diff --git "a/cityscapes/deeplabv3plus_r101_singlestep/20230303_203945.log" "b/cityscapes/deeplabv3plus_r101_singlestep/20230303_203945.log"
new file mode 100644--- /dev/null
+++ "b/cityscapes/deeplabv3plus_r101_singlestep/20230303_203945.log"
@@ -0,0 +1,3148 @@
+2023-03-03 20:39:45,022 - mmseg - INFO - Multi-processing start method is `None`
+2023-03-03 20:39:45,034 - mmseg - INFO - OpenCV num_threads is `128
+2023-03-03 20:39:45,035 - mmseg - INFO - OMP num threads is 1
+2023-03-03 20:39:45,103 - 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+c844fc6
+------------------------------------------------------------
+
+2023-03-03 20:39:45,104 - mmseg - INFO - Distributed training: True
+2023-03-03 20:39:45,728 - mmseg - INFO - Config:
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    type='EncoderDecoderFreeze',
+    pretrained=
+    'pretrained/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.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='DepthwiseSeparableASPPHeadUnetFCHeadSingleStep',
+        pretrained=
+        'pretrained/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth',
+        dim=256,
+        out_dim=256,
+        unet_channels=528,
+        dim_mults=[1, 1, 1],
+        cat_embedding_dim=16,
+        ignore_index=0,
+        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=10000,
+    gamma=0.5,
+    min_lr=1e-06,
+    by_epoch=False)
+runner = dict(type='IterBasedRunner', max_iters=80000)
+checkpoint_config = dict(by_epoch=False, interval=8000, max_keep_ckpts=1)
+evaluation = dict(
+    interval=8000, metric='mIoU', pre_eval=True, save_best='mIoU')
+checkpoint = 'pretrained/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth'
+work_dir = './work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20'
+gpu_ids = range(0, 8)
+auto_resume = True
+
+2023-03-03 20:39:50,049 - mmseg - INFO - Set random seed to 1230368388, deterministic: False
+2023-03-03 20:39:51,472 - mmseg - INFO - Parameters in backbone freezed!
+2023-03-03 20:39:51,473 - mmseg - INFO - Trainable parameters in DepthwiseSeparableASPPHeadUnetFCHeadSingleStep: ['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', 'unet.ups.1.3.1.weight', 'unet.ups.1.3.1.bias', 'unet.ups.2.0.mlp.1.weight', 'unet.ups.2.0.mlp.1.bias', 'unet.ups.2.0.block1.proj.weight', 'unet.ups.2.0.block1.proj.bias', 'unet.ups.2.0.block1.norm.weight', 'unet.ups.2.0.block1.norm.bias', 'unet.ups.2.0.block2.proj.weight', 'unet.ups.2.0.block2.proj.bias', 'unet.ups.2.0.block2.norm.weight', 'unet.ups.2.0.block2.norm.bias', 'unet.ups.2.0.res_conv.weight', 'unet.ups.2.0.res_conv.bias', 'unet.ups.2.1.mlp.1.weight', 'unet.ups.2.1.mlp.1.bias', 'unet.ups.2.1.block1.proj.weight', 'unet.ups.2.1.block1.proj.bias', 'unet.ups.2.1.block1.norm.weight', 'unet.ups.2.1.block1.norm.bias', 'unet.ups.2.1.block2.proj.weight', 'unet.ups.2.1.block2.proj.bias', 'unet.ups.2.1.block2.norm.weight', 'unet.ups.2.1.block2.norm.bias', 'unet.ups.2.1.res_conv.weight', 'unet.ups.2.1.res_conv.bias', 'unet.ups.2.2.fn.fn.to_qkv.weight', 'unet.ups.2.2.fn.fn.to_out.0.weight', 'unet.ups.2.2.fn.fn.to_out.0.bias', 'unet.ups.2.2.fn.fn.to_out.1.g', 'unet.ups.2.2.fn.norm.g', 'unet.ups.2.3.weight', 'unet.ups.2.3.bias', 'unet.mid_block1.mlp.1.weight', 'unet.mid_block1.mlp.1.bias', 'unet.mid_block1.block1.proj.weight', 'unet.mid_block1.block1.proj.bias', 'unet.mid_block1.block1.norm.weight', 'unet.mid_block1.block1.norm.bias', 'unet.mid_block1.block2.proj.weight', 'unet.mid_block1.block2.proj.bias', 'unet.mid_block1.block2.norm.weight', 'unet.mid_block1.block2.norm.bias', 'unet.mid_attn.fn.fn.to_qkv.weight', 'unet.mid_attn.fn.fn.to_out.weight', 'unet.mid_attn.fn.fn.to_out.bias', 'unet.mid_attn.fn.norm.g', 'unet.mid_block2.mlp.1.weight', 'unet.mid_block2.mlp.1.bias', 'unet.mid_block2.block1.proj.weight', 'unet.mid_block2.block1.proj.bias', 'unet.mid_block2.block1.norm.weight', 'unet.mid_block2.block1.norm.bias', 'unet.mid_block2.block2.proj.weight', 'unet.mid_block2.block2.proj.bias', 'unet.mid_block2.block2.norm.weight', 'unet.mid_block2.block2.norm.bias', 'unet.final_res_block.mlp.1.weight', 'unet.final_res_block.mlp.1.bias', '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']
+2023-03-03 20:39:51,473 - mmseg - INFO - Parameters in decode_head freezed!
+2023-03-03 20:39:51,605 - mmseg - INFO - load checkpoint from local path: pretrained/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth
+2023-03-03 20:39:52,210 - mmseg - WARNING - The model and loaded state dict do not match exactly
+
+unexpected key in source state_dict: decode_head.conv_seg.weight, decode_head.conv_seg.bias, 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, auxiliary_head.conv_seg.weight, auxiliary_head.conv_seg.bias, auxiliary_head.convs.0.conv.weight, auxiliary_head.convs.0.bn.weight, auxiliary_head.convs.0.bn.bias, auxiliary_head.convs.0.bn.running_mean, auxiliary_head.convs.0.bn.running_var, auxiliary_head.convs.0.bn.num_batches_tracked
+
+2023-03-03 20:39:52,233 - mmseg - INFO - load checkpoint from local path: pretrained/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth
+2023-03-03 20:39:52,733 - mmseg - WARNING - The model and loaded state dict do not match exactly
+
+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, 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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, 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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, 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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, 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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, auxiliary_head.conv_seg.weight, auxiliary_head.conv_seg.bias, auxiliary_head.convs.0.conv.weight, auxiliary_head.convs.0.bn.weight, auxiliary_head.convs.0.bn.bias, auxiliary_head.convs.0.bn.running_mean, auxiliary_head.convs.0.bn.running_var, auxiliary_head.convs.0.bn.num_batches_tracked
+
+missing keys in source state_dict: 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, 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+
+2023-03-03 20:39:52,797 - mmseg - INFO - EncoderDecoderFreeze(
+  (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': 'pretrained/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth'}
+  (decode_head): DepthwiseSeparableASPPHeadUnetFCHeadSingleStep(
+    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, 256, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
+      (time_mlp): Sequential(
+        (0): SinusoidalPosEmb()
+        (1): Linear(in_features=256, out_features=1024, bias=True)
+        (2): GELU(approximate='none')
+        (3): Linear(in_features=1024, out_features=1024, bias=True)
+      )
+      (downs): ModuleList(
+        (0): ModuleList(
+          (0): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=1024, out_features=512, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Identity()
+          )
+          (1): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=1024, out_features=512, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Identity()
+          )
+          (2): Residual(
+            (fn): PreNorm(
+              (fn): LinearAttention(
+                (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+                (to_out): Sequential(
+                  (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
+                  (1): LayerNorm()
+                )
+              )
+              (norm): LayerNorm()
+            )
+          )
+          (3): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+        )
+        (1): ModuleList(
+          (0): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=1024, out_features=512, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Identity()
+          )
+          (1): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=1024, out_features=512, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Identity()
+          )
+          (2): Residual(
+            (fn): PreNorm(
+              (fn): LinearAttention(
+                (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+                (to_out): Sequential(
+                  (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
+                  (1): LayerNorm()
+                )
+              )
+              (norm): LayerNorm()
+            )
+          )
+          (3): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+        )
+        (2): ModuleList(
+          (0): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=1024, out_features=512, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Identity()
+          )
+          (1): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=1024, out_features=512, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Identity()
+          )
+          (2): Residual(
+            (fn): PreNorm(
+              (fn): LinearAttention(
+                (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+                (to_out): Sequential(
+                  (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
+                  (1): LayerNorm()
+                )
+              )
+              (norm): LayerNorm()
+            )
+          )
+          (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+        )
+      )
+      (ups): ModuleList(
+        (0): ModuleList(
+          (0): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=1024, out_features=512, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (1): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=1024, out_features=512, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (2): Residual(
+            (fn): PreNorm(
+              (fn): LinearAttention(
+                (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+                (to_out): Sequential(
+                  (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
+                  (1): LayerNorm()
+                )
+              )
+              (norm): LayerNorm()
+            )
+          )
+          (3): Sequential(
+            (0): Upsample(scale_factor=2.0, mode=nearest)
+            (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          )
+        )
+        (1): ModuleList(
+          (0): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=1024, out_features=512, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (1): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=1024, out_features=512, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (2): Residual(
+            (fn): PreNorm(
+              (fn): LinearAttention(
+                (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+                (to_out): Sequential(
+                  (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
+                  (1): LayerNorm()
+                )
+              )
+              (norm): LayerNorm()
+            )
+          )
+          (3): Sequential(
+            (0): Upsample(scale_factor=2.0, mode=nearest)
+            (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          )
+        )
+        (2): ModuleList(
+          (0): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=1024, out_features=512, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (1): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=1024, out_features=512, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (2): Residual(
+            (fn): PreNorm(
+              (fn): LinearAttention(
+                (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+                (to_out): Sequential(
+                  (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
+                  (1): LayerNorm()
+                )
+              )
+              (norm): LayerNorm()
+            )
+          )
+          (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+        )
+      )
+      (mid_block1): ResnetBlock(
+        (mlp): Sequential(
+          (0): SiLU()
+          (1): Linear(in_features=1024, out_features=512, bias=True)
+        )
+        (block1): Block(
+          (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+          (act): SiLU()
+        )
+        (block2): Block(
+          (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+          (act): SiLU()
+        )
+        (res_conv): Identity()
+      )
+      (mid_attn): Residual(
+        (fn): PreNorm(
+          (fn): Attention(
+            (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+            (to_out): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (norm): LayerNorm()
+        )
+      )
+      (mid_block2): ResnetBlock(
+        (mlp): Sequential(
+          (0): SiLU()
+          (1): Linear(in_features=1024, out_features=512, bias=True)
+        )
+        (block1): Block(
+          (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+          (act): SiLU()
+        )
+        (block2): Block(
+          (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+          (act): SiLU()
+        )
+        (res_conv): Identity()
+      )
+      (final_res_block): ResnetBlock(
+        (mlp): Sequential(
+          (0): SiLU()
+          (1): Linear(in_features=1024, out_features=512, bias=True)
+        )
+        (block1): Block(
+          (proj): WeightStandardizedConv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+          (act): SiLU()
+        )
+        (block2): Block(
+          (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (norm): GroupNorm(8, 256, eps=1e-05, affine=True)
+          (act): SiLU()
+        )
+        (res_conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
+      )
+      (final_conv): Conv2d(256, 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': 'pretrained/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth'}
+)
+2023-03-03 20:39:52,863 - mmseg - INFO - Loaded 2975 images
+2023-03-03 20:39:53,843 - mmseg - INFO - Loaded 500 images
+2023-03-03 20:39:53,845 - mmseg - INFO - Start running, host: laizeqiang@SH-IDC1-10-140-37-151, work_dir: /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20
+2023-03-03 20:39:53,845 - mmseg - INFO - Hooks will be executed in the following order:
+before_run:
+(VERY_HIGH   ) StepLrUpdaterHook                  
+(NORMAL      ) CheckpointHook                     
+(LOW         ) DistEvalHook                       
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+before_train_epoch:
+(VERY_HIGH   ) StepLrUpdaterHook                  
+(LOW         ) IterTimerHook                      
+(LOW         ) DistEvalHook                       
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+before_train_iter:
+(VERY_HIGH   ) StepLrUpdaterHook                  
+(LOW         ) IterTimerHook                      
+(LOW         ) DistEvalHook                       
+ -------------------- 
+after_train_iter:
+(ABOVE_NORMAL) OptimizerHook                      
+(NORMAL      ) CheckpointHook                     
+(LOW         ) IterTimerHook                      
+(LOW         ) DistEvalHook                       
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+after_train_epoch:
+(NORMAL      ) CheckpointHook                     
+(LOW         ) DistEvalHook                       
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+before_val_epoch:
+(LOW         ) IterTimerHook                      
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+before_val_iter:
+(LOW         ) IterTimerHook                      
+ -------------------- 
+after_val_iter:
+(LOW         ) IterTimerHook                      
+ -------------------- 
+after_val_epoch:
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+after_run:
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+2023-03-03 20:39:53,846 - mmseg - INFO - workflow: [('train', 1)], max: 80000 iters
+2023-03-03 20:39:53,846 - mmseg - INFO - Checkpoints will be saved to /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20 by HardDiskBackend.
+2023-03-03 20:40:39,827 - mmseg - INFO - Iter [50/80000]	lr: 7.350e-06, eta: 14:35:31, time: 0.657, data_time: 0.014, memory: 67605, decode.loss_ce: 1.9434, decode.acc_seg: 59.1974, loss: 1.9434
+2023-03-03 20:40:54,779 - mmseg - INFO - Iter [100/80000]	lr: 1.485e-05, eta: 10:36:35, time: 0.299, data_time: 0.007, memory: 67605, decode.loss_ce: 0.5979, decode.acc_seg: 89.3834, loss: 0.5979
+2023-03-03 20:41:09,460 - mmseg - INFO - Iter [150/80000]	lr: 2.235e-05, eta: 9:14:23, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.2275, decode.acc_seg: 94.7804, loss: 0.2275
+2023-03-03 20:41:26,506 - mmseg - INFO - Iter [200/80000]	lr: 2.985e-05, eta: 8:48:52, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.1299, decode.acc_seg: 95.9063, loss: 0.1299
+2023-03-03 20:41:41,164 - mmseg - INFO - Iter [250/80000]	lr: 3.735e-05, eta: 8:20:46, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.1126, decode.acc_seg: 95.9313, loss: 0.1126
+2023-03-03 20:41:55,947 - mmseg - INFO - Iter [300/80000]	lr: 4.485e-05, eta: 8:02:30, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.1075, decode.acc_seg: 95.9972, loss: 0.1075
+2023-03-03 20:42:10,602 - mmseg - INFO - Iter [350/80000]	lr: 5.235e-05, eta: 7:48:53, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.1043, decode.acc_seg: 96.0563, loss: 0.1043
+2023-03-03 20:42:27,520 - mmseg - INFO - Iter [400/80000]	lr: 5.985e-05, eta: 7:46:08, time: 0.338, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0986, decode.acc_seg: 96.2080, loss: 0.0986
+2023-03-03 20:42:42,289 - mmseg - INFO - Iter [450/80000]	lr: 6.735e-05, eta: 7:37:35, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0970, decode.acc_seg: 96.3000, loss: 0.0970
+2023-03-03 20:42:56,915 - mmseg - INFO - Iter [500/80000]	lr: 7.485e-05, eta: 7:30:20, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0956, decode.acc_seg: 96.3106, loss: 0.0956
+2023-03-03 20:43:11,490 - mmseg - INFO - Iter [550/80000]	lr: 8.235e-05, eta: 7:24:13, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0947, decode.acc_seg: 96.3613, loss: 0.0947
+2023-03-03 20:43:28,427 - mmseg - INFO - Iter [600/80000]	lr: 8.985e-05, eta: 7:24:18, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0933, decode.acc_seg: 96.3710, loss: 0.0933
+2023-03-03 20:43:43,092 - mmseg - INFO - Iter [650/80000]	lr: 9.735e-05, eta: 7:19:42, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0926, decode.acc_seg: 96.3667, loss: 0.0926
+2023-03-03 20:43:57,823 - mmseg - INFO - Iter [700/80000]	lr: 1.049e-04, eta: 7:15:51, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0924, decode.acc_seg: 96.4190, loss: 0.0924
+2023-03-03 20:44:14,721 - mmseg - INFO - Iter [750/80000]	lr: 1.124e-04, eta: 7:16:18, time: 0.338, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0911, decode.acc_seg: 96.4214, loss: 0.0911
+2023-03-03 20:44:29,345 - mmseg - INFO - Iter [800/80000]	lr: 1.199e-04, eta: 7:12:54, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0942, decode.acc_seg: 96.3410, loss: 0.0942
+2023-03-03 20:44:43,947 - mmseg - INFO - Iter [850/80000]	lr: 1.274e-04, eta: 7:09:50, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0902, decode.acc_seg: 96.4662, loss: 0.0902
+2023-03-03 20:44:58,536 - mmseg - INFO - Iter [900/80000]	lr: 1.349e-04, eta: 7:07:04, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0935, decode.acc_seg: 96.3513, loss: 0.0935
+2023-03-03 20:45:15,545 - mmseg - INFO - Iter [950/80000]	lr: 1.424e-04, eta: 7:07:55, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0929, decode.acc_seg: 96.4352, loss: 0.0929
+2023-03-03 20:45:30,205 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 20:45:30,205 - mmseg - INFO - Iter [1000/80000]	lr: 1.499e-04, eta: 7:05:34, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0959, decode.acc_seg: 96.2780, loss: 0.0959
+2023-03-03 20:45:44,862 - mmseg - INFO - Iter [1050/80000]	lr: 1.500e-04, eta: 7:03:25, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0900, decode.acc_seg: 96.4786, loss: 0.0900
+2023-03-03 20:45:59,419 - mmseg - INFO - Iter [1100/80000]	lr: 1.500e-04, eta: 7:01:19, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0935, decode.acc_seg: 96.3664, loss: 0.0935
+2023-03-03 20:46:16,346 - mmseg - INFO - Iter [1150/80000]	lr: 1.500e-04, eta: 7:02:05, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0918, decode.acc_seg: 96.4127, loss: 0.0918
+2023-03-03 20:46:31,020 - mmseg - INFO - Iter [1200/80000]	lr: 1.500e-04, eta: 7:00:18, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0886, decode.acc_seg: 96.5238, loss: 0.0886
+2023-03-03 20:46:45,757 - mmseg - INFO - Iter [1250/80000]	lr: 1.500e-04, eta: 6:58:42, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0883, decode.acc_seg: 96.5800, loss: 0.0883
+2023-03-03 20:47:00,595 - mmseg - INFO - Iter [1300/80000]	lr: 1.500e-04, eta: 6:57:19, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0873, decode.acc_seg: 96.5723, loss: 0.0873
+2023-03-03 20:47:17,704 - mmseg - INFO - Iter [1350/80000]	lr: 1.500e-04, eta: 6:58:13, time: 0.342, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0867, decode.acc_seg: 96.6390, loss: 0.0867
+2023-03-03 20:47:32,279 - mmseg - INFO - Iter [1400/80000]	lr: 1.500e-04, eta: 6:56:40, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0883, decode.acc_seg: 96.5316, loss: 0.0883
+2023-03-03 20:47:46,904 - mmseg - INFO - Iter [1450/80000]	lr: 1.500e-04, eta: 6:55:15, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0863, decode.acc_seg: 96.6172, loss: 0.0863
+2023-03-03 20:48:04,064 - mmseg - INFO - Iter [1500/80000]	lr: 1.500e-04, eta: 6:56:07, time: 0.343, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0898, decode.acc_seg: 96.5129, loss: 0.0898
+2023-03-03 20:48:18,964 - mmseg - INFO - Iter [1550/80000]	lr: 1.500e-04, eta: 6:55:00, time: 0.298, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0896, decode.acc_seg: 96.5096, loss: 0.0896
+2023-03-03 20:48:33,625 - mmseg - INFO - Iter [1600/80000]	lr: 1.500e-04, eta: 6:53:45, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0862, decode.acc_seg: 96.5945, loss: 0.0862
+2023-03-03 20:48:48,380 - mmseg - INFO - Iter [1650/80000]	lr: 1.500e-04, eta: 6:52:38, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0856, decode.acc_seg: 96.6088, loss: 0.0856
+2023-03-03 20:49:05,325 - mmseg - INFO - Iter [1700/80000]	lr: 1.500e-04, eta: 6:53:15, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0909, decode.acc_seg: 96.4592, loss: 0.0909
+2023-03-03 20:49:20,041 - mmseg - INFO - Iter [1750/80000]	lr: 1.500e-04, eta: 6:52:09, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0880, decode.acc_seg: 96.5435, loss: 0.0880
+2023-03-03 20:49:34,623 - mmseg - INFO - Iter [1800/80000]	lr: 1.500e-04, eta: 6:51:00, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0888, decode.acc_seg: 96.5137, loss: 0.0888
+2023-03-03 20:49:49,229 - mmseg - INFO - Iter [1850/80000]	lr: 1.500e-04, eta: 6:49:55, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0898, decode.acc_seg: 96.5114, loss: 0.0898
+2023-03-03 20:50:06,249 - mmseg - INFO - Iter [1900/80000]	lr: 1.500e-04, eta: 6:50:32, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0889, decode.acc_seg: 96.4363, loss: 0.0889
+2023-03-03 20:50:20,908 - mmseg - INFO - Iter [1950/80000]	lr: 1.500e-04, eta: 6:49:32, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0895, decode.acc_seg: 96.4833, loss: 0.0895
+2023-03-03 20:50:35,562 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 20:50:35,562 - mmseg - INFO - Iter [2000/80000]	lr: 1.500e-04, eta: 6:48:34, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0896, decode.acc_seg: 96.5174, loss: 0.0896
+2023-03-03 20:50:52,578 - mmseg - INFO - Iter [2050/80000]	lr: 1.500e-04, eta: 6:49:08, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0857, decode.acc_seg: 96.6437, loss: 0.0857
+2023-03-03 20:51:07,307 - mmseg - INFO - Iter [2100/80000]	lr: 1.500e-04, eta: 6:48:14, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0865, decode.acc_seg: 96.5910, loss: 0.0865
+2023-03-03 20:51:21,953 - mmseg - INFO - Iter [2150/80000]	lr: 1.500e-04, eta: 6:47:20, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0903, decode.acc_seg: 96.4951, loss: 0.0903
+2023-03-03 20:51:36,589 - mmseg - INFO - Iter [2200/80000]	lr: 1.500e-04, eta: 6:46:26, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0875, decode.acc_seg: 96.5607, loss: 0.0875
+2023-03-03 20:51:53,487 - mmseg - INFO - Iter [2250/80000]	lr: 1.500e-04, eta: 6:46:53, time: 0.338, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0904, decode.acc_seg: 96.4650, loss: 0.0904
+2023-03-03 20:52:08,355 - mmseg - INFO - Iter [2300/80000]	lr: 1.500e-04, eta: 6:46:09, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0867, decode.acc_seg: 96.6157, loss: 0.0867
+2023-03-03 20:52:22,982 - mmseg - INFO - Iter [2350/80000]	lr: 1.500e-04, eta: 6:45:19, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0851, decode.acc_seg: 96.6261, loss: 0.0851
+2023-03-03 20:52:37,744 - mmseg - INFO - Iter [2400/80000]	lr: 1.500e-04, eta: 6:44:34, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0924, decode.acc_seg: 96.4582, loss: 0.0924
+2023-03-03 20:52:54,632 - mmseg - INFO - Iter [2450/80000]	lr: 1.500e-04, eta: 6:44:58, time: 0.338, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0852, decode.acc_seg: 96.6733, loss: 0.0852
+2023-03-03 20:53:09,265 - mmseg - INFO - Iter [2500/80000]	lr: 1.500e-04, eta: 6:44:10, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0901, decode.acc_seg: 96.4915, loss: 0.0901
+2023-03-03 20:53:23,940 - mmseg - INFO - Iter [2550/80000]	lr: 1.500e-04, eta: 6:43:25, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0841, decode.acc_seg: 96.6862, loss: 0.0841
+2023-03-03 20:53:38,477 - mmseg - INFO - Iter [2600/80000]	lr: 1.500e-04, eta: 6:42:37, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0882, decode.acc_seg: 96.4968, loss: 0.0882
+2023-03-03 20:53:55,581 - mmseg - INFO - Iter [2650/80000]	lr: 1.500e-04, eta: 6:43:05, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0857, decode.acc_seg: 96.6405, loss: 0.0857
+2023-03-03 20:54:10,197 - mmseg - INFO - Iter [2700/80000]	lr: 1.500e-04, eta: 6:42:20, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0894, decode.acc_seg: 96.4850, loss: 0.0894
+2023-03-03 20:54:24,874 - mmseg - INFO - Iter [2750/80000]	lr: 1.500e-04, eta: 6:41:38, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0884, decode.acc_seg: 96.5566, loss: 0.0884
+2023-03-03 20:54:41,910 - mmseg - INFO - Iter [2800/80000]	lr: 1.500e-04, eta: 6:42:02, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0876, decode.acc_seg: 96.5946, loss: 0.0876
+2023-03-03 20:54:56,780 - mmseg - INFO - Iter [2850/80000]	lr: 1.500e-04, eta: 6:41:26, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0867, decode.acc_seg: 96.5906, loss: 0.0867
+2023-03-03 20:55:11,511 - mmseg - INFO - Iter [2900/80000]	lr: 1.500e-04, eta: 6:40:47, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0897, decode.acc_seg: 96.5038, loss: 0.0897
+2023-03-03 20:55:26,104 - mmseg - INFO - Iter [2950/80000]	lr: 1.500e-04, eta: 6:40:05, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0855, decode.acc_seg: 96.6477, loss: 0.0855
+2023-03-03 20:55:43,621 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 20:55:43,621 - mmseg - INFO - Iter [3000/80000]	lr: 1.500e-04, eta: 6:40:40, time: 0.350, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0872, decode.acc_seg: 96.5753, loss: 0.0872
+2023-03-03 20:55:58,338 - mmseg - INFO - Iter [3050/80000]	lr: 1.500e-04, eta: 6:40:01, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0856, decode.acc_seg: 96.6087, loss: 0.0856
+2023-03-03 20:56:12,906 - mmseg - INFO - Iter [3100/80000]	lr: 1.500e-04, eta: 6:39:20, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0881, decode.acc_seg: 96.5534, loss: 0.0881
+2023-03-03 20:56:27,506 - mmseg - INFO - Iter [3150/80000]	lr: 1.500e-04, eta: 6:38:41, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0869, decode.acc_seg: 96.5911, loss: 0.0869
+2023-03-03 20:56:44,426 - mmseg - INFO - Iter [3200/80000]	lr: 1.500e-04, eta: 6:38:58, time: 0.338, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0875, decode.acc_seg: 96.5574, loss: 0.0875
+2023-03-03 20:56:59,016 - mmseg - INFO - Iter [3250/80000]	lr: 1.500e-04, eta: 6:38:19, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0875, decode.acc_seg: 96.5690, loss: 0.0875
+2023-03-03 20:57:13,824 - mmseg - INFO - Iter [3300/80000]	lr: 1.500e-04, eta: 6:37:46, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7635, loss: 0.0827
+2023-03-03 20:57:30,823 - mmseg - INFO - Iter [3350/80000]	lr: 1.500e-04, eta: 6:38:03, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0867, decode.acc_seg: 96.5906, loss: 0.0867
+2023-03-03 20:57:45,494 - mmseg - INFO - Iter [3400/80000]	lr: 1.500e-04, eta: 6:37:27, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0888, decode.acc_seg: 96.5167, loss: 0.0888
+2023-03-03 20:58:00,055 - mmseg - INFO - Iter [3450/80000]	lr: 1.500e-04, eta: 6:36:49, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0889, decode.acc_seg: 96.4854, loss: 0.0889
+2023-03-03 20:58:14,628 - mmseg - INFO - Iter [3500/80000]	lr: 1.500e-04, eta: 6:36:12, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0841, decode.acc_seg: 96.6787, loss: 0.0841
+2023-03-03 20:58:31,570 - mmseg - INFO - Iter [3550/80000]	lr: 1.500e-04, eta: 6:36:27, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0837, decode.acc_seg: 96.6599, loss: 0.0837
+2023-03-03 20:58:46,193 - mmseg - INFO - Iter [3600/80000]	lr: 1.500e-04, eta: 6:35:51, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0847, decode.acc_seg: 96.7094, loss: 0.0847
+2023-03-03 20:59:00,735 - mmseg - INFO - Iter [3650/80000]	lr: 1.500e-04, eta: 6:35:15, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0843, decode.acc_seg: 96.6534, loss: 0.0843
+2023-03-03 20:59:15,445 - mmseg - INFO - Iter [3700/80000]	lr: 1.500e-04, eta: 6:34:42, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0867, decode.acc_seg: 96.5967, loss: 0.0867
+2023-03-03 20:59:32,488 - mmseg - INFO - Iter [3750/80000]	lr: 1.500e-04, eta: 6:34:58, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0869, decode.acc_seg: 96.5920, loss: 0.0869
+2023-03-03 20:59:47,059 - mmseg - INFO - Iter [3800/80000]	lr: 1.500e-04, eta: 6:34:23, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0863, decode.acc_seg: 96.6007, loss: 0.0863
+2023-03-03 21:00:01,765 - mmseg - INFO - Iter [3850/80000]	lr: 1.500e-04, eta: 6:33:51, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0859, decode.acc_seg: 96.6234, loss: 0.0859
+2023-03-03 21:00:16,529 - mmseg - INFO - Iter [3900/80000]	lr: 1.500e-04, eta: 6:33:21, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0850, decode.acc_seg: 96.6506, loss: 0.0850
+2023-03-03 21:00:33,589 - mmseg - INFO - Iter [3950/80000]	lr: 1.500e-04, eta: 6:33:35, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0857, decode.acc_seg: 96.6333, loss: 0.0857
+2023-03-03 21:00:48,208 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 21:00:48,208 - mmseg - INFO - Iter [4000/80000]	lr: 1.500e-04, eta: 6:33:02, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0915, decode.acc_seg: 96.4077, loss: 0.0915
+2023-03-03 21:01:02,819 - mmseg - INFO - Iter [4050/80000]	lr: 1.500e-04, eta: 6:32:30, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0838, decode.acc_seg: 96.7171, loss: 0.0838
+2023-03-03 21:01:19,914 - mmseg - INFO - Iter [4100/80000]	lr: 1.500e-04, eta: 6:32:44, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0863, decode.acc_seg: 96.6428, loss: 0.0863
+2023-03-03 21:01:34,458 - mmseg - INFO - Iter [4150/80000]	lr: 1.500e-04, eta: 6:32:11, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0849, decode.acc_seg: 96.6789, loss: 0.0849
+2023-03-03 21:01:49,011 - mmseg - INFO - Iter [4200/80000]	lr: 1.500e-04, eta: 6:31:38, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0847, decode.acc_seg: 96.6622, loss: 0.0847
+2023-03-03 21:02:03,575 - mmseg - INFO - Iter [4250/80000]	lr: 1.500e-04, eta: 6:31:05, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0873, decode.acc_seg: 96.5608, loss: 0.0873
+2023-03-03 21:02:20,525 - mmseg - INFO - Iter [4300/80000]	lr: 1.500e-04, eta: 6:31:16, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0880, decode.acc_seg: 96.6081, loss: 0.0880
+2023-03-03 21:02:35,188 - mmseg - INFO - Iter [4350/80000]	lr: 1.500e-04, eta: 6:30:46, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0859, decode.acc_seg: 96.6336, loss: 0.0859
+2023-03-03 21:02:49,920 - mmseg - INFO - Iter [4400/80000]	lr: 1.500e-04, eta: 6:30:17, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0822, decode.acc_seg: 96.7872, loss: 0.0822
+2023-03-03 21:03:04,556 - mmseg - INFO - Iter [4450/80000]	lr: 1.500e-04, eta: 6:29:47, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0874, decode.acc_seg: 96.5715, loss: 0.0874
+2023-03-03 21:03:21,583 - mmseg - INFO - Iter [4500/80000]	lr: 1.500e-04, eta: 6:29:57, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0902, decode.acc_seg: 96.4753, loss: 0.0902
+2023-03-03 21:03:36,138 - mmseg - INFO - Iter [4550/80000]	lr: 1.500e-04, eta: 6:29:26, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0881, decode.acc_seg: 96.5542, loss: 0.0881
+2023-03-03 21:03:50,779 - mmseg - INFO - Iter [4600/80000]	lr: 1.500e-04, eta: 6:28:57, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0868, decode.acc_seg: 96.6392, loss: 0.0868
+2023-03-03 21:04:05,369 - mmseg - INFO - Iter [4650/80000]	lr: 1.500e-04, eta: 6:28:27, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0828, decode.acc_seg: 96.7127, loss: 0.0828
+2023-03-03 21:04:22,468 - mmseg - INFO - Iter [4700/80000]	lr: 1.500e-04, eta: 6:28:38, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0843, decode.acc_seg: 96.6877, loss: 0.0843
+2023-03-03 21:04:37,248 - mmseg - INFO - Iter [4750/80000]	lr: 1.500e-04, eta: 6:28:11, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0834, decode.acc_seg: 96.7205, loss: 0.0834
+2023-03-03 21:04:52,070 - mmseg - INFO - Iter [4800/80000]	lr: 1.500e-04, eta: 6:27:46, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0897, decode.acc_seg: 96.4795, loss: 0.0897
+2023-03-03 21:05:09,244 - mmseg - INFO - Iter [4850/80000]	lr: 1.500e-04, eta: 6:27:56, time: 0.343, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0873, decode.acc_seg: 96.5233, loss: 0.0873
+2023-03-03 21:05:23,933 - mmseg - INFO - Iter [4900/80000]	lr: 1.500e-04, eta: 6:27:29, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0879, decode.acc_seg: 96.5584, loss: 0.0879
+2023-03-03 21:05:38,505 - mmseg - INFO - Iter [4950/80000]	lr: 1.500e-04, eta: 6:27:00, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0831, decode.acc_seg: 96.7257, loss: 0.0831
+2023-03-03 21:05:53,063 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 21:05:53,063 - mmseg - INFO - Iter [5000/80000]	lr: 1.500e-04, eta: 6:26:30, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0870, decode.acc_seg: 96.5746, loss: 0.0870
+2023-03-03 21:06:10,142 - mmseg - INFO - Iter [5050/80000]	lr: 1.500e-04, eta: 6:26:39, time: 0.342, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0862, decode.acc_seg: 96.6355, loss: 0.0862
+2023-03-03 21:06:24,869 - mmseg - INFO - Iter [5100/80000]	lr: 1.500e-04, eta: 6:26:12, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0836, decode.acc_seg: 96.6941, loss: 0.0836
+2023-03-03 21:06:39,561 - mmseg - INFO - Iter [5150/80000]	lr: 1.500e-04, eta: 6:25:46, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0847, decode.acc_seg: 96.6664, loss: 0.0847
+2023-03-03 21:06:54,224 - mmseg - INFO - Iter [5200/80000]	lr: 1.500e-04, eta: 6:25:19, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0859, decode.acc_seg: 96.6041, loss: 0.0859
+2023-03-03 21:07:11,416 - mmseg - INFO - Iter [5250/80000]	lr: 1.500e-04, eta: 6:25:28, time: 0.344, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0843, decode.acc_seg: 96.6679, loss: 0.0843
+2023-03-03 21:07:25,982 - mmseg - INFO - Iter [5300/80000]	lr: 1.500e-04, eta: 6:25:00, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0822, decode.acc_seg: 96.7697, loss: 0.0822
+2023-03-03 21:07:40,542 - mmseg - INFO - Iter [5350/80000]	lr: 1.500e-04, eta: 6:24:32, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0908, decode.acc_seg: 96.4930, loss: 0.0908
+2023-03-03 21:07:57,494 - mmseg - INFO - Iter [5400/80000]	lr: 1.500e-04, eta: 6:24:37, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0856, decode.acc_seg: 96.6104, loss: 0.0856
+2023-03-03 21:08:12,078 - mmseg - INFO - Iter [5450/80000]	lr: 1.500e-04, eta: 6:24:09, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0856, decode.acc_seg: 96.6574, loss: 0.0856
+2023-03-03 21:08:26,636 - mmseg - INFO - Iter [5500/80000]	lr: 1.500e-04, eta: 6:23:42, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0879, decode.acc_seg: 96.5493, loss: 0.0879
+2023-03-03 21:08:41,336 - mmseg - INFO - Iter [5550/80000]	lr: 1.500e-04, eta: 6:23:16, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0836, decode.acc_seg: 96.6809, loss: 0.0836
+2023-03-03 21:08:58,357 - mmseg - INFO - Iter [5600/80000]	lr: 1.500e-04, eta: 6:23:22, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0880, decode.acc_seg: 96.5377, loss: 0.0880
+2023-03-03 21:09:13,184 - mmseg - INFO - Iter [5650/80000]	lr: 1.500e-04, eta: 6:22:58, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0865, decode.acc_seg: 96.5801, loss: 0.0865
+2023-03-03 21:09:27,836 - mmseg - INFO - Iter [5700/80000]	lr: 1.500e-04, eta: 6:22:32, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0856, decode.acc_seg: 96.6026, loss: 0.0856
+2023-03-03 21:09:42,470 - mmseg - INFO - Iter [5750/80000]	lr: 1.500e-04, eta: 6:22:06, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0832, decode.acc_seg: 96.7116, loss: 0.0832
+2023-03-03 21:09:59,605 - mmseg - INFO - Iter [5800/80000]	lr: 1.500e-04, eta: 6:22:12, time: 0.343, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0832, decode.acc_seg: 96.7049, loss: 0.0832
+2023-03-03 21:10:14,210 - mmseg - INFO - Iter [5850/80000]	lr: 1.500e-04, eta: 6:21:46, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.7866, loss: 0.0812
+2023-03-03 21:10:28,954 - mmseg - INFO - Iter [5900/80000]	lr: 1.500e-04, eta: 6:21:22, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0857, decode.acc_seg: 96.6016, loss: 0.0857
+2023-03-03 21:10:43,543 - mmseg - INFO - Iter [5950/80000]	lr: 1.500e-04, eta: 6:20:56, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0856, decode.acc_seg: 96.6240, loss: 0.0856
+2023-03-03 21:11:00,689 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 21:11:00,690 - mmseg - INFO - Iter [6000/80000]	lr: 1.500e-04, eta: 6:21:02, time: 0.343, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0828, decode.acc_seg: 96.7273, loss: 0.0828
+2023-03-03 21:11:15,380 - mmseg - INFO - Iter [6050/80000]	lr: 1.500e-04, eta: 6:20:37, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0882, decode.acc_seg: 96.5259, loss: 0.0882
+2023-03-03 21:11:30,201 - mmseg - INFO - Iter [6100/80000]	lr: 1.500e-04, eta: 6:20:14, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0872, decode.acc_seg: 96.5827, loss: 0.0872
+2023-03-03 21:11:47,376 - mmseg - INFO - Iter [6150/80000]	lr: 1.500e-04, eta: 6:20:19, time: 0.343, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0845, decode.acc_seg: 96.6753, loss: 0.0845
+2023-03-03 21:12:02,016 - mmseg - INFO - Iter [6200/80000]	lr: 1.500e-04, eta: 6:19:54, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0838, decode.acc_seg: 96.6455, loss: 0.0838
+2023-03-03 21:12:16,838 - mmseg - INFO - Iter [6250/80000]	lr: 1.500e-04, eta: 6:19:31, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.7546, loss: 0.0819
+2023-03-03 21:12:31,532 - mmseg - INFO - Iter [6300/80000]	lr: 1.500e-04, eta: 6:19:07, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0880, decode.acc_seg: 96.5500, loss: 0.0880
+2023-03-03 21:12:48,619 - mmseg - INFO - Iter [6350/80000]	lr: 1.500e-04, eta: 6:19:11, time: 0.342, data_time: 0.054, memory: 67605, decode.loss_ce: 0.0830, decode.acc_seg: 96.7306, loss: 0.0830
+2023-03-03 21:13:03,238 - mmseg - INFO - Iter [6400/80000]	lr: 1.500e-04, eta: 6:18:46, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0859, decode.acc_seg: 96.6428, loss: 0.0859
+2023-03-03 21:13:17,881 - mmseg - INFO - Iter [6450/80000]	lr: 1.500e-04, eta: 6:18:22, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0831, decode.acc_seg: 96.6875, loss: 0.0831
+2023-03-03 21:13:32,649 - mmseg - INFO - Iter [6500/80000]	lr: 1.500e-04, eta: 6:17:59, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.7305, loss: 0.0821
+2023-03-03 21:13:49,642 - mmseg - INFO - Iter [6550/80000]	lr: 1.500e-04, eta: 6:18:01, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0885, decode.acc_seg: 96.5355, loss: 0.0885
+2023-03-03 21:14:04,203 - mmseg - INFO - Iter [6600/80000]	lr: 1.500e-04, eta: 6:17:35, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0848, decode.acc_seg: 96.6432, loss: 0.0848
+2023-03-03 21:14:18,782 - mmseg - INFO - Iter [6650/80000]	lr: 1.500e-04, eta: 6:17:11, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0835, decode.acc_seg: 96.6818, loss: 0.0835
+2023-03-03 21:14:35,766 - mmseg - INFO - Iter [6700/80000]	lr: 1.500e-04, eta: 6:17:12, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0826, decode.acc_seg: 96.7330, loss: 0.0826
+2023-03-03 21:14:50,671 - mmseg - INFO - Iter [6750/80000]	lr: 1.500e-04, eta: 6:16:51, time: 0.298, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7456, loss: 0.0827
+2023-03-03 21:15:05,297 - mmseg - INFO - Iter [6800/80000]	lr: 1.500e-04, eta: 6:16:27, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0839, decode.acc_seg: 96.6935, loss: 0.0839
+2023-03-03 21:15:19,857 - mmseg - INFO - Iter [6850/80000]	lr: 1.500e-04, eta: 6:16:02, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0850, decode.acc_seg: 96.6315, loss: 0.0850
+2023-03-03 21:15:36,865 - mmseg - INFO - Iter [6900/80000]	lr: 1.500e-04, eta: 6:16:04, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0862, decode.acc_seg: 96.6278, loss: 0.0862
+2023-03-03 21:15:51,546 - mmseg - INFO - Iter [6950/80000]	lr: 1.500e-04, eta: 6:15:40, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0855, decode.acc_seg: 96.6508, loss: 0.0855
+2023-03-03 21:16:06,369 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 21:16:06,369 - mmseg - INFO - Iter [7000/80000]	lr: 1.500e-04, eta: 6:15:18, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0833, decode.acc_seg: 96.6754, loss: 0.0833
+2023-03-03 21:16:20,938 - mmseg - INFO - Iter [7050/80000]	lr: 1.500e-04, eta: 6:14:54, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0842, decode.acc_seg: 96.6657, loss: 0.0842
+2023-03-03 21:16:37,937 - mmseg - INFO - Iter [7100/80000]	lr: 1.500e-04, eta: 6:14:55, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0817, decode.acc_seg: 96.7726, loss: 0.0817
+2023-03-03 21:16:52,638 - mmseg - INFO - Iter [7150/80000]	lr: 1.500e-04, eta: 6:14:32, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0848, decode.acc_seg: 96.6261, loss: 0.0848
+2023-03-03 21:17:07,305 - mmseg - INFO - Iter [7200/80000]	lr: 1.500e-04, eta: 6:14:09, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0892, decode.acc_seg: 96.5449, loss: 0.0892
+2023-03-03 21:17:22,058 - mmseg - INFO - Iter [7250/80000]	lr: 1.500e-04, eta: 6:13:47, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0899, decode.acc_seg: 96.4910, loss: 0.0899
+2023-03-03 21:17:39,075 - mmseg - INFO - Iter [7300/80000]	lr: 1.500e-04, eta: 6:13:47, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0872, decode.acc_seg: 96.5804, loss: 0.0872
+2023-03-03 21:17:53,869 - mmseg - INFO - Iter [7350/80000]	lr: 1.500e-04, eta: 6:13:26, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0837, decode.acc_seg: 96.7056, loss: 0.0837
+2023-03-03 21:18:08,559 - mmseg - INFO - Iter [7400/80000]	lr: 1.500e-04, eta: 6:13:03, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0833, decode.acc_seg: 96.6921, loss: 0.0833
+2023-03-03 21:18:25,551 - mmseg - INFO - Iter [7450/80000]	lr: 1.500e-04, eta: 6:13:03, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0835, decode.acc_seg: 96.6945, loss: 0.0835
+2023-03-03 21:18:40,229 - mmseg - INFO - Iter [7500/80000]	lr: 1.500e-04, eta: 6:12:41, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0843, decode.acc_seg: 96.6832, loss: 0.0843
+2023-03-03 21:18:54,922 - mmseg - INFO - Iter [7550/80000]	lr: 1.500e-04, eta: 6:12:18, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7260, loss: 0.0827
+2023-03-03 21:19:09,576 - mmseg - INFO - Iter [7600/80000]	lr: 1.500e-04, eta: 6:11:55, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0848, decode.acc_seg: 96.6127, loss: 0.0848
+2023-03-03 21:19:26,729 - mmseg - INFO - Iter [7650/80000]	lr: 1.500e-04, eta: 6:11:56, time: 0.343, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0852, decode.acc_seg: 96.6502, loss: 0.0852
+2023-03-03 21:19:41,509 - mmseg - INFO - Iter [7700/80000]	lr: 1.500e-04, eta: 6:11:35, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0866, decode.acc_seg: 96.6447, loss: 0.0866
+2023-03-03 21:19:56,139 - mmseg - INFO - Iter [7750/80000]	lr: 1.500e-04, eta: 6:11:12, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.7849, loss: 0.0819
+2023-03-03 21:20:10,762 - mmseg - INFO - Iter [7800/80000]	lr: 1.500e-04, eta: 6:10:49, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0826, decode.acc_seg: 96.7127, loss: 0.0826
+2023-03-03 21:20:27,866 - mmseg - INFO - Iter [7850/80000]	lr: 1.500e-04, eta: 6:10:50, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0860, decode.acc_seg: 96.6290, loss: 0.0860
+2023-03-03 21:20:42,587 - mmseg - INFO - Iter [7900/80000]	lr: 1.500e-04, eta: 6:10:28, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0834, decode.acc_seg: 96.6875, loss: 0.0834
+2023-03-03 21:20:57,233 - mmseg - INFO - Iter [7950/80000]	lr: 1.500e-04, eta: 6:10:05, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0889, decode.acc_seg: 96.5498, loss: 0.0889
+2023-03-03 21:21:14,164 - mmseg - INFO - Saving checkpoint at 8000 iterations
+2023-03-03 21:21:16,064 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 21:21:16,065 - mmseg - INFO - Iter [8000/80000]	lr: 1.500e-04, eta: 6:10:21, time: 0.377, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0870, decode.acc_seg: 96.6073, loss: 0.0870
+2023-03-03 21:21:55,465 - mmseg - INFO - per class results:
+2023-03-03 21:21:55,467 - mmseg - INFO - 
++---------------+-------+-------+
+|     Class     |  IoU  |  Acc  |
++---------------+-------+-------+
+|   background  |  nan  |  nan  |
+|      road     | 98.49 | 99.14 |
+|    sidewalk   | 87.01 | 93.71 |
+|    building   | 93.52 | 96.43 |
+|      wall     |  55.4 | 62.99 |
+|     fence     | 64.11 | 73.86 |
+|      pole     |  69.8 | 80.96 |
+| traffic light | 74.15 | 83.75 |
+|  traffic sign | 81.75 | 86.97 |
+|   vegetation  | 92.87 | 97.58 |
+|    terrain    | 63.95 |  72.7 |
+|      sky      | 95.46 | 98.03 |
+|     person    | 84.51 | 92.89 |
+|     rider     | 67.81 | 80.02 |
+|      car      | 95.96 | 98.19 |
+|     truck     | 84.53 | 89.49 |
+|      bus      | 91.47 | 96.05 |
+|     train     | 84.98 | 90.01 |
+|   motorcycle  | 71.92 |  81.0 |
+|    bicycle    | 79.96 | 91.47 |
++---------------+-------+-------+
+2023-03-03 21:21:55,467 - mmseg - INFO - Summary:
+2023-03-03 21:21:55,467 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 96.57 | 80.93 | 87.64 |
++-------+-------+-------+
+2023-03-03 21:21:57,486 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_8000.pth.
