2023-01-05 23:14:11,035 - mmseg - INFO - Multi-processing start method is `None` 2023-01-05 23:14:11,036 - mmseg - INFO - OpenCV num_threads is `64 2023-01-05 23:14:11,077 - mmseg - INFO - Environment info: ------------------------------------------------------------ sys.platform: linux Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] CUDA available: True GPU 0,1,2,3,4,5,6,7: Tesla V100-SXM2-16GB-N CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.3, V11.3.109 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 1.11.0 PyTorch compiling details: PyTorch built with: - GCC 7.3 - 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 v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.3 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_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 8.2 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.12.0 OpenCV: 4.6.0 MMCV: 1.7.0 MMCV Compiler: GCC 9.3 MMCV CUDA Compiler: 11.3 MMSegmentation: 0.29.1+08f5eeb ------------------------------------------------------------ 2023-01-05 23:14:11,078 - mmseg - INFO - Distributed training: True 2023-01-05 23:14:11,509 - mmseg - INFO - Config: embed_dims = [64, 128, 250, 320] norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( type='EncoderDecoder', pretrained=None, backbone=dict( type='SimplifiedMixTransformer', in_channels=3, embed_dims=[64, 128, 250, 320], num_stages=4, num_layers=[3, 10, 16, 5], num_heads=[1, 2, 5, 8], patch_sizes=[7, 3, 3, 3], strides=[4, 2, 2, 2], sr_ratios=[8, 4, 2, 1], out_indices=(0, 1, 2, 3), mlp_ratios=[8, 8, 4, 4], qkv_bias=True, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1, norm_cfg=dict(type='SyncBN', requires_grad=True)), decode_head=dict( type='DESTHead', in_channels=[64, 128, 250, 320], in_index=[0, 1, 2, 3], channels=64, dropout_ratio=0.1, num_classes=19, norm_cfg=dict(type='SyncBN', requires_grad=True), align_corners=False, loss_decode=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), train_cfg=dict(), test_cfg=dict(mode='slide', crop_size=(1024, 1024), stride=(768, 768))) dataset_type = 'CityscapesDataset' data_root = 'data/cityscapes/' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) crop_size = (1024, 1024) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)), dict(type='RandomCrop', crop_size=(1024, 1024), cat_max_ratio=0.75), dict(type='RandomFlip', prob=0.5), dict(type='PhotoMetricDistortion'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size=(1024, 1024), pad_val=0, seg_pad_val=255), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_semantic_seg']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 1024), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ] data = dict( samples_per_gpu=1, workers_per_gpu=1, train=dict( type='CityscapesDataset', data_root='data/cityscapes/', img_dir='leftImg8bit/train', ann_dir='gtFine/train', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)), dict( type='RandomCrop', crop_size=(1024, 1024), cat_max_ratio=0.75), dict(type='RandomFlip', prob=0.5), dict(type='PhotoMetricDistortion'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size=(1024, 1024), pad_val=0, seg_pad_val=255), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_semantic_seg']) ]), val=dict( type='CityscapesDataset', data_root='data/cityscapes/', img_dir='leftImg8bit/val', ann_dir='gtFine/val', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 1024), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ]), test=dict( type='CityscapesDataset', data_root='data/cityscapes/', img_dir='leftImg8bit/val', ann_dir='gtFine/val', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 1024), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ])) log_config = dict( interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)]) dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)] cudnn_benchmark = True optimizer = dict( type='AdamW', lr=6e-05, betas=(0.9, 0.999), weight_decay=0.01, paramwise_cfg=dict( custom_keys=dict( pos_block=dict(decay_mult=0.0), norm=dict(decay_mult=0.0), head=dict(lr_mult=1.0)))) optimizer_config = dict() lr_config = dict( policy='poly', warmup='linear', warmup_iters=1500, warmup_ratio=1e-06, power=1.0, min_lr=0.0, by_epoch=False) runner = dict(type='IterBasedRunner', max_iters=160000) checkpoint_config = dict(by_epoch=False, interval=16000) evaluation = dict(interval=16000, metric='mIoU', pre_eval=True) work_dir = '/workspace/result/train_log' gpu_ids = range(0, 8) auto_resume = False 2023-01-05 23:14:17,815 - mmseg - INFO - Set random seed to 1026735116, deterministic: False 2023-01-05 23:14:19,656 - mmseg - INFO - initialize DESTHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} Name of parameter - Initialization information backbone.layers.0.0.projection.weight - torch.Size([64, 3, 7, 7]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.0.projection.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.0.norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.0.norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.norm1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.norm1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.attn.q.weight - torch.Size([64, 64, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.attn.q.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.attn.k.weight - torch.Size([64, 64, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.attn.k.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.attn.proj.weight - torch.Size([64, 64, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.attn.proj.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.attn.sr.weight - torch.Size([64, 64, 8, 8]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.attn.sr.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.attn.norm1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.attn.norm1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.norm2.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.norm2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.ffn.dwconv.dwconv.weight - torch.Size([512, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.ffn.dwconv.dwconv.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.ffn.pre_layers.0.weight - torch.Size([512, 64, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.ffn.pre_layers.0.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.ffn.pre_layers.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.ffn.pre_layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.ffn.post_layers.0.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.ffn.post_layers.0.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.ffn.post_layers.3.weight - torch.Size([64, 512, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.0.ffn.post_layers.3.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.norm1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.norm1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.attn.q.weight - torch.Size([64, 64, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.attn.q.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.attn.k.weight - torch.Size([64, 64, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.attn.k.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.attn.proj.weight - torch.Size([64, 64, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.attn.proj.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.attn.sr.weight - torch.Size([64, 64, 8, 8]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.attn.sr.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.attn.norm1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.attn.norm1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.norm2.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.norm2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.ffn.dwconv.dwconv.weight - torch.Size([512, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.ffn.dwconv.dwconv.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.ffn.pre_layers.0.weight - torch.Size([512, 64, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.ffn.pre_layers.0.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.ffn.pre_layers.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.ffn.pre_layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.ffn.post_layers.0.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.ffn.post_layers.0.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.ffn.post_layers.3.weight - torch.Size([64, 512, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.1.ffn.post_layers.3.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.norm1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.norm1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.attn.q.weight - torch.Size([64, 64, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.attn.q.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.attn.k.weight - torch.Size([64, 64, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.attn.k.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.attn.proj.weight - torch.Size([64, 64, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.attn.proj.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.attn.sr.weight - torch.Size([64, 64, 8, 8]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.attn.sr.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.attn.norm1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.attn.norm1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.norm2.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.norm2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.ffn.dwconv.dwconv.weight - torch.Size([512, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.ffn.dwconv.dwconv.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.ffn.pre_layers.0.weight - torch.Size([512, 64, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.ffn.pre_layers.0.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.ffn.pre_layers.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.ffn.pre_layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.ffn.post_layers.0.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.ffn.post_layers.0.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.ffn.post_layers.3.weight - torch.Size([64, 512, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.1.2.ffn.post_layers.3.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.2.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.0.2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.0.projection.weight - torch.Size([128, 64, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.0.projection.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.0.norm.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.0.norm.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.attn.q.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.attn.q.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.attn.k.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.attn.k.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.attn.proj.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.attn.proj.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.attn.sr.weight - torch.Size([128, 128, 4, 4]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.attn.sr.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.attn.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.attn.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.norm2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.norm2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.ffn.dwconv.dwconv.weight - torch.Size([1024, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.ffn.dwconv.dwconv.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.ffn.pre_layers.0.weight - torch.Size([1024, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.ffn.pre_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.ffn.pre_layers.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.ffn.pre_layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.ffn.post_layers.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.ffn.post_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.ffn.post_layers.3.weight - torch.Size([128, 1024, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.0.ffn.post_layers.3.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.attn.q.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.attn.q.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.attn.k.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.attn.k.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.attn.proj.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.attn.proj.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.attn.sr.weight - torch.Size([128, 128, 4, 4]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.attn.sr.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.attn.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.attn.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.norm2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.norm2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.ffn.dwconv.dwconv.weight - torch.Size([1024, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.ffn.dwconv.dwconv.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.ffn.pre_layers.0.weight - torch.Size([1024, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.ffn.pre_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.ffn.pre_layers.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.ffn.pre_layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.ffn.post_layers.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.ffn.post_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.ffn.post_layers.3.weight - torch.Size([128, 1024, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.1.ffn.post_layers.3.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.attn.q.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.attn.q.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.attn.k.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.attn.k.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.attn.proj.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.attn.proj.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.attn.sr.weight - torch.Size([128, 128, 4, 4]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.attn.sr.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.attn.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.attn.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.norm2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.norm2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.ffn.dwconv.dwconv.weight - torch.Size([1024, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.ffn.dwconv.dwconv.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.ffn.pre_layers.0.weight - torch.Size([1024, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.ffn.pre_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.ffn.pre_layers.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.ffn.pre_layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.ffn.post_layers.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.ffn.post_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.ffn.post_layers.3.weight - torch.Size([128, 1024, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.2.ffn.post_layers.3.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.attn.q.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.attn.q.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.attn.k.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.attn.k.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.attn.proj.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.attn.proj.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.attn.sr.weight - torch.Size([128, 128, 4, 4]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.attn.sr.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.attn.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.attn.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.norm2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.norm2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.ffn.dwconv.dwconv.weight - torch.Size([1024, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.ffn.dwconv.dwconv.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.ffn.pre_layers.0.weight - torch.Size([1024, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.ffn.pre_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.ffn.pre_layers.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.ffn.pre_layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.ffn.post_layers.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.ffn.post_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.ffn.post_layers.3.weight - torch.Size([128, 1024, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.3.ffn.post_layers.3.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.attn.q.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.attn.q.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.attn.k.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.attn.k.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.attn.proj.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.attn.proj.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.attn.sr.weight - torch.Size([128, 128, 4, 4]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.attn.sr.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.attn.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.attn.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.norm2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.norm2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.ffn.dwconv.dwconv.weight - torch.Size([1024, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.ffn.dwconv.dwconv.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.ffn.pre_layers.0.weight - torch.Size([1024, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.ffn.pre_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.ffn.pre_layers.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.ffn.pre_layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.ffn.post_layers.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.ffn.post_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.ffn.post_layers.3.weight - torch.Size([128, 1024, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.4.ffn.post_layers.3.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.attn.q.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.attn.q.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.attn.k.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.attn.k.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.attn.proj.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.attn.proj.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.attn.sr.weight - torch.Size([128, 128, 4, 4]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.attn.sr.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.attn.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.attn.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.norm2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.norm2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.ffn.dwconv.dwconv.weight - torch.Size([1024, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.ffn.dwconv.dwconv.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.ffn.pre_layers.0.weight - torch.Size([1024, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.ffn.pre_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.ffn.pre_layers.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.ffn.pre_layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.ffn.post_layers.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.ffn.post_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.ffn.post_layers.3.weight - torch.Size([128, 1024, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.5.ffn.post_layers.3.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.attn.q.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.attn.q.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.attn.k.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.attn.k.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.attn.proj.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.attn.proj.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.attn.sr.weight - torch.Size([128, 128, 4, 4]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.attn.sr.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.attn.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.attn.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.norm2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.norm2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.ffn.dwconv.dwconv.weight - torch.Size([1024, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.ffn.dwconv.dwconv.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.ffn.pre_layers.0.weight - torch.Size([1024, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.ffn.pre_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.ffn.pre_layers.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.ffn.pre_layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.ffn.post_layers.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.ffn.post_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.ffn.post_layers.3.weight - torch.Size([128, 1024, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.6.ffn.post_layers.3.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.attn.q.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.attn.q.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.attn.k.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.attn.k.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.attn.proj.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.attn.proj.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.attn.sr.weight - torch.Size([128, 128, 4, 4]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.attn.sr.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.attn.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.attn.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.norm2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.norm2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.ffn.dwconv.dwconv.weight - torch.Size([1024, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.ffn.dwconv.dwconv.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.ffn.pre_layers.0.weight - torch.Size([1024, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.ffn.pre_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.ffn.pre_layers.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.ffn.pre_layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.ffn.post_layers.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.ffn.post_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.ffn.post_layers.3.weight - torch.Size([128, 1024, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.7.ffn.post_layers.3.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.attn.q.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.attn.q.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.attn.k.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.attn.k.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.attn.proj.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.attn.proj.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.attn.sr.weight - torch.Size([128, 128, 4, 4]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.attn.sr.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.attn.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.attn.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.norm2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.norm2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.ffn.dwconv.dwconv.weight - torch.Size([1024, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.ffn.dwconv.dwconv.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.ffn.pre_layers.0.weight - torch.Size([1024, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.ffn.pre_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.ffn.pre_layers.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.ffn.pre_layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.ffn.post_layers.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.ffn.post_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.ffn.post_layers.3.weight - torch.Size([128, 1024, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.8.ffn.post_layers.3.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.attn.q.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.attn.q.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.attn.k.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.attn.k.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.attn.proj.weight - torch.Size([128, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.attn.proj.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.attn.sr.weight - torch.Size([128, 128, 4, 4]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.attn.sr.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.attn.norm1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.attn.norm1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.norm2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.norm2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.ffn.dwconv.dwconv.weight - torch.Size([1024, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.ffn.dwconv.dwconv.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.ffn.pre_layers.0.weight - torch.Size([1024, 128, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.ffn.pre_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.ffn.pre_layers.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.ffn.pre_layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.ffn.post_layers.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.ffn.post_layers.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.ffn.post_layers.3.weight - torch.Size([128, 1024, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.1.9.ffn.post_layers.3.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.1.2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.0.projection.weight - torch.Size([250, 128, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.0.projection.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.0.norm.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.0.norm.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.attn.q.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.attn.q.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.attn.k.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.attn.k.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.attn.proj.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.attn.proj.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.attn.sr.weight - torch.Size([250, 250, 2, 2]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.attn.sr.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.attn.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.attn.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.norm2.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.norm2.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.ffn.dwconv.dwconv.weight - torch.Size([1000, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.ffn.dwconv.dwconv.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.ffn.pre_layers.0.weight - torch.Size([1000, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.ffn.pre_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.ffn.pre_layers.1.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.ffn.pre_layers.1.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.ffn.post_layers.0.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.ffn.post_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.ffn.post_layers.3.weight - torch.Size([250, 1000, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.0.ffn.post_layers.3.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.attn.q.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.attn.q.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.attn.k.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.attn.k.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.attn.proj.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.attn.proj.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.attn.sr.weight - torch.Size([250, 250, 2, 2]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.attn.sr.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.attn.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.attn.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.norm2.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.norm2.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.ffn.dwconv.dwconv.weight - torch.Size([1000, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.ffn.dwconv.dwconv.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.ffn.pre_layers.0.weight - torch.Size([1000, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.ffn.pre_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.ffn.pre_layers.1.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.ffn.pre_layers.1.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.ffn.post_layers.0.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.ffn.post_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.ffn.post_layers.3.weight - torch.Size([250, 1000, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.1.ffn.post_layers.3.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.attn.q.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.attn.q.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.attn.k.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.attn.k.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.attn.proj.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.attn.proj.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.attn.sr.weight - torch.Size([250, 250, 2, 2]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.attn.sr.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.attn.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.attn.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.norm2.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.norm2.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.ffn.dwconv.dwconv.weight - torch.Size([1000, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.ffn.dwconv.dwconv.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.ffn.pre_layers.0.weight - torch.Size([1000, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.ffn.pre_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.ffn.pre_layers.1.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.ffn.pre_layers.1.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.ffn.post_layers.0.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.ffn.post_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.ffn.post_layers.3.weight - torch.Size([250, 1000, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.2.ffn.post_layers.3.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.attn.q.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.attn.q.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.attn.k.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.attn.k.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.attn.proj.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.attn.proj.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.attn.sr.weight - torch.Size([250, 250, 2, 2]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.attn.sr.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.attn.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.attn.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.norm2.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.norm2.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.ffn.dwconv.dwconv.weight - torch.Size([1000, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.ffn.dwconv.dwconv.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.ffn.pre_layers.0.weight - torch.Size([1000, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.ffn.pre_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.ffn.pre_layers.1.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.ffn.pre_layers.1.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.ffn.post_layers.0.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.ffn.post_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.ffn.post_layers.3.weight - torch.Size([250, 1000, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.3.ffn.post_layers.3.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.attn.q.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.attn.q.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.attn.k.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.attn.k.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.attn.proj.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.attn.proj.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.attn.sr.weight - torch.Size([250, 250, 2, 2]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.attn.sr.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.attn.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.attn.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.norm2.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.norm2.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.ffn.dwconv.dwconv.weight - torch.Size([1000, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.ffn.dwconv.dwconv.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.ffn.pre_layers.0.weight - torch.Size([1000, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.ffn.pre_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.ffn.pre_layers.1.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.ffn.pre_layers.1.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.ffn.post_layers.0.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.ffn.post_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.ffn.post_layers.3.weight - torch.Size([250, 1000, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.4.ffn.post_layers.3.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.attn.q.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.attn.q.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.attn.k.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.attn.k.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.attn.proj.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.attn.proj.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.attn.sr.weight - torch.Size([250, 250, 2, 2]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.attn.sr.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.attn.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.attn.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.norm2.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.norm2.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.ffn.dwconv.dwconv.weight - torch.Size([1000, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.ffn.dwconv.dwconv.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.ffn.pre_layers.0.weight - torch.Size([1000, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.ffn.pre_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.ffn.pre_layers.1.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.ffn.pre_layers.1.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.ffn.post_layers.0.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.ffn.post_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.ffn.post_layers.3.weight - torch.Size([250, 1000, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.5.ffn.post_layers.3.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.attn.q.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.attn.q.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.attn.k.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.attn.k.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.attn.proj.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.attn.proj.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.attn.sr.weight - torch.Size([250, 250, 2, 2]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.attn.sr.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.attn.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.attn.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.norm2.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.norm2.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.ffn.dwconv.dwconv.weight - torch.Size([1000, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.ffn.dwconv.dwconv.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.ffn.pre_layers.0.weight - torch.Size([1000, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.ffn.pre_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.ffn.pre_layers.1.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.ffn.pre_layers.1.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.ffn.post_layers.0.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.ffn.post_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.ffn.post_layers.3.weight - torch.Size([250, 1000, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.6.ffn.post_layers.3.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.attn.q.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.attn.q.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.attn.k.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.attn.k.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.attn.proj.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.attn.proj.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.attn.sr.weight - torch.Size([250, 250, 2, 2]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.attn.sr.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.attn.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.attn.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.norm2.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.norm2.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.ffn.dwconv.dwconv.weight - torch.Size([1000, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.ffn.dwconv.dwconv.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.ffn.pre_layers.0.weight - torch.Size([1000, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.ffn.pre_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.ffn.pre_layers.1.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.ffn.pre_layers.1.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.ffn.post_layers.0.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.ffn.post_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.ffn.post_layers.3.weight - torch.Size([250, 1000, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.7.ffn.post_layers.3.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.attn.q.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.attn.q.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.attn.k.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.attn.k.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.attn.proj.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.attn.proj.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.attn.sr.weight - torch.Size([250, 250, 2, 2]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.attn.sr.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.attn.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.attn.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.norm2.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.norm2.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.ffn.dwconv.dwconv.weight - torch.Size([1000, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.ffn.dwconv.dwconv.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.ffn.pre_layers.0.weight - torch.Size([1000, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.ffn.pre_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.ffn.pre_layers.1.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.ffn.pre_layers.1.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.ffn.post_layers.0.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.ffn.post_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.ffn.post_layers.3.weight - torch.Size([250, 1000, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.8.ffn.post_layers.3.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.attn.q.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.attn.q.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.attn.k.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.attn.k.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.attn.proj.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.attn.proj.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.attn.sr.weight - torch.Size([250, 250, 2, 2]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.attn.sr.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.attn.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.attn.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.norm2.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.norm2.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.ffn.dwconv.dwconv.weight - torch.Size([1000, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.ffn.dwconv.dwconv.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.ffn.pre_layers.0.weight - torch.Size([1000, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.ffn.pre_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.ffn.pre_layers.1.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.ffn.pre_layers.1.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.ffn.post_layers.0.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.ffn.post_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.ffn.post_layers.3.weight - torch.Size([250, 1000, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.9.ffn.post_layers.3.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.attn.q.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.attn.q.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.attn.k.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.attn.k.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.attn.proj.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.attn.proj.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.attn.sr.weight - torch.Size([250, 250, 2, 2]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.attn.sr.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.attn.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.attn.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.norm2.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.norm2.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.ffn.dwconv.dwconv.weight - torch.Size([1000, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.ffn.dwconv.dwconv.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.ffn.pre_layers.0.weight - torch.Size([1000, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.ffn.pre_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.ffn.pre_layers.1.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.ffn.pre_layers.1.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.ffn.post_layers.0.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.ffn.post_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.ffn.post_layers.3.weight - torch.Size([250, 1000, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.10.ffn.post_layers.3.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.attn.q.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.attn.q.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.attn.k.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.attn.k.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.attn.proj.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.attn.proj.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.attn.sr.weight - torch.Size([250, 250, 2, 2]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.attn.sr.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.attn.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.attn.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.norm2.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.norm2.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.ffn.dwconv.dwconv.weight - torch.Size([1000, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.ffn.dwconv.dwconv.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.ffn.pre_layers.0.weight - torch.Size([1000, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.ffn.pre_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.ffn.pre_layers.1.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.ffn.pre_layers.1.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.ffn.post_layers.0.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.ffn.post_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.ffn.post_layers.3.weight - torch.Size([250, 1000, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.11.ffn.post_layers.3.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.attn.q.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.attn.q.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.attn.k.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.attn.k.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.attn.proj.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.attn.proj.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.attn.sr.weight - torch.Size([250, 250, 2, 2]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.attn.sr.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.attn.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.attn.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.norm2.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.norm2.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.ffn.dwconv.dwconv.weight - torch.Size([1000, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.ffn.dwconv.dwconv.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.ffn.pre_layers.0.weight - torch.Size([1000, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.ffn.pre_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.ffn.pre_layers.1.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.ffn.pre_layers.1.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.ffn.post_layers.0.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.ffn.post_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.ffn.post_layers.3.weight - torch.Size([250, 1000, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.12.ffn.post_layers.3.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.attn.q.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.attn.q.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.attn.k.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.attn.k.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.attn.proj.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.attn.proj.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.attn.sr.weight - torch.Size([250, 250, 2, 2]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.attn.sr.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.attn.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.attn.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.norm2.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.norm2.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.ffn.dwconv.dwconv.weight - torch.Size([1000, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.ffn.dwconv.dwconv.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.ffn.pre_layers.0.weight - torch.Size([1000, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.ffn.pre_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.ffn.pre_layers.1.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.ffn.pre_layers.1.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.ffn.post_layers.0.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.ffn.post_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.ffn.post_layers.3.weight - torch.Size([250, 1000, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.13.ffn.post_layers.3.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.attn.q.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.attn.q.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.attn.k.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.attn.k.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.attn.proj.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.attn.proj.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.attn.sr.weight - torch.Size([250, 250, 2, 2]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.attn.sr.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.attn.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.attn.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.norm2.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.norm2.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.ffn.dwconv.dwconv.weight - torch.Size([1000, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.ffn.dwconv.dwconv.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.ffn.pre_layers.0.weight - torch.Size([1000, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.ffn.pre_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.ffn.pre_layers.1.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.ffn.pre_layers.1.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.ffn.post_layers.0.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.ffn.post_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.ffn.post_layers.3.weight - torch.Size([250, 1000, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.14.ffn.post_layers.3.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.attn.q.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.attn.q.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.attn.k.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.attn.k.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.attn.proj.weight - torch.Size([250, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.attn.proj.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.attn.sr.weight - torch.Size([250, 250, 2, 2]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.attn.sr.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.attn.norm1.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.attn.norm1.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.norm2.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.norm2.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.ffn.dwconv.dwconv.weight - torch.Size([1000, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.ffn.dwconv.dwconv.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.ffn.pre_layers.0.weight - torch.Size([1000, 250, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.ffn.pre_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.ffn.pre_layers.1.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.ffn.pre_layers.1.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.ffn.post_layers.0.weight - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.ffn.post_layers.0.bias - torch.Size([1000]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.ffn.post_layers.3.weight - torch.Size([250, 1000, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.1.15.ffn.post_layers.3.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.2.weight - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.2.2.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.0.projection.weight - torch.Size([320, 250, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.0.projection.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.0.norm.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.0.norm.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.norm1.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.norm1.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.attn.q.weight - torch.Size([320, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.attn.q.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.attn.k.weight - torch.Size([320, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.attn.k.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.attn.proj.weight - torch.Size([320, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.attn.proj.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.norm2.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.norm2.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.ffn.dwconv.dwconv.weight - torch.Size([1280, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.ffn.dwconv.dwconv.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.ffn.pre_layers.0.weight - torch.Size([1280, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.ffn.pre_layers.0.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.ffn.pre_layers.1.weight - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.ffn.pre_layers.1.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.ffn.post_layers.0.weight - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.ffn.post_layers.0.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.ffn.post_layers.3.weight - torch.Size([320, 1280, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.0.ffn.post_layers.3.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.norm1.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.norm1.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.attn.q.weight - torch.Size([320, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.attn.q.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.attn.k.weight - torch.Size([320, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.attn.k.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.attn.proj.weight - torch.Size([320, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.attn.proj.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.norm2.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.norm2.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.ffn.dwconv.dwconv.weight - torch.Size([1280, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.ffn.dwconv.dwconv.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.ffn.pre_layers.0.weight - torch.Size([1280, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.ffn.pre_layers.0.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.ffn.pre_layers.1.weight - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.ffn.pre_layers.1.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.ffn.post_layers.0.weight - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.ffn.post_layers.0.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.ffn.post_layers.3.weight - torch.Size([320, 1280, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.1.ffn.post_layers.3.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.norm1.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.norm1.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.attn.q.weight - torch.Size([320, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.attn.q.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.attn.k.weight - torch.Size([320, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.attn.k.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.attn.proj.weight - torch.Size([320, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.attn.proj.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.norm2.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.norm2.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.ffn.dwconv.dwconv.weight - torch.Size([1280, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.ffn.dwconv.dwconv.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.ffn.pre_layers.0.weight - torch.Size([1280, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.ffn.pre_layers.0.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.ffn.pre_layers.1.weight - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.ffn.pre_layers.1.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.ffn.post_layers.0.weight - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.ffn.post_layers.0.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.ffn.post_layers.3.weight - torch.Size([320, 1280, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.2.ffn.post_layers.3.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.norm1.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.norm1.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.attn.q.weight - torch.Size([320, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.attn.q.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.attn.k.weight - torch.Size([320, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.attn.k.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.attn.proj.weight - torch.Size([320, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.attn.proj.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.norm2.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.norm2.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.ffn.dwconv.dwconv.weight - torch.Size([1280, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.ffn.dwconv.dwconv.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.ffn.pre_layers.0.weight - torch.Size([1280, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.ffn.pre_layers.0.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.ffn.pre_layers.1.weight - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.ffn.pre_layers.1.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.ffn.post_layers.0.weight - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.ffn.post_layers.0.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.ffn.post_layers.3.weight - torch.Size([320, 1280, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.3.ffn.post_layers.3.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.norm1.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.norm1.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.attn.q.weight - torch.Size([320, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.attn.q.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.attn.k.weight - torch.Size([320, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.attn.k.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.attn.proj.weight - torch.Size([320, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.attn.proj.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.norm2.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.norm2.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.ffn.dwconv.dwconv.weight - torch.Size([1280, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.ffn.dwconv.dwconv.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.ffn.pre_layers.0.weight - torch.Size([1280, 320, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.ffn.pre_layers.0.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.ffn.pre_layers.1.weight - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.ffn.pre_layers.1.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.ffn.post_layers.0.weight - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.ffn.post_layers.0.bias - torch.Size([1280]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.ffn.post_layers.3.weight - torch.Size([320, 1280, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.1.4.ffn.post_layers.3.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.2.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.layers.3.2.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.conv_seg.weight - torch.Size([19, 64, 1, 1]): NormalInit: mean=0, std=0.01, bias=0 decode_head.conv_seg.bias - torch.Size([19]): NormalInit: mean=0, std=0.01, bias=0 decode_head.convs.0.conv.weight - torch.Size([64, 64, 1, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.convs.0.conv.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.convs.1.conv.weight - torch.Size([128, 128, 1, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.convs.1.conv.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.convs.2.conv.weight - torch.Size([250, 250, 1, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.convs.2.conv.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.convs.3.conv.weight - torch.Size([320, 320, 1, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.convs.3.conv.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.fuse_convs.0.conv.weight - torch.Size([64, 192, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.fuse_convs.0.conv.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.fuse_convs.1.conv.weight - torch.Size([128, 378, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.fuse_convs.1.conv.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.fuse_convs.2.conv.weight - torch.Size([250, 570, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.fuse_convs.2.conv.bias - torch.Size([250]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.fuse_convs.3.conv.weight - torch.Size([320, 320, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.fuse_convs.3.conv.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder 2023-01-05 23:14:19,675 - mmseg - INFO - EncoderDecoder( (backbone): SimplifiedMixTransformer( (layers): ModuleList( (0): ModuleList( (0): SimplifiedPatchEmbed( (projection): Conv2d(3, 64, kernel_size=(7, 7), stride=(4, 4), padding=(3, 3)) (norm): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (1): ModuleList( (0): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(64, 64, kernel_size=(1,), stride=(1,)) (k): Conv1d(64, 64, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(64, 64, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(64, 64, kernel_size=(8, 8), stride=(8, 8)) (norm1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512) ) (pre_layers): Sequential( (0): Conv1d(64, 512, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(512, 64, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (1): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(64, 64, kernel_size=(1,), stride=(1,)) (k): Conv1d(64, 64, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(64, 64, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(64, 64, kernel_size=(8, 8), stride=(8, 8)) (norm1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512) ) (pre_layers): Sequential( (0): Conv1d(64, 512, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(512, 64, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (2): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(64, 64, kernel_size=(1,), stride=(1,)) (k): Conv1d(64, 64, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(64, 64, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(64, 64, kernel_size=(8, 8), stride=(8, 8)) (norm1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512) ) (pre_layers): Sequential( (0): Conv1d(64, 512, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(512, 64, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) ) (2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (1): ModuleList( (0): SimplifiedPatchEmbed( (projection): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (norm): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (1): ModuleList( (0): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (k): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4)) (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024) ) (pre_layers): Sequential( (0): Conv1d(128, 1024, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1024, 128, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (1): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (k): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4)) (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024) ) (pre_layers): Sequential( (0): Conv1d(128, 1024, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1024, 128, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (2): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (k): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4)) (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024) ) (pre_layers): Sequential( (0): Conv1d(128, 1024, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1024, 128, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (3): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (k): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4)) (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024) ) (pre_layers): Sequential( (0): Conv1d(128, 1024, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1024, 128, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (4): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (k): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4)) (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024) ) (pre_layers): Sequential( (0): Conv1d(128, 1024, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1024, 128, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (5): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (k): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4)) (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024) ) (pre_layers): Sequential( (0): Conv1d(128, 1024, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1024, 128, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (6): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (k): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4)) (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024) ) (pre_layers): Sequential( (0): Conv1d(128, 1024, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1024, 128, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (7): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (k): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4)) (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024) ) (pre_layers): Sequential( (0): Conv1d(128, 1024, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1024, 128, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (8): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (k): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4)) (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024) ) (pre_layers): Sequential( (0): Conv1d(128, 1024, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1024, 128, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (9): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (k): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4)) (norm1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024) ) (pre_layers): Sequential( (0): Conv1d(128, 1024, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1024, 128, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) ) (2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (2): ModuleList( (0): SimplifiedPatchEmbed( (projection): Conv2d(128, 250, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (norm): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (1): ModuleList( (0): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (k): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(250, 250, kernel_size=(2, 2), stride=(2, 2)) (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1000, 1000, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1000) ) (pre_layers): Sequential( (0): Conv1d(250, 1000, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1000, 250, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (1): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (k): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(250, 250, kernel_size=(2, 2), stride=(2, 2)) (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1000, 1000, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1000) ) (pre_layers): Sequential( (0): Conv1d(250, 1000, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1000, 250, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (2): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (k): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(250, 250, kernel_size=(2, 2), stride=(2, 2)) (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1000, 1000, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1000) ) (pre_layers): Sequential( (0): Conv1d(250, 1000, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1000, 250, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (3): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (k): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(250, 250, kernel_size=(2, 2), stride=(2, 2)) (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1000, 1000, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1000) ) (pre_layers): Sequential( (0): Conv1d(250, 1000, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1000, 250, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (4): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (k): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(250, 250, kernel_size=(2, 2), stride=(2, 2)) (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1000, 1000, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1000) ) (pre_layers): Sequential( (0): Conv1d(250, 1000, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1000, 250, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (5): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (k): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(250, 250, kernel_size=(2, 2), stride=(2, 2)) (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1000, 1000, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1000) ) (pre_layers): Sequential( (0): Conv1d(250, 1000, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1000, 250, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (6): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (k): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(250, 250, kernel_size=(2, 2), stride=(2, 2)) (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1000, 1000, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1000) ) (pre_layers): Sequential( (0): Conv1d(250, 1000, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1000, 250, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (7): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (k): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(250, 250, kernel_size=(2, 2), stride=(2, 2)) (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1000, 1000, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1000) ) (pre_layers): Sequential( (0): Conv1d(250, 1000, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1000, 250, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (8): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (k): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(250, 250, kernel_size=(2, 2), stride=(2, 2)) (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1000, 1000, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1000) ) (pre_layers): Sequential( (0): Conv1d(250, 1000, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1000, 250, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (9): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (k): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(250, 250, kernel_size=(2, 2), stride=(2, 2)) (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1000, 1000, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1000) ) (pre_layers): Sequential( (0): Conv1d(250, 1000, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1000, 250, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (10): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (k): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(250, 250, kernel_size=(2, 2), stride=(2, 2)) (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1000, 1000, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1000) ) (pre_layers): Sequential( (0): Conv1d(250, 1000, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1000, 250, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (11): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (k): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(250, 250, kernel_size=(2, 2), stride=(2, 2)) (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1000, 1000, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1000) ) (pre_layers): Sequential( (0): Conv1d(250, 1000, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1000, 250, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (12): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (k): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(250, 250, kernel_size=(2, 2), stride=(2, 2)) (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1000, 1000, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1000) ) (pre_layers): Sequential( (0): Conv1d(250, 1000, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1000, 250, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (13): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (k): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(250, 250, kernel_size=(2, 2), stride=(2, 2)) (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1000, 1000, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1000) ) (pre_layers): Sequential( (0): Conv1d(250, 1000, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1000, 250, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (14): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (k): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(250, 250, kernel_size=(2, 2), stride=(2, 2)) (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1000, 1000, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1000) ) (pre_layers): Sequential( (0): Conv1d(250, 1000, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1000, 250, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (15): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (k): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(250, 250, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (sr): Conv2d(250, 250, kernel_size=(2, 2), stride=(2, 2)) (norm1): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1000, 1000, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1000) ) (pre_layers): Sequential( (0): Conv1d(250, 1000, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1000, 250, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) ) (2): SyncBatchNorm(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (3): ModuleList( (0): SimplifiedPatchEmbed( (projection): Conv2d(250, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (norm): SyncBatchNorm(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (1): ModuleList( (0): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(320, 320, kernel_size=(1,), stride=(1,)) (k): Conv1d(320, 320, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(320, 320, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1280) ) (pre_layers): Sequential( (0): Conv1d(320, 1280, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1280, 320, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (1): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(320, 320, kernel_size=(1,), stride=(1,)) (k): Conv1d(320, 320, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(320, 320, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1280) ) (pre_layers): Sequential( (0): Conv1d(320, 1280, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1280, 320, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (2): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(320, 320, kernel_size=(1,), stride=(1,)) (k): Conv1d(320, 320, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(320, 320, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1280) ) (pre_layers): Sequential( (0): Conv1d(320, 1280, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1280, 320, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (3): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(320, 320, kernel_size=(1,), stride=(1,)) (k): Conv1d(320, 320, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(320, 320, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1280) ) (pre_layers): Sequential( (0): Conv1d(320, 1280, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1280, 320, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (4): SimpliefiedTransformerEncoderLayer( (norm1): SyncBatchNorm(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (attn): SimplifiedAttention( (q): Conv1d(320, 320, kernel_size=(1,), stride=(1,)) (k): Conv1d(320, 320, kernel_size=(1,), stride=(1,)) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Conv1d(320, 320, kernel_size=(1,), stride=(1,)) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): DropPath() ) (norm2): SyncBatchNorm(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (ffn): MixFFN( (dwconv): DWConv( (dwconv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1280) ) (pre_layers): Sequential( (0): Conv1d(320, 1280, kernel_size=(1,), stride=(1,)) (1): SyncBatchNorm(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (post_layers): Sequential( (0): SyncBatchNorm(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU() (2): Dropout(p=0.0, inplace=False) (3): Conv1d(1280, 320, kernel_size=(1,), stride=(1,)) (4): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) ) (2): SyncBatchNorm(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) ) (decode_head): DESTHead( input_transform=multiple_select, ignore_index=255, align_corners=False (loss_decode): CrossEntropyLoss(avg_non_ignore=False) (conv_seg): Conv2d(64, 19, kernel_size=(1, 1), stride=(1, 1)) (dropout): Dropout2d(p=0.1, inplace=False) (convs): ModuleList( (0): ConvModule( (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) (activate): ReLU(inplace=True) ) (1): ConvModule( (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) (activate): ReLU(inplace=True) ) (2): ConvModule( (conv): Conv2d(250, 250, kernel_size=(1, 1), stride=(1, 1)) (activate): ReLU(inplace=True) ) (3): ConvModule( (conv): Conv2d(320, 320, kernel_size=(1, 1), stride=(1, 1)) (activate): ReLU(inplace=True) ) ) (fuse_convs): ModuleList( (0): ConvModule( (conv): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activate): ReLU(inplace=True) ) (1): ConvModule( (conv): Conv2d(378, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activate): ReLU(inplace=True) ) (2): ConvModule( (conv): Conv2d(570, 250, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activate): ReLU(inplace=True) ) (3): ConvModule( (conv): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activate): ReLU(inplace=True) ) ) (upsample): ModuleList( (0): Sequential( (0): Upsample(scale_factor=2.0, mode=bilinear) ) (1): Sequential( (0): Upsample(scale_factor=2.0, mode=bilinear) ) (2): Sequential( (0): Upsample(scale_factor=2.0, mode=bilinear) ) (3): Sequential( (0): Upsample(scale_factor=2.0, mode=bilinear) ) ) ) init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} ) 2023-01-05 23:14:19,807 - mmseg - INFO - Loaded 2975 images 2023-01-05 23:14:21,026 - mmseg - INFO - Loaded 500 images 2023-01-05 23:14:21,028 - mmseg - INFO - Start running, host: root@3920386, work_dir: /workspace/result/train_log 2023-01-05 23:14:21,028 - mmseg - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) PolyLrUpdaterHook (NORMAL ) CheckpointHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_train_epoch: (VERY_HIGH ) PolyLrUpdaterHook (LOW ) IterTimerHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_train_iter: (VERY_HIGH ) PolyLrUpdaterHook (LOW ) IterTimerHook (LOW ) DistEvalHook -------------------- after_train_iter: (ABOVE_NORMAL) OptimizerHook (NORMAL ) CheckpointHook (LOW ) IterTimerHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- after_train_epoch: (NORMAL ) CheckpointHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_val_epoch: (LOW ) IterTimerHook (VERY_LOW ) TextLoggerHook -------------------- before_val_iter: (LOW ) IterTimerHook -------------------- after_val_iter: (LOW ) IterTimerHook -------------------- after_val_epoch: (VERY_LOW ) TextLoggerHook -------------------- after_run: (VERY_LOW ) TextLoggerHook -------------------- 2023-01-05 23:14:21,028 - mmseg - INFO - workflow: [('train', 1)], max: 160000 iters 2023-01-05 23:14:21,029 - mmseg - INFO - Checkpoints will be saved to /workspace/result/train_log by HardDiskBackend. 