diff --git "a/cityscapes/deeplabv3plus_r101_multistep/20230304_140046.log" "b/cityscapes/deeplabv3plus_r101_multistep/20230304_140046.log"
new file mode 100644--- /dev/null
+++ "b/cityscapes/deeplabv3plus_r101_multistep/20230304_140046.log"
@@ -0,0 +1,5091 @@
+2023-03-04 14:00:46,465 - mmseg - INFO - Multi-processing start method is `None`
+2023-03-04 14:00:46,483 - mmseg - INFO - OpenCV num_threads is `128
+2023-03-04 14:00:46,484 - mmseg - INFO - OMP num threads is 1
+2023-03-04 14:00:46,532 - mmseg - INFO - Environment info:
+------------------------------------------------------------
+sys.platform: linux
+Python: 3.7.16 (default, Jan 17 2023, 22:20:44) [GCC 11.2.0]
+CUDA available: True
+GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB
+CUDA_HOME: /mnt/petrelfs/laizeqiang/miniconda3/envs/torch
+NVCC: Cuda compilation tools, release 11.6, V11.6.124
+GCC: gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44)
+PyTorch: 1.13.1
+PyTorch compiling details: PyTorch built with:
+  - GCC 9.3
+  - C++ Version: 201402
+  - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
+  - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
+  - OpenMP 201511 (a.k.a. OpenMP 4.5)
+  - LAPACK is enabled (usually provided by MKL)
+  - NNPACK is enabled
+  - CPU capability usage: AVX2
+  - CUDA Runtime 11.6
+  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
+  - CuDNN 8.3.2  (built against CUDA 11.5)
+  - Magma 2.6.1
+  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, 
+
+TorchVision: 0.14.1
+OpenCV: 4.7.0
+MMCV: 1.7.1
+MMCV Compiler: GCC 9.3
+MMCV CUDA Compiler: 11.6
+MMSegmentation: 0.30.0+d4f0cb3
+------------------------------------------------------------
+
+2023-03-04 14:00:46,532 - mmseg - INFO - Distributed training: True
+2023-03-04 14:00:47,250 - mmseg - INFO - Config:
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    type='EncoderDecoderDiffusion',
+    pretrained=
+    'work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20/latest.pth',
+    backbone=dict(
+        type='ResNetV1cCustomInitWeights',
+        depth=101,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        dilations=(1, 1, 2, 4),
+        strides=(1, 2, 1, 1),
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        style='pytorch',
+        contract_dilation=True),
+    decode_head=dict(
+        type='DepthwiseSeparableASPPHeadUnetFCHeadMultiStep',
+        pretrained=
+        'work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20/latest.pth',
+        dim=128,
+        out_dim=256,
+        unet_channels=528,
+        dim_mults=[1, 1, 1],
+        cat_embedding_dim=16,
+        ignore_index=0,
+        diffusion_timesteps=100,
+        collect_timesteps=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 99],
+        in_channels=2048,
+        in_index=3,
+        channels=512,
+        dilations=(1, 12, 24, 36),
+        c1_in_channels=256,
+        c1_channels=48,
+        dropout_ratio=0.1,
+        num_classes=20,
+        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=None,
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'),
+    freeze_parameters=['backbone', 'decode_head'])
+dataset_type = 'Cityscapes20Dataset'
+data_root = 'data/cityscapes/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+crop_size = (512, 1024)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotationsCityscapes20'),
+    dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=(512, 1024), 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, 1024), pad_val=0, seg_pad_val=0),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(2048, 1024),
+        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=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='Cityscapes20Dataset',
+        data_root='data/cityscapes/',
+        img_dir='leftImg8bit/train',
+        ann_dir='gtFine/train',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotationsCityscapes20'),
+            dict(
+                type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
+            dict(type='RandomCrop', crop_size=(512, 1024), 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, 1024), pad_val=0, seg_pad_val=0),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img', 'gt_semantic_seg'])
+        ]),
+    val=dict(
+        type='Cityscapes20Dataset',
+        data_root='data/cityscapes/',
+        img_dir='leftImg8bit/val',
+        ann_dir='gtFine/val',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(2048, 1024),
+                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='Cityscapes20Dataset',
+        data_root='data/cityscapes/',
+        img_dir='leftImg8bit/val',
+        ann_dir='gtFine/val',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(2048, 1024),
+                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 = dict(
+    type='AdamW', lr=0.00015, betas=[0.9, 0.96], weight_decay=0.045)
+optimizer_config = dict()
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=1e-06,
+    step=20000,
+    gamma=0.5,
+    min_lr=1e-06,
+    by_epoch=False)
+runner = dict(type='IterBasedRunner', max_iters=160000)
+checkpoint_config = dict(by_epoch=False, interval=16000, max_keep_ckpts=1)
+evaluation = dict(
+    interval=16000, metric='mIoU', pre_eval=True, save_best='mIoU')
+checkpoint = 'work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20/latest.pth'
+custom_hooks = [
+    dict(
+        type='ConstantMomentumEMAHook',
+        momentum=0.01,
+        interval=25,
+        eval_interval=16000,
+        auto_resume=True,
+        priority=49)
+]
+work_dir = './work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune'
+gpu_ids = range(0, 8)
+auto_resume = True
+
+2023-03-04 14:00:51,465 - mmseg - INFO - Set random seed to 1382369470, deterministic: False
+2023-03-04 14:00:52,657 - mmseg - INFO - Parameters in backbone freezed!
+2023-03-04 14:00:52,658 - mmseg - INFO - Trainable parameters in DepthwiseSeparableASPPHeadUnetFCHeadMultiStep: ['unet.init_conv.weight', 'unet.init_conv.bias', 'unet.time_mlp.1.weight', 'unet.time_mlp.1.bias', 'unet.time_mlp.3.weight', 'unet.time_mlp.3.bias', 'unet.downs.0.0.mlp.1.weight', 'unet.downs.0.0.mlp.1.bias', 'unet.downs.0.0.block1.proj.weight', 'unet.downs.0.0.block1.proj.bias', 'unet.downs.0.0.block1.norm.weight', 'unet.downs.0.0.block1.norm.bias', 'unet.downs.0.0.block2.proj.weight', 'unet.downs.0.0.block2.proj.bias', 'unet.downs.0.0.block2.norm.weight', 'unet.downs.0.0.block2.norm.bias', 'unet.downs.0.1.mlp.1.weight', 'unet.downs.0.1.mlp.1.bias', 'unet.downs.0.1.block1.proj.weight', 'unet.downs.0.1.block1.proj.bias', 'unet.downs.0.1.block1.norm.weight', 'unet.downs.0.1.block1.norm.bias', 'unet.downs.0.1.block2.proj.weight', 'unet.downs.0.1.block2.proj.bias', 'unet.downs.0.1.block2.norm.weight', 'unet.downs.0.1.block2.norm.bias', 'unet.downs.0.2.fn.fn.to_qkv.weight', 'unet.downs.0.2.fn.fn.to_out.0.weight', 'unet.downs.0.2.fn.fn.to_out.0.bias', 'unet.downs.0.2.fn.fn.to_out.1.g', 'unet.downs.0.2.fn.norm.g', 'unet.downs.0.3.weight', 'unet.downs.0.3.bias', 'unet.downs.1.0.mlp.1.weight', 'unet.downs.1.0.mlp.1.bias', 'unet.downs.1.0.block1.proj.weight', 'unet.downs.1.0.block1.proj.bias', 'unet.downs.1.0.block1.norm.weight', 'unet.downs.1.0.block1.norm.bias', 'unet.downs.1.0.block2.proj.weight', 'unet.downs.1.0.block2.proj.bias', 'unet.downs.1.0.block2.norm.weight', 'unet.downs.1.0.block2.norm.bias', 'unet.downs.1.1.mlp.1.weight', 'unet.downs.1.1.mlp.1.bias', 'unet.downs.1.1.block1.proj.weight', 'unet.downs.1.1.block1.proj.bias', 'unet.downs.1.1.block1.norm.weight', 'unet.downs.1.1.block1.norm.bias', 'unet.downs.1.1.block2.proj.weight', 'unet.downs.1.1.block2.proj.bias', 'unet.downs.1.1.block2.norm.weight', 'unet.downs.1.1.block2.norm.bias', 'unet.downs.1.2.fn.fn.to_qkv.weight', 'unet.downs.1.2.fn.fn.to_out.0.weight', 'unet.downs.1.2.fn.fn.to_out.0.bias', 'unet.downs.1.2.fn.fn.to_out.1.g', 'unet.downs.1.2.fn.norm.g', 'unet.downs.1.3.weight', 'unet.downs.1.3.bias', 'unet.downs.2.0.mlp.1.weight', 'unet.downs.2.0.mlp.1.bias', 'unet.downs.2.0.block1.proj.weight', 'unet.downs.2.0.block1.proj.bias', 'unet.downs.2.0.block1.norm.weight', 'unet.downs.2.0.block1.norm.bias', 'unet.downs.2.0.block2.proj.weight', 'unet.downs.2.0.block2.proj.bias', 'unet.downs.2.0.block2.norm.weight', 'unet.downs.2.0.block2.norm.bias', 'unet.downs.2.1.mlp.1.weight', 'unet.downs.2.1.mlp.1.bias', 'unet.downs.2.1.block1.proj.weight', 'unet.downs.2.1.block1.proj.bias', 'unet.downs.2.1.block1.norm.weight', 'unet.downs.2.1.block1.norm.bias', 'unet.downs.2.1.block2.proj.weight', 'unet.downs.2.1.block2.proj.bias', 'unet.downs.2.1.block2.norm.weight', 'unet.downs.2.1.block2.norm.bias', 'unet.downs.2.2.fn.fn.to_qkv.weight', 'unet.downs.2.2.fn.fn.to_out.0.weight', 'unet.downs.2.2.fn.fn.to_out.0.bias', 'unet.downs.2.2.fn.fn.to_out.1.g', 'unet.downs.2.2.fn.norm.g', 'unet.downs.2.3.weight', 'unet.downs.2.3.bias', 'unet.ups.0.0.mlp.1.weight', 'unet.ups.0.0.mlp.1.bias', 'unet.ups.0.0.block1.proj.weight', 'unet.ups.0.0.block1.proj.bias', 'unet.ups.0.0.block1.norm.weight', 'unet.ups.0.0.block1.norm.bias', 'unet.ups.0.0.block2.proj.weight', 'unet.ups.0.0.block2.proj.bias', 'unet.ups.0.0.block2.norm.weight', 'unet.ups.0.0.block2.norm.bias', 'unet.ups.0.0.res_conv.weight', 'unet.ups.0.0.res_conv.bias', 'unet.ups.0.1.mlp.1.weight', 'unet.ups.0.1.mlp.1.bias', 'unet.ups.0.1.block1.proj.weight', 'unet.ups.0.1.block1.proj.bias', 'unet.ups.0.1.block1.norm.weight', 'unet.ups.0.1.block1.norm.bias', 'unet.ups.0.1.block2.proj.weight', 'unet.ups.0.1.block2.proj.bias', 'unet.ups.0.1.block2.norm.weight', 'unet.ups.0.1.block2.norm.bias', 'unet.ups.0.1.res_conv.weight', 'unet.ups.0.1.res_conv.bias', 'unet.ups.0.2.fn.fn.to_qkv.weight', 'unet.ups.0.2.fn.fn.to_out.0.weight', 'unet.ups.0.2.fn.fn.to_out.0.bias', 'unet.ups.0.2.fn.fn.to_out.1.g', 'unet.ups.0.2.fn.norm.g', 'unet.ups.0.3.1.weight', 'unet.ups.0.3.1.bias', 'unet.ups.1.0.mlp.1.weight', 'unet.ups.1.0.mlp.1.bias', 'unet.ups.1.0.block1.proj.weight', 'unet.ups.1.0.block1.proj.bias', 'unet.ups.1.0.block1.norm.weight', 'unet.ups.1.0.block1.norm.bias', 'unet.ups.1.0.block2.proj.weight', 'unet.ups.1.0.block2.proj.bias', 'unet.ups.1.0.block2.norm.weight', 'unet.ups.1.0.block2.norm.bias', 'unet.ups.1.0.res_conv.weight', 'unet.ups.1.0.res_conv.bias', 'unet.ups.1.1.mlp.1.weight', 'unet.ups.1.1.mlp.1.bias', 'unet.ups.1.1.block1.proj.weight', 'unet.ups.1.1.block1.proj.bias', 'unet.ups.1.1.block1.norm.weight', 'unet.ups.1.1.block1.norm.bias', 'unet.ups.1.1.block2.proj.weight', 'unet.ups.1.1.block2.proj.bias', 'unet.ups.1.1.block2.norm.weight', 'unet.ups.1.1.block2.norm.bias', 'unet.ups.1.1.res_conv.weight', 'unet.ups.1.1.res_conv.bias', 'unet.ups.1.2.fn.fn.to_qkv.weight', 'unet.ups.1.2.fn.fn.to_out.0.weight', 'unet.ups.1.2.fn.fn.to_out.0.bias', 'unet.ups.1.2.fn.fn.to_out.1.g', 'unet.ups.1.2.fn.norm.g', 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'unet.final_res_block.block1.proj.weight', 'unet.final_res_block.block1.proj.bias', 'unet.final_res_block.block1.norm.weight', 'unet.final_res_block.block1.norm.bias', 'unet.final_res_block.block2.proj.weight', 'unet.final_res_block.block2.proj.bias', 'unet.final_res_block.block2.norm.weight', 'unet.final_res_block.block2.norm.bias', 'unet.final_res_block.res_conv.weight', 'unet.final_res_block.res_conv.bias', 'unet.final_conv.weight', 'unet.final_conv.bias', 'conv_seg_new.weight', 'conv_seg_new.bias']
+2023-03-04 14:00:52,658 - mmseg - INFO - Parameters in decode_head freezed!
+2023-03-04 14:00:52,691 - mmseg - INFO - load checkpoint from local path: work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20/latest.pth
+2023-03-04 14:00:54,286 - mmseg - WARNING - The model and loaded state dict do not match exactly
+
+unexpected key in source state_dict: decode_head.image_pool.1.conv.weight, decode_head.image_pool.1.bn.weight, decode_head.image_pool.1.bn.bias, decode_head.image_pool.1.bn.running_mean, decode_head.image_pool.1.bn.running_var, decode_head.image_pool.1.bn.num_batches_tracked, decode_head.aspp_modules.0.conv.weight, decode_head.aspp_modules.0.bn.weight, decode_head.aspp_modules.0.bn.bias, decode_head.aspp_modules.0.bn.running_mean, decode_head.aspp_modules.0.bn.running_var, decode_head.aspp_modules.0.bn.num_batches_tracked, decode_head.aspp_modules.1.depthwise_conv.conv.weight, decode_head.aspp_modules.1.depthwise_conv.bn.weight, decode_head.aspp_modules.1.depthwise_conv.bn.bias, decode_head.aspp_modules.1.depthwise_conv.bn.running_mean, decode_head.aspp_modules.1.depthwise_conv.bn.running_var, decode_head.aspp_modules.1.depthwise_conv.bn.num_batches_tracked, decode_head.aspp_modules.1.pointwise_conv.conv.weight, decode_head.aspp_modules.1.pointwise_conv.bn.weight, decode_head.aspp_modules.1.pointwise_conv.bn.bias, decode_head.aspp_modules.1.pointwise_conv.bn.running_mean, decode_head.aspp_modules.1.pointwise_conv.bn.running_var, decode_head.aspp_modules.1.pointwise_conv.bn.num_batches_tracked, decode_head.aspp_modules.2.depthwise_conv.conv.weight, decode_head.aspp_modules.2.depthwise_conv.bn.weight, decode_head.aspp_modules.2.depthwise_conv.bn.bias, decode_head.aspp_modules.2.depthwise_conv.bn.running_mean, decode_head.aspp_modules.2.depthwise_conv.bn.running_var, decode_head.aspp_modules.2.depthwise_conv.bn.num_batches_tracked, decode_head.aspp_modules.2.pointwise_conv.conv.weight, decode_head.aspp_modules.2.pointwise_conv.bn.weight, decode_head.aspp_modules.2.pointwise_conv.bn.bias, decode_head.aspp_modules.2.pointwise_conv.bn.running_mean, decode_head.aspp_modules.2.pointwise_conv.bn.running_var, decode_head.aspp_modules.2.pointwise_conv.bn.num_batches_tracked, decode_head.aspp_modules.3.depthwise_conv.conv.weight, decode_head.aspp_modules.3.depthwise_conv.bn.weight, decode_head.aspp_modules.3.depthwise_conv.bn.bias, decode_head.aspp_modules.3.depthwise_conv.bn.running_mean, decode_head.aspp_modules.3.depthwise_conv.bn.running_var, decode_head.aspp_modules.3.depthwise_conv.bn.num_batches_tracked, decode_head.aspp_modules.3.pointwise_conv.conv.weight, decode_head.aspp_modules.3.pointwise_conv.bn.weight, decode_head.aspp_modules.3.pointwise_conv.bn.bias, decode_head.aspp_modules.3.pointwise_conv.bn.running_mean, decode_head.aspp_modules.3.pointwise_conv.bn.running_var, decode_head.aspp_modules.3.pointwise_conv.bn.num_batches_tracked, decode_head.bottleneck.conv.weight, decode_head.bottleneck.bn.weight, decode_head.bottleneck.bn.bias, decode_head.bottleneck.bn.running_mean, decode_head.bottleneck.bn.running_var, decode_head.bottleneck.bn.num_batches_tracked, decode_head.c1_bottleneck.conv.weight, decode_head.c1_bottleneck.bn.weight, decode_head.c1_bottleneck.bn.bias, decode_head.c1_bottleneck.bn.running_mean, decode_head.c1_bottleneck.bn.running_var, decode_head.c1_bottleneck.bn.num_batches_tracked, decode_head.sep_bottleneck.0.depthwise_conv.conv.weight, decode_head.sep_bottleneck.0.depthwise_conv.bn.weight, decode_head.sep_bottleneck.0.depthwise_conv.bn.bias, decode_head.sep_bottleneck.0.depthwise_conv.bn.running_mean, decode_head.sep_bottleneck.0.depthwise_conv.bn.running_var, decode_head.sep_bottleneck.0.depthwise_conv.bn.num_batches_tracked, decode_head.sep_bottleneck.0.pointwise_conv.conv.weight, decode_head.sep_bottleneck.0.pointwise_conv.bn.weight, decode_head.sep_bottleneck.0.pointwise_conv.bn.bias, decode_head.sep_bottleneck.0.pointwise_conv.bn.running_mean, decode_head.sep_bottleneck.0.pointwise_conv.bn.running_var, decode_head.sep_bottleneck.0.pointwise_conv.bn.num_batches_tracked, decode_head.sep_bottleneck.1.depthwise_conv.conv.weight, decode_head.sep_bottleneck.1.depthwise_conv.bn.weight, decode_head.sep_bottleneck.1.depthwise_conv.bn.bias, decode_head.sep_bottleneck.1.depthwise_conv.bn.running_mean, decode_head.sep_bottleneck.1.depthwise_conv.bn.running_var, decode_head.sep_bottleneck.1.depthwise_conv.bn.num_batches_tracked, decode_head.sep_bottleneck.1.pointwise_conv.conv.weight, decode_head.sep_bottleneck.1.pointwise_conv.bn.weight, decode_head.sep_bottleneck.1.pointwise_conv.bn.bias, decode_head.sep_bottleneck.1.pointwise_conv.bn.running_mean, decode_head.sep_bottleneck.1.pointwise_conv.bn.running_var, decode_head.sep_bottleneck.1.pointwise_conv.bn.num_batches_tracked, decode_head.unet.init_conv.weight, decode_head.unet.init_conv.bias, decode_head.unet.time_mlp.1.weight, decode_head.unet.time_mlp.1.bias, decode_head.unet.time_mlp.3.weight, decode_head.unet.time_mlp.3.bias, decode_head.unet.downs.0.0.mlp.1.weight, decode_head.unet.downs.0.0.mlp.1.bias, decode_head.unet.downs.0.0.block1.proj.weight, decode_head.unet.downs.0.0.block1.proj.bias, decode_head.unet.downs.0.0.block1.norm.weight, decode_head.unet.downs.0.0.block1.norm.bias, decode_head.unet.downs.0.0.block2.proj.weight, decode_head.unet.downs.0.0.block2.proj.bias, decode_head.unet.downs.0.0.block2.norm.weight, decode_head.unet.downs.0.0.block2.norm.bias, decode_head.unet.downs.0.1.mlp.1.weight, decode_head.unet.downs.0.1.mlp.1.bias, decode_head.unet.downs.0.1.block1.proj.weight, decode_head.unet.downs.0.1.block1.proj.bias, decode_head.unet.downs.0.1.block1.norm.weight, decode_head.unet.downs.0.1.block1.norm.bias, decode_head.unet.downs.0.1.block2.proj.weight, decode_head.unet.downs.0.1.block2.proj.bias, decode_head.unet.downs.0.1.block2.norm.weight, decode_head.unet.downs.0.1.block2.norm.bias, decode_head.unet.downs.0.2.fn.fn.to_qkv.weight, decode_head.unet.downs.0.2.fn.fn.to_out.0.weight, decode_head.unet.downs.0.2.fn.fn.to_out.0.bias, decode_head.unet.downs.0.2.fn.fn.to_out.1.g, decode_head.unet.downs.0.2.fn.norm.g, decode_head.unet.downs.0.3.weight, decode_head.unet.downs.0.3.bias, decode_head.unet.downs.1.0.mlp.1.weight, decode_head.unet.downs.1.0.mlp.1.bias, decode_head.unet.downs.1.0.block1.proj.weight, decode_head.unet.downs.1.0.block1.proj.bias, decode_head.unet.downs.1.0.block1.norm.weight, decode_head.unet.downs.1.0.block1.norm.bias, decode_head.unet.downs.1.0.block2.proj.weight, decode_head.unet.downs.1.0.block2.proj.bias, decode_head.unet.downs.1.0.block2.norm.weight, decode_head.unet.downs.1.0.block2.norm.bias, decode_head.unet.downs.1.1.mlp.1.weight, decode_head.unet.downs.1.1.mlp.1.bias, decode_head.unet.downs.1.1.block1.proj.weight, decode_head.unet.downs.1.1.block1.proj.bias, decode_head.unet.downs.1.1.block1.norm.weight, decode_head.unet.downs.1.1.block1.norm.bias, decode_head.unet.downs.1.1.block2.proj.weight, decode_head.unet.downs.1.1.block2.proj.bias, decode_head.unet.downs.1.1.block2.norm.weight, decode_head.unet.downs.1.1.block2.norm.bias, decode_head.unet.downs.1.2.fn.fn.to_qkv.weight, decode_head.unet.downs.1.2.fn.fn.to_out.0.weight, decode_head.unet.downs.1.2.fn.fn.to_out.0.bias, decode_head.unet.downs.1.2.fn.fn.to_out.1.g, decode_head.unet.downs.1.2.fn.norm.g, decode_head.unet.downs.1.3.weight, decode_head.unet.downs.1.3.bias, decode_head.unet.downs.2.0.mlp.1.weight, decode_head.unet.downs.2.0.mlp.1.bias, decode_head.unet.downs.2.0.block1.proj.weight, decode_head.unet.downs.2.0.block1.proj.bias, decode_head.unet.downs.2.0.block1.norm.weight, decode_head.unet.downs.2.0.block1.norm.bias, decode_head.unet.downs.2.0.block2.proj.weight, decode_head.unet.downs.2.0.block2.proj.bias, decode_head.unet.downs.2.0.block2.norm.weight, decode_head.unet.downs.2.0.block2.norm.bias, decode_head.unet.downs.2.1.mlp.1.weight, decode_head.unet.downs.2.1.mlp.1.bias, decode_head.unet.downs.2.1.block1.proj.weight, decode_head.unet.downs.2.1.block1.proj.bias, decode_head.unet.downs.2.1.block1.norm.weight, decode_head.unet.downs.2.1.block1.norm.bias, decode_head.unet.downs.2.1.block2.proj.weight, decode_head.unet.downs.2.1.block2.proj.bias, decode_head.unet.downs.2.1.block2.norm.weight, decode_head.unet.downs.2.1.block2.norm.bias, decode_head.unet.downs.2.2.fn.fn.to_qkv.weight, decode_head.unet.downs.2.2.fn.fn.to_out.0.weight, decode_head.unet.downs.2.2.fn.fn.to_out.0.bias, decode_head.unet.downs.2.2.fn.fn.to_out.1.g, decode_head.unet.downs.2.2.fn.norm.g, decode_head.unet.downs.2.3.weight, decode_head.unet.downs.2.3.bias, decode_head.unet.ups.0.0.mlp.1.weight, decode_head.unet.ups.0.0.mlp.1.bias, decode_head.unet.ups.0.0.block1.proj.weight, decode_head.unet.ups.0.0.block1.proj.bias, decode_head.unet.ups.0.0.block1.norm.weight, decode_head.unet.ups.0.0.block1.norm.bias, decode_head.unet.ups.0.0.block2.proj.weight, decode_head.unet.ups.0.0.block2.proj.bias, decode_head.unet.ups.0.0.block2.norm.weight, decode_head.unet.ups.0.0.block2.norm.bias, decode_head.unet.ups.0.0.res_conv.weight, decode_head.unet.ups.0.0.res_conv.bias, decode_head.unet.ups.0.1.mlp.1.weight, decode_head.unet.ups.0.1.mlp.1.bias, decode_head.unet.ups.0.1.block1.proj.weight, decode_head.unet.ups.0.1.block1.proj.bias, decode_head.unet.ups.0.1.block1.norm.weight, decode_head.unet.ups.0.1.block1.norm.bias, decode_head.unet.ups.0.1.block2.proj.weight, decode_head.unet.ups.0.1.block2.proj.bias, decode_head.unet.ups.0.1.block2.norm.weight, decode_head.unet.ups.0.1.block2.norm.bias, decode_head.unet.ups.0.1.res_conv.weight, decode_head.unet.ups.0.1.res_conv.bias, decode_head.unet.ups.0.2.fn.fn.to_qkv.weight, decode_head.unet.ups.0.2.fn.fn.to_out.0.weight, decode_head.unet.ups.0.2.fn.fn.to_out.0.bias, decode_head.unet.ups.0.2.fn.fn.to_out.1.g, decode_head.unet.ups.0.2.fn.norm.g, decode_head.unet.ups.0.3.1.weight, decode_head.unet.ups.0.3.1.bias, decode_head.unet.ups.1.0.mlp.1.weight, decode_head.unet.ups.1.0.mlp.1.bias, decode_head.unet.ups.1.0.block1.proj.weight, decode_head.unet.ups.1.0.block1.proj.bias, decode_head.unet.ups.1.0.block1.norm.weight, decode_head.unet.ups.1.0.block1.norm.bias, decode_head.unet.ups.1.0.block2.proj.weight, decode_head.unet.ups.1.0.block2.proj.bias, decode_head.unet.ups.1.0.block2.norm.weight, decode_head.unet.ups.1.0.block2.norm.bias, decode_head.unet.ups.1.0.res_conv.weight, decode_head.unet.ups.1.0.res_conv.bias, decode_head.unet.ups.1.1.mlp.1.weight, decode_head.unet.ups.1.1.mlp.1.bias, decode_head.unet.ups.1.1.block1.proj.weight, decode_head.unet.ups.1.1.block1.proj.bias, decode_head.unet.ups.1.1.block1.norm.weight, decode_head.unet.ups.1.1.block1.norm.bias, decode_head.unet.ups.1.1.block2.proj.weight, decode_head.unet.ups.1.1.block2.proj.bias, decode_head.unet.ups.1.1.block2.norm.weight, decode_head.unet.ups.1.1.block2.norm.bias, decode_head.unet.ups.1.1.res_conv.weight, decode_head.unet.ups.1.1.res_conv.bias, decode_head.unet.ups.1.2.fn.fn.to_qkv.weight, decode_head.unet.ups.1.2.fn.fn.to_out.0.weight, decode_head.unet.ups.1.2.fn.fn.to_out.0.bias, decode_head.unet.ups.1.2.fn.fn.to_out.1.g, decode_head.unet.ups.1.2.fn.norm.g, decode_head.unet.ups.1.3.1.weight, decode_head.unet.ups.1.3.1.bias, decode_head.unet.ups.2.0.mlp.1.weight, decode_head.unet.ups.2.0.mlp.1.bias, decode_head.unet.ups.2.0.block1.proj.weight, decode_head.unet.ups.2.0.block1.proj.bias, decode_head.unet.ups.2.0.block1.norm.weight, decode_head.unet.ups.2.0.block1.norm.bias, decode_head.unet.ups.2.0.block2.proj.weight, decode_head.unet.ups.2.0.block2.proj.bias, decode_head.unet.ups.2.0.block2.norm.weight, decode_head.unet.ups.2.0.block2.norm.bias, decode_head.unet.ups.2.0.res_conv.weight, decode_head.unet.ups.2.0.res_conv.bias, decode_head.unet.ups.2.1.mlp.1.weight, decode_head.unet.ups.2.1.mlp.1.bias, decode_head.unet.ups.2.1.block1.proj.weight, decode_head.unet.ups.2.1.block1.proj.bias, decode_head.unet.ups.2.1.block1.norm.weight, decode_head.unet.ups.2.1.block1.norm.bias, decode_head.unet.ups.2.1.block2.proj.weight, decode_head.unet.ups.2.1.block2.proj.bias, decode_head.unet.ups.2.1.block2.norm.weight, decode_head.unet.ups.2.1.block2.norm.bias, decode_head.unet.ups.2.1.res_conv.weight, decode_head.unet.ups.2.1.res_conv.bias, decode_head.unet.ups.2.2.fn.fn.to_qkv.weight, decode_head.unet.ups.2.2.fn.fn.to_out.0.weight, decode_head.unet.ups.2.2.fn.fn.to_out.0.bias, decode_head.unet.ups.2.2.fn.fn.to_out.1.g, decode_head.unet.ups.2.2.fn.norm.g, decode_head.unet.ups.2.3.weight, decode_head.unet.ups.2.3.bias, decode_head.unet.mid_block1.mlp.1.weight, decode_head.unet.mid_block1.mlp.1.bias, decode_head.unet.mid_block1.block1.proj.weight, decode_head.unet.mid_block1.block1.proj.bias, decode_head.unet.mid_block1.block1.norm.weight, decode_head.unet.mid_block1.block1.norm.bias, decode_head.unet.mid_block1.block2.proj.weight, decode_head.unet.mid_block1.block2.proj.bias, decode_head.unet.mid_block1.block2.norm.weight, decode_head.unet.mid_block1.block2.norm.bias, decode_head.unet.mid_attn.fn.fn.to_qkv.weight, decode_head.unet.mid_attn.fn.fn.to_out.weight, decode_head.unet.mid_attn.fn.fn.to_out.bias, decode_head.unet.mid_attn.fn.norm.g, decode_head.unet.mid_block2.mlp.1.weight, decode_head.unet.mid_block2.mlp.1.bias, decode_head.unet.mid_block2.block1.proj.weight, decode_head.unet.mid_block2.block1.proj.bias, decode_head.unet.mid_block2.block1.norm.weight, decode_head.unet.mid_block2.block1.norm.bias, decode_head.unet.mid_block2.block2.proj.weight, decode_head.unet.mid_block2.block2.proj.bias, decode_head.unet.mid_block2.block2.norm.weight, decode_head.unet.mid_block2.block2.norm.bias, decode_head.unet.final_res_block.mlp.1.weight, decode_head.unet.final_res_block.mlp.1.bias, decode_head.unet.final_res_block.block1.proj.weight, decode_head.unet.final_res_block.block1.proj.bias, decode_head.unet.final_res_block.block1.norm.weight, decode_head.unet.final_res_block.block1.norm.bias, decode_head.unet.final_res_block.block2.proj.weight, decode_head.unet.final_res_block.block2.proj.bias, decode_head.unet.final_res_block.block2.norm.weight, decode_head.unet.final_res_block.block2.norm.bias, decode_head.unet.final_res_block.res_conv.weight, decode_head.unet.final_res_block.res_conv.bias, decode_head.unet.final_conv.weight, decode_head.unet.final_conv.bias, decode_head.conv_seg_new.weight, decode_head.conv_seg_new.bias, decode_head.embed.weight
+
+2023-03-04 14:00:54,310 - mmseg - INFO - load checkpoint from local path: work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20/latest.pth
+2023-03-04 14:00:55,223 - mmseg - WARNING - The model and loaded state dict do not match exactly
+
+size mismatch for unet.init_conv.weight: copying a param with shape torch.Size([256, 528, 7, 7]) from checkpoint, the shape in current model is torch.Size([128, 528, 7, 7]).
+size mismatch for unet.init_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.time_mlp.1.weight: copying a param with shape torch.Size([1024, 256]) from checkpoint, the shape in current model is torch.Size([512, 128]).
+size mismatch for unet.time_mlp.1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([512]).
+size mismatch for unet.time_mlp.3.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([512, 512]).
+size mismatch for unet.time_mlp.3.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([512]).
+size mismatch for unet.downs.0.0.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
+size mismatch for unet.downs.0.0.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
+size mismatch for unet.downs.0.0.block1.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.downs.0.0.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.0.0.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.0.0.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.0.0.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.downs.0.0.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.0.0.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.0.0.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.0.1.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
+size mismatch for unet.downs.0.1.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
+size mismatch for unet.downs.0.1.block1.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.downs.0.1.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.0.1.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.0.1.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.0.1.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.downs.0.1.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.0.1.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.0.1.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.0.2.fn.fn.to_qkv.weight: copying a param with shape torch.Size([384, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([384, 128, 1, 1]).
+size mismatch for unet.downs.0.2.fn.fn.to_out.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]).
+size mismatch for unet.downs.0.2.fn.fn.to_out.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.0.2.fn.fn.to_out.1.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]).
+size mismatch for unet.downs.0.2.fn.norm.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]).
+size mismatch for unet.downs.0.3.weight: copying a param with shape torch.Size([256, 256, 4, 4]) from checkpoint, the shape in current model is torch.Size([128, 128, 4, 4]).
+size mismatch for unet.downs.0.3.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.1.0.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
+size mismatch for unet.downs.1.0.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
+size mismatch for unet.downs.1.0.block1.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.downs.1.0.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.1.0.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.1.0.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.1.0.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.downs.1.0.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.1.0.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.1.0.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.1.1.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
+size mismatch for unet.downs.1.1.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
+size mismatch for unet.downs.1.1.block1.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.downs.1.1.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.1.1.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.1.1.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.1.1.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.downs.1.1.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.1.1.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.1.1.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.1.2.fn.fn.to_qkv.weight: copying a param with shape torch.Size([384, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([384, 128, 1, 1]).
+size mismatch for unet.downs.1.2.fn.fn.to_out.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]).
+size mismatch for unet.downs.1.2.fn.fn.to_out.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.1.2.fn.fn.to_out.1.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]).
+size mismatch for unet.downs.1.2.fn.norm.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]).
+size mismatch for unet.downs.1.3.weight: copying a param with shape torch.Size([256, 256, 4, 4]) from checkpoint, the shape in current model is torch.Size([128, 128, 4, 4]).
+size mismatch for unet.downs.1.3.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.2.0.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
+size mismatch for unet.downs.2.0.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
+size mismatch for unet.downs.2.0.block1.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.downs.2.0.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.2.0.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.2.0.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.2.0.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.downs.2.0.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.2.0.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.2.0.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.2.1.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
+size mismatch for unet.downs.2.1.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
+size mismatch for unet.downs.2.1.block1.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.downs.2.1.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.2.1.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.2.1.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.2.1.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.downs.2.1.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.2.1.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.2.1.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.2.2.fn.fn.to_qkv.weight: copying a param with shape torch.Size([384, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([384, 128, 1, 1]).
+size mismatch for unet.downs.2.2.fn.fn.to_out.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]).
+size mismatch for unet.downs.2.2.fn.fn.to_out.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.downs.2.2.fn.fn.to_out.1.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]).
+size mismatch for unet.downs.2.2.fn.norm.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]).
+size mismatch for unet.downs.2.3.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.downs.2.3.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.0.0.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
+size mismatch for unet.ups.0.0.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
+size mismatch for unet.ups.0.0.block1.proj.weight: copying a param with shape torch.Size([256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]).
+size mismatch for unet.ups.0.0.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.0.0.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.0.0.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.0.0.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.ups.0.0.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.0.0.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.0.0.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.0.0.res_conv.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).
+size mismatch for unet.ups.0.0.res_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.0.1.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
+size mismatch for unet.ups.0.1.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
+size mismatch for unet.ups.0.1.block1.proj.weight: copying a param with shape torch.Size([256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]).
+size mismatch for unet.ups.0.1.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.0.1.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.0.1.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.0.1.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.ups.0.1.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.0.1.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.0.1.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.0.1.res_conv.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).
+size mismatch for unet.ups.0.1.res_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.0.2.fn.fn.to_qkv.weight: copying a param with shape torch.Size([384, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([384, 128, 1, 1]).
+size mismatch for unet.ups.0.2.fn.fn.to_out.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]).
+size mismatch for unet.ups.0.2.fn.fn.to_out.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.0.2.fn.fn.to_out.1.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]).
+size mismatch for unet.ups.0.2.fn.norm.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]).
+size mismatch for unet.ups.0.3.1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.ups.0.3.1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.1.0.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
+size mismatch for unet.ups.1.0.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
+size mismatch for unet.ups.1.0.block1.proj.weight: copying a param with shape torch.Size([256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]).
+size mismatch for unet.ups.1.0.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.1.0.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.1.0.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.1.0.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.ups.1.0.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.1.0.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.1.0.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.1.0.res_conv.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).
+size mismatch for unet.ups.1.0.res_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.1.1.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
+size mismatch for unet.ups.1.1.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
+size mismatch for unet.ups.1.1.block1.proj.weight: copying a param with shape torch.Size([256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]).
+size mismatch for unet.ups.1.1.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.1.1.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.1.1.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.1.1.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.ups.1.1.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.1.1.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.1.1.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.1.1.res_conv.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).
+size mismatch for unet.ups.1.1.res_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.1.2.fn.fn.to_qkv.weight: copying a param with shape torch.Size([384, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([384, 128, 1, 1]).
+size mismatch for unet.ups.1.2.fn.fn.to_out.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]).
+size mismatch for unet.ups.1.2.fn.fn.to_out.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.1.2.fn.fn.to_out.1.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]).
+size mismatch for unet.ups.1.2.fn.norm.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]).
+size mismatch for unet.ups.1.3.1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.ups.1.3.1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.2.0.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
+size mismatch for unet.ups.2.0.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
+size mismatch for unet.ups.2.0.block1.proj.weight: copying a param with shape torch.Size([256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]).
+size mismatch for unet.ups.2.0.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.2.0.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.2.0.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.2.0.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.ups.2.0.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.2.0.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.2.0.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.2.0.res_conv.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).
+size mismatch for unet.ups.2.0.res_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.2.1.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
+size mismatch for unet.ups.2.1.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
+size mismatch for unet.ups.2.1.block1.proj.weight: copying a param with shape torch.Size([256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]).
+size mismatch for unet.ups.2.1.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.2.1.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.2.1.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.2.1.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.ups.2.1.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.2.1.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.2.1.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.2.1.res_conv.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).
+size mismatch for unet.ups.2.1.res_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.2.2.fn.fn.to_qkv.weight: copying a param with shape torch.Size([384, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([384, 128, 1, 1]).
+size mismatch for unet.ups.2.2.fn.fn.to_out.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]).
+size mismatch for unet.ups.2.2.fn.fn.to_out.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.ups.2.2.fn.fn.to_out.1.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]).
+size mismatch for unet.ups.2.2.fn.norm.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]).
+size mismatch for unet.ups.2.3.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.ups.2.3.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.mid_block1.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
+size mismatch for unet.mid_block1.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
+size mismatch for unet.mid_block1.block1.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.mid_block1.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.mid_block1.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.mid_block1.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.mid_block1.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.mid_block1.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.mid_block1.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.mid_block1.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.mid_attn.fn.fn.to_qkv.weight: copying a param with shape torch.Size([384, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([384, 128, 1, 1]).
+size mismatch for unet.mid_attn.fn.fn.to_out.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]).
+size mismatch for unet.mid_attn.fn.fn.to_out.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.mid_attn.fn.norm.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]).
+size mismatch for unet.mid_block2.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
+size mismatch for unet.mid_block2.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
+size mismatch for unet.mid_block2.block1.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.mid_block2.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.mid_block2.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.mid_block2.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.mid_block2.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.mid_block2.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.mid_block2.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.mid_block2.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.final_res_block.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
+size mismatch for unet.final_res_block.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
+size mismatch for unet.final_res_block.block1.proj.weight: copying a param with shape torch.Size([256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]).
+size mismatch for unet.final_res_block.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.final_res_block.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.final_res_block.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.final_res_block.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
+size mismatch for unet.final_res_block.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.final_res_block.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.final_res_block.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.final_res_block.res_conv.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).
+size mismatch for unet.final_res_block.res_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
+size mismatch for unet.final_conv.weight: copying a param with shape torch.Size([256, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 128, 1, 1]).
+unexpected key in source state_dict: backbone.stem.0.weight, backbone.stem.1.weight, backbone.stem.1.bias, backbone.stem.1.running_mean, backbone.stem.1.running_var, backbone.stem.1.num_batches_tracked, backbone.stem.3.weight, backbone.stem.4.weight, backbone.stem.4.bias, backbone.stem.4.running_mean, backbone.stem.4.running_var, backbone.stem.4.num_batches_tracked, backbone.stem.6.weight, backbone.stem.7.weight, backbone.stem.7.bias, backbone.stem.7.running_mean, backbone.stem.7.running_var, backbone.stem.7.num_batches_tracked, backbone.layer1.0.conv1.weight, backbone.layer1.0.bn1.weight, backbone.layer1.0.bn1.bias, backbone.layer1.0.bn1.running_mean, backbone.layer1.0.bn1.running_var, backbone.layer1.0.bn1.num_batches_tracked, backbone.layer1.0.conv2.weight, backbone.layer1.0.bn2.weight, backbone.layer1.0.bn2.bias, backbone.layer1.0.bn2.running_mean, backbone.layer1.0.bn2.running_var, backbone.layer1.0.bn2.num_batches_tracked, backbone.layer1.0.conv3.weight, backbone.layer1.0.bn3.weight, backbone.layer1.0.bn3.bias, backbone.layer1.0.bn3.running_mean, backbone.layer1.0.bn3.running_var, backbone.layer1.0.bn3.num_batches_tracked, backbone.layer1.0.downsample.0.weight, backbone.layer1.0.downsample.1.weight, backbone.layer1.0.downsample.1.bias, backbone.layer1.0.downsample.1.running_mean, backbone.layer1.0.downsample.1.running_var, backbone.layer1.0.downsample.1.num_batches_tracked, backbone.layer1.1.conv1.weight, backbone.layer1.1.bn1.weight, backbone.layer1.1.bn1.bias, backbone.layer1.1.bn1.running_mean, backbone.layer1.1.bn1.running_var, backbone.layer1.1.bn1.num_batches_tracked, backbone.layer1.1.conv2.weight, backbone.layer1.1.bn2.weight, backbone.layer1.1.bn2.bias, backbone.layer1.1.bn2.running_mean, backbone.layer1.1.bn2.running_var, backbone.layer1.1.bn2.num_batches_tracked, backbone.layer1.1.conv3.weight, backbone.layer1.1.bn3.weight, backbone.layer1.1.bn3.bias, backbone.layer1.1.bn3.running_mean, backbone.layer1.1.bn3.running_var, backbone.layer1.1.bn3.num_batches_tracked, backbone.layer1.2.conv1.weight, backbone.layer1.2.bn1.weight, backbone.layer1.2.bn1.bias, backbone.layer1.2.bn1.running_mean, backbone.layer1.2.bn1.running_var, backbone.layer1.2.bn1.num_batches_tracked, backbone.layer1.2.conv2.weight, backbone.layer1.2.bn2.weight, backbone.layer1.2.bn2.bias, backbone.layer1.2.bn2.running_mean, backbone.layer1.2.bn2.running_var, backbone.layer1.2.bn2.num_batches_tracked, backbone.layer1.2.conv3.weight, backbone.layer1.2.bn3.weight, backbone.layer1.2.bn3.bias, backbone.layer1.2.bn3.running_mean, backbone.layer1.2.bn3.running_var, backbone.layer1.2.bn3.num_batches_tracked, backbone.layer2.0.conv1.weight, backbone.layer2.0.bn1.weight, backbone.layer2.0.bn1.bias, backbone.layer2.0.bn1.running_mean, backbone.layer2.0.bn1.running_var, backbone.layer2.0.bn1.num_batches_tracked, backbone.layer2.0.conv2.weight, backbone.layer2.0.bn2.weight, backbone.layer2.0.bn2.bias, backbone.layer2.0.bn2.running_mean, backbone.layer2.0.bn2.running_var, backbone.layer2.0.bn2.num_batches_tracked, backbone.layer2.0.conv3.weight, backbone.layer2.0.bn3.weight, backbone.layer2.0.bn3.bias, backbone.layer2.0.bn3.running_mean, backbone.layer2.0.bn3.running_var, backbone.layer2.0.bn3.num_batches_tracked, backbone.layer2.0.downsample.0.weight, backbone.layer2.0.downsample.1.weight, backbone.layer2.0.downsample.1.bias, backbone.layer2.0.downsample.1.running_mean, backbone.layer2.0.downsample.1.running_var, backbone.layer2.0.downsample.1.num_batches_tracked, backbone.layer2.1.conv1.weight, backbone.layer2.1.bn1.weight, backbone.layer2.1.bn1.bias, backbone.layer2.1.bn1.running_mean, backbone.layer2.1.bn1.running_var, backbone.layer2.1.bn1.num_batches_tracked, backbone.layer2.1.conv2.weight, backbone.layer2.1.bn2.weight, backbone.layer2.1.bn2.bias, backbone.layer2.1.bn2.running_mean, backbone.layer2.1.bn2.running_var, backbone.layer2.1.bn2.num_batches_tracked, backbone.layer2.1.conv3.weight, backbone.layer2.1.bn3.weight, backbone.layer2.1.bn3.bias, backbone.layer2.1.bn3.running_mean, backbone.layer2.1.bn3.running_var, backbone.layer2.1.bn3.num_batches_tracked, backbone.layer2.2.conv1.weight, backbone.layer2.2.bn1.weight, backbone.layer2.2.bn1.bias, backbone.layer2.2.bn1.running_mean, backbone.layer2.2.bn1.running_var, backbone.layer2.2.bn1.num_batches_tracked, backbone.layer2.2.conv2.weight, backbone.layer2.2.bn2.weight, backbone.layer2.2.bn2.bias, backbone.layer2.2.bn2.running_mean, backbone.layer2.2.bn2.running_var, backbone.layer2.2.bn2.num_batches_tracked, backbone.layer2.2.conv3.weight, backbone.layer2.2.bn3.weight, backbone.layer2.2.bn3.bias, backbone.layer2.2.bn3.running_mean, backbone.layer2.2.bn3.running_var, backbone.layer2.2.bn3.num_batches_tracked, backbone.layer2.3.conv1.weight, backbone.layer2.3.bn1.weight, backbone.layer2.3.bn1.bias, backbone.layer2.3.bn1.running_mean, backbone.layer2.3.bn1.running_var, backbone.layer2.3.bn1.num_batches_tracked, backbone.layer2.3.conv2.weight, backbone.layer2.3.bn2.weight, backbone.layer2.3.bn2.bias, backbone.layer2.3.bn2.running_mean, backbone.layer2.3.bn2.running_var, backbone.layer2.3.bn2.num_batches_tracked, backbone.layer2.3.conv3.weight, backbone.layer2.3.bn3.weight, backbone.layer2.3.bn3.bias, backbone.layer2.3.bn3.running_mean, backbone.layer2.3.bn3.running_var, backbone.layer2.3.bn3.num_batches_tracked, backbone.layer3.0.conv1.weight, backbone.layer3.0.bn1.weight, backbone.layer3.0.bn1.bias, backbone.layer3.0.bn1.running_mean, backbone.layer3.0.bn1.running_var, backbone.layer3.0.bn1.num_batches_tracked, backbone.layer3.0.conv2.weight, backbone.layer3.0.bn2.weight, backbone.layer3.0.bn2.bias, backbone.layer3.0.bn2.running_mean, backbone.layer3.0.bn2.running_var, backbone.layer3.0.bn2.num_batches_tracked, backbone.layer3.0.conv3.weight, backbone.layer3.0.bn3.weight, backbone.layer3.0.bn3.bias, backbone.layer3.0.bn3.running_mean, backbone.layer3.0.bn3.running_var, backbone.layer3.0.bn3.num_batches_tracked, backbone.layer3.0.downsample.0.weight, backbone.layer3.0.downsample.1.weight, backbone.layer3.0.downsample.1.bias, backbone.layer3.0.downsample.1.running_mean, backbone.layer3.0.downsample.1.running_var, backbone.layer3.0.downsample.1.num_batches_tracked, backbone.layer3.1.conv1.weight, backbone.layer3.1.bn1.weight, backbone.layer3.1.bn1.bias, backbone.layer3.1.bn1.running_mean, backbone.layer3.1.bn1.running_var, backbone.layer3.1.bn1.num_batches_tracked, backbone.layer3.1.conv2.weight, backbone.layer3.1.bn2.weight, backbone.layer3.1.bn2.bias, backbone.layer3.1.bn2.running_mean, backbone.layer3.1.bn2.running_var, backbone.layer3.1.bn2.num_batches_tracked, backbone.layer3.1.conv3.weight, backbone.layer3.1.bn3.weight, backbone.layer3.1.bn3.bias, backbone.layer3.1.bn3.running_mean, backbone.layer3.1.bn3.running_var, backbone.layer3.1.bn3.num_batches_tracked, backbone.layer3.2.conv1.weight, backbone.layer3.2.bn1.weight, backbone.layer3.2.bn1.bias, backbone.layer3.2.bn1.running_mean, backbone.layer3.2.bn1.running_var, backbone.layer3.2.bn1.num_batches_tracked, backbone.layer3.2.conv2.weight, backbone.layer3.2.bn2.weight, backbone.layer3.2.bn2.bias, backbone.layer3.2.bn2.running_mean, backbone.layer3.2.bn2.running_var, backbone.layer3.2.bn2.num_batches_tracked, backbone.layer3.2.conv3.weight, backbone.layer3.2.bn3.weight, backbone.layer3.2.bn3.bias, backbone.layer3.2.bn3.running_mean, backbone.layer3.2.bn3.running_var, backbone.layer3.2.bn3.num_batches_tracked, backbone.layer3.3.conv1.weight, backbone.layer3.3.bn1.weight, backbone.layer3.3.bn1.bias, backbone.layer3.3.bn1.running_mean, backbone.layer3.3.bn1.running_var, backbone.layer3.3.bn1.num_batches_tracked, backbone.layer3.3.conv2.weight, backbone.layer3.3.bn2.weight, backbone.layer3.3.bn2.bias, backbone.layer3.3.bn2.running_mean, backbone.layer3.3.bn2.running_var, backbone.layer3.3.bn2.num_batches_tracked, backbone.layer3.3.conv3.weight, backbone.layer3.3.bn3.weight, backbone.layer3.3.bn3.bias, backbone.layer3.3.bn3.running_mean, backbone.layer3.3.bn3.running_var, backbone.layer3.3.bn3.num_batches_tracked, backbone.layer3.4.conv1.weight, backbone.layer3.4.bn1.weight, backbone.layer3.4.bn1.bias, backbone.layer3.4.bn1.running_mean, backbone.layer3.4.bn1.running_var, backbone.layer3.4.bn1.num_batches_tracked, backbone.layer3.4.conv2.weight, backbone.layer3.4.bn2.weight, backbone.layer3.4.bn2.bias, backbone.layer3.4.bn2.running_mean, backbone.layer3.4.bn2.running_var, backbone.layer3.4.bn2.num_batches_tracked, backbone.layer3.4.conv3.weight, backbone.layer3.4.bn3.weight, backbone.layer3.4.bn3.bias, backbone.layer3.4.bn3.running_mean, backbone.layer3.4.bn3.running_var, backbone.layer3.4.bn3.num_batches_tracked, backbone.layer3.5.conv1.weight, backbone.layer3.5.bn1.weight, backbone.layer3.5.bn1.bias, backbone.layer3.5.bn1.running_mean, backbone.layer3.5.bn1.running_var, backbone.layer3.5.bn1.num_batches_tracked, backbone.layer3.5.conv2.weight, backbone.layer3.5.bn2.weight, backbone.layer3.5.bn2.bias, backbone.layer3.5.bn2.running_mean, backbone.layer3.5.bn2.running_var, backbone.layer3.5.bn2.num_batches_tracked, backbone.layer3.5.conv3.weight, backbone.layer3.5.bn3.weight, backbone.layer3.5.bn3.bias, backbone.layer3.5.bn3.running_mean, backbone.layer3.5.bn3.running_var, backbone.layer3.5.bn3.num_batches_tracked, backbone.layer3.6.conv1.weight, backbone.layer3.6.bn1.weight, backbone.layer3.6.bn1.bias, backbone.layer3.6.bn1.running_mean, backbone.layer3.6.bn1.running_var, backbone.layer3.6.bn1.num_batches_tracked, backbone.layer3.6.conv2.weight, backbone.layer3.6.bn2.weight, backbone.layer3.6.bn2.bias, backbone.layer3.6.bn2.running_mean, backbone.layer3.6.bn2.running_var, backbone.layer3.6.bn2.num_batches_tracked, backbone.layer3.6.conv3.weight, backbone.layer3.6.bn3.weight, backbone.layer3.6.bn3.bias, backbone.layer3.6.bn3.running_mean, backbone.layer3.6.bn3.running_var, backbone.layer3.6.bn3.num_batches_tracked, backbone.layer3.7.conv1.weight, backbone.layer3.7.bn1.weight, backbone.layer3.7.bn1.bias, backbone.layer3.7.bn1.running_mean, backbone.layer3.7.bn1.running_var, backbone.layer3.7.bn1.num_batches_tracked, backbone.layer3.7.conv2.weight, backbone.layer3.7.bn2.weight, backbone.layer3.7.bn2.bias, backbone.layer3.7.bn2.running_mean, backbone.layer3.7.bn2.running_var, backbone.layer3.7.bn2.num_batches_tracked, backbone.layer3.7.conv3.weight, backbone.layer3.7.bn3.weight, backbone.layer3.7.bn3.bias, backbone.layer3.7.bn3.running_mean, backbone.layer3.7.bn3.running_var, backbone.layer3.7.bn3.num_batches_tracked, backbone.layer3.8.conv1.weight, backbone.layer3.8.bn1.weight, backbone.layer3.8.bn1.bias, backbone.layer3.8.bn1.running_mean, backbone.layer3.8.bn1.running_var, backbone.layer3.8.bn1.num_batches_tracked, backbone.layer3.8.conv2.weight, backbone.layer3.8.bn2.weight, backbone.layer3.8.bn2.bias, backbone.layer3.8.bn2.running_mean, backbone.layer3.8.bn2.running_var, backbone.layer3.8.bn2.num_batches_tracked, backbone.layer3.8.conv3.weight, backbone.layer3.8.bn3.weight, backbone.layer3.8.bn3.bias, backbone.layer3.8.bn3.running_mean, backbone.layer3.8.bn3.running_var, backbone.layer3.8.bn3.num_batches_tracked, backbone.layer3.9.conv1.weight, backbone.layer3.9.bn1.weight, backbone.layer3.9.bn1.bias, backbone.layer3.9.bn1.running_mean, backbone.layer3.9.bn1.running_var, backbone.layer3.9.bn1.num_batches_tracked, backbone.layer3.9.conv2.weight, backbone.layer3.9.bn2.weight, backbone.layer3.9.bn2.bias, backbone.layer3.9.bn2.running_mean, backbone.layer3.9.bn2.running_var, backbone.layer3.9.bn2.num_batches_tracked, backbone.layer3.9.conv3.weight, backbone.layer3.9.bn3.weight, backbone.layer3.9.bn3.bias, backbone.layer3.9.bn3.running_mean, backbone.layer3.9.bn3.running_var, backbone.layer3.9.bn3.num_batches_tracked, backbone.layer3.10.conv1.weight, backbone.layer3.10.bn1.weight, backbone.layer3.10.bn1.bias, backbone.layer3.10.bn1.running_mean, backbone.layer3.10.bn1.running_var, backbone.layer3.10.bn1.num_batches_tracked, backbone.layer3.10.conv2.weight, backbone.layer3.10.bn2.weight, backbone.layer3.10.bn2.bias, backbone.layer3.10.bn2.running_mean, backbone.layer3.10.bn2.running_var, backbone.layer3.10.bn2.num_batches_tracked, backbone.layer3.10.conv3.weight, backbone.layer3.10.bn3.weight, backbone.layer3.10.bn3.bias, backbone.layer3.10.bn3.running_mean, backbone.layer3.10.bn3.running_var, backbone.layer3.10.bn3.num_batches_tracked, backbone.layer3.11.conv1.weight, backbone.layer3.11.bn1.weight, backbone.layer3.11.bn1.bias, backbone.layer3.11.bn1.running_mean, backbone.layer3.11.bn1.running_var, backbone.layer3.11.bn1.num_batches_tracked, backbone.layer3.11.conv2.weight, backbone.layer3.11.bn2.weight, backbone.layer3.11.bn2.bias, backbone.layer3.11.bn2.running_mean, backbone.layer3.11.bn2.running_var, backbone.layer3.11.bn2.num_batches_tracked, backbone.layer3.11.conv3.weight, backbone.layer3.11.bn3.weight, backbone.layer3.11.bn3.bias, backbone.layer3.11.bn3.running_mean, backbone.layer3.11.bn3.running_var, backbone.layer3.11.bn3.num_batches_tracked, backbone.layer3.12.conv1.weight, backbone.layer3.12.bn1.weight, backbone.layer3.12.bn1.bias, backbone.layer3.12.bn1.running_mean, backbone.layer3.12.bn1.running_var, backbone.layer3.12.bn1.num_batches_tracked, backbone.layer3.12.conv2.weight, backbone.layer3.12.bn2.weight, backbone.layer3.12.bn2.bias, backbone.layer3.12.bn2.running_mean, backbone.layer3.12.bn2.running_var, backbone.layer3.12.bn2.num_batches_tracked, backbone.layer3.12.conv3.weight, backbone.layer3.12.bn3.weight, backbone.layer3.12.bn3.bias, backbone.layer3.12.bn3.running_mean, backbone.layer3.12.bn3.running_var, backbone.layer3.12.bn3.num_batches_tracked, backbone.layer3.13.conv1.weight, backbone.layer3.13.bn1.weight, backbone.layer3.13.bn1.bias, backbone.layer3.13.bn1.running_mean, backbone.layer3.13.bn1.running_var, backbone.layer3.13.bn1.num_batches_tracked, backbone.layer3.13.conv2.weight, backbone.layer3.13.bn2.weight, backbone.layer3.13.bn2.bias, backbone.layer3.13.bn2.running_mean, backbone.layer3.13.bn2.running_var, backbone.layer3.13.bn2.num_batches_tracked, backbone.layer3.13.conv3.weight, backbone.layer3.13.bn3.weight, backbone.layer3.13.bn3.bias, backbone.layer3.13.bn3.running_mean, backbone.layer3.13.bn3.running_var, backbone.layer3.13.bn3.num_batches_tracked, backbone.layer3.14.conv1.weight, backbone.layer3.14.bn1.weight, backbone.layer3.14.bn1.bias, backbone.layer3.14.bn1.running_mean, backbone.layer3.14.bn1.running_var, backbone.layer3.14.bn1.num_batches_tracked, backbone.layer3.14.conv2.weight, backbone.layer3.14.bn2.weight, backbone.layer3.14.bn2.bias, backbone.layer3.14.bn2.running_mean, backbone.layer3.14.bn2.running_var, backbone.layer3.14.bn2.num_batches_tracked, backbone.layer3.14.conv3.weight, backbone.layer3.14.bn3.weight, backbone.layer3.14.bn3.bias, backbone.layer3.14.bn3.running_mean, backbone.layer3.14.bn3.running_var, backbone.layer3.14.bn3.num_batches_tracked, backbone.layer3.15.conv1.weight, backbone.layer3.15.bn1.weight, backbone.layer3.15.bn1.bias, backbone.layer3.15.bn1.running_mean, backbone.layer3.15.bn1.running_var, backbone.layer3.15.bn1.num_batches_tracked, backbone.layer3.15.conv2.weight, backbone.layer3.15.bn2.weight, backbone.layer3.15.bn2.bias, backbone.layer3.15.bn2.running_mean, backbone.layer3.15.bn2.running_var, backbone.layer3.15.bn2.num_batches_tracked, backbone.layer3.15.conv3.weight, backbone.layer3.15.bn3.weight, backbone.layer3.15.bn3.bias, backbone.layer3.15.bn3.running_mean, backbone.layer3.15.bn3.running_var, backbone.layer3.15.bn3.num_batches_tracked, backbone.layer3.16.conv1.weight, backbone.layer3.16.bn1.weight, backbone.layer3.16.bn1.bias, backbone.layer3.16.bn1.running_mean, backbone.layer3.16.bn1.running_var, backbone.layer3.16.bn1.num_batches_tracked, backbone.layer3.16.conv2.weight, backbone.layer3.16.bn2.weight, backbone.layer3.16.bn2.bias, backbone.layer3.16.bn2.running_mean, backbone.layer3.16.bn2.running_var, backbone.layer3.16.bn2.num_batches_tracked, backbone.layer3.16.conv3.weight, backbone.layer3.16.bn3.weight, backbone.layer3.16.bn3.bias, backbone.layer3.16.bn3.running_mean, backbone.layer3.16.bn3.running_var, backbone.layer3.16.bn3.num_batches_tracked, backbone.layer3.17.conv1.weight, backbone.layer3.17.bn1.weight, backbone.layer3.17.bn1.bias, backbone.layer3.17.bn1.running_mean, backbone.layer3.17.bn1.running_var, backbone.layer3.17.bn1.num_batches_tracked, backbone.layer3.17.conv2.weight, backbone.layer3.17.bn2.weight, backbone.layer3.17.bn2.bias, backbone.layer3.17.bn2.running_mean, backbone.layer3.17.bn2.running_var, backbone.layer3.17.bn2.num_batches_tracked, backbone.layer3.17.conv3.weight, backbone.layer3.17.bn3.weight, backbone.layer3.17.bn3.bias, backbone.layer3.17.bn3.running_mean, backbone.layer3.17.bn3.running_var, backbone.layer3.17.bn3.num_batches_tracked, backbone.layer3.18.conv1.weight, backbone.layer3.18.bn1.weight, backbone.layer3.18.bn1.bias, backbone.layer3.18.bn1.running_mean, backbone.layer3.18.bn1.running_var, backbone.layer3.18.bn1.num_batches_tracked, backbone.layer3.18.conv2.weight, backbone.layer3.18.bn2.weight, backbone.layer3.18.bn2.bias, backbone.layer3.18.bn2.running_mean, backbone.layer3.18.bn2.running_var, backbone.layer3.18.bn2.num_batches_tracked, backbone.layer3.18.conv3.weight, backbone.layer3.18.bn3.weight, backbone.layer3.18.bn3.bias, backbone.layer3.18.bn3.running_mean, backbone.layer3.18.bn3.running_var, backbone.layer3.18.bn3.num_batches_tracked, backbone.layer3.19.conv1.weight, backbone.layer3.19.bn1.weight, backbone.layer3.19.bn1.bias, backbone.layer3.19.bn1.running_mean, backbone.layer3.19.bn1.running_var, backbone.layer3.19.bn1.num_batches_tracked, backbone.layer3.19.conv2.weight, backbone.layer3.19.bn2.weight, backbone.layer3.19.bn2.bias, backbone.layer3.19.bn2.running_mean, backbone.layer3.19.bn2.running_var, backbone.layer3.19.bn2.num_batches_tracked, backbone.layer3.19.conv3.weight, backbone.layer3.19.bn3.weight, backbone.layer3.19.bn3.bias, backbone.layer3.19.bn3.running_mean, backbone.layer3.19.bn3.running_var, backbone.layer3.19.bn3.num_batches_tracked, backbone.layer3.20.conv1.weight, backbone.layer3.20.bn1.weight, backbone.layer3.20.bn1.bias, backbone.layer3.20.bn1.running_mean, backbone.layer3.20.bn1.running_var, backbone.layer3.20.bn1.num_batches_tracked, backbone.layer3.20.conv2.weight, backbone.layer3.20.bn2.weight, backbone.layer3.20.bn2.bias, backbone.layer3.20.bn2.running_mean, backbone.layer3.20.bn2.running_var, backbone.layer3.20.bn2.num_batches_tracked, backbone.layer3.20.conv3.weight, backbone.layer3.20.bn3.weight, backbone.layer3.20.bn3.bias, backbone.layer3.20.bn3.running_mean, backbone.layer3.20.bn3.running_var, backbone.layer3.20.bn3.num_batches_tracked, backbone.layer3.21.conv1.weight, backbone.layer3.21.bn1.weight, backbone.layer3.21.bn1.bias, backbone.layer3.21.bn1.running_mean, backbone.layer3.21.bn1.running_var, backbone.layer3.21.bn1.num_batches_tracked, backbone.layer3.21.conv2.weight, backbone.layer3.21.bn2.weight, backbone.layer3.21.bn2.bias, backbone.layer3.21.bn2.running_mean, backbone.layer3.21.bn2.running_var, backbone.layer3.21.bn2.num_batches_tracked, backbone.layer3.21.conv3.weight, backbone.layer3.21.bn3.weight, backbone.layer3.21.bn3.bias, backbone.layer3.21.bn3.running_mean, backbone.layer3.21.bn3.running_var, backbone.layer3.21.bn3.num_batches_tracked, backbone.layer3.22.conv1.weight, backbone.layer3.22.bn1.weight, backbone.layer3.22.bn1.bias, backbone.layer3.22.bn1.running_mean, backbone.layer3.22.bn1.running_var, backbone.layer3.22.bn1.num_batches_tracked, backbone.layer3.22.conv2.weight, backbone.layer3.22.bn2.weight, backbone.layer3.22.bn2.bias, backbone.layer3.22.bn2.running_mean, backbone.layer3.22.bn2.running_var, backbone.layer3.22.bn2.num_batches_tracked, backbone.layer3.22.conv3.weight, backbone.layer3.22.bn3.weight, backbone.layer3.22.bn3.bias, backbone.layer3.22.bn3.running_mean, backbone.layer3.22.bn3.running_var, backbone.layer3.22.bn3.num_batches_tracked, backbone.layer4.0.conv1.weight, backbone.layer4.0.bn1.weight, backbone.layer4.0.bn1.bias, backbone.layer4.0.bn1.running_mean, backbone.layer4.0.bn1.running_var, backbone.layer4.0.bn1.num_batches_tracked, backbone.layer4.0.conv2.weight, backbone.layer4.0.bn2.weight, backbone.layer4.0.bn2.bias, backbone.layer4.0.bn2.running_mean, backbone.layer4.0.bn2.running_var, backbone.layer4.0.bn2.num_batches_tracked, backbone.layer4.0.conv3.weight, backbone.layer4.0.bn3.weight, backbone.layer4.0.bn3.bias, backbone.layer4.0.bn3.running_mean, backbone.layer4.0.bn3.running_var, backbone.layer4.0.bn3.num_batches_tracked, backbone.layer4.0.downsample.0.weight, backbone.layer4.0.downsample.1.weight, backbone.layer4.0.downsample.1.bias, backbone.layer4.0.downsample.1.running_mean, backbone.layer4.0.downsample.1.running_var, backbone.layer4.0.downsample.1.num_batches_tracked, backbone.layer4.1.conv1.weight, backbone.layer4.1.bn1.weight, backbone.layer4.1.bn1.bias, backbone.layer4.1.bn1.running_mean, backbone.layer4.1.bn1.running_var, backbone.layer4.1.bn1.num_batches_tracked, backbone.layer4.1.conv2.weight, backbone.layer4.1.bn2.weight, backbone.layer4.1.bn2.bias, backbone.layer4.1.bn2.running_mean, backbone.layer4.1.bn2.running_var, backbone.layer4.1.bn2.num_batches_tracked, backbone.layer4.1.conv3.weight, backbone.layer4.1.bn3.weight, backbone.layer4.1.bn3.bias, backbone.layer4.1.bn3.running_mean, backbone.layer4.1.bn3.running_var, backbone.layer4.1.bn3.num_batches_tracked, backbone.layer4.2.conv1.weight, backbone.layer4.2.bn1.weight, backbone.layer4.2.bn1.bias, backbone.layer4.2.bn1.running_mean, backbone.layer4.2.bn1.running_var, backbone.layer4.2.bn1.num_batches_tracked, backbone.layer4.2.conv2.weight, backbone.layer4.2.bn2.weight, backbone.layer4.2.bn2.bias, backbone.layer4.2.bn2.running_mean, backbone.layer4.2.bn2.running_var, backbone.layer4.2.bn2.num_batches_tracked, backbone.layer4.2.conv3.weight, backbone.layer4.2.bn3.weight, backbone.layer4.2.bn3.bias, backbone.layer4.2.bn3.running_mean, backbone.layer4.2.bn3.running_var, backbone.layer4.2.bn3.num_batches_tracked
+
+missing keys in source state_dict: log_cumprod_at, log_cumprod_bt
+
+2023-03-04 14:00:55,254 - mmseg - INFO - EncoderDecoderDiffusion(
+  (backbone): ResNetV1cCustomInitWeights(
+    (stem): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+      (1): SyncBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (2): ReLU(inplace=True)
+      (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (4): SyncBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (5): ReLU(inplace=True)
+      (6): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (7): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (8): ReLU(inplace=True)
+    )
+    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
+    (layer1): ResLayer(
+      (0): Bottleneck(
+        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+        (bn2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+        (downsample): Sequential(
+          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          (1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        )
+      )
+      (1): Bottleneck(
+        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+        (bn2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (2): Bottleneck(
+        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+        (bn2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+    )
+    (layer2): ResLayer(
+      (0): Bottleneck(
+        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+        (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+        (downsample): Sequential(
+          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
+          (1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        )
+      )
+      (1): Bottleneck(
+        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+        (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (2): Bottleneck(
+        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+        (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (3): Bottleneck(
+        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+        (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+    )
+    (layer3): ResLayer(
+      (0): Bottleneck(
+        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+        (downsample): Sequential(
+          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          (1): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        )
+      )
+      (1): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (2): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (3): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (4): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (5): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (6): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (7): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (8): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (9): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (10): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (11): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (12): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (13): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (14): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (15): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (16): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (17): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (18): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (19): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (20): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (21): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (22): Bottleneck(
+        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+    )
+    (layer4): ResLayer(
+      (0): Bottleneck(
+        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
+        (bn2): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+        (downsample): Sequential(
+          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          (1): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        )
+      )
+      (1): Bottleneck(
+        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)
+        (bn2): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+      (2): Bottleneck(
+        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)
+        (bn2): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn3): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+      )
+    )
+  )
+  init_cfg={'type': 'Pretrained', 'checkpoint': 'work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20/latest.pth'}
+  (decode_head): DepthwiseSeparableASPPHeadUnetFCHeadMultiStep(
+    input_transform=None, ignore_index=0, align_corners=False
+    (loss_decode): CrossEntropyLoss(avg_non_ignore=False)
+    (conv_seg): None
+    (dropout): Dropout2d(p=0.1, inplace=False)
+    (image_pool): Sequential(
+      (0): AdaptiveAvgPool2d(output_size=1)
+      (1): ConvModule(
+        (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (activate): ReLU(inplace=True)
+      )
+    )
+    (aspp_modules): DepthwiseSeparableASPPModule(
+      (0): ConvModule(
+        (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (activate): ReLU(inplace=True)
+      )
+      (1): DepthwiseSeparableConvModule(
+        (depthwise_conv): ConvModule(
+          (conv): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(12, 12), dilation=(12, 12), groups=2048, bias=False)
+          (bn): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (activate): ReLU(inplace=True)
+        )
+        (pointwise_conv): ConvModule(
+          (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (activate): ReLU(inplace=True)
+        )
+      )
+      (2): DepthwiseSeparableConvModule(
+        (depthwise_conv): ConvModule(
+          (conv): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(24, 24), dilation=(24, 24), groups=2048, bias=False)
+          (bn): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (activate): ReLU(inplace=True)
+        )
+        (pointwise_conv): ConvModule(
+          (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (activate): ReLU(inplace=True)
+        )
+      )
+      (3): DepthwiseSeparableConvModule(
+        (depthwise_conv): ConvModule(
+          (conv): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(36, 36), dilation=(36, 36), groups=2048, bias=False)
+          (bn): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (activate): ReLU(inplace=True)
+        )
+        (pointwise_conv): ConvModule(
+          (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (activate): ReLU(inplace=True)
+        )
+      )
+    )
+    (bottleneck): ConvModule(
+      (conv): Conv2d(2560, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (activate): ReLU(inplace=True)
+    )
+    (c1_bottleneck): ConvModule(
+      (conv): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn): SyncBatchNorm(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (activate): ReLU(inplace=True)
+    )
+    (sep_bottleneck): Sequential(
+      (0): DepthwiseSeparableConvModule(
+        (depthwise_conv): ConvModule(
+          (conv): Conv2d(560, 560, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=560, bias=False)
+          (bn): SyncBatchNorm(560, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (activate): ReLU(inplace=True)
+        )
+        (pointwise_conv): ConvModule(
+          (conv): Conv2d(560, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (activate): ReLU(inplace=True)
+        )
+      )
+      (1): DepthwiseSeparableConvModule(
+        (depthwise_conv): ConvModule(
+          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)
+          (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (activate): ReLU(inplace=True)
+        )
+        (pointwise_conv): ConvModule(
+          (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (activate): ReLU(inplace=True)
+        )
+      )
+    )
+    (unet): Unet(
+      (init_conv): Conv2d(528, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
+      (time_mlp): Sequential(
+        (0): SinusoidalPosEmb()
+        (1): Linear(in_features=128, out_features=512, bias=True)
+        (2): GELU(approximate='none')
+        (3): Linear(in_features=512, out_features=512, bias=True)
+      )
+      (downs): ModuleList(
+        (0): ModuleList(
+          (0): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Identity()
+          )
+          (1): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Identity()
+          )
+          (2): Residual(
+            (fn): PreNorm(
+              (fn): LinearAttention(
+                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+                (to_out): Sequential(
+                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
+                  (1): LayerNorm()
+                )
+              )
+              (norm): LayerNorm()
+            )
+          )
+          (3): Conv2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+        )
+        (1): ModuleList(
+          (0): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Identity()
+          )
+          (1): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Identity()
+          )
+          (2): Residual(
+            (fn): PreNorm(
+              (fn): LinearAttention(
+                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+                (to_out): Sequential(
+                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
+                  (1): LayerNorm()
+                )
+              )
+              (norm): LayerNorm()
+            )
+          )
+          (3): Conv2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+        )
+        (2): ModuleList(
+          (0): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Identity()
+          )
+          (1): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Identity()
+          )
+          (2): Residual(
+            (fn): PreNorm(
+              (fn): LinearAttention(
+                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+                (to_out): Sequential(
+                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
+                  (1): LayerNorm()
+                )
+              )
+              (norm): LayerNorm()
+            )
+          )
+          (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+        )
+      )
+      (ups): ModuleList(
+        (0): ModuleList(
+          (0): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (1): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (2): Residual(
+            (fn): PreNorm(
+              (fn): LinearAttention(
+                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+                (to_out): Sequential(
+                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
+                  (1): LayerNorm()
+                )
+              )
+              (norm): LayerNorm()
+            )
+          )
+          (3): Sequential(
+            (0): Upsample(scale_factor=2.0, mode=nearest)
+            (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          )
+        )
+        (1): ModuleList(
+          (0): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (1): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (2): Residual(
+            (fn): PreNorm(
+              (fn): LinearAttention(
+                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+                (to_out): Sequential(
+                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
+                  (1): LayerNorm()
+                )
+              )
+              (norm): LayerNorm()
+            )
+          )
+          (3): Sequential(
+            (0): Upsample(scale_factor=2.0, mode=nearest)
+            (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          )
+        )
+        (2): ModuleList(
+          (0): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (1): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (2): Residual(
+            (fn): PreNorm(
+              (fn): LinearAttention(
+                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+                (to_out): Sequential(
+                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
+                  (1): LayerNorm()
+                )
+              )
+              (norm): LayerNorm()
+            )
+          )
+          (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+        )
+      )
+      (mid_block1): ResnetBlock(
+        (mlp): Sequential(
+          (0): SiLU()
+          (1): Linear(in_features=512, out_features=256, bias=True)
+        )
+        (block1): Block(
+          (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+          (act): SiLU()
+        )
+        (block2): Block(
+          (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+          (act): SiLU()
+        )
+        (res_conv): Identity()
+      )
+      (mid_attn): Residual(
+        (fn): PreNorm(
+          (fn): Attention(
+            (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+            (to_out): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (norm): LayerNorm()
+        )
+      )
+      (mid_block2): ResnetBlock(
+        (mlp): Sequential(
+          (0): SiLU()
+          (1): Linear(in_features=512, out_features=256, bias=True)
+        )
+        (block1): Block(
+          (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+          (act): SiLU()
+        )
+        (block2): Block(
+          (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+          (act): SiLU()
+        )
+        (res_conv): Identity()
+      )
+      (final_res_block): ResnetBlock(
+        (mlp): Sequential(
+          (0): SiLU()
+          (1): Linear(in_features=512, out_features=256, bias=True)
+        )
+        (block1): Block(
+          (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+          (act): SiLU()
+        )
+        (block2): Block(
+          (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+          (act): SiLU()
+        )
+        (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
+      )
+      (final_conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
+    )
+    (conv_seg_new): Conv2d(256, 20, kernel_size=(1, 1), stride=(1, 1))
+    (embed): Embedding(20, 16)
+  )
+  init_cfg={'type': 'Pretrained', 'checkpoint': 'work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20/latest.pth'}
+)
+2023-03-04 14:00:55,329 - mmseg - INFO - Loaded 2975 images
+2023-03-04 14:00:59,382 - mmseg - INFO - Loaded 500 images
+2023-03-04 14:00:59,384 - mmseg - INFO - Start running, host: laizeqiang@SH-IDC1-10-140-37-155, work_dir: /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune
+2023-03-04 14:00:59,385 - mmseg - INFO - Hooks will be executed in the following order:
+before_run:
+(VERY_HIGH   ) StepLrUpdaterHook                  
+(49          ) ConstantMomentumEMAHook            
+(NORMAL      ) CheckpointHook                     
+(LOW         ) DistEvalHookMultiSteps             
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+before_train_epoch:
+(VERY_HIGH   ) StepLrUpdaterHook                  
+(LOW         ) IterTimerHook                      
+(LOW         ) DistEvalHookMultiSteps             
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+before_train_iter:
+(VERY_HIGH   ) StepLrUpdaterHook                  
+(49          ) ConstantMomentumEMAHook            
+(LOW         ) IterTimerHook                      
+(LOW         ) DistEvalHookMultiSteps             
+ -------------------- 
+after_train_iter:
+(ABOVE_NORMAL) OptimizerHook                      
+(49          ) ConstantMomentumEMAHook            
+(NORMAL      ) CheckpointHook                     
+(LOW         ) IterTimerHook                      
+(LOW         ) DistEvalHookMultiSteps             
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+after_train_epoch:
+(NORMAL      ) CheckpointHook                     
+(LOW         ) DistEvalHookMultiSteps             
+(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                     
+ -------------------- 
+2023-03-04 14:00:59,385 - mmseg - INFO - workflow: [('train', 1)], max: 160000 iters
+2023-03-04 14:00:59,468 - mmseg - INFO - Checkpoints will be saved to /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune by HardDiskBackend.