+2023-03-03 21:21:57,486 - mmseg - INFO - Best mIoU is 0.8093 at 8000 iter.
+2023-03-03 21:21:57,486 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 21:21:57,487 - mmseg - INFO - Iter(val) [63]	aAcc: 0.9657, mIoU: 0.8093, mAcc: 0.8764, IoU.background: nan, IoU.road: 0.9849, IoU.sidewalk: 0.8701, IoU.building: 0.9352, IoU.wall: 0.5540, IoU.fence: 0.6411, IoU.pole: 0.6980, IoU.traffic light: 0.7415, IoU.traffic sign: 0.8175, IoU.vegetation: 0.9287, IoU.terrain: 0.6395, IoU.sky: 0.9546, IoU.person: 0.8451, IoU.rider: 0.6781, IoU.car: 0.9596, IoU.truck: 0.8453, IoU.bus: 0.9147, IoU.train: 0.8498, IoU.motorcycle: 0.7192, IoU.bicycle: 0.7996, Acc.background: nan, Acc.road: 0.9914, Acc.sidewalk: 0.9371, Acc.building: 0.9643, Acc.wall: 0.6299, Acc.fence: 0.7386, Acc.pole: 0.8096, Acc.traffic light: 0.8375, Acc.traffic sign: 0.8697, Acc.vegetation: 0.9758, Acc.terrain: 0.7270, Acc.sky: 0.9803, Acc.person: 0.9289, Acc.rider: 0.8002, Acc.car: 0.9819, Acc.truck: 0.8949, Acc.bus: 0.9605, Acc.train: 0.9001, Acc.motorcycle: 0.8100, Acc.bicycle: 0.9147
+2023-03-03 21:22:12,567 - mmseg - INFO - Iter [8050/80000]	lr: 1.500e-04, eta: 6:16:13, time: 1.130, data_time: 0.836, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7499, loss: 0.0827
+2023-03-03 21:22:27,312 - mmseg - INFO - Iter [8100/80000]	lr: 1.500e-04, eta: 6:15:48, time: 0.295, data_time: 0.008, memory: 67605, decode.loss_ce: 0.0846, decode.acc_seg: 96.6754, loss: 0.0846
+2023-03-03 21:22:41,963 - mmseg - INFO - Iter [8150/80000]	lr: 1.500e-04, eta: 6:15:24, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0842, decode.acc_seg: 96.6954, loss: 0.0842
+2023-03-03 21:22:58,986 - mmseg - INFO - Iter [8200/80000]	lr: 1.500e-04, eta: 6:15:20, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0838, decode.acc_seg: 96.7057, loss: 0.0838
+2023-03-03 21:23:14,006 - mmseg - INFO - Iter [8250/80000]	lr: 1.500e-04, eta: 6:14:58, time: 0.300, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0856, decode.acc_seg: 96.6049, loss: 0.0856
+2023-03-03 21:23:28,801 - mmseg - INFO - Iter [8300/80000]	lr: 1.500e-04, eta: 6:14:35, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0842, decode.acc_seg: 96.6898, loss: 0.0842
+2023-03-03 21:23:43,538 - mmseg - INFO - Iter [8350/80000]	lr: 1.500e-04, eta: 6:14:11, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0841, decode.acc_seg: 96.6609, loss: 0.0841
+2023-03-03 21:24:00,814 - mmseg - INFO - Iter [8400/80000]	lr: 1.500e-04, eta: 6:14:09, time: 0.346, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0824, decode.acc_seg: 96.7602, loss: 0.0824
+2023-03-03 21:24:15,569 - mmseg - INFO - Iter [8450/80000]	lr: 1.500e-04, eta: 6:13:46, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0843, decode.acc_seg: 96.6649, loss: 0.0843
+2023-03-03 21:24:30,316 - mmseg - INFO - Iter [8500/80000]	lr: 1.500e-04, eta: 6:13:22, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0843, decode.acc_seg: 96.6365, loss: 0.0843
+2023-03-03 21:24:44,914 - mmseg - INFO - Iter [8550/80000]	lr: 1.500e-04, eta: 6:12:58, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0825, decode.acc_seg: 96.7521, loss: 0.0825
+2023-03-03 21:25:01,964 - mmseg - INFO - Iter [8600/80000]	lr: 1.500e-04, eta: 6:12:54, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0845, decode.acc_seg: 96.6540, loss: 0.0845
+2023-03-03 21:25:16,548 - mmseg - INFO - Iter [8650/80000]	lr: 1.500e-04, eta: 6:12:29, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0872, decode.acc_seg: 96.5916, loss: 0.0872
+2023-03-03 21:25:31,296 - mmseg - INFO - Iter [8700/80000]	lr: 1.500e-04, eta: 6:12:06, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0887, decode.acc_seg: 96.4894, loss: 0.0887
+2023-03-03 21:25:48,369 - mmseg - INFO - Iter [8750/80000]	lr: 1.500e-04, eta: 6:12:02, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0834, decode.acc_seg: 96.7322, loss: 0.0834
+2023-03-03 21:26:02,999 - mmseg - INFO - Iter [8800/80000]	lr: 1.500e-04, eta: 6:11:38, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.8071, loss: 0.0812
+2023-03-03 21:26:17,582 - mmseg - INFO - Iter [8850/80000]	lr: 1.500e-04, eta: 6:11:13, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0862, decode.acc_seg: 96.5347, loss: 0.0862
+2023-03-03 21:26:32,369 - mmseg - INFO - Iter [8900/80000]	lr: 1.500e-04, eta: 6:10:51, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0854, decode.acc_seg: 96.6223, loss: 0.0854
+2023-03-03 21:26:49,506 - mmseg - INFO - Iter [8950/80000]	lr: 1.500e-04, eta: 6:10:47, time: 0.343, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0915, decode.acc_seg: 96.4401, loss: 0.0915
+2023-03-03 21:27:04,211 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 21:27:04,211 - mmseg - INFO - Iter [9000/80000]	lr: 1.500e-04, eta: 6:10:24, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0853, decode.acc_seg: 96.6431, loss: 0.0853
+2023-03-03 21:27:18,821 - mmseg - INFO - Iter [9050/80000]	lr: 1.500e-04, eta: 6:10:00, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0828, decode.acc_seg: 96.7185, loss: 0.0828
+2023-03-03 21:27:33,581 - mmseg - INFO - Iter [9100/80000]	lr: 1.500e-04, eta: 6:09:38, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7180, loss: 0.0827
+2023-03-03 21:27:50,641 - mmseg - INFO - Iter [9150/80000]	lr: 1.500e-04, eta: 6:09:33, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0855, decode.acc_seg: 96.6637, loss: 0.0855
+2023-03-03 21:28:05,450 - mmseg - INFO - Iter [9200/80000]	lr: 1.500e-04, eta: 6:09:11, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0836, decode.acc_seg: 96.6821, loss: 0.0836
+2023-03-03 21:28:20,090 - mmseg - INFO - Iter [9250/80000]	lr: 1.500e-04, eta: 6:08:47, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0834, decode.acc_seg: 96.6918, loss: 0.0834
+2023-03-03 21:28:34,765 - mmseg - INFO - Iter [9300/80000]	lr: 1.500e-04, eta: 6:08:24, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0838, decode.acc_seg: 96.7185, loss: 0.0838
+2023-03-03 21:28:51,713 - mmseg - INFO - Iter [9350/80000]	lr: 1.500e-04, eta: 6:08:19, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0813, decode.acc_seg: 96.8230, loss: 0.0813
+2023-03-03 21:29:06,299 - mmseg - INFO - Iter [9400/80000]	lr: 1.500e-04, eta: 6:07:55, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0847, decode.acc_seg: 96.6701, loss: 0.0847
+2023-03-03 21:29:20,963 - mmseg - INFO - Iter [9450/80000]	lr: 1.500e-04, eta: 6:07:32, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0858, decode.acc_seg: 96.5842, loss: 0.0858
+2023-03-03 21:29:37,937 - mmseg - INFO - Iter [9500/80000]	lr: 1.500e-04, eta: 6:07:27, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0876, decode.acc_seg: 96.5245, loss: 0.0876
+2023-03-03 21:29:52,675 - mmseg - INFO - Iter [9550/80000]	lr: 1.500e-04, eta: 6:07:04, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0830, decode.acc_seg: 96.7137, loss: 0.0830
+2023-03-03 21:30:07,333 - mmseg - INFO - Iter [9600/80000]	lr: 1.500e-04, eta: 6:06:42, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0833, decode.acc_seg: 96.7064, loss: 0.0833
+2023-03-03 21:30:21,972 - mmseg - INFO - Iter [9650/80000]	lr: 1.500e-04, eta: 6:06:19, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0833, decode.acc_seg: 96.7159, loss: 0.0833
+2023-03-03 21:30:38,999 - mmseg - INFO - Iter [9700/80000]	lr: 1.500e-04, eta: 6:06:13, time: 0.341, data_time: 0.054, memory: 67605, decode.loss_ce: 0.0855, decode.acc_seg: 96.6385, loss: 0.0855
+2023-03-03 21:30:53,681 - mmseg - INFO - Iter [9750/80000]	lr: 1.500e-04, eta: 6:05:51, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.8302, loss: 0.0797
+2023-03-03 21:31:08,319 - mmseg - INFO - Iter [9800/80000]	lr: 1.500e-04, eta: 6:05:28, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0842, decode.acc_seg: 96.7023, loss: 0.0842
+2023-03-03 21:31:22,893 - mmseg - INFO - Iter [9850/80000]	lr: 1.500e-04, eta: 6:05:05, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0844, decode.acc_seg: 96.6445, loss: 0.0844
+2023-03-03 21:31:40,112 - mmseg - INFO - Iter [9900/80000]	lr: 1.500e-04, eta: 6:05:01, time: 0.344, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0845, decode.acc_seg: 96.6689, loss: 0.0845
+2023-03-03 21:31:54,830 - mmseg - INFO - Iter [9950/80000]	lr: 1.500e-04, eta: 6:04:39, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0851, decode.acc_seg: 96.5965, loss: 0.0851
+2023-03-03 21:32:09,630 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 21:32:09,630 - mmseg - INFO - Iter [10000/80000]	lr: 1.500e-04, eta: 6:04:18, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0842, decode.acc_seg: 96.6415, loss: 0.0842
+2023-03-03 21:32:26,664 - mmseg - INFO - Iter [10050/80000]	lr: 7.500e-05, eta: 6:04:12, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.6965, loss: 0.0827
+2023-03-03 21:32:41,323 - mmseg - INFO - Iter [10100/80000]	lr: 7.500e-05, eta: 6:03:50, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0809, decode.acc_seg: 96.7754, loss: 0.0809
+2023-03-03 21:32:56,125 - mmseg - INFO - Iter [10150/80000]	lr: 7.500e-05, eta: 6:03:28, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0862, decode.acc_seg: 96.6183, loss: 0.0862
+2023-03-03 21:33:10,968 - mmseg - INFO - Iter [10200/80000]	lr: 7.500e-05, eta: 6:03:08, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0860, decode.acc_seg: 96.6081, loss: 0.0860
+2023-03-03 21:33:28,014 - mmseg - INFO - Iter [10250/80000]	lr: 7.500e-05, eta: 6:03:02, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0851, decode.acc_seg: 96.6117, loss: 0.0851
+2023-03-03 21:33:42,613 - mmseg - INFO - Iter [10300/80000]	lr: 7.500e-05, eta: 6:02:39, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0846, decode.acc_seg: 96.6759, loss: 0.0846
+2023-03-03 21:33:57,289 - mmseg - INFO - Iter [10350/80000]	lr: 7.500e-05, eta: 6:02:17, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0844, decode.acc_seg: 96.6966, loss: 0.0844
+2023-03-03 21:34:11,944 - mmseg - INFO - Iter [10400/80000]	lr: 7.500e-05, eta: 6:01:55, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0867, decode.acc_seg: 96.5897, loss: 0.0867
+2023-03-03 21:34:28,886 - mmseg - INFO - Iter [10450/80000]	lr: 7.500e-05, eta: 6:01:49, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0809, decode.acc_seg: 96.8148, loss: 0.0809
+2023-03-03 21:34:43,697 - mmseg - INFO - Iter [10500/80000]	lr: 7.500e-05, eta: 6:01:28, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0898, decode.acc_seg: 96.5790, loss: 0.0898
+2023-03-03 21:34:58,287 - mmseg - INFO - Iter [10550/80000]	lr: 7.500e-05, eta: 6:01:05, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0832, decode.acc_seg: 96.7284, loss: 0.0832
+2023-03-03 21:35:12,977 - mmseg - INFO - Iter [10600/80000]	lr: 7.500e-05, eta: 6:00:44, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0825, decode.acc_seg: 96.7583, loss: 0.0825
+2023-03-03 21:35:30,067 - mmseg - INFO - Iter [10650/80000]	lr: 7.500e-05, eta: 6:00:38, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.7845, loss: 0.0814
+2023-03-03 21:35:44,723 - mmseg - INFO - Iter [10700/80000]	lr: 7.500e-05, eta: 6:00:16, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0836, decode.acc_seg: 96.6997, loss: 0.0836
+2023-03-03 21:35:59,478 - mmseg - INFO - Iter [10750/80000]	lr: 7.500e-05, eta: 5:59:55, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.7386, loss: 0.0819
+2023-03-03 21:36:16,447 - mmseg - INFO - Iter [10800/80000]	lr: 7.500e-05, eta: 5:59:49, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.8578, loss: 0.0797
+2023-03-03 21:36:31,076 - mmseg - INFO - Iter [10850/80000]	lr: 7.500e-05, eta: 5:59:27, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8122, loss: 0.0802
+2023-03-03 21:36:45,701 - mmseg - INFO - Iter [10900/80000]	lr: 7.500e-05, eta: 5:59:05, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7323, loss: 0.0815
+2023-03-03 21:37:00,424 - mmseg - INFO - Iter [10950/80000]	lr: 7.500e-05, eta: 5:58:44, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0822, decode.acc_seg: 96.7351, loss: 0.0822
+2023-03-03 21:37:17,689 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 21:37:17,689 - mmseg - INFO - Iter [11000/80000]	lr: 7.500e-05, eta: 5:58:39, time: 0.345, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0858, decode.acc_seg: 96.5784, loss: 0.0858
+2023-03-03 21:37:32,419 - mmseg - INFO - Iter [11050/80000]	lr: 7.500e-05, eta: 5:58:18, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0838, decode.acc_seg: 96.7151, loss: 0.0838
+2023-03-03 21:37:47,243 - mmseg - INFO - Iter [11100/80000]	lr: 7.500e-05, eta: 5:57:58, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.7991, loss: 0.0805
+2023-03-03 21:38:01,820 - mmseg - INFO - Iter [11150/80000]	lr: 7.500e-05, eta: 5:57:36, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0835, decode.acc_seg: 96.6952, loss: 0.0835
+2023-03-03 21:38:18,923 - mmseg - INFO - Iter [11200/80000]	lr: 7.500e-05, eta: 5:57:30, time: 0.342, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0841, decode.acc_seg: 96.7132, loss: 0.0841
+2023-03-03 21:38:33,516 - mmseg - INFO - Iter [11250/80000]	lr: 7.500e-05, eta: 5:57:08, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.8042, loss: 0.0814
+2023-03-03 21:38:48,163 - mmseg - INFO - Iter [11300/80000]	lr: 7.500e-05, eta: 5:56:47, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0839, decode.acc_seg: 96.6848, loss: 0.0839
+2023-03-03 21:39:05,059 - mmseg - INFO - Iter [11350/80000]	lr: 7.500e-05, eta: 5:56:39, time: 0.338, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0826, decode.acc_seg: 96.6896, loss: 0.0826
+2023-03-03 21:39:19,666 - mmseg - INFO - Iter [11400/80000]	lr: 7.500e-05, eta: 5:56:18, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.7948, loss: 0.0803
+2023-03-03 21:39:34,337 - mmseg - INFO - Iter [11450/80000]	lr: 7.500e-05, eta: 5:55:56, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0847, decode.acc_seg: 96.6661, loss: 0.0847
+2023-03-03 21:39:49,148 - mmseg - INFO - Iter [11500/80000]	lr: 7.500e-05, eta: 5:55:36, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0838, decode.acc_seg: 96.7021, loss: 0.0838
+2023-03-03 21:40:06,121 - mmseg - INFO - Iter [11550/80000]	lr: 7.500e-05, eta: 5:55:29, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0853, decode.acc_seg: 96.6192, loss: 0.0853
+2023-03-03 21:40:20,718 - mmseg - INFO - Iter [11600/80000]	lr: 7.500e-05, eta: 5:55:08, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0833, decode.acc_seg: 96.7012, loss: 0.0833
+2023-03-03 21:40:35,405 - mmseg - INFO - Iter [11650/80000]	lr: 7.500e-05, eta: 5:54:47, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0823, decode.acc_seg: 96.7322, loss: 0.0823
+2023-03-03 21:40:50,032 - mmseg - INFO - Iter [11700/80000]	lr: 7.500e-05, eta: 5:54:26, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0838, decode.acc_seg: 96.6444, loss: 0.0838
+2023-03-03 21:41:07,197 - mmseg - INFO - Iter [11750/80000]	lr: 7.500e-05, eta: 5:54:19, time: 0.343, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0838, decode.acc_seg: 96.6857, loss: 0.0838
+2023-03-03 21:41:21,777 - mmseg - INFO - Iter [11800/80000]	lr: 7.500e-05, eta: 5:53:58, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0825, decode.acc_seg: 96.6891, loss: 0.0825
+2023-03-03 21:41:36,498 - mmseg - INFO - Iter [11850/80000]	lr: 7.500e-05, eta: 5:53:38, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0809, decode.acc_seg: 96.8126, loss: 0.0809
+2023-03-03 21:41:51,181 - mmseg - INFO - Iter [11900/80000]	lr: 7.500e-05, eta: 5:53:17, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0847, decode.acc_seg: 96.6647, loss: 0.0847
+2023-03-03 21:42:08,235 - mmseg - INFO - Iter [11950/80000]	lr: 7.500e-05, eta: 5:53:10, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0834, decode.acc_seg: 96.6798, loss: 0.0834
+2023-03-03 21:42:22,828 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 21:42:22,828 - mmseg - INFO - Iter [12000/80000]	lr: 7.500e-05, eta: 5:52:49, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0853, decode.acc_seg: 96.5943, loss: 0.0853
+2023-03-03 21:42:37,417 - mmseg - INFO - Iter [12050/80000]	lr: 7.500e-05, eta: 5:52:28, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0857, decode.acc_seg: 96.6361, loss: 0.0857
+2023-03-03 21:42:54,498 - mmseg - INFO - Iter [12100/80000]	lr: 7.500e-05, eta: 5:52:21, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0823, decode.acc_seg: 96.7404, loss: 0.0823
+2023-03-03 21:43:09,095 - mmseg - INFO - Iter [12150/80000]	lr: 7.500e-05, eta: 5:52:00, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8478, loss: 0.0795
+2023-03-03 21:43:23,678 - mmseg - INFO - Iter [12200/80000]	lr: 7.500e-05, eta: 5:51:39, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0823, decode.acc_seg: 96.7635, loss: 0.0823
+2023-03-03 21:43:38,254 - mmseg - INFO - Iter [12250/80000]	lr: 7.500e-05, eta: 5:51:18, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0833, decode.acc_seg: 96.6960, loss: 0.0833
+2023-03-03 21:43:55,275 - mmseg - INFO - Iter [12300/80000]	lr: 7.500e-05, eta: 5:51:10, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.7492, loss: 0.0818
+2023-03-03 21:44:09,977 - mmseg - INFO - Iter [12350/80000]	lr: 7.500e-05, eta: 5:50:50, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.8225, loss: 0.0805
+2023-03-03 21:44:24,627 - mmseg - INFO - Iter [12400/80000]	lr: 7.500e-05, eta: 5:50:29, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0830, decode.acc_seg: 96.8104, loss: 0.0830
+2023-03-03 21:44:39,274 - mmseg - INFO - Iter [12450/80000]	lr: 7.500e-05, eta: 5:50:09, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0855, decode.acc_seg: 96.6558, loss: 0.0855
+2023-03-03 21:44:56,314 - mmseg - INFO - Iter [12500/80000]	lr: 7.500e-05, eta: 5:50:01, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0835, decode.acc_seg: 96.7446, loss: 0.0835
+2023-03-03 21:45:11,014 - mmseg - INFO - Iter [12550/80000]	lr: 7.500e-05, eta: 5:49:41, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0838, decode.acc_seg: 96.6836, loss: 0.0838
+2023-03-03 21:45:25,592 - mmseg - INFO - Iter [12600/80000]	lr: 7.500e-05, eta: 5:49:21, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0828, decode.acc_seg: 96.7017, loss: 0.0828
+2023-03-03 21:45:42,690 - mmseg - INFO - Iter [12650/80000]	lr: 7.500e-05, eta: 5:49:13, time: 0.342, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7483, loss: 0.0815
+2023-03-03 21:45:57,437 - mmseg - INFO - Iter [12700/80000]	lr: 7.500e-05, eta: 5:48:53, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0843, decode.acc_seg: 96.6553, loss: 0.0843
+2023-03-03 21:46:12,217 - mmseg - INFO - Iter [12750/80000]	lr: 7.500e-05, eta: 5:48:34, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0840, decode.acc_seg: 96.6708, loss: 0.0840
+2023-03-03 21:46:26,906 - mmseg - INFO - Iter [12800/80000]	lr: 7.500e-05, eta: 5:48:14, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0876, decode.acc_seg: 96.5805, loss: 0.0876
+2023-03-03 21:46:43,856 - mmseg - INFO - Iter [12850/80000]	lr: 7.500e-05, eta: 5:48:05, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0844, decode.acc_seg: 96.6791, loss: 0.0844
+2023-03-03 21:46:58,539 - mmseg - INFO - Iter [12900/80000]	lr: 7.500e-05, eta: 5:47:45, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0853, decode.acc_seg: 96.6722, loss: 0.0853
+2023-03-03 21:47:13,125 - mmseg - INFO - Iter [12950/80000]	lr: 7.500e-05, eta: 5:47:25, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0852, decode.acc_seg: 96.6237, loss: 0.0852
+2023-03-03 21:47:27,771 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 21:47:27,771 - mmseg - INFO - Iter [13000/80000]	lr: 7.500e-05, eta: 5:47:05, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0825, decode.acc_seg: 96.7100, loss: 0.0825
+2023-03-03 21:47:44,767 - mmseg - INFO - Iter [13050/80000]	lr: 7.500e-05, eta: 5:46:57, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8649, loss: 0.0792
+2023-03-03 21:47:59,345 - mmseg - INFO - Iter [13100/80000]	lr: 7.500e-05, eta: 5:46:36, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0838, decode.acc_seg: 96.7534, loss: 0.0838
+2023-03-03 21:48:14,024 - mmseg - INFO - Iter [13150/80000]	lr: 7.500e-05, eta: 5:46:16, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0830, decode.acc_seg: 96.7018, loss: 0.0830
+2023-03-03 21:48:28,769 - mmseg - INFO - Iter [13200/80000]	lr: 7.500e-05, eta: 5:45:57, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8979, loss: 0.0794
+2023-03-03 21:48:45,953 - mmseg - INFO - Iter [13250/80000]	lr: 7.500e-05, eta: 5:45:49, time: 0.344, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.8160, loss: 0.0801
+2023-03-03 21:49:00,625 - mmseg - INFO - Iter [13300/80000]	lr: 7.500e-05, eta: 5:45:29, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0825, decode.acc_seg: 96.7104, loss: 0.0825
+2023-03-03 21:49:15,191 - mmseg - INFO - Iter [13350/80000]	lr: 7.500e-05, eta: 5:45:09, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7090, loss: 0.0827
+2023-03-03 21:49:32,167 - mmseg - INFO - Iter [13400/80000]	lr: 7.500e-05, eta: 5:45:01, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0824, decode.acc_seg: 96.7160, loss: 0.0824
+2023-03-03 21:49:46,781 - mmseg - INFO - Iter [13450/80000]	lr: 7.500e-05, eta: 5:44:41, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0831, decode.acc_seg: 96.7368, loss: 0.0831
+2023-03-03 21:50:01,482 - mmseg - INFO - Iter [13500/80000]	lr: 7.500e-05, eta: 5:44:21, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0846, decode.acc_seg: 96.6529, loss: 0.0846
+2023-03-03 21:50:16,055 - mmseg - INFO - Iter [13550/80000]	lr: 7.500e-05, eta: 5:44:01, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8403, loss: 0.0794
+2023-03-03 21:50:33,043 - mmseg - INFO - Iter [13600/80000]	lr: 7.500e-05, eta: 5:43:52, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0861, decode.acc_seg: 96.6137, loss: 0.0861
+2023-03-03 21:50:47,738 - mmseg - INFO - Iter [13650/80000]	lr: 7.500e-05, eta: 5:43:33, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0848, decode.acc_seg: 96.6324, loss: 0.0848
+2023-03-03 21:51:02,360 - mmseg - INFO - Iter [13700/80000]	lr: 7.500e-05, eta: 5:43:13, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0845, decode.acc_seg: 96.6538, loss: 0.0845
+2023-03-03 21:51:17,221 - mmseg - INFO - Iter [13750/80000]	lr: 7.500e-05, eta: 5:42:54, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0826, decode.acc_seg: 96.7335, loss: 0.0826
+2023-03-03 21:51:34,460 - mmseg - INFO - Iter [13800/80000]	lr: 7.500e-05, eta: 5:42:47, time: 0.345, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0816, decode.acc_seg: 96.7706, loss: 0.0816
+2023-03-03 21:51:49,283 - mmseg - INFO - Iter [13850/80000]	lr: 7.500e-05, eta: 5:42:28, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0824, decode.acc_seg: 96.6970, loss: 0.0824
+2023-03-03 21:52:04,001 - mmseg - INFO - Iter [13900/80000]	lr: 7.500e-05, eta: 5:42:08, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0838, decode.acc_seg: 96.6939, loss: 0.0838
+2023-03-03 21:52:18,702 - mmseg - INFO - Iter [13950/80000]	lr: 7.500e-05, eta: 5:41:49, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0820, decode.acc_seg: 96.7569, loss: 0.0820
+2023-03-03 21:52:35,743 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 21:52:35,743 - mmseg - INFO - Iter [14000/80000]	lr: 7.500e-05, eta: 5:41:40, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.8120, loss: 0.0821
+2023-03-03 21:52:50,336 - mmseg - INFO - Iter [14050/80000]	lr: 7.500e-05, eta: 5:41:20, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0839, decode.acc_seg: 96.6773, loss: 0.0839
+2023-03-03 21:53:04,945 - mmseg - INFO - Iter [14100/80000]	lr: 7.500e-05, eta: 5:41:01, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0844, decode.acc_seg: 96.6839, loss: 0.0844
+2023-03-03 21:53:21,872 - mmseg - INFO - Iter [14150/80000]	lr: 7.500e-05, eta: 5:40:52, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0840, decode.acc_seg: 96.6647, loss: 0.0840
+2023-03-03 21:53:36,545 - mmseg - INFO - Iter [14200/80000]	lr: 7.500e-05, eta: 5:40:32, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0826, decode.acc_seg: 96.7774, loss: 0.0826
+2023-03-03 21:53:51,099 - mmseg - INFO - Iter [14250/80000]	lr: 7.500e-05, eta: 5:40:12, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0820, decode.acc_seg: 96.7072, loss: 0.0820
+2023-03-03 21:54:05,683 - mmseg - INFO - Iter [14300/80000]	lr: 7.500e-05, eta: 5:39:52, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0847, decode.acc_seg: 96.6202, loss: 0.0847
+2023-03-03 21:54:22,785 - mmseg - INFO - Iter [14350/80000]	lr: 7.500e-05, eta: 5:39:44, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0838, decode.acc_seg: 96.6629, loss: 0.0838
+2023-03-03 21:54:37,413 - mmseg - INFO - Iter [14400/80000]	lr: 7.500e-05, eta: 5:39:24, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0849, decode.acc_seg: 96.6711, loss: 0.0849
+2023-03-03 21:54:52,063 - mmseg - INFO - Iter [14450/80000]	lr: 7.500e-05, eta: 5:39:05, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.7560, loss: 0.0821
+2023-03-03 21:55:06,632 - mmseg - INFO - Iter [14500/80000]	lr: 7.500e-05, eta: 5:38:45, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7226, loss: 0.0827
+2023-03-03 21:55:23,550 - mmseg - INFO - Iter [14550/80000]	lr: 7.500e-05, eta: 5:38:36, time: 0.338, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8719, loss: 0.0790
+2023-03-03 21:55:38,336 - mmseg - INFO - Iter [14600/80000]	lr: 7.500e-05, eta: 5:38:17, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0823, decode.acc_seg: 96.7336, loss: 0.0823
+2023-03-03 21:55:52,913 - mmseg - INFO - Iter [14650/80000]	lr: 7.500e-05, eta: 5:37:57, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.7766, loss: 0.0810
+2023-03-03 21:56:10,030 - mmseg - INFO - Iter [14700/80000]	lr: 7.500e-05, eta: 5:37:49, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0835, decode.acc_seg: 96.6857, loss: 0.0835
+2023-03-03 21:56:24,607 - mmseg - INFO - Iter [14750/80000]	lr: 7.500e-05, eta: 5:37:29, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0820, decode.acc_seg: 96.7583, loss: 0.0820
+2023-03-03 21:56:39,470 - mmseg - INFO - Iter [14800/80000]	lr: 7.500e-05, eta: 5:37:11, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0869, decode.acc_seg: 96.5510, loss: 0.0869
+2023-03-03 21:56:54,063 - mmseg - INFO - Iter [14850/80000]	lr: 7.500e-05, eta: 5:36:51, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0831, decode.acc_seg: 96.7199, loss: 0.0831
+2023-03-03 21:57:10,989 - mmseg - INFO - Iter [14900/80000]	lr: 7.500e-05, eta: 5:36:42, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0829, decode.acc_seg: 96.6919, loss: 0.0829
+2023-03-03 21:57:25,582 - mmseg - INFO - Iter [14950/80000]	lr: 7.500e-05, eta: 5:36:22, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0844, decode.acc_seg: 96.6653, loss: 0.0844
+2023-03-03 21:57:40,193 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 21:57:40,193 - mmseg - INFO - Iter [15000/80000]	lr: 7.500e-05, eta: 5:36:03, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0877, decode.acc_seg: 96.5412, loss: 0.0877
+2023-03-03 21:57:54,959 - mmseg - INFO - Iter [15050/80000]	lr: 7.500e-05, eta: 5:35:44, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.8276, loss: 0.0797
+2023-03-03 21:58:11,924 - mmseg - INFO - Iter [15100/80000]	lr: 7.500e-05, eta: 5:35:35, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.8045, loss: 0.0805
+2023-03-03 21:58:26,702 - mmseg - INFO - Iter [15150/80000]	lr: 7.500e-05, eta: 5:35:16, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8592, loss: 0.0793
+2023-03-03 21:58:41,333 - mmseg - INFO - Iter [15200/80000]	lr: 7.500e-05, eta: 5:34:57, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0830, decode.acc_seg: 96.6941, loss: 0.0830
+2023-03-03 21:58:56,000 - mmseg - INFO - Iter [15250/80000]	lr: 7.500e-05, eta: 5:34:38, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0824, decode.acc_seg: 96.7155, loss: 0.0824
+2023-03-03 21:59:13,026 - mmseg - INFO - Iter [15300/80000]	lr: 7.500e-05, eta: 5:34:29, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0835, decode.acc_seg: 96.7009, loss: 0.0835
+2023-03-03 21:59:27,958 - mmseg - INFO - Iter [15350/80000]	lr: 7.500e-05, eta: 5:34:11, time: 0.299, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0832, decode.acc_seg: 96.7324, loss: 0.0832
+2023-03-03 21:59:42,600 - mmseg - INFO - Iter [15400/80000]	lr: 7.500e-05, eta: 5:33:52, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0825, decode.acc_seg: 96.7075, loss: 0.0825
+2023-03-03 21:59:59,667 - mmseg - INFO - Iter [15450/80000]	lr: 7.500e-05, eta: 5:33:43, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0841, decode.acc_seg: 96.7059, loss: 0.0841
+2023-03-03 22:00:14,366 - mmseg - INFO - Iter [15500/80000]	lr: 7.500e-05, eta: 5:33:24, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0840, decode.acc_seg: 96.6267, loss: 0.0840
+2023-03-03 22:00:29,103 - mmseg - INFO - Iter [15550/80000]	lr: 7.500e-05, eta: 5:33:05, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0829, decode.acc_seg: 96.6749, loss: 0.0829
+2023-03-03 22:00:43,723 - mmseg - INFO - Iter [15600/80000]	lr: 7.500e-05, eta: 5:32:46, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7333, loss: 0.0811
+2023-03-03 22:01:00,687 - mmseg - INFO - Iter [15650/80000]	lr: 7.500e-05, eta: 5:32:37, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7316, loss: 0.0827
+2023-03-03 22:01:15,455 - mmseg - INFO - Iter [15700/80000]	lr: 7.500e-05, eta: 5:32:18, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0804, decode.acc_seg: 96.8026, loss: 0.0804
+2023-03-03 22:01:30,112 - mmseg - INFO - Iter [15750/80000]	lr: 7.500e-05, eta: 5:31:59, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0831, decode.acc_seg: 96.7620, loss: 0.0831
+2023-03-03 22:01:44,695 - mmseg - INFO - Iter [15800/80000]	lr: 7.500e-05, eta: 5:31:40, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8164, loss: 0.0796
+2023-03-03 22:02:01,703 - mmseg - INFO - Iter [15850/80000]	lr: 7.500e-05, eta: 5:31:31, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0828, decode.acc_seg: 96.6762, loss: 0.0828
+2023-03-03 22:02:16,700 - mmseg - INFO - Iter [15900/80000]	lr: 7.500e-05, eta: 5:31:13, time: 0.300, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0823, decode.acc_seg: 96.7139, loss: 0.0823
+2023-03-03 22:02:31,440 - mmseg - INFO - Iter [15950/80000]	lr: 7.500e-05, eta: 5:30:54, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0857, decode.acc_seg: 96.6378, loss: 0.0857
+2023-03-03 22:02:48,349 - mmseg - INFO - Saving checkpoint at 16000 iterations
+2023-03-03 22:02:50,252 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 22:02:50,252 - mmseg - INFO - Iter [16000/80000]	lr: 7.500e-05, eta: 5:30:52, time: 0.376, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0842, decode.acc_seg: 96.6499, loss: 0.0842
+2023-03-03 22:03:16,061 - mmseg - INFO - per class results:
+2023-03-03 22:03:16,062 - mmseg - INFO - 
++---------------+-------+-------+
+|     Class     |  IoU  |  Acc  |
++---------------+-------+-------+
+|   background  |  nan  |  nan  |
+|      road     | 98.51 |  99.2 |
+|    sidewalk   | 87.16 | 93.61 |
+|    building   | 93.39 | 96.36 |
+|      wall     | 52.02 | 56.46 |
+|     fence     | 64.52 |  74.1 |
+|      pole     |  70.1 | 84.54 |
+| traffic light | 74.98 | 85.66 |
+|  traffic sign | 82.14 | 90.01 |
+|   vegetation  | 92.85 | 97.27 |
+|    terrain    | 64.04 |  72.7 |
+|      sky      | 95.23 | 98.45 |
+|     person    | 84.82 | 92.77 |
+|     rider     | 67.24 | 83.22 |
+|      car      | 95.99 | 97.96 |
+|     truck     | 83.84 |  91.0 |
+|      bus      | 92.19 | 95.27 |
+|     train     | 85.84 | 89.68 |
+|   motorcycle  | 71.56 | 83.57 |
+|    bicycle    | 79.76 | 90.54 |
++---------------+-------+-------+
+2023-03-03 22:03:16,062 - mmseg - INFO - Summary:
+2023-03-03 22:03:16,062 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 96.55 | 80.85 | 88.02 |
++-------+-------+-------+
+2023-03-03 22:03:16,062 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 22:03:16,062 - mmseg - INFO - Iter(val) [63]	aAcc: 0.9655, mIoU: 0.8085, mAcc: 0.8802, IoU.background: nan, IoU.road: 0.9851, IoU.sidewalk: 0.8716, IoU.building: 0.9339, IoU.wall: 0.5202, IoU.fence: 0.6452, IoU.pole: 0.7010, IoU.traffic light: 0.7498, IoU.traffic sign: 0.8214, IoU.vegetation: 0.9285, IoU.terrain: 0.6404, IoU.sky: 0.9523, IoU.person: 0.8482, IoU.rider: 0.6724, IoU.car: 0.9599, IoU.truck: 0.8384, IoU.bus: 0.9219, IoU.train: 0.8584, IoU.motorcycle: 0.7156, IoU.bicycle: 0.7976, Acc.background: nan, Acc.road: 0.9920, Acc.sidewalk: 0.9361, Acc.building: 0.9636, Acc.wall: 0.5646, Acc.fence: 0.7410, Acc.pole: 0.8454, Acc.traffic light: 0.8566, Acc.traffic sign: 0.9001, Acc.vegetation: 0.9727, Acc.terrain: 0.7270, Acc.sky: 0.9845, Acc.person: 0.9277, Acc.rider: 0.8322, Acc.car: 0.9796, Acc.truck: 0.9100, Acc.bus: 0.9527, Acc.train: 0.8968, Acc.motorcycle: 0.8357, Acc.bicycle: 0.9054
+2023-03-03 22:03:31,068 - mmseg - INFO - Iter [16050/80000]	lr: 7.500e-05, eta: 5:32:17, time: 0.816, data_time: 0.524, memory: 67605, decode.loss_ce: 0.0847, decode.acc_seg: 96.6392, loss: 0.0847
+2023-03-03 22:03:45,821 - mmseg - INFO - Iter [16100/80000]	lr: 7.500e-05, eta: 5:31:59, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.7853, loss: 0.0807
+2023-03-03 22:04:00,494 - mmseg - INFO - Iter [16150/80000]	lr: 7.500e-05, eta: 5:31:39, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0820, decode.acc_seg: 96.7373, loss: 0.0820
+2023-03-03 22:04:17,569 - mmseg - INFO - Iter [16200/80000]	lr: 7.500e-05, eta: 5:31:30, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0850, decode.acc_seg: 96.6483, loss: 0.0850
+2023-03-03 22:04:32,186 - mmseg - INFO - Iter [16250/80000]	lr: 7.500e-05, eta: 5:31:10, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7310, loss: 0.0815
+2023-03-03 22:04:46,921 - mmseg - INFO - Iter [16300/80000]	lr: 7.500e-05, eta: 5:30:51, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.7730, loss: 0.0807
+2023-03-03 22:05:01,538 - mmseg - INFO - Iter [16350/80000]	lr: 7.500e-05, eta: 5:30:32, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0830, decode.acc_seg: 96.7483, loss: 0.0830
+2023-03-03 22:05:18,456 - mmseg - INFO - Iter [16400/80000]	lr: 7.500e-05, eta: 5:30:22, time: 0.338, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0839, decode.acc_seg: 96.6484, loss: 0.0839
+2023-03-03 22:05:33,007 - mmseg - INFO - Iter [16450/80000]	lr: 7.500e-05, eta: 5:30:02, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7828, loss: 0.0815
+2023-03-03 22:05:47,725 - mmseg - INFO - Iter [16500/80000]	lr: 7.500e-05, eta: 5:29:43, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0822, decode.acc_seg: 96.7418, loss: 0.0822
+2023-03-03 22:06:02,312 - mmseg - INFO - Iter [16550/80000]	lr: 7.500e-05, eta: 5:29:24, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7109, loss: 0.0827
+2023-03-03 22:06:19,298 - mmseg - INFO - Iter [16600/80000]	lr: 7.500e-05, eta: 5:29:14, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0767, decode.acc_seg: 96.9474, loss: 0.0767
+2023-03-03 22:06:33,916 - mmseg - INFO - Iter [16650/80000]	lr: 7.500e-05, eta: 5:28:54, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0847, decode.acc_seg: 96.6553, loss: 0.0847
+2023-03-03 22:06:48,665 - mmseg - INFO - Iter [16700/80000]	lr: 7.500e-05, eta: 5:28:36, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0836, decode.acc_seg: 96.6856, loss: 0.0836
+2023-03-03 22:07:05,688 - mmseg - INFO - Iter [16750/80000]	lr: 7.500e-05, eta: 5:28:26, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0838, decode.acc_seg: 96.6761, loss: 0.0838
+2023-03-03 22:07:20,365 - mmseg - INFO - Iter [16800/80000]	lr: 7.500e-05, eta: 5:28:07, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7785, loss: 0.0811
+2023-03-03 22:07:34,951 - mmseg - INFO - Iter [16850/80000]	lr: 7.500e-05, eta: 5:27:47, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7173, loss: 0.0827
+2023-03-03 22:07:49,545 - mmseg - INFO - Iter [16900/80000]	lr: 7.500e-05, eta: 5:27:28, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7792, loss: 0.0811
+2023-03-03 22:08:06,558 - mmseg - INFO - Iter [16950/80000]	lr: 7.500e-05, eta: 5:27:18, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0830, decode.acc_seg: 96.7266, loss: 0.0830
+2023-03-03 22:08:21,232 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 22:08:21,233 - mmseg - INFO - Iter [17000/80000]	lr: 7.500e-05, eta: 5:26:59, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0823, decode.acc_seg: 96.7098, loss: 0.0823
+2023-03-03 22:08:35,861 - mmseg - INFO - Iter [17050/80000]	lr: 7.500e-05, eta: 5:26:40, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7617, loss: 0.0815
+2023-03-03 22:08:50,485 - mmseg - INFO - Iter [17100/80000]	lr: 7.500e-05, eta: 5:26:21, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.7992, loss: 0.0812
+2023-03-03 22:09:07,600 - mmseg - INFO - Iter [17150/80000]	lr: 7.500e-05, eta: 5:26:11, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8098, loss: 0.0799
+2023-03-03 22:09:22,360 - mmseg - INFO - Iter [17200/80000]	lr: 7.500e-05, eta: 5:25:52, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.7852, loss: 0.0808
+2023-03-03 22:09:37,140 - mmseg - INFO - Iter [17250/80000]	lr: 7.500e-05, eta: 5:25:34, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.7842, loss: 0.0806
+2023-03-03 22:09:54,216 - mmseg - INFO - Iter [17300/80000]	lr: 7.500e-05, eta: 5:25:24, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0823, decode.acc_seg: 96.7547, loss: 0.0823
+2023-03-03 22:10:08,874 - mmseg - INFO - Iter [17350/80000]	lr: 7.500e-05, eta: 5:25:05, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0829, decode.acc_seg: 96.7060, loss: 0.0829
+2023-03-03 22:10:23,534 - mmseg - INFO - Iter [17400/80000]	lr: 7.500e-05, eta: 5:24:46, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7823, loss: 0.0811
+2023-03-03 22:10:38,241 - mmseg - INFO - Iter [17450/80000]	lr: 7.500e-05, eta: 5:24:28, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.7879, loss: 0.0818
+2023-03-03 22:10:55,189 - mmseg - INFO - Iter [17500/80000]	lr: 7.500e-05, eta: 5:24:17, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0817, decode.acc_seg: 96.7625, loss: 0.0817
+2023-03-03 22:11:09,791 - mmseg - INFO - Iter [17550/80000]	lr: 7.500e-05, eta: 5:23:58, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0813, decode.acc_seg: 96.7281, loss: 0.0813
+2023-03-03 22:11:24,458 - mmseg - INFO - Iter [17600/80000]	lr: 7.500e-05, eta: 5:23:39, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0817, decode.acc_seg: 96.7359, loss: 0.0817
+2023-03-03 22:11:39,139 - mmseg - INFO - Iter [17650/80000]	lr: 7.500e-05, eta: 5:23:21, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.7601, loss: 0.0814
+2023-03-03 22:11:56,118 - mmseg - INFO - Iter [17700/80000]	lr: 7.500e-05, eta: 5:23:10, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8251, loss: 0.0798
+2023-03-03 22:12:10,821 - mmseg - INFO - Iter [17750/80000]	lr: 7.500e-05, eta: 5:22:51, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0867, decode.acc_seg: 96.6212, loss: 0.0867
+2023-03-03 22:12:25,471 - mmseg - INFO - Iter [17800/80000]	lr: 7.500e-05, eta: 5:22:33, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.7500, loss: 0.0821
+2023-03-03 22:12:40,040 - mmseg - INFO - Iter [17850/80000]	lr: 7.500e-05, eta: 5:22:14, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0839, decode.acc_seg: 96.6451, loss: 0.0839
+2023-03-03 22:12:57,202 - mmseg - INFO - Iter [17900/80000]	lr: 7.500e-05, eta: 5:22:04, time: 0.343, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.6995, loss: 0.0821
+2023-03-03 22:13:11,779 - mmseg - INFO - Iter [17950/80000]	lr: 7.500e-05, eta: 5:21:45, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0832, decode.acc_seg: 96.6991, loss: 0.0832
+2023-03-03 22:13:26,373 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 22:13:26,373 - mmseg - INFO - Iter [18000/80000]	lr: 7.500e-05, eta: 5:21:26, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0823, decode.acc_seg: 96.7677, loss: 0.0823
+2023-03-03 22:13:43,584 - mmseg - INFO - Iter [18050/80000]	lr: 7.500e-05, eta: 5:21:16, time: 0.344, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7755, loss: 0.0811
+2023-03-03 22:13:58,215 - mmseg - INFO - Iter [18100/80000]	lr: 7.500e-05, eta: 5:20:57, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8661, loss: 0.0794
+2023-03-03 22:14:12,822 - mmseg - INFO - Iter [18150/80000]	lr: 7.500e-05, eta: 5:20:39, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0826, decode.acc_seg: 96.6840, loss: 0.0826
+2023-03-03 22:14:27,474 - mmseg - INFO - Iter [18200/80000]	lr: 7.500e-05, eta: 5:20:20, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.7937, loss: 0.0812
+2023-03-03 22:14:44,541 - mmseg - INFO - Iter [18250/80000]	lr: 7.500e-05, eta: 5:20:09, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0809, decode.acc_seg: 96.8063, loss: 0.0809
+2023-03-03 22:14:59,330 - mmseg - INFO - Iter [18300/80000]	lr: 7.500e-05, eta: 5:19:51, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0826, decode.acc_seg: 96.7003, loss: 0.0826
+2023-03-03 22:15:14,025 - mmseg - INFO - Iter [18350/80000]	lr: 7.500e-05, eta: 5:19:33, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0816, decode.acc_seg: 96.7630, loss: 0.0816
+2023-03-03 22:15:28,682 - mmseg - INFO - Iter [18400/80000]	lr: 7.500e-05, eta: 5:19:14, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0889, decode.acc_seg: 96.5983, loss: 0.0889
