diff --git "a/deeplabv3plus_r101_singlestep/20230303_203941.log" "b/deeplabv3plus_r101_singlestep/20230303_203941.log"
new file mode 100644--- /dev/null
+++ "b/deeplabv3plus_r101_singlestep/20230303_203941.log"
@@ -0,0 +1,4462 @@
+2023-03-03 20:39:41,990 - mmseg - INFO - Multi-processing start method is `None`
+2023-03-03 20:39:42,003 - mmseg - INFO - OpenCV num_threads is `128
+2023-03-03 20:39:42,003 - mmseg - INFO - OMP num threads is 1
+2023-03-03 20:39:42,058 - 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 20:39:42,058 - mmseg - INFO - Distributed training: True
+2023-03-03 20:39:42,772 - mmseg - INFO - Config:
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    type='EncoderDecoderFreeze',
+    pretrained=
+    'pretrained/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.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_512x512_160k_ade20k_20200615_123232-38ed86bb.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=151,
+        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 = 'ADE20K151Dataset'
+data_root = 'data/ade/ADEChallengeData2016'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+crop_size = (512, 512)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', reduce_zero_label=False),
+    dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=0),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(2048, 512),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=16, pad_val=0, seg_pad_val=0),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+    train=dict(
+        type='ADE20K151Dataset',
+        data_root='data/ade/ADEChallengeData2016',
+        img_dir='images/training',
+        ann_dir='annotations/training',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', reduce_zero_label=False),
+            dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
+            dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
+            dict(type='RandomFlip', prob=0.5),
+            dict(type='PhotoMetricDistortion'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=0),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img', 'gt_semantic_seg'])
+        ]),
+    val=dict(
+        type='ADE20K151Dataset',
+        data_root='data/ade/ADEChallengeData2016',
+        img_dir='images/validation',
+        ann_dir='annotations/validation',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(2048, 512),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(
+                        type='Pad', size_divisor=16, pad_val=0, seg_pad_val=0),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]),
+    test=dict(
+        type='ADE20K151Dataset',
+        data_root='data/ade/ADEChallengeData2016',
+        img_dir='images/validation',
+        ann_dir='annotations/validation',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(2048, 512),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(
+                        type='Pad', size_divisor=16, pad_val=0, seg_pad_val=0),
+                    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_512x512_160k_ade20k_20200615_123232-38ed86bb.pth'
+work_dir = './work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151'
+gpu_ids = range(0, 8)
+auto_resume = True
+
+2023-03-03 20:39:47,122 - mmseg - INFO - Set random seed to 147409490, deterministic: False
+2023-03-03 20:39:48,584 - mmseg - INFO - Parameters in backbone freezed!
+2023-03-03 20:39:48,585 - 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 20:39:48,585 - mmseg - INFO - Parameters in decode_head freezed!
+2023-03-03 20:39:48,630 - mmseg - INFO - load checkpoint from local path: pretrained/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth
+2023-03-03 20:39:49,150 - 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 20:39:49,170 - mmseg - INFO - load checkpoint from local path: pretrained/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth
+2023-03-03 20:39:49,665 - 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, backbone.layer2.3.conv2.weight, backbone.layer2.3.bn2.weight, backbone.layer2.3.bn2.bias, backbone.layer2.3.bn2.running_mean, backbone.layer2.3.bn2.running_var, backbone.layer2.3.bn2.num_batches_tracked, backbone.layer2.3.conv3.weight, backbone.layer2.3.bn3.weight, backbone.layer2.3.bn3.bias, backbone.layer2.3.bn3.running_mean, backbone.layer2.3.bn3.running_var, backbone.layer2.3.bn3.num_batches_tracked, backbone.layer3.0.conv1.weight, backbone.layer3.0.bn1.weight, backbone.layer3.0.bn1.bias, backbone.layer3.0.bn1.running_mean, backbone.layer3.0.bn1.running_var, backbone.layer3.0.bn1.num_batches_tracked, backbone.layer3.0.conv2.weight, backbone.layer3.0.bn2.weight, backbone.layer3.0.bn2.bias, backbone.layer3.0.bn2.running_mean, backbone.layer3.0.bn2.running_var, backbone.layer3.0.bn2.num_batches_tracked, backbone.layer3.0.conv3.weight, backbone.layer3.0.bn3.weight, backbone.layer3.0.bn3.bias, backbone.layer3.0.bn3.running_mean, backbone.layer3.0.bn3.running_var, 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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.layer3.22.conv3.weight, backbone.layer3.22.bn3.weight, backbone.layer3.22.bn3.bias, backbone.layer3.22.bn3.running_mean, backbone.layer3.22.bn3.running_var, backbone.layer3.22.bn3.num_batches_tracked, backbone.layer4.0.conv1.weight, backbone.layer4.0.bn1.weight, backbone.layer4.0.bn1.bias, backbone.layer4.0.bn1.running_mean, backbone.layer4.0.bn1.running_var, backbone.layer4.0.bn1.num_batches_tracked, backbone.layer4.0.conv2.weight, backbone.layer4.0.bn2.weight, backbone.layer4.0.bn2.bias, backbone.layer4.0.bn2.running_mean, backbone.layer4.0.bn2.running_var, backbone.layer4.0.bn2.num_batches_tracked, backbone.layer4.0.conv3.weight, backbone.layer4.0.bn3.weight, backbone.layer4.0.bn3.bias, backbone.layer4.0.bn3.running_mean, backbone.layer4.0.bn3.running_var, backbone.layer4.0.bn3.num_batches_tracked, backbone.layer4.0.downsample.0.weight, backbone.layer4.0.downsample.1.weight, backbone.layer4.0.downsample.1.bias, backbone.layer4.0.downsample.1.running_mean, 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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 20:39:49,708 - 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_512x512_160k_ade20k_20200615_123232-38ed86bb.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, 151, kernel_size=(1, 1), stride=(1, 1))
+    (embed): Embedding(151, 16)
+  )
+  init_cfg={'type': 'Pretrained', 'checkpoint': 'pretrained/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth'}
+)
+2023-03-03 20:39:50,511 - mmseg - INFO - Loaded 20210 images
+2023-03-03 20:39:51,718 - mmseg - INFO - Loaded 2000 images
+2023-03-03 20:39:51,720 - mmseg - INFO - Start running, host: laizeqiang@SH-IDC1-10-140-37-152, work_dir: /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151
+2023-03-03 20:39:51,720 - 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 20:39:51,720 - mmseg - INFO - workflow: [('train', 1)], max: 80000 iters
+2023-03-03 20:39:51,721 - mmseg - INFO - Checkpoints will be saved to /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151 by HardDiskBackend.
+2023-03-03 20:40:45,552 - mmseg - INFO - Iter [50/80000]	lr: 7.350e-06, eta: 12:23:39, time: 0.558, data_time: 0.016, memory: 39544, decode.loss_ce: 3.5598, decode.acc_seg: 26.4415, loss: 3.5598
+2023-03-03 20:41:00,491 - mmseg - INFO - Iter [100/80000]	lr: 1.485e-05, eta: 9:30:32, time: 0.299, data_time: 0.007, memory: 39544, decode.loss_ce: 2.1244, decode.acc_seg: 58.3822, loss: 2.1244
+2023-03-03 20:41:15,601 - mmseg - INFO - Iter [150/80000]	lr: 2.235e-05, eta: 8:34:10, time: 0.302, data_time: 0.007, memory: 39544, decode.loss_ce: 1.2879, decode.acc_seg: 73.6113, loss: 1.2879
+2023-03-03 20:41:30,056 - mmseg - INFO - Iter [200/80000]	lr: 2.985e-05, eta: 8:01:30, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.8581, decode.acc_seg: 80.6673, loss: 0.8581
+2023-03-03 20:41:44,474 - mmseg - INFO - Iter [250/80000]	lr: 3.735e-05, eta: 7:41:37, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.6163, decode.acc_seg: 84.8843, loss: 0.6163
+2023-03-03 20:41:59,179 - mmseg - INFO - Iter [300/80000]	lr: 4.485e-05, eta: 7:29:33, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.4717, decode.acc_seg: 87.5084, loss: 0.4717
+2023-03-03 20:42:13,667 - mmseg - INFO - Iter [350/80000]	lr: 5.235e-05, eta: 7:20:02, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.3920, decode.acc_seg: 88.2327, loss: 0.3920
+2023-03-03 20:42:28,066 - mmseg - INFO - Iter [400/80000]	lr: 5.985e-05, eta: 7:12:33, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.3345, decode.acc_seg: 89.5493, loss: 0.3345
+2023-03-03 20:42:42,620 - mmseg - INFO - Iter [450/80000]	lr: 6.735e-05, eta: 7:07:07, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.3161, decode.acc_seg: 89.3609, loss: 0.3161
+2023-03-03 20:42:57,172 - mmseg - INFO - Iter [500/80000]	lr: 7.485e-05, eta: 7:02:44, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.3010, decode.acc_seg: 89.4298, loss: 0.3010
+2023-03-03 20:43:11,674 - mmseg - INFO - Iter [550/80000]	lr: 8.235e-05, eta: 6:58:58, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2869, decode.acc_seg: 89.9164, loss: 0.2869
+2023-03-03 20:43:26,382 - mmseg - INFO - Iter [600/80000]	lr: 8.985e-05, eta: 6:56:15, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2740, decode.acc_seg: 89.9384, loss: 0.2740
+2023-03-03 20:43:43,518 - mmseg - INFO - Iter [650/80000]	lr: 9.735e-05, eta: 6:58:51, time: 0.343, data_time: 0.057, memory: 39544, decode.loss_ce: 0.2763, decode.acc_seg: 89.9713, loss: 0.2763
+2023-03-03 20:43:58,180 - mmseg - INFO - Iter [700/80000]	lr: 1.049e-04, eta: 6:56:22, time: 0.293, data_time: 0.008, memory: 39544, decode.loss_ce: 0.2747, decode.acc_seg: 89.9510, loss: 0.2747
+2023-03-03 20:44:12,753 - mmseg - INFO - Iter [750/80000]	lr: 1.124e-04, eta: 6:54:02, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2623, decode.acc_seg: 90.4293, loss: 0.2623
+2023-03-03 20:44:27,306 - mmseg - INFO - Iter [800/80000]	lr: 1.199e-04, eta: 6:51:55, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2608, decode.acc_seg: 90.2657, loss: 0.2608
+2023-03-03 20:44:41,912 - mmseg - INFO - Iter [850/80000]	lr: 1.274e-04, eta: 6:50:07, time: 0.292, data_time: 0.008, memory: 39544, decode.loss_ce: 0.2559, decode.acc_seg: 90.2935, loss: 0.2559
+2023-03-03 20:44:56,546 - mmseg - INFO - Iter [900/80000]	lr: 1.349e-04, eta: 6:48:31, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2618, decode.acc_seg: 90.3285, loss: 0.2618
+2023-03-03 20:45:11,037 - mmseg - INFO - Iter [950/80000]	lr: 1.424e-04, eta: 6:46:52, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2622, decode.acc_seg: 90.3918, loss: 0.2622
+2023-03-03 20:45:25,681 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 20:45:25,681 - mmseg - INFO - Iter [1000/80000]	lr: 1.499e-04, eta: 6:45:34, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2591, decode.acc_seg: 90.3405, loss: 0.2591
+2023-03-03 20:45:40,180 - mmseg - INFO - Iter [1050/80000]	lr: 1.500e-04, eta: 6:44:11, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2585, decode.acc_seg: 90.5195, loss: 0.2585
+2023-03-03 20:45:54,723 - mmseg - INFO - Iter [1100/80000]	lr: 1.500e-04, eta: 6:42:57, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2597, decode.acc_seg: 90.2241, loss: 0.2597
+2023-03-03 20:46:09,263 - mmseg - INFO - Iter [1150/80000]	lr: 1.500e-04, eta: 6:41:48, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2441, decode.acc_seg: 90.8717, loss: 0.2441
+2023-03-03 20:46:23,734 - mmseg - INFO - Iter [1200/80000]	lr: 1.500e-04, eta: 6:40:39, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2435, decode.acc_seg: 90.7060, loss: 0.2435
+2023-03-03 20:46:38,376 - mmseg - INFO - Iter [1250/80000]	lr: 1.500e-04, eta: 6:39:45, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2419, decode.acc_seg: 90.7513, loss: 0.2419
+2023-03-03 20:46:55,395 - mmseg - INFO - Iter [1300/80000]	lr: 1.500e-04, eta: 6:41:18, time: 0.340, data_time: 0.056, memory: 39544, decode.loss_ce: 0.2391, decode.acc_seg: 90.9306, loss: 0.2391
+2023-03-03 20:47:09,847 - mmseg - INFO - Iter [1350/80000]	lr: 1.500e-04, eta: 6:40:14, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2583, decode.acc_seg: 90.3600, loss: 0.2583
+2023-03-03 20:47:24,356 - mmseg - INFO - Iter [1400/80000]	lr: 1.500e-04, eta: 6:39:16, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2463, decode.acc_seg: 90.7443, loss: 0.2463
+2023-03-03 20:47:38,958 - mmseg - INFO - Iter [1450/80000]	lr: 1.500e-04, eta: 6:38:26, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2442, decode.acc_seg: 90.8104, loss: 0.2442
+2023-03-03 20:47:53,502 - mmseg - INFO - Iter [1500/80000]	lr: 1.500e-04, eta: 6:37:36, time: 0.291, data_time: 0.008, memory: 39544, decode.loss_ce: 0.2357, decode.acc_seg: 91.0354, loss: 0.2357
+2023-03-03 20:48:08,119 - mmseg - INFO - Iter [1550/80000]	lr: 1.500e-04, eta: 6:36:51, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2459, decode.acc_seg: 90.6844, loss: 0.2459
+2023-03-03 20:48:22,649 - mmseg - INFO - Iter [1600/80000]	lr: 1.500e-04, eta: 6:36:04, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2475, decode.acc_seg: 90.5964, loss: 0.2475
+2023-03-03 20:48:37,201 - mmseg - INFO - Iter [1650/80000]	lr: 1.500e-04, eta: 6:35:20, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2312, decode.acc_seg: 91.0055, loss: 0.2312
+2023-03-03 20:48:51,740 - mmseg - INFO - Iter [1700/80000]	lr: 1.500e-04, eta: 6:34:38, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2410, decode.acc_seg: 90.8174, loss: 0.2410
+2023-03-03 20:49:06,248 - mmseg - INFO - Iter [1750/80000]	lr: 1.500e-04, eta: 6:33:55, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2231, decode.acc_seg: 91.4248, loss: 0.2231
+2023-03-03 20:49:20,845 - mmseg - INFO - Iter [1800/80000]	lr: 1.500e-04, eta: 6:33:18, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2346, decode.acc_seg: 90.9537, loss: 0.2346
+2023-03-03 20:49:35,398 - mmseg - INFO - Iter [1850/80000]	lr: 1.500e-04, eta: 6:32:40, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2367, decode.acc_seg: 90.7575, loss: 0.2367
+2023-03-03 20:49:52,293 - mmseg - INFO - Iter [1900/80000]	lr: 1.500e-04, eta: 6:33:40, time: 0.338, data_time: 0.056, memory: 39544, decode.loss_ce: 0.2335, decode.acc_seg: 90.9782, loss: 0.2335
+2023-03-03 20:50:06,788 - mmseg - INFO - Iter [1950/80000]	lr: 1.500e-04, eta: 6:33:00, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2300, decode.acc_seg: 91.0451, loss: 0.2300
+2023-03-03 20:50:21,288 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 20:50:21,288 - mmseg - INFO - Iter [2000/80000]	lr: 1.500e-04, eta: 6:32:21, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2300, decode.acc_seg: 91.2050, loss: 0.2300
+2023-03-03 20:50:35,773 - mmseg - INFO - Iter [2050/80000]	lr: 1.500e-04, eta: 6:31:43, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2326, decode.acc_seg: 90.9816, loss: 0.2326
+2023-03-03 20:50:50,299 - mmseg - INFO - Iter [2100/80000]	lr: 1.500e-04, eta: 6:31:07, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2336, decode.acc_seg: 90.9539, loss: 0.2336
+2023-03-03 20:51:04,728 - mmseg - INFO - Iter [2150/80000]	lr: 1.500e-04, eta: 6:30:29, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2341, decode.acc_seg: 91.0447, loss: 0.2341
+2023-03-03 20:51:19,227 - mmseg - INFO - Iter [2200/80000]	lr: 1.500e-04, eta: 6:29:55, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2327, decode.acc_seg: 90.9099, loss: 0.2327
+2023-03-03 20:51:33,738 - mmseg - INFO - Iter [2250/80000]	lr: 1.500e-04, eta: 6:29:22, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2344, decode.acc_seg: 90.8458, loss: 0.2344
+2023-03-03 20:51:48,311 - mmseg - INFO - Iter [2300/80000]	lr: 1.500e-04, eta: 6:28:51, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2260, decode.acc_seg: 91.2486, loss: 0.2260
+2023-03-03 20:52:02,816 - mmseg - INFO - Iter [2350/80000]	lr: 1.500e-04, eta: 6:28:20, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2201, decode.acc_seg: 91.4102, loss: 0.2201
+2023-03-03 20:52:17,247 - mmseg - INFO - Iter [2400/80000]	lr: 1.500e-04, eta: 6:27:46, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2262, decode.acc_seg: 91.1899, loss: 0.2262
+2023-03-03 20:52:31,795 - mmseg - INFO - Iter [2450/80000]	lr: 1.500e-04, eta: 6:27:17, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2301, decode.acc_seg: 91.1879, loss: 0.2301
+2023-03-03 20:52:46,226 - mmseg - INFO - Iter [2500/80000]	lr: 1.500e-04, eta: 6:26:45, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2213, decode.acc_seg: 91.3750, loss: 0.2213
+2023-03-03 20:53:03,346 - mmseg - INFO - Iter [2550/80000]	lr: 1.500e-04, eta: 6:27:35, time: 0.342, data_time: 0.053, memory: 39544, decode.loss_ce: 0.2217, decode.acc_seg: 91.4940, loss: 0.2217
+2023-03-03 20:53:17,812 - mmseg - INFO - Iter [2600/80000]	lr: 1.500e-04, eta: 6:27:04, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2307, decode.acc_seg: 91.1856, loss: 0.2307
+2023-03-03 20:53:32,250 - mmseg - INFO - Iter [2650/80000]	lr: 1.500e-04, eta: 6:26:33, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2296, decode.acc_seg: 91.1523, loss: 0.2296
+2023-03-03 20:53:46,729 - mmseg - INFO - Iter [2700/80000]	lr: 1.500e-04, eta: 6:26:03, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2260, decode.acc_seg: 91.3178, loss: 0.2260
+2023-03-03 20:54:01,297 - mmseg - INFO - Iter [2750/80000]	lr: 1.500e-04, eta: 6:25:36, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2301, decode.acc_seg: 91.0385, loss: 0.2301
+2023-03-03 20:54:15,728 - mmseg - INFO - Iter [2800/80000]	lr: 1.500e-04, eta: 6:25:06, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2190, decode.acc_seg: 91.4960, loss: 0.2190
+2023-03-03 20:54:30,193 - mmseg - INFO - Iter [2850/80000]	lr: 1.500e-04, eta: 6:24:38, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2321, decode.acc_seg: 91.2245, loss: 0.2321
+2023-03-03 20:54:44,613 - mmseg - INFO - Iter [2900/80000]	lr: 1.500e-04, eta: 6:24:08, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2332, decode.acc_seg: 91.0292, loss: 0.2332
+2023-03-03 20:54:59,005 - mmseg - INFO - Iter [2950/80000]	lr: 1.500e-04, eta: 6:23:39, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2186, decode.acc_seg: 91.3791, loss: 0.2186
+2023-03-03 20:55:13,501 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 20:55:13,502 - mmseg - INFO - Iter [3000/80000]	lr: 1.500e-04, eta: 6:23:13, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2198, decode.acc_seg: 91.4972, loss: 0.2198
+2023-03-03 20:55:27,957 - mmseg - INFO - Iter [3050/80000]	lr: 1.500e-04, eta: 6:22:46, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2269, decode.acc_seg: 91.2342, loss: 0.2269
+2023-03-03 20:55:42,338 - mmseg - INFO - Iter [3100/80000]	lr: 1.500e-04, eta: 6:22:17, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2227, decode.acc_seg: 91.1750, loss: 0.2227
+2023-03-03 20:55:56,822 - mmseg - INFO - Iter [3150/80000]	lr: 1.500e-04, eta: 6:21:52, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2176, decode.acc_seg: 91.4224, loss: 0.2176
+2023-03-03 20:56:13,807 - mmseg - INFO - Iter [3200/80000]	lr: 1.500e-04, eta: 6:22:27, time: 0.340, data_time: 0.056, memory: 39544, decode.loss_ce: 0.2198, decode.acc_seg: 91.3442, loss: 0.2198
+2023-03-03 20:56:28,275 - mmseg - INFO - Iter [3250/80000]	lr: 1.500e-04, eta: 6:22:01, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2281, decode.acc_seg: 91.1654, loss: 0.2281
+2023-03-03 20:56:42,795 - mmseg - INFO - Iter [3300/80000]	lr: 1.500e-04, eta: 6:21:36, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2204, decode.acc_seg: 91.4208, loss: 0.2204
+2023-03-03 20:56:57,319 - mmseg - INFO - Iter [3350/80000]	lr: 1.500e-04, eta: 6:21:12, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2174, decode.acc_seg: 91.5542, loss: 0.2174
+2023-03-03 20:57:11,707 - mmseg - INFO - Iter [3400/80000]	lr: 1.500e-04, eta: 6:20:45, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2279, decode.acc_seg: 91.1691, loss: 0.2279
+2023-03-03 20:57:26,138 - mmseg - INFO - Iter [3450/80000]	lr: 1.500e-04, eta: 6:20:20, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2285, decode.acc_seg: 91.0973, loss: 0.2285
+2023-03-03 20:57:40,574 - mmseg - INFO - Iter [3500/80000]	lr: 1.500e-04, eta: 6:19:55, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2262, decode.acc_seg: 91.0474, loss: 0.2262
+2023-03-03 20:57:55,026 - mmseg - INFO - Iter [3550/80000]	lr: 1.500e-04, eta: 6:19:30, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2277, decode.acc_seg: 91.2837, loss: 0.2277
+2023-03-03 20:58:09,560 - mmseg - INFO - Iter [3600/80000]	lr: 1.500e-04, eta: 6:19:07, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2212, decode.acc_seg: 91.3929, loss: 0.2212
+2023-03-03 20:58:24,096 - mmseg - INFO - Iter [3650/80000]	lr: 1.500e-04, eta: 6:18:45, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2226, decode.acc_seg: 91.2071, loss: 0.2226
+2023-03-03 20:58:38,541 - mmseg - INFO - Iter [3700/80000]	lr: 1.500e-04, eta: 6:18:21, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2228, decode.acc_seg: 91.2868, loss: 0.2228
+2023-03-03 20:58:53,050 - mmseg - INFO - Iter [3750/80000]	lr: 1.500e-04, eta: 6:17:59, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2214, decode.acc_seg: 91.2800, loss: 0.2214
+2023-03-03 20:59:10,030 - mmseg - INFO - Iter [3800/80000]	lr: 1.500e-04, eta: 6:18:26, time: 0.340, data_time: 0.057, memory: 39544, decode.loss_ce: 0.2196, decode.acc_seg: 91.4036, loss: 0.2196
+2023-03-03 20:59:24,462 - mmseg - INFO - Iter [3850/80000]	lr: 1.500e-04, eta: 6:18:02, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2315, decode.acc_seg: 91.0809, loss: 0.2315
+2023-03-03 20:59:38,921 - mmseg - INFO - Iter [3900/80000]	lr: 1.500e-04, eta: 6:17:39, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2219, decode.acc_seg: 91.2939, loss: 0.2219
+2023-03-03 20:59:53,405 - mmseg - INFO - Iter [3950/80000]	lr: 1.500e-04, eta: 6:17:16, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2204, decode.acc_seg: 91.4525, loss: 0.2204
+2023-03-03 21:00:07,862 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 21:00:07,862 - mmseg - INFO - Iter [4000/80000]	lr: 1.500e-04, eta: 6:16:53, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2166, decode.acc_seg: 91.5000, loss: 0.2166
+2023-03-03 21:00:22,281 - mmseg - INFO - Iter [4050/80000]	lr: 1.500e-04, eta: 6:16:30, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2274, decode.acc_seg: 91.1082, loss: 0.2274
+2023-03-03 21:00:36,851 - mmseg - INFO - Iter [4100/80000]	lr: 1.500e-04, eta: 6:16:09, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2154, decode.acc_seg: 91.6453, loss: 0.2154
+2023-03-03 21:00:51,327 - mmseg - INFO - Iter [4150/80000]	lr: 1.500e-04, eta: 6:15:47, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2295, decode.acc_seg: 91.1932, loss: 0.2295
+2023-03-03 21:01:05,737 - mmseg - INFO - Iter [4200/80000]	lr: 1.500e-04, eta: 6:15:24, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2281, decode.acc_seg: 91.1640, loss: 0.2281
+2023-03-03 21:01:20,252 - mmseg - INFO - Iter [4250/80000]	lr: 1.500e-04, eta: 6:15:03, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2273, decode.acc_seg: 91.1179, loss: 0.2273
+2023-03-03 21:01:34,684 - mmseg - INFO - Iter [4300/80000]	lr: 1.500e-04, eta: 6:14:41, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2195, decode.acc_seg: 91.3571, loss: 0.2195
+2023-03-03 21:01:49,335 - mmseg - INFO - Iter [4350/80000]	lr: 1.500e-04, eta: 6:14:23, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2252, decode.acc_seg: 91.2511, loss: 0.2252
+2023-03-03 21:02:03,762 - mmseg - INFO - Iter [4400/80000]	lr: 1.500e-04, eta: 6:14:01, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2217, decode.acc_seg: 91.4655, loss: 0.2217
+2023-03-03 21:02:20,730 - mmseg - INFO - Iter [4450/80000]	lr: 1.500e-04, eta: 6:14:22, time: 0.339, data_time: 0.054, memory: 39544, decode.loss_ce: 0.2217, decode.acc_seg: 91.3304, loss: 0.2217
+2023-03-03 21:02:35,176 - mmseg - INFO - Iter [4500/80000]	lr: 1.500e-04, eta: 6:14:00, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2196, decode.acc_seg: 91.5225, loss: 0.2196
+2023-03-03 21:02:49,661 - mmseg - INFO - Iter [4550/80000]	lr: 1.500e-04, eta: 6:13:39, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2154, decode.acc_seg: 91.5304, loss: 0.2154
+2023-03-03 21:03:04,246 - mmseg - INFO - Iter [4600/80000]	lr: 1.500e-04, eta: 6:13:19, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2260, decode.acc_seg: 91.0415, loss: 0.2260
+2023-03-03 21:03:18,703 - mmseg - INFO - Iter [4650/80000]	lr: 1.500e-04, eta: 6:12:58, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2236, decode.acc_seg: 91.3299, loss: 0.2236
+2023-03-03 21:03:33,112 - mmseg - INFO - Iter [4700/80000]	lr: 1.500e-04, eta: 6:12:36, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2276, decode.acc_seg: 91.0787, loss: 0.2276
+2023-03-03 21:03:47,543 - mmseg - INFO - Iter [4750/80000]	lr: 1.500e-04, eta: 6:12:15, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2332, decode.acc_seg: 90.9522, loss: 0.2332
+2023-03-03 21:04:01,982 - mmseg - INFO - Iter [4800/80000]	lr: 1.500e-04, eta: 6:11:54, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2205, decode.acc_seg: 91.3302, loss: 0.2205
+2023-03-03 21:04:16,384 - mmseg - INFO - Iter [4850/80000]	lr: 1.500e-04, eta: 6:11:32, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2127, decode.acc_seg: 91.3973, loss: 0.2127
+2023-03-03 21:04:30,989 - mmseg - INFO - Iter [4900/80000]	lr: 1.500e-04, eta: 6:11:14, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2170, decode.acc_seg: 91.4873, loss: 0.2170
+2023-03-03 21:04:45,434 - mmseg - INFO - Iter [4950/80000]	lr: 1.500e-04, eta: 6:10:53, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2142, decode.acc_seg: 91.5172, loss: 0.2142
+2023-03-03 21:04:59,884 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 21:04:59,885 - mmseg - INFO - Iter [5000/80000]	lr: 1.500e-04, eta: 6:10:33, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2116, decode.acc_seg: 91.6393, loss: 0.2116
+2023-03-03 21:05:16,901 - mmseg - INFO - Iter [5050/80000]	lr: 1.500e-04, eta: 6:10:50, time: 0.340, data_time: 0.055, memory: 39544, decode.loss_ce: 0.2334, decode.acc_seg: 91.1643, loss: 0.2334
+2023-03-03 21:05:31,493 - mmseg - INFO - Iter [5100/80000]	lr: 1.500e-04, eta: 6:10:32, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2100, decode.acc_seg: 91.6614, loss: 0.2100
+2023-03-03 21:05:45,985 - mmseg - INFO - Iter [5150/80000]	lr: 1.500e-04, eta: 6:10:12, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2177, decode.acc_seg: 91.4262, loss: 0.2177
+2023-03-03 21:06:00,423 - mmseg - INFO - Iter [5200/80000]	lr: 1.500e-04, eta: 6:09:51, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2231, decode.acc_seg: 91.2192, loss: 0.2231
+2023-03-03 21:06:14,986 - mmseg - INFO - Iter [5250/80000]	lr: 1.500e-04, eta: 6:09:33, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2108, decode.acc_seg: 91.6540, loss: 0.2108
+2023-03-03 21:06:29,384 - mmseg - INFO - Iter [5300/80000]	lr: 1.500e-04, eta: 6:09:12, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2223, decode.acc_seg: 91.4052, loss: 0.2223
+2023-03-03 21:06:43,855 - mmseg - INFO - Iter [5350/80000]	lr: 1.500e-04, eta: 6:08:52, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2204, decode.acc_seg: 91.5398, loss: 0.2204
+2023-03-03 21:06:58,301 - mmseg - INFO - Iter [5400/80000]	lr: 1.500e-04, eta: 6:08:32, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2221, decode.acc_seg: 91.5203, loss: 0.2221
+2023-03-03 21:07:12,732 - mmseg - INFO - Iter [5450/80000]	lr: 1.500e-04, eta: 6:08:12, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2172, decode.acc_seg: 91.3089, loss: 0.2172
+2023-03-03 21:07:27,198 - mmseg - INFO - Iter [5500/80000]	lr: 1.500e-04, eta: 6:07:52, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2225, decode.acc_seg: 91.1451, loss: 0.2225
+2023-03-03 21:07:41,621 - mmseg - INFO - Iter [5550/80000]	lr: 1.500e-04, eta: 6:07:32, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2222, decode.acc_seg: 91.3315, loss: 0.2222
+2023-03-03 21:07:56,245 - mmseg - INFO - Iter [5600/80000]	lr: 1.500e-04, eta: 6:07:15, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2185, decode.acc_seg: 91.4833, loss: 0.2185
+2023-03-03 21:08:10,784 - mmseg - INFO - Iter [5650/80000]	lr: 1.500e-04, eta: 6:06:56, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2271, decode.acc_seg: 91.1335, loss: 0.2271
+2023-03-03 21:08:27,770 - mmseg - INFO - Iter [5700/80000]	lr: 1.500e-04, eta: 6:07:10, time: 0.340, data_time: 0.055, memory: 39544, decode.loss_ce: 0.2135, decode.acc_seg: 91.4956, loss: 0.2135
+2023-03-03 21:08:42,162 - mmseg - INFO - Iter [5750/80000]	lr: 1.500e-04, eta: 6:06:50, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2198, decode.acc_seg: 91.4721, loss: 0.2198
+2023-03-03 21:08:56,655 - mmseg - INFO - Iter [5800/80000]	lr: 1.500e-04, eta: 6:06:31, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2155, decode.acc_seg: 91.6101, loss: 0.2155
+2023-03-03 21:09:11,127 - mmseg - INFO - Iter [5850/80000]	lr: 1.500e-04, eta: 6:06:11, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2233, decode.acc_seg: 91.2912, loss: 0.2233
+2023-03-03 21:09:25,675 - mmseg - INFO - Iter [5900/80000]	lr: 1.500e-04, eta: 6:05:53, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2219, decode.acc_seg: 91.4192, loss: 0.2219
+2023-03-03 21:09:40,209 - mmseg - INFO - Iter [5950/80000]	lr: 1.500e-04, eta: 6:05:35, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2152, decode.acc_seg: 91.5641, loss: 0.2152
+2023-03-03 21:09:54,619 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 21:09:54,619 - mmseg - INFO - Iter [6000/80000]	lr: 1.500e-04, eta: 6:05:15, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2148, decode.acc_seg: 91.5323, loss: 0.2148
+2023-03-03 21:10:09,097 - mmseg - INFO - Iter [6050/80000]	lr: 1.500e-04, eta: 6:04:56, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2134, decode.acc_seg: 91.6340, loss: 0.2134
+2023-03-03 21:10:23,557 - mmseg - INFO - Iter [6100/80000]	lr: 1.500e-04, eta: 6:04:37, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2192, decode.acc_seg: 91.4612, loss: 0.2192
+2023-03-03 21:10:38,014 - mmseg - INFO - Iter [6150/80000]	lr: 1.500e-04, eta: 6:04:18, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2245, decode.acc_seg: 91.2432, loss: 0.2245
+2023-03-03 21:10:52,443 - mmseg - INFO - Iter [6200/80000]	lr: 1.500e-04, eta: 6:03:59, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2174, decode.acc_seg: 91.4780, loss: 0.2174
+2023-03-03 21:11:06,896 - mmseg - INFO - Iter [6250/80000]	lr: 1.500e-04, eta: 6:03:40, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2330, decode.acc_seg: 91.0710, loss: 0.2330
+2023-03-03 21:11:21,456 - mmseg - INFO - Iter [6300/80000]	lr: 1.500e-04, eta: 6:03:23, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2200, decode.acc_seg: 91.2911, loss: 0.2200
+2023-03-03 21:11:38,411 - mmseg - INFO - Iter [6350/80000]	lr: 1.500e-04, eta: 6:03:33, time: 0.339, data_time: 0.056, memory: 39544, decode.loss_ce: 0.2176, decode.acc_seg: 91.3258, loss: 0.2176
+2023-03-03 21:11:52,925 - mmseg - INFO - Iter [6400/80000]	lr: 1.500e-04, eta: 6:03:15, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2208, decode.acc_seg: 91.5350, loss: 0.2208
+2023-03-03 21:12:07,354 - mmseg - INFO - Iter [6450/80000]	lr: 1.500e-04, eta: 6:02:56, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2155, decode.acc_seg: 91.5218, loss: 0.2155
+2023-03-03 21:12:21,843 - mmseg - INFO - Iter [6500/80000]	lr: 1.500e-04, eta: 6:02:37, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2089, decode.acc_seg: 91.8525, loss: 0.2089
+2023-03-03 21:12:36,278 - mmseg - INFO - Iter [6550/80000]	lr: 1.500e-04, eta: 6:02:18, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2211, decode.acc_seg: 91.2530, loss: 0.2211
+2023-03-03 21:12:50,728 - mmseg - INFO - Iter [6600/80000]	lr: 1.500e-04, eta: 6:02:00, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2125, decode.acc_seg: 91.6622, loss: 0.2125
+2023-03-03 21:13:05,136 - mmseg - INFO - Iter [6650/80000]	lr: 1.500e-04, eta: 6:01:41, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2171, decode.acc_seg: 91.4595, loss: 0.2171
+2023-03-03 21:13:19,671 - mmseg - INFO - Iter [6700/80000]	lr: 1.500e-04, eta: 6:01:23, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2153, decode.acc_seg: 91.6000, loss: 0.2153
+2023-03-03 21:13:34,144 - mmseg - INFO - Iter [6750/80000]	lr: 1.500e-04, eta: 6:01:05, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2090, decode.acc_seg: 91.7361, loss: 0.2090
+2023-03-03 21:13:48,596 - mmseg - INFO - Iter [6800/80000]	lr: 1.500e-04, eta: 6:00:46, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2240, decode.acc_seg: 91.0978, loss: 0.2240
+2023-03-03 21:14:03,088 - mmseg - INFO - Iter [6850/80000]	lr: 1.500e-04, eta: 6:00:28, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2143, decode.acc_seg: 91.5865, loss: 0.2143
+2023-03-03 21:14:17,780 - mmseg - INFO - Iter [6900/80000]	lr: 1.500e-04, eta: 6:00:13, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2130, decode.acc_seg: 91.5212, loss: 0.2130
+2023-03-03 21:14:34,906 - mmseg - INFO - Iter [6950/80000]	lr: 1.500e-04, eta: 6:00:23, time: 0.343, data_time: 0.057, memory: 39544, decode.loss_ce: 0.2233, decode.acc_seg: 91.3396, loss: 0.2233
+2023-03-03 21:14:49,371 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 21:14:49,372 - mmseg - INFO - Iter [7000/80000]	lr: 1.500e-04, eta: 6:00:04, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2178, decode.acc_seg: 91.6191, loss: 0.2178
+2023-03-03 21:15:03,784 - mmseg - INFO - Iter [7050/80000]	lr: 1.500e-04, eta: 5:59:45, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2183, decode.acc_seg: 91.4211, loss: 0.2183
+2023-03-03 21:15:18,252 - mmseg - INFO - Iter [7100/80000]	lr: 1.500e-04, eta: 5:59:27, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2098, decode.acc_seg: 91.7454, loss: 0.2098
+2023-03-03 21:15:32,711 - mmseg - INFO - Iter [7150/80000]	lr: 1.500e-04, eta: 5:59:09, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2155, decode.acc_seg: 91.5806, loss: 0.2155
+2023-03-03 21:15:47,188 - mmseg - INFO - Iter [7200/80000]	lr: 1.500e-04, eta: 5:58:51, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2045, decode.acc_seg: 91.7584, loss: 0.2045
+2023-03-03 21:16:01,672 - mmseg - INFO - Iter [7250/80000]	lr: 1.500e-04, eta: 5:58:33, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2236, decode.acc_seg: 91.3505, loss: 0.2236
+2023-03-03 21:16:16,182 - mmseg - INFO - Iter [7300/80000]	lr: 1.500e-04, eta: 5:58:16, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2131, decode.acc_seg: 91.5531, loss: 0.2131
+2023-03-03 21:16:30,550 - mmseg - INFO - Iter [7350/80000]	lr: 1.500e-04, eta: 5:57:57, time: 0.287, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2183, decode.acc_seg: 91.4929, loss: 0.2183
+2023-03-03 21:16:44,985 - mmseg - INFO - Iter [7400/80000]	lr: 1.500e-04, eta: 5:57:39, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2230, decode.acc_seg: 91.3840, loss: 0.2230
+2023-03-03 21:16:59,606 - mmseg - INFO - Iter [7450/80000]	lr: 1.500e-04, eta: 5:57:22, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2062, decode.acc_seg: 91.7753, loss: 0.2062
+2023-03-03 21:17:14,106 - mmseg - INFO - Iter [7500/80000]	lr: 1.500e-04, eta: 5:57:05, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2119, decode.acc_seg: 91.4698, loss: 0.2119
+2023-03-03 21:17:28,781 - mmseg - INFO - Iter [7550/80000]	lr: 1.500e-04, eta: 5:56:49, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2194, decode.acc_seg: 91.3233, loss: 0.2194
+2023-03-03 21:17:46,019 - mmseg - INFO - Iter [7600/80000]	lr: 1.500e-04, eta: 5:56:58, time: 0.345, data_time: 0.054, memory: 39544, decode.loss_ce: 0.2206, decode.acc_seg: 91.4040, loss: 0.2206
+2023-03-03 21:18:00,509 - mmseg - INFO - Iter [7650/80000]	lr: 1.500e-04, eta: 5:56:40, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2237, decode.acc_seg: 91.2081, loss: 0.2237
+2023-03-03 21:18:15,029 - mmseg - INFO - Iter [7700/80000]	lr: 1.500e-04, eta: 5:56:23, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2148, decode.acc_seg: 91.4479, loss: 0.2148
+2023-03-03 21:18:29,507 - mmseg - INFO - Iter [7750/80000]	lr: 1.500e-04, eta: 5:56:05, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2161, decode.acc_seg: 91.4975, loss: 0.2161
+2023-03-03 21:18:43,931 - mmseg - INFO - Iter [7800/80000]	lr: 1.500e-04, eta: 5:55:47, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2211, decode.acc_seg: 91.2720, loss: 0.2211
+2023-03-03 21:18:58,361 - mmseg - INFO - Iter [7850/80000]	lr: 1.500e-04, eta: 5:55:29, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2112, decode.acc_seg: 91.5562, loss: 0.2112
+2023-03-03 21:19:12,941 - mmseg - INFO - Iter [7900/80000]	lr: 1.500e-04, eta: 5:55:12, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2186, decode.acc_seg: 91.3620, loss: 0.2186
+2023-03-03 21:19:27,450 - mmseg - INFO - Iter [7950/80000]	lr: 1.500e-04, eta: 5:54:55, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2112, decode.acc_seg: 91.7605, loss: 0.2112
+2023-03-03 21:19:41,886 - mmseg - INFO - Saving checkpoint at 8000 iterations
+2023-03-03 21:19:43,798 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 21:19:43,798 - mmseg - INFO - Iter [8000/80000]	lr: 1.500e-04, eta: 5:54:54, time: 0.327, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2199, decode.acc_seg: 91.4431, loss: 0.2199
+2023-03-03 21:21:21,463 - mmseg - INFO - per class results:
+2023-03-03 21:21:21,469 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        | 74.68 |  89.3 |
+|       building      | 80.95 |  92.4 |
+|         sky         | 93.99 |  96.2 |
+|        floor        | 79.28 | 91.18 |
+|         tree        | 71.62 |  90.1 |
+|       ceiling       | 81.89 | 89.77 |
+|         road        | 81.48 | 87.93 |
+|         bed         | 87.84 | 94.45 |
+|      windowpane     | 59.73 | 72.66 |
+|        grass        | 66.26 | 77.55 |
+|       cabinet       | 57.27 | 76.41 |
+|       sidewalk      | 65.11 | 81.68 |
+|        person       | 78.14 | 87.97 |
+|        earth        |  33.5 | 51.85 |
+|         door        |  46.7 |  62.8 |
+|        table        | 60.81 | 75.28 |
+|       mountain      | 50.16 | 66.68 |
+|        plant        | 48.94 | 60.12 |
+|       curtain       | 69.47 | 81.85 |
+|        chair        | 57.28 | 72.66 |
+|         car         | 82.72 |  90.2 |
+|        water        | 48.14 | 61.88 |
+|       painting      | 68.55 | 83.37 |
+|         sofa        | 63.33 | 86.26 |
+|        shelf        | 37.48 | 50.75 |
+|        house        | 35.09 | 38.76 |
+|         sea         | 42.71 | 65.95 |
+|        mirror       | 60.93 | 66.62 |
+|         rug         | 49.01 | 52.56 |
+|        field        |  29.7 | 51.16 |
+|       armchair      | 40.54 |  50.4 |
+|         seat        |  53.8 | 71.28 |
+|        fence        | 41.27 | 54.24 |
+|         desk        | 48.56 | 62.35 |
+|         rock        | 27.97 | 44.35 |
+|       wardrobe      | 49.53 | 71.87 |
+|         lamp        | 62.51 | 73.81 |
+|       bathtub       | 74.52 | 80.82 |
+|       railing       | 30.83 | 43.16 |
+|       cushion       | 45.32 | 50.38 |
+|         base        |  28.3 | 47.51 |
+|         box         | 18.05 |  20.5 |
+|        column       | 42.99 | 59.32 |
+|      signboard      | 36.57 | 50.92 |
+|   chest of drawers  | 38.66 | 51.12 |
+|       counter       | 24.24 | 28.58 |
+|         sand        | 33.45 | 46.26 |
+|         sink        | 70.35 | 79.88 |
+|      skyscraper     | 46.43 | 51.19 |
+|      fireplace      | 65.89 | 80.59 |
+|     refrigerator    |  74.3 |  89.7 |
+|      grandstand     | 40.02 | 65.95 |
+|         path        |  14.9 | 23.67 |
+|        stairs       | 32.57 | 45.49 |
+|        runway       | 62.64 | 82.91 |
+|         case        | 53.76 | 73.37 |
+|      pool table     | 92.29 | 95.33 |
+|        pillow       | 54.28 | 69.25 |
+|     screen door     | 53.34 | 59.06 |
+|       stairway      | 25.45 | 28.41 |
+|        river        | 10.63 | 20.34 |
+|        bridge       | 46.54 | 51.23 |
+|       bookcase      | 41.29 | 59.78 |
+|        blind        | 41.52 | 45.38 |
+|     coffee table    | 64.05 |  81.9 |
+|        toilet       | 85.81 | 87.91 |
+|        flower       | 30.43 | 42.37 |
+|         book        | 45.26 | 65.09 |
+|         hill        |  8.03 |  9.77 |
+|        bench        | 44.58 | 49.57 |
+|      countertop     | 49.14 | 56.43 |
+|        stove        | 71.09 | 74.87 |
+|         palm        | 50.38 | 70.37 |
+|    kitchen island   | 43.62 | 64.01 |
+|       computer      | 55.55 | 62.43 |
+|     swivel chair    | 37.31 | 43.47 |
+|         boat        | 36.64 | 38.71 |
+|         bar         | 18.29 | 19.07 |
+|    arcade machine   | 30.46 | 32.48 |
+|        hovel        | 34.58 | 52.65 |
+|         bus         | 87.49 | 89.92 |
+|        towel        | 57.57 | 65.84 |
+|        light        | 47.09 | 49.79 |
+|        truck        | 31.82 | 43.29 |
+|        tower        | 23.03 | 30.13 |
+|      chandelier     | 65.63 | 79.63 |
+|        awning       |  18.5 | 19.55 |
+|     streetlight     | 24.71 | 30.04 |
+|        booth        | 32.82 | 33.06 |
+| television receiver |  67.3 | 76.56 |
+|       airplane      | 50.67 | 71.23 |
+|      dirt track     |  6.92 | 23.23 |
+|       apparel       | 28.73 | 38.79 |
+|         pole        | 23.96 | 32.53 |
+|         land        | 13.03 | 17.81 |
+|      bannister      |  4.33 |  5.73 |
+|      escalator      | 20.24 | 20.84 |
+|       ottoman       | 49.42 | 61.69 |
+|        bottle       | 14.15 | 21.21 |
+|        buffet       | 43.65 | 48.56 |
+|        poster       |  24.5 | 28.05 |
+|        stage        | 16.43 | 20.62 |
+|         van         | 47.54 | 60.27 |
+|         ship        | 18.72 | 23.63 |
+|       fountain      | 11.86 | 12.09 |
+|    conveyer belt    |  80.6 | 85.83 |
+|        canopy       | 12.36 | 13.27 |
+|        washer       | 65.02 | 65.14 |
+|      plaything      | 18.33 | 20.63 |
+|    swimming pool    | 36.17 |  39.8 |
+|        stool        | 36.73 | 45.42 |
+|        barrel       | 41.21 | 64.63 |
+|        basket       | 26.83 | 33.93 |
+|      waterfall      | 69.11 | 88.47 |
+|         tent        | 94.64 | 97.44 |
+|         bag         |  3.94 |  4.07 |
+|       minibike      | 61.76 | 70.38 |
+|        cradle       |  80.7 | 91.81 |
+|         oven        | 26.04 | 72.99 |
+|         ball        | 45.66 | 57.05 |
+|         food        |  55.1 | 73.16 |
+|         step        | 11.28 | 12.25 |
+|         tank        | 42.79 | 43.44 |
+|      trade name     | 24.69 | 28.49 |
+|      microwave      | 30.81 | 31.27 |
+|         pot         | 40.21 |  48.5 |
+|        animal       | 52.59 | 62.68 |
+|       bicycle       | 42.84 | 59.99 |
+|         lake        | 60.34 | 62.83 |
+|      dishwasher     | 77.17 | 80.94 |
+|        screen       | 68.02 | 83.51 |
+|       blanket       | 13.76 | 15.37 |
+|      sculpture      | 35.72 | 56.36 |
+|         hood        | 55.67 | 61.56 |
+|        sconce       | 37.18 | 42.38 |
+|         vase        | 35.56 | 56.58 |
+|    traffic light    | 23.37 | 29.25 |
+|         tray        |  4.86 |  8.88 |
+|        ashcan       | 38.12 | 46.09 |
+|         fan         | 54.84 | 74.68 |
+|         pier        | 14.24 | 17.01 |
+|      crt screen     |  3.22 |  8.38 |
+|        plate        | 41.34 | 53.32 |
+|       monitor       | 23.56 | 30.95 |
+|    bulletin board   | 42.64 | 52.26 |
+|        shower       |  0.96 |  5.12 |
+|       radiator      | 41.75 | 46.65 |
+|        glass        | 13.19 | 15.03 |
+|        clock        | 21.65 | 23.15 |
+|         flag        |  32.0 | 34.36 |
++---------------------+-------+-------+
+2023-03-03 21:21:21,470 - mmseg - INFO - Summary:
+2023-03-03 21:21:21,470 - mmseg - INFO - 
++-------+------+------+
+|  aAcc | mIoU | mAcc |
++-------+------+------+
+| 81.22 | 44.4 | 54.3 |
++-------+------+------+
+2023-03-03 21:21:23,405 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_8000.pth.
+2023-03-03 21:21:23,406 - mmseg - INFO - Best mIoU is 0.4440 at 8000 iter.
