09 |
|        tower        | 33.77 | 50.55 |
|      chandelier     | 46.36 | 61.87 |
|        awning       | 18.59 | 21.94 |
|     streetlight     |  5.24 |  5.91 |
|        booth        | 30.08 | 36.79 |
| television receiver | 51.86 |  66.4 |
|       airplane      | 36.65 | 50.92 |
|      dirt track     | 12.21 | 25.29 |
|       apparel       | 13.49 |  17.9 |
|         pole        |  6.33 | 11.32 |
|         land        |  0.37 |  0.41 |
|      bannister      |  1.17 |  1.85 |
|      escalator      |  47.9 | 71.57 |
|       ottoman       |  13.5 | 15.85 |
|        bottle       | 13.51 | 16.87 |
|        buffet       | 31.12 | 45.76 |
|        poster       | 10.53 | 13.08 |
|        stage        |  5.17 | 12.58 |
|         van         | 23.58 | 27.56 |
|         ship        | 52.52 | 69.59 |
|       fountain      |  6.2  |  7.01 |
|    conveyer belt    | 50.01 | 64.82 |
|        canopy       | 10.38 | 15.04 |
|        washer       | 59.58 | 62.53 |
|      plaything      |  8.3  | 15.84 |
|    swimming pool    |  3.82 |  4.87 |
|        stool        | 11.74 | 13.81 |
|        barrel       | 26.95 | 62.92 |
|        basket       | 15.17 | 18.59 |
|      waterfall      | 25.22 | 32.59 |
|         tent        | 90.07 | 95.96 |
|         bag         |  3.01 |  3.33 |
|       minibike      | 35.14 | 43.47 |
|        cradle       | 58.35 | 84.56 |
|         oven        | 17.48 | 23.27 |
|         ball        | 35.24 | 45.31 |
|         food        | 36.53 | 45.88 |
|         step        |  1.86 |  2.39 |
|         tank        | 28.06 | 33.52 |
|      trade name     | 16.21 | 18.84 |
|      microwave      | 24.78 |  27.9 |
|         pot         | 23.06 | 28.78 |
|        animal       | 46.38 | 51.41 |
|       bicycle       | 33.48 | 62.02 |
|         lake        |  0.17 |  0.18 |
|      dishwasher     | 23.23 | 28.66 |
|        screen       | 53.46 | 79.29 |
|       blanket       |  0.64 |  0.67 |
|      sculpture      | 29.01 | 48.59 |
|         hood        | 28.26 | 33.28 |
|        sconce       |  13.4 | 14.22 |
|         vase        | 16.42 | 25.09 |
|    traffic light    |  9.27 | 11.94 |
|         tray        |  1.17 |  2.84 |
|        ashcan       | 17.73 | 24.64 |
|         fan         | 27.49 | 34.13 |
|         pier        | 45.21 | 56.43 |
|      crt screen     |  0.0  |  0.0  |
|        plate        | 27.09 | 33.54 |
|       monitor       | 15.82 | 24.01 |
|    bulletin board   | 29.42 | 33.05 |
|        shower       |  0.0  |  0.0  |
|       radiator      | 35.41 | 43.26 |
|        glass        |  1.95 |  2.04 |
|        clock        |  7.95 |  8.95 |
|         flag        | 15.48 | 16.58 |
+---------------------+-------+-------+
2022-11-04 14:10:25,003 - mmseg - INFO - Summary:
2022-11-04 14:10:25,004 - mmseg - INFO - 
+-------+-------+-------+
|  aAcc |  mIoU |  mAcc |
+-------+-------+-------+
| 75.02 | 33.38 | 44.24 |
+-------+-------+-------+
2022-11-04 14:10:25,005 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 14:10:25,005 - mmseg - INFO - Iter(val) [250]	aAcc: 0.7502, mIoU: 0.3338, mAcc: 0.4424, IoU.wall: 0.6642, IoU.building: 0.7507, IoU.sky: 0.8851, IoU.floor: 0.7114, IoU.tree: 0.6748, IoU.ceiling: 0.7507, IoU.road: 0.7637, IoU.bed : 0.7505, IoU.windowpane: 0.5061, IoU.grass: 0.6180, IoU.cabinet: 0.4769, IoU.sidewalk: 0.5048, IoU.person: 0.6375, IoU.earth: 0.2837, IoU.door: 0.2618, IoU.table: 0.4098, IoU.mountain: 0.5457, IoU.plant: 0.4289, IoU.curtain: 0.5586, IoU.chair: 0.3878, IoU.car: 0.7152, IoU.water: 0.4258, IoU.painting: 0.5469, IoU.sofa: 0.4580, IoU.shelf: 0.3013, IoU.house: 0.3396, IoU.sea: 0.3866, IoU.mirror: 0.4382, IoU.rug: 0.4820, IoU.field: 0.2424, IoU.armchair: 0.2702, IoU.seat: 0.4773, IoU.fence: 0.2470, IoU.desk: 0.3194, IoU.rock: 0.4312, IoU.wardrobe: 0.3856, IoU.lamp: 0.3603, IoU.bathtub: 0.5131, IoU.railing: 0.2629, IoU.cushion: 0.3122, IoU.base: 0.1610, IoU.box: 0.1493, IoU.column: 0.3071, IoU.signboard: 0.2062, IoU.chest of drawers: 0.2771, IoU.counter: 0.1920, IoU.sand: 0.2979, IoU.sink: 0.4466, IoU.skyscraper: 0.6582, IoU.fireplace: 0.5167, IoU.refrigerator: 0.4884, IoU.grandstand: 0.3209, IoU.path: 0.1177, IoU.stairs: 0.2341, IoU.runway: 0.7435, IoU.case: 0.4553, IoU.pool table: 0.8012, IoU.pillow: 0.3617, IoU.screen door: 0.5257, IoU.stairway: 0.3025, IoU.river: 0.1949, IoU.bridge: 0.5008, IoU.bookcase: 0.2476, IoU.blind: 0.2069, IoU.coffee table: 0.4184, IoU.toilet: 0.6340, IoU.flower: 0.2637, IoU.book: 0.3426, IoU.hill: 0.0431, IoU.bench: 0.3007, IoU.countertop: 0.4651, IoU.stove: 0.4830, IoU.palm: 0.3524, IoU.kitchen island: 0.1733, IoU.computer: 0.4348, IoU.swivel chair: 0.3540, IoU.boat: 0.4786, IoU.bar: 0.2227, IoU.arcade machine: 0.2507, IoU.hovel: 0.2143, IoU.bus: 0.6964, IoU.towel: 0.3531, IoU.light: 0.1535, IoU.truck: 0.0403, IoU.tower: 0.3377, IoU.chandelier: 0.4636, IoU.awning: 0.1859, IoU.streetlight: 0.0524, IoU.booth: 0.3008, IoU.television receiver: 0.5186, IoU.airplane: 0.3665, IoU.dirt track: 0.1221, IoU.apparel: 0.1349, IoU.pole: 0.0633, IoU.land: 0.0037, IoU.bannister: 0.0117, IoU.escalator: 0.4790, IoU.ottoman: 0.1350, IoU.bottle: 0.1351, IoU.buffet: 0.3112, IoU.poster: 0.1053, IoU.stage: 0.0517, IoU.van: 0.2358, IoU.ship: 0.5252, IoU.fountain: 0.0620, IoU.conveyer belt: 0.5001, IoU.canopy: 0.1038, IoU.washer: 0.5958, IoU.plaything: 0.0830, IoU.swimming pool: 0.0382, IoU.stool: 0.1174, IoU.barrel: 0.2695, IoU.basket: 0.1517, IoU.waterfall: 0.2522, IoU.tent: 0.9007, IoU.bag: 0.0301, IoU.minibike: 0.3514, IoU.cradle: 0.5835, IoU.oven: 0.1748, IoU.ball: 0.3524, IoU.food: 0.3653, IoU.step: 0.0186, IoU.tank: 0.2806, IoU.trade name: 0.1621, IoU.microwave: 0.2478, IoU.pot: 0.2306, IoU.animal: 0.4638, IoU.bicycle: 0.3348, IoU.lake: 0.0017, IoU.dishwasher: 0.2323, IoU.screen: 0.5346, IoU.blanket: 0.0064, IoU.sculpture: 0.2901, IoU.hood: 0.2826, IoU.sconce: 0.1340, IoU.vase: 0.1642, IoU.traffic light: 0.0927, IoU.tray: 0.0117, IoU.ashcan: 0.1773, IoU.fan: 0.2749, IoU.pier: 0.4521, IoU.crt screen: 0.0000, IoU.plate: 0.2709, IoU.monitor: 0.1582, IoU.bulletin board: 0.2942, IoU.shower: 0.0000, IoU.radiator: 0.3541, IoU.glass: 0.0195, IoU.clock: 0.0795, IoU.flag: 0.1548, Acc.wall: 0.8262, Acc.building: 0.8952, Acc.sky: 0.9335, Acc.floor: 0.8427, Acc.tree: 0.8462, Acc.ceiling: 0.8561, Acc.road: 0.8749, Acc.bed : 0.8976, Acc.windowpane: 0.6689, Acc.grass: 0.8673, Acc.cabinet: 0.5964, Acc.sidewalk: 0.6301, Acc.person: 0.8273, Acc.earth: 0.3661, Acc.door: 0.4237, Acc.table: 0.5726, Acc.mountain: 0.6761, Acc.plant: 0.5325, Acc.curtain: 0.7415, Acc.chair: 0.5526, Acc.car: 0.8493, Acc.water: 0.6198, Acc.painting: 0.6851, Acc.sofa: 0.6321, Acc.shelf: 0.4601, Acc.house: 0.4529, Acc.sea: 0.6000, Acc.mirror: 0.5279, Acc.rug: 0.5690, Acc.field: 0.3832, Acc.armchair: 0.4376, Acc.seat: 0.6576, Acc.fence: 0.4676, Acc.desk: 0.4576, Acc.rock: 0.6886, Acc.wardrobe: 0.6261, Acc.lamp: 0.4817, Acc.bathtub: 0.6675, Acc.railing: 0.3744, Acc.cushion: 0.4083, Acc.base: 0.2652, Acc.box: 0.2161, Acc.column: 0.3882, Acc.signboard: 0.2951, Acc.chest of drawers: 0.4647, Acc.counter: 0.2471, Acc.sand: 0.4380, Acc.sink: 0.5454, Acc.skyscraper: 0.7830, Acc.fireplace: 0.7486, Acc.refrigerator: 0.6349, Acc.grandstand: 0.6193, Acc.path: 0.1534, Acc.stairs: 0.2808, Acc.runway: 0.8999, Acc.case: 0.6215, Acc.pool table: 0.8777, Acc.pillow: 0.4489, Acc.screen door: 0.7538, Acc.stairway: 0.4349, Acc.river: 0.3815, Acc.bridge: 0.6512, Acc.bookcase: 0.3903, Acc.blind: 0.2401, Acc.coffee table: 0.6884, Acc.toilet: 0.7879, Acc.flower: 0.4245, Acc.book: 0.4961, Acc.hill: 0.0780, Acc.bench: 0.3991, Acc.countertop: 0.6217, Acc.stove: 0.6042, Acc.palm: 0.5139, Acc.kitchen island: 0.3245, Acc.computer: 0.6015, Acc.swivel chair: 0.4786, Acc.boat: 0.5558, Acc.bar: 0.2802, Acc.arcade machine: 0.2730, Acc.hovel: 0.3770, Acc.bus: 0.8272, Acc.towel: 0.4508, Acc.light: 0.1697, Acc.truck: 0.0509, Acc.tower: 0.5055, Acc.chandelier: 0.6187, Acc.awning: 0.2194, Acc.streetlight: 0.0591, Acc.booth: 0.3679, Acc.television receiver: 0.6640, Acc.airplane: 0.5092, Acc.dirt track: 0.2529, Acc.apparel: 0.1790, Acc.pole: 0.1132, Acc.land: 0.0041, Acc.bannister: 0.0185, Acc.escalator: 0.7157, Acc.ottoman: 0.1585, Acc.bottle: 0.1687, Acc.buffet: 0.4576, Acc.poster: 0.1308, Acc.stage: 0.1258, Acc.van: 0.2756, Acc.ship: 0.6959, Acc.fountain: 0.0701, Acc.conveyer belt: 0.6482, Acc.canopy: 0.1504, Acc.washer: 0.6253, Acc.plaything: 0.1584, Acc.swimming pool: 0.0487, Acc.stool: 0.1381, Acc.barrel: 0.6292, Acc.basket: 0.1859, Acc.waterfall: 0.3259, Acc.tent: 0.9596, Acc.bag: 0.0333, Acc.minibike: 0.4347, Acc.cradle: 0.8456, Acc.oven: 0.2327, Acc.ball: 0.4531, Acc.food: 0.4588, Acc.step: 0.0239, Acc.tank: 0.3352, Acc.trade name: 0.1884, Acc.microwave: 0.2790, Acc.pot: 0.2878, Acc.animal: 0.5141, Acc.bicycle: 0.6202, Acc.lake: 0.0018, Acc.dishwasher: 0.2866, Acc.screen: 0.7929, Acc.blanket: 0.0067, Acc.sculpture: 0.4859, Acc.hood: 0.3328, Acc.sconce: 0.1422, Acc.vase: 0.2509, Acc.traffic light: 0.1194, Acc.tray: 0.0284, Acc.ashcan: 0.2464, Acc.fan: 0.3413, Acc.pier: 0.5643, Acc.crt screen: 0.0000, Acc.plate: 0.3354, Acc.monitor: 0.2401, Acc.bulletin board: 0.3305, Acc.shower: 0.0000, Acc.radiator: 0.4326, Acc.glass: 0.0204, Acc.clock: 0.0895, Acc.flag: 0.1658
2022-11-04 14:10:42,097 - mmseg - INFO - Iter [40050/80000]	lr: 6.424e-05, eta: 3:52:05, time: 0.528, data_time: 0.196, memory: 12988, decode.loss_ce: 0.4939, decode.acc_seg: 81.0670, aux.loss_ce: 0.2740, aux.acc_seg: 75.2512, loss: 0.7679
2022-11-04 14:10:59,053 - mmseg - INFO - Iter [40100/80000]	lr: 6.416e-05, eta: 3:51:47, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4900, decode.acc_seg: 81.2383, aux.loss_ce: 0.2710, aux.acc_seg: 75.5272, loss: 0.7611
2022-11-04 14:11:15,987 - mmseg - INFO - Iter [40150/80000]	lr: 6.409e-05, eta: 3:51:29, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4891, decode.acc_seg: 81.0691, aux.loss_ce: 0.2782, aux.acc_seg: 74.9920, loss: 0.7672
2022-11-04 14:11:33,272 - mmseg - INFO - Iter [40200/80000]	lr: 6.402e-05, eta: 3:51:11, time: 0.346, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4799, decode.acc_seg: 81.6630, aux.loss_ce: 0.2712, aux.acc_seg: 75.9815, loss: 0.7511
2022-11-04 14:11:50,267 - mmseg - INFO - Iter [40250/80000]	lr: 6.395e-05, eta: 3:50:54, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4708, decode.acc_seg: 81.6366, aux.loss_ce: 0.2631, aux.acc_seg: 76.1339, loss: 0.7339
2022-11-04 14:12:07,228 - mmseg - INFO - Iter [40300/80000]	lr: 6.387e-05, eta: 3:50:36, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4910, decode.acc_seg: 81.0663, aux.loss_ce: 0.2765, aux.acc_seg: 75.4141, loss: 0.7675
2022-11-04 14:12:24,247 - mmseg - INFO - Iter [40350/80000]	lr: 6.380e-05, eta: 3:50:18, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4788, decode.acc_seg: 81.7019, aux.loss_ce: 0.2674, aux.acc_seg: 75.8929, loss: 0.7462
2022-11-04 14:12:44,493 - mmseg - INFO - Iter [40400/80000]	lr: 6.373e-05, eta: 3:50:03, time: 0.405, data_time: 0.067, memory: 12988, decode.loss_ce: 0.4713, decode.acc_seg: 81.7183, aux.loss_ce: 0.2676, aux.acc_seg: 75.7618, loss: 0.7389
2022-11-04 14:13:01,524 - mmseg - INFO - Iter [40450/80000]	lr: 6.366e-05, eta: 3:49:45, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4679, decode.acc_seg: 81.8462, aux.loss_ce: 0.2591, aux.acc_seg: 76.4637, loss: 0.7270
2022-11-04 14:13:18,469 - mmseg - INFO - Iter [40500/80000]	lr: 6.358e-05, eta: 3:49:28, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4665, decode.acc_seg: 81.7931, aux.loss_ce: 0.2655, aux.acc_seg: 75.7156, loss: 0.7320
2022-11-04 14:13:35,699 - mmseg - INFO - Iter [40550/80000]	lr: 6.351e-05, eta: 3:49:10, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4889, decode.acc_seg: 80.8762, aux.loss_ce: 0.2695, aux.acc_seg: 75.3406, loss: 0.7584
2022-11-04 14:13:52,701 - mmseg - INFO - Iter [40600/80000]	lr: 6.344e-05, eta: 3:48:52, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4969, decode.acc_seg: 81.1483, aux.loss_ce: 0.2841, aux.acc_seg: 74.6733, loss: 0.7810
2022-11-04 14:14:09,715 - mmseg - INFO - Iter [40650/80000]	lr: 6.337e-05, eta: 3:48:34, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4696, decode.acc_seg: 82.0982, aux.loss_ce: 0.2655, aux.acc_seg: 76.3819, loss: 0.7351
2022-11-04 14:14:26,810 - mmseg - INFO - Iter [40700/80000]	lr: 6.329e-05, eta: 3:48:16, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4639, decode.acc_seg: 81.7240, aux.loss_ce: 0.2597, aux.acc_seg: 76.0881, loss: 0.7237
2022-11-04 14:14:43,866 - mmseg - INFO - Iter [40750/80000]	lr: 6.322e-05, eta: 3:47:59, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4934, decode.acc_seg: 80.8003, aux.loss_ce: 0.2712, aux.acc_seg: 75.2187, loss: 0.7646
2022-11-04 14:15:00,753 - mmseg - INFO - Iter [40800/80000]	lr: 6.315e-05, eta: 3:47:41, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4748, decode.acc_seg: 81.6625, aux.loss_ce: 0.2641, aux.acc_seg: 75.9313, loss: 0.7389
2022-11-04 14:15:18,133 - mmseg - INFO - Iter [40850/80000]	lr: 6.308e-05, eta: 3:47:23, time: 0.348, data_time: 0.011, memory: 12988, decode.loss_ce: 0.4660, decode.acc_seg: 81.8924, aux.loss_ce: 0.2623, aux.acc_seg: 76.1360, loss: 0.7283
2022-11-04 14:15:35,113 - mmseg - INFO - Iter [40900/80000]	lr: 6.300e-05, eta: 3:47:05, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4866, decode.acc_seg: 81.2530, aux.loss_ce: 0.2719, aux.acc_seg: 75.6660, loss: 0.7585
2022-11-04 14:15:52,041 - mmseg - INFO - Iter [40950/80000]	lr: 6.293e-05, eta: 3:46:48, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4850, decode.acc_seg: 81.5792, aux.loss_ce: 0.2728, aux.acc_seg: 75.5631, loss: 0.7579
2022-11-04 14:16:09,044 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 14:16:09,045 - mmseg - INFO - Iter [41000/80000]	lr: 6.286e-05, eta: 3:46:30, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4779, decode.acc_seg: 81.2060, aux.loss_ce: 0.2686, aux.acc_seg: 75.4373, loss: 0.7465
2022-11-04 14:16:28,737 - mmseg - INFO - Iter [41050/80000]	lr: 6.279e-05, eta: 3:46:14, time: 0.394, data_time: 0.069, memory: 12988, decode.loss_ce: 0.4751, decode.acc_seg: 81.6873, aux.loss_ce: 0.2613, aux.acc_seg: 76.3537, loss: 0.7364
2022-11-04 14:16:45,964 - mmseg - INFO - Iter [41100/80000]	lr: 6.271e-05, eta: 3:45:57, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4499, decode.acc_seg: 82.5445, aux.loss_ce: 0.2545, aux.acc_seg: 76.8657, loss: 0.7044
2022-11-04 14:17:02,852 - mmseg - INFO - Iter [41150/80000]	lr: 6.264e-05, eta: 3:45:39, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4790, decode.acc_seg: 81.4736, aux.loss_ce: 0.2699, aux.acc_seg: 75.8813, loss: 0.7489
2022-11-04 14:17:19,658 - mmseg - INFO - Iter [41200/80000]	lr: 6.257e-05, eta: 3:45:21, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4801, decode.acc_seg: 81.6877, aux.loss_ce: 0.2691, aux.acc_seg: 76.1261, loss: 0.7492
2022-11-04 14:17:36,584 - mmseg - INFO - Iter [41250/80000]	lr: 6.250e-05, eta: 3:45:03, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4843, decode.acc_seg: 81.0490, aux.loss_ce: 0.2718, aux.acc_seg: 75.5773, loss: 0.7561
2022-11-04 14:17:53,490 - mmseg - INFO - Iter [41300/80000]	lr: 6.242e-05, eta: 3:44:45, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4743, decode.acc_seg: 81.8821, aux.loss_ce: 0.2685, aux.acc_seg: 75.9630, loss: 0.7429
2022-11-04 14:18:10,574 - mmseg - INFO - Iter [41350/80000]	lr: 6.235e-05, eta: 3:44:27, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4827, decode.acc_seg: 81.2494, aux.loss_ce: 0.2710, aux.acc_seg: 75.2629, loss: 0.7537
2022-11-04 14:18:27,753 - mmseg - INFO - Iter [41400/80000]	lr: 6.228e-05, eta: 3:44:10, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4850, decode.acc_seg: 81.1563, aux.loss_ce: 0.2694, aux.acc_seg: 75.6079, loss: 0.7544
2022-11-04 14:18:44,726 - mmseg - INFO - Iter [41450/80000]	lr: 6.221e-05, eta: 3:43:52, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4690, decode.acc_seg: 81.7056, aux.loss_ce: 0.2664, aux.acc_seg: 75.4837, loss: 0.7354
2022-11-04 14:19:01,867 - mmseg - INFO - Iter [41500/80000]	lr: 6.213e-05, eta: 3:43:34, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4634, decode.acc_seg: 81.6726, aux.loss_ce: 0.2584, aux.acc_seg: 76.4059, loss: 0.7217
2022-11-04 14:19:18,546 - mmseg - INFO - Iter [41550/80000]	lr: 6.206e-05, eta: 3:43:16, time: 0.334, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4788, decode.acc_seg: 81.3360, aux.loss_ce: 0.2661, aux.acc_seg: 75.9278, loss: 0.7449
2022-11-04 14:19:35,765 - mmseg - INFO - Iter [41600/80000]	lr: 6.199e-05, eta: 3:42:58, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4859, decode.acc_seg: 81.3841, aux.loss_ce: 0.2758, aux.acc_seg: 75.6233, loss: 0.7617
2022-11-04 14:19:55,867 - mmseg - INFO - Iter [41650/80000]	lr: 6.192e-05, eta: 3:42:43, time: 0.402, data_time: 0.062, memory: 12988, decode.loss_ce: 0.4795, decode.acc_seg: 81.5549, aux.loss_ce: 0.2639, aux.acc_seg: 76.2807, loss: 0.7434
2022-11-04 14:20:12,906 - mmseg - INFO - Iter [41700/80000]	lr: 6.184e-05, eta: 3:42:26, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4759, decode.acc_seg: 81.6550, aux.loss_ce: 0.2743, aux.acc_seg: 75.3185, loss: 0.7502
2022-11-04 14:20:29,871 - mmseg - INFO - Iter [41750/80000]	lr: 6.177e-05, eta: 3:42:08, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4916, decode.acc_seg: 81.0334, aux.loss_ce: 0.2749, aux.acc_seg: 75.3253, loss: 0.7665
2022-11-04 14:20:46,777 - mmseg - INFO - Iter [41800/80000]	lr: 6.170e-05, eta: 3:41:50, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4884, decode.acc_seg: 80.7515, aux.loss_ce: 0.2731, aux.acc_seg: 75.2142, loss: 0.7615
2022-11-04 14:21:03,989 - mmseg - INFO - Iter [41850/80000]	lr: 6.162e-05, eta: 3:41:32, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4825, decode.acc_seg: 81.5586, aux.loss_ce: 0.2704, aux.acc_seg: 75.7299, loss: 0.7529
2022-11-04 14:21:20,876 - mmseg - INFO - Iter [41900/80000]	lr: 6.155e-05, eta: 3:41:14, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4692, decode.acc_seg: 82.1452, aux.loss_ce: 0.2599, aux.acc_seg: 76.9266, loss: 0.7291
2022-11-04 14:21:38,198 - mmseg - INFO - Iter [41950/80000]	lr: 6.148e-05, eta: 3:40:57, time: 0.346, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4662, decode.acc_seg: 82.1353, aux.loss_ce: 0.2551, aux.acc_seg: 76.7345, loss: 0.7213
2022-11-04 14:21:55,224 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 14:21:55,224 - mmseg - INFO - Iter [42000/80000]	lr: 6.141e-05, eta: 3:40:39, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4732, decode.acc_seg: 81.4850, aux.loss_ce: 0.2625, aux.acc_seg: 76.2621, loss: 0.7357
2022-11-04 14:22:12,483 - mmseg - INFO - Iter [42050/80000]	lr: 6.133e-05, eta: 3:40:22, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4774, decode.acc_seg: 81.4817, aux.loss_ce: 0.2660, aux.acc_seg: 75.6626, loss: 0.7434
2022-11-04 14:22:29,533 - mmseg - INFO - Iter [42100/80000]	lr: 6.126e-05, eta: 3:40:04, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4601, decode.acc_seg: 81.8558, aux.loss_ce: 0.2593, aux.acc_seg: 76.0120, loss: 0.7194
2022-11-04 14:22:46,487 - mmseg - INFO - Iter [42150/80000]	lr: 6.119e-05, eta: 3:39:46, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4844, decode.acc_seg: 81.2168, aux.loss_ce: 0.2675, aux.acc_seg: 75.7488, loss: 0.7519
2022-11-04 14:23:03,553 - mmseg - INFO - Iter [42200/80000]	lr: 6.112e-05, eta: 3:39:28, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4726, decode.acc_seg: 81.7120, aux.loss_ce: 0.2637, aux.acc_seg: 76.0491, loss: 0.7363
2022-11-04 14:23:20,696 - mmseg - INFO - Iter [42250/80000]	lr: 6.104e-05, eta: 3:39:11, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.5011, decode.acc_seg: 81.1182, aux.loss_ce: 0.2788, aux.acc_seg: 75.2376, loss: 0.7799
2022-11-04 14:23:40,832 - mmseg - INFO - Iter [42300/80000]	lr: 6.097e-05, eta: 3:38:56, time: 0.403, data_time: 0.066, memory: 12988, decode.loss_ce: 0.4791, decode.acc_seg: 81.6350, aux.loss_ce: 0.2715, aux.acc_seg: 75.3085, loss: 0.7506
2022-11-04 14:23:57,795 - mmseg - INFO - Iter [42350/80000]	lr: 6.090e-05, eta: 3:38:38, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4842, decode.acc_seg: 81.4947, aux.loss_ce: 0.2747, aux.acc_seg: 75.5083, loss: 0.7589
2022-11-04 14:24:14,674 - mmseg - INFO - Iter [42400/80000]	lr: 6.082e-05, eta: 3:38:20, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4779, decode.acc_seg: 81.5497, aux.loss_ce: 0.2635, aux.acc_seg: 75.9896, loss: 0.7414
2022-11-04 14:24:31,638 - mmseg - INFO - Iter [42450/80000]	lr: 6.075e-05, eta: 3:38:02, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4578, decode.acc_seg: 82.2546, aux.loss_ce: 0.2590, aux.acc_seg: 76.5982, loss: 0.7168
2022-11-04 14:24:48,578 - mmseg - INFO - Iter [42500/80000]	lr: 6.068e-05, eta: 3:37:44, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4644, decode.acc_seg: 81.8589, aux.loss_ce: 0.2627, aux.acc_seg: 76.1812, loss: 0.7271
2022-11-04 14:25:05,356 - mmseg - INFO - Iter [42550/80000]	lr: 6.061e-05, eta: 3:37:26, time: 0.336, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4496, decode.acc_seg: 82.5638, aux.loss_ce: 0.2582, aux.acc_seg: 76.7459, loss: 0.7078
2022-11-04 14:25:22,530 - mmseg - INFO - Iter [42600/80000]	lr: 6.053e-05, eta: 3:37:09, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4607, decode.acc_seg: 82.1577, aux.loss_ce: 0.2644, aux.acc_seg: 76.2190, loss: 0.7251
2022-11-04 14:25:39,582 - mmseg - INFO - Iter [42650/80000]	lr: 6.046e-05, eta: 3:36:51, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4773, decode.acc_seg: 81.3171, aux.loss_ce: 0.2634, aux.acc_seg: 76.0850, loss: 0.7407
2022-11-04 14:25:56,617 - mmseg - INFO - Iter [42700/80000]	lr: 6.039e-05, eta: 3:36:33, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4776, decode.acc_seg: 81.4107, aux.loss_ce: 0.2681, aux.acc_seg: 75.7797, loss: 0.7457
2022-11-04 14:26:13,873 - mmseg - INFO - Iter [42750/80000]	lr: 6.031e-05, eta: 3:36:16, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4820, decode.acc_seg: 81.4122, aux.loss_ce: 0.2757, aux.acc_seg: 74.8979, loss: 0.7577
2022-11-04 14:26:31,217 - mmseg - INFO - Iter [42800/80000]	lr: 6.024e-05, eta: 3:35:58, time: 0.347, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4847, decode.acc_seg: 81.3384, aux.loss_ce: 0.2710, aux.acc_seg: 75.8972, loss: 0.7557
2022-11-04 14:26:48,298 - mmseg - INFO - Iter [42850/80000]	lr: 6.017e-05, eta: 3:35:40, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4791, decode.acc_seg: 81.3399, aux.loss_ce: 0.2715, aux.acc_seg: 75.4838, loss: 0.7506
2022-11-04 14:27:05,523 - mmseg - INFO - Iter [42900/80000]	lr: 6.010e-05, eta: 3:35:23, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4848, decode.acc_seg: 81.2204, aux.loss_ce: 0.2660, aux.acc_seg: 75.9092, loss: 0.7508
2022-11-04 14:27:25,479 - mmseg - INFO - Iter [42950/80000]	lr: 6.002e-05, eta: 3:35:08, time: 0.399, data_time: 0.068, memory: 12988, decode.loss_ce: 0.4692, decode.acc_seg: 82.0762, aux.loss_ce: 0.2635, aux.acc_seg: 76.1911, loss: 0.7327
2022-11-04 14:27:42,507 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 14:27:42,507 - mmseg - INFO - Iter [43000/80000]	lr: 5.995e-05, eta: 3:34:50, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4814, decode.acc_seg: 81.3865, aux.loss_ce: 0.2708, aux.acc_seg: 75.7337, loss: 0.7522
2022-11-04 14:27:59,527 - mmseg - INFO - Iter [43050/80000]	lr: 5.988e-05, eta: 3:34:32, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4760, decode.acc_seg: 81.5561, aux.loss_ce: 0.2628, aux.acc_seg: 76.1317, loss: 0.7389
2022-11-04 14:28:16,426 - mmseg - INFO - Iter [43100/80000]	lr: 5.980e-05, eta: 3:34:14, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4597, decode.acc_seg: 81.6910, aux.loss_ce: 0.2594, aux.acc_seg: 76.2964, loss: 0.7191
2022-11-04 14:28:33,347 - mmseg - INFO - Iter [43150/80000]	lr: 5.973e-05, eta: 3:33:56, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4598, decode.acc_seg: 81.9805, aux.loss_ce: 0.2569, aux.acc_seg: 76.4231, loss: 0.7167
2022-11-04 14:28:50,441 - mmseg - INFO - Iter [43200/80000]	lr: 5.966e-05, eta: 3:33:39, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4803, decode.acc_seg: 81.6734, aux.loss_ce: 0.2696, aux.acc_seg: 76.1051, loss: 0.7499
2022-11-04 14:29:07,575 - mmseg - INFO - Iter [43250/80000]	lr: 5.959e-05, eta: 3:33:21, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4803, decode.acc_seg: 81.2315, aux.loss_ce: 0.2715, aux.acc_seg: 75.5152, loss: 0.7517
2022-11-04 14:29:24,645 - mmseg - INFO - Iter [43300/80000]	lr: 5.951e-05, eta: 3:33:03, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4825, decode.acc_seg: 81.3862, aux.loss_ce: 0.2780, aux.acc_seg: 74.9881, loss: 0.7605
2022-11-04 14:29:41,808 - mmseg - INFO - Iter [43350/80000]	lr: 5.944e-05, eta: 3:32:46, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4705, decode.acc_seg: 81.6214, aux.loss_ce: 0.2677, aux.acc_seg: 75.8949, loss: 0.7381
2022-11-04 14:29:58,817 - mmseg - INFO - Iter [43400/80000]	lr: 5.937e-05, eta: 3:32:28, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4854, decode.acc_seg: 81.0531, aux.loss_ce: 0.2748, aux.acc_seg: 74.9449, loss: 0.7602
2022-11-04 14:30:15,942 - mmseg - INFO - Iter [43450/80000]	lr: 5.929e-05, eta: 3:32:10, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4718, decode.acc_seg: 81.8485, aux.loss_ce: 0.2618, aux.acc_seg: 76.6871, loss: 0.7336
2022-11-04 14:30:32,895 - mmseg - INFO - Iter [43500/80000]	lr: 5.922e-05, eta: 3:31:52, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4642, decode.acc_seg: 81.7795, aux.loss_ce: 0.2652, aux.acc_seg: 76.0730, loss: 0.7294
2022-11-04 14:30:52,788 - mmseg - INFO - Iter [43550/80000]	lr: 5.915e-05, eta: 3:31:37, time: 0.398, data_time: 0.061, memory: 12988, decode.loss_ce: 0.4784, decode.acc_seg: 81.4668, aux.loss_ce: 0.2657, aux.acc_seg: 75.7768, loss: 0.7441
2022-11-04 14:31:09,816 - mmseg - INFO - Iter [43600/80000]	lr: 5.907e-05, eta: 3:31:19, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4530, decode.acc_seg: 82.3714, aux.loss_ce: 0.2576, aux.acc_seg: 76.5843, loss: 0.7106
2022-11-04 14:31:26,835 - mmseg - INFO - Iter [43650/80000]	lr: 5.900e-05, eta: 3:31:02, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4755, decode.acc_seg: 81.5751, aux.loss_ce: 0.2666, aux.acc_seg: 75.6519, loss: 0.7420
2022-11-04 14:31:43,989 - mmseg - INFO - Iter [43700/80000]	lr: 5.893e-05, eta: 3:30:44, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4664, decode.acc_seg: 82.0952, aux.loss_ce: 0.2602, aux.acc_seg: 76.8499, loss: 0.7266
2022-11-04 14:32:00,962 - mmseg - INFO - Iter [43750/80000]	lr: 5.886e-05, eta: 3:30:26, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4740, decode.acc_seg: 81.6900, aux.loss_ce: 0.2649, aux.acc_seg: 75.6932, loss: 0.7389
2022-11-04 14:32:17,876 - mmseg - INFO - Iter [43800/80000]	lr: 5.878e-05, eta: 3:30:08, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4495, decode.acc_seg: 82.5713, aux.loss_ce: 0.2548, aux.acc_seg: 76.7407, loss: 0.7042
2022-11-04 14:32:34,690 - mmseg - INFO - Iter [43850/80000]	lr: 5.871e-05, eta: 3:29:50, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4658, decode.acc_seg: 81.8053, aux.loss_ce: 0.2631, aux.acc_seg: 76.0998, loss: 0.7289
2022-11-04 14:32:51,648 - mmseg - INFO - Iter [43900/80000]	lr: 5.864e-05, eta: 3:29:33, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4654, decode.acc_seg: 81.9970, aux.loss_ce: 0.2628, aux.acc_seg: 76.3267, loss: 0.7283
2022-11-04 14:33:08,485 - mmseg - INFO - Iter [43950/80000]	lr: 5.856e-05, eta: 3:29:15, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4773, decode.acc_seg: 81.4386, aux.loss_ce: 0.2699, aux.acc_seg: 75.8797, loss: 0.7473
2022-11-04 14:33:25,757 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 14:33:25,758 - mmseg - INFO - Iter [44000/80000]	lr: 5.849e-05, eta: 3:28:57, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4695, decode.acc_seg: 81.5422, aux.loss_ce: 0.2630, aux.acc_seg: 75.9581, loss: 0.7325
2022-11-04 14:33:43,036 - mmseg - INFO - Iter [44050/80000]	lr: 5.842e-05, eta: 3:28:40, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4776, decode.acc_seg: 81.5068, aux.loss_ce: 0.2739, aux.acc_seg: 75.1561, loss: 0.7515
2022-11-04 14:33:59,936 - mmseg - INFO - Iter [44100/80000]	lr: 5.834e-05, eta: 3:28:22, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4670, decode.acc_seg: 81.9044, aux.loss_ce: 0.2589, aux.acc_seg: 76.6604, loss: 0.7259
2022-11-04 14:34:16,847 - mmseg - INFO - Iter [44150/80000]	lr: 5.827e-05, eta: 3:28:04, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4647, decode.acc_seg: 81.8830, aux.loss_ce: 0.2655, aux.acc_seg: 75.9527, loss: 0.7302
2022-11-04 14:34:36,918 - mmseg - INFO - Iter [44200/80000]	lr: 5.820e-05, eta: 3:27:49, time: 0.401, data_time: 0.065, memory: 12988, decode.loss_ce: 0.4730, decode.acc_seg: 82.0863, aux.loss_ce: 0.2609, aux.acc_seg: 76.7785, loss: 0.7339
2022-11-04 14:34:54,097 - mmseg - INFO - Iter [44250/80000]	lr: 5.812e-05, eta: 3:27:31, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4523, decode.acc_seg: 82.4061, aux.loss_ce: 0.2507, aux.acc_seg: 77.0249, loss: 0.7030
2022-11-04 14:35:11,137 - mmseg - INFO - Iter [44300/80000]	lr: 5.805e-05, eta: 3:27:13, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4710, decode.acc_seg: 81.7398, aux.loss_ce: 0.2707, aux.acc_seg: 75.5990, loss: 0.7417
2022-11-04 14:35:28,301 - mmseg - INFO - Iter [44350/80000]	lr: 5.798e-05, eta: 3:26:56, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4683, decode.acc_seg: 81.8292, aux.loss_ce: 0.2656, aux.acc_seg: 76.1696, loss: 0.7339
2022-11-04 14:35:45,013 - mmseg - INFO - Iter [44400/80000]	lr: 5.791e-05, eta: 3:26:38, time: 0.334, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4602, decode.acc_seg: 82.2214, aux.loss_ce: 0.2624, aux.acc_seg: 76.1896, loss: 0.7226
2022-11-04 14:36:02,048 - mmseg - INFO - Iter [44450/80000]	lr: 5.783e-05, eta: 3:26:20, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4575, decode.acc_seg: 82.1836, aux.loss_ce: 0.2634, aux.acc_seg: 76.3698, loss: 0.7209
2022-11-04 14:36:19,102 - mmseg - INFO - Iter [44500/80000]	lr: 5.776e-05, eta: 3:26:02, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4690, decode.acc_seg: 81.6653, aux.loss_ce: 0.2618, aux.acc_seg: 76.1413, loss: 0.7307
2022-11-04 14:36:36,101 - mmseg - INFO - Iter [44550/80000]	lr: 5.769e-05, eta: 3:25:45, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4536, decode.acc_seg: 82.3500, aux.loss_ce: 0.2552, aux.acc_seg: 76.9400, loss: 0.7088
2022-11-04 14:36:53,125 - mmseg - INFO - Iter [44600/80000]	lr: 5.761e-05, eta: 3:25:27, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4815, decode.acc_seg: 81.3735, aux.loss_ce: 0.2686, aux.acc_seg: 75.7011, loss: 0.7502
2022-11-04 14:37:10,317 - mmseg - INFO - Iter [44650/80000]	lr: 5.754e-05, eta: 3:25:09, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4653, decode.acc_seg: 81.8269, aux.loss_ce: 0.2607, aux.acc_seg: 76.2536, loss: 0.7260
2022-11-04 14:37:27,126 - mmseg - INFO - Iter [44700/80000]	lr: 5.747e-05, eta: 3:24:51, time: 0.336, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4788, decode.acc_seg: 81.3460, aux.loss_ce: 0.2672, aux.acc_seg: 75.9777, loss: 0.7460
2022-11-04 14:37:44,202 - mmseg - INFO - Iter [44750/80000]	lr: 5.739e-05, eta: 3:24:34, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4728, decode.acc_seg: 81.6242, aux.loss_ce: 0.2658, aux.acc_seg: 75.7198, loss: 0.7386
2022-11-04 14:38:01,123 - mmseg - INFO - Iter [44800/80000]	lr: 5.732e-05, eta: 3:24:16, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4874, decode.acc_seg: 81.5576, aux.loss_ce: 0.2724, aux.acc_seg: 75.7151, loss: 0.7598
2022-11-04 14:38:21,019 - mmseg - INFO - Iter [44850/80000]	lr: 5.725e-05, eta: 3:24:01, time: 0.398, data_time: 0.061, memory: 12988, decode.loss_ce: 0.4704, decode.acc_seg: 81.6287, aux.loss_ce: 0.2654, aux.acc_seg: 76.0549, loss: 0.7357
2022-11-04 14:38:38,096 - mmseg - INFO - Iter [44900/80000]	lr: 5.717e-05, eta: 3:23:43, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4600, decode.acc_seg: 82.2072, aux.loss_ce: 0.2591, aux.acc_seg: 76.7173, loss: 0.7191
2022-11-04 14:38:55,267 - mmseg - INFO - Iter [44950/80000]	lr: 5.710e-05, eta: 3:23:25, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4723, decode.acc_seg: 81.7508, aux.loss_ce: 0.2681, aux.acc_seg: 75.9561, loss: 0.7404
2022-11-04 14:39:12,563 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 14:39:12,563 - mmseg - INFO - Iter [45000/80000]	lr: 5.703e-05, eta: 3:23:08, time: 0.346, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4827, decode.acc_seg: 81.3542, aux.loss_ce: 0.2713, aux.acc_seg: 75.3820, loss: 0.7540