+2023-03-03 22:15:45,824 - mmseg - INFO - Iter [18450/80000]	lr: 7.500e-05, eta: 5:19:04, time: 0.343, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0804, decode.acc_seg: 96.7924, loss: 0.0804
+2023-03-03 22:16:00,407 - mmseg - INFO - Iter [18500/80000]	lr: 7.500e-05, eta: 5:18:45, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0835, decode.acc_seg: 96.6885, loss: 0.0835
+2023-03-03 22:16:15,021 - mmseg - INFO - Iter [18550/80000]	lr: 7.500e-05, eta: 5:18:27, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0831, decode.acc_seg: 96.7514, loss: 0.0831
+2023-03-03 22:16:29,584 - mmseg - INFO - Iter [18600/80000]	lr: 7.500e-05, eta: 5:18:08, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0849, decode.acc_seg: 96.6884, loss: 0.0849
+2023-03-03 22:16:46,718 - mmseg - INFO - Iter [18650/80000]	lr: 7.500e-05, eta: 5:17:58, time: 0.343, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0847, decode.acc_seg: 96.6868, loss: 0.0847
+2023-03-03 22:17:01,312 - mmseg - INFO - Iter [18700/80000]	lr: 7.500e-05, eta: 5:17:39, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0840, decode.acc_seg: 96.6358, loss: 0.0840
+2023-03-03 22:17:15,926 - mmseg - INFO - Iter [18750/80000]	lr: 7.500e-05, eta: 5:17:20, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0837, decode.acc_seg: 96.6760, loss: 0.0837
+2023-03-03 22:17:33,025 - mmseg - INFO - Iter [18800/80000]	lr: 7.500e-05, eta: 5:17:10, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0837, decode.acc_seg: 96.6957, loss: 0.0837
+2023-03-03 22:17:47,737 - mmseg - INFO - Iter [18850/80000]	lr: 7.500e-05, eta: 5:16:52, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0809, decode.acc_seg: 96.7530, loss: 0.0809
+2023-03-03 22:18:02,546 - mmseg - INFO - Iter [18900/80000]	lr: 7.500e-05, eta: 5:16:34, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0839, decode.acc_seg: 96.6840, loss: 0.0839
+2023-03-03 22:18:17,119 - mmseg - INFO - Iter [18950/80000]	lr: 7.500e-05, eta: 5:16:15, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8694, loss: 0.0790
+2023-03-03 22:18:34,069 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 22:18:34,069 - mmseg - INFO - Iter [19000/80000]	lr: 7.500e-05, eta: 5:16:04, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0846, decode.acc_seg: 96.6504, loss: 0.0846
+2023-03-03 22:18:48,792 - mmseg - INFO - Iter [19050/80000]	lr: 7.500e-05, eta: 5:15:46, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0828, decode.acc_seg: 96.7048, loss: 0.0828
+2023-03-03 22:19:03,394 - mmseg - INFO - Iter [19100/80000]	lr: 7.500e-05, eta: 5:15:27, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0832, decode.acc_seg: 96.7099, loss: 0.0832
+2023-03-03 22:19:17,960 - mmseg - INFO - Iter [19150/80000]	lr: 7.500e-05, eta: 5:15:09, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8342, loss: 0.0795
+2023-03-03 22:19:35,114 - mmseg - INFO - Iter [19200/80000]	lr: 7.500e-05, eta: 5:14:58, time: 0.343, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.7711, loss: 0.0803
+2023-03-03 22:19:49,706 - mmseg - INFO - Iter [19250/80000]	lr: 7.500e-05, eta: 5:14:40, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0825, decode.acc_seg: 96.6910, loss: 0.0825
+2023-03-03 22:20:04,370 - mmseg - INFO - Iter [19300/80000]	lr: 7.500e-05, eta: 5:14:21, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0828, decode.acc_seg: 96.6994, loss: 0.0828
+2023-03-03 22:20:21,409 - mmseg - INFO - Iter [19350/80000]	lr: 7.500e-05, eta: 5:14:11, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0846, decode.acc_seg: 96.6463, loss: 0.0846
+2023-03-03 22:20:36,057 - mmseg - INFO - Iter [19400/80000]	lr: 7.500e-05, eta: 5:13:52, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0829, decode.acc_seg: 96.7291, loss: 0.0829
+2023-03-03 22:20:50,735 - mmseg - INFO - Iter [19450/80000]	lr: 7.500e-05, eta: 5:13:34, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0813, decode.acc_seg: 96.7258, loss: 0.0813
+2023-03-03 22:21:05,361 - mmseg - INFO - Iter [19500/80000]	lr: 7.500e-05, eta: 5:13:16, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0824, decode.acc_seg: 96.6892, loss: 0.0824
+2023-03-03 22:21:22,432 - mmseg - INFO - Iter [19550/80000]	lr: 7.500e-05, eta: 5:13:05, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.7539, loss: 0.0814
+2023-03-03 22:21:37,121 - mmseg - INFO - Iter [19600/80000]	lr: 7.500e-05, eta: 5:12:47, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0779, decode.acc_seg: 96.8872, loss: 0.0779
+2023-03-03 22:21:51,756 - mmseg - INFO - Iter [19650/80000]	lr: 7.500e-05, eta: 5:12:28, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0845, decode.acc_seg: 96.6748, loss: 0.0845
+2023-03-03 22:22:06,354 - mmseg - INFO - Iter [19700/80000]	lr: 7.500e-05, eta: 5:12:10, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0863, decode.acc_seg: 96.6649, loss: 0.0863
+2023-03-03 22:22:23,365 - mmseg - INFO - Iter [19750/80000]	lr: 7.500e-05, eta: 5:11:59, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0830, decode.acc_seg: 96.6790, loss: 0.0830
+2023-03-03 22:22:38,039 - mmseg - INFO - Iter [19800/80000]	lr: 7.500e-05, eta: 5:11:41, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0822, decode.acc_seg: 96.7342, loss: 0.0822
+2023-03-03 22:22:52,711 - mmseg - INFO - Iter [19850/80000]	lr: 7.500e-05, eta: 5:11:23, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7679, loss: 0.0811
+2023-03-03 22:23:07,348 - mmseg - INFO - Iter [19900/80000]	lr: 7.500e-05, eta: 5:11:04, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.7372, loss: 0.0819
+2023-03-03 22:23:24,326 - mmseg - INFO - Iter [19950/80000]	lr: 7.500e-05, eta: 5:10:53, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.7707, loss: 0.0819
+2023-03-03 22:23:39,054 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 22:23:39,054 - mmseg - INFO - Iter [20000/80000]	lr: 7.500e-05, eta: 5:10:35, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.7569, loss: 0.0807
+2023-03-03 22:23:53,833 - mmseg - INFO - Iter [20050/80000]	lr: 3.750e-05, eta: 5:10:18, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0849, decode.acc_seg: 96.6677, loss: 0.0849
+2023-03-03 22:24:10,779 - mmseg - INFO - Iter [20100/80000]	lr: 3.750e-05, eta: 5:10:06, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0848, decode.acc_seg: 96.6204, loss: 0.0848
+2023-03-03 22:24:25,476 - mmseg - INFO - Iter [20150/80000]	lr: 3.750e-05, eta: 5:09:48, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8055, loss: 0.0798
+2023-03-03 22:24:40,089 - mmseg - INFO - Iter [20200/80000]	lr: 3.750e-05, eta: 5:09:30, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0835, decode.acc_seg: 96.6892, loss: 0.0835
+2023-03-03 22:24:54,690 - mmseg - INFO - Iter [20250/80000]	lr: 3.750e-05, eta: 5:09:12, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.7668, loss: 0.0819
+2023-03-03 22:25:11,627 - mmseg - INFO - Iter [20300/80000]	lr: 3.750e-05, eta: 5:09:00, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8625, loss: 0.0790
+2023-03-03 22:25:26,313 - mmseg - INFO - Iter [20350/80000]	lr: 3.750e-05, eta: 5:08:42, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.7939, loss: 0.0814
+2023-03-03 22:25:40,891 - mmseg - INFO - Iter [20400/80000]	lr: 3.750e-05, eta: 5:08:24, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0824, decode.acc_seg: 96.7431, loss: 0.0824
+2023-03-03 22:25:55,606 - mmseg - INFO - Iter [20450/80000]	lr: 3.750e-05, eta: 5:08:06, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.7472, loss: 0.0821
+2023-03-03 22:26:12,665 - mmseg - INFO - Iter [20500/80000]	lr: 3.750e-05, eta: 5:07:55, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0828, decode.acc_seg: 96.7012, loss: 0.0828
+2023-03-03 22:26:27,351 - mmseg - INFO - Iter [20550/80000]	lr: 3.750e-05, eta: 5:07:37, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0847, decode.acc_seg: 96.6266, loss: 0.0847
+2023-03-03 22:26:41,994 - mmseg - INFO - Iter [20600/80000]	lr: 3.750e-05, eta: 5:07:19, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.7946, loss: 0.0808
+2023-03-03 22:26:58,895 - mmseg - INFO - Iter [20650/80000]	lr: 3.750e-05, eta: 5:07:08, time: 0.338, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0820, decode.acc_seg: 96.7410, loss: 0.0820
+2023-03-03 22:27:13,641 - mmseg - INFO - Iter [20700/80000]	lr: 3.750e-05, eta: 5:06:50, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.8633, loss: 0.0783
+2023-03-03 22:27:28,554 - mmseg - INFO - Iter [20750/80000]	lr: 3.750e-05, eta: 5:06:33, time: 0.298, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0835, decode.acc_seg: 96.7416, loss: 0.0835
+2023-03-03 22:27:43,124 - mmseg - INFO - Iter [20800/80000]	lr: 3.750e-05, eta: 5:06:14, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.7521, loss: 0.0812
+2023-03-03 22:28:00,115 - mmseg - INFO - Iter [20850/80000]	lr: 3.750e-05, eta: 5:06:03, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8225, loss: 0.0799
+2023-03-03 22:28:14,737 - mmseg - INFO - Iter [20900/80000]	lr: 3.750e-05, eta: 5:05:45, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8474, loss: 0.0796
+2023-03-03 22:28:29,434 - mmseg - INFO - Iter [20950/80000]	lr: 3.750e-05, eta: 5:05:27, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0849, decode.acc_seg: 96.6304, loss: 0.0849
+2023-03-03 22:28:44,031 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 22:28:44,031 - mmseg - INFO - Iter [21000/80000]	lr: 3.750e-05, eta: 5:05:09, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.7770, loss: 0.0808
+2023-03-03 22:29:01,037 - mmseg - INFO - Iter [21050/80000]	lr: 3.750e-05, eta: 5:04:58, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8438, loss: 0.0799
+2023-03-03 22:29:15,647 - mmseg - INFO - Iter [21100/80000]	lr: 3.750e-05, eta: 5:04:40, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0841, decode.acc_seg: 96.7096, loss: 0.0841
+2023-03-03 22:29:30,232 - mmseg - INFO - Iter [21150/80000]	lr: 3.750e-05, eta: 5:04:21, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.7800, loss: 0.0812
+2023-03-03 22:29:44,960 - mmseg - INFO - Iter [21200/80000]	lr: 3.750e-05, eta: 5:04:04, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7749, loss: 0.0811
+2023-03-03 22:30:01,910 - mmseg - INFO - Iter [21250/80000]	lr: 3.750e-05, eta: 5:03:52, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0862, decode.acc_seg: 96.5992, loss: 0.0862
+2023-03-03 22:30:16,552 - mmseg - INFO - Iter [21300/80000]	lr: 3.750e-05, eta: 5:03:34, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0787, decode.acc_seg: 96.8389, loss: 0.0787
+2023-03-03 22:30:31,201 - mmseg - INFO - Iter [21350/80000]	lr: 3.750e-05, eta: 5:03:16, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8475, loss: 0.0785
+2023-03-03 22:30:48,221 - mmseg - INFO - Iter [21400/80000]	lr: 3.750e-05, eta: 5:03:05, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.7842, loss: 0.0812
+2023-03-03 22:31:02,796 - mmseg - INFO - Iter [21450/80000]	lr: 3.750e-05, eta: 5:02:47, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0813, decode.acc_seg: 96.8070, loss: 0.0813
+2023-03-03 22:31:17,364 - mmseg - INFO - Iter [21500/80000]	lr: 3.750e-05, eta: 5:02:29, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.7787, loss: 0.0814
+2023-03-03 22:31:31,951 - mmseg - INFO - Iter [21550/80000]	lr: 3.750e-05, eta: 5:02:11, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8583, loss: 0.0793
+2023-03-03 22:31:49,033 - mmseg - INFO - Iter [21600/80000]	lr: 3.750e-05, eta: 5:01:59, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0804, decode.acc_seg: 96.8175, loss: 0.0804
+2023-03-03 22:32:03,643 - mmseg - INFO - Iter [21650/80000]	lr: 3.750e-05, eta: 5:01:42, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8955, loss: 0.0785
+2023-03-03 22:32:18,244 - mmseg - INFO - Iter [21700/80000]	lr: 3.750e-05, eta: 5:01:24, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.7850, loss: 0.0806
+2023-03-03 22:32:32,988 - mmseg - INFO - Iter [21750/80000]	lr: 3.750e-05, eta: 5:01:06, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0804, decode.acc_seg: 96.8577, loss: 0.0804
+2023-03-03 22:32:50,031 - mmseg - INFO - Iter [21800/80000]	lr: 3.750e-05, eta: 5:00:55, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0866, decode.acc_seg: 96.6404, loss: 0.0866
+2023-03-03 22:33:04,629 - mmseg - INFO - Iter [21850/80000]	lr: 3.750e-05, eta: 5:00:37, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0822, decode.acc_seg: 96.7519, loss: 0.0822
+2023-03-03 22:33:19,316 - mmseg - INFO - Iter [21900/80000]	lr: 3.750e-05, eta: 5:00:19, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0853, decode.acc_seg: 96.6308, loss: 0.0853
+2023-03-03 22:33:36,292 - mmseg - INFO - Iter [21950/80000]	lr: 3.750e-05, eta: 5:00:07, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7492, loss: 0.0815
+2023-03-03 22:33:51,012 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 22:33:51,012 - mmseg - INFO - Iter [22000/80000]	lr: 3.750e-05, eta: 4:59:50, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0822, decode.acc_seg: 96.7051, loss: 0.0822
+2023-03-03 22:34:05,700 - mmseg - INFO - Iter [22050/80000]	lr: 3.750e-05, eta: 4:59:32, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0813, decode.acc_seg: 96.7437, loss: 0.0813
+2023-03-03 22:34:20,267 - mmseg - INFO - Iter [22100/80000]	lr: 3.750e-05, eta: 4:59:14, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0816, decode.acc_seg: 96.7475, loss: 0.0816
+2023-03-03 22:34:37,333 - mmseg - INFO - Iter [22150/80000]	lr: 3.750e-05, eta: 4:59:03, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0809, decode.acc_seg: 96.7460, loss: 0.0809
+2023-03-03 22:34:51,964 - mmseg - INFO - Iter [22200/80000]	lr: 3.750e-05, eta: 4:58:45, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0804, decode.acc_seg: 96.7958, loss: 0.0804
+2023-03-03 22:35:06,568 - mmseg - INFO - Iter [22250/80000]	lr: 3.750e-05, eta: 4:58:27, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0826, decode.acc_seg: 96.7135, loss: 0.0826
+2023-03-03 22:35:21,121 - mmseg - INFO - Iter [22300/80000]	lr: 3.750e-05, eta: 4:58:09, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.8006, loss: 0.0805
+2023-03-03 22:35:38,161 - mmseg - INFO - Iter [22350/80000]	lr: 3.750e-05, eta: 4:57:58, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8175, loss: 0.0802
+2023-03-03 22:35:52,801 - mmseg - INFO - Iter [22400/80000]	lr: 3.750e-05, eta: 4:57:40, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8227, loss: 0.0795
+2023-03-03 22:36:07,369 - mmseg - INFO - Iter [22450/80000]	lr: 3.750e-05, eta: 4:57:22, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0836, decode.acc_seg: 96.6754, loss: 0.0836
+2023-03-03 22:36:21,970 - mmseg - INFO - Iter [22500/80000]	lr: 3.750e-05, eta: 4:57:04, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0816, decode.acc_seg: 96.7636, loss: 0.0816
+2023-03-03 22:36:39,015 - mmseg - INFO - Iter [22550/80000]	lr: 3.750e-05, eta: 4:56:53, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0833, decode.acc_seg: 96.6962, loss: 0.0833
+2023-03-03 22:36:53,647 - mmseg - INFO - Iter [22600/80000]	lr: 3.750e-05, eta: 4:56:35, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.8381, loss: 0.0801
+2023-03-03 22:37:08,311 - mmseg - INFO - Iter [22650/80000]	lr: 3.750e-05, eta: 4:56:17, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0836, decode.acc_seg: 96.6772, loss: 0.0836
+2023-03-03 22:37:25,302 - mmseg - INFO - Iter [22700/80000]	lr: 3.750e-05, eta: 4:56:05, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8313, loss: 0.0795
+2023-03-03 22:37:39,978 - mmseg - INFO - Iter [22750/80000]	lr: 3.750e-05, eta: 4:55:48, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0831, decode.acc_seg: 96.6963, loss: 0.0831
+2023-03-03 22:37:54,613 - mmseg - INFO - Iter [22800/80000]	lr: 3.750e-05, eta: 4:55:30, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.7850, loss: 0.0814
+2023-03-03 22:38:09,268 - mmseg - INFO - Iter [22850/80000]	lr: 3.750e-05, eta: 4:55:13, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8538, loss: 0.0784
+2023-03-03 22:38:26,228 - mmseg - INFO - Iter [22900/80000]	lr: 3.750e-05, eta: 4:55:01, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8510, loss: 0.0793
+2023-03-03 22:38:41,048 - mmseg - INFO - Iter [22950/80000]	lr: 3.750e-05, eta: 4:54:44, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0825, decode.acc_seg: 96.7345, loss: 0.0825
+2023-03-03 22:38:55,618 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 22:38:55,618 - mmseg - INFO - Iter [23000/80000]	lr: 3.750e-05, eta: 4:54:26, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7722, loss: 0.0811
+2023-03-03 22:39:10,238 - mmseg - INFO - Iter [23050/80000]	lr: 3.750e-05, eta: 4:54:08, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0837, decode.acc_seg: 96.6970, loss: 0.0837
+2023-03-03 22:39:27,202 - mmseg - INFO - Iter [23100/80000]	lr: 3.750e-05, eta: 4:53:56, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0820, decode.acc_seg: 96.7832, loss: 0.0820
+2023-03-03 22:39:41,781 - mmseg - INFO - Iter [23150/80000]	lr: 3.750e-05, eta: 4:53:38, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.7520, loss: 0.0806
+2023-03-03 22:39:56,362 - mmseg - INFO - Iter [23200/80000]	lr: 3.750e-05, eta: 4:53:21, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8728, loss: 0.0784
+2023-03-03 22:40:10,982 - mmseg - INFO - Iter [23250/80000]	lr: 3.750e-05, eta: 4:53:03, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7266, loss: 0.0827
+2023-03-03 22:40:28,063 - mmseg - INFO - Iter [23300/80000]	lr: 3.750e-05, eta: 4:52:51, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8221, loss: 0.0793
+2023-03-03 22:40:42,924 - mmseg - INFO - Iter [23350/80000]	lr: 3.750e-05, eta: 4:52:34, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0830, decode.acc_seg: 96.6969, loss: 0.0830
+2023-03-03 22:40:57,738 - mmseg - INFO - Iter [23400/80000]	lr: 3.750e-05, eta: 4:52:17, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0844, decode.acc_seg: 96.6694, loss: 0.0844
+2023-03-03 22:41:14,717 - mmseg - INFO - Iter [23450/80000]	lr: 3.750e-05, eta: 4:52:05, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0837, decode.acc_seg: 96.7328, loss: 0.0837
+2023-03-03 22:41:29,443 - mmseg - INFO - Iter [23500/80000]	lr: 3.750e-05, eta: 4:51:48, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.8265, loss: 0.0808
+2023-03-03 22:41:44,048 - mmseg - INFO - Iter [23550/80000]	lr: 3.750e-05, eta: 4:51:30, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0844, decode.acc_seg: 96.6426, loss: 0.0844
+2023-03-03 22:41:59,107 - mmseg - INFO - Iter [23600/80000]	lr: 3.750e-05, eta: 4:51:14, time: 0.301, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0831, decode.acc_seg: 96.6879, loss: 0.0831
+2023-03-03 22:42:16,162 - mmseg - INFO - Iter [23650/80000]	lr: 3.750e-05, eta: 4:51:02, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7926, loss: 0.0811
+2023-03-03 22:42:30,986 - mmseg - INFO - Iter [23700/80000]	lr: 3.750e-05, eta: 4:50:45, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.8403, loss: 0.0807
+2023-03-03 22:42:45,541 - mmseg - INFO - Iter [23750/80000]	lr: 3.750e-05, eta: 4:50:27, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.7083, loss: 0.0821
+2023-03-03 22:43:00,278 - mmseg - INFO - Iter [23800/80000]	lr: 3.750e-05, eta: 4:50:10, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.7328, loss: 0.0819
+2023-03-03 22:43:17,259 - mmseg - INFO - Iter [23850/80000]	lr: 3.750e-05, eta: 4:49:58, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0832, decode.acc_seg: 96.6895, loss: 0.0832
+2023-03-03 22:43:31,950 - mmseg - INFO - Iter [23900/80000]	lr: 3.750e-05, eta: 4:49:41, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.7949, loss: 0.0803
+2023-03-03 22:43:46,544 - mmseg - INFO - Iter [23950/80000]	lr: 3.750e-05, eta: 4:49:23, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.9115, loss: 0.0786
+2023-03-03 22:44:03,458 - mmseg - INFO - Saving checkpoint at 24000 iterations
+2023-03-03 22:44:05,320 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 22:44:05,320 - mmseg - INFO - Iter [24000/80000]	lr: 3.750e-05, eta: 4:49:15, time: 0.376, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.7981, loss: 0.0806
+2023-03-03 22:44:31,173 - mmseg - INFO - per class results:
+2023-03-03 22:44:31,174 - mmseg - INFO - 
++---------------+-------+-------+
+|     Class     |  IoU  |  Acc  |
++---------------+-------+-------+
+|   background  |  nan  |  nan  |
+|      road     |  98.5 | 99.17 |
+|    sidewalk   | 87.11 | 93.69 |
+|    building   | 93.62 | 97.18 |
+|      wall     | 56.13 | 62.89 |
+|     fence     | 64.86 | 75.33 |
+|      pole     | 70.86 | 81.46 |
+| traffic light | 74.69 | 82.66 |
+|  traffic sign |  82.4 | 89.09 |
+|   vegetation  | 93.12 | 96.81 |
+|    terrain    | 63.85 | 72.94 |
+|      sky      | 95.37 | 98.23 |
+|     person    | 84.85 | 93.13 |
+|     rider     | 67.53 | 79.58 |
+|      car      | 96.04 | 98.04 |
+|     truck     | 85.29 | 91.68 |
+|      bus      | 91.54 | 96.61 |
+|     train     | 83.91 | 87.31 |
+|   motorcycle  | 70.89 | 83.37 |
+|    bicycle    | 80.45 | 89.49 |
++---------------+-------+-------+
+2023-03-03 22:44:31,174 - mmseg - INFO - Summary:
+2023-03-03 22:44:31,174 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 96.64 | 81.11 | 87.82 |
++-------+-------+-------+
+2023-03-03 22:44:31,236 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20/best_mIoU_iter_8000.pth was removed
+2023-03-03 22:44:33,013 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_24000.pth.
+2023-03-03 22:44:33,014 - mmseg - INFO - Best mIoU is 0.8111 at 24000 iter.
+2023-03-03 22:44:33,014 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 22:44:33,014 - mmseg - INFO - Iter(val) [63]	aAcc: 0.9664, mIoU: 0.8111, mAcc: 0.8782, IoU.background: nan, IoU.road: 0.9850, IoU.sidewalk: 0.8711, IoU.building: 0.9362, IoU.wall: 0.5613, IoU.fence: 0.6486, IoU.pole: 0.7086, IoU.traffic light: 0.7469, IoU.traffic sign: 0.8240, IoU.vegetation: 0.9312, IoU.terrain: 0.6385, IoU.sky: 0.9537, IoU.person: 0.8485, IoU.rider: 0.6753, IoU.car: 0.9604, IoU.truck: 0.8529, IoU.bus: 0.9154, IoU.train: 0.8391, IoU.motorcycle: 0.7089, IoU.bicycle: 0.8045, Acc.background: nan, Acc.road: 0.9917, Acc.sidewalk: 0.9369, Acc.building: 0.9718, Acc.wall: 0.6289, Acc.fence: 0.7533, Acc.pole: 0.8146, Acc.traffic light: 0.8266, Acc.traffic sign: 0.8909, Acc.vegetation: 0.9681, Acc.terrain: 0.7294, Acc.sky: 0.9823, Acc.person: 0.9313, Acc.rider: 0.7958, Acc.car: 0.9804, Acc.truck: 0.9168, Acc.bus: 0.9661, Acc.train: 0.8731, Acc.motorcycle: 0.8337, Acc.bicycle: 0.8949
+2023-03-03 22:44:47,949 - mmseg - INFO - Iter [24050/80000]	lr: 3.750e-05, eta: 4:50:03, time: 0.853, data_time: 0.562, memory: 67605, decode.loss_ce: 0.0777, decode.acc_seg: 96.9118, loss: 0.0777
+2023-03-03 22:45:02,802 - mmseg - INFO - Iter [24100/80000]	lr: 3.750e-05, eta: 4:49:46, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0846, decode.acc_seg: 96.6123, loss: 0.0846
+2023-03-03 22:45:17,430 - mmseg - INFO - Iter [24150/80000]	lr: 3.750e-05, eta: 4:49:28, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.7936, loss: 0.0803
+2023-03-03 22:45:34,495 - mmseg - INFO - Iter [24200/80000]	lr: 3.750e-05, eta: 4:49:16, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0830, decode.acc_seg: 96.7343, loss: 0.0830
+2023-03-03 22:45:49,153 - mmseg - INFO - Iter [24250/80000]	lr: 3.750e-05, eta: 4:48:58, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8206, loss: 0.0802
+2023-03-03 22:46:03,897 - mmseg - INFO - Iter [24300/80000]	lr: 3.750e-05, eta: 4:48:41, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0826, decode.acc_seg: 96.7485, loss: 0.0826
+2023-03-03 22:46:18,524 - mmseg - INFO - Iter [24350/80000]	lr: 3.750e-05, eta: 4:48:23, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.7779, loss: 0.0803
+2023-03-03 22:46:35,478 - mmseg - INFO - Iter [24400/80000]	lr: 3.750e-05, eta: 4:48:11, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0824, decode.acc_seg: 96.7423, loss: 0.0824
+2023-03-03 22:46:50,109 - mmseg - INFO - Iter [24450/80000]	lr: 3.750e-05, eta: 4:47:53, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0844, decode.acc_seg: 96.6628, loss: 0.0844
+2023-03-03 22:47:04,916 - mmseg - INFO - Iter [24500/80000]	lr: 3.750e-05, eta: 4:47:36, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.7930, loss: 0.0818
+2023-03-03 22:47:19,657 - mmseg - INFO - Iter [24550/80000]	lr: 3.750e-05, eta: 4:47:19, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8281, loss: 0.0799
+2023-03-03 22:47:36,770 - mmseg - INFO - Iter [24600/80000]	lr: 3.750e-05, eta: 4:47:07, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0809, decode.acc_seg: 96.7902, loss: 0.0809
+2023-03-03 22:47:51,389 - mmseg - INFO - Iter [24650/80000]	lr: 3.750e-05, eta: 4:46:49, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0834, decode.acc_seg: 96.6697, loss: 0.0834
+2023-03-03 22:48:05,970 - mmseg - INFO - Iter [24700/80000]	lr: 3.750e-05, eta: 4:46:31, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.8882, loss: 0.0801
+2023-03-03 22:48:22,886 - mmseg - INFO - Iter [24750/80000]	lr: 3.750e-05, eta: 4:46:19, time: 0.338, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0822, decode.acc_seg: 96.7415, loss: 0.0822
+2023-03-03 22:48:37,506 - mmseg - INFO - Iter [24800/80000]	lr: 3.750e-05, eta: 4:46:01, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.7836, loss: 0.0812
+2023-03-03 22:48:52,194 - mmseg - INFO - Iter [24850/80000]	lr: 3.750e-05, eta: 4:45:44, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.7779, loss: 0.0808
+2023-03-03 22:49:06,777 - mmseg - INFO - Iter [24900/80000]	lr: 3.750e-05, eta: 4:45:26, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.7131, loss: 0.0819
+2023-03-03 22:49:23,705 - mmseg - INFO - Iter [24950/80000]	lr: 3.750e-05, eta: 4:45:14, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0835, decode.acc_seg: 96.6690, loss: 0.0835
+2023-03-03 22:49:38,321 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 22:49:38,321 - mmseg - INFO - Iter [25000/80000]	lr: 3.750e-05, eta: 4:44:56, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0843, decode.acc_seg: 96.6491, loss: 0.0843
+2023-03-03 22:49:53,009 - mmseg - INFO - Iter [25050/80000]	lr: 3.750e-05, eta: 4:44:39, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0804, decode.acc_seg: 96.7740, loss: 0.0804
+2023-03-03 22:50:07,735 - mmseg - INFO - Iter [25100/80000]	lr: 3.750e-05, eta: 4:44:21, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.7909, loss: 0.0803
+2023-03-03 22:50:24,880 - mmseg - INFO - Iter [25150/80000]	lr: 3.750e-05, eta: 4:44:09, time: 0.343, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0832, decode.acc_seg: 96.6750, loss: 0.0832
+2023-03-03 22:50:39,538 - mmseg - INFO - Iter [25200/80000]	lr: 3.750e-05, eta: 4:43:52, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8359, loss: 0.0792
+2023-03-03 22:50:54,190 - mmseg - INFO - Iter [25250/80000]	lr: 3.750e-05, eta: 4:43:34, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0820, decode.acc_seg: 96.7256, loss: 0.0820
+2023-03-03 22:51:11,297 - mmseg - INFO - Iter [25300/80000]	lr: 3.750e-05, eta: 4:43:22, time: 0.342, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0779, decode.acc_seg: 96.9422, loss: 0.0779
+2023-03-03 22:51:26,176 - mmseg - INFO - Iter [25350/80000]	lr: 3.750e-05, eta: 4:43:05, time: 0.298, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.8058, loss: 0.0815
+2023-03-03 22:51:40,796 - mmseg - INFO - Iter [25400/80000]	lr: 3.750e-05, eta: 4:42:48, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0826, decode.acc_seg: 96.6852, loss: 0.0826
+2023-03-03 22:51:55,404 - mmseg - INFO - Iter [25450/80000]	lr: 3.750e-05, eta: 4:42:30, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0809, decode.acc_seg: 96.7895, loss: 0.0809
+2023-03-03 22:52:12,418 - mmseg - INFO - Iter [25500/80000]	lr: 3.750e-05, eta: 4:42:18, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0852, decode.acc_seg: 96.6224, loss: 0.0852
+2023-03-03 22:52:27,057 - mmseg - INFO - Iter [25550/80000]	lr: 3.750e-05, eta: 4:42:00, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.8330, loss: 0.0801
+2023-03-03 22:52:41,649 - mmseg - INFO - Iter [25600/80000]	lr: 3.750e-05, eta: 4:41:43, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0849, decode.acc_seg: 96.6373, loss: 0.0849
+2023-03-03 22:52:56,209 - mmseg - INFO - Iter [25650/80000]	lr: 3.750e-05, eta: 4:41:25, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.8713, loss: 0.0786
+2023-03-03 22:53:13,211 - mmseg - INFO - Iter [25700/80000]	lr: 3.750e-05, eta: 4:41:13, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0834, decode.acc_seg: 96.7674, loss: 0.0834
+2023-03-03 22:53:28,008 - mmseg - INFO - Iter [25750/80000]	lr: 3.750e-05, eta: 4:40:56, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0825, decode.acc_seg: 96.7526, loss: 0.0825
+2023-03-03 22:53:42,596 - mmseg - INFO - Iter [25800/80000]	lr: 3.750e-05, eta: 4:40:38, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0845, decode.acc_seg: 96.6533, loss: 0.0845
+2023-03-03 22:53:57,190 - mmseg - INFO - Iter [25850/80000]	lr: 3.750e-05, eta: 4:40:21, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7817, loss: 0.0811
+2023-03-03 22:54:14,185 - mmseg - INFO - Iter [25900/80000]	lr: 3.750e-05, eta: 4:40:08, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.7243, loss: 0.0810
+2023-03-03 22:54:28,791 - mmseg - INFO - Iter [25950/80000]	lr: 3.750e-05, eta: 4:39:51, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0829, decode.acc_seg: 96.7606, loss: 0.0829
+2023-03-03 22:54:43,510 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 22:54:43,510 - mmseg - INFO - Iter [26000/80000]	lr: 3.750e-05, eta: 4:39:33, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0834, decode.acc_seg: 96.7256, loss: 0.0834
+2023-03-03 22:55:00,502 - mmseg - INFO - Iter [26050/80000]	lr: 3.750e-05, eta: 4:39:21, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.8159, loss: 0.0801
+2023-03-03 22:55:15,230 - mmseg - INFO - Iter [26100/80000]	lr: 3.750e-05, eta: 4:39:04, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.7681, loss: 0.0812
+2023-03-03 22:55:29,819 - mmseg - INFO - Iter [26150/80000]	lr: 3.750e-05, eta: 4:38:46, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0831, decode.acc_seg: 96.7128, loss: 0.0831
+2023-03-03 22:55:44,427 - mmseg - INFO - Iter [26200/80000]	lr: 3.750e-05, eta: 4:38:29, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8167, loss: 0.0802
+2023-03-03 22:56:01,420 - mmseg - INFO - Iter [26250/80000]	lr: 3.750e-05, eta: 4:38:16, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0843, decode.acc_seg: 96.6886, loss: 0.0843
+2023-03-03 22:56:16,093 - mmseg - INFO - Iter [26300/80000]	lr: 3.750e-05, eta: 4:37:59, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8782, loss: 0.0791
+2023-03-03 22:56:30,830 - mmseg - INFO - Iter [26350/80000]	lr: 3.750e-05, eta: 4:37:42, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8106, loss: 0.0799
+2023-03-03 22:56:45,426 - mmseg - INFO - Iter [26400/80000]	lr: 3.750e-05, eta: 4:37:24, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0816, decode.acc_seg: 96.7295, loss: 0.0816
+2023-03-03 22:57:02,433 - mmseg - INFO - Iter [26450/80000]	lr: 3.750e-05, eta: 4:37:12, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8115, loss: 0.0795
+2023-03-03 22:57:17,104 - mmseg - INFO - Iter [26500/80000]	lr: 3.750e-05, eta: 4:36:55, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.7484, loss: 0.0818
+2023-03-03 22:57:31,855 - mmseg - INFO - Iter [26550/80000]	lr: 3.750e-05, eta: 4:36:38, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0834, decode.acc_seg: 96.6680, loss: 0.0834
+2023-03-03 22:57:48,782 - mmseg - INFO - Iter [26600/80000]	lr: 3.750e-05, eta: 4:36:25, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0833, decode.acc_seg: 96.6968, loss: 0.0833
+2023-03-03 22:58:03,472 - mmseg - INFO - Iter [26650/80000]	lr: 3.750e-05, eta: 4:36:08, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8525, loss: 0.0790
+2023-03-03 22:58:18,081 - mmseg - INFO - Iter [26700/80000]	lr: 3.750e-05, eta: 4:35:50, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.7316, loss: 0.0818
+2023-03-03 22:58:32,670 - mmseg - INFO - Iter [26750/80000]	lr: 3.750e-05, eta: 4:35:33, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0835, decode.acc_seg: 96.7077, loss: 0.0835
+2023-03-03 22:58:49,641 - mmseg - INFO - Iter [26800/80000]	lr: 3.750e-05, eta: 4:35:20, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0868, decode.acc_seg: 96.7301, loss: 0.0868
+2023-03-03 22:59:04,255 - mmseg - INFO - Iter [26850/80000]	lr: 3.750e-05, eta: 4:35:03, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0822, decode.acc_seg: 96.7269, loss: 0.0822
+2023-03-03 22:59:18,868 - mmseg - INFO - Iter [26900/80000]	lr: 3.750e-05, eta: 4:34:46, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7544, loss: 0.0811
+2023-03-03 22:59:33,431 - mmseg - INFO - Iter [26950/80000]	lr: 3.750e-05, eta: 4:34:28, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.7454, loss: 0.0808
+2023-03-03 22:59:50,399 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 22:59:50,399 - mmseg - INFO - Iter [27000/80000]	lr: 3.750e-05, eta: 4:34:15, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.7802, loss: 0.0806
+2023-03-03 23:00:05,108 - mmseg - INFO - Iter [27050/80000]	lr: 3.750e-05, eta: 4:33:58, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8501, loss: 0.0791
+2023-03-03 23:00:19,706 - mmseg - INFO - Iter [27100/80000]	lr: 3.750e-05, eta: 4:33:41, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0822, decode.acc_seg: 96.7213, loss: 0.0822
+2023-03-03 23:00:34,337 - mmseg - INFO - Iter [27150/80000]	lr: 3.750e-05, eta: 4:33:24, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.8117, loss: 0.0801
+2023-03-03 23:00:51,409 - mmseg - INFO - Iter [27200/80000]	lr: 3.750e-05, eta: 4:33:11, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8483, loss: 0.0795
+2023-03-03 23:01:06,144 - mmseg - INFO - Iter [27250/80000]	lr: 3.750e-05, eta: 4:32:54, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.8131, loss: 0.0805
+2023-03-03 23:01:20,772 - mmseg - INFO - Iter [27300/80000]	lr: 3.750e-05, eta: 4:32:37, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0836, decode.acc_seg: 96.6661, loss: 0.0836
+2023-03-03 23:01:38,181 - mmseg - INFO - Iter [27350/80000]	lr: 3.750e-05, eta: 4:32:25, time: 0.348, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.7060, loss: 0.0821
+2023-03-03 23:01:52,838 - mmseg - INFO - Iter [27400/80000]	lr: 3.750e-05, eta: 4:32:08, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.7508, loss: 0.0808
+2023-03-03 23:02:07,435 - mmseg - INFO - Iter [27450/80000]	lr: 3.750e-05, eta: 4:31:51, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0842, decode.acc_seg: 96.6940, loss: 0.0842
+2023-03-03 23:02:22,158 - mmseg - INFO - Iter [27500/80000]	lr: 3.750e-05, eta: 4:31:34, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.7285, loss: 0.0819
+2023-03-03 23:02:39,156 - mmseg - INFO - Iter [27550/80000]	lr: 3.750e-05, eta: 4:31:21, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0822, decode.acc_seg: 96.7324, loss: 0.0822
+2023-03-03 23:02:53,984 - mmseg - INFO - Iter [27600/80000]	lr: 3.750e-05, eta: 4:31:04, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0781, decode.acc_seg: 96.8507, loss: 0.0781
+2023-03-03 23:03:08,805 - mmseg - INFO - Iter [27650/80000]	lr: 3.750e-05, eta: 4:30:47, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7985, loss: 0.0811
+2023-03-03 23:03:23,433 - mmseg - INFO - Iter [27700/80000]	lr: 3.750e-05, eta: 4:30:30, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0837, decode.acc_seg: 96.6916, loss: 0.0837
+2023-03-03 23:03:40,393 - mmseg - INFO - Iter [27750/80000]	lr: 3.750e-05, eta: 4:30:17, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0825, decode.acc_seg: 96.7168, loss: 0.0825
+2023-03-03 23:03:54,962 - mmseg - INFO - Iter [27800/80000]	lr: 3.750e-05, eta: 4:30:00, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.7786, loss: 0.0812
+2023-03-03 23:04:09,529 - mmseg - INFO - Iter [27850/80000]	lr: 3.750e-05, eta: 4:29:43, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0830, decode.acc_seg: 96.7175, loss: 0.0830
+2023-03-03 23:04:24,163 - mmseg - INFO - Iter [27900/80000]	lr: 3.750e-05, eta: 4:29:25, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.7849, loss: 0.0803
+2023-03-03 23:04:41,179 - mmseg - INFO - Iter [27950/80000]	lr: 3.750e-05, eta: 4:29:13, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.7957, loss: 0.0805
+2023-03-03 23:04:55,846 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 23:04:55,846 - mmseg - INFO - Iter [28000/80000]	lr: 3.750e-05, eta: 4:28:56, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8061, loss: 0.0802
+2023-03-03 23:05:10,516 - mmseg - INFO - Iter [28050/80000]	lr: 3.750e-05, eta: 4:28:39, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.8122, loss: 0.0805
+2023-03-03 23:05:27,558 - mmseg - INFO - Iter [28100/80000]	lr: 3.750e-05, eta: 4:28:26, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0820, decode.acc_seg: 96.7355, loss: 0.0820
+2023-03-03 23:05:42,229 - mmseg - INFO - Iter [28150/80000]	lr: 3.750e-05, eta: 4:28:09, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0816, decode.acc_seg: 96.7793, loss: 0.0816
+2023-03-03 23:05:56,901 - mmseg - INFO - Iter [28200/80000]	lr: 3.750e-05, eta: 4:27:52, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.7171, loss: 0.0821
+2023-03-03 23:06:11,782 - mmseg - INFO - Iter [28250/80000]	lr: 3.750e-05, eta: 4:27:35, time: 0.298, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7461, loss: 0.0815
+2023-03-03 23:06:28,888 - mmseg - INFO - Iter [28300/80000]	lr: 3.750e-05, eta: 4:27:22, time: 0.342, data_time: 0.054, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.7699, loss: 0.0808
+2023-03-03 23:06:43,559 - mmseg - INFO - Iter [28350/80000]	lr: 3.750e-05, eta: 4:27:05, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0804, decode.acc_seg: 96.7768, loss: 0.0804
+2023-03-03 23:06:58,182 - mmseg - INFO - Iter [28400/80000]	lr: 3.750e-05, eta: 4:26:48, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0816, decode.acc_seg: 96.7584, loss: 0.0816
+2023-03-03 23:07:12,787 - mmseg - INFO - Iter [28450/80000]	lr: 3.750e-05, eta: 4:26:31, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8912, loss: 0.0795
+2023-03-03 23:07:29,914 - mmseg - INFO - Iter [28500/80000]	lr: 3.750e-05, eta: 4:26:19, time: 0.343, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0823, decode.acc_seg: 96.6957, loss: 0.0823
+2023-03-03 23:07:44,527 - mmseg - INFO - Iter [28550/80000]	lr: 3.750e-05, eta: 4:26:01, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8097, loss: 0.0799
+2023-03-03 23:07:59,158 - mmseg - INFO - Iter [28600/80000]	lr: 3.750e-05, eta: 4:25:44, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0847, decode.acc_seg: 96.6332, loss: 0.0847
+2023-03-03 23:08:16,213 - mmseg - INFO - Iter [28650/80000]	lr: 3.750e-05, eta: 4:25:32, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0868, decode.acc_seg: 96.5409, loss: 0.0868
+2023-03-03 23:08:30,837 - mmseg - INFO - Iter [28700/80000]	lr: 3.750e-05, eta: 4:25:14, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0837, decode.acc_seg: 96.6496, loss: 0.0837
+2023-03-03 23:08:45,418 - mmseg - INFO - Iter [28750/80000]	lr: 3.750e-05, eta: 4:24:57, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.7783, loss: 0.0819
+2023-03-03 23:09:00,082 - mmseg - INFO - Iter [28800/80000]	lr: 3.750e-05, eta: 4:24:40, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8485, loss: 0.0790
+2023-03-03 23:09:17,100 - mmseg - INFO - Iter [28850/80000]	lr: 3.750e-05, eta: 4:24:27, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8704, loss: 0.0796
+2023-03-03 23:09:31,822 - mmseg - INFO - Iter [28900/80000]	lr: 3.750e-05, eta: 4:24:10, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.8535, loss: 0.0797
+2023-03-03 23:09:46,553 - mmseg - INFO - Iter [28950/80000]	lr: 3.750e-05, eta: 4:23:54, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0830, decode.acc_seg: 96.6701, loss: 0.0830
+2023-03-03 23:10:01,237 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 23:10:01,238 - mmseg - INFO - Iter [29000/80000]	lr: 3.750e-05, eta: 4:23:37, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0824, decode.acc_seg: 96.7332, loss: 0.0824
+2023-03-03 23:10:18,316 - mmseg - INFO - Iter [29050/80000]	lr: 3.750e-05, eta: 4:23:24, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8307, loss: 0.0790
+2023-03-03 23:10:33,057 - mmseg - INFO - Iter [29100/80000]	lr: 3.750e-05, eta: 4:23:07, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0787, decode.acc_seg: 96.8750, loss: 0.0787
+2023-03-03 23:10:47,658 - mmseg - INFO - Iter [29150/80000]	lr: 3.750e-05, eta: 4:22:50, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7398, loss: 0.0827
+2023-03-03 23:11:02,246 - mmseg - INFO - Iter [29200/80000]	lr: 3.750e-05, eta: 4:22:33, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0816, decode.acc_seg: 96.7394, loss: 0.0816
+2023-03-03 23:11:19,218 - mmseg - INFO - Iter [29250/80000]	lr: 3.750e-05, eta: 4:22:20, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8157, loss: 0.0792
+2023-03-03 23:11:33,866 - mmseg - INFO - Iter [29300/80000]	lr: 3.750e-05, eta: 4:22:03, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.7704, loss: 0.0807