+2023-03-04 14:01:12,229 - mmseg - INFO - Swap parameters (before train) before iter [1]
+2023-03-04 14:01:47,680 - mmseg - INFO - Iter [50/160000]	lr: 7.350e-06, eta: 1 day, 7:45:18, time: 0.715, data_time: 0.014, memory: 67646, decode.loss_ce: 2.2516, decode.acc_seg: 50.3335, loss: 2.2516
+2023-03-04 14:02:01,644 - mmseg - INFO - Iter [100/160000]	lr: 1.485e-05, eta: 22:04:29, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 1.2953, decode.acc_seg: 81.9477, loss: 1.2953
+2023-03-04 14:02:15,408 - mmseg - INFO - Iter [150/160000]	lr: 2.235e-05, eta: 18:47:08, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.6263, decode.acc_seg: 87.4114, loss: 0.6263
+2023-03-04 14:02:31,434 - mmseg - INFO - Iter [200/160000]	lr: 2.985e-05, eta: 17:38:30, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.3013, decode.acc_seg: 93.3725, loss: 0.3013
+2023-03-04 14:02:45,253 - mmseg - INFO - Iter [250/160000]	lr: 3.735e-05, eta: 16:33:44, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.1517, decode.acc_seg: 95.5157, loss: 0.1517
+2023-03-04 14:02:58,896 - mmseg - INFO - Iter [300/160000]	lr: 4.485e-05, eta: 15:48:52, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.1183, decode.acc_seg: 95.7889, loss: 0.1183
+2023-03-04 14:03:12,505 - mmseg - INFO - Iter [350/160000]	lr: 5.235e-05, eta: 15:16:31, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.1053, decode.acc_seg: 96.0703, loss: 0.1053
+2023-03-04 14:03:28,502 - mmseg - INFO - Iter [400/160000]	lr: 5.985e-05, eta: 15:08:05, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.1026, decode.acc_seg: 96.1086, loss: 0.1026
+2023-03-04 14:03:42,443 - mmseg - INFO - Iter [450/160000]	lr: 6.735e-05, eta: 14:49:13, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0957, decode.acc_seg: 96.3330, loss: 0.0957
+2023-03-04 14:03:56,189 - mmseg - INFO - Iter [500/160000]	lr: 7.485e-05, eta: 14:33:08, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0948, decode.acc_seg: 96.3629, loss: 0.0948
+2023-03-04 14:04:09,940 - mmseg - INFO - Iter [550/160000]	lr: 8.235e-05, eta: 14:20:01, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0937, decode.acc_seg: 96.3647, loss: 0.0937
+2023-03-04 14:04:26,040 - mmseg - INFO - Iter [600/160000]	lr: 8.985e-05, eta: 14:19:23, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0947, decode.acc_seg: 96.3183, loss: 0.0947
+2023-03-04 14:04:39,671 - mmseg - INFO - Iter [650/160000]	lr: 9.735e-05, eta: 14:08:43, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0909, decode.acc_seg: 96.4393, loss: 0.0909
+2023-03-04 14:04:53,310 - mmseg - INFO - Iter [700/160000]	lr: 1.049e-04, eta: 13:59:35, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0952, decode.acc_seg: 96.2830, loss: 0.0952
+2023-03-04 14:05:09,487 - mmseg - INFO - Iter [750/160000]	lr: 1.124e-04, eta: 14:00:37, time: 0.324, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0895, decode.acc_seg: 96.4966, loss: 0.0895
+2023-03-04 14:05:23,089 - mmseg - INFO - Iter [800/160000]	lr: 1.199e-04, eta: 13:52:56, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0942, decode.acc_seg: 96.4190, loss: 0.0942
+2023-03-04 14:05:36,804 - mmseg - INFO - Iter [850/160000]	lr: 1.274e-04, eta: 13:46:27, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0893, decode.acc_seg: 96.4906, loss: 0.0893
+2023-03-04 14:05:50,406 - mmseg - INFO - Iter [900/160000]	lr: 1.349e-04, eta: 13:40:24, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0905, decode.acc_seg: 96.4191, loss: 0.0905
+2023-03-04 14:06:06,690 - mmseg - INFO - Iter [950/160000]	lr: 1.424e-04, eta: 13:42:25, time: 0.326, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0877, decode.acc_seg: 96.5846, loss: 0.0877
+2023-03-04 14:06:20,307 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 14:06:20,307 - mmseg - INFO - Iter [1000/160000]	lr: 1.499e-04, eta: 13:37:08, time: 0.272, data_time: 0.006, memory: 67646, decode.loss_ce: 0.0869, decode.acc_seg: 96.6022, loss: 0.0869
+2023-03-04 14:06:33,801 - mmseg - INFO - Iter [1050/160000]	lr: 1.500e-04, eta: 13:32:01, time: 0.270, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0912, decode.acc_seg: 96.4504, loss: 0.0912
+2023-03-04 14:06:47,348 - mmseg - INFO - Iter [1100/160000]	lr: 1.500e-04, eta: 13:27:29, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0863, decode.acc_seg: 96.5929, loss: 0.0863
+2023-03-04 14:07:03,247 - mmseg - INFO - Iter [1150/160000]	lr: 1.500e-04, eta: 13:28:44, time: 0.318, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0897, decode.acc_seg: 96.4747, loss: 0.0897
+2023-03-04 14:07:17,004 - mmseg - INFO - Iter [1200/160000]	lr: 1.500e-04, eta: 13:25:08, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0900, decode.acc_seg: 96.4493, loss: 0.0900
+2023-03-04 14:07:30,707 - mmseg - INFO - Iter [1250/160000]	lr: 1.500e-04, eta: 13:21:41, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0901, decode.acc_seg: 96.4486, loss: 0.0901
+2023-03-04 14:07:45,071 - mmseg - INFO - Iter [1300/160000]	lr: 1.500e-04, eta: 13:19:50, time: 0.287, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0894, decode.acc_seg: 96.4955, loss: 0.0894
+2023-03-04 14:08:00,974 - mmseg - INFO - Iter [1350/160000]	lr: 1.500e-04, eta: 13:21:07, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0906, decode.acc_seg: 96.4449, loss: 0.0906
+2023-03-04 14:08:14,547 - mmseg - INFO - Iter [1400/160000]	lr: 1.500e-04, eta: 13:17:53, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.7552, loss: 0.0830
+2023-03-04 14:08:28,201 - mmseg - INFO - Iter [1450/160000]	lr: 1.500e-04, eta: 13:15:01, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0891, decode.acc_seg: 96.4741, loss: 0.0891
+2023-03-04 14:08:44,431 - mmseg - INFO - Iter [1500/160000]	lr: 1.500e-04, eta: 13:16:51, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0877, decode.acc_seg: 96.5690, loss: 0.0877
+2023-03-04 14:08:57,959 - mmseg - INFO - Iter [1550/160000]	lr: 1.500e-04, eta: 13:13:57, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0878, decode.acc_seg: 96.5335, loss: 0.0878
+2023-03-04 14:09:11,590 - mmseg - INFO - Iter [1600/160000]	lr: 1.500e-04, eta: 13:11:23, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0886, decode.acc_seg: 96.5147, loss: 0.0886
+2023-03-04 14:09:25,343 - mmseg - INFO - Iter [1650/160000]	lr: 1.500e-04, eta: 13:09:10, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0864, decode.acc_seg: 96.6126, loss: 0.0864
+2023-03-04 14:09:41,555 - mmseg - INFO - Iter [1700/160000]	lr: 1.500e-04, eta: 13:10:52, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0893, decode.acc_seg: 96.4610, loss: 0.0893
+2023-03-04 14:09:55,281 - mmseg - INFO - Iter [1750/160000]	lr: 1.500e-04, eta: 13:08:42, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0861, decode.acc_seg: 96.5909, loss: 0.0861
+2023-03-04 14:10:08,927 - mmseg - INFO - Iter [1800/160000]	lr: 1.500e-04, eta: 13:06:33, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0879, decode.acc_seg: 96.5562, loss: 0.0879
+2023-03-04 14:10:22,754 - mmseg - INFO - Iter [1850/160000]	lr: 1.500e-04, eta: 13:04:45, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0880, decode.acc_seg: 96.5511, loss: 0.0880
+2023-03-04 14:10:38,655 - mmseg - INFO - Iter [1900/160000]	lr: 1.500e-04, eta: 13:05:55, time: 0.318, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0904, decode.acc_seg: 96.4264, loss: 0.0904
+2023-03-04 14:10:52,488 - mmseg - INFO - Iter [1950/160000]	lr: 1.500e-04, eta: 13:04:12, time: 0.277, data_time: 0.006, memory: 67646, decode.loss_ce: 0.0849, decode.acc_seg: 96.6477, loss: 0.0849
+2023-03-04 14:11:06,117 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 14:11:06,118 - mmseg - INFO - Iter [2000/160000]	lr: 1.500e-04, eta: 13:02:18, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0866, decode.acc_seg: 96.6141, loss: 0.0866
+2023-03-04 14:11:22,166 - mmseg - INFO - Iter [2050/160000]	lr: 1.500e-04, eta: 13:03:35, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0891, decode.acc_seg: 96.5176, loss: 0.0891
+2023-03-04 14:11:35,704 - mmseg - INFO - Iter [2100/160000]	lr: 1.500e-04, eta: 13:01:39, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0863, decode.acc_seg: 96.6257, loss: 0.0863
+2023-03-04 14:11:49,455 - mmseg - INFO - Iter [2150/160000]	lr: 1.500e-04, eta: 13:00:04, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0885, decode.acc_seg: 96.5025, loss: 0.0885
+2023-03-04 14:12:03,495 - mmseg - INFO - Iter [2200/160000]	lr: 1.500e-04, eta: 12:58:52, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0839, decode.acc_seg: 96.6887, loss: 0.0839
+2023-03-04 14:12:19,440 - mmseg - INFO - Iter [2250/160000]	lr: 1.500e-04, eta: 12:59:57, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7776, loss: 0.0810
+2023-03-04 14:12:33,076 - mmseg - INFO - Iter [2300/160000]	lr: 1.500e-04, eta: 12:58:20, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0903, decode.acc_seg: 96.4879, loss: 0.0903
+2023-03-04 14:12:46,705 - mmseg - INFO - Iter [2350/160000]	lr: 1.500e-04, eta: 12:56:45, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0873, decode.acc_seg: 96.5847, loss: 0.0873
+2023-03-04 14:13:00,584 - mmseg - INFO - Iter [2400/160000]	lr: 1.500e-04, eta: 12:55:32, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0878, decode.acc_seg: 96.5449, loss: 0.0878
+2023-03-04 14:13:16,426 - mmseg - INFO - Iter [2450/160000]	lr: 1.500e-04, eta: 12:56:27, time: 0.317, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0867, decode.acc_seg: 96.6105, loss: 0.0867
+2023-03-04 14:13:30,367 - mmseg - INFO - Iter [2500/160000]	lr: 1.500e-04, eta: 12:55:19, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0878, decode.acc_seg: 96.5712, loss: 0.0878
+2023-03-04 14:13:43,995 - mmseg - INFO - Iter [2550/160000]	lr: 1.500e-04, eta: 12:53:54, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0862, decode.acc_seg: 96.6223, loss: 0.0862
+2023-03-04 14:13:57,496 - mmseg - INFO - Iter [2600/160000]	lr: 1.500e-04, eta: 12:52:24, time: 0.270, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0850, decode.acc_seg: 96.6629, loss: 0.0850
+2023-03-04 14:14:13,552 - mmseg - INFO - Iter [2650/160000]	lr: 1.500e-04, eta: 12:53:28, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0820, decode.acc_seg: 96.7190, loss: 0.0820
+2023-03-04 14:14:27,045 - mmseg - INFO - Iter [2700/160000]	lr: 1.500e-04, eta: 12:52:00, time: 0.270, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0835, decode.acc_seg: 96.7175, loss: 0.0835
+2023-03-04 14:14:40,614 - mmseg - INFO - Iter [2750/160000]	lr: 1.500e-04, eta: 12:50:40, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0857, decode.acc_seg: 96.5799, loss: 0.0857
+2023-03-04 14:14:56,660 - mmseg - INFO - Iter [2800/160000]	lr: 1.500e-04, eta: 12:51:40, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0859, decode.acc_seg: 96.6412, loss: 0.0859
+2023-03-04 14:15:10,337 - mmseg - INFO - Iter [2850/160000]	lr: 1.500e-04, eta: 12:50:28, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0875, decode.acc_seg: 96.5386, loss: 0.0875
+2023-03-04 14:15:24,073 - mmseg - INFO - Iter [2900/160000]	lr: 1.500e-04, eta: 12:49:20, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0918, decode.acc_seg: 96.4458, loss: 0.0918
+2023-03-04 14:15:37,682 - mmseg - INFO - Iter [2950/160000]	lr: 1.500e-04, eta: 12:48:08, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0837, decode.acc_seg: 96.6949, loss: 0.0837
+2023-03-04 14:15:53,534 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 14:15:53,534 - mmseg - INFO - Iter [3000/160000]	lr: 1.500e-04, eta: 12:48:55, time: 0.317, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7328, loss: 0.0825
+2023-03-04 14:16:07,408 - mmseg - INFO - Iter [3050/160000]	lr: 1.500e-04, eta: 12:47:58, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0833, decode.acc_seg: 96.7328, loss: 0.0833
+2023-03-04 14:16:20,999 - mmseg - INFO - Iter [3100/160000]	lr: 1.500e-04, eta: 12:46:48, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0856, decode.acc_seg: 96.6395, loss: 0.0856
+2023-03-04 14:16:34,667 - mmseg - INFO - Iter [3150/160000]	lr: 1.500e-04, eta: 12:45:44, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7812, loss: 0.0818
+2023-03-04 14:16:50,756 - mmseg - INFO - Iter [3200/160000]	lr: 1.500e-04, eta: 12:46:40, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0879, decode.acc_seg: 96.5561, loss: 0.0879
+2023-03-04 14:17:04,485 - mmseg - INFO - Iter [3250/160000]	lr: 1.500e-04, eta: 12:45:40, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0856, decode.acc_seg: 96.6414, loss: 0.0856
+2023-03-04 14:17:18,068 - mmseg - INFO - Iter [3300/160000]	lr: 1.500e-04, eta: 12:44:35, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0908, decode.acc_seg: 96.4379, loss: 0.0908
+2023-03-04 14:17:34,189 - mmseg - INFO - Iter [3350/160000]	lr: 1.500e-04, eta: 12:45:29, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0854, decode.acc_seg: 96.6288, loss: 0.0854
+2023-03-04 14:17:47,892 - mmseg - INFO - Iter [3400/160000]	lr: 1.500e-04, eta: 12:44:31, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0856, decode.acc_seg: 96.6641, loss: 0.0856
+2023-03-04 14:18:01,638 - mmseg - INFO - Iter [3450/160000]	lr: 1.500e-04, eta: 12:43:35, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0857, decode.acc_seg: 96.6538, loss: 0.0857
+2023-03-04 14:18:15,306 - mmseg - INFO - Iter [3500/160000]	lr: 1.500e-04, eta: 12:42:37, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0875, decode.acc_seg: 96.5091, loss: 0.0875
+2023-03-04 14:18:31,411 - mmseg - INFO - Iter [3550/160000]	lr: 1.500e-04, eta: 12:43:28, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0847, decode.acc_seg: 96.6580, loss: 0.0847
+2023-03-04 14:18:45,269 - mmseg - INFO - Iter [3600/160000]	lr: 1.500e-04, eta: 12:42:40, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0845, decode.acc_seg: 96.6681, loss: 0.0845
+2023-03-04 14:18:58,914 - mmseg - INFO - Iter [3650/160000]	lr: 1.500e-04, eta: 12:41:43, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0874, decode.acc_seg: 96.5242, loss: 0.0874
+2023-03-04 14:19:12,660 - mmseg - INFO - Iter [3700/160000]	lr: 1.500e-04, eta: 12:40:52, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0860, decode.acc_seg: 96.6175, loss: 0.0860
+2023-03-04 14:19:28,658 - mmseg - INFO - Iter [3750/160000]	lr: 1.500e-04, eta: 12:41:35, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0840, decode.acc_seg: 96.6607, loss: 0.0840
+2023-03-04 14:19:42,354 - mmseg - INFO - Iter [3800/160000]	lr: 1.500e-04, eta: 12:40:42, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0858, decode.acc_seg: 96.6012, loss: 0.0858
+2023-03-04 14:19:56,129 - mmseg - INFO - Iter [3850/160000]	lr: 1.500e-04, eta: 12:39:54, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.7138, loss: 0.0831
+2023-03-04 14:20:09,823 - mmseg - INFO - Iter [3900/160000]	lr: 1.500e-04, eta: 12:39:03, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0863, decode.acc_seg: 96.6121, loss: 0.0863
+2023-03-04 14:20:25,724 - mmseg - INFO - Iter [3950/160000]	lr: 1.500e-04, eta: 12:39:40, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0858, decode.acc_seg: 96.5749, loss: 0.0858
+2023-03-04 14:20:39,693 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 14:20:39,693 - mmseg - INFO - Iter [4000/160000]	lr: 1.500e-04, eta: 12:39:01, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0846, decode.acc_seg: 96.6758, loss: 0.0846
+2023-03-04 14:20:53,423 - mmseg - INFO - Iter [4050/160000]	lr: 1.500e-04, eta: 12:38:13, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7580, loss: 0.0823
+2023-03-04 14:21:09,421 - mmseg - INFO - Iter [4100/160000]	lr: 1.500e-04, eta: 12:38:52, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0833, decode.acc_seg: 96.6829, loss: 0.0833
+2023-03-04 14:21:23,027 - mmseg - INFO - Iter [4150/160000]	lr: 1.500e-04, eta: 12:38:00, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0859, decode.acc_seg: 96.6342, loss: 0.0859
+2023-03-04 14:21:36,582 - mmseg - INFO - Iter [4200/160000]	lr: 1.500e-04, eta: 12:37:07, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0845, decode.acc_seg: 96.6650, loss: 0.0845
+2023-03-04 14:21:50,211 - mmseg - INFO - Iter [4250/160000]	lr: 1.500e-04, eta: 12:36:17, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0857, decode.acc_seg: 96.5976, loss: 0.0857
+2023-03-04 14:22:06,151 - mmseg - INFO - Iter [4300/160000]	lr: 1.500e-04, eta: 12:36:52, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0853, decode.acc_seg: 96.6342, loss: 0.0853
+2023-03-04 14:22:19,907 - mmseg - INFO - Iter [4350/160000]	lr: 1.500e-04, eta: 12:36:08, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0860, decode.acc_seg: 96.6512, loss: 0.0860
+2023-03-04 14:22:33,583 - mmseg - INFO - Iter [4400/160000]	lr: 1.500e-04, eta: 12:35:22, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0879, decode.acc_seg: 96.5235, loss: 0.0879
+2023-03-04 14:22:47,133 - mmseg - INFO - Iter [4450/160000]	lr: 1.500e-04, eta: 12:34:32, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.8082, loss: 0.0802
+2023-03-04 14:23:03,251 - mmseg - INFO - Iter [4500/160000]	lr: 1.500e-04, eta: 12:35:11, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0846, decode.acc_seg: 96.6385, loss: 0.0846
+2023-03-04 14:23:16,857 - mmseg - INFO - Iter [4550/160000]	lr: 1.500e-04, eta: 12:34:24, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0877, decode.acc_seg: 96.5589, loss: 0.0877
+2023-03-04 14:23:30,665 - mmseg - INFO - Iter [4600/160000]	lr: 1.500e-04, eta: 12:33:43, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0853, decode.acc_seg: 96.6373, loss: 0.0853
+2023-03-04 14:23:44,715 - mmseg - INFO - Iter [4650/160000]	lr: 1.500e-04, eta: 12:33:13, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0874, decode.acc_seg: 96.5324, loss: 0.0874
+2023-03-04 14:24:00,749 - mmseg - INFO - Iter [4700/160000]	lr: 1.500e-04, eta: 12:33:47, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0841, decode.acc_seg: 96.6588, loss: 0.0841
+2023-03-04 14:24:14,614 - mmseg - INFO - Iter [4750/160000]	lr: 1.500e-04, eta: 12:33:10, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0868, decode.acc_seg: 96.6005, loss: 0.0868
+2023-03-04 14:24:28,198 - mmseg - INFO - Iter [4800/160000]	lr: 1.500e-04, eta: 12:32:24, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0850, decode.acc_seg: 96.6375, loss: 0.0850
+2023-03-04 14:24:44,134 - mmseg - INFO - Iter [4850/160000]	lr: 1.500e-04, eta: 12:32:54, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7713, loss: 0.0814
+2023-03-04 14:24:57,891 - mmseg - INFO - Iter [4900/160000]	lr: 1.500e-04, eta: 12:32:14, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0888, decode.acc_seg: 96.5330, loss: 0.0888
+2023-03-04 14:25:11,590 - mmseg - INFO - Iter [4950/160000]	lr: 1.500e-04, eta: 12:31:33, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0840, decode.acc_seg: 96.6382, loss: 0.0840
+2023-03-04 14:25:25,466 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 14:25:25,466 - mmseg - INFO - Iter [5000/160000]	lr: 1.500e-04, eta: 12:30:58, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0861, decode.acc_seg: 96.5823, loss: 0.0861
+2023-03-04 14:25:41,343 - mmseg - INFO - Iter [5050/160000]	lr: 1.500e-04, eta: 12:31:24, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0841, decode.acc_seg: 96.6737, loss: 0.0841
+2023-03-04 14:25:55,189 - mmseg - INFO - Iter [5100/160000]	lr: 1.500e-04, eta: 12:30:48, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0821, decode.acc_seg: 96.7523, loss: 0.0821
+2023-03-04 14:26:08,835 - mmseg - INFO - Iter [5150/160000]	lr: 1.500e-04, eta: 12:30:07, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0834, decode.acc_seg: 96.7283, loss: 0.0834
+2023-03-04 14:26:22,543 - mmseg - INFO - Iter [5200/160000]	lr: 1.500e-04, eta: 12:29:28, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0863, decode.acc_seg: 96.5855, loss: 0.0863
+2023-03-04 14:26:38,774 - mmseg - INFO - Iter [5250/160000]	lr: 1.500e-04, eta: 12:30:04, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0861, decode.acc_seg: 96.5221, loss: 0.0861
+2023-03-04 14:26:52,583 - mmseg - INFO - Iter [5300/160000]	lr: 1.500e-04, eta: 12:29:28, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0878, decode.acc_seg: 96.5389, loss: 0.0878
+2023-03-04 14:27:06,777 - mmseg - INFO - Iter [5350/160000]	lr: 1.500e-04, eta: 12:29:03, time: 0.284, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0854, decode.acc_seg: 96.6435, loss: 0.0854
+2023-03-04 14:27:22,968 - mmseg - INFO - Iter [5400/160000]	lr: 1.500e-04, eta: 12:29:36, time: 0.324, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0889, decode.acc_seg: 96.4893, loss: 0.0889
+2023-03-04 14:27:36,656 - mmseg - INFO - Iter [5450/160000]	lr: 1.500e-04, eta: 12:28:57, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0862, decode.acc_seg: 96.5672, loss: 0.0862
+2023-03-04 14:27:50,871 - mmseg - INFO - Iter [5500/160000]	lr: 1.500e-04, eta: 12:28:34, time: 0.284, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0843, decode.acc_seg: 96.6848, loss: 0.0843
+2023-03-04 14:28:04,960 - mmseg - INFO - Iter [5550/160000]	lr: 1.500e-04, eta: 12:28:07, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0861, decode.acc_seg: 96.5870, loss: 0.0861
+2023-03-04 14:28:20,991 - mmseg - INFO - Iter [5600/160000]	lr: 1.500e-04, eta: 12:28:34, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0847, decode.acc_seg: 96.6532, loss: 0.0847
+2023-03-04 14:28:35,010 - mmseg - INFO - Iter [5650/160000]	lr: 1.500e-04, eta: 12:28:05, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0869, decode.acc_seg: 96.5464, loss: 0.0869
+2023-03-04 14:28:48,597 - mmseg - INFO - Iter [5700/160000]	lr: 1.500e-04, eta: 12:27:24, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7616, loss: 0.0816
+2023-03-04 14:29:02,234 - mmseg - INFO - Iter [5750/160000]	lr: 1.500e-04, eta: 12:26:46, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0867, decode.acc_seg: 96.5750, loss: 0.0867
+2023-03-04 14:29:18,395 - mmseg - INFO - Iter [5800/160000]	lr: 1.500e-04, eta: 12:27:15, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0846, decode.acc_seg: 96.6489, loss: 0.0846
+2023-03-04 14:29:32,032 - mmseg - INFO - Iter [5850/160000]	lr: 1.500e-04, eta: 12:26:37, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0880, decode.acc_seg: 96.5656, loss: 0.0880
+2023-03-04 14:29:45,771 - mmseg - INFO - Iter [5900/160000]	lr: 1.500e-04, eta: 12:26:01, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0844, decode.acc_seg: 96.6234, loss: 0.0844
+2023-03-04 14:29:59,540 - mmseg - INFO - Iter [5950/160000]	lr: 1.500e-04, eta: 12:25:27, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7201, loss: 0.0823
+2023-03-04 14:30:15,466 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 14:30:15,466 - mmseg - INFO - Iter [6000/160000]	lr: 1.500e-04, eta: 12:25:49, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0836, decode.acc_seg: 96.6983, loss: 0.0836
+2023-03-04 14:30:29,499 - mmseg - INFO - Iter [6050/160000]	lr: 1.500e-04, eta: 12:25:22, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0857, decode.acc_seg: 96.5872, loss: 0.0857
+2023-03-04 14:30:43,346 - mmseg - INFO - Iter [6100/160000]	lr: 1.500e-04, eta: 12:24:50, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0842, decode.acc_seg: 96.6874, loss: 0.0842
+2023-03-04 14:30:59,309 - mmseg - INFO - Iter [6150/160000]	lr: 1.500e-04, eta: 12:25:12, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0837, decode.acc_seg: 96.7133, loss: 0.0837
+2023-03-04 14:31:13,157 - mmseg - INFO - Iter [6200/160000]	lr: 1.500e-04, eta: 12:24:40, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0845, decode.acc_seg: 96.6893, loss: 0.0845
+2023-03-04 14:31:26,994 - mmseg - INFO - Iter [6250/160000]	lr: 1.500e-04, eta: 12:24:09, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0826, decode.acc_seg: 96.7178, loss: 0.0826
+2023-03-04 14:31:40,591 - mmseg - INFO - Iter [6300/160000]	lr: 1.500e-04, eta: 12:23:32, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0833, decode.acc_seg: 96.6889, loss: 0.0833
+2023-03-04 14:31:56,882 - mmseg - INFO - Iter [6350/160000]	lr: 1.500e-04, eta: 12:24:00, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0820, decode.acc_seg: 96.7281, loss: 0.0820
+2023-03-04 14:32:10,441 - mmseg - INFO - Iter [6400/160000]	lr: 1.500e-04, eta: 12:23:22, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0824, decode.acc_seg: 96.7074, loss: 0.0824
+2023-03-04 14:32:24,135 - mmseg - INFO - Iter [6450/160000]	lr: 1.500e-04, eta: 12:22:48, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0855, decode.acc_seg: 96.6424, loss: 0.0855
+2023-03-04 14:32:37,800 - mmseg - INFO - Iter [6500/160000]	lr: 1.500e-04, eta: 12:22:14, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.7227, loss: 0.0831
+2023-03-04 14:32:53,840 - mmseg - INFO - Iter [6550/160000]	lr: 1.500e-04, eta: 12:22:35, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0863, decode.acc_seg: 96.5883, loss: 0.0863
+2023-03-04 14:33:07,488 - mmseg - INFO - Iter [6600/160000]	lr: 1.500e-04, eta: 12:22:00, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7720, loss: 0.0816
+2023-03-04 14:33:21,167 - mmseg - INFO - Iter [6650/160000]	lr: 1.500e-04, eta: 12:21:27, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0889, decode.acc_seg: 96.5100, loss: 0.0889
+2023-03-04 14:33:37,061 - mmseg - INFO - Iter [6700/160000]	lr: 1.500e-04, eta: 12:21:44, time: 0.318, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0858, decode.acc_seg: 96.6413, loss: 0.0858
+2023-03-04 14:33:50,819 - mmseg - INFO - Iter [6750/160000]	lr: 1.500e-04, eta: 12:21:12, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0886, decode.acc_seg: 96.5324, loss: 0.0886
+2023-03-04 14:34:04,377 - mmseg - INFO - Iter [6800/160000]	lr: 1.500e-04, eta: 12:20:36, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0828, decode.acc_seg: 96.7352, loss: 0.0828
+2023-03-04 14:34:18,217 - mmseg - INFO - Iter [6850/160000]	lr: 1.500e-04, eta: 12:20:07, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0879, decode.acc_seg: 96.5480, loss: 0.0879
+2023-03-04 14:34:34,280 - mmseg - INFO - Iter [6900/160000]	lr: 1.500e-04, eta: 12:20:27, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0852, decode.acc_seg: 96.6760, loss: 0.0852
+2023-03-04 14:34:47,867 - mmseg - INFO - Iter [6950/160000]	lr: 1.500e-04, eta: 12:19:52, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0819, decode.acc_seg: 96.7329, loss: 0.0819
+2023-03-04 14:35:01,713 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 14:35:01,714 - mmseg - INFO - Iter [7000/160000]	lr: 1.500e-04, eta: 12:19:23, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0835, decode.acc_seg: 96.7188, loss: 0.0835
+2023-03-04 14:35:15,258 - mmseg - INFO - Iter [7050/160000]	lr: 1.500e-04, eta: 12:18:48, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0857, decode.acc_seg: 96.6131, loss: 0.0857
+2023-03-04 14:35:31,504 - mmseg - INFO - Iter [7100/160000]	lr: 1.500e-04, eta: 12:19:12, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0867, decode.acc_seg: 96.5692, loss: 0.0867
+2023-03-04 14:35:45,304 - mmseg - INFO - Iter [7150/160000]	lr: 1.500e-04, eta: 12:18:42, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0867, decode.acc_seg: 96.5798, loss: 0.0867
+2023-03-04 14:35:59,037 - mmseg - INFO - Iter [7200/160000]	lr: 1.500e-04, eta: 12:18:11, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.7988, loss: 0.0806
+2023-03-04 14:36:12,757 - mmseg - INFO - Iter [7250/160000]	lr: 1.500e-04, eta: 12:17:40, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0848, decode.acc_seg: 96.6567, loss: 0.0848
+2023-03-04 14:36:28,889 - mmseg - INFO - Iter [7300/160000]	lr: 1.500e-04, eta: 12:18:00, time: 0.323, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0862, decode.acc_seg: 96.6014, loss: 0.0862
+2023-03-04 14:36:42,766 - mmseg - INFO - Iter [7350/160000]	lr: 1.500e-04, eta: 12:17:33, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0853, decode.acc_seg: 96.6269, loss: 0.0853
+2023-03-04 14:36:56,316 - mmseg - INFO - Iter [7400/160000]	lr: 1.500e-04, eta: 12:16:59, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0845, decode.acc_seg: 96.6018, loss: 0.0845
+2023-03-04 14:37:12,406 - mmseg - INFO - Iter [7450/160000]	lr: 1.500e-04, eta: 12:17:17, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0843, decode.acc_seg: 96.6582, loss: 0.0843
+2023-03-04 14:37:26,103 - mmseg - INFO - Iter [7500/160000]	lr: 1.500e-04, eta: 12:16:46, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0854, decode.acc_seg: 96.6075, loss: 0.0854
+2023-03-04 14:37:40,025 - mmseg - INFO - Iter [7550/160000]	lr: 1.500e-04, eta: 12:16:20, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.7778, loss: 0.0817
+2023-03-04 14:37:53,730 - mmseg - INFO - Iter [7600/160000]	lr: 1.500e-04, eta: 12:15:50, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0856, decode.acc_seg: 96.6447, loss: 0.0856
+2023-03-04 14:38:09,798 - mmseg - INFO - Iter [7650/160000]	lr: 1.500e-04, eta: 12:16:07, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.6830, loss: 0.0831
+2023-03-04 14:38:23,661 - mmseg - INFO - Iter [7700/160000]	lr: 1.500e-04, eta: 12:15:40, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0874, decode.acc_seg: 96.5633, loss: 0.0874
+2023-03-04 14:38:37,467 - mmseg - INFO - Iter [7750/160000]	lr: 1.500e-04, eta: 12:15:12, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.7759, loss: 0.0799
+2023-03-04 14:38:51,322 - mmseg - INFO - Iter [7800/160000]	lr: 1.500e-04, eta: 12:14:45, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0856, decode.acc_seg: 96.6466, loss: 0.0856
+2023-03-04 14:39:07,274 - mmseg - INFO - Iter [7850/160000]	lr: 1.500e-04, eta: 12:14:59, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0878, decode.acc_seg: 96.6123, loss: 0.0878
+2023-03-04 14:39:21,147 - mmseg - INFO - Iter [7900/160000]	lr: 1.500e-04, eta: 12:14:33, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7935, loss: 0.0813
+2023-03-04 14:39:34,715 - mmseg - INFO - Iter [7950/160000]	lr: 1.500e-04, eta: 12:14:01, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0878, decode.acc_seg: 96.5181, loss: 0.0878
+2023-03-04 14:39:50,861 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 14:39:50,861 - mmseg - INFO - Iter [8000/160000]	lr: 1.500e-04, eta: 12:14:18, time: 0.323, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0877, decode.acc_seg: 96.5531, loss: 0.0877
+2023-03-04 14:40:04,959 - mmseg - INFO - Iter [8050/160000]	lr: 1.500e-04, eta: 12:13:56, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7649, loss: 0.0816
+2023-03-04 14:40:18,666 - mmseg - INFO - Iter [8100/160000]	lr: 1.500e-04, eta: 12:13:27, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8744, loss: 0.0795
+2023-03-04 14:40:32,645 - mmseg - INFO - Iter [8150/160000]	lr: 1.500e-04, eta: 12:13:03, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0870, decode.acc_seg: 96.5735, loss: 0.0870
+2023-03-04 14:40:48,649 - mmseg - INFO - Iter [8200/160000]	lr: 1.500e-04, eta: 12:13:17, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.7002, loss: 0.0831
+2023-03-04 14:41:02,349 - mmseg - INFO - Iter [8250/160000]	lr: 1.500e-04, eta: 12:12:47, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0869, decode.acc_seg: 96.5940, loss: 0.0869
+2023-03-04 14:41:16,230 - mmseg - INFO - Iter [8300/160000]	lr: 1.500e-04, eta: 12:12:22, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.7115, loss: 0.0830
+2023-03-04 14:41:29,980 - mmseg - INFO - Iter [8350/160000]	lr: 1.500e-04, eta: 12:11:54, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0829, decode.acc_seg: 96.7501, loss: 0.0829
+2023-03-04 14:41:45,900 - mmseg - INFO - Iter [8400/160000]	lr: 1.500e-04, eta: 12:12:06, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0827, decode.acc_seg: 96.7041, loss: 0.0827
+2023-03-04 14:42:00,030 - mmseg - INFO - Iter [8450/160000]	lr: 1.500e-04, eta: 12:11:45, time: 0.283, data_time: 0.006, memory: 67646, decode.loss_ce: 0.0845, decode.acc_seg: 96.6430, loss: 0.0845
+2023-03-04 14:42:13,620 - mmseg - INFO - Iter [8500/160000]	lr: 1.500e-04, eta: 12:11:14, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0832, decode.acc_seg: 96.7270, loss: 0.0832
+2023-03-04 14:42:27,286 - mmseg - INFO - Iter [8550/160000]	lr: 1.500e-04, eta: 12:10:45, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0838, decode.acc_seg: 96.6506, loss: 0.0838
+2023-03-04 14:42:43,503 - mmseg - INFO - Iter [8600/160000]	lr: 1.500e-04, eta: 12:11:01, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0835, decode.acc_seg: 96.6862, loss: 0.0835
+2023-03-04 14:42:57,349 - mmseg - INFO - Iter [8650/160000]	lr: 1.500e-04, eta: 12:10:36, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0839, decode.acc_seg: 96.7198, loss: 0.0839
+2023-03-04 14:43:10,916 - mmseg - INFO - Iter [8700/160000]	lr: 1.500e-04, eta: 12:10:05, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.7089, loss: 0.0830
+2023-03-04 14:43:27,171 - mmseg - INFO - Iter [8750/160000]	lr: 1.500e-04, eta: 12:10:21, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0837, decode.acc_seg: 96.7063, loss: 0.0837
+2023-03-04 14:43:40,915 - mmseg - INFO - Iter [8800/160000]	lr: 1.500e-04, eta: 12:09:54, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0835, decode.acc_seg: 96.6982, loss: 0.0835
+2023-03-04 14:43:54,614 - mmseg - INFO - Iter [8850/160000]	lr: 1.500e-04, eta: 12:09:27, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.7213, loss: 0.0830
+2023-03-04 14:44:08,208 - mmseg - INFO - Iter [8900/160000]	lr: 1.500e-04, eta: 12:08:57, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7582, loss: 0.0823
+2023-03-04 14:44:24,593 - mmseg - INFO - Iter [8950/160000]	lr: 1.500e-04, eta: 12:09:15, time: 0.328, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.8187, loss: 0.0807
+2023-03-04 14:44:38,233 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 14:44:38,233 - mmseg - INFO - Iter [9000/160000]	lr: 1.500e-04, eta: 12:08:46, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0837, decode.acc_seg: 96.7421, loss: 0.0837
+2023-03-04 14:44:52,212 - mmseg - INFO - Iter [9050/160000]	lr: 1.500e-04, eta: 12:08:23, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0856, decode.acc_seg: 96.6334, loss: 0.0856
+2023-03-04 14:45:05,807 - mmseg - INFO - Iter [9100/160000]	lr: 1.500e-04, eta: 12:07:54, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0872, decode.acc_seg: 96.5622, loss: 0.0872
+2023-03-04 14:45:22,025 - mmseg - INFO - Iter [9150/160000]	lr: 1.500e-04, eta: 12:08:09, time: 0.324, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7817, loss: 0.0816
+2023-03-04 14:45:35,828 - mmseg - INFO - Iter [9200/160000]	lr: 1.500e-04, eta: 12:07:43, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0826, decode.acc_seg: 96.7475, loss: 0.0826
+2023-03-04 14:45:49,350 - mmseg - INFO - Iter [9250/160000]	lr: 1.500e-04, eta: 12:07:13, time: 0.270, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.7223, loss: 0.0830
+2023-03-04 14:46:03,022 - mmseg - INFO - Iter [9300/160000]	lr: 1.500e-04, eta: 12:06:46, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7700, loss: 0.0811
+2023-03-04 14:46:19,173 - mmseg - INFO - Iter [9350/160000]	lr: 1.500e-04, eta: 12:06:58, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0867, decode.acc_seg: 96.6028, loss: 0.0867
+2023-03-04 14:46:33,170 - mmseg - INFO - Iter [9400/160000]	lr: 1.500e-04, eta: 12:06:36, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0847, decode.acc_seg: 96.6683, loss: 0.0847
+2023-03-04 14:46:46,960 - mmseg - INFO - Iter [9450/160000]	lr: 1.500e-04, eta: 12:06:11, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.7522, loss: 0.0817
+2023-03-04 14:47:03,311 - mmseg - INFO - Iter [9500/160000]	lr: 1.500e-04, eta: 12:06:26, time: 0.327, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7962, loss: 0.0810
+2023-03-04 14:47:17,080 - mmseg - INFO - Iter [9550/160000]	lr: 1.500e-04, eta: 12:06:00, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0847, decode.acc_seg: 96.6385, loss: 0.0847
+2023-03-04 14:47:31,011 - mmseg - INFO - Iter [9600/160000]	lr: 1.500e-04, eta: 12:05:37, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0866, decode.acc_seg: 96.6012, loss: 0.0866
+2023-03-04 14:47:44,655 - mmseg - INFO - Iter [9650/160000]	lr: 1.500e-04, eta: 12:05:10, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0834, decode.acc_seg: 96.6970, loss: 0.0834
+2023-03-04 14:48:00,567 - mmseg - INFO - Iter [9700/160000]	lr: 1.500e-04, eta: 12:05:18, time: 0.318, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0865, decode.acc_seg: 96.6030, loss: 0.0865
+2023-03-04 14:48:14,106 - mmseg - INFO - Iter [9750/160000]	lr: 1.500e-04, eta: 12:04:49, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0850, decode.acc_seg: 96.6016, loss: 0.0850
+2023-03-04 14:48:27,671 - mmseg - INFO - Iter [9800/160000]	lr: 1.500e-04, eta: 12:04:20, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0861, decode.acc_seg: 96.6205, loss: 0.0861
+2023-03-04 14:48:41,262 - mmseg - INFO - Iter [9850/160000]	lr: 1.500e-04, eta: 12:03:53, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7916, loss: 0.0810
+2023-03-04 14:48:57,506 - mmseg - INFO - Iter [9900/160000]	lr: 1.500e-04, eta: 12:04:05, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.6683, loss: 0.0831
+2023-03-04 14:49:11,239 - mmseg - INFO - Iter [9950/160000]	lr: 1.500e-04, eta: 12:03:39, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0843, decode.acc_seg: 96.7137, loss: 0.0843
+2023-03-04 14:49:25,043 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 14:49:25,043 - mmseg - INFO - Iter [10000/160000]	lr: 1.500e-04, eta: 12:03:15, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7688, loss: 0.0813
+2023-03-04 14:49:41,048 - mmseg - INFO - Iter [10050/160000]	lr: 1.500e-04, eta: 12:03:24, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.7421, loss: 0.0830
+2023-03-04 14:49:54,640 - mmseg - INFO - Iter [10100/160000]	lr: 1.500e-04, eta: 12:02:56, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8147, loss: 0.0799
+2023-03-04 14:50:08,263 - mmseg - INFO - Iter [10150/160000]	lr: 1.500e-04, eta: 12:02:29, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0853, decode.acc_seg: 96.5951, loss: 0.0853
+2023-03-04 14:50:21,966 - mmseg - INFO - Iter [10200/160000]	lr: 1.500e-04, eta: 12:02:03, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7373, loss: 0.0823
+2023-03-04 14:50:37,924 - mmseg - INFO - Iter [10250/160000]	lr: 1.500e-04, eta: 12:02:11, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8419, loss: 0.0799
+2023-03-04 14:50:51,778 - mmseg - INFO - Iter [10300/160000]	lr: 1.500e-04, eta: 12:01:47, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0839, decode.acc_seg: 96.6691, loss: 0.0839
+2023-03-04 14:51:05,552 - mmseg - INFO - Iter [10350/160000]	lr: 1.500e-04, eta: 12:01:23, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0827, decode.acc_seg: 96.7519, loss: 0.0827
+2023-03-04 14:51:19,850 - mmseg - INFO - Iter [10400/160000]	lr: 1.500e-04, eta: 12:01:06, time: 0.286, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0819, decode.acc_seg: 96.7374, loss: 0.0819
+2023-03-04 14:51:35,780 - mmseg - INFO - Iter [10450/160000]	lr: 1.500e-04, eta: 12:01:13, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0827, decode.acc_seg: 96.7109, loss: 0.0827
+2023-03-04 14:51:49,393 - mmseg - INFO - Iter [10500/160000]	lr: 1.500e-04, eta: 12:00:46, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0821, decode.acc_seg: 96.7286, loss: 0.0821
+2023-03-04 14:52:02,990 - mmseg - INFO - Iter [10550/160000]	lr: 1.500e-04, eta: 12:00:19, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.7199, loss: 0.0831
+2023-03-04 14:52:16,869 - mmseg - INFO - Iter [10600/160000]	lr: 1.500e-04, eta: 11:59:57, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0849, decode.acc_seg: 96.6599, loss: 0.0849
+2023-03-04 14:52:32,894 - mmseg - INFO - Iter [10650/160000]	lr: 1.500e-04, eta: 12:00:04, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0843, decode.acc_seg: 96.6719, loss: 0.0843
+2023-03-04 14:52:47,024 - mmseg - INFO - Iter [10700/160000]	lr: 1.500e-04, eta: 11:59:45, time: 0.283, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0838, decode.acc_seg: 96.6819, loss: 0.0838
+2023-03-04 14:53:00,890 - mmseg - INFO - Iter [10750/160000]	lr: 1.500e-04, eta: 11:59:22, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8630, loss: 0.0796
+2023-03-04 14:53:17,074 - mmseg - INFO - Iter [10800/160000]	lr: 1.500e-04, eta: 11:59:32, time: 0.324, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0844, decode.acc_seg: 96.6747, loss: 0.0844
+2023-03-04 14:53:30,632 - mmseg - INFO - Iter [10850/160000]	lr: 1.500e-04, eta: 11:59:05, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0861, decode.acc_seg: 96.5548, loss: 0.0861
+2023-03-04 14:53:44,282 - mmseg - INFO - Iter [10900/160000]	lr: 1.500e-04, eta: 11:58:39, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7324, loss: 0.0818
+2023-03-04 14:53:58,090 - mmseg - INFO - Iter [10950/160000]	lr: 1.500e-04, eta: 11:58:16, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0896, decode.acc_seg: 96.4455, loss: 0.0896
+2023-03-04 14:54:14,345 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 14:54:14,345 - mmseg - INFO - Iter [11000/160000]	lr: 1.500e-04, eta: 11:58:25, time: 0.325, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0822, decode.acc_seg: 96.7377, loss: 0.0822
+2023-03-04 14:54:28,128 - mmseg - INFO - Iter [11050/160000]	lr: 1.500e-04, eta: 11:58:02, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0872, decode.acc_seg: 96.5621, loss: 0.0872
+2023-03-04 14:54:42,051 - mmseg - INFO - Iter [11100/160000]	lr: 1.500e-04, eta: 11:57:40, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0848, decode.acc_seg: 96.6537, loss: 0.0848
+2023-03-04 14:54:55,634 - mmseg - INFO - Iter [11150/160000]	lr: 1.500e-04, eta: 11:57:14, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0857, decode.acc_seg: 96.6093, loss: 0.0857
+2023-03-04 14:55:11,583 - mmseg - INFO - Iter [11200/160000]	lr: 1.500e-04, eta: 11:57:19, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7758, loss: 0.0810
+2023-03-04 14:55:25,252 - mmseg - INFO - Iter [11250/160000]	lr: 1.500e-04, eta: 11:56:54, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.7987, loss: 0.0798
+2023-03-04 14:55:39,029 - mmseg - INFO - Iter [11300/160000]	lr: 1.500e-04, eta: 11:56:31, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.6974, loss: 0.0830
+2023-03-04 14:55:55,077 - mmseg - INFO - Iter [11350/160000]	lr: 1.500e-04, eta: 11:56:37, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0855, decode.acc_seg: 96.6765, loss: 0.0855
+2023-03-04 14:56:08,729 - mmseg - INFO - Iter [11400/160000]	lr: 1.500e-04, eta: 11:56:12, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0840, decode.acc_seg: 96.6858, loss: 0.0840
+2023-03-04 14:56:23,054 - mmseg - INFO - Iter [11450/160000]	lr: 1.500e-04, eta: 11:55:56, time: 0.287, data_time: 0.006, memory: 67646, decode.loss_ce: 0.0846, decode.acc_seg: 96.6521, loss: 0.0846
+2023-03-04 14:56:36,703 - mmseg - INFO - Iter [11500/160000]	lr: 1.500e-04, eta: 11:55:31, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0840, decode.acc_seg: 96.6380, loss: 0.0840
+2023-03-04 14:56:52,686 - mmseg - INFO - Iter [11550/160000]	lr: 1.500e-04, eta: 11:55:36, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8154, loss: 0.0804
+2023-03-04 14:57:06,769 - mmseg - INFO - Iter [11600/160000]	lr: 1.500e-04, eta: 11:55:17, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7446, loss: 0.0823
+2023-03-04 14:57:20,525 - mmseg - INFO - Iter [11650/160000]	lr: 1.500e-04, eta: 11:54:54, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0856, decode.acc_seg: 96.6131, loss: 0.0856
+2023-03-04 14:57:34,523 - mmseg - INFO - Iter [11700/160000]	lr: 1.500e-04, eta: 11:54:33, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7182, loss: 0.0825
+2023-03-04 14:57:50,467 - mmseg - INFO - Iter [11750/160000]	lr: 1.500e-04, eta: 11:54:38, time: 0.319, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0839, decode.acc_seg: 96.6740, loss: 0.0839
+2023-03-04 14:58:04,409 - mmseg - INFO - Iter [11800/160000]	lr: 1.500e-04, eta: 11:54:17, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0851, decode.acc_seg: 96.6426, loss: 0.0851
+2023-03-04 14:58:18,082 - mmseg - INFO - Iter [11850/160000]	lr: 1.500e-04, eta: 11:53:52, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0845, decode.acc_seg: 96.6557, loss: 0.0845
+2023-03-04 14:58:31,813 - mmseg - INFO - Iter [11900/160000]	lr: 1.500e-04, eta: 11:53:29, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0834, decode.acc_seg: 96.7160, loss: 0.0834
+2023-03-04 14:58:47,975 - mmseg - INFO - Iter [11950/160000]	lr: 1.500e-04, eta: 11:53:36, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0860, decode.acc_seg: 96.6350, loss: 0.0860
+2023-03-04 14:59:01,824 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 14:59:01,824 - mmseg - INFO - Iter [12000/160000]	lr: 1.500e-04, eta: 11:53:14, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.7939, loss: 0.0803
+2023-03-04 14:59:15,540 - mmseg - INFO - Iter [12050/160000]	lr: 1.500e-04, eta: 11:52:50, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7823, loss: 0.0818
+2023-03-04 14:59:31,437 - mmseg - INFO - Iter [12100/160000]	lr: 1.500e-04, eta: 11:52:53, time: 0.318, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0824, decode.acc_seg: 96.7053, loss: 0.0824
+2023-03-04 14:59:45,786 - mmseg - INFO - Iter [12150/160000]	lr: 1.500e-04, eta: 11:52:38, time: 0.287, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0855, decode.acc_seg: 96.5909, loss: 0.0855
+2023-03-04 14:59:59,420 - mmseg - INFO - Iter [12200/160000]	lr: 1.500e-04, eta: 11:52:13, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0836, decode.acc_seg: 96.7237, loss: 0.0836
+2023-03-04 15:00:13,150 - mmseg - INFO - Iter [12250/160000]	lr: 1.500e-04, eta: 11:51:50, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0841, decode.acc_seg: 96.6835, loss: 0.0841
+2023-03-04 15:00:29,122 - mmseg - INFO - Iter [12300/160000]	lr: 1.500e-04, eta: 11:51:53, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0844, decode.acc_seg: 96.6556, loss: 0.0844
+2023-03-04 15:00:42,895 - mmseg - INFO - Iter [12350/160000]	lr: 1.500e-04, eta: 11:51:31, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0875, decode.acc_seg: 96.5530, loss: 0.0875
+2023-03-04 15:00:56,513 - mmseg - INFO - Iter [12400/160000]	lr: 1.500e-04, eta: 11:51:06, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0845, decode.acc_seg: 96.6222, loss: 0.0845
+2023-03-04 15:01:10,106 - mmseg - INFO - Iter [12450/160000]	lr: 1.500e-04, eta: 11:50:42, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.7736, loss: 0.0807
+2023-03-04 15:01:26,367 - mmseg - INFO - Iter [12500/160000]	lr: 1.500e-04, eta: 11:50:49, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0856, decode.acc_seg: 96.5919, loss: 0.0856
+2023-03-04 15:01:39,987 - mmseg - INFO - Iter [12550/160000]	lr: 1.500e-04, eta: 11:50:24, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0840, decode.acc_seg: 96.6775, loss: 0.0840
+2023-03-04 15:01:53,530 - mmseg - INFO - Iter [12600/160000]	lr: 1.500e-04, eta: 11:49:59, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0847, decode.acc_seg: 96.6478, loss: 0.0847
+2023-03-04 15:02:09,615 - mmseg - INFO - Iter [12650/160000]	lr: 1.500e-04, eta: 11:50:04, time: 0.322, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7064, loss: 0.0825
+2023-03-04 15:02:23,491 - mmseg - INFO - Iter [12700/160000]	lr: 1.500e-04, eta: 11:49:43, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7859, loss: 0.0813
+2023-03-04 15:02:37,660 - mmseg - INFO - Iter [12750/160000]	lr: 1.500e-04, eta: 11:49:25, time: 0.283, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0836, decode.acc_seg: 96.6778, loss: 0.0836
+2023-03-04 15:02:51,544 - mmseg - INFO - Iter [12800/160000]	lr: 1.500e-04, eta: 11:49:04, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8498, loss: 0.0800
+2023-03-04 15:03:07,946 - mmseg - INFO - Iter [12850/160000]	lr: 1.500e-04, eta: 11:49:12, time: 0.328, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0872, decode.acc_seg: 96.6109, loss: 0.0872
+2023-03-04 15:03:21,747 - mmseg - INFO - Iter [12900/160000]	lr: 1.500e-04, eta: 11:48:50, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0842, decode.acc_seg: 96.6807, loss: 0.0842
+2023-03-04 15:03:35,534 - mmseg - INFO - Iter [12950/160000]	lr: 1.500e-04, eta: 11:48:28, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0820, decode.acc_seg: 96.7422, loss: 0.0820
+2023-03-04 15:03:49,299 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 15:03:49,300 - mmseg - INFO - Iter [13000/160000]	lr: 1.500e-04, eta: 11:48:06, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0847, decode.acc_seg: 96.6049, loss: 0.0847
+2023-03-04 15:04:05,458 - mmseg - INFO - Iter [13050/160000]	lr: 1.500e-04, eta: 11:48:10, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0822, decode.acc_seg: 96.7289, loss: 0.0822
+2023-03-04 15:04:19,043 - mmseg - INFO - Iter [13100/160000]	lr: 1.500e-04, eta: 11:47:46, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0875, decode.acc_seg: 96.6327, loss: 0.0875
+2023-03-04 15:04:33,052 - mmseg - INFO - Iter [13150/160000]	lr: 1.500e-04, eta: 11:47:27, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0826, decode.acc_seg: 96.7240, loss: 0.0826
+2023-03-04 15:04:46,712 - mmseg - INFO - Iter [13200/160000]	lr: 1.500e-04, eta: 11:47:03, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0839, decode.acc_seg: 96.6917, loss: 0.0839
+2023-03-04 15:05:02,836 - mmseg - INFO - Iter [13250/160000]	lr: 1.500e-04, eta: 11:47:07, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0836, decode.acc_seg: 96.7073, loss: 0.0836
+2023-03-04 15:05:16,696 - mmseg - INFO - Iter [13300/160000]	lr: 1.500e-04, eta: 11:46:46, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0846, decode.acc_seg: 96.6562, loss: 0.0846
+2023-03-04 15:05:30,247 - mmseg - INFO - Iter [13350/160000]	lr: 1.500e-04, eta: 11:46:22, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8493, loss: 0.0791
+2023-03-04 15:05:46,203 - mmseg - INFO - Iter [13400/160000]	lr: 1.500e-04, eta: 11:46:24, time: 0.319, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0847, decode.acc_seg: 96.6549, loss: 0.0847
+2023-03-04 15:05:59,953 - mmseg - INFO - Iter [13450/160000]	lr: 1.500e-04, eta: 11:46:02, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0833, decode.acc_seg: 96.7213, loss: 0.0833
+2023-03-04 15:06:13,595 - mmseg - INFO - Iter [13500/160000]	lr: 1.500e-04, eta: 11:45:39, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0826, decode.acc_seg: 96.7029, loss: 0.0826
+2023-03-04 15:06:27,563 - mmseg - INFO - Iter [13550/160000]	lr: 1.500e-04, eta: 11:45:19, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7160, loss: 0.0825
+2023-03-04 15:06:43,751 - mmseg - INFO - Iter [13600/160000]	lr: 1.500e-04, eta: 11:45:23, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0859, decode.acc_seg: 96.5916, loss: 0.0859
+2023-03-04 15:06:57,490 - mmseg - INFO - Iter [13650/160000]	lr: 1.500e-04, eta: 11:45:01, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0839, decode.acc_seg: 96.7089, loss: 0.0839
+2023-03-04 15:07:11,243 - mmseg - INFO - Iter [13700/160000]	lr: 1.500e-04, eta: 11:44:39, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0835, decode.acc_seg: 96.6825, loss: 0.0835
+2023-03-04 15:07:24,823 - mmseg - INFO - Iter [13750/160000]	lr: 1.500e-04, eta: 11:44:16, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0851, decode.acc_seg: 96.6204, loss: 0.0851
+2023-03-04 15:07:40,775 - mmseg - INFO - Iter [13800/160000]	lr: 1.500e-04, eta: 11:44:17, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7505, loss: 0.0816
+2023-03-04 15:07:54,455 - mmseg - INFO - Iter [13850/160000]	lr: 1.500e-04, eta: 11:43:54, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.8022, loss: 0.0815
+2023-03-04 15:08:08,028 - mmseg - INFO - Iter [13900/160000]	lr: 1.500e-04, eta: 11:43:31, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.7101, loss: 0.0830
+2023-03-04 15:08:22,666 - mmseg - INFO - Iter [13950/160000]	lr: 1.500e-04, eta: 11:43:18, time: 0.293, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7055, loss: 0.0823
+2023-03-04 15:08:38,998 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 15:08:38,998 - mmseg - INFO - Iter [14000/160000]	lr: 1.500e-04, eta: 11:43:24, time: 0.327, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7744, loss: 0.0816
+2023-03-04 15:08:52,794 - mmseg - INFO - Iter [14050/160000]	lr: 1.500e-04, eta: 11:43:02, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0838, decode.acc_seg: 96.6870, loss: 0.0838
+2023-03-04 15:09:06,459 - mmseg - INFO - Iter [14100/160000]	lr: 1.500e-04, eta: 11:42:40, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0838, decode.acc_seg: 96.6876, loss: 0.0838
+2023-03-04 15:09:22,375 - mmseg - INFO - Iter [14150/160000]	lr: 1.500e-04, eta: 11:42:40, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.7971, loss: 0.0807
+2023-03-04 15:09:36,263 - mmseg - INFO - Iter [14200/160000]	lr: 1.500e-04, eta: 11:42:20, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.7832, loss: 0.0817
+2023-03-04 15:09:50,000 - mmseg - INFO - Iter [14250/160000]	lr: 1.500e-04, eta: 11:41:58, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0853, decode.acc_seg: 96.6282, loss: 0.0853
+2023-03-04 15:10:03,528 - mmseg - INFO - Iter [14300/160000]	lr: 1.500e-04, eta: 11:41:34, time: 0.270, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0848, decode.acc_seg: 96.6398, loss: 0.0848
+2023-03-04 15:10:19,834 - mmseg - INFO - Iter [14350/160000]	lr: 1.500e-04, eta: 11:41:39, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.6871, loss: 0.0831
+2023-03-04 15:10:33,527 - mmseg - INFO - Iter [14400/160000]	lr: 1.500e-04, eta: 11:41:17, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7935, loss: 0.0811
+2023-03-04 15:10:47,156 - mmseg - INFO - Iter [14450/160000]	lr: 1.500e-04, eta: 11:40:54, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.7459, loss: 0.0830
+2023-03-04 15:11:00,730 - mmseg - INFO - Iter [14500/160000]	lr: 1.500e-04, eta: 11:40:31, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0861, decode.acc_seg: 96.6007, loss: 0.0861
+2023-03-04 15:11:16,974 - mmseg - INFO - Iter [14550/160000]	lr: 1.500e-04, eta: 11:40:34, time: 0.325, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8525, loss: 0.0794
+2023-03-04 15:11:30,747 - mmseg - INFO - Iter [14600/160000]	lr: 1.500e-04, eta: 11:40:13, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0844, decode.acc_seg: 96.6472, loss: 0.0844
+2023-03-04 15:11:44,384 - mmseg - INFO - Iter [14650/160000]	lr: 1.500e-04, eta: 11:39:51, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.6810, loss: 0.0830
+2023-03-04 15:12:00,420 - mmseg - INFO - Iter [14700/160000]	lr: 1.500e-04, eta: 11:39:52, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.7840, loss: 0.0807
+2023-03-04 15:12:14,114 - mmseg - INFO - Iter [14750/160000]	lr: 1.500e-04, eta: 11:39:30, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0826, decode.acc_seg: 96.7597, loss: 0.0826
+2023-03-04 15:12:27,756 - mmseg - INFO - Iter [14800/160000]	lr: 1.500e-04, eta: 11:39:08, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0834, decode.acc_seg: 96.6773, loss: 0.0834
+2023-03-04 15:12:41,601 - mmseg - INFO - Iter [14850/160000]	lr: 1.500e-04, eta: 11:38:47, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0845, decode.acc_seg: 96.6391, loss: 0.0845
+2023-03-04 15:12:57,439 - mmseg - INFO - Iter [14900/160000]	lr: 1.500e-04, eta: 11:38:47, time: 0.317, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0819, decode.acc_seg: 96.7741, loss: 0.0819
+2023-03-04 15:13:11,064 - mmseg - INFO - Iter [14950/160000]	lr: 1.500e-04, eta: 11:38:24, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0841, decode.acc_seg: 96.6827, loss: 0.0841
+2023-03-04 15:13:24,694 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 15:13:24,694 - mmseg - INFO - Iter [15000/160000]	lr: 1.500e-04, eta: 11:38:02, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0821, decode.acc_seg: 96.6882, loss: 0.0821
+2023-03-04 15:13:38,397 - mmseg - INFO - Iter [15050/160000]	lr: 1.500e-04, eta: 11:37:40, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0861, decode.acc_seg: 96.6304, loss: 0.0861
+2023-03-04 15:13:54,434 - mmseg - INFO - Iter [15100/160000]	lr: 1.500e-04, eta: 11:37:41, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0822, decode.acc_seg: 96.7486, loss: 0.0822
+2023-03-04 15:14:08,286 - mmseg - INFO - Iter [15150/160000]	lr: 1.500e-04, eta: 11:37:21, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0884, decode.acc_seg: 96.4935, loss: 0.0884
+2023-03-04 15:14:21,890 - mmseg - INFO - Iter [15200/160000]	lr: 1.500e-04, eta: 11:36:59, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7746, loss: 0.0814
+2023-03-04 15:14:35,599 - mmseg - INFO - Iter [15250/160000]	lr: 1.500e-04, eta: 11:36:37, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0863, decode.acc_seg: 96.5854, loss: 0.0863
+2023-03-04 15:14:51,562 - mmseg - INFO - Iter [15300/160000]	lr: 1.500e-04, eta: 11:36:37, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0872, decode.acc_seg: 96.5374, loss: 0.0872
+2023-03-04 15:15:05,319 - mmseg - INFO - Iter [15350/160000]	lr: 1.500e-04, eta: 11:36:16, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0835, decode.acc_seg: 96.7008, loss: 0.0835
+2023-03-04 15:15:19,049 - mmseg - INFO - Iter [15400/160000]	lr: 1.500e-04, eta: 11:35:55, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0829, decode.acc_seg: 96.7258, loss: 0.0829
+2023-03-04 15:15:35,117 - mmseg - INFO - Iter [15450/160000]	lr: 1.500e-04, eta: 11:35:56, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0841, decode.acc_seg: 96.6909, loss: 0.0841
+2023-03-04 15:15:48,669 - mmseg - INFO - Iter [15500/160000]	lr: 1.500e-04, eta: 11:35:33, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.7879, loss: 0.0806
+2023-03-04 15:16:02,334 - mmseg - INFO - Iter [15550/160000]	lr: 1.500e-04, eta: 11:35:12, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0824, decode.acc_seg: 96.6985, loss: 0.0824
+2023-03-04 15:16:16,034 - mmseg - INFO - Iter [15600/160000]	lr: 1.500e-04, eta: 11:34:50, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0826, decode.acc_seg: 96.7707, loss: 0.0826
+2023-03-04 15:16:32,110 - mmseg - INFO - Iter [15650/160000]	lr: 1.500e-04, eta: 11:34:51, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0864, decode.acc_seg: 96.6230, loss: 0.0864
+2023-03-04 15:16:45,737 - mmseg - INFO - Iter [15700/160000]	lr: 1.500e-04, eta: 11:34:29, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8205, loss: 0.0799
+2023-03-04 15:16:59,419 - mmseg - INFO - Iter [15750/160000]	lr: 1.500e-04, eta: 11:34:08, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0842, decode.acc_seg: 96.6868, loss: 0.0842
+2023-03-04 15:17:12,961 - mmseg - INFO - Iter [15800/160000]	lr: 1.500e-04, eta: 11:33:45, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0852, decode.acc_seg: 96.6144, loss: 0.0852
+2023-03-04 15:17:28,880 - mmseg - INFO - Iter [15850/160000]	lr: 1.500e-04, eta: 11:33:44, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7756, loss: 0.0808
+2023-03-04 15:17:42,765 - mmseg - INFO - Iter [15900/160000]	lr: 1.500e-04, eta: 11:33:25, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7025, loss: 0.0823
+2023-03-04 15:17:56,388 - mmseg - INFO - Iter [15950/160000]	lr: 1.500e-04, eta: 11:33:03, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8434, loss: 0.0792
+2023-03-04 15:18:12,442 - mmseg - INFO - Swap parameters (after train) after iter [16000]
+2023-03-04 15:18:12,463 - mmseg - INFO - Saving checkpoint at 16000 iterations
+2023-03-04 15:18:14,349 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 15:18:14,349 - mmseg - INFO - Iter [16000/160000]	lr: 1.500e-04, eta: 11:33:20, time: 0.359, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0834, decode.acc_seg: 96.6967, loss: 0.0834
+2023-03-04 15:33:24,581 - mmseg - INFO - per class results:
+2023-03-04 15:33:24,582 - mmseg - INFO - 
++---------------+-------------------------------------------------------------------+
+|     Class     |                IoU 0,10,20,30,40,50,60,70,80,90,99                |
++---------------+-------------------------------------------------------------------+
+|   background  |            nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan            |
+|      road     |    98.5,98.5,98.5,98.5,98.5,98.5,98.49,98.49,98.49,98.49,98.49    |
+|    sidewalk   |    87.11,87.11,87.11,87.1,87.1,87.1,87.11,87.1,87.1,87.1,87.08    |
+|    building   | 93.56,93.56,93.56,93.56,93.56,93.56,93.56,93.56,93.55,93.55,93.56 |
+|      wall     | 53.71,53.68,53.66,53.61,53.58,53.51,53.48,53.44,53.39,53.36,53.58 |
+|     fence     |  64.88,64.87,64.86,64.85,64.83,64.83,64.83,64.8,64.73,64.72,64.82 |
+|      pole     | 70.86,70.86,70.86,70.86,70.86,70.86,70.84,70.84,70.85,70.84,70.83 |
+| traffic light |   75.11,75.1,75.1,75.09,75.1,75.12,75.14,75.13,75.12,75.15,75.02  |
+|  traffic sign | 82.44,82.44,82.45,82.43,82.43,82.45,82.46,82.46,82.46,82.47,82.41 |
+|   vegetation  | 93.05,93.05,93.04,93.04,93.04,93.04,93.04,93.04,93.03,93.03,93.04 |
+|    terrain    | 64.21,64.21,64.18,64.19,64.17,64.18,64.18,64.15,64.14,64.13,64.15 |
+|      sky      | 95.27,95.26,95.26,95.27,95.27,95.28,95.28,95.28,95.28,95.28,95.28 |
+|     person    | 84.88,84.88,84.88,84.87,84.87,84.85,84.86,84.86,84.85,84.84,84.87 |
+|     rider     |  67.93,67.92,67.93,67.93,67.92,67.9,67.94,67.89,67.86,67.88,67.91 |
+|      car      | 96.01,96.01,96.01,96.01,96.01,96.01,96.01,96.01,96.01,96.01,95.99 |
+|     truck     | 84.99,85.03,85.05,85.03,85.04,84.98,84.95,84.95,84.89,84.83,84.78 |
+|      bus      |  92.24,92.23,92.24,92.2,92.23,92.22,92.19,92.22,92.19,92.18,92.26 |
+|     train     |    85.48,85.51,85.51,85.53,85.5,85.5,85.45,85.5,85.45,85.5,85.6   |
+|   motorcycle  |  72.19,72.18,72.18,72.19,72.2,72.14,72.14,72.15,72.14,72.17,72.15 |
+|    bicycle    |   80.45,80.45,80.46,80.45,80.43,80.44,80.4,80.39,80.4,80.4,80.36  |
++---------------+-------------------------------------------------------------------+
+2023-03-04 15:33:24,583 - mmseg - INFO - Summary:
+2023-03-04 15:33:24,583 - mmseg - INFO - 
++---------------------------------------------------------------+
+|              mIoU 0,10,20,30,40,50,60,70,80,90,99             |
++---------------------------------------------------------------+
+| 81.2,81.2,81.2,81.2,81.19,81.18,81.17,81.17,81.16,81.15,81.17 |
++---------------------------------------------------------------+
+2023-03-04 15:33:26,345 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_16000.pth.
+2023-03-04 15:33:26,346 - mmseg - INFO - Best mIoU is 0.8117 at 16000 iter.
+2023-03-04 15:33:26,346 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 15:33:26,346 - mmseg - INFO - Iter(val) [63]	mIoU: [0.812, 0.812, 0.812, 0.812, 0.8119, 0.8118, 0.8117, 0.8117, 0.8116, 0.8115, 0.8117], copy_paste: 81.2,81.2,81.2,81.2,81.19,81.18,81.17,81.17,81.16,81.15,81.17
+2023-03-04 15:33:26,352 - mmseg - INFO - Swap parameters (before train) before iter [16001]
+2023-03-04 15:33:40,606 - mmseg - INFO - Iter [16050/160000]	lr: 1.500e-04, eta: 13:49:24, time: 18.525, data_time: 18.249, memory: 67646, decode.loss_ce: 0.0838, decode.acc_seg: 96.6955, loss: 0.0838
+2023-03-04 15:33:54,481 - mmseg - INFO - Iter [16100/160000]	lr: 1.500e-04, eta: 13:48:36, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0853, decode.acc_seg: 96.6186, loss: 0.0853
+2023-03-04 15:34:08,191 - mmseg - INFO - Iter [16150/160000]	lr: 1.500e-04, eta: 13:47:47, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8264, loss: 0.0804
+2023-03-04 15:34:24,152 - mmseg - INFO - Iter [16200/160000]	lr: 1.500e-04, eta: 13:47:18, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7580, loss: 0.0818
+2023-03-04 15:34:37,776 - mmseg - INFO - Iter [16250/160000]	lr: 1.500e-04, eta: 13:46:29, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0827, decode.acc_seg: 96.7128, loss: 0.0827
+2023-03-04 15:34:51,580 - mmseg - INFO - Iter [16300/160000]	lr: 1.500e-04, eta: 13:45:41, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7493, loss: 0.0810
+2023-03-04 15:35:05,261 - mmseg - INFO - Iter [16350/160000]	lr: 1.500e-04, eta: 13:44:52, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0833, decode.acc_seg: 96.6999, loss: 0.0833
+2023-03-04 15:35:21,341 - mmseg - INFO - Iter [16400/160000]	lr: 1.500e-04, eta: 13:44:25, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0883, decode.acc_seg: 96.5634, loss: 0.0883
+2023-03-04 15:35:35,040 - mmseg - INFO - Iter [16450/160000]	lr: 1.500e-04, eta: 13:43:37, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0846, decode.acc_seg: 96.6638, loss: 0.0846
+2023-03-04 15:35:48,635 - mmseg - INFO - Iter [16500/160000]	lr: 1.500e-04, eta: 13:42:49, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0829, decode.acc_seg: 96.6862, loss: 0.0829
+2023-03-04 15:36:02,165 - mmseg - INFO - Iter [16550/160000]	lr: 1.500e-04, eta: 13:42:00, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.7469, loss: 0.0830
+2023-03-04 15:36:18,258 - mmseg - INFO - Iter [16600/160000]	lr: 1.500e-04, eta: 13:41:33, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.6845, loss: 0.0830
+2023-03-04 15:36:32,018 - mmseg - INFO - Iter [16650/160000]	lr: 1.500e-04, eta: 13:40:46, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7960, loss: 0.0810
+2023-03-04 15:36:46,152 - mmseg - INFO - Iter [16700/160000]	lr: 1.500e-04, eta: 13:40:03, time: 0.283, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0853, decode.acc_seg: 96.6613, loss: 0.0853
+2023-03-04 15:37:02,145 - mmseg - INFO - Iter [16750/160000]	lr: 1.500e-04, eta: 13:39:36, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0833, decode.acc_seg: 96.7252, loss: 0.0833
+2023-03-04 15:37:16,187 - mmseg - INFO - Iter [16800/160000]	lr: 1.500e-04, eta: 13:38:52, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0843, decode.acc_seg: 96.6814, loss: 0.0843
+2023-03-04 15:37:29,906 - mmseg - INFO - Iter [16850/160000]	lr: 1.500e-04, eta: 13:38:06, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0834, decode.acc_seg: 96.6917, loss: 0.0834
+2023-03-04 15:37:43,460 - mmseg - INFO - Iter [16900/160000]	lr: 1.500e-04, eta: 13:37:18, time: 0.271, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8125, loss: 0.0801
+2023-03-04 15:37:59,454 - mmseg - INFO - Iter [16950/160000]	lr: 1.500e-04, eta: 13:36:51, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.7551, loss: 0.0812
+2023-03-04 15:38:13,086 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 15:38:13,086 - mmseg - INFO - Iter [17000/160000]	lr: 1.500e-04, eta: 13:36:05, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0836, decode.acc_seg: 96.6976, loss: 0.0836
+2023-03-04 15:38:26,816 - mmseg - INFO - Iter [17050/160000]	lr: 1.500e-04, eta: 13:35:19, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.7975, loss: 0.0804
+2023-03-04 15:38:40,394 - mmseg - INFO - Iter [17100/160000]	lr: 1.500e-04, eta: 13:34:32, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0856, decode.acc_seg: 96.6692, loss: 0.0856
+2023-03-04 15:38:56,376 - mmseg - INFO - Iter [17150/160000]	lr: 1.500e-04, eta: 13:34:06, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0833, decode.acc_seg: 96.6657, loss: 0.0833
+2023-03-04 15:39:10,127 - mmseg - INFO - Iter [17200/160000]	lr: 1.500e-04, eta: 13:33:21, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7382, loss: 0.0823
+2023-03-04 15:39:24,017 - mmseg - INFO - Iter [17250/160000]	lr: 1.500e-04, eta: 13:32:38, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7851, loss: 0.0813
+2023-03-04 15:39:40,188 - mmseg - INFO - Iter [17300/160000]	lr: 1.500e-04, eta: 13:32:13, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0848, decode.acc_seg: 96.6556, loss: 0.0848
+2023-03-04 15:39:53,928 - mmseg - INFO - Iter [17350/160000]	lr: 1.500e-04, eta: 13:31:29, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7204, loss: 0.0823
+2023-03-04 15:40:07,561 - mmseg - INFO - Iter [17400/160000]	lr: 1.500e-04, eta: 13:30:43, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0835, decode.acc_seg: 96.7291, loss: 0.0835
+2023-03-04 15:40:21,643 - mmseg - INFO - Iter [17450/160000]	lr: 1.500e-04, eta: 13:30:02, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7963, loss: 0.0814
+2023-03-04 15:40:37,914 - mmseg - INFO - Iter [17500/160000]	lr: 1.500e-04, eta: 13:29:39, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0855, decode.acc_seg: 96.5938, loss: 0.0855
+2023-03-04 15:40:51,728 - mmseg - INFO - Iter [17550/160000]	lr: 1.500e-04, eta: 13:28:55, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0842, decode.acc_seg: 96.6888, loss: 0.0842
+2023-03-04 15:41:05,333 - mmseg - INFO - Iter [17600/160000]	lr: 1.500e-04, eta: 13:28:11, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0839, decode.acc_seg: 96.6680, loss: 0.0839
+2023-03-04 15:41:19,069 - mmseg - INFO - Iter [17650/160000]	lr: 1.500e-04, eta: 13:27:27, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0844, decode.acc_seg: 96.6448, loss: 0.0844
+2023-03-04 15:41:35,322 - mmseg - INFO - Iter [17700/160000]	lr: 1.500e-04, eta: 13:27:04, time: 0.325, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.7893, loss: 0.0798
+2023-03-04 15:41:49,120 - mmseg - INFO - Iter [17750/160000]	lr: 1.500e-04, eta: 13:26:21, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0826, decode.acc_seg: 96.7498, loss: 0.0826
+2023-03-04 15:42:02,769 - mmseg - INFO - Iter [17800/160000]	lr: 1.500e-04, eta: 13:25:37, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0837, decode.acc_seg: 96.6906, loss: 0.0837
+2023-03-04 15:42:16,356 - mmseg - INFO - Iter [17850/160000]	lr: 1.500e-04, eta: 13:24:53, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0856, decode.acc_seg: 96.6213, loss: 0.0856
+2023-03-04 15:42:32,323 - mmseg - INFO - Iter [17900/160000]	lr: 1.500e-04, eta: 13:24:28, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0842, decode.acc_seg: 96.6699, loss: 0.0842
+2023-03-04 15:42:45,983 - mmseg - INFO - Iter [17950/160000]	lr: 1.500e-04, eta: 13:23:45, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8038, loss: 0.0797
+2023-03-04 15:42:59,555 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 15:42:59,555 - mmseg - INFO - Iter [18000/160000]	lr: 1.500e-04, eta: 13:23:01, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0852, decode.acc_seg: 96.6364, loss: 0.0852
+2023-03-04 15:43:15,586 - mmseg - INFO - Iter [18050/160000]	lr: 1.500e-04, eta: 13:22:36, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0822, decode.acc_seg: 96.7699, loss: 0.0822
+2023-03-04 15:43:29,178 - mmseg - INFO - Iter [18100/160000]	lr: 1.500e-04, eta: 13:21:53, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7511, loss: 0.0816
+2023-03-04 15:43:43,007 - mmseg - INFO - Iter [18150/160000]	lr: 1.500e-04, eta: 13:21:12, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8857, loss: 0.0783
+2023-03-04 15:43:56,686 - mmseg - INFO - Iter [18200/160000]	lr: 1.500e-04, eta: 13:20:29, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0855, decode.acc_seg: 96.6690, loss: 0.0855
+2023-03-04 15:44:12,748 - mmseg - INFO - Iter [18250/160000]	lr: 1.500e-04, eta: 13:20:06, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0883, decode.acc_seg: 96.4802, loss: 0.0883
+2023-03-04 15:44:26,344 - mmseg - INFO - Iter [18300/160000]	lr: 1.500e-04, eta: 13:19:23, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0841, decode.acc_seg: 96.6645, loss: 0.0841
+2023-03-04 15:44:39,957 - mmseg - INFO - Iter [18350/160000]	lr: 1.500e-04, eta: 13:18:40, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0850, decode.acc_seg: 96.6281, loss: 0.0850
+2023-03-04 15:44:53,792 - mmseg - INFO - Iter [18400/160000]	lr: 1.500e-04, eta: 13:18:00, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0833, decode.acc_seg: 96.6965, loss: 0.0833
+2023-03-04 15:45:09,872 - mmseg - INFO - Iter [18450/160000]	lr: 1.500e-04, eta: 13:17:36, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.6964, loss: 0.0823
+2023-03-04 15:45:23,510 - mmseg - INFO - Iter [18500/160000]	lr: 1.500e-04, eta: 13:16:55, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0828, decode.acc_seg: 96.7333, loss: 0.0828
+2023-03-04 15:45:37,441 - mmseg - INFO - Iter [18550/160000]	lr: 1.500e-04, eta: 13:16:15, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8238, loss: 0.0801
+2023-03-04 15:45:51,021 - mmseg - INFO - Iter [18600/160000]	lr: 1.500e-04, eta: 13:15:33, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7310, loss: 0.0823
+2023-03-04 15:46:07,229 - mmseg - INFO - Iter [18650/160000]	lr: 1.500e-04, eta: 13:15:11, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0826, decode.acc_seg: 96.7369, loss: 0.0826
+2023-03-04 15:46:20,788 - mmseg - INFO - Iter [18700/160000]	lr: 1.500e-04, eta: 13:14:29, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0824, decode.acc_seg: 96.7150, loss: 0.0824
+2023-03-04 15:46:34,493 - mmseg - INFO - Iter [18750/160000]	lr: 1.500e-04, eta: 13:13:48, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0837, decode.acc_seg: 96.7077, loss: 0.0837
+2023-03-04 15:46:50,411 - mmseg - INFO - Iter [18800/160000]	lr: 1.500e-04, eta: 13:13:24, time: 0.318, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0834, decode.acc_seg: 96.6865, loss: 0.0834
+2023-03-04 15:47:04,349 - mmseg - INFO - Iter [18850/160000]	lr: 1.500e-04, eta: 13:12:46, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0860, decode.acc_seg: 96.6458, loss: 0.0860
+2023-03-04 15:47:18,294 - mmseg - INFO - Iter [18900/160000]	lr: 1.500e-04, eta: 13:12:07, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7934, loss: 0.0815
+2023-03-04 15:47:31,870 - mmseg - INFO - Iter [18950/160000]	lr: 1.500e-04, eta: 13:11:26, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0833, decode.acc_seg: 96.6803, loss: 0.0833
+2023-03-04 15:47:48,154 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 15:47:48,154 - mmseg - INFO - Iter [19000/160000]	lr: 1.500e-04, eta: 13:11:05, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0876, decode.acc_seg: 96.5387, loss: 0.0876
+2023-03-04 15:48:01,728 - mmseg - INFO - Iter [19050/160000]	lr: 1.500e-04, eta: 13:10:24, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8126, loss: 0.0804
+2023-03-04 15:48:15,299 - mmseg - INFO - Iter [19100/160000]	lr: 1.500e-04, eta: 13:09:43, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0861, decode.acc_seg: 96.6468, loss: 0.0861
+2023-03-04 15:48:29,018 - mmseg - INFO - Iter [19150/160000]	lr: 1.500e-04, eta: 13:09:04, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0819, decode.acc_seg: 96.7474, loss: 0.0819
+2023-03-04 15:48:44,995 - mmseg - INFO - Iter [19200/160000]	lr: 1.500e-04, eta: 13:08:41, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.7232, loss: 0.0830
+2023-03-04 15:48:58,601 - mmseg - INFO - Iter [19250/160000]	lr: 1.500e-04, eta: 13:08:01, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0850, decode.acc_seg: 96.6004, loss: 0.0850
+2023-03-04 15:49:12,287 - mmseg - INFO - Iter [19300/160000]	lr: 1.500e-04, eta: 13:07:21, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0829, decode.acc_seg: 96.6808, loss: 0.0829
+2023-03-04 15:49:28,258 - mmseg - INFO - Iter [19350/160000]	lr: 1.500e-04, eta: 13:06:59, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0827, decode.acc_seg: 96.7075, loss: 0.0827
+2023-03-04 15:49:41,979 - mmseg - INFO - Iter [19400/160000]	lr: 1.500e-04, eta: 13:06:20, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0819, decode.acc_seg: 96.7318, loss: 0.0819
+2023-03-04 15:49:55,614 - mmseg - INFO - Iter [19450/160000]	lr: 1.500e-04, eta: 13:05:40, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7237, loss: 0.0825
+2023-03-04 15:50:09,301 - mmseg - INFO - Iter [19500/160000]	lr: 1.500e-04, eta: 13:05:01, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7644, loss: 0.0813
+2023-03-04 15:50:25,278 - mmseg - INFO - Iter [19550/160000]	lr: 1.500e-04, eta: 13:04:39, time: 0.320, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0838, decode.acc_seg: 96.6811, loss: 0.0838
+2023-03-04 15:50:39,428 - mmseg - INFO - Iter [19600/160000]	lr: 1.500e-04, eta: 13:04:03, time: 0.283, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0838, decode.acc_seg: 96.6724, loss: 0.0838
+2023-03-04 15:50:52,987 - mmseg - INFO - Iter [19650/160000]	lr: 1.500e-04, eta: 13:03:24, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.8098, loss: 0.0810
+2023-03-04 15:51:06,755 - mmseg - INFO - Iter [19700/160000]	lr: 1.500e-04, eta: 13:02:46, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0821, decode.acc_seg: 96.7459, loss: 0.0821
+2023-03-04 15:51:22,843 - mmseg - INFO - Iter [19750/160000]	lr: 1.500e-04, eta: 13:02:24, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7850, loss: 0.0811
+2023-03-04 15:51:37,274 - mmseg - INFO - Iter [19800/160000]	lr: 1.500e-04, eta: 13:01:51, time: 0.289, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7911, loss: 0.0815
+2023-03-04 15:51:50,841 - mmseg - INFO - Iter [19850/160000]	lr: 1.500e-04, eta: 13:01:12, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8758, loss: 0.0789
+2023-03-04 15:52:04,801 - mmseg - INFO - Iter [19900/160000]	lr: 1.500e-04, eta: 13:00:36, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0827, decode.acc_seg: 96.6907, loss: 0.0827
+2023-03-04 15:52:21,225 - mmseg - INFO - Iter [19950/160000]	lr: 1.500e-04, eta: 13:00:17, time: 0.328, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0861, decode.acc_seg: 96.5889, loss: 0.0861
+2023-03-04 15:52:35,088 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 15:52:35,088 - mmseg - INFO - Iter [20000/160000]	lr: 1.500e-04, eta: 12:59:41, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8307, loss: 0.0798
+2023-03-04 15:52:48,677 - mmseg - INFO - Iter [20050/160000]	lr: 7.500e-05, eta: 12:59:02, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.7992, loss: 0.0831
+2023-03-04 15:53:04,713 - mmseg - INFO - Iter [20100/160000]	lr: 7.500e-05, eta: 12:58:41, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0822, decode.acc_seg: 96.7545, loss: 0.0822
+2023-03-04 15:53:18,282 - mmseg - INFO - Iter [20150/160000]	lr: 7.500e-05, eta: 12:58:02, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8560, loss: 0.0797
+2023-03-04 15:53:32,108 - mmseg - INFO - Iter [20200/160000]	lr: 7.500e-05, eta: 12:57:26, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0851, decode.acc_seg: 96.6134, loss: 0.0851
+2023-03-04 15:53:45,853 - mmseg - INFO - Iter [20250/160000]	lr: 7.500e-05, eta: 12:56:49, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7689, loss: 0.0816
+2023-03-04 15:54:01,926 - mmseg - INFO - Iter [20300/160000]	lr: 7.500e-05, eta: 12:56:28, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7830, loss: 0.0815
+2023-03-04 15:54:15,656 - mmseg - INFO - Iter [20350/160000]	lr: 7.500e-05, eta: 12:55:51, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.7975, loss: 0.0801
+2023-03-04 15:54:29,201 - mmseg - INFO - Iter [20400/160000]	lr: 7.500e-05, eta: 12:55:13, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.7586, loss: 0.0830
+2023-03-04 15:54:43,243 - mmseg - INFO - Iter [20450/160000]	lr: 7.500e-05, eta: 12:54:39, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.7628, loss: 0.0805
+2023-03-04 15:54:59,273 - mmseg - INFO - Iter [20500/160000]	lr: 7.500e-05, eta: 12:54:18, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.7812, loss: 0.0812
+2023-03-04 15:55:12,870 - mmseg - INFO - Iter [20550/160000]	lr: 7.500e-05, eta: 12:53:40, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7952, loss: 0.0811
+2023-03-04 15:55:26,651 - mmseg - INFO - Iter [20600/160000]	lr: 7.500e-05, eta: 12:53:04, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0819, decode.acc_seg: 96.7120, loss: 0.0819
+2023-03-04 15:55:42,712 - mmseg - INFO - Iter [20650/160000]	lr: 7.500e-05, eta: 12:52:44, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.7569, loss: 0.0817
+2023-03-04 15:55:56,648 - mmseg - INFO - Iter [20700/160000]	lr: 7.500e-05, eta: 12:52:09, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0844, decode.acc_seg: 96.6597, loss: 0.0844
+2023-03-04 15:56:10,757 - mmseg - INFO - Iter [20750/160000]	lr: 7.500e-05, eta: 12:51:35, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7458, loss: 0.0825
+2023-03-04 15:56:24,705 - mmseg - INFO - Iter [20800/160000]	lr: 7.500e-05, eta: 12:51:01, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7456, loss: 0.0818
+2023-03-04 15:56:41,048 - mmseg - INFO - Iter [20850/160000]	lr: 7.500e-05, eta: 12:50:42, time: 0.327, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8492, loss: 0.0799
+2023-03-04 15:56:54,767 - mmseg - INFO - Iter [20900/160000]	lr: 7.500e-05, eta: 12:50:06, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8039, loss: 0.0798
+2023-03-04 15:57:08,575 - mmseg - INFO - Iter [20950/160000]	lr: 7.500e-05, eta: 12:49:31, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0832, decode.acc_seg: 96.7163, loss: 0.0832
+2023-03-04 15:57:22,460 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 15:57:22,461 - mmseg - INFO - Iter [21000/160000]	lr: 7.500e-05, eta: 12:48:57, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0837, decode.acc_seg: 96.6934, loss: 0.0837
+2023-03-04 15:57:38,487 - mmseg - INFO - Iter [21050/160000]	lr: 7.500e-05, eta: 12:48:36, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0828, decode.acc_seg: 96.6603, loss: 0.0828
+2023-03-04 15:57:52,127 - mmseg - INFO - Iter [21100/160000]	lr: 7.500e-05, eta: 12:48:00, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0862, decode.acc_seg: 96.6487, loss: 0.0862
+2023-03-04 15:58:05,715 - mmseg - INFO - Iter [21150/160000]	lr: 7.500e-05, eta: 12:47:24, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7411, loss: 0.0823
+2023-03-04 15:58:19,537 - mmseg - INFO - Iter [21200/160000]	lr: 7.500e-05, eta: 12:46:49, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.7936, loss: 0.0801
+2023-03-04 15:58:35,863 - mmseg - INFO - Iter [21250/160000]	lr: 7.500e-05, eta: 12:46:31, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8343, loss: 0.0800
+2023-03-04 15:58:49,488 - mmseg - INFO - Iter [21300/160000]	lr: 7.500e-05, eta: 12:45:55, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0832, decode.acc_seg: 96.7004, loss: 0.0832
+2023-03-04 15:59:03,234 - mmseg - INFO - Iter [21350/160000]	lr: 7.500e-05, eta: 12:45:21, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7111, loss: 0.0825
+2023-03-04 15:59:19,235 - mmseg - INFO - Iter [21400/160000]	lr: 7.500e-05, eta: 12:45:00, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0853, decode.acc_seg: 96.6254, loss: 0.0853
+2023-03-04 15:59:32,860 - mmseg - INFO - Iter [21450/160000]	lr: 7.500e-05, eta: 12:44:25, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0834, decode.acc_seg: 96.7165, loss: 0.0834
+2023-03-04 15:59:46,869 - mmseg - INFO - Iter [21500/160000]	lr: 7.500e-05, eta: 12:43:52, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8832, loss: 0.0784
+2023-03-04 16:00:00,662 - mmseg - INFO - Iter [21550/160000]	lr: 7.500e-05, eta: 12:43:18, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7524, loss: 0.0816
+2023-03-04 16:00:16,601 - mmseg - INFO - Iter [21600/160000]	lr: 7.500e-05, eta: 12:42:57, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7814, loss: 0.0808
+2023-03-04 16:00:30,218 - mmseg - INFO - Iter [21650/160000]	lr: 7.500e-05, eta: 12:42:22, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8453, loss: 0.0793
+2023-03-04 16:00:43,963 - mmseg - INFO - Iter [21700/160000]	lr: 7.500e-05, eta: 12:41:48, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0837, decode.acc_seg: 96.6714, loss: 0.0837
+2023-03-04 16:00:57,881 - mmseg - INFO - Iter [21750/160000]	lr: 7.500e-05, eta: 12:41:15, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7411, loss: 0.0815
+2023-03-04 16:01:13,775 - mmseg - INFO - Iter [21800/160000]	lr: 7.500e-05, eta: 12:40:54, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7813, loss: 0.0811
+2023-03-04 16:01:27,518 - mmseg - INFO - Iter [21850/160000]	lr: 7.500e-05, eta: 12:40:20, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7618, loss: 0.0815
+2023-03-04 16:01:41,319 - mmseg - INFO - Iter [21900/160000]	lr: 7.500e-05, eta: 12:39:46, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0822, decode.acc_seg: 96.7109, loss: 0.0822
+2023-03-04 16:01:57,372 - mmseg - INFO - Iter [21950/160000]	lr: 7.500e-05, eta: 12:39:27, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7898, loss: 0.0810
+2023-03-04 16:02:11,037 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 16:02:11,037 - mmseg - INFO - Iter [22000/160000]	lr: 7.500e-05, eta: 12:38:53, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.7747, loss: 0.0817
+2023-03-04 16:02:24,899 - mmseg - INFO - Iter [22050/160000]	lr: 7.500e-05, eta: 12:38:20, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7404, loss: 0.0823
+2023-03-04 16:02:38,854 - mmseg - INFO - Iter [22100/160000]	lr: 7.500e-05, eta: 12:37:48, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7388, loss: 0.0825
+2023-03-04 16:02:54,909 - mmseg - INFO - Iter [22150/160000]	lr: 7.500e-05, eta: 12:37:28, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.7557, loss: 0.0812
+2023-03-04 16:03:08,635 - mmseg - INFO - Iter [22200/160000]	lr: 7.500e-05, eta: 12:36:55, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0824, decode.acc_seg: 96.7544, loss: 0.0824
+2023-03-04 16:03:22,472 - mmseg - INFO - Iter [22250/160000]	lr: 7.500e-05, eta: 12:36:22, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0836, decode.acc_seg: 96.6767, loss: 0.0836
+2023-03-04 16:03:36,036 - mmseg - INFO - Iter [22300/160000]	lr: 7.500e-05, eta: 12:35:48, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.7758, loss: 0.0809
+2023-03-04 16:03:52,608 - mmseg - INFO - Iter [22350/160000]	lr: 7.500e-05, eta: 12:35:32, time: 0.331, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.7815, loss: 0.0812
+2023-03-04 16:04:06,502 - mmseg - INFO - Iter [22400/160000]	lr: 7.500e-05, eta: 12:34:59, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.7062, loss: 0.0830
+2023-03-04 16:04:20,379 - mmseg - INFO - Iter [22450/160000]	lr: 7.500e-05, eta: 12:34:27, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0822, decode.acc_seg: 96.7475, loss: 0.0822
+2023-03-04 16:04:34,054 - mmseg - INFO - Iter [22500/160000]	lr: 7.500e-05, eta: 12:33:54, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0833, decode.acc_seg: 96.6984, loss: 0.0833
+2023-03-04 16:04:50,184 - mmseg - INFO - Iter [22550/160000]	lr: 7.500e-05, eta: 12:33:35, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8507, loss: 0.0796
+2023-03-04 16:05:04,394 - mmseg - INFO - Iter [22600/160000]	lr: 7.500e-05, eta: 12:33:05, time: 0.284, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0846, decode.acc_seg: 96.6583, loss: 0.0846
+2023-03-04 16:05:18,120 - mmseg - INFO - Iter [22650/160000]	lr: 7.500e-05, eta: 12:32:32, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7721, loss: 0.0808
+2023-03-04 16:05:34,177 - mmseg - INFO - Iter [22700/160000]	lr: 7.500e-05, eta: 12:32:14, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7795, loss: 0.0811
+2023-03-04 16:05:47,770 - mmseg - INFO - Iter [22750/160000]	lr: 7.500e-05, eta: 12:31:40, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8635, loss: 0.0786
+2023-03-04 16:06:01,482 - mmseg - INFO - Iter [22800/160000]	lr: 7.500e-05, eta: 12:31:07, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7715, loss: 0.0816
+2023-03-04 16:06:15,245 - mmseg - INFO - Iter [22850/160000]	lr: 7.500e-05, eta: 12:30:35, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0852, decode.acc_seg: 96.6277, loss: 0.0852
+2023-03-04 16:06:31,403 - mmseg - INFO - Iter [22900/160000]	lr: 7.500e-05, eta: 12:30:17, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8341, loss: 0.0786
+2023-03-04 16:06:45,085 - mmseg - INFO - Iter [22950/160000]	lr: 7.500e-05, eta: 12:29:44, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0829, decode.acc_seg: 96.7106, loss: 0.0829
+2023-03-04 16:06:58,801 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 16:06:58,801 - mmseg - INFO - Iter [23000/160000]	lr: 7.500e-05, eta: 12:29:12, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7900, loss: 0.0814
+2023-03-04 16:07:12,937 - mmseg - INFO - Iter [23050/160000]	lr: 7.500e-05, eta: 12:28:42, time: 0.283, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0855, decode.acc_seg: 96.6157, loss: 0.0855
+2023-03-04 16:07:29,034 - mmseg - INFO - Iter [23100/160000]	lr: 7.500e-05, eta: 12:28:24, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.7253, loss: 0.0801
+2023-03-04 16:07:42,748 - mmseg - INFO - Iter [23150/160000]	lr: 7.500e-05, eta: 12:27:51, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.8675, loss: 0.0778
+2023-03-04 16:07:56,292 - mmseg - INFO - Iter [23200/160000]	lr: 7.500e-05, eta: 12:27:18, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7437, loss: 0.0814
+2023-03-04 16:08:10,057 - mmseg - INFO - Iter [23250/160000]	lr: 7.500e-05, eta: 12:26:46, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.8207, loss: 0.0805
+2023-03-04 16:08:26,117 - mmseg - INFO - Iter [23300/160000]	lr: 7.500e-05, eta: 12:26:28, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8293, loss: 0.0800
+2023-03-04 16:08:39,798 - mmseg - INFO - Iter [23350/160000]	lr: 7.500e-05, eta: 12:25:56, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.8005, loss: 0.0816
+2023-03-04 16:08:53,488 - mmseg - INFO - Iter [23400/160000]	lr: 7.500e-05, eta: 12:25:24, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0829, decode.acc_seg: 96.7402, loss: 0.0829
+2023-03-04 16:09:09,560 - mmseg - INFO - Iter [23450/160000]	lr: 7.500e-05, eta: 12:25:06, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.8230, loss: 0.0802
+2023-03-04 16:09:23,197 - mmseg - INFO - Iter [23500/160000]	lr: 7.500e-05, eta: 12:24:33, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0851, decode.acc_seg: 96.7078, loss: 0.0851
+2023-03-04 16:09:36,780 - mmseg - INFO - Iter [23550/160000]	lr: 7.500e-05, eta: 12:24:01, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0822, decode.acc_seg: 96.7779, loss: 0.0822
+2023-03-04 16:09:50,456 - mmseg - INFO - Iter [23600/160000]	lr: 7.500e-05, eta: 12:23:29, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0849, decode.acc_seg: 96.6327, loss: 0.0849
+2023-03-04 16:10:06,675 - mmseg - INFO - Iter [23650/160000]	lr: 7.500e-05, eta: 12:23:12, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.8023, loss: 0.0807
+2023-03-04 16:10:20,331 - mmseg - INFO - Iter [23700/160000]	lr: 7.500e-05, eta: 12:22:40, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0863, decode.acc_seg: 96.5857, loss: 0.0863