2023-01-05 23:15:04,682 - mmseg - INFO - Iter [50/160000] lr: 1.959e-06, eta: 1 day, 11:09:52, time: 0.791, data_time: 0.019, memory: 11582, decode.loss_ce: 2.4200, decode.acc_seg: 9.3548, loss: 2.4200 2023-01-05 23:15:38,627 - mmseg - INFO - Iter [100/160000] lr: 3.958e-06, eta: 1 day, 8:38:06, time: 0.678, data_time: 0.013, memory: 11582, decode.loss_ce: 2.3717, decode.acc_seg: 15.8254, loss: 2.3717 2023-01-05 23:16:13,008 - mmseg - INFO - Iter [150/160000] lr: 5.955e-06, eta: 1 day, 7:56:25, time: 0.689, data_time: 0.014, memory: 11582, decode.loss_ce: 2.2535, decode.acc_seg: 36.1381, loss: 2.2535 2023-01-05 23:16:46,026 - mmseg - INFO - Iter [200/160000] lr: 7.950e-06, eta: 1 day, 7:16:34, time: 0.660, data_time: 0.013, memory: 11582, decode.loss_ce: 1.9101, decode.acc_seg: 47.6852, loss: 1.9101 2023-01-05 23:17:19,638 - mmseg - INFO - Iter [250/160000] lr: 9.945e-06, eta: 1 day, 6:58:44, time: 0.672, data_time: 0.013, memory: 11582, decode.loss_ce: 1.5313, decode.acc_seg: 49.3929, loss: 1.5313 2023-01-05 23:17:53,792 - mmseg - INFO - Iter [300/160000] lr: 1.194e-05, eta: 1 day, 6:51:26, time: 0.683, data_time: 0.013, memory: 11582, decode.loss_ce: 1.3350, decode.acc_seg: 52.9082, loss: 1.3350 2023-01-05 23:18:27,824 - mmseg - INFO - Iter [350/160000] lr: 1.393e-05, eta: 1 day, 6:45:13, time: 0.681, data_time: 0.013, memory: 11582, decode.loss_ce: 1.2788, decode.acc_seg: 54.8781, loss: 1.2788 2023-01-05 23:19:03,939 - mmseg - INFO - Iter [400/160000] lr: 1.592e-05, eta: 1 day, 6:54:14, time: 0.722, data_time: 0.058, memory: 11582, decode.loss_ce: 1.1631, decode.acc_seg: 59.1797, loss: 1.1631 2023-01-05 23:19:37,819 - mmseg - INFO - Iter [450/160000] lr: 1.791e-05, eta: 1 day, 6:47:50, time: 0.677, data_time: 0.013, memory: 11582, decode.loss_ce: 1.0697, decode.acc_seg: 63.1159, loss: 1.0697 2023-01-05 23:20:10,677 - mmseg - INFO - Iter [500/160000] lr: 1.990e-05, eta: 1 day, 6:37:15, time: 0.657, data_time: 0.013, memory: 11582, decode.loss_ce: 1.0137, decode.acc_seg: 63.8978, loss: 1.0137 2023-01-05 23:20:44,232 - mmseg - INFO - Iter [550/160000] lr: 2.188e-05, eta: 1 day, 6:31:48, time: 0.671, data_time: 0.013, memory: 11582, decode.loss_ce: 0.9449, decode.acc_seg: 67.2090, loss: 0.9449 2023-01-05 23:21:17,581 - mmseg - INFO - Iter [600/160000] lr: 2.387e-05, eta: 1 day, 6:26:17, time: 0.667, data_time: 0.013, memory: 11582, decode.loss_ce: 0.9204, decode.acc_seg: 69.2137, loss: 0.9204 2023-01-05 23:21:51,892 - mmseg - INFO - Iter [650/160000] lr: 2.585e-05, eta: 1 day, 6:25:28, time: 0.686, data_time: 0.013, memory: 11582, decode.loss_ce: 0.8298, decode.acc_seg: 71.6303, loss: 0.8298 2023-01-05 23:22:25,506 - mmseg - INFO - Iter [700/160000] lr: 2.784e-05, eta: 1 day, 6:22:03, time: 0.672, data_time: 0.013, memory: 11582, decode.loss_ce: 0.8541, decode.acc_seg: 71.6859, loss: 0.8541 2023-01-05 23:23:01,710 - mmseg - INFO - Iter [750/160000] lr: 2.982e-05, eta: 1 day, 6:28:08, time: 0.724, data_time: 0.059, memory: 11582, decode.loss_ce: 0.8079, decode.acc_seg: 72.0288, loss: 0.8079 2023-01-05 23:23:37,489 - mmseg - INFO - Iter [800/160000] lr: 3.180e-05, eta: 1 day, 6:31:51, time: 0.715, data_time: 0.013, memory: 11582, decode.loss_ce: 0.7405, decode.acc_seg: 74.2031, loss: 0.7405 2023-01-05 23:24:12,324 - mmseg - INFO - Iter [850/160000] lr: 3.378e-05, eta: 1 day, 6:32:26, time: 0.698, data_time: 0.014, memory: 11582, decode.loss_ce: 0.7826, decode.acc_seg: 74.1336, loss: 0.7826 2023-01-05 23:24:47,800 - mmseg - INFO - Iter [900/160000] lr: 3.576e-05, eta: 1 day, 6:34:36, time: 0.709, data_time: 0.013, memory: 11582, decode.loss_ce: 0.7444, decode.acc_seg: 74.4108, loss: 0.7444 2023-01-05 23:25:22,221 - mmseg - INFO - Iter [950/160000] lr: 3.773e-05, eta: 1 day, 6:33:24, time: 0.687, data_time: 0.014, memory: 11582, decode.loss_ce: 0.7257, decode.acc_seg: 75.6401, loss: 0.7257 2023-01-05 23:25:55,068 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-05 23:25:55,069 - mmseg - INFO - Iter [1000/160000] lr: 3.971e-05, eta: 1 day, 6:28:20, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.7242, decode.acc_seg: 75.8912, loss: 0.7242 2023-01-05 23:26:28,429 - mmseg - INFO - Iter [1050/160000] lr: 4.168e-05, eta: 1 day, 6:24:54, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.6549, decode.acc_seg: 77.2796, loss: 0.6549 2023-01-05 23:27:01,423 - mmseg - INFO - Iter [1100/160000] lr: 4.366e-05, eta: 1 day, 6:20:47, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.6327, decode.acc_seg: 78.4114, loss: 0.6327 2023-01-05 23:27:37,462 - mmseg - INFO - Iter [1150/160000] lr: 4.563e-05, eta: 1 day, 6:24:06, time: 0.721, data_time: 0.058, memory: 11582, decode.loss_ce: 0.6768, decode.acc_seg: 76.6220, loss: 0.6768 2023-01-05 23:28:12,343 - mmseg - INFO - Iter [1200/160000] lr: 4.760e-05, eta: 1 day, 6:24:22, time: 0.697, data_time: 0.013, memory: 11582, decode.loss_ce: 0.6372, decode.acc_seg: 77.9969, loss: 0.6372 2023-01-05 23:28:46,212 - mmseg - INFO - Iter [1250/160000] lr: 4.957e-05, eta: 1 day, 6:22:32, time: 0.677, data_time: 0.015, memory: 11582, decode.loss_ce: 0.6361, decode.acc_seg: 77.8902, loss: 0.6361 2023-01-05 23:29:20,457 - mmseg - INFO - Iter [1300/160000] lr: 5.154e-05, eta: 1 day, 6:21:39, time: 0.686, data_time: 0.014, memory: 11582, decode.loss_ce: 0.5926, decode.acc_seg: 79.5022, loss: 0.5926 2023-01-05 23:29:53,736 - mmseg - INFO - Iter [1350/160000] lr: 5.351e-05, eta: 1 day, 6:18:43, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.6116, decode.acc_seg: 78.9453, loss: 0.6116 2023-01-05 23:30:28,982 - mmseg - INFO - Iter [1400/160000] lr: 5.547e-05, eta: 1 day, 6:19:51, time: 0.706, data_time: 0.014, memory: 11582, decode.loss_ce: 0.6064, decode.acc_seg: 79.0581, loss: 0.6064 2023-01-05 23:31:02,051 - mmseg - INFO - Iter [1450/160000] lr: 5.744e-05, eta: 1 day, 6:16:48, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.6156, decode.acc_seg: 78.9207, loss: 0.6156 2023-01-05 23:31:39,587 - mmseg - INFO - Iter [1500/160000] lr: 5.940e-05, eta: 1 day, 6:21:48, time: 0.751, data_time: 0.058, memory: 11582, decode.loss_ce: 0.5629, decode.acc_seg: 80.0610, loss: 0.5629 2023-01-05 23:32:12,393 - mmseg - INFO - Iter [1550/160000] lr: 5.942e-05, eta: 1 day, 6:18:19, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.5730, decode.acc_seg: 80.0778, loss: 0.5730 2023-01-05 23:32:46,042 - mmseg - INFO - Iter [1600/160000] lr: 5.940e-05, eta: 1 day, 6:16:26, time: 0.673, data_time: 0.014, memory: 11582, decode.loss_ce: 0.5555, decode.acc_seg: 80.8108, loss: 0.5555 2023-01-05 23:33:18,886 - mmseg - INFO - Iter [1650/160000] lr: 5.938e-05, eta: 1 day, 6:13:26, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.5502, decode.acc_seg: 81.4890, loss: 0.5502 2023-01-05 23:33:53,511 - mmseg - INFO - Iter [1700/160000] lr: 5.936e-05, eta: 1 day, 6:13:13, time: 0.692, data_time: 0.013, memory: 11582, decode.loss_ce: 0.5164, decode.acc_seg: 81.8121, loss: 0.5164 2023-01-05 23:34:28,513 - mmseg - INFO - Iter [1750/160000] lr: 5.934e-05, eta: 1 day, 6:13:40, time: 0.701, data_time: 0.014, memory: 11582, decode.loss_ce: 0.5598, decode.acc_seg: 80.6520, loss: 0.5598 2023-01-05 23:35:01,174 - mmseg - INFO - Iter [1800/160000] lr: 5.933e-05, eta: 1 day, 6:10:34, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.5382, decode.acc_seg: 81.1123, loss: 0.5382 2023-01-05 23:35:35,006 - mmseg - INFO - Iter [1850/160000] lr: 5.931e-05, eta: 1 day, 6:09:15, time: 0.676, data_time: 0.013, memory: 11582, decode.loss_ce: 0.5129, decode.acc_seg: 81.8853, loss: 0.5129 2023-01-05 23:36:12,503 - mmseg - INFO - Iter [1900/160000] lr: 5.929e-05, eta: 1 day, 6:13:02, time: 0.749, data_time: 0.059, memory: 11582, decode.loss_ce: 0.4934, decode.acc_seg: 82.6591, loss: 0.4934 2023-01-05 23:36:45,606 - mmseg - INFO - Iter [1950/160000] lr: 5.927e-05, eta: 1 day, 6:10:47, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.5121, decode.acc_seg: 82.3266, loss: 0.5121 2023-01-05 23:37:18,366 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-05 23:37:18,366 - mmseg - INFO - Iter [2000/160000] lr: 5.925e-05, eta: 1 day, 6:08:05, time: 0.655, data_time: 0.013, memory: 11582, decode.loss_ce: 0.5161, decode.acc_seg: 82.1390, loss: 0.5161 2023-01-05 23:37:51,176 - mmseg - INFO - Iter [2050/160000] lr: 5.923e-05, eta: 1 day, 6:05:34, time: 0.656, data_time: 0.013, memory: 11582, decode.loss_ce: 0.5034, decode.acc_seg: 82.8475, loss: 0.5034 2023-01-05 23:38:23,936 - mmseg - INFO - Iter [2100/160000] lr: 5.921e-05, eta: 1 day, 6:03:04, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.5027, decode.acc_seg: 82.6011, loss: 0.5027 2023-01-05 23:38:57,695 - mmseg - INFO - Iter [2150/160000] lr: 5.919e-05, eta: 1 day, 6:01:53, time: 0.675, data_time: 0.013, memory: 11582, decode.loss_ce: 0.4806, decode.acc_seg: 83.4126, loss: 0.4806 2023-01-05 23:39:30,670 - mmseg - INFO - Iter [2200/160000] lr: 5.918e-05, eta: 1 day, 5:59:45, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.4525, decode.acc_seg: 83.6122, loss: 0.4525 2023-01-05 23:40:07,069 - mmseg - INFO - Iter [2250/160000] lr: 5.916e-05, eta: 1 day, 6:01:43, time: 0.728, data_time: 0.059, memory: 11582, decode.loss_ce: 0.4748, decode.acc_seg: 82.7528, loss: 0.4748 2023-01-05 23:40:39,821 - mmseg - INFO - Iter [2300/160000] lr: 5.914e-05, eta: 1 day, 5:59:28, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.4277, decode.acc_seg: 84.7814, loss: 0.4277 2023-01-05 23:41:14,168 - mmseg - INFO - Iter [2350/160000] lr: 5.912e-05, eta: 1 day, 5:59:02, time: 0.687, data_time: 0.014, memory: 11582, decode.loss_ce: 0.4653, decode.acc_seg: 83.9492, loss: 0.4653 2023-01-05 23:41:47,067 - mmseg - INFO - Iter [2400/160000] lr: 5.910e-05, eta: 1 day, 5:56:59, time: 0.658, data_time: 0.013, memory: 11582, decode.loss_ce: 0.4451, decode.acc_seg: 83.9393, loss: 0.4451 2023-01-05 23:42:20,272 - mmseg - INFO - Iter [2450/160000] lr: 5.908e-05, eta: 1 day, 5:55:20, time: 0.664, data_time: 0.013, memory: 11582, decode.loss_ce: 0.4567, decode.acc_seg: 83.6383, loss: 0.4567 2023-01-05 23:42:53,763 - mmseg - INFO - Iter [2500/160000] lr: 5.906e-05, eta: 1 day, 5:54:00, time: 0.669, data_time: 0.013, memory: 11582, decode.loss_ce: 0.4494, decode.acc_seg: 83.8347, loss: 0.4494 2023-01-05 23:43:29,068 - mmseg - INFO - Iter [2550/160000] lr: 5.904e-05, eta: 1 day, 5:54:38, time: 0.707, data_time: 0.014, memory: 11582, decode.loss_ce: 0.4097, decode.acc_seg: 85.4019, loss: 0.4097 2023-01-05 23:44:01,887 - mmseg - INFO - Iter [2600/160000] lr: 5.903e-05, eta: 1 day, 5:52:41, time: 0.656, data_time: 0.013, memory: 11582, decode.loss_ce: 0.4232, decode.acc_seg: 85.1990, loss: 0.4232 2023-01-05 23:44:37,010 - mmseg - INFO - Iter [2650/160000] lr: 5.901e-05, eta: 1 day, 5:53:03, time: 0.702, data_time: 0.058, memory: 11582, decode.loss_ce: 0.4173, decode.acc_seg: 85.2652, loss: 0.4173 2023-01-05 23:45:09,856 - mmseg - INFO - Iter [2700/160000] lr: 5.899e-05, eta: 1 day, 5:51:11, time: 0.657, data_time: 0.013, memory: 11582, decode.loss_ce: 0.4504, decode.acc_seg: 84.2363, loss: 0.4504 2023-01-05 23:45:42,580 - mmseg - INFO - Iter [2750/160000] lr: 5.897e-05, eta: 1 day, 5:49:15, time: 0.655, data_time: 0.013, memory: 11582, decode.loss_ce: 0.4257, decode.acc_seg: 85.1395, loss: 0.4257 2023-01-05 23:46:16,328 - mmseg - INFO - Iter [2800/160000] lr: 5.895e-05, eta: 1 day, 5:48:19, time: 0.675, data_time: 0.013, memory: 11582, decode.loss_ce: 0.3814, decode.acc_seg: 86.4007, loss: 0.3814 2023-01-05 23:46:49,259 - mmseg - INFO - Iter [2850/160000] lr: 5.893e-05, eta: 1 day, 5:46:39, time: 0.658, data_time: 0.013, memory: 11582, decode.loss_ce: 0.4423, decode.acc_seg: 84.5084, loss: 0.4423 2023-01-05 23:47:24,396 - mmseg - INFO - Iter [2900/160000] lr: 5.891e-05, eta: 1 day, 5:46:58, time: 0.702, data_time: 0.013, memory: 11582, decode.loss_ce: 0.3716, decode.acc_seg: 86.6381, loss: 0.3716 2023-01-05 23:47:58,418 - mmseg - INFO - Iter [2950/160000] lr: 5.889e-05, eta: 1 day, 5:46:17, time: 0.680, data_time: 0.014, memory: 11582, decode.loss_ce: 0.4359, decode.acc_seg: 84.4556, loss: 0.4359 2023-01-05 23:48:33,427 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-05 23:48:33,428 - mmseg - INFO - Iter [3000/160000] lr: 5.888e-05, eta: 1 day, 5:46:33, time: 0.702, data_time: 0.060, memory: 11582, decode.loss_ce: 0.3869, decode.acc_seg: 86.3804, loss: 0.3869 2023-01-05 23:49:06,224 - mmseg - INFO - Iter [3050/160000] lr: 5.886e-05, eta: 1 day, 5:44:50, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.4048, decode.acc_seg: 85.6410, loss: 0.4048 2023-01-05 23:49:39,598 - mmseg - INFO - Iter [3100/160000] lr: 5.884e-05, eta: 1 day, 5:43:38, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.4101, decode.acc_seg: 85.8451, loss: 0.4101 2023-01-05 23:50:12,506 - mmseg - INFO - Iter [3150/160000] lr: 5.882e-05, eta: 1 day, 5:42:04, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3432, decode.acc_seg: 87.6209, loss: 0.3432 2023-01-05 23:50:47,145 - mmseg - INFO - Iter [3200/160000] lr: 5.880e-05, eta: 1 day, 5:41:55, time: 0.692, data_time: 0.013, memory: 11582, decode.loss_ce: 0.3717, decode.acc_seg: 86.4455, loss: 0.3717 2023-01-05 23:51:22,609 - mmseg - INFO - Iter [3250/160000] lr: 5.878e-05, eta: 1 day, 5:42:27, time: 0.709, data_time: 0.014, memory: 11582, decode.loss_ce: 0.4073, decode.acc_seg: 85.1543, loss: 0.4073 2023-01-05 23:51:56,219 - mmseg - INFO - Iter [3300/160000] lr: 5.876e-05, eta: 1 day, 5:41:31, time: 0.673, data_time: 0.014, memory: 11582, decode.loss_ce: 0.4221, decode.acc_seg: 84.9550, loss: 0.4221 2023-01-05 23:52:31,489 - mmseg - INFO - Iter [3350/160000] lr: 5.874e-05, eta: 1 day, 5:41:52, time: 0.705, data_time: 0.059, memory: 11582, decode.loss_ce: 0.3866, decode.acc_seg: 86.0185, loss: 0.3866 2023-01-05 23:53:04,140 - mmseg - INFO - Iter [3400/160000] lr: 5.873e-05, eta: 1 day, 5:40:09, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3760, decode.acc_seg: 86.4985, loss: 0.3760 2023-01-05 23:53:37,491 - mmseg - INFO - Iter [3450/160000] lr: 5.871e-05, eta: 1 day, 5:39:01, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3903, decode.acc_seg: 86.1401, loss: 0.3903 2023-01-05 23:54:11,451 - mmseg - INFO - Iter [3500/160000] lr: 5.869e-05, eta: 1 day, 5:38:21, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3770, decode.acc_seg: 86.3322, loss: 0.3770 2023-01-05 23:54:44,549 - mmseg - INFO - Iter [3550/160000] lr: 5.867e-05, eta: 1 day, 5:37:03, time: 0.662, data_time: 0.013, memory: 11582, decode.loss_ce: 0.3860, decode.acc_seg: 86.3182, loss: 0.3860 2023-01-05 23:55:17,470 - mmseg - INFO - Iter [3600/160000] lr: 5.865e-05, eta: 1 day, 5:35:39, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3912, decode.acc_seg: 85.9913, loss: 0.3912 2023-01-05 23:55:50,934 - mmseg - INFO - Iter [3650/160000] lr: 5.863e-05, eta: 1 day, 5:34:40, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3829, decode.acc_seg: 86.3143, loss: 0.3829 2023-01-05 23:56:24,023 - mmseg - INFO - Iter [3700/160000] lr: 5.861e-05, eta: 1 day, 5:33:25, time: 0.662, data_time: 0.013, memory: 11582, decode.loss_ce: 0.3808, decode.acc_seg: 86.0070, loss: 0.3808 2023-01-05 23:57:00,511 - mmseg - INFO - Iter [3750/160000] lr: 5.859e-05, eta: 1 day, 5:34:31, time: 0.729, data_time: 0.057, memory: 11582, decode.loss_ce: 0.3568, decode.acc_seg: 86.8180, loss: 0.3568 2023-01-05 23:57:34,581 - mmseg - INFO - Iter [3800/160000] lr: 5.858e-05, eta: 1 day, 5:33:57, time: 0.681, data_time: 0.015, memory: 11582, decode.loss_ce: 0.3481, decode.acc_seg: 87.4024, loss: 0.3481 2023-01-05 23:58:09,832 - mmseg - INFO - Iter [3850/160000] lr: 5.856e-05, eta: 1 day, 5:34:12, time: 0.706, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3297, decode.acc_seg: 88.3244, loss: 0.3297 2023-01-05 23:58:43,023 - mmseg - INFO - Iter [3900/160000] lr: 5.854e-05, eta: 1 day, 5:33:02, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3958, decode.acc_seg: 86.1090, loss: 0.3958 2023-01-05 23:59:17,480 - mmseg - INFO - Iter [3950/160000] lr: 5.852e-05, eta: 1 day, 5:32:41, time: 0.688, data_time: 0.013, memory: 11582, decode.loss_ce: 0.3274, decode.acc_seg: 87.8990, loss: 0.3274 2023-01-05 23:59:50,186 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-05 23:59:50,187 - mmseg - INFO - Iter [4000/160000] lr: 5.850e-05, eta: 1 day, 5:31:16, time: 0.655, data_time: 0.015, memory: 11582, decode.loss_ce: 0.3447, decode.acc_seg: 87.7652, loss: 0.3447 2023-01-06 00:00:22,867 - mmseg - INFO - Iter [4050/160000] lr: 5.848e-05, eta: 1 day, 5:29:48, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3414, decode.acc_seg: 87.5814, loss: 0.3414 2023-01-06 00:01:00,408 - mmseg - INFO - Iter [4100/160000] lr: 5.846e-05, eta: 1 day, 5:31:27, time: 0.751, data_time: 0.059, memory: 11582, decode.loss_ce: 0.3504, decode.acc_seg: 87.0039, loss: 0.3504 2023-01-06 00:01:33,153 - mmseg - INFO - Iter [4150/160000] lr: 5.844e-05, eta: 1 day, 5:30:02, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3514, decode.acc_seg: 87.1337, loss: 0.3514 2023-01-06 00:02:06,443 - mmseg - INFO - Iter [4200/160000] lr: 5.843e-05, eta: 1 day, 5:29:00, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3079, decode.acc_seg: 88.8485, loss: 0.3079 2023-01-06 00:02:39,107 - mmseg - INFO - Iter [4250/160000] lr: 5.841e-05, eta: 1 day, 5:27:34, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3668, decode.acc_seg: 86.8240, loss: 0.3668 2023-01-06 00:03:13,225 - mmseg - INFO - Iter [4300/160000] lr: 5.839e-05, eta: 1 day, 5:27:03, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3175, decode.acc_seg: 88.0231, loss: 0.3175 2023-01-06 00:03:47,091 - mmseg - INFO - Iter [4350/160000] lr: 5.837e-05, eta: 1 day, 5:26:22, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3582, decode.acc_seg: 87.1345, loss: 0.3582 2023-01-06 00:04:19,758 - mmseg - INFO - Iter [4400/160000] lr: 5.835e-05, eta: 1 day, 5:24:59, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3457, decode.acc_seg: 87.4234, loss: 0.3457 2023-01-06 00:04:52,640 - mmseg - INFO - Iter [4450/160000] lr: 5.833e-05, eta: 1 day, 5:23:45, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3169, decode.acc_seg: 88.4699, loss: 0.3169 2023-01-06 00:05:27,697 - mmseg - INFO - Iter [4500/160000] lr: 5.831e-05, eta: 1 day, 5:23:47, time: 0.701, data_time: 0.059, memory: 11582, decode.loss_ce: 0.3409, decode.acc_seg: 87.4027, loss: 0.3409 2023-01-06 00:06:00,426 - mmseg - INFO - Iter [4550/160000] lr: 5.829e-05, eta: 1 day, 5:22:29, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3283, decode.acc_seg: 87.7748, loss: 0.3283 2023-01-06 00:06:33,642 - mmseg - INFO - Iter [4600/160000] lr: 5.828e-05, eta: 1 day, 5:21:28, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3185, decode.acc_seg: 88.2300, loss: 0.3185 2023-01-06 00:07:06,679 - mmseg - INFO - Iter [4650/160000] lr: 5.826e-05, eta: 1 day, 5:20:21, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3211, decode.acc_seg: 88.4601, loss: 0.3211 2023-01-06 00:07:39,311 - mmseg - INFO - Iter [4700/160000] lr: 5.824e-05, eta: 1 day, 5:19:02, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3483, decode.acc_seg: 87.5185, loss: 0.3483 2023-01-06 00:08:12,012 - mmseg - INFO - Iter [4750/160000] lr: 5.822e-05, eta: 1 day, 5:17:46, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3555, decode.acc_seg: 87.1468, loss: 0.3555 2023-01-06 00:08:46,815 - mmseg - INFO - Iter [4800/160000] lr: 5.820e-05, eta: 1 day, 5:17:39, time: 0.696, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3100, decode.acc_seg: 88.4563, loss: 0.3100 2023-01-06 00:09:22,453 - mmseg - INFO - Iter [4850/160000] lr: 5.818e-05, eta: 1 day, 5:17:59, time: 0.713, data_time: 0.059, memory: 11582, decode.loss_ce: 0.3488, decode.acc_seg: 87.5539, loss: 0.3488 2023-01-06 00:09:56,074 - mmseg - INFO - Iter [4900/160000] lr: 5.816e-05, eta: 1 day, 5:17:13, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3107, decode.acc_seg: 88.3328, loss: 0.3107 2023-01-06 00:10:29,324 - mmseg - INFO - Iter [4950/160000] lr: 5.814e-05, eta: 1 day, 5:16:16, time: 0.665, data_time: 0.013, memory: 11582, decode.loss_ce: 0.3271, decode.acc_seg: 88.1734, loss: 0.3271 2023-01-06 00:11:02,969 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 00:11:02,969 - mmseg - INFO - Iter [5000/160000] lr: 5.813e-05, eta: 1 day, 5:15:31, time: 0.673, data_time: 0.013, memory: 11582, decode.loss_ce: 0.3123, decode.acc_seg: 88.3713, loss: 0.3123 2023-01-06 00:11:35,893 - mmseg - INFO - Iter [5050/160000] lr: 5.811e-05, eta: 1 day, 5:14:25, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3319, decode.acc_seg: 88.0679, loss: 0.3319 2023-01-06 00:12:08,408 - mmseg - INFO - Iter [5100/160000] lr: 5.809e-05, eta: 1 day, 5:13:07, time: 0.650, data_time: 0.013, memory: 11582, decode.loss_ce: 0.3529, decode.acc_seg: 87.5355, loss: 0.3529 2023-01-06 00:12:41,468 - mmseg - INFO - Iter [5150/160000] lr: 5.807e-05, eta: 1 day, 5:12:06, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3189, decode.acc_seg: 88.4696, loss: 0.3189 2023-01-06 00:13:14,500 - mmseg - INFO - Iter [5200/160000] lr: 5.805e-05, eta: 1 day, 5:11:05, time: 0.661, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2915, decode.acc_seg: 89.2669, loss: 0.2915 2023-01-06 00:13:49,237 - mmseg - INFO - Iter [5250/160000] lr: 5.803e-05, eta: 1 day, 5:10:54, time: 0.695, data_time: 0.058, memory: 11582, decode.loss_ce: 0.2932, decode.acc_seg: 89.3284, loss: 0.2932 2023-01-06 00:14:21,941 - mmseg - INFO - Iter [5300/160000] lr: 5.801e-05, eta: 1 day, 5:09:44, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2964, decode.acc_seg: 89.0862, loss: 0.2964 2023-01-06 00:14:56,415 - mmseg - INFO - Iter [5350/160000] lr: 5.799e-05, eta: 1 day, 5:09:25, time: 0.689, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3073, decode.acc_seg: 88.5421, loss: 0.3073 2023-01-06 00:15:29,378 - mmseg - INFO - Iter [5400/160000] lr: 5.798e-05, eta: 1 day, 5:08:24, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3111, decode.acc_seg: 88.4147, loss: 0.3111 2023-01-06 00:16:01,729 - mmseg - INFO - Iter [5450/160000] lr: 5.796e-05, eta: 1 day, 5:07:06, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3010, decode.acc_seg: 88.8364, loss: 0.3010 2023-01-06 00:16:35,292 - mmseg - INFO - Iter [5500/160000] lr: 5.794e-05, eta: 1 day, 5:06:22, time: 0.671, data_time: 0.013, memory: 11582, decode.loss_ce: 0.3574, decode.acc_seg: 87.2220, loss: 0.3574 2023-01-06 00:17:11,227 - mmseg - INFO - Iter [5550/160000] lr: 5.792e-05, eta: 1 day, 5:06:43, time: 0.718, data_time: 0.013, memory: 11582, decode.loss_ce: 0.3115, decode.acc_seg: 88.8359, loss: 0.3115 2023-01-06 00:17:46,763 - mmseg - INFO - Iter [5600/160000] lr: 5.790e-05, eta: 1 day, 5:06:55, time: 0.712, data_time: 0.060, memory: 11582, decode.loss_ce: 0.3184, decode.acc_seg: 88.5525, loss: 0.3184 2023-01-06 00:18:20,555 - mmseg - INFO - Iter [5650/160000] lr: 5.788e-05, eta: 1 day, 5:06:15, time: 0.675, data_time: 0.013, memory: 11582, decode.loss_ce: 0.3079, decode.acc_seg: 89.0364, loss: 0.3079 2023-01-06 00:18:53,673 - mmseg - INFO - Iter [5700/160000] lr: 5.786e-05, eta: 1 day, 5:05:20, time: 0.663, data_time: 0.018, memory: 11582, decode.loss_ce: 0.3406, decode.acc_seg: 87.4288, loss: 0.3406 2023-01-06 00:19:27,944 - mmseg - INFO - Iter [5750/160000] lr: 5.784e-05, eta: 1 day, 5:04:56, time: 0.686, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3052, decode.acc_seg: 88.3222, loss: 0.3052 2023-01-06 00:20:00,706 - mmseg - INFO - Iter [5800/160000] lr: 5.783e-05, eta: 1 day, 5:03:49, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2767, decode.acc_seg: 89.5944, loss: 0.2767 2023-01-06 00:20:34,625 - mmseg - INFO - Iter [5850/160000] lr: 5.781e-05, eta: 1 day, 5:03:15, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2979, decode.acc_seg: 89.0163, loss: 0.2979 2023-01-06 00:21:10,574 - mmseg - INFO - Iter [5900/160000] lr: 5.779e-05, eta: 1 day, 5:03:34, time: 0.719, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2897, decode.acc_seg: 89.2295, loss: 0.2897 2023-01-06 00:21:43,377 - mmseg - INFO - Iter [5950/160000] lr: 5.777e-05, eta: 1 day, 5:02:32, time: 0.657, data_time: 0.015, memory: 11582, decode.loss_ce: 0.3008, decode.acc_seg: 89.0187, loss: 0.3008 2023-01-06 00:22:19,073 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 00:22:19,074 - mmseg - INFO - Iter [6000/160000] lr: 5.775e-05, eta: 1 day, 5:02:43, time: 0.714, data_time: 0.059, memory: 11582, decode.loss_ce: 0.2844, decode.acc_seg: 89.2802, loss: 0.2844 2023-01-06 00:22:55,215 - mmseg - INFO - Iter [6050/160000] lr: 5.773e-05, eta: 1 day, 5:03:04, time: 0.722, data_time: 0.013, memory: 11582, decode.loss_ce: 0.3023, decode.acc_seg: 89.1593, loss: 0.3023 2023-01-06 00:23:30,236 - mmseg - INFO - Iter [6100/160000] lr: 5.771e-05, eta: 1 day, 5:02:56, time: 0.700, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3165, decode.acc_seg: 88.3105, loss: 0.3165 2023-01-06 00:24:05,286 - mmseg - INFO - Iter [6150/160000] lr: 5.769e-05, eta: 1 day, 5:02:50, time: 0.702, data_time: 0.014, memory: 11582, decode.loss_ce: 0.3061, decode.acc_seg: 88.4607, loss: 0.3061 2023-01-06 00:24:37,920 - mmseg - INFO - Iter [6200/160000] lr: 5.768e-05, eta: 1 day, 5:01:43, time: 0.653, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2971, decode.acc_seg: 88.9065, loss: 0.2971 2023-01-06 00:25:10,820 - mmseg - INFO - Iter [6250/160000] lr: 5.766e-05, eta: 1 day, 5:00:41, time: 0.657, data_time: 0.013, memory: 11582, decode.loss_ce: 0.3191, decode.acc_seg: 88.4474, loss: 0.3191 2023-01-06 00:25:45,604 - mmseg - INFO - Iter [6300/160000] lr: 5.764e-05, eta: 1 day, 5:00:28, time: 0.697, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2815, decode.acc_seg: 89.5188, loss: 0.2815 2023-01-06 00:26:20,861 - mmseg - INFO - Iter [6350/160000] lr: 5.762e-05, eta: 1 day, 5:00:25, time: 0.705, data_time: 0.059, memory: 11582, decode.loss_ce: 0.3150, decode.acc_seg: 88.3413, loss: 0.3150 2023-01-06 00:26:53,877 - mmseg - INFO - Iter [6400/160000] lr: 5.760e-05, eta: 1 day, 4:59:28, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2838, decode.acc_seg: 89.3102, loss: 0.2838 2023-01-06 00:27:27,755 - mmseg - INFO - Iter [6450/160000] lr: 5.758e-05, eta: 1 day, 4:58:52, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2816, decode.acc_seg: 89.3295, loss: 0.2816 2023-01-06 00:28:02,572 - mmseg - INFO - Iter [6500/160000] lr: 5.756e-05, eta: 1 day, 4:58:37, time: 0.695, data_time: 0.013, memory: 11582, decode.loss_ce: 0.3022, decode.acc_seg: 88.8883, loss: 0.3022 2023-01-06 00:28:35,396 - mmseg - INFO - Iter [6550/160000] lr: 5.754e-05, eta: 1 day, 4:57:37, time: 0.657, data_time: 0.015, memory: 11582, decode.loss_ce: 0.3038, decode.acc_seg: 89.0656, loss: 0.3038 2023-01-06 00:29:08,099 - mmseg - INFO - Iter [6600/160000] lr: 5.753e-05, eta: 1 day, 4:56:33, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2998, decode.acc_seg: 89.1349, loss: 0.2998 2023-01-06 00:29:42,134 - mmseg - INFO - Iter [6650/160000] lr: 5.751e-05, eta: 1 day, 4:56:01, time: 0.681, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2785, decode.acc_seg: 89.8729, loss: 0.2785 2023-01-06 00:30:18,768 - mmseg - INFO - Iter [6700/160000] lr: 5.749e-05, eta: 1 day, 4:56:27, time: 0.732, data_time: 0.058, memory: 11582, decode.loss_ce: 0.2917, decode.acc_seg: 88.6670, loss: 0.2917 2023-01-06 00:30:53,372 - mmseg - INFO - Iter [6750/160000] lr: 5.747e-05, eta: 1 day, 4:56:08, time: 0.693, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2912, decode.acc_seg: 89.2439, loss: 0.2912 2023-01-06 00:31:26,062 - mmseg - INFO - Iter [6800/160000] lr: 5.745e-05, eta: 1 day, 4:55:05, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2792, decode.acc_seg: 89.4769, loss: 0.2792 2023-01-06 00:32:00,338 - mmseg - INFO - Iter [6850/160000] lr: 5.743e-05, eta: 1 day, 4:54:37, time: 0.685, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2767, decode.acc_seg: 89.7301, loss: 0.2767 2023-01-06 00:32:35,702 - mmseg - INFO - Iter [6900/160000] lr: 5.741e-05, eta: 1 day, 4:54:34, time: 0.708, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2806, decode.acc_seg: 89.4222, loss: 0.2806 2023-01-06 00:33:08,808 - mmseg - INFO - Iter [6950/160000] lr: 5.739e-05, eta: 1 day, 4:53:41, time: 0.662, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2960, decode.acc_seg: 88.9467, loss: 0.2960 2023-01-06 00:33:42,377 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 00:33:42,378 - mmseg - INFO - Iter [7000/160000] lr: 5.738e-05, eta: 1 day, 4:52:58, time: 0.671, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2713, decode.acc_seg: 89.4691, loss: 0.2713 2023-01-06 00:34:15,577 - mmseg - INFO - Iter [7050/160000] lr: 5.736e-05, eta: 1 day, 4:52:07, time: 0.664, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2644, decode.acc_seg: 90.1812, loss: 0.2644 2023-01-06 00:34:51,028 - mmseg - INFO - Iter [7100/160000] lr: 5.734e-05, eta: 1 day, 4:52:05, time: 0.709, data_time: 0.059, memory: 11582, decode.loss_ce: 0.2813, decode.acc_seg: 89.8008, loss: 0.2813 2023-01-06 00:35:23,797 - mmseg - INFO - Iter [7150/160000] lr: 5.732e-05, eta: 1 day, 4:51:05, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2880, decode.acc_seg: 89.1261, loss: 0.2880 2023-01-06 00:35:56,728 - mmseg - INFO - Iter [7200/160000] lr: 5.730e-05, eta: 1 day, 4:50:09, time: 0.659, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2661, decode.acc_seg: 89.9000, loss: 0.2661 2023-01-06 00:36:32,529 - mmseg - INFO - Iter [7250/160000] lr: 5.728e-05, eta: 1 day, 4:50:13, time: 0.716, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2660, decode.acc_seg: 89.9212, loss: 0.2660 2023-01-06 00:37:08,400 - mmseg - INFO - Iter [7300/160000] lr: 5.726e-05, eta: 1 day, 4:50:19, time: 0.717, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2830, decode.acc_seg: 89.6977, loss: 0.2830 2023-01-06 00:37:41,419 - mmseg - INFO - Iter [7350/160000] lr: 5.724e-05, eta: 1 day, 4:49:24, time: 0.660, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2554, decode.acc_seg: 90.4142, loss: 0.2554 2023-01-06 00:38:14,242 - mmseg - INFO - Iter [7400/160000] lr: 5.723e-05, eta: 1 day, 4:48:27, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2840, decode.acc_seg: 89.4021, loss: 0.2840 2023-01-06 00:38:50,136 - mmseg - INFO - Iter [7450/160000] lr: 5.721e-05, eta: 1 day, 4:48:32, time: 0.718, data_time: 0.058, memory: 11582, decode.loss_ce: 0.2909, decode.acc_seg: 88.9713, loss: 0.2909 2023-01-06 00:39:22,847 - mmseg - INFO - Iter [7500/160000] lr: 5.719e-05, eta: 1 day, 4:47:32, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2594, decode.acc_seg: 89.9878, loss: 0.2594 2023-01-06 00:39:57,475 - mmseg - INFO - Iter [7550/160000] lr: 5.717e-05, eta: 1 day, 4:47:11, time: 0.693, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2514, decode.acc_seg: 90.6361, loss: 0.2514 2023-01-06 00:40:31,837 - mmseg - INFO - Iter [7600/160000] lr: 5.715e-05, eta: 1 day, 4:46:44, time: 0.687, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2626, decode.acc_seg: 90.1483, loss: 0.2626 2023-01-06 00:41:04,797 - mmseg - INFO - Iter [7650/160000] lr: 5.713e-05, eta: 1 day, 4:45:50, time: 0.659, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2581, decode.acc_seg: 90.2575, loss: 0.2581 2023-01-06 00:41:37,558 - mmseg - INFO - Iter [7700/160000] lr: 5.711e-05, eta: 1 day, 4:44:51, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2700, decode.acc_seg: 89.7449, loss: 0.2700 2023-01-06 00:42:10,384 - mmseg - INFO - Iter [7750/160000] lr: 5.709e-05, eta: 1 day, 4:43:55, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2677, decode.acc_seg: 90.1221, loss: 0.2677 2023-01-06 00:42:43,338 - mmseg - INFO - Iter [7800/160000] lr: 5.708e-05, eta: 1 day, 4:43:01, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2756, decode.acc_seg: 89.6200, loss: 0.2756 2023-01-06 00:43:18,893 - mmseg - INFO - Iter [7850/160000] lr: 5.706e-05, eta: 1 day, 4:42:58, time: 0.712, data_time: 0.060, memory: 11582, decode.loss_ce: 0.2621, decode.acc_seg: 90.1832, loss: 0.2621 2023-01-06 00:43:51,794 - mmseg - INFO - Iter [7900/160000] lr: 5.704e-05, eta: 1 day, 4:42:03, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2458, decode.acc_seg: 90.9496, loss: 0.2458 2023-01-06 00:44:25,182 - mmseg - INFO - Iter [7950/160000] lr: 5.702e-05, eta: 1 day, 4:41:18, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2642, decode.acc_seg: 90.1256, loss: 0.2642 2023-01-06 00:44:58,733 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 00:44:58,734 - mmseg - INFO - Iter [8000/160000] lr: 5.700e-05, eta: 1 day, 4:40:36, time: 0.671, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2774, decode.acc_seg: 89.5833, loss: 0.2774 2023-01-06 00:45:31,891 - mmseg - INFO - Iter [8050/160000] lr: 5.698e-05, eta: 1 day, 4:39:48, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2659, decode.acc_seg: 90.0293, loss: 0.2659 2023-01-06 00:46:05,294 - mmseg - INFO - Iter [8100/160000] lr: 5.696e-05, eta: 1 day, 4:39:03, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2775, decode.acc_seg: 89.7877, loss: 0.2775 2023-01-06 00:46:39,589 - mmseg - INFO - Iter [8150/160000] lr: 5.694e-05, eta: 1 day, 4:38:35, time: 0.685, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2627, decode.acc_seg: 90.2157, loss: 0.2627 2023-01-06 00:47:15,445 - mmseg - INFO - Iter [8200/160000] lr: 5.693e-05, eta: 1 day, 4:38:37, time: 0.718, data_time: 0.060, memory: 11582, decode.loss_ce: 0.2368, decode.acc_seg: 90.7913, loss: 0.2368 2023-01-06 00:47:48,191 - mmseg - INFO - Iter [8250/160000] lr: 5.691e-05, eta: 1 day, 4:37:41, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2370, decode.acc_seg: 90.8456, loss: 0.2370 2023-01-06 00:48:21,288 - mmseg - INFO - Iter [8300/160000] lr: 5.689e-05, eta: 1 day, 4:36:51, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2438, decode.acc_seg: 90.6632, loss: 0.2438 2023-01-06 00:48:56,859 - mmseg - INFO - Iter [8350/160000] lr: 5.687e-05, eta: 1 day, 4:36:45, time: 0.711, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2454, decode.acc_seg: 90.6265, loss: 0.2454 2023-01-06 00:49:30,559 - mmseg - INFO - Iter [8400/160000] lr: 5.685e-05, eta: 1 day, 4:36:08, time: 0.675, data_time: 0.015, memory: 11582, decode.loss_ce: 0.2598, decode.acc_seg: 90.0428, loss: 0.2598 2023-01-06 00:50:05,590 - mmseg - INFO - Iter [8450/160000] lr: 5.683e-05, eta: 1 day, 4:35:53, time: 0.700, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2354, decode.acc_seg: 91.1389, loss: 0.2354 2023-01-06 00:50:38,639 - mmseg - INFO - Iter [8500/160000] lr: 5.681e-05, eta: 1 day, 4:35:02, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2757, decode.acc_seg: 89.7222, loss: 0.2757 2023-01-06 00:51:14,244 - mmseg - INFO - Iter [8550/160000] lr: 5.679e-05, eta: 1 day, 4:34:58, time: 0.712, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2357, decode.acc_seg: 90.7966, loss: 0.2357 2023-01-06 00:51:50,268 - mmseg - INFO - Iter [8600/160000] lr: 5.678e-05, eta: 1 day, 4:35:00, time: 0.720, data_time: 0.058, memory: 11582, decode.loss_ce: 0.2446, decode.acc_seg: 90.5618, loss: 0.2446 2023-01-06 00:52:24,823 - mmseg - INFO - Iter [8650/160000] lr: 5.676e-05, eta: 1 day, 4:34:36, time: 0.691, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2567, decode.acc_seg: 90.4528, loss: 0.2567 2023-01-06 00:52:57,962 - mmseg - INFO - Iter [8700/160000] lr: 5.674e-05, eta: 1 day, 4:33:46, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2469, decode.acc_seg: 90.4514, loss: 0.2469 2023-01-06 00:53:31,355 - mmseg - INFO - Iter [8750/160000] lr: 5.672e-05, eta: 1 day, 4:33:03, time: 0.669, data_time: 0.015, memory: 11582, decode.loss_ce: 0.2239, decode.acc_seg: 91.2829, loss: 0.2239 2023-01-06 00:54:04,048 - mmseg - INFO - Iter [8800/160000] lr: 5.670e-05, eta: 1 day, 4:32:07, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2372, decode.acc_seg: 90.7668, loss: 0.2372 2023-01-06 00:54:36,867 - mmseg - INFO - Iter [8850/160000] lr: 5.668e-05, eta: 1 day, 4:31:13, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2717, decode.acc_seg: 89.5668, loss: 0.2717 2023-01-06 00:55:09,648 - mmseg - INFO - Iter [8900/160000] lr: 5.666e-05, eta: 1 day, 4:30:19, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2609, decode.acc_seg: 90.0988, loss: 0.2609 2023-01-06 00:55:46,051 - mmseg - INFO - Iter [8950/160000] lr: 5.664e-05, eta: 1 day, 4:30:26, time: 0.728, data_time: 0.058, memory: 11582, decode.loss_ce: 0.2413, decode.acc_seg: 90.9873, loss: 0.2413 2023-01-06 00:56:19,371 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 00:56:19,372 - mmseg - INFO - Iter [9000/160000] lr: 5.663e-05, eta: 1 day, 4:29:41, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2589, decode.acc_seg: 90.1305, loss: 0.2589 2023-01-06 00:56:52,220 - mmseg - INFO - Iter [9050/160000] lr: 5.661e-05, eta: 1 day, 4:28:49, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2380, decode.acc_seg: 90.9989, loss: 0.2380 2023-01-06 00:57:25,122 - mmseg - INFO - Iter [9100/160000] lr: 5.659e-05, eta: 1 day, 4:27:57, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2395, decode.acc_seg: 90.7839, loss: 0.2395 2023-01-06 00:57:59,604 - mmseg - INFO - Iter [9150/160000] lr: 5.657e-05, eta: 1 day, 4:27:31, time: 0.689, data_time: 0.015, memory: 11582, decode.loss_ce: 0.2333, decode.acc_seg: 91.0310, loss: 0.2333 2023-01-06 00:58:33,206 - mmseg - INFO - Iter [9200/160000] lr: 5.655e-05, eta: 1 day, 4:26:52, time: 0.673, data_time: 0.015, memory: 11582, decode.loss_ce: 0.2553, decode.acc_seg: 90.3027, loss: 0.2553 2023-01-06 00:59:07,209 - mmseg - INFO - Iter [9250/160000] lr: 5.653e-05, eta: 1 day, 4:26:19, time: 0.680, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2587, decode.acc_seg: 90.0772, loss: 0.2587 2023-01-06 00:59:41,038 - mmseg - INFO - Iter [9300/160000] lr: 5.651e-05, eta: 1 day, 4:25:43, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2561, decode.acc_seg: 89.9111, loss: 0.2561 2023-01-06 01:00:15,948 - mmseg - INFO - Iter [9350/160000] lr: 5.649e-05, eta: 1 day, 4:25:24, time: 0.698, data_time: 0.059, memory: 11582, decode.loss_ce: 0.2553, decode.acc_seg: 90.4930, loss: 0.2553 2023-01-06 01:00:48,894 - mmseg - INFO - Iter [9400/160000] lr: 5.648e-05, eta: 1 day, 4:24:34, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2769, decode.acc_seg: 89.7904, loss: 0.2769 2023-01-06 01:01:22,811 - mmseg - INFO - Iter [9450/160000] lr: 5.646e-05, eta: 1 day, 4:24:00, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2424, decode.acc_seg: 90.8375, loss: 0.2424 2023-01-06 01:01:56,500 - mmseg - INFO - Iter [9500/160000] lr: 5.644e-05, eta: 1 day, 4:23:21, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2419, decode.acc_seg: 91.0075, loss: 0.2419 2023-01-06 01:02:31,285 - mmseg - INFO - Iter [9550/160000] lr: 5.642e-05, eta: 1 day, 4:23:01, time: 0.696, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2488, decode.acc_seg: 90.4022, loss: 0.2488 2023-01-06 01:03:05,088 - mmseg - INFO - Iter [9600/160000] lr: 5.640e-05, eta: 1 day, 4:22:23, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2563, decode.acc_seg: 90.1843, loss: 0.2563 2023-01-06 01:03:39,130 - mmseg - INFO - Iter [9650/160000] lr: 5.638e-05, eta: 1 day, 4:21:51, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2285, decode.acc_seg: 91.5677, loss: 0.2285 2023-01-06 01:04:14,330 - mmseg - INFO - Iter [9700/160000] lr: 5.636e-05, eta: 1 day, 4:21:37, time: 0.704, data_time: 0.059, memory: 11582, decode.loss_ce: 0.2533, decode.acc_seg: 90.5854, loss: 0.2533 2023-01-06 01:04:47,516 - mmseg - INFO - Iter [9750/160000] lr: 5.634e-05, eta: 1 day, 4:20:50, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2443, decode.acc_seg: 90.7972, loss: 0.2443 2023-01-06 01:05:22,345 - mmseg - INFO - Iter [9800/160000] lr: 5.633e-05, eta: 1 day, 4:20:30, time: 0.697, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2607, decode.acc_seg: 90.3701, loss: 0.2607 2023-01-06 01:05:55,052 - mmseg - INFO - Iter [9850/160000] lr: 5.631e-05, eta: 1 day, 4:19:37, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2365, decode.acc_seg: 91.0885, loss: 0.2365 2023-01-06 01:06:27,682 - mmseg - INFO - Iter [9900/160000] lr: 5.629e-05, eta: 1 day, 4:18:43, time: 0.653, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2443, decode.acc_seg: 90.3935, loss: 0.2443 2023-01-06 01:07:00,438 - mmseg - INFO - Iter [9950/160000] lr: 5.627e-05, eta: 1 day, 4:17:51, time: 0.655, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2312, decode.acc_seg: 91.1534, loss: 0.2312 2023-01-06 01:07:33,853 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 01:07:33,854 - mmseg - INFO - Iter [10000/160000] lr: 5.625e-05, eta: 1 day, 4:17:09, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2182, decode.acc_seg: 91.5799, loss: 0.2182 2023-01-06 01:08:09,701 - mmseg - INFO - Iter [10050/160000] lr: 5.623e-05, eta: 1 day, 4:17:03, time: 0.716, data_time: 0.058, memory: 11582, decode.loss_ce: 0.2314, decode.acc_seg: 91.2036, loss: 0.2314 2023-01-06 01:08:44,036 - mmseg - INFO - Iter [10100/160000] lr: 5.621e-05, eta: 1 day, 4:16:35, time: 0.687, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2533, decode.acc_seg: 90.4849, loss: 0.2533 2023-01-06 01:09:17,604 - mmseg - INFO - Iter [10150/160000] lr: 5.619e-05, eta: 1 day, 4:15:56, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2508, decode.acc_seg: 90.3679, loss: 0.2508 2023-01-06 01:09:50,714 - mmseg - INFO - Iter [10200/160000] lr: 5.618e-05, eta: 1 day, 4:15:09, time: 0.662, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2423, decode.acc_seg: 90.7310, loss: 0.2423 2023-01-06 01:10:23,422 - mmseg - INFO - Iter [10250/160000] lr: 5.616e-05, eta: 1 day, 4:14:17, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2374, decode.acc_seg: 90.5730, loss: 0.2374 2023-01-06 01:10:59,040 - mmseg - INFO - Iter [10300/160000] lr: 5.614e-05, eta: 1 day, 4:14:08, time: 0.712, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2314, decode.acc_seg: 91.3866, loss: 0.2314 2023-01-06 01:11:32,453 - mmseg - INFO - Iter [10350/160000] lr: 5.612e-05, eta: 1 day, 4:13:26, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2539, decode.acc_seg: 90.2133, loss: 0.2539 2023-01-06 01:12:06,458 - mmseg - INFO - Iter [10400/160000] lr: 5.610e-05, eta: 1 day, 4:12:52, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2328, decode.acc_seg: 91.0936, loss: 0.2328 2023-01-06 01:12:43,045 - mmseg - INFO - Iter [10450/160000] lr: 5.608e-05, eta: 1 day, 4:12:56, time: 0.732, data_time: 0.060, memory: 11582, decode.loss_ce: 0.2064, decode.acc_seg: 91.8117, loss: 0.2064 2023-01-06 01:13:18,302 - mmseg - INFO - Iter [10500/160000] lr: 5.606e-05, eta: 1 day, 4:12:41, time: 0.706, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2498, decode.acc_seg: 90.6325, loss: 0.2498 2023-01-06 01:13:51,230 - mmseg - INFO - Iter [10550/160000] lr: 5.604e-05, eta: 1 day, 4:11:52, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2278, decode.acc_seg: 91.3749, loss: 0.2278 2023-01-06 01:14:24,250 - mmseg - INFO - Iter [10600/160000] lr: 5.603e-05, eta: 1 day, 4:11:05, time: 0.660, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2234, decode.acc_seg: 91.5657, loss: 0.2234 2023-01-06 01:14:57,659 - mmseg - INFO - Iter [10650/160000] lr: 5.601e-05, eta: 1 day, 4:10:23, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2297, decode.acc_seg: 91.4047, loss: 0.2297 2023-01-06 01:15:30,562 - mmseg - INFO - Iter [10700/160000] lr: 5.599e-05, eta: 1 day, 4:09:35, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2276, decode.acc_seg: 91.3301, loss: 0.2276 2023-01-06 01:16:03,415 - mmseg - INFO - Iter [10750/160000] lr: 5.597e-05, eta: 1 day, 4:08:46, time: 0.657, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2387, decode.acc_seg: 90.8559, loss: 0.2387 2023-01-06 01:16:39,294 - mmseg - INFO - Iter [10800/160000] lr: 5.595e-05, eta: 1 day, 4:08:38, time: 0.718, data_time: 0.058, memory: 11582, decode.loss_ce: 0.2411, decode.acc_seg: 90.7911, loss: 0.2411 2023-01-06 01:17:13,526 - mmseg - INFO - Iter [10850/160000] lr: 5.593e-05, eta: 1 day, 4:08:08, time: 0.685, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2032, decode.acc_seg: 91.7936, loss: 0.2032 2023-01-06 01:17:46,545 - mmseg - INFO - Iter [10900/160000] lr: 5.591e-05, eta: 1 day, 4:07:21, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2268, decode.acc_seg: 91.3589, loss: 0.2268 2023-01-06 01:18:20,286 - mmseg - INFO - Iter [10950/160000] lr: 5.589e-05, eta: 1 day, 4:06:45, time: 0.675, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2345, decode.acc_seg: 91.1232, loss: 0.2345 2023-01-06 01:18:53,794 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 01:18:53,795 - mmseg - INFO - Iter [11000/160000] lr: 5.588e-05, eta: 1 day, 4:06:04, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2235, decode.acc_seg: 91.3622, loss: 0.2235 2023-01-06 01:19:29,172 - mmseg - INFO - Iter [11050/160000] lr: 5.586e-05, eta: 1 day, 4:05:49, time: 0.707, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2351, decode.acc_seg: 91.0807, loss: 0.2351 2023-01-06 01:20:03,184 - mmseg - INFO - Iter [11100/160000] lr: 5.584e-05, eta: 1 day, 4:05:16, time: 0.680, data_time: 0.015, memory: 11582, decode.loss_ce: 0.2365, decode.acc_seg: 90.9168, loss: 0.2365 2023-01-06 01:20:36,671 - mmseg - INFO - Iter [11150/160000] lr: 5.582e-05, eta: 1 day, 4:04:37, time: 0.671, data_time: 0.015, memory: 11582, decode.loss_ce: 0.2488, decode.acc_seg: 90.6726, loss: 0.2488 2023-01-06 01:21:12,301 - mmseg - INFO - Iter [11200/160000] lr: 5.580e-05, eta: 1 day, 4:04:25, time: 0.712, data_time: 0.059, memory: 11582, decode.loss_ce: 0.2320, decode.acc_seg: 91.2153, loss: 0.2320 2023-01-06 01:21:46,594 - mmseg - INFO - Iter [11250/160000] lr: 5.578e-05, eta: 1 day, 4:03:55, time: 0.686, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2591, decode.acc_seg: 90.1831, loss: 0.2591 2023-01-06 01:22:20,027 - mmseg - INFO - Iter [11300/160000] lr: 5.576e-05, eta: 1 day, 4:03:14, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2228, decode.acc_seg: 91.3552, loss: 0.2228 2023-01-06 01:22:55,302 - mmseg - INFO - Iter [11350/160000] lr: 5.574e-05, eta: 1 day, 4:02:58, time: 0.706, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2288, decode.acc_seg: 91.3075, loss: 0.2288 2023-01-06 01:23:28,000 - mmseg - INFO - Iter [11400/160000] lr: 5.573e-05, eta: 1 day, 4:02:07, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2191, decode.acc_seg: 91.4821, loss: 0.2191 2023-01-06 01:24:01,034 - mmseg - INFO - Iter [11450/160000] lr: 5.571e-05, eta: 1 day, 4:01:21, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2321, decode.acc_seg: 91.1747, loss: 0.2321 2023-01-06 01:24:34,611 - mmseg - INFO - Iter [11500/160000] lr: 5.569e-05, eta: 1 day, 4:00:42, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2079, decode.acc_seg: 92.0196, loss: 0.2079 2023-01-06 01:25:10,550 - mmseg - INFO - Iter [11550/160000] lr: 5.567e-05, eta: 1 day, 4:00:34, time: 0.719, data_time: 0.059, memory: 11582, decode.loss_ce: 0.2079, decode.acc_seg: 92.1007, loss: 0.2079 2023-01-06 01:25:43,769 - mmseg - INFO - Iter [11600/160000] lr: 5.565e-05, eta: 1 day, 3:59:50, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2363, decode.acc_seg: 90.9616, loss: 0.2363 2023-01-06 01:26:17,327 - mmseg - INFO - Iter [11650/160000] lr: 5.563e-05, eta: 1 day, 3:59:11, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2312, decode.acc_seg: 91.2516, loss: 0.2312 2023-01-06 01:26:50,274 - mmseg - INFO - Iter [11700/160000] lr: 5.561e-05, eta: 1 day, 3:58:25, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2322, decode.acc_seg: 91.0386, loss: 0.2322 2023-01-06 01:27:22,946 - mmseg - INFO - Iter [11750/160000] lr: 5.559e-05, eta: 1 day, 3:57:34, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2485, decode.acc_seg: 90.6568, loss: 0.2485 2023-01-06 01:27:58,035 - mmseg - INFO - Iter [11800/160000] lr: 5.558e-05, eta: 1 day, 3:57:15, time: 0.702, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2304, decode.acc_seg: 91.1118, loss: 0.2304 2023-01-06 01:28:30,909 - mmseg - INFO - Iter [11850/160000] lr: 5.556e-05, eta: 1 day, 3:56:27, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2154, decode.acc_seg: 91.5231, loss: 0.2154 2023-01-06 01:29:04,909 - mmseg - INFO - Iter [11900/160000] lr: 5.554e-05, eta: 1 day, 3:55:54, time: 0.680, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2227, decode.acc_seg: 91.6417, loss: 0.2227 2023-01-06 01:29:41,073 - mmseg - INFO - Iter [11950/160000] lr: 5.552e-05, eta: 1 day, 3:55:48, time: 0.723, data_time: 0.059, memory: 11582, decode.loss_ce: 0.2103, decode.acc_seg: 91.8039, loss: 0.2103 2023-01-06 01:30:13,957 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 01:30:13,958 - mmseg - INFO - Iter [12000/160000] lr: 5.550e-05, eta: 1 day, 3:55:00, time: 0.658, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2095, decode.acc_seg: 91.9394, loss: 0.2095 2023-01-06 01:30:48,860 - mmseg - INFO - Iter [12050/160000] lr: 5.548e-05, eta: 1 day, 3:54:38, time: 0.698, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2062, decode.acc_seg: 92.2163, loss: 0.2062 2023-01-06 01:31:23,204 - mmseg - INFO - Iter [12100/160000] lr: 5.546e-05, eta: 1 day, 3:54:09, time: 0.687, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2044, decode.acc_seg: 91.9372, loss: 0.2044 2023-01-06 01:31:57,450 - mmseg - INFO - Iter [12150/160000] lr: 5.544e-05, eta: 1 day, 3:53:38, time: 0.685, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2169, decode.acc_seg: 91.4326, loss: 0.2169 2023-01-06 01:32:30,672 - mmseg - INFO - Iter [12200/160000] lr: 5.543e-05, eta: 1 day, 3:52:55, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2132, decode.acc_seg: 92.1829, loss: 0.2132 2023-01-06 01:33:05,094 - mmseg - INFO - Iter [12250/160000] lr: 5.541e-05, eta: 1 day, 3:52:27, time: 0.688, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2170, decode.acc_seg: 91.5000, loss: 0.2170 2023-01-06 01:33:41,339 - mmseg - INFO - Iter [12300/160000] lr: 5.539e-05, eta: 1 day, 3:52:20, time: 0.725, data_time: 0.060, memory: 11582, decode.loss_ce: 0.2081, decode.acc_seg: 91.9769, loss: 0.2081 2023-01-06 01:34:14,014 - mmseg - INFO - Iter [12350/160000] lr: 5.537e-05, eta: 1 day, 3:51:31, time: 0.654, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1953, decode.acc_seg: 92.3408, loss: 0.1953 2023-01-06 01:34:46,925 - mmseg - INFO - Iter [12400/160000] lr: 5.535e-05, eta: 1 day, 3:50:45, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2068, decode.acc_seg: 92.2200, loss: 0.2068 2023-01-06 01:35:21,717 - mmseg - INFO - Iter [12450/160000] lr: 5.533e-05, eta: 1 day, 3:50:21, time: 0.696, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2209, decode.acc_seg: 91.6825, loss: 0.2209 2023-01-06 01:35:54,573 - mmseg - INFO - Iter [12500/160000] lr: 5.531e-05, eta: 1 day, 3:49:34, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2429, decode.acc_seg: 90.9528, loss: 0.2429 2023-01-06 01:36:28,053 - mmseg - INFO - Iter [12550/160000] lr: 5.529e-05, eta: 1 day, 3:48:53, time: 0.669, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2250, decode.acc_seg: 91.2586, loss: 0.2250 2023-01-06 01:37:03,854 - mmseg - INFO - Iter [12600/160000] lr: 5.528e-05, eta: 1 day, 3:48:42, time: 0.717, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2122, decode.acc_seg: 91.6914, loss: 0.2122 2023-01-06 01:37:40,025 - mmseg - INFO - Iter [12650/160000] lr: 5.526e-05, eta: 1 day, 3:48:33, time: 0.723, data_time: 0.058, memory: 11582, decode.loss_ce: 0.2193, decode.acc_seg: 91.4284, loss: 0.2193 2023-01-06 01:38:14,426 - mmseg - INFO - Iter [12700/160000] lr: 5.524e-05, eta: 1 day, 3:48:04, time: 0.688, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2138, decode.acc_seg: 91.6600, loss: 0.2138 2023-01-06 01:38:47,474 - mmseg - INFO - Iter [12750/160000] lr: 5.522e-05, eta: 1 day, 3:47:20, time: 0.661, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2116, decode.acc_seg: 91.7157, loss: 0.2116 2023-01-06 01:39:22,957 - mmseg - INFO - Iter [12800/160000] lr: 5.520e-05, eta: 1 day, 3:47:03, time: 0.709, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2047, decode.acc_seg: 91.8799, loss: 0.2047 2023-01-06 01:39:56,789 - mmseg - INFO - Iter [12850/160000] lr: 5.518e-05, eta: 1 day, 3:46:28, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2129, decode.acc_seg: 91.6687, loss: 0.2129 2023-01-06 01:40:30,939 - mmseg - INFO - Iter [12900/160000] lr: 5.516e-05, eta: 1 day, 3:45:56, time: 0.683, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2367, decode.acc_seg: 91.1586, loss: 0.2367 2023-01-06 01:41:04,339 - mmseg - INFO - Iter [12950/160000] lr: 5.514e-05, eta: 1 day, 3:45:15, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2228, decode.acc_seg: 91.4902, loss: 0.2228 2023-01-06 01:41:37,063 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 01:41:37,063 - mmseg - INFO - Iter [13000/160000] lr: 5.513e-05, eta: 1 day, 3:44:27, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2187, decode.acc_seg: 91.5810, loss: 0.2187 2023-01-06 01:42:12,878 - mmseg - INFO - Iter [13050/160000] lr: 5.511e-05, eta: 1 day, 3:44:14, time: 0.716, data_time: 0.059, memory: 11582, decode.loss_ce: 0.2169, decode.acc_seg: 91.4783, loss: 0.2169 2023-01-06 01:42:47,002 - mmseg - INFO - Iter [13100/160000] lr: 5.509e-05, eta: 1 day, 3:43:41, time: 0.682, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2018, decode.acc_seg: 92.0375, loss: 0.2018 2023-01-06 01:43:19,732 - mmseg - INFO - Iter [13150/160000] lr: 5.507e-05, eta: 1 day, 3:42:54, time: 0.655, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2059, decode.acc_seg: 91.9600, loss: 0.2059 2023-01-06 01:43:53,771 - mmseg - INFO - Iter [13200/160000] lr: 5.505e-05, eta: 1 day, 3:42:20, time: 0.681, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2108, decode.acc_seg: 91.6174, loss: 0.2108 2023-01-06 01:44:26,914 - mmseg - INFO - Iter [13250/160000] lr: 5.503e-05, eta: 1 day, 3:41:37, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1913, decode.acc_seg: 92.5385, loss: 0.1913 2023-01-06 01:45:01,108 - mmseg - INFO - Iter [13300/160000] lr: 5.501e-05, eta: 1 day, 3:41:06, time: 0.684, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2196, decode.acc_seg: 91.7405, loss: 0.2196 2023-01-06 01:45:33,978 - mmseg - INFO - Iter [13350/160000] lr: 5.499e-05, eta: 1 day, 3:40:19, time: 0.656, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2217, decode.acc_seg: 91.4778, loss: 0.2217 2023-01-06 01:46:10,682 - mmseg - INFO - Iter [13400/160000] lr: 5.498e-05, eta: 1 day, 3:40:16, time: 0.735, data_time: 0.059, memory: 11582, decode.loss_ce: 0.2118, decode.acc_seg: 92.0212, loss: 0.2118 2023-01-06 01:46:44,885 - mmseg - INFO - Iter [13450/160000] lr: 5.496e-05, eta: 1 day, 3:39:44, time: 0.683, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2027, decode.acc_seg: 92.0361, loss: 0.2027 2023-01-06 01:47:18,584 - mmseg - INFO - Iter [13500/160000] lr: 5.494e-05, eta: 1 day, 3:39:07, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2090, decode.acc_seg: 91.9472, loss: 0.2090 2023-01-06 01:47:51,715 - mmseg - INFO - Iter [13550/160000] lr: 5.492e-05, eta: 1 day, 3:38:24, time: 0.662, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2232, decode.acc_seg: 91.4215, loss: 0.2232 2023-01-06 01:48:24,705 - mmseg - INFO - Iter [13600/160000] lr: 5.490e-05, eta: 1 day, 3:37:39, time: 0.659, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1955, decode.acc_seg: 92.1415, loss: 0.1955 2023-01-06 01:48:59,388 - mmseg - INFO - Iter [13650/160000] lr: 5.488e-05, eta: 1 day, 3:37:13, time: 0.693, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2046, decode.acc_seg: 92.0552, loss: 0.2046 2023-01-06 01:49:33,174 - mmseg - INFO - Iter [13700/160000] lr: 5.486e-05, eta: 1 day, 3:36:37, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2051, decode.acc_seg: 91.8928, loss: 0.2051 2023-01-06 01:50:06,756 - mmseg - INFO - Iter [13750/160000] lr: 5.484e-05, eta: 1 day, 3:35:59, time: 0.672, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2050, decode.acc_seg: 92.2333, loss: 0.2050 2023-01-06 01:50:43,450 - mmseg - INFO - Iter [13800/160000] lr: 5.483e-05, eta: 1 day, 3:35:54, time: 0.734, data_time: 0.059, memory: 11582, decode.loss_ce: 0.2010, decode.acc_seg: 92.0405, loss: 0.2010 2023-01-06 01:51:17,242 - mmseg - INFO - Iter [13850/160000] lr: 5.481e-05, eta: 1 day, 3:35:18, time: 0.676, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2170, decode.acc_seg: 91.7153, loss: 0.2170 2023-01-06 01:51:52,890 - mmseg - INFO - Iter [13900/160000] lr: 5.479e-05, eta: 1 day, 3:35:01, time: 0.712, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2206, decode.acc_seg: 91.5701, loss: 0.2206 2023-01-06 01:52:27,217 - mmseg - INFO - Iter [13950/160000] lr: 5.477e-05, eta: 1 day, 3:34:31, time: 0.687, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1974, decode.acc_seg: 92.4937, loss: 0.1974 2023-01-06 01:53:00,117 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 01:53:00,118 - mmseg - INFO - Iter [14000/160000] lr: 5.475e-05, eta: 1 day, 3:33:46, time: 0.659, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1810, decode.acc_seg: 92.8788, loss: 0.1810 2023-01-06 01:53:33,076 - mmseg - INFO - Iter [14050/160000] lr: 5.473e-05, eta: 1 day, 3:33:01, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2090, decode.acc_seg: 91.9794, loss: 0.2090 2023-01-06 01:54:06,706 - mmseg - INFO - Iter [14100/160000] lr: 5.471e-05, eta: 1 day, 3:32:23, time: 0.673, data_time: 0.015, memory: 11582, decode.loss_ce: 0.2124, decode.acc_seg: 91.6728, loss: 0.2124 2023-01-06 01:54:44,673 - mmseg - INFO - Iter [14150/160000] lr: 5.469e-05, eta: 1 day, 3:32:31, time: 0.760, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1955, decode.acc_seg: 92.0045, loss: 0.1955 2023-01-06 01:55:19,694 - mmseg - INFO - Iter [14200/160000] lr: 5.468e-05, eta: 1 day, 3:32:07, time: 0.699, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1973, decode.acc_seg: 92.2954, loss: 0.1973 2023-01-06 01:55:53,657 - mmseg - INFO - Iter [14250/160000] lr: 5.466e-05, eta: 1 day, 3:31:33, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1999, decode.acc_seg: 92.0977, loss: 0.1999 2023-01-06 01:56:29,265 - mmseg - INFO - Iter [14300/160000] lr: 5.464e-05, eta: 1 day, 3:31:15, time: 0.712, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2110, decode.acc_seg: 91.6973, loss: 0.2110 2023-01-06 01:57:03,427 - mmseg - INFO - Iter [14350/160000] lr: 5.462e-05, eta: 1 day, 3:30:43, time: 0.684, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2097, decode.acc_seg: 92.1686, loss: 0.2097 2023-01-06 01:57:38,284 - mmseg - INFO - Iter [14400/160000] lr: 5.460e-05, eta: 1 day, 3:30:18, time: 0.697, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1985, decode.acc_seg: 92.5712, loss: 0.1985 2023-01-06 01:58:11,042 - mmseg - INFO - Iter [14450/160000] lr: 5.458e-05, eta: 1 day, 3:29:31, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1924, decode.acc_seg: 92.6489, loss: 0.1924 2023-01-06 01:58:43,716 - mmseg - INFO - Iter [14500/160000] lr: 5.456e-05, eta: 1 day, 3:28:44, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2111, decode.acc_seg: 91.8976, loss: 0.2111 2023-01-06 01:59:18,859 - mmseg - INFO - Iter [14550/160000] lr: 5.454e-05, eta: 1 day, 3:28:21, time: 0.703, data_time: 0.060, memory: 11582, decode.loss_ce: 0.2032, decode.acc_seg: 91.7059, loss: 0.2032 2023-01-06 01:59:52,552 - mmseg - INFO - Iter [14600/160000] lr: 5.453e-05, eta: 1 day, 3:27:44, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2052, decode.acc_seg: 92.1932, loss: 0.2052 2023-01-06 02:00:28,391 - mmseg - INFO - Iter [14650/160000] lr: 5.451e-05, eta: 1 day, 3:27:28, time: 0.716, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2066, decode.acc_seg: 91.9540, loss: 0.2066 2023-01-06 02:01:02,181 - mmseg - INFO - Iter [14700/160000] lr: 5.449e-05, eta: 1 day, 3:26:53, time: 0.677, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1890, decode.acc_seg: 92.6322, loss: 0.1890 2023-01-06 02:01:35,101 - mmseg - INFO - Iter [14750/160000] lr: 5.447e-05, eta: 1 day, 3:26:08, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2200, decode.acc_seg: 91.8424, loss: 0.2200 2023-01-06 02:02:07,717 - mmseg - INFO - Iter [14800/160000] lr: 5.445e-05, eta: 1 day, 3:25:20, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2271, decode.acc_seg: 91.4237, loss: 0.2271 2023-01-06 02:02:41,261 - mmseg - INFO - Iter [14850/160000] lr: 5.443e-05, eta: 1 day, 3:24:41, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2199, decode.acc_seg: 91.3194, loss: 0.2199 2023-01-06 02:03:19,071 - mmseg - INFO - Iter [14900/160000] lr: 5.441e-05, eta: 1 day, 3:24:45, time: 0.757, data_time: 0.059, memory: 11582, decode.loss_ce: 0.2142, decode.acc_seg: 91.7477, loss: 0.2142 2023-01-06 02:03:51,998 - mmseg - INFO - Iter [14950/160000] lr: 5.439e-05, eta: 1 day, 3:24:01, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1924, decode.acc_seg: 92.4950, loss: 0.1924 2023-01-06 02:04:24,936 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 02:04:24,937 - mmseg - INFO - Iter [15000/160000] lr: 5.438e-05, eta: 1 day, 3:23:16, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2126, decode.acc_seg: 91.7142, loss: 0.2126 2023-01-06 02:04:59,757 - mmseg - INFO - Iter [15050/160000] lr: 5.436e-05, eta: 1 day, 3:22:50, time: 0.696, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1957, decode.acc_seg: 92.3987, loss: 0.1957 2023-01-06 02:05:35,172 - mmseg - INFO - Iter [15100/160000] lr: 5.434e-05, eta: 1 day, 3:22:30, time: 0.708, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2099, decode.acc_seg: 91.9081, loss: 0.2099 2023-01-06 02:06:09,377 - mmseg - INFO - Iter [15150/160000] lr: 5.432e-05, eta: 1 day, 3:21:58, time: 0.684, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2001, decode.acc_seg: 92.1779, loss: 0.2001 2023-01-06 02:06:42,123 - mmseg - INFO - Iter [15200/160000] lr: 5.430e-05, eta: 1 day, 3:21:12, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1989, decode.acc_seg: 92.3754, loss: 0.1989 2023-01-06 02:07:15,617 - mmseg - INFO - Iter [15250/160000] lr: 5.428e-05, eta: 1 day, 3:20:32, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1887, decode.acc_seg: 92.6571, loss: 0.1887 2023-01-06 02:07:52,014 - mmseg - INFO - Iter [15300/160000] lr: 5.426e-05, eta: 1 day, 3:20:22, time: 0.729, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1911, decode.acc_seg: 92.5106, loss: 0.1911 2023-01-06 02:08:26,010 - mmseg - INFO - Iter [15350/160000] lr: 5.424e-05, eta: 1 day, 3:19:47, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1967, decode.acc_seg: 92.0777, loss: 0.1967 2023-01-06 02:08:59,423 - mmseg - INFO - Iter [15400/160000] lr: 5.423e-05, eta: 1 day, 3:19:08, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1911, decode.acc_seg: 92.6293, loss: 0.1911 2023-01-06 02:09:34,399 - mmseg - INFO - Iter [15450/160000] lr: 5.421e-05, eta: 1 day, 3:18:43, time: 0.699, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1932, decode.acc_seg: 92.4253, loss: 0.1932 2023-01-06 02:10:07,416 - mmseg - INFO - Iter [15500/160000] lr: 5.419e-05, eta: 1 day, 3:18:00, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1918, decode.acc_seg: 92.4603, loss: 0.1918 2023-01-06 02:10:40,610 - mmseg - INFO - Iter [15550/160000] lr: 5.417e-05, eta: 1 day, 3:17:18, time: 0.664, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2085, decode.acc_seg: 92.0861, loss: 0.2085 2023-01-06 02:11:15,268 - mmseg - INFO - Iter [15600/160000] lr: 5.415e-05, eta: 1 day, 3:16:50, time: 0.693, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1919, decode.acc_seg: 92.5104, loss: 0.1919 2023-01-06 02:11:51,511 - mmseg - INFO - Iter [15650/160000] lr: 5.413e-05, eta: 1 day, 3:16:37, time: 0.725, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1897, decode.acc_seg: 92.6134, loss: 0.1897 2023-01-06 02:12:24,298 - mmseg - INFO - Iter [15700/160000] lr: 5.411e-05, eta: 1 day, 3:15:51, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1856, decode.acc_seg: 92.6140, loss: 0.1856 2023-01-06 02:12:57,615 - mmseg - INFO - Iter [15750/160000] lr: 5.409e-05, eta: 1 day, 3:15:11, time: 0.666, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1868, decode.acc_seg: 92.6980, loss: 0.1868 2023-01-06 02:13:31,122 - mmseg - INFO - Iter [15800/160000] lr: 5.408e-05, eta: 1 day, 3:14:32, time: 0.670, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2049, decode.acc_seg: 92.2738, loss: 0.2049 2023-01-06 02:14:04,907 - mmseg - INFO - Iter [15850/160000] lr: 5.406e-05, eta: 1 day, 3:13:56, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2047, decode.acc_seg: 91.9286, loss: 0.2047 2023-01-06 02:14:37,459 - mmseg - INFO - Iter [15900/160000] lr: 5.404e-05, eta: 1 day, 3:13:09, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2131, decode.acc_seg: 91.6564, loss: 0.2131 2023-01-06 02:15:10,125 - mmseg - INFO - Iter [15950/160000] lr: 5.402e-05, eta: 1 day, 3:12:23, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2010, decode.acc_seg: 92.2212, loss: 0.2010 2023-01-06 02:15:46,071 - mmseg - INFO - Saving checkpoint at 16000 iterations 2023-01-06 02:15:52,546 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 02:15:52,547 - mmseg - INFO - Iter [16000/160000] lr: 5.400e-05, eta: 1 day, 3:13:05, time: 0.848, data_time: 0.059, memory: 11582, decode.loss_ce: 0.2121, decode.acc_seg: 91.7626, loss: 0.2121 2023-01-06 02:16:31,952 - mmseg - INFO - per class results: 2023-01-06 02:16:31,955 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 96.63 | 97.93 | | sidewalk | 75.51 | 87.82 | | building | 87.45 | 94.39 | | wall | 34.5 | 39.9 | | fence | 41.02 | 55.04 | | pole | 47.96 | 58.36 | | traffic light | 36.53 | 39.81 | | traffic sign | 57.77 | 64.27 | | vegetation | 89.3 | 95.45 | | terrain | 53.05 | 69.44 | | sky | 91.21 | 97.33 | | person | 63.62 | 84.06 | | rider | 22.14 | 24.97 | | car | 89.58 | 94.86 | | truck | 29.24 | 31.71 | | bus | 29.23 | 34.88 | | train | 23.51 | 71.22 | | motorcycle | 20.66 | 24.05 | | bicycle | 61.07 | 78.72 | +---------------+-------+-------+ 2023-01-06 02:16:31,955 - mmseg - INFO - Summary: 2023-01-06 02:16:31,956 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 93.01 | 55.26 | 65.49 | +-------+-------+-------+ 2023-01-06 02:16:31,957 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 02:16:31,957 - mmseg - INFO - Iter(val) [63] aAcc: 0.9301, mIoU: 0.5526, mAcc: 0.6549, IoU.road: 0.9663, IoU.sidewalk: 0.7551, IoU.building: 0.8745, IoU.wall: 0.3450, IoU.fence: 0.4102, IoU.pole: 0.4796, IoU.traffic light: 0.3653, IoU.traffic sign: 0.5777, IoU.vegetation: 0.8930, IoU.terrain: 0.5305, IoU.sky: 0.9121, IoU.person: 0.6362, IoU.rider: 0.2214, IoU.car: 0.8958, IoU.truck: 0.2924, IoU.bus: 0.2923, IoU.train: 0.2351, IoU.motorcycle: 0.2066, IoU.bicycle: 0.6107, Acc.road: 0.9793, Acc.sidewalk: 0.8782, Acc.building: 0.9439, Acc.wall: 0.3990, Acc.fence: 0.5504, Acc.pole: 0.5836, Acc.traffic light: 0.3981, Acc.traffic sign: 0.6427, Acc.vegetation: 0.9545, Acc.terrain: 0.6944, Acc.sky: 0.9733, Acc.person: 0.8406, Acc.rider: 0.2497, Acc.car: 0.9486, Acc.truck: 0.3171, Acc.bus: 0.3488, Acc.train: 0.7122, Acc.motorcycle: 0.2405, Acc.bicycle: 0.7872 2023-01-06 02:17:05,552 - mmseg - INFO - Iter [16050/160000] lr: 5.398e-05, eta: 1 day, 3:18:21, time: 1.460, data_time: 0.801, memory: 11582, decode.loss_ce: 0.1820, decode.acc_seg: 92.6087, loss: 0.1820 2023-01-06 02:17:38,351 - mmseg - INFO - Iter [16100/160000] lr: 5.396e-05, eta: 1 day, 3:17:34, time: 0.656, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2044, decode.acc_seg: 91.9991, loss: 0.2044 2023-01-06 02:18:11,045 - mmseg - INFO - Iter [16150/160000] lr: 5.394e-05, eta: 1 day, 3:16:47, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1963, decode.acc_seg: 92.6260, loss: 0.1963 2023-01-06 02:18:44,017 - mmseg - INFO - Iter [16200/160000] lr: 5.393e-05, eta: 1 day, 3:16:02, time: 0.659, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1912, decode.acc_seg: 92.3515, loss: 0.1912 2023-01-06 02:19:17,094 - mmseg - INFO - Iter [16250/160000] lr: 5.391e-05, eta: 1 day, 3:15:19, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1925, decode.acc_seg: 92.3203, loss: 0.1925 2023-01-06 02:19:49,967 - mmseg - INFO - Iter [16300/160000] lr: 5.389e-05, eta: 1 day, 3:14:34, time: 0.657, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1948, decode.acc_seg: 92.5408, loss: 0.1948 2023-01-06 02:20:24,049 - mmseg - INFO - Iter [16350/160000] lr: 5.387e-05, eta: 1 day, 3:13:59, time: 0.681, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1902, decode.acc_seg: 92.5181, loss: 0.1902 2023-01-06 02:20:59,708 - mmseg - INFO - Iter [16400/160000] lr: 5.385e-05, eta: 1 day, 3:13:39, time: 0.714, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1931, decode.acc_seg: 92.6214, loss: 0.1931 2023-01-06 02:21:32,666 - mmseg - INFO - Iter [16450/160000] lr: 5.383e-05, eta: 1 day, 3:12:55, time: 0.659, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2023, decode.acc_seg: 92.3343, loss: 0.2023 2023-01-06 02:22:05,349 - mmseg - INFO - Iter [16500/160000] lr: 5.381e-05, eta: 1 day, 3:12:08, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1826, decode.acc_seg: 92.6961, loss: 0.1826 2023-01-06 02:22:39,201 - mmseg - INFO - Iter [16550/160000] lr: 5.379e-05, eta: 1 day, 3:11:31, time: 0.677, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1999, decode.acc_seg: 92.1523, loss: 0.1999 2023-01-06 02:23:12,435 - mmseg - INFO - Iter [16600/160000] lr: 5.378e-05, eta: 1 day, 3:10:49, time: 0.664, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1868, decode.acc_seg: 92.5934, loss: 0.1868 2023-01-06 02:23:46,508 - mmseg - INFO - Iter [16650/160000] lr: 5.376e-05, eta: 1 day, 3:10:15, time: 0.682, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1811, decode.acc_seg: 92.8281, loss: 0.1811 2023-01-06 02:24:19,730 - mmseg - INFO - Iter [16700/160000] lr: 5.374e-05, eta: 1 day, 3:09:33, time: 0.664, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2079, decode.acc_seg: 91.9926, loss: 0.2079 2023-01-06 02:24:55,595 - mmseg - INFO - Iter [16750/160000] lr: 5.372e-05, eta: 1 day, 3:09:14, time: 0.718, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1896, decode.acc_seg: 92.5403, loss: 0.1896 2023-01-06 02:25:30,696 - mmseg - INFO - Iter [16800/160000] lr: 5.370e-05, eta: 1 day, 3:08:48, time: 0.701, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1992, decode.acc_seg: 92.1483, loss: 0.1992 2023-01-06 02:26:05,505 - mmseg - INFO - Iter [16850/160000] lr: 5.368e-05, eta: 1 day, 3:08:20, time: 0.697, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1934, decode.acc_seg: 92.6041, loss: 0.1934 2023-01-06 02:26:38,397 - mmseg - INFO - Iter [16900/160000] lr: 5.366e-05, eta: 1 day, 3:07:36, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2046, decode.acc_seg: 91.8765, loss: 0.2046 2023-01-06 02:27:11,254 - mmseg - INFO - Iter [16950/160000] lr: 5.364e-05, eta: 1 day, 3:06:51, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1956, decode.acc_seg: 92.5841, loss: 0.1956 2023-01-06 02:27:45,270 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 02:27:45,270 - mmseg - INFO - Iter [17000/160000] lr: 5.363e-05, eta: 1 day, 3:06:16, time: 0.680, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1679, decode.acc_seg: 93.0911, loss: 0.1679 2023-01-06 02:28:19,473 - mmseg - INFO - Iter [17050/160000] lr: 5.361e-05, eta: 1 day, 3:05:42, time: 0.684, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1958, decode.acc_seg: 92.4294, loss: 0.1958 2023-01-06 02:28:52,156 - mmseg - INFO - Iter [17100/160000] lr: 5.359e-05, eta: 1 day, 3:04:56, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1789, decode.acc_seg: 92.7137, loss: 0.1789 2023-01-06 02:29:28,451 - mmseg - INFO - Iter [17150/160000] lr: 5.357e-05, eta: 1 day, 3:04:40, time: 0.725, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1936, decode.acc_seg: 92.4108, loss: 0.1936 2023-01-06 02:30:02,289 - mmseg - INFO - Iter [17200/160000] lr: 5.355e-05, eta: 1 day, 3:04:04, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1850, decode.acc_seg: 92.8904, loss: 0.1850 2023-01-06 02:30:37,897 - mmseg - INFO - Iter [17250/160000] lr: 5.353e-05, eta: 1 day, 3:03:42, time: 0.712, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1920, decode.acc_seg: 92.7005, loss: 0.1920 2023-01-06 02:31:12,424 - mmseg - INFO - Iter [17300/160000] lr: 5.351e-05, eta: 1 day, 3:03:11, time: 0.690, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1865, decode.acc_seg: 92.7438, loss: 0.1865 2023-01-06 02:31:45,797 - mmseg - INFO - Iter [17350/160000] lr: 5.349e-05, eta: 1 day, 3:02:31, time: 0.667, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1806, decode.acc_seg: 92.6735, loss: 0.1806 2023-01-06 02:32:18,470 - mmseg - INFO - Iter [17400/160000] lr: 5.348e-05, eta: 1 day, 3:01:45, time: 0.653, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1834, decode.acc_seg: 92.5473, loss: 0.1834 2023-01-06 02:32:51,018 - mmseg - INFO - Iter [17450/160000] lr: 5.346e-05, eta: 1 day, 3:00:58, time: 0.651, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1657, decode.acc_seg: 93.5883, loss: 0.1657 2023-01-06 02:33:26,344 - mmseg - INFO - Iter [17500/160000] lr: 5.344e-05, eta: 1 day, 3:00:33, time: 0.706, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1768, decode.acc_seg: 93.1666, loss: 0.1768 2023-01-06 02:33:59,182 - mmseg - INFO - Iter [17550/160000] lr: 5.342e-05, eta: 1 day, 2:59:49, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1834, decode.acc_seg: 92.8020, loss: 0.1834 2023-01-06 02:34:31,796 - mmseg - INFO - Iter [17600/160000] lr: 5.340e-05, eta: 1 day, 2:59:03, time: 0.652, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1639, decode.acc_seg: 93.3448, loss: 0.1639 2023-01-06 02:35:04,566 - mmseg - INFO - Iter [17650/160000] lr: 5.338e-05, eta: 1 day, 2:58:18, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1915, decode.acc_seg: 92.4870, loss: 0.1915 2023-01-06 02:35:40,600 - mmseg - INFO - Iter [17700/160000] lr: 5.336e-05, eta: 1 day, 2:57:59, time: 0.720, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1847, decode.acc_seg: 92.6280, loss: 0.1847 2023-01-06 02:36:15,371 - mmseg - INFO - Iter [17750/160000] lr: 5.334e-05, eta: 1 day, 2:57:30, time: 0.695, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1641, decode.acc_seg: 93.4178, loss: 0.1641 2023-01-06 02:36:47,883 - mmseg - INFO - Iter [17800/160000] lr: 5.333e-05, eta: 1 day, 2:56:44, time: 0.651, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1748, decode.acc_seg: 92.9284, loss: 0.1748 2023-01-06 02:37:21,786 - mmseg - INFO - Iter [17850/160000] lr: 5.331e-05, eta: 1 day, 2:56:08, time: 0.678, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1855, decode.acc_seg: 92.7098, loss: 0.1855 2023-01-06 02:37:56,827 - mmseg - INFO - Iter [17900/160000] lr: 5.329e-05, eta: 1 day, 2:55:41, time: 0.701, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1957, decode.acc_seg: 92.6584, loss: 0.1957 2023-01-06 02:38:31,824 - mmseg - INFO - Iter [17950/160000] lr: 5.327e-05, eta: 1 day, 2:55:14, time: 0.699, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1912, decode.acc_seg: 92.2834, loss: 0.1912 2023-01-06 02:39:06,131 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 02:39:06,131 - mmseg - INFO - Iter [18000/160000] lr: 5.325e-05, eta: 1 day, 2:54:42, time: 0.687, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1866, decode.acc_seg: 92.5948, loss: 0.1866 2023-01-06 02:39:40,194 - mmseg - INFO - Iter [18050/160000] lr: 5.323e-05, eta: 1 day, 2:54:07, time: 0.680, data_time: 0.014, memory: 11582, decode.loss_ce: 0.2123, decode.acc_seg: 91.6511, loss: 0.2123 2023-01-06 02:40:14,973 - mmseg - INFO - Iter [18100/160000] lr: 5.321e-05, eta: 1 day, 2:53:38, time: 0.697, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1668, decode.acc_seg: 93.4437, loss: 0.1668 2023-01-06 02:40:47,590 - mmseg - INFO - Iter [18150/160000] lr: 5.319e-05, eta: 1 day, 2:52:52, time: 0.652, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1768, decode.acc_seg: 92.9374, loss: 0.1768 2023-01-06 02:41:21,710 - mmseg - INFO - Iter [18200/160000] lr: 5.318e-05, eta: 1 day, 2:52:18, time: 0.682, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1742, decode.acc_seg: 93.2140, loss: 0.1742 2023-01-06 02:41:57,888 - mmseg - INFO - Iter [18250/160000] lr: 5.316e-05, eta: 1 day, 2:52:00, time: 0.723, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1762, decode.acc_seg: 93.0371, loss: 0.1762 2023-01-06 02:42:32,043 - mmseg - INFO - Iter [18300/160000] lr: 5.314e-05, eta: 1 day, 2:51:26, time: 0.684, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1762, decode.acc_seg: 92.9942, loss: 0.1762 2023-01-06 02:43:05,512 - mmseg - INFO - Iter [18350/160000] lr: 5.312e-05, eta: 1 day, 2:50:47, time: 0.669, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1704, decode.acc_seg: 93.1931, loss: 0.1704 2023-01-06 02:43:38,049 - mmseg - INFO - Iter [18400/160000] lr: 5.310e-05, eta: 1 day, 2:50:01, time: 0.651, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1835, decode.acc_seg: 92.6985, loss: 0.1835 2023-01-06 02:44:11,455 - mmseg - INFO - Iter [18450/160000] lr: 5.308e-05, eta: 1 day, 2:49:21, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1631, decode.acc_seg: 93.4832, loss: 0.1631 2023-01-06 02:44:45,297 - mmseg - INFO - Iter [18500/160000] lr: 5.306e-05, eta: 1 day, 2:48:45, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1908, decode.acc_seg: 92.4631, loss: 0.1908 2023-01-06 02:45:18,672 - mmseg - INFO - Iter [18550/160000] lr: 5.304e-05, eta: 1 day, 2:48:06, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1751, decode.acc_seg: 92.9821, loss: 0.1751 2023-01-06 02:45:52,357 - mmseg - INFO - Iter [18600/160000] lr: 5.303e-05, eta: 1 day, 2:47:28, time: 0.674, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1762, decode.acc_seg: 92.8155, loss: 0.1762 2023-01-06 02:46:29,152 - mmseg - INFO - Iter [18650/160000] lr: 5.301e-05, eta: 1 day, 2:47:14, time: 0.735, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1897, decode.acc_seg: 92.6765, loss: 0.1897 2023-01-06 02:47:04,620 - mmseg - INFO - Iter [18700/160000] lr: 5.299e-05, eta: 1 day, 2:46:50, time: 0.710, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1745, decode.acc_seg: 93.2805, loss: 0.1745 2023-01-06 02:47:38,710 - mmseg - INFO - Iter [18750/160000] lr: 5.297e-05, eta: 1 day, 2:46:16, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1639, decode.acc_seg: 93.6153, loss: 0.1639 2023-01-06 02:48:12,743 - mmseg - INFO - Iter [18800/160000] lr: 5.295e-05, eta: 1 day, 2:45:42, time: 0.681, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1889, decode.acc_seg: 92.7078, loss: 0.1889 2023-01-06 02:48:45,909 - mmseg - INFO - Iter [18850/160000] lr: 5.293e-05, eta: 1 day, 2:45:00, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1907, decode.acc_seg: 92.5060, loss: 0.1907 2023-01-06 02:49:20,432 - mmseg - INFO - Iter [18900/160000] lr: 5.291e-05, eta: 1 day, 2:44:29, time: 0.690, data_time: 0.020, memory: 11582, decode.loss_ce: 0.1963, decode.acc_seg: 92.0426, loss: 0.1963 2023-01-06 02:49:54,884 - mmseg - INFO - Iter [18950/160000] lr: 5.289e-05, eta: 1 day, 2:43:57, time: 0.688, data_time: 0.013, memory: 11582, decode.loss_ce: 0.2176, decode.acc_seg: 91.9328, loss: 0.2176 2023-01-06 02:50:30,543 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 02:50:30,543 - mmseg - INFO - Iter [19000/160000] lr: 5.288e-05, eta: 1 day, 2:43:35, time: 0.714, data_time: 0.059, memory: 11582, decode.loss_ce: 0.2051, decode.acc_seg: 92.1974, loss: 0.2051 2023-01-06 02:51:04,452 - mmseg - INFO - Iter [19050/160000] lr: 5.286e-05, eta: 1 day, 2:42:59, time: 0.678, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1842, decode.acc_seg: 92.7064, loss: 0.1842 2023-01-06 02:51:38,109 - mmseg - INFO - Iter [19100/160000] lr: 5.284e-05, eta: 1 day, 2:42:22, time: 0.673, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1755, decode.acc_seg: 93.0599, loss: 0.1755 2023-01-06 02:52:11,304 - mmseg - INFO - Iter [19150/160000] lr: 5.282e-05, eta: 1 day, 2:41:41, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1784, decode.acc_seg: 92.8512, loss: 0.1784 2023-01-06 02:52:44,281 - mmseg - INFO - Iter [19200/160000] lr: 5.280e-05, eta: 1 day, 2:40:59, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1888, decode.acc_seg: 92.7682, loss: 0.1888 2023-01-06 02:53:18,651 - mmseg - INFO - Iter [19250/160000] lr: 5.278e-05, eta: 1 day, 2:40:26, time: 0.686, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1734, decode.acc_seg: 92.8676, loss: 0.1734 2023-01-06 02:53:51,829 - mmseg - INFO - Iter [19300/160000] lr: 5.276e-05, eta: 1 day, 2:39:45, time: 0.665, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1810, decode.acc_seg: 92.8829, loss: 0.1810 2023-01-06 02:54:27,009 - mmseg - INFO - Iter [19350/160000] lr: 5.274e-05, eta: 1 day, 2:39:19, time: 0.703, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1666, decode.acc_seg: 93.5779, loss: 0.1666 2023-01-06 02:55:00,597 - mmseg - INFO - Iter [19400/160000] lr: 5.273e-05, eta: 1 day, 2:38:41, time: 0.673, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1712, decode.acc_seg: 93.2642, loss: 0.1712 2023-01-06 02:55:33,305 - mmseg - INFO - Iter [19450/160000] lr: 5.271e-05, eta: 1 day, 2:37:57, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1813, decode.acc_seg: 92.9363, loss: 0.1813 2023-01-06 02:56:06,277 - mmseg - INFO - Iter [19500/160000] lr: 5.269e-05, eta: 1 day, 2:37:15, time: 0.659, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1735, decode.acc_seg: 93.1260, loss: 0.1735 2023-01-06 02:56:39,232 - mmseg - INFO - Iter [19550/160000] lr: 5.267e-05, eta: 1 day, 2:36:32, time: 0.659, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1841, decode.acc_seg: 92.8055, loss: 0.1841 2023-01-06 02:57:12,955 - mmseg - INFO - Iter [19600/160000] lr: 5.265e-05, eta: 1 day, 2:35:55, time: 0.673, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1806, decode.acc_seg: 92.6730, loss: 0.1806 2023-01-06 02:57:45,736 - mmseg - INFO - Iter [19650/160000] lr: 5.263e-05, eta: 1 day, 2:35:12, time: 0.657, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1679, decode.acc_seg: 93.3578, loss: 0.1679 2023-01-06 02:58:18,504 - mmseg - INFO - Iter [19700/160000] lr: 5.261e-05, eta: 1 day, 2:34:28, time: 0.655, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1829, decode.acc_seg: 92.7560, loss: 0.1829 2023-01-06 02:58:55,006 - mmseg - INFO - Iter [19750/160000] lr: 5.259e-05, eta: 1 day, 2:34:11, time: 0.730, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1796, decode.acc_seg: 93.0194, loss: 0.1796 2023-01-06 02:59:28,095 - mmseg - INFO - Iter [19800/160000] lr: 5.258e-05, eta: 1 day, 2:33:30, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1887, decode.acc_seg: 92.7469, loss: 0.1887 2023-01-06 03:00:02,459 - mmseg - INFO - Iter [19850/160000] lr: 5.256e-05, eta: 1 day, 2:32:58, time: 0.687, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1817, decode.acc_seg: 92.8555, loss: 0.1817 2023-01-06 03:00:36,542 - mmseg - INFO - Iter [19900/160000] lr: 5.254e-05, eta: 1 day, 2:32:24, time: 0.682, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1682, decode.acc_seg: 93.1648, loss: 0.1682 2023-01-06 03:01:09,656 - mmseg - INFO - Iter [19950/160000] lr: 5.252e-05, eta: 1 day, 2:31:42, time: 0.661, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1681, decode.acc_seg: 93.3027, loss: 0.1681 2023-01-06 03:01:43,266 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 03:01:43,266 - mmseg - INFO - Iter [20000/160000] lr: 5.250e-05, eta: 1 day, 2:31:05, time: 0.673, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1782, decode.acc_seg: 92.9571, loss: 0.1782 2023-01-06 03:02:16,672 - mmseg - INFO - Iter [20050/160000] lr: 5.248e-05, eta: 1 day, 2:30:26, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1820, decode.acc_seg: 93.0332, loss: 0.1820 2023-01-06 03:02:52,139 - mmseg - INFO - Iter [20100/160000] lr: 5.246e-05, eta: 1 day, 2:30:02, time: 0.709, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1670, decode.acc_seg: 93.5233, loss: 0.1670 2023-01-06 03:03:25,130 - mmseg - INFO - Iter [20150/160000] lr: 5.244e-05, eta: 1 day, 2:29:20, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1603, decode.acc_seg: 93.5941, loss: 0.1603 2023-01-06 03:04:01,098 - mmseg - INFO - Iter [20200/160000] lr: 5.243e-05, eta: 1 day, 2:28:58, time: 0.719, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1636, decode.acc_seg: 93.4895, loss: 0.1636 2023-01-06 03:04:35,159 - mmseg - INFO - Iter [20250/160000] lr: 5.241e-05, eta: 1 day, 2:28:24, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1890, decode.acc_seg: 92.3488, loss: 0.1890 2023-01-06 03:05:08,066 - mmseg - INFO - Iter [20300/160000] lr: 5.239e-05, eta: 1 day, 2:27:42, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1927, decode.acc_seg: 92.6962, loss: 0.1927 2023-01-06 03:05:43,313 - mmseg - INFO - Iter [20350/160000] lr: 5.237e-05, eta: 1 day, 2:27:16, time: 0.704, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1760, decode.acc_seg: 93.1984, loss: 0.1760 2023-01-06 03:06:18,901 - mmseg - INFO - Iter [20400/160000] lr: 5.235e-05, eta: 1 day, 2:26:52, time: 0.713, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1614, decode.acc_seg: 93.5819, loss: 0.1614 2023-01-06 03:06:52,116 - mmseg - INFO - Iter [20450/160000] lr: 5.233e-05, eta: 1 day, 2:26:12, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1721, decode.acc_seg: 93.2202, loss: 0.1721 2023-01-06 03:07:28,080 - mmseg - INFO - Iter [20500/160000] lr: 5.231e-05, eta: 1 day, 2:25:50, time: 0.718, data_time: 0.059, memory: 11582, decode.loss_ce: 0.2019, decode.acc_seg: 92.0705, loss: 0.2019 2023-01-06 03:08:00,923 - mmseg - INFO - Iter [20550/160000] lr: 5.229e-05, eta: 1 day, 2:25:08, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1745, decode.acc_seg: 93.1098, loss: 0.1745 2023-01-06 03:08:33,511 - mmseg - INFO - Iter [20600/160000] lr: 5.228e-05, eta: 1 day, 2:24:23, time: 0.652, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1798, decode.acc_seg: 92.9529, loss: 0.1798 2023-01-06 03:09:07,460 - mmseg - INFO - Iter [20650/160000] lr: 5.226e-05, eta: 1 day, 2:23:48, time: 0.679, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1763, decode.acc_seg: 92.9367, loss: 0.1763 2023-01-06 03:09:40,401 - mmseg - INFO - Iter [20700/160000] lr: 5.224e-05, eta: 1 day, 2:23:06, time: 0.659, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1467, decode.acc_seg: 93.9704, loss: 0.1467 2023-01-06 03:10:15,392 - mmseg - INFO - Iter [20750/160000] lr: 5.222e-05, eta: 1 day, 2:22:38, time: 0.699, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1726, decode.acc_seg: 93.0240, loss: 0.1726 2023-01-06 03:10:50,462 - mmseg - INFO - Iter [20800/160000] lr: 5.220e-05, eta: 1 day, 2:22:11, time: 0.702, data_time: 0.016, memory: 11582, decode.loss_ce: 0.1809, decode.acc_seg: 92.8797, loss: 0.1809 2023-01-06 03:11:26,342 - mmseg - INFO - Iter [20850/160000] lr: 5.218e-05, eta: 1 day, 2:21:48, time: 0.717, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1824, decode.acc_seg: 92.9946, loss: 0.1824 2023-01-06 03:11:59,958 - mmseg - INFO - Iter [20900/160000] lr: 5.216e-05, eta: 1 day, 2:21:11, time: 0.673, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1648, decode.acc_seg: 93.3460, loss: 0.1648 2023-01-06 03:12:32,577 - mmseg - INFO - Iter [20950/160000] lr: 5.214e-05, eta: 1 day, 2:20:27, time: 0.652, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1715, decode.acc_seg: 93.2387, loss: 0.1715 2023-01-06 03:13:05,950 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 03:13:05,951 - mmseg - INFO - Iter [21000/160000] lr: 5.213e-05, eta: 1 day, 2:19:48, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1697, decode.acc_seg: 93.3177, loss: 0.1697 2023-01-06 03:13:40,650 - mmseg - INFO - Iter [21050/160000] lr: 5.211e-05, eta: 1 day, 2:19:18, time: 0.694, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1796, decode.acc_seg: 93.0166, loss: 0.1796 2023-01-06 03:14:13,838 - mmseg - INFO - Iter [21100/160000] lr: 5.209e-05, eta: 1 day, 2:18:38, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1732, decode.acc_seg: 93.2760, loss: 0.1732 2023-01-06 03:14:48,026 - mmseg - INFO - Iter [21150/160000] lr: 5.207e-05, eta: 1 day, 2:18:05, time: 0.684, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1706, decode.acc_seg: 93.2924, loss: 0.1706 2023-01-06 03:15:21,799 - mmseg - INFO - Iter [21200/160000] lr: 5.205e-05, eta: 1 day, 2:17:28, time: 0.675, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1739, decode.acc_seg: 93.1032, loss: 0.1739 2023-01-06 03:15:58,220 - mmseg - INFO - Iter [21250/160000] lr: 5.203e-05, eta: 1 day, 2:17:09, time: 0.728, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1672, decode.acc_seg: 93.4284, loss: 0.1672 2023-01-06 03:16:31,891 - mmseg - INFO - Iter [21300/160000] lr: 5.201e-05, eta: 1 day, 2:16:33, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1550, decode.acc_seg: 93.8505, loss: 0.1550 2023-01-06 03:17:05,863 - mmseg - INFO - Iter [21350/160000] lr: 5.199e-05, eta: 1 day, 2:15:57, time: 0.678, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1583, decode.acc_seg: 93.5475, loss: 0.1583 2023-01-06 03:17:41,719 - mmseg - INFO - Iter [21400/160000] lr: 5.198e-05, eta: 1 day, 2:15:35, time: 0.718, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1723, decode.acc_seg: 93.0215, loss: 0.1723 2023-01-06 03:18:16,246 - mmseg - INFO - Iter [21450/160000] lr: 5.196e-05, eta: 1 day, 2:15:03, time: 0.690, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1719, decode.acc_seg: 93.2805, loss: 0.1719 2023-01-06 03:18:51,202 - mmseg - INFO - Iter [21500/160000] lr: 5.194e-05, eta: 1 day, 2:14:35, time: 0.699, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1965, decode.acc_seg: 92.3601, loss: 0.1965 2023-01-06 03:19:24,942 - mmseg - INFO - Iter [21550/160000] lr: 5.192e-05, eta: 1 day, 2:13:58, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1760, decode.acc_seg: 93.0423, loss: 0.1760 2023-01-06 03:20:00,091 - mmseg - INFO - Iter [21600/160000] lr: 5.190e-05, eta: 1 day, 2:13:31, time: 0.704, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1694, decode.acc_seg: 93.3443, loss: 0.1694 2023-01-06 03:20:33,133 - mmseg - INFO - Iter [21650/160000] lr: 5.188e-05, eta: 1 day, 2:12:50, time: 0.661, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1573, decode.acc_seg: 93.7662, loss: 0.1573 2023-01-06 03:21:07,098 - mmseg - INFO - Iter [21700/160000] lr: 5.186e-05, eta: 1 day, 2:12:15, time: 0.679, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1580, decode.acc_seg: 93.7821, loss: 0.1580 2023-01-06 03:21:40,537 - mmseg - INFO - Iter [21750/160000] lr: 5.184e-05, eta: 1 day, 2:11:36, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1709, decode.acc_seg: 93.1263, loss: 0.1709 2023-01-06 03:22:13,579 - mmseg - INFO - Iter [21800/160000] lr: 5.183e-05, eta: 1 day, 2:10:56, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1586, decode.acc_seg: 93.7731, loss: 0.1586 2023-01-06 03:22:47,556 - mmseg - INFO - Iter [21850/160000] lr: 5.181e-05, eta: 1 day, 2:10:21, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1640, decode.acc_seg: 93.5707, loss: 0.1640 2023-01-06 03:23:20,617 - mmseg - INFO - Iter [21900/160000] lr: 5.179e-05, eta: 1 day, 2:09:40, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1689, decode.acc_seg: 93.1736, loss: 0.1689 2023-01-06 03:23:55,675 - mmseg - INFO - Iter [21950/160000] lr: 5.177e-05, eta: 1 day, 2:09:12, time: 0.701, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1675, decode.acc_seg: 93.4552, loss: 0.1675 2023-01-06 03:24:28,444 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 03:24:28,445 - mmseg - INFO - Iter [22000/160000] lr: 5.175e-05, eta: 1 day, 2:08:30, time: 0.655, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1658, decode.acc_seg: 93.5962, loss: 0.1658 2023-01-06 03:25:00,998 - mmseg - INFO - Iter [22050/160000] lr: 5.173e-05, eta: 1 day, 2:07:46, time: 0.651, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1755, decode.acc_seg: 93.1416, loss: 0.1755 2023-01-06 03:25:33,912 - mmseg - INFO - Iter [22100/160000] lr: 5.171e-05, eta: 1 day, 2:07:05, time: 0.658, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1649, decode.acc_seg: 93.4596, loss: 0.1649 2023-01-06 03:26:06,839 - mmseg - INFO - Iter [22150/160000] lr: 5.169e-05, eta: 1 day, 2:06:23, time: 0.659, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1676, decode.acc_seg: 93.1645, loss: 0.1676 2023-01-06 03:26:41,106 - mmseg - INFO - Iter [22200/160000] lr: 5.168e-05, eta: 1 day, 2:05:50, time: 0.684, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1706, decode.acc_seg: 93.1257, loss: 0.1706 2023-01-06 03:27:14,438 - mmseg - INFO - Iter [22250/160000] lr: 5.166e-05, eta: 1 day, 2:05:11, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1637, decode.acc_seg: 93.3879, loss: 0.1637 2023-01-06 03:27:47,092 - mmseg - INFO - Iter [22300/160000] lr: 5.164e-05, eta: 1 day, 2:04:29, time: 0.653, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1515, decode.acc_seg: 93.7242, loss: 0.1515 2023-01-06 03:28:21,946 - mmseg - INFO - Iter [22350/160000] lr: 5.162e-05, eta: 1 day, 2:03:59, time: 0.697, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1639, decode.acc_seg: 93.3274, loss: 0.1639 2023-01-06 03:28:54,484 - mmseg - INFO - Iter [22400/160000] lr: 5.160e-05, eta: 1 day, 2:03:16, time: 0.651, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1903, decode.acc_seg: 92.7822, loss: 0.1903 2023-01-06 03:29:27,988 - mmseg - INFO - Iter [22450/160000] lr: 5.158e-05, eta: 1 day, 2:02:38, time: 0.670, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1918, decode.acc_seg: 92.6791, loss: 0.1918 2023-01-06 03:30:02,031 - mmseg - INFO - Iter [22500/160000] lr: 5.156e-05, eta: 1 day, 2:02:04, time: 0.681, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1745, decode.acc_seg: 93.1328, loss: 0.1745 2023-01-06 03:30:34,779 - mmseg - INFO - Iter [22550/160000] lr: 5.154e-05, eta: 1 day, 2:01:21, time: 0.655, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1683, decode.acc_seg: 93.4357, loss: 0.1683 2023-01-06 03:31:09,069 - mmseg - INFO - Iter [22600/160000] lr: 5.153e-05, eta: 1 day, 2:00:49, time: 0.686, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1692, decode.acc_seg: 93.2527, loss: 0.1692 2023-01-06 03:31:41,586 - mmseg - INFO - Iter [22650/160000] lr: 5.151e-05, eta: 1 day, 2:00:05, time: 0.650, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1945, decode.acc_seg: 92.4744, loss: 0.1945 2023-01-06 03:32:17,316 - mmseg - INFO - Iter [22700/160000] lr: 5.149e-05, eta: 1 day, 1:59:41, time: 0.715, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1772, decode.acc_seg: 92.9899, loss: 0.1772 2023-01-06 03:32:51,694 - mmseg - INFO - Iter [22750/160000] lr: 5.147e-05, eta: 1 day, 1:59:09, time: 0.688, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1665, decode.acc_seg: 93.5990, loss: 0.1665 2023-01-06 03:33:24,411 - mmseg - INFO - Iter [22800/160000] lr: 5.145e-05, eta: 1 day, 1:58:26, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1513, decode.acc_seg: 94.1356, loss: 0.1513 2023-01-06 03:33:58,683 - mmseg - INFO - Iter [22850/160000] lr: 5.143e-05, eta: 1 day, 1:57:54, time: 0.685, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1717, decode.acc_seg: 93.1433, loss: 0.1717 2023-01-06 03:34:34,501 - mmseg - INFO - Iter [22900/160000] lr: 5.141e-05, eta: 1 day, 1:57:30, time: 0.715, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1731, decode.acc_seg: 93.1500, loss: 0.1731 2023-01-06 03:35:07,848 - mmseg - INFO - Iter [22950/160000] lr: 5.139e-05, eta: 1 day, 1:56:51, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1981, decode.acc_seg: 92.3162, loss: 0.1981 2023-01-06 03:35:40,899 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 03:35:40,899 - mmseg - INFO - Iter [23000/160000] lr: 5.138e-05, eta: 1 day, 1:56:11, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1824, decode.acc_seg: 93.0302, loss: 0.1824 2023-01-06 03:36:13,675 - mmseg - INFO - Iter [23050/160000] lr: 5.136e-05, eta: 1 day, 1:55:29, time: 0.655, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1875, decode.acc_seg: 92.9650, loss: 0.1875 2023-01-06 03:36:48,739 - mmseg - INFO - Iter [23100/160000] lr: 5.134e-05, eta: 1 day, 1:55:01, time: 0.702, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1789, decode.acc_seg: 92.9315, loss: 0.1789 2023-01-06 03:37:23,947 - mmseg - INFO - Iter [23150/160000] lr: 5.132e-05, eta: 1 day, 1:54:34, time: 0.703, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1689, decode.acc_seg: 93.4091, loss: 0.1689 2023-01-06 03:37:59,547 - mmseg - INFO - Iter [23200/160000] lr: 5.130e-05, eta: 1 day, 1:54:08, time: 0.712, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1681, decode.acc_seg: 93.2737, loss: 0.1681 2023-01-06 03:38:35,820 - mmseg - INFO - Iter [23250/160000] lr: 5.128e-05, eta: 1 day, 1:53:47, time: 0.725, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1605, decode.acc_seg: 93.3911, loss: 0.1605 2023-01-06 03:39:08,862 - mmseg - INFO - Iter [23300/160000] lr: 5.126e-05, eta: 1 day, 1:53:07, time: 0.662, data_time: 0.023, memory: 11582, decode.loss_ce: 0.1730, decode.acc_seg: 93.2324, loss: 0.1730 2023-01-06 03:39:41,647 - mmseg - INFO - Iter [23350/160000] lr: 5.124e-05, eta: 1 day, 1:52:25, time: 0.656, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1536, decode.acc_seg: 93.7805, loss: 0.1536 2023-01-06 03:40:15,623 - mmseg - INFO - Iter [23400/160000] lr: 5.123e-05, eta: 1 day, 1:51:51, time: 0.680, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1643, decode.acc_seg: 93.4710, loss: 0.1643 2023-01-06 03:40:50,980 - mmseg - INFO - Iter [23450/160000] lr: 5.121e-05, eta: 1 day, 1:51:24, time: 0.707, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1760, decode.acc_seg: 93.2653, loss: 0.1760 2023-01-06 03:41:24,418 - mmseg - INFO - Iter [23500/160000] lr: 5.119e-05, eta: 1 day, 1:50:46, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1610, decode.acc_seg: 93.5909, loss: 0.1610 2023-01-06 03:41:58,343 - mmseg - INFO - Iter [23550/160000] lr: 5.117e-05, eta: 1 day, 1:50:11, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1611, decode.acc_seg: 93.5056, loss: 0.1611 2023-01-06 03:42:32,226 - mmseg - INFO - Iter [23600/160000] lr: 5.115e-05, eta: 1 day, 1:49:36, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1749, decode.acc_seg: 93.1793, loss: 0.1749 2023-01-06 03:43:05,205 - mmseg - INFO - Iter [23650/160000] lr: 5.113e-05, eta: 1 day, 1:48:56, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1602, decode.acc_seg: 93.6670, loss: 0.1602 2023-01-06 03:43:38,604 - mmseg - INFO - Iter [23700/160000] lr: 5.111e-05, eta: 1 day, 1:48:17, time: 0.667, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1551, decode.acc_seg: 93.7842, loss: 0.1551 2023-01-06 03:44:12,931 - mmseg - INFO - Iter [23750/160000] lr: 5.109e-05, eta: 1 day, 1:47:45, time: 0.687, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1670, decode.acc_seg: 93.6069, loss: 0.1670 2023-01-06 03:44:49,121 - mmseg - INFO - Iter [23800/160000] lr: 5.108e-05, eta: 1 day, 1:47:23, time: 0.724, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1596, decode.acc_seg: 93.6699, loss: 0.1596 2023-01-06 03:45:24,615 - mmseg - INFO - Iter [23850/160000] lr: 5.106e-05, eta: 1 day, 1:46:57, time: 0.711, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1650, decode.acc_seg: 93.3062, loss: 0.1650 2023-01-06 03:45:58,324 - mmseg - INFO - Iter [23900/160000] lr: 5.104e-05, eta: 1 day, 1:46:20, time: 0.673, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1642, decode.acc_seg: 93.2499, loss: 0.1642 2023-01-06 03:46:32,631 - mmseg - INFO - Iter [23950/160000] lr: 5.102e-05, eta: 1 day, 1:45:48, time: 0.687, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1609, decode.acc_seg: 93.3941, loss: 0.1609 2023-01-06 03:47:07,871 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 03:47:07,871 - mmseg - INFO - Iter [24000/160000] lr: 5.100e-05, eta: 1 day, 1:45:20, time: 0.705, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1543, decode.acc_seg: 93.9471, loss: 0.1543 2023-01-06 03:47:41,815 - mmseg - INFO - Iter [24050/160000] lr: 5.098e-05, eta: 1 day, 1:44:45, time: 0.679, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1654, decode.acc_seg: 93.4121, loss: 0.1654 2023-01-06 03:48:16,794 - mmseg - INFO - Iter [24100/160000] lr: 5.096e-05, eta: 1 day, 1:44:16, time: 0.700, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1671, decode.acc_seg: 93.3275, loss: 0.1671 2023-01-06 03:48:51,639 - mmseg - INFO - Iter [24150/160000] lr: 5.094e-05, eta: 1 day, 1:43:46, time: 0.696, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1610, decode.acc_seg: 93.5478, loss: 0.1610 2023-01-06 03:49:27,757 - mmseg - INFO - Iter [24200/160000] lr: 5.093e-05, eta: 1 day, 1:43:24, time: 0.723, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1576, decode.acc_seg: 93.7528, loss: 0.1576 2023-01-06 03:50:00,614 - mmseg - INFO - Iter [24250/160000] lr: 5.091e-05, eta: 1 day, 1:42:43, time: 0.657, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1558, decode.acc_seg: 93.8284, loss: 0.1558 2023-01-06 03:50:33,305 - mmseg - INFO - Iter [24300/160000] lr: 5.089e-05, eta: 1 day, 1:42:01, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1585, decode.acc_seg: 93.7228, loss: 0.1585 2023-01-06 03:51:08,895 - mmseg - INFO - Iter [24350/160000] lr: 5.087e-05, eta: 1 day, 1:41:35, time: 0.712, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1675, decode.acc_seg: 93.3955, loss: 0.1675 2023-01-06 03:51:42,119 - mmseg - INFO - Iter [24400/160000] lr: 5.085e-05, eta: 1 day, 1:40:56, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1663, decode.acc_seg: 93.2499, loss: 0.1663 2023-01-06 03:52:15,357 - mmseg - INFO - Iter [24450/160000] lr: 5.083e-05, eta: 1 day, 1:40:17, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1694, decode.acc_seg: 93.3258, loss: 0.1694 2023-01-06 03:52:49,127 - mmseg - INFO - Iter [24500/160000] lr: 5.081e-05, eta: 1 day, 1:39:41, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1531, decode.acc_seg: 93.8589, loss: 0.1531 2023-01-06 03:53:23,019 - mmseg - INFO - Iter [24550/160000] lr: 5.079e-05, eta: 1 day, 1:39:06, time: 0.677, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1544, decode.acc_seg: 93.9727, loss: 0.1544 2023-01-06 03:53:59,052 - mmseg - INFO - Iter [24600/160000] lr: 5.078e-05, eta: 1 day, 1:38:43, time: 0.722, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1539, decode.acc_seg: 93.9159, loss: 0.1539 2023-01-06 03:54:31,541 - mmseg - INFO - Iter [24650/160000] lr: 5.076e-05, eta: 1 day, 1:38:00, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1697, decode.acc_seg: 93.3108, loss: 0.1697 2023-01-06 03:55:05,613 - mmseg - INFO - Iter [24700/160000] lr: 5.074e-05, eta: 1 day, 1:37:26, time: 0.681, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1800, decode.acc_seg: 93.1197, loss: 0.1800 2023-01-06 03:55:38,320 - mmseg - INFO - Iter [24750/160000] lr: 5.072e-05, eta: 1 day, 1:36:44, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1686, decode.acc_seg: 93.3578, loss: 0.1686 2023-01-06 03:56:11,146 - mmseg - INFO - Iter [24800/160000] lr: 5.070e-05, eta: 1 day, 1:36:03, time: 0.656, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1527, decode.acc_seg: 93.7504, loss: 0.1527 2023-01-06 03:56:44,434 - mmseg - INFO - Iter [24850/160000] lr: 5.068e-05, eta: 1 day, 1:35:25, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1586, decode.acc_seg: 93.6195, loss: 0.1586 2023-01-06 03:57:18,240 - mmseg - INFO - Iter [24900/160000] lr: 5.066e-05, eta: 1 day, 1:34:49, time: 0.676, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1590, decode.acc_seg: 93.9162, loss: 0.1590 2023-01-06 03:57:52,999 - mmseg - INFO - Iter [24950/160000] lr: 5.064e-05, eta: 1 day, 1:34:19, time: 0.695, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1534, decode.acc_seg: 94.0774, loss: 0.1534 2023-01-06 03:58:25,799 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 03:58:25,800 - mmseg - INFO - Iter [25000/160000] lr: 5.063e-05, eta: 1 day, 1:33:38, time: 0.656, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1516, decode.acc_seg: 93.7125, loss: 0.1516 2023-01-06 03:58:58,982 - mmseg - INFO - Iter [25050/160000] lr: 5.061e-05, eta: 1 day, 1:32:59, time: 0.664, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1749, decode.acc_seg: 93.1114, loss: 0.1749 2023-01-06 03:59:31,733 - mmseg - INFO - Iter [25100/160000] lr: 5.059e-05, eta: 1 day, 1:32:18, time: 0.655, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1712, decode.acc_seg: 93.3235, loss: 0.1712 2023-01-06 04:00:05,250 - mmseg - INFO - Iter [25150/160000] lr: 5.057e-05, eta: 1 day, 1:31:41, time: 0.670, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1581, decode.acc_seg: 93.6559, loss: 0.1581 2023-01-06 04:00:38,829 - mmseg - INFO - Iter [25200/160000] lr: 5.055e-05, eta: 1 day, 1:31:04, time: 0.672, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1673, decode.acc_seg: 93.4630, loss: 0.1673 2023-01-06 04:01:12,154 - mmseg - INFO - Iter [25250/160000] lr: 5.053e-05, eta: 1 day, 1:30:26, time: 0.666, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1570, decode.acc_seg: 93.6941, loss: 0.1570 2023-01-06 04:01:48,148 - mmseg - INFO - Iter [25300/160000] lr: 5.051e-05, eta: 1 day, 1:30:02, time: 0.720, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1590, decode.acc_seg: 93.7642, loss: 0.1590 2023-01-06 04:02:20,873 - mmseg - INFO - Iter [25350/160000] lr: 5.049e-05, eta: 1 day, 1:29:21, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1541, decode.acc_seg: 94.0481, loss: 0.1541 2023-01-06 04:02:53,707 - mmseg - INFO - Iter [25400/160000] lr: 5.048e-05, eta: 1 day, 1:28:40, time: 0.657, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1532, decode.acc_seg: 93.9760, loss: 0.1532 2023-01-06 04:03:27,319 - mmseg - INFO - Iter [25450/160000] lr: 5.046e-05, eta: 1 day, 1:28:03, time: 0.672, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1688, decode.acc_seg: 93.1407, loss: 0.1688 2023-01-06 04:04:01,712 - mmseg - INFO - Iter [25500/160000] lr: 5.044e-05, eta: 1 day, 1:27:31, time: 0.688, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1511, decode.acc_seg: 93.9799, loss: 0.1511 2023-01-06 04:04:35,020 - mmseg - INFO - Iter [25550/160000] lr: 5.042e-05, eta: 1 day, 1:26:53, time: 0.665, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1559, decode.acc_seg: 93.8002, loss: 0.1559 2023-01-06 04:05:08,709 - mmseg - INFO - Iter [25600/160000] lr: 5.040e-05, eta: 1 day, 1:26:17, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1798, decode.acc_seg: 92.9631, loss: 0.1798 2023-01-06 04:05:41,862 - mmseg - INFO - Iter [25650/160000] lr: 5.038e-05, eta: 1 day, 1:25:38, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1790, decode.acc_seg: 93.0590, loss: 0.1790 2023-01-06 04:06:17,276 - mmseg - INFO - Iter [25700/160000] lr: 5.036e-05, eta: 1 day, 1:25:11, time: 0.708, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1692, decode.acc_seg: 93.3503, loss: 0.1692 2023-01-06 04:06:51,510 - mmseg - INFO - Iter [25750/160000] lr: 5.034e-05, eta: 1 day, 1:24:38, time: 0.684, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1614, decode.acc_seg: 93.7211, loss: 0.1614 2023-01-06 04:07:26,065 - mmseg - INFO - Iter [25800/160000] lr: 5.033e-05, eta: 1 day, 1:24:06, time: 0.692, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1617, decode.acc_seg: 93.5131, loss: 0.1617 2023-01-06 04:08:00,252 - mmseg - INFO - Iter [25850/160000] lr: 5.031e-05, eta: 1 day, 1:23:33, time: 0.684, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1560, decode.acc_seg: 93.6416, loss: 0.1560 2023-01-06 04:08:33,359 - mmseg - INFO - Iter [25900/160000] lr: 5.029e-05, eta: 1 day, 1:22:54, time: 0.662, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1599, decode.acc_seg: 93.6164, loss: 0.1599 2023-01-06 04:09:05,980 - mmseg - INFO - Iter [25950/160000] lr: 5.027e-05, eta: 1 day, 1:22:12, time: 0.653, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1524, decode.acc_seg: 93.7857, loss: 0.1524 2023-01-06 04:09:40,305 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 04:09:40,306 - mmseg - INFO - Iter [26000/160000] lr: 5.025e-05, eta: 1 day, 1:21:39, time: 0.686, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1645, decode.acc_seg: 93.3081, loss: 0.1645 2023-01-06 04:10:16,479 - mmseg - INFO - Iter [26050/160000] lr: 5.023e-05, eta: 1 day, 1:21:16, time: 0.723, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1544, decode.acc_seg: 93.9721, loss: 0.1544 2023-01-06 04:10:49,168 - mmseg - INFO - Iter [26100/160000] lr: 5.021e-05, eta: 1 day, 1:20:35, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1509, decode.acc_seg: 94.0821, loss: 0.1509 2023-01-06 04:11:21,939 - mmseg - INFO - Iter [26150/160000] lr: 5.019e-05, eta: 1 day, 1:19:54, time: 0.655, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1460, decode.acc_seg: 94.1088, loss: 0.1460 2023-01-06 04:11:56,710 - mmseg - INFO - Iter [26200/160000] lr: 5.018e-05, eta: 1 day, 1:19:24, time: 0.695, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1540, decode.acc_seg: 93.9628, loss: 0.1540 2023-01-06 04:12:29,617 - mmseg - INFO - Iter [26250/160000] lr: 5.016e-05, eta: 1 day, 1:18:44, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1511, decode.acc_seg: 93.8802, loss: 0.1511 2023-01-06 04:13:02,558 - mmseg - INFO - Iter [26300/160000] lr: 5.014e-05, eta: 1 day, 1:18:04, time: 0.659, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1549, decode.acc_seg: 93.7059, loss: 0.1549 2023-01-06 04:13:35,301 - mmseg - INFO - Iter [26350/160000] lr: 5.012e-05, eta: 1 day, 1:17:23, time: 0.655, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1453, decode.acc_seg: 94.0682, loss: 0.1453 2023-01-06 04:14:08,093 - mmseg - INFO - Iter [26400/160000] lr: 5.010e-05, eta: 1 day, 1:16:43, time: 0.656, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1501, decode.acc_seg: 93.8784, loss: 0.1501 2023-01-06 04:14:43,618 - mmseg - INFO - Iter [26450/160000] lr: 5.008e-05, eta: 1 day, 1:16:16, time: 0.710, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1662, decode.acc_seg: 93.5234, loss: 0.1662 2023-01-06 04:15:16,181 - mmseg - INFO - Iter [26500/160000] lr: 5.006e-05, eta: 1 day, 1:15:35, time: 0.651, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1664, decode.acc_seg: 93.3235, loss: 0.1664 2023-01-06 04:15:49,831 - mmseg - INFO - Iter [26550/160000] lr: 5.004e-05, eta: 1 day, 1:14:58, time: 0.673, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1647, decode.acc_seg: 93.3563, loss: 0.1647 2023-01-06 04:16:23,478 - mmseg - INFO - Iter [26600/160000] lr: 5.003e-05, eta: 1 day, 1:14:22, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1504, decode.acc_seg: 94.0150, loss: 0.1504 2023-01-06 04:16:58,821 - mmseg - INFO - Iter [26650/160000] lr: 5.001e-05, eta: 1 day, 1:13:55, time: 0.708, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1438, decode.acc_seg: 94.1229, loss: 0.1438 2023-01-06 04:17:32,311 - mmseg - INFO - Iter [26700/160000] lr: 4.999e-05, eta: 1 day, 1:13:18, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1503, decode.acc_seg: 93.8267, loss: 0.1503 2023-01-06 04:18:06,075 - mmseg - INFO - Iter [26750/160000] lr: 4.997e-05, eta: 1 day, 1:12:42, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1555, decode.acc_seg: 93.6463, loss: 0.1555 2023-01-06 04:18:41,939 - mmseg - INFO - Iter [26800/160000] lr: 4.995e-05, eta: 1 day, 1:12:17, time: 0.718, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1572, decode.acc_seg: 93.7167, loss: 0.1572 2023-01-06 04:19:15,991 - mmseg - INFO - Iter [26850/160000] lr: 4.993e-05, eta: 1 day, 1:11:43, time: 0.681, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1577, decode.acc_seg: 93.4871, loss: 0.1577 2023-01-06 04:19:48,909 - mmseg - INFO - Iter [26900/160000] lr: 4.991e-05, eta: 1 day, 1:11:03, time: 0.658, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1635, decode.acc_seg: 93.4390, loss: 0.1635 2023-01-06 04:20:23,333 - mmseg - INFO - Iter [26950/160000] lr: 4.989e-05, eta: 1 day, 1:10:31, time: 0.689, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1463, decode.acc_seg: 94.1505, loss: 0.1463 2023-01-06 04:20:55,858 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 04:20:55,859 - mmseg - INFO - Iter [27000/160000] lr: 4.988e-05, eta: 1 day, 1:09:50, time: 0.650, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1528, decode.acc_seg: 93.9716, loss: 0.1528 2023-01-06 04:21:28,385 - mmseg - INFO - Iter [27050/160000] lr: 4.986e-05, eta: 1 day, 1:09:08, time: 0.651, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1628, decode.acc_seg: 93.6828, loss: 0.1628 2023-01-06 04:22:01,777 - mmseg - INFO - Iter [27100/160000] lr: 4.984e-05, eta: 1 day, 1:08:30, time: 0.667, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1427, decode.acc_seg: 94.2285, loss: 0.1427 2023-01-06 04:22:37,095 - mmseg - INFO - Iter [27150/160000] lr: 4.982e-05, eta: 1 day, 1:08:03, time: 0.707, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1374, decode.acc_seg: 94.4003, loss: 0.1374 2023-01-06 04:23:14,385 - mmseg - INFO - Iter [27200/160000] lr: 4.980e-05, eta: 1 day, 1:07:45, time: 0.746, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1463, decode.acc_seg: 94.1607, loss: 0.1463 2023-01-06 04:23:48,672 - mmseg - INFO - Iter [27250/160000] lr: 4.978e-05, eta: 1 day, 1:07:12, time: 0.686, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1480, decode.acc_seg: 93.9892, loss: 0.1480 2023-01-06 04:24:22,437 - mmseg - INFO - Iter [27300/160000] lr: 4.976e-05, eta: 1 day, 1:06:36, time: 0.675, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1558, decode.acc_seg: 93.9988, loss: 0.1558 2023-01-06 04:24:54,965 - mmseg - INFO - Iter [27350/160000] lr: 4.974e-05, eta: 1 day, 1:05:55, time: 0.651, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1954, decode.acc_seg: 92.4040, loss: 0.1954 2023-01-06 04:25:28,100 - mmseg - INFO - Iter [27400/160000] lr: 4.973e-05, eta: 1 day, 1:05:16, time: 0.663, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1819, decode.acc_seg: 92.6335, loss: 0.1819 2023-01-06 04:26:02,674 - mmseg - INFO - Iter [27450/160000] lr: 4.971e-05, eta: 1 day, 1:04:44, time: 0.691, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1710, decode.acc_seg: 93.3769, loss: 0.1710 2023-01-06 04:26:36,537 - mmseg - INFO - Iter [27500/160000] lr: 4.969e-05, eta: 1 day, 1:04:10, time: 0.677, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1515, decode.acc_seg: 93.9278, loss: 0.1515 2023-01-06 04:27:11,436 - mmseg - INFO - Iter [27550/160000] lr: 4.967e-05, eta: 1 day, 1:03:39, time: 0.698, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1404, decode.acc_seg: 94.4161, loss: 0.1404 2023-01-06 04:27:44,903 - mmseg - INFO - Iter [27600/160000] lr: 4.965e-05, eta: 1 day, 1:03:03, time: 0.669, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1535, decode.acc_seg: 93.9750, loss: 0.1535 2023-01-06 04:28:18,947 - mmseg - INFO - Iter [27650/160000] lr: 4.963e-05, eta: 1 day, 1:02:28, time: 0.681, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1640, decode.acc_seg: 93.3334, loss: 0.1640 2023-01-06 04:28:53,802 - mmseg - INFO - Iter [27700/160000] lr: 4.961e-05, eta: 1 day, 1:01:58, time: 0.696, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1642, decode.acc_seg: 93.5958, loss: 0.1642 2023-01-06 04:29:26,449 - mmseg - INFO - Iter [27750/160000] lr: 4.959e-05, eta: 1 day, 1:01:17, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1488, decode.acc_seg: 93.9529, loss: 0.1488 2023-01-06 04:29:59,153 - mmseg - INFO - Iter [27800/160000] lr: 4.958e-05, eta: 1 day, 1:00:37, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1430, decode.acc_seg: 94.1584, loss: 0.1430 2023-01-06 04:30:31,826 - mmseg - INFO - Iter [27850/160000] lr: 4.956e-05, eta: 1 day, 0:59:56, time: 0.653, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1468, decode.acc_seg: 93.9686, loss: 0.1468 2023-01-06 04:31:04,441 - mmseg - INFO - Iter [27900/160000] lr: 4.954e-05, eta: 1 day, 0:59:16, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1764, decode.acc_seg: 92.9921, loss: 0.1764 2023-01-06 04:31:39,443 - mmseg - INFO - Iter [27950/160000] lr: 4.952e-05, eta: 1 day, 0:58:46, time: 0.700, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1520, decode.acc_seg: 94.0360, loss: 0.1520 2023-01-06 04:32:13,132 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 04:32:13,133 - mmseg - INFO - Iter [28000/160000] lr: 4.950e-05, eta: 1 day, 0:58:10, time: 0.673, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1602, decode.acc_seg: 93.4881, loss: 0.1602 2023-01-06 04:32:48,548 - mmseg - INFO - Iter [28050/160000] lr: 4.948e-05, eta: 1 day, 0:57:43, time: 0.709, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1496, decode.acc_seg: 94.1660, loss: 0.1496 2023-01-06 04:33:23,974 - mmseg - INFO - Iter [28100/160000] lr: 4.946e-05, eta: 1 day, 0:57:15, time: 0.708, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1588, decode.acc_seg: 93.8053, loss: 0.1588 2023-01-06 04:33:56,662 - mmseg - INFO - Iter [28150/160000] lr: 4.944e-05, eta: 1 day, 0:56:34, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1508, decode.acc_seg: 94.0398, loss: 0.1508 2023-01-06 04:34:30,297 - mmseg - INFO - Iter [28200/160000] lr: 4.943e-05, eta: 1 day, 0:55:58, time: 0.672, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1464, decode.acc_seg: 94.2180, loss: 0.1464 2023-01-06 04:35:05,785 - mmseg - INFO - Iter [28250/160000] lr: 4.941e-05, eta: 1 day, 0:55:31, time: 0.711, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1552, decode.acc_seg: 93.8883, loss: 0.1552 2023-01-06 04:35:41,890 - mmseg - INFO - Iter [28300/160000] lr: 4.939e-05, eta: 1 day, 0:55:07, time: 0.722, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1439, decode.acc_seg: 94.1348, loss: 0.1439 2023-01-06 04:36:17,270 - mmseg - INFO - Iter [28350/160000] lr: 4.937e-05, eta: 1 day, 0:54:39, time: 0.708, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1612, decode.acc_seg: 93.6725, loss: 0.1612 2023-01-06 04:36:49,921 - mmseg - INFO - Iter [28400/160000] lr: 4.935e-05, eta: 1 day, 0:53:58, time: 0.653, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1438, decode.acc_seg: 94.2509, loss: 0.1438 2023-01-06 04:37:24,089 - mmseg - INFO - Iter [28450/160000] lr: 4.933e-05, eta: 1 day, 0:53:25, time: 0.683, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1634, decode.acc_seg: 93.3906, loss: 0.1634 2023-01-06 04:37:57,385 - mmseg - INFO - Iter [28500/160000] lr: 4.931e-05, eta: 1 day, 0:52:47, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1538, decode.acc_seg: 93.8799, loss: 0.1538 2023-01-06 04:38:31,687 - mmseg - INFO - Iter [28550/160000] lr: 4.929e-05, eta: 1 day, 0:52:14, time: 0.686, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1535, decode.acc_seg: 93.9343, loss: 0.1535 2023-01-06 04:39:04,344 - mmseg - INFO - Iter [28600/160000] lr: 4.928e-05, eta: 1 day, 0:51:34, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1565, decode.acc_seg: 93.8408, loss: 0.1565 2023-01-06 04:39:40,123 - mmseg - INFO - Iter [28650/160000] lr: 4.926e-05, eta: 1 day, 0:51:07, time: 0.715, data_time: 0.057, memory: 11582, decode.loss_ce: 0.1508, decode.acc_seg: 93.7165, loss: 0.1508 2023-01-06 04:40:14,141 - mmseg - INFO - Iter [28700/160000] lr: 4.924e-05, eta: 1 day, 0:50:33, time: 0.681, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1490, decode.acc_seg: 94.1946, loss: 0.1490 2023-01-06 04:40:49,748 - mmseg - INFO - Iter [28750/160000] lr: 4.922e-05, eta: 1 day, 0:50:06, time: 0.711, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1484, decode.acc_seg: 93.9990, loss: 0.1484 2023-01-06 04:41:25,686 - mmseg - INFO - Iter [28800/160000] lr: 4.920e-05, eta: 1 day, 0:49:41, time: 0.720, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1442, decode.acc_seg: 94.3150, loss: 0.1442 2023-01-06 04:41:58,111 - mmseg - INFO - Iter [28850/160000] lr: 4.918e-05, eta: 1 day, 0:48:59, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1481, decode.acc_seg: 94.0069, loss: 0.1481 2023-01-06 04:42:31,380 - mmseg - INFO - Iter [28900/160000] lr: 4.916e-05, eta: 1 day, 0:48:22, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1420, decode.acc_seg: 94.2346, loss: 0.1420 2023-01-06 04:43:05,542 - mmseg - INFO - Iter [28950/160000] lr: 4.914e-05, eta: 1 day, 0:47:48, time: 0.682, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1603, decode.acc_seg: 93.5323, loss: 0.1603 2023-01-06 04:43:39,121 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 04:43:39,122 - mmseg - INFO - Iter [29000/160000] lr: 4.913e-05, eta: 1 day, 0:47:12, time: 0.672, data_time: 0.016, memory: 11582, decode.loss_ce: 0.1535, decode.acc_seg: 93.7815, loss: 0.1535 2023-01-06 04:44:16,449 - mmseg - INFO - Iter [29050/160000] lr: 4.911e-05, eta: 1 day, 0:46:52, time: 0.746, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1635, decode.acc_seg: 93.5150, loss: 0.1635 2023-01-06 04:44:50,116 - mmseg - INFO - Iter [29100/160000] lr: 4.909e-05, eta: 1 day, 0:46:17, time: 0.674, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1627, decode.acc_seg: 93.5414, loss: 0.1627 2023-01-06 04:45:23,763 - mmseg - INFO - Iter [29150/160000] lr: 4.907e-05, eta: 1 day, 0:45:40, time: 0.672, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1516, decode.acc_seg: 93.6673, loss: 0.1516 2023-01-06 04:45:58,975 - mmseg - INFO - Iter [29200/160000] lr: 4.905e-05, eta: 1 day, 0:45:12, time: 0.704, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1529, decode.acc_seg: 93.7500, loss: 0.1529 2023-01-06 04:46:32,437 - mmseg - INFO - Iter [29250/160000] lr: 4.903e-05, eta: 1 day, 0:44:35, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1505, decode.acc_seg: 93.9994, loss: 0.1505 2023-01-06 04:47:05,132 - mmseg - INFO - Iter [29300/160000] lr: 4.901e-05, eta: 1 day, 0:43:55, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1756, decode.acc_seg: 93.2686, loss: 0.1756 2023-01-06 04:47:38,703 - mmseg - INFO - Iter [29350/160000] lr: 4.899e-05, eta: 1 day, 0:43:19, time: 0.671, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1560, decode.acc_seg: 93.7882, loss: 0.1560 2023-01-06 04:48:13,580 - mmseg - INFO - Iter [29400/160000] lr: 4.898e-05, eta: 1 day, 0:42:48, time: 0.698, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1448, decode.acc_seg: 94.3179, loss: 0.1448 2023-01-06 04:48:46,695 - mmseg - INFO - Iter [29450/160000] lr: 4.896e-05, eta: 1 day, 0:42:10, time: 0.662, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1404, decode.acc_seg: 94.2573, loss: 0.1404 2023-01-06 04:49:20,426 - mmseg - INFO - Iter [29500/160000] lr: 4.894e-05, eta: 1 day, 0:41:34, time: 0.675, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1494, decode.acc_seg: 94.0812, loss: 0.1494 2023-01-06 04:49:54,714 - mmseg - INFO - Iter [29550/160000] lr: 4.892e-05, eta: 1 day, 0:41:01, time: 0.686, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1651, decode.acc_seg: 93.6572, loss: 0.1651 2023-01-06 04:50:27,226 - mmseg - INFO - Iter [29600/160000] lr: 4.890e-05, eta: 1 day, 0:40:20, time: 0.650, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1527, decode.acc_seg: 93.8709, loss: 0.1527 2023-01-06 04:51:00,584 - mmseg - INFO - Iter [29650/160000] lr: 4.888e-05, eta: 1 day, 0:39:43, time: 0.667, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1589, decode.acc_seg: 93.7920, loss: 0.1589 2023-01-06 04:51:33,428 - mmseg - INFO - Iter [29700/160000] lr: 4.886e-05, eta: 1 day, 0:39:04, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1370, decode.acc_seg: 94.2670, loss: 0.1370 2023-01-06 04:52:06,656 - mmseg - INFO - Iter [29750/160000] lr: 4.884e-05, eta: 1 day, 0:38:26, time: 0.665, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1525, decode.acc_seg: 93.8596, loss: 0.1525 2023-01-06 04:52:43,500 - mmseg - INFO - Iter [29800/160000] lr: 4.883e-05, eta: 1 day, 0:38:04, time: 0.736, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1505, decode.acc_seg: 93.9303, loss: 0.1505 2023-01-06 04:53:18,924 - mmseg - INFO - Iter [29850/160000] lr: 4.881e-05, eta: 1 day, 0:37:36, time: 0.708, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1492, decode.acc_seg: 93.9544, loss: 0.1492 2023-01-06 04:53:52,040 - mmseg - INFO - Iter [29900/160000] lr: 4.879e-05, eta: 1 day, 0:36:58, time: 0.663, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1472, decode.acc_seg: 94.1053, loss: 0.1472 2023-01-06 04:54:25,038 - mmseg - INFO - Iter [29950/160000] lr: 4.877e-05, eta: 1 day, 0:36:19, time: 0.659, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1417, decode.acc_seg: 94.1576, loss: 0.1417 2023-01-06 04:54:59,940 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 04:54:59,940 - mmseg - INFO - Iter [30000/160000] lr: 4.875e-05, eta: 1 day, 0:35:49, time: 0.698, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1470, decode.acc_seg: 94.1562, loss: 0.1470 2023-01-06 04:55:33,838 - mmseg - INFO - Iter [30050/160000] lr: 4.873e-05, eta: 1 day, 0:35:14, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1317, decode.acc_seg: 94.6503, loss: 0.1317 2023-01-06 04:56:06,618 - mmseg - INFO - Iter [30100/160000] lr: 4.871e-05, eta: 1 day, 0:34:35, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1570, decode.acc_seg: 93.7792, loss: 0.1570 2023-01-06 04:56:43,335 - mmseg - INFO - Iter [30150/160000] lr: 4.869e-05, eta: 1 day, 0:34:12, time: 0.734, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1555, decode.acc_seg: 93.9007, loss: 0.1555 2023-01-06 04:57:16,007 - mmseg - INFO - Iter [30200/160000] lr: 4.868e-05, eta: 1 day, 0:33:32, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1791, decode.acc_seg: 93.0932, loss: 0.1791 2023-01-06 04:57:48,852 - mmseg - INFO - Iter [30250/160000] lr: 4.866e-05, eta: 1 day, 0:32:53, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1634, decode.acc_seg: 93.5803, loss: 0.1634 2023-01-06 04:58:21,399 - mmseg - INFO - Iter [30300/160000] lr: 4.864e-05, eta: 1 day, 0:32:12, time: 0.651, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1577, decode.acc_seg: 93.8733, loss: 0.1577 2023-01-06 04:58:54,109 - mmseg - INFO - Iter [30350/160000] lr: 4.862e-05, eta: 1 day, 0:31:33, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1530, decode.acc_seg: 93.9396, loss: 0.1530 2023-01-06 04:59:27,496 - mmseg - INFO - Iter [30400/160000] lr: 4.860e-05, eta: 1 day, 0:30:56, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1520, decode.acc_seg: 93.9522, loss: 0.1520 2023-01-06 05:00:02,221 - mmseg - INFO - Iter [30450/160000] lr: 4.858e-05, eta: 1 day, 0:30:24, time: 0.694, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1460, decode.acc_seg: 94.2368, loss: 0.1460 2023-01-06 05:00:36,695 - mmseg - INFO - Iter [30500/160000] lr: 4.856e-05, eta: 1 day, 0:29:52, time: 0.690, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1537, decode.acc_seg: 93.8960, loss: 0.1537 2023-01-06 05:01:11,534 - mmseg - INFO - Iter [30550/160000] lr: 4.854e-05, eta: 1 day, 0:29:22, time: 0.697, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1496, decode.acc_seg: 93.9391, loss: 0.1496 2023-01-06 05:01:44,380 - mmseg - INFO - Iter [30600/160000] lr: 4.853e-05, eta: 1 day, 0:28:42, time: 0.656, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1396, decode.acc_seg: 94.2629, loss: 0.1396 2023-01-06 05:02:18,202 - mmseg - INFO - Iter [30650/160000] lr: 4.851e-05, eta: 1 day, 0:28:07, time: 0.678, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1518, decode.acc_seg: 93.8382, loss: 0.1518 2023-01-06 05:02:51,986 - mmseg - INFO - Iter [30700/160000] lr: 4.849e-05, eta: 1 day, 0:27:32, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1734, decode.acc_seg: 93.3251, loss: 0.1734 2023-01-06 05:03:25,641 - mmseg - INFO - Iter [30750/160000] lr: 4.847e-05, eta: 1 day, 0:26:56, time: 0.672, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1540, decode.acc_seg: 93.8355, loss: 0.1540 2023-01-06 05:04:01,262 - mmseg - INFO - Iter [30800/160000] lr: 4.845e-05, eta: 1 day, 0:26:29, time: 0.713, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1426, decode.acc_seg: 94.2126, loss: 0.1426 2023-01-06 05:04:33,754 - mmseg - INFO - Iter [30850/160000] lr: 4.843e-05, eta: 1 day, 0:25:48, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1397, decode.acc_seg: 94.3638, loss: 0.1397 2023-01-06 05:05:08,541 - mmseg - INFO - Iter [30900/160000] lr: 4.841e-05, eta: 1 day, 0:25:17, time: 0.696, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1420, decode.acc_seg: 94.2793, loss: 0.1420 2023-01-06 05:05:41,778 - mmseg - INFO - Iter [30950/160000] lr: 4.839e-05, eta: 1 day, 0:24:40, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1447, decode.acc_seg: 94.1591, loss: 0.1447 2023-01-06 05:06:14,711 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 05:06:14,712 - mmseg - INFO - Iter [31000/160000] lr: 4.838e-05, eta: 1 day, 0:24:01, time: 0.659, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1474, decode.acc_seg: 94.1940, loss: 0.1474 2023-01-06 05:06:48,789 - mmseg - INFO - Iter [31050/160000] lr: 4.836e-05, eta: 1 day, 0:23:27, time: 0.681, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1506, decode.acc_seg: 94.1028, loss: 0.1506 2023-01-06 05:07:21,307 - mmseg - INFO - Iter [31100/160000] lr: 4.834e-05, eta: 1 day, 0:22:47, time: 0.650, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1468, decode.acc_seg: 94.2057, loss: 0.1468 2023-01-06 05:07:56,471 - mmseg - INFO - Iter [31150/160000] lr: 4.832e-05, eta: 1 day, 0:22:17, time: 0.702, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1677, decode.acc_seg: 93.3166, loss: 0.1677 2023-01-06 05:08:30,487 - mmseg - INFO - Iter [31200/160000] lr: 4.830e-05, eta: 1 day, 0:21:43, time: 0.681, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1538, decode.acc_seg: 93.8576, loss: 0.1538 2023-01-06 05:09:05,446 - mmseg - INFO - Iter [31250/160000] lr: 4.828e-05, eta: 1 day, 0:21:13, time: 0.699, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1595, decode.acc_seg: 93.5169, loss: 0.1595 2023-01-06 05:09:39,725 - mmseg - INFO - Iter [31300/160000] lr: 4.826e-05, eta: 1 day, 0:20:40, time: 0.685, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1386, decode.acc_seg: 94.3136, loss: 0.1386 2023-01-06 05:10:13,701 - mmseg - INFO - Iter [31350/160000] lr: 4.824e-05, eta: 1 day, 0:20:06, time: 0.680, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1314, decode.acc_seg: 94.7058, loss: 0.1314 2023-01-06 05:10:47,236 - mmseg - INFO - Iter [31400/160000] lr: 4.823e-05, eta: 1 day, 0:19:29, time: 0.670, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1325, decode.acc_seg: 94.5226, loss: 0.1325 2023-01-06 05:11:22,331 - mmseg - INFO - Iter [31450/160000] lr: 4.821e-05, eta: 1 day, 0:19:00, time: 0.703, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1450, decode.acc_seg: 94.0477, loss: 0.1450 2023-01-06 05:11:54,927 - mmseg - INFO - Iter [31500/160000] lr: 4.819e-05, eta: 1 day, 0:18:20, time: 0.652, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1407, decode.acc_seg: 94.3213, loss: 0.1407 2023-01-06 05:12:28,612 - mmseg - INFO - Iter [31550/160000] lr: 4.817e-05, eta: 1 day, 0:17:44, time: 0.673, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1354, decode.acc_seg: 94.4283, loss: 0.1354 2023-01-06 05:13:01,994 - mmseg - INFO - Iter [31600/160000] lr: 4.815e-05, eta: 1 day, 0:17:07, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1422, decode.acc_seg: 94.0915, loss: 0.1422 2023-01-06 05:13:37,350 - mmseg - INFO - Iter [31650/160000] lr: 4.813e-05, eta: 1 day, 0:16:39, time: 0.708, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1419, decode.acc_seg: 94.2566, loss: 0.1419 2023-01-06 05:14:10,724 - mmseg - INFO - Iter [31700/160000] lr: 4.811e-05, eta: 1 day, 0:16:02, time: 0.667, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1380, decode.acc_seg: 94.4127, loss: 0.1380 2023-01-06 05:14:43,599 - mmseg - INFO - Iter [31750/160000] lr: 4.809e-05, eta: 1 day, 0:15:23, time: 0.658, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1550, decode.acc_seg: 93.8077, loss: 0.1550 2023-01-06 05:15:17,218 - mmseg - INFO - Iter [31800/160000] lr: 4.808e-05, eta: 1 day, 0:14:47, time: 0.672, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1574, decode.acc_seg: 93.6936, loss: 0.1574 2023-01-06 05:15:49,667 - mmseg - INFO - Iter [31850/160000] lr: 4.806e-05, eta: 1 day, 0:14:07, time: 0.649, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1406, decode.acc_seg: 94.2419, loss: 0.1406 2023-01-06 05:16:23,053 - mmseg - INFO - Iter [31900/160000] lr: 4.804e-05, eta: 1 day, 0:13:30, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1327, decode.acc_seg: 94.4954, loss: 0.1327 2023-01-06 05:16:55,565 - mmseg - INFO - Iter [31950/160000] lr: 4.802e-05, eta: 1 day, 0:12:50, time: 0.650, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1475, decode.acc_seg: 94.1672, loss: 0.1475 2023-01-06 05:17:30,607 - mmseg - INFO - Saving checkpoint at 32000 iterations 2023-01-06 05:17:36,633 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 05:17:36,634 - mmseg - INFO - Iter [32000/160000] lr: 4.800e-05, eta: 1 day, 0:12:44, time: 0.821, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1361, decode.acc_seg: 94.5487, loss: 0.1361 2023-01-06 05:18:12,241 - mmseg - INFO - per class results: 2023-01-06 05:18:12,243 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 97.14 | 98.38 | | sidewalk | 78.45 | 89.53 | | building | 89.56 | 95.01 | | wall | 47.23 | 56.73 | | fence | 45.29 | 61.86 | | pole | 53.4 | 62.12 | | traffic light | 51.99 | 59.55 | | traffic sign | 66.21 | 72.65 | | vegetation | 90.73 | 96.46 | | terrain | 53.91 | 57.95 | | sky | 93.87 | 97.6 | | person | 70.93 | 86.17 | | rider | 37.79 | 42.93 | | car | 89.82 | 97.3 | | truck | 36.82 | 40.93 | | bus | 49.09 | 54.91 | | train | 40.87 | 51.33 | | motorcycle | 29.05 | 32.33 | | bicycle | 65.81 | 84.34 | +---------------+-------+-------+ 2023-01-06 05:18:12,244 - mmseg - INFO - Summary: 2023-01-06 05:18:12,244 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 94.18 | 62.52 | 70.42 | +-------+-------+-------+ 2023-01-06 05:18:12,245 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 05:18:12,245 - mmseg - INFO - Iter(val) [63] aAcc: 0.9418, mIoU: 0.6252, mAcc: 0.7042, IoU.road: 0.9714, IoU.sidewalk: 0.7845, IoU.building: 0.8956, IoU.wall: 0.4723, IoU.fence: 0.4529, IoU.pole: 0.5340, IoU.traffic light: 0.5199, IoU.traffic sign: 0.6621, IoU.vegetation: 0.9073, IoU.terrain: 0.5391, IoU.sky: 0.9387, IoU.person: 0.7093, IoU.rider: 0.3779, IoU.car: 0.8982, IoU.truck: 0.3682, IoU.bus: 0.4909, IoU.train: 0.4087, IoU.motorcycle: 0.2905, IoU.bicycle: 0.6581, Acc.road: 0.9838, Acc.sidewalk: 0.8953, Acc.building: 0.9501, Acc.wall: 0.5673, Acc.fence: 0.6186, Acc.pole: 0.6212, Acc.traffic light: 0.5955, Acc.traffic sign: 0.7265, Acc.vegetation: 0.9646, Acc.terrain: 0.5795, Acc.sky: 0.9760, Acc.person: 0.8617, Acc.rider: 0.4293, Acc.car: 0.9730, Acc.truck: 0.4093, Acc.bus: 0.5491, Acc.train: 0.5133, Acc.motorcycle: 0.3233, Acc.bicycle: 0.8434 2023-01-06 05:18:45,966 - mmseg - INFO - Iter [32050/160000] lr: 4.798e-05, eta: 1 day, 0:14:31, time: 1.387, data_time: 0.726, memory: 11582, decode.loss_ce: 0.1414, decode.acc_seg: 94.1963, loss: 0.1414 2023-01-06 05:19:20,418 - mmseg - INFO - Iter [32100/160000] lr: 4.796e-05, eta: 1 day, 0:13:58, time: 0.689, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1498, decode.acc_seg: 93.9287, loss: 0.1498 2023-01-06 05:19:53,410 - mmseg - INFO - Iter [32150/160000] lr: 4.794e-05, eta: 1 day, 0:13:20, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1391, decode.acc_seg: 94.3923, loss: 0.1391 2023-01-06 05:20:26,660 - mmseg - INFO - Iter [32200/160000] lr: 4.793e-05, eta: 1 day, 0:12:42, time: 0.665, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1418, decode.acc_seg: 94.3488, loss: 0.1418 2023-01-06 05:20:59,382 - mmseg - INFO - Iter [32250/160000] lr: 4.791e-05, eta: 1 day, 0:12:03, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1465, decode.acc_seg: 94.1775, loss: 0.1465 2023-01-06 05:21:33,087 - mmseg - INFO - Iter [32300/160000] lr: 4.789e-05, eta: 1 day, 0:11:27, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1407, decode.acc_seg: 94.2530, loss: 0.1407 2023-01-06 05:22:05,435 - mmseg - INFO - Iter [32350/160000] lr: 4.787e-05, eta: 1 day, 0:10:46, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1458, decode.acc_seg: 94.2207, loss: 0.1458 2023-01-06 05:22:41,116 - mmseg - INFO - Iter [32400/160000] lr: 4.785e-05, eta: 1 day, 0:10:18, time: 0.714, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1374, decode.acc_seg: 94.6363, loss: 0.1374 2023-01-06 05:23:13,736 - mmseg - INFO - Iter [32450/160000] lr: 4.783e-05, eta: 1 day, 0:09:38, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1375, decode.acc_seg: 94.4287, loss: 0.1375 2023-01-06 05:23:47,678 - mmseg - INFO - Iter [32500/160000] lr: 4.781e-05, eta: 1 day, 0:09:04, time: 0.679, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1402, decode.acc_seg: 94.4051, loss: 0.1402 2023-01-06 05:24:20,430 - mmseg - INFO - Iter [32550/160000] lr: 4.779e-05, eta: 1 day, 0:08:24, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1493, decode.acc_seg: 94.1907, loss: 0.1493 2023-01-06 05:24:53,187 - mmseg - INFO - Iter [32600/160000] lr: 4.778e-05, eta: 1 day, 0:07:45, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1414, decode.acc_seg: 94.3817, loss: 0.1414 2023-01-06 05:25:28,739 - mmseg - INFO - Iter [32650/160000] lr: 4.776e-05, eta: 1 day, 0:07:16, time: 0.710, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1686, decode.acc_seg: 93.3994, loss: 0.1686 2023-01-06 05:26:04,797 - mmseg - INFO - Iter [32700/160000] lr: 4.774e-05, eta: 1 day, 0:06:50, time: 0.721, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1441, decode.acc_seg: 94.2191, loss: 0.1441 2023-01-06 05:26:41,479 - mmseg - INFO - Iter [32750/160000] lr: 4.772e-05, eta: 1 day, 0:06:26, time: 0.734, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1410, decode.acc_seg: 94.3717, loss: 0.1410 2023-01-06 05:27:13,932 - mmseg - INFO - Iter [32800/160000] lr: 4.770e-05, eta: 1 day, 0:05:46, time: 0.649, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1574, decode.acc_seg: 93.9065, loss: 0.1574 2023-01-06 05:27:46,737 - mmseg - INFO - Iter [32850/160000] lr: 4.768e-05, eta: 1 day, 0:05:06, time: 0.656, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1396, decode.acc_seg: 94.2377, loss: 0.1396 2023-01-06 05:28:20,236 - mmseg - INFO - Iter [32900/160000] lr: 4.766e-05, eta: 1 day, 0:04:30, time: 0.669, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1420, decode.acc_seg: 94.3160, loss: 0.1420 2023-01-06 05:28:54,946 - mmseg - INFO - Iter [32950/160000] lr: 4.764e-05, eta: 1 day, 0:03:58, time: 0.695, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1342, decode.acc_seg: 94.6760, loss: 0.1342 2023-01-06 05:29:29,572 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 05:29:29,572 - mmseg - INFO - Iter [33000/160000] lr: 4.763e-05, eta: 1 day, 0:03:26, time: 0.691, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1332, decode.acc_seg: 94.4813, loss: 0.1332 2023-01-06 05:30:03,458 - mmseg - INFO - Iter [33050/160000] lr: 4.761e-05, eta: 1 day, 0:02:51, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1422, decode.acc_seg: 94.1998, loss: 0.1422 2023-01-06 05:30:38,068 - mmseg - INFO - Iter [33100/160000] lr: 4.759e-05, eta: 1 day, 0:02:19, time: 0.692, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1422, decode.acc_seg: 94.4097, loss: 0.1422 2023-01-06 05:31:13,549 - mmseg - INFO - Iter [33150/160000] lr: 4.757e-05, eta: 1 day, 0:01:50, time: 0.709, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1352, decode.acc_seg: 94.6205, loss: 0.1352 2023-01-06 05:31:46,992 - mmseg - INFO - Iter [33200/160000] lr: 4.755e-05, eta: 1 day, 0:01:14, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1416, decode.acc_seg: 94.1352, loss: 0.1416 2023-01-06 05:32:22,184 - mmseg - INFO - Iter [33250/160000] lr: 4.753e-05, eta: 1 day, 0:00:44, time: 0.705, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1492, decode.acc_seg: 93.7931, loss: 0.1492 2023-01-06 05:32:54,832 - mmseg - INFO - Iter [33300/160000] lr: 4.751e-05, eta: 1 day, 0:00:04, time: 0.653, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1441, decode.acc_seg: 94.2763, loss: 0.1441 2023-01-06 05:33:27,519 - mmseg - INFO - Iter [33350/160000] lr: 4.749e-05, eta: 23:59:25, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1400, decode.acc_seg: 94.3635, loss: 0.1400 2023-01-06 05:34:01,655 - mmseg - INFO - Iter [33400/160000] lr: 4.748e-05, eta: 23:58:51, time: 0.682, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1439, decode.acc_seg: 94.1097, loss: 0.1439 2023-01-06 05:34:35,839 - mmseg - INFO - Iter [33450/160000] lr: 4.746e-05, eta: 23:58:17, time: 0.685, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1418, decode.acc_seg: 94.0928, loss: 0.1418 2023-01-06 05:35:11,868 - mmseg - INFO - Iter [33500/160000] lr: 4.744e-05, eta: 23:57:50, time: 0.721, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1389, decode.acc_seg: 94.4376, loss: 0.1389 2023-01-06 05:35:45,409 - mmseg - INFO - Iter [33550/160000] lr: 4.742e-05, eta: 23:57:14, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1456, decode.acc_seg: 94.3296, loss: 0.1456 2023-01-06 05:36:21,046 - mmseg - INFO - Iter [33600/160000] lr: 4.740e-05, eta: 23:56:46, time: 0.713, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1387, decode.acc_seg: 94.1818, loss: 0.1387 2023-01-06 05:36:54,385 - mmseg - INFO - Iter [33650/160000] lr: 4.738e-05, eta: 23:56:09, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1520, decode.acc_seg: 94.0169, loss: 0.1520 2023-01-06 05:37:27,068 - mmseg - INFO - Iter [33700/160000] lr: 4.736e-05, eta: 23:55:29, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1445, decode.acc_seg: 94.2064, loss: 0.1445 2023-01-06 05:38:00,236 - mmseg - INFO - Iter [33750/160000] lr: 4.734e-05, eta: 23:54:52, time: 0.663, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1623, decode.acc_seg: 93.7686, loss: 0.1623 2023-01-06 05:38:35,341 - mmseg - INFO - Iter [33800/160000] lr: 4.733e-05, eta: 23:54:21, time: 0.701, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1576, decode.acc_seg: 93.9658, loss: 0.1576 2023-01-06 05:39:08,938 - mmseg - INFO - Iter [33850/160000] lr: 4.731e-05, eta: 23:53:45, time: 0.673, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1488, decode.acc_seg: 94.0930, loss: 0.1488 2023-01-06 05:39:44,708 - mmseg - INFO - Iter [33900/160000] lr: 4.729e-05, eta: 23:53:18, time: 0.715, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1332, decode.acc_seg: 94.6071, loss: 0.1332 2023-01-06 05:40:17,541 - mmseg - INFO - Iter [33950/160000] lr: 4.727e-05, eta: 23:52:39, time: 0.657, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1304, decode.acc_seg: 94.4933, loss: 0.1304 2023-01-06 05:40:50,672 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 05:40:50,673 - mmseg - INFO - Iter [34000/160000] lr: 4.725e-05, eta: 23:52:01, time: 0.663, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1412, decode.acc_seg: 94.2912, loss: 0.1412 2023-01-06 05:41:23,524 - mmseg - INFO - Iter [34050/160000] lr: 4.723e-05, eta: 23:51:22, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1516, decode.acc_seg: 93.9271, loss: 0.1516 2023-01-06 05:41:56,231 - mmseg - INFO - Iter [34100/160000] lr: 4.721e-05, eta: 23:50:43, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1358, decode.acc_seg: 94.5658, loss: 0.1358 2023-01-06 05:42:29,095 - mmseg - INFO - Iter [34150/160000] lr: 4.719e-05, eta: 23:50:05, time: 0.657, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1367, decode.acc_seg: 94.4893, loss: 0.1367 2023-01-06 05:43:01,766 - mmseg - INFO - Iter [34200/160000] lr: 4.718e-05, eta: 23:49:25, time: 0.653, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1481, decode.acc_seg: 94.1418, loss: 0.1481 2023-01-06 05:43:38,085 - mmseg - INFO - Iter [34250/160000] lr: 4.716e-05, eta: 23:48:59, time: 0.726, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1384, decode.acc_seg: 94.3657, loss: 0.1384 2023-01-06 05:44:12,263 - mmseg - INFO - Iter [34300/160000] lr: 4.714e-05, eta: 23:48:26, time: 0.684, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1417, decode.acc_seg: 94.2462, loss: 0.1417 2023-01-06 05:44:46,855 - mmseg - INFO - Iter [34350/160000] lr: 4.712e-05, eta: 23:47:53, time: 0.692, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1417, decode.acc_seg: 94.2857, loss: 0.1417 2023-01-06 05:45:19,586 - mmseg - INFO - Iter [34400/160000] lr: 4.710e-05, eta: 23:47:14, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1442, decode.acc_seg: 93.9694, loss: 0.1442 2023-01-06 05:45:54,129 - mmseg - INFO - Iter [34450/160000] lr: 4.708e-05, eta: 23:46:42, time: 0.690, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1314, decode.acc_seg: 94.7035, loss: 0.1314 2023-01-06 05:46:28,090 - mmseg - INFO - Iter [34500/160000] lr: 4.706e-05, eta: 23:46:07, time: 0.679, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1549, decode.acc_seg: 93.8506, loss: 0.1549 2023-01-06 05:47:01,860 - mmseg - INFO - Iter [34550/160000] lr: 4.704e-05, eta: 23:45:32, time: 0.676, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1387, decode.acc_seg: 94.3844, loss: 0.1387 2023-01-06 05:47:37,288 - mmseg - INFO - Iter [34600/160000] lr: 4.703e-05, eta: 23:45:03, time: 0.708, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1337, decode.acc_seg: 94.4646, loss: 0.1337 2023-01-06 05:48:10,220 - mmseg - INFO - Iter [34650/160000] lr: 4.701e-05, eta: 23:44:24, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1413, decode.acc_seg: 94.2841, loss: 0.1413 2023-01-06 05:48:45,294 - mmseg - INFO - Iter [34700/160000] lr: 4.699e-05, eta: 23:43:54, time: 0.701, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1377, decode.acc_seg: 94.5499, loss: 0.1377 2023-01-06 05:49:17,883 - mmseg - INFO - Iter [34750/160000] lr: 4.697e-05, eta: 23:43:14, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1409, decode.acc_seg: 94.4214, loss: 0.1409 2023-01-06 05:49:52,000 - mmseg - INFO - Iter [34800/160000] lr: 4.695e-05, eta: 23:42:40, time: 0.682, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1414, decode.acc_seg: 94.3510, loss: 0.1414 2023-01-06 05:50:25,256 - mmseg - INFO - Iter [34850/160000] lr: 4.693e-05, eta: 23:42:03, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1477, decode.acc_seg: 93.9070, loss: 0.1477 2023-01-06 05:50:59,931 - mmseg - INFO - Iter [34900/160000] lr: 4.691e-05, eta: 23:41:31, time: 0.693, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1540, decode.acc_seg: 93.7983, loss: 0.1540 2023-01-06 05:51:35,413 - mmseg - INFO - Iter [34950/160000] lr: 4.689e-05, eta: 23:41:02, time: 0.710, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1285, decode.acc_seg: 94.7595, loss: 0.1285 2023-01-06 05:52:12,710 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 05:52:12,711 - mmseg - INFO - Iter [35000/160000] lr: 4.688e-05, eta: 23:40:39, time: 0.746, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1430, decode.acc_seg: 94.6222, loss: 0.1430 2023-01-06 05:52:48,371 - mmseg - INFO - Iter [35050/160000] lr: 4.686e-05, eta: 23:40:11, time: 0.714, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1396, decode.acc_seg: 94.5456, loss: 0.1396 2023-01-06 05:53:21,008 - mmseg - INFO - Iter [35100/160000] lr: 4.684e-05, eta: 23:39:32, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1422, decode.acc_seg: 94.2922, loss: 0.1422 2023-01-06 05:53:56,642 - mmseg - INFO - Iter [35150/160000] lr: 4.682e-05, eta: 23:39:03, time: 0.712, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1310, decode.acc_seg: 94.7058, loss: 0.1310 2023-01-06 05:54:29,978 - mmseg - INFO - Iter [35200/160000] lr: 4.680e-05, eta: 23:38:26, time: 0.668, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1321, decode.acc_seg: 94.6459, loss: 0.1321 2023-01-06 05:55:02,491 - mmseg - INFO - Iter [35250/160000] lr: 4.678e-05, eta: 23:37:46, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1347, decode.acc_seg: 94.3920, loss: 0.1347 2023-01-06 05:55:36,071 - mmseg - INFO - Iter [35300/160000] lr: 4.676e-05, eta: 23:37:11, time: 0.673, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1439, decode.acc_seg: 94.3302, loss: 0.1439 2023-01-06 05:56:11,365 - mmseg - INFO - Iter [35350/160000] lr: 4.674e-05, eta: 23:36:41, time: 0.706, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1478, decode.acc_seg: 93.9231, loss: 0.1478 2023-01-06 05:56:46,617 - mmseg - INFO - Iter [35400/160000] lr: 4.673e-05, eta: 23:36:11, time: 0.705, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1374, decode.acc_seg: 94.4178, loss: 0.1374 2023-01-06 05:57:20,553 - mmseg - INFO - Iter [35450/160000] lr: 4.671e-05, eta: 23:35:36, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1348, decode.acc_seg: 94.5725, loss: 0.1348 2023-01-06 05:57:53,151 - mmseg - INFO - Iter [35500/160000] lr: 4.669e-05, eta: 23:34:57, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1316, decode.acc_seg: 94.6175, loss: 0.1316 2023-01-06 05:58:26,461 - mmseg - INFO - Iter [35550/160000] lr: 4.667e-05, eta: 23:34:20, time: 0.666, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1330, decode.acc_seg: 94.6846, loss: 0.1330 2023-01-06 05:58:59,104 - mmseg - INFO - Iter [35600/160000] lr: 4.665e-05, eta: 23:33:41, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1299, decode.acc_seg: 94.7505, loss: 0.1299 2023-01-06 05:59:32,727 - mmseg - INFO - Iter [35650/160000] lr: 4.663e-05, eta: 23:33:05, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1411, decode.acc_seg: 94.5556, loss: 0.1411 2023-01-06 06:00:06,596 - mmseg - INFO - Iter [35700/160000] lr: 4.661e-05, eta: 23:32:30, time: 0.678, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1364, decode.acc_seg: 94.5020, loss: 0.1364 2023-01-06 06:00:42,381 - mmseg - INFO - Iter [35750/160000] lr: 4.659e-05, eta: 23:32:02, time: 0.716, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1394, decode.acc_seg: 94.3182, loss: 0.1394 2023-01-06 06:01:15,606 - mmseg - INFO - Iter [35800/160000] lr: 4.658e-05, eta: 23:31:25, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1522, decode.acc_seg: 94.2453, loss: 0.1522 2023-01-06 06:01:48,314 - mmseg - INFO - Iter [35850/160000] lr: 4.656e-05, eta: 23:30:46, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1460, decode.acc_seg: 94.3599, loss: 0.1460 2023-01-06 06:02:21,932 - mmseg - INFO - Iter [35900/160000] lr: 4.654e-05, eta: 23:30:10, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1413, decode.acc_seg: 94.2319, loss: 0.1413 2023-01-06 06:02:54,879 - mmseg - INFO - Iter [35950/160000] lr: 4.652e-05, eta: 23:29:32, time: 0.660, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1379, decode.acc_seg: 94.4681, loss: 0.1379 2023-01-06 06:03:28,210 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 06:03:28,211 - mmseg - INFO - Iter [36000/160000] lr: 4.650e-05, eta: 23:28:55, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1299, decode.acc_seg: 94.6539, loss: 0.1299 2023-01-06 06:04:03,145 - mmseg - INFO - Iter [36050/160000] lr: 4.648e-05, eta: 23:28:24, time: 0.698, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1356, decode.acc_seg: 94.4967, loss: 0.1356 2023-01-06 06:04:40,802 - mmseg - INFO - Iter [36100/160000] lr: 4.646e-05, eta: 23:28:02, time: 0.754, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1468, decode.acc_seg: 94.0459, loss: 0.1468 2023-01-06 06:05:13,651 - mmseg - INFO - Iter [36150/160000] lr: 4.644e-05, eta: 23:27:24, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1292, decode.acc_seg: 94.8352, loss: 0.1292 2023-01-06 06:05:46,493 - mmseg - INFO - Iter [36200/160000] lr: 4.643e-05, eta: 23:26:46, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1462, decode.acc_seg: 94.0668, loss: 0.1462 2023-01-06 06:06:21,127 - mmseg - INFO - Iter [36250/160000] lr: 4.641e-05, eta: 23:26:14, time: 0.693, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1547, decode.acc_seg: 93.8120, loss: 0.1547 2023-01-06 06:06:54,045 - mmseg - INFO - Iter [36300/160000] lr: 4.639e-05, eta: 23:25:35, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1396, decode.acc_seg: 94.2979, loss: 0.1396 2023-01-06 06:07:26,697 - mmseg - INFO - Iter [36350/160000] lr: 4.637e-05, eta: 23:24:56, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1313, decode.acc_seg: 94.6171, loss: 0.1313 2023-01-06 06:07:59,403 - mmseg - INFO - Iter [36400/160000] lr: 4.635e-05, eta: 23:24:18, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1481, decode.acc_seg: 94.0936, loss: 0.1481 2023-01-06 06:08:32,924 - mmseg - INFO - Iter [36450/160000] lr: 4.633e-05, eta: 23:23:42, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1610, decode.acc_seg: 93.7663, loss: 0.1610 2023-01-06 06:09:08,982 - mmseg - INFO - Iter [36500/160000] lr: 4.631e-05, eta: 23:23:14, time: 0.721, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1438, decode.acc_seg: 94.3730, loss: 0.1438 2023-01-06 06:09:42,747 - mmseg - INFO - Iter [36550/160000] lr: 4.629e-05, eta: 23:22:39, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1417, decode.acc_seg: 94.3170, loss: 0.1417 2023-01-06 06:10:18,874 - mmseg - INFO - Iter [36600/160000] lr: 4.628e-05, eta: 23:22:12, time: 0.722, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1448, decode.acc_seg: 94.2737, loss: 0.1448 2023-01-06 06:10:52,833 - mmseg - INFO - Iter [36650/160000] lr: 4.626e-05, eta: 23:21:37, time: 0.680, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1499, decode.acc_seg: 93.9324, loss: 0.1499 2023-01-06 06:11:26,864 - mmseg - INFO - Iter [36700/160000] lr: 4.624e-05, eta: 23:21:03, time: 0.680, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1409, decode.acc_seg: 94.4152, loss: 0.1409 2023-01-06 06:11:59,561 - mmseg - INFO - Iter [36750/160000] lr: 4.622e-05, eta: 23:20:24, time: 0.655, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1416, decode.acc_seg: 94.3375, loss: 0.1416 2023-01-06 06:12:33,565 - mmseg - INFO - Iter [36800/160000] lr: 4.620e-05, eta: 23:19:50, time: 0.680, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1282, decode.acc_seg: 94.6524, loss: 0.1282 2023-01-06 06:13:09,877 - mmseg - INFO - Iter [36850/160000] lr: 4.618e-05, eta: 23:19:23, time: 0.725, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1369, decode.acc_seg: 94.5526, loss: 0.1369 2023-01-06 06:13:44,657 - mmseg - INFO - Iter [36900/160000] lr: 4.616e-05, eta: 23:18:51, time: 0.696, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1182, decode.acc_seg: 95.1478, loss: 0.1182 2023-01-06 06:14:18,398 - mmseg - INFO - Iter [36950/160000] lr: 4.614e-05, eta: 23:18:16, time: 0.675, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1305, decode.acc_seg: 94.8202, loss: 0.1305 2023-01-06 06:14:50,923 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 06:14:50,924 - mmseg - INFO - Iter [37000/160000] lr: 4.613e-05, eta: 23:17:37, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1456, decode.acc_seg: 94.1205, loss: 0.1456 2023-01-06 06:15:24,409 - mmseg - INFO - Iter [37050/160000] lr: 4.611e-05, eta: 23:17:01, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1451, decode.acc_seg: 94.2825, loss: 0.1451 2023-01-06 06:15:58,239 - mmseg - INFO - Iter [37100/160000] lr: 4.609e-05, eta: 23:16:26, time: 0.677, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1300, decode.acc_seg: 94.7368, loss: 0.1300 2023-01-06 06:16:32,582 - mmseg - INFO - Iter [37150/160000] lr: 4.607e-05, eta: 23:15:53, time: 0.688, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1397, decode.acc_seg: 94.4055, loss: 0.1397 2023-01-06 06:17:06,853 - mmseg - INFO - Iter [37200/160000] lr: 4.605e-05, eta: 23:15:19, time: 0.686, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1284, decode.acc_seg: 94.6876, loss: 0.1284 2023-01-06 06:17:43,704 - mmseg - INFO - Iter [37250/160000] lr: 4.603e-05, eta: 23:14:54, time: 0.736, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1228, decode.acc_seg: 95.0383, loss: 0.1228 2023-01-06 06:18:19,656 - mmseg - INFO - Iter [37300/160000] lr: 4.601e-05, eta: 23:14:26, time: 0.720, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1440, decode.acc_seg: 94.2017, loss: 0.1440 2023-01-06 06:18:52,327 - mmseg - INFO - Iter [37350/160000] lr: 4.599e-05, eta: 23:13:47, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1342, decode.acc_seg: 94.5140, loss: 0.1342 2023-01-06 06:19:25,988 - mmseg - INFO - Iter [37400/160000] lr: 4.598e-05, eta: 23:13:12, time: 0.673, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1284, decode.acc_seg: 94.7871, loss: 0.1284 2023-01-06 06:19:59,096 - mmseg - INFO - Iter [37450/160000] lr: 4.596e-05, eta: 23:12:35, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1336, decode.acc_seg: 94.6154, loss: 0.1336 2023-01-06 06:20:32,838 - mmseg - INFO - Iter [37500/160000] lr: 4.594e-05, eta: 23:11:59, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1325, decode.acc_seg: 94.4847, loss: 0.1325 2023-01-06 06:21:06,089 - mmseg - INFO - Iter [37550/160000] lr: 4.592e-05, eta: 23:11:23, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1487, decode.acc_seg: 94.0661, loss: 0.1487 2023-01-06 06:21:40,882 - mmseg - INFO - Iter [37600/160000] lr: 4.590e-05, eta: 23:10:51, time: 0.696, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1538, decode.acc_seg: 93.7934, loss: 0.1538 2023-01-06 06:22:13,699 - mmseg - INFO - Iter [37650/160000] lr: 4.588e-05, eta: 23:10:13, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1415, decode.acc_seg: 94.3115, loss: 0.1415 2023-01-06 06:22:47,994 - mmseg - INFO - Iter [37700/160000] lr: 4.586e-05, eta: 23:09:39, time: 0.686, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1451, decode.acc_seg: 94.2073, loss: 0.1451 2023-01-06 06:23:21,281 - mmseg - INFO - Iter [37750/160000] lr: 4.584e-05, eta: 23:09:02, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1286, decode.acc_seg: 94.8162, loss: 0.1286 2023-01-06 06:23:56,308 - mmseg - INFO - Iter [37800/160000] lr: 4.583e-05, eta: 23:08:31, time: 0.701, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1240, decode.acc_seg: 95.0025, loss: 0.1240 2023-01-06 06:24:30,243 - mmseg - INFO - Iter [37850/160000] lr: 4.581e-05, eta: 23:07:57, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1288, decode.acc_seg: 94.8634, loss: 0.1288 2023-01-06 06:25:03,480 - mmseg - INFO - Iter [37900/160000] lr: 4.579e-05, eta: 23:07:20, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1299, decode.acc_seg: 94.6605, loss: 0.1299 2023-01-06 06:25:38,777 - mmseg - INFO - Iter [37950/160000] lr: 4.577e-05, eta: 23:06:50, time: 0.707, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1402, decode.acc_seg: 94.4868, loss: 0.1402 2023-01-06 06:26:11,534 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 06:26:11,535 - mmseg - INFO - Iter [38000/160000] lr: 4.575e-05, eta: 23:06:11, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1367, decode.acc_seg: 94.5772, loss: 0.1367 2023-01-06 06:26:44,451 - mmseg - INFO - Iter [38050/160000] lr: 4.573e-05, eta: 23:05:34, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1307, decode.acc_seg: 94.5378, loss: 0.1307 2023-01-06 06:27:17,013 - mmseg - INFO - Iter [38100/160000] lr: 4.571e-05, eta: 23:04:55, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1323, decode.acc_seg: 94.7000, loss: 0.1323 2023-01-06 06:27:52,160 - mmseg - INFO - Iter [38150/160000] lr: 4.569e-05, eta: 23:04:24, time: 0.703, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1339, decode.acc_seg: 94.6772, loss: 0.1339 2023-01-06 06:28:25,232 - mmseg - INFO - Iter [38200/160000] lr: 4.568e-05, eta: 23:03:47, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1469, decode.acc_seg: 94.1801, loss: 0.1469 2023-01-06 06:28:58,224 - mmseg - INFO - Iter [38250/160000] lr: 4.566e-05, eta: 23:03:09, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1395, decode.acc_seg: 94.3290, loss: 0.1395 2023-01-06 06:29:32,474 - mmseg - INFO - Iter [38300/160000] lr: 4.564e-05, eta: 23:02:35, time: 0.685, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1410, decode.acc_seg: 94.3406, loss: 0.1410 2023-01-06 06:30:09,883 - mmseg - INFO - Iter [38350/160000] lr: 4.562e-05, eta: 23:02:12, time: 0.748, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1365, decode.acc_seg: 94.6023, loss: 0.1365 2023-01-06 06:30:45,748 - mmseg - INFO - Iter [38400/160000] lr: 4.560e-05, eta: 23:01:43, time: 0.717, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1272, decode.acc_seg: 94.6761, loss: 0.1272 2023-01-06 06:31:20,778 - mmseg - INFO - Iter [38450/160000] lr: 4.558e-05, eta: 23:01:12, time: 0.701, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1310, decode.acc_seg: 94.8173, loss: 0.1310 2023-01-06 06:31:53,898 - mmseg - INFO - Iter [38500/160000] lr: 4.556e-05, eta: 23:00:35, time: 0.663, data_time: 0.016, memory: 11582, decode.loss_ce: 0.1324, decode.acc_seg: 94.5091, loss: 0.1324 2023-01-06 06:32:27,373 - mmseg - INFO - Iter [38550/160000] lr: 4.554e-05, eta: 22:59:59, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1345, decode.acc_seg: 94.5491, loss: 0.1345 2023-01-06 06:33:00,284 - mmseg - INFO - Iter [38600/160000] lr: 4.553e-05, eta: 22:59:21, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1384, decode.acc_seg: 94.2722, loss: 0.1384 2023-01-06 06:33:33,296 - mmseg - INFO - Iter [38650/160000] lr: 4.551e-05, eta: 22:58:44, time: 0.660, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1387, decode.acc_seg: 94.5089, loss: 0.1387 2023-01-06 06:34:10,236 - mmseg - INFO - Iter [38700/160000] lr: 4.549e-05, eta: 22:58:19, time: 0.738, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1324, decode.acc_seg: 94.5929, loss: 0.1324 2023-01-06 06:34:43,383 - mmseg - INFO - Iter [38750/160000] lr: 4.547e-05, eta: 22:57:42, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1409, decode.acc_seg: 94.1910, loss: 0.1409 2023-01-06 06:35:17,531 - mmseg - INFO - Iter [38800/160000] lr: 4.545e-05, eta: 22:57:08, time: 0.683, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1289, decode.acc_seg: 94.8328, loss: 0.1289 2023-01-06 06:35:50,366 - mmseg - INFO - Iter [38850/160000] lr: 4.543e-05, eta: 22:56:30, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1364, decode.acc_seg: 94.5401, loss: 0.1364 2023-01-06 06:36:23,487 - mmseg - INFO - Iter [38900/160000] lr: 4.541e-05, eta: 22:55:53, time: 0.663, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1298, decode.acc_seg: 94.6452, loss: 0.1298 2023-01-06 06:36:56,306 - mmseg - INFO - Iter [38950/160000] lr: 4.539e-05, eta: 22:55:15, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1389, decode.acc_seg: 94.4615, loss: 0.1389 2023-01-06 06:37:29,881 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 06:37:29,881 - mmseg - INFO - Iter [39000/160000] lr: 4.538e-05, eta: 22:54:39, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1385, decode.acc_seg: 94.3278, loss: 0.1385 2023-01-06 06:38:04,039 - mmseg - INFO - Iter [39050/160000] lr: 4.536e-05, eta: 22:54:05, time: 0.683, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1300, decode.acc_seg: 94.6654, loss: 0.1300 2023-01-06 06:38:40,411 - mmseg - INFO - Iter [39100/160000] lr: 4.534e-05, eta: 22:53:38, time: 0.727, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1291, decode.acc_seg: 94.8499, loss: 0.1291 2023-01-06 06:39:12,837 - mmseg - INFO - Iter [39150/160000] lr: 4.532e-05, eta: 22:52:59, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1298, decode.acc_seg: 94.7333, loss: 0.1298 2023-01-06 06:39:45,405 - mmseg - INFO - Iter [39200/160000] lr: 4.530e-05, eta: 22:52:20, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1336, decode.acc_seg: 94.5699, loss: 0.1336 2023-01-06 06:40:20,821 - mmseg - INFO - Iter [39250/160000] lr: 4.528e-05, eta: 22:51:50, time: 0.708, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1316, decode.acc_seg: 94.5888, loss: 0.1316 2023-01-06 06:40:55,403 - mmseg - INFO - Iter [39300/160000] lr: 4.526e-05, eta: 22:51:18, time: 0.693, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1402, decode.acc_seg: 94.3891, loss: 0.1402 2023-01-06 06:41:30,310 - mmseg - INFO - Iter [39350/160000] lr: 4.524e-05, eta: 22:50:46, time: 0.697, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1206, decode.acc_seg: 95.0912, loss: 0.1206 2023-01-06 06:42:05,481 - mmseg - INFO - Iter [39400/160000] lr: 4.523e-05, eta: 22:50:15, time: 0.704, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1246, decode.acc_seg: 95.0128, loss: 0.1246 2023-01-06 06:42:43,369 - mmseg - INFO - Iter [39450/160000] lr: 4.521e-05, eta: 22:49:53, time: 0.758, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1290, decode.acc_seg: 94.7709, loss: 0.1290 2023-01-06 06:43:17,458 - mmseg - INFO - Iter [39500/160000] lr: 4.519e-05, eta: 22:49:19, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1309, decode.acc_seg: 94.7555, loss: 0.1309 2023-01-06 06:43:50,436 - mmseg - INFO - Iter [39550/160000] lr: 4.517e-05, eta: 22:48:41, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1310, decode.acc_seg: 94.6879, loss: 0.1310 2023-01-06 06:44:23,279 - mmseg - INFO - Iter [39600/160000] lr: 4.515e-05, eta: 22:48:03, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1369, decode.acc_seg: 94.2718, loss: 0.1369 2023-01-06 06:44:56,679 - mmseg - INFO - Iter [39650/160000] lr: 4.513e-05, eta: 22:47:27, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1217, decode.acc_seg: 94.9831, loss: 0.1217 2023-01-06 06:45:29,518 - mmseg - INFO - Iter [39700/160000] lr: 4.511e-05, eta: 22:46:49, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1273, decode.acc_seg: 94.7178, loss: 0.1273 2023-01-06 06:46:01,952 - mmseg - INFO - Iter [39750/160000] lr: 4.509e-05, eta: 22:46:10, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1312, decode.acc_seg: 94.6708, loss: 0.1312 2023-01-06 06:46:37,029 - mmseg - INFO - Iter [39800/160000] lr: 4.508e-05, eta: 22:45:39, time: 0.702, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1285, decode.acc_seg: 94.6557, loss: 0.1285 2023-01-06 06:47:13,264 - mmseg - INFO - Iter [39850/160000] lr: 4.506e-05, eta: 22:45:12, time: 0.725, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1397, decode.acc_seg: 94.3047, loss: 0.1397 2023-01-06 06:47:46,850 - mmseg - INFO - Iter [39900/160000] lr: 4.504e-05, eta: 22:44:36, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1310, decode.acc_seg: 94.6707, loss: 0.1310 2023-01-06 06:48:20,200 - mmseg - INFO - Iter [39950/160000] lr: 4.502e-05, eta: 22:44:00, time: 0.668, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1441, decode.acc_seg: 94.3341, loss: 0.1441 2023-01-06 06:48:52,670 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 06:48:52,670 - mmseg - INFO - Iter [40000/160000] lr: 4.500e-05, eta: 22:43:21, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1389, decode.acc_seg: 94.3920, loss: 0.1389 2023-01-06 06:49:25,192 - mmseg - INFO - Iter [40050/160000] lr: 4.498e-05, eta: 22:42:42, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1377, decode.acc_seg: 94.4531, loss: 0.1377 2023-01-06 06:49:58,923 - mmseg - INFO - Iter [40100/160000] lr: 4.496e-05, eta: 22:42:07, time: 0.674, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1233, decode.acc_seg: 94.9731, loss: 0.1233 2023-01-06 06:50:33,870 - mmseg - INFO - Iter [40150/160000] lr: 4.494e-05, eta: 22:41:35, time: 0.698, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1320, decode.acc_seg: 94.6621, loss: 0.1320 2023-01-06 06:51:09,571 - mmseg - INFO - Iter [40200/160000] lr: 4.493e-05, eta: 22:41:06, time: 0.715, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1532, decode.acc_seg: 93.8895, loss: 0.1532 2023-01-06 06:51:44,023 - mmseg - INFO - Iter [40250/160000] lr: 4.491e-05, eta: 22:40:33, time: 0.688, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1414, decode.acc_seg: 94.2695, loss: 0.1414 2023-01-06 06:52:18,646 - mmseg - INFO - Iter [40300/160000] lr: 4.489e-05, eta: 22:40:01, time: 0.693, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1280, decode.acc_seg: 94.8093, loss: 0.1280 2023-01-06 06:52:51,241 - mmseg - INFO - Iter [40350/160000] lr: 4.487e-05, eta: 22:39:22, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1246, decode.acc_seg: 94.8427, loss: 0.1246 2023-01-06 06:53:24,456 - mmseg - INFO - Iter [40400/160000] lr: 4.485e-05, eta: 22:38:46, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1425, decode.acc_seg: 94.1909, loss: 0.1425 2023-01-06 06:53:56,991 - mmseg - INFO - Iter [40450/160000] lr: 4.483e-05, eta: 22:38:07, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1274, decode.acc_seg: 94.7545, loss: 0.1274 2023-01-06 06:54:29,597 - mmseg - INFO - Iter [40500/160000] lr: 4.481e-05, eta: 22:37:28, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1425, decode.acc_seg: 94.4013, loss: 0.1425 2023-01-06 06:55:04,391 - mmseg - INFO - Iter [40550/160000] lr: 4.479e-05, eta: 22:36:56, time: 0.695, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1324, decode.acc_seg: 94.9791, loss: 0.1324 2023-01-06 06:55:39,303 - mmseg - INFO - Iter [40600/160000] lr: 4.478e-05, eta: 22:36:25, time: 0.699, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1293, decode.acc_seg: 94.7339, loss: 0.1293 2023-01-06 06:56:12,344 - mmseg - INFO - Iter [40650/160000] lr: 4.476e-05, eta: 22:35:48, time: 0.661, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1262, decode.acc_seg: 94.9128, loss: 0.1262 2023-01-06 06:56:45,275 - mmseg - INFO - Iter [40700/160000] lr: 4.474e-05, eta: 22:35:10, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1269, decode.acc_seg: 94.9095, loss: 0.1269 2023-01-06 06:57:20,109 - mmseg - INFO - Iter [40750/160000] lr: 4.472e-05, eta: 22:34:38, time: 0.697, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1239, decode.acc_seg: 94.7666, loss: 0.1239 2023-01-06 06:57:52,516 - mmseg - INFO - Iter [40800/160000] lr: 4.470e-05, eta: 22:33:59, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1264, decode.acc_seg: 94.7862, loss: 0.1264 2023-01-06 06:58:25,959 - mmseg - INFO - Iter [40850/160000] lr: 4.468e-05, eta: 22:33:23, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1284, decode.acc_seg: 94.7231, loss: 0.1284 2023-01-06 06:58:59,266 - mmseg - INFO - Iter [40900/160000] lr: 4.466e-05, eta: 22:32:47, time: 0.667, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1234, decode.acc_seg: 94.9037, loss: 0.1234 2023-01-06 06:59:36,595 - mmseg - INFO - Iter [40950/160000] lr: 4.464e-05, eta: 22:32:22, time: 0.746, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1221, decode.acc_seg: 95.2556, loss: 0.1221 2023-01-06 07:00:09,523 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 07:00:09,524 - mmseg - INFO - Iter [41000/160000] lr: 4.463e-05, eta: 22:31:45, time: 0.659, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1331, decode.acc_seg: 94.6953, loss: 0.1331 2023-01-06 07:00:42,663 - mmseg - INFO - Iter [41050/160000] lr: 4.461e-05, eta: 22:31:08, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1284, decode.acc_seg: 94.6995, loss: 0.1284 2023-01-06 07:01:17,189 - mmseg - INFO - Iter [41100/160000] lr: 4.459e-05, eta: 22:30:36, time: 0.691, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1281, decode.acc_seg: 94.7596, loss: 0.1281 2023-01-06 07:01:49,853 - mmseg - INFO - Iter [41150/160000] lr: 4.457e-05, eta: 22:29:57, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1291, decode.acc_seg: 94.7298, loss: 0.1291 2023-01-06 07:02:24,239 - mmseg - INFO - Iter [41200/160000] lr: 4.455e-05, eta: 22:29:24, time: 0.687, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1334, decode.acc_seg: 94.4967, loss: 0.1334 2023-01-06 07:02:59,214 - mmseg - INFO - Iter [41250/160000] lr: 4.453e-05, eta: 22:28:53, time: 0.700, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1413, decode.acc_seg: 94.6139, loss: 0.1413 2023-01-06 07:03:36,214 - mmseg - INFO - Iter [41300/160000] lr: 4.451e-05, eta: 22:28:27, time: 0.740, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1372, decode.acc_seg: 94.3634, loss: 0.1372 2023-01-06 07:04:10,173 - mmseg - INFO - Iter [41350/160000] lr: 4.449e-05, eta: 22:27:53, time: 0.679, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1410, decode.acc_seg: 94.1684, loss: 0.1410 2023-01-06 07:04:44,963 - mmseg - INFO - Iter [41400/160000] lr: 4.448e-05, eta: 22:27:21, time: 0.697, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1300, decode.acc_seg: 94.7290, loss: 0.1300 2023-01-06 07:05:19,368 - mmseg - INFO - Iter [41450/160000] lr: 4.446e-05, eta: 22:26:47, time: 0.688, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1208, decode.acc_seg: 94.9768, loss: 0.1208 2023-01-06 07:05:52,191 - mmseg - INFO - Iter [41500/160000] lr: 4.444e-05, eta: 22:26:10, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1175, decode.acc_seg: 95.0783, loss: 0.1175 2023-01-06 07:06:24,479 - mmseg - INFO - Iter [41550/160000] lr: 4.442e-05, eta: 22:25:31, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1211, decode.acc_seg: 95.1159, loss: 0.1211 2023-01-06 07:06:57,011 - mmseg - INFO - Iter [41600/160000] lr: 4.440e-05, eta: 22:24:52, time: 0.650, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1231, decode.acc_seg: 94.9073, loss: 0.1231 2023-01-06 07:07:29,687 - mmseg - INFO - Iter [41650/160000] lr: 4.438e-05, eta: 22:24:14, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1412, decode.acc_seg: 94.4515, loss: 0.1412 2023-01-06 07:08:05,504 - mmseg - INFO - Iter [41700/160000] lr: 4.436e-05, eta: 22:23:45, time: 0.716, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1331, decode.acc_seg: 94.6852, loss: 0.1331 2023-01-06 07:08:41,398 - mmseg - INFO - Iter [41750/160000] lr: 4.434e-05, eta: 22:23:16, time: 0.718, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1395, decode.acc_seg: 94.4493, loss: 0.1395 2023-01-06 07:09:14,580 - mmseg - INFO - Iter [41800/160000] lr: 4.433e-05, eta: 22:22:39, time: 0.665, data_time: 0.023, memory: 11582, decode.loss_ce: 0.1331, decode.acc_seg: 94.7136, loss: 0.1331 2023-01-06 07:09:47,905 - mmseg - INFO - Iter [41850/160000] lr: 4.431e-05, eta: 22:22:03, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1371, decode.acc_seg: 94.5444, loss: 0.1371 2023-01-06 07:10:20,360 - mmseg - INFO - Iter [41900/160000] lr: 4.429e-05, eta: 22:21:25, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1265, decode.acc_seg: 95.0106, loss: 0.1265 2023-01-06 07:10:53,480 - mmseg - INFO - Iter [41950/160000] lr: 4.427e-05, eta: 22:20:48, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1312, decode.acc_seg: 94.8541, loss: 0.1312 2023-01-06 07:11:26,205 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 07:11:26,206 - mmseg - INFO - Iter [42000/160000] lr: 4.425e-05, eta: 22:20:10, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1262, decode.acc_seg: 95.0194, loss: 0.1262 2023-01-06 07:12:01,460 - mmseg - INFO - Iter [42050/160000] lr: 4.423e-05, eta: 22:19:39, time: 0.705, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1286, decode.acc_seg: 94.8799, loss: 0.1286 2023-01-06 07:12:35,273 - mmseg - INFO - Iter [42100/160000] lr: 4.421e-05, eta: 22:19:04, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1260, decode.acc_seg: 94.8631, loss: 0.1260 2023-01-06 07:13:09,299 - mmseg - INFO - Iter [42150/160000] lr: 4.419e-05, eta: 22:18:30, time: 0.682, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1377, decode.acc_seg: 94.4229, loss: 0.1377 2023-01-06 07:13:41,998 - mmseg - INFO - Iter [42200/160000] lr: 4.418e-05, eta: 22:17:52, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1358, decode.acc_seg: 94.4828, loss: 0.1358 2023-01-06 07:14:14,380 - mmseg - INFO - Iter [42250/160000] lr: 4.416e-05, eta: 22:17:13, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1257, decode.acc_seg: 94.5907, loss: 0.1257 2023-01-06 07:14:48,146 - mmseg - INFO - Iter [42300/160000] lr: 4.414e-05, eta: 22:16:38, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1311, decode.acc_seg: 94.7214, loss: 0.1311 2023-01-06 07:15:22,542 - mmseg - INFO - Iter [42350/160000] lr: 4.412e-05, eta: 22:16:05, time: 0.689, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1261, decode.acc_seg: 94.9160, loss: 0.1261 2023-01-06 07:15:57,176 - mmseg - INFO - Iter [42400/160000] lr: 4.410e-05, eta: 22:15:33, time: 0.692, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1250, decode.acc_seg: 94.9541, loss: 0.1250 2023-01-06 07:16:32,154 - mmseg - INFO - Iter [42450/160000] lr: 4.408e-05, eta: 22:15:01, time: 0.700, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1303, decode.acc_seg: 94.7852, loss: 0.1303 2023-01-06 07:17:06,557 - mmseg - INFO - Iter [42500/160000] lr: 4.406e-05, eta: 22:14:28, time: 0.687, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1282, decode.acc_seg: 94.8108, loss: 0.1282 2023-01-06 07:17:40,043 - mmseg - INFO - Iter [42550/160000] lr: 4.404e-05, eta: 22:13:52, time: 0.670, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1305, decode.acc_seg: 94.7604, loss: 0.1305 2023-01-06 07:18:14,233 - mmseg - INFO - Iter [42600/160000] lr: 4.403e-05, eta: 22:13:19, time: 0.684, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1267, decode.acc_seg: 94.9616, loss: 0.1267 2023-01-06 07:18:46,815 - mmseg - INFO - Iter [42650/160000] lr: 4.401e-05, eta: 22:12:41, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1231, decode.acc_seg: 94.9591, loss: 0.1231 2023-01-06 07:19:19,298 - mmseg - INFO - Iter [42700/160000] lr: 4.399e-05, eta: 22:12:02, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1326, decode.acc_seg: 94.7086, loss: 0.1326 2023-01-06 07:19:52,929 - mmseg - INFO - Iter [42750/160000] lr: 4.397e-05, eta: 22:11:27, time: 0.673, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1275, decode.acc_seg: 94.6351, loss: 0.1275 2023-01-06 07:20:29,316 - mmseg - INFO - Iter [42800/160000] lr: 4.395e-05, eta: 22:10:59, time: 0.728, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1212, decode.acc_seg: 95.0075, loss: 0.1212 2023-01-06 07:21:04,503 - mmseg - INFO - Iter [42850/160000] lr: 4.393e-05, eta: 22:10:28, time: 0.704, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1250, decode.acc_seg: 94.9493, loss: 0.1250 2023-01-06 07:21:37,197 - mmseg - INFO - Iter [42900/160000] lr: 4.391e-05, eta: 22:09:50, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1344, decode.acc_seg: 94.6286, loss: 0.1344 2023-01-06 07:22:10,353 - mmseg - INFO - Iter [42950/160000] lr: 4.389e-05, eta: 22:09:14, time: 0.664, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1319, decode.acc_seg: 94.6970, loss: 0.1319 2023-01-06 07:22:44,669 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 07:22:44,670 - mmseg - INFO - Iter [43000/160000] lr: 4.388e-05, eta: 22:08:40, time: 0.685, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1399, decode.acc_seg: 94.4605, loss: 0.1399 2023-01-06 07:23:19,368 - mmseg - INFO - Iter [43050/160000] lr: 4.386e-05, eta: 22:08:08, time: 0.695, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1243, decode.acc_seg: 95.0082, loss: 0.1243 2023-01-06 07:23:51,874 - mmseg - INFO - Iter [43100/160000] lr: 4.384e-05, eta: 22:07:30, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1355, decode.acc_seg: 94.4261, loss: 0.1355 2023-01-06 07:24:24,776 - mmseg - INFO - Iter [43150/160000] lr: 4.382e-05, eta: 22:06:52, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1246, decode.acc_seg: 94.8741, loss: 0.1246 2023-01-06 07:25:00,562 - mmseg - INFO - Iter [43200/160000] lr: 4.380e-05, eta: 22:06:23, time: 0.717, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1321, decode.acc_seg: 94.6964, loss: 0.1321 2023-01-06 07:25:33,022 - mmseg - INFO - Iter [43250/160000] lr: 4.378e-05, eta: 22:05:45, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1304, decode.acc_seg: 94.7165, loss: 0.1304 2023-01-06 07:26:07,279 - mmseg - INFO - Iter [43300/160000] lr: 4.376e-05, eta: 22:05:11, time: 0.684, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1291, decode.acc_seg: 94.6923, loss: 0.1291 2023-01-06 07:26:42,953 - mmseg - INFO - Iter [43350/160000] lr: 4.374e-05, eta: 22:04:41, time: 0.714, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1256, decode.acc_seg: 94.9689, loss: 0.1256 2023-01-06 07:27:15,609 - mmseg - INFO - Iter [43400/160000] lr: 4.373e-05, eta: 22:04:03, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1404, decode.acc_seg: 94.4374, loss: 0.1404 2023-01-06 07:27:47,951 - mmseg - INFO - Iter [43450/160000] lr: 4.371e-05, eta: 22:03:25, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1292, decode.acc_seg: 94.8668, loss: 0.1292 2023-01-06 07:28:21,152 - mmseg - INFO - Iter [43500/160000] lr: 4.369e-05, eta: 22:02:48, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1250, decode.acc_seg: 94.9268, loss: 0.1250 2023-01-06 07:28:57,486 - mmseg - INFO - Iter [43550/160000] lr: 4.367e-05, eta: 22:02:20, time: 0.728, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1302, decode.acc_seg: 94.7551, loss: 0.1302 2023-01-06 07:29:31,889 - mmseg - INFO - Iter [43600/160000] lr: 4.365e-05, eta: 22:01:47, time: 0.688, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1297, decode.acc_seg: 94.7128, loss: 0.1297 2023-01-06 07:30:06,285 - mmseg - INFO - Iter [43650/160000] lr: 4.363e-05, eta: 22:01:14, time: 0.687, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1303, decode.acc_seg: 94.7404, loss: 0.1303 2023-01-06 07:30:40,263 - mmseg - INFO - Iter [43700/160000] lr: 4.361e-05, eta: 22:00:40, time: 0.681, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1163, decode.acc_seg: 95.2686, loss: 0.1163 2023-01-06 07:31:13,296 - mmseg - INFO - Iter [43750/160000] lr: 4.359e-05, eta: 22:00:03, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1270, decode.acc_seg: 94.8484, loss: 0.1270 2023-01-06 07:31:47,906 - mmseg - INFO - Iter [43800/160000] lr: 4.358e-05, eta: 21:59:30, time: 0.692, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1285, decode.acc_seg: 94.6901, loss: 0.1285 2023-01-06 07:32:20,700 - mmseg - INFO - Iter [43850/160000] lr: 4.356e-05, eta: 21:58:53, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1264, decode.acc_seg: 94.9659, loss: 0.1264 2023-01-06 07:32:55,717 - mmseg - INFO - Iter [43900/160000] lr: 4.354e-05, eta: 21:58:21, time: 0.701, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1243, decode.acc_seg: 94.9632, loss: 0.1243 2023-01-06 07:33:28,498 - mmseg - INFO - Iter [43950/160000] lr: 4.352e-05, eta: 21:57:44, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1258, decode.acc_seg: 94.9432, loss: 0.1258 2023-01-06 07:34:01,719 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 07:34:01,720 - mmseg - INFO - Iter [44000/160000] lr: 4.350e-05, eta: 21:57:07, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1229, decode.acc_seg: 94.9921, loss: 0.1229 2023-01-06 07:34:35,671 - mmseg - INFO - Iter [44050/160000] lr: 4.348e-05, eta: 21:56:33, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1249, decode.acc_seg: 94.8920, loss: 0.1249 2023-01-06 07:35:08,739 - mmseg - INFO - Iter [44100/160000] lr: 4.346e-05, eta: 21:55:56, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1238, decode.acc_seg: 94.9359, loss: 0.1238 2023-01-06 07:35:41,166 - mmseg - INFO - Iter [44150/160000] lr: 4.344e-05, eta: 21:55:18, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1358, decode.acc_seg: 94.5894, loss: 0.1358 2023-01-06 07:36:13,634 - mmseg - INFO - Iter [44200/160000] lr: 4.343e-05, eta: 21:54:40, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1258, decode.acc_seg: 94.7987, loss: 0.1258 2023-01-06 07:36:47,419 - mmseg - INFO - Iter [44250/160000] lr: 4.341e-05, eta: 21:54:05, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1276, decode.acc_seg: 94.9372, loss: 0.1276 2023-01-06 07:37:23,242 - mmseg - INFO - Iter [44300/160000] lr: 4.339e-05, eta: 21:53:36, time: 0.717, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1248, decode.acc_seg: 95.0377, loss: 0.1248 2023-01-06 07:37:56,885 - mmseg - INFO - Iter [44350/160000] lr: 4.337e-05, eta: 21:53:00, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1299, decode.acc_seg: 94.6188, loss: 0.1299 2023-01-06 07:38:32,826 - mmseg - INFO - Iter [44400/160000] lr: 4.335e-05, eta: 21:52:31, time: 0.719, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1344, decode.acc_seg: 94.6914, loss: 0.1344 2023-01-06 07:39:06,165 - mmseg - INFO - Iter [44450/160000] lr: 4.333e-05, eta: 21:51:55, time: 0.668, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1248, decode.acc_seg: 94.8634, loss: 0.1248 2023-01-06 07:39:39,022 - mmseg - INFO - Iter [44500/160000] lr: 4.331e-05, eta: 21:51:18, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1250, decode.acc_seg: 95.0389, loss: 0.1250 2023-01-06 07:40:12,461 - mmseg - INFO - Iter [44550/160000] lr: 4.329e-05, eta: 21:50:42, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1268, decode.acc_seg: 94.8839, loss: 0.1268 2023-01-06 07:40:45,416 - mmseg - INFO - Iter [44600/160000] lr: 4.328e-05, eta: 21:50:06, time: 0.660, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1218, decode.acc_seg: 95.0998, loss: 0.1218 2023-01-06 07:41:21,186 - mmseg - INFO - Iter [44650/160000] lr: 4.326e-05, eta: 21:49:36, time: 0.714, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1190, decode.acc_seg: 95.0679, loss: 0.1190 2023-01-06 07:41:53,839 - mmseg - INFO - Iter [44700/160000] lr: 4.324e-05, eta: 21:48:58, time: 0.654, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1157, decode.acc_seg: 95.3507, loss: 0.1157 2023-01-06 07:42:26,763 - mmseg - INFO - Iter [44750/160000] lr: 4.322e-05, eta: 21:48:21, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1267, decode.acc_seg: 94.8713, loss: 0.1267 2023-01-06 07:43:00,336 - mmseg - INFO - Iter [44800/160000] lr: 4.320e-05, eta: 21:47:46, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1239, decode.acc_seg: 94.8870, loss: 0.1239 2023-01-06 07:43:32,581 - mmseg - INFO - Iter [44850/160000] lr: 4.318e-05, eta: 21:47:07, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1183, decode.acc_seg: 95.1943, loss: 0.1183 2023-01-06 07:44:05,094 - mmseg - INFO - Iter [44900/160000] lr: 4.316e-05, eta: 21:46:29, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1305, decode.acc_seg: 94.6831, loss: 0.1305 2023-01-06 07:44:37,598 - mmseg - INFO - Iter [44950/160000] lr: 4.314e-05, eta: 21:45:51, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1209, decode.acc_seg: 95.0316, loss: 0.1209 2023-01-06 07:45:10,215 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 07:45:10,215 - mmseg - INFO - Iter [45000/160000] lr: 4.313e-05, eta: 21:45:14, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1235, decode.acc_seg: 94.8766, loss: 0.1235 2023-01-06 07:45:45,477 - mmseg - INFO - Iter [45050/160000] lr: 4.311e-05, eta: 21:44:42, time: 0.704, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1277, decode.acc_seg: 94.7497, loss: 0.1277 2023-01-06 07:46:21,122 - mmseg - INFO - Iter [45100/160000] lr: 4.309e-05, eta: 21:44:12, time: 0.713, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1313, decode.acc_seg: 94.6193, loss: 0.1313 2023-01-06 07:46:55,360 - mmseg - INFO - Iter [45150/160000] lr: 4.307e-05, eta: 21:43:39, time: 0.686, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1216, decode.acc_seg: 95.1238, loss: 0.1216 2023-01-06 07:47:27,931 - mmseg - INFO - Iter [45200/160000] lr: 4.305e-05, eta: 21:43:01, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1187, decode.acc_seg: 95.1349, loss: 0.1187 2023-01-06 07:48:02,691 - mmseg - INFO - Iter [45250/160000] lr: 4.303e-05, eta: 21:42:29, time: 0.694, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1312, decode.acc_seg: 94.5414, loss: 0.1312 2023-01-06 07:48:35,015 - mmseg - INFO - Iter [45300/160000] lr: 4.301e-05, eta: 21:41:50, time: 0.647, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1304, decode.acc_seg: 94.6825, loss: 0.1304 2023-01-06 07:49:07,587 - mmseg - INFO - Iter [45350/160000] lr: 4.299e-05, eta: 21:41:13, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1243, decode.acc_seg: 94.8279, loss: 0.1243 2023-01-06 07:49:42,387 - mmseg - INFO - Iter [45400/160000] lr: 4.298e-05, eta: 21:40:41, time: 0.696, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1294, decode.acc_seg: 94.8173, loss: 0.1294 2023-01-06 07:50:15,913 - mmseg - INFO - Iter [45450/160000] lr: 4.296e-05, eta: 21:40:05, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1234, decode.acc_seg: 95.0712, loss: 0.1234 2023-01-06 07:50:51,695 - mmseg - INFO - Iter [45500/160000] lr: 4.294e-05, eta: 21:39:35, time: 0.716, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1336, decode.acc_seg: 94.6987, loss: 0.1336 2023-01-06 07:51:26,126 - mmseg - INFO - Iter [45550/160000] lr: 4.292e-05, eta: 21:39:02, time: 0.690, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1202, decode.acc_seg: 94.9724, loss: 0.1202 2023-01-06 07:52:00,020 - mmseg - INFO - Iter [45600/160000] lr: 4.290e-05, eta: 21:38:28, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1177, decode.acc_seg: 95.1677, loss: 0.1177 2023-01-06 07:52:33,569 - mmseg - INFO - Iter [45650/160000] lr: 4.288e-05, eta: 21:37:53, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1311, decode.acc_seg: 94.7349, loss: 0.1311 2023-01-06 07:53:06,872 - mmseg - INFO - Iter [45700/160000] lr: 4.286e-05, eta: 21:37:17, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1258, decode.acc_seg: 94.8796, loss: 0.1258 2023-01-06 07:53:40,123 - mmseg - INFO - Iter [45750/160000] lr: 4.284e-05, eta: 21:36:41, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1399, decode.acc_seg: 94.4672, loss: 0.1399 2023-01-06 07:54:16,652 - mmseg - INFO - Iter [45800/160000] lr: 4.283e-05, eta: 21:36:13, time: 0.731, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1218, decode.acc_seg: 95.1231, loss: 0.1218 2023-01-06 07:54:49,119 - mmseg - INFO - Iter [45850/160000] lr: 4.281e-05, eta: 21:35:35, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1266, decode.acc_seg: 94.8893, loss: 0.1266 2023-01-06 07:55:22,579 - mmseg - INFO - Iter [45900/160000] lr: 4.279e-05, eta: 21:34:59, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1330, decode.acc_seg: 94.7486, loss: 0.1330 2023-01-06 07:55:58,033 - mmseg - INFO - Iter [45950/160000] lr: 4.277e-05, eta: 21:34:29, time: 0.710, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1331, decode.acc_seg: 94.7924, loss: 0.1331 2023-01-06 07:56:30,536 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 07:56:30,536 - mmseg - INFO - Iter [46000/160000] lr: 4.275e-05, eta: 21:33:51, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1279, decode.acc_seg: 94.7769, loss: 0.1279 2023-01-06 07:57:02,897 - mmseg - INFO - Iter [46050/160000] lr: 4.273e-05, eta: 21:33:13, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1355, decode.acc_seg: 94.5530, loss: 0.1355 2023-01-06 07:57:38,057 - mmseg - INFO - Iter [46100/160000] lr: 4.271e-05, eta: 21:32:41, time: 0.702, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1266, decode.acc_seg: 94.9716, loss: 0.1266 2023-01-06 07:58:13,211 - mmseg - INFO - Iter [46150/160000] lr: 4.269e-05, eta: 21:32:10, time: 0.704, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1249, decode.acc_seg: 95.0052, loss: 0.1249 2023-01-06 07:58:46,284 - mmseg - INFO - Iter [46200/160000] lr: 4.268e-05, eta: 21:31:33, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1234, decode.acc_seg: 94.8608, loss: 0.1234 2023-01-06 07:59:20,439 - mmseg - INFO - Iter [46250/160000] lr: 4.266e-05, eta: 21:31:00, time: 0.684, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1274, decode.acc_seg: 94.7230, loss: 0.1274 2023-01-06 07:59:52,930 - mmseg - INFO - Iter [46300/160000] lr: 4.264e-05, eta: 21:30:22, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1149, decode.acc_seg: 95.3028, loss: 0.1149 2023-01-06 08:00:25,301 - mmseg - INFO - Iter [46350/160000] lr: 4.262e-05, eta: 21:29:44, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1211, decode.acc_seg: 95.0280, loss: 0.1211 2023-01-06 08:00:57,704 - mmseg - INFO - Iter [46400/160000] lr: 4.260e-05, eta: 21:29:06, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1252, decode.acc_seg: 94.8907, loss: 0.1252 2023-01-06 08:01:30,161 - mmseg - INFO - Iter [46450/160000] lr: 4.258e-05, eta: 21:28:28, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1189, decode.acc_seg: 95.1660, loss: 0.1189 2023-01-06 08:02:04,086 - mmseg - INFO - Iter [46500/160000] lr: 4.256e-05, eta: 21:27:53, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1307, decode.acc_seg: 94.6938, loss: 0.1307 2023-01-06 08:02:39,476 - mmseg - INFO - Iter [46550/160000] lr: 4.254e-05, eta: 21:27:23, time: 0.707, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1244, decode.acc_seg: 94.9414, loss: 0.1244 2023-01-06 08:03:13,046 - mmseg - INFO - Iter [46600/160000] lr: 4.253e-05, eta: 21:26:47, time: 0.672, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1234, decode.acc_seg: 95.0336, loss: 0.1234 2023-01-06 08:03:45,474 - mmseg - INFO - Iter [46650/160000] lr: 4.251e-05, eta: 21:26:09, time: 0.649, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1245, decode.acc_seg: 94.9697, loss: 0.1245 2023-01-06 08:04:17,817 - mmseg - INFO - Iter [46700/160000] lr: 4.249e-05, eta: 21:25:31, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1403, decode.acc_seg: 94.2200, loss: 0.1403 2023-01-06 08:04:50,264 - mmseg - INFO - Iter [46750/160000] lr: 4.247e-05, eta: 21:24:53, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1360, decode.acc_seg: 94.5986, loss: 0.1360 2023-01-06 08:05:23,516 - mmseg - INFO - Iter [46800/160000] lr: 4.245e-05, eta: 21:24:18, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1353, decode.acc_seg: 94.5966, loss: 0.1353 2023-01-06 08:05:56,841 - mmseg - INFO - Iter [46850/160000] lr: 4.243e-05, eta: 21:23:42, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1155, decode.acc_seg: 95.1608, loss: 0.1155 2023-01-06 08:06:32,731 - mmseg - INFO - Iter [46900/160000] lr: 4.241e-05, eta: 21:23:12, time: 0.719, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1161, decode.acc_seg: 95.1753, loss: 0.1161 2023-01-06 08:07:05,829 - mmseg - INFO - Iter [46950/160000] lr: 4.239e-05, eta: 21:22:36, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1179, decode.acc_seg: 95.2456, loss: 0.1179 2023-01-06 08:07:40,340 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 08:07:40,340 - mmseg - INFO - Iter [47000/160000] lr: 4.238e-05, eta: 21:22:03, time: 0.689, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1261, decode.acc_seg: 94.9574, loss: 0.1261 2023-01-06 08:08:14,757 - mmseg - INFO - Iter [47050/160000] lr: 4.236e-05, eta: 21:21:30, time: 0.689, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1223, decode.acc_seg: 95.1409, loss: 0.1223 2023-01-06 08:08:47,017 - mmseg - INFO - Iter [47100/160000] lr: 4.234e-05, eta: 21:20:52, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1218, decode.acc_seg: 95.1842, loss: 0.1218 2023-01-06 08:09:19,799 - mmseg - INFO - Iter [47150/160000] lr: 4.232e-05, eta: 21:20:15, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1106, decode.acc_seg: 95.4360, loss: 0.1106 2023-01-06 08:09:53,284 - mmseg - INFO - Iter [47200/160000] lr: 4.230e-05, eta: 21:19:39, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1333, decode.acc_seg: 94.7907, loss: 0.1333 2023-01-06 08:10:29,272 - mmseg - INFO - Iter [47250/160000] lr: 4.228e-05, eta: 21:19:10, time: 0.721, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1125, decode.acc_seg: 95.3044, loss: 0.1125 2023-01-06 08:11:03,043 - mmseg - INFO - Iter [47300/160000] lr: 4.226e-05, eta: 21:18:35, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1119, decode.acc_seg: 95.3267, loss: 0.1119 2023-01-06 08:11:37,609 - mmseg - INFO - Iter [47350/160000] lr: 4.224e-05, eta: 21:18:02, time: 0.692, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1242, decode.acc_seg: 95.0785, loss: 0.1242 2023-01-06 08:12:11,137 - mmseg - INFO - Iter [47400/160000] lr: 4.223e-05, eta: 21:17:27, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1248, decode.acc_seg: 94.9504, loss: 0.1248 2023-01-06 08:12:45,651 - mmseg - INFO - Iter [47450/160000] lr: 4.221e-05, eta: 21:16:54, time: 0.691, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1130, decode.acc_seg: 