+2023-03-03 21:21:23,406 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 21:21:23,406 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8122, mIoU: 0.4440, mAcc: 0.5430, IoU.background: nan, IoU.wall: 0.7468, IoU.building: 0.8095, IoU.sky: 0.9399, IoU.floor: 0.7928, IoU.tree: 0.7162, IoU.ceiling: 0.8189, IoU.road: 0.8148, IoU.bed : 0.8784, IoU.windowpane: 0.5973, IoU.grass: 0.6626, IoU.cabinet: 0.5727, IoU.sidewalk: 0.6511, IoU.person: 0.7814, IoU.earth: 0.3350, IoU.door: 0.4670, IoU.table: 0.6081, IoU.mountain: 0.5016, IoU.plant: 0.4894, IoU.curtain: 0.6947, IoU.chair: 0.5728, IoU.car: 0.8272, IoU.water: 0.4814, IoU.painting: 0.6855, IoU.sofa: 0.6333, IoU.shelf: 0.3748, IoU.house: 0.3509, IoU.sea: 0.4271, IoU.mirror: 0.6093, IoU.rug: 0.4901, IoU.field: 0.2970, IoU.armchair: 0.4054, IoU.seat: 0.5380, IoU.fence: 0.4127, IoU.desk: 0.4856, IoU.rock: 0.2797, IoU.wardrobe: 0.4953, IoU.lamp: 0.6251, IoU.bathtub: 0.7452, IoU.railing: 0.3083, IoU.cushion: 0.4532, IoU.base: 0.2830, IoU.box: 0.1805, IoU.column: 0.4299, IoU.signboard: 0.3657, IoU.chest of drawers: 0.3866, IoU.counter: 0.2424, IoU.sand: 0.3345, IoU.sink: 0.7035, IoU.skyscraper: 0.4643, IoU.fireplace: 0.6589, IoU.refrigerator: 0.7430, IoU.grandstand: 0.4002, IoU.path: 0.1490, IoU.stairs: 0.3257, IoU.runway: 0.6264, IoU.case: 0.5376, IoU.pool table: 0.9229, IoU.pillow: 0.5428, IoU.screen door: 0.5334, IoU.stairway: 0.2545, IoU.river: 0.1063, IoU.bridge: 0.4654, IoU.bookcase: 0.4129, IoU.blind: 0.4152, IoU.coffee table: 0.6405, IoU.toilet: 0.8581, IoU.flower: 0.3043, IoU.book: 0.4526, IoU.hill: 0.0803, IoU.bench: 0.4458, IoU.countertop: 0.4914, IoU.stove: 0.7109, IoU.palm: 0.5038, IoU.kitchen island: 0.4362, IoU.computer: 0.5555, IoU.swivel chair: 0.3731, IoU.boat: 0.3664, IoU.bar: 0.1829, IoU.arcade machine: 0.3046, IoU.hovel: 0.3458, IoU.bus: 0.8749, IoU.towel: 0.5757, IoU.light: 0.4709, IoU.truck: 0.3182, IoU.tower: 0.2303, IoU.chandelier: 0.6563, IoU.awning: 0.1850, IoU.streetlight: 0.2471, IoU.booth: 0.3282, IoU.television receiver: 0.6730, IoU.airplane: 0.5067, IoU.dirt track: 0.0692, IoU.apparel: 0.2873, IoU.pole: 0.2396, IoU.land: 0.1303, IoU.bannister: 0.0433, IoU.escalator: 0.2024, IoU.ottoman: 0.4942, IoU.bottle: 0.1415, IoU.buffet: 0.4365, IoU.poster: 0.2450, IoU.stage: 0.1643, IoU.van: 0.4754, IoU.ship: 0.1872, IoU.fountain: 0.1186, IoU.conveyer belt: 0.8060, IoU.canopy: 0.1236, IoU.washer: 0.6502, IoU.plaything: 0.1833, IoU.swimming pool: 0.3617, IoU.stool: 0.3673, IoU.barrel: 0.4121, IoU.basket: 0.2683, IoU.waterfall: 0.6911, IoU.tent: 0.9464, IoU.bag: 0.0394, IoU.minibike: 0.6176, IoU.cradle: 0.8070, IoU.oven: 0.2604, IoU.ball: 0.4566, IoU.food: 0.5510, IoU.step: 0.1128, IoU.tank: 0.4279, IoU.trade name: 0.2469, IoU.microwave: 0.3081, IoU.pot: 0.4021, IoU.animal: 0.5259, IoU.bicycle: 0.4284, IoU.lake: 0.6034, IoU.dishwasher: 0.7717, IoU.screen: 0.6802, IoU.blanket: 0.1376, IoU.sculpture: 0.3572, IoU.hood: 0.5567, IoU.sconce: 0.3718, IoU.vase: 0.3556, IoU.traffic light: 0.2337, IoU.tray: 0.0486, IoU.ashcan: 0.3812, IoU.fan: 0.5484, IoU.pier: 0.1424, IoU.crt screen: 0.0322, IoU.plate: 0.4134, IoU.monitor: 0.2356, IoU.bulletin board: 0.4264, IoU.shower: 0.0096, IoU.radiator: 0.4175, IoU.glass: 0.1319, IoU.clock: 0.2165, IoU.flag: 0.3200, Acc.background: nan, Acc.wall: 0.8930, Acc.building: 0.9240, Acc.sky: 0.9620, Acc.floor: 0.9118, Acc.tree: 0.9010, Acc.ceiling: 0.8977, Acc.road: 0.8793, Acc.bed : 0.9445, Acc.windowpane: 0.7266, Acc.grass: 0.7755, Acc.cabinet: 0.7641, Acc.sidewalk: 0.8168, Acc.person: 0.8797, Acc.earth: 0.5185, Acc.door: 0.6280, Acc.table: 0.7528, Acc.mountain: 0.6668, Acc.plant: 0.6012, Acc.curtain: 0.8185, Acc.chair: 0.7266, Acc.car: 0.9020, Acc.water: 0.6188, Acc.painting: 0.8337, Acc.sofa: 0.8626, Acc.shelf: 0.5075, Acc.house: 0.3876, Acc.sea: 0.6595, Acc.mirror: 0.6662, Acc.rug: 0.5256, Acc.field: 0.5116, Acc.armchair: 0.5040, Acc.seat: 0.7128, Acc.fence: 0.5424, Acc.desk: 0.6235, Acc.rock: 0.4435, Acc.wardrobe: 0.7187, Acc.lamp: 0.7381, Acc.bathtub: 0.8082, Acc.railing: 0.4316, Acc.cushion: 0.5038, Acc.base: 0.4751, Acc.box: 0.2050, Acc.column: 0.5932, Acc.signboard: 0.5092, Acc.chest of drawers: 0.5112, Acc.counter: 0.2858, Acc.sand: 0.4626, Acc.sink: 0.7988, Acc.skyscraper: 0.5119, Acc.fireplace: 0.8059, Acc.refrigerator: 0.8970, Acc.grandstand: 0.6595, Acc.path: 0.2367, Acc.stairs: 0.4549, Acc.runway: 0.8291, Acc.case: 0.7337, Acc.pool table: 0.9533, Acc.pillow: 0.6925, Acc.screen door: 0.5906, Acc.stairway: 0.2841, Acc.river: 0.2034, Acc.bridge: 0.5123, Acc.bookcase: 0.5978, Acc.blind: 0.4538, Acc.coffee table: 0.8190, Acc.toilet: 0.8791, Acc.flower: 0.4237, Acc.book: 0.6509, Acc.hill: 0.0977, Acc.bench: 0.4957, Acc.countertop: 0.5643, Acc.stove: 0.7487, Acc.palm: 0.7037, Acc.kitchen island: 0.6401, Acc.computer: 0.6243, Acc.swivel chair: 0.4347, Acc.boat: 0.3871, Acc.bar: 0.1907, Acc.arcade machine: 0.3248, Acc.hovel: 0.5265, Acc.bus: 0.8992, Acc.towel: 0.6584, Acc.light: 0.4979, Acc.truck: 0.4329, Acc.tower: 0.3013, Acc.chandelier: 0.7963, Acc.awning: 0.1955, Acc.streetlight: 0.3004, Acc.booth: 0.3306, Acc.television receiver: 0.7656, Acc.airplane: 0.7123, Acc.dirt track: 0.2323, Acc.apparel: 0.3879, Acc.pole: 0.3253, Acc.land: 0.1781, Acc.bannister: 0.0573, Acc.escalator: 0.2084, Acc.ottoman: 0.6169, Acc.bottle: 0.2121, Acc.buffet: 0.4856, Acc.poster: 0.2805, Acc.stage: 0.2062, Acc.van: 0.6027, Acc.ship: 0.2363, Acc.fountain: 0.1209, Acc.conveyer belt: 0.8583, Acc.canopy: 0.1327, Acc.washer: 0.6514, Acc.plaything: 0.2063, Acc.swimming pool: 0.3980, Acc.stool: 0.4542, Acc.barrel: 0.6463, Acc.basket: 0.3393, Acc.waterfall: 0.8847, Acc.tent: 0.9744, Acc.bag: 0.0407, Acc.minibike: 0.7038, Acc.cradle: 0.9181, Acc.oven: 0.7299, Acc.ball: 0.5705, Acc.food: 0.7316, Acc.step: 0.1225, Acc.tank: 0.4344, Acc.trade name: 0.2849, Acc.microwave: 0.3127, Acc.pot: 0.4850, Acc.animal: 0.6268, Acc.bicycle: 0.5999, Acc.lake: 0.6283, Acc.dishwasher: 0.8094, Acc.screen: 0.8351, Acc.blanket: 0.1537, Acc.sculpture: 0.5636, Acc.hood: 0.6156, Acc.sconce: 0.4238, Acc.vase: 0.5658, Acc.traffic light: 0.2925, Acc.tray: 0.0888, Acc.ashcan: 0.4609, Acc.fan: 0.7468, Acc.pier: 0.1701, Acc.crt screen: 0.0838, Acc.plate: 0.5332, Acc.monitor: 0.3095, Acc.bulletin board: 0.5226, Acc.shower: 0.0512, Acc.radiator: 0.4665, Acc.glass: 0.1503, Acc.clock: 0.2315, Acc.flag: 0.3436
+2023-03-03 21:21:38,339 - mmseg - INFO - Iter [8050/80000]	lr: 1.500e-04, eta: 6:09:31, time: 2.291, data_time: 2.000, memory: 39544, decode.loss_ce: 0.2163, decode.acc_seg: 91.5101, loss: 0.2163
+2023-03-03 21:21:52,934 - mmseg - INFO - Iter [8100/80000]	lr: 1.500e-04, eta: 6:09:09, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2154, decode.acc_seg: 91.4131, loss: 0.2154
+2023-03-03 21:22:07,556 - mmseg - INFO - Iter [8150/80000]	lr: 1.500e-04, eta: 6:08:46, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2184, decode.acc_seg: 91.5115, loss: 0.2184
+2023-03-03 21:22:22,122 - mmseg - INFO - Iter [8200/80000]	lr: 1.500e-04, eta: 6:08:24, time: 0.291, data_time: 0.008, memory: 39544, decode.loss_ce: 0.2164, decode.acc_seg: 91.5528, loss: 0.2164
+2023-03-03 21:22:39,298 - mmseg - INFO - Iter [8250/80000]	lr: 1.500e-04, eta: 6:08:24, time: 0.344, data_time: 0.055, memory: 39544, decode.loss_ce: 0.2114, decode.acc_seg: 91.6728, loss: 0.2114
+2023-03-03 21:22:53,879 - mmseg - INFO - Iter [8300/80000]	lr: 1.500e-04, eta: 6:08:01, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2249, decode.acc_seg: 91.2468, loss: 0.2249
+2023-03-03 21:23:08,349 - mmseg - INFO - Iter [8350/80000]	lr: 1.500e-04, eta: 6:07:38, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2124, decode.acc_seg: 91.6407, loss: 0.2124
+2023-03-03 21:23:22,799 - mmseg - INFO - Iter [8400/80000]	lr: 1.500e-04, eta: 6:07:14, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2160, decode.acc_seg: 91.5880, loss: 0.2160
+2023-03-03 21:23:37,281 - mmseg - INFO - Iter [8450/80000]	lr: 1.500e-04, eta: 6:06:51, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2166, decode.acc_seg: 91.5377, loss: 0.2166
+2023-03-03 21:23:51,798 - mmseg - INFO - Iter [8500/80000]	lr: 1.500e-04, eta: 6:06:29, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2133, decode.acc_seg: 91.5524, loss: 0.2133
+2023-03-03 21:24:06,350 - mmseg - INFO - Iter [8550/80000]	lr: 1.500e-04, eta: 6:06:06, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2098, decode.acc_seg: 91.6660, loss: 0.2098
+2023-03-03 21:24:20,874 - mmseg - INFO - Iter [8600/80000]	lr: 1.500e-04, eta: 6:05:44, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2156, decode.acc_seg: 91.3725, loss: 0.2156
+2023-03-03 21:24:35,358 - mmseg - INFO - Iter [8650/80000]	lr: 1.500e-04, eta: 6:05:21, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2185, decode.acc_seg: 91.4537, loss: 0.2185
+2023-03-03 21:24:49,926 - mmseg - INFO - Iter [8700/80000]	lr: 1.500e-04, eta: 6:05:00, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2194, decode.acc_seg: 91.4302, loss: 0.2194
+2023-03-03 21:25:04,412 - mmseg - INFO - Iter [8750/80000]	lr: 1.500e-04, eta: 6:04:37, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2162, decode.acc_seg: 91.4249, loss: 0.2162
+2023-03-03 21:25:18,956 - mmseg - INFO - Iter [8800/80000]	lr: 1.500e-04, eta: 6:04:15, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2162, decode.acc_seg: 91.5798, loss: 0.2162
+2023-03-03 21:25:36,206 - mmseg - INFO - Iter [8850/80000]	lr: 1.500e-04, eta: 6:04:15, time: 0.345, data_time: 0.054, memory: 39544, decode.loss_ce: 0.2123, decode.acc_seg: 91.6778, loss: 0.2123
+2023-03-03 21:25:50,729 - mmseg - INFO - Iter [8900/80000]	lr: 1.500e-04, eta: 6:03:53, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2198, decode.acc_seg: 91.3897, loss: 0.2198
+2023-03-03 21:26:05,291 - mmseg - INFO - Iter [8950/80000]	lr: 1.500e-04, eta: 6:03:31, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2081, decode.acc_seg: 91.7920, loss: 0.2081
+2023-03-03 21:26:19,777 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 21:26:19,777 - mmseg - INFO - Iter [9000/80000]	lr: 1.500e-04, eta: 6:03:09, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2147, decode.acc_seg: 91.6088, loss: 0.2147
+2023-03-03 21:26:34,187 - mmseg - INFO - Iter [9050/80000]	lr: 1.500e-04, eta: 6:02:47, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2059, decode.acc_seg: 91.8584, loss: 0.2059
+2023-03-03 21:26:48,678 - mmseg - INFO - Iter [9100/80000]	lr: 1.500e-04, eta: 6:02:25, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2102, decode.acc_seg: 91.6377, loss: 0.2102
+2023-03-03 21:27:03,195 - mmseg - INFO - Iter [9150/80000]	lr: 1.500e-04, eta: 6:02:03, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2067, decode.acc_seg: 91.7695, loss: 0.2067
+2023-03-03 21:27:17,652 - mmseg - INFO - Iter [9200/80000]	lr: 1.500e-04, eta: 6:01:41, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2065, decode.acc_seg: 91.8292, loss: 0.2065
+2023-03-03 21:27:32,181 - mmseg - INFO - Iter [9250/80000]	lr: 1.500e-04, eta: 6:01:19, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2176, decode.acc_seg: 91.5797, loss: 0.2176
+2023-03-03 21:27:46,643 - mmseg - INFO - Iter [9300/80000]	lr: 1.500e-04, eta: 6:00:58, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2022, decode.acc_seg: 91.9443, loss: 0.2022
+2023-03-03 21:28:01,236 - mmseg - INFO - Iter [9350/80000]	lr: 1.500e-04, eta: 6:00:37, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2166, decode.acc_seg: 91.4460, loss: 0.2166
+2023-03-03 21:28:15,669 - mmseg - INFO - Iter [9400/80000]	lr: 1.500e-04, eta: 6:00:15, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2142, decode.acc_seg: 91.4547, loss: 0.2142
+2023-03-03 21:28:30,207 - mmseg - INFO - Iter [9450/80000]	lr: 1.500e-04, eta: 5:59:54, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2087, decode.acc_seg: 91.6430, loss: 0.2087
+2023-03-03 21:28:47,435 - mmseg - INFO - Iter [9500/80000]	lr: 1.500e-04, eta: 5:59:53, time: 0.345, data_time: 0.053, memory: 39544, decode.loss_ce: 0.2059, decode.acc_seg: 91.7189, loss: 0.2059
+2023-03-03 21:29:01,934 - mmseg - INFO - Iter [9550/80000]	lr: 1.500e-04, eta: 5:59:31, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2140, decode.acc_seg: 91.6229, loss: 0.2140
+2023-03-03 21:29:16,433 - mmseg - INFO - Iter [9600/80000]	lr: 1.500e-04, eta: 5:59:10, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2103, decode.acc_seg: 91.6863, loss: 0.2103
+2023-03-03 21:29:30,911 - mmseg - INFO - Iter [9650/80000]	lr: 1.500e-04, eta: 5:58:49, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2235, decode.acc_seg: 91.3956, loss: 0.2235
+2023-03-03 21:29:45,482 - mmseg - INFO - Iter [9700/80000]	lr: 1.500e-04, eta: 5:58:28, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2096, decode.acc_seg: 91.5597, loss: 0.2096
+2023-03-03 21:29:59,996 - mmseg - INFO - Iter [9750/80000]	lr: 1.500e-04, eta: 5:58:07, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2058, decode.acc_seg: 91.8324, loss: 0.2058
+2023-03-03 21:30:14,482 - mmseg - INFO - Iter [9800/80000]	lr: 1.500e-04, eta: 5:57:46, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2124, decode.acc_seg: 91.5833, loss: 0.2124
+2023-03-03 21:30:28,904 - mmseg - INFO - Iter [9850/80000]	lr: 1.500e-04, eta: 5:57:25, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2044, decode.acc_seg: 91.9166, loss: 0.2044
+2023-03-03 21:30:43,491 - mmseg - INFO - Iter [9900/80000]	lr: 1.500e-04, eta: 5:57:05, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2125, decode.acc_seg: 91.8247, loss: 0.2125
+2023-03-03 21:30:57,968 - mmseg - INFO - Iter [9950/80000]	lr: 1.500e-04, eta: 5:56:44, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2130, decode.acc_seg: 91.6133, loss: 0.2130
+2023-03-03 21:31:12,467 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 21:31:12,468 - mmseg - INFO - Iter [10000/80000]	lr: 1.500e-04, eta: 5:56:23, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2123, decode.acc_seg: 91.6285, loss: 0.2123
+2023-03-03 21:31:27,053 - mmseg - INFO - Iter [10050/80000]	lr: 7.500e-05, eta: 5:56:03, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2019, decode.acc_seg: 92.0249, loss: 0.2019
+2023-03-03 21:31:44,017 - mmseg - INFO - Iter [10100/80000]	lr: 7.500e-05, eta: 5:55:59, time: 0.339, data_time: 0.053, memory: 39544, decode.loss_ce: 0.2132, decode.acc_seg: 91.4259, loss: 0.2132
+2023-03-03 21:31:58,536 - mmseg - INFO - Iter [10150/80000]	lr: 7.500e-05, eta: 5:55:39, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2048, decode.acc_seg: 91.8665, loss: 0.2048
+2023-03-03 21:32:13,020 - mmseg - INFO - Iter [10200/80000]	lr: 7.500e-05, eta: 5:55:18, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2045, decode.acc_seg: 91.8324, loss: 0.2045
+2023-03-03 21:32:27,576 - mmseg - INFO - Iter [10250/80000]	lr: 7.500e-05, eta: 5:54:58, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2120, decode.acc_seg: 91.5136, loss: 0.2120
+2023-03-03 21:32:42,219 - mmseg - INFO - Iter [10300/80000]	lr: 7.500e-05, eta: 5:54:38, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2050, decode.acc_seg: 91.7615, loss: 0.2050
+2023-03-03 21:32:56,706 - mmseg - INFO - Iter [10350/80000]	lr: 7.500e-05, eta: 5:54:18, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2062, decode.acc_seg: 91.6581, loss: 0.2062
+2023-03-03 21:33:11,316 - mmseg - INFO - Iter [10400/80000]	lr: 7.500e-05, eta: 5:53:58, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2148, decode.acc_seg: 91.4726, loss: 0.2148
+2023-03-03 21:33:25,942 - mmseg - INFO - Iter [10450/80000]	lr: 7.500e-05, eta: 5:53:39, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1957, decode.acc_seg: 92.0942, loss: 0.1957
+2023-03-03 21:33:40,391 - mmseg - INFO - Iter [10500/80000]	lr: 7.500e-05, eta: 5:53:18, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2092, decode.acc_seg: 91.6516, loss: 0.2092
+2023-03-03 21:33:54,855 - mmseg - INFO - Iter [10550/80000]	lr: 7.500e-05, eta: 5:52:58, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2039, decode.acc_seg: 91.8155, loss: 0.2039
+2023-03-03 21:34:09,364 - mmseg - INFO - Iter [10600/80000]	lr: 7.500e-05, eta: 5:52:38, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1996, decode.acc_seg: 92.0943, loss: 0.1996
+2023-03-03 21:34:23,923 - mmseg - INFO - Iter [10650/80000]	lr: 7.500e-05, eta: 5:52:18, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2074, decode.acc_seg: 91.8210, loss: 0.2074
+2023-03-03 21:34:38,386 - mmseg - INFO - Iter [10700/80000]	lr: 7.500e-05, eta: 5:51:58, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1993, decode.acc_seg: 91.9881, loss: 0.1993
+2023-03-03 21:34:55,387 - mmseg - INFO - Iter [10750/80000]	lr: 7.500e-05, eta: 5:51:54, time: 0.340, data_time: 0.055, memory: 39544, decode.loss_ce: 0.1984, decode.acc_seg: 92.0703, loss: 0.1984
+2023-03-03 21:35:09,915 - mmseg - INFO - Iter [10800/80000]	lr: 7.500e-05, eta: 5:51:34, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2082, decode.acc_seg: 91.6743, loss: 0.2082
+2023-03-03 21:35:24,559 - mmseg - INFO - Iter [10850/80000]	lr: 7.500e-05, eta: 5:51:15, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2073, decode.acc_seg: 91.8134, loss: 0.2073
+2023-03-03 21:35:38,969 - mmseg - INFO - Iter [10900/80000]	lr: 7.500e-05, eta: 5:50:54, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1968, decode.acc_seg: 92.0870, loss: 0.1968
+2023-03-03 21:35:53,436 - mmseg - INFO - Iter [10950/80000]	lr: 7.500e-05, eta: 5:50:34, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2027, decode.acc_seg: 91.8560, loss: 0.2027
+2023-03-03 21:36:08,018 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 21:36:08,019 - mmseg - INFO - Iter [11000/80000]	lr: 7.500e-05, eta: 5:50:15, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2098, decode.acc_seg: 91.8430, loss: 0.2098
+2023-03-03 21:36:22,472 - mmseg - INFO - Iter [11050/80000]	lr: 7.500e-05, eta: 5:49:55, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2006, decode.acc_seg: 92.0045, loss: 0.2006
+2023-03-03 21:36:36,939 - mmseg - INFO - Iter [11100/80000]	lr: 7.500e-05, eta: 5:49:35, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2010, decode.acc_seg: 92.0507, loss: 0.2010
+2023-03-03 21:36:51,389 - mmseg - INFO - Iter [11150/80000]	lr: 7.500e-05, eta: 5:49:15, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2153, decode.acc_seg: 91.6129, loss: 0.2153
+2023-03-03 21:37:05,814 - mmseg - INFO - Iter [11200/80000]	lr: 7.500e-05, eta: 5:48:55, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2103, decode.acc_seg: 91.7240, loss: 0.2103
+2023-03-03 21:37:20,403 - mmseg - INFO - Iter [11250/80000]	lr: 7.500e-05, eta: 5:48:36, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2060, decode.acc_seg: 91.7105, loss: 0.2060
+2023-03-03 21:37:34,896 - mmseg - INFO - Iter [11300/80000]	lr: 7.500e-05, eta: 5:48:16, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1988, decode.acc_seg: 92.0135, loss: 0.1988
+2023-03-03 21:37:49,302 - mmseg - INFO - Iter [11350/80000]	lr: 7.500e-05, eta: 5:47:56, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2058, decode.acc_seg: 91.6465, loss: 0.2058
+2023-03-03 21:38:06,306 - mmseg - INFO - Iter [11400/80000]	lr: 7.500e-05, eta: 5:47:52, time: 0.340, data_time: 0.054, memory: 39544, decode.loss_ce: 0.2003, decode.acc_seg: 91.9222, loss: 0.2003
+2023-03-03 21:38:20,758 - mmseg - INFO - Iter [11450/80000]	lr: 7.500e-05, eta: 5:47:32, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1999, decode.acc_seg: 91.9795, loss: 0.1999
+2023-03-03 21:38:35,244 - mmseg - INFO - Iter [11500/80000]	lr: 7.500e-05, eta: 5:47:13, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2091, decode.acc_seg: 91.6926, loss: 0.2091
+2023-03-03 21:38:49,876 - mmseg - INFO - Iter [11550/80000]	lr: 7.500e-05, eta: 5:46:54, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2064, decode.acc_seg: 91.8007, loss: 0.2064
+2023-03-03 21:39:04,342 - mmseg - INFO - Iter [11600/80000]	lr: 7.500e-05, eta: 5:46:34, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2032, decode.acc_seg: 91.9245, loss: 0.2032
+2023-03-03 21:39:18,898 - mmseg - INFO - Iter [11650/80000]	lr: 7.500e-05, eta: 5:46:15, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2049, decode.acc_seg: 91.6981, loss: 0.2049
+2023-03-03 21:39:33,580 - mmseg - INFO - Iter [11700/80000]	lr: 7.500e-05, eta: 5:45:57, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2039, decode.acc_seg: 91.8719, loss: 0.2039
+2023-03-03 21:39:48,086 - mmseg - INFO - Iter [11750/80000]	lr: 7.500e-05, eta: 5:45:38, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2089, decode.acc_seg: 91.5628, loss: 0.2089
+2023-03-03 21:40:02,534 - mmseg - INFO - Iter [11800/80000]	lr: 7.500e-05, eta: 5:45:19, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1952, decode.acc_seg: 92.0432, loss: 0.1952
+2023-03-03 21:40:16,958 - mmseg - INFO - Iter [11850/80000]	lr: 7.500e-05, eta: 5:44:59, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1976, decode.acc_seg: 92.0216, loss: 0.1976
+2023-03-03 21:40:31,427 - mmseg - INFO - Iter [11900/80000]	lr: 7.500e-05, eta: 5:44:40, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2032, decode.acc_seg: 91.9131, loss: 0.2032
+2023-03-03 21:40:45,872 - mmseg - INFO - Iter [11950/80000]	lr: 7.500e-05, eta: 5:44:20, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2030, decode.acc_seg: 91.9689, loss: 0.2030
+2023-03-03 21:41:02,915 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 21:41:02,916 - mmseg - INFO - Iter [12000/80000]	lr: 7.500e-05, eta: 5:44:16, time: 0.341, data_time: 0.055, memory: 39544, decode.loss_ce: 0.2015, decode.acc_seg: 91.8195, loss: 0.2015
+2023-03-03 21:41:17,572 - mmseg - INFO - Iter [12050/80000]	lr: 7.500e-05, eta: 5:43:57, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2040, decode.acc_seg: 91.8912, loss: 0.2040
+2023-03-03 21:41:32,098 - mmseg - INFO - Iter [12100/80000]	lr: 7.500e-05, eta: 5:43:39, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1985, decode.acc_seg: 92.0229, loss: 0.1985
+2023-03-03 21:41:46,523 - mmseg - INFO - Iter [12150/80000]	lr: 7.500e-05, eta: 5:43:19, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2030, decode.acc_seg: 91.9691, loss: 0.2030
+2023-03-03 21:42:01,049 - mmseg - INFO - Iter [12200/80000]	lr: 7.500e-05, eta: 5:43:00, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2016, decode.acc_seg: 91.7839, loss: 0.2016
+2023-03-03 21:42:15,512 - mmseg - INFO - Iter [12250/80000]	lr: 7.500e-05, eta: 5:42:41, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2045, decode.acc_seg: 91.7619, loss: 0.2045
+2023-03-03 21:42:30,112 - mmseg - INFO - Iter [12300/80000]	lr: 7.500e-05, eta: 5:42:23, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2096, decode.acc_seg: 91.7703, loss: 0.2096
+2023-03-03 21:42:44,587 - mmseg - INFO - Iter [12350/80000]	lr: 7.500e-05, eta: 5:42:04, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2083, decode.acc_seg: 91.7495, loss: 0.2083
+2023-03-03 21:42:58,998 - mmseg - INFO - Iter [12400/80000]	lr: 7.500e-05, eta: 5:41:45, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2061, decode.acc_seg: 91.8853, loss: 0.2061
+2023-03-03 21:43:13,646 - mmseg - INFO - Iter [12450/80000]	lr: 7.500e-05, eta: 5:41:27, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2051, decode.acc_seg: 91.8122, loss: 0.2051
+2023-03-03 21:43:28,180 - mmseg - INFO - Iter [12500/80000]	lr: 7.500e-05, eta: 5:41:08, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2048, decode.acc_seg: 91.9464, loss: 0.2048
+2023-03-03 21:43:42,684 - mmseg - INFO - Iter [12550/80000]	lr: 7.500e-05, eta: 5:40:49, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2036, decode.acc_seg: 91.7655, loss: 0.2036
+2023-03-03 21:43:57,152 - mmseg - INFO - Iter [12600/80000]	lr: 7.500e-05, eta: 5:40:30, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2080, decode.acc_seg: 91.6065, loss: 0.2080
+2023-03-03 21:44:14,221 - mmseg - INFO - Iter [12650/80000]	lr: 7.500e-05, eta: 5:40:25, time: 0.341, data_time: 0.055, memory: 39544, decode.loss_ce: 0.2032, decode.acc_seg: 91.8515, loss: 0.2032
+2023-03-03 21:44:28,740 - mmseg - INFO - Iter [12700/80000]	lr: 7.500e-05, eta: 5:40:07, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2044, decode.acc_seg: 91.8342, loss: 0.2044
+2023-03-03 21:44:43,328 - mmseg - INFO - Iter [12750/80000]	lr: 7.500e-05, eta: 5:39:49, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2005, decode.acc_seg: 91.8831, loss: 0.2005
+2023-03-03 21:44:58,002 - mmseg - INFO - Iter [12800/80000]	lr: 7.500e-05, eta: 5:39:31, time: 0.294, data_time: 0.008, memory: 39544, decode.loss_ce: 0.2052, decode.acc_seg: 91.6378, loss: 0.2052
+2023-03-03 21:45:12,518 - mmseg - INFO - Iter [12850/80000]	lr: 7.500e-05, eta: 5:39:12, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2026, decode.acc_seg: 91.9428, loss: 0.2026
+2023-03-03 21:45:26,978 - mmseg - INFO - Iter [12900/80000]	lr: 7.500e-05, eta: 5:38:54, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2117, decode.acc_seg: 91.6708, loss: 0.2117
+2023-03-03 21:45:41,513 - mmseg - INFO - Iter [12950/80000]	lr: 7.500e-05, eta: 5:38:35, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2049, decode.acc_seg: 91.8596, loss: 0.2049
+2023-03-03 21:45:55,915 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 21:45:55,915 - mmseg - INFO - Iter [13000/80000]	lr: 7.500e-05, eta: 5:38:16, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2104, decode.acc_seg: 91.8108, loss: 0.2104
+2023-03-03 21:46:10,461 - mmseg - INFO - Iter [13050/80000]	lr: 7.500e-05, eta: 5:37:58, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2021, decode.acc_seg: 91.7605, loss: 0.2021
+2023-03-03 21:46:24,896 - mmseg - INFO - Iter [13100/80000]	lr: 7.500e-05, eta: 5:37:39, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2007, decode.acc_seg: 92.0236, loss: 0.2007
+2023-03-03 21:46:39,340 - mmseg - INFO - Iter [13150/80000]	lr: 7.500e-05, eta: 5:37:21, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1962, decode.acc_seg: 92.1209, loss: 0.1962
+2023-03-03 21:46:53,857 - mmseg - INFO - Iter [13200/80000]	lr: 7.500e-05, eta: 5:37:02, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2024, decode.acc_seg: 91.8396, loss: 0.2024
+2023-03-03 21:47:08,245 - mmseg - INFO - Iter [13250/80000]	lr: 7.500e-05, eta: 5:36:43, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1990, decode.acc_seg: 92.1231, loss: 0.1990
+2023-03-03 21:47:25,322 - mmseg - INFO - Iter [13300/80000]	lr: 7.500e-05, eta: 5:36:38, time: 0.342, data_time: 0.054, memory: 39544, decode.loss_ce: 0.2013, decode.acc_seg: 92.0844, loss: 0.2013
+2023-03-03 21:47:39,783 - mmseg - INFO - Iter [13350/80000]	lr: 7.500e-05, eta: 5:36:20, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2001, decode.acc_seg: 91.8964, loss: 0.2001
+2023-03-03 21:47:54,680 - mmseg - INFO - Iter [13400/80000]	lr: 7.500e-05, eta: 5:36:03, time: 0.298, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2044, decode.acc_seg: 91.8869, loss: 0.2044
+2023-03-03 21:48:09,203 - mmseg - INFO - Iter [13450/80000]	lr: 7.500e-05, eta: 5:35:45, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2095, decode.acc_seg: 91.6851, loss: 0.2095
+2023-03-03 21:48:23,766 - mmseg - INFO - Iter [13500/80000]	lr: 7.500e-05, eta: 5:35:27, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2022, decode.acc_seg: 91.9914, loss: 0.2022
+2023-03-03 21:48:38,161 - mmseg - INFO - Iter [13550/80000]	lr: 7.500e-05, eta: 5:35:08, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1995, decode.acc_seg: 91.9260, loss: 0.1995
+2023-03-03 21:48:52,634 - mmseg - INFO - Iter [13600/80000]	lr: 7.500e-05, eta: 5:34:50, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1985, decode.acc_seg: 92.0415, loss: 0.1985
+2023-03-03 21:49:07,227 - mmseg - INFO - Iter [13650/80000]	lr: 7.500e-05, eta: 5:34:32, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2113, decode.acc_seg: 91.4441, loss: 0.2113
+2023-03-03 21:49:21,792 - mmseg - INFO - Iter [13700/80000]	lr: 7.500e-05, eta: 5:34:14, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1950, decode.acc_seg: 92.1657, loss: 0.1950
+2023-03-03 21:49:36,352 - mmseg - INFO - Iter [13750/80000]	lr: 7.500e-05, eta: 5:33:57, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1995, decode.acc_seg: 92.0858, loss: 0.1995
+2023-03-03 21:49:50,869 - mmseg - INFO - Iter [13800/80000]	lr: 7.500e-05, eta: 5:33:39, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1985, decode.acc_seg: 92.0113, loss: 0.1985
+2023-03-03 21:50:05,344 - mmseg - INFO - Iter [13850/80000]	lr: 7.500e-05, eta: 5:33:20, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2053, decode.acc_seg: 91.9028, loss: 0.2053
+2023-03-03 21:50:22,478 - mmseg - INFO - Iter [13900/80000]	lr: 7.500e-05, eta: 5:33:15, time: 0.343, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1951, decode.acc_seg: 91.9552, loss: 0.1951
+2023-03-03 21:50:37,086 - mmseg - INFO - Iter [13950/80000]	lr: 7.500e-05, eta: 5:32:57, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1967, decode.acc_seg: 92.0424, loss: 0.1967
+2023-03-03 21:50:51,742 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 21:50:51,743 - mmseg - INFO - Iter [14000/80000]	lr: 7.500e-05, eta: 5:32:40, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1981, decode.acc_seg: 92.1216, loss: 0.1981
+2023-03-03 21:51:06,248 - mmseg - INFO - Iter [14050/80000]	lr: 7.500e-05, eta: 5:32:22, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2030, decode.acc_seg: 91.9819, loss: 0.2030
+2023-03-03 21:51:20,794 - mmseg - INFO - Iter [14100/80000]	lr: 7.500e-05, eta: 5:32:04, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2112, decode.acc_seg: 91.5844, loss: 0.2112
+2023-03-03 21:51:35,421 - mmseg - INFO - Iter [14150/80000]	lr: 7.500e-05, eta: 5:31:47, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2062, decode.acc_seg: 91.8496, loss: 0.2062
+2023-03-03 21:51:49,848 - mmseg - INFO - Iter [14200/80000]	lr: 7.500e-05, eta: 5:31:28, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2067, decode.acc_seg: 91.6899, loss: 0.2067
+2023-03-03 21:52:04,267 - mmseg - INFO - Iter [14250/80000]	lr: 7.500e-05, eta: 5:31:10, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2045, decode.acc_seg: 91.8968, loss: 0.2045
+2023-03-03 21:52:18,765 - mmseg - INFO - Iter [14300/80000]	lr: 7.500e-05, eta: 5:30:52, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2016, decode.acc_seg: 91.8261, loss: 0.2016
+2023-03-03 21:52:33,370 - mmseg - INFO - Iter [14350/80000]	lr: 7.500e-05, eta: 5:30:35, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2082, decode.acc_seg: 91.8238, loss: 0.2082
+2023-03-03 21:52:47,818 - mmseg - INFO - Iter [14400/80000]	lr: 7.500e-05, eta: 5:30:17, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1998, decode.acc_seg: 92.0505, loss: 0.1998
+2023-03-03 21:53:02,221 - mmseg - INFO - Iter [14450/80000]	lr: 7.500e-05, eta: 5:29:58, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1997, decode.acc_seg: 91.9397, loss: 0.1997
+2023-03-03 21:53:16,921 - mmseg - INFO - Iter [14500/80000]	lr: 7.500e-05, eta: 5:29:41, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2072, decode.acc_seg: 91.5899, loss: 0.2072
+2023-03-03 21:53:34,019 - mmseg - INFO - Iter [14550/80000]	lr: 7.500e-05, eta: 5:29:35, time: 0.342, data_time: 0.055, memory: 39544, decode.loss_ce: 0.2021, decode.acc_seg: 91.9634, loss: 0.2021
+2023-03-03 21:53:48,601 - mmseg - INFO - Iter [14600/80000]	lr: 7.500e-05, eta: 5:29:18, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1946, decode.acc_seg: 92.1456, loss: 0.1946
+2023-03-03 21:54:03,045 - mmseg - INFO - Iter [14650/80000]	lr: 7.500e-05, eta: 5:29:00, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2025, decode.acc_seg: 91.9019, loss: 0.2025
+2023-03-03 21:54:17,539 - mmseg - INFO - Iter [14700/80000]	lr: 7.500e-05, eta: 5:28:42, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1981, decode.acc_seg: 91.9798, loss: 0.1981
+2023-03-03 21:54:32,211 - mmseg - INFO - Iter [14750/80000]	lr: 7.500e-05, eta: 5:28:25, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2005, decode.acc_seg: 92.0032, loss: 0.2005
+2023-03-03 21:54:46,694 - mmseg - INFO - Iter [14800/80000]	lr: 7.500e-05, eta: 5:28:07, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1983, decode.acc_seg: 92.0224, loss: 0.1983
+2023-03-03 21:55:01,241 - mmseg - INFO - Iter [14850/80000]	lr: 7.500e-05, eta: 5:27:50, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2089, decode.acc_seg: 91.4877, loss: 0.2089
+2023-03-03 21:55:15,661 - mmseg - INFO - Iter [14900/80000]	lr: 7.500e-05, eta: 5:27:32, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2047, decode.acc_seg: 91.6465, loss: 0.2047
+2023-03-03 21:55:30,193 - mmseg - INFO - Iter [14950/80000]	lr: 7.500e-05, eta: 5:27:14, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2021, decode.acc_seg: 91.9273, loss: 0.2021
+2023-03-03 21:55:44,774 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 21:55:44,775 - mmseg - INFO - Iter [15000/80000]	lr: 7.500e-05, eta: 5:26:57, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2078, decode.acc_seg: 91.8577, loss: 0.2078
+2023-03-03 21:55:59,216 - mmseg - INFO - Iter [15050/80000]	lr: 7.500e-05, eta: 5:26:39, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2092, decode.acc_seg: 91.8085, loss: 0.2092
+2023-03-03 21:56:13,660 - mmseg - INFO - Iter [15100/80000]	lr: 7.500e-05, eta: 5:26:21, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2058, decode.acc_seg: 91.9337, loss: 0.2058
+2023-03-03 21:56:30,730 - mmseg - INFO - Iter [15150/80000]	lr: 7.500e-05, eta: 5:26:14, time: 0.341, data_time: 0.057, memory: 39544, decode.loss_ce: 0.1958, decode.acc_seg: 92.1912, loss: 0.1958
+2023-03-03 21:56:45,250 - mmseg - INFO - Iter [15200/80000]	lr: 7.500e-05, eta: 5:25:57, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2033, decode.acc_seg: 92.0194, loss: 0.2033
+2023-03-03 21:57:00,037 - mmseg - INFO - Iter [15250/80000]	lr: 7.500e-05, eta: 5:25:40, time: 0.296, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1955, decode.acc_seg: 92.1500, loss: 0.1955
+2023-03-03 21:57:14,589 - mmseg - INFO - Iter [15300/80000]	lr: 7.500e-05, eta: 5:25:23, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2051, decode.acc_seg: 91.9107, loss: 0.2051
+2023-03-03 21:57:29,194 - mmseg - INFO - Iter [15350/80000]	lr: 7.500e-05, eta: 5:25:06, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2052, decode.acc_seg: 91.7373, loss: 0.2052
+2023-03-03 21:57:43,619 - mmseg - INFO - Iter [15400/80000]	lr: 7.500e-05, eta: 5:24:48, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2100, decode.acc_seg: 91.6451, loss: 0.2100
+2023-03-03 21:57:58,184 - mmseg - INFO - Iter [15450/80000]	lr: 7.500e-05, eta: 5:24:31, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2067, decode.acc_seg: 91.6714, loss: 0.2067
+2023-03-03 21:58:12,612 - mmseg - INFO - Iter [15500/80000]	lr: 7.500e-05, eta: 5:24:13, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2049, decode.acc_seg: 91.7628, loss: 0.2049
+2023-03-03 21:58:27,154 - mmseg - INFO - Iter [15550/80000]	lr: 7.500e-05, eta: 5:23:56, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2048, decode.acc_seg: 91.7980, loss: 0.2048
+2023-03-03 21:58:41,619 - mmseg - INFO - Iter [15600/80000]	lr: 7.500e-05, eta: 5:23:38, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1996, decode.acc_seg: 92.1017, loss: 0.1996
+2023-03-03 21:58:56,091 - mmseg - INFO - Iter [15650/80000]	lr: 7.500e-05, eta: 5:23:21, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2000, decode.acc_seg: 91.8750, loss: 0.2000
+2023-03-03 21:59:10,624 - mmseg - INFO - Iter [15700/80000]	lr: 7.500e-05, eta: 5:23:03, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2099, decode.acc_seg: 91.6794, loss: 0.2099
+2023-03-03 21:59:25,147 - mmseg - INFO - Iter [15750/80000]	lr: 7.500e-05, eta: 5:22:46, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2052, decode.acc_seg: 91.9921, loss: 0.2052
+2023-03-03 21:59:42,230 - mmseg - INFO - Iter [15800/80000]	lr: 7.500e-05, eta: 5:22:39, time: 0.342, data_time: 0.053, memory: 39544, decode.loss_ce: 0.2018, decode.acc_seg: 91.9128, loss: 0.2018
+2023-03-03 21:59:56,744 - mmseg - INFO - Iter [15850/80000]	lr: 7.500e-05, eta: 5:22:22, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2043, decode.acc_seg: 91.9814, loss: 0.2043
+2023-03-03 22:00:11,305 - mmseg - INFO - Iter [15900/80000]	lr: 7.500e-05, eta: 5:22:05, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2042, decode.acc_seg: 91.7338, loss: 0.2042
+2023-03-03 22:00:25,931 - mmseg - INFO - Iter [15950/80000]	lr: 7.500e-05, eta: 5:21:48, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2102, decode.acc_seg: 91.5328, loss: 0.2102
+2023-03-03 22:00:40,503 - mmseg - INFO - Saving checkpoint at 16000 iterations
+2023-03-03 22:00:42,683 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 22:00:42,684 - mmseg - INFO - Iter [16000/80000]	lr: 7.500e-05, eta: 5:21:39, time: 0.335, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1995, decode.acc_seg: 91.9435, loss: 0.1995
+2023-03-03 22:01:02,615 - mmseg - INFO - per class results:
+2023-03-03 22:01:02,621 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        | 75.59 | 88.73 |
+|       building      |  81.0 | 91.97 |
+|         sky         | 94.08 |  96.7 |
+|        floor        | 79.93 | 90.82 |
+|         tree        | 71.83 | 88.75 |
+|       ceiling       | 81.54 | 87.83 |
+|         road        | 81.61 | 88.88 |
+|         bed         | 87.66 | 95.21 |
+|      windowpane     | 60.58 | 76.69 |
+|        grass        |  67.1 | 82.04 |
+|       cabinet       | 59.11 |  69.1 |
+|       sidewalk      | 65.24 | 81.49 |
+|        person       | 78.82 | 91.52 |
+|        earth        | 33.52 |  50.4 |
+|         door        |  47.1 | 64.92 |
+|        table        | 59.58 |  79.9 |
+|       mountain      | 51.18 | 70.31 |
+|        plant        | 49.81 | 63.17 |
+|       curtain       |  69.3 | 82.45 |
+|        chair        | 57.12 | 72.06 |
+|         car         | 82.67 | 90.54 |
+|        water        | 47.58 | 63.03 |
+|       painting      | 68.31 | 84.22 |
+|         sofa        | 64.94 |  84.4 |
+|        shelf        | 39.18 | 55.09 |
+|        house        | 42.78 | 51.21 |
+|         sea         | 42.94 | 66.73 |
+|        mirror       | 63.76 | 71.32 |
+|         rug         | 54.57 | 59.82 |
+|        field        | 25.76 | 35.18 |
+|       armchair      | 41.03 | 50.18 |
+|         seat        | 53.59 | 76.73 |
+|        fence        | 39.84 | 54.91 |
+|         desk        | 48.81 | 65.93 |
+|         rock        | 28.25 | 43.72 |
+|       wardrobe      | 47.89 | 65.17 |
+|         lamp        | 63.33 | 73.91 |
+|       bathtub       | 73.94 | 79.21 |
+|       railing       | 29.05 | 37.38 |
+|       cushion       | 54.36 | 71.25 |
+|         base        | 26.89 | 38.66 |
+|         box         |  23.3 | 31.25 |
+|        column       |  43.6 | 52.99 |
+|      signboard      | 33.85 | 49.36 |
+|   chest of drawers  | 40.57 |  60.8 |
+|       counter       | 25.29 | 33.72 |
+|         sand        | 33.11 | 46.45 |
+|         sink        | 69.63 | 80.13 |
+|      skyscraper     | 47.41 | 53.17 |
+|      fireplace      |  66.2 |  83.2 |
+|     refrigerator    | 75.61 | 83.29 |
+|      grandstand     | 42.26 | 65.66 |
+|         path        |  17.8 | 25.03 |
+|        stairs       | 30.43 | 47.77 |
+|        runway       | 62.87 | 80.73 |
+|         case        | 48.16 | 67.57 |
+|      pool table     | 92.49 | 95.37 |
+|        pillow       | 51.83 | 58.29 |
+|     screen door     | 49.95 | 53.83 |
+|       stairway      | 26.76 | 31.52 |
+|        river        |  9.75 | 18.41 |
+|        bridge       | 46.16 | 49.87 |
+|       bookcase      | 40.21 |  54.5 |
+|        blind        | 47.84 | 56.55 |
+|     coffee table    | 65.05 | 82.29 |
+|        toilet       | 85.91 | 90.36 |
+|        flower       | 32.02 | 47.67 |
+|         book        | 42.57 | 71.03 |
+|         hill        |  8.02 |  9.58 |
+|        bench        | 44.26 | 51.26 |
+|      countertop     | 52.36 | 69.43 |
+|        stove        | 73.46 | 79.12 |
+|         palm        | 49.73 | 71.19 |
+|    kitchen island   | 46.35 |  67.6 |
+|       computer      | 56.39 | 64.55 |
+|     swivel chair    | 40.79 | 51.02 |
+|         boat        | 40.45 | 44.54 |
+|         bar         | 26.33 | 28.98 |
+|    arcade machine   | 22.77 | 23.75 |
+|        hovel        | 31.72 | 35.68 |
+|         bus         | 87.84 | 91.89 |
+|        towel        | 56.81 | 64.51 |
+|        light        | 56.16 | 67.92 |
+|        truck        | 31.48 | 38.43 |
+|        tower        | 24.43 | 35.02 |
+|      chandelier     | 65.93 | 83.17 |
+|        awning       | 22.98 | 25.83 |
+|     streetlight     | 27.95 | 38.05 |
+|        booth        | 47.96 | 49.55 |
+| television receiver | 68.52 | 79.77 |
+|       airplane      | 52.43 | 65.52 |
+|      dirt track     |  9.43 | 18.43 |
+|       apparel       | 28.83 | 47.03 |
+|         pole        | 23.82 | 34.21 |
+|         land        | 11.03 | 17.62 |
+|      bannister      |  4.68 |  6.8  |
+|      escalator      | 23.86 | 25.13 |
+|       ottoman       | 47.06 | 55.59 |
+|        bottle       | 15.85 | 24.75 |
+|        buffet       | 56.99 | 69.76 |
+|        poster       | 27.55 | 35.64 |
+|        stage        |  17.6 | 24.42 |
+|         van         | 45.56 | 68.25 |
+|         ship        | 25.62 | 32.76 |
+|       fountain      |  6.72 |  6.84 |
+|    conveyer belt    | 80.65 |  87.2 |
+|        canopy       | 13.06 | 14.63 |
+|        washer       | 66.37 | 66.58 |
+|      plaything      | 22.52 | 32.43 |
+|    swimming pool    | 41.46 | 48.85 |
+|        stool        | 39.44 | 57.72 |
+|        barrel       | 38.53 | 66.72 |
+|        basket       | 26.02 |  38.2 |
+|      waterfall      | 52.97 | 61.52 |
+|         tent        | 94.12 | 97.07 |
+|         bag         | 12.05 | 15.94 |
+|       minibike      | 60.47 | 75.83 |
+|        cradle       | 78.77 | 95.72 |
+|         oven        | 26.68 | 63.51 |
+|         ball        | 45.89 | 60.63 |
+|         food        | 49.82 |  56.5 |
+|         step        | 14.79 | 18.15 |
+|         tank        | 43.03 | 43.45 |
+|      trade name     | 27.86 | 37.05 |
+|      microwave      |  37.7 | 40.83 |
+|         pot         | 38.61 | 46.38 |
+|        animal       |  49.5 | 51.76 |
+|       bicycle       | 45.01 | 72.66 |
+|         lake        | 59.84 | 63.36 |
+|      dishwasher     | 76.11 | 82.03 |
+|        screen       | 58.54 | 68.71 |
+|       blanket       |  7.03 |  7.44 |
+|      sculpture      | 35.81 | 60.07 |
+|         hood        | 55.59 | 66.57 |
+|        sconce       | 41.01 |  48.8 |
+|         vase        | 36.49 |  54.2 |
+|    traffic light    | 31.17 | 49.51 |
+|         tray        |  5.37 | 11.23 |
+|        ashcan       |  38.7 | 51.92 |
+|         fan         | 55.49 | 75.42 |
+|         pier        |  9.39 | 10.17 |
+|      crt screen     |  6.0  | 22.09 |
+|        plate        | 36.59 | 46.27 |
+|       monitor       |  7.98 | 10.04 |
+|    bulletin board   | 47.92 | 55.71 |
+|        shower       |  0.63 |  1.1  |
+|       radiator      | 44.88 | 52.06 |
+|        glass        | 10.58 | 11.36 |
+|        clock        | 24.36 | 32.26 |
+|         flag        | 37.89 |  44.2 |
++---------------------+-------+-------+
+2023-03-03 22:01:02,621 - mmseg - INFO - Summary:
+2023-03-03 22:01:02,621 - mmseg - INFO - 
++------+-------+-------+
+| aAcc |  mIoU |  mAcc |
++------+-------+-------+
+| 81.5 | 45.03 | 55.87 |
++------+-------+-------+
+2023-03-03 22:01:02,686 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151/best_mIoU_iter_8000.pth was removed
+2023-03-03 22:01:04,586 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_16000.pth.
+2023-03-03 22:01:04,587 - mmseg - INFO - Best mIoU is 0.4503 at 16000 iter.
+2023-03-03 22:01:04,587 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 22:01:04,587 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8150, mIoU: 0.4503, mAcc: 0.5587, IoU.background: nan, IoU.wall: 0.7559, IoU.building: 0.8100, IoU.sky: 0.9408, IoU.floor: 0.7993, IoU.tree: 0.7183, IoU.ceiling: 0.8154, IoU.road: 0.8161, IoU.bed : 0.8766, IoU.windowpane: 0.6058, IoU.grass: 0.6710, IoU.cabinet: 0.5911, IoU.sidewalk: 0.6524, IoU.person: 0.7882, IoU.earth: 0.3352, IoU.door: 0.4710, IoU.table: 0.5958, IoU.mountain: 0.5118, IoU.plant: 0.4981, IoU.curtain: 0.6930, IoU.chair: 0.5712, IoU.car: 0.8267, IoU.water: 0.4758, IoU.painting: 0.6831, IoU.sofa: 0.6494, IoU.shelf: 0.3918, IoU.house: 0.4278, IoU.sea: 0.4294, IoU.mirror: 0.6376, IoU.rug: 0.5457, IoU.field: 0.2576, IoU.armchair: 0.4103, IoU.seat: 0.5359, IoU.fence: 0.3984, IoU.desk: 0.4881, IoU.rock: 0.2825, IoU.wardrobe: 0.4789, IoU.lamp: 0.6333, IoU.bathtub: 0.7394, IoU.railing: 0.2905, IoU.cushion: 0.5436, IoU.base: 0.2689, IoU.box: 0.2330, IoU.column: 0.4360, IoU.signboard: 0.3385, IoU.chest of drawers: 0.4057, IoU.counter: 0.2529, IoU.sand: 0.3311, IoU.sink: 0.6963, IoU.skyscraper: 0.4741, IoU.fireplace: 0.6620, IoU.refrigerator: 0.7561, IoU.grandstand: 0.4226, IoU.path: 0.1780, IoU.stairs: 0.3043, IoU.runway: 0.6287, IoU.case: 0.4816, IoU.pool table: 0.9249, IoU.pillow: 0.5183, IoU.screen door: 0.4995, IoU.stairway: 0.2676, IoU.river: 0.0975, IoU.bridge: 0.4616, IoU.bookcase: 0.4021, IoU.blind: 0.4784, IoU.coffee table: 0.6505, IoU.toilet: 0.8591, IoU.flower: 0.3202, IoU.book: 0.4257, IoU.hill: 0.0802, IoU.bench: 0.4426, IoU.countertop: 0.5236, IoU.stove: 0.7346, IoU.palm: 0.4973, IoU.kitchen island: 0.4635, IoU.computer: 0.5639, IoU.swivel chair: 0.4079, IoU.boat: 0.4045, IoU.bar: 0.2633, IoU.arcade machine: 0.2277, IoU.hovel: 0.3172, IoU.bus: 0.8784, IoU.towel: 0.5681, IoU.light: 0.5616, IoU.truck: 0.3148, IoU.tower: 0.2443, IoU.chandelier: 0.6593, IoU.awning: 0.2298, IoU.streetlight: 0.2795, IoU.booth: 0.4796, IoU.television receiver: 0.6852, IoU.airplane: 0.5243, IoU.dirt track: 0.0943, IoU.apparel: 0.2883, IoU.pole: 0.2382, IoU.land: 0.1103, IoU.bannister: 0.0468, IoU.escalator: 0.2386, IoU.ottoman: 0.4706, IoU.bottle: 0.1585, IoU.buffet: 0.5699, IoU.poster: 0.2755, IoU.stage: 0.1760, IoU.van: 0.4556, IoU.ship: 0.2562, IoU.fountain: 0.0672, IoU.conveyer belt: 0.8065, IoU.canopy: 0.1306, IoU.washer: 0.6637, IoU.plaything: 0.2252, IoU.swimming pool: 0.4146, IoU.stool: 0.3944, IoU.barrel: 0.3853, IoU.basket: 0.2602, IoU.waterfall: 0.5297, IoU.tent: 0.9412, IoU.bag: 0.1205, IoU.minibike: 0.6047, IoU.cradle: 0.7877, IoU.oven: 0.2668, IoU.ball: 0.4589, IoU.food: 0.4982, IoU.step: 0.1479, IoU.tank: 0.4303, IoU.trade name: 0.2786, IoU.microwave: 0.3770, IoU.pot: 0.3861, IoU.animal: 0.4950, IoU.bicycle: 0.4501, IoU.lake: 0.5984, IoU.dishwasher: 0.7611, IoU.screen: 0.5854, IoU.blanket: 0.0703, IoU.sculpture: 0.3581, IoU.hood: 0.5559, IoU.sconce: 0.4101, IoU.vase: 0.3649, IoU.traffic light: 0.3117, IoU.tray: 0.0537, IoU.ashcan: 0.3870, IoU.fan: 0.5549, IoU.pier: 0.0939, IoU.crt screen: 0.0600, IoU.plate: 0.3659, IoU.monitor: 0.0798, IoU.bulletin board: 0.4792, IoU.shower: 0.0063, IoU.radiator: 0.4488, IoU.glass: 0.1058, IoU.clock: 0.2436, IoU.flag: 0.3789, Acc.background: nan, Acc.wall: 0.8873, Acc.building: 0.9197, Acc.sky: 0.9670, Acc.floor: 0.9082, Acc.tree: 0.8875, Acc.ceiling: 0.8783, Acc.road: 0.8888, Acc.bed : 0.9521, Acc.windowpane: 0.7669, Acc.grass: 0.8204, Acc.cabinet: 0.6910, Acc.sidewalk: 0.8149, Acc.person: 0.9152, Acc.earth: 0.5040, Acc.door: 0.6492, Acc.table: 0.7990, Acc.mountain: 0.7031, Acc.plant: 0.6317, Acc.curtain: 0.8245, Acc.chair: 0.7206, Acc.car: 0.9054, Acc.water: 0.6303, Acc.painting: 0.8422, Acc.sofa: 0.8440, Acc.shelf: 0.5509, Acc.house: 0.5121, Acc.sea: 0.6673, Acc.mirror: 0.7132, Acc.rug: 0.5982, Acc.field: 0.3518, Acc.armchair: 0.5018, Acc.seat: 0.7673, Acc.fence: 0.5491, Acc.desk: 0.6593, Acc.rock: 0.4372, Acc.wardrobe: 0.6517, Acc.lamp: 0.7391, Acc.bathtub: 0.7921, Acc.railing: 0.3738, Acc.cushion: 0.7125, Acc.base: 0.3866, Acc.box: 0.3125, Acc.column: 0.5299, Acc.signboard: 0.4936, Acc.chest of drawers: 0.6080, Acc.counter: 0.3372, Acc.sand: 0.4645, Acc.sink: 0.8013, Acc.skyscraper: 0.5317, Acc.fireplace: 0.8320, Acc.refrigerator: 0.8329, Acc.grandstand: 0.6566, Acc.path: 0.2503, Acc.stairs: 0.4777, Acc.runway: 0.8073, Acc.case: 0.6757, Acc.pool table: 0.9537, Acc.pillow: 0.5829, Acc.screen door: 0.5383, Acc.stairway: 0.3152, Acc.river: 0.1841, Acc.bridge: 0.4987, Acc.bookcase: 0.5450, Acc.blind: 0.5655, Acc.coffee table: 0.8229, Acc.toilet: 0.9036, Acc.flower: 0.4767, Acc.book: 0.7103, Acc.hill: 0.0958, Acc.bench: 0.5126, Acc.countertop: 0.6943, Acc.stove: 0.7912, Acc.palm: 0.7119, Acc.kitchen island: 0.6760, Acc.computer: 0.6455, Acc.swivel chair: 0.5102, Acc.boat: 0.4454, Acc.bar: 0.2898, Acc.arcade machine: 0.2375, Acc.hovel: 0.3568, Acc.bus: 0.9189, Acc.towel: 0.6451, Acc.light: 0.6792, Acc.truck: 0.3843, Acc.tower: 0.3502, Acc.chandelier: 0.8317, Acc.awning: 0.2583, Acc.streetlight: 0.3805, Acc.booth: 0.4955, Acc.television receiver: 0.7977, Acc.airplane: 0.6552, Acc.dirt track: 0.1843, Acc.apparel: 0.4703, Acc.pole: 0.3421, Acc.land: 0.1762, Acc.bannister: 0.0680, Acc.escalator: 0.2513, Acc.ottoman: 0.5559, Acc.bottle: 0.2475, Acc.buffet: 0.6976, Acc.poster: 0.3564, Acc.stage: 0.2442, Acc.van: 0.6825, Acc.ship: 0.3276, Acc.fountain: 0.0684, Acc.conveyer belt: 0.8720, Acc.canopy: 0.1463, Acc.washer: 0.6658, Acc.plaything: 0.3243, Acc.swimming pool: 0.4885, Acc.stool: 0.5772, Acc.barrel: 0.6672, Acc.basket: 0.3820, Acc.waterfall: 0.6152, Acc.tent: 0.9707, Acc.bag: 0.1594, Acc.minibike: 0.7583, Acc.cradle: 0.9572, Acc.oven: 0.6351, Acc.ball: 0.6063, Acc.food: 0.5650, Acc.step: 0.1815, Acc.tank: 0.4345, Acc.trade name: 0.3705, Acc.microwave: 0.4083, Acc.pot: 0.4638, Acc.animal: 0.5176, Acc.bicycle: 0.7266, Acc.lake: 0.6336, Acc.dishwasher: 0.8203, Acc.screen: 0.6871, Acc.blanket: 0.0744, Acc.sculpture: 0.6007, Acc.hood: 0.6657, Acc.sconce: 0.4880, Acc.vase: 0.5420, Acc.traffic light: 0.4951, Acc.tray: 0.1123, Acc.ashcan: 0.5192, Acc.fan: 0.7542, Acc.pier: 0.1017, Acc.crt screen: 0.2209, Acc.plate: 0.4627, Acc.monitor: 0.1004, Acc.bulletin board: 0.5571, Acc.shower: 0.0110, Acc.radiator: 0.5206, Acc.glass: 0.1136, Acc.clock: 0.3226, Acc.flag: 0.4420
+2023-03-03 22:01:19,447 - mmseg - INFO - Iter [16050/80000]	lr: 7.500e-05, eta: 5:22:51, time: 0.735, data_time: 0.446, memory: 39544, decode.loss_ce: 0.2041, decode.acc_seg: 91.8488, loss: 0.2041
+2023-03-03 22:01:34,075 - mmseg - INFO - Iter [16100/80000]	lr: 7.500e-05, eta: 5:22:33, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2078, decode.acc_seg: 91.7118, loss: 0.2078
+2023-03-03 22:01:48,575 - mmseg - INFO - Iter [16150/80000]	lr: 7.500e-05, eta: 5:22:16, time: 0.290, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1994, decode.acc_seg: 92.0736, loss: 0.1994
+2023-03-03 22:02:03,078 - mmseg - INFO - Iter [16200/80000]	lr: 7.500e-05, eta: 5:21:58, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1958, decode.acc_seg: 92.0869, loss: 0.1958
+2023-03-03 22:02:17,550 - mmseg - INFO - Iter [16250/80000]	lr: 7.500e-05, eta: 5:21:40, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1975, decode.acc_seg: 91.9657, loss: 0.1975
+2023-03-03 22:02:32,128 - mmseg - INFO - Iter [16300/80000]	lr: 7.500e-05, eta: 5:21:23, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1956, decode.acc_seg: 92.1816, loss: 0.1956
+2023-03-03 22:02:46,590 - mmseg - INFO - Iter [16350/80000]	lr: 7.500e-05, eta: 5:21:05, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1995, decode.acc_seg: 91.9827, loss: 0.1995
+2023-03-03 22:03:00,953 - mmseg - INFO - Iter [16400/80000]	lr: 7.500e-05, eta: 5:20:47, time: 0.287, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2051, decode.acc_seg: 91.8256, loss: 0.2051
+2023-03-03 22:03:18,162 - mmseg - INFO - Iter [16450/80000]	lr: 7.500e-05, eta: 5:20:40, time: 0.344, data_time: 0.055, memory: 39544, decode.loss_ce: 0.2043, decode.acc_seg: 91.8404, loss: 0.2043
+2023-03-03 22:03:32,718 - mmseg - INFO - Iter [16500/80000]	lr: 7.500e-05, eta: 5:20:23, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1987, decode.acc_seg: 92.2215, loss: 0.1987
+2023-03-03 22:03:47,283 - mmseg - INFO - Iter [16550/80000]	lr: 7.500e-05, eta: 5:20:05, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2032, decode.acc_seg: 91.8992, loss: 0.2032
+2023-03-03 22:04:01,937 - mmseg - INFO - Iter [16600/80000]	lr: 7.500e-05, eta: 5:19:48, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2108, decode.acc_seg: 91.6330, loss: 0.2108
+2023-03-03 22:04:16,485 - mmseg - INFO - Iter [16650/80000]	lr: 7.500e-05, eta: 5:19:31, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2018, decode.acc_seg: 91.9614, loss: 0.2018
+2023-03-03 22:04:31,023 - mmseg - INFO - Iter [16700/80000]	lr: 7.500e-05, eta: 5:19:14, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2019, decode.acc_seg: 91.9573, loss: 0.2019
+2023-03-03 22:04:45,537 - mmseg - INFO - Iter [16750/80000]	lr: 7.500e-05, eta: 5:18:56, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2068, decode.acc_seg: 91.7573, loss: 0.2068
+2023-03-03 22:05:00,072 - mmseg - INFO - Iter [16800/80000]	lr: 7.500e-05, eta: 5:18:39, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1984, decode.acc_seg: 92.1655, loss: 0.1984
+2023-03-03 22:05:14,687 - mmseg - INFO - Iter [16850/80000]	lr: 7.500e-05, eta: 5:18:22, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2030, decode.acc_seg: 91.8794, loss: 0.2030
+2023-03-03 22:05:29,258 - mmseg - INFO - Iter [16900/80000]	lr: 7.500e-05, eta: 5:18:04, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2072, decode.acc_seg: 91.9572, loss: 0.2072
+2023-03-03 22:05:43,743 - mmseg - INFO - Iter [16950/80000]	lr: 7.500e-05, eta: 5:17:47, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2015, decode.acc_seg: 91.9108, loss: 0.2015
+2023-03-03 22:05:58,292 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 22:05:58,292 - mmseg - INFO - Iter [17000/80000]	lr: 7.500e-05, eta: 5:17:30, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2063, decode.acc_seg: 91.8097, loss: 0.2063
+2023-03-03 22:06:15,249 - mmseg - INFO - Iter [17050/80000]	lr: 7.500e-05, eta: 5:17:21, time: 0.339, data_time: 0.053, memory: 39544, decode.loss_ce: 0.1958, decode.acc_seg: 92.0109, loss: 0.1958
+2023-03-03 22:06:29,847 - mmseg - INFO - Iter [17100/80000]	lr: 7.500e-05, eta: 5:17:04, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2048, decode.acc_seg: 91.7931, loss: 0.2048
+2023-03-03 22:06:44,443 - mmseg - INFO - Iter [17150/80000]	lr: 7.500e-05, eta: 5:16:47, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1948, decode.acc_seg: 92.1984, loss: 0.1948
+2023-03-03 22:06:58,914 - mmseg - INFO - Iter [17200/80000]	lr: 7.500e-05, eta: 5:16:30, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1993, decode.acc_seg: 92.0203, loss: 0.1993
+2023-03-03 22:07:13,452 - mmseg - INFO - Iter [17250/80000]	lr: 7.500e-05, eta: 5:16:13, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2023, decode.acc_seg: 91.9116, loss: 0.2023
+2023-03-03 22:07:27,926 - mmseg - INFO - Iter [17300/80000]	lr: 7.500e-05, eta: 5:15:55, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2010, decode.acc_seg: 92.0184, loss: 0.2010
+2023-03-03 22:07:42,487 - mmseg - INFO - Iter [17350/80000]	lr: 7.500e-05, eta: 5:15:38, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2032, decode.acc_seg: 91.9277, loss: 0.2032
+2023-03-03 22:07:57,046 - mmseg - INFO - Iter [17400/80000]	lr: 7.500e-05, eta: 5:15:21, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1947, decode.acc_seg: 92.1621, loss: 0.1947
+2023-03-03 22:08:11,549 - mmseg - INFO - Iter [17450/80000]	lr: 7.500e-05, eta: 5:15:04, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2041, decode.acc_seg: 91.6899, loss: 0.2041
+2023-03-03 22:08:26,065 - mmseg - INFO - Iter [17500/80000]	lr: 7.500e-05, eta: 5:14:46, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2047, decode.acc_seg: 91.9340, loss: 0.2047
+2023-03-03 22:08:40,598 - mmseg - INFO - Iter [17550/80000]	lr: 7.500e-05, eta: 5:14:29, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2044, decode.acc_seg: 91.8787, loss: 0.2044
+2023-03-03 22:08:55,204 - mmseg - INFO - Iter [17600/80000]	lr: 7.500e-05, eta: 5:14:12, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1939, decode.acc_seg: 92.1525, loss: 0.1939
+2023-03-03 22:09:09,795 - mmseg - INFO - Iter [17650/80000]	lr: 7.500e-05, eta: 5:13:55, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1996, decode.acc_seg: 92.0596, loss: 0.1996
+2023-03-03 22:09:26,792 - mmseg - INFO - Iter [17700/80000]	lr: 7.500e-05, eta: 5:13:47, time: 0.340, data_time: 0.054, memory: 39544, decode.loss_ce: 0.2055, decode.acc_seg: 91.6429, loss: 0.2055
+2023-03-03 22:09:41,311 - mmseg - INFO - Iter [17750/80000]	lr: 7.500e-05, eta: 5:13:30, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1975, decode.acc_seg: 92.0760, loss: 0.1975
+2023-03-03 22:09:56,094 - mmseg - INFO - Iter [17800/80000]	lr: 7.500e-05, eta: 5:13:14, time: 0.296, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2028, decode.acc_seg: 92.0585, loss: 0.2028
+2023-03-03 22:10:10,624 - mmseg - INFO - Iter [17850/80000]	lr: 7.500e-05, eta: 5:12:56, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2052, decode.acc_seg: 91.8070, loss: 0.2052
+2023-03-03 22:10:25,072 - mmseg - INFO - Iter [17900/80000]	lr: 7.500e-05, eta: 5:12:39, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2005, decode.acc_seg: 91.9172, loss: 0.2005
+2023-03-03 22:10:39,522 - mmseg - INFO - Iter [17950/80000]	lr: 7.500e-05, eta: 5:12:22, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2044, decode.acc_seg: 91.8658, loss: 0.2044
+2023-03-03 22:10:53,999 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 22:10:53,999 - mmseg - INFO - Iter [18000/80000]	lr: 7.500e-05, eta: 5:12:04, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2071, decode.acc_seg: 91.8311, loss: 0.2071
+2023-03-03 22:11:08,465 - mmseg - INFO - Iter [18050/80000]	lr: 7.500e-05, eta: 5:11:47, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2032, decode.acc_seg: 91.8023, loss: 0.2032
+2023-03-03 22:11:23,004 - mmseg - INFO - Iter [18100/80000]	lr: 7.500e-05, eta: 5:11:30, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2015, decode.acc_seg: 91.9092, loss: 0.2015
+2023-03-03 22:11:37,645 - mmseg - INFO - Iter [18150/80000]	lr: 7.500e-05, eta: 5:11:13, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2060, decode.acc_seg: 91.7714, loss: 0.2060
+2023-03-03 22:11:52,102 - mmseg - INFO - Iter [18200/80000]	lr: 7.500e-05, eta: 5:10:56, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2036, decode.acc_seg: 91.8096, loss: 0.2036
+2023-03-03 22:12:06,656 - mmseg - INFO - Iter [18250/80000]	lr: 7.500e-05, eta: 5:10:39, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2036, decode.acc_seg: 91.7492, loss: 0.2036
+2023-03-03 22:12:23,813 - mmseg - INFO - Iter [18300/80000]	lr: 7.500e-05, eta: 5:10:31, time: 0.343, data_time: 0.057, memory: 39544, decode.loss_ce: 0.2107, decode.acc_seg: 91.6646, loss: 0.2107
+2023-03-03 22:12:38,340 - mmseg - INFO - Iter [18350/80000]	lr: 7.500e-05, eta: 5:10:14, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2041, decode.acc_seg: 91.7645, loss: 0.2041
+2023-03-03 22:12:52,875 - mmseg - INFO - Iter [18400/80000]	lr: 7.500e-05, eta: 5:09:57, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1962, decode.acc_seg: 92.1064, loss: 0.1962
+2023-03-03 22:13:07,373 - mmseg - INFO - Iter [18450/80000]	lr: 7.500e-05, eta: 5:09:40, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1970, decode.acc_seg: 92.1341, loss: 0.1970
+2023-03-03 22:13:22,105 - mmseg - INFO - Iter [18500/80000]	lr: 7.500e-05, eta: 5:09:24, time: 0.295, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2063, decode.acc_seg: 91.8072, loss: 0.2063
+2023-03-03 22:13:36,613 - mmseg - INFO - Iter [18550/80000]	lr: 7.500e-05, eta: 5:09:07, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2022, decode.acc_seg: 91.8440, loss: 0.2022
+2023-03-03 22:13:51,076 - mmseg - INFO - Iter [18600/80000]	lr: 7.500e-05, eta: 5:08:49, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2015, decode.acc_seg: 91.8838, loss: 0.2015
+2023-03-03 22:14:05,625 - mmseg - INFO - Iter [18650/80000]	lr: 7.500e-05, eta: 5:08:33, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2042, decode.acc_seg: 91.7741, loss: 0.2042
+2023-03-03 22:14:20,411 - mmseg - INFO - Iter [18700/80000]	lr: 7.500e-05, eta: 5:08:17, time: 0.296, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2018, decode.acc_seg: 92.0067, loss: 0.2018
+2023-03-03 22:14:34,875 - mmseg - INFO - Iter [18750/80000]	lr: 7.500e-05, eta: 5:07:59, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1961, decode.acc_seg: 92.1242, loss: 0.1961
+2023-03-03 22:14:49,449 - mmseg - INFO - Iter [18800/80000]	lr: 7.500e-05, eta: 5:07:43, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2040, decode.acc_seg: 91.8434, loss: 0.2040
+2023-03-03 22:15:04,030 - mmseg - INFO - Iter [18850/80000]	lr: 7.500e-05, eta: 5:07:26, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1981, decode.acc_seg: 92.1694, loss: 0.1981
+2023-03-03 22:15:18,524 - mmseg - INFO - Iter [18900/80000]	lr: 7.500e-05, eta: 5:07:09, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2043, decode.acc_seg: 91.8655, loss: 0.2043
+2023-03-03 22:15:35,532 - mmseg - INFO - Iter [18950/80000]	lr: 7.500e-05, eta: 5:07:00, time: 0.340, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1999, decode.acc_seg: 92.1943, loss: 0.1999
+2023-03-03 22:15:50,062 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 22:15:50,063 - mmseg - INFO - Iter [19000/80000]	lr: 7.500e-05, eta: 5:06:43, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2077, decode.acc_seg: 91.7160, loss: 0.2077
+2023-03-03 22:16:04,561 - mmseg - INFO - Iter [19050/80000]	lr: 7.500e-05, eta: 5:06:26, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2016, decode.acc_seg: 91.6865, loss: 0.2016
+2023-03-03 22:16:19,061 - mmseg - INFO - Iter [19100/80000]	lr: 7.500e-05, eta: 5:06:09, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2026, decode.acc_seg: 91.8581, loss: 0.2026
+2023-03-03 22:16:33,795 - mmseg - INFO - Iter [19150/80000]	lr: 7.500e-05, eta: 5:05:53, time: 0.295, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1999, decode.acc_seg: 92.1053, loss: 0.1999
+2023-03-03 22:16:48,299 - mmseg - INFO - Iter [19200/80000]	lr: 7.500e-05, eta: 5:05:36, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1981, decode.acc_seg: 91.8719, loss: 0.1981
+2023-03-03 22:17:02,791 - mmseg - INFO - Iter [19250/80000]	lr: 7.500e-05, eta: 5:05:19, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2050, decode.acc_seg: 91.6845, loss: 0.2050
+2023-03-03 22:17:17,377 - mmseg - INFO - Iter [19300/80000]	lr: 7.500e-05, eta: 5:05:03, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1962, decode.acc_seg: 92.2032, loss: 0.1962
+2023-03-03 22:17:31,915 - mmseg - INFO - Iter [19350/80000]	lr: 7.500e-05, eta: 5:04:46, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1974, decode.acc_seg: 92.1245, loss: 0.1974
+2023-03-03 22:17:46,406 - mmseg - INFO - Iter [19400/80000]	lr: 7.500e-05, eta: 5:04:29, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2036, decode.acc_seg: 91.9932, loss: 0.2036
+2023-03-03 22:18:01,034 - mmseg - INFO - Iter [19450/80000]	lr: 7.500e-05, eta: 5:04:13, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1968, decode.acc_seg: 92.0187, loss: 0.1968
+2023-03-03 22:18:15,480 - mmseg - INFO - Iter [19500/80000]	lr: 7.500e-05, eta: 5:03:56, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1979, decode.acc_seg: 92.0666, loss: 0.1979
+2023-03-03 22:18:29,953 - mmseg - INFO - Iter [19550/80000]	lr: 7.500e-05, eta: 5:03:39, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1972, decode.acc_seg: 92.0449, loss: 0.1972
+2023-03-03 22:18:47,090 - mmseg - INFO - Iter [19600/80000]	lr: 7.500e-05, eta: 5:03:30, time: 0.343, data_time: 0.055, memory: 39544, decode.loss_ce: 0.1973, decode.acc_seg: 92.1636, loss: 0.1973
+2023-03-03 22:19:01,752 - mmseg - INFO - Iter [19650/80000]	lr: 7.500e-05, eta: 5:03:14, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2094, decode.acc_seg: 91.7522, loss: 0.2094
+2023-03-03 22:19:16,274 - mmseg - INFO - Iter [19700/80000]	lr: 7.500e-05, eta: 5:02:57, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1970, decode.acc_seg: 92.0909, loss: 0.1970
+2023-03-03 22:19:30,733 - mmseg - INFO - Iter [19750/80000]	lr: 7.500e-05, eta: 5:02:40, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1988, decode.acc_seg: 92.1909, loss: 0.1988
+2023-03-03 22:19:45,316 - mmseg - INFO - Iter [19800/80000]	lr: 7.500e-05, eta: 5:02:23, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1998, decode.acc_seg: 92.1046, loss: 0.1998
+2023-03-03 22:19:59,846 - mmseg - INFO - Iter [19850/80000]	lr: 7.500e-05, eta: 5:02:07, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1970, decode.acc_seg: 91.9679, loss: 0.1970
+2023-03-03 22:20:14,448 - mmseg - INFO - Iter [19900/80000]	lr: 7.500e-05, eta: 5:01:50, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1977, decode.acc_seg: 92.0420, loss: 0.1977
+2023-03-03 22:20:28,966 - mmseg - INFO - Iter [19950/80000]	lr: 7.500e-05, eta: 5:01:33, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2006, decode.acc_seg: 91.9159, loss: 0.2006
+2023-03-03 22:20:43,511 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 22:20:43,511 - mmseg - INFO - Iter [20000/80000]	lr: 7.500e-05, eta: 5:01:17, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1973, decode.acc_seg: 92.0965, loss: 0.1973
+2023-03-03 22:20:58,076 - mmseg - INFO - Iter [20050/80000]	lr: 3.750e-05, eta: 5:01:00, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1965, decode.acc_seg: 92.0633, loss: 0.1965