+2023-03-04 16:10:34,068 - mmseg - INFO - Iter [23750/160000]	lr: 7.500e-05, eta: 12:22:09, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7386, loss: 0.0813
+2023-03-04 16:10:47,841 - mmseg - INFO - Iter [23800/160000]	lr: 7.500e-05, eta: 12:21:38, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8296, loss: 0.0797
+2023-03-04 16:11:03,833 - mmseg - INFO - Iter [23850/160000]	lr: 7.500e-05, eta: 12:21:19, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.7563, loss: 0.0831
+2023-03-04 16:11:17,588 - mmseg - INFO - Iter [23900/160000]	lr: 7.500e-05, eta: 12:20:48, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7629, loss: 0.0808
+2023-03-04 16:11:31,715 - mmseg - INFO - Iter [23950/160000]	lr: 7.500e-05, eta: 12:20:19, time: 0.283, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0832, decode.acc_seg: 96.7032, loss: 0.0832
+2023-03-04 16:11:47,871 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 16:11:47,871 - mmseg - INFO - Iter [24000/160000]	lr: 7.500e-05, eta: 12:20:02, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.7836, loss: 0.0803
+2023-03-04 16:12:01,517 - mmseg - INFO - Iter [24050/160000]	lr: 7.500e-05, eta: 12:19:31, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0853, decode.acc_seg: 96.6299, loss: 0.0853
+2023-03-04 16:12:15,116 - mmseg - INFO - Iter [24100/160000]	lr: 7.500e-05, eta: 12:18:59, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.8019, loss: 0.0807
+2023-03-04 16:12:28,709 - mmseg - INFO - Iter [24150/160000]	lr: 7.500e-05, eta: 12:18:27, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8889, loss: 0.0784
+2023-03-04 16:12:44,790 - mmseg - INFO - Iter [24200/160000]	lr: 7.500e-05, eta: 12:18:10, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0849, decode.acc_seg: 96.6421, loss: 0.0849
+2023-03-04 16:12:58,436 - mmseg - INFO - Iter [24250/160000]	lr: 7.500e-05, eta: 12:17:39, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0826, decode.acc_seg: 96.6910, loss: 0.0826
+2023-03-04 16:13:12,071 - mmseg - INFO - Iter [24300/160000]	lr: 7.500e-05, eta: 12:17:07, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0899, decode.acc_seg: 96.5241, loss: 0.0899
+2023-03-04 16:13:25,894 - mmseg - INFO - Iter [24350/160000]	lr: 7.500e-05, eta: 12:16:37, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8968, loss: 0.0779
+2023-03-04 16:13:41,888 - mmseg - INFO - Iter [24400/160000]	lr: 7.500e-05, eta: 12:16:19, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7065, loss: 0.0815
+2023-03-04 16:13:55,516 - mmseg - INFO - Iter [24450/160000]	lr: 7.500e-05, eta: 12:15:48, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8377, loss: 0.0796
+2023-03-04 16:14:09,341 - mmseg - INFO - Iter [24500/160000]	lr: 7.500e-05, eta: 12:15:18, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0821, decode.acc_seg: 96.7445, loss: 0.0821
+2023-03-04 16:14:23,260 - mmseg - INFO - Iter [24550/160000]	lr: 7.500e-05, eta: 12:14:49, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0826, decode.acc_seg: 96.7206, loss: 0.0826
+2023-03-04 16:14:39,321 - mmseg - INFO - Iter [24600/160000]	lr: 7.500e-05, eta: 12:14:32, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0826, decode.acc_seg: 96.6973, loss: 0.0826
+2023-03-04 16:14:52,928 - mmseg - INFO - Iter [24650/160000]	lr: 7.500e-05, eta: 12:14:01, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0841, decode.acc_seg: 96.6775, loss: 0.0841
+2023-03-04 16:15:06,765 - mmseg - INFO - Iter [24700/160000]	lr: 7.500e-05, eta: 12:13:31, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7713, loss: 0.0814
+2023-03-04 16:15:22,696 - mmseg - INFO - Iter [24750/160000]	lr: 7.500e-05, eta: 12:13:13, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8183, loss: 0.0803
+2023-03-04 16:15:36,797 - mmseg - INFO - Iter [24800/160000]	lr: 7.500e-05, eta: 12:12:45, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0850, decode.acc_seg: 96.6672, loss: 0.0850
+2023-03-04 16:15:50,550 - mmseg - INFO - Iter [24850/160000]	lr: 7.500e-05, eta: 12:12:15, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8569, loss: 0.0795
+2023-03-04 16:16:04,224 - mmseg - INFO - Iter [24900/160000]	lr: 7.500e-05, eta: 12:11:45, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8273, loss: 0.0785
+2023-03-04 16:16:20,130 - mmseg - INFO - Iter [24950/160000]	lr: 7.500e-05, eta: 12:11:27, time: 0.318, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0819, decode.acc_seg: 96.7593, loss: 0.0819
+2023-03-04 16:16:33,725 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 16:16:33,725 - mmseg - INFO - Iter [25000/160000]	lr: 7.500e-05, eta: 12:10:56, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7135, loss: 0.0816
+2023-03-04 16:16:47,462 - mmseg - INFO - Iter [25050/160000]	lr: 7.500e-05, eta: 12:10:26, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8304, loss: 0.0800
+2023-03-04 16:17:01,108 - mmseg - INFO - Iter [25100/160000]	lr: 7.500e-05, eta: 12:09:56, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.7857, loss: 0.0806
+2023-03-04 16:17:17,041 - mmseg - INFO - Iter [25150/160000]	lr: 7.500e-05, eta: 12:09:38, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8511, loss: 0.0790
+2023-03-04 16:17:30,735 - mmseg - INFO - Iter [25200/160000]	lr: 7.500e-05, eta: 12:09:09, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0826, decode.acc_seg: 96.6842, loss: 0.0826
+2023-03-04 16:17:44,397 - mmseg - INFO - Iter [25250/160000]	lr: 7.500e-05, eta: 12:08:39, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0821, decode.acc_seg: 96.7326, loss: 0.0821
+2023-03-04 16:18:00,363 - mmseg - INFO - Iter [25300/160000]	lr: 7.500e-05, eta: 12:08:21, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8323, loss: 0.0793
+2023-03-04 16:18:14,019 - mmseg - INFO - Iter [25350/160000]	lr: 7.500e-05, eta: 12:07:51, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0849, decode.acc_seg: 96.6655, loss: 0.0849
+2023-03-04 16:18:27,741 - mmseg - INFO - Iter [25400/160000]	lr: 7.500e-05, eta: 12:07:22, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.7924, loss: 0.0803
+2023-03-04 16:18:41,407 - mmseg - INFO - Iter [25450/160000]	lr: 7.500e-05, eta: 12:06:52, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0827, decode.acc_seg: 96.6991, loss: 0.0827
+2023-03-04 16:18:57,368 - mmseg - INFO - Iter [25500/160000]	lr: 7.500e-05, eta: 12:06:35, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0858, decode.acc_seg: 96.5716, loss: 0.0858
+2023-03-04 16:19:11,168 - mmseg - INFO - Iter [25550/160000]	lr: 7.500e-05, eta: 12:06:06, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7572, loss: 0.0810
+2023-03-04 16:19:25,112 - mmseg - INFO - Iter [25600/160000]	lr: 7.500e-05, eta: 12:05:38, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.7765, loss: 0.0806
+2023-03-04 16:19:38,753 - mmseg - INFO - Iter [25650/160000]	lr: 7.500e-05, eta: 12:05:08, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8842, loss: 0.0784
+2023-03-04 16:19:54,716 - mmseg - INFO - Iter [25700/160000]	lr: 7.500e-05, eta: 12:04:51, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7771, loss: 0.0810
+2023-03-04 16:20:08,412 - mmseg - INFO - Iter [25750/160000]	lr: 7.500e-05, eta: 12:04:22, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7082, loss: 0.0825
+2023-03-04 16:20:22,071 - mmseg - INFO - Iter [25800/160000]	lr: 7.500e-05, eta: 12:03:52, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8090, loss: 0.0801
+2023-03-04 16:20:35,696 - mmseg - INFO - Iter [25850/160000]	lr: 7.500e-05, eta: 12:03:23, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.7995, loss: 0.0798
+2023-03-04 16:20:51,784 - mmseg - INFO - Iter [25900/160000]	lr: 7.500e-05, eta: 12:03:06, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.7911, loss: 0.0803
+2023-03-04 16:21:05,451 - mmseg - INFO - Iter [25950/160000]	lr: 7.500e-05, eta: 12:02:37, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8340, loss: 0.0801
+2023-03-04 16:21:19,205 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 16:21:19,205 - mmseg - INFO - Iter [26000/160000]	lr: 7.500e-05, eta: 12:02:08, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8316, loss: 0.0801
+2023-03-04 16:21:35,401 - mmseg - INFO - Iter [26050/160000]	lr: 7.500e-05, eta: 12:01:52, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.9094, loss: 0.0782
+2023-03-04 16:21:49,163 - mmseg - INFO - Iter [26100/160000]	lr: 7.500e-05, eta: 12:01:24, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.6797, loss: 0.0831
+2023-03-04 16:22:03,067 - mmseg - INFO - Iter [26150/160000]	lr: 7.500e-05, eta: 12:00:56, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.7942, loss: 0.0806
+2023-03-04 16:22:16,796 - mmseg - INFO - Iter [26200/160000]	lr: 7.500e-05, eta: 12:00:27, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7551, loss: 0.0815
+2023-03-04 16:22:32,887 - mmseg - INFO - Iter [26250/160000]	lr: 7.500e-05, eta: 12:00:11, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8418, loss: 0.0804
+2023-03-04 16:22:46,616 - mmseg - INFO - Iter [26300/160000]	lr: 7.500e-05, eta: 11:59:43, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0886, decode.acc_seg: 96.5846, loss: 0.0886
+2023-03-04 16:23:00,361 - mmseg - INFO - Iter [26350/160000]	lr: 7.500e-05, eta: 11:59:14, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.7317, loss: 0.0831
+2023-03-04 16:23:14,435 - mmseg - INFO - Iter [26400/160000]	lr: 7.500e-05, eta: 11:58:48, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7671, loss: 0.0814
+2023-03-04 16:23:30,510 - mmseg - INFO - Iter [26450/160000]	lr: 7.500e-05, eta: 11:58:31, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0837, decode.acc_seg: 96.6588, loss: 0.0837
+2023-03-04 16:23:44,408 - mmseg - INFO - Iter [26500/160000]	lr: 7.500e-05, eta: 11:58:04, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7339, loss: 0.0818
+2023-03-04 16:23:58,443 - mmseg - INFO - Iter [26550/160000]	lr: 7.500e-05, eta: 11:57:37, time: 0.281, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8652, loss: 0.0790
+2023-03-04 16:24:14,413 - mmseg - INFO - Iter [26600/160000]	lr: 7.500e-05, eta: 11:57:20, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8819, loss: 0.0792
+2023-03-04 16:24:28,036 - mmseg - INFO - Iter [26650/160000]	lr: 7.500e-05, eta: 11:56:51, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.7936, loss: 0.0809
+2023-03-04 16:24:41,629 - mmseg - INFO - Iter [26700/160000]	lr: 7.500e-05, eta: 11:56:23, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.7874, loss: 0.0817
+2023-03-04 16:24:55,303 - mmseg - INFO - Iter [26750/160000]	lr: 7.500e-05, eta: 11:55:54, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.8265, loss: 0.0802
+2023-03-04 16:25:11,283 - mmseg - INFO - Iter [26800/160000]	lr: 7.500e-05, eta: 11:55:37, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7227, loss: 0.0818
+2023-03-04 16:25:25,013 - mmseg - INFO - Iter [26850/160000]	lr: 7.500e-05, eta: 11:55:09, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.7863, loss: 0.0804
+2023-03-04 16:25:38,701 - mmseg - INFO - Iter [26900/160000]	lr: 7.500e-05, eta: 11:54:41, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8216, loss: 0.0790
+2023-03-04 16:25:52,262 - mmseg - INFO - Iter [26950/160000]	lr: 7.500e-05, eta: 11:54:13, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8489, loss: 0.0799
+2023-03-04 16:26:08,216 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 16:26:08,216 - mmseg - INFO - Iter [27000/160000]	lr: 7.500e-05, eta: 11:53:56, time: 0.319, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8725, loss: 0.0786
+2023-03-04 16:26:22,316 - mmseg - INFO - Iter [27050/160000]	lr: 7.500e-05, eta: 11:53:30, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8611, loss: 0.0794
+2023-03-04 16:26:36,453 - mmseg - INFO - Iter [27100/160000]	lr: 7.500e-05, eta: 11:53:04, time: 0.283, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7797, loss: 0.0811
+2023-03-04 16:26:50,096 - mmseg - INFO - Iter [27150/160000]	lr: 7.500e-05, eta: 11:52:36, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0864, decode.acc_seg: 96.5898, loss: 0.0864
+2023-03-04 16:27:06,031 - mmseg - INFO - Iter [27200/160000]	lr: 7.500e-05, eta: 11:52:19, time: 0.319, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8555, loss: 0.0785
+2023-03-04 16:27:19,727 - mmseg - INFO - Iter [27250/160000]	lr: 7.500e-05, eta: 11:51:51, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.7620, loss: 0.0812
+2023-03-04 16:27:33,427 - mmseg - INFO - Iter [27300/160000]	lr: 7.500e-05, eta: 11:51:24, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7725, loss: 0.0813
+2023-03-04 16:27:49,369 - mmseg - INFO - Iter [27350/160000]	lr: 7.500e-05, eta: 11:51:07, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8392, loss: 0.0804
+2023-03-04 16:28:03,088 - mmseg - INFO - Iter [27400/160000]	lr: 7.500e-05, eta: 11:50:39, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0837, decode.acc_seg: 96.6720, loss: 0.0837
+2023-03-04 16:28:16,685 - mmseg - INFO - Iter [27450/160000]	lr: 7.500e-05, eta: 11:50:11, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7751, loss: 0.0810
+2023-03-04 16:28:30,496 - mmseg - INFO - Iter [27500/160000]	lr: 7.500e-05, eta: 11:49:44, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0848, decode.acc_seg: 96.6680, loss: 0.0848
+2023-03-04 16:28:46,517 - mmseg - INFO - Iter [27550/160000]	lr: 7.500e-05, eta: 11:49:28, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0838, decode.acc_seg: 96.6569, loss: 0.0838
+2023-03-04 16:29:00,379 - mmseg - INFO - Iter [27600/160000]	lr: 7.500e-05, eta: 11:49:01, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8271, loss: 0.0804
+2023-03-04 16:29:14,050 - mmseg - INFO - Iter [27650/160000]	lr: 7.500e-05, eta: 11:48:34, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7388, loss: 0.0818
+2023-03-04 16:29:28,419 - mmseg - INFO - Iter [27700/160000]	lr: 7.500e-05, eta: 11:48:10, time: 0.287, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8313, loss: 0.0797
+2023-03-04 16:29:44,569 - mmseg - INFO - Iter [27750/160000]	lr: 7.500e-05, eta: 11:47:54, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7421, loss: 0.0815
+2023-03-04 16:29:58,396 - mmseg - INFO - Iter [27800/160000]	lr: 7.500e-05, eta: 11:47:27, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7609, loss: 0.0814
+2023-03-04 16:30:12,136 - mmseg - INFO - Iter [27850/160000]	lr: 7.500e-05, eta: 11:47:00, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.8289, loss: 0.0815
+2023-03-04 16:30:25,870 - mmseg - INFO - Iter [27900/160000]	lr: 7.500e-05, eta: 11:46:33, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7375, loss: 0.0825
+2023-03-04 16:30:42,356 - mmseg - INFO - Iter [27950/160000]	lr: 7.500e-05, eta: 11:46:19, time: 0.330, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8451, loss: 0.0795
+2023-03-04 16:30:56,081 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 16:30:56,081 - mmseg - INFO - Iter [28000/160000]	lr: 7.500e-05, eta: 11:45:52, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0849, decode.acc_seg: 96.6168, loss: 0.0849
+2023-03-04 16:31:10,215 - mmseg - INFO - Iter [28050/160000]	lr: 7.500e-05, eta: 11:45:27, time: 0.283, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8275, loss: 0.0798
+2023-03-04 16:31:26,311 - mmseg - INFO - Iter [28100/160000]	lr: 7.500e-05, eta: 11:45:12, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.7553, loss: 0.0806
+2023-03-04 16:31:39,917 - mmseg - INFO - Iter [28150/160000]	lr: 7.500e-05, eta: 11:44:44, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0840, decode.acc_seg: 96.7014, loss: 0.0840
+2023-03-04 16:31:53,553 - mmseg - INFO - Iter [28200/160000]	lr: 7.500e-05, eta: 11:44:17, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.7701, loss: 0.0794
+2023-03-04 16:32:07,287 - mmseg - INFO - Iter [28250/160000]	lr: 7.500e-05, eta: 11:43:50, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.8097, loss: 0.0802
+2023-03-04 16:32:23,219 - mmseg - INFO - Iter [28300/160000]	lr: 7.500e-05, eta: 11:43:34, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.8749, loss: 0.0776
+2023-03-04 16:32:36,862 - mmseg - INFO - Iter [28350/160000]	lr: 7.500e-05, eta: 11:43:07, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0840, decode.acc_seg: 96.6829, loss: 0.0840
+2023-03-04 16:32:50,447 - mmseg - INFO - Iter [28400/160000]	lr: 7.500e-05, eta: 11:42:39, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.7784, loss: 0.0807
+2023-03-04 16:33:04,282 - mmseg - INFO - Iter [28450/160000]	lr: 7.500e-05, eta: 11:42:13, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.9009, loss: 0.0788
+2023-03-04 16:33:20,889 - mmseg - INFO - Iter [28500/160000]	lr: 7.500e-05, eta: 11:42:00, time: 0.332, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8592, loss: 0.0782
+2023-03-04 16:33:34,619 - mmseg - INFO - Iter [28550/160000]	lr: 7.500e-05, eta: 11:41:33, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0820, decode.acc_seg: 96.7309, loss: 0.0820
+2023-03-04 16:33:48,231 - mmseg - INFO - Iter [28600/160000]	lr: 7.500e-05, eta: 11:41:06, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8272, loss: 0.0803
+2023-03-04 16:34:04,364 - mmseg - INFO - Iter [28650/160000]	lr: 7.500e-05, eta: 11:40:51, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0820, decode.acc_seg: 96.7580, loss: 0.0820
+2023-03-04 16:34:17,998 - mmseg - INFO - Iter [28700/160000]	lr: 7.500e-05, eta: 11:40:24, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7899, loss: 0.0814
+2023-03-04 16:34:31,575 - mmseg - INFO - Iter [28750/160000]	lr: 7.500e-05, eta: 11:39:57, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8387, loss: 0.0798
+2023-03-04 16:34:45,146 - mmseg - INFO - Iter [28800/160000]	lr: 7.500e-05, eta: 11:39:30, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.7635, loss: 0.0817
+2023-03-04 16:35:01,151 - mmseg - INFO - Iter [28850/160000]	lr: 7.500e-05, eta: 11:39:14, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0840, decode.acc_seg: 96.6714, loss: 0.0840
+2023-03-04 16:35:15,285 - mmseg - INFO - Iter [28900/160000]	lr: 7.500e-05, eta: 11:38:49, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0827, decode.acc_seg: 96.6983, loss: 0.0827
+2023-03-04 16:35:29,085 - mmseg - INFO - Iter [28950/160000]	lr: 7.500e-05, eta: 11:38:24, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.7182, loss: 0.0830
+2023-03-04 16:35:42,659 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 16:35:42,659 - mmseg - INFO - Iter [29000/160000]	lr: 7.500e-05, eta: 11:37:57, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.7996, loss: 0.0801
+2023-03-04 16:35:58,876 - mmseg - INFO - Iter [29050/160000]	lr: 7.500e-05, eta: 11:37:42, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0837, decode.acc_seg: 96.7057, loss: 0.0837
+2023-03-04 16:36:12,833 - mmseg - INFO - Iter [29100/160000]	lr: 7.500e-05, eta: 11:37:17, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.7927, loss: 0.0809
+2023-03-04 16:36:26,404 - mmseg - INFO - Iter [29150/160000]	lr: 7.500e-05, eta: 11:36:50, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.6761, loss: 0.0830
+2023-03-04 16:36:40,068 - mmseg - INFO - Iter [29200/160000]	lr: 7.500e-05, eta: 11:36:24, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.7728, loss: 0.0812
+2023-03-04 16:36:56,368 - mmseg - INFO - Iter [29250/160000]	lr: 7.500e-05, eta: 11:36:09, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.8022, loss: 0.0812
+2023-03-04 16:37:10,132 - mmseg - INFO - Iter [29300/160000]	lr: 7.500e-05, eta: 11:35:43, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.7999, loss: 0.0807
+2023-03-04 16:37:23,933 - mmseg - INFO - Iter [29350/160000]	lr: 7.500e-05, eta: 11:35:18, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.8222, loss: 0.0808
+2023-03-04 16:37:39,863 - mmseg - INFO - Iter [29400/160000]	lr: 7.500e-05, eta: 11:35:01, time: 0.319, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7755, loss: 0.0810
+2023-03-04 16:37:53,686 - mmseg - INFO - Iter [29450/160000]	lr: 7.500e-05, eta: 11:34:36, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7160, loss: 0.0825
+2023-03-04 16:38:07,363 - mmseg - INFO - Iter [29500/160000]	lr: 7.500e-05, eta: 11:34:10, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7944, loss: 0.0810
+2023-03-04 16:38:21,177 - mmseg - INFO - Iter [29550/160000]	lr: 7.500e-05, eta: 11:33:44, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8411, loss: 0.0787
+2023-03-04 16:38:37,266 - mmseg - INFO - Iter [29600/160000]	lr: 7.500e-05, eta: 11:33:29, time: 0.322, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7845, loss: 0.0813
+2023-03-04 16:38:50,911 - mmseg - INFO - Iter [29650/160000]	lr: 7.500e-05, eta: 11:33:03, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7640, loss: 0.0815
+2023-03-04 16:39:04,953 - mmseg - INFO - Iter [29700/160000]	lr: 7.500e-05, eta: 11:32:39, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0829, decode.acc_seg: 96.6918, loss: 0.0829
+2023-03-04 16:39:18,611 - mmseg - INFO - Iter [29750/160000]	lr: 7.500e-05, eta: 11:32:13, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0827, decode.acc_seg: 96.6880, loss: 0.0827
+2023-03-04 16:39:34,633 - mmseg - INFO - Iter [29800/160000]	lr: 7.500e-05, eta: 11:31:57, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8654, loss: 0.0793
+2023-03-04 16:39:48,380 - mmseg - INFO - Iter [29850/160000]	lr: 7.500e-05, eta: 11:31:32, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.7986, loss: 0.0794
+2023-03-04 16:40:02,307 - mmseg - INFO - Iter [29900/160000]	lr: 7.500e-05, eta: 11:31:07, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0837, decode.acc_seg: 96.7050, loss: 0.0837
+2023-03-04 16:40:18,384 - mmseg - INFO - Iter [29950/160000]	lr: 7.500e-05, eta: 11:30:52, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.7846, loss: 0.0807
+2023-03-04 16:40:31,949 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 16:40:31,950 - mmseg - INFO - Iter [30000/160000]	lr: 7.500e-05, eta: 11:30:25, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8235, loss: 0.0793
+2023-03-04 16:40:45,568 - mmseg - INFO - Iter [30050/160000]	lr: 7.500e-05, eta: 11:29:59, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8525, loss: 0.0790
+2023-03-04 16:40:59,304 - mmseg - INFO - Iter [30100/160000]	lr: 7.500e-05, eta: 11:29:34, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7580, loss: 0.0810
+2023-03-04 16:41:15,484 - mmseg - INFO - Iter [30150/160000]	lr: 7.500e-05, eta: 11:29:19, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8145, loss: 0.0801
+2023-03-04 16:41:29,064 - mmseg - INFO - Iter [30200/160000]	lr: 7.500e-05, eta: 11:28:53, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7516, loss: 0.0813
+2023-03-04 16:41:42,879 - mmseg - INFO - Iter [30250/160000]	lr: 7.500e-05, eta: 11:28:28, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8178, loss: 0.0801
+2023-03-04 16:41:56,412 - mmseg - INFO - Iter [30300/160000]	lr: 7.500e-05, eta: 11:28:02, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7711, loss: 0.0813
+2023-03-04 16:42:12,623 - mmseg - INFO - Iter [30350/160000]	lr: 7.500e-05, eta: 11:27:47, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.7625, loss: 0.0817
+2023-03-04 16:42:26,583 - mmseg - INFO - Iter [30400/160000]	lr: 7.500e-05, eta: 11:27:23, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8118, loss: 0.0804
+2023-03-04 16:42:40,526 - mmseg - INFO - Iter [30450/160000]	lr: 7.500e-05, eta: 11:26:59, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.8109, loss: 0.0807
+2023-03-04 16:42:54,134 - mmseg - INFO - Iter [30500/160000]	lr: 7.500e-05, eta: 11:26:33, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8200, loss: 0.0803
+2023-03-04 16:43:10,294 - mmseg - INFO - Iter [30550/160000]	lr: 7.500e-05, eta: 11:26:18, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.7909, loss: 0.0804
+2023-03-04 16:43:24,013 - mmseg - INFO - Iter [30600/160000]	lr: 7.500e-05, eta: 11:25:53, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8489, loss: 0.0788
+2023-03-04 16:43:37,757 - mmseg - INFO - Iter [30650/160000]	lr: 7.500e-05, eta: 11:25:28, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0833, decode.acc_seg: 96.7095, loss: 0.0833
+2023-03-04 16:43:53,737 - mmseg - INFO - Iter [30700/160000]	lr: 7.500e-05, eta: 11:25:13, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.9036, loss: 0.0784
+2023-03-04 16:44:07,344 - mmseg - INFO - Iter [30750/160000]	lr: 7.500e-05, eta: 11:24:47, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.6942, loss: 0.0831
+2023-03-04 16:44:21,277 - mmseg - INFO - Iter [30800/160000]	lr: 7.500e-05, eta: 11:24:23, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8806, loss: 0.0782
+2023-03-04 16:44:34,978 - mmseg - INFO - Iter [30850/160000]	lr: 7.500e-05, eta: 11:23:58, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7996, loss: 0.0808
+2023-03-04 16:44:51,111 - mmseg - INFO - Iter [30900/160000]	lr: 7.500e-05, eta: 11:23:43, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.7881, loss: 0.0802
+2023-03-04 16:45:04,822 - mmseg - INFO - Iter [30950/160000]	lr: 7.500e-05, eta: 11:23:18, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7923, loss: 0.0825
+2023-03-04 16:45:18,406 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 16:45:18,406 - mmseg - INFO - Iter [31000/160000]	lr: 7.500e-05, eta: 11:22:53, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0859, decode.acc_seg: 96.5819, loss: 0.0859
+2023-03-04 16:45:31,999 - mmseg - INFO - Iter [31050/160000]	lr: 7.500e-05, eta: 11:22:27, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.7863, loss: 0.0802
+2023-03-04 16:45:48,067 - mmseg - INFO - Iter [31100/160000]	lr: 7.500e-05, eta: 11:22:12, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8966, loss: 0.0788
+2023-03-04 16:46:01,729 - mmseg - INFO - Iter [31150/160000]	lr: 7.500e-05, eta: 11:21:47, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.8391, loss: 0.0802
+2023-03-04 16:46:15,673 - mmseg - INFO - Iter [31200/160000]	lr: 7.500e-05, eta: 11:21:23, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8581, loss: 0.0794
+2023-03-04 16:46:31,599 - mmseg - INFO - Iter [31250/160000]	lr: 7.500e-05, eta: 11:21:08, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0852, decode.acc_seg: 96.7191, loss: 0.0852
+2023-03-04 16:46:45,256 - mmseg - INFO - Iter [31300/160000]	lr: 7.500e-05, eta: 11:20:43, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8495, loss: 0.0787
+2023-03-04 16:46:58,834 - mmseg - INFO - Iter [31350/160000]	lr: 7.500e-05, eta: 11:20:17, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0820, decode.acc_seg: 96.7522, loss: 0.0820
+2023-03-04 16:47:12,494 - mmseg - INFO - Iter [31400/160000]	lr: 7.500e-05, eta: 11:19:52, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0828, decode.acc_seg: 96.7240, loss: 0.0828
+2023-03-04 16:47:28,733 - mmseg - INFO - Iter [31450/160000]	lr: 7.500e-05, eta: 11:19:38, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7445, loss: 0.0815
+2023-03-04 16:47:42,375 - mmseg - INFO - Iter [31500/160000]	lr: 7.500e-05, eta: 11:19:13, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7258, loss: 0.0825
+2023-03-04 16:47:56,121 - mmseg - INFO - Iter [31550/160000]	lr: 7.500e-05, eta: 11:18:49, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7052, loss: 0.0816
+2023-03-04 16:48:09,963 - mmseg - INFO - Iter [31600/160000]	lr: 7.500e-05, eta: 11:18:25, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8139, loss: 0.0801
+2023-03-04 16:48:26,008 - mmseg - INFO - Iter [31650/160000]	lr: 7.500e-05, eta: 11:18:10, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.7999, loss: 0.0809
+2023-03-04 16:48:39,601 - mmseg - INFO - Iter [31700/160000]	lr: 7.500e-05, eta: 11:17:45, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9026, loss: 0.0773
+2023-03-04 16:48:53,494 - mmseg - INFO - Iter [31750/160000]	lr: 7.500e-05, eta: 11:17:21, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0839, decode.acc_seg: 96.6944, loss: 0.0839
+2023-03-04 16:49:07,244 - mmseg - INFO - Iter [31800/160000]	lr: 7.500e-05, eta: 11:16:57, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7865, loss: 0.0810
+2023-03-04 16:49:23,357 - mmseg - INFO - Iter [31850/160000]	lr: 7.500e-05, eta: 11:16:42, time: 0.322, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0821, decode.acc_seg: 96.7703, loss: 0.0821
+2023-03-04 16:49:37,125 - mmseg - INFO - Iter [31900/160000]	lr: 7.500e-05, eta: 11:16:18, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0819, decode.acc_seg: 96.7776, loss: 0.0819
+2023-03-04 16:49:51,265 - mmseg - INFO - Iter [31950/160000]	lr: 7.500e-05, eta: 11:15:55, time: 0.283, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7952, loss: 0.0811
+2023-03-04 16:50:07,279 - mmseg - INFO - Swap parameters (after train) after iter [32000]
+2023-03-04 16:50:07,299 - mmseg - INFO - Saving checkpoint at 32000 iterations
+2023-03-04 16:50:09,224 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 16:50:09,224 - mmseg - INFO - Iter [32000/160000]	lr: 7.500e-05, eta: 11:15:48, time: 0.359, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0824, decode.acc_seg: 96.7290, loss: 0.0824
+2023-03-04 17:05:05,931 - mmseg - INFO - per class results:
+2023-03-04 17:05:05,933 - mmseg - INFO - 
++---------------+-------------------------------------------------------------------+
+|     Class     |                IoU 0,10,20,30,40,50,60,70,80,90,99                |
++---------------+-------------------------------------------------------------------+
+|   background  |            nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan            |
+|      road     | 98.51,98.51,98.52,98.52,98.52,98.52,98.52,98.52,98.52,98.52,98.52 |
+|    sidewalk   |  87.2,87.21,87.21,87.22,87.22,87.23,87.23,87.23,87.24,87.24,87.23 |
+|    building   | 93.62,93.62,93.62,93.62,93.62,93.61,93.61,93.61,93.61,93.61,93.61 |
+|      wall     |  54.27,54.25,54.2,54.19,54.18,54.17,54.15,54.14,54.15,54.16,53.97 |
+|     fence     |  65.29,65.3,65.28,65.28,65.26,65.26,65.25,65.24,65.24,65.22,65.24 |
+|      pole     | 71.16,71.17,71.17,71.17,71.17,71.16,71.17,71.17,71.16,71.16,71.18 |
+| traffic light | 75.36,75.36,75.37,75.36,75.34,75.34,75.36,75.36,75.34,75.35,75.28 |
+|  traffic sign | 82.67,82.68,82.68,82.67,82.66,82.68,82.67,82.68,82.68,82.68,82.65 |
+|   vegetation  | 93.09,93.08,93.08,93.09,93.09,93.08,93.09,93.09,93.09,93.09,93.09 |
+|    terrain    |  64.38,64.39,64.44,64.45,64.46,64.47,64.5,64.54,64.56,64.59,64.6  |
+|      sky      | 95.32,95.32,95.31,95.32,95.32,95.31,95.32,95.32,95.32,95.32,95.32 |
+|     person    |     85.0,85.0,85.0,85.0,84.99,84.99,85.0,84.99,85.0,85.0,84.98    |
+|     rider     |   68.0,68.0,68.0,68.02,67.98,67.98,67.96,67.96,67.99,67.97,67.93  |
+|      car      | 96.05,96.05,96.05,96.05,96.05,96.05,96.05,96.05,96.05,96.05,96.05 |
+|     truck     |  85.85,85.87,85.86,85.83,85.84,85.92,85.94,85.95,85.89,85.89,85.8 |
+|      bus      |   92.32,92.32,92.33,92.32,92.3,92.3,92.3,92.3,92.28,92.26,92.34   |
+|     train     |   85.6,85.6,85.61,85.6,85.65,85.62,85.66,85.7,85.69,85.73,85.62   |
+|   motorcycle  |  72.13,72.13,72.17,72.15,72.2,72.17,72.2,72.24,72.18,72.21,72.15  |
+|    bicycle    | 80.57,80.57,80.57,80.58,80.56,80.56,80.55,80.55,80.54,80.53,80.57 |
++---------------+-------------------------------------------------------------------+
+2023-03-04 17:05:05,933 - mmseg - INFO - Summary:
+2023-03-04 17:05:05,933 - mmseg - INFO - 
++---------------------------------------------------------------+
+|              mIoU 0,10,20,30,40,50,60,70,80,90,99             |
++---------------------------------------------------------------+
+| 81.39,81.39,81.39,81.39,81.39,81.39,81.4,81.4,81.4,81.4,81.37 |
++---------------------------------------------------------------+
+2023-03-04 17:05:05,994 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune/best_mIoU_iter_16000.pth was removed
+2023-03-04 17:05:07,821 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_32000.pth.
+2023-03-04 17:05:07,821 - mmseg - INFO - Best mIoU is 0.8137 at 32000 iter.
+2023-03-04 17:05:07,822 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 17:05:07,822 - mmseg - INFO - Iter(val) [63]	mIoU: [0.8139, 0.8139, 0.8139, 0.8139, 0.8139, 0.8139, 0.814, 0.814, 0.814, 0.814, 0.8137], copy_paste: 81.39,81.39,81.39,81.39,81.39,81.39,81.4,81.4,81.4,81.4,81.37
+2023-03-04 17:05:07,831 - mmseg - INFO - Swap parameters (before train) before iter [32001]
+2023-03-04 17:05:22,243 - mmseg - INFO - Iter [32050/160000]	lr: 7.500e-05, eta: 12:15:14, time: 18.260, data_time: 17.980, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7967, loss: 0.0823
+2023-03-04 17:05:36,298 - mmseg - INFO - Iter [32100/160000]	lr: 7.500e-05, eta: 12:14:44, time: 0.281, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7264, loss: 0.0811
+2023-03-04 17:05:50,161 - mmseg - INFO - Iter [32150/160000]	lr: 7.500e-05, eta: 12:14:13, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.7880, loss: 0.0807
+2023-03-04 17:06:06,332 - mmseg - INFO - Iter [32200/160000]	lr: 7.500e-05, eta: 12:13:52, time: 0.323, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0832, decode.acc_seg: 96.6708, loss: 0.0832
+2023-03-04 17:06:20,020 - mmseg - INFO - Iter [32250/160000]	lr: 7.500e-05, eta: 12:13:20, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8809, loss: 0.0789
+2023-03-04 17:06:33,979 - mmseg - INFO - Iter [32300/160000]	lr: 7.500e-05, eta: 12:12:50, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.8121, loss: 0.0808
+2023-03-04 17:06:47,920 - mmseg - INFO - Iter [32350/160000]	lr: 7.500e-05, eta: 12:12:20, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7521, loss: 0.0813
+2023-03-04 17:07:04,067 - mmseg - INFO - Iter [32400/160000]	lr: 7.500e-05, eta: 12:11:59, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0832, decode.acc_seg: 96.7551, loss: 0.0832
+2023-03-04 17:07:17,880 - mmseg - INFO - Iter [32450/160000]	lr: 7.500e-05, eta: 12:11:28, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8307, loss: 0.0791
+2023-03-04 17:07:31,704 - mmseg - INFO - Iter [32500/160000]	lr: 7.500e-05, eta: 12:10:58, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.7772, loss: 0.0802
+2023-03-04 17:07:45,271 - mmseg - INFO - Iter [32550/160000]	lr: 7.500e-05, eta: 12:10:26, time: 0.271, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.7844, loss: 0.0809
+2023-03-04 17:08:01,348 - mmseg - INFO - Iter [32600/160000]	lr: 7.500e-05, eta: 12:10:05, time: 0.322, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.7882, loss: 0.0802
+2023-03-04 17:08:15,081 - mmseg - INFO - Iter [32650/160000]	lr: 7.500e-05, eta: 12:09:34, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8891, loss: 0.0782
+2023-03-04 17:08:28,874 - mmseg - INFO - Iter [32700/160000]	lr: 7.500e-05, eta: 12:09:04, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7465, loss: 0.0815
+2023-03-04 17:08:45,095 - mmseg - INFO - Iter [32750/160000]	lr: 7.500e-05, eta: 12:08:43, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.8431, loss: 0.0807
+2023-03-04 17:08:58,807 - mmseg - INFO - Iter [32800/160000]	lr: 7.500e-05, eta: 12:08:12, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7196, loss: 0.0825
+2023-03-04 17:09:12,882 - mmseg - INFO - Iter [32850/160000]	lr: 7.500e-05, eta: 12:07:43, time: 0.281, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0824, decode.acc_seg: 96.7404, loss: 0.0824
+2023-03-04 17:09:26,539 - mmseg - INFO - Iter [32900/160000]	lr: 7.500e-05, eta: 12:07:12, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.6860, loss: 0.0831
+2023-03-04 17:09:42,682 - mmseg - INFO - Iter [32950/160000]	lr: 7.500e-05, eta: 12:06:51, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.7592, loss: 0.0804
+2023-03-04 17:09:56,803 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 17:09:56,803 - mmseg - INFO - Iter [33000/160000]	lr: 7.500e-05, eta: 12:06:22, time: 0.282, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7469, loss: 0.0814
+2023-03-04 17:10:10,619 - mmseg - INFO - Iter [33050/160000]	lr: 7.500e-05, eta: 12:05:52, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7230, loss: 0.0825
+2023-03-04 17:10:24,244 - mmseg - INFO - Iter [33100/160000]	lr: 7.500e-05, eta: 12:05:22, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8238, loss: 0.0800
+2023-03-04 17:10:40,257 - mmseg - INFO - Iter [33150/160000]	lr: 7.500e-05, eta: 12:05:00, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7983, loss: 0.0811
+2023-03-04 17:10:53,900 - mmseg - INFO - Iter [33200/160000]	lr: 7.500e-05, eta: 12:04:29, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.7654, loss: 0.0817
+2023-03-04 17:11:07,777 - mmseg - INFO - Iter [33250/160000]	lr: 7.500e-05, eta: 12:04:00, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.7382, loss: 0.0812
+2023-03-04 17:11:23,867 - mmseg - INFO - Iter [33300/160000]	lr: 7.500e-05, eta: 12:03:39, time: 0.322, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0857, decode.acc_seg: 96.6273, loss: 0.0857
+2023-03-04 17:11:37,549 - mmseg - INFO - Iter [33350/160000]	lr: 7.500e-05, eta: 12:03:09, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8811, loss: 0.0783
+2023-03-04 17:11:51,254 - mmseg - INFO - Iter [33400/160000]	lr: 7.500e-05, eta: 12:02:38, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0851, decode.acc_seg: 96.6096, loss: 0.0851
+2023-03-04 17:12:05,090 - mmseg - INFO - Iter [33450/160000]	lr: 7.500e-05, eta: 12:02:09, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0827, decode.acc_seg: 96.7355, loss: 0.0827
+2023-03-04 17:12:21,264 - mmseg - INFO - Iter [33500/160000]	lr: 7.500e-05, eta: 12:01:48, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7222, loss: 0.0823
+2023-03-04 17:12:35,360 - mmseg - INFO - Iter [33550/160000]	lr: 7.500e-05, eta: 12:01:20, time: 0.282, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0821, decode.acc_seg: 96.7631, loss: 0.0821
+2023-03-04 17:12:49,096 - mmseg - INFO - Iter [33600/160000]	lr: 7.500e-05, eta: 12:00:50, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8338, loss: 0.0794
+2023-03-04 17:13:02,771 - mmseg - INFO - Iter [33650/160000]	lr: 7.500e-05, eta: 12:00:20, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0849, decode.acc_seg: 96.6952, loss: 0.0849
+2023-03-04 17:13:18,825 - mmseg - INFO - Iter [33700/160000]	lr: 7.500e-05, eta: 11:59:59, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8272, loss: 0.0796
+2023-03-04 17:13:32,602 - mmseg - INFO - Iter [33750/160000]	lr: 7.500e-05, eta: 11:59:29, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.7938, loss: 0.0804
+2023-03-04 17:13:46,545 - mmseg - INFO - Iter [33800/160000]	lr: 7.500e-05, eta: 11:59:00, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.7662, loss: 0.0817
+2023-03-04 17:14:00,246 - mmseg - INFO - Iter [33850/160000]	lr: 7.500e-05, eta: 11:58:31, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8043, loss: 0.0803
+2023-03-04 17:14:16,241 - mmseg - INFO - Iter [33900/160000]	lr: 7.500e-05, eta: 11:58:10, time: 0.320, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0821, decode.acc_seg: 96.7403, loss: 0.0821
+2023-03-04 17:14:30,400 - mmseg - INFO - Iter [33950/160000]	lr: 7.500e-05, eta: 11:57:42, time: 0.283, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7599, loss: 0.0818
+2023-03-04 17:14:44,332 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 17:14:44,333 - mmseg - INFO - Iter [34000/160000]	lr: 7.500e-05, eta: 11:57:13, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7710, loss: 0.0815
+2023-03-04 17:15:00,437 - mmseg - INFO - Iter [34050/160000]	lr: 7.500e-05, eta: 11:56:52, time: 0.322, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0833, decode.acc_seg: 96.7425, loss: 0.0833
+2023-03-04 17:15:14,215 - mmseg - INFO - Iter [34100/160000]	lr: 7.500e-05, eta: 11:56:23, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8367, loss: 0.0804
+2023-03-04 17:15:27,952 - mmseg - INFO - Iter [34150/160000]	lr: 7.500e-05, eta: 11:55:53, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9264, loss: 0.0774
+2023-03-04 17:15:41,674 - mmseg - INFO - Iter [34200/160000]	lr: 7.500e-05, eta: 11:55:24, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0842, decode.acc_seg: 96.6649, loss: 0.0842
+2023-03-04 17:15:57,923 - mmseg - INFO - Iter [34250/160000]	lr: 7.500e-05, eta: 11:55:04, time: 0.325, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0819, decode.acc_seg: 96.7448, loss: 0.0819
+2023-03-04 17:16:11,776 - mmseg - INFO - Iter [34300/160000]	lr: 7.500e-05, eta: 11:54:35, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7769, loss: 0.0816
+2023-03-04 17:16:25,502 - mmseg - INFO - Iter [34350/160000]	lr: 7.500e-05, eta: 11:54:06, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.7955, loss: 0.0809
+2023-03-04 17:16:39,260 - mmseg - INFO - Iter [34400/160000]	lr: 7.500e-05, eta: 11:53:37, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0821, decode.acc_seg: 96.7263, loss: 0.0821
+2023-03-04 17:16:55,371 - mmseg - INFO - Iter [34450/160000]	lr: 7.500e-05, eta: 11:53:17, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7687, loss: 0.0813
+2023-03-04 17:17:09,028 - mmseg - INFO - Iter [34500/160000]	lr: 7.500e-05, eta: 11:52:47, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8198, loss: 0.0801
+2023-03-04 17:17:23,124 - mmseg - INFO - Iter [34550/160000]	lr: 7.500e-05, eta: 11:52:19, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8163, loss: 0.0789
+2023-03-04 17:17:39,190 - mmseg - INFO - Iter [34600/160000]	lr: 7.500e-05, eta: 11:51:59, time: 0.322, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0819, decode.acc_seg: 96.7294, loss: 0.0819
+2023-03-04 17:17:53,168 - mmseg - INFO - Iter [34650/160000]	lr: 7.500e-05, eta: 11:51:31, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0826, decode.acc_seg: 96.7793, loss: 0.0826
+2023-03-04 17:18:06,863 - mmseg - INFO - Iter [34700/160000]	lr: 7.500e-05, eta: 11:51:02, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8189, loss: 0.0804
+2023-03-04 17:18:20,464 - mmseg - INFO - Iter [34750/160000]	lr: 7.500e-05, eta: 11:50:32, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8587, loss: 0.0788
+2023-03-04 17:18:36,380 - mmseg - INFO - Iter [34800/160000]	lr: 7.500e-05, eta: 11:50:11, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.8226, loss: 0.0806
+2023-03-04 17:18:50,195 - mmseg - INFO - Iter [34850/160000]	lr: 7.500e-05, eta: 11:49:43, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0844, decode.acc_seg: 96.6462, loss: 0.0844
+2023-03-04 17:19:03,902 - mmseg - INFO - Iter [34900/160000]	lr: 7.500e-05, eta: 11:49:14, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7447, loss: 0.0814
+2023-03-04 17:19:17,578 - mmseg - INFO - Iter [34950/160000]	lr: 7.500e-05, eta: 11:48:45, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7432, loss: 0.0818
+2023-03-04 17:19:33,892 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 17:19:33,892 - mmseg - INFO - Iter [35000/160000]	lr: 7.500e-05, eta: 11:48:26, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7841, loss: 0.0808
+2023-03-04 17:19:47,479 - mmseg - INFO - Iter [35050/160000]	lr: 7.500e-05, eta: 11:47:56, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.7921, loss: 0.0807
+2023-03-04 17:20:01,064 - mmseg - INFO - Iter [35100/160000]	lr: 7.500e-05, eta: 11:47:27, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.7979, loss: 0.0797
+2023-03-04 17:20:14,761 - mmseg - INFO - Iter [35150/160000]	lr: 7.500e-05, eta: 11:46:59, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0819, decode.acc_seg: 96.7664, loss: 0.0819
+2023-03-04 17:20:30,764 - mmseg - INFO - Iter [35200/160000]	lr: 7.500e-05, eta: 11:46:38, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.8003, loss: 0.0805
+2023-03-04 17:20:44,758 - mmseg - INFO - Iter [35250/160000]	lr: 7.500e-05, eta: 11:46:10, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8219, loss: 0.0791
+2023-03-04 17:20:58,502 - mmseg - INFO - Iter [35300/160000]	lr: 7.500e-05, eta: 11:45:42, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8732, loss: 0.0789
+2023-03-04 17:21:14,440 - mmseg - INFO - Iter [35350/160000]	lr: 7.500e-05, eta: 11:45:21, time: 0.319, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0826, decode.acc_seg: 96.6914, loss: 0.0826
+2023-03-04 17:21:28,287 - mmseg - INFO - Iter [35400/160000]	lr: 7.500e-05, eta: 11:44:53, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0837, decode.acc_seg: 96.8083, loss: 0.0837
+2023-03-04 17:21:42,022 - mmseg - INFO - Iter [35450/160000]	lr: 7.500e-05, eta: 11:44:25, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.7539, loss: 0.0817
+2023-03-04 17:21:56,559 - mmseg - INFO - Iter [35500/160000]	lr: 7.500e-05, eta: 11:44:00, time: 0.291, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7908, loss: 0.0816
+2023-03-04 17:22:12,768 - mmseg - INFO - Iter [35550/160000]	lr: 7.500e-05, eta: 11:43:40, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0835, decode.acc_seg: 96.8115, loss: 0.0835
+2023-03-04 17:22:26,377 - mmseg - INFO - Iter [35600/160000]	lr: 7.500e-05, eta: 11:43:11, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8838, loss: 0.0786
+2023-03-04 17:22:40,073 - mmseg - INFO - Iter [35650/160000]	lr: 7.500e-05, eta: 11:42:43, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.8883, loss: 0.0778
+2023-03-04 17:22:53,828 - mmseg - INFO - Iter [35700/160000]	lr: 7.500e-05, eta: 11:42:15, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.8213, loss: 0.0802
+2023-03-04 17:23:09,834 - mmseg - INFO - Iter [35750/160000]	lr: 7.500e-05, eta: 11:41:55, time: 0.320, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9270, loss: 0.0769
+2023-03-04 17:23:23,569 - mmseg - INFO - Iter [35800/160000]	lr: 7.500e-05, eta: 11:41:27, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8083, loss: 0.0800
+2023-03-04 17:23:37,228 - mmseg - INFO - Iter [35850/160000]	lr: 7.500e-05, eta: 11:40:58, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7657, loss: 0.0814
+2023-03-04 17:23:53,202 - mmseg - INFO - Iter [35900/160000]	lr: 7.500e-05, eta: 11:40:38, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7450, loss: 0.0813
+2023-03-04 17:24:07,207 - mmseg - INFO - Iter [35950/160000]	lr: 7.500e-05, eta: 11:40:11, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0824, decode.acc_seg: 96.7275, loss: 0.0824
+2023-03-04 17:24:20,778 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 17:24:20,778 - mmseg - INFO - Iter [36000/160000]	lr: 7.500e-05, eta: 11:39:42, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.7984, loss: 0.0807
+2023-03-04 17:24:34,469 - mmseg - INFO - Iter [36050/160000]	lr: 7.500e-05, eta: 11:39:14, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8206, loss: 0.0803
+2023-03-04 17:24:50,409 - mmseg - INFO - Iter [36100/160000]	lr: 7.500e-05, eta: 11:38:54, time: 0.319, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8297, loss: 0.0797
+2023-03-04 17:25:04,709 - mmseg - INFO - Iter [36150/160000]	lr: 7.500e-05, eta: 11:38:28, time: 0.286, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0826, decode.acc_seg: 96.7168, loss: 0.0826
+2023-03-04 17:25:18,403 - mmseg - INFO - Iter [36200/160000]	lr: 7.500e-05, eta: 11:38:00, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.7857, loss: 0.0800
+2023-03-04 17:25:32,228 - mmseg - INFO - Iter [36250/160000]	lr: 7.500e-05, eta: 11:37:33, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.7919, loss: 0.0802
+2023-03-04 17:25:48,205 - mmseg - INFO - Iter [36300/160000]	lr: 7.500e-05, eta: 11:37:13, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8823, loss: 0.0795
+2023-03-04 17:26:02,440 - mmseg - INFO - Iter [36350/160000]	lr: 7.500e-05, eta: 11:36:47, time: 0.285, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8939, loss: 0.0787
+2023-03-04 17:26:16,065 - mmseg - INFO - Iter [36400/160000]	lr: 7.500e-05, eta: 11:36:19, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0828, decode.acc_seg: 96.7439, loss: 0.0828
+2023-03-04 17:26:29,675 - mmseg - INFO - Iter [36450/160000]	lr: 7.500e-05, eta: 11:35:51, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0845, decode.acc_seg: 96.6638, loss: 0.0845
+2023-03-04 17:26:45,663 - mmseg - INFO - Iter [36500/160000]	lr: 7.500e-05, eta: 11:35:31, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7640, loss: 0.0810
+2023-03-04 17:26:59,334 - mmseg - INFO - Iter [36550/160000]	lr: 7.500e-05, eta: 11:35:03, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.7762, loss: 0.0812
+2023-03-04 17:27:13,149 - mmseg - INFO - Iter [36600/160000]	lr: 7.500e-05, eta: 11:34:36, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0832, decode.acc_seg: 96.7128, loss: 0.0832
+2023-03-04 17:27:29,149 - mmseg - INFO - Iter [36650/160000]	lr: 7.500e-05, eta: 11:34:16, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0836, decode.acc_seg: 96.6855, loss: 0.0836
+2023-03-04 17:27:43,042 - mmseg - INFO - Iter [36700/160000]	lr: 7.500e-05, eta: 11:33:49, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8604, loss: 0.0784
+2023-03-04 17:27:56,734 - mmseg - INFO - Iter [36750/160000]	lr: 7.500e-05, eta: 11:33:21, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0820, decode.acc_seg: 96.7826, loss: 0.0820
+2023-03-04 17:28:10,329 - mmseg - INFO - Iter [36800/160000]	lr: 7.500e-05, eta: 11:32:53, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8023, loss: 0.0801
+2023-03-04 17:28:26,494 - mmseg - INFO - Iter [36850/160000]	lr: 7.500e-05, eta: 11:32:34, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0824, decode.acc_seg: 96.7333, loss: 0.0824
+2023-03-04 17:28:40,238 - mmseg - INFO - Iter [36900/160000]	lr: 7.500e-05, eta: 11:32:07, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0834, decode.acc_seg: 96.7439, loss: 0.0834
+2023-03-04 17:28:54,049 - mmseg - INFO - Iter [36950/160000]	lr: 7.500e-05, eta: 11:31:40, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7166, loss: 0.0825
+2023-03-04 17:29:08,021 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 17:29:08,022 - mmseg - INFO - Iter [37000/160000]	lr: 7.500e-05, eta: 11:31:13, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.8059, loss: 0.0805
+2023-03-04 17:29:24,088 - mmseg - INFO - Iter [37050/160000]	lr: 7.500e-05, eta: 11:30:54, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0850, decode.acc_seg: 96.6871, loss: 0.0850
+2023-03-04 17:29:37,649 - mmseg - INFO - Iter [37100/160000]	lr: 7.500e-05, eta: 11:30:26, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.7840, loss: 0.0801
+2023-03-04 17:29:51,372 - mmseg - INFO - Iter [37150/160000]	lr: 7.500e-05, eta: 11:29:59, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.7789, loss: 0.0812
+2023-03-04 17:30:04,926 - mmseg - INFO - Iter [37200/160000]	lr: 7.500e-05, eta: 11:29:31, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8561, loss: 0.0797
+2023-03-04 17:30:20,989 - mmseg - INFO - Iter [37250/160000]	lr: 7.500e-05, eta: 11:29:12, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8236, loss: 0.0801
+2023-03-04 17:30:34,621 - mmseg - INFO - Iter [37300/160000]	lr: 7.500e-05, eta: 11:28:44, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0833, decode.acc_seg: 96.7025, loss: 0.0833
+2023-03-04 17:30:48,556 - mmseg - INFO - Iter [37350/160000]	lr: 7.500e-05, eta: 11:28:18, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7565, loss: 0.0813
+2023-03-04 17:31:04,513 - mmseg - INFO - Iter [37400/160000]	lr: 7.500e-05, eta: 11:27:58, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.7726, loss: 0.0802
+2023-03-04 17:31:18,106 - mmseg - INFO - Iter [37450/160000]	lr: 7.500e-05, eta: 11:27:31, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7491, loss: 0.0818
+2023-03-04 17:31:31,949 - mmseg - INFO - Iter [37500/160000]	lr: 7.500e-05, eta: 11:27:04, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8545, loss: 0.0783
+2023-03-04 17:31:45,617 - mmseg - INFO - Iter [37550/160000]	lr: 7.500e-05, eta: 11:26:37, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7422, loss: 0.0815
+2023-03-04 17:32:01,704 - mmseg - INFO - Iter [37600/160000]	lr: 7.500e-05, eta: 11:26:18, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8008, loss: 0.0787
+2023-03-04 17:32:15,340 - mmseg - INFO - Iter [37650/160000]	lr: 7.500e-05, eta: 11:25:50, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.6989, loss: 0.0830
+2023-03-04 17:32:29,042 - mmseg - INFO - Iter [37700/160000]	lr: 7.500e-05, eta: 11:25:24, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0819, decode.acc_seg: 96.7500, loss: 0.0819
+2023-03-04 17:32:42,762 - mmseg - INFO - Iter [37750/160000]	lr: 7.500e-05, eta: 11:24:57, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8421, loss: 0.0790
+2023-03-04 17:32:58,925 - mmseg - INFO - Iter [37800/160000]	lr: 7.500e-05, eta: 11:24:38, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8026, loss: 0.0798
+2023-03-04 17:33:12,543 - mmseg - INFO - Iter [37850/160000]	lr: 7.500e-05, eta: 11:24:11, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0846, decode.acc_seg: 96.6680, loss: 0.0846
+2023-03-04 17:33:26,231 - mmseg - INFO - Iter [37900/160000]	lr: 7.500e-05, eta: 11:23:44, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.7617, loss: 0.0806
+2023-03-04 17:33:42,502 - mmseg - INFO - Iter [37950/160000]	lr: 7.500e-05, eta: 11:23:25, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7736, loss: 0.0810
+2023-03-04 17:33:56,094 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 17:33:56,094 - mmseg - INFO - Iter [38000/160000]	lr: 7.500e-05, eta: 11:22:58, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0826, decode.acc_seg: 96.7603, loss: 0.0826
+2023-03-04 17:34:09,669 - mmseg - INFO - Iter [38050/160000]	lr: 7.500e-05, eta: 11:22:31, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.7813, loss: 0.0800
+2023-03-04 17:34:23,344 - mmseg - INFO - Iter [38100/160000]	lr: 7.500e-05, eta: 11:22:04, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8567, loss: 0.0789
+2023-03-04 17:34:39,311 - mmseg - INFO - Iter [38150/160000]	lr: 7.500e-05, eta: 11:21:45, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0842, decode.acc_seg: 96.6580, loss: 0.0842
+2023-03-04 17:34:53,008 - mmseg - INFO - Iter [38200/160000]	lr: 7.500e-05, eta: 11:21:18, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9275, loss: 0.0778
+2023-03-04 17:35:06,663 - mmseg - INFO - Iter [38250/160000]	lr: 7.500e-05, eta: 11:20:52, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.7357, loss: 0.0830
+2023-03-04 17:35:20,361 - mmseg - INFO - Iter [38300/160000]	lr: 7.500e-05, eta: 11:20:25, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8487, loss: 0.0798
+2023-03-04 17:35:36,924 - mmseg - INFO - Iter [38350/160000]	lr: 7.500e-05, eta: 11:20:08, time: 0.331, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.6820, loss: 0.0831
+2023-03-04 17:35:50,641 - mmseg - INFO - Iter [38400/160000]	lr: 7.500e-05, eta: 11:19:41, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.8189, loss: 0.0805
+2023-03-04 17:36:04,821 - mmseg - INFO - Iter [38450/160000]	lr: 7.500e-05, eta: 11:19:16, time: 0.284, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7893, loss: 0.0808
+2023-03-04 17:36:18,764 - mmseg - INFO - Iter [38500/160000]	lr: 7.500e-05, eta: 11:18:51, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7469, loss: 0.0816
+2023-03-04 17:36:34,928 - mmseg - INFO - Iter [38550/160000]	lr: 7.500e-05, eta: 11:18:32, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9490, loss: 0.0770
+2023-03-04 17:36:48,595 - mmseg - INFO - Iter [38600/160000]	lr: 7.500e-05, eta: 11:18:05, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7651, loss: 0.0818
+2023-03-04 17:37:02,514 - mmseg - INFO - Iter [38650/160000]	lr: 7.500e-05, eta: 11:17:40, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.7832, loss: 0.0796
+2023-03-04 17:37:18,515 - mmseg - INFO - Iter [38700/160000]	lr: 7.500e-05, eta: 11:17:21, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0829, decode.acc_seg: 96.7234, loss: 0.0829
+2023-03-04 17:37:32,195 - mmseg - INFO - Iter [38750/160000]	lr: 7.500e-05, eta: 11:16:54, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7926, loss: 0.0810
+2023-03-04 17:37:45,781 - mmseg - INFO - Iter [38800/160000]	lr: 7.500e-05, eta: 11:16:28, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7790, loss: 0.0816
+2023-03-04 17:37:59,484 - mmseg - INFO - Iter [38850/160000]	lr: 7.500e-05, eta: 11:16:01, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8650, loss: 0.0790
+2023-03-04 17:38:15,470 - mmseg - INFO - Iter [38900/160000]	lr: 7.500e-05, eta: 11:15:42, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8399, loss: 0.0795
+2023-03-04 17:38:29,159 - mmseg - INFO - Iter [38950/160000]	lr: 7.500e-05, eta: 11:15:16, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7778, loss: 0.0818
+2023-03-04 17:38:42,760 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 17:38:42,761 - mmseg - INFO - Iter [39000/160000]	lr: 7.500e-05, eta: 11:14:50, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7756, loss: 0.0813
+2023-03-04 17:38:56,364 - mmseg - INFO - Iter [39050/160000]	lr: 7.500e-05, eta: 11:14:23, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0849, decode.acc_seg: 96.6512, loss: 0.0849
+2023-03-04 17:39:12,391 - mmseg - INFO - Iter [39100/160000]	lr: 7.500e-05, eta: 11:14:04, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8860, loss: 0.0781
+2023-03-04 17:39:26,229 - mmseg - INFO - Iter [39150/160000]	lr: 7.500e-05, eta: 11:13:39, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0820, decode.acc_seg: 96.7393, loss: 0.0820
+2023-03-04 17:39:39,938 - mmseg - INFO - Iter [39200/160000]	lr: 7.500e-05, eta: 11:13:13, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9221, loss: 0.0777
+2023-03-04 17:39:55,820 - mmseg - INFO - Iter [39250/160000]	lr: 7.500e-05, eta: 11:12:53, time: 0.318, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8638, loss: 0.0783
+2023-03-04 17:40:09,720 - mmseg - INFO - Iter [39300/160000]	lr: 7.500e-05, eta: 11:12:28, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8254, loss: 0.0803
+2023-03-04 17:40:23,448 - mmseg - INFO - Iter [39350/160000]	lr: 7.500e-05, eta: 11:12:02, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.8298, loss: 0.0816
+2023-03-04 17:40:37,090 - mmseg - INFO - Iter [39400/160000]	lr: 7.500e-05, eta: 11:11:36, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0841, decode.acc_seg: 96.6969, loss: 0.0841
+2023-03-04 17:40:52,986 - mmseg - INFO - Iter [39450/160000]	lr: 7.500e-05, eta: 11:11:17, time: 0.318, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7653, loss: 0.0825
+2023-03-04 17:41:06,528 - mmseg - INFO - Iter [39500/160000]	lr: 7.500e-05, eta: 11:10:51, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.8074, loss: 0.0807
+2023-03-04 17:41:20,116 - mmseg - INFO - Iter [39550/160000]	lr: 7.500e-05, eta: 11:10:24, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0822, decode.acc_seg: 96.7884, loss: 0.0822
+2023-03-04 17:41:33,661 - mmseg - INFO - Iter [39600/160000]	lr: 7.500e-05, eta: 11:09:58, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8015, loss: 0.0803
+2023-03-04 17:41:49,576 - mmseg - INFO - Iter [39650/160000]	lr: 7.500e-05, eta: 11:09:39, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8656, loss: 0.0790
+2023-03-04 17:42:03,211 - mmseg - INFO - Iter [39700/160000]	lr: 7.500e-05, eta: 11:09:13, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8654, loss: 0.0794
+2023-03-04 17:42:16,971 - mmseg - INFO - Iter [39750/160000]	lr: 7.500e-05, eta: 11:08:47, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0861, decode.acc_seg: 96.5984, loss: 0.0861
+2023-03-04 17:42:30,556 - mmseg - INFO - Iter [39800/160000]	lr: 7.500e-05, eta: 11:08:21, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8629, loss: 0.0792
+2023-03-04 17:42:47,022 - mmseg - INFO - Iter [39850/160000]	lr: 7.500e-05, eta: 11:08:04, time: 0.329, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8214, loss: 0.0801
+2023-03-04 17:43:00,726 - mmseg - INFO - Iter [39900/160000]	lr: 7.500e-05, eta: 11:07:38, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7565, loss: 0.0818
+2023-03-04 17:43:14,321 - mmseg - INFO - Iter [39950/160000]	lr: 7.500e-05, eta: 11:07:13, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7414, loss: 0.0816
+2023-03-04 17:43:30,535 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 17:43:30,535 - mmseg - INFO - Iter [40000/160000]	lr: 7.500e-05, eta: 11:06:54, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.7778, loss: 0.0801
+2023-03-04 17:43:44,544 - mmseg - INFO - Iter [40050/160000]	lr: 3.750e-05, eta: 11:06:30, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.8161, loss: 0.0808
+2023-03-04 17:43:58,218 - mmseg - INFO - Iter [40100/160000]	lr: 3.750e-05, eta: 11:06:04, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.7784, loss: 0.0801
+2023-03-04 17:44:11,987 - mmseg - INFO - Iter [40150/160000]	lr: 3.750e-05, eta: 11:05:39, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0833, decode.acc_seg: 96.7199, loss: 0.0833
+2023-03-04 17:44:28,726 - mmseg - INFO - Iter [40200/160000]	lr: 3.750e-05, eta: 11:05:22, time: 0.335, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7530, loss: 0.0815
+2023-03-04 17:44:42,467 - mmseg - INFO - Iter [40250/160000]	lr: 3.750e-05, eta: 11:04:57, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8316, loss: 0.0801
+2023-03-04 17:44:56,036 - mmseg - INFO - Iter [40300/160000]	lr: 3.750e-05, eta: 11:04:31, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8691, loss: 0.0788
+2023-03-04 17:45:09,731 - mmseg - INFO - Iter [40350/160000]	lr: 3.750e-05, eta: 11:04:06, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8451, loss: 0.0801
+2023-03-04 17:45:26,087 - mmseg - INFO - Iter [40400/160000]	lr: 3.750e-05, eta: 11:03:48, time: 0.327, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7862, loss: 0.0808
+2023-03-04 17:45:39,691 - mmseg - INFO - Iter [40450/160000]	lr: 3.750e-05, eta: 11:03:23, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 96.9841, loss: 0.0754
+2023-03-04 17:45:53,537 - mmseg - INFO - Iter [40500/160000]	lr: 3.750e-05, eta: 11:02:58, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0844, decode.acc_seg: 96.8150, loss: 0.0844
+2023-03-04 17:46:09,621 - mmseg - INFO - Iter [40550/160000]	lr: 3.750e-05, eta: 11:02:39, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8780, loss: 0.0788
+2023-03-04 17:46:23,578 - mmseg - INFO - Iter [40600/160000]	lr: 3.750e-05, eta: 11:02:15, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8675, loss: 0.0792
+2023-03-04 17:46:37,215 - mmseg - INFO - Iter [40650/160000]	lr: 3.750e-05, eta: 11:01:49, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.8153, loss: 0.0813
+2023-03-04 17:46:50,872 - mmseg - INFO - Iter [40700/160000]	lr: 3.750e-05, eta: 11:01:24, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0832, decode.acc_seg: 96.7621, loss: 0.0832
+2023-03-04 17:47:07,037 - mmseg - INFO - Iter [40750/160000]	lr: 3.750e-05, eta: 11:01:06, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8001, loss: 0.0800
+2023-03-04 17:47:20,782 - mmseg - INFO - Iter [40800/160000]	lr: 3.750e-05, eta: 11:00:41, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9419, loss: 0.0780
+2023-03-04 17:47:34,329 - mmseg - INFO - Iter [40850/160000]	lr: 3.750e-05, eta: 11:00:15, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8411, loss: 0.0786
+2023-03-04 17:47:48,070 - mmseg - INFO - Iter [40900/160000]	lr: 3.750e-05, eta: 10:59:50, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8474, loss: 0.0789
+2023-03-04 17:48:04,484 - mmseg - INFO - Iter [40950/160000]	lr: 3.750e-05, eta: 10:59:33, time: 0.328, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.7841, loss: 0.0806
+2023-03-04 17:48:18,217 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 17:48:18,217 - mmseg - INFO - Iter [41000/160000]	lr: 3.750e-05, eta: 10:59:08, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.7839, loss: 0.0807
+2023-03-04 17:48:31,876 - mmseg - INFO - Iter [41050/160000]	lr: 3.750e-05, eta: 10:58:43, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8872, loss: 0.0793
+2023-03-04 17:48:45,589 - mmseg - INFO - Iter [41100/160000]	lr: 3.750e-05, eta: 10:58:18, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7739, loss: 0.0815
+2023-03-04 17:49:01,971 - mmseg - INFO - Iter [41150/160000]	lr: 3.750e-05, eta: 10:58:01, time: 0.328, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9477, loss: 0.0773
+2023-03-04 17:49:15,717 - mmseg - INFO - Iter [41200/160000]	lr: 3.750e-05, eta: 10:57:36, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8667, loss: 0.0790
+2023-03-04 17:49:29,337 - mmseg - INFO - Iter [41250/160000]	lr: 3.750e-05, eta: 10:57:11, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8518, loss: 0.0794
+2023-03-04 17:49:45,282 - mmseg - INFO - Iter [41300/160000]	lr: 3.750e-05, eta: 10:56:52, time: 0.319, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8252, loss: 0.0796
+2023-03-04 17:49:59,079 - mmseg - INFO - Iter [41350/160000]	lr: 3.750e-05, eta: 10:56:27, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.7964, loss: 0.0803
+2023-03-04 17:50:12,721 - mmseg - INFO - Iter [41400/160000]	lr: 3.750e-05, eta: 10:56:02, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8687, loss: 0.0795
+2023-03-04 17:50:26,389 - mmseg - INFO - Iter [41450/160000]	lr: 3.750e-05, eta: 10:55:37, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8354, loss: 0.0800
+2023-03-04 17:50:42,254 - mmseg - INFO - Iter [41500/160000]	lr: 3.750e-05, eta: 10:55:19, time: 0.317, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8638, loss: 0.0790
+2023-03-04 17:50:55,882 - mmseg - INFO - Iter [41550/160000]	lr: 3.750e-05, eta: 10:54:54, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8175, loss: 0.0792
+2023-03-04 17:51:09,511 - mmseg - INFO - Iter [41600/160000]	lr: 3.750e-05, eta: 10:54:29, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0822, decode.acc_seg: 96.7019, loss: 0.0822
+2023-03-04 17:51:23,376 - mmseg - INFO - Iter [41650/160000]	lr: 3.750e-05, eta: 10:54:04, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7784, loss: 0.0813
+2023-03-04 17:51:39,283 - mmseg - INFO - Iter [41700/160000]	lr: 3.750e-05, eta: 10:53:46, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8843, loss: 0.0785
+2023-03-04 17:51:52,922 - mmseg - INFO - Iter [41750/160000]	lr: 3.750e-05, eta: 10:53:21, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7607, loss: 0.0813
+2023-03-04 17:52:06,763 - mmseg - INFO - Iter [41800/160000]	lr: 3.750e-05, eta: 10:52:57, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7761, loss: 0.0814
+2023-03-04 17:52:20,366 - mmseg - INFO - Iter [41850/160000]	lr: 3.750e-05, eta: 10:52:32, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8821, loss: 0.0793
+2023-03-04 17:52:36,614 - mmseg - INFO - Iter [41900/160000]	lr: 3.750e-05, eta: 10:52:14, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8164, loss: 0.0799
+2023-03-04 17:52:50,275 - mmseg - INFO - Iter [41950/160000]	lr: 3.750e-05, eta: 10:51:50, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0832, decode.acc_seg: 96.7375, loss: 0.0832
+2023-03-04 17:53:04,258 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 17:53:04,259 - mmseg - INFO - Iter [42000/160000]	lr: 3.750e-05, eta: 10:51:26, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7336, loss: 0.0823
+2023-03-04 17:53:20,278 - mmseg - INFO - Iter [42050/160000]	lr: 3.750e-05, eta: 10:51:08, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8484, loss: 0.0783
+2023-03-04 17:53:33,858 - mmseg - INFO - Iter [42100/160000]	lr: 3.750e-05, eta: 10:50:43, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0886, decode.acc_seg: 96.7168, loss: 0.0886
+2023-03-04 17:53:47,683 - mmseg - INFO - Iter [42150/160000]	lr: 3.750e-05, eta: 10:50:18, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.7863, loss: 0.0803
+2023-03-04 17:54:01,371 - mmseg - INFO - Iter [42200/160000]	lr: 3.750e-05, eta: 10:49:54, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9166, loss: 0.0776
+2023-03-04 17:54:17,817 - mmseg - INFO - Iter [42250/160000]	lr: 3.750e-05, eta: 10:49:37, time: 0.329, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.9038, loss: 0.0785
+2023-03-04 17:54:31,523 - mmseg - INFO - Iter [42300/160000]	lr: 3.750e-05, eta: 10:49:13, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.8259, loss: 0.0805
+2023-03-04 17:54:45,135 - mmseg - INFO - Iter [42350/160000]	lr: 3.750e-05, eta: 10:48:48, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8834, loss: 0.0780
+2023-03-04 17:54:58,890 - mmseg - INFO - Iter [42400/160000]	lr: 3.750e-05, eta: 10:48:24, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0838, decode.acc_seg: 96.6887, loss: 0.0838
+2023-03-04 17:55:14,843 - mmseg - INFO - Iter [42450/160000]	lr: 3.750e-05, eta: 10:48:05, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8225, loss: 0.0791
+2023-03-04 17:55:28,485 - mmseg - INFO - Iter [42500/160000]	lr: 3.750e-05, eta: 10:47:41, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.8011, loss: 0.0810
+2023-03-04 17:55:42,311 - mmseg - INFO - Iter [42550/160000]	lr: 3.750e-05, eta: 10:47:17, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8428, loss: 0.0796
+2023-03-04 17:55:58,668 - mmseg - INFO - Iter [42600/160000]	lr: 3.750e-05, eta: 10:47:00, time: 0.327, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.7953, loss: 0.0806
+2023-03-04 17:56:12,311 - mmseg - INFO - Iter [42650/160000]	lr: 3.750e-05, eta: 10:46:35, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.7847, loss: 0.0807
+2023-03-04 17:56:25,929 - mmseg - INFO - Iter [42700/160000]	lr: 3.750e-05, eta: 10:46:11, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7735, loss: 0.0815
+2023-03-04 17:56:39,518 - mmseg - INFO - Iter [42750/160000]	lr: 3.750e-05, eta: 10:45:46, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8603, loss: 0.0786
+2023-03-04 17:56:55,757 - mmseg - INFO - Iter [42800/160000]	lr: 3.750e-05, eta: 10:45:29, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7635, loss: 0.0814
+2023-03-04 17:57:09,396 - mmseg - INFO - Iter [42850/160000]	lr: 3.750e-05, eta: 10:45:05, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8763, loss: 0.0789
+2023-03-04 17:57:22,997 - mmseg - INFO - Iter [42900/160000]	lr: 3.750e-05, eta: 10:44:40, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0828, decode.acc_seg: 96.7776, loss: 0.0828
+2023-03-04 17:57:36,687 - mmseg - INFO - Iter [42950/160000]	lr: 3.750e-05, eta: 10:44:16, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8638, loss: 0.0780
+2023-03-04 17:57:52,730 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 17:57:52,730 - mmseg - INFO - Iter [43000/160000]	lr: 3.750e-05, eta: 10:43:58, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8465, loss: 0.0789
+2023-03-04 17:58:06,338 - mmseg - INFO - Iter [43050/160000]	lr: 3.750e-05, eta: 10:43:34, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7814, loss: 0.0816
+2023-03-04 17:58:20,049 - mmseg - INFO - Iter [43100/160000]	lr: 3.750e-05, eta: 10:43:10, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7467, loss: 0.0825
+2023-03-04 17:58:33,682 - mmseg - INFO - Iter [43150/160000]	lr: 3.750e-05, eta: 10:42:45, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8893, loss: 0.0780
+2023-03-04 17:58:49,738 - mmseg - INFO - Iter [43200/160000]	lr: 3.750e-05, eta: 10:42:28, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8085, loss: 0.0804
+2023-03-04 17:59:03,318 - mmseg - INFO - Iter [43250/160000]	lr: 3.750e-05, eta: 10:42:03, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8361, loss: 0.0801
+2023-03-04 17:59:17,049 - mmseg - INFO - Iter [43300/160000]	lr: 3.750e-05, eta: 10:41:39, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8389, loss: 0.0797
+2023-03-04 17:59:32,985 - mmseg - INFO - Iter [43350/160000]	lr: 3.750e-05, eta: 10:41:21, time: 0.319, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.8184, loss: 0.0807
+2023-03-04 17:59:46,999 - mmseg - INFO - Iter [43400/160000]	lr: 3.750e-05, eta: 10:40:58, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7727, loss: 0.0808
+2023-03-04 18:00:00,718 - mmseg - INFO - Iter [43450/160000]	lr: 3.750e-05, eta: 10:40:34, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.7801, loss: 0.0805
+2023-03-04 18:00:14,616 - mmseg - INFO - Iter [43500/160000]	lr: 3.750e-05, eta: 10:40:11, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.8015, loss: 0.0808
+2023-03-04 18:00:30,673 - mmseg - INFO - Iter [43550/160000]	lr: 3.750e-05, eta: 10:39:53, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8163, loss: 0.0804
+2023-03-04 18:00:44,332 - mmseg - INFO - Iter [43600/160000]	lr: 3.750e-05, eta: 10:39:29, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0845, decode.acc_seg: 96.8496, loss: 0.0845
+2023-03-04 18:00:58,239 - mmseg - INFO - Iter [43650/160000]	lr: 3.750e-05, eta: 10:39:06, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.7811, loss: 0.0805
+2023-03-04 18:01:12,101 - mmseg - INFO - Iter [43700/160000]	lr: 3.750e-05, eta: 10:38:42, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8421, loss: 0.0792
+2023-03-04 18:01:28,435 - mmseg - INFO - Iter [43750/160000]	lr: 3.750e-05, eta: 10:38:25, time: 0.327, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7640, loss: 0.0816
+2023-03-04 18:01:42,166 - mmseg - INFO - Iter [43800/160000]	lr: 3.750e-05, eta: 10:38:02, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.7770, loss: 0.0807
+2023-03-04 18:01:55,883 - mmseg - INFO - Iter [43850/160000]	lr: 3.750e-05, eta: 10:37:38, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.7818, loss: 0.0809
+2023-03-04 18:02:12,131 - mmseg - INFO - Iter [43900/160000]	lr: 3.750e-05, eta: 10:37:21, time: 0.325, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8990, loss: 0.0787
+2023-03-04 18:02:25,855 - mmseg - INFO - Iter [43950/160000]	lr: 3.750e-05, eta: 10:36:57, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9084, loss: 0.0776
+2023-03-04 18:02:39,811 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 18:02:39,811 - mmseg - INFO - Iter [44000/160000]	lr: 3.750e-05, eta: 10:36:34, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8719, loss: 0.0780
+2023-03-04 18:02:53,441 - mmseg - INFO - Iter [44050/160000]	lr: 3.750e-05, eta: 10:36:10, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0828, decode.acc_seg: 96.7215, loss: 0.0828
+2023-03-04 18:03:09,775 - mmseg - INFO - Iter [44100/160000]	lr: 3.750e-05, eta: 10:35:53, time: 0.327, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.8203, loss: 0.0807
+2023-03-04 18:03:23,442 - mmseg - INFO - Iter [44150/160000]	lr: 3.750e-05, eta: 10:35:30, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0839, decode.acc_seg: 96.6050, loss: 0.0839
+2023-03-04 18:03:37,202 - mmseg - INFO - Iter [44200/160000]	lr: 3.750e-05, eta: 10:35:06, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8803, loss: 0.0782
+2023-03-04 18:03:50,891 - mmseg - INFO - Iter [44250/160000]	lr: 3.750e-05, eta: 10:34:42, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9115, loss: 0.0779
+2023-03-04 18:04:07,062 - mmseg - INFO - Iter [44300/160000]	lr: 3.750e-05, eta: 10:34:25, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8061, loss: 0.0800
+2023-03-04 18:04:20,964 - mmseg - INFO - Iter [44350/160000]	lr: 3.750e-05, eta: 10:34:02, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.8831, loss: 0.0769
+2023-03-04 18:04:34,734 - mmseg - INFO - Iter [44400/160000]	lr: 3.750e-05, eta: 10:33:39, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.7657, loss: 0.0817
+2023-03-04 18:04:48,409 - mmseg - INFO - Iter [44450/160000]	lr: 3.750e-05, eta: 10:33:15, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7964, loss: 0.0810
+2023-03-04 18:05:04,381 - mmseg - INFO - Iter [44500/160000]	lr: 3.750e-05, eta: 10:32:57, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7394, loss: 0.0811
+2023-03-04 18:05:18,003 - mmseg - INFO - Iter [44550/160000]	lr: 3.750e-05, eta: 10:32:34, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7952, loss: 0.0815
+2023-03-04 18:05:32,067 - mmseg - INFO - Iter [44600/160000]	lr: 3.750e-05, eta: 10:32:11, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8302, loss: 0.0803
+2023-03-04 18:05:48,204 - mmseg - INFO - Iter [44650/160000]	lr: 3.750e-05, eta: 10:31:54, time: 0.323, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8971, loss: 0.0782
+2023-03-04 18:06:01,804 - mmseg - INFO - Iter [44700/160000]	lr: 3.750e-05, eta: 10:31:30, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.8072, loss: 0.0808
+2023-03-04 18:06:15,467 - mmseg - INFO - Iter [44750/160000]	lr: 3.750e-05, eta: 10:31:06, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8576, loss: 0.0793
+2023-03-04 18:06:29,231 - mmseg - INFO - Iter [44800/160000]	lr: 3.750e-05, eta: 10:30:43, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8549, loss: 0.0785
+2023-03-04 18:06:45,248 - mmseg - INFO - Iter [44850/160000]	lr: 3.750e-05, eta: 10:30:26, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.7824, loss: 0.0806
+2023-03-04 18:06:58,881 - mmseg - INFO - Iter [44900/160000]	lr: 3.750e-05, eta: 10:30:02, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9306, loss: 0.0780
+2023-03-04 18:07:12,460 - mmseg - INFO - Iter [44950/160000]	lr: 3.750e-05, eta: 10:29:38, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8663, loss: 0.0779
+2023-03-04 18:07:26,152 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 18:07:26,153 - mmseg - INFO - Iter [45000/160000]	lr: 3.750e-05, eta: 10:29:15, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8116, loss: 0.0800
+2023-03-04 18:07:42,039 - mmseg - INFO - Iter [45050/160000]	lr: 3.750e-05, eta: 10:28:57, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.7980, loss: 0.0804
+2023-03-04 18:07:56,171 - mmseg - INFO - Iter [45100/160000]	lr: 3.750e-05, eta: 10:28:35, time: 0.283, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9509, loss: 0.0762
+2023-03-04 18:08:10,039 - mmseg - INFO - Iter [45150/160000]	lr: 3.750e-05, eta: 10:28:12, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.8138, loss: 0.0805
+2023-03-04 18:08:25,954 - mmseg - INFO - Iter [45200/160000]	lr: 3.750e-05, eta: 10:27:54, time: 0.318, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9244, loss: 0.0770
+2023-03-04 18:08:39,643 - mmseg - INFO - Iter [45250/160000]	lr: 3.750e-05, eta: 10:27:31, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8561, loss: 0.0784
+2023-03-04 18:08:53,347 - mmseg - INFO - Iter [45300/160000]	lr: 3.750e-05, eta: 10:27:08, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8734, loss: 0.0796
+2023-03-04 18:09:06,936 - mmseg - INFO - Iter [45350/160000]	lr: 3.750e-05, eta: 10:26:44, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8839, loss: 0.0782
+2023-03-04 18:09:23,016 - mmseg - INFO - Iter [45400/160000]	lr: 3.750e-05, eta: 10:26:27, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8592, loss: 0.0798
+2023-03-04 18:09:36,644 - mmseg - INFO - Iter [45450/160000]	lr: 3.750e-05, eta: 10:26:04, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8512, loss: 0.0793
+2023-03-04 18:09:50,392 - mmseg - INFO - Iter [45500/160000]	lr: 3.750e-05, eta: 10:25:41, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8718, loss: 0.0781
+2023-03-04 18:10:04,222 - mmseg - INFO - Iter [45550/160000]	lr: 3.750e-05, eta: 10:25:18, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0829, decode.acc_seg: 96.6558, loss: 0.0829
+2023-03-04 18:10:20,511 - mmseg - INFO - Iter [45600/160000]	lr: 3.750e-05, eta: 10:25:01, time: 0.326, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.9104, loss: 0.0782
+2023-03-04 18:10:34,293 - mmseg - INFO - Iter [45650/160000]	lr: 3.750e-05, eta: 10:24:38, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0845, decode.acc_seg: 96.7231, loss: 0.0845
+2023-03-04 18:10:48,173 - mmseg - INFO - Iter [45700/160000]	lr: 3.750e-05, eta: 10:24:16, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.9008, loss: 0.0782
+2023-03-04 18:11:01,776 - mmseg - INFO - Iter [45750/160000]	lr: 3.750e-05, eta: 10:23:52, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7842, loss: 0.0810
+2023-03-04 18:11:17,917 - mmseg - INFO - Iter [45800/160000]	lr: 3.750e-05, eta: 10:23:35, time: 0.323, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8158, loss: 0.0804
+2023-03-04 18:11:31,521 - mmseg - INFO - Iter [45850/160000]	lr: 3.750e-05, eta: 10:23:12, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8685, loss: 0.0788
+2023-03-04 18:11:45,172 - mmseg - INFO - Iter [45900/160000]	lr: 3.750e-05, eta: 10:22:49, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0822, decode.acc_seg: 96.7419, loss: 0.0822
+2023-03-04 18:12:01,247 - mmseg - INFO - Iter [45950/160000]	lr: 3.750e-05, eta: 10:22:32, time: 0.322, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.8342, loss: 0.0810
+2023-03-04 18:12:14,840 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 18:12:14,840 - mmseg - INFO - Iter [46000/160000]	lr: 3.750e-05, eta: 10:22:09, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8713, loss: 0.0786
+2023-03-04 18:12:28,609 - mmseg - INFO - Iter [46050/160000]	lr: 3.750e-05, eta: 10:21:46, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0835, decode.acc_seg: 96.6951, loss: 0.0835
+2023-03-04 18:12:42,285 - mmseg - INFO - Iter [46100/160000]	lr: 3.750e-05, eta: 10:21:23, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7999, loss: 0.0810
+2023-03-04 18:12:58,356 - mmseg - INFO - Iter [46150/160000]	lr: 3.750e-05, eta: 10:21:06, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9135, loss: 0.0774
+2023-03-04 18:13:12,382 - mmseg - INFO - Iter [46200/160000]	lr: 3.750e-05, eta: 10:20:43, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0824, decode.acc_seg: 96.7432, loss: 0.0824
+2023-03-04 18:13:25,986 - mmseg - INFO - Iter [46250/160000]	lr: 3.750e-05, eta: 10:20:20, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8695, loss: 0.0779
+2023-03-04 18:13:39,754 - mmseg - INFO - Iter [46300/160000]	lr: 3.750e-05, eta: 10:19:58, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.8953, loss: 0.0775
+2023-03-04 18:13:55,754 - mmseg - INFO - Iter [46350/160000]	lr: 3.750e-05, eta: 10:19:40, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8132, loss: 0.0804
+2023-03-04 18:14:09,412 - mmseg - INFO - Iter [46400/160000]	lr: 3.750e-05, eta: 10:19:17, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.7706, loss: 0.0817
+2023-03-04 18:14:23,208 - mmseg - INFO - Iter [46450/160000]	lr: 3.750e-05, eta: 10:18:55, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8226, loss: 0.0797
+2023-03-04 18:14:36,822 - mmseg - INFO - Iter [46500/160000]	lr: 3.750e-05, eta: 10:18:32, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7959, loss: 0.0818
+2023-03-04 18:14:52,803 - mmseg - INFO - Iter [46550/160000]	lr: 3.750e-05, eta: 10:18:14, time: 0.320, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.8001, loss: 0.0809
+2023-03-04 18:15:06,603 - mmseg - INFO - Iter [46600/160000]	lr: 3.750e-05, eta: 10:17:52, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8460, loss: 0.0799
+2023-03-04 18:15:20,316 - mmseg - INFO - Iter [46650/160000]	lr: 3.750e-05, eta: 10:17:29, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.8298, loss: 0.0810
+2023-03-04 18:15:36,287 - mmseg - INFO - Iter [46700/160000]	lr: 3.750e-05, eta: 10:17:12, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8006, loss: 0.0791
+2023-03-04 18:15:49,950 - mmseg - INFO - Iter [46750/160000]	lr: 3.750e-05, eta: 10:16:49, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7637, loss: 0.0816
+2023-03-04 18:16:03,592 - mmseg - INFO - Iter [46800/160000]	lr: 3.750e-05, eta: 10:16:26, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.8102, loss: 0.0807
+2023-03-04 18:16:17,281 - mmseg - INFO - Iter [46850/160000]	lr: 3.750e-05, eta: 10:16:03, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9023, loss: 0.0778
+2023-03-04 18:16:33,361 - mmseg - INFO - Iter [46900/160000]	lr: 3.750e-05, eta: 10:15:47, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.7945, loss: 0.0806
+2023-03-04 18:16:47,101 - mmseg - INFO - Iter [46950/160000]	lr: 3.750e-05, eta: 10:15:24, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8873, loss: 0.0781
+2023-03-04 18:17:01,028 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 18:17:01,028 - mmseg - INFO - Iter [47000/160000]	lr: 3.750e-05, eta: 10:15:02, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0753, decode.acc_seg: 96.9951, loss: 0.0753
+2023-03-04 18:17:14,915 - mmseg - INFO - Iter [47050/160000]	lr: 3.750e-05, eta: 10:14:40, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0832, decode.acc_seg: 96.7293, loss: 0.0832
+2023-03-04 18:17:30,826 - mmseg - INFO - Iter [47100/160000]	lr: 3.750e-05, eta: 10:14:22, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.7901, loss: 0.0801
+2023-03-04 18:17:44,643 - mmseg - INFO - Iter [47150/160000]	lr: 3.750e-05, eta: 10:14:00, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8027, loss: 0.0800
+2023-03-04 18:17:58,285 - mmseg - INFO - Iter [47200/160000]	lr: 3.750e-05, eta: 10:13:37, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9544, loss: 0.0764
+2023-03-04 18:18:14,214 - mmseg - INFO - Iter [47250/160000]	lr: 3.750e-05, eta: 10:13:20, time: 0.319, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9207, loss: 0.0779
+2023-03-04 18:18:28,064 - mmseg - INFO - Iter [47300/160000]	lr: 3.750e-05, eta: 10:12:58, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0833, decode.acc_seg: 96.7160, loss: 0.0833
+2023-03-04 18:18:42,145 - mmseg - INFO - Iter [47350/160000]	lr: 3.750e-05, eta: 10:12:36, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8768, loss: 0.0779
+2023-03-04 18:18:55,951 - mmseg - INFO - Iter [47400/160000]	lr: 3.750e-05, eta: 10:12:14, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0821, decode.acc_seg: 96.7463, loss: 0.0821
+2023-03-04 18:19:12,033 - mmseg - INFO - Iter [47450/160000]	lr: 3.750e-05, eta: 10:11:57, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8096, loss: 0.0804
+2023-03-04 18:19:25,649 - mmseg - INFO - Iter [47500/160000]	lr: 3.750e-05, eta: 10:11:34, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8440, loss: 0.0792
+2023-03-04 18:19:39,318 - mmseg - INFO - Iter [47550/160000]	lr: 3.750e-05, eta: 10:11:12, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.8917, loss: 0.0777
+2023-03-04 18:19:53,147 - mmseg - INFO - Iter [47600/160000]	lr: 3.750e-05, eta: 10:10:50, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8710, loss: 0.0786
+2023-03-04 18:20:09,117 - mmseg - INFO - Iter [47650/160000]	lr: 3.750e-05, eta: 10:10:33, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.7996, loss: 0.0812
+2023-03-04 18:20:22,823 - mmseg - INFO - Iter [47700/160000]	lr: 3.750e-05, eta: 10:10:10, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8465, loss: 0.0797
+2023-03-04 18:20:36,505 - mmseg - INFO - Iter [47750/160000]	lr: 3.750e-05, eta: 10:09:48, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8527, loss: 0.0796
+2023-03-04 18:20:50,119 - mmseg - INFO - Iter [47800/160000]	lr: 3.750e-05, eta: 10:09:25, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9098, loss: 0.0772
+2023-03-04 18:21:06,137 - mmseg - INFO - Iter [47850/160000]	lr: 3.750e-05, eta: 10:09:08, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7344, loss: 0.0815
+2023-03-04 18:21:19,824 - mmseg - INFO - Iter [47900/160000]	lr: 3.750e-05, eta: 10:08:46, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.8040, loss: 0.0802
+2023-03-04 18:21:33,537 - mmseg - INFO - Iter [47950/160000]	lr: 3.750e-05, eta: 10:08:23, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8555, loss: 0.0795
+2023-03-04 18:21:49,519 - mmseg - INFO - Swap parameters (after train) after iter [48000]
+2023-03-04 18:21:49,541 - mmseg - INFO - Saving checkpoint at 48000 iterations
+2023-03-04 18:21:51,293 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 18:21:51,293 - mmseg - INFO - Iter [48000/160000]	lr: 3.750e-05, eta: 10:08:11, time: 0.355, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.8039, loss: 0.0815
+2023-03-04 18:36:48,183 - mmseg - INFO - per class results:
+2023-03-04 18:36:48,184 - mmseg - INFO - 
++---------------+-------------------------------------------------------------------+
+|     Class     |                IoU 0,10,20,30,40,50,60,70,80,90,99                |
++---------------+-------------------------------------------------------------------+
+|   background  |            nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan            |
+|      road     | 98.53,98.53,98.53,98.53,98.53,98.53,98.53,98.53,98.53,98.53,98.53 |
+|    sidewalk   | 87.33,87.33,87.33,87.34,87.33,87.34,87.34,87.34,87.33,87.33,87.35 |
+|    building   |       93.6,93.6,93.6,93.6,93.6,93.6,93.6,93.6,93.6,93.6,93.6      |
+|      wall     |  54.25,54.25,54.24,54.25,54.28,54.26,54.25,54.23,54.27,54.28,54.2 |
+|     fence     | 65.38,65.38,65.39,65.38,65.37,65.37,65.37,65.37,65.37,65.35,65.36 |
+|      pole     |    71.18,71.19,71.19,71.2,71.19,71.2,71.2,71.2,71.2,71.2,71.21    |
+| traffic light | 75.49,75.48,75.47,75.48,75.46,75.46,75.48,75.45,75.46,75.46,75.42 |
+|  traffic sign |    82.6,82.6,82.6,82.6,82.59,82.59,82.6,82.61,82.6,82.61,82.58    |
+|   vegetation  | 93.07,93.07,93.08,93.07,93.07,93.08,93.07,93.07,93.07,93.07,93.08 |
+|    terrain    |  64.67,64.7,64.72,64.71,64.73,64.74,64.75,64.75,64.76,64.75,64.78 |
+|      sky      |      95.31,95.3,95.3,95.3,95.3,95.3,95.3,95.3,95.29,95.3,95.3     |
+|     person    | 84.98,84.98,84.99,84.98,84.97,84.97,84.97,84.98,84.97,84.96,84.96 |
+|     rider     |  68.02,68.01,68.02,68.04,68.01,68.01,67.99,68.0,68.01,67.99,67.99 |
+|      car      | 96.05,96.06,96.05,96.05,96.05,96.05,96.05,96.05,96.06,96.06,96.06 |
+|     truck     |  86.04,86.1,86.1,86.07,86.14,86.18,86.18,86.25,86.24,86.25,86.26  |
+|      bus      |   92.37,92.4,92.4,92.39,92.38,92.38,92.39,92.39,92.42,92.42,92.4  |
+|     train     | 85.76,85.79,85.76,85.81,85.79,85.78,85.78,85.78,85.84,85.88,85.69 |
+|   motorcycle  | 72.14,72.14,72.16,72.16,72.14,72.16,72.16,72.14,72.19,72.18,72.17 |
+|    bicycle    | 80.52,80.53,80.53,80.54,80.52,80.52,80.53,80.53,80.53,80.53,80.54 |
++---------------+-------------------------------------------------------------------+
+2023-03-04 18:36:48,184 - mmseg - INFO - Summary:
+2023-03-04 18:36:48,185 - mmseg - INFO - 
++-------------------------------------------------------------------+
+|                mIoU 0,10,20,30,40,50,60,70,80,90,99               |
++-------------------------------------------------------------------+
+| 81.44,81.45,81.44,81.45,81.44,81.45,81.45,81.45,81.46,81.46,81.44 |
++-------------------------------------------------------------------+
+2023-03-04 18:36:48,243 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune/best_mIoU_iter_32000.pth was removed
+2023-03-04 18:36:49,967 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_48000.pth.