2022-11-04 14:39:29,432 - mmseg - INFO - Iter [45050/80000]	lr: 5.695e-05, eta: 3:22:50, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4591, decode.acc_seg: 81.8181, aux.loss_ce: 0.2544, aux.acc_seg: 76.1781, loss: 0.7135
2022-11-04 14:39:46,454 - mmseg - INFO - Iter [45100/80000]	lr: 5.688e-05, eta: 3:22:32, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4515, decode.acc_seg: 82.4722, aux.loss_ce: 0.2577, aux.acc_seg: 76.6507, loss: 0.7092
2022-11-04 14:40:03,674 - mmseg - INFO - Iter [45150/80000]	lr: 5.681e-05, eta: 3:22:15, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4492, decode.acc_seg: 82.5657, aux.loss_ce: 0.2557, aux.acc_seg: 76.8372, loss: 0.7049
2022-11-04 14:40:20,561 - mmseg - INFO - Iter [45200/80000]	lr: 5.673e-05, eta: 3:21:57, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4629, decode.acc_seg: 82.4572, aux.loss_ce: 0.2608, aux.acc_seg: 76.6375, loss: 0.7237
2022-11-04 14:40:37,708 - mmseg - INFO - Iter [45250/80000]	lr: 5.666e-05, eta: 3:21:39, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4618, decode.acc_seg: 82.1623, aux.loss_ce: 0.2638, aux.acc_seg: 76.1484, loss: 0.7255
2022-11-04 14:40:54,846 - mmseg - INFO - Iter [45300/80000]	lr: 5.659e-05, eta: 3:21:22, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4721, decode.acc_seg: 81.8057, aux.loss_ce: 0.2660, aux.acc_seg: 75.5598, loss: 0.7381
2022-11-04 14:41:11,893 - mmseg - INFO - Iter [45350/80000]	lr: 5.651e-05, eta: 3:21:04, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4462, decode.acc_seg: 82.2631, aux.loss_ce: 0.2522, aux.acc_seg: 76.8226, loss: 0.6984
2022-11-04 14:41:28,721 - mmseg - INFO - Iter [45400/80000]	lr: 5.644e-05, eta: 3:20:46, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4874, decode.acc_seg: 81.5109, aux.loss_ce: 0.2729, aux.acc_seg: 75.9620, loss: 0.7603
2022-11-04 14:41:48,956 - mmseg - INFO - Iter [45450/80000]	lr: 5.637e-05, eta: 3:20:31, time: 0.405, data_time: 0.061, memory: 12988, decode.loss_ce: 0.4841, decode.acc_seg: 81.3938, aux.loss_ce: 0.2735, aux.acc_seg: 75.3396, loss: 0.7576
2022-11-04 14:42:06,026 - mmseg - INFO - Iter [45500/80000]	lr: 5.629e-05, eta: 3:20:13, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4355, decode.acc_seg: 82.8695, aux.loss_ce: 0.2510, aux.acc_seg: 76.8005, loss: 0.6866
2022-11-04 14:42:23,329 - mmseg - INFO - Iter [45550/80000]	lr: 5.622e-05, eta: 3:19:56, time: 0.346, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4496, decode.acc_seg: 82.4403, aux.loss_ce: 0.2565, aux.acc_seg: 76.5962, loss: 0.7061
2022-11-04 14:42:40,390 - mmseg - INFO - Iter [45600/80000]	lr: 5.615e-05, eta: 3:19:38, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4582, decode.acc_seg: 82.2531, aux.loss_ce: 0.2586, aux.acc_seg: 76.4687, loss: 0.7168
2022-11-04 14:42:57,357 - mmseg - INFO - Iter [45650/80000]	lr: 5.607e-05, eta: 3:19:20, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4527, decode.acc_seg: 82.3244, aux.loss_ce: 0.2587, aux.acc_seg: 76.4213, loss: 0.7115
2022-11-04 14:43:14,293 - mmseg - INFO - Iter [45700/80000]	lr: 5.600e-05, eta: 3:19:02, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4635, decode.acc_seg: 82.0194, aux.loss_ce: 0.2615, aux.acc_seg: 76.3307, loss: 0.7250
2022-11-04 14:43:31,374 - mmseg - INFO - Iter [45750/80000]	lr: 5.593e-05, eta: 3:18:45, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4407, decode.acc_seg: 82.5071, aux.loss_ce: 0.2530, aux.acc_seg: 76.6613, loss: 0.6936
2022-11-04 14:43:48,385 - mmseg - INFO - Iter [45800/80000]	lr: 5.585e-05, eta: 3:18:27, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4753, decode.acc_seg: 82.0239, aux.loss_ce: 0.2683, aux.acc_seg: 76.3765, loss: 0.7437
2022-11-04 14:44:05,489 - mmseg - INFO - Iter [45850/80000]	lr: 5.578e-05, eta: 3:18:09, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4728, decode.acc_seg: 81.6327, aux.loss_ce: 0.2655, aux.acc_seg: 75.5528, loss: 0.7383
2022-11-04 14:44:22,451 - mmseg - INFO - Iter [45900/80000]	lr: 5.570e-05, eta: 3:17:52, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4576, decode.acc_seg: 82.0843, aux.loss_ce: 0.2570, aux.acc_seg: 76.8149, loss: 0.7146
2022-11-04 14:44:39,397 - mmseg - INFO - Iter [45950/80000]	lr: 5.563e-05, eta: 3:17:34, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4678, decode.acc_seg: 81.8415, aux.loss_ce: 0.2662, aux.acc_seg: 75.6113, loss: 0.7340
2022-11-04 14:44:56,442 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 14:44:56,443 - mmseg - INFO - Iter [46000/80000]	lr: 5.556e-05, eta: 3:17:16, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4839, decode.acc_seg: 81.5204, aux.loss_ce: 0.2730, aux.acc_seg: 75.7103, loss: 0.7569
2022-11-04 14:45:13,364 - mmseg - INFO - Iter [46050/80000]	lr: 5.548e-05, eta: 3:16:59, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4740, decode.acc_seg: 81.8827, aux.loss_ce: 0.2645, aux.acc_seg: 76.3148, loss: 0.7385
2022-11-04 14:45:33,639 - mmseg - INFO - Iter [46100/80000]	lr: 5.541e-05, eta: 3:16:43, time: 0.405, data_time: 0.073, memory: 12988, decode.loss_ce: 0.4546, decode.acc_seg: 82.2483, aux.loss_ce: 0.2548, aux.acc_seg: 76.9724, loss: 0.7094
2022-11-04 14:45:50,502 - mmseg - INFO - Iter [46150/80000]	lr: 5.534e-05, eta: 3:16:25, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4562, decode.acc_seg: 82.3400, aux.loss_ce: 0.2598, aux.acc_seg: 76.3770, loss: 0.7160
2022-11-04 14:46:07,524 - mmseg - INFO - Iter [46200/80000]	lr: 5.526e-05, eta: 3:16:08, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4390, decode.acc_seg: 82.6523, aux.loss_ce: 0.2483, aux.acc_seg: 76.9834, loss: 0.6873
2022-11-04 14:46:24,671 - mmseg - INFO - Iter [46250/80000]	lr: 5.519e-05, eta: 3:15:50, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4618, decode.acc_seg: 82.0738, aux.loss_ce: 0.2639, aux.acc_seg: 76.2613, loss: 0.7257
2022-11-04 14:46:41,556 - mmseg - INFO - Iter [46300/80000]	lr: 5.512e-05, eta: 3:15:32, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4631, decode.acc_seg: 82.0719, aux.loss_ce: 0.2598, aux.acc_seg: 76.5349, loss: 0.7229
2022-11-04 14:46:58,535 - mmseg - INFO - Iter [46350/80000]	lr: 5.504e-05, eta: 3:15:15, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4606, decode.acc_seg: 81.9225, aux.loss_ce: 0.2530, aux.acc_seg: 76.8628, loss: 0.7136
2022-11-04 14:47:15,278 - mmseg - INFO - Iter [46400/80000]	lr: 5.497e-05, eta: 3:14:57, time: 0.335, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4493, decode.acc_seg: 82.2840, aux.loss_ce: 0.2570, aux.acc_seg: 76.5069, loss: 0.7062
2022-11-04 14:47:32,324 - mmseg - INFO - Iter [46450/80000]	lr: 5.490e-05, eta: 3:14:39, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4520, decode.acc_seg: 82.6268, aux.loss_ce: 0.2580, aux.acc_seg: 76.8418, loss: 0.7100
2022-11-04 14:47:49,134 - mmseg - INFO - Iter [46500/80000]	lr: 5.482e-05, eta: 3:14:21, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4728, decode.acc_seg: 81.9984, aux.loss_ce: 0.2675, aux.acc_seg: 76.1491, loss: 0.7402
2022-11-04 14:48:06,362 - mmseg - INFO - Iter [46550/80000]	lr: 5.475e-05, eta: 3:14:04, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4827, decode.acc_seg: 81.3974, aux.loss_ce: 0.2760, aux.acc_seg: 75.0394, loss: 0.7586
2022-11-04 14:48:23,396 - mmseg - INFO - Iter [46600/80000]	lr: 5.467e-05, eta: 3:13:46, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4686, decode.acc_seg: 81.9510, aux.loss_ce: 0.2650, aux.acc_seg: 76.4904, loss: 0.7336
2022-11-04 14:48:40,693 - mmseg - INFO - Iter [46650/80000]	lr: 5.460e-05, eta: 3:13:29, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4669, decode.acc_seg: 81.7506, aux.loss_ce: 0.2646, aux.acc_seg: 76.0858, loss: 0.7315
2022-11-04 14:49:00,558 - mmseg - INFO - Iter [46700/80000]	lr: 5.453e-05, eta: 3:13:13, time: 0.397, data_time: 0.062, memory: 12988, decode.loss_ce: 0.4750, decode.acc_seg: 82.0313, aux.loss_ce: 0.2682, aux.acc_seg: 76.0115, loss: 0.7432
2022-11-04 14:49:17,419 - mmseg - INFO - Iter [46750/80000]	lr: 5.445e-05, eta: 3:12:55, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4534, decode.acc_seg: 82.4038, aux.loss_ce: 0.2582, aux.acc_seg: 76.8670, loss: 0.7117
2022-11-04 14:49:34,736 - mmseg - INFO - Iter [46800/80000]	lr: 5.438e-05, eta: 3:12:38, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4613, decode.acc_seg: 82.1221, aux.loss_ce: 0.2587, aux.acc_seg: 76.4940, loss: 0.7200
2022-11-04 14:49:51,901 - mmseg - INFO - Iter [46850/80000]	lr: 5.431e-05, eta: 3:12:20, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4689, decode.acc_seg: 81.8527, aux.loss_ce: 0.2688, aux.acc_seg: 75.5661, loss: 0.7376
2022-11-04 14:50:09,107 - mmseg - INFO - Iter [46900/80000]	lr: 5.423e-05, eta: 3:12:02, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4500, decode.acc_seg: 82.3105, aux.loss_ce: 0.2602, aux.acc_seg: 76.4222, loss: 0.7102
2022-11-04 14:50:26,201 - mmseg - INFO - Iter [46950/80000]	lr: 5.416e-05, eta: 3:11:45, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4733, decode.acc_seg: 82.1148, aux.loss_ce: 0.2641, aux.acc_seg: 76.4313, loss: 0.7373
2022-11-04 14:50:43,257 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 14:50:43,258 - mmseg - INFO - Iter [47000/80000]	lr: 5.408e-05, eta: 3:11:27, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4541, decode.acc_seg: 82.2778, aux.loss_ce: 0.2576, aux.acc_seg: 76.2917, loss: 0.7116
2022-11-04 14:51:00,264 - mmseg - INFO - Iter [47050/80000]	lr: 5.401e-05, eta: 3:11:10, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4556, decode.acc_seg: 82.2238, aux.loss_ce: 0.2592, aux.acc_seg: 76.1799, loss: 0.7147
2022-11-04 14:51:17,329 - mmseg - INFO - Iter [47100/80000]	lr: 5.394e-05, eta: 3:10:52, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4532, decode.acc_seg: 81.8339, aux.loss_ce: 0.2585, aux.acc_seg: 76.0010, loss: 0.7117
2022-11-04 14:51:34,344 - mmseg - INFO - Iter [47150/80000]	lr: 5.386e-05, eta: 3:10:34, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4487, decode.acc_seg: 82.6608, aux.loss_ce: 0.2551, aux.acc_seg: 76.9881, loss: 0.7038
2022-11-04 14:51:51,506 - mmseg - INFO - Iter [47200/80000]	lr: 5.379e-05, eta: 3:10:17, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4594, decode.acc_seg: 82.2539, aux.loss_ce: 0.2587, aux.acc_seg: 76.6664, loss: 0.7181
2022-11-04 14:52:08,830 - mmseg - INFO - Iter [47250/80000]	lr: 5.372e-05, eta: 3:09:59, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4638, decode.acc_seg: 81.9581, aux.loss_ce: 0.2650, aux.acc_seg: 76.5108, loss: 0.7288
2022-11-04 14:52:25,912 - mmseg - INFO - Iter [47300/80000]	lr: 5.364e-05, eta: 3:09:42, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4785, decode.acc_seg: 81.6150, aux.loss_ce: 0.2643, aux.acc_seg: 75.8906, loss: 0.7428
2022-11-04 14:52:45,962 - mmseg - INFO - Iter [47350/80000]	lr: 5.357e-05, eta: 3:09:26, time: 0.401, data_time: 0.063, memory: 12988, decode.loss_ce: 0.4619, decode.acc_seg: 82.0588, aux.loss_ce: 0.2626, aux.acc_seg: 76.2002, loss: 0.7245
2022-11-04 14:53:03,141 - mmseg - INFO - Iter [47400/80000]	lr: 5.349e-05, eta: 3:09:08, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4637, decode.acc_seg: 82.2699, aux.loss_ce: 0.2616, aux.acc_seg: 76.5162, loss: 0.7252
2022-11-04 14:53:20,427 - mmseg - INFO - Iter [47450/80000]	lr: 5.342e-05, eta: 3:08:51, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4514, decode.acc_seg: 82.7005, aux.loss_ce: 0.2601, aux.acc_seg: 76.6203, loss: 0.7115
2022-11-04 14:53:37,441 - mmseg - INFO - Iter [47500/80000]	lr: 5.335e-05, eta: 3:08:33, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4578, decode.acc_seg: 82.1367, aux.loss_ce: 0.2582, aux.acc_seg: 76.3990, loss: 0.7159
2022-11-04 14:53:54,570 - mmseg - INFO - Iter [47550/80000]	lr: 5.327e-05, eta: 3:08:16, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4465, decode.acc_seg: 82.8047, aux.loss_ce: 0.2506, aux.acc_seg: 77.3390, loss: 0.6972
2022-11-04 14:54:11,680 - mmseg - INFO - Iter [47600/80000]	lr: 5.320e-05, eta: 3:07:58, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4650, decode.acc_seg: 81.8599, aux.loss_ce: 0.2620, aux.acc_seg: 75.8360, loss: 0.7270
2022-11-04 14:54:28,591 - mmseg - INFO - Iter [47650/80000]	lr: 5.313e-05, eta: 3:07:40, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4628, decode.acc_seg: 82.2337, aux.loss_ce: 0.2642, aux.acc_seg: 76.4192, loss: 0.7270
2022-11-04 14:54:45,571 - mmseg - INFO - Iter [47700/80000]	lr: 5.305e-05, eta: 3:07:23, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4659, decode.acc_seg: 82.1260, aux.loss_ce: 0.2613, aux.acc_seg: 76.5097, loss: 0.7272
2022-11-04 14:55:02,686 - mmseg - INFO - Iter [47750/80000]	lr: 5.298e-05, eta: 3:07:05, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4650, decode.acc_seg: 82.1181, aux.loss_ce: 0.2715, aux.acc_seg: 75.9537, loss: 0.7364
2022-11-04 14:55:19,788 - mmseg - INFO - Iter [47800/80000]	lr: 5.290e-05, eta: 3:06:47, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4531, decode.acc_seg: 82.1545, aux.loss_ce: 0.2591, aux.acc_seg: 76.3773, loss: 0.7121
2022-11-04 14:55:37,069 - mmseg - INFO - Iter [47850/80000]	lr: 5.283e-05, eta: 3:06:30, time: 0.346, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4666, decode.acc_seg: 81.6197, aux.loss_ce: 0.2626, aux.acc_seg: 75.8101, loss: 0.7292
2022-11-04 14:55:54,137 - mmseg - INFO - Iter [47900/80000]	lr: 5.276e-05, eta: 3:06:12, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4546, decode.acc_seg: 82.1034, aux.loss_ce: 0.2532, aux.acc_seg: 76.6992, loss: 0.7078
2022-11-04 14:56:11,006 - mmseg - INFO - Iter [47950/80000]	lr: 5.268e-05, eta: 3:05:55, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4364, decode.acc_seg: 82.8641, aux.loss_ce: 0.2500, aux.acc_seg: 77.3649, loss: 0.6863
2022-11-04 14:56:31,049 - mmseg - INFO - Saving checkpoint at 48000 iterations
2022-11-04 14:56:31,315 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 14:56:31,315 - mmseg - INFO - Iter [48000/80000]	lr: 5.261e-05, eta: 3:05:39, time: 0.406, data_time: 0.060, memory: 12988, decode.loss_ce: 0.4522, decode.acc_seg: 82.3772, aux.loss_ce: 0.2569, aux.acc_seg: 76.6795, loss: 0.7091
2022-11-04 14:56:40,639 - mmseg - INFO - per class results:
2022-11-04 14:56:40,646 - mmseg - INFO - 
+---------------------+-------+-------+
|        Class        |  IoU  |  Acc  |
+---------------------+-------+-------+
|         wall        | 66.68 | 82.82 |
|       building      | 75.04 | 87.14 |
|         sky         | 88.66 | 93.59 |
|        floor        | 70.73 | 85.49 |
|         tree        | 66.91 | 83.35 |
|       ceiling       | 74.96 | 85.76 |
|         road        | 75.35 | 87.18 |
|         bed         | 74.96 |  90.1 |
|      windowpane     | 50.84 | 66.78 |
|        grass        | 60.96 | 79.26 |
|       cabinet       | 48.13 | 60.33 |
|       sidewalk      |  49.8 | 65.42 |
|        person       | 64.36 | 81.15 |
|        earth        | 29.96 | 41.91 |
|         door        | 27.12 | 42.75 |
|        table        | 40.55 | 56.03 |
|       mountain      | 57.01 | 71.52 |
|        plant        | 40.73 | 49.93 |
|       curtain       | 56.07 | 73.87 |
|        chair        | 37.34 | 51.42 |
|         car         | 71.86 | 86.25 |
|        water        | 43.88 | 61.02 |
|       painting      | 55.86 | 73.48 |
|         sofa        | 46.09 | 65.74 |
|        shelf        | 32.94 | 49.31 |
|        house        | 39.97 |  69.8 |
|         sea         |  36.7 | 59.77 |
|        mirror       | 43.29 | 51.12 |
|         rug         | 50.89 | 61.36 |
|        field        | 26.34 | 42.54 |
|       armchair      | 27.57 | 45.08 |
|         seat        | 46.72 | 65.43 |
|        fence        | 26.42 | 44.24 |
|         desk        | 31.64 | 52.28 |
|         rock        | 42.04 | 73.06 |
|       wardrobe      | 38.03 | 57.57 |
|         lamp        |  37.3 | 49.55 |
|       bathtub       | 53.57 | 66.64 |
|       railing       | 24.36 | 43.11 |
|       cushion       | 27.38 | 32.62 |
|         base        | 17.28 | 28.69 |
|         box         | 14.16 | 19.96 |
|        column       | 27.27 | 34.66 |
|      signboard      | 21.79 | 31.28 |
|   chest of drawers  | 27.75 | 52.58 |
|       counter       |  28.9 | 39.06 |
|         sand        | 27.31 |  43.5 |
|         sink        | 46.19 | 60.59 |
|      skyscraper     | 65.45 | 79.22 |
|      fireplace      | 55.32 |  77.4 |
|     refrigerator    | 48.45 | 63.89 |
|      grandstand     | 43.47 | 62.68 |
|         path        |  9.84 | 13.89 |
|        stairs       | 26.84 | 33.28 |
|        runway       | 74.17 | 88.77 |
|         case        | 48.69 | 59.27 |
|      pool table     | 79.46 | 90.34 |
|        pillow       | 39.31 | 54.31 |
|     screen door     | 54.26 | 64.05 |
|       stairway      | 33.45 | 41.51 |
|        river        | 13.22 | 22.93 |
|        bridge       | 32.22 | 42.45 |
|       bookcase      | 26.88 | 40.07 |
|        blind        | 16.16 | 17.74 |
|     coffee table    | 40.95 | 70.55 |
|        toilet       | 65.39 | 79.56 |
|        flower       |  28.7 |  47.1 |
|         book        | 37.87 | 57.69 |
|         hill        |  5.35 |  8.54 |
|        bench        | 31.38 | 41.94 |
|      countertop     | 45.92 |  65.0 |
|        stove        | 46.56 | 56.37 |
|         palm        | 35.27 | 47.75 |
|    kitchen island   | 19.26 | 42.17 |
|       computer      | 44.41 | 57.78 |
|     swivel chair    | 28.23 | 38.34 |
|         boat        | 54.83 | 62.33 |
|         bar         | 24.17 | 29.77 |
|    arcade machine   | 31.79 | 36.05 |
|        hovel        | 25.34 | 31.36 |
|         bus         | 72.33 | 85.61 |
|        towel        | 38.66 | 49.53 |
|        light        | 13.72 | 14.85 |
|        truck        |  9.73 | 12.48 |
|        tower        | 37.93 | 52.32 |
|      chandelier     | 47.27 | 59.33 |
|        awning       | 19.93 | 23.65 |
|     streetlight     |  6.21 |  7.13 |
|        booth        | 31.92 | 35.08 |
| television receiver | 48.63 | 69.17 |
|       airplane      | 45.48 | 53.23 |
|      dirt track     |  13.1 | 24.36 |
|       apparel       | 16.52 | 24.63 |
|         pole        |  6.27 |  11.9 |
|         land        |  0.03 |  0.03 |
|      bannister      |  2.28 |  3.7  |
|      escalator      | 44.69 | 65.97 |
|       ottoman       | 19.69 | 24.01 |
|        bottle       | 10.24 | 11.87 |
|        buffet       |  36.4 | 48.57 |
|        poster       | 18.27 | 25.14 |
|        stage        |  6.27 | 14.86 |
|         van         | 23.86 |  27.7 |
|         ship        | 19.59 | 19.92 |
|       fountain      | 18.83 | 19.56 |
|    conveyer belt    | 45.26 | 63.96 |
|        canopy       | 15.02 | 22.56 |
|        washer       | 59.34 | 62.54 |
|      plaything      |  6.82 | 16.59 |
|    swimming pool    | 28.68 | 41.85 |
|        stool        | 19.89 | 27.09 |
|        barrel       | 22.04 | 61.55 |
|        basket       | 15.53 | 18.39 |
|      waterfall      | 38.52 | 48.34 |
|         tent        | 89.53 | 95.21 |
|         bag         |  4.92 |  5.68 |
|       minibike      | 32.78 | 38.78 |
|        cradle       | 60.07 | 89.85 |
|         oven        |  25.5 | 42.09 |
|         ball        | 18.68 | 22.33 |
|         food        | 47.14 | 64.08 |
|         step        |  3.5  |  3.95 |
|         tank        | 24.57 | 26.54 |
|      trade name     | 20.18 | 25.66 |
|      microwave      | 26.42 | 28.97 |
|         pot         | 19.74 | 23.34 |
|        animal       |  45.6 | 48.89 |
|       bicycle       | 32.28 | 56.81 |
|         lake        |  48.3 | 48.53 |
|      dishwasher     | 30.87 | 44.49 |
|        screen       |  56.7 | 76.54 |
|       blanket       |  0.67 |  0.72 |
|      sculpture      | 26.71 | 34.04 |
|         hood        | 30.47 | 36.17 |
|        sconce       | 17.92 | 20.09 |
|         vase        | 18.15 | 24.67 |
|    traffic light    | 11.85 | 16.52 |
|         tray        |  0.96 |  1.58 |
|        ashcan       | 18.62 | 26.43 |
|         fan         | 30.81 |  38.3 |
|         pier        | 41.58 | 51.18 |
|      crt screen     |  0.18 |  0.47 |
|        plate        |  19.1 |  21.3 |
|       monitor       | 33.84 | 41.37 |
|    bulletin board   | 34.51 | 40.48 |
|        shower       |  0.0  |  0.0  |
|       radiator      | 32.19 | 40.74 |
|        glass        |  0.48 |  0.49 |
|        clock        |  7.88 |  9.1  |
|         flag        | 19.74 | 22.27 |
+---------------------+-------+-------+
2022-11-04 14:56:40,646 - mmseg - INFO - Summary:
2022-11-04 14:56:40,647 - mmseg - INFO - 
+-------+-------+-------+
|  aAcc |  mIoU |  mAcc |
+-------+-------+-------+
| 75.11 | 34.62 | 45.57 |
+-------+-------+-------+
2022-11-04 14:56:40,648 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 14:56:40,648 - mmseg - INFO - Iter(val) [250]	aAcc: 0.7511, mIoU: 0.3462, mAcc: 0.4557, IoU.wall: 0.6668, IoU.building: 0.7504, IoU.sky: 0.8866, IoU.floor: 0.7073, IoU.tree: 0.6691, IoU.ceiling: 0.7496, IoU.road: 0.7535, IoU.bed : 0.7496, IoU.windowpane: 0.5084, IoU.grass: 0.6096, IoU.cabinet: 0.4813, IoU.sidewalk: 0.4980, IoU.person: 0.6436, IoU.earth: 0.2996, IoU.door: 0.2712, IoU.table: 0.4055, IoU.mountain: 0.5701, IoU.plant: 0.4073, IoU.curtain: 0.5607, IoU.chair: 0.3734, IoU.car: 0.7186, IoU.water: 0.4388, IoU.painting: 0.5586, IoU.sofa: 0.4609, IoU.shelf: 0.3294, IoU.house: 0.3997, IoU.sea: 0.3670, IoU.mirror: 0.4329, IoU.rug: 0.5089, IoU.field: 0.2634, IoU.armchair: 0.2757, IoU.seat: 0.4672, IoU.fence: 0.2642, IoU.desk: 0.3164, IoU.rock: 0.4204, IoU.wardrobe: 0.3803, IoU.lamp: 0.3730, IoU.bathtub: 0.5357, IoU.railing: 0.2436, IoU.cushion: 0.2738, IoU.base: 0.1728, IoU.box: 0.1416, IoU.column: 0.2727, IoU.signboard: 0.2179, IoU.chest of drawers: 0.2775, IoU.counter: 0.2890, IoU.sand: 0.2731, IoU.sink: 0.4619, IoU.skyscraper: 0.6545, IoU.fireplace: 0.5532, IoU.refrigerator: 0.4845, IoU.grandstand: 0.4347, IoU.path: 0.0984, IoU.stairs: 0.2684, IoU.runway: 0.7417, IoU.case: 0.4869, IoU.pool table: 0.7946, IoU.pillow: 0.3931, IoU.screen door: 0.5426, IoU.stairway: 0.3345, IoU.river: 0.1322, IoU.bridge: 0.3222, IoU.bookcase: 0.2688, IoU.blind: 0.1616, IoU.coffee table: 0.4095, IoU.toilet: 0.6539, IoU.flower: 0.2870, IoU.book: 0.3787, IoU.hill: 0.0535, IoU.bench: 0.3138, IoU.countertop: 0.4592, IoU.stove: 0.4656, IoU.palm: 0.3527, IoU.kitchen island: 0.1926, IoU.computer: 0.4441, IoU.swivel chair: 0.2823, IoU.boat: 0.5483, IoU.bar: 0.2417, IoU.arcade machine: 0.3179, IoU.hovel: 0.2534, IoU.bus: 0.7233, IoU.towel: 0.3866, IoU.light: 0.1372, IoU.truck: 0.0973, IoU.tower: 0.3793, IoU.chandelier: 0.4727, IoU.awning: 0.1993, IoU.streetlight: 0.0621, IoU.booth: 0.3192, IoU.television receiver: 0.4863, IoU.airplane: 0.4548, IoU.dirt track: 0.1310, IoU.apparel: 0.1652, IoU.pole: 0.0627, IoU.land: 0.0003, IoU.bannister: 0.0228, IoU.escalator: 0.4469, IoU.ottoman: 0.1969, IoU.bottle: 0.1024, IoU.buffet: 0.3640, IoU.poster: 0.1827, IoU.stage: 0.0627, IoU.van: 0.2386, IoU.ship: 0.1959, IoU.fountain: 0.1883, IoU.conveyer belt: 0.4526, IoU.canopy: 0.1502, IoU.washer: 0.5934, IoU.plaything: 0.0682, IoU.swimming pool: 0.2868, IoU.stool: 0.1989, IoU.barrel: 0.2204, IoU.basket: 0.1553, IoU.waterfall: 0.3852, IoU.tent: 0.8953, IoU.bag: 0.0492, IoU.minibike: 0.3278, IoU.cradle: 0.6007, IoU.oven: 0.2550, IoU.ball: 0.1868, IoU.food: 0.4714, IoU.step: 0.0350, IoU.tank: 0.2457, IoU.trade name: 0.2018, IoU.microwave: 0.2642, IoU.pot: 0.1974, IoU.animal: 0.4560, IoU.bicycle: 0.3228, IoU.lake: 0.4830, IoU.dishwasher: 0.3087, IoU.screen: 0.5670, IoU.blanket: 0.0067, IoU.sculpture: 0.2671, IoU.hood: 0.3047, IoU.sconce: 0.1792, IoU.vase: 0.1815, IoU.traffic light: 0.1185, IoU.tray: 0.0096, IoU.ashcan: 0.1862, IoU.fan: 0.3081, IoU.pier: 0.4158, IoU.crt screen: 0.0018, IoU.plate: 0.1910, IoU.monitor: 0.3384, IoU.bulletin board: 0.3451, IoU.shower: 0.0000, IoU.radiator: 0.3219, IoU.glass: 0.0048, IoU.clock: 0.0788, IoU.flag: 0.1974, Acc.wall: 0.8282, Acc.building: 0.8714, Acc.sky: 0.9359, Acc.floor: 0.8549, Acc.tree: 0.8335, Acc.ceiling: 0.8576, Acc.road: 0.8718, Acc.bed : 0.9010, Acc.windowpane: 0.6678, Acc.grass: 0.7926, Acc.cabinet: 0.6033, Acc.sidewalk: 0.6542, Acc.person: 0.8115, Acc.earth: 0.4191, Acc.door: 0.4275, Acc.table: 0.5603, Acc.mountain: 0.7152, Acc.plant: 0.4993, Acc.curtain: 0.7387, Acc.chair: 0.5142, Acc.car: 0.8625, Acc.water: 0.6102, Acc.painting: 0.7348, Acc.sofa: 0.6574, Acc.shelf: 0.4931, Acc.house: 0.6980, Acc.sea: 0.5977, Acc.mirror: 0.5112, Acc.rug: 0.6136, Acc.field: 0.4254, Acc.armchair: 0.4508, Acc.seat: 0.6543, Acc.fence: 0.4424, Acc.desk: 0.5228, Acc.rock: 0.7306, Acc.wardrobe: 0.5757, Acc.lamp: 0.4955, Acc.bathtub: 0.6664, Acc.railing: 0.4311, Acc.cushion: 0.3262, Acc.base: 0.2869, Acc.box: 0.1996, Acc.column: 0.3466, Acc.signboard: 0.3128, Acc.chest of drawers: 0.5258, Acc.counter: 0.3906, Acc.sand: 0.4350, Acc.sink: 0.6059, Acc.skyscraper: 0.7922, Acc.fireplace: 0.7740, Acc.refrigerator: 0.6389, Acc.grandstand: 0.6268, Acc.path: 0.1389, Acc.stairs: 0.3328, Acc.runway: 0.8877, Acc.case: 0.5927, Acc.pool table: 0.9034, Acc.pillow: 0.5431, Acc.screen door: 0.6405, Acc.stairway: 0.4151, Acc.river: 0.2293, Acc.bridge: 0.4245, Acc.bookcase: 0.4007, Acc.blind: 0.1774, Acc.coffee table: 0.7055, Acc.toilet: 0.7956, Acc.flower: 0.4710, Acc.book: 0.5769, Acc.hill: 0.0854, Acc.bench: 0.4194, Acc.countertop: 0.6500, Acc.stove: 0.5637, Acc.palm: 0.4775, Acc.kitchen island: 0.4217, Acc.computer: 0.5778, Acc.swivel chair: 0.3834, Acc.boat: 0.6233, Acc.bar: 0.2977, Acc.arcade machine: 0.3605, Acc.hovel: 0.3136, Acc.bus: 0.8561, Acc.towel: 0.4953, Acc.light: 0.1485, Acc.truck: 0.1248, Acc.tower: 0.5232, Acc.chandelier: 0.5933, Acc.awning: 0.2365, Acc.streetlight: 0.0713, Acc.booth: 0.3508, Acc.television receiver: 0.6917, Acc.airplane: 0.5323, Acc.dirt track: 0.2436, Acc.apparel: 0.2463, Acc.pole: 0.1190, Acc.land: 0.0003, Acc.bannister: 0.0370, Acc.escalator: 0.6597, Acc.ottoman: 0.2401, Acc.bottle: 0.1187, Acc.buffet: 0.4857, Acc.poster: 0.2514, Acc.stage: 0.1486, Acc.van: 0.2770, Acc.ship: 0.1992, Acc.fountain: 0.1956, Acc.conveyer belt: 0.6396, Acc.canopy: 0.2256, Acc.washer: 0.6254, Acc.plaything: 0.1659, Acc.swimming pool: 0.4185, Acc.stool: 0.2709, Acc.barrel: 0.6155, Acc.basket: 0.1839, Acc.waterfall: 0.4834, Acc.tent: 0.9521, Acc.bag: 0.0568, Acc.minibike: 0.3878, Acc.cradle: 0.8985, Acc.oven: 0.4209, Acc.ball: 0.2233, Acc.food: 0.6408, Acc.step: 0.0395, Acc.tank: 0.2654, Acc.trade name: 0.2566, Acc.microwave: 0.2897, Acc.pot: 0.2334, Acc.animal: 0.4889, Acc.bicycle: 0.5681, Acc.lake: 0.4853, Acc.dishwasher: 0.4449, Acc.screen: 0.7654, Acc.blanket: 0.0072, Acc.sculpture: 0.3404, Acc.hood: 0.3617, Acc.sconce: 0.2009, Acc.vase: 0.2467, Acc.traffic light: 0.1652, Acc.tray: 0.0158, Acc.ashcan: 0.2643, Acc.fan: 0.3830, Acc.pier: 0.5118, Acc.crt screen: 0.0047, Acc.plate: 0.2130, Acc.monitor: 0.4137, Acc.bulletin board: 0.4048, Acc.shower: 0.0000, Acc.radiator: 0.4074, Acc.glass: 0.0049, Acc.clock: 0.0910, Acc.flag: 0.2227
2022-11-04 14:56:57,581 - mmseg - INFO - Iter [48050/80000]	lr: 5.253e-05, eta: 3:05:28, time: 0.525, data_time: 0.196, memory: 12988, decode.loss_ce: 0.4443, decode.acc_seg: 82.5027, aux.loss_ce: 0.2532, aux.acc_seg: 76.7893, loss: 0.6974
2022-11-04 14:57:14,674 - mmseg - INFO - Iter [48100/80000]	lr: 5.246e-05, eta: 3:05:10, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4551, decode.acc_seg: 82.4638, aux.loss_ce: 0.2650, aux.acc_seg: 76.2377, loss: 0.7201
2022-11-04 14:57:31,622 - mmseg - INFO - Iter [48150/80000]	lr: 5.239e-05, eta: 3:04:52, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4701, decode.acc_seg: 81.8978, aux.loss_ce: 0.2656, aux.acc_seg: 76.0708, loss: 0.7357
2022-11-04 14:57:48,748 - mmseg - INFO - Iter [48200/80000]	lr: 5.231e-05, eta: 3:04:35, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4641, decode.acc_seg: 81.9147, aux.loss_ce: 0.2601, aux.acc_seg: 76.3434, loss: 0.7242
2022-11-04 14:58:05,662 - mmseg - INFO - Iter [48250/80000]	lr: 5.224e-05, eta: 3:04:17, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4474, decode.acc_seg: 82.5222, aux.loss_ce: 0.2525, aux.acc_seg: 77.0725, loss: 0.6999
2022-11-04 14:58:22,649 - mmseg - INFO - Iter [48300/80000]	lr: 5.216e-05, eta: 3:03:59, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4525, decode.acc_seg: 82.2601, aux.loss_ce: 0.2600, aux.acc_seg: 76.2205, loss: 0.7125
2022-11-04 14:58:39,629 - mmseg - INFO - Iter [48350/80000]	lr: 5.209e-05, eta: 3:03:41, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4663, decode.acc_seg: 81.7998, aux.loss_ce: 0.2590, aux.acc_seg: 76.1873, loss: 0.7253
2022-11-04 14:58:56,749 - mmseg - INFO - Iter [48400/80000]	lr: 5.202e-05, eta: 3:03:24, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4736, decode.acc_seg: 81.8816, aux.loss_ce: 0.2721, aux.acc_seg: 75.6330, loss: 0.7458
2022-11-04 14:59:13,839 - mmseg - INFO - Iter [48450/80000]	lr: 5.194e-05, eta: 3:03:06, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4504, decode.acc_seg: 82.3339, aux.loss_ce: 0.2580, aux.acc_seg: 76.4299, loss: 0.7085
2022-11-04 14:59:30,904 - mmseg - INFO - Iter [48500/80000]	lr: 5.187e-05, eta: 3:02:49, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4444, decode.acc_seg: 82.6253, aux.loss_ce: 0.2490, aux.acc_seg: 77.3663, loss: 0.6935
2022-11-04 14:59:48,053 - mmseg - INFO - Iter [48550/80000]	lr: 5.179e-05, eta: 3:02:31, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4643, decode.acc_seg: 82.0479, aux.loss_ce: 0.2647, aux.acc_seg: 76.1265, loss: 0.7290
2022-11-04 15:00:08,052 - mmseg - INFO - Iter [48600/80000]	lr: 5.172e-05, eta: 3:02:15, time: 0.400, data_time: 0.069, memory: 12988, decode.loss_ce: 0.4440, decode.acc_seg: 82.6459, aux.loss_ce: 0.2523, aux.acc_seg: 76.6279, loss: 0.6962
2022-11-04 15:00:24,929 - mmseg - INFO - Iter [48650/80000]	lr: 5.164e-05, eta: 3:01:58, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4762, decode.acc_seg: 81.5131, aux.loss_ce: 0.2671, aux.acc_seg: 75.7919, loss: 0.7434
2022-11-04 15:00:42,054 - mmseg - INFO - Iter [48700/80000]	lr: 5.157e-05, eta: 3:01:40, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4524, decode.acc_seg: 82.4684, aux.loss_ce: 0.2576, aux.acc_seg: 76.8891, loss: 0.7100
2022-11-04 15:00:59,214 - mmseg - INFO - Iter [48750/80000]	lr: 5.150e-05, eta: 3:01:22, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4473, decode.acc_seg: 82.3223, aux.loss_ce: 0.2565, aux.acc_seg: 76.2702, loss: 0.7038
2022-11-04 15:01:16,479 - mmseg - INFO - Iter [48800/80000]	lr: 5.142e-05, eta: 3:01:05, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4764, decode.acc_seg: 81.6003, aux.loss_ce: 0.2672, aux.acc_seg: 75.9823, loss: 0.7436
2022-11-04 15:01:33,474 - mmseg - INFO - Iter [48850/80000]	lr: 5.135e-05, eta: 3:00:47, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4738, decode.acc_seg: 81.9750, aux.loss_ce: 0.2690, aux.acc_seg: 75.9316, loss: 0.7428
2022-11-04 15:01:50,667 - mmseg - INFO - Iter [48900/80000]	lr: 5.127e-05, eta: 3:00:30, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4438, decode.acc_seg: 82.5164, aux.loss_ce: 0.2538, aux.acc_seg: 76.6677, loss: 0.6976
2022-11-04 15:02:07,470 - mmseg - INFO - Iter [48950/80000]	lr: 5.120e-05, eta: 3:00:12, time: 0.336, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4664, decode.acc_seg: 82.2198, aux.loss_ce: 0.2656, aux.acc_seg: 76.3354, loss: 0.7320
2022-11-04 15:02:24,550 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 15:02:24,550 - mmseg - INFO - Iter [49000/80000]	lr: 5.113e-05, eta: 2:59:54, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4282, decode.acc_seg: 83.1194, aux.loss_ce: 0.2480, aux.acc_seg: 77.1084, loss: 0.6763
2022-11-04 15:02:41,522 - mmseg - INFO - Iter [49050/80000]	lr: 5.105e-05, eta: 2:59:36, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4600, decode.acc_seg: 82.2695, aux.loss_ce: 0.2591, aux.acc_seg: 76.4171, loss: 0.7192
2022-11-04 15:02:58,850 - mmseg - INFO - Iter [49100/80000]	lr: 5.098e-05, eta: 2:59:19, time: 0.347, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4560, decode.acc_seg: 82.3793, aux.loss_ce: 0.2587, aux.acc_seg: 76.5663, loss: 0.7147
2022-11-04 15:03:15,767 - mmseg - INFO - Iter [49150/80000]	lr: 5.090e-05, eta: 2:59:01, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4425, decode.acc_seg: 82.6595, aux.loss_ce: 0.2545, aux.acc_seg: 76.8597, loss: 0.6969
2022-11-04 15:03:32,844 - mmseg - INFO - Iter [49200/80000]	lr: 5.083e-05, eta: 2:58:44, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4503, decode.acc_seg: 82.6821, aux.loss_ce: 0.2596, aux.acc_seg: 76.5624, loss: 0.7099
2022-11-04 15:03:53,095 - mmseg - INFO - Iter [49250/80000]	lr: 5.075e-05, eta: 2:58:28, time: 0.405, data_time: 0.060, memory: 12988, decode.loss_ce: 0.4485, decode.acc_seg: 82.4218, aux.loss_ce: 0.2526, aux.acc_seg: 77.0025, loss: 0.7012
