2023-02-16 15:29:09,751 - mmseg - INFO - Multi-processing start method is `None` 2023-02-16 15:29:09,752 - mmseg - INFO - OpenCV num_threads is `112 2023-02-16 15:29:09,752 - mmseg - INFO - OMP num threads is 1 2023-02-16 15:29:09,791 - mmseg - INFO - Environment info: ------------------------------------------------------------ sys.platform: linux Python: 3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0] CUDA available: True GPU 0,1,2,3: A100-SXM-80GB CUDA_HOME: /usr/local/cuda NVCC: Build cuda_11.2.r11.2/compiler.29618528_0 GCC: gcc (GCC) 5.4.0 PyTorch: 1.9.0+cu111 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb) - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.1 - 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_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 - CuDNN 8.0.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, 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 -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-variable -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=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.10.0+cu111 OpenCV: 4.6.0 MMCV: 1.4.2 MMCV Compiler: GCC 7.3 MMCV CUDA Compiler: 11.1 MMSegmentation: 0.29.0+ ------------------------------------------------------------ 2023-02-16 15:29:09,791 - mmseg - INFO - Distributed training: True 2023-02-16 15:29:10,221 - mmseg - INFO - Config: dataset_type = 'ADE20KDataset' data_root = 'data/ade/ADEChallengeData2016' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) crop_size = (512, 512) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', reduce_zero_label=True), dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)), dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75), dict(type='RandomFlip', prob=0.5), dict(type='PhotoMetricDistortion'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_semantic_seg']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 512), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ] data = dict( samples_per_gpu=4, workers_per_gpu=4, train=dict( type='ADE20KDataset', data_root='data/ade/ADEChallengeData2016', img_dir='images/training', ann_dir='annotations/training', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', reduce_zero_label=True), dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)), dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75), dict(type='RandomFlip', prob=0.5), dict(type='PhotoMetricDistortion'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_semantic_seg']) ]), val=dict( type='ADE20KDataset', data_root='data/ade/ADEChallengeData2016', img_dir='images/validation', ann_dir='annotations/validation', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 512), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ]), test=dict( type='ADE20KDataset', data_root='data/ade/ADEChallengeData2016', img_dir='images/validation', ann_dir='annotations/validation', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 512), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='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)))) optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2)) 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=1000, max_keep_ckpts=1) evaluation = dict( interval=16000, metric='mIoU', pre_eval=True, save_best='mIoU') checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth' norm_cfg = dict(type='SyncBN', requires_grad=True) backbone_norm_cfg = dict(type='LN', requires_grad=True) model = dict( type='DiffSegV20', bit_scale=0.01, pretrained=None, backbone=dict( type='SwinTransformer', init_cfg=dict( type='Pretrained', checkpoint= 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth' ), pretrain_img_size=224, in_channels=3, embed_dims=96, patch_size=4, window_size=7, mlp_ratio=4, depths=[2, 2, 18, 2], num_heads=[3, 6, 12, 24], strides=(4, 2, 2, 2), out_indices=(0, 1, 2, 3), qkv_bias=True, qk_scale=None, patch_norm=True, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.3, use_abs_pos_embed=False, act_cfg=dict(type='GELU'), norm_cfg=dict(type='LN', requires_grad=True)), neck=[ dict( type='FPN', in_channels=[96, 192, 384, 768], out_channels=256, act_cfg=None, norm_cfg=dict(type='GN', num_groups=32), num_outs=4), dict( type='MultiStageMerging', in_channels=[256, 256, 256, 256], out_channels=256, kernel_size=1, norm_cfg=dict(type='GN', num_groups=32), act_cfg=None) ], auxiliary_head=dict( type='FCNHead', in_channels=256, in_index=0, channels=256, num_convs=1, concat_input=False, dropout_ratio=0.1, num_classes=150, norm_cfg=dict(type='SyncBN', requires_grad=True), align_corners=False, loss_decode=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), decode_head=dict( type='DeformableHeadWithTime', in_channels=[256], channels=256, in_index=[0], dropout_ratio=0.0, num_classes=150, norm_cfg=dict(type='SyncBN', requires_grad=True), align_corners=False, num_feature_levels=1, encoder=dict( type='DetrTransformerEncoder', num_layers=6, transformerlayers=dict( type='BaseTransformerLayer', use_time_mlp=True, attn_cfgs=dict( type='MultiScaleDeformableAttention', embed_dims=256, num_levels=1, num_heads=8, dropout=0.0), ffn_cfgs=dict( type='FFN', embed_dims=256, feedforward_channels=1024, ffn_drop=0.0, act_cfg=dict(type='GELU')), operation_order=('self_attn', 'norm', 'ffn', 'norm'))), positional_encoding=dict( type='SinePositionalEncoding', num_feats=128, normalize=True, offset=-0.5), loss_decode=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), train_cfg=dict(), test_cfg=dict(mode='whole')) work_dir = './work_dirs/diffseg_swin_s_4x4_512x512_160k_ade20k_v20' gpu_ids = range(0, 4) auto_resume = False 2023-02-16 15:29:14,804 - mmseg - INFO - Set random seed to 1832030961, deterministic: True 2023-02-16 15:29:15,247 - mmseg - INFO - load checkpoint from http path: https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth 2023-02-16 15:30:21,393 - mmseg - WARNING - The model and loaded state dict do not match exactly unexpected key in source state_dict: norm.weight, norm.bias, stages.0.blocks.1.attn_mask, stages.1.blocks.1.attn_mask, stages.2.blocks.1.attn_mask, stages.2.blocks.3.attn_mask, stages.2.blocks.5.attn_mask, stages.2.blocks.7.attn_mask, stages.2.blocks.9.attn_mask, stages.2.blocks.11.attn_mask, stages.2.blocks.13.attn_mask, stages.2.blocks.15.attn_mask, stages.2.blocks.17.attn_mask missing keys in source state_dict: norm0.weight, norm0.bias, norm1.weight, norm1.bias, norm2.weight, norm2.bias, norm3.weight, norm3.bias 2023-02-16 15:30:21,413 - mmseg - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'} 2023-02-16 15:30:21,429 - mmseg - INFO - initialize MultiStageMerging with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'} 2023-02-16 15:30:21,480 - mmseg - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} Name of parameter - Initialization information backbone.patch_embed.projection.weight - torch.Size([96, 3, 4, 4]): Initialized by user-defined `init_weights` in SwinTransformer backbone.patch_embed.projection.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.patch_embed.norm.weight - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.patch_embed.norm.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.norm1.weight - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.norm1.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.attn.w_msa.relative_position_bias_table - torch.Size([169, 3]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.attn.w_msa.qkv.weight - torch.Size([288, 96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.attn.w_msa.qkv.bias - torch.Size([288]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.attn.w_msa.proj.weight - torch.Size([96, 96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.attn.w_msa.proj.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.norm2.weight - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.norm2.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.ffn.layers.0.0.weight - torch.Size([384, 96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.ffn.layers.0.0.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.ffn.layers.1.weight - torch.Size([96, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.ffn.layers.1.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.norm1.weight - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.norm1.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.attn.w_msa.relative_position_bias_table - torch.Size([169, 3]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.attn.w_msa.qkv.weight - torch.Size([288, 96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.attn.w_msa.qkv.bias - torch.Size([288]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.attn.w_msa.proj.weight - torch.Size([96, 96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.attn.w_msa.proj.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.norm2.weight - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.norm2.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.ffn.layers.0.0.weight - torch.Size([384, 96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.ffn.layers.0.0.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.ffn.layers.1.weight - torch.Size([96, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.ffn.layers.1.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.downsample.norm.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.downsample.norm.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.downsample.reduction.weight - torch.Size([192, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.norm1.weight - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.norm1.bias - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.attn.w_msa.relative_position_bias_table - torch.Size([169, 6]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.attn.w_msa.qkv.weight - torch.Size([576, 192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.attn.w_msa.qkv.bias - torch.Size([576]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.attn.w_msa.proj.weight - torch.Size([192, 192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.attn.w_msa.proj.bias - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.norm2.weight - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.norm2.bias - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.ffn.layers.0.0.weight - torch.Size([768, 192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.ffn.layers.0.0.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.ffn.layers.1.weight - torch.Size([192, 768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.ffn.layers.1.bias - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.norm1.weight - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.norm1.bias - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.attn.w_msa.relative_position_bias_table - torch.Size([169, 6]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.attn.w_msa.qkv.weight - torch.Size([576, 192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.attn.w_msa.qkv.bias - torch.Size([576]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.attn.w_msa.proj.weight - torch.Size([192, 192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.attn.w_msa.proj.bias - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.norm2.weight - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.norm2.bias - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.ffn.layers.0.0.weight - torch.Size([768, 192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.ffn.layers.0.0.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.ffn.layers.1.weight - torch.Size([192, 768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.ffn.layers.1.bias - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.downsample.norm.weight - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.downsample.norm.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.downsample.reduction.weight - torch.Size([384, 768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.downsample.norm.weight - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.downsample.norm.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.downsample.reduction.weight - torch.Size([768, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.norm1.weight - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.norm1.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.attn.w_msa.relative_position_bias_table - torch.Size([169, 24]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.attn.w_msa.qkv.weight - torch.Size([2304, 768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.attn.w_msa.qkv.bias - torch.Size([2304]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.attn.w_msa.proj.weight - torch.Size([768, 768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.attn.w_msa.proj.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.norm2.weight - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.norm2.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.ffn.layers.0.0.weight - torch.Size([3072, 768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.ffn.layers.0.0.bias - torch.Size([3072]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.ffn.layers.1.weight - torch.Size([768, 3072]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.ffn.layers.1.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.norm1.weight - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.norm1.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.attn.w_msa.relative_position_bias_table - torch.Size([169, 24]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.attn.w_msa.qkv.weight - torch.Size([2304, 768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.attn.w_msa.qkv.bias - torch.Size([2304]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.attn.w_msa.proj.weight - torch.Size([768, 768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.attn.w_msa.proj.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.norm2.weight - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.norm2.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.ffn.layers.0.0.weight - torch.Size([3072, 768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.ffn.layers.0.0.bias - torch.Size([3072]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.ffn.layers.1.weight - torch.Size([768, 3072]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.ffn.layers.1.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.norm0.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of DiffSegV20 backbone.norm0.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of DiffSegV20 backbone.norm1.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DiffSegV20 backbone.norm1.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DiffSegV20 backbone.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of DiffSegV20 backbone.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of DiffSegV20 backbone.norm3.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of DiffSegV20 backbone.norm3.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.lateral_convs.0.conv.weight - torch.Size([256, 96, 1, 1]): XavierInit: gain=1, distribution=uniform, bias=0 neck.0.lateral_convs.0.gn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.lateral_convs.0.gn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.lateral_convs.1.conv.weight - torch.Size([256, 192, 1, 1]): XavierInit: gain=1, distribution=uniform, bias=0 neck.0.lateral_convs.1.gn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.lateral_convs.1.gn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.lateral_convs.2.conv.weight - torch.Size([256, 384, 1, 1]): XavierInit: gain=1, distribution=uniform, bias=0 neck.0.lateral_convs.2.gn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.lateral_convs.2.gn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.lateral_convs.3.conv.weight - torch.Size([256, 768, 1, 1]): XavierInit: gain=1, distribution=uniform, bias=0 neck.0.lateral_convs.3.gn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.lateral_convs.3.gn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.fpn_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): XavierInit: gain=1, distribution=uniform, bias=0 neck.0.fpn_convs.0.gn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.fpn_convs.0.gn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.fpn_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): XavierInit: gain=1, distribution=uniform, bias=0 neck.0.fpn_convs.1.gn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.fpn_convs.1.gn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.fpn_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): XavierInit: gain=1, distribution=uniform, bias=0 neck.0.fpn_convs.2.gn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.fpn_convs.2.gn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.fpn_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): XavierInit: gain=1, distribution=uniform, bias=0 neck.0.fpn_convs.3.gn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.fpn_convs.3.gn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.1.down.conv.weight - torch.Size([256, 1024, 1, 1]): Initialized by user-defined `init_weights` in ConvModule neck.1.down.gn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.1.down.gn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.conv_seg.weight - torch.Size([150, 256, 1, 1]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.conv_seg.bias - torch.Size([150]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.attentions.0.sampling_offsets.weight - torch.Size([64, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.attentions.0.sampling_offsets.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.attentions.0.attention_weights.weight - torch.Size([32, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.attentions.0.attention_weights.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.attentions.0.value_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.0.attentions.0.value_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.attentions.0.output_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.0.attentions.0.output_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.time_mlp.1.weight - torch.Size([512, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.0.time_mlp.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.ffns.0.layers.0.0.weight - torch.Size([1024, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.0.ffns.0.layers.0.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.ffns.0.layers.1.weight - torch.Size([256, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.0.ffns.0.layers.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.norms.0.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.norms.0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.norms.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.norms.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.attentions.0.sampling_offsets.weight - torch.Size([64, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.attentions.0.sampling_offsets.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.attentions.0.attention_weights.weight - torch.Size([32, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.attentions.0.attention_weights.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.attentions.0.value_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.1.attentions.0.value_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.attentions.0.output_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.1.attentions.0.output_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.time_mlp.1.weight - torch.Size([512, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.1.time_mlp.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.ffns.0.layers.0.0.weight - torch.Size([1024, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.1.ffns.0.layers.0.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.ffns.0.layers.1.weight - torch.Size([256, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.1.ffns.0.layers.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.norms.0.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.norms.0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.norms.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.norms.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.attentions.0.sampling_offsets.weight - torch.Size([64, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.attentions.0.sampling_offsets.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.attentions.0.attention_weights.weight - torch.Size([32, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.attentions.0.attention_weights.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.attentions.0.value_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.2.attentions.0.value_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.attentions.0.output_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.2.attentions.0.output_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.time_mlp.1.weight - torch.Size([512, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.2.time_mlp.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.ffns.0.layers.0.0.weight - torch.Size([1024, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.2.ffns.0.layers.0.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.ffns.0.layers.1.weight - torch.Size([256, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.2.ffns.0.layers.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.norms.0.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.norms.0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.norms.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.norms.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.attentions.0.sampling_offsets.weight - torch.Size([64, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.attentions.0.sampling_offsets.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.attentions.0.attention_weights.weight - torch.Size([32, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.attentions.0.attention_weights.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.attentions.0.value_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.3.attentions.0.value_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.attentions.0.output_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.3.attentions.0.output_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.time_mlp.1.weight - torch.Size([512, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.3.time_mlp.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.ffns.0.layers.0.0.weight - torch.Size([1024, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.3.ffns.0.layers.0.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.ffns.0.layers.1.weight - torch.Size([256, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.3.ffns.0.layers.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.norms.0.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.norms.0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.norms.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.norms.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.attentions.0.sampling_offsets.weight - torch.Size([64, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.attentions.0.sampling_offsets.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.attentions.0.attention_weights.weight - torch.Size([32, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.attentions.0.attention_weights.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.attentions.0.value_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.4.attentions.0.value_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.attentions.0.output_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.4.attentions.0.output_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.time_mlp.1.weight - torch.Size([512, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.4.time_mlp.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.ffns.0.layers.0.0.weight - torch.Size([1024, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.4.ffns.0.layers.0.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.ffns.0.layers.1.weight - torch.Size([256, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.4.ffns.0.layers.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.norms.0.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.norms.0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.norms.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.norms.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.attentions.0.sampling_offsets.weight - torch.Size([64, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.attentions.0.sampling_offsets.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.attentions.0.attention_weights.weight - torch.Size([32, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.attentions.0.attention_weights.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.attentions.0.value_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.5.attentions.0.value_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.attentions.0.output_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.5.attentions.0.output_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.time_mlp.1.weight - torch.Size([512, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.5.time_mlp.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.ffns.0.layers.0.0.weight - torch.Size([1024, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.5.ffns.0.layers.0.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.ffns.0.layers.1.weight - torch.Size([256, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.5.ffns.0.layers.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.norms.0.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.norms.0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.norms.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.norms.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 auxiliary_head.conv_seg.weight - torch.Size([150, 256, 1, 1]): NormalInit: mean=0, std=0.01, bias=0 auxiliary_head.conv_seg.bias - torch.Size([150]): NormalInit: mean=0, std=0.01, bias=0 auxiliary_head.convs.0.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of DiffSegV20 auxiliary_head.convs.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 auxiliary_head.convs.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 embedding_table.weight - torch.Size([151, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 transform.conv.weight - torch.Size([256, 512, 1, 1]): Initialized by user-defined `init_weights` in ConvModule transform.conv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 time_mlp.0.weights - torch.Size([8]): The value is the same before and after calling `init_weights` of DiffSegV20 time_mlp.1.weight - torch.Size([1024, 17]): The value is the same before and after calling `init_weights` of DiffSegV20 time_mlp.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of DiffSegV20 time_mlp.3.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of DiffSegV20 time_mlp.3.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of DiffSegV20 2023-02-16 15:30:21,487 - mmseg - INFO - DiffSegV20( (backbone): SwinTransformer( (patch_embed): PatchEmbed( (adap_padding): AdaptivePadding() (projection): Conv2d(3, 96, kernel_size=(4, 4), stride=(4, 4)) (norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True) ) (drop_after_pos): Dropout(p=0.0, inplace=False) (stages): ModuleList( (0): SwinBlockSequence( (blocks): ModuleList( (0): SwinBlock( (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=96, out_features=288, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=96, out_features=96, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=96, out_features=384, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=384, out_features=96, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (1): SwinBlock( (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=96, out_features=288, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=96, out_features=96, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=96, out_features=384, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=384, out_features=96, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) ) (downsample): PatchMerging( (adap_padding): AdaptivePadding() (sampler): Unfold(kernel_size=(2, 2), dilation=(1, 1), padding=(0, 0), stride=(2, 2)) (norm): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (reduction): Linear(in_features=384, out_features=192, bias=False) ) ) (1): SwinBlockSequence( (blocks): ModuleList( (0): SwinBlock( (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=192, out_features=576, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=192, out_features=192, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=192, out_features=768, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=768, out_features=192, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (1): SwinBlock( (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=192, out_features=576, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=192, out_features=192, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=192, out_features=768, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=768, out_features=192, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) ) (downsample): PatchMerging( (adap_padding): AdaptivePadding() (sampler): Unfold(kernel_size=(2, 2), dilation=(1, 1), padding=(0, 0), stride=(2, 2)) (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (reduction): Linear(in_features=768, out_features=384, bias=False) ) ) (2): SwinBlockSequence( (blocks): ModuleList( (0): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (1): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (2): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (3): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (4): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (5): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (6): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (7): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (8): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (9): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (10): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (11): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (12): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (13): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (14): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (15): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (16): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (17): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) ) (downsample): PatchMerging( (adap_padding): AdaptivePadding() (sampler): Unfold(kernel_size=(2, 2), dilation=(1, 1), padding=(0, 0), stride=(2, 2)) (norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True) (reduction): Linear(in_features=1536, out_features=768, bias=False) ) ) (3): SwinBlockSequence( (blocks): ModuleList( (0): SwinBlock( (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=768, out_features=2304, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=768, out_features=768, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=768, out_features=3072, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=3072, out_features=768, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (1): SwinBlock( (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=768, out_features=2304, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=768, out_features=768, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=768, out_features=3072, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=3072, out_features=768, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) ) ) ) (norm0): LayerNorm((96,), eps=1e-05, elementwise_affine=True) (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (norm3): LayerNorm((768,), eps=1e-05, elementwise_affine=True) ) init_cfg={'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth'} (neck): Sequential( (0): FPN( (lateral_convs): ModuleList( (0): ConvModule( (conv): Conv2d(96, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (gn): GroupNorm(32, 256, eps=1e-05, affine=True) ) (1): ConvModule( (conv): Conv2d(192, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (gn): GroupNorm(32, 256, eps=1e-05, affine=True) ) (2): ConvModule( (conv): Conv2d(384, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (gn): GroupNorm(32, 256, eps=1e-05, affine=True) ) (3): ConvModule( (conv): Conv2d(768, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (gn): GroupNorm(32, 256, eps=1e-05, affine=True) ) ) (fpn_convs): ModuleList( (0): ConvModule( (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (gn): GroupNorm(32, 256, eps=1e-05, affine=True) ) (1): ConvModule( (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (gn): GroupNorm(32, 256, eps=1e-05, affine=True) ) (2): ConvModule( (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (gn): GroupNorm(32, 256, eps=1e-05, affine=True) ) (3): ConvModule( (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (gn): GroupNorm(32, 256, eps=1e-05, affine=True) ) ) ) init_cfg={'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'} (1): MultiStageMerging( (down): ConvModule( (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (gn): GroupNorm(32, 256, eps=1e-05, affine=True) ) ) init_cfg={'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'} ) (decode_head): DeformableHeadWithTime( input_transform=multiple_select, ignore_index=255, align_corners=False (loss_decode): CrossEntropyLoss(avg_non_ignore=False) (conv_seg): Conv2d(256, 150, kernel_size=(1, 1), stride=(1, 1)) (encoder): DetrTransformerEncoder( (layers): ModuleList( (0): BaseTransformerLayer( (attentions): ModuleList( (0): MultiScaleDeformableAttention( (dropout): Dropout(p=0.0, inplace=False) (sampling_offsets): Linear(in_features=256, out_features=64, bias=True) (attention_weights): Linear(in_features=256, out_features=32, bias=True) (value_proj): Linear(in_features=256, out_features=256, bias=True) (output_proj): Linear(in_features=256, out_features=256, bias=True) ) ) (time_mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (ffns): ModuleList( (0): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=256, out_features=1024, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1024, out_features=256, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) ) ) (1): BaseTransformerLayer( (attentions): ModuleList( (0): MultiScaleDeformableAttention( (dropout): Dropout(p=0.0, inplace=False) (sampling_offsets): Linear(in_features=256, out_features=64, bias=True) (attention_weights): Linear(in_features=256, out_features=32, bias=True) (value_proj): Linear(in_features=256, out_features=256, bias=True) (output_proj): Linear(in_features=256, out_features=256, bias=True) ) ) (time_mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (ffns): ModuleList( (0): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=256, out_features=1024, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1024, out_features=256, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) ) ) (2): BaseTransformerLayer( (attentions): ModuleList( (0): MultiScaleDeformableAttention( (dropout): Dropout(p=0.0, inplace=False) (sampling_offsets): Linear(in_features=256, out_features=64, bias=True) (attention_weights): Linear(in_features=256, out_features=32, bias=True) (value_proj): Linear(in_features=256, out_features=256, bias=True) (output_proj): Linear(in_features=256, out_features=256, bias=True) ) ) (time_mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (ffns): ModuleList( (0): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=256, out_features=1024, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1024, out_features=256, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) ) ) (3): BaseTransformerLayer( (attentions): ModuleList( (0): MultiScaleDeformableAttention( (dropout): Dropout(p=0.0, inplace=False) (sampling_offsets): Linear(in_features=256, out_features=64, bias=True) (attention_weights): Linear(in_features=256, out_features=32, bias=True) (value_proj): Linear(in_features=256, out_features=256, bias=True) (output_proj): Linear(in_features=256, out_features=256, bias=True) ) ) (time_mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (ffns): ModuleList( (0): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=256, out_features=1024, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1024, out_features=256, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) ) ) (4): BaseTransformerLayer( (attentions): ModuleList( (0): MultiScaleDeformableAttention( (dropout): Dropout(p=0.0, inplace=False) (sampling_offsets): Linear(in_features=256, out_features=64, bias=True) (attention_weights): Linear(in_features=256, out_features=32, bias=True) (value_proj): Linear(in_features=256, out_features=256, bias=True) (output_proj): Linear(in_features=256, out_features=256, bias=True) ) ) (time_mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (ffns): ModuleList( (0): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=256, out_features=1024, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1024, out_features=256, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) ) ) (5): BaseTransformerLayer( (attentions): ModuleList( (0): MultiScaleDeformableAttention( (dropout): Dropout(p=0.0, inplace=False) (sampling_offsets): Linear(in_features=256, out_features=64, bias=True) (attention_weights): Linear(in_features=256, out_features=32, bias=True) (value_proj): Linear(in_features=256, out_features=256, bias=True) (output_proj): Linear(in_features=256, out_features=256, bias=True) ) ) (time_mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (ffns): ModuleList( (0): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=256, out_features=1024, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1024, out_features=256, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) ) ) ) ) (positional_encoding): SinePositionalEncoding(num_feats=128, temperature=10000, normalize=True, scale=6.283185307179586, eps=1e-06) ) init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} (auxiliary_head): FCNHead( input_transform=None, ignore_index=255, align_corners=False (loss_decode): CrossEntropyLoss(avg_non_ignore=False) (conv_seg): Conv2d(256, 150, kernel_size=(1, 1), stride=(1, 1)) (dropout): Dropout2d(p=0.1, inplace=False) (convs): Sequential( (0): ConvModule( (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) ) init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} (embedding_table): Embedding(151, 256) (transform): ConvModule( (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) ) (time_mlp): Sequential( (0): LearnedSinusoidalPosEmb() (1): Linear(in_features=17, out_features=1024, bias=True) (2): GELU() (3): Linear(in_features=1024, out_features=1024, bias=True) ) ) 2023-02-16 15:30:21,497 - mmseg - INFO - Model size:216.93 2023-02-16 15:30:21,794 - mmseg - INFO - Loaded 20210 images 2023-02-16 15:30:22,633 - mmseg - INFO - Loaded 2000 images 2023-02-16 15:30:22,634 - 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 -------------------- 2023-02-16 15:30:22,634 - mmseg - INFO - workflow: [('train', 1)], max: 160000 iters 2023-02-16 15:30:54,783 - mmseg - INFO - Iter [50/160000] lr: 1.959e-06, eta: 17:16:34, time: 0.389, data_time: 0.008, memory: 16600, decode.loss_ce: 4.2635, decode.acc_seg: 1.7310, aux.loss_ce: 1.6152, aux.acc_seg: 1.1971, loss: 5.8787, grad_norm: 15.6240 2023-02-16 15:31:12,909 - mmseg - INFO - Iter [100/160000] lr: 3.958e-06, eta: 16:41:12, time: 0.363, data_time: 0.005, memory: 16600, decode.loss_ce: 3.3957, decode.acc_seg: 16.1327, aux.loss_ce: 1.6148, aux.acc_seg: 1.5967, loss: 5.0105, grad_norm: 10.5189 2023-02-16 15:31:31,308 - mmseg - INFO - Iter [150/160000] lr: 5.955e-06, eta: 16:34:02, time: 0.368, data_time: 0.005, memory: 16600, decode.loss_ce: 2.8471, decode.acc_seg: 28.7756, aux.loss_ce: 1.6148, aux.acc_seg: 3.4198, loss: 4.4619, grad_norm: 8.7869 2023-02-16 15:31:49,327 - mmseg - INFO - Iter [200/160000] lr: 7.950e-06, eta: 16:25:14, time: 0.360, data_time: 0.005, memory: 16600, decode.loss_ce: 2.4273, decode.acc_seg: 39.5627, aux.loss_ce: 1.5798, aux.acc_seg: 12.1062, loss: 4.0071, grad_norm: 8.7350 2023-02-16 15:32:07,601 - mmseg - INFO - Iter [250/160000] lr: 9.945e-06, eta: 16:22:33, time: 0.365, data_time: 0.005, memory: 16600, decode.loss_ce: 2.1393, decode.acc_seg: 47.3001, aux.loss_ce: 1.5580, aux.acc_seg: 33.1089, loss: 3.6973, grad_norm: 8.2318 2023-02-16 15:32:25,852 - mmseg - INFO - Iter [300/160000] lr: 1.194e-05, eta: 16:20:28, time: 0.365, data_time: 0.005, memory: 16600, decode.loss_ce: 1.9062, decode.acc_seg: 51.7650, aux.loss_ce: 1.4764, aux.acc_seg: 41.7532, loss: 3.3826, grad_norm: 8.4227 2023-02-16 15:32:43,761 - mmseg - INFO - Iter [350/160000] lr: 1.393e-05, eta: 16:16:17, time: 0.358, data_time: 0.005, memory: 16600, decode.loss_ce: 1.7631, decode.acc_seg: 55.0544, aux.loss_ce: 1.4171, aux.acc_seg: 44.2052, loss: 3.1801, grad_norm: 8.2505 2023-02-16 15:33:01,911 - mmseg - INFO - Iter [400/160000] lr: 1.592e-05, eta: 16:14:40, time: 0.363, data_time: 0.005, memory: 16600, decode.loss_ce: 1.6168, decode.acc_seg: 56.5588, aux.loss_ce: 1.3461, aux.acc_seg: 44.3415, loss: 2.9629, grad_norm: 7.9341 2023-02-16 15:33:20,191 - mmseg - INFO - Iter [450/160000] lr: 1.791e-05, eta: 16:14:07, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 1.4533, decode.acc_seg: 59.7469, aux.loss_ce: 1.2662, aux.acc_seg: 47.8204, loss: 2.7194, grad_norm: 8.7154 2023-02-16 15:33:38,300 - mmseg - INFO - Iter [500/160000] lr: 1.990e-05, eta: 16:12:42, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 1.5555, decode.acc_seg: 58.6627, aux.loss_ce: 1.2462, aux.acc_seg: 46.3373, loss: 2.8017, grad_norm: 8.8290 2023-02-16 15:33:56,093 - mmseg - INFO - Iter [550/160000] lr: 2.188e-05, eta: 16:09:59, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 1.4085, decode.acc_seg: 61.8737, aux.loss_ce: 1.1881, aux.acc_seg: 49.4678, loss: 2.5966, grad_norm: 7.9806 2023-02-16 15:34:14,502 - mmseg - INFO - Iter [600/160000] lr: 2.387e-05, eta: 16:10:22, time: 0.368, data_time: 0.006, memory: 16600, decode.loss_ce: 1.4220, decode.acc_seg: 60.2484, aux.loss_ce: 1.1192, aux.acc_seg: 48.4600, loss: 2.5412, grad_norm: 8.5362 2023-02-16 15:34:32,607 - mmseg - INFO - Iter [650/160000] lr: 2.585e-05, eta: 16:09:25, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 1.3974, decode.acc_seg: 60.2687, aux.loss_ce: 1.0666, aux.acc_seg: 49.3592, loss: 2.4640, grad_norm: 8.8391 2023-02-16 15:34:50,417 - mmseg - INFO - Iter [700/160000] lr: 2.784e-05, eta: 16:07:27, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 1.3081, decode.acc_seg: 62.1009, aux.loss_ce: 1.0149, aux.acc_seg: 50.9641, loss: 2.3229, grad_norm: 8.3763 2023-02-16 15:35:08,265 - mmseg - INFO - Iter [750/160000] lr: 2.982e-05, eta: 16:05:49, time: 0.357, data_time: 0.005, memory: 16600, decode.loss_ce: 1.2601, decode.acc_seg: 62.9319, aux.loss_ce: 0.9828, aux.acc_seg: 51.2120, loss: 2.2429, grad_norm: 9.6669 2023-02-16 15:35:26,186 - mmseg - INFO - Iter [800/160000] lr: 3.180e-05, eta: 16:04:37, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 1.2489, decode.acc_seg: 63.1749, aux.loss_ce: 0.9364, aux.acc_seg: 52.5911, loss: 2.1852, grad_norm: 8.1735 2023-02-16 15:35:44,186 - mmseg - INFO - Iter [850/160000] lr: 3.378e-05, eta: 16:03:42, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 1.2456, decode.acc_seg: 62.5928, aux.loss_ce: 0.8940, aux.acc_seg: 52.8289, loss: 2.1396, grad_norm: 7.7240 2023-02-16 15:36:02,167 - mmseg - INFO - Iter [900/160000] lr: 3.576e-05, eta: 16:02:54, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 1.1922, decode.acc_seg: 63.7429, aux.loss_ce: 0.8514, aux.acc_seg: 53.5639, loss: 2.0436, grad_norm: 7.7157 2023-02-16 15:36:20,559 - mmseg - INFO - Iter [950/160000] lr: 3.773e-05, eta: 16:03:15, time: 0.368, data_time: 0.006, memory: 16600, decode.loss_ce: 1.2116, decode.acc_seg: 63.8267, aux.loss_ce: 0.8262, aux.acc_seg: 54.2507, loss: 2.0377, grad_norm: 7.7811 2023-02-16 15:36:38,368 - mmseg - INFO - Saving checkpoint at 1000 iterations 2023-02-16 15:36:39,415 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 15:36:39,416 - mmseg - INFO - Iter [1000/160000] lr: 3.971e-05, eta: 16:04:46, time: 0.377, data_time: 0.006, memory: 16600, decode.loss_ce: 1.1554, decode.acc_seg: 65.7789, aux.loss_ce: 0.7981, aux.acc_seg: 56.1297, loss: 1.9535, grad_norm: 7.8055 2023-02-16 15:36:57,236 - mmseg - INFO - Iter [1050/160000] lr: 4.168e-05, eta: 16:03:30, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 1.1778, decode.acc_seg: 63.8470, aux.loss_ce: 0.7590, aux.acc_seg: 55.6680, loss: 1.9368, grad_norm: 8.4789 2023-02-16 15:37:15,105 - mmseg - INFO - Iter [1100/160000] lr: 4.366e-05, eta: 16:02:26, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 1.1438, decode.acc_seg: 64.7229, aux.loss_ce: 0.7472, aux.acc_seg: 55.9457, loss: 1.8910, grad_norm: 7.5400 2023-02-16 15:37:32,938 - mmseg - INFO - Iter [1150/160000] lr: 4.563e-05, eta: 16:01:21, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 1.1440, decode.acc_seg: 64.6102, aux.loss_ce: 0.7206, aux.acc_seg: 56.4637, loss: 1.8646, grad_norm: 8.1286 2023-02-16 15:37:50,693 - mmseg - INFO - Iter [1200/160000] lr: 4.760e-05, eta: 16:00:10, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 1.1599, decode.acc_seg: 64.2348, aux.loss_ce: 0.7259, aux.acc_seg: 56.1571, loss: 1.8858, grad_norm: 8.8501 2023-02-16 15:38:08,534 - mmseg - INFO - Iter [1250/160000] lr: 4.957e-05, eta: 15:59:14, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 1.0948, decode.acc_seg: 65.3189, aux.loss_ce: 0.6801, aux.acc_seg: 57.2786, loss: 1.7749, grad_norm: 7.8417 2023-02-16 15:38:29,075 - mmseg - INFO - Iter [1300/160000] lr: 5.154e-05, eta: 16:03:51, time: 0.411, data_time: 0.050, memory: 16600, decode.loss_ce: 1.0561, decode.acc_seg: 66.2708, aux.loss_ce: 0.6475, aux.acc_seg: 59.5672, loss: 1.7036, grad_norm: 7.7176 2023-02-16 15:38:46,991 - mmseg - INFO - Iter [1350/160000] lr: 5.351e-05, eta: 16:02:54, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 1.0100, decode.acc_seg: 67.4213, aux.loss_ce: 0.6163, aux.acc_seg: 61.5178, loss: 1.6263, grad_norm: 6.9742 2023-02-16 15:39:05,090 - mmseg - INFO - Iter [1400/160000] lr: 5.547e-05, eta: 16:02:26, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 1.0755, decode.acc_seg: 65.5633, aux.loss_ce: 0.6203, aux.acc_seg: 59.8389, loss: 1.6958, grad_norm: 7.8029 2023-02-16 15:39:22,930 - mmseg - INFO - Iter [1450/160000] lr: 5.744e-05, eta: 16:01:28, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 1.1014, decode.acc_seg: 64.8825, aux.loss_ce: 0.6143, aux.acc_seg: 60.3848, loss: 1.7157, grad_norm: 8.2588 2023-02-16 15:39:40,915 - mmseg - INFO - Iter [1500/160000] lr: 5.940e-05, eta: 16:00:48, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 1.0601, decode.acc_seg: 65.7736, aux.loss_ce: 0.5974, aux.acc_seg: 60.9588, loss: 1.6576, grad_norm: 7.5453 2023-02-16 15:39:58,838 - mmseg - INFO - Iter [1550/160000] lr: 5.942e-05, eta: 16:00:02, time: 0.358, data_time: 0.005, memory: 16600, decode.loss_ce: 1.0511, decode.acc_seg: 66.0350, aux.loss_ce: 0.5725, aux.acc_seg: 62.1418, loss: 1.6237, grad_norm: 8.6911 2023-02-16 15:40:16,808 - mmseg - INFO - Iter [1600/160000] lr: 5.940e-05, eta: 15:59:24, time: 0.359, data_time: 0.005, memory: 16600, decode.loss_ce: 1.0875, decode.acc_seg: 65.2823, aux.loss_ce: 0.5890, aux.acc_seg: 61.0540, loss: 1.6764, grad_norm: 7.4948 2023-02-16 15:40:34,568 - mmseg - INFO - Iter [1650/160000] lr: 5.938e-05, eta: 15:58:26, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 1.0377, decode.acc_seg: 65.2742, aux.loss_ce: 0.5623, aux.acc_seg: 60.8236, loss: 1.5999, grad_norm: 7.8446 2023-02-16 15:40:52,790 - mmseg - INFO - Iter [1700/160000] lr: 5.936e-05, eta: 15:58:14, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 1.0962, decode.acc_seg: 64.7156, aux.loss_ce: 0.5819, aux.acc_seg: 60.7491, loss: 1.6781, grad_norm: 8.6108 2023-02-16 15:41:10,761 - mmseg - INFO - Iter [1750/160000] lr: 5.934e-05, eta: 15:57:39, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 1.0416, decode.acc_seg: 66.4312, aux.loss_ce: 0.5573, aux.acc_seg: 63.3030, loss: 1.5989, grad_norm: 7.5580 2023-02-16 15:41:28,527 - mmseg - INFO - Iter [1800/160000] lr: 5.933e-05, eta: 15:56:46, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.9854, decode.acc_seg: 67.3856, aux.loss_ce: 0.5420, aux.acc_seg: 63.1646, loss: 1.5274, grad_norm: 6.8298 2023-02-16 15:41:46,436 - mmseg - INFO - Iter [1850/160000] lr: 5.931e-05, eta: 15:56:08, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 1.0607, decode.acc_seg: 66.0368, aux.loss_ce: 0.5455, aux.acc_seg: 62.8711, loss: 1.6062, grad_norm: 7.8047 2023-02-16 15:42:04,192 - mmseg - INFO - Iter [1900/160000] lr: 5.929e-05, eta: 15:55:18, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.9843, decode.acc_seg: 67.0504, aux.loss_ce: 0.5186, aux.acc_seg: 63.9709, loss: 1.5029, grad_norm: 7.2752 2023-02-16 15:42:22,633 - mmseg - INFO - Iter [1950/160000] lr: 5.927e-05, eta: 15:55:22, time: 0.368, data_time: 0.006, memory: 16600, decode.loss_ce: 0.9853, decode.acc_seg: 67.9918, aux.loss_ce: 0.5132, aux.acc_seg: 64.6883, loss: 1.4985, grad_norm: 7.2541 2023-02-16 15:42:40,523 - mmseg - INFO - Saving checkpoint at 2000 iterations 2023-02-16 15:42:41,602 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 15:42:41,602 - mmseg - INFO - Iter [2000/160000] lr: 5.925e-05, eta: 15:56:13, time: 0.380, data_time: 0.006, memory: 16600, decode.loss_ce: 0.9369, decode.acc_seg: 68.3710, aux.loss_ce: 0.5012, aux.acc_seg: 64.7037, loss: 1.4380, grad_norm: 6.8087 2023-02-16 15:42:59,350 - mmseg - INFO - Iter [2050/160000] lr: 5.923e-05, eta: 15:55:24, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 1.0300, decode.acc_seg: 66.2476, aux.loss_ce: 0.5377, aux.acc_seg: 62.4356, loss: 1.5677, grad_norm: 7.6219 2023-02-16 15:43:17,245 - mmseg - INFO - Iter [2100/160000] lr: 5.921e-05, eta: 15:54:47, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.9817, decode.acc_seg: 67.5440, aux.loss_ce: 0.5053, aux.acc_seg: 64.1883, loss: 1.4870, grad_norm: 7.4459 2023-02-16 15:43:35,076 - mmseg - INFO - Iter [2150/160000] lr: 5.919e-05, eta: 15:54:06, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.9797, decode.acc_seg: 67.5370, aux.loss_ce: 0.4913, aux.acc_seg: 65.1680, loss: 1.4710, grad_norm: 7.1372 2023-02-16 15:43:53,215 - mmseg - INFO - Iter [2200/160000] lr: 5.918e-05, eta: 15:53:48, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.9738, decode.acc_seg: 67.7851, aux.loss_ce: 0.5015, aux.acc_seg: 63.8675, loss: 1.4753, grad_norm: 6.5732 2023-02-16 15:44:11,228 - mmseg - INFO - Iter [2250/160000] lr: 5.916e-05, eta: 15:53:21, time: 0.360, data_time: 0.005, memory: 16600, decode.loss_ce: 0.9461, decode.acc_seg: 67.9007, aux.loss_ce: 0.4752, aux.acc_seg: 65.4454, loss: 1.4212, grad_norm: 6.4702 2023-02-16 15:44:28,966 - mmseg - INFO - Iter [2300/160000] lr: 5.914e-05, eta: 15:52:36, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.9776, decode.acc_seg: 67.4598, aux.loss_ce: 0.4897, aux.acc_seg: 64.2493, loss: 1.4673, grad_norm: 7.1159 2023-02-16 15:44:47,487 - mmseg - INFO - Iter [2350/160000] lr: 5.912e-05, eta: 15:52:45, time: 0.370, data_time: 0.005, memory: 16600, decode.loss_ce: 0.9706, decode.acc_seg: 68.3222, aux.loss_ce: 0.4907, aux.acc_seg: 65.1012, loss: 1.4613, grad_norm: 6.7859 2023-02-16 15:45:05,249 - mmseg - INFO - Iter [2400/160000] lr: 5.910e-05, eta: 15:52:03, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.9513, decode.acc_seg: 68.4909, aux.loss_ce: 0.4808, aux.acc_seg: 65.4996, loss: 1.4322, grad_norm: 6.8970 2023-02-16 15:45:23,288 - mmseg - INFO - Iter [2450/160000] lr: 5.908e-05, eta: 15:51:39, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.9484, decode.acc_seg: 67.7333, aux.loss_ce: 0.4576, aux.acc_seg: 65.5063, loss: 1.4060, grad_norm: 7.4562 2023-02-16 15:45:42,085 - mmseg - INFO - Iter [2500/160000] lr: 5.906e-05, eta: 15:52:03, time: 0.376, data_time: 0.007, memory: 16600, decode.loss_ce: 0.9353, decode.acc_seg: 67.8772, aux.loss_ce: 0.4696, aux.acc_seg: 64.3591, loss: 1.4049, grad_norm: 7.9614 2023-02-16 15:46:02,417 - mmseg - INFO - Iter [2550/160000] lr: 5.904e-05, eta: 15:54:01, time: 0.407, data_time: 0.054, memory: 16600, decode.loss_ce: 0.9455, decode.acc_seg: 68.6398, aux.loss_ce: 0.4668, aux.acc_seg: 66.3785, loss: 1.4122, grad_norm: 7.3430 2023-02-16 15:46:20,626 - mmseg - INFO - Iter [2600/160000] lr: 5.903e-05, eta: 15:53:45, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.9571, decode.acc_seg: 68.3601, aux.loss_ce: 0.4768, aux.acc_seg: 65.9355, loss: 1.4339, grad_norm: 7.1596 2023-02-16 15:46:38,438 - mmseg - INFO - Iter [2650/160000] lr: 5.901e-05, eta: 15:53:05, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.9501, decode.acc_seg: 68.0761, aux.loss_ce: 0.4774, aux.acc_seg: 64.7514, loss: 1.4275, grad_norm: 7.1623 2023-02-16 15:46:56,362 - mmseg - INFO - Iter [2700/160000] lr: 5.899e-05, eta: 15:52:32, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8936, decode.acc_seg: 69.9943, aux.loss_ce: 0.4421, aux.acc_seg: 67.5205, loss: 1.3357, grad_norm: 6.6902 2023-02-16 15:47:14,216 - mmseg - INFO - Iter [2750/160000] lr: 5.897e-05, eta: 15:51:56, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8698, decode.acc_seg: 70.7072, aux.loss_ce: 0.4319, aux.acc_seg: 67.7194, loss: 1.3017, grad_norm: 7.2605 2023-02-16 15:47:32,377 - mmseg - INFO - Iter [2800/160000] lr: 5.895e-05, eta: 15:51:38, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.9009, decode.acc_seg: 68.9565, aux.loss_ce: 0.4336, aux.acc_seg: 66.7420, loss: 1.3345, grad_norm: 6.4547 2023-02-16 15:47:50,321 - mmseg - INFO - Iter [2850/160000] lr: 5.893e-05, eta: 15:51:07, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8753, decode.acc_seg: 69.4652, aux.loss_ce: 0.4378, aux.acc_seg: 66.2703, loss: 1.3131, grad_norm: 6.0669 2023-02-16 15:48:08,083 - mmseg - INFO - Iter [2900/160000] lr: 5.891e-05, eta: 15:50:28, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8863, decode.acc_seg: 69.8184, aux.loss_ce: 0.4330, aux.acc_seg: 67.7008, loss: 1.3193, grad_norm: 6.3743 2023-02-16 15:48:25,891 - mmseg - INFO - Iter [2950/160000] lr: 5.889e-05, eta: 15:49:52, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8887, decode.acc_seg: 69.2489, aux.loss_ce: 0.4342, aux.acc_seg: 66.5307, loss: 1.3230, grad_norm: 6.3248 2023-02-16 15:48:44,078 - mmseg - INFO - Saving checkpoint at 3000 iterations 2023-02-16 15:48:45,153 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 15:48:45,153 - mmseg - INFO - Iter [3000/160000] lr: 5.888e-05, eta: 15:50:32, time: 0.385, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8599, decode.acc_seg: 70.0827, aux.loss_ce: 0.4213, aux.acc_seg: 67.6147, loss: 1.2812, grad_norm: 7.1994 2023-02-16 15:49:03,005 - mmseg - INFO - Iter [3050/160000] lr: 5.886e-05, eta: 15:49:58, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.9323, decode.acc_seg: 68.0600, aux.loss_ce: 0.4394, aux.acc_seg: 66.6027, loss: 1.3717, grad_norm: 8.2968 2023-02-16 15:49:21,184 - mmseg - INFO - Iter [3100/160000] lr: 5.884e-05, eta: 15:49:41, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8383, decode.acc_seg: 70.5678, aux.loss_ce: 0.4161, aux.acc_seg: 68.0002, loss: 1.2544, grad_norm: 6.4132 2023-02-16 15:49:39,051 - mmseg - INFO - Iter [3150/160000] lr: 5.882e-05, eta: 15:49:08, time: 0.357, data_time: 0.005, memory: 16600, decode.loss_ce: 0.8976, decode.acc_seg: 69.8895, aux.loss_ce: 0.4290, aux.acc_seg: 67.2106, loss: 1.3266, grad_norm: 6.7967 2023-02-16 15:49:56,999 - mmseg - INFO - Iter [3200/160000] lr: 5.880e-05, eta: 15:48:40, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8289, decode.acc_seg: 71.7726, aux.loss_ce: 0.3963, aux.acc_seg: 69.5983, loss: 1.2252, grad_norm: 7.6433 2023-02-16 15:50:15,032 - mmseg - INFO - Iter [3250/160000] lr: 5.878e-05, eta: 15:48:16, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8442, decode.acc_seg: 70.4358, aux.loss_ce: 0.4144, aux.acc_seg: 67.9792, loss: 1.2587, grad_norm: 7.6354 2023-02-16 15:50:33,376 - mmseg - INFO - Iter [3300/160000] lr: 5.876e-05, eta: 15:48:07, time: 0.367, data_time: 0.006, memory: 16600, decode.loss_ce: 0.9301, decode.acc_seg: 68.0045, aux.loss_ce: 0.4380, aux.acc_seg: 66.3522, loss: 1.3680, grad_norm: 7.0129 2023-02-16 15:50:51,335 - mmseg - INFO - Iter [3350/160000] lr: 5.874e-05, eta: 15:47:40, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8436, decode.acc_seg: 69.8371, aux.loss_ce: 0.3870, aux.acc_seg: 69.0719, loss: 1.2307, grad_norm: 6.3327 2023-02-16 15:51:09,411 - mmseg - INFO - Iter [3400/160000] lr: 5.873e-05, eta: 15:47:19, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8624, decode.acc_seg: 70.1042, aux.loss_ce: 0.4025, aux.acc_seg: 68.7219, loss: 1.2649, grad_norm: 7.5323 2023-02-16 15:51:27,610 - mmseg - INFO - Iter [3450/160000] lr: 5.871e-05, eta: 15:47:02, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8957, decode.acc_seg: 68.8610, aux.loss_ce: 0.4226, aux.acc_seg: 66.6458, loss: 1.3183, grad_norm: 7.6503 2023-02-16 15:51:45,718 - mmseg - INFO - Iter [3500/160000] lr: 5.869e-05, eta: 15:46:43, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.9001, decode.acc_seg: 69.5715, aux.loss_ce: 0.4151, aux.acc_seg: 67.8072, loss: 1.3152, grad_norm: 7.5593 2023-02-16 15:52:03,841 - mmseg - INFO - Iter [3550/160000] lr: 5.867e-05, eta: 15:46:23, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8678, decode.acc_seg: 69.3170, aux.loss_ce: 0.3948, aux.acc_seg: 68.1253, loss: 1.2626, grad_norm: 6.8982 2023-02-16 15:52:21,632 - mmseg - INFO - Iter [3600/160000] lr: 5.865e-05, eta: 15:45:50, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8552, decode.acc_seg: 71.0329, aux.loss_ce: 0.4060, aux.acc_seg: 68.7954, loss: 1.2612, grad_norm: 6.4516 2023-02-16 15:52:39,490 - mmseg - INFO - Iter [3650/160000] lr: 5.863e-05, eta: 15:45:19, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8402, decode.acc_seg: 71.0476, aux.loss_ce: 0.3892, aux.acc_seg: 69.6190, loss: 1.2294, grad_norm: 7.0809 2023-02-16 15:52:57,234 - mmseg - INFO - Iter [3700/160000] lr: 5.861e-05, eta: 15:44:44, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8281, decode.acc_seg: 70.4982, aux.loss_ce: 0.3898, aux.acc_seg: 68.6635, loss: 1.2180, grad_norm: 6.8731 2023-02-16 15:53:15,096 - mmseg - INFO - Iter [3750/160000] lr: 5.859e-05, eta: 15:44:14, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8651, decode.acc_seg: 70.0069, aux.loss_ce: 0.3956, aux.acc_seg: 68.4870, loss: 1.2608, grad_norm: 6.4822 2023-02-16 15:53:35,442 - mmseg - INFO - Iter [3800/160000] lr: 5.858e-05, eta: 15:45:27, time: 0.407, data_time: 0.052, memory: 16600, decode.loss_ce: 0.8210, decode.acc_seg: 71.8630, aux.loss_ce: 0.3781, aux.acc_seg: 69.6018, loss: 1.1991, grad_norm: 6.4517 2023-02-16 15:53:53,897 - mmseg - INFO - Iter [3850/160000] lr: 5.856e-05, eta: 15:45:22, time: 0.370, data_time: 0.007, memory: 16600, decode.loss_ce: 0.8412, decode.acc_seg: 71.2579, aux.loss_ce: 0.4013, aux.acc_seg: 68.8654, loss: 1.2425, grad_norm: 6.8756 2023-02-16 15:54:11,813 - mmseg - INFO - Iter [3900/160000] lr: 5.854e-05, eta: 15:44:54, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7978, decode.acc_seg: 71.4851, aux.loss_ce: 0.3769, aux.acc_seg: 69.8556, loss: 1.1747, grad_norm: 6.2603 2023-02-16 15:54:29,693 - mmseg - INFO - Iter [3950/160000] lr: 5.852e-05, eta: 15:44:25, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8114, decode.acc_seg: 71.6175, aux.loss_ce: 0.3831, aux.acc_seg: 69.3110, loss: 1.1945, grad_norm: 6.8593 2023-02-16 15:54:47,611 - mmseg - INFO - Saving checkpoint at 4000 iterations 2023-02-16 15:54:48,704 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 15:54:48,704 - mmseg - INFO - Iter [4000/160000] lr: 5.850e-05, eta: 15:44:40, time: 0.380, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7985, decode.acc_seg: 71.8680, aux.loss_ce: 0.3631, aux.acc_seg: 70.6400, loss: 1.1616, grad_norm: 6.7195 2023-02-16 15:55:06,512 - mmseg - INFO - Iter [4050/160000] lr: 5.848e-05, eta: 15:44:08, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8102, decode.acc_seg: 72.4005, aux.loss_ce: 0.3843, aux.acc_seg: 70.0898, loss: 1.1945, grad_norm: 6.4429 2023-02-16 15:55:24,518 - mmseg - INFO - Iter [4100/160000] lr: 5.846e-05, eta: 15:43:44, time: 0.360, data_time: 0.005, memory: 16600, decode.loss_ce: 0.8076, decode.acc_seg: 71.1407, aux.loss_ce: 0.3801, aux.acc_seg: 68.9559, loss: 1.1876, grad_norm: 6.2179 2023-02-16 15:55:42,312 - mmseg - INFO - Iter [4150/160000] lr: 5.844e-05, eta: 15:43:12, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7981, decode.acc_seg: 72.2349, aux.loss_ce: 0.3654, aux.acc_seg: 70.6745, loss: 1.1635, grad_norm: 6.7942 2023-02-16 15:56:00,414 - mmseg - INFO - Iter [4200/160000] lr: 5.843e-05, eta: 15:42:52, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7879, decode.acc_seg: 71.8788, aux.loss_ce: 0.3656, aux.acc_seg: 69.7815, loss: 1.1536, grad_norm: 7.4486 2023-02-16 15:56:18,178 - mmseg - INFO - Iter [4250/160000] lr: 5.841e-05, eta: 15:42:19, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8549, decode.acc_seg: 71.2819, aux.loss_ce: 0.3790, aux.acc_seg: 70.2102, loss: 1.2339, grad_norm: 8.0889 2023-02-16 15:56:36,230 - mmseg - INFO - Iter [4300/160000] lr: 5.839e-05, eta: 15:41:57, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8763, decode.acc_seg: 69.7816, aux.loss_ce: 0.3986, aux.acc_seg: 67.6071, loss: 1.2749, grad_norm: 7.4129 2023-02-16 15:56:54,422 - mmseg - INFO - Iter [4350/160000] lr: 5.837e-05, eta: 15:41:41, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8456, decode.acc_seg: 70.3673, aux.loss_ce: 0.3833, aux.acc_seg: 68.8126, loss: 1.2289, grad_norm: 6.7541 2023-02-16 15:57:12,531 - mmseg - INFO - Iter [4400/160000] lr: 5.835e-05, eta: 15:41:21, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7950, decode.acc_seg: 72.7290, aux.loss_ce: 0.3609, aux.acc_seg: 70.9959, loss: 1.1559, grad_norm: 7.7781 2023-02-16 15:57:30,416 - mmseg - INFO - Iter [4450/160000] lr: 5.833e-05, eta: 15:40:54, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8256, decode.acc_seg: 71.0939, aux.loss_ce: 0.3765, aux.acc_seg: 69.5401, loss: 1.2021, grad_norm: 7.3957 2023-02-16 15:57:48,296 - mmseg - INFO - Iter [4500/160000] lr: 5.831e-05, eta: 15:40:26, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8011, decode.acc_seg: 72.2001, aux.loss_ce: 0.3688, aux.acc_seg: 70.2986, loss: 1.1699, grad_norm: 6.7133 2023-02-16 15:58:06,489 - mmseg - INFO - Iter [4550/160000] lr: 5.829e-05, eta: 15:40:10, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8013, decode.acc_seg: 70.5299, aux.loss_ce: 0.3719, aux.acc_seg: 68.4422, loss: 1.1732, grad_norm: 6.4723 2023-02-16 15:58:24,418 - mmseg - INFO - Iter [4600/160000] lr: 5.828e-05, eta: 15:39:45, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8259, decode.acc_seg: 71.6073, aux.loss_ce: 0.3766, aux.acc_seg: 69.4324, loss: 1.2025, grad_norm: 6.8411 2023-02-16 15:58:42,331 - mmseg - INFO - Iter [4650/160000] lr: 5.826e-05, eta: 15:39:19, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8019, decode.acc_seg: 71.4010, aux.loss_ce: 0.3578, aux.acc_seg: 70.0350, loss: 1.1597, grad_norm: 6.2055 2023-02-16 15:59:00,290 - mmseg - INFO - Iter [4700/160000] lr: 5.824e-05, eta: 15:38:55, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8024, decode.acc_seg: 71.7284, aux.loss_ce: 0.3613, aux.acc_seg: 70.3304, loss: 1.1637, grad_norm: 6.8486 2023-02-16 15:59:18,109 - mmseg - INFO - Iter [4750/160000] lr: 5.822e-05, eta: 15:38:26, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.8216, decode.acc_seg: 71.5928, aux.loss_ce: 0.3649, aux.acc_seg: 70.4964, loss: 1.1865, grad_norm: 6.0751 2023-02-16 15:59:35,959 - mmseg - INFO - Iter [4800/160000] lr: 5.820e-05, eta: 15:37:59, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7833, decode.acc_seg: 72.3335, aux.loss_ce: 0.3523, aux.acc_seg: 70.7046, loss: 1.1355, grad_norm: 6.6509 2023-02-16 15:59:54,439 - mmseg - INFO - Iter [4850/160000] lr: 5.818e-05, eta: 15:37:52, time: 0.370, data_time: 0.007, memory: 16600, decode.loss_ce: 0.7725, decode.acc_seg: 71.7562, aux.loss_ce: 0.3540, aux.acc_seg: 69.9194, loss: 1.1265, grad_norm: 6.4494 2023-02-16 16:00:12,336 - mmseg - INFO - Iter [4900/160000] lr: 5.816e-05, eta: 15:37:26, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7741, decode.acc_seg: 72.9495, aux.loss_ce: 0.3512, aux.acc_seg: 71.0650, loss: 1.1253, grad_norm: 5.9143 2023-02-16 16:00:30,513 - mmseg - INFO - Iter [4950/160000] lr: 5.814e-05, eta: 15:37:09, time: 0.364, data_time: 0.007, memory: 16600, decode.loss_ce: 0.7995, decode.acc_seg: 71.4558, aux.loss_ce: 0.3528, aux.acc_seg: 70.3919, loss: 1.1523, grad_norm: 6.6508 2023-02-16 16:00:48,548 - mmseg - INFO - Saving checkpoint at 5000 iterations 2023-02-16 16:00:49,702 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 16:00:49,703 - mmseg - INFO - Iter [5000/160000] lr: 5.813e-05, eta: 15:37:24, time: 0.384, data_time: 0.007, memory: 16600, decode.loss_ce: 0.7459, decode.acc_seg: 74.2006, aux.loss_ce: 0.3460, aux.acc_seg: 71.8119, loss: 1.0919, grad_norm: 6.3291 2023-02-16 16:01:07,715 - mmseg - INFO - Iter [5050/160000] lr: 5.811e-05, eta: 15:37:02, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7921, decode.acc_seg: 72.1666, aux.loss_ce: 0.3500, aux.acc_seg: 70.9409, loss: 1.1421, grad_norm: 7.2487 2023-02-16 16:01:28,159 - mmseg - INFO - Iter [5100/160000] lr: 5.809e-05, eta: 15:37:54, time: 0.409, data_time: 0.052, memory: 16600, decode.loss_ce: 0.7762, decode.acc_seg: 73.3105, aux.loss_ce: 0.3533, aux.acc_seg: 71.1143, loss: 1.1295, grad_norm: 6.6635 2023-02-16 16:01:46,147 - mmseg - INFO - Iter [5150/160000] lr: 5.807e-05, eta: 15:37:30, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7895, decode.acc_seg: 72.4731, aux.loss_ce: 0.3486, aux.acc_seg: 71.1512, loss: 1.1381, grad_norm: 6.6612 2023-02-16 