95.3843, loss: 0.1130 2023-01-06 08:13:18,097 - mmseg - INFO - Iter [47500/160000] lr: 4.219e-05, eta: 21:16:16, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1227, decode.acc_seg: 94.9514, loss: 0.1227 2023-01-06 08:13:53,492 - mmseg - INFO - Iter [47550/160000] lr: 4.217e-05, eta: 21:15:46, time: 0.707, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1259, decode.acc_seg: 95.0067, loss: 0.1259 2023-01-06 08:14:27,565 - mmseg - INFO - Iter [47600/160000] lr: 4.215e-05, eta: 21:15:12, time: 0.682, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1241, decode.acc_seg: 94.8775, loss: 0.1241 2023-01-06 08:15:02,051 - mmseg - INFO - Iter [47650/160000] lr: 4.213e-05, eta: 21:14:39, time: 0.690, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1163, decode.acc_seg: 95.1504, loss: 0.1163 2023-01-06 08:15:35,149 - mmseg - INFO - Iter [47700/160000] lr: 4.211e-05, eta: 21:14:02, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1187, decode.acc_seg: 95.0708, loss: 0.1187 2023-01-06 08:16:07,972 - mmseg - INFO - Iter [47750/160000] lr: 4.209e-05, eta: 21:13:26, time: 0.657, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1227, decode.acc_seg: 94.8841, loss: 0.1227 2023-01-06 08:16:42,882 - mmseg - INFO - Iter [47800/160000] lr: 4.208e-05, eta: 21:12:53, time: 0.697, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1164, decode.acc_seg: 95.2047, loss: 0.1164 2023-01-06 08:17:16,273 - mmseg - INFO - Iter [47850/160000] lr: 4.206e-05, eta: 21:12:18, time: 0.669, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1176, decode.acc_seg: 95.0859, loss: 0.1176 2023-01-06 08:17:49,699 - mmseg - INFO - Iter [47900/160000] lr: 4.204e-05, eta: 21:11:43, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1214, decode.acc_seg: 95.1499, loss: 0.1214 2023-01-06 08:18:22,237 - mmseg - INFO - Iter [47950/160000] lr: 4.202e-05, eta: 21:11:05, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1259, decode.acc_seg: 94.7792, loss: 0.1259 2023-01-06 08:18:56,899 - mmseg - INFO - Saving checkpoint at 48000 iterations 2023-01-06 08:19:02,914 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 08:19:02,915 - mmseg - INFO - Iter [48000/160000] lr: 4.200e-05, eta: 21:10:47, time: 0.814, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1211, decode.acc_seg: 94.9862, loss: 0.1211 2023-01-06 08:19:38,548 - mmseg - INFO - per class results: 2023-01-06 08:19:38,551 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 97.6 | 98.51 | | sidewalk | 80.07 | 91.68 | | building | 90.75 | 94.83 | | wall | 50.75 | 58.85 | | fence | 51.76 | 72.19 | | pole | 57.31 | 67.28 | | traffic light | 59.22 | 69.34 | | traffic sign | 68.63 | 79.52 | | vegetation | 91.35 | 96.56 | | terrain | 55.1 | 63.47 | | sky | 94.14 | 97.7 | | person | 73.54 | 89.75 | | rider | 42.51 | 48.49 | | car | 92.14 | 96.98 | | truck | 54.28 | 67.03 | | bus | 51.44 | 56.92 | | train | 48.37 | 61.42 | | motorcycle | 35.04 | 38.81 | | bicycle | 68.07 | 87.92 | +---------------+-------+-------+ 2023-01-06 08:19:38,551 - mmseg - INFO - Summary: 2023-01-06 08:19:38,551 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 94.78 | 66.42 | 75.64 | +-------+-------+-------+ 2023-01-06 08:19:38,552 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 08:19:38,552 - mmseg - INFO - Iter(val) [63] aAcc: 0.9478, mIoU: 0.6642, mAcc: 0.7564, IoU.road: 0.9760, IoU.sidewalk: 0.8007, IoU.building: 0.9075, IoU.wall: 0.5075, IoU.fence: 0.5176, IoU.pole: 0.5731, IoU.traffic light: 0.5922, IoU.traffic sign: 0.6863, IoU.vegetation: 0.9135, IoU.terrain: 0.5510, IoU.sky: 0.9414, IoU.person: 0.7354, IoU.rider: 0.4251, IoU.car: 0.9214, IoU.truck: 0.5428, IoU.bus: 0.5144, IoU.train: 0.4837, IoU.motorcycle: 0.3504, IoU.bicycle: 0.6807, Acc.road: 0.9851, Acc.sidewalk: 0.9168, Acc.building: 0.9483, Acc.wall: 0.5885, Acc.fence: 0.7219, Acc.pole: 0.6728, Acc.traffic light: 0.6934, Acc.traffic sign: 0.7952, Acc.vegetation: 0.9656, Acc.terrain: 0.6347, Acc.sky: 0.9770, Acc.person: 0.8975, Acc.rider: 0.4849, Acc.car: 0.9698, Acc.truck: 0.6703, Acc.bus: 0.5692, Acc.train: 0.6142, Acc.motorcycle: 0.3881, Acc.bicycle: 0.8792 2023-01-06 08:20:11,405 - mmseg - INFO - Iter [48050/160000] lr: 4.198e-05, eta: 21:11:33, time: 1.370, data_time: 0.727, memory: 11582, decode.loss_ce: 0.1189, decode.acc_seg: 95.0450, loss: 0.1189 2023-01-06 08:20:45,252 - mmseg - INFO - Iter [48100/160000] lr: 4.196e-05, eta: 21:10:58, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1199, decode.acc_seg: 95.1010, loss: 0.1199 2023-01-06 08:21:17,713 - mmseg - INFO - Iter [48150/160000] lr: 4.194e-05, eta: 21:10:20, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1151, decode.acc_seg: 95.2900, loss: 0.1151 2023-01-06 08:21:50,308 - mmseg - INFO - Iter [48200/160000] lr: 4.193e-05, eta: 21:09:43, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1167, decode.acc_seg: 95.1045, loss: 0.1167 2023-01-06 08:22:23,475 - mmseg - INFO - Iter [48250/160000] lr: 4.191e-05, eta: 21:09:07, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1335, decode.acc_seg: 94.7986, loss: 0.1335 2023-01-06 08:22:57,839 - mmseg - INFO - Iter [48300/160000] lr: 4.189e-05, eta: 21:08:33, time: 0.688, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1391, decode.acc_seg: 94.3948, loss: 0.1391 2023-01-06 08:23:31,136 - mmseg - INFO - Iter [48350/160000] lr: 4.187e-05, eta: 21:07:57, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1235, decode.acc_seg: 94.7800, loss: 0.1235 2023-01-06 08:24:08,701 - mmseg - INFO - Iter [48400/160000] lr: 4.185e-05, eta: 21:07:31, time: 0.750, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1188, decode.acc_seg: 95.2056, loss: 0.1188 2023-01-06 08:24:42,353 - mmseg - INFO - Iter [48450/160000] lr: 4.183e-05, eta: 21:06:56, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1178, decode.acc_seg: 95.2963, loss: 0.1178 2023-01-06 08:25:14,946 - mmseg - INFO - Iter [48500/160000] lr: 4.181e-05, eta: 21:06:19, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1178, decode.acc_seg: 95.0720, loss: 0.1178 2023-01-06 08:25:47,385 - mmseg - INFO - Iter [48550/160000] lr: 4.179e-05, eta: 21:05:41, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1248, decode.acc_seg: 95.0360, loss: 0.1248 2023-01-06 08:26:19,759 - mmseg - INFO - Iter [48600/160000] lr: 4.178e-05, eta: 21:05:03, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1182, decode.acc_seg: 95.2098, loss: 0.1182 2023-01-06 08:26:53,422 - mmseg - INFO - Iter [48650/160000] lr: 4.176e-05, eta: 21:04:28, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1122, decode.acc_seg: 95.2940, loss: 0.1122 2023-01-06 08:27:26,413 - mmseg - INFO - Iter [48700/160000] lr: 4.174e-05, eta: 21:03:52, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1222, decode.acc_seg: 95.0446, loss: 0.1222 2023-01-06 08:28:01,460 - mmseg - INFO - Iter [48750/160000] lr: 4.172e-05, eta: 21:03:20, time: 0.700, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1283, decode.acc_seg: 94.9195, loss: 0.1283 2023-01-06 08:28:34,584 - mmseg - INFO - Iter [48800/160000] lr: 4.170e-05, eta: 21:02:43, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1141, decode.acc_seg: 95.1553, loss: 0.1141 2023-01-06 08:29:07,003 - mmseg - INFO - Iter [48850/160000] lr: 4.168e-05, eta: 21:02:06, time: 0.648, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1218, decode.acc_seg: 95.0203, loss: 0.1218 2023-01-06 08:29:39,582 - mmseg - INFO - Iter [48900/160000] lr: 4.166e-05, eta: 21:01:28, time: 0.652, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1159, decode.acc_seg: 95.3536, loss: 0.1159 2023-01-06 08:30:12,297 - mmseg - INFO - Iter [48950/160000] lr: 4.164e-05, eta: 21:00:51, time: 0.653, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1219, decode.acc_seg: 95.0177, loss: 0.1219 2023-01-06 08:30:45,669 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 08:30:45,669 - mmseg - INFO - Iter [49000/160000] lr: 4.163e-05, eta: 21:00:15, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1189, decode.acc_seg: 95.1572, loss: 0.1189 2023-01-06 08:31:18,321 - mmseg - INFO - Iter [49050/160000] lr: 4.161e-05, eta: 20:59:38, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1292, decode.acc_seg: 94.9164, loss: 0.1292 2023-01-06 08:31:50,946 - mmseg - INFO - Iter [49100/160000] lr: 4.159e-05, eta: 20:59:01, time: 0.652, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1203, decode.acc_seg: 95.1505, loss: 0.1203 2023-01-06 08:32:26,842 - mmseg - INFO - Iter [49150/160000] lr: 4.157e-05, eta: 20:58:31, time: 0.717, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1154, decode.acc_seg: 95.2899, loss: 0.1154 2023-01-06 08:33:00,701 - mmseg - INFO - Iter [49200/160000] lr: 4.155e-05, eta: 20:57:56, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1148, decode.acc_seg: 95.1648, loss: 0.1148 2023-01-06 08:33:33,109 - mmseg - INFO - Iter [49250/160000] lr: 4.153e-05, eta: 20:57:19, time: 0.648, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1186, decode.acc_seg: 95.1888, loss: 0.1186 2023-01-06 08:34:05,871 - mmseg - INFO - Iter [49300/160000] lr: 4.151e-05, eta: 20:56:42, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1147, decode.acc_seg: 95.1311, loss: 0.1147 2023-01-06 08:34:41,342 - mmseg - INFO - Iter [49350/160000] lr: 4.149e-05, eta: 20:56:11, time: 0.710, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1135, decode.acc_seg: 95.3430, loss: 0.1135 2023-01-06 08:35:14,616 - mmseg - INFO - Iter [49400/160000] lr: 4.148e-05, eta: 20:55:35, time: 0.665, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1237, decode.acc_seg: 94.9096, loss: 0.1237 2023-01-06 08:35:47,208 - mmseg - INFO - Iter [49450/160000] lr: 4.146e-05, eta: 20:54:58, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1219, decode.acc_seg: 95.0519, loss: 0.1219 2023-01-06 08:36:22,737 - mmseg - INFO - Iter [49500/160000] lr: 4.144e-05, eta: 20:54:27, time: 0.711, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1201, decode.acc_seg: 95.2390, loss: 0.1201 2023-01-06 08:36:55,101 - mmseg - INFO - Iter [49550/160000] lr: 4.142e-05, eta: 20:53:49, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1206, decode.acc_seg: 95.0154, loss: 0.1206 2023-01-06 08:37:28,385 - mmseg - INFO - Iter [49600/160000] lr: 4.140e-05, eta: 20:53:13, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1225, decode.acc_seg: 95.0697, loss: 0.1225 2023-01-06 08:38:01,888 - mmseg - INFO - Iter [49650/160000] lr: 4.138e-05, eta: 20:52:38, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1249, decode.acc_seg: 94.9431, loss: 0.1249 2023-01-06 08:38:34,703 - mmseg - INFO - Iter [49700/160000] lr: 4.136e-05, eta: 20:52:01, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1379, decode.acc_seg: 94.6924, loss: 0.1379 2023-01-06 08:39:07,121 - mmseg - INFO - Iter [49750/160000] lr: 4.134e-05, eta: 20:51:24, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1281, decode.acc_seg: 94.6858, loss: 0.1281 2023-01-06 08:39:41,433 - mmseg - INFO - Iter [49800/160000] lr: 4.133e-05, eta: 20:50:50, time: 0.686, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1224, decode.acc_seg: 95.0235, loss: 0.1224 2023-01-06 08:40:16,082 - mmseg - INFO - Iter [49850/160000] lr: 4.131e-05, eta: 20:50:17, time: 0.693, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1352, decode.acc_seg: 94.7057, loss: 0.1352 2023-01-06 08:40:49,497 - mmseg - INFO - Iter [49900/160000] lr: 4.129e-05, eta: 20:49:42, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1249, decode.acc_seg: 95.0183, loss: 0.1249 2023-01-06 08:41:24,125 - mmseg - INFO - Iter [49950/160000] lr: 4.127e-05, eta: 20:49:09, time: 0.693, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1298, decode.acc_seg: 94.8390, loss: 0.1298 2023-01-06 08:41:57,773 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 08:41:57,774 - mmseg - INFO - Iter [50000/160000] lr: 4.125e-05, eta: 20:48:34, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1185, decode.acc_seg: 95.1869, loss: 0.1185 2023-01-06 08:42:30,942 - mmseg - INFO - Iter [50050/160000] lr: 4.123e-05, eta: 20:47:58, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1167, decode.acc_seg: 95.1810, loss: 0.1167 2023-01-06 08:43:03,476 - mmseg - INFO - Iter [50100/160000] lr: 4.121e-05, eta: 20:47:21, time: 0.650, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1184, decode.acc_seg: 95.1165, loss: 0.1184 2023-01-06 08:43:36,879 - mmseg - INFO - Iter [50150/160000] lr: 4.119e-05, eta: 20:46:45, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1185, decode.acc_seg: 95.0790, loss: 0.1185 2023-01-06 08:44:10,298 - mmseg - INFO - Iter [50200/160000] lr: 4.118e-05, eta: 20:46:10, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1208, decode.acc_seg: 95.1419, loss: 0.1208 2023-01-06 08:44:45,315 - mmseg - INFO - Iter [50250/160000] lr: 4.116e-05, eta: 20:45:38, time: 0.700, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1212, decode.acc_seg: 95.0863, loss: 0.1212 2023-01-06 08:45:17,531 - mmseg - INFO - Iter [50300/160000] lr: 4.114e-05, eta: 20:45:00, time: 0.644, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1091, decode.acc_seg: 95.4337, loss: 0.1091 2023-01-06 08:45:51,529 - mmseg - INFO - Iter [50350/160000] lr: 4.112e-05, eta: 20:44:26, time: 0.679, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1171, decode.acc_seg: 95.2798, loss: 0.1171 2023-01-06 08:46:24,495 - mmseg - INFO - Iter [50400/160000] lr: 4.110e-05, eta: 20:43:49, time: 0.660, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1113, decode.acc_seg: 95.3994, loss: 0.1113 2023-01-06 08:46:56,877 - mmseg - INFO - Iter [50450/160000] lr: 4.108e-05, eta: 20:43:12, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1167, decode.acc_seg: 95.1588, loss: 0.1167 2023-01-06 08:47:30,572 - mmseg - INFO - Iter [50500/160000] lr: 4.106e-05, eta: 20:42:37, time: 0.674, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1105, decode.acc_seg: 95.4058, loss: 0.1105 2023-01-06 08:48:03,239 - mmseg - INFO - Iter [50550/160000] lr: 4.104e-05, eta: 20:42:00, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1302, decode.acc_seg: 94.7965, loss: 0.1302 2023-01-06 08:48:37,838 - mmseg - INFO - Iter [50600/160000] lr: 4.103e-05, eta: 20:41:27, time: 0.692, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1282, decode.acc_seg: 94.8214, loss: 0.1282 2023-01-06 08:49:10,797 - mmseg - INFO - Iter [50650/160000] lr: 4.101e-05, eta: 20:40:51, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1200, decode.acc_seg: 95.0545, loss: 0.1200 2023-01-06 08:49:43,650 - mmseg - INFO - Iter [50700/160000] lr: 4.099e-05, eta: 20:40:14, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1152, decode.acc_seg: 95.2268, loss: 0.1152 2023-01-06 08:50:16,084 - mmseg - INFO - Iter [50750/160000] lr: 4.097e-05, eta: 20:39:37, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1289, decode.acc_seg: 94.9039, loss: 0.1289 2023-01-06 08:50:50,242 - mmseg - INFO - Iter [50800/160000] lr: 4.095e-05, eta: 20:39:03, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1178, decode.acc_seg: 95.1375, loss: 0.1178 2023-01-06 08:51:24,014 - mmseg - INFO - Iter [50850/160000] lr: 4.093e-05, eta: 20:38:28, time: 0.676, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1264, decode.acc_seg: 94.8939, loss: 0.1264 2023-01-06 08:51:59,845 - mmseg - INFO - Iter [50900/160000] lr: 4.091e-05, eta: 20:37:58, time: 0.716, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1135, decode.acc_seg: 95.2477, loss: 0.1135 2023-01-06 08:52:32,551 - mmseg - INFO - Iter [50950/160000] lr: 4.089e-05, eta: 20:37:21, time: 0.655, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1179, decode.acc_seg: 95.1767, loss: 0.1179 2023-01-06 08:53:09,618 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 08:53:09,618 - mmseg - INFO - Iter [51000/160000] lr: 4.088e-05, eta: 20:36:53, time: 0.741, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1152, decode.acc_seg: 95.3296, loss: 0.1152 2023-01-06 08:53:42,620 - mmseg - INFO - Iter [51050/160000] lr: 4.086e-05, eta: 20:36:17, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1098, decode.acc_seg: 95.4694, loss: 0.1098 2023-01-06 08:54:16,482 - mmseg - INFO - Iter [51100/160000] lr: 4.084e-05, eta: 20:35:43, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1159, decode.acc_seg: 95.3332, loss: 0.1159 2023-01-06 08:54:49,575 - mmseg - INFO - Iter [51150/160000] lr: 4.082e-05, eta: 20:35:07, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1209, decode.acc_seg: 94.9491, loss: 0.1209 2023-01-06 08:55:23,859 - mmseg - INFO - Iter [51200/160000] lr: 4.080e-05, eta: 20:34:33, time: 0.686, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1120, decode.acc_seg: 95.2950, loss: 0.1120 2023-01-06 08:55:59,106 - mmseg - INFO - Iter [51250/160000] lr: 4.078e-05, eta: 20:34:02, time: 0.705, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1158, decode.acc_seg: 95.2075, loss: 0.1158 2023-01-06 08:56:31,502 - mmseg - INFO - Iter [51300/160000] lr: 4.076e-05, eta: 20:33:24, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1172, decode.acc_seg: 95.2696, loss: 0.1172 2023-01-06 08:57:06,525 - mmseg - INFO - Iter [51350/160000] lr: 4.074e-05, eta: 20:32:52, time: 0.700, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1174, decode.acc_seg: 95.0587, loss: 0.1174 2023-01-06 08:57:39,308 - mmseg - INFO - Iter [51400/160000] lr: 4.073e-05, eta: 20:32:15, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1282, decode.acc_seg: 94.6943, loss: 0.1282 2023-01-06 08:58:11,957 - mmseg - INFO - Iter [51450/160000] lr: 4.071e-05, eta: 20:31:38, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1261, decode.acc_seg: 94.9282, loss: 0.1261 2023-01-06 08:58:44,975 - mmseg - INFO - Iter [51500/160000] lr: 4.069e-05, eta: 20:31:02, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1148, decode.acc_seg: 95.2784, loss: 0.1148 2023-01-06 08:59:17,481 - mmseg - INFO - Iter [51550/160000] lr: 4.067e-05, eta: 20:30:25, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1119, decode.acc_seg: 95.2399, loss: 0.1119 2023-01-06 08:59:49,876 - mmseg - INFO - Iter [51600/160000] lr: 4.065e-05, eta: 20:29:48, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1190, decode.acc_seg: 95.1098, loss: 0.1190 2023-01-06 09:00:22,227 - mmseg - INFO - Iter [51650/160000] lr: 4.063e-05, eta: 20:29:10, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1097, decode.acc_seg: 95.4403, loss: 0.1097 2023-01-06 09:00:54,663 - mmseg - INFO - Iter [51700/160000] lr: 4.061e-05, eta: 20:28:33, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1172, decode.acc_seg: 95.1560, loss: 0.1172 2023-01-06 09:01:29,318 - mmseg - INFO - Iter [51750/160000] lr: 4.059e-05, eta: 20:28:00, time: 0.692, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1159, decode.acc_seg: 95.2639, loss: 0.1159 2023-01-06 09:02:04,483 - mmseg - INFO - Iter [51800/160000] lr: 4.058e-05, eta: 20:27:28, time: 0.704, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1157, decode.acc_seg: 95.2042, loss: 0.1157 2023-01-06 09:02:38,067 - mmseg - INFO - Iter [51850/160000] lr: 4.056e-05, eta: 20:26:53, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1196, decode.acc_seg: 95.1317, loss: 0.1196 2023-01-06 09:03:10,697 - mmseg - INFO - Iter [51900/160000] lr: 4.054e-05, eta: 20:26:16, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1174, decode.acc_seg: 95.2090, loss: 0.1174 2023-01-06 09:03:45,529 - mmseg - INFO - Iter [51950/160000] lr: 4.052e-05, eta: 20:25:44, time: 0.696, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1225, decode.acc_seg: 95.1015, loss: 0.1225 2023-01-06 09:04:19,898 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 09:04:19,899 - mmseg - INFO - Iter [52000/160000] lr: 4.050e-05, eta: 20:25:11, time: 0.688, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1154, decode.acc_seg: 95.4055, loss: 0.1154 2023-01-06 09:04:52,012 - mmseg - INFO - Iter [52050/160000] lr: 4.048e-05, eta: 20:24:33, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1149, decode.acc_seg: 95.3120, loss: 0.1149 2023-01-06 09:05:27,866 - mmseg - INFO - Iter [52100/160000] lr: 4.046e-05, eta: 20:24:02, time: 0.717, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1147, decode.acc_seg: 95.2086, loss: 0.1147 2023-01-06 09:06:00,898 - mmseg - INFO - Iter [52150/160000] lr: 4.044e-05, eta: 20:23:26, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1188, decode.acc_seg: 95.2060, loss: 0.1188 2023-01-06 09:06:33,328 - mmseg - INFO - Iter [52200/160000] lr: 4.043e-05, eta: 20:22:49, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1195, decode.acc_seg: 95.1337, loss: 0.1195 2023-01-06 09:07:07,445 - mmseg - INFO - Iter [52250/160000] lr: 4.041e-05, eta: 20:22:15, time: 0.682, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1177, decode.acc_seg: 95.1618, loss: 0.1177 2023-01-06 09:07:39,858 - mmseg - INFO - Iter [52300/160000] lr: 4.039e-05, eta: 20:21:38, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1143, decode.acc_seg: 95.2918, loss: 0.1143 2023-01-06 09:08:12,445 - mmseg - INFO - Iter [52350/160000] lr: 4.037e-05, eta: 20:21:01, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1160, decode.acc_seg: 95.2813, loss: 0.1160 2023-01-06 09:08:44,801 - mmseg - INFO - Iter [52400/160000] lr: 4.035e-05, eta: 20:20:23, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1116, decode.acc_seg: 95.3401, loss: 0.1116 2023-01-06 09:09:17,221 - mmseg - INFO - Iter [52450/160000] lr: 4.033e-05, eta: 20:19:46, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1212, decode.acc_seg: 95.0409, loss: 0.1212 2023-01-06 09:09:51,853 - mmseg - INFO - Iter [52500/160000] lr: 4.031e-05, eta: 20:19:13, time: 0.693, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1077, decode.acc_seg: 95.4350, loss: 0.1077 2023-01-06 09:10:24,758 - mmseg - INFO - Iter [52550/160000] lr: 4.029e-05, eta: 20:18:37, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1166, decode.acc_seg: 95.3024, loss: 0.1166 2023-01-06 09:10:58,370 - mmseg - INFO - Iter [52600/160000] lr: 4.028e-05, eta: 20:18:02, time: 0.673, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1184, decode.acc_seg: 95.1564, loss: 0.1184 2023-01-06 09:11:33,140 - mmseg - INFO - Iter [52650/160000] lr: 4.026e-05, eta: 20:17:29, time: 0.694, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1273, decode.acc_seg: 94.8092, loss: 0.1273 2023-01-06 09:12:08,379 - mmseg - INFO - Iter [52700/160000] lr: 4.024e-05, eta: 20:16:58, time: 0.706, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1105, decode.acc_seg: 95.3196, loss: 0.1105 2023-01-06 09:12:40,814 - mmseg - INFO - Iter [52750/160000] lr: 4.022e-05, eta: 20:16:21, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1199, decode.acc_seg: 95.0496, loss: 0.1199 2023-01-06 09:13:15,329 - mmseg - INFO - Iter [52800/160000] lr: 4.020e-05, eta: 20:15:47, time: 0.689, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1133, decode.acc_seg: 95.3082, loss: 0.1133 2023-01-06 09:13:50,366 - mmseg - INFO - Iter [52850/160000] lr: 4.018e-05, eta: 20:15:16, time: 0.702, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1320, decode.acc_seg: 94.7294, loss: 0.1320 2023-01-06 09:14:23,464 - mmseg - INFO - Iter [52900/160000] lr: 4.016e-05, eta: 20:14:40, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1250, decode.acc_seg: 94.9446, loss: 0.1250 2023-01-06 09:14:56,734 - mmseg - INFO - Iter [52950/160000] lr: 4.014e-05, eta: 20:14:04, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1140, decode.acc_seg: 95.2982, loss: 0.1140 2023-01-06 09:15:29,290 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 09:15:29,290 - mmseg - INFO - Iter [53000/160000] lr: 4.013e-05, eta: 20:13:27, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1247, decode.acc_seg: 94.9568, loss: 0.1247 2023-01-06 09:16:02,660 - mmseg - INFO - Iter [53050/160000] lr: 4.011e-05, eta: 20:12:52, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1145, decode.acc_seg: 95.3692, loss: 0.1145 2023-01-06 09:16:35,320 - mmseg - INFO - Iter [53100/160000] lr: 4.009e-05, eta: 20:12:15, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1229, decode.acc_seg: 95.0261, loss: 0.1229 2023-01-06 09:17:07,606 - mmseg - INFO - Iter [53150/160000] lr: 4.007e-05, eta: 20:11:38, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1093, decode.acc_seg: 95.6003, loss: 0.1093 2023-01-06 09:17:43,904 - mmseg - INFO - Iter [53200/160000] lr: 4.005e-05, eta: 20:11:08, time: 0.726, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1165, decode.acc_seg: 95.2270, loss: 0.1165 2023-01-06 09:18:16,391 - mmseg - INFO - Iter [53250/160000] lr: 4.003e-05, eta: 20:10:31, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1287, decode.acc_seg: 94.8907, loss: 0.1287 2023-01-06 09:18:48,826 - mmseg - INFO - Iter [53300/160000] lr: 4.001e-05, eta: 20:09:54, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1122, decode.acc_seg: 95.3616, loss: 0.1122 2023-01-06 09:19:21,266 - mmseg - INFO - Iter [53350/160000] lr: 3.999e-05, eta: 20:09:17, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1228, decode.acc_seg: 95.0703, loss: 0.1228 2023-01-06 09:19:53,633 - mmseg - INFO - Iter [53400/160000] lr: 3.998e-05, eta: 20:08:39, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1109, decode.acc_seg: 95.3180, loss: 0.1109 2023-01-06 09:20:28,503 - mmseg - INFO - Iter [53450/160000] lr: 3.996e-05, eta: 20:08:07, time: 0.697, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1173, decode.acc_seg: 95.2547, loss: 0.1173 2023-01-06 09:21:00,792 - mmseg - INFO - Iter [53500/160000] lr: 3.994e-05, eta: 20:07:30, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1092, decode.acc_seg: 95.4521, loss: 0.1092 2023-01-06 09:21:33,933 - mmseg - INFO - Iter [53550/160000] lr: 3.992e-05, eta: 20:06:54, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1172, decode.acc_seg: 95.1696, loss: 0.1172 2023-01-06 09:22:08,842 - mmseg - INFO - Iter [53600/160000] lr: 3.990e-05, eta: 20:06:22, time: 0.699, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1113, decode.acc_seg: 95.3973, loss: 0.1113 2023-01-06 09:22:41,475 - mmseg - INFO - Iter [53650/160000] lr: 3.988e-05, eta: 20:05:45, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1135, decode.acc_seg: 95.3267, loss: 0.1135 2023-01-06 09:23:14,254 - mmseg - INFO - Iter [53700/160000] lr: 3.986e-05, eta: 20:05:08, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1136, decode.acc_seg: 95.4097, loss: 0.1136 2023-01-06 09:23:46,869 - mmseg - INFO - Iter [53750/160000] lr: 3.984e-05, eta: 20:04:32, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1149, decode.acc_seg: 95.2930, loss: 0.1149 2023-01-06 09:24:21,368 - mmseg - INFO - Iter [53800/160000] lr: 3.983e-05, eta: 20:03:59, time: 0.691, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1174, decode.acc_seg: 95.0929, loss: 0.1174 2023-01-06 09:24:54,665 - mmseg - INFO - Iter [53850/160000] lr: 3.981e-05, eta: 20:03:23, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1144, decode.acc_seg: 95.3715, loss: 0.1144 2023-01-06 09:25:27,731 - mmseg - INFO - Iter [53900/160000] lr: 3.979e-05, eta: 20:02:47, time: 0.662, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1268, decode.acc_seg: 94.9168, loss: 0.1268 2023-01-06 09:26:02,312 - mmseg - INFO - Iter [53950/160000] lr: 3.977e-05, eta: 20:02:14, time: 0.692, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1259, decode.acc_seg: 94.9280, loss: 0.1259 2023-01-06 09:26:36,625 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 09:26:36,625 - mmseg - INFO - Iter [54000/160000] lr: 3.975e-05, eta: 20:01:41, time: 0.686, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1261, decode.acc_seg: 95.0686, loss: 0.1261 2023-01-06 09:27:08,939 - mmseg - INFO - Iter [54050/160000] lr: 3.973e-05, eta: 20:01:04, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1189, decode.acc_seg: 95.1920, loss: 0.1189 2023-01-06 09:27:42,155 - mmseg - INFO - Iter [54100/160000] lr: 3.971e-05, eta: 20:00:28, time: 0.664, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1121, decode.acc_seg: 95.4616, loss: 0.1121 2023-01-06 09:28:15,854 - mmseg - INFO - Iter [54150/160000] lr: 3.969e-05, eta: 19:59:53, time: 0.673, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1197, decode.acc_seg: 95.0570, loss: 0.1197 2023-01-06 09:28:49,694 - mmseg - INFO - Iter [54200/160000] lr: 3.968e-05, eta: 19:59:19, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1138, decode.acc_seg: 95.2527, loss: 0.1138 2023-01-06 09:29:24,295 - mmseg - INFO - Iter [54250/160000] lr: 3.966e-05, eta: 19:58:46, time: 0.692, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1168, decode.acc_seg: 95.1746, loss: 0.1168 2023-01-06 09:29:57,746 - mmseg - INFO - Iter [54300/160000] lr: 3.964e-05, eta: 19:58:11, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1144, decode.acc_seg: 95.2327, loss: 0.1144 2023-01-06 09:30:32,497 - mmseg - INFO - Iter [54350/160000] lr: 3.962e-05, eta: 19:57:39, time: 0.695, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1118, decode.acc_seg: 95.4438, loss: 0.1118 2023-01-06 09:31:04,799 - mmseg - INFO - Iter [54400/160000] lr: 3.960e-05, eta: 19:57:01, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1154, decode.acc_seg: 95.2537, loss: 0.1154 2023-01-06 09:31:40,450 - mmseg - INFO - Iter [54450/160000] lr: 3.958e-05, eta: 19:56:30, time: 0.712, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1232, decode.acc_seg: 95.0341, loss: 0.1232 2023-01-06 09:32:16,211 - mmseg - INFO - Iter [54500/160000] lr: 3.956e-05, eta: 19:56:00, time: 0.715, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1142, decode.acc_seg: 95.3018, loss: 0.1142 2023-01-06 09:32:51,967 - mmseg - INFO - Iter [54550/160000] lr: 3.954e-05, eta: 19:55:29, time: 0.716, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1073, decode.acc_seg: 95.5476, loss: 0.1073 2023-01-06 09:33:24,241 - mmseg - INFO - Iter [54600/160000] lr: 3.953e-05, eta: 19:54:52, time: 0.645, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1211, decode.acc_seg: 94.9009, loss: 0.1211 2023-01-06 09:33:56,701 - mmseg - INFO - Iter [54650/160000] lr: 3.951e-05, eta: 19:54:15, time: 0.649, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1113, decode.acc_seg: 95.4144, loss: 0.1113 2023-01-06 09:34:32,777 - mmseg - INFO - Iter [54700/160000] lr: 3.949e-05, eta: 19:53:45, time: 0.721, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1177, decode.acc_seg: 95.1032, loss: 0.1177 2023-01-06 09:35:06,013 - mmseg - INFO - Iter [54750/160000] lr: 3.947e-05, eta: 19:53:09, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1229, decode.acc_seg: 95.0409, loss: 0.1229 2023-01-06 09:35:40,383 - mmseg - INFO - Iter [54800/160000] lr: 3.945e-05, eta: 19:52:36, time: 0.686, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1042, decode.acc_seg: 95.7207, loss: 0.1042 2023-01-06 09:36:14,111 - mmseg - INFO - Iter [54850/160000] lr: 3.943e-05, eta: 19:52:01, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1135, decode.acc_seg: 95.1739, loss: 0.1135 2023-01-06 09:36:47,696 - mmseg - INFO - Iter [54900/160000] lr: 3.941e-05, eta: 19:51:27, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1318, decode.acc_seg: 94.7544, loss: 0.1318 2023-01-06 09:37:20,511 - mmseg - INFO - Iter [54950/160000] lr: 3.939e-05, eta: 19:50:50, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1296, decode.acc_seg: 94.8109, loss: 0.1296 2023-01-06 09:37:54,364 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 09:37:54,364 - mmseg - INFO - Iter [55000/160000] lr: 3.938e-05, eta: 19:50:16, time: 0.676, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1149, decode.acc_seg: 95.3297, loss: 0.1149 2023-01-06 09:38:28,852 - mmseg - INFO - Iter [55050/160000] lr: 3.936e-05, eta: 19:49:43, time: 0.691, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1154, decode.acc_seg: 95.1941, loss: 0.1154 2023-01-06 09:39:04,364 - mmseg - INFO - Iter [55100/160000] lr: 3.934e-05, eta: 19:49:12, time: 0.709, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1141, decode.acc_seg: 95.2185, loss: 0.1141 2023-01-06 09:39:37,817 - mmseg - INFO - Iter [55150/160000] lr: 3.932e-05, eta: 19:48:37, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1161, decode.acc_seg: 95.1159, loss: 0.1161 2023-01-06 09:40:12,759 - mmseg - INFO - Iter [55200/160000] lr: 3.930e-05, eta: 19:48:04, time: 0.698, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1250, decode.acc_seg: 94.8039, loss: 0.1250 2023-01-06 09:40:48,028 - mmseg - INFO - Iter [55250/160000] lr: 3.928e-05, eta: 19:47:33, time: 0.706, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1123, decode.acc_seg: 95.3979, loss: 0.1123 2023-01-06 09:41:20,931 - mmseg - INFO - Iter [55300/160000] lr: 3.926e-05, eta: 19:46:57, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1112, decode.acc_seg: 95.3800, loss: 0.1112 2023-01-06 09:41:54,684 - mmseg - INFO - Iter [55350/160000] lr: 3.924e-05, eta: 19:46:22, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1128, decode.acc_seg: 95.4159, loss: 0.1128 2023-01-06 09:42:27,009 - mmseg - INFO - Iter [55400/160000] lr: 3.923e-05, eta: 19:45:45, time: 0.648, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1188, decode.acc_seg: 95.0936, loss: 0.1188 2023-01-06 09:43:01,493 - mmseg - INFO - Iter [55450/160000] lr: 3.921e-05, eta: 19:45:12, time: 0.690, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1136, decode.acc_seg: 95.2560, loss: 0.1136 2023-01-06 09:43:33,760 - mmseg - INFO - Iter [55500/160000] lr: 3.919e-05, eta: 19:44:35, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1198, decode.acc_seg: 95.0145, loss: 0.1198 2023-01-06 09:44:06,926 - mmseg - INFO - Iter [55550/160000] lr: 3.917e-05, eta: 19:43:59, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1071, decode.acc_seg: 95.5743, loss: 0.1071 2023-01-06 09:44:40,744 - mmseg - INFO - Iter [55600/160000] lr: 3.915e-05, eta: 19:43:25, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1107, decode.acc_seg: 95.4069, loss: 0.1107 2023-01-06 09:45:13,628 - mmseg - INFO - Iter [55650/160000] lr: 3.913e-05, eta: 19:42:48, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1141, decode.acc_seg: 95.4536, loss: 0.1141 2023-01-06 09:45:48,052 - mmseg - INFO - Iter [55700/160000] lr: 3.911e-05, eta: 19:42:15, time: 0.690, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1112, decode.acc_seg: 95.3420, loss: 0.1112 2023-01-06 09:46:21,353 - mmseg - INFO - Iter [55750/160000] lr: 3.909e-05, eta: 19:41:40, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1158, decode.acc_seg: 95.2786, loss: 0.1158 2023-01-06 09:46:55,235 - mmseg - INFO - Iter [55800/160000] lr: 3.908e-05, eta: 19:41:06, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1129, decode.acc_seg: 95.3032, loss: 0.1129 2023-01-06 09:47:30,788 - mmseg - INFO - Iter [55850/160000] lr: 3.906e-05, eta: 19:40:35, time: 0.710, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1035, decode.acc_seg: 95.6670, loss: 0.1035 2023-01-06 09:48:04,314 - mmseg - INFO - Iter [55900/160000] lr: 3.904e-05, eta: 19:40:00, time: 0.671, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1027, decode.acc_seg: 95.7266, loss: 0.1027 2023-01-06 09:48:36,515 - mmseg - INFO - Iter [55950/160000] lr: 3.902e-05, eta: 19:39:22, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1107, decode.acc_seg: 95.3869, loss: 0.1107 2023-01-06 09:49:08,777 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 09:49:08,777 - mmseg - INFO - Iter [56000/160000] lr: 3.900e-05, eta: 19:38:45, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1180, decode.acc_seg: 95.2880, loss: 0.1180 2023-01-06 09:49:41,640 - mmseg - INFO - Iter [56050/160000] lr: 3.898e-05, eta: 19:38:09, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1085, decode.acc_seg: 95.6249, loss: 0.1085 2023-01-06 09:50:14,505 - mmseg - INFO - Iter [56100/160000] lr: 3.896e-05, eta: 19:37:33, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1065, decode.acc_seg: 95.4864, loss: 0.1065 2023-01-06 09:50:47,313 - mmseg - INFO - Iter [56150/160000] lr: 3.894e-05, eta: 19:36:57, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1127, decode.acc_seg: 95.3699, loss: 0.1127 2023-01-06 09:51:23,772 - mmseg - INFO - Iter [56200/160000] lr: 3.893e-05, eta: 19:36:27, time: 0.729, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1099, decode.acc_seg: 95.4644, loss: 0.1099 2023-01-06 09:51:56,550 - mmseg - INFO - Iter [56250/160000] lr: 3.891e-05, eta: 19:35:51, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1071, decode.acc_seg: 95.4828, loss: 0.1071 2023-01-06 09:52:30,224 - mmseg - INFO - Iter [56300/160000] lr: 3.889e-05, eta: 19:35:16, time: 0.672, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1077, decode.acc_seg: 95.5147, loss: 0.1077 2023-01-06 09:53:03,199 - mmseg - INFO - Iter [56350/160000] lr: 3.887e-05, eta: 19:34:41, time: 0.660, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1028, decode.acc_seg: 95.7385, loss: 0.1028 2023-01-06 09:53:36,200 - mmseg - INFO - Iter [56400/160000] lr: 3.885e-05, eta: 19:34:05, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1057, decode.acc_seg: 95.4939, loss: 0.1057 2023-01-06 09:54:10,940 - mmseg - INFO - Iter [56450/160000] lr: 3.883e-05, eta: 19:33:32, time: 0.695, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1151, decode.acc_seg: 95.2126, loss: 0.1151 2023-01-06 09:54:43,296 - mmseg - INFO - Iter [56500/160000] lr: 3.881e-05, eta: 19:32:55, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1206, decode.acc_seg: 95.0967, loss: 0.1206 2023-01-06 09:55:19,531 - mmseg - INFO - Iter [56550/160000] lr: 3.879e-05, eta: 19:32:25, time: 0.724, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1201, decode.acc_seg: 95.1803, loss: 0.1201 2023-01-06 09:55:51,778 - mmseg - INFO - Iter [56600/160000] lr: 3.878e-05, eta: 19:31:48, time: 0.646, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1190, decode.acc_seg: 95.0132, loss: 0.1190 2023-01-06 09:56:25,106 - mmseg - INFO - Iter [56650/160000] lr: 3.876e-05, eta: 19:31:13, time: 0.666, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1264, decode.acc_seg: 94.8107, loss: 0.1264 2023-01-06 09:56:59,708 - mmseg - INFO - Iter [56700/160000] lr: 3.874e-05, eta: 19:30:40, time: 0.692, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1110, decode.acc_seg: 95.3866, loss: 0.1110 2023-01-06 09:57:32,034 - mmseg - INFO - Iter [56750/160000] lr: 3.872e-05, eta: 19:30:03, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1245, decode.acc_seg: 95.0782, loss: 0.1245 2023-01-06 09:58:05,291 - mmseg - INFO - Iter [56800/160000] lr: 3.870e-05, eta: 19:29:27, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1158, decode.acc_seg: 95.3122, loss: 0.1158 2023-01-06 09:58:40,929 - mmseg - INFO - Iter [56850/160000] lr: 3.868e-05, eta: 19:28:56, time: 0.712, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1158, decode.acc_seg: 95.1665, loss: 0.1158 2023-01-06 09:59:16,658 - mmseg - INFO - Iter [56900/160000] lr: 3.866e-05, eta: 19:28:26, time: 0.716, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1164, decode.acc_seg: 95.2890, loss: 0.1164 2023-01-06 09:59:52,417 - mmseg - INFO - Iter [56950/160000] lr: 3.864e-05, eta: 19:27:55, time: 0.715, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1071, decode.acc_seg: 95.6236, loss: 0.1071 2023-01-06 10:00:26,330 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 10:00:26,330 - mmseg - INFO - Iter [57000/160000] lr: 3.863e-05, eta: 19:27:21, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1090, decode.acc_seg: 95.5123, loss: 0.1090 2023-01-06 10:00:59,210 - mmseg - INFO - Iter [57050/160000] lr: 3.861e-05, eta: 19:26:45, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1056, decode.acc_seg: 95.7272, loss: 0.1056 2023-01-06 10:01:32,457 - mmseg - INFO - Iter [57100/160000] lr: 3.859e-05, eta: 19:26:09, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1197, decode.acc_seg: 94.9512, loss: 0.1197 2023-01-06 10:02:05,565 - mmseg - INFO - Iter [57150/160000] lr: 3.857e-05, eta: 19:25:34, time: 0.663, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1271, decode.acc_seg: 94.8508, loss: 0.1271 2023-01-06 10:02:38,406 - mmseg - INFO - Iter [57200/160000] lr: 3.855e-05, eta: 19:24:57, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1266, decode.acc_seg: 94.8942, loss: 0.1266 2023-01-06 10:03:14,088 - mmseg - INFO - Iter [57250/160000] lr: 3.853e-05, eta: 19:24:27, time: 0.715, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1373, decode.acc_seg: 94.5741, loss: 0.1373 2023-01-06 10:03:49,089 - mmseg - INFO - Iter [57300/160000] lr: 3.851e-05, eta: 19:23:54, time: 0.700, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1141, decode.acc_seg: 95.3869, loss: 0.1141 2023-01-06 10:04:22,844 - mmseg - INFO - Iter [57350/160000] lr: 3.849e-05, eta: 19:23:20, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1165, decode.acc_seg: 95.2302, loss: 0.1165 2023-01-06 10:04:58,134 - mmseg - INFO - Iter [57400/160000] lr: 3.848e-05, eta: 19:22:48, time: 0.706, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1147, decode.acc_seg: 95.2731, loss: 0.1147 2023-01-06 10:05:33,067 - mmseg - INFO - Iter [57450/160000] lr: 3.846e-05, eta: 19:22:16, time: 0.699, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1122, decode.acc_seg: 95.2245, loss: 0.1122 2023-01-06 10:06:06,567 - mmseg - INFO - Iter [57500/160000] lr: 3.844e-05, eta: 19:21:41, time: 0.671, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1050, decode.acc_seg: 95.6211, loss: 0.1050 2023-01-06 10:06:39,892 - mmseg - INFO - Iter [57550/160000] lr: 3.842e-05, eta: 19:21:06, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1121, decode.acc_seg: 95.4724, loss: 0.1121 2023-01-06 10:07:12,915 - mmseg - INFO - Iter [57600/160000] lr: 3.840e-05, eta: 19:20:30, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1095, decode.acc_seg: 95.4542, loss: 0.1095 2023-01-06 10:07:46,205 - mmseg - INFO - Iter [57650/160000] lr: 3.838e-05, eta: 19:19:55, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1019, decode.acc_seg: 95.8849, loss: 0.1019 2023-01-06 10:08:22,037 - mmseg - INFO - Iter [57700/160000] lr: 3.836e-05, eta: 19:19:24, time: 0.716, data_time: 0.066, memory: 11582, decode.loss_ce: 0.1026, decode.acc_seg: 95.7427, loss: 0.1026 2023-01-06 10:08:55,261 - mmseg - INFO - Iter [57750/160000] lr: 3.834e-05, eta: 19:18:49, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1067, decode.acc_seg: 95.5543, loss: 0.1067 2023-01-06 10:09:27,570 - mmseg - INFO - Iter [57800/160000] lr: 3.833e-05, eta: 19:18:12, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1127, decode.acc_seg: 95.3803, loss: 0.1127 2023-01-06 10:10:01,830 - mmseg - INFO - Iter [57850/160000] lr: 3.831e-05, eta: 19:17:38, time: 0.684, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1116, decode.acc_seg: 95.2381, loss: 0.1116 2023-01-06 10:10:36,520 - mmseg - INFO - Iter [57900/160000] lr: 3.829e-05, eta: 19:17:05, time: 0.695, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1236, decode.acc_seg: 94.9717, loss: 0.1236 2023-01-06 10:11:08,813 - mmseg - INFO - Iter [57950/160000] lr: 3.827e-05, eta: 19:16:28, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1058, decode.acc_seg: 95.5685, loss: 0.1058 2023-01-06 10:11:41,265 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 10:11:41,266 - mmseg - INFO - Iter [58000/160000] lr: 3.825e-05, eta: 19:15:52, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1064, decode.acc_seg: 95.5073, loss: 0.1064 2023-01-06 10:12:17,409 - mmseg - INFO - Iter [58050/160000] lr: 3.823e-05, eta: 19:15:21, time: 0.723, data_time: 0.063, memory: 11582, decode.loss_ce: 0.1146, decode.acc_seg: 95.2565, loss: 0.1146 2023-01-06 10:12:50,236 - mmseg - INFO - Iter [58100/160000] lr: 3.821e-05, eta: 19:14:45, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1121, decode.acc_seg: 95.3332, loss: 0.1121 2023-01-06 10:13:24,796 - mmseg - INFO - Iter [58150/160000] lr: 3.819e-05, eta: 19:14:12, time: 0.691, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1098, decode.acc_seg: 95.5074, loss: 0.1098 2023-01-06 10:13:58,775 - mmseg - INFO - Iter [58200/160000] lr: 3.818e-05, eta: 19:13:38, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1126, decode.acc_seg: 95.4050, loss: 0.1126 2023-01-06 10:14:33,470 - mmseg - INFO - Iter [58250/160000] lr: 3.816e-05, eta: 19:13:05, time: 0.694, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1176, decode.acc_seg: 95.2150, loss: 0.1176 2023-01-06 10:15:06,122 - mmseg - INFO - Iter [58300/160000] lr: 3.814e-05, eta: 19:12:29, time: 0.654, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1129, decode.acc_seg: 95.3724, loss: 0.1129 2023-01-06 10:15:39,327 - mmseg - INFO - Iter [58350/160000] lr: 3.812e-05, eta: 19:11:54, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1103, decode.acc_seg: 95.4614, loss: 0.1103 2023-01-06 10:16:13,523 - mmseg - INFO - Iter [58400/160000] lr: 3.810e-05, eta: 19:11:20, time: 0.683, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1052, decode.acc_seg: 95.6813, loss: 0.1052 2023-01-06 10:16:49,675 - mmseg - INFO - Iter [58450/160000] lr: 3.808e-05, eta: 19:10:50, time: 0.724, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1159, decode.acc_seg: 95.4323, loss: 0.1159 2023-01-06 10:17:22,086 - mmseg - INFO - Iter [58500/160000] lr: 3.806e-05, eta: 19:10:13, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1145, decode.acc_seg: 95.2685, loss: 0.1145 2023-01-06 10:17:55,123 - mmseg - INFO - Iter [58550/160000] lr: 3.804e-05, eta: 19:09:37, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1063, decode.acc_seg: 95.5838, loss: 0.1063 2023-01-06 10:18:30,671 - mmseg - INFO - Iter [58600/160000] lr: 3.803e-05, eta: 19:09:06, time: 0.711, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1020, decode.acc_seg: 95.5703, loss: 0.1020 2023-01-06 10:19:05,432 - mmseg - INFO - Iter [58650/160000] lr: 3.801e-05, eta: 19:08:33, time: 0.695, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1172, decode.acc_seg: 95.1800, loss: 0.1172 2023-01-06 10:19:39,863 - mmseg - INFO - Iter [58700/160000] lr: 3.799e-05, eta: 19:08:00, time: 0.689, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1162, decode.acc_seg: 95.3309, loss: 0.1162 2023-01-06 10:20:15,248 - mmseg - INFO - Iter [58750/160000] lr: 3.797e-05, eta: 19:07:29, time: 0.708, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1026, decode.acc_seg: 95.6393, loss: 0.1026 2023-01-06 10:20:51,825 - mmseg - INFO - Iter [58800/160000] lr: 3.795e-05, eta: 19:06:59, time: 0.732, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1152, decode.acc_seg: 95.4262, loss: 0.1152 2023-01-06 10:21:27,312 - mmseg - INFO - Iter [58850/160000] lr: 3.793e-05, eta: 19:06:28, time: 0.710, data_time: 0.024, memory: 11582, decode.loss_ce: 0.1024, decode.acc_seg: 95.7600, loss: 0.1024 2023-01-06 10:21:59,729 - mmseg - INFO - Iter [58900/160000] lr: 3.791e-05, eta: 19:05:51, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1013, decode.acc_seg: 95.6924, loss: 0.1013 2023-01-06 10:22:31,890 - mmseg - INFO - Iter [58950/160000] lr: 3.789e-05, eta: 19:05:14, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1074, decode.acc_seg: 95.5933, loss: 0.1074 2023-01-06 10:23:04,350 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 10:23:04,351 - mmseg - INFO - Iter [59000/160000] lr: 3.788e-05, eta: 19:04:37, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1097, decode.acc_seg: 95.3020, loss: 0.1097 2023-01-06 10:23:37,246 - mmseg - INFO - Iter [59050/160000] lr: 3.786e-05, eta: 19:04:01, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1133, decode.acc_seg: 95.3150, loss: 0.1133 2023-01-06 10:24:09,734 - mmseg - INFO - Iter [59100/160000] lr: 3.784e-05, eta: 19:03:25, time: 0.651, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1347, decode.acc_seg: 94.6442, loss: 0.1347 2023-01-06 10:24:46,143 - mmseg - INFO - Iter [59150/160000] lr: 3.782e-05, eta: 19:02:55, time: 0.728, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1245, decode.acc_seg: 95.0217, loss: 0.1245 2023-01-06 10:25:19,492 - mmseg - INFO - Iter [59200/160000] lr: 3.780e-05, eta: 19:02:20, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1100, decode.acc_seg: 95.4340, loss: 0.1100 2023-01-06 10:25:53,788 - mmseg - INFO - Iter [59250/160000] lr: 3.778e-05, eta: 19:01:46, time: 0.685, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1089, decode.acc_seg: 95.5073, loss: 0.1089 2023-01-06 10:26:26,699 - mmseg - INFO - Iter [59300/160000] lr: 3.776e-05, eta: 19:01:10, time: 0.659, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1145, decode.acc_seg: 95.2987, loss: 0.1145 2023-01-06 10:26:59,150 - mmseg - INFO - Iter [59350/160000] lr: 3.774e-05, eta: 19:00:34, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1102, decode.acc_seg: 95.5503, loss: 0.1102 2023-01-06 10:27:33,529 - mmseg - INFO - Iter [59400/160000] lr: 3.773e-05, eta: 19:00:00, time: 0.688, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1058, decode.acc_seg: 95.6372, loss: 0.1058 2023-01-06 10:28:07,807 - mmseg - INFO - Iter [59450/160000] lr: 3.771e-05, eta: 18:59:27, time: 0.685, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1162, decode.acc_seg: 95.2780, loss: 0.1162 2023-01-06 10:28:41,046 - mmseg - INFO - Iter [59500/160000] lr: 3.769e-05, eta: 18:58:51, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1128, decode.acc_seg: 95.3648, loss: 0.1128 2023-01-06 10:29:17,428 - mmseg - INFO - Iter [59550/160000] lr: 3.767e-05, eta: 18:58:21, time: 0.728, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1068, decode.acc_seg: 95.6291, loss: 0.1068 2023-01-06 10:29:50,470 - mmseg - INFO - Iter [59600/160000] lr: 3.765e-05, eta: 18:57:46, time: 0.662, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1117, decode.acc_seg: 95.3904, loss: 0.1117 2023-01-06 10:30:22,714 - mmseg - INFO - Iter [59650/160000] lr: 3.763e-05, eta: 18:57:09, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1088, decode.acc_seg: 95.4378, loss: 0.1088 2023-01-06 10:30:56,909 - mmseg - INFO - Iter [59700/160000] lr: 3.761e-05, eta: 18:56:35, time: 0.684, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1121, decode.acc_seg: 95.4224, loss: 0.1121 2023-01-06 10:31:29,351 - mmseg - INFO - Iter [59750/160000] lr: 3.759e-05, eta: 18:55:59, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1345, decode.acc_seg: 95.0511, loss: 0.1345 2023-01-06 10:32:01,511 - mmseg - INFO - Iter [59800/160000] lr: 3.758e-05, eta: 18:55:22, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1210, decode.acc_seg: 95.0263, loss: 0.1210 2023-01-06 10:32:34,244 - mmseg - INFO - Iter [59850/160000] lr: 3.756e-05, eta: 18:54:46, time: 0.655, data_time: 0.022, memory: 11582, decode.loss_ce: 0.1166, decode.acc_seg: 95.1294, loss: 0.1166 2023-01-06 10:33:09,571 - mmseg - INFO - Iter [59900/160000] lr: 3.754e-05, eta: 18:54:14, time: 0.707, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1159, decode.acc_seg: 95.2244, loss: 0.1159 2023-01-06 10:33:44,280 - mmseg - INFO - Iter [59950/160000] lr: 3.752e-05, eta: 18:53:41, time: 0.694, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1205, decode.acc_seg: 95.1317, loss: 0.1205 2023-01-06 10:34:18,097 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 10:34:18,097 - mmseg - INFO - Iter [60000/160000] lr: 3.750e-05, eta: 18:53:07, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1192, decode.acc_seg: 95.2256, loss: 0.1192 2023-01-06 10:34:51,740 - mmseg - INFO - Iter [60050/160000] lr: 3.748e-05, eta: 18:52:32, time: 0.673, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1164, decode.acc_seg: 95.3385, loss: 0.1164 2023-01-06 10:35:25,307 - mmseg - INFO - Iter [60100/160000] lr: 3.746e-05, eta: 18:51:57, time: 0.672, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1082, decode.acc_seg: 95.5944, loss: 0.1082 2023-01-06 10:35:58,450 - mmseg - INFO - Iter [60150/160000] lr: 3.744e-05, eta: 18:51:22, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1207, decode.acc_seg: 95.0931, loss: 0.1207 2023-01-06 10:36:30,756 - mmseg - INFO - Iter [60200/160000] lr: 3.743e-05, eta: 18:50:45, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1111, decode.acc_seg: 95.4061, loss: 0.1111 2023-01-06 10:37:04,483 - mmseg - INFO - Iter [60250/160000] lr: 3.741e-05, eta: 18:50:11, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1074, decode.acc_seg: 95.6253, loss: 0.1074 2023-01-06 10:37:39,181 - mmseg - INFO - Iter [60300/160000] lr: 3.739e-05, eta: 18:49:38, time: 0.694, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1169, decode.acc_seg: 95.2345, loss: 0.1169 2023-01-06 10:38:12,682 - mmseg - INFO - Iter [60350/160000] lr: 3.737e-05, eta: 18:49:03, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1128, decode.acc_seg: 95.3823, loss: 0.1128 2023-01-06 10:38:47,252 - mmseg - INFO - Iter [60400/160000] lr: 3.735e-05, eta: 18:48:30, time: 0.691, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1094, decode.acc_seg: 95.5561, loss: 0.1094 2023-01-06 10:39:22,631 - mmseg - INFO - Iter [60450/160000] lr: 3.733e-05, eta: 18:47:58, time: 0.708, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1096, decode.acc_seg: 95.4615, loss: 0.1096 2023-01-06 10:39:58,689 - mmseg - INFO - Iter [60500/160000] lr: 3.731e-05, eta: 18:47:28, time: 0.721, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1105, decode.acc_seg: 95.4624, loss: 0.1105 2023-01-06 10:40:31,717 - mmseg - INFO - Iter [60550/160000] lr: 3.729e-05, eta: 18:46:52, time: 0.661, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1098, decode.acc_seg: 95.3978, loss: 0.1098 2023-01-06 10:41:04,010 - mmseg - INFO - Iter [60600/160000] lr: 3.728e-05, eta: 18:46:15, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1078, decode.acc_seg: 95.4833, loss: 0.1078 2023-01-06 10:41:39,910 - mmseg - INFO - Iter [60650/160000] lr: 3.726e-05, eta: 18:45:45, time: 0.719, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0993, decode.acc_seg: 95.8338, loss: 0.0993 2023-01-06 10:42:12,235 - mmseg - INFO - Iter [60700/160000] lr: 3.724e-05, eta: 18:45:08, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1047, decode.acc_seg: 95.6884, loss: 0.1047 2023-01-06 10:42:44,447 - mmseg - INFO - Iter [60750/160000] lr: 3.722e-05, eta: 18:44:31, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1148, decode.acc_seg: 95.2508, loss: 0.1148 2023-01-06 10:43:17,727 - mmseg - INFO - Iter [60800/160000] lr: 3.720e-05, eta: 18:43:56, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1106, decode.acc_seg: 95.4457, loss: 0.1106 2023-01-06 10:43:50,583 - mmseg - INFO - Iter [60850/160000] lr: 3.718e-05, eta: 18:43:20, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1084, decode.acc_seg: 95.5362, loss: 0.1084 2023-01-06 10:44:24,988 - mmseg - INFO - Iter [60900/160000] lr: 3.716e-05, eta: 18:42:47, time: 0.688, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1110, decode.acc_seg: 95.4209, loss: 0.1110 2023-01-06 10:44:57,235 - mmseg - INFO - Iter [60950/160000] lr: 3.714e-05, eta: 18:42:10, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1096, decode.acc_seg: 95.3552, loss: 0.1096 2023-01-06 10:45:32,749 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 10:45:32,749 - mmseg - INFO - Iter [61000/160000] lr: 3.713e-05, eta: 18:41:38, time: 0.709, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1033, decode.acc_seg: 95.6077, loss: 0.1033 2023-01-06 10:46:09,637 - mmseg - INFO - Iter [61050/160000] lr: 3.711e-05, eta: 18:41:09, time: 0.738, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1171, decode.acc_seg: 95.2108, loss: 0.1171 2023-01-06 10:46:43,470 - mmseg - INFO - Iter [61100/160000] lr: 3.709e-05, eta: 18:40:35, time: 0.677, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1029, decode.acc_seg: 95.7479, loss: 0.1029 2023-01-06 10:47:16,327 - mmseg - INFO - Iter [61150/160000] lr: 3.707e-05, eta: 18:39:59, time: 0.658, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1007, decode.acc_seg: 95.7534, loss: 0.1007 2023-01-06 10:47:49,640 - mmseg - INFO - Iter [61200/160000] lr: 3.705e-05, eta: 18:39:24, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1008, decode.acc_seg: 95.8138, loss: 0.1008 2023-01-06 10:48:21,756 - mmseg - INFO - Iter [61250/160000] lr: 3.703e-05, eta: 18:38:47, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0995, decode.acc_seg: 95.9249, loss: 0.0995 2023-01-06 10:48:54,751 - mmseg - INFO - Iter [61300/160000] lr: 3.701e-05, eta: 18:38:11, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1071, decode.acc_seg: 95.5165, loss: 0.1071 2023-01-06 10:49:27,979 - mmseg - INFO - Iter [61350/160000] lr: 3.699e-05, eta: 18:37:36, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1100, decode.acc_seg: 95.5361, loss: 0.1100 2023-01-06 10:50:02,764 - mmseg - INFO - Iter [61400/160000] lr: 3.698e-05, eta: 18:37:03, time: 0.696, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1022, decode.acc_seg: 95.7336, loss: 0.1022 2023-01-06 10:50:36,665 - mmseg - INFO - Iter [61450/160000] lr: 3.696e-05, eta: 18:36:29, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1021, decode.acc_seg: 95.7683, loss: 0.1021 2023-01-06 10:51:09,383 - mmseg - INFO - Iter [61500/160000] lr: 3.694e-05, eta: 18:35:53, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1056, decode.acc_seg: 95.7238, loss: 0.1056 2023-01-06 10:51:43,433 - mmseg - INFO - Iter [61550/160000] lr: 3.692e-05, eta: 18:35:19, time: 0.681, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1227, decode.acc_seg: 95.0488, loss: 0.1227 2023-01-06 10:52:15,913 - mmseg - INFO - Iter [61600/160000] lr: 3.690e-05, eta: 18:34:43, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1052, decode.acc_seg: 95.6776, loss: 0.1052 2023-01-06 10:52:51,249 - mmseg - INFO - Iter [61650/160000] lr: 3.688e-05, eta: 18:34:11, time: 0.708, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1017, decode.acc_seg: 95.7121, loss: 0.1017 2023-01-06 10:53:24,417 - mmseg - INFO - Iter [61700/160000] lr: 3.686e-05, eta: 18:33:36, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1028, decode.acc_seg: 95.6487, loss: 0.1028 2023-01-06 10:53:59,075 - mmseg - INFO - Iter [61750/160000] lr: 3.684e-05, eta: 18:33:03, time: 0.692, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1050, decode.acc_seg: 95.5693, loss: 0.1050 2023-01-06 10:54:34,882 - mmseg - INFO - Iter [61800/160000] lr: 3.683e-05, eta: 18:32:32, time: 0.716, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1054, decode.acc_seg: 95.6532, loss: 0.1054 2023-01-06 10:55:08,944 - mmseg - INFO - Iter [61850/160000] lr: 3.681e-05, eta: 18:31:58, time: 0.682, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1026, decode.acc_seg: 95.6595, loss: 0.1026 2023-01-06 10:55:43,507 - mmseg - INFO - Iter [61900/160000] lr: 3.679e-05, eta: 18:31:25, time: 0.690, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1073, decode.acc_seg: 95.5813, loss: 0.1073 2023-01-06 10:56:18,613 - mmseg - INFO - Iter [61950/160000] lr: 3.677e-05, eta: 18:30:53, time: 0.702, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0992, decode.acc_seg: 95.7419, loss: 0.0992 2023-01-06 10:56:52,285 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 10:56:52,285 - mmseg - INFO - Iter [62000/160000] lr: 3.675e-05, eta: 18:30:18, time: 0.674, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1106, decode.acc_seg: 95.3862, loss: 0.1106 2023-01-06 10:57:25,194 - mmseg - INFO - Iter [62050/160000] lr: 3.673e-05, eta: 18:29:42, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1160, decode.acc_seg: 95.1754, loss: 0.1160 2023-01-06 10:57:57,977 - mmseg - INFO - Iter [62100/160000] lr: 3.671e-05, eta: 18:29:07, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1196, decode.acc_seg: 95.1951, loss: 0.1196 2023-01-06 10:58:33,592 - mmseg - INFO - Iter [62150/160000] lr: 3.669e-05, eta: 18:28:35, time: 0.711, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1081, decode.acc_seg: 95.4759, loss: 0.1081 2023-01-06 10:59:06,556 - mmseg - INFO - Iter [62200/160000] lr: 3.668e-05, eta: 18:28:00, time: 0.660, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1147, decode.acc_seg: 95.4049, loss: 0.1147 2023-01-06 10:59:40,501 - mmseg - INFO - Iter [62250/160000] lr: 3.666e-05, eta: 18:27:26, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1145, decode.acc_seg: 95.3032, loss: 0.1145 2023-01-06 11:00:14,821 - mmseg - INFO - Iter [62300/160000] lr: 3.664e-05, eta: 18:26:52, time: 0.686, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1047, decode.acc_seg: 95.6818, loss: 0.1047 2023-01-06 11:00:48,824 - mmseg - INFO - Iter [62350/160000] lr: 3.662e-05, eta: 18:26:18, time: 0.681, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1041, decode.acc_seg: 95.5833, loss: 0.1041 2023-01-06 11:01:21,728 - mmseg - INFO - Iter [62400/160000] lr: 3.660e-05, eta: 18:25:42, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1081, decode.acc_seg: 95.6121, loss: 0.1081 2023-01-06 11:01:55,094 - mmseg - INFO - Iter [62450/160000] lr: 3.658e-05, eta: 18:25:07, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1064, decode.acc_seg: 95.5477, loss: 0.1064 2023-01-06 11:02:31,173 - mmseg - INFO - Iter [62500/160000] lr: 3.656e-05, eta: 18:24:37, time: 0.722, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1023, decode.acc_seg: 95.7063, loss: 0.1023 2023-01-06 11:03:05,366 - mmseg - INFO - Iter [62550/160000] lr: 3.654e-05, eta: 18:24:03, time: 0.683, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1087, decode.acc_seg: 95.6042, loss: 0.1087 2023-01-06 11:03:37,714 - mmseg - INFO - Iter [62600/160000] lr: 3.653e-05, eta: 18:23:27, time: 0.648, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1126, decode.acc_seg: 95.3646, loss: 0.1126 2023-01-06 11:04:09,947 - mmseg - INFO - Iter [62650/160000] lr: 3.651e-05, eta: 18:22:50, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1086, decode.acc_seg: 95.4587, loss: 0.1086 2023-01-06 11:04:42,200 - mmseg - INFO - Iter [62700/160000] lr: 3.649e-05, eta: 18:22:13, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0995, decode.acc_seg: 95.8960, loss: 0.0995 2023-01-06 11:05:14,744 - mmseg - INFO - Iter [62750/160000] lr: 3.647e-05, eta: 18:21:37, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1044, decode.acc_seg: 95.6610, loss: 0.1044 2023-01-06 11:05:46,987 - mmseg - INFO - Iter [62800/160000] lr: 3.645e-05, eta: 18:21:00, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1107, decode.acc_seg: 95.4717, loss: 0.1107 2023-01-06 11:06:19,423 - mmseg - INFO - Iter [62850/160000] lr: 3.643e-05, eta: 18:20:24, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1158, decode.acc_seg: 95.3526, loss: 0.1158 2023-01-06 11:06:54,134 - mmseg - INFO - Iter [62900/160000] lr: 3.641e-05, eta: 18:19:51, time: 0.695, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1049, decode.acc_seg: 95.5874, loss: 0.1049 2023-01-06 11:07:27,307 - mmseg - INFO - Iter [62950/160000] lr: 3.639e-05, eta: 18:19:16, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1039, decode.acc_seg: 95.5965, loss: 0.1039 2023-01-06 11:07:59,624 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 11:07:59,625 - mmseg - INFO - Iter [63000/160000] lr: 3.638e-05, eta: 18:18:39, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1006, decode.acc_seg: 95.7719, loss: 0.1006 2023-01-06 11:08:33,128 - mmseg - INFO - Iter [63050/160000] lr: 3.636e-05, eta: 18:18:05, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1024, decode.acc_seg: 95.6930, loss: 0.1024 2023-01-06 11:09:07,549 - mmseg - INFO - Iter [63100/160000] lr: 3.634e-05, eta: 18:17:31, time: 0.688, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1078, decode.acc_seg: 95.5373, loss: 0.1078 2023-01-06 11:09:41,096 - mmseg - INFO - Iter [63150/160000] lr: 3.632e-05, eta: 18:16:57, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1095, decode.acc_seg: 95.5401, loss: 0.1095 2023-01-06 11:10:13,986 - mmseg - INFO - Iter [63200/160000] lr: 3.630e-05, eta: 18:16:21, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1036, decode.acc_seg: 95.6735, loss: 0.1036 2023-01-06 11:10:51,871 - mmseg - INFO - Iter [63250/160000] lr: 3.628e-05, eta: 18:15:53, time: 0.757, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1010, decode.acc_seg: 95.7837, loss: 0.1010 2023-01-06 11:11:24,714 - mmseg - INFO - Iter [63300/160000] lr: 3.626e-05, eta: 18:15:17, time: 0.658, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1030, decode.acc_seg: 95.6390, loss: 0.1030 2023-01-06 11:11:58,349 - mmseg - INFO - Iter [63350/160000] lr: 3.624e-05, eta: 18:14:43, time: 0.673, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0976, decode.acc_seg: 95.9033, loss: 0.0976 2023-01-06 11:12:31,452 - mmseg - INFO - Iter [63400/160000] lr: 3.623e-05, eta: 18:14:07, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0988, decode.acc_seg: 95.8341, loss: 0.0988 2023-01-06 11:13:07,263 - mmseg - INFO - Iter [63450/160000] lr: 3.621e-05, eta: 18:13:36, time: 0.715, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1144, decode.acc_seg: 95.2709, loss: 0.1144 2023-01-06 11:13:40,687 - mmseg - INFO - Iter [63500/160000] lr: 3.619e-05, eta: 18:13:01, time: 0.669, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1176, decode.acc_seg: 95.1758, loss: 0.1176 2023-01-06 11:14:14,754 - mmseg - INFO - Iter [63550/160000] lr: 3.617e-05, eta: 18:12:27, time: 0.680, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1175, decode.acc_seg: 95.3814, loss: 0.1175 2023-01-06 11:14:48,965 - mmseg - INFO - Iter [63600/160000] lr: 3.615e-05, eta: 18:11:54, time: 0.685, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1060, decode.acc_seg: 95.5212, loss: 0.1060 2023-01-06 11:15:24,476 - mmseg - INFO - Iter [63650/160000] lr: 3.613e-05, eta: 18:11:22, time: 0.710, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1056, decode.acc_seg: 95.5437, loss: 0.1056 2023-01-06 11:15:57,067 - mmseg - INFO - Iter [63700/160000] lr: 3.611e-05, eta: 18:10:46, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1090, decode.acc_seg: 95.6950, loss: 0.1090 2023-01-06 11:16:30,373 - mmseg - INFO - Iter [63750/160000] lr: 3.609e-05, eta: 18:10:11, time: 0.666, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1078, decode.acc_seg: 95.5240, loss: 0.1078 2023-01-06 11:17:03,383 - mmseg - INFO - Iter [63800/160000] lr: 3.608e-05, eta: 18:09:36, time: 0.661, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1082, decode.acc_seg: 95.6637, loss: 0.1082 2023-01-06 11:17:36,257 - mmseg - INFO - Iter [63850/160000] lr: 3.606e-05, eta: 18:09:00, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1108, decode.acc_seg: 95.5026, loss: 0.1108 2023-01-06 11:18:09,771 - mmseg - INFO - Iter [63900/160000] lr: 3.604e-05, eta: 18:08:25, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1066, decode.acc_seg: 95.5186, loss: 0.1066 2023-01-06 11:18:42,834 - mmseg - INFO - Iter [63950/160000] lr: 3.602e-05, eta: 18:07:50, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1044, decode.acc_seg: 95.4569, loss: 0.1044 2023-01-06 11:19:17,313 - mmseg - INFO - Saving checkpoint at 64000 iterations 2023-01-06 11:19:23,337 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 11:19:23,337 - mmseg - INFO - Iter [64000/160000] lr: 3.600e-05, eta: 18:07:26, time: 0.810, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1079, decode.acc_seg: 95.5244, loss: 0.1079 2023-01-06 11:19:58,987 - mmseg - INFO - per class results: 2023-01-06 11:19:58,990 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 97.68 | 98.79 | | sidewalk | 81.28 | 90.15 | | building | 91.45 | 96.31 | | wall | 49.89 | 57.11 | | fence | 55.06 | 68.08 | | pole | 60.89 | 72.94 | | traffic light | 61.86 | 71.16 | | traffic sign | 72.83 | 79.67 | | vegetation | 91.91 | 96.29 | | terrain | 60.47 | 67.07 | | sky | 94.22 | 98.11 | | person | 76.37 | 86.2 | | rider | 51.91 | 65.36 | | car | 93.27 | 97.17 | | truck | 55.91 | 63.01 | | bus | 71.02 | 81.58 | | train | 62.15 | 75.69 | | motorcycle | 38.96 | 43.59 | | bicycle | 69.98 | 87.59 | +---------------+-------+-------+ 2023-01-06 11:19:58,990 - mmseg - INFO - Summary: 2023-01-06 11:19:58,991 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 95.27 | 70.37 | 78.73 | +-------+-------+-------+ 2023-01-06 11:19:58,992 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 11:19:58,992 - mmseg - INFO - Iter(val) [63] aAcc: 0.9527, mIoU: 0.7037, mAcc: 0.7873, IoU.road: 0.9768, IoU.sidewalk: 0.8128, IoU.building: 0.9145, IoU.wall: 0.4989, IoU.fence: 0.5506, IoU.pole: 0.6089, IoU.traffic light: 0.6186, IoU.traffic sign: 0.7283, IoU.vegetation: 0.9191, IoU.terrain: 0.6047, IoU.sky: 0.9422, IoU.person: 0.7637, IoU.rider: 0.5191, IoU.car: 0.9327, IoU.truck: 0.5591, IoU.bus: 0.7102, IoU.train: 0.6215, IoU.motorcycle: 0.3896, IoU.bicycle: 0.6998, Acc.road: 0.9879, Acc.sidewalk: 0.9015, Acc.building: 0.9631, Acc.wall: 0.5711, Acc.fence: 0.6808, Acc.pole: 0.7294, Acc.traffic light: 0.7116, Acc.traffic sign: 0.7967, Acc.vegetation: 0.9629, Acc.terrain: 0.6707, Acc.sky: 0.9811, Acc.person: 0.8620, Acc.rider: 0.6536, Acc.car: 0.9717, Acc.truck: 0.6301, Acc.bus: 0.8158, Acc.train: 0.7569, Acc.motorcycle: 0.4359, Acc.bicycle: 0.8759 2023-01-06 11:20:31,205 - mmseg - INFO - Iter [64050/160000] lr: 3.598e-05, eta: 18:07:43, time: 1.357, data_time: 0.727, memory: 11582, decode.loss_ce: 0.1061, decode.acc_seg: 95.6121, loss: 0.1061 2023-01-06 11:21:03,422 - mmseg - INFO - Iter [64100/160000] lr: 3.596e-05, eta: 18:07:06, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1103, decode.acc_seg: 95.4576, loss: 0.1103 2023-01-06 11:21:35,750 - mmseg - INFO - Iter [64150/160000] lr: 3.594e-05, eta: 18:06:29, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1013, decode.acc_seg: 95.7096, loss: 0.1013 2023-01-06 11:22:09,273 - mmseg - INFO - Iter [64200/160000] lr: 3.593e-05, eta: 18:05:55, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1063, decode.acc_seg: 95.6394, loss: 0.1063 2023-01-06 11:22:41,591 - mmseg - INFO - Iter [64250/160000] lr: 3.591e-05, eta: 18:05:18, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1027, decode.acc_seg: 95.7289, loss: 0.1027 2023-01-06 11:23:13,834 - mmseg - INFO - Iter [64300/160000] lr: 3.589e-05, eta: 18:04:42, time: 0.645, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1093, decode.acc_seg: 95.4036, loss: 0.1093 2023-01-06 11:23:47,281 - mmseg - INFO - Iter [64350/160000] lr: 3.587e-05, eta: 18:04:07, time: 0.669, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1078, decode.acc_seg: 95.5300, loss: 0.1078 2023-01-06 11:24:21,811 - mmseg - INFO - Iter [64400/160000] lr: 3.585e-05, eta: 18:03:33, time: 0.691, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1106, decode.acc_seg: 95.4072, loss: 0.1106 2023-01-06 11:24:54,551 - mmseg - INFO - Iter [64450/160000] lr: 3.583e-05, eta: 18:02:58, time: 0.655, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1054, decode.acc_seg: 95.6664, loss: 0.1054 2023-01-06 11:25:26,871 - mmseg - INFO - Iter [64500/160000] lr: 3.581e-05, eta: 18:02:21, time: 0.646, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1119, decode.acc_seg: 95.4406, loss: 0.1119 2023-01-06 11:26:00,982 - mmseg - INFO - Iter [64550/160000] lr: 3.579e-05, eta: 18:01:47, time: 0.681, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1147, decode.acc_seg: 95.4406, loss: 0.1147 2023-01-06 11:26:36,053 - mmseg - INFO - Iter [64600/160000] lr: 3.578e-05, eta: 18:01:15, time: 0.701, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1063, decode.acc_seg: 95.6197, loss: 0.1063 2023-01-06 11:27:09,562 - mmseg - INFO - Iter [64650/160000] lr: 3.576e-05, eta: 18:00:40, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1128, decode.acc_seg: 95.3817, loss: 0.1128 2023-01-06 11:27:43,849 - mmseg - INFO - Iter [64700/160000] lr: 3.574e-05, eta: 18:00:07, time: 0.686, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1038, decode.acc_seg: 95.6931, loss: 0.1038 2023-01-06 11:28:18,610 - mmseg - INFO - Iter [64750/160000] lr: 3.572e-05, eta: 17:59:34, time: 0.695, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1144, decode.acc_seg: 95.4458, loss: 0.1144 2023-01-06 11:28:50,998 - mmseg - INFO - Iter [64800/160000] lr: 3.570e-05, eta: 17:58:57, time: 0.648, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1024, decode.acc_seg: 95.7001, loss: 0.1024 2023-01-06 11:29:24,183 - mmseg - INFO - Iter [64850/160000] lr: 3.568e-05, eta: 17:58:22, time: 0.664, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1085, decode.acc_seg: 95.5178, loss: 0.1085 2023-01-06 11:29:56,529 - mmseg - INFO - Iter [64900/160000] lr: 3.566e-05, eta: 17:57:46, time: 0.647, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1125, decode.acc_seg: 95.4955, loss: 0.1125 2023-01-06 11:30:28,600 - mmseg - INFO - Iter [64950/160000] lr: 3.564e-05, eta: 17:57:09, time: 0.641, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1014, decode.acc_seg: 95.8614, loss: 0.1014 2023-01-06 11:31:01,320 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 11:31:01,321 - mmseg - INFO - Iter [65000/160000] lr: 3.563e-05, eta: 17:56:33, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1011, decode.acc_seg: 95.6905, loss: 0.1011 2023-01-06 11:31:34,898 - mmseg - INFO - Iter [65050/160000] lr: 3.561e-05, eta: 17:55:58, time: 0.672, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1038, decode.acc_seg: 95.6170, loss: 0.1038 2023-01-06 11:32:07,125 - mmseg - INFO - Iter [65100/160000] lr: 3.559e-05, eta: 17:55:22, time: 0.644, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1150, decode.acc_seg: 95.3856, loss: 0.1150 2023-01-06 11:32:42,624 - mmseg - INFO - Iter [65150/160000] lr: 3.557e-05, eta: 17:54:50, time: 0.710, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1089, decode.acc_seg: 95.5609, loss: 0.1089 2023-01-06 11:33:14,804 - mmseg - INFO - Iter [65200/160000] lr: 3.555e-05, eta: 17:54:13, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1074, decode.acc_seg: 95.5429, loss: 0.1074 2023-01-06 11:33:47,615 - mmseg - INFO - Iter [65250/160000] lr: 3.553e-05, eta: 17:53:38, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1096, decode.acc_seg: 95.5217, loss: 0.1096 2023-01-06 11:34:20,060 - mmseg - INFO - Iter [65300/160000] lr: 3.551e-05, eta: 17:53:01, time: 0.649, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1041, decode.acc_seg: 95.6154, loss: 0.1041 2023-01-06 11:34:52,808 - mmseg - INFO - Iter [65350/160000] lr: 3.549e-05, eta: 17:52:26, time: 0.655, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1088, decode.acc_seg: 95.6006, loss: 0.1088 2023-01-06 11:35:25,177 - mmseg - INFO - Iter [65400/160000] lr: 3.548e-05, eta: 17:51:49, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1060, decode.acc_seg: 95.6459, loss: 0.1060 2023-01-06 11:35:58,781 - mmseg - INFO - Iter [65450/160000] lr: 3.546e-05, eta: 17:51:15, time: 0.672, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1036, decode.acc_seg: 95.6617, loss: 0.1036 2023-01-06 11:36:34,151 - mmseg - INFO - Iter [65500/160000] lr: 3.544e-05, eta: 17:50:43, time: 0.707, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1029, decode.acc_seg: 95.8224, loss: 0.1029 2023-01-06 11:37:07,123 - mmseg - INFO - Iter [65550/160000] lr: 3.542e-05, eta: 17:50:07, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1002, decode.acc_seg: 95.8534, loss: 0.1002 2023-01-06 11:37:40,446 - mmseg - INFO - Iter [65600/160000] lr: 3.540e-05, eta: 17:49:32, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1070, decode.acc_seg: 95.6547, loss: 0.1070 2023-01-06 11:38:14,046 - mmseg - INFO - Iter [65650/160000] lr: 3.538e-05, eta: 17:48:58, time: 0.672, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1050, decode.acc_seg: 95.6793, loss: 0.1050 2023-01-06 11:38:47,056 - mmseg - INFO - Iter [65700/160000] lr: 3.536e-05, eta: 17:48:22, time: 0.659, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1109, decode.acc_seg: 95.3849, loss: 0.1109 2023-01-06 11:39:20,620 - mmseg - INFO - Iter [65750/160000] lr: 3.534e-05, eta: 17:47:48, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1018, decode.acc_seg: 95.7729, loss: 0.1018 2023-01-06 11:39:53,094 - mmseg - INFO - Iter [65800/160000] lr: 3.533e-05, eta: 17:47:12, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0980, decode.acc_seg: 95.8437, loss: 0.0980 2023-01-06 11:40:29,669 - mmseg - INFO - Iter [65850/160000] lr: 3.531e-05, eta: 17:46:41, time: 0.732, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1071, decode.acc_seg: 95.5779, loss: 0.1071 2023-01-06 11:41:01,951 - mmseg - INFO - Iter [65900/160000] lr: 3.529e-05, eta: 17:46:05, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1066, decode.acc_seg: 95.5601, loss: 0.1066 2023-01-06 11:41:34,896 - mmseg - INFO - Iter [65950/160000] lr: 3.527e-05, eta: 17:45:29, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1039, decode.acc_seg: 95.6473, loss: 0.1039 2023-01-06 11:42:08,511 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 11:42:08,511 - mmseg - INFO - Iter [66000/160000] lr: 3.525e-05, eta: 17:44:55, time: 0.673, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0976, decode.acc_seg: 95.8907, loss: 0.0976 2023-01-06 11:42:40,987 - mmseg - INFO - Iter [66050/160000] lr: 3.523e-05, eta: 17:44:19, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1000, decode.acc_seg: 95.8950, loss: 0.1000 2023-01-06 11:43:15,191 - mmseg - INFO - Iter [66100/160000] lr: 3.521e-05, eta: 17:43:45, time: 0.683, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1061, decode.acc_seg: 95.7310, loss: 0.1061 2023-01-06 11:43:51,097 - mmseg - INFO - Iter [66150/160000] lr: 3.519e-05, eta: 17:43:14, time: 0.718, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1006, decode.acc_seg: 95.7568, loss: 0.1006 2023-01-06 11:44:26,972 - mmseg - INFO - Iter [66200/160000] lr: 3.518e-05, eta: 17:42:42, time: 0.718, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0965, decode.acc_seg: 96.0054, loss: 0.0965 2023-01-06 11:45:03,686 - mmseg - INFO - Iter [66250/160000] lr: 3.516e-05, eta: 17:42:12, time: 0.735, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1119, decode.acc_seg: 95.4763, loss: 0.1119 2023-01-06 11:45:35,881 - mmseg - INFO - Iter [66300/160000] lr: 3.514e-05, eta: 17:41:36, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1079, decode.acc_seg: 95.5992, loss: 0.1079 2023-01-06 11:46:09,209 - mmseg - INFO - Iter [66350/160000] lr: 3.512e-05, eta: 17:41:01, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1053, decode.acc_seg: 95.7023, loss: 0.1053 2023-01-06 11:46:42,117 - mmseg - INFO - Iter [66400/160000] lr: 3.510e-05, eta: 17:40:25, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0949, decode.acc_seg: 96.0082, loss: 0.0949 2023-01-06 11:47:14,341 - mmseg - INFO - Iter [66450/160000] lr: 3.508e-05, eta: 17:39:49, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1057, decode.acc_seg: 95.5421, loss: 0.1057 2023-01-06 11:47:48,764 - mmseg - INFO - Iter [66500/160000] lr: 3.506e-05, eta: 17:39:16, time: 0.688, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1048, decode.acc_seg: 95.5968, loss: 0.1048 2023-01-06 11:48:24,160 - mmseg - INFO - Iter [66550/160000] lr: 3.504e-05, eta: 17:38:43, time: 0.707, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1081, decode.acc_seg: 95.4473, loss: 0.1081 2023-01-06 11:48:59,362 - mmseg - INFO - Iter [66600/160000] lr: 3.503e-05, eta: 17:38:11, time: 0.705, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1134, decode.acc_seg: 95.3869, loss: 0.1134 2023-01-06 11:49:33,538 - mmseg - INFO - Iter [66650/160000] lr: 3.501e-05, eta: 17:37:37, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1144, decode.acc_seg: 95.3232, loss: 0.1144 2023-01-06 11:50:08,277 - mmseg - INFO - Iter [66700/160000] lr: 3.499e-05, eta: 17:37:04, time: 0.695, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1056, decode.acc_seg: 95.5506, loss: 0.1056 2023-01-06 11:50:43,497 - mmseg - INFO - Iter [66750/160000] lr: 3.497e-05, eta: 17:36:32, time: 0.705, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1070, decode.acc_seg: 95.6036, loss: 0.1070 2023-01-06 11:51:17,247 - mmseg - INFO - Iter [66800/160000] lr: 3.495e-05, eta: 17:35:58, time: 0.675, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1068, decode.acc_seg: 95.5787, loss: 0.1068 2023-01-06 11:51:50,720 - mmseg - INFO - Iter [66850/160000] lr: 3.493e-05, eta: 17:35:23, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1026, decode.acc_seg: 95.8405, loss: 0.1026 2023-01-06 11:52:23,065 - mmseg - INFO - Iter [66900/160000] lr: 3.491e-05, eta: 17:34:47, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0975, decode.acc_seg: 95.8959, loss: 0.0975 2023-01-06 11:52:57,987 - mmseg - INFO - Iter [66950/160000] lr: 3.489e-05, eta: 17:34:14, time: 0.697, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1086, decode.acc_seg: 95.6081, loss: 0.1086 2023-01-06 11:53:33,526 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 11:53:33,527 - mmseg - INFO - Iter [67000/160000] lr: 3.488e-05, eta: 17:33:42, time: 0.712, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1175, decode.acc_seg: 95.3118, loss: 0.1175 2023-01-06 11:54:05,945 - mmseg - INFO - Iter [67050/160000] lr: 3.486e-05, eta: 17:33:06, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1086, decode.acc_seg: 95.6146, loss: 0.1086 2023-01-06 11:54:41,437 - mmseg - INFO - Iter [67100/160000] lr: 3.484e-05, eta: 17:32:34, time: 0.710, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1004, decode.acc_seg: 95.8128, loss: 0.1004 2023-01-06 11:55:14,457 - mmseg - INFO - Iter [67150/160000] lr: 3.482e-05, eta: 17:31:59, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1025, decode.acc_seg: 95.6683, loss: 0.1025 2023-01-06 11:55:48,119 - mmseg - INFO - Iter [67200/160000] lr: 3.480e-05, eta: 17:31:24, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1056, decode.acc_seg: 95.6815, loss: 0.1056 2023-01-06 11:56:22,367 - mmseg - INFO - Iter [67250/160000] lr: 3.478e-05, eta: 17:30:51, time: 0.686, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1122, decode.acc_seg: 95.3388, loss: 0.1122 2023-01-06 11:56:56,239 - mmseg - INFO - Iter [67300/160000] lr: 3.476e-05, eta: 17:30:17, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1069, decode.acc_seg: 95.5307, loss: 0.1069 2023-01-06 11:57:32,579 - mmseg - INFO - Iter [67350/160000] lr: 3.474e-05, eta: 17:29:46, time: 0.727, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1037, decode.acc_seg: 95.7498, loss: 0.1037 2023-01-06 11:58:07,265 - mmseg - INFO - Iter [67400/160000] lr: 3.473e-05, eta: 17:29:13, time: 0.695, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1002, decode.acc_seg: 95.7733, loss: 0.1002 2023-01-06 11:58:39,719 - mmseg - INFO - Iter [67450/160000] lr: 3.471e-05, eta: 17:28:37, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1091, decode.acc_seg: 95.4833, loss: 0.1091 2023-01-06 11:59:14,132 - mmseg - INFO - Iter [67500/160000] lr: 3.469e-05, eta: 17:28:03, time: 0.687, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1113, decode.acc_seg: 95.3355, loss: 0.1113 2023-01-06 11:59:48,424 - mmseg - INFO - Iter [67550/160000] lr: 3.467e-05, eta: 17:27:30, time: 0.686, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1092, decode.acc_seg: 95.4570, loss: 0.1092 2023-01-06 12:00:23,117 - mmseg - INFO - Iter [67600/160000] lr: 3.465e-05, eta: 17:26:57, time: 0.694, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1104, decode.acc_seg: 95.4240, loss: 0.1104 2023-01-06 12:00:57,303 - mmseg - INFO - Iter [67650/160000] lr: 3.463e-05, eta: 17:26:23, time: 0.685, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1127, decode.acc_seg: 95.4185, loss: 0.1127 2023-01-06 12:01:31,299 - mmseg - INFO - Iter [67700/160000] lr: 3.461e-05, eta: 17:25:49, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1076, decode.acc_seg: 95.4795, loss: 0.1076 2023-01-06 12:02:07,787 - mmseg - INFO - Iter [67750/160000] lr: 3.459e-05, eta: 17:25:19, time: 0.731, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1220, decode.acc_seg: 95.0416, loss: 0.1220 2023-01-06 12:02:40,006 - mmseg - INFO - Iter [67800/160000] lr: 3.458e-05, eta: 17:24:42, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1311, decode.acc_seg: 94.8291, loss: 0.1311 2023-01-06 12:03:13,083 - mmseg - INFO - Iter [67850/160000] lr: 3.456e-05, eta: 17:24:07, time: 0.661, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1137, decode.acc_seg: 95.3654, loss: 0.1137 2023-01-06 12:03:45,367 - mmseg - INFO - Iter [67900/160000] lr: 3.454e-05, eta: 17:23:31, time: 0.647, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1034, decode.acc_seg: 95.6143, loss: 0.1034 2023-01-06 12:04:17,763 - mmseg - INFO - Iter [67950/160000] lr: 3.452e-05, eta: 17:22:54, time: 0.648, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1176, decode.acc_seg: 95.2520, loss: 0.1176 2023-01-06 12:04:50,912 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 12:04:50,913 - mmseg - INFO - Iter [68000/160000] lr: 3.450e-05, eta: 17:22:19, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1075, decode.acc_seg: 95.6290, loss: 0.1075 2023-01-06 12:05:23,170 - mmseg - INFO - Iter [68050/160000] lr: 3.448e-05, eta: 17:21:43, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1082, decode.acc_seg: 95.5046, loss: 0.1082 2023-01-06 12:05:57,667 - mmseg - INFO - Iter [68100/160000] lr: 3.446e-05, eta: 17:21:10, time: 0.690, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1025, decode.acc_seg: 95.6583, loss: 0.1025 2023-01-06 12:06:30,428 - mmseg - INFO - Iter [68150/160000] lr: 3.444e-05, eta: 17:20:34, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1018, decode.acc_seg: 95.7677, loss: 0.1018 2023-01-06 12:07:03,518 - mmseg - INFO - Iter [68200/160000] lr: 3.443e-05, eta: 17:19:59, time: 0.663, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1053, decode.acc_seg: 95.6445, loss: 0.1053 2023-01-06 12:07:36,110 - mmseg - INFO - Iter [68250/160000] lr: 3.441e-05, eta: 17:19:23, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0977, decode.acc_seg: 95.8882, loss: 0.0977 2023-01-06 12:08:08,458 - mmseg - INFO - Iter [68300/160000] lr: 3.439e-05, eta: 17:18:47, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1025, decode.acc_seg: 95.7498, loss: 0.1025 2023-01-06 12:08:42,533 - mmseg - INFO - Iter [68350/160000] lr: 3.437e-05, eta: 17:18:13, time: 0.681, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1082, decode.acc_seg: 95.6385, loss: 0.1082 2023-01-06 12:09:15,781 - mmseg - INFO - Iter [68400/160000] lr: 3.435e-05, eta: 17:17:38, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0983, decode.acc_seg: 96.0508, loss: 0.0983 2023-01-06 12:09:51,045 - mmseg - INFO - Iter [68450/160000] lr: 3.433e-05, eta: 17:17:06, time: 0.705, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1015, decode.acc_seg: 95.7663, loss: 0.1015 2023-01-06 12:10:25,509 - mmseg - INFO - Iter [68500/160000] lr: 3.431e-05, eta: 17:16:32, time: 0.688, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0957, decode.acc_seg: 95.9722, loss: 0.0957 2023-01-06 12:11:00,308 - mmseg - INFO - Iter [68550/160000] lr: 3.429e-05, eta: 17:15:59, time: 0.697, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1022, decode.acc_seg: 95.7427, loss: 0.1022 2023-01-06 12:11:33,790 - mmseg - INFO - Iter [68600/160000] lr: 3.428e-05, eta: 17:15:25, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1025, decode.acc_seg: 95.8229, loss: 0.1025 2023-01-06 12:12:06,042 - mmseg - INFO - Iter [68650/160000] lr: 3.426e-05, eta: 17:14:48, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1028, decode.acc_seg: 95.8023, loss: 0.1028 2023-01-06 12:12:38,468 - mmseg - INFO - Iter [68700/160000] lr: 3.424e-05, eta: 17:14:12, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0983, decode.acc_seg: 95.8454, loss: 0.0983 2023-01-06 12:13:10,863 - mmseg - INFO - Iter [68750/160000] lr: 3.422e-05, eta: 17:13:36, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1048, decode.acc_seg: 95.5976, loss: 0.1048 2023-01-06 12:13:43,109 - mmseg - INFO - Iter [68800/160000] lr: 3.420e-05, eta: 17:13:00, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1055, decode.acc_seg: 95.6188, loss: 0.1055 2023-01-06 12:14:17,770 - mmseg - INFO - Iter [68850/160000] lr: 3.418e-05, eta: 17:12:27, time: 0.693, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1069, decode.acc_seg: 95.5589, loss: 0.1069 2023-01-06 12:14:50,323 - mmseg - INFO - Iter [68900/160000] lr: 3.416e-05, eta: 17:11:51, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1146, decode.acc_seg: 95.1710, loss: 0.1146 2023-01-06 12:15:24,140 - mmseg - INFO - Iter [68950/160000] lr: 3.414e-05, eta: 17:11:17, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1046, decode.acc_seg: 95.6574, loss: 0.1046 2023-01-06 12:15:56,665 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 12:15:56,666 - mmseg - INFO - Iter [69000/160000] lr: 3.413e-05, eta: 17:10:41, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1050, decode.acc_seg: 95.6451, loss: 0.1050 2023-01-06 12:16:32,483 - mmseg - INFO - Iter [69050/160000] lr: 3.411e-05, eta: 17:10:09, time: 0.716, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1087, decode.acc_seg: 95.5574, loss: 0.1087 2023-01-06 12:17:07,819 - mmseg - INFO - Iter [69100/160000] lr: 3.409e-05, eta: 17:09:37, time: 0.708, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0996, decode.acc_seg: 95.7542, loss: 0.0996 2023-01-06 12:17:41,052 - mmseg - INFO - Iter [69150/160000] lr: 3.407e-05, eta: 17:09:02, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1060, decode.acc_seg: 95.6067, loss: 0.1060 2023-01-06 12:18:15,469 - mmseg - INFO - Iter [69200/160000] lr: 3.405e-05, eta: 17:08:29, time: 0.688, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0935, decode.acc_seg: 96.0499, loss: 0.0935 2023-01-06 12:18:47,663 - mmseg - INFO - Iter [69250/160000] lr: 3.403e-05, eta: 17:07:53, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1018, decode.acc_seg: 95.6918, loss: 0.1018 2023-01-06 12:19:20,995 - mmseg - INFO - Iter [69300/160000] lr: 3.401e-05, eta: 17:07:18, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1112, decode.acc_seg: 95.5024, loss: 0.1112 2023-01-06 12:19:55,086 - mmseg - INFO - Iter [69350/160000] lr: 3.399e-05, eta: 17:06:44, time: 0.681, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1104, decode.acc_seg: 95.4850, loss: 0.1104 2023-01-06 12:20:27,717 - mmseg - INFO - Iter [69400/160000] lr: 3.398e-05, eta: 17:06:08, time: 0.653, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1024, decode.acc_seg: 95.8023, loss: 0.1024 2023-01-06 12:21:01,108 - mmseg - INFO - Iter [69450/160000] lr: 3.396e-05, eta: 17:05:33, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1053, decode.acc_seg: 95.5092, loss: 0.1053 2023-01-06 12:21:34,420 - mmseg - INFO - Iter [69500/160000] lr: 3.394e-05, eta: 17:04:59, time: 0.666, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1069, decode.acc_seg: 95.5312, loss: 0.1069 2023-01-06 12:22:07,286 - mmseg - INFO - Iter [69550/160000] lr: 3.392e-05, eta: 17:04:23, time: 0.658, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1062, decode.acc_seg: 95.7295, loss: 0.1062 2023-01-06 12:22:41,768 - mmseg - INFO - Iter [69600/160000] lr: 3.390e-05, eta: 17:03:50, time: 0.690, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1140, decode.acc_seg: 95.3292, loss: 0.1140 2023-01-06 12:23:14,766 - mmseg - INFO - Iter [69650/160000] lr: 3.388e-05, eta: 17:03:15, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1009, decode.acc_seg: 95.8740, loss: 0.1009 2023-01-06 12:23:48,311 - mmseg - INFO - Iter [69700/160000] lr: 3.386e-05, eta: 17:02:40, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0969, decode.acc_seg: 96.0304, loss: 0.0969 2023-01-06 12:24:20,547 - mmseg - INFO - Iter [69750/160000] lr: 3.384e-05, eta: 17:02:04, time: 0.646, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0964, decode.acc_seg: 96.0121, loss: 0.0964 2023-01-06 12:24:55,442 - mmseg - INFO - Iter [69800/160000] lr: 3.383e-05, eta: 17:01:31, time: 0.698, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1003, decode.acc_seg: 95.8632, loss: 0.1003 2023-01-06 12:25:29,771 - mmseg - INFO - Iter [69850/160000] lr: 3.381e-05, eta: 17:00:57, time: 0.686, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1160, decode.acc_seg: 