2022-11-04 15:04:10,326 - mmseg - INFO - Iter [49300/80000]	lr: 5.068e-05, eta: 2:58:11, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4580, decode.acc_seg: 82.3814, aux.loss_ce: 0.2595, aux.acc_seg: 76.8544, loss: 0.7175
2022-11-04 15:04:27,338 - mmseg - INFO - Iter [49350/80000]	lr: 5.061e-05, eta: 2:57:53, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4504, decode.acc_seg: 82.1730, aux.loss_ce: 0.2515, aux.acc_seg: 76.8653, loss: 0.7019
2022-11-04 15:04:44,500 - mmseg - INFO - Iter [49400/80000]	lr: 5.053e-05, eta: 2:57:35, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4453, decode.acc_seg: 82.3325, aux.loss_ce: 0.2517, aux.acc_seg: 76.7011, loss: 0.6971
2022-11-04 15:05:01,774 - mmseg - INFO - Iter [49450/80000]	lr: 5.046e-05, eta: 2:57:18, time: 0.345, data_time: 0.018, memory: 12988, decode.loss_ce: 0.4469, decode.acc_seg: 82.6538, aux.loss_ce: 0.2587, aux.acc_seg: 76.6548, loss: 0.7056
2022-11-04 15:05:18,559 - mmseg - INFO - Iter [49500/80000]	lr: 5.038e-05, eta: 2:57:00, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4378, decode.acc_seg: 83.0667, aux.loss_ce: 0.2520, aux.acc_seg: 77.4896, loss: 0.6897
2022-11-04 15:05:35,466 - mmseg - INFO - Iter [49550/80000]	lr: 5.031e-05, eta: 2:56:42, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4608, decode.acc_seg: 81.9068, aux.loss_ce: 0.2596, aux.acc_seg: 76.2240, loss: 0.7204
2022-11-04 15:05:52,606 - mmseg - INFO - Iter [49600/80000]	lr: 5.023e-05, eta: 2:56:25, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4489, decode.acc_seg: 82.4599, aux.loss_ce: 0.2559, aux.acc_seg: 76.6878, loss: 0.7047
2022-11-04 15:06:09,577 - mmseg - INFO - Iter [49650/80000]	lr: 5.016e-05, eta: 2:56:07, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4611, decode.acc_seg: 81.8062, aux.loss_ce: 0.2624, aux.acc_seg: 75.8982, loss: 0.7235
2022-11-04 15:06:26,721 - mmseg - INFO - Iter [49700/80000]	lr: 5.009e-05, eta: 2:55:49, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4590, decode.acc_seg: 82.2061, aux.loss_ce: 0.2696, aux.acc_seg: 75.4872, loss: 0.7286
2022-11-04 15:06:43,755 - mmseg - INFO - Iter [49750/80000]	lr: 5.001e-05, eta: 2:55:32, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4584, decode.acc_seg: 82.2268, aux.loss_ce: 0.2567, aux.acc_seg: 76.8681, loss: 0.7151
2022-11-04 15:07:00,771 - mmseg - INFO - Iter [49800/80000]	lr: 4.994e-05, eta: 2:55:14, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4482, decode.acc_seg: 82.5816, aux.loss_ce: 0.2586, aux.acc_seg: 76.5675, loss: 0.7068
2022-11-04 15:07:21,031 - mmseg - INFO - Iter [49850/80000]	lr: 4.986e-05, eta: 2:54:58, time: 0.405, data_time: 0.064, memory: 12988, decode.loss_ce: 0.4741, decode.acc_seg: 81.7833, aux.loss_ce: 0.2680, aux.acc_seg: 76.1344, loss: 0.7420
2022-11-04 15:07:37,895 - mmseg - INFO - Iter [49900/80000]	lr: 4.979e-05, eta: 2:54:41, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4414, decode.acc_seg: 82.9842, aux.loss_ce: 0.2518, aux.acc_seg: 77.0767, loss: 0.6932
2022-11-04 15:07:54,854 - mmseg - INFO - Iter [49950/80000]	lr: 4.971e-05, eta: 2:54:23, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4508, decode.acc_seg: 82.4987, aux.loss_ce: 0.2579, aux.acc_seg: 76.4671, loss: 0.7087
2022-11-04 15:08:11,865 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 15:08:11,865 - mmseg - INFO - Iter [50000/80000]	lr: 4.964e-05, eta: 2:54:05, time: 0.340, data_time: 0.014, memory: 12988, decode.loss_ce: 0.4370, decode.acc_seg: 82.9609, aux.loss_ce: 0.2527, aux.acc_seg: 77.2294, loss: 0.6897
2022-11-04 15:08:28,826 - mmseg - INFO - Iter [50050/80000]	lr: 4.956e-05, eta: 2:53:48, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4394, decode.acc_seg: 82.6778, aux.loss_ce: 0.2566, aux.acc_seg: 76.4336, loss: 0.6959
2022-11-04 15:08:45,770 - mmseg - INFO - Iter [50100/80000]	lr: 4.949e-05, eta: 2:53:30, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4520, decode.acc_seg: 82.3473, aux.loss_ce: 0.2591, aux.acc_seg: 76.2769, loss: 0.7112
2022-11-04 15:09:02,925 - mmseg - INFO - Iter [50150/80000]	lr: 4.942e-05, eta: 2:53:12, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4414, decode.acc_seg: 82.7225, aux.loss_ce: 0.2560, aux.acc_seg: 77.0054, loss: 0.6975
2022-11-04 15:09:20,042 - mmseg - INFO - Iter [50200/80000]	lr: 4.934e-05, eta: 2:52:55, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4459, decode.acc_seg: 82.7334, aux.loss_ce: 0.2547, aux.acc_seg: 77.0278, loss: 0.7007
2022-11-04 15:09:37,209 - mmseg - INFO - Iter [50250/80000]	lr: 4.927e-05, eta: 2:52:37, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4542, decode.acc_seg: 82.4239, aux.loss_ce: 0.2588, aux.acc_seg: 76.4965, loss: 0.7130
2022-11-04 15:09:54,400 - mmseg - INFO - Iter [50300/80000]	lr: 4.919e-05, eta: 2:52:20, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4326, decode.acc_seg: 82.9418, aux.loss_ce: 0.2486, aux.acc_seg: 77.0323, loss: 0.6812
2022-11-04 15:10:11,628 - mmseg - INFO - Iter [50350/80000]	lr: 4.912e-05, eta: 2:52:02, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4505, decode.acc_seg: 82.5209, aux.loss_ce: 0.2532, aux.acc_seg: 76.9727, loss: 0.7036
2022-11-04 15:10:28,808 - mmseg - INFO - Iter [50400/80000]	lr: 4.904e-05, eta: 2:51:45, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4585, decode.acc_seg: 82.1891, aux.loss_ce: 0.2606, aux.acc_seg: 76.4685, loss: 0.7191
2022-11-04 15:10:45,789 - mmseg - INFO - Iter [50450/80000]	lr: 4.897e-05, eta: 2:51:27, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4412, decode.acc_seg: 82.5231, aux.loss_ce: 0.2551, aux.acc_seg: 76.3695, loss: 0.6963
2022-11-04 15:11:05,787 - mmseg - INFO - Iter [50500/80000]	lr: 4.889e-05, eta: 2:51:11, time: 0.400, data_time: 0.069, memory: 12988, decode.loss_ce: 0.4631, decode.acc_seg: 82.0296, aux.loss_ce: 0.2616, aux.acc_seg: 76.4560, loss: 0.7248
2022-11-04 15:11:22,937 - mmseg - INFO - Iter [50550/80000]	lr: 4.882e-05, eta: 2:50:54, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4724, decode.acc_seg: 82.0200, aux.loss_ce: 0.2651, aux.acc_seg: 76.3867, loss: 0.7375
2022-11-04 15:11:39,937 - mmseg - INFO - Iter [50600/80000]	lr: 4.874e-05, eta: 2:50:36, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4377, decode.acc_seg: 82.9332, aux.loss_ce: 0.2543, aux.acc_seg: 76.9284, loss: 0.6920
2022-11-04 15:11:56,903 - mmseg - INFO - Iter [50650/80000]	lr: 4.867e-05, eta: 2:50:18, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4595, decode.acc_seg: 82.0425, aux.loss_ce: 0.2604, aux.acc_seg: 76.0729, loss: 0.7198
2022-11-04 15:12:13,805 - mmseg - INFO - Iter [50700/80000]	lr: 4.860e-05, eta: 2:50:01, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4476, decode.acc_seg: 81.9387, aux.loss_ce: 0.2546, aux.acc_seg: 76.4849, loss: 0.7022
2022-11-04 15:12:30,707 - mmseg - INFO - Iter [50750/80000]	lr: 4.852e-05, eta: 2:49:43, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4546, decode.acc_seg: 82.5657, aux.loss_ce: 0.2629, aux.acc_seg: 76.4841, loss: 0.7174
2022-11-04 15:12:47,856 - mmseg - INFO - Iter [50800/80000]	lr: 4.845e-05, eta: 2:49:25, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4507, decode.acc_seg: 82.4197, aux.loss_ce: 0.2582, aux.acc_seg: 76.5288, loss: 0.7089
2022-11-04 15:13:04,953 - mmseg - INFO - Iter [50850/80000]	lr: 4.837e-05, eta: 2:49:08, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4399, decode.acc_seg: 82.9642, aux.loss_ce: 0.2535, aux.acc_seg: 76.7550, loss: 0.6934
2022-11-04 15:13:22,127 - mmseg - INFO - Iter [50900/80000]	lr: 4.830e-05, eta: 2:48:50, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4546, decode.acc_seg: 82.5103, aux.loss_ce: 0.2552, aux.acc_seg: 77.0952, loss: 0.7098
2022-11-04 15:13:39,189 - mmseg - INFO - Iter [50950/80000]	lr: 4.822e-05, eta: 2:48:33, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4344, decode.acc_seg: 82.9586, aux.loss_ce: 0.2539, aux.acc_seg: 76.9793, loss: 0.6883
2022-11-04 15:13:56,226 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 15:13:56,227 - mmseg - INFO - Iter [51000/80000]	lr: 4.815e-05, eta: 2:48:15, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4551, decode.acc_seg: 82.2330, aux.loss_ce: 0.2623, aux.acc_seg: 76.0691, loss: 0.7175
2022-11-04 15:14:13,273 - mmseg - INFO - Iter [51050/80000]	lr: 4.807e-05, eta: 2:47:57, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4227, decode.acc_seg: 83.4125, aux.loss_ce: 0.2460, aux.acc_seg: 77.5193, loss: 0.6688
2022-11-04 15:14:30,277 - mmseg - INFO - Iter [51100/80000]	lr: 4.800e-05, eta: 2:47:40, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4498, decode.acc_seg: 82.3013, aux.loss_ce: 0.2516, aux.acc_seg: 77.2128, loss: 0.7014
2022-11-04 15:14:50,294 - mmseg - INFO - Iter [51150/80000]	lr: 4.792e-05, eta: 2:47:24, time: 0.400, data_time: 0.066, memory: 12988, decode.loss_ce: 0.4432, decode.acc_seg: 82.6433, aux.loss_ce: 0.2556, aux.acc_seg: 76.8568, loss: 0.6988
2022-11-04 15:15:07,150 - mmseg - INFO - Iter [51200/80000]	lr: 4.785e-05, eta: 2:47:06, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4474, decode.acc_seg: 82.5425, aux.loss_ce: 0.2587, aux.acc_seg: 76.3694, loss: 0.7061
2022-11-04 15:15:24,185 - mmseg - INFO - Iter [51250/80000]	lr: 4.777e-05, eta: 2:46:49, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4443, decode.acc_seg: 82.9199, aux.loss_ce: 0.2573, aux.acc_seg: 76.9143, loss: 0.7016
2022-11-04 15:15:41,789 - mmseg - INFO - Iter [51300/80000]	lr: 4.770e-05, eta: 2:46:31, time: 0.352, data_time: 0.020, memory: 12988, decode.loss_ce: 0.4406, decode.acc_seg: 82.8418, aux.loss_ce: 0.2532, aux.acc_seg: 77.2932, loss: 0.6937
2022-11-04 15:15:58,822 - mmseg - INFO - Iter [51350/80000]	lr: 4.762e-05, eta: 2:46:14, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4374, decode.acc_seg: 82.6323, aux.loss_ce: 0.2566, aux.acc_seg: 76.5539, loss: 0.6940
2022-11-04 15:16:15,880 - mmseg - INFO - Iter [51400/80000]	lr: 4.755e-05, eta: 2:45:56, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4435, decode.acc_seg: 82.8090, aux.loss_ce: 0.2552, aux.acc_seg: 77.1030, loss: 0.6986
2022-11-04 15:16:32,921 - mmseg - INFO - Iter [51450/80000]	lr: 4.747e-05, eta: 2:45:38, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4492, decode.acc_seg: 82.8417, aux.loss_ce: 0.2619, aux.acc_seg: 76.5228, loss: 0.7111
2022-11-04 15:16:49,921 - mmseg - INFO - Iter [51500/80000]	lr: 4.740e-05, eta: 2:45:21, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4576, decode.acc_seg: 82.3986, aux.loss_ce: 0.2659, aux.acc_seg: 76.4768, loss: 0.7236
2022-11-04 15:17:07,042 - mmseg - INFO - Iter [51550/80000]	lr: 4.732e-05, eta: 2:45:03, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4576, decode.acc_seg: 81.7808, aux.loss_ce: 0.2605, aux.acc_seg: 75.8839, loss: 0.7181
2022-11-04 15:17:24,284 - mmseg - INFO - Iter [51600/80000]	lr: 4.725e-05, eta: 2:44:46, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4490, decode.acc_seg: 82.2707, aux.loss_ce: 0.2537, aux.acc_seg: 76.2400, loss: 0.7027
2022-11-04 15:17:41,952 - mmseg - INFO - Iter [51650/80000]	lr: 4.717e-05, eta: 2:44:28, time: 0.353, data_time: 0.022, memory: 12988, decode.loss_ce: 0.4459, decode.acc_seg: 82.4450, aux.loss_ce: 0.2533, aux.acc_seg: 76.8911, loss: 0.6992
2022-11-04 15:17:58,956 - mmseg - INFO - Iter [51700/80000]	lr: 4.710e-05, eta: 2:44:11, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4576, decode.acc_seg: 81.9788, aux.loss_ce: 0.2534, aux.acc_seg: 76.3578, loss: 0.7111
2022-11-04 15:18:18,881 - mmseg - INFO - Iter [51750/80000]	lr: 4.703e-05, eta: 2:43:55, time: 0.398, data_time: 0.062, memory: 12988, decode.loss_ce: 0.4474, decode.acc_seg: 82.5145, aux.loss_ce: 0.2534, aux.acc_seg: 77.0075, loss: 0.7008
2022-11-04 15:18:35,710 - mmseg - INFO - Iter [51800/80000]	lr: 4.695e-05, eta: 2:43:37, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4433, decode.acc_seg: 82.6045, aux.loss_ce: 0.2550, aux.acc_seg: 77.1214, loss: 0.6983
2022-11-04 15:18:52,799 - mmseg - INFO - Iter [51850/80000]	lr: 4.688e-05, eta: 2:43:19, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4386, decode.acc_seg: 82.7006, aux.loss_ce: 0.2527, aux.acc_seg: 76.5144, loss: 0.6913
2022-11-04 15:19:10,029 - mmseg - INFO - Iter [51900/80000]	lr: 4.680e-05, eta: 2:43:02, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4589, decode.acc_seg: 82.0261, aux.loss_ce: 0.2644, aux.acc_seg: 75.9905, loss: 0.7234
2022-11-04 15:19:26,873 - mmseg - INFO - Iter [51950/80000]	lr: 4.673e-05, eta: 2:42:44, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4428, decode.acc_seg: 82.4863, aux.loss_ce: 0.2571, aux.acc_seg: 75.7811, loss: 0.6999
2022-11-04 15:19:43,971 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 15:19:43,971 - mmseg - INFO - Iter [52000/80000]	lr: 4.665e-05, eta: 2:42:27, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4390, decode.acc_seg: 82.6394, aux.loss_ce: 0.2543, aux.acc_seg: 76.5112, loss: 0.6933
2022-11-04 15:20:00,920 - mmseg - INFO - Iter [52050/80000]	lr: 4.658e-05, eta: 2:42:09, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4509, decode.acc_seg: 82.1478, aux.loss_ce: 0.2550, aux.acc_seg: 76.4119, loss: 0.7059
2022-11-04 15:20:17,780 - mmseg - INFO - Iter [52100/80000]	lr: 4.650e-05, eta: 2:41:51, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4461, decode.acc_seg: 82.5624, aux.loss_ce: 0.2513, aux.acc_seg: 76.7536, loss: 0.6974
2022-11-04 15:20:35,043 - mmseg - INFO - Iter [52150/80000]	lr: 4.643e-05, eta: 2:41:34, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4498, decode.acc_seg: 82.2928, aux.loss_ce: 0.2575, aux.acc_seg: 76.5726, loss: 0.7073
2022-11-04 15:20:51,928 - mmseg - INFO - Iter [52200/80000]	lr: 4.635e-05, eta: 2:41:16, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4397, decode.acc_seg: 82.8949, aux.loss_ce: 0.2530, aux.acc_seg: 77.3091, loss: 0.6926
2022-11-04 15:21:09,104 - mmseg - INFO - Iter [52250/80000]	lr: 4.628e-05, eta: 2:40:59, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4415, decode.acc_seg: 82.5120, aux.loss_ce: 0.2544, aux.acc_seg: 76.5668, loss: 0.6959
2022-11-04 15:21:26,300 - mmseg - INFO - Iter [52300/80000]	lr: 4.620e-05, eta: 2:40:41, time: 0.344, data_time: 0.011, memory: 12988, decode.loss_ce: 0.4273, decode.acc_seg: 83.1752, aux.loss_ce: 0.2469, aux.acc_seg: 77.3326, loss: 0.6742
2022-11-04 15:21:43,212 - mmseg - INFO - Iter [52350/80000]	lr: 4.613e-05, eta: 2:40:23, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4309, decode.acc_seg: 83.2200, aux.loss_ce: 0.2499, aux.acc_seg: 77.3633, loss: 0.6809
2022-11-04 15:22:03,408 - mmseg - INFO - Iter [52400/80000]	lr: 4.605e-05, eta: 2:40:08, time: 0.404, data_time: 0.071, memory: 12988, decode.loss_ce: 0.4467, decode.acc_seg: 82.6089, aux.loss_ce: 0.2585, aux.acc_seg: 76.3735, loss: 0.7052
2022-11-04 15:22:20,446 - mmseg - INFO - Iter [52450/80000]	lr: 4.598e-05, eta: 2:39:50, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4426, decode.acc_seg: 82.7836, aux.loss_ce: 0.2534, aux.acc_seg: 77.0563, loss: 0.6960
2022-11-04 15:22:37,455 - mmseg - INFO - Iter [52500/80000]	lr: 4.590e-05, eta: 2:39:32, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4446, decode.acc_seg: 82.6662, aux.loss_ce: 0.2602, aux.acc_seg: 76.3550, loss: 0.7048
2022-11-04 15:22:54,394 - mmseg - INFO - Iter [52550/80000]	lr: 4.583e-05, eta: 2:39:15, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4668, decode.acc_seg: 82.3424, aux.loss_ce: 0.2632, aux.acc_seg: 76.6781, loss: 0.7301
2022-11-04 15:23:11,595 - mmseg - INFO - Iter [52600/80000]	lr: 4.575e-05, eta: 2:38:57, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4377, decode.acc_seg: 83.0901, aux.loss_ce: 0.2542, aux.acc_seg: 77.3593, loss: 0.6919
2022-11-04 15:23:28,611 - mmseg - INFO - Iter [52650/80000]	lr: 4.567e-05, eta: 2:38:40, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4574, decode.acc_seg: 81.9652, aux.loss_ce: 0.2614, aux.acc_seg: 76.0231, loss: 0.7189
2022-11-04 15:23:45,642 - mmseg - INFO - Iter [52700/80000]	lr: 4.560e-05, eta: 2:38:22, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4400, decode.acc_seg: 82.7767, aux.loss_ce: 0.2521, aux.acc_seg: 77.1397, loss: 0.6921
2022-11-04 15:24:02,444 - mmseg - INFO - Iter [52750/80000]	lr: 4.552e-05, eta: 2:38:04, time: 0.336, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4557, decode.acc_seg: 82.2359, aux.loss_ce: 0.2613, aux.acc_seg: 76.0144, loss: 0.7170
2022-11-04 15:24:19,576 - mmseg - INFO - Iter [52800/80000]	lr: 4.545e-05, eta: 2:37:47, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4309, decode.acc_seg: 83.2105, aux.loss_ce: 0.2484, aux.acc_seg: 77.0941, loss: 0.6793
2022-11-04 15:24:36,667 - mmseg - INFO - Iter [52850/80000]	lr: 4.537e-05, eta: 2:37:29, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4557, decode.acc_seg: 82.3298, aux.loss_ce: 0.2496, aux.acc_seg: 77.3261, loss: 0.7052
2022-11-04 15:24:53,736 - mmseg - INFO - Iter [52900/80000]	lr: 4.530e-05, eta: 2:37:12, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4727, decode.acc_seg: 81.6655, aux.loss_ce: 0.2740, aux.acc_seg: 75.5202, loss: 0.7468
2022-11-04 15:25:10,965 - mmseg - INFO - Iter [52950/80000]	lr: 4.522e-05, eta: 2:36:54, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4447, decode.acc_seg: 82.5254, aux.loss_ce: 0.2570, aux.acc_seg: 76.9350, loss: 0.7016
2022-11-04 15:25:27,773 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 15:25:27,774 - mmseg - INFO - Iter [53000/80000]	lr: 4.515e-05, eta: 2:36:36, time: 0.336, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4410, decode.acc_seg: 82.8111, aux.loss_ce: 0.2524, aux.acc_seg: 77.0310, loss: 0.6935
2022-11-04 15:25:47,813 - mmseg - INFO - Iter [53050/80000]	lr: 4.507e-05, eta: 2:36:20, time: 0.401, data_time: 0.065, memory: 12988, decode.loss_ce: 0.4331, decode.acc_seg: 82.9220, aux.loss_ce: 0.2501, aux.acc_seg: 77.4127, loss: 0.6832
2022-11-04 15:26:04,966 - mmseg - INFO - Iter [53100/80000]	lr: 4.500e-05, eta: 2:36:03, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4491, decode.acc_seg: 82.5669, aux.loss_ce: 0.2550, aux.acc_seg: 77.0066, loss: 0.7041
2022-11-04 15:26:21,797 - mmseg - INFO - Iter [53150/80000]	lr: 4.492e-05, eta: 2:35:45, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4448, decode.acc_seg: 82.6370, aux.loss_ce: 0.2547, aux.acc_seg: 76.9843, loss: 0.6995
2022-11-04 15:26:38,758 - mmseg - INFO - Iter [53200/80000]	lr: 4.485e-05, eta: 2:35:27, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4464, decode.acc_seg: 82.7403, aux.loss_ce: 0.2529, aux.acc_seg: 76.9455, loss: 0.6992
2022-11-04 15:26:55,899 - mmseg - INFO - Iter [53250/80000]	lr: 4.477e-05, eta: 2:35:10, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4514, decode.acc_seg: 82.6103, aux.loss_ce: 0.2609, aux.acc_seg: 76.3496, loss: 0.7124
2022-11-04 15:27:13,203 - mmseg - INFO - Iter [53300/80000]	lr: 4.470e-05, eta: 2:34:52, time: 0.346, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4414, decode.acc_seg: 82.7467, aux.loss_ce: 0.2514, aux.acc_seg: 77.2961, loss: 0.6929
2022-11-04 15:27:30,147 - mmseg - INFO - Iter [53350/80000]	lr: 4.462e-05, eta: 2:34:35, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4264, decode.acc_seg: 83.5264, aux.loss_ce: 0.2452, aux.acc_seg: 77.8731, loss: 0.6716
2022-11-04 15:27:47,148 - mmseg - INFO - Iter [53400/80000]	lr: 4.455e-05, eta: 2:34:17, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4501, decode.acc_seg: 82.4015, aux.loss_ce: 0.2592, aux.acc_seg: 76.5520, loss: 0.7093
2022-11-04 15:28:04,170 - mmseg - INFO - Iter [53450/80000]	lr: 4.447e-05, eta: 2:34:00, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4406, decode.acc_seg: 82.5828, aux.loss_ce: 0.2454, aux.acc_seg: 77.3740, loss: 0.6860
2022-11-04 15:28:21,230 - mmseg - INFO - Iter [53500/80000]	lr: 4.440e-05, eta: 2:33:42, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4293, decode.acc_seg: 83.0350, aux.loss_ce: 0.2557, aux.acc_seg: 76.7454, loss: 0.6850
2022-11-04 15:28:38,271 - mmseg - INFO - Iter [53550/80000]	lr: 4.432e-05, eta: 2:33:25, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4471, decode.acc_seg: 82.7899, aux.loss_ce: 0.2522, aux.acc_seg: 77.3325, loss: 0.6992
2022-11-04 15:28:55,499 - mmseg - INFO - Iter [53600/80000]	lr: 4.424e-05, eta: 2:33:07, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4555, decode.acc_seg: 82.2026, aux.loss_ce: 0.2584, aux.acc_seg: 76.4281, loss: 0.7140
2022-11-04 15:29:15,394 - mmseg - INFO - Iter [53650/80000]	lr: 4.417e-05, eta: 2:32:51, time: 0.398, data_time: 0.053, memory: 12988, decode.loss_ce: 0.4496, decode.acc_seg: 82.6734, aux.loss_ce: 0.2532, aux.acc_seg: 76.9890, loss: 0.7028
2022-11-04 15:29:32,605 - mmseg - INFO - Iter [53700/80000]	lr: 4.409e-05, eta: 2:32:33, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4396, decode.acc_seg: 82.8309, aux.loss_ce: 0.2509, aux.acc_seg: 76.9463, loss: 0.6906
2022-11-04 15:29:49,648 - mmseg - INFO - Iter [53750/80000]	lr: 4.402e-05, eta: 2:32:16, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4423, decode.acc_seg: 82.7731, aux.loss_ce: 0.2518, aux.acc_seg: 77.1719, loss: 0.6941
2022-11-04 15:30:06,815 - mmseg - INFO - Iter [53800/80000]	lr: 4.394e-05, eta: 2:31:58, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4276, decode.acc_seg: 82.9434, aux.loss_ce: 0.2494, aux.acc_seg: 76.8737, loss: 0.6770
2022-11-04 15:30:24,161 - mmseg - INFO - Iter [53850/80000]	lr: 4.387e-05, eta: 2:31:41, time: 0.347, data_time: 0.011, memory: 12988, decode.loss_ce: 0.4334, decode.acc_seg: 82.7491, aux.loss_ce: 0.2488, aux.acc_seg: 76.9946, loss: 0.6821
2022-11-04 15:30:41,686 - mmseg - INFO - Iter [53900/80000]	lr: 4.379e-05, eta: 2:31:23, time: 0.350, data_time: 0.022, memory: 12988, decode.loss_ce: 0.4503, decode.acc_seg: 82.5429, aux.loss_ce: 0.2584, aux.acc_seg: 76.8956, loss: 0.7087
2022-11-04 15:30:58,676 - mmseg - INFO - Iter [53950/80000]	lr: 4.372e-05, eta: 2:31:06, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4378, decode.acc_seg: 82.8831, aux.loss_ce: 0.2530, aux.acc_seg: 77.1850, loss: 0.6908
2022-11-04 15:31:15,904 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 15:31:15,904 - mmseg - INFO - Iter [54000/80000]	lr: 4.364e-05, eta: 2:30:48, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4409, decode.acc_seg: 83.1154, aux.loss_ce: 0.2450, aux.acc_seg: 77.9109, loss: 0.6859
2022-11-04 15:31:32,797 - mmseg - INFO - Iter [54050/80000]	lr: 4.357e-05, eta: 2:30:31, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4373, decode.acc_seg: 82.8523, aux.loss_ce: 0.2472, aux.acc_seg: 77.4196, loss: 0.6845
2022-11-04 15:31:50,020 - mmseg - INFO - Iter [54100/80000]	lr: 4.349e-05, eta: 2:30:13, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4476, decode.acc_seg: 82.3523, aux.loss_ce: 0.2612, aux.acc_seg: 75.9341, loss: 0.7088
2022-11-04 15:32:07,081 - mmseg - INFO - Iter [54150/80000]	lr: 4.341e-05, eta: 2:29:56, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4389, decode.acc_seg: 82.7351, aux.loss_ce: 0.2542, aux.acc_seg: 76.9782, loss: 0.6931
2022-11-04 15:32:23,941 - mmseg - INFO - Iter [54200/80000]	lr: 4.334e-05, eta: 2:29:38, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4498, decode.acc_seg: 82.5492, aux.loss_ce: 0.2544, aux.acc_seg: 76.9843, loss: 0.7042
2022-11-04 15:32:41,117 - mmseg - INFO - Iter [54250/80000]	lr: 4.326e-05, eta: 2:29:21, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4315, decode.acc_seg: 83.2598, aux.loss_ce: 0.2573, aux.acc_seg: 76.8704, loss: 0.6888
2022-11-04 15:33:01,078 - mmseg - INFO - Iter [54300/80000]	lr: 4.319e-05, eta: 2:29:04, time: 0.399, data_time: 0.070, memory: 12988, decode.loss_ce: 0.4356, decode.acc_seg: 82.9183, aux.loss_ce: 0.2528, aux.acc_seg: 76.9954, loss: 0.6884
2022-11-04 15:33:18,214 - mmseg - INFO - Iter [54350/80000]	lr: 4.311e-05, eta: 2:28:47, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4311, decode.acc_seg: 83.2058, aux.loss_ce: 0.2464, aux.acc_seg: 77.3037, loss: 0.6775
2022-11-04 15:33:35,334 - mmseg - INFO - Iter [54400/80000]	lr: 4.304e-05, eta: 2:28:29, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4458, decode.acc_seg: 82.7195, aux.loss_ce: 0.2590, aux.acc_seg: 76.6411, loss: 0.7049
2022-11-04 15:33:52,495 - mmseg - INFO - Iter [54450/80000]	lr: 4.296e-05, eta: 2:28:12, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4347, decode.acc_seg: 82.9599, aux.loss_ce: 0.2542, aux.acc_seg: 76.8949, loss: 0.6889
2022-11-04 15:34:09,740 - mmseg - INFO - Iter [54500/80000]	lr: 4.288e-05, eta: 2:27:54, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4315, decode.acc_seg: 83.1472, aux.loss_ce: 0.2520, aux.acc_seg: 76.7134, loss: 0.6835
2022-11-04 15:34:27,014 - mmseg - INFO - Iter [54550/80000]	lr: 4.281e-05, eta: 2:27:37, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4476, decode.acc_seg: 82.7054, aux.loss_ce: 0.2548, aux.acc_seg: 77.1126, loss: 0.7025
2022-11-04 15:34:44,204 - mmseg - INFO - Iter [54600/80000]	lr: 4.273e-05, eta: 2:27:19, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4371, decode.acc_seg: 82.9521, aux.loss_ce: 0.2494, aux.acc_seg: 77.2964, loss: 0.6865
2022-11-04 15:35:01,391 - mmseg - INFO - Iter [54650/80000]	lr: 4.266e-05, eta: 2:27:02, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4385, decode.acc_seg: 83.0181, aux.loss_ce: 0.2567, aux.acc_seg: 77.0372, loss: 0.6953
2022-11-04 15:35:18,695 - mmseg - INFO - Iter [54700/80000]	lr: 4.258e-05, eta: 2:26:44, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4505, decode.acc_seg: 82.3433, aux.loss_ce: 0.2610, aux.acc_seg: 76.3298, loss: 0.7116
2022-11-04 15:35:35,800 - mmseg - INFO - Iter [54750/80000]	lr: 4.251e-05, eta: 2:26:27, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4437, decode.acc_seg: 82.7280, aux.loss_ce: 0.2570, aux.acc_seg: 76.6249, loss: 0.7007
2022-11-04 15:35:53,007 - mmseg - INFO - Iter [54800/80000]	lr: 4.243e-05, eta: 2:26:09, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4199, decode.acc_seg: 83.2759, aux.loss_ce: 0.2434, aux.acc_seg: 77.7328, loss: 0.6633
2022-11-04 15:36:09,777 - mmseg - INFO - Iter [54850/80000]	lr: 4.235e-05, eta: 2:25:52, time: 0.335, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4587, decode.acc_seg: 81.8596, aux.loss_ce: 0.2635, aux.acc_seg: 75.9575, loss: 0.7222
2022-11-04 15:36:29,596 - mmseg - INFO - Iter [54900/80000]	lr: 4.228e-05, eta: 2:25:35, time: 0.396, data_time: 0.063, memory: 12988, decode.loss_ce: 0.4453, decode.acc_seg: 82.3153, aux.loss_ce: 0.2592, aux.acc_seg: 75.9333, loss: 0.7045
2022-11-04 15:36:46,716 - mmseg - INFO - Iter [54950/80000]	lr: 4.220e-05, eta: 2:25:18, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4316, decode.acc_seg: 83.0976, aux.loss_ce: 0.2455, aux.acc_seg: 77.4648, loss: 0.6771
2022-11-04 15:37:03,689 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 15:37:03,690 - mmseg - INFO - Iter [55000/80000]	lr: 4.213e-05, eta: 2:25:00, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4242, decode.acc_seg: 83.1138, aux.loss_ce: 0.2422, aux.acc_seg: 77.5558, loss: 0.6664
2022-11-04 15:37:20,511 - mmseg - INFO - Iter [55050/80000]	lr: 4.205e-05, eta: 2:24:43, time: 0.336, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4246, decode.acc_seg: 83.1227, aux.loss_ce: 0.2440, aux.acc_seg: 77.5996, loss: 0.6685
2022-11-04 15:37:37,308 - mmseg - INFO - Iter [55100/80000]	lr: 4.198e-05, eta: 2:24:25, time: 0.336, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4359, decode.acc_seg: 83.2317, aux.loss_ce: 0.2474, aux.acc_seg: 77.9487, loss: 0.6833
2022-11-04 15:37:54,557 - mmseg - INFO - Iter [55150/80000]	lr: 4.190e-05, eta: 2:24:07, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4417, decode.acc_seg: 82.6494, aux.loss_ce: 0.2591, aux.acc_seg: 76.4213, loss: 0.7008
2022-11-04 15:38:11,406 - mmseg - INFO - Iter [55200/80000]	lr: 4.182e-05, eta: 2:23:50, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4584, decode.acc_seg: 82.1742, aux.loss_ce: 0.2610, aux.acc_seg: 76.4304, loss: 0.7193
2022-11-04 15:38:28,352 - mmseg - INFO - Iter [55250/80000]	lr: 4.175e-05, eta: 2:23:32, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4291, decode.acc_seg: 83.1582, aux.loss_ce: 0.2507, aux.acc_seg: 77.1623, loss: 0.6797
2022-11-04 15:38:45,498 - mmseg - INFO - Iter [55300/80000]	lr: 4.167e-05, eta: 2:23:15, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4464, decode.acc_seg: 82.6498, aux.loss_ce: 0.2571, aux.acc_seg: 76.7694, loss: 0.7035
2022-11-04 15:39:02,274 - mmseg - INFO - Iter [55350/80000]	lr: 4.160e-05, eta: 2:22:57, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4383, decode.acc_seg: 82.8787, aux.loss_ce: 0.2528, aux.acc_seg: 76.9864, loss: 0.6911
2022-11-04 15:39:19,303 - mmseg - INFO - Iter [55400/80000]	lr: 4.152e-05, eta: 2:22:39, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4201, decode.acc_seg: 83.4841, aux.loss_ce: 0.2414, aux.acc_seg: 78.0123, loss: 0.6615
2022-11-04 15:39:36,184 - mmseg - INFO - Iter [55450/80000]	lr: 4.144e-05, eta: 2:22:22, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4313, decode.acc_seg: 83.2418, aux.loss_ce: 0.2481, aux.acc_seg: 77.1969, loss: 0.6795
2022-11-04 15:39:53,421 - mmseg - INFO - Iter [55500/80000]	lr: 4.137e-05, eta: 2:22:04, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4385, decode.acc_seg: 82.6616, aux.loss_ce: 0.2563, aux.acc_seg: 76.3985, loss: 0.6948
2022-11-04 15:40:13,325 - mmseg - INFO - Iter [55550/80000]	lr: 4.129e-05, eta: 2:21:48, time: 0.398, data_time: 0.066, memory: 12988, decode.loss_ce: 0.4174, decode.acc_seg: 83.5316, aux.loss_ce: 0.2433, aux.acc_seg: 77.8032, loss: 0.6606
2022-11-04 15:40:30,534 - mmseg - INFO - Iter [55600/80000]	lr: 4.122e-05, eta: 2:21:31, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4394, decode.acc_seg: 82.7951, aux.loss_ce: 0.2593, aux.acc_seg: 76.4859, loss: 0.6988
2022-11-04 15:40:47,557 - mmseg - INFO - Iter [55650/80000]	lr: 4.114e-05, eta: 2:21:13, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4269, decode.acc_seg: 83.2567, aux.loss_ce: 0.2518, aux.acc_seg: 76.9566, loss: 0.6787
2022-11-04 15:41:04,694 - mmseg - INFO - Iter [55700/80000]	lr: 4.106e-05, eta: 2:20:55, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4437, decode.acc_seg: 82.7133, aux.loss_ce: 0.2560, aux.acc_seg: 76.8961, loss: 0.6996
2022-11-04 15:41:21,805 - mmseg - INFO - Iter [55750/80000]	lr: 4.099e-05, eta: 2:20:38, time: 0.342, data_time: 0.011, memory: 12988, decode.loss_ce: 0.4410, decode.acc_seg: 82.6484, aux.loss_ce: 0.2519, aux.acc_seg: 76.7875, loss: 0.6929
2022-11-04 15:41:39,088 - mmseg - INFO - Iter [55800/80000]	lr: 4.091e-05, eta: 2:20:21, time: 0.346, data_time: 0.012, memory: 12988, decode.loss_ce: 0.4283, decode.acc_seg: 82.9993, aux.loss_ce: 0.2525, aux.acc_seg: 76.7297, loss: 0.6808
2022-11-04 15:41:56,024 - mmseg - INFO - Iter [55850/80000]	lr: 4.084e-05, eta: 2:20:03, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4254, decode.acc_seg: 83.3142, aux.loss_ce: 0.2529, aux.acc_seg: 76.7929, loss: 0.6783
2022-11-04 15:42:13,304 - mmseg - INFO - Iter [55900/80000]	lr: 4.076e-05, eta: 2:19:45, time: 0.346, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4199, decode.acc_seg: 83.5633, aux.loss_ce: 0.2415, aux.acc_seg: 77.8285, loss: 0.6614
2022-11-04 15:42:30,477 - mmseg - INFO - Iter [55950/80000]	lr: 4.068e-05, eta: 2:19:28, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4372, decode.acc_seg: 83.0108, aux.loss_ce: 0.2510, aux.acc_seg: 77.2448, loss: 0.6882
2022-11-04 15:42:47,353 - mmseg - INFO - Saving checkpoint at 56000 iterations
2022-11-04 15:42:47,598 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 15:42:47,599 - mmseg - INFO - Iter [56000/80000]	lr: 4.061e-05, eta: 2:19:10, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4312, decode.acc_seg: 82.9654, aux.loss_ce: 0.2474, aux.acc_seg: 77.5883, loss: 0.6786
2022-11-04 15:42:56,961 - mmseg - INFO - per class results:
2022-11-04 15:42:56,967 - mmseg - INFO - 
+---------------------+-------+-------+
|        Class        |  IoU  |  Acc  |
+---------------------+-------+-------+
|         wall        |  66.8 | 82.91 |
|       building      |  75.5 | 88.61 |
|         sky         | 88.65 | 92.88 |
|        floor        | 71.57 | 84.52 |
|         tree        | 67.78 | 84.98 |
|       ceiling       | 75.65 | 85.56 |
|         road        | 74.23 | 80.79 |
|         bed         | 74.63 | 90.48 |
|      windowpane     | 50.73 | 67.46 |
|        grass        | 61.89 |  82.2 |
|       cabinet       |  48.7 | 60.49 |
|       sidewalk      | 51.72 |  73.5 |
|        person       |  63.6 | 82.09 |
|        earth        | 31.68 | 44.99 |
|         door        | 25.72 | 41.13 |
|        table        | 40.93 | 58.18 |
|       mountain      | 56.63 | 70.29 |