+2023-03-03 23:11:48,541 - mmseg - INFO - Iter [29350/80000]	lr: 3.750e-05, eta: 4:21:46, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8366, loss: 0.0802
+2023-03-03 23:12:05,491 - mmseg - INFO - Iter [29400/80000]	lr: 3.750e-05, eta: 4:21:33, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.7981, loss: 0.0803
+2023-03-03 23:12:20,096 - mmseg - INFO - Iter [29450/80000]	lr: 3.750e-05, eta: 4:21:16, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0833, decode.acc_seg: 96.7225, loss: 0.0833
+2023-03-03 23:12:34,717 - mmseg - INFO - Iter [29500/80000]	lr: 3.750e-05, eta: 4:20:59, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.7954, loss: 0.0810
+2023-03-03 23:12:49,487 - mmseg - INFO - Iter [29550/80000]	lr: 3.750e-05, eta: 4:20:42, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.7713, loss: 0.0814
+2023-03-03 23:13:06,549 - mmseg - INFO - Iter [29600/80000]	lr: 3.750e-05, eta: 4:20:29, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0839, decode.acc_seg: 96.7076, loss: 0.0839
+2023-03-03 23:13:21,142 - mmseg - INFO - Iter [29650/80000]	lr: 3.750e-05, eta: 4:20:12, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.7804, loss: 0.0810
+2023-03-03 23:13:35,802 - mmseg - INFO - Iter [29700/80000]	lr: 3.750e-05, eta: 4:19:55, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7571, loss: 0.0815
+2023-03-03 23:13:50,442 - mmseg - INFO - Iter [29750/80000]	lr: 3.750e-05, eta: 4:19:38, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.8345, loss: 0.0807
+2023-03-03 23:14:07,383 - mmseg - INFO - Iter [29800/80000]	lr: 3.750e-05, eta: 4:19:25, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.8203, loss: 0.0803
+2023-03-03 23:14:22,046 - mmseg - INFO - Iter [29850/80000]	lr: 3.750e-05, eta: 4:19:08, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7653, loss: 0.0811
+2023-03-03 23:14:36,660 - mmseg - INFO - Iter [29900/80000]	lr: 3.750e-05, eta: 4:18:51, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8235, loss: 0.0784
+2023-03-03 23:14:53,671 - mmseg - INFO - Iter [29950/80000]	lr: 3.750e-05, eta: 4:18:38, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.8436, loss: 0.0805
+2023-03-03 23:15:08,418 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 23:15:08,418 - mmseg - INFO - Iter [30000/80000]	lr: 3.750e-05, eta: 4:18:22, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.7573, loss: 0.0806
+2023-03-03 23:15:23,084 - mmseg - INFO - Iter [30050/80000]	lr: 1.875e-05, eta: 4:18:05, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.7784, loss: 0.0821
+2023-03-03 23:15:37,674 - mmseg - INFO - Iter [30100/80000]	lr: 1.875e-05, eta: 4:17:48, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0831, decode.acc_seg: 96.7273, loss: 0.0831
+2023-03-03 23:15:54,617 - mmseg - INFO - Iter [30150/80000]	lr: 1.875e-05, eta: 4:17:35, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8370, loss: 0.0789
+2023-03-03 23:16:09,208 - mmseg - INFO - Iter [30200/80000]	lr: 1.875e-05, eta: 4:17:18, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8882, loss: 0.0790
+2023-03-03 23:16:23,881 - mmseg - INFO - Iter [30250/80000]	lr: 1.875e-05, eta: 4:17:01, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8199, loss: 0.0802
+2023-03-03 23:16:38,560 - mmseg - INFO - Iter [30300/80000]	lr: 1.875e-05, eta: 4:16:44, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.7300, loss: 0.0818
+2023-03-03 23:16:55,872 - mmseg - INFO - Iter [30350/80000]	lr: 1.875e-05, eta: 4:16:31, time: 0.346, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8517, loss: 0.0799
+2023-03-03 23:17:10,422 - mmseg - INFO - Iter [30400/80000]	lr: 1.875e-05, eta: 4:16:14, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8226, loss: 0.0790
+2023-03-03 23:17:24,985 - mmseg - INFO - Iter [30450/80000]	lr: 1.875e-05, eta: 4:15:57, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.7939, loss: 0.0799
+2023-03-03 23:17:39,747 - mmseg - INFO - Iter [30500/80000]	lr: 1.875e-05, eta: 4:15:41, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8377, loss: 0.0790
+2023-03-03 23:17:56,943 - mmseg - INFO - Iter [30550/80000]	lr: 1.875e-05, eta: 4:15:28, time: 0.344, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8739, loss: 0.0788
+2023-03-03 23:18:11,548 - mmseg - INFO - Iter [30600/80000]	lr: 1.875e-05, eta: 4:15:11, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7101, loss: 0.0827
+2023-03-03 23:18:26,231 - mmseg - INFO - Iter [30650/80000]	lr: 1.875e-05, eta: 4:14:54, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0804, decode.acc_seg: 96.7769, loss: 0.0804
+2023-03-03 23:18:43,314 - mmseg - INFO - Iter [30700/80000]	lr: 1.875e-05, eta: 4:14:41, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.7952, loss: 0.0802
+2023-03-03 23:18:57,895 - mmseg - INFO - Iter [30750/80000]	lr: 1.875e-05, eta: 4:14:24, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0772, decode.acc_seg: 96.9033, loss: 0.0772
+2023-03-03 23:19:12,494 - mmseg - INFO - Iter [30800/80000]	lr: 1.875e-05, eta: 4:14:07, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7307, loss: 0.0827
+2023-03-03 23:19:27,153 - mmseg - INFO - Iter [30850/80000]	lr: 1.875e-05, eta: 4:13:50, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0848, decode.acc_seg: 96.6189, loss: 0.0848
+2023-03-03 23:19:44,227 - mmseg - INFO - Iter [30900/80000]	lr: 1.875e-05, eta: 4:13:37, time: 0.342, data_time: 0.054, memory: 67605, decode.loss_ce: 0.0804, decode.acc_seg: 96.7813, loss: 0.0804
+2023-03-03 23:19:59,008 - mmseg - INFO - Iter [30950/80000]	lr: 1.875e-05, eta: 4:13:21, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0862, decode.acc_seg: 96.5945, loss: 0.0862
+2023-03-03 23:20:13,590 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 23:20:13,590 - mmseg - INFO - Iter [31000/80000]	lr: 1.875e-05, eta: 4:13:04, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8542, loss: 0.0790
+2023-03-03 23:20:28,228 - mmseg - INFO - Iter [31050/80000]	lr: 1.875e-05, eta: 4:12:47, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0817, decode.acc_seg: 96.7516, loss: 0.0817
+2023-03-03 23:20:45,159 - mmseg - INFO - Iter [31100/80000]	lr: 1.875e-05, eta: 4:12:34, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8277, loss: 0.0790
+2023-03-03 23:20:59,908 - mmseg - INFO - Iter [31150/80000]	lr: 1.875e-05, eta: 4:12:17, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.7666, loss: 0.0810
+2023-03-03 23:21:14,520 - mmseg - INFO - Iter [31200/80000]	lr: 1.875e-05, eta: 4:12:00, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0825, decode.acc_seg: 96.7142, loss: 0.0825
+2023-03-03 23:21:31,549 - mmseg - INFO - Iter [31250/80000]	lr: 1.875e-05, eta: 4:11:47, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8187, loss: 0.0802
+2023-03-03 23:21:46,242 - mmseg - INFO - Iter [31300/80000]	lr: 1.875e-05, eta: 4:11:30, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.8022, loss: 0.0810
+2023-03-03 23:22:00,857 - mmseg - INFO - Iter [31350/80000]	lr: 1.875e-05, eta: 4:11:14, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0773, decode.acc_seg: 96.9334, loss: 0.0773
+2023-03-03 23:22:15,474 - mmseg - INFO - Iter [31400/80000]	lr: 1.875e-05, eta: 4:10:57, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.7961, loss: 0.0796
+2023-03-03 23:22:32,510 - mmseg - INFO - Iter [31450/80000]	lr: 1.875e-05, eta: 4:10:44, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7412, loss: 0.0815
+2023-03-03 23:22:47,097 - mmseg - INFO - Iter [31500/80000]	lr: 1.875e-05, eta: 4:10:27, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0782, decode.acc_seg: 96.8872, loss: 0.0782
+2023-03-03 23:23:01,806 - mmseg - INFO - Iter [31550/80000]	lr: 1.875e-05, eta: 4:10:10, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8347, loss: 0.0791
+2023-03-03 23:23:16,463 - mmseg - INFO - Iter [31600/80000]	lr: 1.875e-05, eta: 4:09:53, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.7546, loss: 0.0818
+2023-03-03 23:23:33,389 - mmseg - INFO - Iter [31650/80000]	lr: 1.875e-05, eta: 4:09:40, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8268, loss: 0.0800
+2023-03-03 23:23:48,019 - mmseg - INFO - Iter [31700/80000]	lr: 1.875e-05, eta: 4:09:23, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.9027, loss: 0.0786
+2023-03-03 23:24:02,628 - mmseg - INFO - Iter [31750/80000]	lr: 1.875e-05, eta: 4:09:06, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.7859, loss: 0.0797
+2023-03-03 23:24:17,493 - mmseg - INFO - Iter [31800/80000]	lr: 1.875e-05, eta: 4:08:50, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7554, loss: 0.0815
+2023-03-03 23:24:34,666 - mmseg - INFO - Iter [31850/80000]	lr: 1.875e-05, eta: 4:08:37, time: 0.343, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8167, loss: 0.0790
+2023-03-03 23:24:49,332 - mmseg - INFO - Iter [31900/80000]	lr: 1.875e-05, eta: 4:08:20, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8526, loss: 0.0788
+2023-03-03 23:25:03,939 - mmseg - INFO - Iter [31950/80000]	lr: 1.875e-05, eta: 4:08:03, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8366, loss: 0.0788
+2023-03-03 23:25:20,924 - mmseg - INFO - Saving checkpoint at 32000 iterations
+2023-03-03 23:25:22,838 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 23:25:22,838 - mmseg - INFO - Iter [32000/80000]	lr: 1.875e-05, eta: 4:07:53, time: 0.378, data_time: 0.054, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7631, loss: 0.0827
+2023-03-03 23:25:48,589 - mmseg - INFO - per class results:
+2023-03-03 23:25:48,590 - mmseg - INFO - 
++---------------+-------+-------+
+|     Class     |  IoU  |  Acc  |
++---------------+-------+-------+
+|   background  |  nan  |  nan  |
+|      road     | 98.52 | 99.13 |
+|    sidewalk   | 87.26 | 93.98 |
+|    building   |  93.6 |  96.8 |
+|      wall     | 55.23 | 61.08 |
+|     fence     | 65.21 | 76.12 |
+|      pole     | 70.83 | 82.65 |
+| traffic light | 75.27 |  86.7 |
+|  traffic sign | 82.58 | 89.35 |
+|   vegetation  | 93.06 | 97.01 |
+|    terrain    | 64.41 | 74.39 |
+|      sky      | 95.26 | 98.44 |
+|     person    | 84.96 | 92.46 |
+|     rider     | 67.97 | 80.49 |
+|      car      | 96.02 | 98.05 |
+|     truck     | 85.36 | 92.04 |
+|      bus      | 92.46 | 96.05 |
+|     train     | 85.32 | 91.74 |
+|   motorcycle  | 71.73 | 81.69 |
+|    bicycle    | 80.49 | 91.07 |
++---------------+-------+-------+
+2023-03-03 23:25:48,590 - mmseg - INFO - Summary:
+2023-03-03 23:25:48,591 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 96.63 | 81.34 | 88.38 |
++-------+-------+-------+
+2023-03-03 23:25:48,654 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20/best_mIoU_iter_24000.pth was removed
+2023-03-03 23:25:50,497 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_32000.pth.
+2023-03-03 23:25:50,498 - mmseg - INFO - Best mIoU is 0.8134 at 32000 iter.
+2023-03-03 23:25:50,498 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 23:25:50,498 - mmseg - INFO - Iter(val) [63]	aAcc: 0.9663, mIoU: 0.8134, mAcc: 0.8838, IoU.background: nan, IoU.road: 0.9852, IoU.sidewalk: 0.8726, IoU.building: 0.9360, IoU.wall: 0.5523, IoU.fence: 0.6521, IoU.pole: 0.7083, IoU.traffic light: 0.7527, IoU.traffic sign: 0.8258, IoU.vegetation: 0.9306, IoU.terrain: 0.6441, IoU.sky: 0.9526, IoU.person: 0.8496, IoU.rider: 0.6797, IoU.car: 0.9602, IoU.truck: 0.8536, IoU.bus: 0.9246, IoU.train: 0.8532, IoU.motorcycle: 0.7173, IoU.bicycle: 0.8049, Acc.background: nan, Acc.road: 0.9913, Acc.sidewalk: 0.9398, Acc.building: 0.9680, Acc.wall: 0.6108, Acc.fence: 0.7612, Acc.pole: 0.8265, Acc.traffic light: 0.8670, Acc.traffic sign: 0.8935, Acc.vegetation: 0.9701, Acc.terrain: 0.7439, Acc.sky: 0.9844, Acc.person: 0.9246, Acc.rider: 0.8049, Acc.car: 0.9805, Acc.truck: 0.9204, Acc.bus: 0.9605, Acc.train: 0.9174, Acc.motorcycle: 0.8169, Acc.bicycle: 0.9107
+2023-03-03 23:26:05,477 - mmseg - INFO - Iter [32050/80000]	lr: 1.875e-05, eta: 4:08:18, time: 0.853, data_time: 0.561, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.7889, loss: 0.0799
+2023-03-03 23:26:20,185 - mmseg - INFO - Iter [32100/80000]	lr: 1.875e-05, eta: 4:08:01, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8545, loss: 0.0791
+2023-03-03 23:26:35,068 - mmseg - INFO - Iter [32150/80000]	lr: 1.875e-05, eta: 4:07:45, time: 0.298, data_time: 0.008, memory: 67605, decode.loss_ce: 0.0776, decode.acc_seg: 96.9087, loss: 0.0776
+2023-03-03 23:26:52,089 - mmseg - INFO - Iter [32200/80000]	lr: 1.875e-05, eta: 4:07:31, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0787, decode.acc_seg: 96.8659, loss: 0.0787
+2023-03-03 23:27:06,808 - mmseg - INFO - Iter [32250/80000]	lr: 1.875e-05, eta: 4:07:15, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0828, decode.acc_seg: 96.7382, loss: 0.0828
+2023-03-03 23:27:21,461 - mmseg - INFO - Iter [32300/80000]	lr: 1.875e-05, eta: 4:06:58, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0825, decode.acc_seg: 96.7453, loss: 0.0825
+2023-03-03 23:27:36,266 - mmseg - INFO - Iter [32350/80000]	lr: 1.875e-05, eta: 4:06:41, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0825, decode.acc_seg: 96.7173, loss: 0.0825
+2023-03-03 23:27:53,500 - mmseg - INFO - Iter [32400/80000]	lr: 1.875e-05, eta: 4:06:28, time: 0.345, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8669, loss: 0.0794
+2023-03-03 23:28:08,113 - mmseg - INFO - Iter [32450/80000]	lr: 1.875e-05, eta: 4:06:11, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8067, loss: 0.0799
+2023-03-03 23:28:22,721 - mmseg - INFO - Iter [32500/80000]	lr: 1.875e-05, eta: 4:05:55, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0816, decode.acc_seg: 96.7834, loss: 0.0816
+2023-03-03 23:28:37,370 - mmseg - INFO - Iter [32550/80000]	lr: 1.875e-05, eta: 4:05:38, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.7549, loss: 0.0819
+2023-03-03 23:28:54,474 - mmseg - INFO - Iter [32600/80000]	lr: 1.875e-05, eta: 4:05:24, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.7926, loss: 0.0802
+2023-03-03 23:29:09,092 - mmseg - INFO - Iter [32650/80000]	lr: 1.875e-05, eta: 4:05:08, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0829, decode.acc_seg: 96.7589, loss: 0.0829
+2023-03-03 23:29:23,768 - mmseg - INFO - Iter [32700/80000]	lr: 1.875e-05, eta: 4:04:51, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0837, decode.acc_seg: 96.6326, loss: 0.0837
+2023-03-03 23:29:40,804 - mmseg - INFO - Iter [32750/80000]	lr: 1.875e-05, eta: 4:04:37, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7651, loss: 0.0815
+2023-03-03 23:29:55,442 - mmseg - INFO - Iter [32800/80000]	lr: 1.875e-05, eta: 4:04:21, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.7334, loss: 0.0818
+2023-03-03 23:30:10,102 - mmseg - INFO - Iter [32850/80000]	lr: 1.875e-05, eta: 4:04:04, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.7336, loss: 0.0821
+2023-03-03 23:30:24,699 - mmseg - INFO - Iter [32900/80000]	lr: 1.875e-05, eta: 4:03:47, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0820, decode.acc_seg: 96.7206, loss: 0.0820
+2023-03-03 23:30:41,682 - mmseg - INFO - Iter [32950/80000]	lr: 1.875e-05, eta: 4:03:34, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8132, loss: 0.0802
+2023-03-03 23:30:56,374 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 23:30:56,374 - mmseg - INFO - Iter [33000/80000]	lr: 1.875e-05, eta: 4:03:17, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8319, loss: 0.0788
+2023-03-03 23:31:10,974 - mmseg - INFO - Iter [33050/80000]	lr: 1.875e-05, eta: 4:03:00, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0831, decode.acc_seg: 96.7161, loss: 0.0831
+2023-03-03 23:31:25,668 - mmseg - INFO - Iter [33100/80000]	lr: 1.875e-05, eta: 4:02:43, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8116, loss: 0.0800
+2023-03-03 23:31:42,717 - mmseg - INFO - Iter [33150/80000]	lr: 1.875e-05, eta: 4:02:30, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8434, loss: 0.0790
+2023-03-03 23:31:57,294 - mmseg - INFO - Iter [33200/80000]	lr: 1.875e-05, eta: 4:02:13, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0809, decode.acc_seg: 96.7441, loss: 0.0809
+2023-03-03 23:32:11,870 - mmseg - INFO - Iter [33250/80000]	lr: 1.875e-05, eta: 4:01:56, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.7818, loss: 0.0800
+2023-03-03 23:32:28,785 - mmseg - INFO - Iter [33300/80000]	lr: 1.875e-05, eta: 4:01:43, time: 0.338, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0826, decode.acc_seg: 96.7288, loss: 0.0826
+2023-03-03 23:32:43,384 - mmseg - INFO - Iter [33350/80000]	lr: 1.875e-05, eta: 4:01:26, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8349, loss: 0.0790
+2023-03-03 23:32:57,996 - mmseg - INFO - Iter [33400/80000]	lr: 1.875e-05, eta: 4:01:09, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8208, loss: 0.0796
+2023-03-03 23:33:12,707 - mmseg - INFO - Iter [33450/80000]	lr: 1.875e-05, eta: 4:00:52, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.6904, loss: 0.0819
+2023-03-03 23:33:29,782 - mmseg - INFO - Iter [33500/80000]	lr: 1.875e-05, eta: 4:00:39, time: 0.341, data_time: 0.054, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.8481, loss: 0.0783
+2023-03-03 23:33:44,616 - mmseg - INFO - Iter [33550/80000]	lr: 1.875e-05, eta: 4:00:22, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8279, loss: 0.0793
+2023-03-03 23:33:59,251 - mmseg - INFO - Iter [33600/80000]	lr: 1.875e-05, eta: 4:00:06, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0824, decode.acc_seg: 96.7394, loss: 0.0824
+2023-03-03 23:34:14,022 - mmseg - INFO - Iter [33650/80000]	lr: 1.875e-05, eta: 3:59:49, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0822, decode.acc_seg: 96.7317, loss: 0.0822
+2023-03-03 23:34:30,996 - mmseg - INFO - Iter [33700/80000]	lr: 1.875e-05, eta: 3:59:36, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8153, loss: 0.0794
+2023-03-03 23:34:45,652 - mmseg - INFO - Iter [33750/80000]	lr: 1.875e-05, eta: 3:59:19, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0829, decode.acc_seg: 96.7117, loss: 0.0829
+2023-03-03 23:35:00,262 - mmseg - INFO - Iter [33800/80000]	lr: 1.875e-05, eta: 3:59:02, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0820, decode.acc_seg: 96.7601, loss: 0.0820
+2023-03-03 23:35:14,843 - mmseg - INFO - Iter [33850/80000]	lr: 1.875e-05, eta: 3:58:45, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.8016, loss: 0.0801
+2023-03-03 23:35:31,910 - mmseg - INFO - Iter [33900/80000]	lr: 1.875e-05, eta: 3:58:32, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0828, decode.acc_seg: 96.7197, loss: 0.0828
+2023-03-03 23:35:46,540 - mmseg - INFO - Iter [33950/80000]	lr: 1.875e-05, eta: 3:58:15, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8416, loss: 0.0789
+2023-03-03 23:36:01,159 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 23:36:01,159 - mmseg - INFO - Iter [34000/80000]	lr: 1.875e-05, eta: 3:57:58, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0751, decode.acc_seg: 97.0253, loss: 0.0751
+2023-03-03 23:36:18,158 - mmseg - INFO - Iter [34050/80000]	lr: 1.875e-05, eta: 3:57:45, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0830, decode.acc_seg: 96.7859, loss: 0.0830
+2023-03-03 23:36:32,734 - mmseg - INFO - Iter [34100/80000]	lr: 1.875e-05, eta: 3:57:28, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0773, decode.acc_seg: 96.9367, loss: 0.0773
+2023-03-03 23:36:47,764 - mmseg - INFO - Iter [34150/80000]	lr: 1.875e-05, eta: 3:57:12, time: 0.301, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7638, loss: 0.0815
+2023-03-03 23:37:02,371 - mmseg - INFO - Iter [34200/80000]	lr: 1.875e-05, eta: 3:56:55, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0813, decode.acc_seg: 96.7656, loss: 0.0813
+2023-03-03 23:37:19,359 - mmseg - INFO - Iter [34250/80000]	lr: 1.875e-05, eta: 3:56:42, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0831, decode.acc_seg: 96.7101, loss: 0.0831
+2023-03-03 23:37:33,921 - mmseg - INFO - Iter [34300/80000]	lr: 1.875e-05, eta: 3:56:25, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7397, loss: 0.0815
+2023-03-03 23:37:48,830 - mmseg - INFO - Iter [34350/80000]	lr: 1.875e-05, eta: 3:56:08, time: 0.298, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8483, loss: 0.0789
+2023-03-03 23:38:03,464 - mmseg - INFO - Iter [34400/80000]	lr: 1.875e-05, eta: 3:55:52, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0779, decode.acc_seg: 96.9064, loss: 0.0779
+2023-03-03 23:38:20,435 - mmseg - INFO - Iter [34450/80000]	lr: 1.875e-05, eta: 3:55:38, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.7809, loss: 0.0807
+2023-03-03 23:38:35,128 - mmseg - INFO - Iter [34500/80000]	lr: 1.875e-05, eta: 3:55:22, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0833, decode.acc_seg: 96.6812, loss: 0.0833
+2023-03-03 23:38:50,064 - mmseg - INFO - Iter [34550/80000]	lr: 1.875e-05, eta: 3:55:05, time: 0.299, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.9045, loss: 0.0783
+2023-03-03 23:39:07,037 - mmseg - INFO - Iter [34600/80000]	lr: 1.875e-05, eta: 3:54:52, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0834, decode.acc_seg: 96.7485, loss: 0.0834
+2023-03-03 23:39:21,883 - mmseg - INFO - Iter [34650/80000]	lr: 1.875e-05, eta: 3:54:35, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8180, loss: 0.0798
+2023-03-03 23:39:36,522 - mmseg - INFO - Iter [34700/80000]	lr: 1.875e-05, eta: 3:54:19, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8349, loss: 0.0789
+2023-03-03 23:39:51,107 - mmseg - INFO - Iter [34750/80000]	lr: 1.875e-05, eta: 3:54:02, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.7571, loss: 0.0819
+2023-03-03 23:40:08,121 - mmseg - INFO - Iter [34800/80000]	lr: 1.875e-05, eta: 3:53:48, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8257, loss: 0.0795
+2023-03-03 23:40:22,755 - mmseg - INFO - Iter [34850/80000]	lr: 1.875e-05, eta: 3:53:32, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8560, loss: 0.0791
+2023-03-03 23:40:37,498 - mmseg - INFO - Iter [34900/80000]	lr: 1.875e-05, eta: 3:53:15, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0825, decode.acc_seg: 96.7482, loss: 0.0825
+2023-03-03 23:40:52,135 - mmseg - INFO - Iter [34950/80000]	lr: 1.875e-05, eta: 3:52:58, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.8064, loss: 0.0797
+2023-03-03 23:41:09,085 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 23:41:09,085 - mmseg - INFO - Iter [35000/80000]	lr: 1.875e-05, eta: 3:52:45, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.7260, loss: 0.0821
+2023-03-03 23:41:23,731 - mmseg - INFO - Iter [35050/80000]	lr: 1.875e-05, eta: 3:52:28, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7786, loss: 0.0811
+2023-03-03 23:41:38,362 - mmseg - INFO - Iter [35100/80000]	lr: 1.875e-05, eta: 3:52:11, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0836, decode.acc_seg: 96.6967, loss: 0.0836
+2023-03-03 23:41:53,099 - mmseg - INFO - Iter [35150/80000]	lr: 1.875e-05, eta: 3:51:55, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8918, loss: 0.0784
+2023-03-03 23:42:10,117 - mmseg - INFO - Iter [35200/80000]	lr: 1.875e-05, eta: 3:51:41, time: 0.340, data_time: 0.054, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.8159, loss: 0.0801
+2023-03-03 23:42:24,747 - mmseg - INFO - Iter [35250/80000]	lr: 1.875e-05, eta: 3:51:25, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8851, loss: 0.0790
+2023-03-03 23:42:39,361 - mmseg - INFO - Iter [35300/80000]	lr: 1.875e-05, eta: 3:51:08, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.7486, loss: 0.0812
+2023-03-03 23:42:56,345 - mmseg - INFO - Iter [35350/80000]	lr: 1.875e-05, eta: 3:50:54, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0809, decode.acc_seg: 96.7629, loss: 0.0809
+2023-03-03 23:43:10,941 - mmseg - INFO - Iter [35400/80000]	lr: 1.875e-05, eta: 3:50:38, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0854, decode.acc_seg: 96.6254, loss: 0.0854
+2023-03-03 23:43:25,545 - mmseg - INFO - Iter [35450/80000]	lr: 1.875e-05, eta: 3:50:21, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.7973, loss: 0.0808
+2023-03-03 23:43:40,188 - mmseg - INFO - Iter [35500/80000]	lr: 1.875e-05, eta: 3:50:04, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7831, loss: 0.0811
+2023-03-03 23:43:57,274 - mmseg - INFO - Iter [35550/80000]	lr: 1.875e-05, eta: 3:49:51, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0804, decode.acc_seg: 96.8221, loss: 0.0804
+2023-03-03 23:44:11,983 - mmseg - INFO - Iter [35600/80000]	lr: 1.875e-05, eta: 3:49:34, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0817, decode.acc_seg: 96.7337, loss: 0.0817
+2023-03-03 23:44:26,635 - mmseg - INFO - Iter [35650/80000]	lr: 1.875e-05, eta: 3:49:18, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0844, decode.acc_seg: 96.6515, loss: 0.0844
+2023-03-03 23:44:41,240 - mmseg - INFO - Iter [35700/80000]	lr: 1.875e-05, eta: 3:49:01, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8580, loss: 0.0802
+2023-03-03 23:44:58,361 - mmseg - INFO - Iter [35750/80000]	lr: 1.875e-05, eta: 3:48:48, time: 0.342, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8748, loss: 0.0785
+2023-03-03 23:45:13,000 - mmseg - INFO - Iter [35800/80000]	lr: 1.875e-05, eta: 3:48:31, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.7259, loss: 0.0821
+2023-03-03 23:45:27,900 - mmseg - INFO - Iter [35850/80000]	lr: 1.875e-05, eta: 3:48:15, time: 0.298, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.7659, loss: 0.0810
+2023-03-03 23:45:44,973 - mmseg - INFO - Iter [35900/80000]	lr: 1.875e-05, eta: 3:48:01, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.8536, loss: 0.0786
+2023-03-03 23:45:59,631 - mmseg - INFO - Iter [35950/80000]	lr: 1.875e-05, eta: 3:47:45, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0842, decode.acc_seg: 96.6668, loss: 0.0842
+2023-03-03 23:46:14,349 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 23:46:14,349 - mmseg - INFO - Iter [36000/80000]	lr: 1.875e-05, eta: 3:47:28, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0779, decode.acc_seg: 96.8590, loss: 0.0779
+2023-03-03 23:46:28,939 - mmseg - INFO - Iter [36050/80000]	lr: 1.875e-05, eta: 3:47:12, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8007, loss: 0.0798
+2023-03-03 23:46:45,875 - mmseg - INFO - Iter [36100/80000]	lr: 1.875e-05, eta: 3:46:58, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.8159, loss: 0.0810
+2023-03-03 23:47:00,528 - mmseg - INFO - Iter [36150/80000]	lr: 1.875e-05, eta: 3:46:41, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.7303, loss: 0.0808
+2023-03-03 23:47:15,169 - mmseg - INFO - Iter [36200/80000]	lr: 1.875e-05, eta: 3:46:25, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0777, decode.acc_seg: 96.9105, loss: 0.0777
+2023-03-03 23:47:29,759 - mmseg - INFO - Iter [36250/80000]	lr: 1.875e-05, eta: 3:46:08, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8633, loss: 0.0790
+2023-03-03 23:47:46,821 - mmseg - INFO - Iter [36300/80000]	lr: 1.875e-05, eta: 3:45:54, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7914, loss: 0.0811
+2023-03-03 23:48:01,709 - mmseg - INFO - Iter [36350/80000]	lr: 1.875e-05, eta: 3:45:38, time: 0.298, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0835, decode.acc_seg: 96.6606, loss: 0.0835
+2023-03-03 23:48:16,272 - mmseg - INFO - Iter [36400/80000]	lr: 1.875e-05, eta: 3:45:22, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0774, decode.acc_seg: 96.9040, loss: 0.0774
+2023-03-03 23:48:30,887 - mmseg - INFO - Iter [36450/80000]	lr: 1.875e-05, eta: 3:45:05, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0836, decode.acc_seg: 96.6638, loss: 0.0836
+2023-03-03 23:48:47,827 - mmseg - INFO - Iter [36500/80000]	lr: 1.875e-05, eta: 3:44:51, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.8055, loss: 0.0803
+2023-03-03 23:49:02,636 - mmseg - INFO - Iter [36550/80000]	lr: 1.875e-05, eta: 3:44:35, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0816, decode.acc_seg: 96.7764, loss: 0.0816
+2023-03-03 23:49:17,281 - mmseg - INFO - Iter [36600/80000]	lr: 1.875e-05, eta: 3:44:18, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0787, decode.acc_seg: 96.8420, loss: 0.0787
+2023-03-03 23:49:34,296 - mmseg - INFO - Iter [36650/80000]	lr: 1.875e-05, eta: 3:44:05, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8009, loss: 0.0795
+2023-03-03 23:49:48,945 - mmseg - INFO - Iter [36700/80000]	lr: 1.875e-05, eta: 3:43:48, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0822, decode.acc_seg: 96.7358, loss: 0.0822
+2023-03-03 23:50:03,619 - mmseg - INFO - Iter [36750/80000]	lr: 1.875e-05, eta: 3:43:32, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8187, loss: 0.0802
+2023-03-03 23:50:18,195 - mmseg - INFO - Iter [36800/80000]	lr: 1.875e-05, eta: 3:43:15, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8465, loss: 0.0788
+2023-03-03 23:50:35,226 - mmseg - INFO - Iter [36850/80000]	lr: 1.875e-05, eta: 3:43:01, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0823, decode.acc_seg: 96.7162, loss: 0.0823
+2023-03-03 23:50:49,973 - mmseg - INFO - Iter [36900/80000]	lr: 1.875e-05, eta: 3:42:45, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7903, loss: 0.0811
+2023-03-03 23:51:04,628 - mmseg - INFO - Iter [36950/80000]	lr: 1.875e-05, eta: 3:42:28, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.7694, loss: 0.0818
+2023-03-03 23:51:19,262 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 23:51:19,262 - mmseg - INFO - Iter [37000/80000]	lr: 1.875e-05, eta: 3:42:12, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8054, loss: 0.0795
+2023-03-03 23:51:36,252 - mmseg - INFO - Iter [37050/80000]	lr: 1.875e-05, eta: 3:41:58, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.7479, loss: 0.0806
+2023-03-03 23:51:50,871 - mmseg - INFO - Iter [37100/80000]	lr: 1.875e-05, eta: 3:41:42, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.7682, loss: 0.0807
+2023-03-03 23:52:05,431 - mmseg - INFO - Iter [37150/80000]	lr: 1.875e-05, eta: 3:41:25, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0780, decode.acc_seg: 96.8756, loss: 0.0780
+2023-03-03 23:52:20,121 - mmseg - INFO - Iter [37200/80000]	lr: 1.875e-05, eta: 3:41:09, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8892, loss: 0.0785
+2023-03-03 23:52:37,093 - mmseg - INFO - Iter [37250/80000]	lr: 1.875e-05, eta: 3:40:55, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8135, loss: 0.0799
+2023-03-03 23:52:51,785 - mmseg - INFO - Iter [37300/80000]	lr: 1.875e-05, eta: 3:40:38, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0841, decode.acc_seg: 96.6647, loss: 0.0841
+2023-03-03 23:53:06,479 - mmseg - INFO - Iter [37350/80000]	lr: 1.875e-05, eta: 3:40:22, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0787, decode.acc_seg: 96.8321, loss: 0.0787
+2023-03-03 23:53:23,461 - mmseg - INFO - Iter [37400/80000]	lr: 1.875e-05, eta: 3:40:08, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0813, decode.acc_seg: 96.7422, loss: 0.0813
+2023-03-03 23:53:38,105 - mmseg - INFO - Iter [37450/80000]	lr: 1.875e-05, eta: 3:39:52, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7873, loss: 0.0827
+2023-03-03 23:53:52,845 - mmseg - INFO - Iter [37500/80000]	lr: 1.875e-05, eta: 3:39:35, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.7934, loss: 0.0802
+2023-03-03 23:54:07,459 - mmseg - INFO - Iter [37550/80000]	lr: 1.875e-05, eta: 3:39:19, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0831, decode.acc_seg: 96.7488, loss: 0.0831
+2023-03-03 23:54:24,401 - mmseg - INFO - Iter [37600/80000]	lr: 1.875e-05, eta: 3:39:05, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8833, loss: 0.0785
+2023-03-03 23:54:39,064 - mmseg - INFO - Iter [37650/80000]	lr: 1.875e-05, eta: 3:38:48, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8828, loss: 0.0784
+2023-03-03 23:54:53,620 - mmseg - INFO - Iter [37700/80000]	lr: 1.875e-05, eta: 3:38:32, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0823, decode.acc_seg: 96.7484, loss: 0.0823
+2023-03-03 23:55:08,447 - mmseg - INFO - Iter [37750/80000]	lr: 1.875e-05, eta: 3:38:16, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8470, loss: 0.0794
+2023-03-03 23:55:25,436 - mmseg - INFO - Iter [37800/80000]	lr: 1.875e-05, eta: 3:38:02, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8542, loss: 0.0791
+2023-03-03 23:55:40,011 - mmseg - INFO - Iter [37850/80000]	lr: 1.875e-05, eta: 3:37:45, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.7544, loss: 0.0818
+2023-03-03 23:55:54,690 - mmseg - INFO - Iter [37900/80000]	lr: 1.875e-05, eta: 3:37:29, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0828, decode.acc_seg: 96.7152, loss: 0.0828
+2023-03-03 23:56:11,620 - mmseg - INFO - Iter [37950/80000]	lr: 1.875e-05, eta: 3:37:15, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0823, decode.acc_seg: 96.7404, loss: 0.0823
+2023-03-03 23:56:26,338 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-03 23:56:26,339 - mmseg - INFO - Iter [38000/80000]	lr: 1.875e-05, eta: 3:36:59, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8741, loss: 0.0788
+2023-03-03 23:56:40,916 - mmseg - INFO - Iter [38050/80000]	lr: 1.875e-05, eta: 3:36:42, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8180, loss: 0.0795
+2023-03-03 23:56:55,498 - mmseg - INFO - Iter [38100/80000]	lr: 1.875e-05, eta: 3:36:25, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0839, decode.acc_seg: 96.6655, loss: 0.0839
+2023-03-03 23:57:12,436 - mmseg - INFO - Iter [38150/80000]	lr: 1.875e-05, eta: 3:36:12, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.8095, loss: 0.0806
+2023-03-03 23:57:27,175 - mmseg - INFO - Iter [38200/80000]	lr: 1.875e-05, eta: 3:35:55, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.7609, loss: 0.0806
+2023-03-03 23:57:41,817 - mmseg - INFO - Iter [38250/80000]	lr: 1.875e-05, eta: 3:35:39, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0777, decode.acc_seg: 96.8893, loss: 0.0777
+2023-03-03 23:57:56,644 - mmseg - INFO - Iter [38300/80000]	lr: 1.875e-05, eta: 3:35:23, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0829, decode.acc_seg: 96.6611, loss: 0.0829
+2023-03-03 23:58:13,610 - mmseg - INFO - Iter [38350/80000]	lr: 1.875e-05, eta: 3:35:09, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.7959, loss: 0.0801
+2023-03-03 23:58:28,228 - mmseg - INFO - Iter [38400/80000]	lr: 1.875e-05, eta: 3:34:52, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0813, decode.acc_seg: 96.7626, loss: 0.0813
+2023-03-03 23:58:42,886 - mmseg - INFO - Iter [38450/80000]	lr: 1.875e-05, eta: 3:34:36, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.8067, loss: 0.0803
+2023-03-03 23:58:57,534 - mmseg - INFO - Iter [38500/80000]	lr: 1.875e-05, eta: 3:34:19, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.7333, loss: 0.0818
+2023-03-03 23:59:14,616 - mmseg - INFO - Iter [38550/80000]	lr: 1.875e-05, eta: 3:34:06, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0835, decode.acc_seg: 96.6950, loss: 0.0835
+2023-03-03 23:59:29,349 - mmseg - INFO - Iter [38600/80000]	lr: 1.875e-05, eta: 3:33:49, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.7194, loss: 0.0821
+2023-03-03 23:59:43,953 - mmseg - INFO - Iter [38650/80000]	lr: 1.875e-05, eta: 3:33:33, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0780, decode.acc_seg: 96.8993, loss: 0.0780
+2023-03-04 00:00:00,881 - mmseg - INFO - Iter [38700/80000]	lr: 1.875e-05, eta: 3:33:19, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0851, decode.acc_seg: 96.6639, loss: 0.0851
+2023-03-04 00:00:15,686 - mmseg - INFO - Iter [38750/80000]	lr: 1.875e-05, eta: 3:33:03, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.8569, loss: 0.0783
+2023-03-04 00:00:30,319 - mmseg - INFO - Iter [38800/80000]	lr: 1.875e-05, eta: 3:32:46, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0843, decode.acc_seg: 96.6406, loss: 0.0843
+2023-03-04 00:00:44,908 - mmseg - INFO - Iter [38850/80000]	lr: 1.875e-05, eta: 3:32:30, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.8740, loss: 0.0786
+2023-03-04 00:01:01,916 - mmseg - INFO - Iter [38900/80000]	lr: 1.875e-05, eta: 3:32:16, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0834, decode.acc_seg: 96.6553, loss: 0.0834
+2023-03-04 00:01:16,559 - mmseg - INFO - Iter [38950/80000]	lr: 1.875e-05, eta: 3:32:00, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0820, decode.acc_seg: 96.7201, loss: 0.0820
+2023-03-04 00:01:31,239 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 00:01:31,239 - mmseg - INFO - Iter [39000/80000]	lr: 1.875e-05, eta: 3:31:43, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.7516, loss: 0.0818
+2023-03-04 00:01:45,941 - mmseg - INFO - Iter [39050/80000]	lr: 1.875e-05, eta: 3:31:27, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8408, loss: 0.0794
+2023-03-04 00:02:03,060 - mmseg - INFO - Iter [39100/80000]	lr: 1.875e-05, eta: 3:31:13, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.7277, loss: 0.0819
+2023-03-04 00:02:17,714 - mmseg - INFO - Iter [39150/80000]	lr: 1.875e-05, eta: 3:30:57, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0770, decode.acc_seg: 96.9026, loss: 0.0770
+2023-03-04 00:02:32,320 - mmseg - INFO - Iter [39200/80000]	lr: 1.875e-05, eta: 3:30:40, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.7501, loss: 0.0821
+2023-03-04 00:02:49,262 - mmseg - INFO - Iter [39250/80000]	lr: 1.875e-05, eta: 3:30:26, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0813, decode.acc_seg: 96.7579, loss: 0.0813
+2023-03-04 00:03:03,962 - mmseg - INFO - Iter [39300/80000]	lr: 1.875e-05, eta: 3:30:10, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.8443, loss: 0.0783
+2023-03-04 00:03:18,607 - mmseg - INFO - Iter [39350/80000]	lr: 1.875e-05, eta: 3:29:54, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0820, decode.acc_seg: 96.7567, loss: 0.0820
+2023-03-04 00:03:33,209 - mmseg - INFO - Iter [39400/80000]	lr: 1.875e-05, eta: 3:29:37, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8851, loss: 0.0795
+2023-03-04 00:03:50,282 - mmseg - INFO - Iter [39450/80000]	lr: 1.875e-05, eta: 3:29:23, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8144, loss: 0.0798
+2023-03-04 00:04:04,948 - mmseg - INFO - Iter [39500/80000]	lr: 1.875e-05, eta: 3:29:07, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0775, decode.acc_seg: 96.8938, loss: 0.0775
+2023-03-04 00:04:19,505 - mmseg - INFO - Iter [39550/80000]	lr: 1.875e-05, eta: 3:28:51, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.7374, loss: 0.0821
+2023-03-04 00:04:34,151 - mmseg - INFO - Iter [39600/80000]	lr: 1.875e-05, eta: 3:28:34, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.7519, loss: 0.0812
+2023-03-04 00:04:51,163 - mmseg - INFO - Iter [39650/80000]	lr: 1.875e-05, eta: 3:28:20, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0816, decode.acc_seg: 96.7057, loss: 0.0816
+2023-03-04 00:05:05,912 - mmseg - INFO - Iter [39700/80000]	lr: 1.875e-05, eta: 3:28:04, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0879, decode.acc_seg: 96.7032, loss: 0.0879
+2023-03-04 00:05:20,536 - mmseg - INFO - Iter [39750/80000]	lr: 1.875e-05, eta: 3:27:48, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8483, loss: 0.0784
+2023-03-04 00:05:35,129 - mmseg - INFO - Iter [39800/80000]	lr: 1.875e-05, eta: 3:27:31, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8445, loss: 0.0791
+2023-03-04 00:05:52,087 - mmseg - INFO - Iter [39850/80000]	lr: 1.875e-05, eta: 3:27:17, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0831, decode.acc_seg: 96.6678, loss: 0.0831
+2023-03-04 00:06:06,833 - mmseg - INFO - Iter [39900/80000]	lr: 1.875e-05, eta: 3:27:01, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8380, loss: 0.0794
+2023-03-04 00:06:21,490 - mmseg - INFO - Iter [39950/80000]	lr: 1.875e-05, eta: 3:26:45, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.7715, loss: 0.0812
+2023-03-04 00:06:38,571 - mmseg - INFO - Saving checkpoint at 40000 iterations
+2023-03-04 00:06:40,498 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 00:06:40,498 - mmseg - INFO - Iter [40000/80000]	lr: 1.875e-05, eta: 3:26:33, time: 0.380, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8185, loss: 0.0795
+2023-03-04 00:07:06,324 - mmseg - INFO - per class results:
+2023-03-04 00:07:06,325 - mmseg - INFO - 
++---------------+-------+-------+
+|     Class     |  IoU  |  Acc  |
++---------------+-------+-------+
+|   background  |  nan  |  nan  |
+|      road     | 98.53 | 99.16 |
+|    sidewalk   | 87.18 | 93.83 |
+|    building   | 93.57 |  97.1 |
+|      wall     | 54.72 | 59.97 |
+|     fence     |  65.1 | 75.52 |
+|      pole     |  71.1 | 82.56 |
+| traffic light | 75.58 | 87.07 |
+|  traffic sign | 82.72 | 89.88 |
+|   vegetation  |  93.1 | 96.82 |
+|    terrain    | 63.84 | 72.65 |
+|      sky      | 95.34 | 98.36 |
+|     person    | 84.86 | 93.46 |
+|     rider     | 67.83 | 80.14 |
+|      car      | 96.05 | 98.01 |
+|     truck     | 86.59 | 91.48 |
+|      bus      | 92.86 | 95.75 |
+|     train     | 86.25 | 91.18 |
+|   motorcycle  | 71.94 | 81.31 |
+|    bicycle    | 80.58 |  90.3 |
++---------------+-------+-------+
+2023-03-04 00:07:06,325 - mmseg - INFO - Summary:
+2023-03-04 00:07:06,325 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 96.64 | 81.46 | 88.13 |
++-------+-------+-------+
+2023-03-04 00:07:06,394 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20/best_mIoU_iter_32000.pth was removed
+2023-03-04 00:07:08,289 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_40000.pth.