+2023-03-04 18:36:49,968 - mmseg - INFO - Best mIoU is 0.8144 at 48000 iter.
+2023-03-04 18:36:49,968 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 18:36:49,968 - mmseg - INFO - Iter(val) [63]	mIoU: [0.8144, 0.8145, 0.8144, 0.8145, 0.8144, 0.8145, 0.8145, 0.8145, 0.8146, 0.8146, 0.8144], copy_paste: 81.44,81.45,81.44,81.45,81.44,81.45,81.45,81.45,81.46,81.46,81.44
+2023-03-04 18:36:49,974 - mmseg - INFO - Swap parameters (before train) before iter [48001]
+2023-03-04 18:37:03,988 - mmseg - INFO - Iter [48050/160000]	lr: 3.750e-05, eta: 10:42:43, time: 18.254, data_time: 17.982, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8822, loss: 0.0795
+2023-03-04 18:37:17,946 - mmseg - INFO - Iter [48100/160000]	lr: 3.750e-05, eta: 10:42:18, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.8063, loss: 0.0807
+2023-03-04 18:37:31,764 - mmseg - INFO - Iter [48150/160000]	lr: 3.750e-05, eta: 10:41:53, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8504, loss: 0.0789
+2023-03-04 18:37:47,936 - mmseg - INFO - Iter [48200/160000]	lr: 3.750e-05, eta: 10:41:33, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.8421, loss: 0.0810
+2023-03-04 18:38:02,255 - mmseg - INFO - Iter [48250/160000]	lr: 3.750e-05, eta: 10:41:09, time: 0.287, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8360, loss: 0.0794
+2023-03-04 18:38:15,950 - mmseg - INFO - Iter [48300/160000]	lr: 3.750e-05, eta: 10:40:44, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7476, loss: 0.0816
+2023-03-04 18:38:29,605 - mmseg - INFO - Iter [48350/160000]	lr: 3.750e-05, eta: 10:40:18, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9153, loss: 0.0780
+2023-03-04 18:38:45,728 - mmseg - INFO - Iter [48400/160000]	lr: 3.750e-05, eta: 10:39:59, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8075, loss: 0.0798
+2023-03-04 18:38:59,336 - mmseg - INFO - Iter [48450/160000]	lr: 3.750e-05, eta: 10:39:33, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.8021, loss: 0.0805
+2023-03-04 18:39:12,989 - mmseg - INFO - Iter [48500/160000]	lr: 3.750e-05, eta: 10:39:08, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7401, loss: 0.0823
+2023-03-04 18:39:29,131 - mmseg - INFO - Iter [48550/160000]	lr: 3.750e-05, eta: 10:38:48, time: 0.323, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8317, loss: 0.0803
+2023-03-04 18:39:43,262 - mmseg - INFO - Iter [48600/160000]	lr: 3.750e-05, eta: 10:38:24, time: 0.283, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8377, loss: 0.0790
+2023-03-04 18:39:57,019 - mmseg - INFO - Iter [48650/160000]	lr: 3.750e-05, eta: 10:37:59, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8560, loss: 0.0797
+2023-03-04 18:40:10,712 - mmseg - INFO - Iter [48700/160000]	lr: 3.750e-05, eta: 10:37:34, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9102, loss: 0.0778
+2023-03-04 18:40:26,807 - mmseg - INFO - Iter [48750/160000]	lr: 3.750e-05, eta: 10:37:14, time: 0.322, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7149, loss: 0.0823
+2023-03-04 18:40:40,595 - mmseg - INFO - Iter [48800/160000]	lr: 3.750e-05, eta: 10:36:49, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.8819, loss: 0.0808
+2023-03-04 18:40:54,265 - mmseg - INFO - Iter [48850/160000]	lr: 3.750e-05, eta: 10:36:24, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7959, loss: 0.0814
+2023-03-04 18:41:07,881 - mmseg - INFO - Iter [48900/160000]	lr: 3.750e-05, eta: 10:35:59, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7711, loss: 0.0810
+2023-03-04 18:41:23,805 - mmseg - INFO - Iter [48950/160000]	lr: 3.750e-05, eta: 10:35:39, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8084, loss: 0.0804
+2023-03-04 18:41:37,412 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 18:41:37,412 - mmseg - INFO - Iter [49000/160000]	lr: 3.750e-05, eta: 10:35:14, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8207, loss: 0.0800
+2023-03-04 18:41:51,221 - mmseg - INFO - Iter [49050/160000]	lr: 3.750e-05, eta: 10:34:49, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9054, loss: 0.0770
+2023-03-04 18:42:04,945 - mmseg - INFO - Iter [49100/160000]	lr: 3.750e-05, eta: 10:34:24, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0748, decode.acc_seg: 97.0072, loss: 0.0748
+2023-03-04 18:42:21,135 - mmseg - INFO - Iter [49150/160000]	lr: 3.750e-05, eta: 10:34:05, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0843, decode.acc_seg: 96.6834, loss: 0.0843
+2023-03-04 18:42:34,702 - mmseg - INFO - Iter [49200/160000]	lr: 3.750e-05, eta: 10:33:39, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8908, loss: 0.0779
+2023-03-04 18:42:48,673 - mmseg - INFO - Iter [49250/160000]	lr: 3.750e-05, eta: 10:33:15, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8123, loss: 0.0794
+2023-03-04 18:43:04,608 - mmseg - INFO - Iter [49300/160000]	lr: 3.750e-05, eta: 10:32:55, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8097, loss: 0.0798
+2023-03-04 18:43:18,183 - mmseg - INFO - Iter [49350/160000]	lr: 3.750e-05, eta: 10:32:30, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8453, loss: 0.0794
+2023-03-04 18:43:32,177 - mmseg - INFO - Iter [49400/160000]	lr: 3.750e-05, eta: 10:32:06, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7755, loss: 0.0810
+2023-03-04 18:43:45,969 - mmseg - INFO - Iter [49450/160000]	lr: 3.750e-05, eta: 10:31:41, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.8331, loss: 0.0809
+2023-03-04 18:44:01,867 - mmseg - INFO - Iter [49500/160000]	lr: 3.750e-05, eta: 10:31:21, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7757, loss: 0.0811
+2023-03-04 18:44:15,445 - mmseg - INFO - Iter [49550/160000]	lr: 3.750e-05, eta: 10:30:56, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7965, loss: 0.0811
+2023-03-04 18:44:28,995 - mmseg - INFO - Iter [49600/160000]	lr: 3.750e-05, eta: 10:30:31, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8564, loss: 0.0796
+2023-03-04 18:44:42,764 - mmseg - INFO - Iter [49650/160000]	lr: 3.750e-05, eta: 10:30:06, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.7580, loss: 0.0803
+2023-03-04 18:44:58,835 - mmseg - INFO - Iter [49700/160000]	lr: 3.750e-05, eta: 10:29:47, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8283, loss: 0.0797
+2023-03-04 18:45:12,616 - mmseg - INFO - Iter [49750/160000]	lr: 3.750e-05, eta: 10:29:22, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8521, loss: 0.0797
+2023-03-04 18:45:26,289 - mmseg - INFO - Iter [49800/160000]	lr: 3.750e-05, eta: 10:28:57, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9285, loss: 0.0773
+2023-03-04 18:45:42,225 - mmseg - INFO - Iter [49850/160000]	lr: 3.750e-05, eta: 10:28:38, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8468, loss: 0.0789
+2023-03-04 18:45:55,935 - mmseg - INFO - Iter [49900/160000]	lr: 3.750e-05, eta: 10:28:13, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7667, loss: 0.0818
+2023-03-04 18:46:09,555 - mmseg - INFO - Iter [49950/160000]	lr: 3.750e-05, eta: 10:27:48, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8717, loss: 0.0791
+2023-03-04 18:46:23,120 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 18:46:23,120 - mmseg - INFO - Iter [50000/160000]	lr: 3.750e-05, eta: 10:27:23, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0849, decode.acc_seg: 96.7111, loss: 0.0849
+2023-03-04 18:46:39,200 - mmseg - INFO - Iter [50050/160000]	lr: 3.750e-05, eta: 10:27:04, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7718, loss: 0.0808
+2023-03-04 18:46:52,973 - mmseg - INFO - Iter [50100/160000]	lr: 3.750e-05, eta: 10:26:40, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.8000, loss: 0.0806
+2023-03-04 18:47:06,631 - mmseg - INFO - Iter [50150/160000]	lr: 3.750e-05, eta: 10:26:15, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8210, loss: 0.0800
+2023-03-04 18:47:20,402 - mmseg - INFO - Iter [50200/160000]	lr: 3.750e-05, eta: 10:25:50, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.8086, loss: 0.0813
+2023-03-04 18:47:36,392 - mmseg - INFO - Iter [50250/160000]	lr: 3.750e-05, eta: 10:25:31, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.8110, loss: 0.0805
+2023-03-04 18:47:50,242 - mmseg - INFO - Iter [50300/160000]	lr: 3.750e-05, eta: 10:25:07, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8756, loss: 0.0788
+2023-03-04 18:48:04,108 - mmseg - INFO - Iter [50350/160000]	lr: 3.750e-05, eta: 10:24:43, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7141, loss: 0.0818
+2023-03-04 18:48:17,927 - mmseg - INFO - Iter [50400/160000]	lr: 3.750e-05, eta: 10:24:18, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8460, loss: 0.0789
+2023-03-04 18:48:33,841 - mmseg - INFO - Iter [50450/160000]	lr: 3.750e-05, eta: 10:23:59, time: 0.318, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.8218, loss: 0.0806
+2023-03-04 18:48:47,631 - mmseg - INFO - Iter [50500/160000]	lr: 3.750e-05, eta: 10:23:35, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8123, loss: 0.0803
+2023-03-04 18:49:01,215 - mmseg - INFO - Iter [50550/160000]	lr: 3.750e-05, eta: 10:23:10, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8625, loss: 0.0785
+2023-03-04 18:49:17,820 - mmseg - INFO - Iter [50600/160000]	lr: 3.750e-05, eta: 10:22:52, time: 0.332, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8446, loss: 0.0792
+2023-03-04 18:49:31,842 - mmseg - INFO - Iter [50650/160000]	lr: 3.750e-05, eta: 10:22:28, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8298, loss: 0.0794
+2023-03-04 18:49:45,432 - mmseg - INFO - Iter [50700/160000]	lr: 3.750e-05, eta: 10:22:03, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7949, loss: 0.0813
+2023-03-04 18:49:59,104 - mmseg - INFO - Iter [50750/160000]	lr: 3.750e-05, eta: 10:21:39, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7856, loss: 0.0811
+2023-03-04 18:50:15,359 - mmseg - INFO - Iter [50800/160000]	lr: 3.750e-05, eta: 10:21:20, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9406, loss: 0.0766
+2023-03-04 18:50:29,152 - mmseg - INFO - Iter [50850/160000]	lr: 3.750e-05, eta: 10:20:56, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.7717, loss: 0.0804
+2023-03-04 18:50:42,735 - mmseg - INFO - Iter [50900/160000]	lr: 3.750e-05, eta: 10:20:32, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8447, loss: 0.0798
+2023-03-04 18:50:56,328 - mmseg - INFO - Iter [50950/160000]	lr: 3.750e-05, eta: 10:20:07, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8926, loss: 0.0781
+2023-03-04 18:51:12,383 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 18:51:12,383 - mmseg - INFO - Iter [51000/160000]	lr: 3.750e-05, eta: 10:19:48, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0820, decode.acc_seg: 96.7536, loss: 0.0820
+2023-03-04 18:51:26,050 - mmseg - INFO - Iter [51050/160000]	lr: 3.750e-05, eta: 10:19:24, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8234, loss: 0.0796
+2023-03-04 18:51:39,623 - mmseg - INFO - Iter [51100/160000]	lr: 3.750e-05, eta: 10:18:59, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8883, loss: 0.0783
+2023-03-04 18:51:53,230 - mmseg - INFO - Iter [51150/160000]	lr: 3.750e-05, eta: 10:18:35, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7610, loss: 0.0808
+2023-03-04 18:52:09,426 - mmseg - INFO - Iter [51200/160000]	lr: 3.750e-05, eta: 10:18:16, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8463, loss: 0.0794
+2023-03-04 18:52:22,972 - mmseg - INFO - Iter [51250/160000]	lr: 3.750e-05, eta: 10:17:51, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8931, loss: 0.0783
+2023-03-04 18:52:36,936 - mmseg - INFO - Iter [51300/160000]	lr: 3.750e-05, eta: 10:17:28, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8544, loss: 0.0801
+2023-03-04 18:52:52,934 - mmseg - INFO - Iter [51350/160000]	lr: 3.750e-05, eta: 10:17:09, time: 0.320, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.9050, loss: 0.0786
+2023-03-04 18:53:06,646 - mmseg - INFO - Iter [51400/160000]	lr: 3.750e-05, eta: 10:16:44, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0819, decode.acc_seg: 96.7586, loss: 0.0819
+2023-03-04 18:53:20,343 - mmseg - INFO - Iter [51450/160000]	lr: 3.750e-05, eta: 10:16:20, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8419, loss: 0.0795
+2023-03-04 18:53:34,366 - mmseg - INFO - Iter [51500/160000]	lr: 3.750e-05, eta: 10:15:57, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8844, loss: 0.0792
+2023-03-04 18:53:50,249 - mmseg - INFO - Iter [51550/160000]	lr: 3.750e-05, eta: 10:15:38, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7620, loss: 0.0814
+2023-03-04 18:54:03,794 - mmseg - INFO - Iter [51600/160000]	lr: 3.750e-05, eta: 10:15:13, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8220, loss: 0.0798
+2023-03-04 18:54:17,428 - mmseg - INFO - Iter [51650/160000]	lr: 3.750e-05, eta: 10:14:49, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7884, loss: 0.0823
+2023-03-04 18:54:31,562 - mmseg - INFO - Iter [51700/160000]	lr: 3.750e-05, eta: 10:14:26, time: 0.283, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9084, loss: 0.0773
+2023-03-04 18:54:47,886 - mmseg - INFO - Iter [51750/160000]	lr: 3.750e-05, eta: 10:14:08, time: 0.327, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9275, loss: 0.0770
+2023-03-04 18:55:01,535 - mmseg - INFO - Iter [51800/160000]	lr: 3.750e-05, eta: 10:13:44, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.7933, loss: 0.0804
+2023-03-04 18:55:15,129 - mmseg - INFO - Iter [51850/160000]	lr: 3.750e-05, eta: 10:13:19, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8324, loss: 0.0795
+2023-03-04 18:55:31,403 - mmseg - INFO - Iter [51900/160000]	lr: 3.750e-05, eta: 10:13:01, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8856, loss: 0.0788
+2023-03-04 18:55:45,068 - mmseg - INFO - Iter [51950/160000]	lr: 3.750e-05, eta: 10:12:37, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.9273, loss: 0.0782
+2023-03-04 18:55:58,895 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 18:55:58,895 - mmseg - INFO - Iter [52000/160000]	lr: 3.750e-05, eta: 10:12:13, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9170, loss: 0.0777
+2023-03-04 18:56:12,710 - mmseg - INFO - Iter [52050/160000]	lr: 3.750e-05, eta: 10:11:50, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.9014, loss: 0.0792
+2023-03-04 18:56:28,991 - mmseg - INFO - Iter [52100/160000]	lr: 3.750e-05, eta: 10:11:31, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7730, loss: 0.0814
+2023-03-04 18:56:43,483 - mmseg - INFO - Iter [52150/160000]	lr: 3.750e-05, eta: 10:11:09, time: 0.290, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.7842, loss: 0.0803
+2023-03-04 18:56:57,086 - mmseg - INFO - Iter [52200/160000]	lr: 3.750e-05, eta: 10:10:45, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9132, loss: 0.0777
+2023-03-04 18:57:10,788 - mmseg - INFO - Iter [52250/160000]	lr: 3.750e-05, eta: 10:10:21, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0836, decode.acc_seg: 96.7291, loss: 0.0836
+2023-03-04 18:57:26,837 - mmseg - INFO - Iter [52300/160000]	lr: 3.750e-05, eta: 10:10:02, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8404, loss: 0.0800
+2023-03-04 18:57:40,419 - mmseg - INFO - Iter [52350/160000]	lr: 3.750e-05, eta: 10:09:38, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9742, loss: 0.0762
+2023-03-04 18:57:54,003 - mmseg - INFO - Iter [52400/160000]	lr: 3.750e-05, eta: 10:09:14, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8221, loss: 0.0787
+2023-03-04 18:58:07,708 - mmseg - INFO - Iter [52450/160000]	lr: 3.750e-05, eta: 10:08:50, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7888, loss: 0.0808
+2023-03-04 18:58:23,849 - mmseg - INFO - Iter [52500/160000]	lr: 3.750e-05, eta: 10:08:32, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7794, loss: 0.0808
+2023-03-04 18:58:37,610 - mmseg - INFO - Iter [52550/160000]	lr: 3.750e-05, eta: 10:08:08, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8277, loss: 0.0794
+2023-03-04 18:58:51,477 - mmseg - INFO - Iter [52600/160000]	lr: 3.750e-05, eta: 10:07:45, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0828, decode.acc_seg: 96.7282, loss: 0.0828
+2023-03-04 18:59:07,804 - mmseg - INFO - Iter [52650/160000]	lr: 3.750e-05, eta: 10:07:27, time: 0.327, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7864, loss: 0.0808
+2023-03-04 18:59:21,493 - mmseg - INFO - Iter [52700/160000]	lr: 3.750e-05, eta: 10:07:03, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7932, loss: 0.0814
+2023-03-04 18:59:35,125 - mmseg - INFO - Iter [52750/160000]	lr: 3.750e-05, eta: 10:06:39, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8574, loss: 0.0795
+2023-03-04 18:59:48,925 - mmseg - INFO - Iter [52800/160000]	lr: 3.750e-05, eta: 10:06:16, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8364, loss: 0.0794
+2023-03-04 19:00:05,072 - mmseg - INFO - Iter [52850/160000]	lr: 3.750e-05, eta: 10:05:57, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0870, decode.acc_seg: 96.6318, loss: 0.0870
+2023-03-04 19:00:18,925 - mmseg - INFO - Iter [52900/160000]	lr: 3.750e-05, eta: 10:05:34, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8577, loss: 0.0799
+2023-03-04 19:00:32,494 - mmseg - INFO - Iter [52950/160000]	lr: 3.750e-05, eta: 10:05:10, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.9119, loss: 0.0781
+2023-03-04 19:00:46,203 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 19:00:46,203 - mmseg - INFO - Iter [53000/160000]	lr: 3.750e-05, eta: 10:04:46, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8406, loss: 0.0789
+2023-03-04 19:01:02,586 - mmseg - INFO - Iter [53050/160000]	lr: 3.750e-05, eta: 10:04:28, time: 0.328, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8176, loss: 0.0795
+2023-03-04 19:01:16,337 - mmseg - INFO - Iter [53100/160000]	lr: 3.750e-05, eta: 10:04:05, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.7657, loss: 0.0817
+2023-03-04 19:01:29,987 - mmseg - INFO - Iter [53150/160000]	lr: 3.750e-05, eta: 10:03:41, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.8181, loss: 0.0808
+2023-03-04 19:01:46,046 - mmseg - INFO - Iter [53200/160000]	lr: 3.750e-05, eta: 10:03:23, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 97.0031, loss: 0.0752
+2023-03-04 19:01:59,890 - mmseg - INFO - Iter [53250/160000]	lr: 3.750e-05, eta: 10:02:59, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8600, loss: 0.0796
+2023-03-04 19:02:13,956 - mmseg - INFO - Iter [53300/160000]	lr: 3.750e-05, eta: 10:02:37, time: 0.281, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8104, loss: 0.0800
+2023-03-04 19:02:27,615 - mmseg - INFO - Iter [53350/160000]	lr: 3.750e-05, eta: 10:02:13, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8773, loss: 0.0784
+2023-03-04 19:02:43,665 - mmseg - INFO - Iter [53400/160000]	lr: 3.750e-05, eta: 10:01:54, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7548, loss: 0.0818
+2023-03-04 19:02:57,495 - mmseg - INFO - Iter [53450/160000]	lr: 3.750e-05, eta: 10:01:31, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8365, loss: 0.0803
+2023-03-04 19:03:11,162 - mmseg - INFO - Iter [53500/160000]	lr: 3.750e-05, eta: 10:01:08, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9015, loss: 0.0775
+2023-03-04 19:03:24,752 - mmseg - INFO - Iter [53550/160000]	lr: 3.750e-05, eta: 10:00:44, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0835, decode.acc_seg: 96.7110, loss: 0.0835
+2023-03-04 19:03:40,768 - mmseg - INFO - Iter [53600/160000]	lr: 3.750e-05, eta: 10:00:26, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8255, loss: 0.0800
+2023-03-04 19:03:54,524 - mmseg - INFO - Iter [53650/160000]	lr: 3.750e-05, eta: 10:00:02, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9363, loss: 0.0776
+2023-03-04 19:04:08,095 - mmseg - INFO - Iter [53700/160000]	lr: 3.750e-05, eta: 9:59:39, time: 0.271, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7622, loss: 0.0810
+2023-03-04 19:04:21,711 - mmseg - INFO - Iter [53750/160000]	lr: 3.750e-05, eta: 9:59:15, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0824, decode.acc_seg: 96.7361, loss: 0.0824
+2023-03-04 19:04:37,830 - mmseg - INFO - Iter [53800/160000]	lr: 3.750e-05, eta: 9:58:57, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7661, loss: 0.0815
+2023-03-04 19:04:51,478 - mmseg - INFO - Iter [53850/160000]	lr: 3.750e-05, eta: 9:58:33, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8187, loss: 0.0797
+2023-03-04 19:05:05,166 - mmseg - INFO - Iter [53900/160000]	lr: 3.750e-05, eta: 9:58:10, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0833, decode.acc_seg: 96.7528, loss: 0.0833
+2023-03-04 19:05:21,428 - mmseg - INFO - Iter [53950/160000]	lr: 3.750e-05, eta: 9:57:52, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.8258, loss: 0.0809
+2023-03-04 19:05:35,164 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 19:05:35,164 - mmseg - INFO - Iter [54000/160000]	lr: 3.750e-05, eta: 9:57:29, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8214, loss: 0.0800
+2023-03-04 19:05:49,441 - mmseg - INFO - Iter [54050/160000]	lr: 3.750e-05, eta: 9:57:07, time: 0.285, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7470, loss: 0.0810
+2023-03-04 19:06:03,133 - mmseg - INFO - Iter [54100/160000]	lr: 3.750e-05, eta: 9:56:44, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.7904, loss: 0.0807
+2023-03-04 19:06:19,371 - mmseg - INFO - Iter [54150/160000]	lr: 3.750e-05, eta: 9:56:25, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8828, loss: 0.0779
+2023-03-04 19:06:33,228 - mmseg - INFO - Iter [54200/160000]	lr: 3.750e-05, eta: 9:56:02, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.7634, loss: 0.0802
+2023-03-04 19:06:46,957 - mmseg - INFO - Iter [54250/160000]	lr: 3.750e-05, eta: 9:55:39, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.7684, loss: 0.0812
+2023-03-04 19:07:00,765 - mmseg - INFO - Iter [54300/160000]	lr: 3.750e-05, eta: 9:55:16, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9297, loss: 0.0772
+2023-03-04 19:07:16,872 - mmseg - INFO - Iter [54350/160000]	lr: 3.750e-05, eta: 9:54:58, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.9018, loss: 0.0784
+2023-03-04 19:07:30,612 - mmseg - INFO - Iter [54400/160000]	lr: 3.750e-05, eta: 9:54:35, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.7804, loss: 0.0803
+2023-03-04 19:07:44,566 - mmseg - INFO - Iter [54450/160000]	lr: 3.750e-05, eta: 9:54:12, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.7941, loss: 0.0806
+2023-03-04 19:08:00,677 - mmseg - INFO - Iter [54500/160000]	lr: 3.750e-05, eta: 9:53:54, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0821, decode.acc_seg: 96.7220, loss: 0.0821
+2023-03-04 19:08:14,563 - mmseg - INFO - Iter [54550/160000]	lr: 3.750e-05, eta: 9:53:31, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8224, loss: 0.0794
+2023-03-04 19:08:28,288 - mmseg - INFO - Iter [54600/160000]	lr: 3.750e-05, eta: 9:53:08, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8280, loss: 0.0798
+2023-03-04 19:08:41,912 - mmseg - INFO - Iter [54650/160000]	lr: 3.750e-05, eta: 9:52:45, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.7332, loss: 0.0812
+2023-03-04 19:08:57,823 - mmseg - INFO - Iter [54700/160000]	lr: 3.750e-05, eta: 9:52:26, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7855, loss: 0.0811
+2023-03-04 19:09:11,843 - mmseg - INFO - Iter [54750/160000]	lr: 3.750e-05, eta: 9:52:04, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7538, loss: 0.0818
+2023-03-04 19:09:25,518 - mmseg - INFO - Iter [54800/160000]	lr: 3.750e-05, eta: 9:51:41, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8444, loss: 0.0794
+2023-03-04 19:09:39,160 - mmseg - INFO - Iter [54850/160000]	lr: 3.750e-05, eta: 9:51:18, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8534, loss: 0.0786
+2023-03-04 19:09:55,300 - mmseg - INFO - Iter [54900/160000]	lr: 3.750e-05, eta: 9:51:00, time: 0.323, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8434, loss: 0.0798
+2023-03-04 19:10:08,910 - mmseg - INFO - Iter [54950/160000]	lr: 3.750e-05, eta: 9:50:37, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.9034, loss: 0.0782
+2023-03-04 19:10:22,894 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 19:10:22,895 - mmseg - INFO - Iter [55000/160000]	lr: 3.750e-05, eta: 9:50:14, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8307, loss: 0.0799
+2023-03-04 19:10:36,529 - mmseg - INFO - Iter [55050/160000]	lr: 3.750e-05, eta: 9:49:51, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8143, loss: 0.0794
+2023-03-04 19:10:52,524 - mmseg - INFO - Iter [55100/160000]	lr: 3.750e-05, eta: 9:49:33, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8288, loss: 0.0800
+2023-03-04 19:11:06,279 - mmseg - INFO - Iter [55150/160000]	lr: 3.750e-05, eta: 9:49:10, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.7769, loss: 0.0802
+2023-03-04 19:11:20,001 - mmseg - INFO - Iter [55200/160000]	lr: 3.750e-05, eta: 9:48:47, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0838, decode.acc_seg: 96.6826, loss: 0.0838
+2023-03-04 19:11:36,015 - mmseg - INFO - Iter [55250/160000]	lr: 3.750e-05, eta: 9:48:29, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7589, loss: 0.0825
+2023-03-04 19:11:49,875 - mmseg - INFO - Iter [55300/160000]	lr: 3.750e-05, eta: 9:48:06, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.8220, loss: 0.0806
+2023-03-04 19:12:03,541 - mmseg - INFO - Iter [55350/160000]	lr: 3.750e-05, eta: 9:47:43, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9298, loss: 0.0769
+2023-03-04 19:12:17,179 - mmseg - INFO - Iter [55400/160000]	lr: 3.750e-05, eta: 9:47:20, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.7875, loss: 0.0809
+2023-03-04 19:12:33,132 - mmseg - INFO - Iter [55450/160000]	lr: 3.750e-05, eta: 9:47:02, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8365, loss: 0.0789
+2023-03-04 19:12:46,735 - mmseg - INFO - Iter [55500/160000]	lr: 3.750e-05, eta: 9:46:39, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8460, loss: 0.0799
+2023-03-04 19:13:00,440 - mmseg - INFO - Iter [55550/160000]	lr: 3.750e-05, eta: 9:46:16, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9031, loss: 0.0773
+2023-03-04 19:13:14,052 - mmseg - INFO - Iter [55600/160000]	lr: 3.750e-05, eta: 9:45:53, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0822, decode.acc_seg: 96.7115, loss: 0.0822
+2023-03-04 19:13:30,239 - mmseg - INFO - Iter [55650/160000]	lr: 3.750e-05, eta: 9:45:35, time: 0.324, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.8024, loss: 0.0805
+2023-03-04 19:13:43,809 - mmseg - INFO - Iter [55700/160000]	lr: 3.750e-05, eta: 9:45:12, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8455, loss: 0.0799
+2023-03-04 19:13:57,460 - mmseg - INFO - Iter [55750/160000]	lr: 3.750e-05, eta: 9:44:49, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8910, loss: 0.0782
+2023-03-04 19:14:11,207 - mmseg - INFO - Iter [55800/160000]	lr: 3.750e-05, eta: 9:44:27, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8028, loss: 0.0803
+2023-03-04 19:14:27,448 - mmseg - INFO - Iter [55850/160000]	lr: 3.750e-05, eta: 9:44:09, time: 0.325, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.8156, loss: 0.0806
+2023-03-04 19:14:41,181 - mmseg - INFO - Iter [55900/160000]	lr: 3.750e-05, eta: 9:43:46, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8751, loss: 0.0799
+2023-03-04 19:14:55,274 - mmseg - INFO - Iter [55950/160000]	lr: 3.750e-05, eta: 9:43:24, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9445, loss: 0.0774
+2023-03-04 19:15:11,333 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 19:15:11,333 - mmseg - INFO - Iter [56000/160000]	lr: 3.750e-05, eta: 9:43:06, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.7742, loss: 0.0812
+2023-03-04 19:15:25,037 - mmseg - INFO - Iter [56050/160000]	lr: 3.750e-05, eta: 9:42:44, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8517, loss: 0.0797
+2023-03-04 19:15:39,156 - mmseg - INFO - Iter [56100/160000]	lr: 3.750e-05, eta: 9:42:22, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0822, decode.acc_seg: 96.7485, loss: 0.0822
+2023-03-04 19:15:53,179 - mmseg - INFO - Iter [56150/160000]	lr: 3.750e-05, eta: 9:42:00, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9403, loss: 0.0762
+2023-03-04 19:16:09,239 - mmseg - INFO - Iter [56200/160000]	lr: 3.750e-05, eta: 9:41:42, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8737, loss: 0.0779
+2023-03-04 19:16:22,849 - mmseg - INFO - Iter [56250/160000]	lr: 3.750e-05, eta: 9:41:19, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0821, decode.acc_seg: 96.7672, loss: 0.0821
+2023-03-04 19:16:36,639 - mmseg - INFO - Iter [56300/160000]	lr: 3.750e-05, eta: 9:40:57, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.8925, loss: 0.0776
+2023-03-04 19:16:50,283 - mmseg - INFO - Iter [56350/160000]	lr: 3.750e-05, eta: 9:40:34, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.8191, loss: 0.0809
+2023-03-04 19:17:06,541 - mmseg - INFO - Iter [56400/160000]	lr: 3.750e-05, eta: 9:40:16, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.9032, loss: 0.0783
+2023-03-04 19:17:20,510 - mmseg - INFO - Iter [56450/160000]	lr: 3.750e-05, eta: 9:39:54, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9495, loss: 0.0772
+2023-03-04 19:17:34,100 - mmseg - INFO - Iter [56500/160000]	lr: 3.750e-05, eta: 9:39:31, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9240, loss: 0.0776
+2023-03-04 19:17:50,186 - mmseg - INFO - Iter [56550/160000]	lr: 3.750e-05, eta: 9:39:13, time: 0.322, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8293, loss: 0.0799
+2023-03-04 19:18:04,160 - mmseg - INFO - Iter [56600/160000]	lr: 3.750e-05, eta: 9:38:51, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0828, decode.acc_seg: 96.7271, loss: 0.0828
+2023-03-04 19:18:18,190 - mmseg - INFO - Iter [56650/160000]	lr: 3.750e-05, eta: 9:38:30, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8763, loss: 0.0784
+2023-03-04 19:18:31,789 - mmseg - INFO - Iter [56700/160000]	lr: 3.750e-05, eta: 9:38:07, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7736, loss: 0.0813
+2023-03-04 19:18:47,727 - mmseg - INFO - Iter [56750/160000]	lr: 3.750e-05, eta: 9:37:49, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8088, loss: 0.0796
+2023-03-04 19:19:01,362 - mmseg - INFO - Iter [56800/160000]	lr: 3.750e-05, eta: 9:37:26, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8469, loss: 0.0793
+2023-03-04 19:19:15,254 - mmseg - INFO - Iter [56850/160000]	lr: 3.750e-05, eta: 9:37:04, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.7883, loss: 0.0798
+2023-03-04 19:19:28,783 - mmseg - INFO - Iter [56900/160000]	lr: 3.750e-05, eta: 9:36:41, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0820, decode.acc_seg: 96.7637, loss: 0.0820
+2023-03-04 19:19:44,694 - mmseg - INFO - Iter [56950/160000]	lr: 3.750e-05, eta: 9:36:23, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8770, loss: 0.0790
+2023-03-04 19:19:58,409 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 19:19:58,409 - mmseg - INFO - Iter [57000/160000]	lr: 3.750e-05, eta: 9:36:01, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8104, loss: 0.0801
+2023-03-04 19:20:12,414 - mmseg - INFO - Iter [57050/160000]	lr: 3.750e-05, eta: 9:35:39, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0828, decode.acc_seg: 96.7260, loss: 0.0828
+2023-03-04 19:20:26,175 - mmseg - INFO - Iter [57100/160000]	lr: 3.750e-05, eta: 9:35:17, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7793, loss: 0.0816
+2023-03-04 19:20:42,224 - mmseg - INFO - Iter [57150/160000]	lr: 3.750e-05, eta: 9:34:59, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.7770, loss: 0.0804
+2023-03-04 19:20:55,853 - mmseg - INFO - Iter [57200/160000]	lr: 3.750e-05, eta: 9:34:36, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8926, loss: 0.0789
+2023-03-04 19:21:09,637 - mmseg - INFO - Iter [57250/160000]	lr: 3.750e-05, eta: 9:34:14, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0842, decode.acc_seg: 96.6988, loss: 0.0842
+2023-03-04 19:21:25,990 - mmseg - INFO - Iter [57300/160000]	lr: 3.750e-05, eta: 9:33:57, time: 0.327, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8102, loss: 0.0803
+2023-03-04 19:21:39,657 - mmseg - INFO - Iter [57350/160000]	lr: 3.750e-05, eta: 9:33:34, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9657, loss: 0.0766
+2023-03-04 19:21:53,316 - mmseg - INFO - Iter [57400/160000]	lr: 3.750e-05, eta: 9:33:12, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8103, loss: 0.0803
+2023-03-04 19:22:07,006 - mmseg - INFO - Iter [57450/160000]	lr: 3.750e-05, eta: 9:32:50, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8358, loss: 0.0792
+2023-03-04 19:22:23,260 - mmseg - INFO - Iter [57500/160000]	lr: 3.750e-05, eta: 9:32:32, time: 0.325, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7841, loss: 0.0818
+2023-03-04 19:22:36,854 - mmseg - INFO - Iter [57550/160000]	lr: 3.750e-05, eta: 9:32:10, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7663, loss: 0.0814
+2023-03-04 19:22:50,432 - mmseg - INFO - Iter [57600/160000]	lr: 3.750e-05, eta: 9:31:47, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.7974, loss: 0.0796
+2023-03-04 19:23:04,330 - mmseg - INFO - Iter [57650/160000]	lr: 3.750e-05, eta: 9:31:25, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9438, loss: 0.0774
+2023-03-04 19:23:20,969 - mmseg - INFO - Iter [57700/160000]	lr: 3.750e-05, eta: 9:31:08, time: 0.333, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7826, loss: 0.0811
+2023-03-04 19:23:34,680 - mmseg - INFO - Iter [57750/160000]	lr: 3.750e-05, eta: 9:30:46, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8677, loss: 0.0790
+2023-03-04 19:23:48,590 - mmseg - INFO - Iter [57800/160000]	lr: 3.750e-05, eta: 9:30:25, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8044, loss: 0.0799
+2023-03-04 19:24:04,580 - mmseg - INFO - Iter [57850/160000]	lr: 3.750e-05, eta: 9:30:06, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8739, loss: 0.0799
+2023-03-04 19:24:18,165 - mmseg - INFO - Iter [57900/160000]	lr: 3.750e-05, eta: 9:29:44, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7445, loss: 0.0818
+2023-03-04 19:24:32,120 - mmseg - INFO - Iter [57950/160000]	lr: 3.750e-05, eta: 9:29:23, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8943, loss: 0.0780
+2023-03-04 19:24:45,836 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 19:24:45,836 - mmseg - INFO - Iter [58000/160000]	lr: 3.750e-05, eta: 9:29:00, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8622, loss: 0.0793
+2023-03-04 19:25:02,016 - mmseg - INFO - Iter [58050/160000]	lr: 3.750e-05, eta: 9:28:43, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.7781, loss: 0.0817
+2023-03-04 19:25:15,939 - mmseg - INFO - Iter [58100/160000]	lr: 3.750e-05, eta: 9:28:21, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9297, loss: 0.0762
+2023-03-04 19:25:30,116 - mmseg - INFO - Iter [58150/160000]	lr: 3.750e-05, eta: 9:28:00, time: 0.283, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0827, decode.acc_seg: 96.7918, loss: 0.0827
+2023-03-04 19:25:44,067 - mmseg - INFO - Iter [58200/160000]	lr: 3.750e-05, eta: 9:27:38, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8752, loss: 0.0781
+2023-03-04 19:26:00,077 - mmseg - INFO - Iter [58250/160000]	lr: 3.750e-05, eta: 9:27:20, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9839, loss: 0.0762
+2023-03-04 19:26:14,172 - mmseg - INFO - Iter [58300/160000]	lr: 3.750e-05, eta: 9:26:59, time: 0.282, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7756, loss: 0.0810
+2023-03-04 19:26:27,814 - mmseg - INFO - Iter [58350/160000]	lr: 3.750e-05, eta: 9:26:37, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9315, loss: 0.0770
+2023-03-04 19:26:41,536 - mmseg - INFO - Iter [58400/160000]	lr: 3.750e-05, eta: 9:26:15, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0829, decode.acc_seg: 96.7027, loss: 0.0829
+2023-03-04 19:26:57,600 - mmseg - INFO - Iter [58450/160000]	lr: 3.750e-05, eta: 9:25:57, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8307, loss: 0.0789
+2023-03-04 19:27:11,579 - mmseg - INFO - Iter [58500/160000]	lr: 3.750e-05, eta: 9:25:36, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8456, loss: 0.0795
+2023-03-04 19:27:25,439 - mmseg - INFO - Iter [58550/160000]	lr: 3.750e-05, eta: 9:25:14, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8939, loss: 0.0779
+2023-03-04 19:27:41,474 - mmseg - INFO - Iter [58600/160000]	lr: 3.750e-05, eta: 9:24:56, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8857, loss: 0.0781
+2023-03-04 19:27:55,216 - mmseg - INFO - Iter [58650/160000]	lr: 3.750e-05, eta: 9:24:34, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8465, loss: 0.0797
+2023-03-04 19:28:08,975 - mmseg - INFO - Iter [58700/160000]	lr: 3.750e-05, eta: 9:24:12, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.6778, loss: 0.0831
+2023-03-04 19:28:22,670 - mmseg - INFO - Iter [58750/160000]	lr: 3.750e-05, eta: 9:23:50, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8349, loss: 0.0791
+2023-03-04 19:28:38,714 - mmseg - INFO - Iter [58800/160000]	lr: 3.750e-05, eta: 9:23:33, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8855, loss: 0.0780
+2023-03-04 19:28:52,566 - mmseg - INFO - Iter [58850/160000]	lr: 3.750e-05, eta: 9:23:11, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8919, loss: 0.0781
+2023-03-04 19:29:06,337 - mmseg - INFO - Iter [58900/160000]	lr: 3.750e-05, eta: 9:22:49, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.8541, loss: 0.0814
+2023-03-04 19:29:19,931 - mmseg - INFO - Iter [58950/160000]	lr: 3.750e-05, eta: 9:22:27, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8207, loss: 0.0796
+2023-03-04 19:29:36,201 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 19:29:36,201 - mmseg - INFO - Iter [59000/160000]	lr: 3.750e-05, eta: 9:22:10, time: 0.325, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9606, loss: 0.0766
+2023-03-04 19:29:49,901 - mmseg - INFO - Iter [59050/160000]	lr: 3.750e-05, eta: 9:21:48, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.8012, loss: 0.0802
+2023-03-04 19:30:03,492 - mmseg - INFO - Iter [59100/160000]	lr: 3.750e-05, eta: 9:21:26, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0824, decode.acc_seg: 96.7485, loss: 0.0824
+2023-03-04 19:30:19,591 - mmseg - INFO - Iter [59150/160000]	lr: 3.750e-05, eta: 9:21:08, time: 0.322, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7609, loss: 0.0823
+2023-03-04 19:30:33,480 - mmseg - INFO - Iter [59200/160000]	lr: 3.750e-05, eta: 9:20:47, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8224, loss: 0.0796
+2023-03-04 19:30:47,033 - mmseg - INFO - Iter [59250/160000]	lr: 3.750e-05, eta: 9:20:25, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8164, loss: 0.0800
+2023-03-04 19:31:00,658 - mmseg - INFO - Iter [59300/160000]	lr: 3.750e-05, eta: 9:20:03, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9810, loss: 0.0759
+2023-03-04 19:31:16,671 - mmseg - INFO - Iter [59350/160000]	lr: 3.750e-05, eta: 9:19:45, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9074, loss: 0.0774
+2023-03-04 19:31:30,655 - mmseg - INFO - Iter [59400/160000]	lr: 3.750e-05, eta: 9:19:24, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8208, loss: 0.0799
+2023-03-04 19:31:44,283 - mmseg - INFO - Iter [59450/160000]	lr: 3.750e-05, eta: 9:19:02, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8232, loss: 0.0797
+2023-03-04 19:31:57,919 - mmseg - INFO - Iter [59500/160000]	lr: 3.750e-05, eta: 9:18:40, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8188, loss: 0.0796
+2023-03-04 19:32:14,035 - mmseg - INFO - Iter [59550/160000]	lr: 3.750e-05, eta: 9:18:23, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8515, loss: 0.0799
+2023-03-04 19:32:27,639 - mmseg - INFO - Iter [59600/160000]	lr: 3.750e-05, eta: 9:18:01, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8537, loss: 0.0785
+2023-03-04 19:32:41,294 - mmseg - INFO - Iter [59650/160000]	lr: 3.750e-05, eta: 9:17:39, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.7549, loss: 0.0807
+2023-03-04 19:32:55,050 - mmseg - INFO - Iter [59700/160000]	lr: 3.750e-05, eta: 9:17:17, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9107, loss: 0.0774
+2023-03-04 19:33:11,173 - mmseg - INFO - Iter [59750/160000]	lr: 3.750e-05, eta: 9:17:00, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0821, decode.acc_seg: 96.6990, loss: 0.0821
+2023-03-04 19:33:24,941 - mmseg - INFO - Iter [59800/160000]	lr: 3.750e-05, eta: 9:16:38, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.9127, loss: 0.0787
+2023-03-04 19:33:38,488 - mmseg - INFO - Iter [59850/160000]	lr: 3.750e-05, eta: 9:16:16, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8826, loss: 0.0790
+2023-03-04 19:33:54,601 - mmseg - INFO - Iter [59900/160000]	lr: 3.750e-05, eta: 9:15:59, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0829, decode.acc_seg: 96.7338, loss: 0.0829
+2023-03-04 19:34:08,550 - mmseg - INFO - Iter [59950/160000]	lr: 3.750e-05, eta: 9:15:38, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8505, loss: 0.0799
+2023-03-04 19:34:22,112 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 19:34:22,112 - mmseg - INFO - Iter [60000/160000]	lr: 3.750e-05, eta: 9:15:16, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8445, loss: 0.0795
+2023-03-04 19:34:36,063 - mmseg - INFO - Iter [60050/160000]	lr: 1.875e-05, eta: 9:14:55, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0821, decode.acc_seg: 96.7460, loss: 0.0821
+2023-03-04 19:34:51,974 - mmseg - INFO - Iter [60100/160000]	lr: 1.875e-05, eta: 9:14:37, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.8246, loss: 0.0808
+2023-03-04 19:35:05,631 - mmseg - INFO - Iter [60150/160000]	lr: 1.875e-05, eta: 9:14:15, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9430, loss: 0.0768
+2023-03-04 19:35:19,428 - mmseg - INFO - Iter [60200/160000]	lr: 1.875e-05, eta: 9:13:54, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0750, decode.acc_seg: 97.0039, loss: 0.0750
+2023-03-04 19:35:33,173 - mmseg - INFO - Iter [60250/160000]	lr: 1.875e-05, eta: 9:13:32, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0736, decode.acc_seg: 97.0486, loss: 0.0736
+2023-03-04 19:35:49,263 - mmseg - INFO - Iter [60300/160000]	lr: 1.875e-05, eta: 9:13:15, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8609, loss: 0.0788
+2023-03-04 19:36:03,229 - mmseg - INFO - Iter [60350/160000]	lr: 1.875e-05, eta: 9:12:54, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0832, decode.acc_seg: 96.6719, loss: 0.0832
+2023-03-04 19:36:16,866 - mmseg - INFO - Iter [60400/160000]	lr: 1.875e-05, eta: 9:12:32, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9745, loss: 0.0763
+2023-03-04 19:36:30,450 - mmseg - INFO - Iter [60450/160000]	lr: 1.875e-05, eta: 9:12:10, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9186, loss: 0.0780
+2023-03-04 19:36:46,451 - mmseg - INFO - Iter [60500/160000]	lr: 1.875e-05, eta: 9:11:53, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0749, decode.acc_seg: 97.0172, loss: 0.0749
+2023-03-04 19:37:00,490 - mmseg - INFO - Iter [60550/160000]	lr: 1.875e-05, eta: 9:11:32, time: 0.281, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9045, loss: 0.0770
+2023-03-04 19:37:14,324 - mmseg - INFO - Iter [60600/160000]	lr: 1.875e-05, eta: 9:11:10, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.7545, loss: 0.0812
+2023-03-04 19:37:30,462 - mmseg - INFO - Iter [60650/160000]	lr: 1.875e-05, eta: 9:10:53, time: 0.323, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9762, loss: 0.0764
+2023-03-04 19:37:44,103 - mmseg - INFO - Iter [60700/160000]	lr: 1.875e-05, eta: 9:10:31, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8614, loss: 0.0789
+2023-03-04 19:37:57,690 - mmseg - INFO - Iter [60750/160000]	lr: 1.875e-05, eta: 9:10:10, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8808, loss: 0.0788
+2023-03-04 19:38:11,324 - mmseg - INFO - Iter [60800/160000]	lr: 1.875e-05, eta: 9:09:48, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8387, loss: 0.0796
+2023-03-04 19:38:27,481 - mmseg - INFO - Iter [60850/160000]	lr: 1.875e-05, eta: 9:09:31, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9039, loss: 0.0777
+2023-03-04 19:38:41,053 - mmseg - INFO - Iter [60900/160000]	lr: 1.875e-05, eta: 9:09:09, time: 0.271, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9511, loss: 0.0765
+2023-03-04 19:38:54,744 - mmseg - INFO - Iter [60950/160000]	lr: 1.875e-05, eta: 9:08:48, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8613, loss: 0.0785
+2023-03-04 19:39:08,355 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 19:39:08,356 - mmseg - INFO - Iter [61000/160000]	lr: 1.875e-05, eta: 9:08:26, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8311, loss: 0.0803
+2023-03-04 19:39:24,294 - mmseg - INFO - Iter [61050/160000]	lr: 1.875e-05, eta: 9:08:09, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8346, loss: 0.0798
+2023-03-04 19:39:38,131 - mmseg - INFO - Iter [61100/160000]	lr: 1.875e-05, eta: 9:07:48, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9490, loss: 0.0779
+2023-03-04 19:39:51,843 - mmseg - INFO - Iter [61150/160000]	lr: 1.875e-05, eta: 9:07:26, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0751, decode.acc_seg: 97.0036, loss: 0.0751
+2023-03-04 19:40:07,827 - mmseg - INFO - Iter [61200/160000]	lr: 1.875e-05, eta: 9:07:09, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8508, loss: 0.0790
+2023-03-04 19:40:21,469 - mmseg - INFO - Iter [61250/160000]	lr: 1.875e-05, eta: 9:06:47, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9438, loss: 0.0772
+2023-03-04 19:40:35,250 - mmseg - INFO - Iter [61300/160000]	lr: 1.875e-05, eta: 9:06:26, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.8276, loss: 0.0818
+2023-03-04 19:40:48,813 - mmseg - INFO - Iter [61350/160000]	lr: 1.875e-05, eta: 9:06:05, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8733, loss: 0.0787
+2023-03-04 19:41:05,110 - mmseg - INFO - Iter [61400/160000]	lr: 1.875e-05, eta: 9:05:47, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9434, loss: 0.0772
+2023-03-04 19:41:19,135 - mmseg - INFO - Iter [61450/160000]	lr: 1.875e-05, eta: 9:05:27, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 97.0083, loss: 0.0754
+2023-03-04 19:41:32,835 - mmseg - INFO - Iter [61500/160000]	lr: 1.875e-05, eta: 9:05:05, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7571, loss: 0.0811
+2023-03-04 19:41:46,444 - mmseg - INFO - Iter [61550/160000]	lr: 1.875e-05, eta: 9:04:44, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8720, loss: 0.0790
+2023-03-04 19:42:02,356 - mmseg - INFO - Iter [61600/160000]	lr: 1.875e-05, eta: 9:04:26, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7873, loss: 0.0823
+2023-03-04 19:42:15,981 - mmseg - INFO - Iter [61650/160000]	lr: 1.875e-05, eta: 9:04:05, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.7936, loss: 0.0817
+2023-03-04 19:42:29,566 - mmseg - INFO - Iter [61700/160000]	lr: 1.875e-05, eta: 9:03:44, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9392, loss: 0.0767
+2023-03-04 19:42:43,279 - mmseg - INFO - Iter [61750/160000]	lr: 1.875e-05, eta: 9:03:22, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0827, decode.acc_seg: 96.7217, loss: 0.0827
+2023-03-04 19:42:59,453 - mmseg - INFO - Iter [61800/160000]	lr: 1.875e-05, eta: 9:03:05, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0902, decode.acc_seg: 96.6868, loss: 0.0902
+2023-03-04 19:43:13,262 - mmseg - INFO - Iter [61850/160000]	lr: 1.875e-05, eta: 9:02:44, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.9068, loss: 0.0784
+2023-03-04 19:43:26,916 - mmseg - INFO - Iter [61900/160000]	lr: 1.875e-05, eta: 9:02:23, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9168, loss: 0.0777
+2023-03-04 19:43:43,226 - mmseg - INFO - Iter [61950/160000]	lr: 1.875e-05, eta: 9:02:06, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9711, loss: 0.0757
+2023-03-04 19:43:57,081 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 19:43:57,081 - mmseg - INFO - Iter [62000/160000]	lr: 1.875e-05, eta: 9:01:45, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8795, loss: 0.0786
+2023-03-04 19:44:10,951 - mmseg - INFO - Iter [62050/160000]	lr: 1.875e-05, eta: 9:01:24, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.7962, loss: 0.0796
+2023-03-04 19:44:24,763 - mmseg - INFO - Iter [62100/160000]	lr: 1.875e-05, eta: 9:01:03, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.9124, loss: 0.0782
+2023-03-04 19:44:40,945 - mmseg - INFO - Iter [62150/160000]	lr: 1.875e-05, eta: 9:00:46, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.7765, loss: 0.0803
+2023-03-04 19:44:54,551 - mmseg - INFO - Iter [62200/160000]	lr: 1.875e-05, eta: 9:00:25, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9582, loss: 0.0762
+2023-03-04 19:45:08,136 - mmseg - INFO - Iter [62250/160000]	lr: 1.875e-05, eta: 9:00:03, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.8354, loss: 0.0802
+2023-03-04 19:45:21,834 - mmseg - INFO - Iter [62300/160000]	lr: 1.875e-05, eta: 8:59:42, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8727, loss: 0.0782
+2023-03-04 19:45:37,850 - mmseg - INFO - Iter [62350/160000]	lr: 1.875e-05, eta: 8:59:25, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9120, loss: 0.0773
+2023-03-04 19:45:51,488 - mmseg - INFO - Iter [62400/160000]	lr: 1.875e-05, eta: 8:59:04, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8890, loss: 0.0785
+2023-03-04 19:46:05,075 - mmseg - INFO - Iter [62450/160000]	lr: 1.875e-05, eta: 8:58:43, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9467, loss: 0.0765
+2023-03-04 19:46:21,042 - mmseg - INFO - Iter [62500/160000]	lr: 1.875e-05, eta: 8:58:25, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9431, loss: 0.0777
+2023-03-04 19:46:34,889 - mmseg - INFO - Iter [62550/160000]	lr: 1.875e-05, eta: 8:58:04, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8695, loss: 0.0784
+2023-03-04 19:46:48,497 - mmseg - INFO - Iter [62600/160000]	lr: 1.875e-05, eta: 8:57:43, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9633, loss: 0.0767
+2023-03-04 19:47:02,104 - mmseg - INFO - Iter [62650/160000]	lr: 1.875e-05, eta: 8:57:22, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9212, loss: 0.0773
+2023-03-04 19:47:18,405 - mmseg - INFO - Iter [62700/160000]	lr: 1.875e-05, eta: 8:57:05, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9485, loss: 0.0764
+2023-03-04 19:47:32,261 - mmseg - INFO - Iter [62750/160000]	lr: 1.875e-05, eta: 8:56:44, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0819, decode.acc_seg: 96.7719, loss: 0.0819
+2023-03-04 19:47:46,055 - mmseg - INFO - Iter [62800/160000]	lr: 1.875e-05, eta: 8:56:23, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9654, loss: 0.0768
+2023-03-04 19:47:59,678 - mmseg - INFO - Iter [62850/160000]	lr: 1.875e-05, eta: 8:56:02, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9190, loss: 0.0769
+2023-03-04 19:48:15,994 - mmseg - INFO - Iter [62900/160000]	lr: 1.875e-05, eta: 8:55:45, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7981, loss: 0.0808
+2023-03-04 19:48:29,658 - mmseg - INFO - Iter [62950/160000]	lr: 1.875e-05, eta: 8:55:24, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.7757, loss: 0.0809
+2023-03-04 19:48:43,455 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 19:48:43,455 - mmseg - INFO - Iter [63000/160000]	lr: 1.875e-05, eta: 8:55:04, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8755, loss: 0.0785
+2023-03-04 19:48:57,109 - mmseg - INFO - Iter [63050/160000]	lr: 1.875e-05, eta: 8:54:43, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.7895, loss: 0.0812
+2023-03-04 19:49:13,140 - mmseg - INFO - Iter [63100/160000]	lr: 1.875e-05, eta: 8:54:25, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8828, loss: 0.0781
+2023-03-04 19:49:26,813 - mmseg - INFO - Iter [63150/160000]	lr: 1.875e-05, eta: 8:54:04, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9190, loss: 0.0774
+2023-03-04 19:49:40,439 - mmseg - INFO - Iter [63200/160000]	lr: 1.875e-05, eta: 8:53:43, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.8998, loss: 0.0776
+2023-03-04 19:49:56,356 - mmseg - INFO - Iter [63250/160000]	lr: 1.875e-05, eta: 8:53:26, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8955, loss: 0.0786
+2023-03-04 19:50:10,126 - mmseg - INFO - Iter [63300/160000]	lr: 1.875e-05, eta: 8:53:05, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8618, loss: 0.0792
+2023-03-04 19:50:23,766 - mmseg - INFO - Iter [63350/160000]	lr: 1.875e-05, eta: 8:52:44, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.7921, loss: 0.0798
+2023-03-04 19:50:37,353 - mmseg - INFO - Iter [63400/160000]	lr: 1.875e-05, eta: 8:52:23, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.8287, loss: 0.0807
+2023-03-04 19:50:53,380 - mmseg - INFO - Iter [63450/160000]	lr: 1.875e-05, eta: 8:52:06, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.9302, loss: 0.0784
+2023-03-04 19:51:07,033 - mmseg - INFO - Iter [63500/160000]	lr: 1.875e-05, eta: 8:51:45, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0753, decode.acc_seg: 97.0106, loss: 0.0753
+2023-03-04 19:51:20,682 - mmseg - INFO - Iter [63550/160000]	lr: 1.875e-05, eta: 8:51:24, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9185, loss: 0.0771
+2023-03-04 19:51:34,498 - mmseg - INFO - Iter [63600/160000]	lr: 1.875e-05, eta: 8:51:03, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9405, loss: 0.0772
+2023-03-04 19:51:50,638 - mmseg - INFO - Iter [63650/160000]	lr: 1.875e-05, eta: 8:50:46, time: 0.323, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8466, loss: 0.0793
+2023-03-04 19:52:04,182 - mmseg - INFO - Iter [63700/160000]	lr: 1.875e-05, eta: 8:50:25, time: 0.271, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8767, loss: 0.0787
+2023-03-04 19:52:17,818 - mmseg - INFO - Iter [63750/160000]	lr: 1.875e-05, eta: 8:50:04, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.7646, loss: 0.0806
+2023-03-04 19:52:33,882 - mmseg - INFO - Iter [63800/160000]	lr: 1.875e-05, eta: 8:49:47, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8605, loss: 0.0792
+2023-03-04 19:52:47,766 - mmseg - INFO - Iter [63850/160000]	lr: 1.875e-05, eta: 8:49:26, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9130, loss: 0.0775
+2023-03-04 19:53:01,369 - mmseg - INFO - Iter [63900/160000]	lr: 1.875e-05, eta: 8:49:06, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8264, loss: 0.0792
+2023-03-04 19:53:14,974 - mmseg - INFO - Iter [63950/160000]	lr: 1.875e-05, eta: 8:48:45, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8936, loss: 0.0786
+2023-03-04 19:53:31,156 - mmseg - INFO - Swap parameters (after train) after iter [64000]
+2023-03-04 19:53:31,176 - mmseg - INFO - Saving checkpoint at 64000 iterations
+2023-03-04 19:53:32,989 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 19:53:32,989 - mmseg - INFO - Iter [64000/160000]	lr: 1.875e-05, eta: 8:48:30, time: 0.361, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8887, loss: 0.0786
+2023-03-04 20:08:29,552 - mmseg - INFO - per class results:
+2023-03-04 20:08:29,554 - mmseg - INFO - 
++---------------+-------------------------------------------------------------------+
+|     Class     |                IoU 0,10,20,30,40,50,60,70,80,90,99                |
++---------------+-------------------------------------------------------------------+
+|   background  |            nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan            |
+|      road     | 98.55,98.55,98.55,98.55,98.55,98.55,98.55,98.55,98.55,98.55,98.55 |
+|    sidewalk   | 87.45,87.45,87.45,87.46,87.47,87.47,87.45,87.47,87.46,87.45,87.47 |
+|    building   | 93.61,93.61,93.61,93.61,93.61,93.61,93.61,93.61,93.61,93.61,93.61 |
+|      wall     | 54.61,54.66,54.69,54.72,54.74,54.76,54.76,54.75,54.81,54.81,54.85 |
+|     fence     |   65.27,65.29,65.3,65.3,65.27,65.28,65.29,65.27,65.3,65.28,65.28  |
+|      pole     |   71.28,71.29,71.28,71.3,71.31,71.3,71.3,71.31,71.31,71.31,71.31  |
+| traffic light |  75.53,75.54,75.55,75.54,75.54,75.54,75.51,75.52,75.53,75.54,75.5 |
+|  traffic sign |  82.67,82.68,82.68,82.68,82.69,82.69,82.69,82.69,82.7,82.7,82.69  |
+|   vegetation  |     93.09,93.09,93.09,93.1,93.1,93.1,93.1,93.1,93.1,93.1,93.1     |
+|    terrain    |  64.71,64.69,64.76,64.8,64.81,64.84,64.82,64.82,64.85,64.87,64.84 |
+|      sky      |  95.29,95.29,95.28,95.29,95.29,95.29,95.29,95.29,95.29,95.3,95.29 |
+|     person    |    85.0,85.0,85.0,85.0,84.99,84.99,84.99,84.99,84.99,84.98,85.0   |
+|     rider     |   67.89,67.9,67.9,67.88,67.88,67.88,67.9,67.89,67.89,67.86,67.92  |
+|      car      | 96.07,96.07,96.07,96.07,96.07,96.07,96.08,96.07,96.07,96.08,96.08 |
+|     truck     |   86.46,86.49,86.47,86.5,86.5,86.5,86.57,86.56,86.54,86.59,86.57  |
+|      bus      | 92.34,92.36,92.34,92.36,92.33,92.33,92.35,92.35,92.35,92.35,92.36 |
+|     train     |  85.57,85.61,85.58,85.6,85.61,85.62,85.62,85.65,85.65,85.69,85.69 |
+|   motorcycle  |  72.07,72.13,72.14,72.09,72.1,72.11,72.15,72.14,72.07,72.07,72.14 |
+|    bicycle    | 80.54,80.55,80.55,80.55,80.56,80.56,80.56,80.56,80.56,80.56,80.58 |
++---------------+-------------------------------------------------------------------+
+2023-03-04 20:08:29,554 - mmseg - INFO - Summary:
+2023-03-04 20:08:29,554 - mmseg - INFO - 
++----------------------------------------------------------------+
+|              mIoU 0,10,20,30,40,50,60,70,80,90,99              |
++----------------------------------------------------------------+
+| 81.47,81.49,81.49,81.49,81.5,81.5,81.5,81.51,81.51,81.51,81.52 |
++----------------------------------------------------------------+
+2023-03-04 20:08:29,614 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune/best_mIoU_iter_48000.pth was removed
+2023-03-04 20:08:31,371 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_64000.pth.
+2023-03-04 20:08:31,372 - mmseg - INFO - Best mIoU is 0.8152 at 64000 iter.