+2023-03-03 22:21:12,678 - mmseg - INFO - Iter [20100/80000]	lr: 3.750e-05, eta: 5:00:44, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2062, decode.acc_seg: 91.9366, loss: 0.2062
+2023-03-03 22:21:27,164 - mmseg - INFO - Iter [20150/80000]	lr: 3.750e-05, eta: 5:00:27, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1955, decode.acc_seg: 92.1160, loss: 0.1955
+2023-03-03 22:21:44,118 - mmseg - INFO - Iter [20200/80000]	lr: 3.750e-05, eta: 5:00:18, time: 0.339, data_time: 0.055, memory: 39544, decode.loss_ce: 0.2000, decode.acc_seg: 91.9183, loss: 0.2000
+2023-03-03 22:21:58,724 - mmseg - INFO - Iter [20250/80000]	lr: 3.750e-05, eta: 5:00:01, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1866, decode.acc_seg: 92.4440, loss: 0.1866
+2023-03-03 22:22:13,168 - mmseg - INFO - Iter [20300/80000]	lr: 3.750e-05, eta: 4:59:44, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1952, decode.acc_seg: 92.0600, loss: 0.1952
+2023-03-03 22:22:27,649 - mmseg - INFO - Iter [20350/80000]	lr: 3.750e-05, eta: 4:59:27, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2004, decode.acc_seg: 92.0358, loss: 0.2004
+2023-03-03 22:22:42,216 - mmseg - INFO - Iter [20400/80000]	lr: 3.750e-05, eta: 4:59:11, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1937, decode.acc_seg: 92.0490, loss: 0.1937
+2023-03-03 22:22:56,718 - mmseg - INFO - Iter [20450/80000]	lr: 3.750e-05, eta: 4:58:54, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1957, decode.acc_seg: 92.1265, loss: 0.1957
+2023-03-03 22:23:11,213 - mmseg - INFO - Iter [20500/80000]	lr: 3.750e-05, eta: 4:58:38, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2005, decode.acc_seg: 92.0494, loss: 0.2005
+2023-03-03 22:23:25,782 - mmseg - INFO - Iter [20550/80000]	lr: 3.750e-05, eta: 4:58:21, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1870, decode.acc_seg: 92.4562, loss: 0.1870
+2023-03-03 22:23:40,272 - mmseg - INFO - Iter [20600/80000]	lr: 3.750e-05, eta: 4:58:04, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1896, decode.acc_seg: 92.3138, loss: 0.1896
+2023-03-03 22:23:54,831 - mmseg - INFO - Iter [20650/80000]	lr: 3.750e-05, eta: 4:57:48, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1928, decode.acc_seg: 92.2473, loss: 0.1928
+2023-03-03 22:24:09,294 - mmseg - INFO - Iter [20700/80000]	lr: 3.750e-05, eta: 4:57:31, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1954, decode.acc_seg: 92.0414, loss: 0.1954
+2023-03-03 22:24:23,810 - mmseg - INFO - Iter [20750/80000]	lr: 3.750e-05, eta: 4:57:15, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1974, decode.acc_seg: 91.9350, loss: 0.1974
+2023-03-03 22:24:38,275 - mmseg - INFO - Iter [20800/80000]	lr: 3.750e-05, eta: 4:56:58, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1989, decode.acc_seg: 92.1278, loss: 0.1989
+2023-03-03 22:24:55,268 - mmseg - INFO - Iter [20850/80000]	lr: 3.750e-05, eta: 4:56:48, time: 0.340, data_time: 0.057, memory: 39544, decode.loss_ce: 0.1968, decode.acc_seg: 91.9520, loss: 0.1968
+2023-03-03 22:25:09,832 - mmseg - INFO - Iter [20900/80000]	lr: 3.750e-05, eta: 4:56:32, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2044, decode.acc_seg: 91.8267, loss: 0.2044
+2023-03-03 22:25:24,334 - mmseg - INFO - Iter [20950/80000]	lr: 3.750e-05, eta: 4:56:15, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1921, decode.acc_seg: 92.3268, loss: 0.1921
+2023-03-03 22:25:38,903 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 22:25:38,903 - mmseg - INFO - Iter [21000/80000]	lr: 3.750e-05, eta: 4:55:59, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1932, decode.acc_seg: 92.1031, loss: 0.1932
+2023-03-03 22:25:53,441 - mmseg - INFO - Iter [21050/80000]	lr: 3.750e-05, eta: 4:55:42, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2019, decode.acc_seg: 91.8519, loss: 0.2019
+2023-03-03 22:26:07,973 - mmseg - INFO - Iter [21100/80000]	lr: 3.750e-05, eta: 4:55:26, time: 0.291, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1951, decode.acc_seg: 92.1569, loss: 0.1951
+2023-03-03 22:26:22,458 - mmseg - INFO - Iter [21150/80000]	lr: 3.750e-05, eta: 4:55:09, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1887, decode.acc_seg: 92.4509, loss: 0.1887
+2023-03-03 22:26:37,017 - mmseg - INFO - Iter [21200/80000]	lr: 3.750e-05, eta: 4:54:53, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1952, decode.acc_seg: 92.3163, loss: 0.1952
+2023-03-03 22:26:51,587 - mmseg - INFO - Iter [21250/80000]	lr: 3.750e-05, eta: 4:54:37, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1954, decode.acc_seg: 92.1038, loss: 0.1954
+2023-03-03 22:27:06,207 - mmseg - INFO - Iter [21300/80000]	lr: 3.750e-05, eta: 4:54:20, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1943, decode.acc_seg: 92.1878, loss: 0.1943
+2023-03-03 22:27:20,797 - mmseg - INFO - Iter [21350/80000]	lr: 3.750e-05, eta: 4:54:04, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1976, decode.acc_seg: 92.1717, loss: 0.1976
+2023-03-03 22:27:35,249 - mmseg - INFO - Iter [21400/80000]	lr: 3.750e-05, eta: 4:53:47, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1998, decode.acc_seg: 92.0939, loss: 0.1998
+2023-03-03 22:27:49,649 - mmseg - INFO - Iter [21450/80000]	lr: 3.750e-05, eta: 4:53:31, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1956, decode.acc_seg: 92.0141, loss: 0.1956
+2023-03-03 22:28:06,744 - mmseg - INFO - Iter [21500/80000]	lr: 3.750e-05, eta: 4:53:21, time: 0.342, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1972, decode.acc_seg: 92.1260, loss: 0.1972
+2023-03-03 22:28:21,215 - mmseg - INFO - Iter [21550/80000]	lr: 3.750e-05, eta: 4:53:05, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1872, decode.acc_seg: 92.3610, loss: 0.1872
+2023-03-03 22:28:35,791 - mmseg - INFO - Iter [21600/80000]	lr: 3.750e-05, eta: 4:52:48, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1881, decode.acc_seg: 92.2909, loss: 0.1881
+2023-03-03 22:28:50,211 - mmseg - INFO - Iter [21650/80000]	lr: 3.750e-05, eta: 4:52:32, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1959, decode.acc_seg: 92.0591, loss: 0.1959
+2023-03-03 22:29:04,694 - mmseg - INFO - Iter [21700/80000]	lr: 3.750e-05, eta: 4:52:15, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1988, decode.acc_seg: 92.0528, loss: 0.1988
+2023-03-03 22:29:19,191 - mmseg - INFO - Iter [21750/80000]	lr: 3.750e-05, eta: 4:51:58, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1953, decode.acc_seg: 92.1715, loss: 0.1953
+2023-03-03 22:29:33,782 - mmseg - INFO - Iter [21800/80000]	lr: 3.750e-05, eta: 4:51:42, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2033, decode.acc_seg: 91.9331, loss: 0.2033
+2023-03-03 22:29:48,359 - mmseg - INFO - Iter [21850/80000]	lr: 3.750e-05, eta: 4:51:26, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1963, decode.acc_seg: 92.0509, loss: 0.1963
+2023-03-03 22:30:02,783 - mmseg - INFO - Iter [21900/80000]	lr: 3.750e-05, eta: 4:51:09, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1886, decode.acc_seg: 92.2937, loss: 0.1886
+2023-03-03 22:30:17,274 - mmseg - INFO - Iter [21950/80000]	lr: 3.750e-05, eta: 4:50:53, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1996, decode.acc_seg: 92.0986, loss: 0.1996
+2023-03-03 22:30:31,855 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 22:30:31,855 - mmseg - INFO - Iter [22000/80000]	lr: 3.750e-05, eta: 4:50:37, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1979, decode.acc_seg: 92.0822, loss: 0.1979
+2023-03-03 22:30:46,434 - mmseg - INFO - Iter [22050/80000]	lr: 3.750e-05, eta: 4:50:20, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1965, decode.acc_seg: 92.0566, loss: 0.1965
+2023-03-03 22:31:03,501 - mmseg - INFO - Iter [22100/80000]	lr: 3.750e-05, eta: 4:50:11, time: 0.341, data_time: 0.057, memory: 39544, decode.loss_ce: 0.1960, decode.acc_seg: 92.1441, loss: 0.1960
+2023-03-03 22:31:18,234 - mmseg - INFO - Iter [22150/80000]	lr: 3.750e-05, eta: 4:49:55, time: 0.295, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1985, decode.acc_seg: 92.1326, loss: 0.1985
+2023-03-03 22:31:32,676 - mmseg - INFO - Iter [22200/80000]	lr: 3.750e-05, eta: 4:49:38, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1898, decode.acc_seg: 92.2389, loss: 0.1898
+2023-03-03 22:31:47,280 - mmseg - INFO - Iter [22250/80000]	lr: 3.750e-05, eta: 4:49:22, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2000, decode.acc_seg: 92.0289, loss: 0.2000
+2023-03-03 22:32:01,771 - mmseg - INFO - Iter [22300/80000]	lr: 3.750e-05, eta: 4:49:06, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1903, decode.acc_seg: 92.3583, loss: 0.1903
+2023-03-03 22:32:16,170 - mmseg - INFO - Iter [22350/80000]	lr: 3.750e-05, eta: 4:48:49, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2017, decode.acc_seg: 91.9136, loss: 0.2017
+2023-03-03 22:32:30,732 - mmseg - INFO - Iter [22400/80000]	lr: 3.750e-05, eta: 4:48:33, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1907, decode.acc_seg: 92.2909, loss: 0.1907
+2023-03-03 22:32:45,312 - mmseg - INFO - Iter [22450/80000]	lr: 3.750e-05, eta: 4:48:17, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1943, decode.acc_seg: 92.2168, loss: 0.1943
+2023-03-03 22:32:59,783 - mmseg - INFO - Iter [22500/80000]	lr: 3.750e-05, eta: 4:48:00, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1989, decode.acc_seg: 91.8374, loss: 0.1989
+2023-03-03 22:33:14,286 - mmseg - INFO - Iter [22550/80000]	lr: 3.750e-05, eta: 4:47:44, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2006, decode.acc_seg: 92.0228, loss: 0.2006
+2023-03-03 22:33:28,811 - mmseg - INFO - Iter [22600/80000]	lr: 3.750e-05, eta: 4:47:28, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1963, decode.acc_seg: 92.1011, loss: 0.1963
+2023-03-03 22:33:43,283 - mmseg - INFO - Iter [22650/80000]	lr: 3.750e-05, eta: 4:47:11, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1964, decode.acc_seg: 92.0534, loss: 0.1964
+2023-03-03 22:33:57,754 - mmseg - INFO - Iter [22700/80000]	lr: 3.750e-05, eta: 4:46:55, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1939, decode.acc_seg: 92.2414, loss: 0.1939
+2023-03-03 22:34:14,875 - mmseg - INFO - Iter [22750/80000]	lr: 3.750e-05, eta: 4:46:45, time: 0.342, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1952, decode.acc_seg: 92.0605, loss: 0.1952
+2023-03-03 22:34:29,503 - mmseg - INFO - Iter [22800/80000]	lr: 3.750e-05, eta: 4:46:29, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1955, decode.acc_seg: 92.1552, loss: 0.1955
+2023-03-03 22:34:43,995 - mmseg - INFO - Iter [22850/80000]	lr: 3.750e-05, eta: 4:46:13, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1916, decode.acc_seg: 92.2885, loss: 0.1916
+2023-03-03 22:34:58,613 - mmseg - INFO - Iter [22900/80000]	lr: 3.750e-05, eta: 4:45:57, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1958, decode.acc_seg: 92.0318, loss: 0.1958
+2023-03-03 22:35:13,081 - mmseg - INFO - Iter [22950/80000]	lr: 3.750e-05, eta: 4:45:40, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1930, decode.acc_seg: 92.1086, loss: 0.1930
+2023-03-03 22:35:27,659 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 22:35:27,659 - mmseg - INFO - Iter [23000/80000]	lr: 3.750e-05, eta: 4:45:24, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1946, decode.acc_seg: 92.2426, loss: 0.1946
+2023-03-03 22:35:42,150 - mmseg - INFO - Iter [23050/80000]	lr: 3.750e-05, eta: 4:45:08, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2012, decode.acc_seg: 91.8439, loss: 0.2012
+2023-03-03 22:35:56,661 - mmseg - INFO - Iter [23100/80000]	lr: 3.750e-05, eta: 4:44:51, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1948, decode.acc_seg: 92.0851, loss: 0.1948
+2023-03-03 22:36:11,260 - mmseg - INFO - Iter [23150/80000]	lr: 3.750e-05, eta: 4:44:35, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1996, decode.acc_seg: 91.9509, loss: 0.1996
+2023-03-03 22:36:25,860 - mmseg - INFO - Iter [23200/80000]	lr: 3.750e-05, eta: 4:44:19, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1970, decode.acc_seg: 92.0728, loss: 0.1970
+2023-03-03 22:36:40,360 - mmseg - INFO - Iter [23250/80000]	lr: 3.750e-05, eta: 4:44:03, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2064, decode.acc_seg: 91.7476, loss: 0.2064
+2023-03-03 22:36:54,930 - mmseg - INFO - Iter [23300/80000]	lr: 3.750e-05, eta: 4:43:47, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1902, decode.acc_seg: 92.3783, loss: 0.1902
+2023-03-03 22:37:12,028 - mmseg - INFO - Iter [23350/80000]	lr: 3.750e-05, eta: 4:43:37, time: 0.342, data_time: 0.055, memory: 39544, decode.loss_ce: 0.1913, decode.acc_seg: 92.3203, loss: 0.1913
+2023-03-03 22:37:26,634 - mmseg - INFO - Iter [23400/80000]	lr: 3.750e-05, eta: 4:43:21, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1935, decode.acc_seg: 92.1422, loss: 0.1935
+2023-03-03 22:37:41,073 - mmseg - INFO - Iter [23450/80000]	lr: 3.750e-05, eta: 4:43:05, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1955, decode.acc_seg: 92.2073, loss: 0.1955
+2023-03-03 22:37:55,563 - mmseg - INFO - Iter [23500/80000]	lr: 3.750e-05, eta: 4:42:48, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1959, decode.acc_seg: 92.1380, loss: 0.1959
+2023-03-03 22:38:10,192 - mmseg - INFO - Iter [23550/80000]	lr: 3.750e-05, eta: 4:42:32, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1904, decode.acc_seg: 92.1800, loss: 0.1904
+2023-03-03 22:38:24,699 - mmseg - INFO - Iter [23600/80000]	lr: 3.750e-05, eta: 4:42:16, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1940, decode.acc_seg: 92.1463, loss: 0.1940
+2023-03-03 22:38:39,122 - mmseg - INFO - Iter [23650/80000]	lr: 3.750e-05, eta: 4:42:00, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1976, decode.acc_seg: 92.0653, loss: 0.1976
+2023-03-03 22:38:53,680 - mmseg - INFO - Iter [23700/80000]	lr: 3.750e-05, eta: 4:41:44, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1961, decode.acc_seg: 92.2046, loss: 0.1961
+2023-03-03 22:39:08,371 - mmseg - INFO - Iter [23750/80000]	lr: 3.750e-05, eta: 4:41:28, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2001, decode.acc_seg: 91.9896, loss: 0.2001
+2023-03-03 22:39:22,879 - mmseg - INFO - Iter [23800/80000]	lr: 3.750e-05, eta: 4:41:12, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1965, decode.acc_seg: 91.9679, loss: 0.1965
+2023-03-03 22:39:37,450 - mmseg - INFO - Iter [23850/80000]	lr: 3.750e-05, eta: 4:40:55, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1896, decode.acc_seg: 92.1959, loss: 0.1896
+2023-03-03 22:39:52,018 - mmseg - INFO - Iter [23900/80000]	lr: 3.750e-05, eta: 4:40:39, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2005, decode.acc_seg: 91.9791, loss: 0.2005
+2023-03-03 22:40:06,578 - mmseg - INFO - Iter [23950/80000]	lr: 3.750e-05, eta: 4:40:23, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2000, decode.acc_seg: 91.9557, loss: 0.2000
+2023-03-03 22:40:23,596 - mmseg - INFO - Saving checkpoint at 24000 iterations
+2023-03-03 22:40:25,521 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 22:40:25,521 - mmseg - INFO - Iter [24000/80000]	lr: 3.750e-05, eta: 4:40:18, time: 0.379, data_time: 0.055, memory: 39544, decode.loss_ce: 0.2001, decode.acc_seg: 91.9212, loss: 0.2001
+2023-03-03 22:40:45,363 - mmseg - INFO - per class results:
+2023-03-03 22:40:45,369 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        | 76.02 | 87.92 |
+|       building      | 80.41 | 93.45 |
+|         sky         | 94.17 | 97.22 |
+|        floor        | 80.24 | 90.88 |
+|         tree        | 72.66 | 87.15 |
+|       ceiling       | 82.15 | 91.71 |
+|         road        | 82.08 |  89.5 |
+|         bed         | 88.36 | 94.95 |
+|      windowpane     |  60.6 | 78.03 |
+|        grass        | 65.55 | 80.99 |
+|       cabinet       | 59.05 |  73.8 |
+|       sidewalk      | 65.49 | 80.88 |
+|        person       | 78.86 | 90.86 |
+|        earth        | 34.43 | 50.93 |
+|         door        | 47.93 | 63.13 |
+|        table        |  61.3 | 76.29 |
+|       mountain      | 52.07 | 66.59 |
+|        plant        | 49.18 | 60.93 |
+|       curtain       | 69.29 | 80.68 |
+|        chair        | 57.86 | 70.69 |
+|         car         | 83.13 | 89.43 |
+|        water        | 46.46 |  62.7 |
+|       painting      |  69.3 | 83.01 |
+|         sofa        | 64.79 | 81.93 |
+|        shelf        | 38.89 |  52.2 |
+|        house        | 38.89 | 42.93 |
+|         sea         | 45.25 | 74.32 |
+|        mirror       | 64.62 | 73.82 |
+|         rug         | 56.28 | 62.44 |
+|        field        | 28.42 | 43.89 |
+|       armchair      | 44.46 | 60.46 |
+|         seat        | 54.02 | 78.75 |
+|        fence        | 40.98 | 54.58 |
+|         desk        | 48.21 | 72.08 |
+|         rock        | 31.42 | 47.89 |
+|       wardrobe      |  49.5 | 69.48 |
+|         lamp        | 63.09 | 76.51 |
+|       bathtub       | 75.53 | 82.76 |
+|       railing       | 32.96 | 48.37 |
+|       cushion       | 54.63 | 70.58 |
+|         base        | 26.96 | 40.32 |
+|         box         | 24.05 | 33.23 |
+|        column       | 42.09 | 50.49 |
+|      signboard      | 34.24 |  45.7 |
+|   chest of drawers  | 40.48 | 58.97 |
+|       counter       | 28.91 | 44.88 |
+|         sand        | 33.24 | 46.57 |
+|         sink        | 70.35 | 81.22 |
+|      skyscraper     | 47.34 | 53.34 |
+|      fireplace      | 67.01 | 82.73 |
+|     refrigerator    | 77.27 | 83.26 |
+|      grandstand     | 40.72 | 63.35 |
+|         path        | 16.09 | 24.13 |
+|        stairs       | 33.12 | 41.76 |
+|        runway       | 63.46 | 83.03 |
+|         case        | 49.26 | 65.65 |
+|      pool table     | 92.47 | 95.85 |
+|        pillow       | 55.03 | 63.56 |
+|     screen door     | 65.82 | 74.61 |
+|       stairway      | 24.85 | 30.97 |
+|        river        | 10.71 | 17.55 |
+|        bridge       | 41.75 | 45.08 |
+|       bookcase      | 40.78 | 51.49 |
+|        blind        | 45.25 | 50.64 |
+|     coffee table    | 65.96 | 78.91 |
+|        toilet       | 85.92 | 90.42 |
+|        flower       |  29.5 | 43.28 |
+|         book        | 46.51 |  67.4 |
+|         hill        |  8.63 | 10.47 |
+|        bench        | 41.99 | 53.34 |
+|      countertop     | 54.12 | 72.37 |
+|        stove        | 71.84 |  78.9 |
+|         palm        | 50.22 | 69.98 |
+|    kitchen island   | 45.92 | 70.86 |
+|       computer      | 56.53 | 62.72 |
+|     swivel chair    |  45.0 | 60.39 |
+|         boat        | 38.03 | 41.37 |
+|         bar         | 27.06 |  30.8 |
+|    arcade machine   | 24.85 |  25.6 |
+|        hovel        | 31.89 | 34.34 |
+|         bus         | 88.34 | 92.56 |
+|        towel        | 57.62 | 66.06 |
+|        light        | 54.52 | 60.31 |
+|        truck        | 33.98 | 43.69 |
+|        tower        | 20.77 | 26.28 |
+|      chandelier     | 66.09 |  78.5 |
+|        awning       | 23.83 | 26.53 |
+|     streetlight     | 27.37 | 35.31 |
+|        booth        | 53.86 | 56.18 |
+| television receiver | 68.45 | 79.34 |
+|       airplane      | 49.64 | 69.66 |
+|      dirt track     |  8.46 | 21.72 |
+|       apparel       | 31.09 | 41.54 |
+|         pole        | 24.52 | 38.19 |
+|         land        | 11.69 |  16.7 |
+|      bannister      |  5.86 |  7.96 |
+|      escalator      | 23.44 | 24.17 |
+|       ottoman       | 47.02 | 59.16 |
+|        bottle       | 15.95 |  24.0 |
+|        buffet       | 45.44 | 51.38 |
+|        poster       | 26.83 |  37.5 |
+|        stage        |  17.7 | 22.94 |
+|         van         | 48.08 | 63.94 |
+|         ship        | 37.52 | 49.16 |
+|       fountain      |  5.98 |  6.02 |
+|    conveyer belt    | 76.17 | 87.85 |
+|        canopy       | 15.47 | 18.53 |
+|        washer       | 66.29 | 67.09 |
+|      plaything      | 21.79 |  30.5 |
+|    swimming pool    | 43.53 | 52.65 |
+|        stool        | 41.07 | 55.29 |
+|        barrel       | 38.31 |  64.4 |
+|        basket       | 26.75 | 38.49 |
+|      waterfall      | 48.67 | 56.16 |
+|         tent        | 94.82 | 97.68 |
+|         bag         | 11.28 | 13.69 |
+|       minibike      | 60.12 | 66.05 |
+|        cradle       | 78.99 | 97.62 |
+|         oven        | 27.85 | 60.09 |
+|         ball        | 45.92 | 57.24 |
+|         food        | 55.91 | 68.08 |
+|         step        | 15.54 | 20.61 |
+|         tank        | 41.97 | 42.16 |
+|      trade name     | 22.32 | 24.59 |
+|      microwave      | 37.98 | 40.86 |
+|         pot         | 39.46 | 47.02 |
+|        animal       | 50.15 | 53.53 |
+|       bicycle       | 44.37 | 68.37 |
+|         lake        | 61.85 | 63.31 |
+|      dishwasher     |  77.5 | 82.79 |
+|        screen       | 68.85 | 86.84 |
+|       blanket       | 11.42 | 13.52 |
+|      sculpture      | 37.83 | 59.18 |
+|         hood        | 56.04 | 70.26 |
+|        sconce       | 42.55 | 51.97 |
+|         vase        | 36.58 | 52.16 |
+|    traffic light    | 30.18 | 45.73 |
+|         tray        |  6.06 | 11.97 |
+|        ashcan       | 38.05 | 48.08 |
+|         fan         | 57.88 |  70.9 |
+|         pier        | 15.71 | 18.29 |
+|      crt screen     |  4.22 | 10.73 |
+|        plate        | 38.31 | 47.83 |
+|       monitor       | 22.75 | 27.51 |
+|    bulletin board   | 46.53 | 56.23 |
+|        shower       |  1.38 |  2.25 |
+|       radiator      | 45.49 | 52.99 |
+|        glass        |  12.6 | 13.96 |
+|        clock        | 24.66 | 29.96 |
+|         flag        | 37.52 | 41.25 |
++---------------------+-------+-------+
+2023-03-03 22:40:45,369 - mmseg - INFO - Summary:
+2023-03-03 22:40:45,370 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 81.78 | 45.71 | 56.26 |
++-------+-------+-------+
+2023-03-03 22:40:45,439 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151/best_mIoU_iter_16000.pth was removed
+2023-03-03 22:40:47,247 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_24000.pth.
+2023-03-03 22:40:47,247 - mmseg - INFO - Best mIoU is 0.4571 at 24000 iter.
+2023-03-03 22:40:47,247 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 22:40:47,248 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8178, mIoU: 0.4571, mAcc: 0.5626, IoU.background: nan, IoU.wall: 0.7602, IoU.building: 0.8041, IoU.sky: 0.9417, IoU.floor: 0.8024, IoU.tree: 0.7266, IoU.ceiling: 0.8215, IoU.road: 0.8208, IoU.bed : 0.8836, IoU.windowpane: 0.6060, IoU.grass: 0.6555, IoU.cabinet: 0.5905, IoU.sidewalk: 0.6549, IoU.person: 0.7886, IoU.earth: 0.3443, IoU.door: 0.4793, IoU.table: 0.6130, IoU.mountain: 0.5207, IoU.plant: 0.4918, IoU.curtain: 0.6929, IoU.chair: 0.5786, IoU.car: 0.8313, IoU.water: 0.4646, IoU.painting: 0.6930, IoU.sofa: 0.6479, IoU.shelf: 0.3889, IoU.house: 0.3889, IoU.sea: 0.4525, IoU.mirror: 0.6462, IoU.rug: 0.5628, IoU.field: 0.2842, IoU.armchair: 0.4446, IoU.seat: 0.5402, IoU.fence: 0.4098, IoU.desk: 0.4821, IoU.rock: 0.3142, IoU.wardrobe: 0.4950, IoU.lamp: 0.6309, IoU.bathtub: 0.7553, IoU.railing: 0.3296, IoU.cushion: 0.5463, IoU.base: 0.2696, IoU.box: 0.2405, IoU.column: 0.4209, IoU.signboard: 0.3424, IoU.chest of drawers: 0.4048, IoU.counter: 0.2891, IoU.sand: 0.3324, IoU.sink: 0.7035, IoU.skyscraper: 0.4734, IoU.fireplace: 0.6701, IoU.refrigerator: 0.7727, IoU.grandstand: 0.4072, IoU.path: 0.1609, IoU.stairs: 0.3312, IoU.runway: 0.6346, IoU.case: 0.4926, IoU.pool table: 0.9247, IoU.pillow: 0.5503, IoU.screen door: 0.6582, IoU.stairway: 0.2485, IoU.river: 0.1071, IoU.bridge: 0.4175, IoU.bookcase: 0.4078, IoU.blind: 0.4525, IoU.coffee table: 0.6596, IoU.toilet: 0.8592, IoU.flower: 0.2950, IoU.book: 0.4651, IoU.hill: 0.0863, IoU.bench: 0.4199, IoU.countertop: 0.5412, IoU.stove: 0.7184, IoU.palm: 0.5022, IoU.kitchen island: 0.4592, IoU.computer: 0.5653, IoU.swivel chair: 0.4500, IoU.boat: 0.3803, IoU.bar: 0.2706, IoU.arcade machine: 0.2485, IoU.hovel: 0.3189, IoU.bus: 0.8834, IoU.towel: 0.5762, IoU.light: 0.5452, IoU.truck: 0.3398, IoU.tower: 0.2077, IoU.chandelier: 0.6609, IoU.awning: 0.2383, IoU.streetlight: 0.2737, IoU.booth: 0.5386, IoU.television receiver: 0.6845, IoU.airplane: 0.4964, IoU.dirt track: 0.0846, IoU.apparel: 0.3109, IoU.pole: 0.2452, IoU.land: 0.1169, IoU.bannister: 0.0586, IoU.escalator: 0.2344, IoU.ottoman: 0.4702, IoU.bottle: 0.1595, IoU.buffet: 0.4544, IoU.poster: 0.2683, IoU.stage: 0.1770, IoU.van: 0.4808, IoU.ship: 0.3752, IoU.fountain: 0.0598, IoU.conveyer belt: 0.7617, IoU.canopy: 0.1547, IoU.washer: 0.6629, IoU.plaything: 0.2179, IoU.swimming pool: 0.4353, IoU.stool: 0.4107, IoU.barrel: 0.3831, IoU.basket: 0.2675, IoU.waterfall: 0.4867, IoU.tent: 0.9482, IoU.bag: 0.1128, IoU.minibike: 0.6012, IoU.cradle: 0.7899, IoU.oven: 0.2785, IoU.ball: 0.4592, IoU.food: 0.5591, IoU.step: 0.1554, IoU.tank: 0.4197, IoU.trade name: 0.2232, IoU.microwave: 0.3798, IoU.pot: 0.3946, IoU.animal: 0.5015, IoU.bicycle: 0.4437, IoU.lake: 0.6185, IoU.dishwasher: 0.7750, IoU.screen: 0.6885, IoU.blanket: 0.1142, IoU.sculpture: 0.3783, IoU.hood: 0.5604, IoU.sconce: 0.4255, IoU.vase: 0.3658, IoU.traffic light: 0.3018, IoU.tray: 0.0606, IoU.ashcan: 0.3805, IoU.fan: 0.5788, IoU.pier: 0.1571, IoU.crt screen: 0.0422, IoU.plate: 0.3831, IoU.monitor: 0.2275, IoU.bulletin board: 0.4653, IoU.shower: 0.0138, IoU.radiator: 0.4549, IoU.glass: 0.1260, IoU.clock: 0.2466, IoU.flag: 0.3752, Acc.background: nan, Acc.wall: 0.8792, Acc.building: 0.9345, Acc.sky: 0.9722, Acc.floor: 0.9088, Acc.tree: 0.8715, Acc.ceiling: 0.9171, Acc.road: 0.8950, Acc.bed : 0.9495, Acc.windowpane: 0.7803, Acc.grass: 0.8099, Acc.cabinet: 0.7380, Acc.sidewalk: 0.8088, Acc.person: 0.9086, Acc.earth: 0.5093, Acc.door: 0.6313, Acc.table: 0.7629, Acc.mountain: 0.6659, Acc.plant: 0.6093, Acc.curtain: 0.8068, Acc.chair: 0.7069, Acc.car: 0.8943, Acc.water: 0.6270, Acc.painting: 0.8301, Acc.sofa: 0.8193, Acc.shelf: 0.5220, Acc.house: 0.4293, Acc.sea: 0.7432, Acc.mirror: 0.7382, Acc.rug: 0.6244, Acc.field: 0.4389, Acc.armchair: 0.6046, Acc.seat: 0.7875, Acc.fence: 0.5458, Acc.desk: 0.7208, Acc.rock: 0.4789, Acc.wardrobe: 0.6948, Acc.lamp: 0.7651, Acc.bathtub: 0.8276, Acc.railing: 0.4837, Acc.cushion: 0.7058, Acc.base: 0.4032, Acc.box: 0.3323, Acc.column: 0.5049, Acc.signboard: 0.4570, Acc.chest of drawers: 0.5897, Acc.counter: 0.4488, Acc.sand: 0.4657, Acc.sink: 0.8122, Acc.skyscraper: 0.5334, Acc.fireplace: 0.8273, Acc.refrigerator: 0.8326, Acc.grandstand: 0.6335, Acc.path: 0.2413, Acc.stairs: 0.4176, Acc.runway: 0.8303, Acc.case: 0.6565, Acc.pool table: 0.9585, Acc.pillow: 0.6356, Acc.screen door: 0.7461, Acc.stairway: 0.3097, Acc.river: 0.1755, Acc.bridge: 0.4508, Acc.bookcase: 0.5149, Acc.blind: 0.5064, Acc.coffee table: 0.7891, Acc.toilet: 0.9042, Acc.flower: 0.4328, Acc.book: 0.6740, Acc.hill: 0.1047, Acc.bench: 0.5334, Acc.countertop: 0.7237, Acc.stove: 0.7890, Acc.palm: 0.6998, Acc.kitchen island: 0.7086, Acc.computer: 0.6272, Acc.swivel chair: 0.6039, Acc.boat: 0.4137, Acc.bar: 0.3080, Acc.arcade machine: 0.2560, Acc.hovel: 0.3434, Acc.bus: 0.9256, Acc.towel: 0.6606, Acc.light: 0.6031, Acc.truck: 0.4369, Acc.tower: 0.2628, Acc.chandelier: 0.7850, Acc.awning: 0.2653, Acc.streetlight: 0.3531, Acc.booth: 0.5618, Acc.television receiver: 0.7934, Acc.airplane: 0.6966, Acc.dirt track: 0.2172, Acc.apparel: 0.4154, Acc.pole: 0.3819, Acc.land: 0.1670, Acc.bannister: 0.0796, Acc.escalator: 0.2417, Acc.ottoman: 0.5916, Acc.bottle: 0.2400, Acc.buffet: 0.5138, Acc.poster: 0.3750, Acc.stage: 0.2294, Acc.van: 0.6394, Acc.ship: 0.4916, Acc.fountain: 0.0602, Acc.conveyer belt: 0.8785, Acc.canopy: 0.1853, Acc.washer: 0.6709, Acc.plaything: 0.3050, Acc.swimming pool: 0.5265, Acc.stool: 0.5529, Acc.barrel: 0.6440, Acc.basket: 0.3849, Acc.waterfall: 0.5616, Acc.tent: 0.9768, Acc.bag: 0.1369, Acc.minibike: 0.6605, Acc.cradle: 0.9762, Acc.oven: 0.6009, Acc.ball: 0.5724, Acc.food: 0.6808, Acc.step: 0.2061, Acc.tank: 0.4216, Acc.trade name: 0.2459, Acc.microwave: 0.4086, Acc.pot: 0.4702, Acc.animal: 0.5353, Acc.bicycle: 0.6837, Acc.lake: 0.6331, Acc.dishwasher: 0.8279, Acc.screen: 0.8684, Acc.blanket: 0.1352, Acc.sculpture: 0.5918, Acc.hood: 0.7026, Acc.sconce: 0.5197, Acc.vase: 0.5216, Acc.traffic light: 0.4573, Acc.tray: 0.1197, Acc.ashcan: 0.4808, Acc.fan: 0.7090, Acc.pier: 0.1829, Acc.crt screen: 0.1073, Acc.plate: 0.4783, Acc.monitor: 0.2751, Acc.bulletin board: 0.5623, Acc.shower: 0.0225, Acc.radiator: 0.5299, Acc.glass: 0.1396, Acc.clock: 0.2996, Acc.flag: 0.4125
+2023-03-03 22:41:02,268 - mmseg - INFO - Iter [24050/80000]	lr: 3.750e-05, eta: 4:40:53, time: 0.735, data_time: 0.442, memory: 39544, decode.loss_ce: 0.1911, decode.acc_seg: 92.2189, loss: 0.1911
+2023-03-03 22:41:16,951 - mmseg - INFO - Iter [24100/80000]	lr: 3.750e-05, eta: 4:40:37, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1987, decode.acc_seg: 92.0151, loss: 0.1987
+2023-03-03 22:41:31,499 - mmseg - INFO - Iter [24150/80000]	lr: 3.750e-05, eta: 4:40:21, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1969, decode.acc_seg: 92.1403, loss: 0.1969
+2023-03-03 22:41:46,011 - mmseg - INFO - Iter [24200/80000]	lr: 3.750e-05, eta: 4:40:05, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1941, decode.acc_seg: 92.1425, loss: 0.1941
+2023-03-03 22:42:00,508 - mmseg - INFO - Iter [24250/80000]	lr: 3.750e-05, eta: 4:39:48, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1876, decode.acc_seg: 92.2695, loss: 0.1876
+2023-03-03 22:42:15,057 - mmseg - INFO - Iter [24300/80000]	lr: 3.750e-05, eta: 4:39:32, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1957, decode.acc_seg: 92.1820, loss: 0.1957
+2023-03-03 22:42:29,646 - mmseg - INFO - Iter [24350/80000]	lr: 3.750e-05, eta: 4:39:16, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2003, decode.acc_seg: 92.0744, loss: 0.2003
+2023-03-03 22:42:44,084 - mmseg - INFO - Iter [24400/80000]	lr: 3.750e-05, eta: 4:38:59, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1975, decode.acc_seg: 91.9975, loss: 0.1975
+2023-03-03 22:42:58,571 - mmseg - INFO - Iter [24450/80000]	lr: 3.750e-05, eta: 4:38:43, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1947, decode.acc_seg: 92.1908, loss: 0.1947
+2023-03-03 22:43:13,123 - mmseg - INFO - Iter [24500/80000]	lr: 3.750e-05, eta: 4:38:27, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1903, decode.acc_seg: 92.3406, loss: 0.1903
+2023-03-03 22:43:27,662 - mmseg - INFO - Iter [24550/80000]	lr: 3.750e-05, eta: 4:38:11, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2003, decode.acc_seg: 92.0957, loss: 0.2003
+2023-03-03 22:43:42,174 - mmseg - INFO - Iter [24600/80000]	lr: 3.750e-05, eta: 4:37:54, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1934, decode.acc_seg: 92.1165, loss: 0.1934
+2023-03-03 22:43:59,157 - mmseg - INFO - Iter [24650/80000]	lr: 3.750e-05, eta: 4:37:44, time: 0.340, data_time: 0.056, memory: 39544, decode.loss_ce: 0.1899, decode.acc_seg: 92.3616, loss: 0.1899
+2023-03-03 22:44:13,661 - mmseg - INFO - Iter [24700/80000]	lr: 3.750e-05, eta: 4:37:27, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1956, decode.acc_seg: 92.0873, loss: 0.1956
+2023-03-03 22:44:28,079 - mmseg - INFO - Iter [24750/80000]	lr: 3.750e-05, eta: 4:37:11, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2010, decode.acc_seg: 91.8074, loss: 0.2010
+2023-03-03 22:44:42,521 - mmseg - INFO - Iter [24800/80000]	lr: 3.750e-05, eta: 4:36:55, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1917, decode.acc_seg: 92.2979, loss: 0.1917
+2023-03-03 22:44:56,984 - mmseg - INFO - Iter [24850/80000]	lr: 3.750e-05, eta: 4:36:38, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1969, decode.acc_seg: 92.1620, loss: 0.1969
+2023-03-03 22:45:11,424 - mmseg - INFO - Iter [24900/80000]	lr: 3.750e-05, eta: 4:36:22, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1979, decode.acc_seg: 92.0986, loss: 0.1979
+2023-03-03 22:45:25,765 - mmseg - INFO - Iter [24950/80000]	lr: 3.750e-05, eta: 4:36:05, time: 0.287, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2008, decode.acc_seg: 91.9746, loss: 0.2008
+2023-03-03 22:45:40,170 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 22:45:40,170 - mmseg - INFO - Iter [25000/80000]	lr: 3.750e-05, eta: 4:35:49, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1914, decode.acc_seg: 92.2071, loss: 0.1914
+2023-03-03 22:45:54,705 - mmseg - INFO - Iter [25050/80000]	lr: 3.750e-05, eta: 4:35:33, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1972, decode.acc_seg: 92.2242, loss: 0.1972
+2023-03-03 22:46:09,299 - mmseg - INFO - Iter [25100/80000]	lr: 3.750e-05, eta: 4:35:17, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1931, decode.acc_seg: 92.1503, loss: 0.1931
+2023-03-03 22:46:23,805 - mmseg - INFO - Iter [25150/80000]	lr: 3.750e-05, eta: 4:35:00, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1888, decode.acc_seg: 92.3519, loss: 0.1888
+2023-03-03 22:46:38,448 - mmseg - INFO - Iter [25200/80000]	lr: 3.750e-05, eta: 4:34:44, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1971, decode.acc_seg: 92.0476, loss: 0.1971
+2023-03-03 22:46:55,449 - mmseg - INFO - Iter [25250/80000]	lr: 3.750e-05, eta: 4:34:34, time: 0.340, data_time: 0.056, memory: 39544, decode.loss_ce: 0.1947, decode.acc_seg: 92.1148, loss: 0.1947
+2023-03-03 22:47:10,075 - mmseg - INFO - Iter [25300/80000]	lr: 3.750e-05, eta: 4:34:18, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1873, decode.acc_seg: 92.3982, loss: 0.1873
+2023-03-03 22:47:24,624 - mmseg - INFO - Iter [25350/80000]	lr: 3.750e-05, eta: 4:34:02, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2018, decode.acc_seg: 91.8696, loss: 0.2018
+2023-03-03 22:47:39,112 - mmseg - INFO - Iter [25400/80000]	lr: 3.750e-05, eta: 4:33:45, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1967, decode.acc_seg: 92.0615, loss: 0.1967
+2023-03-03 22:47:53,711 - mmseg - INFO - Iter [25450/80000]	lr: 3.750e-05, eta: 4:33:29, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1960, decode.acc_seg: 92.1173, loss: 0.1960
+2023-03-03 22:48:08,188 - mmseg - INFO - Iter [25500/80000]	lr: 3.750e-05, eta: 4:33:13, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1980, decode.acc_seg: 92.1832, loss: 0.1980
+2023-03-03 22:48:22,911 - mmseg - INFO - Iter [25550/80000]	lr: 3.750e-05, eta: 4:32:57, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1986, decode.acc_seg: 92.1103, loss: 0.1986
+2023-03-03 22:48:37,486 - mmseg - INFO - Iter [25600/80000]	lr: 3.750e-05, eta: 4:32:41, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1918, decode.acc_seg: 92.2975, loss: 0.1918
+2023-03-03 22:48:51,962 - mmseg - INFO - Iter [25650/80000]	lr: 3.750e-05, eta: 4:32:25, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1979, decode.acc_seg: 92.3095, loss: 0.1979
+2023-03-03 22:49:06,375 - mmseg - INFO - Iter [25700/80000]	lr: 3.750e-05, eta: 4:32:09, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1941, decode.acc_seg: 92.1672, loss: 0.1941
+2023-03-03 22:49:20,927 - mmseg - INFO - Iter [25750/80000]	lr: 3.750e-05, eta: 4:31:53, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1938, decode.acc_seg: 92.0850, loss: 0.1938
+2023-03-03 22:49:35,381 - mmseg - INFO - Iter [25800/80000]	lr: 3.750e-05, eta: 4:31:36, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2039, decode.acc_seg: 91.8252, loss: 0.2039
+2023-03-03 22:49:49,843 - mmseg - INFO - Iter [25850/80000]	lr: 3.750e-05, eta: 4:31:20, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1943, decode.acc_seg: 92.2221, loss: 0.1943
+2023-03-03 22:50:06,821 - mmseg - INFO - Iter [25900/80000]	lr: 3.750e-05, eta: 4:31:09, time: 0.340, data_time: 0.057, memory: 39544, decode.loss_ce: 0.1890, decode.acc_seg: 92.3952, loss: 0.1890
+2023-03-03 22:50:21,433 - mmseg - INFO - Iter [25950/80000]	lr: 3.750e-05, eta: 4:30:53, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1973, decode.acc_seg: 92.1683, loss: 0.1973
+2023-03-03 22:50:35,912 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 22:50:35,913 - mmseg - INFO - Iter [26000/80000]	lr: 3.750e-05, eta: 4:30:37, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1936, decode.acc_seg: 92.0751, loss: 0.1936
+2023-03-03 22:50:50,430 - mmseg - INFO - Iter [26050/80000]	lr: 3.750e-05, eta: 4:30:21, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1891, decode.acc_seg: 92.3622, loss: 0.1891
+2023-03-03 22:51:04,891 - mmseg - INFO - Iter [26100/80000]	lr: 3.750e-05, eta: 4:30:05, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1935, decode.acc_seg: 92.2266, loss: 0.1935
+2023-03-03 22:51:19,398 - mmseg - INFO - Iter [26150/80000]	lr: 3.750e-05, eta: 4:29:49, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2037, decode.acc_seg: 91.9637, loss: 0.2037
+2023-03-03 22:51:33,964 - mmseg - INFO - Iter [26200/80000]	lr: 3.750e-05, eta: 4:29:33, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1906, decode.acc_seg: 92.1810, loss: 0.1906
+2023-03-03 22:51:48,433 - mmseg - INFO - Iter [26250/80000]	lr: 3.750e-05, eta: 4:29:17, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1932, decode.acc_seg: 92.1540, loss: 0.1932
+2023-03-03 22:52:02,983 - mmseg - INFO - Iter [26300/80000]	lr: 3.750e-05, eta: 4:29:01, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1863, decode.acc_seg: 92.5077, loss: 0.1863
+2023-03-03 22:52:17,542 - mmseg - INFO - Iter [26350/80000]	lr: 3.750e-05, eta: 4:28:45, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1936, decode.acc_seg: 92.1093, loss: 0.1936
+2023-03-03 22:52:32,049 - mmseg - INFO - Iter [26400/80000]	lr: 3.750e-05, eta: 4:28:28, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1935, decode.acc_seg: 92.2080, loss: 0.1935
+2023-03-03 22:52:46,478 - mmseg - INFO - Iter [26450/80000]	lr: 3.750e-05, eta: 4:28:12, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1924, decode.acc_seg: 92.3452, loss: 0.1924
+2023-03-03 22:53:00,953 - mmseg - INFO - Iter [26500/80000]	lr: 3.750e-05, eta: 4:27:56, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2040, decode.acc_seg: 91.8324, loss: 0.2040
+2023-03-03 22:53:18,028 - mmseg - INFO - Iter [26550/80000]	lr: 3.750e-05, eta: 4:27:45, time: 0.341, data_time: 0.056, memory: 39544, decode.loss_ce: 0.1950, decode.acc_seg: 92.1428, loss: 0.1950
+2023-03-03 22:53:32,687 - mmseg - INFO - Iter [26600/80000]	lr: 3.750e-05, eta: 4:27:29, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1919, decode.acc_seg: 92.2416, loss: 0.1919
+2023-03-03 22:53:47,208 - mmseg - INFO - Iter [26650/80000]	lr: 3.750e-05, eta: 4:27:13, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1912, decode.acc_seg: 92.4153, loss: 0.1912
+2023-03-03 22:54:01,834 - mmseg - INFO - Iter [26700/80000]	lr: 3.750e-05, eta: 4:26:58, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1901, decode.acc_seg: 92.2331, loss: 0.1901
+2023-03-03 22:54:16,412 - mmseg - INFO - Iter [26750/80000]	lr: 3.750e-05, eta: 4:26:42, time: 0.292, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1955, decode.acc_seg: 92.0715, loss: 0.1955
+2023-03-03 22:54:30,922 - mmseg - INFO - Iter [26800/80000]	lr: 3.750e-05, eta: 4:26:26, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1913, decode.acc_seg: 92.0954, loss: 0.1913
+2023-03-03 22:54:45,503 - mmseg - INFO - Iter [26850/80000]	lr: 3.750e-05, eta: 4:26:10, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1998, decode.acc_seg: 92.0290, loss: 0.1998
+2023-03-03 22:54:59,991 - mmseg - INFO - Iter [26900/80000]	lr: 3.750e-05, eta: 4:25:54, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1946, decode.acc_seg: 92.1425, loss: 0.1946
+2023-03-03 22:55:14,479 - mmseg - INFO - Iter [26950/80000]	lr: 3.750e-05, eta: 4:25:37, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1881, decode.acc_seg: 92.3500, loss: 0.1881
+2023-03-03 22:55:28,891 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 22:55:28,891 - mmseg - INFO - Iter [27000/80000]	lr: 3.750e-05, eta: 4:25:21, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1917, decode.acc_seg: 92.3051, loss: 0.1917
+2023-03-03 22:55:43,429 - mmseg - INFO - Iter [27050/80000]	lr: 3.750e-05, eta: 4:25:05, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1991, decode.acc_seg: 92.0065, loss: 0.1991
+2023-03-03 22:55:57,925 - mmseg - INFO - Iter [27100/80000]	lr: 3.750e-05, eta: 4:24:49, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2021, decode.acc_seg: 91.9772, loss: 0.2021
+2023-03-03 22:56:14,951 - mmseg - INFO - Iter [27150/80000]	lr: 3.750e-05, eta: 4:24:38, time: 0.341, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1955, decode.acc_seg: 92.1317, loss: 0.1955
+2023-03-03 22:56:29,519 - mmseg - INFO - Iter [27200/80000]	lr: 3.750e-05, eta: 4:24:22, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1902, decode.acc_seg: 92.3439, loss: 0.1902
+2023-03-03 22:56:44,066 - mmseg - INFO - Iter [27250/80000]	lr: 3.750e-05, eta: 4:24:06, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1985, decode.acc_seg: 92.0662, loss: 0.1985
+2023-03-03 22:56:58,499 - mmseg - INFO - Iter [27300/80000]	lr: 3.750e-05, eta: 4:23:50, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1880, decode.acc_seg: 92.3047, loss: 0.1880
+2023-03-03 22:57:12,951 - mmseg - INFO - Iter [27350/80000]	lr: 3.750e-05, eta: 4:23:34, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1880, decode.acc_seg: 92.3101, loss: 0.1880
+2023-03-03 22:57:27,404 - mmseg - INFO - Iter [27400/80000]	lr: 3.750e-05, eta: 4:23:18, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1976, decode.acc_seg: 92.0132, loss: 0.1976
+2023-03-03 22:57:41,888 - mmseg - INFO - Iter [27450/80000]	lr: 3.750e-05, eta: 4:23:02, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1934, decode.acc_seg: 92.0983, loss: 0.1934
+2023-03-03 22:57:56,344 - mmseg - INFO - Iter [27500/80000]	lr: 3.750e-05, eta: 4:22:46, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1938, decode.acc_seg: 92.1588, loss: 0.1938
+2023-03-03 22:58:10,903 - mmseg - INFO - Iter [27550/80000]	lr: 3.750e-05, eta: 4:22:30, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1900, decode.acc_seg: 92.3364, loss: 0.1900
+2023-03-03 22:58:25,368 - mmseg - INFO - Iter [27600/80000]	lr: 3.750e-05, eta: 4:22:14, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1885, decode.acc_seg: 92.3931, loss: 0.1885
+2023-03-03 22:58:39,923 - mmseg - INFO - Iter [27650/80000]	lr: 3.750e-05, eta: 4:21:58, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1947, decode.acc_seg: 92.1416, loss: 0.1947
+2023-03-03 22:58:54,405 - mmseg - INFO - Iter [27700/80000]	lr: 3.750e-05, eta: 4:21:42, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2004, decode.acc_seg: 92.1783, loss: 0.2004
+2023-03-03 22:59:08,891 - mmseg - INFO - Iter [27750/80000]	lr: 3.750e-05, eta: 4:21:26, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1956, decode.acc_seg: 92.0797, loss: 0.1956
+2023-03-03 22:59:25,986 - mmseg - INFO - Iter [27800/80000]	lr: 3.750e-05, eta: 4:21:15, time: 0.342, data_time: 0.053, memory: 39544, decode.loss_ce: 0.1906, decode.acc_seg: 92.4465, loss: 0.1906
+2023-03-03 22:59:40,611 - mmseg - INFO - Iter [27850/80000]	lr: 3.750e-05, eta: 4:20:59, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1890, decode.acc_seg: 92.3390, loss: 0.1890
+2023-03-03 22:59:55,137 - mmseg - INFO - Iter [27900/80000]	lr: 3.750e-05, eta: 4:20:43, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2026, decode.acc_seg: 91.9911, loss: 0.2026
+2023-03-03 23:00:09,640 - mmseg - INFO - Iter [27950/80000]	lr: 3.750e-05, eta: 4:20:27, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1911, decode.acc_seg: 92.2314, loss: 0.1911
+2023-03-03 23:00:24,098 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 23:00:24,098 - mmseg - INFO - Iter [28000/80000]	lr: 3.750e-05, eta: 4:20:11, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1910, decode.acc_seg: 92.3131, loss: 0.1910
+2023-03-03 23:00:38,653 - mmseg - INFO - Iter [28050/80000]	lr: 3.750e-05, eta: 4:19:55, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1923, decode.acc_seg: 92.1602, loss: 0.1923
+2023-03-03 23:00:53,155 - mmseg - INFO - Iter [28100/80000]	lr: 3.750e-05, eta: 4:19:39, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1911, decode.acc_seg: 92.1348, loss: 0.1911
+2023-03-03 23:01:07,673 - mmseg - INFO - Iter [28150/80000]	lr: 3.750e-05, eta: 4:19:23, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1945, decode.acc_seg: 92.2430, loss: 0.1945
+2023-03-03 23:01:22,137 - mmseg - INFO - Iter [28200/80000]	lr: 3.750e-05, eta: 4:19:07, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2014, decode.acc_seg: 91.8533, loss: 0.2014
+2023-03-03 23:01:36,590 - mmseg - INFO - Iter [28250/80000]	lr: 3.750e-05, eta: 4:18:51, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1954, decode.acc_seg: 92.2076, loss: 0.1954
+2023-03-03 23:01:51,159 - mmseg - INFO - Iter [28300/80000]	lr: 3.750e-05, eta: 4:18:36, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2062, decode.acc_seg: 91.7584, loss: 0.2062
+2023-03-03 23:02:05,587 - mmseg - INFO - Iter [28350/80000]	lr: 3.750e-05, eta: 4:18:20, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1974, decode.acc_seg: 92.1455, loss: 0.1974
+2023-03-03 23:02:22,675 - mmseg - INFO - Iter [28400/80000]	lr: 3.750e-05, eta: 4:18:08, time: 0.342, data_time: 0.053, memory: 39544, decode.loss_ce: 0.2016, decode.acc_seg: 92.0545, loss: 0.2016
+2023-03-03 23:02:37,209 - mmseg - INFO - Iter [28450/80000]	lr: 3.750e-05, eta: 4:17:52, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1928, decode.acc_seg: 92.2428, loss: 0.1928
+2023-03-03 23:02:51,821 - mmseg - INFO - Iter [28500/80000]	lr: 3.750e-05, eta: 4:17:37, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1934, decode.acc_seg: 92.1999, loss: 0.1934
+2023-03-03 23:03:06,334 - mmseg - INFO - Iter [28550/80000]	lr: 3.750e-05, eta: 4:17:21, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2015, decode.acc_seg: 91.8233, loss: 0.2015
+2023-03-03 23:03:20,840 - mmseg - INFO - Iter [28600/80000]	lr: 3.750e-05, eta: 4:17:05, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1900, decode.acc_seg: 92.4363, loss: 0.1900
+2023-03-03 23:03:35,391 - mmseg - INFO - Iter [28650/80000]	lr: 3.750e-05, eta: 4:16:49, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1946, decode.acc_seg: 92.1441, loss: 0.1946
+2023-03-03 23:03:49,909 - mmseg - INFO - Iter [28700/80000]	lr: 3.750e-05, eta: 4:16:33, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1896, decode.acc_seg: 92.3739, loss: 0.1896
+2023-03-03 23:04:04,475 - mmseg - INFO - Iter [28750/80000]	lr: 3.750e-05, eta: 4:16:17, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1942, decode.acc_seg: 92.1062, loss: 0.1942
+2023-03-03 23:04:18,994 - mmseg - INFO - Iter [28800/80000]	lr: 3.750e-05, eta: 4:16:02, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1935, decode.acc_seg: 92.3013, loss: 0.1935
+2023-03-03 23:04:33,554 - mmseg - INFO - Iter [28850/80000]	lr: 3.750e-05, eta: 4:15:46, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1938, decode.acc_seg: 92.1948, loss: 0.1938
+2023-03-03 23:04:48,080 - mmseg - INFO - Iter [28900/80000]	lr: 3.750e-05, eta: 4:15:30, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1905, decode.acc_seg: 92.1560, loss: 0.1905
+2023-03-03 23:05:02,577 - mmseg - INFO - Iter [28950/80000]	lr: 3.750e-05, eta: 4:15:14, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1946, decode.acc_seg: 92.0957, loss: 0.1946
+2023-03-03 23:05:17,114 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 23:05:17,114 - mmseg - INFO - Iter [29000/80000]	lr: 3.750e-05, eta: 4:14:58, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1925, decode.acc_seg: 92.2216, loss: 0.1925
+2023-03-03 23:05:34,222 - mmseg - INFO - Iter [29050/80000]	lr: 3.750e-05, eta: 4:14:47, time: 0.342, data_time: 0.053, memory: 39544, decode.loss_ce: 0.1831, decode.acc_seg: 92.4796, loss: 0.1831
+2023-03-03 23:05:48,893 - mmseg - INFO - Iter [29100/80000]	lr: 3.750e-05, eta: 4:14:31, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1919, decode.acc_seg: 92.2627, loss: 0.1919
+2023-03-03 23:06:03,413 - mmseg - INFO - Iter [29150/80000]	lr: 3.750e-05, eta: 4:14:16, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1905, decode.acc_seg: 92.3395, loss: 0.1905
+2023-03-03 23:06:17,911 - mmseg - INFO - Iter [29200/80000]	lr: 3.750e-05, eta: 4:14:00, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1979, decode.acc_seg: 91.9924, loss: 0.1979
+2023-03-03 23:06:32,379 - mmseg - INFO - Iter [29250/80000]	lr: 3.750e-05, eta: 4:13:44, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1995, decode.acc_seg: 92.0298, loss: 0.1995
+2023-03-03 23:06:46,888 - mmseg - INFO - Iter [29300/80000]	lr: 3.750e-05, eta: 4:13:28, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1914, decode.acc_seg: 92.1802, loss: 0.1914
+2023-03-03 23:07:01,365 - mmseg - INFO - Iter [29350/80000]	lr: 3.750e-05, eta: 4:13:12, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1870, decode.acc_seg: 92.3035, loss: 0.1870
+2023-03-03 23:07:15,981 - mmseg - INFO - Iter [29400/80000]	lr: 3.750e-05, eta: 4:12:56, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2008, decode.acc_seg: 91.9170, loss: 0.2008
+2023-03-03 23:07:30,583 - mmseg - INFO - Iter [29450/80000]	lr: 3.750e-05, eta: 4:12:41, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1952, decode.acc_seg: 92.0025, loss: 0.1952
+2023-03-03 23:07:45,233 - mmseg - INFO - Iter [29500/80000]	lr: 3.750e-05, eta: 4:12:25, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1937, decode.acc_seg: 92.1945, loss: 0.1937
+2023-03-03 23:07:59,870 - mmseg - INFO - Iter [29550/80000]	lr: 3.750e-05, eta: 4:12:09, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1957, decode.acc_seg: 92.0780, loss: 0.1957
+2023-03-03 23:08:14,374 - mmseg - INFO - Iter [29600/80000]	lr: 3.750e-05, eta: 4:11:54, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1804, decode.acc_seg: 92.4773, loss: 0.1804
+2023-03-03 23:08:28,881 - mmseg - INFO - Iter [29650/80000]	lr: 3.750e-05, eta: 4:11:38, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1904, decode.acc_seg: 92.3782, loss: 0.1904
+2023-03-03 23:08:45,828 - mmseg - INFO - Iter [29700/80000]	lr: 3.750e-05, eta: 4:11:26, time: 0.339, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1981, decode.acc_seg: 92.0331, loss: 0.1981
+2023-03-03 23:09:00,399 - mmseg - INFO - Iter [29750/80000]	lr: 3.750e-05, eta: 4:11:10, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1907, decode.acc_seg: 92.1693, loss: 0.1907
+2023-03-03 23:09:14,970 - mmseg - INFO - Iter [29800/80000]	lr: 3.750e-05, eta: 4:10:55, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2045, decode.acc_seg: 91.7861, loss: 0.2045
+2023-03-03 23:09:29,617 - mmseg - INFO - Iter [29850/80000]	lr: 3.750e-05, eta: 4:10:39, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1903, decode.acc_seg: 92.2615, loss: 0.1903
+2023-03-03 23:09:44,148 - mmseg - INFO - Iter [29900/80000]	lr: 3.750e-05, eta: 4:10:23, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2018, decode.acc_seg: 91.8596, loss: 0.2018
+2023-03-03 23:09:58,568 - mmseg - INFO - Iter [29950/80000]	lr: 3.750e-05, eta: 4:10:07, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1917, decode.acc_seg: 92.3314, loss: 0.1917
+2023-03-03 23:10:13,164 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 23:10:13,164 - mmseg - INFO - Iter [30000/80000]	lr: 3.750e-05, eta: 4:09:52, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1926, decode.acc_seg: 92.1023, loss: 0.1926
+2023-03-03 23:10:27,641 - mmseg - INFO - Iter [30050/80000]	lr: 1.875e-05, eta: 4:09:36, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1926, decode.acc_seg: 92.1854, loss: 0.1926
+2023-03-03 23:10:42,105 - mmseg - INFO - Iter [30100/80000]	lr: 1.875e-05, eta: 4:09:20, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1958, decode.acc_seg: 92.0533, loss: 0.1958
+2023-03-03 23:10:56,610 - mmseg - INFO - Iter [30150/80000]	lr: 1.875e-05, eta: 4:09:04, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1849, decode.acc_seg: 92.4376, loss: 0.1849
+2023-03-03 23:11:11,188 - mmseg - INFO - Iter [30200/80000]	lr: 1.875e-05, eta: 4:08:49, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1861, decode.acc_seg: 92.4412, loss: 0.1861
+2023-03-03 23:11:25,832 - mmseg - INFO - Iter [30250/80000]	lr: 1.875e-05, eta: 4:08:33, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1937, decode.acc_seg: 92.1582, loss: 0.1937
+2023-03-03 23:11:42,924 - mmseg - INFO - Iter [30300/80000]	lr: 1.875e-05, eta: 4:08:21, time: 0.342, data_time: 0.055, memory: 39544, decode.loss_ce: 0.1883, decode.acc_seg: 92.4054, loss: 0.1883
+2023-03-03 23:11:57,525 - mmseg - INFO - Iter [30350/80000]	lr: 1.875e-05, eta: 4:08:06, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2026, decode.acc_seg: 91.9255, loss: 0.2026
+2023-03-03 23:12:11,994 - mmseg - INFO - Iter [30400/80000]	lr: 1.875e-05, eta: 4:07:50, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1896, decode.acc_seg: 92.4266, loss: 0.1896
+2023-03-03 23:12:26,519 - mmseg - INFO - Iter [30450/80000]	lr: 1.875e-05, eta: 4:07:34, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1932, decode.acc_seg: 92.1603, loss: 0.1932
+2023-03-03 23:12:41,075 - mmseg - INFO - Iter [30500/80000]	lr: 1.875e-05, eta: 4:07:19, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1916, decode.acc_seg: 92.3163, loss: 0.1916
+2023-03-03 23:12:55,561 - mmseg - INFO - Iter [30550/80000]	lr: 1.875e-05, eta: 4:07:03, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1842, decode.acc_seg: 92.6084, loss: 0.1842
+2023-03-03 23:13:10,028 - mmseg - INFO - Iter [30600/80000]	lr: 1.875e-05, eta: 4:06:47, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1903, decode.acc_seg: 92.3662, loss: 0.1903
+2023-03-03 23:13:24,654 - mmseg - INFO - Iter [30650/80000]	lr: 1.875e-05, eta: 4:06:31, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1952, decode.acc_seg: 92.1836, loss: 0.1952
+2023-03-03 23:13:39,171 - mmseg - INFO - Iter [30700/80000]	lr: 1.875e-05, eta: 4:06:16, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1944, decode.acc_seg: 92.0544, loss: 0.1944
+2023-03-03 23:13:53,604 - mmseg - INFO - Iter [30750/80000]	lr: 1.875e-05, eta: 4:06:00, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1980, decode.acc_seg: 92.0036, loss: 0.1980
+2023-03-03 23:14:08,137 - mmseg - INFO - Iter [30800/80000]	lr: 1.875e-05, eta: 4:05:44, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1978, decode.acc_seg: 92.0288, loss: 0.1978
+2023-03-03 23:14:22,676 - mmseg - INFO - Iter [30850/80000]	lr: 1.875e-05, eta: 4:05:28, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1933, decode.acc_seg: 92.1726, loss: 0.1933
+2023-03-03 23:14:37,112 - mmseg - INFO - Iter [30900/80000]	lr: 1.875e-05, eta: 4:05:12, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1959, decode.acc_seg: 92.1336, loss: 0.1959
+2023-03-03 23:14:54,205 - mmseg - INFO - Iter [30950/80000]	lr: 1.875e-05, eta: 4:05:01, time: 0.342, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1927, decode.acc_seg: 92.1379, loss: 0.1927
+2023-03-03 23:15:08,750 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 23:15:08,750 - mmseg - INFO - Iter [31000/80000]	lr: 1.875e-05, eta: 4:04:45, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1924, decode.acc_seg: 92.1481, loss: 0.1924
+2023-03-03 23:15:23,353 - mmseg - INFO - Iter [31050/80000]	lr: 1.875e-05, eta: 4:04:30, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1904, decode.acc_seg: 92.3179, loss: 0.1904
+2023-03-03 23:15:37,890 - mmseg - INFO - Iter [31100/80000]	lr: 1.875e-05, eta: 4:04:14, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1901, decode.acc_seg: 92.2820, loss: 0.1901
+2023-03-03 23:15:52,464 - mmseg - INFO - Iter [31150/80000]	lr: 1.875e-05, eta: 4:03:58, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1901, decode.acc_seg: 92.3201, loss: 0.1901
+2023-03-03 23:16:06,977 - mmseg - INFO - Iter [31200/80000]	lr: 1.875e-05, eta: 4:03:43, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1872, decode.acc_seg: 92.3588, loss: 0.1872
+2023-03-03 23:16:21,663 - mmseg - INFO - Iter [31250/80000]	lr: 1.875e-05, eta: 4:03:27, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1903, decode.acc_seg: 92.3166, loss: 0.1903
+2023-03-03 23:16:36,143 - mmseg - INFO - Iter [31300/80000]	lr: 1.875e-05, eta: 4:03:11, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1864, decode.acc_seg: 92.3458, loss: 0.1864
+2023-03-03 23:16:50,663 - mmseg - INFO - Iter [31350/80000]	lr: 1.875e-05, eta: 4:02:56, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1936, decode.acc_seg: 92.0826, loss: 0.1936
+2023-03-03 23:17:05,133 - mmseg - INFO - Iter [31400/80000]	lr: 1.875e-05, eta: 4:02:40, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1889, decode.acc_seg: 92.3945, loss: 0.1889
+2023-03-03 23:17:19,586 - mmseg - INFO - Iter [31450/80000]	lr: 1.875e-05, eta: 4:02:24, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1922, decode.acc_seg: 92.1653, loss: 0.1922
+2023-03-03 23:17:34,032 - mmseg - INFO - Iter [31500/80000]	lr: 1.875e-05, eta: 4:02:08, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1973, decode.acc_seg: 92.0802, loss: 0.1973
+2023-03-03 23:17:48,459 - mmseg - INFO - Iter [31550/80000]	lr: 1.875e-05, eta: 4:01:52, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1944, decode.acc_seg: 92.1847, loss: 0.1944
+2023-03-03 23:18:05,628 - mmseg - INFO - Iter [31600/80000]	lr: 1.875e-05, eta: 4:01:41, time: 0.343, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1877, decode.acc_seg: 92.3723, loss: 0.1877
+2023-03-03 23:18:20,226 - mmseg - INFO - Iter [31650/80000]	lr: 1.875e-05, eta: 4:01:25, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1905, decode.acc_seg: 92.3274, loss: 0.1905
+2023-03-03 23:18:34,846 - mmseg - INFO - Iter [31700/80000]	lr: 1.875e-05, eta: 4:01:10, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1875, decode.acc_seg: 92.2609, loss: 0.1875
+2023-03-03 23:18:49,414 - mmseg - INFO - Iter [31750/80000]	lr: 1.875e-05, eta: 4:00:54, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1900, decode.acc_seg: 92.1462, loss: 0.1900
+2023-03-03 23:19:03,877 - mmseg - INFO - Iter [31800/80000]	lr: 1.875e-05, eta: 4:00:38, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1888, decode.acc_seg: 92.3357, loss: 0.1888
+2023-03-03 23:19:18,302 - mmseg - INFO - Iter [31850/80000]	lr: 1.875e-05, eta: 4:00:23, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1907, decode.acc_seg: 92.4672, loss: 0.1907
+2023-03-03 23:19:32,776 - mmseg - INFO - Iter [31900/80000]	lr: 1.875e-05, eta: 4:00:07, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1959, decode.acc_seg: 92.3093, loss: 0.1959
+2023-03-03 23:19:47,200 - mmseg - INFO - Iter [31950/80000]	lr: 1.875e-05, eta: 3:59:51, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1907, decode.acc_seg: 92.5401, loss: 0.1907
+2023-03-03 23:20:01,683 - mmseg - INFO - Saving checkpoint at 32000 iterations
+2023-03-03 23:20:03,791 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 23:20:03,791 - mmseg - INFO - Iter [32000/80000]	lr: 1.875e-05, eta: 3:59:38, time: 0.332, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1965, decode.acc_seg: 92.0142, loss: 0.1965
+2023-03-03 23:20:23,336 - mmseg - INFO - per class results:
+2023-03-03 23:20:23,342 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        | 76.12 |  88.2 |
+|       building      | 81.07 | 93.12 |
+|         sky         | 94.15 | 97.35 |
+|        floor        | 79.65 | 91.73 |
+|         tree        |  72.6 | 87.31 |
+|       ceiling       |  82.4 | 90.97 |
+|         road        | 82.17 | 89.55 |
+|         bed         | 88.52 | 95.07 |
+|      windowpane     | 61.03 | 77.62 |
+|        grass        | 66.01 | 80.43 |
+|       cabinet       | 59.11 | 72.23 |
+|       sidewalk      |  65.6 | 79.98 |
+|        person       | 79.06 | 91.53 |
+|        earth        | 32.36 | 45.18 |
+|         door        | 47.57 | 62.02 |
+|        table        | 61.04 | 77.08 |
+|       mountain      | 51.76 | 68.51 |
+|        plant        | 50.19 | 63.52 |
+|       curtain       |  70.5 | 81.85 |
+|        chair        | 57.65 | 73.07 |
+|         car         | 83.04 | 89.65 |
+|        water        | 47.44 | 64.07 |
+|       painting      | 69.46 |  84.3 |
+|         sofa        | 64.71 | 81.85 |
+|        shelf        | 40.41 | 56.39 |
+|        house        | 44.62 | 50.62 |
+|         sea         | 46.28 | 74.27 |
+|        mirror       | 64.88 | 73.98 |
+|         rug         | 52.27 |  56.2 |
+|        field        | 28.49 | 47.27 |
+|       armchair      | 44.36 | 59.97 |
+|         seat        | 55.04 | 78.39 |
+|        fence        | 41.02 | 57.21 |
+|         desk        |  48.8 |  71.2 |
+|         rock        |  30.6 |  47.1 |
+|       wardrobe      | 48.48 | 67.65 |
+|         lamp        | 63.88 | 76.31 |
+|       bathtub       | 74.98 | 81.52 |
+|       railing       | 31.42 | 43.68 |
+|       cushion       | 54.68 | 69.72 |
+|         base        | 27.11 | 37.28 |
+|         box         | 25.12 | 33.59 |
+|        column       | 44.29 | 55.27 |
+|      signboard      | 35.46 |  48.5 |
+|   chest of drawers  |  38.6 | 58.64 |
+|       counter       | 27.54 | 40.74 |
+|         sand        | 30.23 | 45.58 |
+|         sink        | 70.44 | 81.47 |
+|      skyscraper     | 47.97 | 54.78 |
+|      fireplace      |  66.3 | 84.95 |
+|     refrigerator    | 76.49 | 83.08 |
+|      grandstand     | 41.15 | 65.67 |
+|         path        | 16.27 | 25.67 |
+|        stairs       | 32.52 | 41.63 |
+|        runway       | 63.41 | 83.32 |
+|         case        | 49.36 |  66.5 |
+|      pool table     | 92.72 | 95.64 |
+|        pillow       |  55.0 | 63.56 |
+|     screen door     | 59.85 | 65.15 |
+|       stairway      | 25.94 | 32.41 |
+|        river        |  8.94 | 13.66 |
+|        bridge       | 50.35 | 56.49 |
+|       bookcase      |  38.4 | 49.74 |
+|        blind        | 47.17 | 53.24 |
+|     coffee table    | 66.29 | 78.77 |
+|        toilet       | 86.47 | 90.28 |
+|        flower       | 32.29 |  47.3 |
+|         book        | 47.23 | 68.62 |
+|         hill        |  8.08 |  9.34 |
+|        bench        | 42.95 | 53.38 |
+|      countertop     | 52.85 | 69.56 |
+|        stove        | 73.47 | 78.66 |
+|         palm        | 50.73 | 72.08 |
+|    kitchen island   | 47.35 | 73.28 |
+|       computer      | 57.01 | 64.41 |
+|     swivel chair    | 42.72 | 53.46 |
+|         boat        |  39.3 | 42.04 |
+|         bar         | 27.04 | 29.99 |
+|    arcade machine   |  28.3 | 30.63 |
+|        hovel        | 30.76 | 32.76 |
+|         bus         | 88.02 |  91.9 |
+|        towel        | 59.36 | 67.86 |
+|        light        | 55.35 | 61.41 |
+|        truck        | 33.53 | 44.12 |
+|        tower        | 22.84 | 31.55 |
+|      chandelier     | 66.22 | 79.59 |
+|        awning       | 24.85 | 27.89 |
+|     streetlight     | 27.49 | 35.36 |
+|        booth        | 52.99 | 55.14 |
+| television receiver | 68.07 | 79.14 |
+|       airplane      | 50.33 | 69.35 |
+|      dirt track     |  7.87 | 21.76 |
+|       apparel       | 28.59 | 44.16 |
+|         pole        | 23.57 | 33.66 |
+|         land        | 12.55 |  18.0 |
+|      bannister      |  7.67 | 12.81 |
+|      escalator      |  23.1 |  23.8 |
+|       ottoman       | 49.25 | 63.35 |
+|        bottle       | 18.56 | 30.67 |
+|        buffet       | 48.67 | 56.28 |
+|        poster       | 27.89 |  33.6 |
+|        stage        | 19.01 | 26.11 |
+|         van         | 47.69 | 60.36 |
+|         ship        | 38.29 | 49.39 |
+|       fountain      |  7.07 |  7.14 |
+|    conveyer belt    | 79.36 | 87.87 |
+|        canopy       | 15.63 | 18.57 |
+|        washer       | 65.74 | 66.01 |
+|      plaything      | 21.85 |  28.7 |
+|    swimming pool    | 36.95 | 41.23 |
+|        stool        |  40.1 | 52.45 |
+|        barrel       | 40.73 | 64.25 |
+|        basket       | 27.89 | 36.04 |
+|      waterfall      |  51.9 | 60.03 |
+|         tent        |  94.4 |  97.8 |
+|         bag         | 11.89 | 15.26 |
+|       minibike      | 60.81 | 72.73 |
+|        cradle       | 80.45 | 97.28 |
+|         oven        | 27.72 |  65.2 |
+|         ball        | 46.55 |  59.1 |
+|         food        | 54.97 | 65.23 |
+|         step        | 14.08 | 17.85 |
+|         tank        | 42.22 | 42.48 |
+|      trade name     | 25.28 | 29.96 |
+|      microwave      | 35.73 | 37.51 |
+|         pot         |  41.1 | 49.72 |
+|        animal       | 51.56 | 54.85 |
+|       bicycle       | 45.09 | 67.56 |
+|         lake        | 60.25 | 63.33 |
+|      dishwasher     | 76.94 | 81.06 |
+|        screen       | 68.58 | 85.71 |
+|       blanket       | 12.79 | 14.78 |
+|      sculpture      | 33.93 | 64.05 |
+|         hood        | 55.61 |  70.6 |
+|        sconce       | 40.37 | 46.81 |
+|         vase        | 35.69 | 53.62 |
+|    traffic light    | 30.17 | 43.73 |
+|         tray        |  5.97 | 11.41 |
+|        ashcan       | 37.86 | 47.85 |
+|         fan         | 57.74 | 71.64 |
+|         pier        | 11.83 | 13.41 |
+|      crt screen     |  4.16 | 10.77 |
+|        plate        | 36.05 | 43.94 |
+|       monitor       | 18.04 | 24.31 |
+|    bulletin board   | 45.83 |  57.0 |
+|        shower       |  1.68 |  2.97 |
+|       radiator      | 47.01 | 54.97 |
+|        glass        | 11.43 | 12.27 |
+|        clock        | 24.54 | 31.03 |
+|         flag        | 38.57 | 42.14 |
++---------------------+-------+-------+
+2023-03-03 23:20:23,342 - mmseg - INFO - Summary:
+2023-03-03 23:20:23,342 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 81.86 | 45.82 | 56.46 |
++-------+-------+-------+
+2023-03-03 23:20:23,404 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151/best_mIoU_iter_24000.pth was removed
+2023-03-03 23:20:25,412 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_32000.pth.