+2023-03-04 00:07:08,289 - mmseg - INFO - Best mIoU is 0.8146 at 40000 iter.
+2023-03-04 00:07:08,289 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 00:07:08,289 - mmseg - INFO - Iter(val) [63]	aAcc: 0.9664, mIoU: 0.8146, mAcc: 0.8813, IoU.background: nan, IoU.road: 0.9853, IoU.sidewalk: 0.8718, IoU.building: 0.9357, IoU.wall: 0.5472, IoU.fence: 0.6510, IoU.pole: 0.7110, IoU.traffic light: 0.7558, IoU.traffic sign: 0.8272, IoU.vegetation: 0.9310, IoU.terrain: 0.6384, IoU.sky: 0.9534, IoU.person: 0.8486, IoU.rider: 0.6783, IoU.car: 0.9605, IoU.truck: 0.8659, IoU.bus: 0.9286, IoU.train: 0.8625, IoU.motorcycle: 0.7194, IoU.bicycle: 0.8058, Acc.background: nan, Acc.road: 0.9916, Acc.sidewalk: 0.9383, Acc.building: 0.9710, Acc.wall: 0.5997, Acc.fence: 0.7552, Acc.pole: 0.8256, Acc.traffic light: 0.8707, Acc.traffic sign: 0.8988, Acc.vegetation: 0.9682, Acc.terrain: 0.7265, Acc.sky: 0.9836, Acc.person: 0.9346, Acc.rider: 0.8014, Acc.car: 0.9801, Acc.truck: 0.9148, Acc.bus: 0.9575, Acc.train: 0.9118, Acc.motorcycle: 0.8131, Acc.bicycle: 0.9030
+2023-03-04 00:07:23,204 - mmseg - INFO - Iter [40050/80000]	lr: 9.375e-06, eta: 3:26:44, time: 0.854, data_time: 0.564, memory: 67605, decode.loss_ce: 0.0813, decode.acc_seg: 96.7611, loss: 0.0813
+2023-03-04 00:07:37,893 - mmseg - INFO - Iter [40100/80000]	lr: 9.375e-06, eta: 3:26:28, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8740, loss: 0.0790
+2023-03-04 00:07:52,520 - mmseg - INFO - Iter [40150/80000]	lr: 9.375e-06, eta: 3:26:12, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.8034, loss: 0.0806
+2023-03-04 00:08:09,527 - mmseg - INFO - Iter [40200/80000]	lr: 9.375e-06, eta: 3:25:58, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0775, decode.acc_seg: 96.9126, loss: 0.0775
+2023-03-04 00:08:24,216 - mmseg - INFO - Iter [40250/80000]	lr: 9.375e-06, eta: 3:25:41, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.8119, loss: 0.0812
+2023-03-04 00:08:38,792 - mmseg - INFO - Iter [40300/80000]	lr: 9.375e-06, eta: 3:25:25, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0828, decode.acc_seg: 96.6996, loss: 0.0828
+2023-03-04 00:08:53,487 - mmseg - INFO - Iter [40350/80000]	lr: 9.375e-06, eta: 3:25:08, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.7610, loss: 0.0818
+2023-03-04 00:09:10,457 - mmseg - INFO - Iter [40400/80000]	lr: 9.375e-06, eta: 3:24:54, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0773, decode.acc_seg: 96.9311, loss: 0.0773
+2023-03-04 00:09:25,238 - mmseg - INFO - Iter [40450/80000]	lr: 9.375e-06, eta: 3:24:38, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.8893, loss: 0.0786
+2023-03-04 00:09:39,800 - mmseg - INFO - Iter [40500/80000]	lr: 9.375e-06, eta: 3:24:22, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.7781, loss: 0.0800
+2023-03-04 00:09:56,725 - mmseg - INFO - Iter [40550/80000]	lr: 9.375e-06, eta: 3:24:07, time: 0.338, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0781, decode.acc_seg: 96.9205, loss: 0.0781
+2023-03-04 00:10:11,623 - mmseg - INFO - Iter [40600/80000]	lr: 9.375e-06, eta: 3:23:51, time: 0.298, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8152, loss: 0.0802
+2023-03-04 00:10:26,184 - mmseg - INFO - Iter [40650/80000]	lr: 9.375e-06, eta: 3:23:35, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8372, loss: 0.0794
+2023-03-04 00:10:40,854 - mmseg - INFO - Iter [40700/80000]	lr: 9.375e-06, eta: 3:23:19, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.7051, loss: 0.0819
+2023-03-04 00:10:57,797 - mmseg - INFO - Iter [40750/80000]	lr: 9.375e-06, eta: 3:23:04, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8515, loss: 0.0790
+2023-03-04 00:11:12,443 - mmseg - INFO - Iter [40800/80000]	lr: 9.375e-06, eta: 3:22:48, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0830, decode.acc_seg: 96.7456, loss: 0.0830
+2023-03-04 00:11:26,998 - mmseg - INFO - Iter [40850/80000]	lr: 9.375e-06, eta: 3:22:32, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0779, decode.acc_seg: 96.8665, loss: 0.0779
+2023-03-04 00:11:41,650 - mmseg - INFO - Iter [40900/80000]	lr: 9.375e-06, eta: 3:22:15, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8481, loss: 0.0790
+2023-03-04 00:11:58,586 - mmseg - INFO - Iter [40950/80000]	lr: 9.375e-06, eta: 3:22:01, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.8137, loss: 0.0805
+2023-03-04 00:12:13,247 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 00:12:13,247 - mmseg - INFO - Iter [41000/80000]	lr: 9.375e-06, eta: 3:21:45, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0768, decode.acc_seg: 96.9235, loss: 0.0768
+2023-03-04 00:12:27,847 - mmseg - INFO - Iter [41050/80000]	lr: 9.375e-06, eta: 3:21:28, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8339, loss: 0.0794
+2023-03-04 00:12:42,473 - mmseg - INFO - Iter [41100/80000]	lr: 9.375e-06, eta: 3:21:12, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0847, decode.acc_seg: 96.7353, loss: 0.0847
+2023-03-04 00:12:59,470 - mmseg - INFO - Iter [41150/80000]	lr: 9.375e-06, eta: 3:20:58, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0823, decode.acc_seg: 96.7287, loss: 0.0823
+2023-03-04 00:13:14,088 - mmseg - INFO - Iter [41200/80000]	lr: 9.375e-06, eta: 3:20:41, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.7983, loss: 0.0803
+2023-03-04 00:13:28,704 - mmseg - INFO - Iter [41250/80000]	lr: 9.375e-06, eta: 3:20:25, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.7457, loss: 0.0814
+2023-03-04 00:13:45,657 - mmseg - INFO - Iter [41300/80000]	lr: 9.375e-06, eta: 3:20:11, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8337, loss: 0.0799
+2023-03-04 00:14:00,346 - mmseg - INFO - Iter [41350/80000]	lr: 9.375e-06, eta: 3:19:55, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8686, loss: 0.0791
+2023-03-04 00:14:15,093 - mmseg - INFO - Iter [41400/80000]	lr: 9.375e-06, eta: 3:19:38, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0774, decode.acc_seg: 96.8678, loss: 0.0774
+2023-03-04 00:14:29,666 - mmseg - INFO - Iter [41450/80000]	lr: 9.375e-06, eta: 3:19:22, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.7674, loss: 0.0802
+2023-03-04 00:14:46,845 - mmseg - INFO - Iter [41500/80000]	lr: 9.375e-06, eta: 3:19:08, time: 0.344, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7700, loss: 0.0811
+2023-03-04 00:15:01,452 - mmseg - INFO - Iter [41550/80000]	lr: 9.375e-06, eta: 3:18:52, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0804, decode.acc_seg: 96.7934, loss: 0.0804
+2023-03-04 00:15:16,039 - mmseg - INFO - Iter [41600/80000]	lr: 9.375e-06, eta: 3:18:35, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0844, decode.acc_seg: 96.7186, loss: 0.0844
+2023-03-04 00:15:30,655 - mmseg - INFO - Iter [41650/80000]	lr: 9.375e-06, eta: 3:18:19, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.8124, loss: 0.0801
+2023-03-04 00:15:47,569 - mmseg - INFO - Iter [41700/80000]	lr: 9.375e-06, eta: 3:18:05, time: 0.338, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0787, decode.acc_seg: 96.8401, loss: 0.0787
+2023-03-04 00:16:02,215 - mmseg - INFO - Iter [41750/80000]	lr: 9.375e-06, eta: 3:17:48, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0774, decode.acc_seg: 96.9009, loss: 0.0774
+2023-03-04 00:16:16,858 - mmseg - INFO - Iter [41800/80000]	lr: 9.375e-06, eta: 3:17:32, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8449, loss: 0.0789
+2023-03-04 00:16:31,614 - mmseg - INFO - Iter [41850/80000]	lr: 9.375e-06, eta: 3:17:16, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0770, decode.acc_seg: 96.9233, loss: 0.0770
+2023-03-04 00:16:48,882 - mmseg - INFO - Iter [41900/80000]	lr: 9.375e-06, eta: 3:17:02, time: 0.345, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.7701, loss: 0.0814
+2023-03-04 00:17:03,488 - mmseg - INFO - Iter [41950/80000]	lr: 9.375e-06, eta: 3:16:46, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8554, loss: 0.0792
+2023-03-04 00:17:18,076 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 00:17:18,077 - mmseg - INFO - Iter [42000/80000]	lr: 9.375e-06, eta: 3:16:29, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8643, loss: 0.0785
+2023-03-04 00:17:35,013 - mmseg - INFO - Iter [42050/80000]	lr: 9.375e-06, eta: 3:16:15, time: 0.339, data_time: 0.054, memory: 67605, decode.loss_ce: 0.0809, decode.acc_seg: 96.7202, loss: 0.0809
+2023-03-04 00:17:49,852 - mmseg - INFO - Iter [42100/80000]	lr: 9.375e-06, eta: 3:15:59, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8130, loss: 0.0799
+2023-03-04 00:18:04,443 - mmseg - INFO - Iter [42150/80000]	lr: 9.375e-06, eta: 3:15:43, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0817, decode.acc_seg: 96.7621, loss: 0.0817
+2023-03-04 00:18:19,050 - mmseg - INFO - Iter [42200/80000]	lr: 9.375e-06, eta: 3:15:26, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.8531, loss: 0.0797
+2023-03-04 00:18:35,989 - mmseg - INFO - Iter [42250/80000]	lr: 9.375e-06, eta: 3:15:12, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.8451, loss: 0.0786
+2023-03-04 00:18:50,686 - mmseg - INFO - Iter [42300/80000]	lr: 9.375e-06, eta: 3:14:56, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8458, loss: 0.0789
+2023-03-04 00:19:05,279 - mmseg - INFO - Iter [42350/80000]	lr: 9.375e-06, eta: 3:14:39, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8403, loss: 0.0796
+2023-03-04 00:19:19,847 - mmseg - INFO - Iter [42400/80000]	lr: 9.375e-06, eta: 3:14:23, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0758, decode.acc_seg: 96.9624, loss: 0.0758
+2023-03-04 00:19:36,906 - mmseg - INFO - Iter [42450/80000]	lr: 9.375e-06, eta: 3:14:09, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0846, decode.acc_seg: 96.6593, loss: 0.0846
+2023-03-04 00:19:51,492 - mmseg - INFO - Iter [42500/80000]	lr: 9.375e-06, eta: 3:13:53, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0835, decode.acc_seg: 96.6507, loss: 0.0835
+2023-03-04 00:20:06,267 - mmseg - INFO - Iter [42550/80000]	lr: 9.375e-06, eta: 3:13:36, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8211, loss: 0.0792
+2023-03-04 00:20:23,193 - mmseg - INFO - Iter [42600/80000]	lr: 9.375e-06, eta: 3:13:22, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.7727, loss: 0.0803
+2023-03-04 00:20:37,883 - mmseg - INFO - Iter [42650/80000]	lr: 9.375e-06, eta: 3:13:06, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8513, loss: 0.0792
+2023-03-04 00:20:52,506 - mmseg - INFO - Iter [42700/80000]	lr: 9.375e-06, eta: 3:12:50, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.8244, loss: 0.0805
+2023-03-04 00:21:07,187 - mmseg - INFO - Iter [42750/80000]	lr: 9.375e-06, eta: 3:12:33, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0809, decode.acc_seg: 96.7804, loss: 0.0809
+2023-03-04 00:21:24,117 - mmseg - INFO - Iter [42800/80000]	lr: 9.375e-06, eta: 3:12:19, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.8817, loss: 0.0783
+2023-03-04 00:21:38,874 - mmseg - INFO - Iter [42850/80000]	lr: 9.375e-06, eta: 3:12:03, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0817, decode.acc_seg: 96.7444, loss: 0.0817
+2023-03-04 00:21:53,556 - mmseg - INFO - Iter [42900/80000]	lr: 9.375e-06, eta: 3:11:47, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0777, decode.acc_seg: 96.9339, loss: 0.0777
+2023-03-04 00:22:08,197 - mmseg - INFO - Iter [42950/80000]	lr: 9.375e-06, eta: 3:11:31, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.7864, loss: 0.0803
+2023-03-04 00:22:25,310 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 00:22:25,310 - mmseg - INFO - Iter [43000/80000]	lr: 9.375e-06, eta: 3:11:16, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.7839, loss: 0.0814
+2023-03-04 00:22:40,059 - mmseg - INFO - Iter [43050/80000]	lr: 9.375e-06, eta: 3:11:00, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0776, decode.acc_seg: 96.8673, loss: 0.0776
+2023-03-04 00:22:54,633 - mmseg - INFO - Iter [43100/80000]	lr: 9.375e-06, eta: 3:10:44, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0766, decode.acc_seg: 96.9821, loss: 0.0766
+2023-03-04 00:23:09,259 - mmseg - INFO - Iter [43150/80000]	lr: 9.375e-06, eta: 3:10:28, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.8138, loss: 0.0806
+2023-03-04 00:23:26,271 - mmseg - INFO - Iter [43200/80000]	lr: 9.375e-06, eta: 3:10:13, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8298, loss: 0.0785
+2023-03-04 00:23:40,894 - mmseg - INFO - Iter [43250/80000]	lr: 9.375e-06, eta: 3:09:57, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0826, decode.acc_seg: 96.7305, loss: 0.0826
+2023-03-04 00:23:55,654 - mmseg - INFO - Iter [43300/80000]	lr: 9.375e-06, eta: 3:09:41, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.9111, loss: 0.0783
+2023-03-04 00:24:12,784 - mmseg - INFO - Iter [43350/80000]	lr: 9.375e-06, eta: 3:09:27, time: 0.343, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.8857, loss: 0.0786
+2023-03-04 00:24:27,502 - mmseg - INFO - Iter [43400/80000]	lr: 9.375e-06, eta: 3:09:11, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0813, decode.acc_seg: 96.7834, loss: 0.0813
+2023-03-04 00:24:42,129 - mmseg - INFO - Iter [43450/80000]	lr: 9.375e-06, eta: 3:08:55, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0772, decode.acc_seg: 96.9425, loss: 0.0772
+2023-03-04 00:24:56,792 - mmseg - INFO - Iter [43500/80000]	lr: 9.375e-06, eta: 3:08:38, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8665, loss: 0.0792
+2023-03-04 00:25:13,727 - mmseg - INFO - Iter [43550/80000]	lr: 9.375e-06, eta: 3:08:24, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0804, decode.acc_seg: 96.8089, loss: 0.0804
+2023-03-04 00:25:28,516 - mmseg - INFO - Iter [43600/80000]	lr: 9.375e-06, eta: 3:08:08, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8328, loss: 0.0788
+2023-03-04 00:25:43,211 - mmseg - INFO - Iter [43650/80000]	lr: 9.375e-06, eta: 3:07:52, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0776, decode.acc_seg: 96.9017, loss: 0.0776
+2023-03-04 00:25:57,785 - mmseg - INFO - Iter [43700/80000]	lr: 9.375e-06, eta: 3:07:35, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0781, decode.acc_seg: 96.8621, loss: 0.0781
+2023-03-04 00:26:14,870 - mmseg - INFO - Iter [43750/80000]	lr: 9.375e-06, eta: 3:07:21, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.7010, loss: 0.0821
+2023-03-04 00:26:29,542 - mmseg - INFO - Iter [43800/80000]	lr: 9.375e-06, eta: 3:07:05, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0771, decode.acc_seg: 96.9339, loss: 0.0771
+2023-03-04 00:26:44,425 - mmseg - INFO - Iter [43850/80000]	lr: 9.375e-06, eta: 3:06:49, time: 0.298, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0843, decode.acc_seg: 96.6757, loss: 0.0843
+2023-03-04 00:27:01,529 - mmseg - INFO - Iter [43900/80000]	lr: 9.375e-06, eta: 3:06:35, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0846, decode.acc_seg: 96.6431, loss: 0.0846
+2023-03-04 00:27:16,155 - mmseg - INFO - Iter [43950/80000]	lr: 9.375e-06, eta: 3:06:19, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8305, loss: 0.0785
+2023-03-04 00:27:30,732 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 00:27:30,732 - mmseg - INFO - Iter [44000/80000]	lr: 9.375e-06, eta: 3:06:02, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8292, loss: 0.0798
+2023-03-04 00:27:45,338 - mmseg - INFO - Iter [44050/80000]	lr: 9.375e-06, eta: 3:05:46, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8773, loss: 0.0784
+2023-03-04 00:28:02,230 - mmseg - INFO - Iter [44100/80000]	lr: 9.375e-06, eta: 3:05:32, time: 0.338, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8091, loss: 0.0796
+2023-03-04 00:28:16,942 - mmseg - INFO - Iter [44150/80000]	lr: 9.375e-06, eta: 3:05:16, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8194, loss: 0.0800
+2023-03-04 00:28:31,586 - mmseg - INFO - Iter [44200/80000]	lr: 9.375e-06, eta: 3:04:59, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0771, decode.acc_seg: 96.9146, loss: 0.0771
+2023-03-04 00:28:46,354 - mmseg - INFO - Iter [44250/80000]	lr: 9.375e-06, eta: 3:04:43, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8330, loss: 0.0791
+2023-03-04 00:29:03,380 - mmseg - INFO - Iter [44300/80000]	lr: 9.375e-06, eta: 3:04:29, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0777, decode.acc_seg: 96.8991, loss: 0.0777
+2023-03-04 00:29:18,036 - mmseg - INFO - Iter [44350/80000]	lr: 9.375e-06, eta: 3:04:13, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0752, decode.acc_seg: 96.9880, loss: 0.0752
+2023-03-04 00:29:32,631 - mmseg - INFO - Iter [44400/80000]	lr: 9.375e-06, eta: 3:03:57, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0780, decode.acc_seg: 96.8664, loss: 0.0780
+2023-03-04 00:29:47,516 - mmseg - INFO - Iter [44450/80000]	lr: 9.375e-06, eta: 3:03:41, time: 0.298, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.7851, loss: 0.0803
+2023-03-04 00:30:04,984 - mmseg - INFO - Iter [44500/80000]	lr: 9.375e-06, eta: 3:03:27, time: 0.349, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0816, decode.acc_seg: 96.7659, loss: 0.0816
+2023-03-04 00:30:19,678 - mmseg - INFO - Iter [44550/80000]	lr: 9.375e-06, eta: 3:03:11, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.7787, loss: 0.0797
+2023-03-04 00:30:34,275 - mmseg - INFO - Iter [44600/80000]	lr: 9.375e-06, eta: 3:02:54, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8748, loss: 0.0784
+2023-03-04 00:30:51,284 - mmseg - INFO - Iter [44650/80000]	lr: 9.375e-06, eta: 3:02:40, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8542, loss: 0.0791
+2023-03-04 00:31:05,879 - mmseg - INFO - Iter [44700/80000]	lr: 9.375e-06, eta: 3:02:24, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0779, decode.acc_seg: 96.8882, loss: 0.0779
+2023-03-04 00:31:20,557 - mmseg - INFO - Iter [44750/80000]	lr: 9.375e-06, eta: 3:02:08, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.8435, loss: 0.0786
+2023-03-04 00:31:35,242 - mmseg - INFO - Iter [44800/80000]	lr: 9.375e-06, eta: 3:01:51, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.8414, loss: 0.0803
+2023-03-04 00:31:52,283 - mmseg - INFO - Iter [44850/80000]	lr: 9.375e-06, eta: 3:01:37, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8573, loss: 0.0794
+2023-03-04 00:32:07,005 - mmseg - INFO - Iter [44900/80000]	lr: 9.375e-06, eta: 3:01:21, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8127, loss: 0.0794
+2023-03-04 00:32:21,616 - mmseg - INFO - Iter [44950/80000]	lr: 9.375e-06, eta: 3:01:05, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.8107, loss: 0.0801
+2023-03-04 00:32:36,260 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 00:32:36,260 - mmseg - INFO - Iter [45000/80000]	lr: 9.375e-06, eta: 3:00:49, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7127, loss: 0.0827
+2023-03-04 00:32:53,244 - mmseg - INFO - Iter [45050/80000]	lr: 9.375e-06, eta: 3:00:34, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.7976, loss: 0.0807
+2023-03-04 00:33:07,934 - mmseg - INFO - Iter [45100/80000]	lr: 9.375e-06, eta: 3:00:18, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.7941, loss: 0.0810
+2023-03-04 00:33:22,521 - mmseg - INFO - Iter [45150/80000]	lr: 9.375e-06, eta: 3:00:02, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8272, loss: 0.0789
+2023-03-04 00:33:39,455 - mmseg - INFO - Iter [45200/80000]	lr: 9.375e-06, eta: 2:59:48, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0841, decode.acc_seg: 96.6445, loss: 0.0841
+2023-03-04 00:33:54,201 - mmseg - INFO - Iter [45250/80000]	lr: 9.375e-06, eta: 2:59:32, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0779, decode.acc_seg: 96.8828, loss: 0.0779
+2023-03-04 00:34:08,847 - mmseg - INFO - Iter [45300/80000]	lr: 9.375e-06, eta: 2:59:15, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0774, decode.acc_seg: 96.9104, loss: 0.0774
+2023-03-04 00:34:23,521 - mmseg - INFO - Iter [45350/80000]	lr: 9.375e-06, eta: 2:58:59, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.7956, loss: 0.0798
+2023-03-04 00:34:40,448 - mmseg - INFO - Iter [45400/80000]	lr: 9.375e-06, eta: 2:58:45, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8519, loss: 0.0802
+2023-03-04 00:34:55,179 - mmseg - INFO - Iter [45450/80000]	lr: 9.375e-06, eta: 2:58:29, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8830, loss: 0.0785
+2023-03-04 00:35:09,874 - mmseg - INFO - Iter [45500/80000]	lr: 9.375e-06, eta: 2:58:13, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0773, decode.acc_seg: 96.9216, loss: 0.0773
+2023-03-04 00:35:24,542 - mmseg - INFO - Iter [45550/80000]	lr: 9.375e-06, eta: 2:57:57, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8146, loss: 0.0800
+2023-03-04 00:35:41,640 - mmseg - INFO - Iter [45600/80000]	lr: 9.375e-06, eta: 2:57:42, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.7681, loss: 0.0802
+2023-03-04 00:35:56,286 - mmseg - INFO - Iter [45650/80000]	lr: 9.375e-06, eta: 2:57:26, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8540, loss: 0.0798
+2023-03-04 00:36:10,926 - mmseg - INFO - Iter [45700/80000]	lr: 9.375e-06, eta: 2:57:10, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.7530, loss: 0.0803
+2023-03-04 00:36:25,489 - mmseg - INFO - Iter [45750/80000]	lr: 9.375e-06, eta: 2:56:54, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0832, decode.acc_seg: 96.7222, loss: 0.0832
+2023-03-04 00:36:42,505 - mmseg - INFO - Iter [45800/80000]	lr: 9.375e-06, eta: 2:56:39, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.7662, loss: 0.0812
+2023-03-04 00:36:57,104 - mmseg - INFO - Iter [45850/80000]	lr: 9.375e-06, eta: 2:56:23, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.6951, loss: 0.0815
+2023-03-04 00:37:11,772 - mmseg - INFO - Iter [45900/80000]	lr: 9.375e-06, eta: 2:56:07, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8082, loss: 0.0800
+2023-03-04 00:37:28,842 - mmseg - INFO - Iter [45950/80000]	lr: 9.375e-06, eta: 2:55:53, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.8603, loss: 0.0797
+2023-03-04 00:37:43,589 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 00:37:43,590 - mmseg - INFO - Iter [46000/80000]	lr: 9.375e-06, eta: 2:55:37, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.8604, loss: 0.0786
+2023-03-04 00:37:58,214 - mmseg - INFO - Iter [46050/80000]	lr: 9.375e-06, eta: 2:55:21, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8768, loss: 0.0788
+2023-03-04 00:38:13,236 - mmseg - INFO - Iter [46100/80000]	lr: 9.375e-06, eta: 2:55:05, time: 0.300, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8588, loss: 0.0792
+2023-03-04 00:38:30,258 - mmseg - INFO - Iter [46150/80000]	lr: 9.375e-06, eta: 2:54:50, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0780, decode.acc_seg: 96.9013, loss: 0.0780
+2023-03-04 00:38:44,946 - mmseg - INFO - Iter [46200/80000]	lr: 9.375e-06, eta: 2:54:34, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8201, loss: 0.0793
+2023-03-04 00:38:59,519 - mmseg - INFO - Iter [46250/80000]	lr: 9.375e-06, eta: 2:54:18, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8625, loss: 0.0785
+2023-03-04 00:39:14,124 - mmseg - INFO - Iter [46300/80000]	lr: 9.375e-06, eta: 2:54:02, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.8037, loss: 0.0803
+2023-03-04 00:39:31,137 - mmseg - INFO - Iter [46350/80000]	lr: 9.375e-06, eta: 2:53:48, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8145, loss: 0.0795
+2023-03-04 00:39:45,711 - mmseg - INFO - Iter [46400/80000]	lr: 9.375e-06, eta: 2:53:31, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.8320, loss: 0.0786
+2023-03-04 00:40:00,331 - mmseg - INFO - Iter [46450/80000]	lr: 9.375e-06, eta: 2:53:15, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8724, loss: 0.0789
+2023-03-04 00:40:14,968 - mmseg - INFO - Iter [46500/80000]	lr: 9.375e-06, eta: 2:52:59, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.7588, loss: 0.0805
+2023-03-04 00:40:31,933 - mmseg - INFO - Iter [46550/80000]	lr: 9.375e-06, eta: 2:52:45, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0764, decode.acc_seg: 96.9178, loss: 0.0764
+2023-03-04 00:40:46,509 - mmseg - INFO - Iter [46600/80000]	lr: 9.375e-06, eta: 2:52:29, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0822, decode.acc_seg: 96.7777, loss: 0.0822
+2023-03-04 00:41:01,202 - mmseg - INFO - Iter [46650/80000]	lr: 9.375e-06, eta: 2:52:13, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8531, loss: 0.0790
+2023-03-04 00:41:18,253 - mmseg - INFO - Iter [46700/80000]	lr: 9.375e-06, eta: 2:51:58, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0824, decode.acc_seg: 96.7243, loss: 0.0824
+2023-03-04 00:41:32,840 - mmseg - INFO - Iter [46750/80000]	lr: 9.375e-06, eta: 2:51:42, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7439, loss: 0.0815
+2023-03-04 00:41:47,440 - mmseg - INFO - Iter [46800/80000]	lr: 9.375e-06, eta: 2:51:26, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0813, decode.acc_seg: 96.7657, loss: 0.0813
+2023-03-04 00:42:02,163 - mmseg - INFO - Iter [46850/80000]	lr: 9.375e-06, eta: 2:51:10, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8639, loss: 0.0789
+2023-03-04 00:42:19,294 - mmseg - INFO - Iter [46900/80000]	lr: 9.375e-06, eta: 2:50:56, time: 0.343, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.7950, loss: 0.0807
+2023-03-04 00:42:33,856 - mmseg - INFO - Iter [46950/80000]	lr: 9.375e-06, eta: 2:50:39, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8649, loss: 0.0789
+2023-03-04 00:42:48,444 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 00:42:48,444 - mmseg - INFO - Iter [47000/80000]	lr: 9.375e-06, eta: 2:50:23, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8634, loss: 0.0788
+2023-03-04 00:43:03,028 - mmseg - INFO - Iter [47050/80000]	lr: 9.375e-06, eta: 2:50:07, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0828, decode.acc_seg: 96.7018, loss: 0.0828
+2023-03-04 00:43:20,229 - mmseg - INFO - Iter [47100/80000]	lr: 9.375e-06, eta: 2:49:53, time: 0.344, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7886, loss: 0.0811
+2023-03-04 00:43:34,876 - mmseg - INFO - Iter [47150/80000]	lr: 9.375e-06, eta: 2:49:37, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0817, decode.acc_seg: 96.7425, loss: 0.0817
+2023-03-04 00:43:49,558 - mmseg - INFO - Iter [47200/80000]	lr: 9.375e-06, eta: 2:49:21, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7636, loss: 0.0811
+2023-03-04 00:44:06,685 - mmseg - INFO - Iter [47250/80000]	lr: 9.375e-06, eta: 2:49:06, time: 0.343, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8246, loss: 0.0794
+2023-03-04 00:44:21,322 - mmseg - INFO - Iter [47300/80000]	lr: 9.375e-06, eta: 2:48:50, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.7631, loss: 0.0814
+2023-03-04 00:44:35,945 - mmseg - INFO - Iter [47350/80000]	lr: 9.375e-06, eta: 2:48:34, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0837, decode.acc_seg: 96.7896, loss: 0.0837
+2023-03-04 00:44:50,638 - mmseg - INFO - Iter [47400/80000]	lr: 9.375e-06, eta: 2:48:18, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8617, loss: 0.0798
+2023-03-04 00:45:07,599 - mmseg - INFO - Iter [47450/80000]	lr: 9.375e-06, eta: 2:48:04, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0756, decode.acc_seg: 96.9782, loss: 0.0756
+2023-03-04 00:45:22,431 - mmseg - INFO - Iter [47500/80000]	lr: 9.375e-06, eta: 2:47:48, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0774, decode.acc_seg: 96.9195, loss: 0.0774
+2023-03-04 00:45:37,009 - mmseg - INFO - Iter [47550/80000]	lr: 9.375e-06, eta: 2:47:32, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7486, loss: 0.0811
+2023-03-04 00:45:51,731 - mmseg - INFO - Iter [47600/80000]	lr: 9.375e-06, eta: 2:47:16, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.7696, loss: 0.0805
+2023-03-04 00:46:08,698 - mmseg - INFO - Iter [47650/80000]	lr: 9.375e-06, eta: 2:47:01, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0778, decode.acc_seg: 96.8994, loss: 0.0778
+2023-03-04 00:46:23,317 - mmseg - INFO - Iter [47700/80000]	lr: 9.375e-06, eta: 2:46:45, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.7505, loss: 0.0814
+2023-03-04 00:46:37,941 - mmseg - INFO - Iter [47750/80000]	lr: 9.375e-06, eta: 2:46:29, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.7905, loss: 0.0807
+2023-03-04 00:46:52,508 - mmseg - INFO - Iter [47800/80000]	lr: 9.375e-06, eta: 2:46:13, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8777, loss: 0.0784
+2023-03-04 00:47:09,607 - mmseg - INFO - Iter [47850/80000]	lr: 9.375e-06, eta: 2:45:58, time: 0.342, data_time: 0.054, memory: 67605, decode.loss_ce: 0.0820, decode.acc_seg: 96.7784, loss: 0.0820
+2023-03-04 00:47:24,237 - mmseg - INFO - Iter [47900/80000]	lr: 9.375e-06, eta: 2:45:42, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8365, loss: 0.0790
+2023-03-04 00:47:38,858 - mmseg - INFO - Iter [47950/80000]	lr: 9.375e-06, eta: 2:45:26, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.8587, loss: 0.0783
+2023-03-04 00:47:55,898 - mmseg - INFO - Saving checkpoint at 48000 iterations
+2023-03-04 00:47:57,798 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 00:47:57,798 - mmseg - INFO - Iter [48000/80000]	lr: 9.375e-06, eta: 2:45:13, time: 0.379, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.8070, loss: 0.0810
+2023-03-04 00:48:23,616 - mmseg - INFO - per class results:
+2023-03-04 00:48:23,617 - mmseg - INFO - 
++---------------+-------+-------+
+|     Class     |  IoU  |  Acc  |
++---------------+-------+-------+
+|   background  |  nan  |  nan  |
+|      road     | 98.53 | 99.13 |
+|    sidewalk   | 87.37 | 94.07 |
+|    building   | 93.63 | 96.96 |
+|      wall     | 54.47 | 60.01 |
+|     fence     |  65.3 | 75.86 |
+|      pole     | 71.14 | 82.45 |
+| traffic light | 75.41 |  85.6 |
+|  traffic sign | 82.64 | 89.28 |
+|   vegetation  | 93.09 | 96.97 |
+|    terrain    | 64.67 | 75.45 |
+|      sky      | 95.44 | 98.17 |
+|     person    | 85.01 | 92.81 |
+|     rider     |  67.8 | 79.93 |
+|      car      | 96.04 | 98.14 |
+|     truck     | 85.86 | 92.14 |
+|      bus      | 92.56 | 95.81 |
+|     train     | 85.82 | 89.83 |
+|   motorcycle  | 71.98 | 82.44 |
+|    bicycle    | 80.57 | 90.57 |
++---------------+-------+-------+
+2023-03-04 00:48:23,617 - mmseg - INFO - Summary:
+2023-03-04 00:48:23,618 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 96.65 | 81.44 | 88.19 |
++-------+-------+-------+
+2023-03-04 00:48:23,618 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 00:48:23,618 - mmseg - INFO - Iter(val) [63]	aAcc: 0.9665, mIoU: 0.8144, mAcc: 0.8819, IoU.background: nan, IoU.road: 0.9853, IoU.sidewalk: 0.8737, IoU.building: 0.9363, IoU.wall: 0.5447, IoU.fence: 0.6530, IoU.pole: 0.7114, IoU.traffic light: 0.7541, IoU.traffic sign: 0.8264, IoU.vegetation: 0.9309, IoU.terrain: 0.6467, IoU.sky: 0.9544, IoU.person: 0.8501, IoU.rider: 0.6780, IoU.car: 0.9604, IoU.truck: 0.8586, IoU.bus: 0.9256, IoU.train: 0.8582, IoU.motorcycle: 0.7198, IoU.bicycle: 0.8057, Acc.background: nan, Acc.road: 0.9913, Acc.sidewalk: 0.9407, Acc.building: 0.9696, Acc.wall: 0.6001, Acc.fence: 0.7586, Acc.pole: 0.8245, Acc.traffic light: 0.8560, Acc.traffic sign: 0.8928, Acc.vegetation: 0.9697, Acc.terrain: 0.7545, Acc.sky: 0.9817, Acc.person: 0.9281, Acc.rider: 0.7993, Acc.car: 0.9814, Acc.truck: 0.9214, Acc.bus: 0.9581, Acc.train: 0.8983, Acc.motorcycle: 0.8244, Acc.bicycle: 0.9057
+2023-03-04 00:48:38,601 - mmseg - INFO - Iter [48050/80000]	lr: 9.375e-06, eta: 2:45:15, time: 0.816, data_time: 0.524, memory: 67605, decode.loss_ce: 0.0817, decode.acc_seg: 96.7833, loss: 0.0817
+2023-03-04 00:48:53,393 - mmseg - INFO - Iter [48100/80000]	lr: 9.375e-06, eta: 2:44:59, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0778, decode.acc_seg: 96.9043, loss: 0.0778
+2023-03-04 00:49:08,194 - mmseg - INFO - Iter [48150/80000]	lr: 9.375e-06, eta: 2:44:43, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.8231, loss: 0.0806
+2023-03-04 00:49:25,412 - mmseg - INFO - Iter [48200/80000]	lr: 9.375e-06, eta: 2:44:28, time: 0.344, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8178, loss: 0.0802
+2023-03-04 00:49:40,038 - mmseg - INFO - Iter [48250/80000]	lr: 9.375e-06, eta: 2:44:12, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7777, loss: 0.0811
+2023-03-04 00:49:54,642 - mmseg - INFO - Iter [48300/80000]	lr: 9.375e-06, eta: 2:43:56, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0839, decode.acc_seg: 96.6685, loss: 0.0839
+2023-03-04 00:50:09,394 - mmseg - INFO - Iter [48350/80000]	lr: 9.375e-06, eta: 2:43:40, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0824, decode.acc_seg: 96.7589, loss: 0.0824
+2023-03-04 00:50:26,327 - mmseg - INFO - Iter [48400/80000]	lr: 9.375e-06, eta: 2:43:25, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.8071, loss: 0.0797
+2023-03-04 00:50:40,958 - mmseg - INFO - Iter [48450/80000]	lr: 9.375e-06, eta: 2:43:09, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0809, decode.acc_seg: 96.7932, loss: 0.0809
+2023-03-04 00:50:55,583 - mmseg - INFO - Iter [48500/80000]	lr: 9.375e-06, eta: 2:42:53, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0769, decode.acc_seg: 96.9383, loss: 0.0769
+2023-03-04 00:51:12,555 - mmseg - INFO - Iter [48550/80000]	lr: 9.375e-06, eta: 2:42:39, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0828, decode.acc_seg: 96.7463, loss: 0.0828
+2023-03-04 00:51:27,322 - mmseg - INFO - Iter [48600/80000]	lr: 9.375e-06, eta: 2:42:23, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8966, loss: 0.0784
+2023-03-04 00:51:41,968 - mmseg - INFO - Iter [48650/80000]	lr: 9.375e-06, eta: 2:42:06, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0813, decode.acc_seg: 96.7547, loss: 0.0813
+2023-03-04 00:51:56,660 - mmseg - INFO - Iter [48700/80000]	lr: 9.375e-06, eta: 2:41:50, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.8155, loss: 0.0803
+2023-03-04 00:52:13,733 - mmseg - INFO - Iter [48750/80000]	lr: 9.375e-06, eta: 2:41:36, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0754, decode.acc_seg: 96.9645, loss: 0.0754
+2023-03-04 00:52:28,529 - mmseg - INFO - Iter [48800/80000]	lr: 9.375e-06, eta: 2:41:20, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0779, decode.acc_seg: 96.8815, loss: 0.0779
+2023-03-04 00:52:43,092 - mmseg - INFO - Iter [48850/80000]	lr: 9.375e-06, eta: 2:41:04, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8538, loss: 0.0784
+2023-03-04 00:52:57,819 - mmseg - INFO - Iter [48900/80000]	lr: 9.375e-06, eta: 2:40:48, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0767, decode.acc_seg: 96.9592, loss: 0.0767
+2023-03-04 00:53:14,802 - mmseg - INFO - Iter [48950/80000]	lr: 9.375e-06, eta: 2:40:33, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0780, decode.acc_seg: 96.8704, loss: 0.0780
+2023-03-04 00:53:29,390 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 00:53:29,390 - mmseg - INFO - Iter [49000/80000]	lr: 9.375e-06, eta: 2:40:17, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.7806, loss: 0.0818
+2023-03-04 00:53:43,977 - mmseg - INFO - Iter [49050/80000]	lr: 9.375e-06, eta: 2:40:01, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8203, loss: 0.0800
+2023-03-04 00:53:58,568 - mmseg - INFO - Iter [49100/80000]	lr: 9.375e-06, eta: 2:39:45, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0787, decode.acc_seg: 96.8740, loss: 0.0787
+2023-03-04 00:54:15,624 - mmseg - INFO - Iter [49150/80000]	lr: 9.375e-06, eta: 2:39:30, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.7667, loss: 0.0812
+2023-03-04 00:54:30,411 - mmseg - INFO - Iter [49200/80000]	lr: 9.375e-06, eta: 2:39:14, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.7504, loss: 0.0821
+2023-03-04 00:54:45,189 - mmseg - INFO - Iter [49250/80000]	lr: 9.375e-06, eta: 2:38:58, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8282, loss: 0.0799
+2023-03-04 00:55:02,215 - mmseg - INFO - Iter [49300/80000]	lr: 9.375e-06, eta: 2:38:44, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0850, decode.acc_seg: 96.6455, loss: 0.0850
+2023-03-04 00:55:16,816 - mmseg - INFO - Iter [49350/80000]	lr: 9.375e-06, eta: 2:38:28, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.7951, loss: 0.0806
+2023-03-04 00:55:31,416 - mmseg - INFO - Iter [49400/80000]	lr: 9.375e-06, eta: 2:38:12, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.8099, loss: 0.0810
+2023-03-04 00:55:46,011 - mmseg - INFO - Iter [49450/80000]	lr: 9.375e-06, eta: 2:37:56, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0804, decode.acc_seg: 96.8407, loss: 0.0804
+2023-03-04 00:56:03,075 - mmseg - INFO - Iter [49500/80000]	lr: 9.375e-06, eta: 2:37:41, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8507, loss: 0.0784
+2023-03-04 00:56:17,802 - mmseg - INFO - Iter [49550/80000]	lr: 9.375e-06, eta: 2:37:25, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.8204, loss: 0.0797
+2023-03-04 00:56:32,730 - mmseg - INFO - Iter [49600/80000]	lr: 9.375e-06, eta: 2:37:09, time: 0.299, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8423, loss: 0.0789
+2023-03-04 00:56:47,400 - mmseg - INFO - Iter [49650/80000]	lr: 9.375e-06, eta: 2:36:53, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8347, loss: 0.0795
+2023-03-04 00:57:04,817 - mmseg - INFO - Iter [49700/80000]	lr: 9.375e-06, eta: 2:36:39, time: 0.348, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.8061, loss: 0.0805
+2023-03-04 00:57:19,397 - mmseg - INFO - Iter [49750/80000]	lr: 9.375e-06, eta: 2:36:23, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.7773, loss: 0.0819
+2023-03-04 00:57:34,101 - mmseg - INFO - Iter [49800/80000]	lr: 9.375e-06, eta: 2:36:07, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0823, decode.acc_seg: 96.7259, loss: 0.0823
+2023-03-04 00:57:51,129 - mmseg - INFO - Iter [49850/80000]	lr: 9.375e-06, eta: 2:35:52, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8596, loss: 0.0790
+2023-03-04 00:58:05,761 - mmseg - INFO - Iter [49900/80000]	lr: 9.375e-06, eta: 2:35:36, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0804, decode.acc_seg: 96.8380, loss: 0.0804
+2023-03-04 00:58:20,453 - mmseg - INFO - Iter [49950/80000]	lr: 9.375e-06, eta: 2:35:20, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8630, loss: 0.0802
+2023-03-04 00:58:35,133 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 00:58:35,133 - mmseg - INFO - Iter [50000/80000]	lr: 9.375e-06, eta: 2:35:04, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.7889, loss: 0.0802
+2023-03-04 00:58:52,046 - mmseg - INFO - Iter [50050/80000]	lr: 4.687e-06, eta: 2:34:50, time: 0.338, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.7679, loss: 0.0808