95.4596, loss: 0.1160 2023-01-06 12:26:02,385 - mmseg - INFO - Iter [69900/160000] lr: 3.379e-05, eta: 17:00:22, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1073, decode.acc_seg: 95.5756, loss: 0.1073 2023-01-06 12:26:39,152 - mmseg - INFO - Iter [69950/160000] lr: 3.377e-05, eta: 16:59:51, time: 0.735, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0985, decode.acc_seg: 95.8138, loss: 0.0985 2023-01-06 12:27:11,479 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 12:27:11,479 - mmseg - INFO - Iter [70000/160000] lr: 3.375e-05, eta: 16:59:15, time: 0.647, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0999, decode.acc_seg: 95.8906, loss: 0.0999 2023-01-06 12:27:43,848 - mmseg - INFO - Iter [70050/160000] lr: 3.373e-05, eta: 16:58:39, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1031, decode.acc_seg: 95.6464, loss: 0.1031 2023-01-06 12:28:16,971 - mmseg - INFO - Iter [70100/160000] lr: 3.371e-05, eta: 16:58:04, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1039, decode.acc_seg: 95.7658, loss: 0.1039 2023-01-06 12:28:51,942 - mmseg - INFO - Iter [70150/160000] lr: 3.369e-05, eta: 16:57:31, time: 0.700, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1113, decode.acc_seg: 95.4838, loss: 0.1113 2023-01-06 12:29:26,124 - mmseg - INFO - Iter [70200/160000] lr: 3.368e-05, eta: 16:56:58, time: 0.684, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1082, decode.acc_seg: 95.4996, loss: 0.1082 2023-01-06 12:30:00,328 - mmseg - INFO - Iter [70250/160000] lr: 3.366e-05, eta: 16:56:24, time: 0.683, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1149, decode.acc_seg: 95.3509, loss: 0.1149 2023-01-06 12:30:33,195 - mmseg - INFO - Iter [70300/160000] lr: 3.364e-05, eta: 16:55:49, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1057, decode.acc_seg: 95.5761, loss: 0.1057 2023-01-06 12:31:07,946 - mmseg - INFO - Iter [70350/160000] lr: 3.362e-05, eta: 16:55:16, time: 0.695, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1102, decode.acc_seg: 95.4961, loss: 0.1102 2023-01-06 12:31:42,785 - mmseg - INFO - Iter [70400/160000] lr: 3.360e-05, eta: 16:54:43, time: 0.696, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1023, decode.acc_seg: 95.7904, loss: 0.1023 2023-01-06 12:32:16,099 - mmseg - INFO - Iter [70450/160000] lr: 3.358e-05, eta: 16:54:08, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1090, decode.acc_seg: 95.5463, loss: 0.1090 2023-01-06 12:32:49,060 - mmseg - INFO - Iter [70500/160000] lr: 3.356e-05, eta: 16:53:33, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0985, decode.acc_seg: 95.8455, loss: 0.0985 2023-01-06 12:33:21,348 - mmseg - INFO - Iter [70550/160000] lr: 3.354e-05, eta: 16:52:57, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1018, decode.acc_seg: 95.7904, loss: 0.1018 2023-01-06 12:33:54,979 - mmseg - INFO - Iter [70600/160000] lr: 3.353e-05, eta: 16:52:22, time: 0.672, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1093, decode.acc_seg: 95.5598, loss: 0.1093 2023-01-06 12:34:28,262 - mmseg - INFO - Iter [70650/160000] lr: 3.351e-05, eta: 16:51:47, time: 0.666, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1019, decode.acc_seg: 95.7478, loss: 0.1019 2023-01-06 12:35:02,587 - mmseg - INFO - Iter [70700/160000] lr: 3.349e-05, eta: 16:51:14, time: 0.687, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1009, decode.acc_seg: 95.8594, loss: 0.1009 2023-01-06 12:35:35,746 - mmseg - INFO - Iter [70750/160000] lr: 3.347e-05, eta: 16:50:39, time: 0.663, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1022, decode.acc_seg: 95.6750, loss: 0.1022 2023-01-06 12:36:09,590 - mmseg - INFO - Iter [70800/160000] lr: 3.345e-05, eta: 16:50:05, time: 0.676, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0985, decode.acc_seg: 95.9425, loss: 0.0985 2023-01-06 12:36:42,036 - mmseg - INFO - Iter [70850/160000] lr: 3.343e-05, eta: 16:49:29, time: 0.650, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1000, decode.acc_seg: 95.8881, loss: 0.1000 2023-01-06 12:37:15,977 - mmseg - INFO - Iter [70900/160000] lr: 3.341e-05, eta: 16:48:55, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0910, decode.acc_seg: 96.1671, loss: 0.0910 2023-01-06 12:37:49,780 - mmseg - INFO - Iter [70950/160000] lr: 3.339e-05, eta: 16:48:21, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1024, decode.acc_seg: 95.7423, loss: 0.1024 2023-01-06 12:38:23,870 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 12:38:23,871 - mmseg - INFO - Iter [71000/160000] lr: 3.338e-05, eta: 16:47:47, time: 0.683, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1029, decode.acc_seg: 95.6476, loss: 0.1029 2023-01-06 12:38:57,152 - mmseg - INFO - Iter [71050/160000] lr: 3.336e-05, eta: 16:47:12, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0960, decode.acc_seg: 95.9886, loss: 0.0960 2023-01-06 12:39:32,274 - mmseg - INFO - Iter [71100/160000] lr: 3.334e-05, eta: 16:46:39, time: 0.702, data_time: 0.060, memory: 11582, decode.loss_ce: 0.1026, decode.acc_seg: 95.6798, loss: 0.1026 2023-01-06 12:40:05,406 - mmseg - INFO - Iter [71150/160000] lr: 3.332e-05, eta: 16:46:04, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0990, decode.acc_seg: 95.9119, loss: 0.0990 2023-01-06 12:40:38,384 - mmseg - INFO - Iter [71200/160000] lr: 3.330e-05, eta: 16:45:29, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0966, decode.acc_seg: 95.9649, loss: 0.0966 2023-01-06 12:41:12,003 - mmseg - INFO - Iter [71250/160000] lr: 3.328e-05, eta: 16:44:55, time: 0.672, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1084, decode.acc_seg: 95.5981, loss: 0.1084 2023-01-06 12:41:44,692 - mmseg - INFO - Iter [71300/160000] lr: 3.326e-05, eta: 16:44:19, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1040, decode.acc_seg: 95.7233, loss: 0.1040 2023-01-06 12:42:17,746 - mmseg - INFO - Iter [71350/160000] lr: 3.324e-05, eta: 16:43:44, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0973, decode.acc_seg: 95.8865, loss: 0.0973 2023-01-06 12:42:49,865 - mmseg - INFO - Iter [71400/160000] lr: 3.323e-05, eta: 16:43:08, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1012, decode.acc_seg: 95.7738, loss: 0.1012 2023-01-06 12:43:24,362 - mmseg - INFO - Iter [71450/160000] lr: 3.321e-05, eta: 16:42:35, time: 0.690, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0975, decode.acc_seg: 95.9540, loss: 0.0975 2023-01-06 12:43:56,547 - mmseg - INFO - Iter [71500/160000] lr: 3.319e-05, eta: 16:41:58, time: 0.645, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0998, decode.acc_seg: 95.6754, loss: 0.0998 2023-01-06 12:44:29,641 - mmseg - INFO - Iter [71550/160000] lr: 3.317e-05, eta: 16:41:23, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0974, decode.acc_seg: 95.9232, loss: 0.0974 2023-01-06 12:45:03,386 - mmseg - INFO - Iter [71600/160000] lr: 3.315e-05, eta: 16:40:49, time: 0.675, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0972, decode.acc_seg: 96.0328, loss: 0.0972 2023-01-06 12:45:36,664 - mmseg - INFO - Iter [71650/160000] lr: 3.313e-05, eta: 16:40:14, time: 0.666, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0955, decode.acc_seg: 95.9244, loss: 0.0955 2023-01-06 12:46:09,554 - mmseg - INFO - Iter [71700/160000] lr: 3.311e-05, eta: 16:39:39, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0973, decode.acc_seg: 95.9230, loss: 0.0973 2023-01-06 12:46:43,005 - mmseg - INFO - Iter [71750/160000] lr: 3.309e-05, eta: 16:39:04, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0998, decode.acc_seg: 95.7706, loss: 0.0998 2023-01-06 12:47:18,281 - mmseg - INFO - Iter [71800/160000] lr: 3.308e-05, eta: 16:38:32, time: 0.706, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1114, decode.acc_seg: 95.3901, loss: 0.1114 2023-01-06 12:47:52,194 - mmseg - INFO - Iter [71850/160000] lr: 3.306e-05, eta: 16:37:58, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1079, decode.acc_seg: 95.4620, loss: 0.1079 2023-01-06 12:48:25,367 - mmseg - INFO - Iter [71900/160000] lr: 3.304e-05, eta: 16:37:23, time: 0.664, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1039, decode.acc_seg: 95.6423, loss: 0.1039 2023-01-06 12:48:57,513 - mmseg - INFO - Iter [71950/160000] lr: 3.302e-05, eta: 16:36:47, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1058, decode.acc_seg: 95.5612, loss: 0.1058 2023-01-06 12:49:29,803 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 12:49:29,804 - mmseg - INFO - Iter [72000/160000] lr: 3.300e-05, eta: 16:36:11, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1076, decode.acc_seg: 95.3392, loss: 0.1076 2023-01-06 12:50:03,170 - mmseg - INFO - Iter [72050/160000] lr: 3.298e-05, eta: 16:35:36, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1038, decode.acc_seg: 95.6896, loss: 0.1038 2023-01-06 12:50:38,582 - mmseg - INFO - Iter [72100/160000] lr: 3.296e-05, eta: 16:35:04, time: 0.709, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1103, decode.acc_seg: 95.4606, loss: 0.1103 2023-01-06 12:51:10,835 - mmseg - INFO - Iter [72150/160000] lr: 3.294e-05, eta: 16:34:28, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0986, decode.acc_seg: 95.8960, loss: 0.0986 2023-01-06 12:51:45,789 - mmseg - INFO - Iter [72200/160000] lr: 3.293e-05, eta: 16:33:55, time: 0.699, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1024, decode.acc_seg: 95.8002, loss: 0.1024 2023-01-06 12:52:18,284 - mmseg - INFO - Iter [72250/160000] lr: 3.291e-05, eta: 16:33:20, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1001, decode.acc_seg: 95.9142, loss: 0.1001 2023-01-06 12:52:51,074 - mmseg - INFO - Iter [72300/160000] lr: 3.289e-05, eta: 16:32:44, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1056, decode.acc_seg: 95.6971, loss: 0.1056 2023-01-06 12:53:26,565 - mmseg - INFO - Iter [72350/160000] lr: 3.287e-05, eta: 16:32:12, time: 0.710, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0989, decode.acc_seg: 95.7904, loss: 0.0989 2023-01-06 12:53:59,300 - mmseg - INFO - Iter [72400/160000] lr: 3.285e-05, eta: 16:31:37, time: 0.656, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0993, decode.acc_seg: 95.7856, loss: 0.0993 2023-01-06 12:54:32,399 - mmseg - INFO - Iter [72450/160000] lr: 3.283e-05, eta: 16:31:02, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1033, decode.acc_seg: 95.7879, loss: 0.1033 2023-01-06 12:55:05,645 - mmseg - INFO - Iter [72500/160000] lr: 3.281e-05, eta: 16:30:27, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1109, decode.acc_seg: 95.4131, loss: 0.1109 2023-01-06 12:55:40,862 - mmseg - INFO - Iter [72550/160000] lr: 3.279e-05, eta: 16:29:54, time: 0.705, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0906, decode.acc_seg: 96.1467, loss: 0.0906 2023-01-06 12:56:13,174 - mmseg - INFO - Iter [72600/160000] lr: 3.278e-05, eta: 16:29:18, time: 0.646, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0990, decode.acc_seg: 95.8281, loss: 0.0990 2023-01-06 12:56:45,958 - mmseg - INFO - Iter [72650/160000] lr: 3.276e-05, eta: 16:28:43, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1013, decode.acc_seg: 95.7884, loss: 0.1013 2023-01-06 12:57:20,756 - mmseg - INFO - Iter [72700/160000] lr: 3.274e-05, eta: 16:28:10, time: 0.697, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1017, decode.acc_seg: 95.7403, loss: 0.1017 2023-01-06 12:57:54,530 - mmseg - INFO - Iter [72750/160000] lr: 3.272e-05, eta: 16:27:36, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1054, decode.acc_seg: 95.7196, loss: 0.1054 2023-01-06 12:58:27,829 - mmseg - INFO - Iter [72800/160000] lr: 3.270e-05, eta: 16:27:01, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1042, decode.acc_seg: 95.8443, loss: 0.1042 2023-01-06 12:59:01,155 - mmseg - INFO - Iter [72850/160000] lr: 3.268e-05, eta: 16:26:26, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1158, decode.acc_seg: 95.5726, loss: 0.1158 2023-01-06 12:59:36,619 - mmseg - INFO - Iter [72900/160000] lr: 3.266e-05, eta: 16:25:54, time: 0.708, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1055, decode.acc_seg: 95.6816, loss: 0.1055 2023-01-06 13:00:11,271 - mmseg - INFO - Iter [72950/160000] lr: 3.264e-05, eta: 16:25:21, time: 0.694, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1004, decode.acc_seg: 95.8040, loss: 0.1004 2023-01-06 13:00:43,688 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 13:00:43,688 - mmseg - INFO - Iter [73000/160000] lr: 3.263e-05, eta: 16:24:45, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1009, decode.acc_seg: 95.6833, loss: 0.1009 2023-01-06 13:01:16,050 - mmseg - INFO - Iter [73050/160000] lr: 3.261e-05, eta: 16:24:10, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1026, decode.acc_seg: 95.8261, loss: 0.1026 2023-01-06 13:01:50,676 - mmseg - INFO - Iter [73100/160000] lr: 3.259e-05, eta: 16:23:36, time: 0.693, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0909, decode.acc_seg: 96.2143, loss: 0.0909 2023-01-06 13:02:25,503 - mmseg - INFO - Iter [73150/160000] lr: 3.257e-05, eta: 16:23:03, time: 0.697, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0988, decode.acc_seg: 95.9186, loss: 0.0988 2023-01-06 13:02:59,568 - mmseg - INFO - Iter [73200/160000] lr: 3.255e-05, eta: 16:22:30, time: 0.681, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1033, decode.acc_seg: 95.7557, loss: 0.1033 2023-01-06 13:03:32,881 - mmseg - INFO - Iter [73250/160000] lr: 3.253e-05, eta: 16:21:55, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1019, decode.acc_seg: 95.7919, loss: 0.1019 2023-01-06 13:04:07,406 - mmseg - INFO - Iter [73300/160000] lr: 3.251e-05, eta: 16:21:22, time: 0.690, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0919, decode.acc_seg: 96.0279, loss: 0.0919 2023-01-06 13:04:39,912 - mmseg - INFO - Iter [73350/160000] lr: 3.249e-05, eta: 16:20:46, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0999, decode.acc_seg: 95.8374, loss: 0.0999 2023-01-06 13:05:14,247 - mmseg - INFO - Iter [73400/160000] lr: 3.248e-05, eta: 16:20:12, time: 0.686, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0967, decode.acc_seg: 95.8278, loss: 0.0967 2023-01-06 13:05:46,452 - mmseg - INFO - Iter [73450/160000] lr: 3.246e-05, eta: 16:19:36, time: 0.645, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0967, decode.acc_seg: 96.0352, loss: 0.0967 2023-01-06 13:06:20,270 - mmseg - INFO - Iter [73500/160000] lr: 3.244e-05, eta: 16:19:02, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1041, decode.acc_seg: 95.6644, loss: 0.1041 2023-01-06 13:06:53,776 - mmseg - INFO - Iter [73550/160000] lr: 3.242e-05, eta: 16:18:28, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0993, decode.acc_seg: 95.7795, loss: 0.0993 2023-01-06 13:07:27,560 - mmseg - INFO - Iter [73600/160000] lr: 3.240e-05, eta: 16:17:54, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0947, decode.acc_seg: 95.9666, loss: 0.0947 2023-01-06 13:08:00,873 - mmseg - INFO - Iter [73650/160000] lr: 3.238e-05, eta: 16:17:19, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0963, decode.acc_seg: 95.9761, loss: 0.0963 2023-01-06 13:08:37,296 - mmseg - INFO - Iter [73700/160000] lr: 3.236e-05, eta: 16:16:48, time: 0.729, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1039, decode.acc_seg: 95.7564, loss: 0.1039 2023-01-06 13:09:09,644 - mmseg - INFO - Iter [73750/160000] lr: 3.234e-05, eta: 16:16:12, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1070, decode.acc_seg: 95.6091, loss: 0.1070 2023-01-06 13:09:42,464 - mmseg - INFO - Iter [73800/160000] lr: 3.233e-05, eta: 16:15:37, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1037, decode.acc_seg: 95.7578, loss: 0.1037 2023-01-06 13:10:14,766 - mmseg - INFO - Iter [73850/160000] lr: 3.231e-05, eta: 16:15:01, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0939, decode.acc_seg: 96.1143, loss: 0.0939 2023-01-06 13:10:48,107 - mmseg - INFO - Iter [73900/160000] lr: 3.229e-05, eta: 16:14:26, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1026, decode.acc_seg: 95.6903, loss: 0.1026 2023-01-06 13:11:22,105 - mmseg - INFO - Iter [73950/160000] lr: 3.227e-05, eta: 16:13:52, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1009, decode.acc_seg: 95.7335, loss: 0.1009 2023-01-06 13:11:54,406 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 13:11:54,407 - mmseg - INFO - Iter [74000/160000] lr: 3.225e-05, eta: 16:13:16, time: 0.647, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0988, decode.acc_seg: 95.9212, loss: 0.0988 2023-01-06 13:12:30,582 - mmseg - INFO - Iter [74050/160000] lr: 3.223e-05, eta: 16:12:45, time: 0.723, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1082, decode.acc_seg: 95.4060, loss: 0.1082 2023-01-06 13:13:02,953 - mmseg - INFO - Iter [74100/160000] lr: 3.221e-05, eta: 16:12:09, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0906, decode.acc_seg: 96.1540, loss: 0.0906 2023-01-06 13:13:35,113 - mmseg - INFO - Iter [74150/160000] lr: 3.219e-05, eta: 16:11:33, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0967, decode.acc_seg: 95.9976, loss: 0.0967 2023-01-06 13:14:10,156 - mmseg - INFO - Iter [74200/160000] lr: 3.218e-05, eta: 16:11:00, time: 0.700, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1041, decode.acc_seg: 95.6938, loss: 0.1041 2023-01-06 13:14:44,350 - mmseg - INFO - Iter [74250/160000] lr: 3.216e-05, eta: 16:10:27, time: 0.685, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0942, decode.acc_seg: 96.0139, loss: 0.0942 2023-01-06 13:15:16,629 - mmseg - INFO - Iter [74300/160000] lr: 3.214e-05, eta: 16:09:51, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0994, decode.acc_seg: 95.8618, loss: 0.0994 2023-01-06 13:15:49,311 - mmseg - INFO - Iter [74350/160000] lr: 3.212e-05, eta: 16:09:16, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1015, decode.acc_seg: 95.6572, loss: 0.1015 2023-01-06 13:16:22,934 - mmseg - INFO - Iter [74400/160000] lr: 3.210e-05, eta: 16:08:41, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1088, decode.acc_seg: 95.6718, loss: 0.1088 2023-01-06 13:16:58,484 - mmseg - INFO - Iter [74450/160000] lr: 3.208e-05, eta: 16:08:09, time: 0.710, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1048, decode.acc_seg: 95.6856, loss: 0.1048 2023-01-06 13:17:34,294 - mmseg - INFO - Iter [74500/160000] lr: 3.206e-05, eta: 16:07:37, time: 0.716, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0970, decode.acc_seg: 95.8015, loss: 0.0970 2023-01-06 13:18:09,467 - mmseg - INFO - Iter [74550/160000] lr: 3.204e-05, eta: 16:07:05, time: 0.704, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0943, decode.acc_seg: 96.0469, loss: 0.0943 2023-01-06 13:18:44,100 - mmseg - INFO - Iter [74600/160000] lr: 3.203e-05, eta: 16:06:32, time: 0.692, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1012, decode.acc_seg: 95.7356, loss: 0.1012 2023-01-06 13:19:19,336 - mmseg - INFO - Iter [74650/160000] lr: 3.201e-05, eta: 16:05:59, time: 0.705, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0962, decode.acc_seg: 96.0868, loss: 0.0962 2023-01-06 13:19:55,244 - mmseg - INFO - Iter [74700/160000] lr: 3.199e-05, eta: 16:05:27, time: 0.718, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0985, decode.acc_seg: 95.8804, loss: 0.0985 2023-01-06 13:20:30,180 - mmseg - INFO - Iter [74750/160000] lr: 3.197e-05, eta: 16:04:55, time: 0.700, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0992, decode.acc_seg: 95.7440, loss: 0.0992 2023-01-06 13:21:05,428 - mmseg - INFO - Iter [74800/160000] lr: 3.195e-05, eta: 16:04:22, time: 0.705, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0918, decode.acc_seg: 96.0902, loss: 0.0918 2023-01-06 13:21:37,717 - mmseg - INFO - Iter [74850/160000] lr: 3.193e-05, eta: 16:03:46, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0921, decode.acc_seg: 96.0741, loss: 0.0921 2023-01-06 13:22:11,956 - mmseg - INFO - Iter [74900/160000] lr: 3.191e-05, eta: 16:03:13, time: 0.685, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0976, decode.acc_seg: 95.9741, loss: 0.0976 2023-01-06 13:22:44,617 - mmseg - INFO - Iter [74950/160000] lr: 3.189e-05, eta: 16:02:37, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0934, decode.acc_seg: 96.1301, loss: 0.0934 2023-01-06 13:23:17,828 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 13:23:17,828 - mmseg - INFO - Iter [75000/160000] lr: 3.188e-05, eta: 16:02:02, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0907, decode.acc_seg: 96.0866, loss: 0.0907 2023-01-06 13:23:51,791 - mmseg - INFO - Iter [75050/160000] lr: 3.186e-05, eta: 16:01:28, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1089, decode.acc_seg: 95.4294, loss: 0.1089 2023-01-06 13:24:25,011 - mmseg - INFO - Iter [75100/160000] lr: 3.184e-05, eta: 16:00:54, time: 0.665, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0953, decode.acc_seg: 96.0072, loss: 0.0953 2023-01-06 13:24:59,976 - mmseg - INFO - Iter [75150/160000] lr: 3.182e-05, eta: 16:00:21, time: 0.699, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0916, decode.acc_seg: 96.1651, loss: 0.0916 2023-01-06 13:25:32,289 - mmseg - INFO - Iter [75200/160000] lr: 3.180e-05, eta: 15:59:45, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0887, decode.acc_seg: 96.1480, loss: 0.0887 2023-01-06 13:26:05,578 - mmseg - INFO - Iter [75250/160000] lr: 3.178e-05, eta: 15:59:10, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1043, decode.acc_seg: 95.7484, loss: 0.1043 2023-01-06 13:26:39,681 - mmseg - INFO - Iter [75300/160000] lr: 3.176e-05, eta: 15:58:37, time: 0.683, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1071, decode.acc_seg: 95.5095, loss: 0.1071 2023-01-06 13:27:12,502 - mmseg - INFO - Iter [75350/160000] lr: 3.174e-05, eta: 15:58:01, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1048, decode.acc_seg: 95.6825, loss: 0.1048 2023-01-06 13:27:45,543 - mmseg - INFO - Iter [75400/160000] lr: 3.173e-05, eta: 15:57:26, time: 0.662, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0994, decode.acc_seg: 95.7543, loss: 0.0994 2023-01-06 13:28:18,094 - mmseg - INFO - Iter [75450/160000] lr: 3.171e-05, eta: 15:56:51, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0984, decode.acc_seg: 95.8591, loss: 0.0984 2023-01-06 13:28:51,742 - mmseg - INFO - Iter [75500/160000] lr: 3.169e-05, eta: 15:56:16, time: 0.674, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0998, decode.acc_seg: 95.8065, loss: 0.0998 2023-01-06 13:29:28,188 - mmseg - INFO - Iter [75550/160000] lr: 3.167e-05, eta: 15:55:45, time: 0.728, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0947, decode.acc_seg: 96.0178, loss: 0.0947 2023-01-06 13:30:00,846 - mmseg - INFO - Iter [75600/160000] lr: 3.165e-05, eta: 15:55:10, time: 0.654, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1006, decode.acc_seg: 95.7690, loss: 0.1006 2023-01-06 13:30:33,174 - mmseg - INFO - Iter [75650/160000] lr: 3.163e-05, eta: 15:54:34, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0979, decode.acc_seg: 95.7562, loss: 0.0979 2023-01-06 13:31:07,097 - mmseg - INFO - Iter [75700/160000] lr: 3.161e-05, eta: 15:54:00, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1022, decode.acc_seg: 95.7672, loss: 0.1022 2023-01-06 13:31:41,778 - mmseg - INFO - Iter [75750/160000] lr: 3.159e-05, eta: 15:53:27, time: 0.694, data_time: 0.021, memory: 11582, decode.loss_ce: 0.0951, decode.acc_seg: 95.9774, loss: 0.0951 2023-01-06 13:32:14,158 - mmseg - INFO - Iter [75800/160000] lr: 3.158e-05, eta: 15:52:51, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0924, decode.acc_seg: 96.0058, loss: 0.0924 2023-01-06 13:32:47,384 - mmseg - INFO - Iter [75850/160000] lr: 3.156e-05, eta: 15:52:17, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1010, decode.acc_seg: 95.8242, loss: 0.1010 2023-01-06 13:33:22,198 - mmseg - INFO - Iter [75900/160000] lr: 3.154e-05, eta: 15:51:44, time: 0.697, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1039, decode.acc_seg: 95.7901, loss: 0.1039 2023-01-06 13:33:55,408 - mmseg - INFO - Iter [75950/160000] lr: 3.152e-05, eta: 15:51:09, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1002, decode.acc_seg: 95.7810, loss: 0.1002 2023-01-06 13:34:28,930 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 13:34:28,931 - mmseg - INFO - Iter [76000/160000] lr: 3.150e-05, eta: 15:50:34, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0965, decode.acc_seg: 95.9527, loss: 0.0965 2023-01-06 13:35:01,978 - mmseg - INFO - Iter [76050/160000] lr: 3.148e-05, eta: 15:49:59, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0935, decode.acc_seg: 96.1084, loss: 0.0935 2023-01-06 13:35:34,576 - mmseg - INFO - Iter [76100/160000] lr: 3.146e-05, eta: 15:49:24, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1060, decode.acc_seg: 95.7382, loss: 0.1060 2023-01-06 13:36:07,478 - mmseg - INFO - Iter [76150/160000] lr: 3.144e-05, eta: 15:48:49, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1095, decode.acc_seg: 95.4829, loss: 0.1095 2023-01-06 13:36:41,376 - mmseg - INFO - Iter [76200/160000] lr: 3.143e-05, eta: 15:48:15, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1047, decode.acc_seg: 95.6562, loss: 0.1047 2023-01-06 13:37:13,824 - mmseg - INFO - Iter [76250/160000] lr: 3.141e-05, eta: 15:47:39, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1028, decode.acc_seg: 95.7724, loss: 0.1028 2023-01-06 13:37:49,124 - mmseg - INFO - Iter [76300/160000] lr: 3.139e-05, eta: 15:47:07, time: 0.706, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0974, decode.acc_seg: 95.9598, loss: 0.0974 2023-01-06 13:38:21,473 - mmseg - INFO - Iter [76350/160000] lr: 3.137e-05, eta: 15:46:31, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0986, decode.acc_seg: 95.8297, loss: 0.0986 2023-01-06 13:38:55,243 - mmseg - INFO - Iter [76400/160000] lr: 3.135e-05, eta: 15:45:57, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1039, decode.acc_seg: 95.6712, loss: 0.1039 2023-01-06 13:39:27,868 - mmseg - INFO - Iter [76450/160000] lr: 3.133e-05, eta: 15:45:22, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0963, decode.acc_seg: 95.9342, loss: 0.0963 2023-01-06 13:40:02,285 - mmseg - INFO - Iter [76500/160000] lr: 3.131e-05, eta: 15:44:48, time: 0.688, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0944, decode.acc_seg: 95.9707, loss: 0.0944 2023-01-06 13:40:34,755 - mmseg - INFO - Iter [76550/160000] lr: 3.129e-05, eta: 15:44:13, time: 0.650, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0880, decode.acc_seg: 96.2899, loss: 0.0880 2023-01-06 13:41:07,489 - mmseg - INFO - Iter [76600/160000] lr: 3.128e-05, eta: 15:43:37, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0987, decode.acc_seg: 95.8793, loss: 0.0987 2023-01-06 13:41:44,318 - mmseg - INFO - Iter [76650/160000] lr: 3.126e-05, eta: 15:43:07, time: 0.738, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0963, decode.acc_seg: 95.9896, loss: 0.0963 2023-01-06 13:42:17,490 - mmseg - INFO - Iter [76700/160000] lr: 3.124e-05, eta: 15:42:32, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0953, decode.acc_seg: 96.0479, loss: 0.0953 2023-01-06 13:42:51,853 - mmseg - INFO - Iter [76750/160000] lr: 3.122e-05, eta: 15:41:58, time: 0.688, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0970, decode.acc_seg: 95.8656, loss: 0.0970 2023-01-06 13:43:26,508 - mmseg - INFO - Iter [76800/160000] lr: 3.120e-05, eta: 15:41:25, time: 0.692, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0928, decode.acc_seg: 96.1433, loss: 0.0928 2023-01-06 13:43:59,640 - mmseg - INFO - Iter [76850/160000] lr: 3.118e-05, eta: 15:40:50, time: 0.664, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0960, decode.acc_seg: 95.8786, loss: 0.0960 2023-01-06 13:44:32,229 - mmseg - INFO - Iter [76900/160000] lr: 3.116e-05, eta: 15:40:15, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0954, decode.acc_seg: 96.0429, loss: 0.0954 2023-01-06 13:45:05,240 - mmseg - INFO - Iter [76950/160000] lr: 3.114e-05, eta: 15:39:40, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1071, decode.acc_seg: 95.7320, loss: 0.1071 2023-01-06 13:45:37,921 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 13:45:37,921 - mmseg - INFO - Iter [77000/160000] lr: 3.113e-05, eta: 15:39:05, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0956, decode.acc_seg: 96.0167, loss: 0.0956 2023-01-06 13:46:12,402 - mmseg - INFO - Iter [77050/160000] lr: 3.111e-05, eta: 15:38:31, time: 0.690, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1035, decode.acc_seg: 95.8264, loss: 0.1035 2023-01-06 13:46:47,615 - mmseg - INFO - Iter [77100/160000] lr: 3.109e-05, eta: 15:37:59, time: 0.704, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0977, decode.acc_seg: 95.9687, loss: 0.0977 2023-01-06 13:47:22,610 - mmseg - INFO - Iter [77150/160000] lr: 3.107e-05, eta: 15:37:26, time: 0.700, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1005, decode.acc_seg: 95.8713, loss: 0.1005 2023-01-06 13:47:54,862 - mmseg - INFO - Iter [77200/160000] lr: 3.105e-05, eta: 15:36:50, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1043, decode.acc_seg: 95.7898, loss: 0.1043 2023-01-06 13:48:28,279 - mmseg - INFO - Iter [77250/160000] lr: 3.103e-05, eta: 15:36:16, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0964, decode.acc_seg: 95.8887, loss: 0.0964 2023-01-06 13:49:00,533 - mmseg - INFO - Iter [77300/160000] lr: 3.101e-05, eta: 15:35:40, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0983, decode.acc_seg: 95.9085, loss: 0.0983 2023-01-06 13:49:32,714 - mmseg - INFO - Iter [77350/160000] lr: 3.099e-05, eta: 15:35:04, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0990, decode.acc_seg: 95.8542, loss: 0.0990 2023-01-06 13:50:07,604 - mmseg - INFO - Iter [77400/160000] lr: 3.098e-05, eta: 15:34:31, time: 0.698, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1091, decode.acc_seg: 95.6220, loss: 0.1091 2023-01-06 13:50:40,937 - mmseg - INFO - Iter [77450/160000] lr: 3.096e-05, eta: 15:33:56, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0982, decode.acc_seg: 95.8763, loss: 0.0982 2023-01-06 13:51:16,793 - mmseg - INFO - Iter [77500/160000] lr: 3.094e-05, eta: 15:33:25, time: 0.717, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0949, decode.acc_seg: 96.0258, loss: 0.0949 2023-01-06 13:51:51,968 - mmseg - INFO - Iter [77550/160000] lr: 3.092e-05, eta: 15:32:52, time: 0.704, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1002, decode.acc_seg: 95.8195, loss: 0.1002 2023-01-06 13:52:25,821 - mmseg - INFO - Iter [77600/160000] lr: 3.090e-05, eta: 15:32:18, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1026, decode.acc_seg: 95.6356, loss: 0.1026 2023-01-06 13:52:59,765 - mmseg - INFO - Iter [77650/160000] lr: 3.088e-05, eta: 15:31:44, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1012, decode.acc_seg: 95.6016, loss: 0.1012 2023-01-06 13:53:32,468 - mmseg - INFO - Iter [77700/160000] lr: 3.086e-05, eta: 15:31:09, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0960, decode.acc_seg: 96.0410, loss: 0.0960 2023-01-06 13:54:10,226 - mmseg - INFO - Iter [77750/160000] lr: 3.084e-05, eta: 15:30:39, time: 0.755, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1090, decode.acc_seg: 95.5341, loss: 0.1090 2023-01-06 13:54:45,038 - mmseg - INFO - Iter [77800/160000] lr: 3.083e-05, eta: 15:30:06, time: 0.696, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0930, decode.acc_seg: 96.0581, loss: 0.0930 2023-01-06 13:55:17,174 - mmseg - INFO - Iter [77850/160000] lr: 3.081e-05, eta: 15:29:30, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0964, decode.acc_seg: 95.9662, loss: 0.0964 2023-01-06 13:55:49,581 - mmseg - INFO - Iter [77900/160000] lr: 3.079e-05, eta: 15:28:54, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0980, decode.acc_seg: 95.9395, loss: 0.0980 2023-01-06 13:56:22,516 - mmseg - INFO - Iter [77950/160000] lr: 3.077e-05, eta: 15:28:19, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0999, decode.acc_seg: 95.8136, loss: 0.0999 2023-01-06 13:56:55,360 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 13:56:55,361 - mmseg - INFO - Iter [78000/160000] lr: 3.075e-05, eta: 15:27:44, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0916, decode.acc_seg: 96.0772, loss: 0.0916 2023-01-06 13:57:27,559 - mmseg - INFO - Iter [78050/160000] lr: 3.073e-05, eta: 15:27:09, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1040, decode.acc_seg: 95.5385, loss: 0.1040 2023-01-06 13:57:59,724 - mmseg - INFO - Iter [78100/160000] lr: 3.071e-05, eta: 15:26:33, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1101, decode.acc_seg: 95.4709, loss: 0.1101 2023-01-06 13:58:34,669 - mmseg - INFO - Iter [78150/160000] lr: 3.069e-05, eta: 15:26:00, time: 0.699, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0996, decode.acc_seg: 95.7378, loss: 0.0996 2023-01-06 13:59:06,999 - mmseg - INFO - Iter [78200/160000] lr: 3.068e-05, eta: 15:25:24, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0995, decode.acc_seg: 95.8866, loss: 0.0995 2023-01-06 13:59:39,223 - mmseg - INFO - Iter [78250/160000] lr: 3.066e-05, eta: 15:24:48, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0979, decode.acc_seg: 95.8656, loss: 0.0979 2023-01-06 14:00:12,586 - mmseg - INFO - Iter [78300/160000] lr: 3.064e-05, eta: 15:24:14, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0985, decode.acc_seg: 95.9489, loss: 0.0985 2023-01-06 14:00:45,310 - mmseg - INFO - Iter [78350/160000] lr: 3.062e-05, eta: 15:23:39, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0999, decode.acc_seg: 95.8616, loss: 0.0999 2023-01-06 14:01:17,603 - mmseg - INFO - Iter [78400/160000] lr: 3.060e-05, eta: 15:23:03, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0966, decode.acc_seg: 95.9958, loss: 0.0966 2023-01-06 14:01:49,745 - mmseg - INFO - Iter [78450/160000] lr: 3.058e-05, eta: 15:22:27, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0939, decode.acc_seg: 95.9619, loss: 0.0939 2023-01-06 14:02:24,648 - mmseg - INFO - Iter [78500/160000] lr: 3.056e-05, eta: 15:21:54, time: 0.698, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0913, decode.acc_seg: 96.1918, loss: 0.0913 2023-01-06 14:02:57,047 - mmseg - INFO - Iter [78550/160000] lr: 3.054e-05, eta: 15:21:19, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1032, decode.acc_seg: 95.8415, loss: 0.1032 2023-01-06 14:03:29,736 - mmseg - INFO - Iter [78600/160000] lr: 3.053e-05, eta: 15:20:44, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1024, decode.acc_seg: 95.6348, loss: 0.1024 2023-01-06 14:04:02,884 - mmseg - INFO - Iter [78650/160000] lr: 3.051e-05, eta: 15:20:09, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0968, decode.acc_seg: 95.9597, loss: 0.0968 2023-01-06 14:04:35,583 - mmseg - INFO - Iter [78700/160000] lr: 3.049e-05, eta: 15:19:34, time: 0.655, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0993, decode.acc_seg: 95.8529, loss: 0.0993 2023-01-06 14:05:10,420 - mmseg - INFO - Iter [78750/160000] lr: 3.047e-05, eta: 15:19:01, time: 0.696, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0944, decode.acc_seg: 96.0169, loss: 0.0944 2023-01-06 14:05:42,846 - mmseg - INFO - Iter [78800/160000] lr: 3.045e-05, eta: 15:18:25, time: 0.649, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0925, decode.acc_seg: 96.1382, loss: 0.0925 2023-01-06 14:06:15,372 - mmseg - INFO - Iter [78850/160000] lr: 3.043e-05, eta: 15:17:50, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0916, decode.acc_seg: 96.1224, loss: 0.0916 2023-01-06 14:06:50,120 - mmseg - INFO - Iter [78900/160000] lr: 3.041e-05, eta: 15:17:17, time: 0.695, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1010, decode.acc_seg: 95.8561, loss: 0.1010 2023-01-06 14:07:24,495 - mmseg - INFO - Iter [78950/160000] lr: 3.039e-05, eta: 15:16:43, time: 0.687, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1074, decode.acc_seg: 95.6327, loss: 0.1074 2023-01-06 14:07:57,867 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 14:07:57,868 - mmseg - INFO - Iter [79000/160000] lr: 3.038e-05, eta: 15:16:09, time: 0.668, data_time: 0.015, memory: 11582, decode.loss_ce: 0.1071, decode.acc_seg: 95.5108, loss: 0.1071 2023-01-06 14:08:31,295 - mmseg - INFO - Iter [79050/160000] lr: 3.036e-05, eta: 15:15:34, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1123, decode.acc_seg: 95.5051, loss: 0.1123 2023-01-06 14:09:04,767 - mmseg - INFO - Iter [79100/160000] lr: 3.034e-05, eta: 15:15:00, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0971, decode.acc_seg: 95.8693, loss: 0.0971 2023-01-06 14:09:39,861 - mmseg - INFO - Iter [79150/160000] lr: 3.032e-05, eta: 15:14:27, time: 0.700, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0934, decode.acc_seg: 95.9956, loss: 0.0934 2023-01-06 14:10:13,039 - mmseg - INFO - Iter [79200/160000] lr: 3.030e-05, eta: 15:13:52, time: 0.665, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0997, decode.acc_seg: 95.9137, loss: 0.0997 2023-01-06 14:10:48,988 - mmseg - INFO - Iter [79250/160000] lr: 3.028e-05, eta: 15:13:20, time: 0.718, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0963, decode.acc_seg: 95.9710, loss: 0.0963 2023-01-06 14:11:21,879 - mmseg - INFO - Iter [79300/160000] lr: 3.026e-05, eta: 15:12:46, time: 0.659, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0918, decode.acc_seg: 96.2053, loss: 0.0918 2023-01-06 14:11:55,692 - mmseg - INFO - Iter [79350/160000] lr: 3.024e-05, eta: 15:12:11, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0966, decode.acc_seg: 95.9276, loss: 0.0966 2023-01-06 14:12:29,219 - mmseg - INFO - Iter [79400/160000] lr: 3.023e-05, eta: 15:11:37, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0979, decode.acc_seg: 95.9041, loss: 0.0979 2023-01-06 14:13:04,614 - mmseg - INFO - Iter [79450/160000] lr: 3.021e-05, eta: 15:11:05, time: 0.708, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0993, decode.acc_seg: 95.8297, loss: 0.0993 2023-01-06 14:13:38,126 - mmseg - INFO - Iter [79500/160000] lr: 3.019e-05, eta: 15:10:30, time: 0.671, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0955, decode.acc_seg: 95.9994, loss: 0.0955 2023-01-06 14:14:10,468 - mmseg - INFO - Iter [79550/160000] lr: 3.017e-05, eta: 15:09:55, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1014, decode.acc_seg: 95.6915, loss: 0.1014 2023-01-06 14:14:43,553 - mmseg - INFO - Iter [79600/160000] lr: 3.015e-05, eta: 15:09:20, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0933, decode.acc_seg: 96.1075, loss: 0.0933 2023-01-06 14:15:20,096 - mmseg - INFO - Iter [79650/160000] lr: 3.013e-05, eta: 15:08:49, time: 0.730, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0970, decode.acc_seg: 95.9972, loss: 0.0970 2023-01-06 14:15:54,627 - mmseg - INFO - Iter [79700/160000] lr: 3.011e-05, eta: 15:08:15, time: 0.692, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0947, decode.acc_seg: 96.0605, loss: 0.0947 2023-01-06 14:16:27,647 - mmseg - INFO - Iter [79750/160000] lr: 3.009e-05, eta: 15:07:41, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1029, decode.acc_seg: 95.7273, loss: 0.1029 2023-01-06 14:16:59,905 - mmseg - INFO - Iter [79800/160000] lr: 3.008e-05, eta: 15:07:05, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1140, decode.acc_seg: 95.2924, loss: 0.1140 2023-01-06 14:17:32,446 - mmseg - INFO - Iter [79850/160000] lr: 3.006e-05, eta: 15:06:30, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0982, decode.acc_seg: 95.7745, loss: 0.0982 2023-01-06 14:18:04,814 - mmseg - INFO - Iter [79900/160000] lr: 3.004e-05, eta: 15:05:54, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1032, decode.acc_seg: 95.7870, loss: 0.1032 2023-01-06 14:18:37,264 - mmseg - INFO - Iter [79950/160000] lr: 3.002e-05, eta: 15:05:19, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0953, decode.acc_seg: 95.9823, loss: 0.0953 2023-01-06 14:19:11,953 - mmseg - INFO - Saving checkpoint at 80000 iterations 2023-01-06 14:19:17,971 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 14:19:17,971 - mmseg - INFO - Iter [80000/160000] lr: 3.000e-05, eta: 15:04:52, time: 0.814, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0952, decode.acc_seg: 95.7809, loss: 0.0952 2023-01-06 14:19:53,727 - mmseg - INFO - per class results: 2023-01-06 14:19:53,729 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 97.62 | 98.91 | | sidewalk | 80.59 | 89.72 | | building | 91.56 | 96.28 | | wall | 50.55 | 57.05 | | fence | 53.22 | 61.9 | | pole | 61.86 | 74.16 | | traffic light | 65.02 | 76.21 | | traffic sign | 73.13 | 79.64 | | vegetation | 92.06 | 96.18 | | terrain | 62.11 | 69.6 | | sky | 93.94 | 98.39 | | person | 76.84 | 89.85 | | rider | 52.22 | 61.87 | | car | 93.75 | 97.12 | | truck | 64.51 | 71.07 | | bus | 74.87 | 85.38 | | train | 61.7 | 80.73 | | motorcycle | 44.53 | 51.05 | | bicycle | 72.29 | 86.31 | +---------------+-------+-------+ 2023-01-06 14:19:53,729 - mmseg - INFO - Summary: 2023-01-06 14:19:53,730 - mmseg - INFO - +-------+------+-------+ | aAcc | mIoU | mAcc | +-------+------+-------+ | 95.35 | 71.7 | 80.07 | +-------+------+-------+ 2023-01-06 14:19:53,730 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 14:19:53,731 - mmseg - INFO - Iter(val) [63] aAcc: 0.9535, mIoU: 0.7170, mAcc: 0.8007, IoU.road: 0.9762, IoU.sidewalk: 0.8059, IoU.building: 0.9156, IoU.wall: 0.5055, IoU.fence: 0.5322, IoU.pole: 0.6186, IoU.traffic light: 0.6502, IoU.traffic sign: 0.7313, IoU.vegetation: 0.9206, IoU.terrain: 0.6211, IoU.sky: 0.9394, IoU.person: 0.7684, IoU.rider: 0.5222, IoU.car: 0.9375, IoU.truck: 0.6451, IoU.bus: 0.7487, IoU.train: 0.6170, IoU.motorcycle: 0.4453, IoU.bicycle: 0.7229, Acc.road: 0.9891, Acc.sidewalk: 0.8972, Acc.building: 0.9628, Acc.wall: 0.5705, Acc.fence: 0.6190, Acc.pole: 0.7416, Acc.traffic light: 0.7621, Acc.traffic sign: 0.7964, Acc.vegetation: 0.9618, Acc.terrain: 0.6960, Acc.sky: 0.9839, Acc.person: 0.8985, Acc.rider: 0.6187, Acc.car: 0.9712, Acc.truck: 0.7107, Acc.bus: 0.8538, Acc.train: 0.8073, Acc.motorcycle: 0.5105, Acc.bicycle: 0.8631 2023-01-06 14:20:28,117 - mmseg - INFO - Iter [80050/160000] lr: 2.998e-05, eta: 15:04:54, time: 1.402, data_time: 0.728, memory: 11582, decode.loss_ce: 0.0943, decode.acc_seg: 96.0621, loss: 0.0943 2023-01-06 14:21:02,693 - mmseg - INFO - Iter [80100/160000] lr: 2.996e-05, eta: 15:04:20, time: 0.693, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0937, decode.acc_seg: 96.0860, loss: 0.0937 2023-01-06 14:21:35,410 - mmseg - INFO - Iter [80150/160000] lr: 2.994e-05, eta: 15:03:45, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0950, decode.acc_seg: 96.0070, loss: 0.0950 2023-01-06 14:22:07,892 - mmseg - INFO - Iter [80200/160000] lr: 2.993e-05, eta: 15:03:10, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0928, decode.acc_seg: 96.1051, loss: 0.0928 2023-01-06 14:22:41,471 - mmseg - INFO - Iter [80250/160000] lr: 2.991e-05, eta: 15:02:35, time: 0.671, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0939, decode.acc_seg: 96.0102, loss: 0.0939 2023-01-06 14:23:14,343 - mmseg - INFO - Iter [80300/160000] lr: 2.989e-05, eta: 15:02:00, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1004, decode.acc_seg: 95.7443, loss: 0.1004 2023-01-06 14:23:46,474 - mmseg - INFO - Iter [80350/160000] lr: 2.987e-05, eta: 15:01:25, time: 0.643, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0987, decode.acc_seg: 95.9016, loss: 0.0987 2023-01-06 14:24:23,704 - mmseg - INFO - Iter [80400/160000] lr: 2.985e-05, eta: 15:00:54, time: 0.744, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1079, decode.acc_seg: 95.5881, loss: 0.1079 2023-01-06 14:24:57,673 - mmseg - INFO - Iter [80450/160000] lr: 2.983e-05, eta: 15:00:20, time: 0.680, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0965, decode.acc_seg: 96.0045, loss: 0.0965 2023-01-06 14:25:32,576 - mmseg - INFO - Iter [80500/160000] lr: 2.981e-05, eta: 14:59:47, time: 0.697, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1042, decode.acc_seg: 95.6980, loss: 0.1042 2023-01-06 14:26:05,704 - mmseg - INFO - Iter [80550/160000] lr: 2.979e-05, eta: 14:59:12, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0986, decode.acc_seg: 95.8773, loss: 0.0986 2023-01-06 14:26:37,893 - mmseg - INFO - Iter [80600/160000] lr: 2.978e-05, eta: 14:58:37, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0986, decode.acc_seg: 95.8946, loss: 0.0986 2023-01-06 14:27:11,632 - mmseg - INFO - Iter [80650/160000] lr: 2.976e-05, eta: 14:58:02, time: 0.675, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1038, decode.acc_seg: 95.6249, loss: 0.1038 2023-01-06 14:27:45,326 - mmseg - INFO - Iter [80700/160000] lr: 2.974e-05, eta: 14:57:28, time: 0.674, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0931, decode.acc_seg: 96.0195, loss: 0.0931 2023-01-06 14:28:21,801 - mmseg - INFO - Iter [80750/160000] lr: 2.972e-05, eta: 14:56:57, time: 0.729, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0950, decode.acc_seg: 95.9407, loss: 0.0950 2023-01-06 14:28:56,583 - mmseg - INFO - Iter [80800/160000] lr: 2.970e-05, eta: 14:56:24, time: 0.696, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0948, decode.acc_seg: 96.0365, loss: 0.0948 2023-01-06 14:29:31,295 - mmseg - INFO - Iter [80850/160000] lr: 2.968e-05, eta: 14:55:50, time: 0.695, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0953, decode.acc_seg: 96.0676, loss: 0.0953 2023-01-06 14:30:05,866 - mmseg - INFO - Iter [80900/160000] lr: 2.966e-05, eta: 14:55:17, time: 0.691, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0979, decode.acc_seg: 95.9908, loss: 0.0979 2023-01-06 14:30:38,136 - mmseg - INFO - Iter [80950/160000] lr: 2.964e-05, eta: 14:54:41, time: 0.645, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0968, decode.acc_seg: 95.9775, loss: 0.0968 2023-01-06 14:31:11,467 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 14:31:11,467 - mmseg - INFO - Iter [81000/160000] lr: 2.963e-05, eta: 14:54:07, time: 0.667, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0936, decode.acc_seg: 95.9611, loss: 0.0936 2023-01-06 14:31:43,615 - mmseg - INFO - Iter [81050/160000] lr: 2.961e-05, eta: 14:53:31, time: 0.643, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0989, decode.acc_seg: 95.9160, loss: 0.0989 2023-01-06 14:32:19,268 - mmseg - INFO - Iter [81100/160000] lr: 2.959e-05, eta: 14:52:59, time: 0.712, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0915, decode.acc_seg: 96.1480, loss: 0.0915 2023-01-06 14:32:53,183 - mmseg - INFO - Iter [81150/160000] lr: 2.957e-05, eta: 14:52:25, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0901, decode.acc_seg: 96.1780, loss: 0.0901 2023-01-06 14:33:27,325 - mmseg - INFO - Iter [81200/160000] lr: 2.955e-05, eta: 14:51:51, time: 0.683, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0891, decode.acc_seg: 96.1073, loss: 0.0891 2023-01-06 14:34:00,680 - mmseg - INFO - Iter [81250/160000] lr: 2.953e-05, eta: 14:51:17, time: 0.667, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0901, decode.acc_seg: 96.2012, loss: 0.0901 2023-01-06 14:34:34,489 - mmseg - INFO - Iter [81300/160000] lr: 2.951e-05, eta: 14:50:42, time: 0.676, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0972, decode.acc_seg: 95.8951, loss: 0.0972 2023-01-06 14:35:07,030 - mmseg - INFO - Iter [81350/160000] lr: 2.949e-05, eta: 14:50:07, time: 0.651, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0898, decode.acc_seg: 96.0898, loss: 0.0898 2023-01-06 14:35:40,045 - mmseg - INFO - Iter [81400/160000] lr: 2.948e-05, eta: 14:49:32, time: 0.660, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0995, decode.acc_seg: 95.9962, loss: 0.0995 2023-01-06 14:36:14,210 - mmseg - INFO - Iter [81450/160000] lr: 2.946e-05, eta: 14:48:58, time: 0.683, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0924, decode.acc_seg: 96.1395, loss: 0.0924 2023-01-06 14:36:49,013 - mmseg - INFO - Iter [81500/160000] lr: 2.944e-05, eta: 14:48:25, time: 0.696, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0958, decode.acc_seg: 95.9842, loss: 0.0958 2023-01-06 14:37:23,563 - mmseg - INFO - Iter [81550/160000] lr: 2.942e-05, eta: 14:47:52, time: 0.691, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0922, decode.acc_seg: 96.0721, loss: 0.0922 2023-01-06 14:37:56,034 - mmseg - INFO - Iter [81600/160000] lr: 2.940e-05, eta: 14:47:17, time: 0.649, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0930, decode.acc_seg: 96.1735, loss: 0.0930 2023-01-06 14:38:29,152 - mmseg - INFO - Iter [81650/160000] lr: 2.938e-05, eta: 14:46:42, time: 0.662, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0881, decode.acc_seg: 96.0839, loss: 0.0881 2023-01-06 14:39:01,507 - mmseg - INFO - Iter [81700/160000] lr: 2.936e-05, eta: 14:46:06, time: 0.646, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0977, decode.acc_seg: 95.8917, loss: 0.0977 2023-01-06 14:39:35,124 - mmseg - INFO - Iter [81750/160000] lr: 2.934e-05, eta: 14:45:32, time: 0.673, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0982, decode.acc_seg: 95.9764, loss: 0.0982 2023-01-06 14:40:07,216 - mmseg - INFO - Iter [81800/160000] lr: 2.933e-05, eta: 14:44:56, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0968, decode.acc_seg: 95.9357, loss: 0.0968 2023-01-06 14:40:43,375 - mmseg - INFO - Iter [81850/160000] lr: 2.931e-05, eta: 14:44:25, time: 0.723, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1014, decode.acc_seg: 95.7704, loss: 0.1014 2023-01-06 14:41:15,798 - mmseg - INFO - Iter [81900/160000] lr: 2.929e-05, eta: 14:43:49, time: 0.648, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0905, decode.acc_seg: 96.1575, loss: 0.0905 2023-01-06 14:41:49,070 - mmseg - INFO - Iter [81950/160000] lr: 2.927e-05, eta: 14:43:15, time: 0.666, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1068, decode.acc_seg: 95.5750, loss: 0.1068 2023-01-06 14:42:21,280 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 14:42:21,281 - mmseg - INFO - Iter [82000/160000] lr: 2.925e-05, eta: 14:42:39, time: 0.644, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0980, decode.acc_seg: 95.8618, loss: 0.0980 2023-01-06 14:42:54,410 - mmseg - INFO - Iter [82050/160000] lr: 2.923e-05, eta: 14:42:04, time: 0.663, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1006, decode.acc_seg: 95.8398, loss: 0.1006 2023-01-06 14:43:27,699 - mmseg - INFO - Iter [82100/160000] lr: 2.921e-05, eta: 14:41:30, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0983, decode.acc_seg: 95.8841, loss: 0.0983 2023-01-06 14:44:00,424 - mmseg - INFO - Iter [82150/160000] lr: 2.919e-05, eta: 14:40:54, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1078, decode.acc_seg: 95.5331, loss: 0.1078 2023-01-06 14:44:34,513 - mmseg - INFO - Iter [82200/160000] lr: 2.918e-05, eta: 14:40:21, time: 0.683, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0935, decode.acc_seg: 96.0049, loss: 0.0935 2023-01-06 14:45:09,883 - mmseg - INFO - Iter [82250/160000] lr: 2.916e-05, eta: 14:39:48, time: 0.708, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0976, decode.acc_seg: 95.8864, loss: 0.0976 2023-01-06 14:45:42,407 - mmseg - INFO - Iter [82300/160000] lr: 2.914e-05, eta: 14:39:13, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0939, decode.acc_seg: 96.0988, loss: 0.0939 2023-01-06 14:46:14,867 - mmseg - INFO - Iter [82350/160000] lr: 2.912e-05, eta: 14:38:37, time: 0.649, data_time: 0.018, memory: 11582, decode.loss_ce: 0.0904, decode.acc_seg: 96.0706, loss: 0.0904 2023-01-06 14:46:47,445 - mmseg - INFO - Iter [82400/160000] lr: 2.910e-05, eta: 14:38:02, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0941, decode.acc_seg: 96.0801, loss: 0.0941 2023-01-06 14:47:20,409 - mmseg - INFO - Iter [82450/160000] lr: 2.908e-05, eta: 14:37:27, time: 0.659, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0977, decode.acc_seg: 95.9225, loss: 0.0977 2023-01-06 14:47:53,248 - mmseg - INFO - Iter [82500/160000] lr: 2.906e-05, eta: 14:36:52, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1010, decode.acc_seg: 95.7597, loss: 0.1010 2023-01-06 14:48:26,222 - mmseg - INFO - Iter [82550/160000] lr: 2.904e-05, eta: 14:36:18, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0999, decode.acc_seg: 95.8081, loss: 0.0999 2023-01-06 14:49:01,166 - mmseg - INFO - Iter [82600/160000] lr: 2.903e-05, eta: 14:35:45, time: 0.699, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0857, decode.acc_seg: 96.3888, loss: 0.0857 2023-01-06 14:49:35,111 - mmseg - INFO - Iter [82650/160000] lr: 2.901e-05, eta: 14:35:11, time: 0.679, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1084, decode.acc_seg: 95.7720, loss: 0.1084 2023-01-06 14:50:08,379 - mmseg - INFO - Iter [82700/160000] lr: 2.899e-05, eta: 14:34:36, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1055, decode.acc_seg: 95.6848, loss: 0.1055 2023-01-06 14:50:41,119 - mmseg - INFO - Iter [82750/160000] lr: 2.897e-05, eta: 14:34:01, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1026, decode.acc_seg: 95.7535, loss: 0.1026 2023-01-06 14:51:13,244 - mmseg - INFO - Iter [82800/160000] lr: 2.895e-05, eta: 14:33:25, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1001, decode.acc_seg: 95.7771, loss: 0.1001 2023-01-06 14:51:46,659 - mmseg - INFO - Iter [82850/160000] lr: 2.893e-05, eta: 14:32:51, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0961, decode.acc_seg: 95.9393, loss: 0.0961 2023-01-06 14:52:18,838 - mmseg - INFO - Iter [82900/160000] lr: 2.891e-05, eta: 14:32:15, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0967, decode.acc_seg: 95.9594, loss: 0.0967 2023-01-06 14:52:52,046 - mmseg - INFO - Iter [82950/160000] lr: 2.889e-05, eta: 14:31:41, time: 0.664, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0984, decode.acc_seg: 95.8757, loss: 0.0984 2023-01-06 14:53:27,244 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 14:53:27,244 - mmseg - INFO - Iter [83000/160000] lr: 2.888e-05, eta: 14:31:08, time: 0.704, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1011, decode.acc_seg: 95.8703, loss: 0.1011 2023-01-06 14:53:59,989 - mmseg - INFO - Iter [83050/160000] lr: 2.886e-05, eta: 14:30:33, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0961, decode.acc_seg: 95.9006, loss: 0.0961 2023-01-06 14:54:33,318 - mmseg - INFO - Iter [83100/160000] lr: 2.884e-05, eta: 14:29:58, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0942, decode.acc_seg: 95.9381, loss: 0.0942 2023-01-06 14:55:06,856 - mmseg - INFO - Iter [83150/160000] lr: 2.882e-05, eta: 14:29:24, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0929, decode.acc_seg: 96.1492, loss: 0.0929 2023-01-06 14:55:39,051 - mmseg - INFO - Iter [83200/160000] lr: 2.880e-05, eta: 14:28:48, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0932, decode.acc_seg: 96.1182, loss: 0.0932 2023-01-06 14:56:12,435 - mmseg - INFO - Iter [83250/160000] lr: 2.878e-05, eta: 14:28:14, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1024, decode.acc_seg: 95.7925, loss: 0.1024 2023-01-06 14:56:47,833 - mmseg - INFO - Iter [83300/160000] lr: 2.876e-05, eta: 14:27:41, time: 0.708, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1053, decode.acc_seg: 95.7319, loss: 0.1053 2023-01-06 14:57:25,410 - mmseg - INFO - Iter [83350/160000] lr: 2.874e-05, eta: 14:27:11, time: 0.750, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0893, decode.acc_seg: 96.2296, loss: 0.0893 2023-01-06 14:58:00,466 - mmseg - INFO - Iter [83400/160000] lr: 2.873e-05, eta: 14:26:38, time: 0.702, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0939, decode.acc_seg: 95.9715, loss: 0.0939 2023-01-06 14:58:32,951 - mmseg - INFO - Iter [83450/160000] lr: 2.871e-05, eta: 14:26:03, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0923, decode.acc_seg: 96.0775, loss: 0.0923 2023-01-06 14:59:06,990 - mmseg - INFO - Iter [83500/160000] lr: 2.869e-05, eta: 14:25:29, time: 0.681, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0902, decode.acc_seg: 96.2211, loss: 0.0902 2023-01-06 14:59:39,452 - mmseg - INFO - Iter [83550/160000] lr: 2.867e-05, eta: 14:24:53, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0883, decode.acc_seg: 96.2012, loss: 0.0883 2023-01-06 15:00:11,671 - mmseg - INFO - Iter [83600/160000] lr: 2.865e-05, eta: 14:24:18, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0909, decode.acc_seg: 96.2061, loss: 0.0909 2023-01-06 15:00:46,115 - mmseg - INFO - Iter [83650/160000] lr: 2.863e-05, eta: 14:23:45, time: 0.689, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0924, decode.acc_seg: 96.1848, loss: 0.0924 2023-01-06 15:01:19,498 - mmseg - INFO - Iter [83700/160000] lr: 2.861e-05, eta: 14:23:10, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0932, decode.acc_seg: 96.0593, loss: 0.0932 2023-01-06 15:01:54,878 - mmseg - INFO - Iter [83750/160000] lr: 2.859e-05, eta: 14:22:37, time: 0.707, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0954, decode.acc_seg: 95.8667, loss: 0.0954 2023-01-06 15:02:27,251 - mmseg - INFO - Iter [83800/160000] lr: 2.858e-05, eta: 14:22:02, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0978, decode.acc_seg: 95.9293, loss: 0.0978 2023-01-06 15:03:00,604 - mmseg - INFO - Iter [83850/160000] lr: 2.856e-05, eta: 14:21:28, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0929, decode.acc_seg: 96.1374, loss: 0.0929 2023-01-06 15:03:35,047 - mmseg - INFO - Iter [83900/160000] lr: 2.854e-05, eta: 14:20:54, time: 0.689, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0950, decode.acc_seg: 96.0371, loss: 0.0950 2023-01-06 15:04:08,550 - mmseg - INFO - Iter [83950/160000] lr: 2.852e-05, eta: 14:20:20, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0911, decode.acc_seg: 96.1306, loss: 0.0911 2023-01-06 15:04:40,779 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 15:04:40,779 - mmseg - INFO - Iter [84000/160000] lr: 2.850e-05, eta: 14:19:44, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0888, decode.acc_seg: 96.2266, loss: 0.0888 2023-01-06 15:05:14,910 - mmseg - INFO - Iter [84050/160000] lr: 2.848e-05, eta: 14:19:11, time: 0.683, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0899, decode.acc_seg: 96.2207, loss: 0.0899 2023-01-06 15:05:49,824 - mmseg - INFO - Iter [84100/160000] lr: 2.846e-05, eta: 14:18:37, time: 0.697, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0961, decode.acc_seg: 96.0636, loss: 0.0961 2023-01-06 15:06:24,343 - mmseg - INFO - Iter [84150/160000] lr: 2.844e-05, eta: 14:18:04, time: 0.691, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0923, decode.acc_seg: 95.9982, loss: 0.0923 2023-01-06 15:06:56,560 - mmseg - INFO - Iter [84200/160000] lr: 2.843e-05, eta: 14:17:29, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1082, decode.acc_seg: 95.6443, loss: 0.1082 2023-01-06 15:07:28,964 - mmseg - INFO - Iter [84250/160000] lr: 2.841e-05, eta: 14:16:53, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0923, decode.acc_seg: 96.1032, loss: 0.0923 2023-01-06 15:08:01,512 - mmseg - INFO - Iter [84300/160000] lr: 2.839e-05, eta: 14:16:18, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0954, decode.acc_seg: 95.9626, loss: 0.0954 2023-01-06 15:08:34,883 - mmseg - INFO - Iter [84350/160000] lr: 2.837e-05, eta: 14:15:44, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0933, decode.acc_seg: 96.0724, loss: 0.0933 2023-01-06 15:09:07,066 - mmseg - INFO - Iter [84400/160000] lr: 2.835e-05, eta: 14:15:08, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0965, decode.acc_seg: 96.0750, loss: 0.0965 2023-01-06 15:09:44,393 - mmseg - INFO - Iter [84450/160000] lr: 2.833e-05, eta: 14:14:37, time: 0.746, data_time: 0.058, memory: 11582, decode.loss_ce: 0.1003, decode.acc_seg: 95.8893, loss: 0.1003 2023-01-06 15:10:17,536 - mmseg - INFO - Iter [84500/160000] lr: 2.831e-05, eta: 14:14:03, time: 0.664, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0973, decode.acc_seg: 95.9522, loss: 0.0973 2023-01-06 15:10:51,424 - mmseg - INFO - Iter [84550/160000] lr: 2.829e-05, eta: 14:13:29, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0881, decode.acc_seg: 96.2794, loss: 0.0881 2023-01-06 15:11:24,507 - mmseg - INFO - Iter [84600/160000] lr: 2.828e-05, eta: 14:12:54, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0936, decode.acc_seg: 96.0345, loss: 0.0936 2023-01-06 15:11:56,982 - mmseg - INFO - Iter [84650/160000] lr: 2.826e-05, eta: 14:12:19, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0914, decode.acc_seg: 96.1868, loss: 0.0914 2023-01-06 15:12:30,360 - mmseg - INFO - Iter [84700/160000] lr: 2.824e-05, eta: 14:11:44, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0976, decode.acc_seg: 95.9577, loss: 0.0976 2023-01-06 15:13:03,473 - mmseg - INFO - Iter [84750/160000] lr: 2.822e-05, eta: 14:11:10, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0954, decode.acc_seg: 95.9123, loss: 0.0954 2023-01-06 15:13:36,616 - mmseg - INFO - Iter [84800/160000] lr: 2.820e-05, eta: 14:10:35, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0917, decode.acc_seg: 96.0605, loss: 0.0917 2023-01-06 15:14:12,692 - mmseg - INFO - Iter [84850/160000] lr: 2.818e-05, eta: 14:10:03, time: 0.722, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0899, decode.acc_seg: 96.1957, loss: 0.0899 2023-01-06 15:14:46,560 - mmseg - INFO - Iter [84900/160000] lr: 2.816e-05, eta: 14:09:29, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0885, decode.acc_seg: 96.2630, loss: 0.0885 2023-01-06 15:15:18,851 - mmseg - INFO - Iter [84950/160000] lr: 2.814e-05, eta: 14:08:54, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0924, decode.acc_seg: 96.0470, loss: 0.0924 2023-01-06 15:15:51,445 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 15:15:51,446 - mmseg - INFO - Iter [85000/160000] lr: 2.813e-05, eta: 14:08:18, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1051, decode.acc_seg: 95.6833, loss: 0.1051 2023-01-06 15:16:23,900 - mmseg - INFO - Iter [85050/160000] lr: 2.811e-05, eta: 14:07:43, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0968, decode.acc_seg: 95.8920, loss: 0.0968 2023-01-06 15:16:57,386 - mmseg - INFO - Iter [85100/160000] lr: 2.809e-05, eta: 14:07:09, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0935, decode.acc_seg: 96.0514, loss: 0.0935 2023-01-06 15:17:30,801 - mmseg - INFO - Iter [85150/160000] lr: 2.807e-05, eta: 14:06:34, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0959, decode.acc_seg: 95.9744, loss: 0.0959 2023-01-06 15:18:06,003 - mmseg - INFO - Iter [85200/160000] lr: 2.805e-05, eta: 14:06:02, time: 0.705, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0953, decode.acc_seg: 96.0283, loss: 0.0953 2023-01-06 15:18:40,420 - mmseg - INFO - Iter [85250/160000] lr: 2.803e-05, eta: 14:05:28, time: 0.688, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0921, decode.acc_seg: 96.1054, loss: 0.0921 2023-01-06 15:19:12,938 - mmseg - INFO - Iter [85300/160000] lr: 2.801e-05, eta: 14:04:53, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1016, decode.acc_seg: 95.7301, loss: 0.1016 2023-01-06 15:19:46,722 - mmseg - INFO - Iter [85350/160000] lr: 2.799e-05, eta: 14:04:19, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0932, decode.acc_seg: 96.0678, loss: 0.0932 2023-01-06 15:20:19,011 - mmseg - INFO - Iter [85400/160000] lr: 2.798e-05, eta: 14:03:44, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0973, decode.acc_seg: 95.9299, loss: 0.0973 2023-01-06 15:20:51,309 - mmseg - INFO - Iter [85450/160000] lr: 2.796e-05, eta: 14:03:08, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0935, decode.acc_seg: 96.1041, loss: 0.0935 2023-01-06 15:21:24,161 - mmseg - INFO - Iter [85500/160000] lr: 2.794e-05, eta: 14:02:33, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0926, decode.acc_seg: 96.1174, loss: 0.0926 2023-01-06 15:21:58,382 - mmseg - INFO - Iter [85550/160000] lr: 2.792e-05, eta: 14:02:00, time: 0.685, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0948, decode.acc_seg: 96.0508, loss: 0.0948 2023-01-06 15:22:34,297 - mmseg - INFO - Iter [85600/160000] lr: 2.790e-05, eta: 14:01:27, time: 0.717, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0903, decode.acc_seg: 96.2038, loss: 0.0903 2023-01-06 15:23:08,189 - mmseg - INFO - Iter [85650/160000] lr: 2.788e-05, eta: 14:00:53, time: 0.679, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0887, decode.acc_seg: 96.1905, loss: 0.0887 2023-01-06 15:23:41,184 - mmseg - INFO - Iter [85700/160000] lr: 2.786e-05, eta: 14:00:19, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0889, decode.acc_seg: 96.2009, loss: 0.0889 2023-01-06 15:24:15,728 - mmseg - INFO - Iter [85750/160000] lr: 2.784e-05, eta: 13:59:45, time: 0.690, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0954, decode.acc_seg: 95.9832, loss: 0.0954 2023-01-06 15:24:48,868 - mmseg - INFO - Iter [85800/160000] lr: 2.783e-05, eta: 13:59:11, time: 0.664, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0856, decode.acc_seg: 96.2017, loss: 0.0856 2023-01-06 15:25:22,024 - mmseg - INFO - Iter [85850/160000] lr: 2.781e-05, eta: 13:58:36, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0938, decode.acc_seg: 96.1530, loss: 0.0938 2023-01-06 15:25:56,311 - mmseg - INFO - Iter [85900/160000] lr: 2.779e-05, eta: 13:58:03, time: 0.687, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0974, decode.acc_seg: 95.8681, loss: 0.0974 2023-01-06 15:26:32,806 - mmseg - INFO - Iter [85950/160000] lr: 2.777e-05, eta: 13:57:31, time: 0.730, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0928, decode.acc_seg: 96.0919, loss: 0.0928 2023-01-06 15:27:08,359 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 15:27:08,360 - mmseg - INFO - Iter [86000/160000] lr: 2.775e-05, eta: 13:56:58, time: 0.710, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0867, decode.acc_seg: 96.4137, loss: 0.0867 2023-01-06 15:27:40,547 - mmseg - INFO - Iter [86050/160000] lr: 2.773e-05, eta: 13:56:23, time: 0.645, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0933, decode.acc_seg: 96.0379, loss: 0.0933 2023-01-06 15:28:14,428 - mmseg - INFO - Iter [86100/160000] lr: 2.771e-05, eta: 13:55:49, time: 0.677, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0877, decode.acc_seg: 96.2191, loss: 0.0877 2023-01-06 15:28:47,081 - mmseg - INFO - Iter [86150/160000] lr: 2.769e-05, eta: 13:55:14, time: 0.654, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0925, decode.acc_seg: 96.1237, loss: 0.0925 2023-01-06 15:29:19,824 - mmseg - INFO - Iter [86200/160000] lr: 2.768e-05, eta: 13:54:39, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1004, decode.acc_seg: 95.9315, loss: 0.1004 2023-01-06 15:29:53,890 - mmseg - INFO - Iter [86250/160000] lr: 2.766e-05, eta: 13:54:05, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0975, decode.acc_seg: 95.8780, loss: 0.0975 2023-01-06 15:30:27,428 - mmseg - INFO - Iter [86300/160000] lr: 2.764e-05, eta: 13:53:31, time: 0.671, data_time: 0.013, memory: 11582, decode.loss_ce: 0.1092, decode.acc_seg: 95.5771, loss: 0.1092 2023-01-06 15:31:01,849 - mmseg - INFO - Iter [86350/160000] lr: 2.762e-05, eta: 13:52:57, time: 0.688, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0920, decode.acc_seg: 96.0991, loss: 0.0920 2023-01-06 15:31:34,082 - mmseg - INFO - Iter [86400/160000] lr: 2.760e-05, eta: 13:52:22, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0944, decode.acc_seg: 96.0937, loss: 0.0944 2023-01-06 15:32:08,209 - mmseg - INFO - Iter [86450/160000] lr: 2.758e-05, eta: 13:51:48, time: 0.683, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0927, decode.acc_seg: 96.0960, loss: 0.0927 2023-01-06 15:32:42,003 - mmseg - INFO - Iter [86500/160000] lr: 2.756e-05, eta: 13:51:14, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0930, decode.acc_seg: 96.0671, loss: 0.0930 2023-01-06 15:33:15,531 - mmseg - INFO - Iter [86550/160000] lr: 2.754e-05, eta: 13:50:40, time: 0.671, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0907, decode.acc_seg: 96.1512, loss: 0.0907 2023-01-06 15:33:47,770 - mmseg - INFO - Iter [86600/160000] lr: 2.753e-05, eta: 13:50:04, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0916, decode.acc_seg: 96.1035, loss: 0.0916 2023-01-06 15:34:20,295 - mmseg - INFO - Iter [86650/160000] lr: 2.751e-05, eta: 13:49:29, time: 0.651, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0900, decode.acc_seg: 96.1888, loss: 0.0900 2023-01-06 15:34:57,509 - mmseg - INFO - Iter [86700/160000] lr: 2.749e-05, eta: 13:48:58, time: 0.744, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0946, decode.acc_seg: 95.8871, loss: 0.0946 2023-01-06 15:35:30,009 - mmseg - INFO - Iter [86750/160000] lr: 2.747e-05, eta: 13:48:23, time: 0.649, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0855, decode.acc_seg: 96.2471, loss: 0.0855 2023-01-06 15:36:03,333 - mmseg - INFO - Iter [86800/160000] lr: 2.745e-05, eta: 13:47:49, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0938, decode.acc_seg: 96.0969, loss: 0.0938 2023-01-06 15:36:36,995 - mmseg - INFO - Iter [86850/160000] lr: 2.743e-05, eta: 13:47:14, time: 0.673, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0917, decode.acc_seg: 96.1792, loss: 0.0917 2023-01-06 15:37:09,571 - mmseg - INFO - Iter [86900/160000] lr: 2.741e-05, eta: 13:46:39, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0927, decode.acc_seg: 96.1439, loss: 0.0927 2023-01-06 15:37:42,974 - mmseg - INFO - Iter [86950/160000] lr: 2.739e-05, eta: 13:46:05, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0888, decode.acc_seg: 96.2237, loss: 0.0888 2023-01-06 15:38:16,013 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 15:38:16,014 - mmseg - INFO - Iter [87000/160000] lr: 2.738e-05, eta: 13:45:30, time: 0.661, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0898, decode.acc_seg: 96.2591, loss: 0.0898 2023-01-06 15:38:50,586 - mmseg - INFO - Iter [87050/160000] lr: 2.736e-05, eta: 13:44:57, time: 0.691, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0867, decode.acc_seg: 96.3322, loss: 0.0867 2023-01-06 15:39:23,016 - mmseg - INFO - Iter [87100/160000] lr: 2.734e-05, eta: 13:44:22, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0886, decode.acc_seg: 96.2473, loss: 0.0886 2023-01-06 15:39:55,850 - mmseg - INFO - Iter [87150/160000] lr: 2.732e-05, eta: 13:43:47, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0897, decode.acc_seg: 96.1947, loss: 0.0897 2023-01-06 15:40:30,046 - mmseg - INFO - Iter [87200/160000] lr: 2.730e-05, eta: 13:43:13, time: 0.683, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0913, decode.acc_seg: 96.1591, loss: 0.0913 2023-01-06 15:41:05,483 - mmseg - INFO - Iter [87250/160000] lr: 2.728e-05, eta: 13:42:41, time: 0.709, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0895, decode.acc_seg: 96.2010, loss: 0.0895 2023-01-06 15:41:39,060 - mmseg - INFO - Iter [87300/160000] lr: 2.726e-05, eta: 13:42:06, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0936, decode.acc_seg: 96.1718, loss: 0.0936 2023-01-06 15:42:12,179 - mmseg - INFO - Iter [87350/160000] lr: 2.724e-05, eta: 13:41:32, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0970, decode.acc_seg: 96.0786, loss: 0.0970 2023-01-06 15:42:44,665 - mmseg - INFO - Iter [87400/160000] lr: 2.723e-05, eta: 13:40:57, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1032, decode.acc_seg: 95.8877, loss: 0.1032 2023-01-06 15:43:20,191 - mmseg - INFO - Iter [87450/160000] lr: 2.721e-05, eta: 13:40:24, time: 0.710, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0912, decode.acc_seg: 96.1034, loss: 0.0912 2023-01-06 15:43:53,235 - mmseg - INFO - Iter [87500/160000] lr: 2.719e-05, eta: 13:39:49, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1021, decode.acc_seg: 95.9026, loss: 0.1021 2023-01-06 15:44:26,644 - mmseg - INFO - Iter [87550/160000] lr: 2.717e-05, eta: 13:39:15, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0990, decode.acc_seg: 95.7723, loss: 0.0990 2023-01-06 15:45:00,284 - mmseg - INFO - Iter [87600/160000] lr: 2.715e-05, eta: 13:38:41, time: 0.674, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0940, decode.acc_seg: 96.1422, loss: 0.0940 2023-01-06 15:45:35,603 - mmseg - INFO - Iter [87650/160000] lr: 2.713e-05, eta: 13:38:08, time: 0.705, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0916, decode.acc_seg: 96.1605, loss: 0.0916 2023-01-06 15:46:08,842 - mmseg - INFO - Iter [87700/160000] lr: 2.711e-05, eta: 13:37:34, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0937, decode.acc_seg: 95.9605, loss: 0.0937 2023-01-06 15:46:41,567 - mmseg - INFO - Iter [87750/160000] lr: 2.709e-05, eta: 13:36:59, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0921, decode.acc_seg: 96.1957, loss: 0.0921 2023-01-06 15:47:17,746 - mmseg - INFO - Iter [87800/160000] lr: 2.708e-05, eta: 13:36:27, time: 0.723, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0932, decode.acc_seg: 96.0164, loss: 0.0932 2023-01-06 15:47:50,941 - mmseg - INFO - Iter [87850/160000] lr: 2.706e-05, eta: 13:35:52, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0969, decode.acc_seg: 95.9626, loss: 0.0969 2023-01-06 15:48:24,232 - mmseg - INFO - Iter [87900/160000] lr: 2.704e-05, eta: 13:35:18, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0962, decode.acc_seg: 95.9652, loss: 0.0962 2023-01-06 15:48:57,245 - mmseg - INFO - Iter [87950/160000] lr: 2.702e-05, eta: 13:34:43, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0927, decode.acc_seg: 96.0646, loss: 0.0927 2023-01-06 15:49:29,510 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 15:49:29,510 - mmseg - INFO - Iter [88000/160000] lr: 2.700e-05, eta: 13:34:08, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0930, decode.acc_seg: 96.0049, loss: 0.0930 2023-01-06 15:50:02,637 - mmseg - INFO - Iter [88050/160000] lr: 2.698e-05, eta: 13:33:33, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0886, decode.acc_seg: 96.3511, loss: 0.0886 2023-01-06 15:50:36,482 - mmseg - INFO - Iter [88100/160000] lr: 2.696e-05, eta: 13:32:59, time: 0.676, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0910, decode.acc_seg: 96.1852, loss: 0.0910 2023-01-06 15:51:11,379 - mmseg - INFO - Iter [88150/160000] lr: 2.694e-05, eta: 13:32:26, time: 0.698, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0975, decode.acc_seg: 95.9721, loss: 0.0975 2023-01-06 15:51:47,284 - mmseg - INFO - Iter [88200/160000] lr: 2.693e-05, eta: 13:31:54, time: 0.719, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0944, decode.acc_seg: 96.1369, loss: 0.0944 2023-01-06 15:52:20,002 - mmseg - INFO - Iter [88250/160000] lr: 2.691e-05, eta: 13:31:19, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0889, decode.acc_seg: 96.2115, loss: 0.0889 2023-01-06 15:52:52,576 - mmseg - INFO - Iter [88300/160000] lr: 2.689e-05, eta: 13:30:44, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0943, decode.acc_seg: 95.9618, loss: 0.0943 2023-01-06 15:53:25,675 - mmseg - INFO - Iter [88350/160000] lr: 2.687e-05, eta: 13:30:09, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0896, decode.acc_seg: 96.2819, loss: 0.0896 2023-01-06 15:53:59,605 - mmseg - INFO - Iter [88400/160000] lr: 2.685e-05, eta: 13:29:35, time: 0.678, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0927, decode.acc_seg: 96.0902, loss: 0.0927 2023-01-06 15:54:32,465 - mmseg - INFO - Iter [88450/160000] lr: 2.683e-05, eta: 13:29:01, time: 0.658, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0876, decode.acc_seg: 96.2247, loss: 0.0876 2023-01-06 15:55:06,318 - mmseg - INFO - Iter [88500/160000] lr: 2.681e-05, eta: 13:28:27, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0902, decode.acc_seg: 96.1798, loss: 0.0902 2023-01-06 15:55:41,422 - mmseg - INFO - Iter [88550/160000] lr: 2.679e-05, eta: 13:27:54, time: 0.701, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0948, decode.acc_seg: 96.0498, loss: 0.0948 2023-01-06 15:56:14,455 - mmseg - INFO - Iter [88600/160000] lr: 2.678e-05, eta: 13:27:19, time: 0.661, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0844, decode.acc_seg: 96.3521, loss: 0.0844 2023-01-06 15:56:46,632 - mmseg - INFO - Iter [88650/160000] lr: 2.676e-05, eta: 13:26:44, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0884, decode.acc_seg: 96.2569, loss: 0.0884 2023-01-06 15:57:18,967 - mmseg - INFO - Iter [88700/160000] lr: 2.674e-05, eta: 13:26:08, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0979, decode.acc_seg: 95.9670, loss: 0.0979 2023-01-06 15:57:53,747 - mmseg - INFO - Iter [88750/160000] lr: 2.672e-05, eta: 13:25:35, time: 0.696, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0907, decode.acc_seg: 96.2084, loss: 0.0907 2023-01-06 15:58:26,510 - mmseg - INFO - Iter [88800/160000] lr: 2.670e-05, eta: 13:25:00, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0898, decode.acc_seg: 96.2130, loss: 0.0898 2023-01-06 15:58:59,149 - mmseg - INFO - Iter [88850/160000] lr: 2.668e-05, eta: 13:24:25, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0858, decode.acc_seg: 96.3253, loss: 0.0858 2023-01-06 15:59:31,345 - mmseg - INFO - Iter [88900/160000] lr: 2.666e-05, eta: 13:23:50, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1037, decode.acc_seg: 95.8400, loss: 0.1037 2023-01-06 16:00:08,307 - mmseg - INFO - Iter [88950/160000] lr: 2.664e-05, eta: 13:23:19, time: 0.738, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0960, decode.acc_seg: 95.9467, loss: 0.0960 2023-01-06 16:00:41,448 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 16:00:41,449 - mmseg - INFO - Iter [89000/160000] lr: 2.663e-05, eta: 13:22:44, time: 0.663, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0916, decode.acc_seg: 96.2782, loss: 0.0916 2023-01-06 16:01:14,669 - mmseg - INFO - Iter [89050/160000] lr: 2.661e-05, eta: 13:22:10, time: 0.665, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0981, decode.acc_seg: 95.8499, loss: 0.0981 2023-01-06 16:01:46,798 - mmseg - INFO - Iter [89100/160000] lr: 2.659e-05, eta: 13:21:34, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0993, decode.acc_seg: 95.8530, loss: 0.0993 2023-01-06 16:02:18,896 - mmseg - INFO - Iter [89150/160000] lr: 2.657e-05, eta: 13:20:59, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0931, decode.acc_seg: 96.0501, loss: 0.0931 2023-01-06 16:02:51,814 - mmseg - INFO - Iter [89200/160000] lr: 2.655e-05, eta: 13:20:24, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0958, decode.acc_seg: 95.9965, loss: 0.0958 2023-01-06 16:03:25,504 - mmseg - INFO - Iter [89250/160000] lr: 2.653e-05, eta: 13:19:50, time: 0.674, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0911, decode.acc_seg: 96.1157, loss: 0.0911 2023-01-06 16:04:01,268 - mmseg - INFO - Iter [89300/160000] lr: 2.651e-05, eta: 13:19:18, time: 0.715, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1047, decode.acc_seg: 95.7142, loss: 0.1047 2023-01-06 16:04:34,689 - mmseg - INFO - Iter [89350/160000] lr: 2.649e-05, eta: 13:18:43, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0959, decode.acc_seg: 95.9470, loss: 0.0959 2023-01-06 16:05:07,332 - mmseg - INFO - Iter [89400/160000] lr: 2.648e-05, eta: 13:18:08, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0936, decode.acc_seg: 95.9414, loss: 0.0936 2023-01-06 16:05:40,396 - mmseg - INFO - Iter [89450/160000] lr: 2.646e-05, eta: 13:17:34, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0909, decode.acc_seg: 96.0705, loss: 0.0909 2023-01-06 16:06:12,830 - mmseg - INFO - Iter [89500/160000] lr: 2.644e-05, eta: 13:16:59, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0930, decode.acc_seg: 96.1372, loss: 0.0930 2023-01-06 16:06:48,605 - mmseg - INFO - Iter [89550/160000] lr: 2.642e-05, eta: 13:16:26, time: 0.716, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0930, decode.acc_seg: 96.1050, loss: 0.0930 2023-01-06 16:07:22,382 - mmseg - INFO - Iter [89600/160000] lr: 2.640e-05, eta: 13:15:52, time: 0.676, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0839, decode.acc_seg: 96.3786, loss: 0.0839 2023-01-06 16:07:56,556 - mmseg - INFO - Iter [89650/160000] lr: 2.638e-05, eta: 13:15:19, time: 0.683, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0882, decode.acc_seg: 96.3107, loss: 0.0882 2023-01-06 16:08:31,450 - mmseg - INFO - Iter [89700/160000] lr: 2.636e-05, eta: 13:14:45, time: 0.699, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0878, decode.acc_seg: 96.2887, loss: 0.0878 2023-01-06 16:09:03,529 - mmseg - INFO - Iter [89750/160000] lr: 2.634e-05, eta: 13:14:10, time: 0.641, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0871, decode.acc_seg: 96.3092, loss: 0.0871 2023-01-06 16:09:36,287 - mmseg - INFO - Iter [89800/160000] lr: 2.633e-05, eta: 13:13:35, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0844, decode.acc_seg: 96.4277, loss: 0.0844 2023-01-06 16:10:09,516 - mmseg - INFO - Iter [89850/160000] lr: 2.631e-05, eta: 13:13:01, time: 0.665, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0866, decode.acc_seg: 96.2697, loss: 0.0866 2023-01-06 16:10:41,855 - mmseg - INFO - Iter [89900/160000] lr: 2.629e-05, eta: 13:12:26, time: 0.648, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0878, decode.acc_seg: 96.2401, loss: 0.0878 2023-01-06 16:11:14,304 - mmseg - INFO - Iter [89950/160000] lr: 2.627e-05, eta: 13:11:51, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0901, decode.acc_seg: 96.0822, loss: 0.0901 2023-01-06 16:11:47,717 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 16:11:47,717 - mmseg - INFO - Iter [90000/160000] lr: 2.625e-05, eta: 13:11:16, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0931, decode.acc_seg: 96.1187, loss: 0.0931 2023-01-06 16:12:25,554 - mmseg - INFO - Iter [90050/160000] lr: 2.623e-05, eta: 13:10:45, time: 0.756, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0879, decode.acc_seg: 96.2445, loss: 0.0879 2023-01-06 16:12:59,256 - mmseg - INFO - Iter [90100/160000] lr: 2.621e-05, eta: 13:10:11, time: 0.675, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0915, decode.acc_seg: 96.1816, loss: 0.0915 2023-01-06 16:13:31,930 - mmseg - INFO - Iter [90150/160000] lr: 2.619e-05, eta: 13:09:36, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0860, decode.acc_seg: 96.3286, loss: 0.0860 2023-01-06 16:14:06,930 - mmseg - INFO - Iter [90200/160000] lr: 2.618e-05, eta: 13:09:03, time: 0.701, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0880, decode.acc_seg: 96.2182, loss: 0.0880 2023-01-06 16:14:39,851 - mmseg - INFO - Iter [90250/160000] lr: 2.616e-05, eta: 13:08:29, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0902, decode.acc_seg: 96.1139, loss: 0.0902 2023-01-06 16:15:12,694 - mmseg - INFO - Iter [90300/160000] lr: 2.614e-05, eta: 13:07:54, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0906, decode.acc_seg: 96.1711, loss: 0.0906 2023-01-06 16:15:46,300 - mmseg - INFO - Iter [90350/160000] lr: 2.612e-05, eta: 13:07:20, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0887, decode.acc_seg: 96.2157, loss: 0.0887 2023-01-06 16:16:22,234 - mmseg - INFO - Iter [90400/160000] lr: 2.610e-05, eta: 13:06:48, time: 0.719, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0912, decode.acc_seg: 96.2241, loss: 0.0912 2023-01-06 16:16:54,469 - mmseg - INFO - Iter [90450/160000] lr: 2.608e-05, eta: 13:06:12, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0893, decode.acc_seg: 96.2653, loss: 0.0893 2023-01-06 16:17:27,251 - mmseg - INFO - Iter [90500/160000] lr: 2.606e-05, eta: 13:05:38, time: 0.656, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0913, decode.acc_seg: 96.1440, loss: 0.0913 2023-01-06 16:18:01,922 - mmseg - INFO - Iter [90550/160000] lr: 2.604e-05, eta: 13:05:04, time: 0.692, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0835, decode.acc_seg: 96.4492, loss: 0.0835 2023-01-06 16:18:34,619 - mmseg - INFO - Iter [90600/160000] lr: 2.603e-05, eta: 13:04:29, time: 0.655, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0937, decode.acc_seg: 95.9811, loss: 0.0937 2023-01-06 16:19:06,971 - mmseg - INFO - Iter [90650/160000] lr: 2.601e-05, eta: 13:03:54, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0983, decode.acc_seg: 95.8704, loss: 0.0983 2023-01-06 16:19:40,451 - mmseg - INFO - Iter [90700/160000] lr: 2.599e-05, eta: 13:03:20, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0876, decode.acc_seg: 96.2653, loss: 0.0876 2023-01-06 16:20:12,921 - mmseg - INFO - Iter [90750/160000] lr: 2.597e-05, eta: 13:02:45, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0883, decode.acc_seg: 96.2609, loss: 0.0883 2023-01-06 16:20:47,306 - mmseg - INFO - Iter [90800/160000] lr: 2.595e-05, eta: 13:02:11, time: 0.688, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0824, decode.acc_seg: 96.4693, loss: 0.0824 2023-01-06 16:21:20,592 - mmseg - INFO - Iter [90850/160000] lr: 2.593e-05, eta: 13:01:37, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0906, decode.acc_seg: 96.2038, loss: 0.0906 2023-01-06 16:21:53,117 - mmseg - INFO - Iter [90900/160000] lr: 2.591e-05, eta: 13:01:02, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0911, decode.acc_seg: 96.2128, loss: 0.0911 2023-01-06 16:22:25,621 - mmseg - INFO - Iter [90950/160000] lr: 2.589e-05, eta: 13:00:27, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0899, decode.acc_seg: 96.1214, loss: 0.0899 2023-01-06 16:22:58,648 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 16:22:58,649 - mmseg - INFO - Iter [91000/160000] lr: 2.588e-05, eta: 12:59:53, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0868, decode.acc_seg: 96.3129, loss: 0.0868 2023-01-06 16:23:31,145 - mmseg - INFO - Iter [91050/160000] lr: 2.586e-05, eta: 12:59:18, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0866, decode.acc_seg: 96.3180, loss: 0.0866 2023-01-06 16:24:03,757 - mmseg - INFO - Iter [91100/160000] lr: 2.584e-05, eta: 12:58:43, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0871, decode.acc_seg: 96.3045, loss: 0.0871 2023-01-06 16:24:38,445 - mmseg - INFO - Iter [91150/160000] lr: 2.582e-05, eta: 12:58:09, time: 0.694, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0956, decode.acc_seg: 95.9986, loss: 0.0956 2023-01-06 16:25:11,475 - mmseg - INFO - Iter [91200/160000] lr: 2.580e-05, eta: 12:57:35, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0890, decode.acc_seg: 96.2509, loss: 0.0890 2023-01-06 16:25:43,804 - mmseg - INFO - Iter [91250/160000] lr: 2.578e-05, eta: 12:57:00, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0910, decode.acc_seg: 96.1398, loss: 0.0910 2023-01-06 16:26:16,088 - mmseg - INFO - Iter [91300/160000] lr: 2.576e-05, eta: 12:56:25, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0937, decode.acc_seg: 96.0252, loss: 0.0937 2023-01-06 16:26:48,279 - mmseg - INFO - Iter [91350/160000] lr: 2.574e-05, eta: 12:55:49, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0897, decode.acc_seg: 96.1923, loss: 0.0897 2023-01-06 16:27:20,487 - mmseg - INFO - Iter [91400/160000] lr: 2.573e-05, eta: 12:55:14, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0897, decode.acc_seg: 96.1950, loss: 0.0897 2023-01-06 16:27:53,231 - mmseg - INFO - Iter [91450/160000] lr: 2.571e-05, eta: 12:54:39, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0846, decode.acc_seg: 96.4241, loss: 0.0846 2023-01-06 16:28:25,394 - mmseg - INFO - Iter [91500/160000] lr: 2.569e-05, eta: 12:54:04, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0930, decode.acc_seg: 96.0911, loss: 0.0930 2023-01-06 16:28:59,734 - mmseg - INFO - Iter [91550/160000] lr: 2.567e-05, eta: 12:53:31, time: 0.687, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0958, decode.acc_seg: 95.9557, loss: 0.0958 2023-01-06 16:29:32,683 - mmseg - INFO - Iter [91600/160000] lr: 2.565e-05, eta: 12:52:56, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0916, decode.acc_seg: 96.1319, loss: 0.0916 2023-01-06 16:30:04,874 - mmseg - INFO - Iter [91650/160000] lr: 2.563e-05, eta: 12:52:21, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0945, decode.acc_seg: 95.9453, loss: 0.0945 2023-01-06 16:30:37,150 - mmseg - INFO - Iter [91700/160000] lr: 2.561e-05, eta: 12:51:46, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0891, decode.acc_seg: 96.1413, loss: 0.0891 2023-01-06 16:31:09,593 - mmseg - INFO - Iter [91750/160000] lr: 2.559e-05, eta: 12:51:11, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0872, decode.acc_seg: 96.2902, loss: 0.0872 2023-01-06 16:31:44,015 - mmseg - INFO - Iter [91800/160000] lr: 2.558e-05, eta: 12:50:37, time: 0.688, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0924, decode.acc_seg: 96.1358, loss: 0.0924 2023-01-06 16:32:16,868 - mmseg - INFO - Iter [91850/160000] lr: 2.556e-05, eta: 12:50:03, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0913, decode.acc_seg: 96.1910, loss: 0.0913 2023-01-06 16:32:51,619 - mmseg - INFO - Iter [91900/160000] lr: 2.554e-05, eta: 12:49:29, time: 0.695, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0859, decode.acc_seg: 96.3386, loss: 0.0859 2023-01-06 16:33:23,881 - mmseg - INFO - Iter [91950/160000] lr: 2.552e-05, eta: 12:48:54, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0834, decode.acc_seg: 96.4465, loss: 0.0834 2023-01-06 16:33:56,801 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 16:33:56,802 - mmseg - INFO - Iter [92000/160000] lr: 2.550e-05, eta: 12:48:20, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0915, decode.acc_seg: 96.1708, loss: 0.0915 2023-01-06 16:34:30,348 - mmseg - INFO - Iter [92050/160000] lr: 2.548e-05, eta: 12:47:45, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0935, decode.acc_seg: 96.0954, loss: 0.0935 2023-01-06 16:35:04,980 - mmseg - INFO - Iter [92100/160000] lr: 2.546e-05, eta: 12:47:12, time: 0.694, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0903, decode.acc_seg: 96.1506, loss: 0.0903 2023-01-06 16:35:37,825 - mmseg - INFO - Iter [92150/160000] lr: 2.544e-05, eta: 12:46:37, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0988, decode.acc_seg: 95.9614, loss: 0.0988 2023-01-06 16:36:11,403 - mmseg - INFO - Iter [92200/160000] lr: 2.543e-05, eta: 12:46:03, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0974, decode.acc_seg: 95.9095, loss: 0.0974 2023-01-06 16:36:44,950 - mmseg - INFO - Iter [92250/160000] lr: 2.541e-05, eta: 12:45:29, time: 0.672, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0896, decode.acc_seg: 96.0738, loss: 0.0896 2023-01-06 16:37:20,088 - mmseg - INFO - Iter [92300/160000] lr: 2.539e-05, eta: 12:44:56, time: 0.703, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0929, decode.acc_seg: 96.0136, loss: 0.0929 2023-01-06 16:37:53,185 - mmseg - INFO - Iter [92350/160000] lr: 2.537e-05, eta: 12:44:22, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0903, decode.acc_seg: 96.1292, loss: 0.0903 2023-01-06 16:38:27,760 - mmseg - INFO - Iter [92400/160000] lr: 2.535e-05, eta: 12:43:48, time: 0.692, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0858, decode.acc_seg: 96.3125, loss: 0.0858 2023-01-06 16:39:00,678 - mmseg - INFO - Iter [92450/160000] lr: 2.533e-05, eta: 12:43:14, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0908, decode.acc_seg: 96.1208, loss: 0.0908 2023-01-06 16:39:34,836 - mmseg - INFO - Iter [92500/160000] lr: 2.531e-05, eta: 12:42:40, time: 0.683, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0874, decode.acc_seg: 96.2213, loss: 0.0874 2023-01-06 16:40:10,067 - mmseg - INFO - Iter [92550/160000] lr: 2.529e-05, eta: 12:42:07, time: 0.705, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0891, decode.acc_seg: 96.1769, loss: 0.0891 2023-01-06 16:40:42,387 - mmseg - INFO - Iter [92600/160000] lr: 2.528e-05, eta: 12:41:32, time: 0.646, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0904, decode.acc_seg: 96.1943, loss: 0.0904 2023-01-06 16:41:18,901 - mmseg - INFO - Iter [92650/160000] lr: 2.526e-05, eta: 12:41:00, time: 0.730, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0873, decode.acc_seg: 96.3251, loss: 0.0873 2023-01-06 16:41:51,336 - mmseg - INFO - Iter [92700/160000] lr: 2.524e-05, eta: 12:40:25, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0861, decode.acc_seg: 96.3780, loss: 0.0861 2023-01-06 16:42:23,496 - mmseg - INFO - Iter [92750/160000] lr: 2.522e-05, eta: 12:39:50, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0930, decode.acc_seg: 96.1695, loss: 0.0930 2023-01-06 16:42:57,243 - mmseg - INFO - Iter [92800/160000] lr: 