+2023-03-03 23:20:25,413 - mmseg - INFO - Best mIoU is 0.4582 at 32000 iter.
+2023-03-03 23:20:25,413 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 23:20:25,413 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8186, mIoU: 0.4582, mAcc: 0.5646, IoU.background: nan, IoU.wall: 0.7612, IoU.building: 0.8107, IoU.sky: 0.9415, IoU.floor: 0.7965, IoU.tree: 0.7260, IoU.ceiling: 0.8240, IoU.road: 0.8217, IoU.bed : 0.8852, IoU.windowpane: 0.6103, IoU.grass: 0.6601, IoU.cabinet: 0.5911, IoU.sidewalk: 0.6560, IoU.person: 0.7906, IoU.earth: 0.3236, IoU.door: 0.4757, IoU.table: 0.6104, IoU.mountain: 0.5176, IoU.plant: 0.5019, IoU.curtain: 0.7050, IoU.chair: 0.5765, IoU.car: 0.8304, IoU.water: 0.4744, IoU.painting: 0.6946, IoU.sofa: 0.6471, IoU.shelf: 0.4041, IoU.house: 0.4462, IoU.sea: 0.4628, IoU.mirror: 0.6488, IoU.rug: 0.5227, IoU.field: 0.2849, IoU.armchair: 0.4436, IoU.seat: 0.5504, IoU.fence: 0.4102, IoU.desk: 0.4880, IoU.rock: 0.3060, IoU.wardrobe: 0.4848, IoU.lamp: 0.6388, IoU.bathtub: 0.7498, IoU.railing: 0.3142, IoU.cushion: 0.5468, IoU.base: 0.2711, IoU.box: 0.2512, IoU.column: 0.4429, IoU.signboard: 0.3546, IoU.chest of drawers: 0.3860, IoU.counter: 0.2754, IoU.sand: 0.3023, IoU.sink: 0.7044, IoU.skyscraper: 0.4797, IoU.fireplace: 0.6630, IoU.refrigerator: 0.7649, IoU.grandstand: 0.4115, IoU.path: 0.1627, IoU.stairs: 0.3252, IoU.runway: 0.6341, IoU.case: 0.4936, IoU.pool table: 0.9272, IoU.pillow: 0.5500, IoU.screen door: 0.5985, IoU.stairway: 0.2594, IoU.river: 0.0894, IoU.bridge: 0.5035, IoU.bookcase: 0.3840, IoU.blind: 0.4717, IoU.coffee table: 0.6629, IoU.toilet: 0.8647, IoU.flower: 0.3229, IoU.book: 0.4723, IoU.hill: 0.0808, IoU.bench: 0.4295, IoU.countertop: 0.5285, IoU.stove: 0.7347, IoU.palm: 0.5073, IoU.kitchen island: 0.4735, IoU.computer: 0.5701, IoU.swivel chair: 0.4272, IoU.boat: 0.3930, IoU.bar: 0.2704, IoU.arcade machine: 0.2830, IoU.hovel: 0.3076, IoU.bus: 0.8802, IoU.towel: 0.5936, IoU.light: 0.5535, IoU.truck: 0.3353, IoU.tower: 0.2284, IoU.chandelier: 0.6622, IoU.awning: 0.2485, IoU.streetlight: 0.2749, IoU.booth: 0.5299, IoU.television receiver: 0.6807, IoU.airplane: 0.5033, IoU.dirt track: 0.0787, IoU.apparel: 0.2859, IoU.pole: 0.2357, IoU.land: 0.1255, IoU.bannister: 0.0767, IoU.escalator: 0.2310, IoU.ottoman: 0.4925, IoU.bottle: 0.1856, IoU.buffet: 0.4867, IoU.poster: 0.2789, IoU.stage: 0.1901, IoU.van: 0.4769, IoU.ship: 0.3829, IoU.fountain: 0.0707, IoU.conveyer belt: 0.7936, IoU.canopy: 0.1563, IoU.washer: 0.6574, IoU.plaything: 0.2185, IoU.swimming pool: 0.3695, IoU.stool: 0.4010, IoU.barrel: 0.4073, IoU.basket: 0.2789, IoU.waterfall: 0.5190, IoU.tent: 0.9440, IoU.bag: 0.1189, IoU.minibike: 0.6081, IoU.cradle: 0.8045, IoU.oven: 0.2772, IoU.ball: 0.4655, IoU.food: 0.5497, IoU.step: 0.1408, IoU.tank: 0.4222, IoU.trade name: 0.2528, IoU.microwave: 0.3573, IoU.pot: 0.4110, IoU.animal: 0.5156, IoU.bicycle: 0.4509, IoU.lake: 0.6025, IoU.dishwasher: 0.7694, IoU.screen: 0.6858, IoU.blanket: 0.1279, IoU.sculpture: 0.3393, IoU.hood: 0.5561, IoU.sconce: 0.4037, IoU.vase: 0.3569, IoU.traffic light: 0.3017, IoU.tray: 0.0597, IoU.ashcan: 0.3786, IoU.fan: 0.5774, IoU.pier: 0.1183, IoU.crt screen: 0.0416, IoU.plate: 0.3605, IoU.monitor: 0.1804, IoU.bulletin board: 0.4583, IoU.shower: 0.0168, IoU.radiator: 0.4701, IoU.glass: 0.1143, IoU.clock: 0.2454, IoU.flag: 0.3857, Acc.background: nan, Acc.wall: 0.8820, Acc.building: 0.9312, Acc.sky: 0.9735, Acc.floor: 0.9173, Acc.tree: 0.8731, Acc.ceiling: 0.9097, Acc.road: 0.8955, Acc.bed : 0.9507, Acc.windowpane: 0.7762, Acc.grass: 0.8043, Acc.cabinet: 0.7223, Acc.sidewalk: 0.7998, Acc.person: 0.9153, Acc.earth: 0.4518, Acc.door: 0.6202, Acc.table: 0.7708, Acc.mountain: 0.6851, Acc.plant: 0.6352, Acc.curtain: 0.8185, Acc.chair: 0.7307, Acc.car: 0.8965, Acc.water: 0.6407, Acc.painting: 0.8430, Acc.sofa: 0.8185, Acc.shelf: 0.5639, Acc.house: 0.5062, Acc.sea: 0.7427, Acc.mirror: 0.7398, Acc.rug: 0.5620, Acc.field: 0.4727, Acc.armchair: 0.5997, Acc.seat: 0.7839, Acc.fence: 0.5721, Acc.desk: 0.7120, Acc.rock: 0.4710, Acc.wardrobe: 0.6765, Acc.lamp: 0.7631, Acc.bathtub: 0.8152, Acc.railing: 0.4368, Acc.cushion: 0.6972, Acc.base: 0.3728, Acc.box: 0.3359, Acc.column: 0.5527, Acc.signboard: 0.4850, Acc.chest of drawers: 0.5864, Acc.counter: 0.4074, Acc.sand: 0.4558, Acc.sink: 0.8147, Acc.skyscraper: 0.5478, Acc.fireplace: 0.8495, Acc.refrigerator: 0.8308, Acc.grandstand: 0.6567, Acc.path: 0.2567, Acc.stairs: 0.4163, Acc.runway: 0.8332, Acc.case: 0.6650, Acc.pool table: 0.9564, Acc.pillow: 0.6356, Acc.screen door: 0.6515, Acc.stairway: 0.3241, Acc.river: 0.1366, Acc.bridge: 0.5649, Acc.bookcase: 0.4974, Acc.blind: 0.5324, Acc.coffee table: 0.7877, Acc.toilet: 0.9028, Acc.flower: 0.4730, Acc.book: 0.6862, Acc.hill: 0.0934, Acc.bench: 0.5338, Acc.countertop: 0.6956, Acc.stove: 0.7866, Acc.palm: 0.7208, Acc.kitchen island: 0.7328, Acc.computer: 0.6441, Acc.swivel chair: 0.5346, Acc.boat: 0.4204, Acc.bar: 0.2999, Acc.arcade machine: 0.3063, Acc.hovel: 0.3276, Acc.bus: 0.9190, Acc.towel: 0.6786, Acc.light: 0.6141, Acc.truck: 0.4412, Acc.tower: 0.3155, Acc.chandelier: 0.7959, Acc.awning: 0.2789, Acc.streetlight: 0.3536, Acc.booth: 0.5514, Acc.television receiver: 0.7914, Acc.airplane: 0.6935, Acc.dirt track: 0.2176, Acc.apparel: 0.4416, Acc.pole: 0.3366, Acc.land: 0.1800, Acc.bannister: 0.1281, Acc.escalator: 0.2380, Acc.ottoman: 0.6335, Acc.bottle: 0.3067, Acc.buffet: 0.5628, Acc.poster: 0.3360, Acc.stage: 0.2611, Acc.van: 0.6036, Acc.ship: 0.4939, Acc.fountain: 0.0714, Acc.conveyer belt: 0.8787, Acc.canopy: 0.1857, Acc.washer: 0.6601, Acc.plaything: 0.2870, Acc.swimming pool: 0.4123, Acc.stool: 0.5245, Acc.barrel: 0.6425, Acc.basket: 0.3604, Acc.waterfall: 0.6003, Acc.tent: 0.9780, Acc.bag: 0.1526, Acc.minibike: 0.7273, Acc.cradle: 0.9728, Acc.oven: 0.6520, Acc.ball: 0.5910, Acc.food: 0.6523, Acc.step: 0.1785, Acc.tank: 0.4248, Acc.trade name: 0.2996, Acc.microwave: 0.3751, Acc.pot: 0.4972, Acc.animal: 0.5485, Acc.bicycle: 0.6756, Acc.lake: 0.6333, Acc.dishwasher: 0.8106, Acc.screen: 0.8571, Acc.blanket: 0.1478, Acc.sculpture: 0.6405, Acc.hood: 0.7060, Acc.sconce: 0.4681, Acc.vase: 0.5362, Acc.traffic light: 0.4373, Acc.tray: 0.1141, Acc.ashcan: 0.4785, Acc.fan: 0.7164, Acc.pier: 0.1341, Acc.crt screen: 0.1077, Acc.plate: 0.4394, Acc.monitor: 0.2431, Acc.bulletin board: 0.5700, Acc.shower: 0.0297, Acc.radiator: 0.5497, Acc.glass: 0.1227, Acc.clock: 0.3103, Acc.flag: 0.4214
+2023-03-03 23:20:40,307 - mmseg - INFO - Iter [32050/80000]	lr: 1.875e-05, eta: 3:59:56, time: 0.730, data_time: 0.440, memory: 39544, decode.loss_ce: 0.1947, decode.acc_seg: 92.1003, loss: 0.1947
+2023-03-03 23:20:54,969 - mmseg - INFO - Iter [32100/80000]	lr: 1.875e-05, eta: 3:59:40, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1834, decode.acc_seg: 92.4878, loss: 0.1834
+2023-03-03 23:21:09,480 - mmseg - INFO - Iter [32150/80000]	lr: 1.875e-05, eta: 3:59:24, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1913, decode.acc_seg: 92.2034, loss: 0.1913
+2023-03-03 23:21:26,409 - mmseg - INFO - Iter [32200/80000]	lr: 1.875e-05, eta: 3:59:12, time: 0.339, data_time: 0.056, memory: 39544, decode.loss_ce: 0.1843, decode.acc_seg: 92.3409, loss: 0.1843
+2023-03-03 23:21:40,893 - mmseg - INFO - Iter [32250/80000]	lr: 1.875e-05, eta: 3:58:56, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1913, decode.acc_seg: 92.3056, loss: 0.1913
+2023-03-03 23:21:55,416 - mmseg - INFO - Iter [32300/80000]	lr: 1.875e-05, eta: 3:58:41, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1947, decode.acc_seg: 92.1599, loss: 0.1947
+2023-03-03 23:22:09,926 - mmseg - INFO - Iter [32350/80000]	lr: 1.875e-05, eta: 3:58:25, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1956, decode.acc_seg: 92.1847, loss: 0.1956
+2023-03-03 23:22:24,510 - mmseg - INFO - Iter [32400/80000]	lr: 1.875e-05, eta: 3:58:09, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1875, decode.acc_seg: 92.4298, loss: 0.1875
+2023-03-03 23:22:39,034 - mmseg - INFO - Iter [32450/80000]	lr: 1.875e-05, eta: 3:57:54, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1908, decode.acc_seg: 92.3414, loss: 0.1908
+2023-03-03 23:22:53,650 - mmseg - INFO - Iter [32500/80000]	lr: 1.875e-05, eta: 3:57:38, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1903, decode.acc_seg: 92.3194, loss: 0.1903
+2023-03-03 23:23:08,176 - mmseg - INFO - Iter [32550/80000]	lr: 1.875e-05, eta: 3:57:22, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1955, decode.acc_seg: 92.2475, loss: 0.1955
+2023-03-03 23:23:22,676 - mmseg - INFO - Iter [32600/80000]	lr: 1.875e-05, eta: 3:57:07, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1873, decode.acc_seg: 92.4134, loss: 0.1873
+2023-03-03 23:23:37,076 - mmseg - INFO - Iter [32650/80000]	lr: 1.875e-05, eta: 3:56:51, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1944, decode.acc_seg: 92.0632, loss: 0.1944
+2023-03-03 23:23:51,469 - mmseg - INFO - Iter [32700/80000]	lr: 1.875e-05, eta: 3:56:35, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1924, decode.acc_seg: 92.2238, loss: 0.1924
+2023-03-03 23:24:05,940 - mmseg - INFO - Iter [32750/80000]	lr: 1.875e-05, eta: 3:56:19, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1934, decode.acc_seg: 92.3472, loss: 0.1934
+2023-03-03 23:24:20,359 - mmseg - INFO - Iter [32800/80000]	lr: 1.875e-05, eta: 3:56:03, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1938, decode.acc_seg: 92.1115, loss: 0.1938
+2023-03-03 23:24:37,419 - mmseg - INFO - Iter [32850/80000]	lr: 1.875e-05, eta: 3:55:51, time: 0.341, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1938, decode.acc_seg: 92.2156, loss: 0.1938
+2023-03-03 23:24:51,899 - mmseg - INFO - Iter [32900/80000]	lr: 1.875e-05, eta: 3:55:35, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1904, decode.acc_seg: 92.3667, loss: 0.1904
+2023-03-03 23:25:06,526 - mmseg - INFO - Iter [32950/80000]	lr: 1.875e-05, eta: 3:55:20, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1941, decode.acc_seg: 92.0351, loss: 0.1941
+2023-03-03 23:25:21,005 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 23:25:21,005 - mmseg - INFO - Iter [33000/80000]	lr: 1.875e-05, eta: 3:55:04, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1941, decode.acc_seg: 92.3046, loss: 0.1941
+2023-03-03 23:25:35,633 - mmseg - INFO - Iter [33050/80000]	lr: 1.875e-05, eta: 3:54:48, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1879, decode.acc_seg: 92.3250, loss: 0.1879
+2023-03-03 23:25:50,258 - mmseg - INFO - Iter [33100/80000]	lr: 1.875e-05, eta: 3:54:33, time: 0.292, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1869, decode.acc_seg: 92.4013, loss: 0.1869
+2023-03-03 23:26:04,751 - mmseg - INFO - Iter [33150/80000]	lr: 1.875e-05, eta: 3:54:17, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1960, decode.acc_seg: 92.2246, loss: 0.1960
+2023-03-03 23:26:19,177 - mmseg - INFO - Iter [33200/80000]	lr: 1.875e-05, eta: 3:54:01, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1902, decode.acc_seg: 92.3141, loss: 0.1902
+2023-03-03 23:26:33,680 - mmseg - INFO - Iter [33250/80000]	lr: 1.875e-05, eta: 3:53:46, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2005, decode.acc_seg: 91.9722, loss: 0.2005
+2023-03-03 23:26:48,135 - mmseg - INFO - Iter [33300/80000]	lr: 1.875e-05, eta: 3:53:30, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1918, decode.acc_seg: 92.1929, loss: 0.1918
+2023-03-03 23:27:02,620 - mmseg - INFO - Iter [33350/80000]	lr: 1.875e-05, eta: 3:53:14, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1911, decode.acc_seg: 92.2791, loss: 0.1911
+2023-03-03 23:27:17,085 - mmseg - INFO - Iter [33400/80000]	lr: 1.875e-05, eta: 3:52:58, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1859, decode.acc_seg: 92.2887, loss: 0.1859
+2023-03-03 23:27:34,230 - mmseg - INFO - Iter [33450/80000]	lr: 1.875e-05, eta: 3:52:46, time: 0.343, data_time: 0.052, memory: 39544, decode.loss_ce: 0.1953, decode.acc_seg: 92.2607, loss: 0.1953
+2023-03-03 23:27:48,684 - mmseg - INFO - Iter [33500/80000]	lr: 1.875e-05, eta: 3:52:31, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1855, decode.acc_seg: 92.3901, loss: 0.1855
+2023-03-03 23:28:03,429 - mmseg - INFO - Iter [33550/80000]	lr: 1.875e-05, eta: 3:52:15, time: 0.295, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1915, decode.acc_seg: 92.1801, loss: 0.1915
+2023-03-03 23:28:17,946 - mmseg - INFO - Iter [33600/80000]	lr: 1.875e-05, eta: 3:52:00, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1828, decode.acc_seg: 92.5151, loss: 0.1828
+2023-03-03 23:28:32,376 - mmseg - INFO - Iter [33650/80000]	lr: 1.875e-05, eta: 3:51:44, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1973, decode.acc_seg: 92.1689, loss: 0.1973
+2023-03-03 23:28:46,902 - mmseg - INFO - Iter [33700/80000]	lr: 1.875e-05, eta: 3:51:28, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1939, decode.acc_seg: 92.2131, loss: 0.1939
+2023-03-03 23:29:01,407 - mmseg - INFO - Iter [33750/80000]	lr: 1.875e-05, eta: 3:51:13, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1847, decode.acc_seg: 92.4417, loss: 0.1847
+2023-03-03 23:29:15,886 - mmseg - INFO - Iter [33800/80000]	lr: 1.875e-05, eta: 3:50:57, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1959, decode.acc_seg: 92.1502, loss: 0.1959
+2023-03-03 23:29:30,266 - mmseg - INFO - Iter [33850/80000]	lr: 1.875e-05, eta: 3:50:41, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1993, decode.acc_seg: 91.8984, loss: 0.1993
+2023-03-03 23:29:44,688 - mmseg - INFO - Iter [33900/80000]	lr: 1.875e-05, eta: 3:50:25, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1836, decode.acc_seg: 92.5988, loss: 0.1836
+2023-03-03 23:29:59,306 - mmseg - INFO - Iter [33950/80000]	lr: 1.875e-05, eta: 3:50:10, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1924, decode.acc_seg: 92.3034, loss: 0.1924
+2023-03-03 23:30:13,796 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 23:30:13,796 - mmseg - INFO - Iter [34000/80000]	lr: 1.875e-05, eta: 3:49:54, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1895, decode.acc_seg: 92.2627, loss: 0.1895
+2023-03-03 23:30:28,266 - mmseg - INFO - Iter [34050/80000]	lr: 1.875e-05, eta: 3:49:38, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2011, decode.acc_seg: 91.8888, loss: 0.2011
+2023-03-03 23:30:45,168 - mmseg - INFO - Iter [34100/80000]	lr: 1.875e-05, eta: 3:49:26, time: 0.338, data_time: 0.055, memory: 39544, decode.loss_ce: 0.2033, decode.acc_seg: 91.7872, loss: 0.2033
+2023-03-03 23:30:59,687 - mmseg - INFO - Iter [34150/80000]	lr: 1.875e-05, eta: 3:49:10, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1980, decode.acc_seg: 92.0301, loss: 0.1980
+2023-03-03 23:31:14,275 - mmseg - INFO - Iter [34200/80000]	lr: 1.875e-05, eta: 3:48:55, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1964, decode.acc_seg: 92.1179, loss: 0.1964
+2023-03-03 23:31:28,708 - mmseg - INFO - Iter [34250/80000]	lr: 1.875e-05, eta: 3:48:39, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1975, decode.acc_seg: 92.0435, loss: 0.1975
+2023-03-03 23:31:43,111 - mmseg - INFO - Iter [34300/80000]	lr: 1.875e-05, eta: 3:48:23, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1886, decode.acc_seg: 92.2533, loss: 0.1886
+2023-03-03 23:31:57,560 - mmseg - INFO - Iter [34350/80000]	lr: 1.875e-05, eta: 3:48:07, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1913, decode.acc_seg: 92.2575, loss: 0.1913
+2023-03-03 23:32:12,156 - mmseg - INFO - Iter [34400/80000]	lr: 1.875e-05, eta: 3:47:52, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1843, decode.acc_seg: 92.5422, loss: 0.1843
+2023-03-03 23:32:26,701 - mmseg - INFO - Iter [34450/80000]	lr: 1.875e-05, eta: 3:47:36, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1882, decode.acc_seg: 92.4307, loss: 0.1882
+2023-03-03 23:32:41,196 - mmseg - INFO - Iter [34500/80000]	lr: 1.875e-05, eta: 3:47:21, time: 0.290, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1839, decode.acc_seg: 92.6083, loss: 0.1839
+2023-03-03 23:32:55,693 - mmseg - INFO - Iter [34550/80000]	lr: 1.875e-05, eta: 3:47:05, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1904, decode.acc_seg: 92.3237, loss: 0.1904
+2023-03-03 23:33:10,191 - mmseg - INFO - Iter [34600/80000]	lr: 1.875e-05, eta: 3:46:49, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1867, decode.acc_seg: 92.4876, loss: 0.1867
+2023-03-03 23:33:24,765 - mmseg - INFO - Iter [34650/80000]	lr: 1.875e-05, eta: 3:46:34, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1916, decode.acc_seg: 92.2132, loss: 0.1916
+2023-03-03 23:33:39,246 - mmseg - INFO - Iter [34700/80000]	lr: 1.875e-05, eta: 3:46:18, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1923, decode.acc_seg: 92.1770, loss: 0.1923
+2023-03-03 23:33:56,296 - mmseg - INFO - Iter [34750/80000]	lr: 1.875e-05, eta: 3:46:06, time: 0.341, data_time: 0.055, memory: 39544, decode.loss_ce: 0.1867, decode.acc_seg: 92.3908, loss: 0.1867
+2023-03-03 23:34:10,791 - mmseg - INFO - Iter [34800/80000]	lr: 1.875e-05, eta: 3:45:50, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1936, decode.acc_seg: 92.1998, loss: 0.1936
+2023-03-03 23:34:25,371 - mmseg - INFO - Iter [34850/80000]	lr: 1.875e-05, eta: 3:45:35, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1868, decode.acc_seg: 92.5250, loss: 0.1868
+2023-03-03 23:34:39,859 - mmseg - INFO - Iter [34900/80000]	lr: 1.875e-05, eta: 3:45:19, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2034, decode.acc_seg: 91.9749, loss: 0.2034
+2023-03-03 23:34:54,322 - mmseg - INFO - Iter [34950/80000]	lr: 1.875e-05, eta: 3:45:04, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1905, decode.acc_seg: 92.2576, loss: 0.1905
+2023-03-03 23:35:08,802 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 23:35:08,802 - mmseg - INFO - Iter [35000/80000]	lr: 1.875e-05, eta: 3:44:48, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1897, decode.acc_seg: 92.2126, loss: 0.1897
+2023-03-03 23:35:23,243 - mmseg - INFO - Iter [35050/80000]	lr: 1.875e-05, eta: 3:44:32, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1915, decode.acc_seg: 92.3038, loss: 0.1915
+2023-03-03 23:35:37,823 - mmseg - INFO - Iter [35100/80000]	lr: 1.875e-05, eta: 3:44:17, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1846, decode.acc_seg: 92.4288, loss: 0.1846
+2023-03-03 23:35:52,368 - mmseg - INFO - Iter [35150/80000]	lr: 1.875e-05, eta: 3:44:01, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1922, decode.acc_seg: 92.2365, loss: 0.1922
+2023-03-03 23:36:06,965 - mmseg - INFO - Iter [35200/80000]	lr: 1.875e-05, eta: 3:43:46, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1875, decode.acc_seg: 92.4162, loss: 0.1875
+2023-03-03 23:36:21,615 - mmseg - INFO - Iter [35250/80000]	lr: 1.875e-05, eta: 3:43:30, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1926, decode.acc_seg: 92.1102, loss: 0.1926
+2023-03-03 23:36:36,065 - mmseg - INFO - Iter [35300/80000]	lr: 1.875e-05, eta: 3:43:15, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1892, decode.acc_seg: 92.4474, loss: 0.1892
+2023-03-03 23:36:53,206 - mmseg - INFO - Iter [35350/80000]	lr: 1.875e-05, eta: 3:43:02, time: 0.343, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1896, decode.acc_seg: 92.3009, loss: 0.1896
+2023-03-03 23:37:07,708 - mmseg - INFO - Iter [35400/80000]	lr: 1.875e-05, eta: 3:42:47, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1939, decode.acc_seg: 92.0866, loss: 0.1939
+2023-03-03 23:37:22,182 - mmseg - INFO - Iter [35450/80000]	lr: 1.875e-05, eta: 3:42:31, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1994, decode.acc_seg: 91.9888, loss: 0.1994
+2023-03-03 23:37:36,661 - mmseg - INFO - Iter [35500/80000]	lr: 1.875e-05, eta: 3:42:15, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1913, decode.acc_seg: 92.4048, loss: 0.1913
+2023-03-03 23:37:51,156 - mmseg - INFO - Iter [35550/80000]	lr: 1.875e-05, eta: 3:42:00, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1959, decode.acc_seg: 92.0182, loss: 0.1959
+2023-03-03 23:38:05,684 - mmseg - INFO - Iter [35600/80000]	lr: 1.875e-05, eta: 3:41:44, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1817, decode.acc_seg: 92.5544, loss: 0.1817
+2023-03-03 23:38:20,219 - mmseg - INFO - Iter [35650/80000]	lr: 1.875e-05, eta: 3:41:29, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1881, decode.acc_seg: 92.2749, loss: 0.1881
+2023-03-03 23:38:34,620 - mmseg - INFO - Iter [35700/80000]	lr: 1.875e-05, eta: 3:41:13, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1881, decode.acc_seg: 92.1817, loss: 0.1881
+2023-03-03 23:38:49,149 - mmseg - INFO - Iter [35750/80000]	lr: 1.875e-05, eta: 3:40:58, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1867, decode.acc_seg: 92.4756, loss: 0.1867
+2023-03-03 23:39:03,638 - mmseg - INFO - Iter [35800/80000]	lr: 1.875e-05, eta: 3:40:42, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1858, decode.acc_seg: 92.4916, loss: 0.1858
+2023-03-03 23:39:18,071 - mmseg - INFO - Iter [35850/80000]	lr: 1.875e-05, eta: 3:40:26, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1906, decode.acc_seg: 92.4332, loss: 0.1906
+2023-03-03 23:39:32,537 - mmseg - INFO - Iter [35900/80000]	lr: 1.875e-05, eta: 3:40:11, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1947, decode.acc_seg: 92.0665, loss: 0.1947
+2023-03-03 23:39:47,097 - mmseg - INFO - Iter [35950/80000]	lr: 1.875e-05, eta: 3:39:55, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1884, decode.acc_seg: 92.4045, loss: 0.1884
+2023-03-03 23:40:04,063 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 23:40:04,063 - mmseg - INFO - Iter [36000/80000]	lr: 1.875e-05, eta: 3:39:43, time: 0.339, data_time: 0.057, memory: 39544, decode.loss_ce: 0.2002, decode.acc_seg: 91.8951, loss: 0.2002
+2023-03-03 23:40:18,594 - mmseg - INFO - Iter [36050/80000]	lr: 1.875e-05, eta: 3:39:27, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1865, decode.acc_seg: 92.4643, loss: 0.1865
+2023-03-03 23:40:33,097 - mmseg - INFO - Iter [36100/80000]	lr: 1.875e-05, eta: 3:39:12, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1965, decode.acc_seg: 92.1155, loss: 0.1965
+2023-03-03 23:40:47,682 - mmseg - INFO - Iter [36150/80000]	lr: 1.875e-05, eta: 3:38:56, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1838, decode.acc_seg: 92.5402, loss: 0.1838
+2023-03-03 23:41:02,148 - mmseg - INFO - Iter [36200/80000]	lr: 1.875e-05, eta: 3:38:40, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1876, decode.acc_seg: 92.4292, loss: 0.1876
+2023-03-03 23:41:16,634 - mmseg - INFO - Iter [36250/80000]	lr: 1.875e-05, eta: 3:38:25, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1981, decode.acc_seg: 91.9820, loss: 0.1981
+2023-03-03 23:41:31,180 - mmseg - INFO - Iter [36300/80000]	lr: 1.875e-05, eta: 3:38:09, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1888, decode.acc_seg: 92.3758, loss: 0.1888
+2023-03-03 23:41:45,753 - mmseg - INFO - Iter [36350/80000]	lr: 1.875e-05, eta: 3:37:54, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1897, decode.acc_seg: 92.3797, loss: 0.1897
+2023-03-03 23:42:00,313 - mmseg - INFO - Iter [36400/80000]	lr: 1.875e-05, eta: 3:37:38, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1916, decode.acc_seg: 92.2493, loss: 0.1916
+2023-03-03 23:42:14,942 - mmseg - INFO - Iter [36450/80000]	lr: 1.875e-05, eta: 3:37:23, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1904, decode.acc_seg: 92.3033, loss: 0.1904
+2023-03-03 23:42:29,476 - mmseg - INFO - Iter [36500/80000]	lr: 1.875e-05, eta: 3:37:08, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1867, decode.acc_seg: 92.3323, loss: 0.1867
+2023-03-03 23:42:43,974 - mmseg - INFO - Iter [36550/80000]	lr: 1.875e-05, eta: 3:36:52, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1961, decode.acc_seg: 92.1971, loss: 0.1961
+2023-03-03 23:43:01,240 - mmseg - INFO - Iter [36600/80000]	lr: 1.875e-05, eta: 3:36:40, time: 0.345, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1897, decode.acc_seg: 92.3355, loss: 0.1897
+2023-03-03 23:43:15,758 - mmseg - INFO - Iter [36650/80000]	lr: 1.875e-05, eta: 3:36:24, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1888, decode.acc_seg: 92.2269, loss: 0.1888
+2023-03-03 23:43:30,359 - mmseg - INFO - Iter [36700/80000]	lr: 1.875e-05, eta: 3:36:09, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1869, decode.acc_seg: 92.4514, loss: 0.1869
+2023-03-03 23:43:44,870 - mmseg - INFO - Iter [36750/80000]	lr: 1.875e-05, eta: 3:35:53, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1877, decode.acc_seg: 92.4384, loss: 0.1877
+2023-03-03 23:43:59,407 - mmseg - INFO - Iter [36800/80000]	lr: 1.875e-05, eta: 3:35:38, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1914, decode.acc_seg: 92.2652, loss: 0.1914
+2023-03-03 23:44:13,920 - mmseg - INFO - Iter [36850/80000]	lr: 1.875e-05, eta: 3:35:22, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1893, decode.acc_seg: 92.3130, loss: 0.1893
+2023-03-03 23:44:28,524 - mmseg - INFO - Iter [36900/80000]	lr: 1.875e-05, eta: 3:35:07, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1931, decode.acc_seg: 92.1924, loss: 0.1931
+2023-03-03 23:44:43,024 - mmseg - INFO - Iter [36950/80000]	lr: 1.875e-05, eta: 3:34:51, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1918, decode.acc_seg: 92.1323, loss: 0.1918
+2023-03-03 23:44:57,615 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 23:44:57,615 - mmseg - INFO - Iter [37000/80000]	lr: 1.875e-05, eta: 3:34:36, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1980, decode.acc_seg: 92.0052, loss: 0.1980
+2023-03-03 23:45:12,147 - mmseg - INFO - Iter [37050/80000]	lr: 1.875e-05, eta: 3:34:20, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1847, decode.acc_seg: 92.5081, loss: 0.1847
+2023-03-03 23:45:26,677 - mmseg - INFO - Iter [37100/80000]	lr: 1.875e-05, eta: 3:34:05, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1936, decode.acc_seg: 92.0657, loss: 0.1936
+2023-03-03 23:45:41,199 - mmseg - INFO - Iter [37150/80000]	lr: 1.875e-05, eta: 3:33:50, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1878, decode.acc_seg: 92.5014, loss: 0.1878
+2023-03-03 23:45:55,724 - mmseg - INFO - Iter [37200/80000]	lr: 1.875e-05, eta: 3:33:34, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1884, decode.acc_seg: 92.4722, loss: 0.1884
+2023-03-03 23:46:12,954 - mmseg - INFO - Iter [37250/80000]	lr: 1.875e-05, eta: 3:33:22, time: 0.345, data_time: 0.053, memory: 39544, decode.loss_ce: 0.1872, decode.acc_seg: 92.4329, loss: 0.1872
+2023-03-03 23:46:27,433 - mmseg - INFO - Iter [37300/80000]	lr: 1.875e-05, eta: 3:33:06, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1928, decode.acc_seg: 92.3441, loss: 0.1928
+2023-03-03 23:46:41,956 - mmseg - INFO - Iter [37350/80000]	lr: 1.875e-05, eta: 3:32:51, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1969, decode.acc_seg: 92.1025, loss: 0.1969
+2023-03-03 23:46:56,618 - mmseg - INFO - Iter [37400/80000]	lr: 1.875e-05, eta: 3:32:35, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1857, decode.acc_seg: 92.4253, loss: 0.1857
+2023-03-03 23:47:11,139 - mmseg - INFO - Iter [37450/80000]	lr: 1.875e-05, eta: 3:32:20, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1921, decode.acc_seg: 92.3014, loss: 0.1921
+2023-03-03 23:47:25,623 - mmseg - INFO - Iter [37500/80000]	lr: 1.875e-05, eta: 3:32:04, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1916, decode.acc_seg: 92.2150, loss: 0.1916
+2023-03-03 23:47:40,124 - mmseg - INFO - Iter [37550/80000]	lr: 1.875e-05, eta: 3:31:49, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1877, decode.acc_seg: 92.2538, loss: 0.1877
+2023-03-03 23:47:54,595 - mmseg - INFO - Iter [37600/80000]	lr: 1.875e-05, eta: 3:31:33, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1924, decode.acc_seg: 92.2200, loss: 0.1924
+2023-03-03 23:48:09,230 - mmseg - INFO - Iter [37650/80000]	lr: 1.875e-05, eta: 3:31:18, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1926, decode.acc_seg: 92.2796, loss: 0.1926
+2023-03-03 23:48:23,823 - mmseg - INFO - Iter [37700/80000]	lr: 1.875e-05, eta: 3:31:03, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1937, decode.acc_seg: 92.3530, loss: 0.1937
+2023-03-03 23:48:38,347 - mmseg - INFO - Iter [37750/80000]	lr: 1.875e-05, eta: 3:30:47, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1893, decode.acc_seg: 92.3847, loss: 0.1893
+2023-03-03 23:48:52,891 - mmseg - INFO - Iter [37800/80000]	lr: 1.875e-05, eta: 3:30:32, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1919, decode.acc_seg: 92.3222, loss: 0.1919
+2023-03-03 23:49:07,510 - mmseg - INFO - Iter [37850/80000]	lr: 1.875e-05, eta: 3:30:16, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1960, decode.acc_seg: 92.1229, loss: 0.1960
+2023-03-03 23:49:24,569 - mmseg - INFO - Iter [37900/80000]	lr: 1.875e-05, eta: 3:30:04, time: 0.341, data_time: 0.057, memory: 39544, decode.loss_ce: 0.1865, decode.acc_seg: 92.4986, loss: 0.1865
+2023-03-03 23:49:39,080 - mmseg - INFO - Iter [37950/80000]	lr: 1.875e-05, eta: 3:29:48, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1903, decode.acc_seg: 92.2964, loss: 0.1903
+2023-03-03 23:49:53,598 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 23:49:53,599 - mmseg - INFO - Iter [38000/80000]	lr: 1.875e-05, eta: 3:29:33, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1901, decode.acc_seg: 92.2566, loss: 0.1901
+2023-03-03 23:50:08,147 - mmseg - INFO - Iter [38050/80000]	lr: 1.875e-05, eta: 3:29:17, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1940, decode.acc_seg: 92.1355, loss: 0.1940
+2023-03-03 23:50:22,737 - mmseg - INFO - Iter [38100/80000]	lr: 1.875e-05, eta: 3:29:02, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1983, decode.acc_seg: 91.9252, loss: 0.1983
+2023-03-03 23:50:37,282 - mmseg - INFO - Iter [38150/80000]	lr: 1.875e-05, eta: 3:28:46, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1842, decode.acc_seg: 92.5268, loss: 0.1842
+2023-03-03 23:50:51,739 - mmseg - INFO - Iter [38200/80000]	lr: 1.875e-05, eta: 3:28:31, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1902, decode.acc_seg: 92.4188, loss: 0.1902
+2023-03-03 23:51:06,316 - mmseg - INFO - Iter [38250/80000]	lr: 1.875e-05, eta: 3:28:16, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1907, decode.acc_seg: 92.2558, loss: 0.1907
+2023-03-03 23:51:20,992 - mmseg - INFO - Iter [38300/80000]	lr: 1.875e-05, eta: 3:28:00, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1910, decode.acc_seg: 92.3373, loss: 0.1910
+2023-03-03 23:51:35,590 - mmseg - INFO - Iter [38350/80000]	lr: 1.875e-05, eta: 3:27:45, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1920, decode.acc_seg: 92.1729, loss: 0.1920
+2023-03-03 23:51:50,348 - mmseg - INFO - Iter [38400/80000]	lr: 1.875e-05, eta: 3:27:30, time: 0.295, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1967, decode.acc_seg: 92.0503, loss: 0.1967
+2023-03-03 23:52:04,749 - mmseg - INFO - Iter [38450/80000]	lr: 1.875e-05, eta: 3:27:14, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1855, decode.acc_seg: 92.5945, loss: 0.1855
+2023-03-03 23:52:21,806 - mmseg - INFO - Iter [38500/80000]	lr: 1.875e-05, eta: 3:27:01, time: 0.341, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1968, decode.acc_seg: 92.0936, loss: 0.1968
+2023-03-03 23:52:36,389 - mmseg - INFO - Iter [38550/80000]	lr: 1.875e-05, eta: 3:26:46, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1855, decode.acc_seg: 92.3853, loss: 0.1855
+2023-03-03 23:52:50,950 - mmseg - INFO - Iter [38600/80000]	lr: 1.875e-05, eta: 3:26:31, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1900, decode.acc_seg: 92.3050, loss: 0.1900
+2023-03-03 23:53:05,516 - mmseg - INFO - Iter [38650/80000]	lr: 1.875e-05, eta: 3:26:15, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1926, decode.acc_seg: 92.1778, loss: 0.1926
+2023-03-03 23:53:20,016 - mmseg - INFO - Iter [38700/80000]	lr: 1.875e-05, eta: 3:26:00, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1893, decode.acc_seg: 92.3356, loss: 0.1893
+2023-03-03 23:53:34,575 - mmseg - INFO - Iter [38750/80000]	lr: 1.875e-05, eta: 3:25:44, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1932, decode.acc_seg: 92.2372, loss: 0.1932
+2023-03-03 23:53:49,155 - mmseg - INFO - Iter [38800/80000]	lr: 1.875e-05, eta: 3:25:29, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1854, decode.acc_seg: 92.3651, loss: 0.1854
+2023-03-03 23:54:03,652 - mmseg - INFO - Iter [38850/80000]	lr: 1.875e-05, eta: 3:25:14, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1922, decode.acc_seg: 92.2267, loss: 0.1922
+2023-03-03 23:54:18,112 - mmseg - INFO - Iter [38900/80000]	lr: 1.875e-05, eta: 3:24:58, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1958, decode.acc_seg: 92.1575, loss: 0.1958
+2023-03-03 23:54:32,570 - mmseg - INFO - Iter [38950/80000]	lr: 1.875e-05, eta: 3:24:43, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1968, decode.acc_seg: 92.2217, loss: 0.1968
+2023-03-03 23:54:47,036 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 23:54:47,037 - mmseg - INFO - Iter [39000/80000]	lr: 1.875e-05, eta: 3:24:27, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1920, decode.acc_seg: 92.2513, loss: 0.1920
+2023-03-03 23:55:01,640 - mmseg - INFO - Iter [39050/80000]	lr: 1.875e-05, eta: 3:24:12, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1997, decode.acc_seg: 91.9100, loss: 0.1997
+2023-03-03 23:55:16,332 - mmseg - INFO - Iter [39100/80000]	lr: 1.875e-05, eta: 3:23:57, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1879, decode.acc_seg: 92.4060, loss: 0.1879
+2023-03-03 23:55:33,408 - mmseg - INFO - Iter [39150/80000]	lr: 1.875e-05, eta: 3:23:44, time: 0.342, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1892, decode.acc_seg: 92.4051, loss: 0.1892
+2023-03-03 23:55:47,986 - mmseg - INFO - Iter [39200/80000]	lr: 1.875e-05, eta: 3:23:28, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1830, decode.acc_seg: 92.5917, loss: 0.1830
+2023-03-03 23:56:02,547 - mmseg - INFO - Iter [39250/80000]	lr: 1.875e-05, eta: 3:23:13, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1945, decode.acc_seg: 92.2583, loss: 0.1945
+2023-03-03 23:56:17,020 - mmseg - INFO - Iter [39300/80000]	lr: 1.875e-05, eta: 3:22:58, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1915, decode.acc_seg: 92.2310, loss: 0.1915
+2023-03-03 23:56:31,422 - mmseg - INFO - Iter [39350/80000]	lr: 1.875e-05, eta: 3:22:42, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1852, decode.acc_seg: 92.5058, loss: 0.1852
+2023-03-03 23:56:45,889 - mmseg - INFO - Iter [39400/80000]	lr: 1.875e-05, eta: 3:22:27, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1908, decode.acc_seg: 92.2066, loss: 0.1908
+2023-03-03 23:57:00,343 - mmseg - INFO - Iter [39450/80000]	lr: 1.875e-05, eta: 3:22:11, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1863, decode.acc_seg: 92.4101, loss: 0.1863
+2023-03-03 23:57:14,773 - mmseg - INFO - Iter [39500/80000]	lr: 1.875e-05, eta: 3:21:56, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1856, decode.acc_seg: 92.4877, loss: 0.1856
+2023-03-03 23:57:29,353 - mmseg - INFO - Iter [39550/80000]	lr: 1.875e-05, eta: 3:21:40, time: 0.292, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1965, decode.acc_seg: 92.1117, loss: 0.1965
+2023-03-03 23:57:43,793 - mmseg - INFO - Iter [39600/80000]	lr: 1.875e-05, eta: 3:21:25, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1887, decode.acc_seg: 92.3966, loss: 0.1887
+2023-03-03 23:57:58,309 - mmseg - INFO - Iter [39650/80000]	lr: 1.875e-05, eta: 3:21:09, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1898, decode.acc_seg: 92.4064, loss: 0.1898
+2023-03-03 23:58:12,852 - mmseg - INFO - Iter [39700/80000]	lr: 1.875e-05, eta: 3:20:54, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1872, decode.acc_seg: 92.4039, loss: 0.1872
+2023-03-03 23:58:27,446 - mmseg - INFO - Iter [39750/80000]	lr: 1.875e-05, eta: 3:20:39, time: 0.292, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1910, decode.acc_seg: 92.2784, loss: 0.1910
+2023-03-03 23:58:44,460 - mmseg - INFO - Iter [39800/80000]	lr: 1.875e-05, eta: 3:20:26, time: 0.340, data_time: 0.057, memory: 39544, decode.loss_ce: 0.1871, decode.acc_seg: 92.4212, loss: 0.1871
+2023-03-03 23:58:58,953 - mmseg - INFO - Iter [39850/80000]	lr: 1.875e-05, eta: 3:20:10, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1962, decode.acc_seg: 92.0780, loss: 0.1962
+2023-03-03 23:59:13,431 - mmseg - INFO - Iter [39900/80000]	lr: 1.875e-05, eta: 3:19:55, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1929, decode.acc_seg: 92.2441, loss: 0.1929
+2023-03-03 23:59:27,928 - mmseg - INFO - Iter [39950/80000]	lr: 1.875e-05, eta: 3:19:39, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1839, decode.acc_seg: 92.4960, loss: 0.1839
+2023-03-03 23:59:42,364 - mmseg - INFO - Saving checkpoint at 40000 iterations
+2023-03-03 23:59:44,353 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-03 23:59:44,353 - mmseg - INFO - Iter [40000/80000]	lr: 1.875e-05, eta: 3:19:26, time: 0.329, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1947, decode.acc_seg: 92.2081, loss: 0.1947
+2023-03-04 00:00:03,983 - mmseg - INFO - per class results:
+2023-03-04 00:00:03,989 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        | 76.18 | 88.32 |
+|       building      | 81.28 | 92.24 |
+|         sky         | 94.16 | 97.03 |
+|        floor        | 79.61 | 91.97 |
+|         tree        | 72.79 | 88.75 |
+|       ceiling       | 82.61 | 91.58 |
+|         road        | 82.48 | 89.05 |
+|         bed         | 88.55 | 94.99 |
+|      windowpane     | 61.16 |  76.9 |
+|        grass        | 66.84 | 83.16 |
+|       cabinet       | 59.54 | 71.19 |
+|       sidewalk      | 66.43 | 81.99 |
+|        person       |  79.2 | 90.92 |
+|        earth        | 33.34 | 48.25 |
+|         door        | 48.02 |  64.7 |
+|        table        | 61.56 | 77.44 |
+|       mountain      | 50.96 | 66.41 |
+|        plant        | 49.62 | 62.17 |
+|       curtain       | 70.21 | 81.38 |
+|        chair        | 57.49 | 68.88 |
+|         car         | 83.31 | 90.13 |
+|        water        | 47.27 | 62.99 |
+|       painting      | 69.36 |  83.7 |
+|         sofa        | 64.69 | 85.55 |
+|        shelf        | 39.37 | 52.84 |
+|        house        | 44.79 | 54.08 |
+|         sea         | 43.95 |  69.5 |
+|        mirror       | 64.87 | 73.63 |
+|         rug         | 55.05 | 60.54 |
+|        field        | 28.09 | 41.25 |
+|       armchair      | 42.66 | 53.96 |
+|         seat        | 53.29 | 77.86 |
+|        fence        | 41.57 | 58.26 |
+|         desk        | 48.72 |  70.2 |
+|         rock        | 31.49 | 47.66 |
+|       wardrobe      | 48.94 | 70.54 |
+|         lamp        | 63.89 | 74.84 |
+|       bathtub       | 75.63 | 81.57 |
+|       railing       | 31.02 | 43.32 |
+|       cushion       | 55.27 | 69.51 |
+|         base        | 28.79 | 44.14 |
+|         box         |  24.7 | 33.96 |
+|        column       | 44.72 | 56.18 |
+|      signboard      | 36.63 | 51.03 |
+|   chest of drawers  |  41.4 | 61.87 |
+|       counter       |  29.5 | 44.06 |
+|         sand        | 31.97 | 48.75 |
+|         sink        | 70.58 | 81.83 |
+|      skyscraper     | 47.81 | 54.45 |
+|      fireplace      | 66.53 | 83.79 |
+|     refrigerator    | 77.44 | 83.77 |
+|      grandstand     | 41.08 | 63.23 |
+|         path        | 15.71 | 22.94 |
+|        stairs       | 31.98 | 41.07 |
+|        runway       | 64.14 | 83.69 |
+|         case        | 49.41 | 71.53 |
+|      pool table     |  92.6 | 95.81 |
+|        pillow       | 55.85 | 64.58 |
+|     screen door     | 62.91 | 69.48 |
+|       stairway      | 24.59 | 31.49 |
+|        river        |  9.03 | 16.23 |
+|        bridge       | 50.06 | 54.97 |
+|       bookcase      | 41.81 | 55.39 |
+|        blind        |  49.8 | 58.17 |
+|     coffee table    | 66.74 | 80.09 |
+|        toilet       | 86.59 | 89.56 |
+|        flower       | 30.37 | 44.96 |
+|         book        | 46.58 | 66.45 |
+|         hill        |  7.37 |  8.45 |
+|        bench        | 44.43 | 52.45 |
+|      countertop     | 53.27 | 69.77 |
+|        stove        | 73.51 | 80.49 |
+|         palm        | 50.84 | 68.75 |
+|    kitchen island   | 47.07 | 74.39 |
+|       computer      | 57.24 | 65.69 |
+|     swivel chair    |  42.9 | 56.47 |
+|         boat        | 39.98 | 43.94 |
+|         bar         | 26.05 | 28.67 |
+|    arcade machine   | 26.36 | 29.84 |
+|        hovel        | 30.61 | 32.66 |
+|         bus         | 88.23 | 92.25 |
+|        towel        | 60.18 | 69.26 |
+|        light        | 56.07 | 63.17 |
+|        truck        | 34.35 | 46.66 |
+|        tower        |  23.6 | 32.01 |
+|      chandelier     | 66.44 | 81.62 |
+|        awning       | 24.48 | 27.34 |
+|     streetlight     |  28.3 | 36.12 |
+|        booth        | 55.26 | 57.28 |
+| television receiver | 68.65 |  79.9 |
+|       airplane      |  50.4 | 71.64 |
+|      dirt track     |  8.54 | 20.88 |
+|       apparel       | 29.86 |  46.3 |
+|         pole        | 23.53 | 33.54 |
+|         land        | 11.47 | 17.66 |
+|      bannister      |  4.28 |  5.86 |
+|      escalator      | 22.24 | 22.95 |
+|       ottoman       | 48.29 | 57.16 |
+|        bottle       | 17.69 | 29.47 |
+|        buffet       | 57.98 | 71.14 |
+|        poster       | 28.37 | 39.13 |
+|        stage        | 18.38 | 24.92 |
+|         van         |  48.5 | 62.44 |
+|         ship        |  41.3 | 54.02 |
+|       fountain      |  6.62 |  6.71 |
+|    conveyer belt    | 77.22 | 86.85 |
+|        canopy       | 14.25 | 15.78 |
+|        washer       | 66.39 | 66.86 |
+|      plaything      | 21.91 | 28.45 |
+|    swimming pool    | 42.01 | 49.24 |
+|        stool        | 41.55 | 58.57 |
+|        barrel       |  44.2 | 64.17 |
+|        basket       |  27.7 | 37.33 |
+|      waterfall      | 57.84 | 69.35 |
+|         tent        |  94.7 | 97.51 |
+|         bag         |  11.4 | 13.83 |
+|       minibike      | 62.46 | 72.25 |
+|        cradle       | 80.09 | 98.12 |
+|         oven        | 28.93 | 61.23 |
+|         ball        | 46.57 | 60.86 |
+|         food        | 52.36 | 63.57 |
+|         step        | 13.63 | 18.01 |
+|         tank        | 41.44 | 41.62 |
+|      trade name     | 25.73 | 30.18 |
+|      microwave      | 37.74 | 40.15 |
+|         pot         | 40.05 | 47.05 |
+|        animal       | 50.02 | 52.81 |
+|       bicycle       | 45.92 | 71.12 |
+|         lake        | 57.63 | 63.37 |
+|      dishwasher     | 77.34 | 82.53 |
+|        screen       | 67.35 | 86.92 |
+|       blanket       | 12.25 | 14.42 |
+|      sculpture      | 36.47 | 62.85 |
+|         hood        | 57.09 | 70.18 |
+|        sconce       | 41.05 | 48.26 |
+|         vase        | 36.55 | 52.44 |
+|    traffic light    | 29.62 |  45.4 |
+|         tray        |  5.94 |  9.78 |
+|        ashcan       | 38.31 |  49.4 |
+|         fan         | 57.72 | 70.05 |
+|         pier        | 18.83 | 21.74 |
+|      crt screen     |  4.06 | 11.45 |
+|        plate        | 39.29 | 49.05 |
+|       monitor       | 20.17 | 26.57 |
+|    bulletin board   |  45.4 | 56.81 |
+|        shower       |  2.46 |  3.57 |
+|       radiator      | 46.16 | 53.86 |
+|        glass        | 12.18 | 13.33 |
+|        clock        |  24.8 |  29.4 |
+|         flag        | 38.93 | 43.15 |
++---------------------+-------+-------+
+2023-03-04 00:00:03,989 - mmseg - INFO - Summary:
+2023-03-04 00:00:03,989 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 81.96 | 46.21 | 57.02 |
++-------+-------+-------+
+2023-03-04 00:00:04,050 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151/best_mIoU_iter_32000.pth was removed
+2023-03-04 00:00:05,969 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_40000.pth.
+2023-03-04 00:00:05,970 - mmseg - INFO - Best mIoU is 0.4621 at 40000 iter.