+2023-03-04 20:08:31,372 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 20:08:31,372 - mmseg - INFO - Iter(val) [63]	mIoU: [0.8147, 0.8149, 0.8149, 0.8149, 0.815, 0.815, 0.815, 0.8151, 0.8151, 0.8151, 0.8152], copy_paste: 81.47,81.49,81.49,81.49,81.5,81.5,81.5,81.51,81.51,81.51,81.52
+2023-03-04 20:08:31,378 - mmseg - INFO - Swap parameters (before train) before iter [64001]
+2023-03-04 20:08:45,488 - mmseg - INFO - Iter [64050/160000]	lr: 1.875e-05, eta: 9:10:36, time: 18.250, data_time: 17.977, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8374, loss: 0.0797
+2023-03-04 20:08:59,308 - mmseg - INFO - Iter [64100/160000]	lr: 1.875e-05, eta: 9:10:14, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.8222, loss: 0.0807
+2023-03-04 20:09:13,159 - mmseg - INFO - Iter [64150/160000]	lr: 1.875e-05, eta: 9:09:52, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8189, loss: 0.0795
+2023-03-04 20:09:29,215 - mmseg - INFO - Iter [64200/160000]	lr: 1.875e-05, eta: 9:09:33, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8404, loss: 0.0784
+2023-03-04 20:09:42,860 - mmseg - INFO - Iter [64250/160000]	lr: 1.875e-05, eta: 9:09:10, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8509, loss: 0.0789
+2023-03-04 20:09:56,453 - mmseg - INFO - Iter [64300/160000]	lr: 1.875e-05, eta: 9:08:48, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9316, loss: 0.0763
+2023-03-04 20:10:10,043 - mmseg - INFO - Iter [64350/160000]	lr: 1.875e-05, eta: 9:08:25, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8450, loss: 0.0796
+2023-03-04 20:10:25,983 - mmseg - INFO - Iter [64400/160000]	lr: 1.875e-05, eta: 9:08:06, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8812, loss: 0.0783
+2023-03-04 20:10:39,706 - mmseg - INFO - Iter [64450/160000]	lr: 1.875e-05, eta: 9:07:44, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8103, loss: 0.0798
+2023-03-04 20:10:53,446 - mmseg - INFO - Iter [64500/160000]	lr: 1.875e-05, eta: 9:07:21, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9222, loss: 0.0771
+2023-03-04 20:11:09,395 - mmseg - INFO - Iter [64550/160000]	lr: 1.875e-05, eta: 9:07:02, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0827, decode.acc_seg: 96.7355, loss: 0.0827
+2023-03-04 20:11:23,335 - mmseg - INFO - Iter [64600/160000]	lr: 1.875e-05, eta: 9:06:40, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.8398, loss: 0.0809
+2023-03-04 20:11:37,227 - mmseg - INFO - Iter [64650/160000]	lr: 1.875e-05, eta: 9:06:18, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8019, loss: 0.0799
+2023-03-04 20:11:51,052 - mmseg - INFO - Iter [64700/160000]	lr: 1.875e-05, eta: 9:05:56, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.7611, loss: 0.0806
+2023-03-04 20:12:07,326 - mmseg - INFO - Iter [64750/160000]	lr: 1.875e-05, eta: 9:05:37, time: 0.326, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.8185, loss: 0.0806
+2023-03-04 20:12:21,001 - mmseg - INFO - Iter [64800/160000]	lr: 1.875e-05, eta: 9:05:15, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8310, loss: 0.0797
+2023-03-04 20:12:34,718 - mmseg - INFO - Iter [64850/160000]	lr: 1.875e-05, eta: 9:04:53, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8654, loss: 0.0781
+2023-03-04 20:12:48,550 - mmseg - INFO - Iter [64900/160000]	lr: 1.875e-05, eta: 9:04:31, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.8233, loss: 0.0805
+2023-03-04 20:13:04,580 - mmseg - INFO - Iter [64950/160000]	lr: 1.875e-05, eta: 9:04:12, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0749, decode.acc_seg: 96.9884, loss: 0.0749
+2023-03-04 20:13:18,212 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 20:13:18,212 - mmseg - INFO - Iter [65000/160000]	lr: 1.875e-05, eta: 9:03:50, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7669, loss: 0.0816
+2023-03-04 20:13:31,956 - mmseg - INFO - Iter [65050/160000]	lr: 1.875e-05, eta: 9:03:27, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8249, loss: 0.0793
+2023-03-04 20:13:45,702 - mmseg - INFO - Iter [65100/160000]	lr: 1.875e-05, eta: 9:03:05, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0756, decode.acc_seg: 96.9843, loss: 0.0756
+2023-03-04 20:14:01,813 - mmseg - INFO - Iter [65150/160000]	lr: 1.875e-05, eta: 9:02:46, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7446, loss: 0.0813
+2023-03-04 20:14:15,488 - mmseg - INFO - Iter [65200/160000]	lr: 1.875e-05, eta: 9:02:24, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.7896, loss: 0.0794
+2023-03-04 20:14:29,071 - mmseg - INFO - Iter [65250/160000]	lr: 1.875e-05, eta: 9:02:02, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.9009, loss: 0.0787
+2023-03-04 20:14:45,191 - mmseg - INFO - Iter [65300/160000]	lr: 1.875e-05, eta: 9:01:43, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.8227, loss: 0.0811
+2023-03-04 20:14:59,064 - mmseg - INFO - Iter [65350/160000]	lr: 1.875e-05, eta: 9:01:21, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9878, loss: 0.0760
+2023-03-04 20:15:12,755 - mmseg - INFO - Iter [65400/160000]	lr: 1.875e-05, eta: 9:00:59, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8088, loss: 0.0800
+2023-03-04 20:15:26,468 - mmseg - INFO - Iter [65450/160000]	lr: 1.875e-05, eta: 9:00:37, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8967, loss: 0.0781
+2023-03-04 20:15:42,798 - mmseg - INFO - Iter [65500/160000]	lr: 1.875e-05, eta: 9:00:19, time: 0.327, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8527, loss: 0.0789
+2023-03-04 20:15:56,655 - mmseg - INFO - Iter [65550/160000]	lr: 1.875e-05, eta: 8:59:57, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.8213, loss: 0.0807
+2023-03-04 20:16:10,260 - mmseg - INFO - Iter [65600/160000]	lr: 1.875e-05, eta: 8:59:34, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8534, loss: 0.0797
+2023-03-04 20:16:24,306 - mmseg - INFO - Iter [65650/160000]	lr: 1.875e-05, eta: 8:59:13, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0747, decode.acc_seg: 97.0180, loss: 0.0747
+2023-03-04 20:16:40,210 - mmseg - INFO - Iter [65700/160000]	lr: 1.875e-05, eta: 8:58:54, time: 0.318, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9251, loss: 0.0773
+2023-03-04 20:16:53,912 - mmseg - INFO - Iter [65750/160000]	lr: 1.875e-05, eta: 8:58:32, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9371, loss: 0.0771
+2023-03-04 20:17:07,490 - mmseg - INFO - Iter [65800/160000]	lr: 1.875e-05, eta: 8:58:10, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8584, loss: 0.0791
+2023-03-04 20:17:23,613 - mmseg - INFO - Iter [65850/160000]	lr: 1.875e-05, eta: 8:57:51, time: 0.322, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7707, loss: 0.0808
+2023-03-04 20:17:37,274 - mmseg - INFO - Iter [65900/160000]	lr: 1.875e-05, eta: 8:57:29, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8035, loss: 0.0799
+2023-03-04 20:17:50,943 - mmseg - INFO - Iter [65950/160000]	lr: 1.875e-05, eta: 8:57:07, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.8157, loss: 0.0811
+2023-03-04 20:18:04,736 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 20:18:04,736 - mmseg - INFO - Iter [66000/160000]	lr: 1.875e-05, eta: 8:56:45, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9141, loss: 0.0778
+2023-03-04 20:18:20,827 - mmseg - INFO - Iter [66050/160000]	lr: 1.875e-05, eta: 8:56:26, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8714, loss: 0.0783
+2023-03-04 20:18:34,495 - mmseg - INFO - Iter [66100/160000]	lr: 1.875e-05, eta: 8:56:04, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.8537, loss: 0.0802
+2023-03-04 20:18:48,218 - mmseg - INFO - Iter [66150/160000]	lr: 1.875e-05, eta: 8:55:42, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8725, loss: 0.0780
+2023-03-04 20:19:01,870 - mmseg - INFO - Iter [66200/160000]	lr: 1.875e-05, eta: 8:55:20, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.8106, loss: 0.0808
+2023-03-04 20:19:17,832 - mmseg - INFO - Iter [66250/160000]	lr: 1.875e-05, eta: 8:55:02, time: 0.319, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9191, loss: 0.0780
+2023-03-04 20:19:31,570 - mmseg - INFO - Iter [66300/160000]	lr: 1.875e-05, eta: 8:54:40, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8452, loss: 0.0785
+2023-03-04 20:19:45,209 - mmseg - INFO - Iter [66350/160000]	lr: 1.875e-05, eta: 8:54:18, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8346, loss: 0.0800
+2023-03-04 20:19:58,785 - mmseg - INFO - Iter [66400/160000]	lr: 1.875e-05, eta: 8:53:55, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0853, decode.acc_seg: 96.6080, loss: 0.0853
+2023-03-04 20:20:14,966 - mmseg - INFO - Iter [66450/160000]	lr: 1.875e-05, eta: 8:53:37, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8935, loss: 0.0782
+2023-03-04 20:20:28,542 - mmseg - INFO - Iter [66500/160000]	lr: 1.875e-05, eta: 8:53:15, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9790, loss: 0.0764
+2023-03-04 20:20:42,140 - mmseg - INFO - Iter [66550/160000]	lr: 1.875e-05, eta: 8:52:53, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9199, loss: 0.0765
+2023-03-04 20:20:58,131 - mmseg - INFO - Iter [66600/160000]	lr: 1.875e-05, eta: 8:52:34, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9192, loss: 0.0777
+2023-03-04 20:21:11,903 - mmseg - INFO - Iter [66650/160000]	lr: 1.875e-05, eta: 8:52:12, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9760, loss: 0.0762
+2023-03-04 20:21:25,698 - mmseg - INFO - Iter [66700/160000]	lr: 1.875e-05, eta: 8:51:51, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8505, loss: 0.0786
+2023-03-04 20:21:39,459 - mmseg - INFO - Iter [66750/160000]	lr: 1.875e-05, eta: 8:51:29, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8885, loss: 0.0784
+2023-03-04 20:21:55,490 - mmseg - INFO - Iter [66800/160000]	lr: 1.875e-05, eta: 8:51:10, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9474, loss: 0.0770
+2023-03-04 20:22:09,343 - mmseg - INFO - Iter [66850/160000]	lr: 1.875e-05, eta: 8:50:49, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8028, loss: 0.0800
+2023-03-04 20:22:23,168 - mmseg - INFO - Iter [66900/160000]	lr: 1.875e-05, eta: 8:50:27, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8442, loss: 0.0788
+2023-03-04 20:22:36,941 - mmseg - INFO - Iter [66950/160000]	lr: 1.875e-05, eta: 8:50:05, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.8581, loss: 0.0777
+2023-03-04 20:22:52,933 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 20:22:52,933 - mmseg - INFO - Iter [67000/160000]	lr: 1.875e-05, eta: 8:49:47, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8246, loss: 0.0796
+2023-03-04 20:23:06,640 - mmseg - INFO - Iter [67050/160000]	lr: 1.875e-05, eta: 8:49:25, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8885, loss: 0.0787
+2023-03-04 20:23:20,203 - mmseg - INFO - Iter [67100/160000]	lr: 1.875e-05, eta: 8:49:03, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0879, decode.acc_seg: 96.6342, loss: 0.0879
+2023-03-04 20:23:36,296 - mmseg - INFO - Iter [67150/160000]	lr: 1.875e-05, eta: 8:48:45, time: 0.322, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.8412, loss: 0.0802
+2023-03-04 20:23:50,155 - mmseg - INFO - Iter [67200/160000]	lr: 1.875e-05, eta: 8:48:23, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0735, decode.acc_seg: 97.0405, loss: 0.0735
+2023-03-04 20:24:03,794 - mmseg - INFO - Iter [67250/160000]	lr: 1.875e-05, eta: 8:48:01, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8724, loss: 0.0786
+2023-03-04 20:24:17,509 - mmseg - INFO - Iter [67300/160000]	lr: 1.875e-05, eta: 8:47:40, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9553, loss: 0.0768
+2023-03-04 20:24:33,889 - mmseg - INFO - Iter [67350/160000]	lr: 1.875e-05, eta: 8:47:21, time: 0.328, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9694, loss: 0.0762
+2023-03-04 20:24:47,572 - mmseg - INFO - Iter [67400/160000]	lr: 1.875e-05, eta: 8:47:00, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.8960, loss: 0.0776
+2023-03-04 20:25:01,225 - mmseg - INFO - Iter [67450/160000]	lr: 1.875e-05, eta: 8:46:38, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8622, loss: 0.0785
+2023-03-04 20:25:15,071 - mmseg - INFO - Iter [67500/160000]	lr: 1.875e-05, eta: 8:46:16, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8926, loss: 0.0786
+2023-03-04 20:25:31,252 - mmseg - INFO - Iter [67550/160000]	lr: 1.875e-05, eta: 8:45:58, time: 0.324, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8835, loss: 0.0789
+2023-03-04 20:25:44,894 - mmseg - INFO - Iter [67600/160000]	lr: 1.875e-05, eta: 8:45:36, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8051, loss: 0.0793
+2023-03-04 20:25:59,090 - mmseg - INFO - Iter [67650/160000]	lr: 1.875e-05, eta: 8:45:15, time: 0.284, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8541, loss: 0.0783
+2023-03-04 20:26:12,894 - mmseg - INFO - Iter [67700/160000]	lr: 1.875e-05, eta: 8:44:54, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7611, loss: 0.0814
+2023-03-04 20:26:29,218 - mmseg - INFO - Iter [67750/160000]	lr: 1.875e-05, eta: 8:44:36, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0840, decode.acc_seg: 96.7658, loss: 0.0840
+2023-03-04 20:26:42,969 - mmseg - INFO - Iter [67800/160000]	lr: 1.875e-05, eta: 8:44:14, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8847, loss: 0.0783
+2023-03-04 20:26:56,909 - mmseg - INFO - Iter [67850/160000]	lr: 1.875e-05, eta: 8:43:53, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8823, loss: 0.0787
+2023-03-04 20:27:13,056 - mmseg - INFO - Iter [67900/160000]	lr: 1.875e-05, eta: 8:43:35, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8263, loss: 0.0804
+2023-03-04 20:27:26,670 - mmseg - INFO - Iter [67950/160000]	lr: 1.875e-05, eta: 8:43:13, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7627, loss: 0.0810
+2023-03-04 20:27:40,315 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 20:27:40,316 - mmseg - INFO - Iter [68000/160000]	lr: 1.875e-05, eta: 8:42:51, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.9055, loss: 0.0781
+2023-03-04 20:27:54,014 - mmseg - INFO - Iter [68050/160000]	lr: 1.875e-05, eta: 8:42:30, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8913, loss: 0.0783
+2023-03-04 20:28:10,001 - mmseg - INFO - Iter [68100/160000]	lr: 1.875e-05, eta: 8:42:11, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8981, loss: 0.0782
+2023-03-04 20:28:23,649 - mmseg - INFO - Iter [68150/160000]	lr: 1.875e-05, eta: 8:41:50, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8437, loss: 0.0794
+2023-03-04 20:28:37,244 - mmseg - INFO - Iter [68200/160000]	lr: 1.875e-05, eta: 8:41:28, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8445, loss: 0.0787
+2023-03-04 20:28:51,088 - mmseg - INFO - Iter [68250/160000]	lr: 1.875e-05, eta: 8:41:07, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8995, loss: 0.0782
+2023-03-04 20:29:07,408 - mmseg - INFO - Iter [68300/160000]	lr: 1.875e-05, eta: 8:40:49, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8725, loss: 0.0793
+2023-03-04 20:29:21,029 - mmseg - INFO - Iter [68350/160000]	lr: 1.875e-05, eta: 8:40:27, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.7690, loss: 0.0801
+2023-03-04 20:29:34,730 - mmseg - INFO - Iter [68400/160000]	lr: 1.875e-05, eta: 8:40:06, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8183, loss: 0.0798
+2023-03-04 20:29:50,743 - mmseg - INFO - Iter [68450/160000]	lr: 1.875e-05, eta: 8:39:47, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9963, loss: 0.0758
+2023-03-04 20:30:04,439 - mmseg - INFO - Iter [68500/160000]	lr: 1.875e-05, eta: 8:39:26, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7833, loss: 0.0810
+2023-03-04 20:30:18,278 - mmseg - INFO - Iter [68550/160000]	lr: 1.875e-05, eta: 8:39:04, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8753, loss: 0.0783
+2023-03-04 20:30:31,994 - mmseg - INFO - Iter [68600/160000]	lr: 1.875e-05, eta: 8:38:43, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8782, loss: 0.0787
+2023-03-04 20:30:47,991 - mmseg - INFO - Iter [68650/160000]	lr: 1.875e-05, eta: 8:38:25, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9350, loss: 0.0767
+2023-03-04 20:31:01,638 - mmseg - INFO - Iter [68700/160000]	lr: 1.875e-05, eta: 8:38:03, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7613, loss: 0.0816
+2023-03-04 20:31:15,374 - mmseg - INFO - Iter [68750/160000]	lr: 1.875e-05, eta: 8:37:42, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9623, loss: 0.0758
+2023-03-04 20:31:29,038 - mmseg - INFO - Iter [68800/160000]	lr: 1.875e-05, eta: 8:37:20, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8694, loss: 0.0786
+2023-03-04 20:31:45,032 - mmseg - INFO - Iter [68850/160000]	lr: 1.875e-05, eta: 8:37:02, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8706, loss: 0.0793
+2023-03-04 20:31:58,921 - mmseg - INFO - Iter [68900/160000]	lr: 1.875e-05, eta: 8:36:41, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8999, loss: 0.0784
+2023-03-04 20:32:12,493 - mmseg - INFO - Iter [68950/160000]	lr: 1.875e-05, eta: 8:36:19, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.8608, loss: 0.0802
+2023-03-04 20:32:26,145 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 20:32:26,145 - mmseg - INFO - Iter [69000/160000]	lr: 1.875e-05, eta: 8:35:58, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8645, loss: 0.0780
+2023-03-04 20:32:42,208 - mmseg - INFO - Iter [69050/160000]	lr: 1.875e-05, eta: 8:35:39, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7398, loss: 0.0814
+2023-03-04 20:32:55,831 - mmseg - INFO - Iter [69100/160000]	lr: 1.875e-05, eta: 8:35:18, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9049, loss: 0.0774
+2023-03-04 20:33:09,569 - mmseg - INFO - Iter [69150/160000]	lr: 1.875e-05, eta: 8:34:57, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8244, loss: 0.0803
+2023-03-04 20:33:25,540 - mmseg - INFO - Iter [69200/160000]	lr: 1.875e-05, eta: 8:34:38, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9029, loss: 0.0780
+2023-03-04 20:33:39,228 - mmseg - INFO - Iter [69250/160000]	lr: 1.875e-05, eta: 8:34:17, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9640, loss: 0.0762
+2023-03-04 20:33:52,950 - mmseg - INFO - Iter [69300/160000]	lr: 1.875e-05, eta: 8:33:56, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8392, loss: 0.0798
+2023-03-04 20:34:06,587 - mmseg - INFO - Iter [69350/160000]	lr: 1.875e-05, eta: 8:33:34, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8285, loss: 0.0800
+2023-03-04 20:34:22,576 - mmseg - INFO - Iter [69400/160000]	lr: 1.875e-05, eta: 8:33:16, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.7936, loss: 0.0798
+2023-03-04 20:34:36,386 - mmseg - INFO - Iter [69450/160000]	lr: 1.875e-05, eta: 8:32:55, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9099, loss: 0.0777
+2023-03-04 20:34:50,113 - mmseg - INFO - Iter [69500/160000]	lr: 1.875e-05, eta: 8:32:33, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9610, loss: 0.0762
+2023-03-04 20:35:04,021 - mmseg - INFO - Iter [69550/160000]	lr: 1.875e-05, eta: 8:32:12, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.9245, loss: 0.0782
+2023-03-04 20:35:20,071 - mmseg - INFO - Iter [69600/160000]	lr: 1.875e-05, eta: 8:31:54, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8734, loss: 0.0785
+2023-03-04 20:35:33,707 - mmseg - INFO - Iter [69650/160000]	lr: 1.875e-05, eta: 8:31:33, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0829, decode.acc_seg: 96.7178, loss: 0.0829
+2023-03-04 20:35:47,548 - mmseg - INFO - Iter [69700/160000]	lr: 1.875e-05, eta: 8:31:12, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9754, loss: 0.0762
+2023-03-04 20:36:01,448 - mmseg - INFO - Iter [69750/160000]	lr: 1.875e-05, eta: 8:30:51, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9001, loss: 0.0779
+2023-03-04 20:36:17,438 - mmseg - INFO - Iter [69800/160000]	lr: 1.875e-05, eta: 8:30:33, time: 0.320, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9596, loss: 0.0762
+2023-03-04 20:36:31,238 - mmseg - INFO - Iter [69850/160000]	lr: 1.875e-05, eta: 8:30:12, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8793, loss: 0.0787
+2023-03-04 20:36:44,888 - mmseg - INFO - Iter [69900/160000]	lr: 1.875e-05, eta: 8:29:50, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 96.9789, loss: 0.0755
+2023-03-04 20:37:01,349 - mmseg - INFO - Iter [69950/160000]	lr: 1.875e-05, eta: 8:29:33, time: 0.329, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9247, loss: 0.0771
+2023-03-04 20:37:15,426 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 20:37:15,426 - mmseg - INFO - Iter [70000/160000]	lr: 1.875e-05, eta: 8:29:12, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8440, loss: 0.0793
+2023-03-04 20:37:29,203 - mmseg - INFO - Iter [70050/160000]	lr: 1.875e-05, eta: 8:28:51, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8140, loss: 0.0795
+2023-03-04 20:37:42,993 - mmseg - INFO - Iter [70100/160000]	lr: 1.875e-05, eta: 8:28:30, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8598, loss: 0.0795
+2023-03-04 20:37:59,047 - mmseg - INFO - Iter [70150/160000]	lr: 1.875e-05, eta: 8:28:12, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8100, loss: 0.0797
+2023-03-04 20:38:12,671 - mmseg - INFO - Iter [70200/160000]	lr: 1.875e-05, eta: 8:27:50, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8343, loss: 0.0785
+2023-03-04 20:38:26,414 - mmseg - INFO - Iter [70250/160000]	lr: 1.875e-05, eta: 8:27:29, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.8093, loss: 0.0802
+2023-03-04 20:38:40,458 - mmseg - INFO - Iter [70300/160000]	lr: 1.875e-05, eta: 8:27:09, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8591, loss: 0.0779
+2023-03-04 20:38:56,420 - mmseg - INFO - Iter [70350/160000]	lr: 1.875e-05, eta: 8:26:50, time: 0.320, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9582, loss: 0.0760
+2023-03-04 20:39:10,009 - mmseg - INFO - Iter [70400/160000]	lr: 1.875e-05, eta: 8:26:29, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0824, decode.acc_seg: 96.7434, loss: 0.0824
+2023-03-04 20:39:23,743 - mmseg - INFO - Iter [70450/160000]	lr: 1.875e-05, eta: 8:26:08, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8798, loss: 0.0786
+2023-03-04 20:39:39,830 - mmseg - INFO - Iter [70500/160000]	lr: 1.875e-05, eta: 8:25:50, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 96.9515, loss: 0.0754
+2023-03-04 20:39:53,480 - mmseg - INFO - Iter [70550/160000]	lr: 1.875e-05, eta: 8:25:29, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0743, decode.acc_seg: 96.9959, loss: 0.0743
+2023-03-04 20:40:07,124 - mmseg - INFO - Iter [70600/160000]	lr: 1.875e-05, eta: 8:25:08, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9540, loss: 0.0774
+2023-03-04 20:40:20,722 - mmseg - INFO - Iter [70650/160000]	lr: 1.875e-05, eta: 8:24:47, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8869, loss: 0.0787
+2023-03-04 20:40:36,639 - mmseg - INFO - Iter [70700/160000]	lr: 1.875e-05, eta: 8:24:28, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8790, loss: 0.0780
+2023-03-04 20:40:50,251 - mmseg - INFO - Iter [70750/160000]	lr: 1.875e-05, eta: 8:24:07, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.8689, loss: 0.0772
+2023-03-04 20:41:03,835 - mmseg - INFO - Iter [70800/160000]	lr: 1.875e-05, eta: 8:23:46, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9797, loss: 0.0760
+2023-03-04 20:41:17,511 - mmseg - INFO - Iter [70850/160000]	lr: 1.875e-05, eta: 8:23:25, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0830, decode.acc_seg: 96.7291, loss: 0.0830
+2023-03-04 20:41:33,421 - mmseg - INFO - Iter [70900/160000]	lr: 1.875e-05, eta: 8:23:07, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7805, loss: 0.0810
+2023-03-04 20:41:47,212 - mmseg - INFO - Iter [70950/160000]	lr: 1.875e-05, eta: 8:22:46, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8246, loss: 0.0800
+2023-03-04 20:42:00,937 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 20:42:00,937 - mmseg - INFO - Iter [71000/160000]	lr: 1.875e-05, eta: 8:22:25, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8676, loss: 0.0784
+2023-03-04 20:42:14,502 - mmseg - INFO - Iter [71050/160000]	lr: 1.875e-05, eta: 8:22:04, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8790, loss: 0.0785
+2023-03-04 20:42:30,653 - mmseg - INFO - Iter [71100/160000]	lr: 1.875e-05, eta: 8:21:46, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.9029, loss: 0.0785
+2023-03-04 20:42:44,352 - mmseg - INFO - Iter [71150/160000]	lr: 1.875e-05, eta: 8:21:25, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8511, loss: 0.0788
+2023-03-04 20:42:58,253 - mmseg - INFO - Iter [71200/160000]	lr: 1.875e-05, eta: 8:21:04, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8277, loss: 0.0787
+2023-03-04 20:43:14,310 - mmseg - INFO - Iter [71250/160000]	lr: 1.875e-05, eta: 8:20:46, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9374, loss: 0.0769
+2023-03-04 20:43:28,015 - mmseg - INFO - Iter [71300/160000]	lr: 1.875e-05, eta: 8:20:25, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9061, loss: 0.0774
+2023-03-04 20:43:42,868 - mmseg - INFO - Iter [71350/160000]	lr: 1.875e-05, eta: 8:20:06, time: 0.297, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.7612, loss: 0.0805
+2023-03-04 20:43:57,117 - mmseg - INFO - Iter [71400/160000]	lr: 1.875e-05, eta: 8:19:45, time: 0.285, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.8912, loss: 0.0771
+2023-03-04 20:44:13,074 - mmseg - INFO - Iter [71450/160000]	lr: 1.875e-05, eta: 8:19:27, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9364, loss: 0.0763
+2023-03-04 20:44:27,120 - mmseg - INFO - Iter [71500/160000]	lr: 1.875e-05, eta: 8:19:07, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8406, loss: 0.0787
+2023-03-04 20:44:40,831 - mmseg - INFO - Iter [71550/160000]	lr: 1.875e-05, eta: 8:18:46, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9026, loss: 0.0774
+2023-03-04 20:44:54,496 - mmseg - INFO - Iter [71600/160000]	lr: 1.875e-05, eta: 8:18:25, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9222, loss: 0.0779
+2023-03-04 20:45:10,684 - mmseg - INFO - Iter [71650/160000]	lr: 1.875e-05, eta: 8:18:07, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.9012, loss: 0.0782
+2023-03-04 20:45:24,674 - mmseg - INFO - Iter [71700/160000]	lr: 1.875e-05, eta: 8:17:47, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8040, loss: 0.0796
+2023-03-04 20:45:38,398 - mmseg - INFO - Iter [71750/160000]	lr: 1.875e-05, eta: 8:17:26, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7761, loss: 0.0808
+2023-03-04 20:45:54,556 - mmseg - INFO - Iter [71800/160000]	lr: 1.875e-05, eta: 8:17:08, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9502, loss: 0.0761
+2023-03-04 20:46:08,158 - mmseg - INFO - Iter [71850/160000]	lr: 1.875e-05, eta: 8:16:47, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9367, loss: 0.0761
+2023-03-04 20:46:21,798 - mmseg - INFO - Iter [71900/160000]	lr: 1.875e-05, eta: 8:16:26, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9039, loss: 0.0768
+2023-03-04 20:46:35,546 - mmseg - INFO - Iter [71950/160000]	lr: 1.875e-05, eta: 8:16:05, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8107, loss: 0.0801
+2023-03-04 20:46:51,551 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 20:46:51,551 - mmseg - INFO - Iter [72000/160000]	lr: 1.875e-05, eta: 8:15:47, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0751, decode.acc_seg: 96.9678, loss: 0.0751
+2023-03-04 20:47:05,103 - mmseg - INFO - Iter [72050/160000]	lr: 1.875e-05, eta: 8:15:26, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8859, loss: 0.0798
+2023-03-04 20:47:18,748 - mmseg - INFO - Iter [72100/160000]	lr: 1.875e-05, eta: 8:15:05, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 96.9856, loss: 0.0752
+2023-03-04 20:47:32,463 - mmseg - INFO - Iter [72150/160000]	lr: 1.875e-05, eta: 8:14:45, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8638, loss: 0.0797
+2023-03-04 20:47:48,358 - mmseg - INFO - Iter [72200/160000]	lr: 1.875e-05, eta: 8:14:27, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.9147, loss: 0.0786
+2023-03-04 20:48:01,994 - mmseg - INFO - Iter [72250/160000]	lr: 1.875e-05, eta: 8:14:06, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8785, loss: 0.0784
+2023-03-04 20:48:15,586 - mmseg - INFO - Iter [72300/160000]	lr: 1.875e-05, eta: 8:13:45, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8102, loss: 0.0799
+2023-03-04 20:48:29,398 - mmseg - INFO - Iter [72350/160000]	lr: 1.875e-05, eta: 8:13:24, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8277, loss: 0.0797
+2023-03-04 20:48:45,453 - mmseg - INFO - Iter [72400/160000]	lr: 1.875e-05, eta: 8:13:06, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9526, loss: 0.0764
+2023-03-04 20:48:59,405 - mmseg - INFO - Iter [72450/160000]	lr: 1.875e-05, eta: 8:12:46, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9088, loss: 0.0771
+2023-03-04 20:49:13,094 - mmseg - INFO - Iter [72500/160000]	lr: 1.875e-05, eta: 8:12:25, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.8020, loss: 0.0802
+2023-03-04 20:49:29,484 - mmseg - INFO - Iter [72550/160000]	lr: 1.875e-05, eta: 8:12:08, time: 0.328, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0748, decode.acc_seg: 96.9923, loss: 0.0748
+2023-03-04 20:49:43,085 - mmseg - INFO - Iter [72600/160000]	lr: 1.875e-05, eta: 8:11:47, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8852, loss: 0.0793
+2023-03-04 20:49:56,794 - mmseg - INFO - Iter [72650/160000]	lr: 1.875e-05, eta: 8:11:26, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8161, loss: 0.0793
+2023-03-04 20:50:10,621 - mmseg - INFO - Iter [72700/160000]	lr: 1.875e-05, eta: 8:11:06, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8590, loss: 0.0791
+2023-03-04 20:50:26,916 - mmseg - INFO - Iter [72750/160000]	lr: 1.875e-05, eta: 8:10:48, time: 0.326, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8887, loss: 0.0787
+2023-03-04 20:50:40,633 - mmseg - INFO - Iter [72800/160000]	lr: 1.875e-05, eta: 8:10:27, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8740, loss: 0.0783
+2023-03-04 20:50:55,102 - mmseg - INFO - Iter [72850/160000]	lr: 1.875e-05, eta: 8:10:08, time: 0.289, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.7968, loss: 0.0799
+2023-03-04 20:51:08,740 - mmseg - INFO - Iter [72900/160000]	lr: 1.875e-05, eta: 8:09:47, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.8824, loss: 0.0778
+2023-03-04 20:51:24,775 - mmseg - INFO - Iter [72950/160000]	lr: 1.875e-05, eta: 8:09:29, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9281, loss: 0.0776
+2023-03-04 20:51:38,435 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 20:51:38,435 - mmseg - INFO - Iter [73000/160000]	lr: 1.875e-05, eta: 8:09:08, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.9335, loss: 0.0787
+2023-03-04 20:51:52,197 - mmseg - INFO - Iter [73050/160000]	lr: 1.875e-05, eta: 8:08:48, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.9000, loss: 0.0783
+2023-03-04 20:52:08,707 - mmseg - INFO - Iter [73100/160000]	lr: 1.875e-05, eta: 8:08:30, time: 0.330, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9462, loss: 0.0767
+2023-03-04 20:52:22,499 - mmseg - INFO - Iter [73150/160000]	lr: 1.875e-05, eta: 8:08:10, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8374, loss: 0.0781
+2023-03-04 20:52:36,387 - mmseg - INFO - Iter [73200/160000]	lr: 1.875e-05, eta: 8:07:50, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.7877, loss: 0.0812
+2023-03-04 20:52:49,998 - mmseg - INFO - Iter [73250/160000]	lr: 1.875e-05, eta: 8:07:29, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8461, loss: 0.0795
+2023-03-04 20:53:06,165 - mmseg - INFO - Iter [73300/160000]	lr: 1.875e-05, eta: 8:07:11, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8789, loss: 0.0787
+2023-03-04 20:53:20,247 - mmseg - INFO - Iter [73350/160000]	lr: 1.875e-05, eta: 8:06:51, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8196, loss: 0.0800
+2023-03-04 20:53:34,077 - mmseg - INFO - Iter [73400/160000]	lr: 1.875e-05, eta: 8:06:31, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9037, loss: 0.0779
+2023-03-04 20:53:47,645 - mmseg - INFO - Iter [73450/160000]	lr: 1.875e-05, eta: 8:06:10, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8461, loss: 0.0799
+2023-03-04 20:54:03,799 - mmseg - INFO - Iter [73500/160000]	lr: 1.875e-05, eta: 8:05:52, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8551, loss: 0.0794
+2023-03-04 20:54:17,894 - mmseg - INFO - Iter [73550/160000]	lr: 1.875e-05, eta: 8:05:32, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.8182, loss: 0.0805
+2023-03-04 20:54:31,480 - mmseg - INFO - Iter [73600/160000]	lr: 1.875e-05, eta: 8:05:11, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0833, decode.acc_seg: 96.6735, loss: 0.0833
+2023-03-04 20:54:45,109 - mmseg - INFO - Iter [73650/160000]	lr: 1.875e-05, eta: 8:04:51, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 97.0147, loss: 0.0752
+2023-03-04 20:55:01,237 - mmseg - INFO - Iter [73700/160000]	lr: 1.875e-05, eta: 8:04:33, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0727, decode.acc_seg: 97.0794, loss: 0.0727
+2023-03-04 20:55:14,998 - mmseg - INFO - Iter [73750/160000]	lr: 1.875e-05, eta: 8:04:13, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7642, loss: 0.0814
+2023-03-04 20:55:28,702 - mmseg - INFO - Iter [73800/160000]	lr: 1.875e-05, eta: 8:03:52, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7895, loss: 0.0810
+2023-03-04 20:55:44,836 - mmseg - INFO - Iter [73850/160000]	lr: 1.875e-05, eta: 8:03:34, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9645, loss: 0.0766
+2023-03-04 20:55:58,619 - mmseg - INFO - Iter [73900/160000]	lr: 1.875e-05, eta: 8:03:14, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9665, loss: 0.0767
+2023-03-04 20:56:12,214 - mmseg - INFO - Iter [73950/160000]	lr: 1.875e-05, eta: 8:02:53, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8479, loss: 0.0801
+2023-03-04 20:56:25,803 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 20:56:25,803 - mmseg - INFO - Iter [74000/160000]	lr: 1.875e-05, eta: 8:02:33, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.8438, loss: 0.0823
+2023-03-04 20:56:41,836 - mmseg - INFO - Iter [74050/160000]	lr: 1.875e-05, eta: 8:02:15, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8794, loss: 0.0783
+2023-03-04 20:56:55,422 - mmseg - INFO - Iter [74100/160000]	lr: 1.875e-05, eta: 8:01:54, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.7691, loss: 0.0805
+2023-03-04 20:57:09,255 - mmseg - INFO - Iter [74150/160000]	lr: 1.875e-05, eta: 8:01:34, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9848, loss: 0.0775
+2023-03-04 20:57:23,045 - mmseg - INFO - Iter [74200/160000]	lr: 1.875e-05, eta: 8:01:14, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.9010, loss: 0.0782
+2023-03-04 20:57:39,049 - mmseg - INFO - Iter [74250/160000]	lr: 1.875e-05, eta: 8:00:56, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8675, loss: 0.0791
+2023-03-04 20:57:53,121 - mmseg - INFO - Iter [74300/160000]	lr: 1.875e-05, eta: 8:00:36, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8558, loss: 0.0792
+2023-03-04 20:58:06,865 - mmseg - INFO - Iter [74350/160000]	lr: 1.875e-05, eta: 8:00:16, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8858, loss: 0.0788
+2023-03-04 20:58:20,541 - mmseg - INFO - Iter [74400/160000]	lr: 1.875e-05, eta: 7:59:55, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8939, loss: 0.0783
+2023-03-04 20:58:36,655 - mmseg - INFO - Iter [74450/160000]	lr: 1.875e-05, eta: 7:59:38, time: 0.322, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9117, loss: 0.0779
+2023-03-04 20:58:50,274 - mmseg - INFO - Iter [74500/160000]	lr: 1.875e-05, eta: 7:59:17, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8567, loss: 0.0791
+2023-03-04 20:59:04,168 - mmseg - INFO - Iter [74550/160000]	lr: 1.875e-05, eta: 7:58:57, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9274, loss: 0.0766
+2023-03-04 20:59:20,271 - mmseg - INFO - Iter [74600/160000]	lr: 1.875e-05, eta: 7:58:39, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9330, loss: 0.0776
+2023-03-04 20:59:34,198 - mmseg - INFO - Iter [74650/160000]	lr: 1.875e-05, eta: 7:58:19, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9646, loss: 0.0762
+2023-03-04 20:59:48,230 - mmseg - INFO - Iter [74700/160000]	lr: 1.875e-05, eta: 7:57:59, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8421, loss: 0.0790
+2023-03-04 21:00:01,983 - mmseg - INFO - Iter [74750/160000]	lr: 1.875e-05, eta: 7:57:39, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8103, loss: 0.0794
+2023-03-04 21:00:18,452 - mmseg - INFO - Iter [74800/160000]	lr: 1.875e-05, eta: 7:57:22, time: 0.329, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8920, loss: 0.0789
+2023-03-04 21:00:32,071 - mmseg - INFO - Iter [74850/160000]	lr: 1.875e-05, eta: 7:57:01, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8397, loss: 0.0792
+2023-03-04 21:00:45,768 - mmseg - INFO - Iter [74900/160000]	lr: 1.875e-05, eta: 7:56:41, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.9051, loss: 0.0781
+2023-03-04 21:00:59,488 - mmseg - INFO - Iter [74950/160000]	lr: 1.875e-05, eta: 7:56:21, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8078, loss: 0.0801
+2023-03-04 21:01:15,567 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 21:01:15,567 - mmseg - INFO - Iter [75000/160000]	lr: 1.875e-05, eta: 7:56:03, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8606, loss: 0.0791
+2023-03-04 21:01:29,304 - mmseg - INFO - Iter [75050/160000]	lr: 1.875e-05, eta: 7:55:43, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9011, loss: 0.0780
+2023-03-04 21:01:42,886 - mmseg - INFO - Iter [75100/160000]	lr: 1.875e-05, eta: 7:55:22, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8821, loss: 0.0788
+2023-03-04 21:01:58,967 - mmseg - INFO - Iter [75150/160000]	lr: 1.875e-05, eta: 7:55:05, time: 0.322, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8328, loss: 0.0796
+2023-03-04 21:02:12,683 - mmseg - INFO - Iter [75200/160000]	lr: 1.875e-05, eta: 7:54:44, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7772, loss: 0.0811
+2023-03-04 21:02:26,518 - mmseg - INFO - Iter [75250/160000]	lr: 1.875e-05, eta: 7:54:24, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0824, decode.acc_seg: 96.7492, loss: 0.0824
+2023-03-04 21:02:40,338 - mmseg - INFO - Iter [75300/160000]	lr: 1.875e-05, eta: 7:54:04, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0824, decode.acc_seg: 96.7621, loss: 0.0824
+2023-03-04 21:02:56,290 - mmseg - INFO - Iter [75350/160000]	lr: 1.875e-05, eta: 7:53:46, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9001, loss: 0.0779
+2023-03-04 21:03:09,946 - mmseg - INFO - Iter [75400/160000]	lr: 1.875e-05, eta: 7:53:26, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8226, loss: 0.0801
+2023-03-04 21:03:23,767 - mmseg - INFO - Iter [75450/160000]	lr: 1.875e-05, eta: 7:53:06, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.8934, loss: 0.0778
+2023-03-04 21:03:37,392 - mmseg - INFO - Iter [75500/160000]	lr: 1.875e-05, eta: 7:52:46, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8316, loss: 0.0798
+2023-03-04 21:03:53,495 - mmseg - INFO - Iter [75550/160000]	lr: 1.875e-05, eta: 7:52:28, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8273, loss: 0.0791
+2023-03-04 21:04:07,216 - mmseg - INFO - Iter [75600/160000]	lr: 1.875e-05, eta: 7:52:08, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.8916, loss: 0.0776
+2023-03-04 21:04:20,877 - mmseg - INFO - Iter [75650/160000]	lr: 1.875e-05, eta: 7:51:48, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8986, loss: 0.0780
+2023-03-04 21:04:34,519 - mmseg - INFO - Iter [75700/160000]	lr: 1.875e-05, eta: 7:51:27, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.9063, loss: 0.0784
+2023-03-04 21:04:50,740 - mmseg - INFO - Iter [75750/160000]	lr: 1.875e-05, eta: 7:51:10, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9667, loss: 0.0764
+2023-03-04 21:05:04,405 - mmseg - INFO - Iter [75800/160000]	lr: 1.875e-05, eta: 7:50:50, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8104, loss: 0.0803
+2023-03-04 21:05:18,231 - mmseg - INFO - Iter [75850/160000]	lr: 1.875e-05, eta: 7:50:30, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9822, loss: 0.0757
+2023-03-04 21:05:34,206 - mmseg - INFO - Iter [75900/160000]	lr: 1.875e-05, eta: 7:50:12, time: 0.320, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8092, loss: 0.0799
+2023-03-04 21:05:47,847 - mmseg - INFO - Iter [75950/160000]	lr: 1.875e-05, eta: 7:49:52, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8440, loss: 0.0804
+2023-03-04 21:06:01,517 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 21:06:01,517 - mmseg - INFO - Iter [76000/160000]	lr: 1.875e-05, eta: 7:49:32, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9328, loss: 0.0765
+2023-03-04 21:06:15,205 - mmseg - INFO - Iter [76050/160000]	lr: 1.875e-05, eta: 7:49:11, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0748, decode.acc_seg: 97.0048, loss: 0.0748
+2023-03-04 21:06:31,172 - mmseg - INFO - Iter [76100/160000]	lr: 1.875e-05, eta: 7:48:54, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8771, loss: 0.0790
+2023-03-04 21:06:44,842 - mmseg - INFO - Iter [76150/160000]	lr: 1.875e-05, eta: 7:48:34, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8867, loss: 0.0784
+2023-03-04 21:06:58,638 - mmseg - INFO - Iter [76200/160000]	lr: 1.875e-05, eta: 7:48:14, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8426, loss: 0.0794
+2023-03-04 21:07:12,221 - mmseg - INFO - Iter [76250/160000]	lr: 1.875e-05, eta: 7:47:53, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0750, decode.acc_seg: 97.0086, loss: 0.0750
+2023-03-04 21:07:28,350 - mmseg - INFO - Iter [76300/160000]	lr: 1.875e-05, eta: 7:47:36, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9286, loss: 0.0765
+2023-03-04 21:07:42,273 - mmseg - INFO - Iter [76350/160000]	lr: 1.875e-05, eta: 7:47:16, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8245, loss: 0.0797
+2023-03-04 21:07:55,943 - mmseg - INFO - Iter [76400/160000]	lr: 1.875e-05, eta: 7:46:56, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8465, loss: 0.0796
+2023-03-04 21:08:12,313 - mmseg - INFO - Iter [76450/160000]	lr: 1.875e-05, eta: 7:46:39, time: 0.327, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8165, loss: 0.0801
+2023-03-04 21:08:26,728 - mmseg - INFO - Iter [76500/160000]	lr: 1.875e-05, eta: 7:46:19, time: 0.288, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.7876, loss: 0.0809
+2023-03-04 21:08:40,577 - mmseg - INFO - Iter [76550/160000]	lr: 1.875e-05, eta: 7:45:59, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0828, decode.acc_seg: 96.7492, loss: 0.0828
+2023-03-04 21:08:54,214 - mmseg - INFO - Iter [76600/160000]	lr: 1.875e-05, eta: 7:45:39, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8658, loss: 0.0787
+2023-03-04 21:09:10,363 - mmseg - INFO - Iter [76650/160000]	lr: 1.875e-05, eta: 7:45:22, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.9373, loss: 0.0781
+2023-03-04 21:09:24,139 - mmseg - INFO - Iter [76700/160000]	lr: 1.875e-05, eta: 7:45:02, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.8964, loss: 0.0770
+2023-03-04 21:09:37,969 - mmseg - INFO - Iter [76750/160000]	lr: 1.875e-05, eta: 7:44:42, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8051, loss: 0.0797
+2023-03-04 21:09:51,685 - mmseg - INFO - Iter [76800/160000]	lr: 1.875e-05, eta: 7:44:22, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9614, loss: 0.0761
+2023-03-04 21:10:07,860 - mmseg - INFO - Iter [76850/160000]	lr: 1.875e-05, eta: 7:44:05, time: 0.324, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.9055, loss: 0.0782
+2023-03-04 21:10:22,003 - mmseg - INFO - Iter [76900/160000]	lr: 1.875e-05, eta: 7:43:45, time: 0.283, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8789, loss: 0.0780
+2023-03-04 21:10:35,925 - mmseg - INFO - Iter [76950/160000]	lr: 1.875e-05, eta: 7:43:25, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9470, loss: 0.0761
+2023-03-04 21:10:49,602 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 21:10:49,602 - mmseg - INFO - Iter [77000/160000]	lr: 1.875e-05, eta: 7:43:05, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0844, decode.acc_seg: 96.6470, loss: 0.0844
+2023-03-04 21:11:05,601 - mmseg - INFO - Iter [77050/160000]	lr: 1.875e-05, eta: 7:42:48, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8745, loss: 0.0785
+2023-03-04 21:11:19,252 - mmseg - INFO - Iter [77100/160000]	lr: 1.875e-05, eta: 7:42:28, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8768, loss: 0.0789
+2023-03-04 21:11:32,985 - mmseg - INFO - Iter [77150/160000]	lr: 1.875e-05, eta: 7:42:08, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8576, loss: 0.0792
+2023-03-04 21:11:49,154 - mmseg - INFO - Iter [77200/160000]	lr: 1.875e-05, eta: 7:41:50, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.8878, loss: 0.0807
+2023-03-04 21:12:02,783 - mmseg - INFO - Iter [77250/160000]	lr: 1.875e-05, eta: 7:41:30, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8667, loss: 0.0789
+2023-03-04 21:12:16,375 - mmseg - INFO - Iter [77300/160000]	lr: 1.875e-05, eta: 7:41:10, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9568, loss: 0.0760
+2023-03-04 21:12:30,058 - mmseg - INFO - Iter [77350/160000]	lr: 1.875e-05, eta: 7:40:50, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8466, loss: 0.0788
+2023-03-04 21:12:46,142 - mmseg - INFO - Iter [77400/160000]	lr: 1.875e-05, eta: 7:40:33, time: 0.322, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8765, loss: 0.0790
+2023-03-04 21:12:59,807 - mmseg - INFO - Iter [77450/160000]	lr: 1.875e-05, eta: 7:40:13, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.9272, loss: 0.0787
+2023-03-04 21:13:13,564 - mmseg - INFO - Iter [77500/160000]	lr: 1.875e-05, eta: 7:39:53, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9118, loss: 0.0777
+2023-03-04 21:13:27,138 - mmseg - INFO - Iter [77550/160000]	lr: 1.875e-05, eta: 7:39:33, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9481, loss: 0.0766
+2023-03-04 21:13:43,086 - mmseg - INFO - Iter [77600/160000]	lr: 1.875e-05, eta: 7:39:15, time: 0.319, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.7459, loss: 0.0800
+2023-03-04 21:13:56,643 - mmseg - INFO - Iter [77650/160000]	lr: 1.875e-05, eta: 7:38:55, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.9168, loss: 0.0782
+2023-03-04 21:14:10,208 - mmseg - INFO - Iter [77700/160000]	lr: 1.875e-05, eta: 7:38:35, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8548, loss: 0.0798
+2023-03-04 21:14:26,267 - mmseg - INFO - Iter [77750/160000]	lr: 1.875e-05, eta: 7:38:18, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8463, loss: 0.0782
+2023-03-04 21:14:40,068 - mmseg - INFO - Iter [77800/160000]	lr: 1.875e-05, eta: 7:37:58, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8782, loss: 0.0781
+2023-03-04 21:14:53,678 - mmseg - INFO - Iter [77850/160000]	lr: 1.875e-05, eta: 7:37:38, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8457, loss: 0.0796
+2023-03-04 21:15:07,433 - mmseg - INFO - Iter [77900/160000]	lr: 1.875e-05, eta: 7:37:18, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.7755, loss: 0.0817
+2023-03-04 21:15:23,440 - mmseg - INFO - Iter [77950/160000]	lr: 1.875e-05, eta: 7:37:01, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.8900, loss: 0.0778
+2023-03-04 21:15:37,334 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 21:15:37,334 - mmseg - INFO - Iter [78000/160000]	lr: 1.875e-05, eta: 7:36:41, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0753, decode.acc_seg: 96.9984, loss: 0.0753
+2023-03-04 21:15:51,305 - mmseg - INFO - Iter [78050/160000]	lr: 1.875e-05, eta: 7:36:21, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.8926, loss: 0.0778
+2023-03-04 21:16:04,936 - mmseg - INFO - Iter [78100/160000]	lr: 1.875e-05, eta: 7:36:01, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0827, decode.acc_seg: 96.8625, loss: 0.0827
+2023-03-04 21:16:20,945 - mmseg - INFO - Iter [78150/160000]	lr: 1.875e-05, eta: 7:35:44, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.7893, loss: 0.0806
+2023-03-04 21:16:34,628 - mmseg - INFO - Iter [78200/160000]	lr: 1.875e-05, eta: 7:35:24, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8645, loss: 0.0797
+2023-03-04 21:16:48,413 - mmseg - INFO - Iter [78250/160000]	lr: 1.875e-05, eta: 7:35:04, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8955, loss: 0.0782
+2023-03-04 21:17:02,067 - mmseg - INFO - Iter [78300/160000]	lr: 1.875e-05, eta: 7:34:45, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8741, loss: 0.0788
+2023-03-04 21:17:18,089 - mmseg - INFO - Iter [78350/160000]	lr: 1.875e-05, eta: 7:34:27, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.9086, loss: 0.0783
+2023-03-04 21:17:31,706 - mmseg - INFO - Iter [78400/160000]	lr: 1.875e-05, eta: 7:34:07, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9763, loss: 0.0759
+2023-03-04 21:17:45,536 - mmseg - INFO - Iter [78450/160000]	lr: 1.875e-05, eta: 7:33:48, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8358, loss: 0.0789
+2023-03-04 21:18:01,591 - mmseg - INFO - Iter [78500/160000]	lr: 1.875e-05, eta: 7:33:30, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8926, loss: 0.0780
+2023-03-04 21:18:15,336 - mmseg - INFO - Iter [78550/160000]	lr: 1.875e-05, eta: 7:33:10, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9038, loss: 0.0776
+2023-03-04 21:18:29,134 - mmseg - INFO - Iter [78600/160000]	lr: 1.875e-05, eta: 7:32:51, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.8775, loss: 0.0775
+2023-03-04 21:18:43,032 - mmseg - INFO - Iter [78650/160000]	lr: 1.875e-05, eta: 7:32:31, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 96.9894, loss: 0.0752
+2023-03-04 21:18:59,002 - mmseg - INFO - Iter [78700/160000]	lr: 1.875e-05, eta: 7:32:14, time: 0.319, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8003, loss: 0.0791
+2023-03-04 21:19:13,033 - mmseg - INFO - Iter [78750/160000]	lr: 1.875e-05, eta: 7:31:54, time: 0.281, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9479, loss: 0.0766
+2023-03-04 21:19:26,778 - mmseg - INFO - Iter [78800/160000]	lr: 1.875e-05, eta: 7:31:35, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8461, loss: 0.0789
+2023-03-04 21:19:40,547 - mmseg - INFO - Iter [78850/160000]	lr: 1.875e-05, eta: 7:31:15, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0860, decode.acc_seg: 96.6832, loss: 0.0860
+2023-03-04 21:19:56,771 - mmseg - INFO - Iter [78900/160000]	lr: 1.875e-05, eta: 7:30:58, time: 0.325, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8717, loss: 0.0787
+2023-03-04 21:20:10,422 - mmseg - INFO - Iter [78950/160000]	lr: 1.875e-05, eta: 7:30:38, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.8003, loss: 0.0802
+2023-03-04 21:20:24,167 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 21:20:24,167 - mmseg - INFO - Iter [79000/160000]	lr: 1.875e-05, eta: 7:30:18, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9285, loss: 0.0773
+2023-03-04 21:20:37,804 - mmseg - INFO - Iter [79050/160000]	lr: 1.875e-05, eta: 7:29:59, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7574, loss: 0.0813
+2023-03-04 21:20:54,004 - mmseg - INFO - Iter [79100/160000]	lr: 1.875e-05, eta: 7:29:41, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.7879, loss: 0.0805
+2023-03-04 21:21:07,952 - mmseg - INFO - Iter [79150/160000]	lr: 1.875e-05, eta: 7:29:22, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8942, loss: 0.0781
+2023-03-04 21:21:21,532 - mmseg - INFO - Iter [79200/160000]	lr: 1.875e-05, eta: 7:29:02, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0744, decode.acc_seg: 97.0421, loss: 0.0744
+2023-03-04 21:21:37,825 - mmseg - INFO - Iter [79250/160000]	lr: 1.875e-05, eta: 7:28:45, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9499, loss: 0.0769
+2023-03-04 21:21:51,427 - mmseg - INFO - Iter [79300/160000]	lr: 1.875e-05, eta: 7:28:25, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8270, loss: 0.0799
+2023-03-04 21:22:05,153 - mmseg - INFO - Iter [79350/160000]	lr: 1.875e-05, eta: 7:28:06, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8234, loss: 0.0791
+2023-03-04 21:22:19,260 - mmseg - INFO - Iter [79400/160000]	lr: 1.875e-05, eta: 7:27:46, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8920, loss: 0.0780
+2023-03-04 21:22:35,649 - mmseg - INFO - Iter [79450/160000]	lr: 1.875e-05, eta: 7:27:29, time: 0.328, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9557, loss: 0.0780
+2023-03-04 21:22:49,587 - mmseg - INFO - Iter [79500/160000]	lr: 1.875e-05, eta: 7:27:10, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9124, loss: 0.0776
+2023-03-04 21:23:03,338 - mmseg - INFO - Iter [79550/160000]	lr: 1.875e-05, eta: 7:26:50, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.8331, loss: 0.0805
+2023-03-04 21:23:17,142 - mmseg - INFO - Iter [79600/160000]	lr: 1.875e-05, eta: 7:26:31, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.8663, loss: 0.0776
+2023-03-04 21:23:33,055 - mmseg - INFO - Iter [79650/160000]	lr: 1.875e-05, eta: 7:26:13, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7832, loss: 0.0811
+2023-03-04 21:23:46,893 - mmseg - INFO - Iter [79700/160000]	lr: 1.875e-05, eta: 7:25:54, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9255, loss: 0.0772
+2023-03-04 21:24:00,839 - mmseg - INFO - Iter [79750/160000]	lr: 1.875e-05, eta: 7:25:34, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0756, decode.acc_seg: 96.9486, loss: 0.0756
+2023-03-04 21:24:16,926 - mmseg - INFO - Iter [79800/160000]	lr: 1.875e-05, eta: 7:25:17, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0819, decode.acc_seg: 96.8027, loss: 0.0819
+2023-03-04 21:24:30,484 - mmseg - INFO - Iter [79850/160000]	lr: 1.875e-05, eta: 7:24:57, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8761, loss: 0.0785
+2023-03-04 21:24:44,739 - mmseg - INFO - Iter [79900/160000]	lr: 1.875e-05, eta: 7:24:38, time: 0.285, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8206, loss: 0.0788
+2023-03-04 21:24:58,701 - mmseg - INFO - Iter [79950/160000]	lr: 1.875e-05, eta: 7:24:19, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9100, loss: 0.0772
+2023-03-04 21:25:14,865 - mmseg - INFO - Swap parameters (after train) after iter [80000]
+2023-03-04 21:25:14,885 - mmseg - INFO - Saving checkpoint at 80000 iterations
+2023-03-04 21:25:16,823 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 21:25:16,823 - mmseg - INFO - Iter [80000/160000]	lr: 1.875e-05, eta: 7:24:04, time: 0.362, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.7905, loss: 0.0801
+2023-03-04 21:40:07,624 - mmseg - INFO - per class results:
+2023-03-04 21:40:07,625 - mmseg - INFO - 
++---------------+-------------------------------------------------------------------+
+|     Class     |                IoU 0,10,20,30,40,50,60,70,80,90,99                |
++---------------+-------------------------------------------------------------------+
+|   background  |            nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan            |
+|      road     | 98.55,98.55,98.55,98.55,98.55,98.55,98.55,98.55,98.55,98.55,98.55 |
+|    sidewalk   |     87.5,87.49,87.49,87.5,87.51,87.5,87.5,87.5,87.5,87.5,87.48    |
+|    building   |   93.6,93.6,93.6,93.61,93.61,93.61,93.61,93.61,93.61,93.61,93.62  |
+|      wall     |  54.69,54.75,54.75,54.77,54.8,54.83,54.82,54.85,54.91,54.92,54.94 |
+|     fence     | 65.32,65.34,65.36,65.37,65.38,65.42,65.39,65.41,65.45,65.47,65.39 |
+|      pole     |   71.38,71.4,71.39,71.4,71.41,71.4,71.41,71.41,71.42,71.42,71.43  |
+| traffic light | 75.56,75.56,75.56,75.54,75.57,75.57,75.54,75.56,75.56,75.57,75.54 |
+|  traffic sign | 82.73,82.73,82.73,82.73,82.74,82.73,82.73,82.74,82.74,82.75,82.75 |
+|   vegetation  |   93.09,93.09,93.09,93.09,93.1,93.1,93.1,93.1,93.11,93.11,93.11   |
+|    terrain    |   64.84,64.84,64.87,64.9,64.9,64.91,64.89,64.9,64.9,64.89,64.88   |
+|      sky      | 95.28,95.28,95.28,95.28,95.28,95.28,95.28,95.28,95.29,95.29,95.29 |
+|     person    | 85.02,85.03,85.02,85.02,85.02,85.02,85.03,85.02,85.02,85.02,85.02 |
+|     rider     | 67.87,67.87,67.88,67.88,67.86,67.87,67.92,67.89,67.88,67.89,67.89 |
+|      car      |  96.09,96.09,96.09,96.09,96.09,96.09,96.09,96.09,96.09,96.1,96.1  |
+|     truck     |  86.67,86.68,86.68,86.7,86.68,86.72,86.74,86.72,86.69,86.78,86.73 |
+|      bus      | 92.56,92.58,92.58,92.57,92.57,92.56,92.58,92.57,92.57,92.57,92.58 |
+|     train     |    85.81,85.8,85.8,85.82,85.82,85.85,85.9,85.84,85.9,85.93,85.9   |
+|   motorcycle  |  72.16,72.18,72.16,72.17,72.14,72.16,72.11,72.1,72.17,72.17,72.11 |
+|    bicycle    | 80.56,80.57,80.57,80.58,80.57,80.57,80.57,80.57,80.58,80.58,80.58 |
++---------------+-------------------------------------------------------------------+
+2023-03-04 21:40:07,625 - mmseg - INFO - Summary:
+2023-03-04 21:40:07,625 - mmseg - INFO - 
++-------------------------------------------------------------------+
+|                mIoU 0,10,20,30,40,50,60,70,80,90,99               |
++-------------------------------------------------------------------+
+| 81.54,81.55,81.55,81.56,81.56,81.56,81.57,81.57,81.58,81.58,81.57 |
++-------------------------------------------------------------------+
+2023-03-04 21:40:07,687 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune/best_mIoU_iter_64000.pth was removed
+2023-03-04 21:40:09,266 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_80000.pth.
+2023-03-04 21:40:09,266 - mmseg - INFO - Best mIoU is 0.8157 at 80000 iter.
+2023-03-04 21:40:09,266 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 21:40:09,266 - mmseg - INFO - Iter(val) [63]	mIoU: [0.8154, 0.8155, 0.8155, 0.8156, 0.8156, 0.8156, 0.8157, 0.8157, 0.8158, 0.8158, 0.8157], copy_paste: 81.54,81.55,81.55,81.56,81.56,81.56,81.57,81.57,81.58,81.58,81.57
+2023-03-04 21:40:09,272 - mmseg - INFO - Swap parameters (before train) before iter [80001]
+2023-03-04 21:40:23,414 - mmseg - INFO - Iter [80050/160000]	lr: 9.375e-06, eta: 7:38:36, time: 18.132, data_time: 17.858, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9048, loss: 0.0777
+2023-03-04 21:40:37,591 - mmseg - INFO - Iter [80100/160000]	lr: 9.375e-06, eta: 7:38:16, time: 0.283, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9543, loss: 0.0767
+2023-03-04 21:40:51,451 - mmseg - INFO - Iter [80150/160000]	lr: 9.375e-06, eta: 7:37:55, time: 0.277, data_time: 0.009, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7839, loss: 0.0810
+2023-03-04 21:41:07,746 - mmseg - INFO - Iter [80200/160000]	lr: 9.375e-06, eta: 7:37:37, time: 0.326, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8690, loss: 0.0789
+2023-03-04 21:41:21,540 - mmseg - INFO - Iter [80250/160000]	lr: 9.375e-06, eta: 7:37:17, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8914, loss: 0.0793
+2023-03-04 21:41:35,455 - mmseg - INFO - Iter [80300/160000]	lr: 9.375e-06, eta: 7:36:56, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9078, loss: 0.0774
+2023-03-04 21:41:49,148 - mmseg - INFO - Iter [80350/160000]	lr: 9.375e-06, eta: 7:36:35, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0751, decode.acc_seg: 97.0051, loss: 0.0751
+2023-03-04 21:42:05,136 - mmseg - INFO - Iter [80400/160000]	lr: 9.375e-06, eta: 7:36:17, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.8761, loss: 0.0775
+2023-03-04 21:42:18,713 - mmseg - INFO - Iter [80450/160000]	lr: 9.375e-06, eta: 7:35:56, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9124, loss: 0.0773
+2023-03-04 21:42:32,443 - mmseg - INFO - Iter [80500/160000]	lr: 9.375e-06, eta: 7:35:36, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8614, loss: 0.0801
+2023-03-04 21:42:48,496 - mmseg - INFO - Iter [80550/160000]	lr: 9.375e-06, eta: 7:35:17, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9793, loss: 0.0769
+2023-03-04 21:43:02,401 - mmseg - INFO - Iter [80600/160000]	lr: 9.375e-06, eta: 7:34:57, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9636, loss: 0.0760
+2023-03-04 21:43:16,086 - mmseg - INFO - Iter [80650/160000]	lr: 9.375e-06, eta: 7:34:36, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8523, loss: 0.0798
+2023-03-04 21:43:29,737 - mmseg - INFO - Iter [80700/160000]	lr: 9.375e-06, eta: 7:34:16, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9571, loss: 0.0768
+2023-03-04 21:43:45,749 - mmseg - INFO - Iter [80750/160000]	lr: 9.375e-06, eta: 7:33:57, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0753, decode.acc_seg: 97.0120, loss: 0.0753
+2023-03-04 21:43:59,400 - mmseg - INFO - Iter [80800/160000]	lr: 9.375e-06, eta: 7:33:37, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9158, loss: 0.0778
+2023-03-04 21:44:13,126 - mmseg - INFO - Iter [80850/160000]	lr: 9.375e-06, eta: 7:33:16, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8569, loss: 0.0793
+2023-03-04 21:44:26,824 - mmseg - INFO - Iter [80900/160000]	lr: 9.375e-06, eta: 7:32:55, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8412, loss: 0.0792
+2023-03-04 21:44:43,019 - mmseg - INFO - Iter [80950/160000]	lr: 9.375e-06, eta: 7:32:37, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8268, loss: 0.0797
+2023-03-04 21:44:56,722 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 21:44:56,722 - mmseg - INFO - Iter [81000/160000]	lr: 9.375e-06, eta: 7:32:17, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9457, loss: 0.0765
+2023-03-04 21:45:10,507 - mmseg - INFO - Iter [81050/160000]	lr: 9.375e-06, eta: 7:31:56, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9286, loss: 0.0772
+2023-03-04 21:45:26,722 - mmseg - INFO - Iter [81100/160000]	lr: 9.375e-06, eta: 7:31:38, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8298, loss: 0.0794
+2023-03-04 21:45:40,367 - mmseg - INFO - Iter [81150/160000]	lr: 9.375e-06, eta: 7:31:18, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8892, loss: 0.0787
+2023-03-04 21:45:53,982 - mmseg - INFO - Iter [81200/160000]	lr: 9.375e-06, eta: 7:30:57, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.8858, loss: 0.0777
+2023-03-04 21:46:07,923 - mmseg - INFO - Iter [81250/160000]	lr: 9.375e-06, eta: 7:30:37, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8598, loss: 0.0787
+2023-03-04 21:46:23,892 - mmseg - INFO - Iter [81300/160000]	lr: 9.375e-06, eta: 7:30:18, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.8919, loss: 0.0777
+2023-03-04 21:46:37,602 - mmseg - INFO - Iter [81350/160000]	lr: 9.375e-06, eta: 7:29:58, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.8049, loss: 0.0805
+2023-03-04 21:46:51,452 - mmseg - INFO - Iter [81400/160000]	lr: 9.375e-06, eta: 7:29:37, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8203, loss: 0.0798
+2023-03-04 21:47:05,239 - mmseg - INFO - Iter [81450/160000]	lr: 9.375e-06, eta: 7:29:17, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9196, loss: 0.0774
+2023-03-04 21:47:21,877 - mmseg - INFO - Iter [81500/160000]	lr: 9.375e-06, eta: 7:28:59, time: 0.333, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8577, loss: 0.0787
+2023-03-04 21:47:35,462 - mmseg - INFO - Iter [81550/160000]	lr: 9.375e-06, eta: 7:28:39, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9374, loss: 0.0770
+2023-03-04 21:47:49,296 - mmseg - INFO - Iter [81600/160000]	lr: 9.375e-06, eta: 7:28:18, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7929, loss: 0.0815
+2023-03-04 21:48:03,039 - mmseg - INFO - Iter [81650/160000]	lr: 9.375e-06, eta: 7:27:58, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8441, loss: 0.0787
+2023-03-04 21:48:19,135 - mmseg - INFO - Iter [81700/160000]	lr: 9.375e-06, eta: 7:27:40, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0751, decode.acc_seg: 97.0144, loss: 0.0751
+2023-03-04 21:48:32,885 - mmseg - INFO - Iter [81750/160000]	lr: 9.375e-06, eta: 7:27:19, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0745, decode.acc_seg: 97.0258, loss: 0.0745
+2023-03-04 21:48:46,681 - mmseg - INFO - Iter [81800/160000]	lr: 9.375e-06, eta: 7:26:59, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8593, loss: 0.0796
+2023-03-04 21:49:02,603 - mmseg - INFO - Iter [81850/160000]	lr: 9.375e-06, eta: 7:26:41, time: 0.318, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8975, loss: 0.0779
+2023-03-04 21:49:16,569 - mmseg - INFO - Iter [81900/160000]	lr: 9.375e-06, eta: 7:26:21, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8733, loss: 0.0783
+2023-03-04 21:49:30,240 - mmseg - INFO - Iter [81950/160000]	lr: 9.375e-06, eta: 7:26:00, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0756, decode.acc_seg: 97.0011, loss: 0.0756
+2023-03-04 21:49:44,447 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 21:49:44,447 - mmseg - INFO - Iter [82000/160000]	lr: 9.375e-06, eta: 7:25:40, time: 0.284, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9926, loss: 0.0757
+2023-03-04 21:50:00,463 - mmseg - INFO - Iter [82050/160000]	lr: 9.375e-06, eta: 7:25:22, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8592, loss: 0.0789
+2023-03-04 21:50:14,041 - mmseg - INFO - Iter [82100/160000]	lr: 9.375e-06, eta: 7:25:01, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8619, loss: 0.0795
+2023-03-04 21:50:27,703 - mmseg - INFO - Iter [82150/160000]	lr: 9.375e-06, eta: 7:24:41, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8172, loss: 0.0801
+2023-03-04 21:50:41,237 - mmseg - INFO - Iter [82200/160000]	lr: 9.375e-06, eta: 7:24:20, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9432, loss: 0.0770
+2023-03-04 21:50:57,156 - mmseg - INFO - Iter [82250/160000]	lr: 9.375e-06, eta: 7:24:02, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8241, loss: 0.0799
+2023-03-04 21:51:10,861 - mmseg - INFO - Iter [82300/160000]	lr: 9.375e-06, eta: 7:23:42, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8297, loss: 0.0804
+2023-03-04 21:51:25,097 - mmseg - INFO - Iter [82350/160000]	lr: 9.375e-06, eta: 7:23:22, time: 0.285, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9051, loss: 0.0775
+2023-03-04 21:51:41,206 - mmseg - INFO - Iter [82400/160000]	lr: 9.375e-06, eta: 7:23:04, time: 0.322, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9494, loss: 0.0761
+2023-03-04 21:51:55,083 - mmseg - INFO - Iter [82450/160000]	lr: 9.375e-06, eta: 7:22:44, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.9105, loss: 0.0784
+2023-03-04 21:52:08,796 - mmseg - INFO - Iter [82500/160000]	lr: 9.375e-06, eta: 7:22:23, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8547, loss: 0.0784
+2023-03-04 21:52:22,541 - mmseg - INFO - Iter [82550/160000]	lr: 9.375e-06, eta: 7:22:03, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.9219, loss: 0.0795
+2023-03-04 21:52:38,595 - mmseg - INFO - Iter [82600/160000]	lr: 9.375e-06, eta: 7:21:45, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0756, decode.acc_seg: 97.0016, loss: 0.0756
+2023-03-04 21:52:52,297 - mmseg - INFO - Iter [82650/160000]	lr: 9.375e-06, eta: 7:21:25, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7912, loss: 0.0810
+2023-03-04 21:53:06,013 - mmseg - INFO - Iter [82700/160000]	lr: 9.375e-06, eta: 7:21:04, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 97.0085, loss: 0.0755
+2023-03-04 21:53:19,824 - mmseg - INFO - Iter [82750/160000]	lr: 9.375e-06, eta: 7:20:44, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9182, loss: 0.0764
+2023-03-04 21:53:35,797 - mmseg - INFO - Iter [82800/160000]	lr: 9.375e-06, eta: 7:20:26, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.7796, loss: 0.0798
+2023-03-04 21:53:49,369 - mmseg - INFO - Iter [82850/160000]	lr: 9.375e-06, eta: 7:20:05, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9629, loss: 0.0761
+2023-03-04 21:54:03,459 - mmseg - INFO - Iter [82900/160000]	lr: 9.375e-06, eta: 7:19:46, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9199, loss: 0.0778
+2023-03-04 21:54:17,124 - mmseg - INFO - Iter [82950/160000]	lr: 9.375e-06, eta: 7:19:25, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8352, loss: 0.0795
+2023-03-04 21:54:33,112 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 21:54:33,112 - mmseg - INFO - Iter [83000/160000]	lr: 9.375e-06, eta: 7:19:07, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.9102, loss: 0.0786
+2023-03-04 21:54:46,882 - mmseg - INFO - Iter [83050/160000]	lr: 9.375e-06, eta: 7:18:47, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7634, loss: 0.0814
+2023-03-04 21:55:00,505 - mmseg - INFO - Iter [83100/160000]	lr: 9.375e-06, eta: 7:18:27, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8086, loss: 0.0793
+2023-03-04 21:55:16,588 - mmseg - INFO - Iter [83150/160000]	lr: 9.375e-06, eta: 7:18:08, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 96.9857, loss: 0.0752
+2023-03-04 21:55:30,179 - mmseg - INFO - Iter [83200/160000]	lr: 9.375e-06, eta: 7:17:48, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9593, loss: 0.0768
+2023-03-04 21:55:43,808 - mmseg - INFO - Iter [83250/160000]	lr: 9.375e-06, eta: 7:17:28, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.8616, loss: 0.0776
+2023-03-04 21:55:57,947 - mmseg - INFO - Iter [83300/160000]	lr: 9.375e-06, eta: 7:17:08, time: 0.283, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8822, loss: 0.0785
+2023-03-04 21:56:14,023 - mmseg - INFO - Iter [83350/160000]	lr: 9.375e-06, eta: 7:16:50, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8394, loss: 0.0792
+2023-03-04 21:56:27,622 - mmseg - INFO - Iter [83400/160000]	lr: 9.375e-06, eta: 7:16:30, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9855, loss: 0.0762
+2023-03-04 21:56:41,640 - mmseg - INFO - Iter [83450/160000]	lr: 9.375e-06, eta: 7:16:10, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.8714, loss: 0.0778
+2023-03-04 21:56:55,213 - mmseg - INFO - Iter [83500/160000]	lr: 9.375e-06, eta: 7:15:49, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8148, loss: 0.0803
+2023-03-04 21:57:11,552 - mmseg - INFO - Iter [83550/160000]	lr: 9.375e-06, eta: 7:15:32, time: 0.327, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8159, loss: 0.0799
+2023-03-04 21:57:25,143 - mmseg - INFO - Iter [83600/160000]	lr: 9.375e-06, eta: 7:15:11, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8564, loss: 0.0789
+2023-03-04 21:57:38,801 - mmseg - INFO - Iter [83650/160000]	lr: 9.375e-06, eta: 7:14:51, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9078, loss: 0.0765
+2023-03-04 21:57:52,458 - mmseg - INFO - Iter [83700/160000]	lr: 9.375e-06, eta: 7:14:31, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8844, loss: 0.0795
+2023-03-04 21:58:08,398 - mmseg - INFO - Iter [83750/160000]	lr: 9.375e-06, eta: 7:14:13, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0813, decode.acc_seg: 96.7647, loss: 0.0813
+2023-03-04 21:58:22,330 - mmseg - INFO - Iter [83800/160000]	lr: 9.375e-06, eta: 7:13:53, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8952, loss: 0.0784
+2023-03-04 21:58:36,272 - mmseg - INFO - Iter [83850/160000]	lr: 9.375e-06, eta: 7:13:33, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9600, loss: 0.0771
+2023-03-04 21:58:52,636 - mmseg - INFO - Iter [83900/160000]	lr: 9.375e-06, eta: 7:13:15, time: 0.327, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7446, loss: 0.0825
+2023-03-04 21:59:06,285 - mmseg - INFO - Iter [83950/160000]	lr: 9.375e-06, eta: 7:12:55, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9586, loss: 0.0763
+2023-03-04 21:59:20,083 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 21:59:20,084 - mmseg - INFO - Iter [84000/160000]	lr: 9.375e-06, eta: 7:12:35, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8946, loss: 0.0785
+2023-03-04 21:59:33,949 - mmseg - INFO - Iter [84050/160000]	lr: 9.375e-06, eta: 7:12:15, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9501, loss: 0.0771
+2023-03-04 21:59:49,924 - mmseg - INFO - Iter [84100/160000]	lr: 9.375e-06, eta: 7:11:57, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.8840, loss: 0.0767
+2023-03-04 22:00:03,534 - mmseg - INFO - Iter [84150/160000]	lr: 9.375e-06, eta: 7:11:37, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8807, loss: 0.0793
+2023-03-04 22:00:17,586 - mmseg - INFO - Iter [84200/160000]	lr: 9.375e-06, eta: 7:11:17, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8457, loss: 0.0791
+2023-03-04 22:00:31,157 - mmseg - INFO - Iter [84250/160000]	lr: 9.375e-06, eta: 7:10:57, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8542, loss: 0.0798
+2023-03-04 22:00:47,194 - mmseg - INFO - Iter [84300/160000]	lr: 9.375e-06, eta: 7:10:39, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9370, loss: 0.0763
+2023-03-04 22:01:01,546 - mmseg - INFO - Iter [84350/160000]	lr: 9.375e-06, eta: 7:10:19, time: 0.287, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9488, loss: 0.0765
+2023-03-04 22:01:15,246 - mmseg - INFO - Iter [84400/160000]	lr: 9.375e-06, eta: 7:09:59, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8767, loss: 0.0791
+2023-03-04 22:01:31,278 - mmseg - INFO - Iter [84450/160000]	lr: 9.375e-06, eta: 7:09:41, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8030, loss: 0.0801
+2023-03-04 22:01:44,902 - mmseg - INFO - Iter [84500/160000]	lr: 9.375e-06, eta: 7:09:21, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8637, loss: 0.0788
+2023-03-04 22:01:58,598 - mmseg - INFO - Iter [84550/160000]	lr: 9.375e-06, eta: 7:09:01, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9247, loss: 0.0776
+2023-03-04 22:02:12,337 - mmseg - INFO - Iter [84600/160000]	lr: 9.375e-06, eta: 7:08:41, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9604, loss: 0.0770
+2023-03-04 22:02:28,453 - mmseg - INFO - Iter [84650/160000]	lr: 9.375e-06, eta: 7:08:23, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8560, loss: 0.0803
+2023-03-04 22:02:43,035 - mmseg - INFO - Iter [84700/160000]	lr: 9.375e-06, eta: 7:08:04, time: 0.291, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8644, loss: 0.0791
+2023-03-04 22:02:56,924 - mmseg - INFO - Iter [84750/160000]	lr: 9.375e-06, eta: 7:07:44, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0851, decode.acc_seg: 96.7171, loss: 0.0851
+2023-03-04 22:03:10,784 - mmseg - INFO - Iter [84800/160000]	lr: 9.375e-06, eta: 7:07:24, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9225, loss: 0.0767
+2023-03-04 22:03:26,834 - mmseg - INFO - Iter [84850/160000]	lr: 9.375e-06, eta: 7:07:06, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9088, loss: 0.0775
+2023-03-04 22:03:40,665 - mmseg - INFO - Iter [84900/160000]	lr: 9.375e-06, eta: 7:06:46, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8359, loss: 0.0792
+2023-03-04 22:03:54,288 - mmseg - INFO - Iter [84950/160000]	lr: 9.375e-06, eta: 7:06:26, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8318, loss: 0.0790
+2023-03-04 22:04:07,879 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 22:04:07,880 - mmseg - INFO - Iter [85000/160000]	lr: 9.375e-06, eta: 7:06:06, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8676, loss: 0.0783
+2023-03-04 22:04:24,068 - mmseg - INFO - Iter [85050/160000]	lr: 9.375e-06, eta: 7:05:48, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8865, loss: 0.0784
+2023-03-04 22:04:37,698 - mmseg - INFO - Iter [85100/160000]	lr: 9.375e-06, eta: 7:05:28, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0748, decode.acc_seg: 97.0303, loss: 0.0748
+2023-03-04 22:04:51,414 - mmseg - INFO - Iter [85150/160000]	lr: 9.375e-06, eta: 7:05:08, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8733, loss: 0.0784
+2023-03-04 22:05:07,417 - mmseg - INFO - Iter [85200/160000]	lr: 9.375e-06, eta: 7:04:50, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9809, loss: 0.0759
+2023-03-04 22:05:21,097 - mmseg - INFO - Iter [85250/160000]	lr: 9.375e-06, eta: 7:04:30, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8773, loss: 0.0786
+2023-03-04 22:05:34,859 - mmseg - INFO - Iter [85300/160000]	lr: 9.375e-06, eta: 7:04:10, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9294, loss: 0.0763
+2023-03-04 22:05:48,586 - mmseg - INFO - Iter [85350/160000]	lr: 9.375e-06, eta: 7:03:50, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0749, decode.acc_seg: 97.0168, loss: 0.0749
+2023-03-04 22:06:04,559 - mmseg - INFO - Iter [85400/160000]	lr: 9.375e-06, eta: 7:03:32, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0749, decode.acc_seg: 97.0102, loss: 0.0749
+2023-03-04 22:06:18,170 - mmseg - INFO - Iter [85450/160000]	lr: 9.375e-06, eta: 7:03:12, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.8300, loss: 0.0817
+2023-03-04 22:06:31,825 - mmseg - INFO - Iter [85500/160000]	lr: 9.375e-06, eta: 7:02:52, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9109, loss: 0.0769
+2023-03-04 22:06:45,600 - mmseg - INFO - Iter [85550/160000]	lr: 9.375e-06, eta: 7:02:33, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9212, loss: 0.0777
+2023-03-04 22:07:01,559 - mmseg - INFO - Iter [85600/160000]	lr: 9.375e-06, eta: 7:02:15, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9289, loss: 0.0776
+2023-03-04 22:07:15,335 - mmseg - INFO - Iter [85650/160000]	lr: 9.375e-06, eta: 7:01:55, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.7852, loss: 0.0803
+2023-03-04 22:07:29,043 - mmseg - INFO - Iter [85700/160000]	lr: 9.375e-06, eta: 7:01:35, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8515, loss: 0.0798
+2023-03-04 22:07:44,988 - mmseg - INFO - Iter [85750/160000]	lr: 9.375e-06, eta: 7:01:17, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9015, loss: 0.0776
+2023-03-04 22:07:58,690 - mmseg - INFO - Iter [85800/160000]	lr: 9.375e-06, eta: 7:00:57, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8593, loss: 0.0786
+2023-03-04 22:08:12,432 - mmseg - INFO - Iter [85850/160000]	lr: 9.375e-06, eta: 7:00:37, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8415, loss: 0.0792
+2023-03-04 22:08:26,015 - mmseg - INFO - Iter [85900/160000]	lr: 9.375e-06, eta: 7:00:17, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8798, loss: 0.0786
+2023-03-04 22:08:42,172 - mmseg - INFO - Iter [85950/160000]	lr: 9.375e-06, eta: 6:59:59, time: 0.323, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9759, loss: 0.0760
+2023-03-04 22:08:55,923 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 22:08:55,923 - mmseg - INFO - Iter [86000/160000]	lr: 9.375e-06, eta: 6:59:40, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8789, loss: 0.0783
+2023-03-04 22:09:09,562 - mmseg - INFO - Iter [86050/160000]	lr: 9.375e-06, eta: 6:59:20, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8905, loss: 0.0783
+2023-03-04 22:09:23,306 - mmseg - INFO - Iter [86100/160000]	lr: 9.375e-06, eta: 6:59:00, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9355, loss: 0.0779
+2023-03-04 22:09:39,547 - mmseg - INFO - Iter [86150/160000]	lr: 9.375e-06, eta: 6:58:42, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.8965, loss: 0.0775
+2023-03-04 22:09:53,261 - mmseg - INFO - Iter [86200/160000]	lr: 9.375e-06, eta: 6:58:22, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8269, loss: 0.0794
+2023-03-04 22:10:06,903 - mmseg - INFO - Iter [86250/160000]	lr: 9.375e-06, eta: 6:58:03, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.8725, loss: 0.0772
+2023-03-04 22:10:20,467 - mmseg - INFO - Iter [86300/160000]	lr: 9.375e-06, eta: 6:57:43, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9130, loss: 0.0775
+2023-03-04 22:10:36,533 - mmseg - INFO - Iter [86350/160000]	lr: 9.375e-06, eta: 6:57:25, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.8133, loss: 0.0809
+2023-03-04 22:10:50,168 - mmseg - INFO - Iter [86400/160000]	lr: 9.375e-06, eta: 6:57:05, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8952, loss: 0.0786
+2023-03-04 22:11:04,022 - mmseg - INFO - Iter [86450/160000]	lr: 9.375e-06, eta: 6:56:45, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9268, loss: 0.0768
+2023-03-04 22:11:20,341 - mmseg - INFO - Iter [86500/160000]	lr: 9.375e-06, eta: 6:56:28, time: 0.327, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9794, loss: 0.0770
+2023-03-04 22:11:34,165 - mmseg - INFO - Iter [86550/160000]	lr: 9.375e-06, eta: 6:56:08, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9562, loss: 0.0757
+2023-03-04 22:11:48,087 - mmseg - INFO - Iter [86600/160000]	lr: 9.375e-06, eta: 6:55:48, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8131, loss: 0.0798
+2023-03-04 22:12:01,746 - mmseg - INFO - Iter [86650/160000]	lr: 9.375e-06, eta: 6:55:29, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9075, loss: 0.0775
+2023-03-04 22:12:18,052 - mmseg - INFO - Iter [86700/160000]	lr: 9.375e-06, eta: 6:55:11, time: 0.326, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8732, loss: 0.0789
+2023-03-04 22:12:31,789 - mmseg - INFO - Iter [86750/160000]	lr: 9.375e-06, eta: 6:54:51, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9226, loss: 0.0773
+2023-03-04 22:12:45,545 - mmseg - INFO - Iter [86800/160000]	lr: 9.375e-06, eta: 6:54:31, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8597, loss: 0.0793
+2023-03-04 22:12:59,140 - mmseg - INFO - Iter [86850/160000]	lr: 9.375e-06, eta: 6:54:12, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.8901, loss: 0.0774
+2023-03-04 22:13:15,247 - mmseg - INFO - Iter [86900/160000]	lr: 9.375e-06, eta: 6:53:54, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8043, loss: 0.0804
+2023-03-04 22:13:29,214 - mmseg - INFO - Iter [86950/160000]	lr: 9.375e-06, eta: 6:53:34, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 96.9846, loss: 0.0754
+2023-03-04 22:13:43,102 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 22:13:43,102 - mmseg - INFO - Iter [87000/160000]	lr: 9.375e-06, eta: 6:53:15, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9372, loss: 0.0759
+2023-03-04 22:13:59,156 - mmseg - INFO - Iter [87050/160000]	lr: 9.375e-06, eta: 6:52:57, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.8772, loss: 0.0778
+2023-03-04 22:14:12,966 - mmseg - INFO - Iter [87100/160000]	lr: 9.375e-06, eta: 6:52:37, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8609, loss: 0.0795
+2023-03-04 22:14:26,546 - mmseg - INFO - Iter [87150/160000]	lr: 9.375e-06, eta: 6:52:18, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9139, loss: 0.0770
+2023-03-04 22:14:40,142 - mmseg - INFO - Iter [87200/160000]	lr: 9.375e-06, eta: 6:51:58, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9764, loss: 0.0757
+2023-03-04 22:14:56,149 - mmseg - INFO - Iter [87250/160000]	lr: 9.375e-06, eta: 6:51:40, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 97.0069, loss: 0.0752
+2023-03-04 22:15:10,134 - mmseg - INFO - Iter [87300/160000]	lr: 9.375e-06, eta: 6:51:21, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8535, loss: 0.0792
+2023-03-04 22:15:23,719 - mmseg - INFO - Iter [87350/160000]	lr: 9.375e-06, eta: 6:51:01, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9183, loss: 0.0773
+2023-03-04 22:15:37,310 - mmseg - INFO - Iter [87400/160000]	lr: 9.375e-06, eta: 6:50:41, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.7768, loss: 0.0809
+2023-03-04 22:15:53,367 - mmseg - INFO - Iter [87450/160000]	lr: 9.375e-06, eta: 6:50:23, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.9259, loss: 0.0783
+2023-03-04 22:16:06,991 - mmseg - INFO - Iter [87500/160000]	lr: 9.375e-06, eta: 6:50:03, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9632, loss: 0.0762
+2023-03-04 22:16:20,893 - mmseg - INFO - Iter [87550/160000]	lr: 9.375e-06, eta: 6:49:44, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.9370, loss: 0.0788
+2023-03-04 22:16:34,492 - mmseg - INFO - Iter [87600/160000]	lr: 9.375e-06, eta: 6:49:24, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9572, loss: 0.0773
+2023-03-04 22:16:50,443 - mmseg - INFO - Iter [87650/160000]	lr: 9.375e-06, eta: 6:49:06, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8540, loss: 0.0794
+2023-03-04 22:17:04,120 - mmseg - INFO - Iter [87700/160000]	lr: 9.375e-06, eta: 6:48:47, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9837, loss: 0.0766
+2023-03-04 22:17:17,797 - mmseg - INFO - Iter [87750/160000]	lr: 9.375e-06, eta: 6:48:27, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0750, decode.acc_seg: 97.0126, loss: 0.0750
+2023-03-04 22:17:33,769 - mmseg - INFO - Iter [87800/160000]	lr: 9.375e-06, eta: 6:48:09, time: 0.319, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9064, loss: 0.0778
+2023-03-04 22:17:47,580 - mmseg - INFO - Iter [87850/160000]	lr: 9.375e-06, eta: 6:47:50, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9605, loss: 0.0766
+2023-03-04 22:18:01,202 - mmseg - INFO - Iter [87900/160000]	lr: 9.375e-06, eta: 6:47:30, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 96.9664, loss: 0.0754
+2023-03-04 22:18:14,802 - mmseg - INFO - Iter [87950/160000]	lr: 9.375e-06, eta: 6:47:10, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9317, loss: 0.0764
+2023-03-04 22:18:30,767 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 22:18:30,767 - mmseg - INFO - Iter [88000/160000]	lr: 9.375e-06, eta: 6:46:53, time: 0.319, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0744, decode.acc_seg: 97.0120, loss: 0.0744
+2023-03-04 22:18:44,503 - mmseg - INFO - Iter [88050/160000]	lr: 9.375e-06, eta: 6:46:33, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.8140, loss: 0.0805
+2023-03-04 22:18:58,159 - mmseg - INFO - Iter [88100/160000]	lr: 9.375e-06, eta: 6:46:13, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9307, loss: 0.0776
+2023-03-04 22:19:11,701 - mmseg - INFO - Iter [88150/160000]	lr: 9.375e-06, eta: 6:45:54, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8043, loss: 0.0797
+2023-03-04 22:19:27,653 - mmseg - INFO - Iter [88200/160000]	lr: 9.375e-06, eta: 6:45:36, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9359, loss: 0.0761
+2023-03-04 22:19:41,271 - mmseg - INFO - Iter [88250/160000]	lr: 9.375e-06, eta: 6:45:16, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9230, loss: 0.0779
+2023-03-04 22:19:55,261 - mmseg - INFO - Iter [88300/160000]	lr: 9.375e-06, eta: 6:44:57, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8721, loss: 0.0791
+2023-03-04 22:20:08,860 - mmseg - INFO - Iter [88350/160000]	lr: 9.375e-06, eta: 6:44:37, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9752, loss: 0.0766
+2023-03-04 22:20:25,172 - mmseg - INFO - Iter [88400/160000]	lr: 9.375e-06, eta: 6:44:20, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.7771, loss: 0.0817
+2023-03-04 22:20:38,870 - mmseg - INFO - Iter [88450/160000]	lr: 9.375e-06, eta: 6:44:00, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9228, loss: 0.0778
+2023-03-04 22:20:52,474 - mmseg - INFO - Iter [88500/160000]	lr: 9.375e-06, eta: 6:43:41, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8236, loss: 0.0793
+2023-03-04 22:21:08,442 - mmseg - INFO - Iter [88550/160000]	lr: 9.375e-06, eta: 6:43:23, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8574, loss: 0.0784
+2023-03-04 22:21:22,285 - mmseg - INFO - Iter [88600/160000]	lr: 9.375e-06, eta: 6:43:03, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8961, loss: 0.0789
+2023-03-04 22:21:36,073 - mmseg - INFO - Iter [88650/160000]	lr: 9.375e-06, eta: 6:42:44, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 97.0035, loss: 0.0755
+2023-03-04 22:21:49,699 - mmseg - INFO - Iter [88700/160000]	lr: 9.375e-06, eta: 6:42:24, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9825, loss: 0.0759
+2023-03-04 22:22:05,715 - mmseg - INFO - Iter [88750/160000]	lr: 9.375e-06, eta: 6:42:07, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9195, loss: 0.0780
+2023-03-04 22:22:19,520 - mmseg - INFO - Iter [88800/160000]	lr: 9.375e-06, eta: 6:41:47, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9064, loss: 0.0772
+2023-03-04 22:22:33,379 - mmseg - INFO - Iter [88850/160000]	lr: 9.375e-06, eta: 6:41:28, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8814, loss: 0.0788
+2023-03-04 22:22:47,061 - mmseg - INFO - Iter [88900/160000]	lr: 9.375e-06, eta: 6:41:08, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9229, loss: 0.0773
+2023-03-04 22:23:03,009 - mmseg - INFO - Iter [88950/160000]	lr: 9.375e-06, eta: 6:40:51, time: 0.319, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8792, loss: 0.0783
+2023-03-04 22:23:16,696 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 22:23:16,696 - mmseg - INFO - Iter [89000/160000]	lr: 9.375e-06, eta: 6:40:31, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.7615, loss: 0.0809
+2023-03-04 22:23:30,429 - mmseg - INFO - Iter [89050/160000]	lr: 9.375e-06, eta: 6:40:12, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8750, loss: 0.0789
+2023-03-04 22:23:46,751 - mmseg - INFO - Iter [89100/160000]	lr: 9.375e-06, eta: 6:39:54, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9513, loss: 0.0773
+2023-03-04 22:24:00,417 - mmseg - INFO - Iter [89150/160000]	lr: 9.375e-06, eta: 6:39:35, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9735, loss: 0.0761
+2023-03-04 22:24:14,149 - mmseg - INFO - Iter [89200/160000]	lr: 9.375e-06, eta: 6:39:15, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9532, loss: 0.0768
+2023-03-04 22:24:27,870 - mmseg - INFO - Iter [89250/160000]	lr: 9.375e-06, eta: 6:38:56, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7696, loss: 0.0816
+2023-03-04 22:24:44,063 - mmseg - INFO - Iter [89300/160000]	lr: 9.375e-06, eta: 6:38:38, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9267, loss: 0.0776
+2023-03-04 22:24:57,793 - mmseg - INFO - Iter [89350/160000]	lr: 9.375e-06, eta: 6:38:19, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9899, loss: 0.0773
+2023-03-04 22:25:11,617 - mmseg - INFO - Iter [89400/160000]	lr: 9.375e-06, eta: 6:38:00, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8764, loss: 0.0784
+2023-03-04 22:25:25,695 - mmseg - INFO - Iter [89450/160000]	lr: 9.375e-06, eta: 6:37:40, time: 0.282, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8963, loss: 0.0779
+2023-03-04 22:25:42,075 - mmseg - INFO - Iter [89500/160000]	lr: 9.375e-06, eta: 6:37:23, time: 0.328, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8767, loss: 0.0792
+2023-03-04 22:25:55,866 - mmseg - INFO - Iter [89550/160000]	lr: 9.375e-06, eta: 6:37:04, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8171, loss: 0.0799
+2023-03-04 22:26:09,588 - mmseg - INFO - Iter [89600/160000]	lr: 9.375e-06, eta: 6:36:44, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9578, loss: 0.0762
+2023-03-04 22:26:23,265 - mmseg - INFO - Iter [89650/160000]	lr: 9.375e-06, eta: 6:36:25, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9149, loss: 0.0777
+2023-03-04 22:26:39,225 - mmseg - INFO - Iter [89700/160000]	lr: 9.375e-06, eta: 6:36:07, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9111, loss: 0.0775
+2023-03-04 22:26:52,912 - mmseg - INFO - Iter [89750/160000]	lr: 9.375e-06, eta: 6:35:48, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.9080, loss: 0.0784
+2023-03-04 22:27:06,707 - mmseg - INFO - Iter [89800/160000]	lr: 9.375e-06, eta: 6:35:28, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8505, loss: 0.0788
+2023-03-04 22:27:22,910 - mmseg - INFO - Iter [89850/160000]	lr: 9.375e-06, eta: 6:35:11, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8312, loss: 0.0796
+2023-03-04 22:27:37,240 - mmseg - INFO - Iter [89900/160000]	lr: 9.375e-06, eta: 6:34:52, time: 0.287, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.9073, loss: 0.0783
+2023-03-04 22:27:50,803 - mmseg - INFO - Iter [89950/160000]	lr: 9.375e-06, eta: 6:34:33, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9405, loss: 0.0757
+2023-03-04 22:28:04,519 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 22:28:04,519 - mmseg - INFO - Iter [90000/160000]	lr: 9.375e-06, eta: 6:34:13, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7570, loss: 0.0811
+2023-03-04 22:28:20,562 - mmseg - INFO - Iter [90050/160000]	lr: 9.375e-06, eta: 6:33:56, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8924, loss: 0.0780
+2023-03-04 22:28:34,250 - mmseg - INFO - Iter [90100/160000]	lr: 9.375e-06, eta: 6:33:36, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9380, loss: 0.0762
+2023-03-04 22:28:47,981 - mmseg - INFO - Iter [90150/160000]	lr: 9.375e-06, eta: 6:33:17, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9071, loss: 0.0771
+2023-03-04 22:29:01,708 - mmseg - INFO - Iter [90200/160000]	lr: 9.375e-06, eta: 6:32:58, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9044, loss: 0.0773
+2023-03-04 22:29:18,100 - mmseg - INFO - Iter [90250/160000]	lr: 9.375e-06, eta: 6:32:40, time: 0.328, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0750, decode.acc_seg: 97.0044, loss: 0.0750
+2023-03-04 22:29:31,746 - mmseg - INFO - Iter [90300/160000]	lr: 9.375e-06, eta: 6:32:21, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9817, loss: 0.0768
+2023-03-04 22:29:45,409 - mmseg - INFO - Iter [90350/160000]	lr: 9.375e-06, eta: 6:32:02, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9491, loss: 0.0765
+2023-03-04 22:30:01,434 - mmseg - INFO - Iter [90400/160000]	lr: 9.375e-06, eta: 6:31:44, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0745, decode.acc_seg: 97.0057, loss: 0.0745
+2023-03-04 22:30:15,077 - mmseg - INFO - Iter [90450/160000]	lr: 9.375e-06, eta: 6:31:25, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9561, loss: 0.0769
+2023-03-04 22:30:29,178 - mmseg - INFO - Iter [90500/160000]	lr: 9.375e-06, eta: 6:31:06, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.8866, loss: 0.0778
+2023-03-04 22:30:42,808 - mmseg - INFO - Iter [90550/160000]	lr: 9.375e-06, eta: 6:30:46, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9139, loss: 0.0780
+2023-03-04 22:30:58,905 - mmseg - INFO - Iter [90600/160000]	lr: 9.375e-06, eta: 6:30:29, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0819, decode.acc_seg: 96.7789, loss: 0.0819
+2023-03-04 22:31:12,823 - mmseg - INFO - Iter [90650/160000]	lr: 9.375e-06, eta: 6:30:10, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8955, loss: 0.0779
+2023-03-04 22:31:26,450 - mmseg - INFO - Iter [90700/160000]	lr: 9.375e-06, eta: 6:29:50, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8433, loss: 0.0789
+2023-03-04 22:31:40,388 - mmseg - INFO - Iter [90750/160000]	lr: 9.375e-06, eta: 6:29:31, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8670, loss: 0.0786
+2023-03-04 22:31:56,401 - mmseg - INFO - Iter [90800/160000]	lr: 9.375e-06, eta: 6:29:14, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8964, loss: 0.0782
+2023-03-04 22:32:09,993 - mmseg - INFO - Iter [90850/160000]	lr: 9.375e-06, eta: 6:28:54, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9046, loss: 0.0777
+2023-03-04 22:32:23,564 - mmseg - INFO - Iter [90900/160000]	lr: 9.375e-06, eta: 6:28:35, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9534, loss: 0.0771
+2023-03-04 22:32:37,207 - mmseg - INFO - Iter [90950/160000]	lr: 9.375e-06, eta: 6:28:16, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8897, loss: 0.0793
+2023-03-04 22:32:53,621 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 22:32:53,621 - mmseg - INFO - Iter [91000/160000]	lr: 9.375e-06, eta: 6:27:58, time: 0.328, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9579, loss: 0.0765
+2023-03-04 22:33:07,268 - mmseg - INFO - Iter [91050/160000]	lr: 9.375e-06, eta: 6:27:39, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9581, loss: 0.0768
+2023-03-04 22:33:21,180 - mmseg - INFO - Iter [91100/160000]	lr: 9.375e-06, eta: 6:27:20, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8621, loss: 0.0789
+2023-03-04 22:33:37,576 - mmseg - INFO - Iter [91150/160000]	lr: 9.375e-06, eta: 6:27:03, time: 0.328, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8656, loss: 0.0779
+2023-03-04 22:33:51,293 - mmseg - INFO - Iter [91200/160000]	lr: 9.375e-06, eta: 6:26:43, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8882, loss: 0.0782
+2023-03-04 22:34:04,891 - mmseg - INFO - Iter [91250/160000]	lr: 9.375e-06, eta: 6:26:24, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8695, loss: 0.0801
+2023-03-04 22:34:18,582 - mmseg - INFO - Iter [91300/160000]	lr: 9.375e-06, eta: 6:26:05, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8699, loss: 0.0796
+2023-03-04 22:34:34,517 - mmseg - INFO - Iter [91350/160000]	lr: 9.375e-06, eta: 6:25:47, time: 0.319, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.9243, loss: 0.0788
+2023-03-04 22:34:48,261 - mmseg - INFO - Iter [91400/160000]	lr: 9.375e-06, eta: 6:25:28, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0827, decode.acc_seg: 96.6977, loss: 0.0827
+2023-03-04 22:35:01,939 - mmseg - INFO - Iter [91450/160000]	lr: 9.375e-06, eta: 6:25:09, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9790, loss: 0.0761
+2023-03-04 22:35:15,626 - mmseg - INFO - Iter [91500/160000]	lr: 9.375e-06, eta: 6:24:50, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8191, loss: 0.0796
+2023-03-04 22:35:31,657 - mmseg - INFO - Iter [91550/160000]	lr: 9.375e-06, eta: 6:24:32, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9810, loss: 0.0761
+2023-03-04 22:35:45,435 - mmseg - INFO - Iter [91600/160000]	lr: 9.375e-06, eta: 6:24:13, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8824, loss: 0.0780
+2023-03-04 22:35:59,808 - mmseg - INFO - Iter [91650/160000]	lr: 9.375e-06, eta: 6:23:54, time: 0.287, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9256, loss: 0.0765
+2023-03-04 22:36:16,167 - mmseg - INFO - Iter [91700/160000]	lr: 9.375e-06, eta: 6:23:37, time: 0.327, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8704, loss: 0.0788
+2023-03-04 22:36:29,972 - mmseg - INFO - Iter [91750/160000]	lr: 9.375e-06, eta: 6:23:18, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9527, loss: 0.0771
+2023-03-04 22:36:43,599 - mmseg - INFO - Iter [91800/160000]	lr: 9.375e-06, eta: 6:22:59, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9532, loss: 0.0774
+2023-03-04 22:36:57,476 - mmseg - INFO - Iter [91850/160000]	lr: 9.375e-06, eta: 6:22:40, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8901, loss: 0.0789
+2023-03-04 22:37:13,555 - mmseg - INFO - Iter [91900/160000]	lr: 9.375e-06, eta: 6:22:22, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7578, loss: 0.0811
+2023-03-04 22:37:27,441 - mmseg - INFO - Iter [91950/160000]	lr: 9.375e-06, eta: 6:22:03, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9494, loss: 0.0776
+2023-03-04 22:37:41,050 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 22:37:41,050 - mmseg - INFO - Iter [92000/160000]	lr: 9.375e-06, eta: 6:21:44, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9286, loss: 0.0778
+2023-03-04 22:37:54,716 - mmseg - INFO - Iter [92050/160000]	lr: 9.375e-06, eta: 6:21:25, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0749, decode.acc_seg: 97.0389, loss: 0.0749
+2023-03-04 22:38:10,774 - mmseg - INFO - Iter [92100/160000]	lr: 9.375e-06, eta: 6:21:07, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8781, loss: 0.0789
+2023-03-04 22:38:24,466 - mmseg - INFO - Iter [92150/160000]	lr: 9.375e-06, eta: 6:20:48, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9272, loss: 0.0770
+2023-03-04 22:38:38,034 - mmseg - INFO - Iter [92200/160000]	lr: 9.375e-06, eta: 6:20:29, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8393, loss: 0.0793
+2023-03-04 22:38:52,032 - mmseg - INFO - Iter [92250/160000]	lr: 9.375e-06, eta: 6:20:10, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8801, loss: 0.0783
+2023-03-04 22:39:08,101 - mmseg - INFO - Iter [92300/160000]	lr: 9.375e-06, eta: 6:19:53, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8917, loss: 0.0782
+2023-03-04 22:39:21,719 - mmseg - INFO - Iter [92350/160000]	lr: 9.375e-06, eta: 6:19:34, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0741, decode.acc_seg: 97.0440, loss: 0.0741
+2023-03-04 22:39:35,434 - mmseg - INFO - Iter [92400/160000]	lr: 9.375e-06, eta: 6:19:14, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9120, loss: 0.0775
+2023-03-04 22:39:51,452 - mmseg - INFO - Iter [92450/160000]	lr: 9.375e-06, eta: 6:18:57, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9659, loss: 0.0760
+2023-03-04 22:40:05,263 - mmseg - INFO - Iter [92500/160000]	lr: 9.375e-06, eta: 6:18:38, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9099, loss: 0.0771
+2023-03-04 22:40:18,995 - mmseg - INFO - Iter [92550/160000]	lr: 9.375e-06, eta: 6:18:19, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8198, loss: 0.0795
+2023-03-04 22:40:32,933 - mmseg - INFO - Iter [92600/160000]	lr: 9.375e-06, eta: 6:18:00, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9444, loss: 0.0764
+2023-03-04 22:40:49,037 - mmseg - INFO - Iter [92650/160000]	lr: 9.375e-06, eta: 6:17:43, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9714, loss: 0.0761
+2023-03-04 22:41:02,980 - mmseg - INFO - Iter [92700/160000]	lr: 9.375e-06, eta: 6:17:24, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.8100, loss: 0.0807
+2023-03-04 22:41:16,934 - mmseg - INFO - Iter [92750/160000]	lr: 9.375e-06, eta: 6:17:05, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 96.9794, loss: 0.0755
+2023-03-04 22:41:30,906 - mmseg - INFO - Iter [92800/160000]	lr: 9.375e-06, eta: 6:16:46, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9778, loss: 0.0764
+2023-03-04 22:41:47,092 - mmseg - INFO - Iter [92850/160000]	lr: 9.375e-06, eta: 6:16:29, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9925, loss: 0.0757
+2023-03-04 22:42:00,826 - mmseg - INFO - Iter [92900/160000]	lr: 9.375e-06, eta: 6:16:10, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9884, loss: 0.0764
+2023-03-04 22:42:14,570 - mmseg - INFO - Iter [92950/160000]	lr: 9.375e-06, eta: 6:15:51, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9310, loss: 0.0778
+2023-03-04 22:42:28,152 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 22:42:28,152 - mmseg - INFO - Iter [93000/160000]	lr: 9.375e-06, eta: 6:15:31, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9484, loss: 0.0767
+2023-03-04 22:42:44,326 - mmseg - INFO - Iter [93050/160000]	lr: 9.375e-06, eta: 6:15:14, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.9399, loss: 0.0825
+2023-03-04 22:42:58,097 - mmseg - INFO - Iter [93100/160000]	lr: 9.375e-06, eta: 6:14:55, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8770, loss: 0.0786
+2023-03-04 22:43:11,922 - mmseg - INFO - Iter [93150/160000]	lr: 9.375e-06, eta: 6:14:36, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9135, loss: 0.0760
+2023-03-04 22:43:28,174 - mmseg - INFO - Iter [93200/160000]	lr: 9.375e-06, eta: 6:14:19, time: 0.325, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8525, loss: 0.0786
+2023-03-04 22:43:42,118 - mmseg - INFO - Iter [93250/160000]	lr: 9.375e-06, eta: 6:14:00, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9469, loss: 0.0772
+2023-03-04 22:43:55,846 - mmseg - INFO - Iter [93300/160000]	lr: 9.375e-06, eta: 6:13:41, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.8079, loss: 0.0805
+2023-03-04 22:44:09,460 - mmseg - INFO - Iter [93350/160000]	lr: 9.375e-06, eta: 6:13:22, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9073, loss: 0.0779
+2023-03-04 22:44:25,764 - mmseg - INFO - Iter [93400/160000]	lr: 9.375e-06, eta: 6:13:05, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.8918, loss: 0.0778
+2023-03-04 22:44:39,719 - mmseg - INFO - Iter [93450/160000]	lr: 9.375e-06, eta: 6:12:46, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.8132, loss: 0.0802
+2023-03-04 22:44:53,415 - mmseg - INFO - Iter [93500/160000]	lr: 9.375e-06, eta: 6:12:27, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9681, loss: 0.0761
+2023-03-04 22:45:07,138 - mmseg - INFO - Iter [93550/160000]	lr: 9.375e-06, eta: 6:12:08, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9251, loss: 0.0769
+2023-03-04 22:45:23,064 - mmseg - INFO - Iter [93600/160000]	lr: 9.375e-06, eta: 6:11:51, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8868, loss: 0.0783
+2023-03-04 22:45:36,679 - mmseg - INFO - Iter [93650/160000]	lr: 9.375e-06, eta: 6:11:31, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9286, loss: 0.0762
+2023-03-04 22:45:50,312 - mmseg - INFO - Iter [93700/160000]	lr: 9.375e-06, eta: 6:11:12, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9244, loss: 0.0770
+2023-03-04 22:46:06,455 - mmseg - INFO - Iter [93750/160000]	lr: 9.375e-06, eta: 6:10:55, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9159, loss: 0.0773
+2023-03-04 22:46:20,163 - mmseg - INFO - Iter [93800/160000]	lr: 9.375e-06, eta: 6:10:36, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.9137, loss: 0.0794
+2023-03-04 22:46:33,828 - mmseg - INFO - Iter [93850/160000]	lr: 9.375e-06, eta: 6:10:17, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.8875, loss: 0.0771
+2023-03-04 22:46:47,444 - mmseg - INFO - Iter [93900/160000]	lr: 9.375e-06, eta: 6:09:58, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7777, loss: 0.0814
+2023-03-04 22:47:03,376 - mmseg - INFO - Iter [93950/160000]	lr: 9.375e-06, eta: 6:09:41, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8542, loss: 0.0793
+2023-03-04 22:47:17,090 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 22:47:17,090 - mmseg - INFO - Iter [94000/160000]	lr: 9.375e-06, eta: 6:09:22, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9402, loss: 0.0780
+2023-03-04 22:47:30,993 - mmseg - INFO - Iter [94050/160000]	lr: 9.375e-06, eta: 6:09:03, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.8710, loss: 0.0773
+2023-03-04 22:47:44,644 - mmseg - INFO - Iter [94100/160000]	lr: 9.375e-06, eta: 6:08:44, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.8513, loss: 0.0808
+2023-03-04 22:48:00,859 - mmseg - INFO - Iter [94150/160000]	lr: 9.375e-06, eta: 6:08:27, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9352, loss: 0.0767
+2023-03-04 22:48:15,186 - mmseg - INFO - Iter [94200/160000]	lr: 9.375e-06, eta: 6:08:08, time: 0.287, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9736, loss: 0.0764
+2023-03-04 22:48:29,099 - mmseg - INFO - Iter [94250/160000]	lr: 9.375e-06, eta: 6:07:50, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9178, loss: 0.0763
+2023-03-04 22:48:42,657 - mmseg - INFO - Iter [94300/160000]	lr: 9.375e-06, eta: 6:07:30, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8668, loss: 0.0789
+2023-03-04 22:48:58,605 - mmseg - INFO - Iter [94350/160000]	lr: 9.375e-06, eta: 6:07:13, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8333, loss: 0.0795
+2023-03-04 22:49:12,336 - mmseg - INFO - Iter [94400/160000]	lr: 9.375e-06, eta: 6:06:54, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9283, loss: 0.0780
+2023-03-04 22:49:26,070 - mmseg - INFO - Iter [94450/160000]	lr: 9.375e-06, eta: 6:06:35, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9481, loss: 0.0773
+2023-03-04 22:49:42,202 - mmseg - INFO - Iter [94500/160000]	lr: 9.375e-06, eta: 6:06:18, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9479, loss: 0.0772
+2023-03-04 22:49:55,764 - mmseg - INFO - Iter [94550/160000]	lr: 9.375e-06, eta: 6:05:59, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8664, loss: 0.0784
+2023-03-04 22:50:09,552 - mmseg - INFO - Iter [94600/160000]	lr: 9.375e-06, eta: 6:05:40, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8582, loss: 0.0790
+2023-03-04 22:50:23,341 - mmseg - INFO - Iter [94650/160000]	lr: 9.375e-06, eta: 6:05:21, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8315, loss: 0.0793
+2023-03-04 22:50:39,464 - mmseg - INFO - Iter [94700/160000]	lr: 9.375e-06, eta: 6:05:04, time: 0.322, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9279, loss: 0.0767
+2023-03-04 22:50:53,046 - mmseg - INFO - Iter [94750/160000]	lr: 9.375e-06, eta: 6:04:45, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9678, loss: 0.0769
+2023-03-04 22:51:06,694 - mmseg - INFO - Iter [94800/160000]	lr: 9.375e-06, eta: 6:04:26, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8023, loss: 0.0803
+2023-03-04 22:51:20,632 - mmseg - INFO - Iter [94850/160000]	lr: 9.375e-06, eta: 6:04:08, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8511, loss: 0.0801
+2023-03-04 22:51:36,667 - mmseg - INFO - Iter [94900/160000]	lr: 9.375e-06, eta: 6:03:50, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0756, decode.acc_seg: 96.9879, loss: 0.0756
+2023-03-04 22:51:50,366 - mmseg - INFO - Iter [94950/160000]	lr: 9.375e-06, eta: 6:03:31, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9577, loss: 0.0761
+2023-03-04 22:52:03,984 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 22:52:03,985 - mmseg - INFO - Iter [95000/160000]	lr: 9.375e-06, eta: 6:03:13, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8817, loss: 0.0782
+2023-03-04 22:52:19,929 - mmseg - INFO - Iter [95050/160000]	lr: 9.375e-06, eta: 6:02:55, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7652, loss: 0.0818
+2023-03-04 22:52:33,633 - mmseg - INFO - Iter [95100/160000]	lr: 9.375e-06, eta: 6:02:36, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9877, loss: 0.0758
+2023-03-04 22:52:47,255 - mmseg - INFO - Iter [95150/160000]	lr: 9.375e-06, eta: 6:02:17, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9673, loss: 0.0764
+2023-03-04 22:53:00,936 - mmseg - INFO - Iter [95200/160000]	lr: 9.375e-06, eta: 6:01:59, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9197, loss: 0.0776
+2023-03-04 22:53:16,999 - mmseg - INFO - Iter [95250/160000]	lr: 9.375e-06, eta: 6:01:41, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9239, loss: 0.0778
+2023-03-04 22:53:30,700 - mmseg - INFO - Iter [95300/160000]	lr: 9.375e-06, eta: 6:01:23, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 96.9744, loss: 0.0754
+2023-03-04 22:53:44,300 - mmseg - INFO - Iter [95350/160000]	lr: 9.375e-06, eta: 6:01:04, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8553, loss: 0.0797
+2023-03-04 22:53:58,023 - mmseg - INFO - Iter [95400/160000]	lr: 9.375e-06, eta: 6:00:45, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8783, loss: 0.0781
+2023-03-04 22:54:14,077 - mmseg - INFO - Iter [95450/160000]	lr: 9.375e-06, eta: 6:00:28, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9677, loss: 0.0760
+2023-03-04 22:54:27,903 - mmseg - INFO - Iter [95500/160000]	lr: 9.375e-06, eta: 6:00:09, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9542, loss: 0.0767
+2023-03-04 22:54:41,508 - mmseg - INFO - Iter [95550/160000]	lr: 9.375e-06, eta: 5:59:50, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8630, loss: 0.0791
+2023-03-04 22:54:55,513 - mmseg - INFO - Iter [95600/160000]	lr: 9.375e-06, eta: 5:59:31, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.9368, loss: 0.0788
+2023-03-04 22:55:11,891 - mmseg - INFO - Iter [95650/160000]	lr: 9.375e-06, eta: 5:59:14, time: 0.328, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8720, loss: 0.0780
+2023-03-04 22:55:25,845 - mmseg - INFO - Iter [95700/160000]	lr: 9.375e-06, eta: 5:58:56, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8566, loss: 0.0783
+2023-03-04 22:55:39,497 - mmseg - INFO - Iter [95750/160000]	lr: 9.375e-06, eta: 5:58:37, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9258, loss: 0.0774
+2023-03-04 22:55:55,470 - mmseg - INFO - Iter [95800/160000]	lr: 9.375e-06, eta: 5:58:20, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.8686, loss: 0.0831
+2023-03-04 22:56:09,115 - mmseg - INFO - Iter [95850/160000]	lr: 9.375e-06, eta: 5:58:01, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9101, loss: 0.0775
+2023-03-04 22:56:22,788 - mmseg - INFO - Iter [95900/160000]	lr: 9.375e-06, eta: 5:57:42, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0743, decode.acc_seg: 97.0491, loss: 0.0743
+2023-03-04 22:56:36,458 - mmseg - INFO - Iter [95950/160000]	lr: 9.375e-06, eta: 5:57:23, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9518, loss: 0.0769
+2023-03-04 22:56:52,732 - mmseg - INFO - Swap parameters (after train) after iter [96000]
+2023-03-04 22:56:52,752 - mmseg - INFO - Saving checkpoint at 96000 iterations
+2023-03-04 22:56:54,614 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 22:56:54,614 - mmseg - INFO - Iter [96000/160000]	lr: 9.375e-06, eta: 5:57:08, time: 0.363, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8856, loss: 0.0782
+2023-03-04 23:11:52,080 - mmseg - INFO - per class results:
+2023-03-04 23:11:52,082 - mmseg - INFO - 
++---------------+-------------------------------------------------------------------+
+|     Class     |                IoU 0,10,20,30,40,50,60,70,80,90,99                |
++---------------+-------------------------------------------------------------------+
+|   background  |            nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan            |
+|      road     | 98.54,98.54,98.54,98.55,98.55,98.55,98.55,98.55,98.55,98.55,98.54 |
+|    sidewalk   |   87.48,87.48,87.48,87.5,87.5,87.49,87.49,87.51,87.5,87.48,87.46  |
+|    building   | 93.61,93.61,93.61,93.61,93.61,93.62,93.62,93.62,93.62,93.62,93.62 |
+|      wall     |   55.23,55.27,55.3,55.32,55.38,55.39,55.36,55.42,55.5,55.5,55.35  |
+|     fence     |  65.21,65.23,65.27,65.27,65.27,65.29,65.31,65.31,65.33,65.36,65.3 |
+|      pole     | 71.33,71.35,71.34,71.36,71.37,71.37,71.37,71.36,71.38,71.38,71.39 |
+| traffic light | 75.53,75.54,75.56,75.55,75.56,75.57,75.55,75.56,75.56,75.57,75.55 |
+|  traffic sign | 82.73,82.74,82.74,82.74,82.75,82.75,82.75,82.76,82.76,82.77,82.76 |
+|   vegetation  |  93.1,93.11,93.11,93.11,93.12,93.12,93.12,93.12,93.13,93.13,93.13 |
+|    terrain    |  64.71,64.73,64.8,64.82,64.83,64.83,64.84,64.84,64.87,64.87,64.86 |
+|      sky      | 95.29,95.29,95.29,95.29,95.29,95.29,95.29,95.29,95.29,95.29,95.29 |
+|     person    | 85.01,85.02,85.01,85.02,85.01,85.02,85.02,85.01,85.01,85.01,85.01 |
+|     rider     | 67.86,67.87,67.87,67.87,67.86,67.87,67.88,67.85,67.87,67.88,67.85 |
+|      car      |     96.09,96.09,96.09,96.1,96.09,96.1,96.1,96.1,96.1,96.1,96.1    |
+|     truck     | 86.61,86.64,86.61,86.66,86.64,86.63,86.68,86.65,86.62,86.64,86.62 |
+|      bus      | 92.54,92.56,92.55,92.55,92.55,92.56,92.59,92.58,92.55,92.57,92.57 |
+|     train     | 85.72,85.75,85.74,85.79,85.82,85.85,85.84,85.85,85.87,85.92,85.86 |
+|   motorcycle  | 72.27,72.29,72.27,72.29,72.29,72.27,72.28,72.25,72.27,72.27,72.28 |
+|    bicycle    |   80.57,80.58,80.58,80.59,80.59,80.59,80.59,80.59,80.6,80.6,80.6  |
++---------------+-------------------------------------------------------------------+
+2023-03-04 23:11:52,082 - mmseg - INFO - Summary:
+2023-03-04 23:11:52,082 - mmseg - INFO - 
++------------------------------------------------------------------+
+|               mIoU 0,10,20,30,40,50,60,70,80,90,99               |
++------------------------------------------------------------------+
+| 81.55,81.56,81.57,81.58,81.58,81.59,81.59,81.59,81.6,81.61,81.59 |
++------------------------------------------------------------------+
+2023-03-04 23:11:52,132 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune/best_mIoU_iter_80000.pth was removed
+2023-03-04 23:11:54,007 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_96000.pth.