16:02:03,956 - mmseg - INFO - Iter [5200/160000] lr: 5.805e-05, eta: 15:37:01, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7383, decode.acc_seg: 73.7016, aux.loss_ce: 0.3333, aux.acc_seg: 72.6138, loss: 1.0715, grad_norm: 6.5008 2023-02-16 16:02:22,005 - mmseg - INFO - Iter [5250/160000] lr: 5.803e-05, eta: 15:36:40, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.7431, decode.acc_seg: 73.7964, aux.loss_ce: 0.3388, aux.acc_seg: 72.2320, loss: 1.0820, grad_norm: 5.9333 2023-02-16 16:02:39,954 - mmseg - INFO - Iter [5300/160000] lr: 5.801e-05, eta: 15:36:16, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7891, decode.acc_seg: 72.2569, aux.loss_ce: 0.3458, aux.acc_seg: 71.3152, loss: 1.1349, grad_norm: 6.7822 2023-02-16 16:02:57,963 - mmseg - INFO - Iter [5350/160000] lr: 5.799e-05, eta: 15:35:53, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7870, decode.acc_seg: 72.7518, aux.loss_ce: 0.3503, aux.acc_seg: 71.2555, loss: 1.1372, grad_norm: 6.5754 2023-02-16 16:03:16,004 - mmseg - INFO - Iter [5400/160000] lr: 5.798e-05, eta: 15:35:32, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7590, decode.acc_seg: 73.6232, aux.loss_ce: 0.3362, aux.acc_seg: 72.1811, loss: 1.0952, grad_norm: 6.2979 2023-02-16 16:03:33,903 - mmseg - INFO - Iter [5450/160000] lr: 5.796e-05, eta: 15:35:06, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7775, decode.acc_seg: 72.8664, aux.loss_ce: 0.3393, aux.acc_seg: 72.0982, loss: 1.1168, grad_norm: 9.1783 2023-02-16 16:03:52,041 - mmseg - INFO - Iter [5500/160000] lr: 5.794e-05, eta: 15:34:48, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7632, decode.acc_seg: 72.8205, aux.loss_ce: 0.3380, aux.acc_seg: 71.6258, loss: 1.1012, grad_norm: 6.5492 2023-02-16 16:04:10,083 - mmseg - INFO - Iter [5550/160000] lr: 5.792e-05, eta: 15:34:27, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7505, decode.acc_seg: 72.8815, aux.loss_ce: 0.3365, aux.acc_seg: 71.0567, loss: 1.0870, grad_norm: 6.3562 2023-02-16 16:04:28,560 - mmseg - INFO - Iter [5600/160000] lr: 5.790e-05, eta: 15:34:17, time: 0.370, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7572, decode.acc_seg: 73.4715, aux.loss_ce: 0.3340, aux.acc_seg: 72.2638, loss: 1.0912, grad_norm: 6.6511 2023-02-16 16:04:46,455 - mmseg - INFO - Iter [5650/160000] lr: 5.788e-05, eta: 15:33:52, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7567, decode.acc_seg: 72.9418, aux.loss_ce: 0.3332, aux.acc_seg: 72.0967, loss: 1.0899, grad_norm: 6.9529 2023-02-16 16:05:04,263 - mmseg - INFO - Iter [5700/160000] lr: 5.786e-05, eta: 15:33:25, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7609, decode.acc_seg: 73.4237, aux.loss_ce: 0.3338, aux.acc_seg: 72.5968, loss: 1.0947, grad_norm: 6.6315 2023-02-16 16:05:22,234 - mmseg - INFO - Iter [5750/160000] lr: 5.784e-05, eta: 15:33:02, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7726, decode.acc_seg: 72.9505, aux.loss_ce: 0.3413, aux.acc_seg: 71.7969, loss: 1.1139, grad_norm: 6.2019 2023-02-16 16:05:40,181 - mmseg - INFO - Iter [5800/160000] lr: 5.783e-05, eta: 15:32:38, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7837, decode.acc_seg: 72.2013, aux.loss_ce: 0.3391, aux.acc_seg: 71.7608, loss: 1.1228, grad_norm: 6.9699 2023-02-16 16:05:58,140 - mmseg - INFO - Iter [5850/160000] lr: 5.781e-05, eta: 15:32:15, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7630, decode.acc_seg: 73.0890, aux.loss_ce: 0.3363, aux.acc_seg: 71.9321, loss: 1.0993, grad_norm: 7.3056 2023-02-16 16:06:16,075 - mmseg - INFO - Iter [5900/160000] lr: 5.779e-05, eta: 15:31:52, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7796, decode.acc_seg: 72.0509, aux.loss_ce: 0.3392, aux.acc_seg: 71.4206, loss: 1.1188, grad_norm: 7.4954 2023-02-16 16:06:34,041 - mmseg - INFO - Iter [5950/160000] lr: 5.777e-05, eta: 15:31:29, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7705, decode.acc_seg: 73.1122, aux.loss_ce: 0.3407, aux.acc_seg: 72.1203, loss: 1.1112, grad_norm: 6.7080 2023-02-16 16:06:52,096 - mmseg - INFO - Saving checkpoint at 6000 iterations 2023-02-16 16:06:53,256 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 16:06:53,257 - mmseg - INFO - Iter [6000/160000] lr: 5.775e-05, eta: 15:31:39, time: 0.384, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7641, decode.acc_seg: 73.3751, aux.loss_ce: 0.3357, aux.acc_seg: 72.1712, loss: 1.0998, grad_norm: 7.2722 2023-02-16 16:07:11,051 - mmseg - INFO - Iter [6050/160000] lr: 5.773e-05, eta: 15:31:11, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7578, decode.acc_seg: 72.7865, aux.loss_ce: 0.3238, aux.acc_seg: 72.2882, loss: 1.0816, grad_norm: 6.7690 2023-02-16 16:07:29,068 - mmseg - INFO - Iter [6100/160000] lr: 5.771e-05, eta: 15:30:50, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7814, decode.acc_seg: 71.7551, aux.loss_ce: 0.3399, aux.acc_seg: 71.0761, loss: 1.1213, grad_norm: 6.4721 2023-02-16 16:07:46,897 - mmseg - INFO - Iter [6150/160000] lr: 5.769e-05, eta: 15:30:24, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7797, decode.acc_seg: 73.2773, aux.loss_ce: 0.3422, aux.acc_seg: 71.7495, loss: 1.1219, grad_norm: 6.6538 2023-02-16 16:08:04,924 - mmseg - INFO - Iter [6200/160000] lr: 5.768e-05, eta: 15:30:03, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.7353, decode.acc_seg: 73.9115, aux.loss_ce: 0.3220, aux.acc_seg: 72.8373, loss: 1.0572, grad_norm: 5.9456 2023-02-16 16:08:22,806 - mmseg - INFO - Iter [6250/160000] lr: 5.766e-05, eta: 15:29:38, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7503, decode.acc_seg: 73.3162, aux.loss_ce: 0.3232, aux.acc_seg: 72.1040, loss: 1.0735, grad_norm: 7.0673 2023-02-16 16:08:40,733 - mmseg - INFO - Iter [6300/160000] lr: 5.764e-05, eta: 15:29:15, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7349, decode.acc_seg: 73.4060, aux.loss_ce: 0.3212, aux.acc_seg: 72.1528, loss: 1.0561, grad_norm: 6.4909 2023-02-16 16:09:00,832 - mmseg - INFO - Iter [6350/160000] lr: 5.762e-05, eta: 15:29:44, time: 0.402, data_time: 0.053, memory: 16600, decode.loss_ce: 0.7540, decode.acc_seg: 73.3520, aux.loss_ce: 0.3276, aux.acc_seg: 72.1279, loss: 1.0816, grad_norm: 6.2761 2023-02-16 16:09:18,740 - mmseg - INFO - Iter [6400/160000] lr: 5.760e-05, eta: 15:29:20, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7147, decode.acc_seg: 74.0686, aux.loss_ce: 0.3092, aux.acc_seg: 73.4250, loss: 1.0239, grad_norm: 6.4587 2023-02-16 16:09:36,642 - mmseg - INFO - Iter [6450/160000] lr: 5.758e-05, eta: 15:28:56, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7573, decode.acc_seg: 73.3483, aux.loss_ce: 0.3366, aux.acc_seg: 71.7609, loss: 1.0939, grad_norm: 5.8375 2023-02-16 16:09:54,579 - mmseg - INFO - Iter [6500/160000] lr: 5.756e-05, eta: 15:28:33, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7398, decode.acc_seg: 73.9639, aux.loss_ce: 0.3212, aux.acc_seg: 73.3710, loss: 1.0610, grad_norm: 5.9682 2023-02-16 16:10:12,634 - mmseg - INFO - Iter [6550/160000] lr: 5.754e-05, eta: 15:28:13, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7171, decode.acc_seg: 74.1268, aux.loss_ce: 0.3156, aux.acc_seg: 73.1385, loss: 1.0327, grad_norm: 6.7962 2023-02-16 16:10:30,506 - mmseg - INFO - Iter [6600/160000] lr: 5.753e-05, eta: 15:27:48, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7106, decode.acc_seg: 75.3534, aux.loss_ce: 0.3079, aux.acc_seg: 73.9057, loss: 1.0185, grad_norm: 6.9505 2023-02-16 16:10:48,921 - mmseg - INFO - Iter [6650/160000] lr: 5.751e-05, eta: 15:27:36, time: 0.368, data_time: 0.007, memory: 16600, decode.loss_ce: 0.7006, decode.acc_seg: 75.2962, aux.loss_ce: 0.3076, aux.acc_seg: 74.4078, loss: 1.0082, grad_norm: 6.2803 2023-02-16 16:11:06,846 - mmseg - INFO - Iter [6700/160000] lr: 5.749e-05, eta: 15:27:13, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.7117, decode.acc_seg: 74.2412, aux.loss_ce: 0.3125, aux.acc_seg: 72.8672, loss: 1.0242, grad_norm: 6.0264 2023-02-16 16:11:24,877 - mmseg - INFO - Iter [6750/160000] lr: 5.747e-05, eta: 15:26:52, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7471, decode.acc_seg: 72.9422, aux.loss_ce: 0.3210, aux.acc_seg: 72.5527, loss: 1.0681, grad_norm: 6.9665 2023-02-16 16:11:42,825 - mmseg - INFO - Iter [6800/160000] lr: 5.745e-05, eta: 15:26:30, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7120, decode.acc_seg: 74.2053, aux.loss_ce: 0.3124, aux.acc_seg: 73.1512, loss: 1.0244, grad_norm: 6.7648 2023-02-16 16:12:00,867 - mmseg - INFO - Iter [6850/160000] lr: 5.743e-05, eta: 15:26:09, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7152, decode.acc_seg: 74.9098, aux.loss_ce: 0.3101, aux.acc_seg: 73.7296, loss: 1.0253, grad_norm: 6.2519 2023-02-16 16:12:19,190 - mmseg - INFO - Iter [6900/160000] lr: 5.741e-05, eta: 15:25:55, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7424, decode.acc_seg: 73.8815, aux.loss_ce: 0.3149, aux.acc_seg: 73.2127, loss: 1.0573, grad_norm: 7.0544 2023-02-16 16:12:37,109 - mmseg - INFO - Iter [6950/160000] lr: 5.739e-05, eta: 15:25:32, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7512, decode.acc_seg: 73.0692, aux.loss_ce: 0.3251, aux.acc_seg: 72.2888, loss: 1.0763, grad_norm: 6.6347 2023-02-16 16:12:55,305 - mmseg - INFO - Saving checkpoint at 7000 iterations 2023-02-16 16:12:56,417 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 16:12:56,417 - mmseg - INFO - Iter [7000/160000] lr: 5.738e-05, eta: 15:25:39, time: 0.386, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7102, decode.acc_seg: 74.0526, aux.loss_ce: 0.3085, aux.acc_seg: 73.1821, loss: 1.0187, grad_norm: 7.5885 2023-02-16 16:13:14,486 - mmseg - INFO - Iter [7050/160000] lr: 5.736e-05, eta: 15:25:19, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7405, decode.acc_seg: 72.6862, aux.loss_ce: 0.3201, aux.acc_seg: 71.6450, loss: 1.0606, grad_norm: 6.2677 2023-02-16 16:13:32,534 - mmseg - INFO - Iter [7100/160000] lr: 5.734e-05, eta: 15:24:59, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6971, decode.acc_seg: 74.7004, aux.loss_ce: 0.3023, aux.acc_seg: 73.8084, loss: 0.9994, grad_norm: 5.7364 2023-02-16 16:13:50,462 - mmseg - INFO - Iter [7150/160000] lr: 5.732e-05, eta: 15:24:36, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.7105, decode.acc_seg: 74.8743, aux.loss_ce: 0.3067, aux.acc_seg: 74.2260, loss: 1.0172, grad_norm: 7.0413 2023-02-16 16:14:08,850 - mmseg - INFO - Iter [7200/160000] lr: 5.730e-05, eta: 15:24:23, time: 0.368, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7179, decode.acc_seg: 74.1836, aux.loss_ce: 0.3099, aux.acc_seg: 73.5683, loss: 1.0278, grad_norm: 6.5936 2023-02-16 16:14:26,760 - mmseg - INFO - Iter [7250/160000] lr: 5.728e-05, eta: 15:24:00, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7372, decode.acc_seg: 74.3233, aux.loss_ce: 0.3130, aux.acc_seg: 73.7479, loss: 1.0502, grad_norm: 5.8545 2023-02-16 16:14:44,648 - mmseg - INFO - Iter [7300/160000] lr: 5.726e-05, eta: 15:23:36, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6907, decode.acc_seg: 74.7094, aux.loss_ce: 0.2996, aux.acc_seg: 73.9380, loss: 0.9903, grad_norm: 6.0493 2023-02-16 16:15:02,507 - mmseg - INFO - Iter [7350/160000] lr: 5.724e-05, eta: 15:23:12, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7244, decode.acc_seg: 74.1533, aux.loss_ce: 0.3104, aux.acc_seg: 73.4750, loss: 1.0348, grad_norm: 6.2956 2023-02-16 16:15:20,572 - mmseg - INFO - Iter [7400/160000] lr: 5.723e-05, eta: 15:22:53, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6971, decode.acc_seg: 74.7335, aux.loss_ce: 0.2964, aux.acc_seg: 74.0396, loss: 0.9935, grad_norm: 6.3717 2023-02-16 16:15:38,758 - mmseg - INFO - Iter [7450/160000] lr: 5.721e-05, eta: 15:22:35, time: 0.364, data_time: 0.007, memory: 16600, decode.loss_ce: 0.7257, decode.acc_seg: 73.0173, aux.loss_ce: 0.3109, aux.acc_seg: 72.3924, loss: 1.0366, grad_norm: 6.4910 2023-02-16 16:15:56,972 - mmseg - INFO - Iter [7500/160000] lr: 5.719e-05, eta: 15:22:19, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7457, decode.acc_seg: 74.1820, aux.loss_ce: 0.3218, aux.acc_seg: 73.3811, loss: 1.0676, grad_norm: 6.3372 2023-02-16 16:16:14,970 - mmseg - INFO - Iter [7550/160000] lr: 5.717e-05, eta: 15:21:57, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7372, decode.acc_seg: 72.8053, aux.loss_ce: 0.3127, aux.acc_seg: 72.2615, loss: 1.0498, grad_norm: 9.3052 2023-02-16 16:16:35,232 - mmseg - INFO - Iter [7600/160000] lr: 5.715e-05, eta: 15:22:22, time: 0.405, data_time: 0.053, memory: 16600, decode.loss_ce: 0.7127, decode.acc_seg: 74.0536, aux.loss_ce: 0.3032, aux.acc_seg: 73.4865, loss: 1.0159, grad_norm: 6.9171 2023-02-16 16:16:53,152 - mmseg - INFO - Iter [7650/160000] lr: 5.713e-05, eta: 15:21:59, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7151, decode.acc_seg: 74.7945, aux.loss_ce: 0.3079, aux.acc_seg: 73.6586, loss: 1.0230, grad_norm: 6.6656 2023-02-16 16:17:10,971 - mmseg - INFO - Iter [7700/160000] lr: 5.711e-05, eta: 15:21:34, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7290, decode.acc_seg: 73.8461, aux.loss_ce: 0.3174, aux.acc_seg: 72.2122, loss: 1.0464, grad_norm: 6.5982 2023-02-16 16:17:28,960 - mmseg - INFO - Iter [7750/160000] lr: 5.709e-05, eta: 15:21:13, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7043, decode.acc_seg: 74.4083, aux.loss_ce: 0.3094, aux.acc_seg: 73.1336, loss: 1.0137, grad_norm: 6.5508 2023-02-16 16:17:46,971 - mmseg - INFO - Iter [7800/160000] lr: 5.708e-05, eta: 15:20:52, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6545, decode.acc_seg: 75.7387, aux.loss_ce: 0.2858, aux.acc_seg: 74.9838, loss: 0.9403, grad_norm: 6.4517 2023-02-16 16:18:05,073 - mmseg - INFO - Iter [7850/160000] lr: 5.706e-05, eta: 15:20:33, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7214, decode.acc_seg: 73.4915, aux.loss_ce: 0.3101, aux.acc_seg: 72.5854, loss: 1.0315, grad_norm: 7.5231 2023-02-16 16:18:23,142 - mmseg - INFO - Iter [7900/160000] lr: 5.704e-05, eta: 15:20:13, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6700, decode.acc_seg: 75.6293, aux.loss_ce: 0.2943, aux.acc_seg: 74.1765, loss: 0.9643, grad_norm: 5.6334 2023-02-16 16:18:41,074 - mmseg - INFO - Iter [7950/160000] lr: 5.702e-05, eta: 15:19:51, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6933, decode.acc_seg: 75.1502, aux.loss_ce: 0.2996, aux.acc_seg: 74.0518, loss: 0.9929, grad_norm: 6.0731 2023-02-16 16:18:58,818 - mmseg - INFO - Saving checkpoint at 8000 iterations 2023-02-16 16:18:59,978 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 16:18:59,978 - mmseg - INFO - Iter [8000/160000] lr: 5.700e-05, eta: 15:19:47, time: 0.378, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6611, decode.acc_seg: 76.0253, aux.loss_ce: 0.2902, aux.acc_seg: 74.7951, loss: 0.9514, grad_norm: 6.0461 2023-02-16 16:19:18,012 - mmseg - INFO - Iter [8050/160000] lr: 5.698e-05, eta: 15:19:26, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7085, decode.acc_seg: 74.6246, aux.loss_ce: 0.3040, aux.acc_seg: 73.7489, loss: 1.0124, grad_norm: 6.1516 2023-02-16 16:19:35,845 - mmseg - INFO - Iter [8100/160000] lr: 5.696e-05, eta: 15:19:02, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6755, decode.acc_seg: 75.6013, aux.loss_ce: 0.2939, aux.acc_seg: 74.6487, loss: 0.9694, grad_norm: 6.3206 2023-02-16 16:19:53,645 - mmseg - INFO - Iter [8150/160000] lr: 5.694e-05, eta: 15:18:38, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6835, decode.acc_seg: 75.8508, aux.loss_ce: 0.2949, aux.acc_seg: 74.8348, loss: 0.9784, grad_norm: 5.6123 2023-02-16 16:20:11,465 - mmseg - INFO - Iter [8200/160000] lr: 5.693e-05, eta: 15:18:13, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7047, decode.acc_seg: 73.9356, aux.loss_ce: 0.3022, aux.acc_seg: 73.1279, loss: 1.0069, grad_norm: 6.4046 2023-02-16 16:20:29,273 - mmseg - INFO - Iter [8250/160000] lr: 5.691e-05, eta: 15:17:49, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7212, decode.acc_seg: 74.9868, aux.loss_ce: 0.3042, aux.acc_seg: 74.3830, loss: 1.0255, grad_norm: 6.6472 2023-02-16 16:20:47,521 - mmseg - INFO - Iter [8300/160000] lr: 5.689e-05, eta: 15:17:33, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6745, decode.acc_seg: 75.4618, aux.loss_ce: 0.2913, aux.acc_seg: 74.4786, loss: 0.9658, grad_norm: 6.3954 2023-02-16 16:21:05,589 - mmseg - INFO - Iter [8350/160000] lr: 5.687e-05, eta: 15:17:13, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.6651, decode.acc_seg: 75.5986, aux.loss_ce: 0.2859, aux.acc_seg: 74.7560, loss: 0.9510, grad_norm: 6.0877 2023-02-16 16:21:23,715 - mmseg - INFO - Iter [8400/160000] lr: 5.685e-05, eta: 15:16:55, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6883, decode.acc_seg: 75.3199, aux.loss_ce: 0.2934, aux.acc_seg: 74.8253, loss: 0.9817, grad_norm: 6.0989 2023-02-16 16:21:41,609 - mmseg - INFO - Iter [8450/160000] lr: 5.683e-05, eta: 15:16:32, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6878, decode.acc_seg: 74.7132, aux.loss_ce: 0.2929, aux.acc_seg: 74.0600, loss: 0.9807, grad_norm: 6.4983 2023-02-16 16:21:59,522 - mmseg - INFO - Iter [8500/160000] lr: 5.681e-05, eta: 15:16:10, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6711, decode.acc_seg: 75.3007, aux.loss_ce: 0.2864, aux.acc_seg: 74.6988, loss: 0.9575, grad_norm: 6.9526 2023-02-16 16:22:17,553 - mmseg - INFO - Iter [8550/160000] lr: 5.679e-05, eta: 15:15:50, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6891, decode.acc_seg: 75.2370, aux.loss_ce: 0.2958, aux.acc_seg: 74.3629, loss: 0.9849, grad_norm: 6.4785 2023-02-16 16:22:35,355 - mmseg - INFO - Iter [8600/160000] lr: 5.678e-05, eta: 15:15:26, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7075, decode.acc_seg: 74.6038, aux.loss_ce: 0.3048, aux.acc_seg: 73.8144, loss: 1.0123, grad_norm: 6.7086 2023-02-16 16:22:53,215 - mmseg - INFO - Iter [8650/160000] lr: 5.676e-05, eta: 15:15:02, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7142, decode.acc_seg: 74.1892, aux.loss_ce: 0.3040, aux.acc_seg: 73.3248, loss: 1.0182, grad_norm: 6.6882 2023-02-16 16:23:11,211 - mmseg - INFO - Iter [8700/160000] lr: 5.674e-05, eta: 15:14:42, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.6740, decode.acc_seg: 75.5938, aux.loss_ce: 0.2872, aux.acc_seg: 75.1439, loss: 0.9612, grad_norm: 5.9049 2023-02-16 16:23:29,285 - mmseg - INFO - Iter [8750/160000] lr: 5.672e-05, eta: 15:14:23, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6940, decode.acc_seg: 74.8558, aux.loss_ce: 0.2969, aux.acc_seg: 74.0358, loss: 0.9910, grad_norm: 5.7384 2023-02-16 16:23:47,259 - mmseg - INFO - Iter [8800/160000] lr: 5.670e-05, eta: 15:14:02, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.7147, decode.acc_seg: 73.7946, aux.loss_ce: 0.3064, aux.acc_seg: 72.3874, loss: 1.0211, grad_norm: 7.1504 2023-02-16 16:24:07,855 - mmseg - INFO - Iter [8850/160000] lr: 5.668e-05, eta: 15:14:26, time: 0.412, data_time: 0.054, memory: 16600, decode.loss_ce: 0.6241, decode.acc_seg: 76.3100, aux.loss_ce: 0.2741, aux.acc_seg: 75.3997, loss: 0.8982, grad_norm: 5.8610 2023-02-16 16:24:25,719 - mmseg - INFO - Iter [8900/160000] lr: 5.666e-05, eta: 15:14:03, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6975, decode.acc_seg: 75.2254, aux.loss_ce: 0.2916, aux.acc_seg: 74.8173, loss: 0.9892, grad_norm: 6.1387 2023-02-16 16:24:43,535 - mmseg - INFO - Iter [8950/160000] lr: 5.664e-05, eta: 15:13:39, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6669, decode.acc_seg: 76.3677, aux.loss_ce: 0.2871, aux.acc_seg: 75.1245, loss: 0.9540, grad_norm: 6.3813 2023-02-16 16:25:01,604 - mmseg - INFO - Saving checkpoint at 9000 iterations 2023-02-16 16:25:02,751 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 16:25:02,751 - mmseg - INFO - Iter [9000/160000] lr: 5.663e-05, eta: 15:13:39, time: 0.384, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6418, decode.acc_seg: 76.3872, aux.loss_ce: 0.2765, aux.acc_seg: 75.1864, loss: 0.9183, grad_norm: 6.9597 2023-02-16 16:25:20,826 - mmseg - INFO - Iter [9050/160000] lr: 5.661e-05, eta: 15:13:19, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.6303, decode.acc_seg: 76.1499, aux.loss_ce: 0.2704, aux.acc_seg: 75.4464, loss: 0.9008, grad_norm: 6.1173 2023-02-16 16:25:38,945 - mmseg - INFO - Iter [9100/160000] lr: 5.659e-05, eta: 15:13:00, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6410, decode.acc_seg: 76.2423, aux.loss_ce: 0.2770, aux.acc_seg: 75.2930, loss: 0.9180, grad_norm: 6.6875 2023-02-16 16:25:57,272 - mmseg - INFO - Iter [9150/160000] lr: 5.657e-05, eta: 15:12:45, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6124, decode.acc_seg: 76.9521, aux.loss_ce: 0.2689, aux.acc_seg: 75.7300, loss: 0.8814, grad_norm: 6.0406 2023-02-16 16:26:15,440 - mmseg - INFO - Iter [9200/160000] lr: 5.655e-05, eta: 15:12:27, time: 0.364, data_time: 0.007, memory: 16600, decode.loss_ce: 0.6663, decode.acc_seg: 75.6873, aux.loss_ce: 0.2884, aux.acc_seg: 74.4773, loss: 0.9547, grad_norm: 6.0123 2023-02-16 16:26:33,312 - mmseg - INFO - Iter [9250/160000] lr: 5.653e-05, eta: 15:12:05, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6633, decode.acc_seg: 76.1274, aux.loss_ce: 0.2863, aux.acc_seg: 75.0177, loss: 0.9496, grad_norm: 6.1852 2023-02-16 16:26:51,336 - mmseg - INFO - Iter [9300/160000] lr: 5.651e-05, eta: 15:11:44, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6549, decode.acc_seg: 75.6742, aux.loss_ce: 0.2852, aux.acc_seg: 74.5510, loss: 0.9401, grad_norm: 5.8663 2023-02-16 16:27:09,177 - mmseg - INFO - Iter [9350/160000] lr: 5.649e-05, eta: 15:11:21, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6401, decode.acc_seg: 76.2656, aux.loss_ce: 0.2760, aux.acc_seg: 75.3621, loss: 0.9161, grad_norm: 7.0684 2023-02-16 16:27:27,054 - mmseg - INFO - Iter [9400/160000] lr: 5.648e-05, eta: 15:10:59, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6869, decode.acc_seg: 74.7895, aux.loss_ce: 0.2972, aux.acc_seg: 73.6705, loss: 0.9841, grad_norm: 6.6723 2023-02-16 16:27:44,967 - mmseg - INFO - Iter [9450/160000] lr: 5.646e-05, eta: 15:10:37, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6945, decode.acc_seg: 75.0982, aux.loss_ce: 0.2925, aux.acc_seg: 74.4162, loss: 0.9870, grad_norm: 6.0641 2023-02-16 16:28:02,999 - mmseg - INFO - Iter [9500/160000] lr: 5.644e-05, eta: 15:10:17, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6695, decode.acc_seg: 76.1534, aux.loss_ce: 0.2912, aux.acc_seg: 75.1333, loss: 0.9606, grad_norm: 5.8338 2023-02-16 16:28:21,092 - mmseg - INFO - Iter [9550/160000] lr: 5.642e-05, eta: 15:09:58, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6590, decode.acc_seg: 75.0794, aux.loss_ce: 0.2848, aux.acc_seg: 74.1110, loss: 0.9439, grad_norm: 7.4070 2023-02-16 16:28:39,249 - mmseg - INFO - Iter [9600/160000] lr: 5.640e-05, eta: 15:09:40, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6887, decode.acc_seg: 74.6212, aux.loss_ce: 0.2951, aux.acc_seg: 73.4183, loss: 0.9838, grad_norm: 6.3886 2023-02-16 16:28:57,096 - mmseg - INFO - Iter [9650/160000] lr: 5.638e-05, eta: 15:09:17, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6458, decode.acc_seg: 76.3421, aux.loss_ce: 0.2788, aux.acc_seg: 74.9841, loss: 0.9245, grad_norm: 5.9628 2023-02-16 16:29:15,005 - mmseg - INFO - Iter [9700/160000] lr: 5.636e-05, eta: 15:08:55, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6494, decode.acc_seg: 76.6824, aux.loss_ce: 0.2831, aux.acc_seg: 75.2522, loss: 0.9325, grad_norm: 6.3734 2023-02-16 16:29:32,777 - mmseg - INFO - Iter [9750/160000] lr: 5.634e-05, eta: 15:08:32, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6932, decode.acc_seg: 74.6604, aux.loss_ce: 0.2919, aux.acc_seg: 73.9126, loss: 0.9850, grad_norm: 6.0691 2023-02-16 16:29:50,805 - mmseg - INFO - Iter [9800/160000] lr: 5.633e-05, eta: 15:08:12, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6559, decode.acc_seg: 76.4538, aux.loss_ce: 0.2829, aux.acc_seg: 75.4326, loss: 0.9388, grad_norm: 5.7259 2023-02-16 16:30:08,873 - mmseg - INFO - Iter [9850/160000] lr: 5.631e-05, eta: 15:07:52, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6842, decode.acc_seg: 74.5470, aux.loss_ce: 0.2956, aux.acc_seg: 73.3569, loss: 0.9798, grad_norm: 6.4933 2023-02-16 16:30:26,656 - mmseg - INFO - Iter [9900/160000] lr: 5.629e-05, eta: 15:07:29, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6507, decode.acc_seg: 75.6627, aux.loss_ce: 0.2790, aux.acc_seg: 74.6821, loss: 0.9297, grad_norm: 6.2083 2023-02-16 16:30:44,648 - mmseg - INFO - Iter [9950/160000] lr: 5.627e-05, eta: 15:07:09, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6160, decode.acc_seg: 76.8657, aux.loss_ce: 0.2638, aux.acc_seg: 75.9155, loss: 0.8797, grad_norm: 6.9879 2023-02-16 16:31:02,525 - mmseg - INFO - Saving checkpoint at 10000 iterations 2023-02-16 16:31:03,728 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 16:31:03,728 - mmseg - INFO - Iter [10000/160000] lr: 5.625e-05, eta: 15:07:05, time: 0.382, data_time: 0.007, memory: 16600, decode.loss_ce: 0.6812, decode.acc_seg: 75.7718, aux.loss_ce: 0.2939, aux.acc_seg: 74.6700, loss: 0.9751, grad_norm: 7.2413 2023-02-16 16:31:21,465 - mmseg - INFO - Iter [10050/160000] lr: 5.623e-05, eta: 15:06:40, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6394, decode.acc_seg: 77.2725, aux.loss_ce: 0.2728, aux.acc_seg: 76.3730, loss: 0.9122, grad_norm: 6.7866 2023-02-16 16:31:39,283 - mmseg - INFO - Iter [10100/160000] lr: 5.621e-05, eta: 15:06:17, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6530, decode.acc_seg: 76.7332, aux.loss_ce: 0.2792, aux.acc_seg: 75.5059, loss: 0.9322, grad_norm: 5.8730 2023-02-16 16:31:59,599 - mmseg - INFO - Iter [10150/160000] lr: 5.619e-05, eta: 15:06:31, time: 0.406, data_time: 0.053, memory: 16600, decode.loss_ce: 0.6477, decode.acc_seg: 77.2992, aux.loss_ce: 0.2765, aux.acc_seg: 75.9733, loss: 0.9241, grad_norm: 7.5356 2023-02-16 16:32:17,666 - mmseg - INFO - Iter [10200/160000] lr: 5.618e-05, eta: 15:06:12, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6166, decode.acc_seg: 77.1426, aux.loss_ce: 0.2708, aux.acc_seg: 76.0162, loss: 0.8875, grad_norm: 5.6660 2023-02-16 16:32:35,596 - mmseg - INFO - Iter [10250/160000] lr: 5.616e-05, eta: 15:05:51, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6463, decode.acc_seg: 76.4524, aux.loss_ce: 0.2716, aux.acc_seg: 75.8067, loss: 0.9179, grad_norm: 5.2481 2023-02-16 16:32:53,468 - mmseg - INFO - Iter [10300/160000] lr: 5.614e-05, eta: 15:05:29, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6247, decode.acc_seg: 77.0255, aux.loss_ce: 0.2676, aux.acc_seg: 76.4448, loss: 0.8924, grad_norm: 5.5983 2023-02-16 16:33:11,347 - mmseg - INFO - Iter [10350/160000] lr: 5.612e-05, eta: 15:05:07, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6512, decode.acc_seg: 75.9887, aux.loss_ce: 0.2846, aux.acc_seg: 74.7700, loss: 0.9358, grad_norm: 6.2212 2023-02-16 16:33:29,431 - mmseg - INFO - Iter [10400/160000] lr: 5.610e-05, eta: 15:04:48, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6029, decode.acc_seg: 77.4349, aux.loss_ce: 0.2592, aux.acc_seg: 76.8145, loss: 0.8620, grad_norm: 5.4671 2023-02-16 16:33:47,197 - mmseg - INFO - Iter [10450/160000] lr: 5.608e-05, eta: 15:04:24, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6425, decode.acc_seg: 77.5584, aux.loss_ce: 0.2758, aux.acc_seg: 76.3558, loss: 0.9183, grad_norm: 6.9039 2023-02-16 16:34:05,210 - mmseg - INFO - Iter [10500/160000] lr: 5.606e-05, eta: 15:04:04, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6288, decode.acc_seg: 77.0644, aux.loss_ce: 0.2679, aux.acc_seg: 76.3161, loss: 0.8967, grad_norm: 6.1855 2023-02-16 16:34:23,081 - mmseg - INFO - Iter [10550/160000] lr: 5.604e-05, eta: 15:03:42, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6679, decode.acc_seg: 76.1196, aux.loss_ce: 0.2834, aux.acc_seg: 75.4246, loss: 0.9513, grad_norm: 5.7206 2023-02-16 16:34:41,023 - mmseg - INFO - Iter [10600/160000] lr: 5.603e-05, eta: 15:03:21, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6512, decode.acc_seg: 76.3887, aux.loss_ce: 0.2765, aux.acc_seg: 75.6301, loss: 0.9276, grad_norm: 5.4470 2023-02-16 16:34:58,898 - mmseg - INFO - Iter [10650/160000] lr: 5.601e-05, eta: 15:02:59, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6489, decode.acc_seg: 75.9012, aux.loss_ce: 0.2759, aux.acc_seg: 75.3976, loss: 0.9248, grad_norm: 6.7991 2023-02-16 16:35:16,855 - mmseg - INFO - Iter [10700/160000] lr: 5.599e-05, eta: 15:02:39, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6300, decode.acc_seg: 77.1392, aux.loss_ce: 0.2751, aux.acc_seg: 75.8817, loss: 0.9052, grad_norm: 5.8548 2023-02-16 16:35:34,701 - mmseg - INFO - Iter [10750/160000] lr: 5.597e-05, eta: 15:02:16, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6409, decode.acc_seg: 76.8310, aux.loss_ce: 0.2771, aux.acc_seg: 75.5607, loss: 0.9180, grad_norm: 6.4144 2023-02-16 16:35:52,631 - mmseg - INFO - Iter [10800/160000] lr: 5.595e-05, eta: 15:01:55, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6058, decode.acc_seg: 78.0580, aux.loss_ce: 0.2658, aux.acc_seg: 76.4530, loss: 0.8715, grad_norm: 6.1466 2023-02-16 16:36:10,608 - mmseg - INFO - Iter [10850/160000] lr: 5.593e-05, eta: 15:01:35, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6234, decode.acc_seg: 76.6693, aux.loss_ce: 0.2696, aux.acc_seg: 75.2990, loss: 0.8930, grad_norm: 5.6745 2023-02-16 16:36:28,642 - mmseg - INFO - Iter [10900/160000] lr: 5.591e-05, eta: 15:01:16, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6295, decode.acc_seg: 76.7308, aux.loss_ce: 0.2735, aux.acc_seg: 75.5702, loss: 0.9030, grad_norm: 6.1225 2023-02-16 16:36:46,726 - mmseg - INFO - Iter [10950/160000] lr: 5.589e-05, eta: 15:00:57, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6250, decode.acc_seg: 76.9965, aux.loss_ce: 0.2672, aux.acc_seg: 75.6005, loss: 0.8922, grad_norm: 6.9506 2023-02-16 16:37:04,617 - mmseg - INFO - Saving checkpoint at 11000 iterations 2023-02-16 16:37:05,844 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 16:37:05,844 - mmseg - INFO - Iter [11000/160000] lr: 5.588e-05, eta: 15:00:52, time: 0.382, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6591, decode.acc_seg: 75.5851, aux.loss_ce: 0.2825, aux.acc_seg: 74.7335, loss: 0.9416, grad_norm: 6.7081 2023-02-16 16:37:23,833 - mmseg - INFO - Iter [11050/160000] lr: 5.586e-05, eta: 15:00:32, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6301, decode.acc_seg: 76.6630, aux.loss_ce: 0.2740, aux.acc_seg: 75.3864, loss: 0.9041, grad_norm: 6.4876 2023-02-16 16:37:41,683 - mmseg - INFO - Iter [11100/160000] lr: 5.584e-05, eta: 15:00:10, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6258, decode.acc_seg: 77.1323, aux.loss_ce: 0.2698, aux.acc_seg: 75.6617, loss: 0.8956, grad_norm: 6.1154 2023-02-16 16:37:59,465 - mmseg - INFO - Iter [11150/160000] lr: 5.582e-05, eta: 14:59:47, time: 0.356, data_time: 0.007, memory: 16600, decode.loss_ce: 0.6594, decode.acc_seg: 76.2169, aux.loss_ce: 0.2805, aux.acc_seg: 75.5209, loss: 0.9399, grad_norm: 5.7452 2023-02-16 16:38:17,321 - mmseg - INFO - Iter [11200/160000] lr: 5.580e-05, eta: 14:59:25, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6498, decode.acc_seg: 75.5146, aux.loss_ce: 0.2743, aux.acc_seg: 74.9995, loss: 0.9241, grad_norm: 6.1531 2023-02-16 16:38:35,104 - mmseg - INFO - Iter [11250/160000] lr: 5.578e-05, eta: 14:59:02, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6518, decode.acc_seg: 76.8933, aux.loss_ce: 0.2777, aux.acc_seg: 75.6173, loss: 0.9295, grad_norm: 6.0077 2023-02-16 16:38:52,909 - mmseg - INFO - Iter [11300/160000] lr: 5.576e-05, eta: 14:58:40, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6500, decode.acc_seg: 76.0546, aux.loss_ce: 0.2767, aux.acc_seg: 75.4293, loss: 0.9267, grad_norm: 6.4711 2023-02-16 16:39:10,652 - mmseg - INFO - Iter [11350/160000] lr: 5.574e-05, eta: 14:58:17, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6463, decode.acc_seg: 76.6452, aux.loss_ce: 0.2797, aux.acc_seg: 75.5761, loss: 0.9260, grad_norm: 6.1967 2023-02-16 16:39:31,084 - mmseg - INFO - Iter [11400/160000] lr: 5.573e-05, eta: 14:58:29, time: 0.409, data_time: 0.051, memory: 16600, decode.loss_ce: 0.6452, decode.acc_seg: 75.6134, aux.loss_ce: 0.2735, aux.acc_seg: 75.1276, loss: 0.9187, grad_norm: 6.2167 2023-02-16 16:39:49,371 - mmseg - INFO - Iter [11450/160000] lr: 5.571e-05, eta: 14:58:12, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6111, decode.acc_seg: 77.2897, aux.loss_ce: 0.2657, aux.acc_seg: 76.0370, loss: 0.8768, grad_norm: 5.4914 2023-02-16 16:40:07,201 - mmseg - INFO - Iter [11500/160000] lr: 5.569e-05, eta: 14:57:50, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6372, decode.acc_seg: 76.4917, aux.loss_ce: 0.2755, aux.acc_seg: 75.4474, loss: 0.9128, grad_norm: 6.0043 2023-02-16 16:40:25,052 - mmseg - INFO - Iter [11550/160000] lr: 5.567e-05, eta: 14:57:28, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6256, decode.acc_seg: 77.2131, aux.loss_ce: 0.2675, aux.acc_seg: 75.8763, loss: 0.8931, grad_norm: 5.7737 2023-02-16 16:40:42,847 - mmseg - INFO - Iter [11600/160000] lr: 5.565e-05, eta: 14:57:06, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6399, decode.acc_seg: 76.9911, aux.loss_ce: 0.2689, aux.acc_seg: 76.1002, loss: 0.9089, grad_norm: 6.5935 2023-02-16 16:41:00,609 - mmseg - INFO - Iter [11650/160000] lr: 5.563e-05, eta: 14:56:43, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6326, decode.acc_seg: 77.0804, aux.loss_ce: 0.2741, aux.acc_seg: 75.6480, loss: 0.9067, grad_norm: 6.1720 2023-02-16 16:41:18,663 - mmseg - INFO - Iter [11700/160000] lr: 5.561e-05, eta: 14:56:24, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5572, decode.acc_seg: 79.2534, aux.loss_ce: 0.2449, aux.acc_seg: 77.6043, loss: 0.8021, grad_norm: 5.1446 2023-02-16 16:41:36,492 - mmseg - INFO - Iter [11750/160000] lr: 5.559e-05, eta: 14:56:02, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5873, decode.acc_seg: 78.6717, aux.loss_ce: 0.2534, aux.acc_seg: 77.1428, loss: 0.8407, grad_norm: 5.4926 2023-02-16 16:41:54,464 - mmseg - INFO - Iter [11800/160000] lr: 5.558e-05, eta: 14:55:42, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6090, decode.acc_seg: 78.0358, aux.loss_ce: 0.2610, aux.acc_seg: 76.9767, loss: 0.8701, grad_norm: 6.0903 2023-02-16 16:42:12,550 - mmseg - INFO - Iter [11850/160000] lr: 5.556e-05, eta: 14:55:23, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6018, decode.acc_seg: 76.8779, aux.loss_ce: 0.2627, aux.acc_seg: 75.6775, loss: 0.8645, grad_norm: 6.0398 2023-02-16 16:42:30,473 - mmseg - INFO - Iter [11900/160000] lr: 5.554e-05, eta: 14:55:02, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6064, decode.acc_seg: 77.5416, aux.loss_ce: 0.2630, aux.acc_seg: 76.3900, loss: 0.8694, grad_norm: 6.5069 2023-02-16 16:42:48,381 - mmseg - INFO - Iter [11950/160000] lr: 5.552e-05, eta: 14:54:41, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5811, decode.acc_seg: 78.3597, aux.loss_ce: 0.2564, aux.acc_seg: 76.7016, loss: 0.8375, grad_norm: 6.1623 2023-02-16 16:43:06,150 - mmseg - INFO - Saving checkpoint at 12000 iterations 2023-02-16 16:43:07,290 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 16:43:07,290 - mmseg - INFO - Iter [12000/160000] lr: 5.550e-05, eta: 14:54:33, time: 0.378, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5987, decode.acc_seg: 77.9194, aux.loss_ce: 0.2629, aux.acc_seg: 76.3322, loss: 0.8616, grad_norm: 5.9561 2023-02-16 16:43:25,170 - mmseg - INFO - Iter [12050/160000] lr: 5.548e-05, eta: 14:54:12, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6126, decode.acc_seg: 77.4836, aux.loss_ce: 0.2667, aux.acc_seg: 76.0875, loss: 0.8793, grad_norm: 6.4950 2023-02-16 16:43:43,617 - mmseg - INFO - Iter [12100/160000] lr: 5.546e-05, eta: 14:53:57, time: 0.368, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6387, decode.acc_seg: 76.8959, aux.loss_ce: 0.2756, aux.acc_seg: 75.4014, loss: 0.9143, grad_norm: 6.4047 2023-02-16 16:44:01,846 - mmseg - INFO - Iter [12150/160000] lr: 5.544e-05, eta: 14:53:40, time: 0.365, data_time: 0.007, memory: 16600, decode.loss_ce: 0.5892, decode.acc_seg: 78.0886, aux.loss_ce: 0.2588, aux.acc_seg: 76.7142, loss: 0.8480, grad_norm: 5.1266 2023-02-16 16:44:19,635 - mmseg - INFO - Iter [12200/160000] lr: 5.543e-05, eta: 14:53:18, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6364, decode.acc_seg: 77.3479, aux.loss_ce: 0.2718, aux.acc_seg: 75.9591, loss: 0.9082, grad_norm: 6.3001 2023-02-16 16:44:37,579 - mmseg - INFO - Iter [12250/160000] lr: 5.541e-05, eta: 14:52:58, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6068, decode.acc_seg: 77.7763, aux.loss_ce: 0.2633, aux.acc_seg: 76.6317, loss: 0.8700, grad_norm: 5.6416 2023-02-16 16:44:55,557 - mmseg - INFO - Iter [12300/160000] lr: 5.539e-05, eta: 14:52:38, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6144, decode.acc_seg: 77.0689, aux.loss_ce: 0.2660, aux.acc_seg: 75.6703, loss: 0.8804, grad_norm: 6.0246 2023-02-16 16:45:13,468 - mmseg - INFO - Iter [12350/160000] lr: 5.537e-05, eta: 14:52:17, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5965, decode.acc_seg: 78.4496, aux.loss_ce: 0.2553, aux.acc_seg: 77.0692, loss: 0.8518, grad_norm: 5.3888 2023-02-16 16:45:31,260 - mmseg - INFO - Iter [12400/160000] lr: 5.535e-05, eta: 14:51:55, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6557, decode.acc_seg: 76.3376, aux.loss_ce: 0.2869, aux.acc_seg: 74.7223, loss: 0.9426, grad_norm: 5.9993 2023-02-16 16:45:49,062 - mmseg - INFO - Iter [12450/160000] lr: 5.533e-05, eta: 14:51:33, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5834, decode.acc_seg: 78.6497, aux.loss_ce: 0.2563, aux.acc_seg: 76.8601, loss: 0.8397, grad_norm: 5.2820 2023-02-16 16:46:06,897 - mmseg - INFO - Iter [12500/160000] lr: 5.531e-05, eta: 14:51:11, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5997, decode.acc_seg: 77.8071, aux.loss_ce: 0.2562, aux.acc_seg: 76.7112, loss: 0.8559, grad_norm: 5.7112 2023-02-16 16:46:24,807 - mmseg - INFO - Iter [12550/160000] lr: 5.529e-05, eta: 14:50:50, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6078, decode.acc_seg: 77.1977, aux.loss_ce: 0.2625, aux.acc_seg: 76.1380, loss: 0.8703, grad_norm: 6.2171 2023-02-16 16:46:42,825 - mmseg - INFO - Iter [12600/160000] lr: 5.528e-05, eta: 14:50:31, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6443, decode.acc_seg: 76.8256, aux.loss_ce: 0.2741, aux.acc_seg: 75.7697, loss: 0.9184, grad_norm: 6.1281 2023-02-16 16:47:03,098 - mmseg - INFO - Iter [12650/160000] lr: 5.526e-05, eta: 14:50:38, time: 0.405, data_time: 0.050, memory: 16600, decode.loss_ce: 0.6210, decode.acc_seg: 77.1477, aux.loss_ce: 0.2742, aux.acc_seg: 75.4729, loss: 0.8952, grad_norm: 6.2992 2023-02-16 16:47:21,029 - mmseg - INFO - Iter [12700/160000] lr: 5.524e-05, eta: 14:50:17, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6048, decode.acc_seg: 77.6892, aux.loss_ce: 0.2622, aux.acc_seg: 76.3285, loss: 0.8670, grad_norm: 5.9443 2023-02-16 16:47:39,149 - mmseg - INFO - Iter [12750/160000] lr: 5.522e-05, eta: 14:49:59, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5995, decode.acc_seg: 78.8172, aux.loss_ce: 0.2635, aux.acc_seg: 76.9651, loss: 0.8630, grad_norm: 6.1027 2023-02-16 16:47:56,851 - mmseg - INFO - Iter [12800/160000] lr: 5.520e-05, eta: 14:49:36, time: 0.354, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5590, decode.acc_seg: 79.2865, aux.loss_ce: 0.2462, aux.acc_seg: 77.9124, loss: 0.8051, grad_norm: 5.0265 2023-02-16 16:48:14,742 - mmseg - INFO - Iter [12850/160000] lr: 5.518e-05, eta: 14:49:15, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6011, decode.acc_seg: 77.6137, aux.loss_ce: 0.2610, aux.acc_seg: 76.0016, loss: 0.8620, grad_norm: 6.0327 2023-02-16 16:48:32,585 - mmseg - INFO - Iter [12900/160000] lr: 5.516e-05, eta: 14:48:54, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6062, decode.acc_seg: 78.0804, aux.loss_ce: 0.2659, aux.acc_seg: 76.6480, loss: 0.8721, grad_norm: 6.7793 2023-02-16 16:48:50,558 - mmseg - INFO - Iter [12950/160000] lr: 5.514e-05, eta: 14:48:34, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5967, decode.acc_seg: 78.4781, aux.loss_ce: 0.2605, aux.acc_seg: 76.9692, loss: 0.8572, grad_norm: 6.1747 2023-02-16 16:49:08,507 - mmseg - INFO - Saving checkpoint at 13000 iterations 2023-02-16 16:49:09,607 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 16:49:09,607 - mmseg - INFO - Iter [13000/160000] lr: 5.513e-05, eta: 14:48:26, time: 0.381, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5900, decode.acc_seg: 78.1166, aux.loss_ce: 0.2583, aux.acc_seg: 76.4388, loss: 0.8484, grad_norm: 6.4929 2023-02-16 16:49:27,504 - mmseg - INFO - Iter [13050/160000] lr: 5.511e-05, eta: 14:48:05, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6113, decode.acc_seg: 77.8072, aux.loss_ce: 0.2624, aux.acc_seg: 76.6012, loss: 0.8737, grad_norm: 5.8218 2023-02-16 16:49:45,525 - mmseg - INFO - Iter [13100/160000] lr: 5.509e-05, eta: 14:47:46, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5971, decode.acc_seg: 77.8470, aux.loss_ce: 0.2633, aux.acc_seg: 75.9651, loss: 0.8604, grad_norm: 6.1736 2023-02-16 16:50:03,400 - mmseg - INFO - Iter [13150/160000] lr: 5.507e-05, eta: 14:47:25, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5935, decode.acc_seg: 77.8023, aux.loss_ce: 0.2588, aux.acc_seg: 76.4849, loss: 0.8522, grad_norm: 6.2816 2023-02-16 16:50:21,306 - mmseg - INFO - Iter [13200/160000] lr: 5.505e-05, eta: 14:47:04, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5966, decode.acc_seg: 78.1886, aux.loss_ce: 0.2592, aux.acc_seg: 76.8735, loss: 0.8558, grad_norm: 7.3758 2023-02-16 16:50:39,009 - mmseg - INFO - Iter [13250/160000] lr: 5.503e-05, eta: 14:46:42, time: 0.354, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6188, decode.acc_seg: 77.7379, aux.loss_ce: 0.2709, aux.acc_seg: 76.0379, loss: 0.8897, grad_norm: 5.9295 2023-02-16 16:50:57,046 - mmseg - INFO - Iter [13300/160000] lr: 5.501e-05, eta: 14:46:22, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5518, decode.acc_seg: 79.0710, aux.loss_ce: 0.2411, aux.acc_seg: 77.7243, loss: 0.7929, grad_norm: 5.3868 2023-02-16 16:51:14,769 - mmseg - INFO - Iter [13350/160000] lr: 5.499e-05, eta: 14:46:00, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6230, decode.acc_seg: 77.3778, aux.loss_ce: 0.2758, aux.acc_seg: 75.4851, loss: 0.8988, grad_norm: 5.8729 2023-02-16 16:51:32,785 - mmseg - INFO - Iter [13400/160000] lr: 5.498e-05, eta: 14:45:41, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5988, decode.acc_seg: 77.6796, aux.loss_ce: 0.2600, aux.acc_seg: 76.2999, loss: 0.8588, grad_norm: 5.9773 2023-02-16 16:51:50,681 - mmseg - INFO - Iter [13450/160000] lr: 5.496e-05, eta: 14:45:20, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6161, decode.acc_seg: 77.2616, aux.loss_ce: 0.2637, aux.acc_seg: 76.2660, loss: 0.8799, grad_norm: 5.8057 2023-02-16 16:52:08,577 - mmseg - INFO - Iter [13500/160000] lr: 5.494e-05, eta: 14:44:59, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5653, decode.acc_seg: 79.0053, aux.loss_ce: 0.2494, aux.acc_seg: 77.3478, loss: 0.8147, grad_norm: 5.7145 2023-02-16 16:52:26,509 - mmseg - INFO - Iter [13550/160000] lr: 5.492e-05, eta: 14:44:39, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5653, decode.acc_seg: 79.3689, aux.loss_ce: 0.2473, aux.acc_seg: 77.9243, loss: 0.8127, grad_norm: 6.3311 2023-02-16 16:52:44,529 - mmseg - INFO - Iter [13600/160000] lr: 5.490e-05, eta: 14:44:20, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5905, decode.acc_seg: 77.9052, aux.loss_ce: 0.2616, aux.acc_seg: 76.1513, loss: 0.8521, grad_norm: 5.5717 2023-02-16 16:53:02,816 - mmseg - INFO - Iter [13650/160000] lr: 5.488e-05, eta: 14:44:04, time: 0.366, data_time: 0.007, memory: 16600, decode.loss_ce: 0.5759, decode.acc_seg: 79.0412, aux.loss_ce: 0.2552, aux.acc_seg: 77.1373, loss: 0.8311, grad_norm: 6.2223 2023-02-16 16:53:21,255 - mmseg - INFO - Iter [13700/160000] lr: 5.486e-05, eta: 14:43:49, time: 0.369, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5861, decode.acc_seg: 77.8842, aux.loss_ce: 0.2636, aux.acc_seg: 75.2240, loss: 0.8498, grad_norm: 6.3644 2023-02-16 16:53:39,369 - mmseg - INFO - Iter [13750/160000] lr: 5.484e-05, eta: 14:43:31, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5677, decode.acc_seg: 78.8006, aux.loss_ce: 0.2524, aux.acc_seg: 77.1938, loss: 0.8201, grad_norm: 6.1959 2023-02-16 16:53:57,298 - mmseg - INFO - Iter [13800/160000] lr: 5.483e-05, eta: 14:43:11, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5685, decode.acc_seg: 79.1608, aux.loss_ce: 0.2529, aux.acc_seg: 77.3691, loss: 0.8214, grad_norm: 6.6877 2023-02-16 16:54:15,329 - mmseg - INFO - Iter [13850/160000] lr: 5.481e-05, eta: 14:42:51, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.6039, decode.acc_seg: 77.5870, aux.loss_ce: 0.2607, aux.acc_seg: 76.4173, loss: 0.8646, grad_norm: 5.7555 2023-02-16 16:54:35,527 - mmseg - INFO - Iter [13900/160000] lr: 5.479e-05, eta: 14:42:55, time: 0.404, data_time: 0.051, memory: 16600, decode.loss_ce: 0.5825, decode.acc_seg: 78.7319, aux.loss_ce: 0.2563, aux.acc_seg: 77.1096, loss: 0.8388, grad_norm: 6.1909 2023-02-16 16:54:53,312 - mmseg - INFO - Iter [13950/160000] lr: 5.477e-05, eta: 14:42:33, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5759, decode.acc_seg: 78.7971, aux.loss_ce: 0.2574, aux.acc_seg: 76.8002, loss: 0.8332, grad_norm: 6.4947 2023-02-16 16:55:11,473 - mmseg - INFO - Saving checkpoint at 14000 iterations 2023-02-16 16:55:12,571 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 16:55:12,571 - mmseg - INFO - Iter [14000/160000] lr: 5.475e-05, eta: 14:42:27, time: 0.385, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5534, decode.acc_seg: 79.3104, aux.loss_ce: 0.2486, aux.acc_seg: 77.4185, loss: 0.8020, grad_norm: 5.9736 2023-02-16 16:55:30,290 - mmseg - INFO - Iter [14050/160000] lr: 5.473e-05, eta: 14:42:05, time: 0.354, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5720, decode.acc_seg: 78.8128, aux.loss_ce: 0.2508, aux.acc_seg: 77.2768, loss: 0.8228, grad_norm: 6.3499 2023-02-16 16:55:48,182 - mmseg - INFO - Iter [14100/160000] lr: 5.471e-05, eta: 14:41:44, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5750, decode.acc_seg: 79.1262, aux.loss_ce: 0.2518, aux.acc_seg: 77.6092, loss: 0.8268, grad_norm: 6.1325 2023-02-16 16:56:06,117 - mmseg - INFO - Iter [14150/160000] lr: 5.469e-05, eta: 14:41:24, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5632, decode.acc_seg: 78.8597, aux.loss_ce: 0.2454, aux.acc_seg: 77.2547, loss: 0.8086, grad_norm: 6.2013 2023-02-16 16:56:24,390 - mmseg - INFO - Iter [14200/160000] lr: 5.468e-05, eta: 14:41:07, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5229, decode.acc_seg: 80.1174, aux.loss_ce: 0.2339, aux.acc_seg: 78.3435, loss: 0.7567, grad_norm: 5.0320 2023-02-16 16:56:42,175 - mmseg - INFO - Iter [14250/160000] lr: 5.466e-05, eta: 14:40:46, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5944, decode.acc_seg: 78.4065, aux.loss_ce: 0.2650, aux.acc_seg: 76.7103, loss: 0.8594, grad_norm: 6.4929 2023-02-16 16:56:59,905 - mmseg - INFO - Iter [14300/160000] lr: 5.464e-05, eta: 14:40:23, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5527, decode.acc_seg: 79.3054, aux.loss_ce: 0.2466, aux.acc_seg: 77.3318, loss: 0.7993, grad_norm: 5.6169 2023-02-16 16:57:18,059 - mmseg - INFO - Iter [14350/160000] lr: 5.462e-05, eta: 14:40:05, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5908, decode.acc_seg: 77.7678, aux.loss_ce: 0.2569, aux.acc_seg: 76.3225, loss: 0.8476, grad_norm: 5.7340 2023-02-16 16:57:35,887 - mmseg - INFO - Iter [14400/160000] lr: 5.460e-05, eta: 14:39:44, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5666, decode.acc_seg: 78.9918, aux.loss_ce: 0.2549, aux.acc_seg: 77.1375, loss: 0.8215, grad_norm: 5.6114 2023-02-16 16:57:53,749 - mmseg - INFO - Iter [14450/160000] lr: 5.458e-05, eta: 14:39:23, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5764, decode.acc_seg: 77.6225, aux.loss_ce: 0.2590, aux.acc_seg: 75.6499, loss: 0.8354, grad_norm: 5.9964 2023-02-16 16:58:11,899 - mmseg - INFO - Iter [14500/160000] lr: 5.456e-05, eta: 14:39:06, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5896, decode.acc_seg: 78.0206, aux.loss_ce: 0.2624, aux.acc_seg: 76.1518, loss: 0.8520, grad_norm: 5.8689 2023-02-16 16:58:29,753 - mmseg - INFO - Iter [14550/160000] lr: 5.454e-05, eta: 14:38:45, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5749, decode.acc_seg: 78.7321, aux.loss_ce: 0.2584, aux.acc_seg: 76.2839, loss: 0.8333, grad_norm: 5.5529 2023-02-16 16:58:47,455 - mmseg - INFO - Iter [14600/160000] lr: 5.453e-05, eta: 14:38:22, time: 0.354, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5541, decode.acc_seg: 79.4872, aux.loss_ce: 0.2514, aux.acc_seg: 76.9621, loss: 0.8054, grad_norm: 5.4990 2023-02-16 16:59:05,483 - mmseg - INFO - Iter [14650/160000] lr: 5.451e-05, eta: 14:38:03, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5534, decode.acc_seg: 79.8030, aux.loss_ce: 0.2438, aux.acc_seg: 78.0223, loss: 0.7972, grad_norm: 5.5100 2023-02-16 16:59:23,235 - mmseg - INFO - Iter [14700/160000] lr: 5.449e-05, eta: 14:37:42, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5785, decode.acc_seg: 78.7303, aux.loss_ce: 0.2556, aux.acc_seg: 77.1391, loss: 0.8342, grad_norm: 6.0312 2023-02-16 16:59:41,462 - mmseg - INFO - Iter [14750/160000] lr: 5.447e-05, eta: 14:37:24, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5911, decode.acc_seg: 78.0100, aux.loss_ce: 0.2627, aux.acc_seg: 75.6448, loss: 0.8539, grad_norm: 6.3020 2023-02-16 16:59:59,318 - mmseg - INFO - Iter [14800/160000] lr: 5.445e-05, eta: 14:37:04, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5532, decode.acc_seg: 79.9481, aux.loss_ce: 0.2479, aux.acc_seg: 78.0301, loss: 0.8012, grad_norm: 5.1276 2023-02-16 17:00:17,379 - mmseg - INFO - Iter [14850/160000] lr: 5.443e-05, eta: 14:36:45, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5245, decode.acc_seg: 79.7825, aux.loss_ce: 0.2354, aux.acc_seg: 78.0853, loss: 0.7598, grad_norm: 5.9367 2023-02-16 17:00:35,382 - mmseg - INFO - Iter [14900/160000] lr: 5.441e-05, eta: 14:36:26, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5625, decode.acc_seg: 79.0801, aux.loss_ce: 0.2472, aux.acc_seg: 77.3900, loss: 0.8097, grad_norm: 5.5576 2023-02-16 17:00:53,285 - mmseg - INFO - Iter [14950/160000] lr: 5.439e-05, eta: 14:36:05, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5737, decode.acc_seg: 79.0734, aux.loss_ce: 0.2521, aux.acc_seg: 77.4105, loss: 0.8258, grad_norm: 5.8413 2023-02-16 17:01:11,676 - mmseg - INFO - Saving checkpoint at 15000 iterations 2023-02-16 17:01:12,775 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 17:01:12,775 - mmseg - INFO - Iter [15000/160000] lr: 5.438e-05, eta: 14:36:01, time: 0.390, data_time: 0.007, memory: 16600, decode.loss_ce: 0.5778, decode.acc_seg: 78.5181, aux.loss_ce: 0.2583, aux.acc_seg: 76.5666, loss: 0.8360, grad_norm: 5.5789 2023-02-16 17:01:30,469 - mmseg - INFO - Iter [15050/160000] lr: 5.436e-05, eta: 14:35:38, time: 0.354, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5730, decode.acc_seg: 78.9932, aux.loss_ce: 0.2543, aux.acc_seg: 77.0890, loss: 0.8273, grad_norm: 6.2324 2023-02-16 17:01:48,206 - mmseg - INFO - Iter [15100/160000] lr: 5.434e-05, eta: 14:35:17, time: 0.355, data_time: 0.007, memory: 16600, decode.loss_ce: 0.5756, decode.acc_seg: 78.6324, aux.loss_ce: 0.2567, aux.acc_seg: 76.7958, loss: 0.8323, grad_norm: 5.8240 2023-02-16 17:02:05,892 - mmseg - INFO - Iter [15150/160000] lr: 5.432e-05, eta: 14:34:54, time: 0.354, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5658, decode.acc_seg: 78.8899, aux.loss_ce: 0.2566, aux.acc_seg: 76.2055, loss: 0.8224, grad_norm: 5.5931 2023-02-16 17:02:26,113 - mmseg - INFO - Iter [15200/160000] lr: 5.430e-05, eta: 14:34:56, time: 0.404, data_time: 0.054, memory: 16600, decode.loss_ce: 0.5486, decode.acc_seg: 79.5825, aux.loss_ce: 0.2459, aux.acc_seg: 77.5341, loss: 0.7944, grad_norm: 5.6058 2023-02-16 17:02:44,133 - mmseg - INFO - Iter [15250/160000] lr: 5.428e-05, eta: 14:34:37, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5431, decode.acc_seg: 79.9067, aux.loss_ce: 0.2484, aux.acc_seg: 77.3340, loss: 0.7915, grad_norm: 5.8676 2023-02-16 17:03:01,977 - mmseg - INFO - Iter [15300/160000] lr: 5.426e-05, eta: 14:34:16, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5339, decode.acc_seg: 80.5289, aux.loss_ce: 0.2409, aux.acc_seg: 78.3172, loss: 0.7747, grad_norm: 5.4644 2023-02-16 17:03:19,670 - mmseg - INFO - Iter [15350/160000] lr: 5.424e-05, eta: 14:33:54, time: 0.354, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5239, decode.acc_seg: 80.1162, aux.loss_ce: 0.2424, aux.acc_seg: 77.5542, loss: 0.7663, grad_norm: 5.5120 2023-02-16 17:03:37,551 - mmseg - INFO - Iter [15400/160000] lr: 5.423e-05, eta: 14:33:34, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5547, decode.acc_seg: 79.3250, aux.loss_ce: 0.2494, aux.acc_seg: 76.9990, loss: 0.8041, grad_norm: 5.6438 2023-02-16 17:03:55,245 - mmseg - INFO - Iter [15450/160000] lr: 5.421e-05, eta: 14:33:11, time: 0.354, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5500, decode.acc_seg: 79.9095, aux.loss_ce: 0.2519, aux.acc_seg: 77.3250, loss: 0.8019, grad_norm: 5.7880 2023-02-16 17:04:12,960 - mmseg - INFO - Iter [15500/160000] lr: 5.419e-05, eta: 14:32:49, time: 0.354, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5230, decode.acc_seg: 80.5242, aux.loss_ce: 0.2380, aux.acc_seg: 78.5805, loss: 0.7609, grad_norm: 6.0097 2023-02-16 17:04:30,719 - mmseg - INFO - Iter [15550/160000] lr: 5.417e-05, eta: 14:32:28, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5370, decode.acc_seg: 80.3803, aux.loss_ce: 0.2474, aux.acc_seg: 77.5354, loss: 0.7844, grad_norm: 5.2584 2023-02-16 17:04:48,494 - mmseg - INFO - Iter [15600/160000] lr: 5.415e-05, eta: 14:32:07, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5730, decode.acc_seg: 78.9130, aux.loss_ce: 0.2632, aux.acc_seg: 76.3868, loss: 0.8362, grad_norm: 6.7567 2023-02-16 17:05:06,230 - mmseg - INFO - Iter [15650/160000] lr: 5.413e-05, eta: 14:31:45, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5793, decode.acc_seg: 79.1331, aux.loss_ce: 0.2589, aux.acc_seg: 76.9419, loss: 0.8381, grad_norm: 6.1808 2023-02-16 17:05:24,019 - mmseg - INFO - Iter [15700/160000] lr: 5.411e-05, eta: 14:31:24, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5133, decode.acc_seg: 81.0644, aux.loss_ce: 0.2362, aux.acc_seg: 78.5002, loss: 0.7495, grad_norm: 5.3332 2023-02-16 17:05:41,930 - mmseg - INFO - Iter [15750/160000] lr: 5.409e-05, eta: 14:31:04, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.5016, decode.acc_seg: 81.0647, aux.loss_ce: 0.2332, aux.acc_seg: 78.6175, loss: 0.7347, grad_norm: 5.5131 2023-02-16 17:05:59,918 - mmseg - INFO - Iter [15800/160000] lr: 5.408e-05, eta: 14:30:45, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5654, decode.acc_seg: 79.0897, aux.loss_ce: 0.2561, aux.acc_seg: 76.6140, loss: 0.8215, grad_norm: 5.9800 2023-02-16 17:06:17,750 - mmseg - INFO - Iter [15850/160000] lr: 5.406e-05, eta: 14:30:24, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5284, decode.acc_seg: 80.2162, aux.loss_ce: 0.2381, aux.acc_seg: 78.0858, loss: 0.7665, grad_norm: 5.3875 2023-02-16 17:06:35,937 - mmseg - INFO - Iter [15900/160000] lr: 5.404e-05, eta: 14:30:06, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5463, decode.acc_seg: 79.7896, aux.loss_ce: 0.2479, aux.acc_seg: 77.4785, loss: 0.7942, grad_norm: 5.6538 2023-02-16 17:06:53,834 - mmseg - INFO - Iter [15950/160000] lr: 5.402e-05, eta: 14:29:46, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5619, decode.acc_seg: 79.6915, aux.loss_ce: 0.2512, aux.acc_seg: 77.6911, loss: 0.8131, grad_norm: 6.0492 2023-02-16 17:07:11,534 - mmseg - INFO - Saving checkpoint at 16000 iterations 2023-02-16 17:07:12,693 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 17:07:12,693 - mmseg - INFO - Iter [16000/160000] lr: 5.400e-05, eta: 14:29:35, time: 0.377, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5396, decode.acc_seg: 80.1731, aux.loss_ce: 0.2480, aux.acc_seg: 77.9216, loss: 0.7876, grad_norm: 5.9800 2023-02-16 17:07:54,119 - mmseg - INFO - per class results: 2023-02-16 17:07:54,125 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 70.25 | 77.14 | | building | 79.12 | 89.41 | | sky | 92.9 | 97.65 | | floor | 77.45 | 85.93 | | tree | 69.72 | 87.66 | | ceiling | 79.63 | 91.23 | | road | 77.81 | 89.26 | | bed | 83.15 | 86.89 | | windowpane | 51.48 | 65.63 | | grass | 62.8 | 74.74 | | cabinet | 44.88 | 82.01 | | sidewalk | 58.0 | 78.92 | | person | 76.49 | 89.07 | | earth | 38.23 | 66.8 | | door | 30.85 | 65.32 | | table | 46.71 | 75.24 | | mountain | 52.37 | 80.49 | | plant | 37.84 | 40.59 | | curtain | 61.59 | 87.64 | | chair | 41.16 | 54.97 | | car | 77.03 | 88.9 | | water | 51.11 | 72.06 | | painting | 65.43 | 83.15 | | sofa | 54.62 | 66.79 | | shelf | 36.32 | 51.59 | | house | 28.67 | 32.06 | | sea | 54.47 | 84.92 | | mirror | 49.69 | 62.14 | | rug | 56.69 | 69.53 | | field | 19.76 | 26.7 | | armchair | 28.79 | 69.85 | | seat | 52.63 | 69.15 | | fence | 31.56 | 35.99 | | desk | 20.42 | 22.28 | | rock | 19.82 | 21.12 | | wardrobe | 42.93 | 49.76 | | lamp | 49.91 | 57.58 | | bathtub | 52.69 | 67.44 | | railing | 25.05 | 46.29 | | cushion | 50.26 | 69.67 | | base | 21.96 | 29.01 | | box | 14.91 | 17.31 | | column | 33.48 | 36.54 | | signboard | 26.4 | 53.21 | | chest of drawers | 16.08 | 17.5 | | counter | 28.25 | 53.04 | | sand | 34.28 | 44.49 | | sink | 49.45 | 83.0 | | skyscraper | 66.62 | 87.35 | | fireplace | 63.1 | 74.66 | | refrigerator | 48.81 | 82.39 | | grandstand | 40.69 | 64.13 | | path | 2.06 | 2.21 | | stairs | 5.42 | 5.54 | | runway | 66.76 | 94.8 | | case | 50.42 | 59.41 | | pool table | 91.62 | 94.83 | | pillow | 49.68 | 65.07 | | screen door | 58.15 | 73.96 | | stairway | 21.3 | 49.43 | | river | 9.99 | 10.99 | | bridge | 37.94 | 89.15 | | bookcase | 31.12 | 60.81 | | blind | 11.68 | 11.86 | | coffee table | 52.38 | 67.81 | | toilet | 78.66 | 87.85 | | flower | 30.07 | 45.78 | | book | 34.39 | 41.57 | | hill | 0.65 | 0.7 | | bench | 40.83 | 44.95 | | countertop | 47.94 | 64.77 | | stove | 53.71 | 78.82 | | palm | 40.59 | 68.22 | | kitchen island | 29.61 | 59.34 | | computer | 64.43 | 77.22 | | swivel chair | 38.41 | 54.28 | | boat | 36.48 | 48.68 | | bar | 2.01 | 2.04 | | arcade machine | 51.17 | 56.59 | | hovel | 22.22 | 23.86 | | bus | 83.9 | 94.08 | | towel | 43.59 | 69.81 | | light | 42.13 | 65.78 | | truck | 26.32 | 37.19 | | tower | 18.87 | 22.25 | | chandelier | 46.82 | 85.29 | | awning | 15.36 | 16.27 | | streetlight | 14.08 | 19.69 | | booth | 0.71 | 0.74 | | television receiver | 63.69 | 69.25 | | airplane | 55.29 | 63.9 | | dirt track | 3.21 | 3.75 | | apparel | 26.14 | 60.06 | | pole | 5.93 | 7.1 | | land | 6.05 | 14.65 | | bannister | 6.89 | 10.33 | | escalator | 36.67 | 55.86 | | ottoman | 42.58 | 54.5 | | bottle | 31.73 | 71.38 | | buffet | 32.1 | 42.36 | | poster | 3.97 | 4.6 | | stage | 0.06 | 0.07 | | van | 8.47 | 9.29 | | ship | 48.87 | 85.33 | | fountain | 19.2 | 20.24 | | conveyer belt | 48.51 | 87.29 | | canopy | 14.83 | 21.4 | | washer | 64.98 | 80.52 | | plaything | 14.16 | 18.68 | | swimming pool | 34.44 | 50.78 | | stool | 20.35 | 24.48 | | barrel | 57.61 | 61.26 | | basket | 21.82 | 30.66 | | waterfall | 52.65 | 70.14 | | tent | 85.74 | 98.05 | | bag | 8.32 | 9.32 | | minibike | 50.01 | 75.93 | | cradle | 73.28 | 93.57 | | oven | 9.21 | 26.58 | | ball | 34.12 | 65.88 | | food | 49.28 | 52.81 | | step | 2.58 | 2.64 | | tank | 33.39 | 40.03 | | trade name | 0.03 | 0.03 | | microwave | 31.64 | 33.44 | | pot | 30.68 | 36.79 | | animal | 52.1 | 52.71 | | bicycle | 52.08 | 63.73 | | lake | 0.0 | 0.0 | | dishwasher | 57.03 | 65.09 | | screen | 68.24 | 85.8 | | blanket | 2.33 | 2.68 | | sculpture | 45.09 | 62.39 | | hood | 22.35 | 22.78 | | sconce | 22.19 | 29.79 | | vase | 25.42 | 40.21 | | traffic light | 18.03 | 49.4 | | tray | 0.02 | 0.02 | | ashcan | 26.04 | 59.7 | | fan | 39.85 | 45.46 | | pier | 35.59 | 77.26 | | crt screen | 0.0 | 0.0 | | plate | 9.96 | 10.25 | | monitor | 51.97 | 69.92 | | bulletin board | 26.93 | 48.28 | | shower | 0.0 | 0.0 | | radiator | 48.52 | 64.8 | | glass | 0.05 | 0.05 | | clock | 27.28 | 43.65 | | flag | 33.59 | 50.81 | +---------------------+-------+-------+ 2023-02-16 17:07:54,126 - mmseg - INFO - Summary: 2023-02-16 17:07:54,126 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 77.56 | 37.93 | 51.37 | +-------+-------+-------+ 2023-02-16 17:07:55,071 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_16000.pth. 2023-02-16 17:07:55,071 - mmseg - INFO - Best mIoU is 0.3793 at 16000 iter. 2023-02-16 17:07:55,072 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 17:07:55,072 - mmseg - INFO - Iter(val) [500] aAcc: 0.7756, mIoU: 0.3793, mAcc: 0.5137, IoU.wall: 0.7025, IoU.building: 0.7912, IoU.sky: 0.9290, IoU.floor: 0.7745, IoU.tree: 0.6972, IoU.ceiling: 0.7963, IoU.road: 0.7781, IoU.bed : 0.8315, IoU.windowpane: 0.5148, IoU.grass: 0.6280, IoU.cabinet: 0.4488, IoU.sidewalk: 0.5800, IoU.person: 0.7649, IoU.earth: 0.3823, IoU.door: 0.3085, IoU.table: 0.4671, IoU.mountain: 0.5237, IoU.plant: 0.3784, IoU.curtain: 0.6159, IoU.chair: 0.4116, IoU.car: 0.7703, IoU.water: 0.5111, IoU.painting: 0.6543, IoU.sofa: 0.5462, IoU.shelf: 0.3632, IoU.house: 0.2867, IoU.sea: 0.5447, IoU.mirror: 0.4969, IoU.rug: 0.5669, IoU.field: 0.1976, IoU.armchair: 0.2879, IoU.seat: 0.5263, IoU.fence: 0.3156, IoU.desk: 0.2042, IoU.rock: 0.1982, IoU.wardrobe: 0.4293, IoU.lamp: 0.4991, IoU.bathtub: 0.5269, IoU.railing: 0.2505, IoU.cushion: 0.5026, IoU.base: 0.2196, IoU.box: 0.1491, IoU.column: 0.3348, IoU.signboard: 0.2640, IoU.chest of drawers: 0.1608, IoU.counter: 0.2825, IoU.sand: 0.3428, IoU.sink: 0.4945, IoU.skyscraper: 0.6662, IoU.fireplace: 0.6310, IoU.refrigerator: 0.4881, IoU.grandstand: 0.4069, IoU.path: 0.0206, IoU.stairs: 0.0542, IoU.runway: 0.6676, IoU.case: 0.5042, IoU.pool table: 0.9162, IoU.pillow: 0.4968, IoU.screen door: 0.5815, IoU.stairway: 0.2130, IoU.river: 0.0999, IoU.bridge: 0.3794, IoU.bookcase: 0.3112, IoU.blind: 0.1168, IoU.coffee table: 0.5238, IoU.toilet: 0.7866, IoU.flower: 0.3007, IoU.book: 0.3439, IoU.hill: 0.0065, IoU.bench: 0.4083, IoU.countertop: 0.4794, IoU.stove: 0.5371, IoU.palm: 0.4059, IoU.kitchen island: 0.2961, IoU.computer: 0.6443, IoU.swivel chair: 0.3841, IoU.boat: 0.3648, IoU.bar: 0.0201, IoU.arcade machine: 0.5117, IoU.hovel: 0.2222, IoU.bus: 0.8390, IoU.towel: 0.4359, IoU.light: 0.4213, IoU.truck: 0.2632, IoU.tower: 0.1887, IoU.chandelier: 0.4682, IoU.awning: 0.1536, IoU.streetlight: 0.1408, IoU.booth: 0.0071, IoU.television receiver: 0.6369, IoU.airplane: 0.5529, IoU.dirt track: 0.0321, IoU.apparel: 0.2614, IoU.pole: 0.0593, IoU.land: 0.0605, IoU.bannister: 0.0689, IoU.escalator: 0.3667, IoU.ottoman: 0.4258, IoU.bottle: 0.3173, IoU.buffet: 0.3210, IoU.poster: 0.0397, IoU.stage: 0.0006, IoU.van: 0.0847, IoU.ship: 0.4887, IoU.fountain: 0.1920, IoU.conveyer belt: 0.4851, IoU.canopy: 0.1483, IoU.washer: 0.6498, IoU.plaything: 0.1416, IoU.swimming pool: 0.3444, IoU.stool: 0.2035, IoU.barrel: 0.5761, IoU.basket: 0.2182, IoU.waterfall: 0.5265, IoU.tent: 0.8574, IoU.bag: 0.0832, IoU.minibike: 0.5001, IoU.cradle: 0.7328, IoU.oven: 0.0921, IoU.ball: 0.3412, IoU.food: 0.4928, IoU.step: 0.0258, IoU.tank: 0.3339, IoU.trade name: 0.0003, IoU.microwave: 0.3164, IoU.pot: 0.3068, IoU.animal: 0.5210, IoU.bicycle: 0.5208, IoU.lake: 0.0000, IoU.dishwasher: 0.5703, IoU.screen: 0.6824, IoU.blanket: 0.0233, IoU.sculpture: 0.4509, IoU.hood: 0.2235, IoU.sconce: 0.2219, IoU.vase: 0.2542, IoU.traffic light: 0.1803, IoU.tray: 0.0002, IoU.ashcan: 0.2604, IoU.fan: 0.3985, IoU.pier: 0.3559, IoU.crt screen: 0.0000, IoU.plate: 0.0996, IoU.monitor: 0.5197, IoU.bulletin board: 0.2693, IoU.shower: 0.0000, IoU.radiator: 0.4852, IoU.glass: 0.0005, IoU.clock: 0.2728, IoU.flag: 0.3359, Acc.wall: 0.7714, Acc.building: 0.8941, Acc.sky: 0.9765, Acc.floor: 0.8593, Acc.tree: 0.8766, Acc.ceiling: 0.9123, Acc.road: 0.8926, Acc.bed : 0.8689, Acc.windowpane: 0.6563, Acc.grass: 0.7474, Acc.cabinet: 0.8201, Acc.sidewalk: 0.7892, Acc.person: 0.8907, Acc.earth: 0.6680, Acc.door: 0.6532, Acc.table: 0.7524, Acc.mountain: 0.8049, Acc.plant: 0.4059, Acc.curtain: 0.8764, Acc.chair: 0.5497, Acc.car: 0.8890, Acc.water: 0.7206, Acc.painting: 0.8315, Acc.sofa: 0.6679, Acc.shelf: 0.5159, Acc.house: 0.3206, Acc.sea: 0.8492, Acc.mirror: 0.6214, Acc.rug: 0.6953, Acc.field: 0.2670, Acc.armchair: 0.6985, Acc.seat: 0.6915, Acc.fence: 0.3599, Acc.desk: 0.2228, Acc.rock: 0.2112, Acc.wardrobe: 0.4976, Acc.lamp: 0.5758, Acc.bathtub: 0.6744, Acc.railing: 0.4629, Acc.cushion: 0.6967, Acc.base: 0.2901, Acc.box: 0.1731, Acc.column: 0.3654, Acc.signboard: 0.5321, Acc.chest of drawers: 0.1750, Acc.counter: 0.5304, Acc.sand: 0.4449, Acc.sink: 0.8300, Acc.skyscraper: 0.8735, Acc.fireplace: 0.7466, Acc.refrigerator: 0.8239, Acc.grandstand: 0.6413, Acc.path: 0.0221, Acc.stairs: 0.0554, Acc.runway: 0.9480, Acc.case: 0.5941, Acc.pool table: 0.9483, Acc.pillow: 0.6507, Acc.screen door: 0.7396, Acc.stairway: 0.4943, Acc.river: 0.1099, Acc.bridge: 0.8915, Acc.bookcase: 0.6081, Acc.blind: 0.1186, Acc.coffee table: 0.6781, Acc.toilet: 0.8785, Acc.flower: 0.4578, Acc.book: 0.4157, Acc.hill: 0.0070, Acc.bench: 0.4495, Acc.countertop: 0.6477, Acc.stove: 0.7882, Acc.palm: 0.6822, Acc.kitchen island: 0.5934, Acc.computer: 0.7722, Acc.swivel chair: 0.5428, Acc.boat: 0.4868, Acc.bar: 0.0204, Acc.arcade machine: 0.5659, Acc.hovel: 0.2386, Acc.bus: 0.9408, Acc.towel: 0.6981, Acc.light: 0.6578, Acc.truck: 0.3719, Acc.tower: 0.2225, Acc.chandelier: 0.8529, Acc.awning: 0.1627, Acc.streetlight: 0.1969, Acc.booth: 0.0074, Acc.television receiver: 0.6925, Acc.airplane: 0.6390, Acc.dirt track: 0.0375, Acc.apparel: 0.6006, Acc.pole: 0.0710, Acc.land: 0.1465, Acc.bannister: 0.1033, Acc.escalator: 0.5586, Acc.ottoman: 0.5450, Acc.bottle: 0.7138, Acc.buffet: 0.4236, Acc.poster: 0.0460, Acc.stage: 0.0007, Acc.van: 0.0929, Acc.ship: 0.8533, Acc.fountain: 0.2024, Acc.conveyer belt: 