+2023-03-04 00:59:06,896 - mmseg - INFO - Iter [50100/80000]	lr: 4.687e-06, eta: 2:34:34, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8591, loss: 0.0789
+2023-03-04 00:59:21,629 - mmseg - INFO - Iter [50150/80000]	lr: 4.687e-06, eta: 2:34:18, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8365, loss: 0.0798
+2023-03-04 00:59:36,302 - mmseg - INFO - Iter [50200/80000]	lr: 4.687e-06, eta: 2:34:02, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8616, loss: 0.0784
+2023-03-04 00:59:53,302 - mmseg - INFO - Iter [50250/80000]	lr: 4.687e-06, eta: 2:33:47, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0777, decode.acc_seg: 96.8757, loss: 0.0777
+2023-03-04 01:00:07,871 - mmseg - INFO - Iter [50300/80000]	lr: 4.687e-06, eta: 2:33:31, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.8965, loss: 0.0786
+2023-03-04 01:00:22,547 - mmseg - INFO - Iter [50350/80000]	lr: 4.687e-06, eta: 2:33:15, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8636, loss: 0.0789
+2023-03-04 01:00:37,127 - mmseg - INFO - Iter [50400/80000]	lr: 4.687e-06, eta: 2:32:59, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.8470, loss: 0.0797
+2023-03-04 01:00:54,167 - mmseg - INFO - Iter [50450/80000]	lr: 4.687e-06, eta: 2:32:44, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0775, decode.acc_seg: 96.8845, loss: 0.0775
+2023-03-04 01:01:08,753 - mmseg - INFO - Iter [50500/80000]	lr: 4.687e-06, eta: 2:32:28, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0832, decode.acc_seg: 96.7094, loss: 0.0832
+2023-03-04 01:01:23,461 - mmseg - INFO - Iter [50550/80000]	lr: 4.687e-06, eta: 2:32:12, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.9162, loss: 0.0783
+2023-03-04 01:01:40,725 - mmseg - INFO - Iter [50600/80000]	lr: 4.687e-06, eta: 2:31:58, time: 0.345, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.7513, loss: 0.0821
+2023-03-04 01:01:55,432 - mmseg - INFO - Iter [50650/80000]	lr: 4.687e-06, eta: 2:31:42, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8606, loss: 0.0789
+2023-03-04 01:02:10,126 - mmseg - INFO - Iter [50700/80000]	lr: 4.687e-06, eta: 2:31:26, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0776, decode.acc_seg: 96.9083, loss: 0.0776
+2023-03-04 01:02:24,928 - mmseg - INFO - Iter [50750/80000]	lr: 4.687e-06, eta: 2:31:10, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8248, loss: 0.0785
+2023-03-04 01:02:41,844 - mmseg - INFO - Iter [50800/80000]	lr: 4.687e-06, eta: 2:30:55, time: 0.338, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7382, loss: 0.0815
+2023-03-04 01:02:56,556 - mmseg - INFO - Iter [50850/80000]	lr: 4.687e-06, eta: 2:30:39, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8279, loss: 0.0799
+2023-03-04 01:03:11,344 - mmseg - INFO - Iter [50900/80000]	lr: 4.687e-06, eta: 2:30:23, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8664, loss: 0.0792
+2023-03-04 01:03:25,910 - mmseg - INFO - Iter [50950/80000]	lr: 4.687e-06, eta: 2:30:07, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8409, loss: 0.0794
+2023-03-04 01:03:42,968 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 01:03:42,968 - mmseg - INFO - Iter [51000/80000]	lr: 4.687e-06, eta: 2:29:53, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0774, decode.acc_seg: 96.9437, loss: 0.0774
+2023-03-04 01:03:57,562 - mmseg - INFO - Iter [51050/80000]	lr: 4.687e-06, eta: 2:29:37, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.9183, loss: 0.0793
+2023-03-04 01:04:12,167 - mmseg - INFO - Iter [51100/80000]	lr: 4.687e-06, eta: 2:29:21, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8364, loss: 0.0790
+2023-03-04 01:04:26,810 - mmseg - INFO - Iter [51150/80000]	lr: 4.687e-06, eta: 2:29:05, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8366, loss: 0.0796
+2023-03-04 01:04:43,811 - mmseg - INFO - Iter [51200/80000]	lr: 4.687e-06, eta: 2:28:50, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8150, loss: 0.0802
+2023-03-04 01:04:58,504 - mmseg - INFO - Iter [51250/80000]	lr: 4.687e-06, eta: 2:28:34, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.8510, loss: 0.0797
+2023-03-04 01:05:13,197 - mmseg - INFO - Iter [51300/80000]	lr: 4.687e-06, eta: 2:28:18, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7228, loss: 0.0827
+2023-03-04 01:05:30,273 - mmseg - INFO - Iter [51350/80000]	lr: 4.687e-06, eta: 2:28:03, time: 0.342, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.7170, loss: 0.0819
+2023-03-04 01:05:44,882 - mmseg - INFO - Iter [51400/80000]	lr: 4.687e-06, eta: 2:27:47, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8447, loss: 0.0798
+2023-03-04 01:05:59,513 - mmseg - INFO - Iter [51450/80000]	lr: 4.687e-06, eta: 2:27:31, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0776, decode.acc_seg: 96.8927, loss: 0.0776
+2023-03-04 01:06:14,140 - mmseg - INFO - Iter [51500/80000]	lr: 4.687e-06, eta: 2:27:16, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0826, decode.acc_seg: 96.7266, loss: 0.0826
+2023-03-04 01:06:31,135 - mmseg - INFO - Iter [51550/80000]	lr: 4.687e-06, eta: 2:27:01, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0774, decode.acc_seg: 96.8943, loss: 0.0774
+2023-03-04 01:06:45,970 - mmseg - INFO - Iter [51600/80000]	lr: 4.687e-06, eta: 2:26:45, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0832, decode.acc_seg: 96.6997, loss: 0.0832
+2023-03-04 01:07:00,534 - mmseg - INFO - Iter [51650/80000]	lr: 4.687e-06, eta: 2:26:29, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.8352, loss: 0.0797
+2023-03-04 01:07:15,298 - mmseg - INFO - Iter [51700/80000]	lr: 4.687e-06, eta: 2:26:13, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8842, loss: 0.0784
+2023-03-04 01:07:32,317 - mmseg - INFO - Iter [51750/80000]	lr: 4.687e-06, eta: 2:25:58, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8519, loss: 0.0791
+2023-03-04 01:07:46,895 - mmseg - INFO - Iter [51800/80000]	lr: 4.687e-06, eta: 2:25:42, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.8804, loss: 0.0783
+2023-03-04 01:08:01,467 - mmseg - INFO - Iter [51850/80000]	lr: 4.687e-06, eta: 2:25:26, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.7902, loss: 0.0810
+2023-03-04 01:08:18,454 - mmseg - INFO - Iter [51900/80000]	lr: 4.687e-06, eta: 2:25:12, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8547, loss: 0.0794
+2023-03-04 01:08:33,097 - mmseg - INFO - Iter [51950/80000]	lr: 4.687e-06, eta: 2:24:56, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.7985, loss: 0.0799
+2023-03-04 01:08:47,721 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 01:08:47,722 - mmseg - INFO - Iter [52000/80000]	lr: 4.687e-06, eta: 2:24:40, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8199, loss: 0.0799
+2023-03-04 01:09:02,379 - mmseg - INFO - Iter [52050/80000]	lr: 4.687e-06, eta: 2:24:24, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0778, decode.acc_seg: 96.8962, loss: 0.0778
+2023-03-04 01:09:19,538 - mmseg - INFO - Iter [52100/80000]	lr: 4.687e-06, eta: 2:24:09, time: 0.343, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8965, loss: 0.0784
+2023-03-04 01:09:34,284 - mmseg - INFO - Iter [52150/80000]	lr: 4.687e-06, eta: 2:23:53, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7962, loss: 0.0811
+2023-03-04 01:09:48,874 - mmseg - INFO - Iter [52200/80000]	lr: 4.687e-06, eta: 2:23:37, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.7838, loss: 0.0810
+2023-03-04 01:10:03,626 - mmseg - INFO - Iter [52250/80000]	lr: 4.687e-06, eta: 2:23:21, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8120, loss: 0.0796
+2023-03-04 01:10:20,739 - mmseg - INFO - Iter [52300/80000]	lr: 4.687e-06, eta: 2:23:07, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.7817, loss: 0.0814
+2023-03-04 01:10:35,353 - mmseg - INFO - Iter [52350/80000]	lr: 4.687e-06, eta: 2:22:51, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8317, loss: 0.0791
+2023-03-04 01:10:49,923 - mmseg - INFO - Iter [52400/80000]	lr: 4.687e-06, eta: 2:22:35, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8056, loss: 0.0799
+2023-03-04 01:11:04,502 - mmseg - INFO - Iter [52450/80000]	lr: 4.687e-06, eta: 2:22:19, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0777, decode.acc_seg: 96.8814, loss: 0.0777
+2023-03-04 01:11:21,475 - mmseg - INFO - Iter [52500/80000]	lr: 4.687e-06, eta: 2:22:04, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0823, decode.acc_seg: 96.7257, loss: 0.0823
+2023-03-04 01:11:36,057 - mmseg - INFO - Iter [52550/80000]	lr: 4.687e-06, eta: 2:21:48, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0768, decode.acc_seg: 96.8979, loss: 0.0768
+2023-03-04 01:11:50,653 - mmseg - INFO - Iter [52600/80000]	lr: 4.687e-06, eta: 2:21:32, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.8005, loss: 0.0803
+2023-03-04 01:12:07,684 - mmseg - INFO - Iter [52650/80000]	lr: 4.687e-06, eta: 2:21:17, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0773, decode.acc_seg: 96.9147, loss: 0.0773
+2023-03-04 01:12:22,350 - mmseg - INFO - Iter [52700/80000]	lr: 4.687e-06, eta: 2:21:01, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8355, loss: 0.0794
+2023-03-04 01:12:36,931 - mmseg - INFO - Iter [52750/80000]	lr: 4.687e-06, eta: 2:20:46, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8261, loss: 0.0793
+2023-03-04 01:12:51,539 - mmseg - INFO - Iter [52800/80000]	lr: 4.687e-06, eta: 2:20:30, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0761, decode.acc_seg: 96.9542, loss: 0.0761
+2023-03-04 01:13:08,587 - mmseg - INFO - Iter [52850/80000]	lr: 4.687e-06, eta: 2:20:15, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0774, decode.acc_seg: 96.9092, loss: 0.0774
+2023-03-04 01:13:23,212 - mmseg - INFO - Iter [52900/80000]	lr: 4.687e-06, eta: 2:19:59, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0771, decode.acc_seg: 96.8602, loss: 0.0771
+2023-03-04 01:13:37,800 - mmseg - INFO - Iter [52950/80000]	lr: 4.687e-06, eta: 2:19:43, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0775, decode.acc_seg: 96.9013, loss: 0.0775
+2023-03-04 01:13:52,399 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 01:13:52,399 - mmseg - INFO - Iter [53000/80000]	lr: 4.687e-06, eta: 2:19:27, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.8020, loss: 0.0814
+2023-03-04 01:14:09,471 - mmseg - INFO - Iter [53050/80000]	lr: 4.687e-06, eta: 2:19:12, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.8610, loss: 0.0786
+2023-03-04 01:14:24,073 - mmseg - INFO - Iter [53100/80000]	lr: 4.687e-06, eta: 2:18:56, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8749, loss: 0.0789
+2023-03-04 01:14:38,689 - mmseg - INFO - Iter [53150/80000]	lr: 4.687e-06, eta: 2:18:40, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8533, loss: 0.0789
+2023-03-04 01:14:55,803 - mmseg - INFO - Iter [53200/80000]	lr: 4.687e-06, eta: 2:18:26, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0780, decode.acc_seg: 96.8857, loss: 0.0780
+2023-03-04 01:15:10,382 - mmseg - INFO - Iter [53250/80000]	lr: 4.687e-06, eta: 2:18:10, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0778, decode.acc_seg: 96.8712, loss: 0.0778
+2023-03-04 01:15:24,935 - mmseg - INFO - Iter [53300/80000]	lr: 4.687e-06, eta: 2:17:54, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0787, decode.acc_seg: 96.8998, loss: 0.0787
+2023-03-04 01:15:39,503 - mmseg - INFO - Iter [53350/80000]	lr: 4.687e-06, eta: 2:17:38, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8292, loss: 0.0794
+2023-03-04 01:15:56,505 - mmseg - INFO - Iter [53400/80000]	lr: 4.687e-06, eta: 2:17:23, time: 0.340, data_time: 0.054, memory: 67605, decode.loss_ce: 0.0756, decode.acc_seg: 96.9950, loss: 0.0756
+2023-03-04 01:16:11,133 - mmseg - INFO - Iter [53450/80000]	lr: 4.687e-06, eta: 2:17:07, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.7245, loss: 0.0810
+2023-03-04 01:16:25,789 - mmseg - INFO - Iter [53500/80000]	lr: 4.687e-06, eta: 2:16:51, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.7748, loss: 0.0801
+2023-03-04 01:16:40,368 - mmseg - INFO - Iter [53550/80000]	lr: 4.687e-06, eta: 2:16:35, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8400, loss: 0.0795
+2023-03-04 01:16:57,370 - mmseg - INFO - Iter [53600/80000]	lr: 4.687e-06, eta: 2:16:21, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8746, loss: 0.0785
+2023-03-04 01:17:11,986 - mmseg - INFO - Iter [53650/80000]	lr: 4.687e-06, eta: 2:16:05, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0824, decode.acc_seg: 96.6990, loss: 0.0824
+2023-03-04 01:17:26,586 - mmseg - INFO - Iter [53700/80000]	lr: 4.687e-06, eta: 2:15:49, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0780, decode.acc_seg: 96.8974, loss: 0.0780
+2023-03-04 01:17:41,160 - mmseg - INFO - Iter [53750/80000]	lr: 4.687e-06, eta: 2:15:33, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8398, loss: 0.0788
+2023-03-04 01:17:58,433 - mmseg - INFO - Iter [53800/80000]	lr: 4.687e-06, eta: 2:15:18, time: 0.345, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8622, loss: 0.0788
+2023-03-04 01:18:13,109 - mmseg - INFO - Iter [53850/80000]	lr: 4.687e-06, eta: 2:15:02, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0782, decode.acc_seg: 96.9153, loss: 0.0782
+2023-03-04 01:18:27,771 - mmseg - INFO - Iter [53900/80000]	lr: 4.687e-06, eta: 2:14:46, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8777, loss: 0.0785
+2023-03-04 01:18:44,790 - mmseg - INFO - Iter [53950/80000]	lr: 4.687e-06, eta: 2:14:32, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8185, loss: 0.0798
+2023-03-04 01:18:59,448 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 01:18:59,449 - mmseg - INFO - Iter [54000/80000]	lr: 4.687e-06, eta: 2:14:16, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.7851, loss: 0.0806
+2023-03-04 01:19:14,036 - mmseg - INFO - Iter [54050/80000]	lr: 4.687e-06, eta: 2:14:00, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0780, decode.acc_seg: 96.8861, loss: 0.0780
+2023-03-04 01:19:28,625 - mmseg - INFO - Iter [54100/80000]	lr: 4.687e-06, eta: 2:13:44, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8504, loss: 0.0785
+2023-03-04 01:19:45,604 - mmseg - INFO - Iter [54150/80000]	lr: 4.687e-06, eta: 2:13:29, time: 0.340, data_time: 0.054, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8709, loss: 0.0798
+2023-03-04 01:20:00,325 - mmseg - INFO - Iter [54200/80000]	lr: 4.687e-06, eta: 2:13:13, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.7718, loss: 0.0803
+2023-03-04 01:20:14,976 - mmseg - INFO - Iter [54250/80000]	lr: 4.687e-06, eta: 2:12:57, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8689, loss: 0.0789
+2023-03-04 01:20:29,678 - mmseg - INFO - Iter [54300/80000]	lr: 4.687e-06, eta: 2:12:41, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0780, decode.acc_seg: 96.9019, loss: 0.0780
+2023-03-04 01:20:46,655 - mmseg - INFO - Iter [54350/80000]	lr: 4.687e-06, eta: 2:12:27, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0869, decode.acc_seg: 96.6445, loss: 0.0869
+2023-03-04 01:21:01,245 - mmseg - INFO - Iter [54400/80000]	lr: 4.687e-06, eta: 2:12:11, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8112, loss: 0.0793
+2023-03-04 01:21:15,840 - mmseg - INFO - Iter [54450/80000]	lr: 4.687e-06, eta: 2:11:55, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.7485, loss: 0.0810
+2023-03-04 01:21:32,732 - mmseg - INFO - Iter [54500/80000]	lr: 4.687e-06, eta: 2:11:40, time: 0.338, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8265, loss: 0.0793
+2023-03-04 01:21:47,431 - mmseg - INFO - Iter [54550/80000]	lr: 4.687e-06, eta: 2:11:24, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8310, loss: 0.0795
+2023-03-04 01:22:02,056 - mmseg - INFO - Iter [54600/80000]	lr: 4.687e-06, eta: 2:11:08, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0817, decode.acc_seg: 96.7466, loss: 0.0817
+2023-03-04 01:22:16,681 - mmseg - INFO - Iter [54650/80000]	lr: 4.687e-06, eta: 2:10:52, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.8115, loss: 0.0808
+2023-03-04 01:22:33,603 - mmseg - INFO - Iter [54700/80000]	lr: 4.687e-06, eta: 2:10:38, time: 0.338, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0775, decode.acc_seg: 96.9079, loss: 0.0775
+2023-03-04 01:22:48,169 - mmseg - INFO - Iter [54750/80000]	lr: 4.687e-06, eta: 2:10:22, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8941, loss: 0.0784
+2023-03-04 01:23:02,742 - mmseg - INFO - Iter [54800/80000]	lr: 4.687e-06, eta: 2:10:06, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0839, decode.acc_seg: 96.6815, loss: 0.0839
+2023-03-04 01:23:17,457 - mmseg - INFO - Iter [54850/80000]	lr: 4.687e-06, eta: 2:09:50, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8169, loss: 0.0798
+2023-03-04 01:23:34,424 - mmseg - INFO - Iter [54900/80000]	lr: 4.687e-06, eta: 2:09:35, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.7750, loss: 0.0807
+2023-03-04 01:23:49,045 - mmseg - INFO - Iter [54950/80000]	lr: 4.687e-06, eta: 2:09:19, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0760, decode.acc_seg: 96.9862, loss: 0.0760
+2023-03-04 01:24:03,605 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 01:24:03,605 - mmseg - INFO - Iter [55000/80000]	lr: 4.687e-06, eta: 2:09:03, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0769, decode.acc_seg: 96.9181, loss: 0.0769
+2023-03-04 01:24:18,166 - mmseg - INFO - Iter [55050/80000]	lr: 4.687e-06, eta: 2:08:47, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0813, decode.acc_seg: 96.6941, loss: 0.0813
+2023-03-04 01:24:35,379 - mmseg - INFO - Iter [55100/80000]	lr: 4.687e-06, eta: 2:08:33, time: 0.344, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8927, loss: 0.0791
+2023-03-04 01:24:50,005 - mmseg - INFO - Iter [55150/80000]	lr: 4.687e-06, eta: 2:08:17, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0782, decode.acc_seg: 96.8745, loss: 0.0782
+2023-03-04 01:25:04,599 - mmseg - INFO - Iter [55200/80000]	lr: 4.687e-06, eta: 2:08:01, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8402, loss: 0.0784
+2023-03-04 01:25:21,889 - mmseg - INFO - Iter [55250/80000]	lr: 4.687e-06, eta: 2:07:46, time: 0.346, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8397, loss: 0.0793
+2023-03-04 01:25:36,492 - mmseg - INFO - Iter [55300/80000]	lr: 4.687e-06, eta: 2:07:30, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0773, decode.acc_seg: 96.9091, loss: 0.0773
+2023-03-04 01:25:51,129 - mmseg - INFO - Iter [55350/80000]	lr: 4.687e-06, eta: 2:07:14, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7593, loss: 0.0811
+2023-03-04 01:26:05,892 - mmseg - INFO - Iter [55400/80000]	lr: 4.687e-06, eta: 2:06:59, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0822, decode.acc_seg: 96.6721, loss: 0.0822
+2023-03-04 01:26:22,902 - mmseg - INFO - Iter [55450/80000]	lr: 4.687e-06, eta: 2:06:44, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8527, loss: 0.0792
+2023-03-04 01:26:37,572 - mmseg - INFO - Iter [55500/80000]	lr: 4.687e-06, eta: 2:06:28, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0820, decode.acc_seg: 96.7666, loss: 0.0820
+2023-03-04 01:26:52,454 - mmseg - INFO - Iter [55550/80000]	lr: 4.687e-06, eta: 2:06:12, time: 0.298, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0843, decode.acc_seg: 96.6976, loss: 0.0843
+2023-03-04 01:27:07,124 - mmseg - INFO - Iter [55600/80000]	lr: 4.687e-06, eta: 2:05:56, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.8006, loss: 0.0803
+2023-03-04 01:27:24,135 - mmseg - INFO - Iter [55650/80000]	lr: 4.687e-06, eta: 2:05:42, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8251, loss: 0.0793
+2023-03-04 01:27:38,733 - mmseg - INFO - Iter [55700/80000]	lr: 4.687e-06, eta: 2:05:26, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.6940, loss: 0.0827
+2023-03-04 01:27:53,333 - mmseg - INFO - Iter [55750/80000]	lr: 4.687e-06, eta: 2:05:10, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8747, loss: 0.0784
+2023-03-04 01:28:07,935 - mmseg - INFO - Iter [55800/80000]	lr: 4.687e-06, eta: 2:04:54, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8916, loss: 0.0789
+2023-03-04 01:28:25,151 - mmseg - INFO - Iter [55850/80000]	lr: 4.687e-06, eta: 2:04:39, time: 0.344, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0768, decode.acc_seg: 96.9248, loss: 0.0768
+2023-03-04 01:28:39,777 - mmseg - INFO - Iter [55900/80000]	lr: 4.687e-06, eta: 2:04:23, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0804, decode.acc_seg: 96.7871, loss: 0.0804
+2023-03-04 01:28:54,373 - mmseg - INFO - Iter [55950/80000]	lr: 4.687e-06, eta: 2:04:07, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.7766, loss: 0.0808
+2023-03-04 01:29:11,335 - mmseg - INFO - Saving checkpoint at 56000 iterations
+2023-03-04 01:29:13,225 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 01:29:13,225 - mmseg - INFO - Iter [56000/80000]	lr: 4.687e-06, eta: 2:03:53, time: 0.377, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0782, decode.acc_seg: 96.8820, loss: 0.0782
+2023-03-04 01:29:38,953 - mmseg - INFO - per class results:
+2023-03-04 01:29:38,954 - mmseg - INFO - 
++---------------+-------+-------+
+|     Class     |  IoU  |  Acc  |
++---------------+-------+-------+
+|   background  |  nan  |  nan  |
+|      road     | 98.54 | 99.19 |
+|    sidewalk   | 87.31 | 93.75 |
+|    building   | 93.58 | 96.93 |
+|      wall     | 55.18 | 60.52 |
+|     fence     | 64.83 | 74.52 |
+|      pole     | 70.99 | 83.29 |
+| traffic light | 75.42 | 85.93 |
+|  traffic sign | 82.48 | 88.69 |
+|   vegetation  | 93.08 | 96.96 |
+|    terrain    | 64.05 | 73.48 |
+|      sky      | 95.34 | 98.31 |
+|     person    | 84.98 | 92.83 |
+|     rider     | 68.07 | 80.72 |
+|      car      | 96.03 | 98.15 |
+|     truck     |  85.6 | 92.36 |
+|      bus      | 92.25 | 95.93 |
+|     train     | 84.71 | 88.88 |
+|   motorcycle  | 72.02 | 82.01 |
+|    bicycle    | 80.49 | 90.64 |
++---------------+-------+-------+
+2023-03-04 01:29:38,954 - mmseg - INFO - Summary:
+2023-03-04 01:29:38,954 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 96.64 | 81.31 | 88.06 |
++-------+-------+-------+
+2023-03-04 01:29:38,955 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 01:29:38,955 - mmseg - INFO - Iter(val) [63]	aAcc: 0.9664, mIoU: 0.8131, mAcc: 0.8806, IoU.background: nan, IoU.road: 0.9854, IoU.sidewalk: 0.8731, IoU.building: 0.9358, IoU.wall: 0.5518, IoU.fence: 0.6483, IoU.pole: 0.7099, IoU.traffic light: 0.7542, IoU.traffic sign: 0.8248, IoU.vegetation: 0.9308, IoU.terrain: 0.6405, IoU.sky: 0.9534, IoU.person: 0.8498, IoU.rider: 0.6807, IoU.car: 0.9603, IoU.truck: 0.8560, IoU.bus: 0.9225, IoU.train: 0.8471, IoU.motorcycle: 0.7202, IoU.bicycle: 0.8049, Acc.background: nan, Acc.road: 0.9919, Acc.sidewalk: 0.9375, Acc.building: 0.9693, Acc.wall: 0.6052, Acc.fence: 0.7452, Acc.pole: 0.8329, Acc.traffic light: 0.8593, Acc.traffic sign: 0.8869, Acc.vegetation: 0.9696, Acc.terrain: 0.7348, Acc.sky: 0.9831, Acc.person: 0.9283, Acc.rider: 0.8072, Acc.car: 0.9815, Acc.truck: 0.9236, Acc.bus: 0.9593, Acc.train: 0.8888, Acc.motorcycle: 0.8201, Acc.bicycle: 0.9064
+2023-03-04 01:29:53,910 - mmseg - INFO - Iter [56050/80000]	lr: 4.687e-06, eta: 2:03:49, time: 0.814, data_time: 0.522, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8220, loss: 0.0794
+2023-03-04 01:30:08,756 - mmseg - INFO - Iter [56100/80000]	lr: 4.687e-06, eta: 2:03:33, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.7946, loss: 0.0807
+2023-03-04 01:30:23,475 - mmseg - INFO - Iter [56150/80000]	lr: 4.687e-06, eta: 2:03:17, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0825, decode.acc_seg: 96.6756, loss: 0.0825
+2023-03-04 01:30:40,551 - mmseg - INFO - Iter [56200/80000]	lr: 4.687e-06, eta: 2:03:02, time: 0.342, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.8023, loss: 0.0808
+2023-03-04 01:30:55,287 - mmseg - INFO - Iter [56250/80000]	lr: 4.687e-06, eta: 2:02:46, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8254, loss: 0.0788
+2023-03-04 01:31:09,893 - mmseg - INFO - Iter [56300/80000]	lr: 4.687e-06, eta: 2:02:31, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0782, decode.acc_seg: 96.8574, loss: 0.0782
+2023-03-04 01:31:24,683 - mmseg - INFO - Iter [56350/80000]	lr: 4.687e-06, eta: 2:02:15, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8660, loss: 0.0793
+2023-03-04 01:31:41,675 - mmseg - INFO - Iter [56400/80000]	lr: 4.687e-06, eta: 2:02:00, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0772, decode.acc_seg: 96.8808, loss: 0.0772
+2023-03-04 01:31:56,544 - mmseg - INFO - Iter [56450/80000]	lr: 4.687e-06, eta: 2:01:44, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0773, decode.acc_seg: 96.9060, loss: 0.0773
+2023-03-04 01:32:11,351 - mmseg - INFO - Iter [56500/80000]	lr: 4.687e-06, eta: 2:01:28, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8076, loss: 0.0802
+2023-03-04 01:32:28,328 - mmseg - INFO - Iter [56550/80000]	lr: 4.687e-06, eta: 2:01:13, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0773, decode.acc_seg: 96.8900, loss: 0.0773
+2023-03-04 01:32:43,192 - mmseg - INFO - Iter [56600/80000]	lr: 4.687e-06, eta: 2:00:58, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7861, loss: 0.0811
+2023-03-04 01:32:58,105 - mmseg - INFO - Iter [56650/80000]	lr: 4.687e-06, eta: 2:00:42, time: 0.298, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0776, decode.acc_seg: 96.8930, loss: 0.0776
+2023-03-04 01:33:12,668 - mmseg - INFO - Iter [56700/80000]	lr: 4.687e-06, eta: 2:00:26, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.7424, loss: 0.0812
+2023-03-04 01:33:29,719 - mmseg - INFO - Iter [56750/80000]	lr: 4.687e-06, eta: 2:00:11, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.7862, loss: 0.0807
+2023-03-04 01:33:44,426 - mmseg - INFO - Iter [56800/80000]	lr: 4.687e-06, eta: 1:59:55, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8299, loss: 0.0802
+2023-03-04 01:33:59,426 - mmseg - INFO - Iter [56850/80000]	lr: 4.687e-06, eta: 1:59:39, time: 0.300, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8021, loss: 0.0800
+2023-03-04 01:34:14,034 - mmseg - INFO - Iter [56900/80000]	lr: 4.687e-06, eta: 1:59:24, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8532, loss: 0.0792
+2023-03-04 01:34:31,161 - mmseg - INFO - Iter [56950/80000]	lr: 4.687e-06, eta: 1:59:09, time: 0.343, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8426, loss: 0.0790
+2023-03-04 01:34:45,943 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 01:34:45,944 - mmseg - INFO - Iter [57000/80000]	lr: 4.687e-06, eta: 1:58:53, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0809, decode.acc_seg: 96.7849, loss: 0.0809
+2023-03-04 01:35:00,554 - mmseg - INFO - Iter [57050/80000]	lr: 4.687e-06, eta: 1:58:37, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8151, loss: 0.0800
+2023-03-04 01:35:15,099 - mmseg - INFO - Iter [57100/80000]	lr: 4.687e-06, eta: 1:58:21, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8151, loss: 0.0800
+2023-03-04 01:35:32,384 - mmseg - INFO - Iter [57150/80000]	lr: 4.687e-06, eta: 1:58:06, time: 0.346, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8253, loss: 0.0799
+2023-03-04 01:35:47,003 - mmseg - INFO - Iter [57200/80000]	lr: 4.687e-06, eta: 1:57:51, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0820, decode.acc_seg: 96.7785, loss: 0.0820
+2023-03-04 01:36:01,629 - mmseg - INFO - Iter [57250/80000]	lr: 4.687e-06, eta: 1:57:35, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8457, loss: 0.0790
+2023-03-04 01:36:18,719 - mmseg - INFO - Iter [57300/80000]	lr: 4.687e-06, eta: 1:57:20, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8003, loss: 0.0800
+2023-03-04 01:36:33,314 - mmseg - INFO - Iter [57350/80000]	lr: 4.687e-06, eta: 1:57:04, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8171, loss: 0.0795
+2023-03-04 01:36:48,027 - mmseg - INFO - Iter [57400/80000]	lr: 4.687e-06, eta: 1:56:48, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0780, decode.acc_seg: 96.8911, loss: 0.0780
+2023-03-04 01:37:02,668 - mmseg - INFO - Iter [57450/80000]	lr: 4.687e-06, eta: 1:56:32, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0831, decode.acc_seg: 96.7090, loss: 0.0831
+2023-03-04 01:37:19,630 - mmseg - INFO - Iter [57500/80000]	lr: 4.687e-06, eta: 1:56:17, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7059, loss: 0.0827
+2023-03-04 01:37:34,227 - mmseg - INFO - Iter [57550/80000]	lr: 4.687e-06, eta: 1:56:01, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8169, loss: 0.0789
+2023-03-04 01:37:48,851 - mmseg - INFO - Iter [57600/80000]	lr: 4.687e-06, eta: 1:55:46, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0774, decode.acc_seg: 96.9305, loss: 0.0774
+2023-03-04 01:38:03,405 - mmseg - INFO - Iter [57650/80000]	lr: 4.687e-06, eta: 1:55:30, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.8002, loss: 0.0814
+2023-03-04 01:38:20,339 - mmseg - INFO - Iter [57700/80000]	lr: 4.687e-06, eta: 1:55:15, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0772, decode.acc_seg: 96.9046, loss: 0.0772
+2023-03-04 01:38:35,008 - mmseg - INFO - Iter [57750/80000]	lr: 4.687e-06, eta: 1:54:59, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.7886, loss: 0.0803
+2023-03-04 01:38:49,570 - mmseg - INFO - Iter [57800/80000]	lr: 4.687e-06, eta: 1:54:43, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.8187, loss: 0.0797
+2023-03-04 01:39:06,569 - mmseg - INFO - Iter [57850/80000]	lr: 4.687e-06, eta: 1:54:28, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.9033, loss: 0.0786
+2023-03-04 01:39:21,125 - mmseg - INFO - Iter [57900/80000]	lr: 4.687e-06, eta: 1:54:12, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0781, decode.acc_seg: 96.8639, loss: 0.0781
+2023-03-04 01:39:35,871 - mmseg - INFO - Iter [57950/80000]	lr: 4.687e-06, eta: 1:53:57, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.7287, loss: 0.0819
+2023-03-04 01:39:50,607 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 01:39:50,608 - mmseg - INFO - Iter [58000/80000]	lr: 4.687e-06, eta: 1:53:41, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8335, loss: 0.0793
+2023-03-04 01:40:07,655 - mmseg - INFO - Iter [58050/80000]	lr: 4.687e-06, eta: 1:53:26, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.8221, loss: 0.0803
+2023-03-04 01:40:22,340 - mmseg - INFO - Iter [58100/80000]	lr: 4.687e-06, eta: 1:53:10, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8248, loss: 0.0794
+2023-03-04 01:40:36,924 - mmseg - INFO - Iter [58150/80000]	lr: 4.687e-06, eta: 1:52:54, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0779, decode.acc_seg: 96.8470, loss: 0.0779
+2023-03-04 01:40:51,604 - mmseg - INFO - Iter [58200/80000]	lr: 4.687e-06, eta: 1:52:38, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8239, loss: 0.0800
+2023-03-04 01:41:08,582 - mmseg - INFO - Iter [58250/80000]	lr: 4.687e-06, eta: 1:52:23, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0774, decode.acc_seg: 96.8960, loss: 0.0774
+2023-03-04 01:41:23,267 - mmseg - INFO - Iter [58300/80000]	lr: 4.687e-06, eta: 1:52:08, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.8150, loss: 0.0807
+2023-03-04 01:41:38,003 - mmseg - INFO - Iter [58350/80000]	lr: 4.687e-06, eta: 1:51:52, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8242, loss: 0.0799
+2023-03-04 01:41:52,574 - mmseg - INFO - Iter [58400/80000]	lr: 4.687e-06, eta: 1:51:36, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.7962, loss: 0.0803
+2023-03-04 01:42:09,559 - mmseg - INFO - Iter [58450/80000]	lr: 4.687e-06, eta: 1:51:21, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0787, decode.acc_seg: 96.8659, loss: 0.0787
+2023-03-04 01:42:24,135 - mmseg - INFO - Iter [58500/80000]	lr: 4.687e-06, eta: 1:51:05, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0777, decode.acc_seg: 96.9009, loss: 0.0777
+2023-03-04 01:42:38,706 - mmseg - INFO - Iter [58550/80000]	lr: 4.687e-06, eta: 1:50:49, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0767, decode.acc_seg: 96.9568, loss: 0.0767
+2023-03-04 01:42:55,840 - mmseg - INFO - Iter [58600/80000]	lr: 4.687e-06, eta: 1:50:34, time: 0.343, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.7575, loss: 0.0808
+2023-03-04 01:43:10,410 - mmseg - INFO - Iter [58650/80000]	lr: 4.687e-06, eta: 1:50:19, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0804, decode.acc_seg: 96.8068, loss: 0.0804
+2023-03-04 01:43:25,073 - mmseg - INFO - Iter [58700/80000]	lr: 4.687e-06, eta: 1:50:03, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0765, decode.acc_seg: 96.9640, loss: 0.0765
+2023-03-04 01:43:39,795 - mmseg - INFO - Iter [58750/80000]	lr: 4.687e-06, eta: 1:49:47, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8077, loss: 0.0798
+2023-03-04 01:43:56,864 - mmseg - INFO - Iter [58800/80000]	lr: 4.687e-06, eta: 1:49:32, time: 0.342, data_time: 0.054, memory: 67605, decode.loss_ce: 0.0742, decode.acc_seg: 97.0151, loss: 0.0742
+2023-03-04 01:44:11,481 - mmseg - INFO - Iter [58850/80000]	lr: 4.687e-06, eta: 1:49:16, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.8160, loss: 0.0807
+2023-03-04 01:44:26,114 - mmseg - INFO - Iter [58900/80000]	lr: 4.687e-06, eta: 1:49:00, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0813, decode.acc_seg: 96.6492, loss: 0.0813
+2023-03-04 01:44:40,699 - mmseg - INFO - Iter [58950/80000]	lr: 4.687e-06, eta: 1:48:45, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0767, decode.acc_seg: 96.9450, loss: 0.0767
+2023-03-04 01:44:57,698 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 01:44:57,699 - mmseg - INFO - Iter [59000/80000]	lr: 4.687e-06, eta: 1:48:30, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0824, decode.acc_seg: 96.7338, loss: 0.0824
+2023-03-04 01:45:12,265 - mmseg - INFO - Iter [59050/80000]	lr: 4.687e-06, eta: 1:48:14, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0767, decode.acc_seg: 96.9475, loss: 0.0767
+2023-03-04 01:45:26,831 - mmseg - INFO - Iter [59100/80000]	lr: 4.687e-06, eta: 1:47:58, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0781, decode.acc_seg: 96.8913, loss: 0.0781
+2023-03-04 01:45:43,760 - mmseg - INFO - Iter [59150/80000]	lr: 4.687e-06, eta: 1:47:43, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0779, decode.acc_seg: 96.8879, loss: 0.0779
+2023-03-04 01:45:58,661 - mmseg - INFO - Iter [59200/80000]	lr: 4.687e-06, eta: 1:47:27, time: 0.298, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.9019, loss: 0.0784
+2023-03-04 01:46:13,437 - mmseg - INFO - Iter [59250/80000]	lr: 4.687e-06, eta: 1:47:11, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.8341, loss: 0.0806
+2023-03-04 01:46:28,003 - mmseg - INFO - Iter [59300/80000]	lr: 4.687e-06, eta: 1:46:56, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0769, decode.acc_seg: 96.9186, loss: 0.0769
+2023-03-04 01:46:44,969 - mmseg - INFO - Iter [59350/80000]	lr: 4.687e-06, eta: 1:46:41, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.7978, loss: 0.0802
+2023-03-04 01:46:59,615 - mmseg - INFO - Iter [59400/80000]	lr: 4.687e-06, eta: 1:46:25, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.7911, loss: 0.0801
+2023-03-04 01:47:14,316 - mmseg - INFO - Iter [59450/80000]	lr: 4.687e-06, eta: 1:46:09, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0757, decode.acc_seg: 96.9947, loss: 0.0757
+2023-03-04 01:47:28,960 - mmseg - INFO - Iter [59500/80000]	lr: 4.687e-06, eta: 1:45:53, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0779, decode.acc_seg: 96.8747, loss: 0.0779
+2023-03-04 01:47:45,889 - mmseg - INFO - Iter [59550/80000]	lr: 4.687e-06, eta: 1:45:38, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8574, loss: 0.0796
+2023-03-04 01:48:00,466 - mmseg - INFO - Iter [59600/80000]	lr: 4.687e-06, eta: 1:45:23, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8859, loss: 0.0784
+2023-03-04 01:48:15,076 - mmseg - INFO - Iter [59650/80000]	lr: 4.687e-06, eta: 1:45:07, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0845, decode.acc_seg: 96.6604, loss: 0.0845
+2023-03-04 01:48:29,708 - mmseg - INFO - Iter [59700/80000]	lr: 4.687e-06, eta: 1:44:51, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7961, loss: 0.0815
+2023-03-04 01:48:46,813 - mmseg - INFO - Iter [59750/80000]	lr: 4.687e-06, eta: 1:44:36, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0778, decode.acc_seg: 96.9078, loss: 0.0778
+2023-03-04 01:49:01,424 - mmseg - INFO - Iter [59800/80000]	lr: 4.687e-06, eta: 1:44:20, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.8740, loss: 0.0783
+2023-03-04 01:49:16,071 - mmseg - INFO - Iter [59850/80000]	lr: 4.687e-06, eta: 1:44:04, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.7822, loss: 0.0805
+2023-03-04 01:49:33,007 - mmseg - INFO - Iter [59900/80000]	lr: 4.687e-06, eta: 1:43:49, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0813, decode.acc_seg: 96.7989, loss: 0.0813
+2023-03-04 01:49:47,713 - mmseg - INFO - Iter [59950/80000]	lr: 4.687e-06, eta: 1:43:34, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8500, loss: 0.0785
+2023-03-04 01:50:02,268 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 01:50:02,268 - mmseg - INFO - Iter [60000/80000]	lr: 4.687e-06, eta: 1:43:18, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0782, decode.acc_seg: 96.8404, loss: 0.0782
+2023-03-04 01:50:16,936 - mmseg - INFO - Iter [60050/80000]	lr: 2.344e-06, eta: 1:43:02, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8609, loss: 0.0789
+2023-03-04 01:50:34,155 - mmseg - INFO - Iter [60100/80000]	lr: 2.344e-06, eta: 1:42:47, time: 0.344, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7643, loss: 0.0811
+2023-03-04 01:50:49,070 - mmseg - INFO - Iter [60150/80000]	lr: 2.344e-06, eta: 1:42:31, time: 0.298, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.7998, loss: 0.0802
+2023-03-04 01:51:03,634 - mmseg - INFO - Iter [60200/80000]	lr: 2.344e-06, eta: 1:42:16, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0830, decode.acc_seg: 96.7084, loss: 0.0830
+2023-03-04 01:51:18,288 - mmseg - INFO - Iter [60250/80000]	lr: 2.344e-06, eta: 1:42:00, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0767, decode.acc_seg: 96.9326, loss: 0.0767
+2023-03-04 01:51:35,314 - mmseg - INFO - Iter [60300/80000]	lr: 2.344e-06, eta: 1:41:45, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0782, decode.acc_seg: 96.8749, loss: 0.0782
+2023-03-04 01:51:50,217 - mmseg - INFO - Iter [60350/80000]	lr: 2.344e-06, eta: 1:41:29, time: 0.298, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.8256, loss: 0.0801
