2023-01-05 23:21:55,487 - mmseg - INFO - Multi-processing start method is `None` 2023-01-05 23:21:55,488 - mmseg - INFO - OpenCV num_threads is `64 2023-01-05 23:21:55,536 - 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 23:21:55,537 - mmseg - INFO - Distributed training: True 2023-01-05 23:21:55,969 - 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, 8, 12, 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 23:22:02,413 - mmseg - INFO - Set random seed to 1640890893, deterministic: False 2023-01-05 23:22:04,021 - 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.1.2.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.2.2.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 23:22:04,038 - 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() ) ) ) (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() ) ) ) (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 23:22:04,166 - mmseg - INFO - Loaded 2975 images 2023-01-05 23:22:05,181 - mmseg - INFO - Loaded 500 images 2023-01-05 23:22:05,182 - mmseg - INFO - Start running, host: root@3920435, work_dir: /workspace/result/train_log 2023-01-05 23:22:05,182 - 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 23:22:05,183 - mmseg - INFO - workflow: [('train', 1)], max: 160000 iters 2023-01-05 23:22:05,183 - mmseg - INFO - Checkpoints will be saved to /workspace/result/train_log by HardDiskBackend. 2023-01-05 23:22:43,118 - mmseg - INFO - Iter [50/160000] lr: 1.959e-06, eta: 1 day, 6:01:13, time: 0.676, data_time: 0.019, memory: 10576, decode.loss_ce: 2.5351, decode.acc_seg: 2.8322, loss: 2.5351 2023-01-05 23:23:10,761 - mmseg - INFO - Iter [100/160000] lr: 3.958e-06, eta: 1 day, 3:16:55, time: 0.553, data_time: 0.012, memory: 10576, decode.loss_ce: 2.4339, decode.acc_seg: 6.7673, loss: 2.4339 2023-01-05 23:23:38,718 - mmseg - INFO - Iter [150/160000] lr: 5.955e-06, eta: 1 day, 2:26:47, time: 0.558, data_time: 0.012, memory: 10576, decode.loss_ce: 2.2785, decode.acc_seg: 34.0764, loss: 2.2785 2023-01-05 23:24:08,898 - mmseg - INFO - Iter [200/160000] lr: 7.950e-06, eta: 1 day, 2:31:39, time: 0.604, data_time: 0.013, memory: 10576, decode.loss_ce: 1.9110, decode.acc_seg: 47.3219, loss: 1.9110 2023-01-05 23:24:38,700 - mmseg - INFO - Iter [250/160000] lr: 9.945e-06, eta: 1 day, 2:30:15, time: 0.596, data_time: 0.013, memory: 10576, decode.loss_ce: 1.4209, decode.acc_seg: 53.3293, loss: 1.4209 2023-01-05 23:25:07,518 - mmseg - INFO - Iter [300/160000] lr: 1.194e-05, eta: 1 day, 2:20:50, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 1.2460, decode.acc_seg: 56.2269, loss: 1.2460 2023-01-05 23:25:36,460 - mmseg - INFO - Iter [350/160000] lr: 1.393e-05, eta: 1 day, 2:14:36, time: 0.579, data_time: 0.012, memory: 10576, decode.loss_ce: 1.1407, decode.acc_seg: 59.9805, loss: 1.1407 2023-01-05 23:26:07,168 - mmseg - INFO - Iter [400/160000] lr: 1.592e-05, eta: 1 day, 2:21:32, time: 0.614, data_time: 0.057, memory: 10576, decode.loss_ce: 1.0505, decode.acc_seg: 63.4588, loss: 1.0505 2023-01-05 23:26:35,784 - mmseg - INFO - Iter [450/160000] lr: 1.791e-05, eta: 1 day, 2:14:15, time: 0.572, data_time: 0.012, memory: 10576, decode.loss_ce: 0.9710, decode.acc_seg: 67.9201, loss: 0.9710 2023-01-05 23:27:04,302 - mmseg - INFO - Iter [500/160000] lr: 1.990e-05, eta: 1 day, 2:08:12, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.9182, decode.acc_seg: 68.3597, loss: 0.9182 2023-01-05 23:27:32,011 - mmseg - INFO - Iter [550/160000] lr: 2.188e-05, eta: 1 day, 1:58:54, time: 0.553, data_time: 0.012, memory: 10576, decode.loss_ce: 0.9164, decode.acc_seg: 70.3731, loss: 0.9164 2023-01-05 23:28:01,052 - mmseg - INFO - Iter [600/160000] lr: 2.387e-05, eta: 1 day, 1:57:15, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.8516, decode.acc_seg: 71.7499, loss: 0.8516 2023-01-05 23:28:28,579 - mmseg - INFO - Iter [650/160000] lr: 2.585e-05, eta: 1 day, 1:49:31, time: 0.551, data_time: 0.012, memory: 10576, decode.loss_ce: 0.8072, decode.acc_seg: 72.7480, loss: 0.8072 2023-01-05 23:28:57,189 - mmseg - INFO - Iter [700/160000] lr: 2.784e-05, eta: 1 day, 1:46:53, time: 0.572, data_time: 0.012, memory: 10576, decode.loss_ce: 0.7685, decode.acc_seg: 74.0557, loss: 0.7685 2023-01-05 23:29:26,953 - mmseg - INFO - Iter [750/160000] lr: 2.982e-05, eta: 1 day, 1:48:31, time: 0.595, data_time: 0.057, memory: 10576, decode.loss_ce: 0.7639, decode.acc_seg: 73.8051, loss: 0.7639 2023-01-05 23:29:56,888 - mmseg - INFO - Iter [800/160000] lr: 3.180e-05, eta: 1 day, 1:50:33, time: 0.599, data_time: 0.013, memory: 10576, decode.loss_ce: 0.7059, decode.acc_seg: 75.8634, loss: 0.7059 2023-01-05 23:30:25,595 - mmseg - INFO - Iter [850/160000] lr: 3.378e-05, eta: 1 day, 1:48:35, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.7278, decode.acc_seg: 75.1694, loss: 0.7278 2023-01-05 23:30:55,031 - mmseg - INFO - Iter [900/160000] lr: 3.576e-05, eta: 1 day, 1:48:49, time: 0.589, data_time: 0.012, memory: 10576, decode.loss_ce: 0.6852, decode.acc_seg: 76.1697, loss: 0.6852 2023-01-05 23:31:22,664 - mmseg - INFO - Iter [950/160000] lr: 3.773e-05, eta: 1 day, 1:43:56, time: 0.553, data_time: 0.012, memory: 10576, decode.loss_ce: 0.6912, decode.acc_seg: 76.1587, loss: 0.6912 2023-01-05 23:31:52,357 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-05 23:31:52,357 - mmseg - INFO - Iter [1000/160000] lr: 3.971e-05, eta: 1 day, 1:44:52, time: 0.593, data_time: 0.012, memory: 10576, decode.loss_ce: 0.6623, decode.acc_seg: 77.4995, loss: 0.6623 2023-01-05 23:32:20,943 - mmseg - INFO - Iter [1050/160000] lr: 4.168e-05, eta: 1 day, 1:43:02, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.6442, decode.acc_seg: 78.4034, loss: 0.6442 2023-01-05 23:32:50,155 - mmseg - INFO - Iter [1100/160000] lr: 4.366e-05, eta: 1 day, 1:42:41, time: 0.584, data_time: 0.012, memory: 10576, decode.loss_ce: 0.6026, decode.acc_seg: 79.2344, loss: 0.6026 2023-01-05 23:33:21,815 - mmseg - INFO - Iter [1150/160000] lr: 4.563e-05, eta: 1 day, 1:48:04, time: 0.633, data_time: 0.058, memory: 10576, decode.loss_ce: 0.6452, decode.acc_seg: 78.1221, loss: 0.6452 2023-01-05 23:33:49,790 - mmseg - INFO - Iter [1200/160000] lr: 4.760e-05, eta: 1 day, 1:44:51, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.6181, decode.acc_seg: 79.2193, loss: 0.6181 2023-01-05 23:34:18,564 - mmseg - INFO - Iter [1250/160000] lr: 4.957e-05, eta: 1 day, 1:43:24, time: 0.575, data_time: 0.012, memory: 10576, decode.loss_ce: 0.6081, decode.acc_seg: 79.3371, loss: 0.6081 2023-01-05 23:34:48,126 - mmseg - INFO - Iter [1300/160000] lr: 5.154e-05, eta: 1 day, 1:43:43, time: 0.591, data_time: 0.013, memory: 10576, decode.loss_ce: 0.5910, decode.acc_seg: 79.8167, loss: 0.5910 2023-01-05 23:35:17,485 - mmseg - INFO - Iter [1350/160000] lr: 5.351e-05, eta: 1 day, 1:43:40, time: 0.588, data_time: 0.013, memory: 10576, decode.loss_ce: 0.6146, decode.acc_seg: 78.6864, loss: 0.6146 2023-01-05 23:35:46,357 - mmseg - INFO - Iter [1400/160000] lr: 5.547e-05, eta: 1 day, 1:42:30, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.5882, decode.acc_seg: 79.9217, loss: 0.5882 2023-01-05 23:36:13,989 - mmseg - INFO - Iter [1450/160000] lr: 5.744e-05, eta: 1 day, 1:39:16, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.6001, decode.acc_seg: 79.1681, loss: 0.6001 2023-01-05 23:36:44,137 - mmseg - INFO - Iter [1500/160000] lr: 5.940e-05, eta: 1 day, 1:40:33, time: 0.603, data_time: 0.058, memory: 10576, decode.loss_ce: 0.5439, decode.acc_seg: 80.9391, loss: 0.5439 2023-01-05 23:37:12,828 - mmseg - INFO - Iter [1550/160000] lr: 5.942e-05, eta: 1 day, 1:39:14, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.5637, decode.acc_seg: 80.4324, loss: 0.5637 2023-01-05 23:37:41,508 - mmseg - INFO - Iter [1600/160000] lr: 5.940e-05, eta: 1 day, 1:38:03, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.5327, decode.acc_seg: 82.0073, loss: 0.5327 2023-01-05 23:38:10,173 - mmseg - INFO - Iter [1650/160000] lr: 5.938e-05, eta: 1 day, 1:36:47, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.4738, decode.acc_seg: 82.6212, loss: 0.4738 2023-01-05 23:38:38,064 - mmseg - INFO - Iter [1700/160000] lr: 5.936e-05, eta: 1 day, 1:34:22, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.4952, decode.acc_seg: 82.5940, loss: 0.4952 2023-01-05 23:39:07,209 - mmseg - INFO - Iter [1750/160000] lr: 5.934e-05, eta: 1 day, 1:33:59, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.5058, decode.acc_seg: 82.7973, loss: 0.5058 2023-01-05 23:39:35,541 - mmseg - INFO - Iter [1800/160000] lr: 5.933e-05, eta: 1 day, 1:32:27, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.5099, decode.acc_seg: 82.2822, loss: 0.5099 2023-01-05 23:40:04,373 - mmseg - INFO - Iter [1850/160000] lr: 5.931e-05, eta: 1 day, 1:31:36, time: 0.576, data_time: 0.012, memory: 10576, decode.loss_ce: 0.4651, decode.acc_seg: 83.2456, loss: 0.4651 2023-01-05 23:40:36,664 - mmseg - INFO - Iter [1900/160000] lr: 5.929e-05, eta: 1 day, 1:35:35, time: 0.646, data_time: 0.058, memory: 10576, decode.loss_ce: 0.4927, decode.acc_seg: 82.9982, loss: 0.4927 2023-01-05 23:41:06,704 - mmseg - INFO - Iter [1950/160000] lr: 5.927e-05, eta: 1 day, 1:36:19, time: 0.601, data_time: 0.013, memory: 10576, decode.loss_ce: 0.4901, decode.acc_seg: 82.9734, loss: 0.4901 2023-01-05 23:41:34,858 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-05 23:41:34,859 - mmseg - INFO - Iter [2000/160000] lr: 5.925e-05, eta: 1 day, 1:34:31, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.4600, decode.acc_seg: 84.1507, loss: 0.4600 2023-01-05 23:42:04,270 - mmseg - INFO - Iter [2050/160000] lr: 5.923e-05, eta: 1 day, 1:34:22, time: 0.588, data_time: 0.013, memory: 10576, decode.loss_ce: 0.4618, decode.acc_seg: 83.9559, loss: 0.4618 2023-01-05 23:42:33,242 - mmseg - INFO - Iter [2100/160000] lr: 5.921e-05, eta: 1 day, 1:33:44, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.4515, decode.acc_seg: 84.0759, loss: 0.4515 2023-01-05 23:43:00,758 - mmseg - INFO - Iter [2150/160000] lr: 5.919e-05, eta: 1 day, 1:31:15, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.4514, decode.acc_seg: 84.0475, loss: 0.4514 2023-01-05 23:43:29,949 - mmseg - INFO - Iter [2200/160000] lr: 5.918e-05, eta: 1 day, 1:30:52, time: 0.584, data_time: 0.013, memory: 10576, decode.loss_ce: 0.4243, decode.acc_seg: 85.2484, loss: 0.4243 2023-01-05 23:44:01,637 - mmseg - INFO - Iter [2250/160000] lr: 5.916e-05, eta: 1 day, 1:33:22, time: 0.633, data_time: 0.057, memory: 10576, decode.loss_ce: 0.4496, decode.acc_seg: 83.6671, loss: 0.4496 2023-01-05 23:44:31,857 - mmseg - INFO - Iter [2300/160000] lr: 5.914e-05, eta: 1 day, 1:34:05, time: 0.604, data_time: 0.013, memory: 10576, decode.loss_ce: 0.4680, decode.acc_seg: 83.7133, loss: 0.4680 2023-01-05 23:45:02,094 - mmseg - INFO - Iter [2350/160000] lr: 5.912e-05, eta: 1 day, 1:34:47, time: 0.605, data_time: 0.013, memory: 10576, decode.loss_ce: 0.4531, decode.acc_seg: 84.2274, loss: 0.4531 2023-01-05 23:45:30,778 - mmseg - INFO - Iter [2400/160000] lr: 5.910e-05, eta: 1 day, 1:33:46, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.4413, decode.acc_seg: 84.7078, loss: 0.4413 2023-01-05 23:45:58,560 - mmseg - INFO - Iter [2450/160000] lr: 5.908e-05, eta: 1 day, 1:31:45, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.4215, decode.acc_seg: 85.1696, loss: 0.4215 2023-01-05 23:46:26,855 - mmseg - INFO - Iter [2500/160000] lr: 5.906e-05, eta: 1 day, 1:30:19, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.4555, decode.acc_seg: 84.0708, loss: 0.4555 2023-01-05 23:46:56,883 - mmseg - INFO - Iter [2550/160000] lr: 5.904e-05, eta: 1 day, 1:30:44, time: 0.601, data_time: 0.014, memory: 10576, decode.loss_ce: 0.4116, decode.acc_seg: 85.6808, loss: 0.4116 2023-01-05 23:47:25,941 - mmseg - INFO - Iter [2600/160000] lr: 5.903e-05, eta: 1 day, 1:30:08, time: 0.581, data_time: 0.014, memory: 10576, decode.loss_ce: 0.4014, decode.acc_seg: 85.5544, loss: 0.4014 2023-01-05 23:47:55,870 - mmseg - INFO - Iter [2650/160000] lr: 5.901e-05, eta: 1 day, 1:30:27, time: 0.599, data_time: 0.059, memory: 10576, decode.loss_ce: 0.4207, decode.acc_seg: 85.1414, loss: 0.4207 2023-01-05 23:48:24,189 - mmseg - INFO - Iter [2700/160000] lr: 5.899e-05, eta: 1 day, 1:29:05, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.4287, decode.acc_seg: 84.7462, loss: 0.4287 2023-01-05 23:48:53,627 - mmseg - INFO - Iter [2750/160000] lr: 5.897e-05, eta: 1 day, 1:28:52, time: 0.589, data_time: 0.013, memory: 10576, decode.loss_ce: 0.4051, decode.acc_seg: 85.6338, loss: 0.4051 2023-01-05 23:49:22,750 - mmseg - INFO - Iter [2800/160000] lr: 5.895e-05, eta: 1 day, 1:28:20, time: 0.582, data_time: 0.013, memory: 10576, decode.loss_ce: 0.4331, decode.acc_seg: 84.8395, loss: 0.4331 2023-01-05 23:49:51,173 - mmseg - INFO - Iter [2850/160000] lr: 5.893e-05, eta: 1 day, 1:27:12, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3767, decode.acc_seg: 86.4017, loss: 0.3767 2023-01-05 23:50:20,895 - mmseg - INFO - Iter [2900/160000] lr: 5.891e-05, eta: 1 day, 1:27:12, time: 0.594, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3929, decode.acc_seg: 85.9808, loss: 0.3929 2023-01-05 23:50:48,950 - mmseg - INFO - Iter [2950/160000] lr: 5.889e-05, eta: 1 day, 1:25:45, time: 0.562, data_time: 0.014, memory: 10576, decode.loss_ce: 0.4051, decode.acc_seg: 85.5660, loss: 0.4051 2023-01-05 23:51:19,702 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-05 23:51:19,703 - mmseg - INFO - Iter [3000/160000] lr: 5.888e-05, eta: 1 day, 1:26:40, time: 0.615, data_time: 0.058, memory: 10576, decode.loss_ce: 0.3984, decode.acc_seg: 85.4457, loss: 0.3984 2023-01-05 23:51:47,564 - mmseg - INFO - Iter [3050/160000] lr: 5.886e-05, eta: 1 day, 1:25:02, time: 0.556, data_time: 0.012, memory: 10576, decode.loss_ce: 0.3860, decode.acc_seg: 85.9761, loss: 0.3860 2023-01-05 23:52:17,183 - mmseg - INFO - Iter [3100/160000] lr: 5.884e-05, eta: 1 day, 1:24:56, time: 0.592, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3824, decode.acc_seg: 85.7025, loss: 0.3824 2023-01-05 23:52:46,031 - mmseg - INFO - Iter [3150/160000] lr: 5.882e-05, eta: 1 day, 1:24:12, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3820, decode.acc_seg: 86.1845, loss: 0.3820 2023-01-05 23:53:14,413 - mmseg - INFO - Iter [3200/160000] lr: 5.880e-05, eta: 1 day, 1:23:04, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3452, decode.acc_seg: 87.7781, loss: 0.3452 2023-01-05 23:53:42,731 - mmseg - INFO - Iter [3250/160000] lr: 5.878e-05, eta: 1 day, 1:21:56, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3698, decode.acc_seg: 86.7630, loss: 0.3698 2023-01-05 23:54:11,470 - mmseg - INFO - Iter [3300/160000] lr: 5.876e-05, eta: 1 day, 1:21:10, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3752, decode.acc_seg: 86.6157, loss: 0.3752 2023-01-05 23:54:43,097 - mmseg - INFO - Iter [3350/160000] lr: 5.874e-05, eta: 1 day, 1:22:36, time: 0.632, data_time: 0.058, memory: 10576, decode.loss_ce: 0.3760, decode.acc_seg: 86.3571, loss: 0.3760 2023-01-05 23:55:12,920 - mmseg - INFO - Iter [3400/160000] lr: 5.873e-05, eta: 1 day, 1:22:39, time: 0.597, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3739, decode.acc_seg: 86.3760, loss: 0.3739 2023-01-05 23:55:40,286 - mmseg - INFO - Iter [3450/160000] lr: 5.871e-05, eta: 1 day, 1:20:48, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3588, decode.acc_seg: 86.6822, loss: 0.3588 2023-01-05 23:56:08,194 - mmseg - INFO - Iter [3500/160000] lr: 5.869e-05, eta: 1 day, 1:19:22, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3298, decode.acc_seg: 87.8700, loss: 0.3298 2023-01-05 23:56:36,334 - mmseg - INFO - Iter [3550/160000] lr: 5.867e-05, eta: 1 day, 1:18:10, time: 0.563, data_time: 0.014, memory: 10576, decode.loss_ce: 0.3941, decode.acc_seg: 85.9676, loss: 0.3941 2023-01-05 23:57:05,309 - mmseg - INFO - Iter [3600/160000] lr: 5.865e-05, eta: 1 day, 1:17:36, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3815, decode.acc_seg: 86.4174, loss: 0.3815 2023-01-05 23:57:33,525 - mmseg - INFO - Iter [3650/160000] lr: 5.863e-05, eta: 1 day, 1:16:29, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.3519, decode.acc_seg: 87.0921, loss: 0.3519 2023-01-05 23:58:02,546 - mmseg - INFO - Iter [3700/160000] lr: 5.861e-05, eta: 1 day, 1:15:55, time: 0.580, data_time: 0.012, memory: 10576, decode.loss_ce: 0.4004, decode.acc_seg: 85.8781, loss: 0.4004 2023-01-05 23:58:32,243 - mmseg - INFO - Iter [3750/160000] lr: 5.859e-05, eta: 1 day, 1:15:52, time: 0.595, data_time: 0.058, memory: 10576, decode.loss_ce: 0.3565, decode.acc_seg: 87.3998, loss: 0.3565 2023-01-05 23:59:01,529 - mmseg - INFO - Iter [3800/160000] lr: 5.858e-05, eta: 1 day, 1:15:30, time: 0.586, data_time: 0.012, memory: 10576, decode.loss_ce: 0.3441, decode.acc_seg: 87.6238, loss: 0.3441 2023-01-05 23:59:29,321 - mmseg - INFO - Iter [3850/160000] lr: 5.856e-05, eta: 1 day, 1:14:06, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.3615, decode.acc_seg: 86.7288, loss: 0.3615 2023-01-05 23:59:59,243 - mmseg - INFO - Iter [3900/160000] lr: 5.854e-05, eta: 1 day, 1:14:12, time: 0.599, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3562, decode.acc_seg: 86.6693, loss: 0.3562 2023-01-06 00:00:27,217 - mmseg - INFO - Iter [3950/160000] lr: 5.852e-05, eta: 1 day, 1:12:59, time: 0.559, data_time: 0.012, memory: 10576, decode.loss_ce: 0.3608, decode.acc_seg: 86.5922, loss: 0.3608 2023-01-06 00:00:55,375 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 00:00:55,376 - mmseg - INFO - Iter [4000/160000] lr: 5.850e-05, eta: 1 day, 1:11:53, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.3544, decode.acc_seg: 87.0095, loss: 0.3544 2023-01-06 00:01:23,457 - mmseg - INFO - Iter [4050/160000] lr: 5.848e-05, eta: 1 day, 1:10:46, time: 0.562, data_time: 0.012, memory: 10576, decode.loss_ce: 0.3389, decode.acc_seg: 87.5141, loss: 0.3389 2023-01-06 00:01:53,753 - mmseg - INFO - Iter [4100/160000] lr: 5.846e-05, eta: 1 day, 1:11:02, time: 0.605, data_time: 0.057, memory: 10576, decode.loss_ce: 0.3556, decode.acc_seg: 87.1131, loss: 0.3556 2023-01-06 00:02:23,792 - mmseg - INFO - Iter [4150/160000] lr: 5.844e-05, eta: 1 day, 1:11:09, time: 0.601, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3250, decode.acc_seg: 87.9497, loss: 0.3250 2023-01-06 00:02:53,399 - mmseg - INFO - Iter [4200/160000] lr: 5.843e-05, eta: 1 day, 1:11:01, time: 0.593, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3505, decode.acc_seg: 87.5285, loss: 0.3505 2023-01-06 00:03:22,642 - mmseg - INFO - Iter [4250/160000] lr: 5.841e-05, eta: 1 day, 1:10:37, time: 0.585, data_time: 0.012, memory: 10576, decode.loss_ce: 0.3185, decode.acc_seg: 88.4006, loss: 0.3185 2023-01-06 00:03:51,619 - mmseg - INFO - Iter [4300/160000] lr: 5.839e-05, eta: 1 day, 1:10:02, time: 0.579, data_time: 0.012, memory: 10576, decode.loss_ce: 0.3459, decode.acc_seg: 87.1161, loss: 0.3459 2023-01-06 00:04:20,015 - mmseg - INFO - Iter [4350/160000] lr: 5.837e-05, eta: 1 day, 1:09:08, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3434, decode.acc_seg: 87.8361, loss: 0.3434 2023-01-06 00:04:49,367 - mmseg - INFO - Iter [4400/160000] lr: 5.835e-05, eta: 1 day, 1:08:48, time: 0.587, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3480, decode.acc_seg: 87.6710, loss: 0.3480 2023-01-06 00:05:19,025 - mmseg - INFO - Iter [4450/160000] lr: 5.833e-05, eta: 1 day, 1:08:39, time: 0.593, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3625, decode.acc_seg: 86.7319, loss: 0.3625 2023-01-06 00:05:49,498 - mmseg - INFO - Iter [4500/160000] lr: 5.831e-05, eta: 1 day, 1:08:59, time: 0.610, data_time: 0.059, memory: 10576, decode.loss_ce: 0.3190, decode.acc_seg: 88.1765, loss: 0.3190 2023-01-06 00:06:18,008 - mmseg - INFO - Iter [4550/160000] lr: 5.829e-05, eta: 1 day, 1:08:09, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3368, decode.acc_seg: 87.6470, loss: 0.3368 2023-01-06 00:06:45,389 - mmseg - INFO - Iter [4600/160000] lr: 5.828e-05, eta: 1 day, 1:06:42, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3217, decode.acc_seg: 88.0486, loss: 0.3217 2023-01-06 00:07:15,045 - mmseg - INFO - Iter [4650/160000] lr: 5.826e-05, eta: 1 day, 1:06:31, time: 0.592, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3422, decode.acc_seg: 87.9846, loss: 0.3422 2023-01-06 00:07:45,081 - mmseg - INFO - Iter [4700/160000] lr: 5.824e-05, eta: 1 day, 1:06:33, time: 0.601, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3086, decode.acc_seg: 88.4164, loss: 0.3086 2023-01-06 00:08:13,480 - mmseg - INFO - Iter [4750/160000] lr: 5.822e-05, eta: 1 day, 1:05:40, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3295, decode.acc_seg: 87.8761, loss: 0.3295 2023-01-06 00:08:40,945 - mmseg - INFO - Iter [4800/160000] lr: 5.820e-05, eta: 1 day, 1:04:20, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3141, decode.acc_seg: 88.4977, loss: 0.3141 2023-01-06 00:09:11,454 - mmseg - INFO - Iter [4850/160000] lr: 5.818e-05, eta: 1 day, 1:04:36, time: 0.610, data_time: 0.058, memory: 10576, decode.loss_ce: 0.3284, decode.acc_seg: 87.6587, loss: 0.3284 2023-01-06 00:09:39,765 - mmseg - INFO - Iter [4900/160000] lr: 5.816e-05, eta: 1 day, 1:03:43, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.3111, decode.acc_seg: 88.5803, loss: 0.3111 2023-01-06 00:10:09,356 - mmseg - INFO - Iter [4950/160000] lr: 5.814e-05, eta: 1 day, 1:03:28, time: 0.591, data_time: 0.012, memory: 10576, decode.loss_ce: 0.3322, decode.acc_seg: 87.9869, loss: 0.3322 2023-01-06 00:10:38,705 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 00:10:38,706 - mmseg - INFO - Iter [5000/160000] lr: 5.813e-05, eta: 1 day, 1:03:07, time: 0.587, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3354, decode.acc_seg: 87.7252, loss: 0.3354 2023-01-06 00:11:07,900 - mmseg - INFO - Iter [5050/160000] lr: 5.811e-05, eta: 1 day, 1:02:41, time: 0.584, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3088, decode.acc_seg: 88.9358, loss: 0.3088 2023-01-06 00:11:35,508 - mmseg - INFO - Iter [5100/160000] lr: 5.809e-05, eta: 1 day, 1:01:28, time: 0.553, data_time: 0.014, memory: 10576, decode.loss_ce: 0.3239, decode.acc_seg: 88.2288, loss: 0.3239 2023-01-06 00:12:04,681 - mmseg - INFO - Iter [5150/160000] lr: 5.807e-05, eta: 1 day, 1:01:01, time: 0.583, data_time: 0.012, memory: 10576, decode.loss_ce: 0.3055, decode.acc_seg: 88.2691, loss: 0.3055 2023-01-06 00:12:33,201 - mmseg - INFO - Iter [5200/160000] lr: 5.805e-05, eta: 1 day, 1:00:15, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3314, decode.acc_seg: 88.3207, loss: 0.3314 2023-01-06 00:13:04,487 - mmseg - INFO - Iter [5250/160000] lr: 5.803e-05, eta: 1 day, 1:00:52, time: 0.626, data_time: 0.057, memory: 10576, decode.loss_ce: 0.3044, decode.acc_seg: 88.6130, loss: 0.3044 2023-01-06 00:13:32,642 - mmseg - INFO - Iter [5300/160000] lr: 5.801e-05, eta: 1 day, 0:59:54, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3055, decode.acc_seg: 89.1567, loss: 0.3055 2023-01-06 00:14:02,614 - mmseg - INFO - Iter [5350/160000] lr: 5.799e-05, eta: 1 day, 0:59:51, time: 0.599, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2889, decode.acc_seg: 89.3568, loss: 0.2889 2023-01-06 00:14:32,383 - mmseg - INFO - Iter [5400/160000] lr: 5.798e-05, eta: 1 day, 0:59:42, time: 0.596, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3005, decode.acc_seg: 88.9810, loss: 0.3005 2023-01-06 00:15:00,033 - mmseg - INFO - Iter [5450/160000] lr: 5.796e-05, eta: 1 day, 0:58:32, time: 0.553, data_time: 0.012, memory: 10576, decode.loss_ce: 0.3037, decode.acc_seg: 88.8546, loss: 0.3037 2023-01-06 00:15:29,401 - mmseg - INFO - Iter [5500/160000] lr: 5.794e-05, eta: 1 day, 0:58:10, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.3066, decode.acc_seg: 88.5729, loss: 0.3066 2023-01-06 00:15:56,875 - mmseg - INFO - Iter [5550/160000] lr: 5.792e-05, eta: 1 day, 0:56:56, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2980, decode.acc_seg: 89.0051, loss: 0.2980 2023-01-06 00:16:27,548 - mmseg - INFO - Iter [5600/160000] lr: 5.790e-05, eta: 1 day, 0:57:11, time: 0.614, data_time: 0.057, memory: 10576, decode.loss_ce: 0.2648, decode.acc_seg: 89.7974, loss: 0.2648 2023-01-06 00:16:55,731 - mmseg - INFO - Iter [5650/160000] lr: 5.788e-05, eta: 1 day, 0:56:17, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2710, decode.acc_seg: 89.8726, loss: 0.2710 2023-01-06 00:17:23,391 - mmseg - INFO - Iter [5700/160000] lr: 5.786e-05, eta: 1 day, 0:55:09, time: 0.552, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2965, decode.acc_seg: 88.9063, loss: 0.2965 2023-01-06 00:17:51,762 - mmseg - INFO - Iter [5750/160000] lr: 5.784e-05, eta: 1 day, 0:54:22, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3030, decode.acc_seg: 88.4313, loss: 0.3030 2023-01-06 00:18:20,227 - mmseg - INFO - Iter [5800/160000] lr: 5.783e-05, eta: 1 day, 0:53:36, time: 0.569, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2829, decode.acc_seg: 89.6557, loss: 0.2829 2023-01-06 00:18:49,684 - mmseg - INFO - Iter [5850/160000] lr: 5.781e-05, eta: 1 day, 0:53:18, time: 0.590, data_time: 0.022, memory: 10576, decode.loss_ce: 0.3194, decode.acc_seg: 88.3085, loss: 0.3194 2023-01-06 00:19:17,965 - mmseg - INFO - Iter [5900/160000] lr: 5.779e-05, eta: 1 day, 0:52:28, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2875, decode.acc_seg: 89.3610, loss: 0.2875 2023-01-06 00:19:47,747 - mmseg - INFO - Iter [5950/160000] lr: 5.777e-05, eta: 1 day, 0:52:19, time: 0.597, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2757, decode.acc_seg: 89.5365, loss: 0.2757 2023-01-06 00:20:18,685 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 00:20:18,686 - mmseg - INFO - Iter [6000/160000] lr: 5.775e-05, eta: 1 day, 0:52:38, time: 0.619, data_time: 0.057, memory: 10576, decode.loss_ce: 0.3115, decode.acc_seg: 88.4365, loss: 0.3115 2023-01-06 00:20:47,016 - mmseg - INFO - Iter [6050/160000] lr: 5.773e-05, eta: 1 day, 0:51:50, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2948, decode.acc_seg: 89.1271, loss: 0.2948 2023-01-06 00:21:14,757 - mmseg - INFO - Iter [6100/160000] lr: 5.771e-05, eta: 1 day, 0:50:46, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2937, decode.acc_seg: 89.4186, loss: 0.2937 2023-01-06 00:21:42,360 - mmseg - INFO - Iter [6150/160000] lr: 5.769e-05, eta: 1 day, 0:49:42, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2811, decode.acc_seg: 89.5209, loss: 0.2811 2023-01-06 00:22:10,239 - mmseg - INFO - Iter [6200/160000] lr: 5.768e-05, eta: 1 day, 0:48:43, time: 0.557, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2654, decode.acc_seg: 90.0136, loss: 0.2654 2023-01-06 00:22:38,189 - mmseg - INFO - Iter [6250/160000] lr: 5.766e-05, eta: 1 day, 0:47:47, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2839, decode.acc_seg: 89.3468, loss: 0.2839 2023-01-06 00:23:07,008 - mmseg - INFO - Iter [6300/160000] lr: 5.764e-05, eta: 1 day, 0:47:12, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2925, decode.acc_seg: 89.1029, loss: 0.2925 2023-01-06 00:23:36,875 - mmseg - INFO - Iter [6350/160000] lr: 5.762e-05, eta: 1 day, 0:47:05, time: 0.598, data_time: 0.058, memory: 10576, decode.loss_ce: 0.2587, decode.acc_seg: 90.1537, loss: 0.2587 2023-01-06 00:24:04,653 - mmseg - INFO - Iter [6400/160000] lr: 5.760e-05, eta: 1 day, 0:46:05, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2685, decode.acc_seg: 89.8638, loss: 0.2685 2023-01-06 00:24:33,053 - mmseg - INFO - Iter [6450/160000] lr: 5.758e-05, eta: 1 day, 0:45:22, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2814, decode.acc_seg: 89.4740, loss: 0.2814 2023-01-06 00:25:02,338 - mmseg - INFO - Iter [6500/160000] lr: 5.756e-05, eta: 1 day, 0:44:58, time: 0.585, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2841, decode.acc_seg: 89.5973, loss: 0.2841 2023-01-06 00:25:30,985 - mmseg - INFO - Iter [6550/160000] lr: 5.754e-05, eta: 1 day, 0:44:21, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2731, decode.acc_seg: 89.5482, loss: 0.2731 2023-01-06 00:26:00,466 - mmseg - INFO - Iter [6600/160000] lr: 5.753e-05, eta: 1 day, 0:44:03, time: 0.590, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2696, decode.acc_seg: 89.8415, loss: 0.2696 2023-01-06 00:26:28,297 - mmseg - INFO - Iter [6650/160000] lr: 5.751e-05, eta: 1 day, 0:43:06, time: 0.557, data_time: 0.018, memory: 10576, decode.loss_ce: 0.2798, decode.acc_seg: 89.6124, loss: 0.2798 2023-01-06 00:26:58,592 - mmseg - INFO - Iter [6700/160000] lr: 5.749e-05, eta: 1 day, 0:43:06, time: 0.606, data_time: 0.058, memory: 10576, decode.loss_ce: 0.2723, decode.acc_seg: 89.8949, loss: 0.2723 2023-01-06 00:27:27,394 - mmseg - INFO - Iter [6750/160000] lr: 5.747e-05, eta: 1 day, 0:42:31, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2730, decode.acc_seg: 89.6289, loss: 0.2730 2023-01-06 00:27:56,764 - mmseg - INFO - Iter [6800/160000] lr: 5.745e-05, eta: 1 day, 0:42:11, time: 0.588, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2965, decode.acc_seg: 88.9050, loss: 0.2965 2023-01-06 00:28:24,920 - mmseg - INFO - Iter [6850/160000] lr: 5.743e-05, eta: 1 day, 0:41:23, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2753, decode.acc_seg: 89.5098, loss: 0.2753 2023-01-06 00:28:52,667 - mmseg - INFO - Iter [6900/160000] lr: 5.741e-05, eta: 1 day, 0:40:25, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2832, decode.acc_seg: 89.4283, loss: 0.2832 2023-01-06 00:29:22,269 - mmseg - INFO - Iter [6950/160000] lr: 5.739e-05, eta: 1 day, 0:40:09, time: 0.591, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2904, decode.acc_seg: 89.2460, loss: 0.2904 2023-01-06 00:29:51,639 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 00:29:51,640 - mmseg - INFO - Iter [7000/160000] lr: 5.738e-05, eta: 1 day, 0:39:48, time: 0.588, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2735, decode.acc_seg: 89.9141, loss: 0.2735 2023-01-06 00:30:18,996 - mmseg - INFO - Iter [7050/160000] lr: 5.736e-05, eta: 1 day, 0:38:43, time: 0.547, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2460, decode.acc_seg: 90.6476, loss: 0.2460 2023-01-06 00:30:49,286 - mmseg - INFO - Iter [7100/160000] lr: 5.734e-05, eta: 1 day, 0:38:42, time: 0.606, data_time: 0.057, memory: 10576, decode.loss_ce: 0.2594, decode.acc_seg: 90.1303, loss: 0.2594 2023-01-06 00:31:18,570 - mmseg - INFO - Iter [7150/160000] lr: 5.732e-05, eta: 1 day, 0:38:18, time: 0.585, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2785, decode.acc_seg: 89.5568, loss: 0.2785 2023-01-06 00:31:47,007 - mmseg - INFO - Iter [7200/160000] lr: 5.730e-05, eta: 1 day, 0:37:37, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3010, decode.acc_seg: 89.0727, loss: 0.3010 2023-01-06 00:32:15,511 - mmseg - INFO - Iter [7250/160000] lr: 5.728e-05, eta: 1 day, 0:36:57, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2820, decode.acc_seg: 89.3717, loss: 0.2820 2023-01-06 00:32:45,276 - mmseg - INFO - Iter [7300/160000] lr: 5.726e-05, eta: 1 day, 0:36:44, time: 0.595, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2743, decode.acc_seg: 89.6870, loss: 0.2743 2023-01-06 00:33:14,250 - mmseg - INFO - Iter [7350/160000] lr: 5.724e-05, eta: 1 day, 0:36:15, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2635, decode.acc_seg: 90.0726, loss: 0.2635 2023-01-06 00:33:43,247 - mmseg - INFO - Iter [7400/160000] lr: 5.723e-05, eta: 1 day, 0:35:45, time: 0.580, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2824, decode.acc_seg: 89.3554, loss: 0.2824 2023-01-06 00:34:14,327 - mmseg - INFO - Iter [7450/160000] lr: 5.721e-05, eta: 1 day, 0:35:58, time: 0.621, data_time: 0.058, memory: 10576, decode.loss_ce: 0.2648, decode.acc_seg: 90.2395, loss: 0.2648 2023-01-06 00:34:42,716 - mmseg - INFO - Iter [7500/160000] lr: 5.719e-05, eta: 1 day, 0:35:17, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2438, decode.acc_seg: 90.3471, loss: 0.2438 2023-01-06 00:35:12,691 - mmseg - INFO - Iter [7550/160000] lr: 5.717e-05, eta: 1 day, 0:35:06, time: 0.599, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2604, decode.acc_seg: 90.2378, loss: 0.2604 2023-01-06 00:35:42,781 - mmseg - INFO - Iter [7600/160000] lr: 5.715e-05, eta: 1 day, 0:34:58, time: 0.602, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3049, decode.acc_seg: 88.8484, loss: 0.3049 2023-01-06 00:36:10,931 - mmseg - INFO - Iter [7650/160000] lr: 5.713e-05, eta: 1 day, 0:34:12, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.3088, decode.acc_seg: 88.8931, loss: 0.3088 2023-01-06 00:36:39,011 - mmseg - INFO - Iter [7700/160000] lr: 5.711e-05, eta: 1 day, 0:33:24, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2507, decode.acc_seg: 90.4278, loss: 0.2507 2023-01-06 00:37:08,493 - mmseg - INFO - Iter [7750/160000] lr: 5.709e-05, eta: 1 day, 0:33:05, time: 0.590, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2712, decode.acc_seg: 89.7452, loss: 0.2712 2023-01-06 00:37:35,955 - mmseg - INFO - Iter [7800/160000] lr: 5.708e-05, eta: 1 day, 0:32:05, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2630, decode.acc_seg: 90.3568, loss: 0.2630 2023-01-06 00:38:06,411 - mmseg - INFO - Iter [7850/160000] lr: 5.706e-05, eta: 1 day, 0:32:04, time: 0.609, data_time: 0.057, memory: 10576, decode.loss_ce: 0.2296, decode.acc_seg: 91.1830, loss: 0.2296 2023-01-06 00:38:35,887 - mmseg - INFO - Iter [7900/160000] lr: 5.704e-05, eta: 1 day, 0:31:43, time: 0.589, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2540, decode.acc_seg: 90.2259, loss: 0.2540 2023-01-06 00:39:05,449 - mmseg - INFO - Iter [7950/160000] lr: 5.702e-05, eta: 1 day, 0:31:24, time: 0.591, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2450, decode.acc_seg: 90.4586, loss: 0.2450 2023-01-06 00:39:35,587 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 00:39:35,588 - mmseg - INFO - Iter [8000/160000] lr: 5.700e-05, eta: 1 day, 0:31:16, time: 0.603, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2742, decode.acc_seg: 89.9166, loss: 0.2742 2023-01-06 00:40:05,631 - mmseg - INFO - Iter [8050/160000] lr: 5.698e-05, eta: 1 day, 0:31:06, time: 0.601, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2554, decode.acc_seg: 90.3901, loss: 0.2554 2023-01-06 00:40:35,808 - mmseg - INFO - Iter [8100/160000] lr: 5.696e-05, eta: 1 day, 0:30:58, time: 0.603, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2483, decode.acc_seg: 90.6169, loss: 0.2483 2023-01-06 00:41:05,454 - mmseg - INFO - Iter [8150/160000] lr: 5.694e-05, eta: 1 day, 0:30:40, time: 0.593, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2744, decode.acc_seg: 89.8131, loss: 0.2744 2023-01-06 00:41:36,581 - mmseg - INFO - Iter [8200/160000] lr: 5.693e-05, eta: 1 day, 0:30:50, time: 0.623, data_time: 0.059, memory: 10576, decode.loss_ce: 0.2616, decode.acc_seg: 90.2383, loss: 0.2616 2023-01-06 00:42:06,439 - mmseg - INFO - Iter [8250/160000] lr: 5.691e-05, eta: 1 day, 0:30:35, time: 0.597, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2808, decode.acc_seg: 89.4868, loss: 0.2808 2023-01-06 00:42:34,744 - mmseg - INFO - Iter [8300/160000] lr: 5.689e-05, eta: 1 day, 0:29:52, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2759, decode.acc_seg: 89.7762, loss: 0.2759 2023-01-06 00:43:02,191 - mmseg - INFO - Iter [8350/160000] lr: 5.687e-05, eta: 1 day, 0:28:54, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2348, decode.acc_seg: 90.9651, loss: 0.2348 2023-01-06 00:43:30,918 - mmseg - INFO - Iter [8400/160000] lr: 5.685e-05, eta: 1 day, 0:28:19, time: 0.574, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2662, decode.acc_seg: 89.9704, loss: 0.2662 2023-01-06 00:43:59,304 - mmseg - INFO - Iter [8450/160000] lr: 5.683e-05, eta: 1 day, 0:27:38, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2340, decode.acc_seg: 91.0986, loss: 0.2340 2023-01-06 00:44:26,756 - mmseg - INFO - Iter [8500/160000] lr: 5.681e-05, eta: 1 day, 0:26:40, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2630, decode.acc_seg: 90.2181, loss: 0.2630 2023-01-06 00:44:55,508 - mmseg - INFO - Iter [8550/160000] lr: 5.679e-05, eta: 1 day, 0:26:05, time: 0.574, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2555, decode.acc_seg: 90.5196, loss: 0.2555 2023-01-06 00:45:26,182 - mmseg - INFO - Iter [8600/160000] lr: 5.678e-05, eta: 1 day, 0:26:05, time: 0.614, data_time: 0.059, memory: 10576, decode.loss_ce: 0.2366, decode.acc_seg: 90.7499, loss: 0.2366 2023-01-06 00:45:53,809 - mmseg - INFO - Iter [8650/160000] lr: 5.676e-05, eta: 1 day, 0:25:12, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2411, decode.acc_seg: 90.8627, loss: 0.2411 2023-01-06 00:46:21,555 - mmseg - INFO - Iter [8700/160000] lr: 5.674e-05, eta: 1 day, 0:24:19, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2793, decode.acc_seg: 89.6916, loss: 0.2793 2023-01-06 00:46:50,021 - mmseg - INFO - Iter [8750/160000] lr: 5.672e-05, eta: 1 day, 0:23:41, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2568, decode.acc_seg: 89.9987, loss: 0.2568 2023-01-06 00:47:17,874 - mmseg - INFO - Iter [8800/160000] lr: 5.670e-05, eta: 1 day, 0:22:52, time: 0.557, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2262, decode.acc_seg: 91.1580, loss: 0.2262 2023-01-06 00:47:45,318 - mmseg - INFO - Iter [8850/160000] lr: 5.668e-05, eta: 1 day, 0:21:56, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2425, decode.acc_seg: 90.9377, loss: 0.2425 2023-01-06 00:48:14,696 - mmseg - INFO - Iter [8900/160000] lr: 5.666e-05, eta: 1 day, 0:21:33, time: 0.588, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2450, decode.acc_seg: 90.8600, loss: 0.2450 2023-01-06 00:48:45,330 - mmseg - INFO - Iter [8950/160000] lr: 5.664e-05, eta: 1 day, 0:21:31, time: 0.612, data_time: 0.057, memory: 10576, decode.loss_ce: 0.2576, decode.acc_seg: 90.3665, loss: 0.2576 2023-01-06 00:49:14,472 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 00:49:14,473 - mmseg - INFO - Iter [9000/160000] lr: 5.663e-05, eta: 1 day, 0:21:04, time: 0.584, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2458, decode.acc_seg: 90.3823, loss: 0.2458 2023-01-06 00:49:42,371 - mmseg - INFO - Iter [9050/160000] lr: 5.661e-05, eta: 1 day, 0:20:16, time: 0.558, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2235, decode.acc_seg: 91.5359, loss: 0.2235 2023-01-06 00:50:11,942 - mmseg - INFO - Iter [9100/160000] lr: 5.659e-05, eta: 1 day, 0:19:56, time: 0.591, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2594, decode.acc_seg: 90.1434, loss: 0.2594 2023-01-06 00:50:40,632 - mmseg - INFO - Iter [9150/160000] lr: 5.657e-05, eta: 1 day, 0:19:21, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2742, decode.acc_seg: 89.6373, loss: 0.2742 2023-01-06 00:51:09,570 - mmseg - INFO - Iter [9200/160000] lr: 5.655e-05, eta: 1 day, 0:18:51, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2654, decode.acc_seg: 90.4221, loss: 0.2654 2023-01-06 00:51:38,115 - mmseg - INFO - Iter [9250/160000] lr: 5.653e-05, eta: 1 day, 0:18:14, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2317, decode.acc_seg: 91.0379, loss: 0.2317 2023-01-06 00:52:07,799 - mmseg - INFO - Iter [9300/160000] lr: 5.651e-05, eta: 1 day, 0:17:56, time: 0.593, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2266, decode.acc_seg: 91.2320, loss: 0.2266 2023-01-06 00:52:40,167 - mmseg - INFO - Iter [9350/160000] lr: 5.649e-05, eta: 1 day, 0:18:21, time: 0.647, data_time: 0.058, memory: 10576, decode.loss_ce: 0.2384, decode.acc_seg: 90.6846, loss: 0.2384 2023-01-06 00:53:10,394 - mmseg - INFO - Iter [9400/160000] lr: 5.648e-05, eta: 1 day, 0:18:11, time: 0.605, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2539, decode.acc_seg: 90.6704, loss: 0.2539 2023-01-06 00:53:40,279 - mmseg - INFO - Iter [9450/160000] lr: 5.646e-05, eta: 1 day, 0:17:55, time: 0.598, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2374, decode.acc_seg: 90.9828, loss: 0.2374 2023-01-06 00:54:07,749 - mmseg - INFO - Iter [9500/160000] lr: 5.644e-05, eta: 1 day, 0:17:01, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2286, decode.acc_seg: 91.2097, loss: 0.2286 2023-01-06 00:54:35,198 - mmseg - INFO - Iter [9550/160000] lr: 5.642e-05, eta: 1 day, 0:16:07, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2273, decode.acc_seg: 91.2188, loss: 0.2273 2023-01-06 00:55:03,784 - mmseg - INFO - Iter [9600/160000] lr: 5.640e-05, eta: 1 day, 0:15:31, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2396, decode.acc_seg: 90.9101, loss: 0.2396 2023-01-06 00:55:32,460 - mmseg - INFO - Iter [9650/160000] lr: 5.638e-05, eta: 1 day, 0:14:56, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2399, decode.acc_seg: 91.1546, loss: 0.2399 2023-01-06 00:56:03,017 - mmseg - INFO - Iter [9700/160000] lr: 5.636e-05, eta: 1 day, 0:14:51, time: 0.612, data_time: 0.058, memory: 10576, decode.loss_ce: 0.2602, decode.acc_seg: 90.2804, loss: 0.2602 2023-01-06 00:56:30,665 - mmseg - INFO - Iter [9750/160000] lr: 5.634e-05, eta: 1 day, 0:14:01, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2409, decode.acc_seg: 91.0323, loss: 0.2409 2023-01-06 00:57:00,495 - mmseg - INFO - Iter [9800/160000] lr: 5.633e-05, eta: 1 day, 0:13:43, time: 0.596, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2302, decode.acc_seg: 91.1097, loss: 0.2302 2023-01-06 00:57:29,291 - mmseg - INFO - Iter [9850/160000] lr: 5.631e-05, eta: 1 day, 0:13:11, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2611, decode.acc_seg: 90.5433, loss: 0.2611 2023-01-06 00:57:58,220 - mmseg - INFO - Iter [9900/160000] lr: 5.629e-05, eta: 1 day, 0:12:40, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2283, decode.acc_seg: 91.2730, loss: 0.2283 2023-01-06 00:58:26,002 - mmseg - INFO - Iter [9950/160000] lr: 5.627e-05, eta: 1 day, 0:11:52, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2238, decode.acc_seg: 91.3501, loss: 0.2238 2023-01-06 00:58:53,527 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 00:58:53,527 - mmseg - INFO - Iter [10000/160000] lr: 5.625e-05, eta: 1 day, 0:11:00, time: 0.550, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2474, decode.acc_seg: 90.7175, loss: 0.2474 2023-01-06 00:59:24,530 - mmseg - INFO - Iter [10050/160000] lr: 5.623e-05, eta: 1 day, 0:11:01, time: 0.621, data_time: 0.058, memory: 10576, decode.loss_ce: 0.2439, decode.acc_seg: 90.8769, loss: 0.2439 2023-01-06 00:59:52,215 - mmseg - INFO - Iter [10100/160000] lr: 5.621e-05, eta: 1 day, 0:10:12, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2304, decode.acc_seg: 91.0537, loss: 0.2304 2023-01-06 01:00:20,970 - mmseg - INFO - Iter [10150/160000] lr: 5.619e-05, eta: 1 day, 0:09:39, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2543, decode.acc_seg: 90.3999, loss: 0.2543 2023-01-06 01:00:49,213 - mmseg - INFO - Iter [10200/160000] lr: 5.618e-05, eta: 1 day, 0:08:58, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2362, decode.acc_seg: 91.0474, loss: 0.2362 2023-01-06 01:01:18,547 - mmseg - INFO - Iter [10250/160000] lr: 5.616e-05, eta: 1 day, 0:08:34, time: 0.587, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2420, decode.acc_seg: 90.9429, loss: 0.2420 2023-01-06 01:01:47,893 - mmseg - INFO - Iter [10300/160000] lr: 5.614e-05, eta: 1 day, 0:08:10, time: 0.588, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2186, decode.acc_seg: 91.5090, loss: 0.2186 2023-01-06 01:02:16,404 - mmseg - INFO - Iter [10350/160000] lr: 5.612e-05, eta: 1 day, 0:07:33, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2244, decode.acc_seg: 91.4205, loss: 0.2244 2023-01-06 01:02:46,075 - mmseg - INFO - Iter [10400/160000] lr: 5.610e-05, eta: 1 day, 0:07:14, time: 0.593, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2336, decode.acc_seg: 91.2089, loss: 0.2336 2023-01-06 01:03:17,292 - mmseg - INFO - Iter [10450/160000] lr: 5.608e-05, eta: 1 day, 0:07:17, time: 0.625, data_time: 0.058, memory: 10576, decode.loss_ce: 0.2262, decode.acc_seg: 91.3248, loss: 0.2262 2023-01-06 01:03:46,356 - mmseg - INFO - Iter [10500/160000] lr: 5.606e-05, eta: 1 day, 0:06:47, time: 0.581, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2273, decode.acc_seg: 91.2756, loss: 0.2273 2023-01-06 01:04:14,423 - mmseg - INFO - Iter [10550/160000] lr: 5.604e-05, eta: 1 day, 0:06:05, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2223, decode.acc_seg: 91.3885, loss: 0.2223 2023-01-06 01:04:41,863 - mmseg - INFO - Iter [10600/160000] lr: 5.603e-05, eta: 1 day, 0:05:14, time: 0.549, data_time: 0.014, memory: 10576, decode.loss_ce: 0.2368, decode.acc_seg: 91.0132, loss: 0.2368 2023-01-06 01:05:09,307 - mmseg - INFO - Iter [10650/160000] lr: 5.601e-05, eta: 1 day, 0:04:23, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2497, decode.acc_seg: 90.3143, loss: 0.2497 2023-01-06 01:05:38,005 - mmseg - INFO - Iter [10700/160000] lr: 5.599e-05, eta: 1 day, 0:03:49, time: 0.574, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2393, decode.acc_seg: 90.9187, loss: 0.2393 2023-01-06 01:06:06,875 - mmseg - INFO - Iter [10750/160000] lr: 5.597e-05, eta: 1 day, 0:03:18, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2258, decode.acc_seg: 91.4100, loss: 0.2258 2023-01-06 01:06:36,681 - mmseg - INFO - Iter [10800/160000] lr: 5.595e-05, eta: 1 day, 0:03:00, time: 0.597, data_time: 0.059, memory: 10576, decode.loss_ce: 0.2307, decode.acc_seg: 91.0990, loss: 0.2307 2023-01-06 01:07:04,155 - mmseg - INFO - Iter [10850/160000] lr: 5.593e-05, eta: 1 day, 0:02:10, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2337, decode.acc_seg: 91.0817, loss: 0.2337 2023-01-06 01:07:31,919 - mmseg - INFO - Iter [10900/160000] lr: 5.591e-05, eta: 1 day, 0:01:23, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2221, decode.acc_seg: 91.4302, loss: 0.2221 2023-01-06 01:08:01,059 - mmseg - INFO - Iter [10950/160000] lr: 5.589e-05, eta: 1 day, 0:00:57, time: 0.584, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2062, decode.acc_seg: 91.8441, loss: 0.2062 2023-01-06 01:08:29,130 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 01:08:29,131 - mmseg - INFO - Iter [11000/160000] lr: 5.588e-05, eta: 1 day, 0:00:15, time: 0.561, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2275, decode.acc_seg: 91.5572, loss: 0.2275 2023-01-06 01:08:59,215 - mmseg - INFO - Iter [11050/160000] lr: 5.586e-05, eta: 1 day, 0:00:00, time: 0.602, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2487, decode.acc_seg: 90.9199, loss: 0.2487 2023-01-06 01:09:29,379 - mmseg - INFO - Iter [11100/160000] lr: 5.584e-05, eta: 23:59:47, time: 0.603, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2089, decode.acc_seg: 91.6671, loss: 0.2089 2023-01-06 01:09:56,924 - mmseg - INFO - Iter [11150/160000] lr: 5.582e-05, eta: 23:58:58, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2148, decode.acc_seg: 91.5692, loss: 0.2148 2023-01-06 01:10:26,990 - mmseg - INFO - Iter [11200/160000] lr: 5.580e-05, eta: 23:58:44, time: 0.601, data_time: 0.058, memory: 10576, decode.loss_ce: 0.2193, decode.acc_seg: 91.3718, loss: 0.2193 2023-01-06 01:10:56,208 - mmseg - INFO - Iter [11250/160000] lr: 5.578e-05, eta: 23:58:17, time: 0.584, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2282, decode.acc_seg: 91.2276, loss: 0.2282 2023-01-06 01:11:24,468 - mmseg - INFO - Iter [11300/160000] lr: 5.576e-05, eta: 23:57:39, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2220, decode.acc_seg: 91.3557, loss: 0.2220 2023-01-06 01:11:51,757 - mmseg - INFO - Iter [11350/160000] lr: 5.574e-05, eta: 23:56:47, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2134, decode.acc_seg: 91.7057, loss: 0.2134 2023-01-06 01:12:21,180 - mmseg - INFO - Iter [11400/160000] lr: 5.573e-05, eta: 23:56:24, time: 0.588, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2231, decode.acc_seg: 91.1614, loss: 0.2231 2023-01-06 01:12:48,877 - mmseg - INFO - Iter [11450/160000] lr: 5.571e-05, eta: 23:55:38, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2283, decode.acc_seg: 91.6960, loss: 0.2283 2023-01-06 01:13:17,686 - mmseg - INFO - Iter [11500/160000] lr: 5.569e-05, eta: 23:55:06, time: 0.575, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2281, decode.acc_seg: 91.2670, loss: 0.2281 2023-01-06 01:13:49,898 - mmseg - INFO - Iter [11550/160000] lr: 5.567e-05, eta: 23:55:19, time: 0.645, data_time: 0.058, memory: 10576, decode.loss_ce: 0.2224, decode.acc_seg: 91.5775, loss: 0.2224 2023-01-06 01:14:17,482 - mmseg - INFO - Iter [11600/160000] lr: 5.565e-05, eta: 23:54:32, time: 0.552, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2175, decode.acc_seg: 91.6628, loss: 0.2175 2023-01-06 01:14:45,025 - mmseg - INFO - Iter [11650/160000] lr: 5.563e-05, eta: 23:53:44, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2168, decode.acc_seg: 91.3995, loss: 0.2168 2023-01-06 01:15:12,866 - mmseg - INFO - Iter [11700/160000] lr: 5.561e-05, eta: 23:53:00, time: 0.556, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2230, decode.acc_seg: 91.3855, loss: 0.2230 2023-01-06 01:15:40,825 - mmseg - INFO - Iter [11750/160000] lr: 5.559e-05, eta: 23:52:18, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2158, decode.acc_seg: 91.6546, loss: 0.2158 2023-01-06 01:16:10,304 - mmseg - INFO - Iter [11800/160000] lr: 5.558e-05, eta: 23:51:56, time: 0.590, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2073, decode.acc_seg: 92.1004, loss: 0.2073 2023-01-06 01:16:39,428 - mmseg - INFO - Iter [11850/160000] lr: 5.556e-05, eta: 23:51:28, time: 0.582, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2288, decode.acc_seg: 90.9173, loss: 0.2288 2023-01-06 01:17:08,808 - mmseg - INFO - Iter [11900/160000] lr: 5.554e-05, eta: 23:51:04, time: 0.587, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2117, decode.acc_seg: 91.9444, loss: 0.2117 2023-01-06 01:17:39,075 - mmseg - INFO - Iter [11950/160000] lr: 5.552e-05, eta: 23:50:51, time: 0.606, data_time: 0.059, memory: 10576, decode.loss_ce: 0.2129, decode.acc_seg: 91.5676, loss: 0.2129 2023-01-06 01:18:08,335 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 01:18:08,335 - mmseg - INFO - Iter [12000/160000] lr: 5.550e-05, eta: 23:50:26, time: 0.585, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2165, decode.acc_seg: 91.5142, loss: 0.2165 2023-01-06 01:18:35,855 - mmseg - INFO - Iter [12050/160000] lr: 5.548e-05, eta: 23:49:39, time: 0.550, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2140, decode.acc_seg: 91.8526, loss: 0.2140 2023-01-06 01:19:04,602 - mmseg - INFO - Iter [12100/160000] lr: 5.546e-05, eta: 23:49:06, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2141, decode.acc_seg: 91.6665, loss: 0.2141 2023-01-06 01:19:33,089 - mmseg - INFO - Iter [12150/160000] lr: 5.544e-05, eta: 23:48:32, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2179, decode.acc_seg: 91.6794, loss: 0.2179 2023-01-06 01:20:01,363 - mmseg - INFO - Iter [12200/160000] lr: 5.543e-05, eta: 23:47:53, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1949, decode.acc_seg: 92.4097, loss: 0.1949 2023-01-06 01:20:29,043 - mmseg - INFO - Iter [12250/160000] lr: 5.541e-05, eta: 23:47:09, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2092, decode.acc_seg: 92.0047, loss: 0.2092 2023-01-06 01:21:01,315 - mmseg - INFO - Iter [12300/160000] lr: 5.539e-05, eta: 23:47:19, time: 0.645, data_time: 0.058, memory: 10576, decode.loss_ce: 0.2130, decode.acc_seg: 91.8004, loss: 0.2130 2023-01-06 01:21:30,435 - mmseg - INFO - Iter [12350/160000] lr: 5.537e-05, eta: 23:46:52, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2052, decode.acc_seg: 91.9792, loss: 0.2052 2023-01-06 01:22:00,078 - mmseg - INFO - Iter [12400/160000] lr: 5.535e-05, eta: 23:46:31, time: 0.592, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2199, decode.acc_seg: 91.5427, loss: 0.2199 2023-01-06 01:22:29,160 - mmseg - INFO - Iter [12450/160000] lr: 5.533e-05, eta: 23:46:03, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2301, decode.acc_seg: 91.3806, loss: 0.2301 2023-01-06 01:22:57,686 - mmseg - INFO - Iter [12500/160000] lr: 5.531e-05, eta: 23:45:29, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2042, decode.acc_seg: 92.0999, loss: 0.2042 2023-01-06 01:23:27,527 - mmseg - INFO - Iter [12550/160000] lr: 5.529e-05, eta: 23:45:09, time: 0.596, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1962, decode.acc_seg: 92.1529, loss: 0.1962 2023-01-06 01:23:55,922 - mmseg - INFO - Iter [12600/160000] lr: 5.528e-05, eta: 23:44:33, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2298, decode.acc_seg: 91.1690, loss: 0.2298 2023-01-06 01:24:27,248 - mmseg - INFO - Iter [12650/160000] lr: 5.526e-05, eta: 23:44:32, time: 0.627, data_time: 0.058, memory: 10576, decode.loss_ce: 0.2076, decode.acc_seg: 92.2004, loss: 0.2076 2023-01-06 01:24:54,605 - mmseg - INFO - Iter [12700/160000] lr: 5.524e-05, eta: 23:43:44, time: 0.547, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2015, decode.acc_seg: 92.2628, loss: 0.2015 2023-01-06 01:25:22,947 - mmseg - INFO - Iter [12750/160000] lr: 5.522e-05, eta: 23:43:07, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2140, decode.acc_seg: 91.9425, loss: 0.2140 2023-01-06 01:25:53,366 - mmseg - INFO - Iter [12800/160000] lr: 5.520e-05, eta: 23:42:54, time: 0.608, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2298, decode.acc_seg: 91.2585, loss: 0.2298 2023-01-06 01:26:23,307 - mmseg - INFO - Iter [12850/160000] lr: 5.518e-05, eta: 23:42:36, time: 0.599, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2035, decode.acc_seg: 91.8874, loss: 0.2035 2023-01-06 01:26:52,785 - mmseg - INFO - Iter [12900/160000] lr: 5.516e-05, eta: 23:42:13, time: 0.590, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1886, decode.acc_seg: 92.7933, loss: 0.1886 2023-01-06 01:27:21,917 - mmseg - INFO - Iter [12950/160000] lr: 5.514e-05, eta: 23:41:45, time: 0.582, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2090, decode.acc_seg: 91.8243, loss: 0.2090 2023-01-06 01:27:51,135 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 01:27:51,135 - mmseg - INFO - Iter [13000/160000] lr: 5.513e-05, eta: 23:41:18, time: 0.585, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2183, decode.acc_seg: 91.4569, loss: 0.2183 2023-01-06 01:28:22,077 - mmseg - INFO - Iter [13050/160000] lr: 5.511e-05, eta: 23:41:11, time: 0.619, data_time: 0.058, memory: 10576, decode.loss_ce: 0.2101, decode.acc_seg: 91.8122, loss: 0.2101 2023-01-06 01:28:49,907 - mmseg - INFO - Iter [13100/160000] lr: 5.509e-05, eta: 23:40:29, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1975, decode.acc_seg: 92.4625, loss: 0.1975 2023-01-06 01:29:19,350 - mmseg - INFO - Iter [13150/160000] lr: 5.507e-05, eta: 23:40:04, time: 0.588, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1967, decode.acc_seg: 92.3031, loss: 0.1967 2023-01-06 01:29:49,317 - mmseg - INFO - Iter [13200/160000] lr: 5.505e-05, eta: 23:39:46, time: 0.600, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2142, decode.acc_seg: 91.6050, loss: 0.2142 2023-01-06 01:30:18,175 - mmseg - INFO - Iter [13250/160000] lr: 5.503e-05, eta: 23:39:16, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1974, decode.acc_seg: 92.2367, loss: 0.1974 2023-01-06 01:30:46,864 - mmseg - INFO - Iter [13300/160000] lr: 5.501e-05, eta: 23:38:43, time: 0.574, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2110, decode.acc_seg: 91.9210, loss: 0.2110 2023-01-06 01:31:16,345 - mmseg - INFO - Iter [13350/160000] lr: 5.499e-05, eta: 23:38:19, time: 0.589, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2168, decode.acc_seg: 91.5130, loss: 0.2168 2023-01-06 01:31:46,397 - mmseg - INFO - Iter [13400/160000] lr: 5.498e-05, eta: 23:38:01, time: 0.602, data_time: 0.057, memory: 10576, decode.loss_ce: 0.2026, decode.acc_seg: 92.0519, loss: 0.2026 2023-01-06 01:32:14,186 - mmseg - INFO - Iter [13450/160000] lr: 5.496e-05, eta: 23:37:19, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1999, decode.acc_seg: 92.2136, loss: 0.1999 2023-01-06 01:32:43,567 - mmseg - INFO - Iter [13500/160000] lr: 5.494e-05, eta: 23:36:54, time: 0.588, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2419, decode.acc_seg: 90.8441, loss: 0.2419 2023-01-06 01:33:11,266 - mmseg - INFO - Iter [13550/160000] lr: 5.492e-05, eta: 23:36:11, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2360, decode.acc_seg: 91.2885, loss: 0.2360 2023-01-06 01:33:39,033 - mmseg - INFO - Iter [13600/160000] lr: 5.490e-05, eta: 23:35:28, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2095, decode.acc_seg: 91.6965, loss: 0.2095 2023-01-06 01:34:07,268 - mmseg - INFO - Iter [13650/160000] lr: 5.488e-05, eta: 23:34:51, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2113, decode.acc_seg: 92.0607, loss: 0.2113 2023-01-06 01:34:36,621 - mmseg - INFO - Iter [13700/160000] lr: 5.486e-05, eta: 23:34:25, time: 0.586, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1945, decode.acc_seg: 92.1004, loss: 0.1945 2023-01-06 01:35:05,931 - mmseg - INFO - Iter [13750/160000] lr: 5.484e-05, eta: 23:34:00, time: 0.587, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2152, decode.acc_seg: 91.6805, loss: 0.2152 2023-01-06 01:35:36,012 - mmseg - INFO - Iter [13800/160000] lr: 5.483e-05, eta: 23:33:42, time: 0.601, data_time: 0.056, memory: 10576, decode.loss_ce: 0.2219, decode.acc_seg: 91.6477, loss: 0.2219 2023-01-06 01:36:03,693 - mmseg - INFO - Iter [13850/160000] lr: 5.481e-05, eta: 23:32:59, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2137, decode.acc_seg: 91.8566, loss: 0.2137 2023-01-06 01:36:32,334 - mmseg - INFO - Iter [13900/160000] lr: 5.479e-05, eta: 23:32:26, time: 0.572, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2161, decode.acc_seg: 91.8451, loss: 0.2161 2023-01-06 01:37:00,204 - mmseg - INFO - Iter [13950/160000] lr: 5.477e-05, eta: 23:31:46, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2069, decode.acc_seg: 92.1683, loss: 0.2069 2023-01-06 01:37:28,743 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 01:37:28,743 - mmseg - INFO - Iter [14000/160000] lr: 5.475e-05, eta: 23:31:11, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1992, decode.acc_seg: 92.0334, loss: 0.1992 2023-01-06 01:37:56,700 - mmseg - INFO - Iter [14050/160000] lr: 5.473e-05, eta: 23:30:32, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1874, decode.acc_seg: 92.4399, loss: 0.1874 2023-01-06 01:38:25,748 - mmseg - INFO - Iter [14100/160000] lr: 5.471e-05, eta: 23:30:03, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2002, decode.acc_seg: 92.2154, loss: 0.2002 2023-01-06 01:38:56,882 - mmseg - INFO - Iter [14150/160000] lr: 5.469e-05, eta: 23:29:56, time: 0.623, data_time: 0.057, memory: 10576, decode.loss_ce: 0.2056, decode.acc_seg: 91.9083, loss: 0.2056 2023-01-06 01:39:26,246 - mmseg - INFO - Iter [14200/160000] lr: 5.468e-05, eta: 23:29:31, time: 0.587, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2050, decode.acc_seg: 91.9746, loss: 0.2050 2023-01-06 01:39:54,396 - mmseg - INFO - Iter [14250/160000] lr: 5.466e-05, eta: 23:28:54, time: 0.564, data_time: 0.014, memory: 10576, decode.loss_ce: 0.2139, decode.acc_seg: 91.7676, loss: 0.2139 2023-01-06 01:40:21,773 - mmseg - INFO - Iter [14300/160000] lr: 5.464e-05, eta: 23:28:08, time: 0.548, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2022, decode.acc_seg: 92.0823, loss: 0.2022 2023-01-06 01:40:51,073 - mmseg - INFO - Iter [14350/160000] lr: 5.462e-05, eta: 23:27:42, time: 0.585, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1974, decode.acc_seg: 92.1251, loss: 0.1974 2023-01-06 01:41:20,508 - mmseg - INFO - Iter [14400/160000] lr: 5.460e-05, eta: 23:27:18, time: 0.589, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1978, decode.acc_seg: 92.2374, loss: 0.1978 2023-01-06 01:41:49,790 - mmseg - INFO - Iter [14450/160000] lr: 5.458e-05, eta: 23:26:51, time: 0.585, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1909, decode.acc_seg: 92.3353, loss: 0.1909 2023-01-06 01:42:18,374 - mmseg - INFO - Iter [14500/160000] lr: 5.456e-05, eta: 23:26:18, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2016, decode.acc_seg: 92.3046, loss: 0.2016 2023-01-06 01:42:48,877 - mmseg - INFO - Iter [14550/160000] lr: 5.454e-05, eta: 23:26:05, time: 0.610, data_time: 0.058, memory: 10576, decode.loss_ce: 0.2020, decode.acc_seg: 92.1204, loss: 0.2020 2023-01-06 01:43:16,885 - mmseg - INFO - Iter [14600/160000] lr: 5.453e-05, eta: 23:25:26, time: 0.560, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2107, decode.acc_seg: 92.0381, loss: 0.2107 2023-01-06 01:43:44,201 - mmseg - INFO - Iter [14650/160000] lr: 5.451e-05, eta: 23:24:40, time: 0.546, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1966, decode.acc_seg: 92.2324, loss: 0.1966 2023-01-06 01:44:11,587 - mmseg - INFO - Iter [14700/160000] lr: 5.449e-05, eta: 23:23:55, time: 0.548, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1895, decode.acc_seg: 92.6744, loss: 0.1895 2023-01-06 01:44:40,723 - mmseg - INFO - Iter [14750/160000] lr: 5.447e-05, eta: 23:23:27, time: 0.582, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1991, decode.acc_seg: 92.4453, loss: 0.1991 2023-01-06 01:45:09,457 - mmseg - INFO - Iter [14800/160000] lr: 5.445e-05, eta: 23:22:56, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2056, decode.acc_seg: 91.8971, loss: 0.2056 2023-01-06 01:45:38,458 - mmseg - INFO - Iter [14850/160000] lr: 5.443e-05, eta: 23:22:27, time: 0.579, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1971, decode.acc_seg: 92.2889, loss: 0.1971 2023-01-06 01:46:08,294 - mmseg - INFO - Iter [14900/160000] lr: 5.441e-05, eta: 23:22:07, time: 0.598, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1953, decode.acc_seg: 92.3369, loss: 0.1953 2023-01-06 01:46:37,204 - mmseg - INFO - Iter [14950/160000] lr: 5.439e-05, eta: 23:21:36, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1973, decode.acc_seg: 92.3037, loss: 0.1973 2023-01-06 01:47:06,858 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 01:47:06,859 - mmseg - INFO - Iter [15000/160000] lr: 5.438e-05, eta: 23:21:14, time: 0.593, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1936, decode.acc_seg: 92.3873, loss: 0.1936 2023-01-06 01:47:36,888 - mmseg - INFO - Iter [15050/160000] lr: 5.436e-05, eta: 23:20:55, time: 0.601, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1888, decode.acc_seg: 92.6003, loss: 0.1888 2023-01-06 01:48:04,422 - mmseg - INFO - Iter [15100/160000] lr: 5.434e-05, eta: 23:20:12, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2104, decode.acc_seg: 91.8265, loss: 0.2104 2023-01-06 01:48:31,831 - mmseg - INFO - Iter [15150/160000] lr: 5.432e-05, eta: 23:19:28, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2147, decode.acc_seg: 91.7180, loss: 0.2147 2023-01-06 01:48:59,339 - mmseg - INFO - Iter [15200/160000] lr: 5.430e-05, eta: 23:18:45, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2046, decode.acc_seg: 91.9861, loss: 0.2046 2023-01-06 01:49:28,960 - mmseg - INFO - Iter [15250/160000] lr: 5.428e-05, eta: 23:18:22, time: 0.593, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2115, decode.acc_seg: 92.1016, loss: 0.2115 2023-01-06 01:49:58,828 - mmseg - INFO - Iter [15300/160000] lr: 5.426e-05, eta: 23:18:01, time: 0.597, data_time: 0.058, memory: 10576, decode.loss_ce: 0.2010, decode.acc_seg: 92.1066, loss: 0.2010 2023-01-06 01:50:27,712 - mmseg - INFO - Iter [15350/160000] lr: 5.424e-05, eta: 23:17:31, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1850, decode.acc_seg: 92.6694, loss: 0.1850 2023-01-06 01:50:57,897 - mmseg - INFO - Iter [15400/160000] lr: 5.423e-05, eta: 23:17:13, time: 0.604, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1989, decode.acc_seg: 92.2023, loss: 0.1989 2023-01-06 01:51:26,041 - mmseg - INFO - Iter [15450/160000] lr: 5.421e-05, eta: 23:16:37, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1831, decode.acc_seg: 92.6437, loss: 0.1831 2023-01-06 01:51:53,713 - mmseg - INFO - Iter [15500/160000] lr: 5.419e-05, eta: 23:15:56, time: 0.553, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2091, decode.acc_seg: 91.9343, loss: 0.2091 2023-01-06 01:52:22,737 - mmseg - INFO - Iter [15550/160000] lr: 5.417e-05, eta: 23:15:27, time: 0.580, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1865, decode.acc_seg: 92.7267, loss: 0.1865 2023-01-06 01:52:52,957 - mmseg - INFO - Iter [15600/160000] lr: 5.415e-05, eta: 23:15:09, time: 0.604, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1876, decode.acc_seg: 92.4849, loss: 0.1876 2023-01-06 01:53:23,472 - mmseg - INFO - Iter [15650/160000] lr: 5.413e-05, eta: 23:14:55, time: 0.611, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1897, decode.acc_seg: 92.6244, loss: 0.1897 2023-01-06 01:53:53,031 - mmseg - INFO - Iter [15700/160000] lr: 5.411e-05, eta: 23:14:31, time: 0.591, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1848, decode.acc_seg: 92.7855, loss: 0.1848 2023-01-06 01:54:21,556 - mmseg - INFO - Iter [15750/160000] lr: 5.409e-05, eta: 23:13:58, time: 0.571, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1886, decode.acc_seg: 92.6222, loss: 0.1886 2023-01-06 01:54:50,375 - mmseg - INFO - Iter [15800/160000] lr: 5.408e-05, eta: 23:13:27, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1940, decode.acc_seg: 92.2035, loss: 0.1940 2023-01-06 01:55:18,764 - mmseg - INFO - Iter [15850/160000] lr: 5.406e-05, eta: 23:12:52, time: 0.568, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1946, decode.acc_seg: 92.4179, loss: 0.1946 2023-01-06 01:55:47,425 - mmseg - INFO - Iter [15900/160000] lr: 5.404e-05, eta: 23:12:20, time: 0.573, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1988, decode.acc_seg: 92.3450, loss: 0.1988 2023-01-06 01:56:15,041 - mmseg - INFO - Iter [15950/160000] lr: 5.402e-05, eta: 23:11:39, time: 0.553, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1843, decode.acc_seg: 92.6833, loss: 0.1843 2023-01-06 01:56:46,097 - mmseg - INFO - Saving checkpoint at 16000 iterations 2023-01-06 01:56:51,568 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 01:56:51,568 - mmseg - INFO - Iter [16000/160000] lr: 5.400e-05, eta: 23:12:18, time: 0.731, data_time: 0.058, memory: 10576, decode.loss_ce: 0.2143, decode.acc_seg: 92.0279, loss: 0.2143 2023-01-06 01:57:27,710 - mmseg - INFO - per class results: 2023-01-06 01:57:27,713 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 96.52 | 98.07 | | sidewalk | 74.82 | 89.11 | | building | 86.59 | 95.16 | | wall | 15.04 | 15.3 | | fence | 39.13 | 53.29 | | pole | 45.69 | 53.73 | | traffic light | 35.23 | 37.67 | | traffic sign | 53.23 | 57.5 | | vegetation | 89.49 | 94.61 | | terrain | 49.17 | 60.66 | | sky | 90.21 | 98.22 | | person | 63.72 | 82.75 | | rider | 22.27 | 25.62 | | car | 88.66 | 95.26 | | truck | 23.87 | 25.98 | | bus | 41.18 | 50.5 | | train | 14.52 | 16.39 | | motorcycle | 20.19 | 24.19 | | bicycle | 59.59 | 78.64 | +---------------+-------+-------+ 2023-01-06 01:57:27,713 - mmseg - INFO - Summary: 2023-01-06 01:57:27,714 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 92.79 | 53.11 | 60.67 | +-------+-------+-------+ 2023-01-06 01:57:27,715 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 01:57:27,715 - mmseg - INFO - Iter(val) [63] aAcc: 0.9279, mIoU: 0.5311, mAcc: 0.6067, IoU.road: 0.9652, IoU.sidewalk: 0.7482, IoU.building: 0.8659, IoU.wall: 0.1504, IoU.fence: 0.3913, IoU.pole: 0.4569, IoU.traffic light: 0.3523, IoU.traffic sign: 0.5323, IoU.vegetation: 0.8949, IoU.terrain: 0.4917, IoU.sky: 0.9021, IoU.person: 0.6372, IoU.rider: 0.2227, IoU.car: 0.8866, IoU.truck: 0.2387, IoU.bus: 0.4118, IoU.train: 0.1452, IoU.motorcycle: 0.2019, IoU.bicycle: 0.5959, Acc.road: 0.9807, Acc.sidewalk: 0.8911, Acc.building: 0.9516, Acc.wall: 0.1530, Acc.fence: 0.5329, Acc.pole: 0.5373, Acc.traffic light: 0.3767, Acc.traffic sign: 0.5750, Acc.vegetation: 0.9461, Acc.terrain: 0.6066, Acc.sky: 0.9822, Acc.person: 0.8275, Acc.rider: 0.2562, Acc.car: 0.9526, Acc.truck: 0.2598, Acc.bus: 0.5050, Acc.train: 0.1639, Acc.motorcycle: 0.2419, Acc.bicycle: 0.7864 2023-01-06 01:57:56,478 - mmseg - INFO - Iter [16050/160000] lr: 5.398e-05, eta: 23:17:11, time: 1.298, data_time: 0.735, memory: 10576, decode.loss_ce: 0.2218, decode.acc_seg: 91.5942, loss: 0.2218 2023-01-06 01:58:26,080 - mmseg - INFO - Iter [16100/160000] lr: 5.396e-05, eta: 23:16:46, time: 0.591, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1853, decode.acc_seg: 92.3836, loss: 0.1853 2023-01-06 01:58:53,457 - mmseg - INFO - Iter [16150/160000] lr: 5.394e-05, eta: 23:16:02, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1954, decode.acc_seg: 92.4678, loss: 0.1954 2023-01-06 01:59:21,470 - mmseg - INFO - Iter [16200/160000] lr: 5.393e-05, eta: 23:15:23, time: 0.560, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1994, decode.acc_seg: 92.4443, loss: 0.1994 2023-01-06 01:59:49,541 - mmseg - INFO - Iter [16250/160000] lr: 5.391e-05, eta: 23:14:44, time: 0.561, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1854, decode.acc_seg: 92.8029, loss: 0.1854 2023-01-06 02:00:18,679 - mmseg - INFO - Iter [16300/160000] lr: 5.389e-05, eta: 23:14:15, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1782, decode.acc_seg: 93.0145, loss: 0.1782 2023-01-06 02:00:47,372 - mmseg - INFO - Iter [16350/160000] lr: 5.387e-05, eta: 23:13:43, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1826, decode.acc_seg: 92.7376, loss: 0.1826 2023-01-06 02:01:17,456 - mmseg - INFO - Iter [16400/160000] lr: 5.385e-05, eta: 23:13:22, time: 0.602, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1934, decode.acc_seg: 92.5028, loss: 0.1934 2023-01-06 02:01:45,380 - mmseg - INFO - Iter [16450/160000] lr: 5.383e-05, eta: 23:12:43, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1936, decode.acc_seg: 92.5353, loss: 0.1936 2023-01-06 02:02:15,154 - mmseg - INFO - Iter [16500/160000] lr: 5.381e-05, eta: 23:12:20, time: 0.596, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2000, decode.acc_seg: 92.3005, loss: 0.2000 2023-01-06 02:02:43,653 - mmseg - INFO - Iter [16550/160000] lr: 5.379e-05, eta: 23:11:45, time: 0.569, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1725, decode.acc_seg: 93.0057, loss: 0.1725 2023-01-06 02:03:12,415 - mmseg - INFO - Iter [16600/160000] lr: 5.378e-05, eta: 23:11:13, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1824, decode.acc_seg: 92.8297, loss: 0.1824 2023-01-06 02:03:41,487 - mmseg - INFO - Iter [16650/160000] lr: 5.376e-05, eta: 23:10:44, time: 0.581, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1886, decode.acc_seg: 92.5156, loss: 0.1886 2023-01-06 02:04:11,175 - mmseg - INFO - Iter [16700/160000] lr: 5.374e-05, eta: 23:10:20, time: 0.594, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1799, decode.acc_seg: 92.9026, loss: 0.1799 2023-01-06 02:04:42,674 - mmseg - INFO - Iter [16750/160000] lr: 5.372e-05, eta: 23:10:11, time: 0.630, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1859, decode.acc_seg: 92.7681, loss: 0.1859 2023-01-06 02:05:11,082 - mmseg - INFO - Iter [16800/160000] lr: 5.370e-05, eta: 23:09:36, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1927, decode.acc_seg: 92.4722, loss: 0.1927 2023-01-06 02:05:38,399 - mmseg - INFO - Iter [16850/160000] lr: 5.368e-05, eta: 23:08:51, time: 0.546, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1809, decode.acc_seg: 92.9071, loss: 0.1809 2023-01-06 02:06:06,611 - mmseg - INFO - Iter [16900/160000] lr: 5.366e-05, eta: 23:08:15, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1973, decode.acc_seg: 92.4827, loss: 0.1973 2023-01-06 02:06:35,410 - mmseg - INFO - Iter [16950/160000] lr: 5.364e-05, eta: 23:07:43, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1929, decode.acc_seg: 92.6303, loss: 0.1929 2023-01-06 02:07:03,218 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 02:07:03,219 - mmseg - INFO - Iter [17000/160000] lr: 5.363e-05, eta: 23:07:03, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1968, decode.acc_seg: 92.3878, loss: 0.1968 2023-01-06 02:07:32,858 - mmseg - INFO - Iter [17050/160000] lr: 5.361e-05, eta: 23:06:38, time: 0.593, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2227, decode.acc_seg: 91.4021, loss: 0.2227 2023-01-06 02:08:00,352 - mmseg - INFO - Iter [17100/160000] lr: 5.359e-05, eta: 23:05:56, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1887, decode.acc_seg: 92.4213, loss: 0.1887 2023-01-06 02:08:31,082 - mmseg - INFO - Iter [17150/160000] lr: 5.357e-05, eta: 23:05:41, time: 0.615, data_time: 0.057, memory: 10576, decode.loss_ce: 0.2104, decode.acc_seg: 91.6780, loss: 0.2104 2023-01-06 02:08:59,090 - mmseg - INFO - Iter [17200/160000] lr: 5.355e-05, eta: 23:05:02, time: 0.559, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1833, decode.acc_seg: 92.9224, loss: 0.1833 2023-01-06 02:09:28,590 - mmseg - INFO - Iter [17250/160000] lr: 5.353e-05, eta: 23:04:36, time: 0.590, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1900, decode.acc_seg: 92.7770, loss: 0.1900 2023-01-06 02:09:56,896 - mmseg - INFO - Iter [17300/160000] lr: 5.351e-05, eta: 23:04:01, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1765, decode.acc_seg: 92.9960, loss: 0.1765 2023-01-06 02:10:26,189 - mmseg - INFO - Iter [17350/160000] lr: 5.349e-05, eta: 23:03:33, time: 0.587, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1941, decode.acc_seg: 92.3594, loss: 0.1941 2023-01-06 02:10:53,540 - mmseg - INFO - Iter [17400/160000] lr: 5.348e-05, eta: 23:02:50, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1946, decode.acc_seg: 92.4259, loss: 0.1946 2023-01-06 02:11:22,823 - mmseg - INFO - Iter [17450/160000] lr: 5.346e-05, eta: 23:02:22, time: 0.585, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1860, decode.acc_seg: 92.7047, loss: 0.1860 2023-01-06 02:11:54,395 - mmseg - INFO - Iter [17500/160000] lr: 5.344e-05, eta: 23:02:14, time: 0.632, data_time: 0.059, memory: 10576, decode.loss_ce: 0.2087, decode.acc_seg: 92.0458, loss: 0.2087 2023-01-06 02:12:23,293 - mmseg - INFO - Iter [17550/160000] lr: 5.342e-05, eta: 23:01:43, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1816, decode.acc_seg: 93.1112, loss: 0.1816 2023-01-06 02:12:52,420 - mmseg - INFO - Iter [17600/160000] lr: 5.340e-05, eta: 23:01:14, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1814, decode.acc_seg: 92.7159, loss: 0.1814 2023-01-06 02:13:20,826 - mmseg - INFO - Iter [17650/160000] lr: 5.338e-05, eta: 23:00:39, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1770, decode.acc_seg: 93.0076, loss: 0.1770 2023-01-06 02:13:50,954 - mmseg - INFO - Iter [17700/160000] lr: 5.336e-05, eta: 23:00:18, time: 0.603, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1880, decode.acc_seg: 92.6874, loss: 0.1880 2023-01-06 02:14:20,744 - mmseg - INFO - Iter [17750/160000] lr: 5.334e-05, eta: 22:59:55, time: 0.596, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1872, decode.acc_seg: 92.6414, loss: 0.1872 2023-01-06 02:14:48,884 - mmseg - INFO - Iter [17800/160000] lr: 5.333e-05, eta: 22:59:18, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2002, decode.acc_seg: 92.5229, loss: 0.2002 2023-01-06 02:15:18,222 - mmseg - INFO - Iter [17850/160000] lr: 5.331e-05, eta: 22:58:51, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1731, decode.acc_seg: 93.2014, loss: 0.1731 2023-01-06 02:15:50,496 - mmseg - INFO - Iter [17900/160000] lr: 5.329e-05, eta: 22:58:47, time: 0.645, data_time: 0.056, memory: 10576, decode.loss_ce: 0.1790, decode.acc_seg: 93.0639, loss: 0.1790 2023-01-06 02:16:19,375 - mmseg - INFO - Iter [17950/160000] lr: 5.327e-05, eta: 22:58:16, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1873, decode.acc_seg: 92.6199, loss: 0.1873 2023-01-06 02:16:49,118 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 02:16:49,119 - mmseg - INFO - Iter [18000/160000] lr: 5.325e-05, eta: 22:57:52, time: 0.595, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1812, decode.acc_seg: 92.9069, loss: 0.1812 2023-01-06 02:17:16,594 - mmseg - INFO - Iter [18050/160000] lr: 5.323e-05, eta: 22:57:10, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1763, decode.acc_seg: 93.1802, loss: 0.1763 2023-01-06 02:17:44,294 - mmseg - INFO - Iter [18100/160000] lr: 5.321e-05, eta: 22:56:30, time: 0.554, data_time: 0.012, memory: 10576, decode.loss_ce: 0.2225, decode.acc_seg: 91.6970, loss: 0.2225 2023-01-06 02:18:12,604 - mmseg - INFO - Iter [18150/160000] lr: 5.319e-05, eta: 22:55:55, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2076, decode.acc_seg: 91.8619, loss: 0.2076 2023-01-06 02:18:39,963 - mmseg - INFO - Iter [18200/160000] lr: 5.318e-05, eta: 22:55:12, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1899, decode.acc_seg: 92.7539, loss: 0.1899 2023-01-06 02:19:10,008 - mmseg - INFO - Iter [18250/160000] lr: 5.316e-05, eta: 22:54:50, time: 0.601, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1843, decode.acc_seg: 92.8707, loss: 0.1843 2023-01-06 02:19:37,474 - mmseg - INFO - Iter [18300/160000] lr: 5.314e-05, eta: 22:54:08, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1805, decode.acc_seg: 92.9001, loss: 0.1805 2023-01-06 02:20:06,879 - mmseg - INFO - Iter [18350/160000] lr: 5.312e-05, eta: 22:53:41, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1962, decode.acc_seg: 92.5032, loss: 0.1962 2023-01-06 02:20:34,621 - mmseg - INFO - Iter [18400/160000] lr: 5.310e-05, eta: 22:53:02, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2002, decode.acc_seg: 92.2573, loss: 0.2002 2023-01-06 02:21:02,523 - mmseg - INFO - Iter [18450/160000] lr: 5.308e-05, eta: 22:52:24, time: 0.558, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1755, decode.acc_seg: 92.9568, loss: 0.1755 2023-01-06 02:21:30,022 - mmseg - INFO - Iter [18500/160000] lr: 5.306e-05, eta: 22:51:43, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1769, decode.acc_seg: 92.9710, loss: 0.1769 2023-01-06 02:21:59,509 - mmseg - INFO - Iter [18550/160000] lr: 5.304e-05, eta: 22:51:17, time: 0.590, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1700, decode.acc_seg: 93.1699, loss: 0.1700 2023-01-06 02:22:29,644 - mmseg - INFO - Iter [18600/160000] lr: 5.303e-05, eta: 22:50:55, time: 0.603, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1769, decode.acc_seg: 93.0173, loss: 0.1769 2023-01-06 02:23:01,521 - mmseg - INFO - Iter [18650/160000] lr: 5.301e-05, eta: 22:50:47, time: 0.637, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1782, decode.acc_seg: 92.9642, loss: 0.1782 2023-01-06 02:23:30,209 - mmseg - INFO - Iter [18700/160000] lr: 5.299e-05, eta: 22:50:15, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1725, decode.acc_seg: 93.2174, loss: 0.1725 2023-01-06 02:23:58,069 - mmseg - INFO - Iter [18750/160000] lr: 5.297e-05, eta: 22:49:37, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1928, decode.acc_seg: 92.4229, loss: 0.1928 2023-01-06 02:24:25,996 - mmseg - INFO - Iter [18800/160000] lr: 5.295e-05, eta: 22:48:59, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1894, decode.acc_seg: 92.4843, loss: 0.1894 2023-01-06 02:24:56,011 - mmseg - INFO - Iter [18850/160000] lr: 5.293e-05, eta: 22:48:37, time: 0.599, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1658, decode.acc_seg: 93.2881, loss: 0.1658 2023-01-06 02:25:25,725 - mmseg - INFO - Iter [18900/160000] lr: 5.291e-05, eta: 22:48:13, time: 0.595, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1820, decode.acc_seg: 92.8354, loss: 0.1820 2023-01-06 02:25:55,876 - mmseg - INFO - Iter [18950/160000] lr: 5.289e-05, eta: 22:47:51, time: 0.602, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1737, decode.acc_seg: 93.0417, loss: 0.1737 2023-01-06 02:26:26,795 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 02:26:26,796 - mmseg - INFO - Iter [19000/160000] lr: 5.288e-05, eta: 22:47:36, time: 0.619, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1759, decode.acc_seg: 92.8997, loss: 0.1759 2023-01-06 02:26:55,381 - mmseg - INFO - Iter [19050/160000] lr: 5.286e-05, eta: 22:47:03, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1919, decode.acc_seg: 92.3510, loss: 0.1919 2023-01-06 02:27:22,812 - mmseg - INFO - Iter [19100/160000] lr: 5.284e-05, eta: 22:46:22, time: 0.549, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1736, decode.acc_seg: 93.0676, loss: 0.1736 2023-01-06 02:27:51,779 - mmseg - INFO - Iter [19150/160000] lr: 5.282e-05, eta: 22:45:52, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1852, decode.acc_seg: 92.6120, loss: 0.1852 2023-01-06 02:28:21,460 - mmseg - INFO - Iter [19200/160000] lr: 5.280e-05, eta: 22:45:27, time: 0.593, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1816, decode.acc_seg: 92.9209, loss: 0.1816 2023-01-06 02:28:49,309 - mmseg - INFO - Iter [19250/160000] lr: 5.278e-05, eta: 22:44:49, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2006, decode.acc_seg: 92.3313, loss: 0.2006 2023-01-06 02:29:19,304 - mmseg - INFO - Iter [19300/160000] lr: 5.276e-05, eta: 22:44:26, time: 0.600, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1894, decode.acc_seg: 92.4570, loss: 0.1894 2023-01-06 02:29:49,854 - mmseg - INFO - Iter [19350/160000] lr: 5.274e-05, eta: 22:44:08, time: 0.612, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1899, decode.acc_seg: 92.5979, loss: 0.1899 2023-01-06 02:30:18,933 - mmseg - INFO - Iter [19400/160000] lr: 5.273e-05, eta: 22:43:38, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1871, decode.acc_seg: 92.6419, loss: 0.1871 2023-01-06 02:30:47,619 - mmseg - INFO - Iter [19450/160000] lr: 5.271e-05, eta: 22:43:07, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1842, decode.acc_seg: 92.7679, loss: 0.1842 2023-01-06 02:31:15,506 - mmseg - INFO - Iter [19500/160000] lr: 5.269e-05, eta: 22:42:28, time: 0.557, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1714, decode.acc_seg: 