0.8729, Acc.canopy: 0.2140, Acc.washer: 0.8052, Acc.plaything: 0.1868, Acc.swimming pool: 0.5078, Acc.stool: 0.2448, Acc.barrel: 0.6126, Acc.basket: 0.3066, Acc.waterfall: 0.7014, Acc.tent: 0.9805, Acc.bag: 0.0932, Acc.minibike: 0.7593, Acc.cradle: 0.9357, Acc.oven: 0.2658, Acc.ball: 0.6588, Acc.food: 0.5281, Acc.step: 0.0264, Acc.tank: 0.4003, Acc.trade name: 0.0003, Acc.microwave: 0.3344, Acc.pot: 0.3679, Acc.animal: 0.5271, Acc.bicycle: 0.6373, Acc.lake: 0.0000, Acc.dishwasher: 0.6509, Acc.screen: 0.8580, Acc.blanket: 0.0268, Acc.sculpture: 0.6239, Acc.hood: 0.2278, Acc.sconce: 0.2979, Acc.vase: 0.4021, Acc.traffic light: 0.4940, Acc.tray: 0.0002, Acc.ashcan: 0.5970, Acc.fan: 0.4546, Acc.pier: 0.7726, Acc.crt screen: 0.0000, Acc.plate: 0.1025, Acc.monitor: 0.6992, Acc.bulletin board: 0.4828, Acc.shower: 0.0000, Acc.radiator: 0.6480, Acc.glass: 0.0005, Acc.clock: 0.4365, Acc.flag: 0.5081 2023-02-16 17:08:12,960 - mmseg - INFO - Iter [16050/160000] lr: 5.398e-05, eta: 14:35:35, time: 1.205, data_time: 0.854, memory: 16600, decode.loss_ce: 0.5727, decode.acc_seg: 79.6663, aux.loss_ce: 0.2583, aux.acc_seg: 76.9249, loss: 0.8311, grad_norm: 6.3710 2023-02-16 17:08:30,809 - mmseg - INFO - Iter [16100/160000] lr: 5.396e-05, eta: 14:35:13, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5511, decode.acc_seg: 79.1820, aux.loss_ce: 0.2524, aux.acc_seg: 76.1365, loss: 0.8035, grad_norm: 6.6543 2023-02-16 17:08:48,573 - mmseg - INFO - Iter [16150/160000] lr: 5.394e-05, eta: 14:34:50, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5296, decode.acc_seg: 80.4165, aux.loss_ce: 0.2448, aux.acc_seg: 77.8428, loss: 0.7744, grad_norm: 6.2001 2023-02-16 17:09:06,512 - mmseg - INFO - Iter [16200/160000] lr: 5.393e-05, eta: 14:34:30, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5502, decode.acc_seg: 79.7805, aux.loss_ce: 0.2471, aux.acc_seg: 77.2149, loss: 0.7973, grad_norm: 5.8129 2023-02-16 17:09:24,521 - mmseg - INFO - Iter [16250/160000] lr: 5.391e-05, eta: 14:34:09, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5435, decode.acc_seg: 80.0242, aux.loss_ce: 0.2494, aux.acc_seg: 77.1354, loss: 0.7929, grad_norm: 6.1220 2023-02-16 17:09:42,621 - mmseg - INFO - Iter [16300/160000] lr: 5.389e-05, eta: 14:33:50, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5573, decode.acc_seg: 79.2922, aux.loss_ce: 0.2495, aux.acc_seg: 77.0466, loss: 0.8068, grad_norm: 6.4921 2023-02-16 17:10:00,717 - mmseg - INFO - Iter [16350/160000] lr: 5.387e-05, eta: 14:33:30, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5370, decode.acc_seg: 79.4481, aux.loss_ce: 0.2417, aux.acc_seg: 77.4574, loss: 0.7787, grad_norm: 5.5245 2023-02-16 17:10:18,522 - mmseg - INFO - Iter [16400/160000] lr: 5.385e-05, eta: 14:33:08, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5327, decode.acc_seg: 80.1724, aux.loss_ce: 0.2417, aux.acc_seg: 77.6014, loss: 0.7744, grad_norm: 5.9464 2023-02-16 17:10:39,045 - mmseg - INFO - Iter [16450/160000] lr: 5.383e-05, eta: 14:33:10, time: 0.410, data_time: 0.051, memory: 16600, decode.loss_ce: 0.5175, decode.acc_seg: 81.0447, aux.loss_ce: 0.2425, aux.acc_seg: 78.0277, loss: 0.7600, grad_norm: 5.5450 2023-02-16 17:10:56,970 - mmseg - INFO - Iter [16500/160000] lr: 5.381e-05, eta: 14:32:49, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5210, decode.acc_seg: 80.7612, aux.loss_ce: 0.2392, aux.acc_seg: 78.1066, loss: 0.7602, grad_norm: 5.7277 2023-02-16 17:11:14,898 - mmseg - INFO - Iter [16550/160000] lr: 5.379e-05, eta: 14:32:28, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5201, decode.acc_seg: 80.5886, aux.loss_ce: 0.2390, aux.acc_seg: 77.8742, loss: 0.7590, grad_norm: 5.2271 2023-02-16 17:11:33,170 - mmseg - INFO - Iter [16600/160000] lr: 5.378e-05, eta: 14:32:10, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5054, decode.acc_seg: 80.9675, aux.loss_ce: 0.2307, aux.acc_seg: 78.7529, loss: 0.7361, grad_norm: 6.0348 2023-02-16 17:11:51,177 - mmseg - INFO - Iter [16650/160000] lr: 5.376e-05, eta: 14:31:49, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5266, decode.acc_seg: 80.0897, aux.loss_ce: 0.2398, aux.acc_seg: 77.7392, loss: 0.7664, grad_norm: 5.4062 2023-02-16 17:12:09,070 - mmseg - INFO - Iter [16700/160000] lr: 5.374e-05, eta: 14:31:28, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5268, decode.acc_seg: 80.3658, aux.loss_ce: 0.2441, aux.acc_seg: 77.7577, loss: 0.7710, grad_norm: 5.1294 2023-02-16 17:12:27,197 - mmseg - INFO - Iter [16750/160000] lr: 5.372e-05, eta: 14:31:09, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5240, decode.acc_seg: 80.0834, aux.loss_ce: 0.2424, aux.acc_seg: 77.1579, loss: 0.7664, grad_norm: 5.7757 2023-02-16 17:12:45,155 - mmseg - INFO - Iter [16800/160000] lr: 5.370e-05, eta: 14:30:48, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5259, decode.acc_seg: 80.9504, aux.loss_ce: 0.2379, aux.acc_seg: 78.2703, loss: 0.7639, grad_norm: 5.5696 2023-02-16 17:13:03,242 - mmseg - INFO - Iter [16850/160000] lr: 5.368e-05, eta: 14:30:29, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5060, decode.acc_seg: 81.2711, aux.loss_ce: 0.2334, aux.acc_seg: 79.0470, loss: 0.7395, grad_norm: 5.3124 2023-02-16 17:13:21,152 - mmseg - INFO - Iter [16900/160000] lr: 5.366e-05, eta: 14:30:07, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5260, decode.acc_seg: 80.9742, aux.loss_ce: 0.2466, aux.acc_seg: 77.9890, loss: 0.7726, grad_norm: 6.0441 2023-02-16 17:13:39,286 - mmseg - INFO - Iter [16950/160000] lr: 5.364e-05, eta: 14:29:48, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5428, decode.acc_seg: 79.9180, aux.loss_ce: 0.2493, aux.acc_seg: 77.5877, loss: 0.7921, grad_norm: 8.8674 2023-02-16 17:13:57,377 - mmseg - INFO - Saving checkpoint at 17000 iterations 2023-02-16 17:13:58,518 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 17:13:58,518 - mmseg - INFO - Iter [17000/160000] lr: 5.363e-05, eta: 14:29:38, time: 0.385, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5278, decode.acc_seg: 80.5766, aux.loss_ce: 0.2431, aux.acc_seg: 77.9699, loss: 0.7709, grad_norm: 5.5354 2023-02-16 17:14:16,393 - mmseg - INFO - Iter [17050/160000] lr: 5.361e-05, eta: 14:29:17, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5792, decode.acc_seg: 78.5732, aux.loss_ce: 0.2618, aux.acc_seg: 75.4872, loss: 0.8410, grad_norm: 5.8861 2023-02-16 17:14:34,295 - mmseg - INFO - Iter [17100/160000] lr: 5.359e-05, eta: 14:28:56, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5008, decode.acc_seg: 81.1111, aux.loss_ce: 0.2290, aux.acc_seg: 78.7494, loss: 0.7297, grad_norm: 5.4225 2023-02-16 17:14:52,315 - mmseg - INFO - Iter [17150/160000] lr: 5.357e-05, eta: 14:28:36, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5159, decode.acc_seg: 80.6576, aux.loss_ce: 0.2394, aux.acc_seg: 77.8372, loss: 0.7553, grad_norm: 5.8192 2023-02-16 17:15:10,277 - mmseg - INFO - Iter [17200/160000] lr: 5.355e-05, eta: 14:28:15, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5221, decode.acc_seg: 81.2262, aux.loss_ce: 0.2386, aux.acc_seg: 78.5190, loss: 0.7607, grad_norm: 5.7506 2023-02-16 17:15:28,202 - mmseg - INFO - Iter [17250/160000] lr: 5.353e-05, eta: 14:27:54, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5089, decode.acc_seg: 80.7734, aux.loss_ce: 0.2362, aux.acc_seg: 78.0501, loss: 0.7451, grad_norm: 5.3487 2023-02-16 17:15:46,045 - mmseg - INFO - Iter [17300/160000] lr: 5.351e-05, eta: 14:27:33, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4954, decode.acc_seg: 81.3376, aux.loss_ce: 0.2315, aux.acc_seg: 78.4970, loss: 0.7269, grad_norm: 5.1649 2023-02-16 17:16:03,847 - mmseg - INFO - Iter [17350/160000] lr: 5.349e-05, eta: 14:27:11, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4857, decode.acc_seg: 81.9564, aux.loss_ce: 0.2280, aux.acc_seg: 79.3133, loss: 0.7137, grad_norm: 5.5234 2023-02-16 17:16:21,703 - mmseg - INFO - Iter [17400/160000] lr: 5.348e-05, eta: 14:26:50, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5472, decode.acc_seg: 79.0868, aux.loss_ce: 0.2479, aux.acc_seg: 76.6824, loss: 0.7951, grad_norm: 5.2714 2023-02-16 17:16:39,570 - mmseg - INFO - Iter [17450/160000] lr: 5.346e-05, eta: 14:26:28, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5275, decode.acc_seg: 80.4652, aux.loss_ce: 0.2452, aux.acc_seg: 77.5757, loss: 0.7727, grad_norm: 5.5664 2023-02-16 17:16:57,450 - mmseg - INFO - Iter [17500/160000] lr: 5.344e-05, eta: 14:26:07, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5244, decode.acc_seg: 80.9889, aux.loss_ce: 0.2417, aux.acc_seg: 78.0202, loss: 0.7661, grad_norm: 5.4430 2023-02-16 17:17:15,180 - mmseg - INFO - Iter [17550/160000] lr: 5.342e-05, eta: 14:25:45, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5022, decode.acc_seg: 81.3321, aux.loss_ce: 0.2333, aux.acc_seg: 78.6160, loss: 0.7355, grad_norm: 5.5428 2023-02-16 17:17:33,040 - mmseg - INFO - Iter [17600/160000] lr: 5.340e-05, eta: 14:25:24, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5157, decode.acc_seg: 80.3945, aux.loss_ce: 0.2341, aux.acc_seg: 78.2154, loss: 0.7499, grad_norm: 4.8860 2023-02-16 17:17:51,081 - mmseg - INFO - Iter [17650/160000] lr: 5.338e-05, eta: 14:25:04, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5146, decode.acc_seg: 80.4186, aux.loss_ce: 0.2393, aux.acc_seg: 78.0374, loss: 0.7538, grad_norm: 5.7165 2023-02-16 17:18:11,093 - mmseg - INFO - Iter [17700/160000] lr: 5.336e-05, eta: 14:25:00, time: 0.400, data_time: 0.051, memory: 16600, decode.loss_ce: 0.5416, decode.acc_seg: 80.1796, aux.loss_ce: 0.2484, aux.acc_seg: 77.5092, loss: 0.7900, grad_norm: 6.5833 2023-02-16 17:18:28,974 - mmseg - INFO - Iter [17750/160000] lr: 5.334e-05, eta: 14:24:39, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.4978, decode.acc_seg: 80.9722, aux.loss_ce: 0.2322, aux.acc_seg: 78.8480, loss: 0.7300, grad_norm: 5.5525 2023-02-16 17:18:46,958 - mmseg - INFO - Iter [17800/160000] lr: 5.333e-05, eta: 14:24:19, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4874, decode.acc_seg: 81.3712, aux.loss_ce: 0.2310, aux.acc_seg: 78.5197, loss: 0.7184, grad_norm: 5.5075 2023-02-16 17:19:04,833 - mmseg - INFO - Iter [17850/160000] lr: 5.331e-05, eta: 14:23:58, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4749, decode.acc_seg: 82.2643, aux.loss_ce: 0.2199, aux.acc_seg: 79.8534, loss: 0.6948, grad_norm: 4.6815 2023-02-16 17:19:22,622 - mmseg - INFO - Iter [17900/160000] lr: 5.329e-05, eta: 14:23:36, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5129, decode.acc_seg: 81.4084, aux.loss_ce: 0.2398, aux.acc_seg: 78.2388, loss: 0.7527, grad_norm: 5.4100 2023-02-16 17:19:40,528 - mmseg - INFO - Iter [17950/160000] lr: 5.327e-05, eta: 14:23:15, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4870, decode.acc_seg: 81.8405, aux.loss_ce: 0.2300, aux.acc_seg: 78.7565, loss: 0.7169, grad_norm: 4.8441 2023-02-16 17:19:58,299 - mmseg - INFO - Saving checkpoint at 18000 iterations 2023-02-16 17:19:59,400 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 17:19:59,400 - mmseg - INFO - Iter [18000/160000] lr: 5.325e-05, eta: 14:23:02, time: 0.377, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5346, decode.acc_seg: 80.8466, aux.loss_ce: 0.2431, aux.acc_seg: 77.9863, loss: 0.7777, grad_norm: 5.4075 2023-02-16 17:20:17,297 - mmseg - INFO - Iter [18050/160000] lr: 5.323e-05, eta: 14:22:41, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4717, decode.acc_seg: 82.3607, aux.loss_ce: 0.2242, aux.acc_seg: 79.2682, loss: 0.6959, grad_norm: 4.9241 2023-02-16 17:20:35,591 - mmseg - INFO - Iter [18100/160000] lr: 5.321e-05, eta: 14:22:23, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4926, decode.acc_seg: 81.9288, aux.loss_ce: 0.2302, aux.acc_seg: 79.0479, loss: 0.7228, grad_norm: 5.2203 2023-02-16 17:20:53,354 - mmseg - INFO - Iter [18150/160000] lr: 5.319e-05, eta: 14:22:01, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4923, decode.acc_seg: 81.3355, aux.loss_ce: 0.2309, aux.acc_seg: 78.5994, loss: 0.7231, grad_norm: 4.9190 2023-02-16 17:21:11,222 - mmseg - INFO - Iter [18200/160000] lr: 5.318e-05, eta: 14:21:40, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4737, decode.acc_seg: 81.8565, aux.loss_ce: 0.2227, aux.acc_seg: 79.3143, loss: 0.6964, grad_norm: 5.5891 2023-02-16 17:21:29,296 - mmseg - INFO - Iter [18250/160000] lr: 5.316e-05, eta: 14:21:21, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4785, decode.acc_seg: 81.3525, aux.loss_ce: 0.2255, aux.acc_seg: 78.4967, loss: 0.7040, grad_norm: 5.0439 2023-02-16 17:21:47,228 - mmseg - INFO - Iter [18300/160000] lr: 5.314e-05, eta: 14:21:00, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4941, decode.acc_seg: 81.9002, aux.loss_ce: 0.2363, aux.acc_seg: 78.4863, loss: 0.7304, grad_norm: 5.2475 2023-02-16 17:22:05,360 - mmseg - INFO - Iter [18350/160000] lr: 5.312e-05, eta: 14:20:41, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5033, decode.acc_seg: 80.7921, aux.loss_ce: 0.2328, aux.acc_seg: 78.3662, loss: 0.7361, grad_norm: 5.6195 2023-02-16 17:22:23,580 - mmseg - INFO - Iter [18400/160000] lr: 5.310e-05, eta: 14:20:23, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5072, decode.acc_seg: 81.1088, aux.loss_ce: 0.2387, aux.acc_seg: 77.9288, loss: 0.7459, grad_norm: 5.6597 2023-02-16 17:22:41,829 - mmseg - INFO - Iter [18450/160000] lr: 5.308e-05, eta: 14:20:05, time: 0.366, data_time: 0.007, memory: 16600, decode.loss_ce: 0.5142, decode.acc_seg: 80.7977, aux.loss_ce: 0.2359, aux.acc_seg: 78.6017, loss: 0.7501, grad_norm: 5.4122 2023-02-16 17:22:59,893 - mmseg - INFO - Iter [18500/160000] lr: 5.306e-05, eta: 14:19:45, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5057, decode.acc_seg: 80.7576, aux.loss_ce: 0.2353, aux.acc_seg: 78.2897, loss: 0.7410, grad_norm: 4.9495 2023-02-16 17:23:17,881 - mmseg - INFO - Iter [18550/160000] lr: 5.304e-05, eta: 14:19:25, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4952, decode.acc_seg: 81.2711, aux.loss_ce: 0.2303, aux.acc_seg: 78.3119, loss: 0.7254, grad_norm: 5.4999 2023-02-16 17:23:35,887 - mmseg - INFO - Iter [18600/160000] lr: 5.303e-05, eta: 14:19:05, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5339, decode.acc_seg: 80.3956, aux.loss_ce: 0.2538, aux.acc_seg: 77.0141, loss: 0.7877, grad_norm: 5.9844 2023-02-16 17:23:53,868 - mmseg - INFO - Iter [18650/160000] lr: 5.301e-05, eta: 14:18:45, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5155, decode.acc_seg: 80.8435, aux.loss_ce: 0.2375, aux.acc_seg: 78.1524, loss: 0.7530, grad_norm: 5.9651 2023-02-16 17:24:11,773 - mmseg - INFO - Iter [18700/160000] lr: 5.299e-05, eta: 14:18:25, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5070, decode.acc_seg: 81.0602, aux.loss_ce: 0.2361, aux.acc_seg: 78.3093, loss: 0.7431, grad_norm: 5.3586 2023-02-16 17:24:29,984 - mmseg - INFO - Iter [18750/160000] lr: 5.297e-05, eta: 14:18:06, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5252, decode.acc_seg: 80.9279, aux.loss_ce: 0.2410, aux.acc_seg: 77.8591, loss: 0.7662, grad_norm: 5.8382 2023-02-16 17:24:47,921 - mmseg - INFO - Iter [18800/160000] lr: 5.295e-05, eta: 14:17:46, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5237, decode.acc_seg: 81.0893, aux.loss_ce: 0.2448, aux.acc_seg: 78.2990, loss: 0.7685, grad_norm: 7.1311 2023-02-16 17:25:05,746 - mmseg - INFO - Iter [18850/160000] lr: 5.293e-05, eta: 14:17:25, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5177, decode.acc_seg: 80.5713, aux.loss_ce: 0.2422, aux.acc_seg: 77.2669, loss: 0.7600, grad_norm: 5.9981 2023-02-16 17:25:23,924 - mmseg - INFO - Iter [18900/160000] lr: 5.291e-05, eta: 14:17:06, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5290, decode.acc_seg: 80.7591, aux.loss_ce: 0.2457, aux.acc_seg: 77.6834, loss: 0.7748, grad_norm: 5.4334 2023-02-16 17:25:44,211 - mmseg - INFO - Iter [18950/160000] lr: 5.289e-05, eta: 14:17:03, time: 0.406, data_time: 0.051, memory: 16600, decode.loss_ce: 0.4855, decode.acc_seg: 81.3828, aux.loss_ce: 0.2324, aux.acc_seg: 78.1369, loss: 0.7178, grad_norm: 5.0200 2023-02-16 17:26:02,050 - mmseg - INFO - Saving checkpoint at 19000 iterations 2023-02-16 17:26:03,121 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 17:26:03,121 - mmseg - INFO - Iter [19000/160000] lr: 5.288e-05, eta: 14:16:50, time: 0.378, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4841, decode.acc_seg: 81.6289, aux.loss_ce: 0.2298, aux.acc_seg: 78.6530, loss: 0.7139, grad_norm: 5.2321 2023-02-16 17:26:21,179 - mmseg - INFO - Iter [19050/160000] lr: 5.286e-05, eta: 14:16:31, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4823, decode.acc_seg: 82.1632, aux.loss_ce: 0.2284, aux.acc_seg: 79.0467, loss: 0.7107, grad_norm: 5.2054 2023-02-16 17:26:39,085 - mmseg - INFO - Iter [19100/160000] lr: 5.284e-05, eta: 14:16:10, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4775, decode.acc_seg: 82.5267, aux.loss_ce: 0.2263, aux.acc_seg: 79.2758, loss: 0.7038, grad_norm: 4.8384 2023-02-16 17:26:56,771 - mmseg - INFO - Iter [19150/160000] lr: 5.282e-05, eta: 14:15:48, time: 0.354, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5047, decode.acc_seg: 80.7851, aux.loss_ce: 0.2341, aux.acc_seg: 78.0822, loss: 0.7388, grad_norm: 5.7856 2023-02-16 17:27:14,563 - mmseg - INFO - Iter [19200/160000] lr: 5.280e-05, eta: 14:15:26, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5009, decode.acc_seg: 81.4400, aux.loss_ce: 0.2356, aux.acc_seg: 78.0105, loss: 0.7365, grad_norm: 5.4034 2023-02-16 17:27:32,395 - mmseg - INFO - Iter [19250/160000] lr: 5.278e-05, eta: 14:15:05, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5067, decode.acc_seg: 81.3147, aux.loss_ce: 0.2364, aux.acc_seg: 78.5135, loss: 0.7432, grad_norm: 5.3020 2023-02-16 17:27:50,429 - mmseg - INFO - Iter [19300/160000] lr: 5.276e-05, eta: 14:14:46, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4925, decode.acc_seg: 81.8853, aux.loss_ce: 0.2316, aux.acc_seg: 79.0412, loss: 0.7241, grad_norm: 5.8140 2023-02-16 17:28:08,151 - mmseg - INFO - Iter [19350/160000] lr: 5.274e-05, eta: 14:14:24, time: 0.354, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4798, decode.acc_seg: 82.2311, aux.loss_ce: 0.2298, aux.acc_seg: 78.7389, loss: 0.7096, grad_norm: 6.1356 2023-02-16 17:28:25,927 - mmseg - INFO - Iter [19400/160000] lr: 5.273e-05, eta: 14:14:02, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5153, decode.acc_seg: 81.2304, aux.loss_ce: 0.2448, aux.acc_seg: 77.6907, loss: 0.7601, grad_norm: 5.3970 2023-02-16 17:28:43,779 - mmseg - INFO - Iter [19450/160000] lr: 5.271e-05, eta: 14:13:41, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5013, decode.acc_seg: 81.5544, aux.loss_ce: 0.2398, aux.acc_seg: 78.3901, loss: 0.7411, grad_norm: 5.8570 2023-02-16 17:29:01,609 - mmseg - INFO - Iter [19500/160000] lr: 5.269e-05, eta: 14:13:20, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5079, decode.acc_seg: 80.9081, aux.loss_ce: 0.2384, aux.acc_seg: 77.9654, loss: 0.7462, grad_norm: 5.8244 2023-02-16 17:29:19,825 - mmseg - INFO - Iter [19550/160000] lr: 5.267e-05, eta: 14:13:02, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5002, decode.acc_seg: 81.7059, aux.loss_ce: 0.2346, aux.acc_seg: 78.7813, loss: 0.7349, grad_norm: 5.2766 2023-02-16 17:29:38,173 - mmseg - INFO - Iter [19600/160000] lr: 5.265e-05, eta: 14:12:45, time: 0.368, data_time: 0.007, memory: 16600, decode.loss_ce: 0.4818, decode.acc_seg: 82.1011, aux.loss_ce: 0.2278, aux.acc_seg: 78.9663, loss: 0.7097, grad_norm: 5.5935 2023-02-16 17:29:55,937 - mmseg - INFO - Iter [19650/160000] lr: 5.263e-05, eta: 14:12:23, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5064, decode.acc_seg: 81.2604, aux.loss_ce: 0.2345, aux.acc_seg: 78.1694, loss: 0.7408, grad_norm: 5.2953 2023-02-16 17:30:14,255 - mmseg - INFO - Iter [19700/160000] lr: 5.261e-05, eta: 14:12:06, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4912, decode.acc_seg: 81.8430, aux.loss_ce: 0.2333, aux.acc_seg: 78.4350, loss: 0.7245, grad_norm: 4.8811 2023-02-16 17:30:32,524 - mmseg - INFO - Iter [19750/160000] lr: 5.259e-05, eta: 14:11:48, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4743, decode.acc_seg: 81.7812, aux.loss_ce: 0.2259, aux.acc_seg: 78.4938, loss: 0.7002, grad_norm: 5.7633 2023-02-16 17:30:50,399 - mmseg - INFO - Iter [19800/160000] lr: 5.258e-05, eta: 14:11:27, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4927, decode.acc_seg: 81.4989, aux.loss_ce: 0.2326, aux.acc_seg: 78.4341, loss: 0.7253, grad_norm: 4.9337 2023-02-16 17:31:08,386 - mmseg - INFO - Iter [19850/160000] lr: 5.256e-05, eta: 14:11:07, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5146, decode.acc_seg: 81.3372, aux.loss_ce: 0.2392, aux.acc_seg: 78.5271, loss: 0.7538, grad_norm: 5.2527 2023-02-16 17:31:26,502 - mmseg - INFO - Iter [19900/160000] lr: 5.254e-05, eta: 14:10:48, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4726, decode.acc_seg: 82.3330, aux.loss_ce: 0.2242, aux.acc_seg: 79.0599, loss: 0.6969, grad_norm: 4.9180 2023-02-16 17:31:44,161 - mmseg - INFO - Iter [19950/160000] lr: 5.252e-05, eta: 14:10:26, time: 0.353, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4746, decode.acc_seg: 82.4509, aux.loss_ce: 0.2276, aux.acc_seg: 78.9758, loss: 0.7022, grad_norm: 5.8175 2023-02-16 17:32:02,174 - mmseg - INFO - Saving checkpoint at 20000 iterations 2023-02-16 17:32:03,242 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 17:32:03,243 - mmseg - INFO - Iter [20000/160000] lr: 5.250e-05, eta: 14:10:14, time: 0.382, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4715, decode.acc_seg: 82.1669, aux.loss_ce: 0.2190, aux.acc_seg: 79.2433, loss: 0.6905, grad_norm: 5.3172 2023-02-16 17:32:21,266 - mmseg - INFO - Iter [20050/160000] lr: 5.248e-05, eta: 14:09:54, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4905, decode.acc_seg: 81.6595, aux.loss_ce: 0.2361, aux.acc_seg: 77.9735, loss: 0.7266, grad_norm: 5.3089 2023-02-16 17:32:38,997 - mmseg - INFO - Iter [20100/160000] lr: 5.246e-05, eta: 14:09:33, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4869, decode.acc_seg: 81.4054, aux.loss_ce: 0.2317, aux.acc_seg: 78.5344, loss: 0.7186, grad_norm: 4.9504 2023-02-16 17:32:57,032 - mmseg - INFO - Iter [20150/160000] lr: 5.244e-05, eta: 14:09:13, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4591, decode.acc_seg: 82.3118, aux.loss_ce: 0.2207, aux.acc_seg: 79.2611, loss: 0.6798, grad_norm: 4.4172 2023-02-16 17:33:14,925 - mmseg - INFO - Iter [20200/160000] lr: 5.243e-05, eta: 14:08:53, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4866, decode.acc_seg: 82.0767, aux.loss_ce: 0.2320, aux.acc_seg: 78.9147, loss: 0.7185, grad_norm: 5.2490 2023-02-16 17:33:35,254 - mmseg - INFO - Iter [20250/160000] lr: 5.241e-05, eta: 14:08:49, time: 0.407, data_time: 0.049, memory: 16600, decode.loss_ce: 0.4477, decode.acc_seg: 83.2217, aux.loss_ce: 0.2182, aux.acc_seg: 79.3881, loss: 0.6659, grad_norm: 5.1234 2023-02-16 17:33:53,172 - mmseg - INFO - Iter [20300/160000] lr: 5.239e-05, eta: 14:08:29, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4649, decode.acc_seg: 82.3611, aux.loss_ce: 0.2233, aux.acc_seg: 79.3155, loss: 0.6882, grad_norm: 5.1379 2023-02-16 17:34:11,160 - mmseg - INFO - Iter [20350/160000] lr: 5.237e-05, eta: 14:08:09, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.4649, decode.acc_seg: 82.7752, aux.loss_ce: 0.2223, aux.acc_seg: 79.7228, loss: 0.6872, grad_norm: 5.0357 2023-02-16 17:34:28,941 - mmseg - INFO - Iter [20400/160000] lr: 5.235e-05, eta: 14:07:48, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.5025, decode.acc_seg: 82.4758, aux.loss_ce: 0.2417, aux.acc_seg: 79.0985, loss: 0.7441, grad_norm: 5.8348 2023-02-16 17:34:46,706 - mmseg - INFO - Iter [20450/160000] lr: 5.233e-05, eta: 14:07:27, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4775, decode.acc_seg: 82.1476, aux.loss_ce: 0.2293, aux.acc_seg: 78.7633, loss: 0.7068, grad_norm: 5.9233 2023-02-16 17:35:04,616 - mmseg - INFO - Iter [20500/160000] lr: 5.231e-05, eta: 14:07:06, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4658, decode.acc_seg: 82.2703, aux.loss_ce: 0.2272, aux.acc_seg: 78.6360, loss: 0.6931, grad_norm: 5.0316 2023-02-16 17:35:22,394 - mmseg - INFO - Iter [20550/160000] lr: 5.229e-05, eta: 14:06:45, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4609, decode.acc_seg: 82.6373, aux.loss_ce: 0.2161, aux.acc_seg: 79.9007, loss: 0.6770, grad_norm: 5.1602 2023-02-16 17:35:40,415 - mmseg - INFO - Iter [20600/160000] lr: 5.228e-05, eta: 14:06:26, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4823, decode.acc_seg: 82.2836, aux.loss_ce: 0.2352, aux.acc_seg: 78.1650, loss: 0.7175, grad_norm: 5.1176 2023-02-16 17:35:58,374 - mmseg - INFO - Iter [20650/160000] lr: 5.226e-05, eta: 14:06:06, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4823, decode.acc_seg: 82.5371, aux.loss_ce: 0.2373, aux.acc_seg: 78.5367, loss: 0.7196, grad_norm: 4.9423 2023-02-16 17:36:16,599 - mmseg - INFO - Iter [20700/160000] lr: 5.224e-05, eta: 14:05:47, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4805, decode.acc_seg: 82.1256, aux.loss_ce: 0.2303, aux.acc_seg: 78.9672, loss: 0.7109, grad_norm: 5.1198 2023-02-16 17:36:34,554 - mmseg - INFO - Iter [20750/160000] lr: 5.222e-05, eta: 14:05:27, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4855, decode.acc_seg: 82.4876, aux.loss_ce: 0.2363, aux.acc_seg: 78.8840, loss: 0.7218, grad_norm: 5.2938 2023-02-16 17:36:52,415 - mmseg - INFO - Iter [20800/160000] lr: 5.220e-05, eta: 14:05:07, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4617, decode.acc_seg: 82.5148, aux.loss_ce: 0.2207, aux.acc_seg: 79.3962, loss: 0.6823, grad_norm: 5.1859 2023-02-16 17:37:10,297 - mmseg - INFO - Iter [20850/160000] lr: 5.218e-05, eta: 14:04:46, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4710, decode.acc_seg: 82.1183, aux.loss_ce: 0.2185, aux.acc_seg: 79.5376, loss: 0.6895, grad_norm: 5.4209 2023-02-16 17:37:28,222 - mmseg - INFO - Iter [20900/160000] lr: 5.216e-05, eta: 14:04:26, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4819, decode.acc_seg: 81.8917, aux.loss_ce: 0.2332, aux.acc_seg: 78.6192, loss: 0.7152, grad_norm: 5.8785 2023-02-16 17:37:46,138 - mmseg - INFO - Iter [20950/160000] lr: 5.214e-05, eta: 14:04:06, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4629, decode.acc_seg: 82.4145, aux.loss_ce: 0.2201, aux.acc_seg: 79.1733, loss: 0.6830, grad_norm: 5.0374 2023-02-16 17:38:04,000 - mmseg - INFO - Saving checkpoint at 21000 iterations 2023-02-16 17:38:05,076 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 17:38:05,076 - mmseg - INFO - Iter [21000/160000] lr: 5.213e-05, eta: 14:03:53, time: 0.379, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4662, decode.acc_seg: 82.5611, aux.loss_ce: 0.2265, aux.acc_seg: 78.8708, loss: 0.6927, grad_norm: 5.7309 2023-02-16 17:38:23,061 - mmseg - INFO - Iter [21050/160000] lr: 5.211e-05, eta: 14:03:33, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4937, decode.acc_seg: 81.9583, aux.loss_ce: 0.2348, aux.acc_seg: 78.6996, loss: 0.7286, grad_norm: 5.4254 2023-02-16 17:38:41,080 - mmseg - INFO - Iter [21100/160000] lr: 5.209e-05, eta: 14:03:13, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4836, decode.acc_seg: 82.1865, aux.loss_ce: 0.2335, aux.acc_seg: 79.1291, loss: 0.7171, grad_norm: 5.9169 2023-02-16 17:38:58,858 - mmseg - INFO - Iter [21150/160000] lr: 5.207e-05, eta: 14:02:52, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4475, decode.acc_seg: 83.0809, aux.loss_ce: 0.2145, aux.acc_seg: 79.8997, loss: 0.6619, grad_norm: 5.6808 2023-02-16 17:39:16,712 - mmseg - INFO - Iter [21200/160000] lr: 5.205e-05, eta: 14:02:32, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4661, decode.acc_seg: 82.9320, aux.loss_ce: 0.2217, aux.acc_seg: 79.7448, loss: 0.6878, grad_norm: 5.2277 2023-02-16 17:39:34,493 - mmseg - INFO - Iter [21250/160000] lr: 5.203e-05, eta: 14:02:11, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4548, decode.acc_seg: 83.0819, aux.loss_ce: 0.2212, aux.acc_seg: 79.5567, loss: 0.6761, grad_norm: 5.1060 2023-02-16 17:39:52,192 - mmseg - INFO - Iter [21300/160000] lr: 5.201e-05, eta: 14:01:49, time: 0.354, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4755, decode.acc_seg: 82.5144, aux.loss_ce: 0.2286, aux.acc_seg: 79.2857, loss: 0.7041, grad_norm: 6.1616 2023-02-16 17:40:10,092 - mmseg - INFO - Iter [21350/160000] lr: 5.199e-05, eta: 14:01:29, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4678, decode.acc_seg: 82.5264, aux.loss_ce: 0.2215, aux.acc_seg: 79.0917, loss: 0.6893, grad_norm: 5.2596 2023-02-16 17:40:27,811 - mmseg - INFO - Iter [21400/160000] lr: 5.198e-05, eta: 14:01:08, time: 0.354, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4924, decode.acc_seg: 81.8334, aux.loss_ce: 0.2354, aux.acc_seg: 78.3491, loss: 0.7278, grad_norm: 5.2979 2023-02-16 17:40:45,764 - mmseg - INFO - Iter [21450/160000] lr: 5.196e-05, eta: 14:00:48, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4572, decode.acc_seg: 82.6374, aux.loss_ce: 0.2206, aux.acc_seg: 79.4009, loss: 0.6778, grad_norm: 5.4410 2023-02-16 17:41:06,091 - mmseg - INFO - Iter [21500/160000] lr: 5.194e-05, eta: 14:00:43, time: 0.407, data_time: 0.052, memory: 16600, decode.loss_ce: 0.4500, decode.acc_seg: 83.4779, aux.loss_ce: 0.2198, aux.acc_seg: 79.6705, loss: 0.6698, grad_norm: 4.7014 2023-02-16 17:41:24,394 - mmseg - INFO - Iter [21550/160000] lr: 5.192e-05, eta: 14:00:26, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4781, decode.acc_seg: 82.4575, aux.loss_ce: 0.2329, aux.acc_seg: 78.5888, loss: 0.7110, grad_norm: 4.9456 2023-02-16 17:41:42,220 - mmseg - INFO - Iter [21600/160000] lr: 5.190e-05, eta: 14:00:05, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4502, decode.acc_seg: 83.4318, aux.loss_ce: 0.2158, aux.acc_seg: 79.9985, loss: 0.6660, grad_norm: 4.6429 2023-02-16 17:42:00,001 - mmseg - INFO - Iter [21650/160000] lr: 5.188e-05, eta: 13:59:44, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4534, decode.acc_seg: 83.1424, aux.loss_ce: 0.2224, aux.acc_seg: 79.6368, loss: 0.6758, grad_norm: 4.6973 2023-02-16 17:42:17,984 - mmseg - INFO - Iter [21700/160000] lr: 5.186e-05, eta: 13:59:24, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4473, decode.acc_seg: 83.0644, aux.loss_ce: 0.2107, aux.acc_seg: 79.9698, loss: 0.6580, grad_norm: 5.4199 2023-02-16 17:42:35,997 - mmseg - INFO - Iter [21750/160000] lr: 5.184e-05, eta: 13:59:05, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4547, decode.acc_seg: 83.2367, aux.loss_ce: 0.2244, aux.acc_seg: 79.6158, loss: 0.6791, grad_norm: 5.3955 2023-02-16 17:42:54,171 - mmseg - INFO - Iter [21800/160000] lr: 5.183e-05, eta: 13:58:47, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.4549, decode.acc_seg: 83.1029, aux.loss_ce: 0.2220, aux.acc_seg: 79.4122, loss: 0.6769, grad_norm: 4.7282 2023-02-16 17:43:12,500 - mmseg - INFO - Iter [21850/160000] lr: 5.181e-05, eta: 13:58:29, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4658, decode.acc_seg: 82.8267, aux.loss_ce: 0.2232, aux.acc_seg: 79.2629, loss: 0.6890, grad_norm: 5.4171 2023-02-16 17:43:30,361 - mmseg - INFO - Iter [21900/160000] lr: 5.179e-05, eta: 13:58:09, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.4814, decode.acc_seg: 82.3577, aux.loss_ce: 0.2319, aux.acc_seg: 79.0854, loss: 0.7133, grad_norm: 5.3018 2023-02-16 17:43:48,201 - mmseg - INFO - Iter [21950/160000] lr: 5.177e-05, eta: 13:57:48, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4719, decode.acc_seg: 82.3021, aux.loss_ce: 0.2212, aux.acc_seg: 79.3128, loss: 0.6931, grad_norm: 5.0247 2023-02-16 17:44:06,215 - mmseg - INFO - Saving checkpoint at 22000 iterations 2023-02-16 17:44:07,291 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 17:44:07,291 - mmseg - INFO - Iter [22000/160000] lr: 5.175e-05, eta: 13:57:36, time: 0.382, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4381, decode.acc_seg: 83.1720, aux.loss_ce: 0.2111, aux.acc_seg: 80.0976, loss: 0.6492, grad_norm: 4.9862 2023-02-16 17:44:25,065 - mmseg - INFO - Iter [22050/160000] lr: 5.173e-05, eta: 13:57:15, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4431, decode.acc_seg: 83.1660, aux.loss_ce: 0.2134, aux.acc_seg: 80.1608, loss: 0.6564, grad_norm: 4.5754 2023-02-16 17:44:43,024 - mmseg - INFO - Iter [22100/160000] lr: 5.171e-05, eta: 13:56:55, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4599, decode.acc_seg: 82.9664, aux.loss_ce: 0.2255, aux.acc_seg: 79.2225, loss: 0.6853, grad_norm: 4.8245 2023-02-16 17:45:01,115 - mmseg - INFO - Iter [22150/160000] lr: 5.169e-05, eta: 13:56:36, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4631, decode.acc_seg: 82.2872, aux.loss_ce: 0.2199, aux.acc_seg: 79.3092, loss: 0.6830, grad_norm: 5.0890 2023-02-16 17:45:19,034 - mmseg - INFO - Iter [22200/160000] lr: 5.168e-05, eta: 13:56:16, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4527, decode.acc_seg: 83.1489, aux.loss_ce: 0.2186, aux.acc_seg: 79.6852, loss: 0.6713, grad_norm: 4.9723 2023-02-16 17:45:36,885 - mmseg - INFO - Iter [22250/160000] lr: 5.166e-05, eta: 13:55:56, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4785, decode.acc_seg: 81.6163, aux.loss_ce: 0.2324, aux.acc_seg: 78.0960, loss: 0.7110, grad_norm: 5.4459 2023-02-16 17:45:54,927 - mmseg - INFO - Iter [22300/160000] lr: 5.164e-05, eta: 13:55:36, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4755, decode.acc_seg: 82.5513, aux.loss_ce: 0.2277, aux.acc_seg: 79.4611, loss: 0.7033, grad_norm: 5.3947 2023-02-16 17:46:12,729 - mmseg - INFO - Iter [22350/160000] lr: 5.162e-05, eta: 13:55:16, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4401, decode.acc_seg: 83.0950, aux.loss_ce: 0.2125, aux.acc_seg: 79.8111, loss: 0.6526, grad_norm: 4.6559 2023-02-16 17:46:30,635 - mmseg - INFO - Iter [22400/160000] lr: 5.160e-05, eta: 13:54:56, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4792, decode.acc_seg: 82.3318, aux.loss_ce: 0.2305, aux.acc_seg: 79.0106, loss: 0.7097, grad_norm: 5.5486 2023-02-16 17:46:48,879 - mmseg - INFO - Iter [22450/160000] lr: 5.158e-05, eta: 13:54:38, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4736, decode.acc_seg: 82.1146, aux.loss_ce: 0.2267, aux.acc_seg: 78.4688, loss: 0.7003, grad_norm: 5.7517 2023-02-16 17:47:06,655 - mmseg - INFO - Iter [22500/160000] lr: 5.156e-05, eta: 13:54:17, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4198, decode.acc_seg: 84.2062, aux.loss_ce: 0.2119, aux.acc_seg: 80.2421, loss: 0.6317, grad_norm: 4.6762 2023-02-16 17:47:24,479 - mmseg - INFO - Iter [22550/160000] lr: 5.154e-05, eta: 13:53:56, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4457, decode.acc_seg: 83.3720, aux.loss_ce: 0.2193, aux.acc_seg: 79.8482, loss: 0.6651, grad_norm: 5.1936 2023-02-16 17:47:42,313 - mmseg - INFO - Iter [22600/160000] lr: 5.153e-05, eta: 13:53:36, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4840, decode.acc_seg: 81.7995, aux.loss_ce: 0.2371, aux.acc_seg: 77.9184, loss: 0.7211, grad_norm: 6.0791 2023-02-16 17:48:00,286 - mmseg - INFO - Iter [22650/160000] lr: 5.151e-05, eta: 13:53:16, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4491, decode.acc_seg: 83.2462, aux.loss_ce: 0.2205, aux.acc_seg: 79.4298, loss: 0.6696, grad_norm: 4.9678 2023-02-16 17:48:18,449 - mmseg - INFO - Iter [22700/160000] lr: 5.149e-05, eta: 13:52:58, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4906, decode.acc_seg: 82.0298, aux.loss_ce: 0.2353, aux.acc_seg: 78.4896, loss: 0.7259, grad_norm: 5.3101 2023-02-16 17:48:39,243 - mmseg - INFO - Iter [22750/160000] lr: 5.147e-05, eta: 13:52:55, time: 0.416, data_time: 0.053, memory: 16600, decode.loss_ce: 0.4474, decode.acc_seg: 83.3700, aux.loss_ce: 0.2203, aux.acc_seg: 79.7187, loss: 0.6678, grad_norm: 4.8226 2023-02-16 17:48:57,202 - mmseg - INFO - Iter [22800/160000] lr: 5.145e-05, eta: 13:52:35, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4545, decode.acc_seg: 83.2089, aux.loss_ce: 0.2195, aux.acc_seg: 80.0668, loss: 0.6740, grad_norm: 5.0385 2023-02-16 17:49:15,080 - mmseg - INFO - Iter [22850/160000] lr: 5.143e-05, eta: 13:52:15, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.4678, decode.acc_seg: 82.7465, aux.loss_ce: 0.2314, aux.acc_seg: 78.9357, loss: 0.6991, grad_norm: 5.4323 2023-02-16 17:49:33,260 - mmseg - INFO - Iter [22900/160000] lr: 5.141e-05, eta: 13:51:57, time: 0.364, data_time: 0.007, memory: 16600, decode.loss_ce: 0.4370, decode.acc_seg: 83.6311, aux.loss_ce: 0.2137, aux.acc_seg: 79.9873, loss: 0.6508, grad_norm: 5.1937 2023-02-16 17:49:51,123 - mmseg - INFO - Iter [22950/160000] lr: 5.139e-05, eta: 13:51:37, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4612, decode.acc_seg: 82.7388, aux.loss_ce: 0.2251, aux.acc_seg: 79.4705, loss: 0.6862, grad_norm: 5.4433 2023-02-16 17:50:09,054 - mmseg - INFO - Saving checkpoint at 23000 iterations 2023-02-16 17:50:10,224 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 17:50:10,224 - mmseg - INFO - Iter [23000/160000] lr: 5.138e-05, eta: 13:51:24, time: 0.382, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4462, decode.acc_seg: 83.2331, aux.loss_ce: 0.2180, aux.acc_seg: 79.7932, loss: 0.6643, grad_norm: 5.1972 2023-02-16 17:50:28,206 - mmseg - INFO - Iter [23050/160000] lr: 5.136e-05, eta: 13:51:04, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4308, decode.acc_seg: 83.4412, aux.loss_ce: 0.2129, aux.acc_seg: 79.6754, loss: 0.6438, grad_norm: 5.4086 2023-02-16 17:50:46,238 - mmseg - INFO - Iter [23100/160000] lr: 5.134e-05, eta: 13:50:45, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4268, decode.acc_seg: 83.9142, aux.loss_ce: 0.2101, aux.acc_seg: 80.2555, loss: 0.6370, grad_norm: 4.8103 2023-02-16 17:51:04,638 - mmseg - INFO - Iter [23150/160000] lr: 5.132e-05, eta: 13:50:28, time: 0.368, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4450, decode.acc_seg: 83.6360, aux.loss_ce: 0.2204, aux.acc_seg: 79.8235, loss: 0.6654, grad_norm: 5.5999 2023-02-16 17:51:22,570 - mmseg - INFO - Iter [23200/160000] lr: 5.130e-05, eta: 13:50:08, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4343, decode.acc_seg: 84.0037, aux.loss_ce: 0.2148, aux.acc_seg: 79.8292, loss: 0.6491, grad_norm: 5.7502 2023-02-16 17:51:40,521 - mmseg - INFO - Iter [23250/160000] lr: 5.128e-05, eta: 13:49:49, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.4274, decode.acc_seg: 83.9807, aux.loss_ce: 0.2169, aux.acc_seg: 79.7402, loss: 0.6443, grad_norm: 5.1726 2023-02-16 17:51:58,442 - mmseg - INFO - Iter [23300/160000] lr: 5.126e-05, eta: 13:49:29, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4417, decode.acc_seg: 83.1203, aux.loss_ce: 0.2190, aux.acc_seg: 79.3966, loss: 0.6607, grad_norm: 5.5364 2023-02-16 17:52:16,423 - mmseg - INFO - Iter [23350/160000] lr: 5.124e-05, eta: 13:49:09, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4266, decode.acc_seg: 83.9802, aux.loss_ce: 0.2085, aux.acc_seg: 80.3571, loss: 0.6352, grad_norm: 5.0562 2023-02-16 17:52:34,172 - mmseg - INFO - Iter [23400/160000] lr: 5.123e-05, eta: 13:48:48, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4637, decode.acc_seg: 82.4096, aux.loss_ce: 0.2304, aux.acc_seg: 78.5279, loss: 0.6941, grad_norm: 5.1128 2023-02-16 17:52:52,056 - mmseg - INFO - Iter [23450/160000] lr: 5.121e-05, eta: 13:48:28, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4481, decode.acc_seg: 83.8737, aux.loss_ce: 0.2220, aux.acc_seg: 80.1038, loss: 0.6701, grad_norm: 5.9046 2023-02-16 17:53:09,869 - mmseg - INFO - Iter [23500/160000] lr: 5.119e-05, eta: 13:48:08, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4357, decode.acc_seg: 83.5617, aux.loss_ce: 0.2153, aux.acc_seg: 79.7179, loss: 0.6510, grad_norm: 4.8229 2023-02-16 17:53:28,194 - mmseg - INFO - Iter [23550/160000] lr: 5.117e-05, eta: 13:47:50, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4841, decode.acc_seg: 81.7986, aux.loss_ce: 0.2299, aux.acc_seg: 78.6117, loss: 0.7140, grad_norm: 5.6909 2023-02-16 17:53:46,042 - mmseg - INFO - Iter [23600/160000] lr: 5.115e-05, eta: 13:47:30, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4619, decode.acc_seg: 83.3837, aux.loss_ce: 0.2210, aux.acc_seg: 79.9869, loss: 0.6829, grad_norm: 5.8231 2023-02-16 17:54:03,899 - mmseg - INFO - Iter [23650/160000] lr: 5.113e-05, eta: 13:47:10, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4651, decode.acc_seg: 82.7268, aux.loss_ce: 0.2277, aux.acc_seg: 79.4983, loss: 0.6928, grad_norm: 5.3737 2023-02-16 17:54:21,943 - mmseg - INFO - Iter [23700/160000] lr: 5.111e-05, eta: 13:46:51, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4422, decode.acc_seg: 83.5971, aux.loss_ce: 0.2174, aux.acc_seg: 79.9488, loss: 0.6596, grad_norm: 5.0460 2023-02-16 17:54:40,042 - mmseg - INFO - Iter [23750/160000] lr: 5.109e-05, eta: 13:46:32, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4249, decode.acc_seg: 84.0754, aux.loss_ce: 0.2112, aux.acc_seg: 80.0795, loss: 0.6361, grad_norm: 5.0296 2023-02-16 17:54:58,103 - mmseg - INFO - Iter [23800/160000] lr: 5.108e-05, eta: 13:46:13, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4350, decode.acc_seg: 83.9453, aux.loss_ce: 0.2115, aux.acc_seg: 80.8265, loss: 0.6464, grad_norm: 4.6452 2023-02-16 17:55:15,935 - mmseg - INFO - Iter [23850/160000] lr: 5.106e-05, eta: 13:45:53, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4415, decode.acc_seg: 83.1627, aux.loss_ce: 0.2134, aux.acc_seg: 79.7011, loss: 0.6548, grad_norm: 5.0388 2023-02-16 17:55:33,746 - mmseg - INFO - Iter [23900/160000] lr: 5.104e-05, eta: 13:45:32, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4705, decode.acc_seg: 82.6919, aux.loss_ce: 0.2293, aux.acc_seg: 79.0992, loss: 0.6998, grad_norm: 4.7794 2023-02-16 17:55:51,937 - mmseg - INFO - Iter [23950/160000] lr: 5.102e-05, eta: 13:45:14, time: 0.364, data_time: 0.007, memory: 16600, decode.loss_ce: 0.4597, decode.acc_seg: 83.0929, aux.loss_ce: 0.2215, aux.acc_seg: 79.9238, loss: 0.6812, grad_norm: 4.8352 2023-02-16 17:56:12,032 - mmseg - INFO - Saving checkpoint at 24000 iterations 2023-02-16 17:56:13,120 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 17:56:13,120 - mmseg - INFO - Iter [24000/160000] lr: 5.100e-05, eta: 13:45:13, time: 0.424, data_time: 0.050, memory: 16600, decode.loss_ce: 0.4502, decode.acc_seg: 83.2200, aux.loss_ce: 0.2209, aux.acc_seg: 79.6598, loss: 0.6711, grad_norm: 4.9593 2023-02-16 17:56:30,935 - mmseg - INFO - Iter [24050/160000] lr: 5.098e-05, eta: 13:44:52, time: 0.356, data_time: 0.005, memory: 16600, decode.loss_ce: 0.4200, decode.acc_seg: 84.4624, aux.loss_ce: 0.2081, aux.acc_seg: 80.2911, loss: 0.6281, grad_norm: 4.8358 2023-02-16 17:56:48,866 - mmseg - INFO - Iter [24100/160000] lr: 5.096e-05, eta: 13:44:33, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4418, decode.acc_seg: 83.6127, aux.loss_ce: 0.2159, aux.acc_seg: 80.0539, loss: 0.6577, grad_norm: 4.6672 2023-02-16 17:57:06,870 - mmseg - INFO - Iter [24150/160000] lr: 5.094e-05, eta: 13:44:13, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4153, decode.acc_seg: 84.5607, aux.loss_ce: 0.2072, aux.acc_seg: 80.8499, loss: 0.6225, grad_norm: 5.3774 2023-02-16 17:57:24,657 - mmseg - INFO - Iter [24200/160000] lr: 5.093e-05, eta: 13:43:53, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4205, decode.acc_seg: 84.0708, aux.loss_ce: 0.2069, aux.acc_seg: 80.6191, loss: 0.6274, grad_norm: 4.4537 2023-02-16 17:57:42,532 - mmseg - INFO - Iter [24250/160000] lr: 5.091e-05, eta: 13:43:33, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4629, decode.acc_seg: 82.7975, aux.loss_ce: 0.2251, aux.acc_seg: 79.2711, loss: 0.6879, grad_norm: 4.7983 2023-02-16 17:58:00,264 - mmseg - INFO - Iter [24300/160000] lr: 5.089e-05, eta: 13:43:12, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4231, decode.acc_seg: 84.0006, aux.loss_ce: 0.2104, aux.acc_seg: 80.2293, loss: 0.6335, grad_norm: 4.8596 2023-02-16 17:58:18,269 - mmseg - INFO - Iter [24350/160000] lr: 5.087e-05, eta: 13:42:53, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4044, decode.acc_seg: 84.7511, aux.loss_ce: 0.1988, aux.acc_seg: 81.3493, loss: 0.6032, grad_norm: 4.4822 2023-02-16 17:58:36,107 - mmseg - INFO - Iter [24400/160000] lr: 5.085e-05, eta: 13:42:33, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4303, decode.acc_seg: 83.7368, aux.loss_ce: 0.2163, aux.acc_seg: 79.8466, loss: 0.6466, grad_norm: 5.5854 2023-02-16 17:58:53,969 - mmseg - INFO - Iter [24450/160000] lr: 5.083e-05, eta: 13:42:12, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4387, decode.acc_seg: 83.8080, aux.loss_ce: 0.2176, aux.acc_seg: 80.0025, loss: 0.6563, grad_norm: 5.1087 2023-02-16 17:59:11,937 - mmseg - INFO - Iter [24500/160000] lr: 5.081e-05, eta: 13:41:53, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4506, decode.acc_seg: 83.1693, aux.loss_ce: 0.2233, aux.acc_seg: 78.9744, loss: 0.6739, grad_norm: 5.1591 2023-02-16 17:59:29,909 - mmseg - INFO - Iter [24550/160000] lr: 5.079e-05, eta: 13:41:34, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4554, decode.acc_seg: 83.0381, aux.loss_ce: 0.2248, aux.acc_seg: 79.4629, loss: 0.6802, grad_norm: 5.3806 2023-02-16 17:59:47,634 - mmseg - INFO - Iter [24600/160000] lr: 5.078e-05, eta: 13:41:13, time: 0.354, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4469, decode.acc_seg: 83.8739, aux.loss_ce: 0.2238, aux.acc_seg: 80.0864, loss: 0.6707, grad_norm: 5.1000 2023-02-16 18:00:06,062 - mmseg - INFO - Iter [24650/160000] lr: 5.076e-05, eta: 13:40:56, time: 0.368, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4252, decode.acc_seg: 83.8583, aux.loss_ce: 0.2112, aux.acc_seg: 80.3059, loss: 0.6365, grad_norm: 4.7232 2023-02-16 18:00:23,837 - mmseg - INFO - Iter [24700/160000] lr: 5.074e-05, eta: 13:40:35, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4209, decode.acc_seg: 83.6180, aux.loss_ce: 0.2120, aux.acc_seg: 80.0514, loss: 0.6329, grad_norm: 4.7340 2023-02-16 18:00:41,802 - mmseg - INFO - Iter [24750/160000] lr: 5.072e-05, eta: 13:40:16, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4475, decode.acc_seg: 83.5068, aux.loss_ce: 0.2196, aux.acc_seg: 79.9363, loss: 0.6670, grad_norm: 4.9286 2023-02-16 18:00:59,627 - mmseg - INFO - Iter [24800/160000] lr: 5.070e-05, eta: 13:39:56, time: 0.357, data_time: 0.007, memory: 16600, decode.loss_ce: 0.4729, decode.acc_seg: 82.4580, aux.loss_ce: 0.2311, aux.acc_seg: 78.7438, loss: 0.7039, grad_norm: 5.6969 2023-02-16 18:01:17,396 - mmseg - INFO - Iter [24850/160000] lr: 5.068e-05, eta: 13:39:35, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4498, decode.acc_seg: 83.3672, aux.loss_ce: 0.2216, aux.acc_seg: 79.5401, loss: 0.6715, grad_norm: 5.5315 2023-02-16 18:01:35,159 - mmseg - INFO - Iter [24900/160000] lr: 5.066e-05, eta: 13:39:15, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4386, decode.acc_seg: 83.4313, aux.loss_ce: 0.2122, aux.acc_seg: 79.6576, loss: 0.6508, grad_norm: 5.4448 2023-02-16 18:01:53,027 - mmseg - INFO - Iter [24950/160000] lr: 5.064e-05, eta: 13:38:55, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4184, decode.acc_seg: 84.5588, aux.loss_ce: 0.2059, aux.acc_seg: 81.1370, loss: 0.6243, grad_norm: 4.4785 2023-02-16 18:02:10,987 - mmseg - INFO - Saving checkpoint at 25000 iterations 2023-02-16 18:02:12,051 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 18:02:12,052 - mmseg - INFO - Iter [25000/160000] lr: 5.063e-05, eta: 13:38:41, time: 0.381, data_time: 0.007, memory: 16600, decode.loss_ce: 0.4497, decode.acc_seg: 83.2633, aux.loss_ce: 0.2190, aux.acc_seg: 79.7427, loss: 0.6687, grad_norm: 5.3230 2023-02-16 18:02:30,130 - mmseg - INFO - Iter [25050/160000] lr: 5.061e-05, eta: 13:38:22, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4395, decode.acc_seg: 83.5001, aux.loss_ce: 0.2118, aux.acc_seg: 79.8380, loss: 0.6513, grad_norm: 5.2015 2023-02-16 18:02:47,977 - mmseg - INFO - Iter [25100/160000] lr: 5.059e-05, eta: 13:38:02, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4493, decode.acc_seg: 83.3044, aux.loss_ce: 0.2217, aux.acc_seg: 79.4404, loss: 0.6710, grad_norm: 5.1699 2023-02-16 18:03:05,799 - mmseg - INFO - Iter [25150/160000] lr: 5.057e-05, eta: 13:37:42, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4479, decode.acc_seg: 83.4316, aux.loss_ce: 0.2207, aux.acc_seg: 79.7482, loss: 0.6686, grad_norm: 5.0633 2023-02-16 18:03:23,746 - mmseg - INFO - Iter [25200/160000] lr: 5.055e-05, eta: 13:37:22, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4577, decode.acc_seg: 83.3807, aux.loss_ce: 0.2291, aux.acc_seg: 79.3016, loss: 0.6869, grad_norm: 4.8295 2023-02-16 18:03:41,662 - mmseg - INFO - Iter [25250/160000] lr: 5.053e-05, eta: 13:37:03, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4080, decode.acc_seg: 84.6758, aux.loss_ce: 0.2075, aux.acc_seg: 80.7536, loss: 0.6155, grad_norm: 4.9629 2023-02-16 18:04:02,203 - mmseg - INFO - Iter [25300/160000] lr: 5.051e-05, eta: 13:36:57, time: 0.411, data_time: 0.052, memory: 16600, decode.loss_ce: 0.4144, decode.acc_seg: 84.4672, aux.loss_ce: 0.2066, aux.acc_seg: 80.5638, loss: 0.6210, grad_norm: 4.7199 2023-02-16 18:04:20,366 - mmseg - INFO - Iter [25350/160000] lr: 5.049e-05, eta: 13:36:39, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4162, decode.acc_seg: 83.9241, aux.loss_ce: 0.2086, aux.acc_seg: 80.1182, loss: 0.6249, grad_norm: 5.2149 2023-02-16 18:04:38,256 - mmseg - INFO - Iter [25400/160000] lr: 5.048e-05, eta: 13:36:19, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4056, decode.acc_seg: 84.6182, aux.loss_ce: 0.2014, aux.acc_seg: 81.0751, loss: 0.6070, grad_norm: 4.8558 2023-02-16 18:04:56,188 - mmseg - INFO - Iter [25450/160000] lr: 5.046e-05, eta: 13:35:59, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4185, decode.acc_seg: 84.2768, aux.loss_ce: 0.2121, aux.acc_seg: 80.2429, loss: 0.6306, grad_norm: 5.0427 2023-02-16 18:05:14,076 - mmseg - INFO - Iter [25500/160000] lr: 5.044e-05, eta: 13:35:40, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4261, decode.acc_seg: 84.1059, aux.loss_ce: 0.2136, aux.acc_seg: 79.9691, loss: 0.6397, grad_norm: 5.0823 2023-02-16 18:05:31,992 - mmseg - INFO - Iter [25550/160000] lr: 5.042e-05, eta: 13:35:20, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4090, decode.acc_seg: 84.4469, aux.loss_ce: 0.2015, aux.acc_seg: 81.2331, loss: 0.6105, grad_norm: 4.8429 2023-02-16 18:05:49,818 - mmseg - INFO - Iter [25600/160000] lr: 5.040e-05, eta: 13:35:00, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4458, decode.acc_seg: 83.1709, aux.loss_ce: 0.2185, aux.acc_seg: 79.1609, loss: 0.6644, grad_norm: 5.5315 2023-02-16 18:06:07,651 - mmseg - INFO - Iter [25650/160000] lr: 5.038e-05, eta: 13:34:40, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3953, decode.acc_seg: 84.7115, aux.loss_ce: 0.1997, aux.acc_seg: 80.8917, loss: 0.5949, grad_norm: 5.1234 2023-02-16 18:06:25,511 - mmseg - INFO - Iter [25700/160000] lr: 5.036e-05, eta: 13:34:20, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4633, decode.acc_seg: 82.3818, aux.loss_ce: 0.2277, aux.acc_seg: 78.3189, loss: 0.6910, grad_norm: 5.1722 2023-02-16 18:06:43,307 - mmseg - INFO - Iter [25750/160000] lr: 5.034e-05, eta: 13:34:00, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4122, decode.acc_seg: 84.4178, aux.loss_ce: 0.2095, aux.acc_seg: 80.4565, loss: 0.6218, grad_norm: 4.6197 2023-02-16 18:07:01,566 - mmseg - INFO - Iter [25800/160000] lr: 5.033e-05, eta: 13:33:42, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4336, decode.acc_seg: 83.8599, aux.loss_ce: 0.2127, aux.acc_seg: 80.2233, loss: 0.6463, grad_norm: 4.9113 2023-02-16 18:07:19,418 - mmseg - INFO - Iter [25850/160000] lr: 5.031e-05, eta: 13:33:22, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4189, decode.acc_seg: 84.5402, aux.loss_ce: 0.2092, aux.acc_seg: 80.7013, loss: 0.6281, grad_norm: 4.6892 2023-02-16 18:07:37,406 - mmseg - INFO - Iter [25900/160000] lr: 5.029e-05, eta: 13:33:03, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4113, decode.acc_seg: 84.7392, aux.loss_ce: 0.2071, aux.acc_seg: 80.8194, loss: 0.6184, grad_norm: 5.2792 2023-02-16 18:07:55,206 - mmseg - INFO - Iter [25950/160000] lr: 5.027e-05, eta: 13:32:42, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4254, decode.acc_seg: 84.0447, aux.loss_ce: 0.2124, aux.acc_seg: 80.1295, loss: 0.6378, grad_norm: 5.3475 2023-02-16 18:08:13,324 - mmseg - INFO - Saving checkpoint at 26000 iterations 2023-02-16 18:08:14,401 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 18:08:14,401 - mmseg - INFO - Iter [26000/160000] lr: 5.025e-05, eta: 13:32:29, time: 0.384, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4310, decode.acc_seg: 84.0742, aux.loss_ce: 0.2128, aux.acc_seg: 80.4896, loss: 0.6439, grad_norm: 5.5459 2023-02-16 18:08:32,216 - mmseg - INFO - Iter [26050/160000] lr: 5.023e-05, eta: 13:32:09, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4173, decode.acc_seg: 84.3776, aux.loss_ce: 0.2092, aux.acc_seg: 80.3403, loss: 0.6265, grad_norm: 4.8215 2023-02-16 18:08:50,253 - mmseg - INFO - Iter [26100/160000] lr: 5.021e-05, eta: 13:31:50, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4211, decode.acc_seg: 83.6766, aux.loss_ce: 0.2108, aux.acc_seg: 79.7293, loss: 0.6319, grad_norm: 4.6167 2023-02-16 18:09:08,168 - mmseg - INFO - Iter [26150/160000] lr: 5.019e-05, eta: 13:31:31, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4195, decode.acc_seg: 84.3255, aux.loss_ce: 0.2111, aux.acc_seg: 80.3303, loss: 0.6306, grad_norm: 5.2851 2023-02-16 18:09:25,996 - mmseg - INFO - Iter [26200/160000] lr: 5.018e-05, eta: 13:31:11, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4116, decode.acc_seg: 84.1604, aux.loss_ce: 0.2063, aux.acc_seg: 80.7484, loss: 0.6179, grad_norm: 5.2864 2023-02-16 18:09:43,834 - mmseg - INFO - Iter [26250/160000] lr: 5.016e-05, eta: 13:30:51, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4024, decode.acc_seg: 84.4867, aux.loss_ce: 0.2039, aux.acc_seg: 80.4405, loss: 0.6063, grad_norm: 5.3791 2023-02-16 18:10:01,618 - mmseg - INFO - Iter [26300/160000] lr: 5.014e-05, eta: 13:30:30, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4261, decode.acc_seg: 84.2751, aux.loss_ce: 0.2127, aux.acc_seg: 80.3494, loss: 0.6388, grad_norm: 5.6311 2023-02-16 18:10:19,487 - mmseg - INFO - Iter [26350/160000] lr: 5.012e-05, eta: 13:30:11, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4271, decode.acc_seg: 84.0248, aux.loss_ce: 0.2121, aux.acc_seg: 80.2037, loss: 0.6392, grad_norm: 5.0233 2023-02-16 18:10:37,433 - mmseg - INFO - Iter [26400/160000] lr: 5.010e-05, eta: 13:29:51, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4435, decode.acc_seg: 83.6388, aux.loss_ce: 0.2157, aux.acc_seg: 80.1273, loss: 0.6591, grad_norm: 5.2957 2023-02-16 18:10:55,533 - mmseg - INFO - Iter [26450/160000] lr: 5.008e-05, eta: 13:29:33, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4068, decode.acc_seg: 84.8275, aux.loss_ce: 0.2051, aux.acc_seg: 80.7370, loss: 0.6119, grad_norm: 5.0386 2023-02-16 18:11:13,312 - mmseg - INFO - Iter [26500/160000] lr: 5.006e-05, eta: 13:29:12, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4409, decode.acc_seg: 83.7980, aux.loss_ce: 0.2211, aux.acc_seg: 79.7387, loss: 0.6620, grad_norm: 5.1429 2023-02-16 18:11:33,827 - mmseg - INFO - Iter [26550/160000] lr: 5.004e-05, eta: 13:29:06, time: 0.410, data_time: 0.052, memory: 16600, decode.loss_ce: 0.4126, decode.acc_seg: 84.5663, aux.loss_ce: 0.2075, aux.acc_seg: 80.3821, loss: 0.6201, grad_norm: 4.9824 2023-02-16 18:11:51,724 - mmseg - INFO - Iter [26600/160000] lr: 5.003e-05, eta: 13:28:46, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3938, decode.acc_seg: 85.0468, aux.loss_ce: 0.2013, aux.acc_seg: 81.1697, loss: 0.5951, grad_norm: 4.2586 2023-02-16 18:12:09,537 - mmseg - INFO - Iter [26650/160000] lr: 5.001e-05, eta: 13:28:26, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3902, decode.acc_seg: 85.3071, aux.loss_ce: 0.1971, aux.acc_seg: 81.7409, loss: 0.5873, grad_norm: 4.5550 2023-02-16 18:12:27,548 - mmseg - INFO - Iter [26700/160000] lr: 4.999e-05, eta: 13:28:07, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4200, decode.acc_seg: 84.5676, aux.loss_ce: 0.2105, aux.acc_seg: 80.5707, loss: 0.6305, grad_norm: 5.0981 2023-02-16 18:12:45,350 - mmseg - INFO - Iter [26750/160000] lr: 4.997e-05, eta: 13:27:47, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4168, decode.acc_seg: 84.2134, aux.loss_ce: 0.2112, aux.acc_seg: 79.8722, loss: 0.6280, grad_norm: 5.3401 2023-02-16 18:13:03,532 - mmseg - INFO - Iter [26800/160000] lr: 4.995e-05, eta: 13:27:29, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4163, decode.acc_seg: 83.9766, aux.loss_ce: 0.2094, aux.acc_seg: 80.0969, loss: 0.6256, grad_norm: 5.3866 2023-02-16 18:13:21,409 - mmseg - INFO - Iter [26850/160000] lr: 4.993e-05, eta: 13:27:09, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4249, decode.acc_seg: 84.7781, aux.loss_ce: 0.2148, aux.acc_seg: 80.4010, loss: 0.6397, grad_norm: 4.6712 2023-02-16 18:13:39,322 - mmseg - INFO - Iter [26900/160000] lr: 4.991e-05, eta: 13:26:49, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4092, decode.acc_seg: 84.8560, aux.loss_ce: 0.2091, aux.acc_seg: 80.6474, loss: 0.6183, grad_norm: 4.6250 2023-02-16 18:13:57,248 - mmseg - INFO - Iter [26950/160000] lr: 4.989e-05, eta: 13:26:30, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3910, decode.acc_seg: 85.2670, aux.loss_ce: 0.1957, aux.acc_seg: 81.7120, loss: 0.5867, grad_norm: 4.3933 2023-02-16 18:14:15,168 - mmseg - INFO - Saving checkpoint at 27000 iterations 2023-02-16 18:14:16,241 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 18:14:16,241 - mmseg - INFO - Iter [27000/160000] lr: 4.988e-05, eta: 13:26:16, time: 0.380, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4298, decode.acc_seg: 83.8828, aux.loss_ce: 0.2106, aux.acc_seg: 80.1681, loss: 0.6404, grad_norm: 4.8429 2023-02-16 18:14:34,214 - mmseg - INFO - Iter [27050/160000] lr: 4.986e-05, eta: 13:25:57, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3941, decode.acc_seg: 85.2879, aux.loss_ce: 0.2027, aux.acc_seg: 81.1839, loss: 0.5968, grad_norm: 4.9303 2023-02-16 18:14:52,284 - mmseg - INFO - Iter [27100/160000] lr: 4.984e-05, eta: 13:25:38, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4052, decode.acc_seg: 84.6262, aux.loss_ce: 0.2062, aux.acc_seg: 80.7952, loss: 0.6115, grad_norm: 5.1631 2023-02-16 18:15:10,238 - mmseg - INFO - Iter [27150/160000] lr: 4.982e-05, eta: 13:25:18, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.4136, decode.acc_seg: 84.2387, aux.loss_ce: 0.2168, aux.acc_seg: 79.7363, loss: 0.6304, grad_norm: 5.4638 2023-02-16 18:15:28,100 - mmseg - INFO - Iter [27200/160000] lr: 4.980e-05, eta: 13:24:59, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4275, decode.acc_seg: 83.9742, aux.loss_ce: 0.2166, aux.acc_seg: 79.6020, loss: 0.6441, grad_norm: 4.9871 2023-02-16 18:15:46,140 - mmseg - INFO - Iter [27250/160000] lr: 4.978e-05, eta: 13:24:40, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4005, decode.acc_seg: 84.7475, aux.loss_ce: 0.2051, aux.acc_seg: 80.6667, loss: 0.6056, grad_norm: 4.4923 2023-02-16 18:16:04,317 - mmseg - INFO - Iter [27300/160000] lr: 4.976e-05, eta: 13:24:22, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4036, decode.acc_seg: 84.6810, aux.loss_ce: 0.2087, aux.acc_seg: 80.3368, loss: 0.6123, grad_norm: 4.9458 2023-02-16 18:16:22,169 - mmseg - INFO - Iter [27350/160000] lr: 4.974e-05, eta: 13:24:02, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4424, decode.acc_seg: 83.5740, aux.loss_ce: 0.2192, aux.acc_seg: 79.8009, loss: 0.6616, grad_norm: 6.3398 2023-02-16 18:16:39,951 - mmseg - INFO - Iter [27400/160000] lr: 4.973e-05, eta: 13:23:42, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4074, decode.acc_seg: 84.5260, aux.loss_ce: 0.2073, aux.acc_seg: 80.5082, loss: 0.6146, grad_norm: 4.6105 2023-02-16 18:16:58,054 - mmseg - INFO - Iter [27450/160000] lr: 4.971e-05, eta: 13:23:23, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4313, decode.acc_seg: 84.2096, aux.loss_ce: 0.2193, aux.acc_seg: 79.9431, loss: 0.6506, grad_norm: 5.5658 2023-02-16 18:17:16,083 - mmseg - INFO - Iter [27500/160000] lr: 4.969e-05, eta: 13:23:04, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4176, decode.acc_seg: 84.4863, aux.loss_ce: 0.2091, aux.acc_seg: 80.5393, loss: 0.6267, grad_norm: 4.9765 2023-02-16 18:17:33,856 - mmseg - INFO - Iter [27550/160000] lr: 4.967e-05, eta: 13:22:44, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4011, decode.acc_seg: 84.6984, aux.loss_ce: 0.2017, aux.acc_seg: 80.8661, loss: 0.6028, grad_norm: 4.6453 2023-02-16 18:17:51,646 - mmseg - INFO - Iter [27600/160000] lr: 4.965e-05, eta: 13:22:24, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4126, decode.acc_seg: 84.4027, aux.loss_ce: 0.2072, aux.acc_seg: 80.5498, loss: 0.6198, grad_norm: 4.7017 2023-02-16 18:18:09,826 - mmseg - INFO - Iter [27650/160000] lr: 4.963e-05, eta: 13:22:06, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3946, decode.acc_seg: 85.1351, aux.loss_ce: 0.2003, aux.acc_seg: 81.1131, loss: 0.5949, grad_norm: 4.4522 2023-02-16 18:18:27,766 - mmseg - INFO - Iter [27700/160000] lr: 4.961e-05, eta: 13:21:46, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3811, decode.acc_seg: 85.4885, aux.loss_ce: 0.1963, aux.acc_seg: 81.2474, loss: 0.5774, grad_norm: 4.8677 2023-02-16 18:18:45,625 - mmseg - INFO - Iter [27750/160000] lr: 4.959e-05, eta: 13:21:27, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4284, decode.acc_seg: 84.4221, aux.loss_ce: 0.2194, aux.acc_seg: 79.8129, loss: 0.6478, grad_norm: 5.3301 2023-02-16 18:19:05,873 - mmseg - INFO - Iter [27800/160000] lr: 4.958e-05, eta: 13:21:18, time: 0.405, data_time: 0.052, memory: 16600, decode.loss_ce: 0.4175, decode.acc_seg: 84.3931, aux.loss_ce: 0.2153, aux.acc_seg: 79.7161, loss: 0.6328, grad_norm: 4.6154 2023-02-16 18:19:23,703 - mmseg - INFO - Iter [27850/160000] lr: 4.956e-05, eta: 13:20:58, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3928, decode.acc_seg: 85.0572, aux.loss_ce: 0.2025, aux.acc_seg: 80.9387, loss: 0.5953, grad_norm: 4.5190 2023-02-16 18:19:41,671 - mmseg - INFO - Iter [27900/160000] lr: 4.954e-05, eta: 13:20:39, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4069, decode.acc_seg: 84.9733, aux.loss_ce: 0.2103, aux.acc_seg: 80.8443, loss: 0.6172, grad_norm: 4.9388 2023-02-16 18:19:59,673 - mmseg - INFO - Iter [27950/160000] lr: 4.952e-05, eta: 13:20:20, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4033, decode.acc_seg: 84.8411, aux.loss_ce: 0.2071, aux.acc_seg: 80.8799, loss: 0.6104, grad_norm: 4.7589 2023-02-16 18:20:17,761 - mmseg - INFO - Saving checkpoint at 28000 iterations 2023-02-16 18:20:18,830 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 18:20:18,830 - mmseg - INFO - Iter [28000/160000] lr: 4.950e-05, eta: 13:20:07, time: 0.383, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4066, decode.acc_seg: 84.7329, aux.loss_ce: 0.2052, aux.acc_seg: 80.6444, loss: 0.6118, grad_norm: 4.4797 2023-02-16 18:20:36,922 - mmseg - INFO - Iter [28050/160000] lr: 4.948e-05, eta: 13:19:48, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4077, decode.acc_seg: 84.8469, aux.loss_ce: 0.2005, aux.acc_seg: 81.2898, loss: 0.6082, grad_norm: 4.7096 2023-02-16 18:20:55,013 - mmseg - INFO - Iter [28100/160000] lr: 4.946e-05, eta: 13:19:29, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3946, decode.acc_seg: 84.8361, aux.loss_ce: 0.2003, aux.acc_seg: 80.8600, loss: 0.5949, grad_norm: 4.6936 2023-02-16 18:21:12,958 - mmseg - INFO - Iter [28150/160000] lr: 4.944e-05, eta: 13:19:10, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3923, decode.acc_seg: 84.9527, aux.loss_ce: 0.2014, aux.acc_seg: 80.8121, loss: 0.5937, grad_norm: 4.4889 2023-02-16 18:21:30,882 - mmseg - INFO - Iter [28200/160000] lr: 4.943e-05, eta: 13:18:51, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4138, decode.acc_seg: 84.8502, aux.loss_ce: 0.2058, aux.acc_seg: 81.2082, loss: 0.6195, grad_norm: 4.8059 2023-02-16 18:21:48,685 - mmseg - INFO - Iter [28250/160000] lr: 4.941e-05, eta: 13:18:31, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3859, decode.acc_seg: 85.3545, aux.loss_ce: 0.2037, aux.acc_seg: 80.4160, loss: 0.5896, grad_norm: 4.6189 2023-02-16 18:22:06,469 - mmseg - INFO - Iter [28300/160000] lr: 4.939e-05, eta: 13:18:11, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4002, decode.acc_seg: 85.1622, aux.loss_ce: 0.2029, aux.acc_seg: 81.1848, loss: 0.6032, grad_norm: 4.3598 2023-02-16 18:22:24,352 - mmseg - INFO - Iter [28350/160000] lr: 4.937e-05, eta: 13:17:51, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4105, decode.acc_seg: 84.4172, aux.loss_ce: 0.2095, aux.acc_seg: 80.1766, loss: 0.6200, grad_norm: 4.9940 2023-02-16 18:22:42,373 - mmseg - INFO - Iter [28400/160000] lr: 4.935e-05, eta: 13:17:32, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3907, decode.acc_seg: 84.8558, aux.loss_ce: 0.1988, aux.acc_seg: 80.7155, loss: 0.5895, grad_norm: 4.1670 2023-02-16 18:23:00,608 - mmseg - INFO - Iter [28450/160000] lr: 4.933e-05, eta: 13:17:14, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4134, decode.acc_seg: 84.3681, aux.loss_ce: 0.2075, aux.acc_seg: 80.3758, loss: 0.6209, grad_norm: 4.8701 2023-02-16 18:23:18,729 - mmseg - INFO - Iter [28500/160000] lr: 4.931e-05, eta: 13:16:56, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3719, decode.acc_seg: 86.0745, aux.loss_ce: 0.1930, aux.acc_seg: 82.0666, loss: 0.5650, grad_norm: 4.1417 2023-02-16 18:23:36,687 - mmseg - INFO - Iter [28550/160000] lr: 4.929e-05, eta: 13:16:37, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4052, decode.acc_seg: 84.7328, aux.loss_ce: 0.2069, aux.acc_seg: 80.4200, loss: 0.6121, grad_norm: 4.6605 2023-02-16 18:23:54,535 - mmseg - INFO - Iter [28600/160000] lr: 4.928e-05, eta: 13:16:17, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4088, decode.acc_seg: 84.4936, aux.loss_ce: 0.2065, aux.acc_seg: 80.5896, loss: 0.6153, grad_norm: 5.7593 2023-02-16 18:24:12,293 - mmseg - INFO - Iter [28650/160000] lr: 4.926e-05, eta: 13:15:57, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3774, decode.acc_seg: 