+2023-03-04 00:00:05,970 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 00:00:05,970 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8196, mIoU: 0.4621, mAcc: 0.5702, IoU.background: nan, IoU.wall: 0.7618, IoU.building: 0.8128, IoU.sky: 0.9416, IoU.floor: 0.7961, IoU.tree: 0.7279, IoU.ceiling: 0.8261, IoU.road: 0.8248, IoU.bed : 0.8855, IoU.windowpane: 0.6116, IoU.grass: 0.6684, IoU.cabinet: 0.5954, IoU.sidewalk: 0.6643, IoU.person: 0.7920, IoU.earth: 0.3334, IoU.door: 0.4802, IoU.table: 0.6156, IoU.mountain: 0.5096, IoU.plant: 0.4962, IoU.curtain: 0.7021, IoU.chair: 0.5749, IoU.car: 0.8331, IoU.water: 0.4727, IoU.painting: 0.6936, IoU.sofa: 0.6469, IoU.shelf: 0.3937, IoU.house: 0.4479, IoU.sea: 0.4395, IoU.mirror: 0.6487, IoU.rug: 0.5505, IoU.field: 0.2809, IoU.armchair: 0.4266, IoU.seat: 0.5329, IoU.fence: 0.4157, IoU.desk: 0.4872, IoU.rock: 0.3149, IoU.wardrobe: 0.4894, IoU.lamp: 0.6389, IoU.bathtub: 0.7563, IoU.railing: 0.3102, IoU.cushion: 0.5527, IoU.base: 0.2879, IoU.box: 0.2470, IoU.column: 0.4472, IoU.signboard: 0.3663, IoU.chest of drawers: 0.4140, IoU.counter: 0.2950, IoU.sand: 0.3197, IoU.sink: 0.7058, IoU.skyscraper: 0.4781, IoU.fireplace: 0.6653, IoU.refrigerator: 0.7744, IoU.grandstand: 0.4108, IoU.path: 0.1571, IoU.stairs: 0.3198, IoU.runway: 0.6414, IoU.case: 0.4941, IoU.pool table: 0.9260, IoU.pillow: 0.5585, IoU.screen door: 0.6291, IoU.stairway: 0.2459, IoU.river: 0.0903, IoU.bridge: 0.5006, IoU.bookcase: 0.4181, IoU.blind: 0.4980, IoU.coffee table: 0.6674, IoU.toilet: 0.8659, IoU.flower: 0.3037, IoU.book: 0.4658, IoU.hill: 0.0737, IoU.bench: 0.4443, IoU.countertop: 0.5327, IoU.stove: 0.7351, IoU.palm: 0.5084, IoU.kitchen island: 0.4707, IoU.computer: 0.5724, IoU.swivel chair: 0.4290, IoU.boat: 0.3998, IoU.bar: 0.2605, IoU.arcade machine: 0.2636, IoU.hovel: 0.3061, IoU.bus: 0.8823, IoU.towel: 0.6018, IoU.light: 0.5607, IoU.truck: 0.3435, IoU.tower: 0.2360, IoU.chandelier: 0.6644, IoU.awning: 0.2448, IoU.streetlight: 0.2830, IoU.booth: 0.5526, IoU.television receiver: 0.6865, IoU.airplane: 0.5040, IoU.dirt track: 0.0854, IoU.apparel: 0.2986, IoU.pole: 0.2353, IoU.land: 0.1147, IoU.bannister: 0.0428, IoU.escalator: 0.2224, IoU.ottoman: 0.4829, IoU.bottle: 0.1769, IoU.buffet: 0.5798, IoU.poster: 0.2837, IoU.stage: 0.1838, IoU.van: 0.4850, IoU.ship: 0.4130, IoU.fountain: 0.0662, IoU.conveyer belt: 0.7722, IoU.canopy: 0.1425, IoU.washer: 0.6639, IoU.plaything: 0.2191, IoU.swimming pool: 0.4201, IoU.stool: 0.4155, IoU.barrel: 0.4420, IoU.basket: 0.2770, IoU.waterfall: 0.5784, IoU.tent: 0.9470, IoU.bag: 0.1140, IoU.minibike: 0.6246, IoU.cradle: 0.8009, IoU.oven: 0.2893, IoU.ball: 0.4657, IoU.food: 0.5236, IoU.step: 0.1363, IoU.tank: 0.4144, IoU.trade name: 0.2573, IoU.microwave: 0.3774, IoU.pot: 0.4005, IoU.animal: 0.5002, IoU.bicycle: 0.4592, IoU.lake: 0.5763, IoU.dishwasher: 0.7734, IoU.screen: 0.6735, IoU.blanket: 0.1225, IoU.sculpture: 0.3647, IoU.hood: 0.5709, IoU.sconce: 0.4105, IoU.vase: 0.3655, IoU.traffic light: 0.2962, IoU.tray: 0.0594, IoU.ashcan: 0.3831, IoU.fan: 0.5772, IoU.pier: 0.1883, IoU.crt screen: 0.0406, IoU.plate: 0.3929, IoU.monitor: 0.2017, IoU.bulletin board: 0.4540, IoU.shower: 0.0246, IoU.radiator: 0.4616, IoU.glass: 0.1218, IoU.clock: 0.2480, IoU.flag: 0.3893, Acc.background: nan, Acc.wall: 0.8832, Acc.building: 0.9224, Acc.sky: 0.9703, Acc.floor: 0.9197, Acc.tree: 0.8875, Acc.ceiling: 0.9158, Acc.road: 0.8905, Acc.bed : 0.9499, Acc.windowpane: 0.7690, Acc.grass: 0.8316, Acc.cabinet: 0.7119, Acc.sidewalk: 0.8199, Acc.person: 0.9092, Acc.earth: 0.4825, Acc.door: 0.6470, Acc.table: 0.7744, Acc.mountain: 0.6641, Acc.plant: 0.6217, Acc.curtain: 0.8138, Acc.chair: 0.6888, Acc.car: 0.9013, Acc.water: 0.6299, Acc.painting: 0.8370, Acc.sofa: 0.8555, Acc.shelf: 0.5284, Acc.house: 0.5408, Acc.sea: 0.6950, Acc.mirror: 0.7363, Acc.rug: 0.6054, Acc.field: 0.4125, Acc.armchair: 0.5396, Acc.seat: 0.7786, Acc.fence: 0.5826, Acc.desk: 0.7020, Acc.rock: 0.4766, Acc.wardrobe: 0.7054, Acc.lamp: 0.7484, Acc.bathtub: 0.8157, Acc.railing: 0.4332, Acc.cushion: 0.6951, Acc.base: 0.4414, Acc.box: 0.3396, Acc.column: 0.5618, Acc.signboard: 0.5103, Acc.chest of drawers: 0.6187, Acc.counter: 0.4406, Acc.sand: 0.4875, Acc.sink: 0.8183, Acc.skyscraper: 0.5445, Acc.fireplace: 0.8379, Acc.refrigerator: 0.8377, Acc.grandstand: 0.6323, Acc.path: 0.2294, Acc.stairs: 0.4107, Acc.runway: 0.8369, Acc.case: 0.7153, Acc.pool table: 0.9581, Acc.pillow: 0.6458, Acc.screen door: 0.6948, Acc.stairway: 0.3149, Acc.river: 0.1623, Acc.bridge: 0.5497, Acc.bookcase: 0.5539, Acc.blind: 0.5817, Acc.coffee table: 0.8009, Acc.toilet: 0.8956, Acc.flower: 0.4496, Acc.book: 0.6645, Acc.hill: 0.0845, Acc.bench: 0.5245, Acc.countertop: 0.6977, Acc.stove: 0.8049, Acc.palm: 0.6875, Acc.kitchen island: 0.7439, Acc.computer: 0.6569, Acc.swivel chair: 0.5647, Acc.boat: 0.4394, Acc.bar: 0.2867, Acc.arcade machine: 0.2984, Acc.hovel: 0.3266, Acc.bus: 0.9225, Acc.towel: 0.6926, Acc.light: 0.6317, Acc.truck: 0.4666, Acc.tower: 0.3201, Acc.chandelier: 0.8162, Acc.awning: 0.2734, Acc.streetlight: 0.3612, Acc.booth: 0.5728, Acc.television receiver: 0.7990, Acc.airplane: 0.7164, Acc.dirt track: 0.2088, Acc.apparel: 0.4630, Acc.pole: 0.3354, Acc.land: 0.1766, Acc.bannister: 0.0586, Acc.escalator: 0.2295, Acc.ottoman: 0.5716, Acc.bottle: 0.2947, Acc.buffet: 0.7114, Acc.poster: 0.3913, Acc.stage: 0.2492, Acc.van: 0.6244, Acc.ship: 0.5402, Acc.fountain: 0.0671, Acc.conveyer belt: 0.8685, Acc.canopy: 0.1578, Acc.washer: 0.6686, Acc.plaything: 0.2845, Acc.swimming pool: 0.4924, Acc.stool: 0.5857, Acc.barrel: 0.6417, Acc.basket: 0.3733, Acc.waterfall: 0.6935, Acc.tent: 0.9751, Acc.bag: 0.1383, Acc.minibike: 0.7225, Acc.cradle: 0.9812, Acc.oven: 0.6123, Acc.ball: 0.6086, Acc.food: 0.6357, Acc.step: 0.1801, Acc.tank: 0.4162, Acc.trade name: 0.3018, Acc.microwave: 0.4015, Acc.pot: 0.4705, Acc.animal: 0.5281, Acc.bicycle: 0.7112, Acc.lake: 0.6337, Acc.dishwasher: 0.8253, Acc.screen: 0.8692, Acc.blanket: 0.1442, Acc.sculpture: 0.6285, Acc.hood: 0.7018, Acc.sconce: 0.4826, Acc.vase: 0.5244, Acc.traffic light: 0.4540, Acc.tray: 0.0978, Acc.ashcan: 0.4940, Acc.fan: 0.7005, Acc.pier: 0.2174, Acc.crt screen: 0.1145, Acc.plate: 0.4905, Acc.monitor: 0.2657, Acc.bulletin board: 0.5681, Acc.shower: 0.0357, Acc.radiator: 0.5386, Acc.glass: 0.1333, Acc.clock: 0.2940, Acc.flag: 0.4315
+2023-03-04 00:00:20,985 - mmseg - INFO - Iter [40050/80000]	lr: 9.375e-06, eta: 3:19:33, time: 0.733, data_time: 0.440, memory: 39544, decode.loss_ce: 0.1897, decode.acc_seg: 92.3736, loss: 0.1897
+2023-03-04 00:00:35,647 - mmseg - INFO - Iter [40100/80000]	lr: 9.375e-06, eta: 3:19:17, time: 0.293, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1841, decode.acc_seg: 92.5651, loss: 0.1841
+2023-03-04 00:00:50,162 - mmseg - INFO - Iter [40150/80000]	lr: 9.375e-06, eta: 3:19:02, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1850, decode.acc_seg: 92.4000, loss: 0.1850
+2023-03-04 00:01:04,766 - mmseg - INFO - Iter [40200/80000]	lr: 9.375e-06, eta: 3:18:47, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1889, decode.acc_seg: 92.3265, loss: 0.1889
+2023-03-04 00:01:19,358 - mmseg - INFO - Iter [40250/80000]	lr: 9.375e-06, eta: 3:18:31, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1924, decode.acc_seg: 92.0866, loss: 0.1924
+2023-03-04 00:01:33,926 - mmseg - INFO - Iter [40300/80000]	lr: 9.375e-06, eta: 3:18:16, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1918, decode.acc_seg: 92.0353, loss: 0.1918
+2023-03-04 00:01:48,410 - mmseg - INFO - Iter [40350/80000]	lr: 9.375e-06, eta: 3:18:00, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1891, decode.acc_seg: 92.2388, loss: 0.1891
+2023-03-04 00:02:05,472 - mmseg - INFO - Iter [40400/80000]	lr: 9.375e-06, eta: 3:17:47, time: 0.341, data_time: 0.053, memory: 39544, decode.loss_ce: 0.1845, decode.acc_seg: 92.4660, loss: 0.1845
+2023-03-04 00:02:20,010 - mmseg - INFO - Iter [40450/80000]	lr: 9.375e-06, eta: 3:17:32, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1971, decode.acc_seg: 92.1628, loss: 0.1971
+2023-03-04 00:02:34,685 - mmseg - INFO - Iter [40500/80000]	lr: 9.375e-06, eta: 3:17:17, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1825, decode.acc_seg: 92.7050, loss: 0.1825
+2023-03-04 00:02:49,220 - mmseg - INFO - Iter [40550/80000]	lr: 9.375e-06, eta: 3:17:01, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1855, decode.acc_seg: 92.4325, loss: 0.1855
+2023-03-04 00:03:03,993 - mmseg - INFO - Iter [40600/80000]	lr: 9.375e-06, eta: 3:16:46, time: 0.295, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1906, decode.acc_seg: 92.3048, loss: 0.1906
+2023-03-04 00:03:18,489 - mmseg - INFO - Iter [40650/80000]	lr: 9.375e-06, eta: 3:16:31, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1801, decode.acc_seg: 92.7240, loss: 0.1801
+2023-03-04 00:03:33,066 - mmseg - INFO - Iter [40700/80000]	lr: 9.375e-06, eta: 3:16:15, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1833, decode.acc_seg: 92.6399, loss: 0.1833
+2023-03-04 00:03:47,636 - mmseg - INFO - Iter [40750/80000]	lr: 9.375e-06, eta: 3:16:00, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1947, decode.acc_seg: 92.1752, loss: 0.1947
+2023-03-04 00:04:02,204 - mmseg - INFO - Iter [40800/80000]	lr: 9.375e-06, eta: 3:15:44, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1891, decode.acc_seg: 92.3972, loss: 0.1891
+2023-03-04 00:04:16,672 - mmseg - INFO - Iter [40850/80000]	lr: 9.375e-06, eta: 3:15:29, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1848, decode.acc_seg: 92.4438, loss: 0.1848
+2023-03-04 00:04:31,278 - mmseg - INFO - Iter [40900/80000]	lr: 9.375e-06, eta: 3:15:14, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1930, decode.acc_seg: 92.2059, loss: 0.1930
+2023-03-04 00:04:45,806 - mmseg - INFO - Iter [40950/80000]	lr: 9.375e-06, eta: 3:14:58, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1857, decode.acc_seg: 92.5373, loss: 0.1857
+2023-03-04 00:05:00,492 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 00:05:00,492 - mmseg - INFO - Iter [41000/80000]	lr: 9.375e-06, eta: 3:14:43, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1870, decode.acc_seg: 92.3588, loss: 0.1870
+2023-03-04 00:05:17,570 - mmseg - INFO - Iter [41050/80000]	lr: 9.375e-06, eta: 3:14:30, time: 0.342, data_time: 0.052, memory: 39544, decode.loss_ce: 0.1870, decode.acc_seg: 92.3228, loss: 0.1870
+2023-03-04 00:05:31,990 - mmseg - INFO - Iter [41100/80000]	lr: 9.375e-06, eta: 3:14:14, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1892, decode.acc_seg: 92.4424, loss: 0.1892
+2023-03-04 00:05:46,484 - mmseg - INFO - Iter [41150/80000]	lr: 9.375e-06, eta: 3:13:59, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1959, decode.acc_seg: 92.1027, loss: 0.1959
+2023-03-04 00:06:01,037 - mmseg - INFO - Iter [41200/80000]	lr: 9.375e-06, eta: 3:13:44, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1823, decode.acc_seg: 92.5745, loss: 0.1823
+2023-03-04 00:06:15,525 - mmseg - INFO - Iter [41250/80000]	lr: 9.375e-06, eta: 3:13:28, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1937, decode.acc_seg: 92.2368, loss: 0.1937
+2023-03-04 00:06:30,158 - mmseg - INFO - Iter [41300/80000]	lr: 9.375e-06, eta: 3:13:13, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1912, decode.acc_seg: 92.3447, loss: 0.1912
+2023-03-04 00:06:44,649 - mmseg - INFO - Iter [41350/80000]	lr: 9.375e-06, eta: 3:12:57, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1880, decode.acc_seg: 92.4510, loss: 0.1880
+2023-03-04 00:06:59,113 - mmseg - INFO - Iter [41400/80000]	lr: 9.375e-06, eta: 3:12:42, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1936, decode.acc_seg: 92.1205, loss: 0.1936
+2023-03-04 00:07:13,653 - mmseg - INFO - Iter [41450/80000]	lr: 9.375e-06, eta: 3:12:27, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1837, decode.acc_seg: 92.5709, loss: 0.1837
+2023-03-04 00:07:28,088 - mmseg - INFO - Iter [41500/80000]	lr: 9.375e-06, eta: 3:12:11, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1915, decode.acc_seg: 92.2126, loss: 0.1915
+2023-03-04 00:07:42,587 - mmseg - INFO - Iter [41550/80000]	lr: 9.375e-06, eta: 3:11:56, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1924, decode.acc_seg: 92.2243, loss: 0.1924
+2023-03-04 00:07:57,177 - mmseg - INFO - Iter [41600/80000]	lr: 9.375e-06, eta: 3:11:40, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1912, decode.acc_seg: 92.2955, loss: 0.1912
+2023-03-04 00:08:14,110 - mmseg - INFO - Iter [41650/80000]	lr: 9.375e-06, eta: 3:11:27, time: 0.339, data_time: 0.052, memory: 39544, decode.loss_ce: 0.1924, decode.acc_seg: 92.2084, loss: 0.1924
+2023-03-04 00:08:28,683 - mmseg - INFO - Iter [41700/80000]	lr: 9.375e-06, eta: 3:11:12, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1889, decode.acc_seg: 92.4109, loss: 0.1889
+2023-03-04 00:08:43,201 - mmseg - INFO - Iter [41750/80000]	lr: 9.375e-06, eta: 3:10:56, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1848, decode.acc_seg: 92.4405, loss: 0.1848
+2023-03-04 00:08:57,875 - mmseg - INFO - Iter [41800/80000]	lr: 9.375e-06, eta: 3:10:41, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1915, decode.acc_seg: 92.2181, loss: 0.1915
+2023-03-04 00:09:12,447 - mmseg - INFO - Iter [41850/80000]	lr: 9.375e-06, eta: 3:10:26, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1903, decode.acc_seg: 92.4515, loss: 0.1903
+2023-03-04 00:09:27,052 - mmseg - INFO - Iter [41900/80000]	lr: 9.375e-06, eta: 3:10:11, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1904, decode.acc_seg: 92.3287, loss: 0.1904
+2023-03-04 00:09:41,751 - mmseg - INFO - Iter [41950/80000]	lr: 9.375e-06, eta: 3:09:55, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1852, decode.acc_seg: 92.5077, loss: 0.1852
+2023-03-04 00:09:56,253 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 00:09:56,253 - mmseg - INFO - Iter [42000/80000]	lr: 9.375e-06, eta: 3:09:40, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1891, decode.acc_seg: 92.1796, loss: 0.1891
+2023-03-04 00:10:10,757 - mmseg - INFO - Iter [42050/80000]	lr: 9.375e-06, eta: 3:09:25, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1879, decode.acc_seg: 92.3050, loss: 0.1879
+2023-03-04 00:10:25,291 - mmseg - INFO - Iter [42100/80000]	lr: 9.375e-06, eta: 3:09:09, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1869, decode.acc_seg: 92.4520, loss: 0.1869
+2023-03-04 00:10:39,762 - mmseg - INFO - Iter [42150/80000]	lr: 9.375e-06, eta: 3:08:54, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1978, decode.acc_seg: 92.1219, loss: 0.1978
+2023-03-04 00:10:54,318 - mmseg - INFO - Iter [42200/80000]	lr: 9.375e-06, eta: 3:08:38, time: 0.291, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1844, decode.acc_seg: 92.3509, loss: 0.1844
+2023-03-04 00:11:08,768 - mmseg - INFO - Iter [42250/80000]	lr: 9.375e-06, eta: 3:08:23, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1877, decode.acc_seg: 92.3768, loss: 0.1877
+2023-03-04 00:11:25,677 - mmseg - INFO - Iter [42300/80000]	lr: 9.375e-06, eta: 3:08:10, time: 0.338, data_time: 0.053, memory: 39544, decode.loss_ce: 0.1886, decode.acc_seg: 92.3075, loss: 0.1886
+2023-03-04 00:11:40,164 - mmseg - INFO - Iter [42350/80000]	lr: 9.375e-06, eta: 3:07:54, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1868, decode.acc_seg: 92.3834, loss: 0.1868
+2023-03-04 00:11:54,804 - mmseg - INFO - Iter [42400/80000]	lr: 9.375e-06, eta: 3:07:39, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1877, decode.acc_seg: 92.4203, loss: 0.1877
+2023-03-04 00:12:09,436 - mmseg - INFO - Iter [42450/80000]	lr: 9.375e-06, eta: 3:07:24, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1860, decode.acc_seg: 92.5227, loss: 0.1860
+2023-03-04 00:12:23,977 - mmseg - INFO - Iter [42500/80000]	lr: 9.375e-06, eta: 3:07:08, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1813, decode.acc_seg: 92.7780, loss: 0.1813
+2023-03-04 00:12:38,494 - mmseg - INFO - Iter [42550/80000]	lr: 9.375e-06, eta: 3:06:53, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1903, decode.acc_seg: 92.2106, loss: 0.1903
+2023-03-04 00:12:53,086 - mmseg - INFO - Iter [42600/80000]	lr: 9.375e-06, eta: 3:06:38, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1999, decode.acc_seg: 91.9052, loss: 0.1999
+2023-03-04 00:13:07,664 - mmseg - INFO - Iter [42650/80000]	lr: 9.375e-06, eta: 3:06:22, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1975, decode.acc_seg: 92.0114, loss: 0.1975
+2023-03-04 00:13:22,202 - mmseg - INFO - Iter [42700/80000]	lr: 9.375e-06, eta: 3:06:07, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1877, decode.acc_seg: 92.2474, loss: 0.1877
+2023-03-04 00:13:36,671 - mmseg - INFO - Iter [42750/80000]	lr: 9.375e-06, eta: 3:05:52, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1919, decode.acc_seg: 92.2691, loss: 0.1919
+2023-03-04 00:13:51,142 - mmseg - INFO - Iter [42800/80000]	lr: 9.375e-06, eta: 3:05:36, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1906, decode.acc_seg: 92.2500, loss: 0.1906
+2023-03-04 00:14:05,623 - mmseg - INFO - Iter [42850/80000]	lr: 9.375e-06, eta: 3:05:21, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1943, decode.acc_seg: 92.2164, loss: 0.1943
+2023-03-04 00:14:20,155 - mmseg - INFO - Iter [42900/80000]	lr: 9.375e-06, eta: 3:05:06, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1867, decode.acc_seg: 92.3887, loss: 0.1867
+2023-03-04 00:14:37,110 - mmseg - INFO - Iter [42950/80000]	lr: 9.375e-06, eta: 3:04:52, time: 0.339, data_time: 0.053, memory: 39544, decode.loss_ce: 0.1903, decode.acc_seg: 92.2523, loss: 0.1903
+2023-03-04 00:14:51,596 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 00:14:51,596 - mmseg - INFO - Iter [43000/80000]	lr: 9.375e-06, eta: 3:04:37, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1902, decode.acc_seg: 92.3431, loss: 0.1902
+2023-03-04 00:15:06,151 - mmseg - INFO - Iter [43050/80000]	lr: 9.375e-06, eta: 3:04:22, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1893, decode.acc_seg: 92.3568, loss: 0.1893
+2023-03-04 00:15:20,597 - mmseg - INFO - Iter [43100/80000]	lr: 9.375e-06, eta: 3:04:06, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1920, decode.acc_seg: 92.3592, loss: 0.1920
+2023-03-04 00:15:35,203 - mmseg - INFO - Iter [43150/80000]	lr: 9.375e-06, eta: 3:03:51, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1911, decode.acc_seg: 92.2219, loss: 0.1911
+2023-03-04 00:15:49,774 - mmseg - INFO - Iter [43200/80000]	lr: 9.375e-06, eta: 3:03:36, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1903, decode.acc_seg: 92.3843, loss: 0.1903
+2023-03-04 00:16:04,336 - mmseg - INFO - Iter [43250/80000]	lr: 9.375e-06, eta: 3:03:20, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1892, decode.acc_seg: 92.3951, loss: 0.1892
+2023-03-04 00:16:18,942 - mmseg - INFO - Iter [43300/80000]	lr: 9.375e-06, eta: 3:03:05, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1917, decode.acc_seg: 92.3183, loss: 0.1917
+2023-03-04 00:16:33,461 - mmseg - INFO - Iter [43350/80000]	lr: 9.375e-06, eta: 3:02:50, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1867, decode.acc_seg: 92.4405, loss: 0.1867
+2023-03-04 00:16:47,966 - mmseg - INFO - Iter [43400/80000]	lr: 9.375e-06, eta: 3:02:34, time: 0.290, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1933, decode.acc_seg: 92.2488, loss: 0.1933
+2023-03-04 00:17:02,522 - mmseg - INFO - Iter [43450/80000]	lr: 9.375e-06, eta: 3:02:19, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1852, decode.acc_seg: 92.4697, loss: 0.1852
+2023-03-04 00:17:17,105 - mmseg - INFO - Iter [43500/80000]	lr: 9.375e-06, eta: 3:02:04, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1810, decode.acc_seg: 92.5982, loss: 0.1810
+2023-03-04 00:17:34,148 - mmseg - INFO - Iter [43550/80000]	lr: 9.375e-06, eta: 3:01:50, time: 0.341, data_time: 0.056, memory: 39544, decode.loss_ce: 0.1858, decode.acc_seg: 92.4924, loss: 0.1858
+2023-03-04 00:17:48,790 - mmseg - INFO - Iter [43600/80000]	lr: 9.375e-06, eta: 3:01:35, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1927, decode.acc_seg: 92.2491, loss: 0.1927
+2023-03-04 00:18:03,306 - mmseg - INFO - Iter [43650/80000]	lr: 9.375e-06, eta: 3:01:20, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1897, decode.acc_seg: 92.4543, loss: 0.1897
+2023-03-04 00:18:17,836 - mmseg - INFO - Iter [43700/80000]	lr: 9.375e-06, eta: 3:01:05, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1876, decode.acc_seg: 92.4102, loss: 0.1876
+2023-03-04 00:18:32,316 - mmseg - INFO - Iter [43750/80000]	lr: 9.375e-06, eta: 3:00:49, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1904, decode.acc_seg: 92.2711, loss: 0.1904
+2023-03-04 00:18:46,949 - mmseg - INFO - Iter [43800/80000]	lr: 9.375e-06, eta: 3:00:34, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1934, decode.acc_seg: 92.2037, loss: 0.1934
+2023-03-04 00:19:01,413 - mmseg - INFO - Iter [43850/80000]	lr: 9.375e-06, eta: 3:00:19, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1896, decode.acc_seg: 92.3024, loss: 0.1896
+2023-03-04 00:19:15,882 - mmseg - INFO - Iter [43900/80000]	lr: 9.375e-06, eta: 3:00:03, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1900, decode.acc_seg: 92.3230, loss: 0.1900
+2023-03-04 00:19:30,408 - mmseg - INFO - Iter [43950/80000]	lr: 9.375e-06, eta: 2:59:48, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1896, decode.acc_seg: 92.3282, loss: 0.1896
+2023-03-04 00:19:44,917 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 00:19:44,917 - mmseg - INFO - Iter [44000/80000]	lr: 9.375e-06, eta: 2:59:33, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1958, decode.acc_seg: 92.0040, loss: 0.1958
+2023-03-04 00:19:59,440 - mmseg - INFO - Iter [44050/80000]	lr: 9.375e-06, eta: 2:59:17, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1781, decode.acc_seg: 92.7136, loss: 0.1781
+2023-03-04 00:20:13,948 - mmseg - INFO - Iter [44100/80000]	lr: 9.375e-06, eta: 2:59:02, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1855, decode.acc_seg: 92.4291, loss: 0.1855
+2023-03-04 00:20:28,521 - mmseg - INFO - Iter [44150/80000]	lr: 9.375e-06, eta: 2:58:47, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1892, decode.acc_seg: 92.2153, loss: 0.1892
+2023-03-04 00:20:45,769 - mmseg - INFO - Iter [44200/80000]	lr: 9.375e-06, eta: 2:58:33, time: 0.345, data_time: 0.059, memory: 39544, decode.loss_ce: 0.1858, decode.acc_seg: 92.5157, loss: 0.1858
+2023-03-04 00:21:00,390 - mmseg - INFO - Iter [44250/80000]	lr: 9.375e-06, eta: 2:58:18, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1867, decode.acc_seg: 92.4620, loss: 0.1867
+2023-03-04 00:21:14,973 - mmseg - INFO - Iter [44300/80000]	lr: 9.375e-06, eta: 2:58:03, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1878, decode.acc_seg: 92.2864, loss: 0.1878
+2023-03-04 00:21:29,685 - mmseg - INFO - Iter [44350/80000]	lr: 9.375e-06, eta: 2:57:48, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1859, decode.acc_seg: 92.3674, loss: 0.1859
+2023-03-04 00:21:44,198 - mmseg - INFO - Iter [44400/80000]	lr: 9.375e-06, eta: 2:57:32, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1908, decode.acc_seg: 92.2314, loss: 0.1908
+2023-03-04 00:21:58,696 - mmseg - INFO - Iter [44450/80000]	lr: 9.375e-06, eta: 2:57:17, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1947, decode.acc_seg: 92.1618, loss: 0.1947
+2023-03-04 00:22:13,390 - mmseg - INFO - Iter [44500/80000]	lr: 9.375e-06, eta: 2:57:02, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1863, decode.acc_seg: 92.4451, loss: 0.1863
+2023-03-04 00:22:27,827 - mmseg - INFO - Iter [44550/80000]	lr: 9.375e-06, eta: 2:56:47, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1880, decode.acc_seg: 92.3503, loss: 0.1880
+2023-03-04 00:22:42,316 - mmseg - INFO - Iter [44600/80000]	lr: 9.375e-06, eta: 2:56:31, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1827, decode.acc_seg: 92.5618, loss: 0.1827
+2023-03-04 00:22:56,921 - mmseg - INFO - Iter [44650/80000]	lr: 9.375e-06, eta: 2:56:16, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2002, decode.acc_seg: 91.9755, loss: 0.2002
+2023-03-04 00:23:11,446 - mmseg - INFO - Iter [44700/80000]	lr: 9.375e-06, eta: 2:56:01, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1918, decode.acc_seg: 92.3152, loss: 0.1918
+2023-03-04 00:23:26,012 - mmseg - INFO - Iter [44750/80000]	lr: 9.375e-06, eta: 2:55:45, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1865, decode.acc_seg: 92.3811, loss: 0.1865
+2023-03-04 00:23:40,422 - mmseg - INFO - Iter [44800/80000]	lr: 9.375e-06, eta: 2:55:30, time: 0.288, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1862, decode.acc_seg: 92.3866, loss: 0.1862
+2023-03-04 00:23:57,491 - mmseg - INFO - Iter [44850/80000]	lr: 9.375e-06, eta: 2:55:17, time: 0.341, data_time: 0.053, memory: 39544, decode.loss_ce: 0.1926, decode.acc_seg: 92.1853, loss: 0.1926
+2023-03-04 00:24:12,036 - mmseg - INFO - Iter [44900/80000]	lr: 9.375e-06, eta: 2:55:01, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1915, decode.acc_seg: 92.3182, loss: 0.1915
+2023-03-04 00:24:26,620 - mmseg - INFO - Iter [44950/80000]	lr: 9.375e-06, eta: 2:54:46, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1810, decode.acc_seg: 92.6361, loss: 0.1810
+2023-03-04 00:24:41,176 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 00:24:41,176 - mmseg - INFO - Iter [45000/80000]	lr: 9.375e-06, eta: 2:54:31, time: 0.291, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1923, decode.acc_seg: 92.1988, loss: 0.1923
+2023-03-04 00:24:55,815 - mmseg - INFO - Iter [45050/80000]	lr: 9.375e-06, eta: 2:54:16, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1950, decode.acc_seg: 92.3301, loss: 0.1950
+2023-03-04 00:25:10,371 - mmseg - INFO - Iter [45100/80000]	lr: 9.375e-06, eta: 2:54:00, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1980, decode.acc_seg: 92.0728, loss: 0.1980
+2023-03-04 00:25:24,894 - mmseg - INFO - Iter [45150/80000]	lr: 9.375e-06, eta: 2:53:45, time: 0.290, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1833, decode.acc_seg: 92.5307, loss: 0.1833
+2023-03-04 00:25:39,377 - mmseg - INFO - Iter [45200/80000]	lr: 9.375e-06, eta: 2:53:30, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1854, decode.acc_seg: 92.4775, loss: 0.1854
+2023-03-04 00:25:53,940 - mmseg - INFO - Iter [45250/80000]	lr: 9.375e-06, eta: 2:53:15, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1872, decode.acc_seg: 92.4139, loss: 0.1872
+2023-03-04 00:26:08,396 - mmseg - INFO - Iter [45300/80000]	lr: 9.375e-06, eta: 2:52:59, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1917, decode.acc_seg: 92.2454, loss: 0.1917
+2023-03-04 00:26:22,931 - mmseg - INFO - Iter [45350/80000]	lr: 9.375e-06, eta: 2:52:44, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1859, decode.acc_seg: 92.3981, loss: 0.1859
+2023-03-04 00:26:37,326 - mmseg - INFO - Iter [45400/80000]	lr: 9.375e-06, eta: 2:52:29, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1816, decode.acc_seg: 92.6202, loss: 0.1816
+2023-03-04 00:26:54,475 - mmseg - INFO - Iter [45450/80000]	lr: 9.375e-06, eta: 2:52:15, time: 0.343, data_time: 0.055, memory: 39544, decode.loss_ce: 0.1906, decode.acc_seg: 92.2330, loss: 0.1906
+2023-03-04 00:27:08,967 - mmseg - INFO - Iter [45500/80000]	lr: 9.375e-06, eta: 2:52:00, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1901, decode.acc_seg: 92.1956, loss: 0.1901
+2023-03-04 00:27:23,526 - mmseg - INFO - Iter [45550/80000]	lr: 9.375e-06, eta: 2:51:45, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1908, decode.acc_seg: 92.4111, loss: 0.1908
+2023-03-04 00:27:38,017 - mmseg - INFO - Iter [45600/80000]	lr: 9.375e-06, eta: 2:51:29, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1958, decode.acc_seg: 92.1121, loss: 0.1958
+2023-03-04 00:27:52,505 - mmseg - INFO - Iter [45650/80000]	lr: 9.375e-06, eta: 2:51:14, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1853, decode.acc_seg: 92.5235, loss: 0.1853
+2023-03-04 00:28:06,953 - mmseg - INFO - Iter [45700/80000]	lr: 9.375e-06, eta: 2:50:59, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1859, decode.acc_seg: 92.5379, loss: 0.1859
+2023-03-04 00:28:21,493 - mmseg - INFO - Iter [45750/80000]	lr: 9.375e-06, eta: 2:50:43, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1906, decode.acc_seg: 92.2180, loss: 0.1906
+2023-03-04 00:28:36,104 - mmseg - INFO - Iter [45800/80000]	lr: 9.375e-06, eta: 2:50:28, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1939, decode.acc_seg: 92.1302, loss: 0.1939
+2023-03-04 00:28:50,649 - mmseg - INFO - Iter [45850/80000]	lr: 9.375e-06, eta: 2:50:13, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1913, decode.acc_seg: 92.2788, loss: 0.1913
+2023-03-04 00:29:05,211 - mmseg - INFO - Iter [45900/80000]	lr: 9.375e-06, eta: 2:49:58, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1932, decode.acc_seg: 92.3162, loss: 0.1932
+2023-03-04 00:29:19,705 - mmseg - INFO - Iter [45950/80000]	lr: 9.375e-06, eta: 2:49:42, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1828, decode.acc_seg: 92.5608, loss: 0.1828
+2023-03-04 00:29:34,248 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 00:29:34,248 - mmseg - INFO - Iter [46000/80000]	lr: 9.375e-06, eta: 2:49:27, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1830, decode.acc_seg: 92.6207, loss: 0.1830
+2023-03-04 00:29:48,807 - mmseg - INFO - Iter [46050/80000]	lr: 9.375e-06, eta: 2:49:12, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1872, decode.acc_seg: 92.3994, loss: 0.1872
+2023-03-04 00:30:05,966 - mmseg - INFO - Iter [46100/80000]	lr: 9.375e-06, eta: 2:48:59, time: 0.343, data_time: 0.055, memory: 39544, decode.loss_ce: 0.1928, decode.acc_seg: 92.2320, loss: 0.1928
+2023-03-04 00:30:20,455 - mmseg - INFO - Iter [46150/80000]	lr: 9.375e-06, eta: 2:48:43, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1899, decode.acc_seg: 92.3335, loss: 0.1899
+2023-03-04 00:30:34,887 - mmseg - INFO - Iter [46200/80000]	lr: 9.375e-06, eta: 2:48:28, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1940, decode.acc_seg: 92.1215, loss: 0.1940
+2023-03-04 00:30:49,407 - mmseg - INFO - Iter [46250/80000]	lr: 9.375e-06, eta: 2:48:13, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1879, decode.acc_seg: 92.3357, loss: 0.1879
+2023-03-04 00:31:03,933 - mmseg - INFO - Iter [46300/80000]	lr: 9.375e-06, eta: 2:47:57, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1859, decode.acc_seg: 92.2692, loss: 0.1859
+2023-03-04 00:31:18,500 - mmseg - INFO - Iter [46350/80000]	lr: 9.375e-06, eta: 2:47:42, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1884, decode.acc_seg: 92.4254, loss: 0.1884
+2023-03-04 00:31:32,961 - mmseg - INFO - Iter [46400/80000]	lr: 9.375e-06, eta: 2:47:27, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1832, decode.acc_seg: 92.5624, loss: 0.1832
+2023-03-04 00:31:47,453 - mmseg - INFO - Iter [46450/80000]	lr: 9.375e-06, eta: 2:47:12, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1919, decode.acc_seg: 92.2145, loss: 0.1919
+2023-03-04 00:32:01,899 - mmseg - INFO - Iter [46500/80000]	lr: 9.375e-06, eta: 2:46:56, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1874, decode.acc_seg: 92.4259, loss: 0.1874
+2023-03-04 00:32:16,412 - mmseg - INFO - Iter [46550/80000]	lr: 9.375e-06, eta: 2:46:41, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1934, decode.acc_seg: 92.3113, loss: 0.1934
+2023-03-04 00:32:30,953 - mmseg - INFO - Iter [46600/80000]	lr: 9.375e-06, eta: 2:46:26, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1818, decode.acc_seg: 92.5421, loss: 0.1818
+2023-03-04 00:32:45,588 - mmseg - INFO - Iter [46650/80000]	lr: 9.375e-06, eta: 2:46:11, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1886, decode.acc_seg: 92.3573, loss: 0.1886
+2023-03-04 00:33:02,634 - mmseg - INFO - Iter [46700/80000]	lr: 9.375e-06, eta: 2:45:57, time: 0.341, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1918, decode.acc_seg: 92.1664, loss: 0.1918
+2023-03-04 00:33:17,167 - mmseg - INFO - Iter [46750/80000]	lr: 9.375e-06, eta: 2:45:42, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1891, decode.acc_seg: 92.2897, loss: 0.1891
+2023-03-04 00:33:31,688 - mmseg - INFO - Iter [46800/80000]	lr: 9.375e-06, eta: 2:45:27, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1915, decode.acc_seg: 92.2752, loss: 0.1915
+2023-03-04 00:33:46,326 - mmseg - INFO - Iter [46850/80000]	lr: 9.375e-06, eta: 2:45:12, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1958, decode.acc_seg: 92.1223, loss: 0.1958
+2023-03-04 00:34:00,809 - mmseg - INFO - Iter [46900/80000]	lr: 9.375e-06, eta: 2:44:56, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1852, decode.acc_seg: 92.4392, loss: 0.1852
+2023-03-04 00:34:15,360 - mmseg - INFO - Iter [46950/80000]	lr: 9.375e-06, eta: 2:44:41, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1917, decode.acc_seg: 92.2408, loss: 0.1917
+2023-03-04 00:34:29,882 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 00:34:29,882 - mmseg - INFO - Iter [47000/80000]	lr: 9.375e-06, eta: 2:44:26, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1853, decode.acc_seg: 92.4227, loss: 0.1853
+2023-03-04 00:34:44,368 - mmseg - INFO - Iter [47050/80000]	lr: 9.375e-06, eta: 2:44:11, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1991, decode.acc_seg: 92.0464, loss: 0.1991
+2023-03-04 00:34:58,812 - mmseg - INFO - Iter [47100/80000]	lr: 9.375e-06, eta: 2:43:55, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1906, decode.acc_seg: 92.3322, loss: 0.1906
+2023-03-04 00:35:13,400 - mmseg - INFO - Iter [47150/80000]	lr: 9.375e-06, eta: 2:43:40, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1890, decode.acc_seg: 92.1713, loss: 0.1890
+2023-03-04 00:35:27,877 - mmseg - INFO - Iter [47200/80000]	lr: 9.375e-06, eta: 2:43:25, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1827, decode.acc_seg: 92.5354, loss: 0.1827
+2023-03-04 00:35:42,360 - mmseg - INFO - Iter [47250/80000]	lr: 9.375e-06, eta: 2:43:09, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1940, decode.acc_seg: 92.2491, loss: 0.1940
+2023-03-04 00:35:56,831 - mmseg - INFO - Iter [47300/80000]	lr: 9.375e-06, eta: 2:42:54, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1878, decode.acc_seg: 92.3374, loss: 0.1878
+2023-03-04 00:36:13,822 - mmseg - INFO - Iter [47350/80000]	lr: 9.375e-06, eta: 2:42:41, time: 0.340, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1899, decode.acc_seg: 92.2139, loss: 0.1899
+2023-03-04 00:36:28,466 - mmseg - INFO - Iter [47400/80000]	lr: 9.375e-06, eta: 2:42:25, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1891, decode.acc_seg: 92.4041, loss: 0.1891
+2023-03-04 00:36:42,883 - mmseg - INFO - Iter [47450/80000]	lr: 9.375e-06, eta: 2:42:10, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1877, decode.acc_seg: 92.2985, loss: 0.1877
+2023-03-04 00:36:57,348 - mmseg - INFO - Iter [47500/80000]	lr: 9.375e-06, eta: 2:41:55, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1827, decode.acc_seg: 92.5673, loss: 0.1827
+2023-03-04 00:37:11,915 - mmseg - INFO - Iter [47550/80000]	lr: 9.375e-06, eta: 2:41:40, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1883, decode.acc_seg: 92.3702, loss: 0.1883
+2023-03-04 00:37:26,488 - mmseg - INFO - Iter [47600/80000]	lr: 9.375e-06, eta: 2:41:24, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1887, decode.acc_seg: 92.3401, loss: 0.1887
+2023-03-04 00:37:41,038 - mmseg - INFO - Iter [47650/80000]	lr: 9.375e-06, eta: 2:41:09, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1814, decode.acc_seg: 92.6444, loss: 0.1814
+2023-03-04 00:37:55,473 - mmseg - INFO - Iter [47700/80000]	lr: 9.375e-06, eta: 2:40:54, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1861, decode.acc_seg: 92.3749, loss: 0.1861
+2023-03-04 00:38:10,002 - mmseg - INFO - Iter [47750/80000]	lr: 9.375e-06, eta: 2:40:39, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1933, decode.acc_seg: 92.2240, loss: 0.1933
+2023-03-04 00:38:24,570 - mmseg - INFO - Iter [47800/80000]	lr: 9.375e-06, eta: 2:40:24, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1850, decode.acc_seg: 92.6209, loss: 0.1850
+2023-03-04 00:38:39,036 - mmseg - INFO - Iter [47850/80000]	lr: 9.375e-06, eta: 2:40:08, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1809, decode.acc_seg: 92.6345, loss: 0.1809
+2023-03-04 00:38:53,742 - mmseg - INFO - Iter [47900/80000]	lr: 9.375e-06, eta: 2:39:53, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1913, decode.acc_seg: 92.2359, loss: 0.1913
+2023-03-04 00:39:08,122 - mmseg - INFO - Iter [47950/80000]	lr: 9.375e-06, eta: 2:39:38, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1858, decode.acc_seg: 92.4710, loss: 0.1858
+2023-03-04 00:39:25,196 - mmseg - INFO - Saving checkpoint at 48000 iterations
+2023-03-04 00:39:27,171 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 00:39:27,171 - mmseg - INFO - Iter [48000/80000]	lr: 9.375e-06, eta: 2:39:26, time: 0.381, data_time: 0.051, memory: 39544, decode.loss_ce: 0.1834, decode.acc_seg: 92.5858, loss: 0.1834
+2023-03-04 00:39:46,810 - mmseg - INFO - per class results:
+2023-03-04 00:39:46,816 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        | 76.15 | 88.63 |
+|       building      | 81.54 | 92.26 |
+|         sky         | 94.17 | 97.53 |
+|        floor        |  80.0 | 91.22 |
+|         tree        |  72.6 | 87.28 |
+|       ceiling       |  82.4 | 90.78 |
+|         road        | 81.92 | 89.64 |
+|         bed         | 88.44 | 94.92 |
+|      windowpane     | 61.01 | 76.95 |
+|        grass        | 66.31 | 81.46 |
+|       cabinet       | 59.22 | 72.11 |
+|       sidewalk      | 65.85 | 81.29 |
+|        person       | 79.05 | 91.28 |
+|        earth        | 33.53 | 48.07 |
+|         door        | 48.16 | 64.61 |
+|        table        | 61.32 | 77.97 |
+|       mountain      | 51.36 | 67.13 |
+|        plant        | 50.05 | 63.15 |
+|       curtain       | 70.65 | 82.79 |
+|        chair        | 57.76 | 71.45 |
+|         car         | 82.96 | 90.46 |
+|        water        | 47.07 |  62.9 |
+|       painting      | 69.52 |  84.1 |
+|         sofa        | 65.25 | 84.49 |
+|        shelf        | 39.95 | 54.08 |
+|        house        |  44.9 | 52.66 |
+|         sea         |  45.2 |  72.3 |
+|        mirror       | 65.03 | 75.59 |
+|         rug         | 54.52 | 59.48 |
+|        field        | 27.73 | 43.28 |
+|       armchair      | 44.38 | 59.36 |
+|         seat        | 54.16 | 78.22 |
+|        fence        | 41.57 | 56.01 |
+|         desk        | 49.24 | 68.98 |
+|         rock        |  29.1 |  42.4 |
+|       wardrobe      | 49.15 | 71.11 |
+|         lamp        | 63.98 | 75.83 |
+|       bathtub       | 76.64 |  83.1 |
+|       railing       | 31.54 | 42.95 |
+|       cushion       |  54.5 | 66.98 |
+|         base        |  29.7 | 43.56 |
+|         box         | 23.85 | 30.65 |
+|        column       | 45.21 | 57.32 |
+|      signboard      | 36.59 | 50.28 |
+|   chest of drawers  | 39.43 | 56.07 |
+|       counter       | 26.97 | 38.99 |
+|         sand        | 31.99 | 47.95 |
+|         sink        |  69.7 | 82.33 |
+|      skyscraper     | 47.08 | 52.43 |
+|      fireplace      |  66.7 | 83.98 |
+|     refrigerator    | 77.46 |  84.4 |
+|      grandstand     | 40.19 | 62.56 |
+|         path        | 16.63 | 25.47 |
+|        stairs       | 32.35 | 42.42 |
+|        runway       |  63.6 |  82.2 |
+|         case        |  48.1 | 65.14 |
+|      pool table     | 92.77 |  95.8 |
+|        pillow       | 56.68 | 66.55 |
+|     screen door     | 64.84 | 71.65 |
+|       stairway      | 25.26 | 31.81 |
+|        river        |  9.32 | 15.17 |
+|        bridge       |  57.4 | 64.09 |
+|       bookcase      | 41.53 | 54.78 |
+|        blind        | 45.48 | 51.69 |
+|     coffee table    | 66.34 | 81.79 |
+|        toilet       | 85.53 |  91.0 |
+|        flower       | 32.78 | 51.37 |
+|         book        | 46.71 | 65.07 |
+|         hill        |  8.65 |  10.1 |
+|        bench        | 43.69 | 53.14 |
+|      countertop     | 52.83 | 71.36 |
+|        stove        | 73.58 | 80.65 |
+|         palm        | 50.51 | 69.01 |
+|    kitchen island   | 47.33 | 73.01 |
+|       computer      | 57.14 | 64.89 |
+|     swivel chair    | 44.35 | 60.37 |
+|         boat        | 37.21 | 40.47 |
+|         bar         |  27.2 |  30.0 |
+|    arcade machine   | 24.73 | 28.78 |
+|        hovel        |  31.8 | 34.71 |
+|         bus         | 87.92 | 92.78 |
+|        towel        | 59.15 | 68.33 |
+|        light        | 55.17 | 60.93 |
+|        truck        | 35.06 | 46.82 |
+|        tower        | 24.24 |  33.9 |
+|      chandelier     | 66.36 | 81.21 |
+|        awning       | 25.01 |  28.3 |
+|     streetlight     | 28.44 | 36.66 |
+|        booth        | 55.17 | 57.89 |
+| television receiver | 68.01 | 78.04 |
+|       airplane      | 50.43 | 70.14 |
+|      dirt track     |  8.74 | 20.64 |
+|       apparel       | 29.94 | 47.52 |
+|         pole        |  23.7 | 34.28 |
+|         land        | 10.06 | 14.51 |
+|      bannister      |  5.25 |  7.93 |
+|      escalator      | 24.46 | 25.38 |
+|       ottoman       | 48.37 | 56.48 |
+|        bottle       | 16.62 | 27.31 |
+|        buffet       |  50.4 | 59.57 |
+|        poster       | 27.88 | 36.86 |
+|        stage        | 18.61 | 25.24 |
+|         van         | 48.94 | 64.04 |
+|         ship        | 42.11 | 58.13 |
+|       fountain      |  7.03 |  7.17 |
+|    conveyer belt    |  74.7 |  88.3 |
+|        canopy       | 16.35 | 19.28 |
+|        washer       | 66.62 | 67.49 |
+|      plaything      | 22.26 | 30.03 |
+|    swimming pool    | 44.47 | 51.75 |
+|        stool        |  40.8 | 54.35 |
+|        barrel       | 38.16 | 64.72 |
+|        basket       | 27.56 | 37.55 |
+|      waterfall      | 52.98 | 62.06 |
+|         tent        | 94.14 | 97.55 |
+|         bag         | 11.35 | 13.73 |
+|       minibike      | 61.41 | 74.26 |
+|        cradle       | 81.04 | 97.68 |
+|         oven        | 30.68 | 58.13 |
+|         ball        | 46.93 | 62.26 |
+|         food        | 54.05 | 65.69 |
+|         step        | 13.54 | 16.43 |
+|         tank        | 42.09 | 42.44 |
+|      trade name     | 26.22 | 31.02 |
+|      microwave      | 38.28 | 41.33 |
+|         pot         | 40.77 | 49.22 |
+|        animal       | 52.36 | 55.89 |
+|       bicycle       |  46.3 |  71.0 |
+|         lake        | 59.21 |  63.2 |
+|      dishwasher     | 77.32 | 82.97 |
+|        screen       | 65.52 | 84.11 |
+|       blanket       | 12.84 | 14.76 |
+|      sculpture      |  36.1 | 64.15 |
+|         hood        | 57.69 | 70.69 |
+|        sconce       | 43.22 | 52.25 |
+|         vase        | 36.17 | 55.31 |
+|    traffic light    | 30.71 | 46.44 |
+|         tray        |  5.59 | 10.83 |
+|        ashcan       | 38.77 |  50.8 |
+|         fan         | 57.65 | 71.73 |
+|         pier        | 10.93 | 12.52 |
+|      crt screen     |  3.61 |  9.64 |
+|        plate        |  37.9 | 46.59 |
+|       monitor       | 23.75 | 32.54 |
+|    bulletin board   | 44.99 | 57.74 |
+|        shower       |  1.56 |  3.5  |
+|       radiator      | 47.73 | 56.88 |
+|        glass        | 12.32 |  13.6 |
+|        clock        | 24.86 | 32.93 |
+|         flag        | 38.37 | 42.28 |
++---------------------+-------+-------+
+2023-03-04 00:39:46,816 - mmseg - INFO - Summary:
+2023-03-04 00:39:46,816 - mmseg - INFO - 
++-------+-------+------+
+|  aAcc |  mIoU | mAcc |
++-------+-------+------+
+| 81.97 | 46.16 | 57.1 |
++-------+-------+------+
+2023-03-04 00:39:46,817 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 00:39:46,817 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8197, mIoU: 0.4616, mAcc: 0.5710, IoU.background: nan, IoU.wall: 0.7615, IoU.building: 0.8154, IoU.sky: 0.9417, IoU.floor: 0.8000, IoU.tree: 0.7260, IoU.ceiling: 0.8240, IoU.road: 0.8192, IoU.bed : 0.8844, IoU.windowpane: 0.6101, IoU.grass: 0.6631, IoU.cabinet: 0.5922, IoU.sidewalk: 0.6585, IoU.person: 0.7905, IoU.earth: 0.3353, IoU.door: 0.4816, IoU.table: 0.6132, IoU.mountain: 0.5136, IoU.plant: 0.5005, IoU.curtain: 0.7065, IoU.chair: 0.5776, IoU.car: 0.8296, IoU.water: 0.4707, IoU.painting: 0.6952, IoU.sofa: 0.6525, IoU.shelf: 0.3995, IoU.house: 0.4490, IoU.sea: 0.4520, IoU.mirror: 0.6503, IoU.rug: 0.5452, IoU.field: 0.2773, IoU.armchair: 0.4438, IoU.seat: 0.5416, IoU.fence: 0.4157, IoU.desk: 0.4924, IoU.rock: 0.2910, IoU.wardrobe: 0.4915, IoU.lamp: 0.6398, IoU.bathtub: 0.7664, IoU.railing: 0.3154, IoU.cushion: 0.5450, IoU.base: 0.2970, IoU.box: 0.2385, IoU.column: 0.4521, IoU.signboard: 0.3659, IoU.chest of drawers: 0.3943, IoU.counter: 0.2697, IoU.sand: 0.3199, IoU.sink: 0.6970, IoU.skyscraper: 0.4708, IoU.fireplace: 0.6670, IoU.refrigerator: 0.7746, IoU.grandstand: 0.4019, IoU.path: 0.1663, IoU.stairs: 0.3235, IoU.runway: 0.6360, IoU.case: 0.4810, IoU.pool table: 0.9277, IoU.pillow: 0.5668, IoU.screen door: 0.6484, IoU.stairway: 0.2526, IoU.river: 0.0932, IoU.bridge: 0.5740, IoU.bookcase: 0.4153, IoU.blind: 0.4548, IoU.coffee table: 0.6634, IoU.toilet: 0.8553, IoU.flower: 0.3278, IoU.book: 0.4671, IoU.hill: 0.0865, IoU.bench: 0.4369, IoU.countertop: 0.5283, IoU.stove: 0.7358, IoU.palm: 0.5051, IoU.kitchen island: 0.4733, IoU.computer: 0.5714, IoU.swivel chair: 0.4435, IoU.boat: 0.3721, IoU.bar: 0.2720, IoU.arcade machine: 0.2473, IoU.hovel: 0.3180, IoU.bus: 0.8792, IoU.towel: 0.5915, IoU.light: 0.5517, IoU.truck: 0.3506, IoU.tower: 0.2424, IoU.chandelier: 0.6636, IoU.awning: 0.2501, IoU.streetlight: 0.2844, IoU.booth: 0.5517, IoU.television receiver: 0.6801, IoU.airplane: 0.5043, IoU.dirt track: 0.0874, IoU.apparel: 0.2994, IoU.pole: 0.2370, IoU.land: 0.1006, IoU.bannister: 0.0525, IoU.escalator: 0.2446, IoU.ottoman: 0.4837, IoU.bottle: 0.1662, IoU.buffet: 0.5040, IoU.poster: 0.2788, IoU.stage: 0.1861, IoU.van: 0.4894, IoU.ship: 0.4211, IoU.fountain: 0.0703, IoU.conveyer belt: 0.7470, IoU.canopy: 0.1635, IoU.washer: 0.6662, IoU.plaything: 0.2226, IoU.swimming pool: 0.4447, IoU.stool: 0.4080, IoU.barrel: 0.3816, IoU.basket: 0.2756, IoU.waterfall: 0.5298, IoU.tent: 0.9414, IoU.bag: 0.1135, IoU.minibike: 0.6141, IoU.cradle: 0.8104, IoU.oven: 0.3068, IoU.ball: 0.4693, IoU.food: 0.5405, IoU.step: 0.1354, IoU.tank: 0.4209, IoU.trade name: 0.2622, IoU.microwave: 0.3828, IoU.pot: 0.4077, IoU.animal: 0.5236, IoU.bicycle: 0.4630, IoU.lake: 0.5921, IoU.dishwasher: 0.7732, IoU.screen: 0.6552, IoU.blanket: 0.1284, IoU.sculpture: 0.3610, IoU.hood: 0.5769, IoU.sconce: 0.4322, IoU.vase: 0.3617, IoU.traffic light: 0.3071, IoU.tray: 0.0559, IoU.ashcan: 0.3877, IoU.fan: 0.5765, IoU.pier: 0.1093, IoU.crt screen: 0.0361, IoU.plate: 0.3790, IoU.monitor: 0.2375, IoU.bulletin board: 0.4499, IoU.shower: 0.0156, IoU.radiator: 0.4773, IoU.glass: 0.1232, IoU.clock: 0.2486, IoU.flag: 0.3837, Acc.background: nan, Acc.wall: 0.8863, Acc.building: 0.9226, Acc.sky: 0.9753, Acc.floor: 0.9122, Acc.tree: 0.8728, Acc.ceiling: 0.9078, Acc.road: 0.8964, Acc.bed : 0.9492, Acc.windowpane: 0.7695, Acc.grass: 0.8146, Acc.cabinet: 0.7211, Acc.sidewalk: 0.8129, Acc.person: 0.9128, Acc.earth: 0.4807, Acc.door: 0.6461, Acc.table: 0.7797, Acc.mountain: 0.6713, Acc.plant: 0.6315, Acc.curtain: 0.8279, Acc.chair: 0.7145, Acc.car: 0.9046, Acc.water: 0.6290, Acc.painting: 0.8410, Acc.sofa: 0.8449, Acc.shelf: 0.5408, Acc.house: 0.5266, Acc.sea: 0.7230, Acc.mirror: 0.7559, Acc.rug: 0.5948, Acc.field: 0.4328, Acc.armchair: 0.5936, Acc.seat: 0.7822, Acc.fence: 0.5601, Acc.desk: 0.6898, Acc.rock: 0.4240, Acc.wardrobe: 0.7111, Acc.lamp: 0.7583, Acc.bathtub: 0.8310, Acc.railing: 0.4295, Acc.cushion: 0.6698, Acc.base: 0.4356, Acc.box: 0.3065, Acc.column: 0.5732, Acc.signboard: 0.5028, Acc.chest of drawers: 0.5607, Acc.counter: 0.3899, Acc.sand: 0.4795, Acc.sink: 0.8233, Acc.skyscraper: 0.5243, Acc.fireplace: 0.8398, Acc.refrigerator: 0.8440, Acc.grandstand: 0.6256, Acc.path: 0.2547, Acc.stairs: 0.4242, Acc.runway: 0.8220, Acc.case: 0.6514, Acc.pool table: 0.9580, Acc.pillow: 0.6655, Acc.screen door: 0.7165, Acc.stairway: 0.3181, Acc.river: 0.1517, Acc.bridge: 0.6409, Acc.bookcase: 0.5478, Acc.blind: 0.5169, Acc.coffee table: 0.8179, Acc.toilet: 0.9100, Acc.flower: 0.5137, Acc.book: 0.6507, Acc.hill: 0.1010, Acc.bench: 0.5314, Acc.countertop: 0.7136, Acc.stove: 0.8065, Acc.palm: 0.6901, Acc.kitchen island: 0.7301, Acc.computer: 0.6489, Acc.swivel chair: 0.6037, Acc.boat: 0.4047, Acc.bar: 0.3000, Acc.arcade machine: 0.2878, Acc.hovel: 0.3471, Acc.bus: 0.9278, Acc.towel: 0.6833, Acc.light: 0.6093, Acc.truck: 0.4682, Acc.tower: 0.3390, Acc.chandelier: 0.8121, Acc.awning: 0.2830, Acc.streetlight: 0.3666, Acc.booth: 0.5789, Acc.television receiver: 0.7804, Acc.airplane: 0.7014, Acc.dirt track: 0.2064, Acc.apparel: 0.4752, Acc.pole: 0.3428, Acc.land: 0.1451, Acc.bannister: 0.0793, Acc.escalator: 0.2538, Acc.ottoman: 0.5648, Acc.bottle: 0.2731, Acc.buffet: 0.5957, Acc.poster: 0.3686, Acc.stage: 0.2524, Acc.van: 0.6404, Acc.ship: 0.5813, Acc.fountain: 0.0717, Acc.conveyer belt: 0.8830, Acc.canopy: 0.1928, Acc.washer: 0.6749, Acc.plaything: 0.3003, Acc.swimming pool: 0.5175, Acc.stool: 0.5435, Acc.barrel: 0.6472, Acc.basket: 0.3755, Acc.waterfall: 0.6206, Acc.tent: 0.9755, Acc.bag: 0.1373, Acc.minibike: 0.7426, Acc.cradle: 0.9768, Acc.oven: 0.5813, Acc.ball: 0.6226, Acc.food: 0.6569, Acc.step: 0.1643, Acc.tank: 0.4244, Acc.trade name: 0.3102, Acc.microwave: 0.4133, Acc.pot: 0.4922, Acc.animal: 0.5589, Acc.bicycle: 0.7100, Acc.lake: 0.6320, Acc.dishwasher: 0.8297, Acc.screen: 0.8411, Acc.blanket: 0.1476, Acc.sculpture: 0.6415, Acc.hood: 0.7069, Acc.sconce: 0.5225, Acc.vase: 0.5531, Acc.traffic light: 0.4644, Acc.tray: 0.1083, Acc.ashcan: 0.5080, Acc.fan: 0.7173, Acc.pier: 0.1252, Acc.crt screen: 0.0964, Acc.plate: 0.4659, Acc.monitor: 0.3254, Acc.bulletin board: 0.5774, Acc.shower: 0.0350, Acc.radiator: 0.5688, Acc.glass: 0.1360, Acc.clock: 0.3293, Acc.flag: 0.4228
+2023-03-04 00:40:01,875 - mmseg - INFO - Iter [48050/80000]	lr: 9.375e-06, eta: 2:39:24, time: 0.694, data_time: 0.400, memory: 39544, decode.loss_ce: 0.1853, decode.acc_seg: 92.5784, loss: 0.1853
+2023-03-04 00:40:16,473 - mmseg - INFO - Iter [48100/80000]	lr: 9.375e-06, eta: 2:39:09, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1852, decode.acc_seg: 92.4677, loss: 0.1852
+2023-03-04 00:40:31,134 - mmseg - INFO - Iter [48150/80000]	lr: 9.375e-06, eta: 2:38:53, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1890, decode.acc_seg: 92.3235, loss: 0.1890
+2023-03-04 00:40:45,740 - mmseg - INFO - Iter [48200/80000]	lr: 9.375e-06, eta: 2:38:38, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1931, decode.acc_seg: 92.2639, loss: 0.1931
+2023-03-04 00:41:00,356 - mmseg - INFO - Iter [48250/80000]	lr: 9.375e-06, eta: 2:38:23, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1857, decode.acc_seg: 92.4566, loss: 0.1857
+2023-03-04 00:41:14,937 - mmseg - INFO - Iter [48300/80000]	lr: 9.375e-06, eta: 2:38:08, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1879, decode.acc_seg: 92.4632, loss: 0.1879
+2023-03-04 00:41:29,488 - mmseg - INFO - Iter [48350/80000]	lr: 9.375e-06, eta: 2:37:53, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1783, decode.acc_seg: 92.6946, loss: 0.1783
+2023-03-04 00:41:43,968 - mmseg - INFO - Iter [48400/80000]	lr: 9.375e-06, eta: 2:37:37, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1996, decode.acc_seg: 92.0577, loss: 0.1996
+2023-03-04 00:41:58,536 - mmseg - INFO - Iter [48450/80000]	lr: 9.375e-06, eta: 2:37:22, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1903, decode.acc_seg: 92.4412, loss: 0.1903
+2023-03-04 00:42:13,106 - mmseg - INFO - Iter [48500/80000]	lr: 9.375e-06, eta: 2:37:07, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1902, decode.acc_seg: 92.1937, loss: 0.1902
+2023-03-04 00:42:27,648 - mmseg - INFO - Iter [48550/80000]	lr: 9.375e-06, eta: 2:36:52, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1864, decode.acc_seg: 92.4231, loss: 0.1864
+2023-03-04 00:42:44,578 - mmseg - INFO - Iter [48600/80000]	lr: 9.375e-06, eta: 2:36:38, time: 0.339, data_time: 0.056, memory: 39544, decode.loss_ce: 0.1888, decode.acc_seg: 92.2908, loss: 0.1888
+2023-03-04 00:42:59,157 - mmseg - INFO - Iter [48650/80000]	lr: 9.375e-06, eta: 2:36:23, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1925, decode.acc_seg: 92.1326, loss: 0.1925
+2023-03-04 00:43:13,659 - mmseg - INFO - Iter [48700/80000]	lr: 9.375e-06, eta: 2:36:07, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1867, decode.acc_seg: 92.4557, loss: 0.1867
+2023-03-04 00:43:28,150 - mmseg - INFO - Iter [48750/80000]	lr: 9.375e-06, eta: 2:35:52, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1875, decode.acc_seg: 92.3355, loss: 0.1875
+2023-03-04 00:43:42,720 - mmseg - INFO - Iter [48800/80000]	lr: 9.375e-06, eta: 2:35:37, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1871, decode.acc_seg: 92.3757, loss: 0.1871
+2023-03-04 00:43:57,365 - mmseg - INFO - Iter [48850/80000]	lr: 9.375e-06, eta: 2:35:22, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1913, decode.acc_seg: 92.1966, loss: 0.1913
+2023-03-04 00:44:11,895 - mmseg - INFO - Iter [48900/80000]	lr: 9.375e-06, eta: 2:35:07, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1835, decode.acc_seg: 92.4342, loss: 0.1835
+2023-03-04 00:44:26,394 - mmseg - INFO - Iter [48950/80000]	lr: 9.375e-06, eta: 2:34:51, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1987, decode.acc_seg: 91.9149, loss: 0.1987
+2023-03-04 00:44:40,891 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 00:44:40,891 - mmseg - INFO - Iter [49000/80000]	lr: 9.375e-06, eta: 2:34:36, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1878, decode.acc_seg: 92.4769, loss: 0.1878
+2023-03-04 00:44:55,431 - mmseg - INFO - Iter [49050/80000]	lr: 9.375e-06, eta: 2:34:21, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1811, decode.acc_seg: 92.5958, loss: 0.1811
+2023-03-04 00:45:10,060 - mmseg - INFO - Iter [49100/80000]	lr: 9.375e-06, eta: 2:34:06, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1911, decode.acc_seg: 92.2144, loss: 0.1911
+2023-03-04 00:45:24,529 - mmseg - INFO - Iter [49150/80000]	lr: 9.375e-06, eta: 2:33:50, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1878, decode.acc_seg: 92.5102, loss: 0.1878
+2023-03-04 00:45:38,986 - mmseg - INFO - Iter [49200/80000]	lr: 9.375e-06, eta: 2:33:35, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1822, decode.acc_seg: 92.5395, loss: 0.1822
+2023-03-04 00:45:56,057 - mmseg - INFO - Iter [49250/80000]	lr: 9.375e-06, eta: 2:33:21, time: 0.341, data_time: 0.055, memory: 39544, decode.loss_ce: 0.1893, decode.acc_seg: 92.3989, loss: 0.1893
+2023-03-04 00:46:10,512 - mmseg - INFO - Iter [49300/80000]	lr: 9.375e-06, eta: 2:33:06, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1875, decode.acc_seg: 92.5101, loss: 0.1875
+2023-03-04 00:46:24,990 - mmseg - INFO - Iter [49350/80000]	lr: 9.375e-06, eta: 2:32:51, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1834, decode.acc_seg: 92.5492, loss: 0.1834
+2023-03-04 00:46:39,428 - mmseg - INFO - Iter [49400/80000]	lr: 9.375e-06, eta: 2:32:36, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1834, decode.acc_seg: 92.5211, loss: 0.1834
+2023-03-04 00:46:53,905 - mmseg - INFO - Iter [49450/80000]	lr: 9.375e-06, eta: 2:32:20, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1903, decode.acc_seg: 92.3645, loss: 0.1903
+2023-03-04 00:47:08,467 - mmseg - INFO - Iter [49500/80000]	lr: 9.375e-06, eta: 2:32:05, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1852, decode.acc_seg: 92.5355, loss: 0.1852
+2023-03-04 00:47:23,056 - mmseg - INFO - Iter [49550/80000]	lr: 9.375e-06, eta: 2:31:50, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1855, decode.acc_seg: 92.4661, loss: 0.1855
+2023-03-04 00:47:37,723 - mmseg - INFO - Iter [49600/80000]	lr: 9.375e-06, eta: 2:31:35, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1890, decode.acc_seg: 92.1502, loss: 0.1890