+2023-03-04 01:52:04,920 - mmseg - INFO - Iter [60400/80000]	lr: 2.344e-06, eta: 1:41:13, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.7250, loss: 0.0818
+2023-03-04 01:52:19,546 - mmseg - INFO - Iter [60450/80000]	lr: 2.344e-06, eta: 1:40:58, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.8633, loss: 0.0783
+2023-03-04 01:52:36,482 - mmseg - INFO - Iter [60500/80000]	lr: 2.344e-06, eta: 1:40:43, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8643, loss: 0.0788
+2023-03-04 01:52:51,286 - mmseg - INFO - Iter [60550/80000]	lr: 2.344e-06, eta: 1:40:27, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7908, loss: 0.0811
+2023-03-04 01:53:05,979 - mmseg - INFO - Iter [60600/80000]	lr: 2.344e-06, eta: 1:40:11, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0804, decode.acc_seg: 96.7677, loss: 0.0804
+2023-03-04 01:53:22,977 - mmseg - INFO - Iter [60650/80000]	lr: 2.344e-06, eta: 1:39:56, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8448, loss: 0.0784
+2023-03-04 01:53:37,746 - mmseg - INFO - Iter [60700/80000]	lr: 2.344e-06, eta: 1:39:40, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8544, loss: 0.0796
+2023-03-04 01:53:52,327 - mmseg - INFO - Iter [60750/80000]	lr: 2.344e-06, eta: 1:39:25, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0775, decode.acc_seg: 96.9010, loss: 0.0775
+2023-03-04 01:54:06,918 - mmseg - INFO - Iter [60800/80000]	lr: 2.344e-06, eta: 1:39:09, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8499, loss: 0.0788
+2023-03-04 01:54:24,029 - mmseg - INFO - Iter [60850/80000]	lr: 2.344e-06, eta: 1:38:54, time: 0.342, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.7605, loss: 0.0818
+2023-03-04 01:54:38,778 - mmseg - INFO - Iter [60900/80000]	lr: 2.344e-06, eta: 1:38:38, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0777, decode.acc_seg: 96.8825, loss: 0.0777
+2023-03-04 01:54:53,373 - mmseg - INFO - Iter [60950/80000]	lr: 2.344e-06, eta: 1:38:22, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8966, loss: 0.0788
+2023-03-04 01:55:07,931 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 01:55:07,931 - mmseg - INFO - Iter [61000/80000]	lr: 2.344e-06, eta: 1:38:07, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8174, loss: 0.0794
+2023-03-04 01:55:24,964 - mmseg - INFO - Iter [61050/80000]	lr: 2.344e-06, eta: 1:37:52, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8513, loss: 0.0794
+2023-03-04 01:55:39,623 - mmseg - INFO - Iter [61100/80000]	lr: 2.344e-06, eta: 1:37:36, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0837, decode.acc_seg: 96.6558, loss: 0.0837
+2023-03-04 01:55:54,336 - mmseg - INFO - Iter [61150/80000]	lr: 2.344e-06, eta: 1:37:20, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.7839, loss: 0.0807
+2023-03-04 01:56:11,369 - mmseg - INFO - Iter [61200/80000]	lr: 2.344e-06, eta: 1:37:05, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0770, decode.acc_seg: 96.9219, loss: 0.0770
+2023-03-04 01:56:25,986 - mmseg - INFO - Iter [61250/80000]	lr: 2.344e-06, eta: 1:36:49, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8341, loss: 0.0796
+2023-03-04 01:56:40,655 - mmseg - INFO - Iter [61300/80000]	lr: 2.344e-06, eta: 1:36:34, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8298, loss: 0.0796
+2023-03-04 01:56:55,242 - mmseg - INFO - Iter [61350/80000]	lr: 2.344e-06, eta: 1:36:18, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0769, decode.acc_seg: 96.9413, loss: 0.0769
+2023-03-04 01:57:12,419 - mmseg - INFO - Iter [61400/80000]	lr: 2.344e-06, eta: 1:36:03, time: 0.344, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.7696, loss: 0.0805
+2023-03-04 01:57:27,145 - mmseg - INFO - Iter [61450/80000]	lr: 2.344e-06, eta: 1:35:47, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8031, loss: 0.0800
+2023-03-04 01:57:41,789 - mmseg - INFO - Iter [61500/80000]	lr: 2.344e-06, eta: 1:35:31, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0773, decode.acc_seg: 96.9319, loss: 0.0773
+2023-03-04 01:57:56,462 - mmseg - INFO - Iter [61550/80000]	lr: 2.344e-06, eta: 1:35:16, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.8062, loss: 0.0801
+2023-03-04 01:58:13,534 - mmseg - INFO - Iter [61600/80000]	lr: 2.344e-06, eta: 1:35:01, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8208, loss: 0.0792
+2023-03-04 01:58:28,093 - mmseg - INFO - Iter [61650/80000]	lr: 2.344e-06, eta: 1:34:45, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0787, decode.acc_seg: 96.8620, loss: 0.0787
+2023-03-04 01:58:42,672 - mmseg - INFO - Iter [61700/80000]	lr: 2.344e-06, eta: 1:34:29, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8216, loss: 0.0796
+2023-03-04 01:58:57,339 - mmseg - INFO - Iter [61750/80000]	lr: 2.344e-06, eta: 1:34:13, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.7793, loss: 0.0806
+2023-03-04 01:59:14,469 - mmseg - INFO - Iter [61800/80000]	lr: 2.344e-06, eta: 1:33:58, time: 0.343, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0765, decode.acc_seg: 96.9276, loss: 0.0765
+2023-03-04 01:59:29,158 - mmseg - INFO - Iter [61850/80000]	lr: 2.344e-06, eta: 1:33:43, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0768, decode.acc_seg: 96.9296, loss: 0.0768
+2023-03-04 01:59:43,798 - mmseg - INFO - Iter [61900/80000]	lr: 2.344e-06, eta: 1:33:27, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.7973, loss: 0.0808
+2023-03-04 02:00:00,911 - mmseg - INFO - Iter [61950/80000]	lr: 2.344e-06, eta: 1:33:12, time: 0.342, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8365, loss: 0.0795
+2023-03-04 02:00:15,625 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 02:00:15,625 - mmseg - INFO - Iter [62000/80000]	lr: 2.344e-06, eta: 1:32:56, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0809, decode.acc_seg: 96.7446, loss: 0.0809
+2023-03-04 02:00:30,278 - mmseg - INFO - Iter [62050/80000]	lr: 2.344e-06, eta: 1:32:40, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.8527, loss: 0.0805
+2023-03-04 02:00:44,908 - mmseg - INFO - Iter [62100/80000]	lr: 2.344e-06, eta: 1:32:25, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0816, decode.acc_seg: 96.7634, loss: 0.0816
+2023-03-04 02:01:02,027 - mmseg - INFO - Iter [62150/80000]	lr: 2.344e-06, eta: 1:32:10, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7545, loss: 0.0815
+2023-03-04 02:01:16,702 - mmseg - INFO - Iter [62200/80000]	lr: 2.344e-06, eta: 1:31:54, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.7911, loss: 0.0798
+2023-03-04 02:01:31,294 - mmseg - INFO - Iter [62250/80000]	lr: 2.344e-06, eta: 1:31:38, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8425, loss: 0.0784
+2023-03-04 02:01:46,037 - mmseg - INFO - Iter [62300/80000]	lr: 2.344e-06, eta: 1:31:22, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8541, loss: 0.0794
+2023-03-04 02:02:03,080 - mmseg - INFO - Iter [62350/80000]	lr: 2.344e-06, eta: 1:31:07, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0769, decode.acc_seg: 96.9519, loss: 0.0769
+2023-03-04 02:02:17,721 - mmseg - INFO - Iter [62400/80000]	lr: 2.344e-06, eta: 1:30:52, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8271, loss: 0.0798
+2023-03-04 02:02:32,284 - mmseg - INFO - Iter [62450/80000]	lr: 2.344e-06, eta: 1:30:36, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.8600, loss: 0.0786
+2023-03-04 02:02:49,215 - mmseg - INFO - Iter [62500/80000]	lr: 2.344e-06, eta: 1:30:21, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.7774, loss: 0.0799
+2023-03-04 02:03:03,933 - mmseg - INFO - Iter [62550/80000]	lr: 2.344e-06, eta: 1:30:05, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.8045, loss: 0.0810
+2023-03-04 02:03:18,640 - mmseg - INFO - Iter [62600/80000]	lr: 2.344e-06, eta: 1:29:49, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7494, loss: 0.0815
+2023-03-04 02:03:33,236 - mmseg - INFO - Iter [62650/80000]	lr: 2.344e-06, eta: 1:29:34, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.7421, loss: 0.0821
+2023-03-04 02:03:50,335 - mmseg - INFO - Iter [62700/80000]	lr: 2.344e-06, eta: 1:29:19, time: 0.342, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.7771, loss: 0.0805
+2023-03-04 02:04:05,069 - mmseg - INFO - Iter [62750/80000]	lr: 2.344e-06, eta: 1:29:03, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7510, loss: 0.0815
+2023-03-04 02:04:19,819 - mmseg - INFO - Iter [62800/80000]	lr: 2.344e-06, eta: 1:28:47, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0813, decode.acc_seg: 96.7641, loss: 0.0813
+2023-03-04 02:04:34,614 - mmseg - INFO - Iter [62850/80000]	lr: 2.344e-06, eta: 1:28:32, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8526, loss: 0.0798
+2023-03-04 02:04:51,637 - mmseg - INFO - Iter [62900/80000]	lr: 2.344e-06, eta: 1:28:16, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.8474, loss: 0.0801
+2023-03-04 02:05:06,253 - mmseg - INFO - Iter [62950/80000]	lr: 2.344e-06, eta: 1:28:01, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.8082, loss: 0.0805
+2023-03-04 02:05:20,941 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 02:05:20,941 - mmseg - INFO - Iter [63000/80000]	lr: 2.344e-06, eta: 1:27:45, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8694, loss: 0.0794
+2023-03-04 02:05:35,514 - mmseg - INFO - Iter [63050/80000]	lr: 2.344e-06, eta: 1:27:29, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.8605, loss: 0.0786
+2023-03-04 02:05:52,445 - mmseg - INFO - Iter [63100/80000]	lr: 2.344e-06, eta: 1:27:14, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8435, loss: 0.0791
+2023-03-04 02:06:07,079 - mmseg - INFO - Iter [63150/80000]	lr: 2.344e-06, eta: 1:26:59, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.7965, loss: 0.0800
+2023-03-04 02:06:21,687 - mmseg - INFO - Iter [63200/80000]	lr: 2.344e-06, eta: 1:26:43, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8453, loss: 0.0792
+2023-03-04 02:06:38,630 - mmseg - INFO - Iter [63250/80000]	lr: 2.344e-06, eta: 1:26:28, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.8598, loss: 0.0783
+2023-03-04 02:06:53,209 - mmseg - INFO - Iter [63300/80000]	lr: 2.344e-06, eta: 1:26:12, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0816, decode.acc_seg: 96.7243, loss: 0.0816
+2023-03-04 02:07:08,072 - mmseg - INFO - Iter [63350/80000]	lr: 2.344e-06, eta: 1:25:56, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0832, decode.acc_seg: 96.7332, loss: 0.0832
+2023-03-04 02:07:22,695 - mmseg - INFO - Iter [63400/80000]	lr: 2.344e-06, eta: 1:25:41, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.7780, loss: 0.0793
+2023-03-04 02:07:39,636 - mmseg - INFO - Iter [63450/80000]	lr: 2.344e-06, eta: 1:25:25, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8608, loss: 0.0788
+2023-03-04 02:07:54,247 - mmseg - INFO - Iter [63500/80000]	lr: 2.344e-06, eta: 1:25:10, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0823, decode.acc_seg: 96.7171, loss: 0.0823
+2023-03-04 02:08:08,846 - mmseg - INFO - Iter [63550/80000]	lr: 2.344e-06, eta: 1:24:54, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0763, decode.acc_seg: 96.9301, loss: 0.0763
+2023-03-04 02:08:23,517 - mmseg - INFO - Iter [63600/80000]	lr: 2.344e-06, eta: 1:24:38, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8743, loss: 0.0784
+2023-03-04 02:08:40,564 - mmseg - INFO - Iter [63650/80000]	lr: 2.344e-06, eta: 1:24:23, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8920, loss: 0.0788
+2023-03-04 02:08:55,266 - mmseg - INFO - Iter [63700/80000]	lr: 2.344e-06, eta: 1:24:08, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0775, decode.acc_seg: 96.8786, loss: 0.0775
+2023-03-04 02:09:09,849 - mmseg - INFO - Iter [63750/80000]	lr: 2.344e-06, eta: 1:23:52, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0776, decode.acc_seg: 96.8599, loss: 0.0776
+2023-03-04 02:09:26,803 - mmseg - INFO - Iter [63800/80000]	lr: 2.344e-06, eta: 1:23:37, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8638, loss: 0.0795
+2023-03-04 02:09:41,591 - mmseg - INFO - Iter [63850/80000]	lr: 2.344e-06, eta: 1:23:21, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.7534, loss: 0.0818
+2023-03-04 02:09:56,271 - mmseg - INFO - Iter [63900/80000]	lr: 2.344e-06, eta: 1:23:05, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8621, loss: 0.0788
+2023-03-04 02:10:11,041 - mmseg - INFO - Iter [63950/80000]	lr: 2.344e-06, eta: 1:22:50, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.7130, loss: 0.0814
+2023-03-04 02:10:28,055 - mmseg - INFO - Saving checkpoint at 64000 iterations
+2023-03-04 02:10:30,036 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 02:10:30,036 - mmseg - INFO - Iter [64000/80000]	lr: 2.344e-06, eta: 1:22:35, time: 0.380, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.7934, loss: 0.0801
+2023-03-04 02:10:55,811 - mmseg - INFO - per class results:
+2023-03-04 02:10:55,813 - mmseg - INFO - 
++---------------+-------+-------+
+|     Class     |  IoU  |  Acc  |
++---------------+-------+-------+
+|   background  |  nan  |  nan  |
+|      road     | 98.52 | 99.16 |
+|    sidewalk   |  87.3 | 93.93 |
+|    building   | 93.57 | 96.92 |
+|      wall     | 55.46 |  61.1 |
+|     fence     | 65.46 | 75.82 |
+|      pole     | 70.86 | 82.69 |
+| traffic light | 75.47 | 85.46 |
+|  traffic sign | 82.43 | 88.99 |
+|   vegetation  |  93.1 | 97.02 |
+|    terrain    |  64.7 | 74.11 |
+|      sky      | 95.34 | 98.33 |
+|     person    | 84.89 | 92.66 |
+|     rider     | 67.95 | 79.41 |
+|      car      | 96.03 | 98.11 |
+|     truck     |  86.0 | 91.06 |
+|      bus      |  92.5 | 95.71 |
+|     train     | 85.91 | 90.17 |
+|   motorcycle  | 72.12 |  81.9 |
+|    bicycle    | 80.66 | 90.09 |
++---------------+-------+-------+
+2023-03-04 02:10:55,813 - mmseg - INFO - Summary:
+2023-03-04 02:10:55,813 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 96.65 | 81.49 | 88.03 |
++-------+-------+-------+
+2023-03-04 02:10:55,884 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20/best_mIoU_iter_40000.pth was removed
+2023-03-04 02:10:57,812 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_64000.pth.
+2023-03-04 02:10:57,813 - mmseg - INFO - Best mIoU is 0.8149 at 64000 iter.
+2023-03-04 02:10:57,813 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 02:10:57,813 - mmseg - INFO - Iter(val) [63]	aAcc: 0.9665, mIoU: 0.8149, mAcc: 0.8803, IoU.background: nan, IoU.road: 0.9852, IoU.sidewalk: 0.8730, IoU.building: 0.9357, IoU.wall: 0.5546, IoU.fence: 0.6546, IoU.pole: 0.7086, IoU.traffic light: 0.7547, IoU.traffic sign: 0.8243, IoU.vegetation: 0.9310, IoU.terrain: 0.6470, IoU.sky: 0.9534, IoU.person: 0.8489, IoU.rider: 0.6795, IoU.car: 0.9603, IoU.truck: 0.8600, IoU.bus: 0.9250, IoU.train: 0.8591, IoU.motorcycle: 0.7212, IoU.bicycle: 0.8066, Acc.background: nan, Acc.road: 0.9916, Acc.sidewalk: 0.9393, Acc.building: 0.9692, Acc.wall: 0.6110, Acc.fence: 0.7582, Acc.pole: 0.8269, Acc.traffic light: 0.8546, Acc.traffic sign: 0.8899, Acc.vegetation: 0.9702, Acc.terrain: 0.7411, Acc.sky: 0.9833, Acc.person: 0.9266, Acc.rider: 0.7941, Acc.car: 0.9811, Acc.truck: 0.9106, Acc.bus: 0.9571, Acc.train: 0.9017, Acc.motorcycle: 0.8190, Acc.bicycle: 0.9009
+2023-03-04 02:11:12,728 - mmseg - INFO - Iter [64050/80000]	lr: 2.344e-06, eta: 1:22:26, time: 0.854, data_time: 0.563, memory: 67605, decode.loss_ce: 0.0778, decode.acc_seg: 96.9149, loss: 0.0778
+2023-03-04 02:11:27,365 - mmseg - INFO - Iter [64100/80000]	lr: 2.344e-06, eta: 1:22:11, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8212, loss: 0.0802
+2023-03-04 02:11:42,018 - mmseg - INFO - Iter [64150/80000]	lr: 2.344e-06, eta: 1:21:55, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.8745, loss: 0.0786
+2023-03-04 02:11:59,098 - mmseg - INFO - Iter [64200/80000]	lr: 2.344e-06, eta: 1:21:40, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8387, loss: 0.0792
+2023-03-04 02:12:13,805 - mmseg - INFO - Iter [64250/80000]	lr: 2.344e-06, eta: 1:21:24, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8202, loss: 0.0792
+2023-03-04 02:12:28,622 - mmseg - INFO - Iter [64300/80000]	lr: 2.344e-06, eta: 1:21:09, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.7824, loss: 0.0797
+2023-03-04 02:12:43,189 - mmseg - INFO - Iter [64350/80000]	lr: 2.344e-06, eta: 1:20:53, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8655, loss: 0.0792
+2023-03-04 02:13:00,307 - mmseg - INFO - Iter [64400/80000]	lr: 2.344e-06, eta: 1:20:38, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8606, loss: 0.0793
+2023-03-04 02:13:14,909 - mmseg - INFO - Iter [64450/80000]	lr: 2.344e-06, eta: 1:20:22, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0773, decode.acc_seg: 96.9121, loss: 0.0773
+2023-03-04 02:13:29,509 - mmseg - INFO - Iter [64500/80000]	lr: 2.344e-06, eta: 1:20:06, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0778, decode.acc_seg: 96.9170, loss: 0.0778
+2023-03-04 02:13:46,510 - mmseg - INFO - Iter [64550/80000]	lr: 2.344e-06, eta: 1:19:51, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.7735, loss: 0.0812
+2023-03-04 02:14:01,100 - mmseg - INFO - Iter [64600/80000]	lr: 2.344e-06, eta: 1:19:35, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8117, loss: 0.0794
+2023-03-04 02:14:15,747 - mmseg - INFO - Iter [64650/80000]	lr: 2.344e-06, eta: 1:19:20, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0782, decode.acc_seg: 96.8344, loss: 0.0782
+2023-03-04 02:14:30,382 - mmseg - INFO - Iter [64700/80000]	lr: 2.344e-06, eta: 1:19:04, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8694, loss: 0.0796
+2023-03-04 02:14:47,366 - mmseg - INFO - Iter [64750/80000]	lr: 2.344e-06, eta: 1:18:49, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.8646, loss: 0.0786
+2023-03-04 02:15:01,966 - mmseg - INFO - Iter [64800/80000]	lr: 2.344e-06, eta: 1:18:33, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.7550, loss: 0.0808
+2023-03-04 02:15:16,571 - mmseg - INFO - Iter [64850/80000]	lr: 2.344e-06, eta: 1:18:17, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8617, loss: 0.0800
+2023-03-04 02:15:31,206 - mmseg - INFO - Iter [64900/80000]	lr: 2.344e-06, eta: 1:18:02, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8335, loss: 0.0791
+2023-03-04 02:15:48,105 - mmseg - INFO - Iter [64950/80000]	lr: 2.344e-06, eta: 1:17:46, time: 0.338, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.7613, loss: 0.0810
+2023-03-04 02:16:02,855 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 02:16:02,855 - mmseg - INFO - Iter [65000/80000]	lr: 2.344e-06, eta: 1:17:31, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0822, decode.acc_seg: 96.7836, loss: 0.0822
+2023-03-04 02:16:17,504 - mmseg - INFO - Iter [65050/80000]	lr: 2.344e-06, eta: 1:17:15, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8603, loss: 0.0792
+2023-03-04 02:16:32,133 - mmseg - INFO - Iter [65100/80000]	lr: 2.344e-06, eta: 1:16:59, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8461, loss: 0.0788
+2023-03-04 02:16:49,101 - mmseg - INFO - Iter [65150/80000]	lr: 2.344e-06, eta: 1:16:44, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0772, decode.acc_seg: 96.9045, loss: 0.0772
+2023-03-04 02:17:03,670 - mmseg - INFO - Iter [65200/80000]	lr: 2.344e-06, eta: 1:16:28, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8457, loss: 0.0790
+2023-03-04 02:17:18,234 - mmseg - INFO - Iter [65250/80000]	lr: 2.344e-06, eta: 1:16:13, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.8080, loss: 0.0797
+2023-03-04 02:17:35,178 - mmseg - INFO - Iter [65300/80000]	lr: 2.344e-06, eta: 1:15:58, time: 0.339, data_time: 0.054, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.7832, loss: 0.0806
+2023-03-04 02:17:49,850 - mmseg - INFO - Iter [65350/80000]	lr: 2.344e-06, eta: 1:15:42, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.7675, loss: 0.0818
+2023-03-04 02:18:04,448 - mmseg - INFO - Iter [65400/80000]	lr: 2.344e-06, eta: 1:15:26, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0781, decode.acc_seg: 96.8756, loss: 0.0781
+2023-03-04 02:18:19,071 - mmseg - INFO - Iter [65450/80000]	lr: 2.344e-06, eta: 1:15:10, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8653, loss: 0.0784
+2023-03-04 02:18:36,159 - mmseg - INFO - Iter [65500/80000]	lr: 2.344e-06, eta: 1:14:55, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.7941, loss: 0.0796
+2023-03-04 02:18:50,781 - mmseg - INFO - Iter [65550/80000]	lr: 2.344e-06, eta: 1:14:40, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8673, loss: 0.0791
+2023-03-04 02:19:05,447 - mmseg - INFO - Iter [65600/80000]	lr: 2.344e-06, eta: 1:14:24, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8552, loss: 0.0796
+2023-03-04 02:19:20,215 - mmseg - INFO - Iter [65650/80000]	lr: 2.344e-06, eta: 1:14:08, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8451, loss: 0.0790
+2023-03-04 02:19:37,216 - mmseg - INFO - Iter [65700/80000]	lr: 2.344e-06, eta: 1:13:53, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.8332, loss: 0.0797
+2023-03-04 02:19:51,804 - mmseg - INFO - Iter [65750/80000]	lr: 2.344e-06, eta: 1:13:37, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8478, loss: 0.0795
+2023-03-04 02:20:06,541 - mmseg - INFO - Iter [65800/80000]	lr: 2.344e-06, eta: 1:13:22, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0813, decode.acc_seg: 96.7822, loss: 0.0813
+2023-03-04 02:20:23,595 - mmseg - INFO - Iter [65850/80000]	lr: 2.344e-06, eta: 1:13:07, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0762, decode.acc_seg: 96.9524, loss: 0.0762
+2023-03-04 02:20:38,356 - mmseg - INFO - Iter [65900/80000]	lr: 2.344e-06, eta: 1:12:51, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.8558, loss: 0.0783
+2023-03-04 02:20:52,914 - mmseg - INFO - Iter [65950/80000]	lr: 2.344e-06, eta: 1:12:35, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.7877, loss: 0.0796
+2023-03-04 02:21:07,498 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 02:21:07,499 - mmseg - INFO - Iter [66000/80000]	lr: 2.344e-06, eta: 1:12:20, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0772, decode.acc_seg: 96.9104, loss: 0.0772
+2023-03-04 02:21:24,426 - mmseg - INFO - Iter [66050/80000]	lr: 2.344e-06, eta: 1:12:04, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0813, decode.acc_seg: 96.7614, loss: 0.0813
+2023-03-04 02:21:39,039 - mmseg - INFO - Iter [66100/80000]	lr: 2.344e-06, eta: 1:11:49, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0824, decode.acc_seg: 96.7184, loss: 0.0824
+2023-03-04 02:21:53,751 - mmseg - INFO - Iter [66150/80000]	lr: 2.344e-06, eta: 1:11:33, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8274, loss: 0.0796
+2023-03-04 02:22:08,418 - mmseg - INFO - Iter [66200/80000]	lr: 2.344e-06, eta: 1:11:17, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8039, loss: 0.0800
+2023-03-04 02:22:25,357 - mmseg - INFO - Iter [66250/80000]	lr: 2.344e-06, eta: 1:11:02, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8448, loss: 0.0796
+2023-03-04 02:22:40,037 - mmseg - INFO - Iter [66300/80000]	lr: 2.344e-06, eta: 1:10:46, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8435, loss: 0.0791
+2023-03-04 02:22:54,764 - mmseg - INFO - Iter [66350/80000]	lr: 2.344e-06, eta: 1:10:31, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8777, loss: 0.0784
+2023-03-04 02:23:09,472 - mmseg - INFO - Iter [66400/80000]	lr: 2.344e-06, eta: 1:10:15, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.8127, loss: 0.0811
+2023-03-04 02:23:26,404 - mmseg - INFO - Iter [66450/80000]	lr: 2.344e-06, eta: 1:10:00, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0765, decode.acc_seg: 96.9373, loss: 0.0765
+2023-03-04 02:23:41,064 - mmseg - INFO - Iter [66500/80000]	lr: 2.344e-06, eta: 1:09:44, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8297, loss: 0.0799
+2023-03-04 02:23:55,867 - mmseg - INFO - Iter [66550/80000]	lr: 2.344e-06, eta: 1:09:29, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.8070, loss: 0.0815
+2023-03-04 02:24:12,794 - mmseg - INFO - Iter [66600/80000]	lr: 2.344e-06, eta: 1:09:13, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0772, decode.acc_seg: 96.9346, loss: 0.0772
+2023-03-04 02:24:27,532 - mmseg - INFO - Iter [66650/80000]	lr: 2.344e-06, eta: 1:08:58, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.6973, loss: 0.0818
+2023-03-04 02:24:42,119 - mmseg - INFO - Iter [66700/80000]	lr: 2.344e-06, eta: 1:08:42, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0837, decode.acc_seg: 96.6821, loss: 0.0837
+2023-03-04 02:24:56,921 - mmseg - INFO - Iter [66750/80000]	lr: 2.344e-06, eta: 1:08:26, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.9056, loss: 0.0783
+2023-03-04 02:25:13,839 - mmseg - INFO - Iter [66800/80000]	lr: 2.344e-06, eta: 1:08:11, time: 0.338, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0828, decode.acc_seg: 96.7593, loss: 0.0828
+2023-03-04 02:25:28,567 - mmseg - INFO - Iter [66850/80000]	lr: 2.344e-06, eta: 1:07:56, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8996, loss: 0.0785
+2023-03-04 02:25:43,321 - mmseg - INFO - Iter [66900/80000]	lr: 2.344e-06, eta: 1:07:40, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0817, decode.acc_seg: 96.8463, loss: 0.0817
+2023-03-04 02:25:58,016 - mmseg - INFO - Iter [66950/80000]	lr: 2.344e-06, eta: 1:07:24, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0834, decode.acc_seg: 96.6919, loss: 0.0834
+2023-03-04 02:26:15,008 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 02:26:15,008 - mmseg - INFO - Iter [67000/80000]	lr: 2.344e-06, eta: 1:07:09, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.8978, loss: 0.0783
+2023-03-04 02:26:29,576 - mmseg - INFO - Iter [67050/80000]	lr: 2.344e-06, eta: 1:06:53, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0825, decode.acc_seg: 96.7179, loss: 0.0825
+2023-03-04 02:26:44,138 - mmseg - INFO - Iter [67100/80000]	lr: 2.344e-06, eta: 1:06:38, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.8056, loss: 0.0818
+2023-03-04 02:27:01,177 - mmseg - INFO - Iter [67150/80000]	lr: 2.344e-06, eta: 1:06:22, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8305, loss: 0.0791
+2023-03-04 02:27:15,857 - mmseg - INFO - Iter [67200/80000]	lr: 2.344e-06, eta: 1:06:07, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8385, loss: 0.0791
+2023-03-04 02:27:30,499 - mmseg - INFO - Iter [67250/80000]	lr: 2.344e-06, eta: 1:05:51, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0809, decode.acc_seg: 96.7411, loss: 0.0809
+2023-03-04 02:27:45,168 - mmseg - INFO - Iter [67300/80000]	lr: 2.344e-06, eta: 1:05:36, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0772, decode.acc_seg: 96.9355, loss: 0.0772
+2023-03-04 02:28:02,121 - mmseg - INFO - Iter [67350/80000]	lr: 2.344e-06, eta: 1:05:20, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.7853, loss: 0.0812
+2023-03-04 02:28:16,835 - mmseg - INFO - Iter [67400/80000]	lr: 2.344e-06, eta: 1:05:05, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.7638, loss: 0.0810
+2023-03-04 02:28:31,458 - mmseg - INFO - Iter [67450/80000]	lr: 2.344e-06, eta: 1:04:49, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.8036, loss: 0.0807
+2023-03-04 02:28:46,024 - mmseg - INFO - Iter [67500/80000]	lr: 2.344e-06, eta: 1:04:33, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.8164, loss: 0.0805
+2023-03-04 02:29:02,967 - mmseg - INFO - Iter [67550/80000]	lr: 2.344e-06, eta: 1:04:18, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.7490, loss: 0.0814
+2023-03-04 02:29:17,591 - mmseg - INFO - Iter [67600/80000]	lr: 2.344e-06, eta: 1:04:02, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8469, loss: 0.0791
+2023-03-04 02:29:32,285 - mmseg - INFO - Iter [67650/80000]	lr: 2.344e-06, eta: 1:03:47, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0804, decode.acc_seg: 96.8128, loss: 0.0804
+2023-03-04 02:29:46,931 - mmseg - INFO - Iter [67700/80000]	lr: 2.344e-06, eta: 1:03:31, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0758, decode.acc_seg: 96.9926, loss: 0.0758
+2023-03-04 02:30:04,071 - mmseg - INFO - Iter [67750/80000]	lr: 2.344e-06, eta: 1:03:16, time: 0.343, data_time: 0.054, memory: 67605, decode.loss_ce: 0.0780, decode.acc_seg: 96.8651, loss: 0.0780
+2023-03-04 02:30:18,707 - mmseg - INFO - Iter [67800/80000]	lr: 2.344e-06, eta: 1:03:00, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.9039, loss: 0.0783
+2023-03-04 02:30:33,467 - mmseg - INFO - Iter [67850/80000]	lr: 2.344e-06, eta: 1:02:45, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0756, decode.acc_seg: 96.9462, loss: 0.0756
+2023-03-04 02:30:50,413 - mmseg - INFO - Iter [67900/80000]	lr: 2.344e-06, eta: 1:02:29, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8321, loss: 0.0793
+2023-03-04 02:31:04,996 - mmseg - INFO - Iter [67950/80000]	lr: 2.344e-06, eta: 1:02:14, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0839, decode.acc_seg: 96.6871, loss: 0.0839
+2023-03-04 02:31:19,657 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 02:31:19,657 - mmseg - INFO - Iter [68000/80000]	lr: 2.344e-06, eta: 1:01:58, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0762, decode.acc_seg: 96.9552, loss: 0.0762
+2023-03-04 02:31:34,252 - mmseg - INFO - Iter [68050/80000]	lr: 2.344e-06, eta: 1:01:42, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8704, loss: 0.0791
+2023-03-04 02:31:51,279 - mmseg - INFO - Iter [68100/80000]	lr: 2.344e-06, eta: 1:01:27, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0770, decode.acc_seg: 96.9254, loss: 0.0770
+2023-03-04 02:32:05,959 - mmseg - INFO - Iter [68150/80000]	lr: 2.344e-06, eta: 1:01:12, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0782, decode.acc_seg: 96.8570, loss: 0.0782
+2023-03-04 02:32:20,569 - mmseg - INFO - Iter [68200/80000]	lr: 2.344e-06, eta: 1:00:56, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0754, decode.acc_seg: 96.9903, loss: 0.0754
+2023-03-04 02:32:35,448 - mmseg - INFO - Iter [68250/80000]	lr: 2.344e-06, eta: 1:00:40, time: 0.298, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8720, loss: 0.0791
+2023-03-04 02:32:52,761 - mmseg - INFO - Iter [68300/80000]	lr: 2.344e-06, eta: 1:00:25, time: 0.346, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8000, loss: 0.0798
+2023-03-04 02:33:07,540 - mmseg - INFO - Iter [68350/80000]	lr: 2.344e-06, eta: 1:00:10, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8368, loss: 0.0789
+2023-03-04 02:33:22,276 - mmseg - INFO - Iter [68400/80000]	lr: 2.344e-06, eta: 0:59:54, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0821, decode.acc_seg: 96.7366, loss: 0.0821
+2023-03-04 02:33:39,291 - mmseg - INFO - Iter [68450/80000]	lr: 2.344e-06, eta: 0:59:39, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.7876, loss: 0.0806
+2023-03-04 02:33:53,955 - mmseg - INFO - Iter [68500/80000]	lr: 2.344e-06, eta: 0:59:23, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.8352, loss: 0.0797
+2023-03-04 02:34:08,571 - mmseg - INFO - Iter [68550/80000]	lr: 2.344e-06, eta: 0:59:07, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8656, loss: 0.0788
+2023-03-04 02:34:23,167 - mmseg - INFO - Iter [68600/80000]	lr: 2.344e-06, eta: 0:58:52, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0776, decode.acc_seg: 96.9082, loss: 0.0776
+2023-03-04 02:34:40,116 - mmseg - INFO - Iter [68650/80000]	lr: 2.344e-06, eta: 0:58:37, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8184, loss: 0.0795
+2023-03-04 02:34:54,739 - mmseg - INFO - Iter [68700/80000]	lr: 2.344e-06, eta: 0:58:21, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8309, loss: 0.0795
+2023-03-04 02:35:09,435 - mmseg - INFO - Iter [68750/80000]	lr: 2.344e-06, eta: 0:58:05, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0762, decode.acc_seg: 96.9550, loss: 0.0762
+2023-03-04 02:35:24,010 - mmseg - INFO - Iter [68800/80000]	lr: 2.344e-06, eta: 0:57:50, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0782, decode.acc_seg: 96.8937, loss: 0.0782
+2023-03-04 02:35:41,061 - mmseg - INFO - Iter [68850/80000]	lr: 2.344e-06, eta: 0:57:34, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0775, decode.acc_seg: 96.9201, loss: 0.0775
+2023-03-04 02:35:55,720 - mmseg - INFO - Iter [68900/80000]	lr: 2.344e-06, eta: 0:57:19, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0858, decode.acc_seg: 96.6707, loss: 0.0858
+2023-03-04 02:36:10,329 - mmseg - INFO - Iter [68950/80000]	lr: 2.344e-06, eta: 0:57:03, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8667, loss: 0.0796
+2023-03-04 02:36:24,942 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 02:36:24,942 - mmseg - INFO - Iter [69000/80000]	lr: 2.344e-06, eta: 0:56:48, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0809, decode.acc_seg: 96.7781, loss: 0.0809
+2023-03-04 02:36:42,083 - mmseg - INFO - Iter [69050/80000]	lr: 2.344e-06, eta: 0:56:32, time: 0.343, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8207, loss: 0.0800
+2023-03-04 02:36:56,713 - mmseg - INFO - Iter [69100/80000]	lr: 2.344e-06, eta: 0:56:17, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8659, loss: 0.0790
+2023-03-04 02:37:11,280 - mmseg - INFO - Iter [69150/80000]	lr: 2.344e-06, eta: 0:56:01, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0781, decode.acc_seg: 96.8698, loss: 0.0781
+2023-03-04 02:37:28,294 - mmseg - INFO - Iter [69200/80000]	lr: 2.344e-06, eta: 0:55:46, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0776, decode.acc_seg: 96.9047, loss: 0.0776
+2023-03-04 02:37:42,911 - mmseg - INFO - Iter [69250/80000]	lr: 2.344e-06, eta: 0:55:30, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.7845, loss: 0.0803
+2023-03-04 02:37:57,645 - mmseg - INFO - Iter [69300/80000]	lr: 2.344e-06, eta: 0:55:15, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8707, loss: 0.0788
+2023-03-04 02:38:12,234 - mmseg - INFO - Iter [69350/80000]	lr: 2.344e-06, eta: 0:54:59, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0772, decode.acc_seg: 96.8965, loss: 0.0772
+2023-03-04 02:38:29,295 - mmseg - INFO - Iter [69400/80000]	lr: 2.344e-06, eta: 0:54:44, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0822, decode.acc_seg: 96.7364, loss: 0.0822
+2023-03-04 02:38:43,881 - mmseg - INFO - Iter [69450/80000]	lr: 2.344e-06, eta: 0:54:28, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0781, decode.acc_seg: 96.8796, loss: 0.0781
+2023-03-04 02:38:58,567 - mmseg - INFO - Iter [69500/80000]	lr: 2.344e-06, eta: 0:54:12, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8340, loss: 0.0798
+2023-03-04 02:39:13,207 - mmseg - INFO - Iter [69550/80000]	lr: 2.344e-06, eta: 0:53:57, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0815, decode.acc_seg: 96.7523, loss: 0.0815
+2023-03-04 02:39:30,135 - mmseg - INFO - Iter [69600/80000]	lr: 2.344e-06, eta: 0:53:42, time: 0.338, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0787, decode.acc_seg: 96.8864, loss: 0.0787
+2023-03-04 02:39:44,746 - mmseg - INFO - Iter [69650/80000]	lr: 2.344e-06, eta: 0:53:26, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.7841, loss: 0.0814
+2023-03-04 02:39:59,435 - mmseg - INFO - Iter [69700/80000]	lr: 2.344e-06, eta: 0:53:10, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.7580, loss: 0.0806
+2023-03-04 02:40:14,016 - mmseg - INFO - Iter [69750/80000]	lr: 2.344e-06, eta: 0:52:55, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8492, loss: 0.0785
+2023-03-04 02:40:30,984 - mmseg - INFO - Iter [69800/80000]	lr: 2.344e-06, eta: 0:52:39, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8465, loss: 0.0796
+2023-03-04 02:40:45,615 - mmseg - INFO - Iter [69850/80000]	lr: 2.344e-06, eta: 0:52:24, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7517, loss: 0.0811
+2023-03-04 02:41:00,311 - mmseg - INFO - Iter [69900/80000]	lr: 2.344e-06, eta: 0:52:08, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.8902, loss: 0.0783
+2023-03-04 02:41:17,536 - mmseg - INFO - Iter [69950/80000]	lr: 2.344e-06, eta: 0:51:53, time: 0.344, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8706, loss: 0.0788
+2023-03-04 02:41:32,269 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 02:41:32,269 - mmseg - INFO - Iter [70000/80000]	lr: 2.344e-06, eta: 0:51:37, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7515, loss: 0.0811
+2023-03-04 02:41:46,903 - mmseg - INFO - Iter [70050/80000]	lr: 1.172e-06, eta: 0:51:22, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0770, decode.acc_seg: 96.9430, loss: 0.0770
+2023-03-04 02:42:01,498 - mmseg - INFO - Iter [70100/80000]	lr: 1.172e-06, eta: 0:51:06, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8295, loss: 0.0793
+2023-03-04 02:42:18,550 - mmseg - INFO - Iter [70150/80000]	lr: 1.172e-06, eta: 0:50:51, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0809, decode.acc_seg: 96.7279, loss: 0.0809
+2023-03-04 02:42:33,122 - mmseg - INFO - Iter [70200/80000]	lr: 1.172e-06, eta: 0:50:35, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0809, decode.acc_seg: 96.7968, loss: 0.0809
+2023-03-04 02:42:47,735 - mmseg - INFO - Iter [70250/80000]	lr: 1.172e-06, eta: 0:50:20, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0816, decode.acc_seg: 96.7625, loss: 0.0816
+2023-03-04 02:43:02,308 - mmseg - INFO - Iter [70300/80000]	lr: 1.172e-06, eta: 0:50:04, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.8871, loss: 0.0783
+2023-03-04 02:43:19,236 - mmseg - INFO - Iter [70350/80000]	lr: 1.172e-06, eta: 0:49:49, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.8263, loss: 0.0801
+2023-03-04 02:43:33,852 - mmseg - INFO - Iter [70400/80000]	lr: 1.172e-06, eta: 0:49:33, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8154, loss: 0.0800
+2023-03-04 02:43:48,449 - mmseg - INFO - Iter [70450/80000]	lr: 1.172e-06, eta: 0:49:17, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.9059, loss: 0.0783
+2023-03-04 02:44:05,515 - mmseg - INFO - Iter [70500/80000]	lr: 1.172e-06, eta: 0:49:02, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8397, loss: 0.0794
+2023-03-04 02:44:20,154 - mmseg - INFO - Iter [70550/80000]	lr: 1.172e-06, eta: 0:48:47, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0761, decode.acc_seg: 96.9643, loss: 0.0761
+2023-03-04 02:44:34,741 - mmseg - INFO - Iter [70600/80000]	lr: 1.172e-06, eta: 0:48:31, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.8018, loss: 0.0811
+2023-03-04 02:44:49,397 - mmseg - INFO - Iter [70650/80000]	lr: 1.172e-06, eta: 0:48:15, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.7750, loss: 0.0814