85.4031, aux.loss_ce: 0.1917, aux.acc_seg: 81.2645, loss: 0.5691, grad_norm: 4.8534 2023-02-16 18:24:30,296 - mmseg - INFO - Iter [28700/160000] lr: 4.924e-05, eta: 13:15:38, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3959, decode.acc_seg: 85.2623, aux.loss_ce: 0.2000, aux.acc_seg: 81.2441, loss: 0.5960, grad_norm: 4.5961 2023-02-16 18:24:48,067 - mmseg - INFO - Iter [28750/160000] lr: 4.922e-05, eta: 13:15:18, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4146, decode.acc_seg: 84.3317, aux.loss_ce: 0.2106, aux.acc_seg: 80.0981, loss: 0.6252, grad_norm: 4.6540 2023-02-16 18:25:06,142 - mmseg - INFO - Iter [28800/160000] lr: 4.920e-05, eta: 13:14:59, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4031, decode.acc_seg: 84.8624, aux.loss_ce: 0.2149, aux.acc_seg: 79.7454, loss: 0.6180, grad_norm: 5.6472 2023-02-16 18:25:24,057 - mmseg - INFO - Iter [28850/160000] lr: 4.918e-05, eta: 13:14:40, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4367, decode.acc_seg: 83.8133, aux.loss_ce: 0.2170, aux.acc_seg: 79.4088, loss: 0.6537, grad_norm: 5.1467 2023-02-16 18:25:41,835 - mmseg - INFO - Iter [28900/160000] lr: 4.916e-05, eta: 13:14:20, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3992, decode.acc_seg: 85.2117, aux.loss_ce: 0.2085, aux.acc_seg: 80.8376, loss: 0.6077, grad_norm: 5.1765 2023-02-16 18:25:59,959 - mmseg - INFO - Iter [28950/160000] lr: 4.914e-05, eta: 13:14:01, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4047, decode.acc_seg: 84.7652, aux.loss_ce: 0.2041, aux.acc_seg: 80.7005, loss: 0.6088, grad_norm: 5.4949 2023-02-16 18:26:17,833 - mmseg - INFO - Saving checkpoint at 29000 iterations 2023-02-16 18:26:18,901 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 18:26:18,902 - mmseg - INFO - Iter [29000/160000] lr: 4.913e-05, eta: 13:13:47, time: 0.379, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3998, decode.acc_seg: 85.1729, aux.loss_ce: 0.2100, aux.acc_seg: 80.5998, loss: 0.6097, grad_norm: 5.0190 2023-02-16 18:26:38,894 - mmseg - INFO - Iter [29050/160000] lr: 4.911e-05, eta: 13:13:37, time: 0.400, data_time: 0.050, memory: 16600, decode.loss_ce: 0.3917, decode.acc_seg: 84.9091, aux.loss_ce: 0.2014, aux.acc_seg: 81.0664, loss: 0.5931, grad_norm: 4.8246 2023-02-16 18:26:56,715 - mmseg - INFO - Iter [29100/160000] lr: 4.909e-05, eta: 13:13:17, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3735, decode.acc_seg: 85.8367, aux.loss_ce: 0.1940, aux.acc_seg: 81.7575, loss: 0.5675, grad_norm: 4.5818 2023-02-16 18:27:15,092 - mmseg - INFO - Iter [29150/160000] lr: 4.907e-05, eta: 13:12:59, time: 0.368, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3870, decode.acc_seg: 85.1551, aux.loss_ce: 0.1990, aux.acc_seg: 80.8854, loss: 0.5859, grad_norm: 4.6266 2023-02-16 18:27:32,957 - mmseg - INFO - Iter [29200/160000] lr: 4.905e-05, eta: 13:12:40, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3876, decode.acc_seg: 85.2212, aux.loss_ce: 0.2002, aux.acc_seg: 80.8483, loss: 0.5878, grad_norm: 4.4855 2023-02-16 18:27:50,895 - mmseg - INFO - Iter [29250/160000] lr: 4.903e-05, eta: 13:12:21, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3979, decode.acc_seg: 84.6937, aux.loss_ce: 0.1985, aux.acc_seg: 81.1031, loss: 0.5964, grad_norm: 4.7607 2023-02-16 18:28:08,667 - mmseg - INFO - Iter [29300/160000] lr: 4.901e-05, eta: 13:12:01, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3998, decode.acc_seg: 84.9161, aux.loss_ce: 0.2062, aux.acc_seg: 80.6411, loss: 0.6060, grad_norm: 4.7407 2023-02-16 18:28:26,844 - mmseg - INFO - Iter [29350/160000] lr: 4.899e-05, eta: 13:11:42, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3755, decode.acc_seg: 85.4882, aux.loss_ce: 0.1926, aux.acc_seg: 81.4241, loss: 0.5681, grad_norm: 4.9978 2023-02-16 18:28:44,711 - mmseg - INFO - Iter [29400/160000] lr: 4.898e-05, eta: 13:11:23, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3886, decode.acc_seg: 85.5275, aux.loss_ce: 0.1979, aux.acc_seg: 81.2441, loss: 0.5866, grad_norm: 5.2794 2023-02-16 18:29:02,699 - mmseg - INFO - Iter [29450/160000] lr: 4.896e-05, eta: 13:11:04, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3687, decode.acc_seg: 85.9887, aux.loss_ce: 0.1921, aux.acc_seg: 81.7753, loss: 0.5608, grad_norm: 4.5049 2023-02-16 18:29:20,443 - mmseg - INFO - Iter [29500/160000] lr: 4.894e-05, eta: 13:10:44, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3906, decode.acc_seg: 85.2557, aux.loss_ce: 0.1968, aux.acc_seg: 81.5545, loss: 0.5874, grad_norm: 5.7216 2023-02-16 18:29:38,417 - mmseg - INFO - Iter [29550/160000] lr: 4.892e-05, eta: 13:10:25, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4146, decode.acc_seg: 84.1550, aux.loss_ce: 0.2079, aux.acc_seg: 80.6078, loss: 0.6224, grad_norm: 5.1523 2023-02-16 18:29:56,715 - mmseg - INFO - Iter [29600/160000] lr: 4.890e-05, eta: 13:10:07, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3665, decode.acc_seg: 86.0253, aux.loss_ce: 0.1952, aux.acc_seg: 81.5736, loss: 0.5617, grad_norm: 4.6237 2023-02-16 18:30:14,497 - mmseg - INFO - Iter [29650/160000] lr: 4.888e-05, eta: 13:09:47, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3883, decode.acc_seg: 85.2365, aux.loss_ce: 0.2032, aux.acc_seg: 80.8342, loss: 0.5915, grad_norm: 5.6480 2023-02-16 18:30:32,440 - mmseg - INFO - Iter [29700/160000] lr: 4.886e-05, eta: 13:09:28, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4057, decode.acc_seg: 84.8030, aux.loss_ce: 0.2037, aux.acc_seg: 80.9853, loss: 0.6093, grad_norm: 5.3156 2023-02-16 18:30:50,234 - mmseg - INFO - Iter [29750/160000] lr: 4.884e-05, eta: 13:09:08, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3955, decode.acc_seg: 85.5760, aux.loss_ce: 0.2048, aux.acc_seg: 81.0619, loss: 0.6003, grad_norm: 4.5292 2023-02-16 18:31:07,989 - mmseg - INFO - Iter [29800/160000] lr: 4.883e-05, eta: 13:08:48, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3802, decode.acc_seg: 85.4632, aux.loss_ce: 0.1942, aux.acc_seg: 81.5035, loss: 0.5744, grad_norm: 4.3729 2023-02-16 18:31:25,788 - mmseg - INFO - Iter [29850/160000] lr: 4.881e-05, eta: 13:08:28, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4120, decode.acc_seg: 84.7595, aux.loss_ce: 0.2121, aux.acc_seg: 80.4645, loss: 0.6242, grad_norm: 5.6063 2023-02-16 18:31:43,744 - mmseg - INFO - Iter [29900/160000] lr: 4.879e-05, eta: 13:08:09, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3916, decode.acc_seg: 85.2814, aux.loss_ce: 0.2010, aux.acc_seg: 80.9072, loss: 0.5926, grad_norm: 5.2558 2023-02-16 18:32:01,972 - mmseg - INFO - Iter [29950/160000] lr: 4.877e-05, eta: 13:07:51, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3810, decode.acc_seg: 85.6694, aux.loss_ce: 0.2011, aux.acc_seg: 81.3545, loss: 0.5820, grad_norm: 4.9182 2023-02-16 18:32:19,984 - mmseg - INFO - Saving checkpoint at 30000 iterations 2023-02-16 18:32:21,107 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 18:32:21,108 - mmseg - INFO - Iter [30000/160000] lr: 4.875e-05, eta: 13:07:37, time: 0.383, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3973, decode.acc_seg: 85.0042, aux.loss_ce: 0.2063, aux.acc_seg: 80.4008, loss: 0.6037, grad_norm: 4.9017 2023-02-16 18:32:38,914 - mmseg - INFO - Iter [30050/160000] lr: 4.873e-05, eta: 13:07:17, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3711, decode.acc_seg: 85.6133, aux.loss_ce: 0.1899, aux.acc_seg: 81.7702, loss: 0.5609, grad_norm: 4.3638 2023-02-16 18:32:56,730 - mmseg - INFO - Iter [30100/160000] lr: 4.871e-05, eta: 13:06:58, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3994, decode.acc_seg: 84.9629, aux.loss_ce: 0.2124, aux.acc_seg: 80.0793, loss: 0.6118, grad_norm: 4.9314 2023-02-16 18:33:14,511 - mmseg - INFO - Iter [30150/160000] lr: 4.869e-05, eta: 13:06:38, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3911, decode.acc_seg: 85.2190, aux.loss_ce: 0.2047, aux.acc_seg: 80.8274, loss: 0.5959, grad_norm: 4.8890 2023-02-16 18:33:32,776 - mmseg - INFO - Iter [30200/160000] lr: 4.868e-05, eta: 13:06:20, time: 0.365, data_time: 0.007, memory: 16600, decode.loss_ce: 0.4127, decode.acc_seg: 84.8120, aux.loss_ce: 0.2061, aux.acc_seg: 81.1269, loss: 0.6188, grad_norm: 5.0158 2023-02-16 18:33:50,906 - mmseg - INFO - Iter [30250/160000] lr: 4.866e-05, eta: 13:06:02, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3886, decode.acc_seg: 84.7076, aux.loss_ce: 0.2014, aux.acc_seg: 80.4958, loss: 0.5900, grad_norm: 4.7853 2023-02-16 18:34:09,072 - mmseg - INFO - Iter [30300/160000] lr: 4.864e-05, eta: 13:05:43, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.4135, decode.acc_seg: 84.1882, aux.loss_ce: 0.2125, aux.acc_seg: 80.3204, loss: 0.6260, grad_norm: 5.8324 2023-02-16 18:34:29,193 - mmseg - INFO - Iter [30350/160000] lr: 4.862e-05, eta: 13:05:34, time: 0.402, data_time: 0.051, memory: 16600, decode.loss_ce: 0.3895, decode.acc_seg: 85.5006, aux.loss_ce: 0.1996, aux.acc_seg: 81.2047, loss: 0.5891, grad_norm: 4.3634 2023-02-16 18:34:47,598 - mmseg - INFO - Iter [30400/160000] lr: 4.860e-05, eta: 13:05:16, time: 0.368, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3734, decode.acc_seg: 85.9495, aux.loss_ce: 0.1950, aux.acc_seg: 81.5410, loss: 0.5683, grad_norm: 5.0091 2023-02-16 18:35:05,556 - mmseg - INFO - Iter [30450/160000] lr: 4.858e-05, eta: 13:04:57, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3722, decode.acc_seg: 85.6583, aux.loss_ce: 0.1990, aux.acc_seg: 81.0453, loss: 0.5712, grad_norm: 4.9338 2023-02-16 18:35:23,500 - mmseg - INFO - Iter [30500/160000] lr: 4.856e-05, eta: 13:04:38, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3822, decode.acc_seg: 85.8645, aux.loss_ce: 0.1985, aux.acc_seg: 81.2803, loss: 0.5807, grad_norm: 5.4347 2023-02-16 18:35:41,456 - mmseg - INFO - Iter [30550/160000] lr: 4.854e-05, eta: 13:04:19, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3855, decode.acc_seg: 85.3251, aux.loss_ce: 0.1979, aux.acc_seg: 81.5721, loss: 0.5834, grad_norm: 5.0703 2023-02-16 18:35:59,347 - mmseg - INFO - Iter [30600/160000] lr: 4.853e-05, eta: 13:04:00, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3697, decode.acc_seg: 86.0663, aux.loss_ce: 0.1921, aux.acc_seg: 81.6844, loss: 0.5617, grad_norm: 4.2677 2023-02-16 18:36:17,283 - mmseg - INFO - Iter [30650/160000] lr: 4.851e-05, eta: 13:03:40, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3790, decode.acc_seg: 85.2527, aux.loss_ce: 0.1969, aux.acc_seg: 81.0288, loss: 0.5759, grad_norm: 4.2622 2023-02-16 18:36:35,365 - mmseg - INFO - Iter [30700/160000] lr: 4.849e-05, eta: 13:03:22, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3910, decode.acc_seg: 85.1690, aux.loss_ce: 0.2066, aux.acc_seg: 80.5217, loss: 0.5976, grad_norm: 5.0039 2023-02-16 18:36:53,235 - mmseg - INFO - Iter [30750/160000] lr: 4.847e-05, eta: 13:03:02, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3889, decode.acc_seg: 85.3119, aux.loss_ce: 0.2083, aux.acc_seg: 80.2262, loss: 0.5973, grad_norm: 5.0605 2023-02-16 18:37:11,091 - mmseg - INFO - Iter [30800/160000] lr: 4.845e-05, eta: 13:02:43, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3778, decode.acc_seg: 85.4627, aux.loss_ce: 0.1988, aux.acc_seg: 80.9803, loss: 0.5766, grad_norm: 4.5762 2023-02-16 18:37:28,963 - mmseg - INFO - Iter [30850/160000] lr: 4.843e-05, eta: 13:02:23, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3945, decode.acc_seg: 85.1887, aux.loss_ce: 0.2059, aux.acc_seg: 81.0565, loss: 0.6004, grad_norm: 4.7421 2023-02-16 18:37:46,820 - mmseg - INFO - Iter [30900/160000] lr: 4.841e-05, eta: 13:02:04, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3747, decode.acc_seg: 85.5006, aux.loss_ce: 0.1955, aux.acc_seg: 81.3338, loss: 0.5702, grad_norm: 4.7794 2023-02-16 18:38:04,665 - mmseg - INFO - Iter [30950/160000] lr: 4.839e-05, eta: 13:01:44, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3943, decode.acc_seg: 85.1603, aux.loss_ce: 0.2047, aux.acc_seg: 80.7352, loss: 0.5990, grad_norm: 5.3444 2023-02-16 18:38:22,532 - mmseg - INFO - Saving checkpoint at 31000 iterations 2023-02-16 18:38:23,616 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 18:38:23,616 - mmseg - INFO - Iter [31000/160000] lr: 4.838e-05, eta: 13:01:29, time: 0.379, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3959, decode.acc_seg: 84.8717, aux.loss_ce: 0.2030, aux.acc_seg: 80.5422, loss: 0.5989, grad_norm: 4.4830 2023-02-16 18:38:41,517 - mmseg - INFO - Iter [31050/160000] lr: 4.836e-05, eta: 13:01:10, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3720, decode.acc_seg: 85.7394, aux.loss_ce: 0.1936, aux.acc_seg: 81.7202, loss: 0.5656, grad_norm: 4.4048 2023-02-16 18:38:59,294 - mmseg - INFO - Iter [31100/160000] lr: 4.834e-05, eta: 13:00:50, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3799, decode.acc_seg: 85.7826, aux.loss_ce: 0.1967, aux.acc_seg: 81.6703, loss: 0.5766, grad_norm: 4.3376 2023-02-16 18:39:17,050 - mmseg - INFO - Iter [31150/160000] lr: 4.832e-05, eta: 13:00:30, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3675, decode.acc_seg: 85.7969, aux.loss_ce: 0.1898, aux.acc_seg: 81.6035, loss: 0.5572, grad_norm: 4.8553 2023-02-16 18:39:34,901 - mmseg - INFO - Iter [31200/160000] lr: 4.830e-05, eta: 13:00:11, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3962, decode.acc_seg: 84.8208, aux.loss_ce: 0.2091, aux.acc_seg: 80.4903, loss: 0.6053, grad_norm: 5.4064 2023-02-16 18:39:52,754 - mmseg - INFO - Iter [31250/160000] lr: 4.828e-05, eta: 12:59:51, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3799, decode.acc_seg: 85.4363, aux.loss_ce: 0.2011, aux.acc_seg: 80.8893, loss: 0.5810, grad_norm: 4.6360 2023-02-16 18:40:10,650 - mmseg - INFO - Iter [31300/160000] lr: 4.826e-05, eta: 12:59:32, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3885, decode.acc_seg: 85.4825, aux.loss_ce: 0.2023, aux.acc_seg: 81.3441, loss: 0.5907, grad_norm: 5.4303 2023-02-16 18:40:28,651 - mmseg - INFO - Iter [31350/160000] lr: 4.824e-05, eta: 12:59:13, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3662, decode.acc_seg: 85.7207, aux.loss_ce: 0.1908, aux.acc_seg: 81.6855, loss: 0.5570, grad_norm: 4.3986 2023-02-16 18:40:46,997 - mmseg - INFO - Iter [31400/160000] lr: 4.823e-05, eta: 12:58:56, time: 0.367, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3808, decode.acc_seg: 85.7089, aux.loss_ce: 0.1985, aux.acc_seg: 81.4771, loss: 0.5794, grad_norm: 4.5841 2023-02-16 18:41:05,086 - mmseg - INFO - Iter [31450/160000] lr: 4.821e-05, eta: 12:58:37, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3864, decode.acc_seg: 85.6774, aux.loss_ce: 0.2016, aux.acc_seg: 81.3619, loss: 0.5880, grad_norm: 5.2573 2023-02-16 18:41:22,937 - mmseg - INFO - Iter [31500/160000] lr: 4.819e-05, eta: 12:58:18, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3969, decode.acc_seg: 84.9283, aux.loss_ce: 0.2062, aux.acc_seg: 80.6874, loss: 0.6031, grad_norm: 4.9341 2023-02-16 18:41:40,997 - mmseg - INFO - Iter [31550/160000] lr: 4.817e-05, eta: 12:57:59, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3701, decode.acc_seg: 85.6397, aux.loss_ce: 0.1949, aux.acc_seg: 81.1626, loss: 0.5650, grad_norm: 4.5661 2023-02-16 18:42:01,243 - mmseg - INFO - Iter [31600/160000] lr: 4.815e-05, eta: 12:57:49, time: 0.405, data_time: 0.053, memory: 16600, decode.loss_ce: 0.3688, decode.acc_seg: 86.1162, aux.loss_ce: 0.1983, aux.acc_seg: 81.3712, loss: 0.5671, grad_norm: 4.8331 2023-02-16 18:42:19,672 - mmseg - INFO - Iter [31650/160000] lr: 4.813e-05, eta: 12:57:32, time: 0.369, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3648, decode.acc_seg: 86.3565, aux.loss_ce: 0.1870, aux.acc_seg: 82.3848, loss: 0.5519, grad_norm: 4.4484 2023-02-16 18:42:37,686 - mmseg - INFO - Iter [31700/160000] lr: 4.811e-05, eta: 12:57:13, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3793, decode.acc_seg: 85.8362, aux.loss_ce: 0.1962, aux.acc_seg: 81.3472, loss: 0.5755, grad_norm: 4.3006 2023-02-16 18:42:55,644 - mmseg - INFO - Iter [31750/160000] lr: 4.809e-05, eta: 12:56:54, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3639, decode.acc_seg: 86.4102, aux.loss_ce: 0.1913, aux.acc_seg: 81.9433, loss: 0.5552, grad_norm: 4.3811 2023-02-16 18:43:13,559 - mmseg - INFO - Iter [31800/160000] lr: 4.808e-05, eta: 12:56:35, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3639, decode.acc_seg: 86.1348, aux.loss_ce: 0.1924, aux.acc_seg: 81.5709, loss: 0.5563, grad_norm: 4.2731 2023-02-16 18:43:31,454 - mmseg - INFO - Iter [31850/160000] lr: 4.806e-05, eta: 12:56:16, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3665, decode.acc_seg: 86.0256, aux.loss_ce: 0.1964, aux.acc_seg: 81.1204, loss: 0.5629, grad_norm: 4.3768 2023-02-16 18:43:49,331 - mmseg - INFO - Iter [31900/160000] lr: 4.804e-05, eta: 12:55:56, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3594, decode.acc_seg: 86.0914, aux.loss_ce: 0.1883, aux.acc_seg: 82.0720, loss: 0.5478, grad_norm: 5.0556 2023-02-16 18:44:07,388 - mmseg - INFO - Iter [31950/160000] lr: 4.802e-05, eta: 12:55:38, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3678, decode.acc_seg: 85.9989, aux.loss_ce: 0.1899, aux.acc_seg: 81.9901, loss: 0.5577, grad_norm: 4.0850 2023-02-16 18:44:25,478 - mmseg - INFO - Saving checkpoint at 32000 iterations 2023-02-16 18:44:26,556 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 18:44:26,557 - mmseg - INFO - Iter [32000/160000] lr: 4.800e-05, eta: 12:55:24, time: 0.383, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3694, decode.acc_seg: 86.1938, aux.loss_ce: 0.1925, aux.acc_seg: 82.0063, loss: 0.5620, grad_norm: 5.0824 2023-02-16 18:44:55,414 - mmseg - INFO - per class results: 2023-02-16 18:44:55,420 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 73.31 | 82.62 | | building | 80.85 | 90.92 | | sky | 93.17 | 98.21 | | floor | 77.77 | 91.21 | | tree | 73.58 | 84.69 | | ceiling | 81.64 | 88.72 | | road | 79.57 | 84.45 | | bed | 85.06 | 95.7 | | windowpane | 55.95 | 79.4 | | grass | 61.54 | 73.39 | | cabinet | 54.96 | 69.97 | | sidewalk | 60.42 | 86.51 | | person | 78.89 | 88.47 | | earth | 27.81 | 34.15 | | door | 46.96 | 62.83 | | table | 51.96 | 74.11 | | mountain | 56.52 | 70.32 | | plant | 51.61 | 63.93 | | curtain | 70.75 | 82.32 | | chair | 49.65 | 57.62 | | car | 78.92 | 93.56 | | water | 53.56 | 70.56 | | painting | 49.55 | 94.02 | | sofa | 60.24 | 71.99 | | shelf | 39.92 | 68.49 | | house | 43.18 | 49.38 | | sea | 56.21 | 82.92 | | mirror | 60.4 | 68.63 | | rug | 58.34 | 63.62 | | field | 26.97 | 60.69 | | armchair | 35.84 | 56.66 | | seat | 54.52 | 85.29 | | fence | 39.0 | 51.64 | | desk | 45.7 | 61.09 | | rock | 46.49 | 77.7 | | wardrobe | 44.11 | 74.93 | | lamp | 53.21 | 72.1 | | bathtub | 72.46 | 82.45 | | railing | 29.61 | 55.3 | | cushion | 49.15 | 75.07 | | base | 20.85 | 24.34 | | box | 20.74 | 30.08 | | column | 40.91 | 54.48 | | signboard | 31.81 | 50.37 | | chest of drawers | 38.23 | 40.59 | | counter | 24.87 | 31.15 | | sand | 28.37 | 56.09 | | sink | 64.01 | 72.33 | | skyscraper | 58.02 | 93.09 | | fireplace | 62.42 | 89.55 | | refrigerator | 59.72 | 83.63 | | grandstand | 28.46 | 69.55 | | path | 19.54 | 26.89 | | stairs | 28.72 | 35.59 | | runway | 66.95 | 92.53 | | case | 47.21 | 51.13 | | pool table | 92.39 | 94.52 | | pillow | 50.6 | 58.67 | | screen door | 48.68 | 87.8 | | stairway | 26.1 | 37.65 | | river | 3.63 | 3.73 | | bridge | 54.55 | 64.14 | | bookcase | 31.79 | 59.75 | | blind | 28.71 | 29.57 | | coffee table | 53.14 | 62.09 | | toilet | 77.45 | 90.8 | | flower | 40.43 | 57.3 | | book | 37.13 | 50.87 | | hill | 4.32 | 7.42 | | bench | 36.77 | 41.94 | | countertop | 49.74 | 78.53 | | stove | 69.8 | 75.52 | | palm | 42.68 | 57.49 | | kitchen island | 28.4 | 53.38 | | computer | 50.42 | 53.5 | | swivel chair | 38.08 | 49.64 | | boat | 39.98 | 44.76 | | bar | 28.92 | 35.12 | | arcade machine | 59.18 | 64.01 | | hovel | 6.19 | 6.46 | | bus | 82.94 | 88.86 | | towel | 59.08 | 70.84 | | light | 45.0 | 52.47 | | truck | 31.95 | 50.57 | | tower | 46.76 | 69.03 | | chandelier | 58.24 | 84.8 | | awning | 25.34 | 32.78 | | streetlight | 17.29 | 37.32 | | booth | 41.56 | 57.38 | | television receiver | 66.23 | 82.89 | | airplane | 55.09 | 65.93 | | dirt track | 3.85 | 3.85 | | apparel | 33.69 | 47.5 | | pole | 10.69 | 12.83 | | land | 0.39 | 0.5 | | bannister | 14.82 | 22.72 | | escalator | 10.94 | 11.56 | | ottoman | 42.79 | 75.83 | | bottle | 30.83 | 36.14 | | buffet | 40.8 | 50.74 | | poster | 2.37 | 2.62 | | stage | 11.67 | 17.04 | | van | 28.75 | 34.49 | | ship | 31.8 | 48.23 | | fountain | 22.25 | 23.28 | | conveyer belt | 33.42 | 68.85 | | canopy | 33.59 | 51.25 | | washer | 76.02 | 89.5 | | plaything | 16.72 | 34.96 | | swimming pool | 53.61 | 54.21 | | stool | 31.03 | 47.76 | | barrel | 58.7 | 63.82 | | basket | 22.77 | 36.44 | | waterfall | 64.24 | 81.96 | | tent | 92.22 | 98.27 | | bag | 8.18 | 10.19 | | minibike | 60.0 | 77.72 | | cradle | 75.88 | 92.08 | | oven | 18.83 | 46.83 | | ball | 42.13 | 51.43 | | food | 60.81 | 71.67 | | step | 4.99 | 6.57 | | tank | 46.63 | 52.64 | | trade name | 13.52 | 14.76 | | microwave | 51.15 | 63.53 | | pot | 25.48 | 28.3 | | animal | 55.64 | 59.03 | | bicycle | 54.62 | 75.2 | | lake | 56.2 | 87.34 | | dishwasher | 66.84 | 81.47 | | screen | 55.72 | 91.18 | | blanket | 5.46 | 6.58 | | sculpture | 52.31 | 79.28 | | hood | 48.3 | 51.15 | | sconce | 28.84 | 37.21 | | vase | 27.46 | 60.42 | | traffic light | 22.29 | 31.25 | | tray | 4.62 | 5.59 | | ashcan | 38.19 | 48.88 | | fan | 54.3 | 70.24 | | pier | 26.0 | 44.11 | | crt screen | 0.0 | 0.0 | | plate | 47.43 | 55.29 | | monitor | 5.64 | 11.65 | | bulletin board | 14.41 | 15.22 | | shower | 0.0 | 0.0 | | radiator | 49.49 | 54.4 | | glass | 1.54 | 1.55 | | clock | 24.86 | 25.82 | | flag | 28.23 | 33.13 | +---------------------+-------+-------+ 2023-02-16 18:44:55,420 - mmseg - INFO - Summary: 2023-02-16 18:44:55,420 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 80.11 | 42.82 | 55.94 | +-------+-------+-------+ 2023-02-16 18:44:56,445 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_32000.pth. 2023-02-16 18:44:56,446 - mmseg - INFO - Best mIoU is 0.4282 at 32000 iter. 2023-02-16 18:44:56,446 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 18:44:56,446 - mmseg - INFO - Iter(val) [500] aAcc: 0.8011, mIoU: 0.4282, mAcc: 0.5594, IoU.wall: 0.7331, IoU.building: 0.8085, IoU.sky: 0.9317, IoU.floor: 0.7777, IoU.tree: 0.7358, IoU.ceiling: 0.8164, IoU.road: 0.7957, IoU.bed : 0.8506, IoU.windowpane: 0.5595, IoU.grass: 0.6154, IoU.cabinet: 0.5496, IoU.sidewalk: 0.6042, IoU.person: 0.7889, IoU.earth: 0.2781, IoU.door: 0.4696, IoU.table: 0.5196, IoU.mountain: 0.5652, IoU.plant: 0.5161, IoU.curtain: 0.7075, IoU.chair: 0.4965, IoU.car: 0.7892, IoU.water: 0.5356, IoU.painting: 0.4955, IoU.sofa: 0.6024, IoU.shelf: 0.3992, IoU.house: 0.4318, IoU.sea: 0.5621, IoU.mirror: 0.6040, IoU.rug: 0.5834, IoU.field: 0.2697, IoU.armchair: 0.3584, IoU.seat: 0.5452, IoU.fence: 0.3900, IoU.desk: 0.4570, IoU.rock: 0.4649, IoU.wardrobe: 0.4411, IoU.lamp: 0.5321, IoU.bathtub: 0.7246, IoU.railing: 0.2961, IoU.cushion: 0.4915, IoU.base: 0.2085, IoU.box: 0.2074, IoU.column: 0.4091, IoU.signboard: 0.3181, IoU.chest of drawers: 0.3823, IoU.counter: 0.2487, IoU.sand: 0.2837, IoU.sink: 0.6401, IoU.skyscraper: 0.5802, IoU.fireplace: 0.6242, IoU.refrigerator: 0.5972, IoU.grandstand: 0.2846, IoU.path: 0.1954, IoU.stairs: 0.2872, IoU.runway: 0.6695, IoU.case: 0.4721, IoU.pool table: 0.9239, IoU.pillow: 0.5060, IoU.screen door: 0.4868, IoU.stairway: 0.2610, IoU.river: 0.0363, IoU.bridge: 0.5455, IoU.bookcase: 0.3179, IoU.blind: 0.2871, IoU.coffee table: 0.5314, IoU.toilet: 0.7745, IoU.flower: 0.4043, IoU.book: 0.3713, IoU.hill: 0.0432, IoU.bench: 0.3677, IoU.countertop: 0.4974, IoU.stove: 0.6980, IoU.palm: 0.4268, IoU.kitchen island: 0.2840, IoU.computer: 0.5042, IoU.swivel chair: 0.3808, IoU.boat: 0.3998, IoU.bar: 0.2892, IoU.arcade machine: 0.5918, IoU.hovel: 0.0619, IoU.bus: 0.8294, IoU.towel: 0.5908, IoU.light: 0.4500, IoU.truck: 0.3195, IoU.tower: 0.4676, IoU.chandelier: 0.5824, IoU.awning: 0.2534, IoU.streetlight: 0.1729, IoU.booth: 0.4156, IoU.television receiver: 0.6623, IoU.airplane: 0.5509, IoU.dirt track: 0.0385, IoU.apparel: 0.3369, IoU.pole: 0.1069, IoU.land: 0.0039, IoU.bannister: 0.1482, IoU.escalator: 0.1094, IoU.ottoman: 0.4279, IoU.bottle: 0.3083, IoU.buffet: 0.4080, IoU.poster: 0.0237, IoU.stage: 0.1167, IoU.van: 0.2875, IoU.ship: 0.3180, IoU.fountain: 0.2225, IoU.conveyer belt: 0.3342, IoU.canopy: 0.3359, IoU.washer: 0.7602, IoU.plaything: 0.1672, IoU.swimming pool: 0.5361, IoU.stool: 0.3103, IoU.barrel: 0.5870, IoU.basket: 0.2277, IoU.waterfall: 0.6424, IoU.tent: 0.9222, IoU.bag: 0.0818, IoU.minibike: 0.6000, IoU.cradle: 0.7588, IoU.oven: 0.1883, IoU.ball: 0.4213, IoU.food: 0.6081, IoU.step: 0.0499, IoU.tank: 0.4663, IoU.trade name: 0.1352, IoU.microwave: 0.5115, IoU.pot: 0.2548, IoU.animal: 0.5564, IoU.bicycle: 0.5462, IoU.lake: 0.5620, IoU.dishwasher: 0.6684, IoU.screen: 0.5572, IoU.blanket: 0.0546, IoU.sculpture: 0.5231, IoU.hood: 0.4830, IoU.sconce: 0.2884, IoU.vase: 0.2746, IoU.traffic light: 0.2229, IoU.tray: 0.0462, IoU.ashcan: 0.3819, IoU.fan: 0.5430, IoU.pier: 0.2600, IoU.crt screen: 0.0000, IoU.plate: 0.4743, IoU.monitor: 0.0564, IoU.bulletin board: 0.1441, IoU.shower: 0.0000, IoU.radiator: 0.4949, IoU.glass: 0.0154, IoU.clock: 0.2486, IoU.flag: 0.2823, Acc.wall: 0.8262, Acc.building: 0.9092, Acc.sky: 0.9821, Acc.floor: 0.9121, Acc.tree: 0.8469, Acc.ceiling: 0.8872, Acc.road: 0.8445, Acc.bed : 0.9570, Acc.windowpane: 0.7940, Acc.grass: 0.7339, Acc.cabinet: 0.6997, Acc.sidewalk: 0.8651, Acc.person: 0.8847, Acc.earth: 0.3415, Acc.door: 0.6283, Acc.table: 0.7411, Acc.mountain: 0.7032, Acc.plant: 0.6393, Acc.curtain: 0.8232, Acc.chair: 0.5762, Acc.car: 0.9356, Acc.water: 0.7056, Acc.painting: 0.9402, Acc.sofa: 0.7199, Acc.shelf: 0.6849, Acc.house: 0.4938, Acc.sea: 0.8292, Acc.mirror: 0.6863, Acc.rug: 0.6362, Acc.field: 0.6069, Acc.armchair: 0.5666, Acc.seat: 0.8529, Acc.fence: 0.5164, Acc.desk: 0.6109, Acc.rock: 0.7770, Acc.wardrobe: 0.7493, Acc.lamp: 0.7210, Acc.bathtub: 0.8245, Acc.railing: 0.5530, Acc.cushion: 0.7507, Acc.base: 0.2434, Acc.box: 0.3008, Acc.column: 0.5448, Acc.signboard: 0.5037, Acc.chest of drawers: 0.4059, Acc.counter: 0.3115, Acc.sand: 0.5609, Acc.sink: 0.7233, Acc.skyscraper: 0.9309, Acc.fireplace: 0.8955, Acc.refrigerator: 0.8363, Acc.grandstand: 0.6955, Acc.path: 0.2689, Acc.stairs: 0.3559, Acc.runway: 0.9253, Acc.case: 0.5113, Acc.pool table: 0.9452, Acc.pillow: 0.5867, Acc.screen door: 0.8780, Acc.stairway: 0.3765, Acc.river: 0.0373, Acc.bridge: 0.6414, Acc.bookcase: 0.5975, Acc.blind: 0.2957, Acc.coffee table: 0.6209, Acc.toilet: 0.9080, Acc.flower: 0.5730, Acc.book: 0.5087, Acc.hill: 0.0742, Acc.bench: 0.4194, Acc.countertop: 0.7853, Acc.stove: 0.7552, Acc.palm: 0.5749, Acc.kitchen island: 0.5338, Acc.computer: 0.5350, Acc.swivel chair: 0.4964, Acc.boat: 0.4476, Acc.bar: 0.3512, Acc.arcade machine: 0.6401, Acc.hovel: 0.0646, Acc.bus: 0.8886, Acc.towel: 0.7084, Acc.light: 0.5247, Acc.truck: 0.5057, Acc.tower: 0.6903, Acc.chandelier: 0.8480, Acc.awning: 0.3278, Acc.streetlight: 0.3732, Acc.booth: 0.5738, Acc.television receiver: 0.8289, Acc.airplane: 0.6593, Acc.dirt track: 0.0385, Acc.apparel: 0.4750, Acc.pole: 0.1283, Acc.land: 0.0050, Acc.bannister: 0.2272, Acc.escalator: 0.1156, Acc.ottoman: 0.7583, Acc.bottle: 0.3614, Acc.buffet: 0.5074, Acc.poster: 0.0262, Acc.stage: 0.1704, Acc.van: 0.3449, Acc.ship: 0.4823, Acc.fountain: 0.2328, Acc.conveyer belt: 0.6885, Acc.canopy: 0.5125, Acc.washer: 0.8950, Acc.plaything: 0.3496, Acc.swimming pool: 0.5421, Acc.stool: 0.4776, Acc.barrel: 0.6382, Acc.basket: 0.3644, Acc.waterfall: 0.8196, Acc.tent: 0.9827, Acc.bag: 0.1019, Acc.minibike: 0.7772, Acc.cradle: 0.9208, Acc.oven: 0.4683, Acc.ball: 0.5143, Acc.food: 0.7167, Acc.step: 0.0657, Acc.tank: 0.5264, Acc.trade name: 0.1476, Acc.microwave: 0.6353, Acc.pot: 0.2830, Acc.animal: 0.5903, Acc.bicycle: 0.7520, Acc.lake: 0.8734, Acc.dishwasher: 0.8147, Acc.screen: 0.9118, Acc.blanket: 0.0658, Acc.sculpture: 0.7928, Acc.hood: 0.5115, Acc.sconce: 0.3721, Acc.vase: 0.6042, Acc.traffic light: 0.3125, Acc.tray: 0.0559, Acc.ashcan: 0.4888, Acc.fan: 0.7024, Acc.pier: 0.4411, Acc.crt screen: 0.0000, Acc.plate: 0.5529, Acc.monitor: 0.1165, Acc.bulletin board: 0.1522, Acc.shower: 0.0000, Acc.radiator: 0.5440, Acc.glass: 0.0155, Acc.clock: 0.2582, Acc.flag: 0.3313 2023-02-16 18:45:14,264 - mmseg - INFO - Iter [32050/160000] lr: 4.798e-05, eta: 12:57:03, time: 0.954, data_time: 0.604, memory: 16600, decode.loss_ce: 0.3575, decode.acc_seg: 86.5797, aux.loss_ce: 0.1923, aux.acc_seg: 82.1548, loss: 0.5498, grad_norm: 4.7048 2023-02-16 18:45:32,025 - mmseg - INFO - Iter [32100/160000] lr: 4.796e-05, eta: 12:56:43, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3871, decode.acc_seg: 85.4261, aux.loss_ce: 0.1979, aux.acc_seg: 81.3513, loss: 0.5850, grad_norm: 5.6566 2023-02-16 18:45:49,750 - mmseg - INFO - Iter [32150/160000] lr: 4.794e-05, eta: 12:56:23, time: 0.354, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3628, decode.acc_seg: 86.3048, aux.loss_ce: 0.1915, aux.acc_seg: 82.0469, loss: 0.5543, grad_norm: 4.6455 2023-02-16 18:46:07,642 - mmseg - INFO - Iter [32200/160000] lr: 4.793e-05, eta: 12:56:04, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3537, decode.acc_seg: 86.2570, aux.loss_ce: 0.1890, aux.acc_seg: 82.2006, loss: 0.5427, grad_norm: 3.8609 2023-02-16 18:46:25,573 - mmseg - INFO - Iter [32250/160000] lr: 4.791e-05, eta: 12:55:44, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3795, decode.acc_seg: 85.5876, aux.loss_ce: 0.1969, aux.acc_seg: 81.3291, loss: 0.5765, grad_norm: 4.4640 2023-02-16 18:46:43,489 - mmseg - INFO - Iter [32300/160000] lr: 4.789e-05, eta: 12:55:25, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3891, decode.acc_seg: 85.4977, aux.loss_ce: 0.2008, aux.acc_seg: 81.5431, loss: 0.5899, grad_norm: 4.5042 2023-02-16 18:47:01,491 - mmseg - INFO - Iter [32350/160000] lr: 4.787e-05, eta: 12:55:06, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3872, decode.acc_seg: 85.0776, aux.loss_ce: 0.1960, aux.acc_seg: 81.1563, loss: 0.5832, grad_norm: 4.5282 2023-02-16 18:47:19,432 - mmseg - INFO - Iter [32400/160000] lr: 4.785e-05, eta: 12:54:46, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3513, decode.acc_seg: 86.1759, aux.loss_ce: 0.1883, aux.acc_seg: 81.8579, loss: 0.5396, grad_norm: 5.0156 2023-02-16 18:47:37,329 - mmseg - INFO - Iter [32450/160000] lr: 4.783e-05, eta: 12:54:27, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3740, decode.acc_seg: 85.9700, aux.loss_ce: 0.1939, aux.acc_seg: 81.8308, loss: 0.5679, grad_norm: 4.5027 2023-02-16 18:47:55,264 - mmseg - INFO - Iter [32500/160000] lr: 4.781e-05, eta: 12:54:08, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3644, decode.acc_seg: 86.2448, aux.loss_ce: 0.1929, aux.acc_seg: 81.6908, loss: 0.5573, grad_norm: 4.4672 2023-02-16 18:48:13,339 - mmseg - INFO - Iter [32550/160000] lr: 4.779e-05, eta: 12:53:49, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3719, decode.acc_seg: 85.6913, aux.loss_ce: 0.1960, aux.acc_seg: 81.1835, loss: 0.5679, grad_norm: 4.4754 2023-02-16 18:48:31,560 - mmseg - INFO - Iter [32600/160000] lr: 4.778e-05, eta: 12:53:31, time: 0.364, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3705, decode.acc_seg: 85.6542, aux.loss_ce: 0.1929, aux.acc_seg: 81.4123, loss: 0.5634, grad_norm: 4.5829 2023-02-16 18:48:49,607 - mmseg - INFO - Iter [32650/160000] lr: 4.776e-05, eta: 12:53:12, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3895, decode.acc_seg: 85.3243, aux.loss_ce: 0.2014, aux.acc_seg: 81.1460, loss: 0.5909, grad_norm: 4.8541 2023-02-16 18:49:07,546 - mmseg - INFO - Iter [32700/160000] lr: 4.774e-05, eta: 12:52:53, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3669, decode.acc_seg: 85.7549, aux.loss_ce: 0.1894, aux.acc_seg: 81.8373, loss: 0.5563, grad_norm: 4.7396 2023-02-16 18:49:25,564 - mmseg - INFO - Iter [32750/160000] lr: 4.772e-05, eta: 12:52:34, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3761, decode.acc_seg: 85.7029, aux.loss_ce: 0.1985, aux.acc_seg: 81.2020, loss: 0.5747, grad_norm: 4.5161 2023-02-16 18:49:43,731 - mmseg - INFO - Iter [32800/160000] lr: 4.770e-05, eta: 12:52:15, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3565, decode.acc_seg: 86.3817, aux.loss_ce: 0.1886, aux.acc_seg: 82.0301, loss: 0.5451, grad_norm: 3.9589 2023-02-16 18:50:04,000 - mmseg - INFO - Iter [32850/160000] lr: 4.768e-05, eta: 12:52:05, time: 0.405, data_time: 0.049, memory: 16600, decode.loss_ce: 0.3954, decode.acc_seg: 84.7903, aux.loss_ce: 0.2006, aux.acc_seg: 80.6838, loss: 0.5959, grad_norm: 5.1456 2023-02-16 18:50:21,949 - mmseg - INFO - Iter [32900/160000] lr: 4.766e-05, eta: 12:51:46, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3504, decode.acc_seg: 86.3972, aux.loss_ce: 0.1915, aux.acc_seg: 82.1310, loss: 0.5420, grad_norm: 4.4055 2023-02-16 18:50:39,797 - mmseg - INFO - Iter [32950/160000] lr: 4.764e-05, eta: 12:51:26, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3845, decode.acc_seg: 85.5811, aux.loss_ce: 0.1995, aux.acc_seg: 81.1494, loss: 0.5840, grad_norm: 4.2166 2023-02-16 18:50:57,691 - mmseg - INFO - Saving checkpoint at 33000 iterations 2023-02-16 18:50:58,816 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 18:50:58,816 - mmseg - INFO - Iter [33000/160000] lr: 4.763e-05, eta: 12:51:11, time: 0.380, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3230, decode.acc_seg: 87.6569, aux.loss_ce: 0.1769, aux.acc_seg: 82.9806, loss: 0.4999, grad_norm: 4.6155 2023-02-16 18:51:16,797 - mmseg - INFO - Iter [33050/160000] lr: 4.761e-05, eta: 12:50:52, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3484, decode.acc_seg: 86.7566, aux.loss_ce: 0.1868, aux.acc_seg: 82.2193, loss: 0.5353, grad_norm: 4.7662 2023-02-16 18:51:34,605 - mmseg - INFO - Iter [33100/160000] lr: 4.759e-05, eta: 12:50:32, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3720, decode.acc_seg: 86.3441, aux.loss_ce: 0.1996, aux.acc_seg: 81.3739, loss: 0.5717, grad_norm: 4.8419 2023-02-16 18:51:52,731 - mmseg - INFO - Iter [33150/160000] lr: 4.757e-05, eta: 12:50:13, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3582, decode.acc_seg: 86.4891, aux.loss_ce: 0.1870, aux.acc_seg: 82.1728, loss: 0.5452, grad_norm: 4.5093 2023-02-16 18:52:10,642 - mmseg - INFO - Iter [33200/160000] lr: 4.755e-05, eta: 12:49:54, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3527, decode.acc_seg: 86.6487, aux.loss_ce: 0.1885, aux.acc_seg: 82.3060, loss: 0.5412, grad_norm: 4.7882 2023-02-16 18:52:28,779 - mmseg - INFO - Iter [33250/160000] lr: 4.753e-05, eta: 12:49:36, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3586, decode.acc_seg: 86.3195, aux.loss_ce: 0.1953, aux.acc_seg: 81.4114, loss: 0.5539, grad_norm: 4.9398 2023-02-16 18:52:46,788 - mmseg - INFO - Iter [33300/160000] lr: 4.751e-05, eta: 12:49:17, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3612, decode.acc_seg: 86.2011, aux.loss_ce: 0.1960, aux.acc_seg: 81.2194, loss: 0.5572, grad_norm: 5.3554 2023-02-16 18:53:04,765 - mmseg - INFO - Iter [33350/160000] lr: 4.749e-05, eta: 12:48:57, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3821, decode.acc_seg: 85.0386, aux.loss_ce: 0.2013, aux.acc_seg: 80.7924, loss: 0.5834, grad_norm: 5.0852 2023-02-16 18:53:22,719 - mmseg - INFO - Iter [33400/160000] lr: 4.748e-05, eta: 12:48:38, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3704, decode.acc_seg: 85.7643, aux.loss_ce: 0.1944, aux.acc_seg: 81.5105, loss: 0.5648, grad_norm: 4.3477 2023-02-16 18:53:40,629 - mmseg - INFO - Iter [33450/160000] lr: 4.746e-05, eta: 12:48:19, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3613, decode.acc_seg: 86.4925, aux.loss_ce: 0.1915, aux.acc_seg: 81.9539, loss: 0.5528, grad_norm: 5.0996 2023-02-16 18:53:58,504 - mmseg - INFO - Iter [33500/160000] lr: 4.744e-05, eta: 12:47:59, time: 0.357, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3727, decode.acc_seg: 85.7998, aux.loss_ce: 0.1942, aux.acc_seg: 81.7595, loss: 0.5669, grad_norm: 4.7303 2023-02-16 18:54:16,425 - mmseg - INFO - Iter [33550/160000] lr: 4.742e-05, eta: 12:47:40, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3485, decode.acc_seg: 86.5955, aux.loss_ce: 0.1890, aux.acc_seg: 81.9985, loss: 0.5375, grad_norm: 4.7615 2023-02-16 18:54:34,539 - mmseg - INFO - Iter [33600/160000] lr: 4.740e-05, eta: 12:47:22, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3707, decode.acc_seg: 86.2229, aux.loss_ce: 0.1971, aux.acc_seg: 81.4998, loss: 0.5678, grad_norm: 4.5792 2023-02-16 18:54:52,484 - mmseg - INFO - Iter [33650/160000] lr: 4.738e-05, eta: 12:47:02, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3364, decode.acc_seg: 87.2725, aux.loss_ce: 0.1796, aux.acc_seg: 83.0497, loss: 0.5160, grad_norm: 3.9989 2023-02-16 18:55:10,553 - mmseg - INFO - Iter [33700/160000] lr: 4.736e-05, eta: 12:46:44, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3655, decode.acc_seg: 86.3167, aux.loss_ce: 0.1931, aux.acc_seg: 81.4223, loss: 0.5586, grad_norm: 4.5659 2023-02-16 18:55:28,364 - mmseg - INFO - Iter [33750/160000] lr: 4.734e-05, eta: 12:46:24, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3800, decode.acc_seg: 85.5551, aux.loss_ce: 0.1988, aux.acc_seg: 81.1760, loss: 0.5788, grad_norm: 5.0980 2023-02-16 18:55:46,203 - mmseg - INFO - Iter [33800/160000] lr: 4.733e-05, eta: 12:46:04, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3730, decode.acc_seg: 86.0860, aux.loss_ce: 0.1911, aux.acc_seg: 81.9304, loss: 0.5641, grad_norm: 4.2635 2023-02-16 18:56:04,099 - mmseg - INFO - Iter [33850/160000] lr: 4.731e-05, eta: 12:45:45, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3890, decode.acc_seg: 85.4791, aux.loss_ce: 0.2035, aux.acc_seg: 81.1120, loss: 0.5925, grad_norm: 4.8806 2023-02-16 18:56:22,021 - mmseg - INFO - Iter [33900/160000] lr: 4.729e-05, eta: 12:45:26, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3665, decode.acc_seg: 86.0432, aux.loss_ce: 0.1932, aux.acc_seg: 81.5485, loss: 0.5596, grad_norm: 4.5105 2023-02-16 18:56:40,046 - mmseg - INFO - Iter [33950/160000] lr: 4.727e-05, eta: 12:45:07, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3575, decode.acc_seg: 86.3471, aux.loss_ce: 0.1917, aux.acc_seg: 81.7234, loss: 0.5492, grad_norm: 5.1314 2023-02-16 18:56:58,179 - mmseg - INFO - Saving checkpoint at 34000 iterations 2023-02-16 18:56:59,359 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 18:56:59,359 - mmseg - INFO - Iter [34000/160000] lr: 4.725e-05, eta: 12:44:53, time: 0.386, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3690, decode.acc_seg: 85.6723, aux.loss_ce: 0.1927, aux.acc_seg: 81.3049, loss: 0.5617, grad_norm: 4.5348 2023-02-16 18:57:17,301 - mmseg - INFO - Iter [34050/160000] lr: 4.723e-05, eta: 12:44:33, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3583, decode.acc_seg: 86.6611, aux.loss_ce: 0.1916, aux.acc_seg: 82.1362, loss: 0.5499, grad_norm: 4.1455 2023-02-16 18:57:35,402 - mmseg - INFO - Iter [34100/160000] lr: 4.721e-05, eta: 12:44:15, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3684, decode.acc_seg: 86.1138, aux.loss_ce: 0.1944, aux.acc_seg: 81.4641, loss: 0.5628, grad_norm: 4.4336 2023-02-16 18:57:55,704 - mmseg - INFO - Iter [34150/160000] lr: 4.719e-05, eta: 12:44:04, time: 0.406, data_time: 0.053, memory: 16600, decode.loss_ce: 0.3379, decode.acc_seg: 87.1256, aux.loss_ce: 0.1827, aux.acc_seg: 82.5807, loss: 0.5205, grad_norm: 4.8398 2023-02-16 18:58:13,683 - mmseg - INFO - Iter [34200/160000] lr: 4.718e-05, eta: 12:43:45, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3448, decode.acc_seg: 86.8270, aux.loss_ce: 0.1823, aux.acc_seg: 82.8215, loss: 0.5271, grad_norm: 4.6644 2023-02-16 18:58:31,698 - mmseg - INFO - Iter [34250/160000] lr: 4.716e-05, eta: 12:43:26, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3428, decode.acc_seg: 86.3507, aux.loss_ce: 0.1841, aux.acc_seg: 81.8114, loss: 0.5269, grad_norm: 4.4938 2023-02-16 18:58:49,561 - mmseg - INFO - Iter [34300/160000] lr: 4.714e-05, eta: 12:43:07, time: 0.357, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3309, decode.acc_seg: 87.5729, aux.loss_ce: 0.1824, aux.acc_seg: 82.8203, loss: 0.5133, grad_norm: 4.0925 2023-02-16 18:59:07,813 - mmseg - INFO - Iter [34350/160000] lr: 4.712e-05, eta: 12:42:49, time: 0.365, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3652, decode.acc_seg: 86.0985, aux.loss_ce: 0.1910, aux.acc_seg: 81.7793, loss: 0.5562, grad_norm: 4.5696 2023-02-16 18:59:25,898 - mmseg - INFO - Iter [34400/160000] lr: 4.710e-05, eta: 12:42:30, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3499, decode.acc_seg: 86.8091, aux.loss_ce: 0.1846, aux.acc_seg: 82.4444, loss: 0.5346, grad_norm: 4.2759 2023-02-16 18:59:43,904 - mmseg - INFO - Iter [34450/160000] lr: 4.708e-05, eta: 12:42:11, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3620, decode.acc_seg: 86.2435, aux.loss_ce: 0.1926, aux.acc_seg: 81.7629, loss: 0.5546, grad_norm: 4.5301 2023-02-16 19:00:01,964 - mmseg - INFO - Iter [34500/160000] lr: 4.706e-05, eta: 12:41:52, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3673, decode.acc_seg: 86.3877, aux.loss_ce: 0.1917, aux.acc_seg: 82.0064, loss: 0.5591, grad_norm: 4.5984 2023-02-16 19:00:19,935 - mmseg - INFO - Iter [34550/160000] lr: 4.704e-05, eta: 12:41:33, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3358, decode.acc_seg: 87.3431, aux.loss_ce: 0.1798, aux.acc_seg: 82.8005, loss: 0.5156, grad_norm: 4.1169 2023-02-16 19:00:37,857 - mmseg - INFO - Iter [34600/160000] lr: 4.703e-05, eta: 12:41:14, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3449, decode.acc_seg: 86.8918, aux.loss_ce: 0.1846, aux.acc_seg: 82.0166, loss: 0.5295, grad_norm: 4.6073 2023-02-16 19:00:55,731 - mmseg - INFO - Iter [34650/160000] lr: 4.701e-05, eta: 12:40:54, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3772, decode.acc_seg: 85.2507, aux.loss_ce: 0.1999, aux.acc_seg: 80.6641, loss: 0.5771, grad_norm: 4.5153 2023-02-16 19:01:13,677 - mmseg - INFO - Iter [34700/160000] lr: 4.699e-05, eta: 12:40:35, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3575, decode.acc_seg: 86.6366, aux.loss_ce: 0.1919, aux.acc_seg: 81.8152, loss: 0.5494, grad_norm: 4.9881 2023-02-16 19:01:31,669 - mmseg - INFO - Iter [34750/160000] lr: 4.697e-05, eta: 12:40:16, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3474, decode.acc_seg: 87.0553, aux.loss_ce: 0.1838, aux.acc_seg: 82.6000, loss: 0.5311, grad_norm: 4.0689 2023-02-16 19:01:49,511 - mmseg - INFO - Iter [34800/160000] lr: 4.695e-05, eta: 12:39:57, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3613, decode.acc_seg: 86.0479, aux.loss_ce: 0.1905, aux.acc_seg: 81.5821, loss: 0.5519, grad_norm: 5.0405 2023-02-16 19:02:07,289 - mmseg - INFO - Iter [34850/160000] lr: 4.693e-05, eta: 12:39:37, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3617, decode.acc_seg: 86.7362, aux.loss_ce: 0.1854, aux.acc_seg: 82.8504, loss: 0.5471, grad_norm: 5.4030 2023-02-16 19:02:25,367 - mmseg - INFO - Iter [34900/160000] lr: 4.691e-05, eta: 12:39:18, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3670, decode.acc_seg: 86.0316, aux.loss_ce: 0.1937, aux.acc_seg: 81.5104, loss: 0.5607, grad_norm: 4.2921 2023-02-16 19:02:43,200 - mmseg - INFO - Iter [34950/160000] lr: 4.689e-05, eta: 12:38:59, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3657, decode.acc_seg: 86.2620, aux.loss_ce: 0.1973, aux.acc_seg: 81.6543, loss: 0.5630, grad_norm: 4.6123 2023-02-16 19:03:01,012 - mmseg - INFO - Saving checkpoint at 35000 iterations 2023-02-16 19:03:02,088 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 19:03:02,088 - mmseg - INFO - Iter [35000/160000] lr: 4.688e-05, eta: 12:38:43, time: 0.378, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3839, decode.acc_seg: 86.0580, aux.loss_ce: 0.2073, aux.acc_seg: 80.8587, loss: 0.5913, grad_norm: 5.5277 2023-02-16 19:03:19,860 - mmseg - INFO - Iter [35050/160000] lr: 4.686e-05, eta: 12:38:23, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3609, decode.acc_seg: 85.9856, aux.loss_ce: 0.1943, aux.acc_seg: 81.3353, loss: 0.5552, grad_norm: 4.4490 2023-02-16 19:03:37,743 - mmseg - INFO - Iter [35100/160000] lr: 4.684e-05, eta: 12:38:04, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3543, decode.acc_seg: 86.4460, aux.loss_ce: 0.1903, aux.acc_seg: 81.7854, loss: 0.5446, grad_norm: 4.3459 2023-02-16 19:03:55,806 - mmseg - INFO - Iter [35150/160000] lr: 4.682e-05, eta: 12:37:45, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3579, decode.acc_seg: 86.3066, aux.loss_ce: 0.1927, aux.acc_seg: 81.5937, loss: 0.5506, grad_norm: 4.4589 2023-02-16 19:04:14,427 - mmseg - INFO - Iter [35200/160000] lr: 4.680e-05, eta: 12:37:28, time: 0.372, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3604, decode.acc_seg: 86.0669, aux.loss_ce: 0.1889, aux.acc_seg: 81.8583, loss: 0.5493, grad_norm: 4.9459 2023-02-16 19:04:32,468 - mmseg - INFO - Iter [35250/160000] lr: 4.678e-05, eta: 12:37:09, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3682, decode.acc_seg: 86.2288, aux.loss_ce: 0.1896, aux.acc_seg: 81.9336, loss: 0.5578, grad_norm: 4.8557 2023-02-16 19:04:50,281 - mmseg - INFO - Iter [35300/160000] lr: 4.676e-05, eta: 12:36:50, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3283, decode.acc_seg: 87.1870, aux.loss_ce: 0.1790, aux.acc_seg: 82.7916, loss: 0.5073, grad_norm: 4.6257 2023-02-16 19:05:08,232 - mmseg - INFO - Iter [35350/160000] lr: 4.674e-05, eta: 12:36:31, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3622, decode.acc_seg: 85.8615, aux.loss_ce: 0.1956, aux.acc_seg: 81.0999, loss: 0.5577, grad_norm: 5.4375 2023-02-16 19:05:28,604 - mmseg - INFO - Iter [35400/160000] lr: 4.673e-05, eta: 12:36:20, time: 0.407, data_time: 0.054, memory: 16600, decode.loss_ce: 0.3618, decode.acc_seg: 86.3579, aux.loss_ce: 0.1902, aux.acc_seg: 82.2332, loss: 0.5519, grad_norm: 4.5733 2023-02-16 19:05:46,679 - mmseg - INFO - Iter [35450/160000] lr: 4.671e-05, eta: 12:36:01, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3322, decode.acc_seg: 86.9426, aux.loss_ce: 0.1767, aux.acc_seg: 82.6777, loss: 0.5089, grad_norm: 4.1715 2023-02-16 19:06:04,749 - mmseg - INFO - Iter [35500/160000] lr: 4.669e-05, eta: 12:35:43, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3274, decode.acc_seg: 87.2163, aux.loss_ce: 0.1749, aux.acc_seg: 83.5574, loss: 0.5023, grad_norm: 4.1160 2023-02-16 19:06:22,616 - mmseg - INFO - Iter [35550/160000] lr: 4.667e-05, eta: 12:35:23, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3428, decode.acc_seg: 86.5102, aux.loss_ce: 0.1826, aux.acc_seg: 82.3080, loss: 0.5254, grad_norm: 4.3141 2023-02-16 19:06:40,536 - mmseg - INFO - Iter [35600/160000] lr: 4.665e-05, eta: 12:35:04, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3509, decode.acc_seg: 86.4638, aux.loss_ce: 0.1880, aux.acc_seg: 82.1439, loss: 0.5389, grad_norm: 3.7829 2023-02-16 19:06:58,774 - mmseg - INFO - Iter [35650/160000] lr: 4.663e-05, eta: 12:34:46, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3431, decode.acc_seg: 87.3710, aux.loss_ce: 0.1834, aux.acc_seg: 83.1251, loss: 0.5264, grad_norm: 4.1376 2023-02-16 19:07:16,860 - mmseg - INFO - Iter [35700/160000] lr: 4.661e-05, eta: 12:34:27, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3678, decode.acc_seg: 86.0023, aux.loss_ce: 0.1953, aux.acc_seg: 81.3108, loss: 0.5631, grad_norm: 4.6845 2023-02-16 19:07:34,822 - mmseg - INFO - Iter [35750/160000] lr: 4.659e-05, eta: 12:34:08, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3531, decode.acc_seg: 86.6567, aux.loss_ce: 0.1946, aux.acc_seg: 81.8411, loss: 0.5476, grad_norm: 4.9081 2023-02-16 19:07:52,790 - mmseg - INFO - Iter [35800/160000] lr: 4.658e-05, eta: 12:33:49, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3475, decode.acc_seg: 86.6460, aux.loss_ce: 0.1862, aux.acc_seg: 82.4920, loss: 0.5337, grad_norm: 5.0225 2023-02-16 19:08:10,678 - mmseg - INFO - Iter [35850/160000] lr: 4.656e-05, eta: 12:33:30, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3313, decode.acc_seg: 87.2503, aux.loss_ce: 0.1782, aux.acc_seg: 82.7198, loss: 0.5094, grad_norm: 4.7518 2023-02-16 19:08:28,777 - mmseg - INFO - Iter [35900/160000] lr: 4.654e-05, eta: 12:33:11, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3638, decode.acc_seg: 86.1974, aux.loss_ce: 0.1930, aux.acc_seg: 81.4146, loss: 0.5568, grad_norm: 4.7837 2023-02-16 19:08:46,796 - mmseg - INFO - Iter [35950/160000] lr: 4.652e-05, eta: 12:32:53, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3341, decode.acc_seg: 87.3458, aux.loss_ce: 0.1786, aux.acc_seg: 83.2049, loss: 0.5127, grad_norm: 4.2750 2023-02-16 19:09:04,933 - mmseg - INFO - Saving checkpoint at 36000 iterations 2023-02-16 19:09:05,988 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 19:09:05,988 - mmseg - INFO - Iter [36000/160000] lr: 4.650e-05, eta: 12:32:38, time: 0.384, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3480, decode.acc_seg: 87.0537, aux.loss_ce: 0.1859, aux.acc_seg: 82.2916, loss: 0.5338, grad_norm: 4.1695 2023-02-16 19:09:23,721 - mmseg - INFO - Iter [36050/160000] lr: 4.648e-05, eta: 12:32:18, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3469, decode.acc_seg: 86.4138, aux.loss_ce: 0.1845, aux.acc_seg: 82.0090, loss: 0.5314, grad_norm: 4.6802 2023-02-16 19:09:41,713 - mmseg - INFO - Iter [36100/160000] lr: 4.646e-05, eta: 12:31:59, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3300, decode.acc_seg: 87.2973, aux.loss_ce: 0.1845, aux.acc_seg: 82.1328, loss: 0.5144, grad_norm: 4.2818 2023-02-16 19:09:59,770 - mmseg - INFO - Iter [36150/160000] lr: 4.644e-05, eta: 12:31:40, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3486, decode.acc_seg: 86.4648, aux.loss_ce: 0.1853, aux.acc_seg: 81.9603, loss: 0.5339, grad_norm: 4.0530 2023-02-16 19:10:17,561 - mmseg - INFO - Iter [36200/160000] lr: 4.643e-05, eta: 12:31:21, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3495, decode.acc_seg: 86.7159, aux.loss_ce: 0.1920, aux.acc_seg: 81.9062, loss: 0.5416, grad_norm: 4.7441 2023-02-16 19:10:35,398 - mmseg - INFO - Iter [36250/160000] lr: 4.641e-05, eta: 12:31:01, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3733, decode.acc_seg: 86.1306, aux.loss_ce: 0.1976, aux.acc_seg: 81.7041, loss: 0.5709, grad_norm: 5.1239 2023-02-16 19:10:53,299 - mmseg - INFO - Iter [36300/160000] lr: 4.639e-05, eta: 12:30:42, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3664, decode.acc_seg: 86.1709, aux.loss_ce: 0.1935, aux.acc_seg: 81.5709, loss: 0.5599, grad_norm: 4.4448 2023-02-16 19:11:11,291 - mmseg - INFO - Iter [36350/160000] lr: 4.637e-05, eta: 12:30:23, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3435, decode.acc_seg: 86.6798, aux.loss_ce: 0.1869, aux.acc_seg: 82.2547, loss: 0.5304, grad_norm: 4.2952 2023-02-16 19:11:29,199 - mmseg - INFO - Iter [36400/160000] lr: 4.635e-05, eta: 12:30:04, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3483, decode.acc_seg: 86.8974, aux.loss_ce: 0.1888, aux.acc_seg: 82.1197, loss: 0.5370, grad_norm: 4.5191 2023-02-16 19:11:47,257 - mmseg - INFO - Iter [36450/160000] lr: 4.633e-05, eta: 12:29:45, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3443, decode.acc_seg: 86.4876, aux.loss_ce: 0.1855, aux.acc_seg: 82.1703, loss: 0.5297, grad_norm: 4.4796 2023-02-16 19:12:05,616 - mmseg - INFO - Iter [36500/160000] lr: 4.631e-05, eta: 12:29:27, time: 0.367, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3227, decode.acc_seg: 87.7284, aux.loss_ce: 0.1796, aux.acc_seg: 82.8040, loss: 0.5023, grad_norm: 3.8946 2023-02-16 19:12:23,433 - mmseg - INFO - Iter [36550/160000] lr: 4.629e-05, eta: 12:29:08, time: 0.357, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3560, decode.acc_seg: 86.3416, aux.loss_ce: 0.1910, aux.acc_seg: 81.4454, loss: 0.5470, grad_norm: 4.6214 2023-02-16 19:12:41,346 - mmseg - INFO - Iter [36600/160000] lr: 4.628e-05, eta: 12:28:49, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3663, decode.acc_seg: 86.0592, aux.loss_ce: 0.1923, aux.acc_seg: 81.5881, loss: 0.5585, grad_norm: 4.6465 2023-02-16 19:13:01,522 - mmseg - INFO - Iter [36650/160000] lr: 4.626e-05, eta: 12:28:37, time: 0.403, data_time: 0.051, memory: 16600, decode.loss_ce: 0.3637, decode.acc_seg: 86.4468, aux.loss_ce: 0.1933, aux.acc_seg: 82.1689, loss: 0.5570, grad_norm: 4.8218 2023-02-16 19:13:19,633 - mmseg - INFO - Iter [36700/160000] lr: 4.624e-05, eta: 12:28:18, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3148, decode.acc_seg: 88.3271, aux.loss_ce: 0.1707, aux.acc_seg: 84.0302, loss: 0.4855, grad_norm: 3.8194 2023-02-16 19:13:37,465 - mmseg - INFO - Iter [36750/160000] lr: 4.622e-05, eta: 12:27:59, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3343, decode.acc_seg: 87.2389, aux.loss_ce: 0.1825, aux.acc_seg: 82.4203, loss: 0.5168, grad_norm: 3.7875 2023-02-16 19:13:55,515 - mmseg - INFO - Iter [36800/160000] lr: 4.620e-05, eta: 12:27:40, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3421, decode.acc_seg: 86.9746, aux.loss_ce: 0.1850, aux.acc_seg: 82.2621, loss: 0.5271, grad_norm: 4.0780 2023-02-16 19:14:13,549 - mmseg - INFO - Iter [36850/160000] lr: 4.618e-05, eta: 12:27:21, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3348, decode.acc_seg: 87.2759, aux.loss_ce: 0.1803, aux.acc_seg: 83.0538, loss: 0.5151, grad_norm: 3.9234 2023-02-16 19:14:31,512 - mmseg - INFO - Iter [36900/160000] lr: 4.616e-05, eta: 12:27:02, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3182, decode.acc_seg: 88.0197, aux.loss_ce: 0.1744, aux.acc_seg: 83.5033, loss: 0.4925, grad_norm: 3.9535 2023-02-16 19:14:49,454 - mmseg - INFO - Iter [36950/160000] lr: 4.614e-05, eta: 12:26:43, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3580, decode.acc_seg: 87.1121, aux.loss_ce: 0.1918, aux.acc_seg: 82.5843, loss: 0.5498, grad_norm: 4.6854 2023-02-16 19:15:07,528 - mmseg - INFO - Saving checkpoint at 37000 iterations 2023-02-16 19:15:08,603 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 19:15:08,603 - mmseg - INFO - Iter [37000/160000] lr: 4.613e-05, eta: 12:26:28, time: 0.383, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3421, decode.acc_seg: 86.8143, aux.loss_ce: 0.1818, aux.acc_seg: 82.5598, loss: 0.5239, grad_norm: 4.7218 2023-02-16 19:15:26,471 - mmseg - INFO - Iter [37050/160000] lr: 4.611e-05, eta: 12:26:09, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3373, decode.acc_seg: 86.8353, aux.loss_ce: 0.1830, aux.acc_seg: 82.2768, loss: 0.5202, grad_norm: 3.9189 2023-02-16 19:15:44,602 - mmseg - INFO - Iter [37100/160000] lr: 4.609e-05, eta: 12:25:51, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3391, decode.acc_seg: 86.8455, aux.loss_ce: 0.1865, aux.acc_seg: 82.1888, loss: 0.5256, grad_norm: 4.3522 2023-02-16 19:16:02,505 - mmseg - INFO - Iter [37150/160000] lr: 4.607e-05, eta: 12:25:31, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3496, decode.acc_seg: 86.6965, aux.loss_ce: 0.1866, aux.acc_seg: 82.6162, loss: 0.5361, grad_norm: 4.6062 2023-02-16 19:16:20,430 - mmseg - INFO - Iter [37200/160000] lr: 4.605e-05, eta: 12:25:12, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3364, decode.acc_seg: 86.6965, aux.loss_ce: 0.1846, aux.acc_seg: 82.0943, loss: 0.5209, grad_norm: 4.3928 2023-02-16 19:16:38,515 - mmseg - INFO - Iter [37250/160000] lr: 4.603e-05, eta: 12:24:54, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3235, decode.acc_seg: 87.2249, aux.loss_ce: 0.1722, aux.acc_seg: 83.1034, loss: 0.4957, grad_norm: 3.7126 2023-02-16 19:16:56,827 - mmseg - INFO - Iter [37300/160000] lr: 4.601e-05, eta: 12:24:36, time: 0.366, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3492, decode.acc_seg: 86.6371, aux.loss_ce: 0.1945, aux.acc_seg: 81.6936, loss: 0.5437, grad_norm: 4.5681 2023-02-16 19:17:14,669 - mmseg - INFO - Iter [37350/160000] lr: 4.599e-05, eta: 12:24:16, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3220, decode.acc_seg: 87.7343, aux.loss_ce: 0.1719, aux.acc_seg: 83.5554, loss: 0.4939, grad_norm: 3.8731 2023-02-16 19:17:32,855 - mmseg - INFO - Iter [37400/160000] lr: 4.598e-05, eta: 12:23:58, time: 0.364, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3652, decode.acc_seg: 86.0043, aux.loss_ce: 0.1943, aux.acc_seg: 81.3411, loss: 0.5594, grad_norm: 4.5091 2023-02-16 19:17:50,935 - mmseg - INFO - Iter [37450/160000] lr: 4.596e-05, eta: 12:23:39, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3516, decode.acc_seg: 86.7681, aux.loss_ce: 0.1881, aux.acc_seg: 82.4788, loss: 0.5397, grad_norm: 3.9662 2023-02-16 19:18:09,273 - mmseg - INFO - Iter [37500/160000] lr: 4.594e-05, eta: 12:23:22, time: 0.367, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3370, decode.acc_seg: 86.7956, aux.loss_ce: 0.1851, aux.acc_seg: 82.2474, loss: 0.5221, grad_norm: 4.6665 2023-02-16 19:18:27,237 - mmseg - INFO - Iter [37550/160000] lr: 4.592e-05, eta: 12:23:03, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3419, decode.acc_seg: 86.8036, aux.loss_ce: 0.1789, aux.acc_seg: 82.7897, loss: 0.5208, grad_norm: 4.3300 2023-02-16 19:18:45,151 - mmseg - INFO - Iter [37600/160000] lr: 4.590e-05, eta: 12:22:44, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3437, decode.acc_seg: 87.2392, aux.loss_ce: 0.1879, aux.acc_seg: 82.3244, loss: 0.5316, grad_norm: 4.1232 2023-02-16 19:19:03,412 - mmseg - INFO - Iter [37650/160000] lr: 4.588e-05, eta: 12:22:26, time: 0.365, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3299, decode.acc_seg: 87.2115, aux.loss_ce: 0.1806, aux.acc_seg: 82.7059, loss: 0.5104, grad_norm: 3.7427 2023-02-16 19:19:21,502 - mmseg - INFO - Iter [37700/160000] lr: 4.586e-05, eta: 12:22:07, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3349, decode.acc_seg: 87.5695, aux.loss_ce: 0.1783, aux.acc_seg: 83.3960, loss: 0.5131, grad_norm: 3.9027 2023-02-16 19:19:39,391 - mmseg - INFO - Iter [37750/160000] lr: 4.584e-05, eta: 12:21:48, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3561, decode.acc_seg: 86.6420, aux.loss_ce: 0.1894, aux.acc_seg: 82.4246, loss: 0.5456, grad_norm: 4.6694 2023-02-16 19:19:57,240 - mmseg - INFO - Iter [37800/160000] lr: 4.583e-05, eta: 12:21:28, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3572, decode.acc_seg: 86.4868, aux.loss_ce: 0.1912, aux.acc_seg: 81.7787, loss: 0.5484, grad_norm: 4.7606 2023-02-16 19:20:15,352 - mmseg - INFO - Iter [37850/160000] lr: 4.581e-05, eta: 12:21:10, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3485, decode.acc_seg: 86.6224, aux.loss_ce: 0.1872, aux.acc_seg: 81.9970, loss: 0.5358, grad_norm: 4.2245 2023-02-16 19:20:35,963 - mmseg - INFO - Iter [37900/160000] lr: 4.579e-05, eta: 12:20:59, time: 0.412, data_time: 0.052, memory: 16600, decode.loss_ce: 0.3254, decode.acc_seg: 87.6028, aux.loss_ce: 0.1815, aux.acc_seg: 82.8342, loss: 0.5069, grad_norm: 4.2194 2023-02-16 19:20:53,850 - mmseg - INFO - Iter [37950/160000] lr: 4.577e-05, eta: 12:20:40, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3017, decode.acc_seg: 88.2188, aux.loss_ce: 0.1644, aux.acc_seg: 83.8404, loss: 0.4661, grad_norm: 3.7675 2023-02-16 19:21:12,061 - mmseg - INFO - Saving checkpoint at 38000 iterations 2023-02-16 19:21:13,163 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 19:21:13,163 - mmseg - INFO - Iter [38000/160000] lr: 4.575e-05, eta: 12:20:26, time: 0.386, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3384, decode.acc_seg: 87.1602, aux.loss_ce: 0.1794, aux.acc_seg: 83.0943, loss: 0.5178, grad_norm: 4.4361 2023-02-16 19:21:31,029 - mmseg - INFO - Iter [38050/160000] lr: 4.573e-05, eta: 12:20:06, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3212, decode.acc_seg: 87.1387, aux.loss_ce: 0.1751, aux.acc_seg: 82.6372, loss: 0.4963, grad_norm: 3.7504 2023-02-16 19:21:48,959 - mmseg - INFO - Iter [38100/160000] lr: 4.571e-05, eta: 12:19:47, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3271, decode.acc_seg: 87.7391, aux.loss_ce: 0.1773, aux.acc_seg: 83.4641, loss: 0.5044, grad_norm: 4.0380 2023-02-16 19:22:07,241 - mmseg - INFO - Iter [38150/160000] lr: 4.569e-05, eta: 12:19:29, time: 0.366, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3318, decode.acc_seg: 87.5376, aux.loss_ce: 0.1792, aux.acc_seg: 83.2298, loss: 0.5110, grad_norm: 4.5190 2023-02-16 19:22:25,135 - mmseg - INFO - Iter [38200/160000] lr: 4.568e-05, eta: 12:19:10, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3316, decode.acc_seg: 87.5249, aux.loss_ce: 0.1783, aux.acc_seg: 83.1922, loss: 0.5099, grad_norm: 4.2753 2023-02-16 19:22:43,253 - mmseg - INFO - Iter [38250/160000] lr: 4.566e-05, eta: 12:18:51, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3397, decode.acc_seg: 87.3163, aux.loss_ce: 0.1820, aux.acc_seg: 82.7253, loss: 0.5217, grad_norm: 4.1765 2023-02-16 19:23:01,288 - mmseg - INFO - Iter [38300/160000] lr: 4.564e-05, eta: 12:18:33, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3374, decode.acc_seg: 87.0008, aux.loss_ce: 0.1878, aux.acc_seg: 82.2817, loss: 0.5251, grad_norm: 4.7568 2023-02-16 19:23:19,551 - mmseg - INFO - Iter [38350/160000] lr: 4.562e-05, eta: 12:18:15, time: 0.365, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3517, decode.acc_seg: 86.4952, aux.loss_ce: 0.1908, aux.acc_seg: 82.1103, loss: 0.5425, grad_norm: 4.4524 2023-02-16 19:23:37,326 - mmseg - INFO - Iter [38400/160000] lr: 4.560e-05, eta: 12:17:55, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3575, decode.acc_seg: 86.0842, aux.loss_ce: 0.1936, aux.acc_seg: 81.5652, loss: 0.5512, grad_norm: 5.3245 2023-02-16 19:23:55,158 - mmseg - INFO - Iter [38450/160000] lr: 4.558e-05, eta: 12:17:36, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3115, decode.acc_seg: 87.8878, aux.loss_ce: 0.1691, aux.acc_seg: 83.7920, loss: 0.4806, grad_norm: 3.6599 2023-02-16 19:24:12,965 - mmseg - INFO - Iter [38500/160000] lr: 4.556e-05, eta: 12:17:16, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3149, decode.acc_seg: 87.7138, aux.loss_ce: 0.1743, aux.acc_seg: 82.9866, loss: 0.4892, grad_norm: 3.8787 2023-02-16 19:24:31,037 - mmseg - INFO - Iter [38550/160000] lr: 4.554e-05, eta: 12:16:58, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3541, decode.acc_seg: 86.2864, aux.loss_ce: 0.1908, aux.acc_seg: 81.7488, loss: 0.5449, grad_norm: 4.6851 2023-02-16 19:24:49,081 - mmseg - INFO - Iter [38600/160000] lr: 4.553e-05, eta: 12:16:39, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3235, decode.acc_seg: 87.8425, aux.loss_ce: 0.1761, aux.acc_seg: 83.3255, loss: 0.4996, grad_norm: 3.7085 2023-02-16 19:25:06,896 - mmseg - INFO - Iter [38650/160000] lr: 4.551e-05, eta: 12:16:20, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3224, decode.acc_seg: 87.6440, aux.loss_ce: 0.1789, aux.acc_seg: 83.0400, loss: 0.5013, grad_norm: 3.9196 2023-02-16 19:25:24,833 - mmseg - INFO - Iter [38700/160000] lr: 4.549e-05, eta: 12:16:00, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3428, decode.acc_seg: 86.3781, aux.loss_ce: 0.1792, aux.acc_seg: 82.5183, loss: 0.5220, grad_norm: 4.6859 2023-02-16 19:25:42,855 - mmseg - INFO - Iter [38750/160000] lr: 4.547e-05, eta: 12:15:42, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3432, decode.acc_seg: 86.9060, aux.loss_ce: 0.1901, aux.acc_seg: 81.8367, loss: 0.5333, grad_norm: 5.0374 2023-02-16 19:26:00,643 - mmseg - INFO - Iter [38800/160000] lr: 4.545e-05, eta: 12:15:22, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3330, decode.acc_seg: 87.2867, aux.loss_ce: 0.1820, aux.acc_seg: 82.7413, loss: 0.5150, grad_norm: 4.3382 2023-02-16 19:26:18,810 - mmseg - INFO - Iter [38850/160000] lr: 4.543e-05, eta: 12:15:04, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3248, decode.acc_seg: 87.8033, aux.loss_ce: 0.1743, aux.acc_seg: 83.4864, loss: 0.4991, grad_norm: 4.2955 2023-02-16 19:26:36,997 - mmseg - INFO - Iter [38900/160000] lr: 4.541e-05, eta: 12:14:46, time: 0.364, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3393, decode.acc_seg: 86.8771, aux.loss_ce: 0.1812, aux.acc_seg: 82.4802, loss: 0.5204, grad_norm: 5.4651 2023-02-16 19:26:54,989 - mmseg - INFO - Iter [38950/160000] lr: 4.539e-05, eta: 12:14:27, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3388, decode.acc_seg: 87.2414, aux.loss_ce: 0.1813, aux.acc_seg: 82.6758, loss: 0.5201, grad_norm: 4.2060 2023-02-16 19:27:12,803 - mmseg - INFO - Saving checkpoint at 39000 iterations 2023-02-16 19:27:13,895 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 19:27:13,896 - mmseg - INFO - Iter [39000/160000] lr: 4.538e-05, eta: 12:14:11, time: 0.378, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3359, decode.acc_seg: 87.4078, aux.loss_ce: 0.1872, aux.acc_seg: 82.0949, loss: 0.5231, grad_norm: 4.0468 2023-02-16 19:27:31,737 - mmseg - INFO - Iter [39050/160000] lr: 4.536e-05, eta: 12:13:51, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3491, decode.acc_seg: 86.6758, aux.loss_ce: 0.1916, aux.acc_seg: 81.8394, loss: 0.5406, grad_norm: 4.9104 2023-02-16 19:27:49,803 - mmseg - INFO - Iter [39100/160000] lr: 4.534e-05, eta: 12:13:33, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3105, decode.acc_seg: 87.9378, aux.loss_ce: 0.1697, aux.acc_seg: 83.2308, loss: 0.4802, grad_norm: 3.7566 2023-02-16 19:28:07,902 - mmseg - INFO - Iter [39150/160000] lr: 4.532e-05, eta: 12:13:14, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3296, decode.acc_seg: 87.1715, aux.loss_ce: 0.1801, aux.acc_seg: 82.5854, loss: 0.5097, grad_norm: 4.5765 2023-02-16 19:28:28,616 - mmseg - INFO - Iter [39200/160000] lr: 4.530e-05, eta: 12:13:04, time: 0.414, data_time: 0.051, memory: 16600, decode.loss_ce: 0.3385, decode.acc_seg: 87.3292, aux.loss_ce: 0.1800, aux.acc_seg: 83.0614, loss: 0.5185, grad_norm: 3.9753 2023-02-16 19:28:46,702 - mmseg - INFO - Iter [39250/160000] lr: 4.528e-05, eta: 12:12:45, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3232, decode.acc_seg: 87.5478, aux.loss_ce: 0.1788, aux.acc_seg: 82.9401, loss: 0.5021, grad_norm: 4.2475 2023-02-16 19:29:04,669 - mmseg - INFO - Iter [39300/160000] lr: 4.526e-05, eta: 