93.1526, loss: 0.1714 2023-01-06 02:31:44,407 - mmseg - INFO - Iter [19550/160000] lr: 5.267e-05, eta: 22:41:58, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1763, decode.acc_seg: 93.1821, loss: 0.1763 2023-01-06 02:32:12,933 - mmseg - INFO - Iter [19600/160000] lr: 5.265e-05, eta: 22:41:25, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1749, decode.acc_seg: 92.9996, loss: 0.1749 2023-01-06 02:32:41,305 - mmseg - INFO - Iter [19650/160000] lr: 5.263e-05, eta: 22:40:51, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1613, decode.acc_seg: 93.4133, loss: 0.1613 2023-01-06 02:33:09,357 - mmseg - INFO - Iter [19700/160000] lr: 5.261e-05, eta: 22:40:14, time: 0.561, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1883, decode.acc_seg: 92.6608, loss: 0.1883 2023-01-06 02:33:39,579 - mmseg - INFO - Iter [19750/160000] lr: 5.259e-05, eta: 22:39:53, time: 0.604, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1862, decode.acc_seg: 92.6853, loss: 0.1862 2023-01-06 02:34:09,143 - mmseg - INFO - Iter [19800/160000] lr: 5.258e-05, eta: 22:39:27, time: 0.590, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1745, decode.acc_seg: 93.0801, loss: 0.1745 2023-01-06 02:34:36,566 - mmseg - INFO - Iter [19850/160000] lr: 5.256e-05, eta: 22:38:47, time: 0.549, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1771, decode.acc_seg: 93.0488, loss: 0.1771 2023-01-06 02:35:03,879 - mmseg - INFO - Iter [19900/160000] lr: 5.254e-05, eta: 22:38:05, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1809, decode.acc_seg: 92.7898, loss: 0.1809 2023-01-06 02:35:32,308 - mmseg - INFO - Iter [19950/160000] lr: 5.252e-05, eta: 22:37:32, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1775, decode.acc_seg: 92.9944, loss: 0.1775 2023-01-06 02:35:59,832 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 02:35:59,833 - mmseg - INFO - Iter [20000/160000] lr: 5.250e-05, eta: 22:36:51, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1837, decode.acc_seg: 92.7865, loss: 0.1837 2023-01-06 02:36:27,366 - mmseg - INFO - Iter [20050/160000] lr: 5.248e-05, eta: 22:36:12, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1854, decode.acc_seg: 92.8597, loss: 0.1854 2023-01-06 02:36:58,333 - mmseg - INFO - Iter [20100/160000] lr: 5.246e-05, eta: 22:35:56, time: 0.619, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1839, decode.acc_seg: 92.9429, loss: 0.1839 2023-01-06 02:37:26,014 - mmseg - INFO - Iter [20150/160000] lr: 5.244e-05, eta: 22:35:17, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1664, decode.acc_seg: 93.2445, loss: 0.1664 2023-01-06 02:37:55,906 - mmseg - INFO - Iter [20200/160000] lr: 5.243e-05, eta: 22:34:54, time: 0.598, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1692, decode.acc_seg: 93.2213, loss: 0.1692 2023-01-06 02:38:23,222 - mmseg - INFO - Iter [20250/160000] lr: 5.241e-05, eta: 22:34:12, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.2098, decode.acc_seg: 92.1095, loss: 0.2098 2023-01-06 02:38:50,813 - mmseg - INFO - Iter [20300/160000] lr: 5.239e-05, eta: 22:33:33, time: 0.551, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1880, decode.acc_seg: 92.4649, loss: 0.1880 2023-01-06 02:39:18,826 - mmseg - INFO - Iter [20350/160000] lr: 5.237e-05, eta: 22:32:57, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1886, decode.acc_seg: 92.7922, loss: 0.1886 2023-01-06 02:39:48,894 - mmseg - INFO - Iter [20400/160000] lr: 5.235e-05, eta: 22:32:34, time: 0.601, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1725, decode.acc_seg: 93.2855, loss: 0.1725 2023-01-06 02:40:17,291 - mmseg - INFO - Iter [20450/160000] lr: 5.233e-05, eta: 22:32:01, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1627, decode.acc_seg: 93.4356, loss: 0.1627 2023-01-06 02:40:46,807 - mmseg - INFO - Iter [20500/160000] lr: 5.231e-05, eta: 22:31:35, time: 0.590, data_time: 0.057, memory: 10576, decode.loss_ce: 0.2018, decode.acc_seg: 92.2806, loss: 0.2018 2023-01-06 02:41:14,643 - mmseg - INFO - Iter [20550/160000] lr: 5.229e-05, eta: 22:30:58, time: 0.557, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1698, decode.acc_seg: 93.4102, loss: 0.1698 2023-01-06 02:41:43,233 - mmseg - INFO - Iter [20600/160000] lr: 5.228e-05, eta: 22:30:25, time: 0.572, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1821, decode.acc_seg: 92.7140, loss: 0.1821 2023-01-06 02:42:10,708 - mmseg - INFO - Iter [20650/160000] lr: 5.226e-05, eta: 22:29:46, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1619, decode.acc_seg: 93.4957, loss: 0.1619 2023-01-06 02:42:39,150 - mmseg - INFO - Iter [20700/160000] lr: 5.224e-05, eta: 22:29:12, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1691, decode.acc_seg: 93.2827, loss: 0.1691 2023-01-06 02:43:09,149 - mmseg - INFO - Iter [20750/160000] lr: 5.222e-05, eta: 22:28:49, time: 0.600, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1637, decode.acc_seg: 93.6650, loss: 0.1637 2023-01-06 02:43:39,495 - mmseg - INFO - Iter [20800/160000] lr: 5.220e-05, eta: 22:28:29, time: 0.607, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1816, decode.acc_seg: 92.7931, loss: 0.1816 2023-01-06 02:44:11,175 - mmseg - INFO - Iter [20850/160000] lr: 5.218e-05, eta: 22:28:18, time: 0.634, data_time: 0.060, memory: 10576, decode.loss_ce: 0.1672, decode.acc_seg: 93.1246, loss: 0.1672 2023-01-06 02:44:39,121 - mmseg - INFO - Iter [20900/160000] lr: 5.216e-05, eta: 22:27:41, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1854, decode.acc_seg: 92.7824, loss: 0.1854 2023-01-06 02:45:08,995 - mmseg - INFO - Iter [20950/160000] lr: 5.214e-05, eta: 22:27:17, time: 0.597, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1754, decode.acc_seg: 92.9574, loss: 0.1754 2023-01-06 02:45:37,170 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 02:45:37,170 - mmseg - INFO - Iter [21000/160000] lr: 5.213e-05, eta: 22:26:42, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1727, decode.acc_seg: 93.1864, loss: 0.1727 2023-01-06 02:46:06,253 - mmseg - INFO - Iter [21050/160000] lr: 5.211e-05, eta: 22:26:13, time: 0.582, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1703, decode.acc_seg: 93.2004, loss: 0.1703 2023-01-06 02:46:33,558 - mmseg - INFO - Iter [21100/160000] lr: 5.209e-05, eta: 22:25:33, time: 0.546, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1658, decode.acc_seg: 93.4703, loss: 0.1658 2023-01-06 02:47:01,462 - mmseg - INFO - Iter [21150/160000] lr: 5.207e-05, eta: 22:24:56, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1724, decode.acc_seg: 93.1859, loss: 0.1724 2023-01-06 02:47:29,674 - mmseg - INFO - Iter [21200/160000] lr: 5.205e-05, eta: 22:24:21, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1679, decode.acc_seg: 93.5632, loss: 0.1679 2023-01-06 02:47:59,443 - mmseg - INFO - Iter [21250/160000] lr: 5.203e-05, eta: 22:23:57, time: 0.595, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1800, decode.acc_seg: 92.9038, loss: 0.1800 2023-01-06 02:48:28,090 - mmseg - INFO - Iter [21300/160000] lr: 5.201e-05, eta: 22:23:25, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1682, decode.acc_seg: 93.4029, loss: 0.1682 2023-01-06 02:48:57,132 - mmseg - INFO - Iter [21350/160000] lr: 5.199e-05, eta: 22:22:56, time: 0.580, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1697, decode.acc_seg: 93.2594, loss: 0.1697 2023-01-06 02:49:25,026 - mmseg - INFO - Iter [21400/160000] lr: 5.198e-05, eta: 22:22:20, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1790, decode.acc_seg: 92.9173, loss: 0.1790 2023-01-06 02:49:53,386 - mmseg - INFO - Iter [21450/160000] lr: 5.196e-05, eta: 22:21:46, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1636, decode.acc_seg: 93.5132, loss: 0.1636 2023-01-06 02:50:23,400 - mmseg - INFO - Iter [21500/160000] lr: 5.194e-05, eta: 22:21:23, time: 0.600, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1813, decode.acc_seg: 92.8243, loss: 0.1813 2023-01-06 02:50:53,200 - mmseg - INFO - Iter [21550/160000] lr: 5.192e-05, eta: 22:20:59, time: 0.596, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1710, decode.acc_seg: 93.2100, loss: 0.1710 2023-01-06 02:51:24,303 - mmseg - INFO - Iter [21600/160000] lr: 5.190e-05, eta: 22:20:43, time: 0.622, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1776, decode.acc_seg: 92.9561, loss: 0.1776 2023-01-06 02:51:53,846 - mmseg - INFO - Iter [21650/160000] lr: 5.188e-05, eta: 22:20:17, time: 0.592, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1680, decode.acc_seg: 93.2293, loss: 0.1680 2023-01-06 02:52:20,974 - mmseg - INFO - Iter [21700/160000] lr: 5.186e-05, eta: 22:19:36, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1688, decode.acc_seg: 93.4118, loss: 0.1688 2023-01-06 02:52:48,587 - mmseg - INFO - Iter [21750/160000] lr: 5.184e-05, eta: 22:18:57, time: 0.552, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1723, decode.acc_seg: 93.1582, loss: 0.1723 2023-01-06 02:53:17,047 - mmseg - INFO - Iter [21800/160000] lr: 5.183e-05, eta: 22:18:24, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1750, decode.acc_seg: 92.9199, loss: 0.1750 2023-01-06 02:53:46,302 - mmseg - INFO - Iter [21850/160000] lr: 5.181e-05, eta: 22:17:57, time: 0.586, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1706, decode.acc_seg: 93.4274, loss: 0.1706 2023-01-06 02:54:13,659 - mmseg - INFO - Iter [21900/160000] lr: 5.179e-05, eta: 22:17:17, time: 0.546, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1706, decode.acc_seg: 93.4026, loss: 0.1706 2023-01-06 02:54:44,259 - mmseg - INFO - Iter [21950/160000] lr: 5.177e-05, eta: 22:16:58, time: 0.613, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1727, decode.acc_seg: 93.3532, loss: 0.1727 2023-01-06 02:55:13,878 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 02:55:13,878 - mmseg - INFO - Iter [22000/160000] lr: 5.175e-05, eta: 22:16:32, time: 0.592, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1739, decode.acc_seg: 93.1694, loss: 0.1739 2023-01-06 02:55:41,233 - mmseg - INFO - Iter [22050/160000] lr: 5.173e-05, eta: 22:15:52, time: 0.547, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1592, decode.acc_seg: 93.5947, loss: 0.1592 2023-01-06 02:56:08,751 - mmseg - INFO - Iter [22100/160000] lr: 5.171e-05, eta: 22:15:14, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1663, decode.acc_seg: 93.4102, loss: 0.1663 2023-01-06 02:56:38,837 - mmseg - INFO - Iter [22150/160000] lr: 5.169e-05, eta: 22:14:51, time: 0.602, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1777, decode.acc_seg: 93.0137, loss: 0.1777 2023-01-06 02:57:06,103 - mmseg - INFO - Iter [22200/160000] lr: 5.168e-05, eta: 22:14:11, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1752, decode.acc_seg: 93.0744, loss: 0.1752 2023-01-06 02:57:34,451 - mmseg - INFO - Iter [22250/160000] lr: 5.166e-05, eta: 22:13:38, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1701, decode.acc_seg: 93.2011, loss: 0.1701 2023-01-06 02:58:02,244 - mmseg - INFO - Iter [22300/160000] lr: 5.164e-05, eta: 22:13:01, time: 0.556, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1717, decode.acc_seg: 93.0999, loss: 0.1717 2023-01-06 02:58:32,553 - mmseg - INFO - Iter [22350/160000] lr: 5.162e-05, eta: 22:12:39, time: 0.606, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1686, decode.acc_seg: 93.3568, loss: 0.1686 2023-01-06 02:59:01,313 - mmseg - INFO - Iter [22400/160000] lr: 5.160e-05, eta: 22:12:09, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1791, decode.acc_seg: 92.8808, loss: 0.1791 2023-01-06 02:59:28,672 - mmseg - INFO - Iter [22450/160000] lr: 5.158e-05, eta: 22:11:29, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1789, decode.acc_seg: 93.2096, loss: 0.1789 2023-01-06 02:59:57,753 - mmseg - INFO - Iter [22500/160000] lr: 5.156e-05, eta: 22:11:00, time: 0.581, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1624, decode.acc_seg: 93.5667, loss: 0.1624 2023-01-06 03:00:27,109 - mmseg - INFO - Iter [22550/160000] lr: 5.154e-05, eta: 22:10:34, time: 0.588, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1768, decode.acc_seg: 93.2420, loss: 0.1768 2023-01-06 03:00:56,079 - mmseg - INFO - Iter [22600/160000] lr: 5.153e-05, eta: 22:10:04, time: 0.579, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1604, decode.acc_seg: 93.6026, loss: 0.1604 2023-01-06 03:01:24,630 - mmseg - INFO - Iter [22650/160000] lr: 5.151e-05, eta: 22:09:32, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1602, decode.acc_seg: 93.6849, loss: 0.1602 2023-01-06 03:01:54,972 - mmseg - INFO - Iter [22700/160000] lr: 5.149e-05, eta: 22:09:11, time: 0.607, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1784, decode.acc_seg: 93.0567, loss: 0.1784 2023-01-06 03:02:23,242 - mmseg - INFO - Iter [22750/160000] lr: 5.147e-05, eta: 22:08:37, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1691, decode.acc_seg: 93.5598, loss: 0.1691 2023-01-06 03:02:52,885 - mmseg - INFO - Iter [22800/160000] lr: 5.145e-05, eta: 22:08:12, time: 0.592, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1633, decode.acc_seg: 93.6042, loss: 0.1633 2023-01-06 03:03:22,134 - mmseg - INFO - Iter [22850/160000] lr: 5.143e-05, eta: 22:07:44, time: 0.585, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1598, decode.acc_seg: 93.5453, loss: 0.1598 2023-01-06 03:03:49,263 - mmseg - INFO - Iter [22900/160000] lr: 5.141e-05, eta: 22:07:03, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1586, decode.acc_seg: 93.6599, loss: 0.1586 2023-01-06 03:04:17,067 - mmseg - INFO - Iter [22950/160000] lr: 5.139e-05, eta: 22:06:27, time: 0.556, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1759, decode.acc_seg: 93.1799, loss: 0.1759 2023-01-06 03:04:46,602 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 03:04:46,602 - mmseg - INFO - Iter [23000/160000] lr: 5.138e-05, eta: 22:06:01, time: 0.591, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1738, decode.acc_seg: 93.0281, loss: 0.1738 2023-01-06 03:05:14,161 - mmseg - INFO - Iter [23050/160000] lr: 5.136e-05, eta: 22:05:23, time: 0.550, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1734, decode.acc_seg: 92.9639, loss: 0.1734 2023-01-06 03:05:44,009 - mmseg - INFO - Iter [23100/160000] lr: 5.134e-05, eta: 22:04:59, time: 0.598, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1680, decode.acc_seg: 93.3431, loss: 0.1680 2023-01-06 03:06:11,332 - mmseg - INFO - Iter [23150/160000] lr: 5.132e-05, eta: 22:04:20, time: 0.547, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1880, decode.acc_seg: 92.5763, loss: 0.1880 2023-01-06 03:06:40,484 - mmseg - INFO - Iter [23200/160000] lr: 5.130e-05, eta: 22:03:51, time: 0.582, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1592, decode.acc_seg: 93.8786, loss: 0.1592 2023-01-06 03:07:09,603 - mmseg - INFO - Iter [23250/160000] lr: 5.128e-05, eta: 22:03:23, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1759, decode.acc_seg: 93.2519, loss: 0.1759 2023-01-06 03:07:37,018 - mmseg - INFO - Iter [23300/160000] lr: 5.126e-05, eta: 22:02:44, time: 0.548, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1678, decode.acc_seg: 93.4330, loss: 0.1678 2023-01-06 03:08:04,287 - mmseg - INFO - Iter [23350/160000] lr: 5.124e-05, eta: 22:02:05, time: 0.545, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1673, decode.acc_seg: 93.4398, loss: 0.1673 2023-01-06 03:08:32,414 - mmseg - INFO - Iter [23400/160000] lr: 5.123e-05, eta: 22:01:31, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1687, decode.acc_seg: 93.2997, loss: 0.1687 2023-01-06 03:09:03,387 - mmseg - INFO - Iter [23450/160000] lr: 5.121e-05, eta: 22:01:13, time: 0.619, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1777, decode.acc_seg: 92.7612, loss: 0.1777 2023-01-06 03:09:31,426 - mmseg - INFO - Iter [23500/160000] lr: 5.119e-05, eta: 22:00:38, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1601, decode.acc_seg: 93.7597, loss: 0.1601 2023-01-06 03:09:59,771 - mmseg - INFO - Iter [23550/160000] lr: 5.117e-05, eta: 22:00:05, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1620, decode.acc_seg: 93.6643, loss: 0.1620 2023-01-06 03:10:28,941 - mmseg - INFO - Iter [23600/160000] lr: 5.115e-05, eta: 21:59:37, time: 0.584, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1615, decode.acc_seg: 93.4719, loss: 0.1615 2023-01-06 03:10:58,323 - mmseg - INFO - Iter [23650/160000] lr: 5.113e-05, eta: 21:59:10, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1767, decode.acc_seg: 93.1243, loss: 0.1767 2023-01-06 03:11:26,178 - mmseg - INFO - Iter [23700/160000] lr: 5.111e-05, eta: 21:58:35, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1678, decode.acc_seg: 93.2414, loss: 0.1678 2023-01-06 03:11:53,842 - mmseg - INFO - Iter [23750/160000] lr: 5.109e-05, eta: 21:57:58, time: 0.553, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1571, decode.acc_seg: 93.6582, loss: 0.1571 2023-01-06 03:12:22,500 - mmseg - INFO - Iter [23800/160000] lr: 5.108e-05, eta: 21:57:26, time: 0.572, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1762, decode.acc_seg: 93.2264, loss: 0.1762 2023-01-06 03:12:54,639 - mmseg - INFO - Iter [23850/160000] lr: 5.106e-05, eta: 21:57:15, time: 0.643, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1814, decode.acc_seg: 92.9545, loss: 0.1814 2023-01-06 03:13:23,583 - mmseg - INFO - Iter [23900/160000] lr: 5.104e-05, eta: 21:56:46, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1692, decode.acc_seg: 93.5712, loss: 0.1692 2023-01-06 03:13:52,664 - mmseg - INFO - Iter [23950/160000] lr: 5.102e-05, eta: 21:56:17, time: 0.582, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1734, decode.acc_seg: 93.0626, loss: 0.1734 2023-01-06 03:14:20,866 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 03:14:20,866 - mmseg - INFO - Iter [24000/160000] lr: 5.100e-05, eta: 21:55:43, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1606, decode.acc_seg: 93.3943, loss: 0.1606 2023-01-06 03:14:49,950 - mmseg - INFO - Iter [24050/160000] lr: 5.098e-05, eta: 21:55:15, time: 0.581, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1739, decode.acc_seg: 93.0602, loss: 0.1739 2023-01-06 03:15:19,701 - mmseg - INFO - Iter [24100/160000] lr: 5.096e-05, eta: 21:54:50, time: 0.596, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1686, decode.acc_seg: 93.2739, loss: 0.1686 2023-01-06 03:15:47,759 - mmseg - INFO - Iter [24150/160000] lr: 5.094e-05, eta: 21:54:15, time: 0.560, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1698, decode.acc_seg: 93.3942, loss: 0.1698 2023-01-06 03:16:18,679 - mmseg - INFO - Iter [24200/160000] lr: 5.093e-05, eta: 21:53:57, time: 0.618, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1778, decode.acc_seg: 93.1580, loss: 0.1778 2023-01-06 03:16:48,689 - mmseg - INFO - Iter [24250/160000] lr: 5.091e-05, eta: 21:53:34, time: 0.601, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1558, decode.acc_seg: 93.8479, loss: 0.1558 2023-01-06 03:17:17,979 - mmseg - INFO - Iter [24300/160000] lr: 5.089e-05, eta: 21:53:06, time: 0.585, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1507, decode.acc_seg: 93.9183, loss: 0.1507 2023-01-06 03:17:46,179 - mmseg - INFO - Iter [24350/160000] lr: 5.087e-05, eta: 21:52:32, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1563, decode.acc_seg: 93.6141, loss: 0.1563 2023-01-06 03:18:14,688 - mmseg - INFO - Iter [24400/160000] lr: 5.085e-05, eta: 21:52:00, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1547, decode.acc_seg: 93.7011, loss: 0.1547 2023-01-06 03:18:44,781 - mmseg - INFO - Iter [24450/160000] lr: 5.083e-05, eta: 21:51:37, time: 0.602, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1665, decode.acc_seg: 93.5324, loss: 0.1665 2023-01-06 03:19:13,622 - mmseg - INFO - Iter [24500/160000] lr: 5.081e-05, eta: 21:51:07, time: 0.578, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1639, decode.acc_seg: 93.5297, loss: 0.1639 2023-01-06 03:19:41,433 - mmseg - INFO - Iter [24550/160000] lr: 5.079e-05, eta: 21:50:31, time: 0.556, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1740, decode.acc_seg: 93.1050, loss: 0.1740 2023-01-06 03:20:12,647 - mmseg - INFO - Iter [24600/160000] lr: 5.078e-05, eta: 21:50:14, time: 0.624, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1604, decode.acc_seg: 93.8269, loss: 0.1604 2023-01-06 03:20:41,624 - mmseg - INFO - Iter [24650/160000] lr: 5.076e-05, eta: 21:49:45, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1676, decode.acc_seg: 93.4162, loss: 0.1676 2023-01-06 03:21:09,021 - mmseg - INFO - Iter [24700/160000] lr: 5.074e-05, eta: 21:49:07, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1549, decode.acc_seg: 93.5039, loss: 0.1549 2023-01-06 03:21:37,613 - mmseg - INFO - Iter [24750/160000] lr: 5.072e-05, eta: 21:48:36, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1574, decode.acc_seg: 93.8375, loss: 0.1574 2023-01-06 03:22:06,985 - mmseg - INFO - Iter [24800/160000] lr: 5.070e-05, eta: 21:48:08, time: 0.587, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1631, decode.acc_seg: 93.4508, loss: 0.1631 2023-01-06 03:22:36,088 - mmseg - INFO - Iter [24850/160000] lr: 5.068e-05, eta: 21:47:40, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1736, decode.acc_seg: 93.3165, loss: 0.1736 2023-01-06 03:23:03,584 - mmseg - INFO - Iter [24900/160000] lr: 5.066e-05, eta: 21:47:02, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1810, decode.acc_seg: 93.0923, loss: 0.1810 2023-01-06 03:23:34,602 - mmseg - INFO - Iter [24950/160000] lr: 5.064e-05, eta: 21:46:44, time: 0.621, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1670, decode.acc_seg: 93.4813, loss: 0.1670 2023-01-06 03:24:03,505 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 03:24:03,506 - mmseg - INFO - Iter [25000/160000] lr: 5.063e-05, eta: 21:46:15, time: 0.578, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1574, decode.acc_seg: 93.8315, loss: 0.1574 2023-01-06 03:24:31,681 - mmseg - INFO - Iter [25050/160000] lr: 5.061e-05, eta: 21:45:41, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1574, decode.acc_seg: 93.7245, loss: 0.1574 2023-01-06 03:24:59,386 - mmseg - INFO - Iter [25100/160000] lr: 5.059e-05, eta: 21:45:05, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1631, decode.acc_seg: 93.7454, loss: 0.1631 2023-01-06 03:25:27,556 - mmseg - INFO - Iter [25150/160000] lr: 5.057e-05, eta: 21:44:31, time: 0.562, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1681, decode.acc_seg: 93.2750, loss: 0.1681 2023-01-06 03:25:55,848 - mmseg - INFO - Iter [25200/160000] lr: 5.055e-05, eta: 21:43:58, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1718, decode.acc_seg: 93.2247, loss: 0.1718 2023-01-06 03:26:23,056 - mmseg - INFO - Iter [25250/160000] lr: 5.053e-05, eta: 21:43:20, time: 0.544, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1669, decode.acc_seg: 93.3833, loss: 0.1669 2023-01-06 03:26:53,821 - mmseg - INFO - Iter [25300/160000] lr: 5.051e-05, eta: 21:43:00, time: 0.615, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1602, decode.acc_seg: 93.6132, loss: 0.1602 2023-01-06 03:27:22,257 - mmseg - INFO - Iter [25350/160000] lr: 5.049e-05, eta: 21:42:28, time: 0.569, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1708, decode.acc_seg: 93.2203, loss: 0.1708 2023-01-06 03:27:51,070 - mmseg - INFO - Iter [25400/160000] lr: 5.048e-05, eta: 21:41:58, time: 0.576, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1547, decode.acc_seg: 93.7714, loss: 0.1547 2023-01-06 03:28:18,574 - mmseg - INFO - Iter [25450/160000] lr: 5.046e-05, eta: 21:41:21, time: 0.550, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1642, decode.acc_seg: 93.4861, loss: 0.1642 2023-01-06 03:28:47,470 - mmseg - INFO - Iter [25500/160000] lr: 5.044e-05, eta: 21:40:51, time: 0.578, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1615, decode.acc_seg: 93.6286, loss: 0.1615 2023-01-06 03:29:14,655 - mmseg - INFO - Iter [25550/160000] lr: 5.042e-05, eta: 21:40:12, time: 0.544, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1547, decode.acc_seg: 93.7697, loss: 0.1547 2023-01-06 03:29:42,399 - mmseg - INFO - Iter [25600/160000] lr: 5.040e-05, eta: 21:39:37, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1554, decode.acc_seg: 93.8263, loss: 0.1554 2023-01-06 03:30:10,758 - mmseg - INFO - Iter [25650/160000] lr: 5.038e-05, eta: 21:39:04, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1806, decode.acc_seg: 93.2001, loss: 0.1806 2023-01-06 03:30:40,172 - mmseg - INFO - Iter [25700/160000] lr: 5.036e-05, eta: 21:38:37, time: 0.588, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1617, decode.acc_seg: 93.5456, loss: 0.1617 2023-01-06 03:31:07,659 - mmseg - INFO - Iter [25750/160000] lr: 5.034e-05, eta: 21:38:00, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1614, decode.acc_seg: 93.6302, loss: 0.1614 2023-01-06 03:31:36,762 - mmseg - INFO - Iter [25800/160000] lr: 5.033e-05, eta: 21:37:32, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1518, decode.acc_seg: 94.0616, loss: 0.1518 2023-01-06 03:32:04,362 - mmseg - INFO - Iter [25850/160000] lr: 5.031e-05, eta: 21:36:56, time: 0.551, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1543, decode.acc_seg: 93.7876, loss: 0.1543 2023-01-06 03:32:33,743 - mmseg - INFO - Iter [25900/160000] lr: 5.029e-05, eta: 21:36:28, time: 0.588, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1652, decode.acc_seg: 93.3577, loss: 0.1652 2023-01-06 03:33:03,054 - mmseg - INFO - Iter [25950/160000] lr: 5.027e-05, eta: 21:36:01, time: 0.586, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1507, decode.acc_seg: 93.6965, loss: 0.1507 2023-01-06 03:33:30,858 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 03:33:30,859 - mmseg - INFO - Iter [26000/160000] lr: 5.025e-05, eta: 21:35:26, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1515, decode.acc_seg: 93.9569, loss: 0.1515 2023-01-06 03:34:01,827 - mmseg - INFO - Iter [26050/160000] lr: 5.023e-05, eta: 21:35:07, time: 0.619, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1500, decode.acc_seg: 94.0678, loss: 0.1500 2023-01-06 03:34:30,079 - mmseg - INFO - Iter [26100/160000] lr: 5.021e-05, eta: 21:34:34, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1627, decode.acc_seg: 93.7119, loss: 0.1627 2023-01-06 03:34:58,732 - mmseg - INFO - Iter [26150/160000] lr: 5.019e-05, eta: 21:34:03, time: 0.572, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1549, decode.acc_seg: 93.9703, loss: 0.1549 2023-01-06 03:35:28,313 - mmseg - INFO - Iter [26200/160000] lr: 5.018e-05, eta: 21:33:37, time: 0.592, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1523, decode.acc_seg: 93.8355, loss: 0.1523 2023-01-06 03:35:56,762 - mmseg - INFO - Iter [26250/160000] lr: 5.016e-05, eta: 21:33:06, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1640, decode.acc_seg: 93.5343, loss: 0.1640 2023-01-06 03:36:26,278 - mmseg - INFO - Iter [26300/160000] lr: 5.014e-05, eta: 21:32:39, time: 0.590, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1518, decode.acc_seg: 93.8942, loss: 0.1518 2023-01-06 03:36:55,161 - mmseg - INFO - Iter [26350/160000] lr: 5.012e-05, eta: 21:32:10, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1669, decode.acc_seg: 93.3730, loss: 0.1669 2023-01-06 03:37:23,206 - mmseg - INFO - Iter [26400/160000] lr: 5.010e-05, eta: 21:31:35, time: 0.560, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1530, decode.acc_seg: 93.9665, loss: 0.1530 2023-01-06 03:37:53,737 - mmseg - INFO - Iter [26450/160000] lr: 5.008e-05, eta: 21:31:14, time: 0.611, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1624, decode.acc_seg: 93.7580, loss: 0.1624 2023-01-06 03:38:20,900 - mmseg - INFO - Iter [26500/160000] lr: 5.006e-05, eta: 21:30:36, time: 0.543, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1492, decode.acc_seg: 94.0240, loss: 0.1492 2023-01-06 03:38:48,945 - mmseg - INFO - Iter [26550/160000] lr: 5.004e-05, eta: 21:30:02, time: 0.561, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1457, decode.acc_seg: 94.0616, loss: 0.1457 2023-01-06 03:39:16,471 - mmseg - INFO - Iter [26600/160000] lr: 5.003e-05, eta: 21:29:26, time: 0.550, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1569, decode.acc_seg: 93.8495, loss: 0.1569 2023-01-06 03:39:45,419 - mmseg - INFO - Iter [26650/160000] lr: 5.001e-05, eta: 21:28:57, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1694, decode.acc_seg: 93.3122, loss: 0.1694 2023-01-06 03:40:14,953 - mmseg - INFO - Iter [26700/160000] lr: 4.999e-05, eta: 21:28:30, time: 0.590, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1506, decode.acc_seg: 93.8568, loss: 0.1506 2023-01-06 03:40:43,887 - mmseg - INFO - Iter [26750/160000] lr: 4.997e-05, eta: 21:28:01, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1618, decode.acc_seg: 93.8400, loss: 0.1618 2023-01-06 03:41:14,735 - mmseg - INFO - Iter [26800/160000] lr: 4.995e-05, eta: 21:27:41, time: 0.617, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1675, decode.acc_seg: 93.1455, loss: 0.1675 2023-01-06 03:41:44,969 - mmseg - INFO - Iter [26850/160000] lr: 4.993e-05, eta: 21:27:18, time: 0.605, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1672, decode.acc_seg: 93.3851, loss: 0.1672 2023-01-06 03:42:14,994 - mmseg - INFO - Iter [26900/160000] lr: 4.991e-05, eta: 21:26:54, time: 0.601, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1743, decode.acc_seg: 93.1575, loss: 0.1743 2023-01-06 03:42:44,739 - mmseg - INFO - Iter [26950/160000] lr: 4.989e-05, eta: 21:26:29, time: 0.596, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1508, decode.acc_seg: 93.8685, loss: 0.1508 2023-01-06 03:43:12,768 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 03:43:12,769 - mmseg - INFO - Iter [27000/160000] lr: 4.988e-05, eta: 21:25:55, time: 0.560, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1419, decode.acc_seg: 94.3055, loss: 0.1419 2023-01-06 03:43:41,259 - mmseg - INFO - Iter [27050/160000] lr: 4.986e-05, eta: 21:25:24, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1550, decode.acc_seg: 93.8512, loss: 0.1550 2023-01-06 03:44:09,066 - mmseg - INFO - Iter [27100/160000] lr: 4.984e-05, eta: 21:24:48, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1560, decode.acc_seg: 93.7416, loss: 0.1560 2023-01-06 03:44:38,369 - mmseg - INFO - Iter [27150/160000] lr: 4.982e-05, eta: 21:24:21, time: 0.586, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1511, decode.acc_seg: 94.0413, loss: 0.1511 2023-01-06 03:45:08,888 - mmseg - INFO - Iter [27200/160000] lr: 4.980e-05, eta: 21:24:00, time: 0.611, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1503, decode.acc_seg: 94.0539, loss: 0.1503 2023-01-06 03:45:37,234 - mmseg - INFO - Iter [27250/160000] lr: 4.978e-05, eta: 21:23:27, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1482, decode.acc_seg: 94.2708, loss: 0.1482 2023-01-06 03:46:05,932 - mmseg - INFO - Iter [27300/160000] lr: 4.976e-05, eta: 21:22:57, time: 0.574, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1703, decode.acc_seg: 93.4706, loss: 0.1703 2023-01-06 03:46:34,714 - mmseg - INFO - Iter [27350/160000] lr: 4.974e-05, eta: 21:22:27, time: 0.576, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1490, decode.acc_seg: 93.8145, loss: 0.1490 2023-01-06 03:47:01,974 - mmseg - INFO - Iter [27400/160000] lr: 4.973e-05, eta: 21:21:49, time: 0.545, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1469, decode.acc_seg: 93.9472, loss: 0.1469 2023-01-06 03:47:29,883 - mmseg - INFO - Iter [27450/160000] lr: 4.971e-05, eta: 21:21:15, time: 0.557, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1582, decode.acc_seg: 93.7814, loss: 0.1582 2023-01-06 03:47:58,202 - mmseg - INFO - Iter [27500/160000] lr: 4.969e-05, eta: 21:20:43, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1686, decode.acc_seg: 93.5562, loss: 0.1686 2023-01-06 03:48:28,566 - mmseg - INFO - Iter [27550/160000] lr: 4.967e-05, eta: 21:20:20, time: 0.607, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1743, decode.acc_seg: 92.9403, loss: 0.1743 2023-01-06 03:48:56,825 - mmseg - INFO - Iter [27600/160000] lr: 4.965e-05, eta: 21:19:48, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1668, decode.acc_seg: 93.4236, loss: 0.1668 2023-01-06 03:49:26,607 - mmseg - INFO - Iter [27650/160000] lr: 4.963e-05, eta: 21:19:22, time: 0.595, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1532, decode.acc_seg: 93.8486, loss: 0.1532 2023-01-06 03:49:56,444 - mmseg - INFO - Iter [27700/160000] lr: 4.961e-05, eta: 21:18:57, time: 0.597, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1443, decode.acc_seg: 94.0093, loss: 0.1443 2023-01-06 03:50:25,752 - mmseg - INFO - Iter [27750/160000] lr: 4.959e-05, eta: 21:18:30, time: 0.586, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1508, decode.acc_seg: 94.1509, loss: 0.1508 2023-01-06 03:50:53,837 - mmseg - INFO - Iter [27800/160000] lr: 4.958e-05, eta: 21:17:57, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1601, decode.acc_seg: 93.7404, loss: 0.1601 2023-01-06 03:51:22,620 - mmseg - INFO - Iter [27850/160000] lr: 4.956e-05, eta: 21:17:26, time: 0.575, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1650, decode.acc_seg: 93.4271, loss: 0.1650 2023-01-06 03:51:51,737 - mmseg - INFO - Iter [27900/160000] lr: 4.954e-05, eta: 21:16:58, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1523, decode.acc_seg: 93.7086, loss: 0.1523 2023-01-06 03:52:21,198 - mmseg - INFO - Iter [27950/160000] lr: 4.952e-05, eta: 21:16:31, time: 0.589, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1465, decode.acc_seg: 94.0558, loss: 0.1465 2023-01-06 03:52:48,766 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 03:52:48,767 - mmseg - INFO - Iter [28000/160000] lr: 4.950e-05, eta: 21:15:55, time: 0.551, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1426, decode.acc_seg: 94.3427, loss: 0.1426 2023-01-06 03:53:17,196 - mmseg - INFO - Iter [28050/160000] lr: 4.948e-05, eta: 21:15:24, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1609, decode.acc_seg: 93.7985, loss: 0.1609 2023-01-06 03:53:46,240 - mmseg - INFO - Iter [28100/160000] lr: 4.946e-05, eta: 21:14:55, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1545, decode.acc_seg: 93.7456, loss: 0.1545 2023-01-06 03:54:14,894 - mmseg - INFO - Iter [28150/160000] lr: 4.944e-05, eta: 21:14:24, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1542, decode.acc_seg: 93.7986, loss: 0.1542 2023-01-06 03:54:43,495 - mmseg - INFO - Iter [28200/160000] lr: 4.943e-05, eta: 21:13:54, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1554, decode.acc_seg: 93.7961, loss: 0.1554 2023-01-06 03:55:11,062 - mmseg - INFO - Iter [28250/160000] lr: 4.941e-05, eta: 21:13:18, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1640, decode.acc_seg: 93.5185, loss: 0.1640 2023-01-06 03:55:42,078 - mmseg - INFO - Iter [28300/160000] lr: 4.939e-05, eta: 21:12:58, time: 0.619, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1459, decode.acc_seg: 94.1863, loss: 0.1459 2023-01-06 03:56:11,225 - mmseg - INFO - Iter [28350/160000] lr: 4.937e-05, eta: 21:12:30, time: 0.584, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1507, decode.acc_seg: 94.0005, loss: 0.1507 2023-01-06 03:56:40,636 - mmseg - INFO - Iter [28400/160000] lr: 4.935e-05, eta: 21:12:03, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1499, decode.acc_seg: 93.9414, loss: 0.1499 2023-01-06 03:57:09,371 - mmseg - INFO - Iter [28450/160000] lr: 4.933e-05, eta: 21:11:33, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1745, decode.acc_seg: 93.2287, loss: 0.1745 2023-01-06 03:57:38,042 - mmseg - INFO - Iter [28500/160000] lr: 4.931e-05, eta: 21:11:02, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1612, decode.acc_seg: 93.7837, loss: 0.1612 2023-01-06 03:58:06,109 - mmseg - INFO - Iter [28550/160000] lr: 4.929e-05, eta: 21:10:29, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1695, decode.acc_seg: 93.3554, loss: 0.1695 2023-01-06 03:58:34,785 - mmseg - INFO - Iter [28600/160000] lr: 4.928e-05, eta: 21:09:58, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1512, decode.acc_seg: 93.9500, loss: 0.1512 2023-01-06 03:59:05,173 - mmseg - INFO - Iter [28650/160000] lr: 4.926e-05, eta: 21:09:36, time: 0.609, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1592, decode.acc_seg: 93.7968, loss: 0.1592 2023-01-06 03:59:33,813 - mmseg - INFO - Iter [28700/160000] lr: 4.924e-05, eta: 21:09:05, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1443, decode.acc_seg: 94.1044, loss: 0.1443 2023-01-06 04:00:01,031 - mmseg - INFO - Iter [28750/160000] lr: 4.922e-05, eta: 21:08:28, time: 0.544, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1514, decode.acc_seg: 94.0510, loss: 0.1514 2023-01-06 04:00:29,818 - mmseg - INFO - Iter [28800/160000] lr: 4.920e-05, eta: 21:07:58, time: 0.575, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1457, decode.acc_seg: 94.3993, loss: 0.1457 2023-01-06 04:00:59,418 - mmseg - INFO - Iter [28850/160000] lr: 4.918e-05, eta: 21:07:32, time: 0.593, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1545, decode.acc_seg: 93.8063, loss: 0.1545 2023-01-06 04:01:27,719 - mmseg - INFO - Iter [28900/160000] lr: 4.916e-05, eta: 21:07:00, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1680, decode.acc_seg: 93.4893, loss: 0.1680 2023-01-06 04:01:55,431 - mmseg - INFO - Iter [28950/160000] lr: 4.914e-05, eta: 21:06:25, time: 0.554, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1559, decode.acc_seg: 94.0469, loss: 0.1559 2023-01-06 04:02:22,647 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 04:02:22,648 - mmseg - INFO - Iter [29000/160000] lr: 4.913e-05, eta: 21:05:48, time: 0.544, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1681, decode.acc_seg: 93.2392, loss: 0.1681 2023-01-06 04:02:53,085 - mmseg - INFO - Iter [29050/160000] lr: 4.911e-05, eta: 21:05:26, time: 0.609, data_time: 0.056, memory: 10576, decode.loss_ce: 0.1476, decode.acc_seg: 93.9998, loss: 0.1476 2023-01-06 04:03:20,344 - mmseg - INFO - Iter [29100/160000] lr: 4.909e-05, eta: 21:04:49, time: 0.545, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1483, decode.acc_seg: 94.2848, loss: 0.1483 2023-01-06 04:03:48,559 - mmseg - INFO - Iter [29150/160000] lr: 4.907e-05, eta: 21:04:16, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1435, decode.acc_seg: 94.1509, loss: 0.1435 2023-01-06 04:04:17,717 - mmseg - INFO - Iter [29200/160000] lr: 4.905e-05, eta: 21:03:48, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1546, decode.acc_seg: 93.8348, loss: 0.1546 2023-01-06 04:04:46,575 - mmseg - INFO - Iter [29250/160000] lr: 4.903e-05, eta: 21:03:18, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1557, decode.acc_seg: 93.8097, loss: 0.1557 2023-01-06 04:05:15,859 - mmseg - INFO - Iter [29300/160000] lr: 4.901e-05, eta: 21:02:51, time: 0.586, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1430, decode.acc_seg: 94.2254, loss: 0.1430 2023-01-06 04:05:44,133 - mmseg - INFO - Iter [29350/160000] lr: 4.899e-05, eta: 21:02:19, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1439, decode.acc_seg: 94.1812, loss: 0.1439 2023-01-06 04:06:15,037 - mmseg - INFO - Iter [29400/160000] lr: 4.898e-05, eta: 21:01:58, time: 0.618, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1521, decode.acc_seg: 93.9930, loss: 0.1521 2023-01-06 04:06:42,510 - mmseg - INFO - Iter [29450/160000] lr: 4.896e-05, eta: 21:01:23, time: 0.550, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1388, decode.acc_seg: 94.3707, loss: 0.1388 2023-01-06 04:07:11,878 - mmseg - INFO - Iter [29500/160000] lr: 4.894e-05, eta: 21:00:55, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1430, decode.acc_seg: 94.1661, loss: 0.1430 2023-01-06 04:07:41,345 - mmseg - INFO - Iter [29550/160000] lr: 4.892e-05, eta: 21:00:28, time: 0.589, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1470, decode.acc_seg: 93.9997, loss: 0.1470 2023-01-06 04:08:10,130 - mmseg - INFO - Iter [29600/160000] lr: 4.890e-05, eta: 20:59:58, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1468, decode.acc_seg: 94.0361, loss: 0.1468 2023-01-06 04:08:38,512 - mmseg - INFO - Iter [29650/160000] lr: 4.888e-05, eta: 20:59:27, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1619, decode.acc_seg: 93.4666, loss: 0.1619 2023-01-06 04:09:07,195 - mmseg - INFO - Iter [29700/160000] lr: 4.886e-05, eta: 20:58:56, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1749, decode.acc_seg: 93.2167, loss: 0.1749 2023-01-06 04:09:34,466 - mmseg - INFO - Iter [29750/160000] lr: 4.884e-05, eta: 20:58:20, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1538, decode.acc_seg: 94.0400, loss: 0.1538 2023-01-06 04:10:04,529 - mmseg - INFO - Iter [29800/160000] lr: 4.883e-05, eta: 20:57:56, time: 0.601, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1523, decode.acc_seg: 93.8078, loss: 0.1523 2023-01-06 04:10:31,726 - mmseg - INFO - Iter [29850/160000] lr: 4.881e-05, eta: 20:57:19, time: 0.543, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1490, decode.acc_seg: 94.0327, loss: 0.1490 2023-01-06 04:11:00,857 - mmseg - INFO - Iter [29900/160000] lr: 4.879e-05, eta: 20:56:51, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1474, decode.acc_seg: 94.1251, loss: 0.1474 2023-01-06 04:11:28,555 - mmseg - INFO - Iter [29950/160000] lr: 4.877e-05, eta: 20:56:16, time: 0.553, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1466, decode.acc_seg: 94.0131, loss: 0.1466 2023-01-06 04:11:58,191 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 04:11:58,192 - mmseg - INFO - Iter [30000/160000] lr: 4.875e-05, eta: 20:55:50, time: 0.593, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1440, decode.acc_seg: 94.2555, loss: 0.1440 2023-01-06 04:12:27,371 - mmseg - INFO - Iter [30050/160000] lr: 4.873e-05, eta: 20:55:22, time: 0.584, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1445, decode.acc_seg: 94.1202, loss: 0.1445 2023-01-06 04:12:56,722 - mmseg - INFO - Iter [30100/160000] lr: 4.871e-05, eta: 20:54:54, time: 0.588, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1401, decode.acc_seg: 94.2082, loss: 0.1401 2023-01-06 04:13:26,675 - mmseg - INFO - Iter [30150/160000] lr: 4.869e-05, eta: 20:54:30, time: 0.599, data_time: 0.056, memory: 10576, decode.loss_ce: 0.1390, decode.acc_seg: 94.3238, loss: 0.1390 2023-01-06 04:13:54,657 - mmseg - INFO - Iter [30200/160000] lr: 4.868e-05, eta: 20:53:56, time: 0.559, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1498, decode.acc_seg: 94.0215, loss: 0.1498 2023-01-06 04:14:23,873 - mmseg - INFO - Iter [30250/160000] lr: 4.866e-05, eta: 20:53:28, time: 0.585, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1416, decode.acc_seg: 94.3968, loss: 0.1416 2023-01-06 04:14:51,009 - mmseg - INFO - Iter [30300/160000] lr: 4.864e-05, eta: 20:52:51, time: 0.543, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1509, decode.acc_seg: 94.0819, loss: 0.1509 2023-01-06 04:15:18,832 - mmseg - INFO - Iter [30350/160000] lr: 4.862e-05, eta: 20:52:18, time: 0.556, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1479, decode.acc_seg: 94.1978, loss: 0.1479 2023-01-06 04:15:47,045 - mmseg - INFO - Iter [30400/160000] lr: 4.860e-05, eta: 20:51:45, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1533, decode.acc_seg: 93.8579, loss: 0.1533 2023-01-06 04:16:15,501 - mmseg - INFO - Iter [30450/160000] lr: 4.858e-05, eta: 20:51:14, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1612, decode.acc_seg: 93.7136, loss: 0.1612 2023-01-06 04:16:43,047 - mmseg - INFO - Iter [30500/160000] lr: 4.856e-05, eta: 20:50:39, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1547, decode.acc_seg: 93.8660, loss: 0.1547 2023-01-06 04:17:12,763 - mmseg - INFO - Iter [30550/160000] lr: 4.854e-05, eta: 20:50:13, time: 0.594, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1457, decode.acc_seg: 94.1372, loss: 0.1457 2023-01-06 04:17:41,742 - mmseg - INFO - Iter [30600/160000] lr: 4.853e-05, eta: 20:49:44, time: 0.579, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1701, decode.acc_seg: 93.4628, loss: 0.1701 2023-01-06 04:18:11,401 - mmseg - INFO - Iter [30650/160000] lr: 4.851e-05, eta: 20:49:18, time: 0.593, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1584, decode.acc_seg: 93.7516, loss: 0.1584 2023-01-06 04:18:38,943 - mmseg - INFO - Iter [30700/160000] lr: 4.849e-05, eta: 20:48:43, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1562, decode.acc_seg: 93.8133, loss: 0.1562 2023-01-06 04:19:07,327 - mmseg - INFO - Iter [30750/160000] lr: 4.847e-05, eta: 20:48:12, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1463, decode.acc_seg: 94.2580, loss: 0.1463 2023-01-06 04:19:35,722 - mmseg - INFO - Iter [30800/160000] lr: 4.845e-05, eta: 20:47:40, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1538, decode.acc_seg: 93.9641, loss: 0.1538 2023-01-06 04:20:03,773 - mmseg - INFO - Iter [30850/160000] lr: 4.843e-05, eta: 20:47:07, time: 0.560, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1541, decode.acc_seg: 94.0220, loss: 0.1541 2023-01-06 04:20:35,445 - mmseg - INFO - Iter [30900/160000] lr: 4.841e-05, eta: 20:46:50, time: 0.634, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1514, decode.acc_seg: 94.0101, loss: 0.1514 2023-01-06 04:21:04,663 - mmseg - INFO - Iter [30950/160000] lr: 4.839e-05, eta: 20:46:22, time: 0.584, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1606, decode.acc_seg: 93.7229, loss: 0.1606 2023-01-06 04:21:32,681 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 04:21:32,682 - mmseg - INFO - Iter [31000/160000] lr: 4.838e-05, eta: 20:45:49, time: 0.560, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1474, decode.acc_seg: 94.1501, loss: 0.1474 2023-01-06 04:22:00,389 - mmseg - INFO - Iter [31050/160000] lr: 4.836e-05, eta: 20:45:15, time: 0.554, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1353, decode.acc_seg: 94.4525, loss: 0.1353 2023-01-06 04:22:27,877 - mmseg - INFO - Iter [31100/160000] lr: 4.834e-05, eta: 20:44:40, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1448, decode.acc_seg: 94.1843, loss: 0.1448 2023-01-06 04:22:57,548 - mmseg - INFO - Iter [31150/160000] lr: 4.832e-05, eta: 20:44:13, time: 0.593, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1490, decode.acc_seg: 94.0300, loss: 0.1490 2023-01-06 04:23:25,887 - mmseg - INFO - Iter [31200/160000] lr: 4.830e-05, eta: 20:43:42, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1445, decode.acc_seg: 94.2910, loss: 0.1445 2023-01-06 04:23:56,572 - mmseg - INFO - Iter [31250/160000] lr: 4.828e-05, eta: 20:43:20, time: 0.614, data_time: 0.064, memory: 10576, decode.loss_ce: 0.1587, decode.acc_seg: 93.6943, loss: 0.1587 2023-01-06 04:24:23,684 - mmseg - INFO - Iter [31300/160000] lr: 4.826e-05, eta: 20:42:43, time: 0.542, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1470, decode.acc_seg: 94.2461, loss: 0.1470 2023-01-06 04:24:51,169 - mmseg - INFO - Iter [31350/160000] lr: 4.824e-05, eta: 20:42:08, time: 0.550, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1531, decode.acc_seg: 93.8740, loss: 0.1531 2023-01-06 04:25:18,866 - mmseg - INFO - Iter [31400/160000] lr: 4.823e-05, eta: 20:41:34, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1439, decode.acc_seg: 94.2281, loss: 0.1439 2023-01-06 04:25:47,815 - mmseg - INFO - Iter [31450/160000] lr: 4.821e-05, eta: 20:41:05, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1571, decode.acc_seg: 93.7323, loss: 0.1571 2023-01-06 04:26:17,957 - mmseg - INFO - Iter [31500/160000] lr: 4.819e-05, eta: 20:40:41, time: 0.603, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1471, decode.acc_seg: 94.0487, loss: 0.1471 2023-01-06 04:26:47,458 - mmseg - INFO - Iter [31550/160000] lr: 4.817e-05, eta: 20:40:14, time: 0.591, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1554, decode.acc_seg: 93.7352, loss: 0.1554 2023-01-06 04:27:15,704 - mmseg - INFO - Iter [31600/160000] lr: 4.815e-05, eta: 20:39:42, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1532, decode.acc_seg: 93.8927, loss: 0.1532 2023-01-06 04:27:46,459 - mmseg - INFO - Iter [31650/160000] lr: 4.813e-05, eta: 20:39:21, time: 0.615, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1540, decode.acc_seg: 93.8011, loss: 0.1540 2023-01-06 04:28:15,297 - mmseg - INFO - Iter [31700/160000] lr: 4.811e-05, eta: 20:38:51, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1390, decode.acc_seg: 94.2740, loss: 0.1390 2023-01-06 04:28:43,096 - mmseg - INFO - Iter [31750/160000] lr: 4.809e-05, eta: 20:38:17, time: 0.556, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1405, decode.acc_seg: 94.4329, loss: 0.1405 2023-01-06 04:29:11,612 - mmseg - INFO - Iter [31800/160000] lr: 4.808e-05, eta: 20:37:47, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1470, decode.acc_seg: 94.1700, loss: 0.1470 2023-01-06 04:29:38,930 - mmseg - INFO - Iter [31850/160000] lr: 4.806e-05, eta: 20:37:11, time: 0.546, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1567, decode.acc_seg: 93.8714, loss: 0.1567 2023-01-06 04:30:07,507 - mmseg - INFO - Iter [31900/160000] lr: 4.804e-05, eta: 20:36:41, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1522, decode.acc_seg: 93.9537, loss: 0.1522 2023-01-06 04:30:35,884 - mmseg - INFO - Iter [31950/160000] lr: 4.802e-05, eta: 20:36:09, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1482, decode.acc_seg: 93.8868, loss: 0.1482 2023-01-06 04:31:06,205 - mmseg - INFO - Saving checkpoint at 32000 iterations 2023-01-06 04:31:11,345 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 04:31:11,346 - mmseg - INFO - Iter [32000/160000] lr: 4.800e-05, eta: 20:36:06, time: 0.709, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1515, decode.acc_seg: 93.9959, loss: 0.1515 2023-01-06 04:31:43,459 - mmseg - INFO - per class results: 2023-01-06 04:31:43,462 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 97.17 | 98.53 | | sidewalk | 78.26 | 88.55 | | building | 89.62 | 95.53 | | wall | 37.28 | 39.77 | | fence | 46.13 | 66.05 | | pole | 53.46 | 63.3 | | traffic light | 53.54 | 64.26 | | traffic sign | 57.35 | 59.25 | | vegetation | 90.7 | 95.74 | | terrain | 58.13 | 66.15 | | sky | 93.41 | 98.11 | | person | 71.88 | 85.2 | | rider | 45.74 | 56.99 | | car | 91.6 | 97.03 | | truck | 50.6 | 55.89 | | bus | 40.55 | 45.51 | | train | 31.72 | 75.02 | | motorcycle | 36.11 | 41.87 | | bicycle | 65.63 | 87.45 | +---------------+-------+-------+ 2023-01-06 04:31:43,462 - mmseg - INFO - Summary: 2023-01-06 04:31:43,463 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 94.17 | 62.57 | 72.64 | +-------+-------+-------+ 2023-01-06 04:31:43,463 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 04:31:43,464 - mmseg - INFO - Iter(val) [63] aAcc: 0.9417, mIoU: 0.6257, mAcc: 0.7264, IoU.road: 0.9717, IoU.sidewalk: 0.7826, IoU.building: 0.8962, IoU.wall: 0.3728, IoU.fence: 0.4613, IoU.pole: 0.5346, IoU.traffic light: 0.5354, IoU.traffic sign: 0.5735, IoU.vegetation: 0.9070, IoU.terrain: 0.5813, IoU.sky: 0.9341, IoU.person: 0.7188, IoU.rider: 0.4574, IoU.car: 0.9160, IoU.truck: 0.5060, IoU.bus: 0.4055, IoU.train: 0.3172, IoU.motorcycle: 0.3611, IoU.bicycle: 0.6563, Acc.road: 0.9853, Acc.sidewalk: 0.8855, Acc.building: 0.9553, Acc.wall: 0.3977, Acc.fence: 0.6605, Acc.pole: 0.6330, Acc.traffic light: 0.6426, Acc.traffic sign: 0.5925, Acc.vegetation: 0.9574, Acc.terrain: 0.6615, Acc.sky: 0.9811, Acc.person: 0.8520, Acc.rider: 0.5699, Acc.car: 0.9703, Acc.truck: 0.5589, Acc.bus: 0.4551, Acc.train: 0.7502, Acc.motorcycle: 0.4187, Acc.bicycle: 0.8745 2023-01-06 04:32:11,613 - mmseg - INFO - Iter [32050/160000] lr: 4.798e-05, eta: 20:37:42, time: 1.204, data_time: 0.655, memory: 10576, decode.loss_ce: 0.1428, decode.acc_seg: 94.3453, loss: 0.1428 2023-01-06 04:32:39,013 - mmseg - INFO - Iter [32100/160000] lr: 4.796e-05, eta: 20:37:07, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1496, decode.acc_seg: 93.8549, loss: 0.1496 2023-01-06 04:33:06,355 - mmseg - INFO - Iter [32150/160000] lr: 4.794e-05, eta: 20:36:31, time: 0.546, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1473, decode.acc_seg: 93.9835, loss: 0.1473 2023-01-06 04:33:35,950 - mmseg - INFO - Iter [32200/160000] lr: 4.793e-05, eta: 20:36:04, time: 0.592, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1441, decode.acc_seg: 94.2535, loss: 0.1441 2023-01-06 04:34:05,975 - mmseg - INFO - Iter [32250/160000] lr: 4.791e-05, eta: 20:35:39, time: 0.601, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1556, decode.acc_seg: 93.9118, loss: 0.1556 2023-01-06 04:34:33,469 - mmseg - INFO - Iter [32300/160000] lr: 4.789e-05, eta: 20:35:04, time: 0.550, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1527, decode.acc_seg: 93.9888, loss: 0.1527 2023-01-06 04:35:02,575 - mmseg - INFO - Iter [32350/160000] lr: 4.787e-05, eta: 20:34:36, time: 0.581, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1465, decode.acc_seg: 93.9539, loss: 0.1465 2023-01-06 04:35:33,315 - mmseg - INFO - Iter [32400/160000] lr: 4.785e-05, eta: 20:34:13, time: 0.615, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1537, decode.acc_seg: 93.9980, loss: 0.1537 2023-01-06 04:36:02,140 - mmseg - INFO - Iter [32450/160000] lr: 4.783e-05, eta: 20:33:44, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1528, decode.acc_seg: 93.9216, loss: 0.1528 2023-01-06 04:36:30,559 - mmseg - INFO - Iter [32500/160000] lr: 4.781e-05, eta: 20:33:12, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1357, decode.acc_seg: 94.4860, loss: 0.1357 2023-01-06 04:36:58,288 - mmseg - INFO - Iter [32550/160000] lr: 4.779e-05, eta: 20:32:38, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1344, decode.acc_seg: 94.4810, loss: 0.1344 2023-01-06 04:37:25,629 - mmseg - INFO - Iter [32600/160000] lr: 4.778e-05, eta: 20:32:03, time: 0.547, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1372, decode.acc_seg: 94.3594, loss: 0.1372 2023-01-06 04:37:54,695 - mmseg - INFO - Iter [32650/160000] lr: 4.776e-05, eta: 20:31:34, time: 0.581, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1418, decode.acc_seg: 94.3616, loss: 0.1418 2023-01-06 04:38:22,235 - mmseg - INFO - Iter [32700/160000] lr: 4.774e-05, eta: 20:30:59, time: 0.550, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1447, decode.acc_seg: 94.1320, loss: 0.1447 2023-01-06 04:38:52,359 - mmseg - INFO - Iter [32750/160000] lr: 4.772e-05, eta: 20:30:34, time: 0.603, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1415, decode.acc_seg: 94.3464, loss: 0.1415 2023-01-06 04:39:21,120 - mmseg - INFO - Iter [32800/160000] lr: 4.770e-05, eta: 20:30:04, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1481, decode.acc_seg: 94.2712, loss: 0.1481 2023-01-06 04:39:50,521 - mmseg - INFO - Iter [32850/160000] lr: 4.768e-05, eta: 20:29:37, time: 0.589, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1533, decode.acc_seg: 93.7966, loss: 0.1533 2023-01-06 04:40:19,002 - mmseg - INFO - Iter [32900/160000] lr: 4.766e-05, eta: 20:29:06, time: 0.569, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1446, decode.acc_seg: 94.2294, loss: 0.1446 2023-01-06 04:40:47,556 - mmseg - INFO - Iter [32950/160000] lr: 4.764e-05, eta: 20:28:35, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1444, decode.acc_seg: 94.0975, loss: 0.1444 2023-01-06 04:41:15,083 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 04:41:15,084 - mmseg - INFO - Iter [33000/160000] lr: 4.763e-05, eta: 20:28:00, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1440, decode.acc_seg: 94.3713, loss: 0.1440 2023-01-06 04:41:42,652 - mmseg - INFO - Iter [33050/160000] lr: 4.761e-05, eta: 20:27:26, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1600, decode.acc_seg: 93.6451, loss: 0.1600 2023-01-06 04:42:10,113 - mmseg - INFO - Iter [33100/160000] lr: 4.759e-05, eta: 20:26:51, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1399, decode.acc_seg: 94.4499, loss: 0.1399 2023-01-06 04:42:40,014 - mmseg - INFO - Iter [33150/160000] lr: 4.757e-05, eta: 20:26:25, time: 0.597, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1404, decode.acc_seg: 94.4793, loss: 0.1404 2023-01-06 04:43:07,201 - mmseg - INFO - Iter [33200/160000] lr: 4.755e-05, eta: 20:25:49, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1481, decode.acc_seg: 93.9546, loss: 0.1481 2023-01-06 04:43:34,369 - mmseg - INFO - Iter [33250/160000] lr: 4.753e-05, eta: 20:25:13, time: 0.543, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1400, decode.acc_seg: 94.2384, loss: 0.1400 2023-01-06 04:44:02,358 - mmseg - INFO - Iter [33300/160000] lr: 4.751e-05, eta: 20:24:40, time: 0.559, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1573, decode.acc_seg: 93.3312, loss: 0.1573 2023-01-06 04:44:29,594 - mmseg - INFO - Iter [33350/160000] lr: 4.749e-05, eta: 20:24:05, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1373, decode.acc_seg: 94.3828, loss: 0.1373 2023-01-06 04:44:57,729 - mmseg - INFO - Iter [33400/160000] lr: 4.748e-05, eta: 20:23:33, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1398, decode.acc_seg: 94.1816, loss: 0.1398 2023-01-06 04:45:26,243 - mmseg - INFO - Iter [33450/160000] lr: 4.746e-05, eta: 20:23:02, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1454, decode.acc_seg: 94.1363, loss: 0.1454 2023-01-06 04:45:57,445 - mmseg - INFO - Iter [33500/160000] lr: 4.744e-05, eta: 20:22:41, time: 0.624, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1406, decode.acc_seg: 94.3908, loss: 0.1406 2023-01-06 04:46:25,872 - mmseg - INFO - Iter [33550/160000] lr: 4.742e-05, eta: 20:22:10, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1369, decode.acc_seg: 94.4268, loss: 0.1369 2023-01-06 04:46:54,744 - mmseg - INFO - Iter [33600/160000] lr: 4.740e-05, eta: 20:21:40, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1456, decode.acc_seg: 94.1251, loss: 0.1456 2023-01-06 04:47:23,362 - mmseg - INFO - Iter [33650/160000] lr: 4.738e-05, eta: 20:21:10, time: 0.573, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1522, decode.acc_seg: 93.8656, loss: 0.1522 2023-01-06 04:47:51,945 - mmseg - INFO - Iter [33700/160000] lr: 4.736e-05, eta: 20:20:40, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1438, decode.acc_seg: 94.3328, loss: 0.1438 2023-01-06 04:48:19,130 - mmseg - INFO - Iter [33750/160000] lr: 4.734e-05, eta: 20:20:04, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1516, decode.acc_seg: 94.1397, loss: 0.1516 2023-01-06 04:48:48,142 - mmseg - INFO - Iter [33800/160000] lr: 4.733e-05, eta: 20:19:35, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1364, decode.acc_seg: 94.4022, loss: 0.1364 2023-01-06 04:49:15,879 - mmseg - INFO - Iter [33850/160000] lr: 4.731e-05, eta: 20:19:01, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1412, decode.acc_seg: 94.2502, loss: 0.1412 2023-01-06 04:49:46,477 - mmseg - INFO - Iter [33900/160000] lr: 4.729e-05, eta: 20:18:38, time: 0.612, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1433, decode.acc_seg: 94.4220, loss: 0.1433 2023-01-06 04:50:13,498 - mmseg - INFO - Iter [33950/160000] lr: 4.727e-05, eta: 20:18:02, time: 0.540, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1468, decode.acc_seg: 94.2098, loss: 0.1468 2023-01-06 04:50:41,662 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 04:50:41,663 - mmseg - INFO - Iter [34000/160000] lr: 4.725e-05, eta: 20:17:30, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1401, decode.acc_seg: 94.4824, loss: 0.1401 2023-01-06 04:51:09,495 - mmseg - INFO - Iter [34050/160000] lr: 4.723e-05, eta: 20:16:56, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1362, decode.acc_seg: 94.3297, loss: 0.1362 2023-01-06 04:51:36,787 - mmseg - INFO - Iter [34100/160000] lr: 4.721e-05, eta: 20:16:21, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1492, decode.acc_seg: 93.8087, loss: 0.1492 2023-01-06 04:52:04,149 - mmseg - INFO - Iter [34150/160000] lr: 4.719e-05, eta: 20:15:46, time: 0.547, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1493, decode.acc_seg: 93.9921, loss: 0.1493 2023-01-06 04:52:32,644 - mmseg - INFO - Iter [34200/160000] lr: 4.718e-05, eta: 20:15:15, time: 0.569, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1464, decode.acc_seg: 94.1499, loss: 0.1464 2023-01-06 04:53:02,881 - mmseg - INFO - Iter [34250/160000] lr: 4.716e-05, eta: 20:14:51, time: 0.605, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1446, decode.acc_seg: 94.3320, loss: 0.1446 2023-01-06 04:53:32,045 - mmseg - INFO - Iter [34300/160000] lr: 4.714e-05, eta: 20:14:23, time: 0.584, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1353, decode.acc_seg: 94.4856, loss: 0.1353 2023-01-06 04:53:59,689 - mmseg - INFO - Iter [34350/160000] lr: 4.712e-05, eta: 20:13:49, time: 0.552, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1463, decode.acc_seg: 94.1563, loss: 0.1463 2023-01-06 04:54:29,142 - mmseg - INFO - Iter [34400/160000] lr: 4.710e-05, eta: 20:13:22, time: 0.589, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1454, decode.acc_seg: 94.2522, loss: 0.1454 2023-01-06 04:54:56,763 - mmseg - INFO - Iter [34450/160000] lr: 4.708e-05, eta: 20:12:48, time: 0.553, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1409, decode.acc_seg: 94.2844, loss: 0.1409 2023-01-06 04:55:24,329 - mmseg - INFO - Iter [34500/160000] lr: 4.706e-05, eta: 20:12:14, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1272, decode.acc_seg: 94.7311, loss: 0.1272 2023-01-06 04:55:52,502 - mmseg - INFO - Iter [34550/160000] lr: 4.704e-05, eta: 20:11:42, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1443, decode.acc_seg: 94.1750, loss: 0.1443 2023-01-06 04:56:24,040 - mmseg - INFO - Iter [34600/160000] lr: 4.703e-05, eta: 20:11:22, time: 0.631, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1425, decode.acc_seg: 94.1945, loss: 0.1425 2023-01-06 04:56:52,867 - mmseg - INFO - Iter [34650/160000] lr: 4.701e-05, eta: 20:10:52, time: 0.576, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1457, decode.acc_seg: 94.0736, loss: 0.1457 2023-01-06 04:57:22,445 - mmseg - INFO - Iter [34700/160000] lr: 4.699e-05, eta: 20:10:26, time: 0.593, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1386, decode.acc_seg: 94.4172, loss: 0.1386 2023-01-06 04:57:51,375 - mmseg - INFO - Iter [34750/160000] lr: 4.697e-05, eta: 20:09:56, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1359, decode.acc_seg: 94.3931, loss: 0.1359 2023-01-06 04:58:20,920 - mmseg - INFO - Iter [34800/160000] lr: 4.695e-05, eta: 20:09:29, time: 0.591, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1519, decode.acc_seg: 93.9862, loss: 0.1519 2023-01-06 04:58:50,563 - mmseg - INFO - Iter [34850/160000] lr: 4.693e-05, eta: 20:09:03, time: 0.593, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1339, decode.acc_seg: 94.5247, loss: 0.1339 2023-01-06 04:59:20,077 - mmseg - INFO - Iter [34900/160000] lr: 4.691e-05, eta: 20:08:36, time: 0.591, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1392, decode.acc_seg: 94.2920, loss: 0.1392 2023-01-06 04:59:47,122 - mmseg - INFO - Iter [34950/160000] lr: 4.689e-05, eta: 20:08:00, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1376, decode.acc_seg: 94.2975, loss: 0.1376 2023-01-06 05:00:18,772 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 05:00:18,773 - mmseg - INFO - Iter [35000/160000] lr: 4.688e-05, eta: 20:07:41, time: 0.633, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1424, decode.acc_seg: 94.3867, loss: 0.1424 2023-01-06 05:00:46,324 - mmseg - INFO - Iter [35050/160000] lr: 4.686e-05, eta: 20:07:06, time: 0.551, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1392, decode.acc_seg: 94.4298, loss: 0.1392 2023-01-06 05:01:14,870 - mmseg - INFO - Iter [35100/160000] lr: 4.684e-05, eta: 20:06:36, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1303, decode.acc_seg: 94.6029, loss: 0.1303 2023-01-06 05:01:42,658 - mmseg - INFO - Iter [35150/160000] lr: 4.682e-05, eta: 20:06:03, time: 0.556, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1395, decode.acc_seg: 94.2370, loss: 0.1395 2023-01-06 05:02:10,922 - mmseg - INFO - Iter [35200/160000] lr: 4.680e-05, eta: 20:05:31, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1305, decode.acc_seg: 94.6746, loss: 0.1305 2023-01-06 05:02:40,124 - mmseg - INFO - Iter [35250/160000] lr: 4.678e-05, eta: 20:05:03, time: 0.584, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1373, decode.acc_seg: 94.5312, loss: 0.1373 2023-01-06 05:03:07,439 - mmseg - INFO - Iter [35300/160000] lr: 4.676e-05, eta: 20:04:28, time: 0.546, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1436, decode.acc_seg: 94.4272, loss: 0.1436 2023-01-06 05:03:37,136 - mmseg - INFO - Iter [35350/160000] lr: 4.674e-05, eta: 20:04:02, time: 0.594, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1594, decode.acc_seg: 93.6551, loss: 0.1594 2023-01-06 05:04:06,291 - mmseg - INFO - Iter [35400/160000] lr: 4.673e-05, eta: 20:03:33, time: 0.582, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1390, decode.acc_seg: 94.3094, loss: 0.1390 2023-01-06 05:04:35,870 - mmseg - INFO - Iter [35450/160000] lr: 4.671e-05, eta: 20:03:06, time: 0.592, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1429, decode.acc_seg: 94.1935, loss: 0.1429 2023-01-06 05:05:04,025 - mmseg - INFO - Iter [35500/160000] lr: 4.669e-05, eta: 20:02:35, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1393, decode.acc_seg: 94.3414, loss: 0.1393 2023-01-06 05:05:31,698 - mmseg - INFO - Iter [35550/160000] lr: 4.667e-05, eta: 20:02:01, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1448, decode.acc_seg: 94.3382, loss: 0.1448 2023-01-06 05:05:59,777 - mmseg - INFO - Iter [35600/160000] lr: 4.665e-05, eta: 20:01:29, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1213, decode.acc_seg: 94.9527, loss: 0.1213 2023-01-06 05:06:27,254 - mmseg - INFO - Iter [35650/160000] lr: 4.663e-05, eta: 20:00:55, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1393, decode.acc_seg: 94.3767, loss: 0.1393 2023-01-06 05:06:55,297 - mmseg - INFO - Iter [35700/160000] lr: 4.661e-05, eta: 20:00:22, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1427, decode.acc_seg: 94.2510, loss: 0.1427 2023-01-06 05:07:26,072 - mmseg - INFO - Iter [35750/160000] lr: 4.659e-05, eta: 20:00:00, time: 0.616, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1390, decode.acc_seg: 94.4203, loss: 0.1390 2023-01-06 05:07:55,158 - mmseg - INFO - Iter [35800/160000] lr: 4.658e-05, eta: 19:59:31, time: 0.583, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1607, decode.acc_seg: 93.5620, loss: 0.1607 2023-01-06 05:08:24,245 - mmseg - INFO - Iter [35850/160000] lr: 4.656e-05, eta: 19:59:03, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1520, decode.acc_seg: 93.8275, loss: 0.1520 2023-01-06 05:08:53,648 - mmseg - INFO - Iter [35900/160000] lr: 4.654e-05, eta: 19:58:35, time: 0.588, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1357, decode.acc_seg: 94.4143, loss: 0.1357 2023-01-06 05:09:22,771 - mmseg - INFO - Iter [35950/160000] lr: 4.652e-05, eta: 19:58:07, time: 0.583, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1474, decode.acc_seg: 94.2757, loss: 0.1474 2023-01-06 05:09:50,635 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 05:09:50,635 - mmseg - INFO - Iter [36000/160000] lr: 4.650e-05, eta: 19:57:34, time: 0.557, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1399, decode.acc_seg: 94.2459, loss: 0.1399 2023-01-06 05:10:19,691 - mmseg - INFO - Iter [36050/160000] lr: 4.648e-05, eta: 19:57:05, time: 0.581, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1393, decode.acc_seg: 94.5296, loss: 0.1393 2023-01-06 05:10:51,042 - mmseg - INFO - Iter [36100/160000] lr: 4.646e-05, eta: 19:56:44, time: 0.628, data_time: 0.060, memory: 10576, decode.loss_ce: 0.1360, decode.acc_seg: 94.6086, loss: 0.1360 2023-01-06 05:11:19,744 - mmseg - INFO - Iter [36150/160000] lr: 4.644e-05, eta: 19:56:14, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1439, decode.acc_seg: 94.2573, loss: 0.1439 2023-01-06 05:11:47,377 - mmseg - INFO - Iter [36200/160000] lr: 4.643e-05, eta: 19:55:41, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1468, decode.acc_seg: 94.1853, loss: 0.1468 2023-01-06 05:12:14,926 - mmseg - INFO - Iter [36250/160000] lr: 4.641e-05, eta: 19:55:07, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1310, decode.acc_seg: 94.6366, loss: 0.1310 2023-01-06 05:12:43,117 - mmseg - INFO - Iter [36300/160000] lr: 4.639e-05, eta: 19:54:35, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1314, decode.acc_seg: 94.6813, loss: 0.1314 2023-01-06 05:13:11,270 - mmseg - INFO - Iter [36350/160000] lr: 4.637e-05, eta: 19:54:04, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1250, decode.acc_seg: 94.9820, loss: 0.1250 2023-01-06 05:13:40,285 - mmseg - INFO - Iter [36400/160000] lr: 4.635e-05, eta: 19:53:35, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1419, decode.acc_seg: 94.4536, loss: 0.1419 2023-01-06 05:14:08,639 - mmseg - INFO - Iter [36450/160000] lr: 4.633e-05, eta: 19:53:04, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1436, decode.acc_seg: 94.2165, loss: 0.1436 2023-01-06 05:14:39,529 - mmseg - INFO - Iter [36500/160000] lr: 4.631e-05, eta: 19:52:41, time: 0.618, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1373, decode.acc_seg: 94.4708, loss: 0.1373 2023-01-06 05:15:08,083 - mmseg - INFO - Iter [36550/160000] lr: 4.629e-05, eta: 19:52:11, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1442, decode.acc_seg: 94.1521, loss: 0.1442 2023-01-06 05:15:36,629 - mmseg - INFO - Iter [36600/160000] lr: 4.628e-05, eta: 19:51:40, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1310, decode.acc_seg: 94.5604, loss: 0.1310 2023-01-06 05:16:05,232 - mmseg - INFO - Iter [36650/160000] lr: 4.626e-05, eta: 19:51:10, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1480, decode.acc_seg: 94.1423, loss: 0.1480 2023-01-06 05:16:33,877 - mmseg - INFO - Iter [36700/160000] lr: 4.624e-05, eta: 19:50:40, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1400, decode.acc_seg: 94.2862, loss: 0.1400 2023-01-06 05:17:02,254 - mmseg - INFO - Iter [36750/160000] lr: 4.622e-05, eta: 19:50:09, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1317, decode.acc_seg: 94.5003, loss: 0.1317 2023-01-06 05:17:32,344 - mmseg - INFO - Iter [36800/160000] lr: 4.620e-05, eta: 19:49:44, time: 0.602, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1341, decode.acc_seg: 94.5850, loss: 0.1341 2023-01-06 05:18:04,001 - mmseg - INFO - Iter [36850/160000] lr: 4.618e-05, eta: 19:49:24, time: 0.633, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1395, decode.acc_seg: 94.5362, loss: 0.1395 2023-01-06 05:18:32,582 - mmseg - INFO - Iter [36900/160000] lr: 4.616e-05, eta: 19:48:54, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1405, decode.acc_seg: 94.2440, loss: 0.1405 2023-01-06 05:19:00,305 - mmseg - INFO - Iter [36950/160000] lr: 4.614e-05, eta: 19:48:20, time: 0.554, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1402, decode.acc_seg: 94.2983, loss: 0.1402 2023-01-06 05:19:29,407 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 05:19:29,408 - mmseg - INFO - Iter [37000/160000] lr: 4.613e-05, eta: 19:47:52, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1304, decode.acc_seg: 94.8312, loss: 0.1304 2023-01-06 05:19:56,841 - mmseg - INFO - Iter [37050/160000] lr: 4.611e-05, eta: 19:47:18, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1369, decode.acc_seg: 94.6531, loss: 0.1369 2023-01-06 05:20:24,696 - mmseg - INFO - Iter [37100/160000] lr: 4.609e-05, eta: 19:46:45, time: 0.556, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1368, decode.acc_seg: 94.3310, loss: 0.1368 2023-01-06 05:20:53,609 - mmseg - INFO - Iter [37150/160000] lr: 4.607e-05, eta: 19:46:16, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1376, decode.acc_seg: 94.4425, loss: 0.1376 2023-01-06 05:21:22,237 - mmseg - INFO - Iter [37200/160000] lr: 4.605e-05, eta: 19:45:46, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1458, decode.acc_seg: 94.2573, loss: 0.1458 2023-01-06 05:21:52,025 - mmseg - INFO - Iter [37250/160000] lr: 4.603e-05, eta: 19:45:20, time: 0.595, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1291, decode.acc_seg: 94.7272, loss: 0.1291 2023-01-06 05:22:21,217 - mmseg - INFO - Iter [37300/160000] lr: 4.601e-05, eta: 19:44:51, time: 0.584, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1253, decode.acc_seg: 94.8149, loss: 0.1253 2023-01-06 05:22:49,705 - mmseg - INFO - Iter [37350/160000] lr: 4.599e-05, eta: 19:44:21, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1385, decode.acc_seg: 94.2195, loss: 0.1385 2023-01-06 05:23:17,499 - mmseg - INFO - Iter [37400/160000] lr: 4.598e-05, eta: 19:43:48, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1394, decode.acc_seg: 94.3834, loss: 0.1394 2023-01-06 05:23:45,491 - mmseg - INFO - Iter [37450/160000] lr: 4.596e-05, eta: 19:43:16, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1401, decode.acc_seg: 94.3565, loss: 0.1401 2023-01-06 05:24:13,269 - mmseg - INFO - Iter [37500/160000] lr: 4.594e-05, eta: 19:42:43, time: 0.556, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1502, decode.acc_seg: 94.0565, loss: 0.1502 2023-01-06 05:24:41,205 - mmseg - INFO - Iter [37550/160000] lr: 4.592e-05, eta: 19:42:11, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1384, decode.acc_seg: 94.4571, loss: 0.1384 2023-01-06 05:25:11,497 - mmseg - INFO - Iter [37600/160000] lr: 4.590e-05, eta: 19:41:46, time: 0.605, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1395, decode.acc_seg: 94.4651, loss: 0.1395 2023-01-06 05:25:40,417 - mmseg - INFO - Iter [37650/160000] lr: 4.588e-05, eta: 19:41:17, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1387, decode.acc_seg: 94.1930, loss: 0.1387 2023-01-06 05:26:08,369 - mmseg - INFO - Iter [37700/160000] lr: 4.586e-05, eta: 19:40:45, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1286, decode.acc_seg: 94.8446, loss: 0.1286 2023-01-06 05:26:36,915 - mmseg - INFO - Iter [37750/160000] lr: 4.584e-05, eta: 19:40:15, time: 0.572, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1319, decode.acc_seg: 94.7389, loss: 0.1319 2023-01-06 05:27:05,140 - mmseg - INFO - Iter [37800/160000] lr: 4.583e-05, eta: 19:39:43, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1435, decode.acc_seg: 94.2490, loss: 0.1435 2023-01-06 05:27:33,022 - mmseg - INFO - Iter [37850/160000] lr: 4.581e-05, eta: 19:39:11, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1374, decode.acc_seg: 94.4697, loss: 0.1374 2023-01-06 05:28:01,526 - mmseg - INFO - Iter [37900/160000] lr: 4.579e-05, eta: 19:38:40, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1301, decode.acc_seg: 94.6611, loss: 0.1301 2023-01-06 05:28:32,144 - mmseg - INFO - Iter [37950/160000] lr: 4.577e-05, eta: 19:38:17, time: 0.612, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1237, decode.acc_seg: 94.9274, loss: 0.1237 2023-01-06 05:28:59,270 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 05:28:59,271 - mmseg - INFO - Iter [38000/160000] lr: 4.575e-05, eta: 19:37:42, time: 0.543, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1314, decode.acc_seg: 94.6421, loss: 0.1314 2023-01-06 05:29:26,516 - mmseg - INFO - Iter [38050/160000] lr: 4.573e-05, eta: 19:37:07, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1348, decode.acc_seg: 94.6205, loss: 0.1348 2023-01-06 05:29:54,628 - mmseg - INFO - Iter [38100/160000] lr: 4.571e-05, eta: 19:36:36, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1404, decode.acc_seg: 94.4518, loss: 0.1404 2023-01-06 05:30:24,088 - mmseg - INFO - Iter [38150/160000] lr: 4.569e-05, eta: 19:36:08, time: 0.589, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1315, decode.acc_seg: 94.6753, loss: 0.1315 2023-01-06 05:30:53,552 - mmseg - INFO - Iter [38200/160000] lr: 4.568e-05, eta: 19:35:41, time: 0.590, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1452, decode.acc_seg: 94.3287, loss: 0.1452 2023-01-06 05:31:21,846 - mmseg - INFO - Iter [38250/160000] lr: 4.566e-05, eta: 19:35:10, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1391, decode.acc_seg: 94.2319, loss: 0.1391 2023-01-06 05:31:50,108 - mmseg - INFO - Iter [38300/160000] lr: 4.564e-05, eta: 19:34:39, time: 0.566, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1426, decode.acc_seg: 94.2387, loss: 0.1426 2023-01-06 05:32:21,319 - mmseg - INFO - Iter [38350/160000] lr: 4.562e-05, eta: 19:34:17, time: 0.623, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1495, decode.acc_seg: 93.8913, loss: 0.1495 2023-01-06 05:32:50,425 - mmseg - INFO - Iter [38400/160000] lr: 4.560e-05, eta: 19:33:48, time: 0.583, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1342, decode.acc_seg: 94.4732, loss: 0.1342 2023-01-06 05:33:18,903 - mmseg - INFO - Iter [38450/160000] lr: 4.558e-05, eta: 19:33:18, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1345, decode.acc_seg: 94.6008, loss: 0.1345 2023-01-06 05:33:48,325 - mmseg - INFO - Iter [38500/160000] lr: 4.556e-05, eta: 19:32:50, time: 0.588, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1418, decode.acc_seg: 94.4422, loss: 0.1418 2023-01-06 05:34:16,448 - mmseg - INFO - Iter [38550/160000] lr: 4.554e-05, eta: 19:32:19, time: 0.562, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1312, decode.acc_seg: 94.7371, loss: 0.1312 2023-01-06 05:34:45,316 - mmseg - INFO - Iter [38600/160000] lr: 4.553e-05, eta: 19:31:50, time: 0.577, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1274, decode.acc_seg: 94.7754, loss: 0.1274 2023-01-06 05:35:13,917 - mmseg - INFO - Iter [38650/160000] lr: 4.551e-05, eta: 19:31:20, time: 0.573, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1433, decode.acc_seg: 94.4043, loss: 0.1433 2023-01-06 05:35:44,907 - mmseg - INFO - Iter [38700/160000] lr: 4.549e-05, eta: 19:30:57, time: 0.619, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1288, decode.acc_seg: 94.7982, loss: 0.1288 2023-01-06 05:36:14,112 - mmseg - INFO - Iter [38750/160000] lr: 4.547e-05, eta: 19:30:29, time: 0.585, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1279, decode.acc_seg: 94.7429, loss: 0.1279 2023-01-06 05:36:41,332 - mmseg - INFO - Iter [38800/160000] lr: 4.545e-05, eta: 19:29:54, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1317, decode.acc_seg: 94.4930, loss: 0.1317 2023-01-06 05:37:08,460 - mmseg - INFO - Iter [38850/160000] lr: 4.543e-05, eta: 19:29:20, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1308, decode.acc_seg: 94.5742, loss: 0.1308 2023-01-06 05:37:37,468 - mmseg - INFO - Iter [38900/160000] lr: 4.541e-05, eta: 19:28:51, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1340, decode.acc_seg: 94.5983, loss: 0.1340 2023-01-06 05:38:06,020 - mmseg - INFO - Iter [38950/160000] lr: 4.539e-05, eta: 19:28:21, time: 0.571, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1379, decode.acc_seg: 94.4873, loss: 0.1379 2023-01-06 05:38:34,552 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 05:38:34,553 - mmseg - INFO - Iter [39000/160000] lr: 4.538e-05, eta: 19:27:50, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1320, decode.acc_seg: 94.6167, loss: 0.1320 2023-01-06 05:39:02,486 - mmseg - INFO - Iter [39050/160000] lr: 4.536e-05, eta: 19:27:18, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1255, decode.acc_seg: 95.0210, loss: 0.1255 2023-01-06 05:39:32,905 - mmseg - INFO - Iter [39100/160000] lr: 4.534e-05, eta: 19:26:54, time: 0.608, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1364, decode.acc_seg: 94.5980, loss: 0.1364 2023-01-06 05:40:01,535 - mmseg - INFO - Iter [39150/160000] lr: 4.532e-05, eta: 19:26:24, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1333, decode.acc_seg: 94.6627, loss: 0.1333 2023-01-06 05:40:29,286 - mmseg - INFO - Iter [39200/160000] lr: 4.530e-05, eta: 19:25:51, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1282, decode.acc_seg: 94.7508, loss: 0.1282 2023-01-06 05:40:56,655 - mmseg - INFO - Iter [39250/160000] lr: 4.528e-05, eta: 19:25:17, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1379, decode.acc_seg: 94.4188, loss: 0.1379 2023-01-06 05:41:24,635 - mmseg - INFO - Iter [39300/160000] lr: 4.526e-05, eta: 19:24:45, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1459, decode.acc_seg: 94.3334, loss: 0.1459 2023-01-06 05:41:52,574 - mmseg - INFO - Iter [39350/160000] lr: 4.524e-05, eta: 19:24:13, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1334, decode.acc_seg: 94.5859, loss: 0.1334 2023-01-06 05:42:20,403 - mmseg - INFO - Iter [39400/160000] lr: 4.523e-05, eta: 19:23:41, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1316, decode.acc_seg: 94.6760, loss: 0.1316 2023-01-06 05:42:50,170 - mmseg - INFO - Iter [39450/160000] lr: 4.521e-05, eta: 19:23:15, time: 0.595, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1374, decode.acc_seg: 94.3175, loss: 0.1374 2023-01-06 05:43:18,718 - mmseg - INFO - Iter [39500/160000] lr: 4.519e-05, eta: 19:22:44, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1319, decode.acc_seg: 94.6811, loss: 0.1319 2023-01-06 05:43:46,797 - mmseg - INFO - Iter [39550/160000] lr: 4.517e-05, eta: 19:22:13, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1656, decode.acc_seg: 93.7726, loss: 0.1656 2023-01-06 05:44:15,010 - mmseg - INFO - Iter [39600/160000] lr: 4.515e-05, eta: 19:21:42, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1513, decode.acc_seg: 93.8510, loss: 0.1513 2023-01-06 05:44:42,919 - mmseg - INFO - Iter [39650/160000] lr: 4.513e-05, eta: 19:21:09, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1510, decode.acc_seg: 