|        plant        | 42.49 | 54.29 |
|       curtain       | 54.07 | 76.72 |
|        chair        | 37.33 | 50.16 |
|         car         | 72.43 | 85.32 |
|        water        | 42.52 | 59.76 |
|       painting      | 54.42 |  74.0 |
|         sofa        | 46.35 | 68.77 |
|        shelf        | 34.26 | 50.98 |
|        house        | 44.86 | 66.17 |
|         sea         | 42.47 | 69.76 |
|        mirror       | 43.16 | 58.31 |
|         rug         | 50.89 | 61.08 |
|        field        |  30.5 | 38.64 |
|       armchair      | 24.88 | 35.11 |
|         seat        | 49.77 | 65.92 |
|        fence        | 24.72 | 44.93 |
|         desk        | 33.99 | 48.29 |
|         rock        | 45.33 | 67.05 |
|       wardrobe      |  37.4 | 54.41 |
|         lamp        | 36.24 | 47.87 |
|       bathtub       |  54.1 | 65.82 |
|       railing       | 24.54 | 39.67 |
|       cushion       | 29.32 | 35.95 |
|         base        | 13.19 |  21.0 |
|         box         | 15.07 | 21.34 |
|        column       | 24.01 | 30.49 |
|      signboard      | 21.03 | 31.18 |
|   chest of drawers  | 30.41 | 52.61 |
|       counter       | 29.59 | 39.97 |
|         sand        | 28.32 | 43.79 |
|         sink        | 46.72 | 61.52 |
|      skyscraper     | 62.92 | 78.36 |
|      fireplace      |  56.5 | 75.53 |
|     refrigerator    | 49.48 | 65.08 |
|      grandstand     | 41.41 | 55.38 |
|         path        | 11.26 | 16.78 |
|        stairs       | 25.66 | 30.91 |
|        runway       | 71.64 | 86.58 |
|         case        | 52.91 |  62.9 |
|      pool table     | 81.07 | 88.29 |
|        pillow       | 38.53 | 51.89 |
|     screen door     |  45.1 | 58.42 |
|       stairway      | 31.94 | 42.17 |
|        river        | 14.71 | 19.92 |
|        bridge       | 40.51 | 50.93 |
|       bookcase      |  27.0 | 38.53 |
|        blind        | 17.11 | 19.21 |
|     coffee table    | 40.52 | 73.44 |
|        toilet       | 66.26 | 78.09 |
|        flower       | 25.55 | 43.39 |
|         book        | 37.83 | 56.64 |
|         hill        |  4.08 |  7.86 |
|        bench        | 34.77 | 47.51 |
|      countertop     | 45.19 | 64.01 |
|        stove        | 50.29 | 65.92 |
|         palm        | 35.73 | 50.79 |
|    kitchen island   | 16.72 | 42.65 |
|       computer      | 45.34 |  57.9 |
|     swivel chair    | 31.64 | 42.59 |
|         boat        | 59.24 | 70.38 |
|         bar         | 24.31 | 27.63 |
|    arcade machine   | 24.57 |  26.5 |
|        hovel        | 24.25 | 39.18 |
|         bus         | 73.97 | 86.09 |
|        towel        |  38.5 | 52.97 |
|        light        | 15.18 | 16.63 |
|        truck        |  5.63 |  6.56 |
|        tower        |  36.6 | 50.13 |
|      chandelier     |  48.0 | 60.35 |
|        awning       | 19.53 | 25.24 |
|     streetlight     |  6.35 |  7.44 |
|        booth        | 32.83 | 34.97 |
| television receiver | 49.57 | 66.25 |
|       airplane      |  41.1 | 51.24 |
|      dirt track     | 10.68 | 26.73 |
|       apparel       | 18.16 | 27.27 |
|         pole        |  5.05 | 13.34 |
|         land        |  0.0  |  0.0  |
|      bannister      |  1.86 |  2.88 |
|      escalator      | 52.23 | 63.72 |
|       ottoman       | 21.59 | 26.47 |
|        bottle       |  15.9 | 19.32 |
|        buffet       |  39.5 | 50.93 |
|        poster       | 17.22 | 23.36 |
|        stage        |  4.66 | 14.56 |
|         van         | 27.61 | 32.52 |
|         ship        | 81.14 | 84.63 |
|       fountain      | 10.67 | 11.13 |
|    conveyer belt    | 60.92 | 79.65 |
|        canopy       | 11.04 | 14.12 |
|        washer       | 60.36 | 64.77 |
|      plaything      |  6.96 | 17.04 |
|    swimming pool    | 18.34 |  25.0 |
|        stool        | 15.17 | 18.01 |
|        barrel       | 27.79 | 59.51 |
|        basket       | 14.85 | 19.48 |
|      waterfall      | 47.33 | 72.41 |
|         tent        | 89.71 | 95.91 |
|         bag         |  2.53 |  2.97 |
|       minibike      | 32.58 | 37.87 |
|        cradle       | 59.39 | 90.12 |
|         oven        | 14.42 | 30.92 |
|         ball        | 27.89 | 36.21 |
|         food        | 31.79 | 39.01 |
|         step        |  4.44 |  5.35 |
|         tank        | 28.37 | 34.22 |
|      trade name     | 19.21 | 24.15 |
|      microwave      | 26.54 | 29.29 |
|         pot         |  23.6 |  28.3 |
|        animal       |  44.6 | 51.78 |
|       bicycle       |  29.3 | 56.53 |
|         lake        | 52.45 | 56.51 |
|      dishwasher     | 30.56 | 39.81 |
|        screen       | 54.46 | 73.74 |
|       blanket       |  0.7  |  0.75 |
|      sculpture      |  26.0 | 37.49 |
|         hood        | 34.96 | 42.18 |
|        sconce       | 20.02 | 22.69 |
|         vase        | 17.79 | 26.31 |
|    traffic light    | 12.53 | 18.48 |
|         tray        |  1.0  |  1.57 |
|        ashcan       | 18.45 | 25.36 |
|         fan         | 27.62 | 34.37 |
|         pier        | 53.28 | 72.86 |
|      crt screen     |  3.33 |  5.96 |
|        plate        | 28.43 | 33.92 |
|       monitor       | 46.35 | 51.07 |
|    bulletin board   | 36.11 | 48.65 |
|        shower       |  0.0  |  0.0  |
|       radiator      | 37.89 | 47.85 |
|        glass        |  1.96 |  2.03 |
|        clock        |  9.47 |  11.3 |
|         flag        | 20.91 |  22.9 |
+---------------------+-------+-------+
2022-11-04 15:42:56,968 - mmseg - INFO - Summary:
2022-11-04 15:42:56,968 - mmseg - INFO - 
+-------+-------+------+
|  aAcc |  mIoU | mAcc |
+-------+-------+------+
| 75.33 | 35.53 | 46.6 |
+-------+-------+------+
2022-11-04 15:42:56,969 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 15:42:56,969 - mmseg - INFO - Iter(val) [250]	aAcc: 0.7533, mIoU: 0.3553, mAcc: 0.4660, IoU.wall: 0.6680, IoU.building: 0.7550, IoU.sky: 0.8865, IoU.floor: 0.7157, IoU.tree: 0.6778, IoU.ceiling: 0.7565, IoU.road: 0.7423, IoU.bed : 0.7463, IoU.windowpane: 0.5073, IoU.grass: 0.6189, IoU.cabinet: 0.4870, IoU.sidewalk: 0.5172, IoU.person: 0.6360, IoU.earth: 0.3168, IoU.door: 0.2572, IoU.table: 0.4093, IoU.mountain: 0.5663, IoU.plant: 0.4249, IoU.curtain: 0.5407, IoU.chair: 0.3733, IoU.car: 0.7243, IoU.water: 0.4252, IoU.painting: 0.5442, IoU.sofa: 0.4635, IoU.shelf: 0.3426, IoU.house: 0.4486, IoU.sea: 0.4247, IoU.mirror: 0.4316, IoU.rug: 0.5089, IoU.field: 0.3050, IoU.armchair: 0.2488, IoU.seat: 0.4977, IoU.fence: 0.2472, IoU.desk: 0.3399, IoU.rock: 0.4533, IoU.wardrobe: 0.3740, IoU.lamp: 0.3624, IoU.bathtub: 0.5410, IoU.railing: 0.2454, IoU.cushion: 0.2932, IoU.base: 0.1319, IoU.box: 0.1507, IoU.column: 0.2401, IoU.signboard: 0.2103, IoU.chest of drawers: 0.3041, IoU.counter: 0.2959, IoU.sand: 0.2832, IoU.sink: 0.4672, IoU.skyscraper: 0.6292, IoU.fireplace: 0.5650, IoU.refrigerator: 0.4948, IoU.grandstand: 0.4141, IoU.path: 0.1126, IoU.stairs: 0.2566, IoU.runway: 0.7164, IoU.case: 0.5291, IoU.pool table: 0.8107, IoU.pillow: 0.3853, IoU.screen door: 0.4510, IoU.stairway: 0.3194, IoU.river: 0.1471, IoU.bridge: 0.4051, IoU.bookcase: 0.2700, IoU.blind: 0.1711, IoU.coffee table: 0.4052, IoU.toilet: 0.6626, IoU.flower: 0.2555, IoU.book: 0.3783, IoU.hill: 0.0408, IoU.bench: 0.3477, IoU.countertop: 0.4519, IoU.stove: 0.5029, IoU.palm: 0.3573, IoU.kitchen island: 0.1672, IoU.computer: 0.4534, IoU.swivel chair: 0.3164, IoU.boat: 0.5924, IoU.bar: 0.2431, IoU.arcade machine: 0.2457, IoU.hovel: 0.2425, IoU.bus: 0.7397, IoU.towel: 0.3850, IoU.light: 0.1518, IoU.truck: 0.0563, IoU.tower: 0.3660, IoU.chandelier: 0.4800, IoU.awning: 0.1953, IoU.streetlight: 0.0635, IoU.booth: 0.3283, IoU.television receiver: 0.4957, IoU.airplane: 0.4110, IoU.dirt track: 0.1068, IoU.apparel: 0.1816, IoU.pole: 0.0505, IoU.land: 0.0000, IoU.bannister: 0.0186, IoU.escalator: 0.5223, IoU.ottoman: 0.2159, IoU.bottle: 0.1590, IoU.buffet: 0.3950, IoU.poster: 0.1722, IoU.stage: 0.0466, IoU.van: 0.2761, IoU.ship: 0.8114, IoU.fountain: 0.1067, IoU.conveyer belt: 0.6092, IoU.canopy: 0.1104, IoU.washer: 0.6036, IoU.plaything: 0.0696, IoU.swimming pool: 0.1834, IoU.stool: 0.1517, IoU.barrel: 0.2779, IoU.basket: 0.1485, IoU.waterfall: 0.4733, IoU.tent: 0.8971, IoU.bag: 0.0253, IoU.minibike: 0.3258, IoU.cradle: 0.5939, IoU.oven: 0.1442, IoU.ball: 0.2789, IoU.food: 0.3179, IoU.step: 0.0444, IoU.tank: 0.2837, IoU.trade name: 0.1921, IoU.microwave: 0.2654, IoU.pot: 0.2360, IoU.animal: 0.4460, IoU.bicycle: 0.2930, IoU.lake: 0.5245, IoU.dishwasher: 0.3056, IoU.screen: 0.5446, IoU.blanket: 0.0070, IoU.sculpture: 0.2600, IoU.hood: 0.3496, IoU.sconce: 0.2002, IoU.vase: 0.1779, IoU.traffic light: 0.1253, IoU.tray: 0.0100, IoU.ashcan: 0.1845, IoU.fan: 0.2762, IoU.pier: 0.5328, IoU.crt screen: 0.0333, IoU.plate: 0.2843, IoU.monitor: 0.4635, IoU.bulletin board: 0.3611, IoU.shower: 0.0000, IoU.radiator: 0.3789, IoU.glass: 0.0196, IoU.clock: 0.0947, IoU.flag: 0.2091, Acc.wall: 0.8291, Acc.building: 0.8861, Acc.sky: 0.9288, Acc.floor: 0.8452, Acc.tree: 0.8498, Acc.ceiling: 0.8556, Acc.road: 0.8079, Acc.bed : 0.9048, Acc.windowpane: 0.6746, Acc.grass: 0.8220, Acc.cabinet: 0.6049, Acc.sidewalk: 0.7350, Acc.person: 0.8209, Acc.earth: 0.4499, Acc.door: 0.4113, Acc.table: 0.5818, Acc.mountain: 0.7029, Acc.plant: 0.5429, Acc.curtain: 0.7672, Acc.chair: 0.5016, Acc.car: 0.8532, Acc.water: 0.5976, Acc.painting: 0.7400, Acc.sofa: 0.6877, Acc.shelf: 0.5098, Acc.house: 0.6617, Acc.sea: 0.6976, Acc.mirror: 0.5831, Acc.rug: 0.6108, Acc.field: 0.3864, Acc.armchair: 0.3511, Acc.seat: 0.6592, Acc.fence: 0.4493, Acc.desk: 0.4829, Acc.rock: 0.6705, Acc.wardrobe: 0.5441, Acc.lamp: 0.4787, Acc.bathtub: 0.6582, Acc.railing: 0.3967, Acc.cushion: 0.3595, Acc.base: 0.2100, Acc.box: 0.2134, Acc.column: 0.3049, Acc.signboard: 0.3118, Acc.chest of drawers: 0.5261, Acc.counter: 0.3997, Acc.sand: 0.4379, Acc.sink: 0.6152, Acc.skyscraper: 0.7836, Acc.fireplace: 0.7553, Acc.refrigerator: 0.6508, Acc.grandstand: 0.5538, Acc.path: 0.1678, Acc.stairs: 0.3091, Acc.runway: 0.8658, Acc.case: 0.6290, Acc.pool table: 0.8829, Acc.pillow: 0.5189, Acc.screen door: 0.5842, Acc.stairway: 0.4217, Acc.river: 0.1992, Acc.bridge: 0.5093, Acc.bookcase: 0.3853, Acc.blind: 0.1921, Acc.coffee table: 0.7344, Acc.toilet: 0.7809, Acc.flower: 0.4339, Acc.book: 0.5664, Acc.hill: 0.0786, Acc.bench: 0.4751, Acc.countertop: 0.6401, Acc.stove: 0.6592, Acc.palm: 0.5079, Acc.kitchen island: 0.4265, Acc.computer: 0.5790, Acc.swivel chair: 0.4259, Acc.boat: 0.7038, Acc.bar: 0.2763, Acc.arcade machine: 0.2650, Acc.hovel: 0.3918, Acc.bus: 0.8609, Acc.towel: 0.5297, Acc.light: 0.1663, Acc.truck: 0.0656, Acc.tower: 0.5013, Acc.chandelier: 0.6035, Acc.awning: 0.2524, Acc.streetlight: 0.0744, Acc.booth: 0.3497, Acc.television receiver: 0.6625, Acc.airplane: 0.5124, Acc.dirt track: 0.2673, Acc.apparel: 0.2727, Acc.pole: 0.1334, Acc.land: 0.0000, Acc.bannister: 0.0288, Acc.escalator: 0.6372, Acc.ottoman: 0.2647, Acc.bottle: 0.1932, Acc.buffet: 0.5093, Acc.poster: 0.2336, Acc.stage: 0.1456, Acc.van: 0.3252, Acc.ship: 0.8463, Acc.fountain: 0.1113, Acc.conveyer belt: 0.7965, Acc.canopy: 0.1412, Acc.washer: 0.6477, Acc.plaything: 0.1704, Acc.swimming pool: 0.2500, Acc.stool: 0.1801, Acc.barrel: 0.5951, Acc.basket: 0.1948, Acc.waterfall: 0.7241, Acc.tent: 0.9591, Acc.bag: 0.0297, Acc.minibike: 0.3787, Acc.cradle: 0.9012, Acc.oven: 0.3092, Acc.ball: 0.3621, Acc.food: 0.3901, Acc.step: 0.0535, Acc.tank: 0.3422, Acc.trade name: 0.2415, Acc.microwave: 0.2929, Acc.pot: 0.2830, Acc.animal: 0.5178, Acc.bicycle: 0.5653, Acc.lake: 0.5651, Acc.dishwasher: 0.3981, Acc.screen: 0.7374, Acc.blanket: 0.0075, Acc.sculpture: 0.3749, Acc.hood: 0.4218, Acc.sconce: 0.2269, Acc.vase: 0.2631, Acc.traffic light: 0.1848, Acc.tray: 0.0157, Acc.ashcan: 0.2536, Acc.fan: 0.3437, Acc.pier: 0.7286, Acc.crt screen: 0.0596, Acc.plate: 0.3392, Acc.monitor: 0.5107, Acc.bulletin board: 0.4865, Acc.shower: 0.0000, Acc.radiator: 0.4785, Acc.glass: 0.0203, Acc.clock: 0.1130, Acc.flag: 0.2290
2022-11-04 15:43:14,145 - mmseg - INFO - Iter [56050/80000]	lr: 4.053e-05, eta: 2:18:57, time: 0.531, data_time: 0.197, memory: 12988, decode.loss_ce: 0.4398, decode.acc_seg: 82.8286, aux.loss_ce: 0.2552, aux.acc_seg: 76.5460, loss: 0.6950
2022-11-04 15:43:31,259 - mmseg - INFO - Iter [56100/80000]	lr: 4.046e-05, eta: 2:18:39, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4318, decode.acc_seg: 83.2005, aux.loss_ce: 0.2473, aux.acc_seg: 77.5918, loss: 0.6791
2022-11-04 15:43:48,598 - mmseg - INFO - Iter [56150/80000]	lr: 4.038e-05, eta: 2:18:22, time: 0.347, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4351, decode.acc_seg: 82.7442, aux.loss_ce: 0.2496, aux.acc_seg: 76.9434, loss: 0.6847
2022-11-04 15:44:08,850 - mmseg - INFO - Iter [56200/80000]	lr: 4.030e-05, eta: 2:18:06, time: 0.405, data_time: 0.061, memory: 12988, decode.loss_ce: 0.4480, decode.acc_seg: 82.7494, aux.loss_ce: 0.2621, aux.acc_seg: 76.4217, loss: 0.7101
2022-11-04 15:44:25,724 - mmseg - INFO - Iter [56250/80000]	lr: 4.023e-05, eta: 2:17:48, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4316, decode.acc_seg: 83.3945, aux.loss_ce: 0.2482, aux.acc_seg: 77.6298, loss: 0.6798
2022-11-04 15:44:42,899 - mmseg - INFO - Iter [56300/80000]	lr: 4.015e-05, eta: 2:17:31, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4276, decode.acc_seg: 83.1362, aux.loss_ce: 0.2470, aux.acc_seg: 77.7403, loss: 0.6745
2022-11-04 15:44:59,702 - mmseg - INFO - Iter [56350/80000]	lr: 4.007e-05, eta: 2:17:13, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4240, decode.acc_seg: 83.1821, aux.loss_ce: 0.2508, aux.acc_seg: 76.9854, loss: 0.6748
2022-11-04 15:45:16,731 - mmseg - INFO - Iter [56400/80000]	lr: 4.000e-05, eta: 2:16:55, time: 0.341, data_time: 0.011, memory: 12988, decode.loss_ce: 0.4433, decode.acc_seg: 82.7756, aux.loss_ce: 0.2539, aux.acc_seg: 77.1396, loss: 0.6972
2022-11-04 15:45:33,662 - mmseg - INFO - Iter [56450/80000]	lr: 3.992e-05, eta: 2:16:38, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4379, decode.acc_seg: 83.0601, aux.loss_ce: 0.2580, aux.acc_seg: 76.7243, loss: 0.6959
2022-11-04 15:45:50,518 - mmseg - INFO - Iter [56500/80000]	lr: 3.985e-05, eta: 2:16:20, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4456, decode.acc_seg: 82.7089, aux.loss_ce: 0.2580, aux.acc_seg: 76.7603, loss: 0.7036
2022-11-04 15:46:07,323 - mmseg - INFO - Iter [56550/80000]	lr: 3.977e-05, eta: 2:16:03, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4348, decode.acc_seg: 83.1249, aux.loss_ce: 0.2489, aux.acc_seg: 77.5220, loss: 0.6837
2022-11-04 15:46:24,263 - mmseg - INFO - Iter [56600/80000]	lr: 3.969e-05, eta: 2:15:45, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4258, decode.acc_seg: 83.2734, aux.loss_ce: 0.2447, aux.acc_seg: 77.5433, loss: 0.6706
2022-11-04 15:46:41,667 - mmseg - INFO - Iter [56650/80000]	lr: 3.962e-05, eta: 2:15:28, time: 0.348, data_time: 0.018, memory: 12988, decode.loss_ce: 0.4309, decode.acc_seg: 83.3150, aux.loss_ce: 0.2537, aux.acc_seg: 77.2875, loss: 0.6846
2022-11-04 15:46:58,692 - mmseg - INFO - Iter [56700/80000]	lr: 3.954e-05, eta: 2:15:10, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4284, decode.acc_seg: 82.9921, aux.loss_ce: 0.2462, aux.acc_seg: 77.4757, loss: 0.6746
2022-11-04 15:47:15,835 - mmseg - INFO - Iter [56750/80000]	lr: 3.946e-05, eta: 2:14:52, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4234, decode.acc_seg: 83.0506, aux.loss_ce: 0.2501, aux.acc_seg: 77.0621, loss: 0.6735
2022-11-04 15:47:35,755 - mmseg - INFO - Iter [56800/80000]	lr: 3.939e-05, eta: 2:14:36, time: 0.398, data_time: 0.060, memory: 12988, decode.loss_ce: 0.4458, decode.acc_seg: 82.3825, aux.loss_ce: 0.2487, aux.acc_seg: 77.2270, loss: 0.6945
2022-11-04 15:47:52,881 - mmseg - INFO - Iter [56850/80000]	lr: 3.931e-05, eta: 2:14:19, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4264, decode.acc_seg: 83.2353, aux.loss_ce: 0.2462, aux.acc_seg: 77.5244, loss: 0.6727
2022-11-04 15:48:09,747 - mmseg - INFO - Iter [56900/80000]	lr: 3.923e-05, eta: 2:14:01, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4226, decode.acc_seg: 83.4029, aux.loss_ce: 0.2414, aux.acc_seg: 77.6063, loss: 0.6640
2022-11-04 15:48:26,743 - mmseg - INFO - Iter [56950/80000]	lr: 3.916e-05, eta: 2:13:43, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4312, decode.acc_seg: 83.1117, aux.loss_ce: 0.2513, aux.acc_seg: 77.1205, loss: 0.6825
2022-11-04 15:48:43,740 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 15:48:43,740 - mmseg - INFO - Iter [57000/80000]	lr: 3.908e-05, eta: 2:13:26, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4294, decode.acc_seg: 83.0940, aux.loss_ce: 0.2506, aux.acc_seg: 76.8649, loss: 0.6800
2022-11-04 15:49:00,673 - mmseg - INFO - Iter [57050/80000]	lr: 3.901e-05, eta: 2:13:08, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4465, decode.acc_seg: 82.6387, aux.loss_ce: 0.2607, aux.acc_seg: 76.3426, loss: 0.7072
2022-11-04 15:49:17,638 - mmseg - INFO - Iter [57100/80000]	lr: 3.893e-05, eta: 2:12:51, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4335, decode.acc_seg: 82.7361, aux.loss_ce: 0.2488, aux.acc_seg: 76.9162, loss: 0.6823
2022-11-04 15:49:34,577 - mmseg - INFO - Iter [57150/80000]	lr: 3.885e-05, eta: 2:12:33, time: 0.339, data_time: 0.008, memory: 12988, decode.loss_ce: 0.4259, decode.acc_seg: 83.1149, aux.loss_ce: 0.2465, aux.acc_seg: 77.1863, loss: 0.6724
2022-11-04 15:49:51,447 - mmseg - INFO - Iter [57200/80000]	lr: 3.878e-05, eta: 2:12:15, time: 0.337, data_time: 0.008, memory: 12988, decode.loss_ce: 0.4302, decode.acc_seg: 83.0587, aux.loss_ce: 0.2550, aux.acc_seg: 76.7592, loss: 0.6851
2022-11-04 15:50:08,746 - mmseg - INFO - Iter [57250/80000]	lr: 3.870e-05, eta: 2:11:58, time: 0.346, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4199, decode.acc_seg: 83.6434, aux.loss_ce: 0.2427, aux.acc_seg: 77.5964, loss: 0.6626
2022-11-04 15:50:25,763 - mmseg - INFO - Iter [57300/80000]	lr: 3.862e-05, eta: 2:11:40, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4514, decode.acc_seg: 82.3913, aux.loss_ce: 0.2614, aux.acc_seg: 76.6181, loss: 0.7128
2022-11-04 15:50:43,248 - mmseg - INFO - Iter [57350/80000]	lr: 3.855e-05, eta: 2:11:23, time: 0.350, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4250, decode.acc_seg: 83.4241, aux.loss_ce: 0.2483, aux.acc_seg: 77.5241, loss: 0.6733
2022-11-04 15:51:00,258 - mmseg - INFO - Iter [57400/80000]	lr: 3.847e-05, eta: 2:11:05, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4396, decode.acc_seg: 82.5679, aux.loss_ce: 0.2510, aux.acc_seg: 77.0785, loss: 0.6906
2022-11-04 15:51:20,161 - mmseg - INFO - Iter [57450/80000]	lr: 3.839e-05, eta: 2:10:49, time: 0.398, data_time: 0.065, memory: 12988, decode.loss_ce: 0.4369, decode.acc_seg: 82.7949, aux.loss_ce: 0.2549, aux.acc_seg: 76.9144, loss: 0.6918
2022-11-04 15:51:37,189 - mmseg - INFO - Iter [57500/80000]	lr: 3.832e-05, eta: 2:10:31, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4391, decode.acc_seg: 82.8113, aux.loss_ce: 0.2545, aux.acc_seg: 76.8257, loss: 0.6936
2022-11-04 15:51:54,234 - mmseg - INFO - Iter [57550/80000]	lr: 3.824e-05, eta: 2:10:14, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4354, decode.acc_seg: 82.8901, aux.loss_ce: 0.2542, aux.acc_seg: 76.9216, loss: 0.6896
2022-11-04 15:52:11,383 - mmseg - INFO - Iter [57600/80000]	lr: 3.816e-05, eta: 2:09:56, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4330, decode.acc_seg: 83.3527, aux.loss_ce: 0.2523, aux.acc_seg: 77.2101, loss: 0.6852
2022-11-04 15:52:28,484 - mmseg - INFO - Iter [57650/80000]	lr: 3.809e-05, eta: 2:09:39, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4220, decode.acc_seg: 83.6317, aux.loss_ce: 0.2442, aux.acc_seg: 77.6348, loss: 0.6662
2022-11-04 15:52:45,676 - mmseg - INFO - Iter [57700/80000]	lr: 3.801e-05, eta: 2:09:21, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4221, decode.acc_seg: 83.4140, aux.loss_ce: 0.2434, aux.acc_seg: 77.8488, loss: 0.6655
2022-11-04 15:53:02,743 - mmseg - INFO - Iter [57750/80000]	lr: 3.793e-05, eta: 2:09:04, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4305, decode.acc_seg: 83.3742, aux.loss_ce: 0.2476, aux.acc_seg: 77.6656, loss: 0.6781
2022-11-04 15:53:19,858 - mmseg - INFO - Iter [57800/80000]	lr: 3.786e-05, eta: 2:08:46, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4274, decode.acc_seg: 83.3185, aux.loss_ce: 0.2451, aux.acc_seg: 77.7338, loss: 0.6725
2022-11-04 15:53:36,812 - mmseg - INFO - Iter [57850/80000]	lr: 3.778e-05, eta: 2:08:29, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4342, decode.acc_seg: 82.7893, aux.loss_ce: 0.2572, aux.acc_seg: 76.5372, loss: 0.6914
2022-11-04 15:53:53,797 - mmseg - INFO - Iter [57900/80000]	lr: 3.770e-05, eta: 2:08:11, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4175, decode.acc_seg: 83.5508, aux.loss_ce: 0.2485, aux.acc_seg: 77.3936, loss: 0.6660
2022-11-04 15:54:10,943 - mmseg - INFO - Iter [57950/80000]	lr: 3.763e-05, eta: 2:07:54, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4404, decode.acc_seg: 82.6152, aux.loss_ce: 0.2534, aux.acc_seg: 76.3484, loss: 0.6939
2022-11-04 15:54:28,044 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 15:54:28,044 - mmseg - INFO - Iter [58000/80000]	lr: 3.755e-05, eta: 2:07:36, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4324, decode.acc_seg: 83.0282, aux.loss_ce: 0.2504, aux.acc_seg: 77.0044, loss: 0.6828
2022-11-04 15:54:44,783 - mmseg - INFO - Iter [58050/80000]	lr: 3.747e-05, eta: 2:07:19, time: 0.335, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4321, decode.acc_seg: 83.1655, aux.loss_ce: 0.2475, aux.acc_seg: 77.5344, loss: 0.6796
2022-11-04 15:55:05,080 - mmseg - INFO - Iter [58100/80000]	lr: 3.740e-05, eta: 2:07:02, time: 0.406, data_time: 0.059, memory: 12988, decode.loss_ce: 0.4445, decode.acc_seg: 82.5557, aux.loss_ce: 0.2568, aux.acc_seg: 76.5394, loss: 0.7013
2022-11-04 15:55:22,072 - mmseg - INFO - Iter [58150/80000]	lr: 3.732e-05, eta: 2:06:45, time: 0.340, data_time: 0.008, memory: 12988, decode.loss_ce: 0.4202, decode.acc_seg: 83.3119, aux.loss_ce: 0.2424, aux.acc_seg: 77.7019, loss: 0.6626
2022-11-04 15:55:38,901 - mmseg - INFO - Iter [58200/80000]	lr: 3.724e-05, eta: 2:06:27, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4293, decode.acc_seg: 83.0355, aux.loss_ce: 0.2488, aux.acc_seg: 77.4876, loss: 0.6781
2022-11-04 15:55:55,849 - mmseg - INFO - Iter [58250/80000]	lr: 3.716e-05, eta: 2:06:10, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4253, decode.acc_seg: 83.4895, aux.loss_ce: 0.2486, aux.acc_seg: 77.2777, loss: 0.6739
2022-11-04 15:56:13,037 - mmseg - INFO - Iter [58300/80000]	lr: 3.709e-05, eta: 2:05:52, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4307, decode.acc_seg: 83.2797, aux.loss_ce: 0.2455, aux.acc_seg: 77.5197, loss: 0.6762
2022-11-04 15:56:30,225 - mmseg - INFO - Iter [58350/80000]	lr: 3.701e-05, eta: 2:05:35, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4279, decode.acc_seg: 83.2773, aux.loss_ce: 0.2510, aux.acc_seg: 76.9246, loss: 0.6788
2022-11-04 15:56:47,395 - mmseg - INFO - Iter [58400/80000]	lr: 3.693e-05, eta: 2:05:17, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4442, decode.acc_seg: 82.7262, aux.loss_ce: 0.2569, aux.acc_seg: 76.6101, loss: 0.7011
2022-11-04 15:57:04,532 - mmseg - INFO - Iter [58450/80000]	lr: 3.686e-05, eta: 2:05:00, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4303, decode.acc_seg: 83.3229, aux.loss_ce: 0.2555, aux.acc_seg: 77.0262, loss: 0.6858
2022-11-04 15:57:21,732 - mmseg - INFO - Iter [58500/80000]	lr: 3.678e-05, eta: 2:04:42, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4364, decode.acc_seg: 83.0302, aux.loss_ce: 0.2493, aux.acc_seg: 77.4199, loss: 0.6858
2022-11-04 15:57:38,740 - mmseg - INFO - Iter [58550/80000]	lr: 3.670e-05, eta: 2:04:25, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4291, decode.acc_seg: 83.0511, aux.loss_ce: 0.2501, aux.acc_seg: 77.4961, loss: 0.6792
2022-11-04 15:57:55,690 - mmseg - INFO - Iter [58600/80000]	lr: 3.663e-05, eta: 2:04:07, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4314, decode.acc_seg: 83.1009, aux.loss_ce: 0.2477, aux.acc_seg: 77.0920, loss: 0.6792
2022-11-04 15:58:12,475 - mmseg - INFO - Iter [58650/80000]	lr: 3.655e-05, eta: 2:03:49, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4330, decode.acc_seg: 82.7890, aux.loss_ce: 0.2512, aux.acc_seg: 77.1205, loss: 0.6842
2022-11-04 15:58:32,468 - mmseg - INFO - Iter [58700/80000]	lr: 3.647e-05, eta: 2:03:33, time: 0.400, data_time: 0.057, memory: 12988, decode.loss_ce: 0.4231, decode.acc_seg: 83.4931, aux.loss_ce: 0.2429, aux.acc_seg: 77.8678, loss: 0.6661
2022-11-04 15:58:49,728 - mmseg - INFO - Iter [58750/80000]	lr: 3.639e-05, eta: 2:03:15, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4243, decode.acc_seg: 83.1664, aux.loss_ce: 0.2429, aux.acc_seg: 77.6226, loss: 0.6672
2022-11-04 15:59:07,020 - mmseg - INFO - Iter [58800/80000]	lr: 3.632e-05, eta: 2:02:58, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4340, decode.acc_seg: 82.9462, aux.loss_ce: 0.2519, aux.acc_seg: 76.9364, loss: 0.6859
2022-11-04 15:59:24,274 - mmseg - INFO - Iter [58850/80000]	lr: 3.624e-05, eta: 2:02:41, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4234, decode.acc_seg: 83.5746, aux.loss_ce: 0.2488, aux.acc_seg: 77.6525, loss: 0.6723
2022-11-04 15:59:41,388 - mmseg - INFO - Iter [58900/80000]	lr: 3.616e-05, eta: 2:02:23, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4466, decode.acc_seg: 82.5016, aux.loss_ce: 0.2557, aux.acc_seg: 76.9012, loss: 0.7023
2022-11-04 15:59:58,496 - mmseg - INFO - Iter [58950/80000]	lr: 3.609e-05, eta: 2:02:06, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4435, decode.acc_seg: 82.4787, aux.loss_ce: 0.2562, aux.acc_seg: 76.2684, loss: 0.6997
2022-11-04 16:00:15,646 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 16:00:15,647 - mmseg - INFO - Iter [59000/80000]	lr: 3.601e-05, eta: 2:01:48, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4300, decode.acc_seg: 83.3272, aux.loss_ce: 0.2530, aux.acc_seg: 76.9905, loss: 0.6830
2022-11-04 16:00:32,686 - mmseg - INFO - Iter [59050/80000]	lr: 3.593e-05, eta: 2:01:31, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4231, decode.acc_seg: 83.3102, aux.loss_ce: 0.2491, aux.acc_seg: 77.1369, loss: 0.6722
2022-11-04 16:00:49,669 - mmseg - INFO - Iter [59100/80000]	lr: 3.586e-05, eta: 2:01:13, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4279, decode.acc_seg: 82.8425, aux.loss_ce: 0.2420, aux.acc_seg: 77.7249, loss: 0.6700
2022-11-04 16:01:06,584 - mmseg - INFO - Iter [59150/80000]	lr: 3.578e-05, eta: 2:00:55, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4411, decode.acc_seg: 82.9703, aux.loss_ce: 0.2479, aux.acc_seg: 77.4432, loss: 0.6890
2022-11-04 16:01:23,696 - mmseg - INFO - Iter [59200/80000]	lr: 3.570e-05, eta: 2:00:38, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4351, decode.acc_seg: 83.0415, aux.loss_ce: 0.2521, aux.acc_seg: 77.0079, loss: 0.6872
2022-11-04 16:01:40,675 - mmseg - INFO - Iter [59250/80000]	lr: 3.562e-05, eta: 2:00:20, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4320, decode.acc_seg: 83.2121, aux.loss_ce: 0.2496, aux.acc_seg: 77.1441, loss: 0.6816
2022-11-04 16:01:57,722 - mmseg - INFO - Iter [59300/80000]	lr: 3.555e-05, eta: 2:00:03, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4328, decode.acc_seg: 83.2473, aux.loss_ce: 0.2539, aux.acc_seg: 77.2821, loss: 0.6867
2022-11-04 16:02:17,759 - mmseg - INFO - Iter [59350/80000]	lr: 3.547e-05, eta: 1:59:46, time: 0.401, data_time: 0.066, memory: 12988, decode.loss_ce: 0.4209, decode.acc_seg: 83.3831, aux.loss_ce: 0.2432, aux.acc_seg: 77.7938, loss: 0.6640
2022-11-04 16:02:35,017 - mmseg - INFO - Iter [59400/80000]	lr: 3.539e-05, eta: 1:59:29, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4270, decode.acc_seg: 83.2090, aux.loss_ce: 0.2500, aux.acc_seg: 76.8030, loss: 0.6770
2022-11-04 16:02:52,201 - mmseg - INFO - Iter [59450/80000]	lr: 3.531e-05, eta: 1:59:11, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4301, decode.acc_seg: 83.0576, aux.loss_ce: 0.2508, aux.acc_seg: 77.1514, loss: 0.6810
2022-11-04 16:03:09,201 - mmseg - INFO - Iter [59500/80000]	lr: 3.524e-05, eta: 1:58:54, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4056, decode.acc_seg: 83.9729, aux.loss_ce: 0.2384, aux.acc_seg: 77.9022, loss: 0.6440
2022-11-04 16:03:26,177 - mmseg - INFO - Iter [59550/80000]	lr: 3.516e-05, eta: 1:58:36, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4220, decode.acc_seg: 83.1752, aux.loss_ce: 0.2473, aux.acc_seg: 77.1757, loss: 0.6693
2022-11-04 16:03:43,373 - mmseg - INFO - Iter [59600/80000]	lr: 3.508e-05, eta: 1:58:19, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4184, decode.acc_seg: 83.6175, aux.loss_ce: 0.2422, aux.acc_seg: 77.9626, loss: 0.6606
2022-11-04 16:04:00,604 - mmseg - INFO - Iter [59650/80000]	lr: 3.500e-05, eta: 1:58:01, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4339, decode.acc_seg: 83.0844, aux.loss_ce: 0.2521, aux.acc_seg: 76.8897, loss: 0.6860
2022-11-04 16:04:18,015 - mmseg - INFO - Iter [59700/80000]	lr: 3.493e-05, eta: 1:57:44, time: 0.348, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4133, decode.acc_seg: 83.9039, aux.loss_ce: 0.2408, aux.acc_seg: 78.0045, loss: 0.6541
2022-11-04 16:04:35,115 - mmseg - INFO - Iter [59750/80000]	lr: 3.485e-05, eta: 1:57:27, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4285, decode.acc_seg: 83.3224, aux.loss_ce: 0.2557, aux.acc_seg: 76.8867, loss: 0.6842
2022-11-04 16:04:52,208 - mmseg - INFO - Iter [59800/80000]	lr: 3.477e-05, eta: 1:57:09, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4066, decode.acc_seg: 83.6643, aux.loss_ce: 0.2429, aux.acc_seg: 77.5895, loss: 0.6495
2022-11-04 16:05:09,376 - mmseg - INFO - Iter [59850/80000]	lr: 3.470e-05, eta: 1:56:52, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4355, decode.acc_seg: 83.1910, aux.loss_ce: 0.2493, aux.acc_seg: 77.2955, loss: 0.6848
2022-11-04 16:05:26,326 - mmseg - INFO - Iter [59900/80000]	lr: 3.462e-05, eta: 1:56:34, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4402, decode.acc_seg: 83.0672, aux.loss_ce: 0.2528, aux.acc_seg: 77.4992, loss: 0.6929
2022-11-04 16:05:46,427 - mmseg - INFO - Iter [59950/80000]	lr: 3.454e-05, eta: 1:56:17, time: 0.402, data_time: 0.064, memory: 12988, decode.loss_ce: 0.4193, decode.acc_seg: 83.6459, aux.loss_ce: 0.2473, aux.acc_seg: 77.6046, loss: 0.6667
2022-11-04 16:06:03,562 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 16:06:03,562 - mmseg - INFO - Iter [60000/80000]	lr: 3.446e-05, eta: 1:56:00, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4130, decode.acc_seg: 83.3812, aux.loss_ce: 0.2422, aux.acc_seg: 77.3028, loss: 0.6552
2022-11-04 16:06:20,524 - mmseg - INFO - Iter [60050/80000]	lr: 3.438e-05, eta: 1:55:42, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4319, decode.acc_seg: 83.2310, aux.loss_ce: 0.2551, aux.acc_seg: 76.9698, loss: 0.6870
2022-11-04 16:06:37,423 - mmseg - INFO - Iter [60100/80000]	lr: 3.431e-05, eta: 1:55:25, time: 0.338, data_time: 0.011, memory: 12988, decode.loss_ce: 0.4051, decode.acc_seg: 84.1265, aux.loss_ce: 0.2405, aux.acc_seg: 78.1807, loss: 0.6457
2022-11-04 16:06:54,355 - mmseg - INFO - Iter [60150/80000]	lr: 3.423e-05, eta: 1:55:07, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4280, decode.acc_seg: 83.2033, aux.loss_ce: 0.2501, aux.acc_seg: 76.8098, loss: 0.6782
2022-11-04 16:07:11,333 - mmseg - INFO - Iter [60200/80000]	lr: 3.415e-05, eta: 1:54:50, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4196, decode.acc_seg: 83.6470, aux.loss_ce: 0.2474, aux.acc_seg: 77.6151, loss: 0.6669
2022-11-04 16:07:28,185 - mmseg - INFO - Iter [60250/80000]	lr: 3.407e-05, eta: 1:54:32, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4262, decode.acc_seg: 83.4048, aux.loss_ce: 0.2461, aux.acc_seg: 77.6317, loss: 0.6723
2022-11-04 16:07:44,994 - mmseg - INFO - Iter [60300/80000]	lr: 3.400e-05, eta: 1:54:15, time: 0.336, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4442, decode.acc_seg: 82.4807, aux.loss_ce: 0.2598, aux.acc_seg: 76.4848, loss: 0.7040
2022-11-04 16:08:02,149 - mmseg - INFO - Iter [60350/80000]	lr: 3.392e-05, eta: 1:53:57, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4355, decode.acc_seg: 82.9755, aux.loss_ce: 0.2517, aux.acc_seg: 76.8752, loss: 0.6873
2022-11-04 16:08:19,139 - mmseg - INFO - Iter [60400/80000]	lr: 3.384e-05, eta: 1:53:40, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4226, decode.acc_seg: 83.2205, aux.loss_ce: 0.2414, aux.acc_seg: 77.6917, loss: 0.6640
2022-11-04 16:08:36,122 - mmseg - INFO - Iter [60450/80000]	lr: 3.376e-05, eta: 1:53:22, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4271, decode.acc_seg: 83.1284, aux.loss_ce: 0.2537, aux.acc_seg: 76.7448, loss: 0.6808