2.520e-05, eta: 12:39:16, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0892, decode.acc_seg: 96.2358, loss: 0.0892 2023-01-06 16:43:30,966 - mmseg - INFO - Iter [92850/160000] lr: 2.518e-05, eta: 12:38:42, time: 0.675, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0919, decode.acc_seg: 96.1583, loss: 0.0919 2023-01-06 16:44:05,889 - mmseg - INFO - Iter [92900/160000] lr: 2.516e-05, eta: 12:38:09, time: 0.698, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0868, decode.acc_seg: 96.2698, loss: 0.0868 2023-01-06 16:44:40,779 - mmseg - INFO - Iter [92950/160000] lr: 2.514e-05, eta: 12:37:35, time: 0.698, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0859, decode.acc_seg: 96.3770, loss: 0.0859 2023-01-06 16:45:14,976 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 16:45:14,977 - mmseg - INFO - Iter [93000/160000] lr: 2.513e-05, eta: 12:37:02, time: 0.684, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0871, decode.acc_seg: 96.3036, loss: 0.0871 2023-01-06 16:45:51,934 - mmseg - INFO - Iter [93050/160000] lr: 2.511e-05, eta: 12:36:30, time: 0.740, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0918, decode.acc_seg: 96.0545, loss: 0.0918 2023-01-06 16:46:25,188 - mmseg - INFO - Iter [93100/160000] lr: 2.509e-05, eta: 12:35:56, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0857, decode.acc_seg: 96.3544, loss: 0.0857 2023-01-06 16:46:57,486 - mmseg - INFO - Iter [93150/160000] lr: 2.507e-05, eta: 12:35:21, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0892, decode.acc_seg: 96.2430, loss: 0.0892 2023-01-06 16:47:30,265 - mmseg - INFO - Iter [93200/160000] lr: 2.505e-05, eta: 12:34:46, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0959, decode.acc_seg: 95.9847, loss: 0.0959 2023-01-06 16:48:03,009 - mmseg - INFO - Iter [93250/160000] lr: 2.503e-05, eta: 12:34:11, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0890, decode.acc_seg: 96.1817, loss: 0.0890 2023-01-06 16:48:38,628 - mmseg - INFO - Iter [93300/160000] lr: 2.501e-05, eta: 12:33:39, time: 0.711, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0895, decode.acc_seg: 96.2231, loss: 0.0895 2023-01-06 16:49:12,001 - mmseg - INFO - Iter [93350/160000] lr: 2.499e-05, eta: 12:33:04, time: 0.668, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0925, decode.acc_seg: 96.1256, loss: 0.0925 2023-01-06 16:49:47,192 - mmseg - INFO - Iter [93400/160000] lr: 2.498e-05, eta: 12:32:31, time: 0.704, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0894, decode.acc_seg: 96.1769, loss: 0.0894 2023-01-06 16:50:20,975 - mmseg - INFO - Iter [93450/160000] lr: 2.496e-05, eta: 12:31:57, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1088, decode.acc_seg: 95.5985, loss: 0.1088 2023-01-06 16:50:53,487 - mmseg - INFO - Iter [93500/160000] lr: 2.494e-05, eta: 12:31:23, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0901, decode.acc_seg: 96.1755, loss: 0.0901 2023-01-06 16:51:27,691 - mmseg - INFO - Iter [93550/160000] lr: 2.492e-05, eta: 12:30:49, time: 0.683, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0913, decode.acc_seg: 96.0222, loss: 0.0913 2023-01-06 16:52:01,770 - mmseg - INFO - Iter [93600/160000] lr: 2.490e-05, eta: 12:30:15, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0881, decode.acc_seg: 96.3357, loss: 0.0881 2023-01-06 16:52:34,155 - mmseg - INFO - Iter [93650/160000] lr: 2.488e-05, eta: 12:29:40, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1004, decode.acc_seg: 95.8267, loss: 0.1004 2023-01-06 16:53:07,490 - mmseg - INFO - Iter [93700/160000] lr: 2.486e-05, eta: 12:29:06, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0941, decode.acc_seg: 96.0337, loss: 0.0941 2023-01-06 16:53:42,437 - mmseg - INFO - Iter [93750/160000] lr: 2.484e-05, eta: 12:28:33, time: 0.698, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0853, decode.acc_seg: 96.3701, loss: 0.0853 2023-01-06 16:54:17,379 - mmseg - INFO - Iter [93800/160000] lr: 2.483e-05, eta: 12:27:59, time: 0.700, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0832, decode.acc_seg: 96.2424, loss: 0.0832 2023-01-06 16:54:50,311 - mmseg - INFO - Iter [93850/160000] lr: 2.481e-05, eta: 12:27:25, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0897, decode.acc_seg: 96.1437, loss: 0.0897 2023-01-06 16:55:23,819 - mmseg - INFO - Iter [93900/160000] lr: 2.479e-05, eta: 12:26:51, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0862, decode.acc_seg: 96.3748, loss: 0.0862 2023-01-06 16:55:57,511 - mmseg - INFO - Iter [93950/160000] lr: 2.477e-05, eta: 12:26:17, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0931, decode.acc_seg: 96.1270, loss: 0.0931 2023-01-06 16:56:31,587 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 16:56:31,588 - mmseg - INFO - Iter [94000/160000] lr: 2.475e-05, eta: 12:25:43, time: 0.681, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0844, decode.acc_seg: 96.3164, loss: 0.0844 2023-01-06 16:57:05,863 - mmseg - INFO - Iter [94050/160000] lr: 2.473e-05, eta: 12:25:09, time: 0.685, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0885, decode.acc_seg: 96.3370, loss: 0.0885 2023-01-06 16:57:40,181 - mmseg - INFO - Iter [94100/160000] lr: 2.471e-05, eta: 12:24:36, time: 0.686, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0940, decode.acc_seg: 95.9966, loss: 0.0940 2023-01-06 16:58:15,579 - mmseg - INFO - Iter [94150/160000] lr: 2.469e-05, eta: 12:24:03, time: 0.709, data_time: 0.059, memory: 11582, decode.loss_ce: 0.1005, decode.acc_seg: 95.9971, loss: 0.1005 2023-01-06 16:58:48,111 - mmseg - INFO - Iter [94200/160000] lr: 2.468e-05, eta: 12:23:28, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0960, decode.acc_seg: 95.9834, loss: 0.0960 2023-01-06 16:59:21,550 - mmseg - INFO - Iter [94250/160000] lr: 2.466e-05, eta: 12:22:54, time: 0.670, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0876, decode.acc_seg: 96.1513, loss: 0.0876 2023-01-06 16:59:54,085 - mmseg - INFO - Iter [94300/160000] lr: 2.464e-05, eta: 12:22:19, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0879, decode.acc_seg: 96.2652, loss: 0.0879 2023-01-06 17:00:27,743 - mmseg - INFO - Iter [94350/160000] lr: 2.462e-05, eta: 12:21:45, time: 0.673, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0901, decode.acc_seg: 96.2110, loss: 0.0901 2023-01-06 17:01:03,114 - mmseg - INFO - Iter [94400/160000] lr: 2.460e-05, eta: 12:21:12, time: 0.706, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0925, decode.acc_seg: 96.1713, loss: 0.0925 2023-01-06 17:01:36,648 - mmseg - INFO - Iter [94450/160000] lr: 2.458e-05, eta: 12:20:38, time: 0.672, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0913, decode.acc_seg: 96.1135, loss: 0.0913 2023-01-06 17:02:11,175 - mmseg - INFO - Iter [94500/160000] lr: 2.456e-05, eta: 12:20:04, time: 0.691, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0938, decode.acc_seg: 96.2088, loss: 0.0938 2023-01-06 17:02:46,022 - mmseg - INFO - Iter [94550/160000] lr: 2.454e-05, eta: 12:19:31, time: 0.696, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0850, decode.acc_seg: 96.3349, loss: 0.0850 2023-01-06 17:03:19,829 - mmseg - INFO - Iter [94600/160000] lr: 2.453e-05, eta: 12:18:57, time: 0.677, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0860, decode.acc_seg: 96.3836, loss: 0.0860 2023-01-06 17:03:52,906 - mmseg - INFO - Iter [94650/160000] lr: 2.451e-05, eta: 12:18:23, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0855, decode.acc_seg: 96.3771, loss: 0.0855 2023-01-06 17:04:25,181 - mmseg - INFO - Iter [94700/160000] lr: 2.449e-05, eta: 12:17:48, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0882, decode.acc_seg: 96.2963, loss: 0.0882 2023-01-06 17:04:58,276 - mmseg - INFO - Iter [94750/160000] lr: 2.447e-05, eta: 12:17:13, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0878, decode.acc_seg: 96.2904, loss: 0.0878 2023-01-06 17:05:30,535 - mmseg - INFO - Iter [94800/160000] lr: 2.445e-05, eta: 12:16:38, time: 0.645, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0909, decode.acc_seg: 96.2625, loss: 0.0909 2023-01-06 17:06:03,364 - mmseg - INFO - Iter [94850/160000] lr: 2.443e-05, eta: 12:16:04, time: 0.657, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0837, decode.acc_seg: 96.4552, loss: 0.0837 2023-01-06 17:06:38,968 - mmseg - INFO - Iter [94900/160000] lr: 2.441e-05, eta: 12:15:31, time: 0.711, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0838, decode.acc_seg: 96.4092, loss: 0.0838 2023-01-06 17:07:11,707 - mmseg - INFO - Iter [94950/160000] lr: 2.439e-05, eta: 12:14:56, time: 0.656, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0916, decode.acc_seg: 96.1834, loss: 0.0916 2023-01-06 17:07:44,308 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 17:07:44,309 - mmseg - INFO - Iter [95000/160000] lr: 2.438e-05, eta: 12:14:21, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0874, decode.acc_seg: 96.3213, loss: 0.0874 2023-01-06 17:08:17,707 - mmseg - INFO - Iter [95050/160000] lr: 2.436e-05, eta: 12:13:47, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0897, decode.acc_seg: 96.1787, loss: 0.0897 2023-01-06 17:08:52,370 - mmseg - INFO - Iter [95100/160000] lr: 2.434e-05, eta: 12:13:14, time: 0.694, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0861, decode.acc_seg: 96.3477, loss: 0.0861 2023-01-06 17:09:26,186 - mmseg - INFO - Iter [95150/160000] lr: 2.432e-05, eta: 12:12:40, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0865, decode.acc_seg: 96.3259, loss: 0.0865 2023-01-06 17:10:00,161 - mmseg - INFO - Iter [95200/160000] lr: 2.430e-05, eta: 12:12:06, time: 0.679, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0870, decode.acc_seg: 96.3131, loss: 0.0870 2023-01-06 17:10:34,737 - mmseg - INFO - Iter [95250/160000] lr: 2.428e-05, eta: 12:11:33, time: 0.691, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0853, decode.acc_seg: 96.3386, loss: 0.0853 2023-01-06 17:11:07,090 - mmseg - INFO - Iter [95300/160000] lr: 2.426e-05, eta: 12:10:58, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0911, decode.acc_seg: 96.1297, loss: 0.0911 2023-01-06 17:11:39,397 - mmseg - INFO - Iter [95350/160000] lr: 2.424e-05, eta: 12:10:23, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0845, decode.acc_seg: 96.3477, loss: 0.0845 2023-01-06 17:12:11,565 - mmseg - INFO - Iter [95400/160000] lr: 2.423e-05, eta: 12:09:48, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0899, decode.acc_seg: 96.0662, loss: 0.0899 2023-01-06 17:12:45,701 - mmseg - INFO - Iter [95450/160000] lr: 2.421e-05, eta: 12:09:14, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0911, decode.acc_seg: 96.2102, loss: 0.0911 2023-01-06 17:13:19,071 - mmseg - INFO - Iter [95500/160000] lr: 2.419e-05, eta: 12:08:40, time: 0.668, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0856, decode.acc_seg: 96.3708, loss: 0.0856 2023-01-06 17:13:54,406 - mmseg - INFO - Iter [95550/160000] lr: 2.417e-05, eta: 12:08:07, time: 0.706, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0869, decode.acc_seg: 96.3346, loss: 0.0869 2023-01-06 17:14:28,273 - mmseg - INFO - Iter [95600/160000] lr: 2.415e-05, eta: 12:07:33, time: 0.678, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0917, decode.acc_seg: 96.1658, loss: 0.0917 2023-01-06 17:15:03,161 - mmseg - INFO - Iter [95650/160000] lr: 2.413e-05, eta: 12:07:00, time: 0.697, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0855, decode.acc_seg: 96.3707, loss: 0.0855 2023-01-06 17:15:37,694 - mmseg - INFO - Iter [95700/160000] lr: 2.411e-05, eta: 12:06:26, time: 0.692, data_time: 0.014, memory: 11582, decode.loss_ce: 0.1003, decode.acc_seg: 95.8248, loss: 0.1003 2023-01-06 17:16:09,875 - mmseg - INFO - Iter [95750/160000] lr: 2.409e-05, eta: 12:05:51, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0945, decode.acc_seg: 95.9178, loss: 0.0945 2023-01-06 17:16:43,078 - mmseg - INFO - Iter [95800/160000] lr: 2.408e-05, eta: 12:05:17, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0974, decode.acc_seg: 95.9320, loss: 0.0974 2023-01-06 17:17:16,713 - mmseg - INFO - Iter [95850/160000] lr: 2.406e-05, eta: 12:04:43, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0890, decode.acc_seg: 96.2774, loss: 0.0890 2023-01-06 17:17:51,205 - mmseg - INFO - Iter [95900/160000] lr: 2.404e-05, eta: 12:04:09, time: 0.690, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0935, decode.acc_seg: 95.9825, loss: 0.0935 2023-01-06 17:18:25,938 - mmseg - INFO - Iter [95950/160000] lr: 2.402e-05, eta: 12:03:36, time: 0.695, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0943, decode.acc_seg: 96.0606, loss: 0.0943 2023-01-06 17:19:02,105 - mmseg - INFO - Saving checkpoint at 96000 iterations 2023-01-06 17:19:08,022 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 17:19:08,023 - mmseg - INFO - Iter [96000/160000] lr: 2.400e-05, eta: 12:03:07, time: 0.843, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0883, decode.acc_seg: 96.2139, loss: 0.0883 2023-01-06 17:19:43,682 - mmseg - INFO - per class results: 2023-01-06 17:19:43,685 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 97.88 | 98.94 | | sidewalk | 82.07 | 91.04 | | building | 91.61 | 96.55 | | wall | 53.87 | 63.39 | | fence | 54.91 | 66.4 | | pole | 61.8 | 71.67 | | traffic light | 64.89 | 73.29 | | traffic sign | 74.62 | 81.69 | | vegetation | 91.73 | 95.47 | | terrain | 58.19 | 68.94 | | sky | 94.1 | 98.57 | | person | 77.92 | 89.3 | | rider | 53.19 | 63.78 | | car | 93.86 | 97.03 | | truck | 70.71 | 79.54 | | bus | 74.78 | 83.21 | | train | 57.92 | 66.05 | | motorcycle | 42.01 | 46.33 | | bicycle | 71.4 | 89.17 | +---------------+-------+-------+ 2023-01-06 17:19:43,685 - mmseg - INFO - Summary: 2023-01-06 17:19:43,686 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 95.44 | 71.97 | 80.02 | +-------+-------+-------+ 2023-01-06 17:19:43,686 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 17:19:43,687 - mmseg - INFO - Iter(val) [63] aAcc: 0.9544, mIoU: 0.7197, mAcc: 0.8002, IoU.road: 0.9788, IoU.sidewalk: 0.8207, IoU.building: 0.9161, IoU.wall: 0.5387, IoU.fence: 0.5491, IoU.pole: 0.6180, IoU.traffic light: 0.6489, IoU.traffic sign: 0.7462, IoU.vegetation: 0.9173, IoU.terrain: 0.5819, IoU.sky: 0.9410, IoU.person: 0.7792, IoU.rider: 0.5319, IoU.car: 0.9386, IoU.truck: 0.7071, IoU.bus: 0.7478, IoU.train: 0.5792, IoU.motorcycle: 0.4201, IoU.bicycle: 0.7140, Acc.road: 0.9894, Acc.sidewalk: 0.9104, Acc.building: 0.9655, Acc.wall: 0.6339, Acc.fence: 0.6640, Acc.pole: 0.7167, Acc.traffic light: 0.7329, Acc.traffic sign: 0.8169, Acc.vegetation: 0.9547, Acc.terrain: 0.6894, Acc.sky: 0.9857, Acc.person: 0.8930, Acc.rider: 0.6378, Acc.car: 0.9703, Acc.truck: 0.7954, Acc.bus: 0.8321, Acc.train: 0.6605, Acc.motorcycle: 0.4633, Acc.bicycle: 0.8917 2023-01-06 17:20:16,025 - mmseg - INFO - Iter [96050/160000] lr: 2.398e-05, eta: 12:02:56, time: 1.360, data_time: 0.727, memory: 11582, decode.loss_ce: 0.0919, decode.acc_seg: 96.0623, loss: 0.0919 2023-01-06 17:20:48,608 - mmseg - INFO - Iter [96100/160000] lr: 2.396e-05, eta: 12:02:21, time: 0.651, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0990, decode.acc_seg: 95.9311, loss: 0.0990 2023-01-06 17:21:23,770 - mmseg - INFO - Iter [96150/160000] lr: 2.394e-05, eta: 12:01:48, time: 0.704, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0913, decode.acc_seg: 96.1788, loss: 0.0913 2023-01-06 17:21:59,213 - mmseg - INFO - Iter [96200/160000] lr: 2.393e-05, eta: 12:01:15, time: 0.708, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0868, decode.acc_seg: 96.3385, loss: 0.0868 2023-01-06 17:22:33,838 - mmseg - INFO - Iter [96250/160000] lr: 2.391e-05, eta: 12:00:42, time: 0.692, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0899, decode.acc_seg: 96.2203, loss: 0.0899 2023-01-06 17:23:08,282 - mmseg - INFO - Iter [96300/160000] lr: 2.389e-05, eta: 12:00:08, time: 0.690, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0874, decode.acc_seg: 96.2451, loss: 0.0874 2023-01-06 17:23:42,707 - mmseg - INFO - Iter [96350/160000] lr: 2.387e-05, eta: 11:59:35, time: 0.688, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0874, decode.acc_seg: 96.4094, loss: 0.0874 2023-01-06 17:24:15,393 - mmseg - INFO - Iter [96400/160000] lr: 2.385e-05, eta: 11:59:00, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0814, decode.acc_seg: 96.4467, loss: 0.0814 2023-01-06 17:24:47,804 - mmseg - INFO - Iter [96450/160000] lr: 2.383e-05, eta: 11:58:25, time: 0.648, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0885, decode.acc_seg: 96.2793, loss: 0.0885 2023-01-06 17:25:21,540 - mmseg - INFO - Iter [96500/160000] lr: 2.381e-05, eta: 11:57:51, time: 0.674, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0904, decode.acc_seg: 96.1774, loss: 0.0904 2023-01-06 17:25:54,996 - mmseg - INFO - Iter [96550/160000] lr: 2.379e-05, eta: 11:57:17, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0824, decode.acc_seg: 96.5096, loss: 0.0824 2023-01-06 17:26:27,394 - mmseg - INFO - Iter [96600/160000] lr: 2.378e-05, eta: 11:56:42, time: 0.648, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0883, decode.acc_seg: 96.1784, loss: 0.0883 2023-01-06 17:27:01,235 - mmseg - INFO - Iter [96650/160000] lr: 2.376e-05, eta: 11:56:08, time: 0.677, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0868, decode.acc_seg: 96.2746, loss: 0.0868 2023-01-06 17:27:34,454 - mmseg - INFO - Iter [96700/160000] lr: 2.374e-05, eta: 11:55:34, time: 0.664, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0909, decode.acc_seg: 96.2179, loss: 0.0909 2023-01-06 17:28:10,209 - mmseg - INFO - Iter [96750/160000] lr: 2.372e-05, eta: 11:55:01, time: 0.714, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0910, decode.acc_seg: 96.1719, loss: 0.0910 2023-01-06 17:28:45,313 - mmseg - INFO - Iter [96800/160000] lr: 2.370e-05, eta: 11:54:28, time: 0.702, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0917, decode.acc_seg: 96.2259, loss: 0.0917 2023-01-06 17:29:18,246 - mmseg - INFO - Iter [96850/160000] lr: 2.368e-05, eta: 11:53:53, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0860, decode.acc_seg: 96.2969, loss: 0.0860 2023-01-06 17:29:50,481 - mmseg - INFO - Iter [96900/160000] lr: 2.366e-05, eta: 11:53:18, time: 0.645, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0889, decode.acc_seg: 96.2787, loss: 0.0889 2023-01-06 17:30:22,734 - mmseg - INFO - Iter [96950/160000] lr: 2.364e-05, eta: 11:52:43, time: 0.645, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0862, decode.acc_seg: 96.3671, loss: 0.0862 2023-01-06 17:30:55,692 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 17:30:55,692 - mmseg - INFO - Iter [97000/160000] lr: 2.363e-05, eta: 11:52:09, time: 0.658, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0884, decode.acc_seg: 96.2342, loss: 0.0884 2023-01-06 17:31:31,517 - mmseg - INFO - Iter [97050/160000] lr: 2.361e-05, eta: 11:51:36, time: 0.716, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0856, decode.acc_seg: 96.3442, loss: 0.0856 2023-01-06 17:32:06,160 - mmseg - INFO - Iter [97100/160000] lr: 2.359e-05, eta: 11:51:03, time: 0.694, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0908, decode.acc_seg: 96.1862, loss: 0.0908 2023-01-06 17:32:39,668 - mmseg - INFO - Iter [97150/160000] lr: 2.357e-05, eta: 11:50:29, time: 0.670, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0889, decode.acc_seg: 96.2204, loss: 0.0889 2023-01-06 17:33:12,843 - mmseg - INFO - Iter [97200/160000] lr: 2.355e-05, eta: 11:49:54, time: 0.664, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0865, decode.acc_seg: 96.4200, loss: 0.0865 2023-01-06 17:33:45,150 - mmseg - INFO - Iter [97250/160000] lr: 2.353e-05, eta: 11:49:19, time: 0.646, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0862, decode.acc_seg: 96.3355, loss: 0.0862 2023-01-06 17:34:17,353 - mmseg - INFO - Iter [97300/160000] lr: 2.351e-05, eta: 11:48:44, time: 0.644, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0853, decode.acc_seg: 96.3916, loss: 0.0853 2023-01-06 17:34:50,738 - mmseg - INFO - Iter [97350/160000] lr: 2.349e-05, eta: 11:48:10, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0907, decode.acc_seg: 96.2190, loss: 0.0907 2023-01-06 17:35:24,861 - mmseg - INFO - Iter [97400/160000] lr: 2.348e-05, eta: 11:47:36, time: 0.683, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0855, decode.acc_seg: 96.3338, loss: 0.0855 2023-01-06 17:35:57,899 - mmseg - INFO - Iter [97450/160000] lr: 2.346e-05, eta: 11:47:02, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0902, decode.acc_seg: 96.2065, loss: 0.0902 2023-01-06 17:36:33,398 - mmseg - INFO - Iter [97500/160000] lr: 2.344e-05, eta: 11:46:29, time: 0.711, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0853, decode.acc_seg: 96.3553, loss: 0.0853 2023-01-06 17:37:06,223 - mmseg - INFO - Iter [97550/160000] lr: 2.342e-05, eta: 11:45:54, time: 0.657, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0880, decode.acc_seg: 96.2885, loss: 0.0880 2023-01-06 17:37:39,744 - mmseg - INFO - Iter [97600/160000] lr: 2.340e-05, eta: 11:45:20, time: 0.670, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0858, decode.acc_seg: 96.4741, loss: 0.0858 2023-01-06 17:38:15,133 - mmseg - INFO - Iter [97650/160000] lr: 2.338e-05, eta: 11:44:47, time: 0.709, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0871, decode.acc_seg: 96.2096, loss: 0.0871 2023-01-06 17:38:50,102 - mmseg - INFO - Iter [97700/160000] lr: 2.336e-05, eta: 11:44:14, time: 0.699, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0985, decode.acc_seg: 95.8748, loss: 0.0985 2023-01-06 17:39:24,124 - mmseg - INFO - Iter [97750/160000] lr: 2.334e-05, eta: 11:43:40, time: 0.680, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0953, decode.acc_seg: 95.9588, loss: 0.0953 2023-01-06 17:39:57,345 - mmseg - INFO - Iter [97800/160000] lr: 2.333e-05, eta: 11:43:06, time: 0.664, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0910, decode.acc_seg: 96.1635, loss: 0.0910 2023-01-06 17:40:33,424 - mmseg - INFO - Iter [97850/160000] lr: 2.331e-05, eta: 11:42:33, time: 0.721, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0866, decode.acc_seg: 96.3121, loss: 0.0866 2023-01-06 17:41:07,101 - mmseg - INFO - Iter [97900/160000] lr: 2.329e-05, eta: 11:41:59, time: 0.674, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0894, decode.acc_seg: 96.2212, loss: 0.0894 2023-01-06 17:41:39,730 - mmseg - INFO - Iter [97950/160000] lr: 2.327e-05, eta: 11:41:24, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0848, decode.acc_seg: 96.3880, loss: 0.0848 2023-01-06 17:42:14,550 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 17:42:14,551 - mmseg - INFO - Iter [98000/160000] lr: 2.325e-05, eta: 11:40:51, time: 0.697, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0860, decode.acc_seg: 96.3984, loss: 0.0860 2023-01-06 17:42:47,210 - mmseg - INFO - Iter [98050/160000] lr: 2.323e-05, eta: 11:40:16, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0975, decode.acc_seg: 96.0977, loss: 0.0975 2023-01-06 17:43:21,031 - mmseg - INFO - Iter [98100/160000] lr: 2.321e-05, eta: 11:39:42, time: 0.676, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0908, decode.acc_seg: 96.1550, loss: 0.0908 2023-01-06 17:43:54,501 - mmseg - INFO - Iter [98150/160000] lr: 2.319e-05, eta: 11:39:08, time: 0.669, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0896, decode.acc_seg: 96.1842, loss: 0.0896 2023-01-06 17:44:27,882 - mmseg - INFO - Iter [98200/160000] lr: 2.318e-05, eta: 11:38:34, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0830, decode.acc_seg: 96.4078, loss: 0.0830 2023-01-06 17:45:04,261 - mmseg - INFO - Iter [98250/160000] lr: 2.316e-05, eta: 11:38:01, time: 0.728, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0854, decode.acc_seg: 96.3415, loss: 0.0854 2023-01-06 17:45:38,862 - mmseg - INFO - Iter [98300/160000] lr: 2.314e-05, eta: 11:37:28, time: 0.691, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0859, decode.acc_seg: 96.3643, loss: 0.0859 2023-01-06 17:46:11,351 - mmseg - INFO - Iter [98350/160000] lr: 2.312e-05, eta: 11:36:53, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0886, decode.acc_seg: 96.2121, loss: 0.0886 2023-01-06 17:46:43,779 - mmseg - INFO - Iter [98400/160000] lr: 2.310e-05, eta: 11:36:18, time: 0.648, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0901, decode.acc_seg: 96.2610, loss: 0.0901 2023-01-06 17:47:16,903 - mmseg - INFO - Iter [98450/160000] lr: 2.308e-05, eta: 11:35:44, time: 0.662, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0925, decode.acc_seg: 96.1270, loss: 0.0925 2023-01-06 17:47:49,168 - mmseg - INFO - Iter [98500/160000] lr: 2.306e-05, eta: 11:35:09, time: 0.645, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0868, decode.acc_seg: 96.2991, loss: 0.0868 2023-01-06 17:48:22,239 - mmseg - INFO - Iter [98550/160000] lr: 2.304e-05, eta: 11:34:35, time: 0.662, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0857, decode.acc_seg: 96.3549, loss: 0.0857 2023-01-06 17:48:56,825 - mmseg - INFO - Iter [98600/160000] lr: 2.303e-05, eta: 11:34:01, time: 0.692, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0843, decode.acc_seg: 96.3763, loss: 0.0843 2023-01-06 17:49:29,922 - mmseg - INFO - Iter [98650/160000] lr: 2.301e-05, eta: 11:33:27, time: 0.662, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0836, decode.acc_seg: 96.4734, loss: 0.0836 2023-01-06 17:50:03,055 - mmseg - INFO - Iter [98700/160000] lr: 2.299e-05, eta: 11:32:52, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0857, decode.acc_seg: 96.2932, loss: 0.0857 2023-01-06 17:50:35,525 - mmseg - INFO - Iter [98750/160000] lr: 2.297e-05, eta: 11:32:17, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0877, decode.acc_seg: 96.3561, loss: 0.0877 2023-01-06 17:51:10,335 - mmseg - INFO - Iter [98800/160000] lr: 2.295e-05, eta: 11:31:44, time: 0.696, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0835, decode.acc_seg: 96.3385, loss: 0.0835 2023-01-06 17:51:43,055 - mmseg - INFO - Iter [98850/160000] lr: 2.293e-05, eta: 11:31:09, time: 0.655, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0843, decode.acc_seg: 96.4135, loss: 0.0843 2023-01-06 17:52:15,966 - mmseg - INFO - Iter [98900/160000] lr: 2.291e-05, eta: 11:30:35, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0830, decode.acc_seg: 96.4444, loss: 0.0830 2023-01-06 17:52:50,247 - mmseg - INFO - Iter [98950/160000] lr: 2.289e-05, eta: 11:30:01, time: 0.686, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0831, decode.acc_seg: 96.4578, loss: 0.0831 2023-01-06 17:53:26,563 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 17:53:26,563 - mmseg - INFO - Iter [99000/160000] lr: 2.288e-05, eta: 11:29:29, time: 0.726, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0851, decode.acc_seg: 96.3694, loss: 0.0851 2023-01-06 17:53:58,803 - mmseg - INFO - Iter [99050/160000] lr: 2.286e-05, eta: 11:28:54, time: 0.645, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0831, decode.acc_seg: 96.4974, loss: 0.0831 2023-01-06 17:54:31,671 - mmseg - INFO - Iter [99100/160000] lr: 2.284e-05, eta: 11:28:19, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0864, decode.acc_seg: 96.4082, loss: 0.0864 2023-01-06 17:55:05,766 - mmseg - INFO - Iter [99150/160000] lr: 2.282e-05, eta: 11:27:46, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0893, decode.acc_seg: 96.2574, loss: 0.0893 2023-01-06 17:55:38,546 - mmseg - INFO - Iter [99200/160000] lr: 2.280e-05, eta: 11:27:11, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0863, decode.acc_seg: 96.3895, loss: 0.0863 2023-01-06 17:56:12,802 - mmseg - INFO - Iter [99250/160000] lr: 2.278e-05, eta: 11:26:37, time: 0.685, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0824, decode.acc_seg: 96.4331, loss: 0.0824 2023-01-06 17:56:46,847 - mmseg - INFO - Iter [99300/160000] lr: 2.276e-05, eta: 11:26:03, time: 0.680, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0848, decode.acc_seg: 96.3160, loss: 0.0848 2023-01-06 17:57:22,143 - mmseg - INFO - Iter [99350/160000] lr: 2.274e-05, eta: 11:25:30, time: 0.707, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0859, decode.acc_seg: 96.3298, loss: 0.0859 2023-01-06 17:57:57,705 - mmseg - INFO - Iter [99400/160000] lr: 2.273e-05, eta: 11:24:57, time: 0.711, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0874, decode.acc_seg: 96.3157, loss: 0.0874 2023-01-06 17:58:31,212 - mmseg - INFO - Iter [99450/160000] lr: 2.271e-05, eta: 11:24:23, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0898, decode.acc_seg: 96.1083, loss: 0.0898 2023-01-06 17:59:03,854 - mmseg - INFO - Iter [99500/160000] lr: 2.269e-05, eta: 11:23:49, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0924, decode.acc_seg: 96.0942, loss: 0.0924 2023-01-06 17:59:38,222 - mmseg - INFO - Iter [99550/160000] lr: 2.267e-05, eta: 11:23:15, time: 0.688, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0909, decode.acc_seg: 96.1774, loss: 0.0909 2023-01-06 18:00:11,013 - mmseg - INFO - Iter [99600/160000] lr: 2.265e-05, eta: 11:22:40, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0910, decode.acc_seg: 96.2178, loss: 0.0910 2023-01-06 18:00:43,654 - mmseg - INFO - Iter [99650/160000] lr: 2.263e-05, eta: 11:22:06, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0903, decode.acc_seg: 96.1636, loss: 0.0903 2023-01-06 18:01:19,754 - mmseg - INFO - Iter [99700/160000] lr: 2.261e-05, eta: 11:21:33, time: 0.721, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0863, decode.acc_seg: 96.3772, loss: 0.0863 2023-01-06 18:01:52,346 - mmseg - INFO - Iter [99750/160000] lr: 2.259e-05, eta: 11:20:58, time: 0.653, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0834, decode.acc_seg: 96.4803, loss: 0.0834 2023-01-06 18:02:26,603 - mmseg - INFO - Iter [99800/160000] lr: 2.258e-05, eta: 11:20:25, time: 0.684, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0861, decode.acc_seg: 96.4126, loss: 0.0861 2023-01-06 18:03:01,454 - mmseg - INFO - Iter [99850/160000] lr: 2.256e-05, eta: 11:19:51, time: 0.698, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0850, decode.acc_seg: 96.3862, loss: 0.0850 2023-01-06 18:03:34,441 - mmseg - INFO - Iter [99900/160000] lr: 2.254e-05, eta: 11:19:17, time: 0.659, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0842, decode.acc_seg: 96.2232, loss: 0.0842 2023-01-06 18:04:07,556 - mmseg - INFO - Iter [99950/160000] lr: 2.252e-05, eta: 11:18:43, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0824, decode.acc_seg: 96.3683, loss: 0.0824 2023-01-06 18:04:43,361 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 18:04:43,362 - mmseg - INFO - Iter [100000/160000] lr: 2.250e-05, eta: 11:18:10, time: 0.717, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0809, decode.acc_seg: 96.4296, loss: 0.0809 2023-01-06 18:05:16,985 - mmseg - INFO - Iter [100050/160000] lr: 2.248e-05, eta: 11:17:36, time: 0.672, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0862, decode.acc_seg: 96.3993, loss: 0.0862 2023-01-06 18:05:53,331 - mmseg - INFO - Iter [100100/160000] lr: 2.246e-05, eta: 11:17:03, time: 0.728, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0851, decode.acc_seg: 96.4240, loss: 0.0851 2023-01-06 18:06:26,261 - mmseg - INFO - Iter [100150/160000] lr: 2.244e-05, eta: 11:16:29, time: 0.659, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0884, decode.acc_seg: 96.3482, loss: 0.0884 2023-01-06 18:07:00,356 - mmseg - INFO - Iter [100200/160000] lr: 2.243e-05, eta: 11:15:55, time: 0.681, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0864, decode.acc_seg: 96.4158, loss: 0.0864 2023-01-06 18:07:33,480 - mmseg - INFO - Iter [100250/160000] lr: 2.241e-05, eta: 11:15:21, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0874, decode.acc_seg: 96.3303, loss: 0.0874 2023-01-06 18:08:06,633 - mmseg - INFO - Iter [100300/160000] lr: 2.239e-05, eta: 11:14:46, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0831, decode.acc_seg: 96.4492, loss: 0.0831 2023-01-06 18:08:41,609 - mmseg - INFO - Iter [100350/160000] lr: 2.237e-05, eta: 11:14:13, time: 0.700, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0828, decode.acc_seg: 96.4835, loss: 0.0828 2023-01-06 18:09:14,705 - mmseg - INFO - Iter [100400/160000] lr: 2.235e-05, eta: 11:13:39, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0866, decode.acc_seg: 96.4183, loss: 0.0866 2023-01-06 18:09:51,174 - mmseg - INFO - Iter [100450/160000] lr: 2.233e-05, eta: 11:13:06, time: 0.730, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0836, decode.acc_seg: 96.3850, loss: 0.0836 2023-01-06 18:10:23,870 - mmseg - INFO - Iter [100500/160000] lr: 2.231e-05, eta: 11:12:32, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0799, decode.acc_seg: 96.5060, loss: 0.0799 2023-01-06 18:10:57,299 - mmseg - INFO - Iter [100550/160000] lr: 2.229e-05, eta: 11:11:57, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0840, decode.acc_seg: 96.3118, loss: 0.0840 2023-01-06 18:11:29,473 - mmseg - INFO - Iter [100600/160000] lr: 2.228e-05, eta: 11:11:22, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0890, decode.acc_seg: 96.1822, loss: 0.0890 2023-01-06 18:12:02,293 - mmseg - INFO - Iter [100650/160000] lr: 2.226e-05, eta: 11:10:48, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0922, decode.acc_seg: 96.1259, loss: 0.0922 2023-01-06 18:12:36,745 - mmseg - INFO - Iter [100700/160000] lr: 2.224e-05, eta: 11:10:14, time: 0.689, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0866, decode.acc_seg: 96.3872, loss: 0.0866 2023-01-06 18:13:10,361 - mmseg - INFO - Iter [100750/160000] lr: 2.222e-05, eta: 11:09:40, time: 0.673, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0879, decode.acc_seg: 96.2953, loss: 0.0879 2023-01-06 18:13:42,661 - mmseg - INFO - Iter [100800/160000] lr: 2.220e-05, eta: 11:09:05, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0905, decode.acc_seg: 96.1366, loss: 0.0905 2023-01-06 18:14:18,416 - mmseg - INFO - Iter [100850/160000] lr: 2.218e-05, eta: 11:08:33, time: 0.715, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0834, decode.acc_seg: 96.4335, loss: 0.0834 2023-01-06 18:14:52,131 - mmseg - INFO - Iter [100900/160000] lr: 2.216e-05, eta: 11:07:59, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0874, decode.acc_seg: 96.2501, loss: 0.0874 2023-01-06 18:15:25,223 - mmseg - INFO - Iter [100950/160000] lr: 2.214e-05, eta: 11:07:24, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0877, decode.acc_seg: 96.3132, loss: 0.0877 2023-01-06 18:15:57,950 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 18:15:57,951 - mmseg - INFO - Iter [101000/160000] lr: 2.213e-05, eta: 11:06:50, time: 0.655, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0816, decode.acc_seg: 96.4927, loss: 0.0816 2023-01-06 18:16:30,317 - mmseg - INFO - Iter [101050/160000] lr: 2.211e-05, eta: 11:06:15, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0838, decode.acc_seg: 96.3735, loss: 0.0838 2023-01-06 18:17:04,526 - mmseg - INFO - Iter [101100/160000] lr: 2.209e-05, eta: 11:05:41, time: 0.684, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0822, decode.acc_seg: 96.4461, loss: 0.0822 2023-01-06 18:17:38,605 - mmseg - INFO - Iter [101150/160000] lr: 2.207e-05, eta: 11:05:07, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0859, decode.acc_seg: 96.2887, loss: 0.0859 2023-01-06 18:18:13,380 - mmseg - INFO - Iter [101200/160000] lr: 2.205e-05, eta: 11:04:34, time: 0.695, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0879, decode.acc_seg: 96.2627, loss: 0.0879 2023-01-06 18:18:45,947 - mmseg - INFO - Iter [101250/160000] lr: 2.203e-05, eta: 11:03:59, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0827, decode.acc_seg: 96.4867, loss: 0.0827 2023-01-06 18:19:21,786 - mmseg - INFO - Iter [101300/160000] lr: 2.201e-05, eta: 11:03:26, time: 0.717, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0870, decode.acc_seg: 96.3890, loss: 0.0870 2023-01-06 18:19:55,953 - mmseg - INFO - Iter [101350/160000] lr: 2.199e-05, eta: 11:02:53, time: 0.683, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0841, decode.acc_seg: 96.3769, loss: 0.0841 2023-01-06 18:20:30,421 - mmseg - INFO - Iter [101400/160000] lr: 2.198e-05, eta: 11:02:19, time: 0.688, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0831, decode.acc_seg: 96.4973, loss: 0.0831 2023-01-06 18:21:02,987 - mmseg - INFO - Iter [101450/160000] lr: 2.196e-05, eta: 11:01:44, time: 0.652, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0905, decode.acc_seg: 96.2777, loss: 0.0905 2023-01-06 18:21:35,276 - mmseg - INFO - Iter [101500/160000] lr: 2.194e-05, eta: 11:01:09, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0844, decode.acc_seg: 96.4348, loss: 0.0844 2023-01-06 18:22:07,661 - mmseg - INFO - Iter [101550/160000] lr: 2.192e-05, eta: 11:00:35, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0866, decode.acc_seg: 96.3436, loss: 0.0866 2023-01-06 18:22:43,798 - mmseg - INFO - Iter [101600/160000] lr: 2.190e-05, eta: 11:00:02, time: 0.723, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0827, decode.acc_seg: 96.4603, loss: 0.0827 2023-01-06 18:23:19,424 - mmseg - INFO - Iter [101650/160000] lr: 2.188e-05, eta: 10:59:29, time: 0.712, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0879, decode.acc_seg: 96.2784, loss: 0.0879 2023-01-06 18:23:54,166 - mmseg - INFO - Iter [101700/160000] lr: 2.186e-05, eta: 10:58:56, time: 0.695, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0873, decode.acc_seg: 96.2474, loss: 0.0873 2023-01-06 18:24:27,616 - mmseg - INFO - Iter [101750/160000] lr: 2.184e-05, eta: 10:58:22, time: 0.670, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0923, decode.acc_seg: 96.1626, loss: 0.0923 2023-01-06 18:25:01,743 - mmseg - INFO - Iter [101800/160000] lr: 2.183e-05, eta: 10:57:48, time: 0.683, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0963, decode.acc_seg: 95.9766, loss: 0.0963 2023-01-06 18:25:34,890 - mmseg - INFO - Iter [101850/160000] lr: 2.181e-05, eta: 10:57:13, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0898, decode.acc_seg: 96.0751, loss: 0.0898 2023-01-06 18:26:08,978 - mmseg - INFO - Iter [101900/160000] lr: 2.179e-05, eta: 10:56:40, time: 0.682, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0814, decode.acc_seg: 96.3975, loss: 0.0814 2023-01-06 18:26:45,144 - mmseg - INFO - Iter [101950/160000] lr: 2.177e-05, eta: 10:56:07, time: 0.723, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0851, decode.acc_seg: 96.3909, loss: 0.0851 2023-01-06 18:27:17,576 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 18:27:17,577 - mmseg - INFO - Iter [102000/160000] lr: 2.175e-05, eta: 10:55:32, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0898, decode.acc_seg: 96.2296, loss: 0.0898 2023-01-06 18:27:50,307 - mmseg - INFO - Iter [102050/160000] lr: 2.173e-05, eta: 10:54:58, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0973, decode.acc_seg: 96.0285, loss: 0.0973 2023-01-06 18:28:24,581 - mmseg - INFO - Iter [102100/160000] lr: 2.171e-05, eta: 10:54:24, time: 0.685, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0979, decode.acc_seg: 95.9715, loss: 0.0979 2023-01-06 18:28:56,746 - mmseg - INFO - Iter [102150/160000] lr: 2.169e-05, eta: 10:53:49, time: 0.643, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0888, decode.acc_seg: 96.2211, loss: 0.0888 2023-01-06 18:29:29,583 - mmseg - INFO - Iter [102200/160000] lr: 2.168e-05, eta: 10:53:15, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0911, decode.acc_seg: 96.1977, loss: 0.0911 2023-01-06 18:30:02,008 - mmseg - INFO - Iter [102250/160000] lr: 2.166e-05, eta: 10:52:40, time: 0.649, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0934, decode.acc_seg: 96.1443, loss: 0.0934 2023-01-06 18:30:35,769 - mmseg - INFO - Iter [102300/160000] lr: 2.164e-05, eta: 10:52:06, time: 0.674, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0862, decode.acc_seg: 96.2846, loss: 0.0862 2023-01-06 18:31:12,748 - mmseg - INFO - Iter [102350/160000] lr: 2.162e-05, eta: 10:51:34, time: 0.741, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0847, decode.acc_seg: 96.3629, loss: 0.0847 2023-01-06 18:31:44,928 - mmseg - INFO - Iter [102400/160000] lr: 2.160e-05, eta: 10:50:59, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0880, decode.acc_seg: 96.1692, loss: 0.0880 2023-01-06 18:32:19,183 - mmseg - INFO - Iter [102450/160000] lr: 2.158e-05, eta: 10:50:25, time: 0.685, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0851, decode.acc_seg: 96.4450, loss: 0.0851 2023-01-06 18:32:52,661 - mmseg - INFO - Iter [102500/160000] lr: 2.156e-05, eta: 10:49:51, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0819, decode.acc_seg: 96.4470, loss: 0.0819 2023-01-06 18:33:25,061 - mmseg - INFO - Iter [102550/160000] lr: 2.154e-05, eta: 10:49:16, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0840, decode.acc_seg: 96.4151, loss: 0.0840 2023-01-06 18:33:58,079 - mmseg - INFO - Iter [102600/160000] lr: 2.153e-05, eta: 10:48:42, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0808, decode.acc_seg: 96.5167, loss: 0.0808 2023-01-06 18:34:33,447 - mmseg - INFO - Iter [102650/160000] lr: 2.151e-05, eta: 10:48:09, time: 0.707, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0797, decode.acc_seg: 96.5171, loss: 0.0797 2023-01-06 18:35:08,045 - mmseg - INFO - Iter [102700/160000] lr: 2.149e-05, eta: 10:47:35, time: 0.692, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0812, decode.acc_seg: 96.4807, loss: 0.0812 2023-01-06 18:35:41,363 - mmseg - INFO - Iter [102750/160000] lr: 2.147e-05, eta: 10:47:01, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0863, decode.acc_seg: 96.3251, loss: 0.0863 2023-01-06 18:36:15,572 - mmseg - INFO - Iter [102800/160000] lr: 2.145e-05, eta: 10:46:27, time: 0.684, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0808, decode.acc_seg: 96.4628, loss: 0.0808 2023-01-06 18:36:48,787 - mmseg - INFO - Iter [102850/160000] lr: 2.143e-05, eta: 10:45:53, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0793, decode.acc_seg: 96.5836, loss: 0.0793 2023-01-06 18:37:23,282 - mmseg - INFO - Iter [102900/160000] lr: 2.141e-05, eta: 10:45:19, time: 0.689, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0897, decode.acc_seg: 96.1923, loss: 0.0897 2023-01-06 18:37:56,580 - mmseg - INFO - Iter [102950/160000] lr: 2.139e-05, eta: 10:44:45, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0862, decode.acc_seg: 96.4181, loss: 0.0862 2023-01-06 18:38:30,511 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 18:38:30,512 - mmseg - INFO - Iter [103000/160000] lr: 2.138e-05, eta: 10:44:11, time: 0.678, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0845, decode.acc_seg: 96.4598, loss: 0.0845 2023-01-06 18:39:05,870 - mmseg - INFO - Iter [103050/160000] lr: 2.136e-05, eta: 10:43:38, time: 0.708, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0837, decode.acc_seg: 96.3458, loss: 0.0837 2023-01-06 18:39:39,237 - mmseg - INFO - Iter [103100/160000] lr: 2.134e-05, eta: 10:43:04, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0868, decode.acc_seg: 96.3854, loss: 0.0868 2023-01-06 18:40:14,113 - mmseg - INFO - Iter [103150/160000] lr: 2.132e-05, eta: 10:42:31, time: 0.697, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0848, decode.acc_seg: 96.4580, loss: 0.0848 2023-01-06 18:40:49,455 - mmseg - INFO - Iter [103200/160000] lr: 2.130e-05, eta: 10:41:57, time: 0.708, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0822, decode.acc_seg: 96.4812, loss: 0.0822 2023-01-06 18:41:22,854 - mmseg - INFO - Iter [103250/160000] lr: 2.128e-05, eta: 10:41:23, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0837, decode.acc_seg: 96.4583, loss: 0.0837 2023-01-06 18:41:57,080 - mmseg - INFO - Iter [103300/160000] lr: 2.126e-05, eta: 10:40:49, time: 0.684, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0890, decode.acc_seg: 96.2531, loss: 0.0890 2023-01-06 18:42:30,701 - mmseg - INFO - Iter [103350/160000] lr: 2.124e-05, eta: 10:40:15, time: 0.673, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0883, decode.acc_seg: 96.1988, loss: 0.0883 2023-01-06 18:43:02,971 - mmseg - INFO - Iter [103400/160000] lr: 2.123e-05, eta: 10:39:41, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0856, decode.acc_seg: 96.4075, loss: 0.0856 2023-01-06 18:43:38,299 - mmseg - INFO - Iter [103450/160000] lr: 2.121e-05, eta: 10:39:08, time: 0.706, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0830, decode.acc_seg: 96.4213, loss: 0.0830 2023-01-06 18:44:10,451 - mmseg - INFO - Iter [103500/160000] lr: 2.119e-05, eta: 10:38:33, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0808, decode.acc_seg: 96.5245, loss: 0.0808 2023-01-06 18:44:42,727 - mmseg - INFO - Iter [103550/160000] lr: 2.117e-05, eta: 10:37:58, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0826, decode.acc_seg: 96.4915, loss: 0.0826 2023-01-06 18:45:16,380 - mmseg - INFO - Iter [103600/160000] lr: 2.115e-05, eta: 10:37:24, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0904, decode.acc_seg: 96.1635, loss: 0.0904 2023-01-06 18:45:49,430 - mmseg - INFO - Iter [103650/160000] lr: 2.113e-05, eta: 10:36:49, time: 0.662, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0916, decode.acc_seg: 96.0934, loss: 0.0916 2023-01-06 18:46:21,552 - mmseg - INFO - Iter [103700/160000] lr: 2.111e-05, eta: 10:36:15, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0895, decode.acc_seg: 96.1848, loss: 0.0895 2023-01-06 18:46:56,434 - mmseg - INFO - Iter [103750/160000] lr: 2.109e-05, eta: 10:35:41, time: 0.698, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0857, decode.acc_seg: 96.3689, loss: 0.0857 2023-01-06 18:47:32,049 - mmseg - INFO - Iter [103800/160000] lr: 2.108e-05, eta: 10:35:08, time: 0.712, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0818, decode.acc_seg: 96.4795, loss: 0.0818 2023-01-06 18:48:05,382 - mmseg - INFO - Iter [103850/160000] lr: 2.106e-05, eta: 10:34:34, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0817, decode.acc_seg: 96.4596, loss: 0.0817 2023-01-06 18:48:38,575 - mmseg - INFO - Iter [103900/160000] lr: 2.104e-05, eta: 10:34:00, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0795, decode.acc_seg: 96.6056, loss: 0.0795 2023-01-06 18:49:11,886 - mmseg - INFO - Iter [103950/160000] lr: 2.102e-05, eta: 10:33:25, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0820, decode.acc_seg: 96.5211, loss: 0.0820 2023-01-06 18:49:44,130 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 18:49:44,131 - mmseg - INFO - Iter [104000/160000] lr: 2.100e-05, eta: 10:32:51, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0898, decode.acc_seg: 96.2954, loss: 0.0898 2023-01-06 18:50:16,694 - mmseg - INFO - Iter [104050/160000] lr: 2.098e-05, eta: 10:32:16, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0876, decode.acc_seg: 96.3046, loss: 0.0876 2023-01-06 18:50:50,742 - mmseg - INFO - Iter [104100/160000] lr: 2.096e-05, eta: 10:31:42, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0914, decode.acc_seg: 96.1889, loss: 0.0914 2023-01-06 18:51:23,438 - mmseg - INFO - Iter [104150/160000] lr: 2.094e-05, eta: 10:31:08, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0862, decode.acc_seg: 96.3574, loss: 0.0862 2023-01-06 18:51:58,516 - mmseg - INFO - Iter [104200/160000] lr: 2.093e-05, eta: 10:30:34, time: 0.702, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0891, decode.acc_seg: 96.2169, loss: 0.0891 2023-01-06 18:52:30,689 - mmseg - INFO - Iter [104250/160000] lr: 2.091e-05, eta: 10:30:00, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0877, decode.acc_seg: 96.2028, loss: 0.0877 2023-01-06 18:53:02,771 - mmseg - INFO - Iter [104300/160000] lr: 2.089e-05, eta: 10:29:25, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0823, decode.acc_seg: 96.4696, loss: 0.0823 2023-01-06 18:53:35,516 - mmseg - INFO - Iter [104350/160000] lr: 2.087e-05, eta: 10:28:50, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0814, decode.acc_seg: 96.5028, loss: 0.0814 2023-01-06 18:54:09,610 - mmseg - INFO - Iter [104400/160000] lr: 2.085e-05, eta: 10:28:16, time: 0.683, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0878, decode.acc_seg: 96.2222, loss: 0.0878 2023-01-06 18:54:41,842 - mmseg - INFO - Iter [104450/160000] lr: 2.083e-05, eta: 10:27:42, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0772, decode.acc_seg: 96.6725, loss: 0.0772 2023-01-06 18:55:14,353 - mmseg - INFO - Iter [104500/160000] lr: 2.081e-05, eta: 10:27:07, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0803, decode.acc_seg: 96.4847, loss: 0.0803 2023-01-06 18:55:49,538 - mmseg - INFO - Iter [104550/160000] lr: 2.079e-05, eta: 10:26:34, time: 0.703, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0882, decode.acc_seg: 96.2327, loss: 0.0882 2023-01-06 18:56:23,155 - mmseg - INFO - Iter [104600/160000] lr: 2.078e-05, eta: 10:26:00, time: 0.673, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0852, decode.acc_seg: 96.3195, loss: 0.0852 2023-01-06 18:56:56,331 - mmseg - INFO - Iter [104650/160000] lr: 2.076e-05, eta: 10:25:25, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0870, decode.acc_seg: 96.2401, loss: 0.0870 2023-01-06 18:57:29,197 - mmseg - INFO - Iter [104700/160000] lr: 2.074e-05, eta: 10:24:51, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0852, decode.acc_seg: 96.3238, loss: 0.0852 2023-01-06 18:58:01,899 - mmseg - INFO - Iter [104750/160000] lr: 2.072e-05, eta: 10:24:16, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0835, decode.acc_seg: 96.4115, loss: 0.0835 2023-01-06 18:58:35,106 - mmseg - INFO - Iter [104800/160000] lr: 2.070e-05, eta: 10:23:42, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0838, decode.acc_seg: 96.4319, loss: 0.0838 2023-01-06 18:59:09,066 - mmseg - INFO - Iter [104850/160000] lr: 2.068e-05, eta: 10:23:08, time: 0.680, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0849, decode.acc_seg: 96.3670, loss: 0.0849 2023-01-06 18:59:42,785 - mmseg - INFO - Iter [104900/160000] lr: 2.066e-05, eta: 10:22:34, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0852, decode.acc_seg: 96.4568, loss: 0.0852 2023-01-06 19:00:18,354 - mmseg - INFO - Iter [104950/160000] lr: 2.064e-05, eta: 10:22:01, time: 0.711, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0833, decode.acc_seg: 96.3858, loss: 0.0833 2023-01-06 19:00:50,980 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 19:00:50,980 - mmseg - INFO - Iter [105000/160000] lr: 2.063e-05, eta: 10:21:27, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0823, decode.acc_seg: 96.3711, loss: 0.0823 2023-01-06 19:01:23,418 - mmseg - INFO - Iter [105050/160000] lr: 2.061e-05, eta: 10:20:52, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0864, decode.acc_seg: 96.2750, loss: 0.0864 2023-01-06 19:01:56,487 - mmseg - INFO - Iter [105100/160000] lr: 2.059e-05, eta: 10:20:18, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0862, decode.acc_seg: 96.2822, loss: 0.0862 2023-01-06 19:02:28,654 - mmseg - INFO - Iter [105150/160000] lr: 2.057e-05, eta: 10:19:43, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0818, decode.acc_seg: 96.5664, loss: 0.0818 2023-01-06 19:03:01,508 - mmseg - INFO - Iter [105200/160000] lr: 2.055e-05, eta: 10:19:09, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0879, decode.acc_seg: 96.3301, loss: 0.0879 2023-01-06 19:03:33,764 - mmseg - INFO - Iter [105250/160000] lr: 2.053e-05, eta: 10:18:34, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0993, decode.acc_seg: 95.8034, loss: 0.0993 2023-01-06 19:04:08,253 - mmseg - INFO - Iter [105300/160000] lr: 2.051e-05, eta: 10:18:00, time: 0.690, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0909, decode.acc_seg: 96.1700, loss: 0.0909 2023-01-06 19:04:41,392 - mmseg - INFO - Iter [105350/160000] lr: 2.049e-05, eta: 10:17:26, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0848, decode.acc_seg: 96.4264, loss: 0.0848 2023-01-06 19:05:15,055 - mmseg - INFO - Iter [105400/160000] lr: 2.048e-05, eta: 10:16:52, time: 0.673, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0854, decode.acc_seg: 96.3234, loss: 0.0854 2023-01-06 19:05:48,351 - mmseg - INFO - Iter [105450/160000] lr: 2.046e-05, eta: 10:16:18, time: 0.667, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0819, decode.acc_seg: 96.4961, loss: 0.0819 2023-01-06 19:06:20,826 - mmseg - INFO - Iter [105500/160000] lr: 2.044e-05, eta: 10:15:43, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0855, decode.acc_seg: 96.3958, loss: 0.0855 2023-01-06 19:06:53,716 - mmseg - INFO - Iter [105550/160000] lr: 2.042e-05, eta: 10:15:09, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0836, decode.acc_seg: 96.3617, loss: 0.0836 2023-01-06 19:07:26,302 - mmseg - INFO - Iter [105600/160000] lr: 2.040e-05, eta: 10:14:34, time: 0.653, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0840, decode.acc_seg: 96.4006, loss: 0.0840 2023-01-06 19:08:01,687 - mmseg - INFO - Iter [105650/160000] lr: 2.038e-05, eta: 10:14:01, time: 0.708, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0797, decode.acc_seg: 96.5439, loss: 0.0797 2023-01-06 19:08:34,936 - mmseg - INFO - Iter [105700/160000] lr: 2.036e-05, eta: 10:13:27, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0799, decode.acc_seg: 96.5163, loss: 0.0799 2023-01-06 19:09:08,354 - mmseg - INFO - Iter [105750/160000] lr: 2.034e-05, eta: 10:12:53, time: 0.669, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0809, decode.acc_seg: 96.5565, loss: 0.0809 2023-01-06 19:09:41,783 - mmseg - INFO - Iter [105800/160000] lr: 2.033e-05, eta: 10:12:18, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0824, decode.acc_seg: 96.4975, loss: 0.0824 2023-01-06 19:10:15,222 - mmseg - INFO - Iter [105850/160000] lr: 2.031e-05, eta: 10:11:44, time: 0.670, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0882, decode.acc_seg: 96.2324, loss: 0.0882 2023-01-06 19:10:49,391 - mmseg - INFO - Iter [105900/160000] lr: 2.029e-05, eta: 10:11:11, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0891, decode.acc_seg: 96.1537, loss: 0.0891 2023-01-06 19:11:23,339 - mmseg - INFO - Iter [105950/160000] lr: 2.027e-05, eta: 10:10:37, time: 0.680, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0839, decode.acc_seg: 96.3499, loss: 0.0839 2023-01-06 19:11:57,167 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 19:11:57,167 - mmseg - INFO - Iter [106000/160000] lr: 2.025e-05, eta: 10:10:03, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0820, decode.acc_seg: 96.4761, loss: 0.0820 2023-01-06 19:12:31,640 - mmseg - INFO - Iter [106050/160000] lr: 2.023e-05, eta: 10:09:29, time: 0.690, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0855, decode.acc_seg: 96.4304, loss: 0.0855 2023-01-06 19:13:04,629 - mmseg - INFO - Iter [106100/160000] lr: 2.021e-05, eta: 10:08:55, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0842, decode.acc_seg: 96.3016, loss: 0.0842 2023-01-06 19:13:38,152 - mmseg - INFO - Iter [106150/160000] lr: 2.019e-05, eta: 10:08:21, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0825, decode.acc_seg: 96.4441, loss: 0.0825 2023-01-06 19:14:13,887 - mmseg - INFO - Iter [106200/160000] lr: 2.018e-05, eta: 10:07:48, time: 0.715, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0920, decode.acc_seg: 96.0949, loss: 0.0920 2023-01-06 19:14:47,303 - mmseg - INFO - Iter [106250/160000] lr: 2.016e-05, eta: 10:07:14, time: 0.669, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0841, decode.acc_seg: 96.3513, loss: 0.0841 2023-01-06 19:15:19,856 - mmseg - INFO - Iter [106300/160000] lr: 2.014e-05, eta: 10:06:39, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0839, decode.acc_seg: 96.3707, loss: 0.0839 2023-01-06 19:15:52,901 - mmseg - INFO - Iter [106350/160000] lr: 2.012e-05, eta: 10:06:05, time: 0.661, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0849, decode.acc_seg: 96.3577, loss: 0.0849 2023-01-06 19:16:28,496 - mmseg - INFO - Iter [106400/160000] lr: 2.010e-05, eta: 10:05:32, time: 0.712, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0826, decode.acc_seg: 96.4971, loss: 0.0826 2023-01-06 19:17:01,215 - mmseg - INFO - Iter [106450/160000] lr: 2.008e-05, eta: 10:04:57, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0828, decode.acc_seg: 96.5201, loss: 0.0828 2023-01-06 19:17:33,377 - mmseg - INFO - Iter [106500/160000] lr: 2.006e-05, eta: 10:04:23, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0857, decode.acc_seg: 96.3731, loss: 0.0857 2023-01-06 19:18:07,277 - mmseg - INFO - Iter [106550/160000] lr: 2.004e-05, eta: 10:03:49, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0869, decode.acc_seg: 96.3717, loss: 0.0869 2023-01-06 19:18:41,314 - mmseg - INFO - Iter [106600/160000] lr: 2.003e-05, eta: 10:03:15, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0819, decode.acc_seg: 96.5506, loss: 0.0819 2023-01-06 19:19:14,467 - mmseg - INFO - Iter [106650/160000] lr: 2.001e-05, eta: 10:02:41, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0841, decode.acc_seg: 96.4283, loss: 0.0841 2023-01-06 19:19:48,291 - mmseg - INFO - Iter [106700/160000] lr: 1.999e-05, eta: 10:02:07, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0784, decode.acc_seg: 96.6194, loss: 0.0784 2023-01-06 19:20:23,550 - mmseg - INFO - Iter [106750/160000] lr: 1.997e-05, eta: 10:01:33, time: 0.706, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0837, decode.acc_seg: 96.4253, loss: 0.0837 2023-01-06 19:20:58,075 - mmseg - INFO - Iter [106800/160000] lr: 1.995e-05, eta: 10:01:00, time: 0.690, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0852, decode.acc_seg: 96.3113, loss: 0.0852 2023-01-06 19:21:32,104 - mmseg - INFO - Iter [106850/160000] lr: 1.993e-05, eta: 10:00:26, time: 0.681, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0823, decode.acc_seg: 96.5693, loss: 0.0823 2023-01-06 19:22:04,692 - mmseg - INFO - Iter [106900/160000] lr: 1.991e-05, eta: 9:59:51, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0843, decode.acc_seg: 96.4711, loss: 0.0843 2023-01-06 19:22:38,438 - mmseg - INFO - Iter [106950/160000] lr: 1.989e-05, eta: 9:59:18, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0842, decode.acc_seg: 96.3682, loss: 0.0842 2023-01-06 19:23:12,145 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 19:23:12,146 - mmseg - INFO - Iter [107000/160000] lr: 1.988e-05, eta: 9:58:44, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0872, decode.acc_seg: 96.3185, loss: 0.0872 2023-01-06 19:23:44,985 - mmseg - INFO - Iter [107050/160000] lr: 1.986e-05, eta: 9:58:09, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0786, decode.acc_seg: 96.6614, loss: 0.0786 2023-01-06 19:24:18,810 - mmseg - INFO - Iter [107100/160000] lr: 1.984e-05, eta: 9:57:35, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0817, decode.acc_seg: 96.5056, loss: 0.0817 2023-01-06 19:24:55,892 - mmseg - INFO - Iter [107150/160000] lr: 1.982e-05, eta: 9:57:03, time: 0.741, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0825, decode.acc_seg: 96.4758, loss: 0.0825 2023-01-06 19:25:29,789 - mmseg - INFO - Iter [107200/160000] lr: 1.980e-05, eta: 9:56:29, time: 0.679, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0833, decode.acc_seg: 96.3910, loss: 0.0833 2023-01-06 19:26:03,694 - mmseg - INFO - Iter [107250/160000] lr: 1.978e-05, eta: 9:55:55, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0841, decode.acc_seg: 96.4467, loss: 0.0841 2023-01-06 19:26:37,412 - mmseg - INFO - Iter [107300/160000] lr: 1.976e-05, eta: 9:55:21, time: 0.674, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0836, decode.acc_seg: 96.4338, loss: 0.0836 2023-01-06 19:27:11,852 - mmseg - INFO - Iter [107350/160000] lr: 1.974e-05, eta: 9:54:48, time: 0.690, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0799, decode.acc_seg: 96.5721, loss: 0.0799 2023-01-06 19:27:46,710 - mmseg - INFO - Iter [107400/160000] lr: 1.973e-05, eta: 9:54:14, time: 0.697, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0828, decode.acc_seg: 96.5002, loss: 0.0828 2023-01-06 19:28:20,524 - mmseg - INFO - Iter [107450/160000] lr: 1.971e-05, eta: 9:53:40, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0812, decode.acc_seg: 96.5719, loss: 0.0812 2023-01-06 19:28:53,141 - mmseg - INFO - Iter [107500/160000] lr: 1.969e-05, eta: 9:53:06, time: 0.653, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0839, decode.acc_seg: 96.3942, loss: 0.0839 2023-01-06 19:29:28,532 - mmseg - INFO - Iter [107550/160000] lr: 1.967e-05, eta: 9:52:32, time: 0.707, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0801, decode.acc_seg: 96.5160, loss: 0.0801 2023-01-06 19:30:02,495 - mmseg - INFO - Iter [107600/160000] lr: 1.965e-05, eta: 9:51:59, time: 0.679, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0792, decode.acc_seg: 96.6014, loss: 0.0792 2023-01-06 19:30:38,141 - mmseg - INFO - Iter [107650/160000] lr: 1.963e-05, eta: 9:51:26, time: 0.713, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0801, decode.acc_seg: 96.5426, loss: 0.0801 2023-01-06 19:31:13,849 - mmseg - INFO - Iter [107700/160000] lr: 1.961e-05, eta: 9:50:53, time: 0.714, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0868, decode.acc_seg: 96.4171, loss: 0.0868 2023-01-06 19:31:46,624 - mmseg - INFO - Iter [107750/160000] lr: 1.959e-05, eta: 9:50:18, time: 0.656, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0884, decode.acc_seg: 96.1448, loss: 0.0884 2023-01-06 19:32:18,885 - mmseg - INFO - Iter [107800/160000] lr: 1.958e-05, eta: 9:49:43, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0817, decode.acc_seg: 96.5024, loss: 0.0817 2023-01-06 19:32:51,776 - mmseg - INFO - Iter [107850/160000] lr: 1.956e-05, eta: 9:49:09, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0843, decode.acc_seg: 96.4079, loss: 0.0843 2023-01-06 19:33:28,347 - mmseg - INFO - Iter [107900/160000] lr: 1.954e-05, eta: 9:48:36, time: 0.731, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0828, decode.acc_seg: 96.5228, loss: 0.0828 2023-01-06 19:34:02,475 - mmseg - INFO - Iter [107950/160000] lr: 1.952e-05, eta: 9:48:03, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0859, decode.acc_seg: 96.2356, loss: 0.0859 2023-01-06 19:34:35,669 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 19:34:35,669 - mmseg - INFO - Iter [108000/160000] lr: 1.950e-05, eta: 9:47:28, time: 0.664, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0818, decode.acc_seg: 96.4302, loss: 0.0818 2023-01-06 19:35:09,122 - mmseg - INFO - Iter [108050/160000] lr: 1.948e-05, eta: 9:46:54, time: 0.670, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0827, decode.acc_seg: 96.4198, loss: 0.0827 2023-01-06 19:35:42,698 - mmseg - INFO - Iter [108100/160000] lr: 1.946e-05, eta: 9:46:20, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0850, decode.acc_seg: 96.3386, loss: 0.0850 2023-01-06 19:36:16,108 - mmseg - INFO - Iter [108150/160000] lr: 1.944e-05, eta: 9:45:46, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0868, decode.acc_seg: 96.4237, loss: 0.0868 2023-01-06 19:36:50,184 - mmseg - INFO - Iter [108200/160000] lr: 1.943e-05, eta: 9:45:12, time: 0.681, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0868, decode.acc_seg: 96.3010, loss: 0.0868 2023-01-06 19:37:24,106 - mmseg - INFO - Iter [108250/160000] lr: 1.941e-05, eta: 9:44:39, time: 0.679, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0851, decode.acc_seg: 96.3731, loss: 0.0851 2023-01-06 19:38:00,701 - mmseg - INFO - Iter [108300/160000] lr: 1.939e-05, eta: 9:44:06, time: 0.731, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0866, decode.acc_seg: 96.3686, loss: 0.0866 2023-01-06 19:38:34,745 - mmseg - INFO - Iter [108350/160000] lr: 1.937e-05, eta: 9:43:32, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0839, decode.acc_seg: 96.3871, loss: 0.0839 2023-01-06 19:39:08,593 - mmseg - INFO - Iter [108400/160000] lr: 1.935e-05, eta: 9:42:58, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0845, decode.acc_seg: 96.4334, loss: 0.0845 2023-01-06 19:39:40,971 - mmseg - INFO - Iter [108450/160000] lr: 1.933e-05, eta: 9:42:24, time: 0.647, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0844, decode.acc_seg: 96.5065, loss: 0.0844 2023-01-06 19:40:13,117 - mmseg - INFO - Iter [108500/160000] lr: 1.931e-05, eta: 9:41:49, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0838, decode.acc_seg: 96.3771, loss: 0.0838 2023-01-06 19:40:45,530 - mmseg - INFO - Iter [108550/160000] lr: 1.929e-05, eta: 9:41:14, time: 0.648, data_time: 0.018, memory: 11582, decode.loss_ce: 0.0802, decode.acc_seg: 96.5538, loss: 0.0802 2023-01-06 19:41:18,328 - mmseg - INFO - Iter [108600/160000] lr: 1.928e-05, eta: 9:40:40, time: 0.656, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0837, decode.acc_seg: 96.3995, loss: 0.0837 2023-01-06 19:41:54,240 - mmseg - INFO - Iter [108650/160000] lr: 1.926e-05, eta: 9:40:07, time: 0.717, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0852, decode.acc_seg: 96.3281, loss: 0.0852 2023-01-06 19:42:28,605 - mmseg - INFO - Iter [108700/160000] lr: 1.924e-05, eta: 9:39:33, time: 0.687, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0866, decode.acc_seg: 96.3384, loss: 0.0866 2023-01-06 19:43:03,468 - mmseg - INFO - Iter [108750/160000] lr: 1.922e-05, eta: 9:39:00, time: 0.698, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0877, decode.acc_seg: 96.3491, loss: 0.0877 2023-01-06 19:43:38,790 - mmseg - INFO - Iter [108800/160000] lr: 1.920e-05, eta: 9:38:27, time: 0.705, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0799, decode.acc_seg: 96.5556, loss: 0.0799 2023-01-06 19:44:11,745 - mmseg - INFO - Iter [108850/160000] lr: 1.918e-05, eta: 9:37:52, time: 0.660, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0847, decode.acc_seg: 96.4164, loss: 0.0847 2023-01-06 19:44:46,176 - mmseg - INFO - Iter [108900/160000] lr: 1.916e-05, eta: 9:37:19, time: 0.689, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0821, decode.acc_seg: 96.4771, loss: 0.0821 2023-01-06 19:45:19,148 - mmseg - INFO - Iter [108950/160000] lr: 1.914e-05, eta: 9:36:44, time: 0.659, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0837, decode.acc_seg: 96.3889, loss: 0.0837 2023-01-06 19:45:54,262 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 19:45:54,263 - mmseg - INFO - Iter [109000/160000] lr: 1.913e-05, eta: 9:36:11, time: 0.702, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0854, decode.acc_seg: 96.4041, loss: 0.0854 2023-01-06 19:46:28,005 - mmseg - INFO - Iter [109050/160000] lr: 1.911e-05, eta: 9:35:37, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0817, decode.acc_seg: 96.5415, loss: 0.0817 2023-01-06 19:47:00,250 - mmseg - INFO - Iter [109100/160000] lr: 1.909e-05, eta: 9:35:02, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0820, decode.acc_seg: 96.3615, loss: 0.0820 2023-01-06 19:47:33,676 - mmseg - INFO - Iter [109150/160000] lr: 1.907e-05, eta: 9:34:28, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0821, decode.acc_seg: 96.5065, loss: 0.0821 2023-01-06 19:48:06,685 - mmseg - INFO - Iter [109200/160000] lr: 1.905e-05, eta: 9:33:54, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0819, decode.acc_seg: 96.4834, loss: 0.0819 2023-01-06 19:48:39,862 - mmseg - INFO - Iter [109250/160000] lr: 1.903e-05, eta: 9:33:20, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0876, decode.acc_seg: 96.2872, loss: 0.0876 2023-01-06 19:49:12,808 - mmseg - INFO - Iter [109300/160000] lr: 1.901e-05, eta: 9:32:45, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0812, decode.acc_seg: 96.4857, loss: 0.0812 2023-01-06 19:49:46,463 - mmseg - INFO - Iter [109350/160000] lr: 1.899e-05, eta: 9:32:11, time: 0.673, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0822, decode.acc_seg: 96.5193, loss: 0.0822 2023-01-06 19:50:21,450 - mmseg - INFO - Iter [109400/160000] lr: 1.898e-05, eta: 9:31:38, time: 0.700, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0856, decode.acc_seg: 96.3775, loss: 0.0856 2023-01-06 19:50:56,620 - mmseg - INFO - Iter [109450/160000] lr: 1.896e-05, eta: 9:31:05, time: 0.703, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0826, decode.acc_seg: 96.4594, loss: 0.0826 2023-01-06 19:51:29,827 - mmseg - INFO - Iter [109500/160000] lr: 1.894e-05, eta: 9:30:30, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0795, decode.acc_seg: 96.6035, loss: 0.0795 2023-01-06 19:52:02,551 - mmseg - INFO - Iter [109550/160000] lr: 1.892e-05, eta: 9:29:56, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0822, decode.acc_seg: 96.4679, loss: 0.0822 2023-01-06 19:52:34,774 - mmseg - INFO - Iter [109600/160000] lr: 1.890e-05, eta: 9:29:21, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0827, decode.acc_seg: 96.4945, loss: 0.0827 2023-01-06 19:53:06,866 - mmseg - INFO - Iter [109650/160000] lr: 1.888e-05, eta: 9:28:47, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0794, decode.acc_seg: 96.5324, loss: 0.0794 2023-01-06 19:53:39,169 - mmseg - INFO - Iter [109700/160000] lr: 1.886e-05, eta: 9:28:12, time: 0.645, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0807, decode.acc_seg: 96.4319, loss: 0.0807 2023-01-06 19:54:15,835 - mmseg - INFO - Iter [109750/160000] lr: 1.884e-05, eta: 9:27:39, time: 0.734, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0794, decode.acc_seg: 96.5234, loss: 0.0794 2023-01-06 19:54:50,084 - mmseg - INFO - Iter [109800/160000] lr: 1.883e-05, eta: 9:27:06, time: 0.685, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0797, decode.acc_seg: 96.5881, loss: 0.0797 2023-01-06 19:55:23,456 - mmseg - INFO - Iter [109850/160000] lr: 1.881e-05, eta: 9:26:32, time: 0.667, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0844, decode.acc_seg: 96.4102, loss: 0.0844 2023-01-06 19:55:57,150 - mmseg - INFO - Iter [109900/160000] lr: 1.879e-05, eta: 9:25:58, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0829, decode.acc_seg: 96.5165, loss: 0.0829 2023-01-06 19:56:32,255 - mmseg - INFO - Iter [109950/160000] lr: 1.877e-05, eta: 9:25:24, time: 0.702, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0828, decode.acc_seg: 96.4781, loss: 0.0828 2023-01-06 19:57:05,218 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 19:57:05,219 - mmseg - INFO - Iter [110000/160000] lr: 1.875e-05, eta: 9:24:50, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0812, decode.acc_seg: 96.5156, loss: 0.0812 2023-01-06 19:57:38,387 - mmseg - INFO - Iter [110050/160000] lr: 1.873e-05, eta: 9:24:16, time: 0.663, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0765, decode.acc_seg: 96.6616, loss: 0.0765 2023-01-06 19:58:11,169 - mmseg - INFO - Iter [110100/160000] lr: 1.871e-05, eta: 9:23:41, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0796, decode.acc_seg: 96.5601, loss: 0.0796 2023-01-06 19:58:46,236 - mmseg - INFO - Iter [110150/160000] lr: 1.869e-05, eta: 9:23:08, time: 0.701, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0816, decode.acc_seg: 96.4206, loss: 0.0816 2023-01-06 19:59:19,709 - mmseg - INFO - Iter [110200/160000] lr: 1.868e-05, eta: 9:22:34, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0846, decode.acc_seg: 96.4413, loss: 0.0846 2023-01-06 19:59:52,602 - mmseg - INFO - Iter [110250/160000] lr: 1.866e-05, eta: 9:22:00, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0879, decode.acc_seg: 96.2609, loss: 0.0879 2023-01-06 20:00:24,716 - mmseg - INFO - Iter [110300/160000] lr: 1.864e-05, eta: 9:21:25, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0807, decode.acc_seg: 96.5433, loss: 0.0807 2023-01-06 20:00:58,322 - mmseg - INFO - Iter [110350/160000] lr: 1.862e-05, eta: 9:20:51, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0807, decode.acc_seg: 96.5496, loss: 0.0807 2023-01-06 20:01:32,035 - mmseg - INFO - Iter [110400/160000] lr: 1.860e-05, eta: 9:20:17, time: 0.675, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0845, decode.acc_seg: 96.4110, loss: 0.0845 2023-01-06 20:02:04,349 - mmseg - INFO - Iter [110450/160000] lr: 1.858e-05, eta: 9:19:42, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0804, decode.acc_seg: 96.5998, loss: 0.0804 2023-01-06 20:02:39,751 - mmseg - INFO - Iter [110500/160000] lr: 1.856e-05, eta: 9:19:09, time: 0.708, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0820, decode.acc_seg: 96.4963, loss: 0.0820 2023-01-06 20:03:13,517 - mmseg - INFO - Iter [110550/160000] lr: 1.854e-05, eta: 9:18:35, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0850, decode.acc_seg: 96.3681, loss: 0.0850 2023-01-06 20:03:46,983 - mmseg - INFO - Iter [110600/160000] lr: 1.853e-05, eta: 9:18:01, time: 0.670, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0822, decode.acc_seg: 96.5259, loss: 0.0822 2023-01-06 20:04:20,098 - mmseg - INFO - Iter [110650/160000] lr: 1.851e-05, eta: 9:17:27, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0831, decode.acc_seg: 96.3935, loss: 0.0831 2023-01-06 20:04:52,316 - mmseg - INFO - Iter [110700/160000] lr: 1.849e-05, eta: 9:16:52, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0835, decode.acc_seg: 96.4207, loss: 0.0835 2023-01-06 20:05:25,117 - mmseg - INFO - Iter [110750/160000] lr: 1.847e-05, eta: 9:16:18, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0863, decode.acc_seg: 96.4211, loss: 0.0863 2023-01-06 20:05:58,069 - mmseg - INFO - Iter [110800/160000] lr: 1.845e-05, eta: 9:15:44, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0834, decode.acc_seg: 96.3855, loss: 0.0834 2023-01-06 20:06:31,475 - mmseg - INFO - Iter [110850/160000] lr: 1.843e-05, eta: 9:15:09, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0872, decode.acc_seg: 96.3158, loss: 0.0872 2023-01-06 20:07:05,982 - mmseg - INFO - Iter [110900/160000] lr: 1.841e-05, eta: 9:14:36, time: 0.690, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0814, decode.acc_seg: 96.5407, loss: 0.0814 2023-01-06 20:07:39,472 - mmseg - INFO - Iter [110950/160000] lr: 1.839e-05, eta: 9:14:02, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0835, decode.acc_seg: 96.4398, loss: 0.0835 2023-01-06 20:08:13,366 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 20:08:13,367 - mmseg - INFO - Iter [111000/160000] lr: 1.838e-05, eta: 9:13:28, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0862, decode.acc_seg: 96.4120, loss: 0.0862 2023-01-06 20:08:47,476 - mmseg - INFO - Iter [111050/160000] lr: 1.836e-05, eta: 9:12:54, time: 0.683, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0936, decode.acc_seg: 95.9863, loss: 0.0936 2023-01-06 20:09:19,618 - mmseg - INFO - Iter [111100/160000] lr: 1.834e-05, eta: 9:12:19, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0838, decode.acc_seg: 96.4248, loss: 0.0838 2023-01-06 20:09:52,590 - mmseg - INFO - Iter [111150/160000] lr: 1.832e-05, eta: 9:11:45, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0866, decode.acc_seg: 96.4017, loss: 0.0866 2023-01-06 20:10:27,771 - mmseg - INFO - Iter [111200/160000] lr: 1.830e-05, eta: 9:11:12, time: 0.703, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0823, decode.acc_seg: 96.4384, loss: 0.0823 2023-01-06 20:11:03,651 - mmseg - INFO - Iter [111250/160000] lr: 1.828e-05, eta: 9:10:39, time: 0.718, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0825, decode.acc_seg: 96.4308, loss: 0.0825 2023-01-06 20:11:37,641 - mmseg - INFO - Iter [111300/160000] lr: 1.826e-05, eta: 9:10:05, time: 0.681, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0864, decode.acc_seg: 96.2237, loss: 0.0864 2023-01-06 20:12:09,901 - mmseg - INFO - Iter [111350/160000] lr: 1.824e-05, eta: 9:09:30, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0853, decode.acc_seg: 96.4644, loss: 0.0853 2023-01-06 20:12:44,669 - mmseg - INFO - Iter [111400/160000] lr: 1.823e-05, eta: 9:08:57, time: 0.695, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0856, decode.acc_seg: 96.3533, loss: 0.0856 2023-01-06 20:13:19,435 - mmseg - INFO - Iter [111450/160000] lr: 1.821e-05, eta: 9:08:23, time: 0.694, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0826, decode.acc_seg: 96.5757, loss: 0.0826 2023-01-06 20:13:52,146 - mmseg - INFO - Iter [111500/160000] lr: 1.819e-05, eta: 9:07:49, time: 0.655, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0816, decode.acc_seg: 96.5502, loss: 0.0816 2023-01-06 20:14:24,409 - mmseg - INFO - Iter [111550/160000] lr: 1.817e-05, eta: 9:07:14, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0826, decode.acc_seg: 96.5356, loss: 0.0826 2023-01-06 20:14:57,581 - mmseg - INFO - Iter [111600/160000] lr: 1.815e-05, eta: 9:06:40, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0867, decode.acc_seg: 96.4127, loss: 0.0867 2023-01-06 20:15:32,126 - mmseg - INFO - Iter [111650/160000] lr: 1.813e-05, eta: 9:06:07, time: 0.691, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0840, decode.acc_seg: 96.5461, loss: 0.0840 2023-01-06 20:16:06,489 - mmseg - INFO - Iter [111700/160000] lr: 1.811e-05, eta: 9:05:33, time: 0.686, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0828, decode.acc_seg: 96.5044, loss: 0.0828 2023-01-06 20:16:40,653 - mmseg - INFO - Iter [111750/160000] lr: 1.809e-05, eta: 9:04:59, time: 0.684, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0833, decode.acc_seg: 96.4165, loss: 0.0833 2023-01-06 20:17:13,166 - mmseg - INFO - Iter [111800/160000] lr: 1.808e-05, eta: 9:04:25, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0781, decode.acc_seg: 96.6004, loss: 0.0781 2023-01-06 20:17:45,953 - mmseg - INFO - Iter [111850/160000] lr: 1.806e-05, eta: 9:03:50, time: 0.656, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0829, decode.acc_seg: 96.4808, loss: 0.0829 2023-01-06 20:18:19,260 - mmseg - INFO - Iter [111900/160000] lr: 1.804e-05, eta: 9:03:16, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0781, decode.acc_seg: 96.5955, loss: 0.0781 2023-01-06 20:18:53,338 - mmseg - INFO - Iter [111950/160000] lr: 1.802e-05, eta: 9:02:42, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0826, decode.acc_seg: 96.5483, loss: 0.0826 2023-01-06 20:19:30,986 - mmseg - INFO - Saving checkpoint at 112000 iterations 2023-01-06 20:19:37,035 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 20:19:37,036 - mmseg - INFO - Iter [112000/160000] lr: 1.800e-05, eta: 9:02:13, time: 0.875, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0801, decode.acc_seg: 96.5683, loss: 0.0801 2023-01-06 20:20:12,738 - mmseg - INFO - per class results: 2023-01-06 20:20:12,740 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 98.07 | 98.93 | | sidewalk | 83.65 | 91.68 | | building | 92.02 | 96.32 | | wall | 55.63 | 62.74 | | fence | 55.48 | 66.97 | | pole | 63.18 | 75.03 | | traffic light | 66.59 | 78.52 | | traffic sign | 75.11 | 81.98 | | vegetation | 92.24 | 96.57 | | terrain | 61.11 | 69.55 | | sky | 94.48 | 98.16 | | person | 78.62 | 90.22 | | rider | 56.38 | 69.69 | | car | 94.04 | 97.6 | | truck | 69.41 | 79.47 | | bus | 72.36 | 81.02 | | train | 54.2 | 60.08 | | motorcycle | 50.37 | 59.3 | | bicycle | 73.29 | 86.53 | +---------------+-------+-------+ 2023-01-06 20:20:12,740 - mmseg - INFO - Summary: 2023-01-06 20:20:12,741 - mmseg - INFO - +------+-------+-------+ | aAcc | mIoU | mAcc | +------+-------+-------+ | 95.7 | 72.96 | 81.07 | +------+-------+-------+ 2023-01-06 20:20:12,742 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 20:20:12,742 - mmseg - INFO - Iter(val) [63] aAcc: 0.9570, mIoU: 0.7296, mAcc: 0.8107, IoU.road: 0.9807, IoU.sidewalk: 0.8365, IoU.building: 0.9202, IoU.wall: 0.5563, IoU.fence: 0.5548, IoU.pole: 0.6318, IoU.traffic light: 0.6659, IoU.traffic sign: 0.7511, IoU.vegetation: 0.9224, IoU.terrain: 0.6111, IoU.sky: 0.9448, IoU.person: 0.7862, IoU.rider: 0.5638, IoU.car: 0.9404, IoU.truck: 0.6941, IoU.bus: 0.7236, IoU.train: 0.5420, IoU.motorcycle: 0.5037, IoU.bicycle: 0.7329, Acc.road: 0.9893, Acc.sidewalk: 0.9168, Acc.building: 0.9632, Acc.wall: 0.6274, Acc.fence: 0.6697, Acc.pole: 0.7503, Acc.traffic light: 0.7852, Acc.traffic sign: 0.8198, Acc.vegetation: 0.9657, Acc.terrain: 0.6955, Acc.sky: 0.9816, Acc.person: 0.9022, Acc.rider: 0.6969, Acc.car: 0.9760, Acc.truck: 0.7947, Acc.bus: 0.8102, Acc.train: 0.6008, Acc.motorcycle: 0.5930, Acc.bicycle: 0.8653 2023-01-06 20:20:46,813 - mmseg - INFO - Iter [112050/160000] lr: 1.798e-05, eta: 9:01:54, time: 1.395, data_time: 0.727, memory: 11582, decode.loss_ce: 0.0840, decode.acc_seg: 96.4058, loss: 0.0840 2023-01-06 20:21:20,022 - mmseg - INFO - Iter [112100/160000] lr: 1.796e-05, eta: 9:01:20, time: 0.664, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0832, decode.acc_seg: 96.3862, loss: 0.0832 2023-01-06 20:21:53,469 - mmseg - INFO - Iter [112150/160000] lr: 1.794e-05, eta: 9:00:46, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0801, decode.acc_seg: 96.5534, loss: 0.0801 2023-01-06 20:22:26,947 - mmseg - INFO - Iter [112200/160000] lr: 1.793e-05, eta: 9:00:12, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0817, decode.acc_seg: 96.4815, loss: 0.0817 2023-01-06 20:22:59,423 - mmseg - INFO - Iter [112250/160000] lr: 1.791e-05, eta: 8:59:37, time: 0.649, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0811, decode.acc_seg: 96.5113, loss: 0.0811 2023-01-06 20:23:32,357 - mmseg - INFO - Iter [112300/160000] lr: 1.789e-05, eta: 8:59:03, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0798, decode.acc_seg: 96.5485, loss: 0.0798 2023-01-06 20:24:07,989 - mmseg - INFO - Iter [112350/160000] lr: 1.787e-05, eta: 8:58:30, time: 0.713, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0823, decode.acc_seg: 96.5482, loss: 0.0823 2023-01-06 20:24:41,277 - mmseg - INFO - Iter [112400/160000] lr: 1.785e-05, eta: 8:57:56, time: 0.666, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0770, decode.acc_seg: 96.6715, loss: 0.0770 2023-01-06 20:25:14,095 - mmseg - INFO - Iter [112450/160000] lr: 1.783e-05, eta: 8:57:21, time: 0.656, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0906, decode.acc_seg: 96.2674, loss: 0.0906 2023-01-06 20:25:46,272 - mmseg - INFO - Iter [112500/160000] lr: 1.781e-05, eta: 8:56:47, time: 0.643, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0810, decode.acc_seg: 96.5532, loss: 0.0810 2023-01-06 20:26:18,952 - mmseg - INFO - Iter [112550/160000] lr: 1.779e-05, eta: 8:56:12, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0811, decode.acc_seg: 96.5562, loss: 0.0811 2023-01-06 20:26:51,232 - mmseg - INFO - Iter [112600/160000] lr: 1.778e-05, eta: 8:55:38, time: 0.646, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0872, decode.acc_seg: 96.3452, loss: 0.0872 2023-01-06 20:27:25,908 - mmseg - INFO - Iter [112650/160000] lr: 1.776e-05, eta: 8:55:04, time: 0.693, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0818, decode.acc_seg: 96.4638, loss: 0.0818 2023-01-06 20:27:58,230 - mmseg - INFO - Iter [112700/160000] lr: 1.774e-05, eta: 8:54:29, time: 0.646, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0805, decode.acc_seg: 96.6197, loss: 0.0805 2023-01-06 20:28:32,687 - mmseg - INFO - Iter [112750/160000] lr: 1.772e-05, eta: 8:53:56, time: 0.689, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0825, decode.acc_seg: 96.4753, loss: 0.0825 2023-01-06 20:29:04,972 - mmseg - INFO - Iter [112800/160000] lr: 1.770e-05, eta: 8:53:21, time: 0.646, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0874, decode.acc_seg: 96.3257, loss: 0.0874 2023-01-06 20:29:38,367 - mmseg - INFO - Iter [112850/160000] lr: 1.768e-05, eta: 8:52:47, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0811, decode.acc_seg: 96.4808, loss: 0.0811 2023-01-06 20:30:12,426 - mmseg - INFO - Iter [112900/160000] lr: 1.766e-05, eta: 8:52:13, time: 0.681, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0814, decode.acc_seg: 96.5968, loss: 0.0814 2023-01-06 20:30:44,798 - mmseg - INFO - Iter [112950/160000] lr: 1.764e-05, eta: 8:51:39, time: 0.647, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0785, decode.acc_seg: 96.7455, loss: 0.0785 2023-01-06 20:31:17,185 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 20:31:17,185 - mmseg - INFO - Iter [113000/160000] lr: 1.763e-05, eta: 8:51:04, time: 0.648, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0824, decode.acc_seg: 96.4224, loss: 0.0824 2023-01-06 20:31:50,432 - mmseg - INFO - Iter [113050/160000] lr: 1.761e-05, eta: 8:50:30, time: 0.665, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0776, decode.acc_seg: 96.5755, loss: 0.0776 2023-01-06 20:32:24,815 - mmseg - INFO - Iter [113100/160000] lr: 1.759e-05, eta: 8:49:56, time: 0.688, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0823, decode.acc_seg: 96.5326, loss: 0.0823 2023-01-06 20:32:59,318 - mmseg - INFO - Iter [113150/160000] lr: 1.757e-05, eta: 8:49:23, time: 0.689, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0844, decode.acc_seg: 96.4437, loss: 0.0844 2023-01-06 20:33:32,723 - mmseg - INFO - Iter [113200/160000] lr: 1.755e-05, eta: 8:48:49, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0794, decode.acc_seg: 96.6238, loss: 0.0794 2023-01-06 20:34:05,881 - mmseg - INFO - Iter [113250/160000] lr: 1.753e-05, eta: 8:48:14, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0804, decode.acc_seg: 96.6603, loss: 0.0804 2023-01-06 20:34:40,124 - mmseg - INFO - Iter [113300/160000] lr: 1.751e-05, eta: 8:47:41, time: 0.685, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0868, decode.acc_seg: 96.3153, loss: 0.0868 2023-01-06 20:35:14,726 - mmseg - INFO - Iter [113350/160000] lr: 1.749e-05, eta: 8:47:07, time: 0.691, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0830, decode.acc_seg: 96.4392, loss: 0.0830 2023-01-06 20:35:50,616 - mmseg - INFO - Iter [113400/160000] lr: 1.748e-05, eta: 8:46:34, time: 0.718, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0815, decode.acc_seg: 96.4741, loss: 0.0815 2023-01-06 20:36:23,429 - mmseg - INFO - Iter [113450/160000] lr: 1.746e-05, eta: 8:46:00, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0820, decode.acc_seg: 96.5277, loss: 0.0820 2023-01-06 20:36:58,292 - mmseg - INFO - Iter [113500/160000] lr: 1.744e-05, eta: 8:45:26, time: 0.698, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0814, decode.acc_seg: 96.4884, loss: 0.0814 2023-01-06 20:37:30,617 - mmseg - INFO - Iter [113550/160000] lr: 1.742e-05, eta: 8:44:52, time: 0.646, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0813, decode.acc_seg: 96.5102, loss: 0.0813 2023-01-06 20:38:05,549 - mmseg - INFO - Iter [113600/160000] lr: 1.740e-05, eta: 8:44:18, time: 0.699, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0810, decode.acc_seg: 96.5276, loss: 0.0810 2023-01-06 20:38:40,573 - mmseg - INFO - Iter [113650/160000] lr: 1.738e-05, eta: 8:43:45, time: 0.700, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0815, decode.acc_seg: 96.4680, loss: 0.0815 2023-01-06 20:39:14,692 - mmseg - INFO - Iter [113700/160000] lr: 1.736e-05, eta: 8:43:11, time: 0.682, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0804, decode.acc_seg: 96.5630, loss: 0.0804 2023-01-06 20:39:48,986 - mmseg - INFO - Iter [113750/160000] lr: 1.734e-05, eta: 8:42:37, time: 0.685, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0817, decode.acc_seg: 96.4398, loss: 0.0817 2023-01-06 20:40:24,511 - mmseg - INFO - Iter [113800/160000] lr: 1.733e-05, eta: 8:42:04, time: 0.711, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0855, decode.acc_seg: 96.3949, loss: 0.0855 2023-01-06 20:40:59,495 - mmseg - INFO - Iter [113850/160000] lr: 1.731e-05, eta: 8:41:30, time: 0.700, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0802, decode.acc_seg: 96.5921, loss: 0.0802 2023-01-06 20:41:32,471 - mmseg - INFO - Iter [113900/160000] lr: 1.729e-05, eta: 8:40:56, time: 0.660, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0816, decode.acc_seg: 96.5983, loss: 0.0816 2023-01-06 20:42:06,871 - mmseg - INFO - Iter [113950/160000] lr: 1.727e-05, eta: 8:40:22, time: 0.687, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0818, decode.acc_seg: 96.4134, loss: 0.0818 2023-01-06 20:42:42,707 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 20:42:42,708 - mmseg - INFO - Iter [114000/160000] lr: 1.725e-05, eta: 8:39:49, time: 0.717, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0815, decode.acc_seg: 96.4649, loss: 0.0815 2023-01-06 20:43:16,776 - mmseg - INFO - Iter [114050/160000] lr: 1.723e-05, eta: 8:39:15, time: 0.681, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0810, decode.acc_seg: 96.4662, loss: 0.0810 2023-01-06 20:43:50,558 - mmseg - INFO - Iter [114100/160000] lr: 1.721e-05, eta: 8:38:41, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0812, decode.acc_seg: 96.5276, loss: 0.0812 2023-01-06 20:44:23,598 - mmseg - INFO - Iter [114150/160000] lr: 1.719e-05, eta: 8:38:07, time: 0.661, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0894, decode.acc_seg: 96.2358, loss: 0.0894 2023-01-06 20:44:56,189 - mmseg - INFO - Iter [114200/160000] lr: 1.718e-05, eta: 8:37:33, time: 0.652, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0828, decode.acc_seg: 96.4430, loss: 0.0828 2023-01-06 20:45:32,342 - mmseg - INFO - Iter [114250/160000] lr: 1.716e-05, eta: 8:37:00, time: 0.723, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0984, decode.acc_seg: 95.9997, loss: 0.0984 2023-01-06 20:46:04,629 - mmseg - INFO - Iter [114300/160000] lr: 1.714e-05, eta: 8:36:25, time: 0.646, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0845, decode.acc_seg: 96.4509, loss: 0.0845 2023-01-06 20:46:37,669 - mmseg - INFO - Iter [114350/160000] lr: 1.712e-05, eta: 8:35:51, time: 0.661, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0768, decode.acc_seg: 96.7510, loss: 0.0768 2023-01-06 20:47:11,051 - mmseg - INFO - Iter [114400/160000] lr: 1.710e-05, eta: 8:35:17, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0845, decode.acc_seg: 96.4250, loss: 0.0845 2023-01-06 20:47:43,406 - mmseg - INFO - Iter [114450/160000] lr: 1.708e-05, eta: 8:34:42, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0826, decode.acc_seg: 96.4545, loss: 0.0826 2023-01-06 20:48:17,913 - mmseg - INFO - Iter [114500/160000] lr: 1.706e-05, eta: 8:34:09, time: 0.690, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0818, decode.acc_seg: 96.3934, loss: 0.0818 2023-01-06 20:48:53,581 - mmseg - INFO - Iter [114550/160000] lr: 1.704e-05, eta: 8:33:36, time: 0.713, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0834, decode.acc_seg: 96.4729, loss: 0.0834 2023-01-06 20:49:28,055 - mmseg - INFO - Iter [114600/160000] lr: 1.703e-05, eta: 8:33:02, time: 0.689, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0800, decode.acc_seg: 96.5322, loss: 0.0800 2023-01-06 20:50:01,360 - mmseg - INFO - Iter [114650/160000] lr: 1.701e-05, eta: 8:32:28, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0774, decode.acc_seg: 96.7078, loss: 0.0774 2023-01-06 20:50:35,824 - mmseg - INFO - Iter [114700/160000] lr: 1.699e-05, eta: 8:31:54, time: 0.688, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0804, decode.acc_seg: 96.5742, loss: 0.0804 2023-01-06 20:51:08,279 - mmseg - INFO - Iter [114750/160000] lr: 1.697e-05, eta: 8:31:20, time: 0.650, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0814, decode.acc_seg: 96.5408, loss: 0.0814 2023-01-06 20:51:42,532 - mmseg - INFO - Iter [114800/160000] lr: 1.695e-05, eta: 8:30:46, time: 0.685, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0815, decode.acc_seg: 96.5751, loss: 0.0815 2023-01-06 20:52:15,837 - mmseg - INFO - Iter [114850/160000] lr: 1.693e-05, eta: 8:30:12, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0807, decode.acc_seg: 96.5402, loss: 0.0807 2023-01-06 20:52:49,393 - mmseg - INFO - Iter [114900/160000] lr: 1.691e-05, eta: 8:29:38, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0814, decode.acc_seg: 96.6006, loss: 0.0814 2023-01-06 20:53:26,320 - mmseg - INFO - Iter [114950/160000] lr: 1.689e-05, eta: 8:29:05, time: 0.739, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0787, decode.acc_seg: 96.5805, loss: 0.0787 2023-01-06 20:53:59,670 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 20:53:59,670 - mmseg - INFO - Iter [115000/160000] lr: 1.688e-05, eta: 8:28:31, time: 0.667, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0776, decode.acc_seg: 96.6816, loss: 