93.8854, loss: 0.1510 2023-01-06 05:45:12,188 - mmseg - INFO - Iter [39700/160000] lr: 4.511e-05, eta: 19:20:41, time: 0.585, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1312, decode.acc_seg: 94.6705, loss: 0.1312 2023-01-06 05:45:40,524 - mmseg - INFO - Iter [39750/160000] lr: 4.509e-05, eta: 19:20:11, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1352, decode.acc_seg: 94.5656, loss: 0.1352 2023-01-06 05:46:07,824 - mmseg - INFO - Iter [39800/160000] lr: 4.508e-05, eta: 19:19:37, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1356, decode.acc_seg: 94.4895, loss: 0.1356 2023-01-06 05:46:39,792 - mmseg - INFO - Iter [39850/160000] lr: 4.506e-05, eta: 19:19:17, time: 0.639, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1239, decode.acc_seg: 94.8466, loss: 0.1239 2023-01-06 05:47:08,684 - mmseg - INFO - Iter [39900/160000] lr: 4.504e-05, eta: 19:18:48, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1192, decode.acc_seg: 95.1585, loss: 0.1192 2023-01-06 05:47:38,614 - mmseg - INFO - Iter [39950/160000] lr: 4.502e-05, eta: 19:18:22, time: 0.599, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1453, decode.acc_seg: 94.3432, loss: 0.1453 2023-01-06 05:48:07,562 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 05:48:07,563 - mmseg - INFO - Iter [40000/160000] lr: 4.500e-05, eta: 19:17:53, time: 0.580, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1317, decode.acc_seg: 94.4870, loss: 0.1317 2023-01-06 05:48:34,973 - mmseg - INFO - Iter [40050/160000] lr: 4.498e-05, eta: 19:17:19, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1340, decode.acc_seg: 94.5389, loss: 0.1340 2023-01-06 05:49:02,804 - mmseg - INFO - Iter [40100/160000] lr: 4.496e-05, eta: 19:16:47, time: 0.557, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1420, decode.acc_seg: 94.3110, loss: 0.1420 2023-01-06 05:49:31,213 - mmseg - INFO - Iter [40150/160000] lr: 4.494e-05, eta: 19:16:17, time: 0.569, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1371, decode.acc_seg: 94.6200, loss: 0.1371 2023-01-06 05:50:00,812 - mmseg - INFO - Iter [40200/160000] lr: 4.493e-05, eta: 19:15:50, time: 0.591, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1368, decode.acc_seg: 94.4315, loss: 0.1368 2023-01-06 05:50:28,676 - mmseg - INFO - Iter [40250/160000] lr: 4.491e-05, eta: 19:15:17, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1200, decode.acc_seg: 95.1210, loss: 0.1200 2023-01-06 05:50:57,903 - mmseg - INFO - Iter [40300/160000] lr: 4.489e-05, eta: 19:14:49, time: 0.585, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1211, decode.acc_seg: 94.8995, loss: 0.1211 2023-01-06 05:51:26,924 - mmseg - INFO - Iter [40350/160000] lr: 4.487e-05, eta: 19:14:21, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1363, decode.acc_seg: 94.6310, loss: 0.1363 2023-01-06 05:51:54,445 - mmseg - INFO - Iter [40400/160000] lr: 4.485e-05, eta: 19:13:48, time: 0.551, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1480, decode.acc_seg: 94.0615, loss: 0.1480 2023-01-06 05:52:21,537 - mmseg - INFO - Iter [40450/160000] lr: 4.483e-05, eta: 19:13:13, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1404, decode.acc_seg: 94.2654, loss: 0.1404 2023-01-06 05:52:50,519 - mmseg - INFO - Iter [40500/160000] lr: 4.481e-05, eta: 19:12:44, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1297, decode.acc_seg: 94.8438, loss: 0.1297 2023-01-06 05:53:22,218 - mmseg - INFO - Iter [40550/160000] lr: 4.479e-05, eta: 19:12:23, time: 0.634, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1300, decode.acc_seg: 94.8240, loss: 0.1300 2023-01-06 05:53:50,680 - mmseg - INFO - Iter [40600/160000] lr: 4.478e-05, eta: 19:11:53, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1282, decode.acc_seg: 94.8780, loss: 0.1282 2023-01-06 05:54:17,846 - mmseg - INFO - Iter [40650/160000] lr: 4.476e-05, eta: 19:11:19, time: 0.543, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1269, decode.acc_seg: 94.8253, loss: 0.1269 2023-01-06 05:54:47,028 - mmseg - INFO - Iter [40700/160000] lr: 4.474e-05, eta: 19:10:51, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1358, decode.acc_seg: 94.5299, loss: 0.1358 2023-01-06 05:55:15,734 - mmseg - INFO - Iter [40750/160000] lr: 4.472e-05, eta: 19:10:21, time: 0.574, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1460, decode.acc_seg: 94.1504, loss: 0.1460 2023-01-06 05:55:44,509 - mmseg - INFO - Iter [40800/160000] lr: 4.470e-05, eta: 19:09:52, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1268, decode.acc_seg: 94.9382, loss: 0.1268 2023-01-06 05:56:12,869 - mmseg - INFO - Iter [40850/160000] lr: 4.468e-05, eta: 19:09:21, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1273, decode.acc_seg: 94.7649, loss: 0.1273 2023-01-06 05:56:41,585 - mmseg - INFO - Iter [40900/160000] lr: 4.466e-05, eta: 19:08:51, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1272, decode.acc_seg: 94.9638, loss: 0.1272 2023-01-06 05:57:13,764 - mmseg - INFO - Iter [40950/160000] lr: 4.464e-05, eta: 19:08:32, time: 0.644, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1286, decode.acc_seg: 94.7580, loss: 0.1286 2023-01-06 05:57:43,261 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 05:57:43,262 - mmseg - INFO - Iter [41000/160000] lr: 4.463e-05, eta: 19:08:05, time: 0.590, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1281, decode.acc_seg: 94.6227, loss: 0.1281 2023-01-06 05:58:12,937 - mmseg - INFO - Iter [41050/160000] lr: 4.461e-05, eta: 19:07:38, time: 0.594, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1294, decode.acc_seg: 94.8181, loss: 0.1294 2023-01-06 05:58:40,610 - mmseg - INFO - Iter [41100/160000] lr: 4.459e-05, eta: 19:07:05, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1249, decode.acc_seg: 94.7651, loss: 0.1249 2023-01-06 05:59:09,872 - mmseg - INFO - Iter [41150/160000] lr: 4.457e-05, eta: 19:06:37, time: 0.585, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1342, decode.acc_seg: 94.6689, loss: 0.1342 2023-01-06 05:59:38,337 - mmseg - INFO - Iter [41200/160000] lr: 4.455e-05, eta: 19:06:07, time: 0.569, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1227, decode.acc_seg: 94.9273, loss: 0.1227 2023-01-06 06:00:08,235 - mmseg - INFO - Iter [41250/160000] lr: 4.453e-05, eta: 19:05:41, time: 0.598, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1341, decode.acc_seg: 94.6615, loss: 0.1341 2023-01-06 06:00:39,407 - mmseg - INFO - Iter [41300/160000] lr: 4.451e-05, eta: 19:05:18, time: 0.624, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1334, decode.acc_seg: 94.7617, loss: 0.1334 2023-01-06 06:01:07,009 - mmseg - INFO - Iter [41350/160000] lr: 4.449e-05, eta: 19:04:45, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1320, decode.acc_seg: 94.6114, loss: 0.1320 2023-01-06 06:01:34,878 - mmseg - INFO - Iter [41400/160000] lr: 4.448e-05, eta: 19:04:13, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1325, decode.acc_seg: 94.6735, loss: 0.1325 2023-01-06 06:02:02,532 - mmseg - INFO - Iter [41450/160000] lr: 4.446e-05, eta: 19:03:41, time: 0.553, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1317, decode.acc_seg: 94.6794, loss: 0.1317 2023-01-06 06:02:30,937 - mmseg - INFO - Iter [41500/160000] lr: 4.444e-05, eta: 19:03:10, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1207, decode.acc_seg: 94.9445, loss: 0.1207 2023-01-06 06:03:00,361 - mmseg - INFO - Iter [41550/160000] lr: 4.442e-05, eta: 19:02:42, time: 0.588, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1190, decode.acc_seg: 95.1451, loss: 0.1190 2023-01-06 06:03:28,226 - mmseg - INFO - Iter [41600/160000] lr: 4.440e-05, eta: 19:02:10, time: 0.557, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1220, decode.acc_seg: 95.0709, loss: 0.1220 2023-01-06 06:03:57,431 - mmseg - INFO - Iter [41650/160000] lr: 4.438e-05, eta: 19:01:42, time: 0.584, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1388, decode.acc_seg: 94.5303, loss: 0.1388 2023-01-06 06:04:27,537 - mmseg - INFO - Iter [41700/160000] lr: 4.436e-05, eta: 19:01:17, time: 0.603, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1292, decode.acc_seg: 94.8291, loss: 0.1292 2023-01-06 06:04:55,035 - mmseg - INFO - Iter [41750/160000] lr: 4.434e-05, eta: 19:00:44, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1341, decode.acc_seg: 94.6899, loss: 0.1341 2023-01-06 06:05:23,119 - mmseg - INFO - Iter [41800/160000] lr: 4.433e-05, eta: 19:00:12, time: 0.562, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1374, decode.acc_seg: 94.4598, loss: 0.1374 2023-01-06 06:05:50,318 - mmseg - INFO - Iter [41850/160000] lr: 4.431e-05, eta: 18:59:39, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1188, decode.acc_seg: 95.2046, loss: 0.1188 2023-01-06 06:06:18,559 - mmseg - INFO - Iter [41900/160000] lr: 4.429e-05, eta: 18:59:08, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1261, decode.acc_seg: 94.7270, loss: 0.1261 2023-01-06 06:06:46,296 - mmseg - INFO - Iter [41950/160000] lr: 4.427e-05, eta: 18:58:35, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1181, decode.acc_seg: 95.1481, loss: 0.1181 2023-01-06 06:07:14,595 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 06:07:14,596 - mmseg - INFO - Iter [42000/160000] lr: 4.425e-05, eta: 18:58:05, time: 0.567, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1267, decode.acc_seg: 94.7205, loss: 0.1267 2023-01-06 06:07:44,809 - mmseg - INFO - Iter [42050/160000] lr: 4.423e-05, eta: 18:57:39, time: 0.604, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1323, decode.acc_seg: 94.5178, loss: 0.1323 2023-01-06 06:08:12,847 - mmseg - INFO - Iter [42100/160000] lr: 4.421e-05, eta: 18:57:08, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1359, decode.acc_seg: 94.6437, loss: 0.1359 2023-01-06 06:08:41,509 - mmseg - INFO - Iter [42150/160000] lr: 4.419e-05, eta: 18:56:38, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1453, decode.acc_seg: 94.3633, loss: 0.1453 2023-01-06 06:09:10,238 - mmseg - INFO - Iter [42200/160000] lr: 4.418e-05, eta: 18:56:08, time: 0.575, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1343, decode.acc_seg: 94.5938, loss: 0.1343 2023-01-06 06:09:39,876 - mmseg - INFO - Iter [42250/160000] lr: 4.416e-05, eta: 18:55:42, time: 0.593, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1336, decode.acc_seg: 94.5623, loss: 0.1336 2023-01-06 06:10:07,014 - mmseg - INFO - Iter [42300/160000] lr: 4.414e-05, eta: 18:55:08, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1347, decode.acc_seg: 94.6799, loss: 0.1347 2023-01-06 06:10:34,457 - mmseg - INFO - Iter [42350/160000] lr: 4.412e-05, eta: 18:54:35, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1304, decode.acc_seg: 94.6591, loss: 0.1304 2023-01-06 06:11:01,817 - mmseg - INFO - Iter [42400/160000] lr: 4.410e-05, eta: 18:54:01, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1378, decode.acc_seg: 94.3000, loss: 0.1378 2023-01-06 06:11:31,292 - mmseg - INFO - Iter [42450/160000] lr: 4.408e-05, eta: 18:53:34, time: 0.590, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1270, decode.acc_seg: 94.7185, loss: 0.1270 2023-01-06 06:11:59,046 - mmseg - INFO - Iter [42500/160000] lr: 4.406e-05, eta: 18:53:02, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1377, decode.acc_seg: 94.4966, loss: 0.1377 2023-01-06 06:12:27,163 - mmseg - INFO - Iter [42550/160000] lr: 4.404e-05, eta: 18:52:30, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1378, decode.acc_seg: 94.7063, loss: 0.1378 2023-01-06 06:12:55,986 - mmseg - INFO - Iter [42600/160000] lr: 4.403e-05, eta: 18:52:01, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1327, decode.acc_seg: 94.6237, loss: 0.1327 2023-01-06 06:13:23,741 - mmseg - INFO - Iter [42650/160000] lr: 4.401e-05, eta: 18:51:29, time: 0.556, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1317, decode.acc_seg: 94.6755, loss: 0.1317 2023-01-06 06:13:53,123 - mmseg - INFO - Iter [42700/160000] lr: 4.399e-05, eta: 18:51:01, time: 0.587, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1233, decode.acc_seg: 94.9291, loss: 0.1233 2023-01-06 06:14:22,161 - mmseg - INFO - Iter [42750/160000] lr: 4.397e-05, eta: 18:50:33, time: 0.581, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1268, decode.acc_seg: 94.6355, loss: 0.1268 2023-01-06 06:14:51,523 - mmseg - INFO - Iter [42800/160000] lr: 4.395e-05, eta: 18:50:05, time: 0.587, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1375, decode.acc_seg: 94.5224, loss: 0.1375 2023-01-06 06:15:20,582 - mmseg - INFO - Iter [42850/160000] lr: 4.393e-05, eta: 18:49:36, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1335, decode.acc_seg: 94.5251, loss: 0.1335 2023-01-06 06:15:48,609 - mmseg - INFO - Iter [42900/160000] lr: 4.391e-05, eta: 18:49:05, time: 0.561, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1271, decode.acc_seg: 94.7298, loss: 0.1271 2023-01-06 06:16:16,608 - mmseg - INFO - Iter [42950/160000] lr: 4.389e-05, eta: 18:48:34, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1257, decode.acc_seg: 94.8738, loss: 0.1257 2023-01-06 06:16:44,075 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 06:16:44,076 - mmseg - INFO - Iter [43000/160000] lr: 4.388e-05, eta: 18:48:01, time: 0.550, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1230, decode.acc_seg: 94.9170, loss: 0.1230 2023-01-06 06:17:11,730 - mmseg - INFO - Iter [43050/160000] lr: 4.386e-05, eta: 18:47:28, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1261, decode.acc_seg: 94.9003, loss: 0.1261 2023-01-06 06:17:39,218 - mmseg - INFO - Iter [43100/160000] lr: 4.384e-05, eta: 18:46:56, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1339, decode.acc_seg: 94.7329, loss: 0.1339 2023-01-06 06:18:06,503 - mmseg - INFO - Iter [43150/160000] lr: 4.382e-05, eta: 18:46:22, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1281, decode.acc_seg: 94.7336, loss: 0.1281 2023-01-06 06:18:37,190 - mmseg - INFO - Iter [43200/160000] lr: 4.380e-05, eta: 18:45:58, time: 0.614, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1473, decode.acc_seg: 94.4280, loss: 0.1473 2023-01-06 06:19:06,567 - mmseg - INFO - Iter [43250/160000] lr: 4.378e-05, eta: 18:45:30, time: 0.587, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1460, decode.acc_seg: 94.1331, loss: 0.1460 2023-01-06 06:19:36,369 - mmseg - INFO - Iter [43300/160000] lr: 4.376e-05, eta: 18:45:04, time: 0.596, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1323, decode.acc_seg: 94.7956, loss: 0.1323 2023-01-06 06:20:05,514 - mmseg - INFO - Iter [43350/160000] lr: 4.374e-05, eta: 18:44:35, time: 0.583, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1383, decode.acc_seg: 94.6106, loss: 0.1383 2023-01-06 06:20:33,994 - mmseg - INFO - Iter [43400/160000] lr: 4.373e-05, eta: 18:44:05, time: 0.570, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1387, decode.acc_seg: 94.5405, loss: 0.1387 2023-01-06 06:21:00,980 - mmseg - INFO - Iter [43450/160000] lr: 4.371e-05, eta: 18:43:31, time: 0.539, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1348, decode.acc_seg: 94.5933, loss: 0.1348 2023-01-06 06:21:30,598 - mmseg - INFO - Iter [43500/160000] lr: 4.369e-05, eta: 18:43:04, time: 0.592, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1422, decode.acc_seg: 94.1965, loss: 0.1422 2023-01-06 06:22:00,993 - mmseg - INFO - Iter [43550/160000] lr: 4.367e-05, eta: 18:42:39, time: 0.609, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1342, decode.acc_seg: 94.5331, loss: 0.1342 2023-01-06 06:22:30,487 - mmseg - INFO - Iter [43600/160000] lr: 4.365e-05, eta: 18:42:12, time: 0.590, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1280, decode.acc_seg: 94.8481, loss: 0.1280 2023-01-06 06:22:57,956 - mmseg - INFO - Iter [43650/160000] lr: 4.363e-05, eta: 18:41:39, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1251, decode.acc_seg: 94.6497, loss: 0.1251 2023-01-06 06:23:27,281 - mmseg - INFO - Iter [43700/160000] lr: 4.361e-05, eta: 18:41:11, time: 0.587, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1208, decode.acc_seg: 95.0350, loss: 0.1208 2023-01-06 06:23:55,331 - mmseg - INFO - Iter [43750/160000] lr: 4.359e-05, eta: 18:40:40, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1255, decode.acc_seg: 94.7232, loss: 0.1255 2023-01-06 06:24:22,296 - mmseg - INFO - Iter [43800/160000] lr: 4.358e-05, eta: 18:40:06, time: 0.539, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1246, decode.acc_seg: 94.9645, loss: 0.1246 2023-01-06 06:24:49,878 - mmseg - INFO - Iter [43850/160000] lr: 4.356e-05, eta: 18:39:33, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1284, decode.acc_seg: 94.8093, loss: 0.1284 2023-01-06 06:25:19,699 - mmseg - INFO - Iter [43900/160000] lr: 4.354e-05, eta: 18:39:07, time: 0.596, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1318, decode.acc_seg: 94.8069, loss: 0.1318 2023-01-06 06:25:47,814 - mmseg - INFO - Iter [43950/160000] lr: 4.352e-05, eta: 18:38:36, time: 0.563, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1256, decode.acc_seg: 94.9246, loss: 0.1256 2023-01-06 06:26:15,156 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 06:26:15,156 - mmseg - INFO - Iter [44000/160000] lr: 4.350e-05, eta: 18:38:03, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1330, decode.acc_seg: 94.6892, loss: 0.1330 2023-01-06 06:26:43,314 - mmseg - INFO - Iter [44050/160000] lr: 4.348e-05, eta: 18:37:32, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1274, decode.acc_seg: 95.1584, loss: 0.1274 2023-01-06 06:27:11,350 - mmseg - INFO - Iter [44100/160000] lr: 4.346e-05, eta: 18:37:01, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1192, decode.acc_seg: 95.1605, loss: 0.1192 2023-01-06 06:27:40,901 - mmseg - INFO - Iter [44150/160000] lr: 4.344e-05, eta: 18:36:33, time: 0.591, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1311, decode.acc_seg: 94.7529, loss: 0.1311 2023-01-06 06:28:09,486 - mmseg - INFO - Iter [44200/160000] lr: 4.343e-05, eta: 18:36:04, time: 0.572, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1291, decode.acc_seg: 94.6559, loss: 0.1291 2023-01-06 06:28:38,486 - mmseg - INFO - Iter [44250/160000] lr: 4.341e-05, eta: 18:35:35, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1323, decode.acc_seg: 94.6659, loss: 0.1323 2023-01-06 06:29:08,767 - mmseg - INFO - Iter [44300/160000] lr: 4.339e-05, eta: 18:35:10, time: 0.606, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1237, decode.acc_seg: 94.9900, loss: 0.1237 2023-01-06 06:29:36,703 - mmseg - INFO - Iter [44350/160000] lr: 4.337e-05, eta: 18:34:38, time: 0.559, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1171, decode.acc_seg: 95.1643, loss: 0.1171 2023-01-06 06:30:03,708 - mmseg - INFO - Iter [44400/160000] lr: 4.335e-05, eta: 18:34:04, time: 0.540, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1353, decode.acc_seg: 94.7034, loss: 0.1353 2023-01-06 06:30:32,657 - mmseg - INFO - Iter [44450/160000] lr: 4.333e-05, eta: 18:33:35, time: 0.579, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1292, decode.acc_seg: 94.9394, loss: 0.1292 2023-01-06 06:31:00,028 - mmseg - INFO - Iter [44500/160000] lr: 4.331e-05, eta: 18:33:03, time: 0.547, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1424, decode.acc_seg: 94.3668, loss: 0.1424 2023-01-06 06:31:27,794 - mmseg - INFO - Iter [44550/160000] lr: 4.329e-05, eta: 18:32:31, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1275, decode.acc_seg: 94.8410, loss: 0.1275 2023-01-06 06:31:56,507 - mmseg - INFO - Iter [44600/160000] lr: 4.328e-05, eta: 18:32:01, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1216, decode.acc_seg: 95.1072, loss: 0.1216 2023-01-06 06:32:26,448 - mmseg - INFO - Iter [44650/160000] lr: 4.326e-05, eta: 18:31:35, time: 0.599, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1330, decode.acc_seg: 94.4543, loss: 0.1330 2023-01-06 06:32:53,863 - mmseg - INFO - Iter [44700/160000] lr: 4.324e-05, eta: 18:31:02, time: 0.548, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1326, decode.acc_seg: 94.6822, loss: 0.1326 2023-01-06 06:33:22,833 - mmseg - INFO - Iter [44750/160000] lr: 4.322e-05, eta: 18:30:33, time: 0.579, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1181, decode.acc_seg: 95.1780, loss: 0.1181 2023-01-06 06:33:50,702 - mmseg - INFO - Iter [44800/160000] lr: 4.320e-05, eta: 18:30:02, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1233, decode.acc_seg: 95.0279, loss: 0.1233 2023-01-06 06:34:19,014 - mmseg - INFO - Iter [44850/160000] lr: 4.318e-05, eta: 18:29:31, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1406, decode.acc_seg: 94.4878, loss: 0.1406 2023-01-06 06:34:46,226 - mmseg - INFO - Iter [44900/160000] lr: 4.316e-05, eta: 18:28:58, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1217, decode.acc_seg: 95.0554, loss: 0.1217 2023-01-06 06:35:14,563 - mmseg - INFO - Iter [44950/160000] lr: 4.314e-05, eta: 18:28:28, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1294, decode.acc_seg: 94.8240, loss: 0.1294 2023-01-06 06:35:43,955 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 06:35:43,955 - mmseg - INFO - Iter [45000/160000] lr: 4.313e-05, eta: 18:28:00, time: 0.588, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1396, decode.acc_seg: 94.4910, loss: 0.1396 2023-01-06 06:36:14,883 - mmseg - INFO - Iter [45050/160000] lr: 4.311e-05, eta: 18:27:36, time: 0.619, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1297, decode.acc_seg: 94.8174, loss: 0.1297 2023-01-06 06:36:44,878 - mmseg - INFO - Iter [45100/160000] lr: 4.309e-05, eta: 18:27:10, time: 0.601, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1313, decode.acc_seg: 94.6792, loss: 0.1313 2023-01-06 06:37:12,952 - mmseg - INFO - Iter [45150/160000] lr: 4.307e-05, eta: 18:26:39, time: 0.561, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1230, decode.acc_seg: 95.1024, loss: 0.1230 2023-01-06 06:37:40,551 - mmseg - INFO - Iter [45200/160000] lr: 4.305e-05, eta: 18:26:07, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1275, decode.acc_seg: 94.9131, loss: 0.1275 2023-01-06 06:38:08,627 - mmseg - INFO - Iter [45250/160000] lr: 4.303e-05, eta: 18:25:36, time: 0.561, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1270, decode.acc_seg: 94.7552, loss: 0.1270 2023-01-06 06:38:36,842 - mmseg - INFO - Iter [45300/160000] lr: 4.301e-05, eta: 18:25:05, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1351, decode.acc_seg: 94.5556, loss: 0.1351 2023-01-06 06:39:04,610 - mmseg - INFO - Iter [45350/160000] lr: 4.299e-05, eta: 18:24:34, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1271, decode.acc_seg: 94.6527, loss: 0.1271 2023-01-06 06:39:34,867 - mmseg - INFO - Iter [45400/160000] lr: 4.298e-05, eta: 18:24:08, time: 0.605, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1223, decode.acc_seg: 94.9009, loss: 0.1223 2023-01-06 06:40:02,904 - mmseg - INFO - Iter [45450/160000] lr: 4.296e-05, eta: 18:23:37, time: 0.560, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1246, decode.acc_seg: 94.9018, loss: 0.1246 2023-01-06 06:40:31,131 - mmseg - INFO - Iter [45500/160000] lr: 4.294e-05, eta: 18:23:06, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1327, decode.acc_seg: 94.6710, loss: 0.1327 2023-01-06 06:40:58,816 - mmseg - INFO - Iter [45550/160000] lr: 4.292e-05, eta: 18:22:34, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1318, decode.acc_seg: 94.6910, loss: 0.1318 2023-01-06 06:41:27,188 - mmseg - INFO - Iter [45600/160000] lr: 4.290e-05, eta: 18:22:04, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1293, decode.acc_seg: 94.6960, loss: 0.1293 2023-01-06 06:41:54,559 - mmseg - INFO - Iter [45650/160000] lr: 4.288e-05, eta: 18:21:31, time: 0.547, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1317, decode.acc_seg: 94.6872, loss: 0.1317 2023-01-06 06:42:23,144 - mmseg - INFO - Iter [45700/160000] lr: 4.286e-05, eta: 18:21:02, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1248, decode.acc_seg: 94.9226, loss: 0.1248 2023-01-06 06:42:50,753 - mmseg - INFO - Iter [45750/160000] lr: 4.284e-05, eta: 18:20:30, time: 0.552, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1258, decode.acc_seg: 94.9715, loss: 0.1258 2023-01-06 06:43:20,891 - mmseg - INFO - Iter [45800/160000] lr: 4.283e-05, eta: 18:20:04, time: 0.602, data_time: 0.056, memory: 10576, decode.loss_ce: 0.1180, decode.acc_seg: 95.1053, loss: 0.1180 2023-01-06 06:43:48,553 - mmseg - INFO - Iter [45850/160000] lr: 4.281e-05, eta: 18:19:32, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1303, decode.acc_seg: 94.7124, loss: 0.1303 2023-01-06 06:44:17,315 - mmseg - INFO - Iter [45900/160000] lr: 4.279e-05, eta: 18:19:03, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1356, decode.acc_seg: 94.4155, loss: 0.1356 2023-01-06 06:44:46,480 - mmseg - INFO - Iter [45950/160000] lr: 4.277e-05, eta: 18:18:34, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1265, decode.acc_seg: 94.8647, loss: 0.1265 2023-01-06 06:45:15,083 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 06:45:15,084 - mmseg - INFO - Iter [46000/160000] lr: 4.275e-05, eta: 18:18:05, time: 0.573, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1158, decode.acc_seg: 95.3482, loss: 0.1158 2023-01-06 06:45:41,974 - mmseg - INFO - Iter [46050/160000] lr: 4.273e-05, eta: 18:17:31, time: 0.538, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1138, decode.acc_seg: 95.2643, loss: 0.1138 2023-01-06 06:46:11,153 - mmseg - INFO - Iter [46100/160000] lr: 4.271e-05, eta: 18:17:03, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1251, decode.acc_seg: 94.9282, loss: 0.1251 2023-01-06 06:46:40,818 - mmseg - INFO - Iter [46150/160000] lr: 4.269e-05, eta: 18:16:36, time: 0.594, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1250, decode.acc_seg: 95.0738, loss: 0.1250 2023-01-06 06:47:08,702 - mmseg - INFO - Iter [46200/160000] lr: 4.268e-05, eta: 18:16:04, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1306, decode.acc_seg: 94.7579, loss: 0.1306 2023-01-06 06:47:36,822 - mmseg - INFO - Iter [46250/160000] lr: 4.266e-05, eta: 18:15:34, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1170, decode.acc_seg: 95.2514, loss: 0.1170 2023-01-06 06:48:05,910 - mmseg - INFO - Iter [46300/160000] lr: 4.264e-05, eta: 18:15:05, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1175, decode.acc_seg: 95.1613, loss: 0.1175 2023-01-06 06:48:35,521 - mmseg - INFO - Iter [46350/160000] lr: 4.262e-05, eta: 18:14:38, time: 0.592, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1114, decode.acc_seg: 95.3985, loss: 0.1114 2023-01-06 06:49:04,357 - mmseg - INFO - Iter [46400/160000] lr: 4.260e-05, eta: 18:14:09, time: 0.577, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1214, decode.acc_seg: 94.9899, loss: 0.1214 2023-01-06 06:49:33,255 - mmseg - INFO - Iter [46450/160000] lr: 4.258e-05, eta: 18:13:40, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1238, decode.acc_seg: 94.8221, loss: 0.1238 2023-01-06 06:50:01,408 - mmseg - INFO - Iter [46500/160000] lr: 4.256e-05, eta: 18:13:09, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1137, decode.acc_seg: 95.2828, loss: 0.1137 2023-01-06 06:50:32,375 - mmseg - INFO - Iter [46550/160000] lr: 4.254e-05, eta: 18:12:45, time: 0.619, data_time: 0.062, memory: 10576, decode.loss_ce: 0.1232, decode.acc_seg: 95.0640, loss: 0.1232 2023-01-06 06:51:00,042 - mmseg - INFO - Iter [46600/160000] lr: 4.253e-05, eta: 18:12:14, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1407, decode.acc_seg: 94.5140, loss: 0.1407 2023-01-06 06:51:27,311 - mmseg - INFO - Iter [46650/160000] lr: 4.251e-05, eta: 18:11:41, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1357, decode.acc_seg: 94.6763, loss: 0.1357 2023-01-06 06:51:55,657 - mmseg - INFO - Iter [46700/160000] lr: 4.249e-05, eta: 18:11:11, time: 0.568, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1404, decode.acc_seg: 94.3784, loss: 0.1404 2023-01-06 06:52:23,046 - mmseg - INFO - Iter [46750/160000] lr: 4.247e-05, eta: 18:10:38, time: 0.548, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1225, decode.acc_seg: 94.9566, loss: 0.1225 2023-01-06 06:52:52,298 - mmseg - INFO - Iter [46800/160000] lr: 4.245e-05, eta: 18:10:10, time: 0.585, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1215, decode.acc_seg: 95.0250, loss: 0.1215 2023-01-06 06:53:21,078 - mmseg - INFO - Iter [46850/160000] lr: 4.243e-05, eta: 18:09:41, time: 0.575, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1239, decode.acc_seg: 94.8808, loss: 0.1239 2023-01-06 06:53:51,212 - mmseg - INFO - Iter [46900/160000] lr: 4.241e-05, eta: 18:09:15, time: 0.603, data_time: 0.060, memory: 10576, decode.loss_ce: 0.1231, decode.acc_seg: 94.9154, loss: 0.1231 2023-01-06 06:54:20,914 - mmseg - INFO - Iter [46950/160000] lr: 4.239e-05, eta: 18:08:48, time: 0.593, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1234, decode.acc_seg: 95.0244, loss: 0.1234 2023-01-06 06:54:49,733 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 06:54:49,734 - mmseg - INFO - Iter [47000/160000] lr: 4.238e-05, eta: 18:08:19, time: 0.577, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1161, decode.acc_seg: 95.2031, loss: 0.1161 2023-01-06 06:55:18,111 - mmseg - INFO - Iter [47050/160000] lr: 4.236e-05, eta: 18:07:49, time: 0.568, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1221, decode.acc_seg: 95.0345, loss: 0.1221 2023-01-06 06:55:46,062 - mmseg - INFO - Iter [47100/160000] lr: 4.234e-05, eta: 18:07:18, time: 0.559, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1238, decode.acc_seg: 94.7985, loss: 0.1238 2023-01-06 06:56:14,803 - mmseg - INFO - Iter [47150/160000] lr: 4.232e-05, eta: 18:06:48, time: 0.575, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1193, decode.acc_seg: 95.1277, loss: 0.1193 2023-01-06 06:56:42,955 - mmseg - INFO - Iter [47200/160000] lr: 4.230e-05, eta: 18:06:18, time: 0.562, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1202, decode.acc_seg: 95.0540, loss: 0.1202 2023-01-06 06:57:14,175 - mmseg - INFO - Iter [47250/160000] lr: 4.228e-05, eta: 18:05:54, time: 0.625, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1164, decode.acc_seg: 95.0391, loss: 0.1164 2023-01-06 06:57:43,473 - mmseg - INFO - Iter [47300/160000] lr: 4.226e-05, eta: 18:05:26, time: 0.585, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1263, decode.acc_seg: 94.8654, loss: 0.1263 2023-01-06 06:58:13,066 - mmseg - INFO - Iter [47350/160000] lr: 4.224e-05, eta: 18:04:59, time: 0.593, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1281, decode.acc_seg: 94.7147, loss: 0.1281 2023-01-06 06:58:41,956 - mmseg - INFO - Iter [47400/160000] lr: 4.223e-05, eta: 18:04:30, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1223, decode.acc_seg: 95.0862, loss: 0.1223 2023-01-06 06:59:10,185 - mmseg - INFO - Iter [47450/160000] lr: 4.221e-05, eta: 18:04:00, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1148, decode.acc_seg: 95.2725, loss: 0.1148 2023-01-06 06:59:39,363 - mmseg - INFO - Iter [47500/160000] lr: 4.219e-05, eta: 18:03:32, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1171, decode.acc_seg: 95.1540, loss: 0.1171 2023-01-06 07:00:08,099 - mmseg - INFO - Iter [47550/160000] lr: 4.217e-05, eta: 18:03:02, time: 0.575, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1303, decode.acc_seg: 94.7018, loss: 0.1303 2023-01-06 07:00:37,010 - mmseg - INFO - Iter [47600/160000] lr: 4.215e-05, eta: 18:02:34, time: 0.578, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1276, decode.acc_seg: 94.6788, loss: 0.1276 2023-01-06 07:01:07,296 - mmseg - INFO - Iter [47650/160000] lr: 4.213e-05, eta: 18:02:08, time: 0.606, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1314, decode.acc_seg: 94.7427, loss: 0.1314 2023-01-06 07:01:36,065 - mmseg - INFO - Iter [47700/160000] lr: 4.211e-05, eta: 18:01:39, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1306, decode.acc_seg: 94.6026, loss: 0.1306 2023-01-06 07:02:06,146 - mmseg - INFO - Iter [47750/160000] lr: 4.209e-05, eta: 18:01:13, time: 0.602, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1274, decode.acc_seg: 94.8488, loss: 0.1274 2023-01-06 07:02:34,105 - mmseg - INFO - Iter [47800/160000] lr: 4.208e-05, eta: 18:00:42, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1197, decode.acc_seg: 95.0531, loss: 0.1197 2023-01-06 07:03:01,148 - mmseg - INFO - Iter [47850/160000] lr: 4.206e-05, eta: 18:00:08, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1236, decode.acc_seg: 94.9447, loss: 0.1236 2023-01-06 07:03:28,365 - mmseg - INFO - Iter [47900/160000] lr: 4.204e-05, eta: 17:59:35, time: 0.544, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1209, decode.acc_seg: 95.1287, loss: 0.1209 2023-01-06 07:03:56,819 - mmseg - INFO - Iter [47950/160000] lr: 4.202e-05, eta: 17:59:05, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1110, decode.acc_seg: 95.4743, loss: 0.1110 2023-01-06 07:04:28,692 - mmseg - INFO - Saving checkpoint at 48000 iterations 2023-01-06 07:04:34,044 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 07:04:34,045 - mmseg - INFO - Iter [48000/160000] lr: 4.200e-05, eta: 17:58:56, time: 0.745, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1261, decode.acc_seg: 94.9040, loss: 0.1261 2023-01-06 07:05:06,273 - mmseg - INFO - per class results: 2023-01-06 07:05:06,276 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 97.58 | 98.8 | | sidewalk | 80.73 | 89.1 | | building | 90.41 | 95.7 | | wall | 50.43 | 57.53 | | fence | 51.96 | 64.52 | | pole | 54.73 | 61.43 | | traffic light | 57.81 | 68.72 | | traffic sign | 68.67 | 75.06 | | vegetation | 90.83 | 96.23 | | terrain | 57.16 | 71.67 | | sky | 93.83 | 97.81 | | person | 74.78 | 86.28 | | rider | 48.96 | 58.54 | | car | 92.21 | 97.35 | | truck | 52.93 | 62.62 | | bus | 68.25 | 77.8 | | train | 50.88 | 57.4 | | motorcycle | 38.41 | 44.42 | | bicycle | 68.47 | 84.56 | +---------------+-------+-------+ 2023-01-06 07:05:06,276 - mmseg - INFO - Summary: 2023-01-06 07:05:06,277 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 94.81 | 67.84 | 76.08 | +-------+-------+-------+ 2023-01-06 07:05:06,277 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 07:05:06,278 - mmseg - INFO - Iter(val) [63] aAcc: 0.9481, mIoU: 0.6784, mAcc: 0.7608, IoU.road: 0.9758, IoU.sidewalk: 0.8073, IoU.building: 0.9041, IoU.wall: 0.5043, IoU.fence: 0.5196, IoU.pole: 0.5473, IoU.traffic light: 0.5781, IoU.traffic sign: 0.6867, IoU.vegetation: 0.9083, IoU.terrain: 0.5716, IoU.sky: 0.9383, IoU.person: 0.7478, IoU.rider: 0.4896, IoU.car: 0.9221, IoU.truck: 0.5293, IoU.bus: 0.6825, IoU.train: 0.5088, IoU.motorcycle: 0.3841, IoU.bicycle: 0.6847, Acc.road: 0.9880, Acc.sidewalk: 0.8910, Acc.building: 0.9570, Acc.wall: 0.5753, Acc.fence: 0.6452, Acc.pole: 0.6143, Acc.traffic light: 0.6872, Acc.traffic sign: 0.7506, Acc.vegetation: 0.9623, Acc.terrain: 0.7167, Acc.sky: 0.9781, Acc.person: 0.8628, Acc.rider: 0.5854, Acc.car: 0.9735, Acc.truck: 0.6262, Acc.bus: 0.7780, Acc.train: 0.5740, Acc.motorcycle: 0.4442, Acc.bicycle: 0.8456 2023-01-06 07:05:33,976 - mmseg - INFO - Iter [48050/160000] lr: 4.198e-05, eta: 17:59:39, time: 1.198, data_time: 0.658, memory: 10576, decode.loss_ce: 0.1266, decode.acc_seg: 94.9306, loss: 0.1266 2023-01-06 07:06:02,126 - mmseg - INFO - Iter [48100/160000] lr: 4.196e-05, eta: 17:59:09, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1278, decode.acc_seg: 94.7768, loss: 0.1278 2023-01-06 07:06:30,734 - mmseg - INFO - Iter [48150/160000] lr: 4.194e-05, eta: 17:58:39, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1160, decode.acc_seg: 95.1171, loss: 0.1160 2023-01-06 07:06:58,385 - mmseg - INFO - Iter [48200/160000] lr: 4.193e-05, eta: 17:58:07, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1374, decode.acc_seg: 94.4081, loss: 0.1374 2023-01-06 07:07:26,768 - mmseg - INFO - Iter [48250/160000] lr: 4.191e-05, eta: 17:57:37, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1179, decode.acc_seg: 95.0647, loss: 0.1179 2023-01-06 07:07:56,442 - mmseg - INFO - Iter [48300/160000] lr: 4.189e-05, eta: 17:57:10, time: 0.594, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1286, decode.acc_seg: 94.8180, loss: 0.1286 2023-01-06 07:08:24,421 - mmseg - INFO - Iter [48350/160000] lr: 4.187e-05, eta: 17:56:39, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1382, decode.acc_seg: 94.3604, loss: 0.1382 2023-01-06 07:08:55,286 - mmseg - INFO - Iter [48400/160000] lr: 4.185e-05, eta: 17:56:14, time: 0.617, data_time: 0.062, memory: 10576, decode.loss_ce: 0.1368, decode.acc_seg: 94.5497, loss: 0.1368 2023-01-06 07:09:23,585 - mmseg - INFO - Iter [48450/160000] lr: 4.183e-05, eta: 17:55:44, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1283, decode.acc_seg: 94.8941, loss: 0.1283 2023-01-06 07:09:50,751 - mmseg - INFO - Iter [48500/160000] lr: 4.181e-05, eta: 17:55:11, time: 0.544, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1278, decode.acc_seg: 94.7247, loss: 0.1278 2023-01-06 07:10:19,188 - mmseg - INFO - Iter [48550/160000] lr: 4.179e-05, eta: 17:54:41, time: 0.569, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1307, decode.acc_seg: 94.7397, loss: 0.1307 2023-01-06 07:10:48,903 - mmseg - INFO - Iter [48600/160000] lr: 4.178e-05, eta: 17:54:13, time: 0.594, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1214, decode.acc_seg: 94.9152, loss: 0.1214 2023-01-06 07:11:17,156 - mmseg - INFO - Iter [48650/160000] lr: 4.176e-05, eta: 17:53:43, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1241, decode.acc_seg: 95.0242, loss: 0.1241 2023-01-06 07:11:44,475 - mmseg - INFO - Iter [48700/160000] lr: 4.174e-05, eta: 17:53:10, time: 0.546, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1300, decode.acc_seg: 94.8068, loss: 0.1300 2023-01-06 07:12:13,805 - mmseg - INFO - Iter [48750/160000] lr: 4.172e-05, eta: 17:52:42, time: 0.587, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1226, decode.acc_seg: 95.0617, loss: 0.1226 2023-01-06 07:12:42,351 - mmseg - INFO - Iter [48800/160000] lr: 4.170e-05, eta: 17:52:13, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1204, decode.acc_seg: 95.0542, loss: 0.1204 2023-01-06 07:13:11,219 - mmseg - INFO - Iter [48850/160000] lr: 4.168e-05, eta: 17:51:44, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1218, decode.acc_seg: 94.8528, loss: 0.1218 2023-01-06 07:13:38,717 - mmseg - INFO - Iter [48900/160000] lr: 4.166e-05, eta: 17:51:11, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1184, decode.acc_seg: 95.1207, loss: 0.1184 2023-01-06 07:14:06,972 - mmseg - INFO - Iter [48950/160000] lr: 4.164e-05, eta: 17:50:41, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1291, decode.acc_seg: 94.7772, loss: 0.1291 2023-01-06 07:14:34,576 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 07:14:34,577 - mmseg - INFO - Iter [49000/160000] lr: 4.163e-05, eta: 17:50:09, time: 0.552, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1177, decode.acc_seg: 95.3280, loss: 0.1177 2023-01-06 07:15:04,035 - mmseg - INFO - Iter [49050/160000] lr: 4.161e-05, eta: 17:49:41, time: 0.589, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1289, decode.acc_seg: 94.8243, loss: 0.1289 2023-01-06 07:15:31,617 - mmseg - INFO - Iter [49100/160000] lr: 4.159e-05, eta: 17:49:09, time: 0.551, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1305, decode.acc_seg: 94.6591, loss: 0.1305 2023-01-06 07:16:02,532 - mmseg - INFO - Iter [49150/160000] lr: 4.157e-05, eta: 17:48:45, time: 0.618, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1291, decode.acc_seg: 94.7668, loss: 0.1291 2023-01-06 07:16:30,853 - mmseg - INFO - Iter [49200/160000] lr: 4.155e-05, eta: 17:48:15, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1192, decode.acc_seg: 95.1480, loss: 0.1192 2023-01-06 07:16:58,780 - mmseg - INFO - Iter [49250/160000] lr: 4.153e-05, eta: 17:47:44, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1237, decode.acc_seg: 95.0289, loss: 0.1237 2023-01-06 07:17:26,006 - mmseg - INFO - Iter [49300/160000] lr: 4.151e-05, eta: 17:47:11, time: 0.545, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1090, decode.acc_seg: 95.5726, loss: 0.1090 2023-01-06 07:17:54,267 - mmseg - INFO - Iter [49350/160000] lr: 4.149e-05, eta: 17:46:40, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1190, decode.acc_seg: 95.2206, loss: 0.1190 2023-01-06 07:18:21,936 - mmseg - INFO - Iter [49400/160000] lr: 4.148e-05, eta: 17:46:09, time: 0.553, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1242, decode.acc_seg: 95.0895, loss: 0.1242 2023-01-06 07:18:51,647 - mmseg - INFO - Iter [49450/160000] lr: 4.146e-05, eta: 17:45:42, time: 0.595, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1275, decode.acc_seg: 94.7191, loss: 0.1275 2023-01-06 07:19:22,874 - mmseg - INFO - Iter [49500/160000] lr: 4.144e-05, eta: 17:45:18, time: 0.624, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1186, decode.acc_seg: 95.1255, loss: 0.1186 2023-01-06 07:19:51,565 - mmseg - INFO - Iter [49550/160000] lr: 4.142e-05, eta: 17:44:48, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1154, decode.acc_seg: 95.1792, loss: 0.1154 2023-01-06 07:20:21,465 - mmseg - INFO - Iter [49600/160000] lr: 4.140e-05, eta: 17:44:22, time: 0.598, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1243, decode.acc_seg: 94.9771, loss: 0.1243 2023-01-06 07:20:49,978 - mmseg - INFO - Iter [49650/160000] lr: 4.138e-05, eta: 17:43:52, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1370, decode.acc_seg: 94.5236, loss: 0.1370 2023-01-06 07:21:18,269 - mmseg - INFO - Iter [49700/160000] lr: 4.136e-05, eta: 17:43:21, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1300, decode.acc_seg: 94.8911, loss: 0.1300 2023-01-06 07:21:45,395 - mmseg - INFO - Iter [49750/160000] lr: 4.134e-05, eta: 17:42:48, time: 0.542, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1325, decode.acc_seg: 94.7276, loss: 0.1325 2023-01-06 07:22:13,883 - mmseg - INFO - Iter [49800/160000] lr: 4.133e-05, eta: 17:42:19, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1205, decode.acc_seg: 95.1098, loss: 0.1205 2023-01-06 07:22:43,492 - mmseg - INFO - Iter [49850/160000] lr: 4.131e-05, eta: 17:41:51, time: 0.592, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1285, decode.acc_seg: 94.8485, loss: 0.1285 2023-01-06 07:23:11,384 - mmseg - INFO - Iter [49900/160000] lr: 4.129e-05, eta: 17:41:20, time: 0.558, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1287, decode.acc_seg: 94.8022, loss: 0.1287 2023-01-06 07:23:39,044 - mmseg - INFO - Iter [49950/160000] lr: 4.127e-05, eta: 17:40:48, time: 0.552, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1297, decode.acc_seg: 94.8045, loss: 0.1297 2023-01-06 07:24:08,147 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 07:24:08,148 - mmseg - INFO - Iter [50000/160000] lr: 4.125e-05, eta: 17:40:20, time: 0.583, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1228, decode.acc_seg: 95.0074, loss: 0.1228 2023-01-06 07:24:37,286 - mmseg - INFO - Iter [50050/160000] lr: 4.123e-05, eta: 17:39:51, time: 0.583, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1214, decode.acc_seg: 94.9809, loss: 0.1214 2023-01-06 07:25:05,155 - mmseg - INFO - Iter [50100/160000] lr: 4.121e-05, eta: 17:39:20, time: 0.557, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1233, decode.acc_seg: 95.0881, loss: 0.1233 2023-01-06 07:25:33,453 - mmseg - INFO - Iter [50150/160000] lr: 4.119e-05, eta: 17:38:50, time: 0.567, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1326, decode.acc_seg: 94.7249, loss: 0.1326 2023-01-06 07:26:02,181 - mmseg - INFO - Iter [50200/160000] lr: 4.118e-05, eta: 17:38:21, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1176, decode.acc_seg: 95.2776, loss: 0.1176 2023-01-06 07:26:32,217 - mmseg - INFO - Iter [50250/160000] lr: 4.116e-05, eta: 17:37:54, time: 0.601, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1277, decode.acc_seg: 94.8614, loss: 0.1277 2023-01-06 07:27:00,848 - mmseg - INFO - Iter [50300/160000] lr: 4.114e-05, eta: 17:37:25, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1167, decode.acc_seg: 95.2819, loss: 0.1167 2023-01-06 07:27:29,201 - mmseg - INFO - Iter [50350/160000] lr: 4.112e-05, eta: 17:36:54, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1208, decode.acc_seg: 95.1762, loss: 0.1208 2023-01-06 07:27:57,041 - mmseg - INFO - Iter [50400/160000] lr: 4.110e-05, eta: 17:36:23, time: 0.557, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1356, decode.acc_seg: 94.6476, loss: 0.1356 2023-01-06 07:28:25,630 - mmseg - INFO - Iter [50450/160000] lr: 4.108e-05, eta: 17:35:53, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1313, decode.acc_seg: 94.7311, loss: 0.1313 2023-01-06 07:28:53,786 - mmseg - INFO - Iter [50500/160000] lr: 4.106e-05, eta: 17:35:23, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1323, decode.acc_seg: 94.5992, loss: 0.1323 2023-01-06 07:29:22,287 - mmseg - INFO - Iter [50550/160000] lr: 4.104e-05, eta: 17:34:53, time: 0.569, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1244, decode.acc_seg: 95.0225, loss: 0.1244 2023-01-06 07:29:52,138 - mmseg - INFO - Iter [50600/160000] lr: 4.103e-05, eta: 17:34:26, time: 0.598, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1224, decode.acc_seg: 94.8913, loss: 0.1224 2023-01-06 07:30:21,210 - mmseg - INFO - Iter [50650/160000] lr: 4.101e-05, eta: 17:33:58, time: 0.581, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1149, decode.acc_seg: 95.2140, loss: 0.1149 2023-01-06 07:30:49,663 - mmseg - INFO - Iter [50700/160000] lr: 4.099e-05, eta: 17:33:28, time: 0.569, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1103, decode.acc_seg: 95.4083, loss: 0.1103 2023-01-06 07:31:18,487 - mmseg - INFO - Iter [50750/160000] lr: 4.097e-05, eta: 17:32:59, time: 0.576, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1231, decode.acc_seg: 95.0863, loss: 0.1231 2023-01-06 07:31:47,690 - mmseg - INFO - Iter [50800/160000] lr: 4.095e-05, eta: 17:32:30, time: 0.584, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1139, decode.acc_seg: 95.1947, loss: 0.1139 2023-01-06 07:32:14,741 - mmseg - INFO - Iter [50850/160000] lr: 4.093e-05, eta: 17:31:57, time: 0.541, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1172, decode.acc_seg: 95.3060, loss: 0.1172 2023-01-06 07:32:42,353 - mmseg - INFO - Iter [50900/160000] lr: 4.091e-05, eta: 17:31:26, time: 0.552, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1179, decode.acc_seg: 95.1906, loss: 0.1179 2023-01-06 07:33:09,816 - mmseg - INFO - Iter [50950/160000] lr: 4.089e-05, eta: 17:30:54, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1219, decode.acc_seg: 95.2148, loss: 0.1219 2023-01-06 07:33:39,769 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 07:33:39,770 - mmseg - INFO - Iter [51000/160000] lr: 4.088e-05, eta: 17:30:27, time: 0.598, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1072, decode.acc_seg: 95.4899, loss: 0.1072 2023-01-06 07:34:07,886 - mmseg - INFO - Iter [51050/160000] lr: 4.086e-05, eta: 17:29:56, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1204, decode.acc_seg: 95.1271, loss: 0.1204 2023-01-06 07:34:37,663 - mmseg - INFO - Iter [51100/160000] lr: 4.084e-05, eta: 17:29:29, time: 0.596, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1186, decode.acc_seg: 95.1638, loss: 0.1186 2023-01-06 07:35:06,396 - mmseg - INFO - Iter [51150/160000] lr: 4.082e-05, eta: 17:29:00, time: 0.575, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1162, decode.acc_seg: 95.2227, loss: 0.1162 2023-01-06 07:35:33,570 - mmseg - INFO - Iter [51200/160000] lr: 4.080e-05, eta: 17:28:27, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1142, decode.acc_seg: 95.3537, loss: 0.1142 2023-01-06 07:36:01,356 - mmseg - INFO - Iter [51250/160000] lr: 4.078e-05, eta: 17:27:56, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1214, decode.acc_seg: 95.1045, loss: 0.1214 2023-01-06 07:36:28,566 - mmseg - INFO - Iter [51300/160000] lr: 4.076e-05, eta: 17:27:24, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1235, decode.acc_seg: 95.0138, loss: 0.1235 2023-01-06 07:36:58,456 - mmseg - INFO - Iter [51350/160000] lr: 4.074e-05, eta: 17:26:57, time: 0.597, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1184, decode.acc_seg: 95.1712, loss: 0.1184 2023-01-06 07:37:27,628 - mmseg - INFO - Iter [51400/160000] lr: 4.073e-05, eta: 17:26:28, time: 0.584, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1307, decode.acc_seg: 94.8621, loss: 0.1307 2023-01-06 07:37:55,293 - mmseg - INFO - Iter [51450/160000] lr: 4.071e-05, eta: 17:25:57, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1161, decode.acc_seg: 95.0456, loss: 0.1161 2023-01-06 07:38:23,108 - mmseg - INFO - Iter [51500/160000] lr: 4.069e-05, eta: 17:25:26, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1152, decode.acc_seg: 95.2891, loss: 0.1152 2023-01-06 07:38:51,206 - mmseg - INFO - Iter [51550/160000] lr: 4.067e-05, eta: 17:24:55, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1149, decode.acc_seg: 95.2800, loss: 0.1149 2023-01-06 07:39:20,455 - mmseg - INFO - Iter [51600/160000] lr: 4.065e-05, eta: 17:24:27, time: 0.585, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1151, decode.acc_seg: 95.2157, loss: 0.1151 2023-01-06 07:39:47,780 - mmseg - INFO - Iter [51650/160000] lr: 4.063e-05, eta: 17:23:55, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1272, decode.acc_seg: 95.0790, loss: 0.1272 2023-01-06 07:40:15,351 - mmseg - INFO - Iter [51700/160000] lr: 4.061e-05, eta: 17:23:23, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1202, decode.acc_seg: 95.0919, loss: 0.1202 2023-01-06 07:40:44,901 - mmseg - INFO - Iter [51750/160000] lr: 4.059e-05, eta: 17:22:55, time: 0.591, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1289, decode.acc_seg: 94.6678, loss: 0.1289 2023-01-06 07:41:14,276 - mmseg - INFO - Iter [51800/160000] lr: 4.058e-05, eta: 17:22:28, time: 0.588, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1171, decode.acc_seg: 95.1927, loss: 0.1171 2023-01-06 07:41:43,435 - mmseg - INFO - Iter [51850/160000] lr: 4.056e-05, eta: 17:21:59, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1197, decode.acc_seg: 95.0414, loss: 0.1197 2023-01-06 07:42:12,148 - mmseg - INFO - Iter [51900/160000] lr: 4.054e-05, eta: 17:21:30, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1133, decode.acc_seg: 95.2868, loss: 0.1133 2023-01-06 07:42:41,709 - mmseg - INFO - Iter [51950/160000] lr: 4.052e-05, eta: 17:21:02, time: 0.591, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1198, decode.acc_seg: 95.2736, loss: 0.1198 2023-01-06 07:43:09,581 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 07:43:09,581 - mmseg - INFO - Iter [52000/160000] lr: 4.050e-05, eta: 17:20:31, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1161, decode.acc_seg: 95.2093, loss: 0.1161 2023-01-06 07:43:37,121 - mmseg - INFO - Iter [52050/160000] lr: 4.048e-05, eta: 17:19:59, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1401, decode.acc_seg: 94.7053, loss: 0.1401 2023-01-06 07:44:06,922 - mmseg - INFO - Iter [52100/160000] lr: 4.046e-05, eta: 17:19:32, time: 0.596, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1255, decode.acc_seg: 94.9030, loss: 0.1255 2023-01-06 07:44:35,408 - mmseg - INFO - Iter [52150/160000] lr: 4.044e-05, eta: 17:19:03, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1126, decode.acc_seg: 95.3309, loss: 0.1126 2023-01-06 07:45:03,815 - mmseg - INFO - Iter [52200/160000] lr: 4.043e-05, eta: 17:18:33, time: 0.569, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1181, decode.acc_seg: 95.1589, loss: 0.1181 2023-01-06 07:45:31,557 - mmseg - INFO - Iter [52250/160000] lr: 4.041e-05, eta: 17:18:02, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1294, decode.acc_seg: 94.8515, loss: 0.1294 2023-01-06 07:46:00,444 - mmseg - INFO - Iter [52300/160000] lr: 4.039e-05, eta: 17:17:33, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1101, decode.acc_seg: 95.3649, loss: 0.1101 2023-01-06 07:46:27,830 - mmseg - INFO - Iter [52350/160000] lr: 4.037e-05, eta: 17:17:01, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1170, decode.acc_seg: 95.2210, loss: 0.1170 2023-01-06 07:46:54,977 - mmseg - INFO - Iter [52400/160000] lr: 4.035e-05, eta: 17:16:28, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1182, decode.acc_seg: 95.1197, loss: 0.1182 2023-01-06 07:47:23,970 - mmseg - INFO - Iter [52450/160000] lr: 4.033e-05, eta: 17:15:59, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1201, decode.acc_seg: 95.0714, loss: 0.1201 2023-01-06 07:47:54,517 - mmseg - INFO - Iter [52500/160000] lr: 4.031e-05, eta: 17:15:34, time: 0.610, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1201, decode.acc_seg: 95.1227, loss: 0.1201 2023-01-06 07:48:23,816 - mmseg - INFO - Iter [52550/160000] lr: 4.029e-05, eta: 17:15:06, time: 0.587, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1192, decode.acc_seg: 95.1397, loss: 0.1192 2023-01-06 07:48:53,324 - mmseg - INFO - Iter [52600/160000] lr: 4.028e-05, eta: 17:14:38, time: 0.589, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1151, decode.acc_seg: 95.2631, loss: 0.1151 2023-01-06 07:49:22,460 - mmseg - INFO - Iter [52650/160000] lr: 4.026e-05, eta: 17:14:10, time: 0.584, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1182, decode.acc_seg: 95.1935, loss: 0.1182 2023-01-06 07:49:49,694 - mmseg - INFO - Iter [52700/160000] lr: 4.024e-05, eta: 17:13:37, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1156, decode.acc_seg: 95.3352, loss: 0.1156 2023-01-06 07:50:17,491 - mmseg - INFO - Iter [52750/160000] lr: 4.022e-05, eta: 17:13:06, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1181, decode.acc_seg: 95.1036, loss: 0.1181 2023-01-06 07:50:45,293 - mmseg - INFO - Iter [52800/160000] lr: 4.020e-05, eta: 17:12:35, time: 0.557, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1115, decode.acc_seg: 95.4150, loss: 0.1115 2023-01-06 07:51:15,830 - mmseg - INFO - Iter [52850/160000] lr: 4.018e-05, eta: 17:12:10, time: 0.611, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1203, decode.acc_seg: 95.0628, loss: 0.1203 2023-01-06 07:51:44,578 - mmseg - INFO - Iter [52900/160000] lr: 4.016e-05, eta: 17:11:40, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1330, decode.acc_seg: 94.5031, loss: 0.1330 2023-01-06 07:52:12,655 - mmseg - INFO - Iter [52950/160000] lr: 4.014e-05, eta: 17:11:10, time: 0.562, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1280, decode.acc_seg: 94.8691, loss: 0.1280 2023-01-06 07:52:40,707 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 07:52:40,708 - mmseg - INFO - Iter [53000/160000] lr: 4.013e-05, eta: 17:10:39, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1179, decode.acc_seg: 95.0240, loss: 0.1179 2023-01-06 07:53:08,875 - mmseg - INFO - Iter [53050/160000] lr: 4.011e-05, eta: 17:10:09, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1160, decode.acc_seg: 95.1730, loss: 0.1160 2023-01-06 07:53:37,373 - mmseg - INFO - Iter [53100/160000] lr: 4.009e-05, eta: 17:09:39, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1209, decode.acc_seg: 94.9729, loss: 0.1209 2023-01-06 07:54:05,612 - mmseg - INFO - Iter [53150/160000] lr: 4.007e-05, eta: 17:09:09, time: 0.566, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1177, decode.acc_seg: 95.0858, loss: 0.1177 2023-01-06 07:54:36,481 - mmseg - INFO - Iter [53200/160000] lr: 4.005e-05, eta: 17:08:44, time: 0.617, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1159, decode.acc_seg: 95.2779, loss: 0.1159 2023-01-06 07:55:05,494 - mmseg - INFO - Iter [53250/160000] lr: 4.003e-05, eta: 17:08:15, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1232, decode.acc_seg: 94.8150, loss: 0.1232 2023-01-06 07:55:35,341 - mmseg - INFO - Iter [53300/160000] lr: 4.001e-05, eta: 17:07:48, time: 0.597, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1166, decode.acc_seg: 95.2674, loss: 0.1166 2023-01-06 07:56:03,622 - mmseg - INFO - Iter [53350/160000] lr: 3.999e-05, eta: 17:07:18, time: 0.566, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1315, decode.acc_seg: 94.8459, loss: 0.1315 2023-01-06 07:56:33,499 - mmseg - INFO - Iter [53400/160000] lr: 3.998e-05, eta: 17:06:51, time: 0.598, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1228, decode.acc_seg: 94.9211, loss: 0.1228 2023-01-06 07:57:02,320 - mmseg - INFO - Iter [53450/160000] lr: 3.996e-05, eta: 17:06:22, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1247, decode.acc_seg: 94.8925, loss: 0.1247 2023-01-06 07:57:30,286 - mmseg - INFO - Iter [53500/160000] lr: 3.994e-05, eta: 17:05:51, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1182, decode.acc_seg: 95.0422, loss: 0.1182 2023-01-06 07:57:57,435 - mmseg - INFO - Iter [53550/160000] lr: 3.992e-05, eta: 17:05:19, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1142, decode.acc_seg: 95.2775, loss: 0.1142 2023-01-06 07:58:26,704 - mmseg - INFO - Iter [53600/160000] lr: 3.990e-05, eta: 17:04:51, time: 0.585, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1080, decode.acc_seg: 95.4467, loss: 0.1080 2023-01-06 07:58:54,834 - mmseg - INFO - Iter [53650/160000] lr: 3.988e-05, eta: 17:04:20, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1169, decode.acc_seg: 95.1656, loss: 0.1169 2023-01-06 07:59:24,756 - mmseg - INFO - Iter [53700/160000] lr: 3.986e-05, eta: 17:03:54, time: 0.598, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1103, decode.acc_seg: 95.4784, loss: 0.1103 2023-01-06 07:59:53,727 - mmseg - INFO - Iter [53750/160000] lr: 3.984e-05, eta: 17:03:25, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1147, decode.acc_seg: 95.2728, loss: 0.1147 2023-01-06 08:00:20,810 - mmseg - INFO - Iter [53800/160000] lr: 3.983e-05, eta: 17:02:52, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1169, decode.acc_seg: 95.2044, loss: 0.1169 2023-01-06 08:00:49,205 - mmseg - INFO - Iter [53850/160000] lr: 3.981e-05, eta: 17:02:22, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1159, decode.acc_seg: 95.3608, loss: 0.1159 2023-01-06 08:01:16,888 - mmseg - INFO - Iter [53900/160000] lr: 3.979e-05, eta: 17:01:51, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1105, decode.acc_seg: 95.4071, loss: 0.1105 2023-01-06 08:01:48,166 - mmseg - INFO - Iter [53950/160000] lr: 3.977e-05, eta: 17:01:27, time: 0.627, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1143, decode.acc_seg: 95.3023, loss: 0.1143 2023-01-06 08:02:15,443 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 08:02:15,444 - mmseg - INFO - Iter [54000/160000] lr: 3.975e-05, eta: 17:00:55, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1175, decode.acc_seg: 95.2571, loss: 0.1175 2023-01-06 08:02:43,320 - mmseg - INFO - Iter [54050/160000] lr: 3.973e-05, eta: 17:00:24, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1142, decode.acc_seg: 95.2671, loss: 0.1142 2023-01-06 08:03:13,411 - mmseg - INFO - Iter [54100/160000] lr: 3.971e-05, eta: 16:59:57, time: 0.602, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1125, decode.acc_seg: 95.4087, loss: 0.1125 2023-01-06 08:03:40,772 - mmseg - INFO - Iter [54150/160000] lr: 3.969e-05, eta: 16:59:26, time: 0.548, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1125, decode.acc_seg: 95.3876, loss: 0.1125 2023-01-06 08:04:08,808 - mmseg - INFO - Iter [54200/160000] lr: 3.968e-05, eta: 16:58:55, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1145, decode.acc_seg: 95.3475, loss: 0.1145 2023-01-06 08:04:37,010 - mmseg - INFO - Iter [54250/160000] lr: 3.966e-05, eta: 16:58:25, time: 0.564, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1150, decode.acc_seg: 95.2344, loss: 0.1150 2023-01-06 08:05:05,745 - mmseg - INFO - Iter [54300/160000] lr: 3.964e-05, eta: 16:57:56, time: 0.575, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1118, decode.acc_seg: 95.3098, loss: 0.1118 2023-01-06 08:05:37,484 - mmseg - INFO - Iter [54350/160000] lr: 3.962e-05, eta: 16:57:32, time: 0.635, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1154, decode.acc_seg: 95.3541, loss: 0.1154 2023-01-06 08:06:06,411 - mmseg - INFO - Iter [54400/160000] lr: 3.960e-05, eta: 16:57:03, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1182, decode.acc_seg: 95.0750, loss: 0.1182 2023-01-06 08:06:34,475 - mmseg - INFO - Iter [54450/160000] lr: 3.958e-05, eta: 16:56:33, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1219, decode.acc_seg: 94.9269, loss: 0.1219 2023-01-06 08:07:02,951 - mmseg - INFO - Iter [54500/160000] lr: 3.956e-05, eta: 16:56:03, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1179, decode.acc_seg: 94.9581, loss: 0.1179 2023-01-06 08:07:30,934 - mmseg - INFO - Iter [54550/160000] lr: 3.954e-05, eta: 16:55:32, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1221, decode.acc_seg: 94.9309, loss: 0.1221 2023-01-06 08:07:58,201 - mmseg - INFO - Iter [54600/160000] lr: 3.953e-05, eta: 16:55:00, time: 0.545, data_time: 0.015, memory: 10576, decode.loss_ce: 0.1254, decode.acc_seg: 94.8207, loss: 0.1254 2023-01-06 08:08:26,046 - mmseg - INFO - Iter [54650/160000] lr: 3.951e-05, eta: 16:54:30, time: 0.558, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1132, decode.acc_seg: 95.4280, loss: 0.1132 2023-01-06 08:08:55,967 - mmseg - INFO - Iter [54700/160000] lr: 3.949e-05, eta: 16:54:03, time: 0.599, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1230, decode.acc_seg: 95.0017, loss: 0.1230 2023-01-06 08:09:24,850 - mmseg - INFO - Iter [54750/160000] lr: 3.947e-05, eta: 16:53:34, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1128, decode.acc_seg: 95.2771, loss: 0.1128 2023-01-06 08:09:52,244 - mmseg - INFO - Iter [54800/160000] lr: 3.945e-05, eta: 16:53:02, time: 0.549, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1220, decode.acc_seg: 94.9618, loss: 0.1220 2023-01-06 08:10:19,629 - mmseg - INFO - Iter [54850/160000] lr: 3.943e-05, eta: 16:52:30, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1357, decode.acc_seg: 94.6297, loss: 0.1357 2023-01-06 08:10:47,437 - mmseg - INFO - Iter [54900/160000] lr: 3.941e-05, eta: 16:51:59, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1250, decode.acc_seg: 94.8921, loss: 0.1250 2023-01-06 08:11:16,068 - mmseg - INFO - Iter [54950/160000] lr: 3.939e-05, eta: 16:51:30, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1160, decode.acc_seg: 95.1444, loss: 0.1160 2023-01-06 08:11:44,419 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 08:11:44,420 - mmseg - INFO - Iter [55000/160000] lr: 3.938e-05, eta: 16:51:00, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1181, decode.acc_seg: 95.1955, loss: 0.1181 2023-01-06 08:12:12,028 - mmseg - INFO - Iter [55050/160000] lr: 3.936e-05, eta: 16:50:29, time: 0.552, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1168, decode.acc_seg: 95.2723, loss: 0.1168 2023-01-06 08:12:42,403 - mmseg - INFO - Iter [55100/160000] lr: 3.934e-05, eta: 16:50:03, time: 0.607, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1135, decode.acc_seg: 95.3786, loss: 0.1135 2023-01-06 08:13:09,411 - mmseg - INFO - Iter [55150/160000] lr: 3.932e-05, eta: 16:49:30, time: 0.540, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1122, decode.acc_seg: 95.3262, loss: 0.1122 2023-01-06 08:13:39,317 - mmseg - INFO - Iter [55200/160000] lr: 3.930e-05, eta: 16:49:03, time: 0.597, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1026, decode.acc_seg: 95.7692, loss: 0.1026 2023-01-06 08:14:06,923 - mmseg - INFO - Iter [55250/160000] lr: 3.928e-05, eta: 16:48:32, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1162, decode.acc_seg: 95.2872, loss: 0.1162 2023-01-06 08:14:34,106 - mmseg - INFO - Iter [55300/160000] lr: 3.926e-05, eta: 16:48:00, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1139, decode.acc_seg: 95.2983, loss: 0.1139 2023-01-06 08:15:01,627 - mmseg - INFO - Iter [55350/160000] lr: 3.924e-05, eta: 16:47:28, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1156, decode.acc_seg: 95.2393, loss: 0.1156 2023-01-06 08:15:29,930 - mmseg - INFO - Iter [55400/160000] lr: 3.923e-05, eta: 16:46:58, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1183, decode.acc_seg: 95.1440, loss: 0.1183 2023-01-06 08:16:01,005 - mmseg - INFO - Iter [55450/160000] lr: 3.921e-05, eta: 16:46:34, time: 0.621, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1122, decode.acc_seg: 95.4115, loss: 0.1122 2023-01-06 08:16:30,990 - mmseg - INFO - Iter [55500/160000] lr: 3.919e-05, eta: 16:46:07, time: 0.599, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1100, decode.acc_seg: 95.4285, loss: 0.1100 2023-01-06 08:16:59,269 - mmseg - INFO - Iter [55550/160000] lr: 3.917e-05, eta: 16:45:37, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1163, decode.acc_seg: 95.2309, loss: 0.1163 2023-01-06 08:17:28,248 - mmseg - INFO - Iter [55600/160000] lr: 3.915e-05, eta: 16:45:08, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1140, decode.acc_seg: 95.3958, loss: 0.1140 2023-01-06 08:17:56,252 - mmseg - INFO - Iter [55650/160000] lr: 3.913e-05, eta: 16:44:38, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1199, decode.acc_seg: 95.0870, loss: 0.1199 2023-01-06 08:18:26,312 - mmseg - INFO - Iter [55700/160000] lr: 3.911e-05, eta: 16:44:11, time: 0.601, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1224, decode.acc_seg: 94.9107, loss: 0.1224 2023-01-06 08:18:54,901 - mmseg - INFO - Iter [55750/160000] lr: 3.909e-05, eta: 16:43:41, time: 0.572, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1203, decode.acc_seg: 95.1454, loss: 0.1203 2023-01-06 08:19:22,114 - mmseg - INFO - Iter [55800/160000] lr: 3.908e-05, eta: 16:43:09, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1090, decode.acc_seg: 95.5310, loss: 0.1090 2023-01-06 08:19:52,174 - mmseg - INFO - Iter [55850/160000] lr: 3.906e-05, eta: 16:42:43, time: 0.600, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1245, decode.acc_seg: 94.8938, loss: 0.1245 2023-01-06 08:20:19,552 - mmseg - INFO - Iter [55900/160000] lr: 3.904e-05, eta: 16:42:11, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1344, decode.acc_seg: 94.6417, loss: 0.1344 2023-01-06 08:20:47,057 - mmseg - INFO - Iter [55950/160000] lr: 3.902e-05, eta: 16:41:40, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1350, decode.acc_seg: 94.7027, loss: 0.1350 2023-01-06 08:21:14,613 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 08:21:14,614 - mmseg - INFO - Iter [56000/160000] lr: 3.900e-05, eta: 16:41:08, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1191, decode.acc_seg: 95.0999, loss: 0.1191 2023-01-06 08:21:42,804 - mmseg - INFO - Iter [56050/160000] lr: 3.898e-05, eta: 16:40:38, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1274, decode.acc_seg: 94.9815, loss: 0.1274 2023-01-06 08:22:10,662 - mmseg - INFO - Iter [56100/160000] lr: 3.896e-05, eta: 16:40:07, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1133, decode.acc_seg: 95.3331, loss: 0.1133 2023-01-06 08:22:39,324 - mmseg - INFO - Iter [56150/160000] lr: 3.894e-05, eta: 16:39:38, time: 0.572, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1148, decode.acc_seg: 95.0699, loss: 0.1148 2023-01-06 08:23:08,978 - mmseg - INFO - Iter [56200/160000] lr: 3.893e-05, eta: 16:39:11, time: 0.594, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1209, decode.acc_seg: 95.0014, loss: 0.1209 2023-01-06 08:23:37,139 - mmseg - INFO - Iter [56250/160000] lr: 3.891e-05, eta: 16:38:40, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1179, decode.acc_seg: 95.1375, loss: 0.1179 2023-01-06 08:24:06,149 - mmseg - INFO - Iter [56300/160000] lr: 3.889e-05, eta: 16:38:12, time: 0.579, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1083, decode.acc_seg: 95.5083, loss: 0.1083 2023-01-06 08:24:35,189 - mmseg - INFO - Iter [56350/160000] lr: 3.887e-05, eta: 16:37:43, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1180, decode.acc_seg: 95.1613, loss: 0.1180 2023-01-06 08:25:05,252 - mmseg - INFO - Iter [56400/160000] lr: 3.885e-05, eta: 16:37:16, time: 0.601, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1114, decode.acc_seg: 95.3308, loss: 0.1114 2023-01-06 08:25:35,161 - mmseg - INFO - Iter [56450/160000] lr: 3.883e-05, eta: 16:36:50, time: 0.599, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1155, decode.acc_seg: 95.2830, loss: 0.1155 2023-01-06 08:26:03,519 - mmseg - INFO - Iter [56500/160000] lr: 3.881e-05, eta: 16:36:20, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1168, decode.acc_seg: 95.1686, loss: 0.1168 2023-01-06 08:26:34,743 - mmseg - INFO - Iter [56550/160000] lr: 3.879e-05, eta: 16:35:55, time: 0.624, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1171, decode.acc_seg: 95.1127, loss: 0.1171 2023-01-06 08:27:01,777 - mmseg - INFO - Iter [56600/160000] lr: 3.878e-05, eta: 16:35:23, time: 0.541, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1320, decode.acc_seg: 94.6230, loss: 0.1320 2023-01-06 08:27:30,677 - mmseg - INFO - Iter [56650/160000] lr: 3.876e-05, eta: 16:34:54, time: 0.578, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1233, decode.acc_seg: 94.8089, loss: 0.1233 2023-01-06 08:27:59,012 - mmseg - INFO - Iter [56700/160000] lr: 3.874e-05, eta: 16:34:24, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1242, decode.acc_seg: 94.9728, loss: 0.1242 2023-01-06 08:28:27,422 - mmseg - INFO - Iter [56750/160000] lr: 3.872e-05, eta: 16:33:54, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1201, decode.acc_seg: 95.1669, loss: 0.1201 2023-01-06 08:28:55,372 - mmseg - INFO - Iter [56800/160000] lr: 3.870e-05, eta: 16:33:24, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1114, decode.acc_seg: 95.4612, loss: 0.1114 2023-01-06 08:29:24,272 - mmseg - INFO - Iter [56850/160000] lr: 3.868e-05, eta: 16:32:55, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1107, decode.acc_seg: 95.3684, loss: 0.1107 2023-01-06 08:29:53,512 - mmseg - INFO - Iter [56900/160000] lr: 3.866e-05, eta: 16:32:27, time: 0.585, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1164, decode.acc_seg: 95.2675, loss: 0.1164 2023-01-06 08:30:25,376 - mmseg - INFO - Iter [56950/160000] lr: 3.864e-05, eta: 16:32:03, time: 0.637, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1122, decode.acc_seg: 95.3509, loss: 0.1122 2023-01-06 08:30:53,132 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 08:30:53,133 - mmseg - INFO - Iter [57000/160000] lr: 3.863e-05, eta: 16:31:32, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1092, decode.acc_seg: 95.3953, loss: 0.1092 2023-01-06 08:31:20,172 - mmseg - INFO - Iter [57050/160000] lr: 3.861e-05, eta: 16:31:00, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1167, decode.acc_seg: 95.2238, loss: 0.1167 2023-01-06 08:31:47,300 - mmseg - INFO - Iter [57100/160000] lr: 3.859e-05, eta: 16:30:28, time: 0.543, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1160, decode.acc_seg: 95.1553, loss: 0.1160 2023-01-06 08:32:16,448 - mmseg - INFO - Iter [57150/160000] lr: 3.857e-05, eta: 16:30:00, time: 0.582, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1325, decode.acc_seg: 94.4334, loss: 0.1325 2023-01-06 08:32:45,886 - mmseg - INFO - Iter [57200/160000] lr: 3.855e-05, eta: 16:29:32, time: 0.590, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1210, decode.acc_seg: 95.0686, loss: 0.1210 2023-01-06 08:33:14,817 - mmseg - INFO - Iter [57250/160000] lr: 3.853e-05, eta: 16:29:03, time: 0.579, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1251, decode.acc_seg: 94.9163, loss: 0.1251 2023-01-06 08:33:44,201 - mmseg - INFO - Iter [57300/160000] lr: 3.851e-05, eta: 16:28:35, time: 0.587, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1293, decode.acc_seg: 94.5616, loss: 0.1293 2023-01-06 08:34:13,074 - mmseg - INFO - Iter [57350/160000] lr: 3.849e-05, eta: 16:28:06, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1166, decode.acc_seg: 95.2417, loss: 0.1166 2023-01-06 08:34:40,349 - mmseg - INFO - Iter [57400/160000] lr: 3.848e-05, eta: 16:27:35, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1108, decode.acc_seg: 95.3885, loss: 0.1108 2023-01-06 08:35:07,686 - mmseg - INFO - Iter [57450/160000] lr: 3.846e-05, eta: 16:27:03, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1252, decode.acc_seg: 94.9150, loss: 0.1252 2023-01-06 08:35:36,208 - mmseg - INFO - Iter [57500/160000] lr: 3.844e-05, eta: 16:26:33, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1218, decode.acc_seg: 95.0744, loss: 0.1218 2023-01-06 08:36:05,393 - mmseg - INFO - Iter [57550/160000] lr: 3.842e-05, eta: 16:26:05, time: 0.585, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1154, decode.acc_seg: 95.2450, loss: 0.1154 2023-01-06 08:36:32,953 - mmseg - INFO - Iter [57600/160000] lr: 3.840e-05, eta: 16:25:34, time: 0.551, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1116, decode.acc_seg: 95.3712, loss: 0.1116 2023-01-06 08:37:01,892 - mmseg - INFO - Iter [57650/160000] lr: 3.838e-05, eta: 16:25:05, time: 0.579, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1165, decode.acc_seg: 95.1791, loss: 0.1165 2023-01-06 08:37:32,896 - mmseg - INFO - Iter [57700/160000] lr: 3.836e-05, eta: 16:24:40, time: 0.619, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1099, decode.acc_seg: 95.4512, loss: 0.1099 2023-01-06 08:38:02,427 - mmseg - INFO - Iter [57750/160000] lr: 3.834e-05, eta: 16:24:12, time: 0.591, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1117, decode.acc_seg: 95.4163, loss: 0.1117 2023-01-06 08:38:31,217 - mmseg - INFO - Iter [57800/160000] lr: 3.833e-05, eta: 16:23:43, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1126, decode.acc_seg: 95.3454, loss: 0.1126 2023-01-06 08:39:00,527 - mmseg - INFO - Iter [57850/160000] lr: 3.831e-05, eta: 16:23:15, time: 0.586, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1123, decode.acc_seg: 95.3205, loss: 0.1123 2023-01-06 08:39:28,627 - mmseg - INFO - Iter [57900/160000] lr: 3.829e-05, eta: 16:22:45, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1199, decode.acc_seg: 95.0781, loss: 0.1199 2023-01-06 08:39:57,912 - mmseg - INFO - Iter [57950/160000] lr: 3.827e-05, eta: 16:22:17, time: 0.585, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1173, decode.acc_seg: 95.1162, loss: 0.1173 2023-01-06 08:40:26,739 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 08:40:26,739 - mmseg - INFO - Iter [58000/160000] lr: 3.825e-05, eta: 16:21:48, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1107, decode.acc_seg: 95.3684, loss: 0.1107 2023-01-06 08:40:57,359 - mmseg - INFO - Iter [58050/160000] lr: 3.823e-05, eta: 16:21:22, time: 0.612, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1165, decode.acc_seg: 95.3121, loss: 0.1165 2023-01-06 08:41:26,531 - mmseg - INFO - Iter [58100/160000] lr: 3.821e-05, eta: 16:20:54, time: 0.583, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1162, decode.acc_seg: 95.1861, loss: 0.1162 2023-01-06 08:41:54,191 - mmseg - INFO - Iter [58150/160000] lr: 3.819e-05, eta: 16:20:23, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1139, decode.acc_seg: 95.3968, loss: 0.1139 2023-01-06 08:42:21,861 - mmseg - INFO - Iter [58200/160000] lr: 3.818e-05, eta: 16:19:52, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1108, decode.acc_seg: 95.3642, loss: 0.1108 2023-01-06 08:42:50,170 - mmseg - INFO - Iter [58250/160000] lr: 3.816e-05, eta: 16:19:22, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1115, decode.acc_seg: 95.4317, loss: 0.1115 2023-01-06 08:43:17,954 - mmseg - INFO - Iter [58300/160000] lr: 3.814e-05, eta: 16:18:51, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1161, decode.acc_seg: 95.2045, loss: 0.1161 2023-01-06 08:43:46,553 - mmseg - INFO - Iter [58350/160000] lr: 3.812e-05, eta: 16:18:22, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1180, decode.acc_seg: 95.0586, loss: 0.1180 2023-01-06 08:44:14,581 - mmseg - INFO - Iter [58400/160000] lr: 3.810e-05, eta: 16:17:51, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1168, decode.acc_seg: 95.2269, loss: 0.1168 2023-01-06 08:44:45,667 - mmseg - INFO - Iter [58450/160000] lr: 3.808e-05, eta: 16:17:26, time: 0.622, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1114, decode.acc_seg: 95.3968, loss: 0.1114 2023-01-06 08:45:13,921 - mmseg - INFO - Iter [58500/160000] lr: 3.806e-05, eta: 16:16:56, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1170, decode.acc_seg: 95.2299, loss: 0.1170 2023-01-06 08:45:42,216 - mmseg - INFO - Iter [58550/160000] lr: 3.804e-05, eta: 16:16:26, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1133, decode.acc_seg: 95.3851, loss: 0.1133 2023-01-06 08:46:10,646 - mmseg - INFO - Iter [58600/160000] lr: 3.803e-05, eta: 16:15:57, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1171, decode.acc_seg: 95.1165, loss: 0.1171 2023-01-06 08:46:39,312 - mmseg - INFO - Iter [58650/160000] lr: 3.801e-05, eta: 16:15:27, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1279, decode.acc_seg: 94.9065, loss: 0.1279 2023-01-06 08:47:07,084 - mmseg - INFO - Iter [58700/160000] lr: 3.799e-05, eta: 16:14:57, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1215, decode.acc_seg: 94.8236, loss: 0.1215 2023-01-06 08:47:35,793 - mmseg - INFO - Iter [58750/160000] lr: 3.797e-05, eta: 16:14:28, time: 0.574, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1142, decode.acc_seg: 95.2578, loss: 0.1142 2023-01-06 08:48:06,372 - mmseg - INFO - Iter [58800/160000] lr: 3.795e-05, eta: 16:14:02, time: 0.611, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1102, decode.acc_seg: 95.4476, loss: 0.1102 2023-01-06 08:48:34,961 - mmseg - INFO - Iter [58850/160000] lr: 3.793e-05, eta: 16:13:32, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1148, decode.acc_seg: 95.3730, loss: 0.1148 2023-01-06 08:49:03,003 - mmseg - INFO - Iter [58900/160000] lr: 3.791e-05, eta: 16:13:02, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1108, decode.acc_seg: 95.5500, loss: 0.1108 2023-01-06 08:49:31,001 - mmseg - INFO - Iter [58950/160000] lr: 3.789e-05, eta: 16:12:31, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1112, decode.acc_seg: 95.4913, loss: 0.1112 2023-01-06 08:50:00,111 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 08:50:00,112 - mmseg - INFO - Iter [59000/160000] lr: 3.788e-05, eta: 16:12:03, time: 0.582, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1055, decode.acc_seg: 95.5640, loss: 0.1055 2023-01-06 08:50:28,818 - mmseg - INFO - Iter [59050/160000] lr: 3.786e-05, eta: 16:11:34, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1056, decode.acc_seg: 95.5225, loss: 0.1056 2023-01-06 08:50:56,768 - mmseg - INFO - Iter [59100/160000] lr: 3.784e-05, eta: 16:11:03, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1028, decode.acc_seg: 95.6362, loss: 0.1028 2023-01-06 08:51:27,292 - mmseg - INFO - Iter [59150/160000] lr: 3.782e-05, eta: 16:10:37, time: 0.610, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1197, decode.acc_seg: 95.1189, loss: 0.1197 2023-01-06 08:51:55,735 - mmseg - INFO - Iter [59200/160000] lr: 3.780e-05, eta: 16:10:08, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1094, decode.acc_seg: 95.3784, loss: 0.1094 2023-01-06 08:52:23,509 - mmseg - INFO - Iter [59250/160000] lr: 3.778e-05, eta: 16:09:37, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1142, decode.acc_seg: 95.2599, loss: 0.1142 2023-01-06 08:52:53,051 - mmseg - INFO - Iter [59300/160000] lr: 3.776e-05, eta: 16:09:09, time: 0.591, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1231, decode.acc_seg: 95.0435, loss: 0.1231 2023-01-06 08:53:21,950 - mmseg - INFO - Iter [59350/160000] lr: 3.774e-05, eta: 16:08:40, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1070, decode.acc_seg: 95.4964, loss: 0.1070 2023-01-06 08:53:49,720 - mmseg - INFO - Iter [59400/160000] lr: 3.773e-05, eta: 16:08:10, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1076, decode.acc_seg: 95.4920, loss: 0.1076 2023-01-06 08:54:18,412 - mmseg - INFO - Iter [59450/160000] lr: 3.771e-05, eta: 16:07:41, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1203, decode.acc_seg: 95.0060, loss: 0.1203 2023-01-06 08:54:45,813 - mmseg - INFO - Iter [59500/160000] lr: 3.769e-05, eta: 16:07:09, time: 0.548, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1140, decode.acc_seg: 95.3061, loss: 0.1140 2023-01-06 08:55:16,289 - mmseg - INFO - Iter [59550/160000] lr: 3.767e-05, eta: 16:06:43, time: 0.610, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1012, decode.acc_seg: 95.8094, loss: 0.1012 2023-01-06 08:55:46,111 - mmseg - INFO - Iter [59600/160000] lr: 3.765e-05, eta: 16:06:16, time: 0.596, data_time: 0.027, memory: 10576, decode.loss_ce: 0.1093, decode.acc_seg: 95.3856, loss: 0.1093 2023-01-06 08:56:13,953 - mmseg - INFO - Iter [59650/160000] lr: 3.763e-05, eta: 16:05:45, time: 0.558, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1127, decode.acc_seg: 95.4175, loss: 0.1127 2023-01-06 08:56:42,695 - mmseg - INFO - Iter [59700/160000] lr: 3.761e-05, eta: 16:05:16, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1090, decode.acc_seg: 95.5176, loss: 0.1090 2023-01-06 08:57:11,369 - mmseg - INFO - Iter [59750/160000] lr: 3.759e-05, eta: 16:04:47, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1106, decode.acc_seg: 95.4048, loss: 0.1106 2023-01-06 08:57:39,408 - mmseg - INFO - Iter [59800/160000] lr: 3.758e-05, eta: 16:04:17, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1040, decode.acc_seg: 95.6150, loss: 0.1040 2023-01-06 08:58:07,619 - mmseg - INFO - Iter [59850/160000] lr: 3.756e-05, eta: 16:03:47, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1134, decode.acc_seg: 95.2827, loss: 0.1134 