12:12:26, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3121, decode.acc_seg: 87.8116, aux.loss_ce: 0.1717, aux.acc_seg: 83.5878, loss: 0.4838, grad_norm: 3.9786 2023-02-16 19:29:22,657 - mmseg - INFO - Iter [39350/160000] lr: 4.524e-05, eta: 12:12:07, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3375, decode.acc_seg: 87.0671, aux.loss_ce: 0.1877, aux.acc_seg: 82.0714, loss: 0.5252, grad_norm: 4.8317 2023-02-16 19:29:40,599 - mmseg - INFO - Iter [39400/160000] lr: 4.523e-05, eta: 12:11:48, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3215, decode.acc_seg: 87.5122, aux.loss_ce: 0.1755, aux.acc_seg: 82.9288, loss: 0.4970, grad_norm: 4.5788 2023-02-16 19:29:58,442 - mmseg - INFO - Iter [39450/160000] lr: 4.521e-05, eta: 12:11:29, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3197, decode.acc_seg: 87.6865, aux.loss_ce: 0.1756, aux.acc_seg: 83.2164, loss: 0.4953, grad_norm: 3.8873 2023-02-16 19:30:16,388 - mmseg - INFO - Iter [39500/160000] lr: 4.519e-05, eta: 12:11:10, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3379, decode.acc_seg: 87.0735, aux.loss_ce: 0.1820, aux.acc_seg: 82.5846, loss: 0.5199, grad_norm: 4.3714 2023-02-16 19:30:34,430 - mmseg - INFO - Iter [39550/160000] lr: 4.517e-05, eta: 12:10:51, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3280, decode.acc_seg: 87.4517, aux.loss_ce: 0.1834, aux.acc_seg: 82.4715, loss: 0.5114, grad_norm: 3.7114 2023-02-16 19:30:52,438 - mmseg - INFO - Iter [39600/160000] lr: 4.515e-05, eta: 12:10:33, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3250, decode.acc_seg: 87.5839, aux.loss_ce: 0.1730, aux.acc_seg: 83.4577, loss: 0.4980, grad_norm: 3.7138 2023-02-16 19:31:10,252 - mmseg - INFO - Iter [39650/160000] lr: 4.513e-05, eta: 12:10:13, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3085, decode.acc_seg: 88.2334, aux.loss_ce: 0.1753, aux.acc_seg: 83.4323, loss: 0.4837, grad_norm: 4.0082 2023-02-16 19:31:28,155 - mmseg - INFO - Iter [39700/160000] lr: 4.511e-05, eta: 12:09:54, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3377, decode.acc_seg: 86.7851, aux.loss_ce: 0.1823, aux.acc_seg: 82.2904, loss: 0.5199, grad_norm: 4.8785 2023-02-16 19:31:46,683 - mmseg - INFO - Iter [39750/160000] lr: 4.509e-05, eta: 12:09:37, time: 0.371, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3218, decode.acc_seg: 87.8798, aux.loss_ce: 0.1827, aux.acc_seg: 82.6609, loss: 0.5046, grad_norm: 4.1805 2023-02-16 19:32:04,773 - mmseg - INFO - Iter [39800/160000] lr: 4.508e-05, eta: 12:09:18, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3374, decode.acc_seg: 87.2238, aux.loss_ce: 0.1870, aux.acc_seg: 82.3126, loss: 0.5244, grad_norm: 4.4225 2023-02-16 19:32:22,719 - mmseg - INFO - Iter [39850/160000] lr: 4.506e-05, eta: 12:08:59, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3413, decode.acc_seg: 87.0182, aux.loss_ce: 0.1873, aux.acc_seg: 82.3046, loss: 0.5286, grad_norm: 4.5607 2023-02-16 19:32:40,704 - mmseg - INFO - Iter [39900/160000] lr: 4.504e-05, eta: 12:08:41, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3314, decode.acc_seg: 87.5027, aux.loss_ce: 0.1842, aux.acc_seg: 82.8281, loss: 0.5156, grad_norm: 4.4847 2023-02-16 19:32:58,523 - mmseg - INFO - Iter [39950/160000] lr: 4.502e-05, eta: 12:08:21, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3213, decode.acc_seg: 87.8211, aux.loss_ce: 0.1776, aux.acc_seg: 83.1129, loss: 0.4989, grad_norm: 4.2493 2023-02-16 19:33:16,543 - mmseg - INFO - Saving checkpoint at 40000 iterations 2023-02-16 19:33:17,726 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 19:33:17,726 - mmseg - INFO - Iter [40000/160000] lr: 4.500e-05, eta: 12:08:06, time: 0.384, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3209, decode.acc_seg: 87.4988, aux.loss_ce: 0.1749, aux.acc_seg: 83.0457, loss: 0.4958, grad_norm: 4.2153 2023-02-16 19:33:35,629 - mmseg - INFO - Iter [40050/160000] lr: 4.498e-05, eta: 12:07:47, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3295, decode.acc_seg: 87.1761, aux.loss_ce: 0.1775, aux.acc_seg: 82.9052, loss: 0.5070, grad_norm: 4.5122 2023-02-16 19:33:53,443 - mmseg - INFO - Iter [40100/160000] lr: 4.496e-05, eta: 12:07:28, time: 0.356, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3052, decode.acc_seg: 88.0299, aux.loss_ce: 0.1730, aux.acc_seg: 83.0683, loss: 0.4783, grad_norm: 3.8634 2023-02-16 19:34:11,232 - mmseg - INFO - Iter [40150/160000] lr: 4.494e-05, eta: 12:07:08, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3028, decode.acc_seg: 88.4153, aux.loss_ce: 0.1730, aux.acc_seg: 83.2502, loss: 0.4758, grad_norm: 4.1851 2023-02-16 19:34:29,282 - mmseg - INFO - Iter [40200/160000] lr: 4.493e-05, eta: 12:06:49, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3334, decode.acc_seg: 87.1385, aux.loss_ce: 0.1814, aux.acc_seg: 82.6668, loss: 0.5148, grad_norm: 5.4138 2023-02-16 19:34:47,302 - mmseg - INFO - Iter [40250/160000] lr: 4.491e-05, eta: 12:06:31, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3425, decode.acc_seg: 86.7424, aux.loss_ce: 0.1826, aux.acc_seg: 82.3513, loss: 0.5251, grad_norm: 4.4150 2023-02-16 19:35:05,188 - mmseg - INFO - Iter [40300/160000] lr: 4.489e-05, eta: 12:06:12, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3084, decode.acc_seg: 88.0614, aux.loss_ce: 0.1737, aux.acc_seg: 83.3485, loss: 0.4821, grad_norm: 3.8820 2023-02-16 19:35:23,326 - mmseg - INFO - Iter [40350/160000] lr: 4.487e-05, eta: 12:05:53, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3181, decode.acc_seg: 87.8345, aux.loss_ce: 0.1749, aux.acc_seg: 83.5406, loss: 0.4930, grad_norm: 4.1838 2023-02-16 19:35:41,244 - mmseg - INFO - Iter [40400/160000] lr: 4.485e-05, eta: 12:05:34, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3103, decode.acc_seg: 88.0933, aux.loss_ce: 0.1702, aux.acc_seg: 83.4958, loss: 0.4805, grad_norm: 4.1277 2023-02-16 19:36:01,785 - mmseg - INFO - Iter [40450/160000] lr: 4.483e-05, eta: 12:05:23, time: 0.411, data_time: 0.053, memory: 16600, decode.loss_ce: 0.3176, decode.acc_seg: 88.1099, aux.loss_ce: 0.1733, aux.acc_seg: 83.5823, loss: 0.4909, grad_norm: 4.4250 2023-02-16 19:36:19,616 - mmseg - INFO - Iter [40500/160000] lr: 4.481e-05, eta: 12:05:04, time: 0.357, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3386, decode.acc_seg: 87.2944, aux.loss_ce: 0.1772, aux.acc_seg: 83.3800, loss: 0.5158, grad_norm: 4.6476 2023-02-16 19:36:37,563 - mmseg - INFO - Iter [40550/160000] lr: 4.479e-05, eta: 12:04:45, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3136, decode.acc_seg: 88.1165, aux.loss_ce: 0.1791, aux.acc_seg: 83.2680, loss: 0.4928, grad_norm: 3.7869 2023-02-16 19:36:55,569 - mmseg - INFO - Iter [40600/160000] lr: 4.478e-05, eta: 12:04:26, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3175, decode.acc_seg: 87.5696, aux.loss_ce: 0.1747, aux.acc_seg: 82.9992, loss: 0.4922, grad_norm: 3.9834 2023-02-16 19:37:13,457 - mmseg - INFO - Iter [40650/160000] lr: 4.476e-05, eta: 12:04:07, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3184, decode.acc_seg: 87.8908, aux.loss_ce: 0.1729, aux.acc_seg: 83.3363, loss: 0.4912, grad_norm: 4.0581 2023-02-16 19:37:31,276 - mmseg - INFO - Iter [40700/160000] lr: 4.474e-05, eta: 12:03:47, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3132, decode.acc_seg: 87.8126, aux.loss_ce: 0.1702, aux.acc_seg: 83.4190, loss: 0.4834, grad_norm: 4.3805 2023-02-16 19:37:49,709 - mmseg - INFO - Iter [40750/160000] lr: 4.472e-05, eta: 12:03:30, time: 0.369, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3009, decode.acc_seg: 88.1464, aux.loss_ce: 0.1681, aux.acc_seg: 83.5141, loss: 0.4691, grad_norm: 3.9192 2023-02-16 19:38:07,534 - mmseg - INFO - Iter [40800/160000] lr: 4.470e-05, eta: 12:03:11, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3303, decode.acc_seg: 87.5859, aux.loss_ce: 0.1802, aux.acc_seg: 83.0595, loss: 0.5105, grad_norm: 4.1752 2023-02-16 19:38:25,304 - mmseg - INFO - Iter [40850/160000] lr: 4.468e-05, eta: 12:02:51, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3061, decode.acc_seg: 87.9760, aux.loss_ce: 0.1761, aux.acc_seg: 82.9341, loss: 0.4822, grad_norm: 3.9792 2023-02-16 19:38:43,081 - mmseg - INFO - Iter [40900/160000] lr: 4.466e-05, eta: 12:02:32, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3242, decode.acc_seg: 87.5897, aux.loss_ce: 0.1747, aux.acc_seg: 82.9537, loss: 0.4990, grad_norm: 3.9844 2023-02-16 19:39:01,127 - mmseg - INFO - Iter [40950/160000] lr: 4.464e-05, eta: 12:02:13, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3239, decode.acc_seg: 87.3246, aux.loss_ce: 0.1777, aux.acc_seg: 82.8594, loss: 0.5016, grad_norm: 4.0079 2023-02-16 19:39:19,169 - mmseg - INFO - Saving checkpoint at 41000 iterations 2023-02-16 19:39:20,259 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 19:39:20,259 - mmseg - INFO - Iter [41000/160000] lr: 4.463e-05, eta: 12:01:58, time: 0.383, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3273, decode.acc_seg: 87.6814, aux.loss_ce: 0.1815, aux.acc_seg: 82.6308, loss: 0.5088, grad_norm: 4.8398 2023-02-16 19:39:38,363 - mmseg - INFO - Iter [41050/160000] lr: 4.461e-05, eta: 12:01:39, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3197, decode.acc_seg: 87.8449, aux.loss_ce: 0.1773, aux.acc_seg: 82.8737, loss: 0.4969, grad_norm: 4.5669 2023-02-16 19:39:56,424 - mmseg - INFO - Iter [41100/160000] lr: 4.459e-05, eta: 12:01:20, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3179, decode.acc_seg: 87.8272, aux.loss_ce: 0.1759, aux.acc_seg: 83.0921, loss: 0.4938, grad_norm: 4.6931 2023-02-16 19:40:14,405 - mmseg - INFO - Iter [41150/160000] lr: 4.457e-05, eta: 12:01:02, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3131, decode.acc_seg: 88.0584, aux.loss_ce: 0.1732, aux.acc_seg: 83.4519, loss: 0.4863, grad_norm: 4.0250 2023-02-16 19:40:32,265 - mmseg - INFO - Iter [41200/160000] lr: 4.455e-05, eta: 12:00:42, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3249, decode.acc_seg: 87.6848, aux.loss_ce: 0.1786, aux.acc_seg: 83.4623, loss: 0.5036, grad_norm: 4.5621 2023-02-16 19:40:50,609 - mmseg - INFO - Iter [41250/160000] lr: 4.453e-05, eta: 12:00:25, time: 0.367, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3171, decode.acc_seg: 87.6143, aux.loss_ce: 0.1758, aux.acc_seg: 83.1006, loss: 0.4928, grad_norm: 4.1928 2023-02-16 19:41:08,855 - mmseg - INFO - Iter [41300/160000] lr: 4.451e-05, eta: 12:00:07, time: 0.365, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3211, decode.acc_seg: 87.3268, aux.loss_ce: 0.1784, aux.acc_seg: 82.6853, loss: 0.4995, grad_norm: 4.3419 2023-02-16 19:41:26,816 - mmseg - INFO - Iter [41350/160000] lr: 4.449e-05, eta: 11:59:48, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3015, decode.acc_seg: 88.6700, aux.loss_ce: 0.1679, aux.acc_seg: 84.2412, loss: 0.4694, grad_norm: 4.1853 2023-02-16 19:41:44,865 - mmseg - INFO - Iter [41400/160000] lr: 4.448e-05, eta: 11:59:29, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3205, decode.acc_seg: 87.6790, aux.loss_ce: 0.1796, aux.acc_seg: 82.6438, loss: 0.5002, grad_norm: 4.0563 2023-02-16 19:42:02,845 - mmseg - INFO - Iter [41450/160000] lr: 4.446e-05, eta: 11:59:10, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3158, decode.acc_seg: 87.6136, aux.loss_ce: 0.1776, aux.acc_seg: 82.9971, loss: 0.4934, grad_norm: 4.0142 2023-02-16 19:42:20,810 - mmseg - INFO - Iter [41500/160000] lr: 4.444e-05, eta: 11:58:51, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3212, decode.acc_seg: 87.5331, aux.loss_ce: 0.1817, aux.acc_seg: 82.4765, loss: 0.5029, grad_norm: 4.6365 2023-02-16 19:42:38,938 - mmseg - INFO - Iter [41550/160000] lr: 4.442e-05, eta: 11:58:33, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3004, decode.acc_seg: 88.4005, aux.loss_ce: 0.1716, aux.acc_seg: 83.4345, loss: 0.4720, grad_norm: 4.3209 2023-02-16 19:42:56,983 - mmseg - INFO - Iter [41600/160000] lr: 4.440e-05, eta: 11:58:14, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3097, decode.acc_seg: 87.6111, aux.loss_ce: 0.1705, aux.acc_seg: 83.6437, loss: 0.4802, grad_norm: 4.5138 2023-02-16 19:43:14,983 - mmseg - INFO - Iter [41650/160000] lr: 4.438e-05, eta: 11:57:56, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3375, decode.acc_seg: 87.2638, aux.loss_ce: 0.1856, aux.acc_seg: 82.4182, loss: 0.5231, grad_norm: 5.0658 2023-02-16 19:43:35,291 - mmseg - INFO - Iter [41700/160000] lr: 4.436e-05, eta: 11:57:43, time: 0.406, data_time: 0.052, memory: 16600, decode.loss_ce: 0.3183, decode.acc_seg: 87.8874, aux.loss_ce: 0.1787, aux.acc_seg: 82.8081, loss: 0.4970, grad_norm: 3.9576 2023-02-16 19:43:53,456 - mmseg - INFO - Iter [41750/160000] lr: 4.434e-05, eta: 11:57:25, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3145, decode.acc_seg: 88.0396, aux.loss_ce: 0.1742, aux.acc_seg: 83.3975, loss: 0.4887, grad_norm: 3.6123 2023-02-16 19:44:11,415 - mmseg - INFO - Iter [41800/160000] lr: 4.433e-05, eta: 11:57:06, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3056, decode.acc_seg: 88.4618, aux.loss_ce: 0.1696, aux.acc_seg: 84.0340, loss: 0.4752, grad_norm: 3.8407 2023-02-16 19:44:29,347 - mmseg - INFO - Iter [41850/160000] lr: 4.431e-05, eta: 11:56:47, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3121, decode.acc_seg: 87.5234, aux.loss_ce: 0.1750, aux.acc_seg: 82.9564, loss: 0.4871, grad_norm: 3.9074 2023-02-16 19:44:47,435 - mmseg - INFO - Iter [41900/160000] lr: 4.429e-05, eta: 11:56:29, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3069, decode.acc_seg: 88.0446, aux.loss_ce: 0.1674, aux.acc_seg: 83.5548, loss: 0.4743, grad_norm: 3.7598 2023-02-16 19:45:05,519 - mmseg - INFO - Iter [41950/160000] lr: 4.427e-05, eta: 11:56:10, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3233, decode.acc_seg: 87.5488, aux.loss_ce: 0.1783, aux.acc_seg: 82.7401, loss: 0.5017, grad_norm: 4.6066 2023-02-16 19:45:23,692 - mmseg - INFO - Saving checkpoint at 42000 iterations 2023-02-16 19:45:24,803 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 19:45:24,804 - mmseg - INFO - Iter [42000/160000] lr: 4.425e-05, eta: 11:55:55, time: 0.386, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3331, decode.acc_seg: 87.6527, aux.loss_ce: 0.1823, aux.acc_seg: 82.9306, loss: 0.5154, grad_norm: 4.0099 2023-02-16 19:45:42,798 - mmseg - INFO - Iter [42050/160000] lr: 4.423e-05, eta: 11:55:36, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3209, decode.acc_seg: 87.7447, aux.loss_ce: 0.1762, aux.acc_seg: 83.2188, loss: 0.4971, grad_norm: 4.1832 2023-02-16 19:46:00,693 - mmseg - INFO - Iter [42100/160000] lr: 4.421e-05, eta: 11:55:17, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3078, decode.acc_seg: 87.9037, aux.loss_ce: 0.1708, aux.acc_seg: 83.5587, loss: 0.4786, grad_norm: 4.9872 2023-02-16 19:46:18,721 - mmseg - INFO - Iter [42150/160000] lr: 4.419e-05, eta: 11:54:58, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3139, decode.acc_seg: 87.9879, aux.loss_ce: 0.1730, aux.acc_seg: 83.5137, loss: 0.4868, grad_norm: 4.1972 2023-02-16 19:46:36,641 - mmseg - INFO - Iter [42200/160000] lr: 4.418e-05, eta: 11:54:39, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3251, decode.acc_seg: 87.5064, aux.loss_ce: 0.1801, aux.acc_seg: 82.8094, loss: 0.5052, grad_norm: 4.3952 2023-02-16 19:46:54,458 - mmseg - INFO - Iter [42250/160000] lr: 4.416e-05, eta: 11:54:20, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2999, decode.acc_seg: 88.4358, aux.loss_ce: 0.1671, aux.acc_seg: 83.9028, loss: 0.4670, grad_norm: 4.3738 2023-02-16 19:47:12,550 - mmseg - INFO - Iter [42300/160000] lr: 4.414e-05, eta: 11:54:02, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3090, decode.acc_seg: 87.9371, aux.loss_ce: 0.1622, aux.acc_seg: 83.9002, loss: 0.4712, grad_norm: 4.0301 2023-02-16 19:47:30,658 - mmseg - INFO - Iter [42350/160000] lr: 4.412e-05, eta: 11:53:43, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2982, decode.acc_seg: 88.4938, aux.loss_ce: 0.1665, aux.acc_seg: 83.6826, loss: 0.4647, grad_norm: 4.1221 2023-02-16 19:47:48,751 - mmseg - INFO - Iter [42400/160000] lr: 4.410e-05, eta: 11:53:25, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3122, decode.acc_seg: 87.6837, aux.loss_ce: 0.1688, aux.acc_seg: 83.6502, loss: 0.4810, grad_norm: 4.2294 2023-02-16 19:48:06,675 - mmseg - INFO - Iter [42450/160000] lr: 4.408e-05, eta: 11:53:06, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3166, decode.acc_seg: 88.1069, aux.loss_ce: 0.1746, aux.acc_seg: 83.6857, loss: 0.4912, grad_norm: 4.0832 2023-02-16 19:48:24,546 - mmseg - INFO - Iter [42500/160000] lr: 4.406e-05, eta: 11:52:47, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3041, decode.acc_seg: 88.3571, aux.loss_ce: 0.1692, aux.acc_seg: 83.4649, loss: 0.4733, grad_norm: 3.9754 2023-02-16 19:48:42,769 - mmseg - INFO - Iter [42550/160000] lr: 4.404e-05, eta: 11:52:29, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3044, decode.acc_seg: 88.0523, aux.loss_ce: 0.1703, aux.acc_seg: 83.1487, loss: 0.4747, grad_norm: 3.7833 2023-02-16 19:49:00,739 - mmseg - INFO - Iter [42600/160000] lr: 4.403e-05, eta: 11:52:10, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3260, decode.acc_seg: 87.9163, aux.loss_ce: 0.1776, aux.acc_seg: 83.3002, loss: 0.5037, grad_norm: 4.2057 2023-02-16 19:49:18,657 - mmseg - INFO - Iter [42650/160000] lr: 4.401e-05, eta: 11:51:51, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3069, decode.acc_seg: 88.1510, aux.loss_ce: 0.1776, aux.acc_seg: 82.9346, loss: 0.4844, grad_norm: 4.1590 2023-02-16 19:49:36,614 - mmseg - INFO - Iter [42700/160000] lr: 4.399e-05, eta: 11:51:32, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3169, decode.acc_seg: 87.7446, aux.loss_ce: 0.1749, aux.acc_seg: 83.1643, loss: 0.4918, grad_norm: 4.6051 2023-02-16 19:49:54,706 - mmseg - INFO - Iter [42750/160000] lr: 4.397e-05, eta: 11:51:13, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3121, decode.acc_seg: 87.8705, aux.loss_ce: 0.1709, aux.acc_seg: 83.2779, loss: 0.4830, grad_norm: 4.0409 2023-02-16 19:50:12,769 - mmseg - INFO - Iter [42800/160000] lr: 4.395e-05, eta: 11:50:55, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3276, decode.acc_seg: 87.6488, aux.loss_ce: 0.1828, aux.acc_seg: 82.7459, loss: 0.5103, grad_norm: 4.3578 2023-02-16 19:50:30,700 - mmseg - INFO - Iter [42850/160000] lr: 4.393e-05, eta: 11:50:36, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3145, decode.acc_seg: 88.0117, aux.loss_ce: 0.1709, aux.acc_seg: 83.5683, loss: 0.4855, grad_norm: 4.1260 2023-02-16 19:50:48,869 - mmseg - INFO - Iter [42900/160000] lr: 4.391e-05, eta: 11:50:18, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3131, decode.acc_seg: 88.1883, aux.loss_ce: 0.1747, aux.acc_seg: 83.2393, loss: 0.4878, grad_norm: 4.0036 2023-02-16 19:51:09,291 - mmseg - INFO - Iter [42950/160000] lr: 4.389e-05, eta: 11:50:05, time: 0.409, data_time: 0.052, memory: 16600, decode.loss_ce: 0.3077, decode.acc_seg: 87.8096, aux.loss_ce: 0.1711, aux.acc_seg: 83.0508, loss: 0.4788, grad_norm: 3.7937 2023-02-16 19:51:27,529 - mmseg - INFO - Saving checkpoint at 43000 iterations 2023-02-16 19:51:28,621 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 19:51:28,622 - mmseg - INFO - Iter [43000/160000] lr: 4.388e-05, eta: 11:49:50, time: 0.387, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3126, decode.acc_seg: 88.1191, aux.loss_ce: 0.1707, aux.acc_seg: 83.4929, loss: 0.4833, grad_norm: 3.9282 2023-02-16 19:51:46,541 - mmseg - INFO - Iter [43050/160000] lr: 4.386e-05, eta: 11:49:31, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3018, decode.acc_seg: 88.3342, aux.loss_ce: 0.1660, aux.acc_seg: 83.8923, loss: 0.4678, grad_norm: 4.0209 2023-02-16 19:52:04,598 - mmseg - INFO - Iter [43100/160000] lr: 4.384e-05, eta: 11:49:13, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2970, decode.acc_seg: 88.1939, aux.loss_ce: 0.1677, aux.acc_seg: 83.7369, loss: 0.4647, grad_norm: 4.0071 2023-02-16 19:52:22,606 - mmseg - INFO - Iter [43150/160000] lr: 4.382e-05, eta: 11:48:54, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3230, decode.acc_seg: 87.7611, aux.loss_ce: 0.1784, aux.acc_seg: 82.7875, loss: 0.5014, grad_norm: 4.5485 2023-02-16 19:52:40,780 - mmseg - INFO - Iter [43200/160000] lr: 4.380e-05, eta: 11:48:36, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2891, decode.acc_seg: 89.0391, aux.loss_ce: 0.1635, aux.acc_seg: 84.4375, loss: 0.4527, grad_norm: 3.2145 2023-02-16 19:52:59,336 - mmseg - INFO - Iter [43250/160000] lr: 4.378e-05, eta: 11:48:19, time: 0.372, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3018, decode.acc_seg: 88.4118, aux.loss_ce: 0.1669, aux.acc_seg: 83.8655, loss: 0.4687, grad_norm: 4.0120 2023-02-16 19:53:17,244 - mmseg - INFO - Iter [43300/160000] lr: 4.376e-05, eta: 11:48:00, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3034, decode.acc_seg: 88.6300, aux.loss_ce: 0.1683, aux.acc_seg: 84.2370, loss: 0.4718, grad_norm: 4.2303 2023-02-16 19:53:35,860 - mmseg - INFO - Iter [43350/160000] lr: 4.374e-05, eta: 11:47:42, time: 0.372, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3175, decode.acc_seg: 87.6407, aux.loss_ce: 0.1723, aux.acc_seg: 83.1196, loss: 0.4899, grad_norm: 4.9432 2023-02-16 19:53:53,992 - mmseg - INFO - Iter [43400/160000] lr: 4.373e-05, eta: 11:47:24, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2822, decode.acc_seg: 88.6978, aux.loss_ce: 0.1605, aux.acc_seg: 84.0393, loss: 0.4427, grad_norm: 3.8906 2023-02-16 19:54:11,873 - mmseg - INFO - Iter [43450/160000] lr: 4.371e-05, eta: 11:47:05, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3066, decode.acc_seg: 87.9461, aux.loss_ce: 0.1730, aux.acc_seg: 83.1700, loss: 0.4795, grad_norm: 4.1502 2023-02-16 19:54:30,162 - mmseg - INFO - Iter [43500/160000] lr: 4.369e-05, eta: 11:46:47, time: 0.366, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2987, decode.acc_seg: 88.5356, aux.loss_ce: 0.1658, aux.acc_seg: 84.0016, loss: 0.4645, grad_norm: 3.9054 2023-02-16 19:54:48,164 - mmseg - INFO - Iter [43550/160000] lr: 4.367e-05, eta: 11:46:28, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3158, decode.acc_seg: 87.8658, aux.loss_ce: 0.1740, aux.acc_seg: 83.3742, loss: 0.4898, grad_norm: 4.1037 2023-02-16 19:55:06,152 - mmseg - INFO - Iter [43600/160000] lr: 4.365e-05, eta: 11:46:10, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3068, decode.acc_seg: 87.9576, aux.loss_ce: 0.1671, aux.acc_seg: 83.5682, loss: 0.4740, grad_norm: 4.0100 2023-02-16 19:55:24,444 - mmseg - INFO - Iter [43650/160000] lr: 4.363e-05, eta: 11:45:52, time: 0.366, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2974, decode.acc_seg: 88.2088, aux.loss_ce: 0.1696, aux.acc_seg: 82.9917, loss: 0.4671, grad_norm: 3.8311 2023-02-16 19:55:42,543 - mmseg - INFO - Iter [43700/160000] lr: 4.361e-05, eta: 11:45:33, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3090, decode.acc_seg: 88.2719, aux.loss_ce: 0.1789, aux.acc_seg: 83.1388, loss: 0.4879, grad_norm: 4.0442 2023-02-16 19:56:00,459 - mmseg - INFO - Iter [43750/160000] lr: 4.359e-05, eta: 11:45:14, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3159, decode.acc_seg: 88.3784, aux.loss_ce: 0.1789, aux.acc_seg: 83.3155, loss: 0.4948, grad_norm: 5.0226 2023-02-16 19:56:18,499 - mmseg - INFO - Iter [43800/160000] lr: 4.358e-05, eta: 11:44:56, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3063, decode.acc_seg: 88.0661, aux.loss_ce: 0.1715, aux.acc_seg: 83.2531, loss: 0.4778, grad_norm: 3.8212 2023-02-16 19:56:36,508 - mmseg - INFO - Iter [43850/160000] lr: 4.356e-05, eta: 11:44:37, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3144, decode.acc_seg: 87.8046, aux.loss_ce: 0.1743, aux.acc_seg: 83.0485, loss: 0.4887, grad_norm: 3.9830 2023-02-16 19:56:55,007 - mmseg - INFO - Iter [43900/160000] lr: 4.354e-05, eta: 11:44:19, time: 0.370, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3227, decode.acc_seg: 87.3901, aux.loss_ce: 0.1778, aux.acc_seg: 82.4232, loss: 0.5005, grad_norm: 4.4201 2023-02-16 19:57:13,031 - mmseg - INFO - Iter [43950/160000] lr: 4.352e-05, eta: 11:44:01, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3154, decode.acc_seg: 88.0916, aux.loss_ce: 0.1786, aux.acc_seg: 83.0564, loss: 0.4939, grad_norm: 3.9246 2023-02-16 19:57:31,071 - mmseg - INFO - Saving checkpoint at 44000 iterations 2023-02-16 19:57:32,169 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 19:57:32,169 - mmseg - INFO - Iter [44000/160000] lr: 4.350e-05, eta: 11:43:45, time: 0.383, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3109, decode.acc_seg: 87.9929, aux.loss_ce: 0.1693, aux.acc_seg: 83.7716, loss: 0.4802, grad_norm: 4.1399 2023-02-16 19:57:49,987 - mmseg - INFO - Iter [44050/160000] lr: 4.348e-05, eta: 11:43:26, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3059, decode.acc_seg: 88.3766, aux.loss_ce: 0.1731, aux.acc_seg: 83.5052, loss: 0.4790, grad_norm: 3.9952 2023-02-16 19:58:07,815 - mmseg - INFO - Iter [44100/160000] lr: 4.346e-05, eta: 11:43:07, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3254, decode.acc_seg: 87.4590, aux.loss_ce: 0.1828, aux.acc_seg: 82.5621, loss: 0.5082, grad_norm: 4.7243 2023-02-16 19:58:25,784 - mmseg - INFO - Iter [44150/160000] lr: 4.344e-05, eta: 11:42:48, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3008, decode.acc_seg: 88.5395, aux.loss_ce: 0.1679, aux.acc_seg: 83.9415, loss: 0.4687, grad_norm: 4.2735 2023-02-16 19:58:43,733 - mmseg - INFO - Iter [44200/160000] lr: 4.343e-05, eta: 11:42:29, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2988, decode.acc_seg: 88.4677, aux.loss_ce: 0.1702, aux.acc_seg: 83.5380, loss: 0.4690, grad_norm: 3.9202 2023-02-16 19:59:04,335 - mmseg - INFO - Iter [44250/160000] lr: 4.341e-05, eta: 11:42:17, time: 0.412, data_time: 0.054, memory: 16600, decode.loss_ce: 0.3022, decode.acc_seg: 88.1684, aux.loss_ce: 0.1700, aux.acc_seg: 83.3439, loss: 0.4721, grad_norm: 3.9155 2023-02-16 19:59:22,234 - mmseg - INFO - Iter [44300/160000] lr: 4.339e-05, eta: 11:41:58, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3137, decode.acc_seg: 88.0475, aux.loss_ce: 0.1741, aux.acc_seg: 83.4235, loss: 0.4878, grad_norm: 4.1006 2023-02-16 19:59:40,144 - mmseg - INFO - Iter [44350/160000] lr: 4.337e-05, eta: 11:41:39, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3046, decode.acc_seg: 88.2592, aux.loss_ce: 0.1705, aux.acc_seg: 83.5521, loss: 0.4751, grad_norm: 4.2057 2023-02-16 19:59:58,338 - mmseg - INFO - Iter [44400/160000] lr: 4.335e-05, eta: 11:41:21, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3014, decode.acc_seg: 88.4132, aux.loss_ce: 0.1672, aux.acc_seg: 84.0539, loss: 0.4686, grad_norm: 4.2560 2023-02-16 20:00:16,448 - mmseg - INFO - Iter [44450/160000] lr: 4.333e-05, eta: 11:41:02, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3183, decode.acc_seg: 87.8501, aux.loss_ce: 0.1725, aux.acc_seg: 83.4219, loss: 0.4908, grad_norm: 4.7425 2023-02-16 20:00:34,293 - mmseg - INFO - Iter [44500/160000] lr: 4.331e-05, eta: 11:40:43, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2955, decode.acc_seg: 88.8272, aux.loss_ce: 0.1671, aux.acc_seg: 84.1201, loss: 0.4626, grad_norm: 3.7923 2023-02-16 20:00:52,071 - mmseg - INFO - Iter [44550/160000] lr: 4.329e-05, eta: 11:40:24, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2877, decode.acc_seg: 88.8022, aux.loss_ce: 0.1661, aux.acc_seg: 83.5676, loss: 0.4538, grad_norm: 3.7377 2023-02-16 20:01:10,122 - mmseg - INFO - Iter [44600/160000] lr: 4.328e-05, eta: 11:40:05, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2939, decode.acc_seg: 88.3319, aux.loss_ce: 0.1639, aux.acc_seg: 84.1412, loss: 0.4579, grad_norm: 4.0986 2023-02-16 20:01:28,170 - mmseg - INFO - Iter [44650/160000] lr: 4.326e-05, eta: 11:39:47, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2845, decode.acc_seg: 88.7921, aux.loss_ce: 0.1567, aux.acc_seg: 84.4744, loss: 0.4412, grad_norm: 3.6729 2023-02-16 20:01:46,363 - mmseg - INFO - Iter [44700/160000] lr: 4.324e-05, eta: 11:39:29, time: 0.364, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2886, decode.acc_seg: 88.3705, aux.loss_ce: 0.1625, aux.acc_seg: 84.2322, loss: 0.4511, grad_norm: 4.1199 2023-02-16 20:02:04,301 - mmseg - INFO - Iter [44750/160000] lr: 4.322e-05, eta: 11:39:10, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2835, decode.acc_seg: 88.9942, aux.loss_ce: 0.1614, aux.acc_seg: 84.2552, loss: 0.4449, grad_norm: 4.3796 2023-02-16 20:02:22,386 - mmseg - INFO - Iter [44800/160000] lr: 4.320e-05, eta: 11:38:51, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2864, decode.acc_seg: 88.5571, aux.loss_ce: 0.1624, aux.acc_seg: 83.8011, loss: 0.4488, grad_norm: 3.9996 2023-02-16 20:02:40,421 - mmseg - INFO - Iter [44850/160000] lr: 4.318e-05, eta: 11:38:33, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3176, decode.acc_seg: 88.1521, aux.loss_ce: 0.1783, aux.acc_seg: 83.3824, loss: 0.4959, grad_norm: 4.0374 2023-02-16 20:02:58,191 - mmseg - INFO - Iter [44900/160000] lr: 4.316e-05, eta: 11:38:13, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3109, decode.acc_seg: 87.9419, aux.loss_ce: 0.1770, aux.acc_seg: 82.9490, loss: 0.4879, grad_norm: 4.0569 2023-02-16 20:03:16,206 - mmseg - INFO - Iter [44950/160000] lr: 4.314e-05, eta: 11:37:55, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3128, decode.acc_seg: 87.6812, aux.loss_ce: 0.1706, aux.acc_seg: 83.6290, loss: 0.4834, grad_norm: 4.5573 2023-02-16 20:03:34,088 - mmseg - INFO - Saving checkpoint at 45000 iterations 2023-02-16 20:03:35,270 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 20:03:35,270 - mmseg - INFO - Iter [45000/160000] lr: 4.313e-05, eta: 11:37:39, time: 0.381, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3119, decode.acc_seg: 88.0074, aux.loss_ce: 0.1757, aux.acc_seg: 82.8926, loss: 0.4876, grad_norm: 4.4789 2023-02-16 20:03:53,052 - mmseg - INFO - Iter [45050/160000] lr: 4.311e-05, eta: 11:37:19, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2965, decode.acc_seg: 88.4429, aux.loss_ce: 0.1658, aux.acc_seg: 83.9289, loss: 0.4623, grad_norm: 3.9835 2023-02-16 20:04:11,358 - mmseg - INFO - Iter [45100/160000] lr: 4.309e-05, eta: 11:37:01, time: 0.366, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3052, decode.acc_seg: 88.3266, aux.loss_ce: 0.1736, aux.acc_seg: 83.3576, loss: 0.4788, grad_norm: 3.9858 2023-02-16 20:04:29,343 - mmseg - INFO - Iter [45150/160000] lr: 4.307e-05, eta: 11:36:43, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3124, decode.acc_seg: 87.9673, aux.loss_ce: 0.1748, aux.acc_seg: 83.5267, loss: 0.4872, grad_norm: 4.3941 2023-02-16 20:04:47,211 - mmseg - INFO - Iter [45200/160000] lr: 4.305e-05, eta: 11:36:24, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3013, decode.acc_seg: 88.3691, aux.loss_ce: 0.1660, aux.acc_seg: 84.4424, loss: 0.4673, grad_norm: 4.2381 2023-02-16 20:05:05,025 - mmseg - INFO - Iter [45250/160000] lr: 4.303e-05, eta: 11:36:05, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3148, decode.acc_seg: 87.9604, aux.loss_ce: 0.1780, aux.acc_seg: 82.8140, loss: 0.4929, grad_norm: 3.9419 2023-02-16 20:05:22,885 - mmseg - INFO - Iter [45300/160000] lr: 4.301e-05, eta: 11:35:45, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3171, decode.acc_seg: 87.8069, aux.loss_ce: 0.1772, aux.acc_seg: 83.0429, loss: 0.4943, grad_norm: 4.1973 2023-02-16 20:05:40,747 - mmseg - INFO - Iter [45350/160000] lr: 4.299e-05, eta: 11:35:26, time: 0.357, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3057, decode.acc_seg: 88.3428, aux.loss_ce: 0.1716, aux.acc_seg: 83.4671, loss: 0.4773, grad_norm: 4.6373 2023-02-16 20:05:58,700 - mmseg - INFO - Iter [45400/160000] lr: 4.298e-05, eta: 11:35:08, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3113, decode.acc_seg: 88.0785, aux.loss_ce: 0.1743, aux.acc_seg: 83.1485, loss: 0.4856, grad_norm: 4.2406 2023-02-16 20:06:16,679 - mmseg - INFO - Iter [45450/160000] lr: 4.296e-05, eta: 11:34:49, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2972, decode.acc_seg: 88.5856, aux.loss_ce: 0.1710, aux.acc_seg: 83.5110, loss: 0.4681, grad_norm: 3.9209 2023-02-16 20:06:36,954 - mmseg - INFO - Iter [45500/160000] lr: 4.294e-05, eta: 11:34:36, time: 0.405, data_time: 0.052, memory: 16600, decode.loss_ce: 0.2802, decode.acc_seg: 89.1516, aux.loss_ce: 0.1568, aux.acc_seg: 85.2472, loss: 0.4370, grad_norm: 3.7910 2023-02-16 20:06:55,017 - mmseg - INFO - Iter [45550/160000] lr: 4.292e-05, eta: 11:34:17, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2985, decode.acc_seg: 88.2732, aux.loss_ce: 0.1716, aux.acc_seg: 83.1800, loss: 0.4701, grad_norm: 4.0919 2023-02-16 20:07:12,964 - mmseg - INFO - Iter [45600/160000] lr: 4.290e-05, eta: 11:33:59, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2869, decode.acc_seg: 88.9913, aux.loss_ce: 0.1615, aux.acc_seg: 84.2622, loss: 0.4484, grad_norm: 4.1129 2023-02-16 20:07:30,957 - mmseg - INFO - Iter [45650/160000] lr: 4.288e-05, eta: 11:33:40, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3018, decode.acc_seg: 88.2417, aux.loss_ce: 0.1697, aux.acc_seg: 83.5888, loss: 0.4716, grad_norm: 4.0956 2023-02-16 20:07:48,867 - mmseg - INFO - Iter [45700/160000] lr: 4.286e-05, eta: 11:33:21, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2933, decode.acc_seg: 88.5706, aux.loss_ce: 0.1710, aux.acc_seg: 83.5406, loss: 0.4643, grad_norm: 4.1603 2023-02-16 20:08:06,822 - mmseg - INFO - Iter [45750/160000] lr: 4.284e-05, eta: 11:33:02, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2983, decode.acc_seg: 88.5597, aux.loss_ce: 0.1674, aux.acc_seg: 84.1966, loss: 0.4657, grad_norm: 3.8574 2023-02-16 20:08:25,028 - mmseg - INFO - Iter [45800/160000] lr: 4.283e-05, eta: 11:32:44, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2763, decode.acc_seg: 89.4535, aux.loss_ce: 0.1589, aux.acc_seg: 84.8541, loss: 0.4352, grad_norm: 3.8867 2023-02-16 20:08:43,036 - mmseg - INFO - Iter [45850/160000] lr: 4.281e-05, eta: 11:32:25, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2898, decode.acc_seg: 88.9826, aux.loss_ce: 0.1637, aux.acc_seg: 84.5533, loss: 0.4534, grad_norm: 3.8252 2023-02-16 20:09:01,077 - mmseg - INFO - Iter [45900/160000] lr: 4.279e-05, eta: 11:32:07, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3027, decode.acc_seg: 88.5302, aux.loss_ce: 0.1738, aux.acc_seg: 83.7626, loss: 0.4765, grad_norm: 4.6840 2023-02-16 20:09:19,492 - mmseg - INFO - Iter [45950/160000] lr: 4.277e-05, eta: 11:31:49, time: 0.368, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2908, decode.acc_seg: 88.2823, aux.loss_ce: 0.1670, aux.acc_seg: 83.4322, loss: 0.4578, grad_norm: 3.7483 2023-02-16 20:09:37,526 - mmseg - INFO - Saving checkpoint at 46000 iterations 2023-02-16 20:09:38,594 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 20:09:38,595 - mmseg - INFO - Iter [46000/160000] lr: 4.275e-05, eta: 11:31:33, time: 0.383, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2819, decode.acc_seg: 89.0858, aux.loss_ce: 0.1604, aux.acc_seg: 84.4200, loss: 0.4423, grad_norm: 3.6427 2023-02-16 20:09:56,812 - mmseg - INFO - Iter [46050/160000] lr: 4.273e-05, eta: 11:31:15, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3139, decode.acc_seg: 88.1179, aux.loss_ce: 0.1760, aux.acc_seg: 83.2025, loss: 0.4899, grad_norm: 4.2518 2023-02-16 20:10:15,139 - mmseg - INFO - Iter [46100/160000] lr: 4.271e-05, eta: 11:30:57, time: 0.367, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3107, decode.acc_seg: 88.1882, aux.loss_ce: 0.1715, aux.acc_seg: 83.7057, loss: 0.4821, grad_norm: 4.1114 2023-02-16 20:10:33,106 - mmseg - INFO - Iter [46150/160000] lr: 4.269e-05, eta: 11:30:38, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2881, decode.acc_seg: 88.9446, aux.loss_ce: 0.1667, aux.acc_seg: 83.6908, loss: 0.4549, grad_norm: 4.2930 2023-02-16 20:10:51,273 - mmseg - INFO - Iter [46200/160000] lr: 4.268e-05, eta: 11:30:20, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3090, decode.acc_seg: 88.1701, aux.loss_ce: 0.1670, aux.acc_seg: 83.9003, loss: 0.4760, grad_norm: 4.6054 2023-02-16 20:11:09,307 - mmseg - INFO - Iter [46250/160000] lr: 4.266e-05, eta: 11:30:01, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3001, decode.acc_seg: 88.2938, aux.loss_ce: 0.1650, aux.acc_seg: 83.8683, loss: 0.4651, grad_norm: 4.3752 2023-02-16 20:11:27,279 - mmseg - INFO - Iter [46300/160000] lr: 4.264e-05, eta: 11:29:43, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2810, decode.acc_seg: 88.7438, aux.loss_ce: 0.1567, aux.acc_seg: 84.3598, loss: 0.4378, grad_norm: 3.9140 2023-02-16 20:11:45,148 - mmseg - INFO - Iter [46350/160000] lr: 4.262e-05, eta: 11:29:24, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2889, decode.acc_seg: 88.8060, aux.loss_ce: 0.1657, aux.acc_seg: 83.7039, loss: 0.4546, grad_norm: 4.5410 2023-02-16 20:12:03,192 - mmseg - INFO - Iter [46400/160000] lr: 4.260e-05, eta: 11:29:05, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2942, decode.acc_seg: 88.4836, aux.loss_ce: 0.1641, aux.acc_seg: 84.0042, loss: 0.4584, grad_norm: 4.2460 2023-02-16 20:12:21,131 - mmseg - INFO - Iter [46450/160000] lr: 4.258e-05, eta: 11:28:46, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3113, decode.acc_seg: 88.0267, aux.loss_ce: 0.1747, aux.acc_seg: 83.1032, loss: 0.4860, grad_norm: 4.7599 2023-02-16 20:12:39,028 - mmseg - INFO - Iter [46500/160000] lr: 4.256e-05, eta: 11:28:27, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3156, decode.acc_seg: 87.6564, aux.loss_ce: 0.1758, aux.acc_seg: 82.9619, loss: 0.4914, grad_norm: 6.2017 2023-02-16 20:12:57,104 - mmseg - INFO - Iter [46550/160000] lr: 4.254e-05, eta: 11:28:09, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3029, decode.acc_seg: 88.0262, aux.loss_ce: 0.1711, aux.acc_seg: 83.2030, loss: 0.4740, grad_norm: 3.8561 2023-02-16 20:13:15,073 - mmseg - INFO - Iter [46600/160000] lr: 4.253e-05, eta: 11:27:50, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3108, decode.acc_seg: 88.1291, aux.loss_ce: 0.1706, aux.acc_seg: 83.4444, loss: 0.4814, grad_norm: 4.5326 2023-02-16 20:13:33,073 - mmseg - INFO - Iter [46650/160000] lr: 4.251e-05, eta: 11:27:31, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3265, decode.acc_seg: 87.5646, aux.loss_ce: 0.1799, aux.acc_seg: 82.8351, loss: 0.5065, grad_norm: 4.1335 2023-02-16 20:13:51,053 - mmseg - INFO - Iter [46700/160000] lr: 4.249e-05, eta: 11:27:13, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2905, decode.acc_seg: 88.7114, aux.loss_ce: 0.1703, aux.acc_seg: 83.8037, loss: 0.4609, grad_norm: 3.7807 2023-02-16 20:14:11,445 - mmseg - INFO - Iter [46750/160000] lr: 4.247e-05, eta: 11:27:00, time: 0.408, data_time: 0.053, memory: 16600, decode.loss_ce: 0.3001, decode.acc_seg: 88.2169, aux.loss_ce: 0.1706, aux.acc_seg: 83.4825, loss: 0.4706, grad_norm: 3.7157 2023-02-16 20:14:29,344 - mmseg - INFO - Iter [46800/160000] lr: 4.245e-05, eta: 11:26:41, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2879, decode.acc_seg: 88.9901, aux.loss_ce: 0.1628, aux.acc_seg: 84.5338, loss: 0.4508, grad_norm: 3.9575 2023-02-16 20:14:47,586 - mmseg - INFO - Iter [46850/160000] lr: 4.243e-05, eta: 11:26:23, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2734, decode.acc_seg: 89.1785, aux.loss_ce: 0.1617, aux.acc_seg: 84.0491, loss: 0.4351, grad_norm: 3.5924 2023-02-16 20:15:05,713 - mmseg - INFO - Iter [46900/160000] lr: 4.241e-05, eta: 11:26:04, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2810, decode.acc_seg: 89.1296, aux.loss_ce: 0.1602, aux.acc_seg: 84.4227, loss: 0.4411, grad_norm: 4.3307 2023-02-16 20:15:23,727 - mmseg - INFO - Iter [46950/160000] lr: 4.239e-05, eta: 11:25:46, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2998, decode.acc_seg: 88.7143, aux.loss_ce: 0.1711, aux.acc_seg: 83.6966, loss: 0.4709, grad_norm: 3.7607 2023-02-16 20:15:41,506 - mmseg - INFO - Saving checkpoint at 47000 iterations 2023-02-16 20:15:42,565 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 20:15:42,566 - mmseg - INFO - Iter [47000/160000] lr: 4.238e-05, eta: 11:25:29, time: 0.377, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2804, decode.acc_seg: 88.9544, aux.loss_ce: 0.1573, aux.acc_seg: 84.6434, loss: 0.4376, grad_norm: 3.7849 2023-02-16 20:16:00,629 - mmseg - INFO - Iter [47050/160000] lr: 4.236e-05, eta: 11:25:10, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2738, decode.acc_seg: 89.3385, aux.loss_ce: 0.1567, aux.acc_seg: 84.7600, loss: 0.4305, grad_norm: 3.9468 2023-02-16 20:16:18,526 - mmseg - INFO - Iter [47100/160000] lr: 4.234e-05, eta: 11:24:52, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2969, decode.acc_seg: 88.4067, aux.loss_ce: 0.1700, aux.acc_seg: 83.4267, loss: 0.4670, grad_norm: 4.1032 2023-02-16 20:16:36,898 - mmseg - INFO - Iter [47150/160000] lr: 4.232e-05, eta: 11:24:34, time: 0.367, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3025, decode.acc_seg: 88.2671, aux.loss_ce: 0.1711, aux.acc_seg: 83.5064, loss: 0.4736, grad_norm: 4.4950 2023-02-16 20:16:54,869 - mmseg - INFO - Iter [47200/160000] lr: 4.230e-05, eta: 11:24:15, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3003, decode.acc_seg: 88.4983, aux.loss_ce: 0.1683, aux.acc_seg: 83.9121, loss: 0.4687, grad_norm: 4.2018 2023-02-16 20:17:12,933 - mmseg - INFO - Iter [47250/160000] lr: 4.228e-05, eta: 11:23:57, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2957, decode.acc_seg: 88.4933, aux.loss_ce: 0.1702, aux.acc_seg: 83.4918, loss: 0.4659, grad_norm: 4.1867 2023-02-16 20:17:30,801 - mmseg - INFO - Iter [47300/160000] lr: 4.226e-05, eta: 11:23:38, time: 0.357, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2957, decode.acc_seg: 88.9057, aux.loss_ce: 0.1653, aux.acc_seg: 84.5378, loss: 0.4610, grad_norm: 4.1173 2023-02-16 20:17:48,668 - mmseg - INFO - Iter [47350/160000] lr: 4.224e-05, eta: 11:23:19, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3086, decode.acc_seg: 88.1869, aux.loss_ce: 0.1709, aux.acc_seg: 83.7310, loss: 0.4796, grad_norm: 4.9479 2023-02-16 20:18:06,524 - mmseg - INFO - Iter [47400/160000] lr: 4.223e-05, eta: 11:23:00, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2760, decode.acc_seg: 89.2628, aux.loss_ce: 0.1533, aux.acc_seg: 85.2001, loss: 0.4293, grad_norm: 3.5805 2023-02-16 20:18:24,402 - mmseg - INFO - Iter [47450/160000] lr: 4.221e-05, eta: 11:22:41, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2758, decode.acc_seg: 89.0467, aux.loss_ce: 0.1586, aux.acc_seg: 84.3221, loss: 0.4344, grad_norm: 3.8003 2023-02-16 20:18:42,228 - mmseg - INFO - Iter [47500/160000] lr: 4.219e-05, eta: 11:22:22, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2899, decode.acc_seg: 88.6623, aux.loss_ce: 0.1623, aux.acc_seg: 84.2096, loss: 0.4522, grad_norm: 3.5587 2023-02-16 20:19:00,029 - mmseg - INFO - Iter [47550/160000] lr: 4.217e-05, eta: 11:22:02, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3057, decode.acc_seg: 88.1093, aux.loss_ce: 0.1694, aux.acc_seg: 83.7467, loss: 0.4751, grad_norm: 4.5431 2023-02-16 20:19:17,870 - mmseg - INFO - Iter [47600/160000] lr: 4.215e-05, eta: 11:21:43, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2926, decode.acc_seg: 88.5169, aux.loss_ce: 0.1688, aux.acc_seg: 83.4852, loss: 0.4614, grad_norm: 4.0915 2023-02-16 20:19:36,183 - mmseg - INFO - Iter [47650/160000] lr: 4.213e-05, eta: 11:21:25, time: 0.366, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3206, decode.acc_seg: 87.7631, aux.loss_ce: 0.1785, aux.acc_seg: 83.2561, loss: 0.4991, grad_norm: 4.2379 2023-02-16 20:19:54,077 - mmseg - INFO - Iter [47700/160000] lr: 4.211e-05, eta: 11:21:06, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2996, decode.acc_seg: 88.1639, aux.loss_ce: 0.1653, aux.acc_seg: 83.6843, loss: 0.4649, grad_norm: 3.8255 2023-02-16 20:20:12,283 - mmseg - INFO - Iter [47750/160000] lr: 4.209e-05, eta: 11:20:48, time: 0.365, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2987, decode.acc_seg: 88.6486, aux.loss_ce: 0.1703, aux.acc_seg: 83.8061, loss: 0.4689, grad_norm: 3.9423 2023-02-16 20:20:30,236 - mmseg - INFO - Iter [47800/160000] lr: 4.208e-05, eta: 11:20:30, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3042, decode.acc_seg: 88.2031, aux.loss_ce: 0.1726, aux.acc_seg: 83.2518, loss: 0.4768, grad_norm: 4.2344 2023-02-16 20:20:48,618 - mmseg - INFO - Iter [47850/160000] lr: 4.206e-05, eta: 11:20:12, time: 0.368, data_time: 0.007, memory: 16600, decode.loss_ce: 0.3030, decode.acc_seg: 88.5533, aux.loss_ce: 0.1680, aux.acc_seg: 84.3185, loss: 0.4711, grad_norm: 4.2370 2023-02-16 20:21:06,720 - mmseg - INFO - Iter [47900/160000] lr: 4.204e-05, eta: 11:19:53, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2872, decode.acc_seg: 88.8313, aux.loss_ce: 0.1647, aux.acc_seg: 84.0740, loss: 0.4519, grad_norm: 3.9530 2023-02-16 20:21:24,626 - mmseg - INFO - Iter [47950/160000] lr: 4.202e-05, eta: 11:19:35, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2992, decode.acc_seg: 88.4277, aux.loss_ce: 0.1686, aux.acc_seg: 83.6696, loss: 0.4679, grad_norm: 3.8929 2023-02-16 20:21:44,937 - mmseg - INFO - Saving checkpoint at 48000 iterations 2023-02-16 20:21:46,059 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 20:21:46,060 - mmseg - INFO - Iter [48000/160000] lr: 4.200e-05, eta: 11:19:24, time: 0.429, data_time: 0.050, memory: 16600, decode.loss_ce: 0.2903, decode.acc_seg: 88.6790, aux.loss_ce: 0.1649, aux.acc_seg: 83.6169, loss: 0.4552, grad_norm: 4.5342 2023-02-16 20:22:15,075 - mmseg - INFO - per class results: 2023-02-16 20:22:15,081 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 67.38 | 94.17 | | building | 80.01 | 85.86 | | sky | 93.83 | 97.95 | | floor | 75.92 | 90.29 | | tree | 72.74 | 88.3 | | ceiling | 71.07 | 72.89 | | road | 81.98 | 88.62 | | bed | 86.47 | 94.57 | | windowpane | 58.77 | 80.52 | | grass | 67.4 | 80.03 | | cabinet | 55.1 | 62.33 | | sidewalk | 63.84 | 80.85 | | person | 78.32 | 88.71 | | earth | 27.4 | 34.02 | | door | 35.63 | 39.31 | | table | 56.07 | 68.49 | | mountain | 56.56 | 78.07 | | plant | 51.51 | 65.31 | | curtain | 68.22 | 75.7 | | chair | 52.09 | 62.47 | | car | 81.9 | 89.53 | | water | 55.04 | 65.72 | | painting | 68.7 | 80.17 | | sofa | 62.25 | 78.71 | | shelf | 44.63 | 65.93 | | house | 50.39 | 70.16 | | sea | 58.2 | 74.19 | | mirror | 56.1 | 59.28 | | rug | 6.48 | 6.54 | | field | 25.81 | 44.2 | | armchair | 36.14 | 51.23 | | seat | 53.74 | 64.08 | | fence | 28.19 | 34.16 | | desk | 43.29 | 64.14 | | rock | 34.74 | 43.79 | | wardrobe | 44.59 | 62.45 | | lamp | 54.65 | 63.15 | | bathtub | 70.57 | 77.91 | | railing | 25.98 | 32.7 | | cushion | 50.96 | 70.51 | | base | 17.8 | 21.63 | | box | 17.24 | 19.08 | | column | 42.59 | 53.38 | | signboard | 34.24 | 44.82 | | chest of drawers | 42.03 | 56.04 | | counter | 23.39 | 26.69 | | sand | 44.4 | 71.32 | | sink | 70.54 | 75.62 | | skyscraper | 44.59 | 60.52 | | fireplace | 72.19 | 89.26 | | refrigerator | 67.01 | 86.77 | | grandstand | 36.84 | 48.44 | | path | 21.78 | 47.65 | | stairs | 20.18 | 20.7 | | runway | 65.15 | 83.93 | | case | 36.88 | 40.13 | | pool table | 90.86 | 91.81 | | pillow | 54.58 | 64.01 | | screen door | 0.38 | 0.38 | | stairway | 25.3 | 30.4 | | river | 11.59 | 33.36 | | bridge | 58.49 | 74.95 | | bookcase | 30.46 | 35.68 | | blind | 29.53 | 29.99 | | coffee table | 53.33 | 83.9 | | toilet | 82.32 | 86.44 | | flower | 34.35 | 43.2 | | book | 44.0 | 65.32 | | hill | 3.67 | 4.76 | | bench | 40.96 | 52.7 | | countertop | 52.66 | 62.32 | | stove | 68.97 | 81.94 | | palm | 50.26 | 69.78 | | kitchen island | 34.94 | 74.18 | | computer | 63.8 | 68.68 | | swivel chair | 33.67 | 36.72 | | boat | 38.89 | 49.78 | | bar | 29.24 | 32.89 | | arcade machine | 66.6 | 72.23 | | hovel | 7.64 | 8.4 | | bus | 83.11 | 95.47 | | towel | 53.45 | 58.0 | | light | 48.31 | 56.74 | | truck | 18.28 | 24.03 | | tower | 8.0 | 9.82 | | chandelier | 58.12 | 68.24 | | awning | 28.49 | 38.03 | | streetlight | 19.38 | 22.91 | | booth | 28.24 | 28.37 | | television receiver | 67.91 | 76.83 | | airplane | 52.34 | 66.12 | | dirt track | 0.9 | 0.9 | | apparel | 24.75 | 27.56 | | pole | 22.62 | 48.34 | | land | 0.0 | 0.0 | | bannister | 11.73 | 17.62 | | escalator | 14.8 | 15.14 | | ottoman | 43.8 | 63.03 | | bottle | 37.71 | 54.07 | | buffet | 43.6 | 55.14 | | poster | 7.26 | 9.38 | | stage | 10.26 | 13.88 | | van | 46.62 | 56.05 | | ship | 34.91 | 49.22 | | fountain | 20.42 | 21.75 | | conveyer belt | 57.8 | 59.55 | | canopy | 37.6 | 49.58 | | washer | 60.69 | 61.27 | | plaything | 18.98 | 26.3 | | swimming pool | 47.27 | 58.12 | | stool | 28.6 | 36.35 | | barrel | 48.13 | 64.5 | | basket | 29.01 | 45.45 | | waterfall | 59.25 | 77.82 | | tent | 94.24 | 98.1 | | bag | 14.89 | 16.65 | | minibike | 56.13 | 60.63 | | cradle | 75.1 | 95.45 | | oven | 34.17 | 52.36 | | ball | 42.08 | 63.67 | | food | 59.56 | 65.24 | | step | 3.03 | 3.24 | | tank | 30.05 | 34.28 | | trade name | 19.26 | 21.27 | | microwave | 72.61 | 89.11 | | pot | 37.8 | 42.56 | | animal | 49.12 | 51.4 | | bicycle | 49.08 | 58.53 | | lake | 12.85 | 13.47 | | dishwasher | 66.85 | 77.38 | | screen | 68.7 | 85.4 | | blanket | 4.52 | 4.8 | | sculpture | 47.77 | 50.6 | | hood | 55.49 | 66.83 | | sconce | 26.85 | 29.71 | | vase | 32.98 | 46.17 | | traffic light | 26.58 | 34.4 | | tray | 4.27 | 5.49 | | ashcan | 31.0 | 34.76 | | fan | 57.01 | 69.59 | | pier | 23.89 | 40.28 | | crt screen | 6.19 | 11.13 | | plate | 46.36 | 53.72 | | monitor | 4.69 | 5.27 | | bulletin board | 31.42 | 34.25 | | shower | 0.18 | 7.3 | | radiator | 46.01 | 48.82 | | glass | 7.6 | 7.97 | | clock | 23.63 | 24.97 | | flag | 31.28 | 34.74 | +---------------------+-------+-------+ 2023-02-16 20:22:15,081 - mmseg - INFO - Summary: 2023-02-16 20:22:15,081 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 79.39 | 42.54 | 52.15 | +-------+-------+-------+ 2023-02-16 20:22:15,082 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 20:22:15,083 - mmseg - INFO - Iter(val) [500] aAcc: 0.7939, mIoU: 0.4254, mAcc: 0.5215, IoU.wall: 0.6738, IoU.building: 0.8001, IoU.sky: 0.9383, IoU.floor: 0.7592, IoU.tree: 0.7274, IoU.ceiling: 0.7107, IoU.road: 0.8198, IoU.bed : 0.8647, IoU.windowpane: 0.5877, IoU.grass: 0.6740, IoU.cabinet: 0.5510, IoU.sidewalk: 0.6384, IoU.person: 0.7832, IoU.earth: 0.2740, IoU.door: 0.3563, IoU.table: 0.5607, IoU.mountain: 0.5656, IoU.plant: 0.5151, IoU.curtain: 0.6822, IoU.chair: 0.5209, IoU.car: 0.8190, IoU.water: 0.5504, IoU.painting: 0.6870, IoU.sofa: 0.6225, IoU.shelf: 0.4463, IoU.house: 0.5039, IoU.sea: 0.5820, IoU.mirror: 0.5610, IoU.rug: 0.0648, IoU.field: 0.2581, IoU.armchair: 0.3614, IoU.seat: 0.5374, IoU.fence: 0.2819, IoU.desk: 0.4329, IoU.rock: 0.3474, IoU.wardrobe: 0.4459, IoU.lamp: 0.5465, IoU.bathtub: 0.7057, IoU.railing: 0.2598, IoU.cushion: 0.5096, IoU.base: 0.1780, IoU.box: 0.1724, IoU.column: 0.4259, IoU.signboard: 0.3424, IoU.chest of drawers: 0.4203, IoU.counter: 0.2339, IoU.sand: 0.4440, IoU.sink: 0.7054, IoU.skyscraper: 0.4459, IoU.fireplace: 0.7219, IoU.refrigerator: 0.6701, IoU.grandstand: 0.3684, IoU.path: 0.2178, IoU.stairs: 0.2018, IoU.runway: 0.6515, IoU.case: 0.3688, IoU.pool table: 0.9086, IoU.pillow: 0.5458, IoU.screen door: 0.0038, IoU.stairway: 0.2530, IoU.river: 0.1159, IoU.bridge: 0.5849, IoU.bookcase: 0.3046, IoU.blind: 0.2953, IoU.coffee table: 0.5333, IoU.toilet: 0.8232, IoU.flower: 0.3435, IoU.book: 0.4400, IoU.hill: 0.0367, IoU.bench: 0.4096, IoU.countertop: 0.5266, IoU.stove: 0.6897, IoU.palm: 0.5026, IoU.kitchen island: 0.3494, IoU.computer: 0.6380, IoU.swivel chair: 0.3367, IoU.boat: 0.3889, IoU.bar: 0.2924, IoU.arcade machine: 0.6660, IoU.hovel: 0.0764, IoU.bus: 0.8311, IoU.towel: 0.5345, IoU.light: 0.4831, IoU.truck: 0.1828, IoU.tower: 0.0800, IoU.chandelier: 0.5812, IoU.awning: 0.2849, IoU.streetlight: 0.1938, IoU.booth: 0.2824, IoU.television receiver: 0.6791, IoU.airplane: 0.5234, IoU.dirt track: 0.0090, IoU.apparel: 0.2475, IoU.pole: 0.2262, IoU.land: 0.0000, IoU.bannister: 0.1173, IoU.escalator: 0.1480, IoU.ottoman: 0.4380, IoU.bottle: 0.3771, IoU.buffet: 0.4360, IoU.poster: 0.0726, IoU.stage: 0.1026, IoU.van: 0.4662, IoU.ship: 0.3491, IoU.fountain: 0.2042, IoU.conveyer belt: 0.5780, IoU.canopy: 0.3760, IoU.washer: 0.6069, IoU.plaything: 0.1898, IoU.swimming pool: 0.4727, IoU.stool: 0.2860, IoU.barrel: 0.4813, IoU.basket: 0.2901, IoU.waterfall: 0.5925, IoU.tent: 0.9424, IoU.bag: 0.1489, IoU.minibike: 0.5613, IoU.cradle: 0.7510, IoU.oven: 0.3417, IoU.ball: 0.4208, IoU.food: 0.5956, IoU.step: 0.0303, IoU.tank: 0.3005, IoU.trade name: 0.1926, IoU.microwave: 0.7261, IoU.pot: 0.3780, IoU.animal: 0.4912, IoU.bicycle: 0.4908, IoU.lake: 0.1285, IoU.dishwasher: 0.6685, IoU.screen: 0.6870, IoU.blanket: 0.0452, IoU.sculpture: 0.4777, IoU.hood: 0.5549, IoU.sconce: 0.2685, IoU.vase: 0.3298, IoU.traffic light: 0.2658, IoU.tray: 0.0427, IoU.ashcan: 0.3100, IoU.fan: 0.5701, IoU.pier: 0.2389, IoU.crt screen: 0.0619, IoU.plate: 0.4636, IoU.monitor: 0.0469, IoU.bulletin board: 0.3142, IoU.shower: 0.0018, IoU.radiator: 0.4601, IoU.glass: 0.0760, IoU.clock: 0.2363, IoU.flag: 0.3128, Acc.wall: 0.9417, Acc.building: 0.8586, Acc.sky: 0.9795, Acc.floor: 0.9029, Acc.tree: 0.8830, Acc.ceiling: 0.7289, Acc.road: 0.8862, Acc.bed : 0.9457, Acc.windowpane: 0.8052, Acc.grass: 0.8003, Acc.cabinet: 0.6233, Acc.sidewalk: 0.8085, Acc.person: 0.8871, Acc.earth: 0.3402, Acc.door: 0.3931, Acc.table: 0.6849, Acc.mountain: 0.7807, Acc.plant: 0.6531, Acc.curtain: 0.7570, Acc.chair: 0.6247, Acc.car: 0.8953, Acc.water: 0.6572, Acc.painting: 0.8017, Acc.sofa: 0.7871, Acc.shelf: 0.6593, Acc.house: 0.7016, Acc.sea: 0.7419, Acc.mirror: 0.5928, Acc.rug: 0.0654, Acc.field: 0.4420, Acc.armchair: 0.5123, Acc.seat: 0.6408, Acc.fence: 0.3416, Acc.desk: 0.6414, Acc.rock: 0.4379, Acc.wardrobe: 0.6245, Acc.lamp: 0.6315, Acc.bathtub: 0.7791, Acc.railing: 0.3270, Acc.cushion: 0.7051, Acc.base: 0.2163, Acc.box: 0.1908, Acc.column: 0.5338, Acc.signboard: 0.4482, Acc.chest of drawers: 0.5604, Acc.counter: 0.2669, Acc.sand: 0.7132, Acc.sink: 0.7562, Acc.skyscraper: 0.6052, Acc.fireplace: 0.8926, Acc.refrigerator: 0.8677, Acc.grandstand: 0.4844, Acc.path: 0.4765, Acc.stairs: 0.2070, Acc.runway: 0.8393, Acc.case: 0.4013, Acc.pool table: 0.9181, Acc.pillow: 0.6401, Acc.screen door: 0.0038, Acc.stairway: 0.3040, Acc.river: 0.3336, Acc.bridge: 0.7495, Acc.bookcase: 0.3568, Acc.blind: 0.2999, Acc.coffee table: 0.8390, Acc.toilet: 0.8644, Acc.flower: 0.4320, Acc.book: 0.6532, Acc.hill: 0.0476, Acc.bench: 0.5270, Acc.countertop: 0.6232, Acc.stove: 0.8194, Acc.palm: 0.6978, Acc.kitchen island: 0.7418, Acc.computer: 0.6868, Acc.swivel chair: 0.3672, Acc.boat: 0.4978, Acc.bar: 0.3289, Acc.arcade machine: 0.7223, Acc.hovel: 0.0840, Acc.bus: 0.9547, Acc.towel: 0.5800, Acc.light: 0.5674, Acc.truck: 0.2403, Acc.tower: 0.0982, Acc.chandelier: 0.6824, Acc.awning: 0.3803, Acc.streetlight: 0.2291, Acc.booth: 0.2837, Acc.television receiver: 0.7683, Acc.airplane: 0.6612, Acc.dirt track: 0.0090, Acc.apparel: 0.2756, Acc.pole: 0.4834, Acc.land: 0.0000, Acc.bannister: 0.1762, Acc.escalator: 0.1514, Acc.ottoman: 0.6303, Acc.bottle: 0.5407, Acc.buffet: 0.5514, Acc.poster: 0.0938, Acc.stage: 0.1388, Acc.van: 0.5605, Acc.ship: 0.4922, Acc.fountain: 0.2175, Acc.conveyer belt: 0.5955, Acc.canopy: 0.4958, Acc.washer: 0.6127, Acc.plaything: 0.2630, Acc.swimming pool: 0.5812, Acc.stool: 0.3635, Acc.barrel: 0.6450, Acc.basket: 0.4545, Acc.waterfall: 0.7782, Acc.tent: 0.9810, Acc.bag: 0.1665, Acc.minibike: 0.6063, Acc.cradle: 0.9545, Acc.oven: 0.5236, Acc.ball: 0.6367, Acc.food: 0.6524, Acc.step: 0.0324, Acc.tank: 0.3428, Acc.trade name: 0.2127, Acc.microwave: 0.8911, Acc.pot: 0.4256, Acc.animal: 0.5140, Acc.bicycle: 0.5853, Acc.lake: 0.1347, Acc.dishwasher: 0.7738, Acc.screen: 0.8540, Acc.blanket: 0.0480, Acc.sculpture: 0.5060, Acc.hood: 0.6683, Acc.sconce: 0.2971, Acc.vase: 0.4617, Acc.traffic light: 0.3440, Acc.tray: 0.0549, Acc.ashcan: 0.3476, Acc.fan: 0.6959, Acc.pier: 0.4028, Acc.crt screen: 0.1113, Acc.plate: 0.5372, Acc.monitor: 0.0527, Acc.bulletin board: 0.3425, Acc.shower: 0.0730, Acc.radiator: 0.4882, Acc.glass: 0.0797, Acc.clock: 0.2497, Acc.flag: 0.3474 2023-02-16 20:22:33,129 - mmseg - INFO - Iter [48050/160000] lr: 4.198e-05, eta: 11:20:13, time: 0.941, data_time: 0.587, memory: 16600, decode.loss_ce: 0.2882, decode.acc_seg: 88.3105, aux.loss_ce: 0.1565, aux.acc_seg: 84.4829, loss: 0.4446, grad_norm: 4.4436 2023-02-16 20:22:51,096 - mmseg - INFO - Iter [48100/160000] lr: 4.196e-05, eta: 11:19:54, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2639, decode.acc_seg: 89.6132, aux.loss_ce: 0.1547, aux.acc_seg: 84.6756, loss: 0.4187, grad_norm: 3.9727 2023-02-16 20:23:09,028 - mmseg - INFO - Iter [48150/160000] lr: 4.194e-05, eta: 11:19:35, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2942, decode.acc_seg: 88.7807, aux.loss_ce: 0.1722, aux.acc_seg: 83.6681, loss: 0.4664, grad_norm: 4.0418 2023-02-16 20:23:27,214 - mmseg - INFO - Iter [48200/160000] lr: 4.193e-05, eta: 11:19:17, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2831, decode.acc_seg: 88.9217, aux.loss_ce: 0.1647, aux.acc_seg: 83.8212, loss: 0.4478, grad_norm: 3.5938 2023-02-16 20:23:45,166 - mmseg - INFO - Iter [48250/160000] lr: 4.191e-05, eta: 11:18:58, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2836, decode.acc_seg: 88.9366, aux.loss_ce: 0.1612, aux.acc_seg: 84.4075, loss: 0.4448, grad_norm: 3.6074 2023-02-16 20:24:03,155 - mmseg - INFO - Iter [48300/160000] lr: 4.189e-05, eta: 11:18:39, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2997, decode.acc_seg: 88.2775, aux.loss_ce: 0.1650, aux.acc_seg: 84.0214, loss: 0.4647, grad_norm: 4.3391 2023-02-16 20:24:21,157 - mmseg - INFO - Iter [48350/160000] lr: 4.187e-05, eta: 11:18:20, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2840, decode.acc_seg: 88.8441, aux.loss_ce: 0.1640, aux.acc_seg: 83.9202, loss: 0.4480, grad_norm: 4.0782 2023-02-16 20:24:39,178 - mmseg - INFO - Iter [48400/160000] lr: 4.185e-05, eta: 11:18:02, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2967, decode.acc_seg: 88.2152, aux.loss_ce: 0.1676, aux.acc_seg: 83.4987, loss: 0.4643, grad_norm: 4.2140 2023-02-16 20:24:57,099 - mmseg - INFO - Iter [48450/160000] lr: 4.183e-05, eta: 11:17:43, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2795, decode.acc_seg: 89.1681, aux.loss_ce: 0.1600, aux.acc_seg: 84.5797, loss: 0.4396, grad_norm: 3.5379 2023-02-16 20:25:14,982 - mmseg - INFO - Iter [48500/160000] lr: 4.181e-05, eta: 11:17:24, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2915, decode.acc_seg: 88.7793, aux.loss_ce: 0.1618, aux.acc_seg: 84.2862, loss: 0.4533, grad_norm: 4.5309 2023-02-16 20:25:32,808 - mmseg - INFO - Iter [48550/160000] lr: 4.179e-05, eta: 11:17:05, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2847, decode.acc_seg: 88.8621, aux.loss_ce: 0.1625, aux.acc_seg: 84.1924, loss: 0.4472, grad_norm: 4.2799 2023-02-16 20:25:50,927 - mmseg - INFO - Iter [48600/160000] lr: 4.178e-05, eta: 11:16:46, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2946, decode.acc_seg: 88.7424, aux.loss_ce: 0.1630, aux.acc_seg: 84.6144, loss: 0.4576, grad_norm: 3.7835 2023-02-16 20:26:08,881 - mmseg - INFO - Iter [48650/160000] lr: 4.176e-05, eta: 11:16:27, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2967, decode.acc_seg: 88.5298, aux.loss_ce: 0.1694, aux.acc_seg: 83.8687, loss: 0.4662, grad_norm: 4.0777 2023-02-16 20:26:27,277 - mmseg - INFO - Iter [48700/160000] lr: 4.174e-05, eta: 11:16:10, time: 0.368, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2938, decode.acc_seg: 88.7440, aux.loss_ce: 0.1638, aux.acc_seg: 84.1029, loss: 0.4576, grad_norm: 5.2215 2023-02-16 20:26:45,436 - mmseg - INFO - Iter [48750/160000] lr: 4.172e-05, eta: 11:15:51, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2923, decode.acc_seg: 88.7360, aux.loss_ce: 0.1641, aux.acc_seg: 84.4779, loss: 0.4563, grad_norm: 3.8964 2023-02-16 20:27:03,552 - mmseg - INFO - Iter [48800/160000] lr: 4.170e-05, eta: 11:15:33, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3063, decode.acc_seg: 88.7842, aux.loss_ce: 0.1697, aux.acc_seg: 84.0772, loss: 0.4760, grad_norm: 4.5329 2023-02-16 20:27:21,387 - mmseg - INFO - Iter [48850/160000] lr: 4.168e-05, eta: 11:15:14, time: 0.357, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2895, decode.acc_seg: 88.8312, aux.loss_ce: 0.1639, aux.acc_seg: 84.4121, loss: 0.4534, grad_norm: 3.8549 2023-02-16 20:27:39,423 - mmseg - INFO - Iter [48900/160000] lr: 4.166e-05, eta: 11:14:55, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2875, decode.acc_seg: 88.8010, aux.loss_ce: 0.1631, aux.acc_seg: 84.1659, loss: 0.4506, grad_norm: 3.7192 2023-02-16 20:27:57,210 - mmseg - INFO - Iter [48950/160000] lr: 4.164e-05, eta: 11:14:36, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2890, decode.acc_seg: 88.5515, aux.loss_ce: 0.1629, aux.acc_seg: 83.9579, loss: 0.4519, grad_norm: 3.9529 2023-02-16 20:28:15,049 - mmseg - INFO - Saving checkpoint at 49000 iterations 2023-02-16 20:28:16,126 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 20:28:16,126 - mmseg - INFO - Iter [49000/160000] lr: 4.163e-05, eta: 11:14:19, time: 0.378, data_time: 0.006, memory: 16600, decode.loss_ce: 0.3062, decode.acc_seg: 88.3810, aux.loss_ce: 0.1753, aux.acc_seg: 83.2676, loss: 0.4815, grad_norm: 4.5513 2023-02-16 20:28:34,179 - mmseg - INFO - Iter [49050/160000] lr: 4.161e-05, eta: 11:14:00, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2799, decode.acc_seg: 89.0657, aux.loss_ce: 0.1602, aux.acc_seg: 84.4374, loss: 0.4401, grad_norm: 3.7928 2023-02-16 20:28:52,349 - mmseg - INFO - Iter [49100/160000] lr: 4.159e-05, eta: 11:13:42, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2934, decode.acc_seg: 88.6801, aux.loss_ce: 0.1680, aux.acc_seg: 83.5602, loss: 0.4614, grad_norm: 4.2612 2023-02-16 20:29:10,310 - mmseg - INFO - Iter [49150/160000] lr: 4.157e-05, eta: 11:13:23, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2889, decode.acc_seg: 88.4551, aux.loss_ce: 0.1655, aux.acc_seg: 83.6993, loss: 0.4544, grad_norm: 3.5823 2023-02-16 20:29:28,514 - mmseg - INFO - Iter [49200/160000] lr: 4.155e-05, eta: 11:13:05, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2894, decode.acc_seg: 88.7537, aux.loss_ce: 0.1685, aux.acc_seg: 83.5833, loss: 0.4579, grad_norm: 3.5643 2023-02-16 20:29:46,855 - mmseg - INFO - Iter [49250/160000] lr: 4.153e-05, eta: 11:12:47, time: 0.367, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2975, decode.acc_seg: 88.8128, aux.loss_ce: 0.1726, aux.acc_seg: 83.6567, loss: 0.4701, grad_norm: 4.3984 2023-02-16 20:30:07,115 - mmseg - INFO - Iter [49300/160000] lr: 4.151e-05, eta: 11:12:33, time: 0.405, data_time: 0.052, memory: 16600, decode.loss_ce: 0.2737, decode.acc_seg: 89.3968, aux.loss_ce: 0.1526, aux.acc_seg: 85.1949, loss: 0.4263, grad_norm: 3.5268 2023-02-16 20:30:25,378 - mmseg - INFO - Iter [49350/160000] lr: 4.149e-05, eta: 11:12:15, time: 0.365, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2825, decode.acc_seg: 89.0715, aux.loss_ce: 0.1583, aux.acc_seg: 84.5255, loss: 0.4408, grad_norm: 4.3629 2023-02-16 20:30:43,323 - mmseg - INFO - Iter [49400/160000] lr: 4.148e-05, eta: 11:11:56, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2776, decode.acc_seg: 89.1011, aux.loss_ce: 0.1642, aux.acc_seg: 83.8513, loss: 0.4418, grad_norm: 3.5999 2023-02-16 20:31:01,443 - mmseg - INFO - Iter [49450/160000] lr: 4.146e-05, eta: 11:11:38, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2820, decode.acc_seg: 89.4185, aux.loss_ce: 0.1604, aux.acc_seg: 84.2604, loss: 0.4424, grad_norm: 3.7611 2023-02-16 20:31:19,407 - mmseg - INFO - Iter [49500/160000] lr: 4.144e-05, eta: 11:11:19, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2947, decode.acc_seg: 88.8927, aux.loss_ce: 0.1616, aux.acc_seg: 84.7455, loss: 0.4563, grad_norm: 4.7311 2023-02-16 20:31:37,323 - mmseg - INFO - Iter [49550/160000] lr: 4.142e-05, eta: 11:11:00, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2723, decode.acc_seg: 89.3120, aux.loss_ce: 0.1583, aux.acc_seg: 84.4762, loss: 0.4306, grad_norm: 3.3444 2023-02-16 20:31:55,192 - mmseg - INFO - Iter [49600/160000] lr: 4.140e-05, eta: 11:10:41, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2940, decode.acc_seg: 88.7232, aux.loss_ce: 0.1686, aux.acc_seg: 83.9114, loss: 0.4626, grad_norm: 3.5869 2023-02-16 20:32:13,091 - mmseg - INFO - Iter [49650/160000] lr: 4.138e-05, eta: 11:10:22, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2700, decode.acc_seg: 89.5584, aux.loss_ce: 0.1591, aux.acc_seg: 84.5540, loss: 0.4292, grad_norm: 3.7144 2023-02-16 20:32:31,163 - mmseg - INFO - Iter [49700/160000] lr: 4.136e-05, eta: 11:10:04, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2912, decode.acc_seg: 88.9544, aux.loss_ce: 0.1659, aux.acc_seg: 84.3150, loss: 0.4571, grad_norm: 4.1873 2023-02-16 20:32:49,002 - mmseg - INFO - Iter [49750/160000] lr: 4.134e-05, eta: 11:09:45, time: 0.357, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2896, decode.acc_seg: 88.7443, aux.loss_ce: 0.1637, aux.acc_seg: 84.1216, loss: 0.4532, grad_norm: 4.0945 2023-02-16 20:33:07,088 - mmseg - INFO - Iter [49800/160000] lr: 4.133e-05, eta: 11:09:26, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2680, decode.acc_seg: 89.5419, aux.loss_ce: 0.1563, aux.acc_seg: 84.6383, loss: 0.4243, grad_norm: 3.5711 2023-02-16 20:33:24,901 - mmseg - INFO - Iter [49850/160000] lr: 4.131e-05, eta: 11:09:07, time: 0.356, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2841, decode.acc_seg: 89.3600, aux.loss_ce: 0.1617, aux.acc_seg: 84.6756, loss: 0.4457, grad_norm: 3.7778 2023-02-16 20:33:42,867 - mmseg - INFO - Iter [49900/160000] lr: 4.129e-05, eta: 11:08:48, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2751, decode.acc_seg: 89.3793, aux.loss_ce: 0.1577, aux.acc_seg: 84.7706, loss: 0.4328, grad_norm: 3.8073 2023-02-16 20:34:00,899 - mmseg - INFO - Iter [49950/160000] lr: 4.127e-05, eta: 11:08:29, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2888, decode.acc_seg: 88.7630, aux.loss_ce: 0.1697, aux.acc_seg: 83.6244, loss: 0.4585, grad_norm: 4.2754 2023-02-16 20:34:18,797 - mmseg - INFO - Saving checkpoint at 50000 