2022-11-04 16:08:53,037 - mmseg - INFO - Iter [60500/80000]	lr: 3.369e-05, eta: 1:53:05, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4070, decode.acc_seg: 83.6451, aux.loss_ce: 0.2365, aux.acc_seg: 78.0124, loss: 0.6435
2022-11-04 16:09:09,987 - mmseg - INFO - Iter [60550/80000]	lr: 3.361e-05, eta: 1:52:47, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4219, decode.acc_seg: 83.4030, aux.loss_ce: 0.2409, aux.acc_seg: 77.8866, loss: 0.6628
2022-11-04 16:09:30,142 - mmseg - INFO - Iter [60600/80000]	lr: 3.353e-05, eta: 1:52:30, time: 0.403, data_time: 0.061, memory: 12988, decode.loss_ce: 0.4343, decode.acc_seg: 83.1753, aux.loss_ce: 0.2554, aux.acc_seg: 76.8906, loss: 0.6896
2022-11-04 16:09:47,275 - mmseg - INFO - Iter [60650/80000]	lr: 3.345e-05, eta: 1:52:13, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4262, decode.acc_seg: 83.2555, aux.loss_ce: 0.2522, aux.acc_seg: 77.3345, loss: 0.6784
2022-11-04 16:10:04,181 - mmseg - INFO - Iter [60700/80000]	lr: 3.338e-05, eta: 1:51:55, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4161, decode.acc_seg: 83.5948, aux.loss_ce: 0.2417, aux.acc_seg: 77.6360, loss: 0.6579
2022-11-04 16:10:21,287 - mmseg - INFO - Iter [60750/80000]	lr: 3.330e-05, eta: 1:51:38, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4163, decode.acc_seg: 83.5258, aux.loss_ce: 0.2399, aux.acc_seg: 78.0506, loss: 0.6562
2022-11-04 16:10:38,185 - mmseg - INFO - Iter [60800/80000]	lr: 3.322e-05, eta: 1:51:20, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4041, decode.acc_seg: 84.2535, aux.loss_ce: 0.2351, aux.acc_seg: 78.6818, loss: 0.6392
2022-11-04 16:10:55,519 - mmseg - INFO - Iter [60850/80000]	lr: 3.314e-05, eta: 1:51:03, time: 0.347, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4282, decode.acc_seg: 83.2129, aux.loss_ce: 0.2444, aux.acc_seg: 77.5679, loss: 0.6726
2022-11-04 16:11:12,469 - mmseg - INFO - Iter [60900/80000]	lr: 3.306e-05, eta: 1:50:45, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4399, decode.acc_seg: 82.8277, aux.loss_ce: 0.2563, aux.acc_seg: 76.9325, loss: 0.6963
2022-11-04 16:11:29,200 - mmseg - INFO - Iter [60950/80000]	lr: 3.299e-05, eta: 1:50:28, time: 0.335, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4130, decode.acc_seg: 83.6232, aux.loss_ce: 0.2425, aux.acc_seg: 77.6310, loss: 0.6555
2022-11-04 16:11:46,059 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 16:11:46,059 - mmseg - INFO - Iter [61000/80000]	lr: 3.291e-05, eta: 1:50:10, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4248, decode.acc_seg: 83.1952, aux.loss_ce: 0.2492, aux.acc_seg: 77.1830, loss: 0.6740
2022-11-04 16:12:03,208 - mmseg - INFO - Iter [61050/80000]	lr: 3.283e-05, eta: 1:49:53, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4033, decode.acc_seg: 83.9233, aux.loss_ce: 0.2374, aux.acc_seg: 78.1379, loss: 0.6407
2022-11-04 16:12:20,233 - mmseg - INFO - Iter [61100/80000]	lr: 3.275e-05, eta: 1:49:35, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4196, decode.acc_seg: 83.4659, aux.loss_ce: 0.2438, aux.acc_seg: 77.5459, loss: 0.6634
2022-11-04 16:12:37,144 - mmseg - INFO - Iter [61150/80000]	lr: 3.267e-05, eta: 1:49:18, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4430, decode.acc_seg: 82.7807, aux.loss_ce: 0.2604, aux.acc_seg: 76.4734, loss: 0.7034
2022-11-04 16:12:54,538 - mmseg - INFO - Iter [61200/80000]	lr: 3.260e-05, eta: 1:49:00, time: 0.348, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4160, decode.acc_seg: 83.5723, aux.loss_ce: 0.2448, aux.acc_seg: 77.4145, loss: 0.6609
2022-11-04 16:13:14,431 - mmseg - INFO - Iter [61250/80000]	lr: 3.252e-05, eta: 1:48:44, time: 0.398, data_time: 0.062, memory: 12988, decode.loss_ce: 0.4263, decode.acc_seg: 83.4468, aux.loss_ce: 0.2489, aux.acc_seg: 77.5912, loss: 0.6751
2022-11-04 16:13:31,384 - mmseg - INFO - Iter [61300/80000]	lr: 3.244e-05, eta: 1:48:26, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4391, decode.acc_seg: 82.9994, aux.loss_ce: 0.2582, aux.acc_seg: 76.6085, loss: 0.6973
2022-11-04 16:13:48,308 - mmseg - INFO - Iter [61350/80000]	lr: 3.236e-05, eta: 1:48:09, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4263, decode.acc_seg: 83.4668, aux.loss_ce: 0.2483, aux.acc_seg: 77.3968, loss: 0.6746
2022-11-04 16:14:05,325 - mmseg - INFO - Iter [61400/80000]	lr: 3.228e-05, eta: 1:47:51, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4198, decode.acc_seg: 83.5498, aux.loss_ce: 0.2406, aux.acc_seg: 78.2110, loss: 0.6604
2022-11-04 16:14:22,400 - mmseg - INFO - Iter [61450/80000]	lr: 3.221e-05, eta: 1:47:34, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4176, decode.acc_seg: 83.8505, aux.loss_ce: 0.2441, aux.acc_seg: 77.6125, loss: 0.6617
2022-11-04 16:14:39,482 - mmseg - INFO - Iter [61500/80000]	lr: 3.213e-05, eta: 1:47:16, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4316, decode.acc_seg: 83.2106, aux.loss_ce: 0.2467, aux.acc_seg: 77.3199, loss: 0.6783
2022-11-04 16:14:56,643 - mmseg - INFO - Iter [61550/80000]	lr: 3.205e-05, eta: 1:46:59, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4280, decode.acc_seg: 83.3056, aux.loss_ce: 0.2505, aux.acc_seg: 77.3844, loss: 0.6785
2022-11-04 16:15:13,566 - mmseg - INFO - Iter [61600/80000]	lr: 3.197e-05, eta: 1:46:41, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4231, decode.acc_seg: 83.2475, aux.loss_ce: 0.2432, aux.acc_seg: 77.8100, loss: 0.6663
2022-11-04 16:15:30,793 - mmseg - INFO - Iter [61650/80000]	lr: 3.189e-05, eta: 1:46:24, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4235, decode.acc_seg: 83.2680, aux.loss_ce: 0.2478, aux.acc_seg: 77.3307, loss: 0.6714
2022-11-04 16:15:47,835 - mmseg - INFO - Iter [61700/80000]	lr: 3.181e-05, eta: 1:46:06, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4319, decode.acc_seg: 83.0420, aux.loss_ce: 0.2569, aux.acc_seg: 76.5953, loss: 0.6889
2022-11-04 16:16:04,543 - mmseg - INFO - Iter [61750/80000]	lr: 3.174e-05, eta: 1:45:49, time: 0.334, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4169, decode.acc_seg: 83.6849, aux.loss_ce: 0.2455, aux.acc_seg: 77.8746, loss: 0.6625
2022-11-04 16:16:21,543 - mmseg - INFO - Iter [61800/80000]	lr: 3.166e-05, eta: 1:45:31, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4219, decode.acc_seg: 83.0310, aux.loss_ce: 0.2462, aux.acc_seg: 76.9807, loss: 0.6681
2022-11-04 16:16:41,412 - mmseg - INFO - Iter [61850/80000]	lr: 3.158e-05, eta: 1:45:14, time: 0.397, data_time: 0.059, memory: 12988, decode.loss_ce: 0.3950, decode.acc_seg: 84.4158, aux.loss_ce: 0.2320, aux.acc_seg: 78.8922, loss: 0.6271
2022-11-04 16:16:58,693 - mmseg - INFO - Iter [61900/80000]	lr: 3.150e-05, eta: 1:44:57, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4148, decode.acc_seg: 83.4524, aux.loss_ce: 0.2417, aux.acc_seg: 77.5056, loss: 0.6565
2022-11-04 16:17:15,910 - mmseg - INFO - Iter [61950/80000]	lr: 3.142e-05, eta: 1:44:39, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4253, decode.acc_seg: 83.3599, aux.loss_ce: 0.2452, aux.acc_seg: 77.7514, loss: 0.6706
2022-11-04 16:17:32,884 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 16:17:32,884 - mmseg - INFO - Iter [62000/80000]	lr: 3.134e-05, eta: 1:44:22, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4301, decode.acc_seg: 82.9932, aux.loss_ce: 0.2499, aux.acc_seg: 77.1888, loss: 0.6799
2022-11-04 16:17:49,646 - mmseg - INFO - Iter [62050/80000]	lr: 3.127e-05, eta: 1:44:04, time: 0.335, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4191, decode.acc_seg: 83.2956, aux.loss_ce: 0.2433, aux.acc_seg: 77.4955, loss: 0.6624
2022-11-04 16:18:07,135 - mmseg - INFO - Iter [62100/80000]	lr: 3.119e-05, eta: 1:43:47, time: 0.350, data_time: 0.015, memory: 12988, decode.loss_ce: 0.4263, decode.acc_seg: 83.3914, aux.loss_ce: 0.2469, aux.acc_seg: 77.6963, loss: 0.6732
2022-11-04 16:18:24,189 - mmseg - INFO - Iter [62150/80000]	lr: 3.111e-05, eta: 1:43:30, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4300, decode.acc_seg: 82.9898, aux.loss_ce: 0.2480, aux.acc_seg: 76.9670, loss: 0.6780
2022-11-04 16:18:41,293 - mmseg - INFO - Iter [62200/80000]	lr: 3.103e-05, eta: 1:43:12, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4237, decode.acc_seg: 83.4632, aux.loss_ce: 0.2448, aux.acc_seg: 77.6895, loss: 0.6685
2022-11-04 16:18:58,235 - mmseg - INFO - Iter [62250/80000]	lr: 3.095e-05, eta: 1:42:55, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4111, decode.acc_seg: 83.8435, aux.loss_ce: 0.2404, aux.acc_seg: 78.2200, loss: 0.6515
2022-11-04 16:19:15,333 - mmseg - INFO - Iter [62300/80000]	lr: 3.087e-05, eta: 1:42:37, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4135, decode.acc_seg: 83.3419, aux.loss_ce: 0.2415, aux.acc_seg: 77.3385, loss: 0.6549
2022-11-04 16:19:32,212 - mmseg - INFO - Iter [62350/80000]	lr: 3.080e-05, eta: 1:42:20, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4214, decode.acc_seg: 83.5339, aux.loss_ce: 0.2492, aux.acc_seg: 77.3724, loss: 0.6706
2022-11-04 16:19:49,330 - mmseg - INFO - Iter [62400/80000]	lr: 3.072e-05, eta: 1:42:02, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4157, decode.acc_seg: 83.7444, aux.loss_ce: 0.2486, aux.acc_seg: 77.4856, loss: 0.6643
2022-11-04 16:20:07,029 - mmseg - INFO - Iter [62450/80000]	lr: 3.064e-05, eta: 1:41:45, time: 0.354, data_time: 0.021, memory: 12988, decode.loss_ce: 0.4268, decode.acc_seg: 83.4487, aux.loss_ce: 0.2508, aux.acc_seg: 77.2292, loss: 0.6777
2022-11-04 16:20:26,940 - mmseg - INFO - Iter [62500/80000]	lr: 3.056e-05, eta: 1:41:28, time: 0.398, data_time: 0.063, memory: 12988, decode.loss_ce: 0.4287, decode.acc_seg: 83.4782, aux.loss_ce: 0.2502, aux.acc_seg: 77.4106, loss: 0.6789
2022-11-04 16:20:44,088 - mmseg - INFO - Iter [62550/80000]	lr: 3.048e-05, eta: 1:41:11, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4116, decode.acc_seg: 83.7019, aux.loss_ce: 0.2399, aux.acc_seg: 78.0605, loss: 0.6516
2022-11-04 16:21:01,120 - mmseg - INFO - Iter [62600/80000]	lr: 3.040e-05, eta: 1:40:53, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4359, decode.acc_seg: 83.0585, aux.loss_ce: 0.2533, aux.acc_seg: 77.3386, loss: 0.6892
2022-11-04 16:21:18,324 - mmseg - INFO - Iter [62650/80000]	lr: 3.032e-05, eta: 1:40:36, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4309, decode.acc_seg: 83.2975, aux.loss_ce: 0.2462, aux.acc_seg: 77.6573, loss: 0.6771
2022-11-04 16:21:35,674 - mmseg - INFO - Iter [62700/80000]	lr: 3.025e-05, eta: 1:40:18, time: 0.347, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4217, decode.acc_seg: 83.7163, aux.loss_ce: 0.2484, aux.acc_seg: 77.8592, loss: 0.6701
2022-11-04 16:21:52,774 - mmseg - INFO - Iter [62750/80000]	lr: 3.017e-05, eta: 1:40:01, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4051, decode.acc_seg: 83.5978, aux.loss_ce: 0.2398, aux.acc_seg: 77.6352, loss: 0.6449
2022-11-04 16:22:10,084 - mmseg - INFO - Iter [62800/80000]	lr: 3.009e-05, eta: 1:39:43, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4052, decode.acc_seg: 83.7277, aux.loss_ce: 0.2467, aux.acc_seg: 77.3074, loss: 0.6519
2022-11-04 16:22:27,269 - mmseg - INFO - Iter [62850/80000]	lr: 3.001e-05, eta: 1:39:26, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4304, decode.acc_seg: 83.2442, aux.loss_ce: 0.2507, aux.acc_seg: 77.5776, loss: 0.6811
2022-11-04 16:22:44,354 - mmseg - INFO - Iter [62900/80000]	lr: 2.993e-05, eta: 1:39:08, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4309, decode.acc_seg: 83.2609, aux.loss_ce: 0.2503, aux.acc_seg: 77.1335, loss: 0.6811
2022-11-04 16:23:01,233 - mmseg - INFO - Iter [62950/80000]	lr: 2.985e-05, eta: 1:38:51, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4217, decode.acc_seg: 83.4868, aux.loss_ce: 0.2456, aux.acc_seg: 77.7337, loss: 0.6673
2022-11-04 16:23:18,463 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 16:23:18,464 - mmseg - INFO - Iter [63000/80000]	lr: 2.977e-05, eta: 1:38:33, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4252, decode.acc_seg: 83.1345, aux.loss_ce: 0.2470, aux.acc_seg: 77.3905, loss: 0.6722
2022-11-04 16:23:35,690 - mmseg - INFO - Iter [63050/80000]	lr: 2.969e-05, eta: 1:38:16, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4101, decode.acc_seg: 83.7031, aux.loss_ce: 0.2372, aux.acc_seg: 77.8871, loss: 0.6473
2022-11-04 16:23:52,536 - mmseg - INFO - Iter [63100/80000]	lr: 2.962e-05, eta: 1:37:58, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4082, decode.acc_seg: 83.8884, aux.loss_ce: 0.2368, aux.acc_seg: 78.3633, loss: 0.6450
2022-11-04 16:24:12,953 - mmseg - INFO - Iter [63150/80000]	lr: 2.954e-05, eta: 1:37:42, time: 0.408, data_time: 0.062, memory: 12988, decode.loss_ce: 0.4115, decode.acc_seg: 83.7129, aux.loss_ce: 0.2443, aux.acc_seg: 77.6556, loss: 0.6558
2022-11-04 16:24:30,087 - mmseg - INFO - Iter [63200/80000]	lr: 2.946e-05, eta: 1:37:24, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4315, decode.acc_seg: 83.0676, aux.loss_ce: 0.2502, aux.acc_seg: 77.0358, loss: 0.6817
2022-11-04 16:24:47,123 - mmseg - INFO - Iter [63250/80000]	lr: 2.938e-05, eta: 1:37:07, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4258, decode.acc_seg: 83.5238, aux.loss_ce: 0.2455, aux.acc_seg: 77.8786, loss: 0.6713
2022-11-04 16:25:04,098 - mmseg - INFO - Iter [63300/80000]	lr: 2.930e-05, eta: 1:36:49, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4123, decode.acc_seg: 83.9212, aux.loss_ce: 0.2460, aux.acc_seg: 77.5763, loss: 0.6583
2022-11-04 16:25:21,169 - mmseg - INFO - Iter [63350/80000]	lr: 2.922e-05, eta: 1:36:32, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4105, decode.acc_seg: 83.5879, aux.loss_ce: 0.2412, aux.acc_seg: 77.8167, loss: 0.6518
2022-11-04 16:25:38,017 - mmseg - INFO - Iter [63400/80000]	lr: 2.914e-05, eta: 1:36:14, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4181, decode.acc_seg: 83.4620, aux.loss_ce: 0.2463, aux.acc_seg: 77.5762, loss: 0.6644
2022-11-04 16:25:55,039 - mmseg - INFO - Iter [63450/80000]	lr: 2.906e-05, eta: 1:35:57, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4070, decode.acc_seg: 84.1047, aux.loss_ce: 0.2401, aux.acc_seg: 78.3551, loss: 0.6471
2022-11-04 16:26:11,895 - mmseg - INFO - Iter [63500/80000]	lr: 2.898e-05, eta: 1:35:39, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4177, decode.acc_seg: 83.7681, aux.loss_ce: 0.2438, aux.acc_seg: 77.7449, loss: 0.6614
2022-11-04 16:26:28,948 - mmseg - INFO - Iter [63550/80000]	lr: 2.891e-05, eta: 1:35:22, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4240, decode.acc_seg: 83.3194, aux.loss_ce: 0.2503, aux.acc_seg: 77.2508, loss: 0.6742
2022-11-04 16:26:46,213 - mmseg - INFO - Iter [63600/80000]	lr: 2.883e-05, eta: 1:35:04, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4278, decode.acc_seg: 83.2008, aux.loss_ce: 0.2544, aux.acc_seg: 76.9459, loss: 0.6822
2022-11-04 16:27:03,204 - mmseg - INFO - Iter [63650/80000]	lr: 2.875e-05, eta: 1:34:47, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4047, decode.acc_seg: 84.0410, aux.loss_ce: 0.2373, aux.acc_seg: 78.3706, loss: 0.6420
2022-11-04 16:27:20,345 - mmseg - INFO - Iter [63700/80000]	lr: 2.867e-05, eta: 1:34:29, time: 0.343, data_time: 0.011, memory: 12988, decode.loss_ce: 0.4287, decode.acc_seg: 83.3474, aux.loss_ce: 0.2478, aux.acc_seg: 77.4137, loss: 0.6765
2022-11-04 16:27:40,303 - mmseg - INFO - Iter [63750/80000]	lr: 2.859e-05, eta: 1:34:13, time: 0.399, data_time: 0.068, memory: 12988, decode.loss_ce: 0.4103, decode.acc_seg: 83.9673, aux.loss_ce: 0.2354, aux.acc_seg: 78.4767, loss: 0.6457
2022-11-04 16:27:57,254 - mmseg - INFO - Iter [63800/80000]	lr: 2.851e-05, eta: 1:33:55, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4033, decode.acc_seg: 83.9096, aux.loss_ce: 0.2394, aux.acc_seg: 77.9682, loss: 0.6427
2022-11-04 16:28:14,368 - mmseg - INFO - Iter [63850/80000]	lr: 2.843e-05, eta: 1:33:38, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4088, decode.acc_seg: 83.8553, aux.loss_ce: 0.2354, aux.acc_seg: 78.3899, loss: 0.6442
2022-11-04 16:28:31,559 - mmseg - INFO - Iter [63900/80000]	lr: 2.835e-05, eta: 1:33:20, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4103, decode.acc_seg: 83.7962, aux.loss_ce: 0.2435, aux.acc_seg: 77.5709, loss: 0.6538
2022-11-04 16:28:48,935 - mmseg - INFO - Iter [63950/80000]	lr: 2.827e-05, eta: 1:33:03, time: 0.348, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4196, decode.acc_seg: 83.4411, aux.loss_ce: 0.2471, aux.acc_seg: 77.4585, loss: 0.6667
2022-11-04 16:29:06,041 - mmseg - INFO - Saving checkpoint at 64000 iterations
2022-11-04 16:29:06,288 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 16:29:06,288 - mmseg - INFO - Iter [64000/80000]	lr: 2.819e-05, eta: 1:32:46, time: 0.347, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4121, decode.acc_seg: 83.3170, aux.loss_ce: 0.2484, aux.acc_seg: 77.0420, loss: 0.6605
2022-11-04 16:29:15,676 - mmseg - INFO - per class results:
2022-11-04 16:29:15,683 - mmseg - INFO - 
+---------------------+-------+-------+
|        Class        |  IoU  |  Acc  |
+---------------------+-------+-------+
|         wall        |  66.9 | 82.68 |
|       building      | 75.97 | 90.32 |
|         sky         | 88.66 | 93.02 |
|        floor        | 71.66 | 84.46 |
|         tree        | 67.77 | 84.23 |
|       ceiling       | 75.73 | 85.78 |
|         road        | 74.92 | 84.79 |
|         bed         | 75.31 | 89.82 |
|      windowpane     | 51.29 | 68.81 |
|        grass        | 62.31 | 81.87 |
|       cabinet       | 48.59 | 63.96 |
|       sidewalk      | 52.07 | 71.56 |
|        person       | 64.07 | 83.28 |
|        earth        | 31.47 | 42.75 |
|         door        | 26.03 | 38.75 |
|        table        | 40.96 | 59.68 |
|       mountain      | 54.35 | 72.03 |
|        plant        | 44.93 | 55.47 |
|       curtain       | 55.28 | 74.37 |
|        chair        | 38.15 |  52.7 |
|         car         | 72.66 | 86.27 |
|        water        | 43.97 | 62.86 |
|       painting      | 55.04 | 74.22 |
|         sofa        | 46.79 | 66.63 |
|        shelf        | 32.73 | 45.68 |
|        house        |  42.5 | 54.92 |
|         sea         | 36.24 | 57.27 |
|        mirror       | 43.31 | 56.35 |
|         rug         | 50.64 | 61.17 |
|        field        | 27.78 | 37.98 |
|       armchair      | 25.46 |  39.2 |
|         seat        | 49.73 | 70.93 |
|        fence        | 27.14 | 48.22 |
|         desk        | 33.44 |  53.4 |
|         rock        | 45.25 | 68.58 |
|       wardrobe      | 38.46 | 58.95 |
|         lamp        | 37.47 | 48.92 |
|       bathtub       | 55.33 | 64.82 |
|       railing       | 26.42 | 43.61 |
|       cushion       | 33.45 | 44.96 |
|         base        | 18.91 | 26.17 |
|         box         | 16.47 | 25.34 |
|        column       | 25.63 | 32.45 |
|      signboard      |  21.9 | 30.33 |
|   chest of drawers  | 29.94 | 48.15 |
|       counter       | 24.83 | 31.51 |
|         sand        | 25.86 | 37.22 |
|         sink        |  45.7 | 58.34 |
|      skyscraper     |  66.2 | 83.42 |
|      fireplace      | 56.92 | 79.27 |
|     refrigerator    | 50.73 | 65.07 |
|      grandstand     | 37.48 |  58.5 |
|         path        |  8.57 | 11.49 |
|        stairs       | 25.47 | 32.22 |
|        runway       | 73.22 | 86.85 |
|         case        | 45.39 | 51.62 |
|      pool table     | 80.68 | 89.91 |
|        pillow       | 36.54 |  46.4 |
|     screen door     | 55.27 | 67.55 |
|       stairway      | 31.22 | 37.04 |
|        river        | 16.04 | 24.75 |
|        bridge       | 47.41 |  60.7 |
|       bookcase      | 27.09 | 40.01 |
|        blind        | 18.19 | 20.98 |
|     coffee table    | 41.93 | 68.38 |
|        toilet       | 65.65 | 79.62 |
|        flower       | 27.59 | 39.06 |
|         book        | 36.87 | 56.24 |
|         hill        |  7.16 | 11.23 |
|        bench        | 31.76 | 41.06 |
|      countertop     | 46.05 |  66.9 |
|        stove        | 45.35 | 53.86 |
|         palm        | 37.41 | 51.57 |
|    kitchen island   | 15.74 | 32.59 |
|       computer      | 46.24 | 59.84 |
|     swivel chair    | 32.24 | 46.08 |
|         boat        | 59.27 |  71.6 |
|         bar         | 23.97 | 28.63 |
|    arcade machine   | 25.81 | 28.74 |
|        hovel        |  24.2 | 36.65 |
|         bus         | 72.37 | 83.68 |
|        towel        |  41.3 | 51.39 |
|        light        | 13.28 | 14.29 |
|        truck        |  6.64 |  8.36 |
|        tower        | 37.26 | 54.38 |
|      chandelier     | 48.32 | 61.83 |
|        awning       | 18.49 | 22.08 |
|     streetlight     |  6.84 |  7.95 |
|        booth        | 28.28 | 35.55 |
| television receiver | 50.05 | 67.33 |
|       airplane      | 43.64 | 53.46 |
|      dirt track     | 11.85 | 32.82 |
|       apparel       | 19.35 | 25.72 |
|         pole        |  4.31 | 10.11 |
|         land        |  0.01 |  0.01 |
|      bannister      |  1.47 |  2.2  |
|      escalator      | 47.72 | 59.74 |
|       ottoman       | 21.68 | 26.71 |
|        bottle       | 12.94 | 15.19 |
|        buffet       | 38.42 | 52.33 |
|        poster       | 19.32 | 25.06 |
|        stage        |  6.87 | 15.93 |
|         van         | 24.78 | 28.22 |
|         ship        | 50.57 |  51.1 |
|       fountain      | 12.51 | 12.71 |
|    conveyer belt    | 59.49 | 74.82 |
|        canopy       | 13.16 | 15.84 |
|        washer       | 60.71 | 66.09 |
|      plaything      |  8.42 | 16.47 |
|    swimming pool    | 35.74 | 48.55 |
|        stool        | 17.45 | 21.28 |
|        barrel       | 38.21 | 59.65 |
|        basket       | 16.91 | 21.64 |
|      waterfall      | 43.67 | 53.12 |
|         tent        |  87.3 | 94.72 |
|         bag         |  5.38 |  6.58 |
|       minibike      | 35.97 | 42.57 |
|        cradle       | 61.23 | 91.23 |
|         oven        | 11.81 | 31.04 |
|         ball        |  36.3 | 44.02 |
|         food        | 35.07 | 43.37 |
|         step        |  6.16 |  7.25 |
|         tank        |  29.0 | 33.13 |
|      trade name     | 15.28 | 17.27 |
|      microwave      | 26.96 | 30.26 |
|         pot         | 24.82 | 30.14 |
|        animal       | 48.11 | 52.41 |
|       bicycle       | 30.89 |  55.1 |
|         lake        |  5.71 |  6.02 |
|      dishwasher     | 31.28 | 40.48 |
|        screen       | 51.79 | 82.01 |
|       blanket       |  1.21 |  1.32 |
|      sculpture      | 30.62 | 39.71 |
|         hood        | 35.12 | 41.44 |
|        sconce       | 21.85 | 25.06 |
|         vase        | 18.41 | 26.49 |
|    traffic light    | 11.12 | 14.59 |
|         tray        |  1.51 |  3.41 |
|        ashcan       | 17.85 | 26.61 |
|         fan         | 29.22 | 36.66 |
|         pier        | 50.69 | 75.01 |
|      crt screen     |  6.31 |  9.46 |
|        plate        |  30.7 | 35.31 |
|       monitor       | 24.32 | 26.65 |
|    bulletin board   | 31.23 | 39.87 |
|        shower       |  0.0  |  0.0  |
|       radiator      | 37.22 | 47.05 |
|        glass        |  1.77 |  1.83 |
|        clock        |  8.19 |  9.63 |
|         flag        | 20.56 | 23.02 |
+---------------------+-------+-------+
2022-11-04 16:29:15,684 - mmseg - INFO - Summary:
2022-11-04 16:29:15,684 - mmseg - INFO - 
+-------+-------+-------+
|  aAcc |  mIoU |  mAcc |
+-------+-------+-------+
| 75.59 | 35.31 | 46.06 |
+-------+-------+-------+
2022-11-04 16:29:15,685 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 16:29:15,686 - mmseg - INFO - Iter(val) [250]	aAcc: 0.7559, mIoU: 0.3531, mAcc: 0.4606, IoU.wall: 0.6690, IoU.building: 0.7597, IoU.sky: 0.8866, IoU.floor: 0.7166, IoU.tree: 0.6777, IoU.ceiling: 0.7573, IoU.road: 0.7492, IoU.bed : 0.7531, IoU.windowpane: 0.5129, IoU.grass: 0.6231, IoU.cabinet: 0.4859, IoU.sidewalk: 0.5207, IoU.person: 0.6407, IoU.earth: 0.3147, IoU.door: 0.2603, IoU.table: 0.4096, IoU.mountain: 0.5435, IoU.plant: 0.4493, IoU.curtain: 0.5528, IoU.chair: 0.3815, IoU.car: 0.7266, IoU.water: 0.4397, IoU.painting: 0.5504, IoU.sofa: 0.4679, IoU.shelf: 0.3273, IoU.house: 0.4250, IoU.sea: 0.3624, IoU.mirror: 0.4331, IoU.rug: 0.5064, IoU.field: 0.2778, IoU.armchair: 0.2546, IoU.seat: 0.4973, IoU.fence: 0.2714, IoU.desk: 0.3344, IoU.rock: 0.4525, IoU.wardrobe: 0.3846, IoU.lamp: 0.3747, IoU.bathtub: 0.5533, IoU.railing: 0.2642, IoU.cushion: 0.3345, IoU.base: 0.1891, IoU.box: 0.1647, IoU.column: 0.2563, IoU.signboard: 0.2190, IoU.chest of drawers: 0.2994, IoU.counter: 0.2483, IoU.sand: 0.2586, IoU.sink: 0.4570, IoU.skyscraper: 0.6620, IoU.fireplace: 0.5692, IoU.refrigerator: 0.5073, IoU.grandstand: 0.3748, IoU.path: 0.0857, IoU.stairs: 0.2547, IoU.runway: 0.7322, IoU.case: 0.4539, IoU.pool table: 0.8068, IoU.pillow: 0.3654, IoU.screen door: 0.5527, IoU.stairway: 0.3122, IoU.river: 0.1604, IoU.bridge: 0.4741, IoU.bookcase: 0.2709, IoU.blind: 0.1819, IoU.coffee table: 0.4193, IoU.toilet: 0.6565, IoU.flower: 0.2759, IoU.book: 0.3687, IoU.hill: 0.0716, IoU.bench: 0.3176, IoU.countertop: 0.4605, IoU.stove: 0.4535, IoU.palm: 0.3741, IoU.kitchen island: 0.1574, IoU.computer: 0.4624, IoU.swivel chair: 0.3224, IoU.boat: 0.5927, IoU.bar: 0.2397, IoU.arcade machine: 0.2581, IoU.hovel: 0.2420, IoU.bus: 0.7237, IoU.towel: 0.4130, IoU.light: 0.1328, IoU.truck: 0.0664, IoU.tower: 0.3726, IoU.chandelier: 0.4832, IoU.awning: 0.1849, IoU.streetlight: 0.0684, IoU.booth: 0.2828, IoU.television receiver: 0.5005, IoU.airplane: 0.4364, IoU.dirt track: 0.1185, IoU.apparel: 0.1935, IoU.pole: 0.0431, IoU.land: 0.0001, IoU.bannister: 0.0147, IoU.escalator: 0.4772, IoU.ottoman: 0.2168, IoU.bottle: 0.1294, IoU.buffet: 0.3842, IoU.poster: 0.1932, IoU.stage: 0.0687, IoU.van: 0.2478, IoU.ship: 0.5057, IoU.fountain: 0.1251, IoU.conveyer belt: 0.5949, IoU.canopy: 0.1316, IoU.washer: 0.6071, IoU.plaything: 0.0842, IoU.swimming pool: 0.3574, IoU.stool: 0.1745, IoU.barrel: 0.3821, IoU.basket: 0.1691, IoU.waterfall: 0.4367, IoU.tent: 0.8730, IoU.bag: 0.0538, IoU.minibike: 0.3597, IoU.cradle: 0.6123, IoU.oven: 0.1181, IoU.ball: 0.3630, IoU.food: 0.3507, IoU.step: 0.0616, IoU.tank: 0.2900, IoU.trade name: 0.1528, IoU.microwave: 0.2696, IoU.pot: 0.2482, IoU.animal: 0.4811, IoU.bicycle: 0.3089, IoU.lake: 0.0571, IoU.dishwasher: 0.3128, IoU.screen: 0.5179, IoU.blanket: 0.0121, IoU.sculpture: 0.3062, IoU.hood: 0.3512, IoU.sconce: 0.2185, IoU.vase: 0.1841, IoU.traffic light: 0.1112, IoU.tray: 0.0151, IoU.ashcan: 0.1785, IoU.fan: 0.2922, IoU.pier: 0.5069, IoU.crt screen: 0.0631, IoU.plate: 0.3070, IoU.monitor: 0.2432, IoU.bulletin board: 0.3123, IoU.shower: 0.0000, IoU.radiator: 0.3722, IoU.glass: 0.0177, IoU.clock: 0.0819, IoU.flag: 0.2056, Acc.wall: 0.8268, Acc.building: 0.9032, Acc.sky: 0.9302, Acc.floor: 0.8446, Acc.tree: 0.8423, Acc.ceiling: 0.8578, Acc.road: 0.8479, Acc.bed : 0.8982, Acc.windowpane: 0.6881, Acc.grass: 0.8187, Acc.cabinet: 0.6396, Acc.sidewalk: 0.7156, Acc.person: 0.8328, Acc.earth: 0.4275, Acc.door: 0.3875, Acc.table: 0.5968, Acc.mountain: 0.7203, Acc.plant: 0.5547, Acc.curtain: 0.7437, Acc.chair: 0.5270, Acc.car: 0.8627, Acc.water: 0.6286, Acc.painting: 0.7422, Acc.sofa: 0.6663, Acc.shelf: 0.4568, Acc.house: 0.5492, Acc.sea: 0.5727, Acc.mirror: 0.5635, Acc.rug: 0.6117, Acc.field: 0.3798, Acc.armchair: 0.3920, Acc.seat: 0.7093, Acc.fence: 0.4822, Acc.desk: 0.5340, Acc.rock: 0.6858, Acc.wardrobe: 0.5895, Acc.lamp: 0.4892, Acc.bathtub: 0.6482, Acc.railing: 0.4361, Acc.cushion: 0.4496, Acc.base: 0.2617, Acc.box: 0.2534, Acc.column: 0.3245, Acc.signboard: 0.3033, Acc.chest of drawers: 0.4815, Acc.counter: 0.3151, Acc.sand: 0.3722, Acc.sink: 0.5834, Acc.skyscraper: 0.8342, Acc.fireplace: 0.7927, Acc.refrigerator: 0.6507, Acc.grandstand: 0.5850, Acc.path: 0.1149, Acc.stairs: 0.3222, Acc.runway: 0.8685, Acc.case: 0.5162, Acc.pool table: 0.8991, Acc.pillow: 0.4640, Acc.screen door: 0.6755, Acc.stairway: 0.3704, Acc.river: 0.2475, Acc.bridge: 0.6070, Acc.bookcase: 0.4001, Acc.blind: 0.2098, Acc.coffee table: 0.6838, Acc.toilet: 0.7962, Acc.flower: 0.3906, Acc.book: 0.5624, Acc.hill: 0.1123, Acc.bench: 0.4106, Acc.countertop: 0.6690, Acc.stove: 0.5386, Acc.palm: 0.5157, Acc.kitchen island: 0.3259, Acc.computer: 0.5984, Acc.swivel chair: 0.4608, Acc.boat: 0.7160, Acc.bar: 0.2863, Acc.arcade machine: 0.2874, Acc.hovel: 0.3665, Acc.bus: 0.8368, Acc.towel: 0.5139, Acc.light: 0.1429, Acc.truck: 0.0836, Acc.tower: 0.5438, Acc.chandelier: 0.6183, Acc.awning: 0.2208, Acc.streetlight: 0.0795, Acc.booth: 0.3555, Acc.television receiver: 0.6733, Acc.airplane: 0.5346, Acc.dirt track: 0.3282, Acc.apparel: 0.2572, Acc.pole: 0.1011, Acc.land: 0.0001, Acc.bannister: 0.0220, Acc.escalator: 0.5974, Acc.ottoman: 0.2671, Acc.bottle: 0.1519, Acc.buffet: 0.5233, Acc.poster: 0.2506, Acc.stage: 0.1593, Acc.van: 0.2822, Acc.ship: 0.5110, Acc.fountain: 0.1271, Acc.conveyer belt: 0.7482, Acc.canopy: 0.1584, Acc.washer: 0.6609, Acc.plaything: 0.1647, Acc.swimming pool: 0.4855, Acc.stool: 0.2128, Acc.barrel: 0.5965, Acc.basket: 0.2164, Acc.waterfall: 0.5312, Acc.tent: 0.9472, Acc.bag: 0.0658, Acc.minibike: 0.4257, Acc.cradle: 0.9123, Acc.oven: 0.3104, Acc.ball: 0.4402, Acc.food: 0.4337, Acc.step: 0.0725, Acc.tank: 0.3313, Acc.trade name: 0.1727, Acc.microwave: 0.3026, Acc.pot: 0.3014, Acc.animal: 0.5241, Acc.bicycle: 0.5510, Acc.lake: 0.0602, Acc.dishwasher: 0.4048, Acc.screen: 0.8201, Acc.blanket: 0.0132, Acc.sculpture: 0.3971, Acc.hood: 0.4144, Acc.sconce: 0.2506, Acc.vase: 0.2649, Acc.traffic light: 0.1459, Acc.tray: 0.0341, Acc.ashcan: 0.2661, Acc.fan: 0.3666, Acc.pier: 0.7501, Acc.crt screen: 0.0946, Acc.plate: 0.3531, Acc.monitor: 0.2665, Acc.bulletin board: 0.3987, Acc.shower: 0.0000, Acc.radiator: 0.4705, Acc.glass: 0.0183, Acc.clock: 0.0963, Acc.flag: 0.2302
2022-11-04 16:29:32,793 - mmseg - INFO - Iter [64050/80000]	lr: 2.811e-05, eta: 1:32:30, time: 0.530, data_time: 0.197, memory: 12988, decode.loss_ce: 0.4181, decode.acc_seg: 83.6435, aux.loss_ce: 0.2403, aux.acc_seg: 77.7807, loss: 0.6585
2022-11-04 16:29:49,721 - mmseg - INFO - Iter [64100/80000]	lr: 2.803e-05, eta: 1:32:13, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4189, decode.acc_seg: 83.5453, aux.loss_ce: 0.2461, aux.acc_seg: 77.7080, loss: 0.6650
2022-11-04 16:30:07,050 - mmseg - INFO - Iter [64150/80000]	lr: 2.795e-05, eta: 1:31:55, time: 0.347, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4223, decode.acc_seg: 83.2177, aux.loss_ce: 0.2454, aux.acc_seg: 77.2051, loss: 0.6678
2022-11-04 16:30:24,060 - mmseg - INFO - Iter [64200/80000]	lr: 2.788e-05, eta: 1:31:38, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4203, decode.acc_seg: 83.5560, aux.loss_ce: 0.2462, aux.acc_seg: 77.7132, loss: 0.6665
2022-11-04 16:30:41,148 - mmseg - INFO - Iter [64250/80000]	lr: 2.780e-05, eta: 1:31:20, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4266, decode.acc_seg: 83.7050, aux.loss_ce: 0.2461, aux.acc_seg: 77.8678, loss: 0.6727
2022-11-04 16:30:58,340 - mmseg - INFO - Iter [64300/80000]	lr: 2.772e-05, eta: 1:31:03, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4161, decode.acc_seg: 83.5362, aux.loss_ce: 0.2479, aux.acc_seg: 77.3811, loss: 0.6641
2022-11-04 16:31:15,383 - mmseg - INFO - Iter [64350/80000]	lr: 2.764e-05, eta: 1:30:46, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4127, decode.acc_seg: 83.7797, aux.loss_ce: 0.2397, aux.acc_seg: 78.3384, loss: 0.6524
2022-11-04 16:31:35,266 - mmseg - INFO - Iter [64400/80000]	lr: 2.756e-05, eta: 1:30:29, time: 0.398, data_time: 0.065, memory: 12988, decode.loss_ce: 0.4203, decode.acc_seg: 83.4911, aux.loss_ce: 0.2458, aux.acc_seg: 77.4097, loss: 0.6661
2022-11-04 16:31:52,296 - mmseg - INFO - Iter [64450/80000]	lr: 2.748e-05, eta: 1:30:11, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4013, decode.acc_seg: 84.3303, aux.loss_ce: 0.2370, aux.acc_seg: 78.5514, loss: 0.6383
2022-11-04 16:32:09,280 - mmseg - INFO - Iter [64500/80000]	lr: 2.740e-05, eta: 1:29:54, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4061, decode.acc_seg: 84.0673, aux.loss_ce: 0.2392, aux.acc_seg: 78.2260, loss: 0.6454
2022-11-04 16:32:26,137 - mmseg - INFO - Iter [64550/80000]	lr: 2.732e-05, eta: 1:29:36, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4149, decode.acc_seg: 83.4643, aux.loss_ce: 0.2464, aux.acc_seg: 77.5593, loss: 0.6614
2022-11-04 16:32:43,248 - mmseg - INFO - Iter [64600/80000]	lr: 2.724e-05, eta: 1:29:19, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4204, decode.acc_seg: 83.6045, aux.loss_ce: 0.2488, aux.acc_seg: 77.5175, loss: 0.6692
2022-11-04 16:33:00,394 - mmseg - INFO - Iter [64650/80000]	lr: 2.716e-05, eta: 1:29:01, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4228, decode.acc_seg: 83.2771, aux.loss_ce: 0.2462, aux.acc_seg: 77.2245, loss: 0.6690