2023-01-06 08:58:37,026 - mmseg - INFO - Iter [59900/160000] lr: 3.754e-05, eta: 16:03:19, time: 0.588, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1147, decode.acc_seg: 95.1842, loss: 0.1147 2023-01-06 08:59:05,649 - mmseg - INFO - Iter [59950/160000] lr: 3.752e-05, eta: 16:02:49, time: 0.572, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1083, decode.acc_seg: 95.4453, loss: 0.1083 2023-01-06 08:59:33,900 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 08:59:33,901 - mmseg - INFO - Iter [60000/160000] lr: 3.750e-05, eta: 16:02:19, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1117, decode.acc_seg: 95.4448, loss: 0.1117 2023-01-06 09:00:02,380 - mmseg - INFO - Iter [60050/160000] lr: 3.748e-05, eta: 16:01:50, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1049, decode.acc_seg: 95.6889, loss: 0.1049 2023-01-06 09:00:31,299 - mmseg - INFO - Iter [60100/160000] lr: 3.746e-05, eta: 16:01:21, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1169, decode.acc_seg: 95.2749, loss: 0.1169 2023-01-06 09:00:58,329 - mmseg - INFO - Iter [60150/160000] lr: 3.744e-05, eta: 16:00:49, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1145, decode.acc_seg: 95.2415, loss: 0.1145 2023-01-06 09:01:25,568 - mmseg - INFO - Iter [60200/160000] lr: 3.743e-05, eta: 16:00:18, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1096, decode.acc_seg: 95.5263, loss: 0.1096 2023-01-06 09:01:53,064 - mmseg - INFO - Iter [60250/160000] lr: 3.741e-05, eta: 15:59:47, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1157, decode.acc_seg: 95.2571, loss: 0.1157 2023-01-06 09:02:22,805 - mmseg - INFO - Iter [60300/160000] lr: 3.739e-05, eta: 15:59:19, time: 0.595, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1018, decode.acc_seg: 95.6107, loss: 0.1018 2023-01-06 09:02:51,294 - mmseg - INFO - Iter [60350/160000] lr: 3.737e-05, eta: 15:58:50, time: 0.569, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1104, decode.acc_seg: 95.3703, loss: 0.1104 2023-01-06 09:03:20,316 - mmseg - INFO - Iter [60400/160000] lr: 3.735e-05, eta: 15:58:21, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1134, decode.acc_seg: 95.2942, loss: 0.1134 2023-01-06 09:03:47,593 - mmseg - INFO - Iter [60450/160000] lr: 3.733e-05, eta: 15:57:50, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1103, decode.acc_seg: 95.4139, loss: 0.1103 2023-01-06 09:04:16,367 - mmseg - INFO - Iter [60500/160000] lr: 3.731e-05, eta: 15:57:21, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1104, decode.acc_seg: 95.3932, loss: 0.1104 2023-01-06 09:04:44,972 - mmseg - INFO - Iter [60550/160000] lr: 3.729e-05, eta: 15:56:51, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1152, decode.acc_seg: 95.2834, loss: 0.1152 2023-01-06 09:05:14,801 - mmseg - INFO - Iter [60600/160000] lr: 3.728e-05, eta: 15:56:24, time: 0.597, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1011, decode.acc_seg: 95.8025, loss: 0.1011 2023-01-06 09:05:44,398 - mmseg - INFO - Iter [60650/160000] lr: 3.726e-05, eta: 15:55:56, time: 0.592, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1093, decode.acc_seg: 95.5271, loss: 0.1093 2023-01-06 09:06:13,027 - mmseg - INFO - Iter [60700/160000] lr: 3.724e-05, eta: 15:55:27, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1126, decode.acc_seg: 95.4160, loss: 0.1126 2023-01-06 09:06:41,042 - mmseg - INFO - Iter [60750/160000] lr: 3.722e-05, eta: 15:54:57, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1022, decode.acc_seg: 95.6675, loss: 0.1022 2023-01-06 09:07:08,167 - mmseg - INFO - Iter [60800/160000] lr: 3.720e-05, eta: 15:54:25, time: 0.543, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1119, decode.acc_seg: 95.4191, loss: 0.1119 2023-01-06 09:07:36,008 - mmseg - INFO - Iter [60850/160000] lr: 3.718e-05, eta: 15:53:54, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1167, decode.acc_seg: 95.2591, loss: 0.1167 2023-01-06 09:08:04,585 - mmseg - INFO - Iter [60900/160000] lr: 3.716e-05, eta: 15:53:25, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1175, decode.acc_seg: 95.2107, loss: 0.1175 2023-01-06 09:08:32,868 - mmseg - INFO - Iter [60950/160000] lr: 3.714e-05, eta: 15:52:55, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1085, decode.acc_seg: 95.3862, loss: 0.1085 2023-01-06 09:09:01,674 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 09:09:01,675 - mmseg - INFO - Iter [61000/160000] lr: 3.713e-05, eta: 15:52:26, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1024, decode.acc_seg: 95.6385, loss: 0.1024 2023-01-06 09:09:31,404 - mmseg - INFO - Iter [61050/160000] lr: 3.711e-05, eta: 15:51:59, time: 0.594, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1044, decode.acc_seg: 95.6669, loss: 0.1044 2023-01-06 09:09:59,285 - mmseg - INFO - Iter [61100/160000] lr: 3.709e-05, eta: 15:51:29, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1212, decode.acc_seg: 95.1811, loss: 0.1212 2023-01-06 09:10:26,667 - mmseg - INFO - Iter [61150/160000] lr: 3.707e-05, eta: 15:50:57, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1160, decode.acc_seg: 95.3480, loss: 0.1160 2023-01-06 09:10:54,241 - mmseg - INFO - Iter [61200/160000] lr: 3.705e-05, eta: 15:50:26, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1094, decode.acc_seg: 95.4706, loss: 0.1094 2023-01-06 09:11:21,887 - mmseg - INFO - Iter [61250/160000] lr: 3.703e-05, eta: 15:49:56, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1203, decode.acc_seg: 95.1828, loss: 0.1203 2023-01-06 09:11:49,410 - mmseg - INFO - Iter [61300/160000] lr: 3.701e-05, eta: 15:49:25, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1189, decode.acc_seg: 94.9901, loss: 0.1189 2023-01-06 09:12:16,726 - mmseg - INFO - Iter [61350/160000] lr: 3.699e-05, eta: 15:48:53, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1122, decode.acc_seg: 95.3556, loss: 0.1122 2023-01-06 09:12:47,932 - mmseg - INFO - Iter [61400/160000] lr: 3.698e-05, eta: 15:48:28, time: 0.624, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1118, decode.acc_seg: 95.5364, loss: 0.1118 2023-01-06 09:13:15,892 - mmseg - INFO - Iter [61450/160000] lr: 3.696e-05, eta: 15:47:58, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1083, decode.acc_seg: 95.4758, loss: 0.1083 2023-01-06 09:13:45,080 - mmseg - INFO - Iter [61500/160000] lr: 3.694e-05, eta: 15:47:29, time: 0.584, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1099, decode.acc_seg: 95.4996, loss: 0.1099 2023-01-06 09:14:13,900 - mmseg - INFO - Iter [61550/160000] lr: 3.692e-05, eta: 15:47:01, time: 0.576, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1116, decode.acc_seg: 95.3537, loss: 0.1116 2023-01-06 09:14:41,995 - mmseg - INFO - Iter [61600/160000] lr: 3.690e-05, eta: 15:46:30, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1048, decode.acc_seg: 95.6107, loss: 0.1048 2023-01-06 09:15:10,595 - mmseg - INFO - Iter [61650/160000] lr: 3.688e-05, eta: 15:46:01, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1098, decode.acc_seg: 95.4849, loss: 0.1098 2023-01-06 09:15:40,081 - mmseg - INFO - Iter [61700/160000] lr: 3.686e-05, eta: 15:45:33, time: 0.591, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1197, decode.acc_seg: 95.1344, loss: 0.1197 2023-01-06 09:16:07,646 - mmseg - INFO - Iter [61750/160000] lr: 3.684e-05, eta: 15:45:02, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1153, decode.acc_seg: 95.2389, loss: 0.1153 2023-01-06 09:16:38,088 - mmseg - INFO - Iter [61800/160000] lr: 3.683e-05, eta: 15:44:36, time: 0.609, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1142, decode.acc_seg: 95.2785, loss: 0.1142 2023-01-06 09:17:06,496 - mmseg - INFO - Iter [61850/160000] lr: 3.681e-05, eta: 15:44:06, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1083, decode.acc_seg: 95.5777, loss: 0.1083 2023-01-06 09:17:35,471 - mmseg - INFO - Iter [61900/160000] lr: 3.679e-05, eta: 15:43:38, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1142, decode.acc_seg: 95.2784, loss: 0.1142 2023-01-06 09:18:03,044 - mmseg - INFO - Iter [61950/160000] lr: 3.677e-05, eta: 15:43:07, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1210, decode.acc_seg: 95.1231, loss: 0.1210 2023-01-06 09:18:31,977 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 09:18:31,978 - mmseg - INFO - Iter [62000/160000] lr: 3.675e-05, eta: 15:42:38, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1064, decode.acc_seg: 95.6433, loss: 0.1064 2023-01-06 09:19:00,559 - mmseg - INFO - Iter [62050/160000] lr: 3.673e-05, eta: 15:42:09, time: 0.572, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1075, decode.acc_seg: 95.5746, loss: 0.1075 2023-01-06 09:19:28,306 - mmseg - INFO - Iter [62100/160000] lr: 3.671e-05, eta: 15:41:38, time: 0.556, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1030, decode.acc_seg: 95.6069, loss: 0.1030 2023-01-06 09:19:59,126 - mmseg - INFO - Iter [62150/160000] lr: 3.669e-05, eta: 15:41:13, time: 0.616, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1100, decode.acc_seg: 95.4467, loss: 0.1100 2023-01-06 09:20:28,534 - mmseg - INFO - Iter [62200/160000] lr: 3.668e-05, eta: 15:40:45, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1155, decode.acc_seg: 95.3162, loss: 0.1155 2023-01-06 09:20:56,272 - mmseg - INFO - Iter [62250/160000] lr: 3.666e-05, eta: 15:40:14, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1047, decode.acc_seg: 95.6623, loss: 0.1047 2023-01-06 09:21:23,448 - mmseg - INFO - Iter [62300/160000] lr: 3.664e-05, eta: 15:39:42, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1148, decode.acc_seg: 95.2287, loss: 0.1148 2023-01-06 09:21:50,814 - mmseg - INFO - Iter [62350/160000] lr: 3.662e-05, eta: 15:39:11, time: 0.547, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1157, decode.acc_seg: 95.2772, loss: 0.1157 2023-01-06 09:22:19,689 - mmseg - INFO - Iter [62400/160000] lr: 3.660e-05, eta: 15:38:42, time: 0.578, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1108, decode.acc_seg: 95.3414, loss: 0.1108 2023-01-06 09:22:48,658 - mmseg - INFO - Iter [62450/160000] lr: 3.658e-05, eta: 15:38:14, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1084, decode.acc_seg: 95.5090, loss: 0.1084 2023-01-06 09:23:18,873 - mmseg - INFO - Iter [62500/160000] lr: 3.656e-05, eta: 15:37:47, time: 0.604, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1096, decode.acc_seg: 95.5044, loss: 0.1096 2023-01-06 09:23:47,269 - mmseg - INFO - Iter [62550/160000] lr: 3.654e-05, eta: 15:37:17, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1029, decode.acc_seg: 95.6575, loss: 0.1029 2023-01-06 09:24:14,538 - mmseg - INFO - Iter [62600/160000] lr: 3.653e-05, eta: 15:36:46, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1082, decode.acc_seg: 95.5477, loss: 0.1082 2023-01-06 09:24:43,848 - mmseg - INFO - Iter [62650/160000] lr: 3.651e-05, eta: 15:36:18, time: 0.586, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1020, decode.acc_seg: 95.7660, loss: 0.1020 2023-01-06 09:25:12,803 - mmseg - INFO - Iter [62700/160000] lr: 3.649e-05, eta: 15:35:49, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1090, decode.acc_seg: 95.4982, loss: 0.1090 2023-01-06 09:25:40,551 - mmseg - INFO - Iter [62750/160000] lr: 3.647e-05, eta: 15:35:19, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1071, decode.acc_seg: 95.5423, loss: 0.1071 2023-01-06 09:26:09,189 - mmseg - INFO - Iter [62800/160000] lr: 3.645e-05, eta: 15:34:50, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1128, decode.acc_seg: 95.5177, loss: 0.1128 2023-01-06 09:26:37,303 - mmseg - INFO - Iter [62850/160000] lr: 3.643e-05, eta: 15:34:20, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1155, decode.acc_seg: 95.2832, loss: 0.1155 2023-01-06 09:27:07,360 - mmseg - INFO - Iter [62900/160000] lr: 3.641e-05, eta: 15:33:53, time: 0.601, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1093, decode.acc_seg: 95.5878, loss: 0.1093 2023-01-06 09:27:35,740 - mmseg - INFO - Iter [62950/160000] lr: 3.639e-05, eta: 15:33:23, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1137, decode.acc_seg: 95.4246, loss: 0.1137 2023-01-06 09:28:03,763 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 09:28:03,764 - mmseg - INFO - Iter [63000/160000] lr: 3.638e-05, eta: 15:32:53, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1114, decode.acc_seg: 95.3687, loss: 0.1114 2023-01-06 09:28:32,338 - mmseg - INFO - Iter [63050/160000] lr: 3.636e-05, eta: 15:32:24, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1128, decode.acc_seg: 95.3891, loss: 0.1128 2023-01-06 09:28:59,978 - mmseg - INFO - Iter [63100/160000] lr: 3.634e-05, eta: 15:31:53, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1156, decode.acc_seg: 95.1098, loss: 0.1156 2023-01-06 09:29:28,129 - mmseg - INFO - Iter [63150/160000] lr: 3.632e-05, eta: 15:31:23, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0986, decode.acc_seg: 95.8896, loss: 0.0986 2023-01-06 09:29:55,606 - mmseg - INFO - Iter [63200/160000] lr: 3.630e-05, eta: 15:30:52, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1097, decode.acc_seg: 95.5695, loss: 0.1097 2023-01-06 09:30:25,125 - mmseg - INFO - Iter [63250/160000] lr: 3.628e-05, eta: 15:30:24, time: 0.590, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1102, decode.acc_seg: 95.3982, loss: 0.1102 2023-01-06 09:30:53,635 - mmseg - INFO - Iter [63300/160000] lr: 3.626e-05, eta: 15:29:55, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1088, decode.acc_seg: 95.4901, loss: 0.1088 2023-01-06 09:31:21,758 - mmseg - INFO - Iter [63350/160000] lr: 3.624e-05, eta: 15:29:25, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1112, decode.acc_seg: 95.4815, loss: 0.1112 2023-01-06 09:31:51,359 - mmseg - INFO - Iter [63400/160000] lr: 3.623e-05, eta: 15:28:57, time: 0.592, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1079, decode.acc_seg: 95.6249, loss: 0.1079 2023-01-06 09:32:19,528 - mmseg - INFO - Iter [63450/160000] lr: 3.621e-05, eta: 15:28:27, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1106, decode.acc_seg: 95.4405, loss: 0.1106 2023-01-06 09:32:47,142 - mmseg - INFO - Iter [63500/160000] lr: 3.619e-05, eta: 15:27:57, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1031, decode.acc_seg: 95.7189, loss: 0.1031 2023-01-06 09:33:14,744 - mmseg - INFO - Iter [63550/160000] lr: 3.617e-05, eta: 15:27:26, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1140, decode.acc_seg: 95.3551, loss: 0.1140 2023-01-06 09:33:43,341 - mmseg - INFO - Iter [63600/160000] lr: 3.615e-05, eta: 15:26:57, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1090, decode.acc_seg: 95.5961, loss: 0.1090 2023-01-06 09:34:15,384 - mmseg - INFO - Iter [63650/160000] lr: 3.613e-05, eta: 15:26:33, time: 0.641, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1105, decode.acc_seg: 95.3418, loss: 0.1105 2023-01-06 09:34:42,790 - mmseg - INFO - Iter [63700/160000] lr: 3.611e-05, eta: 15:26:02, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1047, decode.acc_seg: 95.6099, loss: 0.1047 2023-01-06 09:35:10,023 - mmseg - INFO - Iter [63750/160000] lr: 3.609e-05, eta: 15:25:30, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1069, decode.acc_seg: 95.5774, loss: 0.1069 2023-01-06 09:35:38,416 - mmseg - INFO - Iter [63800/160000] lr: 3.608e-05, eta: 15:25:01, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1071, decode.acc_seg: 95.5603, loss: 0.1071 2023-01-06 09:36:06,474 - mmseg - INFO - Iter [63850/160000] lr: 3.606e-05, eta: 15:24:31, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1141, decode.acc_seg: 95.2568, loss: 0.1141 2023-01-06 09:36:35,638 - mmseg - INFO - Iter [63900/160000] lr: 3.604e-05, eta: 15:24:02, time: 0.583, data_time: 0.020, memory: 10576, decode.loss_ce: 0.1122, decode.acc_seg: 95.3960, loss: 0.1122 2023-01-06 09:37:04,533 - mmseg - INFO - Iter [63950/160000] lr: 3.602e-05, eta: 15:23:33, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1066, decode.acc_seg: 95.6255, loss: 0.1066 2023-01-06 09:37:36,044 - mmseg - INFO - Saving checkpoint at 64000 iterations 2023-01-06 09:37:41,162 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 09:37:41,163 - mmseg - INFO - Iter [64000/160000] lr: 3.600e-05, eta: 15:23:16, time: 0.733, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1096, decode.acc_seg: 95.5947, loss: 0.1096 2023-01-06 09:38:13,338 - mmseg - INFO - per class results: 2023-01-06 09:38:13,341 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 97.6 | 98.47 | | sidewalk | 81.38 | 91.54 | | building | 91.19 | 95.52 | | wall | 49.45 | 54.58 | | fence | 52.13 | 71.15 | | pole | 59.2 | 70.2 | | traffic light | 61.47 | 73.49 | | traffic sign | 71.65 | 80.29 | | vegetation | 91.44 | 96.7 | | terrain | 60.15 | 67.71 | | sky | 94.09 | 98.15 | | person | 75.97 | 85.45 | | rider | 51.98 | 65.98 | | car | 92.07 | 97.6 | | truck | 61.5 | 70.44 | | bus | 60.04 | 67.26 | | train | 51.53 | 54.46 | | motorcycle | 38.61 | 62.84 | | bicycle | 68.8 | 81.53 | +---------------+-------+-------+ 2023-01-06 09:38:13,342 - mmseg - INFO - Summary: 2023-01-06 09:38:13,342 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 95.04 | 68.96 | 78.07 | +-------+-------+-------+ 2023-01-06 09:38:13,343 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 09:38:13,344 - mmseg - INFO - Iter(val) [63] aAcc: 0.9504, mIoU: 0.6896, mAcc: 0.7807, IoU.road: 0.9760, IoU.sidewalk: 0.8138, IoU.building: 0.9119, IoU.wall: 0.4945, IoU.fence: 0.5213, IoU.pole: 0.5920, IoU.traffic light: 0.6147, IoU.traffic sign: 0.7165, IoU.vegetation: 0.9144, IoU.terrain: 0.6015, IoU.sky: 0.9409, IoU.person: 0.7597, IoU.rider: 0.5198, IoU.car: 0.9207, IoU.truck: 0.6150, IoU.bus: 0.6004, IoU.train: 0.5153, IoU.motorcycle: 0.3861, IoU.bicycle: 0.6880, Acc.road: 0.9847, Acc.sidewalk: 0.9154, Acc.building: 0.9552, Acc.wall: 0.5458, Acc.fence: 0.7115, Acc.pole: 0.7020, Acc.traffic light: 0.7349, Acc.traffic sign: 0.8029, Acc.vegetation: 0.9670, Acc.terrain: 0.6771, Acc.sky: 0.9815, Acc.person: 0.8545, Acc.rider: 0.6598, Acc.car: 0.9760, Acc.truck: 0.7044, Acc.bus: 0.6726, Acc.train: 0.5446, Acc.motorcycle: 0.6284, Acc.bicycle: 0.8153 2023-01-06 09:38:41,240 - mmseg - INFO - Iter [64050/160000] lr: 3.598e-05, eta: 15:23:34, time: 1.201, data_time: 0.656, memory: 10576, decode.loss_ce: 0.1045, decode.acc_seg: 95.6267, loss: 0.1045 2023-01-06 09:39:10,641 - mmseg - INFO - Iter [64100/160000] lr: 3.596e-05, eta: 15:23:06, time: 0.588, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1067, decode.acc_seg: 95.6340, loss: 0.1067 2023-01-06 09:39:38,912 - mmseg - INFO - Iter [64150/160000] lr: 3.594e-05, eta: 15:22:36, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1273, decode.acc_seg: 95.1204, loss: 0.1273 2023-01-06 09:40:07,265 - mmseg - INFO - Iter [64200/160000] lr: 3.593e-05, eta: 15:22:07, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1054, decode.acc_seg: 95.7238, loss: 0.1054 2023-01-06 09:40:35,577 - mmseg - INFO - Iter [64250/160000] lr: 3.591e-05, eta: 15:21:37, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1049, decode.acc_seg: 95.6757, loss: 0.1049 2023-01-06 09:41:04,137 - mmseg - INFO - Iter [64300/160000] lr: 3.589e-05, eta: 15:21:08, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1063, decode.acc_seg: 95.6552, loss: 0.1063 2023-01-06 09:41:33,289 - mmseg - INFO - Iter [64350/160000] lr: 3.587e-05, eta: 15:20:39, time: 0.583, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1004, decode.acc_seg: 95.6510, loss: 0.1004 2023-01-06 09:42:02,934 - mmseg - INFO - Iter [64400/160000] lr: 3.585e-05, eta: 15:20:11, time: 0.592, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1086, decode.acc_seg: 95.5693, loss: 0.1086 2023-01-06 09:42:31,262 - mmseg - INFO - Iter [64450/160000] lr: 3.583e-05, eta: 15:19:42, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1006, decode.acc_seg: 95.7103, loss: 0.1006 2023-01-06 09:43:00,377 - mmseg - INFO - Iter [64500/160000] lr: 3.581e-05, eta: 15:19:13, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1058, decode.acc_seg: 95.6176, loss: 0.1058 2023-01-06 09:43:27,462 - mmseg - INFO - Iter [64550/160000] lr: 3.579e-05, eta: 15:18:42, time: 0.542, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1221, decode.acc_seg: 95.2788, loss: 0.1221 2023-01-06 09:43:54,711 - mmseg - INFO - Iter [64600/160000] lr: 3.578e-05, eta: 15:18:10, time: 0.545, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1258, decode.acc_seg: 94.8184, loss: 0.1258 2023-01-06 09:44:22,256 - mmseg - INFO - Iter [64650/160000] lr: 3.576e-05, eta: 15:17:40, time: 0.550, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1215, decode.acc_seg: 95.0555, loss: 0.1215 2023-01-06 09:44:49,940 - mmseg - INFO - Iter [64700/160000] lr: 3.574e-05, eta: 15:17:09, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1127, decode.acc_seg: 95.4467, loss: 0.1127 2023-01-06 09:45:21,126 - mmseg - INFO - Iter [64750/160000] lr: 3.572e-05, eta: 15:16:43, time: 0.624, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1016, decode.acc_seg: 95.7976, loss: 0.1016 2023-01-06 09:45:48,437 - mmseg - INFO - Iter [64800/160000] lr: 3.570e-05, eta: 15:16:12, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1175, decode.acc_seg: 95.1937, loss: 0.1175 2023-01-06 09:46:15,868 - mmseg - INFO - Iter [64850/160000] lr: 3.568e-05, eta: 15:15:41, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1080, decode.acc_seg: 95.4839, loss: 0.1080 2023-01-06 09:46:43,259 - mmseg - INFO - Iter [64900/160000] lr: 3.566e-05, eta: 15:15:10, time: 0.548, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1046, decode.acc_seg: 95.5344, loss: 0.1046 2023-01-06 09:47:10,835 - mmseg - INFO - Iter [64950/160000] lr: 3.564e-05, eta: 15:14:40, time: 0.552, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1120, decode.acc_seg: 95.4626, loss: 0.1120 2023-01-06 09:47:38,829 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 09:47:38,829 - mmseg - INFO - Iter [65000/160000] lr: 3.563e-05, eta: 15:14:09, time: 0.560, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1067, decode.acc_seg: 95.5601, loss: 0.1067 2023-01-06 09:48:08,034 - mmseg - INFO - Iter [65050/160000] lr: 3.561e-05, eta: 15:13:41, time: 0.584, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1125, decode.acc_seg: 95.4784, loss: 0.1125 2023-01-06 09:48:36,633 - mmseg - INFO - Iter [65100/160000] lr: 3.559e-05, eta: 15:13:12, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1065, decode.acc_seg: 95.6095, loss: 0.1065 2023-01-06 09:49:06,269 - mmseg - INFO - Iter [65150/160000] lr: 3.557e-05, eta: 15:12:44, time: 0.593, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1103, decode.acc_seg: 95.4111, loss: 0.1103 2023-01-06 09:49:33,298 - mmseg - INFO - Iter [65200/160000] lr: 3.555e-05, eta: 15:12:12, time: 0.541, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1052, decode.acc_seg: 95.5892, loss: 0.1052 2023-01-06 09:50:00,430 - mmseg - INFO - Iter [65250/160000] lr: 3.553e-05, eta: 15:11:41, time: 0.542, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1195, decode.acc_seg: 95.1846, loss: 0.1195 2023-01-06 09:50:27,470 - mmseg - INFO - Iter [65300/160000] lr: 3.551e-05, eta: 15:11:10, time: 0.541, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1067, decode.acc_seg: 95.5247, loss: 0.1067 2023-01-06 09:50:54,667 - mmseg - INFO - Iter [65350/160000] lr: 3.549e-05, eta: 15:10:38, time: 0.544, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1053, decode.acc_seg: 95.6134, loss: 0.1053 2023-01-06 09:51:23,665 - mmseg - INFO - Iter [65400/160000] lr: 3.548e-05, eta: 15:10:10, time: 0.579, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1092, decode.acc_seg: 95.4770, loss: 0.1092 2023-01-06 09:51:52,496 - mmseg - INFO - Iter [65450/160000] lr: 3.546e-05, eta: 15:09:41, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1213, decode.acc_seg: 95.2238, loss: 0.1213 2023-01-06 09:52:24,020 - mmseg - INFO - Iter [65500/160000] lr: 3.544e-05, eta: 15:09:16, time: 0.630, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1067, decode.acc_seg: 95.6106, loss: 0.1067 2023-01-06 09:52:51,202 - mmseg - INFO - Iter [65550/160000] lr: 3.542e-05, eta: 15:08:44, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1104, decode.acc_seg: 95.3357, loss: 0.1104 2023-01-06 09:53:18,435 - mmseg - INFO - Iter [65600/160000] lr: 3.540e-05, eta: 15:08:13, time: 0.545, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1053, decode.acc_seg: 95.5666, loss: 0.1053 2023-01-06 09:53:45,681 - mmseg - INFO - Iter [65650/160000] lr: 3.538e-05, eta: 15:07:42, time: 0.545, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1102, decode.acc_seg: 95.4315, loss: 0.1102 2023-01-06 09:54:13,521 - mmseg - INFO - Iter [65700/160000] lr: 3.536e-05, eta: 15:07:12, time: 0.557, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1128, decode.acc_seg: 95.4581, loss: 0.1128 2023-01-06 09:54:42,842 - mmseg - INFO - Iter [65750/160000] lr: 3.534e-05, eta: 15:06:43, time: 0.586, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1097, decode.acc_seg: 95.3974, loss: 0.1097 2023-01-06 09:55:10,074 - mmseg - INFO - Iter [65800/160000] lr: 3.533e-05, eta: 15:06:12, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1088, decode.acc_seg: 95.4143, loss: 0.1088 2023-01-06 09:55:39,409 - mmseg - INFO - Iter [65850/160000] lr: 3.531e-05, eta: 15:05:44, time: 0.587, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1091, decode.acc_seg: 95.4717, loss: 0.1091 2023-01-06 09:56:06,665 - mmseg - INFO - Iter [65900/160000] lr: 3.529e-05, eta: 15:05:13, time: 0.545, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1041, decode.acc_seg: 95.6800, loss: 0.1041 2023-01-06 09:56:33,606 - mmseg - INFO - Iter [65950/160000] lr: 3.527e-05, eta: 15:04:41, time: 0.539, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1016, decode.acc_seg: 95.7804, loss: 0.1016 2023-01-06 09:57:01,376 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 09:57:01,377 - mmseg - INFO - Iter [66000/160000] lr: 3.525e-05, eta: 15:04:11, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1043, decode.acc_seg: 95.6782, loss: 0.1043 2023-01-06 09:57:30,034 - mmseg - INFO - Iter [66050/160000] lr: 3.523e-05, eta: 15:03:42, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1052, decode.acc_seg: 95.5883, loss: 0.1052 2023-01-06 09:57:57,578 - mmseg - INFO - Iter [66100/160000] lr: 3.521e-05, eta: 15:03:11, time: 0.551, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1248, decode.acc_seg: 95.1141, loss: 0.1248 2023-01-06 09:58:25,300 - mmseg - INFO - Iter [66150/160000] lr: 3.519e-05, eta: 15:02:41, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1138, decode.acc_seg: 95.2958, loss: 0.1138 2023-01-06 09:58:54,582 - mmseg - INFO - Iter [66200/160000] lr: 3.518e-05, eta: 15:02:12, time: 0.585, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1082, decode.acc_seg: 95.4490, loss: 0.1082 2023-01-06 09:59:24,783 - mmseg - INFO - Iter [66250/160000] lr: 3.516e-05, eta: 15:01:45, time: 0.605, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1161, decode.acc_seg: 95.2177, loss: 0.1161 2023-01-06 09:59:54,653 - mmseg - INFO - Iter [66300/160000] lr: 3.514e-05, eta: 15:01:18, time: 0.597, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1109, decode.acc_seg: 95.4416, loss: 0.1109 2023-01-06 10:00:22,472 - mmseg - INFO - Iter [66350/160000] lr: 3.512e-05, eta: 15:00:48, time: 0.556, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1046, decode.acc_seg: 95.5301, loss: 0.1046 2023-01-06 10:00:52,065 - mmseg - INFO - Iter [66400/160000] lr: 3.510e-05, eta: 15:00:20, time: 0.591, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1039, decode.acc_seg: 95.7121, loss: 0.1039 2023-01-06 10:01:20,013 - mmseg - INFO - Iter [66450/160000] lr: 3.508e-05, eta: 14:59:50, time: 0.560, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1037, decode.acc_seg: 95.6706, loss: 0.1037 2023-01-06 10:01:49,419 - mmseg - INFO - Iter [66500/160000] lr: 3.506e-05, eta: 14:59:22, time: 0.588, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1012, decode.acc_seg: 95.6991, loss: 0.1012 2023-01-06 10:02:17,553 - mmseg - INFO - Iter [66550/160000] lr: 3.504e-05, eta: 14:58:52, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1020, decode.acc_seg: 95.6339, loss: 0.1020 2023-01-06 10:02:48,400 - mmseg - INFO - Iter [66600/160000] lr: 3.503e-05, eta: 14:58:26, time: 0.618, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1083, decode.acc_seg: 95.4795, loss: 0.1083 2023-01-06 10:03:16,276 - mmseg - INFO - Iter [66650/160000] lr: 3.501e-05, eta: 14:57:55, time: 0.558, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1044, decode.acc_seg: 95.6776, loss: 0.1044 2023-01-06 10:03:44,224 - mmseg - INFO - Iter [66700/160000] lr: 3.499e-05, eta: 14:57:25, time: 0.559, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1003, decode.acc_seg: 95.8118, loss: 0.1003 2023-01-06 10:04:11,916 - mmseg - INFO - Iter [66750/160000] lr: 3.497e-05, eta: 14:56:55, time: 0.554, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1021, decode.acc_seg: 95.7143, loss: 0.1021 2023-01-06 10:04:41,157 - mmseg - INFO - Iter [66800/160000] lr: 3.495e-05, eta: 14:56:26, time: 0.584, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1002, decode.acc_seg: 95.6930, loss: 0.1002 2023-01-06 10:05:09,310 - mmseg - INFO - Iter [66850/160000] lr: 3.493e-05, eta: 14:55:57, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1080, decode.acc_seg: 95.5857, loss: 0.1080 2023-01-06 10:05:37,876 - mmseg - INFO - Iter [66900/160000] lr: 3.491e-05, eta: 14:55:27, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0990, decode.acc_seg: 95.8492, loss: 0.0990 2023-01-06 10:06:05,494 - mmseg - INFO - Iter [66950/160000] lr: 3.489e-05, eta: 14:54:57, time: 0.552, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1011, decode.acc_seg: 95.7648, loss: 0.1011 2023-01-06 10:06:35,535 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 10:06:35,535 - mmseg - INFO - Iter [67000/160000] lr: 3.488e-05, eta: 14:54:30, time: 0.600, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1076, decode.acc_seg: 95.5553, loss: 0.1076 2023-01-06 10:07:04,536 - mmseg - INFO - Iter [67050/160000] lr: 3.486e-05, eta: 14:54:01, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1126, decode.acc_seg: 95.4255, loss: 0.1126 2023-01-06 10:07:32,753 - mmseg - INFO - Iter [67100/160000] lr: 3.484e-05, eta: 14:53:31, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0986, decode.acc_seg: 95.8465, loss: 0.0986 2023-01-06 10:08:01,546 - mmseg - INFO - Iter [67150/160000] lr: 3.482e-05, eta: 14:53:02, time: 0.575, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1078, decode.acc_seg: 95.5409, loss: 0.1078 2023-01-06 10:08:29,788 - mmseg - INFO - Iter [67200/160000] lr: 3.480e-05, eta: 14:52:33, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1040, decode.acc_seg: 95.7598, loss: 0.1040 2023-01-06 10:08:57,875 - mmseg - INFO - Iter [67250/160000] lr: 3.478e-05, eta: 14:52:03, time: 0.562, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1135, decode.acc_seg: 95.2254, loss: 0.1135 2023-01-06 10:09:25,893 - mmseg - INFO - Iter [67300/160000] lr: 3.476e-05, eta: 14:51:33, time: 0.560, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1075, decode.acc_seg: 95.5198, loss: 0.1075 2023-01-06 10:09:56,296 - mmseg - INFO - Iter [67350/160000] lr: 3.474e-05, eta: 14:51:06, time: 0.608, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1112, decode.acc_seg: 95.4512, loss: 0.1112 2023-01-06 10:10:24,449 - mmseg - INFO - Iter [67400/160000] lr: 3.473e-05, eta: 14:50:36, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1095, decode.acc_seg: 95.4153, loss: 0.1095 2023-01-06 10:10:51,943 - mmseg - INFO - Iter [67450/160000] lr: 3.471e-05, eta: 14:50:05, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1080, decode.acc_seg: 95.6377, loss: 0.1080 2023-01-06 10:11:18,917 - mmseg - INFO - Iter [67500/160000] lr: 3.469e-05, eta: 14:49:34, time: 0.539, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1089, decode.acc_seg: 95.4631, loss: 0.1089 2023-01-06 10:11:46,125 - mmseg - INFO - Iter [67550/160000] lr: 3.467e-05, eta: 14:49:03, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1063, decode.acc_seg: 95.6156, loss: 0.1063 2023-01-06 10:12:13,548 - mmseg - INFO - Iter [67600/160000] lr: 3.465e-05, eta: 14:48:32, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1109, decode.acc_seg: 95.4504, loss: 0.1109 2023-01-06 10:12:42,802 - mmseg - INFO - Iter [67650/160000] lr: 3.463e-05, eta: 14:48:04, time: 0.585, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1148, decode.acc_seg: 95.4271, loss: 0.1148 2023-01-06 10:13:10,804 - mmseg - INFO - Iter [67700/160000] lr: 3.461e-05, eta: 14:47:34, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1041, decode.acc_seg: 95.6850, loss: 0.1041 2023-01-06 10:13:40,752 - mmseg - INFO - Iter [67750/160000] lr: 3.459e-05, eta: 14:47:06, time: 0.599, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1114, decode.acc_seg: 95.3609, loss: 0.1114 2023-01-06 10:14:09,921 - mmseg - INFO - Iter [67800/160000] lr: 3.458e-05, eta: 14:46:38, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1182, decode.acc_seg: 95.1930, loss: 0.1182 2023-01-06 10:14:39,514 - mmseg - INFO - Iter [67850/160000] lr: 3.456e-05, eta: 14:46:10, time: 0.593, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1126, decode.acc_seg: 95.3526, loss: 0.1126 2023-01-06 10:15:08,495 - mmseg - INFO - Iter [67900/160000] lr: 3.454e-05, eta: 14:45:41, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1069, decode.acc_seg: 95.6047, loss: 0.1069 2023-01-06 10:15:37,137 - mmseg - INFO - Iter [67950/160000] lr: 3.452e-05, eta: 14:45:12, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1097, decode.acc_seg: 95.4832, loss: 0.1097 2023-01-06 10:16:05,005 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 10:16:05,006 - mmseg - INFO - Iter [68000/160000] lr: 3.450e-05, eta: 14:44:42, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1045, decode.acc_seg: 95.7868, loss: 0.1045 2023-01-06 10:16:34,363 - mmseg - INFO - Iter [68050/160000] lr: 3.448e-05, eta: 14:44:14, time: 0.587, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1088, decode.acc_seg: 95.4648, loss: 0.1088 2023-01-06 10:17:05,663 - mmseg - INFO - Iter [68100/160000] lr: 3.446e-05, eta: 14:43:48, time: 0.626, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1051, decode.acc_seg: 95.4981, loss: 0.1051 2023-01-06 10:17:34,279 - mmseg - INFO - Iter [68150/160000] lr: 3.444e-05, eta: 14:43:19, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1059, decode.acc_seg: 95.5265, loss: 0.1059 2023-01-06 10:18:02,763 - mmseg - INFO - Iter [68200/160000] lr: 3.443e-05, eta: 14:42:50, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1250, decode.acc_seg: 94.8824, loss: 0.1250 2023-01-06 10:18:30,804 - mmseg - INFO - Iter [68250/160000] lr: 3.441e-05, eta: 14:42:20, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1121, decode.acc_seg: 95.4796, loss: 0.1121 2023-01-06 10:18:58,952 - mmseg - INFO - Iter [68300/160000] lr: 3.439e-05, eta: 14:41:50, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1066, decode.acc_seg: 95.3751, loss: 0.1066 2023-01-06 10:19:27,333 - mmseg - INFO - Iter [68350/160000] lr: 3.437e-05, eta: 14:41:21, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1113, decode.acc_seg: 95.4120, loss: 0.1113 2023-01-06 10:19:55,987 - mmseg - INFO - Iter [68400/160000] lr: 3.435e-05, eta: 14:40:52, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1060, decode.acc_seg: 95.6815, loss: 0.1060 2023-01-06 10:20:26,424 - mmseg - INFO - Iter [68450/160000] lr: 3.433e-05, eta: 14:40:25, time: 0.609, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1013, decode.acc_seg: 95.8385, loss: 0.1013 2023-01-06 10:20:53,754 - mmseg - INFO - Iter [68500/160000] lr: 3.431e-05, eta: 14:39:54, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1015, decode.acc_seg: 95.7775, loss: 0.1015 2023-01-06 10:21:21,812 - mmseg - INFO - Iter [68550/160000] lr: 3.429e-05, eta: 14:39:24, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1100, decode.acc_seg: 95.4315, loss: 0.1100 2023-01-06 10:21:50,128 - mmseg - INFO - Iter [68600/160000] lr: 3.428e-05, eta: 14:38:55, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1098, decode.acc_seg: 95.3933, loss: 0.1098 2023-01-06 10:22:18,088 - mmseg - INFO - Iter [68650/160000] lr: 3.426e-05, eta: 14:38:24, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1146, decode.acc_seg: 95.3552, loss: 0.1146 2023-01-06 10:22:47,867 - mmseg - INFO - Iter [68700/160000] lr: 3.424e-05, eta: 14:37:57, time: 0.596, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1108, decode.acc_seg: 95.5218, loss: 0.1108 2023-01-06 10:23:16,028 - mmseg - INFO - Iter [68750/160000] lr: 3.422e-05, eta: 14:37:27, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1092, decode.acc_seg: 95.4977, loss: 0.1092 2023-01-06 10:23:44,271 - mmseg - INFO - Iter [68800/160000] lr: 3.420e-05, eta: 14:36:57, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1059, decode.acc_seg: 95.6168, loss: 0.1059 2023-01-06 10:24:14,335 - mmseg - INFO - Iter [68850/160000] lr: 3.418e-05, eta: 14:36:30, time: 0.602, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0985, decode.acc_seg: 95.9235, loss: 0.0985 2023-01-06 10:24:42,095 - mmseg - INFO - Iter [68900/160000] lr: 3.416e-05, eta: 14:36:00, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1062, decode.acc_seg: 95.4360, loss: 0.1062 2023-01-06 10:25:09,532 - mmseg - INFO - Iter [68950/160000] lr: 3.414e-05, eta: 14:35:29, time: 0.548, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1003, decode.acc_seg: 95.7599, loss: 0.1003 2023-01-06 10:25:38,821 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 10:25:38,822 - mmseg - INFO - Iter [69000/160000] lr: 3.413e-05, eta: 14:35:01, time: 0.586, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1051, decode.acc_seg: 95.6819, loss: 0.1051 2023-01-06 10:26:07,953 - mmseg - INFO - Iter [69050/160000] lr: 3.411e-05, eta: 14:34:33, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0981, decode.acc_seg: 95.9717, loss: 0.0981 2023-01-06 10:26:37,636 - mmseg - INFO - Iter [69100/160000] lr: 3.409e-05, eta: 14:34:05, time: 0.594, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1143, decode.acc_seg: 95.2659, loss: 0.1143 2023-01-06 10:27:07,011 - mmseg - INFO - Iter [69150/160000] lr: 3.407e-05, eta: 14:33:37, time: 0.587, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1023, decode.acc_seg: 95.7248, loss: 0.1023 2023-01-06 10:27:37,180 - mmseg - INFO - Iter [69200/160000] lr: 3.405e-05, eta: 14:33:09, time: 0.604, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1010, decode.acc_seg: 95.8936, loss: 0.1010 2023-01-06 10:28:04,354 - mmseg - INFO - Iter [69250/160000] lr: 3.403e-05, eta: 14:32:38, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1041, decode.acc_seg: 95.6917, loss: 0.1041 2023-01-06 10:28:32,866 - mmseg - INFO - Iter [69300/160000] lr: 3.401e-05, eta: 14:32:09, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1057, decode.acc_seg: 95.5217, loss: 0.1057 2023-01-06 10:29:01,600 - mmseg - INFO - Iter [69350/160000] lr: 3.399e-05, eta: 14:31:40, time: 0.575, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1031, decode.acc_seg: 95.7335, loss: 0.1031 2023-01-06 10:29:29,725 - mmseg - INFO - Iter [69400/160000] lr: 3.398e-05, eta: 14:31:10, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0979, decode.acc_seg: 95.9609, loss: 0.0979 2023-01-06 10:29:58,891 - mmseg - INFO - Iter [69450/160000] lr: 3.396e-05, eta: 14:30:42, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1003, decode.acc_seg: 95.8511, loss: 0.1003 2023-01-06 10:30:27,085 - mmseg - INFO - Iter [69500/160000] lr: 3.394e-05, eta: 14:30:12, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1033, decode.acc_seg: 95.6259, loss: 0.1033 2023-01-06 10:30:55,370 - mmseg - INFO - Iter [69550/160000] lr: 3.392e-05, eta: 14:29:43, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1011, decode.acc_seg: 95.7903, loss: 0.1011 2023-01-06 10:31:26,088 - mmseg - INFO - Iter [69600/160000] lr: 3.390e-05, eta: 14:29:16, time: 0.614, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1011, decode.acc_seg: 95.7496, loss: 0.1011 2023-01-06 10:31:54,833 - mmseg - INFO - Iter [69650/160000] lr: 3.388e-05, eta: 14:28:47, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1058, decode.acc_seg: 95.6978, loss: 0.1058 2023-01-06 10:32:22,636 - mmseg - INFO - Iter [69700/160000] lr: 3.386e-05, eta: 14:28:17, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1004, decode.acc_seg: 95.7485, loss: 0.1004 2023-01-06 10:32:50,734 - mmseg - INFO - Iter [69750/160000] lr: 3.384e-05, eta: 14:27:47, time: 0.563, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1003, decode.acc_seg: 95.7637, loss: 0.1003 2023-01-06 10:33:19,003 - mmseg - INFO - Iter [69800/160000] lr: 3.383e-05, eta: 14:27:18, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1040, decode.acc_seg: 95.5923, loss: 0.1040 2023-01-06 10:33:48,221 - mmseg - INFO - Iter [69850/160000] lr: 3.381e-05, eta: 14:26:49, time: 0.584, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1047, decode.acc_seg: 95.5763, loss: 0.1047 2023-01-06 10:34:16,293 - mmseg - INFO - Iter [69900/160000] lr: 3.379e-05, eta: 14:26:19, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1013, decode.acc_seg: 95.7280, loss: 0.1013 2023-01-06 10:34:46,378 - mmseg - INFO - Iter [69950/160000] lr: 3.377e-05, eta: 14:25:52, time: 0.602, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1082, decode.acc_seg: 95.4573, loss: 0.1082 2023-01-06 10:35:15,180 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 10:35:15,182 - mmseg - INFO - Iter [70000/160000] lr: 3.375e-05, eta: 14:25:23, time: 0.575, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1045, decode.acc_seg: 95.7467, loss: 0.1045 2023-01-06 10:35:44,901 - mmseg - INFO - Iter [70050/160000] lr: 3.373e-05, eta: 14:24:55, time: 0.594, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0962, decode.acc_seg: 95.9102, loss: 0.0962 2023-01-06 10:36:13,505 - mmseg - INFO - Iter [70100/160000] lr: 3.371e-05, eta: 14:24:26, time: 0.573, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1013, decode.acc_seg: 95.8961, loss: 0.1013 2023-01-06 10:36:40,767 - mmseg - INFO - Iter [70150/160000] lr: 3.369e-05, eta: 14:23:56, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1043, decode.acc_seg: 95.8532, loss: 0.1043 2023-01-06 10:37:09,829 - mmseg - INFO - Iter [70200/160000] lr: 3.368e-05, eta: 14:23:27, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1091, decode.acc_seg: 95.3943, loss: 0.1091 2023-01-06 10:37:39,129 - mmseg - INFO - Iter [70250/160000] lr: 3.366e-05, eta: 14:22:59, time: 0.587, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1157, decode.acc_seg: 95.3083, loss: 0.1157 2023-01-06 10:38:07,684 - mmseg - INFO - Iter [70300/160000] lr: 3.364e-05, eta: 14:22:29, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1101, decode.acc_seg: 95.3304, loss: 0.1101 2023-01-06 10:38:37,834 - mmseg - INFO - Iter [70350/160000] lr: 3.362e-05, eta: 14:22:02, time: 0.603, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1027, decode.acc_seg: 95.6921, loss: 0.1027 2023-01-06 10:39:07,230 - mmseg - INFO - Iter [70400/160000] lr: 3.360e-05, eta: 14:21:34, time: 0.588, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1079, decode.acc_seg: 95.5452, loss: 0.1079 2023-01-06 10:39:37,510 - mmseg - INFO - Iter [70450/160000] lr: 3.358e-05, eta: 14:21:07, time: 0.606, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0991, decode.acc_seg: 95.8372, loss: 0.0991 2023-01-06 10:40:05,932 - mmseg - INFO - Iter [70500/160000] lr: 3.356e-05, eta: 14:20:38, time: 0.569, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1031, decode.acc_seg: 95.6772, loss: 0.1031 2023-01-06 10:40:33,741 - mmseg - INFO - Iter [70550/160000] lr: 3.354e-05, eta: 14:20:08, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1077, decode.acc_seg: 95.4339, loss: 0.1077 2023-01-06 10:41:00,995 - mmseg - INFO - Iter [70600/160000] lr: 3.353e-05, eta: 14:19:37, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1138, decode.acc_seg: 95.3595, loss: 0.1138 2023-01-06 10:41:29,404 - mmseg - INFO - Iter [70650/160000] lr: 3.351e-05, eta: 14:19:07, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1046, decode.acc_seg: 95.6649, loss: 0.1046 2023-01-06 10:41:59,320 - mmseg - INFO - Iter [70700/160000] lr: 3.349e-05, eta: 14:18:40, time: 0.597, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1061, decode.acc_seg: 95.4697, loss: 0.1061 2023-01-06 10:42:26,380 - mmseg - INFO - Iter [70750/160000] lr: 3.347e-05, eta: 14:18:09, time: 0.542, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1022, decode.acc_seg: 95.6064, loss: 0.1022 2023-01-06 10:42:54,246 - mmseg - INFO - Iter [70800/160000] lr: 3.345e-05, eta: 14:17:39, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1050, decode.acc_seg: 95.6312, loss: 0.1050 2023-01-06 10:43:22,125 - mmseg - INFO - Iter [70850/160000] lr: 3.343e-05, eta: 14:17:08, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1030, decode.acc_seg: 95.7642, loss: 0.1030 2023-01-06 10:43:50,479 - mmseg - INFO - Iter [70900/160000] lr: 3.341e-05, eta: 14:16:39, time: 0.567, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1069, decode.acc_seg: 95.6306, loss: 0.1069 2023-01-06 10:44:18,679 - mmseg - INFO - Iter [70950/160000] lr: 3.339e-05, eta: 14:16:09, time: 0.565, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1029, decode.acc_seg: 95.6834, loss: 0.1029 2023-01-06 10:44:47,482 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 10:44:47,482 - mmseg - INFO - Iter [71000/160000] lr: 3.338e-05, eta: 14:15:41, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1073, decode.acc_seg: 95.3937, loss: 0.1073 2023-01-06 10:45:16,308 - mmseg - INFO - Iter [71050/160000] lr: 3.336e-05, eta: 14:15:12, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1141, decode.acc_seg: 95.3725, loss: 0.1141 2023-01-06 10:45:47,166 - mmseg - INFO - Iter [71100/160000] lr: 3.334e-05, eta: 14:14:45, time: 0.617, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0991, decode.acc_seg: 95.8676, loss: 0.0991 2023-01-06 10:46:14,352 - mmseg - INFO - Iter [71150/160000] lr: 3.332e-05, eta: 14:14:14, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0995, decode.acc_seg: 95.7662, loss: 0.0995 2023-01-06 10:46:42,377 - mmseg - INFO - Iter [71200/160000] lr: 3.330e-05, eta: 14:13:45, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1021, decode.acc_seg: 95.6533, loss: 0.1021 2023-01-06 10:47:11,520 - mmseg - INFO - Iter [71250/160000] lr: 3.328e-05, eta: 14:13:16, time: 0.582, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1006, decode.acc_seg: 95.8263, loss: 0.1006 2023-01-06 10:47:40,399 - mmseg - INFO - Iter [71300/160000] lr: 3.326e-05, eta: 14:12:47, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0979, decode.acc_seg: 95.9488, loss: 0.0979 2023-01-06 10:48:07,998 - mmseg - INFO - Iter [71350/160000] lr: 3.324e-05, eta: 14:12:17, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1030, decode.acc_seg: 95.7378, loss: 0.1030 2023-01-06 10:48:35,416 - mmseg - INFO - Iter [71400/160000] lr: 3.323e-05, eta: 14:11:46, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1087, decode.acc_seg: 95.5965, loss: 0.1087 2023-01-06 10:49:05,860 - mmseg - INFO - Iter [71450/160000] lr: 3.321e-05, eta: 14:11:19, time: 0.608, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1055, decode.acc_seg: 95.6007, loss: 0.1055 2023-01-06 10:49:33,776 - mmseg - INFO - Iter [71500/160000] lr: 3.319e-05, eta: 14:10:49, time: 0.558, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1024, decode.acc_seg: 95.7797, loss: 0.1024 2023-01-06 10:50:00,807 - mmseg - INFO - Iter [71550/160000] lr: 3.317e-05, eta: 14:10:18, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1031, decode.acc_seg: 95.6666, loss: 0.1031 2023-01-06 10:50:28,337 - mmseg - INFO - Iter [71600/160000] lr: 3.315e-05, eta: 14:09:48, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1134, decode.acc_seg: 95.2913, loss: 0.1134 2023-01-06 10:50:56,823 - mmseg - INFO - Iter [71650/160000] lr: 3.313e-05, eta: 14:09:19, time: 0.570, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1158, decode.acc_seg: 95.1932, loss: 0.1158 2023-01-06 10:51:25,379 - mmseg - INFO - Iter [71700/160000] lr: 3.311e-05, eta: 14:08:49, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1079, decode.acc_seg: 95.5086, loss: 0.1079 2023-01-06 10:51:53,403 - mmseg - INFO - Iter [71750/160000] lr: 3.309e-05, eta: 14:08:20, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1092, decode.acc_seg: 95.4363, loss: 0.1092 2023-01-06 10:52:22,846 - mmseg - INFO - Iter [71800/160000] lr: 3.308e-05, eta: 14:07:52, time: 0.589, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1060, decode.acc_seg: 95.6561, loss: 0.1060 2023-01-06 10:52:50,123 - mmseg - INFO - Iter [71850/160000] lr: 3.306e-05, eta: 14:07:21, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1048, decode.acc_seg: 95.6053, loss: 0.1048 2023-01-06 10:53:17,788 - mmseg - INFO - Iter [71900/160000] lr: 3.304e-05, eta: 14:06:50, time: 0.553, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1003, decode.acc_seg: 95.7939, loss: 0.1003 2023-01-06 10:53:45,609 - mmseg - INFO - Iter [71950/160000] lr: 3.302e-05, eta: 14:06:20, time: 0.557, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1040, decode.acc_seg: 95.6914, loss: 0.1040 2023-01-06 10:54:13,022 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 10:54:13,023 - mmseg - INFO - Iter [72000/160000] lr: 3.300e-05, eta: 14:05:50, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1044, decode.acc_seg: 95.6399, loss: 0.1044 2023-01-06 10:54:40,885 - mmseg - INFO - Iter [72050/160000] lr: 3.298e-05, eta: 14:05:20, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1079, decode.acc_seg: 95.6575, loss: 0.1079 2023-01-06 10:55:10,021 - mmseg - INFO - Iter [72100/160000] lr: 3.296e-05, eta: 14:04:51, time: 0.583, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1098, decode.acc_seg: 95.3563, loss: 0.1098 2023-01-06 10:55:39,685 - mmseg - INFO - Iter [72150/160000] lr: 3.294e-05, eta: 14:04:23, time: 0.593, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1016, decode.acc_seg: 95.7654, loss: 0.1016 2023-01-06 10:56:09,621 - mmseg - INFO - Iter [72200/160000] lr: 3.293e-05, eta: 14:03:56, time: 0.599, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1034, decode.acc_seg: 95.7100, loss: 0.1034 2023-01-06 10:56:38,912 - mmseg - INFO - Iter [72250/160000] lr: 3.291e-05, eta: 14:03:28, time: 0.587, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1045, decode.acc_seg: 95.7259, loss: 0.1045 2023-01-06 10:57:08,707 - mmseg - INFO - Iter [72300/160000] lr: 3.289e-05, eta: 14:03:00, time: 0.595, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1042, decode.acc_seg: 95.7741, loss: 0.1042 2023-01-06 10:57:37,260 - mmseg - INFO - Iter [72350/160000] lr: 3.287e-05, eta: 14:02:31, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1035, decode.acc_seg: 95.7547, loss: 0.1035 2023-01-06 10:58:06,017 - mmseg - INFO - Iter [72400/160000] lr: 3.285e-05, eta: 14:02:02, time: 0.575, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1013, decode.acc_seg: 95.8150, loss: 0.1013 2023-01-06 10:58:33,940 - mmseg - INFO - Iter [72450/160000] lr: 3.283e-05, eta: 14:01:32, time: 0.559, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0967, decode.acc_seg: 95.9918, loss: 0.0967 2023-01-06 10:59:02,652 - mmseg - INFO - Iter [72500/160000] lr: 3.281e-05, eta: 14:01:03, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0941, decode.acc_seg: 96.0178, loss: 0.0941 2023-01-06 10:59:32,415 - mmseg - INFO - Iter [72550/160000] lr: 3.279e-05, eta: 14:00:35, time: 0.596, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1041, decode.acc_seg: 95.6520, loss: 0.1041 2023-01-06 10:59:59,979 - mmseg - INFO - Iter [72600/160000] lr: 3.278e-05, eta: 14:00:05, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1018, decode.acc_seg: 95.8137, loss: 0.1018 2023-01-06 11:00:28,344 - mmseg - INFO - Iter [72650/160000] lr: 3.276e-05, eta: 13:59:35, time: 0.567, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1015, decode.acc_seg: 95.7314, loss: 0.1015 2023-01-06 11:00:57,133 - mmseg - INFO - Iter [72700/160000] lr: 3.274e-05, eta: 13:59:07, time: 0.576, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0994, decode.acc_seg: 95.6982, loss: 0.0994 2023-01-06 11:01:25,513 - mmseg - INFO - Iter [72750/160000] lr: 3.272e-05, eta: 13:58:37, time: 0.568, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0945, decode.acc_seg: 96.0712, loss: 0.0945 2023-01-06 11:01:52,897 - mmseg - INFO - Iter [72800/160000] lr: 3.270e-05, eta: 13:58:07, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1127, decode.acc_seg: 95.2963, loss: 0.1127 2023-01-06 11:02:20,354 - mmseg - INFO - Iter [72850/160000] lr: 3.268e-05, eta: 13:57:36, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1138, decode.acc_seg: 95.4469, loss: 0.1138 2023-01-06 11:02:47,747 - mmseg - INFO - Iter [72900/160000] lr: 3.266e-05, eta: 13:57:06, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1070, decode.acc_seg: 95.5698, loss: 0.1070 2023-01-06 11:03:17,823 - mmseg - INFO - Iter [72950/160000] lr: 3.264e-05, eta: 13:56:38, time: 0.602, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0994, decode.acc_seg: 95.8622, loss: 0.0994 2023-01-06 11:03:46,477 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 11:03:46,478 - mmseg - INFO - Iter [73000/160000] lr: 3.263e-05, eta: 13:56:09, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1172, decode.acc_seg: 95.5239, loss: 0.1172 2023-01-06 11:04:14,609 - mmseg - INFO - Iter [73050/160000] lr: 3.261e-05, eta: 13:55:40, time: 0.563, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1153, decode.acc_seg: 95.2229, loss: 0.1153 2023-01-06 11:04:42,893 - mmseg - INFO - Iter [73100/160000] lr: 3.259e-05, eta: 13:55:10, time: 0.567, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1037, decode.acc_seg: 95.7942, loss: 0.1037 2023-01-06 11:05:10,340 - mmseg - INFO - Iter [73150/160000] lr: 3.257e-05, eta: 13:54:40, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1053, decode.acc_seg: 95.6860, loss: 0.1053 2023-01-06 11:05:38,599 - mmseg - INFO - Iter [73200/160000] lr: 3.255e-05, eta: 13:54:10, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1118, decode.acc_seg: 95.4989, loss: 0.1118 2023-01-06 11:06:07,191 - mmseg - INFO - Iter [73250/160000] lr: 3.253e-05, eta: 13:53:41, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1082, decode.acc_seg: 95.4493, loss: 0.1082 2023-01-06 11:06:36,565 - mmseg - INFO - Iter [73300/160000] lr: 3.251e-05, eta: 13:53:13, time: 0.588, data_time: 0.060, memory: 10576, decode.loss_ce: 0.1061, decode.acc_seg: 95.6488, loss: 0.1061 2023-01-06 11:07:04,904 - mmseg - INFO - Iter [73350/160000] lr: 3.249e-05, eta: 13:52:43, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0989, decode.acc_seg: 95.8508, loss: 0.0989 2023-01-06 11:07:33,673 - mmseg - INFO - Iter [73400/160000] lr: 3.248e-05, eta: 13:52:14, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1127, decode.acc_seg: 95.3027, loss: 0.1127 2023-01-06 11:08:01,846 - mmseg - INFO - Iter [73450/160000] lr: 3.246e-05, eta: 13:51:45, time: 0.564, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1032, decode.acc_seg: 95.7509, loss: 0.1032 2023-01-06 11:08:29,311 - mmseg - INFO - Iter [73500/160000] lr: 3.244e-05, eta: 13:51:14, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0950, decode.acc_seg: 96.0042, loss: 0.0950 2023-01-06 11:08:58,549 - mmseg - INFO - Iter [73550/160000] lr: 3.242e-05, eta: 13:50:46, time: 0.585, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1024, decode.acc_seg: 95.6976, loss: 0.1024 2023-01-06 11:09:27,950 - mmseg - INFO - Iter [73600/160000] lr: 3.240e-05, eta: 13:50:18, time: 0.587, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1040, decode.acc_seg: 95.6086, loss: 0.1040 2023-01-06 11:09:58,016 - mmseg - INFO - Iter [73650/160000] lr: 3.238e-05, eta: 13:49:50, time: 0.601, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1012, decode.acc_seg: 95.8446, loss: 0.1012 2023-01-06 11:10:28,479 - mmseg - INFO - Iter [73700/160000] lr: 3.236e-05, eta: 13:49:24, time: 0.610, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0960, decode.acc_seg: 95.9856, loss: 0.0960 2023-01-06 11:10:56,608 - mmseg - INFO - Iter [73750/160000] lr: 3.234e-05, eta: 13:48:54, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1048, decode.acc_seg: 95.5651, loss: 0.1048 2023-01-06 11:11:25,980 - mmseg - INFO - Iter [73800/160000] lr: 3.233e-05, eta: 13:48:26, time: 0.587, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0989, decode.acc_seg: 95.8249, loss: 0.0989 2023-01-06 11:11:54,005 - mmseg - INFO - Iter [73850/160000] lr: 3.231e-05, eta: 13:47:56, time: 0.560, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0972, decode.acc_seg: 95.9173, loss: 0.0972 2023-01-06 11:12:21,818 - mmseg - INFO - Iter [73900/160000] lr: 3.229e-05, eta: 13:47:26, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1098, decode.acc_seg: 95.3808, loss: 0.1098 2023-01-06 11:12:50,351 - mmseg - INFO - Iter [73950/160000] lr: 3.227e-05, eta: 13:46:57, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1024, decode.acc_seg: 95.6584, loss: 0.1024 2023-01-06 11:13:18,181 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 11:13:18,182 - mmseg - INFO - Iter [74000/160000] lr: 3.225e-05, eta: 13:46:27, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1043, decode.acc_seg: 95.6431, loss: 0.1043 2023-01-06 11:13:48,602 - mmseg - INFO - Iter [74050/160000] lr: 3.223e-05, eta: 13:46:00, time: 0.608, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1079, decode.acc_seg: 95.5161, loss: 0.1079 2023-01-06 11:14:18,059 - mmseg - INFO - Iter [74100/160000] lr: 3.221e-05, eta: 13:45:32, time: 0.589, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1001, decode.acc_seg: 95.6958, loss: 0.1001 2023-01-06 11:14:47,457 - mmseg - INFO - Iter [74150/160000] lr: 3.219e-05, eta: 13:45:03, time: 0.588, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1067, decode.acc_seg: 95.6999, loss: 0.1067 2023-01-06 11:15:15,397 - mmseg - INFO - Iter [74200/160000] lr: 3.218e-05, eta: 13:44:34, time: 0.560, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1019, decode.acc_seg: 95.8133, loss: 0.1019 2023-01-06 11:15:43,143 - mmseg - INFO - Iter [74250/160000] lr: 3.216e-05, eta: 13:44:04, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0977, decode.acc_seg: 95.8906, loss: 0.0977 2023-01-06 11:16:10,893 - mmseg - INFO - Iter [74300/160000] lr: 3.214e-05, eta: 13:43:33, time: 0.556, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1019, decode.acc_seg: 95.8791, loss: 0.1019 2023-01-06 11:16:40,441 - mmseg - INFO - Iter [74350/160000] lr: 3.212e-05, eta: 13:43:05, time: 0.591, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1035, decode.acc_seg: 95.6711, loss: 0.1035 2023-01-06 11:17:07,581 - mmseg - INFO - Iter [74400/160000] lr: 3.210e-05, eta: 13:42:35, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0993, decode.acc_seg: 95.9431, loss: 0.0993 2023-01-06 11:17:38,957 - mmseg - INFO - Iter [74450/160000] lr: 3.208e-05, eta: 13:42:09, time: 0.627, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0992, decode.acc_seg: 95.8716, loss: 0.0992 2023-01-06 11:18:07,339 - mmseg - INFO - Iter [74500/160000] lr: 3.206e-05, eta: 13:41:39, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1005, decode.acc_seg: 95.8985, loss: 0.1005 2023-01-06 11:18:34,369 - mmseg - INFO - Iter [74550/160000] lr: 3.204e-05, eta: 13:41:09, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1017, decode.acc_seg: 95.7637, loss: 0.1017 2023-01-06 11:19:02,437 - mmseg - INFO - Iter [74600/160000] lr: 3.203e-05, eta: 13:40:39, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0995, decode.acc_seg: 95.8345, loss: 0.0995 2023-01-06 11:19:29,873 - mmseg - INFO - Iter [74650/160000] lr: 3.201e-05, eta: 13:40:08, time: 0.549, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1030, decode.acc_seg: 95.7306, loss: 0.1030 2023-01-06 11:19:58,434 - mmseg - INFO - Iter [74700/160000] lr: 3.199e-05, eta: 13:39:39, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1034, decode.acc_seg: 95.7265, loss: 0.1034 2023-01-06 11:20:26,589 - mmseg - INFO - Iter [74750/160000] lr: 3.197e-05, eta: 13:39:10, time: 0.563, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0985, decode.acc_seg: 95.8897, loss: 0.0985 2023-01-06 11:20:56,975 - mmseg - INFO - Iter [74800/160000] lr: 3.195e-05, eta: 13:38:43, time: 0.609, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0969, decode.acc_seg: 95.9618, loss: 0.0969 2023-01-06 11:21:26,041 - mmseg - INFO - Iter [74850/160000] lr: 3.193e-05, eta: 13:38:14, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1003, decode.acc_seg: 95.8072, loss: 0.1003 2023-01-06 11:21:55,484 - mmseg - INFO - Iter [74900/160000] lr: 3.191e-05, eta: 13:37:46, time: 0.589, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0946, decode.acc_seg: 96.0554, loss: 0.0946 2023-01-06 11:22:23,950 - mmseg - INFO - Iter [74950/160000] lr: 3.189e-05, eta: 13:37:17, time: 0.570, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0923, decode.acc_seg: 96.0150, loss: 0.0923 2023-01-06 11:22:51,476 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 11:22:51,476 - mmseg - INFO - Iter [75000/160000] lr: 3.188e-05, eta: 13:36:46, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0992, decode.acc_seg: 95.9074, loss: 0.0992 2023-01-06 11:23:18,780 - mmseg - INFO - Iter [75050/160000] lr: 3.186e-05, eta: 13:36:16, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1038, decode.acc_seg: 95.6651, loss: 0.1038 2023-01-06 11:23:48,771 - mmseg - INFO - Iter [75100/160000] lr: 3.184e-05, eta: 13:35:48, time: 0.599, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1061, decode.acc_seg: 95.6279, loss: 0.1061 2023-01-06 11:24:20,443 - mmseg - INFO - Iter [75150/160000] lr: 3.182e-05, eta: 13:35:23, time: 0.634, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0961, decode.acc_seg: 95.9101, loss: 0.0961 2023-01-06 11:24:48,640 - mmseg - INFO - Iter [75200/160000] lr: 3.180e-05, eta: 13:34:53, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1092, decode.acc_seg: 95.4945, loss: 0.1092 2023-01-06 11:25:16,145 - mmseg - INFO - Iter [75250/160000] lr: 3.178e-05, eta: 13:34:23, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0969, decode.acc_seg: 95.9902, loss: 0.0969 2023-01-06 11:25:43,845 - mmseg - INFO - Iter [75300/160000] lr: 3.176e-05, eta: 13:33:53, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0973, decode.acc_seg: 95.9114, loss: 0.0973 2023-01-06 11:26:13,021 - mmseg - INFO - Iter [75350/160000] lr: 3.174e-05, eta: 13:33:24, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1014, decode.acc_seg: 95.8146, loss: 0.1014 2023-01-06 11:26:41,910 - mmseg - INFO - Iter [75400/160000] lr: 3.173e-05, eta: 13:32:56, time: 0.579, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1063, decode.acc_seg: 95.5210, loss: 0.1063 2023-01-06 11:27:09,305 - mmseg - INFO - Iter [75450/160000] lr: 3.171e-05, eta: 13:32:25, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1007, decode.acc_seg: 95.7632, loss: 0.1007 2023-01-06 11:27:36,416 - mmseg - INFO - Iter [75500/160000] lr: 3.169e-05, eta: 13:31:54, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0974, decode.acc_seg: 95.9713, loss: 0.0974 2023-01-06 11:28:05,785 - mmseg - INFO - Iter [75550/160000] lr: 3.167e-05, eta: 13:31:26, time: 0.588, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1008, decode.acc_seg: 95.7164, loss: 0.1008 2023-01-06 11:28:34,882 - mmseg - INFO - Iter [75600/160000] lr: 3.165e-05, eta: 13:30:58, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0988, decode.acc_seg: 95.8747, loss: 0.0988 2023-01-06 11:29:03,939 - mmseg - INFO - Iter [75650/160000] lr: 3.163e-05, eta: 13:30:29, time: 0.581, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0998, decode.acc_seg: 95.8832, loss: 0.0998 2023-01-06 11:29:32,553 - mmseg - INFO - Iter [75700/160000] lr: 3.161e-05, eta: 13:30:00, time: 0.573, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0961, decode.acc_seg: 95.9189, loss: 0.0961 2023-01-06 11:30:00,298 - mmseg - INFO - Iter [75750/160000] lr: 3.159e-05, eta: 13:29:30, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1001, decode.acc_seg: 95.8424, loss: 0.1001 2023-01-06 11:30:28,301 - mmseg - INFO - Iter [75800/160000] lr: 3.158e-05, eta: 13:29:00, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1101, decode.acc_seg: 95.6024, loss: 0.1101 2023-01-06 11:30:55,678 - mmseg - INFO - Iter [75850/160000] lr: 3.156e-05, eta: 13:28:30, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1067, decode.acc_seg: 95.6991, loss: 0.1067 2023-01-06 11:31:25,466 - mmseg - INFO - Iter [75900/160000] lr: 3.154e-05, eta: 13:28:02, time: 0.596, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0992, decode.acc_seg: 95.8099, loss: 0.0992 2023-01-06 11:31:53,382 - mmseg - INFO - Iter [75950/160000] lr: 3.152e-05, eta: 13:27:32, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0977, decode.acc_seg: 95.8803, loss: 0.0977 2023-01-06 11:32:22,814 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 11:32:22,814 - mmseg - INFO - Iter [76000/160000] lr: 3.150e-05, eta: 13:27:04, time: 0.588, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1005, decode.acc_seg: 95.8596, loss: 0.1005 2023-01-06 11:32:51,276 - mmseg - INFO - Iter [76050/160000] lr: 3.148e-05, eta: 13:26:35, time: 0.569, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1240, decode.acc_seg: 94.9362, loss: 0.1240 2023-01-06 11:33:19,389 - mmseg - INFO - Iter [76100/160000] lr: 3.146e-05, eta: 13:26:05, time: 0.563, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1086, decode.acc_seg: 95.6223, loss: 0.1086 2023-01-06 11:33:48,045 - mmseg - INFO - Iter [76150/160000] lr: 3.144e-05, eta: 13:25:36, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1054, decode.acc_seg: 95.6922, loss: 0.1054 2023-01-06 11:34:17,324 - mmseg - INFO - Iter [76200/160000] lr: 3.143e-05, eta: 13:25:08, time: 0.585, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1125, decode.acc_seg: 95.4479, loss: 0.1125 2023-01-06 11:34:47,413 - mmseg - INFO - Iter [76250/160000] lr: 3.141e-05, eta: 13:24:40, time: 0.602, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1058, decode.acc_seg: 95.5562, loss: 0.1058 2023-01-06 11:35:17,214 - mmseg - INFO - Iter [76300/160000] lr: 3.139e-05, eta: 13:24:13, time: 0.597, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0962, decode.acc_seg: 95.9149, loss: 0.0962 2023-01-06 11:35:44,434 - mmseg - INFO - Iter [76350/160000] lr: 3.137e-05, eta: 13:23:42, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1016, decode.acc_seg: 95.7270, loss: 0.1016 2023-01-06 11:36:11,602 - mmseg - INFO - Iter [76400/160000] lr: 3.135e-05, eta: 13:23:12, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0958, decode.acc_seg: 96.0398, loss: 0.0958 2023-01-06 11:36:39,405 - mmseg - INFO - Iter [76450/160000] lr: 3.133e-05, eta: 13:22:42, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1087, decode.acc_seg: 95.6960, loss: 0.1087 2023-01-06 11:37:07,635 - mmseg - INFO - Iter [76500/160000] lr: 3.131e-05, eta: 13:22:12, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1008, decode.acc_seg: 95.8099, loss: 0.1008 2023-01-06 11:37:36,942 - mmseg - INFO - Iter [76550/160000] lr: 3.129e-05, eta: 13:21:44, time: 0.585, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0945, decode.acc_seg: 96.0601, loss: 0.0945 2023-01-06 11:38:06,271 - mmseg - INFO - Iter [76600/160000] lr: 3.128e-05, eta: 13:21:16, time: 0.586, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1049, decode.acc_seg: 95.7876, loss: 0.1049 2023-01-06 11:38:36,989 - mmseg - INFO - Iter [76650/160000] lr: 3.126e-05, eta: 13:20:49, time: 0.615, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1049, decode.acc_seg: 95.5597, loss: 0.1049 2023-01-06 11:39:05,514 - mmseg - INFO - Iter [76700/160000] lr: 3.124e-05, eta: 13:20:20, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0975, decode.acc_seg: 95.8954, loss: 0.0975 2023-01-06 11:39:32,853 - mmseg - INFO - Iter [76750/160000] lr: 3.122e-05, eta: 13:19:49, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1052, decode.acc_seg: 95.6864, loss: 0.1052 2023-01-06 11:40:00,709 - mmseg - INFO - Iter [76800/160000] lr: 3.120e-05, eta: 13:19:19, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1027, decode.acc_seg: 95.7310, loss: 0.1027 2023-01-06 11:40:28,084 - mmseg - INFO - Iter [76850/160000] lr: 3.118e-05, eta: 13:18:49, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0965, decode.acc_seg: 95.9385, loss: 0.0965 2023-01-06 11:40:55,664 - mmseg - INFO - Iter [76900/160000] lr: 3.116e-05, eta: 13:18:19, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0997, decode.acc_seg: 95.7736, loss: 0.0997 2023-01-06 11:41:24,833 - mmseg - INFO - Iter [76950/160000] lr: 3.114e-05, eta: 13:17:50, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1031, decode.acc_seg: 95.7433, loss: 0.1031 2023-01-06 11:41:52,820 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 11:41:52,821 - mmseg - INFO - Iter [77000/160000] lr: 3.113e-05, eta: 13:17:21, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0991, decode.acc_seg: 95.8609, loss: 0.0991 2023-01-06 11:42:22,223 - mmseg - INFO - Iter [77050/160000] lr: 3.111e-05, eta: 13:16:52, time: 0.588, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0979, decode.acc_seg: 95.7903, loss: 0.0979 2023-01-06 11:42:50,522 - mmseg - INFO - Iter [77100/160000] lr: 3.109e-05, eta: 13:16:23, time: 0.566, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0952, decode.acc_seg: 95.9745, loss: 0.0952 2023-01-06 11:43:18,749 - mmseg - INFO - Iter [77150/160000] lr: 3.107e-05, eta: 13:15:54, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1039, decode.acc_seg: 95.7995, loss: 0.1039 2023-01-06 11:43:47,134 - mmseg - INFO - Iter [77200/160000] lr: 3.105e-05, eta: 13:15:24, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1074, decode.acc_seg: 95.4654, loss: 0.1074 2023-01-06 11:44:16,177 - mmseg - INFO - Iter [77250/160000] lr: 3.103e-05, eta: 13:14:56, time: 0.582, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1021, decode.acc_seg: 95.6922, loss: 0.1021 2023-01-06 11:44:44,797 - mmseg - INFO - Iter [77300/160000] lr: 3.101e-05, eta: 13:14:27, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1042, decode.acc_seg: 95.6358, loss: 0.1042 2023-01-06 11:45:14,558 - mmseg - INFO - Iter [77350/160000] lr: 3.099e-05, eta: 13:13:59, time: 0.594, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1006, decode.acc_seg: 95.7960, loss: 0.1006 2023-01-06 11:45:45,283 - mmseg - INFO - Iter [77400/160000] lr: 3.098e-05, eta: 13:13:32, time: 0.615, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0942, decode.acc_seg: 96.0343, loss: 0.0942 2023-01-06 11:46:14,541 - mmseg - INFO - Iter [77450/160000] lr: 3.096e-05, eta: 13:13:04, time: 0.586, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0955, decode.acc_seg: 96.0171, loss: 0.0955 2023-01-06 11:46:41,700 - mmseg - INFO - Iter [77500/160000] lr: 3.094e-05, eta: 13:12:33, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1219, decode.acc_seg: 95.2104, loss: 0.1219 2023-01-06 11:47:10,990 - mmseg - INFO - Iter [77550/160000] lr: 3.092e-05, eta: 13:12:05, time: 0.585, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0980, decode.acc_seg: 95.8336, loss: 0.0980 2023-01-06 11:47:39,724 - mmseg - INFO - Iter [77600/160000] lr: 3.090e-05, eta: 13:11:36, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0989, decode.acc_seg: 95.8977, loss: 0.0989 2023-01-06 11:48:07,897 - mmseg - INFO - Iter [77650/160000] lr: 3.088e-05, eta: 13:11:06, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1044, decode.acc_seg: 95.6070, loss: 0.1044 2023-01-06 11:48:35,925 - mmseg - INFO - Iter [77700/160000] lr: 3.086e-05, eta: 13:10:37, time: 0.561, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0975, decode.acc_seg: 95.8058, loss: 0.0975 2023-01-06 11:49:06,709 - mmseg - INFO - Iter [77750/160000] lr: 3.084e-05, eta: 13:10:10, time: 0.616, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1072, decode.acc_seg: 95.6490, loss: 0.1072 2023-01-06 11:49:34,817 - mmseg - INFO - Iter [77800/160000] lr: 3.083e-05, eta: 13:09:40, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0959, decode.acc_seg: 95.9556, loss: 0.0959 2023-01-06 11:50:03,133 - mmseg - INFO - Iter [77850/160000] lr: 3.081e-05, eta: 13:09:11, time: 0.567, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0941, decode.acc_seg: 96.0462, loss: 0.0941 2023-01-06 11:50:31,967 - mmseg - INFO - Iter [77900/160000] lr: 3.079e-05, eta: 13:08:42, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1013, decode.acc_seg: 95.8237, loss: 0.1013 2023-01-06 11:51:01,081 - mmseg - INFO - Iter [77950/160000] lr: 3.077e-05, eta: 13:08:14, time: 0.582, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0938, decode.acc_seg: 96.0039, loss: 0.0938 2023-01-06 11:51:28,100 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 11:51:28,100 - mmseg - INFO - Iter [78000/160000] lr: 3.075e-05, eta: 13:07:43, time: 0.541, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1021, decode.acc_seg: 95.8803, loss: 0.1021 2023-01-06 11:51:55,406 - mmseg - INFO - Iter [78050/160000] lr: 3.073e-05, eta: 13:07:13, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0984, decode.acc_seg: 95.7890, loss: 0.0984 2023-01-06 11:52:24,477 - mmseg - INFO - Iter [78100/160000] lr: 3.071e-05, eta: 13:06:44, time: 0.582, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1007, decode.acc_seg: 95.6304, loss: 0.1007 2023-01-06 11:52:54,337 - mmseg - INFO - Iter [78150/160000] lr: 3.069e-05, eta: 13:06:16, time: 0.597, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1008, decode.acc_seg: 95.7244, loss: 0.1008 2023-01-06 11:53:23,776 - mmseg - INFO - Iter [78200/160000] lr: 3.068e-05, eta: 13:05:48, time: 0.588, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0958, decode.acc_seg: 95.8969, loss: 0.0958 2023-01-06 11:53:52,186 - mmseg - INFO - Iter [78250/160000] lr: 3.066e-05, eta: 13:05:19, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0977, decode.acc_seg: 95.9249, loss: 0.0977 2023-01-06 11:54:20,177 - mmseg - INFO - Iter [78300/160000] lr: 3.064e-05, eta: 13:04:49, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1043, decode.acc_seg: 95.6352, loss: 0.1043 2023-01-06 11:54:48,876 - mmseg - INFO - Iter [78350/160000] lr: 3.062e-05, eta: 13:04:20, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0985, decode.acc_seg: 95.9325, loss: 0.0985 2023-01-06 11:55:16,384 - mmseg - INFO - Iter [78400/160000] lr: 3.060e-05, eta: 13:03:50, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1045, decode.acc_seg: 95.6286, loss: 0.1045 2023-01-06 11:55:44,574 - mmseg - INFO - Iter [78450/160000] lr: 3.058e-05, eta: 13:03:21, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0987, decode.acc_seg: 95.9207, loss: 0.0987 2023-01-06 11:56:16,340 - mmseg - INFO - Iter [78500/160000] lr: 3.056e-05, eta: 13:02:55, time: 0.635, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0984, decode.acc_seg: 95.9455, loss: 0.0984 2023-01-06 11:56:43,974 - mmseg - INFO - Iter [78550/160000] lr: 3.054e-05, eta: 13:02:25, time: 0.553, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0987, decode.acc_seg: 95.9091, loss: 0.0987 2023-01-06 11:57:11,803 - mmseg - INFO - Iter [78600/160000] lr: 3.053e-05, eta: 13:01:55, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1030, decode.acc_seg: 95.7217, loss: 0.1030 2023-01-06 11:57:40,550 - mmseg - INFO - Iter [78650/160000] lr: 3.051e-05, eta: 13:01:26, time: 0.575, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0940, decode.acc_seg: 96.0336, loss: 0.0940 2023-01-06 11:58:09,978 - mmseg - INFO - Iter [78700/160000] lr: 3.049e-05, eta: 13:00:58, time: 0.589, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0970, decode.acc_seg: 95.9694, loss: 0.0970 2023-01-06 11:58:38,646 - mmseg - INFO - Iter [78750/160000] lr: 3.047e-05, eta: 13:00:29, time: 0.574, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1036, decode.acc_seg: 95.6847, loss: 0.1036 2023-01-06 11:59:05,645 - mmseg - INFO - Iter [78800/160000] lr: 3.045e-05, eta: 12:59:58, time: 0.540, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1061, decode.acc_seg: 95.6358, loss: 0.1061 2023-01-06 11:59:33,043 - mmseg - INFO - Iter [78850/160000] lr: 3.043e-05, eta: 12:59:28, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1040, decode.acc_seg: 95.7384, loss: 0.1040 2023-01-06 12:00:03,870 - mmseg - INFO - Iter [78900/160000] lr: 3.041e-05, eta: 12:59:01, time: 0.616, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0939, decode.acc_seg: 96.0368, loss: 0.0939 2023-01-06 12:00:31,702 - mmseg - INFO - Iter [78950/160000] lr: 3.039e-05, eta: 12:58:31, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0937, decode.acc_seg: 96.0678, loss: 0.0937 2023-01-06 12:01:00,204 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 12:01:00,205 - mmseg - INFO - Iter [79000/160000] lr: 3.038e-05, eta: 12:58:02, time: 0.570, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1041, decode.acc_seg: 95.6273, loss: 0.1041 2023-01-06 12:01:28,523 - mmseg - INFO - Iter [79050/160000] lr: 3.036e-05, eta: 12:57:33, time: 0.566, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0994, decode.acc_seg: 95.9596, loss: 0.0994 2023-01-06 12:01:57,601 - mmseg - INFO - Iter [79100/160000] lr: 3.034e-05, eta: 12:57:04, time: 0.582, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0986, decode.acc_seg: 95.7924, loss: 0.0986 2023-01-06 12:02:25,197 - mmseg - INFO - Iter [79150/160000] lr: 3.032e-05, eta: 12:56:34, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0963, decode.acc_seg: 95.8687, loss: 0.0963 2023-01-06 12:02:53,531 - mmseg - INFO - Iter [79200/160000] lr: 3.030e-05, eta: 12:56:05, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1047, decode.acc_seg: 95.7176, loss: 0.1047 2023-01-06 12:03:24,000 - mmseg - INFO - Iter [79250/160000] lr: 3.028e-05, eta: 12:55:38, time: 0.610, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1029, decode.acc_seg: 95.7760, loss: 0.1029 2023-01-06 12:03:51,755 - mmseg - INFO - Iter [79300/160000] lr: 3.026e-05, eta: 12:55:08, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1009, decode.acc_seg: 95.8061, loss: 0.1009 2023-01-06 12:04:19,958 - mmseg - INFO - Iter [79350/160000] lr: 3.024e-05, eta: 12:54:39, time: 0.565, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0929, decode.acc_seg: 96.1240, loss: 0.0929 2023-01-06 12:04:49,142 - mmseg - INFO - Iter [79400/160000] lr: 3.023e-05, eta: 12:54:10, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0933, decode.acc_seg: 95.9772, loss: 0.0933 2023-01-06 12:05:18,626 - mmseg - INFO - Iter [79450/160000] lr: 3.021e-05, eta: 12:53:42, time: 0.590, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1042, decode.acc_seg: 95.6844, loss: 0.1042 2023-01-06 12:05:47,248 - mmseg - INFO - Iter [79500/160000] lr: 3.019e-05, eta: 12:53:13, time: 0.572, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0964, decode.acc_seg: 95.9166, loss: 0.0964 2023-01-06 12:06:16,751 - mmseg - INFO - Iter [79550/160000] lr: 3.017e-05, eta: 12:52:45, time: 0.590, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0975, decode.acc_seg: 95.8309, loss: 0.0975 2023-01-06 12:06:45,939 - mmseg - INFO - Iter [79600/160000] lr: 3.015e-05, eta: 12:52:16, time: 0.585, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0925, decode.acc_seg: 96.0142, loss: 0.0925 2023-01-06 12:07:15,461 - mmseg - INFO - Iter [79650/160000] lr: 3.013e-05, eta: 12:51:48, time: 0.590, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0948, decode.acc_seg: 96.0287, loss: 0.0948 2023-01-06 12:07:45,179 - mmseg - INFO - Iter [79700/160000] lr: 3.011e-05, eta: 12:51:20, time: 0.594, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0904, decode.acc_seg: 96.2213, loss: 0.0904 2023-01-06 12:08:13,379 - mmseg - INFO - Iter [79750/160000] lr: 3.009e-05, eta: 12:50:51, time: 0.565, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0991, decode.acc_seg: 95.7667, loss: 0.0991 2023-01-06 12:08:40,588 - mmseg - INFO - Iter [79800/160000] lr: 3.008e-05, eta: 12:50:21, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0958, decode.acc_seg: 95.9689, loss: 0.0958 2023-01-06 12:09:08,045 - mmseg - INFO - Iter [79850/160000] lr: 3.006e-05, eta: 12:49:50, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1065, decode.acc_seg: 95.6133, loss: 0.1065 2023-01-06 12:09:36,755 - mmseg - INFO - Iter [79900/160000] lr: 3.004e-05, eta: 12:49:21, time: 0.574, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1041, decode.acc_seg: 95.6534, loss: 0.1041 2023-01-06 12:10:04,454 - mmseg - INFO - Iter [79950/160000] lr: 3.002e-05, eta: 12:48:51, time: 0.555, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1006, decode.acc_seg: 95.7188, loss: 0.1006 2023-01-06 12:10:35,228 - mmseg - INFO - Saving checkpoint at 80000 iterations 2023-01-06 12:10:40,437 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 12:10:40,437 - mmseg - INFO - Iter [80000/160000] lr: 3.000e-05, eta: 12:48:30, time: 0.720, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1179, decode.acc_seg: 95.4720, loss: 0.1179 2023-01-06 12:11:12,592 - mmseg - INFO - per class results: 2023-01-06 12:11:12,595 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 97.31 | 98.8 | | sidewalk | 79.96 | 87.97 | | building | 89.21 | 