iterations 2023-02-16 20:34:19,887 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 20:34:19,887 - mmseg - INFO - Iter [50000/160000] lr: 4.125e-05, eta: 11:08:13, time: 0.380, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2714, decode.acc_seg: 89.4941, aux.loss_ce: 0.1597, aux.acc_seg: 84.2477, loss: 0.4311, grad_norm: 3.8117 2023-02-16 20:34:37,817 - mmseg - INFO - Iter [50050/160000] lr: 4.123e-05, eta: 11:07:54, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2781, decode.acc_seg: 89.5163, aux.loss_ce: 0.1596, aux.acc_seg: 84.7457, loss: 0.4377, grad_norm: 3.9065 2023-02-16 20:34:55,670 - mmseg - INFO - Iter [50100/160000] lr: 4.121e-05, eta: 11:07:35, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2797, decode.acc_seg: 89.2269, aux.loss_ce: 0.1614, aux.acc_seg: 84.2413, loss: 0.4411, grad_norm: 3.8142 2023-02-16 20:35:13,696 - mmseg - INFO - Iter [50150/160000] lr: 4.119e-05, eta: 11:07:16, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2847, decode.acc_seg: 88.9969, aux.loss_ce: 0.1617, aux.acc_seg: 84.1221, loss: 0.4464, grad_norm: 3.8177 2023-02-16 20:35:31,935 - mmseg - INFO - Iter [50200/160000] lr: 4.118e-05, eta: 11:06:58, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2898, decode.acc_seg: 88.5921, aux.loss_ce: 0.1631, aux.acc_seg: 83.8522, loss: 0.4529, grad_norm: 4.0883 2023-02-16 20:35:49,907 - mmseg - INFO - Iter [50250/160000] lr: 4.116e-05, eta: 11:06:39, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2900, decode.acc_seg: 88.4283, aux.loss_ce: 0.1661, aux.acc_seg: 83.5979, loss: 0.4561, grad_norm: 4.9128 2023-02-16 20:36:08,044 - mmseg - INFO - Iter [50300/160000] lr: 4.114e-05, eta: 11:06:21, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2769, decode.acc_seg: 89.2351, aux.loss_ce: 0.1678, aux.acc_seg: 83.8516, loss: 0.4447, grad_norm: 4.4208 2023-02-16 20:36:25,969 - mmseg - INFO - Iter [50350/160000] lr: 4.112e-05, eta: 11:06:02, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2988, decode.acc_seg: 88.5574, aux.loss_ce: 0.1685, aux.acc_seg: 83.7129, loss: 0.4673, grad_norm: 3.9886 2023-02-16 20:36:43,942 - mmseg - INFO - Iter [50400/160000] lr: 4.110e-05, eta: 11:05:43, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2712, decode.acc_seg: 89.2483, aux.loss_ce: 0.1605, aux.acc_seg: 84.1094, loss: 0.4317, grad_norm: 3.5499 2023-02-16 20:37:01,900 - mmseg - INFO - Iter [50450/160000] lr: 4.108e-05, eta: 11:05:25, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2904, decode.acc_seg: 88.4746, aux.loss_ce: 0.1647, aux.acc_seg: 83.8412, loss: 0.4551, grad_norm: 3.9888 2023-02-16 20:37:19,745 - mmseg - INFO - Iter [50500/160000] lr: 4.106e-05, eta: 11:05:05, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2851, decode.acc_seg: 89.0598, aux.loss_ce: 0.1649, aux.acc_seg: 84.2167, loss: 0.4500, grad_norm: 4.2119 2023-02-16 20:37:40,138 - mmseg - INFO - Iter [50550/160000] lr: 4.104e-05, eta: 11:04:52, time: 0.408, data_time: 0.052, memory: 16600, decode.loss_ce: 0.2791, decode.acc_seg: 89.2001, aux.loss_ce: 0.1616, aux.acc_seg: 84.5523, loss: 0.4407, grad_norm: 3.6847 2023-02-16 20:37:58,529 - mmseg - INFO - Iter [50600/160000] lr: 4.103e-05, eta: 11:04:34, time: 0.368, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2849, decode.acc_seg: 89.1748, aux.loss_ce: 0.1684, aux.acc_seg: 84.0991, loss: 0.4533, grad_norm: 4.3929 2023-02-16 20:38:16,421 - mmseg - INFO - Iter [50650/160000] lr: 4.101e-05, eta: 11:04:15, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2824, decode.acc_seg: 88.6842, aux.loss_ce: 0.1618, aux.acc_seg: 83.9940, loss: 0.4442, grad_norm: 4.7342 2023-02-16 20:38:34,474 - mmseg - INFO - Iter [50700/160000] lr: 4.099e-05, eta: 11:03:57, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2749, decode.acc_seg: 89.4297, aux.loss_ce: 0.1566, aux.acc_seg: 84.9533, loss: 0.4315, grad_norm: 3.7556 2023-02-16 20:38:52,221 - mmseg - INFO - Iter [50750/160000] lr: 4.097e-05, eta: 11:03:37, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2932, decode.acc_seg: 88.6913, aux.loss_ce: 0.1629, aux.acc_seg: 84.4030, loss: 0.4562, grad_norm: 3.9310 2023-02-16 20:39:10,699 - mmseg - INFO - Iter [50800/160000] lr: 4.095e-05, eta: 11:03:20, time: 0.370, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2745, decode.acc_seg: 89.3424, aux.loss_ce: 0.1587, aux.acc_seg: 84.6918, loss: 0.4331, grad_norm: 3.4912 2023-02-16 20:39:28,738 - mmseg - INFO - Iter [50850/160000] lr: 4.093e-05, eta: 11:03:01, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2873, decode.acc_seg: 88.8756, aux.loss_ce: 0.1630, aux.acc_seg: 84.3151, loss: 0.4503, grad_norm: 4.0780 2023-02-16 20:39:46,770 - mmseg - INFO - Iter [50900/160000] lr: 4.091e-05, eta: 11:02:42, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2848, decode.acc_seg: 89.0097, aux.loss_ce: 0.1672, aux.acc_seg: 83.9519, loss: 0.4519, grad_norm: 3.8876 2023-02-16 20:40:04,949 - mmseg - INFO - Iter [50950/160000] lr: 4.089e-05, eta: 11:02:24, time: 0.364, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2710, decode.acc_seg: 89.5547, aux.loss_ce: 0.1568, aux.acc_seg: 84.7935, loss: 0.4277, grad_norm: 3.8077 2023-02-16 20:40:23,001 - mmseg - INFO - Saving checkpoint at 51000 iterations 2023-02-16 20:40:24,303 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 20:40:24,303 - mmseg - INFO - Iter [51000/160000] lr: 4.088e-05, eta: 11:02:08, time: 0.387, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2703, decode.acc_seg: 89.3686, aux.loss_ce: 0.1565, aux.acc_seg: 84.7364, loss: 0.4268, grad_norm: 4.3302 2023-02-16 20:40:42,334 - mmseg - INFO - Iter [51050/160000] lr: 4.086e-05, eta: 11:01:50, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2743, decode.acc_seg: 89.3728, aux.loss_ce: 0.1573, aux.acc_seg: 84.6983, loss: 0.4316, grad_norm: 4.1568 2023-02-16 20:41:00,536 - mmseg - INFO - Iter [51100/160000] lr: 4.084e-05, eta: 11:01:31, time: 0.364, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2691, decode.acc_seg: 89.1476, aux.loss_ce: 0.1557, aux.acc_seg: 84.2680, loss: 0.4248, grad_norm: 3.8734 2023-02-16 20:41:18,706 - mmseg - INFO - Iter [51150/160000] lr: 4.082e-05, eta: 11:01:13, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2868, decode.acc_seg: 88.4436, aux.loss_ce: 0.1602, aux.acc_seg: 83.9647, loss: 0.4471, grad_norm: 3.8783 2023-02-16 20:41:36,540 - mmseg - INFO - Iter [51200/160000] lr: 4.080e-05, eta: 11:00:54, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2808, decode.acc_seg: 89.0585, aux.loss_ce: 0.1603, aux.acc_seg: 84.2936, loss: 0.4411, grad_norm: 4.3742 2023-02-16 20:41:54,316 - mmseg - INFO - Iter [51250/160000] lr: 4.078e-05, eta: 11:00:35, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2639, decode.acc_seg: 89.7746, aux.loss_ce: 0.1544, aux.acc_seg: 85.0516, loss: 0.4183, grad_norm: 3.5886 2023-02-16 20:42:12,364 - mmseg - INFO - Iter [51300/160000] lr: 4.076e-05, eta: 11:00:16, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2910, decode.acc_seg: 88.4758, aux.loss_ce: 0.1630, aux.acc_seg: 83.8347, loss: 0.4540, grad_norm: 4.2574 2023-02-16 20:42:30,311 - mmseg - INFO - Iter [51350/160000] lr: 4.074e-05, eta: 10:59:57, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2789, decode.acc_seg: 89.2517, aux.loss_ce: 0.1622, aux.acc_seg: 84.4365, loss: 0.4412, grad_norm: 4.1205 2023-02-16 20:42:48,516 - mmseg - INFO - Iter [51400/160000] lr: 4.073e-05, eta: 10:59:39, time: 0.364, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2551, decode.acc_seg: 90.1166, aux.loss_ce: 0.1504, aux.acc_seg: 85.2110, loss: 0.4055, grad_norm: 3.3925 2023-02-16 20:43:06,363 - mmseg - INFO - Iter [51450/160000] lr: 4.071e-05, eta: 10:59:20, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2735, decode.acc_seg: 89.1446, aux.loss_ce: 0.1567, aux.acc_seg: 84.7016, loss: 0.4301, grad_norm: 3.8917 2023-02-16 20:43:24,380 - mmseg - INFO - Iter [51500/160000] lr: 4.069e-05, eta: 10:59:01, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2911, decode.acc_seg: 89.3108, aux.loss_ce: 0.1684, aux.acc_seg: 84.3587, loss: 0.4595, grad_norm: 4.5083 2023-02-16 20:43:42,232 - mmseg - INFO - Iter [51550/160000] lr: 4.067e-05, eta: 10:58:42, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2636, decode.acc_seg: 89.9617, aux.loss_ce: 0.1579, aux.acc_seg: 84.9723, loss: 0.4216, grad_norm: 3.5797 2023-02-16 20:44:00,246 - mmseg - INFO - Iter [51600/160000] lr: 4.065e-05, eta: 10:58:24, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2551, decode.acc_seg: 89.8469, aux.loss_ce: 0.1552, aux.acc_seg: 84.5718, loss: 0.4102, grad_norm: 4.2643 2023-02-16 20:44:18,176 - mmseg - INFO - Iter [51650/160000] lr: 4.063e-05, eta: 10:58:05, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2970, decode.acc_seg: 88.6872, aux.loss_ce: 0.1640, aux.acc_seg: 84.2659, loss: 0.4610, grad_norm: 4.0983 2023-02-16 20:44:36,072 - mmseg - INFO - Iter [51700/160000] lr: 4.061e-05, eta: 10:57:46, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2796, decode.acc_seg: 89.3958, aux.loss_ce: 0.1591, aux.acc_seg: 84.8436, loss: 0.4387, grad_norm: 4.1418 2023-02-16 20:44:54,210 - mmseg - INFO - Iter [51750/160000] lr: 4.059e-05, eta: 10:57:28, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2906, decode.acc_seg: 88.8794, aux.loss_ce: 0.1682, aux.acc_seg: 84.0032, loss: 0.4588, grad_norm: 4.7951 2023-02-16 20:45:14,628 - mmseg - INFO - Iter [51800/160000] lr: 4.058e-05, eta: 10:57:14, time: 0.409, data_time: 0.056, memory: 16600, decode.loss_ce: 0.2759, decode.acc_seg: 89.2635, aux.loss_ce: 0.1566, aux.acc_seg: 84.7328, loss: 0.4325, grad_norm: 3.9712 2023-02-16 20:45:32,671 - mmseg - INFO - Iter [51850/160000] lr: 4.056e-05, eta: 10:56:55, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2798, decode.acc_seg: 89.1527, aux.loss_ce: 0.1660, aux.acc_seg: 84.0115, loss: 0.4458, grad_norm: 3.9924 2023-02-16 20:45:50,742 - mmseg - INFO - Iter [51900/160000] lr: 4.054e-05, eta: 10:56:37, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2726, decode.acc_seg: 89.3225, aux.loss_ce: 0.1521, aux.acc_seg: 85.2597, loss: 0.4247, grad_norm: 3.6070 2023-02-16 20:46:08,817 - mmseg - INFO - Iter [51950/160000] lr: 4.052e-05, eta: 10:56:18, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2733, decode.acc_seg: 89.3046, aux.loss_ce: 0.1546, aux.acc_seg: 85.1524, loss: 0.4278, grad_norm: 3.4940 2023-02-16 20:46:26,974 - mmseg - INFO - Saving checkpoint at 52000 iterations 2023-02-16 20:46:28,098 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 20:46:28,098 - mmseg - INFO - Iter [52000/160000] lr: 4.050e-05, eta: 10:56:02, time: 0.386, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2791, decode.acc_seg: 89.2548, aux.loss_ce: 0.1551, aux.acc_seg: 84.7015, loss: 0.4341, grad_norm: 3.7977 2023-02-16 20:46:46,099 - mmseg - INFO - Iter [52050/160000] lr: 4.048e-05, eta: 10:55:44, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2810, decode.acc_seg: 88.9364, aux.loss_ce: 0.1582, aux.acc_seg: 84.6035, loss: 0.4392, grad_norm: 3.7808 2023-02-16 20:47:04,013 - mmseg - INFO - Iter [52100/160000] lr: 4.046e-05, eta: 10:55:25, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2807, decode.acc_seg: 88.8743, aux.loss_ce: 0.1565, aux.acc_seg: 84.7338, loss: 0.4372, grad_norm: 4.1893 2023-02-16 20:47:22,311 - mmseg - INFO - Iter [52150/160000] lr: 4.044e-05, eta: 10:55:07, time: 0.366, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2659, decode.acc_seg: 89.6361, aux.loss_ce: 0.1549, aux.acc_seg: 85.0398, loss: 0.4207, grad_norm: 3.2095 2023-02-16 20:47:40,165 - mmseg - INFO - Iter [52200/160000] lr: 4.043e-05, eta: 10:54:48, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2907, decode.acc_seg: 89.2552, aux.loss_ce: 0.1659, aux.acc_seg: 84.4628, loss: 0.4566, grad_norm: 3.5534 2023-02-16 20:47:58,170 - mmseg - INFO - Iter [52250/160000] lr: 4.041e-05, eta: 10:54:29, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2970, decode.acc_seg: 88.7555, aux.loss_ce: 0.1694, aux.acc_seg: 84.0699, loss: 0.4664, grad_norm: 4.1647 2023-02-16 20:48:16,269 - mmseg - INFO - Iter [52300/160000] lr: 4.039e-05, eta: 10:54:11, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2806, decode.acc_seg: 88.8842, aux.loss_ce: 0.1622, aux.acc_seg: 84.1030, loss: 0.4428, grad_norm: 3.9820 2023-02-16 20:48:34,362 - mmseg - INFO - Iter [52350/160000] lr: 4.037e-05, eta: 10:53:52, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2834, decode.acc_seg: 89.0616, aux.loss_ce: 0.1630, aux.acc_seg: 84.1987, loss: 0.4465, grad_norm: 4.6466 2023-02-16 20:48:52,321 - mmseg - INFO - Iter [52400/160000] lr: 4.035e-05, eta: 10:53:33, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2694, decode.acc_seg: 89.6383, aux.loss_ce: 0.1558, aux.acc_seg: 85.0415, loss: 0.4252, grad_norm: 3.7801 2023-02-16 20:49:10,248 - mmseg - INFO - Iter [52450/160000] lr: 4.033e-05, eta: 10:53:14, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2678, decode.acc_seg: 89.3779, aux.loss_ce: 0.1560, aux.acc_seg: 84.9485, loss: 0.4238, grad_norm: 3.9809 2023-02-16 20:49:28,130 - mmseg - INFO - Iter [52500/160000] lr: 4.031e-05, eta: 10:52:56, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2756, decode.acc_seg: 89.2975, aux.loss_ce: 0.1546, aux.acc_seg: 84.8258, loss: 0.4302, grad_norm: 3.8765 2023-02-16 20:49:46,167 - mmseg - INFO - Iter [52550/160000] lr: 4.029e-05, eta: 10:52:37, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2728, decode.acc_seg: 89.5797, aux.loss_ce: 0.1572, aux.acc_seg: 84.9329, loss: 0.4300, grad_norm: 3.9796 2023-02-16 20:50:04,403 - mmseg - INFO - Iter [52600/160000] lr: 4.028e-05, eta: 10:52:19, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2670, decode.acc_seg: 89.7543, aux.loss_ce: 0.1551, aux.acc_seg: 84.9055, loss: 0.4221, grad_norm: 3.7821 2023-02-16 20:50:22,223 - mmseg - INFO - Iter [52650/160000] lr: 4.026e-05, eta: 10:52:00, time: 0.356, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2924, decode.acc_seg: 88.7075, aux.loss_ce: 0.1659, aux.acc_seg: 83.8354, loss: 0.4583, grad_norm: 4.1441 2023-02-16 20:50:40,386 - mmseg - INFO - Iter [52700/160000] lr: 4.024e-05, eta: 10:51:41, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2666, decode.acc_seg: 89.6997, aux.loss_ce: 0.1608, aux.acc_seg: 84.6575, loss: 0.4273, grad_norm: 3.8382 2023-02-16 20:50:58,256 - mmseg - INFO - Iter [52750/160000] lr: 4.022e-05, eta: 10:51:22, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2859, decode.acc_seg: 88.9383, aux.loss_ce: 0.1630, aux.acc_seg: 84.0184, loss: 0.4488, grad_norm: 3.8423 2023-02-16 20:51:16,243 - mmseg - INFO - Iter [52800/160000] lr: 4.020e-05, eta: 10:51:04, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2749, decode.acc_seg: 89.1855, aux.loss_ce: 0.1601, aux.acc_seg: 84.3592, loss: 0.4350, grad_norm: 3.9045 2023-02-16 20:51:34,365 - mmseg - INFO - Iter [52850/160000] lr: 4.018e-05, eta: 10:50:45, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2620, decode.acc_seg: 90.1226, aux.loss_ce: 0.1503, aux.acc_seg: 85.4480, loss: 0.4123, grad_norm: 4.1072 2023-02-16 20:51:52,263 - mmseg - INFO - Iter [52900/160000] lr: 4.016e-05, eta: 10:50:26, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2844, decode.acc_seg: 89.0835, aux.loss_ce: 0.1658, aux.acc_seg: 84.4194, loss: 0.4502, grad_norm: 4.4026 2023-02-16 20:52:10,237 - mmseg - INFO - Iter [52950/160000] lr: 4.014e-05, eta: 10:50:08, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2825, decode.acc_seg: 89.2272, aux.loss_ce: 0.1667, aux.acc_seg: 83.9209, loss: 0.4493, grad_norm: 4.3868 2023-02-16 20:52:28,248 - mmseg - INFO - Saving checkpoint at 53000 iterations 2023-02-16 20:52:29,449 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 20:52:29,449 - mmseg - INFO - Iter [53000/160000] lr: 4.013e-05, eta: 10:49:52, time: 0.384, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2814, decode.acc_seg: 89.0734, aux.loss_ce: 0.1671, aux.acc_seg: 84.0874, loss: 0.4485, grad_norm: 4.3047 2023-02-16 20:52:49,740 - mmseg - INFO - Iter [53050/160000] lr: 4.011e-05, eta: 10:49:37, time: 0.405, data_time: 0.052, memory: 16600, decode.loss_ce: 0.2826, decode.acc_seg: 89.0811, aux.loss_ce: 0.1597, aux.acc_seg: 84.6251, loss: 0.4423, grad_norm: 4.3190 2023-02-16 20:53:07,852 - mmseg - INFO - Iter [53100/160000] lr: 4.009e-05, eta: 10:49:19, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2718, decode.acc_seg: 89.4164, aux.loss_ce: 0.1554, aux.acc_seg: 84.7914, loss: 0.4273, grad_norm: 4.1736 2023-02-16 20:53:26,035 - mmseg - INFO - Iter [53150/160000] lr: 4.007e-05, eta: 10:49:01, time: 0.364, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2715, decode.acc_seg: 89.4728, aux.loss_ce: 0.1561, aux.acc_seg: 84.7283, loss: 0.4276, grad_norm: 3.6022 2023-02-16 20:53:43,976 - mmseg - INFO - Iter [53200/160000] lr: 4.005e-05, eta: 10:48:42, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2745, decode.acc_seg: 89.0645, aux.loss_ce: 0.1591, aux.acc_seg: 84.3441, loss: 0.4336, grad_norm: 3.7615 2023-02-16 20:54:02,029 - mmseg - INFO - Iter [53250/160000] lr: 4.003e-05, eta: 10:48:23, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2721, decode.acc_seg: 89.2964, aux.loss_ce: 0.1567, aux.acc_seg: 84.9714, loss: 0.4288, grad_norm: 3.4463 2023-02-16 20:54:20,335 - mmseg - INFO - Iter [53300/160000] lr: 4.001e-05, eta: 10:48:05, time: 0.366, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2762, decode.acc_seg: 89.1434, aux.loss_ce: 0.1526, aux.acc_seg: 85.3535, loss: 0.4288, grad_norm: 3.6189 2023-02-16 20:54:38,292 - mmseg - INFO - Iter [53350/160000] lr: 3.999e-05, eta: 10:47:47, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2521, decode.acc_seg: 90.1334, aux.loss_ce: 0.1484, aux.acc_seg: 85.3559, loss: 0.4005, grad_norm: 3.4963 2023-02-16 20:54:56,461 - mmseg - INFO - Iter [53400/160000] lr: 3.998e-05, eta: 10:47:28, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2738, decode.acc_seg: 89.3242, aux.loss_ce: 0.1599, aux.acc_seg: 84.2495, loss: 0.4337, grad_norm: 3.6181 2023-02-16 20:55:14,524 - mmseg - INFO - Iter [53450/160000] lr: 3.996e-05, eta: 10:47:10, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2590, decode.acc_seg: 89.9397, aux.loss_ce: 0.1535, aux.acc_seg: 85.1445, loss: 0.4125, grad_norm: 3.2932 2023-02-16 20:55:32,561 - mmseg - INFO - Iter [53500/160000] lr: 3.994e-05, eta: 10:46:51, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2671, decode.acc_seg: 89.4389, aux.loss_ce: 0.1555, aux.acc_seg: 84.7450, loss: 0.4226, grad_norm: 3.6322 2023-02-16 20:55:50,572 - mmseg - INFO - Iter [53550/160000] lr: 3.992e-05, eta: 10:46:32, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2800, decode.acc_seg: 88.8581, aux.loss_ce: 0.1610, aux.acc_seg: 84.3044, loss: 0.4410, grad_norm: 3.9163 2023-02-16 20:56:08,690 - mmseg - INFO - Iter [53600/160000] lr: 3.990e-05, eta: 10:46:14, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2835, decode.acc_seg: 89.1024, aux.loss_ce: 0.1593, aux.acc_seg: 84.6089, loss: 0.4428, grad_norm: 4.3741 2023-02-16 20:56:26,615 - mmseg - INFO - Iter [53650/160000] lr: 3.988e-05, eta: 10:45:55, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2595, decode.acc_seg: 89.9604, aux.loss_ce: 0.1560, aux.acc_seg: 84.7890, loss: 0.4155, grad_norm: 3.9399 2023-02-16 20:56:44,427 - mmseg - INFO - Iter [53700/160000] lr: 3.986e-05, eta: 10:45:36, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2793, decode.acc_seg: 89.3320, aux.loss_ce: 0.1608, aux.acc_seg: 84.6091, loss: 0.4401, grad_norm: 3.9749 2023-02-16 20:57:02,903 - mmseg - INFO - Iter [53750/160000] lr: 3.984e-05, eta: 10:45:19, time: 0.370, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2633, decode.acc_seg: 89.7850, aux.loss_ce: 0.1551, aux.acc_seg: 85.0380, loss: 0.4184, grad_norm: 3.3568 2023-02-16 20:57:20,840 - mmseg - INFO - Iter [53800/160000] lr: 3.983e-05, eta: 10:45:00, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2707, decode.acc_seg: 89.2550, aux.loss_ce: 0.1505, aux.acc_seg: 85.1648, loss: 0.4212, grad_norm: 3.3727 2023-02-16 20:57:38,896 - mmseg - INFO - Iter [53850/160000] lr: 3.981e-05, eta: 10:44:41, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2795, decode.acc_seg: 88.9218, aux.loss_ce: 0.1625, aux.acc_seg: 84.0358, loss: 0.4420, grad_norm: 4.0895 2023-02-16 20:57:56,840 - mmseg - INFO - Iter [53900/160000] lr: 3.979e-05, eta: 10:44:22, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2794, decode.acc_seg: 89.1786, aux.loss_ce: 0.1609, aux.acc_seg: 84.3769, loss: 0.4402, grad_norm: 3.6825 2023-02-16 20:58:15,201 - mmseg - INFO - Iter [53950/160000] lr: 3.977e-05, eta: 10:44:04, time: 0.367, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2698, decode.acc_seg: 89.3327, aux.loss_ce: 0.1547, aux.acc_seg: 85.0707, loss: 0.4245, grad_norm: 3.9465 2023-02-16 20:58:33,365 - mmseg - INFO - Saving checkpoint at 54000 iterations 2023-02-16 20:58:34,490 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 20:58:34,490 - mmseg - INFO - Iter [54000/160000] lr: 3.975e-05, eta: 10:43:48, time: 0.386, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2689, decode.acc_seg: 89.5476, aux.loss_ce: 0.1561, aux.acc_seg: 84.9766, loss: 0.4249, grad_norm: 3.7024 2023-02-16 20:58:52,320 - mmseg - INFO - Iter [54050/160000] lr: 3.973e-05, eta: 10:43:29, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2834, decode.acc_seg: 89.2292, aux.loss_ce: 0.1600, aux.acc_seg: 84.9054, loss: 0.4434, grad_norm: 4.2870 2023-02-16 20:59:10,217 - mmseg - INFO - Iter [54100/160000] lr: 3.971e-05, eta: 10:43:11, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2860, decode.acc_seg: 88.8878, aux.loss_ce: 0.1615, aux.acc_seg: 84.0479, loss: 0.4475, grad_norm: 3.5144 2023-02-16 20:59:28,184 - mmseg - INFO - Iter [54150/160000] lr: 3.969e-05, eta: 10:42:52, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2598, decode.acc_seg: 89.8590, aux.loss_ce: 0.1513, aux.acc_seg: 85.1622, loss: 0.4111, grad_norm: 3.8943 2023-02-16 20:59:46,128 - mmseg - INFO - Iter [54200/160000] lr: 3.968e-05, eta: 10:42:33, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2738, decode.acc_seg: 89.1418, aux.loss_ce: 0.1572, aux.acc_seg: 84.4369, loss: 0.4310, grad_norm: 3.5898 2023-02-16 21:00:04,095 - mmseg - INFO - Iter [54250/160000] lr: 3.966e-05, eta: 10:42:14, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2662, decode.acc_seg: 89.5171, aux.loss_ce: 0.1561, aux.acc_seg: 84.6347, loss: 0.4223, grad_norm: 3.9479 2023-02-16 21:00:21,970 - mmseg - INFO - Iter [54300/160000] lr: 3.964e-05, eta: 10:41:55, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2658, decode.acc_seg: 89.5648, aux.loss_ce: 0.1580, aux.acc_seg: 84.3296, loss: 0.4238, grad_norm: 3.5796 2023-02-16 21:00:42,300 - mmseg - INFO - Iter [54350/160000] lr: 3.962e-05, eta: 10:41:41, time: 0.407, data_time: 0.053, memory: 16600, decode.loss_ce: 0.2647, decode.acc_seg: 89.6982, aux.loss_ce: 0.1516, aux.acc_seg: 85.2788, loss: 0.4163, grad_norm: 3.2389 2023-02-16 21:01:00,260 - mmseg - INFO - Iter [54400/160000] lr: 3.960e-05, eta: 10:41:23, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2682, decode.acc_seg: 89.4852, aux.loss_ce: 0.1571, aux.acc_seg: 84.5752, loss: 0.4253, grad_norm: 3.9885 2023-02-16 21:01:18,201 - mmseg - INFO - Iter [54450/160000] lr: 3.958e-05, eta: 10:41:04, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2490, decode.acc_seg: 90.2981, aux.loss_ce: 0.1452, aux.acc_seg: 85.7161, loss: 0.3943, grad_norm: 3.2959 2023-02-16 21:01:36,139 - mmseg - INFO - Iter [54500/160000] lr: 3.956e-05, eta: 10:40:45, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2637, decode.acc_seg: 89.6310, aux.loss_ce: 0.1527, aux.acc_seg: 85.1101, loss: 0.4164, grad_norm: 3.3857 2023-02-16 21:01:54,116 - mmseg - INFO - Iter [54550/160000] lr: 3.954e-05, eta: 10:40:26, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2542, decode.acc_seg: 90.3254, aux.loss_ce: 0.1531, aux.acc_seg: 85.3229, loss: 0.4073, grad_norm: 3.3816 2023-02-16 21:02:12,088 - mmseg - INFO - Iter [54600/160000] lr: 3.953e-05, eta: 10:40:08, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2645, decode.acc_seg: 89.7417, aux.loss_ce: 0.1517, aux.acc_seg: 85.2795, loss: 0.4162, grad_norm: 3.4298 2023-02-16 21:02:30,127 - mmseg - INFO - Iter [54650/160000] lr: 3.951e-05, eta: 10:39:49, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2665, decode.acc_seg: 89.5287, aux.loss_ce: 0.1619, aux.acc_seg: 84.1481, loss: 0.4284, grad_norm: 3.5977 2023-02-16 21:02:48,294 - mmseg - INFO - Iter [54700/160000] lr: 3.949e-05, eta: 10:39:31, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2668, decode.acc_seg: 89.4272, aux.loss_ce: 0.1573, aux.acc_seg: 84.6514, loss: 0.4241, grad_norm: 3.9768 2023-02-16 21:03:06,285 - mmseg - INFO - Iter [54750/160000] lr: 3.947e-05, eta: 10:39:12, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2694, decode.acc_seg: 89.5518, aux.loss_ce: 0.1564, aux.acc_seg: 84.7669, loss: 0.4259, grad_norm: 4.3497 2023-02-16 21:03:24,170 - mmseg - INFO - Iter [54800/160000] lr: 3.945e-05, eta: 10:38:53, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2564, decode.acc_seg: 89.8626, aux.loss_ce: 0.1518, aux.acc_seg: 85.1547, loss: 0.4082, grad_norm: 3.3327 2023-02-16 21:03:42,056 - mmseg - INFO - Iter [54850/160000] lr: 3.943e-05, eta: 10:38:34, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2672, decode.acc_seg: 89.4084, aux.loss_ce: 0.1626, aux.acc_seg: 84.2390, loss: 0.4298, grad_norm: 3.7446 2023-02-16 21:04:00,079 - mmseg - INFO - Iter [54900/160000] lr: 3.941e-05, eta: 10:38:16, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2712, decode.acc_seg: 89.4788, aux.loss_ce: 0.1552, aux.acc_seg: 84.7491, loss: 0.4264, grad_norm: 3.4972 2023-02-16 21:04:17,913 - mmseg - INFO - Iter [54950/160000] lr: 3.939e-05, eta: 10:37:57, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2638, decode.acc_seg: 89.6569, aux.loss_ce: 0.1566, aux.acc_seg: 84.7980, loss: 0.4204, grad_norm: 4.0301 2023-02-16 21:04:36,082 - mmseg - INFO - Saving checkpoint at 55000 iterations 2023-02-16 21:04:37,187 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 21:04:37,187 - mmseg - INFO - Iter [55000/160000] lr: 3.938e-05, eta: 10:37:41, time: 0.385, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2619, decode.acc_seg: 89.6903, aux.loss_ce: 0.1484, aux.acc_seg: 85.5988, loss: 0.4103, grad_norm: 3.6709 2023-02-16 21:04:55,276 - mmseg - INFO - Iter [55050/160000] lr: 3.936e-05, eta: 10:37:22, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2554, decode.acc_seg: 90.0805, aux.loss_ce: 0.1506, aux.acc_seg: 85.2038, loss: 0.4061, grad_norm: 3.6975 2023-02-16 21:05:13,322 - mmseg - INFO - Iter [55100/160000] lr: 3.934e-05, eta: 10:37:04, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2714, decode.acc_seg: 89.2811, aux.loss_ce: 0.1598, aux.acc_seg: 84.5400, loss: 0.4312, grad_norm: 4.0064 2023-02-16 21:05:31,600 - mmseg - INFO - Iter [55150/160000] lr: 3.932e-05, eta: 10:36:45, time: 0.366, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2894, decode.acc_seg: 88.8811, aux.loss_ce: 0.1642, aux.acc_seg: 84.3526, loss: 0.4536, grad_norm: 5.2755 2023-02-16 21:05:49,662 - mmseg - INFO - Iter [55200/160000] lr: 3.930e-05, eta: 10:36:27, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2781, decode.acc_seg: 89.1372, aux.loss_ce: 0.1582, aux.acc_seg: 84.4604, loss: 0.4363, grad_norm: 4.0857 2023-02-16 21:06:07,704 - mmseg - INFO - Iter [55250/160000] lr: 3.928e-05, eta: 10:36:08, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2841, decode.acc_seg: 89.0127, aux.loss_ce: 0.1662, aux.acc_seg: 84.4074, loss: 0.4503, grad_norm: 4.1076 2023-02-16 21:06:25,549 - mmseg - INFO - Iter [55300/160000] lr: 3.926e-05, eta: 10:35:49, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2691, decode.acc_seg: 89.6842, aux.loss_ce: 0.1525, aux.acc_seg: 85.2605, loss: 0.4216, grad_norm: 3.7068 2023-02-16 21:06:43,517 - mmseg - INFO - Iter [55350/160000] lr: 3.924e-05, eta: 10:35:31, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2693, decode.acc_seg: 89.3813, aux.loss_ce: 0.1614, aux.acc_seg: 83.9610, loss: 0.4307, grad_norm: 4.2156 2023-02-16 21:07:01,676 - mmseg - INFO - Iter [55400/160000] lr: 3.923e-05, eta: 10:35:12, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2692, decode.acc_seg: 89.4984, aux.loss_ce: 0.1524, aux.acc_seg: 85.1922, loss: 0.4216, grad_norm: 3.9913 2023-02-16 21:07:19,587 - mmseg - INFO - Iter [55450/160000] lr: 3.921e-05, eta: 10:34:54, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2690, decode.acc_seg: 89.5626, aux.loss_ce: 0.1554, aux.acc_seg: 84.9522, loss: 0.4244, grad_norm: 4.2735 2023-02-16 21:07:37,469 - mmseg - INFO - Iter [55500/160000] lr: 3.919e-05, eta: 10:34:35, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2647, decode.acc_seg: 89.6856, aux.loss_ce: 0.1520, aux.acc_seg: 85.2361, loss: 0.4167, grad_norm: 3.4979 2023-02-16 21:07:55,389 - mmseg - INFO - Iter [55550/160000] lr: 3.917e-05, eta: 10:34:16, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2640, decode.acc_seg: 89.6165, aux.loss_ce: 0.1548, aux.acc_seg: 84.7312, loss: 0.4188, grad_norm: 3.8458 2023-02-16 21:08:15,915 - mmseg - INFO - Iter [55600/160000] lr: 3.915e-05, eta: 10:34:02, time: 0.411, data_time: 0.054, memory: 16600, decode.loss_ce: 0.2443, decode.acc_seg: 90.2852, aux.loss_ce: 0.1427, aux.acc_seg: 86.2952, loss: 0.3869, grad_norm: 3.6038 2023-02-16 21:08:33,917 - mmseg - INFO - Iter [55650/160000] lr: 3.913e-05, eta: 10:33:44, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2424, decode.acc_seg: 90.1179, aux.loss_ce: 0.1412, aux.acc_seg: 85.9960, loss: 0.3837, grad_norm: 3.2031 2023-02-16 21:08:52,084 - mmseg - INFO - Iter [55700/160000] lr: 3.911e-05, eta: 10:33:25, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2603, decode.acc_seg: 89.5345, aux.loss_ce: 0.1512, aux.acc_seg: 84.9665, loss: 0.4116, grad_norm: 4.3559 2023-02-16 21:09:10,328 - mmseg - INFO - Iter [55750/160000] lr: 3.909e-05, eta: 10:33:07, time: 0.365, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2588, decode.acc_seg: 90.2057, aux.loss_ce: 0.1511, aux.acc_seg: 85.7470, loss: 0.4099, grad_norm: 3.1918 2023-02-16 21:09:28,436 - mmseg - INFO - Iter [55800/160000] lr: 3.908e-05, eta: 10:32:49, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2596, decode.acc_seg: 89.9108, aux.loss_ce: 0.1510, aux.acc_seg: 85.2676, loss: 0.4106, grad_norm: 3.4116 2023-02-16 21:09:46,565 - mmseg - INFO - Iter [55850/160000] lr: 3.906e-05, eta: 10:32:30, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2818, decode.acc_seg: 89.0689, aux.loss_ce: 0.1626, aux.acc_seg: 84.3925, loss: 0.4443, grad_norm: 4.5947 2023-02-16 21:10:04,589 - mmseg - INFO - Iter [55900/160000] lr: 3.904e-05, eta: 10:32:12, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2425, decode.acc_seg: 90.1372, aux.loss_ce: 0.1422, aux.acc_seg: 85.8577, loss: 0.3847, grad_norm: 3.8053 2023-02-16 21:10:23,008 - mmseg - INFO - Iter [55950/160000] lr: 3.902e-05, eta: 10:31:54, time: 0.368, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2734, decode.acc_seg: 89.4656, aux.loss_ce: 0.1584, aux.acc_seg: 84.5815, loss: 0.4319, grad_norm: 3.6744 2023-02-16 21:10:40,940 - mmseg - INFO - Saving checkpoint at 56000 iterations 2023-02-16 21:10:42,093 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 21:10:42,093 - mmseg - INFO - Iter [56000/160000] lr: 3.900e-05, eta: 10:31:37, time: 0.382, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2653, decode.acc_seg: 89.8517, aux.loss_ce: 0.1540, aux.acc_seg: 85.0331, loss: 0.4193, grad_norm: 3.9156 2023-02-16 21:11:00,035 - mmseg - INFO - Iter [56050/160000] lr: 3.898e-05, eta: 10:31:18, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2509, decode.acc_seg: 90.1274, aux.loss_ce: 0.1486, aux.acc_seg: 85.5947, loss: 0.3995, grad_norm: 3.6200 2023-02-16 21:11:18,118 - mmseg - INFO - Iter [56100/160000] lr: 3.896e-05, eta: 10:31:00, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2594, decode.acc_seg: 89.7991, aux.loss_ce: 0.1510, aux.acc_seg: 85.1637, loss: 0.4105, grad_norm: 3.5029 2023-02-16 21:11:36,267 - mmseg - INFO - Iter [56150/160000] lr: 3.894e-05, eta: 10:30:42, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2536, decode.acc_seg: 90.1089, aux.loss_ce: 0.1503, aux.acc_seg: 85.3258, loss: 0.4039, grad_norm: 3.4503 2023-02-16 21:11:54,326 - mmseg - INFO - Iter [56200/160000] lr: 3.893e-05, eta: 10:30:23, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2582, decode.acc_seg: 89.9351, aux.loss_ce: 0.1524, aux.acc_seg: 85.1096, loss: 0.4106, grad_norm: 3.9001 2023-02-16 21:12:12,574 - mmseg - INFO - Iter [56250/160000] lr: 3.891e-05, eta: 10:30:05, time: 0.365, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2602, decode.acc_seg: 89.8845, aux.loss_ce: 0.1543, aux.acc_seg: 85.0817, loss: 0.4145, grad_norm: 3.9423 2023-02-16 21:12:30,575 - mmseg - INFO - Iter [56300/160000] lr: 3.889e-05, eta: 10:29:46, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2603, decode.acc_seg: 90.0229, aux.loss_ce: 0.1553, aux.acc_seg: 85.0316, loss: 0.4156, grad_norm: 3.6185 2023-02-16 21:12:48,624 - mmseg - INFO - Iter [56350/160000] lr: 3.887e-05, eta: 10:29:28, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2514, decode.acc_seg: 90.1325, aux.loss_ce: 0.1477, aux.acc_seg: 85.4196, loss: 0.3991, grad_norm: 3.8071 2023-02-16 21:13:06,778 - mmseg - INFO - Iter [56400/160000] lr: 3.885e-05, eta: 10:29:09, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2694, decode.acc_seg: 89.7569, aux.loss_ce: 0.1573, aux.acc_seg: 85.1070, loss: 0.4267, grad_norm: 3.6750 2023-02-16 21:13:24,796 - mmseg - INFO - Iter [56450/160000] lr: 3.883e-05, eta: 10:28:51, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2629, decode.acc_seg: 89.6574, aux.loss_ce: 0.1477, aux.acc_seg: 85.5717, loss: 0.4106, grad_norm: 3.5877 2023-02-16 21:13:42,873 - mmseg - INFO - Iter [56500/160000] lr: 3.881e-05, eta: 10:28:32, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2623, decode.acc_seg: 89.5495, aux.loss_ce: 0.1524, aux.acc_seg: 85.0239, loss: 0.4147, grad_norm: 3.9229 2023-02-16 21:14:01,284 - mmseg - INFO - Iter [56550/160000] lr: 3.879e-05, eta: 10:28:14, time: 0.368, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2533, decode.acc_seg: 90.1954, aux.loss_ce: 0.1538, aux.acc_seg: 85.3220, loss: 0.4071, grad_norm: 3.7866 2023-02-16 21:14:19,173 - mmseg - INFO - Iter [56600/160000] lr: 3.878e-05, eta: 10:27:56, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2749, decode.acc_seg: 89.1389, aux.loss_ce: 0.1570, aux.acc_seg: 84.5948, loss: 0.4319, grad_norm: 3.7603 2023-02-16 21:14:37,737 - mmseg - INFO - Iter [56650/160000] lr: 3.876e-05, eta: 10:27:38, time: 0.371, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2598, decode.acc_seg: 89.8749, aux.loss_ce: 0.1513, aux.acc_seg: 85.0928, loss: 0.4110, grad_norm: 4.9543 2023-02-16 21:14:55,783 - mmseg - INFO - Iter [56700/160000] lr: 3.874e-05, eta: 10:27:20, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2614, decode.acc_seg: 89.8023, aux.loss_ce: 0.1619, aux.acc_seg: 84.4899, loss: 0.4233, grad_norm: 3.8472 2023-02-16 21:15:13,839 - mmseg - INFO - Iter [56750/160000] lr: 3.872e-05, eta: 10:27:01, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2509, decode.acc_seg: 89.9795, aux.loss_ce: 0.1487, aux.acc_seg: 85.0647, loss: 0.3996, grad_norm: 3.5104 2023-02-16 21:15:31,734 - mmseg - INFO - Iter [56800/160000] lr: 3.870e-05, eta: 10:26:42, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2735, decode.acc_seg: 89.3541, aux.loss_ce: 0.1597, aux.acc_seg: 84.6296, loss: 0.4332, grad_norm: 4.2007 2023-02-16 21:15:52,063 - mmseg - INFO - Iter [56850/160000] lr: 3.868e-05, eta: 10:26:28, time: 0.407, data_time: 0.052, memory: 16600, decode.loss_ce: 0.2736, decode.acc_seg: 89.0831, aux.loss_ce: 0.1617, aux.acc_seg: 84.1252, loss: 0.4353, grad_norm: 3.7697 2023-02-16 21:16:10,178 - mmseg - INFO - Iter [56900/160000] lr: 3.866e-05, eta: 10:26:09, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2477, decode.acc_seg: 90.1555, aux.loss_ce: 0.1442, aux.acc_seg: 85.7660, loss: 0.3919, grad_norm: 3.8166 2023-02-16 21:16:28,088 - mmseg - INFO - Iter [56950/160000] lr: 3.864e-05, eta: 10:25:51, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2400, decode.acc_seg: 90.2985, aux.loss_ce: 0.1419, aux.acc_seg: 85.7637, loss: 0.3818, grad_norm: 3.3191 2023-02-16 21:16:45,958 - mmseg - INFO - Saving checkpoint at 57000 iterations 2023-02-16 21:16:47,068 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 21:16:47,068 - mmseg - INFO - Iter [57000/160000] lr: 3.863e-05, eta: 10:25:34, time: 0.380, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2678, decode.acc_seg: 89.7203, aux.loss_ce: 0.1541, aux.acc_seg: 85.0736, loss: 0.4218, grad_norm: 4.3409 2023-02-16 21:17:05,006 - mmseg - INFO - Iter [57050/160000] lr: 3.861e-05, eta: 10:25:15, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2571, decode.acc_seg: 89.9263, aux.loss_ce: 0.1495, aux.acc_seg: 85.5002, loss: 0.4066, grad_norm: 4.0707 2023-02-16 21:17:23,116 - mmseg - INFO - Iter [57100/160000] lr: 3.859e-05, eta: 10:24:57, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2583, decode.acc_seg: 89.9895, aux.loss_ce: 0.1510, aux.acc_seg: 85.0686, loss: 0.4092, grad_norm: 4.0515 2023-02-16 21:17:41,496 - mmseg - INFO - Iter [57150/160000] lr: 3.857e-05, eta: 10:24:39, time: 0.368, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2563, decode.acc_seg: 89.9861, aux.loss_ce: 0.1479, aux.acc_seg: 85.4363, loss: 0.4042, grad_norm: 3.7035 2023-02-16 21:17:59,565 - mmseg - INFO - Iter [57200/160000] lr: 3.855e-05, eta: 10:24:20, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2558, decode.acc_seg: 90.0332, aux.loss_ce: 0.1502, aux.acc_seg: 85.6216, loss: 0.4061, grad_norm: 4.0770 2023-02-16 21:18:17,461 - mmseg - INFO - Iter [57250/160000] lr: 3.853e-05, eta: 10:24:01, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2720, decode.acc_seg: 89.5076, aux.loss_ce: 0.1560, aux.acc_seg: 84.8763, loss: 0.4279, grad_norm: 3.9393 2023-02-16 21:18:35,244 - mmseg - INFO - Iter [57300/160000] lr: 3.851e-05, eta: 10:23:42, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2637, decode.acc_seg: 89.6020, aux.loss_ce: 0.1607, aux.acc_seg: 84.1433, loss: 0.4245, grad_norm: 3.6820 2023-02-16 21:18:53,489 - mmseg - INFO - Iter [57350/160000] lr: 3.849e-05, eta: 10:23:24, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2536, decode.acc_seg: 90.0945, aux.loss_ce: 0.1517, aux.acc_seg: 85.2389, loss: 0.4053, grad_norm: 3.2112 2023-02-16 21:19:11,386 - mmseg - INFO - Iter [57400/160000] lr: 3.848e-05, eta: 10:23:05, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2722, decode.acc_seg: 89.2982, aux.loss_ce: 0.1600, aux.acc_seg: 84.6137, loss: 0.4322, grad_norm: 3.9820 2023-02-16 21:19:29,266 - mmseg - INFO - Iter [57450/160000] lr: 3.846e-05, eta: 10:22:47, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2572, decode.acc_seg: 89.9800, aux.loss_ce: 0.1508, aux.acc_seg: 85.2852, loss: 0.4081, grad_norm: 3.6520 2023-02-16 21:19:47,332 - mmseg - INFO - Iter [57500/160000] lr: 3.844e-05, eta: 10:22:28, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2621, decode.acc_seg: 89.7698, aux.loss_ce: 0.1571, aux.acc_seg: 84.8015, loss: 0.4192, grad_norm: 3.6950 2023-02-16 21:20:05,361 - mmseg - INFO - Iter [57550/160000] lr: 3.842e-05, eta: 10:22:10, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2518, decode.acc_seg: 90.1699, aux.loss_ce: 0.1460, aux.acc_seg: 85.8303, loss: 0.3978, grad_norm: 3.7972 2023-02-16 21:20:23,462 - mmseg - INFO - Iter [57600/160000] lr: 3.840e-05, eta: 10:21:51, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2617, decode.acc_seg: 89.5786, aux.loss_ce: 0.1554, aux.acc_seg: 84.6662, loss: 0.4171, grad_norm: 3.6730 2023-02-16 21:20:41,288 - mmseg - INFO - Iter [57650/160000] lr: 3.838e-05, eta: 10:21:32, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2746, decode.acc_seg: 89.3296, aux.loss_ce: 0.1568, aux.acc_seg: 84.8478, loss: 0.4314, grad_norm: 4.0261 2023-02-16 21:20:59,254 - mmseg - INFO - Iter [57700/160000] lr: 3.836e-05, eta: 10:21:14, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2456, decode.acc_seg: 90.3732, aux.loss_ce: 0.1440, aux.acc_seg: 85.8793, loss: 0.3896, grad_norm: 3.1345 2023-02-16 21:21:17,063 - mmseg - INFO - Iter [57750/160000] lr: 3.834e-05, eta: 10:20:55, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2659, decode.acc_seg: 89.6997, aux.loss_ce: 0.1575, aux.acc_seg: 84.9939, loss: 0.4234, grad_norm: 3.9312 2023-02-16 21:21:34,996 - mmseg - INFO - Iter [57800/160000] lr: 3.833e-05, eta: 10:20:36, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2461, decode.acc_seg: 90.3705, aux.loss_ce: 0.1479, aux.acc_seg: 85.6128, loss: 0.3940, grad_norm: 3.2797 2023-02-16 21:21:52,828 - mmseg - INFO - Iter [57850/160000] lr: 3.831e-05, eta: 10:20:17, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2540, decode.acc_seg: 90.0286, aux.loss_ce: 0.1499, aux.acc_seg: 85.5827, loss: 0.4038, grad_norm: 4.2065 2023-02-16 21:22:10,863 - mmseg - INFO - Iter [57900/160000] lr: 3.829e-05, eta: 10:19:58, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2504, decode.acc_seg: 90.2580, aux.loss_ce: 0.1526, aux.acc_seg: 85.1249, loss: 0.4030, grad_norm: 3.5068 2023-02-16 21:22:28,709 - mmseg - INFO - Iter [57950/160000] lr: 3.827e-05, eta: 10:19:40, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2653, decode.acc_seg: 89.8161, aux.loss_ce: 0.1590, aux.acc_seg: 84.8521, loss: 0.4244, grad_norm: 3.7672 2023-02-16 21:22:46,539 - mmseg - INFO - Saving checkpoint at 58000 iterations 2023-02-16 21:22:47,683 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 21:22:47,683 - mmseg - INFO - Iter [58000/160000] lr: 3.825e-05, eta: 10:19:23, time: 0.379, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2663, decode.acc_seg: 89.6406, aux.loss_ce: 0.1621, aux.acc_seg: 84.2919, loss: 0.4284, grad_norm: 3.7846 2023-02-16 21:23:05,974 - mmseg - INFO - Iter [58050/160000] lr: 3.823e-05, eta: 10:19:05, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2644, decode.acc_seg: 89.9257, aux.loss_ce: 0.1545, aux.acc_seg: 85.0846, loss: 0.4189, grad_norm: 3.9104 2023-02-16 21:23:26,313 - mmseg - INFO - Iter [58100/160000] lr: 3.821e-05, eta: 10:18:50, time: 0.406, data_time: 0.055, memory: 16600, decode.loss_ce: 0.2608, decode.acc_seg: 89.8454, aux.loss_ce: 0.1574, aux.acc_seg: 84.6692, loss: 0.4181, grad_norm: 3.5674 2023-02-16 21:23:44,265 - mmseg - INFO - Iter [58150/160000] lr: 3.819e-05, eta: 10:18:31, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2438, decode.acc_seg: 90.4239, aux.loss_ce: 0.1463, aux.acc_seg: 85.6316, loss: 0.3901, grad_norm: 3.2020 2023-02-16 21:24:02,203 - mmseg - INFO - Iter [58200/160000] lr: 3.818e-05, eta: 10:18:13, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2487, decode.acc_seg: 90.5063, aux.loss_ce: 0.1474, aux.acc_seg: 85.8525, loss: 0.3961, grad_norm: 3.9234 2023-02-16 21:24:20,280 - mmseg - INFO - Iter [58250/160000] lr: 3.816e-05, eta: 10:17:54, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2602, decode.acc_seg: 89.6717, aux.loss_ce: 0.1492, aux.acc_seg: 85.1738, loss: 0.4094, grad_norm: 3.9875 2023-02-16 21:24:38,254 - mmseg - INFO - Iter [58300/160000] lr: 3.814e-05, eta: 10:17:36, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2710, decode.acc_seg: 89.1079, aux.loss_ce: 0.1576, aux.acc_seg: 84.4145, loss: 0.4285, grad_norm: 3.9953 2023-02-16 21:24:56,205 - mmseg - INFO - Iter [58350/160000] lr: 3.812e-05, eta: 10:17:17, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2565, decode.acc_seg: 90.1197, aux.loss_ce: 0.1509, aux.acc_seg: 85.1622, loss: 0.4074, grad_norm: 3.2921 2023-02-16 21:25:14,192 - mmseg - INFO - Iter [58400/160000] lr: 3.810e-05, eta: 10:16:58, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2754, decode.acc_seg: 89.3972, aux.loss_ce: 0.1572, aux.acc_seg: 84.9501, loss: 0.4326, grad_norm: 4.1947 2023-02-16 21:25:32,062 - mmseg - INFO - Iter [58450/160000] lr: 3.808e-05, eta: 10:16:39, time: 0.357, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2445, decode.acc_seg: 90.3998, aux.loss_ce: 0.1463, aux.acc_seg: 85.8241, loss: 0.3908, grad_norm: 4.1181 2023-02-16 21:25:49,977 - mmseg - INFO - Iter [58500/160000] lr: 3.806e-05, eta: 10:16:21, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2575, decode.acc_seg: 89.8077, aux.loss_ce: 0.1457, aux.acc_seg: 85.5646, loss: 0.4032, grad_norm: 3.2905 2023-02-16 21:26:08,030 - mmseg - INFO - Iter [58550/160000] lr: 3.804e-05, eta: 10:16:02, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2643, decode.acc_seg: 90.0044, aux.loss_ce: 0.1568, aux.acc_seg: 85.2310, loss: 0.4211, grad_norm: 3.7301 2023-02-16 21:26:25,796 - mmseg - INFO - Iter [58600/160000] lr: 3.803e-05, eta: 10:15:43, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2502, decode.acc_seg: 90.2904, aux.loss_ce: 0.1507, aux.acc_seg: 85.2759, loss: 0.4009, grad_norm: 3.5560 2023-02-16 21:26:43,625 - mmseg - INFO - Iter [58650/160000] lr: 3.801e-05, eta: 10:15:24, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2369, decode.acc_seg: 90.5281, aux.loss_ce: 0.1462, aux.acc_seg: 85.4222, loss: 0.3831, grad_norm: 3.0841 2023-02-16 21:27:01,890 - mmseg - INFO - Iter [58700/160000] lr: 3.799e-05, eta: 10:15:06, time: 0.365, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2432, decode.acc_seg: 90.6017, aux.loss_ce: 0.1478, aux.acc_seg: 85.7876, loss: 0.3910, grad_norm: 3.6959 2023-02-16 21:27:19,858 - mmseg - INFO - Iter [58750/160000] lr: 3.797e-05, eta: 10:14:48, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2724, decode.acc_seg: 89.5936, aux.loss_ce: 0.1615, aux.acc_seg: 84.6282, loss: 0.4339, grad_norm: 3.8877 2023-02-16 21:27:37,704 - mmseg - INFO - Iter [58800/160000] lr: 3.795e-05, eta: 10:14:29, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2670, decode.acc_seg: 89.6698, aux.loss_ce: 0.1509, aux.acc_seg: 85.2454, loss: 0.4178, grad_norm: 3.6284 2023-02-16 21:27:55,671 - mmseg - INFO - Iter [58850/160000] lr: 3.793e-05, eta: 10:14:10, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2599, decode.acc_seg: 89.6948, aux.loss_ce: 0.1533, aux.acc_seg: 84.9651, loss: 0.4132, grad_norm: 3.6404 2023-02-16 21:28:13,812 - mmseg - INFO - Iter [58900/160000] lr: 3.791e-05, eta: 10:13:52, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2511, decode.acc_seg: 90.2726, aux.loss_ce: 0.1449, aux.acc_seg: 85.9857, loss: 0.3960, grad_norm: 4.0211 2023-02-16 21:28:31,836 - mmseg - INFO - Iter [58950/160000] lr: 3.789e-05, eta: 10:13:33, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2608, decode.acc_seg: 89.7287, aux.loss_ce: 0.1589, aux.acc_seg: 84.3275, loss: 0.4197, grad_norm: 3.5023 2023-02-16 21:28:50,004 - mmseg - INFO - Saving checkpoint at 59000 iterations 2023-02-16 21:28:51,069 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 21:28:51,069 - mmseg - INFO - Iter [59000/160000] lr: 3.788e-05, eta: 10:13:17, time: 0.385, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2469, decode.acc_seg: 90.2297, aux.loss_ce: 0.1471, aux.acc_seg: 85.4962, loss: 0.3940, grad_norm: 3.5511 2023-02-16 21:29:09,240 - mmseg - INFO - Iter [59050/160000] lr: 3.786e-05, eta: 10:12:58, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2514, decode.acc_seg: 90.1774, aux.loss_ce: 0.1472, aux.acc_seg: 85.7274, loss: 0.3985, grad_norm: 3.6788 2023-02-16 21:29:27,166 - mmseg - INFO - Iter [59100/160000] lr: 3.784e-05, eta: 10:12:40, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2516, decode.acc_seg: 90.0441, aux.loss_ce: 0.1478, aux.acc_seg: 85.5990, loss: 0.3995, grad_norm: 3.3286 2023-02-16 21:29:45,230 - mmseg - INFO - Iter [59150/160000] lr: 3.782e-05, eta: 10:12:21, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2443, decode.acc_seg: 90.4986, aux.loss_ce: 0.1468, aux.acc_seg: 85.9363, loss: 0.3911, grad_norm: 3.2208 2023-02-16 21:30:03,230 - mmseg - INFO - Iter [59200/160000] lr: 3.780e-05, eta: 10:12:03, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2390, decode.acc_seg: 90.5729, aux.loss_ce: 0.1469, aux.acc_seg: 85.6649, loss: 0.3859, grad_norm: 3.3489 2023-02-16 21:30:21,273 - mmseg - INFO - Iter [59250/160000] lr: 3.778e-05, eta: 10:11:44, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2779, decode.acc_seg: 89.3383, aux.loss_ce: 0.1623, aux.acc_seg: 84.3157, loss: 0.4402, grad_norm: 3.9037 2023-02-16 21:30:39,165 - mmseg - INFO - Iter [59300/160000] lr: 3.776e-05, eta: 10:11:25, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2891, decode.acc_seg: 89.0646, aux.loss_ce: 0.1597, aux.acc_seg: 85.0627, loss: 0.4487, grad_norm: 3.6904 2023-02-16 21:30:57,102 - mmseg - INFO - Iter [59350/160000] lr: 3.774e-05, eta: 10:11:07, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2557, decode.acc_seg: 89.6467, aux.loss_ce: 0.1508, aux.acc_seg: 84.8703, loss: 0.4065, grad_norm: 3.6406 2023-02-16 21:31:17,285 - mmseg - INFO - Iter [59400/160000] lr: 3.773e-05, eta: 10:10:52, time: 0.404, data_time: 0.052, memory: 16600, decode.loss_ce: 0.2463, decode.acc_seg: 90.2987, aux.loss_ce: 0.1465, aux.acc_seg: 85.6269, loss: 0.3928, grad_norm: 3.3447 2023-02-16 21:31:35,324 - mmseg - INFO - Iter [59450/160000] lr: 3.771e-05, eta: 10:10:33, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2505, decode.acc_seg: 90.0552, aux.loss_ce: 0.1496, aux.acc_seg: 85.1467, loss: 0.4001, grad_norm: 3.6090 2023-02-16 21:31:53,255 - mmseg - INFO - Iter [59500/160000] lr: 3.769e-05, eta: 10:10:15, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2330, decode.acc_seg: 91.0914, aux.loss_ce: 0.1392, aux.acc_seg: 86.5927, loss: 0.3722, grad_norm: 3.4653 2023-02-16 21:32:11,294 - mmseg - INFO - Iter [59550/160000] lr: 3.767e-05, eta: 10:09:56, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2556, decode.acc_seg: 89.8832, aux.loss_ce: 0.1521, aux.acc_seg: 85.3616, loss: 0.4077, grad_norm: 3.9330 2023-02-16 21:32:29,527 - mmseg - INFO - Iter [59600/160000] lr: 3.765e-05, eta: 10:09:38, time: 0.365, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2472, decode.acc_seg: 90.4387, aux.loss_ce: 0.1487, aux.acc_seg: 85.5330, loss: 0.3959, grad_norm: 3.5700 2023-02-16 21:32:47,497 - mmseg - INFO - Iter [59650/160000] lr: 3.763e-05, eta: 10:09:19, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2518, decode.acc_seg: 90.3218, aux.loss_ce: 0.1516, aux.acc_seg: 85.4216, loss: 0.4034, grad_norm: 3.5618 2023-02-16 21:33:05,646 - mmseg - INFO - Iter [59700/160000] lr: 3.761e-05, eta: 10:09:01, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2534, decode.acc_seg: 90.0435, aux.loss_ce: 0.1520, aux.acc_seg: 85.2307, loss: 0.4054, grad_norm: 3.8061 2023-02-16 21:33:23,518 - mmseg - INFO - Iter [59750/160000] lr: 3.759e-05, eta: 10:08:42, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2456, decode.acc_seg: 90.2097, aux.loss_ce: 0.1465, aux.acc_seg: 85.3767, loss: 0.3921, grad_norm: 3.4330 2023-02-16 21:33:41,497 - mmseg - INFO - Iter [59800/160000] lr: 3.758e-05, eta: 10:08:24, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2648, decode.acc_seg: 89.8788, aux.loss_ce: 0.1493, aux.acc_seg: 85.5376, loss: 0.4141, grad_norm: 4.4979 2023-02-16 21:33:59,881 - mmseg - INFO - Iter [59850/160000] lr: 3.756e-05, eta: 10:08:06, time: 0.368, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2648, decode.acc_seg: 89.8598, aux.loss_ce: 0.1553, aux.acc_seg: 85.2019, loss: 0.4201, grad_norm: 3.7413 2023-02-16 21:34:17,918 - mmseg - INFO - Iter [59900/160000] lr: 3.754e-05, eta: 10:07:47, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2505, decode.acc_seg: 90.0376, aux.loss_ce: 0.1492, aux.acc_seg: 85.1464, loss: 0.3997, grad_norm: 3.6701 2023-02-16 21:34:36,105 - mmseg - INFO - Iter [59950/160000] lr: 3.752e-05, eta: 10:07:29, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2429, decode.acc_seg: 90.6056, aux.loss_ce: 0.1475, aux.acc_seg: 85.7953, loss: 0.3904, grad_norm: 3.8772 2023-02-16 21:34:54,263 - mmseg - INFO - Saving checkpoint at 60000 iterations 2023-02-16 21:34:55,337 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 21:34:55,337 - mmseg - INFO - Iter [60000/160000] lr: 3.750e-05, eta: 10:07:12, time: 0.385, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2382, decode.acc_seg: 90.4730, aux.loss_ce: 0.1406, aux.acc_seg: 86.2598, loss: 0.3788, grad_norm: 3.6191 2023-02-16 21:35:13,688 - mmseg - INFO - Iter [60050/160000] lr: 3.748e-05, eta: 10:06:54, time: 0.367, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2560, decode.acc_seg: 90.0295, aux.loss_ce: 0.1490, aux.acc_seg: 85.3194, loss: 0.4050, grad_norm: 4.1784 2023-02-16 21:35:31,615 - mmseg - INFO - Iter [60100/160000] lr: 3.746e-05, eta: 10:06:36, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2482, decode.acc_seg: 90.2170, aux.loss_ce: 0.1450, aux.acc_seg: 85.8060, loss: 0.3931, grad_norm: 3.4409 2023-02-16 21:35:49,911 - mmseg - INFO - Iter [60150/160000] lr: 3.744e-05, eta: 10:06:17, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2560, decode.acc_seg: 90.0490, aux.loss_ce: 0.1488, aux.acc_seg: 85.5702, loss: 0.4047, grad_norm: 3.7037 2023-02-16 21:36:07,879 - mmseg - INFO - Iter [60200/160000] lr: 3.743e-05, eta: 10:05:59, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2468, decode.acc_seg: 90.2181, aux.loss_ce: 0.1514, aux.acc_seg: 85.1221, loss: 0.3982, grad_norm: 4.3580 2023-02-16 21:36:25,971 - mmseg - INFO - Iter [60250/160000] lr: 3.741e-05, eta: 10:05:40, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2390, decode.acc_seg: 90.5737, aux.loss_ce: 0.1482, aux.acc_seg: 85.4011, loss: 0.3872, grad_norm: 3.0635 2023-02-16 21:36:43,913 - mmseg - INFO - Iter [60300/160000] lr: 3.739e-05, eta: 10:05:22, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2496, decode.acc_seg: 90.2081, aux.loss_ce: 0.1433, aux.acc_seg: 85.8896, loss: 0.3929, grad_norm: 3.9135 2023-02-16 21:37:01,799 - mmseg - INFO - Iter [60350/160000] lr: 3.737e-05, eta: 10:05:03, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2420, decode.acc_seg: 90.6786, aux.loss_ce: 0.1484, aux.acc_seg: 85.7606, loss: 0.3905, grad_norm: 3.2318 2023-02-16 21:37:19,766 - mmseg - INFO - Iter [60400/160000] lr: 3.735e-05, eta: 10:04:44, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2768, decode.acc_seg: 89.5221, aux.loss_ce: 0.1579, aux.acc_seg: 84.9986, loss: 0.4347, grad_norm: 4.2344 2023-02-16 21:37:37,826 - mmseg - INFO - Iter [60450/160000] lr: 3.733e-05, eta: 10:04:26, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2480, decode.acc_seg: 90.2096, aux.loss_ce: 0.1505, aux.acc_seg: 85.2045, loss: 0.3984, grad_norm: 3.6728 2023-02-16 21:37:55,813 - mmseg - INFO - Iter [60500/160000] lr: 3.731e-05, eta: 10:04:07, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2706, decode.acc_seg: 89.7564, aux.loss_ce: 0.1628, aux.acc_seg: 84.4912, loss: 0.4334, grad_norm: 3.9029 2023-02-16 21:38:14,031 - mmseg - INFO - Iter [60550/160000] lr: 3.729e-05, eta: 10:03:49, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2417, decode.acc_seg: 90.3061, aux.loss_ce: 0.1502, aux.acc_seg: 84.8810, loss: 0.3919, grad_norm: 3.5205 2023-02-16 21:38:32,289 - mmseg - INFO - Iter [60600/160000] lr: 3.728e-05, eta: 10:03:31, time: 0.365, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2484, decode.acc_seg: 90.2943, aux.loss_ce: 0.1495, aux.acc_seg: 85.3062, loss: 0.3979, grad_norm: 3.3949 2023-02-16 21:38:52,734 - mmseg - INFO - Iter [60650/160000] lr: 3.726e-05, eta: 10:03:16, time: 0.409, data_time: 0.051, memory: 16600, decode.loss_ce: 0.2478, decode.acc_seg: 90.0293, aux.loss_ce: 0.1468, aux.acc_seg: 85.4587, loss: 0.3946, grad_norm: 3.5663 2023-02-16 21:39:10,803 - mmseg - INFO - Iter [60700/160000] lr: 3.724e-05, eta: 10:02:58, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2505, decode.acc_seg: 90.1929, aux.loss_ce: 0.1433, aux.acc_seg: 86.1237, loss: 0.3939, grad_norm: 4.0766 2023-02-16 21:39:28,808 - mmseg - INFO - Iter [60750/160000] lr: 3.722e-05, eta: 10:02:39, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2565, decode.acc_seg: 89.8480, aux.loss_ce: 0.1472, aux.acc_seg: 85.6491, loss: 0.4037, grad_norm: 4.0975 2023-02-16 21:39:46,672 - mmseg - INFO - Iter [60800/160000] lr: 3.720e-05, eta: 10:02:21, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2499, decode.acc_seg: 90.0612, aux.loss_ce: 0.1463, aux.acc_seg: 85.7064, loss: 0.3962, grad_norm: 3.5167 2023-02-16 21:40:04,949 - mmseg - INFO - Iter [60850/160000] lr: 3.718e-05, eta: 10:02:02, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2497, decode.acc_seg: 90.5028, aux.loss_ce: 0.1489, aux.acc_seg: 85.6868, loss: 0.3986, grad_norm: 3.6145 2023-02-16 21:40:23,084 - mmseg - INFO - Iter [60900/160000] lr: 3.716e-05, eta: 10:01:44, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2450, decode.acc_seg: 90.5709, aux.loss_ce: 0.1480, aux.acc_seg: 85.6187, loss: 0.3930, grad_norm: 3.3008 2023-02-16 21:40:41,172 - mmseg - INFO - Iter [60950/160000] lr: 3.714e-05, eta: 10:01:26, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2427, decode.acc_seg: 90.4408, aux.loss_ce: 0.1447, aux.acc_seg: 85.7650, loss: 0.3874, grad_norm: 3.5163 2023-02-16 21:40:59,042 - mmseg - INFO - Saving checkpoint at 61000 iterations 2023-02-16 21:41:00,158 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 21:41:00,158 - mmseg - INFO - Iter [61000/160000] lr: 3.713e-05, eta: 10:01:09, time: 0.380, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2482, decode.acc_seg: 90.1456, aux.loss_ce: 0.1443, aux.acc_seg: 85.6762, loss: 0.3926, grad_norm: 3.2687 2023-02-16 21:41:18,270 - mmseg - INFO - Iter [61050/160000] lr: 3.711e-05, eta: 10:00:50, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2542, decode.acc_seg: 89.8606, aux.loss_ce: 0.1583, aux.acc_seg: 84.6702, loss: 0.4125, grad_norm: 3.9913 2023-02-16 21:41:36,365 - mmseg - INFO - Iter [61100/160000] lr: 3.709e-05, eta: 10:00:32, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2455, decode.acc_seg: 90.6293, aux.loss_ce: 0.1446, aux.acc_seg: 86.0292, loss: 0.3900, grad_norm: 3.2570 2023-02-16 21:41:54,163 - mmseg - INFO - Iter [61150/160000] lr: 3.707e-05, eta: 10:00:13, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2587, decode.acc_seg: 89.9143, aux.loss_ce: 0.1500, aux.acc_seg: 85.4166, loss: 0.4087, grad_norm: 3.5820 2023-02-16 21:42:12,230 - mmseg - INFO - Iter [61200/160000] lr: 3.705e-05, eta: 9:59:55, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2483, decode.acc_seg: 90.3183, aux.loss_ce: 0.1498, aux.acc_seg: 85.6595, loss: 0.3981, grad_norm: 3.5276 2023-02-16 21:42:30,183 - mmseg - INFO - Iter [61250/160000] lr: 3.703e-05, eta: 9:59:36, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2580, decode.acc_seg: 90.2837, aux.loss_ce: 0.1502, aux.acc_seg: 85.7982, loss: 0.4082, grad_norm: 3.8716 2023-02-16 21:42:48,518 - mmseg - INFO - Iter [61300/160000] lr: 3.701e-05, eta: 9:59:18, time: 0.367, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2500, decode.acc_seg: 90.0354, aux.loss_ce: 0.1453, aux.acc_seg: 85.3224, loss: 0.3953, grad_norm: 3.4187 2023-02-16 21:43:06,471 - mmseg - INFO - Iter [61350/160000] lr: 3.699e-05, eta: 9:58:59, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2439, decode.acc_seg: 90.2603, aux.loss_ce: 0.1470, aux.acc_seg: 85.6054, loss: 0.3909, grad_norm: 3.4352 2023-02-16 21:43:24,533 - mmseg - INFO - Iter [61400/160000] lr: 3.698e-05, eta: 9:58:41, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2624, decode.acc_seg: 89.9897, aux.loss_ce: 0.1531, aux.acc_seg: 85.1354, loss: 0.4156, grad_norm: 3.8730 2023-02-16 21:43:42,530 - mmseg - INFO - Iter [61450/160000] lr: 3.696e-05, eta: 9:58:22, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2488, decode.acc_seg: 90.5417, aux.loss_ce: 0.1505, aux.acc_seg: 85.9466, loss: 0.3993, grad_norm: 3.8960 2023-02-16 21:44:00,671 - mmseg - INFO - Iter [61500/160000] lr: 3.694e-05, eta: 9:58:04, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2564, decode.acc_seg: 89.8483, aux.loss_ce: 0.1499, aux.acc_seg: 85.3419, loss: 0.4063, grad_norm: 3.8278 2023-02-16 21:44:18,550 - mmseg - INFO - Iter [61550/160000] lr: 3.692e-05, eta: 9:57:45, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2517, decode.acc_seg: 90.0094, aux.loss_ce: 0.1478, aux.acc_seg: 85.4733, loss: 0.3995, grad_norm: 3.5305 2023-02-16 21:44:36,353 - mmseg - INFO - Iter [61600/160000] lr: 3.690e-05, eta: 9:57:26, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2459, decode.acc_seg: 90.1636, aux.loss_ce: 0.1511, aux.acc_seg: 85.2393, loss: 0.3969, grad_norm: 3.2030 2023-02-16 21:44:54,432 - mmseg - INFO - Iter [61650/160000] lr: 3.688e-05, eta: 9:57:08, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2425, decode.acc_seg: 90.5974, aux.loss_ce: 0.1490, aux.acc_seg: 85.5746, loss: 0.3915, grad_norm: 3.3988 2023-02-16 21:45:12,493 - mmseg - INFO - Iter [61700/160000] lr: 3.686e-05, eta: 9:56:49, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2516, decode.acc_seg: 90.0503, aux.loss_ce: 0.1539, aux.acc_seg: 84.9971, loss: 0.4055, grad_norm: 3.7838 2023-02-16 21:45:30,568 - mmseg - INFO - Iter [61750/160000] lr: 3.684e-05, eta: 9:56:31, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2556, decode.acc_seg: 89.6476, aux.loss_ce: 0.1496, aux.acc_seg: 84.8986, loss: 0.4052, grad_norm: 3.9914 2023-02-16 21:45:48,647 - mmseg - INFO - Iter [61800/160000] lr: 3.683e-05, eta: 9:56:13, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2577, decode.acc_seg: 89.8148, aux.loss_ce: 0.1565, aux.acc_seg: 84.7523, loss: 0.4143, grad_norm: 3.6216 2023-02-16 21:46:06,859 - mmseg - INFO - Iter [61850/160000] lr: 3.681e-05, eta: 9:55:54, time: 0.364, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2418, decode.acc_seg: 90.6469, aux.loss_ce: 0.1495, aux.acc_seg: 85.3104, loss: 0.3913, grad_norm: 3.5771 2023-02-16 21:46:27,512 - mmseg - INFO - Iter [61900/160000] lr: 3.679e-05, eta: 9:55:40, time: 0.413, data_time: 0.051, memory: 16600, decode.loss_ce: 0.2542, decode.acc_seg: 89.9765, aux.loss_ce: 0.1505, aux.acc_seg: 85.0606, loss: 0.4047, grad_norm: 3.7599 2023-02-16 21:46:45,736 - mmseg - INFO - Iter [61950/160000] lr: 3.677e-05, eta: 9:55:22, time: 0.365, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2418, decode.acc_seg: 90.5521, aux.loss_ce: 0.1440, aux.acc_seg: 85.9470, loss: 0.3859, grad_norm: 3.3659 2023-02-16 21:47:03,748 - mmseg - INFO - Saving checkpoint at 62000 iterations 2023-02-16 21:47:04,895 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 21:47:04,896 - mmseg - INFO - Iter [62000/160000] lr: 3.675e-05, eta: 9:55:05, time: 0.383, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2314, decode.acc_seg: 90.8402, aux.loss_ce: 0.1393, aux.acc_seg: 85.9442, loss: 0.3707, grad_norm: 2.9170 2023-02-16 21:47:22,784 - mmseg - INFO - Iter [62050/160000] lr: 3.673e-05, eta: 9:54:46, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2420, decode.acc_seg: 90.3027, aux.loss_ce: 0.1423, aux.acc_seg: 85.7494, loss: 0.3843, grad_norm: 3.5104 2023-02-16 21:47:40,723 - mmseg - INFO - Iter [62100/160000] lr: 3.671e-05, eta: 9:54:28, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2495, decode.acc_seg: 90.1445, aux.loss_ce: 0.1492, aux.acc_seg: 85.4540, loss: 0.3987, grad_norm: 4.3246 2023-02-16 21:47:58,633 - mmseg - INFO - Iter [62150/160000] lr: 3.669e-05, eta: 9:54:09, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2312, decode.acc_seg: 90.9380, aux.loss_ce: 0.1390, aux.acc_seg: 86.1329, loss: 0.3701, grad_norm: 3.0525 2023-02-16 21:48:16,613 - mmseg - INFO - Iter [62200/160000] lr: 3.668e-05, eta: 9:53:50, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2554, decode.acc_seg: 90.0231, aux.loss_ce: 0.1516, aux.acc_seg: 85.4366, loss: 0.4070, grad_norm: 3.9767 2023-02-16 21:48:34,417 - mmseg - INFO - Iter [62250/160000] lr: 3.666e-05, eta: 9:53:31, time: 0.356, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2568, decode.acc_seg: 89.6345, aux.loss_ce: 0.1517, aux.acc_seg: 84.8440, loss: 0.4085, grad_norm: 3.5553 2023-02-16 21:48:52,278 - mmseg - INFO - Iter [62300/160000] lr: 3.664e-05, eta: 9:53:13, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2350, decode.acc_seg: 90.6972, aux.loss_ce: 0.1358, aux.acc_seg: 86.7143, loss: 0.3707, grad_norm: 3.1289 2023-02-16 21:49:10,240 - mmseg - INFO - Iter [62350/160000] lr: 3.662e-05, eta: 9:52:54, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2425, decode.acc_seg: 90.3933, aux.loss_ce: 0.1468, aux.acc_seg: 85.7183, loss: 0.3893, grad_norm: 3.3756 2023-02-16 21:49:28,370 - mmseg - INFO - Iter [62400/160000] lr: 3.660e-05, eta: 9:52:36, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2526, decode.acc_seg: 90.2926, aux.loss_ce: 0.1480, aux.acc_seg: 85.4107, loss: 0.4007, grad_norm: 3.4785 2023-02-16 21:49:46,437 - mmseg - INFO - Iter [62450/160000] lr: 3.658e-05, eta: 9:52:17, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2440, decode.acc_seg: 90.4851, aux.loss_ce: 0.1406, aux.acc_seg: 86.2784, loss: 0.3846, grad_norm: 3.9500 2023-02-16 21:50:04,235 - mmseg - INFO - Iter [62500/160000] lr: 3.656e-05, eta: 9:51:58, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2499, decode.acc_seg: 90.1523, aux.loss_ce: 0.1497, aux.acc_seg: 85.3977, loss: 0.3996, grad_norm: 3.4792 2023-02-16 21:50:22,430 - mmseg - INFO - Iter [62550/160000] lr: 3.654e-05, eta: 9:51:40, time: 0.364, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2391, decode.acc_seg: 90.6048, aux.loss_ce: 0.1404, aux.acc_seg: 86.2154, loss: 0.3795, grad_norm: 3.1455 2023-02-16 21:50:40,233 - mmseg - INFO - Iter [62600/160000] lr: 3.653e-05, eta: 9:51:21, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2457, decode.acc_seg: 90.3471, aux.loss_ce: 0.1468, aux.acc_seg: 85.7558, loss: 0.3925, grad_norm: 3.4911 2023-02-16 21:50:58,362 - mmseg - INFO - Iter [62650/160000] lr: 3.651e-05, eta: 9:51:03, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2369, decode.acc_seg: 90.4795, aux.loss_ce: 0.1443, aux.acc_seg: 85.6837, loss: 0.3811, grad_norm: 3.3189 2023-02-16 21:51:16,387 - mmseg - INFO - Iter [62700/160000] lr: 3.649e-05, eta: 9:50:44, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2458, decode.acc_seg: 90.4218, aux.loss_ce: 0.1446, aux.acc_seg: 85.7972, loss: 0.3904, grad_norm: 3.9296 2023-02-16 21:51:34,288 - mmseg - INFO - Iter [62750/160000] lr: 3.647e-05, eta: 9:50:26, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2631, decode.acc_seg: 89.6325, aux.loss_ce: 0.1551, aux.acc_seg: 85.0503, loss: 0.4182, grad_norm: 4.1892 2023-02-16 21:51:52,441 - mmseg - INFO - Iter [62800/160000] lr: 3.645e-05, eta: 9:50:07, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2478, decode.acc_seg: 90.1388, aux.loss_ce: 0.1503, aux.acc_seg: 85.2289, loss: 0.3981, grad_norm: 3.3801 2023-02-16 21:52:10,262 - mmseg - INFO - Iter [62850/160000] lr: 3.643e-05, eta: 9:49:49, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2558, decode.acc_seg: 90.0034, aux.loss_ce: 0.1543, aux.acc_seg: 85.1023, loss: 0.4101, grad_norm: 4.1278 2023-02-16 21:52:28,073 - mmseg - INFO - Iter [62900/160000] lr: 3.641e-05, eta: 9:49:30, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2522, decode.acc_seg: 90.2788, aux.loss_ce: 0.1468, aux.acc_seg: 85.8051, loss: 0.3990, grad_norm: 3.7476 2023-02-16 21:52:46,228 - mmseg - INFO - Iter [62950/160000] lr: 3.639e-05, eta: 9:49:12, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2462, decode.acc_seg: 90.2724, aux.loss_ce: 0.1454, aux.acc_seg: 85.7098, loss: 0.3916, grad_norm: 3.5002 2023-02-16 21:53:04,458 - mmseg - INFO - Saving checkpoint at 63000 iterations 2023-02-16 21:53:05,612 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 21:53:05,612 - mmseg - INFO - Iter [63000/160000] lr: 3.638e-05, eta: 9:48:55, time: 0.388, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2331, decode.acc_seg: 90.5939, aux.loss_ce: 0.1377, aux.acc_seg: 86.2962, loss: 0.3708, grad_norm: 3.3866 2023-02-16 21:53:23,666 - mmseg - INFO - Iter [63050/160000] lr: 3.636e-05, eta: 9:48:37, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2382, decode.acc_seg: 90.5775, aux.loss_ce: 0.1480, aux.acc_seg: 85.2723, loss: 0.3862, grad_norm: 3.5337 2023-02-16 21:53:41,816 - mmseg - INFO - Iter [63100/160000] lr: 3.634e-05, eta: 9:48:18, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2489, decode.acc_seg: 90.0669, aux.loss_ce: 0.1475, aux.acc_seg: 85.2621, loss: 0.3964, grad_norm: 3.8976 2023-02-16 21:53:59,860 - mmseg - INFO - Iter [63150/160000] lr: 3.632e-05, eta: 9:48:00, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2510, decode.acc_seg: 90.0973, aux.loss_ce: 0.1492, aux.acc_seg: 85.2073, loss: 0.4001, grad_norm: 3.6890 2023-02-16 21:54:20,223 - mmseg - INFO - Iter [63200/160000] lr: 3.630e-05, eta: 9:47:45, time: 0.407, data_time: 0.051, memory: 16600, decode.loss_ce: 0.2327, decode.acc_seg: 90.9466, aux.loss_ce: 0.1364, aux.acc_seg: 86.8119, loss: 0.3691, grad_norm: 3.4996 2023-02-16 21:54:38,226 - mmseg - INFO - Iter [63250/160000] lr: 3.628e-05, eta: 9:47:26, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2376, decode.acc_seg: 90.4767, aux.loss_ce: 0.1408, aux.acc_seg: 86.1710, loss: 0.3784, grad_norm: 3.7996 2023-02-16 21:54:56,223 - mmseg - INFO - Iter [63300/160000] lr: 3.626e-05, eta: 9:47:08, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2435, decode.acc_seg: 90.4581, aux.loss_ce: 0.1473, aux.acc_seg: 85.7655, loss: 0.3907, grad_norm: 3.9621 2023-02-16 21:55:14,325 - mmseg - INFO - Iter [63350/160000] lr: 3.624e-05, eta: 9:46:49, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2323, decode.acc_seg: 90.5116, aux.loss_ce: 0.1402, aux.acc_seg: 85.9354, loss: 0.3725, grad_norm: 3.2862 2023-02-16 21:55:32,579 - mmseg - INFO - Iter [63400/160000] lr: 3.623e-05, eta: 9:46:31, time: 0.365, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2465, decode.acc_seg: 90.4774, aux.loss_ce: 0.1445, aux.acc_seg: 85.8550, loss: 0.3910, grad_norm: 3.2121 2023-02-16 21:55:50,607 - mmseg - INFO - Iter [63450/160000] lr: 3.621e-05, eta: 9:46:13, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2408, decode.acc_seg: 90.4531, aux.loss_ce: 0.1441, aux.acc_seg: 85.8753, loss: 0.3849, grad_norm: 3.4269 2023-02-16 21:56:08,521 - mmseg - INFO - Iter [63500/160000] lr: 3.619e-05, eta: 9:45:54, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2279, decode.acc_seg: 90.8915, aux.loss_ce: 0.1392, aux.acc_seg: 86.2745, loss: 0.3671, grad_norm: 3.2506 2023-02-16 21:56:26,697 - mmseg - INFO - Iter [63550/160000] lr: 3.617e-05, eta: 9:45:36, time: 0.364, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2386, decode.acc_seg: 90.7859, aux.loss_ce: 0.1453, aux.acc_seg: 86.0411, loss: 0.3838, grad_norm: 3.2516 2023-02-16 21:56:44,520 - mmseg - INFO - Iter [63600/160000] lr: 3.615e-05, eta: 9:45:17, time: 0.356, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2525, decode.acc_seg: 90.1499, aux.loss_ce: 0.1521, aux.acc_seg: 85.2576, loss: 0.4046, grad_norm: 4.0289 2023-02-16 21:57:02,458 - mmseg - INFO - Iter [63650/160000] lr: 3.613e-05, eta: 9:44:58, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2405, decode.acc_seg: 90.5505, aux.loss_ce: 0.1435, aux.acc_seg: 85.9895, loss: 0.3840, grad_norm: 3.1741 2023-02-16 21:57:20,869 - mmseg - INFO - Iter [63700/160000] lr: 3.611e-05, eta: 9:44:40, time: 0.368, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2608, decode.acc_seg: 89.7654, aux.loss_ce: 0.1537, aux.acc_seg: 84.9237, loss: 0.4145, grad_norm: 3.7863 2023-02-16 21:57:38,966 - mmseg - INFO - Iter [63750/160000] lr: 3.609e-05, eta: 9:44:22, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2399, decode.acc_seg: 90.4231, aux.loss_ce: 0.1438, aux.acc_seg: 85.6629, loss: 0.3837, grad_norm: 3.2948 2023-02-16 21:57:56,888 - mmseg - INFO - Iter [63800/160000] lr: 3.608e-05, eta: 9:44:03, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2388, decode.acc_seg: 90.4257, aux.loss_ce: 0.1428, aux.acc_seg: 85.4112, loss: 0.3816, grad_norm: 3.0194 2023-02-16 21:58:14,682 - mmseg - INFO - Iter [63850/160000] lr: 3.606e-05, eta: 9:43:45, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2395, decode.acc_seg: 90.6359, aux.loss_ce: 0.1415, aux.acc_seg: 86.1683, loss: 0.3810, grad_norm: 3.3942 2023-02-16 21:58:32,649 - mmseg - INFO - Iter [63900/160000] lr: 3.604e-05, eta: 9:43:26, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2438, decode.acc_seg: 90.2818, aux.loss_ce: 0.1465, aux.acc_seg: 85.3431, loss: 0.3903, grad_norm: 4.0161 2023-02-16 21:58:50,476 - mmseg - INFO - Iter [63950/160000] lr: 3.602e-05, eta: 9:43:07, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2468, decode.acc_seg: 90.1685, aux.loss_ce: 0.1494, aux.acc_seg: 85.0022, loss: 0.3962, grad_norm: 3.4535 2023-02-16 21:59:08,267 - mmseg - INFO - Saving checkpoint at 64000 iterations 2023-02-16 21:59:09,378 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 21:59:09,379 - mmseg - INFO - Iter [64000/160000] lr: 3.600e-05, eta: 9:42:50, time: 0.378, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2178, decode.acc_seg: 91.1294, aux.loss_ce: 0.1341, aux.acc_seg: 86.6412, loss: 0.3520, grad_norm: 2.8772 2023-02-16 21:59:38,369 - mmseg - INFO - per class results: 2023-02-16 21:59:38,375 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 75.24 | 89.59 | | building | 81.05 | 90.62 | | sky | 93.9 | 96.16 | | floor | 79.66 | 88.88 | | tree | 73.21 | 89.16 | | ceiling | 83.08 | 91.7 | | road | 84.17 | 88.42 | | bed | 87.5 | 94.29 | | windowpane | 61.47 | 80.5 | | grass | 68.35 | 78.58 | | cabinet | 58.72 | 66.3 | | sidewalk | 66.04 | 79.75 | | person | 79.1 | 92.45 | | earth | 38.38 | 57.96 | | door | 42.74 | 48.61 | | table | 56.91 | 78.32 | | mountain | 57.49 | 69.67 | | plant | 51.56 | 63.32 | | curtain | 69.53 | 84.4 | | chair | 53.34 | 65.33 | | car | 81.22 | 90.26 | | water | 53.64 | 70.08 | | painting | 71.19 | 84.2 | | sofa | 60.21 | 71.93 | | shelf | 39.29 | 65.8 | | house | 43.94 | 52.81 | | sea | 53.0 | 79.08 | | mirror | 61.65 | 67.89 | | rug | 62.08 | 71.56 | | field | 24.97 | 35.18 | | armchair | 37.4 | 64.21 | | seat | 60.31 | 81.49 | | fence | 43.99 | 69.02 | | desk | 48.87 | 65.54 | | rock | 42.37 | 56.03 | | wardrobe | 41.36 | 56.37 | | lamp | 58.96 | 67.5 | | bathtub | 69.29 | 74.78 | | railing | 32.48 | 52.2 | | cushion | 57.58 | 71.35 | | base | 23.94 | 30.59 | | box | 25.77 | 35.23 | | column | 43.06 | 60.41 | | signboard | 34.63 | 50.3 | | chest of drawers | 40.43 | 58.84 | | counter | 27.52 | 40.15 | | sand | 39.53 | 73.07 | | sink | 68.31 | 79.84 | | skyscraper | 52.22 | 62.93 | | fireplace | 68.99 | 84.96 | | refrigerator | 72.76 | 80.75 | | grandstand | 34.38 | 59.38 | | path | 24.78 | 32.62 | | stairs | 29.04 | 35.65 | | runway | 70.48 | 94.74 | | case | 21.73 | 23.95 | | pool table | 92.17 | 93.89 | | pillow | 58.0 | 70.96 | | screen door | 52.82 | 60.48 | | stairway | 28.67 | 36.72 | | river | 14.72 | 18.86 | | bridge | 38.51 | 44.55 | | bookcase | 31.06 | 53.87 | | blind | 45.62 | 50.27 | | coffee table | 57.2 | 77.48 | | toilet | 82.01 | 89.11 | | flower | 39.09 | 47.76 | | book | 43.63 | 66.26 | | hill | 8.08 | 17.99 | | bench | 41.64 | 57.39 | | countertop | 57.37 | 75.35 | | stove | 72.2 | 79.91 | | palm | 43.65 | 88.97 | | kitchen island | 39.23 | 75.88 | | computer | 63.45 | 73.83 | | swivel chair | 46.33 | 60.5 | | boat | 53.26 | 76.91 | | bar | 25.17 | 30.51 | | arcade machine | 60.67 | 67.65 | | hovel | 30.6 | 36.5 | | bus | 83.66 | 95.09 | | towel | 62.76 | 69.45 | | light | 48.36 | 53.76 | | truck | 31.78 | 43.77 | | tower | 6.12 | 7.28 | | chandelier | 65.08 | 78.83 | | awning | 24.87 | 28.12 | | streetlight | 21.67 | 26.5 | | booth | 49.08 | 53.16 | | television receiver | 69.04 | 78.91 | | airplane | 56.75 | 62.33 | | dirt track | 6.88 | 7.1 | | apparel | 32.74 | 50.62 | | pole | 22.81 | 34.88 | | land | 2.6 | 4.03 | | bannister | 13.28 | 23.29 | | escalator | 15.59 | 16.09 | | ottoman | 51.38 | 69.09 | | bottle | 36.65 | 47.8 | | buffet | 43.33 | 58.23 | | poster | 22.85 | 34.61 | | stage | 10.07 | 13.8 | | van | 47.21 | 54.17 | | ship | 61.42 | 67.21 | | fountain | 18.82 | 21.52 | | conveyer belt | 69.59 | 85.53 | | canopy | 16.52 | 20.65 | | washer | 66.69 | 69.63 | | plaything | 22.09 | 29.11 | | swimming pool | 63.9 | 67.84 | | stool | 35.6 | 54.89 | | barrel | 19.14 | 64.83 | | basket | 35.63 | 50.55 | | waterfall | 47.92 | 75.15 | | tent | 95.74 | 98.2 | | bag | 12.33 | 14.36 | | minibike | 62.82 | 78.9 | | cradle | 80.23 | 93.37 | | oven | 34.46 | 43.8 | | ball | 27.15 | 31.78 | | food | 53.41 | 61.22 | | step | 11.24 | 17.96 | | tank | 36.0 | 44.96 | | trade name | 26.91 | 30.02 | | microwave | 80.72 | 92.72 | | pot | 39.46 | 49.13 | | animal | 58.77 | 61.64 | | bicycle | 55.18 | 79.53 | | lake | 60.05 | 61.29 | | dishwasher | 67.83 | 82.98 | | screen | 56.57 | 90.68 | | blanket | 9.4 | 11.42 | | sculpture | 52.4 | 71.71 | | hood | 54.27 | 68.24 | | sconce | 42.76 | 54.8 | | vase | 33.65 | 51.85 | | traffic light | 26.31 | 36.29 | | tray | 6.1 | 7.72 | | ashcan | 38.4 | 45.24 | | fan | 57.05 | 74.18 | | pier | 46.34 | 84.82 | | crt screen | 0.02 | 0.04 | | plate | 46.14 | 52.56 | | monitor | 14.44 | 22.74 | | bulletin board | 35.0 | 53.69 | | shower | 0.01 | 0.14 | | radiator | 57.5 | 67.75 | | glass | 5.31 | 5.42 | | clock | 31.65 | 37.5 | | flag | 32.41 | 36.14 | +---------------------+-------+-------+ 2023-02-16 21:59:38,375 - mmseg - INFO - Summary: 2023-02-16 21:59:38,375 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 81.83 | 46.25 | 58.19 | +-------+-------+-------+ 2023-02-16 21:59:39,396 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_64000.pth. 2023-02-16 21:59:39,397 - mmseg - INFO - Best mIoU is 0.4625 at 64000 iter. 2023-02-16 21:59:39,397 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 21:59:39,397 - mmseg - INFO - Iter(val) [500] aAcc: 0.8183, mIoU: 0.4625, mAcc: 0.5819, IoU.wall: 0.7524, IoU.building: 0.8105, IoU.sky: 0.9390, IoU.floor: 0.7966, IoU.tree: 0.7321, IoU.ceiling: 0.8308, IoU.road: 0.8417, IoU.bed : 0.8750, IoU.windowpane: 0.6147, IoU.grass: 0.6835, IoU.cabinet: 0.5872, IoU.sidewalk: 0.6604, IoU.person: 0.7910, IoU.earth: 0.3838, IoU.door: 0.4274, IoU.table: 0.5691, IoU.mountain: 0.5749, IoU.plant: 0.5156, IoU.curtain: 0.6953, IoU.chair: 0.5334, IoU.car: 0.8122, IoU.water: 0.5364, IoU.painting: 0.7119, IoU.sofa: 0.6021, IoU.shelf: 0.3929, IoU.house: 0.4394, IoU.sea: 0.5300, IoU.mirror: 0.6165, IoU.rug: 0.6208, IoU.field: 0.2497, IoU.armchair: 0.3740, IoU.seat: 0.6031, IoU.fence: 0.4399, IoU.desk: 0.4887, IoU.rock: 0.4237, IoU.wardrobe: 0.4136, IoU.lamp: 0.5896, IoU.bathtub: 0.6929, IoU.railing: 0.3248, IoU.cushion: 0.5758, IoU.base: 0.2394, IoU.box: 0.2577, IoU.column: 0.4306, IoU.signboard: 0.3463, IoU.chest of drawers: 0.4043, IoU.counter: 0.2752, IoU.sand: 0.3953, IoU.sink: 0.6831, IoU.skyscraper: 0.5222, IoU.fireplace: 0.6899, IoU.refrigerator: 0.7276, IoU.grandstand: 0.3438, IoU.path: 0.2478, IoU.stairs: 0.2904, IoU.runway: 0.7048, IoU.case: 0.2173, IoU.pool table: 0.9217, IoU.pillow: 0.5800, IoU.screen door: 0.5282, IoU.stairway: 0.2867, IoU.river: 0.1472, IoU.bridge: 0.3851, IoU.bookcase: 0.3106, IoU.blind: 0.4562, IoU.coffee table: 0.5720, IoU.toilet: 0.8201, IoU.flower: 0.3909, IoU.book: 0.4363, IoU.hill: 0.0808, IoU.bench: 0.4164, IoU.countertop: 0.5737, IoU.stove: 0.7220, IoU.palm: 0.4365, IoU.kitchen island: 0.3923, IoU.computer: 0.6345, IoU.swivel chair: 0.4633, IoU.boat: 0.5326, IoU.bar: 0.2517, IoU.arcade machine: 0.6067, IoU.hovel: 0.3060, IoU.bus: 0.8366, IoU.towel: 0.6276, IoU.light: 0.4836, IoU.truck: 0.3178, IoU.tower: 0.0612, IoU.chandelier: 0.6508, IoU.awning: 0.2487, IoU.streetlight: 0.2167, IoU.booth: 0.4908, IoU.television receiver: 0.6904, IoU.airplane: 0.5675, IoU.dirt track: 0.0688, IoU.apparel: 0.3274, IoU.pole: 0.2281, IoU.land: 0.0260, IoU.bannister: 0.1328, IoU.escalator: 0.1559, IoU.ottoman: 0.5138, IoU.bottle: 0.3665, IoU.buffet: 0.4333, IoU.poster: 0.2285, IoU.stage: 0.1007, IoU.van: 0.4721, IoU.ship: 0.6142, IoU.fountain: 0.1882, IoU.conveyer belt: 0.6959, IoU.canopy: 0.1652, IoU.washer: 0.6669, IoU.plaything: 0.2209, IoU.swimming pool: 0.6390, IoU.stool: 0.3560, IoU.barrel: 0.1914, IoU.basket: 0.3563, IoU.waterfall: 0.4792, IoU.tent: 0.9574, IoU.bag: 0.1233, IoU.minibike: 0.6282, IoU.cradle: 0.8023, IoU.oven: 0.3446, IoU.ball: 0.2715, IoU.food: 0.5341, IoU.step: 0.1124, IoU.tank: 0.3600, IoU.trade name: 0.2691, IoU.microwave: 0.8072, IoU.pot: 0.3946, IoU.animal: 0.5877, IoU.bicycle: 0.5518, IoU.lake: 0.6005, IoU.dishwasher: 0.6783, IoU.screen: 0.5657, IoU.blanket: 0.0940, IoU.sculpture: 0.5240, IoU.hood: 0.5427, IoU.sconce: 0.4276, IoU.vase: 0.3365, IoU.traffic light: 0.2631, IoU.tray: 0.0610, IoU.ashcan: 0.3840, IoU.fan: 0.5705, IoU.pier: 0.4634, IoU.crt screen: 0.0002, IoU.plate: 0.4614, IoU.monitor: 0.1444, IoU.bulletin board: 0.3500, IoU.shower: 0.0001, IoU.radiator: 0.5750, IoU.glass: 0.0531, IoU.clock: 0.3165, IoU.flag: 0.3241, Acc.wall: 0.8959, Acc.building: 0.9062, Acc.sky: 0.9616, Acc.floor: 0.8888, Acc.tree: 0.8916, Acc.ceiling: 0.9170, Acc.road: 0.8842, Acc.bed : 0.9429, Acc.windowpane: 0.8050, Acc.grass: 0.7858, Acc.cabinet: 0.6630, Acc.sidewalk: 0.7975, Acc.person: 0.9245, Acc.earth: 0.5796, Acc.door: 0.4861, Acc.table: 0.7832, Acc.mountain: 0.6967, Acc.plant: 0.6332, Acc.curtain: 0.8440, Acc.chair: 0.6533, Acc.car: 0.9026, Acc.water: 0.7008, Acc.painting: 0.8420, Acc.sofa: 0.7193, Acc.shelf: 0.6580, Acc.house: 0.5281, Acc.sea: 0.7908, Acc.mirror: 0.6789, Acc.rug: 0.7156, Acc.field: 0.3518, Acc.armchair: 0.6421, Acc.seat: 0.8149, Acc.fence: 0.6902, Acc.desk: 0.6554, Acc.rock: 0.5603, Acc.wardrobe: 0.5637, Acc.lamp: 0.6750, Acc.bathtub: 0.7478, Acc.railing: 0.5220, Acc.cushion: 0.7135, Acc.base: 0.3059, Acc.box: 0.3523, Acc.column: 0.6041, Acc.signboard: 0.5030, Acc.chest of drawers: 0.5884, Acc.counter: 0.4015, Acc.sand: 0.7307, Acc.sink: 0.7984, Acc.skyscraper: 0.6293, Acc.fireplace: 0.8496, Acc.refrigerator: 0.8075, Acc.grandstand: 0.5938, Acc.path: 0.3262, Acc.stairs: 0.3565, Acc.runway: 0.9474, Acc.case: 0.2395, Acc.pool table: 0.9389, Acc.pillow: 0.7096, Acc.screen door: 0.6048, Acc.stairway: 0.3672, Acc.river: 0.1886, Acc.bridge: 0.4455, Acc.bookcase: 0.5387, Acc.blind: 0.5027, Acc.coffee table: 0.7748, Acc.toilet: 0.8911, Acc.flower: 0.4776, Acc.book: 0.6626, Acc.hill: 0.1799, Acc.bench: 0.5739, Acc.countertop: 0.7535, Acc.stove: 0.7991, Acc.palm: 0.8897, Acc.kitchen island: 0.7588, Acc.computer: 0.7383, Acc.swivel chair: 0.6050, Acc.boat: 0.7691, Acc.bar: 0.3051, Acc.arcade machine: 0.6765, Acc.hovel: 0.3650, Acc.bus: 0.9509, Acc.towel: 0.6945, Acc.light: 0.5376, Acc.truck: 0.4377, Acc.tower: 0.0728, Acc.chandelier: 0.7883, Acc.awning: 0.2812, Acc.streetlight: 0.2650, Acc.booth: 0.5316, Acc.television receiver: 0.7891, Acc.airplane: 0.6233, Acc.dirt track: 0.0710, Acc.apparel: 0.5062, Acc.pole: 0.3488, Acc.land: 0.0403, Acc.bannister: 0.2329, Acc.escalator: 0.1609, Acc.ottoman: 0.6909, Acc.bottle: 0.4780, Acc.buffet: 0.5823, Acc.poster: 0.3461, Acc.stage: 0.1380, Acc.van: 0.5417, Acc.ship: 0.6721, Acc.fountain: 0.2152, Acc.conveyer belt: 0.8553, Acc.canopy: 0.2065, Acc.washer: 0.6963, Acc.plaything: 0.2911, Acc.swimming pool: 0.6784, Acc.stool: 0.5489, Acc.barrel: 0.6483, Acc.basket: 0.5055, Acc.waterfall: 0.7515, Acc.tent: 0.9820, Acc.bag: 0.1436, Acc.minibike: 0.7890, Acc.cradle: 0.9337, Acc.oven: 0.4380, Acc.ball: 0.3178, Acc.food: 0.6122, Acc.step: 0.1796, Acc.tank: 0.4496, Acc.trade name: 0.3002, Acc.microwave: 0.9272, Acc.pot: 0.4913, Acc.animal: 0.6164, Acc.bicycle: 0.7953, Acc.lake: 0.6129, Acc.dishwasher: 0.8298, Acc.screen: 0.9068, Acc.blanket: 0.1142, Acc.sculpture: 0.7171, Acc.hood: 0.6824, Acc.sconce: 0.5480, Acc.vase: 0.5185, Acc.traffic light: 0.3629, Acc.tray: 0.0772, Acc.ashcan: 0.4524, Acc.fan: 0.7418, Acc.pier: 0.8482, Acc.crt screen: 0.0004, Acc.plate: 0.5256, Acc.monitor: 0.2274, Acc.bulletin board: 0.5369, Acc.shower: 0.0014, Acc.radiator: 0.6775, Acc.glass: 0.0542, Acc.clock: 0.3750, Acc.flag: 0.3614 2023-02-16 21:59:57,498 - mmseg - INFO - Iter [64050/160000] lr: 3.598e-05, eta: 9:43:17, time: 0.962, data_time: 0.607, memory: 16600, decode.loss_ce: 0.2468, decode.acc_seg: 90.2628, aux.loss_ce: 0.1543, aux.acc_seg: 85.0125, loss: 0.4012, grad_norm: 4.1481 2023-02-16 22:00:15,516 - mmseg - INFO - Iter [64100/160000] lr: 3.596e-05, eta: 9:42:58, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2360, decode.acc_seg: 90.8290, aux.loss_ce: 0.1422, aux.acc_seg: 86.1784, loss: 0.3781, grad_norm: 3.5916 2023-02-16 22:00:33,635 - mmseg - INFO - Iter [64150/160000] lr: 3.594e-05, eta: 9:42:40, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2513, decode.acc_seg: 90.0761, aux.loss_ce: 0.1518, aux.acc_seg: 84.9843, loss: 0.4031, grad_norm: 3.9335 2023-02-16 22:00:51,580 - mmseg - INFO - Iter [64200/160000] lr: 3.593e-05, eta: 9:42:21, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2437, decode.acc_seg: 90.4831, aux.loss_ce: 0.1490, aux.acc_seg: 85.3856, loss: 0.3926, grad_norm: 3.5365 2023-02-16 22:01:09,614 - mmseg - INFO - Iter [64250/160000] lr: 3.591e-05, eta: 9:42:02, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2297, decode.acc_seg: 90.8966, aux.loss_ce: 0.1390, aux.acc_seg: 86.2595, loss: 0.3687, grad_norm: 2.9984 2023-02-16 22:01:27,664 - mmseg - INFO - Iter [64300/160000] lr: 3.589e-05, eta: 9:41:44, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2286, decode.acc_seg: 90.9536, aux.loss_ce: 0.1436, aux.acc_seg: 85.6211, loss: 0.3722, grad_norm: 3.1644 2023-02-16 22:01:45,957 - mmseg - INFO - Iter [64350/160000] lr: 3.587e-05, eta: 9:41:26, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2452, decode.acc_seg: 90.4090, aux.loss_ce: 0.1463, aux.acc_seg: 85.8477, loss: 0.3915, grad_norm: 5.1658 2023-02-16 22:02:03,906 - mmseg - INFO - Iter [64400/160000] lr: 3.585e-05, eta: 9:41:07, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2469, decode.acc_seg: 90.2990, aux.loss_ce: 0.1510, aux.acc_seg: 85.3052, loss: 0.3979, grad_norm: 4.0725 2023-02-16 22:02:24,441 - mmseg - INFO - Iter [64450/160000] lr: 3.583e-05, eta: 9:40:52, time: 0.411, data_time: 0.050, memory: 16600, decode.loss_ce: 0.2312, decode.acc_seg: 90.8913, aux.loss_ce: 0.1475, aux.acc_seg: 85.3691, loss: 0.3788, grad_norm: 4.1409 2023-02-16 22:02:42,252 - mmseg - INFO - Iter [64500/160000] lr: 3.581e-05, eta: 9:40:33, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2337, decode.acc_seg: 90.6528, aux.loss_ce: 0.1406, aux.acc_seg: 85.8904, loss: 0.3743, grad_norm: 3.4527 2023-02-16 22:03:00,258 - mmseg - INFO - Iter [64550/160000] lr: 3.579e-05, eta: 9:40:15, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2434, decode.acc_seg: 90.6501, aux.loss_ce: 0.1484, aux.acc_seg: 85.6052, loss: 0.3918, grad_norm: 3.8739 2023-02-16 22:03:18,386 - mmseg - INFO - Iter [64600/160000] lr: 3.578e-05, eta: 9:39:56, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2404, decode.acc_seg: 90.6466, aux.loss_ce: 0.1445, aux.acc_seg: 85.9759, loss: 0.3849, grad_norm: 3.3039 2023-02-16 22:03:36,311 - mmseg - INFO - Iter [64650/160000] lr: 3.576e-05, eta: 9:39:38, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2282, decode.acc_seg: 91.0554, aux.loss_ce: 0.1347, aux.acc_seg: 86.7557, loss: 0.3629, grad_norm: 3.2831 2023-02-16 22:03:54,303 - mmseg - INFO - Iter [64700/160000] lr: 3.574e-05, eta: 9:39:19, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2167, decode.acc_seg: 91.4503, aux.loss_ce: 0.1321, aux.acc_seg: 87.0660, loss: 0.3488, grad_norm: 3.1973 2023-02-16 22:04:12,266 - mmseg - INFO - Iter [64750/160000] lr: 3.572e-05, eta: 9:39:00, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2392, decode.acc_seg: 90.4703, aux.loss_ce: 0.1433, aux.acc_seg: 85.7947, loss: 0.3825, grad_norm: 3.0950 2023-02-16 22:04:30,357 - mmseg - INFO - Iter [64800/160000] lr: 3.570e-05, eta: 9:38:42, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2439, decode.acc_seg: 90.3372, aux.loss_ce: 0.1446, aux.acc_seg: 85.7569, loss: 0.3884, grad_norm: 3.5604 2023-02-16 22:04:48,291 - mmseg - INFO - Iter [64850/160000] lr: 3.568e-05, eta: 9:38:23, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2390, decode.acc_seg: 90.5160, aux.loss_ce: 0.1421, aux.acc_seg: 86.2486, loss: 0.3811, grad_norm: 3.3759 2023-02-16 22:05:06,455 - mmseg - INFO - Iter [64900/160000] lr: 3.566e-05, eta: 9:38:05, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2157, decode.acc_seg: 91.4238, aux.loss_ce: 0.1340, aux.acc_seg: 86.5768, loss: 0.3497, grad_norm: 3.4445 2023-02-16 22:05:24,401 - mmseg - INFO - Iter [64950/160000] lr: 3.564e-05, eta: 9:37:46, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2487, decode.acc_seg: 89.9870, aux.loss_ce: 0.1473, aux.acc_seg: 85.1966, loss: 0.3959, grad_norm: 3.4214 2023-02-16 22:05:42,314 - mmseg - INFO - Saving checkpoint at 65000 iterations 2023-02-16 22:05:43,371 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 22:05:43,372 - mmseg - INFO - Iter [65000/160000] lr: 3.563e-05, eta: 9:37:29, time: 0.379, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2337, decode.acc_seg: 90.8479, aux.loss_ce: 0.1413, aux.acc_seg: 86.2736, loss: 0.3750, grad_norm: 3.1144 2023-02-16 22:06:01,332 - mmseg - INFO - Iter [65050/160000] lr: 3.561e-05, eta: 9:37:11, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2326, decode.acc_seg: 90.8072, aux.loss_ce: 0.1411, aux.acc_seg: 85.9545, loss: 0.3737, grad_norm: 3.7093 2023-02-16 22:06:19,314 - mmseg - INFO - Iter [65100/160000] lr: 3.559e-05, eta: 9:36:52, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2491, decode.acc_seg: 90.1507, aux.loss_ce: 0.1484, aux.acc_seg: 85.4665, loss: 0.3975, grad_norm: 4.5397 2023-02-16 22:06:37,568 - mmseg - INFO - Iter [65150/160000] lr: 3.557e-05, eta: 9:36:34, time: 0.365, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2516, decode.acc_seg: 90.2237, aux.loss_ce: 0.1520, aux.acc_seg: 85.3162, loss: 0.4037, grad_norm: 3.8453 2023-02-16 22:06:55,532 - mmseg - INFO - Iter [65200/160000] lr: 3.555e-05, eta: 9:36:15, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2454, decode.acc_seg: 90.2028, aux.loss_ce: 0.1432, aux.acc_seg: 85.6371, loss: 0.3886, grad_norm: 3.3367 2023-02-16 22:07:13,487 - mmseg - INFO - Iter [65250/160000] lr: 3.553e-05, eta: 9:35:56, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2394, decode.acc_seg: 90.3101, aux.loss_ce: 0.1420, aux.acc_seg: 85.8070, loss: 0.3814, grad_norm: 3.4961 2023-02-16 22:07:31,383 - mmseg - INFO - Iter [65300/160000] lr: 3.551e-05, eta: 9:35:38, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2309, decode.acc_seg: 90.8830, aux.loss_ce: 0.1387, aux.acc_seg: 86.1338, loss: 0.3696, grad_norm: 3.6552 2023-02-16 22:07:49,297 - mmseg - INFO - Iter [65350/160000] lr: 3.549e-05, eta: 9:35:19, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2413, decode.acc_seg: 90.3262, aux.loss_ce: 0.1441, aux.acc_seg: 85.7916, loss: 0.3854, grad_norm: 3.3995 2023-02-16 22:08:07,327 - mmseg - INFO - Iter [65400/160000] lr: 3.548e-05, eta: 9:35:00, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2383, decode.acc_seg: 90.7341, aux.loss_ce: 0.1430, aux.acc_seg: 86.1058, loss: 0.3813, grad_norm: 3.4659 2023-02-16 22:08:25,421 - mmseg - INFO - Iter [65450/160000] lr: 3.546e-05, eta: 9:34:42, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2247, decode.acc_seg: 91.0735, aux.loss_ce: 0.1381, aux.acc_seg: 86.3220, loss: 0.3629, grad_norm: 3.3058 2023-02-16 22:08:43,425 - mmseg - INFO - Iter [65500/160000] lr: 3.544e-05, eta: 9:34:23, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2299, decode.acc_seg: 90.7321, aux.loss_ce: 0.1427, aux.acc_seg: 85.6630, loss: 0.3726, grad_norm: 3.5738 2023-02-16 22:09:01,750 - mmseg - INFO - Iter [65550/160000] lr: 3.542e-05, eta: 9:34:05, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2376, decode.acc_seg: 90.7658, aux.loss_ce: 0.1471, aux.acc_seg: 85.6229, loss: 0.3847, grad_norm: 3.6077 2023-02-16 22:09:19,770 - mmseg - INFO - Iter [65600/160000] lr: 3.540e-05, eta: 9:33:47, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2403, decode.acc_seg: 90.3916, aux.loss_ce: 0.1448, aux.acc_seg: 85.5091, loss: 0.3851, grad_norm: 3.4546 2023-02-16 22:09:38,022 - mmseg - INFO - Iter [65650/160000] lr: 3.538e-05, eta: 9:33:29, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2293, decode.acc_seg: 90.9723, aux.loss_ce: 0.1404, aux.acc_seg: 86.2305, loss: 0.3697, grad_norm: 3.0728 2023-02-16 22:09:58,175 - mmseg - INFO - Iter [65700/160000] lr: 3.536e-05, eta: 9:33:13, time: 0.403, data_time: 0.052, memory: 16600, decode.loss_ce: 0.2273, decode.acc_seg: 90.8008, aux.loss_ce: 0.1373, aux.acc_seg: 86.0216, loss: 0.3645, grad_norm: 3.4367 2023-02-16 22:10:16,104 - mmseg - INFO - Iter [65750/160000] lr: 3.534e-05, eta: 9:32:54, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2352, decode.acc_seg: 90.6443, aux.loss_ce: 0.1384, aux.acc_seg: 86.5190, loss: 0.3736, grad_norm: 3.3793 2023-02-16 22:10:33,941 - mmseg - INFO - Iter [65800/160000] lr: 3.533e-05, eta: 9:32:36, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2205, decode.acc_seg: 91.3058, aux.loss_ce: 0.1327, aux.acc_seg: 86.9371, loss: 0.3532, grad_norm: 2.7993 2023-02-16 22:10:52,006 - mmseg - INFO - Iter [65850/160000] lr: 3.531e-05, eta: 9:32:17, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2265, decode.acc_seg: 91.0156, aux.loss_ce: 0.1419, aux.acc_seg: 85.9584, loss: 0.3684, grad_norm: 3.2401 2023-02-16 22:11:09,903 - mmseg - INFO - Iter [65900/160000] lr: 3.529e-05, eta: 9:31:58, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2176, decode.acc_seg: 91.1276, aux.loss_ce: 0.1305, aux.acc_seg: 86.8758, loss: 0.3481, grad_norm: 3.0965 2023-02-16 22:11:28,009 - mmseg - INFO - Iter [65950/160000] lr: 3.527e-05, eta: 9:31:40, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2247, decode.acc_seg: 91.1088, aux.loss_ce: 0.1390, aux.acc_seg: 86.2334, loss: 0.3637, grad_norm: 3.6126 2023-02-16 22:11:46,157 - mmseg - INFO - Saving checkpoint at 66000 iterations 2023-02-16 22:11:47,287 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 22:11:47,287 - mmseg - INFO - Iter [66000/160000] lr: 3.525e-05, eta: 9:31:23, time: 0.386, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2346, decode.acc_seg: 90.7803, aux.loss_ce: 0.1390, aux.acc_seg: 86.6240, loss: 0.3736, grad_norm: 3.2388 2023-02-16 22:12:05,262 - mmseg - INFO - Iter [66050/160000] lr: 3.523e-05, eta: 9:31:05, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2231, decode.acc_seg: 91.2206, aux.loss_ce: 0.1357, aux.acc_seg: 86.7554, loss: 0.3588, grad_norm: 3.2353 2023-02-16 22:12:23,156 - mmseg - INFO - Iter [66100/160000] lr: 3.521e-05, eta: 9:30:46, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2331, decode.acc_seg: 90.7720, aux.loss_ce: 0.1402, aux.acc_seg: 86.1764, loss: 0.3733, grad_norm: 3.7189 2023-02-16 22:12:41,012 - mmseg - INFO - Iter [66150/160000] lr: 3.519e-05, eta: 9:30:27, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2372, decode.acc_seg: 90.6996, aux.loss_ce: 0.1454, aux.acc_seg: 85.9985, loss: 0.3826, grad_norm: 4.0895 2023-02-16 22:12:59,146 - mmseg - INFO - Iter [66200/160000] lr: 3.518e-05, eta: 9:30:09, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2228, decode.acc_seg: 91.0031, aux.loss_ce: 0.1350, aux.acc_seg: 86.5598, loss: 0.3578, grad_norm: 2.8696 2023-02-16 22:13:17,273 - mmseg - INFO - Iter [66250/160000] lr: 3.516e-05, eta: 9:29:50, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2394, decode.acc_seg: 90.6958, aux.loss_ce: 0.1457, aux.acc_seg: 85.7508, loss: 0.3852, grad_norm: 3.3728 2023-02-16 22:13:35,187 - mmseg - INFO - Iter [66300/160000] lr: 3.514e-05, eta: 9:29:32, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2334, decode.acc_seg: 91.0862, aux.loss_ce: 0.1451, aux.acc_seg: 86.0816, loss: 0.3784, grad_norm: 3.4253 2023-02-16 22:13:53,291 - mmseg - INFO - Iter [66350/160000] lr: 3.512e-05, eta: 9:29:13, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2337, decode.acc_seg: 90.7880, aux.loss_ce: 0.1383, aux.acc_seg: 86.3204, loss: 0.3720, grad_norm: 3.5823 2023-02-16 22:14:11,364 - mmseg - INFO - Iter [66400/160000] lr: 3.510e-05, eta: 9:28:55, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2295, decode.acc_seg: 91.0270, aux.loss_ce: 0.1451, aux.acc_seg: 85.9296, loss: 0.3746, grad_norm: 4.1221 2023-02-16 22:14:29,647 - mmseg - INFO - Iter [66450/160000] lr: 3.508e-05, eta: 9:28:37, time: 0.366, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2402, decode.acc_seg: 90.5608, aux.loss_ce: 0.1489, aux.acc_seg: 85.4543, loss: 0.3892, grad_norm: 3.7610 2023-02-16 22:14:47,764 - mmseg - INFO - Iter [66500/160000] lr: 3.506e-05, eta: 9:28:18, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2426, decode.acc_seg: 90.3496, aux.loss_ce: 0.1429, aux.acc_seg: 85.9392, loss: 0.3855, grad_norm: 3.4149 2023-02-16 22:15:06,033 - mmseg - INFO - Iter [66550/160000] lr: 3.504e-05, eta: 9:28:00, time: 0.365, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2305, decode.acc_seg: 90.7817, aux.loss_ce: 0.1444, aux.acc_seg: 85.7436, loss: 0.3749, grad_norm: 3.9747 2023-02-16 22:15:23,953 - mmseg - INFO - Iter [66600/160000] lr: 3.503e-05, eta: 9:27:41, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2349, decode.acc_seg: 90.7239, aux.loss_ce: 0.1439, aux.acc_seg: 85.7099, loss: 0.3788, grad_norm: 4.0362 2023-02-16 22:15:41,923 - mmseg - INFO - Iter [66650/160000] lr: 3.501e-05, eta: 9:27:23, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2374, decode.acc_seg: 90.7121, aux.loss_ce: 0.1407, aux.acc_seg: 86.0131, loss: 0.3781, grad_norm: 3.4709 2023-02-16 22:15:59,951 - mmseg - INFO - Iter [66700/160000] lr: 3.499e-05, eta: 9:27:04, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2435, decode.acc_seg: 90.3237, aux.loss_ce: 0.1434, aux.acc_seg: 85.9055, loss: 0.3868, grad_norm: 3.7419 2023-02-16 22:16:17,886 - mmseg - INFO - Iter [66750/160000] lr: 3.497e-05, eta: 9:26:46, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2540, decode.acc_seg: 90.3712, aux.loss_ce: 0.1456, aux.acc_seg: 85.9233, loss: 0.3996, grad_norm: 4.3773 2023-02-16 22:16:35,923 - mmseg - INFO - Iter [66800/160000] lr: 3.495e-05, eta: 9:26:27, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2275, decode.acc_seg: 91.1485, aux.loss_ce: 0.1416, aux.acc_seg: 86.2642, loss: 0.3691, grad_norm: 3.2620 2023-02-16 22:16:53,781 - mmseg - INFO - Iter [66850/160000] lr: 3.493e-05, eta: 9:26:08, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2394, decode.acc_seg: 90.5715, aux.loss_ce: 0.1408, aux.acc_seg: 86.1313, loss: 0.3802, grad_norm: 4.4770 2023-02-16 22:17:12,048 - mmseg - INFO - Iter [66900/160000] lr: 3.491e-05, eta: 9:25:50, time: 0.365, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2264, decode.acc_seg: 90.8514, aux.loss_ce: 0.1390, aux.acc_seg: 86.0758, loss: 0.3654, grad_norm: 3.6899 2023-02-16 22:17:32,529 - mmseg - INFO - Iter [66950/160000] lr: 3.489e-05, eta: 9:25:35, time: 0.410, data_time: 0.053, memory: 16600, decode.loss_ce: 0.2442, decode.acc_seg: 90.6133, aux.loss_ce: 0.1455, aux.acc_seg: 86.0661, loss: 0.3897, grad_norm: 3.3870 2023-02-16 22:17:50,465 - mmseg - INFO - Saving checkpoint at 67000 iterations 2023-02-16 22:17:51,588 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 22:17:51,588 - mmseg - INFO - Iter [67000/160000] lr: 3.488e-05, eta: 9:25:18, time: 0.381, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2443, decode.acc_seg: 90.6468, aux.loss_ce: 0.1506, aux.acc_seg: 85.5591, loss: 0.3950, grad_norm: 3.9113 2023-02-16 22:18:09,535 - mmseg - INFO - Iter [67050/160000] lr: 3.486e-05, eta: 9:24:59, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2265, decode.acc_seg: 91.0810, aux.loss_ce: 0.1366, aux.acc_seg: 86.7459, loss: 0.3631, grad_norm: 3.5318 2023-02-16 22:18:27,328 - mmseg - INFO - Iter [67100/160000] lr: 3.484e-05, eta: 9:24:40, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2346, decode.acc_seg: 90.7174, aux.loss_ce: 0.1409, aux.acc_seg: 85.8705, loss: 0.3755, grad_norm: 3.6419 2023-02-16 22:18:45,194 - mmseg - INFO - Iter [67150/160000] lr: 3.482e-05, eta: 9:24:22, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2341, decode.acc_seg: 90.5677, aux.loss_ce: 0.1434, aux.acc_seg: 85.4130, loss: 0.3775, grad_norm: 3.5726 2023-02-16 22:19:03,255 - mmseg - INFO - Iter [67200/160000] lr: 3.480e-05, eta: 9:24:03, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2186, decode.acc_seg: 91.2480, aux.loss_ce: 0.1364, aux.acc_seg: 86.2121, loss: 0.3550, grad_norm: 3.5936 2023-02-16 22:19:21,051 - mmseg - INFO - Iter [67250/160000] lr: 3.478e-05, eta: 9:23:44, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2181, decode.acc_seg: 91.5046, aux.loss_ce: 0.1310, aux.acc_seg: 87.1831, loss: 0.3490, grad_norm: 3.2210 2023-02-16 22:19:39,416 - mmseg - INFO - Iter [67300/160000] lr: 3.476e-05, eta: 9:23:26, time: 0.367, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2399, decode.acc_seg: 90.4132, aux.loss_ce: 0.1384, aux.acc_seg: 86.1696, loss: 0.3782, grad_norm: 3.5364 2023-02-16 22:19:57,182 - mmseg - INFO - Iter [67350/160000] lr: 3.474e-05, eta: 9:23:07, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2185, decode.acc_seg: 91.3576, aux.loss_ce: 0.1333, aux.acc_seg: 86.7872, loss: 0.3518, grad_norm: 3.4211 2023-02-16 22:20:14,989 - mmseg - INFO - Iter [67400/160000] lr: 3.473e-05, eta: 9:22:49, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2320, decode.acc_seg: 90.7984, aux.loss_ce: 0.1368, aux.acc_seg: 86.5004, loss: 0.3688, grad_norm: 3.8837 2023-02-16 22:20:32,831 - mmseg - INFO - Iter [67450/160000] lr: 3.471e-05, eta: 9:22:30, time: 0.357, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2293, decode.acc_seg: 90.8897, aux.loss_ce: 0.1432, aux.acc_seg: 86.1922, loss: 0.3725, grad_norm: 3.4557 2023-02-16 22:20:50,982 - mmseg - INFO - Iter [67500/160000] lr: 3.469e-05, eta: 9:22:12, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2264, decode.acc_seg: 91.0820, aux.loss_ce: 0.1336, aux.acc_seg: 86.9733, loss: 0.3600, grad_norm: 2.8745 2023-02-16 22:21:09,153 - mmseg - INFO - Iter [67550/160000] lr: 3.467e-05, eta: 9:21:53, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2320, decode.acc_seg: 90.8689, aux.loss_ce: 0.1450, aux.acc_seg: 85.7963, loss: 0.3770, grad_norm: 3.4119 2023-02-16 22:21:27,186 - mmseg - INFO - Iter [67600/160000] lr: 3.465e-05, eta: 9:21:35, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2402, decode.acc_seg: 90.3379, aux.loss_ce: 0.1458, aux.acc_seg: 85.5614, loss: 0.3860, grad_norm: 3.7518 2023-02-16 22:21:45,028 - mmseg - INFO - Iter [67650/160000] lr: 3.463e-05, eta: 9:21:16, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2243, decode.acc_seg: 90.9203, aux.loss_ce: 0.1371, aux.acc_seg: 86.1429, loss: 0.3613, grad_norm: 3.1538 2023-02-16 22:22:02,954 - mmseg - INFO - Iter [67700/160000] lr: 3.461e-05, eta: 9:20:57, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2491, decode.acc_seg: 90.2510, aux.loss_ce: 0.1487, aux.acc_seg: 85.7538, loss: 0.3978, grad_norm: 4.1666 2023-02-16 22:22:20,988 - mmseg - INFO - Iter [67750/160000] lr: 3.459e-05, eta: 9:20:39, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2357, decode.acc_seg: 90.7330, aux.loss_ce: 0.1389, aux.acc_seg: 86.4070, loss: 0.3746, grad_norm: 3.6224 2023-02-16 22:22:38,722 - mmseg - INFO - Iter [67800/160000] lr: 3.458e-05, eta: 9:20:20, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2235, decode.acc_seg: 91.1536, aux.loss_ce: 0.1396, aux.acc_seg: 86.2491, loss: 0.3632, grad_norm: 3.2180 2023-02-16 22:22:56,632 - mmseg - INFO - Iter [67850/160000] lr: 3.456e-05, eta: 9:20:01, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2264, decode.acc_seg: 90.9815, aux.loss_ce: 0.1372, aux.acc_seg: 86.3985, loss: 0.3636, grad_norm: 3.2076 2023-02-16 22:23:14,512 - mmseg - INFO - Iter [67900/160000] lr: 3.454e-05, eta: 9:19:42, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2386, decode.acc_seg: 90.7063, aux.loss_ce: 0.1489, aux.acc_seg: 85.5232, loss: 0.3876, grad_norm: 3.3332 2023-02-16 22:23:32,531 - mmseg - INFO - Iter [67950/160000] lr: 3.452e-05, eta: 9:19:24, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2310, decode.acc_seg: 90.9176, aux.loss_ce: 0.1419, aux.acc_seg: 86.1143, loss: 0.3729, grad_norm: 3.5699 2023-02-16 22:23:50,704 - mmseg - INFO - Saving checkpoint at 68000 iterations 2023-02-16 22:23:51,833 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 22:23:51,834 - mmseg - INFO - Iter [68000/160000] lr: 3.450e-05, eta: 9:19:07, time: 0.386, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2235, decode.acc_seg: 91.1863, aux.loss_ce: 0.1396, aux.acc_seg: 86.1945, loss: 0.3631, grad_norm: 3.2570 2023-02-16 22:24:09,861 - mmseg - INFO - Iter [68050/160000] lr: 3.448e-05, eta: 9:18:49, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2416, decode.acc_seg: 90.6599, aux.loss_ce: 0.1450, aux.acc_seg: 85.9165, loss: 0.3865, grad_norm: 3.5530 2023-02-16 22:24:27,928 - mmseg - INFO - Iter [68100/160000] lr: 3.446e-05, eta: 9:18:30, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2303, decode.acc_seg: 91.0652, aux.loss_ce: 0.1410, aux.acc_seg: 86.3586, loss: 0.3713, grad_norm: 3.2515 2023-02-16 22:24:45,919 - mmseg - INFO - Iter [68150/160000] lr: 3.444e-05, eta: 9:18:12, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2374, decode.acc_seg: 90.0362, aux.loss_ce: 0.1383, aux.acc_seg: 85.7531, loss: 0.3757, grad_norm: 3.3885 2023-02-16 22:25:03,904 - mmseg - INFO - Iter [68200/160000] lr: 3.443e-05, eta: 9:17:53, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2442, decode.acc_seg: 90.3534, aux.loss_ce: 0.1460, aux.acc_seg: 85.4793, loss: 0.3903, grad_norm: 3.7427 2023-02-16 22:25:24,317 - mmseg - INFO - Iter [68250/160000] lr: 3.441e-05, eta: 9:17:38, time: 0.408, data_time: 0.051, memory: 16600, decode.loss_ce: 0.2133, decode.acc_seg: 91.4750, aux.loss_ce: 0.1313, aux.acc_seg: 86.9044, loss: 0.3446, grad_norm: 3.0918 2023-02-16 22:25:42,275 - mmseg - INFO - Iter [68300/160000] lr: 3.439e-05, eta: 9:17:19, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2245, decode.acc_seg: 90.8533, aux.loss_ce: 0.1402, aux.acc_seg: 86.0652, loss: 0.3648, grad_norm: 3.7859 2023-02-16 22:26:00,199 - mmseg - INFO - Iter [68350/160000] lr: 3.437e-05, eta: 9:17:01, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2470, decode.acc_seg: 90.5447, aux.loss_ce: 0.1470, aux.acc_seg: 85.9727, loss: 0.3939, grad_norm: 3.9279 2023-02-16 22:26:18,122 - mmseg - INFO - Iter [68400/160000] lr: 3.435e-05, eta: 9:16:42, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2365, decode.acc_seg: 90.7565, aux.loss_ce: 0.1394, aux.acc_seg: 86.4839, loss: 0.3760, grad_norm: 3.2688 2023-02-16 22:26:36,073 - mmseg - INFO - Iter [68450/160000] lr: 3.433e-05, eta: 9:16:23, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2158, decode.acc_seg: 91.2657, aux.loss_ce: 0.1323, aux.acc_seg: 86.8748, loss: 0.3481, grad_norm: 3.2816 2023-02-16 22:26:54,502 - mmseg - INFO - Iter [68500/160000] lr: 3.431e-05, eta: 9:16:05, time: 0.369, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2451, decode.acc_seg: 90.5555, aux.loss_ce: 0.1477, aux.acc_seg: 85.8345, loss: 0.3928, grad_norm: 4.1592 2023-02-16 22:27:12,396 - mmseg - INFO - Iter [68550/160000] lr: 3.429e-05, eta: 9:15:47, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2312, decode.acc_seg: 90.8160, aux.loss_ce: 0.1393, aux.acc_seg: 86.4477, loss: 0.3705, grad_norm: 3.3537 2023-02-16 22:27:30,209 - mmseg - INFO - Iter [68600/160000] lr: 3.428e-05, eta: 9:15:28, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2283, decode.acc_seg: 90.8806, aux.loss_ce: 0.1411, aux.acc_seg: 86.0488, loss: 0.3695, grad_norm: 3.4334 2023-02-16 22:27:48,287 - mmseg - INFO - Iter [68650/160000] lr: 3.426e-05, eta: 9:15:09, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2161, decode.acc_seg: 91.5084, aux.loss_ce: 0.1339, aux.acc_seg: 86.9859, loss: 0.3499, grad_norm: 2.7672 2023-02-16 22:28:06,173 - mmseg - INFO - Iter [68700/160000] lr: 3.424e-05, eta: 9:14:51, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2223, decode.acc_seg: 91.0859, aux.loss_ce: 0.1359, aux.acc_seg: 86.4330, loss: 0.3582, grad_norm: 3.2059 2023-02-16 22:28:23,981 - mmseg - INFO - Iter [68750/160000] lr: 3.422e-05, eta: 9:14:32, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2269, decode.acc_seg: 90.9906, aux.loss_ce: 0.1346, aux.acc_seg: 86.6543, loss: 0.3615, grad_norm: 3.5814 2023-02-16 22:28:42,273 - mmseg - INFO - Iter [68800/160000] lr: 3.420e-05, eta: 9:14:14, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2418, decode.acc_seg: 90.4950, aux.loss_ce: 0.1407, aux.acc_seg: 86.2014, loss: 0.3826, grad_norm: 3.2463 2023-02-16 22:29:00,272 - mmseg - INFO - Iter [68850/160000] lr: 3.418e-05, eta: 9:13:55, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2363, decode.acc_seg: 90.9813, aux.loss_ce: 0.1439, aux.acc_seg: 86.2690, loss: 0.3802, grad_norm: 3.4220 2023-02-16 22:29:18,269 - mmseg - INFO - Iter [68900/160000] lr: 3.416e-05, eta: 9:13:37, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2434, decode.acc_seg: 90.7735, aux.loss_ce: 0.1442, aux.acc_seg: 86.2558, loss: 0.3876, grad_norm: 3.4516 2023-02-16 22:29:36,397 - mmseg - INFO - Iter [68950/160000] lr: 3.414e-05, eta: 9:13:18, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2383, decode.acc_seg: 90.6437, aux.loss_ce: 0.1444, aux.acc_seg: 85.7991, loss: 0.3827, grad_norm: 3.2722 2023-02-16 22:29:54,250 - mmseg - INFO - Saving checkpoint at 69000 iterations 2023-02-16 22:29:55,298 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 22:29:55,298 - mmseg - INFO - Iter [69000/160000] lr: 3.413e-05, eta: 9:13:01, time: 0.378, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2324, decode.acc_seg: 90.9216, aux.loss_ce: 0.1433, aux.acc_seg: 86.0069, loss: 0.3757, grad_norm: 3.6768 2023-02-16 22:30:13,366 - mmseg - INFO - Iter [69050/160000] lr: 3.411e-05, eta: 9:12:43, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2349, decode.acc_seg: 90.7417, aux.loss_ce: 0.1391, aux.acc_seg: 86.3449, loss: 0.3740, grad_norm: 3.2219 2023-02-16 22:30:31,315 - mmseg - INFO - Iter [69100/160000] lr: 3.409e-05, eta: 9:12:24, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2170, decode.acc_seg: 91.3322, aux.loss_ce: 0.1322, aux.acc_seg: 86.8373, loss: 0.3492, grad_norm: 3.0910 2023-02-16 22:30:49,399 - mmseg - INFO - Iter [69150/160000] lr: 3.407e-05, eta: 9:12:05, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2320, decode.acc_seg: 90.8655, aux.loss_ce: 0.1399, aux.acc_seg: 86.1659, loss: 0.3720, grad_norm: 3.4649 2023-02-16 22:31:07,291 - mmseg - INFO - Iter [69200/160000] lr: 3.405e-05, eta: 9:11:47, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2313, decode.acc_seg: 90.7238, aux.loss_ce: 0.1433, aux.acc_seg: 85.7669, loss: 0.3746, grad_norm: 3.5766 2023-02-16 22:31:25,122 - mmseg - INFO - Iter [69250/160000] lr: 3.403e-05, eta: 9:11:28, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2262, decode.acc_seg: 91.0346, aux.loss_ce: 0.1371, aux.acc_seg: 86.3694, loss: 0.3632, grad_norm: 3.2225 2023-02-16 22:31:43,078 - mmseg - INFO - Iter [69300/160000] lr: 3.401e-05, eta: 9:11:09, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2091, decode.acc_seg: 91.5521, aux.loss_ce: 0.1324, aux.acc_seg: 86.6557, loss: 0.3415, grad_norm: 3.2518 2023-02-16 22:32:00,974 - mmseg - INFO - Iter [69350/160000] lr: 3.399e-05, eta: 9:10:51, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2319, decode.acc_seg: 90.9962, aux.loss_ce: 0.1399, aux.acc_seg: 86.4839, loss: 0.3718, grad_norm: 3.6450 2023-02-16 22:32:18,988 - mmseg - INFO - Iter [69400/160000] lr: 3.398e-05, eta: 9:10:32, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2388, decode.acc_seg: 90.4419, aux.loss_ce: 0.1459, aux.acc_seg: 85.3759, loss: 0.3848, grad_norm: 4.0358 2023-02-16 22:32:37,241 - mmseg - INFO - Iter [69450/160000] lr: 3.396e-05, eta: 9:10:14, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2158, decode.acc_seg: 91.5174, aux.loss_ce: 0.1353, aux.acc_seg: 86.5586, loss: 0.3511, grad_norm: 2.9575 2023-02-16 22:32:57,415 - mmseg - INFO - Iter [69500/160000] lr: 3.394e-05, eta: 9:09:58, time: 0.404, data_time: 0.053, memory: 16600, decode.loss_ce: 0.2274, decode.acc_seg: 91.0825, aux.loss_ce: 0.1363, aux.acc_seg: 86.5208, loss: 0.3637, grad_norm: 3.4300 2023-02-16 22:33:15,538 - mmseg - INFO - Iter [69550/160000] lr: 3.392e-05, eta: 9:09:40, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2225, decode.acc_seg: 91.2152, aux.loss_ce: 0.1419, aux.acc_seg: 86.1763, loss: 0.3643, grad_norm: 3.3699 2023-02-16 22:33:33,657 - mmseg - INFO - Iter [69600/160000] lr: 3.390e-05, eta: 9:09:22, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2264, decode.acc_seg: 90.8880, aux.loss_ce: 0.1355, aux.acc_seg: 86.2463, loss: 0.3619, grad_norm: 3.0296 2023-02-16 22:33:51,518 - mmseg - INFO - Iter [69650/160000] lr: 3.388e-05, eta: 9:09:03, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2244, decode.acc_seg: 91.2312, aux.loss_ce: 0.1332, aux.acc_seg: 86.9232, loss: 0.3576, grad_norm: 3.4021 2023-02-16 22:34:09,357 - mmseg - INFO - Iter [69700/160000] lr: 3.386e-05, eta: 9:08:44, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2277, decode.acc_seg: 90.9412, aux.loss_ce: 0.1380, aux.acc_seg: 86.6898, loss: 0.3657, grad_norm: 3.4249 2023-02-16 22:34:27,250 - mmseg - INFO - Iter [69750/160000] lr: 3.384e-05, eta: 9:08:26, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2309, decode.acc_seg: 90.8676, aux.loss_ce: 0.1403, aux.acc_seg: 86.1511, loss: 0.3713, grad_norm: 2.9817 2023-02-16 22:34:45,350 - mmseg - INFO - Iter [69800/160000] lr: 3.383e-05, eta: 9:08:07, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2239, decode.acc_seg: 91.3181, aux.loss_ce: 0.1409, aux.acc_seg: 86.5333, loss: 0.3648, grad_norm: 3.6502 2023-02-16 22:35:03,228 - mmseg - INFO - Iter [69850/160000] lr: 3.381e-05, eta: 9:07:48, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2253, decode.acc_seg: 90.9066, aux.loss_ce: 0.1383, aux.acc_seg: 86.1240, loss: 0.3636, grad_norm: 3.5940 2023-02-16 22:35:21,633 - mmseg - INFO - Iter [69900/160000] lr: 3.379e-05, eta: 9:07:30, time: 0.367, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2115, decode.acc_seg: 91.4731, aux.loss_ce: 0.1326, aux.acc_seg: 86.6459, loss: 0.3441, grad_norm: 3.1120 2023-02-16 22:35:39,699 - mmseg - INFO - Iter [69950/160000] lr: 3.377e-05, eta: 9:07:12, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2162, decode.acc_seg: 91.4043, aux.loss_ce: 0.1350, aux.acc_seg: 86.7325, loss: 0.3512, grad_norm: 3.4109 2023-02-16 22:35:57,766 - mmseg - INFO - Saving checkpoint at 70000 iterations 2023-02-16 22:35:58,890 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 22:35:58,890 - mmseg - INFO - Iter [70000/160000] lr: 3.375e-05, eta: 9:06:55, time: 0.384, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2331, decode.acc_seg: 91.0164, aux.loss_ce: 0.1392, aux.acc_seg: 86.7451, loss: 0.3723, grad_norm: 3.4539 2023-02-16 22:36:17,233 - mmseg - INFO - Iter [70050/160000] lr: 3.373e-05, eta: 9:06:37, time: 0.367, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2324, decode.acc_seg: 90.7953, aux.loss_ce: 0.1379, aux.acc_seg: 86.3761, loss: 0.3703, grad_norm: 3.1696 2023-02-16 22:36:35,127 - mmseg - INFO - Iter [70100/160000] lr: 3.371e-05, eta: 9:06:18, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2253, decode.acc_seg: 91.1407, aux.loss_ce: 0.1379, aux.acc_seg: 86.4295, loss: 0.3632, grad_norm: 3.5103 2023-02-16 22:36:53,213 - mmseg - INFO - Iter [70150/160000] lr: 3.369e-05, eta: 9:06:00, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2329, decode.acc_seg: 90.8594, aux.loss_ce: 0.1407, aux.acc_seg: 86.1135, loss: 0.3736, grad_norm: 3.1401 2023-02-16 22:37:11,143 - mmseg - INFO - Iter [70200/160000] lr: 3.368e-05, eta: 9:05:41, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2394, decode.acc_seg: 90.8256, aux.loss_ce: 0.1383, aux.acc_seg: 86.6022, loss: 0.3777, grad_norm: 3.6313 2023-02-16 22:37:29,401 - mmseg - INFO - Iter [70250/160000] lr: 3.366e-05, eta: 9:05:23, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2269, decode.acc_seg: 90.7927, aux.loss_ce: 0.1381, aux.acc_seg: 86.4705, loss: 0.3650, grad_norm: 3.4736 2023-02-16 22:37:47,751 - mmseg - INFO - Iter [70300/160000] lr: 3.364e-05, eta: 9:05:05, time: 0.367, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2153, decode.acc_seg: 91.3898, aux.loss_ce: 0.1300, aux.acc_seg: 87.1169, loss: 0.3453, grad_norm: 3.0243 2023-02-16 22:38:05,687 - mmseg - INFO - Iter [70350/160000] lr: 3.362e-05, eta: 9:04:46, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2085, decode.acc_seg: 91.7752, aux.loss_ce: 0.1268, aux.acc_seg: 87.3301, loss: 0.3353, grad_norm: 2.6353 2023-02-16 22:38:23,657 - mmseg - INFO - Iter [70400/160000] lr: 3.360e-05, eta: 9:04:28, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2253, decode.acc_seg: 91.0979, aux.loss_ce: 0.1328, aux.acc_seg: 86.9417, loss: 0.3582, grad_norm: 3.2216 2023-02-16 22:38:41,572 - mmseg - INFO - Iter [70450/160000] lr: 3.358e-05, eta: 9:04:09, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2282, decode.acc_seg: 90.9930, aux.loss_ce: 0.1413, aux.acc_seg: 86.1622, loss: 0.3695, grad_norm: 3.4189 2023-02-16 22:38:59,800 - mmseg - INFO - Iter [70500/160000] lr: 3.356e-05, eta: 9:03:51, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2236, decode.acc_seg: 91.2859, aux.loss_ce: 0.1356, aux.acc_seg: 86.6905, loss: 0.3592, grad_norm: 3.2894 2023-02-16 22:39:17,667 - mmseg - INFO - Iter [70550/160000] lr: 3.354e-05, eta: 9:03:32, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2418, decode.acc_seg: 90.7182, aux.loss_ce: 0.1496, aux.acc_seg: 85.7454, loss: 0.3914, grad_norm: 3.8894 2023-02-16 22:39:35,491 - mmseg - INFO - Iter [70600/160000] lr: 3.353e-05, eta: 9:03:13, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2182, decode.acc_seg: 91.3127, aux.loss_ce: 0.1341, aux.acc_seg: 86.6480, loss: 0.3523, grad_norm: 3.0701 2023-02-16 22:39:53,317 - mmseg - INFO - Iter [70650/160000] lr: 3.351e-05, eta: 9:02:55, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2305, decode.acc_seg: 90.7547, aux.loss_ce: 0.1417, aux.acc_seg: 85.8787, loss: 0.3721, grad_norm: 3.6856 2023-02-16 22:40:11,378 - mmseg - INFO - Iter [70700/160000] lr: 3.349e-05, eta: 9:02:36, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2291, decode.acc_seg: 91.2712, aux.loss_ce: 0.1430, aux.acc_seg: 86.3519, loss: 0.3720, grad_norm: 3.4950 2023-02-16 22:40:31,570 - mmseg - INFO - Iter [70750/160000] lr: 3.347e-05, eta: 9:02:21, time: 0.404, data_time: 0.052, memory: 16600, decode.loss_ce: 0.2149, decode.acc_seg: 91.4521, aux.loss_ce: 0.1404, aux.acc_seg: 86.1986, loss: 0.3553, grad_norm: 2.9557 2023-02-16 22:40:49,463 - mmseg - INFO - Iter [70800/160000] lr: 3.345e-05, eta: 9:02:02, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2090, decode.acc_seg: 91.6592, aux.loss_ce: 0.1306, aux.acc_seg: 86.6566, loss: 0.3396, grad_norm: 2.9153 2023-02-16 22:41:07,299 - mmseg - INFO - Iter [70850/160000] lr: 3.343e-05, eta: 9:01:43, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2176, decode.acc_seg: 91.4566, aux.loss_ce: 0.1376, aux.acc_seg: 86.4707, loss: 0.3551, grad_norm: 3.4036 2023-02-16 22:41:25,187 - mmseg - INFO - Iter [70900/160000] lr: 3.341e-05, eta: 9:01:24, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2212, decode.acc_seg: 91.4381, aux.loss_ce: 0.1334, aux.acc_seg: 86.9798, loss: 0.3545, grad_norm: 2.9174 2023-02-16 22:41:43,473 - mmseg - INFO - Iter [70950/160000] lr: 3.339e-05, eta: 9:01:06, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2233, decode.acc_seg: 91.2387, aux.loss_ce: 0.1385, aux.acc_seg: 86.5027, loss: 0.3618, grad_norm: 3.4183 2023-02-16 22:42:01,425 - mmseg - INFO - Saving checkpoint at 71000 iterations 2023-02-16 22:42:02,530 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 22:42:02,530 - mmseg - INFO - Iter [71000/160000] lr: 3.338e-05, eta: 9:00:49, time: 0.381, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2163, decode.acc_seg: 91.1947, aux.loss_ce: 0.1319, aux.acc_seg: 86.4897, loss: 0.3482, grad_norm: 3.4166 2023-02-16 22:42:20,638 - mmseg - INFO - Iter [71050/160000] lr: 3.336e-05, eta: 9:00:31, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2063, decode.acc_seg: 91.6698, aux.loss_ce: 0.1293, aux.acc_seg: 86.6707, loss: 0.3355, grad_norm: 2.9181 2023-02-16 22:42:38,512 - mmseg - INFO - Iter [71100/160000] lr: 3.334e-05, eta: 9:00:12, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2246, decode.acc_seg: 90.7571, aux.loss_ce: 0.1369, aux.acc_seg: 86.4221, loss: 0.3615, grad_norm: 3.5674 2023-02-16 22:42:56,399 - mmseg - INFO - Iter [71150/160000] lr: 3.332e-05, eta: 8:59:53, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2347, decode.acc_seg: 91.0884, aux.loss_ce: 0.1417, aux.acc_seg: 86.5482, loss: 0.3764, grad_norm: 3.4986 2023-02-16 22:43:15,121 - mmseg - INFO - Iter [71200/160000] lr: 3.330e-05, eta: 8:59:36, time: 0.374, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2236, decode.acc_seg: 91.0560, aux.loss_ce: 0.1352, aux.acc_seg: 86.6849, loss: 0.3589, grad_norm: 3.3526 2023-02-16 22:43:32,956 - mmseg - INFO - Iter [71250/160000] lr: 3.328e-05, eta: 8:59:17, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2177, decode.acc_seg: 91.2931, aux.loss_ce: 0.1346, aux.acc_seg: 86.7305, loss: 0.3523, grad_norm: 3.6215 2023-02-16 22:43:51,097 - mmseg - INFO - Iter [71300/160000] lr: 3.326e-05, eta: 8:58:59, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2251, decode.acc_seg: 91.1178, aux.loss_ce: 0.1391, aux.acc_seg: 86.1780, loss: 0.3642, grad_norm: 3.2986 2023-02-16 22:44:09,098 - mmseg - INFO - Iter [71350/160000] lr: 3.324e-05, eta: 8:58:40, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2118, decode.acc_seg: 91.3389, aux.loss_ce: 0.1349, aux.acc_seg: 86.3990, loss: 0.3468, grad_norm: 3.0902 2023-02-16 22:44:26,853 - mmseg - INFO - Iter [71400/160000] lr: 3.323e-05, eta: 8:58:21, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2304, decode.acc_seg: 90.8494, aux.loss_ce: 0.1426, aux.acc_seg: 85.8552, loss: 0.3730, grad_norm: 3.3646 2023-02-16 22:44:44,697 - mmseg - INFO - Iter [71450/160000] lr: 3.321e-05, eta: 8:58:03, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2017, decode.acc_seg: 91.7992, aux.loss_ce: 0.1259, aux.acc_seg: 87.3965, loss: 0.3275, grad_norm: 3.2706 2023-02-16 22:45:02,494 - mmseg - INFO - Iter [71500/160000] lr: 3.319e-05, eta: 8:57:44, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2264, decode.acc_seg: 91.1490, aux.loss_ce: 0.1396, aux.acc_seg: 86.3861, loss: 0.3660, grad_norm: 3.3288 2023-02-16 22:45:20,664 - mmseg - INFO - Iter [71550/160000] lr: 3.317e-05, eta: 8:57:26, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2304, decode.acc_seg: 91.2239, aux.loss_ce: 0.1412, aux.acc_seg: 86.5986, loss: 0.3715, grad_norm: 2.9472 2023-02-16 22:45:38,770 - mmseg - INFO - Iter [71600/160000] lr: 3.315e-05, eta: 8:57:07, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2263, decode.acc_seg: 91.0401, aux.loss_ce: 0.1339, aux.acc_seg: 86.7392, loss: 0.3602, grad_norm: 3.2964 2023-02-16 22:45:56,739 - mmseg - INFO - Iter [71650/160000] lr: 3.313e-05, eta: 8:56:49, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2304, decode.acc_seg: 90.7920, aux.loss_ce: 0.1377, aux.acc_seg: 86.5540, loss: 0.3681, grad_norm: 3.2724 2023-02-16 22:46:15,125 - mmseg - INFO - Iter [71700/160000] lr: 3.311e-05, eta: 8:56:31, time: 0.368, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2291, decode.acc_seg: 90.9494, aux.loss_ce: 0.1422, aux.acc_seg: 85.7929, loss: 0.3714, grad_norm: 4.4591 2023-02-16 22:46:33,075 - mmseg - INFO - Iter [71750/160000] lr: 3.309e-05, eta: 8:56:12, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2281, decode.acc_seg: 91.1125, aux.loss_ce: 0.1366, aux.acc_seg: 86.7153, loss: 0.3647, grad_norm: 3.4139 2023-02-16 22:46:51,218 - mmseg - INFO - Iter [71800/160000] lr: 3.308e-05, eta: 8:55:54, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2227, decode.acc_seg: 91.0411, aux.loss_ce: 0.1377, aux.acc_seg: 86.3253, loss: 0.3604, grad_norm: 3.3746 2023-02-16 22:47:09,403 - mmseg - INFO - Iter [71850/160000] lr: 3.306e-05, eta: 8:55:35, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2188, decode.acc_seg: 91.3636, aux.loss_ce: 0.1388, aux.acc_seg: 86.1764, loss: 0.3577, grad_norm: 3.0668 2023-02-16 22:47:27,278 - mmseg - INFO - Iter [71900/160000] lr: 3.304e-05, eta: 8:55:17, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2166, decode.acc_seg: 91.4054, aux.loss_ce: 0.1336, aux.acc_seg: 86.7974, loss: 0.3502, grad_norm: 2.9857 2023-02-16 22:47:45,367 - mmseg - INFO - Iter [71950/160000] lr: 3.302e-05, eta: 8:54:58, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2152, decode.acc_seg: 91.5875, aux.loss_ce: 0.1326, aux.acc_seg: 86.9117, loss: 0.3478, grad_norm: 3.3282 2023-02-16 22:48:05,505 - mmseg - INFO - Saving checkpoint at 72000 iterations 2023-02-16 22:48:06,608 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 22:48:06,608 - mmseg - INFO - Iter [72000/160000] lr: 3.300e-05, eta: 8:54:44, time: 0.425, data_time: 0.052, memory: 16600, decode.loss_ce: 0.2284, decode.acc_seg: 90.9932, aux.loss_ce: 0.1393, aux.acc_seg: 86.2468, loss: 0.3677, grad_norm: 3.0503 2023-02-16 22:48:24,753 - mmseg - INFO - Iter [72050/160000] lr: 3.298e-05, eta: 8:54:26, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2127, decode.acc_seg: 91.5055, aux.loss_ce: 0.1347, aux.acc_seg: 86.7094, loss: 0.3473, grad_norm: 3.4010 2023-02-16 22:48:42,752 - mmseg - INFO - Iter [72100/160000] lr: 3.296e-05, eta: 8:54:07, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2013, decode.acc_seg: 92.0247, aux.loss_ce: 0.1304, aux.acc_seg: 87.1441, loss: 0.3316, grad_norm: 2.9791 2023-02-16 22:49:00,585 - mmseg - INFO - Iter [72150/160000] lr: 3.294e-05, eta: 8:53:48, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2252, decode.acc_seg: 91.0971, aux.loss_ce: 0.1383, aux.acc_seg: 86.2413, loss: 0.3635, grad_norm: 3.2910 2023-02-16 22:49:18,393 - mmseg - INFO - Iter [72200/160000] lr: 3.293e-05, eta: 8:53:30, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2244, decode.acc_seg: 91.2531, aux.loss_ce: 0.1380, aux.acc_seg: 86.6119, loss: 0.3624, grad_norm: 3.2307 2023-02-16 22:49:36,375 - mmseg - INFO - Iter [72250/160000] lr: 3.291e-05, eta: 8:53:11, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2135, decode.acc_seg: 91.6527, aux.loss_ce: 0.1290, aux.acc_seg: 87.3106, loss: 0.3425, grad_norm: 3.5822 2023-02-16 22:49:54,490 - mmseg - INFO - Iter [72300/160000] lr: 3.289e-05, eta: 8:52:53, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2294, decode.acc_seg: 90.9171, aux.loss_ce: 0.1393, aux.acc_seg: 86.3601, loss: 0.3688, grad_norm: 3.7619 2023-02-16 22:50:12,702 - mmseg - INFO - Iter [72350/160000] lr: 3.287e-05, eta: 8:52:34, time: 0.364, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2220, decode.acc_seg: 91.2551, aux.loss_ce: 0.1342, aux.acc_seg: 86.7411, loss: 0.3562, grad_norm: 3.1105 2023-02-16 22:50:30,588 - mmseg - INFO - Iter [72400/160000] lr: 3.285e-05, eta: 8:52:16, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2361, decode.acc_seg: 90.8817, aux.loss_ce: 0.1427, aux.acc_seg: 86.2110, loss: 0.3789, grad_norm: 3.5991 2023-02-16 22:50:48,554 - mmseg - INFO - Iter [72450/160000] lr: 3.283e-05, eta: 8:51:57, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2004, decode.acc_seg: 92.0856, aux.loss_ce: 0.1272, aux.acc_seg: 87.1522, loss: 0.3276, grad_norm: 3.1030 2023-02-16 22:51:06,684 - mmseg - INFO - Iter [72500/160000] lr: 3.281e-05, eta: 8:51:39, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2114, decode.acc_seg: 91.5189, aux.loss_ce: 0.1369, aux.acc_seg: 86.3451, loss: 0.3483, grad_norm: 3.4268 2023-02-16 22:51:24,626 - mmseg - INFO - Iter [72550/160000] lr: 3.279e-05, eta: 8:51:20, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2143, decode.acc_seg: 91.5246, aux.loss_ce: 0.1295, aux.acc_seg: 87.4903, loss: 0.3439, grad_norm: 3.3963 2023-02-16 22:51:42,665 - mmseg - INFO - Iter [72600/160000] lr: 3.278e-05, eta: 8:51:02, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2346, decode.acc_seg: 90.7779, aux.loss_ce: 0.1400, aux.acc_seg: 86.1008, loss: 0.3746, grad_norm: 3.3811 2023-02-16 22:52:00,657 - mmseg - INFO - Iter [72650/160000] lr: 3.276e-05, eta: 8:50:43, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2289, decode.acc_seg: 90.9619, aux.loss_ce: 0.1426, aux.acc_seg: 86.0173, loss: 0.3715, grad_norm: 3.7907 2023-02-16 22:52:18,720 - mmseg - INFO - Iter [72700/160000] lr: 3.274e-05, eta: 8:50:25, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2217, decode.acc_seg: 91.0290, aux.loss_ce: 0.1339, aux.acc_seg: 86.4760, loss: 0.3556, grad_norm: 3.1201 2023-02-16 22:52:36,731 - mmseg - INFO - Iter [72750/160000] lr: 3.272e-05, eta: 8:50:06, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2092, decode.acc_seg: 91.5707, aux.loss_ce: 0.1329, aux.acc_seg: 86.7527, loss: 0.3421, grad_norm: 3.1969 2023-02-16 22:52:54,838 - mmseg - INFO - Iter [72800/160000] lr: 3.270e-05, eta: 8:49:48, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2295, decode.acc_seg: 91.2643, aux.loss_ce: 0.1352, aux.acc_seg: 86.6849, loss: 0.3647, grad_norm: 4.5229 2023-02-16 22:53:12,889 - mmseg - INFO - Iter [72850/160000] lr: 3.268e-05, eta: 8:49:30, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2269, decode.acc_seg: 90.9958, aux.loss_ce: 0.1400, aux.acc_seg: 86.3774, loss: 0.3669, grad_norm: 3.4341 2023-02-16 22:53:30,676 - mmseg - INFO - Iter [72900/160000] lr: 3.266e-05, eta: 8:49:11, time: 0.356, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2189, decode.acc_seg: 91.1888, aux.loss_ce: 0.1381, aux.acc_seg: 86.3448, loss: 0.3570, grad_norm: 3.3519 2023-02-16 22:53:48,582 - mmseg - INFO - Iter [72950/160000] lr: 3.264e-05, eta: 8:48:52, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2246, decode.acc_seg: 91.1309, aux.loss_ce: 0.1387, aux.acc_seg: 86.5407, loss: 0.3633, grad_norm: 3.2379 2023-02-16 22:54:06,439 - mmseg - INFO - Saving checkpoint at 73000 iterations 2023-02-16 22:54:07,522 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 22:54:07,523 - mmseg - INFO - Iter [73000/160000] lr: 3.263e-05, eta: 8:48:35, time: 0.379, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2329, decode.acc_seg: 90.9991, aux.loss_ce: 0.1433, aux.acc_seg: 86.0190, loss: 0.3762, grad_norm: 3.6079 2023-02-16 22:54:25,379 - mmseg - INFO - Iter [73050/160000] lr: 3.261e-05, eta: 8:48:16, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2181, decode.acc_seg: 91.6146, aux.loss_ce: 0.1330, aux.acc_seg: 87.0814, loss: 0.3511, grad_norm: 3.4733 2023-02-16 22:54:43,427 - mmseg - INFO - Iter [73100/160000] lr: 3.259e-05, eta: 8:47:58, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2257, decode.acc_seg: 91.2545, aux.loss_ce: 0.1343, aux.acc_seg: 86.7123, loss: 0.3600, grad_norm: 2.9838 2023-02-16 22:55:01,555 - mmseg - INFO - Iter [73150/160000] lr: 3.257e-05, eta: 8:47:39, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2088, decode.acc_seg: 91.8011, aux.loss_ce: 0.1314, aux.acc_seg: 87.0721, loss: 0.3402, grad_norm: 3.0172 2023-02-16 22:55:19,490 - mmseg - INFO - Iter [73200/160000] lr: 3.255e-05, eta: 8:47:21, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2210, decode.acc_seg: 91.1112, aux.loss_ce: 0.1380, aux.acc_seg: 86.1747, loss: 0.3590, grad_norm: 3.6105 2023-02-16 22:55:37,402 - mmseg - INFO - Iter [73250/160000] lr: 3.253e-05, eta: 8:47:02, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2108, decode.acc_seg: 91.7127, aux.loss_ce: 0.1317, aux.acc_seg: 87.0265, loss: 0.3424, grad_norm: 3.8095 2023-02-16 22:55:57,640 - mmseg - INFO - Iter [73300/160000] lr: 3.251e-05, eta: 8:46:46, time: 0.405, data_time: 0.052, memory: 16600, decode.loss_ce: 0.2256, decode.acc_seg: 91.0678, aux.loss_ce: 0.1413, aux.acc_seg: 86.3721, loss: 0.3669, grad_norm: 3.8464 2023-02-16 22:56:15,581 - mmseg - INFO - Iter [73350/160000] lr: 3.249e-05, eta: 8:46:28, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2170, decode.acc_seg: 91.1909, aux.loss_ce: 0.1355, aux.acc_seg: 86.5625, loss: 0.3525, grad_norm: 4.4985 2023-02-16 22:56:33,613 - mmseg - INFO - Iter [73400/160000] lr: 3.248e-05, eta: 8:46:09, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2101, decode.acc_seg: 91.3763, aux.loss_ce: 0.1277, aux.acc_seg: 87.0450, loss: 0.3377, grad_norm: 