2022-11-04 16:33:17,343 - mmseg - INFO - Iter [64700/80000]	lr: 2.708e-05, eta: 1:28:44, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4173, decode.acc_seg: 83.7003, aux.loss_ce: 0.2422, aux.acc_seg: 77.9176, loss: 0.6595
2022-11-04 16:33:34,260 - mmseg - INFO - Iter [64750/80000]	lr: 2.700e-05, eta: 1:28:26, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4208, decode.acc_seg: 83.2780, aux.loss_ce: 0.2479, aux.acc_seg: 77.3399, loss: 0.6687
2022-11-04 16:33:51,421 - mmseg - INFO - Iter [64800/80000]	lr: 2.692e-05, eta: 1:28:09, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4291, decode.acc_seg: 82.9893, aux.loss_ce: 0.2486, aux.acc_seg: 76.9725, loss: 0.6776
2022-11-04 16:34:08,355 - mmseg - INFO - Iter [64850/80000]	lr: 2.684e-05, eta: 1:27:51, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4075, decode.acc_seg: 83.9591, aux.loss_ce: 0.2445, aux.acc_seg: 77.8139, loss: 0.6520
2022-11-04 16:34:25,469 - mmseg - INFO - Iter [64900/80000]	lr: 2.676e-05, eta: 1:27:34, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4049, decode.acc_seg: 84.0952, aux.loss_ce: 0.2315, aux.acc_seg: 78.7134, loss: 0.6364
2022-11-04 16:34:42,277 - mmseg - INFO - Iter [64950/80000]	lr: 2.668e-05, eta: 1:27:16, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4332, decode.acc_seg: 83.1717, aux.loss_ce: 0.2593, aux.acc_seg: 76.9139, loss: 0.6925
2022-11-04 16:35:02,222 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 16:35:02,222 - mmseg - INFO - Iter [65000/80000]	lr: 2.660e-05, eta: 1:27:00, time: 0.399, data_time: 0.065, memory: 12988, decode.loss_ce: 0.4165, decode.acc_seg: 83.6969, aux.loss_ce: 0.2454, aux.acc_seg: 77.8494, loss: 0.6620
2022-11-04 16:35:19,104 - mmseg - INFO - Iter [65050/80000]	lr: 2.652e-05, eta: 1:26:42, time: 0.338, data_time: 0.011, memory: 12988, decode.loss_ce: 0.4047, decode.acc_seg: 83.8996, aux.loss_ce: 0.2391, aux.acc_seg: 77.7970, loss: 0.6438
2022-11-04 16:35:36,127 - mmseg - INFO - Iter [65100/80000]	lr: 2.644e-05, eta: 1:26:25, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3989, decode.acc_seg: 84.1803, aux.loss_ce: 0.2404, aux.acc_seg: 77.9507, loss: 0.6393
2022-11-04 16:35:53,014 - mmseg - INFO - Iter [65150/80000]	lr: 2.636e-05, eta: 1:26:07, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4239, decode.acc_seg: 83.3902, aux.loss_ce: 0.2436, aux.acc_seg: 77.6013, loss: 0.6675
2022-11-04 16:36:10,232 - mmseg - INFO - Iter [65200/80000]	lr: 2.628e-05, eta: 1:25:50, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3965, decode.acc_seg: 84.2497, aux.loss_ce: 0.2370, aux.acc_seg: 78.0189, loss: 0.6335
2022-11-04 16:36:27,583 - mmseg - INFO - Iter [65250/80000]	lr: 2.620e-05, eta: 1:25:32, time: 0.347, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4297, decode.acc_seg: 83.0213, aux.loss_ce: 0.2532, aux.acc_seg: 76.9213, loss: 0.6829
2022-11-04 16:36:44,690 - mmseg - INFO - Iter [65300/80000]	lr: 2.612e-05, eta: 1:25:15, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4239, decode.acc_seg: 83.4703, aux.loss_ce: 0.2487, aux.acc_seg: 77.4123, loss: 0.6725
2022-11-04 16:37:01,776 - mmseg - INFO - Iter [65350/80000]	lr: 2.604e-05, eta: 1:24:57, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4031, decode.acc_seg: 83.8771, aux.loss_ce: 0.2384, aux.acc_seg: 77.9125, loss: 0.6414
2022-11-04 16:37:18,856 - mmseg - INFO - Iter [65400/80000]	lr: 2.596e-05, eta: 1:24:40, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4240, decode.acc_seg: 83.3459, aux.loss_ce: 0.2493, aux.acc_seg: 77.2214, loss: 0.6733
2022-11-04 16:37:36,098 - mmseg - INFO - Iter [65450/80000]	lr: 2.588e-05, eta: 1:24:22, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4281, decode.acc_seg: 83.2489, aux.loss_ce: 0.2505, aux.acc_seg: 77.1517, loss: 0.6786
2022-11-04 16:37:53,204 - mmseg - INFO - Iter [65500/80000]	lr: 2.580e-05, eta: 1:24:05, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4011, decode.acc_seg: 83.9694, aux.loss_ce: 0.2378, aux.acc_seg: 77.9307, loss: 0.6389
2022-11-04 16:38:10,291 - mmseg - INFO - Iter [65550/80000]	lr: 2.572e-05, eta: 1:23:47, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4281, decode.acc_seg: 83.3283, aux.loss_ce: 0.2452, aux.acc_seg: 77.6303, loss: 0.6733
2022-11-04 16:38:27,620 - mmseg - INFO - Iter [65600/80000]	lr: 2.564e-05, eta: 1:23:30, time: 0.347, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4271, decode.acc_seg: 83.0577, aux.loss_ce: 0.2491, aux.acc_seg: 77.0773, loss: 0.6761
2022-11-04 16:38:47,671 - mmseg - INFO - Iter [65650/80000]	lr: 2.556e-05, eta: 1:23:13, time: 0.401, data_time: 0.060, memory: 12988, decode.loss_ce: 0.4127, decode.acc_seg: 83.6791, aux.loss_ce: 0.2411, aux.acc_seg: 77.6541, loss: 0.6538
2022-11-04 16:39:04,626 - mmseg - INFO - Iter [65700/80000]	lr: 2.548e-05, eta: 1:22:56, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4166, decode.acc_seg: 83.4940, aux.loss_ce: 0.2464, aux.acc_seg: 77.5073, loss: 0.6631
2022-11-04 16:39:22,047 - mmseg - INFO - Iter [65750/80000]	lr: 2.540e-05, eta: 1:22:38, time: 0.348, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4162, decode.acc_seg: 83.3869, aux.loss_ce: 0.2445, aux.acc_seg: 77.3362, loss: 0.6607
2022-11-04 16:39:38,993 - mmseg - INFO - Iter [65800/80000]	lr: 2.532e-05, eta: 1:22:21, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4091, decode.acc_seg: 83.9287, aux.loss_ce: 0.2413, aux.acc_seg: 77.8339, loss: 0.6504
2022-11-04 16:39:56,047 - mmseg - INFO - Iter [65850/80000]	lr: 2.524e-05, eta: 1:22:03, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4130, decode.acc_seg: 83.8252, aux.loss_ce: 0.2408, aux.acc_seg: 78.0215, loss: 0.6538
2022-11-04 16:40:13,240 - mmseg - INFO - Iter [65900/80000]	lr: 2.516e-05, eta: 1:21:46, time: 0.344, data_time: 0.011, memory: 12988, decode.loss_ce: 0.4039, decode.acc_seg: 84.0551, aux.loss_ce: 0.2381, aux.acc_seg: 78.2378, loss: 0.6420
2022-11-04 16:40:30,309 - mmseg - INFO - Iter [65950/80000]	lr: 2.508e-05, eta: 1:21:28, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4132, decode.acc_seg: 83.3786, aux.loss_ce: 0.2440, aux.acc_seg: 77.1846, loss: 0.6572
2022-11-04 16:40:47,352 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 16:40:47,352 - mmseg - INFO - Iter [66000/80000]	lr: 2.500e-05, eta: 1:21:11, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4120, decode.acc_seg: 83.9858, aux.loss_ce: 0.2463, aux.acc_seg: 77.6789, loss: 0.6583
2022-11-04 16:41:04,491 - mmseg - INFO - Iter [66050/80000]	lr: 2.492e-05, eta: 1:20:53, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4187, decode.acc_seg: 83.6413, aux.loss_ce: 0.2456, aux.acc_seg: 77.8039, loss: 0.6644
2022-11-04 16:41:21,513 - mmseg - INFO - Iter [66100/80000]	lr: 2.484e-05, eta: 1:20:36, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4119, decode.acc_seg: 83.7114, aux.loss_ce: 0.2385, aux.acc_seg: 77.8369, loss: 0.6504
2022-11-04 16:41:38,611 - mmseg - INFO - Iter [66150/80000]	lr: 2.476e-05, eta: 1:20:19, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4145, decode.acc_seg: 83.5884, aux.loss_ce: 0.2452, aux.acc_seg: 77.9099, loss: 0.6597
2022-11-04 16:41:55,537 - mmseg - INFO - Iter [66200/80000]	lr: 2.468e-05, eta: 1:20:01, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4121, decode.acc_seg: 83.7690, aux.loss_ce: 0.2442, aux.acc_seg: 77.7437, loss: 0.6562
2022-11-04 16:42:12,550 - mmseg - INFO - Iter [66250/80000]	lr: 2.460e-05, eta: 1:19:44, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4084, decode.acc_seg: 84.1502, aux.loss_ce: 0.2420, aux.acc_seg: 77.9418, loss: 0.6504
2022-11-04 16:42:32,708 - mmseg - INFO - Iter [66300/80000]	lr: 2.452e-05, eta: 1:19:27, time: 0.403, data_time: 0.061, memory: 12988, decode.loss_ce: 0.4160, decode.acc_seg: 83.9756, aux.loss_ce: 0.2413, aux.acc_seg: 78.3312, loss: 0.6573
2022-11-04 16:42:49,938 - mmseg - INFO - Iter [66350/80000]	lr: 2.444e-05, eta: 1:19:09, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4091, decode.acc_seg: 84.0263, aux.loss_ce: 0.2368, aux.acc_seg: 78.1787, loss: 0.6459
2022-11-04 16:43:06,960 - mmseg - INFO - Iter [66400/80000]	lr: 2.436e-05, eta: 1:18:52, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4042, decode.acc_seg: 84.1493, aux.loss_ce: 0.2380, aux.acc_seg: 78.3238, loss: 0.6422
2022-11-04 16:43:24,111 - mmseg - INFO - Iter [66450/80000]	lr: 2.428e-05, eta: 1:18:34, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4031, decode.acc_seg: 84.1817, aux.loss_ce: 0.2373, aux.acc_seg: 78.5918, loss: 0.6403
2022-11-04 16:43:41,221 - mmseg - INFO - Iter [66500/80000]	lr: 2.420e-05, eta: 1:18:17, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4066, decode.acc_seg: 83.9986, aux.loss_ce: 0.2399, aux.acc_seg: 78.1112, loss: 0.6466
2022-11-04 16:43:58,083 - mmseg - INFO - Iter [66550/80000]	lr: 2.411e-05, eta: 1:17:59, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4118, decode.acc_seg: 83.6089, aux.loss_ce: 0.2417, aux.acc_seg: 77.6973, loss: 0.6535
2022-11-04 16:44:15,116 - mmseg - INFO - Iter [66600/80000]	lr: 2.403e-05, eta: 1:17:42, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4225, decode.acc_seg: 83.5289, aux.loss_ce: 0.2497, aux.acc_seg: 77.5286, loss: 0.6722
2022-11-04 16:44:32,322 - mmseg - INFO - Iter [66650/80000]	lr: 2.395e-05, eta: 1:17:24, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4261, decode.acc_seg: 83.3137, aux.loss_ce: 0.2485, aux.acc_seg: 77.5366, loss: 0.6746
2022-11-04 16:44:49,187 - mmseg - INFO - Iter [66700/80000]	lr: 2.387e-05, eta: 1:17:07, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4223, decode.acc_seg: 83.1718, aux.loss_ce: 0.2461, aux.acc_seg: 77.1826, loss: 0.6684
2022-11-04 16:45:06,655 - mmseg - INFO - Iter [66750/80000]	lr: 2.379e-05, eta: 1:16:50, time: 0.349, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4149, decode.acc_seg: 83.6921, aux.loss_ce: 0.2422, aux.acc_seg: 77.7230, loss: 0.6571
2022-11-04 16:45:23,615 - mmseg - INFO - Iter [66800/80000]	lr: 2.371e-05, eta: 1:16:32, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4244, decode.acc_seg: 83.2859, aux.loss_ce: 0.2490, aux.acc_seg: 77.4466, loss: 0.6734
2022-11-04 16:45:40,507 - mmseg - INFO - Iter [66850/80000]	lr: 2.363e-05, eta: 1:16:15, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4027, decode.acc_seg: 83.9926, aux.loss_ce: 0.2372, aux.acc_seg: 77.9775, loss: 0.6398
2022-11-04 16:46:00,404 - mmseg - INFO - Iter [66900/80000]	lr: 2.355e-05, eta: 1:15:58, time: 0.398, data_time: 0.061, memory: 12988, decode.loss_ce: 0.4115, decode.acc_seg: 83.7232, aux.loss_ce: 0.2397, aux.acc_seg: 77.6378, loss: 0.6513
2022-11-04 16:46:17,463 - mmseg - INFO - Iter [66950/80000]	lr: 2.347e-05, eta: 1:15:40, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4216, decode.acc_seg: 83.3501, aux.loss_ce: 0.2480, aux.acc_seg: 77.3749, loss: 0.6696
2022-11-04 16:46:34,477 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 16:46:34,478 - mmseg - INFO - Iter [67000/80000]	lr: 2.339e-05, eta: 1:15:23, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4104, decode.acc_seg: 83.9847, aux.loss_ce: 0.2458, aux.acc_seg: 77.7915, loss: 0.6562
2022-11-04 16:46:51,615 - mmseg - INFO - Iter [67050/80000]	lr: 2.331e-05, eta: 1:15:05, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4342, decode.acc_seg: 83.1497, aux.loss_ce: 0.2493, aux.acc_seg: 77.4036, loss: 0.6835
2022-11-04 16:47:08,658 - mmseg - INFO - Iter [67100/80000]	lr: 2.323e-05, eta: 1:14:48, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4108, decode.acc_seg: 83.7323, aux.loss_ce: 0.2435, aux.acc_seg: 77.9626, loss: 0.6542
2022-11-04 16:47:25,623 - mmseg - INFO - Iter [67150/80000]	lr: 2.314e-05, eta: 1:14:30, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4139, decode.acc_seg: 83.8777, aux.loss_ce: 0.2443, aux.acc_seg: 77.7253, loss: 0.6583
2022-11-04 16:47:42,937 - mmseg - INFO - Iter [67200/80000]	lr: 2.306e-05, eta: 1:14:13, time: 0.346, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3994, decode.acc_seg: 84.1580, aux.loss_ce: 0.2381, aux.acc_seg: 78.3812, loss: 0.6376
2022-11-04 16:47:59,918 - mmseg - INFO - Iter [67250/80000]	lr: 2.298e-05, eta: 1:13:55, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4002, decode.acc_seg: 84.1144, aux.loss_ce: 0.2339, aux.acc_seg: 78.5206, loss: 0.6341
2022-11-04 16:48:16,865 - mmseg - INFO - Iter [67300/80000]	lr: 2.290e-05, eta: 1:13:38, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4186, decode.acc_seg: 83.4717, aux.loss_ce: 0.2397, aux.acc_seg: 78.1687, loss: 0.6583
2022-11-04 16:48:33,767 - mmseg - INFO - Iter [67350/80000]	lr: 2.282e-05, eta: 1:13:21, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4275, decode.acc_seg: 83.4416, aux.loss_ce: 0.2532, aux.acc_seg: 77.2080, loss: 0.6807
2022-11-04 16:48:50,866 - mmseg - INFO - Iter [67400/80000]	lr: 2.274e-05, eta: 1:13:03, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3977, decode.acc_seg: 83.7984, aux.loss_ce: 0.2337, aux.acc_seg: 78.1474, loss: 0.6314
2022-11-04 16:49:07,824 - mmseg - INFO - Iter [67450/80000]	lr: 2.266e-05, eta: 1:12:46, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4090, decode.acc_seg: 83.7853, aux.loss_ce: 0.2425, aux.acc_seg: 77.4217, loss: 0.6514
2022-11-04 16:49:24,832 - mmseg - INFO - Iter [67500/80000]	lr: 2.258e-05, eta: 1:12:28, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4264, decode.acc_seg: 82.9795, aux.loss_ce: 0.2502, aux.acc_seg: 77.2319, loss: 0.6766
2022-11-04 16:49:44,792 - mmseg - INFO - Iter [67550/80000]	lr: 2.249e-05, eta: 1:12:11, time: 0.399, data_time: 0.063, memory: 12988, decode.loss_ce: 0.4057, decode.acc_seg: 84.1885, aux.loss_ce: 0.2369, aux.acc_seg: 78.7379, loss: 0.6426
2022-11-04 16:50:01,597 - mmseg - INFO - Iter [67600/80000]	lr: 2.241e-05, eta: 1:11:54, time: 0.336, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3966, decode.acc_seg: 84.5074, aux.loss_ce: 0.2355, aux.acc_seg: 78.7366, loss: 0.6321
2022-11-04 16:50:18,552 - mmseg - INFO - Iter [67650/80000]	lr: 2.233e-05, eta: 1:11:36, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4111, decode.acc_seg: 83.6998, aux.loss_ce: 0.2407, aux.acc_seg: 77.6444, loss: 0.6518
2022-11-04 16:50:35,343 - mmseg - INFO - Iter [67700/80000]	lr: 2.225e-05, eta: 1:11:19, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4201, decode.acc_seg: 83.8031, aux.loss_ce: 0.2422, aux.acc_seg: 78.3340, loss: 0.6623
2022-11-04 16:50:52,670 - mmseg - INFO - Iter [67750/80000]	lr: 2.217e-05, eta: 1:11:01, time: 0.347, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4155, decode.acc_seg: 83.6292, aux.loss_ce: 0.2480, aux.acc_seg: 77.3240, loss: 0.6635
2022-11-04 16:51:09,680 - mmseg - INFO - Iter [67800/80000]	lr: 2.209e-05, eta: 1:10:44, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4120, decode.acc_seg: 83.8587, aux.loss_ce: 0.2446, aux.acc_seg: 77.7807, loss: 0.6566
2022-11-04 16:51:26,723 - mmseg - INFO - Iter [67850/80000]	lr: 2.201e-05, eta: 1:10:26, time: 0.341, data_time: 0.014, memory: 12988, decode.loss_ce: 0.4115, decode.acc_seg: 83.6574, aux.loss_ce: 0.2429, aux.acc_seg: 77.8988, loss: 0.6545
2022-11-04 16:51:43,661 - mmseg - INFO - Iter [67900/80000]	lr: 2.192e-05, eta: 1:10:09, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4150, decode.acc_seg: 83.7101, aux.loss_ce: 0.2404, aux.acc_seg: 77.9467, loss: 0.6553
2022-11-04 16:52:00,618 - mmseg - INFO - Iter [67950/80000]	lr: 2.184e-05, eta: 1:09:51, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4001, decode.acc_seg: 84.1152, aux.loss_ce: 0.2366, aux.acc_seg: 78.4085, loss: 0.6367
2022-11-04 16:52:17,739 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 16:52:17,739 - mmseg - INFO - Iter [68000/80000]	lr: 2.176e-05, eta: 1:09:34, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3888, decode.acc_seg: 84.7694, aux.loss_ce: 0.2336, aux.acc_seg: 78.5241, loss: 0.6224
2022-11-04 16:52:34,765 - mmseg - INFO - Iter [68050/80000]	lr: 2.168e-05, eta: 1:09:17, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4074, decode.acc_seg: 83.8500, aux.loss_ce: 0.2390, aux.acc_seg: 78.2594, loss: 0.6464
2022-11-04 16:52:51,819 - mmseg - INFO - Iter [68100/80000]	lr: 2.160e-05, eta: 1:08:59, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4187, decode.acc_seg: 83.4623, aux.loss_ce: 0.2481, aux.acc_seg: 77.4708, loss: 0.6669
2022-11-04 16:53:11,849 - mmseg - INFO - Iter [68150/80000]	lr: 2.152e-05, eta: 1:08:42, time: 0.401, data_time: 0.057, memory: 12988, decode.loss_ce: 0.4209, decode.acc_seg: 83.4760, aux.loss_ce: 0.2510, aux.acc_seg: 77.1635, loss: 0.6719
2022-11-04 16:53:29,028 - mmseg - INFO - Iter [68200/80000]	lr: 2.144e-05, eta: 1:08:25, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4080, decode.acc_seg: 83.9655, aux.loss_ce: 0.2400, aux.acc_seg: 77.9428, loss: 0.6480
2022-11-04 16:53:46,070 - mmseg - INFO - Iter [68250/80000]	lr: 2.135e-05, eta: 1:08:07, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4094, decode.acc_seg: 84.0704, aux.loss_ce: 0.2428, aux.acc_seg: 77.7487, loss: 0.6523
2022-11-04 16:54:03,338 - mmseg - INFO - Iter [68300/80000]	lr: 2.127e-05, eta: 1:07:50, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4217, decode.acc_seg: 83.5768, aux.loss_ce: 0.2462, aux.acc_seg: 77.6659, loss: 0.6678
2022-11-04 16:54:20,577 - mmseg - INFO - Iter [68350/80000]	lr: 2.119e-05, eta: 1:07:32, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4055, decode.acc_seg: 84.0600, aux.loss_ce: 0.2356, aux.acc_seg: 78.5395, loss: 0.6411
2022-11-04 16:54:37,602 - mmseg - INFO - Iter [68400/80000]	lr: 2.111e-05, eta: 1:07:15, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4192, decode.acc_seg: 83.3751, aux.loss_ce: 0.2482, aux.acc_seg: 77.1901, loss: 0.6674
2022-11-04 16:54:54,568 - mmseg - INFO - Iter [68450/80000]	lr: 2.103e-05, eta: 1:06:58, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4018, decode.acc_seg: 84.1236, aux.loss_ce: 0.2384, aux.acc_seg: 78.1875, loss: 0.6401
2022-11-04 16:55:11,626 - mmseg - INFO - Iter [68500/80000]	lr: 2.094e-05, eta: 1:06:40, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4098, decode.acc_seg: 83.9215, aux.loss_ce: 0.2413, aux.acc_seg: 77.8895, loss: 0.6511
2022-11-04 16:55:28,856 - mmseg - INFO - Iter [68550/80000]	lr: 2.086e-05, eta: 1:06:23, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4139, decode.acc_seg: 83.7935, aux.loss_ce: 0.2452, aux.acc_seg: 77.7939, loss: 0.6591
2022-11-04 16:55:45,946 - mmseg - INFO - Iter [68600/80000]	lr: 2.078e-05, eta: 1:06:05, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4144, decode.acc_seg: 83.8183, aux.loss_ce: 0.2461, aux.acc_seg: 77.8443, loss: 0.6606
2022-11-04 16:56:02,743 - mmseg - INFO - Iter [68650/80000]	lr: 2.070e-05, eta: 1:05:48, time: 0.336, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4148, decode.acc_seg: 83.6030, aux.loss_ce: 0.2448, aux.acc_seg: 77.7368, loss: 0.6596
2022-11-04 16:56:19,704 - mmseg - INFO - Iter [68700/80000]	lr: 2.062e-05, eta: 1:05:30, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4056, decode.acc_seg: 84.0418, aux.loss_ce: 0.2369, aux.acc_seg: 78.6033, loss: 0.6425
2022-11-04 16:56:36,893 - mmseg - INFO - Iter [68750/80000]	lr: 2.053e-05, eta: 1:05:13, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4181, decode.acc_seg: 83.5681, aux.loss_ce: 0.2520, aux.acc_seg: 76.7787, loss: 0.6701
2022-11-04 16:56:57,090 - mmseg - INFO - Iter [68800/80000]	lr: 2.045e-05, eta: 1:04:56, time: 0.404, data_time: 0.063, memory: 12988, decode.loss_ce: 0.4102, decode.acc_seg: 83.8870, aux.loss_ce: 0.2372, aux.acc_seg: 78.1068, loss: 0.6474
2022-11-04 16:57:14,081 - mmseg - INFO - Iter [68850/80000]	lr: 2.037e-05, eta: 1:04:38, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3985, decode.acc_seg: 84.0607, aux.loss_ce: 0.2386, aux.acc_seg: 77.9007, loss: 0.6371
2022-11-04 16:57:31,304 - mmseg - INFO - Iter [68900/80000]	lr: 2.029e-05, eta: 1:04:21, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3964, decode.acc_seg: 84.1771, aux.loss_ce: 0.2342, aux.acc_seg: 78.5122, loss: 0.6306
2022-11-04 16:57:48,455 - mmseg - INFO - Iter [68950/80000]	lr: 2.021e-05, eta: 1:04:04, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4064, decode.acc_seg: 84.0453, aux.loss_ce: 0.2426, aux.acc_seg: 77.7313, loss: 0.6490
2022-11-04 16:58:05,251 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 16:58:05,252 - mmseg - INFO - Iter [69000/80000]	lr: 2.012e-05, eta: 1:03:46, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4095, decode.acc_seg: 83.7057, aux.loss_ce: 0.2400, aux.acc_seg: 77.8712, loss: 0.6495
2022-11-04 16:58:22,251 - mmseg - INFO - Iter [69050/80000]	lr: 2.004e-05, eta: 1:03:29, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4037, decode.acc_seg: 84.2300, aux.loss_ce: 0.2386, aux.acc_seg: 78.1733, loss: 0.6423
2022-11-04 16:58:39,321 - mmseg - INFO - Iter [69100/80000]	lr: 1.996e-05, eta: 1:03:11, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4128, decode.acc_seg: 84.1169, aux.loss_ce: 0.2404, aux.acc_seg: 78.5227, loss: 0.6531
2022-11-04 16:58:56,420 - mmseg - INFO - Iter [69150/80000]	lr: 1.988e-05, eta: 1:02:54, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4173, decode.acc_seg: 83.4490, aux.loss_ce: 0.2510, aux.acc_seg: 77.2752, loss: 0.6683
2022-11-04 16:59:13,315 - mmseg - INFO - Iter [69200/80000]	lr: 1.979e-05, eta: 1:02:36, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4235, decode.acc_seg: 83.2753, aux.loss_ce: 0.2492, aux.acc_seg: 77.6259, loss: 0.6727
2022-11-04 16:59:30,335 - mmseg - INFO - Iter [69250/80000]	lr: 1.971e-05, eta: 1:02:19, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3964, decode.acc_seg: 84.2378, aux.loss_ce: 0.2336, aux.acc_seg: 78.4767, loss: 0.6301
2022-11-04 16:59:47,182 - mmseg - INFO - Iter [69300/80000]	lr: 1.963e-05, eta: 1:02:01, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3973, decode.acc_seg: 84.4530, aux.loss_ce: 0.2350, aux.acc_seg: 78.4606, loss: 0.6323
2022-11-04 17:00:04,490 - mmseg - INFO - Iter [69350/80000]	lr: 1.955e-05, eta: 1:01:44, time: 0.346, data_time: 0.011, memory: 12988, decode.loss_ce: 0.4108, decode.acc_seg: 83.9268, aux.loss_ce: 0.2471, aux.acc_seg: 77.5033, loss: 0.6579
2022-11-04 17:00:21,654 - mmseg - INFO - Iter [69400/80000]	lr: 1.946e-05, eta: 1:01:27, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4173, decode.acc_seg: 83.4566, aux.loss_ce: 0.2466, aux.acc_seg: 77.1124, loss: 0.6639
2022-11-04 17:00:41,566 - mmseg - INFO - Iter [69450/80000]	lr: 1.938e-05, eta: 1:01:09, time: 0.398, data_time: 0.060, memory: 12988, decode.loss_ce: 0.3986, decode.acc_seg: 84.0837, aux.loss_ce: 0.2405, aux.acc_seg: 78.1060, loss: 0.6392
2022-11-04 17:00:58,527 - mmseg - INFO - Iter [69500/80000]	lr: 1.930e-05, eta: 1:00:52, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4147, decode.acc_seg: 83.6722, aux.loss_ce: 0.2453, aux.acc_seg: 77.5759, loss: 0.6600
2022-11-04 17:01:15,622 - mmseg - INFO - Iter [69550/80000]	lr: 1.922e-05, eta: 1:00:35, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3994, decode.acc_seg: 84.3481, aux.loss_ce: 0.2345, aux.acc_seg: 78.7098, loss: 0.6339
2022-11-04 17:01:32,611 - mmseg - INFO - Iter [69600/80000]	lr: 1.913e-05, eta: 1:00:17, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4118, decode.acc_seg: 83.7487, aux.loss_ce: 0.2433, aux.acc_seg: 77.6080, loss: 0.6551
2022-11-04 17:01:49,591 - mmseg - INFO - Iter [69650/80000]	lr: 1.905e-05, eta: 1:00:00, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4085, decode.acc_seg: 83.8041, aux.loss_ce: 0.2446, aux.acc_seg: 77.5030, loss: 0.6532
2022-11-04 17:02:06,679 - mmseg - INFO - Iter [69700/80000]	lr: 1.897e-05, eta: 0:59:42, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4067, decode.acc_seg: 84.0152, aux.loss_ce: 0.2397, aux.acc_seg: 78.0021, loss: 0.6464
2022-11-04 17:02:23,863 - mmseg - INFO - Iter [69750/80000]	lr: 1.888e-05, eta: 0:59:25, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4005, decode.acc_seg: 84.1528, aux.loss_ce: 0.2342, aux.acc_seg: 78.3164, loss: 0.6346
2022-11-04 17:02:40,980 - mmseg - INFO - Iter [69800/80000]	lr: 1.880e-05, eta: 0:59:07, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4064, decode.acc_seg: 83.9765, aux.loss_ce: 0.2402, aux.acc_seg: 78.2354, loss: 0.6467
2022-11-04 17:02:57,957 - mmseg - INFO - Iter [69850/80000]	lr: 1.872e-05, eta: 0:58:50, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4023, decode.acc_seg: 84.2416, aux.loss_ce: 0.2391, aux.acc_seg: 78.0908, loss: 0.6414
2022-11-04 17:03:15,009 - mmseg - INFO - Iter [69900/80000]	lr: 1.863e-05, eta: 0:58:33, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3963, decode.acc_seg: 84.1473, aux.loss_ce: 0.2342, aux.acc_seg: 78.2812, loss: 0.6305
2022-11-04 17:03:32,185 - mmseg - INFO - Iter [69950/80000]	lr: 1.855e-05, eta: 0:58:15, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4154, decode.acc_seg: 83.5036, aux.loss_ce: 0.2480, aux.acc_seg: 77.1803, loss: 0.6635
2022-11-04 17:03:49,049 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 17:03:49,049 - mmseg - INFO - Iter [70000/80000]	lr: 1.847e-05, eta: 0:57:58, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4049, decode.acc_seg: 84.0195, aux.loss_ce: 0.2395, aux.acc_seg: 77.9217, loss: 0.6444
2022-11-04 17:04:09,006 - mmseg - INFO - Iter [70050/80000]	lr: 1.839e-05, eta: 0:57:41, time: 0.399, data_time: 0.059, memory: 12988, decode.loss_ce: 0.4055, decode.acc_seg: 84.1604, aux.loss_ce: 0.2401, aux.acc_seg: 78.2962, loss: 0.6456
2022-11-04 17:04:26,181 - mmseg - INFO - Iter [70100/80000]	lr: 1.830e-05, eta: 0:57:23, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4067, decode.acc_seg: 83.9578, aux.loss_ce: 0.2470, aux.acc_seg: 77.5054, loss: 0.6537
2022-11-04 17:04:43,336 - mmseg - INFO - Iter [70150/80000]	lr: 1.822e-05, eta: 0:57:06, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4069, decode.acc_seg: 83.7336, aux.loss_ce: 0.2410, aux.acc_seg: 77.6155, loss: 0.6479
2022-11-04 17:05:00,367 - mmseg - INFO - Iter [70200/80000]	lr: 1.814e-05, eta: 0:56:48, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4036, decode.acc_seg: 83.8908, aux.loss_ce: 0.2417, aux.acc_seg: 77.9719, loss: 0.6453
2022-11-04 17:05:17,321 - mmseg - INFO - Iter [70250/80000]	lr: 1.805e-05, eta: 0:56:31, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4107, decode.acc_seg: 83.9656, aux.loss_ce: 0.2398, aux.acc_seg: 78.4353, loss: 0.6505
2022-11-04 17:05:34,277 - mmseg - INFO - Iter [70300/80000]	lr: 1.797e-05, eta: 0:56:13, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4104, decode.acc_seg: 83.7754, aux.loss_ce: 0.2442, aux.acc_seg: 77.5453, loss: 0.6546
2022-11-04 17:05:51,464 - mmseg - INFO - Iter [70350/80000]	lr: 1.789e-05, eta: 0:55:56, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4052, decode.acc_seg: 84.1286, aux.loss_ce: 0.2397, aux.acc_seg: 78.0212, loss: 0.6449
2022-11-04 17:06:08,656 - mmseg - INFO - Iter [70400/80000]	lr: 1.780e-05, eta: 0:55:39, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4089, decode.acc_seg: 83.7307, aux.loss_ce: 0.2418, aux.acc_seg: 77.6741, loss: 0.6507
2022-11-04 17:06:25,653 - mmseg - INFO - Iter [70450/80000]	lr: 1.772e-05, eta: 0:55:21, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3953, decode.acc_seg: 84.5274, aux.loss_ce: 0.2379, aux.acc_seg: 78.1098, loss: 0.6332
2022-11-04 17:06:42,753 - mmseg - INFO - Iter [70500/80000]	lr: 1.764e-05, eta: 0:55:04, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4053, decode.acc_seg: 83.8954, aux.loss_ce: 0.2417, aux.acc_seg: 77.7721, loss: 0.6470
2022-11-04 17:06:59,839 - mmseg - INFO - Iter [70550/80000]	lr: 1.755e-05, eta: 0:54:46, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3980, decode.acc_seg: 84.4021, aux.loss_ce: 0.2337, aux.acc_seg: 78.6041, loss: 0.6316
2022-11-04 17:07:16,857 - mmseg - INFO - Iter [70600/80000]	lr: 1.747e-05, eta: 0:54:29, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4135, decode.acc_seg: 83.9007, aux.loss_ce: 0.2423, aux.acc_seg: 78.2422, loss: 0.6558
2022-11-04 17:07:34,020 - mmseg - INFO - Iter [70650/80000]	lr: 1.738e-05, eta: 0:54:11, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4208, decode.acc_seg: 83.5381, aux.loss_ce: 0.2452, aux.acc_seg: 77.8445, loss: 0.6660
2022-11-04 17:07:53,970 - mmseg - INFO - Iter [70700/80000]	lr: 1.730e-05, eta: 0:53:54, time: 0.399, data_time: 0.064, memory: 12988, decode.loss_ce: 0.4216, decode.acc_seg: 83.7007, aux.loss_ce: 0.2472, aux.acc_seg: 77.6824, loss: 0.6688
2022-11-04 17:08:11,023 - mmseg - INFO - Iter [70750/80000]	lr: 1.722e-05, eta: 0:53:37, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4052, decode.acc_seg: 83.8251, aux.loss_ce: 0.2397, aux.acc_seg: 77.8556, loss: 0.6450
2022-11-04 17:08:28,243 - mmseg - INFO - Iter [70800/80000]	lr: 1.713e-05, eta: 0:53:19, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4106, decode.acc_seg: 83.7588, aux.loss_ce: 0.2409, aux.acc_seg: 78.0157, loss: 0.6515
2022-11-04 17:08:45,592 - mmseg - INFO - Iter [70850/80000]	lr: 1.705e-05, eta: 0:53:02, time: 0.347, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4164, decode.acc_seg: 83.4800, aux.loss_ce: 0.2438, aux.acc_seg: 77.6102, loss: 0.6602
2022-11-04 17:09:02,613 - mmseg - INFO - Iter [70900/80000]	lr: 1.697e-05, eta: 0:52:45, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4014, decode.acc_seg: 84.0013, aux.loss_ce: 0.2375, aux.acc_seg: 78.0559, loss: 0.6389
2022-11-04 17:09:19,516 - mmseg - INFO - Iter [70950/80000]	lr: 1.688e-05, eta: 0:52:27, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4136, decode.acc_seg: 83.7175, aux.loss_ce: 0.2444, aux.acc_seg: 77.7005, loss: 0.6580
2022-11-04 17:09:36,374 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 17:09:36,375 - mmseg - INFO - Iter [71000/80000]	lr: 1.680e-05, eta: 0:52:10, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3978, decode.acc_seg: 84.3619, aux.loss_ce: 0.2366, aux.acc_seg: 78.3787, loss: 0.6344
2022-11-04 17:09:53,547 - mmseg - INFO - Iter [71050/80000]	lr: 1.671e-05, eta: 0:51:52, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4176, decode.acc_seg: 83.6566, aux.loss_ce: 0.2441, aux.acc_seg: 77.6156, loss: 0.6617
2022-11-04 17:10:10,428 - mmseg - INFO - Iter [71100/80000]	lr: 1.663e-05, eta: 0:51:35, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4055, decode.acc_seg: 83.6722, aux.loss_ce: 0.2405, aux.acc_seg: 77.5068, loss: 0.6460
2022-11-04 17:10:27,748 - mmseg - INFO - Iter [71150/80000]	lr: 1.655e-05, eta: 0:51:17, time: 0.346, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4031, decode.acc_seg: 84.1502, aux.loss_ce: 0.2382, aux.acc_seg: 78.5254, loss: 0.6413
2022-11-04 17:10:44,773 - mmseg - INFO - Iter [71200/80000]	lr: 1.646e-05, eta: 0:51:00, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4067, decode.acc_seg: 84.1143, aux.loss_ce: 0.2441, aux.acc_seg: 77.9119, loss: 0.6508
2022-11-04 17:11:01,684 - mmseg - INFO - Iter [71250/80000]	lr: 1.638e-05, eta: 0:50:43, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4017, decode.acc_seg: 84.0983, aux.loss_ce: 0.2395, aux.acc_seg: 77.9308, loss: 0.6412
2022-11-04 17:11:18,420 - mmseg - INFO - Iter [71300/80000]	lr: 1.629e-05, eta: 0:50:25, time: 0.335, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4069, decode.acc_seg: 83.6438, aux.loss_ce: 0.2411, aux.acc_seg: 77.8561, loss: 0.6479
2022-11-04 17:11:38,336 - mmseg - INFO - Iter [71350/80000]	lr: 1.621e-05, eta: 0:50:08, time: 0.398, data_time: 0.061, memory: 12988, decode.loss_ce: 0.4040, decode.acc_seg: 84.0625, aux.loss_ce: 0.2429, aux.acc_seg: 78.0589, loss: 0.6470
2022-11-04 17:11:55,353 - mmseg - INFO - Iter [71400/80000]	lr: 1.612e-05, eta: 0:49:51, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4047, decode.acc_seg: 84.0428, aux.loss_ce: 0.2387, aux.acc_seg: 78.0128, loss: 0.6434
2022-11-04 17:12:12,506 - mmseg - INFO - Iter [71450/80000]	lr: 1.604e-05, eta: 0:49:33, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4076, decode.acc_seg: 83.7078, aux.loss_ce: 0.2437, aux.acc_seg: 77.4902, loss: 0.6512
2022-11-04 17:12:29,544 - mmseg - INFO - Iter [71500/80000]	lr: 1.596e-05, eta: 0:49:16, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4176, decode.acc_seg: 83.7303, aux.loss_ce: 0.2454, aux.acc_seg: 77.7979, loss: 0.6630
2022-11-04 17:12:46,774 - mmseg - INFO - Iter [71550/80000]	lr: 1.587e-05, eta: 0:48:58, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3975, decode.acc_seg: 84.2602, aux.loss_ce: 0.2378, aux.acc_seg: 78.0210, loss: 0.6353
2022-11-04 17:13:04,090 - mmseg - INFO - Iter [71600/80000]	lr: 1.579e-05, eta: 0:48:41, time: 0.346, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4269, decode.acc_seg: 82.9531, aux.loss_ce: 0.2551, aux.acc_seg: 76.5455, loss: 0.6820