95.67 | | wall | 39.27 | 45.17 | | fence | 50.53 | 64.19 | | pole | 54.44 | 60.17 | | traffic light | 54.21 | 58.79 | | traffic sign | 68.03 | 73.76 | | vegetation | 91.05 | 96.29 | | terrain | 60.46 | 71.83 | | sky | 93.68 | 98.1 | | person | 72.76 | 83.97 | | rider | 38.62 | 42.32 | | car | 90.01 | 92.41 | | truck | 42.13 | 85.64 | | bus | 60.18 | 69.28 | | train | 24.56 | 59.49 | | motorcycle | 40.34 | 51.98 | | bicycle | 68.87 | 86.29 | +---------------+-------+-------+ 2023-01-06 12:11:12,595 - mmseg - INFO - Summary: 2023-01-06 12:11:12,595 - mmseg - INFO - +------+-------+-------+ | aAcc | mIoU | mAcc | +------+-------+-------+ | 94.3 | 63.98 | 74.85 | +------+-------+-------+ 2023-01-06 12:11:12,596 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 12:11:12,596 - mmseg - INFO - Iter(val) [63] aAcc: 0.9430, mIoU: 0.6398, mAcc: 0.7485, IoU.road: 0.9731, IoU.sidewalk: 0.7996, IoU.building: 0.8921, IoU.wall: 0.3927, IoU.fence: 0.5053, IoU.pole: 0.5444, IoU.traffic light: 0.5421, IoU.traffic sign: 0.6803, IoU.vegetation: 0.9105, IoU.terrain: 0.6046, IoU.sky: 0.9368, IoU.person: 0.7276, IoU.rider: 0.3862, IoU.car: 0.9001, IoU.truck: 0.4213, IoU.bus: 0.6018, IoU.train: 0.2456, IoU.motorcycle: 0.4034, IoU.bicycle: 0.6887, Acc.road: 0.9880, Acc.sidewalk: 0.8797, Acc.building: 0.9567, Acc.wall: 0.4517, Acc.fence: 0.6419, Acc.pole: 0.6017, Acc.traffic light: 0.5879, Acc.traffic sign: 0.7376, Acc.vegetation: 0.9629, Acc.terrain: 0.7183, Acc.sky: 0.9810, Acc.person: 0.8397, Acc.rider: 0.4232, Acc.car: 0.9241, Acc.truck: 0.8564, Acc.bus: 0.6928, Acc.train: 0.5949, Acc.motorcycle: 0.5198, Acc.bicycle: 0.8629 2023-01-06 12:11:41,718 - mmseg - INFO - Iter [80050/160000] lr: 2.998e-05, eta: 12:48:33, time: 1.225, data_time: 0.656, memory: 10576, decode.loss_ce: 0.1201, decode.acc_seg: 95.1281, loss: 0.1201 2023-01-06 12:12:10,394 - mmseg - INFO - Iter [80100/160000] lr: 2.996e-05, eta: 12:48:04, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1043, decode.acc_seg: 95.5505, loss: 0.1043 2023-01-06 12:12:38,957 - mmseg - INFO - Iter [80150/160000] lr: 2.994e-05, eta: 12:47:35, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0992, decode.acc_seg: 95.9765, loss: 0.0992 2023-01-06 12:13:07,845 - mmseg - INFO - Iter [80200/160000] lr: 2.993e-05, eta: 12:47:07, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1000, decode.acc_seg: 95.8191, loss: 0.1000 2023-01-06 12:13:35,583 - mmseg - INFO - Iter [80250/160000] lr: 2.991e-05, eta: 12:46:37, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0952, decode.acc_seg: 96.0221, loss: 0.0952 2023-01-06 12:14:04,738 - mmseg - INFO - Iter [80300/160000] lr: 2.989e-05, eta: 12:46:08, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1040, decode.acc_seg: 95.7117, loss: 0.1040 2023-01-06 12:14:31,775 - mmseg - INFO - Iter [80350/160000] lr: 2.987e-05, eta: 12:45:37, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1017, decode.acc_seg: 95.6855, loss: 0.1017 2023-01-06 12:15:01,925 - mmseg - INFO - Iter [80400/160000] lr: 2.985e-05, eta: 12:45:10, time: 0.602, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0929, decode.acc_seg: 95.9871, loss: 0.0929 2023-01-06 12:15:31,479 - mmseg - INFO - Iter [80450/160000] lr: 2.983e-05, eta: 12:44:42, time: 0.591, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0973, decode.acc_seg: 95.8900, loss: 0.0973 2023-01-06 12:16:00,637 - mmseg - INFO - Iter [80500/160000] lr: 2.981e-05, eta: 12:44:13, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0950, decode.acc_seg: 95.9233, loss: 0.0950 2023-01-06 12:16:29,828 - mmseg - INFO - Iter [80550/160000] lr: 2.979e-05, eta: 12:43:45, time: 0.584, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0992, decode.acc_seg: 95.8326, loss: 0.0992 2023-01-06 12:16:58,495 - mmseg - INFO - Iter [80600/160000] lr: 2.978e-05, eta: 12:43:16, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1024, decode.acc_seg: 95.7165, loss: 0.1024 2023-01-06 12:17:26,731 - mmseg - INFO - Iter [80650/160000] lr: 2.976e-05, eta: 12:42:46, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0972, decode.acc_seg: 95.9851, loss: 0.0972 2023-01-06 12:17:54,768 - mmseg - INFO - Iter [80700/160000] lr: 2.974e-05, eta: 12:42:17, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1007, decode.acc_seg: 95.8501, loss: 0.1007 2023-01-06 12:18:25,589 - mmseg - INFO - Iter [80750/160000] lr: 2.972e-05, eta: 12:41:50, time: 0.616, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1129, decode.acc_seg: 95.4758, loss: 0.1129 2023-01-06 12:18:53,509 - mmseg - INFO - Iter [80800/160000] lr: 2.970e-05, eta: 12:41:20, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1003, decode.acc_seg: 95.8045, loss: 0.1003 2023-01-06 12:19:22,268 - mmseg - INFO - Iter [80850/160000] lr: 2.968e-05, eta: 12:40:51, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0961, decode.acc_seg: 96.0096, loss: 0.0961 2023-01-06 12:19:49,751 - mmseg - INFO - Iter [80900/160000] lr: 2.966e-05, eta: 12:40:21, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0920, decode.acc_seg: 96.0726, loss: 0.0920 2023-01-06 12:20:18,278 - mmseg - INFO - Iter [80950/160000] lr: 2.964e-05, eta: 12:39:52, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1057, decode.acc_seg: 95.5820, loss: 0.1057 2023-01-06 12:20:47,705 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 12:20:47,706 - mmseg - INFO - Iter [81000/160000] lr: 2.963e-05, eta: 12:39:24, time: 0.588, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0984, decode.acc_seg: 95.8415, loss: 0.0984 2023-01-06 12:21:17,295 - mmseg - INFO - Iter [81050/160000] lr: 2.961e-05, eta: 12:38:55, time: 0.592, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0941, decode.acc_seg: 96.0098, loss: 0.0941 2023-01-06 12:21:46,912 - mmseg - INFO - Iter [81100/160000] lr: 2.959e-05, eta: 12:38:27, time: 0.593, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1039, decode.acc_seg: 95.7123, loss: 0.1039 2023-01-06 12:22:16,559 - mmseg - INFO - Iter [81150/160000] lr: 2.957e-05, eta: 12:37:59, time: 0.593, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0996, decode.acc_seg: 95.8358, loss: 0.0996 2023-01-06 12:22:44,078 - mmseg - INFO - Iter [81200/160000] lr: 2.955e-05, eta: 12:37:29, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0929, decode.acc_seg: 96.0667, loss: 0.0929 2023-01-06 12:23:12,523 - mmseg - INFO - Iter [81250/160000] lr: 2.953e-05, eta: 12:37:00, time: 0.569, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0976, decode.acc_seg: 95.8495, loss: 0.0976 2023-01-06 12:23:39,589 - mmseg - INFO - Iter [81300/160000] lr: 2.951e-05, eta: 12:36:29, time: 0.541, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0956, decode.acc_seg: 95.9896, loss: 0.0956 2023-01-06 12:24:08,398 - mmseg - INFO - Iter [81350/160000] lr: 2.949e-05, eta: 12:36:01, time: 0.575, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0971, decode.acc_seg: 95.9910, loss: 0.0971 2023-01-06 12:24:37,979 - mmseg - INFO - Iter [81400/160000] lr: 2.948e-05, eta: 12:35:32, time: 0.591, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0986, decode.acc_seg: 95.9946, loss: 0.0986 2023-01-06 12:25:06,309 - mmseg - INFO - Iter [81450/160000] lr: 2.946e-05, eta: 12:35:03, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0983, decode.acc_seg: 95.9079, loss: 0.0983 2023-01-06 12:25:36,110 - mmseg - INFO - Iter [81500/160000] lr: 2.944e-05, eta: 12:34:35, time: 0.596, data_time: 0.058, memory: 10576, decode.loss_ce: 0.1008, decode.acc_seg: 95.9673, loss: 0.1008 2023-01-06 12:26:03,629 - mmseg - INFO - Iter [81550/160000] lr: 2.942e-05, eta: 12:34:05, time: 0.550, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0966, decode.acc_seg: 95.9258, loss: 0.0966 2023-01-06 12:26:30,647 - mmseg - INFO - Iter [81600/160000] lr: 2.940e-05, eta: 12:33:35, time: 0.540, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1000, decode.acc_seg: 95.7857, loss: 0.1000 2023-01-06 12:26:58,128 - mmseg - INFO - Iter [81650/160000] lr: 2.938e-05, eta: 12:33:04, time: 0.550, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1023, decode.acc_seg: 95.7530, loss: 0.1023 2023-01-06 12:27:26,216 - mmseg - INFO - Iter [81700/160000] lr: 2.936e-05, eta: 12:32:35, time: 0.561, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1008, decode.acc_seg: 95.8085, loss: 0.1008 2023-01-06 12:27:54,676 - mmseg - INFO - Iter [81750/160000] lr: 2.934e-05, eta: 12:32:06, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0984, decode.acc_seg: 95.8414, loss: 0.0984 2023-01-06 12:28:22,616 - mmseg - INFO - Iter [81800/160000] lr: 2.933e-05, eta: 12:31:36, time: 0.559, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0973, decode.acc_seg: 95.9459, loss: 0.0973 2023-01-06 12:28:53,456 - mmseg - INFO - Iter [81850/160000] lr: 2.931e-05, eta: 12:31:09, time: 0.616, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0990, decode.acc_seg: 95.9011, loss: 0.0990 2023-01-06 12:29:22,530 - mmseg - INFO - Iter [81900/160000] lr: 2.929e-05, eta: 12:30:40, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0953, decode.acc_seg: 95.9822, loss: 0.0953 2023-01-06 12:29:50,438 - mmseg - INFO - Iter [81950/160000] lr: 2.927e-05, eta: 12:30:11, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0984, decode.acc_seg: 95.8031, loss: 0.0984 2023-01-06 12:30:18,552 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 12:30:18,552 - mmseg - INFO - Iter [82000/160000] lr: 2.925e-05, eta: 12:29:41, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1001, decode.acc_seg: 95.8553, loss: 0.1001 2023-01-06 12:30:47,209 - mmseg - INFO - Iter [82050/160000] lr: 2.923e-05, eta: 12:29:12, time: 0.572, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0949, decode.acc_seg: 95.9770, loss: 0.0949 2023-01-06 12:31:15,649 - mmseg - INFO - Iter [82100/160000] lr: 2.921e-05, eta: 12:28:43, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0964, decode.acc_seg: 95.9969, loss: 0.0964 2023-01-06 12:31:44,207 - mmseg - INFO - Iter [82150/160000] lr: 2.919e-05, eta: 12:28:14, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1032, decode.acc_seg: 95.7163, loss: 0.1032 2023-01-06 12:32:13,594 - mmseg - INFO - Iter [82200/160000] lr: 2.918e-05, eta: 12:27:46, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0984, decode.acc_seg: 95.8879, loss: 0.0984 2023-01-06 12:32:43,959 - mmseg - INFO - Iter [82250/160000] lr: 2.916e-05, eta: 12:27:18, time: 0.607, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1032, decode.acc_seg: 95.6924, loss: 0.1032 2023-01-06 12:33:11,622 - mmseg - INFO - Iter [82300/160000] lr: 2.914e-05, eta: 12:26:48, time: 0.554, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0984, decode.acc_seg: 95.8510, loss: 0.0984 2023-01-06 12:33:38,663 - mmseg - INFO - Iter [82350/160000] lr: 2.912e-05, eta: 12:26:18, time: 0.541, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0980, decode.acc_seg: 95.9480, loss: 0.0980 2023-01-06 12:34:08,357 - mmseg - INFO - Iter [82400/160000] lr: 2.910e-05, eta: 12:25:50, time: 0.593, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0939, decode.acc_seg: 95.9786, loss: 0.0939 2023-01-06 12:34:36,951 - mmseg - INFO - Iter [82450/160000] lr: 2.908e-05, eta: 12:25:21, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0912, decode.acc_seg: 96.0968, loss: 0.0912 2023-01-06 12:35:05,647 - mmseg - INFO - Iter [82500/160000] lr: 2.906e-05, eta: 12:24:52, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1052, decode.acc_seg: 95.7524, loss: 0.1052 2023-01-06 12:35:34,808 - mmseg - INFO - Iter [82550/160000] lr: 2.904e-05, eta: 12:24:23, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1029, decode.acc_seg: 95.6598, loss: 0.1029 2023-01-06 12:36:04,247 - mmseg - INFO - Iter [82600/160000] lr: 2.903e-05, eta: 12:23:55, time: 0.590, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0962, decode.acc_seg: 95.9594, loss: 0.0962 2023-01-06 12:36:32,461 - mmseg - INFO - Iter [82650/160000] lr: 2.901e-05, eta: 12:23:25, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1040, decode.acc_seg: 95.7100, loss: 0.1040 2023-01-06 12:36:59,529 - mmseg - INFO - Iter [82700/160000] lr: 2.899e-05, eta: 12:22:55, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0976, decode.acc_seg: 95.8905, loss: 0.0976 2023-01-06 12:37:26,909 - mmseg - INFO - Iter [82750/160000] lr: 2.897e-05, eta: 12:22:25, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0932, decode.acc_seg: 96.0254, loss: 0.0932 2023-01-06 12:37:55,244 - mmseg - INFO - Iter [82800/160000] lr: 2.895e-05, eta: 12:21:55, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1119, decode.acc_seg: 95.6918, loss: 0.1119 2023-01-06 12:38:23,588 - mmseg - INFO - Iter [82850/160000] lr: 2.893e-05, eta: 12:21:26, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0991, decode.acc_seg: 95.7440, loss: 0.0991 2023-01-06 12:38:51,035 - mmseg - INFO - Iter [82900/160000] lr: 2.891e-05, eta: 12:20:56, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0943, decode.acc_seg: 96.0549, loss: 0.0943 2023-01-06 12:39:18,715 - mmseg - INFO - Iter [82950/160000] lr: 2.889e-05, eta: 12:20:26, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1026, decode.acc_seg: 95.6673, loss: 0.1026 2023-01-06 12:39:48,551 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 12:39:48,552 - mmseg - INFO - Iter [83000/160000] lr: 2.888e-05, eta: 12:19:58, time: 0.596, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0883, decode.acc_seg: 96.2456, loss: 0.0883 2023-01-06 12:40:16,221 - mmseg - INFO - Iter [83050/160000] lr: 2.886e-05, eta: 12:19:28, time: 0.554, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0954, decode.acc_seg: 95.9439, loss: 0.0954 2023-01-06 12:40:44,903 - mmseg - INFO - Iter [83100/160000] lr: 2.884e-05, eta: 12:18:59, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0867, decode.acc_seg: 96.1733, loss: 0.0867 2023-01-06 12:41:13,470 - mmseg - INFO - Iter [83150/160000] lr: 2.882e-05, eta: 12:18:30, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0960, decode.acc_seg: 96.0858, loss: 0.0960 2023-01-06 12:41:41,476 - mmseg - INFO - Iter [83200/160000] lr: 2.880e-05, eta: 12:18:01, time: 0.561, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0955, decode.acc_seg: 96.0348, loss: 0.0955 2023-01-06 12:42:10,891 - mmseg - INFO - Iter [83250/160000] lr: 2.878e-05, eta: 12:17:32, time: 0.588, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0916, decode.acc_seg: 96.1245, loss: 0.0916 2023-01-06 12:42:40,043 - mmseg - INFO - Iter [83300/160000] lr: 2.876e-05, eta: 12:17:04, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0926, decode.acc_seg: 96.0274, loss: 0.0926 2023-01-06 12:43:10,264 - mmseg - INFO - Iter [83350/160000] lr: 2.874e-05, eta: 12:16:36, time: 0.605, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0892, decode.acc_seg: 96.2232, loss: 0.0892 2023-01-06 12:43:38,516 - mmseg - INFO - Iter [83400/160000] lr: 2.873e-05, eta: 12:16:07, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0967, decode.acc_seg: 95.9594, loss: 0.0967 2023-01-06 12:44:06,348 - mmseg - INFO - Iter [83450/160000] lr: 2.871e-05, eta: 12:15:37, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0921, decode.acc_seg: 96.0633, loss: 0.0921 2023-01-06 12:44:34,068 - mmseg - INFO - Iter [83500/160000] lr: 2.869e-05, eta: 12:15:07, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1059, decode.acc_seg: 95.5076, loss: 0.1059 2023-01-06 12:45:02,256 - mmseg - INFO - Iter [83550/160000] lr: 2.867e-05, eta: 12:14:38, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0908, decode.acc_seg: 96.1594, loss: 0.0908 2023-01-06 12:45:30,802 - mmseg - INFO - Iter [83600/160000] lr: 2.865e-05, eta: 12:14:09, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1027, decode.acc_seg: 95.7770, loss: 0.1027 2023-01-06 12:45:59,321 - mmseg - INFO - Iter [83650/160000] lr: 2.863e-05, eta: 12:13:40, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1000, decode.acc_seg: 95.7589, loss: 0.1000 2023-01-06 12:46:27,718 - mmseg - INFO - Iter [83700/160000] lr: 2.861e-05, eta: 12:13:11, time: 0.569, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0992, decode.acc_seg: 95.8257, loss: 0.0992 2023-01-06 12:46:57,588 - mmseg - INFO - Iter [83750/160000] lr: 2.859e-05, eta: 12:12:43, time: 0.597, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0928, decode.acc_seg: 96.0150, loss: 0.0928 2023-01-06 12:47:26,563 - mmseg - INFO - Iter [83800/160000] lr: 2.858e-05, eta: 12:12:14, time: 0.580, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0919, decode.acc_seg: 96.0921, loss: 0.0919 2023-01-06 12:47:54,230 - mmseg - INFO - Iter [83850/160000] lr: 2.856e-05, eta: 12:11:44, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0944, decode.acc_seg: 96.0327, loss: 0.0944 2023-01-06 12:48:22,652 - mmseg - INFO - Iter [83900/160000] lr: 2.854e-05, eta: 12:11:15, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0974, decode.acc_seg: 95.9092, loss: 0.0974 2023-01-06 12:48:49,723 - mmseg - INFO - Iter [83950/160000] lr: 2.852e-05, eta: 12:10:45, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0964, decode.acc_seg: 95.9371, loss: 0.0964 2023-01-06 12:49:17,352 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 12:49:17,353 - mmseg - INFO - Iter [84000/160000] lr: 2.850e-05, eta: 12:10:15, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0973, decode.acc_seg: 95.9769, loss: 0.0973 2023-01-06 12:49:46,111 - mmseg - INFO - Iter [84050/160000] lr: 2.848e-05, eta: 12:09:46, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0951, decode.acc_seg: 96.0102, loss: 0.0951 2023-01-06 12:50:15,939 - mmseg - INFO - Iter [84100/160000] lr: 2.846e-05, eta: 12:09:18, time: 0.596, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0913, decode.acc_seg: 96.1805, loss: 0.0913 2023-01-06 12:50:43,189 - mmseg - INFO - Iter [84150/160000] lr: 2.844e-05, eta: 12:08:48, time: 0.545, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0945, decode.acc_seg: 95.9494, loss: 0.0945 2023-01-06 12:51:10,662 - mmseg - INFO - Iter [84200/160000] lr: 2.843e-05, eta: 12:08:17, time: 0.550, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0970, decode.acc_seg: 95.9784, loss: 0.0970 2023-01-06 12:51:38,308 - mmseg - INFO - Iter [84250/160000] lr: 2.841e-05, eta: 12:07:48, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0959, decode.acc_seg: 95.9672, loss: 0.0959 2023-01-06 12:52:06,322 - mmseg - INFO - Iter [84300/160000] lr: 2.839e-05, eta: 12:07:18, time: 0.561, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0925, decode.acc_seg: 95.9246, loss: 0.0925 2023-01-06 12:52:35,711 - mmseg - INFO - Iter [84350/160000] lr: 2.837e-05, eta: 12:06:50, time: 0.587, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0936, decode.acc_seg: 96.0758, loss: 0.0936 2023-01-06 12:53:04,390 - mmseg - INFO - Iter [84400/160000] lr: 2.835e-05, eta: 12:06:21, time: 0.574, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1058, decode.acc_seg: 95.7776, loss: 0.1058 2023-01-06 12:53:34,200 - mmseg - INFO - Iter [84450/160000] lr: 2.833e-05, eta: 12:05:53, time: 0.595, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0991, decode.acc_seg: 95.8208, loss: 0.0991 2023-01-06 12:54:02,555 - mmseg - INFO - Iter [84500/160000] lr: 2.831e-05, eta: 12:05:24, time: 0.568, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0968, decode.acc_seg: 95.9555, loss: 0.0968 2023-01-06 12:54:30,658 - mmseg - INFO - Iter [84550/160000] lr: 2.829e-05, eta: 12:04:54, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0925, decode.acc_seg: 96.1544, loss: 0.0925 2023-01-06 12:54:58,891 - mmseg - INFO - Iter [84600/160000] lr: 2.828e-05, eta: 12:04:25, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0959, decode.acc_seg: 95.9428, loss: 0.0959 2023-01-06 12:55:26,302 - mmseg - INFO - Iter [84650/160000] lr: 2.826e-05, eta: 12:03:55, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0928, decode.acc_seg: 96.1053, loss: 0.0928 2023-01-06 12:55:54,174 - mmseg - INFO - Iter [84700/160000] lr: 2.824e-05, eta: 12:03:25, time: 0.558, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0938, decode.acc_seg: 96.1452, loss: 0.0938 2023-01-06 12:56:22,618 - mmseg - INFO - Iter [84750/160000] lr: 2.822e-05, eta: 12:02:56, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0962, decode.acc_seg: 96.0411, loss: 0.0962 2023-01-06 12:56:51,729 - mmseg - INFO - Iter [84800/160000] lr: 2.820e-05, eta: 12:02:27, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1013, decode.acc_seg: 95.7038, loss: 0.1013 2023-01-06 12:57:22,332 - mmseg - INFO - Iter [84850/160000] lr: 2.818e-05, eta: 12:02:00, time: 0.612, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0941, decode.acc_seg: 96.0343, loss: 0.0941 2023-01-06 12:57:50,640 - mmseg - INFO - Iter [84900/160000] lr: 2.816e-05, eta: 12:01:31, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0938, decode.acc_seg: 96.1162, loss: 0.0938 2023-01-06 12:58:18,523 - mmseg - INFO - Iter [84950/160000] lr: 2.814e-05, eta: 12:01:01, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0932, decode.acc_seg: 96.1046, loss: 0.0932 2023-01-06 12:58:47,320 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 12:58:47,320 - mmseg - INFO - Iter [85000/160000] lr: 2.813e-05, eta: 12:00:32, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0947, decode.acc_seg: 95.8977, loss: 0.0947 2023-01-06 12:59:15,805 - mmseg - INFO - Iter [85050/160000] lr: 2.811e-05, eta: 12:00:03, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0946, decode.acc_seg: 96.0695, loss: 0.0946 2023-01-06 12:59:43,875 - mmseg - INFO - Iter [85100/160000] lr: 2.809e-05, eta: 11:59:34, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1079, decode.acc_seg: 95.5225, loss: 0.1079 2023-01-06 13:00:12,302 - mmseg - INFO - Iter [85150/160000] lr: 2.807e-05, eta: 11:59:04, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1080, decode.acc_seg: 95.6569, loss: 0.1080 2023-01-06 13:00:43,931 - mmseg - INFO - Iter [85200/160000] lr: 2.805e-05, eta: 11:58:38, time: 0.633, data_time: 0.059, memory: 10576, decode.loss_ce: 0.1064, decode.acc_seg: 95.6277, loss: 0.1064 2023-01-06 13:01:11,483 - mmseg - INFO - Iter [85250/160000] lr: 2.803e-05, eta: 11:58:08, time: 0.551, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0928, decode.acc_seg: 96.0621, loss: 0.0928 2023-01-06 13:01:40,675 - mmseg - INFO - Iter [85300/160000] lr: 2.801e-05, eta: 11:57:40, time: 0.584, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0980, decode.acc_seg: 95.9535, loss: 0.0980 2023-01-06 13:02:09,199 - mmseg - INFO - Iter [85350/160000] lr: 2.799e-05, eta: 11:57:11, time: 0.571, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0964, decode.acc_seg: 95.9635, loss: 0.0964 2023-01-06 13:02:36,292 - mmseg - INFO - Iter [85400/160000] lr: 2.798e-05, eta: 11:56:40, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0933, decode.acc_seg: 96.0531, loss: 0.0933 2023-01-06 13:03:03,342 - mmseg - INFO - Iter [85450/160000] lr: 2.796e-05, eta: 11:56:10, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1002, decode.acc_seg: 95.8153, loss: 0.1002 2023-01-06 13:03:31,762 - mmseg - INFO - Iter [85500/160000] lr: 2.794e-05, eta: 11:55:41, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0942, decode.acc_seg: 96.1030, loss: 0.0942 2023-01-06 13:03:59,647 - mmseg - INFO - Iter [85550/160000] lr: 2.792e-05, eta: 11:55:11, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0968, decode.acc_seg: 96.0242, loss: 0.0968 2023-01-06 13:04:29,451 - mmseg - INFO - Iter [85600/160000] lr: 2.790e-05, eta: 11:54:43, time: 0.596, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0948, decode.acc_seg: 96.0404, loss: 0.0948 2023-01-06 13:04:56,720 - mmseg - INFO - Iter [85650/160000] lr: 2.788e-05, eta: 11:54:13, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0911, decode.acc_seg: 96.1111, loss: 0.0911 2023-01-06 13:05:25,459 - mmseg - INFO - Iter [85700/160000] lr: 2.786e-05, eta: 11:53:44, time: 0.575, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0939, decode.acc_seg: 95.9708, loss: 0.0939 2023-01-06 13:05:53,491 - mmseg - INFO - Iter [85750/160000] lr: 2.784e-05, eta: 11:53:15, time: 0.560, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0939, decode.acc_seg: 96.0883, loss: 0.0939 2023-01-06 13:06:21,977 - mmseg - INFO - Iter [85800/160000] lr: 2.783e-05, eta: 11:52:46, time: 0.570, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0956, decode.acc_seg: 96.0137, loss: 0.0956 2023-01-06 13:06:50,334 - mmseg - INFO - Iter [85850/160000] lr: 2.781e-05, eta: 11:52:16, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0929, decode.acc_seg: 96.1063, loss: 0.0929 2023-01-06 13:07:17,934 - mmseg - INFO - Iter [85900/160000] lr: 2.779e-05, eta: 11:51:46, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0951, decode.acc_seg: 95.9946, loss: 0.0951 2023-01-06 13:07:47,745 - mmseg - INFO - Iter [85950/160000] lr: 2.777e-05, eta: 11:51:18, time: 0.596, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0953, decode.acc_seg: 96.0002, loss: 0.0953 2023-01-06 13:08:14,891 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 13:08:14,892 - mmseg - INFO - Iter [86000/160000] lr: 2.775e-05, eta: 11:50:48, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0882, decode.acc_seg: 96.1934, loss: 0.0882 2023-01-06 13:08:43,413 - mmseg - INFO - Iter [86050/160000] lr: 2.773e-05, eta: 11:50:19, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0923, decode.acc_seg: 96.0718, loss: 0.0923 2023-01-06 13:09:12,893 - mmseg - INFO - Iter [86100/160000] lr: 2.771e-05, eta: 11:49:51, time: 0.590, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0957, decode.acc_seg: 96.0830, loss: 0.0957 2023-01-06 13:09:40,372 - mmseg - INFO - Iter [86150/160000] lr: 2.769e-05, eta: 11:49:21, time: 0.550, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0944, decode.acc_seg: 96.1534, loss: 0.0944 2023-01-06 13:10:08,918 - mmseg - INFO - Iter [86200/160000] lr: 2.768e-05, eta: 11:48:52, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0944, decode.acc_seg: 95.8801, loss: 0.0944 2023-01-06 13:10:37,496 - mmseg - INFO - Iter [86250/160000] lr: 2.766e-05, eta: 11:48:23, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0988, decode.acc_seg: 95.8349, loss: 0.0988 2023-01-06 13:11:05,917 - mmseg - INFO - Iter [86300/160000] lr: 2.764e-05, eta: 11:47:54, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0919, decode.acc_seg: 96.1787, loss: 0.0919 2023-01-06 13:11:36,585 - mmseg - INFO - Iter [86350/160000] lr: 2.762e-05, eta: 11:47:26, time: 0.613, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0953, decode.acc_seg: 96.0156, loss: 0.0953 2023-01-06 13:12:04,796 - mmseg - INFO - Iter [86400/160000] lr: 2.760e-05, eta: 11:46:57, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0977, decode.acc_seg: 95.8861, loss: 0.0977 2023-01-06 13:12:32,539 - mmseg - INFO - Iter [86450/160000] lr: 2.758e-05, eta: 11:46:27, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0985, decode.acc_seg: 95.8976, loss: 0.0985 2023-01-06 13:13:00,931 - mmseg - INFO - Iter [86500/160000] lr: 2.756e-05, eta: 11:45:58, time: 0.568, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1025, decode.acc_seg: 95.8349, loss: 0.1025 2023-01-06 13:13:30,557 - mmseg - INFO - Iter [86550/160000] lr: 2.754e-05, eta: 11:45:30, time: 0.592, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0949, decode.acc_seg: 96.0438, loss: 0.0949 2023-01-06 13:13:58,209 - mmseg - INFO - Iter [86600/160000] lr: 2.753e-05, eta: 11:45:00, time: 0.554, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0968, decode.acc_seg: 95.8599, loss: 0.0968 2023-01-06 13:14:25,530 - mmseg - INFO - Iter [86650/160000] lr: 2.751e-05, eta: 11:44:30, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0960, decode.acc_seg: 95.9915, loss: 0.0960 2023-01-06 13:14:56,344 - mmseg - INFO - Iter [86700/160000] lr: 2.749e-05, eta: 11:44:03, time: 0.617, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0951, decode.acc_seg: 95.9879, loss: 0.0951 2023-01-06 13:15:23,482 - mmseg - INFO - Iter [86750/160000] lr: 2.747e-05, eta: 11:43:33, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0959, decode.acc_seg: 95.9319, loss: 0.0959 2023-01-06 13:15:52,572 - mmseg - INFO - Iter [86800/160000] lr: 2.745e-05, eta: 11:43:04, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0936, decode.acc_seg: 96.0527, loss: 0.0936 2023-01-06 13:16:20,980 - mmseg - INFO - Iter [86850/160000] lr: 2.743e-05, eta: 11:42:35, time: 0.568, data_time: 0.014, memory: 10576, decode.loss_ce: 0.1036, decode.acc_seg: 95.6579, loss: 0.1036 2023-01-06 13:16:49,733 - mmseg - INFO - Iter [86900/160000] lr: 2.741e-05, eta: 11:42:06, time: 0.576, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0945, decode.acc_seg: 96.0700, loss: 0.0945 2023-01-06 13:17:18,317 - mmseg - INFO - Iter [86950/160000] lr: 2.739e-05, eta: 11:41:37, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0921, decode.acc_seg: 96.0179, loss: 0.0921 2023-01-06 13:17:45,370 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 13:17:45,371 - mmseg - INFO - Iter [87000/160000] lr: 2.738e-05, eta: 11:41:07, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0950, decode.acc_seg: 96.1584, loss: 0.0950 2023-01-06 13:18:15,187 - mmseg - INFO - Iter [87050/160000] lr: 2.736e-05, eta: 11:40:39, time: 0.596, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0910, decode.acc_seg: 96.1507, loss: 0.0910 2023-01-06 13:18:42,519 - mmseg - INFO - Iter [87100/160000] lr: 2.734e-05, eta: 11:40:09, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0897, decode.acc_seg: 96.1839, loss: 0.0897 2023-01-06 13:19:09,676 - mmseg - INFO - Iter [87150/160000] lr: 2.732e-05, eta: 11:39:39, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0942, decode.acc_seg: 96.0154, loss: 0.0942 2023-01-06 13:19:37,451 - mmseg - INFO - Iter [87200/160000] lr: 2.730e-05, eta: 11:39:09, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0914, decode.acc_seg: 96.1767, loss: 0.0914 2023-01-06 13:20:05,278 - mmseg - INFO - Iter [87250/160000] lr: 2.728e-05, eta: 11:38:39, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1061, decode.acc_seg: 95.6773, loss: 0.1061 2023-01-06 13:20:33,283 - mmseg - INFO - Iter [87300/160000] lr: 2.726e-05, eta: 11:38:10, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1039, decode.acc_seg: 95.7511, loss: 0.1039 2023-01-06 13:21:01,416 - mmseg - INFO - Iter [87350/160000] lr: 2.724e-05, eta: 11:37:41, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0923, decode.acc_seg: 96.0711, loss: 0.0923 2023-01-06 13:21:29,496 - mmseg - INFO - Iter [87400/160000] lr: 2.723e-05, eta: 11:37:11, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0943, decode.acc_seg: 95.9984, loss: 0.0943 2023-01-06 13:21:59,545 - mmseg - INFO - Iter [87450/160000] lr: 2.721e-05, eta: 11:36:43, time: 0.601, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1050, decode.acc_seg: 95.5838, loss: 0.1050 2023-01-06 13:22:26,578 - mmseg - INFO - Iter [87500/160000] lr: 2.719e-05, eta: 11:36:13, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0937, decode.acc_seg: 96.0056, loss: 0.0937 2023-01-06 13:22:54,358 - mmseg - INFO - Iter [87550/160000] lr: 2.717e-05, eta: 11:35:43, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0997, decode.acc_seg: 95.9091, loss: 0.0997 2023-01-06 13:23:22,515 - mmseg - INFO - Iter [87600/160000] lr: 2.715e-05, eta: 11:35:14, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0940, decode.acc_seg: 95.9636, loss: 0.0940 2023-01-06 13:23:51,006 - mmseg - INFO - Iter [87650/160000] lr: 2.713e-05, eta: 11:34:45, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0980, decode.acc_seg: 95.9468, loss: 0.0980 2023-01-06 13:24:19,100 - mmseg - INFO - Iter [87700/160000] lr: 2.711e-05, eta: 11:34:16, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1046, decode.acc_seg: 95.6369, loss: 0.1046 2023-01-06 13:24:47,719 - mmseg - INFO - Iter [87750/160000] lr: 2.709e-05, eta: 11:33:47, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0970, decode.acc_seg: 95.8813, loss: 0.0970 2023-01-06 13:25:17,497 - mmseg - INFO - Iter [87800/160000] lr: 2.708e-05, eta: 11:33:19, time: 0.595, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0993, decode.acc_seg: 95.8634, loss: 0.0993 2023-01-06 13:25:46,820 - mmseg - INFO - Iter [87850/160000] lr: 2.706e-05, eta: 11:32:50, time: 0.587, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0896, decode.acc_seg: 96.2172, loss: 0.0896 2023-01-06 13:26:15,561 - mmseg - INFO - Iter [87900/160000] lr: 2.704e-05, eta: 11:32:21, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0955, decode.acc_seg: 96.0035, loss: 0.0955 2023-01-06 13:26:45,581 - mmseg - INFO - Iter [87950/160000] lr: 2.702e-05, eta: 11:31:54, time: 0.600, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0971, decode.acc_seg: 95.8967, loss: 0.0971 2023-01-06 13:27:13,440 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 13:27:13,440 - mmseg - INFO - Iter [88000/160000] lr: 2.700e-05, eta: 11:31:24, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0915, decode.acc_seg: 96.2528, loss: 0.0915 2023-01-06 13:27:42,160 - mmseg - INFO - Iter [88050/160000] lr: 2.698e-05, eta: 11:30:55, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0908, decode.acc_seg: 96.1809, loss: 0.0908 2023-01-06 13:28:09,663 - mmseg - INFO - Iter [88100/160000] lr: 2.696e-05, eta: 11:30:25, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0903, decode.acc_seg: 96.1450, loss: 0.0903 2023-01-06 13:28:37,117 - mmseg - INFO - Iter [88150/160000] lr: 2.694e-05, eta: 11:29:55, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0900, decode.acc_seg: 96.2061, loss: 0.0900 2023-01-06 13:29:07,417 - mmseg - INFO - Iter [88200/160000] lr: 2.693e-05, eta: 11:29:28, time: 0.606, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0882, decode.acc_seg: 96.2733, loss: 0.0882 2023-01-06 13:29:34,829 - mmseg - INFO - Iter [88250/160000] lr: 2.691e-05, eta: 11:28:58, time: 0.549, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0982, decode.acc_seg: 95.8952, loss: 0.0982 2023-01-06 13:30:03,629 - mmseg - INFO - Iter [88300/160000] lr: 2.689e-05, eta: 11:28:29, time: 0.576, data_time: 0.015, memory: 10576, decode.loss_ce: 0.0954, decode.acc_seg: 95.9186, loss: 0.0954 2023-01-06 13:30:33,132 - mmseg - INFO - Iter [88350/160000] lr: 2.687e-05, eta: 11:28:01, time: 0.590, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0911, decode.acc_seg: 96.1273, loss: 0.0911 2023-01-06 13:31:02,564 - mmseg - INFO - Iter [88400/160000] lr: 2.685e-05, eta: 11:27:32, time: 0.589, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0935, decode.acc_seg: 96.1040, loss: 0.0935 2023-01-06 13:31:32,174 - mmseg - INFO - Iter [88450/160000] lr: 2.683e-05, eta: 11:27:04, time: 0.592, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1074, decode.acc_seg: 95.5556, loss: 0.1074 2023-01-06 13:32:02,301 - mmseg - INFO - Iter [88500/160000] lr: 2.681e-05, eta: 11:26:37, time: 0.603, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1078, decode.acc_seg: 95.6212, loss: 0.1078 2023-01-06 13:32:33,764 - mmseg - INFO - Iter [88550/160000] lr: 2.679e-05, eta: 11:26:10, time: 0.628, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0941, decode.acc_seg: 96.0844, loss: 0.0941 2023-01-06 13:33:02,980 - mmseg - INFO - Iter [88600/160000] lr: 2.678e-05, eta: 11:25:41, time: 0.585, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0920, decode.acc_seg: 96.2039, loss: 0.0920 2023-01-06 13:33:30,579 - mmseg - INFO - Iter [88650/160000] lr: 2.676e-05, eta: 11:25:12, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0925, decode.acc_seg: 95.9723, loss: 0.0925 2023-01-06 13:33:58,160 - mmseg - INFO - Iter [88700/160000] lr: 2.674e-05, eta: 11:24:42, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0929, decode.acc_seg: 96.1099, loss: 0.0929 2023-01-06 13:34:25,714 - mmseg - INFO - Iter [88750/160000] lr: 2.672e-05, eta: 11:24:12, time: 0.550, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0922, decode.acc_seg: 96.1159, loss: 0.0922 2023-01-06 13:34:53,949 - mmseg - INFO - Iter [88800/160000] lr: 2.670e-05, eta: 11:23:43, time: 0.565, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0989, decode.acc_seg: 95.8272, loss: 0.0989 2023-01-06 13:35:22,183 - mmseg - INFO - Iter [88850/160000] lr: 2.668e-05, eta: 11:23:13, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0961, decode.acc_seg: 95.9338, loss: 0.0961 2023-01-06 13:35:52,190 - mmseg - INFO - Iter [88900/160000] lr: 2.666e-05, eta: 11:22:46, time: 0.600, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0958, decode.acc_seg: 95.9679, loss: 0.0958 2023-01-06 13:36:22,438 - mmseg - INFO - Iter [88950/160000] lr: 2.664e-05, eta: 11:22:18, time: 0.605, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0984, decode.acc_seg: 95.9497, loss: 0.0984 2023-01-06 13:36:50,596 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 13:36:50,598 - mmseg - INFO - Iter [89000/160000] lr: 2.663e-05, eta: 11:21:49, time: 0.562, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0901, decode.acc_seg: 96.1568, loss: 0.0901 2023-01-06 13:37:18,431 - mmseg - INFO - Iter [89050/160000] lr: 2.661e-05, eta: 11:21:19, time: 0.557, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0948, decode.acc_seg: 96.0193, loss: 0.0948 2023-01-06 13:37:45,682 - mmseg - INFO - Iter [89100/160000] lr: 2.659e-05, eta: 11:20:49, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0957, decode.acc_seg: 96.0521, loss: 0.0957 2023-01-06 13:38:13,556 - mmseg - INFO - Iter [89150/160000] lr: 2.657e-05, eta: 11:20:19, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0920, decode.acc_seg: 96.0810, loss: 0.0920 2023-01-06 13:38:41,555 - mmseg - INFO - Iter [89200/160000] lr: 2.655e-05, eta: 11:19:50, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0907, decode.acc_seg: 96.1548, loss: 0.0907 2023-01-06 13:39:10,978 - mmseg - INFO - Iter [89250/160000] lr: 2.653e-05, eta: 11:19:22, time: 0.588, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0938, decode.acc_seg: 96.0830, loss: 0.0938 2023-01-06 13:39:41,186 - mmseg - INFO - Iter [89300/160000] lr: 2.651e-05, eta: 11:18:54, time: 0.604, data_time: 0.060, memory: 10576, decode.loss_ce: 0.0943, decode.acc_seg: 96.1040, loss: 0.0943 2023-01-06 13:40:09,739 - mmseg - INFO - Iter [89350/160000] lr: 2.649e-05, eta: 11:18:25, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0940, decode.acc_seg: 95.9703, loss: 0.0940 2023-01-06 13:40:38,247 - mmseg - INFO - Iter [89400/160000] lr: 2.648e-05, eta: 11:17:56, time: 0.571, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0916, decode.acc_seg: 96.0428, loss: 0.0916 2023-01-06 13:41:06,794 - mmseg - INFO - Iter [89450/160000] lr: 2.646e-05, eta: 11:17:27, time: 0.570, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0916, decode.acc_seg: 96.1236, loss: 0.0916 2023-01-06 13:41:35,429 - mmseg - INFO - Iter [89500/160000] lr: 2.644e-05, eta: 11:16:58, time: 0.573, data_time: 0.015, memory: 10576, decode.loss_ce: 0.0963, decode.acc_seg: 95.9648, loss: 0.0963 2023-01-06 13:42:03,459 - mmseg - INFO - Iter [89550/160000] lr: 2.642e-05, eta: 11:16:29, time: 0.561, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0892, decode.acc_seg: 96.2514, loss: 0.0892 2023-01-06 13:42:30,808 - mmseg - INFO - Iter [89600/160000] lr: 2.640e-05, eta: 11:15:59, time: 0.547, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0944, decode.acc_seg: 96.0601, loss: 0.0944 2023-01-06 13:42:58,340 - mmseg - INFO - Iter [89650/160000] lr: 2.638e-05, eta: 11:15:29, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0955, decode.acc_seg: 95.9145, loss: 0.0955 2023-01-06 13:43:29,643 - mmseg - INFO - Iter [89700/160000] lr: 2.636e-05, eta: 11:15:02, time: 0.625, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0982, decode.acc_seg: 95.9998, loss: 0.0982 2023-01-06 13:43:58,499 - mmseg - INFO - Iter [89750/160000] lr: 2.634e-05, eta: 11:14:33, time: 0.577, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0945, decode.acc_seg: 95.9989, loss: 0.0945 2023-01-06 13:44:26,055 - mmseg - INFO - Iter [89800/160000] lr: 2.633e-05, eta: 11:14:03, time: 0.551, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0885, decode.acc_seg: 96.2824, loss: 0.0885 2023-01-06 13:44:55,536 - mmseg - INFO - Iter [89850/160000] lr: 2.631e-05, eta: 11:13:35, time: 0.590, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0911, decode.acc_seg: 96.1942, loss: 0.0911 2023-01-06 13:45:25,700 - mmseg - INFO - Iter [89900/160000] lr: 2.629e-05, eta: 11:13:07, time: 0.603, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0910, decode.acc_seg: 96.1891, loss: 0.0910 2023-01-06 13:45:55,394 - mmseg - INFO - Iter [89950/160000] lr: 2.627e-05, eta: 11:12:39, time: 0.594, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0917, decode.acc_seg: 96.1310, loss: 0.0917 2023-01-06 13:46:25,129 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 13:46:25,130 - mmseg - INFO - Iter [90000/160000] lr: 2.625e-05, eta: 11:12:11, time: 0.594, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0923, decode.acc_seg: 96.0616, loss: 0.0923 2023-01-06 13:46:56,717 - mmseg - INFO - Iter [90050/160000] lr: 2.623e-05, eta: 11:11:44, time: 0.632, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0976, decode.acc_seg: 95.8805, loss: 0.0976 2023-01-06 13:47:25,595 - mmseg - INFO - Iter [90100/160000] lr: 2.621e-05, eta: 11:11:16, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0889, decode.acc_seg: 96.2011, loss: 0.0889 2023-01-06 13:47:54,615 - mmseg - INFO - Iter [90150/160000] lr: 2.619e-05, eta: 11:10:47, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0953, decode.acc_seg: 95.9208, loss: 0.0953 2023-01-06 13:48:22,981 - mmseg - INFO - Iter [90200/160000] lr: 2.618e-05, eta: 11:10:18, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0940, decode.acc_seg: 96.0924, loss: 0.0940 2023-01-06 13:48:50,047 - mmseg - INFO - Iter [90250/160000] lr: 2.616e-05, eta: 11:09:48, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0934, decode.acc_seg: 96.0560, loss: 0.0934 2023-01-06 13:49:17,049 - mmseg - INFO - Iter [90300/160000] lr: 2.614e-05, eta: 11:09:18, time: 0.540, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0885, decode.acc_seg: 96.2910, loss: 0.0885 2023-01-06 13:49:45,372 - mmseg - INFO - Iter [90350/160000] lr: 2.612e-05, eta: 11:08:48, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0909, decode.acc_seg: 96.0723, loss: 0.0909 2023-01-06 13:50:14,658 - mmseg - INFO - Iter [90400/160000] lr: 2.610e-05, eta: 11:08:20, time: 0.586, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0950, decode.acc_seg: 96.1045, loss: 0.0950 2023-01-06 13:50:42,919 - mmseg - INFO - Iter [90450/160000] lr: 2.608e-05, eta: 11:07:51, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0874, decode.acc_seg: 96.2292, loss: 0.0874 2023-01-06 13:51:09,953 - mmseg - INFO - Iter [90500/160000] lr: 2.606e-05, eta: 11:07:21, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0934, decode.acc_seg: 96.1665, loss: 0.0934 2023-01-06 13:51:39,585 - mmseg - INFO - Iter [90550/160000] lr: 2.604e-05, eta: 11:06:52, time: 0.593, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0939, decode.acc_seg: 96.0652, loss: 0.0939 2023-01-06 13:52:06,919 - mmseg - INFO - Iter [90600/160000] lr: 2.603e-05, eta: 11:06:22, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1051, decode.acc_seg: 95.6628, loss: 0.1051 2023-01-06 13:52:35,659 - mmseg - INFO - Iter [90650/160000] lr: 2.601e-05, eta: 11:05:54, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0959, decode.acc_seg: 95.9701, loss: 0.0959 2023-01-06 13:53:04,990 - mmseg - INFO - Iter [90700/160000] lr: 2.599e-05, eta: 11:05:25, time: 0.586, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0880, decode.acc_seg: 96.2672, loss: 0.0880 2023-01-06 13:53:34,095 - mmseg - INFO - Iter [90750/160000] lr: 2.597e-05, eta: 11:04:57, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0948, decode.acc_seg: 96.0530, loss: 0.0948 2023-01-06 13:54:06,369 - mmseg - INFO - Iter [90800/160000] lr: 2.595e-05, eta: 11:04:30, time: 0.645, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0938, decode.acc_seg: 96.0407, loss: 0.0938 2023-01-06 13:54:34,057 - mmseg - INFO - Iter [90850/160000] lr: 2.593e-05, eta: 11:04:01, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0865, decode.acc_seg: 96.3127, loss: 0.0865 2023-01-06 13:55:01,169 - mmseg - INFO - Iter [90900/160000] lr: 2.591e-05, eta: 11:03:31, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0868, decode.acc_seg: 96.3134, loss: 0.0868 2023-01-06 13:55:29,197 - mmseg - INFO - Iter [90950/160000] lr: 2.589e-05, eta: 11:03:01, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0864, decode.acc_seg: 96.2200, loss: 0.0864 2023-01-06 13:55:57,675 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 13:55:57,676 - mmseg - INFO - Iter [91000/160000] lr: 2.588e-05, eta: 11:02:32, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1004, decode.acc_seg: 95.7646, loss: 0.1004 2023-01-06 13:56:26,828 - mmseg - INFO - Iter [91050/160000] lr: 2.586e-05, eta: 11:02:04, time: 0.584, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0888, decode.acc_seg: 96.2242, loss: 0.0888 2023-01-06 13:56:55,148 - mmseg - INFO - Iter [91100/160000] lr: 2.584e-05, eta: 11:01:34, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0901, decode.acc_seg: 96.1743, loss: 0.0901 2023-01-06 13:57:25,876 - mmseg - INFO - Iter [91150/160000] lr: 2.582e-05, eta: 11:01:07, time: 0.615, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0974, decode.acc_seg: 95.9126, loss: 0.0974 2023-01-06 13:57:53,718 - mmseg - INFO - Iter [91200/160000] lr: 2.580e-05, eta: 11:00:38, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0937, decode.acc_seg: 96.0811, loss: 0.0937 2023-01-06 13:58:21,578 - mmseg - INFO - Iter [91250/160000] lr: 2.578e-05, eta: 11:00:08, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0952, decode.acc_seg: 96.0288, loss: 0.0952 2023-01-06 13:58:49,003 - mmseg - INFO - Iter [91300/160000] lr: 2.576e-05, eta: 10:59:38, time: 0.549, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0961, decode.acc_seg: 96.0604, loss: 0.0961 2023-01-06 13:59:18,490 - mmseg - INFO - Iter [91350/160000] lr: 2.574e-05, eta: 10:59:10, time: 0.590, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0936, decode.acc_seg: 96.0089, loss: 0.0936 2023-01-06 13:59:46,439 - mmseg - INFO - Iter [91400/160000] lr: 2.573e-05, eta: 10:58:40, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0902, decode.acc_seg: 96.1624, loss: 0.0902 2023-01-06 14:00:13,582 - mmseg - INFO - Iter [91450/160000] lr: 2.571e-05, eta: 10:58:10, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0903, decode.acc_seg: 96.1914, loss: 0.0903 2023-01-06 14:00:42,648 - mmseg - INFO - Iter [91500/160000] lr: 2.569e-05, eta: 10:57:42, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0946, decode.acc_seg: 95.9958, loss: 0.0946 2023-01-06 14:01:12,640 - mmseg - INFO - Iter [91550/160000] lr: 2.567e-05, eta: 10:57:14, time: 0.599, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0908, decode.acc_seg: 96.1983, loss: 0.0908 2023-01-06 14:01:40,519 - mmseg - INFO - Iter [91600/160000] lr: 2.565e-05, eta: 10:56:44, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0917, decode.acc_seg: 96.0912, loss: 0.0917 2023-01-06 14:02:08,636 - mmseg - INFO - Iter [91650/160000] lr: 2.563e-05, eta: 10:56:15, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0876, decode.acc_seg: 96.3434, loss: 0.0876 2023-01-06 14:02:37,417 - mmseg - INFO - Iter [91700/160000] lr: 2.561e-05, eta: 10:55:46, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0876, decode.acc_seg: 96.2820, loss: 0.0876 2023-01-06 14:03:04,822 - mmseg - INFO - Iter [91750/160000] lr: 2.559e-05, eta: 10:55:16, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0904, decode.acc_seg: 96.1556, loss: 0.0904 2023-01-06 14:03:33,096 - mmseg - INFO - Iter [91800/160000] lr: 2.558e-05, eta: 10:54:47, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0921, decode.acc_seg: 96.1184, loss: 0.0921 2023-01-06 14:04:02,345 - mmseg - INFO - Iter [91850/160000] lr: 2.556e-05, eta: 10:54:19, time: 0.585, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0871, decode.acc_seg: 96.2005, loss: 0.0871 2023-01-06 14:04:32,771 - mmseg - INFO - Iter [91900/160000] lr: 2.554e-05, eta: 10:53:51, time: 0.609, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0894, decode.acc_seg: 96.2240, loss: 0.0894 2023-01-06 14:05:01,109 - mmseg - INFO - Iter [91950/160000] lr: 2.552e-05, eta: 10:53:22, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0876, decode.acc_seg: 96.2550, loss: 0.0876 2023-01-06 14:05:28,837 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 14:05:28,838 - mmseg - INFO - Iter [92000/160000] lr: 2.550e-05, eta: 10:52:52, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0838, decode.acc_seg: 96.4677, loss: 0.0838 2023-01-06 14:05:57,348 - mmseg - INFO - Iter [92050/160000] lr: 2.548e-05, eta: 10:52:23, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0959, decode.acc_seg: 96.0219, loss: 0.0959 2023-01-06 14:06:25,926 - mmseg - INFO - Iter [92100/160000] lr: 2.546e-05, eta: 10:51:54, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0926, decode.acc_seg: 96.0897, loss: 0.0926 2023-01-06 14:06:54,439 - mmseg - INFO - Iter [92150/160000] lr: 2.544e-05, eta: 10:51:25, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0929, decode.acc_seg: 96.0885, loss: 0.0929 2023-01-06 14:07:23,869 - mmseg - INFO - Iter [92200/160000] lr: 2.543e-05, eta: 10:50:57, time: 0.589, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0852, decode.acc_seg: 96.3125, loss: 0.0852 2023-01-06 14:07:52,258 - mmseg - INFO - Iter [92250/160000] lr: 2.541e-05, eta: 10:50:28, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0954, decode.acc_seg: 95.9524, loss: 0.0954 2023-01-06 14:08:22,253 - mmseg - INFO - Iter [92300/160000] lr: 2.539e-05, eta: 10:50:00, time: 0.600, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0935, decode.acc_seg: 96.1215, loss: 0.0935 2023-01-06 14:08:50,061 - mmseg - INFO - Iter [92350/160000] lr: 2.537e-05, eta: 10:49:30, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0888, decode.acc_seg: 96.2383, loss: 0.0888 2023-01-06 14:09:19,091 - mmseg - INFO - Iter [92400/160000] lr: 2.535e-05, eta: 10:49:02, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0898, decode.acc_seg: 96.1845, loss: 0.0898 2023-01-06 14:09:48,229 - mmseg - INFO - Iter [92450/160000] lr: 2.533e-05, eta: 10:48:33, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0839, decode.acc_seg: 96.3595, loss: 0.0839 2023-01-06 14:10:16,252 - mmseg - INFO - Iter [92500/160000] lr: 2.531e-05, eta: 10:48:04, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0938, decode.acc_seg: 95.8935, loss: 0.0938 2023-01-06 14:10:45,796 - mmseg - INFO - Iter [92550/160000] lr: 2.529e-05, eta: 10:47:36, time: 0.590, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0937, decode.acc_seg: 96.0783, loss: 0.0937 2023-01-06 14:11:15,727 - mmseg - INFO - Iter [92600/160000] lr: 2.528e-05, eta: 10:47:08, time: 0.599, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0875, decode.acc_seg: 96.2796, loss: 0.0875 2023-01-06 14:11:46,280 - mmseg - INFO - Iter [92650/160000] lr: 2.526e-05, eta: 10:46:40, time: 0.612, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0932, decode.acc_seg: 96.0513, loss: 0.0932 2023-01-06 14:12:15,100 - mmseg - INFO - Iter [92700/160000] lr: 2.524e-05, eta: 10:46:11, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0948, decode.acc_seg: 96.0462, loss: 0.0948 2023-01-06 14:12:42,929 - mmseg - INFO - Iter [92750/160000] lr: 2.522e-05, eta: 10:45:42, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0924, decode.acc_seg: 96.0923, loss: 0.0924 2023-01-06 14:13:11,532 - mmseg - INFO - Iter [92800/160000] lr: 2.520e-05, eta: 10:45:13, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0946, decode.acc_seg: 96.0893, loss: 0.0946 2023-01-06 14:13:38,715 - mmseg - INFO - Iter [92850/160000] lr: 2.518e-05, eta: 10:44:43, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0900, decode.acc_seg: 96.1534, loss: 0.0900 2023-01-06 14:14:07,254 - mmseg - INFO - Iter [92900/160000] lr: 2.516e-05, eta: 10:44:14, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0951, decode.acc_seg: 95.9023, loss: 0.0951 2023-01-06 14:14:35,130 - mmseg - INFO - Iter [92950/160000] lr: 2.514e-05, eta: 10:43:44, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0908, decode.acc_seg: 96.1290, loss: 0.0908 2023-01-06 14:15:04,223 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 14:15:04,224 - mmseg - INFO - Iter [93000/160000] lr: 2.513e-05, eta: 10:43:16, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0972, decode.acc_seg: 95.8239, loss: 0.0972 2023-01-06 14:15:35,971 - mmseg - INFO - Iter [93050/160000] lr: 2.511e-05, eta: 10:42:49, time: 0.636, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0868, decode.acc_seg: 96.1766, loss: 0.0868 2023-01-06 14:16:03,945 - mmseg - INFO - Iter [93100/160000] lr: 2.509e-05, eta: 10:42:20, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0922, decode.acc_seg: 96.1487, loss: 0.0922 2023-01-06 14:16:31,842 - mmseg - INFO - Iter [93150/160000] lr: 2.507e-05, eta: 10:41:50, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0926, decode.acc_seg: 96.1014, loss: 0.0926 2023-01-06 14:16:59,226 - mmseg - INFO - Iter [93200/160000] lr: 2.505e-05, eta: 10:41:20, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0889, decode.acc_seg: 96.2666, loss: 0.0889 2023-01-06 14:17:27,851 - mmseg - INFO - Iter [93250/160000] lr: 2.503e-05, eta: 10:40:52, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0886, decode.acc_seg: 96.2555, loss: 0.0886 2023-01-06 14:17:55,797 - mmseg - INFO - Iter [93300/160000] lr: 2.501e-05, eta: 10:40:22, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0936, decode.acc_seg: 96.1708, loss: 0.0936 2023-01-06 14:18:24,387 - mmseg - INFO - Iter [93350/160000] lr: 2.499e-05, eta: 10:39:53, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1098, decode.acc_seg: 95.5862, loss: 0.1098 2023-01-06 14:18:55,348 - mmseg - INFO - Iter [93400/160000] lr: 2.498e-05, eta: 10:39:26, time: 0.619, data_time: 0.057, memory: 10576, decode.loss_ce: 0.1001, decode.acc_seg: 95.7940, loss: 0.1001 2023-01-06 14:19:23,592 - mmseg - INFO - Iter [93450/160000] lr: 2.496e-05, eta: 10:38:57, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0924, decode.acc_seg: 96.1130, loss: 0.0924 2023-01-06 14:19:51,782 - mmseg - INFO - Iter [93500/160000] lr: 2.494e-05, eta: 10:38:27, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0936, decode.acc_seg: 96.0864, loss: 0.0936 2023-01-06 14:20:21,649 - mmseg - INFO - Iter [93550/160000] lr: 2.492e-05, eta: 10:37:59, time: 0.597, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1041, decode.acc_seg: 95.9312, loss: 0.1041 2023-01-06 14:20:49,200 - mmseg - INFO - Iter [93600/160000] lr: 2.490e-05, eta: 10:37:30, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0998, decode.acc_seg: 95.8515, loss: 0.0998 2023-01-06 14:21:16,801 - mmseg - INFO - Iter [93650/160000] lr: 2.488e-05, eta: 10:37:00, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0998, decode.acc_seg: 95.7776, loss: 0.0998 2023-01-06 14:21:44,404 - mmseg - INFO - Iter [93700/160000] lr: 2.486e-05, eta: 10:36:30, time: 0.552, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0978, decode.acc_seg: 95.8828, loss: 0.0978 2023-01-06 14:22:14,317 - mmseg - INFO - Iter [93750/160000] lr: 2.484e-05, eta: 10:36:02, time: 0.598, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0902, decode.acc_seg: 96.2810, loss: 0.0902 2023-01-06 14:22:41,400 - mmseg - INFO - Iter [93800/160000] lr: 2.483e-05, eta: 10:35:32, time: 0.542, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0888, decode.acc_seg: 96.1283, loss: 0.0888 2023-01-06 14:23:10,369 - mmseg - INFO - Iter [93850/160000] lr: 2.481e-05, eta: 10:35:04, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0913, decode.acc_seg: 96.2562, loss: 0.0913 2023-01-06 14:23:39,212 - mmseg - INFO - Iter [93900/160000] lr: 2.479e-05, eta: 10:34:35, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0909, decode.acc_seg: 96.1846, loss: 0.0909 2023-01-06 14:24:07,034 - mmseg - INFO - Iter [93950/160000] lr: 2.477e-05, eta: 10:34:05, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0871, decode.acc_seg: 96.2620, loss: 0.0871 2023-01-06 14:24:34,667 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 14:24:34,667 - mmseg - INFO - Iter [94000/160000] lr: 2.475e-05, eta: 10:33:36, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0898, decode.acc_seg: 96.2079, loss: 0.0898 2023-01-06 14:25:01,731 - mmseg - INFO - Iter [94050/160000] lr: 2.473e-05, eta: 10:33:06, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0911, decode.acc_seg: 96.1175, loss: 0.0911 2023-01-06 14:25:28,917 - mmseg - INFO - Iter [94100/160000] lr: 2.471e-05, eta: 10:32:36, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0947, decode.acc_seg: 95.9594, loss: 0.0947 2023-01-06 14:26:00,100 - mmseg - INFO - Iter [94150/160000] lr: 2.469e-05, eta: 10:32:09, time: 0.624, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0943, decode.acc_seg: 95.9965, loss: 0.0943 2023-01-06 14:26:28,056 - mmseg - INFO - Iter [94200/160000] lr: 2.468e-05, eta: 10:31:39, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0927, decode.acc_seg: 96.0865, loss: 0.0927 2023-01-06 14:26:57,455 - mmseg - INFO - Iter [94250/160000] lr: 2.466e-05, eta: 10:31:11, time: 0.588, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0948, decode.acc_seg: 96.0982, loss: 0.0948 2023-01-06 14:27:27,129 - mmseg - INFO - Iter [94300/160000] lr: 2.464e-05, eta: 10:30:43, time: 0.594, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0949, decode.acc_seg: 96.0390, loss: 0.0949 2023-01-06 14:27:55,717 - mmseg - INFO - Iter [94350/160000] lr: 2.462e-05, eta: 10:30:14, time: 0.572, data_time: 0.020, memory: 10576, decode.loss_ce: 0.0856, decode.acc_seg: 96.3091, loss: 0.0856 2023-01-06 14:28:25,237 - mmseg - INFO - Iter [94400/160000] lr: 2.460e-05, eta: 10:29:45, time: 0.590, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0946, decode.acc_seg: 96.0830, loss: 0.0946 2023-01-06 14:28:54,712 - mmseg - INFO - Iter [94450/160000] lr: 2.458e-05, eta: 10:29:17, time: 0.589, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0889, decode.acc_seg: 96.2606, loss: 0.0889 2023-01-06 14:29:26,834 - mmseg - INFO - Iter [94500/160000] lr: 2.456e-05, eta: 10:28:51, time: 0.643, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0901, decode.acc_seg: 96.2011, loss: 0.0901 2023-01-06 14:29:56,341 - mmseg - INFO - Iter [94550/160000] lr: 2.454e-05, eta: 10:28:22, time: 0.590, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0929, decode.acc_seg: 96.1025, loss: 0.0929 2023-01-06 14:30:24,262 - mmseg - INFO - Iter [94600/160000] lr: 2.453e-05, eta: 10:27:53, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0903, decode.acc_seg: 96.2030, loss: 0.0903 2023-01-06 14:30:52,395 - mmseg - INFO - Iter [94650/160000] lr: 2.451e-05, eta: 10:27:24, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0877, decode.acc_seg: 96.2859, loss: 0.0877 2023-01-06 14:31:20,986 - mmseg - INFO - Iter [94700/160000] lr: 2.449e-05, eta: 10:26:55, time: 0.573, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0885, decode.acc_seg: 96.2057, loss: 0.0885 2023-01-06 14:31:49,260 - mmseg - INFO - Iter [94750/160000] lr: 2.447e-05, eta: 10:26:26, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0925, decode.acc_seg: 96.0807, loss: 0.0925 2023-01-06 14:32:16,997 - mmseg - INFO - Iter [94800/160000] lr: 2.445e-05, eta: 10:25:56, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0956, decode.acc_seg: 96.0989, loss: 0.0956 2023-01-06 14:32:45,154 - mmseg - INFO - Iter [94850/160000] lr: 2.443e-05, eta: 10:25:27, time: 0.563, data_time: 0.023, memory: 10576, decode.loss_ce: 0.0882, decode.acc_seg: 96.2795, loss: 0.0882 2023-01-06 14:33:15,788 - mmseg - INFO - Iter [94900/160000] lr: 2.441e-05, eta: 10:24:59, time: 0.613, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0979, decode.acc_seg: 95.8343, loss: 0.0979 2023-01-06 14:33:44,917 - mmseg - INFO - Iter [94950/160000] lr: 2.439e-05, eta: 10:24:31, time: 0.582, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0946, decode.acc_seg: 96.0472, loss: 0.0946 2023-01-06 14:34:14,587 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 14:34:14,587 - mmseg - INFO - Iter [95000/160000] lr: 2.438e-05, eta: 10:24:02, time: 0.593, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0880, decode.acc_seg: 96.3507, loss: 0.0880 2023-01-06 14:34:42,944 - mmseg - INFO - Iter [95050/160000] lr: 2.436e-05, eta: 10:23:33, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0897, decode.acc_seg: 96.2385, loss: 0.0897 2023-01-06 14:35:11,379 - mmseg - INFO - Iter [95100/160000] lr: 2.434e-05, eta: 10:23:04, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0889, decode.acc_seg: 96.3276, loss: 0.0889 2023-01-06 14:35:40,507 - mmseg - INFO - Iter [95150/160000] lr: 2.432e-05, eta: 10:22:36, time: 0.583, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0901, decode.acc_seg: 96.1993, loss: 0.0901 2023-01-06 14:36:07,991 - mmseg - INFO - Iter [95200/160000] lr: 2.430e-05, eta: 10:22:06, time: 0.550, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0891, decode.acc_seg: 96.1690, loss: 0.0891 2023-01-06 14:36:38,286 - mmseg - INFO - Iter [95250/160000] lr: 2.428e-05, eta: 10:21:38, time: 0.606, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0901, decode.acc_seg: 96.1746, loss: 0.0901 2023-01-06 14:37:06,489 - mmseg - INFO - Iter [95300/160000] lr: 2.426e-05, eta: 10:21:09, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0962, decode.acc_seg: 95.9459, loss: 0.0962 2023-01-06 14:37:34,938 - mmseg - INFO - Iter [95350/160000] lr: 2.424e-05, eta: 10:20:40, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0987, decode.acc_seg: 95.5957, loss: 0.0987 2023-01-06 14:38:04,765 - mmseg - INFO - Iter [95400/160000] lr: 2.423e-05, eta: 10:20:12, time: 0.597, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0893, decode.acc_seg: 96.2570, loss: 0.0893 2023-01-06 14:38:32,783 - mmseg - INFO - Iter [95450/160000] lr: 2.421e-05, eta: 10:19:43, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0915, decode.acc_seg: 96.1165, loss: 0.0915 2023-01-06 14:39:02,233 - mmseg - INFO - Iter [95500/160000] lr: 2.419e-05, eta: 10:19:14, time: 0.590, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0869, decode.acc_seg: 96.2617, loss: 0.0869 2023-01-06 14:39:29,209 - mmseg - INFO - Iter [95550/160000] lr: 2.417e-05, eta: 10:18:44, time: 0.540, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0890, decode.acc_seg: 96.2537, loss: 0.0890 2023-01-06 14:39:56,660 - mmseg - INFO - Iter [95600/160000] lr: 2.415e-05, eta: 10:18:14, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0901, decode.acc_seg: 96.1641, loss: 0.0901 2023-01-06 14:40:26,674 - mmseg - INFO - Iter [95650/160000] lr: 2.413e-05, eta: 10:17:46, time: 0.601, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0893, decode.acc_seg: 96.2602, loss: 0.0893 2023-01-06 14:40:54,321 - mmseg - INFO - Iter [95700/160000] lr: 2.411e-05, eta: 10:17:17, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0882, decode.acc_seg: 96.2715, loss: 0.0882 2023-01-06 14:41:23,089 - mmseg - INFO - Iter [95750/160000] lr: 2.409e-05, eta: 10:16:48, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0887, decode.acc_seg: 96.3000, loss: 0.0887 2023-01-06 14:41:50,995 - mmseg - INFO - Iter [95800/160000] lr: 2.408e-05, eta: 10:16:19, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0897, decode.acc_seg: 96.1388, loss: 0.0897 2023-01-06 14:42:19,488 - mmseg - INFO - Iter [95850/160000] lr: 2.406e-05, eta: 10:15:50, time: 0.569, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0898, decode.acc_seg: 96.1703, loss: 0.0898 2023-01-06 14:42:48,835 - mmseg - INFO - Iter [95900/160000] lr: 2.404e-05, eta: 10:15:21, time: 0.587, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0899, decode.acc_seg: 96.1495, loss: 0.0899 2023-01-06 14:43:16,251 - mmseg - INFO - Iter [95950/160000] lr: 2.402e-05, eta: 10:14:51, time: 0.549, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0852, decode.acc_seg: 96.3460, loss: 0.0852 2023-01-06 14:43:46,677 - mmseg - INFO - Saving checkpoint at 96000 iterations 2023-01-06 14:43:51,940 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 14:43:51,941 - mmseg - INFO - Iter [96000/160000] lr: 2.400e-05, eta: 10:14:27, time: 0.714, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0841, decode.acc_seg: 96.3352, loss: 0.0841 2023-01-06 14:44:24,117 - mmseg - INFO - per class results: 2023-01-06 14:44:24,119 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 97.87 | 98.83 | | sidewalk | 83.15 | 90.65 | | building | 92.03 | 96.02 | | wall | 55.72 | 64.85 | | fence | 56.88 | 72.11 | | pole | 61.56 | 70.98 | | traffic light | 64.94 | 76.83 | | traffic sign | 74.57 | 82.12 | | vegetation | 91.94 | 96.5 | | terrain | 62.43 | 76.15 | | sky | 94.33 | 98.14 | | person | 77.94 | 89.35 | | rider | 54.72 | 66.13 | | car | 93.77 | 97.59 | | truck | 66.17 | 75.24 | | bus | 77.83 | 87.58 | | train | 68.33 | 75.19 | | motorcycle | 44.41 | 50.52 | | bicycle | 72.11 | 87.57 | +---------------+-------+-------+ 2023-01-06 14:44:24,119 - mmseg - INFO - Summary: 2023-01-06 14:44:24,120 - mmseg - INFO - +-------+------+------+ | aAcc | mIoU | mAcc | +-------+------+------+ | 95.59 | 73.2 | 81.7 | +-------+------+------+ 2023-01-06 14:44:24,121 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 14:44:24,121 - mmseg - INFO - Iter(val) [63] aAcc: 0.9559, mIoU: 0.7320, mAcc: 0.8170, IoU.road: 0.9787, IoU.sidewalk: 0.8315, IoU.building: 0.9203, IoU.wall: 0.5572, IoU.fence: 0.5688, IoU.pole: 0.6156, IoU.traffic light: 0.6494, IoU.traffic sign: 0.7457, IoU.vegetation: 0.9194, IoU.terrain: 0.6243, IoU.sky: 0.9433, IoU.person: 0.7794, IoU.rider: 0.5472, IoU.car: 0.9377, IoU.truck: 0.6617, IoU.bus: 0.7783, IoU.train: 0.6833, IoU.motorcycle: 0.4441, IoU.bicycle: 0.7211, Acc.road: 0.9883, Acc.sidewalk: 0.9065, Acc.building: 0.9602, Acc.wall: 0.6485, Acc.fence: 0.7211, Acc.pole: 0.7098, Acc.traffic light: 0.7683, Acc.traffic sign: 0.8212, Acc.vegetation: 0.9650, Acc.terrain: 0.7615, Acc.sky: 0.9814, Acc.person: 0.8935, Acc.rider: 0.6613, Acc.car: 0.9759, Acc.truck: 0.7524, Acc.bus: 0.8758, Acc.train: 0.7519, Acc.motorcycle: 0.5052, Acc.bicycle: 0.8757 2023-01-06 14:44:52,405 - mmseg - INFO - Iter [96050/160000] lr: 2.398e-05, eta: 10:14:20, time: 1.209, data_time: 0.656, memory: 10576, decode.loss_ce: 0.0884, decode.acc_seg: 96.1598, loss: 0.0884 2023-01-06 14:45:19,844 - mmseg - INFO - Iter [96100/160000] lr: 2.396e-05, eta: 10:13:50, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0903, decode.acc_seg: 96.2292, loss: 0.0903 2023-01-06 14:45:48,324 - mmseg - INFO - Iter [96150/160000] lr: 2.394e-05, eta: 10:13:21, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0944, decode.acc_seg: 96.1003, loss: 0.0944 2023-01-06 14:46:16,389 - mmseg - INFO - Iter [96200/160000] lr: 2.393e-05, eta: 10:12:51, time: 0.560, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0891, decode.acc_seg: 96.2161, loss: 0.0891 2023-01-06 14:46:44,826 - mmseg - INFO - Iter [96250/160000] lr: 2.391e-05, eta: 10:12:22, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0843, decode.acc_seg: 96.4415, loss: 0.0843 2023-01-06 14:47:13,833 - mmseg - INFO - Iter [96300/160000] lr: 2.389e-05, eta: 10:11:54, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0946, decode.acc_seg: 95.9516, loss: 0.0946 2023-01-06 14:47:44,644 - mmseg - INFO - Iter [96350/160000] lr: 2.387e-05, eta: 10:11:26, time: 0.615, data_time: 0.056, memory: 10576, decode.loss_ce: 0.0879, decode.acc_seg: 96.2596, loss: 0.0879 2023-01-06 14:48:12,507 - mmseg - INFO - Iter [96400/160000] lr: 2.385e-05, eta: 10:10:57, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0909, decode.acc_seg: 96.2048, loss: 0.0909 2023-01-06 14:48:40,487 - mmseg - INFO - Iter [96450/160000] lr: 2.383e-05, eta: 10:10:27, time: 0.560, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0879, decode.acc_seg: 96.2938, loss: 0.0879 2023-01-06 14:49:09,719 - mmseg - INFO - Iter [96500/160000] lr: 2.381e-05, eta: 10:09:59, time: 0.584, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0935, decode.acc_seg: 95.9896, loss: 0.0935 2023-01-06 14:49:39,338 - mmseg - INFO - Iter [96550/160000] lr: 2.379e-05, eta: 10:09:31, time: 0.592, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0871, decode.acc_seg: 96.2993, loss: 0.0871 2023-01-06 14:50:07,826 - mmseg - INFO - Iter [96600/160000] lr: 2.378e-05, eta: 10:09:02, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0903, decode.acc_seg: 96.1572, loss: 0.0903 2023-01-06 14:50:36,213 - mmseg - INFO - Iter [96650/160000] lr: 2.376e-05, eta: 10:08:32, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0960, decode.acc_seg: 96.0464, loss: 0.0960 2023-01-06 14:51:03,298 - mmseg - INFO - Iter [96700/160000] lr: 2.374e-05, eta: 10:08:02, time: 0.542, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0949, decode.acc_seg: 96.0196, loss: 0.0949 2023-01-06 14:51:32,595 - mmseg - INFO - Iter [96750/160000] lr: 2.372e-05, eta: 10:07:34, time: 0.586, data_time: 0.056, memory: 10576, decode.loss_ce: 0.0884, decode.acc_seg: 96.1842, loss: 0.0884 2023-01-06 14:52:00,758 - mmseg - INFO - Iter [96800/160000] lr: 2.370e-05, eta: 10:07:05, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0910, decode.acc_seg: 96.2557, loss: 0.0910 2023-01-06 14:52:28,885 - mmseg - INFO - Iter [96850/160000] lr: 2.368e-05, eta: 10:06:35, time: 0.562, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0932, decode.acc_seg: 96.1698, loss: 0.0932 2023-01-06 14:52:58,032 - mmseg - INFO - Iter [96900/160000] lr: 2.366e-05, eta: 10:06:07, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0854, decode.acc_seg: 96.3066, loss: 0.0854 2023-01-06 14:53:27,092 - mmseg - INFO - Iter [96950/160000] lr: 2.364e-05, eta: 10:05:38, time: 0.582, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0870, decode.acc_seg: 96.3129, loss: 0.0870 2023-01-06 14:53:54,432 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 14:53:54,433 - mmseg - INFO - Iter [97000/160000] lr: 2.363e-05, eta: 10:05:08, time: 0.547, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0903, decode.acc_seg: 96.1385, loss: 0.0903 2023-01-06 14:54:22,514 - mmseg - INFO - Iter [97050/160000] lr: 2.361e-05, eta: 10:04:39, time: 0.562, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0886, decode.acc_seg: 96.2351, loss: 0.0886 2023-01-06 14:54:53,747 - mmseg - INFO - Iter [97100/160000] lr: 2.359e-05, eta: 10:04:12, time: 0.624, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0907, decode.acc_seg: 96.1508, loss: 0.0907 2023-01-06 14:55:22,364 - mmseg - INFO - Iter [97150/160000] lr: 2.357e-05, eta: 10:03:43, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0979, decode.acc_seg: 95.8921, loss: 0.0979 2023-01-06 14:55:51,570 - mmseg - INFO - Iter [97200/160000] lr: 2.355e-05, eta: 10:03:14, time: 0.584, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0870, decode.acc_seg: 96.2590, loss: 0.0870 2023-01-06 14:56:20,739 - mmseg - INFO - Iter [97250/160000] lr: 2.353e-05, eta: 10:02:46, time: 0.583, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0869, decode.acc_seg: 96.2967, loss: 0.0869 2023-01-06 14:56:48,818 - mmseg - INFO - Iter [97300/160000] lr: 2.351e-05, eta: 10:02:16, time: 0.562, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0995, decode.acc_seg: 95.9761, loss: 0.0995 2023-01-06 14:57:18,410 - mmseg - INFO - Iter [97350/160000] lr: 2.349e-05, eta: 10:01:48, time: 0.592, data_time: 0.012, memory: 10576, decode.loss_ce: 0.1021, decode.acc_seg: 95.7536, loss: 0.1021 2023-01-06 14:57:47,641 - mmseg - INFO - Iter [97400/160000] lr: 2.348e-05, eta: 10:01:20, time: 0.585, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0988, decode.acc_seg: 95.8004, loss: 0.0988 2023-01-06 14:58:15,869 - mmseg - INFO - Iter [97450/160000] lr: 2.346e-05, eta: 10:00:50, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0939, decode.acc_seg: 96.1244, loss: 0.0939 2023-01-06 14:58:46,393 - mmseg - INFO - Iter [97500/160000] lr: 2.344e-05, eta: 10:00:23, time: 0.610, data_time: 0.056, memory: 10576, decode.loss_ce: 0.0967, decode.acc_seg: 96.0233, loss: 0.0967 2023-01-06 14:59:14,312 - mmseg - INFO - Iter [97550/160000] lr: 2.342e-05, eta: 9:59:53, time: 0.558, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0900, decode.acc_seg: 96.2427, loss: 0.0900 2023-01-06 14:59:42,417 - mmseg - INFO - Iter [97600/160000] lr: 2.340e-05, eta: 9:59:24, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0945, decode.acc_seg: 96.1080, loss: 0.0945 2023-01-06 15:00:10,751 - mmseg - INFO - Iter [97650/160000] lr: 2.338e-05, eta: 9:58:55, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0911, decode.acc_seg: 96.2695, loss: 0.0911 2023-01-06 15:00:40,455 - mmseg - INFO - Iter [97700/160000] lr: 2.336e-05, eta: 9:58:27, time: 0.594, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0901, decode.acc_seg: 96.2784, loss: 0.0901 2023-01-06 15:01:08,945 - mmseg - INFO - Iter [97750/160000] lr: 2.334e-05, eta: 9:57:58, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0878, decode.acc_seg: 96.2646, loss: 0.0878 2023-01-06 15:01:37,339 - mmseg - INFO - Iter [97800/160000] lr: 2.333e-05, eta: 9:57:28, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0988, decode.acc_seg: 95.9216, loss: 0.0988 2023-01-06 15:02:08,137 - mmseg - INFO - Iter [97850/160000] lr: 2.331e-05, eta: 9:57:01, time: 0.615, data_time: 0.056, memory: 10576, decode.loss_ce: 0.0892, decode.acc_seg: 96.2432, loss: 0.0892 2023-01-06 15:02:36,846 - mmseg - INFO - Iter [97900/160000] lr: 2.329e-05, eta: 9:56:32, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0889, decode.acc_seg: 96.2344, loss: 0.0889 2023-01-06 15:03:06,292 - mmseg - INFO - Iter [97950/160000] lr: 2.327e-05, eta: 9:56:04, time: 0.588, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0895, decode.acc_seg: 96.1386, loss: 0.0895 2023-01-06 15:03:35,064 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 15:03:35,065 - mmseg - INFO - Iter [98000/160000] lr: 2.325e-05, eta: 9:55:35, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0909, decode.acc_seg: 96.1366, loss: 0.0909 2023-01-06 15:04:04,104 - mmseg - INFO - Iter [98050/160000] lr: 2.323e-05, eta: 9:55:06, time: 0.580, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0878, decode.acc_seg: 96.2797, loss: 0.0878 2023-01-06 15:04:31,613 - mmseg - INFO - Iter [98100/160000] lr: 2.321e-05, eta: 9:54:36, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0888, decode.acc_seg: 96.2281, loss: 0.0888 2023-01-06 15:05:00,808 - mmseg - INFO - Iter [98150/160000] lr: 2.319e-05, eta: 9:54:08, time: 0.583, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0873, decode.acc_seg: 96.3075, loss: 0.0873 2023-01-06 15:05:28,258 - mmseg - INFO - Iter [98200/160000] lr: 2.318e-05, eta: 9:53:38, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0876, decode.acc_seg: 96.3330, loss: 0.0876 2023-01-06 15:05:59,519 - mmseg - INFO - Iter [98250/160000] lr: 2.316e-05, eta: 9:53:11, time: 0.626, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0925, decode.acc_seg: 96.1134, loss: 0.0925 2023-01-06 15:06:28,377 - mmseg - INFO - Iter [98300/160000] lr: 2.314e-05, eta: 9:52:42, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0873, decode.acc_seg: 96.3729, loss: 0.0873 2023-01-06 15:06:56,069 - mmseg - INFO - Iter [98350/160000] lr: 2.312e-05, eta: 9:52:13, time: 0.553, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0913, decode.acc_seg: 96.1704, loss: 0.0913 2023-01-06 15:07:23,183 - mmseg - INFO - Iter [98400/160000] lr: 2.310e-05, eta: 9:51:43, time: 0.543, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0861, decode.acc_seg: 96.3346, loss: 0.0861 2023-01-06 15:07:52,476 - mmseg - INFO - Iter [98450/160000] lr: 2.308e-05, eta: 9:51:14, time: 0.586, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0920, decode.acc_seg: 96.2042, loss: 0.0920 2023-01-06 15:08:20,421 - mmseg - INFO - Iter [98500/160000] lr: 2.306e-05, eta: 9:50:45, time: 0.559, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0912, decode.acc_seg: 96.1571, loss: 0.0912 2023-01-06 15:08:49,644 - mmseg - INFO - Iter [98550/160000] lr: 2.304e-05, eta: 9:50:16, time: 0.584, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0873, decode.acc_seg: 96.3366, loss: 0.0873 2023-01-06 15:09:20,195 - mmseg - INFO - Iter [98600/160000] lr: 2.303e-05, eta: 9:49:49, time: 0.612, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0840, decode.acc_seg: 96.4547, loss: 0.0840 2023-01-06 15:09:48,575 - mmseg - INFO - Iter [98650/160000] lr: 2.301e-05, eta: 9:49:19, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0895, decode.acc_seg: 96.2212, loss: 0.0895 2023-01-06 15:10:16,182 - mmseg - INFO - Iter [98700/160000] lr: 2.299e-05, eta: 9:48:50, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0808, decode.acc_seg: 96.4764, loss: 0.0808 2023-01-06 15:10:44,724 - mmseg - INFO - Iter [98750/160000] lr: 2.297e-05, eta: 9:48:21, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0886, decode.acc_seg: 96.2571, loss: 0.0886 2023-01-06 15:11:13,033 - mmseg - INFO - Iter [98800/160000] lr: 2.295e-05, eta: 9:47:52, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0847, decode.acc_seg: 96.3664, loss: 0.0847 2023-01-06 15:11:40,368 - mmseg - INFO - Iter [98850/160000] lr: 2.293e-05, eta: 9:47:22, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0903, decode.acc_seg: 96.1315, loss: 0.0903 2023-01-06 15:12:08,788 - mmseg - INFO - Iter [98900/160000] lr: 2.291e-05, eta: 9:46:53, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0918, decode.acc_seg: 96.1168, loss: 0.0918 2023-01-06 15:12:35,803 - mmseg - INFO - Iter [98950/160000] lr: 2.289e-05, eta: 9:46:23, time: 0.540, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0874, decode.acc_seg: 96.3366, loss: 0.0874 2023-01-06 15:13:05,403 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 15:13:05,404 - mmseg - INFO - Iter [99000/160000] lr: 2.288e-05, eta: 9:45:55, time: 0.591, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0879, decode.acc_seg: 96.3281, loss: 0.0879 2023-01-06 15:13:32,986 - mmseg - INFO - Iter [99050/160000] lr: 2.286e-05, eta: 9:45:25, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0878, decode.acc_seg: 96.1614, loss: 0.0878 2023-01-06 15:14:00,699 - mmseg - INFO - Iter [99100/160000] lr: 2.284e-05, eta: 9:44:56, time: 0.554, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0898, decode.acc_seg: 96.1684, loss: 0.0898 2023-01-06 15:14:29,405 - mmseg - INFO - Iter [99150/160000] lr: 2.282e-05, eta: 9:44:27, time: 0.574, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0952, decode.acc_seg: 95.9728, loss: 0.0952 2023-01-06 15:14:58,455 - mmseg - INFO - Iter [99200/160000] lr: 2.280e-05, eta: 9:43:58, time: 0.581, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0970, decode.acc_seg: 96.0342, loss: 0.0970 2023-01-06 15:15:28,121 - mmseg - INFO - Iter [99250/160000] lr: 2.278e-05, eta: 9:43:30, time: 0.593, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0866, decode.acc_seg: 96.3081, loss: 0.0866 2023-01-06 15:15:57,183 - mmseg - INFO - Iter [99300/160000] lr: 2.276e-05, eta: 9:43:01, time: 0.581, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0853, decode.acc_seg: 96.2507, loss: 0.0853 2023-01-06 15:16:27,325 - mmseg - INFO - Iter [99350/160000] lr: 2.274e-05, eta: 9:42:33, time: 0.602, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0896, decode.acc_seg: 96.2603, loss: 0.0896 2023-01-06 15:16:54,975 - mmseg - INFO - Iter [99400/160000] lr: 2.273e-05, eta: 9:42:04, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0843, decode.acc_seg: 96.3144, loss: 0.0843 2023-01-06 15:17:23,444 - mmseg - INFO - Iter [99450/160000] lr: 2.271e-05, eta: 9:41:35, time: 0.569, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0824, decode.acc_seg: 96.5075, loss: 0.0824 2023-01-06 15:17:51,161 - mmseg - INFO - Iter [99500/160000] lr: 2.269e-05, eta: 9:41:05, time: 0.553, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0916, decode.acc_seg: 96.2073, loss: 0.0916 2023-01-06 15:18:19,711 - mmseg - INFO - Iter [99550/160000] lr: 2.267e-05, eta: 9:40:36, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0871, decode.acc_seg: 96.3568, loss: 0.0871 2023-01-06 15:18:49,787 - mmseg - INFO - Iter [99600/160000] lr: 2.265e-05, eta: 9:40:08, time: 0.602, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0916, decode.acc_seg: 96.1863, loss: 0.0916 2023-01-06 15:19:17,667 - mmseg - INFO - Iter [99650/160000] lr: 2.263e-05, eta: 9:39:39, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0942, decode.acc_seg: 96.0715, loss: 0.0942 2023-01-06 15:19:48,352 - mmseg - INFO - Iter [99700/160000] lr: 2.261e-05, eta: 9:39:11, time: 0.615, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0934, decode.acc_seg: 96.0637, loss: 0.0934 2023-01-06 15:20:17,746 - mmseg - INFO - Iter [99750/160000] lr: 2.259e-05, eta: 9:38:42, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0867, decode.acc_seg: 96.3551, loss: 0.0867 2023-01-06 15:20:47,816 - mmseg - INFO - Iter [99800/160000] lr: 2.258e-05, eta: 9:38:14, time: 0.601, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0945, decode.acc_seg: 96.0525, loss: 0.0945 2023-01-06 15:21:17,098 - mmseg - INFO - Iter [99850/160000] lr: 2.256e-05, eta: 9:37:46, time: 0.586, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0882, decode.acc_seg: 96.3100, loss: 0.0882 2023-01-06 15:21:46,346 - mmseg - INFO - Iter [99900/160000] lr: 2.254e-05, eta: 9:37:17, time: 0.584, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0885, decode.acc_seg: 96.2573, loss: 0.0885 2023-01-06 15:22:15,343 - mmseg - INFO - Iter [99950/160000] lr: 2.252e-05, eta: 9:36:49, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0865, decode.acc_seg: 96.3831, loss: 0.0865 2023-01-06 15:22:42,784 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 15:22:42,784 - mmseg - INFO - Iter [100000/160000] lr: 2.250e-05, eta: 9:36:19, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0911, decode.acc_seg: 96.1243, loss: 0.0911 2023-01-06 15:23:11,162 - mmseg - INFO - Iter [100050/160000] lr: 2.248e-05, eta: 9:35:50, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0885, decode.acc_seg: 96.2558, loss: 0.0885 2023-01-06 15:23:40,752 - mmseg - INFO - Iter [100100/160000] lr: 2.246e-05, eta: 9:35:22, time: 0.593, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0876, decode.acc_seg: 96.2555, loss: 0.0876 2023-01-06 15:24:09,321 - mmseg - INFO - Iter [100150/160000] lr: 2.244e-05, eta: 9:34:53, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0861, decode.acc_seg: 96.2070, loss: 0.0861 2023-01-06 15:24:37,896 - mmseg - INFO - Iter [100200/160000] lr: 2.243e-05, eta: 9:34:24, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0870, decode.acc_seg: 96.2796, loss: 0.0870 2023-01-06 15:25:06,345 - mmseg - INFO - Iter [100250/160000] lr: 2.241e-05, eta: 9:33:55, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0881, decode.acc_seg: 96.1596, loss: 0.0881 2023-01-06 15:25:35,070 - mmseg - INFO - Iter [100300/160000] lr: 2.239e-05, eta: 9:33:26, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0971, decode.acc_seg: 95.9930, loss: 0.0971 2023-01-06 15:26:02,658 - mmseg - INFO - Iter [100350/160000] lr: 2.237e-05, eta: 9:32:56, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0893, decode.acc_seg: 96.2442, loss: 0.0893 2023-01-06 15:26:30,581 - mmseg - INFO - Iter [100400/160000] lr: 2.235e-05, eta: 9:32:27, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0904, decode.acc_seg: 96.1457, loss: 0.0904 2023-01-06 15:27:00,721 - mmseg - INFO - Iter [100450/160000] lr: 2.233e-05, eta: 9:31:59, time: 0.603, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0899, decode.acc_seg: 96.2048, loss: 0.0899 2023-01-06 15:27:28,020 - mmseg - INFO - Iter [100500/160000] lr: 2.231e-05, eta: 9:31:29, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0879, decode.acc_seg: 96.3531, loss: 0.0879 2023-01-06 15:27:56,393 - mmseg - INFO - Iter [100550/160000] lr: 2.229e-05, eta: 9:31:00, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0877, decode.acc_seg: 96.3135, loss: 0.0877 2023-01-06 15:28:23,754 - mmseg - INFO - Iter [100600/160000] lr: 2.228e-05, eta: 9:30:30, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0856, decode.acc_seg: 96.3914, loss: 0.0856 2023-01-06 15:28:52,762 - mmseg - INFO - Iter [100650/160000] lr: 2.226e-05, eta: 9:30:02, time: 0.579, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0927, decode.acc_seg: 96.1658, loss: 0.0927 2023-01-06 15:29:20,562 - mmseg - INFO - Iter [100700/160000] lr: 2.224e-05, eta: 9:29:32, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0843, decode.acc_seg: 96.3206, loss: 0.0843 2023-01-06 15:29:49,017 - mmseg - INFO - Iter [100750/160000] lr: 2.222e-05, eta: 9:29:03, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0909, decode.acc_seg: 96.1524, loss: 0.0909 2023-01-06 15:30:17,623 - mmseg - INFO - Iter [100800/160000] lr: 2.220e-05, eta: 9:28:34, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0904, decode.acc_seg: 96.1375, loss: 0.0904 2023-01-06 15:30:48,421 - mmseg - INFO - Iter [100850/160000] lr: 2.218e-05, eta: 9:28:07, time: 0.616, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0888, decode.acc_seg: 96.2880, loss: 0.0888 2023-01-06 15:31:16,708 - mmseg - INFO - Iter [100900/160000] lr: 2.216e-05, eta: 9:27:38, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0900, decode.acc_seg: 96.2117, loss: 0.0900 2023-01-06 15:31:44,211 - mmseg - INFO - Iter [100950/160000] lr: 2.214e-05, eta: 9:27:08, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0904, decode.acc_seg: 96.2252, loss: 0.0904 2023-01-06 15:32:13,150 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 15:32:13,150 - mmseg - INFO - Iter [101000/160000] lr: 2.213e-05, eta: 9:26:39, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0870, decode.acc_seg: 96.3491, loss: 0.0870 2023-01-06 15:32:41,202 - mmseg - INFO - Iter [101050/160000] lr: 2.211e-05, eta: 9:26:10, time: 0.562, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0891, decode.acc_seg: 96.1788, loss: 0.0891 2023-01-06 15:33:10,221 - mmseg - INFO - Iter [101100/160000] lr: 2.209e-05, eta: 9:25:41, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0851, decode.acc_seg: 96.4065, loss: 0.0851 2023-01-06 15:33:38,893 - mmseg - INFO - Iter [101150/160000] lr: 2.207e-05, eta: 9:25:12, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0837, decode.acc_seg: 96.3250, loss: 0.0837 2023-01-06 15:34:09,373 - mmseg - INFO - Iter [101200/160000] lr: 2.205e-05, eta: 9:24:44, time: 0.609, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0850, decode.acc_seg: 96.3810, loss: 0.0850 2023-01-06 15:34:37,324 - mmseg - INFO - Iter [101250/160000] lr: 2.203e-05, eta: 9:24:15, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0820, decode.acc_seg: 96.4381, loss: 0.0820 2023-01-06 15:35:06,732 - mmseg - INFO - Iter [101300/160000] lr: 2.201e-05, eta: 9:23:47, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0874, decode.acc_seg: 96.3414, loss: 0.0874 2023-01-06 15:35:34,203 - mmseg - INFO - Iter [101350/160000] lr: 2.199e-05, eta: 9:23:17, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0863, decode.acc_seg: 96.4421, loss: 0.0863 2023-01-06 15:36:01,309 - mmseg - INFO - Iter [101400/160000] lr: 2.198e-05, eta: 9:22:47, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0860, decode.acc_seg: 96.3874, loss: 0.0860 2023-01-06 15:36:30,863 - mmseg - INFO - Iter [101450/160000] lr: 2.196e-05, eta: 9:22:19, time: 0.591, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0945, decode.acc_seg: 96.0098, loss: 0.0945 2023-01-06 15:36:59,868 - mmseg - INFO - Iter [101500/160000] lr: 2.194e-05, eta: 9:21:50, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0948, decode.acc_seg: 96.1066, loss: 0.0948 2023-01-06 15:37:28,513 - mmseg - INFO - Iter [101550/160000] lr: 2.192e-05, eta: 9:21:21, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0865, decode.acc_seg: 96.4332, loss: 0.0865 2023-01-06 15:37:57,859 - mmseg - INFO - Iter [101600/160000] lr: 2.190e-05, eta: 9:20:53, time: 0.587, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0879, decode.acc_seg: 96.2838, loss: 0.0879 2023-01-06 15:38:26,962 - mmseg - INFO - Iter [101650/160000] lr: 2.188e-05, eta: 9:20:24, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0893, decode.acc_seg: 96.1775, loss: 0.0893 2023-01-06 15:38:55,372 - mmseg - INFO - Iter [101700/160000] lr: 2.186e-05, eta: 9:19:55, time: 0.569, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0843, decode.acc_seg: 96.4338, loss: 0.0843 2023-01-06 15:39:23,610 - mmseg - INFO - Iter [101750/160000] lr: 2.184e-05, eta: 9:19:26, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0900, decode.acc_seg: 96.2205, loss: 0.0900 2023-01-06 15:39:51,858 - mmseg - INFO - Iter [101800/160000] lr: 2.183e-05, eta: 9:18:57, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0872, decode.acc_seg: 96.3260, loss: 0.0872 2023-01-06 15:40:20,807 - mmseg - INFO - Iter [101850/160000] lr: 2.181e-05, eta: 9:18:28, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0963, decode.acc_seg: 96.0805, loss: 0.0963 2023-01-06 15:40:50,590 - mmseg - INFO - Iter [101900/160000] lr: 2.179e-05, eta: 9:18:00, time: 0.596, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0894, decode.acc_seg: 96.2545, loss: 0.0894 2023-01-06 15:41:21,775 - mmseg - INFO - Iter [101950/160000] lr: 2.177e-05, eta: 9:17:32, time: 0.624, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0916, decode.acc_seg: 96.2073, loss: 0.0916 2023-01-06 15:41:49,635 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 15:41:49,636 - mmseg - INFO - Iter [102000/160000] lr: 2.175e-05, eta: 9:17:03, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0866, decode.acc_seg: 96.2743, loss: 0.0866 2023-01-06 15:42:18,190 - mmseg - INFO - Iter [102050/160000] lr: 2.173e-05, eta: 9:16:34, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0841, decode.acc_seg: 96.4562, loss: 0.0841 2023-01-06 15:42:46,743 - mmseg - INFO - Iter [102100/160000] lr: 2.171e-05, eta: 9:16:05, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0885, decode.acc_seg: 96.3104, loss: 0.0885 2023-01-06 15:43:15,072 - mmseg - INFO - Iter [102150/160000] lr: 2.169e-05, eta: 9:15:36, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0832, decode.acc_seg: 96.4776, loss: 0.0832 2023-01-06 15:43:42,897 - mmseg - INFO - Iter [102200/160000] lr: 2.168e-05, eta: 9:15:07, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0897, decode.acc_seg: 96.2286, loss: 0.0897 2023-01-06 15:44:10,661 - mmseg - INFO - Iter [102250/160000] lr: 2.166e-05, eta: 9:14:37, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0843, decode.acc_seg: 96.4710, loss: 0.0843 2023-01-06 15:44:39,225 - mmseg - INFO - Iter [102300/160000] lr: 2.164e-05, eta: 9:14:08, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0857, decode.acc_seg: 96.3934, loss: 0.0857 2023-01-06 15:45:10,809 - mmseg - INFO - Iter [102350/160000] lr: 2.162e-05, eta: 9:13:41, time: 0.632, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0918, decode.acc_seg: 96.2352, loss: 0.0918 2023-01-06 15:45:38,912 - mmseg - INFO - Iter [102400/160000] lr: 2.160e-05, eta: 9:13:12, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0908, decode.acc_seg: 96.0961, loss: 0.0908 2023-01-06 15:46:06,877 - mmseg - INFO - Iter [102450/160000] lr: 2.158e-05, eta: 9:12:43, time: 0.560, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0864, decode.acc_seg: 96.2964, loss: 0.0864 2023-01-06 15:46:36,613 - mmseg - INFO - Iter [102500/160000] lr: 2.156e-05, eta: 9:12:14, time: 0.594, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0862, decode.acc_seg: 96.2995, loss: 0.0862 2023-01-06 15:47:05,536 - mmseg - INFO - Iter [102550/160000] lr: 2.154e-05, eta: 9:11:45, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0942, decode.acc_seg: 96.0654, loss: 0.0942 2023-01-06 15:47:34,342 - mmseg - INFO - Iter [102600/160000] lr: 2.153e-05, eta: 9:11:17, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0889, decode.acc_seg: 96.3581, loss: 0.0889 2023-01-06 15:48:01,965 - mmseg - INFO - Iter [102650/160000] lr: 2.151e-05, eta: 9:10:47, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0833, decode.acc_seg: 96.3728, loss: 0.0833 2023-01-06 15:48:31,859 - mmseg - INFO - Iter [102700/160000] lr: 2.149e-05, eta: 9:10:19, time: 0.598, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0840, decode.acc_seg: 96.4185, loss: 0.0840 2023-01-06 15:48:59,327 - mmseg - INFO - Iter [102750/160000] lr: 2.147e-05, eta: 9:09:49, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0859, decode.acc_seg: 96.2818, loss: 0.0859 2023-01-06 15:49:26,555 - mmseg - INFO - Iter [102800/160000] lr: 2.145e-05, eta: 9:09:20, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0811, decode.acc_seg: 96.5358, loss: 0.0811 2023-01-06 15:49:53,797 - mmseg - INFO - Iter [102850/160000] lr: 2.143e-05, eta: 9:08:50, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0877, decode.acc_seg: 96.3161, loss: 0.0877 2023-01-06 15:50:21,211 - mmseg - INFO - Iter [102900/160000] lr: 2.141e-05, eta: 9:08:20, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0906, decode.acc_seg: 96.0641, loss: 0.0906 2023-01-06 15:50:48,567 - mmseg - INFO - Iter [102950/160000] lr: 2.139e-05, eta: 9:07:51, time: 0.547, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0884, decode.acc_seg: 96.1817, loss: 0.0884 2023-01-06 15:51:17,326 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 15:51:17,327 - mmseg - INFO - Iter [103000/160000] lr: 2.138e-05, eta: 9:07:22, time: 0.575, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0944, decode.acc_seg: 96.1223, loss: 0.0944 2023-01-06 15:51:48,393 - mmseg - INFO - Iter [103050/160000] lr: 2.136e-05, eta: 9:06:54, time: 0.621, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0956, decode.acc_seg: 96.0044, loss: 0.0956 2023-01-06 15:52:16,038 - mmseg - INFO - Iter [103100/160000] lr: 2.134e-05, eta: 9:06:25, time: 0.552, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0886, decode.acc_seg: 96.1376, loss: 0.0886 2023-01-06 15:52:43,449 - mmseg - INFO - Iter [103150/160000] lr: 2.132e-05, eta: 9:05:55, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0905, decode.acc_seg: 96.2100, loss: 0.0905 2023-01-06 15:53:12,152 - mmseg - INFO - Iter [103200/160000] lr: 2.130e-05, eta: 9:05:27, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0853, decode.acc_seg: 96.4124, loss: 0.0853 2023-01-06 15:53:40,681 - mmseg - INFO - Iter [103250/160000] lr: 2.128e-05, eta: 9:04:58, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0863, decode.acc_seg: 96.3447, loss: 0.0863 2023-01-06 15:54:09,909 - mmseg - INFO - Iter [103300/160000] lr: 2.126e-05, eta: 9:04:29, time: 0.585, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0879, decode.acc_seg: 96.2677, loss: 0.0879 2023-01-06 15:54:37,978 - mmseg - INFO - Iter [103350/160000] lr: 2.124e-05, eta: 9:04:00, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0838, decode.acc_seg: 96.4141, loss: 0.0838 2023-01-06 15:55:06,369 - mmseg - INFO - Iter [103400/160000] lr: 2.123e-05, eta: 9:03:31, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0893, decode.acc_seg: 96.2585, loss: 0.0893 2023-01-06 15:55:36,560 - mmseg - INFO - Iter [103450/160000] lr: 2.121e-05, eta: 9:03:03, time: 0.604, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0942, decode.acc_seg: 96.0153, loss: 0.0942 2023-01-06 15:56:04,456 - mmseg - INFO - Iter [103500/160000] lr: 2.119e-05, eta: 9:02:33, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0828, decode.acc_seg: 96.4068, loss: 0.0828 2023-01-06 15:56:32,229 - mmseg - INFO - Iter [103550/160000] lr: 2.117e-05, eta: 9:02:04, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0861, decode.acc_seg: 96.3086, loss: 0.0861 2023-01-06 15:57:01,935 - mmseg - INFO - Iter [103600/160000] lr: 2.115e-05, eta: 9:01:36, time: 0.595, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0837, decode.acc_seg: 96.4523, loss: 0.0837 2023-01-06 15:57:29,800 - mmseg - INFO - Iter [103650/160000] lr: 2.113e-05, eta: 9:01:06, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0831, decode.acc_seg: 96.4470, loss: 0.0831 2023-01-06 15:57:57,766 - mmseg - INFO - Iter [103700/160000] lr: 2.111e-05, eta: 9:00:37, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0868, decode.acc_seg: 96.3171, loss: 0.0868 2023-01-06 15:58:25,735 - mmseg - INFO - Iter [103750/160000] lr: 2.109e-05, eta: 9:00:08, time: 0.559, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0838, decode.acc_seg: 96.4183, loss: 0.0838 2023-01-06 15:58:56,781 - mmseg - INFO - Iter [103800/160000] lr: 2.108e-05, eta: 8:59:40, time: 0.622, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0860, decode.acc_seg: 96.3752, loss: 0.0860 2023-01-06 15:59:24,457 - mmseg - INFO - Iter [103850/160000] lr: 2.106e-05, eta: 8:59:11, time: 0.553, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0880, decode.acc_seg: 96.2969, loss: 0.0880 2023-01-06 15:59:53,242 - mmseg - INFO - Iter [103900/160000] lr: 2.104e-05, eta: 8:58:42, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0863, decode.acc_seg: 96.2495, loss: 0.0863 2023-01-06 16:00:22,255 - mmseg - INFO - Iter [103950/160000] lr: 2.102e-05, eta: 8:58:13, time: 0.580, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0881, decode.acc_seg: 96.3073, loss: 0.0881 2023-01-06 16:00:51,687 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 16:00:51,688 - mmseg - INFO - Iter [104000/160000] lr: 2.100e-05, eta: 8:57:45, time: 0.589, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0872, decode.acc_seg: 96.2982, loss: 0.0872 2023-01-06 16:01:19,888 - mmseg - INFO - Iter [104050/160000] lr: 2.098e-05, eta: 8:57:16, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0867, decode.acc_seg: 96.2771, loss: 0.0867 2023-01-06 16:01:47,410 - mmseg - INFO - Iter [104100/160000] lr: 2.096e-05, eta: 8:56:46, time: 0.550, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0838, decode.acc_seg: 96.3640, loss: 0.0838 2023-01-06 16:02:16,768 - mmseg - INFO - Iter [104150/160000] lr: 2.094e-05, eta: 8:56:18, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0878, decode.acc_seg: 96.2525, loss: 0.0878 2023-01-06 16:02:47,493 - mmseg - INFO - Iter [104200/160000] lr: 2.093e-05, eta: 8:55:50, time: 0.614, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0820, decode.acc_seg: 96.4810, loss: 0.0820 2023-01-06 16:03:16,587 - mmseg - INFO - Iter [104250/160000] lr: 2.091e-05, eta: 8:55:21, time: 0.582, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0835, decode.acc_seg: 96.4512, loss: 0.0835 2023-01-06 16:03:45,465 - mmseg - INFO - Iter [104300/160000] lr: 2.089e-05, eta: 8:54:52, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0859, decode.acc_seg: 96.3129, loss: 0.0859 2023-01-06 16:04:14,585 - mmseg - INFO - Iter [104350/160000] lr: 2.087e-05, eta: 8:54:24, time: 0.582, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0868, decode.acc_seg: 96.3500, loss: 0.0868 2023-01-06 16:04:43,207 - mmseg - INFO - Iter [104400/160000] lr: 2.085e-05, eta: 8:53:55, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0870, decode.acc_seg: 96.3230, loss: 0.0870 2023-01-06 16:05:12,394 - mmseg - INFO - Iter [104450/160000] lr: 2.083e-05, eta: 8:53:26, time: 0.583, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0817, decode.acc_seg: 96.4409, loss: 0.0817 2023-01-06 16:05:39,800 - mmseg - INFO - Iter [104500/160000] lr: 2.081e-05, eta: 8:52:57, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0875, decode.acc_seg: 96.2949, loss: 0.0875 2023-01-06 16:06:09,276 - mmseg - INFO - Iter [104550/160000] lr: 2.079e-05, eta: 8:52:28, time: 0.590, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0827, decode.acc_seg: 96.4746, loss: 0.0827 2023-01-06 16:06:36,720 - mmseg - INFO - Iter [104600/160000] lr: 2.078e-05, eta: 8:51:59, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0809, decode.acc_seg: 96.4540, loss: 0.0809 2023-01-06 16:07:04,373 - mmseg - INFO - Iter [104650/160000] lr: 2.076e-05, eta: 8:51:29, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0842, decode.acc_seg: 96.4768, loss: 0.0842 2023-01-06 16:07:32,103 - mmseg - INFO - Iter [104700/160000] lr: 2.074e-05, eta: 8:51:00, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0836, decode.acc_seg: 96.4597, loss: 0.0836 2023-01-06 16:08:00,419 - mmseg - INFO - Iter [104750/160000] lr: 2.072e-05, eta: 8:50:31, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0895, decode.acc_seg: 96.2382, loss: 0.0895 2023-01-06 16:08:29,320 - mmseg - INFO - Iter [104800/160000] lr: 2.070e-05, eta: 8:50:02, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0877, decode.acc_seg: 96.2165, loss: 0.0877 2023-01-06 16:08:58,217 - mmseg - INFO - Iter [104850/160000] lr: 2.068e-05, eta: 8:49:33, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0872, decode.acc_seg: 96.2585, loss: 0.0872 2023-01-06 16:09:26,203 - mmseg - INFO - Iter [104900/160000] lr: 2.066e-05, eta: 8:49:04, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0850, decode.acc_seg: 96.4314, loss: 0.0850 2023-01-06 16:09:56,532 - mmseg - INFO - Iter [104950/160000] lr: 2.064e-05, eta: 8:48:36, time: 0.606, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0860, decode.acc_seg: 96.2890, loss: 0.0860 2023-01-06 16:10:25,742 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 16:10:25,743 - mmseg - INFO - Iter [105000/160000] lr: 2.063e-05, eta: 8:48:08, time: 0.584, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0888, decode.acc_seg: 96.2705, loss: 0.0888 2023-01-06 16:10:53,870 - mmseg - INFO - Iter [105050/160000] lr: 2.061e-05, eta: 8:47:38, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0889, decode.acc_seg: 96.1488, loss: 0.0889 2023-01-06 16:11:21,240 - mmseg - INFO - Iter [105100/160000] lr: 2.059e-05, eta: 8:47:09, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0923, decode.acc_seg: 96.2602, loss: 0.0923 2023-01-06 16:11:49,894 - mmseg - INFO - Iter [105150/160000] lr: 2.057e-05, eta: 8:46:40, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0873, decode.acc_seg: 96.2776, loss: 0.0873 2023-01-06 16:12:18,303 - mmseg - INFO - Iter [105200/160000] lr: 2.055e-05, eta: 8:46:11, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0858, decode.acc_seg: 96.2739, loss: 0.0858 2023-01-06 16:12:46,576 - mmseg - INFO - Iter [105250/160000] lr: 2.053e-05, eta: 8:45:42, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0863, decode.acc_seg: 96.3279, loss: 0.0863 2023-01-06 16:13:16,434 - mmseg - INFO - Iter [105300/160000] lr: 2.051e-05, eta: 8:45:14, time: 0.597, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0900, decode.acc_seg: 96.1746, loss: 0.0900 2023-01-06 16:13:43,611 - mmseg - INFO - Iter [105350/160000] lr: 2.049e-05, eta: 8:44:44, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0853, decode.acc_seg: 96.3918, loss: 0.0853 2023-01-06 16:14:11,382 - mmseg - INFO - Iter [105400/160000] lr: 2.048e-05, eta: 8:44:15, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0847, decode.acc_seg: 96.2938, loss: 0.0847 2023-01-06 16:14:39,869 - mmseg - INFO - Iter [105450/160000] lr: 2.046e-05, eta: 8:43:46, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0897, decode.acc_seg: 96.1447, loss: 0.0897 2023-01-06 16:15:07,255 - mmseg - INFO - Iter [105500/160000] lr: 2.044e-05, eta: 8:43:16, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0923, decode.acc_seg: 96.0702, loss: 0.0923 2023-01-06 16:15:34,280 - mmseg - INFO - Iter [105550/160000] lr: 2.042e-05, eta: 8:42:46, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0890, decode.acc_seg: 96.2720, loss: 0.0890 2023-01-06 16:16:01,985 - mmseg - INFO - Iter [105600/160000] lr: 2.040e-05, eta: 8:42:17, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0897, decode.acc_seg: 96.1663, loss: 0.0897 2023-01-06 16:16:31,932 - mmseg - INFO - Iter [105650/160000] lr: 2.038e-05, eta: 8:41:49, time: 0.598, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0870, decode.acc_seg: 96.2940, loss: 0.0870 2023-01-06 16:16:59,274 - mmseg - INFO - Iter [105700/160000] lr: 2.036e-05, eta: 8:41:19, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0844, decode.acc_seg: 96.2858, loss: 0.0844 2023-01-06 16:17:26,554 - mmseg - INFO - Iter [105750/160000] lr: 2.034e-05, eta: 8:40:50, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0901, decode.acc_seg: 96.1318, loss: 0.0901 2023-01-06 16:17:54,995 - mmseg - INFO - Iter [105800/160000] lr: 2.033e-05, eta: 8:40:21, time: 0.569, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0873, decode.acc_seg: 96.2665, loss: 0.0873 2023-01-06 16:18:23,777 - mmseg - INFO - Iter [105850/160000] lr: 2.031e-05, eta: 8:39:52, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0918, decode.acc_seg: 96.1840, loss: 0.0918 2023-01-06 16:18:52,604 - mmseg - INFO - Iter [105900/160000] lr: 2.029e-05, eta: 8:39:23, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0854, decode.acc_seg: 96.3906, loss: 0.0854 2023-01-06 16:19:21,073 - mmseg - INFO - Iter [105950/160000] lr: 2.027e-05, eta: 8:38:54, time: 0.569, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0865, decode.acc_seg: 96.3405, loss: 0.0865 2023-01-06 16:19:49,840 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 16:19:49,841 - mmseg - INFO - Iter [106000/160000] lr: 2.025e-05, eta: 8:38:25, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0900, decode.acc_seg: 96.1749, loss: 0.0900 2023-01-06 16:20:19,616 - mmseg - INFO - Iter [106050/160000] lr: 2.023e-05, eta: 8:37:57, time: 0.596, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0877, decode.acc_seg: 96.1989, loss: 0.0877 2023-01-06 16:20:48,585 - mmseg - INFO - Iter [106100/160000] lr: 2.021e-05, eta: 8:37:28, time: 0.579, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0874, decode.acc_seg: 96.3307, loss: 0.0874 2023-01-06 16:21:18,505 - mmseg - INFO - Iter [106150/160000] lr: 2.019e-05, eta: 8:37:00, time: 0.598, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0818, decode.acc_seg: 96.5721, loss: 0.0818 2023-01-06 16:21:48,064 - mmseg - INFO - Iter [106200/160000] lr: 2.018e-05, eta: 8:36:32, time: 0.590, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0844, decode.acc_seg: 96.3844, loss: 0.0844 2023-01-06 16:22:16,316 - mmseg - INFO - Iter [106250/160000] lr: 2.016e-05, eta: 8:36:03, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.1000, decode.acc_seg: 95.8558, loss: 0.1000 2023-01-06 16:22:43,413 - mmseg - INFO - Iter [106300/160000] lr: 2.014e-05, eta: 8:35:33, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0963, decode.acc_seg: 96.0856, loss: 0.0963 2023-01-06 16:23:10,591 - mmseg - INFO - Iter [106350/160000] lr: 2.012e-05, eta: 8:35:03, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0890, decode.acc_seg: 96.1817, loss: 0.0890 2023-01-06 16:23:40,491 - mmseg - INFO - Iter [106400/160000] lr: 2.010e-05, eta: 8:34:35, time: 0.598, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0833, decode.acc_seg: 96.4096, loss: 0.0833 2023-01-06 16:24:08,473 - mmseg - INFO - Iter [106450/160000] lr: 2.008e-05, eta: 8:34:06, time: 0.560, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0880, decode.acc_seg: 96.2097, loss: 0.0880 2023-01-06 16:24:35,603 - mmseg - INFO - Iter [106500/160000] lr: 2.006e-05, eta: 8:33:36, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0872, decode.acc_seg: 96.3628, loss: 0.0872 2023-01-06 16:25:04,186 - mmseg - INFO - Iter [106550/160000] lr: 2.004e-05, eta: 8:33:07, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0865, decode.acc_seg: 96.2685, loss: 0.0865 2023-01-06 16:25:33,578 - mmseg - INFO - Iter [106600/160000] lr: 2.003e-05, eta: 8:32:39, time: 0.589, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0874, decode.acc_seg: 96.3620, loss: 0.0874 2023-01-06 16:26:02,021 - mmseg - INFO - Iter [106650/160000] lr: 2.001e-05, eta: 8:32:10, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0828, decode.acc_seg: 96.4975, loss: 0.0828 2023-01-06 16:26:30,826 - mmseg - INFO - Iter [106700/160000] lr: 1.999e-05, eta: 8:31:41, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0836, decode.acc_seg: 96.3563, loss: 0.0836 2023-01-06 16:27:00,056 - mmseg - INFO - Iter [106750/160000] lr: 1.997e-05, eta: 8:31:12, time: 0.585, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0906, decode.acc_seg: 96.1447, loss: 0.0906 2023-01-06 16:27:30,994 - mmseg - INFO - Iter [106800/160000] lr: 1.995e-05, eta: 8:30:45, time: 0.619, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0842, decode.acc_seg: 96.3539, loss: 0.0842 2023-01-06 16:27:59,490 - mmseg - INFO - Iter [106850/160000] lr: 1.993e-05, eta: 8:30:16, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0852, decode.acc_seg: 96.3779, loss: 0.0852 2023-01-06 16:28:27,220 - mmseg - INFO - Iter [106900/160000] lr: 1.991e-05, eta: 8:29:46, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0847, decode.acc_seg: 96.4870, loss: 0.0847 2023-01-06 16:28:56,602 - mmseg - INFO - Iter [106950/160000] lr: 1.989e-05, eta: 8:29:18, time: 0.588, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0888, decode.acc_seg: 96.2131, loss: 0.0888 2023-01-06 16:29:24,185 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 16:29:24,185 - mmseg - INFO - Iter [107000/160000] lr: 1.988e-05, eta: 8:28:48, time: 0.552, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0845, decode.acc_seg: 96.4530, loss: 0.0845 2023-01-06 16:29:51,384 - mmseg - INFO - Iter [107050/160000] lr: 1.986e-05, eta: 8:28:19, time: 0.544, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0868, decode.acc_seg: 96.3746, loss: 0.0868 2023-01-06 16:30:21,249 - mmseg - INFO - Iter [107100/160000] lr: 1.984e-05, eta: 8:27:51, time: 0.597, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0879, decode.acc_seg: 96.3606, loss: 0.0879 2023-01-06 16:30:50,947 - mmseg - INFO - Iter [107150/160000] lr: 1.982e-05, eta: 8:27:22, time: 0.594, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0818, decode.acc_seg: 96.5277, loss: 0.0818 2023-01-06 16:31:18,667 - mmseg - INFO - Iter [107200/160000] lr: 1.980e-05, eta: 8:26:53, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0823, decode.acc_seg: 96.4180, loss: 0.0823 2023-01-06 16:31:46,290 - mmseg - INFO - Iter [107250/160000] lr: 1.978e-05, eta: 8:26:23, time: 0.552, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0896, decode.acc_seg: 96.2587, loss: 0.0896 2023-01-06 16:32:13,524 - mmseg - INFO - Iter [107300/160000] lr: 1.976e-05, eta: 8:25:54, time: 0.545, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0914, decode.acc_seg: 96.1329, loss: 0.0914 2023-01-06 16:32:41,625 - mmseg - INFO - Iter [107350/160000] lr: 1.974e-05, eta: 8:25:25, time: 0.562, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0832, decode.acc_seg: 96.4855, loss: 0.0832 2023-01-06 16:33:10,689 - mmseg - INFO - Iter [107400/160000] lr: 1.973e-05, eta: 8:24:56, time: 0.581, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0853, decode.acc_seg: 96.3794, loss: 0.0853 2023-01-06 16:33:37,921 - mmseg - INFO - Iter [107450/160000] lr: 1.971e-05, eta: 8:24:26, time: 0.544, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0939, decode.acc_seg: 96.0884, loss: 0.0939 2023-01-06 16:34:07,066 - mmseg - INFO - Iter [107500/160000] lr: 1.969e-05, eta: 8:23:58, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0838, decode.acc_seg: 96.3932, loss: 0.0838 2023-01-06 16:34:37,162 - mmseg - INFO - Iter [107550/160000] lr: 1.967e-05, eta: 8:23:30, time: 0.603, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0847, decode.acc_seg: 96.3810, loss: 0.0847 2023-01-06 16:35:05,143 - mmseg - INFO - Iter [107600/160000] lr: 1.965e-05, eta: 8:23:01, time: 0.559, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0870, decode.acc_seg: 96.3652, loss: 0.0870 2023-01-06 16:35:33,012 - mmseg - INFO - Iter [107650/160000] lr: 1.963e-05, eta: 8:22:31, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0831, decode.acc_seg: 96.2701, loss: 0.0831 2023-01-06 16:36:00,809 - mmseg - INFO - Iter [107700/160000] lr: 1.961e-05, eta: 8:22:02, time: 0.556, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0840, decode.acc_seg: 96.4119, loss: 0.0840 2023-01-06 16:36:30,044 - mmseg - INFO - Iter [107750/160000] lr: 1.959e-05, eta: 8:21:33, time: 0.585, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0824, decode.acc_seg: 96.4442, loss: 0.0824 2023-01-06 16:36:58,778 - mmseg - INFO - Iter [107800/160000] lr: 1.958e-05, eta: 8:21:05, time: 0.574, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0814, decode.acc_seg: 96.4999, loss: 0.0814 2023-01-06 16:37:26,024 - mmseg - INFO - Iter [107850/160000] lr: 1.956e-05, eta: 8:20:35, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0843, decode.acc_seg: 96.3375, loss: 0.0843 2023-01-06 16:37:55,685 - mmseg - INFO - Iter [107900/160000] lr: 1.954e-05, eta: 8:20:07, time: 0.593, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0886, decode.acc_seg: 96.2703, loss: 0.0886 2023-01-06 16:38:24,033 - mmseg - INFO - Iter [107950/160000] lr: 1.952e-05, eta: 8:19:38, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0914, decode.acc_seg: 96.2274, loss: 0.0914 2023-01-06 16:38:52,391 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 16:38:52,392 - mmseg - INFO - Iter [108000/160000] lr: 1.950e-05, eta: 8:19:09, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0850, decode.acc_seg: 96.3783, loss: 0.0850 2023-01-06 16:39:20,811 - mmseg - INFO - Iter [108050/160000] lr: 1.948e-05, eta: 8:18:40, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0885, decode.acc_seg: 96.3096, loss: 0.0885 2023-01-06 16:39:48,540 - mmseg - INFO - Iter [108100/160000] lr: 1.946e-05, eta: 8:18:10, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0841, decode.acc_seg: 96.3868, loss: 0.0841 2023-01-06 16:40:17,260 - mmseg - INFO - Iter [108150/160000] lr: 1.944e-05, eta: 8:17:42, time: 0.574, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0915, decode.acc_seg: 96.1753, loss: 0.0915 2023-01-06 16:40:45,592 - mmseg - INFO - Iter [108200/160000] lr: 1.943e-05, eta: 8:17:12, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0861, decode.acc_seg: 96.4126, loss: 0.0861 2023-01-06 16:41:15,448 - mmseg - INFO - Iter [108250/160000] lr: 1.941e-05, eta: 8:16:44, time: 0.597, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0819, decode.acc_seg: 96.4947, loss: 0.0819 2023-01-06 16:41:46,151 - mmseg - INFO - Iter [108300/160000] lr: 1.939e-05, eta: 8:16:16, time: 0.613, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0854, decode.acc_seg: 96.3893, loss: 0.0854 2023-01-06 16:42:14,466 - mmseg - INFO - Iter [108350/160000] lr: 1.937e-05, eta: 8:15:47, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0822, decode.acc_seg: 96.4619, loss: 0.0822 2023-01-06 16:42:42,382 - mmseg - INFO - Iter [108400/160000] lr: 1.935e-05, eta: 8:15:18, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0824, decode.acc_seg: 96.5230, loss: 0.0824 2023-01-06 16:43:09,964 - mmseg - INFO - Iter [108450/160000] lr: 1.933e-05, eta: 8:14:49, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0875, decode.acc_seg: 96.3810, loss: 0.0875 2023-01-06 16:43:38,078 - mmseg - INFO - Iter [108500/160000] lr: 1.931e-05, eta: 8:14:20, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0847, decode.acc_seg: 96.2773, loss: 0.0847 2023-01-06 16:44:05,630 - mmseg - INFO - Iter [108550/160000] lr: 1.929e-05, eta: 8:13:50, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0885, decode.acc_seg: 96.2656, loss: 0.0885 2023-01-06 16:44:34,324 - mmseg - INFO - Iter [108600/160000] lr: 1.928e-05, eta: 8:13:21, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0846, decode.acc_seg: 96.3778, loss: 0.0846 2023-01-06 16:45:06,087 - mmseg - INFO - Iter [108650/160000] lr: 1.926e-05, eta: 8:12:54, time: 0.635, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0826, decode.acc_seg: 96.4444, loss: 0.0826 2023-01-06 16:45:33,516 - mmseg - INFO - Iter [108700/160000] lr: 1.924e-05, eta: 8:12:24, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0810, decode.acc_seg: 96.5078, loss: 0.0810 2023-01-06 16:46:00,621 - mmseg - INFO - Iter [108750/160000] lr: 1.922e-05, eta: 8:11:55, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0836, decode.acc_seg: 96.4468, loss: 0.0836 2023-01-06 16:46:28,770 - mmseg - INFO - Iter [108800/160000] lr: 1.920e-05, eta: 8:11:26, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0865, decode.acc_seg: 96.2838, loss: 0.0865 2023-01-06 16:46:55,971 - mmseg - INFO - Iter [108850/160000] lr: 1.918e-05, eta: 8:10:56, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0909, decode.acc_seg: 96.1172, loss: 0.0909 2023-01-06 16:47:24,532 - mmseg - INFO - Iter [108900/160000] lr: 1.916e-05, eta: 8:10:27, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0837, decode.acc_seg: 96.3384, loss: 0.0837 2023-01-06 16:47:53,003 - mmseg - INFO - Iter [108950/160000] lr: 1.914e-05, eta: 8:09:58, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0850, decode.acc_seg: 96.3075, loss: 0.0850 2023-01-06 16:48:23,242 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 16:48:23,242 - mmseg - INFO - Iter [109000/160000] lr: 1.913e-05, eta: 8:09:30, time: 0.605, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0903, decode.acc_seg: 96.1725, loss: 0.0903 2023-01-06 16:48:51,545 - mmseg - INFO - Iter [109050/160000] lr: 1.911e-05, eta: 8:09:01, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0895, decode.acc_seg: 96.2524, loss: 0.0895 2023-01-06 16:49:20,660 - mmseg - INFO - Iter [109100/160000] lr: 1.909e-05, eta: 8:08:33, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0859, decode.acc_seg: 96.4217, loss: 0.0859 2023-01-06 16:49:49,193 - mmseg - INFO - Iter [109150/160000] lr: 1.907e-05, eta: 8:08:04, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0810, decode.acc_seg: 96.5772, loss: 0.0810 2023-01-06 16:50:17,016 - mmseg - INFO - Iter [109200/160000] lr: 1.905e-05, eta: 8:07:34, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0904, decode.acc_seg: 96.2700, loss: 0.0904 2023-01-06 16:50:44,695 - mmseg - INFO - Iter [109250/160000] lr: 1.903e-05, eta: 8:07:05, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0839, decode.acc_seg: 96.3944, loss: 0.0839 2023-01-06 16:51:12,293 - mmseg - INFO - Iter [109300/160000] lr: 1.901e-05, eta: 8:06:36, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0922, decode.acc_seg: 96.0970, loss: 0.0922 2023-01-06 16:51:40,443 - mmseg - INFO - Iter [109350/160000] lr: 1.899e-05, eta: 8:06:07, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0870, decode.acc_seg: 96.3410, loss: 0.0870 2023-01-06 16:52:10,769 - mmseg - INFO - Iter [109400/160000] lr: 1.898e-05, eta: 8:05:39, time: 0.607, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0882, decode.acc_seg: 96.2927, loss: 0.0882 2023-01-06 16:52:39,351 - mmseg - INFO - Iter [109450/160000] lr: 1.896e-05, eta: 8:05:10, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0831, decode.acc_seg: 96.4281, loss: 0.0831 2023-01-06 16:53:08,024 - mmseg - INFO - Iter [109500/160000] lr: 1.894e-05, eta: 8:04:41, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0826, decode.acc_seg: 96.4196, loss: 0.0826 2023-01-06 16:53:36,550 - mmseg - INFO - Iter [109550/160000] lr: 1.892e-05, eta: 8:04:12, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0870, decode.acc_seg: 96.2461, loss: 0.0870 2023-01-06 16:54:05,134 - mmseg - INFO - Iter [109600/160000] lr: 1.890e-05, eta: 8:03:43, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0899, decode.acc_seg: 96.1018, loss: 0.0899 2023-01-06 16:54:33,279 - mmseg - INFO - Iter [109650/160000] lr: 1.888e-05, eta: 8:03:14, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0886, decode.acc_seg: 96.2767, loss: 0.0886 2023-01-06 16:55:02,515 - mmseg - INFO - Iter [109700/160000] lr: 1.886e-05, eta: 8:02:45, time: 0.585, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0834, decode.acc_seg: 96.3946, loss: 0.0834 2023-01-06 16:55:31,866 - mmseg - INFO - Iter [109750/160000] lr: 1.884e-05, eta: 8:02:17, time: 0.587, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0893, decode.acc_seg: 96.2373, loss: 0.0893 2023-01-06 16:55:59,786 - mmseg - INFO - Iter [109800/160000] lr: 1.883e-05, eta: 8:01:48, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0827, decode.acc_seg: 96.4996, loss: 0.0827 2023-01-06 16:56:27,451 - mmseg - INFO - Iter [109850/160000] lr: 1.881e-05, eta: 8:01:18, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0835, decode.acc_seg: 96.3621, loss: 0.0835 2023-01-06 16:56:55,626 - mmseg - INFO - Iter [109900/160000] lr: 1.879e-05, eta: 8:00:49, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0837, decode.acc_seg: 96.3963, loss: 0.0837 2023-01-06 16:57:24,446 - mmseg - INFO - Iter [109950/160000] lr: 1.877e-05, eta: 8:00:20, time: 0.576, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0827, decode.acc_seg: 96.4636, loss: 0.0827 2023-01-06 16:57:52,207 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 16:57:52,207 - mmseg - INFO - Iter [110000/160000] lr: 1.875e-05, eta: 7:59:51, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0857, decode.acc_seg: 96.3380, loss: 0.0857 2023-01-06 16:58:19,299 - mmseg - INFO - Iter [110050/160000] lr: 1.873e-05, eta: 7:59:22, time: 0.542, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0871, decode.acc_seg: 96.3530, loss: 0.0871 2023-01-06 16:58:47,791 - mmseg - INFO - Iter [110100/160000] lr: 1.871e-05, eta: 7:58:53, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0854, decode.acc_seg: 96.3116, loss: 0.0854 2023-01-06 16:59:18,989 - mmseg - INFO - Iter [110150/160000] lr: 1.869e-05, eta: 7:58:25, time: 0.624, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0831, decode.acc_seg: 96.4580, loss: 0.0831 2023-01-06 16:59:47,930 - mmseg - INFO - Iter [110200/160000] lr: 1.868e-05, eta: 7:57:56, time: 0.578, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0832, decode.acc_seg: 96.4300, loss: 0.0832 2023-01-06 17:00:15,673 - mmseg - INFO - Iter [110250/160000] lr: 1.866e-05, eta: 7:57:27, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0878, decode.acc_seg: 96.1667, loss: 0.0878 2023-01-06 17:00:43,787 - mmseg - INFO - Iter [110300/160000] lr: 1.864e-05, eta: 7:56:58, time: 0.562, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0904, decode.acc_seg: 96.1705, loss: 0.0904 2023-01-06 17:01:11,205 - mmseg - INFO - Iter [110350/160000] lr: 1.862e-05, eta: 7:56:29, time: 0.548, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0835, decode.acc_seg: 96.3667, loss: 0.0835 2023-01-06 17:01:40,318 - mmseg - INFO - Iter [110400/160000] lr: 1.860e-05, eta: 7:56:00, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0858, decode.acc_seg: 96.2679, loss: 0.0858 2023-01-06 17:02:08,477 - mmseg - INFO - Iter [110450/160000] lr: 1.858e-05, eta: 7:55:31, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0830, decode.acc_seg: 96.4545, loss: 0.0830 2023-01-06 17:02:39,145 - mmseg - INFO - Iter [110500/160000] lr: 1.856e-05, eta: 7:55:03, time: 0.613, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0881, decode.acc_seg: 96.3311, loss: 0.0881 2023-01-06 17:03:07,188 - mmseg - INFO - Iter [110550/160000] lr: 1.854e-05, eta: 7:54:34, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0944, decode.acc_seg: 96.1915, loss: 0.0944 2023-01-06 17:03:35,743 - mmseg - INFO - Iter [110600/160000] lr: 1.853e-05, eta: 7:54:05, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0841, decode.acc_seg: 96.3674, loss: 0.0841 2023-01-06 17:04:03,071 - mmseg - INFO - Iter [110650/160000] lr: 1.851e-05, eta: 7:53:35, time: 0.547, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0843, decode.acc_seg: 96.3974, loss: 0.0843 2023-01-06 17:04:32,070 - mmseg - INFO - Iter [110700/160000] lr: 1.849e-05, eta: 7:53:07, time: 0.580, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0928, decode.acc_seg: 96.0436, loss: 0.0928 2023-01-06 17:05:00,703 - mmseg - INFO - Iter [110750/160000] lr: 1.847e-05, eta: 7:52:38, time: 0.572, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0816, decode.acc_seg: 96.5532, loss: 0.0816 2023-01-06 17:05:28,574 - mmseg - INFO - Iter [110800/160000] lr: 1.845e-05, eta: 7:52:09, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0879, decode.acc_seg: 96.2928, loss: 0.0879 2023-01-06 17:05:55,686 - mmseg - INFO - Iter [110850/160000] lr: 1.843e-05, eta: 7:51:39, time: 0.542, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0868, decode.acc_seg: 96.3226, loss: 0.0868 2023-01-06 17:06:26,358 - mmseg - INFO - Iter [110900/160000] lr: 1.841e-05, eta: 7:51:11, time: 0.613, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0800, decode.acc_seg: 96.5639, loss: 0.0800 2023-01-06 17:06:53,807 - mmseg - INFO - Iter [110950/160000] lr: 1.839e-05, eta: 7:50:42, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0837, decode.acc_seg: 96.4152, loss: 0.0837 2023-01-06 17:07:22,050 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 17:07:22,051 - mmseg - INFO - Iter [111000/160000] lr: 1.838e-05, eta: 7:50:13, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0800, decode.acc_seg: 96.5608, loss: 0.0800 2023-01-06 17:07:51,189 - mmseg - INFO - Iter [111050/160000] lr: 1.836e-05, eta: 7:49:44, time: 0.582, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0887, decode.acc_seg: 96.2125, loss: 0.0887 2023-01-06 17:08:19,206 - mmseg - INFO - Iter [111100/160000] lr: 1.834e-05, eta: 7:49:15, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0877, decode.acc_seg: 96.3048, loss: 0.0877 2023-01-06 17:08:46,264 - mmseg - INFO - Iter [111150/160000] lr: 1.832e-05, eta: 7:48:45, time: 0.541, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0863, decode.acc_seg: 96.3654, loss: 0.0863 2023-01-06 17:09:13,504 - mmseg - INFO - Iter [111200/160000] lr: 1.830e-05, eta: 7:48:16, time: 0.545, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0877, decode.acc_seg: 96.3377, loss: 0.0877 2023-01-06 17:09:43,646 - mmseg - INFO - Iter [111250/160000] lr: 1.828e-05, eta: 7:47:48, time: 0.603, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0823, decode.acc_seg: 96.4936, loss: 0.0823 2023-01-06 17:10:12,837 - mmseg - INFO - Iter [111300/160000] lr: 1.826e-05, eta: 7:47:19, time: 0.583, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0815, decode.acc_seg: 96.4508, loss: 0.0815 2023-01-06 17:10:41,336 - mmseg - INFO - Iter [111350/160000] lr: 1.824e-05, eta: 7:46:50, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0818, decode.acc_seg: 96.4677, loss: 0.0818 2023-01-06 17:11:10,430 - mmseg - INFO - Iter [111400/160000] lr: 1.823e-05, eta: 7:46:22, time: 0.581, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0804, decode.acc_seg: 96.6005, loss: 0.0804 2023-01-06 17:11:39,101 - mmseg - INFO - Iter [111450/160000] lr: 1.821e-05, eta: 7:45:53, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0815, decode.acc_seg: 96.4826, loss: 0.0815 2023-01-06 17:12:07,261 - mmseg - INFO - Iter [111500/160000] lr: 1.819e-05, eta: 7:45:24, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0861, decode.acc_seg: 96.3179, loss: 0.0861 2023-01-06 17:12:36,063 - mmseg - INFO - Iter [111550/160000] lr: 1.817e-05, eta: 7:44:55, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0790, decode.acc_seg: 96.5764, loss: 0.0790 2023-01-06 17:13:03,096 - mmseg - INFO - Iter [111600/160000] lr: 1.815e-05, eta: 7:44:25, time: 0.541, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0920, decode.acc_seg: 96.1919, loss: 0.0920 2023-01-06 17:13:33,159 - mmseg - INFO - Iter [111650/160000] lr: 1.813e-05, eta: 7:43:57, time: 0.601, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0882, decode.acc_seg: 96.3533, loss: 0.0882 2023-01-06 17:14:00,155 - mmseg - INFO - Iter [111700/160000] lr: 1.811e-05, eta: 7:43:28, time: 0.540, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0857, decode.acc_seg: 96.3434, loss: 0.0857 2023-01-06 17:14:27,837 - mmseg - INFO - Iter [111750/160000] lr: 1.809e-05, eta: 7:42:58, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0821, decode.acc_seg: 96.3737, loss: 0.0821 2023-01-06 17:14:55,908 - mmseg - INFO - Iter [111800/160000] lr: 1.808e-05, eta: 7:42:29, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0841, decode.acc_seg: 96.4044, loss: 0.0841 2023-01-06 17:15:23,772 - mmseg - INFO - Iter [111850/160000] lr: 1.806e-05, eta: 7:42:00, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0836, decode.acc_seg: 96.3780, loss: 0.0836 2023-01-06 17:15:51,801 - mmseg - INFO - Iter [111900/160000] lr: 1.804e-05, eta: 7:41:31, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0871, decode.acc_seg: 96.3063, loss: 0.0871 2023-01-06 17:16:21,149 - mmseg - INFO - Iter [111950/160000] lr: 1.802e-05, eta: 7:41:02, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0847, decode.acc_seg: 96.4667, loss: 0.0847 2023-01-06 17:16:51,940 - mmseg - INFO - Saving checkpoint at 112000 iterations 2023-01-06 17:16:57,109 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 17:16:57,109 - mmseg - INFO - Iter [112000/160000] lr: 1.800e-05, eta: 7:40:37, time: 0.719, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0822, decode.acc_seg: 96.4577, loss: 0.0822 2023-01-06 17:17:29,439 - mmseg - INFO - per class results: 2023-01-06 17:17:29,442 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 98.0 | 99.11 | | sidewalk | 83.72 | 90.54 | | building | 92.11 | 96.12 | | wall | 54.47 | 63.36 | | fence | 56.54 | 68.82 | | pole | 63.0 | 74.16 | | traffic light | 66.73 | 78.1 | | traffic sign | 75.71 | 83.64 | | vegetation | 91.98 | 96.29 | | terrain | 62.5 | 75.89 | | sky | 94.64 | 98.06 | | person | 78.77 | 89.48 | | rider | 55.82 | 67.41 | | car | 93.77 | 97.56 | | truck | 69.29 | 74.65 | | bus | 76.27 | 83.7 | | train | 64.9 | 75.47 | | motorcycle | 48.61 | 61.25 | | bicycle | 73.26 | 87.23 | +---------------+-------+-------+ 2023-01-06 17:17:29,442 - mmseg - INFO - Summary: 2023-01-06 17:17:29,443 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 95.68 | 73.69 | 82.15 | +-------+-------+-------+ 2023-01-06 17:17:29,443 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 17:17:29,443 - mmseg - INFO - Iter(val) [63] aAcc: 0.9568, mIoU: 0.7369, mAcc: 0.8215, IoU.road: 0.9800, IoU.sidewalk: 0.8372, IoU.building: 0.9211, IoU.wall: 0.5447, IoU.fence: 0.5654, IoU.pole: 0.6300, IoU.traffic light: 0.6673, IoU.traffic sign: 0.7571, IoU.vegetation: 0.9198, IoU.terrain: 0.6250, IoU.sky: 0.9464, IoU.person: 0.7877, IoU.rider: 0.5582, IoU.car: 0.9377, IoU.truck: 0.6929, IoU.bus: 0.7627, IoU.train: 0.6490, IoU.motorcycle: 0.4861, IoU.bicycle: 0.7326, Acc.road: 0.9911, Acc.sidewalk: 0.9054, Acc.building: 0.9612, Acc.wall: 0.6336, Acc.fence: 0.6882, Acc.pole: 0.7416, Acc.traffic light: 0.7810, Acc.traffic sign: 0.8364, Acc.vegetation: 0.9629, Acc.terrain: 0.7589, Acc.sky: 0.9806, Acc.person: 0.8948, Acc.rider: 0.6741, Acc.car: 0.9756, Acc.truck: 0.7465, Acc.bus: 0.8370, Acc.train: 0.7547, Acc.motorcycle: 0.6125, Acc.bicycle: 0.8723 2023-01-06 17:17:58,549 - mmseg - INFO - Iter [112050/160000] lr: 1.798e-05, eta: 7:40:22, time: 1.229, data_time: 0.658, memory: 10576, decode.loss_ce: 0.0894, decode.acc_seg: 96.1657, loss: 0.0894 2023-01-06 17:18:25,744 - mmseg - INFO - Iter [112100/160000] lr: 1.796e-05, eta: 7:39:52, time: 0.544, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0814, decode.acc_seg: 96.5357, loss: 0.0814 2023-01-06 17:18:54,244 - mmseg - INFO - Iter [112150/160000] lr: 1.794e-05, eta: 7:39:23, time: 0.569, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0896, decode.acc_seg: 96.2211, loss: 0.0896 2023-01-06 17:19:22,451 - mmseg - INFO - Iter [112200/160000] lr: 1.793e-05, eta: 7:38:54, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0795, decode.acc_seg: 96.6208, loss: 0.0795 2023-01-06 17:19:51,582 - mmseg - INFO - Iter [112250/160000] lr: 1.791e-05, eta: 7:38:26, time: 0.583, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0836, decode.acc_seg: 96.4643, loss: 0.0836 2023-01-06 17:20:20,625 - mmseg - INFO - Iter [112300/160000] lr: 1.789e-05, eta: 7:37:57, time: 0.581, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0941, decode.acc_seg: 96.0605, loss: 0.0941 2023-01-06 17:20:49,878 - mmseg - INFO - Iter [112350/160000] lr: 1.787e-05, eta: 7:37:28, time: 0.585, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0861, decode.acc_seg: 96.2840, loss: 0.0861 2023-01-06 17:21:19,293 - mmseg - INFO - Iter [112400/160000] lr: 1.785e-05, eta: 7:37:00, time: 0.588, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0861, decode.acc_seg: 96.3551, loss: 0.0861 2023-01-06 17:21:47,261 - mmseg - INFO - Iter [112450/160000] lr: 1.783e-05, eta: 7:36:31, time: 0.559, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0833, decode.acc_seg: 96.4628, loss: 0.0833 2023-01-06 17:22:15,028 - mmseg - INFO - Iter [112500/160000] lr: 1.781e-05, eta: 7:36:01, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0871, decode.acc_seg: 96.3485, loss: 0.0871 2023-01-06 17:22:44,532 - mmseg - INFO - Iter [112550/160000] lr: 1.779e-05, eta: 7:35:33, time: 0.590, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0865, decode.acc_seg: 96.3793, loss: 0.0865 2023-01-06 17:23:12,984 - mmseg - INFO - Iter [112600/160000] lr: 1.778e-05, eta: 7:35:04, time: 0.569, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0823, decode.acc_seg: 96.3229, loss: 0.0823 2023-01-06 17:23:42,106 - mmseg - INFO - Iter [112650/160000] lr: 1.776e-05, eta: 7:34:35, time: 0.582, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0826, decode.acc_seg: 96.4845, loss: 0.0826 2023-01-06 17:24:10,659 - mmseg - INFO - Iter [112700/160000] lr: 1.774e-05, eta: 7:34:06, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0846, decode.acc_seg: 96.5044, loss: 0.0846 2023-01-06 17:24:41,138 - mmseg - INFO - Iter [112750/160000] lr: 1.772e-05, eta: 7:33:38, time: 0.610, data_time: 0.056, memory: 10576, decode.loss_ce: 0.0818, decode.acc_seg: 96.4313, loss: 0.0818 2023-01-06 17:25:08,589 - mmseg - INFO - Iter [112800/160000] lr: 1.770e-05, eta: 7:33:09, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0780, decode.acc_seg: 96.5984, loss: 0.0780 2023-01-06 17:25:36,622 - mmseg - INFO - Iter [112850/160000] lr: 1.768e-05, eta: 7:32:40, time: 0.561, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0816, decode.acc_seg: 96.5033, loss: 0.0816 2023-01-06 17:26:04,353 - mmseg - INFO - Iter [112900/160000] lr: 1.766e-05, eta: 7:32:11, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0876, decode.acc_seg: 96.3467, loss: 0.0876 2023-01-06 17:26:32,526 - mmseg - INFO - Iter [112950/160000] lr: 1.764e-05, eta: 7:31:42, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0882, decode.acc_seg: 96.2554, loss: 0.0882 2023-01-06 17:27:00,251 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 17:27:00,252 - mmseg - INFO - Iter [113000/160000] lr: 1.763e-05, eta: 7:31:12, time: 0.554, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0812, decode.acc_seg: 96.5185, loss: 0.0812 2023-01-06 17:27:28,491 - mmseg - INFO - Iter [113050/160000] lr: 1.761e-05, eta: 7:30:43, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0840, decode.acc_seg: 96.3961, loss: 0.0840 2023-01-06 17:27:59,421 - mmseg - INFO - Iter [113100/160000] lr: 1.759e-05, eta: 7:30:15, time: 0.619, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0796, decode.acc_seg: 96.4372, loss: 0.0796 2023-01-06 17:28:27,009 - mmseg - INFO - Iter [113150/160000] lr: 1.757e-05, eta: 7:29:46, time: 0.552, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0831, decode.acc_seg: 96.4935, loss: 0.0831 2023-01-06 17:28:54,974 - mmseg - INFO - Iter [113200/160000] lr: 1.755e-05, eta: 7:29:17, time: 0.558, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0831, decode.acc_seg: 96.4273, loss: 0.0831 2023-01-06 17:29:23,238 - mmseg - INFO - Iter [113250/160000] lr: 1.753e-05, eta: 7:28:48, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0888, decode.acc_seg: 96.3054, loss: 0.0888 2023-01-06 17:29:50,920 - mmseg - INFO - Iter [113300/160000] lr: 1.751e-05, eta: 7:28:19, time: 0.554, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0805, decode.acc_seg: 96.5515, loss: 0.0805 2023-01-06 17:30:18,460 - mmseg - INFO - Iter [113350/160000] lr: 1.749e-05, eta: 7:27:49, time: 0.551, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0872, decode.acc_seg: 96.3324, loss: 0.0872 2023-01-06 17:30:45,889 - mmseg - INFO - Iter [113400/160000] lr: 1.748e-05, eta: 7:27:20, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0812, decode.acc_seg: 96.5171, loss: 0.0812 2023-01-06 17:31:13,693 - mmseg - INFO - Iter [113450/160000] lr: 1.746e-05, eta: 7:26:51, time: 0.556, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0808, decode.acc_seg: 96.5136, loss: 0.0808 2023-01-06 17:31:42,948 - mmseg - INFO - Iter [113500/160000] lr: 1.744e-05, eta: 7:26:22, time: 0.585, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0888, decode.acc_seg: 96.2817, loss: 0.0888 2023-01-06 17:32:11,811 - mmseg - INFO - Iter [113550/160000] lr: 1.742e-05, eta: 7:25:53, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0869, decode.acc_seg: 96.2475, loss: 0.0869 2023-01-06 17:32:40,378 - mmseg - INFO - Iter [113600/160000] lr: 1.740e-05, eta: 7:25:24, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0857, decode.acc_seg: 96.3544, loss: 0.0857 2023-01-06 17:33:08,451 - mmseg - INFO - Iter [113650/160000] lr: 1.738e-05, eta: 7:24:55, time: 0.562, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0811, decode.acc_seg: 96.4716, loss: 0.0811 2023-01-06 17:33:35,596 - mmseg - INFO - Iter [113700/160000] lr: 1.736e-05, eta: 7:24:26, time: 0.543, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0840, decode.acc_seg: 96.3276, loss: 0.0840 2023-01-06 17:34:03,055 - mmseg - INFO - Iter [113750/160000] lr: 1.734e-05, eta: 7:23:57, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0806, decode.acc_seg: 96.5029, loss: 0.0806 2023-01-06 17:34:30,338 - mmseg - INFO - Iter [113800/160000] lr: 1.733e-05, eta: 7:23:27, time: 0.545, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0840, decode.acc_seg: 96.4470, loss: 0.0840 2023-01-06 17:34:59,980 - mmseg - INFO - Iter [113850/160000] lr: 1.731e-05, eta: 7:22:59, time: 0.594, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0839, decode.acc_seg: 96.4887, loss: 0.0839 2023-01-06 17:35:29,551 - mmseg - INFO - Iter [113900/160000] lr: 1.729e-05, eta: 7:22:30, time: 0.591, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0834, decode.acc_seg: 96.4262, loss: 0.0834 2023-01-06 17:35:57,407 - mmseg - INFO - Iter [113950/160000] lr: 1.727e-05, eta: 7:22:01, time: 0.557, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0806, decode.acc_seg: 96.5829, loss: 0.0806 2023-01-06 17:36:24,723 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 17:36:24,723 - mmseg - INFO - Iter [114000/160000] lr: 1.725e-05, eta: 7:21:32, time: 0.546, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0868, decode.acc_seg: 96.2883, loss: 0.0868 2023-01-06 17:36:52,395 - mmseg - INFO - Iter [114050/160000] lr: 1.723e-05, eta: 7:21:02, time: 0.553, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0860, decode.acc_seg: 96.3590, loss: 0.0860 2023-01-06 17:37:21,648 - mmseg - INFO - Iter [114100/160000] lr: 1.721e-05, eta: 7:20:34, time: 0.585, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0846, decode.acc_seg: 96.4187, loss: 0.0846 2023-01-06 17:37:49,878 - mmseg - INFO - Iter [114150/160000] lr: 1.719e-05, eta: 7:20:05, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0906, decode.acc_seg: 96.2512, loss: 0.0906 2023-01-06 17:38:19,566 - mmseg - INFO - Iter [114200/160000] lr: 1.718e-05, eta: 7:19:36, time: 0.593, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0843, decode.acc_seg: 96.4328, loss: 0.0843 2023-01-06 17:38:50,643 - mmseg - INFO - Iter [114250/160000] lr: 1.716e-05, eta: 7:19:08, time: 0.622, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0850, decode.acc_seg: 96.4226, loss: 0.0850 2023-01-06 17:39:19,770 - mmseg - INFO - Iter [114300/160000] lr: 1.714e-05, eta: 7:18:40, time: 0.582, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0841, decode.acc_seg: 96.4654, loss: 0.0841 2023-01-06 17:39:47,479 - mmseg - INFO - Iter [114350/160000] lr: 1.712e-05, eta: 7:18:10, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0799, decode.acc_seg: 96.5484, loss: 0.0799 2023-01-06 17:40:14,961 - mmseg - INFO - Iter [114400/160000] lr: 1.710e-05, eta: 7:17:41, time: 0.550, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0840, decode.acc_seg: 96.4224, loss: 0.0840 2023-01-06 17:40:43,739 - mmseg - INFO - Iter [114450/160000] lr: 1.708e-05, eta: 7:17:12, time: 0.576, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0897, decode.acc_seg: 96.2459, loss: 0.0897 2023-01-06 17:41:10,682 - mmseg - INFO - Iter [114500/160000] lr: 1.706e-05, eta: 7:16:43, time: 0.539, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0876, decode.acc_seg: 96.2844, loss: 0.0876 2023-01-06 17:41:38,866 - mmseg - INFO - Iter [114550/160000] lr: 1.704e-05, eta: 7:16:14, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0814, decode.acc_seg: 96.4944, loss: 0.0814 2023-01-06 17:42:09,167 - mmseg - INFO - Iter [114600/160000] lr: 1.703e-05, eta: 7:15:46, time: 0.606, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0851, decode.acc_seg: 96.3726, loss: 0.0851 2023-01-06 17:42:37,747 - mmseg - INFO - Iter [114650/160000] lr: 1.701e-05, eta: 7:15:17, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0855, decode.acc_seg: 96.2949, loss: 0.0855 2023-01-06 17:43:07,131 - mmseg - INFO - Iter [114700/160000] lr: 1.699e-05, eta: 7:14:48, time: 0.588, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0867, decode.acc_seg: 96.3807, loss: 0.0867 2023-01-06 17:43:35,584 - mmseg - INFO - Iter [114750/160000] lr: 1.697e-05, eta: 7:14:19, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0838, decode.acc_seg: 96.4221, loss: 0.0838 2023-01-06 17:44:03,626 - mmseg - INFO - Iter [114800/160000] lr: 1.695e-05, eta: 7:13:50, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0818, decode.acc_seg: 96.4320, loss: 0.0818 2023-01-06 17:44:30,676 - mmseg - INFO - Iter [114850/160000] lr: 1.693e-05, eta: 7:13:21, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0822, decode.acc_seg: 96.4055, loss: 0.0822 2023-01-06 17:44:58,264 - mmseg - INFO - Iter [114900/160000] lr: 1.691e-05, eta: 7:12:51, time: 0.552, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0816, decode.acc_seg: 96.4861, loss: 0.0816 2023-01-06 17:45:27,969 - mmseg - INFO - Iter [114950/160000] lr: 1.689e-05, eta: 7:12:23, time: 0.594, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0871, decode.acc_seg: 96.3416, loss: 0.0871 2023-01-06 17:45:55,910 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 17:45:55,910 - mmseg - INFO - Iter [115000/160000] lr: 1.688e-05, eta: 7:11:54, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0841, decode.acc_seg: 96.4478, loss: 0.0841 2023-01-06 17:46:23,600 - mmseg - INFO - Iter [115050/160000] lr: 1.686e-05, eta: 7:11:25, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0854, decode.acc_seg: 96.3796, loss: 0.0854 2023-01-06 17:46:50,669 - mmseg - INFO - Iter [115100/160000] lr: 1.684e-05, eta: 7:10:55, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0863, decode.acc_seg: 96.2838, loss: 0.0863 2023-01-06 17:47:18,762 - mmseg - INFO - Iter [115150/160000] lr: 1.682e-05, eta: 7:10:26, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0792, decode.acc_seg: 96.5624, loss: 0.0792 2023-01-06 17:47:46,552 - mmseg - INFO - Iter [115200/160000] lr: 1.680e-05, eta: 7:09:57, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0798, decode.acc_seg: 96.5281, loss: 0.0798 2023-01-06 17:48:14,022 - mmseg - INFO - Iter [115250/160000] lr: 1.678e-05, eta: 7:09:28, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0839, decode.acc_seg: 96.4324, loss: 0.0839 2023-01-06 17:48:43,105 - mmseg - INFO - Iter [115300/160000] lr: 1.676e-05, eta: 7:08:59, time: 0.582, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0873, decode.acc_seg: 96.2911, loss: 0.0873 2023-01-06 17:49:13,396 - mmseg - INFO - Iter [115350/160000] lr: 1.674e-05, eta: 7:08:31, time: 0.606, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0866, decode.acc_seg: 96.2704, loss: 0.0866 2023-01-06 17:49:41,777 - mmseg - INFO - Iter [115400/160000] lr: 1.673e-05, eta: 7:08:02, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0840, decode.acc_seg: 96.4888, loss: 0.0840 2023-01-06 17:50:08,903 - mmseg - INFO - Iter [115450/160000] lr: 1.671e-05, eta: 7:07:32, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0789, decode.acc_seg: 96.6607, loss: 0.0789 2023-01-06 17:50:36,112 - mmseg - INFO - Iter [115500/160000] lr: 1.669e-05, eta: 7:07:03, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0864, decode.acc_seg: 96.4477, loss: 0.0864 2023-01-06 17:51:04,283 - mmseg - INFO - Iter [115550/160000] lr: 1.667e-05, eta: 7:06:34, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0829, decode.acc_seg: 96.4664, loss: 0.0829 2023-01-06 17:51:32,060 - mmseg - INFO - Iter [115600/160000] lr: 1.665e-05, eta: 7:06:05, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0833, decode.acc_seg: 96.4919, loss: 0.0833 2023-01-06 17:51:59,499 - mmseg - INFO - Iter [115650/160000] lr: 1.663e-05, eta: 7:05:35, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0819, decode.acc_seg: 96.3735, loss: 0.0819 2023-01-06 17:52:30,987 - mmseg - INFO - Iter [115700/160000] lr: 1.661e-05, eta: 7:05:08, time: 0.630, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0853, decode.acc_seg: 96.4549, loss: 0.0853 2023-01-06 17:53:00,586 - mmseg - INFO - Iter [115750/160000] lr: 1.659e-05, eta: 7:04:39, time: 0.592, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0875, decode.acc_seg: 96.3412, loss: 0.0875 2023-01-06 17:53:29,251 - mmseg - INFO - Iter [115800/160000] lr: 1.658e-05, eta: 7:04:10, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0827, decode.acc_seg: 96.4461, loss: 0.0827 2023-01-06 17:53:56,221 - mmseg - INFO - Iter [115850/160000] lr: 1.656e-05, eta: 7:03:41, time: 0.539, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0807, decode.acc_seg: 96.5184, loss: 0.0807 2023-01-06 17:54:23,176 - mmseg - INFO - Iter [115900/160000] lr: 1.654e-05, eta: 7:03:11, time: 0.539, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0813, decode.acc_seg: 96.5558, loss: 0.0813 2023-01-06 17:54:50,886 - mmseg - INFO - Iter [115950/160000] lr: 1.652e-05, eta: 7:02:42, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0809, decode.acc_seg: 96.5327, loss: 0.0809 2023-01-06 17:55:18,081 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 17:55:18,081 - mmseg - INFO - Iter [116000/160000] lr: 1.650e-05, eta: 7:02:13, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0849, decode.acc_seg: 96.3753, loss: 0.0849 2023-01-06 17:55:46,935 - mmseg - INFO - Iter [116050/160000] lr: 1.648e-05, eta: 7:01:44, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0792, decode.acc_seg: 96.5509, loss: 0.0792 2023-01-06 17:56:16,780 - mmseg - INFO - Iter [116100/160000] lr: 1.646e-05, eta: 7:01:16, time: 0.596, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0843, decode.acc_seg: 96.4499, loss: 0.0843 2023-01-06 17:56:44,761 - mmseg - INFO - Iter [116150/160000] lr: 1.644e-05, eta: 7:00:46, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0841, decode.acc_seg: 96.3423, loss: 0.0841 2023-01-06 17:57:14,074 - mmseg - INFO - Iter [116200/160000] lr: 1.643e-05, eta: 7:00:18, time: 0.586, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0833, decode.acc_seg: 96.5007, loss: 0.0833 2023-01-06 17:57:43,027 - mmseg - INFO - Iter [116250/160000] lr: 1.641e-05, eta: 6:59:49, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0847, decode.acc_seg: 96.4699, loss: 0.0847 2023-01-06 17:58:10,667 - mmseg - INFO - Iter [116300/160000] lr: 1.639e-05, eta: 6:59:20, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0831, decode.acc_seg: 96.4825, loss: 0.0831 2023-01-06 17:58:39,979 - mmseg - INFO - Iter [116350/160000] lr: 1.637e-05, eta: 6:58:51, time: 0.586, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0798, decode.acc_seg: 96.5387, loss: 0.0798 2023-01-06 17:59:08,319 - mmseg - INFO - Iter [116400/160000] lr: 1.635e-05, eta: 6:58:22, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0845, decode.acc_seg: 96.4387, loss: 0.0845 2023-01-06 17:59:38,870 - mmseg - INFO - Iter [116450/160000] lr: 1.633e-05, eta: 6:57:54, time: 0.611, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0843, decode.acc_seg: 96.3864, loss: 0.0843 2023-01-06 18:00:07,349 - mmseg - INFO - Iter [116500/160000] lr: 1.631e-05, eta: 6:57:25, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0801, decode.acc_seg: 96.6076, loss: 0.0801 2023-01-06 18:00:36,184 - mmseg - INFO - Iter [116550/160000] lr: 1.629e-05, eta: 6:56:57, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0867, decode.acc_seg: 96.2940, loss: 0.0867 2023-01-06 18:01:03,961 - mmseg - INFO - Iter [116600/160000] lr: 1.628e-05, eta: 6:56:27, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0809, decode.acc_seg: 96.4242, loss: 0.0809 2023-01-06 18:01:32,520 - mmseg - INFO - Iter [116650/160000] lr: 1.626e-05, eta: 6:55:59, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0761, decode.acc_seg: 96.7230, loss: 0.0761 2023-01-06 18:02:00,837 - mmseg - INFO - Iter [116700/160000] lr: 1.624e-05, eta: 6:55:30, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0824, decode.acc_seg: 96.5041, loss: 0.0824 2023-01-06 18:02:28,969 - mmseg - INFO - Iter [116750/160000] lr: 1.622e-05, eta: 6:55:01, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0800, decode.acc_seg: 96.5296, loss: 0.0800 2023-01-06 18:02:56,792 - mmseg - INFO - Iter [116800/160000] lr: 1.620e-05, eta: 6:54:31, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0876, decode.acc_seg: 96.2759, loss: 0.0876 2023-01-06 18:03:26,952 - mmseg - INFO - Iter [116850/160000] lr: 1.618e-05, eta: 6:54:03, time: 0.602, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0827, decode.acc_seg: 96.5091, loss: 0.0827 2023-01-06 18:03:55,939 - mmseg - INFO - Iter [116900/160000] lr: 1.616e-05, eta: 6:53:34, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0780, decode.acc_seg: 96.6589, loss: 0.0780 2023-01-06 18:04:24,803 - mmseg - INFO - Iter [116950/160000] lr: 1.614e-05, eta: 6:53:06, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0832, decode.acc_seg: 96.4273, loss: 0.0832 2023-01-06 18:04:52,974 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 18:04:52,974 - mmseg - INFO - Iter [117000/160000] lr: 1.613e-05, eta: 6:52:37, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0809, decode.acc_seg: 96.5708, loss: 0.0809 2023-01-06 18:05:21,402 - mmseg - INFO - Iter [117050/160000] lr: 1.611e-05, eta: 6:52:08, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0864, decode.acc_seg: 96.3207, loss: 0.0864 2023-01-06 18:05:49,868 - mmseg - INFO - Iter [117100/160000] lr: 1.609e-05, eta: 6:51:39, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0827, decode.acc_seg: 96.4810, loss: 0.0827 2023-01-06 18:06:17,530 - mmseg - INFO - Iter [117150/160000] lr: 1.607e-05, eta: 6:51:10, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0825, decode.acc_seg: 96.4940, loss: 0.0825 2023-01-06 18:06:47,682 - mmseg - INFO - Iter [117200/160000] lr: 1.605e-05, eta: 6:50:41, time: 0.603, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0821, decode.acc_seg: 96.4367, loss: 0.0821 2023-01-06 18:07:16,261 - mmseg - INFO - Iter [117250/160000] lr: 1.603e-05, eta: 6:50:12, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0827, decode.acc_seg: 96.4862, loss: 0.0827 2023-01-06 18:07:43,852 - mmseg - INFO - Iter [117300/160000] lr: 1.601e-05, eta: 6:49:43, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0849, decode.acc_seg: 96.3850, loss: 0.0849 2023-01-06 18:08:11,539 - mmseg - INFO - Iter [117350/160000] lr: 1.599e-05, eta: 6:49:14, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0784, decode.acc_seg: 96.6054, loss: 0.0784 2023-01-06 18:08:39,111 - mmseg - INFO - Iter [117400/160000] lr: 1.598e-05, eta: 6:48:45, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0805, decode.acc_seg: 96.5697, loss: 0.0805 2023-01-06 18:09:06,830 - mmseg - INFO - Iter [117450/160000] lr: 1.596e-05, eta: 6:48:16, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0849, decode.acc_seg: 96.3103, loss: 0.0849 2023-01-06 18:09:35,268 - mmseg - INFO - Iter [117500/160000] lr: 1.594e-05, eta: 6:47:47, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0802, decode.acc_seg: 96.6574, loss: 0.0802 2023-01-06 18:10:02,676 - mmseg - INFO - Iter [117550/160000] lr: 1.592e-05, eta: 6:47:17, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0819, decode.acc_seg: 96.3981, loss: 0.0819 2023-01-06 18:10:32,854 - mmseg - INFO - Iter [117600/160000] lr: 1.590e-05, eta: 6:46:49, time: 0.604, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0779, decode.acc_seg: 96.6069, loss: 0.0779 2023-01-06 18:11:00,400 - mmseg - INFO - Iter [117650/160000] lr: 1.588e-05, eta: 6:46:20, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0820, decode.acc_seg: 96.5238, loss: 0.0820 2023-01-06 18:11:27,984 - mmseg - INFO - Iter [117700/160000] lr: 1.586e-05, eta: 6:45:51, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0814, decode.acc_seg: 96.4574, loss: 0.0814 2023-01-06 18:11:57,152 - mmseg - INFO - Iter [117750/160000] lr: 1.584e-05, eta: 6:45:22, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0800, decode.acc_seg: 96.5848, loss: 0.0800 2023-01-06 18:12:25,144 - mmseg - INFO - Iter [117800/160000] lr: 1.583e-05, eta: 6:44:53, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0810, decode.acc_seg: 96.5134, loss: 0.0810 2023-01-06 18:12:53,480 - mmseg - INFO - Iter [117850/160000] lr: 1.581e-05, eta: 6:44:24, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0808, decode.acc_seg: 96.4984, loss: 0.0808 2023-01-06 18:13:22,219 - mmseg - INFO - Iter [117900/160000] lr: 1.579e-05, eta: 6:43:55, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0812, decode.acc_seg: 96.4914, loss: 0.0812 2023-01-06 18:13:52,120 - mmseg - INFO - Iter [117950/160000] lr: 1.577e-05, eta: 6:43:27, time: 0.597, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0783, decode.acc_seg: 96.6709, loss: 0.0783 2023-01-06 18:14:19,738 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 18:14:19,739 - mmseg - INFO - Iter [118000/160000] lr: 1.575e-05, eta: 6:42:58, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0813, decode.acc_seg: 96.3541, loss: 0.0813 2023-01-06 18:14:49,813 - mmseg - INFO - Iter [118050/160000] lr: 1.573e-05, eta: 6:42:29, time: 0.601, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0815, decode.acc_seg: 96.4473, loss: 0.0815 2023-01-06 18:15:18,236 - mmseg - INFO - Iter [118100/160000] lr: 1.571e-05, eta: 6:42:00, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0787, decode.acc_seg: 96.5046, loss: 0.0787 2023-01-06 18:15:46,043 - mmseg - INFO - Iter [118150/160000] lr: 1.569e-05, eta: 6:41:31, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0808, decode.acc_seg: 96.4752, loss: 0.0808 2023-01-06 18:16:14,501 - mmseg - INFO - Iter [118200/160000] lr: 1.568e-05, eta: 6:41:02, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0805, decode.acc_seg: 96.5861, loss: 0.0805 2023-01-06 18:16:42,124 - mmseg - INFO - Iter [118250/160000] lr: 1.566e-05, eta: 6:40:33, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0805, decode.acc_seg: 96.5727, loss: 0.0805 2023-01-06 18:17:12,316 - mmseg - INFO - Iter [118300/160000] lr: 1.564e-05, eta: 6:40:05, time: 0.603, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0853, decode.acc_seg: 96.4045, loss: 0.0853 2023-01-06 18:17:40,828 - mmseg - INFO - Iter [118350/160000] lr: 1.562e-05, eta: 6:39:36, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0879, decode.acc_seg: 96.3403, loss: 0.0879 2023-01-06 18:18:09,381 - mmseg - INFO - Iter [118400/160000] lr: 1.560e-05, eta: 6:39:07, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0810, decode.acc_seg: 96.4038, loss: 0.0810 2023-01-06 18:18:38,190 - mmseg - INFO - Iter [118450/160000] lr: 1.558e-05, eta: 6:38:38, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0811, decode.acc_seg: 96.6101, loss: 0.0811 2023-01-06 18:19:05,253 - mmseg - INFO - Iter [118500/160000] lr: 1.556e-05, eta: 6:38:09, time: 0.542, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0821, decode.acc_seg: 96.4782, loss: 0.0821 2023-01-06 18:19:33,542 - mmseg - INFO - Iter [118550/160000] lr: 1.554e-05, eta: 6:37:40, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0802, decode.acc_seg: 96.5998, loss: 0.0802 2023-01-06 18:20:03,000 - mmseg - INFO - Iter [118600/160000] lr: 1.553e-05, eta: 6:37:12, time: 0.589, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0891, decode.acc_seg: 96.2904, loss: 0.0891 2023-01-06 18:20:32,286 - mmseg - INFO - Iter [118650/160000] lr: 1.551e-05, eta: 6:36:43, time: 0.587, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0875, decode.acc_seg: 96.3189, loss: 0.0875 2023-01-06 18:21:01,983 - mmseg - INFO - Iter [118700/160000] lr: 1.549e-05, eta: 6:36:14, time: 0.594, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0790, decode.acc_seg: 96.5838, loss: 0.0790 2023-01-06 18:21:30,696 - mmseg - INFO - Iter [118750/160000] lr: 1.547e-05, eta: 6:35:46, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0962, decode.acc_seg: 96.1314, loss: 0.0962 2023-01-06 18:21:59,309 - mmseg - INFO - Iter [118800/160000] lr: 1.545e-05, eta: 6:35:17, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0871, decode.acc_seg: 96.2802, loss: 0.0871 2023-01-06 18:22:27,401 - mmseg - INFO - Iter [118850/160000] lr: 1.543e-05, eta: 6:34:48, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0885, decode.acc_seg: 96.2221, loss: 0.0885 2023-01-06 18:22:55,984 - mmseg - INFO - Iter [118900/160000] lr: 1.541e-05, eta: 6:34:19, time: 0.572, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0836, decode.acc_seg: 96.4113, loss: 0.0836 2023-01-06 18:23:24,334 - mmseg - INFO - Iter [118950/160000] lr: 1.539e-05, eta: 6:33:50, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0854, decode.acc_seg: 96.2794, loss: 0.0854 2023-01-06 18:23:53,359 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 18:23:53,360 - mmseg - INFO - Iter [119000/160000] lr: 1.538e-05, eta: 6:33:21, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0765, decode.acc_seg: 96.7426, loss: 0.0765 2023-01-06 18:24:22,686 - mmseg - INFO - Iter [119050/160000] lr: 1.536e-05, eta: 6:32:53, time: 0.586, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0811, decode.acc_seg: 96.6226, loss: 0.0811 2023-01-06 18:24:50,176 - mmseg - INFO - Iter [119100/160000] lr: 1.534e-05, eta: 6:32:24, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0789, decode.acc_seg: 96.6117, loss: 0.0789 2023-01-06 18:25:17,457 - mmseg - INFO - Iter [119150/160000] lr: 1.532e-05, eta: 6:31:54, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0794, decode.acc_seg: 96.6063, loss: 0.0794 2023-01-06 18:25:47,145 - mmseg - INFO - Iter [119200/160000] lr: 1.530e-05, eta: 6:31:26, time: 0.594, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0824, decode.acc_seg: 96.4823, loss: 0.0824 2023-01-06 18:26:16,223 - mmseg - INFO - Iter [119250/160000] lr: 1.528e-05, eta: 6:30:57, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0816, decode.acc_seg: 96.4642, loss: 0.0816 2023-01-06 18:26:43,739 - mmseg - INFO - Iter [119300/160000] lr: 1.526e-05, eta: 6:30:28, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0917, decode.acc_seg: 96.1978, loss: 0.0917 2023-01-06 18:27:11,771 - mmseg - INFO - Iter [119350/160000] lr: 1.524e-05, eta: 6:29:59, time: 0.561, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0855, decode.acc_seg: 96.2262, loss: 0.0855 2023-01-06 18:27:40,333 - mmseg - INFO - Iter [119400/160000] lr: 1.523e-05, eta: 6:29:30, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0799, decode.acc_seg: 96.5780, loss: 0.0799 2023-01-06 18:28:10,778 - mmseg - INFO - Iter [119450/160000] lr: 1.521e-05, eta: 6:29:02, time: 0.608, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0799, decode.acc_seg: 96.4760, loss: 0.0799 2023-01-06 18:28:40,243 - mmseg - INFO - Iter [119500/160000] lr: 1.519e-05, eta: 6:28:33, time: 0.590, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0829, decode.acc_seg: 96.4582, loss: 0.0829 2023-01-06 18:29:09,170 - mmseg - INFO - Iter [119550/160000] lr: 1.517e-05, eta: 6:28:04, time: 0.578, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0834, decode.acc_seg: 96.3982, loss: 0.0834 2023-01-06 18:29:36,894 - mmseg - INFO - Iter [119600/160000] lr: 1.515e-05, eta: 6:27:35, time: 0.554, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0782, decode.acc_seg: 96.5735, loss: 0.0782 2023-01-06 18:30:05,227 - mmseg - INFO - Iter [119650/160000] lr: 1.513e-05, eta: 6:27:06, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0805, decode.acc_seg: 96.6017, loss: 0.0805 2023-01-06 18:30:34,559 - mmseg - INFO - Iter [119700/160000] lr: 1.511e-05, eta: 6:26:38, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0783, decode.acc_seg: 96.6320, loss: 0.0783 2023-01-06 18:31:03,249 - mmseg - INFO - Iter [119750/160000] lr: 1.509e-05, eta: 6:26:09, time: 0.574, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0807, decode.acc_seg: 96.5656, loss: 0.0807 2023-01-06 18:31:34,584 - mmseg - INFO - Iter [119800/160000] lr: 1.508e-05, eta: 6:25:41, time: 0.627, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0798, decode.acc_seg: 96.5456, loss: 0.0798 2023-01-06 18:32:02,744 - mmseg - INFO - Iter [119850/160000] lr: 1.506e-05, eta: 6:25:12, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0798, decode.acc_seg: 96.5701, loss: 0.0798 2023-01-06 18:32:31,321 - mmseg - INFO - Iter [119900/160000] lr: 1.504e-05, eta: 6:24:43, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0779, decode.acc_seg: 96.7390, loss: 0.0779 2023-01-06 18:33:00,621 - mmseg - INFO - Iter [119950/160000] lr: 1.502e-05, eta: 6:24:15, time: 0.585, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0802, decode.acc_seg: 96.5493, loss: 0.0802 2023-01-06 18:33:28,339 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 18:33:28,340 - mmseg - INFO - Iter [120000/160000] lr: 1.500e-05, eta: 6:23:45, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0790, decode.acc_seg: 96.4896, loss: 0.0790 2023-01-06 18:33:56,750 - mmseg - INFO - Iter [120050/160000] lr: 1.498e-05, eta: 6:23:17, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0777, decode.acc_seg: 96.5841, loss: 0.0777 2023-01-06 18:34:24,753 - mmseg - INFO - Iter [120100/160000] lr: 1.496e-05, eta: 6:22:48, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0819, decode.acc_seg: 96.5296, loss: 0.0819 2023-01-06 18:34:52,475 - mmseg - INFO - Iter [120150/160000] lr: 1.494e-05, eta: 6:22:18, time: 0.554, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0792, decode.acc_seg: 96.5651, loss: 0.0792 2023-01-06 18:35:22,739 - mmseg - INFO - Iter [120200/160000] lr: 1.493e-05, eta: 6:21:50, time: 