+2023-03-04 02:45:06,379 - mmseg - INFO - Iter [70700/80000]	lr: 1.172e-06, eta: 0:48:00, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0769, decode.acc_seg: 96.9174, loss: 0.0769
+2023-03-04 02:45:21,007 - mmseg - INFO - Iter [70750/80000]	lr: 1.172e-06, eta: 0:47:45, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0855, decode.acc_seg: 96.7281, loss: 0.0855
+2023-03-04 02:45:35,932 - mmseg - INFO - Iter [70800/80000]	lr: 1.172e-06, eta: 0:47:29, time: 0.298, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0769, decode.acc_seg: 96.9145, loss: 0.0769
+2023-03-04 02:45:50,517 - mmseg - INFO - Iter [70850/80000]	lr: 1.172e-06, eta: 0:47:13, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.8108, loss: 0.0808
+2023-03-04 02:46:07,532 - mmseg - INFO - Iter [70900/80000]	lr: 1.172e-06, eta: 0:46:58, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0763, decode.acc_seg: 96.9521, loss: 0.0763
+2023-03-04 02:46:22,270 - mmseg - INFO - Iter [70950/80000]	lr: 1.172e-06, eta: 0:46:42, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0765, decode.acc_seg: 96.9342, loss: 0.0765
+2023-03-04 02:46:36,999 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 02:46:37,000 - mmseg - INFO - Iter [71000/80000]	lr: 1.172e-06, eta: 0:46:27, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0774, decode.acc_seg: 96.8925, loss: 0.0774
+2023-03-04 02:46:51,676 - mmseg - INFO - Iter [71050/80000]	lr: 1.172e-06, eta: 0:46:11, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.7770, loss: 0.0806
+2023-03-04 02:47:08,677 - mmseg - INFO - Iter [71100/80000]	lr: 1.172e-06, eta: 0:45:56, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.8488, loss: 0.0801
+2023-03-04 02:47:23,306 - mmseg - INFO - Iter [71150/80000]	lr: 1.172e-06, eta: 0:45:40, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8136, loss: 0.0800
+2023-03-04 02:47:38,007 - mmseg - INFO - Iter [71200/80000]	lr: 1.172e-06, eta: 0:45:25, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8804, loss: 0.0796
+2023-03-04 02:47:55,114 - mmseg - INFO - Iter [71250/80000]	lr: 1.172e-06, eta: 0:45:10, time: 0.342, data_time: 0.054, memory: 67605, decode.loss_ce: 0.0828, decode.acc_seg: 96.7311, loss: 0.0828
+2023-03-04 02:48:09,735 - mmseg - INFO - Iter [71300/80000]	lr: 1.172e-06, eta: 0:44:54, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0776, decode.acc_seg: 96.9493, loss: 0.0776
+2023-03-04 02:48:24,399 - mmseg - INFO - Iter [71350/80000]	lr: 1.172e-06, eta: 0:44:38, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.7942, loss: 0.0807
+2023-03-04 02:48:39,150 - mmseg - INFO - Iter [71400/80000]	lr: 1.172e-06, eta: 0:44:23, time: 0.295, data_time: 0.008, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.7767, loss: 0.0810
+2023-03-04 02:48:56,088 - mmseg - INFO - Iter [71450/80000]	lr: 1.172e-06, eta: 0:44:08, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8331, loss: 0.0793
+2023-03-04 02:49:10,672 - mmseg - INFO - Iter [71500/80000]	lr: 1.172e-06, eta: 0:43:52, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.7672, loss: 0.0810
+2023-03-04 02:49:25,243 - mmseg - INFO - Iter [71550/80000]	lr: 1.172e-06, eta: 0:43:36, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.7755, loss: 0.0805
+2023-03-04 02:49:39,841 - mmseg - INFO - Iter [71600/80000]	lr: 1.172e-06, eta: 0:43:21, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0769, decode.acc_seg: 96.9289, loss: 0.0769
+2023-03-04 02:49:56,854 - mmseg - INFO - Iter [71650/80000]	lr: 1.172e-06, eta: 0:43:05, time: 0.340, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0771, decode.acc_seg: 96.8771, loss: 0.0771
+2023-03-04 02:50:11,405 - mmseg - INFO - Iter [71700/80000]	lr: 1.172e-06, eta: 0:42:50, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0779, decode.acc_seg: 96.8893, loss: 0.0779
+2023-03-04 02:50:25,976 - mmseg - INFO - Iter [71750/80000]	lr: 1.172e-06, eta: 0:42:34, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8456, loss: 0.0793
+2023-03-04 02:50:43,186 - mmseg - INFO - Iter [71800/80000]	lr: 1.172e-06, eta: 0:42:19, time: 0.344, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8534, loss: 0.0792
+2023-03-04 02:50:57,789 - mmseg - INFO - Iter [71850/80000]	lr: 1.172e-06, eta: 0:42:03, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8665, loss: 0.0794
+2023-03-04 02:51:12,627 - mmseg - INFO - Iter [71900/80000]	lr: 1.172e-06, eta: 0:41:48, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0834, decode.acc_seg: 96.7461, loss: 0.0834
+2023-03-04 02:51:27,270 - mmseg - INFO - Iter [71950/80000]	lr: 1.172e-06, eta: 0:41:32, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.8438, loss: 0.0797
+2023-03-04 02:51:44,246 - mmseg - INFO - Saving checkpoint at 72000 iterations
+2023-03-04 02:51:46,280 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 02:51:46,280 - mmseg - INFO - Iter [72000/80000]	lr: 1.172e-06, eta: 0:41:17, time: 0.380, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0781, decode.acc_seg: 96.8696, loss: 0.0781
+2023-03-04 02:52:12,266 - mmseg - INFO - per class results:
+2023-03-04 02:52:12,267 - mmseg - INFO - 
++---------------+-------+-------+
+|     Class     |  IoU  |  Acc  |
++---------------+-------+-------+
+|   background  |  nan  |  nan  |
+|      road     | 98.53 | 99.15 |
+|    sidewalk   | 87.34 | 93.97 |
+|    building   | 93.63 | 96.91 |
+|      wall     | 55.92 |  61.4 |
+|     fence     | 65.34 | 75.91 |
+|      pole     | 70.96 |  82.7 |
+| traffic light | 75.52 |  85.9 |
+|  traffic sign | 82.54 | 88.97 |
+|   vegetation  | 93.12 |  97.0 |
+|    terrain    | 64.72 | 74.72 |
+|      sky      | 95.32 | 98.34 |
+|     person    | 84.85 | 93.09 |
+|     rider     | 67.95 |  79.7 |
+|      car      | 96.06 | 98.13 |
+|     truck     | 85.62 | 91.89 |
+|      bus      | 92.41 | 95.87 |
+|     train     | 85.59 | 90.01 |
+|   motorcycle  | 72.11 |  81.2 |
+|    bicycle    | 80.55 | 90.25 |
++---------------+-------+-------+
+2023-03-04 02:52:12,267 - mmseg - INFO - Summary:
+2023-03-04 02:52:12,267 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 96.66 | 81.48 | 88.16 |
++-------+-------+-------+
+2023-03-04 02:52:12,268 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 02:52:12,268 - mmseg - INFO - Iter(val) [63]	aAcc: 0.9666, mIoU: 0.8148, mAcc: 0.8816, IoU.background: nan, IoU.road: 0.9853, IoU.sidewalk: 0.8734, IoU.building: 0.9363, IoU.wall: 0.5592, IoU.fence: 0.6534, IoU.pole: 0.7096, IoU.traffic light: 0.7552, IoU.traffic sign: 0.8254, IoU.vegetation: 0.9312, IoU.terrain: 0.6472, IoU.sky: 0.9532, IoU.person: 0.8485, IoU.rider: 0.6795, IoU.car: 0.9606, IoU.truck: 0.8562, IoU.bus: 0.9241, IoU.train: 0.8559, IoU.motorcycle: 0.7211, IoU.bicycle: 0.8055, Acc.background: nan, Acc.road: 0.9915, Acc.sidewalk: 0.9397, Acc.building: 0.9691, Acc.wall: 0.6140, Acc.fence: 0.7591, Acc.pole: 0.8270, Acc.traffic light: 0.8590, Acc.traffic sign: 0.8897, Acc.vegetation: 0.9700, Acc.terrain: 0.7472, Acc.sky: 0.9834, Acc.person: 0.9309, Acc.rider: 0.7970, Acc.car: 0.9813, Acc.truck: 0.9189, Acc.bus: 0.9587, Acc.train: 0.9001, Acc.motorcycle: 0.8120, Acc.bicycle: 0.9025
+2023-03-04 02:52:27,226 - mmseg - INFO - Iter [72050/80000]	lr: 1.172e-06, eta: 0:41:04, time: 0.819, data_time: 0.527, memory: 67605, decode.loss_ce: 0.0817, decode.acc_seg: 96.7320, loss: 0.0817
+2023-03-04 02:52:41,903 - mmseg - INFO - Iter [72100/80000]	lr: 1.172e-06, eta: 0:40:49, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8610, loss: 0.0788
+2023-03-04 02:52:56,607 - mmseg - INFO - Iter [72150/80000]	lr: 1.172e-06, eta: 0:40:33, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.8886, loss: 0.0783
+2023-03-04 02:53:13,664 - mmseg - INFO - Iter [72200/80000]	lr: 1.172e-06, eta: 0:40:18, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8010, loss: 0.0802
+2023-03-04 02:53:28,274 - mmseg - INFO - Iter [72250/80000]	lr: 1.172e-06, eta: 0:40:02, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8179, loss: 0.0793
+2023-03-04 02:53:42,962 - mmseg - INFO - Iter [72300/80000]	lr: 1.172e-06, eta: 0:39:47, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0776, decode.acc_seg: 96.8801, loss: 0.0776
+2023-03-04 02:53:57,538 - mmseg - INFO - Iter [72350/80000]	lr: 1.172e-06, eta: 0:39:31, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.8161, loss: 0.0802
+2023-03-04 02:54:14,626 - mmseg - INFO - Iter [72400/80000]	lr: 1.172e-06, eta: 0:39:16, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0755, decode.acc_seg: 97.0061, loss: 0.0755
+2023-03-04 02:54:29,371 - mmseg - INFO - Iter [72450/80000]	lr: 1.172e-06, eta: 0:39:00, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8268, loss: 0.0788
+2023-03-04 02:54:44,016 - mmseg - INFO - Iter [72500/80000]	lr: 1.172e-06, eta: 0:38:45, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8369, loss: 0.0790
+2023-03-04 02:55:01,168 - mmseg - INFO - Iter [72550/80000]	lr: 1.172e-06, eta: 0:38:29, time: 0.343, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0805, decode.acc_seg: 96.7732, loss: 0.0805
+2023-03-04 02:55:15,757 - mmseg - INFO - Iter [72600/80000]	lr: 1.172e-06, eta: 0:38:14, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.8102, loss: 0.0807
+2023-03-04 02:55:30,352 - mmseg - INFO - Iter [72650/80000]	lr: 1.172e-06, eta: 0:37:58, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0782, decode.acc_seg: 96.8964, loss: 0.0782
+2023-03-04 02:55:44,984 - mmseg - INFO - Iter [72700/80000]	lr: 1.172e-06, eta: 0:37:43, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.7690, loss: 0.0807
+2023-03-04 02:56:02,183 - mmseg - INFO - Iter [72750/80000]	lr: 1.172e-06, eta: 0:37:27, time: 0.344, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0775, decode.acc_seg: 96.9081, loss: 0.0775
+2023-03-04 02:56:16,832 - mmseg - INFO - Iter [72800/80000]	lr: 1.172e-06, eta: 0:37:12, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0822, decode.acc_seg: 96.7614, loss: 0.0822
+2023-03-04 02:56:31,659 - mmseg - INFO - Iter [72850/80000]	lr: 1.172e-06, eta: 0:36:56, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0780, decode.acc_seg: 96.9011, loss: 0.0780
+2023-03-04 02:56:46,457 - mmseg - INFO - Iter [72900/80000]	lr: 1.172e-06, eta: 0:36:40, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0759, decode.acc_seg: 96.9964, loss: 0.0759
+2023-03-04 02:57:03,557 - mmseg - INFO - Iter [72950/80000]	lr: 1.172e-06, eta: 0:36:25, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8105, loss: 0.0799
+2023-03-04 02:57:18,138 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 02:57:18,138 - mmseg - INFO - Iter [73000/80000]	lr: 1.172e-06, eta: 0:36:10, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0828, decode.acc_seg: 96.7218, loss: 0.0828
+2023-03-04 02:57:32,939 - mmseg - INFO - Iter [73050/80000]	lr: 1.172e-06, eta: 0:35:54, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8879, loss: 0.0788
+2023-03-04 02:57:49,931 - mmseg - INFO - Iter [73100/80000]	lr: 1.172e-06, eta: 0:35:39, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.9018, loss: 0.0785
+2023-03-04 02:58:04,553 - mmseg - INFO - Iter [73150/80000]	lr: 1.172e-06, eta: 0:35:23, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0763, decode.acc_seg: 96.9461, loss: 0.0763
+2023-03-04 02:58:19,277 - mmseg - INFO - Iter [73200/80000]	lr: 1.172e-06, eta: 0:35:07, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8446, loss: 0.0788
+2023-03-04 02:58:34,031 - mmseg - INFO - Iter [73250/80000]	lr: 1.172e-06, eta: 0:34:52, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.7631, loss: 0.0812
+2023-03-04 02:58:51,121 - mmseg - INFO - Iter [73300/80000]	lr: 1.172e-06, eta: 0:34:37, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0787, decode.acc_seg: 96.8737, loss: 0.0787
+2023-03-04 02:59:05,767 - mmseg - INFO - Iter [73350/80000]	lr: 1.172e-06, eta: 0:34:21, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8730, loss: 0.0788
+2023-03-04 02:59:20,488 - mmseg - INFO - Iter [73400/80000]	lr: 1.172e-06, eta: 0:34:05, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8128, loss: 0.0800
+2023-03-04 02:59:35,125 - mmseg - INFO - Iter [73450/80000]	lr: 1.172e-06, eta: 0:33:50, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8324, loss: 0.0796
+2023-03-04 02:59:52,097 - mmseg - INFO - Iter [73500/80000]	lr: 1.172e-06, eta: 0:33:34, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0826, decode.acc_seg: 96.7887, loss: 0.0826
+2023-03-04 03:00:06,662 - mmseg - INFO - Iter [73550/80000]	lr: 1.172e-06, eta: 0:33:19, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8064, loss: 0.0798
+2023-03-04 03:00:21,529 - mmseg - INFO - Iter [73600/80000]	lr: 1.172e-06, eta: 0:33:03, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0782, decode.acc_seg: 96.8782, loss: 0.0782
+2023-03-04 03:00:36,195 - mmseg - INFO - Iter [73650/80000]	lr: 1.172e-06, eta: 0:32:48, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0782, decode.acc_seg: 96.8733, loss: 0.0782
+2023-03-04 03:00:53,203 - mmseg - INFO - Iter [73700/80000]	lr: 1.172e-06, eta: 0:32:32, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.8834, loss: 0.0783
+2023-03-04 03:01:07,772 - mmseg - INFO - Iter [73750/80000]	lr: 1.172e-06, eta: 0:32:17, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8562, loss: 0.0784
+2023-03-04 03:01:22,414 - mmseg - INFO - Iter [73800/80000]	lr: 1.172e-06, eta: 0:32:01, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0845, decode.acc_seg: 96.7216, loss: 0.0845
+2023-03-04 03:01:39,462 - mmseg - INFO - Iter [73850/80000]	lr: 1.172e-06, eta: 0:31:46, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0843, decode.acc_seg: 96.6378, loss: 0.0843
+2023-03-04 03:01:54,085 - mmseg - INFO - Iter [73900/80000]	lr: 1.172e-06, eta: 0:31:30, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8452, loss: 0.0789
+2023-03-04 03:02:08,715 - mmseg - INFO - Iter [73950/80000]	lr: 1.172e-06, eta: 0:31:15, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8685, loss: 0.0785
+2023-03-04 03:02:23,411 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 03:02:23,411 - mmseg - INFO - Iter [74000/80000]	lr: 1.172e-06, eta: 0:30:59, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0778, decode.acc_seg: 96.8804, loss: 0.0778
+2023-03-04 03:02:40,485 - mmseg - INFO - Iter [74050/80000]	lr: 1.172e-06, eta: 0:30:44, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0775, decode.acc_seg: 96.8679, loss: 0.0775
+2023-03-04 03:02:55,394 - mmseg - INFO - Iter [74100/80000]	lr: 1.172e-06, eta: 0:30:28, time: 0.298, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8499, loss: 0.0793
+2023-03-04 03:03:09,993 - mmseg - INFO - Iter [74150/80000]	lr: 1.172e-06, eta: 0:30:13, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.8026, loss: 0.0803
+2023-03-04 03:03:24,626 - mmseg - INFO - Iter [74200/80000]	lr: 1.172e-06, eta: 0:29:57, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.8858, loss: 0.0786
+2023-03-04 03:03:41,584 - mmseg - INFO - Iter [74250/80000]	lr: 1.172e-06, eta: 0:29:42, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0776, decode.acc_seg: 96.9019, loss: 0.0776
+2023-03-04 03:03:56,234 - mmseg - INFO - Iter [74300/80000]	lr: 1.172e-06, eta: 0:29:26, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0782, decode.acc_seg: 96.8853, loss: 0.0782
+2023-03-04 03:04:10,824 - mmseg - INFO - Iter [74350/80000]	lr: 1.172e-06, eta: 0:29:11, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0782, decode.acc_seg: 96.9085, loss: 0.0782
+2023-03-04 03:04:25,442 - mmseg - INFO - Iter [74400/80000]	lr: 1.172e-06, eta: 0:28:55, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.7833, loss: 0.0806
+2023-03-04 03:04:42,470 - mmseg - INFO - Iter [74450/80000]	lr: 1.172e-06, eta: 0:28:40, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0774, decode.acc_seg: 96.9324, loss: 0.0774
+2023-03-04 03:04:57,269 - mmseg - INFO - Iter [74500/80000]	lr: 1.172e-06, eta: 0:28:24, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8723, loss: 0.0788
+2023-03-04 03:05:11,929 - mmseg - INFO - Iter [74550/80000]	lr: 1.172e-06, eta: 0:28:09, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8144, loss: 0.0800
+2023-03-04 03:05:29,029 - mmseg - INFO - Iter [74600/80000]	lr: 1.172e-06, eta: 0:27:53, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0773, decode.acc_seg: 96.8916, loss: 0.0773
+2023-03-04 03:05:43,762 - mmseg - INFO - Iter [74650/80000]	lr: 1.172e-06, eta: 0:27:38, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8605, loss: 0.0788
+2023-03-04 03:05:58,474 - mmseg - INFO - Iter [74700/80000]	lr: 1.172e-06, eta: 0:27:22, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0787, decode.acc_seg: 96.8625, loss: 0.0787
+2023-03-04 03:06:13,086 - mmseg - INFO - Iter [74750/80000]	lr: 1.172e-06, eta: 0:27:07, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0817, decode.acc_seg: 96.7162, loss: 0.0817
+2023-03-04 03:06:30,171 - mmseg - INFO - Iter [74800/80000]	lr: 1.172e-06, eta: 0:26:51, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0816, decode.acc_seg: 96.7776, loss: 0.0816
+2023-03-04 03:06:44,763 - mmseg - INFO - Iter [74850/80000]	lr: 1.172e-06, eta: 0:26:36, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.7402, loss: 0.0810
+2023-03-04 03:06:59,383 - mmseg - INFO - Iter [74900/80000]	lr: 1.172e-06, eta: 0:26:20, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7334, loss: 0.0811
+2023-03-04 03:07:13,983 - mmseg - INFO - Iter [74950/80000]	lr: 1.172e-06, eta: 0:26:04, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0778, decode.acc_seg: 96.9466, loss: 0.0778
+2023-03-04 03:07:31,004 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 03:07:31,005 - mmseg - INFO - Iter [75000/80000]	lr: 1.172e-06, eta: 0:25:49, time: 0.340, data_time: 0.054, memory: 67605, decode.loss_ce: 0.0822, decode.acc_seg: 96.7434, loss: 0.0822
+2023-03-04 03:07:45,602 - mmseg - INFO - Iter [75050/80000]	lr: 1.172e-06, eta: 0:25:34, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8526, loss: 0.0790
+2023-03-04 03:08:00,598 - mmseg - INFO - Iter [75100/80000]	lr: 1.172e-06, eta: 0:25:18, time: 0.300, data_time: 0.008, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.8112, loss: 0.0797
+2023-03-04 03:08:17,605 - mmseg - INFO - Iter [75150/80000]	lr: 1.172e-06, eta: 0:25:03, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0830, decode.acc_seg: 96.6660, loss: 0.0830
+2023-03-04 03:08:32,461 - mmseg - INFO - Iter [75200/80000]	lr: 1.172e-06, eta: 0:24:47, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.7582, loss: 0.0819
+2023-03-04 03:08:47,060 - mmseg - INFO - Iter [75250/80000]	lr: 1.172e-06, eta: 0:24:31, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0817, decode.acc_seg: 96.7822, loss: 0.0817
+2023-03-04 03:09:01,652 - mmseg - INFO - Iter [75300/80000]	lr: 1.172e-06, eta: 0:24:16, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8553, loss: 0.0789
+2023-03-04 03:09:18,811 - mmseg - INFO - Iter [75350/80000]	lr: 1.172e-06, eta: 0:24:01, time: 0.343, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8183, loss: 0.0798
+2023-03-04 03:09:33,519 - mmseg - INFO - Iter [75400/80000]	lr: 1.172e-06, eta: 0:23:45, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0806, decode.acc_seg: 96.7874, loss: 0.0806
+2023-03-04 03:09:48,294 - mmseg - INFO - Iter [75450/80000]	lr: 1.172e-06, eta: 0:23:29, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0791, decode.acc_seg: 96.8305, loss: 0.0791
+2023-03-04 03:10:02,862 - mmseg - INFO - Iter [75500/80000]	lr: 1.172e-06, eta: 0:23:14, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0765, decode.acc_seg: 96.9330, loss: 0.0765
+2023-03-04 03:10:19,914 - mmseg - INFO - Iter [75550/80000]	lr: 1.172e-06, eta: 0:22:59, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8593, loss: 0.0800
+2023-03-04 03:10:34,628 - mmseg - INFO - Iter [75600/80000]	lr: 1.172e-06, eta: 0:22:43, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0760, decode.acc_seg: 96.9662, loss: 0.0760
+2023-03-04 03:10:49,330 - mmseg - INFO - Iter [75650/80000]	lr: 1.172e-06, eta: 0:22:27, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0766, decode.acc_seg: 96.9475, loss: 0.0766
+2023-03-04 03:11:03,881 - mmseg - INFO - Iter [75700/80000]	lr: 1.172e-06, eta: 0:22:12, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.7897, loss: 0.0810
+2023-03-04 03:11:20,930 - mmseg - INFO - Iter [75750/80000]	lr: 1.172e-06, eta: 0:21:56, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0829, decode.acc_seg: 96.7962, loss: 0.0829
+2023-03-04 03:11:35,581 - mmseg - INFO - Iter [75800/80000]	lr: 1.172e-06, eta: 0:21:41, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.7668, loss: 0.0814
+2023-03-04 03:11:50,267 - mmseg - INFO - Iter [75850/80000]	lr: 1.172e-06, eta: 0:21:25, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0756, decode.acc_seg: 96.9901, loss: 0.0756
+2023-03-04 03:12:07,233 - mmseg - INFO - Iter [75900/80000]	lr: 1.172e-06, eta: 0:21:10, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0790, decode.acc_seg: 96.8122, loss: 0.0790
+2023-03-04 03:12:22,197 - mmseg - INFO - Iter [75950/80000]	lr: 1.172e-06, eta: 0:20:54, time: 0.299, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0788, decode.acc_seg: 96.8505, loss: 0.0788
+2023-03-04 03:12:36,797 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 03:12:36,797 - mmseg - INFO - Iter [76000/80000]	lr: 1.172e-06, eta: 0:20:39, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0771, decode.acc_seg: 96.9424, loss: 0.0771
+2023-03-04 03:12:51,423 - mmseg - INFO - Iter [76050/80000]	lr: 1.172e-06, eta: 0:20:23, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.8085, loss: 0.0801
+2023-03-04 03:13:08,344 - mmseg - INFO - Iter [76100/80000]	lr: 1.172e-06, eta: 0:20:08, time: 0.338, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8239, loss: 0.0799
+2023-03-04 03:13:23,028 - mmseg - INFO - Iter [76150/80000]	lr: 1.172e-06, eta: 0:19:52, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0780, decode.acc_seg: 96.9208, loss: 0.0780
+2023-03-04 03:13:37,665 - mmseg - INFO - Iter [76200/80000]	lr: 1.172e-06, eta: 0:19:37, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.7809, loss: 0.0808
+2023-03-04 03:13:52,299 - mmseg - INFO - Iter [76250/80000]	lr: 1.172e-06, eta: 0:19:21, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0766, decode.acc_seg: 96.9766, loss: 0.0766
+2023-03-04 03:14:09,267 - mmseg - INFO - Iter [76300/80000]	lr: 1.172e-06, eta: 0:19:06, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0816, decode.acc_seg: 96.7624, loss: 0.0816
+2023-03-04 03:14:24,024 - mmseg - INFO - Iter [76350/80000]	lr: 1.172e-06, eta: 0:18:50, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0754, decode.acc_seg: 96.9960, loss: 0.0754
+2023-03-04 03:14:38,703 - mmseg - INFO - Iter [76400/80000]	lr: 1.172e-06, eta: 0:18:35, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0829, decode.acc_seg: 96.7329, loss: 0.0829
+2023-03-04 03:14:55,757 - mmseg - INFO - Iter [76450/80000]	lr: 1.172e-06, eta: 0:18:19, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.8370, loss: 0.0786
+2023-03-04 03:15:10,452 - mmseg - INFO - Iter [76500/80000]	lr: 1.172e-06, eta: 0:18:04, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7648, loss: 0.0811
+2023-03-04 03:15:25,086 - mmseg - INFO - Iter [76550/80000]	lr: 1.172e-06, eta: 0:17:48, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8952, loss: 0.0785
+2023-03-04 03:15:39,699 - mmseg - INFO - Iter [76600/80000]	lr: 1.172e-06, eta: 0:17:33, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8076, loss: 0.0798
+2023-03-04 03:15:56,647 - mmseg - INFO - Iter [76650/80000]	lr: 1.172e-06, eta: 0:17:17, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0768, decode.acc_seg: 96.9686, loss: 0.0768
+2023-03-04 03:16:11,285 - mmseg - INFO - Iter [76700/80000]	lr: 1.172e-06, eta: 0:17:02, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0812, decode.acc_seg: 96.8133, loss: 0.0812
+2023-03-04 03:16:25,972 - mmseg - INFO - Iter [76750/80000]	lr: 1.172e-06, eta: 0:16:46, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0774, decode.acc_seg: 96.8774, loss: 0.0774
+2023-03-04 03:16:40,526 - mmseg - INFO - Iter [76800/80000]	lr: 1.172e-06, eta: 0:16:31, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0775, decode.acc_seg: 96.8909, loss: 0.0775
+2023-03-04 03:16:57,436 - mmseg - INFO - Iter [76850/80000]	lr: 1.172e-06, eta: 0:16:15, time: 0.338, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0765, decode.acc_seg: 96.9338, loss: 0.0765
+2023-03-04 03:17:12,007 - mmseg - INFO - Iter [76900/80000]	lr: 1.172e-06, eta: 0:16:00, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0836, decode.acc_seg: 96.7289, loss: 0.0836
+2023-03-04 03:17:26,715 - mmseg - INFO - Iter [76950/80000]	lr: 1.172e-06, eta: 0:15:44, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0807, decode.acc_seg: 96.7763, loss: 0.0807
+2023-03-04 03:17:41,362 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 03:17:41,362 - mmseg - INFO - Iter [77000/80000]	lr: 1.172e-06, eta: 0:15:29, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0796, decode.acc_seg: 96.8474, loss: 0.0796
+2023-03-04 03:17:58,474 - mmseg - INFO - Iter [77050/80000]	lr: 1.172e-06, eta: 0:15:13, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0772, decode.acc_seg: 96.9285, loss: 0.0772
+2023-03-04 03:18:13,423 - mmseg - INFO - Iter [77100/80000]	lr: 1.172e-06, eta: 0:14:58, time: 0.299, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0769, decode.acc_seg: 96.9673, loss: 0.0769
+2023-03-04 03:18:28,073 - mmseg - INFO - Iter [77150/80000]	lr: 1.172e-06, eta: 0:14:42, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8292, loss: 0.0789
+2023-03-04 03:18:45,017 - mmseg - INFO - Iter [77200/80000]	lr: 1.172e-06, eta: 0:14:27, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.7685, loss: 0.0802
+2023-03-04 03:18:59,637 - mmseg - INFO - Iter [77250/80000]	lr: 1.172e-06, eta: 0:14:11, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0803, decode.acc_seg: 96.8091, loss: 0.0803
+2023-03-04 03:19:14,244 - mmseg - INFO - Iter [77300/80000]	lr: 1.172e-06, eta: 0:13:56, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8918, loss: 0.0792
+2023-03-04 03:19:28,805 - mmseg - INFO - Iter [77350/80000]	lr: 1.172e-06, eta: 0:13:40, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0827, decode.acc_seg: 96.7592, loss: 0.0827
+2023-03-04 03:19:45,792 - mmseg - INFO - Iter [77400/80000]	lr: 1.172e-06, eta: 0:13:25, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8119, loss: 0.0798
+2023-03-04 03:20:00,431 - mmseg - INFO - Iter [77450/80000]	lr: 1.172e-06, eta: 0:13:09, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0801, decode.acc_seg: 96.8385, loss: 0.0801
+2023-03-04 03:20:15,092 - mmseg - INFO - Iter [77500/80000]	lr: 1.172e-06, eta: 0:12:54, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0773, decode.acc_seg: 96.9045, loss: 0.0773
+2023-03-04 03:20:29,807 - mmseg - INFO - Iter [77550/80000]	lr: 1.172e-06, eta: 0:12:38, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8437, loss: 0.0798
+2023-03-04 03:20:46,879 - mmseg - INFO - Iter [77600/80000]	lr: 1.172e-06, eta: 0:12:23, time: 0.341, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8821, loss: 0.0784
+2023-03-04 03:21:01,477 - mmseg - INFO - Iter [77650/80000]	lr: 1.172e-06, eta: 0:12:07, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0775, decode.acc_seg: 96.9009, loss: 0.0775
+2023-03-04 03:21:16,333 - mmseg - INFO - Iter [77700/80000]	lr: 1.172e-06, eta: 0:11:52, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0793, decode.acc_seg: 96.8786, loss: 0.0793
+2023-03-04 03:21:33,263 - mmseg - INFO - Iter [77750/80000]	lr: 1.172e-06, eta: 0:11:37, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0799, decode.acc_seg: 96.8251, loss: 0.0799
+2023-03-04 03:21:47,846 - mmseg - INFO - Iter [77800/80000]	lr: 1.172e-06, eta: 0:11:21, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0776, decode.acc_seg: 96.9013, loss: 0.0776
+2023-03-04 03:22:02,605 - mmseg - INFO - Iter [77850/80000]	lr: 1.172e-06, eta: 0:11:05, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0789, decode.acc_seg: 96.8419, loss: 0.0789
+2023-03-04 03:22:17,260 - mmseg - INFO - Iter [77900/80000]	lr: 1.172e-06, eta: 0:10:50, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8445, loss: 0.0785
+2023-03-04 03:22:34,367 - mmseg - INFO - Iter [77950/80000]	lr: 1.172e-06, eta: 0:10:35, time: 0.342, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0787, decode.acc_seg: 96.8403, loss: 0.0787
+2023-03-04 03:22:48,921 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 03:22:48,921 - mmseg - INFO - Iter [78000/80000]	lr: 1.172e-06, eta: 0:10:19, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0766, decode.acc_seg: 96.9564, loss: 0.0766
+2023-03-04 03:23:03,500 - mmseg - INFO - Iter [78050/80000]	lr: 1.172e-06, eta: 0:10:04, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0766, decode.acc_seg: 96.9575, loss: 0.0766
+2023-03-04 03:23:18,240 - mmseg - INFO - Iter [78100/80000]	lr: 1.172e-06, eta: 0:09:48, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0808, decode.acc_seg: 96.8016, loss: 0.0808
+2023-03-04 03:23:35,451 - mmseg - INFO - Iter [78150/80000]	lr: 1.172e-06, eta: 0:09:33, time: 0.344, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.8626, loss: 0.0783
+2023-03-04 03:23:50,052 - mmseg - INFO - Iter [78200/80000]	lr: 1.172e-06, eta: 0:09:17, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0811, decode.acc_seg: 96.7869, loss: 0.0811
+2023-03-04 03:24:04,747 - mmseg - INFO - Iter [78250/80000]	lr: 1.172e-06, eta: 0:09:02, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0781, decode.acc_seg: 96.8641, loss: 0.0781
+2023-03-04 03:24:19,310 - mmseg - INFO - Iter [78300/80000]	lr: 1.172e-06, eta: 0:08:46, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8204, loss: 0.0794
+2023-03-04 03:24:36,221 - mmseg - INFO - Iter [78350/80000]	lr: 1.172e-06, eta: 0:08:31, time: 0.338, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.9330, loss: 0.0785
+2023-03-04 03:24:50,773 - mmseg - INFO - Iter [78400/80000]	lr: 1.172e-06, eta: 0:08:15, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0768, decode.acc_seg: 96.9239, loss: 0.0768
+2023-03-04 03:25:05,446 - mmseg - INFO - Iter [78450/80000]	lr: 1.172e-06, eta: 0:08:00, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.8891, loss: 0.0786
+2023-03-04 03:25:22,391 - mmseg - INFO - Iter [78500/80000]	lr: 1.172e-06, eta: 0:07:44, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0814, decode.acc_seg: 96.7926, loss: 0.0814
+2023-03-04 03:25:37,203 - mmseg - INFO - Iter [78550/80000]	lr: 1.172e-06, eta: 0:07:29, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0810, decode.acc_seg: 96.7770, loss: 0.0810
+2023-03-04 03:25:52,044 - mmseg - INFO - Iter [78600/80000]	lr: 1.172e-06, eta: 0:07:13, time: 0.297, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.7881, loss: 0.0800
+2023-03-04 03:26:06,676 - mmseg - INFO - Iter [78650/80000]	lr: 1.172e-06, eta: 0:06:58, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0786, decode.acc_seg: 96.8315, loss: 0.0786
+2023-03-04 03:26:23,716 - mmseg - INFO - Iter [78700/80000]	lr: 1.172e-06, eta: 0:06:42, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0794, decode.acc_seg: 96.8630, loss: 0.0794
+2023-03-04 03:26:38,301 - mmseg - INFO - Iter [78750/80000]	lr: 1.172e-06, eta: 0:06:27, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0829, decode.acc_seg: 96.7194, loss: 0.0829
+2023-03-04 03:26:52,887 - mmseg - INFO - Iter [78800/80000]	lr: 1.172e-06, eta: 0:06:11, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0768, decode.acc_seg: 96.9012, loss: 0.0768
+2023-03-04 03:27:07,524 - mmseg - INFO - Iter [78850/80000]	lr: 1.172e-06, eta: 0:05:56, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.8582, loss: 0.0784
+2023-03-04 03:27:24,459 - mmseg - INFO - Iter [78900/80000]	lr: 1.172e-06, eta: 0:05:40, time: 0.339, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0839, decode.acc_seg: 96.7295, loss: 0.0839
+2023-03-04 03:27:39,135 - mmseg - INFO - Iter [78950/80000]	lr: 1.172e-06, eta: 0:05:25, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0819, decode.acc_seg: 96.8111, loss: 0.0819
+2023-03-04 03:27:53,710 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 03:27:53,710 - mmseg - INFO - Iter [79000/80000]	lr: 1.172e-06, eta: 0:05:09, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0762, decode.acc_seg: 96.9583, loss: 0.0762
+2023-03-04 03:28:08,297 - mmseg - INFO - Iter [79050/80000]	lr: 1.172e-06, eta: 0:04:54, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0783, decode.acc_seg: 96.8570, loss: 0.0783
+2023-03-04 03:28:25,423 - mmseg - INFO - Iter [79100/80000]	lr: 1.172e-06, eta: 0:04:38, time: 0.343, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0771, decode.acc_seg: 96.9501, loss: 0.0771
+2023-03-04 03:28:40,029 - mmseg - INFO - Iter [79150/80000]	lr: 1.172e-06, eta: 0:04:23, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0798, decode.acc_seg: 96.8402, loss: 0.0798
+2023-03-04 03:28:54,605 - mmseg - INFO - Iter [79200/80000]	lr: 1.172e-06, eta: 0:04:07, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0818, decode.acc_seg: 96.7532, loss: 0.0818
+2023-03-04 03:29:11,564 - mmseg - INFO - Iter [79250/80000]	lr: 1.172e-06, eta: 0:03:52, time: 0.339, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0759, decode.acc_seg: 96.9432, loss: 0.0759
+2023-03-04 03:29:26,310 - mmseg - INFO - Iter [79300/80000]	lr: 1.172e-06, eta: 0:03:36, time: 0.295, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0802, decode.acc_seg: 96.7635, loss: 0.0802
+2023-03-04 03:29:40,877 - mmseg - INFO - Iter [79350/80000]	lr: 1.172e-06, eta: 0:03:21, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0784, decode.acc_seg: 96.9043, loss: 0.0784
+2023-03-04 03:29:55,596 - mmseg - INFO - Iter [79400/80000]	lr: 1.172e-06, eta: 0:03:05, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0766, decode.acc_seg: 96.9301, loss: 0.0766
+2023-03-04 03:30:12,506 - mmseg - INFO - Iter [79450/80000]	lr: 1.172e-06, eta: 0:02:50, time: 0.338, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0780, decode.acc_seg: 96.9027, loss: 0.0780
+2023-03-04 03:30:27,203 - mmseg - INFO - Iter [79500/80000]	lr: 1.172e-06, eta: 0:02:34, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0800, decode.acc_seg: 96.8364, loss: 0.0800
+2023-03-04 03:30:41,786 - mmseg - INFO - Iter [79550/80000]	lr: 1.172e-06, eta: 0:02:19, time: 0.292, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0795, decode.acc_seg: 96.8627, loss: 0.0795
+2023-03-04 03:30:56,505 - mmseg - INFO - Iter [79600/80000]	lr: 1.172e-06, eta: 0:02:03, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0826, decode.acc_seg: 96.7493, loss: 0.0826
+2023-03-04 03:31:13,519 - mmseg - INFO - Iter [79650/80000]	lr: 1.172e-06, eta: 0:01:48, time: 0.340, data_time: 0.053, memory: 67605, decode.loss_ce: 0.0837, decode.acc_seg: 96.6609, loss: 0.0837
+2023-03-04 03:31:28,167 - mmseg - INFO - Iter [79700/80000]	lr: 1.172e-06, eta: 0:01:32, time: 0.293, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0778, decode.acc_seg: 96.9044, loss: 0.0778
+2023-03-04 03:31:42,731 - mmseg - INFO - Iter [79750/80000]	lr: 1.172e-06, eta: 0:01:17, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0766, decode.acc_seg: 96.9417, loss: 0.0766
+2023-03-04 03:31:59,803 - mmseg - INFO - Iter [79800/80000]	lr: 1.172e-06, eta: 0:01:01, time: 0.341, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0787, decode.acc_seg: 96.8610, loss: 0.0787
+2023-03-04 03:32:14,602 - mmseg - INFO - Iter [79850/80000]	lr: 1.172e-06, eta: 0:00:46, time: 0.296, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0797, decode.acc_seg: 96.7888, loss: 0.0797
+2023-03-04 03:32:29,297 - mmseg - INFO - Iter [79900/80000]	lr: 1.172e-06, eta: 0:00:30, time: 0.294, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0792, decode.acc_seg: 96.8458, loss: 0.0792
+2023-03-04 03:32:43,864 - mmseg - INFO - Iter [79950/80000]	lr: 1.172e-06, eta: 0:00:15, time: 0.291, data_time: 0.007, memory: 67605, decode.loss_ce: 0.0774, decode.acc_seg: 96.9208, loss: 0.0774
+2023-03-04 03:33:01,273 - mmseg - INFO - Saving checkpoint at 80000 iterations
+2023-03-04 03:33:03,132 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 03:33:03,132 - mmseg - INFO - Iter [80000/80000]	lr: 1.172e-06, eta: 0:00:00, time: 0.385, data_time: 0.052, memory: 67605, decode.loss_ce: 0.0785, decode.acc_seg: 96.8863, loss: 0.0785
+2023-03-04 03:33:28,895 - mmseg - INFO - per class results:
+2023-03-04 03:33:28,896 - mmseg - INFO - 
++---------------+-------+-------+
+|     Class     |  IoU  |  Acc  |
++---------------+-------+-------+
+|   background  |  nan  |  nan  |
+|      road     | 98.54 | 99.17 |
+|    sidewalk   |  87.4 | 93.87 |
+|    building   | 93.57 |  96.9 |
+|      wall     | 54.83 |  60.0 |
+|     fence     | 65.11 | 75.31 |
+|      pole     | 70.75 | 82.19 |
+| traffic light | 75.41 | 85.68 |
+|  traffic sign |  82.5 | 89.62 |
+|   vegetation  | 93.05 | 97.08 |
+|    terrain    | 64.74 |  73.9 |
+|      sky      | 95.31 | 98.33 |
+|     person    | 84.74 | 92.95 |
+|     rider     | 67.77 | 80.14 |
+|      car      | 96.02 | 98.05 |
+|     truck     |  86.0 | 92.44 |
+|      bus      | 92.37 | 96.06 |
+|     train     | 85.44 | 89.83 |
+|   motorcycle  | 71.94 | 81.23 |
+|    bicycle    | 80.48 | 90.07 |
++---------------+-------+-------+
+2023-03-04 03:33:28,896 - mmseg - INFO - Summary:
+2023-03-04 03:33:28,896 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 96.64 | 81.37 | 88.04 |
++-------+-------+-------+
+2023-03-04 03:33:28,896 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20.py
+2023-03-04 03:33:28,897 - mmseg - INFO - Iter(val) [63]	aAcc: 0.9664, mIoU: 0.8137, mAcc: 0.8804, IoU.background: nan, IoU.road: 0.9854, IoU.sidewalk: 0.8740, IoU.building: 0.9357, IoU.wall: 0.5483, IoU.fence: 0.6511, IoU.pole: 0.7075, IoU.traffic light: 0.7541, IoU.traffic sign: 0.8250, IoU.vegetation: 0.9305, IoU.terrain: 0.6474, IoU.sky: 0.9531, IoU.person: 0.8474, IoU.rider: 0.6777, IoU.car: 0.9602, IoU.truck: 0.8600, IoU.bus: 0.9237, IoU.train: 0.8544, IoU.motorcycle: 0.7194, IoU.bicycle: 0.8048, Acc.background: nan, Acc.road: 0.9917, Acc.sidewalk: 0.9387, Acc.building: 0.9690, Acc.wall: 0.6000, Acc.fence: 0.7531, Acc.pole: 0.8219, Acc.traffic light: 0.8568, Acc.traffic sign: 0.8962, Acc.vegetation: 0.9708, Acc.terrain: 0.7390, Acc.sky: 0.9833, Acc.person: 0.9295, Acc.rider: 0.8014, Acc.car: 0.9805, Acc.truck: 0.9244, Acc.bus: 0.9606, Acc.train: 0.8983, Acc.motorcycle: 0.8123, Acc.bicycle: 0.9007