2022-11-04 17:13:21,305 - mmseg - INFO - Iter [71650/80000]	lr: 1.570e-05, eta: 0:48:24, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3991, decode.acc_seg: 84.3790, aux.loss_ce: 0.2286, aux.acc_seg: 79.0342, loss: 0.6277
2022-11-04 17:13:38,393 - mmseg - INFO - Iter [71700/80000]	lr: 1.562e-05, eta: 0:48:06, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3943, decode.acc_seg: 84.1203, aux.loss_ce: 0.2366, aux.acc_seg: 78.0373, loss: 0.6309
2022-11-04 17:13:55,266 - mmseg - INFO - Iter [71750/80000]	lr: 1.553e-05, eta: 0:47:49, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3927, decode.acc_seg: 84.3315, aux.loss_ce: 0.2339, aux.acc_seg: 78.4027, loss: 0.6266
2022-11-04 17:14:12,152 - mmseg - INFO - Iter [71800/80000]	lr: 1.545e-05, eta: 0:47:31, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4053, decode.acc_seg: 83.9828, aux.loss_ce: 0.2400, aux.acc_seg: 77.9554, loss: 0.6453
2022-11-04 17:14:29,301 - mmseg - INFO - Iter [71850/80000]	lr: 1.536e-05, eta: 0:47:14, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4012, decode.acc_seg: 84.2303, aux.loss_ce: 0.2424, aux.acc_seg: 78.0329, loss: 0.6436
2022-11-04 17:14:46,223 - mmseg - INFO - Iter [71900/80000]	lr: 1.528e-05, eta: 0:46:56, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3901, decode.acc_seg: 84.6196, aux.loss_ce: 0.2350, aux.acc_seg: 78.1609, loss: 0.6251
2022-11-04 17:15:06,334 - mmseg - INFO - Iter [71950/80000]	lr: 1.519e-05, eta: 0:46:39, time: 0.402, data_time: 0.062, memory: 12988, decode.loss_ce: 0.3923, decode.acc_seg: 84.4454, aux.loss_ce: 0.2318, aux.acc_seg: 78.6734, loss: 0.6241
2022-11-04 17:15:23,288 - mmseg - INFO - Saving checkpoint at 72000 iterations
2022-11-04 17:15:23,603 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 17:15:23,603 - mmseg - INFO - Iter [72000/80000]	lr: 1.511e-05, eta: 0:46:22, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3978, decode.acc_seg: 84.1748, aux.loss_ce: 0.2381, aux.acc_seg: 77.9136, loss: 0.6359
2022-11-04 17:15:33,010 - mmseg - INFO - per class results:
2022-11-04 17:15:33,017 - mmseg - INFO - 
+---------------------+-------+-------+
|        Class        |  IoU  |  Acc  |
+---------------------+-------+-------+
|         wall        | 66.98 | 83.59 |
|       building      | 75.58 | 89.96 |
|         sky         | 88.77 | 93.04 |
|        floor        | 71.87 | 85.44 |
|         tree        | 67.59 |  84.8 |
|       ceiling       | 76.12 | 86.05 |
|         road        | 75.32 | 86.59 |
|         bed         | 76.54 | 90.31 |
|      windowpane     | 51.06 | 69.84 |
|        grass        | 61.56 | 82.71 |
|       cabinet       | 48.21 | 61.14 |
|       sidewalk      | 51.23 |  67.8 |
|        person       | 64.73 | 81.59 |
|        earth        | 28.81 | 38.19 |
|         door        | 25.99 | 40.43 |
|        table        |  41.7 | 57.88 |
|       mountain      | 55.55 | 71.13 |
|        plant        | 43.35 | 53.34 |
|       curtain       | 55.32 | 72.98 |
|        chair        | 38.44 | 53.12 |
|         car         | 72.56 | 86.22 |
|        water        | 39.38 | 56.75 |
|       painting      | 55.74 | 73.98 |
|         sofa        | 47.59 | 71.59 |
|        shelf        | 33.69 | 50.23 |
|        house        | 31.51 | 40.28 |
|         sea         | 36.21 | 59.94 |
|        mirror       | 43.89 | 52.75 |
|         rug         | 48.22 | 56.74 |
|        field        | 29.37 | 42.11 |
|       armchair      | 24.66 |  35.4 |
|         seat        | 49.92 | 70.92 |
|        fence        | 26.34 | 46.63 |
|         desk        | 34.96 | 51.64 |
|         rock        | 46.13 | 68.77 |
|       wardrobe      | 38.54 |  57.0 |
|         lamp        | 36.96 | 47.94 |
|       bathtub       | 54.34 | 66.33 |
|       railing       | 25.16 | 40.06 |
|       cushion       | 33.08 | 43.26 |
|         base        | 15.67 | 21.54 |
|         box         | 16.17 | 22.64 |
|        column       | 23.55 | 28.26 |
|      signboard      | 21.93 | 30.86 |
|   chest of drawers  | 29.77 | 54.04 |
|       counter       | 26.19 | 35.12 |
|         sand        | 28.49 | 41.96 |
|         sink        | 46.66 | 60.28 |
|      skyscraper     | 63.05 | 82.79 |
|      fireplace      | 56.23 | 78.14 |
|     refrigerator    | 50.71 | 66.98 |
|      grandstand     |  37.9 | 56.41 |
|         path        | 12.04 | 16.06 |
|        stairs       | 25.27 | 32.22 |
|        runway       | 72.38 | 85.69 |
|         case        |  51.7 |  61.9 |
|      pool table     | 81.13 | 88.99 |
|        pillow       | 38.03 | 48.52 |
|     screen door     | 51.13 | 61.38 |
|       stairway      | 30.25 | 36.87 |
|        river        | 16.46 | 31.34 |
|        bridge       | 41.58 | 52.13 |
|       bookcase      | 27.92 | 45.32 |
|        blind        | 19.95 | 23.61 |
|     coffee table    | 42.54 | 67.94 |
|        toilet       | 65.51 | 77.92 |
|        flower       | 29.57 | 44.55 |
|         book        | 37.29 | 53.82 |
|         hill        |  5.01 |  8.42 |
|        bench        | 32.99 | 42.47 |
|      countertop     | 45.83 | 62.12 |
|        stove        | 50.06 | 62.81 |
|         palm        | 36.37 | 51.67 |
|    kitchen island   | 15.65 | 31.54 |
|       computer      |  45.6 | 60.27 |
|     swivel chair    | 32.62 | 42.63 |
|         boat        | 56.22 | 65.71 |
|         bar         | 24.92 | 28.53 |
|    arcade machine   |  24.2 | 26.11 |
|        hovel        | 25.19 |  35.2 |
|         bus         | 72.29 | 86.39 |
|        towel        | 40.88 | 51.57 |
|        light        | 13.89 | 14.91 |
|        truck        |  6.83 |  8.38 |
|        tower        | 35.86 | 52.17 |
|      chandelier     | 47.53 | 60.96 |
|        awning       | 15.59 | 20.43 |
|     streetlight     |  6.59 |  7.63 |
|        booth        | 32.65 |  36.3 |
| television receiver | 42.64 | 68.12 |
|       airplane      | 43.56 | 54.96 |
|      dirt track     | 15.17 | 28.79 |
|       apparel       | 18.79 | 26.04 |
|         pole        |  5.77 | 11.31 |
|         land        |  0.0  |  0.0  |
|      bannister      |  1.21 |  1.75 |
|      escalator      |  53.4 | 71.07 |
|       ottoman       | 20.94 |  26.3 |
|        bottle       | 18.64 | 22.98 |
|        buffet       | 37.48 | 50.35 |
|        poster       | 18.82 | 24.38 |
|        stage        |  9.57 | 15.48 |
|         van         | 30.84 |  35.7 |
|         ship        | 27.51 |  29.1 |
|       fountain      | 19.12 | 19.66 |
|    conveyer belt    |  63.0 | 77.18 |
|        canopy       | 16.47 |  19.2 |
|        washer       |  57.0 | 59.76 |
|      plaything      |  9.36 |  16.9 |
|    swimming pool    | 23.27 | 28.14 |
|        stool        | 16.73 | 18.88 |
|        barrel       | 40.74 | 58.69 |
|        basket       |  16.8 | 20.97 |
|      waterfall      | 33.65 | 39.26 |
|         tent        | 87.85 | 94.32 |
|         bag         |  3.75 |  4.67 |
|       minibike      | 36.16 |  41.7 |
|        cradle       | 63.35 | 91.59 |
|         oven        | 18.33 | 32.83 |
|         ball        | 35.51 | 59.09 |
|         food        |  38.9 | 48.43 |
|         step        |  4.33 |  5.22 |
|         tank        | 27.02 |  30.4 |
|      trade name     | 15.45 | 17.69 |
|      microwave      | 27.74 | 30.76 |
|         pot         | 24.12 | 29.45 |
|        animal       | 47.41 | 51.55 |
|       bicycle       | 31.66 | 54.58 |
|         lake        |  4.47 |  6.14 |
|      dishwasher     | 32.18 | 43.57 |
|        screen       | 51.58 | 76.93 |
|       blanket       |  1.18 |  1.28 |
|      sculpture      | 33.25 | 44.03 |
|         hood        | 32.71 | 38.69 |
|        sconce       | 22.76 | 26.47 |
|         vase        | 17.78 | 26.26 |
|    traffic light    | 11.22 | 15.38 |
|         tray        |  0.93 |  1.67 |
|        ashcan       |  17.7 | 27.14 |
|         fan         | 31.57 | 39.79 |
|         pier        | 52.67 | 72.59 |
|      crt screen     |  2.21 |  3.96 |
|        plate        | 32.86 | 39.77 |
|       monitor       | 29.38 | 31.91 |
|    bulletin board   | 31.85 | 39.65 |
|        shower       |  0.0  |  0.0  |
|       radiator      | 36.34 | 44.91 |
|        glass        |  1.66 |  1.71 |
|        clock        |  9.01 | 10.49 |
|         flag        | 21.12 | 24.02 |
+---------------------+-------+-------+
2022-11-04 17:15:33,018 - mmseg - INFO - Summary:
2022-11-04 17:15:33,018 - mmseg - INFO - 
+-------+-------+-------+
|  aAcc |  mIoU |  mAcc |
+-------+-------+-------+
| 75.53 | 35.17 | 45.77 |
+-------+-------+-------+
2022-11-04 17:15:33,019 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 17:15:33,019 - mmseg - INFO - Iter(val) [250]	aAcc: 0.7553, mIoU: 0.3517, mAcc: 0.4577, IoU.wall: 0.6698, IoU.building: 0.7558, IoU.sky: 0.8877, IoU.floor: 0.7187, IoU.tree: 0.6759, IoU.ceiling: 0.7612, IoU.road: 0.7532, IoU.bed : 0.7654, IoU.windowpane: 0.5106, IoU.grass: 0.6156, IoU.cabinet: 0.4821, IoU.sidewalk: 0.5123, IoU.person: 0.6473, IoU.earth: 0.2881, IoU.door: 0.2599, IoU.table: 0.4170, IoU.mountain: 0.5555, IoU.plant: 0.4335, IoU.curtain: 0.5532, IoU.chair: 0.3844, IoU.car: 0.7256, IoU.water: 0.3938, IoU.painting: 0.5574, IoU.sofa: 0.4759, IoU.shelf: 0.3369, IoU.house: 0.3151, IoU.sea: 0.3621, IoU.mirror: 0.4389, IoU.rug: 0.4822, IoU.field: 0.2937, IoU.armchair: 0.2466, IoU.seat: 0.4992, IoU.fence: 0.2634, IoU.desk: 0.3496, IoU.rock: 0.4613, IoU.wardrobe: 0.3854, IoU.lamp: 0.3696, IoU.bathtub: 0.5434, IoU.railing: 0.2516, IoU.cushion: 0.3308, IoU.base: 0.1567, IoU.box: 0.1617, IoU.column: 0.2355, IoU.signboard: 0.2193, IoU.chest of drawers: 0.2977, IoU.counter: 0.2619, IoU.sand: 0.2849, IoU.sink: 0.4666, IoU.skyscraper: 0.6305, IoU.fireplace: 0.5623, IoU.refrigerator: 0.5071, IoU.grandstand: 0.3790, IoU.path: 0.1204, IoU.stairs: 0.2527, IoU.runway: 0.7238, IoU.case: 0.5170, IoU.pool table: 0.8113, IoU.pillow: 0.3803, IoU.screen door: 0.5113, IoU.stairway: 0.3025, IoU.river: 0.1646, IoU.bridge: 0.4158, IoU.bookcase: 0.2792, IoU.blind: 0.1995, IoU.coffee table: 0.4254, IoU.toilet: 0.6551, IoU.flower: 0.2957, IoU.book: 0.3729, IoU.hill: 0.0501, IoU.bench: 0.3299, IoU.countertop: 0.4583, IoU.stove: 0.5006, IoU.palm: 0.3637, IoU.kitchen island: 0.1565, IoU.computer: 0.4560, IoU.swivel chair: 0.3262, IoU.boat: 0.5622, IoU.bar: 0.2492, IoU.arcade machine: 0.2420, IoU.hovel: 0.2519, IoU.bus: 0.7229, IoU.towel: 0.4088, IoU.light: 0.1389, IoU.truck: 0.0683, IoU.tower: 0.3586, IoU.chandelier: 0.4753, IoU.awning: 0.1559, IoU.streetlight: 0.0659, IoU.booth: 0.3265, IoU.television receiver: 0.4264, IoU.airplane: 0.4356, IoU.dirt track: 0.1517, IoU.apparel: 0.1879, IoU.pole: 0.0577, IoU.land: 0.0000, IoU.bannister: 0.0121, IoU.escalator: 0.5340, IoU.ottoman: 0.2094, IoU.bottle: 0.1864, IoU.buffet: 0.3748, IoU.poster: 0.1882, IoU.stage: 0.0957, IoU.van: 0.3084, IoU.ship: 0.2751, IoU.fountain: 0.1912, IoU.conveyer belt: 0.6300, IoU.canopy: 0.1647, IoU.washer: 0.5700, IoU.plaything: 0.0936, IoU.swimming pool: 0.2327, IoU.stool: 0.1673, IoU.barrel: 0.4074, IoU.basket: 0.1680, IoU.waterfall: 0.3365, IoU.tent: 0.8785, IoU.bag: 0.0375, IoU.minibike: 0.3616, IoU.cradle: 0.6335, IoU.oven: 0.1833, IoU.ball: 0.3551, IoU.food: 0.3890, IoU.step: 0.0433, IoU.tank: 0.2702, IoU.trade name: 0.1545, IoU.microwave: 0.2774, IoU.pot: 0.2412, IoU.animal: 0.4741, IoU.bicycle: 0.3166, IoU.lake: 0.0447, IoU.dishwasher: 0.3218, IoU.screen: 0.5158, IoU.blanket: 0.0118, IoU.sculpture: 0.3325, IoU.hood: 0.3271, IoU.sconce: 0.2276, IoU.vase: 0.1778, IoU.traffic light: 0.1122, IoU.tray: 0.0093, IoU.ashcan: 0.1770, IoU.fan: 0.3157, IoU.pier: 0.5267, IoU.crt screen: 0.0221, IoU.plate: 0.3286, IoU.monitor: 0.2938, IoU.bulletin board: 0.3185, IoU.shower: 0.0000, IoU.radiator: 0.3634, IoU.glass: 0.0166, IoU.clock: 0.0901, IoU.flag: 0.2112, Acc.wall: 0.8359, Acc.building: 0.8996, Acc.sky: 0.9304, Acc.floor: 0.8544, Acc.tree: 0.8480, Acc.ceiling: 0.8605, Acc.road: 0.8659, Acc.bed : 0.9031, Acc.windowpane: 0.6984, Acc.grass: 0.8271, Acc.cabinet: 0.6114, Acc.sidewalk: 0.6780, Acc.person: 0.8159, Acc.earth: 0.3819, Acc.door: 0.4043, Acc.table: 0.5788, Acc.mountain: 0.7113, Acc.plant: 0.5334, Acc.curtain: 0.7298, Acc.chair: 0.5312, Acc.car: 0.8622, Acc.water: 0.5675, Acc.painting: 0.7398, Acc.sofa: 0.7159, Acc.shelf: 0.5023, Acc.house: 0.4028, Acc.sea: 0.5994, Acc.mirror: 0.5275, Acc.rug: 0.5674, Acc.field: 0.4211, Acc.armchair: 0.3540, Acc.seat: 0.7092, Acc.fence: 0.4663, Acc.desk: 0.5164, Acc.rock: 0.6877, Acc.wardrobe: 0.5700, Acc.lamp: 0.4794, Acc.bathtub: 0.6633, Acc.railing: 0.4006, Acc.cushion: 0.4326, Acc.base: 0.2154, Acc.box: 0.2264, Acc.column: 0.2826, Acc.signboard: 0.3086, Acc.chest of drawers: 0.5404, Acc.counter: 0.3512, Acc.sand: 0.4196, Acc.sink: 0.6028, Acc.skyscraper: 0.8279, Acc.fireplace: 0.7814, Acc.refrigerator: 0.6698, Acc.grandstand: 0.5641, Acc.path: 0.1606, Acc.stairs: 0.3222, Acc.runway: 0.8569, Acc.case: 0.6190, Acc.pool table: 0.8899, Acc.pillow: 0.4852, Acc.screen door: 0.6138, Acc.stairway: 0.3687, Acc.river: 0.3134, Acc.bridge: 0.5213, Acc.bookcase: 0.4532, Acc.blind: 0.2361, Acc.coffee table: 0.6794, Acc.toilet: 0.7792, Acc.flower: 0.4455, Acc.book: 0.5382, Acc.hill: 0.0842, Acc.bench: 0.4247, Acc.countertop: 0.6212, Acc.stove: 0.6281, Acc.palm: 0.5167, Acc.kitchen island: 0.3154, Acc.computer: 0.6027, Acc.swivel chair: 0.4263, Acc.boat: 0.6571, Acc.bar: 0.2853, Acc.arcade machine: 0.2611, Acc.hovel: 0.3520, Acc.bus: 0.8639, Acc.towel: 0.5157, Acc.light: 0.1491, Acc.truck: 0.0838, Acc.tower: 0.5217, Acc.chandelier: 0.6096, Acc.awning: 0.2043, Acc.streetlight: 0.0763, Acc.booth: 0.3630, Acc.television receiver: 0.6812, Acc.airplane: 0.5496, Acc.dirt track: 0.2879, Acc.apparel: 0.2604, Acc.pole: 0.1131, Acc.land: 0.0000, Acc.bannister: 0.0175, Acc.escalator: 0.7107, Acc.ottoman: 0.2630, Acc.bottle: 0.2298, Acc.buffet: 0.5035, Acc.poster: 0.2438, Acc.stage: 0.1548, Acc.van: 0.3570, Acc.ship: 0.2910, Acc.fountain: 0.1966, Acc.conveyer belt: 0.7718, Acc.canopy: 0.1920, Acc.washer: 0.5976, Acc.plaything: 0.1690, Acc.swimming pool: 0.2814, Acc.stool: 0.1888, Acc.barrel: 0.5869, Acc.basket: 0.2097, Acc.waterfall: 0.3926, Acc.tent: 0.9432, Acc.bag: 0.0467, Acc.minibike: 0.4170, Acc.cradle: 0.9159, Acc.oven: 0.3283, Acc.ball: 0.5909, Acc.food: 0.4843, Acc.step: 0.0522, Acc.tank: 0.3040, Acc.trade name: 0.1769, Acc.microwave: 0.3076, Acc.pot: 0.2945, Acc.animal: 0.5155, Acc.bicycle: 0.5458, Acc.lake: 0.0614, Acc.dishwasher: 0.4357, Acc.screen: 0.7693, Acc.blanket: 0.0128, Acc.sculpture: 0.4403, Acc.hood: 0.3869, Acc.sconce: 0.2647, Acc.vase: 0.2626, Acc.traffic light: 0.1538, Acc.tray: 0.0167, Acc.ashcan: 0.2714, Acc.fan: 0.3979, Acc.pier: 0.7259, Acc.crt screen: 0.0396, Acc.plate: 0.3977, Acc.monitor: 0.3191, Acc.bulletin board: 0.3965, Acc.shower: 0.0000, Acc.radiator: 0.4491, Acc.glass: 0.0171, Acc.clock: 0.1049, Acc.flag: 0.2402
2022-11-04 17:15:50,072 - mmseg - INFO - Iter [72050/80000]	lr: 1.502e-05, eta: 0:46:05, time: 0.529, data_time: 0.198, memory: 12988, decode.loss_ce: 0.3986, decode.acc_seg: 84.4772, aux.loss_ce: 0.2341, aux.acc_seg: 78.5026, loss: 0.6327
2022-11-04 17:16:07,184 - mmseg - INFO - Iter [72100/80000]	lr: 1.494e-05, eta: 0:45:48, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3949, decode.acc_seg: 84.0585, aux.loss_ce: 0.2396, aux.acc_seg: 77.8222, loss: 0.6345
2022-11-04 17:16:24,167 - mmseg - INFO - Iter [72150/80000]	lr: 1.485e-05, eta: 0:45:31, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4022, decode.acc_seg: 84.2707, aux.loss_ce: 0.2409, aux.acc_seg: 78.3029, loss: 0.6432
2022-11-04 17:16:41,128 - mmseg - INFO - Iter [72200/80000]	lr: 1.477e-05, eta: 0:45:13, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3979, decode.acc_seg: 84.0847, aux.loss_ce: 0.2372, aux.acc_seg: 78.2922, loss: 0.6351
2022-11-04 17:16:58,283 - mmseg - INFO - Iter [72250/80000]	lr: 1.468e-05, eta: 0:44:56, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4041, decode.acc_seg: 84.1437, aux.loss_ce: 0.2355, aux.acc_seg: 78.1882, loss: 0.6396
2022-11-04 17:17:15,408 - mmseg - INFO - Iter [72300/80000]	lr: 1.460e-05, eta: 0:44:38, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4319, decode.acc_seg: 83.1791, aux.loss_ce: 0.2488, aux.acc_seg: 77.7421, loss: 0.6807
2022-11-04 17:17:32,272 - mmseg - INFO - Iter [72350/80000]	lr: 1.451e-05, eta: 0:44:21, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4019, decode.acc_seg: 84.1251, aux.loss_ce: 0.2418, aux.acc_seg: 78.2460, loss: 0.6438
2022-11-04 17:17:49,450 - mmseg - INFO - Iter [72400/80000]	lr: 1.443e-05, eta: 0:44:03, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3943, decode.acc_seg: 84.4747, aux.loss_ce: 0.2322, aux.acc_seg: 78.7137, loss: 0.6266
2022-11-04 17:18:06,696 - mmseg - INFO - Iter [72450/80000]	lr: 1.434e-05, eta: 0:43:46, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4183, decode.acc_seg: 83.4858, aux.loss_ce: 0.2459, aux.acc_seg: 77.3265, loss: 0.6642
2022-11-04 17:18:23,610 - mmseg - INFO - Iter [72500/80000]	lr: 1.426e-05, eta: 0:43:29, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4173, decode.acc_seg: 83.7775, aux.loss_ce: 0.2437, aux.acc_seg: 77.9157, loss: 0.6610
2022-11-04 17:18:40,669 - mmseg - INFO - Iter [72550/80000]	lr: 1.417e-05, eta: 0:43:11, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4142, decode.acc_seg: 83.7035, aux.loss_ce: 0.2425, aux.acc_seg: 77.8193, loss: 0.6567
2022-11-04 17:19:00,671 - mmseg - INFO - Iter [72600/80000]	lr: 1.409e-05, eta: 0:42:54, time: 0.400, data_time: 0.056, memory: 12988, decode.loss_ce: 0.4128, decode.acc_seg: 83.8311, aux.loss_ce: 0.2452, aux.acc_seg: 77.5057, loss: 0.6580
2022-11-04 17:19:17,936 - mmseg - INFO - Iter [72650/80000]	lr: 1.400e-05, eta: 0:42:37, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4076, decode.acc_seg: 84.0039, aux.loss_ce: 0.2413, aux.acc_seg: 78.1879, loss: 0.6489
2022-11-04 17:19:35,053 - mmseg - INFO - Iter [72700/80000]	lr: 1.391e-05, eta: 0:42:19, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3986, decode.acc_seg: 84.3921, aux.loss_ce: 0.2366, aux.acc_seg: 78.5421, loss: 0.6352
2022-11-04 17:19:52,382 - mmseg - INFO - Iter [72750/80000]	lr: 1.383e-05, eta: 0:42:02, time: 0.347, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4034, decode.acc_seg: 84.1411, aux.loss_ce: 0.2395, aux.acc_seg: 78.0917, loss: 0.6429
2022-11-04 17:20:09,575 - mmseg - INFO - Iter [72800/80000]	lr: 1.374e-05, eta: 0:41:44, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4025, decode.acc_seg: 83.9448, aux.loss_ce: 0.2368, aux.acc_seg: 78.3561, loss: 0.6393
2022-11-04 17:20:26,646 - mmseg - INFO - Iter [72850/80000]	lr: 1.366e-05, eta: 0:41:27, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4004, decode.acc_seg: 84.0866, aux.loss_ce: 0.2389, aux.acc_seg: 78.0922, loss: 0.6393
2022-11-04 17:20:43,721 - mmseg - INFO - Iter [72900/80000]	lr: 1.357e-05, eta: 0:41:10, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3831, decode.acc_seg: 84.6982, aux.loss_ce: 0.2263, aux.acc_seg: 79.0204, loss: 0.6094
2022-11-04 17:21:00,806 - mmseg - INFO - Iter [72950/80000]	lr: 1.348e-05, eta: 0:40:52, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4021, decode.acc_seg: 84.2984, aux.loss_ce: 0.2415, aux.acc_seg: 78.1509, loss: 0.6436
2022-11-04 17:21:17,997 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 17:21:17,998 - mmseg - INFO - Iter [73000/80000]	lr: 1.340e-05, eta: 0:40:35, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4047, decode.acc_seg: 83.8968, aux.loss_ce: 0.2403, aux.acc_seg: 77.8416, loss: 0.6450
2022-11-04 17:21:34,702 - mmseg - INFO - Iter [73050/80000]	lr: 1.331e-05, eta: 0:40:17, time: 0.334, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4122, decode.acc_seg: 83.7815, aux.loss_ce: 0.2466, aux.acc_seg: 77.7178, loss: 0.6587
2022-11-04 17:21:51,933 - mmseg - INFO - Iter [73100/80000]	lr: 1.323e-05, eta: 0:40:00, time: 0.345, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4004, decode.acc_seg: 84.1071, aux.loss_ce: 0.2301, aux.acc_seg: 78.8716, loss: 0.6305
2022-11-04 17:22:09,119 - mmseg - INFO - Iter [73150/80000]	lr: 1.314e-05, eta: 0:39:42, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4013, decode.acc_seg: 83.8827, aux.loss_ce: 0.2360, aux.acc_seg: 77.9543, loss: 0.6373
2022-11-04 17:22:29,051 - mmseg - INFO - Iter [73200/80000]	lr: 1.305e-05, eta: 0:39:25, time: 0.399, data_time: 0.065, memory: 12988, decode.loss_ce: 0.4056, decode.acc_seg: 84.1517, aux.loss_ce: 0.2409, aux.acc_seg: 78.1192, loss: 0.6465
2022-11-04 17:22:46,211 - mmseg - INFO - Iter [73250/80000]	lr: 1.297e-05, eta: 0:39:08, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4049, decode.acc_seg: 84.1346, aux.loss_ce: 0.2396, aux.acc_seg: 78.3625, loss: 0.6444
2022-11-04 17:23:03,307 - mmseg - INFO - Iter [73300/80000]	lr: 1.288e-05, eta: 0:38:50, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4031, decode.acc_seg: 84.1446, aux.loss_ce: 0.2427, aux.acc_seg: 77.9833, loss: 0.6458
2022-11-04 17:23:20,230 - mmseg - INFO - Iter [73350/80000]	lr: 1.279e-05, eta: 0:38:33, time: 0.338, data_time: 0.008, memory: 12988, decode.loss_ce: 0.3954, decode.acc_seg: 84.4394, aux.loss_ce: 0.2353, aux.acc_seg: 78.5593, loss: 0.6307
2022-11-04 17:23:37,407 - mmseg - INFO - Iter [73400/80000]	lr: 1.271e-05, eta: 0:38:16, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3973, decode.acc_seg: 84.4036, aux.loss_ce: 0.2383, aux.acc_seg: 78.0553, loss: 0.6356
2022-11-04 17:23:54,433 - mmseg - INFO - Iter [73450/80000]	lr: 1.262e-05, eta: 0:37:58, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4047, decode.acc_seg: 84.2080, aux.loss_ce: 0.2405, aux.acc_seg: 78.3289, loss: 0.6452
2022-11-04 17:24:11,146 - mmseg - INFO - Iter [73500/80000]	lr: 1.253e-05, eta: 0:37:41, time: 0.334, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4116, decode.acc_seg: 83.9156, aux.loss_ce: 0.2453, aux.acc_seg: 78.1229, loss: 0.6569
2022-11-04 17:24:28,112 - mmseg - INFO - Iter [73550/80000]	lr: 1.245e-05, eta: 0:37:23, time: 0.339, data_time: 0.011, memory: 12988, decode.loss_ce: 0.3919, decode.acc_seg: 84.5108, aux.loss_ce: 0.2327, aux.acc_seg: 78.7009, loss: 0.6246
2022-11-04 17:24:45,239 - mmseg - INFO - Iter [73600/80000]	lr: 1.236e-05, eta: 0:37:06, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4007, decode.acc_seg: 84.1736, aux.loss_ce: 0.2330, aux.acc_seg: 78.8642, loss: 0.6337
2022-11-04 17:25:02,270 - mmseg - INFO - Iter [73650/80000]	lr: 1.227e-05, eta: 0:36:48, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4014, decode.acc_seg: 84.1632, aux.loss_ce: 0.2396, aux.acc_seg: 78.0565, loss: 0.6410
2022-11-04 17:25:19,167 - mmseg - INFO - Iter [73700/80000]	lr: 1.219e-05, eta: 0:36:31, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3982, decode.acc_seg: 84.0380, aux.loss_ce: 0.2333, aux.acc_seg: 78.1252, loss: 0.6315
2022-11-04 17:25:36,346 - mmseg - INFO - Iter [73750/80000]	lr: 1.210e-05, eta: 0:36:14, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4139, decode.acc_seg: 83.6900, aux.loss_ce: 0.2490, aux.acc_seg: 77.5808, loss: 0.6629
2022-11-04 17:25:53,444 - mmseg - INFO - Iter [73800/80000]	lr: 1.201e-05, eta: 0:35:56, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4180, decode.acc_seg: 83.5814, aux.loss_ce: 0.2412, aux.acc_seg: 78.0282, loss: 0.6592
2022-11-04 17:26:13,887 - mmseg - INFO - Iter [73850/80000]	lr: 1.192e-05, eta: 0:35:39, time: 0.409, data_time: 0.063, memory: 12988, decode.loss_ce: 0.4012, decode.acc_seg: 83.8204, aux.loss_ce: 0.2438, aux.acc_seg: 77.2428, loss: 0.6451
2022-11-04 17:26:31,027 - mmseg - INFO - Iter [73900/80000]	lr: 1.184e-05, eta: 0:35:22, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4184, decode.acc_seg: 83.5406, aux.loss_ce: 0.2472, aux.acc_seg: 77.5665, loss: 0.6655
2022-11-04 17:26:48,116 - mmseg - INFO - Iter [73950/80000]	lr: 1.175e-05, eta: 0:35:04, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3879, decode.acc_seg: 84.7647, aux.loss_ce: 0.2361, aux.acc_seg: 78.5852, loss: 0.6240
2022-11-04 17:27:05,231 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 17:27:05,231 - mmseg - INFO - Iter [74000/80000]	lr: 1.166e-05, eta: 0:34:47, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3982, decode.acc_seg: 84.1980, aux.loss_ce: 0.2368, aux.acc_seg: 78.2721, loss: 0.6350
2022-11-04 17:27:22,106 - mmseg - INFO - Iter [74050/80000]	lr: 1.158e-05, eta: 0:34:29, time: 0.337, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3908, decode.acc_seg: 84.3435, aux.loss_ce: 0.2314, aux.acc_seg: 78.5499, loss: 0.6222
2022-11-04 17:27:39,191 - mmseg - INFO - Iter [74100/80000]	lr: 1.149e-05, eta: 0:34:12, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4024, decode.acc_seg: 84.2170, aux.loss_ce: 0.2426, aux.acc_seg: 78.1586, loss: 0.6450
2022-11-04 17:27:56,114 - mmseg - INFO - Iter [74150/80000]	lr: 1.140e-05, eta: 0:33:54, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4004, decode.acc_seg: 84.2571, aux.loss_ce: 0.2401, aux.acc_seg: 78.2424, loss: 0.6404
2022-11-04 17:28:13,131 - mmseg - INFO - Iter [74200/80000]	lr: 1.131e-05, eta: 0:33:37, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4069, decode.acc_seg: 84.0305, aux.loss_ce: 0.2478, aux.acc_seg: 77.4267, loss: 0.6547
2022-11-04 17:28:30,153 - mmseg - INFO - Iter [74250/80000]	lr: 1.122e-05, eta: 0:33:20, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4119, decode.acc_seg: 83.6768, aux.loss_ce: 0.2431, aux.acc_seg: 77.8795, loss: 0.6550
2022-11-04 17:28:47,015 - mmseg - INFO - Iter [74300/80000]	lr: 1.114e-05, eta: 0:33:02, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3958, decode.acc_seg: 84.3234, aux.loss_ce: 0.2310, aux.acc_seg: 78.7760, loss: 0.6268
2022-11-04 17:29:04,191 - mmseg - INFO - Iter [74350/80000]	lr: 1.105e-05, eta: 0:32:45, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3999, decode.acc_seg: 84.3660, aux.loss_ce: 0.2379, aux.acc_seg: 78.2699, loss: 0.6379
2022-11-04 17:29:21,186 - mmseg - INFO - Iter [74400/80000]	lr: 1.096e-05, eta: 0:32:27, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3980, decode.acc_seg: 83.9728, aux.loss_ce: 0.2338, aux.acc_seg: 78.4950, loss: 0.6318
2022-11-04 17:29:38,323 - mmseg - INFO - Iter [74450/80000]	lr: 1.087e-05, eta: 0:32:10, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4009, decode.acc_seg: 84.0090, aux.loss_ce: 0.2342, aux.acc_seg: 78.0757, loss: 0.6351
2022-11-04 17:29:58,337 - mmseg - INFO - Iter [74500/80000]	lr: 1.078e-05, eta: 0:31:53, time: 0.400, data_time: 0.066, memory: 12988, decode.loss_ce: 0.4074, decode.acc_seg: 84.0639, aux.loss_ce: 0.2484, aux.acc_seg: 77.2324, loss: 0.6558
2022-11-04 17:30:15,523 - mmseg - INFO - Iter [74550/80000]	lr: 1.070e-05, eta: 0:31:35, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4155, decode.acc_seg: 83.6060, aux.loss_ce: 0.2442, aux.acc_seg: 77.8596, loss: 0.6598
2022-11-04 17:30:32,507 - mmseg - INFO - Iter [74600/80000]	lr: 1.061e-05, eta: 0:31:18, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3904, decode.acc_seg: 84.5402, aux.loss_ce: 0.2294, aux.acc_seg: 79.0377, loss: 0.6198
2022-11-04 17:30:49,586 - mmseg - INFO - Iter [74650/80000]	lr: 1.052e-05, eta: 0:31:01, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4077, decode.acc_seg: 84.0026, aux.loss_ce: 0.2471, aux.acc_seg: 77.4592, loss: 0.6548
2022-11-04 17:31:06,684 - mmseg - INFO - Iter [74700/80000]	lr: 1.043e-05, eta: 0:30:43, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3960, decode.acc_seg: 84.3734, aux.loss_ce: 0.2397, aux.acc_seg: 78.1473, loss: 0.6357
2022-11-04 17:31:23,576 - mmseg - INFO - Iter [74750/80000]	lr: 1.034e-05, eta: 0:30:26, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3923, decode.acc_seg: 84.5008, aux.loss_ce: 0.2350, aux.acc_seg: 78.6543, loss: 0.6272
2022-11-04 17:31:40,478 - mmseg - INFO - Iter [74800/80000]	lr: 1.025e-05, eta: 0:30:08, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3864, decode.acc_seg: 84.3987, aux.loss_ce: 0.2342, aux.acc_seg: 78.1513, loss: 0.6206
2022-11-04 17:31:57,702 - mmseg - INFO - Iter [74850/80000]	lr: 1.016e-05, eta: 0:29:51, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3931, decode.acc_seg: 84.6432, aux.loss_ce: 0.2331, aux.acc_seg: 78.6826, loss: 0.6262
2022-11-04 17:32:14,702 - mmseg - INFO - Iter [74900/80000]	lr: 1.008e-05, eta: 0:29:33, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4158, decode.acc_seg: 83.5830, aux.loss_ce: 0.2514, aux.acc_seg: 77.0370, loss: 0.6672
2022-11-04 17:32:32,118 - mmseg - INFO - Iter [74950/80000]	lr: 9.987e-06, eta: 0:29:16, time: 0.348, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3895, decode.acc_seg: 84.2527, aux.loss_ce: 0.2312, aux.acc_seg: 78.5889, loss: 0.6207
2022-11-04 17:32:49,003 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 17:32:49,004 - mmseg - INFO - Iter [75000/80000]	lr: 9.898e-06, eta: 0:28:59, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3877, decode.acc_seg: 84.5741, aux.loss_ce: 0.2303, aux.acc_seg: 78.7754, loss: 0.6180
2022-11-04 17:33:05,788 - mmseg - INFO - Iter [75050/80000]	lr: 9.809e-06, eta: 0:28:41, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3899, decode.acc_seg: 84.4940, aux.loss_ce: 0.2287, aux.acc_seg: 79.1609, loss: 0.6186
2022-11-04 17:33:25,701 - mmseg - INFO - Iter [75100/80000]	lr: 9.720e-06, eta: 0:28:24, time: 0.398, data_time: 0.061, memory: 12988, decode.loss_ce: 0.4145, decode.acc_seg: 83.6331, aux.loss_ce: 0.2421, aux.acc_seg: 77.9649, loss: 0.6566
2022-11-04 17:33:42,614 - mmseg - INFO - Iter [75150/80000]	lr: 9.630e-06, eta: 0:28:07, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3919, decode.acc_seg: 84.4456, aux.loss_ce: 0.2415, aux.acc_seg: 77.8843, loss: 0.6334
2022-11-04 17:33:59,468 - mmseg - INFO - Iter [75200/80000]	lr: 9.541e-06, eta: 0:27:49, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3944, decode.acc_seg: 84.4427, aux.loss_ce: 0.2337, aux.acc_seg: 78.5072, loss: 0.6281
2022-11-04 17:34:16,524 - mmseg - INFO - Iter [75250/80000]	lr: 9.452e-06, eta: 0:27:32, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4065, decode.acc_seg: 84.0842, aux.loss_ce: 0.2435, aux.acc_seg: 77.9207, loss: 0.6500
2022-11-04 17:34:33,732 - mmseg - INFO - Iter [75300/80000]	lr: 9.362e-06, eta: 0:27:14, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3839, decode.acc_seg: 84.9700, aux.loss_ce: 0.2303, aux.acc_seg: 78.8806, loss: 0.6142
2022-11-04 17:34:50,927 - mmseg - INFO - Iter [75350/80000]	lr: 9.272e-06, eta: 0:26:57, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3849, decode.acc_seg: 84.6776, aux.loss_ce: 0.2371, aux.acc_seg: 78.3424, loss: 0.6220
2022-11-04 17:35:07,967 - mmseg - INFO - Iter [75400/80000]	lr: 9.183e-06, eta: 0:26:39, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4043, decode.acc_seg: 84.0366, aux.loss_ce: 0.2382, aux.acc_seg: 78.2075, loss: 0.6425
2022-11-04 17:35:25,045 - mmseg - INFO - Iter [75450/80000]	lr: 9.093e-06, eta: 0:26:22, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4029, decode.acc_seg: 84.2209, aux.loss_ce: 0.2435, aux.acc_seg: 77.7986, loss: 0.6464
2022-11-04 17:35:42,328 - mmseg - INFO - Iter [75500/80000]	lr: 9.003e-06, eta: 0:26:05, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3996, decode.acc_seg: 83.9400, aux.loss_ce: 0.2364, aux.acc_seg: 77.9930, loss: 0.6359
2022-11-04 17:35:59,301 - mmseg - INFO - Iter [75550/80000]	lr: 8.913e-06, eta: 0:25:47, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4084, decode.acc_seg: 84.0702, aux.loss_ce: 0.2442, aux.acc_seg: 77.7749, loss: 0.6526
2022-11-04 17:36:16,317 - mmseg - INFO - Iter [75600/80000]	lr: 8.823e-06, eta: 0:25:30, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4140, decode.acc_seg: 83.9994, aux.loss_ce: 0.2442, aux.acc_seg: 77.8242, loss: 0.6581
2022-11-04 17:36:33,654 - mmseg - INFO - Iter [75650/80000]	lr: 8.732e-06, eta: 0:25:12, time: 0.347, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4089, decode.acc_seg: 83.8398, aux.loss_ce: 0.2433, aux.acc_seg: 78.0333, loss: 0.6522
2022-11-04 17:36:50,750 - mmseg - INFO - Iter [75700/80000]	lr: 8.642e-06, eta: 0:24:55, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3982, decode.acc_seg: 84.2160, aux.loss_ce: 0.2377, aux.acc_seg: 77.9951, loss: 0.6359
2022-11-04 17:37:11,143 - mmseg - INFO - Iter [75750/80000]	lr: 8.551e-06, eta: 0:24:38, time: 0.408, data_time: 0.065, memory: 12988, decode.loss_ce: 0.4052, decode.acc_seg: 83.9580, aux.loss_ce: 0.2369, aux.acc_seg: 78.1228, loss: 0.6422
2022-11-04 17:37:28,047 - mmseg - INFO - Iter [75800/80000]	lr: 8.461e-06, eta: 0:24:20, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3966, decode.acc_seg: 84.2452, aux.loss_ce: 0.2391, aux.acc_seg: 78.2289, loss: 0.6357
2022-11-04 17:37:45,138 - mmseg - INFO - Iter [75850/80000]	lr: 8.370e-06, eta: 0:24:03, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4050, decode.acc_seg: 83.9905, aux.loss_ce: 0.2414, aux.acc_seg: 78.1539, loss: 0.6464