0.0776 2023-01-06 20:54:32,839 - mmseg - INFO - Iter [115050/160000] lr: 1.686e-05, eta: 8:27:57, time: 0.663, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0759, decode.acc_seg: 96.6393, loss: 0.0759 2023-01-06 20:55:05,241 - mmseg - INFO - Iter [115100/160000] lr: 1.684e-05, eta: 8:27:22, time: 0.648, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0808, decode.acc_seg: 96.5302, loss: 0.0808 2023-01-06 20:55:40,287 - mmseg - INFO - Iter [115150/160000] lr: 1.682e-05, eta: 8:26:49, time: 0.701, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0808, decode.acc_seg: 96.5796, loss: 0.0808 2023-01-06 20:56:15,024 - mmseg - INFO - Iter [115200/160000] lr: 1.680e-05, eta: 8:26:15, time: 0.695, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0798, decode.acc_seg: 96.6104, loss: 0.0798 2023-01-06 20:56:47,365 - mmseg - INFO - Iter [115250/160000] lr: 1.678e-05, eta: 8:25:41, time: 0.646, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0751, decode.acc_seg: 96.7278, loss: 0.0751 2023-01-06 20:57:23,151 - mmseg - INFO - Iter [115300/160000] lr: 1.676e-05, eta: 8:25:07, time: 0.716, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0823, decode.acc_seg: 96.4506, loss: 0.0823 2023-01-06 20:57:59,967 - mmseg - INFO - Iter [115350/160000] lr: 1.674e-05, eta: 8:24:35, time: 0.736, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0792, decode.acc_seg: 96.5728, loss: 0.0792 2023-01-06 20:58:34,879 - mmseg - INFO - Iter [115400/160000] lr: 1.673e-05, eta: 8:24:01, time: 0.698, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0765, decode.acc_seg: 96.6470, loss: 0.0765 2023-01-06 20:59:07,850 - mmseg - INFO - Iter [115450/160000] lr: 1.671e-05, eta: 8:23:27, time: 0.659, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0785, decode.acc_seg: 96.6178, loss: 0.0785 2023-01-06 20:59:40,920 - mmseg - INFO - Iter [115500/160000] lr: 1.669e-05, eta: 8:22:53, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0787, decode.acc_seg: 96.6477, loss: 0.0787 2023-01-06 21:00:13,153 - mmseg - INFO - Iter [115550/160000] lr: 1.667e-05, eta: 8:22:18, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0900, decode.acc_seg: 96.1702, loss: 0.0900 2023-01-06 21:00:45,776 - mmseg - INFO - Iter [115600/160000] lr: 1.665e-05, eta: 8:21:44, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0788, decode.acc_seg: 96.5503, loss: 0.0788 2023-01-06 21:01:18,102 - mmseg - INFO - Iter [115650/160000] lr: 1.663e-05, eta: 8:21:09, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0818, decode.acc_seg: 96.5103, loss: 0.0818 2023-01-06 21:01:53,885 - mmseg - INFO - Iter [115700/160000] lr: 1.661e-05, eta: 8:20:36, time: 0.715, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0810, decode.acc_seg: 96.5505, loss: 0.0810 2023-01-06 21:02:29,688 - mmseg - INFO - Iter [115750/160000] lr: 1.659e-05, eta: 8:20:03, time: 0.716, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0810, decode.acc_seg: 96.4812, loss: 0.0810 2023-01-06 21:03:05,618 - mmseg - INFO - Iter [115800/160000] lr: 1.658e-05, eta: 8:19:30, time: 0.719, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0818, decode.acc_seg: 96.4384, loss: 0.0818 2023-01-06 21:03:40,113 - mmseg - INFO - Iter [115850/160000] lr: 1.656e-05, eta: 8:18:56, time: 0.690, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0839, decode.acc_seg: 96.5010, loss: 0.0839 2023-01-06 21:04:13,326 - mmseg - INFO - Iter [115900/160000] lr: 1.654e-05, eta: 8:18:22, time: 0.665, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0816, decode.acc_seg: 96.4591, loss: 0.0816 2023-01-06 21:04:46,389 - mmseg - INFO - Iter [115950/160000] lr: 1.652e-05, eta: 8:17:48, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0779, decode.acc_seg: 96.6777, loss: 0.0779 2023-01-06 21:05:18,697 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 21:05:18,697 - mmseg - INFO - Iter [116000/160000] lr: 1.650e-05, eta: 8:17:13, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0770, decode.acc_seg: 96.7166, loss: 0.0770 2023-01-06 21:05:51,960 - mmseg - INFO - Iter [116050/160000] lr: 1.648e-05, eta: 8:16:39, time: 0.666, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0843, decode.acc_seg: 96.4973, loss: 0.0843 2023-01-06 21:06:27,600 - mmseg - INFO - Iter [116100/160000] lr: 1.646e-05, eta: 8:16:06, time: 0.713, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0795, decode.acc_seg: 96.5954, loss: 0.0795 2023-01-06 21:07:01,124 - mmseg - INFO - Iter [116150/160000] lr: 1.644e-05, eta: 8:15:32, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0781, decode.acc_seg: 96.6400, loss: 0.0781 2023-01-06 21:07:33,421 - mmseg - INFO - Iter [116200/160000] lr: 1.643e-05, eta: 8:14:57, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0807, decode.acc_seg: 96.4944, loss: 0.0807 2023-01-06 21:08:06,981 - mmseg - INFO - Iter [116250/160000] lr: 1.641e-05, eta: 8:14:23, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0797, decode.acc_seg: 96.5678, loss: 0.0797 2023-01-06 21:08:42,521 - mmseg - INFO - Iter [116300/160000] lr: 1.639e-05, eta: 8:13:50, time: 0.710, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0816, decode.acc_seg: 96.5719, loss: 0.0816 2023-01-06 21:09:17,102 - mmseg - INFO - Iter [116350/160000] lr: 1.637e-05, eta: 8:13:16, time: 0.693, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0819, decode.acc_seg: 96.6566, loss: 0.0819 2023-01-06 21:09:49,406 - mmseg - INFO - Iter [116400/160000] lr: 1.635e-05, eta: 8:12:42, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0835, decode.acc_seg: 96.4706, loss: 0.0835 2023-01-06 21:10:24,187 - mmseg - INFO - Iter [116450/160000] lr: 1.633e-05, eta: 8:12:08, time: 0.695, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0830, decode.acc_seg: 96.4234, loss: 0.0830 2023-01-06 21:10:56,759 - mmseg - INFO - Iter [116500/160000] lr: 1.631e-05, eta: 8:11:34, time: 0.652, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0843, decode.acc_seg: 96.4137, loss: 0.0843 2023-01-06 21:11:29,521 - mmseg - INFO - Iter [116550/160000] lr: 1.629e-05, eta: 8:10:59, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0827, decode.acc_seg: 96.4068, loss: 0.0827 2023-01-06 21:12:02,415 - mmseg - INFO - Iter [116600/160000] lr: 1.628e-05, eta: 8:10:25, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0803, decode.acc_seg: 96.6131, loss: 0.0803 2023-01-06 21:12:34,647 - mmseg - INFO - Iter [116650/160000] lr: 1.626e-05, eta: 8:09:51, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0757, decode.acc_seg: 96.6749, loss: 0.0757 2023-01-06 21:13:07,358 - mmseg - INFO - Iter [116700/160000] lr: 1.624e-05, eta: 8:09:16, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0761, decode.acc_seg: 96.6990, loss: 0.0761 2023-01-06 21:13:41,599 - mmseg - INFO - Iter [116750/160000] lr: 1.622e-05, eta: 8:08:43, time: 0.685, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0832, decode.acc_seg: 96.4295, loss: 0.0832 2023-01-06 21:14:14,588 - mmseg - INFO - Iter [116800/160000] lr: 1.620e-05, eta: 8:08:08, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0787, decode.acc_seg: 96.6303, loss: 0.0787 2023-01-06 21:14:50,769 - mmseg - INFO - Iter [116850/160000] lr: 1.618e-05, eta: 8:07:35, time: 0.724, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0807, decode.acc_seg: 96.6123, loss: 0.0807 2023-01-06 21:15:23,925 - mmseg - INFO - Iter [116900/160000] lr: 1.616e-05, eta: 8:07:01, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0782, decode.acc_seg: 96.5890, loss: 0.0782 2023-01-06 21:15:56,811 - mmseg - INFO - Iter [116950/160000] lr: 1.614e-05, eta: 8:06:27, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0810, decode.acc_seg: 96.4720, loss: 0.0810 2023-01-06 21:16:29,091 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 21:16:29,091 - mmseg - INFO - Iter [117000/160000] lr: 1.613e-05, eta: 8:05:52, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0798, decode.acc_seg: 96.5864, loss: 0.0798 2023-01-06 21:17:02,146 - mmseg - INFO - Iter [117050/160000] lr: 1.611e-05, eta: 8:05:18, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0797, decode.acc_seg: 96.6767, loss: 0.0797 2023-01-06 21:17:34,240 - mmseg - INFO - Iter [117100/160000] lr: 1.609e-05, eta: 8:04:44, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0819, decode.acc_seg: 96.5259, loss: 0.0819 2023-01-06 21:18:08,145 - mmseg - INFO - Iter [117150/160000] lr: 1.607e-05, eta: 8:04:10, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0806, decode.acc_seg: 96.5871, loss: 0.0806 2023-01-06 21:18:43,717 - mmseg - INFO - Iter [117200/160000] lr: 1.605e-05, eta: 8:03:36, time: 0.713, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0785, decode.acc_seg: 96.6112, loss: 0.0785 2023-01-06 21:19:16,960 - mmseg - INFO - Iter [117250/160000] lr: 1.603e-05, eta: 8:03:02, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0785, decode.acc_seg: 96.5698, loss: 0.0785 2023-01-06 21:19:51,954 - mmseg - INFO - Iter [117300/160000] lr: 1.601e-05, eta: 8:02:29, time: 0.700, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0816, decode.acc_seg: 96.5331, loss: 0.0816 2023-01-06 21:20:25,171 - mmseg - INFO - Iter [117350/160000] lr: 1.599e-05, eta: 8:01:55, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0739, decode.acc_seg: 96.7760, loss: 0.0739 2023-01-06 21:20:57,381 - mmseg - INFO - Iter [117400/160000] lr: 1.598e-05, eta: 8:01:20, time: 0.644, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0821, decode.acc_seg: 96.4465, loss: 0.0821 2023-01-06 21:21:29,740 - mmseg - INFO - Iter [117450/160000] lr: 1.596e-05, eta: 8:00:46, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0826, decode.acc_seg: 96.4808, loss: 0.0826 2023-01-06 21:22:02,394 - mmseg - INFO - Iter [117500/160000] lr: 1.594e-05, eta: 8:00:11, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0802, decode.acc_seg: 96.5318, loss: 0.0802 2023-01-06 21:22:35,113 - mmseg - INFO - Iter [117550/160000] lr: 1.592e-05, eta: 7:59:37, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0751, decode.acc_seg: 96.7478, loss: 0.0751 2023-01-06 21:23:12,202 - mmseg - INFO - Iter [117600/160000] lr: 1.590e-05, eta: 7:59:04, time: 0.741, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0775, decode.acc_seg: 96.6344, loss: 0.0775 2023-01-06 21:23:44,316 - mmseg - INFO - Iter [117650/160000] lr: 1.588e-05, eta: 7:58:30, time: 0.643, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0809, decode.acc_seg: 96.5688, loss: 0.0809 2023-01-06 21:24:16,565 - mmseg - INFO - Iter [117700/160000] lr: 1.586e-05, eta: 7:57:55, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0769, decode.acc_seg: 96.7519, loss: 0.0769 2023-01-06 21:24:48,905 - mmseg - INFO - Iter [117750/160000] lr: 1.584e-05, eta: 7:57:21, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0762, decode.acc_seg: 96.6323, loss: 0.0762 2023-01-06 21:25:23,344 - mmseg - INFO - Iter [117800/160000] lr: 1.583e-05, eta: 7:56:47, time: 0.689, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0830, decode.acc_seg: 96.4924, loss: 0.0830 2023-01-06 21:25:56,078 - mmseg - INFO - Iter [117850/160000] lr: 1.581e-05, eta: 7:56:13, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0780, decode.acc_seg: 96.7060, loss: 0.0780 2023-01-06 21:26:29,102 - mmseg - INFO - Iter [117900/160000] lr: 1.579e-05, eta: 7:55:38, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0840, decode.acc_seg: 96.4282, loss: 0.0840 2023-01-06 21:27:04,279 - mmseg - INFO - Iter [117950/160000] lr: 1.577e-05, eta: 7:55:05, time: 0.705, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0740, decode.acc_seg: 96.7053, loss: 0.0740 2023-01-06 21:27:36,545 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 21:27:36,545 - mmseg - INFO - Iter [118000/160000] lr: 1.575e-05, eta: 7:54:31, time: 0.645, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0788, decode.acc_seg: 96.5748, loss: 0.0788 2023-01-06 21:28:11,757 - mmseg - INFO - Iter [118050/160000] lr: 1.573e-05, eta: 7:53:57, time: 0.703, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0754, decode.acc_seg: 96.7123, loss: 0.0754 2023-01-06 21:28:45,320 - mmseg - INFO - Iter [118100/160000] lr: 1.571e-05, eta: 7:53:23, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0799, decode.acc_seg: 96.4623, loss: 0.0799 2023-01-06 21:29:17,662 - mmseg - INFO - Iter [118150/160000] lr: 1.569e-05, eta: 7:52:49, time: 0.647, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0777, decode.acc_seg: 96.7084, loss: 0.0777 2023-01-06 21:29:50,452 - mmseg - INFO - Iter [118200/160000] lr: 1.568e-05, eta: 7:52:14, time: 0.656, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0805, decode.acc_seg: 96.5956, loss: 0.0805 2023-01-06 21:30:23,306 - mmseg - INFO - Iter [118250/160000] lr: 1.566e-05, eta: 7:51:40, time: 0.657, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0767, decode.acc_seg: 96.6724, loss: 0.0767 2023-01-06 21:30:57,668 - mmseg - INFO - Iter [118300/160000] lr: 1.564e-05, eta: 7:51:06, time: 0.687, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0815, decode.acc_seg: 96.5396, loss: 0.0815 2023-01-06 21:31:33,213 - mmseg - INFO - Iter [118350/160000] lr: 1.562e-05, eta: 7:50:33, time: 0.710, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0779, decode.acc_seg: 96.6421, loss: 0.0779 2023-01-06 21:32:06,149 - mmseg - INFO - Iter [118400/160000] lr: 1.560e-05, eta: 7:49:59, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0785, decode.acc_seg: 96.5659, loss: 0.0785 2023-01-06 21:32:38,312 - mmseg - INFO - Iter [118450/160000] lr: 1.558e-05, eta: 7:49:24, time: 0.643, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0764, decode.acc_seg: 96.6997, loss: 0.0764 2023-01-06 21:33:11,244 - mmseg - INFO - Iter [118500/160000] lr: 1.556e-05, eta: 7:48:50, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0825, decode.acc_seg: 96.5139, loss: 0.0825 2023-01-06 21:33:44,103 - mmseg - INFO - Iter [118550/160000] lr: 1.554e-05, eta: 7:48:16, time: 0.656, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0784, decode.acc_seg: 96.6599, loss: 0.0784 2023-01-06 21:34:18,366 - mmseg - INFO - Iter [118600/160000] lr: 1.553e-05, eta: 7:47:42, time: 0.685, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0753, decode.acc_seg: 96.7743, loss: 0.0753 2023-01-06 21:34:51,839 - mmseg - INFO - Iter [118650/160000] lr: 1.551e-05, eta: 7:47:08, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0797, decode.acc_seg: 96.6743, loss: 0.0797 2023-01-06 21:35:26,490 - mmseg - INFO - Iter [118700/160000] lr: 1.549e-05, eta: 7:46:34, time: 0.693, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0793, decode.acc_seg: 96.6520, loss: 0.0793 2023-01-06 21:35:59,452 - mmseg - INFO - Iter [118750/160000] lr: 1.547e-05, eta: 7:46:00, time: 0.659, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0813, decode.acc_seg: 96.4905, loss: 0.0813 2023-01-06 21:36:32,123 - mmseg - INFO - Iter [118800/160000] lr: 1.545e-05, eta: 7:45:26, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0819, decode.acc_seg: 96.5396, loss: 0.0819 2023-01-06 21:37:04,264 - mmseg - INFO - Iter [118850/160000] lr: 1.543e-05, eta: 7:44:51, time: 0.643, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0752, decode.acc_seg: 96.7534, loss: 0.0752 2023-01-06 21:37:38,024 - mmseg - INFO - Iter [118900/160000] lr: 1.541e-05, eta: 7:44:17, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0789, decode.acc_seg: 96.5738, loss: 0.0789 2023-01-06 21:38:12,828 - mmseg - INFO - Iter [118950/160000] lr: 1.539e-05, eta: 7:43:44, time: 0.697, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0760, decode.acc_seg: 96.6870, loss: 0.0760 2023-01-06 21:38:44,906 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 21:38:44,907 - mmseg - INFO - Iter [119000/160000] lr: 1.538e-05, eta: 7:43:09, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0795, decode.acc_seg: 96.5530, loss: 0.0795 2023-01-06 21:39:19,710 - mmseg - INFO - Iter [119050/160000] lr: 1.536e-05, eta: 7:42:36, time: 0.696, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0783, decode.acc_seg: 96.5681, loss: 0.0783 2023-01-06 21:39:52,480 - mmseg - INFO - Iter [119100/160000] lr: 1.534e-05, eta: 7:42:02, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0809, decode.acc_seg: 96.5211, loss: 0.0809 2023-01-06 21:40:28,027 - mmseg - INFO - Iter [119150/160000] lr: 1.532e-05, eta: 7:41:28, time: 0.711, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0780, decode.acc_seg: 96.6978, loss: 0.0780 2023-01-06 21:41:00,728 - mmseg - INFO - Iter [119200/160000] lr: 1.530e-05, eta: 7:40:54, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0819, decode.acc_seg: 96.3958, loss: 0.0819 2023-01-06 21:41:35,559 - mmseg - INFO - Iter [119250/160000] lr: 1.528e-05, eta: 7:40:20, time: 0.698, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0783, decode.acc_seg: 96.6181, loss: 0.0783 2023-01-06 21:42:07,768 - mmseg - INFO - Iter [119300/160000] lr: 1.526e-05, eta: 7:39:46, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0761, decode.acc_seg: 96.6480, loss: 0.0761 2023-01-06 21:42:40,253 - mmseg - INFO - Iter [119350/160000] lr: 1.524e-05, eta: 7:39:12, time: 0.649, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0803, decode.acc_seg: 96.5505, loss: 0.0803 2023-01-06 21:43:15,611 - mmseg - INFO - Iter [119400/160000] lr: 1.523e-05, eta: 7:38:38, time: 0.707, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0788, decode.acc_seg: 96.5959, loss: 0.0788 2023-01-06 21:43:51,203 - mmseg - INFO - Iter [119450/160000] lr: 1.521e-05, eta: 7:38:05, time: 0.713, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0753, decode.acc_seg: 96.7165, loss: 0.0753 2023-01-06 21:44:23,493 - mmseg - INFO - Iter [119500/160000] lr: 1.519e-05, eta: 7:37:30, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0765, decode.acc_seg: 96.6347, loss: 0.0765 2023-01-06 21:44:58,582 - mmseg - INFO - Iter [119550/160000] lr: 1.517e-05, eta: 7:36:57, time: 0.702, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0779, decode.acc_seg: 96.6140, loss: 0.0779 2023-01-06 21:45:32,135 - mmseg - INFO - Iter [119600/160000] lr: 1.515e-05, eta: 7:36:23, time: 0.671, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0809, decode.acc_seg: 96.5332, loss: 0.0809 2023-01-06 21:46:04,561 - mmseg - INFO - Iter [119650/160000] lr: 1.513e-05, eta: 7:35:49, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0794, decode.acc_seg: 96.5392, loss: 0.0794 2023-01-06 21:46:38,453 - mmseg - INFO - Iter [119700/160000] lr: 1.511e-05, eta: 7:35:15, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0798, decode.acc_seg: 96.5366, loss: 0.0798 2023-01-06 21:47:10,664 - mmseg - INFO - Iter [119750/160000] lr: 1.509e-05, eta: 7:34:40, time: 0.644, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0801, decode.acc_seg: 96.5622, loss: 0.0801 2023-01-06 21:47:47,479 - mmseg - INFO - Iter [119800/160000] lr: 1.508e-05, eta: 7:34:07, time: 0.736, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0789, decode.acc_seg: 96.6273, loss: 0.0789 2023-01-06 21:48:21,401 - mmseg - INFO - Iter [119850/160000] lr: 1.506e-05, eta: 7:33:33, time: 0.678, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0797, decode.acc_seg: 96.5935, loss: 0.0797 2023-01-06 21:48:55,663 - mmseg - INFO - Iter [119900/160000] lr: 1.504e-05, eta: 7:33:00, time: 0.684, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0776, decode.acc_seg: 96.5927, loss: 0.0776 2023-01-06 21:49:27,911 - mmseg - INFO - Iter [119950/160000] lr: 1.502e-05, eta: 7:32:25, time: 0.646, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0772, decode.acc_seg: 96.6697, loss: 0.0772 2023-01-06 21:50:01,908 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 21:50:01,908 - mmseg - INFO - Iter [120000/160000] lr: 1.500e-05, eta: 7:31:51, time: 0.679, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0775, decode.acc_seg: 96.6306, loss: 0.0775 2023-01-06 21:50:37,770 - mmseg - INFO - Iter [120050/160000] lr: 1.498e-05, eta: 7:31:18, time: 0.717, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0790, decode.acc_seg: 96.6154, loss: 0.0790 2023-01-06 21:51:12,195 - mmseg - INFO - Iter [120100/160000] lr: 1.496e-05, eta: 7:30:44, time: 0.689, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0772, decode.acc_seg: 96.7219, loss: 0.0772 2023-01-06 21:51:44,851 - mmseg - INFO - Iter [120150/160000] lr: 1.494e-05, eta: 7:30:10, time: 0.653, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0788, decode.acc_seg: 96.6621, loss: 0.0788 2023-01-06 21:52:22,747 - mmseg - INFO - Iter [120200/160000] lr: 1.493e-05, eta: 7:29:38, time: 0.757, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0786, decode.acc_seg: 96.5334, loss: 0.0786 2023-01-06 21:52:55,102 - mmseg - INFO - Iter [120250/160000] lr: 1.491e-05, eta: 7:29:03, time: 0.648, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0774, decode.acc_seg: 96.6459, loss: 0.0774 2023-01-06 21:53:27,987 - mmseg - INFO - Iter [120300/160000] lr: 1.489e-05, eta: 7:28:29, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0824, decode.acc_seg: 96.5409, loss: 0.0824 2023-01-06 21:54:02,830 - mmseg - INFO - Iter [120350/160000] lr: 1.487e-05, eta: 7:27:55, time: 0.697, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0759, decode.acc_seg: 96.7148, loss: 0.0759 2023-01-06 21:54:37,308 - mmseg - INFO - Iter [120400/160000] lr: 1.485e-05, eta: 7:27:22, time: 0.690, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0761, decode.acc_seg: 96.7318, loss: 0.0761 2023-01-06 21:55:09,783 - mmseg - INFO - Iter [120450/160000] lr: 1.483e-05, eta: 7:26:47, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0816, decode.acc_seg: 96.5581, loss: 0.0816 2023-01-06 21:55:43,186 - mmseg - INFO - Iter [120500/160000] lr: 1.481e-05, eta: 7:26:13, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0792, decode.acc_seg: 96.5600, loss: 0.0792 2023-01-06 21:56:18,059 - mmseg - INFO - Iter [120550/160000] lr: 1.479e-05, eta: 7:25:40, time: 0.697, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0789, decode.acc_seg: 96.5812, loss: 0.0789 2023-01-06 21:56:52,422 - mmseg - INFO - Iter [120600/160000] lr: 1.478e-05, eta: 7:25:06, time: 0.687, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0789, decode.acc_seg: 96.6383, loss: 0.0789 2023-01-06 21:57:24,561 - mmseg - INFO - Iter [120650/160000] lr: 1.476e-05, eta: 7:24:31, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0792, decode.acc_seg: 96.5763, loss: 0.0792 2023-01-06 21:57:58,537 - mmseg - INFO - Iter [120700/160000] lr: 1.474e-05, eta: 7:23:58, time: 0.680, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0794, decode.acc_seg: 96.6645, loss: 0.0794 2023-01-06 21:58:31,381 - mmseg - INFO - Iter [120750/160000] lr: 1.472e-05, eta: 7:23:23, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0813, decode.acc_seg: 96.4639, loss: 0.0813 2023-01-06 21:59:04,077 - mmseg - INFO - Iter [120800/160000] lr: 1.470e-05, eta: 7:22:49, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0784, decode.acc_seg: 96.6890, loss: 0.0784 2023-01-06 21:59:39,082 - mmseg - INFO - Iter [120850/160000] lr: 1.468e-05, eta: 7:22:16, time: 0.699, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0823, decode.acc_seg: 96.4179, loss: 0.0823 2023-01-06 22:00:11,906 - mmseg - INFO - Iter [120900/160000] lr: 1.466e-05, eta: 7:21:41, time: 0.657, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0795, decode.acc_seg: 96.5817, loss: 0.0795 2023-01-06 22:00:47,919 - mmseg - INFO - Iter [120950/160000] lr: 1.464e-05, eta: 7:21:08, time: 0.720, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0758, decode.acc_seg: 96.6898, loss: 0.0758 2023-01-06 22:01:20,902 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 22:01:20,903 - mmseg - INFO - Iter [121000/160000] lr: 1.463e-05, eta: 7:20:34, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0803, decode.acc_seg: 96.5002, loss: 0.0803 2023-01-06 22:01:56,145 - mmseg - INFO - Iter [121050/160000] lr: 1.461e-05, eta: 7:20:00, time: 0.706, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0762, decode.acc_seg: 96.7421, loss: 0.0762 2023-01-06 22:02:29,538 - mmseg - INFO - Iter [121100/160000] lr: 1.459e-05, eta: 7:19:26, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0813, decode.acc_seg: 96.5084, loss: 0.0813 2023-01-06 22:03:02,146 - mmseg - INFO - Iter [121150/160000] lr: 1.457e-05, eta: 7:18:52, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0792, decode.acc_seg: 96.6104, loss: 0.0792 2023-01-06 22:03:34,829 - mmseg - INFO - Iter [121200/160000] lr: 1.455e-05, eta: 7:18:18, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0813, decode.acc_seg: 96.5131, loss: 0.0813 2023-01-06 22:04:07,066 - mmseg - INFO - Iter [121250/160000] lr: 1.453e-05, eta: 7:17:43, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0813, decode.acc_seg: 96.5110, loss: 0.0813 2023-01-06 22:04:42,613 - mmseg - INFO - Iter [121300/160000] lr: 1.451e-05, eta: 7:17:10, time: 0.711, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0820, decode.acc_seg: 96.3981, loss: 0.0820 2023-01-06 22:05:15,392 - mmseg - INFO - Iter [121350/160000] lr: 1.449e-05, eta: 7:16:36, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0782, decode.acc_seg: 96.6545, loss: 0.0782 2023-01-06 22:05:48,246 - mmseg - INFO - Iter [121400/160000] lr: 1.448e-05, eta: 7:16:02, time: 0.658, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0788, decode.acc_seg: 96.6440, loss: 0.0788 2023-01-06 22:06:21,257 - mmseg - INFO - Iter [121450/160000] lr: 1.446e-05, eta: 7:15:27, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0817, decode.acc_seg: 96.5054, loss: 0.0817 2023-01-06 22:06:54,214 - mmseg - INFO - Iter [121500/160000] lr: 1.444e-05, eta: 7:14:53, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0811, decode.acc_seg: 96.5573, loss: 0.0811 2023-01-06 22:07:26,564 - mmseg - INFO - Iter [121550/160000] lr: 1.442e-05, eta: 7:14:19, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0787, decode.acc_seg: 96.6129, loss: 0.0787 2023-01-06 22:07:58,721 - mmseg - INFO - Iter [121600/160000] lr: 1.440e-05, eta: 7:13:44, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0746, decode.acc_seg: 96.7567, loss: 0.0746 2023-01-06 22:08:33,281 - mmseg - INFO - Iter [121650/160000] lr: 1.438e-05, eta: 7:13:11, time: 0.691, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0836, decode.acc_seg: 96.5315, loss: 0.0836 2023-01-06 22:09:07,249 - mmseg - INFO - Iter [121700/160000] lr: 1.436e-05, eta: 7:12:37, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0783, decode.acc_seg: 96.7129, loss: 0.0783 2023-01-06 22:09:41,797 - mmseg - INFO - Iter [121750/160000] lr: 1.434e-05, eta: 7:12:03, time: 0.691, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0744, decode.acc_seg: 96.7816, loss: 0.0744 2023-01-06 22:10:13,986 - mmseg - INFO - Iter [121800/160000] lr: 1.433e-05, eta: 7:11:29, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0772, decode.acc_seg: 96.6767, loss: 0.0772 2023-01-06 22:10:46,422 - mmseg - INFO - Iter [121850/160000] lr: 1.431e-05, eta: 7:10:54, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0842, decode.acc_seg: 96.4615, loss: 0.0842 2023-01-06 22:11:19,550 - mmseg - INFO - Iter [121900/160000] lr: 1.429e-05, eta: 7:10:20, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0817, decode.acc_seg: 96.5313, loss: 0.0817 2023-01-06 22:11:51,732 - mmseg - INFO - Iter [121950/160000] lr: 1.427e-05, eta: 7:09:46, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0804, decode.acc_seg: 96.5187, loss: 0.0804 2023-01-06 22:12:24,437 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 22:12:24,438 - mmseg - INFO - Iter [122000/160000] lr: 1.425e-05, eta: 7:09:12, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0797, decode.acc_seg: 96.5724, loss: 0.0797 2023-01-06 22:12:59,991 - mmseg - INFO - Iter [122050/160000] lr: 1.423e-05, eta: 7:08:38, time: 0.710, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0814, decode.acc_seg: 96.5040, loss: 0.0814 2023-01-06 22:13:33,221 - mmseg - INFO - Iter [122100/160000] lr: 1.421e-05, eta: 7:08:04, time: 0.666, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0758, decode.acc_seg: 96.7290, loss: 0.0758 2023-01-06 22:14:05,997 - mmseg - INFO - Iter [122150/160000] lr: 1.419e-05, eta: 7:07:30, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0785, decode.acc_seg: 96.5641, loss: 0.0785 2023-01-06 22:14:38,312 - mmseg - INFO - Iter [122200/160000] lr: 1.418e-05, eta: 7:06:56, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0814, decode.acc_seg: 96.5789, loss: 0.0814 2023-01-06 22:15:10,768 - mmseg - INFO - Iter [122250/160000] lr: 1.416e-05, eta: 7:06:21, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0795, decode.acc_seg: 96.5588, loss: 0.0795 2023-01-06 22:15:44,951 - mmseg - INFO - Iter [122300/160000] lr: 1.414e-05, eta: 7:05:47, time: 0.684, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0774, decode.acc_seg: 96.6927, loss: 0.0774 2023-01-06 22:16:20,449 - mmseg - INFO - Iter [122350/160000] lr: 1.412e-05, eta: 7:05:14, time: 0.710, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0739, decode.acc_seg: 96.8719, loss: 0.0739 2023-01-06 22:16:56,557 - mmseg - INFO - Iter [122400/160000] lr: 1.410e-05, eta: 7:04:41, time: 0.722, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0814, decode.acc_seg: 96.4667, loss: 0.0814 2023-01-06 22:17:29,567 - mmseg - INFO - Iter [122450/160000] lr: 1.408e-05, eta: 7:04:07, time: 0.661, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0763, decode.acc_seg: 96.6624, loss: 0.0763 2023-01-06 22:18:02,564 - mmseg - INFO - Iter [122500/160000] lr: 1.406e-05, eta: 7:03:33, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0754, decode.acc_seg: 96.7838, loss: 0.0754 2023-01-06 22:18:35,862 - mmseg - INFO - Iter [122550/160000] lr: 1.404e-05, eta: 7:02:59, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0806, decode.acc_seg: 96.5487, loss: 0.0806 2023-01-06 22:19:08,352 - mmseg - INFO - Iter [122600/160000] lr: 1.403e-05, eta: 7:02:24, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0763, decode.acc_seg: 96.6097, loss: 0.0763 2023-01-06 22:19:42,071 - mmseg - INFO - Iter [122650/160000] lr: 1.401e-05, eta: 7:01:50, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0760, decode.acc_seg: 96.7101, loss: 0.0760 2023-01-06 22:20:16,036 - mmseg - INFO - Iter [122700/160000] lr: 1.399e-05, eta: 7:01:16, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0761, decode.acc_seg: 96.7488, loss: 0.0761 2023-01-06 22:20:48,515 - mmseg - INFO - Iter [122750/160000] lr: 1.397e-05, eta: 7:00:42, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0767, decode.acc_seg: 96.6245, loss: 0.0767 2023-01-06 22:21:24,203 - mmseg - INFO - Iter [122800/160000] lr: 1.395e-05, eta: 7:00:09, time: 0.714, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0799, decode.acc_seg: 96.5438, loss: 0.0799 2023-01-06 22:21:56,557 - mmseg - INFO - Iter [122850/160000] lr: 1.393e-05, eta: 6:59:34, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0755, decode.acc_seg: 96.7186, loss: 0.0755 2023-01-06 22:22:31,847 - mmseg - INFO - Iter [122900/160000] lr: 1.391e-05, eta: 6:59:01, time: 0.706, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0776, decode.acc_seg: 96.7408, loss: 0.0776 2023-01-06 22:23:04,122 - mmseg - INFO - Iter [122950/160000] lr: 1.389e-05, eta: 6:58:27, time: 0.646, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0746, decode.acc_seg: 96.7648, loss: 0.0746 2023-01-06 22:23:38,949 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 22:23:38,949 - mmseg - INFO - Iter [123000/160000] lr: 1.388e-05, eta: 6:57:53, time: 0.697, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0786, decode.acc_seg: 96.6378, loss: 0.0786 2023-01-06 22:24:11,033 - mmseg - INFO - Iter [123050/160000] lr: 1.386e-05, eta: 6:57:19, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0773, decode.acc_seg: 96.6652, loss: 0.0773 2023-01-06 22:24:44,893 - mmseg - INFO - Iter [123100/160000] lr: 1.384e-05, eta: 6:56:45, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0767, decode.acc_seg: 96.7165, loss: 0.0767 2023-01-06 22:25:20,513 - mmseg - INFO - Iter [123150/160000] lr: 1.382e-05, eta: 6:56:11, time: 0.713, data_time: 0.061, memory: 11582, decode.loss_ce: 0.0793, decode.acc_seg: 96.5320, loss: 0.0793 2023-01-06 22:25:52,947 - mmseg - INFO - Iter [123200/160000] lr: 1.380e-05, eta: 6:55:37, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0796, decode.acc_seg: 96.5745, loss: 0.0796 2023-01-06 22:26:25,198 - mmseg - INFO - Iter [123250/160000] lr: 1.378e-05, eta: 6:55:03, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0743, decode.acc_seg: 96.8349, loss: 0.0743 2023-01-06 22:26:58,010 - mmseg - INFO - Iter [123300/160000] lr: 1.376e-05, eta: 6:54:28, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0801, decode.acc_seg: 96.5381, loss: 0.0801 2023-01-06 22:27:31,734 - mmseg - INFO - Iter [123350/160000] lr: 1.374e-05, eta: 6:53:55, time: 0.675, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0745, decode.acc_seg: 96.7070, loss: 0.0745 2023-01-06 22:28:05,488 - mmseg - INFO - Iter [123400/160000] lr: 1.373e-05, eta: 6:53:21, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0798, decode.acc_seg: 96.5663, loss: 0.0798 2023-01-06 22:28:38,636 - mmseg - INFO - Iter [123450/160000] lr: 1.371e-05, eta: 6:52:47, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0764, decode.acc_seg: 96.6986, loss: 0.0764 2023-01-06 22:29:10,839 - mmseg - INFO - Iter [123500/160000] lr: 1.369e-05, eta: 6:52:12, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0761, decode.acc_seg: 96.6103, loss: 0.0761 2023-01-06 22:29:45,557 - mmseg - INFO - Iter [123550/160000] lr: 1.367e-05, eta: 6:51:39, time: 0.694, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0780, decode.acc_seg: 96.6646, loss: 0.0780 2023-01-06 22:30:17,822 - mmseg - INFO - Iter [123600/160000] lr: 1.365e-05, eta: 6:51:04, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0824, decode.acc_seg: 96.5454, loss: 0.0824 2023-01-06 22:30:50,017 - mmseg - INFO - Iter [123650/160000] lr: 1.363e-05, eta: 6:50:30, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0793, decode.acc_seg: 96.5574, loss: 0.0793 2023-01-06 22:31:23,061 - mmseg - INFO - Iter [123700/160000] lr: 1.361e-05, eta: 6:49:56, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0748, decode.acc_seg: 96.7340, loss: 0.0748 2023-01-06 22:31:56,060 - mmseg - INFO - Iter [123750/160000] lr: 1.359e-05, eta: 6:49:22, time: 0.661, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0753, decode.acc_seg: 96.8390, loss: 0.0753 2023-01-06 22:32:30,658 - mmseg - INFO - Iter [123800/160000] lr: 1.358e-05, eta: 6:48:48, time: 0.692, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0808, decode.acc_seg: 96.5550, loss: 0.0808 2023-01-06 22:33:04,142 - mmseg - INFO - Iter [123850/160000] lr: 1.356e-05, eta: 6:48:14, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0740, decode.acc_seg: 96.7103, loss: 0.0740 2023-01-06 22:33:39,909 - mmseg - INFO - Iter [123900/160000] lr: 1.354e-05, eta: 6:47:41, time: 0.715, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0776, decode.acc_seg: 96.5365, loss: 0.0776 2023-01-06 22:34:12,090 - mmseg - INFO - Iter [123950/160000] lr: 1.352e-05, eta: 6:47:06, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0778, decode.acc_seg: 96.7124, loss: 0.0778 2023-01-06 22:34:45,848 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 22:34:45,849 - mmseg - INFO - Iter [124000/160000] lr: 1.350e-05, eta: 6:46:32, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0762, decode.acc_seg: 96.7544, loss: 0.0762 2023-01-06 22:35:18,225 - mmseg - INFO - Iter [124050/160000] lr: 1.348e-05, eta: 6:45:58, time: 0.648, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0791, decode.acc_seg: 96.6820, loss: 0.0791 2023-01-06 22:35:51,426 - mmseg - INFO - Iter [124100/160000] lr: 1.346e-05, eta: 6:45:24, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0804, decode.acc_seg: 96.5915, loss: 0.0804 2023-01-06 22:36:24,151 - mmseg - INFO - Iter [124150/160000] lr: 1.344e-05, eta: 6:44:50, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0793, decode.acc_seg: 96.6182, loss: 0.0793 2023-01-06 22:36:56,277 - mmseg - INFO - Iter [124200/160000] lr: 1.343e-05, eta: 6:44:15, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0843, decode.acc_seg: 96.3611, loss: 0.0843 2023-01-06 22:37:30,916 - mmseg - INFO - Iter [124250/160000] lr: 1.341e-05, eta: 6:43:42, time: 0.693, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0770, decode.acc_seg: 96.6656, loss: 0.0770 2023-01-06 22:38:04,395 - mmseg - INFO - Iter [124300/160000] lr: 1.339e-05, eta: 6:43:08, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0762, decode.acc_seg: 96.6760, loss: 0.0762 2023-01-06 22:38:36,777 - mmseg - INFO - Iter [124350/160000] lr: 1.337e-05, eta: 6:42:33, time: 0.649, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0735, decode.acc_seg: 96.7701, loss: 0.0735 2023-01-06 22:39:11,280 - mmseg - INFO - Iter [124400/160000] lr: 1.335e-05, eta: 6:42:00, time: 0.689, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0781, decode.acc_seg: 96.6702, loss: 0.0781 2023-01-06 22:39:44,922 - mmseg - INFO - Iter [124450/160000] lr: 1.333e-05, eta: 6:41:26, time: 0.673, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0774, decode.acc_seg: 96.6460, loss: 0.0774 2023-01-06 22:40:17,754 - mmseg - INFO - Iter [124500/160000] lr: 1.331e-05, eta: 6:40:51, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0745, decode.acc_seg: 96.7721, loss: 0.0745 2023-01-06 22:40:49,881 - mmseg - INFO - Iter [124550/160000] lr: 1.329e-05, eta: 6:40:17, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0771, decode.acc_seg: 96.6313, loss: 0.0771 2023-01-06 22:41:22,444 - mmseg - INFO - Iter [124600/160000] lr: 1.328e-05, eta: 6:39:43, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0773, decode.acc_seg: 96.6345, loss: 0.0773 2023-01-06 22:41:58,549 - mmseg - INFO - Iter [124650/160000] lr: 1.326e-05, eta: 6:39:10, time: 0.722, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0748, decode.acc_seg: 96.7571, loss: 0.0748 2023-01-06 22:42:30,800 - mmseg - INFO - Iter [124700/160000] lr: 1.324e-05, eta: 6:38:35, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0755, decode.acc_seg: 96.7190, loss: 0.0755 2023-01-06 22:43:04,135 - mmseg - INFO - Iter [124750/160000] lr: 1.322e-05, eta: 6:38:01, time: 0.667, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0774, decode.acc_seg: 96.6998, loss: 0.0774 2023-01-06 22:43:36,881 - mmseg - INFO - Iter [124800/160000] lr: 1.320e-05, eta: 6:37:27, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0758, decode.acc_seg: 96.6648, loss: 0.0758 2023-01-06 22:44:10,852 - mmseg - INFO - Iter [124850/160000] lr: 1.318e-05, eta: 6:36:53, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0774, decode.acc_seg: 96.7616, loss: 0.0774 2023-01-06 22:44:45,068 - mmseg - INFO - Iter [124900/160000] lr: 1.316e-05, eta: 6:36:19, time: 0.685, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0813, decode.acc_seg: 96.6233, loss: 0.0813 2023-01-06 22:45:18,652 - mmseg - INFO - Iter [124950/160000] lr: 1.314e-05, eta: 6:35:45, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0788, decode.acc_seg: 96.6184, loss: 0.0788 2023-01-06 22:45:54,862 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 22:45:54,863 - mmseg - INFO - Iter [125000/160000] lr: 1.313e-05, eta: 6:35:12, time: 0.724, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0779, decode.acc_seg: 96.6108, loss: 0.0779 2023-01-06 22:46:30,604 - mmseg - INFO - Iter [125050/160000] lr: 1.311e-05, eta: 6:34:39, time: 0.714, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0731, decode.acc_seg: 96.8372, loss: 0.0731 2023-01-06 22:47:05,270 - mmseg - INFO - Iter [125100/160000] lr: 1.309e-05, eta: 6:34:05, time: 0.693, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0760, decode.acc_seg: 96.7028, loss: 0.0760 2023-01-06 22:47:40,162 - mmseg - INFO - Iter [125150/160000] lr: 1.307e-05, eta: 6:33:32, time: 0.699, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0775, decode.acc_seg: 96.6495, loss: 0.0775 2023-01-06 22:48:13,665 - mmseg - INFO - Iter [125200/160000] lr: 1.305e-05, eta: 6:32:58, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0813, decode.acc_seg: 96.5870, loss: 0.0813 2023-01-06 22:48:47,119 - mmseg - INFO - Iter [125250/160000] lr: 1.303e-05, eta: 6:32:24, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0829, decode.acc_seg: 96.4806, loss: 0.0829 2023-01-06 22:49:19,521 - mmseg - INFO - Iter [125300/160000] lr: 1.301e-05, eta: 6:31:49, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0783, decode.acc_seg: 96.6450, loss: 0.0783 2023-01-06 22:49:52,011 - mmseg - INFO - Iter [125350/160000] lr: 1.299e-05, eta: 6:31:15, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0759, decode.acc_seg: 96.6907, loss: 0.0759 2023-01-06 22:50:26,351 - mmseg - INFO - Iter [125400/160000] lr: 1.298e-05, eta: 6:30:41, time: 0.687, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0762, decode.acc_seg: 96.7039, loss: 0.0762 2023-01-06 22:50:59,803 - mmseg - INFO - Iter [125450/160000] lr: 1.296e-05, eta: 6:30:07, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0795, decode.acc_seg: 96.5815, loss: 0.0795 2023-01-06 22:51:32,142 - mmseg - INFO - Iter [125500/160000] lr: 1.294e-05, eta: 6:29:33, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0806, decode.acc_seg: 96.6459, loss: 0.0806 2023-01-06 22:52:05,561 - mmseg - INFO - Iter [125550/160000] lr: 1.292e-05, eta: 6:28:59, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0780, decode.acc_seg: 96.6248, loss: 0.0780 2023-01-06 22:52:38,608 - mmseg - INFO - Iter [125600/160000] lr: 1.290e-05, eta: 6:28:25, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0774, decode.acc_seg: 96.6656, loss: 0.0774 2023-01-06 22:53:12,418 - mmseg - INFO - Iter [125650/160000] lr: 1.288e-05, eta: 6:27:51, time: 0.677, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0756, decode.acc_seg: 96.7759, loss: 0.0756 2023-01-06 22:53:47,743 - mmseg - INFO - Iter [125700/160000] lr: 1.286e-05, eta: 6:27:18, time: 0.705, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0768, decode.acc_seg: 96.6933, loss: 0.0768 2023-01-06 22:54:22,834 - mmseg - INFO - Iter [125750/160000] lr: 1.284e-05, eta: 6:26:44, time: 0.703, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0756, decode.acc_seg: 96.7300, loss: 0.0756 2023-01-06 22:54:55,584 - mmseg - INFO - Iter [125800/160000] lr: 1.283e-05, eta: 6:26:10, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0779, decode.acc_seg: 96.6653, loss: 0.0779 2023-01-06 22:55:31,451 - mmseg - INFO - Iter [125850/160000] lr: 1.281e-05, eta: 6:25:37, time: 0.716, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0786, decode.acc_seg: 96.6721, loss: 0.0786 2023-01-06 22:56:04,485 - mmseg - INFO - Iter [125900/160000] lr: 1.279e-05, eta: 6:25:02, time: 0.662, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0783, decode.acc_seg: 96.6570, loss: 0.0783 2023-01-06 22:56:38,035 - mmseg - INFO - Iter [125950/160000] lr: 1.277e-05, eta: 6:24:28, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0732, decode.acc_seg: 96.8362, loss: 0.0732 2023-01-06 22:57:10,964 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 22:57:10,965 - mmseg - INFO - Iter [126000/160000] lr: 1.275e-05, eta: 6:23:54, time: 0.660, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0810, decode.acc_seg: 96.5289, loss: 0.0810 2023-01-06 22:57:45,136 - mmseg - INFO - Iter [126050/160000] lr: 1.273e-05, eta: 6:23:21, time: 0.683, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0785, decode.acc_seg: 96.6889, loss: 0.0785 2023-01-06 22:58:18,505 - mmseg - INFO - Iter [126100/160000] lr: 1.271e-05, eta: 6:22:47, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0764, decode.acc_seg: 96.7132, loss: 0.0764 2023-01-06 22:58:53,608 - mmseg - INFO - Iter [126150/160000] lr: 1.269e-05, eta: 6:22:13, time: 0.702, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0775, decode.acc_seg: 96.6685, loss: 0.0775 2023-01-06 22:59:26,196 - mmseg - INFO - Iter [126200/160000] lr: 1.268e-05, eta: 6:21:39, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0753, decode.acc_seg: 96.6491, loss: 0.0753 2023-01-06 22:59:58,381 - mmseg - INFO - Iter [126250/160000] lr: 1.266e-05, eta: 6:21:04, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0763, decode.acc_seg: 96.6539, loss: 0.0763 2023-01-06 23:00:30,710 - mmseg - INFO - Iter [126300/160000] lr: 1.264e-05, eta: 6:20:30, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0780, decode.acc_seg: 96.6242, loss: 0.0780 2023-01-06 23:01:04,577 - mmseg - INFO - Iter [126350/160000] lr: 1.262e-05, eta: 6:19:56, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0763, decode.acc_seg: 96.7314, loss: 0.0763 2023-01-06 23:01:37,080 - mmseg - INFO - Iter [126400/160000] lr: 1.260e-05, eta: 6:19:22, time: 0.651, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0783, decode.acc_seg: 96.6079, loss: 0.0783 2023-01-06 23:02:09,600 - mmseg - INFO - Iter [126450/160000] lr: 1.258e-05, eta: 6:18:48, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0779, decode.acc_seg: 96.6585, loss: 0.0779 2023-01-06 23:02:44,759 - mmseg - INFO - Iter [126500/160000] lr: 1.256e-05, eta: 6:18:14, time: 0.703, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0729, decode.acc_seg: 96.8794, loss: 0.0729 2023-01-06 23:03:17,332 - mmseg - INFO - Iter [126550/160000] lr: 1.254e-05, eta: 6:17:40, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0760, decode.acc_seg: 96.7874, loss: 0.0760 2023-01-06 23:03:49,615 - mmseg - INFO - Iter [126600/160000] lr: 1.253e-05, eta: 6:17:06, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0728, decode.acc_seg: 96.8392, loss: 0.0728 2023-01-06 23:04:22,774 - mmseg - INFO - Iter [126650/160000] lr: 1.251e-05, eta: 6:16:32, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0720, decode.acc_seg: 96.8632, loss: 0.0720 2023-01-06 23:04:58,233 - mmseg - INFO - Iter [126700/160000] lr: 1.249e-05, eta: 6:15:58, time: 0.709, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0750, decode.acc_seg: 96.6925, loss: 0.0750 2023-01-06 23:05:31,213 - mmseg - INFO - Iter [126750/160000] lr: 1.247e-05, eta: 6:15:24, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0766, decode.acc_seg: 96.6787, loss: 0.0766 2023-01-06 23:06:03,283 - mmseg - INFO - Iter [126800/160000] lr: 1.245e-05, eta: 6:14:50, time: 0.641, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0780, decode.acc_seg: 96.6753, loss: 0.0780 2023-01-06 23:06:37,129 - mmseg - INFO - Iter [126850/160000] lr: 1.243e-05, eta: 6:14:16, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0754, decode.acc_seg: 96.7335, loss: 0.0754 2023-01-06 23:07:14,519 - mmseg - INFO - Iter [126900/160000] lr: 1.241e-05, eta: 6:13:43, time: 0.747, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0770, decode.acc_seg: 96.7011, loss: 0.0770 2023-01-06 23:07:46,986 - mmseg - INFO - Iter [126950/160000] lr: 1.239e-05, eta: 6:13:09, time: 0.650, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0711, decode.acc_seg: 96.8802, loss: 0.0711 2023-01-06 23:08:21,308 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 23:08:21,309 - mmseg - INFO - Iter [127000/160000] lr: 1.238e-05, eta: 6:12:35, time: 0.686, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0787, decode.acc_seg: 96.5771, loss: 0.0787 2023-01-06 23:08:55,224 - mmseg - INFO - Iter [127050/160000] lr: 1.236e-05, eta: 6:12:01, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0745, decode.acc_seg: 96.7534, loss: 0.0745 2023-01-06 23:09:27,511 - mmseg - INFO - Iter [127100/160000] lr: 1.234e-05, eta: 6:11:27, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0812, decode.acc_seg: 96.5505, loss: 0.0812 2023-01-06 23:10:00,782 - mmseg - INFO - Iter [127150/160000] lr: 1.232e-05, eta: 6:10:53, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0749, decode.acc_seg: 96.7732, loss: 0.0749 2023-01-06 23:10:33,391 - mmseg - INFO - Iter [127200/160000] lr: 1.230e-05, eta: 6:10:19, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0757, decode.acc_seg: 96.7574, loss: 0.0757 2023-01-06 23:11:07,918 - mmseg - INFO - Iter [127250/160000] lr: 1.228e-05, eta: 6:09:45, time: 0.690, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0786, decode.acc_seg: 96.6889, loss: 0.0786 2023-01-06 23:11:42,167 - mmseg - INFO - Iter [127300/160000] lr: 1.226e-05, eta: 6:09:11, time: 0.685, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0786, decode.acc_seg: 96.6192, loss: 0.0786 2023-01-06 23:12:14,830 - mmseg - INFO - Iter [127350/160000] lr: 1.224e-05, eta: 6:08:37, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0798, decode.acc_seg: 96.5505, loss: 0.0798 2023-01-06 23:12:47,304 - mmseg - INFO - Iter [127400/160000] lr: 1.223e-05, eta: 6:08:03, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0754, decode.acc_seg: 96.7561, loss: 0.0754 2023-01-06 23:13:19,503 - mmseg - INFO - Iter [127450/160000] lr: 1.221e-05, eta: 6:07:28, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0754, decode.acc_seg: 96.7512, loss: 0.0754 2023-01-06 23:13:51,954 - mmseg - INFO - Iter [127500/160000] lr: 1.219e-05, eta: 6:06:54, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0811, decode.acc_seg: 96.5192, loss: 0.0811 2023-01-06 23:14:24,383 - mmseg - INFO - Iter [127550/160000] lr: 1.217e-05, eta: 6:06:20, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0727, decode.acc_seg: 96.8417, loss: 0.0727 2023-01-06 23:14:58,825 - mmseg - INFO - Iter [127600/160000] lr: 1.215e-05, eta: 6:05:46, time: 0.689, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0752, decode.acc_seg: 96.7960, loss: 0.0752 2023-01-06 23:15:32,809 - mmseg - INFO - Iter [127650/160000] lr: 1.213e-05, eta: 6:05:12, time: 0.679, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0839, decode.acc_seg: 96.4678, loss: 0.0839 2023-01-06 23:16:07,148 - mmseg - INFO - Iter [127700/160000] lr: 1.211e-05, eta: 6:04:39, time: 0.687, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0750, decode.acc_seg: 96.7530, loss: 0.0750 2023-01-06 23:16:40,368 - mmseg - INFO - Iter [127750/160000] lr: 1.209e-05, eta: 6:04:05, time: 0.665, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0790, decode.acc_seg: 96.6328, loss: 0.0790 2023-01-06 23:17:12,542 - mmseg - INFO - Iter [127800/160000] lr: 1.208e-05, eta: 6:03:30, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0758, decode.acc_seg: 96.5858, loss: 0.0758 2023-01-06 23:17:45,546 - mmseg - INFO - Iter [127850/160000] lr: 1.206e-05, eta: 6:02:56, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0777, decode.acc_seg: 96.6278, loss: 0.0777 2023-01-06 23:18:19,093 - mmseg - INFO - Iter [127900/160000] lr: 1.204e-05, eta: 6:02:22, time: 0.672, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0782, decode.acc_seg: 96.6291, loss: 0.0782 2023-01-06 23:18:53,174 - mmseg - INFO - Iter [127950/160000] lr: 1.202e-05, eta: 6:01:49, time: 0.681, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0783, decode.acc_seg: 96.5646, loss: 0.0783 2023-01-06 23:19:27,858 - mmseg - INFO - Saving checkpoint at 128000 iterations 2023-01-06 23:19:33,897 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 23:19:33,897 - mmseg - INFO - Iter [128000/160000] lr: 1.200e-05, eta: 6:01:16, time: 0.815, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0762, decode.acc_seg: 96.7168, loss: 0.0762 2023-01-06 23:20:09,685 - mmseg - INFO - per class results: 2023-01-06 23:20:09,687 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 98.08 | 98.93 | | sidewalk | 83.96 | 91.98 | | building | 92.28 | 96.37 | | wall | 57.43 | 64.51 | | fence | 57.61 | 69.07 | | pole | 63.99 | 75.0 | | traffic light | 67.64 | 78.05 | | traffic sign | 76.7 | 84.23 | | vegetation | 92.39 | 96.69 | | terrain | 64.04 | 73.6 | | sky | 94.62 | 98.27 | | person | 79.46 | 88.79 | | rider | 56.77 | 70.3 | | car | 94.29 | 97.49 | | truck | 71.37 | 80.86 | | bus | 80.75 | 89.4 | | train | 67.22 | 74.68 | | motorcycle | 52.8 | 65.35 | | bicycle | 74.06 | 86.79 | +---------------+-------+-------+ 2023-01-06 23:20:09,687 - mmseg - INFO - Summary: 2023-01-06 23:20:09,688 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 95.86 | 75.02 | 83.18 | +-------+-------+-------+ 2023-01-06 23:20:09,688 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 23:20:09,689 - mmseg - INFO - Iter(val) [63] aAcc: 0.9586, mIoU: 0.7502, mAcc: 0.8318, IoU.road: 0.9808, IoU.sidewalk: 0.8396, IoU.building: 0.9228, IoU.wall: 0.5743, IoU.fence: 0.5761, IoU.pole: 0.6399, IoU.traffic light: 0.6764, IoU.traffic sign: 0.7670, IoU.vegetation: 0.9239, IoU.terrain: 0.6404, IoU.sky: 0.9462, IoU.person: 0.7946, IoU.rider: 0.5677, IoU.car: 0.9429, IoU.truck: 0.7137, IoU.bus: 0.8075, IoU.train: 0.6722, IoU.motorcycle: 0.5280, IoU.bicycle: 0.7406, Acc.road: 0.9893, Acc.sidewalk: 0.9198, Acc.building: 0.9637, Acc.wall: 0.6451, Acc.fence: 0.6907, Acc.pole: 0.7500, Acc.traffic light: 0.7805, Acc.traffic sign: 0.8423, Acc.vegetation: 0.9669, Acc.terrain: 0.7360, Acc.sky: 0.9827, Acc.person: 0.8879, Acc.rider: 0.7030, Acc.car: 0.9749, Acc.truck: 0.8086, Acc.bus: 0.8940, Acc.train: 0.7468, Acc.motorcycle: 0.6535, Acc.bicycle: 0.8679 2023-01-06 23:20:41,839 - mmseg - INFO - Iter [128050/160000] lr: 1.198e-05, eta: 6:00:51, time: 1.359, data_time: 0.729, memory: 11582, decode.loss_ce: 0.0811, decode.acc_seg: 96.5393, loss: 0.0811 2023-01-06 23:21:15,251 - mmseg - INFO - Iter [128100/160000] lr: 1.196e-05, eta: 6:00:17, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0733, decode.acc_seg: 96.7888, loss: 0.0733 2023-01-06 23:21:47,483 - mmseg - INFO - Iter [128150/160000] lr: 1.194e-05, eta: 5:59:43, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0726, decode.acc_seg: 96.8780, loss: 0.0726 2023-01-06 23:22:20,128 - mmseg - INFO - Iter [128200/160000] lr: 1.193e-05, eta: 5:59:08, time: 0.652, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0769, decode.acc_seg: 96.5639, loss: 0.0769 2023-01-06 23:22:53,702 - mmseg - INFO - Iter [128250/160000] lr: 1.191e-05, eta: 5:58:35, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0714, decode.acc_seg: 96.8987, loss: 0.0714 2023-01-06 23:23:27,987 - mmseg - INFO - Iter [128300/160000] lr: 1.189e-05, eta: 5:58:01, time: 0.686, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0780, decode.acc_seg: 96.6553, loss: 0.0780 2023-01-06 23:24:03,200 - mmseg - INFO - Iter [128350/160000] lr: 1.187e-05, eta: 5:57:27, time: 0.704, data_time: 0.057, memory: 11582, decode.loss_ce: 0.0725, decode.acc_seg: 96.8209, loss: 0.0725 2023-01-06 23:24:38,710 - mmseg - INFO - Iter [128400/160000] lr: 1.185e-05, eta: 5:56:54, time: 0.709, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0786, decode.acc_seg: 96.5971, loss: 0.0786 2023-01-06 23:25:13,583 - mmseg - INFO - Iter [128450/160000] lr: 1.183e-05, eta: 5:56:20, time: 0.698, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0788, decode.acc_seg: 96.6032, loss: 0.0788 2023-01-06 23:25:45,970 - mmseg - INFO - Iter [128500/160000] lr: 1.181e-05, eta: 5:55:46, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0754, decode.acc_seg: 96.7075, loss: 0.0754 2023-01-06 23:26:19,524 - mmseg - INFO - Iter [128550/160000] lr: 1.179e-05, eta: 5:55:12, time: 0.671, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0772, decode.acc_seg: 96.6596, loss: 0.0772 2023-01-06 23:26:53,728 - mmseg - INFO - Iter [128600/160000] lr: 1.178e-05, eta: 5:54:38, time: 0.683, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0789, decode.acc_seg: 96.6598, loss: 0.0789 2023-01-06 23:27:26,430 - mmseg - INFO - Iter [128650/160000] lr: 1.176e-05, eta: 5:54:04, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0790, decode.acc_seg: 96.5685, loss: 0.0790 2023-01-06 23:27:59,193 - mmseg - INFO - Iter [128700/160000] lr: 1.174e-05, eta: 5:53:30, time: 0.655, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0727, decode.acc_seg: 96.8249, loss: 0.0727 2023-01-06 23:28:36,462 - mmseg - INFO - Iter [128750/160000] lr: 1.172e-05, eta: 5:52:57, time: 0.745, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0794, decode.acc_seg: 96.6512, loss: 0.0794 2023-01-06 23:29:10,912 - mmseg - INFO - Iter [128800/160000] lr: 1.170e-05, eta: 5:52:23, time: 0.688, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0771, decode.acc_seg: 96.6309, loss: 0.0771 2023-01-06 23:29:43,594 - mmseg - INFO - Iter [128850/160000] lr: 1.168e-05, eta: 5:51:49, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0762, decode.acc_seg: 96.6316, loss: 0.0762 2023-01-06 23:30:18,475 - mmseg - INFO - Iter [128900/160000] lr: 1.166e-05, eta: 5:51:15, time: 0.698, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0746, decode.acc_seg: 96.7846, loss: 0.0746 2023-01-06 23:30:51,416 - mmseg - INFO - Iter [128950/160000] lr: 1.164e-05, eta: 5:50:41, time: 0.659, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0760, decode.acc_seg: 96.7710, loss: 0.0760 2023-01-06 23:31:25,476 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 23:31:25,477 - mmseg - INFO - Iter [129000/160000] lr: 1.163e-05, eta: 5:50:07, time: 0.681, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0748, decode.acc_seg: 96.7188, loss: 0.0748 2023-01-06 23:32:00,697 - mmseg - INFO - Iter [129050/160000] lr: 1.161e-05, eta: 5:49:34, time: 0.704, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0783, decode.acc_seg: 96.6242, loss: 0.0783 2023-01-06 23:32:37,768 - mmseg - INFO - Iter [129100/160000] lr: 1.159e-05, eta: 5:49:00, time: 0.741, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0774, decode.acc_seg: 96.6147, loss: 0.0774 2023-01-06 23:33:11,243 - mmseg - INFO - Iter [129150/160000] lr: 1.157e-05, eta: 5:48:27, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0830, decode.acc_seg: 96.5103, loss: 0.0830 2023-01-06 23:33:44,526 - mmseg - INFO - Iter [129200/160000] lr: 1.155e-05, eta: 5:47:52, time: 0.665, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0762, decode.acc_seg: 96.6716, loss: 0.0762 2023-01-06 23:34:18,689 - mmseg - INFO - Iter [129250/160000] lr: 1.153e-05, eta: 5:47:19, time: 0.683, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0726, decode.acc_seg: 96.8805, loss: 0.0726 2023-01-06 23:34:52,642 - mmseg - INFO - Iter [129300/160000] lr: 1.151e-05, eta: 5:46:45, time: 0.679, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0787, decode.acc_seg: 96.7088, loss: 0.0787 2023-01-06 23:35:26,512 - mmseg - INFO - Iter [129350/160000] lr: 1.149e-05, eta: 5:46:11, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0736, decode.acc_seg: 96.8414, loss: 0.0736 2023-01-06 23:36:00,063 - mmseg - INFO - Iter [129400/160000] lr: 1.148e-05, eta: 5:45:37, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0765, decode.acc_seg: 96.6790, loss: 0.0765 2023-01-06 23:36:32,597 - mmseg - INFO - Iter [129450/160000] lr: 1.146e-05, eta: 5:45:03, time: 0.650, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0746, decode.acc_seg: 96.7678, loss: 0.0746 2023-01-06 23:37:08,329 - mmseg - INFO - Iter [129500/160000] lr: 1.144e-05, eta: 5:44:29, time: 0.716, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0748, decode.acc_seg: 96.7871, loss: 0.0748 2023-01-06 23:37:41,589 - mmseg - INFO - Iter [129550/160000] lr: 1.142e-05, eta: 5:43:55, time: 0.664, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0790, decode.acc_seg: 96.6403, loss: 0.0790 2023-01-06 23:38:14,803 - mmseg - INFO - Iter [129600/160000] lr: 1.140e-05, eta: 5:43:21, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0721, decode.acc_seg: 96.8028, loss: 0.0721 2023-01-06 23:38:48,383 - mmseg - INFO - Iter [129650/160000] lr: 1.138e-05, eta: 5:42:47, time: 0.672, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0763, decode.acc_seg: 96.7598, loss: 0.0763 2023-01-06 23:39:20,776 - mmseg - INFO - Iter [129700/160000] lr: 1.136e-05, eta: 5:42:13, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0806, decode.acc_seg: 96.5573, loss: 0.0806 2023-01-06 23:39:54,540 - mmseg - INFO - Iter [129750/160000] lr: 1.134e-05, eta: 5:41:39, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0754, decode.acc_seg: 96.6987, loss: 0.0754 2023-01-06 23:40:29,810 - mmseg - INFO - Iter [129800/160000] lr: 1.133e-05, eta: 5:41:06, time: 0.706, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0849, decode.acc_seg: 96.4499, loss: 0.0849 2023-01-06 23:41:06,011 - mmseg - INFO - Iter [129850/160000] lr: 1.131e-05, eta: 5:40:32, time: 0.724, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0827, decode.acc_seg: 96.5085, loss: 0.0827 2023-01-06 23:41:38,727 - mmseg - INFO - Iter [129900/160000] lr: 1.129e-05, eta: 5:39:58, time: 0.654, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0798, decode.acc_seg: 96.5641, loss: 0.0798 2023-01-06 23:42:11,398 - mmseg - INFO - Iter [129950/160000] lr: 1.127e-05, eta: 5:39:24, time: 0.652, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0771, decode.acc_seg: 96.6631, loss: 0.0771 2023-01-06 23:42:44,025 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 23:42:44,026 - mmseg - INFO - Iter [130000/160000] lr: 1.125e-05, eta: 5:38:50, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0767, decode.acc_seg: 96.6651, loss: 0.0767 2023-01-06 23:43:17,562 - mmseg - INFO - Iter [130050/160000] lr: 1.123e-05, eta: 5:38:16, time: 0.671, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0758, decode.acc_seg: 96.5846, loss: 0.0758 2023-01-06 23:43:49,615 - mmseg - INFO - Iter [130100/160000] lr: 1.121e-05, eta: 5:37:41, time: 0.641, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0799, decode.acc_seg: 96.5709, loss: 0.0799 2023-01-06 23:44:22,726 - mmseg - INFO - Iter [130150/160000] lr: 1.119e-05, eta: 5:37:07, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0783, decode.acc_seg: 96.6532, loss: 0.0783 2023-01-06 23:44:55,305 - mmseg - INFO - Iter [130200/160000] lr: 1.118e-05, eta: 5:36:33, time: 0.653, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0785, decode.acc_seg: 96.4785, loss: 0.0785 2023-01-06 23:45:32,057 - mmseg - INFO - Iter [130250/160000] lr: 1.116e-05, eta: 5:36:00, time: 0.735, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0740, decode.acc_seg: 96.8154, loss: 0.0740 2023-01-06 23:46:04,682 - mmseg - INFO - Iter [130300/160000] lr: 1.114e-05, eta: 5:35:26, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0739, decode.acc_seg: 96.7664, loss: 0.0739 2023-01-06 23:46:37,012 - mmseg - INFO - Iter [130350/160000] lr: 1.112e-05, eta: 5:34:52, time: 0.646, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0763, decode.acc_seg: 96.7744, loss: 0.0763 2023-01-06 23:47:10,809 - mmseg - INFO - Iter [130400/160000] lr: 1.110e-05, eta: 5:34:18, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0758, decode.acc_seg: 96.6646, loss: 0.0758 2023-01-06 23:47:44,358 - mmseg - INFO - Iter [130450/160000] lr: 1.108e-05, eta: 5:33:44, time: 0.671, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0796, decode.acc_seg: 96.6639, loss: 0.0796 2023-01-06 23:48:16,714 - mmseg - INFO - Iter [130500/160000] lr: 1.106e-05, eta: 5:33:09, time: 0.647, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0751, decode.acc_seg: 96.7856, loss: 0.0751 2023-01-06 23:48:49,803 - mmseg - INFO - Iter [130550/160000] lr: 1.104e-05, eta: 5:32:35, time: 0.661, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0734, decode.acc_seg: 96.7749, loss: 0.0734 2023-01-06 23:49:26,687 - mmseg - INFO - Iter [130600/160000] lr: 1.103e-05, eta: 5:32:02, time: 0.738, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0795, decode.acc_seg: 96.6403, loss: 0.0795 2023-01-06 23:50:01,140 - mmseg - INFO - Iter [130650/160000] lr: 1.101e-05, eta: 5:31:28, time: 0.689, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0743, decode.acc_seg: 96.7882, loss: 0.0743 2023-01-06 23:50:34,423 - mmseg - INFO - Iter [130700/160000] lr: 1.099e-05, eta: 5:30:54, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0753, decode.acc_seg: 96.7064, loss: 0.0753 2023-01-06 23:51:06,564 - mmseg - INFO - Iter [130750/160000] lr: 1.097e-05, eta: 5:30:20, time: 0.643, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0728, decode.acc_seg: 96.8326, loss: 0.0728 2023-01-06 23:51:38,924 - mmseg - INFO - Iter [130800/160000] lr: 1.095e-05, eta: 5:29:46, time: 0.647, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0791, decode.acc_seg: 96.5695, loss: 0.0791 2023-01-06 23:52:11,970 - mmseg - INFO - Iter [130850/160000] lr: 1.093e-05, eta: 5:29:12, time: 0.660, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0748, decode.acc_seg: 96.7274, loss: 0.0748 2023-01-06 23:52:45,499 - mmseg - INFO - Iter [130900/160000] lr: 1.091e-05, eta: 5:28:38, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0741, decode.acc_seg: 96.6953, loss: 0.0741 2023-01-06 23:53:21,101 - mmseg - INFO - Iter [130950/160000] lr: 1.089e-05, eta: 5:28:04, time: 0.712, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0726, decode.acc_seg: 96.8017, loss: 0.0726 2023-01-06 23:53:53,998 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-06 23:53:53,999 - mmseg - INFO - Iter [131000/160000] lr: 1.088e-05, eta: 5:27:30, time: 0.657, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0743, decode.acc_seg: 96.7652, loss: 0.0743 2023-01-06 23:54:27,680 - mmseg - INFO - Iter [131050/160000] lr: 1.086e-05, eta: 5:26:56, time: 0.675, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0726, decode.acc_seg: 96.7843, loss: 0.0726 2023-01-06 23:54:59,950 - mmseg - INFO - Iter [131100/160000] lr: 1.084e-05, eta: 5:26:22, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0759, decode.acc_seg: 96.7028, loss: 0.0759 2023-01-06 23:55:32,144 - mmseg - INFO - Iter [131150/160000] lr: 1.082e-05, eta: 5:25:48, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0760, decode.acc_seg: 96.6698, loss: 0.0760 2023-01-06 23:56:04,797 - mmseg - INFO - Iter [131200/160000] lr: 1.080e-05, eta: 5:25:14, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0752, decode.acc_seg: 96.6922, loss: 0.0752 2023-01-06 23:56:37,242 - mmseg - INFO - Iter [131250/160000] lr: 1.078e-05, eta: 5:24:40, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0718, decode.acc_seg: 96.9464, loss: 0.0718 2023-01-06 23:57:10,973 - mmseg - INFO - Iter [131300/160000] lr: 1.076e-05, eta: 5:24:06, time: 0.675, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0779, decode.acc_seg: 96.5524, loss: 0.0779 2023-01-06 23:57:46,657 - mmseg - INFO - Iter [131350/160000] lr: 1.074e-05, eta: 5:23:32, time: 0.713, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0756, decode.acc_seg: 96.7557, loss: 0.0756 2023-01-06 23:58:21,038 - mmseg - INFO - Iter [131400/160000] lr: 1.073e-05, eta: 5:22:58, time: 0.689, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0738, decode.acc_seg: 96.7663, loss: 0.0738 2023-01-06 23:58:55,104 - mmseg - INFO - Iter [131450/160000] lr: 1.071e-05, eta: 5:22:25, time: 0.681, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0740, decode.acc_seg: 96.8276, loss: 0.0740 2023-01-06 23:59:27,211 - mmseg - INFO - Iter [131500/160000] lr: 1.069e-05, eta: 5:21:50, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0756, decode.acc_seg: 96.7301, loss: 0.0756 2023-01-07 00:00:01,672 - mmseg - INFO - Iter [131550/160000] lr: 1.067e-05, eta: 5:21:17, time: 0.688, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0711, decode.acc_seg: 96.8817, loss: 0.0711 2023-01-07 00:00:36,229 - mmseg - INFO - Iter [131600/160000] lr: 1.065e-05, eta: 5:20:43, time: 0.692, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0745, decode.acc_seg: 96.8037, loss: 0.0745 2023-01-07 00:01:08,665 - mmseg - INFO - Iter [131650/160000] lr: 1.063e-05, eta: 5:20:09, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0785, decode.acc_seg: 96.6448, loss: 0.0785 2023-01-07 00:01:45,249 - mmseg - INFO - Iter [131700/160000] lr: 1.061e-05, eta: 5:19:35, time: 0.731, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0803, decode.acc_seg: 96.5827, loss: 0.0803 2023-01-07 00:02:18,426 - mmseg - INFO - Iter [131750/160000] lr: 1.059e-05, eta: 5:19:01, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0744, decode.acc_seg: 96.7546, loss: 0.0744 2023-01-07 00:02:51,943 - mmseg - INFO - Iter [131800/160000] lr: 1.058e-05, eta: 5:18:27, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0823, decode.acc_seg: 96.4839, loss: 0.0823 2023-01-07 00:03:24,362 - mmseg - INFO - Iter [131850/160000] lr: 1.056e-05, eta: 5:17:53, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0758, decode.acc_seg: 96.6706, loss: 0.0758 2023-01-07 00:03:59,726 - mmseg - INFO - Iter [131900/160000] lr: 1.054e-05, eta: 5:17:20, time: 0.707, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0755, decode.acc_seg: 96.7473, loss: 0.0755 2023-01-07 00:04:34,904 - mmseg - INFO - Iter [131950/160000] lr: 1.052e-05, eta: 5:16:46, time: 0.704, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0766, decode.acc_seg: 96.7182, loss: 0.0766 2023-01-07 00:05:07,843 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 00:05:07,843 - mmseg - INFO - Iter [132000/160000] lr: 1.050e-05, eta: 5:16:12, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0758, decode.acc_seg: 96.7844, loss: 0.0758 2023-01-07 00:05:40,711 - mmseg - INFO - Iter [132050/160000] lr: 1.048e-05, eta: 5:15:38, time: 0.658, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0767, decode.acc_seg: 96.6776, loss: 0.0767 2023-01-07 00:06:16,420 - mmseg - INFO - Iter [132100/160000] lr: 1.046e-05, eta: 5:15:04, time: 0.714, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0763, decode.acc_seg: 96.6653, loss: 0.0763 2023-01-07 00:06:49,468 - mmseg - INFO - Iter [132150/160000] lr: 1.044e-05, eta: 5:14:30, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0771, decode.acc_seg: 96.6524, loss: 0.0771 2023-01-07 00:07:22,310 - mmseg - INFO - Iter [132200/160000] lr: 1.043e-05, eta: 5:13:56, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0737, decode.acc_seg: 96.8163, loss: 0.0737 2023-01-07 00:07:55,462 - mmseg - INFO - Iter [132250/160000] lr: 1.041e-05, eta: 5:13:22, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0752, decode.acc_seg: 96.7939, loss: 0.0752 2023-01-07 00:08:27,716 - mmseg - INFO - Iter [132300/160000] lr: 1.039e-05, eta: 5:12:48, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0754, decode.acc_seg: 96.7155, loss: 0.0754 2023-01-07 00:09:00,047 - mmseg - INFO - Iter [132350/160000] lr: 1.037e-05, eta: 5:12:14, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0736, decode.acc_seg: 96.7337, loss: 0.0736 2023-01-07 00:09:32,298 - mmseg - INFO - Iter [132400/160000] lr: 1.035e-05, eta: 5:11:40, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0718, decode.acc_seg: 96.8088, loss: 0.0718 2023-01-07 00:10:07,954 - mmseg - INFO - Iter [132450/160000] lr: 1.033e-05, eta: 5:11:06, time: 0.713, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0737, decode.acc_seg: 96.8433, loss: 0.0737 2023-01-07 00:10:41,176 - mmseg - INFO - Iter [132500/160000] lr: 1.031e-05, eta: 5:10:32, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0746, decode.acc_seg: 96.7007, loss: 0.0746 2023-01-07 00:11:14,725 - mmseg - INFO - Iter [132550/160000] lr: 1.029e-05, eta: 5:09:58, time: 0.671, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0766, decode.acc_seg: 96.7697, loss: 0.0766 2023-01-07 00:11:50,458 - mmseg - INFO - Iter [132600/160000] lr: 1.028e-05, eta: 5:09:25, time: 0.715, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0785, decode.acc_seg: 96.6386, loss: 0.0785 2023-01-07 00:12:23,837 - mmseg - INFO - Iter [132650/160000] lr: 1.026e-05, eta: 5:08:51, time: 0.669, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0703, decode.acc_seg: 96.8561, loss: 0.0703 2023-01-07 00:12:58,531 - mmseg - INFO - Iter [132700/160000] lr: 1.024e-05, eta: 5:08:17, time: 0.694, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0747, decode.acc_seg: 96.7238, loss: 0.0747 2023-01-07 00:13:31,037 - mmseg - INFO - Iter [132750/160000] lr: 1.022e-05, eta: 5:07:43, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0733, decode.acc_seg: 96.8526, loss: 0.0733 2023-01-07 00:14:06,627 - mmseg - INFO - Iter [132800/160000] lr: 1.020e-05, eta: 5:07:09, time: 0.711, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0725, decode.acc_seg: 96.8163, loss: 0.0725 2023-01-07 00:14:41,587 - mmseg - INFO - Iter [132850/160000] lr: 1.018e-05, eta: 5:06:36, time: 0.700, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0762, decode.acc_seg: 96.6849, loss: 0.0762 2023-01-07 00:15:15,430 - mmseg - INFO - Iter [132900/160000] lr: 1.016e-05, eta: 5:06:02, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0753, decode.acc_seg: 96.7890, loss: 0.0753 2023-01-07 00:15:49,372 - mmseg - INFO - Iter [132950/160000] lr: 1.014e-05, eta: 5:05:28, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0772, decode.acc_seg: 96.6624, loss: 0.0772 2023-01-07 00:16:22,576 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 00:16:22,576 - mmseg - INFO - Iter [133000/160000] lr: 1.013e-05, eta: 5:04:54, time: 0.665, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0731, decode.acc_seg: 96.8045, loss: 0.0731 2023-01-07 00:16:54,987 - mmseg - INFO - Iter [133050/160000] lr: 1.011e-05, eta: 5:04:20, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0733, decode.acc_seg: 96.8757, loss: 0.0733 2023-01-07 00:17:27,657 - mmseg - INFO - Iter [133100/160000] lr: 1.009e-05, eta: 5:03:45, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0752, decode.acc_seg: 96.7625, loss: 0.0752 2023-01-07 00:18:00,327 - mmseg - INFO - Iter [133150/160000] lr: 1.007e-05, eta: 5:03:11, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0790, decode.acc_seg: 96.5419, loss: 0.0790 2023-01-07 00:18:35,868 - mmseg - INFO - Iter [133200/160000] lr: 1.005e-05, eta: 5:02:38, time: 0.711, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0836, decode.acc_seg: 96.4351, loss: 0.0836 2023-01-07 00:19:09,027 - mmseg - INFO - Iter [133250/160000] lr: 1.003e-05, eta: 5:02:04, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0777, decode.acc_seg: 96.6628, loss: 0.0777 2023-01-07 00:19:44,252 - mmseg - INFO - Iter [133300/160000] lr: 1.001e-05, eta: 5:01:30, time: 0.705, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0732, decode.acc_seg: 96.7537, loss: 0.0732 2023-01-07 00:20:17,240 - mmseg - INFO - Iter [133350/160000] lr: 9.994e-06, eta: 5:00:56, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0803, decode.acc_seg: 96.6625, loss: 0.0803 2023-01-07 00:20:52,781 - mmseg - INFO - Iter [133400/160000] lr: 9.975e-06, eta: 5:00:23, time: 0.712, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0796, decode.acc_seg: 96.6264, loss: 0.0796 2023-01-07 00:21:28,318 - mmseg - INFO - Iter [133450/160000] lr: 9.957e-06, eta: 4:59:49, time: 0.711, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0732, decode.acc_seg: 96.7910, loss: 0.0732 2023-01-07 00:22:02,587 - mmseg - INFO - Iter [133500/160000] lr: 9.938e-06, eta: 4:59:15, time: 0.685, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0758, decode.acc_seg: 96.7103, loss: 0.0758 2023-01-07 00:22:37,254 - mmseg - INFO - Iter [133550/160000] lr: 9.919e-06, eta: 4:58:41, time: 0.693, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0772, decode.acc_seg: 96.6649, loss: 0.0772 2023-01-07 00:23:10,292 - mmseg - INFO - Iter [133600/160000] lr: 9.900e-06, eta: 4:58:07, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0736, decode.acc_seg: 96.8020, loss: 0.0736 2023-01-07 00:23:42,705 - mmseg - INFO - Iter [133650/160000] lr: 9.882e-06, eta: 4:57:33, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0758, decode.acc_seg: 96.7428, loss: 0.0758 2023-01-07 00:24:15,175 - mmseg - INFO - Iter [133700/160000] lr: 9.863e-06, eta: 4:56:59, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0745, decode.acc_seg: 96.7203, loss: 0.0745 2023-01-07 00:24:48,916 - mmseg - INFO - Iter [133750/160000] lr: 9.844e-06, eta: 4:56:25, time: 0.676, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0765, decode.acc_seg: 96.6275, loss: 0.0765 2023-01-07 00:25:21,912 - mmseg - INFO - Iter [133800/160000] lr: 9.825e-06, eta: 4:55:51, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0757, decode.acc_seg: 96.6955, loss: 0.0757 2023-01-07 00:25:56,540 - mmseg - INFO - Iter [133850/160000] lr: 9.807e-06, eta: 4:55:17, time: 0.693, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0768, decode.acc_seg: 96.6508, loss: 0.0768 2023-01-07 00:26:28,677 - mmseg - INFO - Iter [133900/160000] lr: 9.788e-06, eta: 4:54:43, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0720, decode.acc_seg: 96.8979, loss: 0.0720 2023-01-07 00:27:04,250 - mmseg - INFO - Iter [133950/160000] lr: 9.769e-06, eta: 4:54:10, time: 0.712, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0767, decode.acc_seg: 96.7408, loss: 0.0767 2023-01-07 00:27:36,358 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 00:27:36,359 - mmseg - INFO - Iter [134000/160000] lr: 9.750e-06, eta: 4:53:35, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0764, decode.acc_seg: 96.6432, loss: 0.0764 2023-01-07 00:28:08,504 - mmseg - INFO - Iter [134050/160000] lr: 9.732e-06, eta: 4:53:01, time: 0.643, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0766, decode.acc_seg: 96.7510, loss: 0.0766 2023-01-07 00:28:41,424 - mmseg - INFO - Iter [134100/160000] lr: 9.713e-06, eta: 4:52:27, time: 0.658, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0746, decode.acc_seg: 96.7397, loss: 0.0746 2023-01-07 00:29:13,531 - mmseg - INFO - Iter [134150/160000] lr: 9.694e-06, eta: 4:51:53, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0733, decode.acc_seg: 96.8881, loss: 0.0733 2023-01-07 00:29:47,989 - mmseg - INFO - Iter [134200/160000] lr: 9.675e-06, eta: 4:51:19, time: 0.688, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0757, decode.acc_seg: 96.6008, loss: 0.0757 2023-01-07 00:30:21,469 - mmseg - INFO - Iter [134250/160000] lr: 9.657e-06, eta: 4:50:45, time: 0.671, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0769, decode.acc_seg: 96.6838, loss: 0.0769 2023-01-07 00:30:55,768 - mmseg - INFO - Iter [134300/160000] lr: 9.638e-06, eta: 4:50:11, time: 0.686, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0749, decode.acc_seg: 96.7545, loss: 0.0749 2023-01-07 00:31:28,031 - mmseg - INFO - Iter [134350/160000] lr: 9.619e-06, eta: 4:49:37, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0725, decode.acc_seg: 96.9196, loss: 0.0725 2023-01-07 00:32:02,897 - mmseg - INFO - Iter [134400/160000] lr: 9.600e-06, eta: 4:49:04, time: 0.696, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0844, decode.acc_seg: 96.4988, loss: 0.0844 2023-01-07 00:32:36,218 - mmseg - INFO - Iter [134450/160000] lr: 9.582e-06, eta: 4:48:30, time: 0.667, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0750, decode.acc_seg: 96.7027, loss: 0.0750 2023-01-07 00:33:09,523 - mmseg - INFO - Iter [134500/160000] lr: 9.563e-06, eta: 4:47:56, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0752, decode.acc_seg: 96.8120, loss: 0.0752 2023-01-07 00:33:44,601 - mmseg - INFO - Iter [134550/160000] lr: 9.544e-06, eta: 4:47:22, time: 0.701, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0727, decode.acc_seg: 96.8564, loss: 0.0727 2023-01-07 00:34:19,071 - mmseg - INFO - Iter [134600/160000] lr: 9.525e-06, eta: 4:46:48, time: 0.688, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0704, decode.acc_seg: 96.8957, loss: 0.0704 2023-01-07 00:34:53,365 - mmseg - INFO - Iter [134650/160000] lr: 9.507e-06, eta: 4:46:14, time: 0.687, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0737, decode.acc_seg: 96.7463, loss: 0.0737 2023-01-07 00:35:28,381 - mmseg - INFO - Iter [134700/160000] lr: 9.488e-06, eta: 4:45:41, time: 0.700, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0764, decode.acc_seg: 96.7506, loss: 0.0764 2023-01-07 00:36:01,229 - mmseg - INFO - Iter [134750/160000] lr: 9.469e-06, eta: 4:45:07, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0740, decode.acc_seg: 96.7550, loss: 0.0740 2023-01-07 00:36:33,377 - mmseg - INFO - Iter [134800/160000] lr: 9.450e-06, eta: 4:44:32, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0777, decode.acc_seg: 96.6128, loss: 0.0777 2023-01-07 00:37:06,210 - mmseg - INFO - Iter [134850/160000] lr: 9.432e-06, eta: 4:43:58, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0692, decode.acc_seg: 96.9088, loss: 0.0692 2023-01-07 00:37:38,805 - mmseg - INFO - Iter [134900/160000] lr: 9.413e-06, eta: 4:43:24, time: 0.652, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0773, decode.acc_seg: 96.6647, loss: 0.0773 2023-01-07 00:38:11,525 - mmseg - INFO - Iter [134950/160000] lr: 9.394e-06, eta: 4:42:50, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0751, decode.acc_seg: 96.8241, loss: 0.0751 2023-01-07 00:38:45,523 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 00:38:45,524 - mmseg - INFO - Iter [135000/160000] lr: 9.375e-06, eta: 4:42:16, time: 0.681, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0746, decode.acc_seg: 96.7909, loss: 0.0746 2023-01-07 00:39:20,032 - mmseg - INFO - Iter [135050/160000] lr: 9.357e-06, eta: 4:41:43, time: 0.690, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0740, decode.acc_seg: 96.6874, loss: 0.0740 2023-01-07 00:39:53,589 - mmseg - INFO - Iter [135100/160000] lr: 9.338e-06, eta: 4:41:09, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0799, decode.acc_seg: 96.5452, loss: 0.0799 2023-01-07 00:40:25,943 - mmseg - INFO - Iter [135150/160000] lr: 9.319e-06, eta: 4:40:35, time: 0.647, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0724, decode.acc_seg: 96.8242, loss: 0.0724 2023-01-07 00:41:00,675 - mmseg - INFO - Iter [135200/160000] lr: 9.300e-06, eta: 4:40:01, time: 0.694, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0737, decode.acc_seg: 96.7618, loss: 0.0737 2023-01-07 00:41:35,205 - mmseg - INFO - Iter [135250/160000] lr: 9.282e-06, eta: 4:39:27, time: 0.691, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0722, decode.acc_seg: 96.8497, loss: 0.0722 2023-01-07 00:42:07,714 - mmseg - INFO - Iter [135300/160000] lr: 9.263e-06, eta: 4:38:53, time: 0.651, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0691, decode.acc_seg: 96.8955, loss: 0.0691 2023-01-07 00:42:41,158 - mmseg - INFO - Iter [135350/160000] lr: 9.244e-06, eta: 4:38:19, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0718, decode.acc_seg: 96.7842, loss: 0.0718 2023-01-07 00:43:15,691 - mmseg - INFO - Iter [135400/160000] lr: 9.225e-06, eta: 4:37:45, time: 0.691, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0766, decode.acc_seg: 96.7581, loss: 0.0766 2023-01-07 00:43:52,371 - mmseg - INFO - Iter [135450/160000] lr: 9.207e-06, eta: 4:37:12, time: 0.734, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0704, decode.acc_seg: 96.8923, loss: 0.0704 2023-01-07 00:44:26,213 - mmseg - INFO - Iter [135500/160000] lr: 9.188e-06, eta: 4:36:38, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0727, decode.acc_seg: 96.8430, loss: 0.0727 2023-01-07 00:44:58,583 - mmseg - INFO - Iter [135550/160000] lr: 9.169e-06, eta: 4:36:04, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0732, decode.acc_seg: 96.7312, loss: 0.0732 2023-01-07 00:45:31,460 - mmseg - INFO - Iter [135600/160000] lr: 9.150e-06, eta: 4:35:30, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0748, decode.acc_seg: 96.8232, loss: 0.0748 2023-01-07 00:46:03,732 - mmseg - INFO - Iter [135650/160000] lr: 9.132e-06, eta: 4:34:56, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0742, decode.acc_seg: 96.7655, loss: 0.0742 2023-01-07 00:46:37,554 - mmseg - INFO - Iter [135700/160000] lr: 9.113e-06, eta: 4:34:22, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0730, decode.acc_seg: 96.8044, loss: 0.0730 2023-01-07 00:47:11,967 - mmseg - INFO - Iter [135750/160000] lr: 9.094e-06, eta: 4:33:48, time: 0.688, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0745, decode.acc_seg: 96.6994, loss: 0.0745 2023-01-07 00:47:47,488 - mmseg - INFO - Iter [135800/160000] lr: 9.075e-06, eta: 4:33:14, time: 0.710, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0756, decode.acc_seg: 96.6514, loss: 0.0756 2023-01-07 00:48:20,151 - mmseg - INFO - Iter [135850/160000] lr: 9.057e-06, eta: 4:32:40, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0729, decode.acc_seg: 96.8323, loss: 0.0729 2023-01-07 00:48:53,820 - mmseg - INFO - Iter [135900/160000] lr: 9.038e-06, eta: 4:32:06, time: 0.673, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0757, decode.acc_seg: 96.6871, loss: 0.0757 2023-01-07 00:49:28,045 - mmseg - INFO - Iter [135950/160000] lr: 9.019e-06, eta: 4:31:33, time: 0.686, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0740, decode.acc_seg: 96.8464, loss: 0.0740 2023-01-07 00:50:02,304 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 00:50:02,305 - mmseg - INFO - Iter [136000/160000] lr: 9.000e-06, eta: 4:30:59, time: 0.685, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0754, decode.acc_seg: 96.7184, loss: 0.0754 2023-01-07 00:50:34,926 - mmseg - INFO - Iter [136050/160000] lr: 8.982e-06, eta: 4:30:25, time: 0.652, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0724, decode.acc_seg: 96.8822, loss: 0.0724 2023-01-07 00:51:07,297 - mmseg - INFO - Iter [136100/160000] lr: 8.963e-06, eta: 4:29:51, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0743, decode.acc_seg: 96.7289, loss: 0.0743 2023-01-07 00:51:39,584 - mmseg - INFO - Iter [136150/160000] lr: 8.944e-06, eta: 4:29:16, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0721, decode.acc_seg: 96.9284, loss: 0.0721 2023-01-07 00:52:14,476 - mmseg - INFO - Iter [136200/160000] lr: 8.925e-06, eta: 4:28:43, time: 0.697, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0715, decode.acc_seg: 96.8813, loss: 0.0715 2023-01-07 00:52:49,707 - mmseg - INFO - Iter [136250/160000] lr: 8.907e-06, eta: 4:28:09, time: 0.704, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0727, decode.acc_seg: 96.7987, loss: 0.0727 2023-01-07 00:53:23,178 - mmseg - INFO - Iter [136300/160000] lr: 8.888e-06, eta: 4:27:35, time: 0.670, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0752, decode.acc_seg: 96.7352, loss: 0.0752 2023-01-07 00:53:56,836 - mmseg - INFO - Iter [136350/160000] lr: 8.869e-06, eta: 4:27:01, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0776, decode.acc_seg: 96.7439, loss: 0.0776 2023-01-07 00:54:30,407 - mmseg - INFO - Iter [136400/160000] lr: 8.850e-06, eta: 4:26:27, time: 0.672, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0778, decode.acc_seg: 96.5904, loss: 0.0778 2023-01-07 00:55:04,122 - mmseg - INFO - Iter [136450/160000] lr: 8.832e-06, eta: 4:25:53, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0764, decode.acc_seg: 96.7157, loss: 0.0764 2023-01-07 00:55:36,373 - mmseg - INFO - Iter [136500/160000] lr: 8.813e-06, eta: 4:25:19, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0816, decode.acc_seg: 96.4833, loss: 0.0816 2023-01-07 00:56:10,960 - mmseg - INFO - Iter [136550/160000] lr: 8.794e-06, eta: 4:24:45, time: 0.692, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0766, decode.acc_seg: 96.6818, loss: 0.0766 2023-01-07 00:56:44,294 - mmseg - INFO - Iter [136600/160000] lr: 8.775e-06, eta: 4:24:12, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0748, decode.acc_seg: 96.7024, loss: 0.0748 2023-01-07 00:57:16,576 - mmseg - INFO - Iter [136650/160000] lr: 8.757e-06, eta: 4:23:37, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0741, decode.acc_seg: 96.7701, loss: 0.0741 2023-01-07 00:57:48,788 - mmseg - INFO - Iter [136700/160000] lr: 8.738e-06, eta: 4:23:03, time: 0.644, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0740, decode.acc_seg: 96.7970, loss: 0.0740 2023-01-07 00:58:23,426 - mmseg - INFO - Iter [136750/160000] lr: 8.719e-06, eta: 4:22:29, time: 0.691, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0758, decode.acc_seg: 96.7380, loss: 0.0758 2023-01-07 00:58:57,516 - mmseg - INFO - Iter [136800/160000] lr: 8.700e-06, eta: 4:21:56, time: 0.682, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0725, decode.acc_seg: 96.7908, loss: 0.0725 2023-01-07 00:59:31,064 - mmseg - INFO - Iter [136850/160000] lr: 8.682e-06, eta: 4:21:22, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0721, decode.acc_seg: 96.8541, loss: 0.0721 2023-01-07 01:00:05,403 - mmseg - INFO - Iter [136900/160000] lr: 8.663e-06, eta: 4:20:48, time: 0.687, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0747, decode.acc_seg: 96.6971, loss: 0.0747 2023-01-07 01:00:38,997 - mmseg - INFO - Iter [136950/160000] lr: 8.644e-06, eta: 4:20:14, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0739, decode.acc_seg: 96.7710, loss: 0.0739 2023-01-07 01:01:11,110 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 01:01:11,111 - mmseg - INFO - Iter [137000/160000] lr: 8.625e-06, eta: 4:19:40, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0758, decode.acc_seg: 96.7660, loss: 0.0758 2023-01-07 01:01:43,750 - mmseg - INFO - Iter [137050/160000] lr: 8.607e-06, eta: 4:19:06, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0746, decode.acc_seg: 96.7810, loss: 0.0746 2023-01-07 01:02:16,005 - mmseg - INFO - Iter [137100/160000] lr: 8.588e-06, eta: 4:18:32, time: 0.645, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0756, decode.acc_seg: 96.7152, loss: 0.0756 2023-01-07 01:02:49,828 - mmseg - INFO - Iter [137150/160000] lr: 8.569e-06, eta: 4:17:58, time: 0.676, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0718, decode.acc_seg: 96.8734, loss: 0.0718 2023-01-07 01:03:23,466 - mmseg - INFO - Iter [137200/160000] lr: 8.550e-06, eta: 4:17:24, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0745, decode.acc_seg: 96.8449, loss: 0.0745 2023-01-07 01:03:58,958 - mmseg - INFO - Iter [137250/160000] lr: 8.532e-06, eta: 4:16:50, time: 0.710, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0738, decode.acc_seg: 96.7898, loss: 0.0738 2023-01-07 01:04:35,201 - mmseg - INFO - Iter [137300/160000] lr: 8.513e-06, eta: 4:16:17, time: 0.724, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0721, decode.acc_seg: 96.8301, loss: 0.0721 2023-01-07 01:05:08,926 - mmseg - INFO - Iter [137350/160000] lr: 8.494e-06, eta: 4:15:43, time: 0.676, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0731, decode.acc_seg: 96.7558, loss: 0.0731 2023-01-07 01:05:43,391 - mmseg - INFO - Iter [137400/160000] lr: 8.475e-06, eta: 4:15:09, time: 0.688, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0749, decode.acc_seg: 96.7614, loss: 0.0749 2023-01-07 01:06:17,132 - mmseg - INFO - Iter [137450/160000] lr: 8.457e-06, eta: 4:14:35, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0765, decode.acc_seg: 96.7342, loss: 0.0765 2023-01-07 01:06:52,228 - mmseg - INFO - Iter [137500/160000] lr: 8.438e-06, eta: 4:14:02, time: 0.702, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0738, decode.acc_seg: 96.6805, loss: 0.0738 2023-01-07 01:07:27,883 - mmseg - INFO - Iter [137550/160000] lr: 8.419e-06, eta: 4:13:28, time: 0.713, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0721, decode.acc_seg: 96.9027, loss: 0.0721 2023-01-07 01:08:01,702 - mmseg - INFO - Iter [137600/160000] lr: 8.400e-06, eta: 4:12:54, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0747, decode.acc_seg: 96.7142, loss: 0.0747 2023-01-07 01:08:36,077 - mmseg - INFO - Iter [137650/160000] lr: 8.382e-06, eta: 4:12:20, time: 0.687, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0708, decode.acc_seg: 96.9180, loss: 0.0708 2023-01-07 01:09:09,652 - mmseg - INFO - Iter [137700/160000] lr: 8.363e-06, eta: 4:11:46, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0796, decode.acc_seg: 96.5417, loss: 0.0796 2023-01-07 01:09:43,648 - mmseg - INFO - Iter [137750/160000] lr: 8.344e-06, eta: 4:11:12, time: 0.680, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0760, decode.acc_seg: 96.6831, loss: 0.0760 2023-01-07 01:10:16,929 - mmseg - INFO - Iter [137800/160000] lr: 8.325e-06, eta: 4:10:39, time: 0.666, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0702, decode.acc_seg: 96.9993, loss: 0.0702 2023-01-07 01:10:50,727 - mmseg - INFO - Iter [137850/160000] lr: 8.307e-06, eta: 4:10:05, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0703, decode.acc_seg: 96.9187, loss: 0.0703 2023-01-07 