+2023-03-04 00:47:52,330 - mmseg - INFO - Iter [49650/80000]	lr: 9.375e-06, eta: 2:31:20, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1936, decode.acc_seg: 92.2877, loss: 0.1936
+2023-03-04 00:48:06,790 - mmseg - INFO - Iter [49700/80000]	lr: 9.375e-06, eta: 2:31:04, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1902, decode.acc_seg: 92.2609, loss: 0.1902
+2023-03-04 00:48:21,285 - mmseg - INFO - Iter [49750/80000]	lr: 9.375e-06, eta: 2:30:49, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1917, decode.acc_seg: 92.3088, loss: 0.1917
+2023-03-04 00:48:35,827 - mmseg - INFO - Iter [49800/80000]	lr: 9.375e-06, eta: 2:30:34, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1898, decode.acc_seg: 92.1544, loss: 0.1898
+2023-03-04 00:48:52,940 - mmseg - INFO - Iter [49850/80000]	lr: 9.375e-06, eta: 2:30:20, time: 0.342, data_time: 0.057, memory: 39544, decode.loss_ce: 0.1956, decode.acc_seg: 92.2019, loss: 0.1956
+2023-03-04 00:49:07,519 - mmseg - INFO - Iter [49900/80000]	lr: 9.375e-06, eta: 2:30:05, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1957, decode.acc_seg: 92.0745, loss: 0.1957
+2023-03-04 00:49:22,020 - mmseg - INFO - Iter [49950/80000]	lr: 9.375e-06, eta: 2:29:50, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1865, decode.acc_seg: 92.4288, loss: 0.1865
+2023-03-04 00:49:36,557 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 00:49:36,557 - mmseg - INFO - Iter [50000/80000]	lr: 9.375e-06, eta: 2:29:35, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1859, decode.acc_seg: 92.4693, loss: 0.1859
+2023-03-04 00:49:51,080 - mmseg - INFO - Iter [50050/80000]	lr: 4.687e-06, eta: 2:29:19, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1877, decode.acc_seg: 92.3779, loss: 0.1877
+2023-03-04 00:50:05,671 - mmseg - INFO - Iter [50100/80000]	lr: 4.687e-06, eta: 2:29:04, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1967, decode.acc_seg: 92.0675, loss: 0.1967
+2023-03-04 00:50:20,097 - mmseg - INFO - Iter [50150/80000]	lr: 4.687e-06, eta: 2:28:49, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1814, decode.acc_seg: 92.4315, loss: 0.1814
+2023-03-04 00:50:34,696 - mmseg - INFO - Iter [50200/80000]	lr: 4.687e-06, eta: 2:28:34, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1877, decode.acc_seg: 92.3995, loss: 0.1877
+2023-03-04 00:50:49,123 - mmseg - INFO - Iter [50250/80000]	lr: 4.687e-06, eta: 2:28:19, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1911, decode.acc_seg: 92.3712, loss: 0.1911
+2023-03-04 00:51:03,585 - mmseg - INFO - Iter [50300/80000]	lr: 4.687e-06, eta: 2:28:03, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1822, decode.acc_seg: 92.6292, loss: 0.1822
+2023-03-04 00:51:18,160 - mmseg - INFO - Iter [50350/80000]	lr: 4.687e-06, eta: 2:27:48, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1844, decode.acc_seg: 92.5016, loss: 0.1844
+2023-03-04 00:51:32,769 - mmseg - INFO - Iter [50400/80000]	lr: 4.687e-06, eta: 2:27:33, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1926, decode.acc_seg: 92.3392, loss: 0.1926
+2023-03-04 00:51:47,473 - mmseg - INFO - Iter [50450/80000]	lr: 4.687e-06, eta: 2:27:18, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1912, decode.acc_seg: 92.2605, loss: 0.1912
+2023-03-04 00:52:04,700 - mmseg - INFO - Iter [50500/80000]	lr: 4.687e-06, eta: 2:27:04, time: 0.345, data_time: 0.056, memory: 39544, decode.loss_ce: 0.1804, decode.acc_seg: 92.6473, loss: 0.1804
+2023-03-04 00:52:19,135 - mmseg - INFO - Iter [50550/80000]	lr: 4.687e-06, eta: 2:26:49, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1913, decode.acc_seg: 92.3556, loss: 0.1913
+2023-03-04 00:52:33,650 - mmseg - INFO - Iter [50600/80000]	lr: 4.687e-06, eta: 2:26:34, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2031, decode.acc_seg: 91.9063, loss: 0.2031
+2023-03-04 00:52:48,110 - mmseg - INFO - Iter [50650/80000]	lr: 4.687e-06, eta: 2:26:19, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1870, decode.acc_seg: 92.5291, loss: 0.1870
+2023-03-04 00:53:02,659 - mmseg - INFO - Iter [50700/80000]	lr: 4.687e-06, eta: 2:26:03, time: 0.291, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1917, decode.acc_seg: 92.2527, loss: 0.1917
+2023-03-04 00:53:17,213 - mmseg - INFO - Iter [50750/80000]	lr: 4.687e-06, eta: 2:25:48, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1944, decode.acc_seg: 92.1284, loss: 0.1944
+2023-03-04 00:53:31,819 - mmseg - INFO - Iter [50800/80000]	lr: 4.687e-06, eta: 2:25:33, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1862, decode.acc_seg: 92.6330, loss: 0.1862
+2023-03-04 00:53:46,350 - mmseg - INFO - Iter [50850/80000]	lr: 4.687e-06, eta: 2:25:18, time: 0.291, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1899, decode.acc_seg: 92.4137, loss: 0.1899
+2023-03-04 00:54:00,871 - mmseg - INFO - Iter [50900/80000]	lr: 4.687e-06, eta: 2:25:03, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1871, decode.acc_seg: 92.3453, loss: 0.1871
+2023-03-04 00:54:15,344 - mmseg - INFO - Iter [50950/80000]	lr: 4.687e-06, eta: 2:24:47, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1829, decode.acc_seg: 92.6494, loss: 0.1829
+2023-03-04 00:54:29,827 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 00:54:29,827 - mmseg - INFO - Iter [51000/80000]	lr: 4.687e-06, eta: 2:24:32, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1941, decode.acc_seg: 92.1014, loss: 0.1941
+2023-03-04 00:54:44,345 - mmseg - INFO - Iter [51050/80000]	lr: 4.687e-06, eta: 2:24:17, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1874, decode.acc_seg: 92.3167, loss: 0.1874
+2023-03-04 00:54:58,797 - mmseg - INFO - Iter [51100/80000]	lr: 4.687e-06, eta: 2:24:02, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1824, decode.acc_seg: 92.5143, loss: 0.1824
+2023-03-04 00:55:16,000 - mmseg - INFO - Iter [51150/80000]	lr: 4.687e-06, eta: 2:23:48, time: 0.344, data_time: 0.058, memory: 39544, decode.loss_ce: 0.1880, decode.acc_seg: 92.3556, loss: 0.1880
+2023-03-04 00:55:30,632 - mmseg - INFO - Iter [51200/80000]	lr: 4.687e-06, eta: 2:23:33, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1944, decode.acc_seg: 92.2971, loss: 0.1944
+2023-03-04 00:55:45,179 - mmseg - INFO - Iter [51250/80000]	lr: 4.687e-06, eta: 2:23:18, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1908, decode.acc_seg: 92.2293, loss: 0.1908
+2023-03-04 00:55:59,754 - mmseg - INFO - Iter [51300/80000]	lr: 4.687e-06, eta: 2:23:03, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1905, decode.acc_seg: 92.3468, loss: 0.1905
+2023-03-04 00:56:14,271 - mmseg - INFO - Iter [51350/80000]	lr: 4.687e-06, eta: 2:22:47, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1843, decode.acc_seg: 92.5068, loss: 0.1843
+2023-03-04 00:56:28,809 - mmseg - INFO - Iter [51400/80000]	lr: 4.687e-06, eta: 2:22:32, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1899, decode.acc_seg: 92.4197, loss: 0.1899
+2023-03-04 00:56:43,452 - mmseg - INFO - Iter [51450/80000]	lr: 4.687e-06, eta: 2:22:17, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1942, decode.acc_seg: 92.2936, loss: 0.1942
+2023-03-04 00:56:57,955 - mmseg - INFO - Iter [51500/80000]	lr: 4.687e-06, eta: 2:22:02, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1802, decode.acc_seg: 92.6594, loss: 0.1802
+2023-03-04 00:57:12,435 - mmseg - INFO - Iter [51550/80000]	lr: 4.687e-06, eta: 2:21:47, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1863, decode.acc_seg: 92.4595, loss: 0.1863
+2023-03-04 00:57:27,067 - mmseg - INFO - Iter [51600/80000]	lr: 4.687e-06, eta: 2:21:31, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1862, decode.acc_seg: 92.3969, loss: 0.1862
+2023-03-04 00:57:41,754 - mmseg - INFO - Iter [51650/80000]	lr: 4.687e-06, eta: 2:21:16, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1852, decode.acc_seg: 92.4767, loss: 0.1852
+2023-03-04 00:57:56,259 - mmseg - INFO - Iter [51700/80000]	lr: 4.687e-06, eta: 2:21:01, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1865, decode.acc_seg: 92.4835, loss: 0.1865
+2023-03-04 00:58:13,339 - mmseg - INFO - Iter [51750/80000]	lr: 4.687e-06, eta: 2:20:47, time: 0.342, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1887, decode.acc_seg: 92.3038, loss: 0.1887
+2023-03-04 00:58:27,981 - mmseg - INFO - Iter [51800/80000]	lr: 4.687e-06, eta: 2:20:32, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1839, decode.acc_seg: 92.4786, loss: 0.1839
+2023-03-04 00:58:42,495 - mmseg - INFO - Iter [51850/80000]	lr: 4.687e-06, eta: 2:20:17, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1887, decode.acc_seg: 92.4492, loss: 0.1887
+2023-03-04 00:58:56,926 - mmseg - INFO - Iter [51900/80000]	lr: 4.687e-06, eta: 2:20:02, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1889, decode.acc_seg: 92.4795, loss: 0.1889
+2023-03-04 00:59:11,363 - mmseg - INFO - Iter [51950/80000]	lr: 4.687e-06, eta: 2:19:47, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1833, decode.acc_seg: 92.5577, loss: 0.1833
+2023-03-04 00:59:25,810 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 00:59:25,810 - mmseg - INFO - Iter [52000/80000]	lr: 4.687e-06, eta: 2:19:31, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1858, decode.acc_seg: 92.3033, loss: 0.1858
+2023-03-04 00:59:40,320 - mmseg - INFO - Iter [52050/80000]	lr: 4.687e-06, eta: 2:19:16, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1912, decode.acc_seg: 92.1769, loss: 0.1912
+2023-03-04 00:59:54,761 - mmseg - INFO - Iter [52100/80000]	lr: 4.687e-06, eta: 2:19:01, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1908, decode.acc_seg: 92.2497, loss: 0.1908
+2023-03-04 01:00:09,205 - mmseg - INFO - Iter [52150/80000]	lr: 4.687e-06, eta: 2:18:46, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1904, decode.acc_seg: 92.4958, loss: 0.1904
+2023-03-04 01:00:23,763 - mmseg - INFO - Iter [52200/80000]	lr: 4.687e-06, eta: 2:18:31, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1911, decode.acc_seg: 92.2406, loss: 0.1911
+2023-03-04 01:00:38,405 - mmseg - INFO - Iter [52250/80000]	lr: 4.687e-06, eta: 2:18:16, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1934, decode.acc_seg: 92.1650, loss: 0.1934
+2023-03-04 01:00:52,895 - mmseg - INFO - Iter [52300/80000]	lr: 4.687e-06, eta: 2:18:00, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1853, decode.acc_seg: 92.4684, loss: 0.1853
+2023-03-04 01:01:07,506 - mmseg - INFO - Iter [52350/80000]	lr: 4.687e-06, eta: 2:17:45, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1877, decode.acc_seg: 92.4505, loss: 0.1877
+2023-03-04 01:01:24,550 - mmseg - INFO - Iter [52400/80000]	lr: 4.687e-06, eta: 2:17:31, time: 0.341, data_time: 0.057, memory: 39544, decode.loss_ce: 0.1841, decode.acc_seg: 92.6008, loss: 0.1841
+2023-03-04 01:01:39,108 - mmseg - INFO - Iter [52450/80000]	lr: 4.687e-06, eta: 2:17:16, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1919, decode.acc_seg: 92.2221, loss: 0.1919
+2023-03-04 01:01:53,712 - mmseg - INFO - Iter [52500/80000]	lr: 4.687e-06, eta: 2:17:01, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1890, decode.acc_seg: 92.2995, loss: 0.1890
+2023-03-04 01:02:08,308 - mmseg - INFO - Iter [52550/80000]	lr: 4.687e-06, eta: 2:16:46, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1831, decode.acc_seg: 92.5803, loss: 0.1831
+2023-03-04 01:02:22,961 - mmseg - INFO - Iter [52600/80000]	lr: 4.687e-06, eta: 2:16:31, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1909, decode.acc_seg: 92.3560, loss: 0.1909
+2023-03-04 01:02:37,463 - mmseg - INFO - Iter [52650/80000]	lr: 4.687e-06, eta: 2:16:16, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1813, decode.acc_seg: 92.5464, loss: 0.1813
+2023-03-04 01:02:52,112 - mmseg - INFO - Iter [52700/80000]	lr: 4.687e-06, eta: 2:16:01, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1837, decode.acc_seg: 92.5359, loss: 0.1837
+2023-03-04 01:03:06,926 - mmseg - INFO - Iter [52750/80000]	lr: 4.687e-06, eta: 2:15:46, time: 0.296, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1924, decode.acc_seg: 92.2296, loss: 0.1924
+2023-03-04 01:03:21,529 - mmseg - INFO - Iter [52800/80000]	lr: 4.687e-06, eta: 2:15:30, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1816, decode.acc_seg: 92.6440, loss: 0.1816
+2023-03-04 01:03:36,179 - mmseg - INFO - Iter [52850/80000]	lr: 4.687e-06, eta: 2:15:15, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1871, decode.acc_seg: 92.5349, loss: 0.1871
+2023-03-04 01:03:50,840 - mmseg - INFO - Iter [52900/80000]	lr: 4.687e-06, eta: 2:15:00, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1768, decode.acc_seg: 92.7483, loss: 0.1768
+2023-03-04 01:04:05,357 - mmseg - INFO - Iter [52950/80000]	lr: 4.687e-06, eta: 2:14:45, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1936, decode.acc_seg: 92.3343, loss: 0.1936
+2023-03-04 01:04:19,943 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 01:04:19,943 - mmseg - INFO - Iter [53000/80000]	lr: 4.687e-06, eta: 2:14:30, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1905, decode.acc_seg: 92.1419, loss: 0.1905
+2023-03-04 01:04:36,988 - mmseg - INFO - Iter [53050/80000]	lr: 4.687e-06, eta: 2:14:16, time: 0.341, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1928, decode.acc_seg: 92.2734, loss: 0.1928
+2023-03-04 01:04:51,460 - mmseg - INFO - Iter [53100/80000]	lr: 4.687e-06, eta: 2:14:01, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1892, decode.acc_seg: 92.4747, loss: 0.1892
+2023-03-04 01:05:05,912 - mmseg - INFO - Iter [53150/80000]	lr: 4.687e-06, eta: 2:13:46, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1859, decode.acc_seg: 92.4234, loss: 0.1859
+2023-03-04 01:05:20,373 - mmseg - INFO - Iter [53200/80000]	lr: 4.687e-06, eta: 2:13:30, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1887, decode.acc_seg: 92.3817, loss: 0.1887
+2023-03-04 01:05:34,898 - mmseg - INFO - Iter [53250/80000]	lr: 4.687e-06, eta: 2:13:15, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1843, decode.acc_seg: 92.5044, loss: 0.1843
+2023-03-04 01:05:49,476 - mmseg - INFO - Iter [53300/80000]	lr: 4.687e-06, eta: 2:13:00, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1876, decode.acc_seg: 92.2860, loss: 0.1876
+2023-03-04 01:06:03,950 - mmseg - INFO - Iter [53350/80000]	lr: 4.687e-06, eta: 2:12:45, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1947, decode.acc_seg: 92.3034, loss: 0.1947
+2023-03-04 01:06:18,415 - mmseg - INFO - Iter [53400/80000]	lr: 4.687e-06, eta: 2:12:30, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1891, decode.acc_seg: 92.2946, loss: 0.1891
+2023-03-04 01:06:32,867 - mmseg - INFO - Iter [53450/80000]	lr: 4.687e-06, eta: 2:12:15, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1875, decode.acc_seg: 92.4107, loss: 0.1875
+2023-03-04 01:06:47,363 - mmseg - INFO - Iter [53500/80000]	lr: 4.687e-06, eta: 2:11:59, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1918, decode.acc_seg: 92.1890, loss: 0.1918
+2023-03-04 01:07:02,053 - mmseg - INFO - Iter [53550/80000]	lr: 4.687e-06, eta: 2:11:44, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1862, decode.acc_seg: 92.6088, loss: 0.1862
+2023-03-04 01:07:16,539 - mmseg - INFO - Iter [53600/80000]	lr: 4.687e-06, eta: 2:11:29, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1807, decode.acc_seg: 92.7033, loss: 0.1807
+2023-03-04 01:07:33,487 - mmseg - INFO - Iter [53650/80000]	lr: 4.687e-06, eta: 2:11:15, time: 0.339, data_time: 0.052, memory: 39544, decode.loss_ce: 0.1934, decode.acc_seg: 92.1667, loss: 0.1934
+2023-03-04 01:07:48,065 - mmseg - INFO - Iter [53700/80000]	lr: 4.687e-06, eta: 2:11:00, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.2024, decode.acc_seg: 91.9640, loss: 0.2024
+2023-03-04 01:08:02,630 - mmseg - INFO - Iter [53750/80000]	lr: 4.687e-06, eta: 2:10:45, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1835, decode.acc_seg: 92.5754, loss: 0.1835
+2023-03-04 01:08:17,145 - mmseg - INFO - Iter [53800/80000]	lr: 4.687e-06, eta: 2:10:30, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1943, decode.acc_seg: 92.1918, loss: 0.1943
+2023-03-04 01:08:31,672 - mmseg - INFO - Iter [53850/80000]	lr: 4.687e-06, eta: 2:10:15, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1904, decode.acc_seg: 92.3762, loss: 0.1904
+2023-03-04 01:08:46,311 - mmseg - INFO - Iter [53900/80000]	lr: 4.687e-06, eta: 2:10:00, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1883, decode.acc_seg: 92.5266, loss: 0.1883
+2023-03-04 01:09:00,914 - mmseg - INFO - Iter [53950/80000]	lr: 4.687e-06, eta: 2:09:45, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1816, decode.acc_seg: 92.4394, loss: 0.1816
+2023-03-04 01:09:15,462 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 01:09:15,462 - mmseg - INFO - Iter [54000/80000]	lr: 4.687e-06, eta: 2:09:29, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1877, decode.acc_seg: 92.4380, loss: 0.1877
+2023-03-04 01:09:30,015 - mmseg - INFO - Iter [54050/80000]	lr: 4.687e-06, eta: 2:09:14, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1908, decode.acc_seg: 92.3043, loss: 0.1908
+2023-03-04 01:09:44,553 - mmseg - INFO - Iter [54100/80000]	lr: 4.687e-06, eta: 2:08:59, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1879, decode.acc_seg: 92.5322, loss: 0.1879
+2023-03-04 01:09:59,043 - mmseg - INFO - Iter [54150/80000]	lr: 4.687e-06, eta: 2:08:44, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1927, decode.acc_seg: 92.2092, loss: 0.1927
+2023-03-04 01:10:13,635 - mmseg - INFO - Iter [54200/80000]	lr: 4.687e-06, eta: 2:08:29, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1915, decode.acc_seg: 92.1272, loss: 0.1915
+2023-03-04 01:10:28,282 - mmseg - INFO - Iter [54250/80000]	lr: 4.687e-06, eta: 2:08:14, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1777, decode.acc_seg: 92.7463, loss: 0.1777
+2023-03-04 01:10:45,401 - mmseg - INFO - Iter [54300/80000]	lr: 4.687e-06, eta: 2:08:00, time: 0.342, data_time: 0.055, memory: 39544, decode.loss_ce: 0.1881, decode.acc_seg: 92.4352, loss: 0.1881
+2023-03-04 01:10:59,981 - mmseg - INFO - Iter [54350/80000]	lr: 4.687e-06, eta: 2:07:45, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1831, decode.acc_seg: 92.4911, loss: 0.1831
+2023-03-04 01:11:14,526 - mmseg - INFO - Iter [54400/80000]	lr: 4.687e-06, eta: 2:07:30, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1850, decode.acc_seg: 92.3871, loss: 0.1850
+2023-03-04 01:11:29,221 - mmseg - INFO - Iter [54450/80000]	lr: 4.687e-06, eta: 2:07:15, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1831, decode.acc_seg: 92.5023, loss: 0.1831
+2023-03-04 01:11:43,741 - mmseg - INFO - Iter [54500/80000]	lr: 4.687e-06, eta: 2:06:59, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1824, decode.acc_seg: 92.6394, loss: 0.1824
+2023-03-04 01:11:58,391 - mmseg - INFO - Iter [54550/80000]	lr: 4.687e-06, eta: 2:06:44, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1804, decode.acc_seg: 92.6530, loss: 0.1804
+2023-03-04 01:12:12,834 - mmseg - INFO - Iter [54600/80000]	lr: 4.687e-06, eta: 2:06:29, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1979, decode.acc_seg: 92.0643, loss: 0.1979
+2023-03-04 01:12:27,340 - mmseg - INFO - Iter [54650/80000]	lr: 4.687e-06, eta: 2:06:14, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1886, decode.acc_seg: 92.3302, loss: 0.1886
+2023-03-04 01:12:41,867 - mmseg - INFO - Iter [54700/80000]	lr: 4.687e-06, eta: 2:05:59, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1827, decode.acc_seg: 92.6781, loss: 0.1827
+2023-03-04 01:12:56,324 - mmseg - INFO - Iter [54750/80000]	lr: 4.687e-06, eta: 2:05:44, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1935, decode.acc_seg: 92.2856, loss: 0.1935
+2023-03-04 01:13:10,815 - mmseg - INFO - Iter [54800/80000]	lr: 4.687e-06, eta: 2:05:29, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1773, decode.acc_seg: 92.6649, loss: 0.1773
+2023-03-04 01:13:25,276 - mmseg - INFO - Iter [54850/80000]	lr: 4.687e-06, eta: 2:05:13, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1871, decode.acc_seg: 92.3360, loss: 0.1871
+2023-03-04 01:13:42,235 - mmseg - INFO - Iter [54900/80000]	lr: 4.687e-06, eta: 2:04:59, time: 0.339, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1852, decode.acc_seg: 92.3813, loss: 0.1852
+2023-03-04 01:13:56,725 - mmseg - INFO - Iter [54950/80000]	lr: 4.687e-06, eta: 2:04:44, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1882, decode.acc_seg: 92.2737, loss: 0.1882
+2023-03-04 01:14:11,187 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 01:14:11,187 - mmseg - INFO - Iter [55000/80000]	lr: 4.687e-06, eta: 2:04:29, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1868, decode.acc_seg: 92.3762, loss: 0.1868
+2023-03-04 01:14:25,712 - mmseg - INFO - Iter [55050/80000]	lr: 4.687e-06, eta: 2:04:14, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1847, decode.acc_seg: 92.6116, loss: 0.1847
+2023-03-04 01:14:40,305 - mmseg - INFO - Iter [55100/80000]	lr: 4.687e-06, eta: 2:03:59, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1959, decode.acc_seg: 92.2574, loss: 0.1959
+2023-03-04 01:14:54,777 - mmseg - INFO - Iter [55150/80000]	lr: 4.687e-06, eta: 2:03:44, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1900, decode.acc_seg: 92.2588, loss: 0.1900
+2023-03-04 01:15:09,366 - mmseg - INFO - Iter [55200/80000]	lr: 4.687e-06, eta: 2:03:29, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1874, decode.acc_seg: 92.4314, loss: 0.1874
+2023-03-04 01:15:23,851 - mmseg - INFO - Iter [55250/80000]	lr: 4.687e-06, eta: 2:03:14, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1857, decode.acc_seg: 92.5230, loss: 0.1857
+2023-03-04 01:15:38,387 - mmseg - INFO - Iter [55300/80000]	lr: 4.687e-06, eta: 2:02:58, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1880, decode.acc_seg: 92.4605, loss: 0.1880
+2023-03-04 01:15:52,873 - mmseg - INFO - Iter [55350/80000]	lr: 4.687e-06, eta: 2:02:43, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1824, decode.acc_seg: 92.6346, loss: 0.1824
+2023-03-04 01:16:07,447 - mmseg - INFO - Iter [55400/80000]	lr: 4.687e-06, eta: 2:02:28, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1899, decode.acc_seg: 92.2358, loss: 0.1899
+2023-03-04 01:16:22,013 - mmseg - INFO - Iter [55450/80000]	lr: 4.687e-06, eta: 2:02:13, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1799, decode.acc_seg: 92.8078, loss: 0.1799
+2023-03-04 01:16:36,468 - mmseg - INFO - Iter [55500/80000]	lr: 4.687e-06, eta: 2:01:58, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1871, decode.acc_seg: 92.5224, loss: 0.1871
+2023-03-04 01:16:53,346 - mmseg - INFO - Iter [55550/80000]	lr: 4.687e-06, eta: 2:01:44, time: 0.338, data_time: 0.055, memory: 39544, decode.loss_ce: 0.1891, decode.acc_seg: 92.4128, loss: 0.1891
+2023-03-04 01:17:07,869 - mmseg - INFO - Iter [55600/80000]	lr: 4.687e-06, eta: 2:01:29, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1894, decode.acc_seg: 92.4557, loss: 0.1894
+2023-03-04 01:17:22,301 - mmseg - INFO - Iter [55650/80000]	lr: 4.687e-06, eta: 2:01:14, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1763, decode.acc_seg: 92.7602, loss: 0.1763
+2023-03-04 01:17:36,876 - mmseg - INFO - Iter [55700/80000]	lr: 4.687e-06, eta: 2:00:58, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1890, decode.acc_seg: 92.2856, loss: 0.1890
+2023-03-04 01:17:51,395 - mmseg - INFO - Iter [55750/80000]	lr: 4.687e-06, eta: 2:00:43, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1839, decode.acc_seg: 92.5331, loss: 0.1839
+2023-03-04 01:18:06,014 - mmseg - INFO - Iter [55800/80000]	lr: 4.687e-06, eta: 2:00:28, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1946, decode.acc_seg: 92.1551, loss: 0.1946
+2023-03-04 01:18:20,574 - mmseg - INFO - Iter [55850/80000]	lr: 4.687e-06, eta: 2:00:13, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1785, decode.acc_seg: 92.7616, loss: 0.1785
+2023-03-04 01:18:35,051 - mmseg - INFO - Iter [55900/80000]	lr: 4.687e-06, eta: 1:59:58, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1794, decode.acc_seg: 92.7342, loss: 0.1794
+2023-03-04 01:18:49,577 - mmseg - INFO - Iter [55950/80000]	lr: 4.687e-06, eta: 1:59:43, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1955, decode.acc_seg: 92.3217, loss: 0.1955
+2023-03-04 01:19:04,202 - mmseg - INFO - Saving checkpoint at 56000 iterations
+2023-03-04 01:19:06,086 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 01:19:06,086 - mmseg - INFO - Iter [56000/80000]	lr: 4.687e-06, eta: 1:59:29, time: 0.330, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1920, decode.acc_seg: 92.1749, loss: 0.1920
+2023-03-04 01:19:25,784 - mmseg - INFO - per class results:
+2023-03-04 01:19:25,790 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        | 76.42 | 87.68 |
+|       building      | 81.32 | 92.33 |
+|         sky         | 94.23 | 97.09 |
+|        floor        | 79.97 | 91.23 |
+|         tree        | 73.04 | 88.13 |
+|       ceiling       | 82.48 | 91.46 |
+|         road        |  82.3 | 89.37 |
+|         bed         | 88.33 | 95.26 |
+|      windowpane     | 60.73 | 78.25 |
+|        grass        | 65.73 | 81.87 |
+|       cabinet       | 59.41 | 73.47 |
+|       sidewalk      | 65.96 | 81.13 |
+|        person       | 79.13 | 91.45 |
+|        earth        | 33.85 | 48.48 |
+|         door        | 47.64 | 65.26 |
+|        table        | 61.31 | 77.27 |
+|       mountain      | 52.21 | 68.24 |
+|        plant        | 50.28 | 63.66 |
+|       curtain       |  69.0 | 81.58 |
+|        chair        | 57.68 | 70.67 |
+|         car         | 83.24 | 90.03 |
+|        water        | 47.18 | 62.98 |
+|       painting      | 69.27 | 84.03 |
+|         sofa        | 65.17 | 83.86 |
+|        shelf        |  39.5 | 54.08 |
+|        house        |  46.0 | 54.98 |
+|         sea         | 43.17 | 69.35 |
+|        mirror       | 64.97 | 74.52 |
+|         rug         | 55.21 | 60.73 |
+|        field        | 28.03 | 43.78 |
+|       armchair      | 44.38 | 59.48 |
+|         seat        |  53.7 |  76.9 |
+|        fence        |  41.4 | 57.08 |
+|         desk        | 49.47 | 69.93 |
+|         rock        | 31.17 | 46.68 |
+|       wardrobe      | 49.58 |  70.0 |
+|         lamp        | 63.82 |  76.2 |
+|       bathtub       | 76.59 | 83.11 |
+|       railing       | 33.26 | 47.89 |
+|       cushion       | 55.46 | 69.75 |
+|         base        | 28.69 | 40.16 |
+|         box         | 23.84 | 32.54 |
+|        column       | 45.49 | 57.18 |
+|      signboard      | 36.04 | 50.01 |
+|   chest of drawers  | 38.87 | 55.57 |
+|       counter       | 29.27 | 42.83 |
+|         sand        | 31.48 | 48.19 |
+|         sink        | 70.16 | 81.07 |
+|      skyscraper     | 47.58 | 55.42 |
+|      fireplace      | 66.75 | 85.58 |
+|     refrigerator    | 77.56 | 83.89 |
+|      grandstand     | 40.37 | 65.66 |
+|         path        | 15.16 |  22.7 |
+|        stairs       | 32.45 | 42.29 |
+|        runway       | 64.07 | 82.91 |
+|         case        | 47.13 | 68.73 |
+|      pool table     | 92.47 | 96.01 |
+|        pillow       |  56.6 | 65.81 |
+|     screen door     | 62.46 | 69.72 |
+|       stairway      | 24.89 | 31.58 |
+|        river        |  9.05 | 16.11 |
+|        bridge       | 49.85 | 54.67 |
+|       bookcase      | 39.53 | 50.71 |
+|        blind        | 44.87 | 51.31 |
+|     coffee table    | 65.97 | 82.17 |
+|        toilet       |  85.8 | 90.96 |
+|        flower       | 31.42 | 46.83 |
+|         book        | 47.06 |  67.7 |
+|         hill        |  8.54 | 10.59 |
+|        bench        | 44.07 | 53.11 |
+|      countertop     | 52.51 | 69.32 |
+|        stove        | 73.78 | 79.73 |
+|         palm        | 50.44 |  70.4 |
+|    kitchen island   | 46.81 | 74.48 |
+|       computer      | 57.39 | 64.96 |
+|     swivel chair    | 44.13 | 61.05 |
+|         boat        | 39.37 | 45.44 |
+|         bar         | 29.88 | 34.57 |
+|    arcade machine   | 25.66 | 27.82 |
+|        hovel        | 29.26 | 31.17 |
+|         bus         | 87.99 | 92.83 |
+|        towel        | 59.01 | 68.58 |
+|        light        | 56.12 | 62.55 |
+|        truck        | 34.69 | 46.56 |
+|        tower        | 23.49 |  33.2 |
+|      chandelier     |  66.2 |  81.9 |
+|        awning       |  23.7 | 26.32 |
+|     streetlight     | 28.65 | 38.19 |
+|        booth        | 55.57 | 58.12 |
+| television receiver | 67.56 | 78.49 |
+|       airplane      | 49.74 | 70.26 |
+|      dirt track     |  6.99 | 20.59 |
+|       apparel       | 28.88 | 44.02 |
+|         pole        | 23.66 | 35.64 |
+|         land        |  7.99 | 11.48 |
+|      bannister      |  4.96 |  6.77 |
+|      escalator      | 23.35 | 24.04 |
+|       ottoman       | 48.22 | 59.15 |
+|        bottle       | 16.62 | 25.85 |
+|        buffet       | 49.46 | 58.07 |
+|        poster       | 27.61 | 38.99 |
+|        stage        | 17.45 | 23.98 |
+|         van         | 48.64 | 63.35 |
+|         ship        | 43.49 | 57.52 |
+|       fountain      |  5.93 |  5.99 |
+|    conveyer belt    | 74.95 | 88.53 |
+|        canopy       | 14.63 | 17.29 |
+|        washer       | 66.85 | 67.36 |
+|      plaything      |  22.4 | 31.97 |
+|    swimming pool    | 43.86 | 53.61 |
+|        stool        | 41.22 | 59.13 |
+|        barrel       | 36.85 | 64.51 |
+|        basket       | 27.31 | 37.53 |
+|      waterfall      | 56.44 | 66.22 |
+|         tent        | 94.49 | 97.35 |
+|         bag         | 11.54 | 14.08 |
+|       minibike      | 62.11 | 72.75 |
+|        cradle       | 79.96 | 97.81 |
+|         oven        | 27.42 | 59.52 |
+|         ball        | 47.01 | 60.24 |
+|         food        | 51.93 | 62.42 |
+|         step        | 13.48 | 17.03 |
+|         tank        | 42.28 | 42.75 |
+|      trade name     |  26.4 |  31.1 |
+|      microwave      | 37.95 | 40.81 |
+|         pot         | 40.89 | 49.14 |
+|        animal       | 51.69 | 55.19 |
+|       bicycle       |  45.3 | 68.11 |
+|         lake        | 59.25 | 63.37 |
+|      dishwasher     | 77.56 | 83.02 |
+|        screen       | 67.17 | 86.04 |
+|       blanket       | 11.91 | 13.39 |
+|      sculpture      | 36.57 | 63.47 |
+|         hood        |  56.0 | 71.62 |
+|        sconce       | 42.24 | 51.06 |
+|         vase        | 36.25 | 50.74 |
+|    traffic light    | 29.76 |  42.7 |
+|         tray        |  5.33 |  9.88 |
+|        ashcan       | 38.36 | 49.07 |
+|         fan         | 57.97 | 70.75 |
+|         pier        | 13.87 | 15.96 |
+|      crt screen     |  3.89 | 11.63 |
+|        plate        | 40.55 | 51.47 |
+|       monitor       | 15.28 | 19.54 |
+|    bulletin board   | 46.22 |  56.8 |
+|        shower       |  1.82 |  2.34 |
+|       radiator      | 47.08 |  55.9 |
+|        glass        | 12.91 | 14.49 |
+|        clock        | 25.04 | 31.35 |
+|         flag        | 38.14 | 42.19 |
++---------------------+-------+-------+
+2023-03-04 01:19:25,790 - mmseg - INFO - Summary:
+2023-03-04 01:19:25,790 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 81.93 | 45.96 | 57.03 |
++-------+-------+-------+
+2023-03-04 01:19:25,791 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 01:19:25,791 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8193, mIoU: 0.4596, mAcc: 0.5703, IoU.background: nan, IoU.wall: 0.7642, IoU.building: 0.8132, IoU.sky: 0.9423, IoU.floor: 0.7997, IoU.tree: 0.7304, IoU.ceiling: 0.8248, IoU.road: 0.8230, IoU.bed : 0.8833, IoU.windowpane: 0.6073, IoU.grass: 0.6573, IoU.cabinet: 0.5941, IoU.sidewalk: 0.6596, IoU.person: 0.7913, IoU.earth: 0.3385, IoU.door: 0.4764, IoU.table: 0.6131, IoU.mountain: 0.5221, IoU.plant: 0.5028, IoU.curtain: 0.6900, IoU.chair: 0.5768, IoU.car: 0.8324, IoU.water: 0.4718, IoU.painting: 0.6927, IoU.sofa: 0.6517, IoU.shelf: 0.3950, IoU.house: 0.4600, IoU.sea: 0.4317, IoU.mirror: 0.6497, IoU.rug: 0.5521, IoU.field: 0.2803, IoU.armchair: 0.4438, IoU.seat: 0.5370, IoU.fence: 0.4140, IoU.desk: 0.4947, IoU.rock: 0.3117, IoU.wardrobe: 0.4958, IoU.lamp: 0.6382, IoU.bathtub: 0.7659, IoU.railing: 0.3326, IoU.cushion: 0.5546, IoU.base: 0.2869, IoU.box: 0.2384, IoU.column: 0.4549, IoU.signboard: 0.3604, IoU.chest of drawers: 0.3887, IoU.counter: 0.2927, IoU.sand: 0.3148, IoU.sink: 0.7016, IoU.skyscraper: 0.4758, IoU.fireplace: 0.6675, IoU.refrigerator: 0.7756, IoU.grandstand: 0.4037, IoU.path: 0.1516, IoU.stairs: 0.3245, IoU.runway: 0.6407, IoU.case: 0.4713, IoU.pool table: 0.9247, IoU.pillow: 0.5660, IoU.screen door: 0.6246, IoU.stairway: 0.2489, IoU.river: 0.0905, IoU.bridge: 0.4985, IoU.bookcase: 0.3953, IoU.blind: 0.4487, IoU.coffee table: 0.6597, IoU.toilet: 0.8580, IoU.flower: 0.3142, IoU.book: 0.4706, IoU.hill: 0.0854, IoU.bench: 0.4407, IoU.countertop: 0.5251, IoU.stove: 0.7378, IoU.palm: 0.5044, IoU.kitchen island: 0.4681, IoU.computer: 0.5739, IoU.swivel chair: 0.4413, IoU.boat: 0.3937, IoU.bar: 0.2988, IoU.arcade machine: 0.2566, IoU.hovel: 0.2926, IoU.bus: 0.8799, IoU.towel: 0.5901, IoU.light: 0.5612, IoU.truck: 0.3469, IoU.tower: 0.2349, IoU.chandelier: 0.6620, IoU.awning: 0.2370, IoU.streetlight: 0.2865, IoU.booth: 0.5557, IoU.television receiver: 0.6756, IoU.airplane: 0.4974, IoU.dirt track: 0.0699, IoU.apparel: 0.2888, IoU.pole: 0.2366, IoU.land: 0.0799, IoU.bannister: 0.0496, IoU.escalator: 0.2335, IoU.ottoman: 0.4822, IoU.bottle: 0.1662, IoU.buffet: 0.4946, IoU.poster: 0.2761, IoU.stage: 0.1745, IoU.van: 0.4864, IoU.ship: 0.4349, IoU.fountain: 0.0593, IoU.conveyer belt: 0.7495, IoU.canopy: 0.1463, IoU.washer: 0.6685, IoU.plaything: 0.2240, IoU.swimming pool: 0.4386, IoU.stool: 0.4122, IoU.barrel: 0.3685, IoU.basket: 0.2731, IoU.waterfall: 0.5644, IoU.tent: 0.9449, IoU.bag: 0.1154, IoU.minibike: 0.6211, IoU.cradle: 0.7996, IoU.oven: 0.2742, IoU.ball: 0.4701, IoU.food: 0.5193, IoU.step: 0.1348, IoU.tank: 0.4228, IoU.trade name: 0.2640, IoU.microwave: 0.3795, IoU.pot: 0.4089, IoU.animal: 0.5169, IoU.bicycle: 0.4530, IoU.lake: 0.5925, IoU.dishwasher: 0.7756, IoU.screen: 0.6717, IoU.blanket: 0.1191, IoU.sculpture: 0.3657, IoU.hood: 0.5600, IoU.sconce: 0.4224, IoU.vase: 0.3625, IoU.traffic light: 0.2976, IoU.tray: 0.0533, IoU.ashcan: 0.3836, IoU.fan: 0.5797, IoU.pier: 0.1387, IoU.crt screen: 0.0389, IoU.plate: 0.4055, IoU.monitor: 0.1528, IoU.bulletin board: 0.4622, IoU.shower: 0.0182, IoU.radiator: 0.4708, IoU.glass: 0.1291, IoU.clock: 0.2504, IoU.flag: 0.3814, Acc.background: nan, Acc.wall: 0.8768, Acc.building: 0.9233, Acc.sky: 0.9709, Acc.floor: 0.9123, Acc.tree: 0.8813, Acc.ceiling: 0.9146, Acc.road: 0.8937, Acc.bed : 0.9526, Acc.windowpane: 0.7825, Acc.grass: 0.8187, Acc.cabinet: 0.7347, Acc.sidewalk: 0.8113, Acc.person: 0.9145, Acc.earth: 0.4848, Acc.door: 0.6526, Acc.table: 0.7727, Acc.mountain: 0.6824, Acc.plant: 0.6366, Acc.curtain: 0.8158, Acc.chair: 0.7067, Acc.car: 0.9003, Acc.water: 0.6298, Acc.painting: 0.8403, Acc.sofa: 0.8386, Acc.shelf: 0.5408, Acc.house: 0.5498, Acc.sea: 0.6935, Acc.mirror: 0.7452, Acc.rug: 0.6073, Acc.field: 0.4378, Acc.armchair: 0.5948, Acc.seat: 0.7690, Acc.fence: 0.5708, Acc.desk: 0.6993, Acc.rock: 0.4668, Acc.wardrobe: 0.7000, Acc.lamp: 0.7620, Acc.bathtub: 0.8311, Acc.railing: 0.4789, Acc.cushion: 0.6975, Acc.base: 0.4016, Acc.box: 0.3254, Acc.column: 0.5718, Acc.signboard: 0.5001, Acc.chest of drawers: 0.5557, Acc.counter: 0.4283, Acc.sand: 0.4819, Acc.sink: 0.8107, Acc.skyscraper: 0.5542, Acc.fireplace: 0.8558, Acc.refrigerator: 0.8389, Acc.grandstand: 0.6566, Acc.path: 0.2270, Acc.stairs: 0.4229, Acc.runway: 0.8291, Acc.case: 0.6873, Acc.pool table: 0.9601, Acc.pillow: 0.6581, Acc.screen door: 0.6972, Acc.stairway: 0.3158, Acc.river: 0.1611, Acc.bridge: 0.5467, Acc.bookcase: 0.5071, Acc.blind: 0.5131, Acc.coffee table: 0.8217, Acc.toilet: 0.9096, Acc.flower: 0.4683, Acc.book: 0.6770, Acc.hill: 0.1059, Acc.bench: 0.5311, Acc.countertop: 0.6932, Acc.stove: 0.7973, Acc.palm: 0.7040, Acc.kitchen island: 0.7448, Acc.computer: 0.6496, Acc.swivel chair: 0.6105, Acc.boat: 0.4544, Acc.bar: 0.3457, Acc.arcade machine: 0.2782, Acc.hovel: 0.3117, Acc.bus: 0.9283, Acc.towel: 0.6858, Acc.light: 0.6255, Acc.truck: 0.4656, Acc.tower: 0.3320, Acc.chandelier: 0.8190, Acc.awning: 0.2632, Acc.streetlight: 0.3819, Acc.booth: 0.5812, Acc.television receiver: 0.7849, Acc.airplane: 0.7026, Acc.dirt track: 0.2059, Acc.apparel: 0.4402, Acc.pole: 0.3564, Acc.land: 0.1148, Acc.bannister: 0.0677, Acc.escalator: 0.2404, Acc.ottoman: 0.5915, Acc.bottle: 0.2585, Acc.buffet: 0.5807, Acc.poster: 0.3899, Acc.stage: 0.2398, Acc.van: 0.6335, Acc.ship: 0.5752, Acc.fountain: 0.0599, Acc.conveyer belt: 0.8853, Acc.canopy: 0.1729, Acc.washer: 0.6736, Acc.plaything: 0.3197, Acc.swimming pool: 0.5361, Acc.stool: 0.5913, Acc.barrel: 0.6451, Acc.basket: 0.3753, Acc.waterfall: 0.6622, Acc.tent: 0.9735, Acc.bag: 0.1408, Acc.minibike: 0.7275, Acc.cradle: 0.9781, Acc.oven: 0.5952, Acc.ball: 0.6024, Acc.food: 0.6242, Acc.step: 0.1703, Acc.tank: 0.4275, Acc.trade name: 0.3110, Acc.microwave: 0.4081, Acc.pot: 0.4914, Acc.animal: 0.5519, Acc.bicycle: 0.6811, Acc.lake: 0.6337, Acc.dishwasher: 0.8302, Acc.screen: 0.8604, Acc.blanket: 0.1339, Acc.sculpture: 0.6347, Acc.hood: 0.7162, Acc.sconce: 0.5106, Acc.vase: 0.5074, Acc.traffic light: 0.4270, Acc.tray: 0.0988, Acc.ashcan: 0.4907, Acc.fan: 0.7075, Acc.pier: 0.1596, Acc.crt screen: 0.1163, Acc.plate: 0.5147, Acc.monitor: 0.1954, Acc.bulletin board: 0.5680, Acc.shower: 0.0234, Acc.radiator: 0.5590, Acc.glass: 0.1449, Acc.clock: 0.3135, Acc.flag: 0.4219
+2023-03-04 01:19:40,674 - mmseg - INFO - Iter [56050/80000]	lr: 4.687e-06, eta: 1:59:22, time: 0.692, data_time: 0.402, memory: 39544, decode.loss_ce: 0.1884, decode.acc_seg: 92.2411, loss: 0.1884
+2023-03-04 01:19:55,377 - mmseg - INFO - Iter [56100/80000]	lr: 4.687e-06, eta: 1:59:07, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1817, decode.acc_seg: 92.4484, loss: 0.1817
+2023-03-04 01:20:10,000 - mmseg - INFO - Iter [56150/80000]	lr: 4.687e-06, eta: 1:58:52, time: 0.292, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1863, decode.acc_seg: 92.4839, loss: 0.1863
+2023-03-04 01:20:27,190 - mmseg - INFO - Iter [56200/80000]	lr: 4.687e-06, eta: 1:58:38, time: 0.344, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1854, decode.acc_seg: 92.5251, loss: 0.1854
+2023-03-04 01:20:41,744 - mmseg - INFO - Iter [56250/80000]	lr: 4.687e-06, eta: 1:58:23, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1851, decode.acc_seg: 92.5635, loss: 0.1851
+2023-03-04 01:20:56,251 - mmseg - INFO - Iter [56300/80000]	lr: 4.687e-06, eta: 1:58:08, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1877, decode.acc_seg: 92.4655, loss: 0.1877
+2023-03-04 01:21:10,800 - mmseg - INFO - Iter [56350/80000]	lr: 4.687e-06, eta: 1:57:53, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1823, decode.acc_seg: 92.5405, loss: 0.1823
+2023-03-04 01:21:25,408 - mmseg - INFO - Iter [56400/80000]	lr: 4.687e-06, eta: 1:57:38, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1921, decode.acc_seg: 92.0360, loss: 0.1921
+2023-03-04 01:21:39,977 - mmseg - INFO - Iter [56450/80000]	lr: 4.687e-06, eta: 1:57:22, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1835, decode.acc_seg: 92.3693, loss: 0.1835
+2023-03-04 01:21:54,416 - mmseg - INFO - Iter [56500/80000]	lr: 4.687e-06, eta: 1:57:07, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1922, decode.acc_seg: 92.1829, loss: 0.1922
+2023-03-04 01:22:08,960 - mmseg - INFO - Iter [56550/80000]	lr: 4.687e-06, eta: 1:56:52, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1878, decode.acc_seg: 92.2916, loss: 0.1878
+2023-03-04 01:22:23,418 - mmseg - INFO - Iter [56600/80000]	lr: 4.687e-06, eta: 1:56:37, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1922, decode.acc_seg: 92.3200, loss: 0.1922
+2023-03-04 01:22:37,947 - mmseg - INFO - Iter [56650/80000]	lr: 4.687e-06, eta: 1:56:22, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1842, decode.acc_seg: 92.6429, loss: 0.1842
+2023-03-04 01:22:52,414 - mmseg - INFO - Iter [56700/80000]	lr: 4.687e-06, eta: 1:56:07, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1828, decode.acc_seg: 92.4635, loss: 0.1828
+2023-03-04 01:23:06,972 - mmseg - INFO - Iter [56750/80000]	lr: 4.687e-06, eta: 1:55:52, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1802, decode.acc_seg: 92.6920, loss: 0.1802
+2023-03-04 01:23:24,090 - mmseg - INFO - Iter [56800/80000]	lr: 4.687e-06, eta: 1:55:37, time: 0.342, data_time: 0.056, memory: 39544, decode.loss_ce: 0.1879, decode.acc_seg: 92.3328, loss: 0.1879
+2023-03-04 01:23:38,721 - mmseg - INFO - Iter [56850/80000]	lr: 4.687e-06, eta: 1:55:22, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1853, decode.acc_seg: 92.5260, loss: 0.1853
+2023-03-04 01:23:53,322 - mmseg - INFO - Iter [56900/80000]	lr: 4.687e-06, eta: 1:55:07, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1885, decode.acc_seg: 92.3530, loss: 0.1885
+2023-03-04 01:24:07,807 - mmseg - INFO - Iter [56950/80000]	lr: 4.687e-06, eta: 1:54:52, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1850, decode.acc_seg: 92.3899, loss: 0.1850
+2023-03-04 01:24:22,269 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 01:24:22,269 - mmseg - INFO - Iter [57000/80000]	lr: 4.687e-06, eta: 1:54:37, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1755, decode.acc_seg: 92.7839, loss: 0.1755
+2023-03-04 01:24:36,853 - mmseg - INFO - Iter [57050/80000]	lr: 4.687e-06, eta: 1:54:22, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1968, decode.acc_seg: 91.9950, loss: 0.1968
+2023-03-04 01:24:51,523 - mmseg - INFO - Iter [57100/80000]	lr: 4.687e-06, eta: 1:54:07, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1921, decode.acc_seg: 92.2714, loss: 0.1921
+2023-03-04 01:25:05,993 - mmseg - INFO - Iter [57150/80000]	lr: 4.687e-06, eta: 1:53:52, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1871, decode.acc_seg: 92.4577, loss: 0.1871
+2023-03-04 01:25:20,538 - mmseg - INFO - Iter [57200/80000]	lr: 4.687e-06, eta: 1:53:37, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1820, decode.acc_seg: 92.6801, loss: 0.1820
+2023-03-04 01:25:35,066 - mmseg - INFO - Iter [57250/80000]	lr: 4.687e-06, eta: 1:53:21, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1858, decode.acc_seg: 92.4899, loss: 0.1858
+2023-03-04 01:25:49,794 - mmseg - INFO - Iter [57300/80000]	lr: 4.687e-06, eta: 1:53:06, time: 0.295, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1813, decode.acc_seg: 92.5358, loss: 0.1813
+2023-03-04 01:26:04,339 - mmseg - INFO - Iter [57350/80000]	lr: 4.687e-06, eta: 1:52:51, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1897, decode.acc_seg: 92.2452, loss: 0.1897
+2023-03-04 01:26:18,871 - mmseg - INFO - Iter [57400/80000]	lr: 4.687e-06, eta: 1:52:36, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1990, decode.acc_seg: 91.9996, loss: 0.1990
+2023-03-04 01:26:35,795 - mmseg - INFO - Iter [57450/80000]	lr: 4.687e-06, eta: 1:52:22, time: 0.338, data_time: 0.056, memory: 39544, decode.loss_ce: 0.1894, decode.acc_seg: 92.2692, loss: 0.1894
+2023-03-04 01:26:50,236 - mmseg - INFO - Iter [57500/80000]	lr: 4.687e-06, eta: 1:52:07, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1884, decode.acc_seg: 92.4013, loss: 0.1884
+2023-03-04 01:27:04,869 - mmseg - INFO - Iter [57550/80000]	lr: 4.687e-06, eta: 1:51:52, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1917, decode.acc_seg: 92.1300, loss: 0.1917
+2023-03-04 01:27:19,389 - mmseg - INFO - Iter [57600/80000]	lr: 4.687e-06, eta: 1:51:37, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1835, decode.acc_seg: 92.5275, loss: 0.1835
+2023-03-04 01:27:33,864 - mmseg - INFO - Iter [57650/80000]	lr: 4.687e-06, eta: 1:51:22, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1858, decode.acc_seg: 92.3830, loss: 0.1858
+2023-03-04 01:27:48,681 - mmseg - INFO - Iter [57700/80000]	lr: 4.687e-06, eta: 1:51:07, time: 0.296, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1954, decode.acc_seg: 92.1962, loss: 0.1954
+2023-03-04 01:28:03,178 - mmseg - INFO - Iter [57750/80000]	lr: 4.687e-06, eta: 1:50:51, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1896, decode.acc_seg: 92.2989, loss: 0.1896
+2023-03-04 01:28:17,965 - mmseg - INFO - Iter [57800/80000]	lr: 4.687e-06, eta: 1:50:36, time: 0.296, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1843, decode.acc_seg: 92.4649, loss: 0.1843
+2023-03-04 01:28:32,462 - mmseg - INFO - Iter [57850/80000]	lr: 4.687e-06, eta: 1:50:21, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1821, decode.acc_seg: 92.6597, loss: 0.1821
+2023-03-04 01:28:46,934 - mmseg - INFO - Iter [57900/80000]	lr: 4.687e-06, eta: 1:50:06, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1895, decode.acc_seg: 92.3103, loss: 0.1895
+2023-03-04 01:29:01,351 - mmseg - INFO - Iter [57950/80000]	lr: 4.687e-06, eta: 1:49:51, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1863, decode.acc_seg: 92.4480, loss: 0.1863
+2023-03-04 01:29:15,754 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 01:29:15,754 - mmseg - INFO - Iter [58000/80000]	lr: 4.687e-06, eta: 1:49:36, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1941, decode.acc_seg: 92.2525, loss: 0.1941
+2023-03-04 01:29:30,285 - mmseg - INFO - Iter [58050/80000]	lr: 4.687e-06, eta: 1:49:21, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1883, decode.acc_seg: 92.3937, loss: 0.1883
+2023-03-04 01:29:47,418 - mmseg - INFO - Iter [58100/80000]	lr: 4.687e-06, eta: 1:49:07, time: 0.343, data_time: 0.056, memory: 39544, decode.loss_ce: 0.1841, decode.acc_seg: 92.4578, loss: 0.1841
+2023-03-04 01:30:01,948 - mmseg - INFO - Iter [58150/80000]	lr: 4.687e-06, eta: 1:48:52, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1858, decode.acc_seg: 92.4398, loss: 0.1858
+2023-03-04 01:30:16,451 - mmseg - INFO - Iter [58200/80000]	lr: 4.687e-06, eta: 1:48:36, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1911, decode.acc_seg: 92.2697, loss: 0.1911
+2023-03-04 01:30:31,023 - mmseg - INFO - Iter [58250/80000]	lr: 4.687e-06, eta: 1:48:21, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1850, decode.acc_seg: 92.4658, loss: 0.1850
+2023-03-04 01:30:45,540 - mmseg - INFO - Iter [58300/80000]	lr: 4.687e-06, eta: 1:48:06, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1935, decode.acc_seg: 92.1229, loss: 0.1935
+2023-03-04 01:31:00,086 - mmseg - INFO - Iter [58350/80000]	lr: 4.687e-06, eta: 1:47:51, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1854, decode.acc_seg: 92.4499, loss: 0.1854
+2023-03-04 01:31:14,829 - mmseg - INFO - Iter [58400/80000]	lr: 4.687e-06, eta: 1:47:36, time: 0.295, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1865, decode.acc_seg: 92.5458, loss: 0.1865
+2023-03-04 01:31:29,302 - mmseg - INFO - Iter [58450/80000]	lr: 4.687e-06, eta: 1:47:21, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1829, decode.acc_seg: 92.4453, loss: 0.1829
+2023-03-04 01:31:43,841 - mmseg - INFO - Iter [58500/80000]	lr: 4.687e-06, eta: 1:47:06, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1828, decode.acc_seg: 92.5803, loss: 0.1828
+2023-03-04 01:31:58,365 - mmseg - INFO - Iter [58550/80000]	lr: 4.687e-06, eta: 1:46:51, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1795, decode.acc_seg: 92.6979, loss: 0.1795
+2023-03-04 01:32:13,047 - mmseg - INFO - Iter [58600/80000]	lr: 4.687e-06, eta: 1:46:36, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1885, decode.acc_seg: 92.3476, loss: 0.1885
+2023-03-04 01:32:27,601 - mmseg - INFO - Iter [58650/80000]	lr: 4.687e-06, eta: 1:46:21, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1879, decode.acc_seg: 92.3127, loss: 0.1879
+2023-03-04 01:32:44,500 - mmseg - INFO - Iter [58700/80000]	lr: 4.687e-06, eta: 1:46:06, time: 0.338, data_time: 0.056, memory: 39544, decode.loss_ce: 0.1817, decode.acc_seg: 92.5292, loss: 0.1817
+2023-03-04 01:32:58,976 - mmseg - INFO - Iter [58750/80000]	lr: 4.687e-06, eta: 1:45:51, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1787, decode.acc_seg: 92.5564, loss: 0.1787
+2023-03-04 01:33:13,477 - mmseg - INFO - Iter [58800/80000]	lr: 4.687e-06, eta: 1:45:36, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1810, decode.acc_seg: 92.5739, loss: 0.1810
+2023-03-04 01:33:27,936 - mmseg - INFO - Iter [58850/80000]	lr: 4.687e-06, eta: 1:45:21, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1896, decode.acc_seg: 92.3892, loss: 0.1896
+2023-03-04 01:33:42,525 - mmseg - INFO - Iter [58900/80000]	lr: 4.687e-06, eta: 1:45:06, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1934, decode.acc_seg: 92.2503, loss: 0.1934
+2023-03-04 01:33:57,048 - mmseg - INFO - Iter [58950/80000]	lr: 4.687e-06, eta: 1:44:51, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1887, decode.acc_seg: 92.5053, loss: 0.1887
+2023-03-04 01:34:11,495 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 01:34:11,495 - mmseg - INFO - Iter [59000/80000]	lr: 4.687e-06, eta: 1:44:36, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1849, decode.acc_seg: 92.5403, loss: 0.1849
+2023-03-04 01:34:26,078 - mmseg - INFO - Iter [59050/80000]	lr: 4.687e-06, eta: 1:44:21, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1964, decode.acc_seg: 91.9874, loss: 0.1964
+2023-03-04 01:34:40,514 - mmseg - INFO - Iter [59100/80000]	lr: 4.687e-06, eta: 1:44:06, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1883, decode.acc_seg: 92.3540, loss: 0.1883
+2023-03-04 01:34:55,090 - mmseg - INFO - Iter [59150/80000]	lr: 4.687e-06, eta: 1:43:51, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1924, decode.acc_seg: 92.1639, loss: 0.1924
+2023-03-04 01:35:09,659 - mmseg - INFO - Iter [59200/80000]	lr: 4.687e-06, eta: 1:43:35, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1863, decode.acc_seg: 92.4479, loss: 0.1863
+2023-03-04 01:35:24,265 - mmseg - INFO - Iter [59250/80000]	lr: 4.687e-06, eta: 1:43:20, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1812, decode.acc_seg: 92.6574, loss: 0.1812
+2023-03-04 01:35:38,818 - mmseg - INFO - Iter [59300/80000]	lr: 4.687e-06, eta: 1:43:05, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1875, decode.acc_seg: 92.4670, loss: 0.1875
+2023-03-04 01:35:55,973 - mmseg - INFO - Iter [59350/80000]	lr: 4.687e-06, eta: 1:42:51, time: 0.343, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1868, decode.acc_seg: 92.4077, loss: 0.1868
+2023-03-04 01:36:10,518 - mmseg - INFO - Iter [59400/80000]	lr: 4.687e-06, eta: 1:42:36, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1899, decode.acc_seg: 92.2819, loss: 0.1899
+2023-03-04 01:36:25,032 - mmseg - INFO - Iter [59450/80000]	lr: 4.687e-06, eta: 1:42:21, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1858, decode.acc_seg: 92.4167, loss: 0.1858
+2023-03-04 01:36:39,542 - mmseg - INFO - Iter [59500/80000]	lr: 4.687e-06, eta: 1:42:06, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1858, decode.acc_seg: 92.5303, loss: 0.1858
+2023-03-04 01:36:53,997 - mmseg - INFO - Iter [59550/80000]	lr: 4.687e-06, eta: 1:41:51, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1853, decode.acc_seg: 92.4593, loss: 0.1853
+2023-03-04 01:37:08,470 - mmseg - INFO - Iter [59600/80000]	lr: 4.687e-06, eta: 1:41:36, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1790, decode.acc_seg: 92.7468, loss: 0.1790
+2023-03-04 01:37:22,981 - mmseg - INFO - Iter [59650/80000]	lr: 4.687e-06, eta: 1:41:21, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1843, decode.acc_seg: 92.5792, loss: 0.1843
+2023-03-04 01:37:37,579 - mmseg - INFO - Iter [59700/80000]	lr: 4.687e-06, eta: 1:41:06, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1863, decode.acc_seg: 92.4576, loss: 0.1863
+2023-03-04 01:37:52,222 - mmseg - INFO - Iter [59750/80000]	lr: 4.687e-06, eta: 1:40:51, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1893, decode.acc_seg: 92.3175, loss: 0.1893
+2023-03-04 01:38:06,684 - mmseg - INFO - Iter [59800/80000]	lr: 4.687e-06, eta: 1:40:35, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1876, decode.acc_seg: 92.2776, loss: 0.1876
+2023-03-04 01:38:21,287 - mmseg - INFO - Iter [59850/80000]	lr: 4.687e-06, eta: 1:40:20, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1865, decode.acc_seg: 92.4366, loss: 0.1865
+2023-03-04 01:38:35,849 - mmseg - INFO - Iter [59900/80000]	lr: 4.687e-06, eta: 1:40:05, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1841, decode.acc_seg: 92.5574, loss: 0.1841
+2023-03-04 01:38:52,871 - mmseg - INFO - Iter [59950/80000]	lr: 4.687e-06, eta: 1:39:51, time: 0.340, data_time: 0.056, memory: 39544, decode.loss_ce: 0.1826, decode.acc_seg: 92.5353, loss: 0.1826
+2023-03-04 01:39:07,398 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 01:39:07,399 - mmseg - INFO - Iter [60000/80000]	lr: 4.687e-06, eta: 1:39:36, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1876, decode.acc_seg: 92.2348, loss: 0.1876
+2023-03-04 01:39:21,907 - mmseg - INFO - Iter [60050/80000]	lr: 2.344e-06, eta: 1:39:21, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1901, decode.acc_seg: 92.3948, loss: 0.1901
+2023-03-04 01:39:36,641 - mmseg - INFO - Iter [60100/80000]	lr: 2.344e-06, eta: 1:39:06, time: 0.295, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1899, decode.acc_seg: 92.2061, loss: 0.1899
+2023-03-04 01:39:51,136 - mmseg - INFO - Iter [60150/80000]	lr: 2.344e-06, eta: 1:38:51, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1835, decode.acc_seg: 92.6235, loss: 0.1835
+2023-03-04 01:40:05,810 - mmseg - INFO - Iter [60200/80000]	lr: 2.344e-06, eta: 1:38:36, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1907, decode.acc_seg: 92.3206, loss: 0.1907
+2023-03-04 01:40:20,448 - mmseg - INFO - Iter [60250/80000]	lr: 2.344e-06, eta: 1:38:21, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1861, decode.acc_seg: 92.4205, loss: 0.1861
+2023-03-04 01:40:34,942 - mmseg - INFO - Iter [60300/80000]	lr: 2.344e-06, eta: 1:38:06, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1815, decode.acc_seg: 92.5617, loss: 0.1815
+2023-03-04 01:40:49,449 - mmseg - INFO - Iter [60350/80000]	lr: 2.344e-06, eta: 1:37:51, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1884, decode.acc_seg: 92.1230, loss: 0.1884
+2023-03-04 01:41:03,974 - mmseg - INFO - Iter [60400/80000]	lr: 2.344e-06, eta: 1:37:35, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1940, decode.acc_seg: 92.1285, loss: 0.1940
+2023-03-04 01:41:18,572 - mmseg - INFO - Iter [60450/80000]	lr: 2.344e-06, eta: 1:37:20, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1911, decode.acc_seg: 92.3699, loss: 0.1911
+2023-03-04 01:41:32,973 - mmseg - INFO - Iter [60500/80000]	lr: 2.344e-06, eta: 1:37:05, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1872, decode.acc_seg: 92.3338, loss: 0.1872
+2023-03-04 01:41:47,534 - mmseg - INFO - Iter [60550/80000]	lr: 2.344e-06, eta: 1:36:50, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1922, decode.acc_seg: 92.0958, loss: 0.1922
+2023-03-04 01:42:04,497 - mmseg - INFO - Iter [60600/80000]	lr: 2.344e-06, eta: 1:36:36, time: 0.339, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1867, decode.acc_seg: 92.4842, loss: 0.1867
+2023-03-04 01:42:18,950 - mmseg - INFO - Iter [60650/80000]	lr: 2.344e-06, eta: 1:36:21, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1817, decode.acc_seg: 92.6518, loss: 0.1817
+2023-03-04 01:42:33,377 - mmseg - INFO - Iter [60700/80000]	lr: 2.344e-06, eta: 1:36:06, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1833, decode.acc_seg: 92.5029, loss: 0.1833
+2023-03-04 01:42:47,773 - mmseg - INFO - Iter [60750/80000]	lr: 2.344e-06, eta: 1:35:51, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1835, decode.acc_seg: 92.4835, loss: 0.1835
+2023-03-04 01:43:02,347 - mmseg - INFO - Iter [60800/80000]	lr: 2.344e-06, eta: 1:35:36, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1819, decode.acc_seg: 92.6270, loss: 0.1819
+2023-03-04 01:43:16,966 - mmseg - INFO - Iter [60850/80000]	lr: 2.344e-06, eta: 1:35:21, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1884, decode.acc_seg: 92.6607, loss: 0.1884
+2023-03-04 01:43:31,475 - mmseg - INFO - Iter [60900/80000]	lr: 2.344e-06, eta: 1:35:05, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1839, decode.acc_seg: 92.4577, loss: 0.1839