+2023-03-04 23:11:54,008 - mmseg - INFO - Best mIoU is 0.8159 at 96000 iter.
+2023-03-04 23:11:54,008 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 23:11:54,008 - mmseg - INFO - Iter(val) [63]	mIoU: [0.8155, 0.8156, 0.8157, 0.8158, 0.8158, 0.8159, 0.8159, 0.8159, 0.816, 0.8161, 0.8159], copy_paste: 81.55,81.56,81.57,81.58,81.58,81.59,81.59,81.59,81.6,81.61,81.59
+2023-03-04 23:11:54,014 - mmseg - INFO - Swap parameters (before train) before iter [96001]
+2023-03-04 23:12:08,226 - mmseg - INFO - Iter [96050/160000]	lr: 9.375e-06, eta: 6:06:48, time: 18.272, data_time: 17.997, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9869, loss: 0.0760
+2023-03-04 23:12:22,140 - mmseg - INFO - Iter [96100/160000]	lr: 9.375e-06, eta: 6:06:29, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9362, loss: 0.0771
+2023-03-04 23:12:36,047 - mmseg - INFO - Iter [96150/160000]	lr: 9.375e-06, eta: 6:06:09, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8747, loss: 0.0786
+2023-03-04 23:12:52,000 - mmseg - INFO - Iter [96200/160000]	lr: 9.375e-06, eta: 6:05:51, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8453, loss: 0.0796
+2023-03-04 23:13:05,576 - mmseg - INFO - Iter [96250/160000]	lr: 9.375e-06, eta: 6:05:31, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9357, loss: 0.0772
+2023-03-04 23:13:19,604 - mmseg - INFO - Iter [96300/160000]	lr: 9.375e-06, eta: 6:05:12, time: 0.281, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8590, loss: 0.0790
+2023-03-04 23:13:35,500 - mmseg - INFO - Iter [96350/160000]	lr: 9.375e-06, eta: 6:04:54, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8958, loss: 0.0786
+2023-03-04 23:13:49,318 - mmseg - INFO - Iter [96400/160000]	lr: 9.375e-06, eta: 6:04:35, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0753, decode.acc_seg: 96.9579, loss: 0.0753
+2023-03-04 23:14:03,157 - mmseg - INFO - Iter [96450/160000]	lr: 9.375e-06, eta: 6:04:15, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8378, loss: 0.0796
+2023-03-04 23:14:17,298 - mmseg - INFO - Iter [96500/160000]	lr: 9.375e-06, eta: 6:03:56, time: 0.283, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9133, loss: 0.0773
+2023-03-04 23:14:33,584 - mmseg - INFO - Iter [96550/160000]	lr: 9.375e-06, eta: 6:03:38, time: 0.326, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9339, loss: 0.0771
+2023-03-04 23:14:47,201 - mmseg - INFO - Iter [96600/160000]	lr: 9.375e-06, eta: 6:03:19, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0745, decode.acc_seg: 97.0684, loss: 0.0745
+2023-03-04 23:15:00,878 - mmseg - INFO - Iter [96650/160000]	lr: 9.375e-06, eta: 6:02:59, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8406, loss: 0.0795
+2023-03-04 23:15:14,835 - mmseg - INFO - Iter [96700/160000]	lr: 9.375e-06, eta: 6:02:40, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8971, loss: 0.0781
+2023-03-04 23:15:30,912 - mmseg - INFO - Iter [96750/160000]	lr: 9.375e-06, eta: 6:02:22, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8089, loss: 0.0790
+2023-03-04 23:15:45,455 - mmseg - INFO - Iter [96800/160000]	lr: 9.375e-06, eta: 6:02:03, time: 0.291, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9214, loss: 0.0768
+2023-03-04 23:15:59,157 - mmseg - INFO - Iter [96850/160000]	lr: 9.375e-06, eta: 6:01:44, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8965, loss: 0.0782
+2023-03-04 23:16:12,799 - mmseg - INFO - Iter [96900/160000]	lr: 9.375e-06, eta: 6:01:24, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0820, decode.acc_seg: 96.7408, loss: 0.0820
+2023-03-04 23:16:28,972 - mmseg - INFO - Iter [96950/160000]	lr: 9.375e-06, eta: 6:01:06, time: 0.323, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 96.9906, loss: 0.0754
+2023-03-04 23:16:42,968 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 23:16:42,968 - mmseg - INFO - Iter [97000/160000]	lr: 9.375e-06, eta: 6:00:47, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8195, loss: 0.0793
+2023-03-04 23:16:56,819 - mmseg - INFO - Iter [97050/160000]	lr: 9.375e-06, eta: 6:00:28, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9125, loss: 0.0774
+2023-03-04 23:17:12,892 - mmseg - INFO - Iter [97100/160000]	lr: 9.375e-06, eta: 6:00:10, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.7979, loss: 0.0809
+2023-03-04 23:17:26,702 - mmseg - INFO - Iter [97150/160000]	lr: 9.375e-06, eta: 5:59:50, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8447, loss: 0.0790
+2023-03-04 23:17:40,638 - mmseg - INFO - Iter [97200/160000]	lr: 9.375e-06, eta: 5:59:31, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9440, loss: 0.0764
+2023-03-04 23:17:54,299 - mmseg - INFO - Iter [97250/160000]	lr: 9.375e-06, eta: 5:59:12, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.9066, loss: 0.0786
+2023-03-04 23:18:10,226 - mmseg - INFO - Iter [97300/160000]	lr: 9.375e-06, eta: 5:58:54, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8769, loss: 0.0792
+2023-03-04 23:18:24,178 - mmseg - INFO - Iter [97350/160000]	lr: 9.375e-06, eta: 5:58:35, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9506, loss: 0.0764
+2023-03-04 23:18:38,078 - mmseg - INFO - Iter [97400/160000]	lr: 9.375e-06, eta: 5:58:15, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8970, loss: 0.0779
+2023-03-04 23:18:51,863 - mmseg - INFO - Iter [97450/160000]	lr: 9.375e-06, eta: 5:57:56, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 96.9751, loss: 0.0755
+2023-03-04 23:19:08,046 - mmseg - INFO - Iter [97500/160000]	lr: 9.375e-06, eta: 5:57:38, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0835, decode.acc_seg: 96.6376, loss: 0.0835
+2023-03-04 23:19:21,935 - mmseg - INFO - Iter [97550/160000]	lr: 9.375e-06, eta: 5:57:19, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8724, loss: 0.0796
+2023-03-04 23:19:35,693 - mmseg - INFO - Iter [97600/160000]	lr: 9.375e-06, eta: 5:56:59, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8099, loss: 0.0798
+2023-03-04 23:19:49,468 - mmseg - INFO - Iter [97650/160000]	lr: 9.375e-06, eta: 5:56:40, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9459, loss: 0.0769
+2023-03-04 23:20:05,439 - mmseg - INFO - Iter [97700/160000]	lr: 9.375e-06, eta: 5:56:22, time: 0.319, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0749, decode.acc_seg: 96.9926, loss: 0.0749
+2023-03-04 23:20:19,211 - mmseg - INFO - Iter [97750/160000]	lr: 9.375e-06, eta: 5:56:03, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.9218, loss: 0.0783
+2023-03-04 23:20:32,838 - mmseg - INFO - Iter [97800/160000]	lr: 9.375e-06, eta: 5:55:44, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8554, loss: 0.0794
+2023-03-04 23:20:48,863 - mmseg - INFO - Iter [97850/160000]	lr: 9.375e-06, eta: 5:55:26, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9653, loss: 0.0762
+2023-03-04 23:21:02,435 - mmseg - INFO - Iter [97900/160000]	lr: 9.375e-06, eta: 5:55:06, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9438, loss: 0.0770
+2023-03-04 23:21:16,249 - mmseg - INFO - Iter [97950/160000]	lr: 9.375e-06, eta: 5:54:47, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8090, loss: 0.0803
+2023-03-04 23:21:29,935 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 23:21:29,935 - mmseg - INFO - Iter [98000/160000]	lr: 9.375e-06, eta: 5:54:28, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8398, loss: 0.0788
+2023-03-04 23:21:46,017 - mmseg - INFO - Iter [98050/160000]	lr: 9.375e-06, eta: 5:54:10, time: 0.322, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.9147, loss: 0.0793
+2023-03-04 23:21:59,701 - mmseg - INFO - Iter [98100/160000]	lr: 9.375e-06, eta: 5:53:50, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0751, decode.acc_seg: 97.0257, loss: 0.0751
+2023-03-04 23:22:13,387 - mmseg - INFO - Iter [98150/160000]	lr: 9.375e-06, eta: 5:53:31, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8572, loss: 0.0781
+2023-03-04 23:22:27,239 - mmseg - INFO - Iter [98200/160000]	lr: 9.375e-06, eta: 5:53:12, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9213, loss: 0.0773
+2023-03-04 23:22:43,270 - mmseg - INFO - Iter [98250/160000]	lr: 9.375e-06, eta: 5:52:54, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8657, loss: 0.0794
+2023-03-04 23:22:56,964 - mmseg - INFO - Iter [98300/160000]	lr: 9.375e-06, eta: 5:52:35, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8537, loss: 0.0798
+2023-03-04 23:23:11,009 - mmseg - INFO - Iter [98350/160000]	lr: 9.375e-06, eta: 5:52:16, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.9045, loss: 0.0785
+2023-03-04 23:23:27,005 - mmseg - INFO - Iter [98400/160000]	lr: 9.375e-06, eta: 5:51:58, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0747, decode.acc_seg: 97.0333, loss: 0.0747
+2023-03-04 23:23:40,694 - mmseg - INFO - Iter [98450/160000]	lr: 9.375e-06, eta: 5:51:38, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8722, loss: 0.0782
+2023-03-04 23:23:54,352 - mmseg - INFO - Iter [98500/160000]	lr: 9.375e-06, eta: 5:51:19, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8726, loss: 0.0789
+2023-03-04 23:24:08,183 - mmseg - INFO - Iter [98550/160000]	lr: 9.375e-06, eta: 5:51:00, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0750, decode.acc_seg: 97.0324, loss: 0.0750
+2023-03-04 23:24:24,180 - mmseg - INFO - Iter [98600/160000]	lr: 9.375e-06, eta: 5:50:42, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.8523, loss: 0.0808
+2023-03-04 23:24:37,848 - mmseg - INFO - Iter [98650/160000]	lr: 9.375e-06, eta: 5:50:23, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9548, loss: 0.0764
+2023-03-04 23:24:51,484 - mmseg - INFO - Iter [98700/160000]	lr: 9.375e-06, eta: 5:50:04, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8120, loss: 0.0804
+2023-03-04 23:25:05,372 - mmseg - INFO - Iter [98750/160000]	lr: 9.375e-06, eta: 5:49:44, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7835, loss: 0.0808
+2023-03-04 23:25:21,279 - mmseg - INFO - Iter [98800/160000]	lr: 9.375e-06, eta: 5:49:26, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9636, loss: 0.0762
+2023-03-04 23:25:35,180 - mmseg - INFO - Iter [98850/160000]	lr: 9.375e-06, eta: 5:49:07, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 96.9872, loss: 0.0752
+2023-03-04 23:25:48,894 - mmseg - INFO - Iter [98900/160000]	lr: 9.375e-06, eta: 5:48:48, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9145, loss: 0.0774
+2023-03-04 23:26:02,554 - mmseg - INFO - Iter [98950/160000]	lr: 9.375e-06, eta: 5:48:29, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8266, loss: 0.0803
+2023-03-04 23:26:18,662 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 23:26:18,662 - mmseg - INFO - Iter [99000/160000]	lr: 9.375e-06, eta: 5:48:11, time: 0.322, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8117, loss: 0.0787
+2023-03-04 23:26:32,376 - mmseg - INFO - Iter [99050/160000]	lr: 9.375e-06, eta: 5:47:52, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9840, loss: 0.0759
+2023-03-04 23:26:46,304 - mmseg - INFO - Iter [99100/160000]	lr: 9.375e-06, eta: 5:47:33, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7778, loss: 0.0823
+2023-03-04 23:27:02,455 - mmseg - INFO - Iter [99150/160000]	lr: 9.375e-06, eta: 5:47:15, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9727, loss: 0.0772
+2023-03-04 23:27:16,209 - mmseg - INFO - Iter [99200/160000]	lr: 9.375e-06, eta: 5:46:56, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8733, loss: 0.0792
+2023-03-04 23:27:29,962 - mmseg - INFO - Iter [99250/160000]	lr: 9.375e-06, eta: 5:46:37, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8196, loss: 0.0790
+2023-03-04 23:27:43,598 - mmseg - INFO - Iter [99300/160000]	lr: 9.375e-06, eta: 5:46:17, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9378, loss: 0.0770
+2023-03-04 23:27:59,519 - mmseg - INFO - Iter [99350/160000]	lr: 9.375e-06, eta: 5:46:00, time: 0.318, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9330, loss: 0.0768
+2023-03-04 23:28:13,282 - mmseg - INFO - Iter [99400/160000]	lr: 9.375e-06, eta: 5:45:40, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9527, loss: 0.0765
+2023-03-04 23:28:26,908 - mmseg - INFO - Iter [99450/160000]	lr: 9.375e-06, eta: 5:45:21, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.8061, loss: 0.0812
+2023-03-04 23:28:40,503 - mmseg - INFO - Iter [99500/160000]	lr: 9.375e-06, eta: 5:45:02, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9058, loss: 0.0780
+2023-03-04 23:28:56,725 - mmseg - INFO - Iter [99550/160000]	lr: 9.375e-06, eta: 5:44:44, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8995, loss: 0.0783
+2023-03-04 23:29:10,368 - mmseg - INFO - Iter [99600/160000]	lr: 9.375e-06, eta: 5:44:25, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.7829, loss: 0.0794
+2023-03-04 23:29:24,096 - mmseg - INFO - Iter [99650/160000]	lr: 9.375e-06, eta: 5:44:06, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9303, loss: 0.0769
+2023-03-04 23:29:40,018 - mmseg - INFO - Iter [99700/160000]	lr: 9.375e-06, eta: 5:43:48, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9966, loss: 0.0758
+2023-03-04 23:29:53,782 - mmseg - INFO - Iter [99750/160000]	lr: 9.375e-06, eta: 5:43:29, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8496, loss: 0.0786
+2023-03-04 23:30:07,333 - mmseg - INFO - Iter [99800/160000]	lr: 9.375e-06, eta: 5:43:10, time: 0.271, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0729, decode.acc_seg: 97.0996, loss: 0.0729
+2023-03-04 23:30:21,326 - mmseg - INFO - Iter [99850/160000]	lr: 9.375e-06, eta: 5:42:51, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8514, loss: 0.0794
+2023-03-04 23:30:37,485 - mmseg - INFO - Iter [99900/160000]	lr: 9.375e-06, eta: 5:42:33, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9546, loss: 0.0764
+2023-03-04 23:30:51,097 - mmseg - INFO - Iter [99950/160000]	lr: 9.375e-06, eta: 5:42:14, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8505, loss: 0.0800
+2023-03-04 23:31:05,191 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 23:31:05,191 - mmseg - INFO - Iter [100000/160000]	lr: 9.375e-06, eta: 5:41:55, time: 0.282, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.8980, loss: 0.0777
+2023-03-04 23:31:19,235 - mmseg - INFO - Iter [100050/160000]	lr: 4.687e-06, eta: 5:41:36, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8513, loss: 0.0786
+2023-03-04 23:31:35,493 - mmseg - INFO - Iter [100100/160000]	lr: 4.687e-06, eta: 5:41:19, time: 0.325, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0747, decode.acc_seg: 96.9877, loss: 0.0747
+2023-03-04 23:31:49,213 - mmseg - INFO - Iter [100150/160000]	lr: 4.687e-06, eta: 5:40:59, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8644, loss: 0.0793
+2023-03-04 23:32:02,826 - mmseg - INFO - Iter [100200/160000]	lr: 4.687e-06, eta: 5:40:40, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0749, decode.acc_seg: 97.0090, loss: 0.0749
+2023-03-04 23:32:16,684 - mmseg - INFO - Iter [100250/160000]	lr: 4.687e-06, eta: 5:40:21, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8517, loss: 0.0796
+2023-03-04 23:32:32,739 - mmseg - INFO - Iter [100300/160000]	lr: 4.687e-06, eta: 5:40:04, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8322, loss: 0.0795
+2023-03-04 23:32:46,444 - mmseg - INFO - Iter [100350/160000]	lr: 4.687e-06, eta: 5:39:44, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9337, loss: 0.0764
+2023-03-04 23:33:00,174 - mmseg - INFO - Iter [100400/160000]	lr: 4.687e-06, eta: 5:39:25, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0819, decode.acc_seg: 96.7354, loss: 0.0819
+2023-03-04 23:33:16,238 - mmseg - INFO - Iter [100450/160000]	lr: 4.687e-06, eta: 5:39:08, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9488, loss: 0.0766
+2023-03-04 23:33:30,093 - mmseg - INFO - Iter [100500/160000]	lr: 4.687e-06, eta: 5:38:49, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.9072, loss: 0.0783
+2023-03-04 23:33:43,926 - mmseg - INFO - Iter [100550/160000]	lr: 4.687e-06, eta: 5:38:30, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9783, loss: 0.0766
+2023-03-04 23:33:57,521 - mmseg - INFO - Iter [100600/160000]	lr: 4.687e-06, eta: 5:38:10, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9967, loss: 0.0765
+2023-03-04 23:34:13,827 - mmseg - INFO - Iter [100650/160000]	lr: 4.687e-06, eta: 5:37:53, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8801, loss: 0.0781
+2023-03-04 23:34:27,527 - mmseg - INFO - Iter [100700/160000]	lr: 4.687e-06, eta: 5:37:34, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8794, loss: 0.0789
+2023-03-04 23:34:41,217 - mmseg - INFO - Iter [100750/160000]	lr: 4.687e-06, eta: 5:37:15, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9784, loss: 0.0762
+2023-03-04 23:34:54,827 - mmseg - INFO - Iter [100800/160000]	lr: 4.687e-06, eta: 5:36:56, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0749, decode.acc_seg: 97.0262, loss: 0.0749
+2023-03-04 23:35:10,719 - mmseg - INFO - Iter [100850/160000]	lr: 4.687e-06, eta: 5:36:38, time: 0.318, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7696, loss: 0.0808
+2023-03-04 23:35:24,295 - mmseg - INFO - Iter [100900/160000]	lr: 4.687e-06, eta: 5:36:19, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9502, loss: 0.0762
+2023-03-04 23:35:38,175 - mmseg - INFO - Iter [100950/160000]	lr: 4.687e-06, eta: 5:36:00, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9483, loss: 0.0772
+2023-03-04 23:35:54,345 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 23:35:54,345 - mmseg - INFO - Iter [101000/160000]	lr: 4.687e-06, eta: 5:35:42, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8697, loss: 0.0791
+2023-03-04 23:36:08,123 - mmseg - INFO - Iter [101050/160000]	lr: 4.687e-06, eta: 5:35:23, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9775, loss: 0.0759
+2023-03-04 23:36:21,808 - mmseg - INFO - Iter [101100/160000]	lr: 4.687e-06, eta: 5:35:04, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8259, loss: 0.0794
+2023-03-04 23:36:35,391 - mmseg - INFO - Iter [101150/160000]	lr: 4.687e-06, eta: 5:34:45, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8708, loss: 0.0794
+2023-03-04 23:36:51,469 - mmseg - INFO - Iter [101200/160000]	lr: 4.687e-06, eta: 5:34:28, time: 0.322, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8991, loss: 0.0779
+2023-03-04 23:37:05,039 - mmseg - INFO - Iter [101250/160000]	lr: 4.687e-06, eta: 5:34:08, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9331, loss: 0.0768
+2023-03-04 23:37:18,887 - mmseg - INFO - Iter [101300/160000]	lr: 4.687e-06, eta: 5:33:49, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9454, loss: 0.0766
+2023-03-04 23:37:32,420 - mmseg - INFO - Iter [101350/160000]	lr: 4.687e-06, eta: 5:33:30, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0736, decode.acc_seg: 97.0622, loss: 0.0736
+2023-03-04 23:37:48,444 - mmseg - INFO - Iter [101400/160000]	lr: 4.687e-06, eta: 5:33:13, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8713, loss: 0.0795
+2023-03-04 23:38:02,014 - mmseg - INFO - Iter [101450/160000]	lr: 4.687e-06, eta: 5:32:54, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8859, loss: 0.0784
+2023-03-04 23:38:15,720 - mmseg - INFO - Iter [101500/160000]	lr: 4.687e-06, eta: 5:32:35, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9248, loss: 0.0775
+2023-03-04 23:38:29,483 - mmseg - INFO - Iter [101550/160000]	lr: 4.687e-06, eta: 5:32:16, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.9219, loss: 0.0798
+2023-03-04 23:38:45,680 - mmseg - INFO - Iter [101600/160000]	lr: 4.687e-06, eta: 5:31:58, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9478, loss: 0.0772
+2023-03-04 23:38:59,312 - mmseg - INFO - Iter [101650/160000]	lr: 4.687e-06, eta: 5:31:39, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.9069, loss: 0.0781
+2023-03-04 23:39:13,318 - mmseg - INFO - Iter [101700/160000]	lr: 4.687e-06, eta: 5:31:20, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0739, decode.acc_seg: 97.0498, loss: 0.0739
+2023-03-04 23:39:29,516 - mmseg - INFO - Iter [101750/160000]	lr: 4.687e-06, eta: 5:31:03, time: 0.324, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8440, loss: 0.0783
+2023-03-04 23:39:43,152 - mmseg - INFO - Iter [101800/160000]	lr: 4.687e-06, eta: 5:30:44, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8785, loss: 0.0790
+2023-03-04 23:39:56,826 - mmseg - INFO - Iter [101850/160000]	lr: 4.687e-06, eta: 5:30:25, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 97.0048, loss: 0.0755
+2023-03-04 23:40:10,512 - mmseg - INFO - Iter [101900/160000]	lr: 4.687e-06, eta: 5:30:06, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 96.9939, loss: 0.0754
+2023-03-04 23:40:26,564 - mmseg - INFO - Iter [101950/160000]	lr: 4.687e-06, eta: 5:29:48, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8987, loss: 0.0780
+2023-03-04 23:40:40,156 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 23:40:40,157 - mmseg - INFO - Iter [102000/160000]	lr: 4.687e-06, eta: 5:29:29, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 96.9973, loss: 0.0755
+2023-03-04 23:40:53,805 - mmseg - INFO - Iter [102050/160000]	lr: 4.687e-06, eta: 5:29:10, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0748, decode.acc_seg: 97.0250, loss: 0.0748
+2023-03-04 23:41:07,718 - mmseg - INFO - Iter [102100/160000]	lr: 4.687e-06, eta: 5:28:51, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 97.0200, loss: 0.0764
+2023-03-04 23:41:23,850 - mmseg - INFO - Iter [102150/160000]	lr: 4.687e-06, eta: 5:28:34, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.8282, loss: 0.0808
+2023-03-04 23:41:37,475 - mmseg - INFO - Iter [102200/160000]	lr: 4.687e-06, eta: 5:28:15, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9085, loss: 0.0766
+2023-03-04 23:41:51,074 - mmseg - INFO - Iter [102250/160000]	lr: 4.687e-06, eta: 5:27:56, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9002, loss: 0.0778
+2023-03-04 23:42:05,374 - mmseg - INFO - Iter [102300/160000]	lr: 4.687e-06, eta: 5:27:37, time: 0.286, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9423, loss: 0.0771
+2023-03-04 23:42:21,527 - mmseg - INFO - Iter [102350/160000]	lr: 4.687e-06, eta: 5:27:20, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9739, loss: 0.0757
+2023-03-04 23:42:35,468 - mmseg - INFO - Iter [102400/160000]	lr: 4.687e-06, eta: 5:27:01, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0817, decode.acc_seg: 96.9098, loss: 0.0817
+2023-03-04 23:42:49,476 - mmseg - INFO - Iter [102450/160000]	lr: 4.687e-06, eta: 5:26:42, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9344, loss: 0.0762
+2023-03-04 23:43:05,644 - mmseg - INFO - Iter [102500/160000]	lr: 4.687e-06, eta: 5:26:25, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9543, loss: 0.0764
+2023-03-04 23:43:19,253 - mmseg - INFO - Iter [102550/160000]	lr: 4.687e-06, eta: 5:26:06, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0823, decode.acc_seg: 96.7852, loss: 0.0823
+2023-03-04 23:43:32,906 - mmseg - INFO - Iter [102600/160000]	lr: 4.687e-06, eta: 5:25:47, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.8752, loss: 0.0774
+2023-03-04 23:43:46,777 - mmseg - INFO - Iter [102650/160000]	lr: 4.687e-06, eta: 5:25:28, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9529, loss: 0.0773
+2023-03-04 23:44:02,783 - mmseg - INFO - Iter [102700/160000]	lr: 4.687e-06, eta: 5:25:11, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9522, loss: 0.0762
+2023-03-04 23:44:16,659 - mmseg - INFO - Iter [102750/160000]	lr: 4.687e-06, eta: 5:24:52, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9721, loss: 0.0766
+2023-03-04 23:44:30,409 - mmseg - INFO - Iter [102800/160000]	lr: 4.687e-06, eta: 5:24:33, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0730, decode.acc_seg: 97.0695, loss: 0.0730
+2023-03-04 23:44:44,309 - mmseg - INFO - Iter [102850/160000]	lr: 4.687e-06, eta: 5:24:14, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8725, loss: 0.0785
+2023-03-04 23:45:00,822 - mmseg - INFO - Iter [102900/160000]	lr: 4.687e-06, eta: 5:23:57, time: 0.331, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8709, loss: 0.0782
+2023-03-04 23:45:14,417 - mmseg - INFO - Iter [102950/160000]	lr: 4.687e-06, eta: 5:23:38, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.7923, loss: 0.0806
+2023-03-04 23:45:28,399 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 23:45:28,399 - mmseg - INFO - Iter [103000/160000]	lr: 4.687e-06, eta: 5:23:19, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7988, loss: 0.0816
+2023-03-04 23:45:44,354 - mmseg - INFO - Iter [103050/160000]	lr: 4.687e-06, eta: 5:23:02, time: 0.319, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8249, loss: 0.0798
+2023-03-04 23:45:58,018 - mmseg - INFO - Iter [103100/160000]	lr: 4.687e-06, eta: 5:22:43, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9796, loss: 0.0759
+2023-03-04 23:46:11,695 - mmseg - INFO - Iter [103150/160000]	lr: 4.687e-06, eta: 5:22:24, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.7495, loss: 0.0812
+2023-03-04 23:46:25,519 - mmseg - INFO - Iter [103200/160000]	lr: 4.687e-06, eta: 5:22:05, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9633, loss: 0.0758
+2023-03-04 23:46:41,960 - mmseg - INFO - Iter [103250/160000]	lr: 4.687e-06, eta: 5:21:48, time: 0.329, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0747, decode.acc_seg: 97.0392, loss: 0.0747
+2023-03-04 23:46:55,909 - mmseg - INFO - Iter [103300/160000]	lr: 4.687e-06, eta: 5:21:29, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9401, loss: 0.0772
+2023-03-04 23:47:09,543 - mmseg - INFO - Iter [103350/160000]	lr: 4.687e-06, eta: 5:21:10, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.7948, loss: 0.0805
+2023-03-04 23:47:23,238 - mmseg - INFO - Iter [103400/160000]	lr: 4.687e-06, eta: 5:20:51, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0756, decode.acc_seg: 96.9643, loss: 0.0756
+2023-03-04 23:47:39,124 - mmseg - INFO - Iter [103450/160000]	lr: 4.687e-06, eta: 5:20:34, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9352, loss: 0.0772
+2023-03-04 23:47:52,974 - mmseg - INFO - Iter [103500/160000]	lr: 4.687e-06, eta: 5:20:15, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8459, loss: 0.0789
+2023-03-04 23:48:06,530 - mmseg - INFO - Iter [103550/160000]	lr: 4.687e-06, eta: 5:19:56, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0749, decode.acc_seg: 97.0861, loss: 0.0749
+2023-03-04 23:48:20,154 - mmseg - INFO - Iter [103600/160000]	lr: 4.687e-06, eta: 5:19:37, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9142, loss: 0.0774
+2023-03-04 23:48:36,300 - mmseg - INFO - Iter [103650/160000]	lr: 4.687e-06, eta: 5:19:20, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9324, loss: 0.0775
+2023-03-04 23:48:50,075 - mmseg - INFO - Iter [103700/160000]	lr: 4.687e-06, eta: 5:19:01, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0737, decode.acc_seg: 97.0287, loss: 0.0737
+2023-03-04 23:49:03,726 - mmseg - INFO - Iter [103750/160000]	lr: 4.687e-06, eta: 5:18:42, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8810, loss: 0.0785
+2023-03-04 23:49:19,885 - mmseg - INFO - Iter [103800/160000]	lr: 4.687e-06, eta: 5:18:25, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9091, loss: 0.0775
+2023-03-04 23:49:33,683 - mmseg - INFO - Iter [103850/160000]	lr: 4.687e-06, eta: 5:18:06, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.8111, loss: 0.0809
+2023-03-04 23:49:47,594 - mmseg - INFO - Iter [103900/160000]	lr: 4.687e-06, eta: 5:17:48, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.7963, loss: 0.0810
+2023-03-04 23:50:01,200 - mmseg - INFO - Iter [103950/160000]	lr: 4.687e-06, eta: 5:17:29, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0726, decode.acc_seg: 97.1064, loss: 0.0726
+2023-03-04 23:50:17,253 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 23:50:17,253 - mmseg - INFO - Iter [104000/160000]	lr: 4.687e-06, eta: 5:17:11, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9580, loss: 0.0768
+2023-03-04 23:50:30,844 - mmseg - INFO - Iter [104050/160000]	lr: 4.687e-06, eta: 5:16:52, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9145, loss: 0.0780
+2023-03-04 23:50:45,066 - mmseg - INFO - Iter [104100/160000]	lr: 4.687e-06, eta: 5:16:34, time: 0.284, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 96.9507, loss: 0.0754
+2023-03-04 23:50:58,773 - mmseg - INFO - Iter [104150/160000]	lr: 4.687e-06, eta: 5:16:15, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8919, loss: 0.0789
+2023-03-04 23:51:14,735 - mmseg - INFO - Iter [104200/160000]	lr: 4.687e-06, eta: 5:15:58, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.9119, loss: 0.0782
+2023-03-04 23:51:28,371 - mmseg - INFO - Iter [104250/160000]	lr: 4.687e-06, eta: 5:15:39, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7968, loss: 0.0808
+2023-03-04 23:51:42,231 - mmseg - INFO - Iter [104300/160000]	lr: 4.687e-06, eta: 5:15:20, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9581, loss: 0.0760
+2023-03-04 23:51:58,276 - mmseg - INFO - Iter [104350/160000]	lr: 4.687e-06, eta: 5:15:03, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 96.9329, loss: 0.0752
+2023-03-04 23:52:11,932 - mmseg - INFO - Iter [104400/160000]	lr: 4.687e-06, eta: 5:14:44, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9303, loss: 0.0765
+2023-03-04 23:52:25,670 - mmseg - INFO - Iter [104450/160000]	lr: 4.687e-06, eta: 5:14:25, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8772, loss: 0.0794
+2023-03-04 23:52:39,374 - mmseg - INFO - Iter [104500/160000]	lr: 4.687e-06, eta: 5:14:07, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9016, loss: 0.0780
+2023-03-04 23:52:55,421 - mmseg - INFO - Iter [104550/160000]	lr: 4.687e-06, eta: 5:13:49, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.8047, loss: 0.0831
+2023-03-04 23:53:09,594 - mmseg - INFO - Iter [104600/160000]	lr: 4.687e-06, eta: 5:13:31, time: 0.283, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.8812, loss: 0.0778
+2023-03-04 23:53:23,553 - mmseg - INFO - Iter [104650/160000]	lr: 4.687e-06, eta: 5:13:12, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8811, loss: 0.0783
+2023-03-04 23:53:37,180 - mmseg - INFO - Iter [104700/160000]	lr: 4.687e-06, eta: 5:12:53, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.8024, loss: 0.0808
+2023-03-04 23:53:53,097 - mmseg - INFO - Iter [104750/160000]	lr: 4.687e-06, eta: 5:12:36, time: 0.318, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8839, loss: 0.0780
+2023-03-04 23:54:06,818 - mmseg - INFO - Iter [104800/160000]	lr: 4.687e-06, eta: 5:12:17, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0738, decode.acc_seg: 97.0172, loss: 0.0738
+2023-03-04 23:54:20,456 - mmseg - INFO - Iter [104850/160000]	lr: 4.687e-06, eta: 5:11:58, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9673, loss: 0.0762
+2023-03-04 23:54:34,129 - mmseg - INFO - Iter [104900/160000]	lr: 4.687e-06, eta: 5:11:40, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8582, loss: 0.0786
+2023-03-04 23:54:50,185 - mmseg - INFO - Iter [104950/160000]	lr: 4.687e-06, eta: 5:11:22, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.9246, loss: 0.0781
+2023-03-04 23:55:03,823 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 23:55:03,823 - mmseg - INFO - Iter [105000/160000]	lr: 4.687e-06, eta: 5:11:03, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8617, loss: 0.0792
+2023-03-04 23:55:17,748 - mmseg - INFO - Iter [105050/160000]	lr: 4.687e-06, eta: 5:10:45, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.8999, loss: 0.0776
+2023-03-04 23:55:33,663 - mmseg - INFO - Iter [105100/160000]	lr: 4.687e-06, eta: 5:10:27, time: 0.318, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9413, loss: 0.0775
+2023-03-04 23:55:47,304 - mmseg - INFO - Iter [105150/160000]	lr: 4.687e-06, eta: 5:10:09, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8143, loss: 0.0803
+2023-03-04 23:56:00,950 - mmseg - INFO - Iter [105200/160000]	lr: 4.687e-06, eta: 5:09:50, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8454, loss: 0.0797
+2023-03-04 23:56:14,695 - mmseg - INFO - Iter [105250/160000]	lr: 4.687e-06, eta: 5:09:31, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9331, loss: 0.0764
+2023-03-04 23:56:30,881 - mmseg - INFO - Iter [105300/160000]	lr: 4.687e-06, eta: 5:09:14, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 97.0116, loss: 0.0757
+2023-03-04 23:56:44,542 - mmseg - INFO - Iter [105350/160000]	lr: 4.687e-06, eta: 5:08:55, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0747, decode.acc_seg: 97.0116, loss: 0.0747
+2023-03-04 23:56:58,170 - mmseg - INFO - Iter [105400/160000]	lr: 4.687e-06, eta: 5:08:37, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9049, loss: 0.0771
+2023-03-04 23:57:12,045 - mmseg - INFO - Iter [105450/160000]	lr: 4.687e-06, eta: 5:08:18, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.7866, loss: 0.0818
+2023-03-04 23:57:28,141 - mmseg - INFO - Iter [105500/160000]	lr: 4.687e-06, eta: 5:08:01, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0734, decode.acc_seg: 97.0642, loss: 0.0734
+2023-03-04 23:57:41,958 - mmseg - INFO - Iter [105550/160000]	lr: 4.687e-06, eta: 5:07:42, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8474, loss: 0.0787
+2023-03-04 23:57:55,687 - mmseg - INFO - Iter [105600/160000]	lr: 4.687e-06, eta: 5:07:23, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9126, loss: 0.0774
+2023-03-04 23:58:11,663 - mmseg - INFO - Iter [105650/160000]	lr: 4.687e-06, eta: 5:07:06, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9527, loss: 0.0772
+2023-03-04 23:58:25,457 - mmseg - INFO - Iter [105700/160000]	lr: 4.687e-06, eta: 5:06:47, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8303, loss: 0.0790
+2023-03-04 23:58:39,441 - mmseg - INFO - Iter [105750/160000]	lr: 4.687e-06, eta: 5:06:29, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9218, loss: 0.0772
+2023-03-04 23:58:53,107 - mmseg - INFO - Iter [105800/160000]	lr: 4.687e-06, eta: 5:06:10, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8772, loss: 0.0779
+2023-03-04 23:59:09,090 - mmseg - INFO - Iter [105850/160000]	lr: 4.687e-06, eta: 5:05:53, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8624, loss: 0.0785
+2023-03-04 23:59:22,690 - mmseg - INFO - Iter [105900/160000]	lr: 4.687e-06, eta: 5:05:34, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9703, loss: 0.0764
+2023-03-04 23:59:36,277 - mmseg - INFO - Iter [105950/160000]	lr: 4.687e-06, eta: 5:05:16, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8875, loss: 0.0779
+2023-03-04 23:59:50,055 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-04 23:59:50,055 - mmseg - INFO - Iter [106000/160000]	lr: 4.687e-06, eta: 5:04:57, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8738, loss: 0.0787
+2023-03-05 00:00:05,960 - mmseg - INFO - Iter [106050/160000]	lr: 4.687e-06, eta: 5:04:40, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9852, loss: 0.0762
+2023-03-05 00:00:19,600 - mmseg - INFO - Iter [106100/160000]	lr: 4.687e-06, eta: 5:04:21, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9410, loss: 0.0773
+2023-03-05 00:00:33,429 - mmseg - INFO - Iter [106150/160000]	lr: 4.687e-06, eta: 5:04:02, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9144, loss: 0.0770
+2023-03-05 00:00:47,420 - mmseg - INFO - Iter [106200/160000]	lr: 4.687e-06, eta: 5:03:44, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.8894, loss: 0.0776
+2023-03-05 00:01:04,017 - mmseg - INFO - Iter [106250/160000]	lr: 4.687e-06, eta: 5:03:27, time: 0.332, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8788, loss: 0.0784
+2023-03-05 00:01:18,101 - mmseg - INFO - Iter [106300/160000]	lr: 4.687e-06, eta: 5:03:09, time: 0.282, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9509, loss: 0.0760
+2023-03-05 00:01:31,986 - mmseg - INFO - Iter [106350/160000]	lr: 4.687e-06, eta: 5:02:50, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.8946, loss: 0.0771
+2023-03-05 00:01:48,079 - mmseg - INFO - Iter [106400/160000]	lr: 4.687e-06, eta: 5:02:33, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0751, decode.acc_seg: 97.0343, loss: 0.0751
+2023-03-05 00:02:01,786 - mmseg - INFO - Iter [106450/160000]	lr: 4.687e-06, eta: 5:02:14, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.8998, loss: 0.0775
+2023-03-05 00:02:15,642 - mmseg - INFO - Iter [106500/160000]	lr: 4.687e-06, eta: 5:01:56, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.9107, loss: 0.0785
+2023-03-05 00:02:29,414 - mmseg - INFO - Iter [106550/160000]	lr: 4.687e-06, eta: 5:01:37, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9370, loss: 0.0761
+2023-03-05 00:02:45,444 - mmseg - INFO - Iter [106600/160000]	lr: 4.687e-06, eta: 5:01:20, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9126, loss: 0.0775
+2023-03-05 00:02:59,060 - mmseg - INFO - Iter [106650/160000]	lr: 4.687e-06, eta: 5:01:01, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8651, loss: 0.0786
+2023-03-05 00:03:12,900 - mmseg - INFO - Iter [106700/160000]	lr: 4.687e-06, eta: 5:00:43, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.8739, loss: 0.0777
+2023-03-05 00:03:26,864 - mmseg - INFO - Iter [106750/160000]	lr: 4.687e-06, eta: 5:00:24, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9265, loss: 0.0769
+2023-03-05 00:03:42,926 - mmseg - INFO - Iter [106800/160000]	lr: 4.687e-06, eta: 5:00:07, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9567, loss: 0.0779
+2023-03-05 00:03:57,146 - mmseg - INFO - Iter [106850/160000]	lr: 4.687e-06, eta: 4:59:49, time: 0.284, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9724, loss: 0.0759
+2023-03-05 00:04:10,787 - mmseg - INFO - Iter [106900/160000]	lr: 4.687e-06, eta: 4:59:30, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7526, loss: 0.0816
+2023-03-05 00:04:24,645 - mmseg - INFO - Iter [106950/160000]	lr: 4.687e-06, eta: 4:59:12, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.8367, loss: 0.0811
+2023-03-05 00:04:40,660 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 00:04:40,660 - mmseg - INFO - Iter [107000/160000]	lr: 4.687e-06, eta: 4:58:54, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.9031, loss: 0.0786
+2023-03-05 00:04:54,261 - mmseg - INFO - Iter [107050/160000]	lr: 4.687e-06, eta: 4:58:36, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8213, loss: 0.0795
+2023-03-05 00:05:08,526 - mmseg - INFO - Iter [107100/160000]	lr: 4.687e-06, eta: 4:58:18, time: 0.285, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9932, loss: 0.0758
+2023-03-05 00:05:24,614 - mmseg - INFO - Iter [107150/160000]	lr: 4.687e-06, eta: 4:58:00, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0749, decode.acc_seg: 96.9937, loss: 0.0749
+2023-03-05 00:05:38,169 - mmseg - INFO - Iter [107200/160000]	lr: 4.687e-06, eta: 4:57:42, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8051, loss: 0.0795
+2023-03-05 00:05:51,807 - mmseg - INFO - Iter [107250/160000]	lr: 4.687e-06, eta: 4:57:23, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8936, loss: 0.0787
+2023-03-05 00:06:05,543 - mmseg - INFO - Iter [107300/160000]	lr: 4.687e-06, eta: 4:57:05, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9500, loss: 0.0765
+2023-03-05 00:06:21,727 - mmseg - INFO - Iter [107350/160000]	lr: 4.687e-06, eta: 4:56:47, time: 0.324, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8586, loss: 0.0790
+2023-03-05 00:06:35,472 - mmseg - INFO - Iter [107400/160000]	lr: 4.687e-06, eta: 4:56:29, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8097, loss: 0.0804
+2023-03-05 00:06:49,220 - mmseg - INFO - Iter [107450/160000]	lr: 4.687e-06, eta: 4:56:10, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8782, loss: 0.0783
+2023-03-05 00:07:02,857 - mmseg - INFO - Iter [107500/160000]	lr: 4.687e-06, eta: 4:55:52, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0747, decode.acc_seg: 97.0346, loss: 0.0747
+2023-03-05 00:07:18,877 - mmseg - INFO - Iter [107550/160000]	lr: 4.687e-06, eta: 4:55:35, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0756, decode.acc_seg: 96.9956, loss: 0.0756
+2023-03-05 00:07:32,472 - mmseg - INFO - Iter [107600/160000]	lr: 4.687e-06, eta: 4:55:16, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8588, loss: 0.0789
+2023-03-05 00:07:46,308 - mmseg - INFO - Iter [107650/160000]	lr: 4.687e-06, eta: 4:54:58, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9615, loss: 0.0770
+2023-03-05 00:08:02,432 - mmseg - INFO - Iter [107700/160000]	lr: 4.687e-06, eta: 4:54:40, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8517, loss: 0.0794
+2023-03-05 00:08:16,221 - mmseg - INFO - Iter [107750/160000]	lr: 4.687e-06, eta: 4:54:22, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9744, loss: 0.0764
+2023-03-05 00:08:30,010 - mmseg - INFO - Iter [107800/160000]	lr: 4.687e-06, eta: 4:54:04, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0846, decode.acc_seg: 96.8323, loss: 0.0846
+2023-03-05 00:08:44,014 - mmseg - INFO - Iter [107850/160000]	lr: 4.687e-06, eta: 4:53:45, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9565, loss: 0.0761
+2023-03-05 00:09:00,051 - mmseg - INFO - Iter [107900/160000]	lr: 4.687e-06, eta: 4:53:28, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8605, loss: 0.0796
+2023-03-05 00:09:14,301 - mmseg - INFO - Iter [107950/160000]	lr: 4.687e-06, eta: 4:53:10, time: 0.285, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9850, loss: 0.0766
+2023-03-05 00:09:28,408 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 00:09:28,409 - mmseg - INFO - Iter [108000/160000]	lr: 4.687e-06, eta: 4:52:52, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9511, loss: 0.0768
+2023-03-05 00:09:41,984 - mmseg - INFO - Iter [108050/160000]	lr: 4.687e-06, eta: 4:52:33, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.9055, loss: 0.0788
+2023-03-05 00:09:57,982 - mmseg - INFO - Iter [108100/160000]	lr: 4.687e-06, eta: 4:52:16, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9128, loss: 0.0773
+2023-03-05 00:10:11,607 - mmseg - INFO - Iter [108150/160000]	lr: 4.687e-06, eta: 4:51:57, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9358, loss: 0.0778
+2023-03-05 00:10:25,652 - mmseg - INFO - Iter [108200/160000]	lr: 4.687e-06, eta: 4:51:39, time: 0.281, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8556, loss: 0.0792
+2023-03-05 00:10:39,217 - mmseg - INFO - Iter [108250/160000]	lr: 4.687e-06, eta: 4:51:20, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.9049, loss: 0.0791
+2023-03-05 00:10:55,189 - mmseg - INFO - Iter [108300/160000]	lr: 4.687e-06, eta: 4:51:03, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0747, decode.acc_seg: 97.0081, loss: 0.0747
+2023-03-05 00:11:08,865 - mmseg - INFO - Iter [108350/160000]	lr: 4.687e-06, eta: 4:50:45, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 96.9958, loss: 0.0754
+2023-03-05 00:11:22,513 - mmseg - INFO - Iter [108400/160000]	lr: 4.687e-06, eta: 4:50:26, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9440, loss: 0.0769
+2023-03-05 00:11:38,689 - mmseg - INFO - Iter [108450/160000]	lr: 4.687e-06, eta: 4:50:09, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8770, loss: 0.0780
+2023-03-05 00:11:52,295 - mmseg - INFO - Iter [108500/160000]	lr: 4.687e-06, eta: 4:49:51, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0748, decode.acc_seg: 97.0070, loss: 0.0748
+2023-03-05 00:12:05,936 - mmseg - INFO - Iter [108550/160000]	lr: 4.687e-06, eta: 4:49:32, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9350, loss: 0.0767
+2023-03-05 00:12:19,711 - mmseg - INFO - Iter [108600/160000]	lr: 4.687e-06, eta: 4:49:14, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9729, loss: 0.0769
+2023-03-05 00:12:35,833 - mmseg - INFO - Iter [108650/160000]	lr: 4.687e-06, eta: 4:48:57, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8511, loss: 0.0789
+2023-03-05 00:12:49,585 - mmseg - INFO - Iter [108700/160000]	lr: 4.687e-06, eta: 4:48:38, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0742, decode.acc_seg: 97.0154, loss: 0.0742
+2023-03-05 00:13:03,183 - mmseg - INFO - Iter [108750/160000]	lr: 4.687e-06, eta: 4:48:20, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9506, loss: 0.0759
+2023-03-05 00:13:16,858 - mmseg - INFO - Iter [108800/160000]	lr: 4.687e-06, eta: 4:48:01, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8212, loss: 0.0798
+2023-03-05 00:13:32,998 - mmseg - INFO - Iter [108850/160000]	lr: 4.687e-06, eta: 4:47:44, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0750, decode.acc_seg: 96.9964, loss: 0.0750
+2023-03-05 00:13:46,674 - mmseg - INFO - Iter [108900/160000]	lr: 4.687e-06, eta: 4:47:26, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 97.0217, loss: 0.0752
+2023-03-05 00:14:00,600 - mmseg - INFO - Iter [108950/160000]	lr: 4.687e-06, eta: 4:47:08, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9691, loss: 0.0759
+2023-03-05 00:14:16,638 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 00:14:16,638 - mmseg - INFO - Iter [109000/160000]	lr: 4.687e-06, eta: 4:46:50, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9452, loss: 0.0767
+2023-03-05 00:14:30,587 - mmseg - INFO - Iter [109050/160000]	lr: 4.687e-06, eta: 4:46:32, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9318, loss: 0.0763
+2023-03-05 00:14:44,275 - mmseg - INFO - Iter [109100/160000]	lr: 4.687e-06, eta: 4:46:14, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8009, loss: 0.0796
+2023-03-05 00:14:58,006 - mmseg - INFO - Iter [109150/160000]	lr: 4.687e-06, eta: 4:45:55, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9292, loss: 0.0779
+2023-03-05 00:15:14,073 - mmseg - INFO - Iter [109200/160000]	lr: 4.687e-06, eta: 4:45:38, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9386, loss: 0.0766
+2023-03-05 00:15:27,877 - mmseg - INFO - Iter [109250/160000]	lr: 4.687e-06, eta: 4:45:20, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8847, loss: 0.0780
+2023-03-05 00:15:41,803 - mmseg - INFO - Iter [109300/160000]	lr: 4.687e-06, eta: 4:45:02, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9336, loss: 0.0770
+2023-03-05 00:15:55,442 - mmseg - INFO - Iter [109350/160000]	lr: 4.687e-06, eta: 4:44:43, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8508, loss: 0.0794
+2023-03-05 00:16:11,689 - mmseg - INFO - Iter [109400/160000]	lr: 4.687e-06, eta: 4:44:26, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0756, decode.acc_seg: 97.0001, loss: 0.0756
+2023-03-05 00:16:25,257 - mmseg - INFO - Iter [109450/160000]	lr: 4.687e-06, eta: 4:44:08, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8739, loss: 0.0783
+2023-03-05 00:16:39,028 - mmseg - INFO - Iter [109500/160000]	lr: 4.687e-06, eta: 4:43:49, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8804, loss: 0.0797
+2023-03-05 00:16:52,587 - mmseg - INFO - Iter [109550/160000]	lr: 4.687e-06, eta: 4:43:31, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 96.9954, loss: 0.0754
+2023-03-05 00:17:08,550 - mmseg - INFO - Iter [109600/160000]	lr: 4.687e-06, eta: 4:43:14, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9574, loss: 0.0767
+2023-03-05 00:17:22,424 - mmseg - INFO - Iter [109650/160000]	lr: 4.687e-06, eta: 4:42:56, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9845, loss: 0.0761
+2023-03-05 00:17:36,374 - mmseg - INFO - Iter [109700/160000]	lr: 4.687e-06, eta: 4:42:37, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9188, loss: 0.0773
+2023-03-05 00:17:52,584 - mmseg - INFO - Iter [109750/160000]	lr: 4.687e-06, eta: 4:42:20, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0743, decode.acc_seg: 97.0481, loss: 0.0743
+2023-03-05 00:18:06,291 - mmseg - INFO - Iter [109800/160000]	lr: 4.687e-06, eta: 4:42:02, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.8318, loss: 0.0802
+2023-03-05 00:18:20,056 - mmseg - INFO - Iter [109850/160000]	lr: 4.687e-06, eta: 4:41:44, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9687, loss: 0.0768
+2023-03-05 00:18:33,633 - mmseg - INFO - Iter [109900/160000]	lr: 4.687e-06, eta: 4:41:25, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0747, decode.acc_seg: 96.9947, loss: 0.0747
+2023-03-05 00:18:49,646 - mmseg - INFO - Iter [109950/160000]	lr: 4.687e-06, eta: 4:41:08, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.7952, loss: 0.0803
+2023-03-05 00:19:03,493 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 00:19:03,494 - mmseg - INFO - Iter [110000/160000]	lr: 4.687e-06, eta: 4:40:50, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9271, loss: 0.0770
+2023-03-05 00:19:17,247 - mmseg - INFO - Iter [110050/160000]	lr: 4.687e-06, eta: 4:40:32, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9213, loss: 0.0771
+2023-03-05 00:19:30,873 - mmseg - INFO - Iter [110100/160000]	lr: 4.687e-06, eta: 4:40:13, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8178, loss: 0.0789
+2023-03-05 00:19:47,606 - mmseg - INFO - Iter [110150/160000]	lr: 4.687e-06, eta: 4:39:56, time: 0.335, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8749, loss: 0.0790
+2023-03-05 00:20:01,297 - mmseg - INFO - Iter [110200/160000]	lr: 4.687e-06, eta: 4:39:38, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0740, decode.acc_seg: 97.0594, loss: 0.0740
+2023-03-05 00:20:15,040 - mmseg - INFO - Iter [110250/160000]	lr: 4.687e-06, eta: 4:39:20, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9395, loss: 0.0763
+2023-03-05 00:20:30,981 - mmseg - INFO - Iter [110300/160000]	lr: 4.687e-06, eta: 4:39:03, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0746, decode.acc_seg: 97.0263, loss: 0.0746
+2023-03-05 00:20:44,647 - mmseg - INFO - Iter [110350/160000]	lr: 4.687e-06, eta: 4:38:44, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8566, loss: 0.0800
+2023-03-05 00:20:58,521 - mmseg - INFO - Iter [110400/160000]	lr: 4.687e-06, eta: 4:38:26, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0749, decode.acc_seg: 97.0081, loss: 0.0749
+2023-03-05 00:21:12,434 - mmseg - INFO - Iter [110450/160000]	lr: 4.687e-06, eta: 4:38:08, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9276, loss: 0.0780
+2023-03-05 00:21:28,590 - mmseg - INFO - Iter [110500/160000]	lr: 4.687e-06, eta: 4:37:51, time: 0.323, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8765, loss: 0.0799
+2023-03-05 00:21:42,308 - mmseg - INFO - Iter [110550/160000]	lr: 4.687e-06, eta: 4:37:33, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9052, loss: 0.0778
+2023-03-05 00:21:56,024 - mmseg - INFO - Iter [110600/160000]	lr: 4.687e-06, eta: 4:37:14, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9674, loss: 0.0776
+2023-03-05 00:22:09,754 - mmseg - INFO - Iter [110650/160000]	lr: 4.687e-06, eta: 4:36:56, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9761, loss: 0.0757
+2023-03-05 00:22:25,842 - mmseg - INFO - Iter [110700/160000]	lr: 4.687e-06, eta: 4:36:39, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9152, loss: 0.0773
+2023-03-05 00:22:39,599 - mmseg - INFO - Iter [110750/160000]	lr: 4.687e-06, eta: 4:36:21, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8198, loss: 0.0804
+2023-03-05 00:22:53,272 - mmseg - INFO - Iter [110800/160000]	lr: 4.687e-06, eta: 4:36:03, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9654, loss: 0.0767
+2023-03-05 00:23:07,020 - mmseg - INFO - Iter [110850/160000]	lr: 4.687e-06, eta: 4:35:44, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9206, loss: 0.0766
+2023-03-05 00:23:23,146 - mmseg - INFO - Iter [110900/160000]	lr: 4.687e-06, eta: 4:35:27, time: 0.323, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8480, loss: 0.0792
+2023-03-05 00:23:36,881 - mmseg - INFO - Iter [110950/160000]	lr: 4.687e-06, eta: 4:35:09, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8662, loss: 0.0784
+2023-03-05 00:23:50,507 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 00:23:50,507 - mmseg - INFO - Iter [111000/160000]	lr: 4.687e-06, eta: 4:34:51, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0745, decode.acc_seg: 97.0047, loss: 0.0745
+2023-03-05 00:24:06,479 - mmseg - INFO - Iter [111050/160000]	lr: 4.687e-06, eta: 4:34:34, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0753, decode.acc_seg: 97.0158, loss: 0.0753
+2023-03-05 00:24:20,329 - mmseg - INFO - Iter [111100/160000]	lr: 4.687e-06, eta: 4:34:15, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8210, loss: 0.0804
+2023-03-05 00:24:33,976 - mmseg - INFO - Iter [111150/160000]	lr: 4.687e-06, eta: 4:33:57, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8990, loss: 0.0787
+2023-03-05 00:24:47,608 - mmseg - INFO - Iter [111200/160000]	lr: 4.687e-06, eta: 4:33:39, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9051, loss: 0.0778
+2023-03-05 00:25:03,738 - mmseg - INFO - Iter [111250/160000]	lr: 4.687e-06, eta: 4:33:22, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8927, loss: 0.0784
+2023-03-05 00:25:17,342 - mmseg - INFO - Iter [111300/160000]	lr: 4.687e-06, eta: 4:33:04, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9678, loss: 0.0769
+2023-03-05 00:25:31,041 - mmseg - INFO - Iter [111350/160000]	lr: 4.687e-06, eta: 4:32:45, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0746, decode.acc_seg: 97.0016, loss: 0.0746
+2023-03-05 00:25:44,633 - mmseg - INFO - Iter [111400/160000]	lr: 4.687e-06, eta: 4:32:27, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.8776, loss: 0.0778
+2023-03-05 00:26:00,909 - mmseg - INFO - Iter [111450/160000]	lr: 4.687e-06, eta: 4:32:10, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8527, loss: 0.0791
+2023-03-05 00:26:14,683 - mmseg - INFO - Iter [111500/160000]	lr: 4.687e-06, eta: 4:31:52, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9105, loss: 0.0770
+2023-03-05 00:26:28,528 - mmseg - INFO - Iter [111550/160000]	lr: 4.687e-06, eta: 4:31:34, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0741, decode.acc_seg: 97.0609, loss: 0.0741
+2023-03-05 00:26:42,090 - mmseg - INFO - Iter [111600/160000]	lr: 4.687e-06, eta: 4:31:16, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 97.0067, loss: 0.0752
+2023-03-05 00:26:58,110 - mmseg - INFO - Iter [111650/160000]	lr: 4.687e-06, eta: 4:30:59, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9111, loss: 0.0776
+2023-03-05 00:27:11,711 - mmseg - INFO - Iter [111700/160000]	lr: 4.687e-06, eta: 4:30:40, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8717, loss: 0.0790
+2023-03-05 00:27:25,350 - mmseg - INFO - Iter [111750/160000]	lr: 4.687e-06, eta: 4:30:22, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9715, loss: 0.0757
+2023-03-05 00:27:41,361 - mmseg - INFO - Iter [111800/160000]	lr: 4.687e-06, eta: 4:30:05, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8662, loss: 0.0794
+2023-03-05 00:27:55,022 - mmseg - INFO - Iter [111850/160000]	lr: 4.687e-06, eta: 4:29:47, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9213, loss: 0.0770
+2023-03-05 00:28:08,832 - mmseg - INFO - Iter [111900/160000]	lr: 4.687e-06, eta: 4:29:29, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.7902, loss: 0.0807
+2023-03-05 00:28:22,503 - mmseg - INFO - Iter [111950/160000]	lr: 4.687e-06, eta: 4:29:11, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9682, loss: 0.0767
+2023-03-05 00:28:38,486 - mmseg - INFO - Swap parameters (after train) after iter [112000]
+2023-03-05 00:28:38,506 - mmseg - INFO - Saving checkpoint at 112000 iterations
+2023-03-05 00:28:40,556 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 00:28:40,556 - mmseg - INFO - Iter [112000/160000]	lr: 4.687e-06, eta: 4:28:54, time: 0.361, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8773, loss: 0.0783
+2023-03-05 00:43:39,440 - mmseg - INFO - per class results:
+2023-03-05 00:43:39,441 - mmseg - INFO - 
++---------------+-------------------------------------------------------------------+
+|     Class     |                IoU 0,10,20,30,40,50,60,70,80,90,99                |
++---------------+-------------------------------------------------------------------+
+|   background  |            nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan            |
+|      road     | 98.56,98.56,98.56,98.56,98.56,98.56,98.56,98.56,98.56,98.56,98.56 |
+|    sidewalk   |  87.58,87.57,87.58,87.6,87.58,87.57,87.57,87.55,87.55,87.55,87.58 |
+|    building   |   93.59,93.59,93.6,93.6,93.6,93.6,93.61,93.61,93.61,93.61,93.62   |
+|      wall     |  55.27,55.32,55.31,55.36,55.42,55.5,55.48,55.44,55.53,55.58,55.55 |
+|     fence     | 64.97,64.99,65.03,65.02,65.07,65.13,65.13,65.14,65.16,65.18,65.32 |
+|      pole     | 71.31,71.32,71.32,71.34,71.34,71.34,71.35,71.35,71.37,71.37,71.36 |
+| traffic light | 75.55,75.54,75.56,75.55,75.58,75.58,75.58,75.57,75.57,75.59,75.56 |
+|  traffic sign |  82.7,82.7,82.71,82.71,82.72,82.73,82.73,82.73,82.74,82.75,82.74  |
+|   vegetation  |  93.1,93.1,93.11,93.11,93.12,93.13,93.13,93.13,93.13,93.14,93.14  |
+|    terrain    |  64.69,64.68,64.71,64.8,64.79,64.84,64.86,64.86,64.87,64.88,64.86 |
+|      sky      | 95.28,95.28,95.27,95.27,95.28,95.28,95.28,95.28,95.28,95.28,95.28 |
+|     person    |     84.99,85.01,85.0,85.0,85.0,85.01,85.01,85.0,85.0,85.0,85.0    |
+|     rider     | 67.84,67.85,67.84,67.84,67.85,67.85,67.85,67.86,67.85,67.83,67.86 |
+|      car      | 96.08,96.08,96.08,96.08,96.08,96.09,96.09,96.09,96.09,96.09,96.09 |
+|     truck     | 86.65,86.66,86.63,86.66,86.65,86.68,86.68,86.67,86.64,86.62,86.55 |
+|      bus      |  92.47,92.5,92.5,92.52,92.51,92.52,92.51,92.52,92.51,92.51,92.55  |
+|     train     | 85.75,85.75,85.75,85.81,85.83,85.86,85.82,85.85,85.84,85.85,85.95 |
+|   motorcycle  |   72.18,72.2,72.2,72.18,72.2,72.17,72.17,72.16,72.17,72.18,72.17  |
+|    bicycle    |  80.54,80.57,80.56,80.57,80.58,80.58,80.58,80.58,80.59,80.6,80.6  |
++---------------+-------------------------------------------------------------------+
+2023-03-05 00:43:39,441 - mmseg - INFO - Summary:
+2023-03-05 00:43:39,441 - mmseg - INFO - 
++------------------------------------------------------------------+
+|               mIoU 0,10,20,30,40,50,60,70,80,90,99               |
++------------------------------------------------------------------+
+| 81.53,81.54,81.54,81.56,81.57,81.58,81.58,81.58,81.58,81.59,81.6 |
++------------------------------------------------------------------+
+2023-03-05 00:43:39,499 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune/best_mIoU_iter_96000.pth was removed
+2023-03-05 00:43:41,115 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_112000.pth.
+2023-03-05 00:43:41,116 - mmseg - INFO - Best mIoU is 0.8160 at 112000 iter.