0.605, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0799, decode.acc_seg: 96.5066, loss: 0.0799 2023-01-06 18:35:51,126 - mmseg - INFO - Iter [120250/160000] lr: 1.491e-05, eta: 6:21:21, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0752, decode.acc_seg: 96.8027, loss: 0.0752 2023-01-06 18:36:19,007 - mmseg - INFO - Iter [120300/160000] lr: 1.489e-05, eta: 6:20:52, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0863, decode.acc_seg: 96.4090, loss: 0.0863 2023-01-06 18:36:47,207 - mmseg - INFO - Iter [120350/160000] lr: 1.487e-05, eta: 6:20:23, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0793, decode.acc_seg: 96.5787, loss: 0.0793 2023-01-06 18:37:15,025 - mmseg - INFO - Iter [120400/160000] lr: 1.485e-05, eta: 6:19:54, time: 0.556, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0828, decode.acc_seg: 96.4275, loss: 0.0828 2023-01-06 18:37:44,743 - mmseg - INFO - Iter [120450/160000] lr: 1.483e-05, eta: 6:19:26, time: 0.594, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0782, decode.acc_seg: 96.5935, loss: 0.0782 2023-01-06 18:38:14,157 - mmseg - INFO - Iter [120500/160000] lr: 1.481e-05, eta: 6:18:57, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0832, decode.acc_seg: 96.3285, loss: 0.0832 2023-01-06 18:38:46,178 - mmseg - INFO - Iter [120550/160000] lr: 1.479e-05, eta: 6:18:29, time: 0.641, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0839, decode.acc_seg: 96.4383, loss: 0.0839 2023-01-06 18:39:14,063 - mmseg - INFO - Iter [120600/160000] lr: 1.478e-05, eta: 6:18:00, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0824, decode.acc_seg: 96.5181, loss: 0.0824 2023-01-06 18:39:42,970 - mmseg - INFO - Iter [120650/160000] lr: 1.476e-05, eta: 6:17:31, time: 0.578, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0811, decode.acc_seg: 96.5569, loss: 0.0811 2023-01-06 18:40:10,666 - mmseg - INFO - Iter [120700/160000] lr: 1.474e-05, eta: 6:17:02, time: 0.554, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0815, decode.acc_seg: 96.5558, loss: 0.0815 2023-01-06 18:40:39,408 - mmseg - INFO - Iter [120750/160000] lr: 1.472e-05, eta: 6:16:33, time: 0.574, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0771, decode.acc_seg: 96.5954, loss: 0.0771 2023-01-06 18:41:07,738 - mmseg - INFO - Iter [120800/160000] lr: 1.470e-05, eta: 6:16:05, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0769, decode.acc_seg: 96.6625, loss: 0.0769 2023-01-06 18:41:36,645 - mmseg - INFO - Iter [120850/160000] lr: 1.468e-05, eta: 6:15:36, time: 0.578, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0806, decode.acc_seg: 96.5574, loss: 0.0806 2023-01-06 18:42:05,032 - mmseg - INFO - Iter [120900/160000] lr: 1.466e-05, eta: 6:15:07, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0797, decode.acc_seg: 96.5754, loss: 0.0797 2023-01-06 18:42:35,672 - mmseg - INFO - Iter [120950/160000] lr: 1.464e-05, eta: 6:14:39, time: 0.613, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0802, decode.acc_seg: 96.5307, loss: 0.0802 2023-01-06 18:43:03,981 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 18:43:03,982 - mmseg - INFO - Iter [121000/160000] lr: 1.463e-05, eta: 6:14:10, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0853, decode.acc_seg: 96.4551, loss: 0.0853 2023-01-06 18:43:32,625 - mmseg - INFO - Iter [121050/160000] lr: 1.461e-05, eta: 6:13:41, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0791, decode.acc_seg: 96.6034, loss: 0.0791 2023-01-06 18:44:01,712 - mmseg - INFO - Iter [121100/160000] lr: 1.459e-05, eta: 6:13:12, time: 0.582, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0832, decode.acc_seg: 96.4930, loss: 0.0832 2023-01-06 18:44:30,720 - mmseg - INFO - Iter [121150/160000] lr: 1.457e-05, eta: 6:12:44, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0798, decode.acc_seg: 96.5851, loss: 0.0798 2023-01-06 18:44:57,687 - mmseg - INFO - Iter [121200/160000] lr: 1.455e-05, eta: 6:12:14, time: 0.540, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0822, decode.acc_seg: 96.5377, loss: 0.0822 2023-01-06 18:45:26,117 - mmseg - INFO - Iter [121250/160000] lr: 1.453e-05, eta: 6:11:45, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0814, decode.acc_seg: 96.5503, loss: 0.0814 2023-01-06 18:45:56,255 - mmseg - INFO - Iter [121300/160000] lr: 1.451e-05, eta: 6:11:17, time: 0.604, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0748, decode.acc_seg: 96.6204, loss: 0.0748 2023-01-06 18:46:24,988 - mmseg - INFO - Iter [121350/160000] lr: 1.449e-05, eta: 6:10:48, time: 0.574, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0792, decode.acc_seg: 96.6112, loss: 0.0792 2023-01-06 18:46:53,853 - mmseg - INFO - Iter [121400/160000] lr: 1.448e-05, eta: 6:10:19, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0800, decode.acc_seg: 96.5036, loss: 0.0800 2023-01-06 18:47:22,469 - mmseg - INFO - Iter [121450/160000] lr: 1.446e-05, eta: 6:09:51, time: 0.572, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0807, decode.acc_seg: 96.5623, loss: 0.0807 2023-01-06 18:47:50,538 - mmseg - INFO - Iter [121500/160000] lr: 1.444e-05, eta: 6:09:22, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0774, decode.acc_seg: 96.6005, loss: 0.0774 2023-01-06 18:48:18,800 - mmseg - INFO - Iter [121550/160000] lr: 1.442e-05, eta: 6:08:53, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0803, decode.acc_seg: 96.5886, loss: 0.0803 2023-01-06 18:48:46,792 - mmseg - INFO - Iter [121600/160000] lr: 1.440e-05, eta: 6:08:24, time: 0.559, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0771, decode.acc_seg: 96.6638, loss: 0.0771 2023-01-06 18:49:18,442 - mmseg - INFO - Iter [121650/160000] lr: 1.438e-05, eta: 6:07:56, time: 0.634, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0779, decode.acc_seg: 96.6998, loss: 0.0779 2023-01-06 18:49:46,376 - mmseg - INFO - Iter [121700/160000] lr: 1.436e-05, eta: 6:07:27, time: 0.559, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0806, decode.acc_seg: 96.5085, loss: 0.0806 2023-01-06 18:50:14,206 - mmseg - INFO - Iter [121750/160000] lr: 1.434e-05, eta: 6:06:58, time: 0.557, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0805, decode.acc_seg: 96.4654, loss: 0.0805 2023-01-06 18:50:42,240 - mmseg - INFO - Iter [121800/160000] lr: 1.433e-05, eta: 6:06:29, time: 0.561, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0963, decode.acc_seg: 95.9602, loss: 0.0963 2023-01-06 18:51:09,795 - mmseg - INFO - Iter [121850/160000] lr: 1.431e-05, eta: 6:05:59, time: 0.551, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0842, decode.acc_seg: 96.4732, loss: 0.0842 2023-01-06 18:51:37,773 - mmseg - INFO - Iter [121900/160000] lr: 1.429e-05, eta: 6:05:30, time: 0.560, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0839, decode.acc_seg: 96.4399, loss: 0.0839 2023-01-06 18:52:04,916 - mmseg - INFO - Iter [121950/160000] lr: 1.427e-05, eta: 6:05:01, time: 0.543, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0818, decode.acc_seg: 96.4377, loss: 0.0818 2023-01-06 18:52:34,188 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 18:52:34,188 - mmseg - INFO - Iter [122000/160000] lr: 1.425e-05, eta: 6:04:32, time: 0.586, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0846, decode.acc_seg: 96.3007, loss: 0.0846 2023-01-06 18:53:05,449 - mmseg - INFO - Iter [122050/160000] lr: 1.423e-05, eta: 6:04:04, time: 0.624, data_time: 0.056, memory: 10576, decode.loss_ce: 0.0867, decode.acc_seg: 96.3534, loss: 0.0867 2023-01-06 18:53:35,693 - mmseg - INFO - Iter [122100/160000] lr: 1.421e-05, eta: 6:03:36, time: 0.605, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0787, decode.acc_seg: 96.6028, loss: 0.0787 2023-01-06 18:54:05,353 - mmseg - INFO - Iter [122150/160000] lr: 1.419e-05, eta: 6:03:08, time: 0.594, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0858, decode.acc_seg: 96.3490, loss: 0.0858 2023-01-06 18:54:35,010 - mmseg - INFO - Iter [122200/160000] lr: 1.418e-05, eta: 6:02:39, time: 0.592, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0833, decode.acc_seg: 96.4893, loss: 0.0833 2023-01-06 18:55:02,756 - mmseg - INFO - Iter [122250/160000] lr: 1.416e-05, eta: 6:02:10, time: 0.556, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0796, decode.acc_seg: 96.6105, loss: 0.0796 2023-01-06 18:55:32,335 - mmseg - INFO - Iter [122300/160000] lr: 1.414e-05, eta: 6:01:41, time: 0.591, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0815, decode.acc_seg: 96.4990, loss: 0.0815 2023-01-06 18:55:59,883 - mmseg - INFO - Iter [122350/160000] lr: 1.412e-05, eta: 6:01:12, time: 0.551, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0804, decode.acc_seg: 96.5105, loss: 0.0804 2023-01-06 18:56:29,358 - mmseg - INFO - Iter [122400/160000] lr: 1.410e-05, eta: 6:00:44, time: 0.589, data_time: 0.056, memory: 10576, decode.loss_ce: 0.0761, decode.acc_seg: 96.7116, loss: 0.0761 2023-01-06 18:56:57,124 - mmseg - INFO - Iter [122450/160000] lr: 1.408e-05, eta: 6:00:15, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0783, decode.acc_seg: 96.5673, loss: 0.0783 2023-01-06 18:57:24,760 - mmseg - INFO - Iter [122500/160000] lr: 1.406e-05, eta: 5:59:45, time: 0.553, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0818, decode.acc_seg: 96.4719, loss: 0.0818 2023-01-06 18:57:52,811 - mmseg - INFO - Iter [122550/160000] lr: 1.404e-05, eta: 5:59:16, time: 0.561, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0786, decode.acc_seg: 96.5612, loss: 0.0786 2023-01-06 18:58:19,973 - mmseg - INFO - Iter [122600/160000] lr: 1.403e-05, eta: 5:58:47, time: 0.543, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0803, decode.acc_seg: 96.5177, loss: 0.0803 2023-01-06 18:58:47,217 - mmseg - INFO - Iter [122650/160000] lr: 1.401e-05, eta: 5:58:18, time: 0.544, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0886, decode.acc_seg: 96.3924, loss: 0.0886 2023-01-06 18:59:15,795 - mmseg - INFO - Iter [122700/160000] lr: 1.399e-05, eta: 5:57:49, time: 0.572, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0757, decode.acc_seg: 96.7610, loss: 0.0757 2023-01-06 18:59:45,241 - mmseg - INFO - Iter [122750/160000] lr: 1.397e-05, eta: 5:57:21, time: 0.588, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0838, decode.acc_seg: 96.4488, loss: 0.0838 2023-01-06 19:00:15,462 - mmseg - INFO - Iter [122800/160000] lr: 1.395e-05, eta: 5:56:52, time: 0.604, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0837, decode.acc_seg: 96.5127, loss: 0.0837 2023-01-06 19:00:44,923 - mmseg - INFO - Iter [122850/160000] lr: 1.393e-05, eta: 5:56:24, time: 0.589, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0808, decode.acc_seg: 96.5599, loss: 0.0808 2023-01-06 19:01:12,226 - mmseg - INFO - Iter [122900/160000] lr: 1.391e-05, eta: 5:55:54, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0794, decode.acc_seg: 96.5097, loss: 0.0794 2023-01-06 19:01:39,561 - mmseg - INFO - Iter [122950/160000] lr: 1.389e-05, eta: 5:55:25, time: 0.547, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0795, decode.acc_seg: 96.5453, loss: 0.0795 2023-01-06 19:02:07,687 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 19:02:07,687 - mmseg - INFO - Iter [123000/160000] lr: 1.388e-05, eta: 5:54:56, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0805, decode.acc_seg: 96.5505, loss: 0.0805 2023-01-06 19:02:35,966 - mmseg - INFO - Iter [123050/160000] lr: 1.386e-05, eta: 5:54:27, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0809, decode.acc_seg: 96.6318, loss: 0.0809 2023-01-06 19:03:03,190 - mmseg - INFO - Iter [123100/160000] lr: 1.384e-05, eta: 5:53:58, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0826, decode.acc_seg: 96.4026, loss: 0.0826 2023-01-06 19:03:33,961 - mmseg - INFO - Iter [123150/160000] lr: 1.382e-05, eta: 5:53:30, time: 0.615, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0797, decode.acc_seg: 96.5927, loss: 0.0797 2023-01-06 19:04:01,242 - mmseg - INFO - Iter [123200/160000] lr: 1.380e-05, eta: 5:53:01, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0830, decode.acc_seg: 96.4939, loss: 0.0830 2023-01-06 19:04:28,705 - mmseg - INFO - Iter [123250/160000] lr: 1.378e-05, eta: 5:52:31, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0809, decode.acc_seg: 96.5415, loss: 0.0809 2023-01-06 19:04:55,904 - mmseg - INFO - Iter [123300/160000] lr: 1.376e-05, eta: 5:52:02, time: 0.543, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0788, decode.acc_seg: 96.6677, loss: 0.0788 2023-01-06 19:05:23,791 - mmseg - INFO - Iter [123350/160000] lr: 1.374e-05, eta: 5:51:33, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0827, decode.acc_seg: 96.4053, loss: 0.0827 2023-01-06 19:05:52,940 - mmseg - INFO - Iter [123400/160000] lr: 1.373e-05, eta: 5:51:04, time: 0.584, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0852, decode.acc_seg: 96.4608, loss: 0.0852 2023-01-06 19:06:21,526 - mmseg - INFO - Iter [123450/160000] lr: 1.371e-05, eta: 5:50:36, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0853, decode.acc_seg: 96.4362, loss: 0.0853 2023-01-06 19:06:48,820 - mmseg - INFO - Iter [123500/160000] lr: 1.369e-05, eta: 5:50:06, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0818, decode.acc_seg: 96.4996, loss: 0.0818 2023-01-06 19:07:18,456 - mmseg - INFO - Iter [123550/160000] lr: 1.367e-05, eta: 5:49:38, time: 0.593, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0788, decode.acc_seg: 96.6730, loss: 0.0788 2023-01-06 19:07:45,693 - mmseg - INFO - Iter [123600/160000] lr: 1.365e-05, eta: 5:49:09, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0817, decode.acc_seg: 96.5968, loss: 0.0817 2023-01-06 19:08:14,257 - mmseg - INFO - Iter [123650/160000] lr: 1.363e-05, eta: 5:48:40, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0819, decode.acc_seg: 96.4268, loss: 0.0819 2023-01-06 19:08:43,786 - mmseg - INFO - Iter [123700/160000] lr: 1.361e-05, eta: 5:48:11, time: 0.591, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0755, decode.acc_seg: 96.7797, loss: 0.0755 2023-01-06 19:09:11,652 - mmseg - INFO - Iter [123750/160000] lr: 1.359e-05, eta: 5:47:42, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0854, decode.acc_seg: 96.4465, loss: 0.0854 2023-01-06 19:09:39,453 - mmseg - INFO - Iter [123800/160000] lr: 1.358e-05, eta: 5:47:13, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0855, decode.acc_seg: 96.3484, loss: 0.0855 2023-01-06 19:10:07,854 - mmseg - INFO - Iter [123850/160000] lr: 1.356e-05, eta: 5:46:44, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0837, decode.acc_seg: 96.4892, loss: 0.0837 2023-01-06 19:10:37,930 - mmseg - INFO - Iter [123900/160000] lr: 1.354e-05, eta: 5:46:16, time: 0.602, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0819, decode.acc_seg: 96.4940, loss: 0.0819 2023-01-06 19:11:05,394 - mmseg - INFO - Iter [123950/160000] lr: 1.352e-05, eta: 5:45:47, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0815, decode.acc_seg: 96.4549, loss: 0.0815 2023-01-06 19:11:33,252 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 19:11:33,252 - mmseg - INFO - Iter [124000/160000] lr: 1.350e-05, eta: 5:45:18, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0796, decode.acc_seg: 96.6363, loss: 0.0796 2023-01-06 19:12:01,920 - mmseg - INFO - Iter [124050/160000] lr: 1.348e-05, eta: 5:44:49, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0800, decode.acc_seg: 96.5417, loss: 0.0800 2023-01-06 19:12:30,426 - mmseg - INFO - Iter [124100/160000] lr: 1.346e-05, eta: 5:44:20, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0779, decode.acc_seg: 96.6515, loss: 0.0779 2023-01-06 19:12:59,599 - mmseg - INFO - Iter [124150/160000] lr: 1.344e-05, eta: 5:43:51, time: 0.583, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0778, decode.acc_seg: 96.6485, loss: 0.0778 2023-01-06 19:13:28,352 - mmseg - INFO - Iter [124200/160000] lr: 1.343e-05, eta: 5:43:23, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0876, decode.acc_seg: 96.3567, loss: 0.0876 2023-01-06 19:14:00,183 - mmseg - INFO - Iter [124250/160000] lr: 1.341e-05, eta: 5:42:55, time: 0.637, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0786, decode.acc_seg: 96.6596, loss: 0.0786 2023-01-06 19:14:29,177 - mmseg - INFO - Iter [124300/160000] lr: 1.339e-05, eta: 5:42:26, time: 0.580, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0773, decode.acc_seg: 96.5645, loss: 0.0773 2023-01-06 19:14:57,752 - mmseg - INFO - Iter [124350/160000] lr: 1.337e-05, eta: 5:41:57, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0796, decode.acc_seg: 96.5967, loss: 0.0796 2023-01-06 19:15:26,114 - mmseg - INFO - Iter [124400/160000] lr: 1.335e-05, eta: 5:41:28, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0853, decode.acc_seg: 96.3539, loss: 0.0853 2023-01-06 19:15:55,480 - mmseg - INFO - Iter [124450/160000] lr: 1.333e-05, eta: 5:41:00, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0816, decode.acc_seg: 96.4256, loss: 0.0816 2023-01-06 19:16:24,414 - mmseg - INFO - Iter [124500/160000] lr: 1.331e-05, eta: 5:40:31, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0812, decode.acc_seg: 96.6339, loss: 0.0812 2023-01-06 19:16:52,219 - mmseg - INFO - Iter [124550/160000] lr: 1.329e-05, eta: 5:40:02, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0773, decode.acc_seg: 96.5269, loss: 0.0773 2023-01-06 19:17:19,330 - mmseg - INFO - Iter [124600/160000] lr: 1.328e-05, eta: 5:39:33, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0874, decode.acc_seg: 96.3518, loss: 0.0874 2023-01-06 19:17:48,655 - mmseg - INFO - Iter [124650/160000] lr: 1.326e-05, eta: 5:39:04, time: 0.586, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0790, decode.acc_seg: 96.5983, loss: 0.0790 2023-01-06 19:18:16,244 - mmseg - INFO - Iter [124700/160000] lr: 1.324e-05, eta: 5:38:35, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0805, decode.acc_seg: 96.5618, loss: 0.0805 2023-01-06 19:18:44,758 - mmseg - INFO - Iter [124750/160000] lr: 1.322e-05, eta: 5:38:06, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0812, decode.acc_seg: 96.4912, loss: 0.0812 2023-01-06 19:19:13,590 - mmseg - INFO - Iter [124800/160000] lr: 1.320e-05, eta: 5:37:37, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0877, decode.acc_seg: 96.3421, loss: 0.0877 2023-01-06 19:19:40,646 - mmseg - INFO - Iter [124850/160000] lr: 1.318e-05, eta: 5:37:08, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0772, decode.acc_seg: 96.6450, loss: 0.0772 2023-01-06 19:20:08,282 - mmseg - INFO - Iter [124900/160000] lr: 1.316e-05, eta: 5:36:39, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0808, decode.acc_seg: 96.5546, loss: 0.0808 2023-01-06 19:20:36,332 - mmseg - INFO - Iter [124950/160000] lr: 1.314e-05, eta: 5:36:10, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0800, decode.acc_seg: 96.6150, loss: 0.0800 2023-01-06 19:21:06,289 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 19:21:06,290 - mmseg - INFO - Iter [125000/160000] lr: 1.313e-05, eta: 5:35:42, time: 0.600, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0850, decode.acc_seg: 96.4618, loss: 0.0850 2023-01-06 19:21:33,817 - mmseg - INFO - Iter [125050/160000] lr: 1.311e-05, eta: 5:35:12, time: 0.550, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0845, decode.acc_seg: 96.4624, loss: 0.0845 2023-01-06 19:22:01,883 - mmseg - INFO - Iter [125100/160000] lr: 1.309e-05, eta: 5:34:43, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0765, decode.acc_seg: 96.6272, loss: 0.0765 2023-01-06 19:22:30,063 - mmseg - INFO - Iter [125150/160000] lr: 1.307e-05, eta: 5:34:14, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0793, decode.acc_seg: 96.5466, loss: 0.0793 2023-01-06 19:22:58,538 - mmseg - INFO - Iter [125200/160000] lr: 1.305e-05, eta: 5:33:46, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0803, decode.acc_seg: 96.5966, loss: 0.0803 2023-01-06 19:23:27,679 - mmseg - INFO - Iter [125250/160000] lr: 1.303e-05, eta: 5:33:17, time: 0.582, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0821, decode.acc_seg: 96.4550, loss: 0.0821 2023-01-06 19:23:55,708 - mmseg - INFO - Iter [125300/160000] lr: 1.301e-05, eta: 5:32:48, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0927, decode.acc_seg: 96.2594, loss: 0.0927 2023-01-06 19:24:23,829 - mmseg - INFO - Iter [125350/160000] lr: 1.299e-05, eta: 5:32:19, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0786, decode.acc_seg: 96.5499, loss: 0.0786 2023-01-06 19:24:54,070 - mmseg - INFO - Iter [125400/160000] lr: 1.298e-05, eta: 5:31:51, time: 0.605, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0816, decode.acc_seg: 96.5525, loss: 0.0816 2023-01-06 19:25:22,983 - mmseg - INFO - Iter [125450/160000] lr: 1.296e-05, eta: 5:31:22, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0841, decode.acc_seg: 96.4091, loss: 0.0841 2023-01-06 19:25:53,039 - mmseg - INFO - Iter [125500/160000] lr: 1.294e-05, eta: 5:30:53, time: 0.601, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0790, decode.acc_seg: 96.6383, loss: 0.0790 2023-01-06 19:26:20,782 - mmseg - INFO - Iter [125550/160000] lr: 1.292e-05, eta: 5:30:24, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0789, decode.acc_seg: 96.6357, loss: 0.0789 2023-01-06 19:26:49,387 - mmseg - INFO - Iter [125600/160000] lr: 1.290e-05, eta: 5:29:56, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0810, decode.acc_seg: 96.5528, loss: 0.0810 2023-01-06 19:27:19,026 - mmseg - INFO - Iter [125650/160000] lr: 1.288e-05, eta: 5:29:27, time: 0.593, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0795, decode.acc_seg: 96.6064, loss: 0.0795 2023-01-06 19:27:47,383 - mmseg - INFO - Iter [125700/160000] lr: 1.286e-05, eta: 5:28:58, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0792, decode.acc_seg: 96.5850, loss: 0.0792 2023-01-06 19:28:17,418 - mmseg - INFO - Iter [125750/160000] lr: 1.284e-05, eta: 5:28:30, time: 0.601, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0828, decode.acc_seg: 96.5243, loss: 0.0828 2023-01-06 19:28:44,462 - mmseg - INFO - Iter [125800/160000] lr: 1.283e-05, eta: 5:28:01, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0778, decode.acc_seg: 96.6215, loss: 0.0778 2023-01-06 19:29:12,996 - mmseg - INFO - Iter [125850/160000] lr: 1.281e-05, eta: 5:27:32, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0784, decode.acc_seg: 96.6379, loss: 0.0784 2023-01-06 19:29:42,030 - mmseg - INFO - Iter [125900/160000] lr: 1.279e-05, eta: 5:27:03, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0783, decode.acc_seg: 96.6473, loss: 0.0783 2023-01-06 19:30:11,411 - mmseg - INFO - Iter [125950/160000] lr: 1.277e-05, eta: 5:26:34, time: 0.587, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0809, decode.acc_seg: 96.5285, loss: 0.0809 2023-01-06 19:30:40,939 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 19:30:40,940 - mmseg - INFO - Iter [126000/160000] lr: 1.275e-05, eta: 5:26:06, time: 0.591, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0822, decode.acc_seg: 96.5205, loss: 0.0822 2023-01-06 19:31:09,982 - mmseg - INFO - Iter [126050/160000] lr: 1.273e-05, eta: 5:25:37, time: 0.581, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0809, decode.acc_seg: 96.5672, loss: 0.0809 2023-01-06 19:31:38,496 - mmseg - INFO - Iter [126100/160000] lr: 1.271e-05, eta: 5:25:08, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0839, decode.acc_seg: 96.4144, loss: 0.0839 2023-01-06 19:32:09,171 - mmseg - INFO - Iter [126150/160000] lr: 1.269e-05, eta: 5:24:40, time: 0.614, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0811, decode.acc_seg: 96.5167, loss: 0.0811 2023-01-06 19:32:37,114 - mmseg - INFO - Iter [126200/160000] lr: 1.268e-05, eta: 5:24:11, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0785, decode.acc_seg: 96.6240, loss: 0.0785 2023-01-06 19:33:05,169 - mmseg - INFO - Iter [126250/160000] lr: 1.266e-05, eta: 5:23:42, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0762, decode.acc_seg: 96.7869, loss: 0.0762 2023-01-06 19:33:34,224 - mmseg - INFO - Iter [126300/160000] lr: 1.264e-05, eta: 5:23:13, time: 0.580, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0828, decode.acc_seg: 96.4913, loss: 0.0828 2023-01-06 19:34:02,151 - mmseg - INFO - Iter [126350/160000] lr: 1.262e-05, eta: 5:22:44, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0826, decode.acc_seg: 96.4083, loss: 0.0826 2023-01-06 19:34:29,270 - mmseg - INFO - Iter [126400/160000] lr: 1.260e-05, eta: 5:22:15, time: 0.542, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0768, decode.acc_seg: 96.6662, loss: 0.0768 2023-01-06 19:34:56,398 - mmseg - INFO - Iter [126450/160000] lr: 1.258e-05, eta: 5:21:46, time: 0.542, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0772, decode.acc_seg: 96.6563, loss: 0.0772 2023-01-06 19:35:27,286 - mmseg - INFO - Iter [126500/160000] lr: 1.256e-05, eta: 5:21:18, time: 0.618, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0793, decode.acc_seg: 96.5859, loss: 0.0793 2023-01-06 19:35:55,719 - mmseg - INFO - Iter [126550/160000] lr: 1.254e-05, eta: 5:20:49, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0776, decode.acc_seg: 96.6432, loss: 0.0776 2023-01-06 19:36:24,056 - mmseg - INFO - Iter [126600/160000] lr: 1.253e-05, eta: 5:20:20, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0791, decode.acc_seg: 96.5899, loss: 0.0791 2023-01-06 19:36:52,452 - mmseg - INFO - Iter [126650/160000] lr: 1.251e-05, eta: 5:19:51, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0780, decode.acc_seg: 96.6769, loss: 0.0780 2023-01-06 19:37:20,854 - mmseg - INFO - Iter [126700/160000] lr: 1.249e-05, eta: 5:19:22, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0780, decode.acc_seg: 96.6124, loss: 0.0780 2023-01-06 19:37:49,888 - mmseg - INFO - Iter [126750/160000] lr: 1.247e-05, eta: 5:18:53, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0794, decode.acc_seg: 96.6295, loss: 0.0794 2023-01-06 19:38:17,414 - mmseg - INFO - Iter [126800/160000] lr: 1.245e-05, eta: 5:18:24, time: 0.550, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0793, decode.acc_seg: 96.5880, loss: 0.0793 2023-01-06 19:38:44,507 - mmseg - INFO - Iter [126850/160000] lr: 1.243e-05, eta: 5:17:55, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0821, decode.acc_seg: 96.4229, loss: 0.0821 2023-01-06 19:39:13,805 - mmseg - INFO - Iter [126900/160000] lr: 1.241e-05, eta: 5:17:27, time: 0.586, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0791, decode.acc_seg: 96.6039, loss: 0.0791 2023-01-06 19:39:40,855 - mmseg - INFO - Iter [126950/160000] lr: 1.239e-05, eta: 5:16:57, time: 0.541, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0790, decode.acc_seg: 96.5439, loss: 0.0790 2023-01-06 19:40:08,153 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 19:40:08,153 - mmseg - INFO - Iter [127000/160000] lr: 1.238e-05, eta: 5:16:28, time: 0.545, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0794, decode.acc_seg: 96.5477, loss: 0.0794 2023-01-06 19:40:36,082 - mmseg - INFO - Iter [127050/160000] lr: 1.236e-05, eta: 5:15:59, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0827, decode.acc_seg: 96.4735, loss: 0.0827 2023-01-06 19:41:04,090 - mmseg - INFO - Iter [127100/160000] lr: 1.234e-05, eta: 5:15:30, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0814, decode.acc_seg: 96.5295, loss: 0.0814 2023-01-06 19:41:32,446 - mmseg - INFO - Iter [127150/160000] lr: 1.232e-05, eta: 5:15:01, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0788, decode.acc_seg: 96.5782, loss: 0.0788 2023-01-06 19:41:59,763 - mmseg - INFO - Iter [127200/160000] lr: 1.230e-05, eta: 5:14:32, time: 0.547, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0769, decode.acc_seg: 96.7356, loss: 0.0769 2023-01-06 19:42:30,190 - mmseg - INFO - Iter [127250/160000] lr: 1.228e-05, eta: 5:14:04, time: 0.609, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0816, decode.acc_seg: 96.5584, loss: 0.0816 2023-01-06 19:42:58,955 - mmseg - INFO - Iter [127300/160000] lr: 1.226e-05, eta: 5:13:35, time: 0.575, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0781, decode.acc_seg: 96.6768, loss: 0.0781 2023-01-06 19:43:26,831 - mmseg - INFO - Iter [127350/160000] lr: 1.224e-05, eta: 5:13:06, time: 0.557, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0787, decode.acc_seg: 96.6212, loss: 0.0787 2023-01-06 19:43:55,608 - mmseg - INFO - Iter [127400/160000] lr: 1.223e-05, eta: 5:12:37, time: 0.575, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0771, decode.acc_seg: 96.6837, loss: 0.0771 2023-01-06 19:44:25,380 - mmseg - INFO - Iter [127450/160000] lr: 1.221e-05, eta: 5:12:09, time: 0.595, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0792, decode.acc_seg: 96.5591, loss: 0.0792 2023-01-06 19:44:53,406 - mmseg - INFO - Iter [127500/160000] lr: 1.219e-05, eta: 5:11:40, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0784, decode.acc_seg: 96.6064, loss: 0.0784 2023-01-06 19:45:22,603 - mmseg - INFO - Iter [127550/160000] lr: 1.217e-05, eta: 5:11:11, time: 0.584, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0785, decode.acc_seg: 96.5952, loss: 0.0785 2023-01-06 19:45:53,141 - mmseg - INFO - Iter [127600/160000] lr: 1.215e-05, eta: 5:10:43, time: 0.611, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0794, decode.acc_seg: 96.6522, loss: 0.0794 2023-01-06 19:46:21,164 - mmseg - INFO - Iter [127650/160000] lr: 1.213e-05, eta: 5:10:14, time: 0.560, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0830, decode.acc_seg: 96.4191, loss: 0.0830 2023-01-06 19:46:48,834 - mmseg - INFO - Iter [127700/160000] lr: 1.211e-05, eta: 5:09:45, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0799, decode.acc_seg: 96.5987, loss: 0.0799 2023-01-06 19:47:17,225 - mmseg - INFO - Iter [127750/160000] lr: 1.209e-05, eta: 5:09:16, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0779, decode.acc_seg: 96.6123, loss: 0.0779 2023-01-06 19:47:44,382 - mmseg - INFO - Iter [127800/160000] lr: 1.208e-05, eta: 5:08:47, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0762, decode.acc_seg: 96.7696, loss: 0.0762 2023-01-06 19:48:11,639 - mmseg - INFO - Iter [127850/160000] lr: 1.206e-05, eta: 5:08:18, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0796, decode.acc_seg: 96.5849, loss: 0.0796 2023-01-06 19:48:38,694 - mmseg - INFO - Iter [127900/160000] lr: 1.204e-05, eta: 5:07:48, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0801, decode.acc_seg: 96.5284, loss: 0.0801 2023-01-06 19:49:07,757 - mmseg - INFO - Iter [127950/160000] lr: 1.202e-05, eta: 5:07:20, time: 0.581, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0819, decode.acc_seg: 96.4840, loss: 0.0819 2023-01-06 19:49:37,034 - mmseg - INFO - Saving checkpoint at 128000 iterations 2023-01-06 19:49:42,261 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 19:49:42,261 - mmseg - INFO - Iter [128000/160000] lr: 1.200e-05, eta: 5:06:52, time: 0.690, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0784, decode.acc_seg: 96.6742, loss: 0.0784 2023-01-06 19:50:14,447 - mmseg - INFO - per class results: 2023-01-06 19:50:14,450 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 98.08 | 99.02 | | sidewalk | 84.32 | 92.14 | | building | 91.97 | 96.3 | | wall | 57.61 | 68.13 | | fence | 57.26 | 69.14 | | pole | 62.67 | 72.73 | | traffic light | 66.5 | 76.42 | | traffic sign | 75.78 | 82.19 | | vegetation | 92.27 | 96.8 | | terrain | 63.04 | 69.0 | | sky | 94.67 | 97.95 | | person | 78.99 | 89.51 | | rider | 56.11 | 67.51 | | car | 93.91 | 97.23 | | truck | 64.88 | 70.25 | | bus | 79.27 | 85.05 | | train | 67.08 | 75.4 | | motorcycle | 48.35 | 56.05 | | bicycle | 73.2 | 87.26 | +---------------+-------+-------+ 2023-01-06 19:50:14,450 - mmseg - INFO - Summary: 2023-01-06 19:50:14,451 - mmseg - INFO - +-------+------+-------+ | aAcc | mIoU | mAcc | +-------+------+-------+ | 95.77 | 74.0 | 81.48 | +-------+------+-------+ 2023-01-06 19:50:14,451 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 19:50:14,451 - mmseg - INFO - Iter(val) [63] aAcc: 0.9577, mIoU: 0.7400, mAcc: 0.8148, IoU.road: 0.9808, IoU.sidewalk: 0.8432, IoU.building: 0.9197, IoU.wall: 0.5761, IoU.fence: 0.5726, IoU.pole: 0.6267, IoU.traffic light: 0.6650, IoU.traffic sign: 0.7578, IoU.vegetation: 0.9227, IoU.terrain: 0.6304, IoU.sky: 0.9467, IoU.person: 0.7899, IoU.rider: 0.5611, IoU.car: 0.9391, IoU.truck: 0.6488, IoU.bus: 0.7927, IoU.train: 0.6708, IoU.motorcycle: 0.4835, IoU.bicycle: 0.7320, Acc.road: 0.9902, Acc.sidewalk: 0.9214, Acc.building: 0.9630, Acc.wall: 0.6813, Acc.fence: 0.6914, Acc.pole: 0.7273, Acc.traffic light: 0.7642, Acc.traffic sign: 0.8219, Acc.vegetation: 0.9680, Acc.terrain: 0.6900, Acc.sky: 0.9795, Acc.person: 0.8951, Acc.rider: 0.6751, Acc.car: 0.9723, Acc.truck: 0.7025, Acc.bus: 0.8505, Acc.train: 0.7540, Acc.motorcycle: 0.5605, Acc.bicycle: 0.8726 2023-01-06 19:50:42,154 - mmseg - INFO - Iter [128050/160000] lr: 1.198e-05, eta: 5:06:31, time: 1.197, data_time: 0.656, memory: 10576, decode.loss_ce: 0.0779, decode.acc_seg: 96.6595, loss: 0.0779 2023-01-06 19:51:10,713 - mmseg - INFO - Iter [128100/160000] lr: 1.196e-05, eta: 5:06:03, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0772, decode.acc_seg: 96.6032, loss: 0.0772 2023-01-06 19:51:38,541 - mmseg - INFO - Iter [128150/160000] lr: 1.194e-05, eta: 5:05:34, time: 0.556, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0808, decode.acc_seg: 96.5293, loss: 0.0808 2023-01-06 19:52:05,583 - mmseg - INFO - Iter [128200/160000] lr: 1.193e-05, eta: 5:05:04, time: 0.541, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0802, decode.acc_seg: 96.5036, loss: 0.0802 2023-01-06 19:52:34,362 - mmseg - INFO - Iter [128250/160000] lr: 1.191e-05, eta: 5:04:36, time: 0.575, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0793, decode.acc_seg: 96.4781, loss: 0.0793 2023-01-06 19:53:02,938 - mmseg - INFO - Iter [128300/160000] lr: 1.189e-05, eta: 5:04:07, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0747, decode.acc_seg: 96.8241, loss: 0.0747 2023-01-06 19:53:32,988 - mmseg - INFO - Iter [128350/160000] lr: 1.187e-05, eta: 5:03:38, time: 0.601, data_time: 0.056, memory: 10576, decode.loss_ce: 0.0766, decode.acc_seg: 96.6842, loss: 0.0766 2023-01-06 19:54:02,028 - mmseg - INFO - Iter [128400/160000] lr: 1.185e-05, eta: 5:03:10, time: 0.580, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0774, decode.acc_seg: 96.6192, loss: 0.0774 2023-01-06 19:54:31,411 - mmseg - INFO - Iter [128450/160000] lr: 1.183e-05, eta: 5:02:41, time: 0.588, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0755, decode.acc_seg: 96.7577, loss: 0.0755 2023-01-06 19:54:59,306 - mmseg - INFO - Iter [128500/160000] lr: 1.181e-05, eta: 5:02:12, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0797, decode.acc_seg: 96.6280, loss: 0.0797 2023-01-06 19:55:27,501 - mmseg - INFO - Iter [128550/160000] lr: 1.179e-05, eta: 5:01:43, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0843, decode.acc_seg: 96.4519, loss: 0.0843 2023-01-06 19:55:55,976 - mmseg - INFO - Iter [128600/160000] lr: 1.178e-05, eta: 5:01:14, time: 0.569, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0826, decode.acc_seg: 96.4880, loss: 0.0826 2023-01-06 19:56:24,202 - mmseg - INFO - Iter [128650/160000] lr: 1.176e-05, eta: 5:00:45, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0819, decode.acc_seg: 96.4875, loss: 0.0819 2023-01-06 19:56:51,652 - mmseg - INFO - Iter [128700/160000] lr: 1.174e-05, eta: 5:00:16, time: 0.548, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0845, decode.acc_seg: 96.3532, loss: 0.0845 2023-01-06 19:57:22,412 - mmseg - INFO - Iter [128750/160000] lr: 1.172e-05, eta: 4:59:48, time: 0.616, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0804, decode.acc_seg: 96.5424, loss: 0.0804 2023-01-06 19:57:50,518 - mmseg - INFO - Iter [128800/160000] lr: 1.170e-05, eta: 4:59:19, time: 0.561, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0739, decode.acc_seg: 96.7626, loss: 0.0739 2023-01-06 19:58:19,765 - mmseg - INFO - Iter [128850/160000] lr: 1.168e-05, eta: 4:58:50, time: 0.586, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0799, decode.acc_seg: 96.5249, loss: 0.0799 2023-01-06 19:58:47,172 - mmseg - INFO - Iter [128900/160000] lr: 1.166e-05, eta: 4:58:21, time: 0.548, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0806, decode.acc_seg: 96.4313, loss: 0.0806 2023-01-06 19:59:14,374 - mmseg - INFO - Iter [128950/160000] lr: 1.164e-05, eta: 4:57:52, time: 0.544, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0774, decode.acc_seg: 96.6899, loss: 0.0774 2023-01-06 19:59:43,620 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 19:59:43,621 - mmseg - INFO - Iter [129000/160000] lr: 1.163e-05, eta: 4:57:23, time: 0.585, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0815, decode.acc_seg: 96.4784, loss: 0.0815 2023-01-06 20:00:10,720 - mmseg - INFO - Iter [129050/160000] lr: 1.161e-05, eta: 4:56:54, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0805, decode.acc_seg: 96.5572, loss: 0.0805 2023-01-06 20:00:40,290 - mmseg - INFO - Iter [129100/160000] lr: 1.159e-05, eta: 4:56:25, time: 0.591, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0766, decode.acc_seg: 96.6835, loss: 0.0766 2023-01-06 20:01:08,390 - mmseg - INFO - Iter [129150/160000] lr: 1.157e-05, eta: 4:55:57, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0745, decode.acc_seg: 96.7602, loss: 0.0745 2023-01-06 20:01:36,538 - mmseg - INFO - Iter [129200/160000] lr: 1.155e-05, eta: 4:55:28, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0810, decode.acc_seg: 96.5978, loss: 0.0810 2023-01-06 20:02:06,386 - mmseg - INFO - Iter [129250/160000] lr: 1.153e-05, eta: 4:54:59, time: 0.597, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0787, decode.acc_seg: 96.6530, loss: 0.0787 2023-01-06 20:02:34,908 - mmseg - INFO - Iter [129300/160000] lr: 1.151e-05, eta: 4:54:30, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0780, decode.acc_seg: 96.6222, loss: 0.0780 2023-01-06 20:03:03,630 - mmseg - INFO - Iter [129350/160000] lr: 1.149e-05, eta: 4:54:01, time: 0.574, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0794, decode.acc_seg: 96.5913, loss: 0.0794 2023-01-06 20:03:31,616 - mmseg - INFO - Iter [129400/160000] lr: 1.148e-05, eta: 4:53:32, time: 0.559, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0813, decode.acc_seg: 96.5629, loss: 0.0813 2023-01-06 20:04:00,113 - mmseg - INFO - Iter [129450/160000] lr: 1.146e-05, eta: 4:53:04, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0777, decode.acc_seg: 96.6388, loss: 0.0777 2023-01-06 20:04:30,519 - mmseg - INFO - Iter [129500/160000] lr: 1.144e-05, eta: 4:52:35, time: 0.608, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0840, decode.acc_seg: 96.3956, loss: 0.0840 2023-01-06 20:04:58,903 - mmseg - INFO - Iter [129550/160000] lr: 1.142e-05, eta: 4:52:06, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0806, decode.acc_seg: 96.5725, loss: 0.0806 2023-01-06 20:05:27,569 - mmseg - INFO - Iter [129600/160000] lr: 1.140e-05, eta: 4:51:38, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0794, decode.acc_seg: 96.6189, loss: 0.0794 2023-01-06 20:05:55,617 - mmseg - INFO - Iter [129650/160000] lr: 1.138e-05, eta: 4:51:09, time: 0.560, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0782, decode.acc_seg: 96.6562, loss: 0.0782 2023-01-06 20:06:23,497 - mmseg - INFO - Iter [129700/160000] lr: 1.136e-05, eta: 4:50:40, time: 0.558, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0800, decode.acc_seg: 96.4876, loss: 0.0800 2023-01-06 20:06:52,191 - mmseg - INFO - Iter [129750/160000] lr: 1.134e-05, eta: 4:50:11, time: 0.574, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0823, decode.acc_seg: 96.4269, loss: 0.0823 2023-01-06 20:07:20,316 - mmseg - INFO - Iter [129800/160000] lr: 1.133e-05, eta: 4:49:42, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0806, decode.acc_seg: 96.5459, loss: 0.0806 2023-01-06 20:07:50,481 - mmseg - INFO - Iter [129850/160000] lr: 1.131e-05, eta: 4:49:13, time: 0.604, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0771, decode.acc_seg: 96.5979, loss: 0.0771 2023-01-06 20:08:17,911 - mmseg - INFO - Iter [129900/160000] lr: 1.129e-05, eta: 4:48:44, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0790, decode.acc_seg: 96.6247, loss: 0.0790 2023-01-06 20:08:45,648 - mmseg - INFO - Iter [129950/160000] lr: 1.127e-05, eta: 4:48:15, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0756, decode.acc_seg: 96.7582, loss: 0.0756 2023-01-06 20:09:14,228 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 20:09:14,229 - mmseg - INFO - Iter [130000/160000] lr: 1.125e-05, eta: 4:47:46, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0806, decode.acc_seg: 96.4873, loss: 0.0806 2023-01-06 20:09:43,140 - mmseg - INFO - Iter [130050/160000] lr: 1.123e-05, eta: 4:47:18, time: 0.579, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0834, decode.acc_seg: 96.4442, loss: 0.0834 2023-01-06 20:10:12,535 - mmseg - INFO - Iter [130100/160000] lr: 1.121e-05, eta: 4:46:49, time: 0.588, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0755, decode.acc_seg: 96.6900, loss: 0.0755 2023-01-06 20:10:41,625 - mmseg - INFO - Iter [130150/160000] lr: 1.119e-05, eta: 4:46:20, time: 0.582, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0835, decode.acc_seg: 96.5302, loss: 0.0835 2023-01-06 20:11:08,670 - mmseg - INFO - Iter [130200/160000] lr: 1.118e-05, eta: 4:45:51, time: 0.541, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0825, decode.acc_seg: 96.4849, loss: 0.0825 2023-01-06 20:11:38,548 - mmseg - INFO - Iter [130250/160000] lr: 1.116e-05, eta: 4:45:23, time: 0.597, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0838, decode.acc_seg: 96.4505, loss: 0.0838 2023-01-06 20:12:05,779 - mmseg - INFO - Iter [130300/160000] lr: 1.114e-05, eta: 4:44:54, time: 0.545, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0782, decode.acc_seg: 96.6047, loss: 0.0782 2023-01-06 20:12:33,426 - mmseg - INFO - Iter [130350/160000] lr: 1.112e-05, eta: 4:44:25, time: 0.553, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0762, decode.acc_seg: 96.7130, loss: 0.0762 2023-01-06 20:13:00,546 - mmseg - INFO - Iter [130400/160000] lr: 1.110e-05, eta: 4:43:55, time: 0.542, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0793, decode.acc_seg: 96.6360, loss: 0.0793 2023-01-06 20:13:28,229 - mmseg - INFO - Iter [130450/160000] lr: 1.108e-05, eta: 4:43:26, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0783, decode.acc_seg: 96.5701, loss: 0.0783 2023-01-06 20:13:55,640 - mmseg - INFO - Iter [130500/160000] lr: 1.106e-05, eta: 4:42:57, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0757, decode.acc_seg: 96.7333, loss: 0.0757 2023-01-06 20:14:22,991 - mmseg - INFO - Iter [130550/160000] lr: 1.104e-05, eta: 4:42:28, time: 0.547, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0736, decode.acc_seg: 96.8156, loss: 0.0736 2023-01-06 20:14:52,364 - mmseg - INFO - Iter [130600/160000] lr: 1.103e-05, eta: 4:42:00, time: 0.587, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0736, decode.acc_seg: 96.7915, loss: 0.0736 2023-01-06 20:15:20,685 - mmseg - INFO - Iter [130650/160000] lr: 1.101e-05, eta: 4:41:31, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0708, decode.acc_seg: 96.9129, loss: 0.0708 2023-01-06 20:15:49,753 - mmseg - INFO - Iter [130700/160000] lr: 1.099e-05, eta: 4:41:02, time: 0.582, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0773, decode.acc_seg: 96.5768, loss: 0.0773 2023-01-06 20:16:16,890 - mmseg - INFO - Iter [130750/160000] lr: 1.097e-05, eta: 4:40:33, time: 0.543, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0777, decode.acc_seg: 96.6366, loss: 0.0777 2023-01-06 20:16:45,135 - mmseg - INFO - Iter [130800/160000] lr: 1.095e-05, eta: 4:40:04, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0745, decode.acc_seg: 96.7671, loss: 0.0745 2023-01-06 20:17:12,629 - mmseg - INFO - Iter [130850/160000] lr: 1.093e-05, eta: 4:39:35, time: 0.550, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0735, decode.acc_seg: 96.7521, loss: 0.0735 2023-01-06 20:17:41,512 - mmseg - INFO - Iter [130900/160000] lr: 1.091e-05, eta: 4:39:06, time: 0.578, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0774, decode.acc_seg: 96.6863, loss: 0.0774 2023-01-06 20:18:12,157 - mmseg - INFO - Iter [130950/160000] lr: 1.089e-05, eta: 4:38:38, time: 0.612, data_time: 0.056, memory: 10576, decode.loss_ce: 0.0768, decode.acc_seg: 96.7346, loss: 0.0768 2023-01-06 20:18:40,729 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 20:18:40,729 - mmseg - INFO - Iter [131000/160000] lr: 1.088e-05, eta: 4:38:09, time: 0.572, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0738, decode.acc_seg: 96.7582, loss: 0.0738 2023-01-06 20:19:08,681 - mmseg - INFO - Iter [131050/160000] lr: 1.086e-05, eta: 4:37:40, time: 0.559, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0778, decode.acc_seg: 96.5781, loss: 0.0778 2023-01-06 20:19:36,859 - mmseg - INFO - Iter [131100/160000] lr: 1.084e-05, eta: 4:37:11, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0801, decode.acc_seg: 96.6252, loss: 0.0801 2023-01-06 20:20:05,968 - mmseg - INFO - Iter [131150/160000] lr: 1.082e-05, eta: 4:36:42, time: 0.582, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0761, decode.acc_seg: 96.7275, loss: 0.0761 2023-01-06 20:20:33,395 - mmseg - INFO - Iter [131200/160000] lr: 1.080e-05, eta: 4:36:13, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0777, decode.acc_seg: 96.6667, loss: 0.0777 2023-01-06 20:21:01,573 - mmseg - INFO - Iter [131250/160000] lr: 1.078e-05, eta: 4:35:44, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0782, decode.acc_seg: 96.6310, loss: 0.0782 2023-01-06 20:21:29,138 - mmseg - INFO - Iter [131300/160000] lr: 1.076e-05, eta: 4:35:15, time: 0.551, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0829, decode.acc_seg: 96.4519, loss: 0.0829 2023-01-06 20:21:59,428 - mmseg - INFO - Iter [131350/160000] lr: 1.074e-05, eta: 4:34:47, time: 0.605, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0802, decode.acc_seg: 96.5904, loss: 0.0802 2023-01-06 20:22:27,758 - mmseg - INFO - Iter [131400/160000] lr: 1.073e-05, eta: 4:34:18, time: 0.568, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0761, decode.acc_seg: 96.6642, loss: 0.0761 2023-01-06 20:22:55,445 - mmseg - INFO - Iter [131450/160000] lr: 1.071e-05, eta: 4:33:49, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0775, decode.acc_seg: 96.6586, loss: 0.0775 2023-01-06 20:23:23,200 - mmseg - INFO - Iter [131500/160000] lr: 1.069e-05, eta: 4:33:20, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0812, decode.acc_seg: 96.5456, loss: 0.0812 2023-01-06 20:23:52,011 - mmseg - INFO - Iter [131550/160000] lr: 1.067e-05, eta: 4:32:51, time: 0.576, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0835, decode.acc_seg: 96.4888, loss: 0.0835 2023-01-06 20:24:20,442 - mmseg - INFO - Iter [131600/160000] lr: 1.065e-05, eta: 4:32:22, time: 0.569, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0769, decode.acc_seg: 96.6126, loss: 0.0769 2023-01-06 20:24:47,608 - mmseg - INFO - Iter [131650/160000] lr: 1.063e-05, eta: 4:31:53, time: 0.543, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0787, decode.acc_seg: 96.5295, loss: 0.0787 2023-01-06 20:25:17,520 - mmseg - INFO - Iter [131700/160000] lr: 1.061e-05, eta: 4:31:25, time: 0.598, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0770, decode.acc_seg: 96.7155, loss: 0.0770 2023-01-06 20:25:45,999 - mmseg - INFO - Iter [131750/160000] lr: 1.059e-05, eta: 4:30:56, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0736, decode.acc_seg: 96.8287, loss: 0.0736 2023-01-06 20:26:13,900 - mmseg - INFO - Iter [131800/160000] lr: 1.058e-05, eta: 4:30:27, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0779, decode.acc_seg: 96.5829, loss: 0.0779 2023-01-06 20:26:40,955 - mmseg - INFO - Iter [131850/160000] lr: 1.056e-05, eta: 4:29:58, time: 0.541, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0784, decode.acc_seg: 96.6213, loss: 0.0784 2023-01-06 20:27:10,015 - mmseg - INFO - Iter [131900/160000] lr: 1.054e-05, eta: 4:29:29, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0775, decode.acc_seg: 96.6502, loss: 0.0775 2023-01-06 20:27:38,079 - mmseg - INFO - Iter [131950/160000] lr: 1.052e-05, eta: 4:29:00, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0759, decode.acc_seg: 96.7287, loss: 0.0759 2023-01-06 20:28:05,181 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 20:28:05,182 - mmseg - INFO - Iter [132000/160000] lr: 1.050e-05, eta: 4:28:31, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0781, decode.acc_seg: 96.6832, loss: 0.0781 2023-01-06 20:28:32,875 - mmseg - INFO - Iter [132050/160000] lr: 1.048e-05, eta: 4:28:02, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0751, decode.acc_seg: 96.7490, loss: 0.0751 2023-01-06 20:29:03,499 - mmseg - INFO - Iter [132100/160000] lr: 1.046e-05, eta: 4:27:34, time: 0.612, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0737, decode.acc_seg: 96.7803, loss: 0.0737 2023-01-06 20:29:33,063 - mmseg - INFO - Iter [132150/160000] lr: 1.044e-05, eta: 4:27:05, time: 0.591, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0796, decode.acc_seg: 96.6168, loss: 0.0796 2023-01-06 20:30:00,522 - mmseg - INFO - Iter [132200/160000] lr: 1.043e-05, eta: 4:26:36, time: 0.549, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0792, decode.acc_seg: 96.5558, loss: 0.0792 2023-01-06 20:30:30,296 - mmseg - INFO - Iter [132250/160000] lr: 1.041e-05, eta: 4:26:07, time: 0.595, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0769, decode.acc_seg: 96.6496, loss: 0.0769 2023-01-06 20:30:59,096 - mmseg - INFO - Iter [132300/160000] lr: 1.039e-05, eta: 4:25:39, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0796, decode.acc_seg: 96.5785, loss: 0.0796 2023-01-06 20:31:28,178 - mmseg - INFO - Iter [132350/160000] lr: 1.037e-05, eta: 4:25:10, time: 0.582, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0765, decode.acc_seg: 96.6818, loss: 0.0765 2023-01-06 20:31:57,130 - mmseg - INFO - Iter [132400/160000] lr: 1.035e-05, eta: 4:24:41, time: 0.579, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0755, decode.acc_seg: 96.7184, loss: 0.0755 2023-01-06 20:32:27,415 - mmseg - INFO - Iter [132450/160000] lr: 1.033e-05, eta: 4:24:13, time: 0.605, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0751, decode.acc_seg: 96.7737, loss: 0.0751 2023-01-06 20:32:56,231 - mmseg - INFO - Iter [132500/160000] lr: 1.031e-05, eta: 4:23:44, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0752, decode.acc_seg: 96.6972, loss: 0.0752 2023-01-06 20:33:23,140 - mmseg - INFO - Iter [132550/160000] lr: 1.029e-05, eta: 4:23:15, time: 0.538, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0798, decode.acc_seg: 96.6805, loss: 0.0798 2023-01-06 20:33:51,271 - mmseg - INFO - Iter [132600/160000] lr: 1.028e-05, eta: 4:22:46, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0796, decode.acc_seg: 96.5622, loss: 0.0796 2023-01-06 20:34:19,230 - mmseg - INFO - Iter [132650/160000] lr: 1.026e-05, eta: 4:22:17, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0789, decode.acc_seg: 96.6537, loss: 0.0789 2023-01-06 20:34:46,340 - mmseg - INFO - Iter [132700/160000] lr: 1.024e-05, eta: 4:21:48, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0813, decode.acc_seg: 96.5263, loss: 0.0813 2023-01-06 20:35:14,311 - mmseg - INFO - Iter [132750/160000] lr: 1.022e-05, eta: 4:21:19, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0803, decode.acc_seg: 96.5939, loss: 0.0803 2023-01-06 20:35:42,490 - mmseg - INFO - Iter [132800/160000] lr: 1.020e-05, eta: 4:20:50, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0782, decode.acc_seg: 96.5962, loss: 0.0782 2023-01-06 20:36:14,334 - mmseg - INFO - Iter [132850/160000] lr: 1.018e-05, eta: 4:20:22, time: 0.636, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0755, decode.acc_seg: 96.7215, loss: 0.0755 2023-01-06 20:36:43,970 - mmseg - INFO - Iter [132900/160000] lr: 1.016e-05, eta: 4:19:53, time: 0.594, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0776, decode.acc_seg: 96.6491, loss: 0.0776 2023-01-06 20:37:11,143 - mmseg - INFO - Iter [132950/160000] lr: 1.014e-05, eta: 4:19:24, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0780, decode.acc_seg: 96.5978, loss: 0.0780 2023-01-06 20:37:39,995 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 20:37:39,995 - mmseg - INFO - Iter [133000/160000] lr: 1.013e-05, eta: 4:18:56, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0768, decode.acc_seg: 96.6820, loss: 0.0768 2023-01-06 20:38:08,277 - mmseg - INFO - Iter [133050/160000] lr: 1.011e-05, eta: 4:18:27, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0769, decode.acc_seg: 96.6064, loss: 0.0769 2023-01-06 20:38:36,081 - mmseg - INFO - Iter [133100/160000] lr: 1.009e-05, eta: 4:17:58, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0779, decode.acc_seg: 96.6546, loss: 0.0779 2023-01-06 20:39:04,450 - mmseg - INFO - Iter [133150/160000] lr: 1.007e-05, eta: 4:17:29, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0758, decode.acc_seg: 96.6784, loss: 0.0758 2023-01-06 20:39:34,314 - mmseg - INFO - Iter [133200/160000] lr: 1.005e-05, eta: 4:17:00, time: 0.597, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0783, decode.acc_seg: 96.5877, loss: 0.0783 2023-01-06 20:40:01,447 - mmseg - INFO - Iter [133250/160000] lr: 1.003e-05, eta: 4:16:31, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0751, decode.acc_seg: 96.7587, loss: 0.0751 2023-01-06 20:40:29,314 - mmseg - INFO - Iter [133300/160000] lr: 1.001e-05, eta: 4:16:02, time: 0.558, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0818, decode.acc_seg: 96.5724, loss: 0.0818 2023-01-06 20:40:57,050 - mmseg - INFO - Iter [133350/160000] lr: 9.994e-06, eta: 4:15:33, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0762, decode.acc_seg: 96.6528, loss: 0.0762 2023-01-06 20:41:25,125 - mmseg - INFO - Iter [133400/160000] lr: 9.975e-06, eta: 4:15:04, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0816, decode.acc_seg: 96.5332, loss: 0.0816 2023-01-06 20:41:54,611 - mmseg - INFO - Iter [133450/160000] lr: 9.957e-06, eta: 4:14:36, time: 0.590, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0763, decode.acc_seg: 96.6979, loss: 0.0763 2023-01-06 20:42:22,957 - mmseg - INFO - Iter [133500/160000] lr: 9.938e-06, eta: 4:14:07, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0777, decode.acc_seg: 96.6899, loss: 0.0777 2023-01-06 20:42:53,139 - mmseg - INFO - Iter [133550/160000] lr: 9.919e-06, eta: 4:13:38, time: 0.603, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0810, decode.acc_seg: 96.5787, loss: 0.0810 2023-01-06 20:43:21,447 - mmseg - INFO - Iter [133600/160000] lr: 9.900e-06, eta: 4:13:10, time: 0.567, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0805, decode.acc_seg: 96.5068, loss: 0.0805 2023-01-06 20:43:49,884 - mmseg - INFO - Iter [133650/160000] lr: 9.882e-06, eta: 4:12:41, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0786, decode.acc_seg: 96.6214, loss: 0.0786 2023-01-06 20:44:18,425 - mmseg - INFO - Iter [133700/160000] lr: 9.863e-06, eta: 4:12:12, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0740, decode.acc_seg: 96.7737, loss: 0.0740 2023-01-06 20:44:46,245 - mmseg - INFO - Iter [133750/160000] lr: 9.844e-06, eta: 4:11:43, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0774, decode.acc_seg: 96.6727, loss: 0.0774 2023-01-06 20:45:14,328 - mmseg - INFO - Iter [133800/160000] lr: 9.825e-06, eta: 4:11:14, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0798, decode.acc_seg: 96.6017, loss: 0.0798 2023-01-06 20:45:43,836 - mmseg - INFO - Iter [133850/160000] lr: 9.807e-06, eta: 4:10:45, time: 0.591, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0789, decode.acc_seg: 96.5747, loss: 0.0789 2023-01-06 20:46:12,425 - mmseg - INFO - Iter [133900/160000] lr: 9.788e-06, eta: 4:10:17, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0797, decode.acc_seg: 96.6391, loss: 0.0797 2023-01-06 20:46:42,798 - mmseg - INFO - Iter [133950/160000] lr: 9.769e-06, eta: 4:09:48, time: 0.607, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0787, decode.acc_seg: 96.6376, loss: 0.0787 2023-01-06 20:47:11,904 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 20:47:11,904 - mmseg - INFO - Iter [134000/160000] lr: 9.750e-06, eta: 4:09:19, time: 0.581, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0787, decode.acc_seg: 96.5390, loss: 0.0787 2023-01-06 20:47:41,519 - mmseg - INFO - Iter [134050/160000] lr: 9.732e-06, eta: 4:08:51, time: 0.592, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0767, decode.acc_seg: 96.6852, loss: 0.0767 2023-01-06 20:48:09,738 - mmseg - INFO - Iter [134100/160000] lr: 9.713e-06, eta: 4:08:22, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0781, decode.acc_seg: 96.5818, loss: 0.0781 2023-01-06 20:48:37,847 - mmseg - INFO - Iter [134150/160000] lr: 9.694e-06, eta: 4:07:53, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0727, decode.acc_seg: 96.7918, loss: 0.0727 2023-01-06 20:49:05,578 - mmseg - INFO - Iter [134200/160000] lr: 9.675e-06, eta: 4:07:24, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0736, decode.acc_seg: 96.8409, loss: 0.0736 2023-01-06 20:49:32,888 - mmseg - INFO - Iter [134250/160000] lr: 9.657e-06, eta: 4:06:55, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0755, decode.acc_seg: 96.6379, loss: 0.0755 2023-01-06 20:50:02,180 - mmseg - INFO - Iter [134300/160000] lr: 9.638e-06, eta: 4:06:26, time: 0.586, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0751, decode.acc_seg: 96.8349, loss: 0.0751 2023-01-06 20:50:31,529 - mmseg - INFO - Iter [134350/160000] lr: 9.619e-06, eta: 4:05:58, time: 0.586, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0780, decode.acc_seg: 96.6933, loss: 0.0780 2023-01-06 20:51:00,422 - mmseg - INFO - Iter [134400/160000] lr: 9.600e-06, eta: 4:05:29, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0788, decode.acc_seg: 96.6458, loss: 0.0788 2023-01-06 20:51:30,014 - mmseg - INFO - Iter [134450/160000] lr: 9.582e-06, eta: 4:05:00, time: 0.592, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0832, decode.acc_seg: 96.5157, loss: 0.0832 2023-01-06 20:51:58,679 - mmseg - INFO - Iter [134500/160000] lr: 9.563e-06, eta: 4:04:32, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0762, decode.acc_seg: 96.6610, loss: 0.0762 2023-01-06 20:52:26,156 - mmseg - INFO - Iter [134550/160000] lr: 9.544e-06, eta: 4:04:03, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0824, decode.acc_seg: 96.4665, loss: 0.0824 2023-01-06 20:52:55,357 - mmseg - INFO - Iter [134600/160000] lr: 9.525e-06, eta: 4:03:34, time: 0.585, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0771, decode.acc_seg: 96.6786, loss: 0.0771 2023-01-06 20:53:22,905 - mmseg - INFO - Iter [134650/160000] lr: 9.507e-06, eta: 4:03:05, time: 0.551, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0787, decode.acc_seg: 96.6039, loss: 0.0787 2023-01-06 20:53:53,703 - mmseg - INFO - Iter [134700/160000] lr: 9.488e-06, eta: 4:02:36, time: 0.615, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0753, decode.acc_seg: 96.6157, loss: 0.0753 2023-01-06 20:54:21,638 - mmseg - INFO - Iter [134750/160000] lr: 9.469e-06, eta: 4:02:08, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0771, decode.acc_seg: 96.6235, loss: 0.0771 2023-01-06 20:54:49,689 - mmseg - INFO - Iter [134800/160000] lr: 9.450e-06, eta: 4:01:39, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0769, decode.acc_seg: 96.7631, loss: 0.0769 2023-01-06 20:55:18,122 - mmseg - INFO - Iter [134850/160000] lr: 9.432e-06, eta: 4:01:10, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0800, decode.acc_seg: 96.6113, loss: 0.0800 2023-01-06 20:55:46,253 - mmseg - INFO - Iter [134900/160000] lr: 9.413e-06, eta: 4:00:41, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0809, decode.acc_seg: 96.5640, loss: 0.0809 2023-01-06 20:56:15,510 - mmseg - INFO - Iter [134950/160000] lr: 9.394e-06, eta: 4:00:12, time: 0.584, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0823, decode.acc_seg: 96.5021, loss: 0.0823 2023-01-06 20:56:44,108 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 20:56:44,109 - mmseg - INFO - Iter [135000/160000] lr: 9.375e-06, eta: 3:59:43, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0754, decode.acc_seg: 96.6874, loss: 0.0754 2023-01-06 20:57:15,375 - mmseg - INFO - Iter [135050/160000] lr: 9.357e-06, eta: 3:59:15, time: 0.625, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0827, decode.acc_seg: 96.4443, loss: 0.0827 2023-01-06 20:57:44,963 - mmseg - INFO - Iter [135100/160000] lr: 9.338e-06, eta: 3:58:47, time: 0.592, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0750, decode.acc_seg: 96.7389, loss: 0.0750 2023-01-06 20:58:13,727 - mmseg - INFO - Iter [135150/160000] lr: 9.319e-06, eta: 3:58:18, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0791, decode.acc_seg: 96.5336, loss: 0.0791 2023-01-06 20:58:41,940 - mmseg - INFO - Iter [135200/160000] lr: 9.300e-06, eta: 3:57:49, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0807, decode.acc_seg: 96.6204, loss: 0.0807 2023-01-06 20:59:08,929 - mmseg - INFO - Iter [135250/160000] lr: 9.282e-06, eta: 3:57:20, time: 0.540, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0766, decode.acc_seg: 96.6868, loss: 0.0766 2023-01-06 20:59:36,222 - mmseg - INFO - Iter [135300/160000] lr: 9.263e-06, eta: 3:56:51, time: 0.546, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0758, decode.acc_seg: 96.6882, loss: 0.0758 2023-01-06 21:00:04,599 - mmseg - INFO - Iter [135350/160000] lr: 9.244e-06, eta: 3:56:22, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0762, decode.acc_seg: 96.7007, loss: 0.0762 2023-01-06 21:00:34,233 - mmseg - INFO - Iter [135400/160000] lr: 9.225e-06, eta: 3:55:53, time: 0.592, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0774, decode.acc_seg: 96.6945, loss: 0.0774 2023-01-06 21:01:04,691 - mmseg - INFO - Iter [135450/160000] lr: 9.207e-06, eta: 3:55:25, time: 0.610, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0771, decode.acc_seg: 96.6699, loss: 0.0771 2023-01-06 21:01:34,064 - mmseg - INFO - Iter [135500/160000] lr: 9.188e-06, eta: 3:54:56, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0754, decode.acc_seg: 96.7121, loss: 0.0754 2023-01-06 21:02:02,454 - mmseg - INFO - Iter [135550/160000] lr: 9.169e-06, eta: 3:54:27, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0773, decode.acc_seg: 96.7106, loss: 0.0773 2023-01-06 21:02:30,052 - mmseg - INFO - Iter [135600/160000] lr: 9.150e-06, eta: 3:53:58, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0777, decode.acc_seg: 96.6070, loss: 0.0777 2023-01-06 21:02:57,252 - mmseg - INFO - Iter [135650/160000] lr: 9.132e-06, eta: 3:53:29, time: 0.544, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0770, decode.acc_seg: 96.6859, loss: 0.0770 2023-01-06 21:03:24,386 - mmseg - INFO - Iter [135700/160000] lr: 9.113e-06, eta: 3:53:00, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0736, decode.acc_seg: 96.7009, loss: 0.0736 2023-01-06 21:03:52,884 - mmseg - INFO - Iter [135750/160000] lr: 9.094e-06, eta: 3:52:31, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0797, decode.acc_seg: 96.5546, loss: 0.0797 2023-01-06 21:04:23,080 - mmseg - INFO - Iter [135800/160000] lr: 9.075e-06, eta: 3:52:03, time: 0.604, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0751, decode.acc_seg: 96.7344, loss: 0.0751 2023-01-06 21:04:52,605 - mmseg - INFO - Iter [135850/160000] lr: 9.057e-06, eta: 3:51:34, time: 0.590, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0794, decode.acc_seg: 96.5755, loss: 0.0794 2023-01-06 21:05:21,186 - mmseg - INFO - Iter [135900/160000] lr: 9.038e-06, eta: 3:51:06, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0797, decode.acc_seg: 96.6844, loss: 0.0797 2023-01-06 21:05:48,321 - mmseg - INFO - Iter [135950/160000] lr: 9.019e-06, eta: 3:50:36, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0774, decode.acc_seg: 96.6850, loss: 0.0774 2023-01-06 21:06:16,466 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 21:06:16,467 - mmseg - INFO - Iter [136000/160000] lr: 9.000e-06, eta: 3:50:08, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0746, decode.acc_seg: 96.8212, loss: 0.0746 2023-01-06 21:06:45,857 - mmseg - INFO - Iter [136050/160000] lr: 8.982e-06, eta: 3:49:39, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0763, decode.acc_seg: 96.7403, loss: 0.0763 2023-01-06 21:07:14,692 - mmseg - INFO - Iter [136100/160000] lr: 8.963e-06, eta: 3:49:10, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0755, decode.acc_seg: 96.6955, loss: 0.0755 2023-01-06 21:07:44,916 - mmseg - INFO - Iter [136150/160000] lr: 8.944e-06, eta: 3:48:42, time: 0.604, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0768, decode.acc_seg: 96.6821, loss: 0.0768 2023-01-06 21:08:15,130 - mmseg - INFO - Iter [136200/160000] lr: 8.925e-06, eta: 3:48:13, time: 0.605, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0737, decode.acc_seg: 96.7633, loss: 0.0737 2023-01-06 21:08:43,377 - mmseg - INFO - Iter [136250/160000] lr: 8.907e-06, eta: 3:47:44, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0747, decode.acc_seg: 96.7417, loss: 0.0747 2023-01-06 21:09:12,338 - mmseg - INFO - Iter [136300/160000] lr: 8.888e-06, eta: 3:47:16, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0786, decode.acc_seg: 96.6438, loss: 0.0786 2023-01-06 21:09:39,846 - mmseg - INFO - Iter [136350/160000] lr: 8.869e-06, eta: 3:46:47, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0772, decode.acc_seg: 96.6652, loss: 0.0772 2023-01-06 21:10:07,083 - mmseg - INFO - Iter [136400/160000] lr: 8.850e-06, eta: 3:46:18, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0762, decode.acc_seg: 96.6955, loss: 0.0762 2023-01-06 21:10:34,587 - mmseg - INFO - Iter [136450/160000] lr: 8.832e-06, eta: 3:45:49, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0833, decode.acc_seg: 96.4871, loss: 0.0833 2023-01-06 21:11:03,161 - mmseg - INFO - Iter [136500/160000] lr: 8.813e-06, eta: 3:45:20, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0785, decode.acc_seg: 96.6858, loss: 0.0785 2023-01-06 21:11:34,564 - mmseg - INFO - Iter [136550/160000] lr: 8.794e-06, eta: 3:44:51, time: 0.627, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0761, decode.acc_seg: 96.7501, loss: 0.0761 2023-01-06 21:12:01,530 - mmseg - INFO - Iter [136600/160000] lr: 8.775e-06, eta: 3:44:22, time: 0.540, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0762, decode.acc_seg: 96.6849, loss: 0.0762 2023-01-06 21:12:29,578 - mmseg - INFO - Iter [136650/160000] lr: 8.757e-06, eta: 3:43:53, time: 0.561, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0733, decode.acc_seg: 96.7094, loss: 0.0733 2023-01-06 21:12:57,028 - mmseg - INFO - Iter [136700/160000] lr: 8.738e-06, eta: 3:43:24, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0789, decode.acc_seg: 96.7230, loss: 0.0789 2023-01-06 21:13:24,787 - mmseg - INFO - Iter [136750/160000] lr: 8.719e-06, eta: 3:42:56, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0778, decode.acc_seg: 96.6734, loss: 0.0778 2023-01-06 21:13:53,914 - mmseg - INFO - Iter [136800/160000] lr: 8.700e-06, eta: 3:42:27, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0769, decode.acc_seg: 96.6615, loss: 0.0769 2023-01-06 21:14:21,372 - mmseg - INFO - Iter [136850/160000] lr: 8.682e-06, eta: 3:41:58, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0737, decode.acc_seg: 96.7973, loss: 0.0737 2023-01-06 21:14:50,665 - mmseg - INFO - Iter [136900/160000] lr: 8.663e-06, eta: 3:41:29, time: 0.586, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0781, decode.acc_seg: 96.6455, loss: 0.0781 2023-01-06 21:15:19,224 - mmseg - INFO - Iter [136950/160000] lr: 8.644e-06, eta: 3:41:00, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0785, decode.acc_seg: 96.6865, loss: 0.0785 2023-01-06 21:15:47,742 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 21:15:47,743 - mmseg - INFO - Iter [137000/160000] lr: 8.625e-06, eta: 3:40:32, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0743, decode.acc_seg: 96.8596, loss: 0.0743 2023-01-06 21:16:15,493 - mmseg - INFO - Iter [137050/160000] lr: 8.607e-06, eta: 3:40:03, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0733, decode.acc_seg: 96.8380, loss: 0.0733 2023-01-06 21:16:43,310 - mmseg - INFO - Iter [137100/160000] lr: 8.588e-06, eta: 3:39:34, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0805, decode.acc_seg: 96.5859, loss: 0.0805 2023-01-06 21:17:12,098 - mmseg - INFO - Iter [137150/160000] lr: 8.569e-06, eta: 3:39:05, time: 0.576, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0753, decode.acc_seg: 96.7081, loss: 0.0753 2023-01-06 21:17:39,885 - mmseg - INFO - Iter [137200/160000] lr: 8.550e-06, eta: 3:38:36, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0757, decode.acc_seg: 96.7358, loss: 0.0757 2023-01-06 21:18:07,268 - mmseg - INFO - Iter [137250/160000] lr: 8.532e-06, eta: 3:38:07, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0777, decode.acc_seg: 96.6220, loss: 0.0777 2023-01-06 21:18:38,077 - mmseg - INFO - Iter [137300/160000] lr: 8.513e-06, eta: 3:37:39, time: 0.616, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0733, decode.acc_seg: 96.7687, loss: 0.0733 2023-01-06 21:19:06,393 - mmseg - INFO - Iter [137350/160000] lr: 8.494e-06, eta: 3:37:10, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0780, decode.acc_seg: 96.6799, loss: 0.0780 2023-01-06 21:19:35,146 - mmseg - INFO - Iter [137400/160000] lr: 8.475e-06, eta: 3:36:41, time: 0.575, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0814, decode.acc_seg: 96.6408, loss: 0.0814 2023-01-06 21:20:04,671 - mmseg - INFO - Iter [137450/160000] lr: 8.457e-06, eta: 3:36:12, time: 0.590, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0790, decode.acc_seg: 96.6194, loss: 0.0790 2023-01-06 21:20:34,043 - mmseg - INFO - Iter [137500/160000] lr: 8.438e-06, eta: 3:35:44, time: 0.587, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0758, decode.acc_seg: 96.7565, loss: 0.0758 2023-01-06 21:21:01,978 - mmseg - INFO - Iter [137550/160000] lr: 8.419e-06, eta: 3:35:15, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0774, decode.acc_seg: 96.7165, loss: 0.0774 2023-01-06 21:21:30,553 - mmseg - INFO - Iter [137600/160000] lr: 8.400e-06, eta: 3:34:46, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0757, decode.acc_seg: 96.7410, loss: 0.0757 2023-01-06 21:22:01,389 - mmseg - INFO - Iter [137650/160000] lr: 8.382e-06, eta: 3:34:18, time: 0.617, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0795, decode.acc_seg: 96.6449, loss: 0.0795 2023-01-06 21:22:30,230 - mmseg - INFO - Iter [137700/160000] lr: 8.363e-06, eta: 3:33:49, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0754, decode.acc_seg: 96.6665, loss: 0.0754 2023-01-06 21:22:59,654 - mmseg - INFO - Iter [137750/160000] lr: 8.344e-06, eta: 3:33:20, time: 0.588, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0808, decode.acc_seg: 96.5127, loss: 0.0808 2023-01-06 21:23:27,604 - mmseg - INFO - Iter [137800/160000] lr: 8.325e-06, eta: 3:32:51, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0822, decode.acc_seg: 96.5102, loss: 0.0822 2023-01-06 21:23:55,778 - mmseg - INFO - Iter [137850/160000] lr: 8.307e-06, eta: 3:32:22, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0784, decode.acc_seg: 96.6351, loss: 0.0784 2023-01-06 21:24:24,005 - mmseg - INFO - Iter [137900/160000] lr: 8.288e-06, eta: 3:31:54, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0745, decode.acc_seg: 96.6942, loss: 0.0745 2023-01-06 21:24:52,026 - mmseg - INFO - Iter [137950/160000] lr: 8.269e-06, eta: 3:31:25, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0755, decode.acc_seg: 96.7443, loss: 0.0755 2023-01-06 21:25:19,199 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 21:25:19,200 - mmseg - INFO - Iter [138000/160000] lr: 8.250e-06, eta: 3:30:56, time: 0.543, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0779, decode.acc_seg: 96.6504, loss: 0.0779 2023-01-06 21:25:48,752 - mmseg - INFO - Iter [138050/160000] lr: 8.232e-06, eta: 3:30:27, time: 0.591, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0734, decode.acc_seg: 96.8273, loss: 0.0734 2023-01-06 21:26:18,100 - mmseg - INFO - Iter [138100/160000] lr: 8.213e-06, eta: 3:29:58, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0776, decode.acc_seg: 96.6338, loss: 0.0776 2023-01-06 21:26:46,473 - mmseg - INFO - Iter [138150/160000] lr: 8.194e-06, eta: 3:29:30, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0814, decode.acc_seg: 96.5453, loss: 0.0814 2023-01-06 21:27:14,776 - mmseg - INFO - Iter [138200/160000] lr: 8.175e-06, eta: 3:29:01, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0770, decode.acc_seg: 96.6775, loss: 0.0770 2023-01-06 21:27:41,943 - mmseg - INFO - Iter [138250/160000] lr: 8.157e-06, eta: 3:28:32, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0735, decode.acc_seg: 96.8322, loss: 0.0735 2023-01-06 21:28:09,712 - mmseg - INFO - Iter [138300/160000] lr: 8.138e-06, eta: 3:28:03, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0773, decode.acc_seg: 96.6782, loss: 0.0773 2023-01-06 21:28:36,973 - mmseg - INFO - Iter [138350/160000] lr: 8.119e-06, eta: 3:27:34, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0757, decode.acc_seg: 96.7474, loss: 0.0757 2023-01-06 21:29:07,334 - mmseg - INFO - Iter [138400/160000] lr: 8.100e-06, eta: 3:27:05, time: 0.607, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0747, decode.acc_seg: 96.8052, loss: 0.0747 2023-01-06 21:29:36,857 - mmseg - INFO - Iter [138450/160000] lr: 8.082e-06, eta: 3:26:37, time: 0.590, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0780, decode.acc_seg: 96.5544, loss: 0.0780 2023-01-06 21:30:05,293 - mmseg - INFO - Iter [138500/160000] lr: 8.063e-06, eta: 3:26:08, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0763, decode.acc_seg: 96.5952, loss: 0.0763 2023-01-06 21:30:33,476 - mmseg - INFO - Iter [138550/160000] lr: 8.044e-06, eta: 3:25:39, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0771, decode.acc_seg: 96.7057, loss: 0.0771 2023-01-06 21:31:00,612 - mmseg - INFO - Iter [138600/160000] lr: 8.025e-06, eta: 3:25:10, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0736, decode.acc_seg: 96.8191, loss: 0.0736 2023-01-06 21:31:28,257 - mmseg - INFO - Iter [138650/160000] lr: 8.007e-06, eta: 3:24:41, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0756, decode.acc_seg: 96.7298, loss: 0.0756 2023-01-06 21:31:56,516 - mmseg - INFO - Iter [138700/160000] lr: 7.988e-06, eta: 3:24:12, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0755, decode.acc_seg: 96.7706, loss: 0.0755 2023-01-06 21:32:25,225 - mmseg - INFO - Iter [138750/160000] lr: 7.969e-06, eta: 3:23:43, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0811, decode.acc_seg: 96.4913, loss: 0.0811 2023-01-06 21:32:55,691 - mmseg - INFO - Iter [138800/160000] lr: 7.950e-06, eta: 3:23:15, time: 0.610, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0742, decode.acc_seg: 96.7749, loss: 0.0742 2023-01-06 21:33:23,432 - mmseg - INFO - Iter [138850/160000] lr: 7.932e-06, eta: 3:22:46, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0740, decode.acc_seg: 96.8175, loss: 0.0740 2023-01-06 21:33:53,094 - mmseg - INFO - Iter [138900/160000] lr: 7.913e-06, eta: 3:22:17, time: 0.593, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0779, decode.acc_seg: 96.6070, loss: 0.0779 2023-01-06 21:34:22,247 - mmseg - INFO - Iter [138950/160000] lr: 7.894e-06, eta: 3:21:49, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0779, decode.acc_seg: 96.6668, loss: 0.0779 2023-01-06 21:34:51,276 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 21:34:51,277 - mmseg - INFO - Iter [139000/160000] lr: 7.875e-06, eta: 3:21:20, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0743, decode.acc_seg: 96.7984, loss: 0.0743 2023-01-06 21:35:19,781 - mmseg - INFO - Iter [139050/160000] lr: 7.857e-06, eta: 3:20:51, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0745, decode.acc_seg: 96.8326, loss: 0.0745 2023-01-06 21:35:49,110 - mmseg - INFO - Iter [139100/160000] lr: 7.838e-06, eta: 3:20:22, time: 0.586, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0801, decode.acc_seg: 96.5319, loss: 0.0801 2023-01-06 21:36:19,900 - mmseg - INFO - Iter [139150/160000] lr: 7.819e-06, eta: 3:19:54, time: 0.617, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0774, decode.acc_seg: 96.6333, loss: 0.0774 2023-01-06 21:36:49,050 - mmseg - INFO - Iter [139200/160000] lr: 7.800e-06, eta: 3:19:25, time: 0.583, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0738, decode.acc_seg: 96.7969, loss: 0.0738 2023-01-06 21:37:16,555 - mmseg - INFO - Iter [139250/160000] lr: 7.782e-06, eta: 3:18:56, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0758, decode.acc_seg: 96.7595, loss: 0.0758 2023-01-06 21:37:45,177 - mmseg - INFO - Iter [139300/160000] lr: 7.763e-06, eta: 3:18:28, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0715, decode.acc_seg: 96.8777, loss: 0.0715 2023-01-06 21:38:15,052 - mmseg - INFO - Iter [139350/160000] lr: 7.744e-06, eta: 3:17:59, time: 0.597, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0787, decode.acc_seg: 96.5772, loss: 0.0787 2023-01-06 21:38:43,816 - mmseg - INFO - Iter [139400/160000] lr: 7.725e-06, eta: 3:17:30, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0762, decode.acc_seg: 96.6705, loss: 0.0762 2023-01-06 21:39:11,954 - mmseg - INFO - Iter [139450/160000] lr: 7.707e-06, eta: 3:17:01, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0753, decode.acc_seg: 96.7021, loss: 0.0753 2023-01-06 21:39:40,276 - mmseg - INFO - Iter [139500/160000] lr: 7.688e-06, eta: 3:16:32, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0760, decode.acc_seg: 96.6861, loss: 0.0760 2023-01-06 21:40:10,083 - mmseg - INFO - Iter [139550/160000] lr: 7.669e-06, eta: 3:16:04, time: 0.596, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0787, decode.acc_seg: 96.5812, loss: 0.0787 2023-01-06 21:40:38,049 - mmseg - INFO - Iter [139600/160000] lr: 7.650e-06, eta: 3:15:35, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0752, decode.acc_seg: 96.7688, loss: 0.0752 2023-01-06 21:41:06,184 - mmseg - INFO - Iter [139650/160000] lr: 7.632e-06, eta: 3:15:06, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0789, decode.acc_seg: 96.6880, loss: 0.0789 2023-01-06 21:41:33,690 - mmseg - INFO - Iter [139700/160000] lr: 7.613e-06, eta: 3:14:37, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0737, decode.acc_seg: 96.7303, loss: 0.0737 2023-01-06 21:42:01,374 - mmseg - INFO - Iter [139750/160000] lr: 7.594e-06, eta: 3:14:08, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0763, decode.acc_seg: 96.6682, loss: 0.0763 2023-01-06 21:42:29,749 - mmseg - INFO - Iter [139800/160000] lr: 7.575e-06, eta: 3:13:39, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0760, decode.acc_seg: 96.7447, loss: 0.0760 2023-01-06 21:42:57,926 - mmseg - INFO - Iter [139850/160000] lr: 7.557e-06, eta: 3:13:11, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0752, decode.acc_seg: 96.8366, loss: 0.0752 2023-01-06 21:43:28,767 - mmseg - INFO - Iter [139900/160000] lr: 7.538e-06, eta: 3:12:42, time: 0.616, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0743, decode.acc_seg: 96.7101, loss: 0.0743 2023-01-06 21:43:56,708 - mmseg - INFO - Iter [139950/160000] lr: 7.519e-06, eta: 3:12:13, time: 0.560, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0739, decode.acc_seg: 96.8405, loss: 0.0739 2023-01-06 21:44:24,018 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 21:44:24,019 - mmseg - INFO - Iter [140000/160000] lr: 7.500e-06, eta: 3:11:44, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0728, decode.acc_seg: 96.8307, loss: 0.0728 2023-01-06 21:44:51,141 - mmseg - INFO - Iter [140050/160000] lr: 7.482e-06, eta: 3:11:15, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0776, decode.acc_seg: 96.5456, loss: 0.0776 2023-01-06 21:45:20,689 - mmseg - INFO - Iter [140100/160000] lr: 7.463e-06, eta: 3:10:47, time: 0.590, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0762, decode.acc_seg: 96.7013, loss: 0.0762 2023-01-06 21:45:48,215 - mmseg - INFO - Iter [140150/160000] lr: 7.444e-06, eta: 3:10:18, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0733, decode.acc_seg: 96.8585, loss: 0.0733 2023-01-06 21:46:16,676 - mmseg - INFO - Iter [140200/160000] lr: 7.425e-06, eta: 3:09:49, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0767, decode.acc_seg: 96.6010, loss: 0.0767 2023-01-06 21:46:47,774 - mmseg - INFO - Iter [140250/160000] lr: 7.407e-06, eta: 3:09:20, time: 0.622, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0772, decode.acc_seg: 96.6610, loss: 0.0772 2023-01-06 21:47:14,888 - mmseg - INFO - Iter [140300/160000] lr: 7.388e-06, eta: 3:08:51, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0801, decode.acc_seg: 96.4970, loss: 0.0801 2023-01-06 21:47:43,228 - mmseg - INFO - Iter [140350/160000] lr: 7.369e-06, eta: 3:08:23, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0811, decode.acc_seg: 96.5483, loss: 0.0811 2023-01-06 21:48:11,843 - mmseg - INFO - Iter [140400/160000] lr: 7.350e-06, eta: 3:07:54, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0731, decode.acc_seg: 96.7707, loss: 0.0731 2023-01-06 21:48:41,454 - mmseg - INFO - Iter [140450/160000] lr: 7.332e-06, eta: 3:07:25, time: 