2022-11-04 17:38:02,400 - mmseg - INFO - Iter [75900/80000]	lr: 8.279e-06, eta: 0:23:46, time: 0.345, data_time: 0.011, memory: 12988, decode.loss_ce: 0.4028, decode.acc_seg: 84.2870, aux.loss_ce: 0.2397, aux.acc_seg: 78.3159, loss: 0.6425
2022-11-04 17:38:19,467 - mmseg - INFO - Iter [75950/80000]	lr: 8.189e-06, eta: 0:23:28, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4027, decode.acc_seg: 84.2189, aux.loss_ce: 0.2373, aux.acc_seg: 78.4201, loss: 0.6400
2022-11-04 17:38:36,370 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 17:38:36,370 - mmseg - INFO - Iter [76000/80000]	lr: 8.098e-06, eta: 0:23:11, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3982, decode.acc_seg: 84.4128, aux.loss_ce: 0.2383, aux.acc_seg: 78.5701, loss: 0.6364
2022-11-04 17:38:53,334 - mmseg - INFO - Iter [76050/80000]	lr: 8.006e-06, eta: 0:22:53, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3953, decode.acc_seg: 84.3300, aux.loss_ce: 0.2356, aux.acc_seg: 78.3578, loss: 0.6309
2022-11-04 17:39:10,282 - mmseg - INFO - Iter [76100/80000]	lr: 7.915e-06, eta: 0:22:36, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3985, decode.acc_seg: 84.3027, aux.loss_ce: 0.2371, aux.acc_seg: 78.1524, loss: 0.6356
2022-11-04 17:39:27,302 - mmseg - INFO - Iter [76150/80000]	lr: 7.824e-06, eta: 0:22:19, time: 0.340, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4081, decode.acc_seg: 83.9100, aux.loss_ce: 0.2398, aux.acc_seg: 77.9588, loss: 0.6479
2022-11-04 17:39:44,507 - mmseg - INFO - Iter [76200/80000]	lr: 7.732e-06, eta: 0:22:01, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3846, decode.acc_seg: 84.6239, aux.loss_ce: 0.2291, aux.acc_seg: 78.9015, loss: 0.6138
2022-11-04 17:40:01,598 - mmseg - INFO - Iter [76250/80000]	lr: 7.641e-06, eta: 0:21:44, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4056, decode.acc_seg: 84.2282, aux.loss_ce: 0.2407, aux.acc_seg: 78.3156, loss: 0.6463
2022-11-04 17:40:18,893 - mmseg - INFO - Iter [76300/80000]	lr: 7.549e-06, eta: 0:21:26, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3998, decode.acc_seg: 84.1215, aux.loss_ce: 0.2360, aux.acc_seg: 78.3899, loss: 0.6358
2022-11-04 17:40:35,376 - mmseg - INFO - Iter [76350/80000]	lr: 7.457e-06, eta: 0:21:09, time: 0.330, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4137, decode.acc_seg: 83.9359, aux.loss_ce: 0.2472, aux.acc_seg: 77.6201, loss: 0.6609
2022-11-04 17:40:55,594 - mmseg - INFO - Iter [76400/80000]	lr: 7.365e-06, eta: 0:20:52, time: 0.404, data_time: 0.064, memory: 12988, decode.loss_ce: 0.4028, decode.acc_seg: 84.2342, aux.loss_ce: 0.2400, aux.acc_seg: 78.2342, loss: 0.6428
2022-11-04 17:41:12,692 - mmseg - INFO - Iter [76450/80000]	lr: 7.273e-06, eta: 0:20:34, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4002, decode.acc_seg: 84.5234, aux.loss_ce: 0.2381, aux.acc_seg: 78.8368, loss: 0.6383
2022-11-04 17:41:29,582 - mmseg - INFO - Iter [76500/80000]	lr: 7.181e-06, eta: 0:20:17, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4080, decode.acc_seg: 83.9070, aux.loss_ce: 0.2427, aux.acc_seg: 77.8095, loss: 0.6508
2022-11-04 17:41:46,742 - mmseg - INFO - Iter [76550/80000]	lr: 7.088e-06, eta: 0:19:59, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3955, decode.acc_seg: 84.3815, aux.loss_ce: 0.2335, aux.acc_seg: 78.5622, loss: 0.6290
2022-11-04 17:42:03,810 - mmseg - INFO - Iter [76600/80000]	lr: 6.996e-06, eta: 0:19:42, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4044, decode.acc_seg: 84.1383, aux.loss_ce: 0.2408, aux.acc_seg: 78.4789, loss: 0.6452
2022-11-04 17:42:20,974 - mmseg - INFO - Iter [76650/80000]	lr: 6.903e-06, eta: 0:19:25, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3951, decode.acc_seg: 84.3591, aux.loss_ce: 0.2361, aux.acc_seg: 78.3224, loss: 0.6313
2022-11-04 17:42:37,843 - mmseg - INFO - Iter [76700/80000]	lr: 6.811e-06, eta: 0:19:07, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3906, decode.acc_seg: 84.4850, aux.loss_ce: 0.2339, aux.acc_seg: 78.4577, loss: 0.6245
2022-11-04 17:42:55,383 - mmseg - INFO - Iter [76750/80000]	lr: 6.718e-06, eta: 0:18:50, time: 0.351, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3960, decode.acc_seg: 84.2290, aux.loss_ce: 0.2371, aux.acc_seg: 77.8306, loss: 0.6331
2022-11-04 17:43:12,594 - mmseg - INFO - Iter [76800/80000]	lr: 6.625e-06, eta: 0:18:32, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4029, decode.acc_seg: 83.7470, aux.loss_ce: 0.2404, aux.acc_seg: 77.5057, loss: 0.6433
2022-11-04 17:43:29,551 - mmseg - INFO - Iter [76850/80000]	lr: 6.531e-06, eta: 0:18:15, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3994, decode.acc_seg: 84.0738, aux.loss_ce: 0.2395, aux.acc_seg: 78.3156, loss: 0.6389
2022-11-04 17:43:46,474 - mmseg - INFO - Iter [76900/80000]	lr: 6.438e-06, eta: 0:17:58, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3872, decode.acc_seg: 84.6092, aux.loss_ce: 0.2305, aux.acc_seg: 78.5555, loss: 0.6177
2022-11-04 17:44:03,680 - mmseg - INFO - Iter [76950/80000]	lr: 6.345e-06, eta: 0:17:40, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3953, decode.acc_seg: 84.2251, aux.loss_ce: 0.2329, aux.acc_seg: 78.7219, loss: 0.6282
2022-11-04 17:44:23,839 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 17:44:23,839 - mmseg - INFO - Iter [77000/80000]	lr: 6.251e-06, eta: 0:17:23, time: 0.403, data_time: 0.059, memory: 12988, decode.loss_ce: 0.4066, decode.acc_seg: 83.9714, aux.loss_ce: 0.2409, aux.acc_seg: 77.8855, loss: 0.6475
2022-11-04 17:44:41,374 - mmseg - INFO - Iter [77050/80000]	lr: 6.157e-06, eta: 0:17:06, time: 0.351, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3969, decode.acc_seg: 84.5279, aux.loss_ce: 0.2378, aux.acc_seg: 78.4235, loss: 0.6347
2022-11-04 17:44:58,150 - mmseg - INFO - Iter [77100/80000]	lr: 6.063e-06, eta: 0:16:48, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4058, decode.acc_seg: 83.8698, aux.loss_ce: 0.2420, aux.acc_seg: 77.8025, loss: 0.6478
2022-11-04 17:45:15,388 - mmseg - INFO - Iter [77150/80000]	lr: 5.969e-06, eta: 0:16:31, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3976, decode.acc_seg: 84.2112, aux.loss_ce: 0.2364, aux.acc_seg: 78.2557, loss: 0.6340
2022-11-04 17:45:32,522 - mmseg - INFO - Iter [77200/80000]	lr: 5.875e-06, eta: 0:16:13, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4099, decode.acc_seg: 83.8396, aux.loss_ce: 0.2478, aux.acc_seg: 77.6307, loss: 0.6577
2022-11-04 17:45:49,570 - mmseg - INFO - Iter [77250/80000]	lr: 5.780e-06, eta: 0:15:56, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3884, decode.acc_seg: 84.6583, aux.loss_ce: 0.2354, aux.acc_seg: 78.4469, loss: 0.6238
2022-11-04 17:46:06,660 - mmseg - INFO - Iter [77300/80000]	lr: 5.686e-06, eta: 0:15:39, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3906, decode.acc_seg: 84.7090, aux.loss_ce: 0.2315, aux.acc_seg: 79.0847, loss: 0.6221
2022-11-04 17:46:23,964 - mmseg - INFO - Iter [77350/80000]	lr: 5.591e-06, eta: 0:15:21, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3890, decode.acc_seg: 84.6606, aux.loss_ce: 0.2342, aux.acc_seg: 78.3706, loss: 0.6232
2022-11-04 17:46:40,898 - mmseg - INFO - Iter [77400/80000]	lr: 5.496e-06, eta: 0:15:04, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4019, decode.acc_seg: 84.2194, aux.loss_ce: 0.2354, aux.acc_seg: 78.6360, loss: 0.6372
2022-11-04 17:46:58,165 - mmseg - INFO - Iter [77450/80000]	lr: 5.401e-06, eta: 0:14:46, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4062, decode.acc_seg: 84.0756, aux.loss_ce: 0.2358, aux.acc_seg: 78.6107, loss: 0.6420
2022-11-04 17:47:15,371 - mmseg - INFO - Iter [77500/80000]	lr: 5.305e-06, eta: 0:14:29, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3952, decode.acc_seg: 84.2102, aux.loss_ce: 0.2349, aux.acc_seg: 78.2142, loss: 0.6301
2022-11-04 17:47:32,522 - mmseg - INFO - Iter [77550/80000]	lr: 5.210e-06, eta: 0:14:12, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4007, decode.acc_seg: 84.2235, aux.loss_ce: 0.2393, aux.acc_seg: 78.1657, loss: 0.6400
2022-11-04 17:47:49,676 - mmseg - INFO - Iter [77600/80000]	lr: 5.114e-06, eta: 0:13:54, time: 0.343, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4113, decode.acc_seg: 83.8320, aux.loss_ce: 0.2475, aux.acc_seg: 77.5090, loss: 0.6588
2022-11-04 17:48:09,801 - mmseg - INFO - Iter [77650/80000]	lr: 5.018e-06, eta: 0:13:37, time: 0.402, data_time: 0.072, memory: 12988, decode.loss_ce: 0.3983, decode.acc_seg: 84.1427, aux.loss_ce: 0.2405, aux.acc_seg: 78.0317, loss: 0.6389
2022-11-04 17:48:26,782 - mmseg - INFO - Iter [77700/80000]	lr: 4.922e-06, eta: 0:13:19, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3988, decode.acc_seg: 84.4371, aux.loss_ce: 0.2411, aux.acc_seg: 77.9822, loss: 0.6399
2022-11-04 17:48:43,793 - mmseg - INFO - Iter [77750/80000]	lr: 4.825e-06, eta: 0:13:02, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4054, decode.acc_seg: 83.7590, aux.loss_ce: 0.2400, aux.acc_seg: 77.6722, loss: 0.6454
2022-11-04 17:49:00,701 - mmseg - INFO - Iter [77800/80000]	lr: 4.729e-06, eta: 0:12:45, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4107, decode.acc_seg: 83.9618, aux.loss_ce: 0.2455, aux.acc_seg: 77.7816, loss: 0.6562
2022-11-04 17:49:17,818 - mmseg - INFO - Iter [77850/80000]	lr: 4.632e-06, eta: 0:12:27, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3914, decode.acc_seg: 84.3833, aux.loss_ce: 0.2315, aux.acc_seg: 78.3721, loss: 0.6228
2022-11-04 17:49:34,921 - mmseg - INFO - Iter [77900/80000]	lr: 4.535e-06, eta: 0:12:10, time: 0.342, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3953, decode.acc_seg: 84.1536, aux.loss_ce: 0.2372, aux.acc_seg: 78.2034, loss: 0.6326
2022-11-04 17:49:51,918 - mmseg - INFO - Iter [77950/80000]	lr: 4.438e-06, eta: 0:11:52, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4029, decode.acc_seg: 84.2408, aux.loss_ce: 0.2382, aux.acc_seg: 78.2641, loss: 0.6411
2022-11-04 17:50:09,172 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 17:50:09,173 - mmseg - INFO - Iter [78000/80000]	lr: 4.340e-06, eta: 0:11:35, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3978, decode.acc_seg: 84.4109, aux.loss_ce: 0.2353, aux.acc_seg: 78.4089, loss: 0.6331
2022-11-04 17:50:26,479 - mmseg - INFO - Iter [78050/80000]	lr: 4.243e-06, eta: 0:11:18, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4022, decode.acc_seg: 83.9980, aux.loss_ce: 0.2384, aux.acc_seg: 78.1210, loss: 0.6406
2022-11-04 17:50:43,662 - mmseg - INFO - Iter [78100/80000]	lr: 4.145e-06, eta: 0:11:00, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3892, decode.acc_seg: 84.5520, aux.loss_ce: 0.2282, aux.acc_seg: 78.8714, loss: 0.6174
2022-11-04 17:51:00,718 - mmseg - INFO - Iter [78150/80000]	lr: 4.046e-06, eta: 0:10:43, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3882, decode.acc_seg: 84.5479, aux.loss_ce: 0.2389, aux.acc_seg: 78.2166, loss: 0.6271
2022-11-04 17:51:17,940 - mmseg - INFO - Iter [78200/80000]	lr: 3.948e-06, eta: 0:10:25, time: 0.344, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4030, decode.acc_seg: 84.1367, aux.loss_ce: 0.2378, aux.acc_seg: 77.9401, loss: 0.6407
2022-11-04 17:51:37,993 - mmseg - INFO - Iter [78250/80000]	lr: 3.849e-06, eta: 0:10:08, time: 0.401, data_time: 0.071, memory: 12988, decode.loss_ce: 0.3825, decode.acc_seg: 84.8264, aux.loss_ce: 0.2270, aux.acc_seg: 78.9743, loss: 0.6095
2022-11-04 17:51:55,041 - mmseg - INFO - Iter [78300/80000]	lr: 3.750e-06, eta: 0:09:51, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3892, decode.acc_seg: 84.6559, aux.loss_ce: 0.2311, aux.acc_seg: 78.9453, loss: 0.6203
2022-11-04 17:52:12,083 - mmseg - INFO - Iter [78350/80000]	lr: 3.651e-06, eta: 0:09:33, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3957, decode.acc_seg: 84.2780, aux.loss_ce: 0.2381, aux.acc_seg: 78.2216, loss: 0.6338
2022-11-04 17:52:29,074 - mmseg - INFO - Iter [78400/80000]	lr: 3.551e-06, eta: 0:09:16, time: 0.340, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4006, decode.acc_seg: 84.1446, aux.loss_ce: 0.2414, aux.acc_seg: 77.7860, loss: 0.6421
2022-11-04 17:52:46,140 - mmseg - INFO - Iter [78450/80000]	lr: 3.451e-06, eta: 0:08:59, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4001, decode.acc_seg: 83.8748, aux.loss_ce: 0.2457, aux.acc_seg: 77.3983, loss: 0.6458
2022-11-04 17:53:03,456 - mmseg - INFO - Iter [78500/80000]	lr: 3.351e-06, eta: 0:08:41, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3958, decode.acc_seg: 84.4128, aux.loss_ce: 0.2411, aux.acc_seg: 77.8528, loss: 0.6369
2022-11-04 17:53:20,313 - mmseg - INFO - Iter [78550/80000]	lr: 3.250e-06, eta: 0:08:24, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3983, decode.acc_seg: 84.2369, aux.loss_ce: 0.2398, aux.acc_seg: 77.9120, loss: 0.6381
2022-11-04 17:53:37,363 - mmseg - INFO - Iter [78600/80000]	lr: 3.149e-06, eta: 0:08:06, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3932, decode.acc_seg: 84.4160, aux.loss_ce: 0.2349, aux.acc_seg: 78.6197, loss: 0.6281
2022-11-04 17:53:54,266 - mmseg - INFO - Iter [78650/80000]	lr: 3.048e-06, eta: 0:07:49, time: 0.338, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3967, decode.acc_seg: 84.3228, aux.loss_ce: 0.2388, aux.acc_seg: 77.9869, loss: 0.6355
2022-11-04 17:54:11,474 - mmseg - INFO - Iter [78700/80000]	lr: 2.946e-06, eta: 0:07:32, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3869, decode.acc_seg: 84.7312, aux.loss_ce: 0.2374, aux.acc_seg: 78.4113, loss: 0.6243
2022-11-04 17:54:28,562 - mmseg - INFO - Iter [78750/80000]	lr: 2.844e-06, eta: 0:07:14, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3981, decode.acc_seg: 84.3228, aux.loss_ce: 0.2376, aux.acc_seg: 78.4461, loss: 0.6357
2022-11-04 17:54:45,822 - mmseg - INFO - Iter [78800/80000]	lr: 2.742e-06, eta: 0:06:57, time: 0.345, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3975, decode.acc_seg: 84.2201, aux.loss_ce: 0.2389, aux.acc_seg: 78.1074, loss: 0.6365
2022-11-04 17:55:02,993 - mmseg - INFO - Iter [78850/80000]	lr: 2.639e-06, eta: 0:06:39, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3972, decode.acc_seg: 84.2147, aux.loss_ce: 0.2330, aux.acc_seg: 78.6182, loss: 0.6302
2022-11-04 17:55:22,961 - mmseg - INFO - Iter [78900/80000]	lr: 2.535e-06, eta: 0:06:22, time: 0.399, data_time: 0.059, memory: 12988, decode.loss_ce: 0.3888, decode.acc_seg: 84.5999, aux.loss_ce: 0.2334, aux.acc_seg: 78.2432, loss: 0.6222
2022-11-04 17:55:39,916 - mmseg - INFO - Iter [78950/80000]	lr: 2.431e-06, eta: 0:06:05, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3983, decode.acc_seg: 84.4085, aux.loss_ce: 0.2338, aux.acc_seg: 78.4878, loss: 0.6321
2022-11-04 17:55:56,999 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 17:55:56,999 - mmseg - INFO - Iter [79000/80000]	lr: 2.327e-06, eta: 0:05:47, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3941, decode.acc_seg: 84.3289, aux.loss_ce: 0.2308, aux.acc_seg: 78.5845, loss: 0.6249
2022-11-04 17:56:14,332 - mmseg - INFO - Iter [79050/80000]	lr: 2.222e-06, eta: 0:05:30, time: 0.347, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4196, decode.acc_seg: 83.5683, aux.loss_ce: 0.2542, aux.acc_seg: 77.0902, loss: 0.6738
2022-11-04 17:56:31,363 - mmseg - INFO - Iter [79100/80000]	lr: 2.117e-06, eta: 0:05:12, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.4014, decode.acc_seg: 83.9558, aux.loss_ce: 0.2354, aux.acc_seg: 78.1202, loss: 0.6368
2022-11-04 17:56:48,284 - mmseg - INFO - Iter [79150/80000]	lr: 2.011e-06, eta: 0:04:55, time: 0.338, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3867, decode.acc_seg: 84.7380, aux.loss_ce: 0.2281, aux.acc_seg: 78.9814, loss: 0.6149
2022-11-04 17:57:05,339 - mmseg - INFO - Iter [79200/80000]	lr: 1.904e-06, eta: 0:04:38, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3900, decode.acc_seg: 84.6745, aux.loss_ce: 0.2307, aux.acc_seg: 78.9176, loss: 0.6206
2022-11-04 17:57:22,512 - mmseg - INFO - Iter [79250/80000]	lr: 1.797e-06, eta: 0:04:20, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4038, decode.acc_seg: 84.0061, aux.loss_ce: 0.2406, aux.acc_seg: 77.8195, loss: 0.6443
2022-11-04 17:57:39,829 - mmseg - INFO - Iter [79300/80000]	lr: 1.689e-06, eta: 0:04:03, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3938, decode.acc_seg: 84.5027, aux.loss_ce: 0.2360, aux.acc_seg: 78.3439, loss: 0.6298
2022-11-04 17:57:56,791 - mmseg - INFO - Iter [79350/80000]	lr: 1.580e-06, eta: 0:03:46, time: 0.339, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3972, decode.acc_seg: 83.9958, aux.loss_ce: 0.2396, aux.acc_seg: 78.0826, loss: 0.6369
2022-11-04 17:58:14,095 - mmseg - INFO - Iter [79400/80000]	lr: 1.470e-06, eta: 0:03:28, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3931, decode.acc_seg: 84.4451, aux.loss_ce: 0.2357, aux.acc_seg: 78.5661, loss: 0.6288
2022-11-04 17:58:30,884 - mmseg - INFO - Iter [79450/80000]	lr: 1.360e-06, eta: 0:03:11, time: 0.336, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3870, decode.acc_seg: 84.7591, aux.loss_ce: 0.2291, aux.acc_seg: 79.1416, loss: 0.6161
2022-11-04 17:58:48,065 - mmseg - INFO - Iter [79500/80000]	lr: 1.248e-06, eta: 0:02:53, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3950, decode.acc_seg: 84.4542, aux.loss_ce: 0.2365, aux.acc_seg: 78.6347, loss: 0.6315
2022-11-04 17:59:08,195 - mmseg - INFO - Iter [79550/80000]	lr: 1.135e-06, eta: 0:02:36, time: 0.403, data_time: 0.063, memory: 12988, decode.loss_ce: 0.4054, decode.acc_seg: 83.9412, aux.loss_ce: 0.2344, aux.acc_seg: 78.4675, loss: 0.6398
2022-11-04 17:59:25,327 - mmseg - INFO - Iter [79600/80000]	lr: 1.021e-06, eta: 0:02:19, time: 0.343, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3849, decode.acc_seg: 84.6316, aux.loss_ce: 0.2246, aux.acc_seg: 79.0520, loss: 0.6094
2022-11-04 17:59:42,388 - mmseg - INFO - Iter [79650/80000]	lr: 9.061e-07, eta: 0:02:01, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.4016, decode.acc_seg: 84.1787, aux.loss_ce: 0.2393, aux.acc_seg: 78.4594, loss: 0.6410
2022-11-04 17:59:59,258 - mmseg - INFO - Iter [79700/80000]	lr: 7.891e-07, eta: 0:01:44, time: 0.337, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3970, decode.acc_seg: 84.2404, aux.loss_ce: 0.2361, aux.acc_seg: 78.2081, loss: 0.6331
2022-11-04 18:00:16,220 - mmseg - INFO - Iter [79750/80000]	lr: 6.700e-07, eta: 0:01:26, time: 0.339, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3982, decode.acc_seg: 84.3130, aux.loss_ce: 0.2383, aux.acc_seg: 78.3862, loss: 0.6365
2022-11-04 18:00:33,419 - mmseg - INFO - Iter [79800/80000]	lr: 5.486e-07, eta: 0:01:09, time: 0.344, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3879, decode.acc_seg: 84.6585, aux.loss_ce: 0.2292, aux.acc_seg: 79.1761, loss: 0.6171
2022-11-04 18:00:50,477 - mmseg - INFO - Iter [79850/80000]	lr: 4.241e-07, eta: 0:00:52, time: 0.341, data_time: 0.009, memory: 12988, decode.loss_ce: 0.3884, decode.acc_seg: 84.6676, aux.loss_ce: 0.2360, aux.acc_seg: 78.3928, loss: 0.6244
2022-11-04 18:01:07,567 - mmseg - INFO - Iter [79900/80000]	lr: 2.953e-07, eta: 0:00:34, time: 0.342, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3934, decode.acc_seg: 84.4902, aux.loss_ce: 0.2309, aux.acc_seg: 79.0631, loss: 0.6243
2022-11-04 18:01:24,609 - mmseg - INFO - Iter [79950/80000]	lr: 1.597e-07, eta: 0:00:17, time: 0.341, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3916, decode.acc_seg: 84.2846, aux.loss_ce: 0.2340, aux.acc_seg: 78.4523, loss: 0.6255
2022-11-04 18:01:41,582 - mmseg - INFO - Saving checkpoint at 80000 iterations
2022-11-04 18:01:41,889 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 18:01:41,889 - mmseg - INFO - Iter [80000/80000]	lr: 4.639e-09, eta: 0:00:00, time: 0.346, data_time: 0.010, memory: 12988, decode.loss_ce: 0.3927, decode.acc_seg: 84.5024, aux.loss_ce: 0.2317, aux.acc_seg: 78.8490, loss: 0.6243
2022-11-04 18:01:51,062 - mmseg - INFO - per class results:
2022-11-04 18:01:51,069 - mmseg - INFO - 
+---------------------+-------+-------+
|        Class        |  IoU  |  Acc  |
+---------------------+-------+-------+
|         wall        | 67.05 | 83.32 |
|       building      | 76.19 | 89.74 |
|         sky         | 88.72 | 93.17 |
|        floor        | 71.71 | 85.68 |
|         tree        | 67.68 |  84.3 |
|       ceiling       | 76.35 | 86.98 |
|         road        | 75.71 | 86.27 |
|         bed         | 76.24 | 90.44 |
|      windowpane     | 50.79 | 67.87 |
|        grass        | 61.65 | 80.65 |
|       cabinet       | 49.23 | 62.33 |
|       sidewalk      | 52.02 | 68.32 |
|        person       | 64.71 | 82.13 |
|        earth        |  30.6 | 40.97 |
|         door        | 26.77 | 42.93 |
|        table        | 41.53 | 57.92 |
|       mountain      | 54.55 | 70.02 |
|        plant        | 44.44 | 56.51 |
|       curtain       |  55.4 | 72.29 |
|        chair        | 38.36 | 53.28 |
|         car         | 72.91 | 86.27 |
|        water        | 41.49 | 60.66 |
|       painting      | 56.16 |  73.0 |
|         sofa        | 46.98 | 68.44 |
|        shelf        | 33.37 | 49.72 |
|        house        | 40.13 | 53.04 |
|         sea         | 34.37 | 56.58 |
|        mirror       | 44.04 | 53.43 |
|         rug         | 48.59 | 56.23 |
|        field        |  28.3 | 42.97 |
|       armchair      | 26.33 | 39.74 |
|         seat        | 47.17 | 69.34 |
|        fence        | 26.17 | 45.95 |
|         desk        |  35.0 | 51.79 |
|         rock        | 44.14 | 68.87 |
|       wardrobe      | 37.54 | 56.82 |
|         lamp        | 37.52 | 49.25 |
|       bathtub       | 54.72 | 67.31 |
|       railing       | 24.67 | 39.06 |
|       cushion       | 33.39 | 43.71 |
|         base        | 16.19 | 23.21 |
|         box         | 16.19 |  23.8 |
|        column       | 26.04 | 32.48 |
|      signboard      | 22.15 | 30.61 |
|   chest of drawers  | 30.77 | 51.33 |
|       counter       | 26.28 | 34.34 |
|         sand        | 26.19 | 38.85 |
|         sink        | 46.47 | 59.95 |
|      skyscraper     | 68.18 | 83.19 |
|      fireplace      | 56.56 | 77.72 |
|     refrigerator    |  50.7 | 66.45 |
|      grandstand     | 38.35 |  58.7 |
|         path        |  9.74 | 12.98 |
|        stairs       | 25.91 | 33.99 |
|        runway       | 71.76 | 85.27 |
|         case        |  48.7 | 58.09 |
|      pool table     | 80.91 | 88.43 |
|        pillow       | 36.96 | 46.33 |
|     screen door     | 50.97 | 62.39 |
|       stairway      | 30.75 |  37.0 |
|        river        | 15.38 | 25.34 |
|        bridge       | 40.79 | 52.78 |
|       bookcase      | 28.21 | 45.15 |
|        blind        | 16.89 | 19.61 |
|     coffee table    | 42.84 | 69.75 |
|        toilet       | 64.86 | 77.76 |
|        flower       | 29.28 | 44.08 |
|         book        | 37.29 |  54.8 |
|         hill        |  5.46 |  8.73 |
|        bench        | 32.03 | 41.46 |
|      countertop     | 47.15 | 64.56 |
|        stove        | 49.49 | 59.49 |
|         palm        | 36.59 | 52.37 |
|    kitchen island   | 18.04 | 39.08 |
|       computer      | 45.37 | 58.83 |
|     swivel chair    | 31.34 | 41.08 |
|         boat        | 56.64 | 64.22 |
|         bar         | 24.15 |  28.1 |
|    arcade machine   | 25.76 | 28.13 |
|        hovel        | 25.33 | 37.48 |
|         bus         | 71.86 |  85.5 |
|        towel        | 42.22 | 52.89 |
|        light        | 13.27 | 14.18 |
|        truck        |  5.7  |  7.38 |
|        tower        |  35.9 | 50.65 |
|      chandelier     | 47.15 | 61.04 |
|        awning       | 14.46 | 18.04 |
|     streetlight     |  6.94 |  8.02 |
|        booth        | 33.26 | 36.49 |
| television receiver | 51.07 | 65.81 |
|       airplane      | 44.54 | 52.72 |
|      dirt track     | 14.74 | 29.84 |
|       apparel       | 17.16 | 23.45 |
|         pole        |  6.52 | 13.39 |
|         land        |  0.0  |  0.0  |
|      bannister      |  2.2  |  3.39 |
|      escalator      | 52.15 | 68.79 |
|       ottoman       | 20.61 | 24.86 |
|        bottle       | 15.99 | 19.82 |
|        buffet       |  37.9 | 51.22 |
|        poster       | 20.46 |  26.3 |
|        stage        |  8.39 | 16.03 |
|         van         | 29.89 | 34.43 |
|         ship        | 37.78 | 38.61 |
|       fountain      | 19.13 | 19.43 |
|    conveyer belt    |  63.7 | 77.17 |
|        canopy       | 15.35 | 17.83 |
|        washer       | 58.29 | 62.29 |
|      plaything      |  8.86 | 16.33 |
|    swimming pool    | 23.87 | 31.51 |
|        stool        | 18.49 | 21.83 |
|        barrel       | 28.31 | 61.12 |
|        basket       | 16.61 | 20.81 |
|      waterfall      | 32.94 | 38.54 |
|         tent        | 90.53 | 95.11 |
|         bag         |  3.12 |  3.63 |
|       minibike      | 37.47 | 44.29 |
|        cradle       | 62.09 | 88.42 |
|         oven        | 13.07 | 33.33 |
|         ball        | 36.17 | 57.63 |
|         food        | 42.24 | 53.35 |
|         step        |  3.94 |  4.85 |
|         tank        | 27.02 | 30.59 |
|      trade name     | 16.68 | 19.72 |
|      microwave      | 27.55 | 30.77 |
|         pot         | 25.82 | 32.08 |
|        animal       | 48.33 | 53.35 |
|       bicycle       | 31.43 | 55.64 |
|         lake        |  3.53 |  4.08 |
|      dishwasher     | 32.89 | 44.21 |
|        screen       | 52.96 | 78.58 |
|       blanket       |  1.49 |  1.61 |
|      sculpture      | 31.01 | 44.67 |
|         hood        |  35.1 | 42.95 |
|        sconce       |  22.6 | 26.17 |
|         vase        | 18.14 |  27.4 |
|    traffic light    | 11.76 | 16.32 |
|         tray        |  1.04 |  2.48 |
|        ashcan       | 18.54 | 25.53 |
|         fan         | 30.22 | 37.41 |
|         pier        | 52.54 | 75.64 |
|      crt screen     |  2.57 |  4.07 |
|        plate        | 32.77 | 40.29 |
|       monitor       | 29.69 | 31.79 |
|    bulletin board   | 31.53 | 39.87 |
|        shower       |  0.0  |  0.0  |
|       radiator      | 34.31 | 42.47 |
|        glass        |  1.56 |  1.61 |
|        clock        |  9.68 | 11.18 |
|         flag        | 18.57 | 20.36 |
+---------------------+-------+-------+
2022-11-04 18:01:51,069 - mmseg - INFO - Summary:
2022-11-04 18:01:51,069 - mmseg - INFO - 
+-------+-------+-------+
|  aAcc |  mIoU |  mAcc |
+-------+-------+-------+
| 75.64 | 35.26 | 45.98 |
+-------+-------+-------+
2022-11-04 18:01:51,070 - mmseg - INFO - Exp name: deeplabv3_metamobile2M_pretrain_512x512_80k_ade20k_256.py
2022-11-04 18:01:51,071 - mmseg - INFO - Iter(val) [250]	aAcc: 0.7564, mIoU: 0.3526, mAcc: 0.4598, IoU.wall: 0.6705, IoU.building: 0.7619, IoU.sky: 0.8872, IoU.floor: 0.7171, IoU.tree: 0.6768, IoU.ceiling: 0.7635, IoU.road: 0.7571, IoU.bed : 0.7624, IoU.windowpane: 0.5079, IoU.grass: 0.6165, IoU.cabinet: 0.4923, IoU.sidewalk: 0.5202, IoU.person: 0.6471, IoU.earth: 0.3060, IoU.door: 0.2677, IoU.table: 0.4153, IoU.mountain: 0.5455, IoU.plant: 0.4444, IoU.curtain: 0.5540, IoU.chair: 0.3836, IoU.car: 0.7291, IoU.water: 0.4149, IoU.painting: 0.5616, IoU.sofa: 0.4698, IoU.shelf: 0.3337, IoU.house: 0.4013, IoU.sea: 0.3437, IoU.mirror: 0.4404, IoU.rug: 0.4859, IoU.field: 0.2830, IoU.armchair: 0.2633, IoU.seat: 0.4717, IoU.fence: 0.2617, IoU.desk: 0.3500, IoU.rock: 0.4414, IoU.wardrobe: 0.3754, IoU.lamp: 0.3752, IoU.bathtub: 0.5472, IoU.railing: 0.2467, IoU.cushion: 0.3339, IoU.base: 0.1619, IoU.box: 0.1619, IoU.column: 0.2604, IoU.signboard: 0.2215, IoU.chest of drawers: 0.3077, IoU.counter: 0.2628, IoU.sand: 0.2619, IoU.sink: 0.4647, IoU.skyscraper: 0.6818, IoU.fireplace: 0.5656, IoU.refrigerator: 0.5070, IoU.grandstand: 0.3835, IoU.path: 0.0974, IoU.stairs: 0.2591, IoU.runway: 0.7176, IoU.case: 0.4870, IoU.pool table: 0.8091, IoU.pillow: 0.3696, IoU.screen door: 0.5097, IoU.stairway: 0.3075, IoU.river: 0.1538, IoU.bridge: 0.4079, IoU.bookcase: 0.2821, IoU.blind: 0.1689, IoU.coffee table: 0.4284, IoU.toilet: 0.6486, IoU.flower: 0.2928, IoU.book: 0.3729, IoU.hill: 0.0546, IoU.bench: 0.3203, IoU.countertop: 0.4715, IoU.stove: 0.4949, IoU.palm: 0.3659, IoU.kitchen island: 0.1804, IoU.computer: 0.4537, IoU.swivel chair: 0.3134, IoU.boat: 0.5664, IoU.bar: 0.2415, IoU.arcade machine: 0.2576, IoU.hovel: 0.2533, IoU.bus: 0.7186, IoU.towel: 0.4222, IoU.light: 0.1327, IoU.truck: 0.0570, IoU.tower: 0.3590, IoU.chandelier: 0.4715, IoU.awning: 0.1446, IoU.streetlight: 0.0694, IoU.booth: 0.3326, IoU.television receiver: 0.5107, IoU.airplane: 0.4454, IoU.dirt track: 0.1474, IoU.apparel: 0.1716, IoU.pole: 0.0652, IoU.land: 0.0000, IoU.bannister: 0.0220, IoU.escalator: 0.5215, IoU.ottoman: 0.2061, IoU.bottle: 0.1599, IoU.buffet: 0.3790, IoU.poster: 0.2046, IoU.stage: 0.0839, IoU.van: 0.2989, IoU.ship: 0.3778, IoU.fountain: 0.1913, IoU.conveyer belt: 0.6370, IoU.canopy: 0.1535, IoU.washer: 0.5829, IoU.plaything: 0.0886, IoU.swimming pool: 0.2387, IoU.stool: 0.1849, IoU.barrel: 0.2831, IoU.basket: 0.1661, IoU.waterfall: 0.3294, IoU.tent: 0.9053, IoU.bag: 0.0312, IoU.minibike: 0.3747, IoU.cradle: 0.6209, IoU.oven: 0.1307, IoU.ball: 0.3617, IoU.food: 0.4224, IoU.step: 0.0394, IoU.tank: 0.2702, IoU.trade name: 0.1668, IoU.microwave: 0.2755, IoU.pot: 0.2582, IoU.animal: 0.4833, IoU.bicycle: 0.3143, IoU.lake: 0.0353, IoU.dishwasher: 0.3289, IoU.screen: 0.5296, IoU.blanket: 0.0149, IoU.sculpture: 0.3101, IoU.hood: 0.3510, IoU.sconce: 0.2260, IoU.vase: 0.1814, IoU.traffic light: 0.1176, IoU.tray: 0.0104, IoU.ashcan: 0.1854, IoU.fan: 0.3022, IoU.pier: 0.5254, IoU.crt screen: 0.0257, IoU.plate: 0.3277, IoU.monitor: 0.2969, IoU.bulletin board: 0.3153, IoU.shower: 0.0000, IoU.radiator: 0.3431, IoU.glass: 0.0156, IoU.clock: 0.0968, IoU.flag: 0.1857, Acc.wall: 0.8332, Acc.building: 0.8974, Acc.sky: 0.9317, Acc.floor: 0.8568, Acc.tree: 0.8430, Acc.ceiling: 0.8698, Acc.road: 0.8627, Acc.bed : 0.9044, Acc.windowpane: 0.6787, Acc.grass: 0.8065, Acc.cabinet: 0.6233, Acc.sidewalk: 0.6832, Acc.person: 0.8213, Acc.earth: 0.4097, Acc.door: 0.4293, Acc.table: 0.5792, Acc.mountain: 0.7002, Acc.plant: 0.5651, Acc.curtain: 0.7229, Acc.chair: 0.5328, Acc.car: 0.8627, Acc.water: 0.6066, Acc.painting: 0.7300, Acc.sofa: 0.6844, Acc.shelf: 0.4972, Acc.house: 0.5304, Acc.sea: 0.5658, Acc.mirror: 0.5343, Acc.rug: 0.5623, Acc.field: 0.4297, Acc.armchair: 0.3974, Acc.seat: 0.6934, Acc.fence: 0.4595, Acc.desk: 0.5179, Acc.rock: 0.6887, Acc.wardrobe: 0.5682, Acc.lamp: 0.4925, Acc.bathtub: 0.6731, Acc.railing: 0.3906, Acc.cushion: 0.4371, Acc.base: 0.2321, Acc.box: 0.2380, Acc.column: 0.3248, Acc.signboard: 0.3061, Acc.chest of drawers: 0.5133, Acc.counter: 0.3434, Acc.sand: 0.3885, Acc.sink: 0.5995, Acc.skyscraper: 0.8319, Acc.fireplace: 0.7772, Acc.refrigerator: 0.6645, Acc.grandstand: 0.5870, Acc.path: 0.1298, Acc.stairs: 0.3399, Acc.runway: 0.8527, Acc.case: 0.5809, Acc.pool table: 0.8843, Acc.pillow: 0.4633, Acc.screen door: 0.6239, Acc.stairway: 0.3700, Acc.river: 0.2534, Acc.bridge: 0.5278, Acc.bookcase: 0.4515, Acc.blind: 0.1961, Acc.coffee table: 0.6975, Acc.toilet: 0.7776, Acc.flower: 0.4408, Acc.book: 0.5480, Acc.hill: 0.0873, Acc.bench: 0.4146, Acc.countertop: 0.6456, Acc.stove: 0.5949, Acc.palm: 0.5237, Acc.kitchen island: 0.3908, Acc.computer: 0.5883, Acc.swivel chair: 0.4108, Acc.boat: 0.6422, Acc.bar: 0.2810, Acc.arcade machine: 0.2813, Acc.hovel: 0.3748, Acc.bus: 0.8550, Acc.towel: 0.5289, Acc.light: 0.1418, Acc.truck: 0.0738, Acc.tower: 0.5065, Acc.chandelier: 0.6104, Acc.awning: 0.1804, Acc.streetlight: 0.0802, Acc.booth: 0.3649, Acc.television receiver: 0.6581, Acc.airplane: 0.5272, Acc.dirt track: 0.2984, Acc.apparel: 0.2345, Acc.pole: 0.1339, Acc.land: 0.0000, Acc.bannister: 0.0339, Acc.escalator: 0.6879, Acc.ottoman: 0.2486, Acc.bottle: 0.1982, Acc.buffet: 0.5122, Acc.poster: 0.2630, Acc.stage: 0.1603, Acc.van: 0.3443, Acc.ship: 0.3861, Acc.fountain: 0.1943, Acc.conveyer belt: 0.7717, Acc.canopy: 0.1783, Acc.washer: 0.6229, Acc.plaything: 0.1633, Acc.swimming pool: 0.3151, Acc.stool: 0.2183, Acc.barrel: 0.6112, Acc.basket: 0.2081, Acc.waterfall: 0.3854, Acc.tent: 0.9511, Acc.bag: 0.0363, Acc.minibike: 0.4429, Acc.cradle: 0.8842, Acc.oven: 0.3333, Acc.ball: 0.5763, Acc.food: 0.5335, Acc.step: 0.0485, Acc.tank: 0.3059, Acc.trade name: 0.1972, Acc.microwave: 0.3077, Acc.pot: 0.3208, Acc.animal: 0.5335, Acc.bicycle: 0.5564, Acc.lake: 0.0408, Acc.dishwasher: 0.4421, Acc.screen: 0.7858, Acc.blanket: 0.0161, Acc.sculpture: 0.4467, Acc.hood: 0.4295, Acc.sconce: 0.2617, Acc.vase: 0.2740, Acc.traffic light: 0.1632, Acc.tray: 0.0248, Acc.ashcan: 0.2553, Acc.fan: 0.3741, Acc.pier: 0.7564, Acc.crt screen: 0.0407, Acc.plate: 0.4029, Acc.monitor: 0.3179, Acc.bulletin board: 0.3987, Acc.shower: 0.0000, Acc.radiator: 0.4247, Acc.glass: 0.0161, Acc.clock: 0.1118, Acc.flag: 0.2036