+2023-03-05 00:43:41,116 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 00:43:41,116 - mmseg - INFO - Iter(val) [63]	mIoU: [0.8153, 0.8154, 0.8154, 0.8156, 0.8157, 0.8158, 0.8158, 0.8158, 0.8158, 0.8159, 0.816], copy_paste: 81.53,81.54,81.54,81.56,81.57,81.58,81.58,81.58,81.58,81.59,81.6
+2023-03-05 00:43:41,124 - mmseg - INFO - Swap parameters (before train) before iter [112001]
+2023-03-05 00:43:55,218 - mmseg - INFO - Iter [112050/160000]	lr: 4.687e-06, eta: 4:35:02, time: 18.293, data_time: 18.020, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8553, loss: 0.0790
+2023-03-05 00:44:09,266 - mmseg - INFO - Iter [112100/160000]	lr: 4.687e-06, eta: 4:34:43, time: 0.281, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0736, decode.acc_seg: 97.0564, loss: 0.0736
+2023-03-05 00:44:23,142 - mmseg - INFO - Iter [112150/160000]	lr: 4.687e-06, eta: 4:34:25, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8568, loss: 0.0794
+2023-03-05 00:44:39,528 - mmseg - INFO - Iter [112200/160000]	lr: 4.687e-06, eta: 4:34:07, time: 0.328, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9523, loss: 0.0769
+2023-03-05 00:44:53,325 - mmseg - INFO - Iter [112250/160000]	lr: 4.687e-06, eta: 4:33:48, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9106, loss: 0.0773
+2023-03-05 00:45:07,354 - mmseg - INFO - Iter [112300/160000]	lr: 4.687e-06, eta: 4:33:30, time: 0.281, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9666, loss: 0.0769
+2023-03-05 00:45:23,389 - mmseg - INFO - Iter [112350/160000]	lr: 4.687e-06, eta: 4:33:12, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0734, decode.acc_seg: 97.0589, loss: 0.0734
+2023-03-05 00:45:37,111 - mmseg - INFO - Iter [112400/160000]	lr: 4.687e-06, eta: 4:32:53, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0733, decode.acc_seg: 97.0386, loss: 0.0733
+2023-03-05 00:45:51,222 - mmseg - INFO - Iter [112450/160000]	lr: 4.687e-06, eta: 4:32:35, time: 0.282, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9031, loss: 0.0767
+2023-03-05 00:46:04,934 - mmseg - INFO - Iter [112500/160000]	lr: 4.687e-06, eta: 4:32:16, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8072, loss: 0.0800
+2023-03-05 00:46:21,138 - mmseg - INFO - Iter [112550/160000]	lr: 4.687e-06, eta: 4:31:59, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8035, loss: 0.0799
+2023-03-05 00:46:34,910 - mmseg - INFO - Iter [112600/160000]	lr: 4.687e-06, eta: 4:31:40, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8846, loss: 0.0786
+2023-03-05 00:46:48,780 - mmseg - INFO - Iter [112650/160000]	lr: 4.687e-06, eta: 4:31:21, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9336, loss: 0.0777
+2023-03-05 00:47:02,545 - mmseg - INFO - Iter [112700/160000]	lr: 4.687e-06, eta: 4:31:03, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9437, loss: 0.0771
+2023-03-05 00:47:18,684 - mmseg - INFO - Iter [112750/160000]	lr: 4.687e-06, eta: 4:30:45, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9068, loss: 0.0776
+2023-03-05 00:47:32,370 - mmseg - INFO - Iter [112800/160000]	lr: 4.687e-06, eta: 4:30:26, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0744, decode.acc_seg: 97.0456, loss: 0.0744
+2023-03-05 00:47:46,063 - mmseg - INFO - Iter [112850/160000]	lr: 4.687e-06, eta: 4:30:08, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9159, loss: 0.0779
+2023-03-05 00:47:59,892 - mmseg - INFO - Iter [112900/160000]	lr: 4.687e-06, eta: 4:29:49, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8968, loss: 0.0786
+2023-03-05 00:48:15,928 - mmseg - INFO - Iter [112950/160000]	lr: 4.687e-06, eta: 4:29:32, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8582, loss: 0.0784
+2023-03-05 00:48:29,685 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 00:48:29,685 - mmseg - INFO - Iter [113000/160000]	lr: 4.687e-06, eta: 4:29:13, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9361, loss: 0.0764
+2023-03-05 00:48:43,448 - mmseg - INFO - Iter [113050/160000]	lr: 4.687e-06, eta: 4:28:54, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8683, loss: 0.0780
+2023-03-05 00:48:59,577 - mmseg - INFO - Iter [113100/160000]	lr: 4.687e-06, eta: 4:28:37, time: 0.323, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.9225, loss: 0.0788
+2023-03-05 00:49:13,284 - mmseg - INFO - Iter [113150/160000]	lr: 4.687e-06, eta: 4:28:18, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.9076, loss: 0.0785
+2023-03-05 00:49:27,093 - mmseg - INFO - Iter [113200/160000]	lr: 4.687e-06, eta: 4:28:00, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9572, loss: 0.0758
+2023-03-05 00:49:40,856 - mmseg - INFO - Iter [113250/160000]	lr: 4.687e-06, eta: 4:27:41, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9350, loss: 0.0759
+2023-03-05 00:49:56,913 - mmseg - INFO - Iter [113300/160000]	lr: 4.687e-06, eta: 4:27:23, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9682, loss: 0.0759
+2023-03-05 00:50:10,519 - mmseg - INFO - Iter [113350/160000]	lr: 4.687e-06, eta: 4:27:05, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8944, loss: 0.0788
+2023-03-05 00:50:24,173 - mmseg - INFO - Iter [113400/160000]	lr: 4.687e-06, eta: 4:26:46, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.9300, loss: 0.0786
+2023-03-05 00:50:37,786 - mmseg - INFO - Iter [113450/160000]	lr: 4.687e-06, eta: 4:26:27, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0750, decode.acc_seg: 97.0188, loss: 0.0750
+2023-03-05 00:50:53,891 - mmseg - INFO - Iter [113500/160000]	lr: 4.687e-06, eta: 4:26:10, time: 0.322, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8177, loss: 0.0795
+2023-03-05 00:51:07,534 - mmseg - INFO - Iter [113550/160000]	lr: 4.687e-06, eta: 4:25:51, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8882, loss: 0.0788
+2023-03-05 00:51:21,097 - mmseg - INFO - Iter [113600/160000]	lr: 4.687e-06, eta: 4:25:32, time: 0.271, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 96.9721, loss: 0.0755
+2023-03-05 00:51:37,011 - mmseg - INFO - Iter [113650/160000]	lr: 4.687e-06, eta: 4:25:15, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9491, loss: 0.0769
+2023-03-05 00:51:50,609 - mmseg - INFO - Iter [113700/160000]	lr: 4.687e-06, eta: 4:24:56, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8408, loss: 0.0792
+2023-03-05 00:52:04,211 - mmseg - INFO - Iter [113750/160000]	lr: 4.687e-06, eta: 4:24:38, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8682, loss: 0.0791
+2023-03-05 00:52:17,986 - mmseg - INFO - Iter [113800/160000]	lr: 4.687e-06, eta: 4:24:19, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 97.0066, loss: 0.0765
+2023-03-05 00:52:34,212 - mmseg - INFO - Iter [113850/160000]	lr: 4.687e-06, eta: 4:24:01, time: 0.325, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9652, loss: 0.0760
+2023-03-05 00:52:48,065 - mmseg - INFO - Iter [113900/160000]	lr: 4.687e-06, eta: 4:23:43, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9579, loss: 0.0762
+2023-03-05 00:53:01,773 - mmseg - INFO - Iter [113950/160000]	lr: 4.687e-06, eta: 4:23:24, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.8877, loss: 0.0771
+2023-03-05 00:53:15,619 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 00:53:15,619 - mmseg - INFO - Iter [114000/160000]	lr: 4.687e-06, eta: 4:23:06, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9242, loss: 0.0771
+2023-03-05 00:53:31,591 - mmseg - INFO - Iter [114050/160000]	lr: 4.687e-06, eta: 4:22:48, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.8891, loss: 0.0777
+2023-03-05 00:53:45,127 - mmseg - INFO - Iter [114100/160000]	lr: 4.687e-06, eta: 4:22:30, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0734, decode.acc_seg: 97.0911, loss: 0.0734
+2023-03-05 00:53:58,722 - mmseg - INFO - Iter [114150/160000]	lr: 4.687e-06, eta: 4:22:11, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8891, loss: 0.0787
+2023-03-05 00:54:12,619 - mmseg - INFO - Iter [114200/160000]	lr: 4.687e-06, eta: 4:21:53, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9261, loss: 0.0765
+2023-03-05 00:54:28,560 - mmseg - INFO - Iter [114250/160000]	lr: 4.687e-06, eta: 4:21:35, time: 0.319, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8796, loss: 0.0794
+2023-03-05 00:54:42,352 - mmseg - INFO - Iter [114300/160000]	lr: 4.687e-06, eta: 4:21:16, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8740, loss: 0.0803
+2023-03-05 00:54:55,987 - mmseg - INFO - Iter [114350/160000]	lr: 4.687e-06, eta: 4:20:58, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9629, loss: 0.0763
+2023-03-05 00:55:12,037 - mmseg - INFO - Iter [114400/160000]	lr: 4.687e-06, eta: 4:20:40, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9854, loss: 0.0757
+2023-03-05 00:55:25,729 - mmseg - INFO - Iter [114450/160000]	lr: 4.687e-06, eta: 4:20:22, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9656, loss: 0.0764
+2023-03-05 00:55:39,407 - mmseg - INFO - Iter [114500/160000]	lr: 4.687e-06, eta: 4:20:03, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9147, loss: 0.0771
+2023-03-05 00:55:53,581 - mmseg - INFO - Iter [114550/160000]	lr: 4.687e-06, eta: 4:19:45, time: 0.283, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9372, loss: 0.0770
+2023-03-05 00:56:09,971 - mmseg - INFO - Iter [114600/160000]	lr: 4.687e-06, eta: 4:19:28, time: 0.328, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9273, loss: 0.0773
+2023-03-05 00:56:23,745 - mmseg - INFO - Iter [114650/160000]	lr: 4.687e-06, eta: 4:19:09, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9576, loss: 0.0765
+2023-03-05 00:56:37,310 - mmseg - INFO - Iter [114700/160000]	lr: 4.687e-06, eta: 4:18:50, time: 0.271, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9697, loss: 0.0762
+2023-03-05 00:56:50,978 - mmseg - INFO - Iter [114750/160000]	lr: 4.687e-06, eta: 4:18:32, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8573, loss: 0.0796
+2023-03-05 00:57:07,047 - mmseg - INFO - Iter [114800/160000]	lr: 4.687e-06, eta: 4:18:14, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8557, loss: 0.0787
+2023-03-05 00:57:20,769 - mmseg - INFO - Iter [114850/160000]	lr: 4.687e-06, eta: 4:17:56, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0740, decode.acc_seg: 97.0632, loss: 0.0740
+2023-03-05 00:57:34,851 - mmseg - INFO - Iter [114900/160000]	lr: 4.687e-06, eta: 4:17:38, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9419, loss: 0.0775
+2023-03-05 00:57:50,977 - mmseg - INFO - Iter [114950/160000]	lr: 4.687e-06, eta: 4:17:20, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8367, loss: 0.0794
+2023-03-05 00:58:04,906 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 00:58:04,906 - mmseg - INFO - Iter [115000/160000]	lr: 4.687e-06, eta: 4:17:02, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 96.9675, loss: 0.0755
+2023-03-05 00:58:18,467 - mmseg - INFO - Iter [115050/160000]	lr: 4.687e-06, eta: 4:16:43, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.9816, loss: 0.0784
+2023-03-05 00:58:32,763 - mmseg - INFO - Iter [115100/160000]	lr: 4.687e-06, eta: 4:16:25, time: 0.286, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0747, decode.acc_seg: 96.9936, loss: 0.0747
+2023-03-05 00:58:48,815 - mmseg - INFO - Iter [115150/160000]	lr: 4.687e-06, eta: 4:16:07, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8672, loss: 0.0788
+2023-03-05 00:59:02,561 - mmseg - INFO - Iter [115200/160000]	lr: 4.687e-06, eta: 4:15:49, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0729, decode.acc_seg: 97.0973, loss: 0.0729
+2023-03-05 00:59:16,230 - mmseg - INFO - Iter [115250/160000]	lr: 4.687e-06, eta: 4:15:30, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8369, loss: 0.0790
+2023-03-05 00:59:30,194 - mmseg - INFO - Iter [115300/160000]	lr: 4.687e-06, eta: 4:15:12, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0749, decode.acc_seg: 97.0031, loss: 0.0749
+2023-03-05 00:59:46,329 - mmseg - INFO - Iter [115350/160000]	lr: 4.687e-06, eta: 4:14:55, time: 0.323, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9979, loss: 0.0761
+2023-03-05 01:00:00,065 - mmseg - INFO - Iter [115400/160000]	lr: 4.687e-06, eta: 4:14:36, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0750, decode.acc_seg: 96.9939, loss: 0.0750
+2023-03-05 01:00:13,830 - mmseg - INFO - Iter [115450/160000]	lr: 4.687e-06, eta: 4:14:18, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9682, loss: 0.0759
+2023-03-05 01:00:27,409 - mmseg - INFO - Iter [115500/160000]	lr: 4.687e-06, eta: 4:13:59, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.7981, loss: 0.0805
+2023-03-05 01:00:43,518 - mmseg - INFO - Iter [115550/160000]	lr: 4.687e-06, eta: 4:13:42, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9323, loss: 0.0773
+2023-03-05 01:00:57,097 - mmseg - INFO - Iter [115600/160000]	lr: 4.687e-06, eta: 4:13:23, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9616, loss: 0.0757
+2023-03-05 01:01:10,774 - mmseg - INFO - Iter [115650/160000]	lr: 4.687e-06, eta: 4:13:05, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0750, decode.acc_seg: 97.0124, loss: 0.0750
+2023-03-05 01:01:26,737 - mmseg - INFO - Iter [115700/160000]	lr: 4.687e-06, eta: 4:12:47, time: 0.319, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9178, loss: 0.0780
+2023-03-05 01:01:40,691 - mmseg - INFO - Iter [115750/160000]	lr: 4.687e-06, eta: 4:12:29, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9058, loss: 0.0776
+2023-03-05 01:01:54,638 - mmseg - INFO - Iter [115800/160000]	lr: 4.687e-06, eta: 4:12:11, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.8905, loss: 0.0775
+2023-03-05 01:02:08,443 - mmseg - INFO - Iter [115850/160000]	lr: 4.687e-06, eta: 4:11:52, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9352, loss: 0.0772
+2023-03-05 01:02:24,474 - mmseg - INFO - Iter [115900/160000]	lr: 4.687e-06, eta: 4:11:35, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0739, decode.acc_seg: 97.0374, loss: 0.0739
+2023-03-05 01:02:38,330 - mmseg - INFO - Iter [115950/160000]	lr: 4.687e-06, eta: 4:11:16, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0750, decode.acc_seg: 97.0148, loss: 0.0750
+2023-03-05 01:02:52,335 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 01:02:52,335 - mmseg - INFO - Iter [116000/160000]	lr: 4.687e-06, eta: 4:10:58, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0750, decode.acc_seg: 97.0042, loss: 0.0750
+2023-03-05 01:03:06,270 - mmseg - INFO - Iter [116050/160000]	lr: 4.687e-06, eta: 4:10:40, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8578, loss: 0.0784
+2023-03-05 01:03:22,278 - mmseg - INFO - Iter [116100/160000]	lr: 4.687e-06, eta: 4:10:22, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.9239, loss: 0.0785
+2023-03-05 01:03:35,876 - mmseg - INFO - Iter [116150/160000]	lr: 4.687e-06, eta: 4:10:04, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9154, loss: 0.0775
+2023-03-05 01:03:49,483 - mmseg - INFO - Iter [116200/160000]	lr: 4.687e-06, eta: 4:09:45, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.8945, loss: 0.0776
+2023-03-05 01:04:03,089 - mmseg - INFO - Iter [116250/160000]	lr: 4.687e-06, eta: 4:09:27, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9425, loss: 0.0773
+2023-03-05 01:04:19,052 - mmseg - INFO - Iter [116300/160000]	lr: 4.687e-06, eta: 4:09:09, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0749, decode.acc_seg: 97.0036, loss: 0.0749
+2023-03-05 01:04:32,921 - mmseg - INFO - Iter [116350/160000]	lr: 4.687e-06, eta: 4:08:51, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8050, loss: 0.0796
+2023-03-05 01:04:46,532 - mmseg - INFO - Iter [116400/160000]	lr: 4.687e-06, eta: 4:08:33, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0751, decode.acc_seg: 97.0226, loss: 0.0751
+2023-03-05 01:05:02,546 - mmseg - INFO - Iter [116450/160000]	lr: 4.687e-06, eta: 4:08:15, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 97.0126, loss: 0.0761
+2023-03-05 01:05:16,201 - mmseg - INFO - Iter [116500/160000]	lr: 4.687e-06, eta: 4:07:57, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9691, loss: 0.0758
+2023-03-05 01:05:29,849 - mmseg - INFO - Iter [116550/160000]	lr: 4.687e-06, eta: 4:07:38, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8848, loss: 0.0790
+2023-03-05 01:05:43,692 - mmseg - INFO - Iter [116600/160000]	lr: 4.687e-06, eta: 4:07:20, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.8980, loss: 0.0775
+2023-03-05 01:05:59,680 - mmseg - INFO - Iter [116650/160000]	lr: 4.687e-06, eta: 4:07:02, time: 0.320, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9628, loss: 0.0760
+2023-03-05 01:06:13,280 - mmseg - INFO - Iter [116700/160000]	lr: 4.687e-06, eta: 4:06:44, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8975, loss: 0.0781
+2023-03-05 01:06:27,048 - mmseg - INFO - Iter [116750/160000]	lr: 4.687e-06, eta: 4:06:26, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.9178, loss: 0.0782
+2023-03-05 01:06:40,746 - mmseg - INFO - Iter [116800/160000]	lr: 4.687e-06, eta: 4:06:07, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8437, loss: 0.0796
+2023-03-05 01:06:56,781 - mmseg - INFO - Iter [116850/160000]	lr: 4.687e-06, eta: 4:05:50, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.9059, loss: 0.0784
+2023-03-05 01:07:10,550 - mmseg - INFO - Iter [116900/160000]	lr: 4.687e-06, eta: 4:05:32, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9867, loss: 0.0758
+2023-03-05 01:07:24,215 - mmseg - INFO - Iter [116950/160000]	lr: 4.687e-06, eta: 4:05:13, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8886, loss: 0.0783
+2023-03-05 01:07:40,192 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 01:07:40,192 - mmseg - INFO - Iter [117000/160000]	lr: 4.687e-06, eta: 4:04:56, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9499, loss: 0.0769
+2023-03-05 01:07:53,877 - mmseg - INFO - Iter [117050/160000]	lr: 4.687e-06, eta: 4:04:37, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8099, loss: 0.0804
+2023-03-05 01:08:07,510 - mmseg - INFO - Iter [117100/160000]	lr: 4.687e-06, eta: 4:04:19, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9575, loss: 0.0764
+2023-03-05 01:08:21,246 - mmseg - INFO - Iter [117150/160000]	lr: 4.687e-06, eta: 4:04:01, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9636, loss: 0.0762
+2023-03-05 01:08:37,465 - mmseg - INFO - Iter [117200/160000]	lr: 4.687e-06, eta: 4:03:43, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9160, loss: 0.0772
+2023-03-05 01:08:51,282 - mmseg - INFO - Iter [117250/160000]	lr: 4.687e-06, eta: 4:03:25, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9343, loss: 0.0775
+2023-03-05 01:09:05,166 - mmseg - INFO - Iter [117300/160000]	lr: 4.687e-06, eta: 4:03:07, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8373, loss: 0.0792
+2023-03-05 01:09:19,040 - mmseg - INFO - Iter [117350/160000]	lr: 4.687e-06, eta: 4:02:49, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9827, loss: 0.0766
+2023-03-05 01:09:35,220 - mmseg - INFO - Iter [117400/160000]	lr: 4.687e-06, eta: 4:02:31, time: 0.324, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0756, decode.acc_seg: 96.9788, loss: 0.0756
+2023-03-05 01:09:48,772 - mmseg - INFO - Iter [117450/160000]	lr: 4.687e-06, eta: 4:02:13, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9475, loss: 0.0774
+2023-03-05 01:10:02,614 - mmseg - INFO - Iter [117500/160000]	lr: 4.687e-06, eta: 4:01:55, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9162, loss: 0.0774
+2023-03-05 01:10:16,344 - mmseg - INFO - Iter [117550/160000]	lr: 4.687e-06, eta: 4:01:36, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9452, loss: 0.0772
+2023-03-05 01:10:32,484 - mmseg - INFO - Iter [117600/160000]	lr: 4.687e-06, eta: 4:01:19, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9156, loss: 0.0771
+2023-03-05 01:10:46,207 - mmseg - INFO - Iter [117650/160000]	lr: 4.687e-06, eta: 4:01:01, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9472, loss: 0.0767
+2023-03-05 01:11:00,319 - mmseg - INFO - Iter [117700/160000]	lr: 4.687e-06, eta: 4:00:42, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.8690, loss: 0.0809
+2023-03-05 01:11:16,613 - mmseg - INFO - Iter [117750/160000]	lr: 4.687e-06, eta: 4:00:25, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8494, loss: 0.0794
+2023-03-05 01:11:30,319 - mmseg - INFO - Iter [117800/160000]	lr: 4.687e-06, eta: 4:00:07, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 96.9989, loss: 0.0752
+2023-03-05 01:11:43,938 - mmseg - INFO - Iter [117850/160000]	lr: 4.687e-06, eta: 3:59:49, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8739, loss: 0.0791
+2023-03-05 01:11:57,687 - mmseg - INFO - Iter [117900/160000]	lr: 4.687e-06, eta: 3:59:30, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.8797, loss: 0.0776
+2023-03-05 01:12:13,743 - mmseg - INFO - Iter [117950/160000]	lr: 4.687e-06, eta: 3:59:13, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.7429, loss: 0.0807
+2023-03-05 01:12:27,652 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 01:12:27,653 - mmseg - INFO - Iter [118000/160000]	lr: 4.687e-06, eta: 3:58:55, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9804, loss: 0.0757
+2023-03-05 01:12:41,381 - mmseg - INFO - Iter [118050/160000]	lr: 4.687e-06, eta: 3:58:36, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9584, loss: 0.0757
+2023-03-05 01:12:55,334 - mmseg - INFO - Iter [118100/160000]	lr: 4.687e-06, eta: 3:58:18, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8694, loss: 0.0794
+2023-03-05 01:13:11,680 - mmseg - INFO - Iter [118150/160000]	lr: 4.687e-06, eta: 3:58:01, time: 0.327, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8422, loss: 0.0794
+2023-03-05 01:13:25,482 - mmseg - INFO - Iter [118200/160000]	lr: 4.687e-06, eta: 3:57:43, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0742, decode.acc_seg: 97.0193, loss: 0.0742
+2023-03-05 01:13:39,115 - mmseg - INFO - Iter [118250/160000]	lr: 4.687e-06, eta: 3:57:24, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9299, loss: 0.0765
+2023-03-05 01:13:55,066 - mmseg - INFO - Iter [118300/160000]	lr: 4.687e-06, eta: 3:57:07, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.8080, loss: 0.0808
+2023-03-05 01:14:08,891 - mmseg - INFO - Iter [118350/160000]	lr: 4.687e-06, eta: 3:56:49, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9220, loss: 0.0775
+2023-03-05 01:14:22,470 - mmseg - INFO - Iter [118400/160000]	lr: 4.687e-06, eta: 3:56:30, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9050, loss: 0.0776
+2023-03-05 01:14:36,043 - mmseg - INFO - Iter [118450/160000]	lr: 4.687e-06, eta: 3:56:12, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8591, loss: 0.0788
+2023-03-05 01:14:52,110 - mmseg - INFO - Iter [118500/160000]	lr: 4.687e-06, eta: 3:55:55, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 96.9608, loss: 0.0754
+2023-03-05 01:15:05,729 - mmseg - INFO - Iter [118550/160000]	lr: 4.687e-06, eta: 3:55:37, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9012, loss: 0.0771
+2023-03-05 01:15:19,612 - mmseg - INFO - Iter [118600/160000]	lr: 4.687e-06, eta: 3:55:18, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9871, loss: 0.0761
+2023-03-05 01:15:33,296 - mmseg - INFO - Iter [118650/160000]	lr: 4.687e-06, eta: 3:55:00, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0750, decode.acc_seg: 97.0003, loss: 0.0750
+2023-03-05 01:15:49,356 - mmseg - INFO - Iter [118700/160000]	lr: 4.687e-06, eta: 3:54:43, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0747, decode.acc_seg: 96.9924, loss: 0.0747
+2023-03-05 01:16:03,172 - mmseg - INFO - Iter [118750/160000]	lr: 4.687e-06, eta: 3:54:25, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.8981, loss: 0.0772
+2023-03-05 01:16:16,858 - mmseg - INFO - Iter [118800/160000]	lr: 4.687e-06, eta: 3:54:06, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 96.9939, loss: 0.0755
+2023-03-05 01:16:30,630 - mmseg - INFO - Iter [118850/160000]	lr: 4.687e-06, eta: 3:53:48, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9158, loss: 0.0778
+2023-03-05 01:16:46,783 - mmseg - INFO - Iter [118900/160000]	lr: 4.687e-06, eta: 3:53:31, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0732, decode.acc_seg: 97.0692, loss: 0.0732
+2023-03-05 01:17:00,424 - mmseg - INFO - Iter [118950/160000]	lr: 4.687e-06, eta: 3:53:13, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.7907, loss: 0.0799
+2023-03-05 01:17:14,076 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 01:17:14,076 - mmseg - INFO - Iter [119000/160000]	lr: 4.687e-06, eta: 3:52:54, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.9042, loss: 0.0785
+2023-03-05 01:17:30,181 - mmseg - INFO - Iter [119050/160000]	lr: 4.687e-06, eta: 3:52:37, time: 0.322, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9819, loss: 0.0762
+2023-03-05 01:17:44,127 - mmseg - INFO - Iter [119100/160000]	lr: 4.687e-06, eta: 3:52:19, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9607, loss: 0.0758
+2023-03-05 01:17:58,219 - mmseg - INFO - Iter [119150/160000]	lr: 4.687e-06, eta: 3:52:01, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8457, loss: 0.0795
+2023-03-05 01:18:11,787 - mmseg - INFO - Iter [119200/160000]	lr: 4.687e-06, eta: 3:51:43, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8917, loss: 0.0781
+2023-03-05 01:18:27,840 - mmseg - INFO - Iter [119250/160000]	lr: 4.687e-06, eta: 3:51:25, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0749, decode.acc_seg: 97.0281, loss: 0.0749
+2023-03-05 01:18:41,720 - mmseg - INFO - Iter [119300/160000]	lr: 4.687e-06, eta: 3:51:07, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.9078, loss: 0.0784
+2023-03-05 01:18:55,906 - mmseg - INFO - Iter [119350/160000]	lr: 4.687e-06, eta: 3:50:49, time: 0.284, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9656, loss: 0.0760
+2023-03-05 01:19:09,749 - mmseg - INFO - Iter [119400/160000]	lr: 4.687e-06, eta: 3:50:31, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.9742, loss: 0.0787
+2023-03-05 01:19:25,899 - mmseg - INFO - Iter [119450/160000]	lr: 4.687e-06, eta: 3:50:14, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 97.0063, loss: 0.0760
+2023-03-05 01:19:39,669 - mmseg - INFO - Iter [119500/160000]	lr: 4.687e-06, eta: 3:49:56, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.8842, loss: 0.0776
+2023-03-05 01:19:53,352 - mmseg - INFO - Iter [119550/160000]	lr: 4.687e-06, eta: 3:49:37, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.7826, loss: 0.0831
+2023-03-05 01:20:09,407 - mmseg - INFO - Iter [119600/160000]	lr: 4.687e-06, eta: 3:49:20, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 97.0016, loss: 0.0758
+2023-03-05 01:20:22,975 - mmseg - INFO - Iter [119650/160000]	lr: 4.687e-06, eta: 3:49:02, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9315, loss: 0.0763
+2023-03-05 01:20:36,558 - mmseg - INFO - Iter [119700/160000]	lr: 4.687e-06, eta: 3:48:44, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9281, loss: 0.0772
+2023-03-05 01:20:50,241 - mmseg - INFO - Iter [119750/160000]	lr: 4.687e-06, eta: 3:48:25, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9301, loss: 0.0767
+2023-03-05 01:21:06,310 - mmseg - INFO - Iter [119800/160000]	lr: 4.687e-06, eta: 3:48:08, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9188, loss: 0.0780
+2023-03-05 01:21:20,597 - mmseg - INFO - Iter [119850/160000]	lr: 4.687e-06, eta: 3:47:50, time: 0.286, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8287, loss: 0.0796
+2023-03-05 01:21:34,358 - mmseg - INFO - Iter [119900/160000]	lr: 4.687e-06, eta: 3:47:32, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.7931, loss: 0.0800
+2023-03-05 01:21:47,962 - mmseg - INFO - Iter [119950/160000]	lr: 4.687e-06, eta: 3:47:14, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8784, loss: 0.0783
+2023-03-05 01:22:03,889 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 01:22:03,889 - mmseg - INFO - Iter [120000/160000]	lr: 4.687e-06, eta: 3:46:57, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8699, loss: 0.0781
+2023-03-05 01:22:17,521 - mmseg - INFO - Iter [120050/160000]	lr: 2.344e-06, eta: 3:46:38, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9457, loss: 0.0768
+2023-03-05 01:22:31,388 - mmseg - INFO - Iter [120100/160000]	lr: 2.344e-06, eta: 3:46:20, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9652, loss: 0.0771
+2023-03-05 01:22:45,459 - mmseg - INFO - Iter [120150/160000]	lr: 2.344e-06, eta: 3:46:02, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8663, loss: 0.0799
+2023-03-05 01:23:01,493 - mmseg - INFO - Iter [120200/160000]	lr: 2.344e-06, eta: 3:45:45, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8566, loss: 0.0789
+2023-03-05 01:23:15,099 - mmseg - INFO - Iter [120250/160000]	lr: 2.344e-06, eta: 3:45:27, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0747, decode.acc_seg: 96.9909, loss: 0.0747
+2023-03-05 01:23:28,762 - mmseg - INFO - Iter [120300/160000]	lr: 2.344e-06, eta: 3:45:09, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8944, loss: 0.0785
+2023-03-05 01:23:45,040 - mmseg - INFO - Iter [120350/160000]	lr: 2.344e-06, eta: 3:44:51, time: 0.326, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8759, loss: 0.0784
+2023-03-05 01:23:58,788 - mmseg - INFO - Iter [120400/160000]	lr: 2.344e-06, eta: 3:44:33, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8820, loss: 0.0788
+2023-03-05 01:24:12,676 - mmseg - INFO - Iter [120450/160000]	lr: 2.344e-06, eta: 3:44:15, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.8527, loss: 0.0778
+2023-03-05 01:24:26,257 - mmseg - INFO - Iter [120500/160000]	lr: 2.344e-06, eta: 3:43:57, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.9056, loss: 0.0781
+2023-03-05 01:24:42,620 - mmseg - INFO - Iter [120550/160000]	lr: 2.344e-06, eta: 3:43:40, time: 0.327, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8801, loss: 0.0790
+2023-03-05 01:24:56,419 - mmseg - INFO - Iter [120600/160000]	lr: 2.344e-06, eta: 3:43:22, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9407, loss: 0.0775
+2023-03-05 01:25:10,071 - mmseg - INFO - Iter [120650/160000]	lr: 2.344e-06, eta: 3:43:04, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8407, loss: 0.0800
+2023-03-05 01:25:23,722 - mmseg - INFO - Iter [120700/160000]	lr: 2.344e-06, eta: 3:42:46, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 97.0122, loss: 0.0754
+2023-03-05 01:25:39,841 - mmseg - INFO - Iter [120750/160000]	lr: 2.344e-06, eta: 3:42:28, time: 0.322, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9284, loss: 0.0762
+2023-03-05 01:25:53,512 - mmseg - INFO - Iter [120800/160000]	lr: 2.344e-06, eta: 3:42:10, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9721, loss: 0.0767
+2023-03-05 01:26:07,170 - mmseg - INFO - Iter [120850/160000]	lr: 2.344e-06, eta: 3:41:52, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8171, loss: 0.0791
+2023-03-05 01:26:20,788 - mmseg - INFO - Iter [120900/160000]	lr: 2.344e-06, eta: 3:41:34, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9619, loss: 0.0764
+2023-03-05 01:26:37,027 - mmseg - INFO - Iter [120950/160000]	lr: 2.344e-06, eta: 3:41:17, time: 0.325, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 96.9697, loss: 0.0755
+2023-03-05 01:26:50,923 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 01:26:50,923 - mmseg - INFO - Iter [121000/160000]	lr: 2.344e-06, eta: 3:40:59, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8438, loss: 0.0796
+2023-03-05 01:27:04,696 - mmseg - INFO - Iter [121050/160000]	lr: 2.344e-06, eta: 3:40:41, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.8878, loss: 0.0775
+2023-03-05 01:27:20,786 - mmseg - INFO - Iter [121100/160000]	lr: 2.344e-06, eta: 3:40:24, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8779, loss: 0.0781
+2023-03-05 01:27:34,763 - mmseg - INFO - Iter [121150/160000]	lr: 2.344e-06, eta: 3:40:06, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9597, loss: 0.0759
+2023-03-05 01:27:48,623 - mmseg - INFO - Iter [121200/160000]	lr: 2.344e-06, eta: 3:39:48, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8855, loss: 0.0779
+2023-03-05 01:28:02,546 - mmseg - INFO - Iter [121250/160000]	lr: 2.344e-06, eta: 3:39:30, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0748, decode.acc_seg: 97.0197, loss: 0.0748
+2023-03-05 01:28:18,787 - mmseg - INFO - Iter [121300/160000]	lr: 2.344e-06, eta: 3:39:12, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9039, loss: 0.0775
+2023-03-05 01:28:32,600 - mmseg - INFO - Iter [121350/160000]	lr: 2.344e-06, eta: 3:38:54, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.8205, loss: 0.0806
+2023-03-05 01:28:46,445 - mmseg - INFO - Iter [121400/160000]	lr: 2.344e-06, eta: 3:38:36, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0839, decode.acc_seg: 96.8430, loss: 0.0839
+2023-03-05 01:29:00,186 - mmseg - INFO - Iter [121450/160000]	lr: 2.344e-06, eta: 3:38:18, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8397, loss: 0.0801
+2023-03-05 01:29:16,231 - mmseg - INFO - Iter [121500/160000]	lr: 2.344e-06, eta: 3:38:01, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9275, loss: 0.0772
+2023-03-05 01:29:29,852 - mmseg - INFO - Iter [121550/160000]	lr: 2.344e-06, eta: 3:37:43, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9346, loss: 0.0768
+2023-03-05 01:29:43,984 - mmseg - INFO - Iter [121600/160000]	lr: 2.344e-06, eta: 3:37:25, time: 0.283, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9479, loss: 0.0768
+2023-03-05 01:30:00,490 - mmseg - INFO - Iter [121650/160000]	lr: 2.344e-06, eta: 3:37:08, time: 0.330, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9417, loss: 0.0771
+2023-03-05 01:30:14,147 - mmseg - INFO - Iter [121700/160000]	lr: 2.344e-06, eta: 3:36:50, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9305, loss: 0.0773
+2023-03-05 01:30:27,985 - mmseg - INFO - Iter [121750/160000]	lr: 2.344e-06, eta: 3:36:32, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8971, loss: 0.0791
+2023-03-05 01:30:41,646 - mmseg - INFO - Iter [121800/160000]	lr: 2.344e-06, eta: 3:36:14, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0829, decode.acc_seg: 96.7670, loss: 0.0829
+2023-03-05 01:30:57,652 - mmseg - INFO - Iter [121850/160000]	lr: 2.344e-06, eta: 3:35:57, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8893, loss: 0.0791
+2023-03-05 01:31:11,216 - mmseg - INFO - Iter [121900/160000]	lr: 2.344e-06, eta: 3:35:39, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8820, loss: 0.0785
+2023-03-05 01:31:24,903 - mmseg - INFO - Iter [121950/160000]	lr: 2.344e-06, eta: 3:35:21, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0748, decode.acc_seg: 97.0285, loss: 0.0748
+2023-03-05 01:31:38,530 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 01:31:38,531 - mmseg - INFO - Iter [122000/160000]	lr: 2.344e-06, eta: 3:35:03, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8752, loss: 0.0791
+2023-03-05 01:31:54,822 - mmseg - INFO - Iter [122050/160000]	lr: 2.344e-06, eta: 3:34:45, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0734, decode.acc_seg: 97.0962, loss: 0.0734
+2023-03-05 01:32:08,495 - mmseg - INFO - Iter [122100/160000]	lr: 2.344e-06, eta: 3:34:27, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9424, loss: 0.0771
+2023-03-05 01:32:22,359 - mmseg - INFO - Iter [122150/160000]	lr: 2.344e-06, eta: 3:34:09, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9195, loss: 0.0771
+2023-03-05 01:32:35,987 - mmseg - INFO - Iter [122200/160000]	lr: 2.344e-06, eta: 3:33:51, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0746, decode.acc_seg: 97.0018, loss: 0.0746
+2023-03-05 01:32:52,119 - mmseg - INFO - Iter [122250/160000]	lr: 2.344e-06, eta: 3:33:34, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9720, loss: 0.0760
+2023-03-05 01:33:05,906 - mmseg - INFO - Iter [122300/160000]	lr: 2.344e-06, eta: 3:33:16, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9631, loss: 0.0763
+2023-03-05 01:33:19,603 - mmseg - INFO - Iter [122350/160000]	lr: 2.344e-06, eta: 3:32:58, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9349, loss: 0.0775
+2023-03-05 01:33:35,969 - mmseg - INFO - Iter [122400/160000]	lr: 2.344e-06, eta: 3:32:41, time: 0.327, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9575, loss: 0.0761
+2023-03-05 01:33:50,111 - mmseg - INFO - Iter [122450/160000]	lr: 2.344e-06, eta: 3:32:23, time: 0.283, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.8000, loss: 0.0807
+2023-03-05 01:34:04,021 - mmseg - INFO - Iter [122500/160000]	lr: 2.344e-06, eta: 3:32:05, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0749, decode.acc_seg: 97.0008, loss: 0.0749
+2023-03-05 01:34:17,712 - mmseg - INFO - Iter [122550/160000]	lr: 2.344e-06, eta: 3:31:47, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9576, loss: 0.0764
+2023-03-05 01:34:33,924 - mmseg - INFO - Iter [122600/160000]	lr: 2.344e-06, eta: 3:31:30, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.8827, loss: 0.0767
+2023-03-05 01:34:47,680 - mmseg - INFO - Iter [122650/160000]	lr: 2.344e-06, eta: 3:31:12, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9430, loss: 0.0766
+2023-03-05 01:35:01,884 - mmseg - INFO - Iter [122700/160000]	lr: 2.344e-06, eta: 3:30:54, time: 0.284, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9315, loss: 0.0774
+2023-03-05 01:35:15,612 - mmseg - INFO - Iter [122750/160000]	lr: 2.344e-06, eta: 3:30:37, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9144, loss: 0.0776
+2023-03-05 01:35:31,529 - mmseg - INFO - Iter [122800/160000]	lr: 2.344e-06, eta: 3:30:19, time: 0.318, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9212, loss: 0.0768
+2023-03-05 01:35:45,161 - mmseg - INFO - Iter [122850/160000]	lr: 2.344e-06, eta: 3:30:01, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9595, loss: 0.0769
+2023-03-05 01:35:58,893 - mmseg - INFO - Iter [122900/160000]	lr: 2.344e-06, eta: 3:29:43, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8595, loss: 0.0785
+2023-03-05 01:36:15,086 - mmseg - INFO - Iter [122950/160000]	lr: 2.344e-06, eta: 3:29:26, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.8969, loss: 0.0778
+2023-03-05 01:36:28,880 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 01:36:28,880 - mmseg - INFO - Iter [123000/160000]	lr: 2.344e-06, eta: 3:29:08, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.8793, loss: 0.0776
+2023-03-05 01:36:42,487 - mmseg - INFO - Iter [123050/160000]	lr: 2.344e-06, eta: 3:28:50, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0740, decode.acc_seg: 97.0834, loss: 0.0740
+2023-03-05 01:36:56,393 - mmseg - INFO - Iter [123100/160000]	lr: 2.344e-06, eta: 3:28:32, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.9054, loss: 0.0783
+2023-03-05 01:37:12,327 - mmseg - INFO - Iter [123150/160000]	lr: 2.344e-06, eta: 3:28:15, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8427, loss: 0.0792
+2023-03-05 01:37:25,957 - mmseg - INFO - Iter [123200/160000]	lr: 2.344e-06, eta: 3:27:57, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9517, loss: 0.0768
+2023-03-05 01:37:39,766 - mmseg - INFO - Iter [123250/160000]	lr: 2.344e-06, eta: 3:27:39, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0741, decode.acc_seg: 97.0375, loss: 0.0741
+2023-03-05 01:37:53,662 - mmseg - INFO - Iter [123300/160000]	lr: 2.344e-06, eta: 3:27:21, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0744, decode.acc_seg: 97.0324, loss: 0.0744
+2023-03-05 01:38:09,901 - mmseg - INFO - Iter [123350/160000]	lr: 2.344e-06, eta: 3:27:04, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9691, loss: 0.0761
+2023-03-05 01:38:23,832 - mmseg - INFO - Iter [123400/160000]	lr: 2.344e-06, eta: 3:26:46, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.8876, loss: 0.0777
+2023-03-05 01:38:37,737 - mmseg - INFO - Iter [123450/160000]	lr: 2.344e-06, eta: 3:26:29, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9118, loss: 0.0780
+2023-03-05 01:38:51,346 - mmseg - INFO - Iter [123500/160000]	lr: 2.344e-06, eta: 3:26:11, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8538, loss: 0.0792
+2023-03-05 01:39:07,345 - mmseg - INFO - Iter [123550/160000]	lr: 2.344e-06, eta: 3:25:53, time: 0.320, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9755, loss: 0.0758
+2023-03-05 01:39:21,109 - mmseg - INFO - Iter [123600/160000]	lr: 2.344e-06, eta: 3:25:36, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8042, loss: 0.0786
+2023-03-05 01:39:34,683 - mmseg - INFO - Iter [123650/160000]	lr: 2.344e-06, eta: 3:25:18, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9735, loss: 0.0759
+2023-03-05 01:39:50,743 - mmseg - INFO - Iter [123700/160000]	lr: 2.344e-06, eta: 3:25:00, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 96.9869, loss: 0.0755
+2023-03-05 01:40:04,371 - mmseg - INFO - Iter [123750/160000]	lr: 2.344e-06, eta: 3:24:42, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8358, loss: 0.0798
+2023-03-05 01:40:18,070 - mmseg - INFO - Iter [123800/160000]	lr: 2.344e-06, eta: 3:24:25, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9210, loss: 0.0775
+2023-03-05 01:40:31,938 - mmseg - INFO - Iter [123850/160000]	lr: 2.344e-06, eta: 3:24:07, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9371, loss: 0.0773
+2023-03-05 01:40:48,223 - mmseg - INFO - Iter [123900/160000]	lr: 2.344e-06, eta: 3:23:50, time: 0.326, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8556, loss: 0.0791
+2023-03-05 01:41:01,862 - mmseg - INFO - Iter [123950/160000]	lr: 2.344e-06, eta: 3:23:32, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9546, loss: 0.0778
+2023-03-05 01:41:15,648 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 01:41:15,648 - mmseg - INFO - Iter [124000/160000]	lr: 2.344e-06, eta: 3:23:14, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9632, loss: 0.0762
+2023-03-05 01:41:29,291 - mmseg - INFO - Iter [124050/160000]	lr: 2.344e-06, eta: 3:22:56, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9447, loss: 0.0767
+2023-03-05 01:41:45,261 - mmseg - INFO - Iter [124100/160000]	lr: 2.344e-06, eta: 3:22:39, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9206, loss: 0.0779
+2023-03-05 01:41:59,367 - mmseg - INFO - Iter [124150/160000]	lr: 2.344e-06, eta: 3:22:21, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0744, decode.acc_seg: 97.0374, loss: 0.0744
+2023-03-05 01:42:12,933 - mmseg - INFO - Iter [124200/160000]	lr: 2.344e-06, eta: 3:22:03, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8955, loss: 0.0784
+2023-03-05 01:42:28,859 - mmseg - INFO - Iter [124250/160000]	lr: 2.344e-06, eta: 3:21:46, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9140, loss: 0.0778
+2023-03-05 01:42:42,963 - mmseg - INFO - Iter [124300/160000]	lr: 2.344e-06, eta: 3:21:28, time: 0.282, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9586, loss: 0.0761
+2023-03-05 01:42:56,691 - mmseg - INFO - Iter [124350/160000]	lr: 2.344e-06, eta: 3:21:10, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9677, loss: 0.0769
+2023-03-05 01:43:10,291 - mmseg - INFO - Iter [124400/160000]	lr: 2.344e-06, eta: 3:20:52, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0806, decode.acc_seg: 96.8106, loss: 0.0806
+2023-03-05 01:43:26,290 - mmseg - INFO - Iter [124450/160000]	lr: 2.344e-06, eta: 3:20:35, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9481, loss: 0.0768
+2023-03-05 01:43:40,121 - mmseg - INFO - Iter [124500/160000]	lr: 2.344e-06, eta: 3:20:17, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9100, loss: 0.0773
+2023-03-05 01:43:53,831 - mmseg - INFO - Iter [124550/160000]	lr: 2.344e-06, eta: 3:20:00, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0814, decode.acc_seg: 96.7827, loss: 0.0814
+2023-03-05 01:44:07,440 - mmseg - INFO - Iter [124600/160000]	lr: 2.344e-06, eta: 3:19:42, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0744, decode.acc_seg: 97.0408, loss: 0.0744
+2023-03-05 01:44:23,388 - mmseg - INFO - Iter [124650/160000]	lr: 2.344e-06, eta: 3:19:24, time: 0.319, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9241, loss: 0.0776
+2023-03-05 01:44:36,997 - mmseg - INFO - Iter [124700/160000]	lr: 2.344e-06, eta: 3:19:07, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0747, decode.acc_seg: 97.0232, loss: 0.0747
+2023-03-05 01:44:50,971 - mmseg - INFO - Iter [124750/160000]	lr: 2.344e-06, eta: 3:18:49, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9751, loss: 0.0768
+2023-03-05 01:45:04,695 - mmseg - INFO - Iter [124800/160000]	lr: 2.344e-06, eta: 3:18:31, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9477, loss: 0.0772
+2023-03-05 01:45:20,688 - mmseg - INFO - Iter [124850/160000]	lr: 2.344e-06, eta: 3:18:14, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0751, decode.acc_seg: 97.0089, loss: 0.0751
+2023-03-05 01:45:34,639 - mmseg - INFO - Iter [124900/160000]	lr: 2.344e-06, eta: 3:17:56, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9030, loss: 0.0772
+2023-03-05 01:45:48,302 - mmseg - INFO - Iter [124950/160000]	lr: 2.344e-06, eta: 3:17:38, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8614, loss: 0.0799
+2023-03-05 01:46:04,379 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 01:46:04,379 - mmseg - INFO - Iter [125000/160000]	lr: 2.344e-06, eta: 3:17:21, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8282, loss: 0.0792
+2023-03-05 01:46:18,049 - mmseg - INFO - Iter [125050/160000]	lr: 2.344e-06, eta: 3:17:03, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9537, loss: 0.0766
+2023-03-05 01:46:31,895 - mmseg - INFO - Iter [125100/160000]	lr: 2.344e-06, eta: 3:16:46, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9874, loss: 0.0763
+2023-03-05 01:46:45,521 - mmseg - INFO - Iter [125150/160000]	lr: 2.344e-06, eta: 3:16:28, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8302, loss: 0.0789
+2023-03-05 01:47:01,499 - mmseg - INFO - Iter [125200/160000]	lr: 2.344e-06, eta: 3:16:11, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0751, decode.acc_seg: 96.9823, loss: 0.0751
+2023-03-05 01:47:15,048 - mmseg - INFO - Iter [125250/160000]	lr: 2.344e-06, eta: 3:15:53, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9836, loss: 0.0764
+2023-03-05 01:47:28,692 - mmseg - INFO - Iter [125300/160000]	lr: 2.344e-06, eta: 3:15:35, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8411, loss: 0.0797
+2023-03-05 01:47:42,284 - mmseg - INFO - Iter [125350/160000]	lr: 2.344e-06, eta: 3:15:17, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.9019, loss: 0.0788
+2023-03-05 01:47:58,235 - mmseg - INFO - Iter [125400/160000]	lr: 2.344e-06, eta: 3:15:00, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.9285, loss: 0.0783
+2023-03-05 01:48:12,139 - mmseg - INFO - Iter [125450/160000]	lr: 2.344e-06, eta: 3:14:42, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.9064, loss: 0.0783
+2023-03-05 01:48:25,859 - mmseg - INFO - Iter [125500/160000]	lr: 2.344e-06, eta: 3:14:24, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 97.0175, loss: 0.0755
+2023-03-05 01:48:39,555 - mmseg - INFO - Iter [125550/160000]	lr: 2.344e-06, eta: 3:14:07, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8537, loss: 0.0789
+2023-03-05 01:48:55,656 - mmseg - INFO - Iter [125600/160000]	lr: 2.344e-06, eta: 3:13:49, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0742, decode.acc_seg: 97.0186, loss: 0.0742
+2023-03-05 01:49:09,247 - mmseg - INFO - Iter [125650/160000]	lr: 2.344e-06, eta: 3:13:32, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0733, decode.acc_seg: 97.0634, loss: 0.0733
+2023-03-05 01:49:23,157 - mmseg - INFO - Iter [125700/160000]	lr: 2.344e-06, eta: 3:13:14, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.7872, loss: 0.0799
+2023-03-05 01:49:39,202 - mmseg - INFO - Iter [125750/160000]	lr: 2.344e-06, eta: 3:12:57, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9616, loss: 0.0766
+2023-03-05 01:49:53,062 - mmseg - INFO - Iter [125800/160000]	lr: 2.344e-06, eta: 3:12:39, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9329, loss: 0.0773
+2023-03-05 01:50:06,725 - mmseg - INFO - Iter [125850/160000]	lr: 2.344e-06, eta: 3:12:21, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8915, loss: 0.0779
+2023-03-05 01:50:20,442 - mmseg - INFO - Iter [125900/160000]	lr: 2.344e-06, eta: 3:12:04, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9774, loss: 0.0761
+2023-03-05 01:50:36,569 - mmseg - INFO - Iter [125950/160000]	lr: 2.344e-06, eta: 3:11:46, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8657, loss: 0.0783
+2023-03-05 01:50:50,215 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 01:50:50,215 - mmseg - INFO - Iter [126000/160000]	lr: 2.344e-06, eta: 3:11:29, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9666, loss: 0.0766
+2023-03-05 01:51:03,863 - mmseg - INFO - Iter [126050/160000]	lr: 2.344e-06, eta: 3:11:11, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9874, loss: 0.0767
+2023-03-05 01:51:17,480 - mmseg - INFO - Iter [126100/160000]	lr: 2.344e-06, eta: 3:10:53, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0864, decode.acc_seg: 96.7431, loss: 0.0864
+2023-03-05 01:51:33,524 - mmseg - INFO - Iter [126150/160000]	lr: 2.344e-06, eta: 3:10:36, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9123, loss: 0.0775
+2023-03-05 01:51:47,295 - mmseg - INFO - Iter [126200/160000]	lr: 2.344e-06, eta: 3:10:18, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9772, loss: 0.0765
+2023-03-05 01:52:01,321 - mmseg - INFO - Iter [126250/160000]	lr: 2.344e-06, eta: 3:10:01, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9005, loss: 0.0778
+2023-03-05 01:52:17,565 - mmseg - INFO - Iter [126300/160000]	lr: 2.344e-06, eta: 3:09:44, time: 0.325, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9813, loss: 0.0763
+2023-03-05 01:52:31,334 - mmseg - INFO - Iter [126350/160000]	lr: 2.344e-06, eta: 3:09:26, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9486, loss: 0.0772
+2023-03-05 01:52:45,064 - mmseg - INFO - Iter [126400/160000]	lr: 2.344e-06, eta: 3:09:08, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8928, loss: 0.0783
+2023-03-05 01:52:58,679 - mmseg - INFO - Iter [126450/160000]	lr: 2.344e-06, eta: 3:08:50, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9123, loss: 0.0772
+2023-03-05 01:53:14,672 - mmseg - INFO - Iter [126500/160000]	lr: 2.344e-06, eta: 3:08:33, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8243, loss: 0.0788
+2023-03-05 01:53:28,299 - mmseg - INFO - Iter [126550/160000]	lr: 2.344e-06, eta: 3:08:15, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9220, loss: 0.0770
+2023-03-05 01:53:42,103 - mmseg - INFO - Iter [126600/160000]	lr: 2.344e-06, eta: 3:07:58, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8239, loss: 0.0799
+2023-03-05 01:53:55,761 - mmseg - INFO - Iter [126650/160000]	lr: 2.344e-06, eta: 3:07:40, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7861, loss: 0.0808
+2023-03-05 01:54:11,752 - mmseg - INFO - Iter [126700/160000]	lr: 2.344e-06, eta: 3:07:23, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0751, decode.acc_seg: 96.9787, loss: 0.0751
+2023-03-05 01:54:25,457 - mmseg - INFO - Iter [126750/160000]	lr: 2.344e-06, eta: 3:07:05, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9365, loss: 0.0767
+2023-03-05 01:54:39,343 - mmseg - INFO - Iter [126800/160000]	lr: 2.344e-06, eta: 3:06:48, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8989, loss: 0.0784
+2023-03-05 01:54:53,110 - mmseg - INFO - Iter [126850/160000]	lr: 2.344e-06, eta: 3:06:30, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8987, loss: 0.0781
+2023-03-05 01:55:09,299 - mmseg - INFO - Iter [126900/160000]	lr: 2.344e-06, eta: 3:06:13, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9627, loss: 0.0758
+2023-03-05 01:55:23,389 - mmseg - INFO - Iter [126950/160000]	lr: 2.344e-06, eta: 3:05:55, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0748, decode.acc_seg: 96.9863, loss: 0.0748
+2023-03-05 01:55:37,101 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 01:55:37,102 - mmseg - INFO - Iter [127000/160000]	lr: 2.344e-06, eta: 3:05:37, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8318, loss: 0.0783
+2023-03-05 01:55:53,124 - mmseg - INFO - Iter [127050/160000]	lr: 2.344e-06, eta: 3:05:20, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9446, loss: 0.0772
+2023-03-05 01:56:06,686 - mmseg - INFO - Iter [127100/160000]	lr: 2.344e-06, eta: 3:05:03, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0730, decode.acc_seg: 97.1087, loss: 0.0730
+2023-03-05 01:56:20,258 - mmseg - INFO - Iter [127150/160000]	lr: 2.344e-06, eta: 3:04:45, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8279, loss: 0.0795
+2023-03-05 01:56:34,022 - mmseg - INFO - Iter [127200/160000]	lr: 2.344e-06, eta: 3:04:27, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9499, loss: 0.0765
+2023-03-05 01:56:50,301 - mmseg - INFO - Iter [127250/160000]	lr: 2.344e-06, eta: 3:04:10, time: 0.326, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9904, loss: 0.0775
+2023-03-05 01:57:03,891 - mmseg - INFO - Iter [127300/160000]	lr: 2.344e-06, eta: 3:03:52, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9047, loss: 0.0774
+2023-03-05 01:57:17,815 - mmseg - INFO - Iter [127350/160000]	lr: 2.344e-06, eta: 3:03:35, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9167, loss: 0.0763
+2023-03-05 01:57:31,531 - mmseg - INFO - Iter [127400/160000]	lr: 2.344e-06, eta: 3:03:17, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9984, loss: 0.0757
+2023-03-05 01:57:47,549 - mmseg - INFO - Iter [127450/160000]	lr: 2.344e-06, eta: 3:03:00, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0744, decode.acc_seg: 97.0282, loss: 0.0744
+2023-03-05 01:58:01,587 - mmseg - INFO - Iter [127500/160000]	lr: 2.344e-06, eta: 3:02:43, time: 0.281, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0792, decode.acc_seg: 96.8535, loss: 0.0792
+2023-03-05 01:58:15,143 - mmseg - INFO - Iter [127550/160000]	lr: 2.344e-06, eta: 3:02:25, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9374, loss: 0.0774
+2023-03-05 01:58:31,229 - mmseg - INFO - Iter [127600/160000]	lr: 2.344e-06, eta: 3:02:08, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9991, loss: 0.0762
+2023-03-05 01:58:45,047 - mmseg - INFO - Iter [127650/160000]	lr: 2.344e-06, eta: 3:01:50, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9017, loss: 0.0773
+2023-03-05 01:58:58,734 - mmseg - INFO - Iter [127700/160000]	lr: 2.344e-06, eta: 3:01:32, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9579, loss: 0.0779
+2023-03-05 01:59:12,462 - mmseg - INFO - Iter [127750/160000]	lr: 2.344e-06, eta: 3:01:15, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0753, decode.acc_seg: 97.0044, loss: 0.0753
+2023-03-05 01:59:28,611 - mmseg - INFO - Iter [127800/160000]	lr: 2.344e-06, eta: 3:00:58, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9494, loss: 0.0767
+2023-03-05 01:59:42,341 - mmseg - INFO - Iter [127850/160000]	lr: 2.344e-06, eta: 3:00:40, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9455, loss: 0.0763
+2023-03-05 01:59:55,999 - mmseg - INFO - Iter [127900/160000]	lr: 2.344e-06, eta: 3:00:22, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8338, loss: 0.0791
+2023-03-05 02:00:09,758 - mmseg - INFO - Iter [127950/160000]	lr: 2.344e-06, eta: 3:00:05, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9590, loss: 0.0771
+2023-03-05 02:00:25,662 - mmseg - INFO - Swap parameters (after train) after iter [128000]
+2023-03-05 02:00:25,683 - mmseg - INFO - Saving checkpoint at 128000 iterations
+2023-03-05 02:00:27,526 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 02:00:27,526 - mmseg - INFO - Iter [128000/160000]	lr: 2.344e-06, eta: 2:59:48, time: 0.355, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9201, loss: 0.0780
+2023-03-05 02:15:24,465 - mmseg - INFO - per class results:
+2023-03-05 02:15:24,466 - mmseg - INFO - 
++---------------+-------------------------------------------------------------------+
+|     Class     |                IoU 0,10,20,30,40,50,60,70,80,90,99                |
++---------------+-------------------------------------------------------------------+
+|   background  |            nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan            |
+|      road     | 98.55,98.55,98.55,98.55,98.55,98.55,98.54,98.54,98.54,98.54,98.55 |
+|    sidewalk   |   87.51,87.5,87.48,87.52,87.5,87.5,87.48,87.47,87.47,87.47,87.49  |
+|    building   |    93.59,93.59,93.59,93.59,93.6,93.6,93.6,93.6,93.6,93.6,93.61    |
+|      wall     | 55.51,55.67,55.66,55.73,55.78,55.84,55.88,55.91,56.07,56.22,56.16 |
+|     fence     |   64.9,64.9,64.96,64.97,64.97,64.99,64.99,64.99,65.0,65.02,65.06  |
+|      pole     |  71.3,71.32,71.31,71.32,71.33,71.33,71.35,71.34,71.35,71.35,71.35 |
+| traffic light | 75.57,75.58,75.57,75.57,75.58,75.57,75.59,75.57,75.57,75.58,75.58 |
+|  traffic sign |  82.69,82.69,82.71,82.7,82.72,82.72,82.73,82.73,82.74,82.74,82.75 |
+|   vegetation  | 93.12,93.12,93.13,93.13,93.14,93.14,93.14,93.14,93.15,93.16,93.16 |
+|    terrain    |   64.6,64.57,64.6,64.71,64.68,64.68,64.7,64.67,64.75,64.77,64.77  |
+|      sky      |  95.28,95.28,95.28,95.29,95.29,95.29,95.29,95.29,95.29,95.3,95.3  |
+|     person    |   85.0,85.01,85.01,85.01,85.01,85.01,85.01,85.0,85.0,85.01,85.01  |
+|     rider     |   67.89,67.89,67.9,67.88,67.89,67.89,67.9,67.88,67.89,67.9,67.88  |
+|      car      |   96.1,96.1,96.1,96.1,96.11,96.11,96.11,96.11,96.11,96.11,96.11   |
+|     truck     |  86.99,87.0,87.05,87.03,87.09,87.08,87.11,87.07,87.13,87.1,87.04  |
+|      bus      |   92.48,92.5,92.5,92.5,92.5,92.52,92.53,92.53,92.54,92.54,92.56   |
+|     train     |  85.63,85.63,85.64,85.72,85.7,85.75,85.75,85.77,85.76,85.79,85.85 |
+|   motorcycle  | 72.18,72.21,72.17,72.16,72.19,72.18,72.21,72.19,72.16,72.17,72.15 |
+|    bicycle    |  80.56,80.56,80.57,80.57,80.57,80.58,80.59,80.59,80.59,80.59,80.6 |
++---------------+-------------------------------------------------------------------+
+2023-03-05 02:15:24,466 - mmseg - INFO - Summary:
+2023-03-05 02:15:24,467 - mmseg - INFO - 
++----------------------------------------------------------------+
+|              mIoU 0,10,20,30,40,50,60,70,80,90,99              |
++----------------------------------------------------------------+
+| 81.55,81.56,81.57,81.58,81.59,81.6,81.6,81.6,81.62,81.63,81.63 |
++----------------------------------------------------------------+
+2023-03-05 02:15:24,529 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune/best_mIoU_iter_112000.pth was removed
+2023-03-05 02:15:26,398 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_128000.pth.
+2023-03-05 02:15:26,399 - mmseg - INFO - Best mIoU is 0.8163 at 128000 iter.
+2023-03-05 02:15:26,399 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 02:15:26,399 - mmseg - INFO - Iter(val) [63]	mIoU: [0.8155, 0.8156, 0.8157, 0.8158, 0.8159, 0.816, 0.816, 0.816, 0.8162, 0.8163, 0.8163], copy_paste: 81.55,81.56,81.57,81.58,81.59,81.6,81.6,81.6,81.62,81.63,81.63
+2023-03-05 02:15:26,406 - mmseg - INFO - Swap parameters (before train) before iter [128001]
+2023-03-05 02:15:40,535 - mmseg - INFO - Iter [128050/160000]	lr: 2.344e-06, eta: 3:03:15, time: 18.260, data_time: 17.987, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9310, loss: 0.0769
+2023-03-05 02:15:54,401 - mmseg - INFO - Iter [128100/160000]	lr: 2.344e-06, eta: 3:02:57, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0751, decode.acc_seg: 96.9872, loss: 0.0751
+2023-03-05 02:16:08,218 - mmseg - INFO - Iter [128150/160000]	lr: 2.344e-06, eta: 3:02:39, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9404, loss: 0.0765
+2023-03-05 02:16:24,246 - mmseg - INFO - Iter [128200/160000]	lr: 2.344e-06, eta: 3:02:21, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9710, loss: 0.0757
+2023-03-05 02:16:38,111 - mmseg - INFO - Iter [128250/160000]	lr: 2.344e-06, eta: 3:02:03, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9353, loss: 0.0777
+2023-03-05 02:16:52,347 - mmseg - INFO - Iter [128300/160000]	lr: 2.344e-06, eta: 3:01:45, time: 0.285, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8644, loss: 0.0790
+2023-03-05 02:17:08,690 - mmseg - INFO - Iter [128350/160000]	lr: 2.344e-06, eta: 3:01:28, time: 0.327, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9443, loss: 0.0760
+2023-03-05 02:17:22,364 - mmseg - INFO - Iter [128400/160000]	lr: 2.344e-06, eta: 3:01:10, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9409, loss: 0.0768
+2023-03-05 02:17:36,061 - mmseg - INFO - Iter [128450/160000]	lr: 2.344e-06, eta: 3:00:52, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9202, loss: 0.0773
+2023-03-05 02:17:49,729 - mmseg - INFO - Iter [128500/160000]	lr: 2.344e-06, eta: 3:00:34, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9112, loss: 0.0775
+2023-03-05 02:18:05,912 - mmseg - INFO - Iter [128550/160000]	lr: 2.344e-06, eta: 3:00:16, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0744, decode.acc_seg: 97.0196, loss: 0.0744
+2023-03-05 02:18:19,585 - mmseg - INFO - Iter [128600/160000]	lr: 2.344e-06, eta: 2:59:58, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8976, loss: 0.0787
+2023-03-05 02:18:33,213 - mmseg - INFO - Iter [128650/160000]	lr: 2.344e-06, eta: 2:59:40, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9563, loss: 0.0767
+2023-03-05 02:18:46,812 - mmseg - INFO - Iter [128700/160000]	lr: 2.344e-06, eta: 2:59:22, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9117, loss: 0.0780
+2023-03-05 02:19:02,839 - mmseg - INFO - Iter [128750/160000]	lr: 2.344e-06, eta: 2:59:05, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8901, loss: 0.0787
+2023-03-05 02:19:16,516 - mmseg - INFO - Iter [128800/160000]	lr: 2.344e-06, eta: 2:58:47, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8686, loss: 0.0790
+2023-03-05 02:19:30,300 - mmseg - INFO - Iter [128850/160000]	lr: 2.344e-06, eta: 2:58:29, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9784, loss: 0.0769
+2023-03-05 02:19:46,444 - mmseg - INFO - Iter [128900/160000]	lr: 2.344e-06, eta: 2:58:11, time: 0.323, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8280, loss: 0.0795
+2023-03-05 02:20:00,078 - mmseg - INFO - Iter [128950/160000]	lr: 2.344e-06, eta: 2:57:53, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8812, loss: 0.0781
+2023-03-05 02:20:13,726 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 02:20:13,727 - mmseg - INFO - Iter [129000/160000]	lr: 2.344e-06, eta: 2:57:35, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9850, loss: 0.0762
+2023-03-05 02:20:27,420 - mmseg - INFO - Iter [129050/160000]	lr: 2.344e-06, eta: 2:57:17, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0739, decode.acc_seg: 97.0238, loss: 0.0739
+2023-03-05 02:20:43,662 - mmseg - INFO - Iter [129100/160000]	lr: 2.344e-06, eta: 2:57:00, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9203, loss: 0.0776
+2023-03-05 02:20:57,330 - mmseg - INFO - Iter [129150/160000]	lr: 2.344e-06, eta: 2:56:42, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0751, decode.acc_seg: 97.0073, loss: 0.0751
+2023-03-05 02:21:10,987 - mmseg - INFO - Iter [129200/160000]	lr: 2.344e-06, eta: 2:56:24, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8457, loss: 0.0793
+2023-03-05 02:21:24,667 - mmseg - INFO - Iter [129250/160000]	lr: 2.344e-06, eta: 2:56:06, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0751, decode.acc_seg: 97.0331, loss: 0.0751
+2023-03-05 02:21:40,710 - mmseg - INFO - Iter [129300/160000]	lr: 2.344e-06, eta: 2:55:48, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8209, loss: 0.0796
+2023-03-05 02:21:54,331 - mmseg - INFO - Iter [129350/160000]	lr: 2.344e-06, eta: 2:55:30, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9332, loss: 0.0772
+2023-03-05 02:22:08,020 - mmseg - INFO - Iter [129400/160000]	lr: 2.344e-06, eta: 2:55:12, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.8918, loss: 0.0778
+2023-03-05 02:22:21,621 - mmseg - INFO - Iter [129450/160000]	lr: 2.344e-06, eta: 2:54:54, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9825, loss: 0.0763
+2023-03-05 02:22:37,813 - mmseg - INFO - Iter [129500/160000]	lr: 2.344e-06, eta: 2:54:37, time: 0.324, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8589, loss: 0.0790
+2023-03-05 02:22:51,443 - mmseg - INFO - Iter [129550/160000]	lr: 2.344e-06, eta: 2:54:19, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8539, loss: 0.0790
+2023-03-05 02:23:05,042 - mmseg - INFO - Iter [129600/160000]	lr: 2.344e-06, eta: 2:54:01, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9439, loss: 0.0773
+2023-03-05 02:23:21,034 - mmseg - INFO - Iter [129650/160000]	lr: 2.344e-06, eta: 2:53:43, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9653, loss: 0.0768
+2023-03-05 02:23:34,774 - mmseg - INFO - Iter [129700/160000]	lr: 2.344e-06, eta: 2:53:25, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9731, loss: 0.0765
+2023-03-05 02:23:48,470 - mmseg - INFO - Iter [129750/160000]	lr: 2.344e-06, eta: 2:53:07, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9401, loss: 0.0761
+2023-03-05 02:24:02,130 - mmseg - INFO - Iter [129800/160000]	lr: 2.344e-06, eta: 2:52:49, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9745, loss: 0.0766
+2023-03-05 02:24:18,060 - mmseg - INFO - Iter [129850/160000]	lr: 2.344e-06, eta: 2:52:32, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9595, loss: 0.0762
+2023-03-05 02:24:31,939 - mmseg - INFO - Iter [129900/160000]	lr: 2.344e-06, eta: 2:52:14, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9636, loss: 0.0767
+2023-03-05 02:24:45,571 - mmseg - INFO - Iter [129950/160000]	lr: 2.344e-06, eta: 2:51:56, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.9045, loss: 0.0782
+2023-03-05 02:24:59,217 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 02:24:59,217 - mmseg - INFO - Iter [130000/160000]	lr: 2.344e-06, eta: 2:51:38, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9560, loss: 0.0776
+2023-03-05 02:25:15,371 - mmseg - INFO - Iter [130050/160000]	lr: 2.344e-06, eta: 2:51:20, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8386, loss: 0.0803
+2023-03-05 02:25:29,080 - mmseg - INFO - Iter [130100/160000]	lr: 2.344e-06, eta: 2:51:03, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 97.0001, loss: 0.0754
+2023-03-05 02:25:42,796 - mmseg - INFO - Iter [130150/160000]	lr: 2.344e-06, eta: 2:50:45, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9623, loss: 0.0759
+2023-03-05 02:25:56,394 - mmseg - INFO - Iter [130200/160000]	lr: 2.344e-06, eta: 2:50:27, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8771, loss: 0.0784
+2023-03-05 02:26:12,476 - mmseg - INFO - Iter [130250/160000]	lr: 2.344e-06, eta: 2:50:09, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.9245, loss: 0.0783
+2023-03-05 02:26:26,134 - mmseg - INFO - Iter [130300/160000]	lr: 2.344e-06, eta: 2:49:51, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.9157, loss: 0.0784
+2023-03-05 02:26:39,921 - mmseg - INFO - Iter [130350/160000]	lr: 2.344e-06, eta: 2:49:33, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9585, loss: 0.0769
+2023-03-05 02:26:56,246 - mmseg - INFO - Iter [130400/160000]	lr: 2.344e-06, eta: 2:49:16, time: 0.327, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0750, decode.acc_seg: 96.9778, loss: 0.0750
+2023-03-05 02:27:09,938 - mmseg - INFO - Iter [130450/160000]	lr: 2.344e-06, eta: 2:48:58, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9354, loss: 0.0774
+2023-03-05 02:27:23,565 - mmseg - INFO - Iter [130500/160000]	lr: 2.344e-06, eta: 2:48:40, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9035, loss: 0.0771
+2023-03-05 02:27:37,506 - mmseg - INFO - Iter [130550/160000]	lr: 2.344e-06, eta: 2:48:22, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.9001, loss: 0.0781
+2023-03-05 02:27:53,708 - mmseg - INFO - Iter [130600/160000]	lr: 2.344e-06, eta: 2:48:05, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8831, loss: 0.0785
+2023-03-05 02:28:07,294 - mmseg - INFO - Iter [130650/160000]	lr: 2.344e-06, eta: 2:47:47, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9889, loss: 0.0765
+2023-03-05 02:28:20,993 - mmseg - INFO - Iter [130700/160000]	lr: 2.344e-06, eta: 2:47:29, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9490, loss: 0.0779
+2023-03-05 02:28:35,062 - mmseg - INFO - Iter [130750/160000]	lr: 2.344e-06, eta: 2:47:11, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8549, loss: 0.0787
+2023-03-05 02:28:51,062 - mmseg - INFO - Iter [130800/160000]	lr: 2.344e-06, eta: 2:46:54, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9223, loss: 0.0767
+2023-03-05 02:29:04,866 - mmseg - INFO - Iter [130850/160000]	lr: 2.344e-06, eta: 2:46:36, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 96.9962, loss: 0.0752
+2023-03-05 02:29:18,483 - mmseg - INFO - Iter [130900/160000]	lr: 2.344e-06, eta: 2:46:18, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8866, loss: 0.0780
+2023-03-05 02:29:34,500 - mmseg - INFO - Iter [130950/160000]	lr: 2.344e-06, eta: 2:46:00, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9471, loss: 0.0762
+2023-03-05 02:29:48,494 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 02:29:48,495 - mmseg - INFO - Iter [131000/160000]	lr: 2.344e-06, eta: 2:45:43, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 96.9931, loss: 0.0755
+2023-03-05 02:30:02,209 - mmseg - INFO - Iter [131050/160000]	lr: 2.344e-06, eta: 2:45:25, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7924, loss: 0.0825
+2023-03-05 02:30:15,886 - mmseg - INFO - Iter [131100/160000]	lr: 2.344e-06, eta: 2:45:07, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8880, loss: 0.0781
+2023-03-05 02:30:31,829 - mmseg - INFO - Iter [131150/160000]	lr: 2.344e-06, eta: 2:44:49, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.7678, loss: 0.0802
+2023-03-05 02:30:45,413 - mmseg - INFO - Iter [131200/160000]	lr: 2.344e-06, eta: 2:44:32, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.8910, loss: 0.0769
+2023-03-05 02:30:59,265 - mmseg - INFO - Iter [131250/160000]	lr: 2.344e-06, eta: 2:44:14, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9259, loss: 0.0780
+2023-03-05 02:31:12,994 - mmseg - INFO - Iter [131300/160000]	lr: 2.344e-06, eta: 2:43:56, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8807, loss: 0.0790
+2023-03-05 02:31:29,037 - mmseg - INFO - Iter [131350/160000]	lr: 2.344e-06, eta: 2:43:38, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 97.0224, loss: 0.0762
+2023-03-05 02:31:42,985 - mmseg - INFO - Iter [131400/160000]	lr: 2.344e-06, eta: 2:43:21, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8234, loss: 0.0797
+2023-03-05 02:31:56,576 - mmseg - INFO - Iter [131450/160000]	lr: 2.344e-06, eta: 2:43:03, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8641, loss: 0.0788
+2023-03-05 02:32:10,150 - mmseg - INFO - Iter [131500/160000]	lr: 2.344e-06, eta: 2:42:45, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9651, loss: 0.0759
+2023-03-05 02:32:26,321 - mmseg - INFO - Iter [131550/160000]	lr: 2.344e-06, eta: 2:42:27, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0801, decode.acc_seg: 96.8246, loss: 0.0801
+2023-03-05 02:32:39,982 - mmseg - INFO - Iter [131600/160000]	lr: 2.344e-06, eta: 2:42:10, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9593, loss: 0.0759
+2023-03-05 02:32:54,014 - mmseg - INFO - Iter [131650/160000]	lr: 2.344e-06, eta: 2:41:52, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0748, decode.acc_seg: 97.0108, loss: 0.0748
+2023-03-05 02:33:10,069 - mmseg - INFO - Iter [131700/160000]	lr: 2.344e-06, eta: 2:41:34, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0743, decode.acc_seg: 97.0476, loss: 0.0743
+2023-03-05 02:33:23,698 - mmseg - INFO - Iter [131750/160000]	lr: 2.344e-06, eta: 2:41:17, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9761, loss: 0.0760
+2023-03-05 02:33:37,301 - mmseg - INFO - Iter [131800/160000]	lr: 2.344e-06, eta: 2:40:59, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9682, loss: 0.0758
+2023-03-05 02:33:51,225 - mmseg - INFO - Iter [131850/160000]	lr: 2.344e-06, eta: 2:40:41, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9260, loss: 0.0767
+2023-03-05 02:34:07,553 - mmseg - INFO - Iter [131900/160000]	lr: 2.344e-06, eta: 2:40:24, time: 0.327, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8391, loss: 0.0789
+2023-03-05 02:34:21,354 - mmseg - INFO - Iter [131950/160000]	lr: 2.344e-06, eta: 2:40:06, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8865, loss: 0.0789
+2023-03-05 02:34:35,261 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 02:34:35,261 - mmseg - INFO - Iter [132000/160000]	lr: 2.344e-06, eta: 2:39:48, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0747, decode.acc_seg: 96.9822, loss: 0.0747
+2023-03-05 02:34:48,858 - mmseg - INFO - Iter [132050/160000]	lr: 2.344e-06, eta: 2:39:30, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8943, loss: 0.0779
+2023-03-05 02:35:04,901 - mmseg - INFO - Iter [132100/160000]	lr: 2.344e-06, eta: 2:39:13, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0744, decode.acc_seg: 96.9991, loss: 0.0744
+2023-03-05 02:35:18,488 - mmseg - INFO - Iter [132150/160000]	lr: 2.344e-06, eta: 2:38:55, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0745, decode.acc_seg: 97.0633, loss: 0.0745
+2023-03-05 02:35:32,160 - mmseg - INFO - Iter [132200/160000]	lr: 2.344e-06, eta: 2:38:37, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9666, loss: 0.0766
+2023-03-05 02:35:48,193 - mmseg - INFO - Iter [132250/160000]	lr: 2.344e-06, eta: 2:38:20, time: 0.321, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8578, loss: 0.0782
+2023-03-05 02:36:01,875 - mmseg - INFO - Iter [132300/160000]	lr: 2.344e-06, eta: 2:38:02, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9487, loss: 0.0767
+2023-03-05 02:36:15,903 - mmseg - INFO - Iter [132350/160000]	lr: 2.344e-06, eta: 2:37:44, time: 0.281, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0751, decode.acc_seg: 97.0042, loss: 0.0751
+2023-03-05 02:36:29,650 - mmseg - INFO - Iter [132400/160000]	lr: 2.344e-06, eta: 2:37:26, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9112, loss: 0.0768
+2023-03-05 02:36:45,811 - mmseg - INFO - Iter [132450/160000]	lr: 2.344e-06, eta: 2:37:09, time: 0.323, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0810, decode.acc_seg: 96.8302, loss: 0.0810
+2023-03-05 02:36:59,647 - mmseg - INFO - Iter [132500/160000]	lr: 2.344e-06, eta: 2:36:51, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8646, loss: 0.0785
+2023-03-05 02:37:13,331 - mmseg - INFO - Iter [132550/160000]	lr: 2.344e-06, eta: 2:36:33, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9507, loss: 0.0760
+2023-03-05 02:37:26,961 - mmseg - INFO - Iter [132600/160000]	lr: 2.344e-06, eta: 2:36:15, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7814, loss: 0.0811
+2023-03-05 02:37:42,932 - mmseg - INFO - Iter [132650/160000]	lr: 2.344e-06, eta: 2:35:58, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8571, loss: 0.0786
+2023-03-05 02:37:56,570 - mmseg - INFO - Iter [132700/160000]	lr: 2.344e-06, eta: 2:35:40, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0753, decode.acc_seg: 96.9875, loss: 0.0753
+2023-03-05 02:38:10,464 - mmseg - INFO - Iter [132750/160000]	lr: 2.344e-06, eta: 2:35:23, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.7000, loss: 0.0831
+2023-03-05 02:38:24,055 - mmseg - INFO - Iter [132800/160000]	lr: 2.344e-06, eta: 2:35:05, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9077, loss: 0.0778
+2023-03-05 02:38:40,142 - mmseg - INFO - Iter [132850/160000]	lr: 2.344e-06, eta: 2:34:47, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 96.9829, loss: 0.0752
+2023-03-05 02:38:53,806 - mmseg - INFO - Iter [132900/160000]	lr: 2.344e-06, eta: 2:34:30, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9489, loss: 0.0766
+2023-03-05 02:39:07,461 - mmseg - INFO - Iter [132950/160000]	lr: 2.344e-06, eta: 2:34:12, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9118, loss: 0.0778
+2023-03-05 02:39:23,610 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 02:39:23,610 - mmseg - INFO - Iter [133000/160000]	lr: 2.344e-06, eta: 2:33:54, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0816, decode.acc_seg: 96.7523, loss: 0.0816
+2023-03-05 02:39:37,228 - mmseg - INFO - Iter [133050/160000]	lr: 2.344e-06, eta: 2:33:37, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9256, loss: 0.0775
+2023-03-05 02:39:50,863 - mmseg - INFO - Iter [133100/160000]	lr: 2.344e-06, eta: 2:33:19, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9801, loss: 0.0758
+2023-03-05 02:40:04,485 - mmseg - INFO - Iter [133150/160000]	lr: 2.344e-06, eta: 2:33:01, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9109, loss: 0.0774
+2023-03-05 02:40:20,770 - mmseg - INFO - Iter [133200/160000]	lr: 2.344e-06, eta: 2:32:44, time: 0.325, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9544, loss: 0.0770
+2023-03-05 02:40:34,440 - mmseg - INFO - Iter [133250/160000]	lr: 2.344e-06, eta: 2:32:26, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9296, loss: 0.0767
+2023-03-05 02:40:48,469 - mmseg - INFO - Iter [133300/160000]	lr: 2.344e-06, eta: 2:32:08, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0833, decode.acc_seg: 96.7031, loss: 0.0833
+2023-03-05 02:41:02,302 - mmseg - INFO - Iter [133350/160000]	lr: 2.344e-06, eta: 2:31:51, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9666, loss: 0.0766
+2023-03-05 02:41:18,313 - mmseg - INFO - Iter [133400/160000]	lr: 2.344e-06, eta: 2:31:33, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8647, loss: 0.0790
+2023-03-05 02:41:32,545 - mmseg - INFO - Iter [133450/160000]	lr: 2.344e-06, eta: 2:31:16, time: 0.285, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0737, decode.acc_seg: 97.0465, loss: 0.0737
+2023-03-05 02:41:46,221 - mmseg - INFO - Iter [133500/160000]	lr: 2.344e-06, eta: 2:30:58, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8508, loss: 0.0787
+2023-03-05 02:42:02,234 - mmseg - INFO - Iter [133550/160000]	lr: 2.344e-06, eta: 2:30:41, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9458, loss: 0.0767
+2023-03-05 02:42:16,424 - mmseg - INFO - Iter [133600/160000]	lr: 2.344e-06, eta: 2:30:23, time: 0.284, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8416, loss: 0.0790
+2023-03-05 02:42:30,082 - mmseg - INFO - Iter [133650/160000]	lr: 2.344e-06, eta: 2:30:05, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9691, loss: 0.0761
+2023-03-05 02:42:43,787 - mmseg - INFO - Iter [133700/160000]	lr: 2.344e-06, eta: 2:29:47, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9606, loss: 0.0763
+2023-03-05 02:42:59,945 - mmseg - INFO - Iter [133750/160000]	lr: 2.344e-06, eta: 2:29:30, time: 0.323, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8759, loss: 0.0786
+2023-03-05 02:43:13,711 - mmseg - INFO - Iter [133800/160000]	lr: 2.344e-06, eta: 2:29:12, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9277, loss: 0.0771
+2023-03-05 02:43:27,483 - mmseg - INFO - Iter [133850/160000]	lr: 2.344e-06, eta: 2:28:55, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9391, loss: 0.0760
+2023-03-05 02:43:41,110 - mmseg - INFO - Iter [133900/160000]	lr: 2.344e-06, eta: 2:28:37, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0733, decode.acc_seg: 97.0711, loss: 0.0733
+2023-03-05 02:43:57,204 - mmseg - INFO - Iter [133950/160000]	lr: 2.344e-06, eta: 2:28:20, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8400, loss: 0.0799
+2023-03-05 02:44:10,894 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 02:44:10,895 - mmseg - INFO - Iter [134000/160000]	lr: 2.344e-06, eta: 2:28:02, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9026, loss: 0.0780
+2023-03-05 02:44:24,658 - mmseg - INFO - Iter [134050/160000]	lr: 2.344e-06, eta: 2:27:44, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8617, loss: 0.0790
+2023-03-05 02:44:38,209 - mmseg - INFO - Iter [134100/160000]	lr: 2.344e-06, eta: 2:27:26, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9616, loss: 0.0768
+2023-03-05 02:44:54,281 - mmseg - INFO - Iter [134150/160000]	lr: 2.344e-06, eta: 2:27:09, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0735, decode.acc_seg: 97.0967, loss: 0.0735
+2023-03-05 02:45:08,073 - mmseg - INFO - Iter [134200/160000]	lr: 2.344e-06, eta: 2:26:51, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9597, loss: 0.0758
+2023-03-05 02:45:21,825 - mmseg - INFO - Iter [134250/160000]	lr: 2.344e-06, eta: 2:26:34, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9220, loss: 0.0769
+2023-03-05 02:45:37,761 - mmseg - INFO - Iter [134300/160000]	lr: 2.344e-06, eta: 2:26:16, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9321, loss: 0.0771
+2023-03-05 02:45:51,499 - mmseg - INFO - Iter [134350/160000]	lr: 2.344e-06, eta: 2:25:59, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9188, loss: 0.0780
+2023-03-05 02:46:05,614 - mmseg - INFO - Iter [134400/160000]	lr: 2.344e-06, eta: 2:25:41, time: 0.282, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9277, loss: 0.0767
+2023-03-05 02:46:19,674 - mmseg - INFO - Iter [134450/160000]	lr: 2.344e-06, eta: 2:25:23, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0744, decode.acc_seg: 97.0544, loss: 0.0744
+2023-03-05 02:46:35,699 - mmseg - INFO - Iter [134500/160000]	lr: 2.344e-06, eta: 2:25:06, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8030, loss: 0.0797
+2023-03-05 02:46:49,526 - mmseg - INFO - Iter [134550/160000]	lr: 2.344e-06, eta: 2:24:48, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0867, decode.acc_seg: 96.6447, loss: 0.0867
+2023-03-05 02:47:03,305 - mmseg - INFO - Iter [134600/160000]	lr: 2.344e-06, eta: 2:24:31, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0738, decode.acc_seg: 97.0543, loss: 0.0738
+2023-03-05 02:47:16,871 - mmseg - INFO - Iter [134650/160000]	lr: 2.344e-06, eta: 2:24:13, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0753, decode.acc_seg: 97.0236, loss: 0.0753
+2023-03-05 02:47:33,168 - mmseg - INFO - Iter [134700/160000]	lr: 2.344e-06, eta: 2:23:56, time: 0.326, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9806, loss: 0.0761
+2023-03-05 02:47:47,130 - mmseg - INFO - Iter [134750/160000]	lr: 2.344e-06, eta: 2:23:38, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8352, loss: 0.0799
+2023-03-05 02:48:00,737 - mmseg - INFO - Iter [134800/160000]	lr: 2.344e-06, eta: 2:23:20, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8481, loss: 0.0795
+2023-03-05 02:48:14,751 - mmseg - INFO - Iter [134850/160000]	lr: 2.344e-06, eta: 2:23:03, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.8589, loss: 0.0778
+2023-03-05 02:48:30,799 - mmseg - INFO - Iter [134900/160000]	lr: 2.344e-06, eta: 2:22:45, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9496, loss: 0.0766
+2023-03-05 02:48:44,369 - mmseg - INFO - Iter [134950/160000]	lr: 2.344e-06, eta: 2:22:28, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9818, loss: 0.0764
+2023-03-05 02:48:58,048 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 02:48:58,048 - mmseg - INFO - Iter [135000/160000]	lr: 2.344e-06, eta: 2:22:10, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9211, loss: 0.0777
+2023-03-05 02:49:14,049 - mmseg - INFO - Iter [135050/160000]	lr: 2.344e-06, eta: 2:21:53, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8327, loss: 0.0797
+2023-03-05 02:49:27,645 - mmseg - INFO - Iter [135100/160000]	lr: 2.344e-06, eta: 2:21:35, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9390, loss: 0.0763
+2023-03-05 02:49:41,559 - mmseg - INFO - Iter [135150/160000]	lr: 2.344e-06, eta: 2:21:17, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 97.0065, loss: 0.0752
+2023-03-05 02:49:55,149 - mmseg - INFO - Iter [135200/160000]	lr: 2.344e-06, eta: 2:21:00, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0730, decode.acc_seg: 97.0633, loss: 0.0730
+2023-03-05 02:50:11,136 - mmseg - INFO - Iter [135250/160000]	lr: 2.344e-06, eta: 2:20:43, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9377, loss: 0.0776
+2023-03-05 02:50:24,806 - mmseg - INFO - Iter [135300/160000]	lr: 2.344e-06, eta: 2:20:25, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 96.9882, loss: 0.0755
+2023-03-05 02:50:38,543 - mmseg - INFO - Iter [135350/160000]	lr: 2.344e-06, eta: 2:20:07, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8963, loss: 0.0781
+2023-03-05 02:50:52,292 - mmseg - INFO - Iter [135400/160000]	lr: 2.344e-06, eta: 2:19:50, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8661, loss: 0.0787
+2023-03-05 02:51:08,236 - mmseg - INFO - Iter [135450/160000]	lr: 2.344e-06, eta: 2:19:32, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8661, loss: 0.0784
+2023-03-05 02:51:21,927 - mmseg - INFO - Iter [135500/160000]	lr: 2.344e-06, eta: 2:19:15, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9368, loss: 0.0770
+2023-03-05 02:51:35,558 - mmseg - INFO - Iter [135550/160000]	lr: 2.344e-06, eta: 2:18:57, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0729, decode.acc_seg: 97.0591, loss: 0.0729
+2023-03-05 02:51:51,554 - mmseg - INFO - Iter [135600/160000]	lr: 2.344e-06, eta: 2:18:40, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9682, loss: 0.0761
+2023-03-05 02:52:05,434 - mmseg - INFO - Iter [135650/160000]	lr: 2.344e-06, eta: 2:18:22, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0815, decode.acc_seg: 96.7982, loss: 0.0815
+2023-03-05 02:52:19,149 - mmseg - INFO - Iter [135700/160000]	lr: 2.344e-06, eta: 2:18:04, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0871, decode.acc_seg: 96.7493, loss: 0.0871
+2023-03-05 02:52:33,147 - mmseg - INFO - Iter [135750/160000]	lr: 2.344e-06, eta: 2:17:47, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9810, loss: 0.0764
+2023-03-05 02:52:49,214 - mmseg - INFO - Iter [135800/160000]	lr: 2.344e-06, eta: 2:17:30, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9179, loss: 0.0767
+2023-03-05 02:53:03,354 - mmseg - INFO - Iter [135850/160000]	lr: 2.344e-06, eta: 2:17:12, time: 0.283, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0756, decode.acc_seg: 96.9743, loss: 0.0756
+2023-03-05 02:53:17,145 - mmseg - INFO - Iter [135900/160000]	lr: 2.344e-06, eta: 2:16:54, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9352, loss: 0.0771
+2023-03-05 02:53:30,798 - mmseg - INFO - Iter [135950/160000]	lr: 2.344e-06, eta: 2:16:37, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0756, decode.acc_seg: 96.9896, loss: 0.0756
+2023-03-05 02:53:47,180 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 02:53:47,180 - mmseg - INFO - Iter [136000/160000]	lr: 2.344e-06, eta: 2:16:20, time: 0.328, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9437, loss: 0.0769
+2023-03-05 02:54:01,061 - mmseg - INFO - Iter [136050/160000]	lr: 2.344e-06, eta: 2:16:02, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9333, loss: 0.0766
+2023-03-05 02:54:14,733 - mmseg - INFO - Iter [136100/160000]	lr: 2.344e-06, eta: 2:15:44, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0745, decode.acc_seg: 97.0423, loss: 0.0745
+2023-03-05 02:54:28,490 - mmseg - INFO - Iter [136150/160000]	lr: 2.344e-06, eta: 2:15:27, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9834, loss: 0.0759
+2023-03-05 02:54:44,460 - mmseg - INFO - Iter [136200/160000]	lr: 2.344e-06, eta: 2:15:10, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9383, loss: 0.0766
+2023-03-05 02:54:58,209 - mmseg - INFO - Iter [136250/160000]	lr: 2.344e-06, eta: 2:14:52, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9235, loss: 0.0775
+2023-03-05 02:55:11,933 - mmseg - INFO - Iter [136300/160000]	lr: 2.344e-06, eta: 2:14:34, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8268, loss: 0.0797
+2023-03-05 02:55:27,854 - mmseg - INFO - Iter [136350/160000]	lr: 2.344e-06, eta: 2:14:17, time: 0.318, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0737, decode.acc_seg: 97.0298, loss: 0.0737
+2023-03-05 02:55:41,544 - mmseg - INFO - Iter [136400/160000]	lr: 2.344e-06, eta: 2:14:00, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8654, loss: 0.0793
+2023-03-05 02:55:55,169 - mmseg - INFO - Iter [136450/160000]	lr: 2.344e-06, eta: 2:13:42, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9580, loss: 0.0775
+2023-03-05 02:56:08,883 - mmseg - INFO - Iter [136500/160000]	lr: 2.344e-06, eta: 2:13:24, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9936, loss: 0.0761
+2023-03-05 02:56:25,019 - mmseg - INFO - Iter [136550/160000]	lr: 2.344e-06, eta: 2:13:07, time: 0.323, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9379, loss: 0.0764
+2023-03-05 02:56:38,718 - mmseg - INFO - Iter [136600/160000]	lr: 2.344e-06, eta: 2:12:50, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.8952, loss: 0.0774
+2023-03-05 02:56:52,683 - mmseg - INFO - Iter [136650/160000]	lr: 2.344e-06, eta: 2:12:32, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9032, loss: 0.0780
+2023-03-05 02:57:06,790 - mmseg - INFO - Iter [136700/160000]	lr: 2.344e-06, eta: 2:12:14, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0818, decode.acc_seg: 96.8539, loss: 0.0818
+2023-03-05 02:57:22,967 - mmseg - INFO - Iter [136750/160000]	lr: 2.344e-06, eta: 2:11:57, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.9021, loss: 0.0784
+2023-03-05 02:57:36,989 - mmseg - INFO - Iter [136800/160000]	lr: 2.344e-06, eta: 2:11:40, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9069, loss: 0.0776
+2023-03-05 02:57:51,096 - mmseg - INFO - Iter [136850/160000]	lr: 2.344e-06, eta: 2:11:22, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0742, decode.acc_seg: 97.0420, loss: 0.0742
+2023-03-05 02:58:07,171 - mmseg - INFO - Iter [136900/160000]	lr: 2.344e-06, eta: 2:11:05, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.7811, loss: 0.0803
+2023-03-05 02:58:21,021 - mmseg - INFO - Iter [136950/160000]	lr: 2.344e-06, eta: 2:10:47, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0738, decode.acc_seg: 97.0501, loss: 0.0738
+2023-03-05 02:58:34,615 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 02:58:34,615 - mmseg - INFO - Iter [137000/160000]	lr: 2.344e-06, eta: 2:10:30, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8676, loss: 0.0790
+2023-03-05 02:58:48,226 - mmseg - INFO - Iter [137050/160000]	lr: 2.344e-06, eta: 2:10:12, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9190, loss: 0.0759
+2023-03-05 02:59:04,290 - mmseg - INFO - Iter [137100/160000]	lr: 2.344e-06, eta: 2:09:55, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0848, decode.acc_seg: 96.8064, loss: 0.0848
+2023-03-05 02:59:17,958 - mmseg - INFO - Iter [137150/160000]	lr: 2.344e-06, eta: 2:09:38, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9598, loss: 0.0778
+2023-03-05 02:59:31,840 - mmseg - INFO - Iter [137200/160000]	lr: 2.344e-06, eta: 2:09:20, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9709, loss: 0.0760
+2023-03-05 02:59:45,433 - mmseg - INFO - Iter [137250/160000]	lr: 2.344e-06, eta: 2:09:02, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 97.0132, loss: 0.0755
+2023-03-05 03:00:01,587 - mmseg - INFO - Iter [137300/160000]	lr: 2.344e-06, eta: 2:08:45, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9457, loss: 0.0768
+2023-03-05 03:00:15,222 - mmseg - INFO - Iter [137350/160000]	lr: 2.344e-06, eta: 2:08:28, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.9592, loss: 0.0781
+2023-03-05 03:00:28,939 - mmseg - INFO - Iter [137400/160000]	lr: 2.344e-06, eta: 2:08:10, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9518, loss: 0.0757
+2023-03-05 03:00:42,839 - mmseg - INFO - Iter [137450/160000]	lr: 2.344e-06, eta: 2:07:53, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7879, loss: 0.0808
+2023-03-05 03:00:58,904 - mmseg - INFO - Iter [137500/160000]	lr: 2.344e-06, eta: 2:07:35, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8117, loss: 0.0797
+2023-03-05 03:01:12,742 - mmseg - INFO - Iter [137550/160000]	lr: 2.344e-06, eta: 2:07:18, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9443, loss: 0.0767
+2023-03-05 03:01:26,727 - mmseg - INFO - Iter [137600/160000]	lr: 2.344e-06, eta: 2:07:00, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0748, decode.acc_seg: 97.0234, loss: 0.0748
+2023-03-05 03:01:42,883 - mmseg - INFO - Iter [137650/160000]	lr: 2.344e-06, eta: 2:06:43, time: 0.323, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9707, loss: 0.0764
+2023-03-05 03:01:56,488 - mmseg - INFO - Iter [137700/160000]	lr: 2.344e-06, eta: 2:06:26, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9474, loss: 0.0761
+2023-03-05 03:02:10,083 - mmseg - INFO - Iter [137750/160000]	lr: 2.344e-06, eta: 2:06:08, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8632, loss: 0.0799
+2023-03-05 03:02:23,719 - mmseg - INFO - Iter [137800/160000]	lr: 2.344e-06, eta: 2:05:51, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8451, loss: 0.0786
+2023-03-05 03:02:39,744 - mmseg - INFO - Iter [137850/160000]	lr: 2.344e-06, eta: 2:05:33, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9740, loss: 0.0764
+2023-03-05 03:02:53,450 - mmseg - INFO - Iter [137900/160000]	lr: 2.344e-06, eta: 2:05:16, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 97.0222, loss: 0.0755
+2023-03-05 03:03:07,182 - mmseg - INFO - Iter [137950/160000]	lr: 2.344e-06, eta: 2:04:58, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0743, decode.acc_seg: 97.0158, loss: 0.0743
+2023-03-05 03:03:20,808 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 03:03:20,808 - mmseg - INFO - Iter [138000/160000]	lr: 2.344e-06, eta: 2:04:41, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9367, loss: 0.0772
+2023-03-05 03:03:36,845 - mmseg - INFO - Iter [138050/160000]	lr: 2.344e-06, eta: 2:04:24, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.7700, loss: 0.0804
+2023-03-05 03:03:50,587 - mmseg - INFO - Iter [138100/160000]	lr: 2.344e-06, eta: 2:04:06, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0749, decode.acc_seg: 96.9904, loss: 0.0749
+2023-03-05 03:04:04,395 - mmseg - INFO - Iter [138150/160000]	lr: 2.344e-06, eta: 2:03:49, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0728, decode.acc_seg: 97.0905, loss: 0.0728
+2023-03-05 03:04:20,330 - mmseg - INFO - Iter [138200/160000]	lr: 2.344e-06, eta: 2:03:31, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0746, decode.acc_seg: 97.0389, loss: 0.0746
+2023-03-05 03:04:34,194 - mmseg - INFO - Iter [138250/160000]	lr: 2.344e-06, eta: 2:03:14, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8839, loss: 0.0779
+2023-03-05 03:04:47,777 - mmseg - INFO - Iter [138300/160000]	lr: 2.344e-06, eta: 2:02:56, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9309, loss: 0.0773
+2023-03-05 03:05:01,558 - mmseg - INFO - Iter [138350/160000]	lr: 2.344e-06, eta: 2:02:39, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 97.0274, loss: 0.0752
+2023-03-05 03:05:18,067 - mmseg - INFO - Iter [138400/160000]	lr: 2.344e-06, eta: 2:02:22, time: 0.330, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8320, loss: 0.0803
+2023-03-05 03:05:32,025 - mmseg - INFO - Iter [138450/160000]	lr: 2.344e-06, eta: 2:02:04, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0734, decode.acc_seg: 97.0491, loss: 0.0734
+2023-03-05 03:05:45,658 - mmseg - INFO - Iter [138500/160000]	lr: 2.344e-06, eta: 2:01:47, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9054, loss: 0.0774
+2023-03-05 03:05:59,400 - mmseg - INFO - Iter [138550/160000]	lr: 2.344e-06, eta: 2:01:29, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.9405, loss: 0.0784
+2023-03-05 03:06:15,951 - mmseg - INFO - Iter [138600/160000]	lr: 2.344e-06, eta: 2:01:12, time: 0.331, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9430, loss: 0.0767
+2023-03-05 03:06:29,884 - mmseg - INFO - Iter [138650/160000]	lr: 2.344e-06, eta: 2:00:55, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8572, loss: 0.0798
+2023-03-05 03:06:43,928 - mmseg - INFO - Iter [138700/160000]	lr: 2.344e-06, eta: 2:00:37, time: 0.281, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9078, loss: 0.0776
+2023-03-05 03:06:57,524 - mmseg - INFO - Iter [138750/160000]	lr: 2.344e-06, eta: 2:00:20, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8643, loss: 0.0779
+2023-03-05 03:07:13,572 - mmseg - INFO - Iter [138800/160000]	lr: 2.344e-06, eta: 2:00:03, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0750, decode.acc_seg: 96.9790, loss: 0.0750
+2023-03-05 03:07:27,794 - mmseg - INFO - Iter [138850/160000]	lr: 2.344e-06, eta: 1:59:45, time: 0.285, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9460, loss: 0.0762
+2023-03-05 03:07:41,490 - mmseg - INFO - Iter [138900/160000]	lr: 2.344e-06, eta: 1:59:28, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 97.0206, loss: 0.0754
+2023-03-05 03:07:57,645 - mmseg - INFO - Iter [138950/160000]	lr: 2.344e-06, eta: 1:59:11, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.9031, loss: 0.0782
+2023-03-05 03:08:11,615 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 03:08:11,615 - mmseg - INFO - Iter [139000/160000]	lr: 2.344e-06, eta: 1:58:53, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0747, decode.acc_seg: 97.0384, loss: 0.0747
+2023-03-05 03:08:25,356 - mmseg - INFO - Iter [139050/160000]	lr: 2.344e-06, eta: 1:58:36, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9444, loss: 0.0773
+2023-03-05 03:08:38,913 - mmseg - INFO - Iter [139100/160000]	lr: 2.344e-06, eta: 1:58:18, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9289, loss: 0.0768
+2023-03-05 03:08:54,954 - mmseg - INFO - Iter [139150/160000]	lr: 2.344e-06, eta: 1:58:01, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0746, decode.acc_seg: 97.0208, loss: 0.0746
+2023-03-05 03:09:09,105 - mmseg - INFO - Iter [139200/160000]	lr: 2.344e-06, eta: 1:57:44, time: 0.283, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8949, loss: 0.0784
+2023-03-05 03:09:22,881 - mmseg - INFO - Iter [139250/160000]	lr: 2.344e-06, eta: 1:57:26, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9300, loss: 0.0767
+2023-03-05 03:09:36,532 - mmseg - INFO - Iter [139300/160000]	lr: 2.344e-06, eta: 1:57:09, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.8920, loss: 0.0778
+2023-03-05 03:09:52,642 - mmseg - INFO - Iter [139350/160000]	lr: 2.344e-06, eta: 1:56:52, time: 0.322, data_time: 0.055, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9145, loss: 0.0773
+2023-03-05 03:10:06,273 - mmseg - INFO - Iter [139400/160000]	lr: 2.344e-06, eta: 1:56:34, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.8793, loss: 0.0772
+2023-03-05 03:10:19,966 - mmseg - INFO - Iter [139450/160000]	lr: 2.344e-06, eta: 1:56:17, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9430, loss: 0.0765
+2023-03-05 03:10:33,808 - mmseg - INFO - Iter [139500/160000]	lr: 2.344e-06, eta: 1:55:59, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9676, loss: 0.0762
+2023-03-05 03:10:50,041 - mmseg - INFO - Iter [139550/160000]	lr: 2.344e-06, eta: 1:55:42, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9570, loss: 0.0764
+2023-03-05 03:11:03,828 - mmseg - INFO - Iter [139600/160000]	lr: 2.344e-06, eta: 1:55:25, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.8474, loss: 0.0779
+2023-03-05 03:11:17,614 - mmseg - INFO - Iter [139650/160000]	lr: 2.344e-06, eta: 1:55:07, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0750, decode.acc_seg: 96.9969, loss: 0.0750
+2023-03-05 03:11:33,716 - mmseg - INFO - Iter [139700/160000]	lr: 2.344e-06, eta: 1:54:50, time: 0.322, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9519, loss: 0.0769
+2023-03-05 03:11:47,488 - mmseg - INFO - Iter [139750/160000]	lr: 2.344e-06, eta: 1:54:33, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9165, loss: 0.0770
+2023-03-05 03:12:01,049 - mmseg - INFO - Iter [139800/160000]	lr: 2.344e-06, eta: 1:54:15, time: 0.271, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 97.0051, loss: 0.0757
+2023-03-05 03:12:14,654 - mmseg - INFO - Iter [139850/160000]	lr: 2.344e-06, eta: 1:53:58, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9978, loss: 0.0759
+2023-03-05 03:12:30,675 - mmseg - INFO - Iter [139900/160000]	lr: 2.344e-06, eta: 1:53:41, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 97.0140, loss: 0.0755
+2023-03-05 03:12:44,407 - mmseg - INFO - Iter [139950/160000]	lr: 2.344e-06, eta: 1:53:23, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9565, loss: 0.0769
+2023-03-05 03:12:58,326 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 03:12:58,326 - mmseg - INFO - Iter [140000/160000]	lr: 2.344e-06, eta: 1:53:06, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 97.0257, loss: 0.0759
+2023-03-05 03:13:12,028 - mmseg - INFO - Iter [140050/160000]	lr: 1.172e-06, eta: 1:52:49, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0751, decode.acc_seg: 96.9753, loss: 0.0751
+2023-03-05 03:13:28,018 - mmseg - INFO - Iter [140100/160000]	lr: 1.172e-06, eta: 1:52:31, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9427, loss: 0.0771
+2023-03-05 03:13:41,670 - mmseg - INFO - Iter [140150/160000]	lr: 1.172e-06, eta: 1:52:14, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9941, loss: 0.0758
+2023-03-05 03:13:55,407 - mmseg - INFO - Iter [140200/160000]	lr: 1.172e-06, eta: 1:51:57, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.8965, loss: 0.0772
+2023-03-05 03:14:11,600 - mmseg - INFO - Iter [140250/160000]	lr: 1.172e-06, eta: 1:51:40, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0751, decode.acc_seg: 97.0229, loss: 0.0751
+2023-03-05 03:14:25,170 - mmseg - INFO - Iter [140300/160000]	lr: 1.172e-06, eta: 1:51:22, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9361, loss: 0.0767
+2023-03-05 03:14:38,919 - mmseg - INFO - Iter [140350/160000]	lr: 1.172e-06, eta: 1:51:05, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9672, loss: 0.0757
+2023-03-05 03:14:52,627 - mmseg - INFO - Iter [140400/160000]	lr: 1.172e-06, eta: 1:50:47, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.8986, loss: 0.0777
+2023-03-05 03:15:08,829 - mmseg - INFO - Iter [140450/160000]	lr: 1.172e-06, eta: 1:50:30, time: 0.324, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0753, decode.acc_seg: 97.0190, loss: 0.0753
+2023-03-05 03:15:22,488 - mmseg - INFO - Iter [140500/160000]	lr: 1.172e-06, eta: 1:50:13, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9738, loss: 0.0764
+2023-03-05 03:15:36,081 - mmseg - INFO - Iter [140550/160000]	lr: 1.172e-06, eta: 1:49:55, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9246, loss: 0.0772
+2023-03-05 03:15:49,972 - mmseg - INFO - Iter [140600/160000]	lr: 1.172e-06, eta: 1:49:38, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9481, loss: 0.0769
+2023-03-05 03:16:06,080 - mmseg - INFO - Iter [140650/160000]	lr: 1.172e-06, eta: 1:49:21, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 97.0043, loss: 0.0754
+2023-03-05 03:16:19,733 - mmseg - INFO - Iter [140700/160000]	lr: 1.172e-06, eta: 1:49:04, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0753, decode.acc_seg: 96.9670, loss: 0.0753
+2023-03-05 03:16:33,393 - mmseg - INFO - Iter [140750/160000]	lr: 1.172e-06, eta: 1:48:46, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9513, loss: 0.0767
+2023-03-05 03:16:47,517 - mmseg - INFO - Iter [140800/160000]	lr: 1.172e-06, eta: 1:48:29, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9151, loss: 0.0780
+2023-03-05 03:17:03,550 - mmseg - INFO - Iter [140850/160000]	lr: 1.172e-06, eta: 1:48:12, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8714, loss: 0.0787
+2023-03-05 03:17:17,207 - mmseg - INFO - Iter [140900/160000]	lr: 1.172e-06, eta: 1:47:54, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9825, loss: 0.0758
+2023-03-05 03:17:31,095 - mmseg - INFO - Iter [140950/160000]	lr: 1.172e-06, eta: 1:47:37, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9733, loss: 0.0761
+2023-03-05 03:17:47,035 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 03:17:47,035 - mmseg - INFO - Iter [141000/160000]	lr: 1.172e-06, eta: 1:47:20, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 96.9945, loss: 0.0752
+2023-03-05 03:18:00,745 - mmseg - INFO - Iter [141050/160000]	lr: 1.172e-06, eta: 1:47:02, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0753, decode.acc_seg: 96.9993, loss: 0.0753
+2023-03-05 03:18:14,749 - mmseg - INFO - Iter [141100/160000]	lr: 1.172e-06, eta: 1:46:45, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.9399, loss: 0.0781
+2023-03-05 03:18:28,352 - mmseg - INFO - Iter [141150/160000]	lr: 1.172e-06, eta: 1:46:28, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.8912, loss: 0.0774
+2023-03-05 03:18:44,312 - mmseg - INFO - Iter [141200/160000]	lr: 1.172e-06, eta: 1:46:11, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 97.0205, loss: 0.0759
+2023-03-05 03:18:58,060 - mmseg - INFO - Iter [141250/160000]	lr: 1.172e-06, eta: 1:45:53, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 97.0011, loss: 0.0765
+2023-03-05 03:19:11,879 - mmseg - INFO - Iter [141300/160000]	lr: 1.172e-06, eta: 1:45:36, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.9000, loss: 0.0791
+2023-03-05 03:19:25,679 - mmseg - INFO - Iter [141350/160000]	lr: 1.172e-06, eta: 1:45:19, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0743, decode.acc_seg: 97.0175, loss: 0.0743
+2023-03-05 03:19:41,667 - mmseg - INFO - Iter [141400/160000]	lr: 1.172e-06, eta: 1:45:01, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8744, loss: 0.0791
+2023-03-05 03:19:55,396 - mmseg - INFO - Iter [141450/160000]	lr: 1.172e-06, eta: 1:44:44, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 96.9706, loss: 0.0755
+2023-03-05 03:20:09,055 - mmseg - INFO - Iter [141500/160000]	lr: 1.172e-06, eta: 1:44:27, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8801, loss: 0.0781
+2023-03-05 03:20:25,205 - mmseg - INFO - Iter [141550/160000]	lr: 1.172e-06, eta: 1:44:10, time: 0.323, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8863, loss: 0.0786
+2023-03-05 03:20:38,782 - mmseg - INFO - Iter [141600/160000]	lr: 1.172e-06, eta: 1:43:52, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.8961, loss: 0.0775
+2023-03-05 03:20:52,467 - mmseg - INFO - Iter [141650/160000]	lr: 1.172e-06, eta: 1:43:35, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9433, loss: 0.0772
+2023-03-05 03:21:06,066 - mmseg - INFO - Iter [141700/160000]	lr: 1.172e-06, eta: 1:43:18, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0731, decode.acc_seg: 97.0751, loss: 0.0731
+2023-03-05 03:21:22,039 - mmseg - INFO - Iter [141750/160000]	lr: 1.172e-06, eta: 1:43:01, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8369, loss: 0.0797
+2023-03-05 03:21:35,773 - mmseg - INFO - Iter [141800/160000]	lr: 1.172e-06, eta: 1:42:43, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9321, loss: 0.0769
+2023-03-05 03:21:49,661 - mmseg - INFO - Iter [141850/160000]	lr: 1.172e-06, eta: 1:42:26, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 97.0148, loss: 0.0755
+2023-03-05 03:22:03,335 - mmseg - INFO - Iter [141900/160000]	lr: 1.172e-06, eta: 1:42:09, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9698, loss: 0.0764
+2023-03-05 03:22:19,428 - mmseg - INFO - Iter [141950/160000]	lr: 1.172e-06, eta: 1:41:52, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8977, loss: 0.0784
+2023-03-05 03:22:33,068 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 03:22:33,069 - mmseg - INFO - Iter [142000/160000]	lr: 1.172e-06, eta: 1:41:34, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0722, decode.acc_seg: 97.1133, loss: 0.0722
+2023-03-05 03:22:46,885 - mmseg - INFO - Iter [142050/160000]	lr: 1.172e-06, eta: 1:41:17, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9655, loss: 0.0760
+2023-03-05 03:23:00,538 - mmseg - INFO - Iter [142100/160000]	lr: 1.172e-06, eta: 1:41:00, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9575, loss: 0.0764
+2023-03-05 03:23:16,642 - mmseg - INFO - Iter [142150/160000]	lr: 1.172e-06, eta: 1:40:42, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8886, loss: 0.0780
+2023-03-05 03:23:30,570 - mmseg - INFO - Iter [142200/160000]	lr: 1.172e-06, eta: 1:40:25, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0742, decode.acc_seg: 97.0440, loss: 0.0742
+2023-03-05 03:23:44,286 - mmseg - INFO - Iter [142250/160000]	lr: 1.172e-06, eta: 1:40:08, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0739, decode.acc_seg: 97.0374, loss: 0.0739
+2023-03-05 03:24:00,266 - mmseg - INFO - Iter [142300/160000]	lr: 1.172e-06, eta: 1:39:51, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9387, loss: 0.0778
+2023-03-05 03:24:14,059 - mmseg - INFO - Iter [142350/160000]	lr: 1.172e-06, eta: 1:39:33, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9727, loss: 0.0779
+2023-03-05 03:24:27,781 - mmseg - INFO - Iter [142400/160000]	lr: 1.172e-06, eta: 1:39:16, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8664, loss: 0.0785
+2023-03-05 03:24:41,403 - mmseg - INFO - Iter [142450/160000]	lr: 1.172e-06, eta: 1:38:59, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9731, loss: 0.0759
+2023-03-05 03:24:57,480 - mmseg - INFO - Iter [142500/160000]	lr: 1.172e-06, eta: 1:38:42, time: 0.322, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 96.9847, loss: 0.0754
+2023-03-05 03:25:11,345 - mmseg - INFO - Iter [142550/160000]	lr: 1.172e-06, eta: 1:38:25, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8583, loss: 0.0791
+2023-03-05 03:25:25,020 - mmseg - INFO - Iter [142600/160000]	lr: 1.172e-06, eta: 1:38:07, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.9068, loss: 0.0791
+2023-03-05 03:25:38,644 - mmseg - INFO - Iter [142650/160000]	lr: 1.172e-06, eta: 1:37:50, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9485, loss: 0.0761
+2023-03-05 03:25:54,588 - mmseg - INFO - Iter [142700/160000]	lr: 1.172e-06, eta: 1:37:33, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8955, loss: 0.0781
+2023-03-05 03:26:08,567 - mmseg - INFO - Iter [142750/160000]	lr: 1.172e-06, eta: 1:37:16, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8895, loss: 0.0788
+2023-03-05 03:26:22,318 - mmseg - INFO - Iter [142800/160000]	lr: 1.172e-06, eta: 1:36:58, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9536, loss: 0.0767
+2023-03-05 03:26:38,569 - mmseg - INFO - Iter [142850/160000]	lr: 1.172e-06, eta: 1:36:41, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8594, loss: 0.0793
+2023-03-05 03:26:52,228 - mmseg - INFO - Iter [142900/160000]	lr: 1.172e-06, eta: 1:36:24, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.9184, loss: 0.0790
+2023-03-05 03:27:05,989 - mmseg - INFO - Iter [142950/160000]	lr: 1.172e-06, eta: 1:36:07, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8257, loss: 0.0788
+2023-03-05 03:27:19,531 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 03:27:19,531 - mmseg - INFO - Iter [143000/160000]	lr: 1.172e-06, eta: 1:35:49, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0743, decode.acc_seg: 97.0396, loss: 0.0743
+2023-03-05 03:27:35,766 - mmseg - INFO - Iter [143050/160000]	lr: 1.172e-06, eta: 1:35:32, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9292, loss: 0.0767
+2023-03-05 03:27:49,378 - mmseg - INFO - Iter [143100/160000]	lr: 1.172e-06, eta: 1:35:15, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9313, loss: 0.0772
+2023-03-05 03:28:02,969 - mmseg - INFO - Iter [143150/160000]	lr: 1.172e-06, eta: 1:34:58, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9730, loss: 0.0763
+2023-03-05 03:28:16,691 - mmseg - INFO - Iter [143200/160000]	lr: 1.172e-06, eta: 1:34:40, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9341, loss: 0.0761
+2023-03-05 03:28:32,661 - mmseg - INFO - Iter [143250/160000]	lr: 1.172e-06, eta: 1:34:23, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0800, decode.acc_seg: 96.8601, loss: 0.0800
+2023-03-05 03:28:46,452 - mmseg - INFO - Iter [143300/160000]	lr: 1.172e-06, eta: 1:34:06, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.8283, loss: 0.0802
+2023-03-05 03:29:00,361 - mmseg - INFO - Iter [143350/160000]	lr: 1.172e-06, eta: 1:33:49, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9155, loss: 0.0769
+2023-03-05 03:29:14,395 - mmseg - INFO - Iter [143400/160000]	lr: 1.172e-06, eta: 1:33:32, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8809, loss: 0.0781
+2023-03-05 03:29:30,569 - mmseg - INFO - Iter [143450/160000]	lr: 1.172e-06, eta: 1:33:15, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9439, loss: 0.0761
+2023-03-05 03:29:44,250 - mmseg - INFO - Iter [143500/160000]	lr: 1.172e-06, eta: 1:32:57, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0831, decode.acc_seg: 96.7753, loss: 0.0831
+2023-03-05 03:29:57,923 - mmseg - INFO - Iter [143550/160000]	lr: 1.172e-06, eta: 1:32:40, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0753, decode.acc_seg: 97.0081, loss: 0.0753
+2023-03-05 03:30:14,143 - mmseg - INFO - Iter [143600/160000]	lr: 1.172e-06, eta: 1:32:23, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 97.0003, loss: 0.0758
+2023-03-05 03:30:27,921 - mmseg - INFO - Iter [143650/160000]	lr: 1.172e-06, eta: 1:32:06, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9521, loss: 0.0769
+2023-03-05 03:30:41,605 - mmseg - INFO - Iter [143700/160000]	lr: 1.172e-06, eta: 1:31:49, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0756, decode.acc_seg: 96.9858, loss: 0.0756
+2023-03-05 03:30:55,481 - mmseg - INFO - Iter [143750/160000]	lr: 1.172e-06, eta: 1:31:31, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8327, loss: 0.0784
+2023-03-05 03:31:11,466 - mmseg - INFO - Iter [143800/160000]	lr: 1.172e-06, eta: 1:31:14, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9345, loss: 0.0766
+2023-03-05 03:31:25,368 - mmseg - INFO - Iter [143850/160000]	lr: 1.172e-06, eta: 1:30:57, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8659, loss: 0.0793
+2023-03-05 03:31:39,076 - mmseg - INFO - Iter [143900/160000]	lr: 1.172e-06, eta: 1:30:40, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 97.0070, loss: 0.0755
+2023-03-05 03:31:52,773 - mmseg - INFO - Iter [143950/160000]	lr: 1.172e-06, eta: 1:30:23, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.7630, loss: 0.0803
+2023-03-05 03:32:08,778 - mmseg - INFO - Swap parameters (after train) after iter [144000]
+2023-03-05 03:32:08,798 - mmseg - INFO - Saving checkpoint at 144000 iterations
+2023-03-05 03:32:10,638 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 03:32:10,638 - mmseg - INFO - Iter [144000/160000]	lr: 1.172e-06, eta: 1:30:06, time: 0.357, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 96.9975, loss: 0.0755
+2023-03-05 03:47:05,800 - mmseg - INFO - per class results:
+2023-03-05 03:47:05,801 - mmseg - INFO - 
++---------------+-------------------------------------------------------------------+
+|     Class     |                IoU 0,10,20,30,40,50,60,70,80,90,99                |
++---------------+-------------------------------------------------------------------+
+|   background  |            nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan            |
+|      road     | 98.55,98.54,98.55,98.55,98.54,98.55,98.54,98.55,98.54,98.54,98.54 |
+|    sidewalk   |  87.55,87.54,87.54,87.55,87.53,87.55,87.52,87.53,87.51,87.51,87.5 |
+|    building   |   93.59,93.6,93.6,93.6,93.61,93.61,93.61,93.61,93.61,93.61,93.61  |
+|      wall     |  55.61,55.76,55.69,55.96,56.07,56.11,56.15,56.2,56.38,56.51,56.4  |
+|     fence     |  65.02,65.03,65.06,65.09,65.09,65.14,65.13,65.16,65.2,65.21,65.14 |
+|      pole     | 71.34,71.35,71.35,71.36,71.37,71.37,71.37,71.38,71.38,71.38,71.38 |
+| traffic light |  75.57,75.57,75.58,75.58,75.59,75.59,75.59,75.6,75.6,75.61,75.58  |
+|  traffic sign |  82.66,82.66,82.67,82.67,82.68,82.68,82.68,82.69,82.7,82.71,82.7  |
+|   vegetation  | 93.13,93.13,93.13,93.14,93.15,93.16,93.16,93.16,93.17,93.18,93.17 |
+|    terrain    | 64.91,64.86,64.89,64.97,64.95,64.98,64.98,65.01,65.01,64.96,64.95 |
+|      sky      | 95.28,95.28,95.28,95.28,95.28,95.28,95.28,95.29,95.29,95.29,95.29 |
+|     person    | 85.02,85.01,85.02,85.02,85.01,85.02,85.01,85.01,85.01,85.02,85.01 |
+|     rider     | 67.87,67.88,67.87,67.86,67.88,67.86,67.85,67.86,67.87,67.84,67.87 |
+|      car      | 96.11,96.11,96.11,96.11,96.11,96.11,96.12,96.11,96.12,96.11,96.12 |
+|     truck     |  87.15,87.22,87.23,87.23,87.21,87.2,87.29,87.21,87.21,87.19,87.15 |
+|      bus      |   92.59,92.6,92.6,92.63,92.62,92.6,92.63,92.63,92.63,92.64,92.63  |
+|     train     |  85.76,85.81,85.84,85.85,85.82,85.85,85.86,85.86,85.85,85.9,85.95 |
+|   motorcycle  | 72.27,72.22,72.27,72.23,72.24,72.25,72.26,72.24,72.22,72.22,72.23 |
+|    bicycle    |   80.57,80.58,80.58,80.58,80.59,80.6,80.6,80.6,80.6,80.61,80.61   |
++---------------+-------------------------------------------------------------------+
+2023-03-05 03:47:05,801 - mmseg - INFO - Summary:
+2023-03-05 03:47:05,801 - mmseg - INFO - 
++-------------------------------------------------------------------+
+|                mIoU 0,10,20,30,40,50,60,70,80,90,99               |
++-------------------------------------------------------------------+
+| 81.61,81.62,81.62,81.65,81.65,81.66,81.66,81.67,81.68,81.69,81.68 |
++-------------------------------------------------------------------+
+2023-03-05 03:47:05,864 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune/best_mIoU_iter_128000.pth was removed
+2023-03-05 03:47:07,573 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_144000.pth.