+2023-03-04 01:43:45,902 - mmseg - INFO - Iter [60950/80000]	lr: 2.344e-06, eta: 1:34:50, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1894, decode.acc_seg: 92.2267, loss: 0.1894
+2023-03-04 01:44:00,503 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 01:44:00,503 - mmseg - INFO - Iter [61000/80000]	lr: 2.344e-06, eta: 1:34:35, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1877, decode.acc_seg: 92.3562, loss: 0.1877
+2023-03-04 01:44:14,942 - mmseg - INFO - Iter [61050/80000]	lr: 2.344e-06, eta: 1:34:20, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1870, decode.acc_seg: 92.4799, loss: 0.1870
+2023-03-04 01:44:29,482 - mmseg - INFO - Iter [61100/80000]	lr: 2.344e-06, eta: 1:34:05, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1911, decode.acc_seg: 92.2694, loss: 0.1911
+2023-03-04 01:44:44,253 - mmseg - INFO - Iter [61150/80000]	lr: 2.344e-06, eta: 1:33:50, time: 0.295, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1863, decode.acc_seg: 92.5513, loss: 0.1863
+2023-03-04 01:44:58,791 - mmseg - INFO - Iter [61200/80000]	lr: 2.344e-06, eta: 1:33:35, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1932, decode.acc_seg: 92.2997, loss: 0.1932
+2023-03-04 01:45:15,804 - mmseg - INFO - Iter [61250/80000]	lr: 2.344e-06, eta: 1:33:21, time: 0.340, data_time: 0.056, memory: 39544, decode.loss_ce: 0.1857, decode.acc_seg: 92.4211, loss: 0.1857
+2023-03-04 01:45:30,243 - mmseg - INFO - Iter [61300/80000]	lr: 2.344e-06, eta: 1:33:06, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1823, decode.acc_seg: 92.6174, loss: 0.1823
+2023-03-04 01:45:44,662 - mmseg - INFO - Iter [61350/80000]	lr: 2.344e-06, eta: 1:32:51, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1945, decode.acc_seg: 92.1278, loss: 0.1945
+2023-03-04 01:45:59,078 - mmseg - INFO - Iter [61400/80000]	lr: 2.344e-06, eta: 1:32:36, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1843, decode.acc_seg: 92.4988, loss: 0.1843
+2023-03-04 01:46:13,573 - mmseg - INFO - Iter [61450/80000]	lr: 2.344e-06, eta: 1:32:21, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1895, decode.acc_seg: 92.4306, loss: 0.1895
+2023-03-04 01:46:28,056 - mmseg - INFO - Iter [61500/80000]	lr: 2.344e-06, eta: 1:32:05, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1856, decode.acc_seg: 92.3475, loss: 0.1856
+2023-03-04 01:46:42,569 - mmseg - INFO - Iter [61550/80000]	lr: 2.344e-06, eta: 1:31:50, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1829, decode.acc_seg: 92.5665, loss: 0.1829
+2023-03-04 01:46:57,001 - mmseg - INFO - Iter [61600/80000]	lr: 2.344e-06, eta: 1:31:35, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1882, decode.acc_seg: 92.3513, loss: 0.1882
+2023-03-04 01:47:11,368 - mmseg - INFO - Iter [61650/80000]	lr: 2.344e-06, eta: 1:31:20, time: 0.287, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1866, decode.acc_seg: 92.4591, loss: 0.1866
+2023-03-04 01:47:25,907 - mmseg - INFO - Iter [61700/80000]	lr: 2.344e-06, eta: 1:31:05, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1866, decode.acc_seg: 92.4576, loss: 0.1866
+2023-03-04 01:47:40,454 - mmseg - INFO - Iter [61750/80000]	lr: 2.344e-06, eta: 1:30:50, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1845, decode.acc_seg: 92.4658, loss: 0.1845
+2023-03-04 01:47:54,845 - mmseg - INFO - Iter [61800/80000]	lr: 2.344e-06, eta: 1:30:35, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1854, decode.acc_seg: 92.5274, loss: 0.1854
+2023-03-04 01:48:11,746 - mmseg - INFO - Iter [61850/80000]	lr: 2.344e-06, eta: 1:30:21, time: 0.338, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1831, decode.acc_seg: 92.6485, loss: 0.1831
+2023-03-04 01:48:26,302 - mmseg - INFO - Iter [61900/80000]	lr: 2.344e-06, eta: 1:30:06, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1776, decode.acc_seg: 92.7731, loss: 0.1776
+2023-03-04 01:48:40,813 - mmseg - INFO - Iter [61950/80000]	lr: 2.344e-06, eta: 1:29:51, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1844, decode.acc_seg: 92.3585, loss: 0.1844
+2023-03-04 01:48:55,374 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 01:48:55,374 - mmseg - INFO - Iter [62000/80000]	lr: 2.344e-06, eta: 1:29:36, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1897, decode.acc_seg: 92.2376, loss: 0.1897
+2023-03-04 01:49:09,911 - mmseg - INFO - Iter [62050/80000]	lr: 2.344e-06, eta: 1:29:20, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1799, decode.acc_seg: 92.6416, loss: 0.1799
+2023-03-04 01:49:24,599 - mmseg - INFO - Iter [62100/80000]	lr: 2.344e-06, eta: 1:29:05, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1815, decode.acc_seg: 92.6221, loss: 0.1815
+2023-03-04 01:49:39,056 - mmseg - INFO - Iter [62150/80000]	lr: 2.344e-06, eta: 1:28:50, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1876, decode.acc_seg: 92.2470, loss: 0.1876
+2023-03-04 01:49:53,558 - mmseg - INFO - Iter [62200/80000]	lr: 2.344e-06, eta: 1:28:35, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1831, decode.acc_seg: 92.5011, loss: 0.1831
+2023-03-04 01:50:08,115 - mmseg - INFO - Iter [62250/80000]	lr: 2.344e-06, eta: 1:28:20, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1821, decode.acc_seg: 92.6662, loss: 0.1821
+2023-03-04 01:50:22,580 - mmseg - INFO - Iter [62300/80000]	lr: 2.344e-06, eta: 1:28:05, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1906, decode.acc_seg: 92.3843, loss: 0.1906
+2023-03-04 01:50:37,337 - mmseg - INFO - Iter [62350/80000]	lr: 2.344e-06, eta: 1:27:50, time: 0.295, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1861, decode.acc_seg: 92.3547, loss: 0.1861
+2023-03-04 01:50:51,834 - mmseg - INFO - Iter [62400/80000]	lr: 2.344e-06, eta: 1:27:35, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1881, decode.acc_seg: 92.3185, loss: 0.1881
+2023-03-04 01:51:06,263 - mmseg - INFO - Iter [62450/80000]	lr: 2.344e-06, eta: 1:27:20, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1896, decode.acc_seg: 92.3089, loss: 0.1896
+2023-03-04 01:51:23,305 - mmseg - INFO - Iter [62500/80000]	lr: 2.344e-06, eta: 1:27:06, time: 0.341, data_time: 0.055, memory: 39544, decode.loss_ce: 0.1916, decode.acc_seg: 92.1887, loss: 0.1916
+2023-03-04 01:51:37,727 - mmseg - INFO - Iter [62550/80000]	lr: 2.344e-06, eta: 1:26:51, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1870, decode.acc_seg: 92.4053, loss: 0.1870
+2023-03-04 01:51:52,328 - mmseg - INFO - Iter [62600/80000]	lr: 2.344e-06, eta: 1:26:36, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1812, decode.acc_seg: 92.5333, loss: 0.1812
+2023-03-04 01:52:06,797 - mmseg - INFO - Iter [62650/80000]	lr: 2.344e-06, eta: 1:26:21, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1856, decode.acc_seg: 92.4407, loss: 0.1856
+2023-03-04 01:52:21,397 - mmseg - INFO - Iter [62700/80000]	lr: 2.344e-06, eta: 1:26:06, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1774, decode.acc_seg: 92.8041, loss: 0.1774
+2023-03-04 01:52:36,032 - mmseg - INFO - Iter [62750/80000]	lr: 2.344e-06, eta: 1:25:51, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1822, decode.acc_seg: 92.6187, loss: 0.1822
+2023-03-04 01:52:50,673 - mmseg - INFO - Iter [62800/80000]	lr: 2.344e-06, eta: 1:25:36, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1892, decode.acc_seg: 92.3615, loss: 0.1892
+2023-03-04 01:53:05,171 - mmseg - INFO - Iter [62850/80000]	lr: 2.344e-06, eta: 1:25:21, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1865, decode.acc_seg: 92.4092, loss: 0.1865
+2023-03-04 01:53:19,731 - mmseg - INFO - Iter [62900/80000]	lr: 2.344e-06, eta: 1:25:05, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1932, decode.acc_seg: 92.3096, loss: 0.1932
+2023-03-04 01:53:34,184 - mmseg - INFO - Iter [62950/80000]	lr: 2.344e-06, eta: 1:24:50, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1898, decode.acc_seg: 92.2874, loss: 0.1898
+2023-03-04 01:53:48,764 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 01:53:48,764 - mmseg - INFO - Iter [63000/80000]	lr: 2.344e-06, eta: 1:24:35, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1981, decode.acc_seg: 92.0515, loss: 0.1981
+2023-03-04 01:54:03,367 - mmseg - INFO - Iter [63050/80000]	lr: 2.344e-06, eta: 1:24:20, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1843, decode.acc_seg: 92.5414, loss: 0.1843
+2023-03-04 01:54:17,821 - mmseg - INFO - Iter [63100/80000]	lr: 2.344e-06, eta: 1:24:05, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1798, decode.acc_seg: 92.6188, loss: 0.1798
+2023-03-04 01:54:34,986 - mmseg - INFO - Iter [63150/80000]	lr: 2.344e-06, eta: 1:23:51, time: 0.343, data_time: 0.053, memory: 39544, decode.loss_ce: 0.1942, decode.acc_seg: 92.1556, loss: 0.1942
+2023-03-04 01:54:49,423 - mmseg - INFO - Iter [63200/80000]	lr: 2.344e-06, eta: 1:23:36, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1847, decode.acc_seg: 92.4887, loss: 0.1847
+2023-03-04 01:55:03,983 - mmseg - INFO - Iter [63250/80000]	lr: 2.344e-06, eta: 1:23:21, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1882, decode.acc_seg: 92.4530, loss: 0.1882
+2023-03-04 01:55:18,688 - mmseg - INFO - Iter [63300/80000]	lr: 2.344e-06, eta: 1:23:06, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1918, decode.acc_seg: 92.1570, loss: 0.1918
+2023-03-04 01:55:33,499 - mmseg - INFO - Iter [63350/80000]	lr: 2.344e-06, eta: 1:22:51, time: 0.296, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1883, decode.acc_seg: 92.4943, loss: 0.1883
+2023-03-04 01:55:47,979 - mmseg - INFO - Iter [63400/80000]	lr: 2.344e-06, eta: 1:22:36, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1865, decode.acc_seg: 92.4601, loss: 0.1865
+2023-03-04 01:56:02,455 - mmseg - INFO - Iter [63450/80000]	lr: 2.344e-06, eta: 1:22:21, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1957, decode.acc_seg: 92.3063, loss: 0.1957
+2023-03-04 01:56:16,970 - mmseg - INFO - Iter [63500/80000]	lr: 2.344e-06, eta: 1:22:06, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1850, decode.acc_seg: 92.5333, loss: 0.1850
+2023-03-04 01:56:31,396 - mmseg - INFO - Iter [63550/80000]	lr: 2.344e-06, eta: 1:21:51, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1852, decode.acc_seg: 92.4446, loss: 0.1852
+2023-03-04 01:56:45,845 - mmseg - INFO - Iter [63600/80000]	lr: 2.344e-06, eta: 1:21:36, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1828, decode.acc_seg: 92.4531, loss: 0.1828
+2023-03-04 01:57:00,272 - mmseg - INFO - Iter [63650/80000]	lr: 2.344e-06, eta: 1:21:21, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1862, decode.acc_seg: 92.5252, loss: 0.1862
+2023-03-04 01:57:14,792 - mmseg - INFO - Iter [63700/80000]	lr: 2.344e-06, eta: 1:21:06, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1866, decode.acc_seg: 92.4398, loss: 0.1866
+2023-03-04 01:57:31,727 - mmseg - INFO - Iter [63750/80000]	lr: 2.344e-06, eta: 1:20:51, time: 0.339, data_time: 0.057, memory: 39544, decode.loss_ce: 0.1853, decode.acc_seg: 92.5225, loss: 0.1853
+2023-03-04 01:57:46,321 - mmseg - INFO - Iter [63800/80000]	lr: 2.344e-06, eta: 1:20:36, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1886, decode.acc_seg: 92.3805, loss: 0.1886
+2023-03-04 01:58:00,776 - mmseg - INFO - Iter [63850/80000]	lr: 2.344e-06, eta: 1:20:21, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1793, decode.acc_seg: 92.6706, loss: 0.1793
+2023-03-04 01:58:15,325 - mmseg - INFO - Iter [63900/80000]	lr: 2.344e-06, eta: 1:20:06, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1859, decode.acc_seg: 92.3550, loss: 0.1859
+2023-03-04 01:58:29,757 - mmseg - INFO - Iter [63950/80000]	lr: 2.344e-06, eta: 1:19:51, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1869, decode.acc_seg: 92.6080, loss: 0.1869
+2023-03-04 01:58:44,250 - mmseg - INFO - Saving checkpoint at 64000 iterations
+2023-03-04 01:58:46,261 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 01:58:46,261 - mmseg - INFO - Iter [64000/80000]	lr: 2.344e-06, eta: 1:19:37, time: 0.330, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1871, decode.acc_seg: 92.3743, loss: 0.1871
+2023-03-04 01:59:05,820 - mmseg - INFO - per class results:
+2023-03-04 01:59:05,830 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        | 76.28 | 88.26 |
+|       building      | 81.47 | 92.49 |
+|         sky         |  94.2 |  97.2 |
+|        floor        | 79.93 | 91.48 |
+|         tree        | 72.94 | 87.39 |
+|       ceiling       |  82.5 | 90.93 |
+|         road        | 81.95 | 89.02 |
+|         bed         | 88.31 | 95.32 |
+|      windowpane     | 61.12 | 77.59 |
+|        grass        | 66.26 | 80.46 |
+|       cabinet       | 59.76 | 72.32 |
+|       sidewalk      | 65.63 | 81.74 |
+|        person       | 79.36 | 90.94 |
+|        earth        | 33.83 | 49.18 |
+|         door        | 48.11 | 64.47 |
+|        table        | 61.46 | 78.34 |
+|       mountain      | 51.62 | 67.48 |
+|        plant        | 50.51 | 64.25 |
+|       curtain       | 70.48 | 82.35 |
+|        chair        | 57.77 | 71.23 |
+|         car         | 83.07 | 90.28 |
+|        water        | 46.82 | 62.56 |
+|       painting      | 69.29 | 84.85 |
+|         sofa        | 65.51 | 84.03 |
+|        shelf        | 40.01 | 55.75 |
+|        house        | 45.65 | 53.23 |
+|         sea         | 46.19 | 74.24 |
+|        mirror       | 64.96 | 74.67 |
+|         rug         | 54.56 | 59.42 |
+|        field        | 28.73 | 44.88 |
+|       armchair      | 44.13 | 58.51 |
+|         seat        | 54.27 | 78.11 |
+|        fence        | 41.28 |  58.5 |
+|         desk        | 50.13 |  67.7 |
+|         rock        | 29.39 | 45.05 |
+|       wardrobe      |  49.5 | 70.46 |
+|         lamp        | 64.02 | 75.88 |
+|       bathtub       | 75.17 | 81.95 |
+|       railing       | 31.23 | 43.23 |
+|       cushion       | 54.63 | 68.61 |
+|         base        | 28.51 | 40.48 |
+|         box         | 24.01 | 31.56 |
+|        column       | 45.34 | 58.43 |
+|      signboard      | 36.16 | 50.74 |
+|   chest of drawers  | 40.15 | 57.28 |
+|       counter       |  27.9 | 41.32 |
+|         sand        | 31.55 |  47.4 |
+|         sink        | 70.53 | 81.31 |
+|      skyscraper     | 45.36 | 55.87 |
+|      fireplace      |  66.1 | 85.08 |
+|     refrigerator    | 77.64 | 84.19 |
+|      grandstand     | 40.73 | 65.66 |
+|         path        | 15.21 | 22.69 |
+|        stairs       | 32.11 | 41.97 |
+|        runway       | 63.83 | 82.35 |
+|         case        | 48.89 | 71.15 |
+|      pool table     | 92.76 | 95.84 |
+|        pillow       | 56.08 | 65.82 |
+|     screen door     | 64.16 | 72.05 |
+|       stairway      |  25.3 |  32.1 |
+|        river        |  9.88 | 16.04 |
+|        bridge       |  61.6 |  68.3 |
+|       bookcase      | 40.69 | 55.71 |
+|        blind        |  48.0 |  55.4 |
+|     coffee table    | 67.09 | 81.16 |
+|        toilet       | 85.81 | 91.09 |
+|        flower       | 31.81 | 46.53 |
+|         book        | 47.42 | 65.23 |
+|         hill        |  8.22 |  9.63 |
+|        bench        | 43.16 | 53.42 |
+|      countertop     | 53.21 | 68.24 |
+|        stove        | 73.33 | 80.73 |
+|         palm        | 51.17 | 72.17 |
+|    kitchen island   | 46.72 | 71.12 |
+|       computer      | 57.42 | 65.15 |
+|     swivel chair    | 44.27 |  60.3 |
+|         boat        | 39.87 | 43.87 |
+|         bar         |  28.1 | 31.79 |
+|    arcade machine   | 31.44 | 35.23 |
+|        hovel        | 31.11 | 33.18 |
+|         bus         | 87.46 | 92.59 |
+|        towel        |  59.6 | 68.48 |
+|        light        | 56.54 |  64.4 |
+|        truck        | 34.17 |  46.7 |
+|        tower        |  24.9 | 34.66 |
+|      chandelier     | 66.18 | 81.38 |
+|        awning       | 23.04 | 25.26 |
+|     streetlight     | 28.33 | 36.64 |
+|        booth        | 54.24 | 56.52 |
+| television receiver | 67.74 | 79.18 |
+|       airplane      |  51.4 | 70.42 |
+|      dirt track     |  7.34 | 19.96 |
+|       apparel       | 28.96 | 44.29 |
+|         pole        | 23.64 | 35.08 |
+|         land        |  8.48 | 11.43 |
+|      bannister      |  4.65 |  6.42 |
+|      escalator      | 23.04 | 23.73 |
+|       ottoman       |  47.5 | 56.44 |
+|        bottle       | 16.55 | 23.89 |
+|        buffet       | 54.79 | 64.99 |
+|        poster       | 27.75 | 38.11 |
+|        stage        | 16.88 | 22.59 |
+|         van         | 48.53 | 63.79 |
+|         ship        | 30.72 | 40.18 |
+|       fountain      |  6.62 |  6.7  |
+|    conveyer belt    | 76.02 | 88.91 |
+|        canopy       | 14.48 | 16.92 |
+|        washer       | 66.98 | 67.43 |
+|      plaything      | 22.41 | 30.14 |
+|    swimming pool    | 42.91 | 52.93 |
+|        stool        | 41.73 | 56.83 |
+|        barrel       | 38.24 | 65.25 |
+|        basket       | 27.88 | 36.94 |
+|      waterfall      | 54.89 | 65.38 |
+|         tent        | 94.84 | 97.44 |
+|         bag         | 11.42 | 13.92 |
+|       minibike      | 62.27 | 74.28 |
+|        cradle       |  80.3 | 96.92 |
+|         oven        | 27.84 | 62.19 |
+|         ball        | 47.15 | 58.61 |
+|         food        | 51.79 | 62.45 |
+|         step        | 13.94 | 17.21 |
+|         tank        | 42.01 | 42.53 |
+|      trade name     | 25.86 | 29.79 |
+|      microwave      | 37.93 | 40.69 |
+|         pot         | 41.35 | 49.86 |
+|        animal       | 51.21 |  54.3 |
+|       bicycle       | 46.33 | 69.62 |
+|         lake        | 59.81 | 63.37 |
+|      dishwasher     | 77.57 | 82.56 |
+|        screen       | 66.27 | 84.77 |
+|       blanket       | 11.57 | 12.97 |
+|      sculpture      | 37.62 | 62.37 |
+|         hood        | 56.91 | 71.49 |
+|        sconce       | 42.73 | 51.23 |
+|         vase        | 36.78 | 51.38 |
+|    traffic light    | 29.97 | 45.16 |
+|         tray        |  6.33 |  11.2 |
+|        ashcan       |  38.7 | 49.75 |
+|         fan         | 57.93 | 72.18 |
+|         pier        | 12.26 | 14.18 |
+|      crt screen     |  4.02 | 10.86 |
+|        plate        | 37.61 | 46.33 |
+|       monitor       | 21.55 | 29.45 |
+|    bulletin board   |  47.2 | 59.41 |
+|        shower       |  1.68 |  2.61 |
+|       radiator      | 46.46 | 54.74 |
+|        glass        | 12.42 | 13.79 |
+|        clock        | 25.03 | 30.73 |
+|         flag        |  37.9 | 40.87 |
++---------------------+-------+-------+
+2023-03-04 01:59:05,830 - mmseg - INFO - Summary:
+2023-03-04 01:59:05,831 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 81.99 | 46.15 | 57.05 |
++-------+-------+-------+
+2023-03-04 01:59:05,832 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 01:59:05,832 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8199, mIoU: 0.4615, mAcc: 0.5705, IoU.background: nan, IoU.wall: 0.7628, IoU.building: 0.8147, IoU.sky: 0.9420, IoU.floor: 0.7993, IoU.tree: 0.7294, IoU.ceiling: 0.8250, IoU.road: 0.8195, IoU.bed : 0.8831, IoU.windowpane: 0.6112, IoU.grass: 0.6626, IoU.cabinet: 0.5976, IoU.sidewalk: 0.6563, IoU.person: 0.7936, IoU.earth: 0.3383, IoU.door: 0.4811, IoU.table: 0.6146, IoU.mountain: 0.5162, IoU.plant: 0.5051, IoU.curtain: 0.7048, IoU.chair: 0.5777, IoU.car: 0.8307, IoU.water: 0.4682, IoU.painting: 0.6929, IoU.sofa: 0.6551, IoU.shelf: 0.4001, IoU.house: 0.4565, IoU.sea: 0.4619, IoU.mirror: 0.6496, IoU.rug: 0.5456, IoU.field: 0.2873, IoU.armchair: 0.4413, IoU.seat: 0.5427, IoU.fence: 0.4128, IoU.desk: 0.5013, IoU.rock: 0.2939, IoU.wardrobe: 0.4950, IoU.lamp: 0.6402, IoU.bathtub: 0.7517, IoU.railing: 0.3123, IoU.cushion: 0.5463, IoU.base: 0.2851, IoU.box: 0.2401, IoU.column: 0.4534, IoU.signboard: 0.3616, IoU.chest of drawers: 0.4015, IoU.counter: 0.2790, IoU.sand: 0.3155, IoU.sink: 0.7053, IoU.skyscraper: 0.4536, IoU.fireplace: 0.6610, IoU.refrigerator: 0.7764, IoU.grandstand: 0.4073, IoU.path: 0.1521, IoU.stairs: 0.3211, IoU.runway: 0.6383, IoU.case: 0.4889, IoU.pool table: 0.9276, IoU.pillow: 0.5608, IoU.screen door: 0.6416, IoU.stairway: 0.2530, IoU.river: 0.0988, IoU.bridge: 0.6160, IoU.bookcase: 0.4069, IoU.blind: 0.4800, IoU.coffee table: 0.6709, IoU.toilet: 0.8581, IoU.flower: 0.3181, IoU.book: 0.4742, IoU.hill: 0.0822, IoU.bench: 0.4316, IoU.countertop: 0.5321, IoU.stove: 0.7333, IoU.palm: 0.5117, IoU.kitchen island: 0.4672, IoU.computer: 0.5742, IoU.swivel chair: 0.4427, IoU.boat: 0.3987, IoU.bar: 0.2810, IoU.arcade machine: 0.3144, IoU.hovel: 0.3111, IoU.bus: 0.8746, IoU.towel: 0.5960, IoU.light: 0.5654, IoU.truck: 0.3417, IoU.tower: 0.2490, IoU.chandelier: 0.6618, IoU.awning: 0.2304, IoU.streetlight: 0.2833, IoU.booth: 0.5424, IoU.television receiver: 0.6774, IoU.airplane: 0.5140, IoU.dirt track: 0.0734, IoU.apparel: 0.2896, IoU.pole: 0.2364, IoU.land: 0.0848, IoU.bannister: 0.0465, IoU.escalator: 0.2304, IoU.ottoman: 0.4750, IoU.bottle: 0.1655, IoU.buffet: 0.5479, IoU.poster: 0.2775, IoU.stage: 0.1688, IoU.van: 0.4853, IoU.ship: 0.3072, IoU.fountain: 0.0662, IoU.conveyer belt: 0.7602, IoU.canopy: 0.1448, IoU.washer: 0.6698, IoU.plaything: 0.2241, IoU.swimming pool: 0.4291, IoU.stool: 0.4173, IoU.barrel: 0.3824, IoU.basket: 0.2788, IoU.waterfall: 0.5489, IoU.tent: 0.9484, IoU.bag: 0.1142, IoU.minibike: 0.6227, IoU.cradle: 0.8030, IoU.oven: 0.2784, IoU.ball: 0.4715, IoU.food: 0.5179, IoU.step: 0.1394, IoU.tank: 0.4201, IoU.trade name: 0.2586, IoU.microwave: 0.3793, IoU.pot: 0.4135, IoU.animal: 0.5121, IoU.bicycle: 0.4633, IoU.lake: 0.5981, IoU.dishwasher: 0.7757, IoU.screen: 0.6627, IoU.blanket: 0.1157, IoU.sculpture: 0.3762, IoU.hood: 0.5691, IoU.sconce: 0.4273, IoU.vase: 0.3678, IoU.traffic light: 0.2997, IoU.tray: 0.0633, IoU.ashcan: 0.3870, IoU.fan: 0.5793, IoU.pier: 0.1226, IoU.crt screen: 0.0402, IoU.plate: 0.3761, IoU.monitor: 0.2155, IoU.bulletin board: 0.4720, IoU.shower: 0.0168, IoU.radiator: 0.4646, IoU.glass: 0.1242, IoU.clock: 0.2503, IoU.flag: 0.3790, Acc.background: nan, Acc.wall: 0.8826, Acc.building: 0.9249, Acc.sky: 0.9720, Acc.floor: 0.9148, Acc.tree: 0.8739, Acc.ceiling: 0.9093, Acc.road: 0.8902, Acc.bed : 0.9532, Acc.windowpane: 0.7759, Acc.grass: 0.8046, Acc.cabinet: 0.7232, Acc.sidewalk: 0.8174, Acc.person: 0.9094, Acc.earth: 0.4918, Acc.door: 0.6447, Acc.table: 0.7834, Acc.mountain: 0.6748, Acc.plant: 0.6425, Acc.curtain: 0.8235, Acc.chair: 0.7123, Acc.car: 0.9028, Acc.water: 0.6256, Acc.painting: 0.8485, Acc.sofa: 0.8403, Acc.shelf: 0.5575, Acc.house: 0.5323, Acc.sea: 0.7424, Acc.mirror: 0.7467, Acc.rug: 0.5942, Acc.field: 0.4488, Acc.armchair: 0.5851, Acc.seat: 0.7811, Acc.fence: 0.5850, Acc.desk: 0.6770, Acc.rock: 0.4505, Acc.wardrobe: 0.7046, Acc.lamp: 0.7588, Acc.bathtub: 0.8195, Acc.railing: 0.4323, Acc.cushion: 0.6861, Acc.base: 0.4048, Acc.box: 0.3156, Acc.column: 0.5843, Acc.signboard: 0.5074, Acc.chest of drawers: 0.5728, Acc.counter: 0.4132, Acc.sand: 0.4740, Acc.sink: 0.8131, Acc.skyscraper: 0.5587, Acc.fireplace: 0.8508, Acc.refrigerator: 0.8419, Acc.grandstand: 0.6566, Acc.path: 0.2269, Acc.stairs: 0.4197, Acc.runway: 0.8235, Acc.case: 0.7115, Acc.pool table: 0.9584, Acc.pillow: 0.6582, Acc.screen door: 0.7205, Acc.stairway: 0.3210, Acc.river: 0.1604, Acc.bridge: 0.6830, Acc.bookcase: 0.5571, Acc.blind: 0.5540, Acc.coffee table: 0.8116, Acc.toilet: 0.9109, Acc.flower: 0.4653, Acc.book: 0.6523, Acc.hill: 0.0963, Acc.bench: 0.5342, Acc.countertop: 0.6824, Acc.stove: 0.8073, Acc.palm: 0.7217, Acc.kitchen island: 0.7112, Acc.computer: 0.6515, Acc.swivel chair: 0.6030, Acc.boat: 0.4387, Acc.bar: 0.3179, Acc.arcade machine: 0.3523, Acc.hovel: 0.3318, Acc.bus: 0.9259, Acc.towel: 0.6848, Acc.light: 0.6440, Acc.truck: 0.4670, Acc.tower: 0.3466, Acc.chandelier: 0.8138, Acc.awning: 0.2526, Acc.streetlight: 0.3664, Acc.booth: 0.5652, Acc.television receiver: 0.7918, Acc.airplane: 0.7042, Acc.dirt track: 0.1996, Acc.apparel: 0.4429, Acc.pole: 0.3508, Acc.land: 0.1143, Acc.bannister: 0.0642, Acc.escalator: 0.2373, Acc.ottoman: 0.5644, Acc.bottle: 0.2389, Acc.buffet: 0.6499, Acc.poster: 0.3811, Acc.stage: 0.2259, Acc.van: 0.6379, Acc.ship: 0.4018, Acc.fountain: 0.0670, Acc.conveyer belt: 0.8891, Acc.canopy: 0.1692, Acc.washer: 0.6743, Acc.plaything: 0.3014, Acc.swimming pool: 0.5293, Acc.stool: 0.5683, Acc.barrel: 0.6525, Acc.basket: 0.3694, Acc.waterfall: 0.6538, Acc.tent: 0.9744, Acc.bag: 0.1392, Acc.minibike: 0.7428, Acc.cradle: 0.9692, Acc.oven: 0.6219, Acc.ball: 0.5861, Acc.food: 0.6245, Acc.step: 0.1721, Acc.tank: 0.4253, Acc.trade name: 0.2979, Acc.microwave: 0.4069, Acc.pot: 0.4986, Acc.animal: 0.5430, Acc.bicycle: 0.6962, Acc.lake: 0.6337, Acc.dishwasher: 0.8256, Acc.screen: 0.8477, Acc.blanket: 0.1297, Acc.sculpture: 0.6237, Acc.hood: 0.7149, Acc.sconce: 0.5123, Acc.vase: 0.5138, Acc.traffic light: 0.4516, Acc.tray: 0.1120, Acc.ashcan: 0.4975, Acc.fan: 0.7218, Acc.pier: 0.1418, Acc.crt screen: 0.1086, Acc.plate: 0.4633, Acc.monitor: 0.2945, Acc.bulletin board: 0.5941, Acc.shower: 0.0261, Acc.radiator: 0.5474, Acc.glass: 0.1379, Acc.clock: 0.3073, Acc.flag: 0.4087
+2023-03-04 01:59:20,851 - mmseg - INFO - Iter [64050/80000]	lr: 2.344e-06, eta: 1:19:27, time: 0.692, data_time: 0.399, memory: 39544, decode.loss_ce: 0.1937, decode.acc_seg: 92.1520, loss: 0.1937
+2023-03-04 01:59:35,745 - mmseg - INFO - Iter [64100/80000]	lr: 2.344e-06, eta: 1:19:12, time: 0.298, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1850, decode.acc_seg: 92.3977, loss: 0.1850
+2023-03-04 01:59:50,248 - mmseg - INFO - Iter [64150/80000]	lr: 2.344e-06, eta: 1:18:57, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1806, decode.acc_seg: 92.6967, loss: 0.1806
+2023-03-04 02:00:04,815 - mmseg - INFO - Iter [64200/80000]	lr: 2.344e-06, eta: 1:18:41, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1838, decode.acc_seg: 92.6375, loss: 0.1838
+2023-03-04 02:00:19,507 - mmseg - INFO - Iter [64250/80000]	lr: 2.344e-06, eta: 1:18:26, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1925, decode.acc_seg: 92.2241, loss: 0.1925
+2023-03-04 02:00:34,028 - mmseg - INFO - Iter [64300/80000]	lr: 2.344e-06, eta: 1:18:11, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1943, decode.acc_seg: 92.3028, loss: 0.1943
+2023-03-04 02:00:48,856 - mmseg - INFO - Iter [64350/80000]	lr: 2.344e-06, eta: 1:17:56, time: 0.297, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1877, decode.acc_seg: 92.2913, loss: 0.1877
+2023-03-04 02:01:05,984 - mmseg - INFO - Iter [64400/80000]	lr: 2.344e-06, eta: 1:17:42, time: 0.343, data_time: 0.056, memory: 39544, decode.loss_ce: 0.1868, decode.acc_seg: 92.3804, loss: 0.1868
+2023-03-04 02:01:20,594 - mmseg - INFO - Iter [64450/80000]	lr: 2.344e-06, eta: 1:17:27, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1849, decode.acc_seg: 92.4808, loss: 0.1849
+2023-03-04 02:01:35,123 - mmseg - INFO - Iter [64500/80000]	lr: 2.344e-06, eta: 1:17:12, time: 0.291, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1808, decode.acc_seg: 92.6116, loss: 0.1808
+2023-03-04 02:01:49,589 - mmseg - INFO - Iter [64550/80000]	lr: 2.344e-06, eta: 1:16:57, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1880, decode.acc_seg: 92.3893, loss: 0.1880
+2023-03-04 02:02:04,115 - mmseg - INFO - Iter [64600/80000]	lr: 2.344e-06, eta: 1:16:42, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1905, decode.acc_seg: 92.4650, loss: 0.1905
+2023-03-04 02:02:18,627 - mmseg - INFO - Iter [64650/80000]	lr: 2.344e-06, eta: 1:16:27, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1828, decode.acc_seg: 92.4565, loss: 0.1828
+2023-03-04 02:02:33,213 - mmseg - INFO - Iter [64700/80000]	lr: 2.344e-06, eta: 1:16:12, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1921, decode.acc_seg: 92.1533, loss: 0.1921
+2023-03-04 02:02:47,893 - mmseg - INFO - Iter [64750/80000]	lr: 2.344e-06, eta: 1:15:57, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1907, decode.acc_seg: 92.2766, loss: 0.1907
+2023-03-04 02:03:02,359 - mmseg - INFO - Iter [64800/80000]	lr: 2.344e-06, eta: 1:15:42, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1858, decode.acc_seg: 92.5918, loss: 0.1858
+2023-03-04 02:03:16,775 - mmseg - INFO - Iter [64850/80000]	lr: 2.344e-06, eta: 1:15:27, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1859, decode.acc_seg: 92.4561, loss: 0.1859
+2023-03-04 02:03:31,291 - mmseg - INFO - Iter [64900/80000]	lr: 2.344e-06, eta: 1:15:12, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1809, decode.acc_seg: 92.5948, loss: 0.1809
+2023-03-04 02:03:45,835 - mmseg - INFO - Iter [64950/80000]	lr: 2.344e-06, eta: 1:14:57, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1864, decode.acc_seg: 92.4737, loss: 0.1864
+2023-03-04 02:04:02,867 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 02:04:02,868 - mmseg - INFO - Iter [65000/80000]	lr: 2.344e-06, eta: 1:14:42, time: 0.341, data_time: 0.057, memory: 39544, decode.loss_ce: 0.1845, decode.acc_seg: 92.6344, loss: 0.1845
+2023-03-04 02:04:17,386 - mmseg - INFO - Iter [65050/80000]	lr: 2.344e-06, eta: 1:14:27, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1840, decode.acc_seg: 92.5927, loss: 0.1840
+2023-03-04 02:04:31,887 - mmseg - INFO - Iter [65100/80000]	lr: 2.344e-06, eta: 1:14:12, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1926, decode.acc_seg: 92.2962, loss: 0.1926
+2023-03-04 02:04:46,373 - mmseg - INFO - Iter [65150/80000]	lr: 2.344e-06, eta: 1:13:57, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1870, decode.acc_seg: 92.3848, loss: 0.1870
+2023-03-04 02:05:00,788 - mmseg - INFO - Iter [65200/80000]	lr: 2.344e-06, eta: 1:13:42, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1861, decode.acc_seg: 92.4684, loss: 0.1861
+2023-03-04 02:05:15,267 - mmseg - INFO - Iter [65250/80000]	lr: 2.344e-06, eta: 1:13:27, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1872, decode.acc_seg: 92.4318, loss: 0.1872
+2023-03-04 02:05:29,689 - mmseg - INFO - Iter [65300/80000]	lr: 2.344e-06, eta: 1:13:12, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1740, decode.acc_seg: 92.8372, loss: 0.1740
+2023-03-04 02:05:44,195 - mmseg - INFO - Iter [65350/80000]	lr: 2.344e-06, eta: 1:12:57, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1806, decode.acc_seg: 92.5979, loss: 0.1806
+2023-03-04 02:05:58,696 - mmseg - INFO - Iter [65400/80000]	lr: 2.344e-06, eta: 1:12:42, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1828, decode.acc_seg: 92.5133, loss: 0.1828
+2023-03-04 02:06:13,328 - mmseg - INFO - Iter [65450/80000]	lr: 2.344e-06, eta: 1:12:27, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1847, decode.acc_seg: 92.3943, loss: 0.1847
+2023-03-04 02:06:27,847 - mmseg - INFO - Iter [65500/80000]	lr: 2.344e-06, eta: 1:12:12, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1904, decode.acc_seg: 92.2099, loss: 0.1904
+2023-03-04 02:06:42,396 - mmseg - INFO - Iter [65550/80000]	lr: 2.344e-06, eta: 1:11:57, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1920, decode.acc_seg: 92.3516, loss: 0.1920
+2023-03-04 02:06:56,947 - mmseg - INFO - Iter [65600/80000]	lr: 2.344e-06, eta: 1:11:42, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1844, decode.acc_seg: 92.5429, loss: 0.1844
+2023-03-04 02:07:14,037 - mmseg - INFO - Iter [65650/80000]	lr: 2.344e-06, eta: 1:11:27, time: 0.342, data_time: 0.057, memory: 39544, decode.loss_ce: 0.1799, decode.acc_seg: 92.6172, loss: 0.1799
+2023-03-04 02:07:28,580 - mmseg - INFO - Iter [65700/80000]	lr: 2.344e-06, eta: 1:11:12, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1867, decode.acc_seg: 92.4706, loss: 0.1867
+2023-03-04 02:07:43,016 - mmseg - INFO - Iter [65750/80000]	lr: 2.344e-06, eta: 1:10:57, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1863, decode.acc_seg: 92.5130, loss: 0.1863
+2023-03-04 02:07:57,581 - mmseg - INFO - Iter [65800/80000]	lr: 2.344e-06, eta: 1:10:42, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1821, decode.acc_seg: 92.5621, loss: 0.1821
+2023-03-04 02:08:12,057 - mmseg - INFO - Iter [65850/80000]	lr: 2.344e-06, eta: 1:10:27, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1916, decode.acc_seg: 92.2586, loss: 0.1916
+2023-03-04 02:08:26,690 - mmseg - INFO - Iter [65900/80000]	lr: 2.344e-06, eta: 1:10:12, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1809, decode.acc_seg: 92.6584, loss: 0.1809
+2023-03-04 02:08:41,235 - mmseg - INFO - Iter [65950/80000]	lr: 2.344e-06, eta: 1:09:57, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1872, decode.acc_seg: 92.4065, loss: 0.1872
+2023-03-04 02:08:55,638 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 02:08:55,638 - mmseg - INFO - Iter [66000/80000]	lr: 2.344e-06, eta: 1:09:42, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1850, decode.acc_seg: 92.3974, loss: 0.1850
+2023-03-04 02:09:10,078 - mmseg - INFO - Iter [66050/80000]	lr: 2.344e-06, eta: 1:09:27, time: 0.289, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1849, decode.acc_seg: 92.4559, loss: 0.1849
+2023-03-04 02:09:24,793 - mmseg - INFO - Iter [66100/80000]	lr: 2.344e-06, eta: 1:09:12, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1850, decode.acc_seg: 92.5615, loss: 0.1850
+2023-03-04 02:09:39,192 - mmseg - INFO - Iter [66150/80000]	lr: 2.344e-06, eta: 1:08:57, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1837, decode.acc_seg: 92.4742, loss: 0.1837
+2023-03-04 02:09:53,772 - mmseg - INFO - Iter [66200/80000]	lr: 2.344e-06, eta: 1:08:42, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1898, decode.acc_seg: 92.3591, loss: 0.1898
+2023-03-04 02:10:08,175 - mmseg - INFO - Iter [66250/80000]	lr: 2.344e-06, eta: 1:08:27, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1841, decode.acc_seg: 92.4670, loss: 0.1841
+2023-03-04 02:10:25,363 - mmseg - INFO - Iter [66300/80000]	lr: 2.344e-06, eta: 1:08:12, time: 0.344, data_time: 0.057, memory: 39544, decode.loss_ce: 0.1913, decode.acc_seg: 92.1947, loss: 0.1913
+2023-03-04 02:10:39,900 - mmseg - INFO - Iter [66350/80000]	lr: 2.344e-06, eta: 1:07:57, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1866, decode.acc_seg: 92.5424, loss: 0.1866
+2023-03-04 02:10:54,372 - mmseg - INFO - Iter [66400/80000]	lr: 2.344e-06, eta: 1:07:42, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1785, decode.acc_seg: 92.6505, loss: 0.1785
+2023-03-04 02:11:08,822 - mmseg - INFO - Iter [66450/80000]	lr: 2.344e-06, eta: 1:07:27, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1863, decode.acc_seg: 92.5114, loss: 0.1863
+2023-03-04 02:11:23,317 - mmseg - INFO - Iter [66500/80000]	lr: 2.344e-06, eta: 1:07:12, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1881, decode.acc_seg: 92.3289, loss: 0.1881
+2023-03-04 02:11:37,777 - mmseg - INFO - Iter [66550/80000]	lr: 2.344e-06, eta: 1:06:57, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1952, decode.acc_seg: 92.2224, loss: 0.1952
+2023-03-04 02:11:52,238 - mmseg - INFO - Iter [66600/80000]	lr: 2.344e-06, eta: 1:06:42, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1874, decode.acc_seg: 92.2854, loss: 0.1874
+2023-03-04 02:12:06,684 - mmseg - INFO - Iter [66650/80000]	lr: 2.344e-06, eta: 1:06:27, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1886, decode.acc_seg: 92.2749, loss: 0.1886
+2023-03-04 02:12:21,075 - mmseg - INFO - Iter [66700/80000]	lr: 2.344e-06, eta: 1:06:12, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1894, decode.acc_seg: 92.2599, loss: 0.1894
+2023-03-04 02:12:35,684 - mmseg - INFO - Iter [66750/80000]	lr: 2.344e-06, eta: 1:05:57, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1769, decode.acc_seg: 92.7584, loss: 0.1769
+2023-03-04 02:12:50,228 - mmseg - INFO - Iter [66800/80000]	lr: 2.344e-06, eta: 1:05:42, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1870, decode.acc_seg: 92.4427, loss: 0.1870
+2023-03-04 02:13:04,743 - mmseg - INFO - Iter [66850/80000]	lr: 2.344e-06, eta: 1:05:27, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1912, decode.acc_seg: 92.1662, loss: 0.1912
+2023-03-04 02:13:21,846 - mmseg - INFO - Iter [66900/80000]	lr: 2.344e-06, eta: 1:05:13, time: 0.342, data_time: 0.056, memory: 39544, decode.loss_ce: 0.1846, decode.acc_seg: 92.3606, loss: 0.1846
+2023-03-04 02:13:36,474 - mmseg - INFO - Iter [66950/80000]	lr: 2.344e-06, eta: 1:04:58, time: 0.293, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1841, decode.acc_seg: 92.5617, loss: 0.1841
+2023-03-04 02:13:50,943 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 02:13:50,944 - mmseg - INFO - Iter [67000/80000]	lr: 2.344e-06, eta: 1:04:43, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1887, decode.acc_seg: 92.3532, loss: 0.1887
+2023-03-04 02:14:05,405 - mmseg - INFO - Iter [67050/80000]	lr: 2.344e-06, eta: 1:04:28, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1827, decode.acc_seg: 92.6497, loss: 0.1827
+2023-03-04 02:14:19,888 - mmseg - INFO - Iter [67100/80000]	lr: 2.344e-06, eta: 1:04:13, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1840, decode.acc_seg: 92.5245, loss: 0.1840
+2023-03-04 02:14:34,360 - mmseg - INFO - Iter [67150/80000]	lr: 2.344e-06, eta: 1:03:58, time: 0.289, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1834, decode.acc_seg: 92.5480, loss: 0.1834
+2023-03-04 02:14:48,764 - mmseg - INFO - Iter [67200/80000]	lr: 2.344e-06, eta: 1:03:42, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1904, decode.acc_seg: 92.1996, loss: 0.1904
+2023-03-04 02:15:03,274 - mmseg - INFO - Iter [67250/80000]	lr: 2.344e-06, eta: 1:03:27, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1895, decode.acc_seg: 92.4703, loss: 0.1895
+2023-03-04 02:15:17,695 - mmseg - INFO - Iter [67300/80000]	lr: 2.344e-06, eta: 1:03:12, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1887, decode.acc_seg: 92.2362, loss: 0.1887
+2023-03-04 02:15:32,294 - mmseg - INFO - Iter [67350/80000]	lr: 2.344e-06, eta: 1:02:57, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1868, decode.acc_seg: 92.4297, loss: 0.1868
+2023-03-04 02:15:46,796 - mmseg - INFO - Iter [67400/80000]	lr: 2.344e-06, eta: 1:02:42, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1864, decode.acc_seg: 92.4759, loss: 0.1864
+2023-03-04 02:16:01,228 - mmseg - INFO - Iter [67450/80000]	lr: 2.344e-06, eta: 1:02:27, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1812, decode.acc_seg: 92.5054, loss: 0.1812
+2023-03-04 02:16:15,796 - mmseg - INFO - Iter [67500/80000]	lr: 2.344e-06, eta: 1:02:12, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1792, decode.acc_seg: 92.7509, loss: 0.1792
+2023-03-04 02:16:32,931 - mmseg - INFO - Iter [67550/80000]	lr: 2.344e-06, eta: 1:01:58, time: 0.343, data_time: 0.057, memory: 39544, decode.loss_ce: 0.1900, decode.acc_seg: 92.3092, loss: 0.1900
+2023-03-04 02:16:47,428 - mmseg - INFO - Iter [67600/80000]	lr: 2.344e-06, eta: 1:01:43, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1898, decode.acc_seg: 92.3989, loss: 0.1898
+2023-03-04 02:17:02,024 - mmseg - INFO - Iter [67650/80000]	lr: 2.344e-06, eta: 1:01:28, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1906, decode.acc_seg: 92.2153, loss: 0.1906
+2023-03-04 02:17:16,539 - mmseg - INFO - Iter [67700/80000]	lr: 2.344e-06, eta: 1:01:13, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1901, decode.acc_seg: 92.2723, loss: 0.1901
+2023-03-04 02:17:31,001 - mmseg - INFO - Iter [67750/80000]	lr: 2.344e-06, eta: 1:00:58, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1826, decode.acc_seg: 92.4616, loss: 0.1826
+2023-03-04 02:17:45,514 - mmseg - INFO - Iter [67800/80000]	lr: 2.344e-06, eta: 1:00:43, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1985, decode.acc_seg: 92.0506, loss: 0.1985
+2023-03-04 02:18:00,202 - mmseg - INFO - Iter [67850/80000]	lr: 2.344e-06, eta: 1:00:28, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1785, decode.acc_seg: 92.6625, loss: 0.1785
+2023-03-04 02:18:14,661 - mmseg - INFO - Iter [67900/80000]	lr: 2.344e-06, eta: 1:00:13, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1863, decode.acc_seg: 92.4992, loss: 0.1863
+2023-03-04 02:18:29,088 - mmseg - INFO - Iter [67950/80000]	lr: 2.344e-06, eta: 0:59:58, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1943, decode.acc_seg: 92.1953, loss: 0.1943
+2023-03-04 02:18:43,660 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 02:18:43,660 - mmseg - INFO - Iter [68000/80000]	lr: 2.344e-06, eta: 0:59:43, time: 0.291, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1851, decode.acc_seg: 92.5045, loss: 0.1851
+2023-03-04 02:18:58,376 - mmseg - INFO - Iter [68050/80000]	lr: 2.344e-06, eta: 0:59:28, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1868, decode.acc_seg: 92.3610, loss: 0.1868
+2023-03-04 02:19:12,938 - mmseg - INFO - Iter [68100/80000]	lr: 2.344e-06, eta: 0:59:13, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1866, decode.acc_seg: 92.3147, loss: 0.1866
+2023-03-04 02:19:29,951 - mmseg - INFO - Iter [68150/80000]	lr: 2.344e-06, eta: 0:58:58, time: 0.340, data_time: 0.055, memory: 39544, decode.loss_ce: 0.1901, decode.acc_seg: 92.2487, loss: 0.1901
+2023-03-04 02:19:44,405 - mmseg - INFO - Iter [68200/80000]	lr: 2.344e-06, eta: 0:58:43, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1862, decode.acc_seg: 92.4711, loss: 0.1862
+2023-03-04 02:19:58,955 - mmseg - INFO - Iter [68250/80000]	lr: 2.344e-06, eta: 0:58:28, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1820, decode.acc_seg: 92.3952, loss: 0.1820
+2023-03-04 02:20:13,543 - mmseg - INFO - Iter [68300/80000]	lr: 2.344e-06, eta: 0:58:13, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1862, decode.acc_seg: 92.4538, loss: 0.1862
+2023-03-04 02:20:28,151 - mmseg - INFO - Iter [68350/80000]	lr: 2.344e-06, eta: 0:57:58, time: 0.292, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1905, decode.acc_seg: 92.3942, loss: 0.1905
+2023-03-04 02:20:42,793 - mmseg - INFO - Iter [68400/80000]	lr: 2.344e-06, eta: 0:57:43, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1816, decode.acc_seg: 92.6335, loss: 0.1816
+2023-03-04 02:20:57,206 - mmseg - INFO - Iter [68450/80000]	lr: 2.344e-06, eta: 0:57:28, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1870, decode.acc_seg: 92.4987, loss: 0.1870
+2023-03-04 02:21:11,811 - mmseg - INFO - Iter [68500/80000]	lr: 2.344e-06, eta: 0:57:13, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1835, decode.acc_seg: 92.5076, loss: 0.1835
+2023-03-04 02:21:26,269 - mmseg - INFO - Iter [68550/80000]	lr: 2.344e-06, eta: 0:56:58, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1869, decode.acc_seg: 92.3633, loss: 0.1869
+2023-03-04 02:21:40,847 - mmseg - INFO - Iter [68600/80000]	lr: 2.344e-06, eta: 0:56:43, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1844, decode.acc_seg: 92.5818, loss: 0.1844
+2023-03-04 02:21:55,516 - mmseg - INFO - Iter [68650/80000]	lr: 2.344e-06, eta: 0:56:28, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1888, decode.acc_seg: 92.3021, loss: 0.1888
+2023-03-04 02:22:10,073 - mmseg - INFO - Iter [68700/80000]	lr: 2.344e-06, eta: 0:56:13, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1829, decode.acc_seg: 92.6381, loss: 0.1829
+2023-03-04 02:22:24,543 - mmseg - INFO - Iter [68750/80000]	lr: 2.344e-06, eta: 0:55:58, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1909, decode.acc_seg: 92.1754, loss: 0.1909
+2023-03-04 02:22:41,708 - mmseg - INFO - Iter [68800/80000]	lr: 2.344e-06, eta: 0:55:44, time: 0.343, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1857, decode.acc_seg: 92.3389, loss: 0.1857
+2023-03-04 02:22:56,372 - mmseg - INFO - Iter [68850/80000]	lr: 2.344e-06, eta: 0:55:29, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1890, decode.acc_seg: 92.4443, loss: 0.1890
+2023-03-04 02:23:10,971 - mmseg - INFO - Iter [68900/80000]	lr: 2.344e-06, eta: 0:55:14, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1900, decode.acc_seg: 92.2303, loss: 0.1900
+2023-03-04 02:23:25,462 - mmseg - INFO - Iter [68950/80000]	lr: 2.344e-06, eta: 0:54:59, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1879, decode.acc_seg: 92.4782, loss: 0.1879
+2023-03-04 02:23:39,922 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 02:23:39,922 - mmseg - INFO - Iter [69000/80000]	lr: 2.344e-06, eta: 0:54:44, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1854, decode.acc_seg: 92.3241, loss: 0.1854
+2023-03-04 02:23:54,496 - mmseg - INFO - Iter [69050/80000]	lr: 2.344e-06, eta: 0:54:29, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1875, decode.acc_seg: 92.4191, loss: 0.1875
+2023-03-04 02:24:09,050 - mmseg - INFO - Iter [69100/80000]	lr: 2.344e-06, eta: 0:54:14, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1757, decode.acc_seg: 92.6814, loss: 0.1757
+2023-03-04 02:24:23,521 - mmseg - INFO - Iter [69150/80000]	lr: 2.344e-06, eta: 0:53:59, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1802, decode.acc_seg: 92.6139, loss: 0.1802
+2023-03-04 02:24:37,995 - mmseg - INFO - Iter [69200/80000]	lr: 2.344e-06, eta: 0:53:44, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1817, decode.acc_seg: 92.4200, loss: 0.1817
+2023-03-04 02:24:52,575 - mmseg - INFO - Iter [69250/80000]	lr: 2.344e-06, eta: 0:53:29, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1920, decode.acc_seg: 92.2234, loss: 0.1920
+2023-03-04 02:25:07,211 - mmseg - INFO - Iter [69300/80000]	lr: 2.344e-06, eta: 0:53:14, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1891, decode.acc_seg: 92.4095, loss: 0.1891
+2023-03-04 02:25:21,802 - mmseg - INFO - Iter [69350/80000]	lr: 2.344e-06, eta: 0:52:59, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1913, decode.acc_seg: 92.2994, loss: 0.1913
+2023-03-04 02:25:36,445 - mmseg - INFO - Iter [69400/80000]	lr: 2.344e-06, eta: 0:52:44, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1888, decode.acc_seg: 92.3839, loss: 0.1888
+2023-03-04 02:25:53,405 - mmseg - INFO - Iter [69450/80000]	lr: 2.344e-06, eta: 0:52:29, time: 0.339, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1838, decode.acc_seg: 92.3512, loss: 0.1838
+2023-03-04 02:26:07,989 - mmseg - INFO - Iter [69500/80000]	lr: 2.344e-06, eta: 0:52:14, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1881, decode.acc_seg: 92.3489, loss: 0.1881
+2023-03-04 02:26:22,572 - mmseg - INFO - Iter [69550/80000]	lr: 2.344e-06, eta: 0:51:59, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1902, decode.acc_seg: 92.2668, loss: 0.1902
+2023-03-04 02:26:37,034 - mmseg - INFO - Iter [69600/80000]	lr: 2.344e-06, eta: 0:51:44, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1823, decode.acc_seg: 92.6068, loss: 0.1823
+2023-03-04 02:26:51,552 - mmseg - INFO - Iter [69650/80000]	lr: 2.344e-06, eta: 0:51:29, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1880, decode.acc_seg: 92.3006, loss: 0.1880
+2023-03-04 02:27:06,157 - mmseg - INFO - Iter [69700/80000]	lr: 2.344e-06, eta: 0:51:14, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1867, decode.acc_seg: 92.3528, loss: 0.1867
+2023-03-04 02:27:20,674 - mmseg - INFO - Iter [69750/80000]	lr: 2.344e-06, eta: 0:50:59, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1872, decode.acc_seg: 92.3121, loss: 0.1872
+2023-03-04 02:27:35,198 - mmseg - INFO - Iter [69800/80000]	lr: 2.344e-06, eta: 0:50:44, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1868, decode.acc_seg: 92.3326, loss: 0.1868
+2023-03-04 02:27:49,689 - mmseg - INFO - Iter [69850/80000]	lr: 2.344e-06, eta: 0:50:29, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1805, decode.acc_seg: 92.7196, loss: 0.1805
+2023-03-04 02:28:04,170 - mmseg - INFO - Iter [69900/80000]	lr: 2.344e-06, eta: 0:50:14, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1829, decode.acc_seg: 92.6590, loss: 0.1829
+2023-03-04 02:28:18,690 - mmseg - INFO - Iter [69950/80000]	lr: 2.344e-06, eta: 0:50:00, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1854, decode.acc_seg: 92.4625, loss: 0.1854
+2023-03-04 02:28:33,241 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 02:28:33,241 - mmseg - INFO - Iter [70000/80000]	lr: 2.344e-06, eta: 0:49:45, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1805, decode.acc_seg: 92.6909, loss: 0.1805
+2023-03-04 02:28:50,228 - mmseg - INFO - Iter [70050/80000]	lr: 1.172e-06, eta: 0:49:30, time: 0.340, data_time: 0.057, memory: 39544, decode.loss_ce: 0.1814, decode.acc_seg: 92.5618, loss: 0.1814
+2023-03-04 02:29:04,729 - mmseg - INFO - Iter [70100/80000]	lr: 1.172e-06, eta: 0:49:15, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1882, decode.acc_seg: 92.3445, loss: 0.1882
+2023-03-04 02:29:19,232 - mmseg - INFO - Iter [70150/80000]	lr: 1.172e-06, eta: 0:49:00, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1940, decode.acc_seg: 92.2726, loss: 0.1940
+2023-03-04 02:29:33,750 - mmseg - INFO - Iter [70200/80000]	lr: 1.172e-06, eta: 0:48:45, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1859, decode.acc_seg: 92.7363, loss: 0.1859
+2023-03-04 02:29:48,469 - mmseg - INFO - Iter [70250/80000]	lr: 1.172e-06, eta: 0:48:30, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1810, decode.acc_seg: 92.5180, loss: 0.1810
+2023-03-04 02:30:02,914 - mmseg - INFO - Iter [70300/80000]	lr: 1.172e-06, eta: 0:48:15, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1879, decode.acc_seg: 92.1922, loss: 0.1879
+2023-03-04 02:30:17,352 - mmseg - INFO - Iter [70350/80000]	lr: 1.172e-06, eta: 0:48:00, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1802, decode.acc_seg: 92.7140, loss: 0.1802
+2023-03-04 02:30:31,881 - mmseg - INFO - Iter [70400/80000]	lr: 1.172e-06, eta: 0:47:45, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1899, decode.acc_seg: 92.3756, loss: 0.1899
+2023-03-04 02:30:46,368 - mmseg - INFO - Iter [70450/80000]	lr: 1.172e-06, eta: 0:47:30, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1867, decode.acc_seg: 92.5487, loss: 0.1867
+2023-03-04 02:31:00,910 - mmseg - INFO - Iter [70500/80000]	lr: 1.172e-06, eta: 0:47:15, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1867, decode.acc_seg: 92.3833, loss: 0.1867
+2023-03-04 02:31:15,587 - mmseg - INFO - Iter [70550/80000]	lr: 1.172e-06, eta: 0:47:00, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1863, decode.acc_seg: 92.5587, loss: 0.1863
+2023-03-04 02:31:30,170 - mmseg - INFO - Iter [70600/80000]	lr: 1.172e-06, eta: 0:46:45, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1820, decode.acc_seg: 92.6409, loss: 0.1820
+2023-03-04 02:31:44,726 - mmseg - INFO - Iter [70650/80000]	lr: 1.172e-06, eta: 0:46:30, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1860, decode.acc_seg: 92.4007, loss: 0.1860
+2023-03-04 02:32:01,804 - mmseg - INFO - Iter [70700/80000]	lr: 1.172e-06, eta: 0:46:16, time: 0.341, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1954, decode.acc_seg: 92.1832, loss: 0.1954
+2023-03-04 02:32:16,318 - mmseg - INFO - Iter [70750/80000]	lr: 1.172e-06, eta: 0:46:01, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1845, decode.acc_seg: 92.5543, loss: 0.1845
+2023-03-04 02:32:30,804 - mmseg - INFO - Iter [70800/80000]	lr: 1.172e-06, eta: 0:45:46, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1896, decode.acc_seg: 92.4497, loss: 0.1896
+2023-03-04 02:32:45,492 - mmseg - INFO - Iter [70850/80000]	lr: 1.172e-06, eta: 0:45:31, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1838, decode.acc_seg: 92.5149, loss: 0.1838
+2023-03-04 02:33:00,020 - mmseg - INFO - Iter [70900/80000]	lr: 1.172e-06, eta: 0:45:16, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1806, decode.acc_seg: 92.6239, loss: 0.1806
+2023-03-04 02:33:14,530 - mmseg - INFO - Iter [70950/80000]	lr: 1.172e-06, eta: 0:45:01, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1889, decode.acc_seg: 92.4271, loss: 0.1889
+2023-03-04 02:33:29,073 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 02:33:29,073 - mmseg - INFO - Iter [71000/80000]	lr: 1.172e-06, eta: 0:44:46, time: 0.291, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1891, decode.acc_seg: 92.3109, loss: 0.1891
+2023-03-04 02:33:43,622 - mmseg - INFO - Iter [71050/80000]	lr: 1.172e-06, eta: 0:44:31, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1876, decode.acc_seg: 92.4501, loss: 0.1876
+2023-03-04 02:33:58,114 - mmseg - INFO - Iter [71100/80000]	lr: 1.172e-06, eta: 0:44:16, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1934, decode.acc_seg: 92.2377, loss: 0.1934
+2023-03-04 02:34:12,648 - mmseg - INFO - Iter [71150/80000]	lr: 1.172e-06, eta: 0:44:01, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1835, decode.acc_seg: 92.5062, loss: 0.1835
+2023-03-04 02:34:27,224 - mmseg - INFO - Iter [71200/80000]	lr: 1.172e-06, eta: 0:43:46, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1821, decode.acc_seg: 92.5398, loss: 0.1821
+2023-03-04 02:34:41,934 - mmseg - INFO - Iter [71250/80000]	lr: 1.172e-06, eta: 0:43:31, time: 0.294, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1861, decode.acc_seg: 92.4223, loss: 0.1861
+2023-03-04 02:34:56,527 - mmseg - INFO - Iter [71300/80000]	lr: 1.172e-06, eta: 0:43:16, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1904, decode.acc_seg: 92.3655, loss: 0.1904
+2023-03-04 02:35:13,828 - mmseg - INFO - Iter [71350/80000]	lr: 1.172e-06, eta: 0:43:01, time: 0.346, data_time: 0.055, memory: 39544, decode.loss_ce: 0.1836, decode.acc_seg: 92.5877, loss: 0.1836
+2023-03-04 02:35:28,396 - mmseg - INFO - Iter [71400/80000]	lr: 1.172e-06, eta: 0:42:46, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1903, decode.acc_seg: 92.3282, loss: 0.1903
+2023-03-04 02:35:42,835 - mmseg - INFO - Iter [71450/80000]	lr: 1.172e-06, eta: 0:42:31, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1769, decode.acc_seg: 92.6597, loss: 0.1769
+2023-03-04 02:35:57,408 - mmseg - INFO - Iter [71500/80000]	lr: 1.172e-06, eta: 0:42:16, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1817, decode.acc_seg: 92.5492, loss: 0.1817
+2023-03-04 02:36:11,973 - mmseg - INFO - Iter [71550/80000]	lr: 1.172e-06, eta: 0:42:01, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1895, decode.acc_seg: 92.3715, loss: 0.1895
+2023-03-04 02:36:26,527 - mmseg - INFO - Iter [71600/80000]	lr: 1.172e-06, eta: 0:41:46, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1822, decode.acc_seg: 92.5410, loss: 0.1822
+2023-03-04 02:36:41,110 - mmseg - INFO - Iter [71650/80000]	lr: 1.172e-06, eta: 0:41:31, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1853, decode.acc_seg: 92.6462, loss: 0.1853
+2023-03-04 02:36:55,593 - mmseg - INFO - Iter [71700/80000]	lr: 1.172e-06, eta: 0:41:16, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1918, decode.acc_seg: 92.3350, loss: 0.1918
+2023-03-04 02:37:10,208 - mmseg - INFO - Iter [71750/80000]	lr: 1.172e-06, eta: 0:41:02, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1863, decode.acc_seg: 92.4577, loss: 0.1863
+2023-03-04 02:37:24,838 - mmseg - INFO - Iter [71800/80000]	lr: 1.172e-06, eta: 0:40:47, time: 0.293, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1875, decode.acc_seg: 92.1949, loss: 0.1875
+2023-03-04 02:37:39,415 - mmseg - INFO - Iter [71850/80000]	lr: 1.172e-06, eta: 0:40:32, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1826, decode.acc_seg: 92.5735, loss: 0.1826
+2023-03-04 02:37:53,904 - mmseg - INFO - Iter [71900/80000]	lr: 1.172e-06, eta: 0:40:17, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1791, decode.acc_seg: 92.8525, loss: 0.1791
+2023-03-04 02:38:10,979 - mmseg - INFO - Iter [71950/80000]	lr: 1.172e-06, eta: 0:40:02, time: 0.342, data_time: 0.055, memory: 39544, decode.loss_ce: 0.1869, decode.acc_seg: 92.4343, loss: 0.1869
+2023-03-04 02:38:25,561 - mmseg - INFO - Saving checkpoint at 72000 iterations
+2023-03-04 02:38:27,600 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 02:38:27,600 - mmseg - INFO - Iter [72000/80000]	lr: 1.172e-06, eta: 0:39:47, time: 0.332, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1813, decode.acc_seg: 92.6922, loss: 0.1813
+2023-03-04 02:38:47,447 - mmseg - INFO - per class results:
+2023-03-04 02:38:47,452 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        | 76.23 |  88.4 |
+|       building      | 81.58 | 92.45 |
+|         sky         | 94.18 | 97.19 |
+|        floor        | 80.14 | 91.27 |
+|         tree        | 73.12 | 87.72 |
+|       ceiling       | 82.46 | 91.55 |
+|         road        | 81.96 | 88.89 |
+|         bed         | 88.43 | 95.32 |
+|      windowpane     | 60.75 | 77.93 |