01:11:23,494 - mmseg - INFO - Iter [137900/160000] lr: 8.288e-06, eta: 4:09:31, time: 0.656, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0726, decode.acc_seg: 96.8441, loss: 0.0726 2023-01-07 01:11:55,791 - mmseg - INFO - Iter [137950/160000] lr: 8.269e-06, eta: 4:08:56, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0752, decode.acc_seg: 96.7234, loss: 0.0752 2023-01-07 01:12:28,743 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 01:12:28,743 - mmseg - INFO - Iter [138000/160000] lr: 8.250e-06, eta: 4:08:22, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0726, decode.acc_seg: 96.7956, loss: 0.0726 2023-01-07 01:13:04,372 - mmseg - INFO - Iter [138050/160000] lr: 8.232e-06, eta: 4:07:49, time: 0.712, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0716, decode.acc_seg: 96.8377, loss: 0.0716 2023-01-07 01:13:36,775 - mmseg - INFO - Iter [138100/160000] lr: 8.213e-06, eta: 4:07:15, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0688, decode.acc_seg: 96.9485, loss: 0.0688 2023-01-07 01:14:09,135 - mmseg - INFO - Iter [138150/160000] lr: 8.194e-06, eta: 4:06:41, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0718, decode.acc_seg: 96.8692, loss: 0.0718 2023-01-07 01:14:41,802 - mmseg - INFO - Iter [138200/160000] lr: 8.175e-06, eta: 4:06:07, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0749, decode.acc_seg: 96.6806, loss: 0.0749 2023-01-07 01:15:16,783 - mmseg - INFO - Iter [138250/160000] lr: 8.157e-06, eta: 4:05:33, time: 0.700, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0765, decode.acc_seg: 96.7166, loss: 0.0765 2023-01-07 01:15:51,080 - mmseg - INFO - Iter [138300/160000] lr: 8.138e-06, eta: 4:04:59, time: 0.685, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0741, decode.acc_seg: 96.7216, loss: 0.0741 2023-01-07 01:16:24,694 - mmseg - INFO - Iter [138350/160000] lr: 8.119e-06, eta: 4:04:25, time: 0.672, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0762, decode.acc_seg: 96.6828, loss: 0.0762 2023-01-07 01:16:59,883 - mmseg - INFO - Iter [138400/160000] lr: 8.100e-06, eta: 4:03:52, time: 0.704, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0799, decode.acc_seg: 96.5994, loss: 0.0799 2023-01-07 01:17:34,166 - mmseg - INFO - Iter [138450/160000] lr: 8.082e-06, eta: 4:03:18, time: 0.687, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0754, decode.acc_seg: 96.7861, loss: 0.0754 2023-01-07 01:18:07,020 - mmseg - INFO - Iter [138500/160000] lr: 8.063e-06, eta: 4:02:44, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0726, decode.acc_seg: 96.8530, loss: 0.0726 2023-01-07 01:18:39,283 - mmseg - INFO - Iter [138550/160000] lr: 8.044e-06, eta: 4:02:10, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0724, decode.acc_seg: 96.7860, loss: 0.0724 2023-01-07 01:19:11,677 - mmseg - INFO - Iter [138600/160000] lr: 8.025e-06, eta: 4:01:35, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0728, decode.acc_seg: 96.8637, loss: 0.0728 2023-01-07 01:19:44,714 - mmseg - INFO - Iter [138650/160000] lr: 8.007e-06, eta: 4:01:01, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0737, decode.acc_seg: 96.8162, loss: 0.0737 2023-01-07 01:20:17,698 - mmseg - INFO - Iter [138700/160000] lr: 7.988e-06, eta: 4:00:27, time: 0.661, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0738, decode.acc_seg: 96.7802, loss: 0.0738 2023-01-07 01:20:49,899 - mmseg - INFO - Iter [138750/160000] lr: 7.969e-06, eta: 3:59:53, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0727, decode.acc_seg: 96.8329, loss: 0.0727 2023-01-07 01:21:24,465 - mmseg - INFO - Iter [138800/160000] lr: 7.950e-06, eta: 3:59:20, time: 0.691, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0744, decode.acc_seg: 96.7441, loss: 0.0744 2023-01-07 01:21:58,450 - mmseg - INFO - Iter [138850/160000] lr: 7.932e-06, eta: 3:58:46, time: 0.681, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0778, decode.acc_seg: 96.6326, loss: 0.0778 2023-01-07 01:22:32,612 - mmseg - INFO - Iter [138900/160000] lr: 7.913e-06, eta: 3:58:12, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0755, decode.acc_seg: 96.6950, loss: 0.0755 2023-01-07 01:23:05,103 - mmseg - INFO - Iter [138950/160000] lr: 7.894e-06, eta: 3:57:38, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0729, decode.acc_seg: 96.7946, loss: 0.0729 2023-01-07 01:23:37,564 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 01:23:37,565 - mmseg - INFO - Iter [139000/160000] lr: 7.875e-06, eta: 3:57:04, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0720, decode.acc_seg: 96.9015, loss: 0.0720 2023-01-07 01:24:09,904 - mmseg - INFO - Iter [139050/160000] lr: 7.857e-06, eta: 3:56:30, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0734, decode.acc_seg: 96.7818, loss: 0.0734 2023-01-07 01:24:45,142 - mmseg - INFO - Iter [139100/160000] lr: 7.838e-06, eta: 3:55:56, time: 0.705, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0773, decode.acc_seg: 96.6662, loss: 0.0773 2023-01-07 01:25:22,149 - mmseg - INFO - Iter [139150/160000] lr: 7.819e-06, eta: 3:55:23, time: 0.739, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0753, decode.acc_seg: 96.7775, loss: 0.0753 2023-01-07 01:25:56,636 - mmseg - INFO - Iter [139200/160000] lr: 7.800e-06, eta: 3:54:49, time: 0.691, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0724, decode.acc_seg: 96.8403, loss: 0.0724 2023-01-07 01:26:30,669 - mmseg - INFO - Iter [139250/160000] lr: 7.782e-06, eta: 3:54:15, time: 0.681, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0728, decode.acc_seg: 96.8449, loss: 0.0728 2023-01-07 01:27:03,223 - mmseg - INFO - Iter [139300/160000] lr: 7.763e-06, eta: 3:53:41, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0750, decode.acc_seg: 96.7673, loss: 0.0750 2023-01-07 01:27:37,285 - mmseg - INFO - Iter [139350/160000] lr: 7.744e-06, eta: 3:53:07, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0746, decode.acc_seg: 96.8172, loss: 0.0746 2023-01-07 01:28:09,985 - mmseg - INFO - Iter [139400/160000] lr: 7.725e-06, eta: 3:52:33, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0728, decode.acc_seg: 96.7251, loss: 0.0728 2023-01-07 01:28:44,211 - mmseg - INFO - Iter [139450/160000] lr: 7.707e-06, eta: 3:51:59, time: 0.684, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0756, decode.acc_seg: 96.7112, loss: 0.0756 2023-01-07 01:29:18,162 - mmseg - INFO - Iter [139500/160000] lr: 7.688e-06, eta: 3:51:25, time: 0.680, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0710, decode.acc_seg: 96.8808, loss: 0.0710 2023-01-07 01:29:53,210 - mmseg - INFO - Iter [139550/160000] lr: 7.669e-06, eta: 3:50:52, time: 0.701, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0699, decode.acc_seg: 96.9124, loss: 0.0699 2023-01-07 01:30:25,631 - mmseg - INFO - Iter [139600/160000] lr: 7.650e-06, eta: 3:50:18, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0729, decode.acc_seg: 96.7375, loss: 0.0729 2023-01-07 01:30:58,787 - mmseg - INFO - Iter [139650/160000] lr: 7.632e-06, eta: 3:49:44, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0700, decode.acc_seg: 96.9598, loss: 0.0700 2023-01-07 01:31:32,633 - mmseg - INFO - Iter [139700/160000] lr: 7.613e-06, eta: 3:49:10, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0734, decode.acc_seg: 96.8596, loss: 0.0734 2023-01-07 01:32:05,334 - mmseg - INFO - Iter [139750/160000] lr: 7.594e-06, eta: 3:48:36, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0726, decode.acc_seg: 96.8476, loss: 0.0726 2023-01-07 01:32:37,732 - mmseg - INFO - Iter [139800/160000] lr: 7.575e-06, eta: 3:48:02, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0738, decode.acc_seg: 96.8118, loss: 0.0738 2023-01-07 01:33:10,743 - mmseg - INFO - Iter [139850/160000] lr: 7.557e-06, eta: 3:47:28, time: 0.661, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0747, decode.acc_seg: 96.6979, loss: 0.0747 2023-01-07 01:33:46,700 - mmseg - INFO - Iter [139900/160000] lr: 7.538e-06, eta: 3:46:54, time: 0.718, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0700, decode.acc_seg: 96.8820, loss: 0.0700 2023-01-07 01:34:21,773 - mmseg - INFO - Iter [139950/160000] lr: 7.519e-06, eta: 3:46:20, time: 0.702, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0720, decode.acc_seg: 96.8653, loss: 0.0720 2023-01-07 01:34:56,218 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 01:34:56,219 - mmseg - INFO - Iter [140000/160000] lr: 7.500e-06, eta: 3:45:47, time: 0.688, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0725, decode.acc_seg: 96.8829, loss: 0.0725 2023-01-07 01:35:30,348 - mmseg - INFO - Iter [140050/160000] lr: 7.482e-06, eta: 3:45:13, time: 0.684, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0772, decode.acc_seg: 96.6458, loss: 0.0772 2023-01-07 01:36:02,731 - mmseg - INFO - Iter [140100/160000] lr: 7.463e-06, eta: 3:44:39, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0773, decode.acc_seg: 96.6861, loss: 0.0773 2023-01-07 01:36:37,007 - mmseg - INFO - Iter [140150/160000] lr: 7.444e-06, eta: 3:44:05, time: 0.685, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0744, decode.acc_seg: 96.7675, loss: 0.0744 2023-01-07 01:37:10,568 - mmseg - INFO - Iter [140200/160000] lr: 7.425e-06, eta: 3:43:31, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0725, decode.acc_seg: 96.8077, loss: 0.0725 2023-01-07 01:37:48,578 - mmseg - INFO - Iter [140250/160000] lr: 7.407e-06, eta: 3:42:58, time: 0.760, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0735, decode.acc_seg: 96.7620, loss: 0.0735 2023-01-07 01:38:21,537 - mmseg - INFO - Iter [140300/160000] lr: 7.388e-06, eta: 3:42:24, time: 0.660, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0700, decode.acc_seg: 96.8987, loss: 0.0700 2023-01-07 01:38:54,288 - mmseg - INFO - Iter [140350/160000] lr: 7.369e-06, eta: 3:41:50, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0702, decode.acc_seg: 96.9058, loss: 0.0702 2023-01-07 01:39:26,398 - mmseg - INFO - Iter [140400/160000] lr: 7.350e-06, eta: 3:41:16, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0705, decode.acc_seg: 96.8362, loss: 0.0705 2023-01-07 01:39:59,987 - mmseg - INFO - Iter [140450/160000] lr: 7.332e-06, eta: 3:40:42, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0768, decode.acc_seg: 96.7183, loss: 0.0768 2023-01-07 01:40:34,568 - mmseg - INFO - Iter [140500/160000] lr: 7.313e-06, eta: 3:40:08, time: 0.692, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0714, decode.acc_seg: 96.9137, loss: 0.0714 2023-01-07 01:41:08,492 - mmseg - INFO - Iter [140550/160000] lr: 7.294e-06, eta: 3:39:34, time: 0.680, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0720, decode.acc_seg: 96.8356, loss: 0.0720 2023-01-07 01:41:40,679 - mmseg - INFO - Iter [140600/160000] lr: 7.275e-06, eta: 3:39:00, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0732, decode.acc_seg: 96.8178, loss: 0.0732 2023-01-07 01:42:15,331 - mmseg - INFO - Iter [140650/160000] lr: 7.257e-06, eta: 3:38:26, time: 0.693, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0719, decode.acc_seg: 96.7962, loss: 0.0719 2023-01-07 01:42:48,787 - mmseg - INFO - Iter [140700/160000] lr: 7.238e-06, eta: 3:37:52, time: 0.669, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0697, decode.acc_seg: 96.9059, loss: 0.0697 2023-01-07 01:43:21,797 - mmseg - INFO - Iter [140750/160000] lr: 7.219e-06, eta: 3:37:18, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0703, decode.acc_seg: 96.8786, loss: 0.0703 2023-01-07 01:43:54,070 - mmseg - INFO - Iter [140800/160000] lr: 7.200e-06, eta: 3:36:44, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0694, decode.acc_seg: 96.9725, loss: 0.0694 2023-01-07 01:44:27,989 - mmseg - INFO - Iter [140850/160000] lr: 7.182e-06, eta: 3:36:10, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0719, decode.acc_seg: 96.8474, loss: 0.0719 2023-01-07 01:45:01,147 - mmseg - INFO - Iter [140900/160000] lr: 7.163e-06, eta: 3:35:36, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0753, decode.acc_seg: 96.7065, loss: 0.0753 2023-01-07 01:45:34,157 - mmseg - INFO - Iter [140950/160000] lr: 7.144e-06, eta: 3:35:02, time: 0.660, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0698, decode.acc_seg: 96.8768, loss: 0.0698 2023-01-07 01:46:09,494 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 01:46:09,494 - mmseg - INFO - Iter [141000/160000] lr: 7.125e-06, eta: 3:34:29, time: 0.708, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0683, decode.acc_seg: 97.0068, loss: 0.0683 2023-01-07 01:46:42,252 - mmseg - INFO - Iter [141050/160000] lr: 7.107e-06, eta: 3:33:55, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0731, decode.acc_seg: 96.7871, loss: 0.0731 2023-01-07 01:47:16,510 - mmseg - INFO - Iter [141100/160000] lr: 7.088e-06, eta: 3:33:21, time: 0.685, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0712, decode.acc_seg: 96.9065, loss: 0.0712 2023-01-07 01:47:49,913 - mmseg - INFO - Iter [141150/160000] lr: 7.069e-06, eta: 3:32:47, time: 0.667, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0763, decode.acc_seg: 96.7377, loss: 0.0763 2023-01-07 01:48:22,934 - mmseg - INFO - Iter [141200/160000] lr: 7.050e-06, eta: 3:32:13, time: 0.660, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0743, decode.acc_seg: 96.8509, loss: 0.0743 2023-01-07 01:48:56,726 - mmseg - INFO - Iter [141250/160000] lr: 7.032e-06, eta: 3:31:39, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0748, decode.acc_seg: 96.8098, loss: 0.0748 2023-01-07 01:49:30,868 - mmseg - INFO - Iter [141300/160000] lr: 7.013e-06, eta: 3:31:05, time: 0.684, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0744, decode.acc_seg: 96.7418, loss: 0.0744 2023-01-07 01:50:02,919 - mmseg - INFO - Iter [141350/160000] lr: 6.994e-06, eta: 3:30:31, time: 0.641, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0747, decode.acc_seg: 96.7563, loss: 0.0747 2023-01-07 01:50:37,622 - mmseg - INFO - Iter [141400/160000] lr: 6.975e-06, eta: 3:29:57, time: 0.694, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0734, decode.acc_seg: 96.7793, loss: 0.0734 2023-01-07 01:51:10,036 - mmseg - INFO - Iter [141450/160000] lr: 6.957e-06, eta: 3:29:23, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0755, decode.acc_seg: 96.7142, loss: 0.0755 2023-01-07 01:51:42,145 - mmseg - INFO - Iter [141500/160000] lr: 6.938e-06, eta: 3:28:49, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0710, decode.acc_seg: 96.9202, loss: 0.0710 2023-01-07 01:52:14,483 - mmseg - INFO - Iter [141550/160000] lr: 6.919e-06, eta: 3:28:15, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0700, decode.acc_seg: 96.9257, loss: 0.0700 2023-01-07 01:52:48,273 - mmseg - INFO - Iter [141600/160000] lr: 6.900e-06, eta: 3:27:41, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0718, decode.acc_seg: 96.8342, loss: 0.0718 2023-01-07 01:53:21,518 - mmseg - INFO - Iter [141650/160000] lr: 6.882e-06, eta: 3:27:07, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0718, decode.acc_seg: 96.9224, loss: 0.0718 2023-01-07 01:53:57,279 - mmseg - INFO - Iter [141700/160000] lr: 6.863e-06, eta: 3:26:34, time: 0.715, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0752, decode.acc_seg: 96.7808, loss: 0.0752 2023-01-07 01:54:32,696 - mmseg - INFO - Iter [141750/160000] lr: 6.844e-06, eta: 3:26:00, time: 0.709, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0724, decode.acc_seg: 96.8105, loss: 0.0724 2023-01-07 01:55:05,013 - mmseg - INFO - Iter [141800/160000] lr: 6.825e-06, eta: 3:25:26, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0743, decode.acc_seg: 96.7918, loss: 0.0743 2023-01-07 01:55:38,770 - mmseg - INFO - Iter [141850/160000] lr: 6.807e-06, eta: 3:24:52, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0746, decode.acc_seg: 96.7485, loss: 0.0746 2023-01-07 01:56:11,032 - mmseg - INFO - Iter [141900/160000] lr: 6.788e-06, eta: 3:24:18, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0718, decode.acc_seg: 96.8826, loss: 0.0718 2023-01-07 01:56:45,559 - mmseg - INFO - Iter [141950/160000] lr: 6.769e-06, eta: 3:23:44, time: 0.690, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0702, decode.acc_seg: 96.8368, loss: 0.0702 2023-01-07 01:57:21,032 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 01:57:21,032 - mmseg - INFO - Iter [142000/160000] lr: 6.750e-06, eta: 3:23:11, time: 0.709, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0747, decode.acc_seg: 96.8003, loss: 0.0747 2023-01-07 01:57:56,282 - mmseg - INFO - Iter [142050/160000] lr: 6.732e-06, eta: 3:22:37, time: 0.706, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0710, decode.acc_seg: 96.9088, loss: 0.0710 2023-01-07 01:58:29,089 - mmseg - INFO - Iter [142100/160000] lr: 6.713e-06, eta: 3:22:03, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0737, decode.acc_seg: 96.7420, loss: 0.0737 2023-01-07 01:59:04,907 - mmseg - INFO - Iter [142150/160000] lr: 6.694e-06, eta: 3:21:29, time: 0.716, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0756, decode.acc_seg: 96.7385, loss: 0.0756 2023-01-07 01:59:37,932 - mmseg - INFO - Iter [142200/160000] lr: 6.675e-06, eta: 3:20:55, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0735, decode.acc_seg: 96.7416, loss: 0.0735 2023-01-07 02:00:10,160 - mmseg - INFO - Iter [142250/160000] lr: 6.657e-06, eta: 3:20:21, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0702, decode.acc_seg: 96.9379, loss: 0.0702 2023-01-07 02:00:44,451 - mmseg - INFO - Iter [142300/160000] lr: 6.638e-06, eta: 3:19:47, time: 0.686, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0731, decode.acc_seg: 96.8849, loss: 0.0731 2023-01-07 02:01:17,533 - mmseg - INFO - Iter [142350/160000] lr: 6.619e-06, eta: 3:19:14, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0747, decode.acc_seg: 96.8309, loss: 0.0747 2023-01-07 02:01:49,930 - mmseg - INFO - Iter [142400/160000] lr: 6.600e-06, eta: 3:18:39, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0730, decode.acc_seg: 96.7315, loss: 0.0730 2023-01-07 02:02:22,133 - mmseg - INFO - Iter [142450/160000] lr: 6.582e-06, eta: 3:18:05, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0701, decode.acc_seg: 96.9592, loss: 0.0701 2023-01-07 02:02:57,164 - mmseg - INFO - Iter [142500/160000] lr: 6.563e-06, eta: 3:17:32, time: 0.700, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0751, decode.acc_seg: 96.7786, loss: 0.0751 2023-01-07 02:03:29,964 - mmseg - INFO - Iter [142550/160000] lr: 6.544e-06, eta: 3:16:58, time: 0.657, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0721, decode.acc_seg: 96.7675, loss: 0.0721 2023-01-07 02:04:02,095 - mmseg - INFO - Iter [142600/160000] lr: 6.525e-06, eta: 3:16:24, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0749, decode.acc_seg: 96.7131, loss: 0.0749 2023-01-07 02:04:35,734 - mmseg - INFO - Iter [142650/160000] lr: 6.507e-06, eta: 3:15:50, time: 0.673, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0756, decode.acc_seg: 96.7190, loss: 0.0756 2023-01-07 02:05:10,267 - mmseg - INFO - Iter [142700/160000] lr: 6.488e-06, eta: 3:15:16, time: 0.691, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0731, decode.acc_seg: 96.8993, loss: 0.0731 2023-01-07 02:05:44,803 - mmseg - INFO - Iter [142750/160000] lr: 6.469e-06, eta: 3:14:42, time: 0.690, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0721, decode.acc_seg: 96.8339, loss: 0.0721 2023-01-07 02:06:20,203 - mmseg - INFO - Iter [142800/160000] lr: 6.450e-06, eta: 3:14:08, time: 0.709, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0673, decode.acc_seg: 97.0357, loss: 0.0673 2023-01-07 02:06:54,684 - mmseg - INFO - Iter [142850/160000] lr: 6.432e-06, eta: 3:13:35, time: 0.690, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0728, decode.acc_seg: 96.8793, loss: 0.0728 2023-01-07 02:07:28,939 - mmseg - INFO - Iter [142900/160000] lr: 6.413e-06, eta: 3:13:01, time: 0.684, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0732, decode.acc_seg: 96.7874, loss: 0.0732 2023-01-07 02:08:02,852 - mmseg - INFO - Iter [142950/160000] lr: 6.394e-06, eta: 3:12:27, time: 0.679, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0688, decode.acc_seg: 96.9749, loss: 0.0688 2023-01-07 02:08:36,119 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 02:08:36,120 - mmseg - INFO - Iter [143000/160000] lr: 6.375e-06, eta: 3:11:53, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0740, decode.acc_seg: 96.7641, loss: 0.0740 2023-01-07 02:09:09,183 - mmseg - INFO - Iter [143050/160000] lr: 6.357e-06, eta: 3:11:19, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0747, decode.acc_seg: 96.8143, loss: 0.0747 2023-01-07 02:09:41,317 - mmseg - INFO - Iter [143100/160000] lr: 6.338e-06, eta: 3:10:45, time: 0.644, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0693, decode.acc_seg: 96.9359, loss: 0.0693 2023-01-07 02:10:15,755 - mmseg - INFO - Iter [143150/160000] lr: 6.319e-06, eta: 3:10:11, time: 0.689, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0746, decode.acc_seg: 96.7379, loss: 0.0746 2023-01-07 02:10:48,355 - mmseg - INFO - Iter [143200/160000] lr: 6.300e-06, eta: 3:09:37, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0765, decode.acc_seg: 96.7442, loss: 0.0765 2023-01-07 02:11:22,986 - mmseg - INFO - Iter [143250/160000] lr: 6.282e-06, eta: 3:09:03, time: 0.693, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0702, decode.acc_seg: 96.8802, loss: 0.0702 2023-01-07 02:11:55,227 - mmseg - INFO - Iter [143300/160000] lr: 6.263e-06, eta: 3:08:29, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0724, decode.acc_seg: 96.8498, loss: 0.0724 2023-01-07 02:12:29,952 - mmseg - INFO - Iter [143350/160000] lr: 6.244e-06, eta: 3:07:56, time: 0.694, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0726, decode.acc_seg: 96.7994, loss: 0.0726 2023-01-07 02:13:03,903 - mmseg - INFO - Iter [143400/160000] lr: 6.225e-06, eta: 3:07:22, time: 0.679, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0729, decode.acc_seg: 96.8927, loss: 0.0729 2023-01-07 02:13:37,448 - mmseg - INFO - Iter [143450/160000] lr: 6.207e-06, eta: 3:06:48, time: 0.672, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0719, decode.acc_seg: 96.8600, loss: 0.0719 2023-01-07 02:14:10,472 - mmseg - INFO - Iter [143500/160000] lr: 6.188e-06, eta: 3:06:14, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0724, decode.acc_seg: 96.8470, loss: 0.0724 2023-01-07 02:14:45,234 - mmseg - INFO - Iter [143550/160000] lr: 6.169e-06, eta: 3:05:40, time: 0.695, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0708, decode.acc_seg: 96.8638, loss: 0.0708 2023-01-07 02:15:19,740 - mmseg - INFO - Iter [143600/160000] lr: 6.150e-06, eta: 3:05:06, time: 0.690, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0771, decode.acc_seg: 96.7893, loss: 0.0771 2023-01-07 02:15:52,077 - mmseg - INFO - Iter [143650/160000] lr: 6.132e-06, eta: 3:04:32, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0707, decode.acc_seg: 96.9120, loss: 0.0707 2023-01-07 02:16:26,724 - mmseg - INFO - Iter [143700/160000] lr: 6.113e-06, eta: 3:03:59, time: 0.694, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0725, decode.acc_seg: 96.8739, loss: 0.0725 2023-01-07 02:17:02,000 - mmseg - INFO - Iter [143750/160000] lr: 6.094e-06, eta: 3:03:25, time: 0.705, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0740, decode.acc_seg: 96.7567, loss: 0.0740 2023-01-07 02:17:35,282 - mmseg - INFO - Iter [143800/160000] lr: 6.075e-06, eta: 3:02:51, time: 0.666, data_time: 0.016, memory: 11582, decode.loss_ce: 0.0734, decode.acc_seg: 96.7693, loss: 0.0734 2023-01-07 02:18:08,515 - mmseg - INFO - Iter [143850/160000] lr: 6.057e-06, eta: 3:02:17, time: 0.665, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0699, decode.acc_seg: 96.9533, loss: 0.0699 2023-01-07 02:18:43,034 - mmseg - INFO - Iter [143900/160000] lr: 6.038e-06, eta: 3:01:43, time: 0.691, data_time: 0.026, memory: 11582, decode.loss_ce: 0.0733, decode.acc_seg: 96.7468, loss: 0.0733 2023-01-07 02:19:16,461 - mmseg - INFO - Iter [143950/160000] lr: 6.019e-06, eta: 3:01:09, time: 0.668, data_time: 0.016, memory: 11582, decode.loss_ce: 0.0762, decode.acc_seg: 96.7398, loss: 0.0762 2023-01-07 02:19:52,346 - mmseg - INFO - Saving checkpoint at 144000 iterations 2023-01-07 02:19:58,547 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 02:19:58,547 - mmseg - INFO - Iter [144000/160000] lr: 6.000e-06, eta: 3:00:36, time: 0.843, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0740, decode.acc_seg: 96.7903, loss: 0.0740 2023-01-07 02:20:34,225 - mmseg - INFO - per class results: 2023-01-07 02:20:34,228 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 98.16 | 99.06 | | sidewalk | 84.67 | 92.0 | | building | 92.2 | 96.45 | | wall | 57.77 | 67.0 | | fence | 58.31 | 70.35 | | pole | 63.44 | 73.32 | | traffic light | 67.41 | 76.62 | | traffic sign | 76.53 | 83.16 | | vegetation | 92.26 | 96.71 | | terrain | 63.57 | 70.23 | | sky | 94.83 | 98.0 | | person | 79.02 | 90.31 | | rider | 54.13 | 64.15 | | car | 94.09 | 97.41 | | truck | 67.52 | 75.39 | | bus | 82.47 | 88.59 | | train | 69.38 | 75.95 | | motorcycle | 49.18 | 60.59 | | bicycle | 73.91 | 86.9 | +---------------+-------+-------+ 2023-01-07 02:20:34,228 - mmseg - INFO - Summary: 2023-01-07 02:20:34,229 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 95.87 | 74.68 | 82.22 | +-------+-------+-------+ 2023-01-07 02:20:34,229 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 02:20:34,230 - mmseg - INFO - Iter(val) [63] aAcc: 0.9587, mIoU: 0.7468, mAcc: 0.8222, IoU.road: 0.9816, IoU.sidewalk: 0.8467, IoU.building: 0.9220, IoU.wall: 0.5777, IoU.fence: 0.5831, IoU.pole: 0.6344, IoU.traffic light: 0.6741, IoU.traffic sign: 0.7653, IoU.vegetation: 0.9226, IoU.terrain: 0.6357, IoU.sky: 0.9483, IoU.person: 0.7902, IoU.rider: 0.5413, IoU.car: 0.9409, IoU.truck: 0.6752, IoU.bus: 0.8247, IoU.train: 0.6938, IoU.motorcycle: 0.4918, IoU.bicycle: 0.7391, Acc.road: 0.9906, Acc.sidewalk: 0.9200, Acc.building: 0.9645, Acc.wall: 0.6700, Acc.fence: 0.7035, Acc.pole: 0.7332, Acc.traffic light: 0.7662, Acc.traffic sign: 0.8316, Acc.vegetation: 0.9671, Acc.terrain: 0.7023, Acc.sky: 0.9800, Acc.person: 0.9031, Acc.rider: 0.6415, Acc.car: 0.9741, Acc.truck: 0.7539, Acc.bus: 0.8859, Acc.train: 0.7595, Acc.motorcycle: 0.6059, Acc.bicycle: 0.8690 2023-01-07 02:21:06,367 - mmseg - INFO - Iter [144050/160000] lr: 5.982e-06, eta: 3:00:06, time: 1.356, data_time: 0.727, memory: 11582, decode.loss_ce: 0.0720, decode.acc_seg: 96.8710, loss: 0.0720 2023-01-07 02:21:39,966 - mmseg - INFO - Iter [144100/160000] lr: 5.963e-06, eta: 2:59:32, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0705, decode.acc_seg: 96.8713, loss: 0.0705 2023-01-07 02:22:12,395 - mmseg - INFO - Iter [144150/160000] lr: 5.944e-06, eta: 2:58:58, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0699, decode.acc_seg: 96.8632, loss: 0.0699 2023-01-07 02:22:44,739 - mmseg - INFO - Iter [144200/160000] lr: 5.925e-06, eta: 2:58:24, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0724, decode.acc_seg: 96.8679, loss: 0.0724 2023-01-07 02:23:18,545 - mmseg - INFO - Iter [144250/160000] lr: 5.907e-06, eta: 2:57:50, time: 0.676, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0747, decode.acc_seg: 96.8070, loss: 0.0747 2023-01-07 02:23:50,970 - mmseg - INFO - Iter [144300/160000] lr: 5.888e-06, eta: 2:57:16, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0743, decode.acc_seg: 96.7319, loss: 0.0743 2023-01-07 02:24:27,372 - mmseg - INFO - Iter [144350/160000] lr: 5.869e-06, eta: 2:56:43, time: 0.728, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0696, decode.acc_seg: 96.9341, loss: 0.0696 2023-01-07 02:24:59,748 - mmseg - INFO - Iter [144400/160000] lr: 5.850e-06, eta: 2:56:09, time: 0.648, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0702, decode.acc_seg: 96.9882, loss: 0.0702 2023-01-07 02:25:32,440 - mmseg - INFO - Iter [144450/160000] lr: 5.832e-06, eta: 2:55:35, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0697, decode.acc_seg: 97.0050, loss: 0.0697 2023-01-07 02:26:04,718 - mmseg - INFO - Iter [144500/160000] lr: 5.813e-06, eta: 2:55:01, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0721, decode.acc_seg: 96.8205, loss: 0.0721 2023-01-07 02:26:36,953 - mmseg - INFO - Iter [144550/160000] lr: 5.794e-06, eta: 2:54:27, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0739, decode.acc_seg: 96.8084, loss: 0.0739 2023-01-07 02:27:09,684 - mmseg - INFO - Iter [144600/160000] lr: 5.775e-06, eta: 2:53:53, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0728, decode.acc_seg: 96.7813, loss: 0.0728 2023-01-07 02:27:42,465 - mmseg - INFO - Iter [144650/160000] lr: 5.757e-06, eta: 2:53:19, time: 0.656, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0707, decode.acc_seg: 96.8858, loss: 0.0707 2023-01-07 02:28:16,580 - mmseg - INFO - Iter [144700/160000] lr: 5.738e-06, eta: 2:52:45, time: 0.682, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0720, decode.acc_seg: 96.8348, loss: 0.0720 2023-01-07 02:28:51,075 - mmseg - INFO - Iter [144750/160000] lr: 5.719e-06, eta: 2:52:11, time: 0.690, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0655, decode.acc_seg: 97.0710, loss: 0.0655 2023-01-07 02:29:23,186 - mmseg - INFO - Iter [144800/160000] lr: 5.700e-06, eta: 2:51:37, time: 0.642, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0739, decode.acc_seg: 96.8160, loss: 0.0739 2023-01-07 02:29:56,565 - mmseg - INFO - Iter [144850/160000] lr: 5.682e-06, eta: 2:51:03, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0722, decode.acc_seg: 96.9445, loss: 0.0722 2023-01-07 02:30:30,287 - mmseg - INFO - Iter [144900/160000] lr: 5.663e-06, eta: 2:50:29, time: 0.674, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0782, decode.acc_seg: 96.6870, loss: 0.0782 2023-01-07 02:31:03,073 - mmseg - INFO - Iter [144950/160000] lr: 5.644e-06, eta: 2:49:55, time: 0.656, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0744, decode.acc_seg: 96.7973, loss: 0.0744 2023-01-07 02:31:35,335 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 02:31:35,335 - mmseg - INFO - Iter [145000/160000] lr: 5.625e-06, eta: 2:49:21, time: 0.645, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0786, decode.acc_seg: 96.6028, loss: 0.0786 2023-01-07 02:32:09,852 - mmseg - INFO - Iter [145050/160000] lr: 5.607e-06, eta: 2:48:47, time: 0.690, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0742, decode.acc_seg: 96.7674, loss: 0.0742 2023-01-07 02:32:44,803 - mmseg - INFO - Iter [145100/160000] lr: 5.588e-06, eta: 2:48:14, time: 0.699, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0714, decode.acc_seg: 96.9475, loss: 0.0714 2023-01-07 02:33:18,070 - mmseg - INFO - Iter [145150/160000] lr: 5.569e-06, eta: 2:47:40, time: 0.665, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0733, decode.acc_seg: 96.7984, loss: 0.0733 2023-01-07 02:33:50,305 - mmseg - INFO - Iter [145200/160000] lr: 5.550e-06, eta: 2:47:06, time: 0.645, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0733, decode.acc_seg: 96.7409, loss: 0.0733 2023-01-07 02:34:23,067 - mmseg - INFO - Iter [145250/160000] lr: 5.532e-06, eta: 2:46:32, time: 0.655, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0739, decode.acc_seg: 96.7806, loss: 0.0739 2023-01-07 02:34:58,330 - mmseg - INFO - Iter [145300/160000] lr: 5.513e-06, eta: 2:45:58, time: 0.705, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0759, decode.acc_seg: 96.7566, loss: 0.0759 2023-01-07 02:35:30,643 - mmseg - INFO - Iter [145350/160000] lr: 5.494e-06, eta: 2:45:24, time: 0.646, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0696, decode.acc_seg: 96.9090, loss: 0.0696 2023-01-07 02:36:03,068 - mmseg - INFO - Iter [145400/160000] lr: 5.475e-06, eta: 2:44:50, time: 0.648, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0729, decode.acc_seg: 96.8050, loss: 0.0729 2023-01-07 02:36:35,698 - mmseg - INFO - Iter [145450/160000] lr: 5.457e-06, eta: 2:44:16, time: 0.653, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0707, decode.acc_seg: 96.8517, loss: 0.0707 2023-01-07 02:37:11,679 - mmseg - INFO - Iter [145500/160000] lr: 5.438e-06, eta: 2:43:42, time: 0.719, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0712, decode.acc_seg: 96.9189, loss: 0.0712 2023-01-07 02:37:45,231 - mmseg - INFO - Iter [145550/160000] lr: 5.419e-06, eta: 2:43:08, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0748, decode.acc_seg: 96.7675, loss: 0.0748 2023-01-07 02:38:20,633 - mmseg - INFO - Iter [145600/160000] lr: 5.400e-06, eta: 2:42:35, time: 0.708, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0716, decode.acc_seg: 96.8731, loss: 0.0716 2023-01-07 02:38:52,987 - mmseg - INFO - Iter [145650/160000] lr: 5.382e-06, eta: 2:42:00, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0768, decode.acc_seg: 96.7191, loss: 0.0768 2023-01-07 02:39:26,085 - mmseg - INFO - Iter [145700/160000] lr: 5.363e-06, eta: 2:41:27, time: 0.662, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0710, decode.acc_seg: 96.8867, loss: 0.0710 2023-01-07 02:39:58,555 - mmseg - INFO - Iter [145750/160000] lr: 5.344e-06, eta: 2:40:53, time: 0.649, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0737, decode.acc_seg: 96.8103, loss: 0.0737 2023-01-07 02:40:30,959 - mmseg - INFO - Iter [145800/160000] lr: 5.325e-06, eta: 2:40:19, time: 0.648, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0701, decode.acc_seg: 96.8894, loss: 0.0701 2023-01-07 02:41:05,483 - mmseg - INFO - Iter [145850/160000] lr: 5.307e-06, eta: 2:39:45, time: 0.690, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0687, decode.acc_seg: 96.9395, loss: 0.0687 2023-01-07 02:41:38,933 - mmseg - INFO - Iter [145900/160000] lr: 5.288e-06, eta: 2:39:11, time: 0.668, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0680, decode.acc_seg: 96.9351, loss: 0.0680 2023-01-07 02:42:12,716 - mmseg - INFO - Iter [145950/160000] lr: 5.269e-06, eta: 2:38:37, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0760, decode.acc_seg: 96.7254, loss: 0.0760 2023-01-07 02:42:45,261 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 02:42:45,261 - mmseg - INFO - Iter [146000/160000] lr: 5.250e-06, eta: 2:38:03, time: 0.651, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0745, decode.acc_seg: 96.7495, loss: 0.0745 2023-01-07 02:43:18,041 - mmseg - INFO - Iter [146050/160000] lr: 5.232e-06, eta: 2:37:29, time: 0.656, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0739, decode.acc_seg: 96.8938, loss: 0.0739 2023-01-07 02:43:51,290 - mmseg - INFO - Iter [146100/160000] lr: 5.213e-06, eta: 2:36:55, time: 0.665, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0726, decode.acc_seg: 96.8122, loss: 0.0726 2023-01-07 02:44:24,663 - mmseg - INFO - Iter [146150/160000] lr: 5.194e-06, eta: 2:36:21, time: 0.667, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0729, decode.acc_seg: 96.8297, loss: 0.0729 2023-01-07 02:45:01,082 - mmseg - INFO - Iter [146200/160000] lr: 5.175e-06, eta: 2:35:47, time: 0.728, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0714, decode.acc_seg: 96.9117, loss: 0.0714 2023-01-07 02:45:34,045 - mmseg - INFO - Iter [146250/160000] lr: 5.157e-06, eta: 2:35:14, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0749, decode.acc_seg: 96.7658, loss: 0.0749 2023-01-07 02:46:07,608 - mmseg - INFO - Iter [146300/160000] lr: 5.138e-06, eta: 2:34:40, time: 0.671, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0730, decode.acc_seg: 96.8389, loss: 0.0730 2023-01-07 02:46:39,961 - mmseg - INFO - Iter [146350/160000] lr: 5.119e-06, eta: 2:34:06, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0735, decode.acc_seg: 96.8061, loss: 0.0735 2023-01-07 02:47:14,763 - mmseg - INFO - Iter [146400/160000] lr: 5.100e-06, eta: 2:33:32, time: 0.695, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0722, decode.acc_seg: 96.7637, loss: 0.0722 2023-01-07 02:47:48,063 - mmseg - INFO - Iter [146450/160000] lr: 5.082e-06, eta: 2:32:58, time: 0.667, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0708, decode.acc_seg: 96.9527, loss: 0.0708 2023-01-07 02:48:21,522 - mmseg - INFO - Iter [146500/160000] lr: 5.063e-06, eta: 2:32:24, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0720, decode.acc_seg: 96.8676, loss: 0.0720 2023-01-07 02:48:54,398 - mmseg - INFO - Iter [146550/160000] lr: 5.044e-06, eta: 2:31:50, time: 0.658, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0725, decode.acc_seg: 96.8275, loss: 0.0725 2023-01-07 02:49:31,005 - mmseg - INFO - Iter [146600/160000] lr: 5.025e-06, eta: 2:31:16, time: 0.732, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0723, decode.acc_seg: 96.8415, loss: 0.0723 2023-01-07 02:50:05,117 - mmseg - INFO - Iter [146650/160000] lr: 5.007e-06, eta: 2:30:43, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0727, decode.acc_seg: 96.8470, loss: 0.0727 2023-01-07 02:50:38,580 - mmseg - INFO - Iter [146700/160000] lr: 4.988e-06, eta: 2:30:09, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0724, decode.acc_seg: 96.8557, loss: 0.0724 2023-01-07 02:51:13,080 - mmseg - INFO - Iter [146750/160000] lr: 4.969e-06, eta: 2:29:35, time: 0.691, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0705, decode.acc_seg: 96.7658, loss: 0.0705 2023-01-07 02:51:47,598 - mmseg - INFO - Iter [146800/160000] lr: 4.950e-06, eta: 2:29:01, time: 0.689, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0702, decode.acc_seg: 96.8707, loss: 0.0702 2023-01-07 02:52:20,856 - mmseg - INFO - Iter [146850/160000] lr: 4.932e-06, eta: 2:28:27, time: 0.666, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0710, decode.acc_seg: 96.8303, loss: 0.0710 2023-01-07 02:52:54,064 - mmseg - INFO - Iter [146900/160000] lr: 4.913e-06, eta: 2:27:53, time: 0.664, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0729, decode.acc_seg: 96.8026, loss: 0.0729 2023-01-07 02:53:28,923 - mmseg - INFO - Iter [146950/160000] lr: 4.894e-06, eta: 2:27:19, time: 0.697, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0699, decode.acc_seg: 96.9290, loss: 0.0699 2023-01-07 02:54:01,182 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 02:54:01,182 - mmseg - INFO - Iter [147000/160000] lr: 4.875e-06, eta: 2:26:45, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0739, decode.acc_seg: 96.8097, loss: 0.0739 2023-01-07 02:54:35,829 - mmseg - INFO - Iter [147050/160000] lr: 4.857e-06, eta: 2:26:12, time: 0.692, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0722, decode.acc_seg: 96.8961, loss: 0.0722 2023-01-07 02:55:08,954 - mmseg - INFO - Iter [147100/160000] lr: 4.838e-06, eta: 2:25:38, time: 0.663, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0719, decode.acc_seg: 96.8476, loss: 0.0719 2023-01-07 02:55:42,439 - mmseg - INFO - Iter [147150/160000] lr: 4.819e-06, eta: 2:25:04, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0733, decode.acc_seg: 96.9192, loss: 0.0733 2023-01-07 02:56:16,851 - mmseg - INFO - Iter [147200/160000] lr: 4.800e-06, eta: 2:24:30, time: 0.688, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0736, decode.acc_seg: 96.7903, loss: 0.0736 2023-01-07 02:56:51,193 - mmseg - INFO - Iter [147250/160000] lr: 4.782e-06, eta: 2:23:56, time: 0.686, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0717, decode.acc_seg: 96.8985, loss: 0.0717 2023-01-07 02:57:25,261 - mmseg - INFO - Iter [147300/160000] lr: 4.763e-06, eta: 2:23:22, time: 0.682, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0720, decode.acc_seg: 96.9125, loss: 0.0720 2023-01-07 02:58:00,232 - mmseg - INFO - Iter [147350/160000] lr: 4.744e-06, eta: 2:22:48, time: 0.699, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0709, decode.acc_seg: 96.8452, loss: 0.0709 2023-01-07 02:58:32,517 - mmseg - INFO - Iter [147400/160000] lr: 4.725e-06, eta: 2:22:14, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0747, decode.acc_seg: 96.7249, loss: 0.0747 2023-01-07 02:59:07,081 - mmseg - INFO - Iter [147450/160000] lr: 4.707e-06, eta: 2:21:41, time: 0.692, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0706, decode.acc_seg: 96.7917, loss: 0.0706 2023-01-07 02:59:40,675 - mmseg - INFO - Iter [147500/160000] lr: 4.688e-06, eta: 2:21:07, time: 0.671, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0704, decode.acc_seg: 96.8099, loss: 0.0704 2023-01-07 03:00:13,163 - mmseg - INFO - Iter [147550/160000] lr: 4.669e-06, eta: 2:20:33, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0704, decode.acc_seg: 96.9276, loss: 0.0704 2023-01-07 03:00:46,072 - mmseg - INFO - Iter [147600/160000] lr: 4.650e-06, eta: 2:19:59, time: 0.657, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0701, decode.acc_seg: 96.8971, loss: 0.0701 2023-01-07 03:01:20,935 - mmseg - INFO - Iter [147650/160000] lr: 4.632e-06, eta: 2:19:25, time: 0.697, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0762, decode.acc_seg: 96.8481, loss: 0.0762 2023-01-07 03:01:56,754 - mmseg - INFO - Iter [147700/160000] lr: 4.613e-06, eta: 2:18:51, time: 0.717, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0751, decode.acc_seg: 96.7958, loss: 0.0751 2023-01-07 03:02:29,338 - mmseg - INFO - Iter [147750/160000] lr: 4.594e-06, eta: 2:18:17, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0756, decode.acc_seg: 96.6902, loss: 0.0756 2023-01-07 03:03:02,177 - mmseg - INFO - Iter [147800/160000] lr: 4.575e-06, eta: 2:17:43, time: 0.658, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0728, decode.acc_seg: 96.8105, loss: 0.0728 2023-01-07 03:03:34,356 - mmseg - INFO - Iter [147850/160000] lr: 4.557e-06, eta: 2:17:09, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0708, decode.acc_seg: 96.9481, loss: 0.0708 2023-01-07 03:04:07,054 - mmseg - INFO - Iter [147900/160000] lr: 4.538e-06, eta: 2:16:35, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0720, decode.acc_seg: 96.8927, loss: 0.0720 2023-01-07 03:04:40,236 - mmseg - INFO - Iter [147950/160000] lr: 4.519e-06, eta: 2:16:02, time: 0.664, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0723, decode.acc_seg: 96.9258, loss: 0.0723 2023-01-07 03:05:15,261 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 03:05:15,262 - mmseg - INFO - Iter [148000/160000] lr: 4.500e-06, eta: 2:15:28, time: 0.701, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0712, decode.acc_seg: 96.9385, loss: 0.0712 2023-01-07 03:05:49,953 - mmseg - INFO - Iter [148050/160000] lr: 4.482e-06, eta: 2:14:54, time: 0.693, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0711, decode.acc_seg: 96.9256, loss: 0.0711 2023-01-07 03:06:26,728 - mmseg - INFO - Iter [148100/160000] lr: 4.463e-06, eta: 2:14:20, time: 0.735, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0696, decode.acc_seg: 96.9418, loss: 0.0696 2023-01-07 03:07:00,373 - mmseg - INFO - Iter [148150/160000] lr: 4.444e-06, eta: 2:13:46, time: 0.673, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0723, decode.acc_seg: 96.8552, loss: 0.0723 2023-01-07 03:07:34,796 - mmseg - INFO - Iter [148200/160000] lr: 4.425e-06, eta: 2:13:13, time: 0.689, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0738, decode.acc_seg: 96.8118, loss: 0.0738 2023-01-07 03:08:07,127 - mmseg - INFO - Iter [148250/160000] lr: 4.407e-06, eta: 2:12:39, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0713, decode.acc_seg: 96.9182, loss: 0.0713 2023-01-07 03:08:41,514 - mmseg - INFO - Iter [148300/160000] lr: 4.388e-06, eta: 2:12:05, time: 0.688, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0757, decode.acc_seg: 96.7540, loss: 0.0757 2023-01-07 03:09:14,974 - mmseg - INFO - Iter [148350/160000] lr: 4.369e-06, eta: 2:11:31, time: 0.669, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0753, decode.acc_seg: 96.8237, loss: 0.0753 2023-01-07 03:09:47,451 - mmseg - INFO - Iter [148400/160000] lr: 4.350e-06, eta: 2:10:57, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0690, decode.acc_seg: 96.9298, loss: 0.0690 2023-01-07 03:10:22,866 - mmseg - INFO - Iter [148450/160000] lr: 4.332e-06, eta: 2:10:23, time: 0.709, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0721, decode.acc_seg: 96.8244, loss: 0.0721 2023-01-07 03:10:57,643 - mmseg - INFO - Iter [148500/160000] lr: 4.313e-06, eta: 2:09:49, time: 0.695, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0729, decode.acc_seg: 96.8629, loss: 0.0729 2023-01-07 03:11:32,742 - mmseg - INFO - Iter [148550/160000] lr: 4.294e-06, eta: 2:09:16, time: 0.701, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0730, decode.acc_seg: 96.7756, loss: 0.0730 2023-01-07 03:12:05,760 - mmseg - INFO - Iter [148600/160000] lr: 4.275e-06, eta: 2:08:42, time: 0.661, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0703, decode.acc_seg: 96.8894, loss: 0.0703 2023-01-07 03:12:38,580 - mmseg - INFO - Iter [148650/160000] lr: 4.257e-06, eta: 2:08:08, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0696, decode.acc_seg: 96.9110, loss: 0.0696 2023-01-07 03:13:14,550 - mmseg - INFO - Iter [148700/160000] lr: 4.238e-06, eta: 2:07:34, time: 0.719, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0708, decode.acc_seg: 96.9126, loss: 0.0708 2023-01-07 03:13:49,193 - mmseg - INFO - Iter [148750/160000] lr: 4.219e-06, eta: 2:07:00, time: 0.693, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0708, decode.acc_seg: 96.9092, loss: 0.0708 2023-01-07 03:14:24,973 - mmseg - INFO - Iter [148800/160000] lr: 4.200e-06, eta: 2:06:26, time: 0.716, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0712, decode.acc_seg: 96.9041, loss: 0.0712 2023-01-07 03:14:59,421 - mmseg - INFO - Iter [148850/160000] lr: 4.182e-06, eta: 2:05:53, time: 0.690, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0704, decode.acc_seg: 96.9200, loss: 0.0704 2023-01-07 03:15:32,124 - mmseg - INFO - Iter [148900/160000] lr: 4.163e-06, eta: 2:05:19, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0716, decode.acc_seg: 96.8244, loss: 0.0716 2023-01-07 03:16:06,073 - mmseg - INFO - Iter [148950/160000] lr: 4.144e-06, eta: 2:04:45, time: 0.679, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0719, decode.acc_seg: 96.9321, loss: 0.0719 2023-01-07 03:16:40,330 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 03:16:40,330 - mmseg - INFO - Iter [149000/160000] lr: 4.125e-06, eta: 2:04:11, time: 0.686, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0684, decode.acc_seg: 96.9660, loss: 0.0684 2023-01-07 03:17:12,467 - mmseg - INFO - Iter [149050/160000] lr: 4.107e-06, eta: 2:03:37, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0726, decode.acc_seg: 96.8333, loss: 0.0726 2023-01-07 03:17:47,284 - mmseg - INFO - Iter [149100/160000] lr: 4.088e-06, eta: 2:03:03, time: 0.695, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0693, decode.acc_seg: 96.9948, loss: 0.0693 2023-01-07 03:18:19,727 - mmseg - INFO - Iter [149150/160000] lr: 4.069e-06, eta: 2:02:29, time: 0.650, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0703, decode.acc_seg: 96.9082, loss: 0.0703 2023-01-07 03:18:54,067 - mmseg - INFO - Iter [149200/160000] lr: 4.050e-06, eta: 2:01:55, time: 0.686, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0711, decode.acc_seg: 96.9055, loss: 0.0711 2023-01-07 03:19:26,810 - mmseg - INFO - Iter [149250/160000] lr: 4.032e-06, eta: 2:01:21, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0699, decode.acc_seg: 96.9195, loss: 0.0699 2023-01-07 03:19:58,958 - mmseg - INFO - Iter [149300/160000] lr: 4.013e-06, eta: 2:00:47, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0691, decode.acc_seg: 97.0011, loss: 0.0691 2023-01-07 03:20:33,554 - mmseg - INFO - Iter [149350/160000] lr: 3.994e-06, eta: 2:00:14, time: 0.691, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0708, decode.acc_seg: 96.9367, loss: 0.0708 2023-01-07 03:21:06,514 - mmseg - INFO - Iter [149400/160000] lr: 3.975e-06, eta: 1:59:40, time: 0.660, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0715, decode.acc_seg: 96.8132, loss: 0.0715 2023-01-07 03:21:40,108 - mmseg - INFO - Iter [149450/160000] lr: 3.957e-06, eta: 1:59:06, time: 0.671, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0720, decode.acc_seg: 96.8886, loss: 0.0720 2023-01-07 03:22:13,484 - mmseg - INFO - Iter [149500/160000] lr: 3.938e-06, eta: 1:58:32, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0747, decode.acc_seg: 96.8206, loss: 0.0747 2023-01-07 03:22:48,418 - mmseg - INFO - Iter [149550/160000] lr: 3.919e-06, eta: 1:57:58, time: 0.698, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0710, decode.acc_seg: 96.8220, loss: 0.0710 2023-01-07 03:23:22,126 - mmseg - INFO - Iter [149600/160000] lr: 3.900e-06, eta: 1:57:24, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0716, decode.acc_seg: 96.8462, loss: 0.0716 2023-01-07 03:23:56,885 - mmseg - INFO - Iter [149650/160000] lr: 3.882e-06, eta: 1:56:50, time: 0.695, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0741, decode.acc_seg: 96.8822, loss: 0.0741 2023-01-07 03:24:30,689 - mmseg - INFO - Iter [149700/160000] lr: 3.863e-06, eta: 1:56:17, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0703, decode.acc_seg: 96.8424, loss: 0.0703 2023-01-07 03:25:06,409 - mmseg - INFO - Iter [149750/160000] lr: 3.844e-06, eta: 1:55:43, time: 0.714, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0709, decode.acc_seg: 96.9458, loss: 0.0709 2023-01-07 03:25:39,683 - mmseg - INFO - Iter [149800/160000] lr: 3.825e-06, eta: 1:55:09, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0730, decode.acc_seg: 96.8058, loss: 0.0730 2023-01-07 03:26:12,580 - mmseg - INFO - Iter [149850/160000] lr: 3.807e-06, eta: 1:54:35, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0690, decode.acc_seg: 96.9778, loss: 0.0690 2023-01-07 03:26:45,020 - mmseg - INFO - Iter [149900/160000] lr: 3.788e-06, eta: 1:54:01, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0731, decode.acc_seg: 96.8175, loss: 0.0731 2023-01-07 03:27:19,586 - mmseg - INFO - Iter [149950/160000] lr: 3.769e-06, eta: 1:53:27, time: 0.690, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0702, decode.acc_seg: 96.9618, loss: 0.0702 2023-01-07 03:27:52,701 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 03:27:52,702 - mmseg - INFO - Iter [150000/160000] lr: 3.750e-06, eta: 1:52:53, time: 0.663, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0715, decode.acc_seg: 96.8496, loss: 0.0715 2023-01-07 03:28:25,337 - mmseg - INFO - Iter [150050/160000] lr: 3.732e-06, eta: 1:52:19, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0692, decode.acc_seg: 96.9022, loss: 0.0692 2023-01-07 03:28:59,904 - mmseg - INFO - Iter [150100/160000] lr: 3.713e-06, eta: 1:51:45, time: 0.691, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0697, decode.acc_seg: 96.8727, loss: 0.0697 2023-01-07 03:29:32,755 - mmseg - INFO - Iter [150150/160000] lr: 3.694e-06, eta: 1:51:12, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0707, decode.acc_seg: 96.8794, loss: 0.0707 2023-01-07 03:30:06,030 - mmseg - INFO - Iter [150200/160000] lr: 3.675e-06, eta: 1:50:38, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0725, decode.acc_seg: 96.8576, loss: 0.0725 2023-01-07 03:30:40,165 - mmseg - INFO - Iter [150250/160000] lr: 3.657e-06, eta: 1:50:04, time: 0.682, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0691, decode.acc_seg: 96.9511, loss: 0.0691 2023-01-07 03:31:18,174 - mmseg - INFO - Iter [150300/160000] lr: 3.638e-06, eta: 1:49:30, time: 0.760, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0734, decode.acc_seg: 96.8056, loss: 0.0734 2023-01-07 03:31:50,912 - mmseg - INFO - Iter [150350/160000] lr: 3.619e-06, eta: 1:48:56, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0704, decode.acc_seg: 96.9006, loss: 0.0704 2023-01-07 03:32:25,203 - mmseg - INFO - Iter [150400/160000] lr: 3.600e-06, eta: 1:48:22, time: 0.685, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0694, decode.acc_seg: 96.9174, loss: 0.0694 2023-01-07 03:32:58,002 - mmseg - INFO - Iter [150450/160000] lr: 3.582e-06, eta: 1:47:48, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0695, decode.acc_seg: 96.9841, loss: 0.0695 2023-01-07 03:33:31,954 - mmseg - INFO - Iter [150500/160000] lr: 3.563e-06, eta: 1:47:15, time: 0.678, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0715, decode.acc_seg: 96.9318, loss: 0.0715 2023-01-07 03:34:04,626 - mmseg - INFO - Iter [150550/160000] lr: 3.544e-06, eta: 1:46:41, time: 0.654, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0715, decode.acc_seg: 96.8638, loss: 0.0715 2023-01-07 03:34:36,863 - mmseg - INFO - Iter [150600/160000] lr: 3.525e-06, eta: 1:46:07, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0688, decode.acc_seg: 96.8937, loss: 0.0688 2023-01-07 03:35:09,897 - mmseg - INFO - Iter [150650/160000] lr: 3.507e-06, eta: 1:45:33, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0685, decode.acc_seg: 96.9544, loss: 0.0685 2023-01-07 03:35:47,729 - mmseg - INFO - Iter [150700/160000] lr: 3.488e-06, eta: 1:44:59, time: 0.757, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0698, decode.acc_seg: 96.9253, loss: 0.0698 2023-01-07 03:36:19,999 - mmseg - INFO - Iter [150750/160000] lr: 3.469e-06, eta: 1:44:25, time: 0.646, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0732, decode.acc_seg: 96.8190, loss: 0.0732 2023-01-07 03:36:54,438 - mmseg - INFO - Iter [150800/160000] lr: 3.450e-06, eta: 1:43:51, time: 0.689, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0719, decode.acc_seg: 96.8945, loss: 0.0719 2023-01-07 03:37:27,293 - mmseg - INFO - Iter [150850/160000] lr: 3.432e-06, eta: 1:43:17, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0748, decode.acc_seg: 96.7246, loss: 0.0748 2023-01-07 03:37:59,695 - mmseg - INFO - Iter [150900/160000] lr: 3.413e-06, eta: 1:42:43, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0744, decode.acc_seg: 96.7635, loss: 0.0744 2023-01-07 03:38:33,483 - mmseg - INFO - Iter [150950/160000] lr: 3.394e-06, eta: 1:42:10, time: 0.676, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0705, decode.acc_seg: 96.9276, loss: 0.0705 2023-01-07 03:39:05,936 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 03:39:05,937 - mmseg - INFO - Iter [151000/160000] lr: 3.375e-06, eta: 1:41:36, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0725, decode.acc_seg: 96.8498, loss: 0.0725 2023-01-07 03:39:41,417 - mmseg - INFO - Iter [151050/160000] lr: 3.357e-06, eta: 1:41:02, time: 0.709, data_time: 0.057, memory: 11582, decode.loss_ce: 0.0698, decode.acc_seg: 96.9735, loss: 0.0698 2023-01-07 03:40:14,553 - mmseg - INFO - Iter [151100/160000] lr: 3.338e-06, eta: 1:40:28, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0726, decode.acc_seg: 96.7674, loss: 0.0726 2023-01-07 03:40:48,991 - mmseg - INFO - Iter [151150/160000] lr: 3.319e-06, eta: 1:39:54, time: 0.688, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0695, decode.acc_seg: 96.9899, loss: 0.0695 2023-01-07 03:41:22,862 - mmseg - INFO - Iter [151200/160000] lr: 3.300e-06, eta: 1:39:20, time: 0.678, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0714, decode.acc_seg: 96.8838, loss: 0.0714 2023-01-07 03:41:55,583 - mmseg - INFO - Iter [151250/160000] lr: 3.282e-06, eta: 1:38:46, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0694, decode.acc_seg: 96.8918, loss: 0.0694 2023-01-07 03:42:29,434 - mmseg - INFO - Iter [151300/160000] lr: 3.263e-06, eta: 1:38:12, time: 0.677, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0701, decode.acc_seg: 96.9634, loss: 0.0701 2023-01-07 03:43:02,068 - mmseg - INFO - Iter [151350/160000] lr: 3.244e-06, eta: 1:37:38, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0712, decode.acc_seg: 96.8390, loss: 0.0712 2023-01-07 03:43:35,277 - mmseg - INFO - Iter [151400/160000] lr: 3.225e-06, eta: 1:37:05, time: 0.665, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0706, decode.acc_seg: 96.9849, loss: 0.0706 2023-01-07 03:44:12,329 - mmseg - INFO - Iter [151450/160000] lr: 3.207e-06, eta: 1:36:31, time: 0.741, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0670, decode.acc_seg: 97.0933, loss: 0.0670 2023-01-07 03:44:44,886 - mmseg - INFO - Iter [151500/160000] lr: 3.188e-06, eta: 1:35:57, time: 0.651, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0722, decode.acc_seg: 96.7850, loss: 0.0722 2023-01-07 03:45:18,468 - mmseg - INFO - Iter [151550/160000] lr: 3.169e-06, eta: 1:35:23, time: 0.672, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0710, decode.acc_seg: 96.8302, loss: 0.0710 2023-01-07 03:45:50,542 - mmseg - INFO - Iter [151600/160000] lr: 3.150e-06, eta: 1:34:49, time: 0.641, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0709, decode.acc_seg: 96.8863, loss: 0.0709 2023-01-07 03:46:22,675 - mmseg - INFO - Iter [151650/160000] lr: 3.132e-06, eta: 1:34:15, time: 0.643, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0705, decode.acc_seg: 96.8735, loss: 0.0705 2023-01-07 03:46:55,700 - mmseg - INFO - Iter [151700/160000] lr: 3.113e-06, eta: 1:33:41, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0736, decode.acc_seg: 96.8715, loss: 0.0736 2023-01-07 03:47:28,030 - mmseg - INFO - Iter [151750/160000] lr: 3.094e-06, eta: 1:33:07, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0719, decode.acc_seg: 96.9038, loss: 0.0719 2023-01-07 03:48:03,224 - mmseg - INFO - Iter [151800/160000] lr: 3.075e-06, eta: 1:32:33, time: 0.704, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0753, decode.acc_seg: 96.7435, loss: 0.0753 2023-01-07 03:48:35,849 - mmseg - INFO - Iter [151850/160000] lr: 3.057e-06, eta: 1:32:00, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0717, decode.acc_seg: 96.8658, loss: 0.0717 2023-01-07 03:49:10,291 - mmseg - INFO - Iter [151900/160000] lr: 3.038e-06, eta: 1:31:26, time: 0.689, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0715, decode.acc_seg: 96.8658, loss: 0.0715 2023-01-07 03:49:45,305 - mmseg - INFO - Iter [151950/160000] lr: 3.019e-06, eta: 1:30:52, time: 0.701, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0699, decode.acc_seg: 96.9272, loss: 0.0699 2023-01-07 03:50:17,946 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 03:50:17,947 - mmseg - INFO - Iter [152000/160000] lr: 3.000e-06, eta: 1:30:18, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0684, decode.acc_seg: 96.9532, loss: 0.0684 2023-01-07 03:50:53,144 - mmseg - INFO - Iter [152050/160000] lr: 2.982e-06, eta: 1:29:44, time: 0.704, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0714, decode.acc_seg: 96.9420, loss: 0.0714 2023-01-07 03:51:25,676 - mmseg - INFO - Iter [152100/160000] lr: 2.963e-06, eta: 1:29:10, time: 0.652, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0730, decode.acc_seg: 96.8830, loss: 0.0730 2023-01-07 03:52:01,250 - mmseg - INFO - Iter [152150/160000] lr: 2.944e-06, eta: 1:28:36, time: 0.711, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0700, decode.acc_seg: 96.9725, loss: 0.0700 2023-01-07 03:52:35,816 - mmseg - INFO - Iter [152200/160000] lr: 2.925e-06, eta: 1:28:03, time: 0.691, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0712, decode.acc_seg: 96.8739, loss: 0.0712 2023-01-07 03:53:10,767 - mmseg - INFO - Iter [152250/160000] lr: 2.907e-06, eta: 1:27:29, time: 0.699, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0652, decode.acc_seg: 97.1212, loss: 0.0652 2023-01-07 03:53:43,055 - mmseg - INFO - Iter [152300/160000] lr: 2.888e-06, eta: 1:26:55, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0734, decode.acc_seg: 96.8300, loss: 0.0734 2023-01-07 03:54:15,919 - mmseg - INFO - Iter [152350/160000] lr: 2.869e-06, eta: 1:26:21, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0721, decode.acc_seg: 96.9034, loss: 0.0721 2023-01-07 03:54:50,424 - mmseg - INFO - Iter [152400/160000] lr: 2.850e-06, eta: 1:25:47, time: 0.690, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0707, decode.acc_seg: 96.8698, loss: 0.0707 2023-01-07 03:55:23,415 - mmseg - INFO - Iter [152450/160000] lr: 2.832e-06, eta: 1:25:13, time: 0.661, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0732, decode.acc_seg: 96.8114, loss: 0.0732 2023-01-07 03:55:57,458 - mmseg - INFO - Iter [152500/160000] lr: 2.813e-06, eta: 1:24:39, time: 0.681, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0705, decode.acc_seg: 96.9298, loss: 0.0705 2023-01-07 03:56:32,883 - mmseg - INFO - Iter [152550/160000] lr: 2.794e-06, eta: 1:24:06, time: 0.708, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0646, decode.acc_seg: 97.1335, loss: 0.0646 2023-01-07 03:57:06,703 - mmseg - INFO - Iter [152600/160000] lr: 2.775e-06, eta: 1:23:32, time: 0.675, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0727, decode.acc_seg: 96.8652, loss: 0.0727 2023-01-07 03:57:40,859 - mmseg - INFO - Iter [152650/160000] lr: 2.757e-06, eta: 1:22:58, time: 0.684, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0714, decode.acc_seg: 96.9197, loss: 0.0714 2023-01-07 03:58:13,101 - mmseg - INFO - Iter [152700/160000] lr: 2.738e-06, eta: 1:22:24, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0686, decode.acc_seg: 96.9426, loss: 0.0686 2023-01-07 03:58:47,417 - mmseg - INFO - Iter [152750/160000] lr: 2.719e-06, eta: 1:21:50, time: 0.685, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0684, decode.acc_seg: 96.9188, loss: 0.0684 2023-01-07 03:59:20,094 - mmseg - INFO - Iter [152800/160000] lr: 2.700e-06, eta: 1:21:16, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0703, decode.acc_seg: 96.9222, loss: 0.0703 2023-01-07 03:59:52,913 - mmseg - INFO - Iter [152850/160000] lr: 2.682e-06, eta: 1:20:42, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0683, decode.acc_seg: 96.9253, loss: 0.0683 2023-01-07 04:00:27,927 - mmseg - INFO - Iter [152900/160000] lr: 2.663e-06, eta: 1:20:08, time: 0.700, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0684, decode.acc_seg: 97.0459, loss: 0.0684 2023-01-07 04:01:02,285 - mmseg - INFO - Iter [152950/160000] lr: 2.644e-06, eta: 1:19:35, time: 0.688, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0698, decode.acc_seg: 96.9715, loss: 0.0698 2023-01-07 04:01:34,950 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 04:01:34,950 - mmseg - INFO - Iter [153000/160000] lr: 2.625e-06, eta: 1:19:01, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0736, decode.acc_seg: 96.8196, loss: 0.0736 2023-01-07 04:02:08,683 - mmseg - INFO - Iter [153050/160000] lr: 2.607e-06, eta: 1:18:27, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0703, decode.acc_seg: 96.9690, loss: 0.0703 2023-01-07 04:02:40,980 - mmseg - INFO - Iter [153100/160000] lr: 2.588e-06, eta: 1:17:53, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0726, decode.acc_seg: 96.7956, loss: 0.0726 2023-01-07 04:03:15,865 - mmseg - INFO - Iter [153150/160000] lr: 2.569e-06, eta: 1:17:19, time: 0.697, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0696, decode.acc_seg: 96.9933, loss: 0.0696 2023-01-07 04:03:49,608 - mmseg - INFO - Iter [153200/160000] lr: 2.550e-06, eta: 1:16:45, time: 0.676, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0708, decode.acc_seg: 96.8837, loss: 0.0708 2023-01-07 04:04:22,594 - mmseg - INFO - Iter [153250/160000] lr: 2.532e-06, eta: 1:16:11, time: 0.660, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0730, decode.acc_seg: 96.8029, loss: 0.0730 2023-01-07 04:04:56,970 - mmseg - INFO - Iter [153300/160000] lr: 2.513e-06, eta: 1:15:37, time: 0.688, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0690, decode.acc_seg: 96.8787, loss: 0.0690 2023-01-07 04:05:29,765 - mmseg - INFO - Iter [153350/160000] lr: 2.494e-06, eta: 1:15:04, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0716, decode.acc_seg: 96.8692, loss: 0.0716 2023-01-07 04:06:02,427 - mmseg - INFO - Iter [153400/160000] lr: 2.475e-06, eta: 1:14:30, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0689, decode.acc_seg: 96.9139, loss: 0.0689 2023-01-07 04:06:35,030 - mmseg - INFO - Iter [153450/160000] lr: 2.457e-06, eta: 1:13:56, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0680, decode.acc_seg: 97.0550, loss: 0.0680 2023-01-07 04:07:07,835 - mmseg - INFO - Iter [153500/160000] lr: 2.438e-06, eta: 1:13:22, time: 0.657, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0686, decode.acc_seg: 96.9741, loss: 0.0686 2023-01-07 04:07:40,483 - mmseg - INFO - Iter [153550/160000] lr: 2.419e-06, eta: 1:12:48, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0680, decode.acc_seg: 97.0079, loss: 0.0680 2023-01-07 04:08:14,506 - mmseg - INFO - Iter [153600/160000] lr: 2.400e-06, eta: 1:12:14, time: 0.680, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0694, decode.acc_seg: 96.9284, loss: 0.0694 2023-01-07 04:08:50,730 - mmseg - INFO - Iter [153650/160000] lr: 2.382e-06, eta: 1:11:40, time: 0.724, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0746, decode.acc_seg: 96.8035, loss: 0.0746 2023-01-07 04:09:22,736 - mmseg - INFO - Iter [153700/160000] lr: 2.363e-06, eta: 1:11:06, time: 0.639, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0701, decode.acc_seg: 96.8771, loss: 0.0701 2023-01-07 04:09:56,531 - mmseg - INFO - Iter [153750/160000] lr: 2.344e-06, eta: 1:10:32, time: 0.676, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0719, decode.acc_seg: 96.8403, loss: 0.0719 2023-01-07 04:10:30,774 - mmseg - INFO - Iter [153800/160000] lr: 2.325e-06, eta: 1:09:59, time: 0.686, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0720, decode.acc_seg: 96.8523, loss: 0.0720 2023-01-07 04:11:03,807 - mmseg - INFO - Iter [153850/160000] lr: 2.307e-06, eta: 1:09:25, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0688, decode.acc_seg: 97.0201, loss: 0.0688 2023-01-07 04:11:35,880 - mmseg - INFO - Iter [153900/160000] lr: 2.288e-06, eta: 1:08:51, time: 0.641, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0681, decode.acc_seg: 96.9404, loss: 0.0681 2023-01-07 04:12:10,867 - mmseg - INFO - Iter [153950/160000] lr: 2.269e-06, eta: 1:08:17, time: 0.699, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0690, decode.acc_seg: 96.9187, loss: 0.0690 2023-01-07 04:12:45,210 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 04:12:45,211 - mmseg - INFO - Iter [154000/160000] lr: 2.250e-06, eta: 1:07:43, time: 0.688, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0703, decode.acc_seg: 96.9232, loss: 0.0703 2023-01-07 04:13:20,215 - mmseg - INFO - Iter [154050/160000] lr: 2.232e-06, eta: 1:07:09, time: 0.700, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0717, decode.acc_seg: 96.8295, loss: 0.0717 2023-01-07 04:13:52,789 - mmseg - INFO - Iter [154100/160000] lr: 2.213e-06, eta: 1:06:35, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0708, decode.acc_seg: 96.9671, loss: 0.0708 2023-01-07 04:14:25,778 - mmseg - INFO - Iter [154150/160000] lr: 2.194e-06, eta: 1:06:01, time: 0.659, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0708, decode.acc_seg: 96.9290, loss: 0.0708 2023-01-07 04:15:00,670 - mmseg - INFO - Iter [154200/160000] lr: 2.175e-06, eta: 1:05:28, time: 0.699, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0714, decode.acc_seg: 96.9758, loss: 0.0714 2023-01-07 04:15:35,113 - mmseg - INFO - Iter [154250/160000] lr: 2.157e-06, eta: 1:04:54, time: 0.689, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0678, decode.acc_seg: 96.9164, loss: 0.0678 2023-01-07 04:16:08,430 - mmseg - INFO - Iter [154300/160000] lr: 2.138e-06, eta: 1:04:20, time: 0.666, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0698, decode.acc_seg: 96.9041, loss: 0.0698 2023-01-07 04:16:41,107 - mmseg - INFO - Iter [154350/160000] lr: 2.119e-06, eta: 1:03:46, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0700, decode.acc_seg: 96.9663, loss: 0.0700 2023-01-07 04:17:15,877 - mmseg - INFO - Iter [154400/160000] lr: 2.100e-06, eta: 1:03:12, time: 0.695, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0712, decode.acc_seg: 96.9080, loss: 0.0712 2023-01-07 04:17:49,527 - mmseg - INFO - Iter [154450/160000] lr: 2.082e-06, eta: 1:02:38, time: 0.673, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0671, decode.acc_seg: 96.9775, loss: 0.0671 2023-01-07 04:18:22,678 - mmseg - INFO - Iter [154500/160000] lr: 2.063e-06, eta: 1:02:04, time: 0.662, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0688, decode.acc_seg: 97.0283, loss: 0.0688 2023-01-07 04:18:58,532 - mmseg - INFO - Iter [154550/160000] lr: 2.044e-06, eta: 1:01:31, time: 0.717, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0712, decode.acc_seg: 96.8573, loss: 0.0712 2023-01-07 04:19:31,898 - mmseg - INFO - Iter [154600/160000] lr: 2.025e-06, eta: 1:00:57, time: 0.668, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0697, decode.acc_seg: 96.9189, loss: 0.0697 2023-01-07 04:20:05,580 - mmseg - INFO - Iter [154650/160000] lr: 2.007e-06, eta: 1:00:23, time: 0.674, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0696, decode.acc_seg: 96.9016, loss: 0.0696 2023-01-07 04:20:37,992 - mmseg - INFO - Iter [154700/160000] lr: 1.988e-06, eta: 0:59:49, time: 0.649, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0729, decode.acc_seg: 96.8792, loss: 0.0729 2023-01-07 04:21:10,814 - mmseg - INFO - Iter [154750/160000] lr: 1.969e-06, eta: 0:59:15, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0707, decode.acc_seg: 96.8496, loss: 0.0707 2023-01-07 04:21:47,763 - mmseg - INFO - Iter [154800/160000] lr: 1.950e-06, eta: 0:58:41, time: 0.739, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0709, decode.acc_seg: 96.8824, loss: 0.0709 2023-01-07 04:22:20,574 - mmseg - INFO - Iter [154850/160000] lr: 1.932e-06, eta: 0:58:07, time: 0.656, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0746, decode.acc_seg: 96.7535, loss: 0.0746 2023-01-07 04:22:53,394 - mmseg - INFO - Iter [154900/160000] lr: 1.913e-06, eta: 0:57:33, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0739, decode.acc_seg: 96.7990, loss: 0.0739 2023-01-07 04:23:28,772 - mmseg - INFO - Iter [154950/160000] lr: 1.894e-06, eta: 0:57:00, time: 0.708, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0698, decode.acc_seg: 96.8245, loss: 0.0698 2023-01-07 04:24:03,333 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 04:24:03,333 - mmseg - INFO - Iter [155000/160000] lr: 1.875e-06, eta: 0:56:26, time: 0.692, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0690, decode.acc_seg: 96.9381, loss: 0.0690 2023-01-07 04:24:35,750 - mmseg - INFO - Iter [155050/160000] lr: 1.857e-06, eta: 0:55:52, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0680, decode.acc_seg: 97.0059, loss: 0.0680 2023-01-07 04:25:09,952 - mmseg - INFO - Iter [155100/160000] lr: 1.838e-06, eta: 0:55:18, time: 0.684, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0699, decode.acc_seg: 96.9948, loss: 0.0699 2023-01-07 04:25:45,743 - mmseg - INFO - Iter [155150/160000] lr: 1.819e-06, eta: 0:54:44, time: 0.715, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0690, decode.acc_seg: 96.8892, loss: 0.0690 2023-01-07 04:26:21,379 - mmseg - INFO - Iter [155200/160000] lr: 1.800e-06, eta: 0:54:10, time: 0.713, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0720, decode.acc_seg: 96.8796, loss: 0.0720 2023-01-07 04:26:54,251 - mmseg - INFO - Iter [155250/160000] lr: 1.782e-06, eta: 0:53:37, time: 0.658, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0721, decode.acc_seg: 96.8459, loss: 0.0721 2023-01-07 04:27:26,996 - mmseg - INFO - Iter [155300/160000] lr: 1.763e-06, eta: 0:53:03, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0705, decode.acc_seg: 96.9179, loss: 0.0705 2023-01-07 04:28:00,726 - mmseg - INFO - Iter [155350/160000] lr: 1.744e-06, eta: 0:52:29, time: 0.674, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0714, decode.acc_seg: 96.8823, loss: 0.0714 2023-01-07 04:28:35,488 - mmseg - INFO - Iter [155400/160000] lr: 1.725e-06, eta: 0:51:55, time: 0.695, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0669, decode.acc_seg: 96.9932, loss: 0.0669 2023-01-07 04:29:08,251 - mmseg - INFO - Iter [155450/160000] lr: 1.707e-06, eta: 0:51:21, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0700, decode.acc_seg: 96.8440, loss: 0.0700 2023-01-07 04:29:42,890 - mmseg - INFO - Iter [155500/160000] lr: 1.688e-06, eta: 0:50:47, time: 0.693, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0722, decode.acc_seg: 96.8517, loss: 0.0722 2023-01-07 04:30:16,702 - mmseg - INFO - Iter [155550/160000] lr: 1.669e-06, eta: 0:50:13, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0693, decode.acc_seg: 96.9815, loss: 0.0693 2023-01-07 04:30:49,174 - mmseg - INFO - Iter [155600/160000] lr: 1.650e-06, eta: 0:49:39, time: 0.650, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0736, decode.acc_seg: 96.8940, loss: 0.0736 2023-01-07 04:31:22,418 - mmseg - INFO - Iter [155650/160000] lr: 1.632e-06, eta: 0:49:06, time: 0.665, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0712, decode.acc_seg: 96.8103, loss: 0.0712 2023-01-07 04:31:55,093 - mmseg - INFO - Iter [155700/160000] lr: 1.613e-06, eta: 0:48:32, time: 0.654, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0740, decode.acc_seg: 96.7340, loss: 0.0740 2023-01-07 04:32:28,976 - mmseg - INFO - Iter [155750/160000] lr: 1.594e-06, eta: 0:47:58, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0676, decode.acc_seg: 97.0750, loss: 0.0676 2023-01-07 04:33:04,151 - mmseg - INFO - Iter [155800/160000] lr: 1.575e-06, eta: 0:47:24, time: 0.704, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0704, decode.acc_seg: 96.9037, loss: 0.0704 2023-01-07 04:33:36,462 - mmseg - INFO - Iter [155850/160000] lr: 1.557e-06, eta: 0:46:50, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0741, decode.acc_seg: 96.8099, loss: 0.0741 2023-01-07 04:34:10,927 - mmseg - INFO - Iter [155900/160000] lr: 1.538e-06, eta: 0:46:16, time: 0.689, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0700, decode.acc_seg: 96.9616, loss: 0.0700 2023-01-07 04:34:43,581 - mmseg - INFO - Iter [155950/160000] lr: 1.519e-06, eta: 0:45:42, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0682, decode.acc_seg: 97.0575, loss: 0.0682 2023-01-07 04:35:16,185 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 04:35:16,186 - mmseg - INFO - Iter [156000/160000] lr: 1.500e-06, eta: 0:45:08, time: 0.652, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0705, decode.acc_seg: 96.9088, loss: 0.0705 2023-01-07 04:35:50,288 - mmseg - INFO - Iter [156050/160000] lr: 1.482e-06, eta: 0:44:35, time: 0.683, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0704, decode.acc_seg: 96.9615, loss: 0.0704 2023-01-07 04:36:24,670 - mmseg - INFO - Iter [156100/160000] lr: 1.463e-06, eta: 0:44:01, time: 0.687, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0687, decode.acc_seg: 96.9382, loss: 0.0687 2023-01-07 04:37:00,480 - mmseg - INFO - Iter [156150/160000] lr: 1.444e-06, eta: 0:43:27, time: 0.716, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0723, decode.acc_seg: 96.8138, loss: 0.0723 2023-01-07 04:37:35,982 - mmseg - INFO - Iter [156200/160000] lr: 1.425e-06, eta: 0:42:53, time: 0.710, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0696, decode.acc_seg: 96.9185, loss: 0.0696 2023-01-07 04:38:11,325 - mmseg - INFO - Iter [156250/160000] lr: 1.407e-06, eta: 0:42:19, time: 0.707, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0734, decode.acc_seg: 96.7785, loss: 0.0734 2023-01-07 04:38:44,828 - mmseg - INFO - Iter [156300/160000] lr: 1.388e-06, eta: 0:41:45, time: 0.671, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0748, decode.acc_seg: 96.7137, loss: 0.0748 2023-01-07 04:39:20,647 - mmseg - INFO - Iter [156350/160000] lr: 1.369e-06, eta: 0:41:12, time: 0.715, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0707, decode.acc_seg: 96.9237, loss: 0.0707 2023-01-07 04:39:54,195 - mmseg - INFO - Iter [156400/160000] lr: 1.350e-06, eta: 0:40:38, time: 0.672, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0718, decode.acc_seg: 96.9176, loss: 0.0718 2023-01-07 04:40:26,252 - mmseg - INFO - Iter [156450/160000] lr: 1.332e-06, eta: 0:40:04, time: 0.641, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0681, decode.acc_seg: 97.0396, loss: 0.0681 2023-01-07 04:40:59,780 - mmseg - INFO - Iter [156500/160000] lr: 1.313e-06, eta: 0:39:30, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0718, decode.acc_seg: 96.8732, loss: 0.0718 2023-01-07 04:41:35,093 - mmseg - INFO - Iter [156550/160000] lr: 1.294e-06, eta: 0:38:56, time: 0.706, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0732, decode.acc_seg: 96.8325, loss: 0.0732 2023-01-07 04:42:10,929 - mmseg - INFO - Iter [156600/160000] lr: 1.275e-06, eta: 0:38:22, time: 0.717, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0674, decode.acc_seg: 97.0236, loss: 0.0674 2023-01-07 04:42:48,252 - mmseg - INFO - Iter [156650/160000] lr: 1.257e-06, eta: 0:37:49, time: 0.746, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0675, decode.acc_seg: 97.0056, loss: 0.0675 2023-01-07 04:43:23,231 - mmseg - INFO - Iter [156700/160000] lr: 1.238e-06, eta: 0:37:15, time: 0.701, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0723, decode.acc_seg: 96.8216, loss: 0.0723 2023-01-07 04:43:57,307 - mmseg - INFO - Iter [156750/160000] lr: 1.219e-06, eta: 0:36:41, time: 0.681, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0706, decode.acc_seg: 96.9277, loss: 0.0706 2023-01-07 04:44:30,198 - mmseg - INFO - Iter [156800/160000] lr: 1.200e-06, eta: 0:36:07, time: 0.658, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0671, decode.acc_seg: 96.9994, loss: 0.0671 2023-01-07 04:45:02,655 - mmseg - INFO - Iter [156850/160000] lr: 1.182e-06, eta: 0:35:33, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0727, decode.acc_seg: 96.8233, loss: 0.0727 2023-01-07 04:45:37,607 - mmseg - INFO - Iter [156900/160000] lr: 1.163e-06, eta: 0:34:59, time: 0.698, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0727, decode.acc_seg: 96.8838, loss: 0.0727 2023-01-07 04:46:11,035 - mmseg - INFO - Iter [156950/160000] lr: 1.144e-06, eta: 0:34:25, time: 0.670, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0719, decode.acc_seg: 96.8872, loss: 0.0719 2023-01-07 04:46:46,663 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 04:46:46,664 - mmseg - INFO - Iter [157000/160000] lr: 1.125e-06, eta: 0:33:51, time: 0.712, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0728, decode.acc_seg: 96.7898, loss: 0.0728 2023-01-07 04:47:20,185 - mmseg - INFO - Iter [157050/160000] lr: 1.107e-06, eta: 0:33:18, time: 0.670, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0690, decode.acc_seg: 96.9357, loss: 0.0690 2023-01-07 04:47:54,444 - mmseg - INFO - Iter [157100/160000] lr: 1.088e-06, eta: 0:32:44, time: 0.685, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0709, decode.acc_seg: 96.8854, loss: 0.0709 2023-01-07 04:48:26,632 - mmseg - INFO - Iter [157150/160000] lr: 1.069e-06, eta: 0:32:10, time: 0.645, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0684, decode.acc_seg: 96.9832, loss: 0.0684 2023-01-07 04:48:59,423 - mmseg - INFO - Iter [157200/160000] lr: 1.050e-06, eta: 0:31:36, time: 0.655, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0714, decode.acc_seg: 96.9156, loss: 0.0714 2023-01-07 04:49:32,413 - mmseg - INFO - Iter [157250/160000] lr: 1.032e-06, eta: 0:31:02, time: 0.661, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0709, decode.acc_seg: 96.8652, loss: 0.0709 2023-01-07 04:50:07,315 - mmseg - INFO - Iter [157300/160000] lr: 1.013e-06, eta: 0:30:28, time: 0.697, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0685, decode.acc_seg: 96.9370, loss: 0.0685 2023-01-07 04:50:42,501 - mmseg - INFO - Iter [157350/160000] lr: 9.941e-07, eta: 0:29:54, time: 0.704, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0702, decode.acc_seg: 96.9666, loss: 0.0702 2023-01-07 04:51:17,620 - mmseg - INFO - Iter [157400/160000] lr: 9.754e-07, eta: 0:29:21, time: 0.703, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0700, decode.acc_seg: 96.9512, loss: 0.0700 2023-01-07 04:51:49,708 - mmseg - INFO - Iter [157450/160000] lr: 9.566e-07, eta: 0:28:47, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0675, decode.acc_seg: 96.9825, loss: 0.0675 2023-01-07 04:52:22,124 - mmseg - INFO - Iter [157500/160000] lr: 9.379e-07, eta: 0:28:13, time: 0.648, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0742, decode.acc_seg: 96.7253, loss: 0.0742 2023-01-07 04:52:54,231 - mmseg - INFO - Iter [157550/160000] lr: 9.191e-07, eta: 0:27:39, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0686, decode.acc_seg: 96.9734, loss: 0.0686 2023-01-07 04:53:26,708 - mmseg - INFO - Iter [157600/160000] lr: 9.004e-07, eta: 0:27:05, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0703, decode.acc_seg: 96.9086, loss: 0.0703 2023-01-07 04:53:59,253 - mmseg - INFO - Iter [157650/160000] lr: 8.816e-07, eta: 0:26:31, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0674, decode.acc_seg: 96.9844, loss: 0.0674 2023-01-07 04:54:32,075 - mmseg - INFO - Iter [157700/160000] lr: 8.629e-07, eta: 0:25:57, time: 0.657, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0666, decode.acc_seg: 97.0136, loss: 0.0666 2023-01-07 04:55:08,096 - mmseg - INFO - Iter [157750/160000] lr: 8.441e-07, eta: 0:25:23, time: 0.720, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0704, decode.acc_seg: 96.9187, loss: 0.0704 2023-01-07 04:55:40,356 - mmseg - INFO - Iter [157800/160000] lr: 8.254e-07, eta: 0:24:49, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0712, decode.acc_seg: 96.9435, loss: 0.0712 2023-01-07 04:56:12,510 - mmseg - INFO - Iter [157850/160000] lr: 8.066e-07, eta: 0:24:16, time: 0.643, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0696, decode.acc_seg: 96.9657, loss: 0.0696 2023-01-07 04:56:46,368 - mmseg - INFO - Iter [157900/160000] lr: 7.879e-07, eta: 0:23:42, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0733, decode.acc_seg: 96.8827, loss: 0.0733 2023-01-07 04:57:20,436 - mmseg - INFO - Iter [157950/160000] lr: 7.691e-07, eta: 0:23:08, time: 0.680, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0697, decode.acc_seg: 96.9202, loss: 0.0697 2023-01-07 04:57:55,027 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 04:57:55,027 - mmseg - INFO - Iter [158000/160000] lr: 7.504e-07, eta: 0:22:34, time: 0.693, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0720, decode.acc_seg: 96.7658, loss: 0.0720 2023-01-07 04:58:28,653 - mmseg - INFO - Iter [158050/160000] lr: 7.316e-07, eta: 0:22:00, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0683, decode.acc_seg: 96.9590, loss: 0.0683 2023-01-07 04:59:03,340 - mmseg - INFO - Iter [158100/160000] lr: 7.129e-07, eta: 0:21:26, time: 0.695, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0697, decode.acc_seg: 96.9547, loss: 0.0697 2023-01-07 04:59:37,867 - mmseg - INFO - Iter [158150/160000] lr: 6.941e-07, eta: 0:20:52, time: 0.691, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0666, decode.acc_seg: 97.0286, loss: 0.0666 2023-01-07 05:00:11,033 - mmseg - INFO - Iter [158200/160000] lr: 6.754e-07, eta: 0:20:19, time: 0.663, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0694, decode.acc_seg: 97.0242, loss: 0.0694 2023-01-07 05:00:43,913 - mmseg - INFO - Iter [158250/160000] lr: 6.566e-07, eta: 0:19:45, time: 0.658, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0699, decode.acc_seg: 96.8845, loss: 0.0699 2023-01-07 05:01:18,173 - mmseg - INFO - Iter [158300/160000] lr: 6.379e-07, eta: 0:19:11, time: 0.684, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0636, decode.acc_seg: 97.1440, loss: 0.0636 2023-01-07 05:01:51,003 - mmseg - INFO - Iter [158350/160000] lr: 6.191e-07, eta: 0:18:37, time: 0.658, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0676, decode.acc_seg: 97.0180, loss: 0.0676 2023-01-07 05:02:24,830 - mmseg - INFO - Iter [158400/160000] lr: 6.004e-07, eta: 0:18:03, time: 0.677, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0726, decode.acc_seg: 96.7882, loss: 0.0726 2023-01-07 05:02:58,217 - mmseg - INFO - Iter [158450/160000] lr: 5.816e-07, eta: 0:17:29, time: 0.668, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0713, decode.acc_seg: 96.8877, loss: 0.0713 2023-01-07 05:03:32,810 - mmseg - INFO - Iter [158500/160000] lr: 5.629e-07, eta: 0:16:55, time: 0.692, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0716, decode.acc_seg: 96.8177, loss: 0.0716 2023-01-07 05:04:05,265 - mmseg - INFO - Iter [158550/160000] lr: 5.441e-07, eta: 0:16:22, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0678, decode.acc_seg: 97.0050, loss: 0.0678 2023-01-07 05:04:37,589 - mmseg - INFO - Iter [158600/160000] lr: 5.254e-07, eta: 0:15:48, time: 0.646, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0694, decode.acc_seg: 96.9825, loss: 0.0694 2023-01-07 05:05:09,807 - mmseg - INFO - Iter [158650/160000] lr: 5.066e-07, eta: 0:15:14, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0713, decode.acc_seg: 96.9029, loss: 0.0713 2023-01-07 05:05:43,453 - mmseg - INFO - Iter [158700/160000] lr: 4.879e-07, eta: 0:14:40, time: 0.672, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0679, decode.acc_seg: 97.0172, loss: 0.0679 2023-01-07 05:06:18,341 - mmseg - INFO - Iter [158750/160000] lr: 4.691e-07, eta: 0:14:06, time: 0.698, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0718, decode.acc_seg: 96.8742, loss: 0.0718 2023-01-07 05:06:50,697 - mmseg - INFO - Iter [158800/160000] lr: 4.504e-07, eta: 0:13:32, time: 0.648, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0705, decode.acc_seg: 96.8092, loss: 0.0705 2023-01-07 05:07:25,277 - mmseg - INFO - Iter [158850/160000] lr: 4.316e-07, eta: 0:12:58, time: 0.692, data_time: 0.059, memory: 11582, decode.loss_ce: 0.0714, decode.acc_seg: 96.8981, loss: 0.0714 2023-01-07 05:07:57,563 - mmseg - INFO - Iter [158900/160000] lr: 4.129e-07, eta: 0:12:24, time: 0.645, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0680, decode.acc_seg: 97.0602, loss: 0.0680 2023-01-07 05:08:31,870 - mmseg - INFO - Iter [158950/160000] lr: 3.941e-07, eta: 0:11:51, time: 0.687, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0711, decode.acc_seg: 96.8908, loss: 0.0711 2023-01-07 05:09:04,302 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 05:09:04,303 - mmseg - INFO - Iter [159000/160000] lr: 3.754e-07, eta: 0:11:17, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0682, decode.acc_seg: 96.9686, loss: 0.0682 2023-01-07 05:09:38,461 - mmseg - INFO - Iter [159050/160000] lr: 3.566e-07, eta: 0:10:43, time: 0.683, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0711, decode.acc_seg: 96.8554, loss: 0.0711 2023-01-07 05:10:13,457 - mmseg - INFO - Iter [159100/160000] lr: 3.379e-07, eta: 0:10:09, time: 0.700, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0694, decode.acc_seg: 96.9209, loss: 0.0694 2023-01-07 05:10:48,149 - mmseg - INFO - Iter [159150/160000] lr: 3.191e-07, eta: 0:09:35, time: 0.693, data_time: 0.013, memory: 11582, decode.loss_ce: 0.0698, decode.acc_seg: 96.9262, loss: 0.0698 2023-01-07 05:11:21,124 - mmseg - INFO - Iter [159200/160000] lr: 3.004e-07, eta: 0:09:01, time: 0.661, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0709, decode.acc_seg: 96.9088, loss: 0.0709 2023-01-07 05:11:56,481 - mmseg - INFO - Iter [159250/160000] lr: 2.816e-07, eta: 0:08:27, time: 0.707, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0689, decode.acc_seg: 96.9867, loss: 0.0689 2023-01-07 05:12:28,821 - mmseg - INFO - Iter [159300/160000] lr: 2.629e-07, eta: 0:07:54, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0725, decode.acc_seg: 96.8626, loss: 0.0725 2023-01-07 05:13:03,248 - mmseg - INFO - Iter [159350/160000] lr: 2.441e-07, eta: 0:07:20, time: 0.689, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0695, decode.acc_seg: 96.9882, loss: 0.0695 2023-01-07 05:13:36,341 - mmseg - INFO - Iter [159400/160000] lr: 2.254e-07, eta: 0:06:46, time: 0.661, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0727, decode.acc_seg: 96.8681, loss: 0.0727 2023-01-07 05:14:10,553 - mmseg - INFO - Iter [159450/160000] lr: 2.066e-07, eta: 0:06:12, time: 0.685, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0713, decode.acc_seg: 96.8404, loss: 0.0713 2023-01-07 05:14:42,657 - mmseg - INFO - Iter [159500/160000] lr: 1.879e-07, eta: 0:05:38, time: 0.642, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0678, decode.acc_seg: 97.0055, loss: 0.0678 2023-01-07 05:15:16,452 - mmseg - INFO - Iter [159550/160000] lr: 1.691e-07, eta: 0:05:04, time: 0.675, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0712, decode.acc_seg: 96.9608, loss: 0.0712 2023-01-07 05:15:52,723 - mmseg - INFO - Iter [159600/160000] lr: 1.504e-07, eta: 0:04:30, time: 0.725, data_time: 0.060, memory: 11582, decode.loss_ce: 0.0699, decode.acc_seg: 96.9102, loss: 0.0699 2023-01-07 05:16:26,623 - mmseg - INFO - Iter [159650/160000] lr: 1.316e-07, eta: 0:03:57, time: 0.678, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0713, decode.acc_seg: 96.8903, loss: 0.0713 2023-01-07 05:16:59,713 - mmseg - INFO - Iter [159700/160000] lr: 1.129e-07, eta: 0:03:23, time: 0.663, data_time: 0.015, memory: 11582, decode.loss_ce: 0.0707, decode.acc_seg: 96.8945, loss: 0.0707 2023-01-07 05:17:32,246 - mmseg - INFO - Iter [159750/160000] lr: 9.413e-08, eta: 0:02:49, time: 0.651, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0691, decode.acc_seg: 97.0012, loss: 0.0691 2023-01-07 05:18:04,894 - mmseg - INFO - Iter [159800/160000] lr: 7.537e-08, eta: 0:02:15, time: 0.653, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0693, decode.acc_seg: 96.9777, loss: 0.0693 2023-01-07 05:18:37,249 - mmseg - INFO - Iter [159850/160000] lr: 5.663e-08, eta: 0:01:41, time: 0.647, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0723, decode.acc_seg: 96.8864, loss: 0.0723 2023-01-07 05:19:09,440 - mmseg - INFO - Iter [159900/160000] lr: 3.787e-08, eta: 0:01:07, time: 0.644, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0690, decode.acc_seg: 96.9616, loss: 0.0690 2023-01-07 05:19:41,888 - mmseg - INFO - Iter [159950/160000] lr: 1.913e-08, eta: 0:00:33, time: 0.649, data_time: 0.014, memory: 11582, decode.loss_ce: 0.0743, decode.acc_seg: 96.8303, loss: 0.0743 2023-01-07 05:20:16,890 - mmseg - INFO - Saving checkpoint at 160000 iterations 2023-01-07 05:20:22,689 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 05:20:22,689 - mmseg - INFO - Iter [160000/160000] lr: 3.750e-10, eta: 0:00:00, time: 0.816, data_time: 0.058, memory: 11582, decode.loss_ce: 0.0734, decode.acc_seg: 96.8283, loss: 0.0734 2023-01-07 05:20:58,391 - mmseg - INFO - per class results: 2023-01-07 05:20:58,394 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 98.17 | 99.05 | | sidewalk | 84.57 | 92.16 | | building | 92.4 | 96.58 | | wall | 58.59 | 66.63 | | fence | 58.06 | 68.74 | | pole | 63.94 | 73.97 | | traffic light | 68.17 | 78.44 | | traffic sign | 77.16 | 84.29 | | vegetation | 92.46 | 96.74 | | terrain | 63.33 | 71.55 | | sky | 95.0 | 98.06 | | person | 79.52 | 90.04 | | rider | 55.62 | 67.29 | | car | 94.27 | 97.36 | | truck | 70.42 | 78.81 | | bus | 82.16 | 88.51 | | train | 68.02 | 74.39 | | motorcycle | 52.47 | 61.78 | | bicycle | 74.22 | 87.64 | +---------------+-------+-------+ 2023-01-07 05:20:58,394 - mmseg - INFO - Summary: 2023-01-07 05:20:58,394 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 95.94 | 75.19 | 82.74 | +-------+-------+-------+ 2023-01-07 05:20:58,395 - mmseg - INFO - Exp name: dest_simpatt-b5_1024x1024_160k_cityscapes.py 2023-01-07 05:20:58,395 - mmseg - INFO - Iter(val) [63] aAcc: 0.9594, mIoU: 0.7519, mAcc: 0.8274, IoU.road: 0.9817, IoU.sidewalk: 0.8457, IoU.building: 0.9240, IoU.wall: 0.5859, IoU.fence: 0.5806, IoU.pole: 0.6394, IoU.traffic light: 0.6817, IoU.traffic sign: 0.7716, IoU.vegetation: 0.9246, IoU.terrain: 0.6333, IoU.sky: 0.9500, IoU.person: 0.7952, IoU.rider: 0.5562, IoU.car: 0.9427, IoU.truck: 0.7042, IoU.bus: 0.8216, IoU.train: 0.6802, IoU.motorcycle: 0.5247, IoU.bicycle: 0.7422, Acc.road: 0.9905, Acc.sidewalk: 0.9216, Acc.building: 0.9658, Acc.wall: 0.6663, Acc.fence: 0.6874, Acc.pole: 0.7397, Acc.traffic light: 0.7844, Acc.traffic sign: 0.8429, Acc.vegetation: 0.9674, Acc.terrain: 0.7155, Acc.sky: 0.9806, Acc.person: 0.9004, Acc.rider: 0.6729, Acc.car: 0.9736, Acc.truck: 0.7881, Acc.bus: 0.8851, Acc.train: 0.7439, Acc.motorcycle: 0.6178, Acc.bicycle: 0.8764