+2023-03-05 03:47:07,574 - mmseg - INFO - Best mIoU is 0.8168 at 144000 iter.
+2023-03-05 03:47:07,574 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 03:47:07,574 - mmseg - INFO - Iter(val) [63]	mIoU: [0.8161, 0.8162, 0.8162, 0.8165, 0.8165, 0.8166, 0.8166, 0.8167, 0.8168, 0.8169, 0.8168], copy_paste: 81.61,81.62,81.62,81.65,81.65,81.66,81.66,81.67,81.68,81.69,81.68
+2023-03-05 03:47:07,580 - mmseg - INFO - Swap parameters (before train) before iter [144001]
+2023-03-05 03:47:21,766 - mmseg - INFO - Iter [144050/160000]	lr: 1.172e-06, eta: 1:31:28, time: 18.223, data_time: 17.948, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 97.0257, loss: 0.0752
+2023-03-05 03:47:35,537 - mmseg - INFO - Iter [144100/160000]	lr: 1.172e-06, eta: 1:31:10, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8756, loss: 0.0783
+2023-03-05 03:47:49,452 - mmseg - INFO - Iter [144150/160000]	lr: 1.172e-06, eta: 1:30:53, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9471, loss: 0.0775
+2023-03-05 03:48:05,526 - mmseg - INFO - Iter [144200/160000]	lr: 1.172e-06, eta: 1:30:36, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.8903, loss: 0.0783
+2023-03-05 03:48:19,312 - mmseg - INFO - Iter [144250/160000]	lr: 1.172e-06, eta: 1:30:18, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0746, decode.acc_seg: 97.0112, loss: 0.0746
+2023-03-05 03:48:33,164 - mmseg - INFO - Iter [144300/160000]	lr: 1.172e-06, eta: 1:30:00, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9216, loss: 0.0778
+2023-03-05 03:48:49,307 - mmseg - INFO - Iter [144350/160000]	lr: 1.172e-06, eta: 1:29:43, time: 0.323, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9204, loss: 0.0779
+2023-03-05 03:49:03,110 - mmseg - INFO - Iter [144400/160000]	lr: 1.172e-06, eta: 1:29:26, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9151, loss: 0.0776
+2023-03-05 03:49:17,042 - mmseg - INFO - Iter [144450/160000]	lr: 1.172e-06, eta: 1:29:08, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8840, loss: 0.0782
+2023-03-05 03:49:30,952 - mmseg - INFO - Iter [144500/160000]	lr: 1.172e-06, eta: 1:28:50, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.9276, loss: 0.0783
+2023-03-05 03:49:47,033 - mmseg - INFO - Iter [144550/160000]	lr: 1.172e-06, eta: 1:28:33, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.9048, loss: 0.0784
+2023-03-05 03:50:00,830 - mmseg - INFO - Iter [144600/160000]	lr: 1.172e-06, eta: 1:28:16, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9486, loss: 0.0764
+2023-03-05 03:50:14,842 - mmseg - INFO - Iter [144650/160000]	lr: 1.172e-06, eta: 1:27:58, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8821, loss: 0.0781
+2023-03-05 03:50:28,622 - mmseg - INFO - Iter [144700/160000]	lr: 1.172e-06, eta: 1:27:40, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0746, decode.acc_seg: 97.0047, loss: 0.0746
+2023-03-05 03:50:44,898 - mmseg - INFO - Iter [144750/160000]	lr: 1.172e-06, eta: 1:27:23, time: 0.325, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9426, loss: 0.0760
+2023-03-05 03:50:58,687 - mmseg - INFO - Iter [144800/160000]	lr: 1.172e-06, eta: 1:27:06, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9660, loss: 0.0768
+2023-03-05 03:51:12,513 - mmseg - INFO - Iter [144850/160000]	lr: 1.172e-06, eta: 1:26:48, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0741, decode.acc_seg: 97.0474, loss: 0.0741
+2023-03-05 03:51:28,728 - mmseg - INFO - Iter [144900/160000]	lr: 1.172e-06, eta: 1:26:31, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0848, decode.acc_seg: 96.8361, loss: 0.0848
+2023-03-05 03:51:42,561 - mmseg - INFO - Iter [144950/160000]	lr: 1.172e-06, eta: 1:26:13, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9787, loss: 0.0758
+2023-03-05 03:51:56,519 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 03:51:56,520 - mmseg - INFO - Iter [145000/160000]	lr: 1.172e-06, eta: 1:25:56, time: 0.279, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.9027, loss: 0.0791
+2023-03-05 03:52:10,210 - mmseg - INFO - Iter [145050/160000]	lr: 1.172e-06, eta: 1:25:38, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.8677, loss: 0.0781
+2023-03-05 03:52:26,240 - mmseg - INFO - Iter [145100/160000]	lr: 1.172e-06, eta: 1:25:21, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0805, decode.acc_seg: 96.8117, loss: 0.0805
+2023-03-05 03:52:40,107 - mmseg - INFO - Iter [145150/160000]	lr: 1.172e-06, eta: 1:25:03, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9550, loss: 0.0777
+2023-03-05 03:52:53,963 - mmseg - INFO - Iter [145200/160000]	lr: 1.172e-06, eta: 1:24:46, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9674, loss: 0.0758
+2023-03-05 03:53:07,641 - mmseg - INFO - Iter [145250/160000]	lr: 1.172e-06, eta: 1:24:28, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.8953, loss: 0.0777
+2023-03-05 03:53:24,007 - mmseg - INFO - Iter [145300/160000]	lr: 1.172e-06, eta: 1:24:11, time: 0.327, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0781, decode.acc_seg: 96.9118, loss: 0.0781
+2023-03-05 03:53:37,800 - mmseg - INFO - Iter [145350/160000]	lr: 1.172e-06, eta: 1:23:54, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.8954, loss: 0.0777
+2023-03-05 03:53:51,580 - mmseg - INFO - Iter [145400/160000]	lr: 1.172e-06, eta: 1:23:36, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9598, loss: 0.0765
+2023-03-05 03:54:05,458 - mmseg - INFO - Iter [145450/160000]	lr: 1.172e-06, eta: 1:23:18, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9475, loss: 0.0775
+2023-03-05 03:54:21,488 - mmseg - INFO - Iter [145500/160000]	lr: 1.172e-06, eta: 1:23:01, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9312, loss: 0.0768
+2023-03-05 03:54:35,134 - mmseg - INFO - Iter [145550/160000]	lr: 1.172e-06, eta: 1:22:44, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8740, loss: 0.0786
+2023-03-05 03:54:48,737 - mmseg - INFO - Iter [145600/160000]	lr: 1.172e-06, eta: 1:22:26, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8516, loss: 0.0793
+2023-03-05 03:55:04,738 - mmseg - INFO - Iter [145650/160000]	lr: 1.172e-06, eta: 1:22:09, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.8582, loss: 0.0807
+2023-03-05 03:55:18,489 - mmseg - INFO - Iter [145700/160000]	lr: 1.172e-06, eta: 1:21:51, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9105, loss: 0.0773
+2023-03-05 03:55:32,421 - mmseg - INFO - Iter [145750/160000]	lr: 1.172e-06, eta: 1:21:34, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9752, loss: 0.0765
+2023-03-05 03:55:46,010 - mmseg - INFO - Iter [145800/160000]	lr: 1.172e-06, eta: 1:21:16, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 97.0239, loss: 0.0755
+2023-03-05 03:56:02,025 - mmseg - INFO - Iter [145850/160000]	lr: 1.172e-06, eta: 1:20:59, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9624, loss: 0.0769
+2023-03-05 03:56:15,973 - mmseg - INFO - Iter [145900/160000]	lr: 1.172e-06, eta: 1:20:42, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9895, loss: 0.0762
+2023-03-05 03:56:29,562 - mmseg - INFO - Iter [145950/160000]	lr: 1.172e-06, eta: 1:20:24, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9171, loss: 0.0774
+2023-03-05 03:56:43,357 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 03:56:43,357 - mmseg - INFO - Iter [146000/160000]	lr: 1.172e-06, eta: 1:20:07, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9694, loss: 0.0762
+2023-03-05 03:56:59,462 - mmseg - INFO - Iter [146050/160000]	lr: 1.172e-06, eta: 1:19:49, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0796, decode.acc_seg: 96.8903, loss: 0.0796
+2023-03-05 03:57:13,059 - mmseg - INFO - Iter [146100/160000]	lr: 1.172e-06, eta: 1:19:32, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9154, loss: 0.0776
+2023-03-05 03:57:26,690 - mmseg - INFO - Iter [146150/160000]	lr: 1.172e-06, eta: 1:19:14, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9312, loss: 0.0775
+2023-03-05 03:57:42,628 - mmseg - INFO - Iter [146200/160000]	lr: 1.172e-06, eta: 1:18:57, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9411, loss: 0.0771
+2023-03-05 03:57:56,406 - mmseg - INFO - Iter [146250/160000]	lr: 1.172e-06, eta: 1:18:39, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8938, loss: 0.0786
+2023-03-05 03:58:10,116 - mmseg - INFO - Iter [146300/160000]	lr: 1.172e-06, eta: 1:18:22, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 96.9921, loss: 0.0755
+2023-03-05 03:58:23,772 - mmseg - INFO - Iter [146350/160000]	lr: 1.172e-06, eta: 1:18:05, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9952, loss: 0.0768
+2023-03-05 03:58:39,703 - mmseg - INFO - Iter [146400/160000]	lr: 1.172e-06, eta: 1:17:47, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9075, loss: 0.0777
+2023-03-05 03:58:53,570 - mmseg - INFO - Iter [146450/160000]	lr: 1.172e-06, eta: 1:17:30, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8580, loss: 0.0790
+2023-03-05 03:59:07,200 - mmseg - INFO - Iter [146500/160000]	lr: 1.172e-06, eta: 1:17:12, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0730, decode.acc_seg: 97.0789, loss: 0.0730
+2023-03-05 03:59:20,970 - mmseg - INFO - Iter [146550/160000]	lr: 1.172e-06, eta: 1:16:55, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9452, loss: 0.0765
+2023-03-05 03:59:36,919 - mmseg - INFO - Iter [146600/160000]	lr: 1.172e-06, eta: 1:16:38, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9512, loss: 0.0773
+2023-03-05 03:59:50,758 - mmseg - INFO - Iter [146650/160000]	lr: 1.172e-06, eta: 1:16:20, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9231, loss: 0.0765
+2023-03-05 04:00:04,425 - mmseg - INFO - Iter [146700/160000]	lr: 1.172e-06, eta: 1:16:03, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8973, loss: 0.0791
+2023-03-05 04:00:18,155 - mmseg - INFO - Iter [146750/160000]	lr: 1.172e-06, eta: 1:15:45, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9648, loss: 0.0759
+2023-03-05 04:00:34,278 - mmseg - INFO - Iter [146800/160000]	lr: 1.172e-06, eta: 1:15:28, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.8969, loss: 0.0771
+2023-03-05 04:00:47,934 - mmseg - INFO - Iter [146850/160000]	lr: 1.172e-06, eta: 1:15:10, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8581, loss: 0.0789
+2023-03-05 04:01:01,525 - mmseg - INFO - Iter [146900/160000]	lr: 1.172e-06, eta: 1:14:53, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9585, loss: 0.0760
+2023-03-05 04:01:17,572 - mmseg - INFO - Iter [146950/160000]	lr: 1.172e-06, eta: 1:14:36, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9009, loss: 0.0777
+2023-03-05 04:01:31,640 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 04:01:31,640 - mmseg - INFO - Iter [147000/160000]	lr: 1.172e-06, eta: 1:14:18, time: 0.281, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9825, loss: 0.0759
+2023-03-05 04:01:45,261 - mmseg - INFO - Iter [147050/160000]	lr: 1.172e-06, eta: 1:14:01, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9661, loss: 0.0766
+2023-03-05 04:01:59,066 - mmseg - INFO - Iter [147100/160000]	lr: 1.172e-06, eta: 1:13:43, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9812, loss: 0.0759
+2023-03-05 04:02:15,119 - mmseg - INFO - Iter [147150/160000]	lr: 1.172e-06, eta: 1:13:26, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9544, loss: 0.0763
+2023-03-05 04:02:29,134 - mmseg - INFO - Iter [147200/160000]	lr: 1.172e-06, eta: 1:13:09, time: 0.281, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0745, decode.acc_seg: 97.0630, loss: 0.0745
+2023-03-05 04:02:42,777 - mmseg - INFO - Iter [147250/160000]	lr: 1.172e-06, eta: 1:12:51, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9131, loss: 0.0773
+2023-03-05 04:02:56,707 - mmseg - INFO - Iter [147300/160000]	lr: 1.172e-06, eta: 1:12:34, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8621, loss: 0.0785
+2023-03-05 04:03:12,814 - mmseg - INFO - Iter [147350/160000]	lr: 1.172e-06, eta: 1:12:17, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0745, decode.acc_seg: 97.0229, loss: 0.0745
+2023-03-05 04:03:26,569 - mmseg - INFO - Iter [147400/160000]	lr: 1.172e-06, eta: 1:11:59, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0802, decode.acc_seg: 96.8214, loss: 0.0802
+2023-03-05 04:03:40,245 - mmseg - INFO - Iter [147450/160000]	lr: 1.172e-06, eta: 1:11:42, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8699, loss: 0.0785
+2023-03-05 04:03:56,915 - mmseg - INFO - Iter [147500/160000]	lr: 1.172e-06, eta: 1:11:25, time: 0.333, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9285, loss: 0.0774
+2023-03-05 04:04:10,693 - mmseg - INFO - Iter [147550/160000]	lr: 1.172e-06, eta: 1:11:07, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9082, loss: 0.0775
+2023-03-05 04:04:24,604 - mmseg - INFO - Iter [147600/160000]	lr: 1.172e-06, eta: 1:10:50, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.9083, loss: 0.0783
+2023-03-05 04:04:38,408 - mmseg - INFO - Iter [147650/160000]	lr: 1.172e-06, eta: 1:10:32, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0807, decode.acc_seg: 96.7424, loss: 0.0807
+2023-03-05 04:04:54,334 - mmseg - INFO - Iter [147700/160000]	lr: 1.172e-06, eta: 1:10:15, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9939, loss: 0.0760
+2023-03-05 04:05:08,000 - mmseg - INFO - Iter [147750/160000]	lr: 1.172e-06, eta: 1:09:58, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0756, decode.acc_seg: 96.9960, loss: 0.0756
+2023-03-05 04:05:21,664 - mmseg - INFO - Iter [147800/160000]	lr: 1.172e-06, eta: 1:09:40, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9030, loss: 0.0778
+2023-03-05 04:05:35,710 - mmseg - INFO - Iter [147850/160000]	lr: 1.172e-06, eta: 1:09:23, time: 0.281, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.8937, loss: 0.0778
+2023-03-05 04:05:51,634 - mmseg - INFO - Iter [147900/160000]	lr: 1.172e-06, eta: 1:09:06, time: 0.318, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8874, loss: 0.0787
+2023-03-05 04:06:05,480 - mmseg - INFO - Iter [147950/160000]	lr: 1.172e-06, eta: 1:08:48, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8816, loss: 0.0785
+2023-03-05 04:06:19,402 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 04:06:19,402 - mmseg - INFO - Iter [148000/160000]	lr: 1.172e-06, eta: 1:08:31, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9285, loss: 0.0772
+2023-03-05 04:06:32,985 - mmseg - INFO - Iter [148050/160000]	lr: 1.172e-06, eta: 1:08:13, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.8848, loss: 0.0776
+2023-03-05 04:06:48,936 - mmseg - INFO - Iter [148100/160000]	lr: 1.172e-06, eta: 1:07:56, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8786, loss: 0.0789
+2023-03-05 04:07:02,565 - mmseg - INFO - Iter [148150/160000]	lr: 1.172e-06, eta: 1:07:39, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 97.0211, loss: 0.0752
+2023-03-05 04:07:16,427 - mmseg - INFO - Iter [148200/160000]	lr: 1.172e-06, eta: 1:07:21, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8980, loss: 0.0791
+2023-03-05 04:07:32,444 - mmseg - INFO - Iter [148250/160000]	lr: 1.172e-06, eta: 1:07:04, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9087, loss: 0.0775
+2023-03-05 04:07:46,091 - mmseg - INFO - Iter [148300/160000]	lr: 1.172e-06, eta: 1:06:47, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9229, loss: 0.0766
+2023-03-05 04:08:00,059 - mmseg - INFO - Iter [148350/160000]	lr: 1.172e-06, eta: 1:06:29, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8586, loss: 0.0789
+2023-03-05 04:08:13,668 - mmseg - INFO - Iter [148400/160000]	lr: 1.172e-06, eta: 1:06:12, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8907, loss: 0.0793
+2023-03-05 04:08:29,769 - mmseg - INFO - Iter [148450/160000]	lr: 1.172e-06, eta: 1:05:55, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9621, loss: 0.0762
+2023-03-05 04:08:43,499 - mmseg - INFO - Iter [148500/160000]	lr: 1.172e-06, eta: 1:05:37, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.9048, loss: 0.0789
+2023-03-05 04:08:57,766 - mmseg - INFO - Iter [148550/160000]	lr: 1.172e-06, eta: 1:05:20, time: 0.285, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0746, decode.acc_seg: 97.0439, loss: 0.0746
+2023-03-05 04:09:11,531 - mmseg - INFO - Iter [148600/160000]	lr: 1.172e-06, eta: 1:05:03, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.8984, loss: 0.0776
+2023-03-05 04:09:28,105 - mmseg - INFO - Iter [148650/160000]	lr: 1.172e-06, eta: 1:04:46, time: 0.331, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0730, decode.acc_seg: 97.0392, loss: 0.0730
+2023-03-05 04:09:41,894 - mmseg - INFO - Iter [148700/160000]	lr: 1.172e-06, eta: 1:04:28, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8909, loss: 0.0782
+2023-03-05 04:09:55,781 - mmseg - INFO - Iter [148750/160000]	lr: 1.172e-06, eta: 1:04:11, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9119, loss: 0.0771
+2023-03-05 04:10:09,544 - mmseg - INFO - Iter [148800/160000]	lr: 1.172e-06, eta: 1:03:53, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9619, loss: 0.0758
+2023-03-05 04:10:25,799 - mmseg - INFO - Iter [148850/160000]	lr: 1.172e-06, eta: 1:03:36, time: 0.325, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8783, loss: 0.0784
+2023-03-05 04:10:39,677 - mmseg - INFO - Iter [148900/160000]	lr: 1.172e-06, eta: 1:03:19, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9369, loss: 0.0769
+2023-03-05 04:10:53,489 - mmseg - INFO - Iter [148950/160000]	lr: 1.172e-06, eta: 1:03:02, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9535, loss: 0.0767
+2023-03-05 04:11:09,930 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 04:11:09,931 - mmseg - INFO - Iter [149000/160000]	lr: 1.172e-06, eta: 1:02:44, time: 0.329, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9307, loss: 0.0767
+2023-03-05 04:11:23,626 - mmseg - INFO - Iter [149050/160000]	lr: 1.172e-06, eta: 1:02:27, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.9126, loss: 0.0783
+2023-03-05 04:11:37,318 - mmseg - INFO - Iter [149100/160000]	lr: 1.172e-06, eta: 1:02:10, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9543, loss: 0.0770
+2023-03-05 04:11:51,077 - mmseg - INFO - Iter [149150/160000]	lr: 1.172e-06, eta: 1:01:52, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9013, loss: 0.0769
+2023-03-05 04:12:07,088 - mmseg - INFO - Iter [149200/160000]	lr: 1.172e-06, eta: 1:01:35, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0744, decode.acc_seg: 97.0360, loss: 0.0744
+2023-03-05 04:12:20,691 - mmseg - INFO - Iter [149250/160000]	lr: 1.172e-06, eta: 1:01:18, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 96.9932, loss: 0.0755
+2023-03-05 04:12:34,413 - mmseg - INFO - Iter [149300/160000]	lr: 1.172e-06, eta: 1:01:00, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8828, loss: 0.0782
+2023-03-05 04:12:48,031 - mmseg - INFO - Iter [149350/160000]	lr: 1.172e-06, eta: 1:00:43, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0829, decode.acc_seg: 96.7008, loss: 0.0829
+2023-03-05 04:13:03,987 - mmseg - INFO - Iter [149400/160000]	lr: 1.172e-06, eta: 1:00:26, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0750, decode.acc_seg: 97.0628, loss: 0.0750
+2023-03-05 04:13:17,663 - mmseg - INFO - Iter [149450/160000]	lr: 1.172e-06, eta: 1:00:09, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 97.0021, loss: 0.0754
+2023-03-05 04:13:31,432 - mmseg - INFO - Iter [149500/160000]	lr: 1.172e-06, eta: 0:59:51, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0748, decode.acc_seg: 97.0194, loss: 0.0748
+2023-03-05 04:13:47,492 - mmseg - INFO - Iter [149550/160000]	lr: 1.172e-06, eta: 0:59:34, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0753, decode.acc_seg: 96.9839, loss: 0.0753
+2023-03-05 04:14:01,120 - mmseg - INFO - Iter [149600/160000]	lr: 1.172e-06, eta: 0:59:17, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8705, loss: 0.0788
+2023-03-05 04:14:14,942 - mmseg - INFO - Iter [149650/160000]	lr: 1.172e-06, eta: 0:58:59, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9678, loss: 0.0761
+2023-03-05 04:14:28,769 - mmseg - INFO - Iter [149700/160000]	lr: 1.172e-06, eta: 0:58:42, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9197, loss: 0.0774
+2023-03-05 04:14:44,809 - mmseg - INFO - Iter [149750/160000]	lr: 1.172e-06, eta: 0:58:25, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.9002, loss: 0.0784
+2023-03-05 04:14:58,667 - mmseg - INFO - Iter [149800/160000]	lr: 1.172e-06, eta: 0:58:07, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0744, decode.acc_seg: 97.0380, loss: 0.0744
+2023-03-05 04:15:12,310 - mmseg - INFO - Iter [149850/160000]	lr: 1.172e-06, eta: 0:57:50, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9753, loss: 0.0757
+2023-03-05 04:15:26,075 - mmseg - INFO - Iter [149900/160000]	lr: 1.172e-06, eta: 0:57:33, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9228, loss: 0.0774
+2023-03-05 04:15:42,222 - mmseg - INFO - Iter [149950/160000]	lr: 1.172e-06, eta: 0:57:16, time: 0.323, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9657, loss: 0.0768
+2023-03-05 04:15:55,870 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 04:15:55,870 - mmseg - INFO - Iter [150000/160000]	lr: 1.172e-06, eta: 0:56:58, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.9301, loss: 0.0785
+2023-03-05 04:16:09,495 - mmseg - INFO - Iter [150050/160000]	lr: 1.172e-06, eta: 0:56:41, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8582, loss: 0.0791
+2023-03-05 04:16:23,717 - mmseg - INFO - Iter [150100/160000]	lr: 1.172e-06, eta: 0:56:24, time: 0.284, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8758, loss: 0.0782
+2023-03-05 04:16:39,863 - mmseg - INFO - Iter [150150/160000]	lr: 1.172e-06, eta: 0:56:07, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9048, loss: 0.0777
+2023-03-05 04:16:53,648 - mmseg - INFO - Iter [150200/160000]	lr: 1.172e-06, eta: 0:55:49, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0751, decode.acc_seg: 97.0318, loss: 0.0751
+2023-03-05 04:17:07,414 - mmseg - INFO - Iter [150250/160000]	lr: 1.172e-06, eta: 0:55:32, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9645, loss: 0.0758
+2023-03-05 04:17:23,461 - mmseg - INFO - Iter [150300/160000]	lr: 1.172e-06, eta: 0:55:15, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9400, loss: 0.0769
+2023-03-05 04:17:37,195 - mmseg - INFO - Iter [150350/160000]	lr: 1.172e-06, eta: 0:54:58, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9347, loss: 0.0764
+2023-03-05 04:17:51,066 - mmseg - INFO - Iter [150400/160000]	lr: 1.172e-06, eta: 0:54:40, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9723, loss: 0.0769
+2023-03-05 04:18:05,065 - mmseg - INFO - Iter [150450/160000]	lr: 1.172e-06, eta: 0:54:23, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0745, decode.acc_seg: 97.0274, loss: 0.0745
+2023-03-05 04:18:21,317 - mmseg - INFO - Iter [150500/160000]	lr: 1.172e-06, eta: 0:54:06, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0787, decode.acc_seg: 96.8888, loss: 0.0787
+2023-03-05 04:18:35,008 - mmseg - INFO - Iter [150550/160000]	lr: 1.172e-06, eta: 0:53:49, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9118, loss: 0.0772
+2023-03-05 04:18:48,817 - mmseg - INFO - Iter [150600/160000]	lr: 1.172e-06, eta: 0:53:31, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9703, loss: 0.0762
+2023-03-05 04:19:02,609 - mmseg - INFO - Iter [150650/160000]	lr: 1.172e-06, eta: 0:53:14, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9420, loss: 0.0761
+2023-03-05 04:19:18,590 - mmseg - INFO - Iter [150700/160000]	lr: 1.172e-06, eta: 0:52:57, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0725, decode.acc_seg: 97.1372, loss: 0.0725
+2023-03-05 04:19:32,290 - mmseg - INFO - Iter [150750/160000]	lr: 1.172e-06, eta: 0:52:39, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0799, decode.acc_seg: 96.8529, loss: 0.0799
+2023-03-05 04:19:45,877 - mmseg - INFO - Iter [150800/160000]	lr: 1.172e-06, eta: 0:52:22, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.9018, loss: 0.0786
+2023-03-05 04:20:01,872 - mmseg - INFO - Iter [150850/160000]	lr: 1.172e-06, eta: 0:52:05, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9594, loss: 0.0762
+2023-03-05 04:20:15,452 - mmseg - INFO - Iter [150900/160000]	lr: 1.172e-06, eta: 0:51:48, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9343, loss: 0.0774
+2023-03-05 04:20:29,124 - mmseg - INFO - Iter [150950/160000]	lr: 1.172e-06, eta: 0:51:30, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 96.9989, loss: 0.0752
+2023-03-05 04:20:42,740 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 04:20:42,740 - mmseg - INFO - Iter [151000/160000]	lr: 1.172e-06, eta: 0:51:13, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0723, decode.acc_seg: 97.1095, loss: 0.0723
+2023-03-05 04:20:58,655 - mmseg - INFO - Iter [151050/160000]	lr: 1.172e-06, eta: 0:50:56, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9621, loss: 0.0759
+2023-03-05 04:21:12,380 - mmseg - INFO - Iter [151100/160000]	lr: 1.172e-06, eta: 0:50:39, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.9001, loss: 0.0788
+2023-03-05 04:21:25,997 - mmseg - INFO - Iter [151150/160000]	lr: 1.172e-06, eta: 0:50:21, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0744, decode.acc_seg: 96.9951, loss: 0.0744
+2023-03-05 04:21:39,764 - mmseg - INFO - Iter [151200/160000]	lr: 1.172e-06, eta: 0:50:04, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0746, decode.acc_seg: 97.0359, loss: 0.0746
+2023-03-05 04:21:55,869 - mmseg - INFO - Iter [151250/160000]	lr: 1.172e-06, eta: 0:49:47, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9815, loss: 0.0757
+2023-03-05 04:22:09,652 - mmseg - INFO - Iter [151300/160000]	lr: 1.172e-06, eta: 0:49:30, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9841, loss: 0.0765
+2023-03-05 04:22:23,210 - mmseg - INFO - Iter [151350/160000]	lr: 1.172e-06, eta: 0:49:13, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0790, decode.acc_seg: 96.8325, loss: 0.0790
+2023-03-05 04:22:36,834 - mmseg - INFO - Iter [151400/160000]	lr: 1.172e-06, eta: 0:48:55, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9492, loss: 0.0762
+2023-03-05 04:22:53,093 - mmseg - INFO - Iter [151450/160000]	lr: 1.172e-06, eta: 0:48:38, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0803, decode.acc_seg: 96.8767, loss: 0.0803
+2023-03-05 04:23:06,776 - mmseg - INFO - Iter [151500/160000]	lr: 1.172e-06, eta: 0:48:21, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9393, loss: 0.0764
+2023-03-05 04:23:20,720 - mmseg - INFO - Iter [151550/160000]	lr: 1.172e-06, eta: 0:48:04, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9252, loss: 0.0771
+2023-03-05 04:23:37,054 - mmseg - INFO - Iter [151600/160000]	lr: 1.172e-06, eta: 0:47:47, time: 0.327, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.8848, loss: 0.0769
+2023-03-05 04:23:50,777 - mmseg - INFO - Iter [151650/160000]	lr: 1.172e-06, eta: 0:47:29, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9759, loss: 0.0771
+2023-03-05 04:24:04,375 - mmseg - INFO - Iter [151700/160000]	lr: 1.172e-06, eta: 0:47:12, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9620, loss: 0.0763
+2023-03-05 04:24:18,046 - mmseg - INFO - Iter [151750/160000]	lr: 1.172e-06, eta: 0:46:55, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.9124, loss: 0.0784
+2023-03-05 04:24:34,077 - mmseg - INFO - Iter [151800/160000]	lr: 1.172e-06, eta: 0:46:38, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9292, loss: 0.0773
+2023-03-05 04:24:47,694 - mmseg - INFO - Iter [151850/160000]	lr: 1.172e-06, eta: 0:46:20, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0725, decode.acc_seg: 97.0994, loss: 0.0725
+2023-03-05 04:25:01,348 - mmseg - INFO - Iter [151900/160000]	lr: 1.172e-06, eta: 0:46:03, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9787, loss: 0.0762
+2023-03-05 04:25:15,082 - mmseg - INFO - Iter [151950/160000]	lr: 1.172e-06, eta: 0:45:46, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8464, loss: 0.0795
+2023-03-05 04:25:31,488 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 04:25:31,489 - mmseg - INFO - Iter [152000/160000]	lr: 1.172e-06, eta: 0:45:29, time: 0.328, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.9244, loss: 0.0782
+2023-03-05 04:25:45,455 - mmseg - INFO - Iter [152050/160000]	lr: 1.172e-06, eta: 0:45:12, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9502, loss: 0.0766
+2023-03-05 04:25:59,253 - mmseg - INFO - Iter [152100/160000]	lr: 1.172e-06, eta: 0:44:54, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9787, loss: 0.0760
+2023-03-05 04:26:15,489 - mmseg - INFO - Iter [152150/160000]	lr: 1.172e-06, eta: 0:44:37, time: 0.325, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9563, loss: 0.0772
+2023-03-05 04:26:29,307 - mmseg - INFO - Iter [152200/160000]	lr: 1.172e-06, eta: 0:44:20, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8474, loss: 0.0791
+2023-03-05 04:26:43,013 - mmseg - INFO - Iter [152250/160000]	lr: 1.172e-06, eta: 0:44:03, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9402, loss: 0.0770
+2023-03-05 04:26:56,787 - mmseg - INFO - Iter [152300/160000]	lr: 1.172e-06, eta: 0:43:46, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8734, loss: 0.0786
+2023-03-05 04:27:13,174 - mmseg - INFO - Iter [152350/160000]	lr: 1.172e-06, eta: 0:43:29, time: 0.328, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9616, loss: 0.0763
+2023-03-05 04:27:27,102 - mmseg - INFO - Iter [152400/160000]	lr: 1.172e-06, eta: 0:43:11, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9228, loss: 0.0770
+2023-03-05 04:27:41,044 - mmseg - INFO - Iter [152450/160000]	lr: 1.172e-06, eta: 0:42:54, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9361, loss: 0.0766
+2023-03-05 04:27:54,717 - mmseg - INFO - Iter [152500/160000]	lr: 1.172e-06, eta: 0:42:37, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8934, loss: 0.0786
+2023-03-05 04:28:10,764 - mmseg - INFO - Iter [152550/160000]	lr: 1.172e-06, eta: 0:42:20, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9268, loss: 0.0770
+2023-03-05 04:28:24,368 - mmseg - INFO - Iter [152600/160000]	lr: 1.172e-06, eta: 0:42:03, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9590, loss: 0.0762
+2023-03-05 04:28:37,976 - mmseg - INFO - Iter [152650/160000]	lr: 1.172e-06, eta: 0:41:45, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9819, loss: 0.0758
+2023-03-05 04:28:52,084 - mmseg - INFO - Iter [152700/160000]	lr: 1.172e-06, eta: 0:41:28, time: 0.282, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.8726, loss: 0.0780
+2023-03-05 04:29:08,454 - mmseg - INFO - Iter [152750/160000]	lr: 1.172e-06, eta: 0:41:11, time: 0.327, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9487, loss: 0.0770
+2023-03-05 04:29:22,201 - mmseg - INFO - Iter [152800/160000]	lr: 1.172e-06, eta: 0:40:54, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0748, decode.acc_seg: 97.0181, loss: 0.0748
+2023-03-05 04:29:35,833 - mmseg - INFO - Iter [152850/160000]	lr: 1.172e-06, eta: 0:40:37, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9089, loss: 0.0778
+2023-03-05 04:29:51,946 - mmseg - INFO - Iter [152900/160000]	lr: 1.172e-06, eta: 0:40:20, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.8905, loss: 0.0776
+2023-03-05 04:30:05,837 - mmseg - INFO - Iter [152950/160000]	lr: 1.172e-06, eta: 0:40:02, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9089, loss: 0.0769
+2023-03-05 04:30:19,720 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 04:30:19,720 - mmseg - INFO - Iter [153000/160000]	lr: 1.172e-06, eta: 0:39:45, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9361, loss: 0.0762
+2023-03-05 04:30:33,429 - mmseg - INFO - Iter [153050/160000]	lr: 1.172e-06, eta: 0:39:28, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9639, loss: 0.0771
+2023-03-05 04:30:49,575 - mmseg - INFO - Iter [153100/160000]	lr: 1.172e-06, eta: 0:39:11, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0729, decode.acc_seg: 97.0554, loss: 0.0729
+2023-03-05 04:31:03,297 - mmseg - INFO - Iter [153150/160000]	lr: 1.172e-06, eta: 0:38:54, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.9282, loss: 0.0788
+2023-03-05 04:31:17,200 - mmseg - INFO - Iter [153200/160000]	lr: 1.172e-06, eta: 0:38:37, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0811, decode.acc_seg: 96.7717, loss: 0.0811
+2023-03-05 04:31:31,387 - mmseg - INFO - Iter [153250/160000]	lr: 1.172e-06, eta: 0:38:19, time: 0.284, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9471, loss: 0.0762
+2023-03-05 04:31:47,512 - mmseg - INFO - Iter [153300/160000]	lr: 1.172e-06, eta: 0:38:02, time: 0.322, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.9196, loss: 0.0776
+2023-03-05 04:32:01,399 - mmseg - INFO - Iter [153350/160000]	lr: 1.172e-06, eta: 0:37:45, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8986, loss: 0.0782
+2023-03-05 04:32:15,390 - mmseg - INFO - Iter [153400/160000]	lr: 1.172e-06, eta: 0:37:28, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9734, loss: 0.0764
+2023-03-05 04:32:28,976 - mmseg - INFO - Iter [153450/160000]	lr: 1.172e-06, eta: 0:37:11, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0809, decode.acc_seg: 96.8404, loss: 0.0809
+2023-03-05 04:32:44,958 - mmseg - INFO - Iter [153500/160000]	lr: 1.172e-06, eta: 0:36:54, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9452, loss: 0.0769
+2023-03-05 04:32:58,554 - mmseg - INFO - Iter [153550/160000]	lr: 1.172e-06, eta: 0:36:37, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9828, loss: 0.0761
+2023-03-05 04:33:12,147 - mmseg - INFO - Iter [153600/160000]	lr: 1.172e-06, eta: 0:36:19, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0744, decode.acc_seg: 97.0047, loss: 0.0744
+2023-03-05 04:33:28,144 - mmseg - INFO - Iter [153650/160000]	lr: 1.172e-06, eta: 0:36:02, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8989, loss: 0.0784
+2023-03-05 04:33:41,848 - mmseg - INFO - Iter [153700/160000]	lr: 1.172e-06, eta: 0:35:45, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0760, decode.acc_seg: 96.9840, loss: 0.0760
+2023-03-05 04:33:55,620 - mmseg - INFO - Iter [153750/160000]	lr: 1.172e-06, eta: 0:35:28, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0776, decode.acc_seg: 96.8874, loss: 0.0776
+2023-03-05 04:34:09,265 - mmseg - INFO - Iter [153800/160000]	lr: 1.172e-06, eta: 0:35:11, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 96.9859, loss: 0.0755
+2023-03-05 04:34:25,271 - mmseg - INFO - Iter [153850/160000]	lr: 1.172e-06, eta: 0:34:54, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0752, decode.acc_seg: 97.0209, loss: 0.0752
+2023-03-05 04:34:39,007 - mmseg - INFO - Iter [153900/160000]	lr: 1.172e-06, eta: 0:34:37, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0794, decode.acc_seg: 96.8470, loss: 0.0794
+2023-03-05 04:34:52,766 - mmseg - INFO - Iter [153950/160000]	lr: 1.172e-06, eta: 0:34:20, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0740, decode.acc_seg: 97.0757, loss: 0.0740
+2023-03-05 04:35:06,497 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 04:35:06,497 - mmseg - INFO - Iter [154000/160000]	lr: 1.172e-06, eta: 0:34:02, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8573, loss: 0.0788
+2023-03-05 04:35:22,534 - mmseg - INFO - Iter [154050/160000]	lr: 1.172e-06, eta: 0:33:45, time: 0.321, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 96.9505, loss: 0.0763
+2023-03-05 04:35:36,177 - mmseg - INFO - Iter [154100/160000]	lr: 1.172e-06, eta: 0:33:28, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0740, decode.acc_seg: 97.0053, loss: 0.0740
+2023-03-05 04:35:49,870 - mmseg - INFO - Iter [154150/160000]	lr: 1.172e-06, eta: 0:33:11, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9288, loss: 0.0777
+2023-03-05 04:36:05,992 - mmseg - INFO - Iter [154200/160000]	lr: 1.172e-06, eta: 0:32:54, time: 0.322, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0795, decode.acc_seg: 96.8389, loss: 0.0795
+2023-03-05 04:36:19,869 - mmseg - INFO - Iter [154250/160000]	lr: 1.172e-06, eta: 0:32:37, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.8923, loss: 0.0773
+2023-03-05 04:36:33,438 - mmseg - INFO - Iter [154300/160000]	lr: 1.172e-06, eta: 0:32:20, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0745, decode.acc_seg: 97.0188, loss: 0.0745
+2023-03-05 04:36:47,223 - mmseg - INFO - Iter [154350/160000]	lr: 1.172e-06, eta: 0:32:03, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9630, loss: 0.0767
+2023-03-05 04:37:03,383 - mmseg - INFO - Iter [154400/160000]	lr: 1.172e-06, eta: 0:31:45, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0768, decode.acc_seg: 96.9354, loss: 0.0768
+2023-03-05 04:37:17,403 - mmseg - INFO - Iter [154450/160000]	lr: 1.172e-06, eta: 0:31:28, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8613, loss: 0.0786
+2023-03-05 04:37:31,054 - mmseg - INFO - Iter [154500/160000]	lr: 1.172e-06, eta: 0:31:11, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9777, loss: 0.0758
+2023-03-05 04:37:44,668 - mmseg - INFO - Iter [154550/160000]	lr: 1.172e-06, eta: 0:30:54, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9523, loss: 0.0769
+2023-03-05 04:38:00,607 - mmseg - INFO - Iter [154600/160000]	lr: 1.172e-06, eta: 0:30:37, time: 0.319, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9661, loss: 0.0767
+2023-03-05 04:38:14,522 - mmseg - INFO - Iter [154650/160000]	lr: 1.172e-06, eta: 0:30:20, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0742, decode.acc_seg: 97.0432, loss: 0.0742
+2023-03-05 04:38:28,310 - mmseg - INFO - Iter [154700/160000]	lr: 1.172e-06, eta: 0:30:03, time: 0.276, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9562, loss: 0.0764
+2023-03-05 04:38:41,964 - mmseg - INFO - Iter [154750/160000]	lr: 1.172e-06, eta: 0:29:46, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9222, loss: 0.0765
+2023-03-05 04:38:58,054 - mmseg - INFO - Iter [154800/160000]	lr: 1.172e-06, eta: 0:29:29, time: 0.322, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0756, decode.acc_seg: 97.0221, loss: 0.0756
+2023-03-05 04:39:11,818 - mmseg - INFO - Iter [154850/160000]	lr: 1.172e-06, eta: 0:29:11, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9439, loss: 0.0766
+2023-03-05 04:39:25,476 - mmseg - INFO - Iter [154900/160000]	lr: 1.172e-06, eta: 0:28:54, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0746, decode.acc_seg: 97.0174, loss: 0.0746
+2023-03-05 04:39:41,556 - mmseg - INFO - Iter [154950/160000]	lr: 1.172e-06, eta: 0:28:37, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9587, loss: 0.0772
+2023-03-05 04:39:55,381 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 04:39:55,381 - mmseg - INFO - Iter [155000/160000]	lr: 1.172e-06, eta: 0:28:20, time: 0.277, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0753, decode.acc_seg: 96.9893, loss: 0.0753
+2023-03-05 04:40:09,116 - mmseg - INFO - Iter [155050/160000]	lr: 1.172e-06, eta: 0:28:03, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0778, decode.acc_seg: 96.9306, loss: 0.0778
+2023-03-05 04:40:22,689 - mmseg - INFO - Iter [155100/160000]	lr: 1.172e-06, eta: 0:27:46, time: 0.271, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9282, loss: 0.0767
+2023-03-05 04:40:38,708 - mmseg - INFO - Iter [155150/160000]	lr: 1.172e-06, eta: 0:27:29, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9617, loss: 0.0767
+2023-03-05 04:40:52,413 - mmseg - INFO - Iter [155200/160000]	lr: 1.172e-06, eta: 0:27:12, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0741, decode.acc_seg: 97.0627, loss: 0.0741
+2023-03-05 04:41:06,183 - mmseg - INFO - Iter [155250/160000]	lr: 1.172e-06, eta: 0:26:55, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9675, loss: 0.0767
+2023-03-05 04:41:20,309 - mmseg - INFO - Iter [155300/160000]	lr: 1.172e-06, eta: 0:26:38, time: 0.283, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9616, loss: 0.0766
+2023-03-05 04:41:36,475 - mmseg - INFO - Iter [155350/160000]	lr: 1.172e-06, eta: 0:26:21, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.9094, loss: 0.0782
+2023-03-05 04:41:50,563 - mmseg - INFO - Iter [155400/160000]	lr: 1.172e-06, eta: 0:26:04, time: 0.281, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0748, decode.acc_seg: 97.0596, loss: 0.0748
+2023-03-05 04:42:04,291 - mmseg - INFO - Iter [155450/160000]	lr: 1.172e-06, eta: 0:25:46, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9485, loss: 0.0762
+2023-03-05 04:42:20,383 - mmseg - INFO - Iter [155500/160000]	lr: 1.172e-06, eta: 0:25:29, time: 0.322, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9756, loss: 0.0761
+2023-03-05 04:42:34,165 - mmseg - INFO - Iter [155550/160000]	lr: 1.172e-06, eta: 0:25:12, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0791, decode.acc_seg: 96.8646, loss: 0.0791
+2023-03-05 04:42:48,030 - mmseg - INFO - Iter [155600/160000]	lr: 1.172e-06, eta: 0:24:55, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9009, loss: 0.0773
+2023-03-05 04:43:01,743 - mmseg - INFO - Iter [155650/160000]	lr: 1.172e-06, eta: 0:24:38, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0756, decode.acc_seg: 96.9854, loss: 0.0756
+2023-03-05 04:43:17,877 - mmseg - INFO - Iter [155700/160000]	lr: 1.172e-06, eta: 0:24:21, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8340, loss: 0.0786
+2023-03-05 04:43:31,983 - mmseg - INFO - Iter [155750/160000]	lr: 1.172e-06, eta: 0:24:04, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0780, decode.acc_seg: 96.9022, loss: 0.0780
+2023-03-05 04:43:45,784 - mmseg - INFO - Iter [155800/160000]	lr: 1.172e-06, eta: 0:23:47, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9147, loss: 0.0766
+2023-03-05 04:43:59,673 - mmseg - INFO - Iter [155850/160000]	lr: 1.172e-06, eta: 0:23:30, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9485, loss: 0.0761
+2023-03-05 04:44:15,611 - mmseg - INFO - Iter [155900/160000]	lr: 1.172e-06, eta: 0:23:13, time: 0.318, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0763, decode.acc_seg: 97.0025, loss: 0.0763
+2023-03-05 04:44:29,543 - mmseg - INFO - Iter [155950/160000]	lr: 1.172e-06, eta: 0:22:56, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0739, decode.acc_seg: 97.0754, loss: 0.0739
+2023-03-05 04:44:43,797 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 04:44:43,797 - mmseg - INFO - Iter [156000/160000]	lr: 1.172e-06, eta: 0:22:39, time: 0.285, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0782, decode.acc_seg: 96.8541, loss: 0.0782
+2023-03-05 04:44:57,679 - mmseg - INFO - Iter [156050/160000]	lr: 1.172e-06, eta: 0:22:22, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0846, decode.acc_seg: 96.7592, loss: 0.0846
+2023-03-05 04:45:13,688 - mmseg - INFO - Iter [156100/160000]	lr: 1.172e-06, eta: 0:22:05, time: 0.320, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9082, loss: 0.0767
+2023-03-05 04:45:27,342 - mmseg - INFO - Iter [156150/160000]	lr: 1.172e-06, eta: 0:21:48, time: 0.273, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 96.9752, loss: 0.0758
+2023-03-05 04:45:41,301 - mmseg - INFO - Iter [156200/160000]	lr: 1.172e-06, eta: 0:21:31, time: 0.279, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8452, loss: 0.0793
+2023-03-05 04:45:57,955 - mmseg - INFO - Iter [156250/160000]	lr: 1.172e-06, eta: 0:21:14, time: 0.333, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0756, decode.acc_seg: 96.9721, loss: 0.0756
+2023-03-05 04:46:11,515 - mmseg - INFO - Iter [156300/160000]	lr: 1.172e-06, eta: 0:20:56, time: 0.271, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0762, decode.acc_seg: 96.9746, loss: 0.0762
+2023-03-05 04:46:25,119 - mmseg - INFO - Iter [156350/160000]	lr: 1.172e-06, eta: 0:20:39, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8949, loss: 0.0784
+2023-03-05 04:46:38,938 - mmseg - INFO - Iter [156400/160000]	lr: 1.172e-06, eta: 0:20:22, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9525, loss: 0.0767
+2023-03-05 04:46:54,924 - mmseg - INFO - Iter [156450/160000]	lr: 1.172e-06, eta: 0:20:05, time: 0.320, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9999, loss: 0.0757
+2023-03-05 04:47:08,615 - mmseg - INFO - Iter [156500/160000]	lr: 1.172e-06, eta: 0:19:48, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9466, loss: 0.0767
+2023-03-05 04:47:22,696 - mmseg - INFO - Iter [156550/160000]	lr: 1.172e-06, eta: 0:19:31, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0757, decode.acc_seg: 96.9833, loss: 0.0757
+2023-03-05 04:47:36,275 - mmseg - INFO - Iter [156600/160000]	lr: 1.172e-06, eta: 0:19:14, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9103, loss: 0.0766
+2023-03-05 04:47:52,271 - mmseg - INFO - Iter [156650/160000]	lr: 1.172e-06, eta: 0:18:57, time: 0.320, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9678, loss: 0.0766
+2023-03-05 04:48:06,180 - mmseg - INFO - Iter [156700/160000]	lr: 1.172e-06, eta: 0:18:40, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0771, decode.acc_seg: 96.9140, loss: 0.0771
+2023-03-05 04:48:19,936 - mmseg - INFO - Iter [156750/160000]	lr: 1.172e-06, eta: 0:18:23, time: 0.275, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0744, decode.acc_seg: 97.0378, loss: 0.0744
+2023-03-05 04:48:36,142 - mmseg - INFO - Iter [156800/160000]	lr: 1.172e-06, eta: 0:18:06, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0753, decode.acc_seg: 97.0135, loss: 0.0753
+2023-03-05 04:48:50,027 - mmseg - INFO - Iter [156850/160000]	lr: 1.172e-06, eta: 0:17:49, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9249, loss: 0.0772
+2023-03-05 04:49:04,169 - mmseg - INFO - Iter [156900/160000]	lr: 1.172e-06, eta: 0:17:32, time: 0.283, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9656, loss: 0.0764
+2023-03-05 04:49:17,865 - mmseg - INFO - Iter [156950/160000]	lr: 1.172e-06, eta: 0:17:15, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9385, loss: 0.0764
+2023-03-05 04:49:33,866 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 04:49:33,867 - mmseg - INFO - Iter [157000/160000]	lr: 1.172e-06, eta: 0:16:58, time: 0.320, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 97.0002, loss: 0.0755
+2023-03-05 04:49:47,567 - mmseg - INFO - Iter [157050/160000]	lr: 1.172e-06, eta: 0:16:41, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.8701, loss: 0.0775
+2023-03-05 04:50:01,162 - mmseg - INFO - Iter [157100/160000]	lr: 1.172e-06, eta: 0:16:24, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0774, decode.acc_seg: 96.9295, loss: 0.0774
+2023-03-05 04:50:15,002 - mmseg - INFO - Iter [157150/160000]	lr: 1.172e-06, eta: 0:16:07, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8104, loss: 0.0798
+2023-03-05 04:50:31,202 - mmseg - INFO - Iter [157200/160000]	lr: 1.172e-06, eta: 0:15:50, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9639, loss: 0.0759
+2023-03-05 04:50:44,849 - mmseg - INFO - Iter [157250/160000]	lr: 1.172e-06, eta: 0:15:33, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9555, loss: 0.0761
+2023-03-05 04:50:58,685 - mmseg - INFO - Iter [157300/160000]	lr: 1.172e-06, eta: 0:15:16, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0798, decode.acc_seg: 96.8449, loss: 0.0798
+2023-03-05 04:51:12,804 - mmseg - INFO - Iter [157350/160000]	lr: 1.172e-06, eta: 0:14:59, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9726, loss: 0.0765
+2023-03-05 04:51:28,981 - mmseg - INFO - Iter [157400/160000]	lr: 1.172e-06, eta: 0:14:42, time: 0.324, data_time: 0.054, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8367, loss: 0.0785
+2023-03-05 04:51:43,076 - mmseg - INFO - Iter [157450/160000]	lr: 1.172e-06, eta: 0:14:25, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0784, decode.acc_seg: 96.8586, loss: 0.0784
+2023-03-05 04:51:56,732 - mmseg - INFO - Iter [157500/160000]	lr: 1.172e-06, eta: 0:14:08, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0742, decode.acc_seg: 97.0105, loss: 0.0742
+2023-03-05 04:52:12,786 - mmseg - INFO - Iter [157550/160000]	lr: 1.172e-06, eta: 0:13:51, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0738, decode.acc_seg: 97.0342, loss: 0.0738
+2023-03-05 04:52:26,493 - mmseg - INFO - Iter [157600/160000]	lr: 1.172e-06, eta: 0:13:34, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0824, decode.acc_seg: 96.7647, loss: 0.0824
+2023-03-05 04:52:40,291 - mmseg - INFO - Iter [157650/160000]	lr: 1.172e-06, eta: 0:13:17, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9571, loss: 0.0767
+2023-03-05 04:52:53,937 - mmseg - INFO - Iter [157700/160000]	lr: 1.172e-06, eta: 0:13:00, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0740, decode.acc_seg: 97.0377, loss: 0.0740
+2023-03-05 04:53:10,079 - mmseg - INFO - Iter [157750/160000]	lr: 1.172e-06, eta: 0:12:43, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0804, decode.acc_seg: 96.8083, loss: 0.0804
+2023-03-05 04:53:24,071 - mmseg - INFO - Iter [157800/160000]	lr: 1.172e-06, eta: 0:12:26, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0797, decode.acc_seg: 96.8256, loss: 0.0797
+2023-03-05 04:53:37,740 - mmseg - INFO - Iter [157850/160000]	lr: 1.172e-06, eta: 0:12:09, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0736, decode.acc_seg: 97.0804, loss: 0.0736
+2023-03-05 04:53:51,334 - mmseg - INFO - Iter [157900/160000]	lr: 1.172e-06, eta: 0:11:52, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.8043, loss: 0.0808
+2023-03-05 04:54:07,368 - mmseg - INFO - Iter [157950/160000]	lr: 1.172e-06, eta: 0:11:35, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9518, loss: 0.0759
+2023-03-05 04:54:21,190 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 04:54:21,190 - mmseg - INFO - Iter [158000/160000]	lr: 1.172e-06, eta: 0:11:18, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7816, loss: 0.0808
+2023-03-05 04:54:34,808 - mmseg - INFO - Iter [158050/160000]	lr: 1.172e-06, eta: 0:11:01, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0734, decode.acc_seg: 97.0566, loss: 0.0734
+2023-03-05 04:54:48,415 - mmseg - INFO - Iter [158100/160000]	lr: 1.172e-06, eta: 0:10:44, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0773, decode.acc_seg: 96.9073, loss: 0.0773
+2023-03-05 04:55:04,614 - mmseg - INFO - Iter [158150/160000]	lr: 1.172e-06, eta: 0:10:27, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8880, loss: 0.0789
+2023-03-05 04:55:18,319 - mmseg - INFO - Iter [158200/160000]	lr: 1.172e-06, eta: 0:10:10, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.9317, loss: 0.0772
+2023-03-05 04:55:31,974 - mmseg - INFO - Iter [158250/160000]	lr: 1.172e-06, eta: 0:09:53, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0775, decode.acc_seg: 96.9162, loss: 0.0775
+2023-03-05 04:55:48,157 - mmseg - INFO - Iter [158300/160000]	lr: 1.172e-06, eta: 0:09:36, time: 0.324, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0772, decode.acc_seg: 96.8973, loss: 0.0772
+2023-03-05 04:56:01,815 - mmseg - INFO - Iter [158350/160000]	lr: 1.172e-06, eta: 0:09:19, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 97.0268, loss: 0.0754
+2023-03-05 04:56:15,440 - mmseg - INFO - Iter [158400/160000]	lr: 1.172e-06, eta: 0:09:02, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0788, decode.acc_seg: 96.8752, loss: 0.0788
+2023-03-05 04:56:29,133 - mmseg - INFO - Iter [158450/160000]	lr: 1.172e-06, eta: 0:08:45, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0761, decode.acc_seg: 96.9723, loss: 0.0761
+2023-03-05 04:56:45,232 - mmseg - INFO - Iter [158500/160000]	lr: 1.172e-06, eta: 0:08:28, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0786, decode.acc_seg: 96.8466, loss: 0.0786
+2023-03-05 04:56:58,833 - mmseg - INFO - Iter [158550/160000]	lr: 1.172e-06, eta: 0:08:11, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0766, decode.acc_seg: 96.9450, loss: 0.0766
+2023-03-05 04:57:12,659 - mmseg - INFO - Iter [158600/160000]	lr: 1.172e-06, eta: 0:07:54, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0751, decode.acc_seg: 97.0211, loss: 0.0751
+2023-03-05 04:57:26,247 - mmseg - INFO - Iter [158650/160000]	lr: 1.172e-06, eta: 0:07:37, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0759, decode.acc_seg: 96.9655, loss: 0.0759
+2023-03-05 04:57:42,456 - mmseg - INFO - Iter [158700/160000]	lr: 1.172e-06, eta: 0:07:20, time: 0.324, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8830, loss: 0.0785
+2023-03-05 04:57:56,362 - mmseg - INFO - Iter [158750/160000]	lr: 1.172e-06, eta: 0:07:03, time: 0.278, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0785, decode.acc_seg: 96.8956, loss: 0.0785
+2023-03-05 04:58:10,056 - mmseg - INFO - Iter [158800/160000]	lr: 1.172e-06, eta: 0:06:46, time: 0.274, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0825, decode.acc_seg: 96.7937, loss: 0.0825
+2023-03-05 04:58:26,189 - mmseg - INFO - Iter [158850/160000]	lr: 1.172e-06, eta: 0:06:29, time: 0.322, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0783, decode.acc_seg: 96.9166, loss: 0.0783
+2023-03-05 04:58:40,027 - mmseg - INFO - Iter [158900/160000]	lr: 1.172e-06, eta: 0:06:12, time: 0.277, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0812, decode.acc_seg: 96.8092, loss: 0.0812
+2023-03-05 04:58:54,010 - mmseg - INFO - Iter [158950/160000]	lr: 1.172e-06, eta: 0:05:55, time: 0.280, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0744, decode.acc_seg: 97.0521, loss: 0.0744
+2023-03-05 04:59:07,618 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 04:59:07,619 - mmseg - INFO - Iter [159000/160000]	lr: 1.172e-06, eta: 0:05:38, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0764, decode.acc_seg: 96.9343, loss: 0.0764
+2023-03-05 04:59:23,829 - mmseg - INFO - Iter [159050/160000]	lr: 1.172e-06, eta: 0:05:21, time: 0.324, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9147, loss: 0.0770
+2023-03-05 04:59:37,544 - mmseg - INFO - Iter [159100/160000]	lr: 1.172e-06, eta: 0:05:04, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0793, decode.acc_seg: 96.8540, loss: 0.0793
+2023-03-05 04:59:51,632 - mmseg - INFO - Iter [159150/160000]	lr: 1.172e-06, eta: 0:04:47, time: 0.282, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 97.0170, loss: 0.0755
+2023-03-05 05:00:05,615 - mmseg - INFO - Iter [159200/160000]	lr: 1.172e-06, eta: 0:04:31, time: 0.280, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0753, decode.acc_seg: 96.9958, loss: 0.0753
+2023-03-05 05:00:21,650 - mmseg - INFO - Iter [159250/160000]	lr: 1.172e-06, eta: 0:04:14, time: 0.321, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0789, decode.acc_seg: 96.8732, loss: 0.0789
+2023-03-05 05:00:35,400 - mmseg - INFO - Iter [159300/160000]	lr: 1.172e-06, eta: 0:03:57, time: 0.275, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0767, decode.acc_seg: 96.9758, loss: 0.0767
+2023-03-05 05:00:49,176 - mmseg - INFO - Iter [159350/160000]	lr: 1.172e-06, eta: 0:03:40, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0777, decode.acc_seg: 96.9175, loss: 0.0777
+2023-03-05 05:01:02,865 - mmseg - INFO - Iter [159400/160000]	lr: 1.172e-06, eta: 0:03:23, time: 0.274, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0754, decode.acc_seg: 97.0010, loss: 0.0754
+2023-03-05 05:01:19,009 - mmseg - INFO - Iter [159450/160000]	lr: 1.172e-06, eta: 0:03:06, time: 0.323, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9617, loss: 0.0769
+2023-03-05 05:01:32,611 - mmseg - INFO - Iter [159500/160000]	lr: 1.172e-06, eta: 0:02:49, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0753, decode.acc_seg: 96.9863, loss: 0.0753
+2023-03-05 05:01:46,429 - mmseg - INFO - Iter [159550/160000]	lr: 1.172e-06, eta: 0:02:32, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0755, decode.acc_seg: 96.9810, loss: 0.0755
+2023-03-05 05:02:02,426 - mmseg - INFO - Iter [159600/160000]	lr: 1.172e-06, eta: 0:02:15, time: 0.320, data_time: 0.052, memory: 67646, decode.loss_ce: 0.0779, decode.acc_seg: 96.9029, loss: 0.0779
+2023-03-05 05:02:16,233 - mmseg - INFO - Iter [159650/160000]	lr: 1.172e-06, eta: 0:01:58, time: 0.276, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0808, decode.acc_seg: 96.7932, loss: 0.0808
+2023-03-05 05:02:29,837 - mmseg - INFO - Iter [159700/160000]	lr: 1.172e-06, eta: 0:01:41, time: 0.272, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0748, decode.acc_seg: 97.0320, loss: 0.0748
+2023-03-05 05:02:43,479 - mmseg - INFO - Iter [159750/160000]	lr: 1.172e-06, eta: 0:01:24, time: 0.273, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0765, decode.acc_seg: 96.9444, loss: 0.0765
+2023-03-05 05:02:59,782 - mmseg - INFO - Iter [159800/160000]	lr: 1.172e-06, eta: 0:01:07, time: 0.326, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0758, decode.acc_seg: 97.0019, loss: 0.0758
+2023-03-05 05:03:13,372 - mmseg - INFO - Iter [159850/160000]	lr: 1.172e-06, eta: 0:00:50, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0770, decode.acc_seg: 96.9130, loss: 0.0770
+2023-03-05 05:03:26,991 - mmseg - INFO - Iter [159900/160000]	lr: 1.172e-06, eta: 0:00:33, time: 0.272, data_time: 0.007, memory: 67646, decode.loss_ce: 0.0749, decode.acc_seg: 96.9955, loss: 0.0749
+2023-03-05 05:03:40,872 - mmseg - INFO - Iter [159950/160000]	lr: 1.172e-06, eta: 0:00:16, time: 0.278, data_time: 0.008, memory: 67646, decode.loss_ce: 0.0769, decode.acc_seg: 96.9422, loss: 0.0769
+2023-03-05 05:03:57,034 - mmseg - INFO - Swap parameters (after train) after iter [160000]
+2023-03-05 05:03:57,054 - mmseg - INFO - Saving checkpoint at 160000 iterations
+2023-03-05 05:03:58,936 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 05:03:58,936 - mmseg - INFO - Iter [160000/160000]	lr: 1.172e-06, eta: 0:00:00, time: 0.361, data_time: 0.053, memory: 67646, decode.loss_ce: 0.0744, decode.acc_seg: 96.9952, loss: 0.0744
+2023-03-05 05:18:54,041 - mmseg - INFO - per class results:
+2023-03-05 05:18:54,042 - mmseg - INFO - 
++---------------+-------------------------------------------------------------------+
+|     Class     |                IoU 0,10,20,30,40,50,60,70,80,90,99                |
++---------------+-------------------------------------------------------------------+
+|   background  |            nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan            |
+|      road     | 98.55,98.55,98.55,98.55,98.55,98.55,98.55,98.55,98.55,98.55,98.55 |
+|    sidewalk   | 87.58,87.57,87.56,87.57,87.55,87.55,87.56,87.56,87.55,87.56,87.52 |
+|    building   | 93.58,93.58,93.58,93.58,93.58,93.59,93.59,93.59,93.59,93.59,93.59 |
+|      wall     |  55.32,55.45,55.47,55.6,55.66,55.67,55.72,55.7,55.86,55.96,56.07  |
+|     fence     |   64.8,64.81,64.81,64.81,64.8,64.84,64.87,64.9,64.86,64.84,64.91  |
+|      pole     | 71.31,71.32,71.31,71.33,71.33,71.33,71.34,71.33,71.34,71.34,71.34 |
+| traffic light | 75.52,75.53,75.54,75.52,75.54,75.53,75.52,75.53,75.54,75.54,75.56 |
+|  traffic sign |  82.69,82.7,82.7,82.71,82.71,82.71,82.72,82.73,82.73,82.73,82.75  |
+|   vegetation  | 93.11,93.11,93.11,93.12,93.13,93.13,93.13,93.13,93.15,93.15,93.15 |
+|    terrain    |  64.72,64.67,64.66,64.73,64.7,64.69,64.67,64.7,64.75,64.73,64.74  |
+|      sky      | 95.27,95.28,95.27,95.28,95.28,95.27,95.27,95.28,95.28,95.27,95.27 |
+|     person    |  85.01,85.01,85.01,85.01,85.01,85.01,85.01,85.01,85.01,85.01,85.0 |
+|     rider     |   67.9,67.89,67.9,67.9,67.89,67.89,67.89,67.88,67.9,67.89,67.87   |
+|      car      |  96.1,96.11,96.11,96.11,96.11,96.11,96.11,96.12,96.12,96.12,96.12 |
+|     truck     | 87.19,87.22,87.23,87.23,87.22,87.23,87.25,87.27,87.26,87.24,87.23 |
+|      bus      | 92.51,92.52,92.52,92.52,92.52,92.53,92.54,92.54,92.55,92.56,92.55 |
+|     train     |   85.66,85.67,85.7,85.7,85.77,85.78,85.77,85.8,85.77,85.8,85.87   |
+|   motorcycle  | 72.17,72.17,72.16,72.15,72.16,72.17,72.17,72.16,72.15,72.14,72.14 |
+|    bicycle    | 80.53,80.55,80.55,80.56,80.56,80.56,80.57,80.57,80.57,80.57,80.58 |
++---------------+-------------------------------------------------------------------+
+2023-03-05 05:18:54,042 - mmseg - INFO - Summary:
+2023-03-05 05:18:54,043 - mmseg - INFO - 
++------------------------------------------------------------------+
+|               mIoU 0,10,20,30,40,50,60,70,80,90,99               |
++------------------------------------------------------------------+
+| 81.55,81.56,81.57,81.58,81.58,81.59,81.59,81.6,81.61,81.61,81.62 |
++------------------------------------------------------------------+
+2023-03-05 05:18:54,043 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_cityscapes20_finetune.py
+2023-03-05 05:18:54,043 - mmseg - INFO - Iter(val) [63]	mIoU: [0.8155, 0.8156, 0.8157, 0.8158, 0.8158, 0.8159, 0.8159, 0.816, 0.8161, 0.8161, 0.8162], copy_paste: 81.55,81.56,81.57,81.58,81.58,81.59,81.59,81.6,81.61,81.61,81.62