+|        grass        |  65.8 | 81.16 |
+|       cabinet       | 59.42 | 72.56 |
+|       sidewalk      | 65.68 | 82.36 |
+|        person       | 79.23 | 90.69 |
+|        earth        |  34.4 | 50.63 |
+|         door        | 47.55 | 62.86 |
+|        table        | 61.68 | 77.77 |
+|       mountain      | 51.82 | 67.37 |
+|        plant        | 50.43 | 63.34 |
+|       curtain       | 69.54 |  81.7 |
+|        chair        |  58.0 | 71.32 |
+|         car         | 83.09 | 90.31 |
+|        water        | 47.81 | 64.17 |
+|       painting      | 69.42 | 84.96 |
+|         sofa        |  65.7 | 84.54 |
+|        shelf        | 39.91 | 55.26 |
+|        house        | 45.34 | 53.63 |
+|         sea         | 43.82 | 68.93 |
+|        mirror       |  65.3 | 75.06 |
+|         rug         |  55.8 |  61.1 |
+|        field        | 27.44 | 42.12 |
+|       armchair      | 44.11 | 57.88 |
+|         seat        | 54.21 | 77.35 |
+|        fence        | 40.89 | 56.28 |
+|         desk        | 50.01 | 69.29 |
+|         rock        | 29.86 |  44.7 |
+|       wardrobe      | 48.86 | 70.21 |
+|         lamp        | 64.03 | 74.97 |
+|       bathtub       | 75.85 | 82.56 |
+|       railing       | 31.51 | 43.64 |
+|       cushion       | 55.01 | 69.55 |
+|         base        | 29.31 | 42.12 |
+|         box         | 24.29 | 31.93 |
+|        column       | 44.98 | 56.47 |
+|      signboard      | 36.09 | 49.39 |
+|   chest of drawers  | 39.28 | 55.28 |
+|       counter       | 26.62 | 38.94 |
+|         sand        |  31.1 | 45.62 |
+|         sink        | 70.29 | 80.05 |
+|      skyscraper     | 46.92 | 55.03 |
+|      fireplace      | 66.84 | 84.94 |
+|     refrigerator    | 78.08 | 83.94 |
+|      grandstand     | 40.86 | 64.87 |
+|         path        | 15.46 | 22.69 |
+|        stairs       | 32.85 | 42.98 |
+|        runway       | 63.66 | 82.07 |
+|         case        | 48.62 | 68.21 |
+|      pool table     | 92.77 |  95.8 |
+|        pillow       | 55.96 | 65.06 |
+|     screen door     | 65.46 |  71.7 |
+|       stairway      | 25.51 | 32.06 |
+|        river        |  8.83 | 15.81 |
+|        bridge       | 62.42 | 69.17 |
+|       bookcase      | 40.29 | 53.45 |
+|        blind        | 45.96 | 53.15 |
+|     coffee table    | 66.39 | 81.99 |
+|        toilet       | 86.25 | 90.36 |
+|        flower       | 31.93 | 47.73 |
+|         book        | 46.54 | 66.41 |
+|         hill        |  7.04 |  7.96 |
+|        bench        | 43.54 | 53.45 |
+|      countertop     | 52.85 | 69.12 |
+|        stove        | 72.68 | 78.71 |
+|         palm        | 51.07 | 71.89 |
+|    kitchen island   | 46.42 | 70.95 |
+|       computer      | 57.39 | 64.81 |
+|     swivel chair    | 44.22 | 57.36 |
+|         boat        |  41.2 | 46.04 |
+|         bar         | 28.12 |  31.5 |
+|    arcade machine   | 27.69 | 30.72 |
+|        hovel        | 31.63 | 34.55 |
+|         bus         | 87.75 | 92.76 |
+|        towel        | 60.06 | 69.77 |
+|        light        | 55.38 | 61.06 |
+|        truck        | 35.31 | 45.98 |
+|        tower        |  24.2 | 34.16 |
+|      chandelier     | 66.17 |  81.1 |
+|        awning       | 24.01 | 26.73 |
+|     streetlight     | 27.99 | 35.97 |
+|        booth        | 52.82 | 54.45 |
+| television receiver | 67.94 | 79.02 |
+|       airplane      | 51.59 | 69.18 |
+|      dirt track     |  6.65 | 19.75 |
+|       apparel       | 28.95 | 45.68 |
+|         pole        | 23.73 | 34.57 |
+|         land        |  8.41 | 11.43 |
+|      bannister      |  4.93 |  6.8  |
+|      escalator      | 23.49 | 24.21 |
+|       ottoman       | 47.62 | 58.38 |
+|        bottle       |  16.3 | 24.28 |
+|        buffet       | 52.25 |  62.0 |
+|        poster       | 27.81 | 36.49 |
+|        stage        | 17.77 |  23.8 |
+|         van         | 48.02 | 61.85 |
+|         ship        | 45.72 | 59.25 |
+|       fountain      |  6.51 |  6.59 |
+|    conveyer belt    | 77.93 | 87.88 |
+|        canopy       | 14.83 | 16.99 |
+|        washer       | 65.93 | 66.36 |
+|      plaything      | 22.16 | 30.56 |
+|    swimming pool    | 46.84 | 57.73 |
+|        stool        | 41.35 | 56.88 |
+|        barrel       | 36.29 | 64.21 |
+|        basket       | 27.73 | 37.63 |
+|      waterfall      | 53.47 | 62.82 |
+|         tent        | 94.59 | 97.52 |
+|         bag         | 11.52 |  14.5 |
+|       minibike      | 62.61 | 73.36 |
+|        cradle       | 79.84 | 96.76 |
+|         oven        | 27.24 | 59.55 |
+|         ball        | 47.51 | 59.44 |
+|         food        |  51.4 | 61.55 |
+|         step        | 13.85 | 17.95 |
+|         tank        | 41.61 | 41.94 |
+|      trade name     | 24.88 | 28.52 |
+|      microwave      | 38.32 | 41.29 |
+|         pot         | 41.34 | 50.13 |
+|        animal       | 52.42 | 55.78 |
+|       bicycle       | 45.58 |  67.7 |
+|         lake        | 58.54 | 63.37 |
+|      dishwasher     | 77.94 | 82.76 |
+|        screen       | 66.41 | 83.62 |
+|       blanket       | 11.78 | 13.61 |
+|      sculpture      | 37.09 | 64.25 |
+|         hood        | 55.67 | 71.79 |
+|        sconce       | 42.28 | 50.25 |
+|         vase        | 37.07 | 53.17 |
+|    traffic light    | 29.95 | 42.81 |
+|         tray        |  6.13 | 11.52 |
+|        ashcan       | 38.37 | 48.72 |
+|         fan         | 57.11 | 70.32 |
+|         pier        | 12.46 | 14.33 |
+|      crt screen     |  4.11 | 11.33 |
+|        plate        | 38.31 | 47.88 |
+|       monitor       | 20.16 | 26.02 |
+|    bulletin board   | 46.14 | 56.87 |
+|        shower       |  1.6  |  2.19 |
+|       radiator      | 45.24 | 52.83 |
+|        glass        | 12.64 | 14.02 |
+|        clock        | 24.94 | 30.12 |
+|         flag        | 37.49 | 40.63 |
++---------------------+-------+-------+
+2023-03-04 02:38:47,452 - mmseg - INFO - Summary:
+2023-03-04 02:38:47,453 - mmseg - INFO - 
++------+-------+-------+
+| aAcc |  mIoU |  mAcc |
++------+-------+-------+
+| 82.0 | 46.13 | 56.85 |
++------+-------+-------+
+2023-03-04 02:38:47,453 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 02:38:47,454 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8200, mIoU: 0.4613, mAcc: 0.5685, IoU.background: nan, IoU.wall: 0.7623, IoU.building: 0.8158, IoU.sky: 0.9418, IoU.floor: 0.8014, IoU.tree: 0.7312, IoU.ceiling: 0.8246, IoU.road: 0.8196, IoU.bed : 0.8843, IoU.windowpane: 0.6075, IoU.grass: 0.6580, IoU.cabinet: 0.5942, IoU.sidewalk: 0.6568, IoU.person: 0.7923, IoU.earth: 0.3440, IoU.door: 0.4755, IoU.table: 0.6168, IoU.mountain: 0.5182, IoU.plant: 0.5043, IoU.curtain: 0.6954, IoU.chair: 0.5800, IoU.car: 0.8309, IoU.water: 0.4781, IoU.painting: 0.6942, IoU.sofa: 0.6570, IoU.shelf: 0.3991, IoU.house: 0.4534, IoU.sea: 0.4382, IoU.mirror: 0.6530, IoU.rug: 0.5580, IoU.field: 0.2744, IoU.armchair: 0.4411, IoU.seat: 0.5421, IoU.fence: 0.4089, IoU.desk: 0.5001, IoU.rock: 0.2986, IoU.wardrobe: 0.4886, IoU.lamp: 0.6403, IoU.bathtub: 0.7585, IoU.railing: 0.3151, IoU.cushion: 0.5501, IoU.base: 0.2931, IoU.box: 0.2429, IoU.column: 0.4498, IoU.signboard: 0.3609, IoU.chest of drawers: 0.3928, IoU.counter: 0.2662, IoU.sand: 0.3110, IoU.sink: 0.7029, IoU.skyscraper: 0.4692, IoU.fireplace: 0.6684, IoU.refrigerator: 0.7808, IoU.grandstand: 0.4086, IoU.path: 0.1546, IoU.stairs: 0.3285, IoU.runway: 0.6366, IoU.case: 0.4862, IoU.pool table: 0.9277, IoU.pillow: 0.5596, IoU.screen door: 0.6546, IoU.stairway: 0.2551, IoU.river: 0.0883, IoU.bridge: 0.6242, IoU.bookcase: 0.4029, IoU.blind: 0.4596, IoU.coffee table: 0.6639, IoU.toilet: 0.8625, IoU.flower: 0.3193, IoU.book: 0.4654, IoU.hill: 0.0704, IoU.bench: 0.4354, IoU.countertop: 0.5285, IoU.stove: 0.7268, IoU.palm: 0.5107, IoU.kitchen island: 0.4642, IoU.computer: 0.5739, IoU.swivel chair: 0.4422, IoU.boat: 0.4120, IoU.bar: 0.2812, IoU.arcade machine: 0.2769, IoU.hovel: 0.3163, IoU.bus: 0.8775, IoU.towel: 0.6006, IoU.light: 0.5538, IoU.truck: 0.3531, IoU.tower: 0.2420, IoU.chandelier: 0.6617, IoU.awning: 0.2401, IoU.streetlight: 0.2799, IoU.booth: 0.5282, IoU.television receiver: 0.6794, IoU.airplane: 0.5159, IoU.dirt track: 0.0665, IoU.apparel: 0.2895, IoU.pole: 0.2373, IoU.land: 0.0841, IoU.bannister: 0.0493, IoU.escalator: 0.2349, IoU.ottoman: 0.4762, IoU.bottle: 0.1630, IoU.buffet: 0.5225, IoU.poster: 0.2781, IoU.stage: 0.1777, IoU.van: 0.4802, IoU.ship: 0.4572, IoU.fountain: 0.0651, IoU.conveyer belt: 0.7793, IoU.canopy: 0.1483, IoU.washer: 0.6593, IoU.plaything: 0.2216, IoU.swimming pool: 0.4684, IoU.stool: 0.4135, IoU.barrel: 0.3629, IoU.basket: 0.2773, IoU.waterfall: 0.5347, IoU.tent: 0.9459, IoU.bag: 0.1152, IoU.minibike: 0.6261, IoU.cradle: 0.7984, IoU.oven: 0.2724, IoU.ball: 0.4751, IoU.food: 0.5140, IoU.step: 0.1385, IoU.tank: 0.4161, IoU.trade name: 0.2488, IoU.microwave: 0.3832, IoU.pot: 0.4134, IoU.animal: 0.5242, IoU.bicycle: 0.4558, IoU.lake: 0.5854, IoU.dishwasher: 0.7794, IoU.screen: 0.6641, IoU.blanket: 0.1178, IoU.sculpture: 0.3709, IoU.hood: 0.5567, IoU.sconce: 0.4228, IoU.vase: 0.3707, IoU.traffic light: 0.2995, IoU.tray: 0.0613, IoU.ashcan: 0.3837, IoU.fan: 0.5711, IoU.pier: 0.1246, IoU.crt screen: 0.0411, IoU.plate: 0.3831, IoU.monitor: 0.2016, IoU.bulletin board: 0.4614, IoU.shower: 0.0160, IoU.radiator: 0.4524, IoU.glass: 0.1264, IoU.clock: 0.2494, IoU.flag: 0.3749, Acc.background: nan, Acc.wall: 0.8840, Acc.building: 0.9245, Acc.sky: 0.9719, Acc.floor: 0.9127, Acc.tree: 0.8772, Acc.ceiling: 0.9155, Acc.road: 0.8889, Acc.bed : 0.9532, Acc.windowpane: 0.7793, Acc.grass: 0.8116, Acc.cabinet: 0.7256, Acc.sidewalk: 0.8236, Acc.person: 0.9069, Acc.earth: 0.5063, Acc.door: 0.6286, Acc.table: 0.7777, Acc.mountain: 0.6737, Acc.plant: 0.6334, Acc.curtain: 0.8170, Acc.chair: 0.7132, Acc.car: 0.9031, Acc.water: 0.6417, Acc.painting: 0.8496, Acc.sofa: 0.8454, Acc.shelf: 0.5526, Acc.house: 0.5363, Acc.sea: 0.6893, Acc.mirror: 0.7506, Acc.rug: 0.6110, Acc.field: 0.4212, Acc.armchair: 0.5788, Acc.seat: 0.7735, Acc.fence: 0.5628, Acc.desk: 0.6929, Acc.rock: 0.4470, Acc.wardrobe: 0.7021, Acc.lamp: 0.7497, Acc.bathtub: 0.8256, Acc.railing: 0.4364, Acc.cushion: 0.6955, Acc.base: 0.4212, Acc.box: 0.3193, Acc.column: 0.5647, Acc.signboard: 0.4939, Acc.chest of drawers: 0.5528, Acc.counter: 0.3894, Acc.sand: 0.4562, Acc.sink: 0.8005, Acc.skyscraper: 0.5503, Acc.fireplace: 0.8494, Acc.refrigerator: 0.8394, Acc.grandstand: 0.6487, Acc.path: 0.2269, Acc.stairs: 0.4298, Acc.runway: 0.8207, Acc.case: 0.6821, Acc.pool table: 0.9580, Acc.pillow: 0.6506, Acc.screen door: 0.7170, Acc.stairway: 0.3206, Acc.river: 0.1581, Acc.bridge: 0.6917, Acc.bookcase: 0.5345, Acc.blind: 0.5315, Acc.coffee table: 0.8199, Acc.toilet: 0.9036, Acc.flower: 0.4773, Acc.book: 0.6641, Acc.hill: 0.0796, Acc.bench: 0.5345, Acc.countertop: 0.6912, Acc.stove: 0.7871, Acc.palm: 0.7189, Acc.kitchen island: 0.7095, Acc.computer: 0.6481, Acc.swivel chair: 0.5736, Acc.boat: 0.4604, Acc.bar: 0.3150, Acc.arcade machine: 0.3072, Acc.hovel: 0.3455, Acc.bus: 0.9276, Acc.towel: 0.6977, Acc.light: 0.6106, Acc.truck: 0.4598, Acc.tower: 0.3416, Acc.chandelier: 0.8110, Acc.awning: 0.2673, Acc.streetlight: 0.3597, Acc.booth: 0.5445, Acc.television receiver: 0.7902, Acc.airplane: 0.6918, Acc.dirt track: 0.1975, Acc.apparel: 0.4568, Acc.pole: 0.3457, Acc.land: 0.1143, Acc.bannister: 0.0680, Acc.escalator: 0.2421, Acc.ottoman: 0.5838, Acc.bottle: 0.2428, Acc.buffet: 0.6200, Acc.poster: 0.3649, Acc.stage: 0.2380, Acc.van: 0.6185, Acc.ship: 0.5925, Acc.fountain: 0.0659, Acc.conveyer belt: 0.8788, Acc.canopy: 0.1699, Acc.washer: 0.6636, Acc.plaything: 0.3056, Acc.swimming pool: 0.5773, Acc.stool: 0.5688, Acc.barrel: 0.6421, Acc.basket: 0.3763, Acc.waterfall: 0.6282, Acc.tent: 0.9752, Acc.bag: 0.1450, Acc.minibike: 0.7336, Acc.cradle: 0.9676, Acc.oven: 0.5955, Acc.ball: 0.5944, Acc.food: 0.6155, Acc.step: 0.1795, Acc.tank: 0.4194, Acc.trade name: 0.2852, Acc.microwave: 0.4129, Acc.pot: 0.5013, Acc.animal: 0.5578, Acc.bicycle: 0.6770, Acc.lake: 0.6337, Acc.dishwasher: 0.8276, Acc.screen: 0.8362, Acc.blanket: 0.1361, Acc.sculpture: 0.6425, Acc.hood: 0.7179, Acc.sconce: 0.5025, Acc.vase: 0.5317, Acc.traffic light: 0.4281, Acc.tray: 0.1152, Acc.ashcan: 0.4872, Acc.fan: 0.7032, Acc.pier: 0.1433, Acc.crt screen: 0.1133, Acc.plate: 0.4788, Acc.monitor: 0.2602, Acc.bulletin board: 0.5687, Acc.shower: 0.0219, Acc.radiator: 0.5283, Acc.glass: 0.1402, Acc.clock: 0.3012, Acc.flag: 0.4063
+2023-03-04 02:39:02,454 - mmseg - INFO - Iter [72050/80000]	lr: 1.172e-06, eta: 0:39:35, time: 0.697, data_time: 0.405, memory: 39544, decode.loss_ce: 0.1951, decode.acc_seg: 92.0991, loss: 0.1951
+2023-03-04 02:39:17,331 - mmseg - INFO - Iter [72100/80000]	lr: 1.172e-06, eta: 0:39:20, time: 0.298, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1872, decode.acc_seg: 92.4075, loss: 0.1872
+2023-03-04 02:39:31,921 - mmseg - INFO - Iter [72150/80000]	lr: 1.172e-06, eta: 0:39:05, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1869, decode.acc_seg: 92.5122, loss: 0.1869
+2023-03-04 02:39:46,467 - mmseg - INFO - Iter [72200/80000]	lr: 1.172e-06, eta: 0:38:50, time: 0.291, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1864, decode.acc_seg: 92.4623, loss: 0.1864
+2023-03-04 02:40:01,106 - mmseg - INFO - Iter [72250/80000]	lr: 1.172e-06, eta: 0:38:35, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1891, decode.acc_seg: 92.3363, loss: 0.1891
+2023-03-04 02:40:15,671 - mmseg - INFO - Iter [72300/80000]	lr: 1.172e-06, eta: 0:38:20, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1958, decode.acc_seg: 92.1459, loss: 0.1958
+2023-03-04 02:40:30,203 - mmseg - INFO - Iter [72350/80000]	lr: 1.172e-06, eta: 0:38:05, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1877, decode.acc_seg: 92.3487, loss: 0.1877
+2023-03-04 02:40:44,692 - mmseg - INFO - Iter [72400/80000]	lr: 1.172e-06, eta: 0:37:50, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1798, decode.acc_seg: 92.6978, loss: 0.1798
+2023-03-04 02:40:59,221 - mmseg - INFO - Iter [72450/80000]	lr: 1.172e-06, eta: 0:37:35, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1846, decode.acc_seg: 92.5259, loss: 0.1846
+2023-03-04 02:41:13,692 - mmseg - INFO - Iter [72500/80000]	lr: 1.172e-06, eta: 0:37:20, time: 0.289, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1864, decode.acc_seg: 92.5303, loss: 0.1864
+2023-03-04 02:41:28,209 - mmseg - INFO - Iter [72550/80000]	lr: 1.172e-06, eta: 0:37:05, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1895, decode.acc_seg: 92.3231, loss: 0.1895
+2023-03-04 02:41:45,172 - mmseg - INFO - Iter [72600/80000]	lr: 1.172e-06, eta: 0:36:50, time: 0.339, data_time: 0.054, memory: 39544, decode.loss_ce: 0.1877, decode.acc_seg: 92.4183, loss: 0.1877
+2023-03-04 02:41:59,651 - mmseg - INFO - Iter [72650/80000]	lr: 1.172e-06, eta: 0:36:35, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1803, decode.acc_seg: 92.7390, loss: 0.1803
+2023-03-04 02:42:14,198 - mmseg - INFO - Iter [72700/80000]	lr: 1.172e-06, eta: 0:36:20, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1887, decode.acc_seg: 92.4058, loss: 0.1887
+2023-03-04 02:42:28,753 - mmseg - INFO - Iter [72750/80000]	lr: 1.172e-06, eta: 0:36:05, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1818, decode.acc_seg: 92.6979, loss: 0.1818
+2023-03-04 02:42:43,283 - mmseg - INFO - Iter [72800/80000]	lr: 1.172e-06, eta: 0:35:50, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1881, decode.acc_seg: 92.3419, loss: 0.1881
+2023-03-04 02:42:57,768 - mmseg - INFO - Iter [72850/80000]	lr: 1.172e-06, eta: 0:35:35, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1795, decode.acc_seg: 92.7054, loss: 0.1795
+2023-03-04 02:43:12,263 - mmseg - INFO - Iter [72900/80000]	lr: 1.172e-06, eta: 0:35:20, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1834, decode.acc_seg: 92.6218, loss: 0.1834
+2023-03-04 02:43:26,848 - mmseg - INFO - Iter [72950/80000]	lr: 1.172e-06, eta: 0:35:05, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1856, decode.acc_seg: 92.5047, loss: 0.1856
+2023-03-04 02:43:41,388 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 02:43:41,388 - mmseg - INFO - Iter [73000/80000]	lr: 1.172e-06, eta: 0:34:50, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1893, decode.acc_seg: 92.1805, loss: 0.1893
+2023-03-04 02:43:55,932 - mmseg - INFO - Iter [73050/80000]	lr: 1.172e-06, eta: 0:34:35, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1826, decode.acc_seg: 92.5359, loss: 0.1826
+2023-03-04 02:44:10,483 - mmseg - INFO - Iter [73100/80000]	lr: 1.172e-06, eta: 0:34:20, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1836, decode.acc_seg: 92.4181, loss: 0.1836
+2023-03-04 02:44:25,083 - mmseg - INFO - Iter [73150/80000]	lr: 1.172e-06, eta: 0:34:05, time: 0.292, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1934, decode.acc_seg: 92.1281, loss: 0.1934
+2023-03-04 02:44:42,157 - mmseg - INFO - Iter [73200/80000]	lr: 1.172e-06, eta: 0:33:51, time: 0.341, data_time: 0.058, memory: 39544, decode.loss_ce: 0.1859, decode.acc_seg: 92.3720, loss: 0.1859
+2023-03-04 02:44:56,720 - mmseg - INFO - Iter [73250/80000]	lr: 1.172e-06, eta: 0:33:36, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1881, decode.acc_seg: 92.4215, loss: 0.1881
+2023-03-04 02:45:11,271 - mmseg - INFO - Iter [73300/80000]	lr: 1.172e-06, eta: 0:33:21, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1941, decode.acc_seg: 92.2613, loss: 0.1941
+2023-03-04 02:45:25,857 - mmseg - INFO - Iter [73350/80000]	lr: 1.172e-06, eta: 0:33:06, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1842, decode.acc_seg: 92.5566, loss: 0.1842
+2023-03-04 02:45:40,410 - mmseg - INFO - Iter [73400/80000]	lr: 1.172e-06, eta: 0:32:51, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1807, decode.acc_seg: 92.6185, loss: 0.1807
+2023-03-04 02:45:54,921 - mmseg - INFO - Iter [73450/80000]	lr: 1.172e-06, eta: 0:32:36, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1831, decode.acc_seg: 92.5590, loss: 0.1831
+2023-03-04 02:46:09,505 - mmseg - INFO - Iter [73500/80000]	lr: 1.172e-06, eta: 0:32:21, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1831, decode.acc_seg: 92.4272, loss: 0.1831
+2023-03-04 02:46:24,044 - mmseg - INFO - Iter [73550/80000]	lr: 1.172e-06, eta: 0:32:06, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1881, decode.acc_seg: 92.3541, loss: 0.1881
+2023-03-04 02:46:38,654 - mmseg - INFO - Iter [73600/80000]	lr: 1.172e-06, eta: 0:31:51, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1892, decode.acc_seg: 92.4157, loss: 0.1892
+2023-03-04 02:46:53,111 - mmseg - INFO - Iter [73650/80000]	lr: 1.172e-06, eta: 0:31:36, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1866, decode.acc_seg: 92.3977, loss: 0.1866
+2023-03-04 02:47:07,660 - mmseg - INFO - Iter [73700/80000]	lr: 1.172e-06, eta: 0:31:21, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1767, decode.acc_seg: 92.7695, loss: 0.1767
+2023-03-04 02:47:22,387 - mmseg - INFO - Iter [73750/80000]	lr: 1.172e-06, eta: 0:31:06, time: 0.295, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1856, decode.acc_seg: 92.4211, loss: 0.1856
+2023-03-04 02:47:36,927 - mmseg - INFO - Iter [73800/80000]	lr: 1.172e-06, eta: 0:30:51, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1922, decode.acc_seg: 92.3586, loss: 0.1922
+2023-03-04 02:47:53,968 - mmseg - INFO - Iter [73850/80000]	lr: 1.172e-06, eta: 0:30:36, time: 0.341, data_time: 0.057, memory: 39544, decode.loss_ce: 0.1826, decode.acc_seg: 92.4778, loss: 0.1826
+2023-03-04 02:48:08,616 - mmseg - INFO - Iter [73900/80000]	lr: 1.172e-06, eta: 0:30:21, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1889, decode.acc_seg: 92.3884, loss: 0.1889
+2023-03-04 02:48:23,422 - mmseg - INFO - Iter [73950/80000]	lr: 1.172e-06, eta: 0:30:06, time: 0.296, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1826, decode.acc_seg: 92.6173, loss: 0.1826
+2023-03-04 02:48:37,878 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 02:48:37,878 - mmseg - INFO - Iter [74000/80000]	lr: 1.172e-06, eta: 0:29:51, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1864, decode.acc_seg: 92.5111, loss: 0.1864
+2023-03-04 02:48:52,482 - mmseg - INFO - Iter [74050/80000]	lr: 1.172e-06, eta: 0:29:36, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1816, decode.acc_seg: 92.6606, loss: 0.1816
+2023-03-04 02:49:07,112 - mmseg - INFO - Iter [74100/80000]	lr: 1.172e-06, eta: 0:29:21, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1951, decode.acc_seg: 92.0175, loss: 0.1951
+2023-03-04 02:49:21,725 - mmseg - INFO - Iter [74150/80000]	lr: 1.172e-06, eta: 0:29:07, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1911, decode.acc_seg: 92.3111, loss: 0.1911
+2023-03-04 02:49:36,302 - mmseg - INFO - Iter [74200/80000]	lr: 1.172e-06, eta: 0:28:52, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1852, decode.acc_seg: 92.5659, loss: 0.1852
+2023-03-04 02:49:50,822 - mmseg - INFO - Iter [74250/80000]	lr: 1.172e-06, eta: 0:28:37, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1835, decode.acc_seg: 92.5102, loss: 0.1835
+2023-03-04 02:50:05,305 - mmseg - INFO - Iter [74300/80000]	lr: 1.172e-06, eta: 0:28:22, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1832, decode.acc_seg: 92.4638, loss: 0.1832
+2023-03-04 02:50:19,844 - mmseg - INFO - Iter [74350/80000]	lr: 1.172e-06, eta: 0:28:07, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1875, decode.acc_seg: 92.5643, loss: 0.1875
+2023-03-04 02:50:34,301 - mmseg - INFO - Iter [74400/80000]	lr: 1.172e-06, eta: 0:27:52, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1864, decode.acc_seg: 92.3906, loss: 0.1864
+2023-03-04 02:50:48,749 - mmseg - INFO - Iter [74450/80000]	lr: 1.172e-06, eta: 0:27:37, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1889, decode.acc_seg: 92.2186, loss: 0.1889
+2023-03-04 02:51:05,888 - mmseg - INFO - Iter [74500/80000]	lr: 1.172e-06, eta: 0:27:22, time: 0.343, data_time: 0.055, memory: 39544, decode.loss_ce: 0.1879, decode.acc_seg: 92.4962, loss: 0.1879
+2023-03-04 02:51:20,511 - mmseg - INFO - Iter [74550/80000]	lr: 1.172e-06, eta: 0:27:07, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1891, decode.acc_seg: 92.4068, loss: 0.1891
+2023-03-04 02:51:35,130 - mmseg - INFO - Iter [74600/80000]	lr: 1.172e-06, eta: 0:26:52, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1825, decode.acc_seg: 92.4655, loss: 0.1825
+2023-03-04 02:51:49,810 - mmseg - INFO - Iter [74650/80000]	lr: 1.172e-06, eta: 0:26:37, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1894, decode.acc_seg: 92.4126, loss: 0.1894
+2023-03-04 02:52:04,248 - mmseg - INFO - Iter [74700/80000]	lr: 1.172e-06, eta: 0:26:22, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1804, decode.acc_seg: 92.5333, loss: 0.1804
+2023-03-04 02:52:18,862 - mmseg - INFO - Iter [74750/80000]	lr: 1.172e-06, eta: 0:26:07, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1868, decode.acc_seg: 92.5307, loss: 0.1868
+2023-03-04 02:52:33,461 - mmseg - INFO - Iter [74800/80000]	lr: 1.172e-06, eta: 0:25:52, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1917, decode.acc_seg: 92.1669, loss: 0.1917
+2023-03-04 02:52:48,080 - mmseg - INFO - Iter [74850/80000]	lr: 1.172e-06, eta: 0:25:37, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1922, decode.acc_seg: 92.3644, loss: 0.1922
+2023-03-04 02:53:02,677 - mmseg - INFO - Iter [74900/80000]	lr: 1.172e-06, eta: 0:25:22, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1840, decode.acc_seg: 92.5579, loss: 0.1840
+2023-03-04 02:53:17,257 - mmseg - INFO - Iter [74950/80000]	lr: 1.172e-06, eta: 0:25:07, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1890, decode.acc_seg: 92.3778, loss: 0.1890
+2023-03-04 02:53:31,958 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 02:53:31,958 - mmseg - INFO - Iter [75000/80000]	lr: 1.172e-06, eta: 0:24:52, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1814, decode.acc_seg: 92.5958, loss: 0.1814
+2023-03-04 02:53:46,399 - mmseg - INFO - Iter [75050/80000]	lr: 1.172e-06, eta: 0:24:37, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1897, decode.acc_seg: 92.2606, loss: 0.1897
+2023-03-04 02:54:03,542 - mmseg - INFO - Iter [75100/80000]	lr: 1.172e-06, eta: 0:24:23, time: 0.343, data_time: 0.056, memory: 39544, decode.loss_ce: 0.1793, decode.acc_seg: 92.6360, loss: 0.1793
+2023-03-04 02:54:18,245 - mmseg - INFO - Iter [75150/80000]	lr: 1.172e-06, eta: 0:24:08, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1889, decode.acc_seg: 92.3127, loss: 0.1889
+2023-03-04 02:54:32,807 - mmseg - INFO - Iter [75200/80000]	lr: 1.172e-06, eta: 0:23:53, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1869, decode.acc_seg: 92.4372, loss: 0.1869
+2023-03-04 02:54:47,241 - mmseg - INFO - Iter [75250/80000]	lr: 1.172e-06, eta: 0:23:38, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1858, decode.acc_seg: 92.3256, loss: 0.1858
+2023-03-04 02:55:01,682 - mmseg - INFO - Iter [75300/80000]	lr: 1.172e-06, eta: 0:23:23, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1798, decode.acc_seg: 92.5846, loss: 0.1798
+2023-03-04 02:55:16,201 - mmseg - INFO - Iter [75350/80000]	lr: 1.172e-06, eta: 0:23:08, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1888, decode.acc_seg: 92.3670, loss: 0.1888
+2023-03-04 02:55:30,750 - mmseg - INFO - Iter [75400/80000]	lr: 1.172e-06, eta: 0:22:53, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1853, decode.acc_seg: 92.3912, loss: 0.1853
+2023-03-04 02:55:45,285 - mmseg - INFO - Iter [75450/80000]	lr: 1.172e-06, eta: 0:22:38, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1817, decode.acc_seg: 92.6297, loss: 0.1817
+2023-03-04 02:55:59,888 - mmseg - INFO - Iter [75500/80000]	lr: 1.172e-06, eta: 0:22:23, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1859, decode.acc_seg: 92.5052, loss: 0.1859
+2023-03-04 02:56:14,531 - mmseg - INFO - Iter [75550/80000]	lr: 1.172e-06, eta: 0:22:08, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1870, decode.acc_seg: 92.3652, loss: 0.1870
+2023-03-04 02:56:29,122 - mmseg - INFO - Iter [75600/80000]	lr: 1.172e-06, eta: 0:21:53, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1871, decode.acc_seg: 92.4734, loss: 0.1871
+2023-03-04 02:56:43,778 - mmseg - INFO - Iter [75650/80000]	lr: 1.172e-06, eta: 0:21:38, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1891, decode.acc_seg: 92.4018, loss: 0.1891
+2023-03-04 02:56:58,406 - mmseg - INFO - Iter [75700/80000]	lr: 1.172e-06, eta: 0:21:23, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1883, decode.acc_seg: 92.2997, loss: 0.1883
+2023-03-04 02:57:15,385 - mmseg - INFO - Iter [75750/80000]	lr: 1.172e-06, eta: 0:21:08, time: 0.340, data_time: 0.057, memory: 39544, decode.loss_ce: 0.1842, decode.acc_seg: 92.5320, loss: 0.1842
+2023-03-04 02:57:29,881 - mmseg - INFO - Iter [75800/80000]	lr: 1.172e-06, eta: 0:20:54, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1780, decode.acc_seg: 92.6752, loss: 0.1780
+2023-03-04 02:57:44,484 - mmseg - INFO - Iter [75850/80000]	lr: 1.172e-06, eta: 0:20:39, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1897, decode.acc_seg: 92.4478, loss: 0.1897
+2023-03-04 02:57:59,137 - mmseg - INFO - Iter [75900/80000]	lr: 1.172e-06, eta: 0:20:24, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1963, decode.acc_seg: 92.0851, loss: 0.1963
+2023-03-04 02:58:13,688 - mmseg - INFO - Iter [75950/80000]	lr: 1.172e-06, eta: 0:20:09, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1913, decode.acc_seg: 92.2146, loss: 0.1913
+2023-03-04 02:58:28,334 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 02:58:28,334 - mmseg - INFO - Iter [76000/80000]	lr: 1.172e-06, eta: 0:19:54, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1776, decode.acc_seg: 92.8093, loss: 0.1776
+2023-03-04 02:58:42,920 - mmseg - INFO - Iter [76050/80000]	lr: 1.172e-06, eta: 0:19:39, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1831, decode.acc_seg: 92.5798, loss: 0.1831
+2023-03-04 02:58:57,522 - mmseg - INFO - Iter [76100/80000]	lr: 1.172e-06, eta: 0:19:24, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1841, decode.acc_seg: 92.5725, loss: 0.1841
+2023-03-04 02:59:12,051 - mmseg - INFO - Iter [76150/80000]	lr: 1.172e-06, eta: 0:19:09, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1786, decode.acc_seg: 92.6797, loss: 0.1786
+2023-03-04 02:59:26,453 - mmseg - INFO - Iter [76200/80000]	lr: 1.172e-06, eta: 0:18:54, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1878, decode.acc_seg: 92.5653, loss: 0.1878
+2023-03-04 02:59:41,003 - mmseg - INFO - Iter [76250/80000]	lr: 1.172e-06, eta: 0:18:39, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1875, decode.acc_seg: 92.4334, loss: 0.1875
+2023-03-04 02:59:55,430 - mmseg - INFO - Iter [76300/80000]	lr: 1.172e-06, eta: 0:18:24, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1807, decode.acc_seg: 92.7957, loss: 0.1807
+2023-03-04 03:00:09,904 - mmseg - INFO - Iter [76350/80000]	lr: 1.172e-06, eta: 0:18:09, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1874, decode.acc_seg: 92.3202, loss: 0.1874
+2023-03-04 03:00:27,107 - mmseg - INFO - Iter [76400/80000]	lr: 1.172e-06, eta: 0:17:54, time: 0.344, data_time: 0.055, memory: 39544, decode.loss_ce: 0.1751, decode.acc_seg: 92.9457, loss: 0.1751
+2023-03-04 03:00:41,764 - mmseg - INFO - Iter [76450/80000]	lr: 1.172e-06, eta: 0:17:39, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1795, decode.acc_seg: 92.6799, loss: 0.1795
+2023-03-04 03:00:56,235 - mmseg - INFO - Iter [76500/80000]	lr: 1.172e-06, eta: 0:17:24, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1933, decode.acc_seg: 92.2355, loss: 0.1933
+2023-03-04 03:01:10,811 - mmseg - INFO - Iter [76550/80000]	lr: 1.172e-06, eta: 0:17:09, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1849, decode.acc_seg: 92.5051, loss: 0.1849
+2023-03-04 03:01:25,345 - mmseg - INFO - Iter [76600/80000]	lr: 1.172e-06, eta: 0:16:54, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1920, decode.acc_seg: 92.1544, loss: 0.1920
+2023-03-04 03:01:39,942 - mmseg - INFO - Iter [76650/80000]	lr: 1.172e-06, eta: 0:16:40, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1904, decode.acc_seg: 92.1326, loss: 0.1904
+2023-03-04 03:01:54,467 - mmseg - INFO - Iter [76700/80000]	lr: 1.172e-06, eta: 0:16:25, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1776, decode.acc_seg: 92.7950, loss: 0.1776
+2023-03-04 03:02:09,067 - mmseg - INFO - Iter [76750/80000]	lr: 1.172e-06, eta: 0:16:10, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1779, decode.acc_seg: 92.6446, loss: 0.1779
+2023-03-04 03:02:23,611 - mmseg - INFO - Iter [76800/80000]	lr: 1.172e-06, eta: 0:15:55, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1777, decode.acc_seg: 92.6088, loss: 0.1777
+2023-03-04 03:02:38,259 - mmseg - INFO - Iter [76850/80000]	lr: 1.172e-06, eta: 0:15:40, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1904, decode.acc_seg: 92.2640, loss: 0.1904
+2023-03-04 03:02:52,828 - mmseg - INFO - Iter [76900/80000]	lr: 1.172e-06, eta: 0:15:25, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1870, decode.acc_seg: 92.3392, loss: 0.1870
+2023-03-04 03:03:07,401 - mmseg - INFO - Iter [76950/80000]	lr: 1.172e-06, eta: 0:15:10, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1892, decode.acc_seg: 92.5199, loss: 0.1892
+2023-03-04 03:03:24,507 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 03:03:24,507 - mmseg - INFO - Iter [77000/80000]	lr: 1.172e-06, eta: 0:14:55, time: 0.342, data_time: 0.058, memory: 39544, decode.loss_ce: 0.1878, decode.acc_seg: 92.4867, loss: 0.1878
+2023-03-04 03:03:39,024 - mmseg - INFO - Iter [77050/80000]	lr: 1.172e-06, eta: 0:14:40, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1768, decode.acc_seg: 92.6865, loss: 0.1768
+2023-03-04 03:03:53,738 - mmseg - INFO - Iter [77100/80000]	lr: 1.172e-06, eta: 0:14:25, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1881, decode.acc_seg: 92.4261, loss: 0.1881
+2023-03-04 03:04:08,541 - mmseg - INFO - Iter [77150/80000]	lr: 1.172e-06, eta: 0:14:10, time: 0.296, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1926, decode.acc_seg: 92.1260, loss: 0.1926
+2023-03-04 03:04:23,151 - mmseg - INFO - Iter [77200/80000]	lr: 1.172e-06, eta: 0:13:55, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1747, decode.acc_seg: 92.8423, loss: 0.1747
+2023-03-04 03:04:37,744 - mmseg - INFO - Iter [77250/80000]	lr: 1.172e-06, eta: 0:13:40, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1882, decode.acc_seg: 92.3425, loss: 0.1882
+2023-03-04 03:04:52,278 - mmseg - INFO - Iter [77300/80000]	lr: 1.172e-06, eta: 0:13:25, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1868, decode.acc_seg: 92.3823, loss: 0.1868
+2023-03-04 03:05:06,834 - mmseg - INFO - Iter [77350/80000]	lr: 1.172e-06, eta: 0:13:11, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1849, decode.acc_seg: 92.2687, loss: 0.1849
+2023-03-04 03:05:21,294 - mmseg - INFO - Iter [77400/80000]	lr: 1.172e-06, eta: 0:12:56, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1901, decode.acc_seg: 92.3094, loss: 0.1901
+2023-03-04 03:05:35,791 - mmseg - INFO - Iter [77450/80000]	lr: 1.172e-06, eta: 0:12:41, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1874, decode.acc_seg: 92.6252, loss: 0.1874
+2023-03-04 03:05:50,242 - mmseg - INFO - Iter [77500/80000]	lr: 1.172e-06, eta: 0:12:26, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1844, decode.acc_seg: 92.4711, loss: 0.1844
+2023-03-04 03:06:04,889 - mmseg - INFO - Iter [77550/80000]	lr: 1.172e-06, eta: 0:12:11, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1909, decode.acc_seg: 92.2647, loss: 0.1909
+2023-03-04 03:06:19,452 - mmseg - INFO - Iter [77600/80000]	lr: 1.172e-06, eta: 0:11:56, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1820, decode.acc_seg: 92.5164, loss: 0.1820
+2023-03-04 03:06:36,512 - mmseg - INFO - Iter [77650/80000]	lr: 1.172e-06, eta: 0:11:41, time: 0.341, data_time: 0.056, memory: 39544, decode.loss_ce: 0.1862, decode.acc_seg: 92.5177, loss: 0.1862
+2023-03-04 03:06:51,026 - mmseg - INFO - Iter [77700/80000]	lr: 1.172e-06, eta: 0:11:26, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1909, decode.acc_seg: 92.1806, loss: 0.1909
+2023-03-04 03:07:05,609 - mmseg - INFO - Iter [77750/80000]	lr: 1.172e-06, eta: 0:11:11, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1842, decode.acc_seg: 92.4530, loss: 0.1842
+2023-03-04 03:07:20,083 - mmseg - INFO - Iter [77800/80000]	lr: 1.172e-06, eta: 0:10:56, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1847, decode.acc_seg: 92.4090, loss: 0.1847
+2023-03-04 03:07:34,712 - mmseg - INFO - Iter [77850/80000]	lr: 1.172e-06, eta: 0:10:41, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1854, decode.acc_seg: 92.5031, loss: 0.1854
+2023-03-04 03:07:49,508 - mmseg - INFO - Iter [77900/80000]	lr: 1.172e-06, eta: 0:10:26, time: 0.296, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1866, decode.acc_seg: 92.4386, loss: 0.1866
+2023-03-04 03:08:04,014 - mmseg - INFO - Iter [77950/80000]	lr: 1.172e-06, eta: 0:10:11, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1832, decode.acc_seg: 92.4241, loss: 0.1832
+2023-03-04 03:08:18,583 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 03:08:18,583 - mmseg - INFO - Iter [78000/80000]	lr: 1.172e-06, eta: 0:09:56, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1820, decode.acc_seg: 92.5859, loss: 0.1820
+2023-03-04 03:08:33,044 - mmseg - INFO - Iter [78050/80000]	lr: 1.172e-06, eta: 0:09:42, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1880, decode.acc_seg: 92.3566, loss: 0.1880
+2023-03-04 03:08:47,480 - mmseg - INFO - Iter [78100/80000]	lr: 1.172e-06, eta: 0:09:27, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1901, decode.acc_seg: 92.3231, loss: 0.1901
+2023-03-04 03:09:01,992 - mmseg - INFO - Iter [78150/80000]	lr: 1.172e-06, eta: 0:09:12, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1832, decode.acc_seg: 92.6554, loss: 0.1832
+2023-03-04 03:09:16,560 - mmseg - INFO - Iter [78200/80000]	lr: 1.172e-06, eta: 0:08:57, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1831, decode.acc_seg: 92.5808, loss: 0.1831
+2023-03-04 03:09:33,583 - mmseg - INFO - Iter [78250/80000]	lr: 1.172e-06, eta: 0:08:42, time: 0.340, data_time: 0.057, memory: 39544, decode.loss_ce: 0.1871, decode.acc_seg: 92.4966, loss: 0.1871
+2023-03-04 03:09:48,172 - mmseg - INFO - Iter [78300/80000]	lr: 1.172e-06, eta: 0:08:27, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1866, decode.acc_seg: 92.4937, loss: 0.1866
+2023-03-04 03:10:02,763 - mmseg - INFO - Iter [78350/80000]	lr: 1.172e-06, eta: 0:08:12, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1905, decode.acc_seg: 92.4075, loss: 0.1905
+2023-03-04 03:10:17,352 - mmseg - INFO - Iter [78400/80000]	lr: 1.172e-06, eta: 0:07:57, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1841, decode.acc_seg: 92.5174, loss: 0.1841
+2023-03-04 03:10:32,072 - mmseg - INFO - Iter [78450/80000]	lr: 1.172e-06, eta: 0:07:42, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1818, decode.acc_seg: 92.5130, loss: 0.1818
+2023-03-04 03:10:46,576 - mmseg - INFO - Iter [78500/80000]	lr: 1.172e-06, eta: 0:07:27, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1805, decode.acc_seg: 92.6929, loss: 0.1805
+2023-03-04 03:11:01,260 - mmseg - INFO - Iter [78550/80000]	lr: 1.172e-06, eta: 0:07:12, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1874, decode.acc_seg: 92.3685, loss: 0.1874
+2023-03-04 03:11:15,842 - mmseg - INFO - Iter [78600/80000]	lr: 1.172e-06, eta: 0:06:57, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1916, decode.acc_seg: 92.1778, loss: 0.1916
+2023-03-04 03:11:30,442 - mmseg - INFO - Iter [78650/80000]	lr: 1.172e-06, eta: 0:06:42, time: 0.292, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1908, decode.acc_seg: 92.2777, loss: 0.1908
+2023-03-04 03:11:44,971 - mmseg - INFO - Iter [78700/80000]	lr: 1.172e-06, eta: 0:06:27, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1860, decode.acc_seg: 92.5063, loss: 0.1860
+2023-03-04 03:11:59,529 - mmseg - INFO - Iter [78750/80000]	lr: 1.172e-06, eta: 0:06:13, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1864, decode.acc_seg: 92.4551, loss: 0.1864
+2023-03-04 03:12:14,008 - mmseg - INFO - Iter [78800/80000]	lr: 1.172e-06, eta: 0:05:58, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1829, decode.acc_seg: 92.5946, loss: 0.1829
+2023-03-04 03:12:28,475 - mmseg - INFO - Iter [78850/80000]	lr: 1.172e-06, eta: 0:05:43, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1845, decode.acc_seg: 92.3745, loss: 0.1845
+2023-03-04 03:12:45,673 - mmseg - INFO - Iter [78900/80000]	lr: 1.172e-06, eta: 0:05:28, time: 0.344, data_time: 0.058, memory: 39544, decode.loss_ce: 0.1757, decode.acc_seg: 92.8012, loss: 0.1757
+2023-03-04 03:13:00,149 - mmseg - INFO - Iter [78950/80000]	lr: 1.172e-06, eta: 0:05:13, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1884, decode.acc_seg: 92.3465, loss: 0.1884
+2023-03-04 03:13:14,659 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 03:13:14,659 - mmseg - INFO - Iter [79000/80000]	lr: 1.172e-06, eta: 0:04:58, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1817, decode.acc_seg: 92.4595, loss: 0.1817
+2023-03-04 03:13:29,166 - mmseg - INFO - Iter [79050/80000]	lr: 1.172e-06, eta: 0:04:43, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1851, decode.acc_seg: 92.5210, loss: 0.1851
+2023-03-04 03:13:43,674 - mmseg - INFO - Iter [79100/80000]	lr: 1.172e-06, eta: 0:04:28, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1833, decode.acc_seg: 92.5269, loss: 0.1833
+2023-03-04 03:13:58,129 - mmseg - INFO - Iter [79150/80000]	lr: 1.172e-06, eta: 0:04:13, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1918, decode.acc_seg: 92.2963, loss: 0.1918
+2023-03-04 03:14:12,548 - mmseg - INFO - Iter [79200/80000]	lr: 1.172e-06, eta: 0:03:58, time: 0.288, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1819, decode.acc_seg: 92.6291, loss: 0.1819
+2023-03-04 03:14:27,227 - mmseg - INFO - Iter [79250/80000]	lr: 1.172e-06, eta: 0:03:43, time: 0.294, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1836, decode.acc_seg: 92.5252, loss: 0.1836
+2023-03-04 03:14:41,767 - mmseg - INFO - Iter [79300/80000]	lr: 1.172e-06, eta: 0:03:28, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1844, decode.acc_seg: 92.5858, loss: 0.1844
+2023-03-04 03:14:56,346 - mmseg - INFO - Iter [79350/80000]	lr: 1.172e-06, eta: 0:03:13, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1861, decode.acc_seg: 92.5183, loss: 0.1861
+2023-03-04 03:15:10,926 - mmseg - INFO - Iter [79400/80000]	lr: 1.172e-06, eta: 0:02:59, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1865, decode.acc_seg: 92.3632, loss: 0.1865
+2023-03-04 03:15:25,374 - mmseg - INFO - Iter [79450/80000]	lr: 1.172e-06, eta: 0:02:44, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1856, decode.acc_seg: 92.5071, loss: 0.1856
+2023-03-04 03:15:39,877 - mmseg - INFO - Iter [79500/80000]	lr: 1.172e-06, eta: 0:02:29, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1926, decode.acc_seg: 92.2768, loss: 0.1926
+2023-03-04 03:15:57,202 - mmseg - INFO - Iter [79550/80000]	lr: 1.172e-06, eta: 0:02:14, time: 0.346, data_time: 0.056, memory: 39544, decode.loss_ce: 0.1877, decode.acc_seg: 92.4010, loss: 0.1877
+2023-03-04 03:16:11,693 - mmseg - INFO - Iter [79600/80000]	lr: 1.172e-06, eta: 0:01:59, time: 0.290, data_time: 0.008, memory: 39544, decode.loss_ce: 0.1819, decode.acc_seg: 92.6297, loss: 0.1819
+2023-03-04 03:16:26,305 - mmseg - INFO - Iter [79650/80000]	lr: 1.172e-06, eta: 0:01:44, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1883, decode.acc_seg: 92.2915, loss: 0.1883
+2023-03-04 03:16:40,800 - mmseg - INFO - Iter [79700/80000]	lr: 1.172e-06, eta: 0:01:29, time: 0.290, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1858, decode.acc_seg: 92.6155, loss: 0.1858
+2023-03-04 03:16:55,354 - mmseg - INFO - Iter [79750/80000]	lr: 1.172e-06, eta: 0:01:14, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1858, decode.acc_seg: 92.4334, loss: 0.1858
+2023-03-04 03:17:09,898 - mmseg - INFO - Iter [79800/80000]	lr: 1.172e-06, eta: 0:00:59, time: 0.291, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1786, decode.acc_seg: 92.5571, loss: 0.1786
+2023-03-04 03:17:24,334 - mmseg - INFO - Iter [79850/80000]	lr: 1.172e-06, eta: 0:00:44, time: 0.289, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1854, decode.acc_seg: 92.3252, loss: 0.1854
+2023-03-04 03:17:38,917 - mmseg - INFO - Iter [79900/80000]	lr: 1.172e-06, eta: 0:00:29, time: 0.292, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1880, decode.acc_seg: 92.3348, loss: 0.1880
+2023-03-04 03:17:53,565 - mmseg - INFO - Iter [79950/80000]	lr: 1.172e-06, eta: 0:00:14, time: 0.293, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1817, decode.acc_seg: 92.6262, loss: 0.1817
+2023-03-04 03:18:08,151 - mmseg - INFO - Saving checkpoint at 80000 iterations
+2023-03-04 03:18:10,117 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 03:18:10,117 - mmseg - INFO - Iter [80000/80000]	lr: 1.172e-06, eta: 0:00:00, time: 0.331, data_time: 0.007, memory: 39544, decode.loss_ce: 0.1857, decode.acc_seg: 92.4978, loss: 0.1857
+2023-03-04 03:18:29,695 - mmseg - INFO - per class results:
+2023-03-04 03:18:29,701 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        |  76.3 | 88.29 |
+|       building      | 81.34 | 92.38 |
+|         sky         | 94.21 | 97.21 |
+|        floor        | 80.01 | 91.45 |
+|         tree        | 72.98 | 87.71 |
+|       ceiling       | 82.52 | 91.57 |
+|         road        | 81.82 | 88.69 |
+|         bed         | 88.37 | 95.11 |
+|      windowpane     | 60.97 | 77.45 |
+|        grass        | 66.22 | 81.19 |
+|       cabinet       | 59.65 | 73.77 |
+|       sidewalk      | 65.77 | 82.79 |
+|        person       | 79.36 | 90.88 |
+|        earth        | 33.38 | 47.61 |
+|         door        | 48.06 |  63.4 |
+|        table        | 61.44 | 77.95 |
+|       mountain      | 51.14 | 68.49 |
+|        plant        |  50.1 | 63.39 |
+|       curtain       | 70.43 | 82.45 |
+|        chair        | 57.75 | 70.64 |
+|         car         | 83.16 | 90.39 |
+|        water        | 46.54 | 62.39 |
+|       painting      | 69.67 | 83.96 |
+|         sofa        | 65.64 | 85.68 |
+|        shelf        | 40.11 | 55.73 |
+|        house        |  43.6 | 50.26 |
+|         sea         | 45.76 | 73.63 |
+|        mirror       | 65.13 | 74.72 |
+|         rug         | 54.93 | 60.03 |
+|        field        | 28.61 | 44.05 |
+|       armchair      |  43.8 | 56.74 |
+|         seat        |  53.8 | 77.19 |
+|        fence        | 41.62 |  56.4 |
+|         desk        | 49.87 | 69.06 |
+|         rock        | 26.45 | 38.21 |
+|       wardrobe      | 48.97 | 66.86 |
+|         lamp        | 63.88 | 76.41 |
+|       bathtub       | 76.04 | 82.45 |
+|       railing       | 31.19 | 42.78 |
+|       cushion       | 53.84 | 66.14 |
+|         base        | 28.17 | 39.27 |
+|         box         | 24.19 | 32.16 |
+|        column       | 45.13 | 55.21 |
+|      signboard      | 36.16 | 50.31 |
+|   chest of drawers  |  38.2 | 52.84 |
+|       counter       | 27.19 | 40.06 |
+|         sand        | 31.03 | 49.57 |
+|         sink        | 70.53 | 81.56 |
+|      skyscraper     | 44.94 | 56.07 |
+|      fireplace      | 66.68 | 85.14 |
+|     refrigerator    | 78.06 | 84.29 |
+|      grandstand     | 40.83 | 64.51 |
+|         path        | 14.99 | 23.21 |
+|        stairs       | 31.38 | 41.25 |
+|        runway       | 64.11 | 82.78 |
+|         case        | 46.72 | 64.46 |
+|      pool table     | 92.73 | 95.88 |
+|        pillow       | 56.32 |  68.3 |
+|     screen door     | 65.61 | 73.04 |
+|       stairway      | 24.78 | 33.14 |
+|        river        |  10.2 |  16.9 |
+|        bridge       | 61.43 | 68.28 |
+|       bookcase      | 40.03 | 52.49 |
+|        blind        | 46.61 | 53.79 |
+|     coffee table    | 66.32 | 82.55 |
+|        toilet       | 85.28 |  90.7 |
+|        flower       | 31.19 | 47.81 |
+|         book        | 47.51 | 68.12 |
+|         hill        |  7.98 |  9.29 |
+|        bench        | 43.86 | 53.18 |
+|      countertop     | 53.61 | 69.91 |
+|        stove        | 73.37 | 79.56 |
+|         palm        | 50.86 | 72.07 |
+|    kitchen island   | 46.48 | 71.85 |
+|       computer      | 57.35 | 65.69 |
+|     swivel chair    | 44.18 | 61.13 |
+|         boat        | 38.95 | 45.75 |
+|         bar         | 27.24 | 30.44 |
+|    arcade machine   | 23.61 | 25.98 |
+|        hovel        | 31.55 | 34.78 |
+|         bus         | 87.91 | 92.48 |
+|        towel        | 59.29 | 69.38 |
+|        light        | 56.16 | 62.73 |
+|        truck        | 34.71 | 44.67 |
+|        tower        | 24.63 | 34.97 |
+|      chandelier     | 66.13 | 80.44 |
+|        awning       | 24.26 | 27.25 |
+|     streetlight     | 28.84 |  38.8 |
+|        booth        | 55.98 | 58.69 |
+| television receiver | 67.65 | 78.21 |
+|       airplane      |  50.3 | 70.16 |
+|      dirt track     |  7.53 | 24.83 |
+|       apparel       | 28.99 | 44.38 |
+|         pole        | 22.72 | 33.11 |
+|         land        |  9.72 |  13.9 |
+|      bannister      |  5.82 |  8.63 |
+|      escalator      | 23.92 |  24.7 |
+|       ottoman       |  48.3 | 59.05 |
+|        bottle       | 15.99 | 25.25 |
+|        buffet       | 49.33 | 57.55 |
+|        poster       | 27.77 | 37.58 |
+|        stage        | 17.77 | 24.44 |
+|         van         | 48.41 | 62.45 |
+|         ship        | 48.97 | 64.78 |
+|       fountain      |  6.21 |  6.3  |
+|    conveyer belt    | 77.16 | 88.34 |
+|        canopy       | 14.68 | 17.19 |
+|        washer       |  66.7 | 67.21 |
+|      plaything      | 23.17 | 31.07 |
+|    swimming pool    | 44.67 | 54.76 |
+|        stool        | 41.74 | 59.13 |
+|        barrel       | 45.89 | 64.24 |
+|        basket       | 27.51 | 37.66 |
+|      waterfall      | 49.63 | 58.03 |
+|         tent        | 94.51 | 97.65 |
+|         bag         | 11.57 | 14.64 |
+|       minibike      | 62.79 | 73.46 |
+|        cradle       | 80.68 | 96.81 |
+|         oven        | 27.69 | 60.02 |
+|         ball        | 46.62 | 59.63 |
+|         food        | 52.85 | 63.45 |
+|         step        | 14.48 | 18.56 |
+|         tank        | 41.97 | 42.37 |
+|      trade name     | 25.59 | 29.59 |
+|      microwave      | 38.12 | 40.95 |
+|         pot         | 41.32 | 50.72 |
+|        animal       | 51.02 | 54.12 |
+|       bicycle       | 45.77 | 68.95 |
+|         lake        | 59.28 | 63.32 |
+|      dishwasher     | 77.76 | 82.92 |
+|        screen       | 66.13 | 84.16 |
+|       blanket       | 12.17 | 13.84 |
+|      sculpture      | 36.65 | 64.75 |
+|         hood        | 55.76 | 71.64 |
+|        sconce       | 42.25 | 50.76 |
+|         vase        | 36.86 | 53.14 |
+|    traffic light    | 30.28 | 47.32 |
+|         tray        |  5.84 | 11.66 |
+|        ashcan       | 38.77 | 49.73 |
+|         fan         |  57.5 | 69.84 |
+|         pier        | 13.96 | 16.14 |
+|      crt screen     |  3.84 | 11.18 |
+|        plate        | 40.59 | 50.69 |
+|       monitor       | 18.45 | 23.92 |
+|    bulletin board   | 46.85 | 57.81 |
+|        shower       |  1.6  |  2.28 |
+|       radiator      | 46.51 | 55.12 |
+|        glass        | 12.36 | 13.59 |
+|        clock        | 24.71 | 30.44 |
+|         flag        | 37.73 | 41.36 |
++---------------------+-------+-------+
+2023-03-04 03:18:29,701 - mmseg - INFO - Summary:
+2023-03-04 03:18:29,701 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 81.94 | 46.13 | 57.05 |
++-------+-------+-------+
+2023-03-04 03:18:29,702 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151.py
+2023-03-04 03:18:29,702 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8194, mIoU: 0.4613, mAcc: 0.5705, IoU.background: nan, IoU.wall: 0.7630, IoU.building: 0.8134, IoU.sky: 0.9421, IoU.floor: 0.8001, IoU.tree: 0.7298, IoU.ceiling: 0.8252, IoU.road: 0.8182, IoU.bed : 0.8837, IoU.windowpane: 0.6097, IoU.grass: 0.6622, IoU.cabinet: 0.5965, IoU.sidewalk: 0.6577, IoU.person: 0.7936, IoU.earth: 0.3338, IoU.door: 0.4806, IoU.table: 0.6144, IoU.mountain: 0.5114, IoU.plant: 0.5010, IoU.curtain: 0.7043, IoU.chair: 0.5775, IoU.car: 0.8316, IoU.water: 0.4654, IoU.painting: 0.6967, IoU.sofa: 0.6564, IoU.shelf: 0.4011, IoU.house: 0.4360, IoU.sea: 0.4576, IoU.mirror: 0.6513, IoU.rug: 0.5493, IoU.field: 0.2861, IoU.armchair: 0.4380, IoU.seat: 0.5380, IoU.fence: 0.4162, IoU.desk: 0.4987, IoU.rock: 0.2645, IoU.wardrobe: 0.4897, IoU.lamp: 0.6388, IoU.bathtub: 0.7604, IoU.railing: 0.3119, IoU.cushion: 0.5384, IoU.base: 0.2817, IoU.box: 0.2419, IoU.column: 0.4513, IoU.signboard: 0.3616, IoU.chest of drawers: 0.3820, IoU.counter: 0.2719, IoU.sand: 0.3103, IoU.sink: 0.7053, IoU.skyscraper: 0.4494, IoU.fireplace: 0.6668, IoU.refrigerator: 0.7806, IoU.grandstand: 0.4083, IoU.path: 0.1499, IoU.stairs: 0.3138, IoU.runway: 0.6411, IoU.case: 0.4672, IoU.pool table: 0.9273, IoU.pillow: 0.5632, IoU.screen door: 0.6561, IoU.stairway: 0.2478, IoU.river: 0.1020, IoU.bridge: 0.6143, IoU.bookcase: 0.4003, IoU.blind: 0.4661, IoU.coffee table: 0.6632, IoU.toilet: 0.8528, IoU.flower: 0.3119, IoU.book: 0.4751, IoU.hill: 0.0798, IoU.bench: 0.4386, IoU.countertop: 0.5361, IoU.stove: 0.7337, IoU.palm: 0.5086, IoU.kitchen island: 0.4648, IoU.computer: 0.5735, IoU.swivel chair: 0.4418, IoU.boat: 0.3895, IoU.bar: 0.2724, IoU.arcade machine: 0.2361, IoU.hovel: 0.3155, IoU.bus: 0.8791, IoU.towel: 0.5929, IoU.light: 0.5616, IoU.truck: 0.3471, IoU.tower: 0.2463, IoU.chandelier: 0.6613, IoU.awning: 0.2426, IoU.streetlight: 0.2884, IoU.booth: 0.5598, IoU.television receiver: 0.6765, IoU.airplane: 0.5030, IoU.dirt track: 0.0753, IoU.apparel: 0.2899, IoU.pole: 0.2272, IoU.land: 0.0972, IoU.bannister: 0.0582, IoU.escalator: 0.2392, IoU.ottoman: 0.4830, IoU.bottle: 0.1599, IoU.buffet: 0.4933, IoU.poster: 0.2777, IoU.stage: 0.1777, IoU.van: 0.4841, IoU.ship: 0.4897, IoU.fountain: 0.0621, IoU.conveyer belt: 0.7716, IoU.canopy: 0.1468, IoU.washer: 0.6670, IoU.plaything: 0.2317, IoU.swimming pool: 0.4467, IoU.stool: 0.4174, IoU.barrel: 0.4589, IoU.basket: 0.2751, IoU.waterfall: 0.4963, IoU.tent: 0.9451, IoU.bag: 0.1157, IoU.minibike: 0.6279, IoU.cradle: 0.8068, IoU.oven: 0.2769, IoU.ball: 0.4662, IoU.food: 0.5285, IoU.step: 0.1448, IoU.tank: 0.4197, IoU.trade name: 0.2559, IoU.microwave: 0.3812, IoU.pot: 0.4132, IoU.animal: 0.5102, IoU.bicycle: 0.4577, IoU.lake: 0.5928, IoU.dishwasher: 0.7776, IoU.screen: 0.6613, IoU.blanket: 0.1217, IoU.sculpture: 0.3665, IoU.hood: 0.5576, IoU.sconce: 0.4225, IoU.vase: 0.3686, IoU.traffic light: 0.3028, IoU.tray: 0.0584, IoU.ashcan: 0.3877, IoU.fan: 0.5750, IoU.pier: 0.1396, IoU.crt screen: 0.0384, IoU.plate: 0.4059, IoU.monitor: 0.1845, IoU.bulletin board: 0.4685, IoU.shower: 0.0160, IoU.radiator: 0.4651, IoU.glass: 0.1236, IoU.clock: 0.2471, IoU.flag: 0.3773, Acc.background: nan, Acc.wall: 0.8829, Acc.building: 0.9238, Acc.sky: 0.9721, Acc.floor: 0.9145, Acc.tree: 0.8771, Acc.ceiling: 0.9157, Acc.road: 0.8869, Acc.bed : 0.9511, Acc.windowpane: 0.7745, Acc.grass: 0.8119, Acc.cabinet: 0.7377, Acc.sidewalk: 0.8279, Acc.person: 0.9088, Acc.earth: 0.4761, Acc.door: 0.6340, Acc.table: 0.7795, Acc.mountain: 0.6849, Acc.plant: 0.6339, Acc.curtain: 0.8245, Acc.chair: 0.7064, Acc.car: 0.9039, Acc.water: 0.6239, Acc.painting: 0.8396, Acc.sofa: 0.8568, Acc.shelf: 0.5573, Acc.house: 0.5026, Acc.sea: 0.7363, Acc.mirror: 0.7472, Acc.rug: 0.6003, Acc.field: 0.4405, Acc.armchair: 0.5674, Acc.seat: 0.7719, Acc.fence: 0.5640, Acc.desk: 0.6906, Acc.rock: 0.3821, Acc.wardrobe: 0.6686, Acc.lamp: 0.7641, Acc.bathtub: 0.8245, Acc.railing: 0.4278, Acc.cushion: 0.6614, Acc.base: 0.3927, Acc.box: 0.3216, Acc.column: 0.5521, Acc.signboard: 0.5031, Acc.chest of drawers: 0.5284, Acc.counter: 0.4006, Acc.sand: 0.4957, Acc.sink: 0.8156, Acc.skyscraper: 0.5607, Acc.fireplace: 0.8514, Acc.refrigerator: 0.8429, Acc.grandstand: 0.6451, Acc.path: 0.2321, Acc.stairs: 0.4125, Acc.runway: 0.8278, Acc.case: 0.6446, Acc.pool table: 0.9588, Acc.pillow: 0.6830, Acc.screen door: 0.7304, Acc.stairway: 0.3314, Acc.river: 0.1690, Acc.bridge: 0.6828, Acc.bookcase: 0.5249, Acc.blind: 0.5379, Acc.coffee table: 0.8255, Acc.toilet: 0.9070, Acc.flower: 0.4781, Acc.book: 0.6812, Acc.hill: 0.0929, Acc.bench: 0.5318, Acc.countertop: 0.6991, Acc.stove: 0.7956, Acc.palm: 0.7207, Acc.kitchen island: 0.7185, Acc.computer: 0.6569, Acc.swivel chair: 0.6113, Acc.boat: 0.4575, Acc.bar: 0.3044, Acc.arcade machine: 0.2598, Acc.hovel: 0.3478, Acc.bus: 0.9248, Acc.towel: 0.6938, Acc.light: 0.6273, Acc.truck: 0.4467, Acc.tower: 0.3497, Acc.chandelier: 0.8044, Acc.awning: 0.2725, Acc.streetlight: 0.3880, Acc.booth: 0.5869, Acc.television receiver: 0.7821, Acc.airplane: 0.7016, Acc.dirt track: 0.2483, Acc.apparel: 0.4438, Acc.pole: 0.3311, Acc.land: 0.1390, Acc.bannister: 0.0863, Acc.escalator: 0.2470, Acc.ottoman: 0.5905, Acc.bottle: 0.2525, Acc.buffet: 0.5755, Acc.poster: 0.3758, Acc.stage: 0.2444, Acc.van: 0.6245, Acc.ship: 0.6478, Acc.fountain: 0.0630, Acc.conveyer belt: 0.8834, Acc.canopy: 0.1719, Acc.washer: 0.6721, Acc.plaything: 0.3107, Acc.swimming pool: 0.5476, Acc.stool: 0.5913, Acc.barrel: 0.6424, Acc.basket: 0.3766, Acc.waterfall: 0.5803, Acc.tent: 0.9765, Acc.bag: 0.1464, Acc.minibike: 0.7346, Acc.cradle: 0.9681, Acc.oven: 0.6002, Acc.ball: 0.5963, Acc.food: 0.6345, Acc.step: 0.1856, Acc.tank: 0.4237, Acc.trade name: 0.2959, Acc.microwave: 0.4095, Acc.pot: 0.5072, Acc.animal: 0.5412, Acc.bicycle: 0.6895, Acc.lake: 0.6332, Acc.dishwasher: 0.8292, Acc.screen: 0.8416, Acc.blanket: 0.1384, Acc.sculpture: 0.6475, Acc.hood: 0.7164, Acc.sconce: 0.5076, Acc.vase: 0.5314, Acc.traffic light: 0.4732, Acc.tray: 0.1166, Acc.ashcan: 0.4973, Acc.fan: 0.6984, Acc.pier: 0.1614, Acc.crt screen: 0.1118, Acc.plate: 0.5069, Acc.monitor: 0.2392, Acc.bulletin board: 0.5781, Acc.shower: 0.0228, Acc.radiator: 0.5512, Acc.glass: 0.1359, Acc.clock: 0.3044, Acc.flag: 0.4136