0.591, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0706, decode.acc_seg: 96.8937, loss: 0.0706 2023-01-06 21:49:09,906 - mmseg - INFO - Iter [140500/160000] lr: 7.313e-06, eta: 3:06:56, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0765, decode.acc_seg: 96.7175, loss: 0.0765 2023-01-06 21:49:37,671 - mmseg - INFO - Iter [140550/160000] lr: 7.294e-06, eta: 3:06:28, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0819, decode.acc_seg: 96.5209, loss: 0.0819 2023-01-06 21:50:07,705 - mmseg - INFO - Iter [140600/160000] lr: 7.275e-06, eta: 3:05:59, time: 0.601, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0763, decode.acc_seg: 96.7035, loss: 0.0763 2023-01-06 21:50:39,363 - mmseg - INFO - Iter [140650/160000] lr: 7.257e-06, eta: 3:05:31, time: 0.634, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0776, decode.acc_seg: 96.6559, loss: 0.0776 2023-01-06 21:51:07,717 - mmseg - INFO - Iter [140700/160000] lr: 7.238e-06, eta: 3:05:02, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0739, decode.acc_seg: 96.8512, loss: 0.0739 2023-01-06 21:51:35,988 - mmseg - INFO - Iter [140750/160000] lr: 7.219e-06, eta: 3:04:33, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0770, decode.acc_seg: 96.6641, loss: 0.0770 2023-01-06 21:52:04,533 - mmseg - INFO - Iter [140800/160000] lr: 7.200e-06, eta: 3:04:04, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0806, decode.acc_seg: 96.5357, loss: 0.0806 2023-01-06 21:52:34,040 - mmseg - INFO - Iter [140850/160000] lr: 7.182e-06, eta: 3:03:35, time: 0.590, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0751, decode.acc_seg: 96.8043, loss: 0.0751 2023-01-06 21:53:01,239 - mmseg - INFO - Iter [140900/160000] lr: 7.163e-06, eta: 3:03:06, time: 0.543, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0759, decode.acc_seg: 96.7485, loss: 0.0759 2023-01-06 21:53:29,452 - mmseg - INFO - Iter [140950/160000] lr: 7.144e-06, eta: 3:02:38, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0746, decode.acc_seg: 96.7423, loss: 0.0746 2023-01-06 21:53:59,962 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 21:53:59,963 - mmseg - INFO - Iter [141000/160000] lr: 7.125e-06, eta: 3:02:09, time: 0.610, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0752, decode.acc_seg: 96.7594, loss: 0.0752 2023-01-06 21:54:28,458 - mmseg - INFO - Iter [141050/160000] lr: 7.107e-06, eta: 3:01:40, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0761, decode.acc_seg: 96.6967, loss: 0.0761 2023-01-06 21:54:56,781 - mmseg - INFO - Iter [141100/160000] lr: 7.088e-06, eta: 3:01:12, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0729, decode.acc_seg: 96.8621, loss: 0.0729 2023-01-06 21:55:25,958 - mmseg - INFO - Iter [141150/160000] lr: 7.069e-06, eta: 3:00:43, time: 0.584, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0744, decode.acc_seg: 96.7960, loss: 0.0744 2023-01-06 21:55:54,009 - mmseg - INFO - Iter [141200/160000] lr: 7.050e-06, eta: 3:00:14, time: 0.561, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0781, decode.acc_seg: 96.7022, loss: 0.0781 2023-01-06 21:56:22,340 - mmseg - INFO - Iter [141250/160000] lr: 7.032e-06, eta: 2:59:45, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0776, decode.acc_seg: 96.6494, loss: 0.0776 2023-01-06 21:56:51,229 - mmseg - INFO - Iter [141300/160000] lr: 7.013e-06, eta: 2:59:16, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0816, decode.acc_seg: 96.5752, loss: 0.0816 2023-01-06 21:57:19,612 - mmseg - INFO - Iter [141350/160000] lr: 6.994e-06, eta: 2:58:48, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0758, decode.acc_seg: 96.7602, loss: 0.0758 2023-01-06 21:57:50,513 - mmseg - INFO - Iter [141400/160000] lr: 6.975e-06, eta: 2:58:19, time: 0.617, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0762, decode.acc_seg: 96.7052, loss: 0.0762 2023-01-06 21:58:18,358 - mmseg - INFO - Iter [141450/160000] lr: 6.957e-06, eta: 2:57:50, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0795, decode.acc_seg: 96.5996, loss: 0.0795 2023-01-06 21:58:45,538 - mmseg - INFO - Iter [141500/160000] lr: 6.938e-06, eta: 2:57:21, time: 0.544, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0775, decode.acc_seg: 96.5744, loss: 0.0775 2023-01-06 21:59:13,867 - mmseg - INFO - Iter [141550/160000] lr: 6.919e-06, eta: 2:56:52, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0760, decode.acc_seg: 96.6810, loss: 0.0760 2023-01-06 21:59:42,061 - mmseg - INFO - Iter [141600/160000] lr: 6.900e-06, eta: 2:56:24, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0738, decode.acc_seg: 96.7168, loss: 0.0738 2023-01-06 22:00:11,003 - mmseg - INFO - Iter [141650/160000] lr: 6.882e-06, eta: 2:55:55, time: 0.579, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0746, decode.acc_seg: 96.7359, loss: 0.0746 2023-01-06 22:00:39,774 - mmseg - INFO - Iter [141700/160000] lr: 6.863e-06, eta: 2:55:26, time: 0.575, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0775, decode.acc_seg: 96.7058, loss: 0.0775 2023-01-06 22:01:10,495 - mmseg - INFO - Iter [141750/160000] lr: 6.844e-06, eta: 2:54:58, time: 0.614, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0766, decode.acc_seg: 96.6636, loss: 0.0766 2023-01-06 22:01:38,897 - mmseg - INFO - Iter [141800/160000] lr: 6.825e-06, eta: 2:54:29, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0730, decode.acc_seg: 96.8030, loss: 0.0730 2023-01-06 22:02:08,501 - mmseg - INFO - Iter [141850/160000] lr: 6.807e-06, eta: 2:54:00, time: 0.593, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0756, decode.acc_seg: 96.6528, loss: 0.0756 2023-01-06 22:02:37,043 - mmseg - INFO - Iter [141900/160000] lr: 6.788e-06, eta: 2:53:31, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0717, decode.acc_seg: 96.9391, loss: 0.0717 2023-01-06 22:03:04,503 - mmseg - INFO - Iter [141950/160000] lr: 6.769e-06, eta: 2:53:02, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0737, decode.acc_seg: 96.8479, loss: 0.0737 2023-01-06 22:03:32,363 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 22:03:32,364 - mmseg - INFO - Iter [142000/160000] lr: 6.750e-06, eta: 2:52:34, time: 0.557, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0721, decode.acc_seg: 96.8591, loss: 0.0721 2023-01-06 22:04:00,120 - mmseg - INFO - Iter [142050/160000] lr: 6.732e-06, eta: 2:52:05, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0771, decode.acc_seg: 96.7305, loss: 0.0771 2023-01-06 22:04:27,252 - mmseg - INFO - Iter [142100/160000] lr: 6.713e-06, eta: 2:51:36, time: 0.543, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0746, decode.acc_seg: 96.7381, loss: 0.0746 2023-01-06 22:04:57,540 - mmseg - INFO - Iter [142150/160000] lr: 6.694e-06, eta: 2:51:07, time: 0.605, data_time: 0.056, memory: 10576, decode.loss_ce: 0.0771, decode.acc_seg: 96.6999, loss: 0.0771 2023-01-06 22:05:27,295 - mmseg - INFO - Iter [142200/160000] lr: 6.675e-06, eta: 2:50:38, time: 0.595, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0740, decode.acc_seg: 96.7783, loss: 0.0740 2023-01-06 22:05:56,868 - mmseg - INFO - Iter [142250/160000] lr: 6.657e-06, eta: 2:50:10, time: 0.591, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0744, decode.acc_seg: 96.7552, loss: 0.0744 2023-01-06 22:06:26,237 - mmseg - INFO - Iter [142300/160000] lr: 6.638e-06, eta: 2:49:41, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0750, decode.acc_seg: 96.7665, loss: 0.0750 2023-01-06 22:06:53,919 - mmseg - INFO - Iter [142350/160000] lr: 6.619e-06, eta: 2:49:12, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0749, decode.acc_seg: 96.7678, loss: 0.0749 2023-01-06 22:07:21,590 - mmseg - INFO - Iter [142400/160000] lr: 6.600e-06, eta: 2:48:43, time: 0.553, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0780, decode.acc_seg: 96.6145, loss: 0.0780 2023-01-06 22:07:50,739 - mmseg - INFO - Iter [142450/160000] lr: 6.582e-06, eta: 2:48:15, time: 0.582, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0752, decode.acc_seg: 96.7531, loss: 0.0752 2023-01-06 22:08:20,770 - mmseg - INFO - Iter [142500/160000] lr: 6.563e-06, eta: 2:47:46, time: 0.601, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0759, decode.acc_seg: 96.7254, loss: 0.0759 2023-01-06 22:08:49,618 - mmseg - INFO - Iter [142550/160000] lr: 6.544e-06, eta: 2:47:17, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0729, decode.acc_seg: 96.8134, loss: 0.0729 2023-01-06 22:09:18,286 - mmseg - INFO - Iter [142600/160000] lr: 6.525e-06, eta: 2:46:48, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0721, decode.acc_seg: 96.8735, loss: 0.0721 2023-01-06 22:09:45,663 - mmseg - INFO - Iter [142650/160000] lr: 6.507e-06, eta: 2:46:20, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0749, decode.acc_seg: 96.7313, loss: 0.0749 2023-01-06 22:10:12,908 - mmseg - INFO - Iter [142700/160000] lr: 6.488e-06, eta: 2:45:51, time: 0.544, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0706, decode.acc_seg: 96.9112, loss: 0.0706 2023-01-06 22:10:42,166 - mmseg - INFO - Iter [142750/160000] lr: 6.469e-06, eta: 2:45:22, time: 0.586, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0789, decode.acc_seg: 96.6556, loss: 0.0789 2023-01-06 22:11:09,432 - mmseg - INFO - Iter [142800/160000] lr: 6.450e-06, eta: 2:44:53, time: 0.545, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0784, decode.acc_seg: 96.6699, loss: 0.0784 2023-01-06 22:11:39,382 - mmseg - INFO - Iter [142850/160000] lr: 6.432e-06, eta: 2:44:24, time: 0.600, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0748, decode.acc_seg: 96.8416, loss: 0.0748 2023-01-06 22:12:07,190 - mmseg - INFO - Iter [142900/160000] lr: 6.413e-06, eta: 2:43:55, time: 0.556, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0755, decode.acc_seg: 96.8511, loss: 0.0755 2023-01-06 22:12:36,238 - mmseg - INFO - Iter [142950/160000] lr: 6.394e-06, eta: 2:43:27, time: 0.581, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0779, decode.acc_seg: 96.7245, loss: 0.0779 2023-01-06 22:13:03,940 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 22:13:03,941 - mmseg - INFO - Iter [143000/160000] lr: 6.375e-06, eta: 2:42:58, time: 0.554, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0777, decode.acc_seg: 96.5959, loss: 0.0777 2023-01-06 22:13:32,536 - mmseg - INFO - Iter [143050/160000] lr: 6.357e-06, eta: 2:42:29, time: 0.572, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0739, decode.acc_seg: 96.7764, loss: 0.0739 2023-01-06 22:14:01,445 - mmseg - INFO - Iter [143100/160000] lr: 6.338e-06, eta: 2:42:00, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0758, decode.acc_seg: 96.7780, loss: 0.0758 2023-01-06 22:14:30,329 - mmseg - INFO - Iter [143150/160000] lr: 6.319e-06, eta: 2:41:32, time: 0.578, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0759, decode.acc_seg: 96.7139, loss: 0.0759 2023-01-06 22:14:58,363 - mmseg - INFO - Iter [143200/160000] lr: 6.300e-06, eta: 2:41:03, time: 0.560, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0760, decode.acc_seg: 96.7529, loss: 0.0760 2023-01-06 22:15:29,099 - mmseg - INFO - Iter [143250/160000] lr: 6.282e-06, eta: 2:40:34, time: 0.615, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0799, decode.acc_seg: 96.6030, loss: 0.0799 2023-01-06 22:15:57,356 - mmseg - INFO - Iter [143300/160000] lr: 6.263e-06, eta: 2:40:05, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0741, decode.acc_seg: 96.8393, loss: 0.0741 2023-01-06 22:16:25,144 - mmseg - INFO - Iter [143350/160000] lr: 6.244e-06, eta: 2:39:37, time: 0.556, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0765, decode.acc_seg: 96.6575, loss: 0.0765 2023-01-06 22:16:52,215 - mmseg - INFO - Iter [143400/160000] lr: 6.225e-06, eta: 2:39:08, time: 0.541, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0750, decode.acc_seg: 96.7502, loss: 0.0750 2023-01-06 22:17:20,397 - mmseg - INFO - Iter [143450/160000] lr: 6.207e-06, eta: 2:38:39, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0791, decode.acc_seg: 96.6410, loss: 0.0791 2023-01-06 22:17:47,632 - mmseg - INFO - Iter [143500/160000] lr: 6.188e-06, eta: 2:38:10, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0798, decode.acc_seg: 96.5778, loss: 0.0798 2023-01-06 22:18:16,381 - mmseg - INFO - Iter [143550/160000] lr: 6.169e-06, eta: 2:37:41, time: 0.575, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0699, decode.acc_seg: 96.9130, loss: 0.0699 2023-01-06 22:18:47,694 - mmseg - INFO - Iter [143600/160000] lr: 6.150e-06, eta: 2:37:13, time: 0.625, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0734, decode.acc_seg: 96.7628, loss: 0.0734 2023-01-06 22:19:16,573 - mmseg - INFO - Iter [143650/160000] lr: 6.132e-06, eta: 2:36:44, time: 0.578, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0708, decode.acc_seg: 96.9476, loss: 0.0708 2023-01-06 22:19:43,628 - mmseg - INFO - Iter [143700/160000] lr: 6.113e-06, eta: 2:36:15, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0744, decode.acc_seg: 96.7279, loss: 0.0744 2023-01-06 22:20:12,147 - mmseg - INFO - Iter [143750/160000] lr: 6.094e-06, eta: 2:35:46, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0757, decode.acc_seg: 96.7204, loss: 0.0757 2023-01-06 22:20:39,279 - mmseg - INFO - Iter [143800/160000] lr: 6.075e-06, eta: 2:35:17, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0736, decode.acc_seg: 96.8748, loss: 0.0736 2023-01-06 22:21:08,594 - mmseg - INFO - Iter [143850/160000] lr: 6.057e-06, eta: 2:34:48, time: 0.586, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0748, decode.acc_seg: 96.7632, loss: 0.0748 2023-01-06 22:21:35,775 - mmseg - INFO - Iter [143900/160000] lr: 6.038e-06, eta: 2:34:20, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0743, decode.acc_seg: 96.7043, loss: 0.0743 2023-01-06 22:22:04,199 - mmseg - INFO - Iter [143950/160000] lr: 6.019e-06, eta: 2:33:51, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0765, decode.acc_seg: 96.7399, loss: 0.0765 2023-01-06 22:22:33,567 - mmseg - INFO - Saving checkpoint at 144000 iterations 2023-01-06 22:22:38,624 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 22:22:38,626 - mmseg - INFO - Iter [144000/160000] lr: 6.000e-06, eta: 2:33:23, time: 0.689, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0767, decode.acc_seg: 96.7019, loss: 0.0767 2023-01-06 22:23:10,914 - mmseg - INFO - per class results: 2023-01-06 22:23:10,917 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 98.1 | 98.96 | | sidewalk | 84.51 | 92.08 | | building | 92.27 | 96.4 | | wall | 58.17 | 69.0 | | fence | 57.99 | 70.31 | | pole | 63.43 | 73.23 | | traffic light | 67.37 | 79.39 | | traffic sign | 76.51 | 84.01 | | vegetation | 92.34 | 96.52 | | terrain | 64.27 | 74.26 | | sky | 94.82 | 97.98 | | person | 79.21 | 90.2 | | rider | 56.46 | 68.51 | | car | 94.26 | 97.6 | | truck | 71.5 | 79.35 | | bus | 77.75 | 87.35 | | train | 58.59 | 62.77 | | motorcycle | 49.77 | 56.39 | | bicycle | 73.95 | 86.99 | +---------------+-------+-------+ 2023-01-06 22:23:10,917 - mmseg - INFO - Summary: 2023-01-06 22:23:10,918 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 95.86 | 74.28 | 82.17 | +-------+-------+-------+ 2023-01-06 22:23:10,918 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 22:23:10,919 - mmseg - INFO - Iter(val) [63] aAcc: 0.9586, mIoU: 0.7428, mAcc: 0.8217, IoU.road: 0.9810, IoU.sidewalk: 0.8451, IoU.building: 0.9227, IoU.wall: 0.5817, IoU.fence: 0.5799, IoU.pole: 0.6343, IoU.traffic light: 0.6737, IoU.traffic sign: 0.7651, IoU.vegetation: 0.9234, IoU.terrain: 0.6427, IoU.sky: 0.9482, IoU.person: 0.7921, IoU.rider: 0.5646, IoU.car: 0.9426, IoU.truck: 0.7150, IoU.bus: 0.7775, IoU.train: 0.5859, IoU.motorcycle: 0.4977, IoU.bicycle: 0.7395, Acc.road: 0.9896, Acc.sidewalk: 0.9208, Acc.building: 0.9640, Acc.wall: 0.6900, Acc.fence: 0.7031, Acc.pole: 0.7323, Acc.traffic light: 0.7939, Acc.traffic sign: 0.8401, Acc.vegetation: 0.9652, Acc.terrain: 0.7426, Acc.sky: 0.9798, Acc.person: 0.9020, Acc.rider: 0.6851, Acc.car: 0.9760, Acc.truck: 0.7935, Acc.bus: 0.8735, Acc.train: 0.6277, Acc.motorcycle: 0.5639, Acc.bicycle: 0.8699 2023-01-06 22:23:39,057 - mmseg - INFO - Iter [144050/160000] lr: 5.982e-06, eta: 2:32:57, time: 1.209, data_time: 0.658, memory: 10576, decode.loss_ce: 0.0754, decode.acc_seg: 96.7462, loss: 0.0754 2023-01-06 22:24:06,628 - mmseg - INFO - Iter [144100/160000] lr: 5.963e-06, eta: 2:32:28, time: 0.551, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0744, decode.acc_seg: 96.7236, loss: 0.0744 2023-01-06 22:24:34,914 - mmseg - INFO - Iter [144150/160000] lr: 5.944e-06, eta: 2:32:00, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0738, decode.acc_seg: 96.7972, loss: 0.0738 2023-01-06 22:25:03,631 - mmseg - INFO - Iter [144200/160000] lr: 5.925e-06, eta: 2:31:31, time: 0.574, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0779, decode.acc_seg: 96.6480, loss: 0.0779 2023-01-06 22:25:32,190 - mmseg - INFO - Iter [144250/160000] lr: 5.907e-06, eta: 2:31:02, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0719, decode.acc_seg: 96.8202, loss: 0.0719 2023-01-06 22:25:59,893 - mmseg - INFO - Iter [144300/160000] lr: 5.888e-06, eta: 2:30:33, time: 0.553, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0732, decode.acc_seg: 96.7674, loss: 0.0732 2023-01-06 22:26:32,073 - mmseg - INFO - Iter [144350/160000] lr: 5.869e-06, eta: 2:30:05, time: 0.645, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0752, decode.acc_seg: 96.7580, loss: 0.0752 2023-01-06 22:27:01,853 - mmseg - INFO - Iter [144400/160000] lr: 5.850e-06, eta: 2:29:36, time: 0.596, data_time: 0.011, memory: 10576, decode.loss_ce: 0.0753, decode.acc_seg: 96.6790, loss: 0.0753 2023-01-06 22:27:29,425 - mmseg - INFO - Iter [144450/160000] lr: 5.832e-06, eta: 2:29:07, time: 0.551, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0756, decode.acc_seg: 96.6806, loss: 0.0756 2023-01-06 22:27:57,338 - mmseg - INFO - Iter [144500/160000] lr: 5.813e-06, eta: 2:28:38, time: 0.558, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0770, decode.acc_seg: 96.6697, loss: 0.0770 2023-01-06 22:28:25,991 - mmseg - INFO - Iter [144550/160000] lr: 5.794e-06, eta: 2:28:10, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0775, decode.acc_seg: 96.7051, loss: 0.0775 2023-01-06 22:28:53,067 - mmseg - INFO - Iter [144600/160000] lr: 5.775e-06, eta: 2:27:41, time: 0.542, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0762, decode.acc_seg: 96.6818, loss: 0.0762 2023-01-06 22:29:21,099 - mmseg - INFO - Iter [144650/160000] lr: 5.757e-06, eta: 2:27:12, time: 0.561, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0761, decode.acc_seg: 96.7748, loss: 0.0761 2023-01-06 22:29:49,909 - mmseg - INFO - Iter [144700/160000] lr: 5.738e-06, eta: 2:26:43, time: 0.576, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0734, decode.acc_seg: 96.8089, loss: 0.0734 2023-01-06 22:30:19,865 - mmseg - INFO - Iter [144750/160000] lr: 5.719e-06, eta: 2:26:14, time: 0.599, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0708, decode.acc_seg: 96.9488, loss: 0.0708 2023-01-06 22:30:47,110 - mmseg - INFO - Iter [144800/160000] lr: 5.700e-06, eta: 2:25:45, time: 0.545, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0789, decode.acc_seg: 96.6677, loss: 0.0789 2023-01-06 22:31:15,824 - mmseg - INFO - Iter [144850/160000] lr: 5.682e-06, eta: 2:25:17, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0775, decode.acc_seg: 96.6921, loss: 0.0775 2023-01-06 22:31:45,611 - mmseg - INFO - Iter [144900/160000] lr: 5.663e-06, eta: 2:24:48, time: 0.596, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0752, decode.acc_seg: 96.6833, loss: 0.0752 2023-01-06 22:32:15,283 - mmseg - INFO - Iter [144950/160000] lr: 5.644e-06, eta: 2:24:19, time: 0.594, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0751, decode.acc_seg: 96.7715, loss: 0.0751 2023-01-06 22:32:42,720 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 22:32:42,721 - mmseg - INFO - Iter [145000/160000] lr: 5.625e-06, eta: 2:23:50, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0763, decode.acc_seg: 96.6133, loss: 0.0763 2023-01-06 22:33:11,300 - mmseg - INFO - Iter [145050/160000] lr: 5.607e-06, eta: 2:23:22, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0775, decode.acc_seg: 96.6998, loss: 0.0775 2023-01-06 22:33:42,002 - mmseg - INFO - Iter [145100/160000] lr: 5.588e-06, eta: 2:22:53, time: 0.615, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0747, decode.acc_seg: 96.7450, loss: 0.0747 2023-01-06 22:34:10,375 - mmseg - INFO - Iter [145150/160000] lr: 5.569e-06, eta: 2:22:24, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0699, decode.acc_seg: 96.8527, loss: 0.0699 2023-01-06 22:34:38,651 - mmseg - INFO - Iter [145200/160000] lr: 5.550e-06, eta: 2:21:55, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0787, decode.acc_seg: 96.6514, loss: 0.0787 2023-01-06 22:35:07,591 - mmseg - INFO - Iter [145250/160000] lr: 5.532e-06, eta: 2:21:27, time: 0.578, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0762, decode.acc_seg: 96.6400, loss: 0.0762 2023-01-06 22:35:37,100 - mmseg - INFO - Iter [145300/160000] lr: 5.513e-06, eta: 2:20:58, time: 0.590, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0745, decode.acc_seg: 96.7750, loss: 0.0745 2023-01-06 22:36:05,638 - mmseg - INFO - Iter [145350/160000] lr: 5.494e-06, eta: 2:20:29, time: 0.572, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0727, decode.acc_seg: 96.8546, loss: 0.0727 2023-01-06 22:36:34,391 - mmseg - INFO - Iter [145400/160000] lr: 5.475e-06, eta: 2:20:00, time: 0.574, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0793, decode.acc_seg: 96.5898, loss: 0.0793 2023-01-06 22:37:03,208 - mmseg - INFO - Iter [145450/160000] lr: 5.457e-06, eta: 2:19:32, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0774, decode.acc_seg: 96.7766, loss: 0.0774 2023-01-06 22:37:32,859 - mmseg - INFO - Iter [145500/160000] lr: 5.438e-06, eta: 2:19:03, time: 0.593, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0757, decode.acc_seg: 96.7227, loss: 0.0757 2023-01-06 22:38:02,168 - mmseg - INFO - Iter [145550/160000] lr: 5.419e-06, eta: 2:18:34, time: 0.586, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0765, decode.acc_seg: 96.6065, loss: 0.0765 2023-01-06 22:38:30,934 - mmseg - INFO - Iter [145600/160000] lr: 5.400e-06, eta: 2:18:05, time: 0.575, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0730, decode.acc_seg: 96.8651, loss: 0.0730 2023-01-06 22:38:59,228 - mmseg - INFO - Iter [145650/160000] lr: 5.382e-06, eta: 2:17:37, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0783, decode.acc_seg: 96.6131, loss: 0.0783 2023-01-06 22:39:27,375 - mmseg - INFO - Iter [145700/160000] lr: 5.363e-06, eta: 2:17:08, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0730, decode.acc_seg: 96.8772, loss: 0.0730 2023-01-06 22:39:56,333 - mmseg - INFO - Iter [145750/160000] lr: 5.344e-06, eta: 2:16:39, time: 0.578, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0726, decode.acc_seg: 96.8237, loss: 0.0726 2023-01-06 22:40:24,103 - mmseg - INFO - Iter [145800/160000] lr: 5.325e-06, eta: 2:16:10, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0759, decode.acc_seg: 96.7137, loss: 0.0759 2023-01-06 22:40:53,666 - mmseg - INFO - Iter [145850/160000] lr: 5.307e-06, eta: 2:15:41, time: 0.591, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0748, decode.acc_seg: 96.7871, loss: 0.0748 2023-01-06 22:41:21,831 - mmseg - INFO - Iter [145900/160000] lr: 5.288e-06, eta: 2:15:13, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0734, decode.acc_seg: 96.8161, loss: 0.0734 2023-01-06 22:41:50,577 - mmseg - INFO - Iter [145950/160000] lr: 5.269e-06, eta: 2:14:44, time: 0.575, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0734, decode.acc_seg: 96.7890, loss: 0.0734 2023-01-06 22:42:19,433 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 22:42:19,433 - mmseg - INFO - Iter [146000/160000] lr: 5.250e-06, eta: 2:14:15, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0755, decode.acc_seg: 96.7855, loss: 0.0755 2023-01-06 22:42:48,446 - mmseg - INFO - Iter [146050/160000] lr: 5.232e-06, eta: 2:13:46, time: 0.580, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0734, decode.acc_seg: 96.7785, loss: 0.0734 2023-01-06 22:43:17,665 - mmseg - INFO - Iter [146100/160000] lr: 5.213e-06, eta: 2:13:18, time: 0.584, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0727, decode.acc_seg: 96.8637, loss: 0.0727 2023-01-06 22:43:45,569 - mmseg - INFO - Iter [146150/160000] lr: 5.194e-06, eta: 2:12:49, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0764, decode.acc_seg: 96.7357, loss: 0.0764 2023-01-06 22:44:16,725 - mmseg - INFO - Iter [146200/160000] lr: 5.175e-06, eta: 2:12:20, time: 0.623, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0755, decode.acc_seg: 96.7046, loss: 0.0755 2023-01-06 22:44:45,691 - mmseg - INFO - Iter [146250/160000] lr: 5.157e-06, eta: 2:11:52, time: 0.579, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0774, decode.acc_seg: 96.6675, loss: 0.0774 2023-01-06 22:45:13,426 - mmseg - INFO - Iter [146300/160000] lr: 5.138e-06, eta: 2:11:23, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0747, decode.acc_seg: 96.7639, loss: 0.0747 2023-01-06 22:45:41,174 - mmseg - INFO - Iter [146350/160000] lr: 5.119e-06, eta: 2:10:54, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0745, decode.acc_seg: 96.7967, loss: 0.0745 2023-01-06 22:46:08,468 - mmseg - INFO - Iter [146400/160000] lr: 5.100e-06, eta: 2:10:25, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0740, decode.acc_seg: 96.8303, loss: 0.0740 2023-01-06 22:46:36,546 - mmseg - INFO - Iter [146450/160000] lr: 5.082e-06, eta: 2:09:56, time: 0.562, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0726, decode.acc_seg: 96.8734, loss: 0.0726 2023-01-06 22:47:04,067 - mmseg - INFO - Iter [146500/160000] lr: 5.063e-06, eta: 2:09:27, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0690, decode.acc_seg: 96.9334, loss: 0.0690 2023-01-06 22:47:32,318 - mmseg - INFO - Iter [146550/160000] lr: 5.044e-06, eta: 2:08:58, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0714, decode.acc_seg: 96.9404, loss: 0.0714 2023-01-06 22:48:02,886 - mmseg - INFO - Iter [146600/160000] lr: 5.025e-06, eta: 2:08:30, time: 0.612, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0774, decode.acc_seg: 96.6986, loss: 0.0774 2023-01-06 22:48:31,308 - mmseg - INFO - Iter [146650/160000] lr: 5.007e-06, eta: 2:08:01, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0697, decode.acc_seg: 96.9632, loss: 0.0697 2023-01-06 22:49:00,011 - mmseg - INFO - Iter [146700/160000] lr: 4.988e-06, eta: 2:07:32, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0744, decode.acc_seg: 96.7613, loss: 0.0744 2023-01-06 22:49:28,986 - mmseg - INFO - Iter [146750/160000] lr: 4.969e-06, eta: 2:07:03, time: 0.580, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0742, decode.acc_seg: 96.7175, loss: 0.0742 2023-01-06 22:49:56,304 - mmseg - INFO - Iter [146800/160000] lr: 4.950e-06, eta: 2:06:34, time: 0.546, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0740, decode.acc_seg: 96.7430, loss: 0.0740 2023-01-06 22:50:24,524 - mmseg - INFO - Iter [146850/160000] lr: 4.932e-06, eta: 2:06:06, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0780, decode.acc_seg: 96.6279, loss: 0.0780 2023-01-06 22:50:53,085 - mmseg - INFO - Iter [146900/160000] lr: 4.913e-06, eta: 2:05:37, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0747, decode.acc_seg: 96.7085, loss: 0.0747 2023-01-06 22:51:24,798 - mmseg - INFO - Iter [146950/160000] lr: 4.894e-06, eta: 2:05:08, time: 0.634, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0714, decode.acc_seg: 96.8466, loss: 0.0714 2023-01-06 22:51:53,100 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 22:51:53,101 - mmseg - INFO - Iter [147000/160000] lr: 4.875e-06, eta: 2:04:40, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0777, decode.acc_seg: 96.7024, loss: 0.0777 2023-01-06 22:52:20,247 - mmseg - INFO - Iter [147050/160000] lr: 4.857e-06, eta: 2:04:11, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0758, decode.acc_seg: 96.7215, loss: 0.0758 2023-01-06 22:52:47,972 - mmseg - INFO - Iter [147100/160000] lr: 4.838e-06, eta: 2:03:42, time: 0.554, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0739, decode.acc_seg: 96.7752, loss: 0.0739 2023-01-06 22:53:17,813 - mmseg - INFO - Iter [147150/160000] lr: 4.819e-06, eta: 2:03:13, time: 0.598, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0746, decode.acc_seg: 96.6807, loss: 0.0746 2023-01-06 22:53:46,511 - mmseg - INFO - Iter [147200/160000] lr: 4.800e-06, eta: 2:02:44, time: 0.574, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0738, decode.acc_seg: 96.7652, loss: 0.0738 2023-01-06 22:54:14,737 - mmseg - INFO - Iter [147250/160000] lr: 4.782e-06, eta: 2:02:16, time: 0.564, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0713, decode.acc_seg: 96.8717, loss: 0.0713 2023-01-06 22:54:44,242 - mmseg - INFO - Iter [147300/160000] lr: 4.763e-06, eta: 2:01:47, time: 0.591, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0723, decode.acc_seg: 96.7893, loss: 0.0723 2023-01-06 22:55:15,180 - mmseg - INFO - Iter [147350/160000] lr: 4.744e-06, eta: 2:01:18, time: 0.618, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0730, decode.acc_seg: 96.8450, loss: 0.0730 2023-01-06 22:55:42,978 - mmseg - INFO - Iter [147400/160000] lr: 4.725e-06, eta: 2:00:49, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0776, decode.acc_seg: 96.6605, loss: 0.0776 2023-01-06 22:56:10,087 - mmseg - INFO - Iter [147450/160000] lr: 4.707e-06, eta: 2:00:20, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0733, decode.acc_seg: 96.8663, loss: 0.0733 2023-01-06 22:56:38,058 - mmseg - INFO - Iter [147500/160000] lr: 4.688e-06, eta: 1:59:52, time: 0.559, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0713, decode.acc_seg: 96.8994, loss: 0.0713 2023-01-06 22:57:06,088 - mmseg - INFO - Iter [147550/160000] lr: 4.669e-06, eta: 1:59:23, time: 0.560, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0718, decode.acc_seg: 96.8572, loss: 0.0718 2023-01-06 22:57:35,828 - mmseg - INFO - Iter [147600/160000] lr: 4.650e-06, eta: 1:58:54, time: 0.596, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0774, decode.acc_seg: 96.6898, loss: 0.0774 2023-01-06 22:58:04,303 - mmseg - INFO - Iter [147650/160000] lr: 4.632e-06, eta: 1:58:25, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0765, decode.acc_seg: 96.7149, loss: 0.0765 2023-01-06 22:58:35,724 - mmseg - INFO - Iter [147700/160000] lr: 4.613e-06, eta: 1:57:57, time: 0.628, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0740, decode.acc_seg: 96.8307, loss: 0.0740 2023-01-06 22:59:03,958 - mmseg - INFO - Iter [147750/160000] lr: 4.594e-06, eta: 1:57:28, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0734, decode.acc_seg: 96.7356, loss: 0.0734 2023-01-06 22:59:31,799 - mmseg - INFO - Iter [147800/160000] lr: 4.575e-06, eta: 1:56:59, time: 0.556, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0719, decode.acc_seg: 96.8972, loss: 0.0719 2023-01-06 22:59:59,656 - mmseg - INFO - Iter [147850/160000] lr: 4.557e-06, eta: 1:56:30, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0742, decode.acc_seg: 96.7993, loss: 0.0742 2023-01-06 23:00:26,617 - mmseg - INFO - Iter [147900/160000] lr: 4.538e-06, eta: 1:56:01, time: 0.540, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0701, decode.acc_seg: 96.8883, loss: 0.0701 2023-01-06 23:00:55,967 - mmseg - INFO - Iter [147950/160000] lr: 4.519e-06, eta: 1:55:33, time: 0.586, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0772, decode.acc_seg: 96.6845, loss: 0.0772 2023-01-06 23:01:24,753 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 23:01:24,754 - mmseg - INFO - Iter [148000/160000] lr: 4.500e-06, eta: 1:55:04, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0759, decode.acc_seg: 96.7031, loss: 0.0759 2023-01-06 23:01:52,867 - mmseg - INFO - Iter [148050/160000] lr: 4.482e-06, eta: 1:54:35, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0734, decode.acc_seg: 96.8303, loss: 0.0734 2023-01-06 23:02:23,493 - mmseg - INFO - Iter [148100/160000] lr: 4.463e-06, eta: 1:54:06, time: 0.612, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0795, decode.acc_seg: 96.5684, loss: 0.0795 2023-01-06 23:02:51,974 - mmseg - INFO - Iter [148150/160000] lr: 4.444e-06, eta: 1:53:38, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0727, decode.acc_seg: 96.8091, loss: 0.0727 2023-01-06 23:03:19,951 - mmseg - INFO - Iter [148200/160000] lr: 4.425e-06, eta: 1:53:09, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0755, decode.acc_seg: 96.7656, loss: 0.0755 2023-01-06 23:03:47,899 - mmseg - INFO - Iter [148250/160000] lr: 4.407e-06, eta: 1:52:40, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0714, decode.acc_seg: 96.8565, loss: 0.0714 2023-01-06 23:04:15,979 - mmseg - INFO - Iter [148300/160000] lr: 4.388e-06, eta: 1:52:11, time: 0.562, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0750, decode.acc_seg: 96.7088, loss: 0.0750 2023-01-06 23:04:43,534 - mmseg - INFO - Iter [148350/160000] lr: 4.369e-06, eta: 1:51:42, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0764, decode.acc_seg: 96.7406, loss: 0.0764 2023-01-06 23:05:11,252 - mmseg - INFO - Iter [148400/160000] lr: 4.350e-06, eta: 1:51:13, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0757, decode.acc_seg: 96.5204, loss: 0.0757 2023-01-06 23:05:41,708 - mmseg - INFO - Iter [148450/160000] lr: 4.332e-06, eta: 1:50:45, time: 0.609, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0757, decode.acc_seg: 96.8059, loss: 0.0757 2023-01-06 23:06:09,847 - mmseg - INFO - Iter [148500/160000] lr: 4.313e-06, eta: 1:50:16, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0744, decode.acc_seg: 96.7138, loss: 0.0744 2023-01-06 23:06:38,183 - mmseg - INFO - Iter [148550/160000] lr: 4.294e-06, eta: 1:49:47, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0739, decode.acc_seg: 96.7898, loss: 0.0739 2023-01-06 23:07:07,971 - mmseg - INFO - Iter [148600/160000] lr: 4.275e-06, eta: 1:49:19, time: 0.596, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0792, decode.acc_seg: 96.6323, loss: 0.0792 2023-01-06 23:07:36,831 - mmseg - INFO - Iter [148650/160000] lr: 4.257e-06, eta: 1:48:50, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0722, decode.acc_seg: 96.8418, loss: 0.0722 2023-01-06 23:08:04,648 - mmseg - INFO - Iter [148700/160000] lr: 4.238e-06, eta: 1:48:21, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0757, decode.acc_seg: 96.7983, loss: 0.0757 2023-01-06 23:08:32,055 - mmseg - INFO - Iter [148750/160000] lr: 4.219e-06, eta: 1:47:52, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0729, decode.acc_seg: 96.7984, loss: 0.0729 2023-01-06 23:08:59,354 - mmseg - INFO - Iter [148800/160000] lr: 4.200e-06, eta: 1:47:23, time: 0.545, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0730, decode.acc_seg: 96.8463, loss: 0.0730 2023-01-06 23:09:29,002 - mmseg - INFO - Iter [148850/160000] lr: 4.182e-06, eta: 1:46:54, time: 0.594, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0700, decode.acc_seg: 96.9792, loss: 0.0700 2023-01-06 23:09:56,913 - mmseg - INFO - Iter [148900/160000] lr: 4.163e-06, eta: 1:46:26, time: 0.558, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0753, decode.acc_seg: 96.7916, loss: 0.0753 2023-01-06 23:10:26,448 - mmseg - INFO - Iter [148950/160000] lr: 4.144e-06, eta: 1:45:57, time: 0.591, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0742, decode.acc_seg: 96.8002, loss: 0.0742 2023-01-06 23:10:55,111 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 23:10:55,112 - mmseg - INFO - Iter [149000/160000] lr: 4.125e-06, eta: 1:45:28, time: 0.573, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0735, decode.acc_seg: 96.8402, loss: 0.0735 2023-01-06 23:11:23,233 - mmseg - INFO - Iter [149050/160000] lr: 4.107e-06, eta: 1:44:59, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0732, decode.acc_seg: 96.7851, loss: 0.0732 2023-01-06 23:11:52,682 - mmseg - INFO - Iter [149100/160000] lr: 4.088e-06, eta: 1:44:31, time: 0.589, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0720, decode.acc_seg: 96.8517, loss: 0.0720 2023-01-06 23:12:21,140 - mmseg - INFO - Iter [149150/160000] lr: 4.069e-06, eta: 1:44:02, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0751, decode.acc_seg: 96.7743, loss: 0.0751 2023-01-06 23:12:51,561 - mmseg - INFO - Iter [149200/160000] lr: 4.050e-06, eta: 1:43:33, time: 0.608, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0756, decode.acc_seg: 96.7540, loss: 0.0756 2023-01-06 23:13:19,007 - mmseg - INFO - Iter [149250/160000] lr: 4.032e-06, eta: 1:43:04, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0749, decode.acc_seg: 96.7442, loss: 0.0749 2023-01-06 23:13:47,873 - mmseg - INFO - Iter [149300/160000] lr: 4.013e-06, eta: 1:42:36, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0786, decode.acc_seg: 96.7634, loss: 0.0786 2023-01-06 23:14:16,263 - mmseg - INFO - Iter [149350/160000] lr: 3.994e-06, eta: 1:42:07, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0788, decode.acc_seg: 96.6597, loss: 0.0788 2023-01-06 23:14:44,456 - mmseg - INFO - Iter [149400/160000] lr: 3.975e-06, eta: 1:41:38, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0723, decode.acc_seg: 96.8376, loss: 0.0723 2023-01-06 23:15:12,794 - mmseg - INFO - Iter [149450/160000] lr: 3.957e-06, eta: 1:41:09, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0728, decode.acc_seg: 96.8010, loss: 0.0728 2023-01-06 23:15:39,994 - mmseg - INFO - Iter [149500/160000] lr: 3.938e-06, eta: 1:40:40, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0741, decode.acc_seg: 96.8254, loss: 0.0741 2023-01-06 23:16:09,981 - mmseg - INFO - Iter [149550/160000] lr: 3.919e-06, eta: 1:40:12, time: 0.600, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0740, decode.acc_seg: 96.8344, loss: 0.0740 2023-01-06 23:16:38,430 - mmseg - INFO - Iter [149600/160000] lr: 3.900e-06, eta: 1:39:43, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0806, decode.acc_seg: 96.5368, loss: 0.0806 2023-01-06 23:17:06,083 - mmseg - INFO - Iter [149650/160000] lr: 3.882e-06, eta: 1:39:14, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0733, decode.acc_seg: 96.8618, loss: 0.0733 2023-01-06 23:17:34,266 - mmseg - INFO - Iter [149700/160000] lr: 3.863e-06, eta: 1:38:45, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0734, decode.acc_seg: 96.7785, loss: 0.0734 2023-01-06 23:18:02,364 - mmseg - INFO - Iter [149750/160000] lr: 3.844e-06, eta: 1:38:16, time: 0.562, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0778, decode.acc_seg: 96.6192, loss: 0.0778 2023-01-06 23:18:31,451 - mmseg - INFO - Iter [149800/160000] lr: 3.825e-06, eta: 1:37:48, time: 0.581, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0752, decode.acc_seg: 96.7298, loss: 0.0752 2023-01-06 23:18:59,015 - mmseg - INFO - Iter [149850/160000] lr: 3.807e-06, eta: 1:37:19, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0772, decode.acc_seg: 96.7117, loss: 0.0772 2023-01-06 23:19:26,287 - mmseg - INFO - Iter [149900/160000] lr: 3.788e-06, eta: 1:36:50, time: 0.545, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0730, decode.acc_seg: 96.8562, loss: 0.0730 2023-01-06 23:19:56,774 - mmseg - INFO - Iter [149950/160000] lr: 3.769e-06, eta: 1:36:21, time: 0.609, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0722, decode.acc_seg: 96.8697, loss: 0.0722 2023-01-06 23:20:25,046 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 23:20:25,047 - mmseg - INFO - Iter [150000/160000] lr: 3.750e-06, eta: 1:35:52, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0750, decode.acc_seg: 96.7618, loss: 0.0750 2023-01-06 23:20:52,431 - mmseg - INFO - Iter [150050/160000] lr: 3.732e-06, eta: 1:35:24, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0700, decode.acc_seg: 96.9040, loss: 0.0700 2023-01-06 23:21:21,979 - mmseg - INFO - Iter [150100/160000] lr: 3.713e-06, eta: 1:34:55, time: 0.590, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0743, decode.acc_seg: 96.7905, loss: 0.0743 2023-01-06 23:21:50,399 - mmseg - INFO - Iter [150150/160000] lr: 3.694e-06, eta: 1:34:26, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0735, decode.acc_seg: 96.7610, loss: 0.0735 2023-01-06 23:22:19,041 - mmseg - INFO - Iter [150200/160000] lr: 3.675e-06, eta: 1:33:57, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0742, decode.acc_seg: 96.7079, loss: 0.0742 2023-01-06 23:22:49,014 - mmseg - INFO - Iter [150250/160000] lr: 3.657e-06, eta: 1:33:29, time: 0.599, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0762, decode.acc_seg: 96.8056, loss: 0.0762 2023-01-06 23:23:19,837 - mmseg - INFO - Iter [150300/160000] lr: 3.638e-06, eta: 1:33:00, time: 0.616, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0753, decode.acc_seg: 96.7644, loss: 0.0753 2023-01-06 23:23:48,698 - mmseg - INFO - Iter [150350/160000] lr: 3.619e-06, eta: 1:32:31, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0722, decode.acc_seg: 96.8986, loss: 0.0722 2023-01-06 23:24:18,738 - mmseg - INFO - Iter [150400/160000] lr: 3.600e-06, eta: 1:32:03, time: 0.601, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0742, decode.acc_seg: 96.8040, loss: 0.0742 2023-01-06 23:24:47,751 - mmseg - INFO - Iter [150450/160000] lr: 3.582e-06, eta: 1:31:34, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0739, decode.acc_seg: 96.6876, loss: 0.0739 2023-01-06 23:25:15,722 - mmseg - INFO - Iter [150500/160000] lr: 3.563e-06, eta: 1:31:05, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0732, decode.acc_seg: 96.8053, loss: 0.0732 2023-01-06 23:25:43,183 - mmseg - INFO - Iter [150550/160000] lr: 3.544e-06, eta: 1:30:36, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0735, decode.acc_seg: 96.8006, loss: 0.0735 2023-01-06 23:26:10,947 - mmseg - INFO - Iter [150600/160000] lr: 3.525e-06, eta: 1:30:07, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0773, decode.acc_seg: 96.7368, loss: 0.0773 2023-01-06 23:26:38,771 - mmseg - INFO - Iter [150650/160000] lr: 3.507e-06, eta: 1:29:38, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0740, decode.acc_seg: 96.7551, loss: 0.0740 2023-01-06 23:27:08,738 - mmseg - INFO - Iter [150700/160000] lr: 3.488e-06, eta: 1:29:10, time: 0.599, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0713, decode.acc_seg: 96.9238, loss: 0.0713 2023-01-06 23:27:37,550 - mmseg - INFO - Iter [150750/160000] lr: 3.469e-06, eta: 1:28:41, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0796, decode.acc_seg: 96.4879, loss: 0.0796 2023-01-06 23:28:06,884 - mmseg - INFO - Iter [150800/160000] lr: 3.450e-06, eta: 1:28:12, time: 0.587, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0768, decode.acc_seg: 96.7134, loss: 0.0768 2023-01-06 23:28:36,357 - mmseg - INFO - Iter [150850/160000] lr: 3.432e-06, eta: 1:27:44, time: 0.589, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0784, decode.acc_seg: 96.6503, loss: 0.0784 2023-01-06 23:29:03,648 - mmseg - INFO - Iter [150900/160000] lr: 3.413e-06, eta: 1:27:15, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0763, decode.acc_seg: 96.6771, loss: 0.0763 2023-01-06 23:29:30,961 - mmseg - INFO - Iter [150950/160000] lr: 3.394e-06, eta: 1:26:46, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0721, decode.acc_seg: 96.9059, loss: 0.0721 2023-01-06 23:29:58,490 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 23:29:58,490 - mmseg - INFO - Iter [151000/160000] lr: 3.375e-06, eta: 1:26:17, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0707, decode.acc_seg: 96.9567, loss: 0.0707 2023-01-06 23:30:30,568 - mmseg - INFO - Iter [151050/160000] lr: 3.357e-06, eta: 1:25:48, time: 0.641, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0719, decode.acc_seg: 96.8739, loss: 0.0719 2023-01-06 23:30:59,197 - mmseg - INFO - Iter [151100/160000] lr: 3.338e-06, eta: 1:25:20, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0765, decode.acc_seg: 96.6598, loss: 0.0765 2023-01-06 23:31:28,113 - mmseg - INFO - Iter [151150/160000] lr: 3.319e-06, eta: 1:24:51, time: 0.579, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0717, decode.acc_seg: 96.8937, loss: 0.0717 2023-01-06 23:31:55,920 - mmseg - INFO - Iter [151200/160000] lr: 3.300e-06, eta: 1:24:22, time: 0.555, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0743, decode.acc_seg: 96.8017, loss: 0.0743 2023-01-06 23:32:23,610 - mmseg - INFO - Iter [151250/160000] lr: 3.282e-06, eta: 1:23:53, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0716, decode.acc_seg: 96.9199, loss: 0.0716 2023-01-06 23:32:52,804 - mmseg - INFO - Iter [151300/160000] lr: 3.263e-06, eta: 1:23:25, time: 0.585, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0732, decode.acc_seg: 96.8212, loss: 0.0732 2023-01-06 23:33:21,347 - mmseg - INFO - Iter [151350/160000] lr: 3.244e-06, eta: 1:22:56, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0715, decode.acc_seg: 96.8599, loss: 0.0715 2023-01-06 23:33:48,564 - mmseg - INFO - Iter [151400/160000] lr: 3.225e-06, eta: 1:22:27, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0741, decode.acc_seg: 96.7671, loss: 0.0741 2023-01-06 23:34:17,966 - mmseg - INFO - Iter [151450/160000] lr: 3.207e-06, eta: 1:21:58, time: 0.589, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0720, decode.acc_seg: 96.8278, loss: 0.0720 2023-01-06 23:34:45,771 - mmseg - INFO - Iter [151500/160000] lr: 3.188e-06, eta: 1:21:29, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0706, decode.acc_seg: 96.9121, loss: 0.0706 2023-01-06 23:35:13,855 - mmseg - INFO - Iter [151550/160000] lr: 3.169e-06, eta: 1:21:01, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0702, decode.acc_seg: 96.9160, loss: 0.0702 2023-01-06 23:35:42,270 - mmseg - INFO - Iter [151600/160000] lr: 3.150e-06, eta: 1:20:32, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0729, decode.acc_seg: 96.7931, loss: 0.0729 2023-01-06 23:36:09,231 - mmseg - INFO - Iter [151650/160000] lr: 3.132e-06, eta: 1:20:03, time: 0.539, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0742, decode.acc_seg: 96.7455, loss: 0.0742 2023-01-06 23:36:37,521 - mmseg - INFO - Iter [151700/160000] lr: 3.113e-06, eta: 1:19:34, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0745, decode.acc_seg: 96.7843, loss: 0.0745 2023-01-06 23:37:05,812 - mmseg - INFO - Iter [151750/160000] lr: 3.094e-06, eta: 1:19:05, time: 0.566, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0739, decode.acc_seg: 96.7710, loss: 0.0739 2023-01-06 23:37:35,865 - mmseg - INFO - Iter [151800/160000] lr: 3.075e-06, eta: 1:18:37, time: 0.600, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0741, decode.acc_seg: 96.7936, loss: 0.0741 2023-01-06 23:38:03,375 - mmseg - INFO - Iter [151850/160000] lr: 3.057e-06, eta: 1:18:08, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0762, decode.acc_seg: 96.6021, loss: 0.0762 2023-01-06 23:38:31,594 - mmseg - INFO - Iter [151900/160000] lr: 3.038e-06, eta: 1:17:39, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0726, decode.acc_seg: 96.7756, loss: 0.0726 2023-01-06 23:38:59,317 - mmseg - INFO - Iter [151950/160000] lr: 3.019e-06, eta: 1:17:10, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0758, decode.acc_seg: 96.7502, loss: 0.0758 2023-01-06 23:39:27,780 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 23:39:27,781 - mmseg - INFO - Iter [152000/160000] lr: 3.000e-06, eta: 1:16:41, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0734, decode.acc_seg: 96.7834, loss: 0.0734 2023-01-06 23:39:55,441 - mmseg - INFO - Iter [152050/160000] lr: 2.982e-06, eta: 1:16:13, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0757, decode.acc_seg: 96.7499, loss: 0.0757 2023-01-06 23:40:24,716 - mmseg - INFO - Iter [152100/160000] lr: 2.963e-06, eta: 1:15:44, time: 0.585, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0709, decode.acc_seg: 96.8712, loss: 0.0709 2023-01-06 23:40:55,312 - mmseg - INFO - Iter [152150/160000] lr: 2.944e-06, eta: 1:15:15, time: 0.613, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0699, decode.acc_seg: 96.9160, loss: 0.0699 2023-01-06 23:41:23,321 - mmseg - INFO - Iter [152200/160000] lr: 2.925e-06, eta: 1:14:46, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0786, decode.acc_seg: 96.5758, loss: 0.0786 2023-01-06 23:41:51,680 - mmseg - INFO - Iter [152250/160000] lr: 2.907e-06, eta: 1:14:18, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0768, decode.acc_seg: 96.7446, loss: 0.0768 2023-01-06 23:42:20,439 - mmseg - INFO - Iter [152300/160000] lr: 2.888e-06, eta: 1:13:49, time: 0.576, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0734, decode.acc_seg: 96.7606, loss: 0.0734 2023-01-06 23:42:48,742 - mmseg - INFO - Iter [152350/160000] lr: 2.869e-06, eta: 1:13:20, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0739, decode.acc_seg: 96.8642, loss: 0.0739 2023-01-06 23:43:16,033 - mmseg - INFO - Iter [152400/160000] lr: 2.850e-06, eta: 1:12:51, time: 0.546, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0724, decode.acc_seg: 96.8274, loss: 0.0724 2023-01-06 23:43:43,595 - mmseg - INFO - Iter [152450/160000] lr: 2.832e-06, eta: 1:12:22, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0723, decode.acc_seg: 96.9023, loss: 0.0723 2023-01-06 23:44:13,284 - mmseg - INFO - Iter [152500/160000] lr: 2.813e-06, eta: 1:11:54, time: 0.593, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0752, decode.acc_seg: 96.8103, loss: 0.0752 2023-01-06 23:44:44,604 - mmseg - INFO - Iter [152550/160000] lr: 2.794e-06, eta: 1:11:25, time: 0.627, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0734, decode.acc_seg: 96.8192, loss: 0.0734 2023-01-06 23:45:14,023 - mmseg - INFO - Iter [152600/160000] lr: 2.775e-06, eta: 1:10:56, time: 0.588, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0751, decode.acc_seg: 96.7269, loss: 0.0751 2023-01-06 23:45:43,001 - mmseg - INFO - Iter [152650/160000] lr: 2.757e-06, eta: 1:10:28, time: 0.580, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0714, decode.acc_seg: 96.8793, loss: 0.0714 2023-01-06 23:46:12,128 - mmseg - INFO - Iter [152700/160000] lr: 2.738e-06, eta: 1:09:59, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0746, decode.acc_seg: 96.7779, loss: 0.0746 2023-01-06 23:46:40,953 - mmseg - INFO - Iter [152750/160000] lr: 2.719e-06, eta: 1:09:30, time: 0.576, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0728, decode.acc_seg: 96.8507, loss: 0.0728 2023-01-06 23:47:09,046 - mmseg - INFO - Iter [152800/160000] lr: 2.700e-06, eta: 1:09:01, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0771, decode.acc_seg: 96.5850, loss: 0.0771 2023-01-06 23:47:36,378 - mmseg - INFO - Iter [152850/160000] lr: 2.682e-06, eta: 1:08:32, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0736, decode.acc_seg: 96.8201, loss: 0.0736 2023-01-06 23:48:07,171 - mmseg - INFO - Iter [152900/160000] lr: 2.663e-06, eta: 1:08:04, time: 0.617, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0733, decode.acc_seg: 96.8875, loss: 0.0733 2023-01-06 23:48:35,164 - mmseg - INFO - Iter [152950/160000] lr: 2.644e-06, eta: 1:07:35, time: 0.560, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0776, decode.acc_seg: 96.6611, loss: 0.0776 2023-01-06 23:49:02,188 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 23:49:02,190 - mmseg - INFO - Iter [153000/160000] lr: 2.625e-06, eta: 1:07:06, time: 0.540, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0710, decode.acc_seg: 96.9056, loss: 0.0710 2023-01-06 23:49:29,316 - mmseg - INFO - Iter [153050/160000] lr: 2.607e-06, eta: 1:06:37, time: 0.543, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0730, decode.acc_seg: 96.7592, loss: 0.0730 2023-01-06 23:49:57,844 - mmseg - INFO - Iter [153100/160000] lr: 2.588e-06, eta: 1:06:09, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0727, decode.acc_seg: 96.7976, loss: 0.0727 2023-01-06 23:50:25,557 - mmseg - INFO - Iter [153150/160000] lr: 2.569e-06, eta: 1:05:40, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0750, decode.acc_seg: 96.7818, loss: 0.0750 2023-01-06 23:50:52,894 - mmseg - INFO - Iter [153200/160000] lr: 2.550e-06, eta: 1:05:11, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0729, decode.acc_seg: 96.7633, loss: 0.0729 2023-01-06 23:51:21,933 - mmseg - INFO - Iter [153250/160000] lr: 2.532e-06, eta: 1:04:42, time: 0.581, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0795, decode.acc_seg: 96.5738, loss: 0.0795 2023-01-06 23:51:52,940 - mmseg - INFO - Iter [153300/160000] lr: 2.513e-06, eta: 1:04:14, time: 0.620, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0763, decode.acc_seg: 96.7436, loss: 0.0763 2023-01-06 23:52:22,456 - mmseg - INFO - Iter [153350/160000] lr: 2.494e-06, eta: 1:03:45, time: 0.590, data_time: 0.014, memory: 10576, decode.loss_ce: 0.0753, decode.acc_seg: 96.7325, loss: 0.0753 2023-01-06 23:52:50,293 - mmseg - INFO - Iter [153400/160000] lr: 2.475e-06, eta: 1:03:16, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0736, decode.acc_seg: 96.8445, loss: 0.0736 2023-01-06 23:53:19,961 - mmseg - INFO - Iter [153450/160000] lr: 2.457e-06, eta: 1:02:47, time: 0.593, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0740, decode.acc_seg: 96.7958, loss: 0.0740 2023-01-06 23:53:47,487 - mmseg - INFO - Iter [153500/160000] lr: 2.438e-06, eta: 1:02:18, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0693, decode.acc_seg: 96.9459, loss: 0.0693 2023-01-06 23:54:15,598 - mmseg - INFO - Iter [153550/160000] lr: 2.419e-06, eta: 1:01:50, time: 0.562, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0731, decode.acc_seg: 96.8341, loss: 0.0731 2023-01-06 23:54:44,027 - mmseg - INFO - Iter [153600/160000] lr: 2.400e-06, eta: 1:01:21, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0763, decode.acc_seg: 96.7044, loss: 0.0763 2023-01-06 23:55:13,990 - mmseg - INFO - Iter [153650/160000] lr: 2.382e-06, eta: 1:00:52, time: 0.599, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0711, decode.acc_seg: 96.8824, loss: 0.0711 2023-01-06 23:55:41,927 - mmseg - INFO - Iter [153700/160000] lr: 2.363e-06, eta: 1:00:23, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0727, decode.acc_seg: 96.8048, loss: 0.0727 2023-01-06 23:56:09,658 - mmseg - INFO - Iter [153750/160000] lr: 2.344e-06, eta: 0:59:55, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0712, decode.acc_seg: 96.8665, loss: 0.0712 2023-01-06 23:56:38,078 - mmseg - INFO - Iter [153800/160000] lr: 2.325e-06, eta: 0:59:26, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0747, decode.acc_seg: 96.7787, loss: 0.0747 2023-01-06 23:57:06,280 - mmseg - INFO - Iter [153850/160000] lr: 2.307e-06, eta: 0:58:57, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0750, decode.acc_seg: 96.7574, loss: 0.0750 2023-01-06 23:57:33,908 - mmseg - INFO - Iter [153900/160000] lr: 2.288e-06, eta: 0:58:28, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0730, decode.acc_seg: 96.8137, loss: 0.0730 2023-01-06 23:58:02,205 - mmseg - INFO - Iter [153950/160000] lr: 2.269e-06, eta: 0:57:59, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0710, decode.acc_seg: 96.8464, loss: 0.0710 2023-01-06 23:58:30,556 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-06 23:58:30,556 - mmseg - INFO - Iter [154000/160000] lr: 2.250e-06, eta: 0:57:31, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0742, decode.acc_seg: 96.8219, loss: 0.0742 2023-01-06 23:59:00,776 - mmseg - INFO - Iter [154050/160000] lr: 2.232e-06, eta: 0:57:02, time: 0.605, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0725, decode.acc_seg: 96.8435, loss: 0.0725 2023-01-06 23:59:27,823 - mmseg - INFO - Iter [154100/160000] lr: 2.213e-06, eta: 0:56:33, time: 0.540, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0767, decode.acc_seg: 96.7448, loss: 0.0767 2023-01-06 23:59:57,104 - mmseg - INFO - Iter [154150/160000] lr: 2.194e-06, eta: 0:56:04, time: 0.586, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0747, decode.acc_seg: 96.7297, loss: 0.0747 2023-01-07 00:00:24,594 - mmseg - INFO - Iter [154200/160000] lr: 2.175e-06, eta: 0:55:36, time: 0.551, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0746, decode.acc_seg: 96.7931, loss: 0.0746 2023-01-07 00:00:52,240 - mmseg - INFO - Iter [154250/160000] lr: 2.157e-06, eta: 0:55:07, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0738, decode.acc_seg: 96.7928, loss: 0.0738 2023-01-07 00:01:20,788 - mmseg - INFO - Iter [154300/160000] lr: 2.138e-06, eta: 0:54:38, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0731, decode.acc_seg: 96.8043, loss: 0.0731 2023-01-07 00:01:49,906 - mmseg - INFO - Iter [154350/160000] lr: 2.119e-06, eta: 0:54:09, time: 0.582, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0728, decode.acc_seg: 96.8477, loss: 0.0728 2023-01-07 00:02:21,733 - mmseg - INFO - Iter [154400/160000] lr: 2.100e-06, eta: 0:53:41, time: 0.637, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0741, decode.acc_seg: 96.6647, loss: 0.0741 2023-01-07 00:02:51,388 - mmseg - INFO - Iter [154450/160000] lr: 2.082e-06, eta: 0:53:12, time: 0.592, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0703, decode.acc_seg: 96.8371, loss: 0.0703 2023-01-07 00:03:19,509 - mmseg - INFO - Iter [154500/160000] lr: 2.063e-06, eta: 0:52:43, time: 0.563, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0765, decode.acc_seg: 96.6858, loss: 0.0765 2023-01-07 00:03:48,518 - mmseg - INFO - Iter [154550/160000] lr: 2.044e-06, eta: 0:52:14, time: 0.580, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0733, decode.acc_seg: 96.8212, loss: 0.0733 2023-01-07 00:04:16,897 - mmseg - INFO - Iter [154600/160000] lr: 2.025e-06, eta: 0:51:46, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0703, decode.acc_seg: 97.0003, loss: 0.0703 2023-01-07 00:04:45,334 - mmseg - INFO - Iter [154650/160000] lr: 2.007e-06, eta: 0:51:17, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0782, decode.acc_seg: 96.6858, loss: 0.0782 2023-01-07 00:05:14,617 - mmseg - INFO - Iter [154700/160000] lr: 1.988e-06, eta: 0:50:48, time: 0.585, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0709, decode.acc_seg: 96.8961, loss: 0.0709 2023-01-07 00:05:42,215 - mmseg - INFO - Iter [154750/160000] lr: 1.969e-06, eta: 0:50:19, time: 0.553, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0727, decode.acc_seg: 96.8200, loss: 0.0727 2023-01-07 00:06:12,000 - mmseg - INFO - Iter [154800/160000] lr: 1.950e-06, eta: 0:49:51, time: 0.595, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0743, decode.acc_seg: 96.7872, loss: 0.0743 2023-01-07 00:06:41,539 - mmseg - INFO - Iter [154850/160000] lr: 1.932e-06, eta: 0:49:22, time: 0.591, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0736, decode.acc_seg: 96.7529, loss: 0.0736 2023-01-07 00:07:08,807 - mmseg - INFO - Iter [154900/160000] lr: 1.913e-06, eta: 0:48:53, time: 0.546, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0789, decode.acc_seg: 96.6191, loss: 0.0789 2023-01-07 00:07:37,987 - mmseg - INFO - Iter [154950/160000] lr: 1.894e-06, eta: 0:48:24, time: 0.584, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0706, decode.acc_seg: 96.8697, loss: 0.0706 2023-01-07 00:08:05,935 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-07 00:08:05,936 - mmseg - INFO - Iter [155000/160000] lr: 1.875e-06, eta: 0:47:55, time: 0.559, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0718, decode.acc_seg: 96.8421, loss: 0.0718 2023-01-07 00:08:34,187 - mmseg - INFO - Iter [155050/160000] lr: 1.857e-06, eta: 0:47:27, time: 0.565, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0706, decode.acc_seg: 96.9439, loss: 0.0706 2023-01-07 00:09:02,438 - mmseg - INFO - Iter [155100/160000] lr: 1.838e-06, eta: 0:46:58, time: 0.565, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0709, decode.acc_seg: 96.8830, loss: 0.0709 2023-01-07 00:09:32,763 - mmseg - INFO - Iter [155150/160000] lr: 1.819e-06, eta: 0:46:29, time: 0.606, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0681, decode.acc_seg: 96.9744, loss: 0.0681 2023-01-07 00:10:01,373 - mmseg - INFO - Iter [155200/160000] lr: 1.800e-06, eta: 0:46:00, time: 0.572, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0735, decode.acc_seg: 96.8145, loss: 0.0735 2023-01-07 00:10:29,481 - mmseg - INFO - Iter [155250/160000] lr: 1.782e-06, eta: 0:45:32, time: 0.562, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0741, decode.acc_seg: 96.7293, loss: 0.0741 2023-01-07 00:10:57,021 - mmseg - INFO - Iter [155300/160000] lr: 1.763e-06, eta: 0:45:03, time: 0.550, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0704, decode.acc_seg: 96.8899, loss: 0.0704 2023-01-07 00:11:24,794 - mmseg - INFO - Iter [155350/160000] lr: 1.744e-06, eta: 0:44:34, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0712, decode.acc_seg: 96.8842, loss: 0.0712 2023-01-07 00:11:53,079 - mmseg - INFO - Iter [155400/160000] lr: 1.725e-06, eta: 0:44:05, time: 0.566, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0725, decode.acc_seg: 96.8064, loss: 0.0725 2023-01-07 00:12:22,152 - mmseg - INFO - Iter [155450/160000] lr: 1.707e-06, eta: 0:43:37, time: 0.581, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0716, decode.acc_seg: 96.8636, loss: 0.0716 2023-01-07 00:12:52,978 - mmseg - INFO - Iter [155500/160000] lr: 1.688e-06, eta: 0:43:08, time: 0.617, data_time: 0.059, memory: 10576, decode.loss_ce: 0.0732, decode.acc_seg: 96.8202, loss: 0.0732 2023-01-07 00:13:21,686 - mmseg - INFO - Iter [155550/160000] lr: 1.669e-06, eta: 0:42:39, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0729, decode.acc_seg: 96.8563, loss: 0.0729 2023-01-07 00:13:50,918 - mmseg - INFO - Iter [155600/160000] lr: 1.650e-06, eta: 0:42:10, time: 0.585, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0739, decode.acc_seg: 96.8746, loss: 0.0739 2023-01-07 00:14:21,018 - mmseg - INFO - Iter [155650/160000] lr: 1.632e-06, eta: 0:41:42, time: 0.602, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0681, decode.acc_seg: 96.9697, loss: 0.0681 2023-01-07 00:14:49,390 - mmseg - INFO - Iter [155700/160000] lr: 1.613e-06, eta: 0:41:13, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0763, decode.acc_seg: 96.7067, loss: 0.0763 2023-01-07 00:15:17,177 - mmseg - INFO - Iter [155750/160000] lr: 1.594e-06, eta: 0:40:44, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0695, decode.acc_seg: 96.8742, loss: 0.0695 2023-01-07 00:15:46,481 - mmseg - INFO - Iter [155800/160000] lr: 1.575e-06, eta: 0:40:15, time: 0.585, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0741, decode.acc_seg: 96.7696, loss: 0.0741 2023-01-07 00:16:15,017 - mmseg - INFO - Iter [155850/160000] lr: 1.557e-06, eta: 0:39:47, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0741, decode.acc_seg: 96.7718, loss: 0.0741 2023-01-07 00:16:45,374 - mmseg - INFO - Iter [155900/160000] lr: 1.538e-06, eta: 0:39:18, time: 0.608, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0726, decode.acc_seg: 96.7622, loss: 0.0726 2023-01-07 00:17:13,228 - mmseg - INFO - Iter [155950/160000] lr: 1.519e-06, eta: 0:38:49, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0707, decode.acc_seg: 96.8758, loss: 0.0707 2023-01-07 00:17:40,307 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-07 00:17:40,308 - mmseg - INFO - Iter [156000/160000] lr: 1.500e-06, eta: 0:38:20, time: 0.542, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0715, decode.acc_seg: 96.8193, loss: 0.0715 2023-01-07 00:18:07,990 - mmseg - INFO - Iter [156050/160000] lr: 1.482e-06, eta: 0:37:51, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0725, decode.acc_seg: 96.8420, loss: 0.0725 2023-01-07 00:18:35,768 - mmseg - INFO - Iter [156100/160000] lr: 1.463e-06, eta: 0:37:23, time: 0.555, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0739, decode.acc_seg: 96.7742, loss: 0.0739 2023-01-07 00:19:05,090 - mmseg - INFO - Iter [156150/160000] lr: 1.444e-06, eta: 0:36:54, time: 0.587, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0722, decode.acc_seg: 96.8001, loss: 0.0722 2023-01-07 00:19:33,181 - mmseg - INFO - Iter [156200/160000] lr: 1.425e-06, eta: 0:36:25, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0719, decode.acc_seg: 96.8916, loss: 0.0719 2023-01-07 00:20:03,658 - mmseg - INFO - Iter [156250/160000] lr: 1.407e-06, eta: 0:35:56, time: 0.609, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0728, decode.acc_seg: 96.8405, loss: 0.0728 2023-01-07 00:20:32,489 - mmseg - INFO - Iter [156300/160000] lr: 1.388e-06, eta: 0:35:28, time: 0.577, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0766, decode.acc_seg: 96.6805, loss: 0.0766 2023-01-07 00:21:01,600 - mmseg - INFO - Iter [156350/160000] lr: 1.369e-06, eta: 0:34:59, time: 0.583, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0737, decode.acc_seg: 96.7702, loss: 0.0737 2023-01-07 00:21:28,792 - mmseg - INFO - Iter [156400/160000] lr: 1.350e-06, eta: 0:34:30, time: 0.544, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0739, decode.acc_seg: 96.8516, loss: 0.0739 2023-01-07 00:21:57,163 - mmseg - INFO - Iter [156450/160000] lr: 1.332e-06, eta: 0:34:01, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0720, decode.acc_seg: 96.8907, loss: 0.0720 2023-01-07 00:22:25,837 - mmseg - INFO - Iter [156500/160000] lr: 1.313e-06, eta: 0:33:33, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0746, decode.acc_seg: 96.7857, loss: 0.0746 2023-01-07 00:22:55,388 - mmseg - INFO - Iter [156550/160000] lr: 1.294e-06, eta: 0:33:04, time: 0.591, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0714, decode.acc_seg: 96.8801, loss: 0.0714 2023-01-07 00:23:23,924 - mmseg - INFO - Iter [156600/160000] lr: 1.275e-06, eta: 0:32:35, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0704, decode.acc_seg: 96.9223, loss: 0.0704 2023-01-07 00:23:54,932 - mmseg - INFO - Iter [156650/160000] lr: 1.257e-06, eta: 0:32:06, time: 0.619, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0740, decode.acc_seg: 96.8204, loss: 0.0740 2023-01-07 00:24:22,804 - mmseg - INFO - Iter [156700/160000] lr: 1.238e-06, eta: 0:31:38, time: 0.557, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0748, decode.acc_seg: 96.8344, loss: 0.0748 2023-01-07 00:24:51,445 - mmseg - INFO - Iter [156750/160000] lr: 1.219e-06, eta: 0:31:09, time: 0.574, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0762, decode.acc_seg: 96.7374, loss: 0.0762 2023-01-07 00:25:19,065 - mmseg - INFO - Iter [156800/160000] lr: 1.200e-06, eta: 0:30:40, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0739, decode.acc_seg: 96.8078, loss: 0.0739 2023-01-07 00:25:48,466 - mmseg - INFO - Iter [156850/160000] lr: 1.182e-06, eta: 0:30:11, time: 0.588, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0712, decode.acc_seg: 96.8831, loss: 0.0712 2023-01-07 00:26:17,316 - mmseg - INFO - Iter [156900/160000] lr: 1.163e-06, eta: 0:29:43, time: 0.577, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0687, decode.acc_seg: 96.9624, loss: 0.0687 2023-01-07 00:26:46,425 - mmseg - INFO - Iter [156950/160000] lr: 1.144e-06, eta: 0:29:14, time: 0.582, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0724, decode.acc_seg: 96.8112, loss: 0.0724 2023-01-07 00:27:16,711 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-07 00:27:16,712 - mmseg - INFO - Iter [157000/160000] lr: 1.125e-06, eta: 0:28:45, time: 0.606, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0729, decode.acc_seg: 96.8556, loss: 0.0729 2023-01-07 00:27:44,413 - mmseg - INFO - Iter [157050/160000] lr: 1.107e-06, eta: 0:28:16, time: 0.554, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0709, decode.acc_seg: 96.8862, loss: 0.0709 2023-01-07 00:28:13,184 - mmseg - INFO - Iter [157100/160000] lr: 1.088e-06, eta: 0:27:48, time: 0.575, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0751, decode.acc_seg: 96.7544, loss: 0.0751 2023-01-07 00:28:41,813 - mmseg - INFO - Iter [157150/160000] lr: 1.069e-06, eta: 0:27:19, time: 0.573, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0734, decode.acc_seg: 96.8299, loss: 0.0734 2023-01-07 00:29:09,625 - mmseg - INFO - Iter [157200/160000] lr: 1.050e-06, eta: 0:26:50, time: 0.556, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0738, decode.acc_seg: 96.7546, loss: 0.0738 2023-01-07 00:29:37,792 - mmseg - INFO - Iter [157250/160000] lr: 1.032e-06, eta: 0:26:21, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0721, decode.acc_seg: 96.8396, loss: 0.0721 2023-01-07 00:30:06,113 - mmseg - INFO - Iter [157300/160000] lr: 1.013e-06, eta: 0:25:52, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0712, decode.acc_seg: 96.8907, loss: 0.0712 2023-01-07 00:30:33,755 - mmseg - INFO - Iter [157350/160000] lr: 9.941e-07, eta: 0:25:24, time: 0.552, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0703, decode.acc_seg: 96.8534, loss: 0.0703 2023-01-07 00:31:05,372 - mmseg - INFO - Iter [157400/160000] lr: 9.754e-07, eta: 0:24:55, time: 0.633, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0701, decode.acc_seg: 96.9250, loss: 0.0701 2023-01-07 00:31:32,924 - mmseg - INFO - Iter [157450/160000] lr: 9.566e-07, eta: 0:24:26, time: 0.550, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0743, decode.acc_seg: 96.8164, loss: 0.0743 2023-01-07 00:32:00,989 - mmseg - INFO - Iter [157500/160000] lr: 9.379e-07, eta: 0:23:57, time: 0.562, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0738, decode.acc_seg: 96.8472, loss: 0.0738 2023-01-07 00:32:29,700 - mmseg - INFO - Iter [157550/160000] lr: 9.191e-07, eta: 0:23:29, time: 0.574, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0700, decode.acc_seg: 96.9483, loss: 0.0700 2023-01-07 00:32:58,163 - mmseg - INFO - Iter [157600/160000] lr: 9.004e-07, eta: 0:23:00, time: 0.568, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0736, decode.acc_seg: 96.8106, loss: 0.0736 2023-01-07 00:33:28,523 - mmseg - INFO - Iter [157650/160000] lr: 8.816e-07, eta: 0:22:31, time: 0.607, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0753, decode.acc_seg: 96.7719, loss: 0.0753 2023-01-07 00:33:56,992 - mmseg - INFO - Iter [157700/160000] lr: 8.629e-07, eta: 0:22:02, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0727, decode.acc_seg: 96.7816, loss: 0.0727 2023-01-07 00:34:28,572 - mmseg - INFO - Iter [157750/160000] lr: 8.441e-07, eta: 0:21:34, time: 0.632, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0736, decode.acc_seg: 96.7715, loss: 0.0736 2023-01-07 00:34:56,048 - mmseg - INFO - Iter [157800/160000] lr: 8.254e-07, eta: 0:21:05, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0695, decode.acc_seg: 96.9980, loss: 0.0695 2023-01-07 00:35:23,411 - mmseg - INFO - Iter [157850/160000] lr: 8.066e-07, eta: 0:20:36, time: 0.548, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0730, decode.acc_seg: 96.8221, loss: 0.0730 2023-01-07 00:35:50,875 - mmseg - INFO - Iter [157900/160000] lr: 7.879e-07, eta: 0:20:07, time: 0.549, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0731, decode.acc_seg: 96.8250, loss: 0.0731 2023-01-07 00:36:18,187 - mmseg - INFO - Iter [157950/160000] lr: 7.691e-07, eta: 0:19:39, time: 0.546, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0707, decode.acc_seg: 96.9223, loss: 0.0707 2023-01-07 00:36:47,260 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-07 00:36:47,261 - mmseg - INFO - Iter [158000/160000] lr: 7.504e-07, eta: 0:19:10, time: 0.581, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0725, decode.acc_seg: 96.8095, loss: 0.0725 2023-01-07 00:37:15,734 - mmseg - INFO - Iter [158050/160000] lr: 7.316e-07, eta: 0:18:41, time: 0.570, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0761, decode.acc_seg: 96.5575, loss: 0.0761 2023-01-07 00:37:45,106 - mmseg - INFO - Iter [158100/160000] lr: 7.129e-07, eta: 0:18:12, time: 0.587, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0724, decode.acc_seg: 96.8694, loss: 0.0724 2023-01-07 00:38:14,897 - mmseg - INFO - Iter [158150/160000] lr: 6.941e-07, eta: 0:17:44, time: 0.595, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0753, decode.acc_seg: 96.7924, loss: 0.0753 2023-01-07 00:38:44,320 - mmseg - INFO - Iter [158200/160000] lr: 6.754e-07, eta: 0:17:15, time: 0.589, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0722, decode.acc_seg: 96.8588, loss: 0.0722 2023-01-07 00:39:12,214 - mmseg - INFO - Iter [158250/160000] lr: 6.566e-07, eta: 0:16:46, time: 0.557, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0744, decode.acc_seg: 96.7831, loss: 0.0744 2023-01-07 00:39:40,281 - mmseg - INFO - Iter [158300/160000] lr: 6.379e-07, eta: 0:16:17, time: 0.562, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0730, decode.acc_seg: 96.8327, loss: 0.0730 2023-01-07 00:40:08,698 - mmseg - INFO - Iter [158350/160000] lr: 6.191e-07, eta: 0:15:49, time: 0.568, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0713, decode.acc_seg: 96.8929, loss: 0.0713 2023-01-07 00:40:37,925 - mmseg - INFO - Iter [158400/160000] lr: 6.004e-07, eta: 0:15:20, time: 0.584, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0703, decode.acc_seg: 96.9203, loss: 0.0703 2023-01-07 00:41:05,332 - mmseg - INFO - Iter [158450/160000] lr: 5.816e-07, eta: 0:14:51, time: 0.549, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0758, decode.acc_seg: 96.7854, loss: 0.0758 2023-01-07 00:41:35,955 - mmseg - INFO - Iter [158500/160000] lr: 5.629e-07, eta: 0:14:22, time: 0.612, data_time: 0.056, memory: 10576, decode.loss_ce: 0.0762, decode.acc_seg: 96.6976, loss: 0.0762 2023-01-07 00:42:04,914 - mmseg - INFO - Iter [158550/160000] lr: 5.441e-07, eta: 0:13:54, time: 0.580, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0736, decode.acc_seg: 96.8342, loss: 0.0736 2023-01-07 00:42:32,254 - mmseg - INFO - Iter [158600/160000] lr: 5.254e-07, eta: 0:13:25, time: 0.546, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0697, decode.acc_seg: 96.9384, loss: 0.0697 2023-01-07 00:43:01,925 - mmseg - INFO - Iter [158650/160000] lr: 5.066e-07, eta: 0:12:56, time: 0.593, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0752, decode.acc_seg: 96.6883, loss: 0.0752 2023-01-07 00:43:31,654 - mmseg - INFO - Iter [158700/160000] lr: 4.879e-07, eta: 0:12:27, time: 0.595, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0729, decode.acc_seg: 96.8098, loss: 0.0729 2023-01-07 00:44:01,018 - mmseg - INFO - Iter [158750/160000] lr: 4.691e-07, eta: 0:11:58, time: 0.586, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0718, decode.acc_seg: 96.7987, loss: 0.0718 2023-01-07 00:44:29,529 - mmseg - INFO - Iter [158800/160000] lr: 4.504e-07, eta: 0:11:30, time: 0.571, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0756, decode.acc_seg: 96.7808, loss: 0.0756 2023-01-07 00:45:01,294 - mmseg - INFO - Iter [158850/160000] lr: 4.316e-07, eta: 0:11:01, time: 0.635, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0716, decode.acc_seg: 96.9073, loss: 0.0716 2023-01-07 00:45:28,199 - mmseg - INFO - Iter [158900/160000] lr: 4.129e-07, eta: 0:10:32, time: 0.538, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0726, decode.acc_seg: 96.7369, loss: 0.0726 2023-01-07 00:45:55,878 - mmseg - INFO - Iter [158950/160000] lr: 3.941e-07, eta: 0:10:03, time: 0.554, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0742, decode.acc_seg: 96.7823, loss: 0.0742 2023-01-07 00:46:24,437 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-07 00:46:24,438 - mmseg - INFO - Iter [159000/160000] lr: 3.754e-07, eta: 0:09:35, time: 0.571, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0739, decode.acc_seg: 96.7567, loss: 0.0739 2023-01-07 00:46:52,581 - mmseg - INFO - Iter [159050/160000] lr: 3.566e-07, eta: 0:09:06, time: 0.562, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0723, decode.acc_seg: 96.8991, loss: 0.0723 2023-01-07 00:47:21,043 - mmseg - INFO - Iter [159100/160000] lr: 3.379e-07, eta: 0:08:37, time: 0.569, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0729, decode.acc_seg: 96.7940, loss: 0.0729 2023-01-07 00:47:48,811 - mmseg - INFO - Iter [159150/160000] lr: 3.191e-07, eta: 0:08:08, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0747, decode.acc_seg: 96.7742, loss: 0.0747 2023-01-07 00:48:16,467 - mmseg - INFO - Iter [159200/160000] lr: 3.004e-07, eta: 0:07:40, time: 0.552, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0730, decode.acc_seg: 96.8334, loss: 0.0730 2023-01-07 00:48:46,585 - mmseg - INFO - Iter [159250/160000] lr: 2.816e-07, eta: 0:07:11, time: 0.602, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0730, decode.acc_seg: 96.8191, loss: 0.0730 2023-01-07 00:49:16,595 - mmseg - INFO - Iter [159300/160000] lr: 2.629e-07, eta: 0:06:42, time: 0.600, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0738, decode.acc_seg: 96.7172, loss: 0.0738 2023-01-07 00:49:44,899 - mmseg - INFO - Iter [159350/160000] lr: 2.441e-07, eta: 0:06:13, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0711, decode.acc_seg: 96.8679, loss: 0.0711 2023-01-07 00:50:12,821 - mmseg - INFO - Iter [159400/160000] lr: 2.254e-07, eta: 0:05:45, time: 0.558, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0745, decode.acc_seg: 96.7846, loss: 0.0745 2023-01-07 00:50:40,936 - mmseg - INFO - Iter [159450/160000] lr: 2.066e-07, eta: 0:05:16, time: 0.562, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0725, decode.acc_seg: 96.9030, loss: 0.0725 2023-01-07 00:51:09,273 - mmseg - INFO - Iter [159500/160000] lr: 1.879e-07, eta: 0:04:47, time: 0.567, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0697, decode.acc_seg: 96.9826, loss: 0.0697 2023-01-07 00:51:37,529 - mmseg - INFO - Iter [159550/160000] lr: 1.691e-07, eta: 0:04:18, time: 0.564, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0738, decode.acc_seg: 96.7828, loss: 0.0738 2023-01-07 00:52:07,670 - mmseg - INFO - Iter [159600/160000] lr: 1.504e-07, eta: 0:03:50, time: 0.604, data_time: 0.058, memory: 10576, decode.loss_ce: 0.0740, decode.acc_seg: 96.7206, loss: 0.0740 2023-01-07 00:52:36,009 - mmseg - INFO - Iter [159650/160000] lr: 1.316e-07, eta: 0:03:21, time: 0.567, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0719, decode.acc_seg: 96.8891, loss: 0.0719 2023-01-07 00:53:04,139 - mmseg - INFO - Iter [159700/160000] lr: 1.129e-07, eta: 0:02:52, time: 0.563, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0714, decode.acc_seg: 96.9154, loss: 0.0714 2023-01-07 00:53:31,217 - mmseg - INFO - Iter [159750/160000] lr: 9.413e-08, eta: 0:02:23, time: 0.542, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0717, decode.acc_seg: 96.8956, loss: 0.0717 2023-01-07 00:53:59,166 - mmseg - INFO - Iter [159800/160000] lr: 7.537e-08, eta: 0:01:55, time: 0.558, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0709, decode.acc_seg: 96.9645, loss: 0.0709 2023-01-07 00:54:28,265 - mmseg - INFO - Iter [159850/160000] lr: 5.663e-08, eta: 0:01:26, time: 0.582, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0731, decode.acc_seg: 96.8154, loss: 0.0731 2023-01-07 00:54:56,041 - mmseg - INFO - Iter [159900/160000] lr: 3.787e-08, eta: 0:00:57, time: 0.556, data_time: 0.013, memory: 10576, decode.loss_ce: 0.0742, decode.acc_seg: 96.7892, loss: 0.0742 2023-01-07 00:55:24,550 - mmseg - INFO - Iter [159950/160000] lr: 1.913e-08, eta: 0:00:28, time: 0.570, data_time: 0.012, memory: 10576, decode.loss_ce: 0.0747, decode.acc_seg: 96.7185, loss: 0.0747 2023-01-07 00:55:55,376 - mmseg - INFO - Saving checkpoint at 160000 iterations 2023-01-07 00:56:00,458 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-07 00:56:00,458 - mmseg - INFO - Iter [160000/160000] lr: 3.750e-10, eta: 0:00:00, time: 0.718, data_time: 0.057, memory: 10576, decode.loss_ce: 0.0735, decode.acc_seg: 96.7759, loss: 0.0735 2023-01-07 00:56:32,569 - mmseg - INFO - per class results: 2023-01-07 00:56:32,572 - mmseg - INFO - +---------------+-------+-------+ | Class | IoU | Acc | +---------------+-------+-------+ | road | 98.11 | 98.93 | | sidewalk | 84.6 | 92.29 | | building | 92.14 | 96.15 | | wall | 57.13 | 67.88 | | fence | 58.08 | 71.57 | | pole | 63.25 | 72.95 | | traffic light | 67.58 | 79.0 | | traffic sign | 76.56 | 83.41 | | vegetation | 92.21 | 96.69 | | terrain | 63.98 | 74.04 | | sky | 94.78 | 98.0 | | person | 79.25 | 90.42 | | rider | 56.65 | 68.64 | | car | 94.28 | 97.47 | | truck | 72.15 | 79.86 | | bus | 78.03 | 84.89 | | train | 67.86 | 76.09 | | motorcycle | 52.6 | 62.15 | | bicycle | 73.85 | 87.42 | +---------------+-------+-------+ 2023-01-07 00:56:32,572 - mmseg - INFO - Summary: 2023-01-07 00:56:32,572 - mmseg - INFO - +-------+------+-------+ | aAcc | mIoU | mAcc | +-------+------+-------+ | 95.84 | 74.9 | 83.04 | +-------+------+-------+ 2023-01-07 00:56:32,573 - mmseg - INFO - Exp name: dest_simpatt-b4_1024x1024_160k_cityscapes.py 2023-01-07 00:56:32,573 - mmseg - INFO - Iter(val) [63] aAcc: 0.9584, mIoU: 0.7490, mAcc: 0.8304, IoU.road: 0.9811, IoU.sidewalk: 0.8460, IoU.building: 0.9214, IoU.wall: 0.5713, IoU.fence: 0.5808, IoU.pole: 0.6325, IoU.traffic light: 0.6758, IoU.traffic sign: 0.7656, IoU.vegetation: 0.9221, IoU.terrain: 0.6398, IoU.sky: 0.9478, IoU.person: 0.7925, IoU.rider: 0.5665, IoU.car: 0.9428, IoU.truck: 0.7215, IoU.bus: 0.7803, IoU.train: 0.6786, IoU.motorcycle: 0.5260, IoU.bicycle: 0.7385, Acc.road: 0.9893, Acc.sidewalk: 0.9229, Acc.building: 0.9615, Acc.wall: 0.6788, Acc.fence: 0.7157, Acc.pole: 0.7295, Acc.traffic light: 0.7900, Acc.traffic sign: 0.8341, Acc.vegetation: 0.9669, Acc.terrain: 0.7404, Acc.sky: 0.9800, Acc.person: 0.9042, Acc.rider: 0.6864, Acc.car: 0.9747, Acc.truck: 0.7986, Acc.bus: 0.8489, Acc.train: 0.7609, Acc.motorcycle: 0.6215, Acc.bicycle: 0.8742