3.3282 2023-02-16 22:56:51,701 - mmseg - INFO - Iter [73450/160000] lr: 3.246e-05, eta: 8:45:51, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2112, decode.acc_seg: 91.6913, aux.loss_ce: 0.1332, aux.acc_seg: 87.3356, loss: 0.3445, grad_norm: 3.5653 2023-02-16 22:57:09,577 - mmseg - INFO - Iter [73500/160000] lr: 3.244e-05, eta: 8:45:32, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2215, decode.acc_seg: 91.2875, aux.loss_ce: 0.1350, aux.acc_seg: 86.9150, loss: 0.3565, grad_norm: 3.2501 2023-02-16 22:57:27,482 - mmseg - INFO - Iter [73550/160000] lr: 3.242e-05, eta: 8:45:14, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2191, decode.acc_seg: 91.4905, aux.loss_ce: 0.1380, aux.acc_seg: 86.4803, loss: 0.3571, grad_norm: 3.1946 2023-02-16 22:57:45,625 - mmseg - INFO - Iter [73600/160000] lr: 3.240e-05, eta: 8:44:55, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2016, decode.acc_seg: 91.6124, aux.loss_ce: 0.1260, aux.acc_seg: 87.1616, loss: 0.3276, grad_norm: 3.2102 2023-02-16 22:58:03,661 - mmseg - INFO - Iter [73650/160000] lr: 3.238e-05, eta: 8:44:37, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2131, decode.acc_seg: 91.5045, aux.loss_ce: 0.1313, aux.acc_seg: 86.9656, loss: 0.3444, grad_norm: 3.3005 2023-02-16 22:58:21,665 - mmseg - INFO - Iter [73700/160000] lr: 3.236e-05, eta: 8:44:18, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2091, decode.acc_seg: 91.5222, aux.loss_ce: 0.1292, aux.acc_seg: 86.9905, loss: 0.3384, grad_norm: 2.8079 2023-02-16 22:58:39,647 - mmseg - INFO - Iter [73750/160000] lr: 3.234e-05, eta: 8:44:00, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2165, decode.acc_seg: 91.3637, aux.loss_ce: 0.1354, aux.acc_seg: 86.8408, loss: 0.3519, grad_norm: 3.1065 2023-02-16 22:58:57,837 - mmseg - INFO - Iter [73800/160000] lr: 3.233e-05, eta: 8:43:42, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2096, decode.acc_seg: 91.6694, aux.loss_ce: 0.1268, aux.acc_seg: 87.3565, loss: 0.3364, grad_norm: 2.9126 2023-02-16 22:59:15,657 - mmseg - INFO - Iter [73850/160000] lr: 3.231e-05, eta: 8:43:23, time: 0.357, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2067, decode.acc_seg: 91.8966, aux.loss_ce: 0.1251, aux.acc_seg: 87.8375, loss: 0.3318, grad_norm: 3.3981 2023-02-16 22:59:33,713 - mmseg - INFO - Iter [73900/160000] lr: 3.229e-05, eta: 8:43:05, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2154, decode.acc_seg: 91.4941, aux.loss_ce: 0.1319, aux.acc_seg: 86.8410, loss: 0.3473, grad_norm: 2.9615 2023-02-16 22:59:51,606 - mmseg - INFO - Iter [73950/160000] lr: 3.227e-05, eta: 8:42:46, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2258, decode.acc_seg: 91.2764, aux.loss_ce: 0.1383, aux.acc_seg: 86.5518, loss: 0.3641, grad_norm: 3.6777 2023-02-16 23:00:09,661 - mmseg - INFO - Saving checkpoint at 74000 iterations 2023-02-16 23:00:10,775 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 23:00:10,775 - mmseg - INFO - Iter [74000/160000] lr: 3.225e-05, eta: 8:42:29, time: 0.383, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2136, decode.acc_seg: 91.3369, aux.loss_ce: 0.1347, aux.acc_seg: 86.4500, loss: 0.3482, grad_norm: 3.1867 2023-02-16 23:00:28,776 - mmseg - INFO - Iter [74050/160000] lr: 3.223e-05, eta: 8:42:10, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2072, decode.acc_seg: 91.8340, aux.loss_ce: 0.1270, aux.acc_seg: 87.4757, loss: 0.3343, grad_norm: 3.2840 2023-02-16 23:00:46,954 - mmseg - INFO - Iter [74100/160000] lr: 3.221e-05, eta: 8:41:52, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2226, decode.acc_seg: 91.0417, aux.loss_ce: 0.1342, aux.acc_seg: 86.7699, loss: 0.3568, grad_norm: 3.5426 2023-02-16 23:01:04,850 - mmseg - INFO - Iter [74150/160000] lr: 3.219e-05, eta: 8:41:33, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2200, decode.acc_seg: 91.3259, aux.loss_ce: 0.1370, aux.acc_seg: 86.6581, loss: 0.3570, grad_norm: 3.2333 2023-02-16 23:01:22,782 - mmseg - INFO - Iter [74200/160000] lr: 3.218e-05, eta: 8:41:15, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2117, decode.acc_seg: 91.6475, aux.loss_ce: 0.1287, aux.acc_seg: 87.2994, loss: 0.3404, grad_norm: 2.8871 2023-02-16 23:01:40,767 - mmseg - INFO - Iter [74250/160000] lr: 3.216e-05, eta: 8:40:56, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2264, decode.acc_seg: 90.9501, aux.loss_ce: 0.1352, aux.acc_seg: 86.6528, loss: 0.3616, grad_norm: 3.1964 2023-02-16 23:01:58,658 - mmseg - INFO - Iter [74300/160000] lr: 3.214e-05, eta: 8:40:38, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2293, decode.acc_seg: 91.0358, aux.loss_ce: 0.1411, aux.acc_seg: 86.2839, loss: 0.3704, grad_norm: 3.9981 2023-02-16 23:02:16,822 - mmseg - INFO - Iter [74350/160000] lr: 3.212e-05, eta: 8:40:19, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2016, decode.acc_seg: 91.9358, aux.loss_ce: 0.1284, aux.acc_seg: 87.4213, loss: 0.3300, grad_norm: 3.0440 2023-02-16 23:02:34,669 - mmseg - INFO - Iter [74400/160000] lr: 3.210e-05, eta: 8:40:01, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2276, decode.acc_seg: 90.9447, aux.loss_ce: 0.1399, aux.acc_seg: 86.0725, loss: 0.3675, grad_norm: 3.5039 2023-02-16 23:02:52,909 - mmseg - INFO - Iter [74450/160000] lr: 3.208e-05, eta: 8:39:43, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2279, decode.acc_seg: 90.8032, aux.loss_ce: 0.1399, aux.acc_seg: 86.1030, loss: 0.3678, grad_norm: 3.8626 2023-02-16 23:03:10,927 - mmseg - INFO - Iter [74500/160000] lr: 3.206e-05, eta: 8:39:24, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2099, decode.acc_seg: 91.3247, aux.loss_ce: 0.1361, aux.acc_seg: 86.1196, loss: 0.3460, grad_norm: 3.3872 2023-02-16 23:03:31,128 - mmseg - INFO - Iter [74550/160000] lr: 3.204e-05, eta: 8:39:08, time: 0.404, data_time: 0.052, memory: 16600, decode.loss_ce: 0.2140, decode.acc_seg: 91.2977, aux.loss_ce: 0.1359, aux.acc_seg: 86.6234, loss: 0.3499, grad_norm: 3.0163 2023-02-16 23:03:49,017 - mmseg - INFO - Iter [74600/160000] lr: 3.203e-05, eta: 8:38:50, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2094, decode.acc_seg: 91.9087, aux.loss_ce: 0.1366, aux.acc_seg: 86.6751, loss: 0.3460, grad_norm: 3.0905 2023-02-16 23:04:06,874 - mmseg - INFO - Iter [74650/160000] lr: 3.201e-05, eta: 8:38:31, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2154, decode.acc_seg: 91.4714, aux.loss_ce: 0.1295, aux.acc_seg: 86.9916, loss: 0.3450, grad_norm: 3.3370 2023-02-16 23:04:24,877 - mmseg - INFO - Iter [74700/160000] lr: 3.199e-05, eta: 8:38:12, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2048, decode.acc_seg: 91.7172, aux.loss_ce: 0.1311, aux.acc_seg: 87.0433, loss: 0.3359, grad_norm: 3.3659 2023-02-16 23:04:42,862 - mmseg - INFO - Iter [74750/160000] lr: 3.197e-05, eta: 8:37:54, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2067, decode.acc_seg: 91.7674, aux.loss_ce: 0.1237, aux.acc_seg: 87.5488, loss: 0.3304, grad_norm: 3.1972 2023-02-16 23:05:00,706 - mmseg - INFO - Iter [74800/160000] lr: 3.195e-05, eta: 8:37:35, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2104, decode.acc_seg: 91.5194, aux.loss_ce: 0.1291, aux.acc_seg: 87.0456, loss: 0.3395, grad_norm: 3.0610 2023-02-16 23:05:18,598 - mmseg - INFO - Iter [74850/160000] lr: 3.193e-05, eta: 8:37:17, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2158, decode.acc_seg: 91.4350, aux.loss_ce: 0.1313, aux.acc_seg: 87.1793, loss: 0.3471, grad_norm: 3.2263 2023-02-16 23:05:36,412 - mmseg - INFO - Iter [74900/160000] lr: 3.191e-05, eta: 8:36:58, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2214, decode.acc_seg: 91.1592, aux.loss_ce: 0.1371, aux.acc_seg: 86.4279, loss: 0.3584, grad_norm: 3.3695 2023-02-16 23:05:54,727 - mmseg - INFO - Iter [74950/160000] lr: 3.189e-05, eta: 8:36:40, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2161, decode.acc_seg: 91.4600, aux.loss_ce: 0.1351, aux.acc_seg: 86.9414, loss: 0.3512, grad_norm: 3.3398 2023-02-16 23:06:12,845 - mmseg - INFO - Saving checkpoint at 75000 iterations 2023-02-16 23:06:13,960 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 23:06:13,960 - mmseg - INFO - Iter [75000/160000] lr: 3.188e-05, eta: 8:36:23, time: 0.385, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2079, decode.acc_seg: 91.4331, aux.loss_ce: 0.1262, aux.acc_seg: 87.0240, loss: 0.3341, grad_norm: 2.9433 2023-02-16 23:06:31,773 - mmseg - INFO - Iter [75050/160000] lr: 3.186e-05, eta: 8:36:04, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2189, decode.acc_seg: 91.2582, aux.loss_ce: 0.1310, aux.acc_seg: 87.1092, loss: 0.3499, grad_norm: 3.4459 2023-02-16 23:06:49,761 - mmseg - INFO - Iter [75100/160000] lr: 3.184e-05, eta: 8:35:46, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2199, decode.acc_seg: 91.0981, aux.loss_ce: 0.1372, aux.acc_seg: 86.4534, loss: 0.3571, grad_norm: 3.4341 2023-02-16 23:07:08,008 - mmseg - INFO - Iter [75150/160000] lr: 3.182e-05, eta: 8:35:27, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2213, decode.acc_seg: 90.9915, aux.loss_ce: 0.1377, aux.acc_seg: 86.2307, loss: 0.3591, grad_norm: 3.3942 2023-02-16 23:07:25,811 - mmseg - INFO - Iter [75200/160000] lr: 3.180e-05, eta: 8:35:09, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2184, decode.acc_seg: 91.2547, aux.loss_ce: 0.1356, aux.acc_seg: 86.6778, loss: 0.3540, grad_norm: 3.3581 2023-02-16 23:07:44,131 - mmseg - INFO - Iter [75250/160000] lr: 3.178e-05, eta: 8:34:51, time: 0.366, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2045, decode.acc_seg: 91.7379, aux.loss_ce: 0.1256, aux.acc_seg: 87.4348, loss: 0.3302, grad_norm: 3.0007 2023-02-16 23:08:02,153 - mmseg - INFO - Iter [75300/160000] lr: 3.176e-05, eta: 8:34:32, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2116, decode.acc_seg: 91.7164, aux.loss_ce: 0.1327, aux.acc_seg: 87.1905, loss: 0.3443, grad_norm: 2.9970 2023-02-16 23:08:20,003 - mmseg - INFO - Iter [75350/160000] lr: 3.174e-05, eta: 8:34:13, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2124, decode.acc_seg: 91.4721, aux.loss_ce: 0.1344, aux.acc_seg: 86.9174, loss: 0.3468, grad_norm: 2.9930 2023-02-16 23:08:38,426 - mmseg - INFO - Iter [75400/160000] lr: 3.173e-05, eta: 8:33:55, time: 0.368, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2045, decode.acc_seg: 91.7343, aux.loss_ce: 0.1268, aux.acc_seg: 87.4267, loss: 0.3313, grad_norm: 3.2527 2023-02-16 23:08:56,452 - mmseg - INFO - Iter [75450/160000] lr: 3.171e-05, eta: 8:33:37, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2099, decode.acc_seg: 91.7199, aux.loss_ce: 0.1331, aux.acc_seg: 86.8762, loss: 0.3429, grad_norm: 3.4439 2023-02-16 23:09:14,637 - mmseg - INFO - Iter [75500/160000] lr: 3.169e-05, eta: 8:33:19, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2284, decode.acc_seg: 90.9717, aux.loss_ce: 0.1381, aux.acc_seg: 86.2321, loss: 0.3665, grad_norm: 2.9197 2023-02-16 23:09:32,755 - mmseg - INFO - Iter [75550/160000] lr: 3.167e-05, eta: 8:33:00, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2095, decode.acc_seg: 91.4901, aux.loss_ce: 0.1335, aux.acc_seg: 86.7392, loss: 0.3430, grad_norm: 2.9818 2023-02-16 23:09:50,659 - mmseg - INFO - Iter [75600/160000] lr: 3.165e-05, eta: 8:32:42, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2163, decode.acc_seg: 91.5947, aux.loss_ce: 0.1352, aux.acc_seg: 86.7254, loss: 0.3515, grad_norm: 3.6308 2023-02-16 23:10:08,805 - mmseg - INFO - Iter [75650/160000] lr: 3.163e-05, eta: 8:32:24, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2172, decode.acc_seg: 91.1211, aux.loss_ce: 0.1362, aux.acc_seg: 86.0778, loss: 0.3534, grad_norm: 3.4390 2023-02-16 23:10:26,789 - mmseg - INFO - Iter [75700/160000] lr: 3.161e-05, eta: 8:32:05, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2054, decode.acc_seg: 91.8601, aux.loss_ce: 0.1249, aux.acc_seg: 87.6221, loss: 0.3303, grad_norm: 3.0284 2023-02-16 23:10:44,786 - mmseg - INFO - Iter [75750/160000] lr: 3.159e-05, eta: 8:31:47, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2153, decode.acc_seg: 91.4766, aux.loss_ce: 0.1339, aux.acc_seg: 86.9364, loss: 0.3492, grad_norm: 3.3310 2023-02-16 23:11:04,895 - mmseg - INFO - Iter [75800/160000] lr: 3.158e-05, eta: 8:31:30, time: 0.402, data_time: 0.051, memory: 16600, decode.loss_ce: 0.2085, decode.acc_seg: 91.5753, aux.loss_ce: 0.1292, aux.acc_seg: 86.9142, loss: 0.3377, grad_norm: 3.0935 2023-02-16 23:11:22,889 - mmseg - INFO - Iter [75850/160000] lr: 3.156e-05, eta: 8:31:12, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2200, decode.acc_seg: 91.2640, aux.loss_ce: 0.1343, aux.acc_seg: 86.6925, loss: 0.3543, grad_norm: 3.2191 2023-02-16 23:11:40,905 - mmseg - INFO - Iter [75900/160000] lr: 3.154e-05, eta: 8:30:53, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2090, decode.acc_seg: 91.3976, aux.loss_ce: 0.1285, aux.acc_seg: 86.9146, loss: 0.3375, grad_norm: 3.2116 2023-02-16 23:11:58,975 - mmseg - INFO - Iter [75950/160000] lr: 3.152e-05, eta: 8:30:35, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2086, decode.acc_seg: 91.6681, aux.loss_ce: 0.1317, aux.acc_seg: 87.0155, loss: 0.3403, grad_norm: 2.9030 2023-02-16 23:12:17,024 - mmseg - INFO - Saving checkpoint at 76000 iterations 2023-02-16 23:12:18,094 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 23:12:18,095 - mmseg - INFO - Iter [76000/160000] lr: 3.150e-05, eta: 8:30:18, time: 0.382, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2150, decode.acc_seg: 91.3702, aux.loss_ce: 0.1315, aux.acc_seg: 87.0245, loss: 0.3466, grad_norm: 3.4980 2023-02-16 23:12:35,973 - mmseg - INFO - Iter [76050/160000] lr: 3.148e-05, eta: 8:29:59, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2153, decode.acc_seg: 91.1150, aux.loss_ce: 0.1282, aux.acc_seg: 86.9182, loss: 0.3436, grad_norm: 3.5775 2023-02-16 23:12:54,070 - mmseg - INFO - Iter [76100/160000] lr: 3.146e-05, eta: 8:29:41, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2182, decode.acc_seg: 91.1433, aux.loss_ce: 0.1369, aux.acc_seg: 86.1018, loss: 0.3551, grad_norm: 3.3555 2023-02-16 23:13:12,447 - mmseg - INFO - Iter [76150/160000] lr: 3.144e-05, eta: 8:29:23, time: 0.368, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2075, decode.acc_seg: 91.9468, aux.loss_ce: 0.1302, aux.acc_seg: 87.3123, loss: 0.3377, grad_norm: 2.9543 2023-02-16 23:13:30,520 - mmseg - INFO - Iter [76200/160000] lr: 3.143e-05, eta: 8:29:04, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2159, decode.acc_seg: 91.5891, aux.loss_ce: 0.1311, aux.acc_seg: 87.0228, loss: 0.3470, grad_norm: 3.8393 2023-02-16 23:13:48,614 - mmseg - INFO - Iter [76250/160000] lr: 3.141e-05, eta: 8:28:46, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2143, decode.acc_seg: 91.4049, aux.loss_ce: 0.1322, aux.acc_seg: 87.0601, loss: 0.3465, grad_norm: 3.0248 2023-02-16 23:14:06,586 - mmseg - INFO - Iter [76300/160000] lr: 3.139e-05, eta: 8:28:28, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2178, decode.acc_seg: 91.5116, aux.loss_ce: 0.1323, aux.acc_seg: 87.0944, loss: 0.3501, grad_norm: 3.5216 2023-02-16 23:14:24,860 - mmseg - INFO - Iter [76350/160000] lr: 3.137e-05, eta: 8:28:09, time: 0.365, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2169, decode.acc_seg: 91.1037, aux.loss_ce: 0.1328, aux.acc_seg: 86.4093, loss: 0.3497, grad_norm: 3.9523 2023-02-16 23:14:42,961 - mmseg - INFO - Iter [76400/160000] lr: 3.135e-05, eta: 8:27:51, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2170, decode.acc_seg: 91.4126, aux.loss_ce: 0.1366, aux.acc_seg: 86.6756, loss: 0.3536, grad_norm: 3.7203 2023-02-16 23:15:01,165 - mmseg - INFO - Iter [76450/160000] lr: 3.133e-05, eta: 8:27:33, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2066, decode.acc_seg: 91.5199, aux.loss_ce: 0.1263, aux.acc_seg: 87.1507, loss: 0.3329, grad_norm: 3.5257 2023-02-16 23:15:19,151 - mmseg - INFO - Iter [76500/160000] lr: 3.131e-05, eta: 8:27:14, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2186, decode.acc_seg: 91.3220, aux.loss_ce: 0.1345, aux.acc_seg: 86.4665, loss: 0.3531, grad_norm: 3.3349 2023-02-16 23:15:37,180 - mmseg - INFO - Iter [76550/160000] lr: 3.129e-05, eta: 8:26:56, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2289, decode.acc_seg: 90.8546, aux.loss_ce: 0.1379, aux.acc_seg: 86.4693, loss: 0.3668, grad_norm: 3.3382 2023-02-16 23:15:55,141 - mmseg - INFO - Iter [76600/160000] lr: 3.128e-05, eta: 8:26:37, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2094, decode.acc_seg: 91.6984, aux.loss_ce: 0.1285, aux.acc_seg: 87.3450, loss: 0.3379, grad_norm: 2.8956 2023-02-16 23:16:13,000 - mmseg - INFO - Iter [76650/160000] lr: 3.126e-05, eta: 8:26:19, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2099, decode.acc_seg: 91.5090, aux.loss_ce: 0.1311, aux.acc_seg: 86.9196, loss: 0.3411, grad_norm: 3.0861 2023-02-16 23:16:31,104 - mmseg - INFO - Iter [76700/160000] lr: 3.124e-05, eta: 8:26:00, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2168, decode.acc_seg: 91.3036, aux.loss_ce: 0.1311, aux.acc_seg: 87.1814, loss: 0.3479, grad_norm: 3.4883 2023-02-16 23:16:48,947 - mmseg - INFO - Iter [76750/160000] lr: 3.122e-05, eta: 8:25:42, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2184, decode.acc_seg: 91.2495, aux.loss_ce: 0.1322, aux.acc_seg: 86.6381, loss: 0.3505, grad_norm: 3.1192 2023-02-16 23:17:06,880 - mmseg - INFO - Iter [76800/160000] lr: 3.120e-05, eta: 8:25:23, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2028, decode.acc_seg: 91.8621, aux.loss_ce: 0.1304, aux.acc_seg: 86.7577, loss: 0.3333, grad_norm: 3.3099 2023-02-16 23:17:24,872 - mmseg - INFO - Iter [76850/160000] lr: 3.118e-05, eta: 8:25:05, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2098, decode.acc_seg: 91.8135, aux.loss_ce: 0.1282, aux.acc_seg: 87.5379, loss: 0.3380, grad_norm: 3.1120 2023-02-16 23:17:42,797 - mmseg - INFO - Iter [76900/160000] lr: 3.116e-05, eta: 8:24:46, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2109, decode.acc_seg: 91.5631, aux.loss_ce: 0.1283, aux.acc_seg: 87.3079, loss: 0.3392, grad_norm: 3.0194 2023-02-16 23:18:00,862 - mmseg - INFO - Iter [76950/160000] lr: 3.114e-05, eta: 8:24:28, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2241, decode.acc_seg: 91.3571, aux.loss_ce: 0.1369, aux.acc_seg: 86.9349, loss: 0.3610, grad_norm: 4.2253 2023-02-16 23:18:18,857 - mmseg - INFO - Saving checkpoint at 77000 iterations 2023-02-16 23:18:20,054 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 23:18:20,054 - mmseg - INFO - Iter [77000/160000] lr: 3.113e-05, eta: 8:24:11, time: 0.384, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2172, decode.acc_seg: 91.2937, aux.loss_ce: 0.1319, aux.acc_seg: 86.9121, loss: 0.3491, grad_norm: 3.0933 2023-02-16 23:18:40,406 - mmseg - INFO - Iter [77050/160000] lr: 3.111e-05, eta: 8:23:55, time: 0.407, data_time: 0.052, memory: 16600, decode.loss_ce: 0.2156, decode.acc_seg: 91.4067, aux.loss_ce: 0.1322, aux.acc_seg: 86.8399, loss: 0.3478, grad_norm: 2.8373 2023-02-16 23:18:58,483 - mmseg - INFO - Iter [77100/160000] lr: 3.109e-05, eta: 8:23:36, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2341, decode.acc_seg: 91.0350, aux.loss_ce: 0.1404, aux.acc_seg: 86.5566, loss: 0.3745, grad_norm: 3.6745 2023-02-16 23:19:16,510 - mmseg - INFO - Iter [77150/160000] lr: 3.107e-05, eta: 8:23:18, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2099, decode.acc_seg: 91.8823, aux.loss_ce: 0.1309, aux.acc_seg: 87.1426, loss: 0.3408, grad_norm: 3.2672 2023-02-16 23:19:34,467 - mmseg - INFO - Iter [77200/160000] lr: 3.105e-05, eta: 8:22:59, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2002, decode.acc_seg: 91.9768, aux.loss_ce: 0.1210, aux.acc_seg: 87.8735, loss: 0.3212, grad_norm: 3.0902 2023-02-16 23:19:52,437 - mmseg - INFO - Iter [77250/160000] lr: 3.103e-05, eta: 8:22:41, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2092, decode.acc_seg: 91.7970, aux.loss_ce: 0.1338, aux.acc_seg: 86.9503, loss: 0.3430, grad_norm: 3.1737 2023-02-16 23:20:10,363 - mmseg - INFO - Iter [77300/160000] lr: 3.101e-05, eta: 8:22:22, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2119, decode.acc_seg: 91.7626, aux.loss_ce: 0.1343, aux.acc_seg: 87.0521, loss: 0.3462, grad_norm: 3.1219 2023-02-16 23:20:28,331 - mmseg - INFO - Iter [77350/160000] lr: 3.099e-05, eta: 8:22:04, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2035, decode.acc_seg: 91.7803, aux.loss_ce: 0.1284, aux.acc_seg: 87.4255, loss: 0.3319, grad_norm: 3.2826 2023-02-16 23:20:46,768 - mmseg - INFO - Iter [77400/160000] lr: 3.098e-05, eta: 8:21:46, time: 0.369, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1970, decode.acc_seg: 92.1721, aux.loss_ce: 0.1205, aux.acc_seg: 88.0704, loss: 0.3175, grad_norm: 2.6663 2023-02-16 23:21:04,791 - mmseg - INFO - Iter [77450/160000] lr: 3.096e-05, eta: 8:21:27, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2175, decode.acc_seg: 91.3452, aux.loss_ce: 0.1313, aux.acc_seg: 87.0733, loss: 0.3488, grad_norm: 3.4257 2023-02-16 23:21:22,701 - mmseg - INFO - Iter [77500/160000] lr: 3.094e-05, eta: 8:21:09, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2177, decode.acc_seg: 91.3370, aux.loss_ce: 0.1330, aux.acc_seg: 86.8957, loss: 0.3507, grad_norm: 4.2652 2023-02-16 23:21:40,811 - mmseg - INFO - Iter [77550/160000] lr: 3.092e-05, eta: 8:20:50, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2003, decode.acc_seg: 91.9199, aux.loss_ce: 0.1322, aux.acc_seg: 86.8449, loss: 0.3325, grad_norm: 3.2161 2023-02-16 23:21:58,658 - mmseg - INFO - Iter [77600/160000] lr: 3.090e-05, eta: 8:20:32, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2126, decode.acc_seg: 91.7408, aux.loss_ce: 0.1327, aux.acc_seg: 87.0722, loss: 0.3453, grad_norm: 3.4276 2023-02-16 23:22:16,550 - mmseg - INFO - Iter [77650/160000] lr: 3.088e-05, eta: 8:20:13, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2166, decode.acc_seg: 91.4298, aux.loss_ce: 0.1347, aux.acc_seg: 86.6975, loss: 0.3513, grad_norm: 2.9720 2023-02-16 23:22:34,473 - mmseg - INFO - Iter [77700/160000] lr: 3.086e-05, eta: 8:19:55, time: 0.358, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2000, decode.acc_seg: 91.9983, aux.loss_ce: 0.1270, aux.acc_seg: 87.2596, loss: 0.3270, grad_norm: 3.1829 2023-02-16 23:22:52,272 - mmseg - INFO - Iter [77750/160000] lr: 3.084e-05, eta: 8:19:36, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2023, decode.acc_seg: 91.9629, aux.loss_ce: 0.1279, aux.acc_seg: 87.3076, loss: 0.3301, grad_norm: 2.9455 2023-02-16 23:23:10,338 - mmseg - INFO - Iter [77800/160000] lr: 3.083e-05, eta: 8:19:18, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2049, decode.acc_seg: 91.7258, aux.loss_ce: 0.1290, aux.acc_seg: 87.1847, loss: 0.3339, grad_norm: 3.0828 2023-02-16 23:23:28,687 - mmseg - INFO - Iter [77850/160000] lr: 3.081e-05, eta: 8:19:00, time: 0.367, data_time: 0.007, memory: 16600, decode.loss_ce: 0.1951, decode.acc_seg: 91.8912, aux.loss_ce: 0.1231, aux.acc_seg: 87.5054, loss: 0.3182, grad_norm: 3.3663 2023-02-16 23:23:46,797 - mmseg - INFO - Iter [77900/160000] lr: 3.079e-05, eta: 8:18:41, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2095, decode.acc_seg: 91.5385, aux.loss_ce: 0.1287, aux.acc_seg: 87.0382, loss: 0.3381, grad_norm: 3.2591 2023-02-16 23:24:04,804 - mmseg - INFO - Iter [77950/160000] lr: 3.077e-05, eta: 8:18:23, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2133, decode.acc_seg: 91.5362, aux.loss_ce: 0.1311, aux.acc_seg: 86.9152, loss: 0.3444, grad_norm: 3.1306 2023-02-16 23:24:22,630 - mmseg - INFO - Saving checkpoint at 78000 iterations 2023-02-16 23:24:23,734 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 23:24:23,734 - mmseg - INFO - Iter [78000/160000] lr: 3.075e-05, eta: 8:18:05, time: 0.379, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2166, decode.acc_seg: 91.5131, aux.loss_ce: 0.1328, aux.acc_seg: 87.1248, loss: 0.3494, grad_norm: 3.4646 2023-02-16 23:24:41,578 - mmseg - INFO - Iter [78050/160000] lr: 3.073e-05, eta: 8:17:47, time: 0.357, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2084, decode.acc_seg: 91.6430, aux.loss_ce: 0.1285, aux.acc_seg: 87.0007, loss: 0.3368, grad_norm: 3.1532 2023-02-16 23:24:59,789 - mmseg - INFO - Iter [78100/160000] lr: 3.071e-05, eta: 8:17:28, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2062, decode.acc_seg: 91.7561, aux.loss_ce: 0.1277, aux.acc_seg: 87.1864, loss: 0.3339, grad_norm: 2.8427 2023-02-16 23:25:17,793 - mmseg - INFO - Iter [78150/160000] lr: 3.069e-05, eta: 8:17:10, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2168, decode.acc_seg: 91.3706, aux.loss_ce: 0.1380, aux.acc_seg: 86.3388, loss: 0.3548, grad_norm: 3.8603 2023-02-16 23:25:35,557 - mmseg - INFO - Iter [78200/160000] lr: 3.068e-05, eta: 8:16:51, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2054, decode.acc_seg: 91.7280, aux.loss_ce: 0.1324, aux.acc_seg: 86.7084, loss: 0.3378, grad_norm: 3.2880 2023-02-16 23:25:53,622 - mmseg - INFO - Iter [78250/160000] lr: 3.066e-05, eta: 8:16:33, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2214, decode.acc_seg: 91.4268, aux.loss_ce: 0.1368, aux.acc_seg: 86.6055, loss: 0.3582, grad_norm: 3.5342 2023-02-16 23:26:11,463 - mmseg - INFO - Iter [78300/160000] lr: 3.064e-05, eta: 8:16:14, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1989, decode.acc_seg: 91.9318, aux.loss_ce: 0.1280, aux.acc_seg: 86.8717, loss: 0.3269, grad_norm: 2.9544 2023-02-16 23:26:31,743 - mmseg - INFO - Iter [78350/160000] lr: 3.062e-05, eta: 8:15:58, time: 0.406, data_time: 0.056, memory: 16600, decode.loss_ce: 0.1941, decode.acc_seg: 91.9808, aux.loss_ce: 0.1238, aux.acc_seg: 87.5091, loss: 0.3179, grad_norm: 3.0695 2023-02-16 23:26:50,052 - mmseg - INFO - Iter [78400/160000] lr: 3.060e-05, eta: 8:15:40, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2253, decode.acc_seg: 90.9293, aux.loss_ce: 0.1409, aux.acc_seg: 86.0405, loss: 0.3662, grad_norm: 3.9812 2023-02-16 23:27:07,851 - mmseg - INFO - Iter [78450/160000] lr: 3.058e-05, eta: 8:15:21, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1985, decode.acc_seg: 91.8972, aux.loss_ce: 0.1234, aux.acc_seg: 87.5749, loss: 0.3219, grad_norm: 3.4063 2023-02-16 23:27:25,646 - mmseg - INFO - Iter [78500/160000] lr: 3.056e-05, eta: 8:15:03, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2034, decode.acc_seg: 91.9414, aux.loss_ce: 0.1295, aux.acc_seg: 87.1320, loss: 0.3330, grad_norm: 2.8268 2023-02-16 23:27:43,581 - mmseg - INFO - Iter [78550/160000] lr: 3.054e-05, eta: 8:14:44, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2082, decode.acc_seg: 91.6975, aux.loss_ce: 0.1329, aux.acc_seg: 87.0176, loss: 0.3411, grad_norm: 3.4867 2023-02-16 23:28:01,490 - mmseg - INFO - Iter [78600/160000] lr: 3.053e-05, eta: 8:14:26, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1995, decode.acc_seg: 91.9821, aux.loss_ce: 0.1290, aux.acc_seg: 87.1828, loss: 0.3285, grad_norm: 2.7884 2023-02-16 23:28:19,514 - mmseg - INFO - Iter [78650/160000] lr: 3.051e-05, eta: 8:14:07, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2112, decode.acc_seg: 91.4901, aux.loss_ce: 0.1321, aux.acc_seg: 87.1394, loss: 0.3433, grad_norm: 3.9754 2023-02-16 23:28:37,305 - mmseg - INFO - Iter [78700/160000] lr: 3.049e-05, eta: 8:13:49, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2179, decode.acc_seg: 91.3395, aux.loss_ce: 0.1347, aux.acc_seg: 86.6233, loss: 0.3526, grad_norm: 3.5988 2023-02-16 23:28:55,289 - mmseg - INFO - Iter [78750/160000] lr: 3.047e-05, eta: 8:13:30, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2042, decode.acc_seg: 92.0361, aux.loss_ce: 0.1272, aux.acc_seg: 87.3659, loss: 0.3315, grad_norm: 2.9832 2023-02-16 23:29:13,144 - mmseg - INFO - Iter [78800/160000] lr: 3.045e-05, eta: 8:13:11, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2122, decode.acc_seg: 91.7470, aux.loss_ce: 0.1290, aux.acc_seg: 87.3018, loss: 0.3411, grad_norm: 3.4575 2023-02-16 23:29:31,167 - mmseg - INFO - Iter [78850/160000] lr: 3.043e-05, eta: 8:12:53, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2160, decode.acc_seg: 91.3693, aux.loss_ce: 0.1320, aux.acc_seg: 86.8365, loss: 0.3480, grad_norm: 3.1627 2023-02-16 23:29:48,937 - mmseg - INFO - Iter [78900/160000] lr: 3.041e-05, eta: 8:12:34, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2070, decode.acc_seg: 91.7965, aux.loss_ce: 0.1287, aux.acc_seg: 87.2056, loss: 0.3357, grad_norm: 2.7694 2023-02-16 23:30:07,165 - mmseg - INFO - Iter [78950/160000] lr: 3.039e-05, eta: 8:12:16, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2085, decode.acc_seg: 91.5749, aux.loss_ce: 0.1254, aux.acc_seg: 87.3485, loss: 0.3339, grad_norm: 3.2624 2023-02-16 23:30:25,228 - mmseg - INFO - Saving checkpoint at 79000 iterations 2023-02-16 23:30:26,300 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 23:30:26,300 - mmseg - INFO - Iter [79000/160000] lr: 3.038e-05, eta: 8:11:59, time: 0.383, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2209, decode.acc_seg: 91.1230, aux.loss_ce: 0.1351, aux.acc_seg: 86.5360, loss: 0.3559, grad_norm: 3.8466 2023-02-16 23:30:44,264 - mmseg - INFO - Iter [79050/160000] lr: 3.036e-05, eta: 8:11:40, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2272, decode.acc_seg: 91.1192, aux.loss_ce: 0.1387, aux.acc_seg: 86.2345, loss: 0.3659, grad_norm: 3.3752 2023-02-16 23:31:02,329 - mmseg - INFO - Iter [79100/160000] lr: 3.034e-05, eta: 8:11:22, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2128, decode.acc_seg: 91.4733, aux.loss_ce: 0.1302, aux.acc_seg: 87.0336, loss: 0.3430, grad_norm: 3.4197 2023-02-16 23:31:20,140 - mmseg - INFO - Iter [79150/160000] lr: 3.032e-05, eta: 8:11:03, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2152, decode.acc_seg: 91.3643, aux.loss_ce: 0.1348, aux.acc_seg: 86.6264, loss: 0.3501, grad_norm: 3.3217 2023-02-16 23:31:38,203 - mmseg - INFO - Iter [79200/160000] lr: 3.030e-05, eta: 8:10:45, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2122, decode.acc_seg: 91.6367, aux.loss_ce: 0.1316, aux.acc_seg: 87.2245, loss: 0.3438, grad_norm: 3.3658 2023-02-16 23:31:56,044 - mmseg - INFO - Iter [79250/160000] lr: 3.028e-05, eta: 8:10:26, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1993, decode.acc_seg: 92.0868, aux.loss_ce: 0.1210, aux.acc_seg: 87.9753, loss: 0.3202, grad_norm: 2.7721 2023-02-16 23:32:13,957 - mmseg - INFO - Iter [79300/160000] lr: 3.026e-05, eta: 8:10:08, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2199, decode.acc_seg: 91.4533, aux.loss_ce: 0.1385, aux.acc_seg: 86.5129, loss: 0.3584, grad_norm: 2.9820 2023-02-16 23:32:31,887 - mmseg - INFO - Iter [79350/160000] lr: 3.024e-05, eta: 8:09:49, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2030, decode.acc_seg: 91.9125, aux.loss_ce: 0.1237, aux.acc_seg: 87.9419, loss: 0.3267, grad_norm: 3.0849 2023-02-16 23:32:49,727 - mmseg - INFO - Iter [79400/160000] lr: 3.023e-05, eta: 8:09:31, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2079, decode.acc_seg: 91.9088, aux.loss_ce: 0.1307, aux.acc_seg: 87.5626, loss: 0.3386, grad_norm: 3.3708 2023-02-16 23:33:08,137 - mmseg - INFO - Iter [79450/160000] lr: 3.021e-05, eta: 8:09:13, time: 0.368, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2214, decode.acc_seg: 91.3958, aux.loss_ce: 0.1356, aux.acc_seg: 86.7891, loss: 0.3570, grad_norm: 3.6301 2023-02-16 23:33:26,573 - mmseg - INFO - Iter [79500/160000] lr: 3.019e-05, eta: 8:08:55, time: 0.369, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2079, decode.acc_seg: 91.9549, aux.loss_ce: 0.1343, aux.acc_seg: 86.7907, loss: 0.3422, grad_norm: 3.1778 2023-02-16 23:33:44,527 - mmseg - INFO - Iter [79550/160000] lr: 3.017e-05, eta: 8:08:36, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2110, decode.acc_seg: 91.6382, aux.loss_ce: 0.1289, aux.acc_seg: 87.2389, loss: 0.3398, grad_norm: 2.8596 2023-02-16 23:34:05,004 - mmseg - INFO - Iter [79600/160000] lr: 3.015e-05, eta: 8:08:20, time: 0.410, data_time: 0.053, memory: 16600, decode.loss_ce: 0.2012, decode.acc_seg: 91.9658, aux.loss_ce: 0.1282, aux.acc_seg: 87.3312, loss: 0.3294, grad_norm: 2.8736 2023-02-16 23:34:22,926 - mmseg - INFO - Iter [79650/160000] lr: 3.013e-05, eta: 8:08:02, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2000, decode.acc_seg: 91.8492, aux.loss_ce: 0.1263, aux.acc_seg: 87.1004, loss: 0.3263, grad_norm: 2.8895 2023-02-16 23:34:40,861 - mmseg - INFO - Iter [79700/160000] lr: 3.011e-05, eta: 8:07:43, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2059, decode.acc_seg: 91.8178, aux.loss_ce: 0.1282, aux.acc_seg: 87.4002, loss: 0.3341, grad_norm: 2.9449 2023-02-16 23:34:59,096 - mmseg - INFO - Iter [79750/160000] lr: 3.009e-05, eta: 8:07:25, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1961, decode.acc_seg: 92.0113, aux.loss_ce: 0.1241, aux.acc_seg: 87.4353, loss: 0.3202, grad_norm: 3.4024 2023-02-16 23:35:17,072 - mmseg - INFO - Iter [79800/160000] lr: 3.008e-05, eta: 8:07:07, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2064, decode.acc_seg: 91.6677, aux.loss_ce: 0.1281, aux.acc_seg: 87.1189, loss: 0.3345, grad_norm: 3.1683 2023-02-16 23:35:35,010 - mmseg - INFO - Iter [79850/160000] lr: 3.006e-05, eta: 8:06:48, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2104, decode.acc_seg: 91.4804, aux.loss_ce: 0.1338, aux.acc_seg: 86.7339, loss: 0.3442, grad_norm: 3.6129 2023-02-16 23:35:53,033 - mmseg - INFO - Iter [79900/160000] lr: 3.004e-05, eta: 8:06:30, time: 0.360, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2103, decode.acc_seg: 91.7743, aux.loss_ce: 0.1333, aux.acc_seg: 86.8000, loss: 0.3437, grad_norm: 3.2443 2023-02-16 23:36:10,975 - mmseg - INFO - Iter [79950/160000] lr: 3.002e-05, eta: 8:06:11, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2068, decode.acc_seg: 91.7570, aux.loss_ce: 0.1276, aux.acc_seg: 87.5451, loss: 0.3343, grad_norm: 3.3683 2023-02-16 23:36:29,056 - mmseg - INFO - Saving checkpoint at 80000 iterations 2023-02-16 23:36:30,179 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 23:36:30,179 - mmseg - INFO - Iter [80000/160000] lr: 3.000e-05, eta: 8:05:54, time: 0.384, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2131, decode.acc_seg: 91.6135, aux.loss_ce: 0.1319, aux.acc_seg: 87.0300, loss: 0.3451, grad_norm: 2.9679 2023-02-16 23:36:59,035 - mmseg - INFO - per class results: 2023-02-16 23:36:59,041 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 75.51 | 87.48 | | building | 80.36 | 92.47 | | sky | 94.07 | 97.23 | | floor | 79.54 | 88.28 | | tree | 73.31 | 82.74 | | ceiling | 83.96 | 90.93 | | road | 83.87 | 88.61 | | bed | 87.17 | 95.24 | | windowpane | 58.82 | 84.03 | | grass | 65.21 | 79.98 | | cabinet | 56.35 | 62.1 | | sidewalk | 63.97 | 81.36 | | person | 78.61 | 91.3 | | earth | 32.84 | 46.4 | | door | 48.8 | 61.21 | | table | 56.22 | 78.69 | | mountain | 59.36 | 75.71 | | plant | 49.77 | 62.23 | | curtain | 71.72 | 79.97 | | chair | 53.97 | 71.78 | | car | 82.36 | 90.43 | | water | 47.31 | 62.42 | | painting | 70.71 | 83.69 | | sofa | 63.99 | 79.58 | | shelf | 42.49 | 72.01 | | house | 45.28 | 54.98 | | sea | 59.6 | 87.57 | | mirror | 64.4 | 70.07 | | rug | 61.59 | 71.47 | | field | 24.85 | 47.32 | | armchair | 38.72 | 59.1 | | seat | 56.81 | 83.56 | | fence | 38.59 | 51.97 | | desk | 44.55 | 74.05 | | rock | 44.21 | 60.01 | | wardrobe | 41.74 | 65.25 | | lamp | 59.88 | 72.39 | | bathtub | 69.17 | 75.33 | | railing | 33.42 | 47.1 | | cushion | 53.47 | 64.1 | | base | 22.83 | 27.1 | | box | 19.45 | 22.75 | | column | 40.76 | 61.45 | | signboard | 34.04 | 49.97 | | chest of drawers | 41.76 | 68.3 | | counter | 24.14 | 30.75 | | sand | 47.67 | 68.94 | | sink | 66.78 | 81.52 | | skyscraper | 51.88 | 64.81 | | fireplace | 68.83 | 75.96 | | refrigerator | 73.23 | 85.11 | | grandstand | 33.17 | 68.33 | | path | 23.19 | 33.8 | | stairs | 28.65 | 33.96 | | runway | 72.07 | 94.32 | | case | 33.24 | 40.73 | | pool table | 90.72 | 91.66 | | pillow | 57.83 | 78.13 | | screen door | 57.16 | 90.32 | | stairway | 29.03 | 39.59 | | river | 9.03 | 24.0 | | bridge | 46.33 | 51.88 | | bookcase | 27.02 | 38.47 | | blind | 48.29 | 52.59 | | coffee table | 51.44 | 82.87 | | toilet | 83.94 | 88.71 | | flower | 39.69 | 59.9 | | book | 41.08 | 66.78 | | hill | 7.43 | 10.57 | | bench | 36.38 | 61.24 | | countertop | 53.46 | 64.38 | | stove | 72.15 | 81.66 | | palm | 50.6 | 80.16 | | kitchen island | 35.26 | 66.43 | | computer | 60.39 | 71.79 | | swivel chair | 40.24 | 50.94 | | boat | 47.86 | 66.41 | | bar | 29.27 | 38.62 | | arcade machine | 18.48 | 18.85 | | hovel | 44.37 | 52.66 | | bus | 82.27 | 95.66 | | towel | 59.03 | 64.67 | | light | 55.77 | 66.62 | | truck | 27.92 | 34.85 | | tower | 8.58 | 12.44 | | chandelier | 65.4 | 80.14 | | awning | 20.65 | 23.12 | | streetlight | 22.45 | 27.76 | | booth | 33.17 | 40.86 | | television receiver | 71.12 | 79.92 | | airplane | 55.06 | 69.6 | | dirt track | 14.01 | 19.47 | | apparel | 34.74 | 44.55 | | pole | 22.34 | 32.01 | | land | 3.3 | 4.17 | | bannister | 13.63 | 17.83 | | escalator | 20.71 | 21.21 | | ottoman | 51.09 | 73.63 | | bottle | 28.68 | 32.58 | | buffet | 48.36 | 56.22 | | poster | 17.82 | 23.66 | | stage | 18.21 | 21.99 | | van | 45.51 | 65.31 | | ship | 8.27 | 9.98 | | fountain | 25.29 | 26.21 | | conveyer belt | 75.96 | 81.83 | | canopy | 17.54 | 19.97 | | washer | 69.95 | 72.31 | | plaything | 20.41 | 35.64 | | swimming pool | 51.27 | 60.05 | | stool | 40.19 | 54.19 | | barrel | 56.69 | 64.41 | | basket | 27.35 | 53.42 | | waterfall | 48.58 | 58.31 | | tent | 96.26 | 98.27 | | bag | 16.46 | 23.96 | | minibike | 58.85 | 66.19 | | cradle | 77.64 | 93.16 | | oven | 33.82 | 35.62 | | ball | 45.96 | 64.99 | | food | 55.42 | 61.06 | | step | 11.99 | 16.07 | | tank | 31.18 | 34.59 | | trade name | 24.85 | 27.45 | | microwave | 67.92 | 75.07 | | pot | 43.18 | 54.7 | | animal | 57.34 | 60.78 | | bicycle | 51.07 | 69.45 | | lake | 48.9 | 52.01 | | dishwasher | 73.53 | 82.26 | | screen | 72.41 | 87.82 | | blanket | 7.35 | 7.89 | | sculpture | 38.48 | 85.57 | | hood | 57.95 | 70.6 | | sconce | 29.6 | 32.0 | | vase | 33.35 | 49.73 | | traffic light | 30.9 | 44.04 | | tray | 3.18 | 3.57 | | ashcan | 38.36 | 43.73 | | fan | 51.71 | 58.44 | | pier | 21.06 | 41.93 | | crt screen | 3.1 | 6.01 | | plate | 47.14 | 59.04 | | monitor | 17.7 | 32.66 | | bulletin board | 38.37 | 48.44 | | shower | 0.0 | 0.0 | | radiator | 58.61 | 62.86 | | glass | 10.65 | 11.15 | | clock | 31.98 | 41.24 | | flag | 43.4 | 50.83 | +---------------------+-------+-------+ 2023-02-16 23:36:59,042 - mmseg - INFO - Summary: 2023-02-16 23:36:59,042 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 81.55 | 45.64 | 57.36 | +-------+-------+-------+ 2023-02-16 23:36:59,043 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 23:36:59,043 - mmseg - INFO - Iter(val) [500] aAcc: 0.8155, mIoU: 0.4564, mAcc: 0.5736, IoU.wall: 0.7551, IoU.building: 0.8036, IoU.sky: 0.9407, IoU.floor: 0.7954, IoU.tree: 0.7331, IoU.ceiling: 0.8396, IoU.road: 0.8387, IoU.bed : 0.8717, IoU.windowpane: 0.5882, IoU.grass: 0.6521, IoU.cabinet: 0.5635, IoU.sidewalk: 0.6397, IoU.person: 0.7861, IoU.earth: 0.3284, IoU.door: 0.4880, IoU.table: 0.5622, IoU.mountain: 0.5936, IoU.plant: 0.4977, IoU.curtain: 0.7172, IoU.chair: 0.5397, IoU.car: 0.8236, IoU.water: 0.4731, IoU.painting: 0.7071, IoU.sofa: 0.6399, IoU.shelf: 0.4249, IoU.house: 0.4528, IoU.sea: 0.5960, IoU.mirror: 0.6440, IoU.rug: 0.6159, IoU.field: 0.2485, IoU.armchair: 0.3872, IoU.seat: 0.5681, IoU.fence: 0.3859, IoU.desk: 0.4455, IoU.rock: 0.4421, IoU.wardrobe: 0.4174, IoU.lamp: 0.5988, IoU.bathtub: 0.6917, IoU.railing: 0.3342, IoU.cushion: 0.5347, IoU.base: 0.2283, IoU.box: 0.1945, IoU.column: 0.4076, IoU.signboard: 0.3404, IoU.chest of drawers: 0.4176, IoU.counter: 0.2414, IoU.sand: 0.4767, IoU.sink: 0.6678, IoU.skyscraper: 0.5188, IoU.fireplace: 0.6883, IoU.refrigerator: 0.7323, IoU.grandstand: 0.3317, IoU.path: 0.2319, IoU.stairs: 0.2865, IoU.runway: 0.7207, IoU.case: 0.3324, IoU.pool table: 0.9072, IoU.pillow: 0.5783, IoU.screen door: 0.5716, IoU.stairway: 0.2903, IoU.river: 0.0903, IoU.bridge: 0.4633, IoU.bookcase: 0.2702, IoU.blind: 0.4829, IoU.coffee table: 0.5144, IoU.toilet: 0.8394, IoU.flower: 0.3969, IoU.book: 0.4108, IoU.hill: 0.0743, IoU.bench: 0.3638, IoU.countertop: 0.5346, IoU.stove: 0.7215, IoU.palm: 0.5060, IoU.kitchen island: 0.3526, IoU.computer: 0.6039, IoU.swivel chair: 0.4024, IoU.boat: 0.4786, IoU.bar: 0.2927, IoU.arcade machine: 0.1848, IoU.hovel: 0.4437, IoU.bus: 0.8227, IoU.towel: 0.5903, IoU.light: 0.5577, IoU.truck: 0.2792, IoU.tower: 0.0858, IoU.chandelier: 0.6540, IoU.awning: 0.2065, IoU.streetlight: 0.2245, IoU.booth: 0.3317, IoU.television receiver: 0.7112, IoU.airplane: 0.5506, IoU.dirt track: 0.1401, IoU.apparel: 0.3474, IoU.pole: 0.2234, IoU.land: 0.0330, IoU.bannister: 0.1363, IoU.escalator: 0.2071, IoU.ottoman: 0.5109, IoU.bottle: 0.2868, IoU.buffet: 0.4836, IoU.poster: 0.1782, IoU.stage: 0.1821, IoU.van: 0.4551, IoU.ship: 0.0827, IoU.fountain: 0.2529, IoU.conveyer belt: 0.7596, IoU.canopy: 0.1754, IoU.washer: 0.6995, IoU.plaything: 0.2041, IoU.swimming pool: 0.5127, IoU.stool: 0.4019, IoU.barrel: 0.5669, IoU.basket: 0.2735, IoU.waterfall: 0.4858, IoU.tent: 0.9626, IoU.bag: 0.1646, IoU.minibike: 0.5885, IoU.cradle: 0.7764, IoU.oven: 0.3382, IoU.ball: 0.4596, IoU.food: 0.5542, IoU.step: 0.1199, IoU.tank: 0.3118, IoU.trade name: 0.2485, IoU.microwave: 0.6792, IoU.pot: 0.4318, IoU.animal: 0.5734, IoU.bicycle: 0.5107, IoU.lake: 0.4890, IoU.dishwasher: 0.7353, IoU.screen: 0.7241, IoU.blanket: 0.0735, IoU.sculpture: 0.3848, IoU.hood: 0.5795, IoU.sconce: 0.2960, IoU.vase: 0.3335, IoU.traffic light: 0.3090, IoU.tray: 0.0318, IoU.ashcan: 0.3836, IoU.fan: 0.5171, IoU.pier: 0.2106, IoU.crt screen: 0.0310, IoU.plate: 0.4714, IoU.monitor: 0.1770, IoU.bulletin board: 0.3837, IoU.shower: 0.0000, IoU.radiator: 0.5861, IoU.glass: 0.1065, IoU.clock: 0.3198, IoU.flag: 0.4340, Acc.wall: 0.8748, Acc.building: 0.9247, Acc.sky: 0.9723, Acc.floor: 0.8828, Acc.tree: 0.8274, Acc.ceiling: 0.9093, Acc.road: 0.8861, Acc.bed : 0.9524, Acc.windowpane: 0.8403, Acc.grass: 0.7998, Acc.cabinet: 0.6210, Acc.sidewalk: 0.8136, Acc.person: 0.9130, Acc.earth: 0.4640, Acc.door: 0.6121, Acc.table: 0.7869, Acc.mountain: 0.7571, Acc.plant: 0.6223, Acc.curtain: 0.7997, Acc.chair: 0.7178, Acc.car: 0.9043, Acc.water: 0.6242, Acc.painting: 0.8369, Acc.sofa: 0.7958, Acc.shelf: 0.7201, Acc.house: 0.5498, Acc.sea: 0.8757, Acc.mirror: 0.7007, Acc.rug: 0.7147, Acc.field: 0.4732, Acc.armchair: 0.5910, Acc.seat: 0.8356, Acc.fence: 0.5197, Acc.desk: 0.7405, Acc.rock: 0.6001, Acc.wardrobe: 0.6525, Acc.lamp: 0.7239, Acc.bathtub: 0.7533, Acc.railing: 0.4710, Acc.cushion: 0.6410, Acc.base: 0.2710, Acc.box: 0.2275, Acc.column: 0.6145, Acc.signboard: 0.4997, Acc.chest of drawers: 0.6830, Acc.counter: 0.3075, Acc.sand: 0.6894, Acc.sink: 0.8152, Acc.skyscraper: 0.6481, Acc.fireplace: 0.7596, Acc.refrigerator: 0.8511, Acc.grandstand: 0.6833, Acc.path: 0.3380, Acc.stairs: 0.3396, Acc.runway: 0.9432, Acc.case: 0.4073, Acc.pool table: 0.9166, Acc.pillow: 0.7813, Acc.screen door: 0.9032, Acc.stairway: 0.3959, Acc.river: 0.2400, Acc.bridge: 0.5188, Acc.bookcase: 0.3847, Acc.blind: 0.5259, Acc.coffee table: 0.8287, Acc.toilet: 0.8871, Acc.flower: 0.5990, Acc.book: 0.6678, Acc.hill: 0.1057, Acc.bench: 0.6124, Acc.countertop: 0.6438, Acc.stove: 0.8166, Acc.palm: 0.8016, Acc.kitchen island: 0.6643, Acc.computer: 0.7179, Acc.swivel chair: 0.5094, Acc.boat: 0.6641, Acc.bar: 0.3862, Acc.arcade machine: 0.1885, Acc.hovel: 0.5266, Acc.bus: 0.9566, Acc.towel: 0.6467, Acc.light: 0.6662, Acc.truck: 0.3485, Acc.tower: 0.1244, Acc.chandelier: 0.8014, Acc.awning: 0.2312, Acc.streetlight: 0.2776, Acc.booth: 0.4086, Acc.television receiver: 0.7992, Acc.airplane: 0.6960, Acc.dirt track: 0.1947, Acc.apparel: 0.4455, Acc.pole: 0.3201, Acc.land: 0.0417, Acc.bannister: 0.1783, Acc.escalator: 0.2121, Acc.ottoman: 0.7363, Acc.bottle: 0.3258, Acc.buffet: 0.5622, Acc.poster: 0.2366, Acc.stage: 0.2199, Acc.van: 0.6531, Acc.ship: 0.0998, Acc.fountain: 0.2621, Acc.conveyer belt: 0.8183, Acc.canopy: 0.1997, Acc.washer: 0.7231, Acc.plaything: 0.3564, Acc.swimming pool: 0.6005, Acc.stool: 0.5419, Acc.barrel: 0.6441, Acc.basket: 0.5342, Acc.waterfall: 0.5831, Acc.tent: 0.9827, Acc.bag: 0.2396, Acc.minibike: 0.6619, Acc.cradle: 0.9316, Acc.oven: 0.3562, Acc.ball: 0.6499, Acc.food: 0.6106, Acc.step: 0.1607, Acc.tank: 0.3459, Acc.trade name: 0.2745, Acc.microwave: 0.7507, Acc.pot: 0.5470, Acc.animal: 0.6078, Acc.bicycle: 0.6945, Acc.lake: 0.5201, Acc.dishwasher: 0.8226, Acc.screen: 0.8782, Acc.blanket: 0.0789, Acc.sculpture: 0.8557, Acc.hood: 0.7060, Acc.sconce: 0.3200, Acc.vase: 0.4973, Acc.traffic light: 0.4404, Acc.tray: 0.0357, Acc.ashcan: 0.4373, Acc.fan: 0.5844, Acc.pier: 0.4193, Acc.crt screen: 0.0601, Acc.plate: 0.5904, Acc.monitor: 0.3266, Acc.bulletin board: 0.4844, Acc.shower: 0.0000, Acc.radiator: 0.6286, Acc.glass: 0.1115, Acc.clock: 0.4124, Acc.flag: 0.5083 2023-02-16 23:37:17,112 - mmseg - INFO - Iter [80050/160000] lr: 2.998e-05, eta: 8:06:04, time: 0.939, data_time: 0.584, memory: 16600, decode.loss_ce: 0.2040, decode.acc_seg: 91.7600, aux.loss_ce: 0.1258, aux.acc_seg: 87.6085, loss: 0.3298, grad_norm: 2.5767 2023-02-16 23:37:35,434 - mmseg - INFO - Iter [80100/160000] lr: 2.996e-05, eta: 8:05:46, time: 0.366, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1972, decode.acc_seg: 92.1527, aux.loss_ce: 0.1193, aux.acc_seg: 87.9993, loss: 0.3165, grad_norm: 2.7528 2023-02-16 23:37:53,770 - mmseg - INFO - Iter [80150/160000] lr: 2.994e-05, eta: 8:05:28, time: 0.367, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2154, decode.acc_seg: 91.4142, aux.loss_ce: 0.1316, aux.acc_seg: 87.0877, loss: 0.3470, grad_norm: 3.3189 2023-02-16 23:38:11,835 - mmseg - INFO - Iter [80200/160000] lr: 2.993e-05, eta: 8:05:10, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2090, decode.acc_seg: 91.5890, aux.loss_ce: 0.1350, aux.acc_seg: 86.7671, loss: 0.3440, grad_norm: 3.8234 2023-02-16 23:38:30,113 - mmseg - INFO - Iter [80250/160000] lr: 2.991e-05, eta: 8:04:51, time: 0.366, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2164, decode.acc_seg: 91.2830, aux.loss_ce: 0.1319, aux.acc_seg: 86.8221, loss: 0.3484, grad_norm: 3.7200 2023-02-16 23:38:48,010 - mmseg - INFO - Iter [80300/160000] lr: 2.989e-05, eta: 8:04:33, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1975, decode.acc_seg: 92.1576, aux.loss_ce: 0.1257, aux.acc_seg: 87.3821, loss: 0.3233, grad_norm: 3.1747 2023-02-16 23:39:05,810 - mmseg - INFO - Iter [80350/160000] lr: 2.987e-05, eta: 8:04:14, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2069, decode.acc_seg: 91.7748, aux.loss_ce: 0.1252, aux.acc_seg: 87.6661, loss: 0.3321, grad_norm: 2.9678 2023-02-16 23:39:24,006 - mmseg - INFO - Iter [80400/160000] lr: 2.985e-05, eta: 8:03:56, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2085, decode.acc_seg: 91.3365, aux.loss_ce: 0.1292, aux.acc_seg: 87.0014, loss: 0.3377, grad_norm: 3.2804 2023-02-16 23:39:42,066 - mmseg - INFO - Iter [80450/160000] lr: 2.983e-05, eta: 8:03:37, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1903, decode.acc_seg: 92.2813, aux.loss_ce: 0.1235, aux.acc_seg: 87.7026, loss: 0.3137, grad_norm: 2.7833 2023-02-16 23:39:59,844 - mmseg - INFO - Iter [80500/160000] lr: 2.981e-05, eta: 8:03:19, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2033, decode.acc_seg: 91.8531, aux.loss_ce: 0.1270, aux.acc_seg: 87.4709, loss: 0.3303, grad_norm: 3.0835 2023-02-16 23:40:17,682 - mmseg - INFO - Iter [80550/160000] lr: 2.979e-05, eta: 8:03:00, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2032, decode.acc_seg: 91.7587, aux.loss_ce: 0.1236, aux.acc_seg: 87.5052, loss: 0.3268, grad_norm: 2.7753 2023-02-16 23:40:35,630 - mmseg - INFO - Iter [80600/160000] lr: 2.978e-05, eta: 8:02:42, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2178, decode.acc_seg: 91.3183, aux.loss_ce: 0.1305, aux.acc_seg: 87.1062, loss: 0.3482, grad_norm: 2.9947 2023-02-16 23:40:53,819 - mmseg - INFO - Iter [80650/160000] lr: 2.976e-05, eta: 8:02:23, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2135, decode.acc_seg: 91.6820, aux.loss_ce: 0.1347, aux.acc_seg: 86.8287, loss: 0.3482, grad_norm: 3.0242 2023-02-16 23:41:11,810 - mmseg - INFO - Iter [80700/160000] lr: 2.974e-05, eta: 8:02:05, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2084, decode.acc_seg: 91.7613, aux.loss_ce: 0.1287, aux.acc_seg: 87.3281, loss: 0.3371, grad_norm: 2.9333 2023-02-16 23:41:29,593 - mmseg - INFO - Iter [80750/160000] lr: 2.972e-05, eta: 8:01:46, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1990, decode.acc_seg: 92.0238, aux.loss_ce: 0.1281, aux.acc_seg: 86.9263, loss: 0.3271, grad_norm: 3.6253 2023-02-16 23:41:47,445 - mmseg - INFO - Iter [80800/160000] lr: 2.970e-05, eta: 8:01:28, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2055, decode.acc_seg: 91.8015, aux.loss_ce: 0.1288, aux.acc_seg: 86.9827, loss: 0.3343, grad_norm: 3.4554 2023-02-16 23:42:07,533 - mmseg - INFO - Iter [80850/160000] lr: 2.968e-05, eta: 8:01:11, time: 0.402, data_time: 0.053, memory: 16600, decode.loss_ce: 0.2119, decode.acc_seg: 91.7331, aux.loss_ce: 0.1327, aux.acc_seg: 87.0742, loss: 0.3447, grad_norm: 3.6196 2023-02-16 23:42:25,571 - mmseg - INFO - Iter [80900/160000] lr: 2.966e-05, eta: 8:00:53, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2013, decode.acc_seg: 91.7141, aux.loss_ce: 0.1244, aux.acc_seg: 87.3166, loss: 0.3257, grad_norm: 3.6441 2023-02-16 23:42:43,316 - mmseg - INFO - Iter [80950/160000] lr: 2.964e-05, eta: 8:00:34, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1977, decode.acc_seg: 92.1626, aux.loss_ce: 0.1304, aux.acc_seg: 87.1340, loss: 0.3280, grad_norm: 2.7767 2023-02-16 23:43:01,296 - mmseg - INFO - Saving checkpoint at 81000 iterations 2023-02-16 23:43:02,382 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 23:43:02,382 - mmseg - INFO - Iter [81000/160000] lr: 2.963e-05, eta: 8:00:17, time: 0.381, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2014, decode.acc_seg: 92.0364, aux.loss_ce: 0.1302, aux.acc_seg: 87.0020, loss: 0.3316, grad_norm: 3.9000 2023-02-16 23:43:20,127 - mmseg - INFO - Iter [81050/160000] lr: 2.961e-05, eta: 7:59:58, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2101, decode.acc_seg: 91.5472, aux.loss_ce: 0.1268, aux.acc_seg: 87.3724, loss: 0.3369, grad_norm: 3.0093 2023-02-16 23:43:38,004 - mmseg - INFO - Iter [81100/160000] lr: 2.959e-05, eta: 7:59:39, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1987, decode.acc_seg: 91.9730, aux.loss_ce: 0.1270, aux.acc_seg: 87.2590, loss: 0.3257, grad_norm: 3.1766 2023-02-16 23:43:55,944 - mmseg - INFO - Iter [81150/160000] lr: 2.957e-05, eta: 7:59:21, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1992, decode.acc_seg: 92.1113, aux.loss_ce: 0.1291, aux.acc_seg: 87.1341, loss: 0.3284, grad_norm: 3.7661 2023-02-16 23:44:13,797 - mmseg - INFO - Iter [81200/160000] lr: 2.955e-05, eta: 7:59:02, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2084, decode.acc_seg: 91.9830, aux.loss_ce: 0.1282, aux.acc_seg: 87.5992, loss: 0.3366, grad_norm: 3.0223 2023-02-16 23:44:31,981 - mmseg - INFO - Iter [81250/160000] lr: 2.953e-05, eta: 7:58:44, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2101, decode.acc_seg: 91.5721, aux.loss_ce: 0.1313, aux.acc_seg: 86.9908, loss: 0.3414, grad_norm: 3.4584 2023-02-16 23:44:49,955 - mmseg - INFO - Iter [81300/160000] lr: 2.951e-05, eta: 7:58:25, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2054, decode.acc_seg: 91.8459, aux.loss_ce: 0.1287, aux.acc_seg: 87.4770, loss: 0.3341, grad_norm: 3.3139 2023-02-16 23:45:07,971 - mmseg - INFO - Iter [81350/160000] lr: 2.949e-05, eta: 7:58:07, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2036, decode.acc_seg: 91.8586, aux.loss_ce: 0.1331, aux.acc_seg: 86.9217, loss: 0.3367, grad_norm: 3.0961 2023-02-16 23:45:25,857 - mmseg - INFO - Iter [81400/160000] lr: 2.948e-05, eta: 7:57:48, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1910, decode.acc_seg: 92.3931, aux.loss_ce: 0.1206, aux.acc_seg: 88.0258, loss: 0.3116, grad_norm: 2.8442 2023-02-16 23:45:43,762 - mmseg - INFO - Iter [81450/160000] lr: 2.946e-05, eta: 7:57:30, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2017, decode.acc_seg: 91.8855, aux.loss_ce: 0.1283, aux.acc_seg: 87.1372, loss: 0.3299, grad_norm: 3.0133 2023-02-16 23:46:01,826 - mmseg - INFO - Iter [81500/160000] lr: 2.944e-05, eta: 7:57:11, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1854, decode.acc_seg: 92.6758, aux.loss_ce: 0.1196, aux.acc_seg: 88.1871, loss: 0.3050, grad_norm: 3.2138 2023-02-16 23:46:19,840 - mmseg - INFO - Iter [81550/160000] lr: 2.942e-05, eta: 7:56:53, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1944, decode.acc_seg: 92.1257, aux.loss_ce: 0.1271, aux.acc_seg: 87.2578, loss: 0.3215, grad_norm: 3.0393 2023-02-16 23:46:37,731 - mmseg - INFO - Iter [81600/160000] lr: 2.940e-05, eta: 7:56:34, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1987, decode.acc_seg: 91.7671, aux.loss_ce: 0.1235, aux.acc_seg: 87.5210, loss: 0.3222, grad_norm: 3.1741 2023-02-16 23:46:55,954 - mmseg - INFO - Iter [81650/160000] lr: 2.938e-05, eta: 7:56:16, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2052, decode.acc_seg: 91.9139, aux.loss_ce: 0.1258, aux.acc_seg: 87.5470, loss: 0.3310, grad_norm: 2.9028 2023-02-16 23:47:13,968 - mmseg - INFO - Iter [81700/160000] lr: 2.936e-05, eta: 7:55:58, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2158, decode.acc_seg: 91.4143, aux.loss_ce: 0.1370, aux.acc_seg: 86.1930, loss: 0.3528, grad_norm: 3.5867 2023-02-16 23:47:31,861 - mmseg - INFO - Iter [81750/160000] lr: 2.934e-05, eta: 7:55:39, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2122, decode.acc_seg: 91.3701, aux.loss_ce: 0.1308, aux.acc_seg: 86.9822, loss: 0.3430, grad_norm: 3.3680 2023-02-16 23:47:49,735 - mmseg - INFO - Iter [81800/160000] lr: 2.933e-05, eta: 7:55:20, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2044, decode.acc_seg: 92.0022, aux.loss_ce: 0.1321, aux.acc_seg: 86.9336, loss: 0.3364, grad_norm: 3.7812 2023-02-16 23:48:07,843 - mmseg - INFO - Iter [81850/160000] lr: 2.931e-05, eta: 7:55:02, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1973, decode.acc_seg: 91.9321, aux.loss_ce: 0.1230, aux.acc_seg: 87.5810, loss: 0.3203, grad_norm: 3.1349 2023-02-16 23:48:25,917 - mmseg - INFO - Iter [81900/160000] lr: 2.929e-05, eta: 7:54:44, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2127, decode.acc_seg: 91.5595, aux.loss_ce: 0.1399, aux.acc_seg: 86.4738, loss: 0.3526, grad_norm: 3.4759 2023-02-16 23:48:43,758 - mmseg - INFO - Iter [81950/160000] lr: 2.927e-05, eta: 7:54:25, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1994, decode.acc_seg: 92.1709, aux.loss_ce: 0.1253, aux.acc_seg: 87.5231, loss: 0.3247, grad_norm: 2.8688 2023-02-16 23:49:01,836 - mmseg - INFO - Saving checkpoint at 82000 iterations 2023-02-16 23:49:02,902 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 23:49:02,903 - mmseg - INFO - Iter [82000/160000] lr: 2.925e-05, eta: 7:54:08, time: 0.383, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2067, decode.acc_seg: 91.6986, aux.loss_ce: 0.1257, aux.acc_seg: 87.5026, loss: 0.3323, grad_norm: 4.4575 2023-02-16 23:49:20,902 - mmseg - INFO - Iter [82050/160000] lr: 2.923e-05, eta: 7:53:49, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2095, decode.acc_seg: 91.6837, aux.loss_ce: 0.1292, aux.acc_seg: 87.1136, loss: 0.3387, grad_norm: 3.4116 2023-02-16 23:49:41,173 - mmseg - INFO - Iter [82100/160000] lr: 2.921e-05, eta: 7:53:33, time: 0.405, data_time: 0.053, memory: 16600, decode.loss_ce: 0.2085, decode.acc_seg: 91.6515, aux.loss_ce: 0.1317, aux.acc_seg: 87.0288, loss: 0.3402, grad_norm: 3.5005 2023-02-16 23:49:59,044 - mmseg - INFO - Iter [82150/160000] lr: 2.919e-05, eta: 7:53:14, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1982, decode.acc_seg: 92.1658, aux.loss_ce: 0.1264, aux.acc_seg: 87.6337, loss: 0.3246, grad_norm: 3.6028 2023-02-16 23:50:16,891 - mmseg - INFO - Iter [82200/160000] lr: 2.918e-05, eta: 7:52:56, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2031, decode.acc_seg: 91.7505, aux.loss_ce: 0.1264, aux.acc_seg: 87.4754, loss: 0.3295, grad_norm: 3.2903 2023-02-16 23:50:34,654 - mmseg - INFO - Iter [82250/160000] lr: 2.916e-05, eta: 7:52:37, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2022, decode.acc_seg: 91.9252, aux.loss_ce: 0.1263, aux.acc_seg: 87.5099, loss: 0.3285, grad_norm: 3.0494 2023-02-16 23:50:52,489 - mmseg - INFO - Iter [82300/160000] lr: 2.914e-05, eta: 7:52:18, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2122, decode.acc_seg: 91.5305, aux.loss_ce: 0.1341, aux.acc_seg: 86.8036, loss: 0.3464, grad_norm: 3.2017 2023-02-16 23:51:10,402 - mmseg - INFO - Iter [82350/160000] lr: 2.912e-05, eta: 7:52:00, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2031, decode.acc_seg: 91.9335, aux.loss_ce: 0.1304, aux.acc_seg: 87.3750, loss: 0.3335, grad_norm: 3.2047 2023-02-16 23:51:28,402 - mmseg - INFO - Iter [82400/160000] lr: 2.910e-05, eta: 7:51:41, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2067, decode.acc_seg: 91.7463, aux.loss_ce: 0.1270, aux.acc_seg: 87.4879, loss: 0.3337, grad_norm: 3.0031 2023-02-16 23:51:46,319 - mmseg - INFO - Iter [82450/160000] lr: 2.908e-05, eta: 7:51:23, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2007, decode.acc_seg: 91.9082, aux.loss_ce: 0.1280, aux.acc_seg: 87.2906, loss: 0.3288, grad_norm: 3.4913 2023-02-16 23:52:04,417 - mmseg - INFO - Iter [82500/160000] lr: 2.906e-05, eta: 7:51:05, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2018, decode.acc_seg: 91.9315, aux.loss_ce: 0.1295, aux.acc_seg: 86.9425, loss: 0.3313, grad_norm: 3.0055 2023-02-16 23:52:22,357 - mmseg - INFO - Iter [82550/160000] lr: 2.904e-05, eta: 7:50:46, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2077, decode.acc_seg: 91.6745, aux.loss_ce: 0.1284, aux.acc_seg: 87.2488, loss: 0.3361, grad_norm: 3.4217 2023-02-16 23:52:40,194 - mmseg - INFO - Iter [82600/160000] lr: 2.903e-05, eta: 7:50:27, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2119, decode.acc_seg: 91.4103, aux.loss_ce: 0.1311, aux.acc_seg: 86.8717, loss: 0.3430, grad_norm: 3.5046 2023-02-16 23:52:58,093 - mmseg - INFO - Iter [82650/160000] lr: 2.901e-05, eta: 7:50:09, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2207, decode.acc_seg: 91.2466, aux.loss_ce: 0.1353, aux.acc_seg: 86.5152, loss: 0.3560, grad_norm: 3.8448 2023-02-16 23:53:15,866 - mmseg - INFO - Iter [82700/160000] lr: 2.899e-05, eta: 7:49:50, time: 0.355, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1887, decode.acc_seg: 92.4235, aux.loss_ce: 0.1214, aux.acc_seg: 87.9013, loss: 0.3101, grad_norm: 2.6104 2023-02-16 23:53:34,027 - mmseg - INFO - Iter [82750/160000] lr: 2.897e-05, eta: 7:49:32, time: 0.363, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1999, decode.acc_seg: 92.0247, aux.loss_ce: 0.1315, aux.acc_seg: 87.0725, loss: 0.3314, grad_norm: 3.0466 2023-02-16 23:53:52,257 - mmseg - INFO - Iter [82800/160000] lr: 2.895e-05, eta: 7:49:14, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2077, decode.acc_seg: 91.6531, aux.loss_ce: 0.1283, aux.acc_seg: 87.3596, loss: 0.3360, grad_norm: 3.1776 2023-02-16 23:54:10,230 - mmseg - INFO - Iter [82850/160000] lr: 2.893e-05, eta: 7:48:55, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1938, decode.acc_seg: 92.2932, aux.loss_ce: 0.1212, aux.acc_seg: 88.0031, loss: 0.3150, grad_norm: 2.9970 2023-02-16 23:54:28,056 - mmseg - INFO - Iter [82900/160000] lr: 2.891e-05, eta: 7:48:37, time: 0.357, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2134, decode.acc_seg: 91.4903, aux.loss_ce: 0.1299, aux.acc_seg: 87.1478, loss: 0.3433, grad_norm: 3.3706 2023-02-16 23:54:46,307 - mmseg - INFO - Iter [82950/160000] lr: 2.889e-05, eta: 7:48:18, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1936, decode.acc_seg: 92.0850, aux.loss_ce: 0.1220, aux.acc_seg: 87.6891, loss: 0.3155, grad_norm: 2.8204 2023-02-16 23:55:04,415 - mmseg - INFO - Saving checkpoint at 83000 iterations 2023-02-16 23:55:05,487 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-16 23:55:05,487 - mmseg - INFO - Iter [83000/160000] lr: 2.888e-05, eta: 7:48:01, time: 0.384, data_time: 0.007, memory: 16600, decode.loss_ce: 0.1874, decode.acc_seg: 92.5229, aux.loss_ce: 0.1177, aux.acc_seg: 88.3750, loss: 0.3051, grad_norm: 2.8872 2023-02-16 23:55:23,403 - mmseg - INFO - Iter [83050/160000] lr: 2.886e-05, eta: 7:47:42, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2024, decode.acc_seg: 92.0607, aux.loss_ce: 0.1297, aux.acc_seg: 87.4327, loss: 0.3321, grad_norm: 2.7509 2023-02-16 23:55:41,276 - mmseg - INFO - Iter [83100/160000] lr: 2.884e-05, eta: 7:47:24, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2027, decode.acc_seg: 91.9341, aux.loss_ce: 0.1290, aux.acc_seg: 87.3736, loss: 0.3317, grad_norm: 3.3655 2023-02-16 23:55:59,138 - mmseg - INFO - Iter [83150/160000] lr: 2.882e-05, eta: 7:47:05, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1958, decode.acc_seg: 91.8709, aux.loss_ce: 0.1226, aux.acc_seg: 87.5120, loss: 0.3184, grad_norm: 3.1513 2023-02-16 23:56:17,015 - mmseg - INFO - Iter [83200/160000] lr: 2.880e-05, eta: 7:46:47, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2018, decode.acc_seg: 91.9466, aux.loss_ce: 0.1276, aux.acc_seg: 87.4987, loss: 0.3294, grad_norm: 3.3458 2023-02-16 23:56:34,833 - mmseg - INFO - Iter [83250/160000] lr: 2.878e-05, eta: 7:46:28, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2027, decode.acc_seg: 92.0010, aux.loss_ce: 0.1273, aux.acc_seg: 87.5948, loss: 0.3300, grad_norm: 3.0344 2023-02-16 23:56:53,034 - mmseg - INFO - Iter [83300/160000] lr: 2.876e-05, eta: 7:46:10, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1927, decode.acc_seg: 92.1151, aux.loss_ce: 0.1251, aux.acc_seg: 87.3990, loss: 0.3178, grad_norm: 3.3429 2023-02-16 23:57:11,120 - mmseg - INFO - Iter [83350/160000] lr: 2.874e-05, eta: 7:45:52, time: 0.362, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2016, decode.acc_seg: 91.6472, aux.loss_ce: 0.1263, aux.acc_seg: 87.3097, loss: 0.3279, grad_norm: 3.1666 2023-02-16 23:57:31,447 - mmseg - INFO - Iter [83400/160000] lr: 2.873e-05, eta: 7:45:35, time: 0.407, data_time: 0.052, memory: 16600, decode.loss_ce: 0.1983, decode.acc_seg: 92.0674, aux.loss_ce: 0.1245, aux.acc_seg: 87.4213, loss: 0.3228, grad_norm: 2.7085 2023-02-16 23:57:49,278 - mmseg - INFO - Iter [83450/160000] lr: 2.871e-05, eta: 7:45:17, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2086, decode.acc_seg: 91.5210, aux.loss_ce: 0.1287, aux.acc_seg: 87.0766, loss: 0.3373, grad_norm: 3.6609 2023-02-16 23:58:07,205 - mmseg - INFO - Iter [83500/160000] lr: 2.869e-05, eta: 7:44:58, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1932, decode.acc_seg: 92.1724, aux.loss_ce: 0.1234, aux.acc_seg: 87.4627, loss: 0.3166, grad_norm: 2.9983 2023-02-16 23:58:25,207 - mmseg - INFO - Iter [83550/160000] lr: 2.867e-05, eta: 7:44:40, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2064, decode.acc_seg: 91.8283, aux.loss_ce: 0.1299, aux.acc_seg: 87.1312, loss: 0.3363, grad_norm: 3.7038 2023-02-16 23:58:43,286 - mmseg - INFO - Iter [83600/160000] lr: 2.865e-05, eta: 7:44:21, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.1869, decode.acc_seg: 92.4160, aux.loss_ce: 0.1197, aux.acc_seg: 87.9499, loss: 0.3066, grad_norm: 2.8563 2023-02-16 23:59:01,165 - mmseg - INFO - Iter [83650/160000] lr: 2.863e-05, eta: 7:44:03, time: 0.358, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1931, decode.acc_seg: 92.0185, aux.loss_ce: 0.1265, aux.acc_seg: 87.2783, loss: 0.3195, grad_norm: 2.7785 2023-02-16 23:59:19,204 - mmseg - INFO - Iter [83700/160000] lr: 2.861e-05, eta: 7:43:44, time: 0.361, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1987, decode.acc_seg: 91.7905, aux.loss_ce: 0.1234, aux.acc_seg: 87.3996, loss: 0.3221, grad_norm: 3.1974 2023-02-16 23:59:37,041 - mmseg - INFO - Iter [83750/160000] lr: 2.859e-05, eta: 7:43:26, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1991, decode.acc_seg: 92.0579, aux.loss_ce: 0.1255, aux.acc_seg: 87.4283, loss: 0.3246, grad_norm: 3.1859 2023-02-16 23:59:55,102 - mmseg - INFO - Iter [83800/160000] lr: 2.858e-05, eta: 7:43:07, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2106, decode.acc_seg: 91.5430, aux.loss_ce: 0.1320, aux.acc_seg: 86.8940, loss: 0.3426, grad_norm: 3.7399 2023-02-17 00:00:13,275 - mmseg - INFO - Iter [83850/160000] lr: 2.856e-05, eta: 7:42:49, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.1939, decode.acc_seg: 92.3457, aux.loss_ce: 0.1261, aux.acc_seg: 87.5747, loss: 0.3200, grad_norm: 3.1419 2023-02-17 00:00:31,373 - mmseg - INFO - Iter [83900/160000] lr: 2.854e-05, eta: 7:42:31, time: 0.362, data_time: 0.007, memory: 16600, decode.loss_ce: 0.1960, decode.acc_seg: 92.1209, aux.loss_ce: 0.1226, aux.acc_seg: 87.6818, loss: 0.3187, grad_norm: 2.8090 2023-02-17 00:00:49,604 - mmseg - INFO - Iter [83950/160000] lr: 2.852e-05, eta: 7:42:12, time: 0.365, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2108, decode.acc_seg: 91.7030, aux.loss_ce: 0.1230, aux.acc_seg: 87.9478, loss: 0.3338, grad_norm: 3.4462 2023-02-17 00:01:07,497 - mmseg - INFO - Saving checkpoint at 84000 iterations 2023-02-17 00:01:08,571 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 00:01:08,572 - mmseg - INFO - Iter [84000/160000] lr: 2.850e-05, eta: 7:41:55, time: 0.379, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2056, decode.acc_seg: 91.7447, aux.loss_ce: 0.1282, aux.acc_seg: 87.1764, loss: 0.3337, grad_norm: 2.9508 2023-02-17 00:01:26,553 - mmseg - INFO - Iter [84050/160000] lr: 2.848e-05, eta: 7:41:36, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1980, decode.acc_seg: 91.9176, aux.loss_ce: 0.1229, aux.acc_seg: 87.5607, loss: 0.3209, grad_norm: 3.0739 2023-02-17 00:01:44,368 - mmseg - INFO - Iter [84100/160000] lr: 2.846e-05, eta: 7:41:18, time: 0.356, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2011, decode.acc_seg: 92.0340, aux.loss_ce: 0.1248, aux.acc_seg: 87.6737, loss: 0.3258, grad_norm: 2.9086 2023-02-17 00:02:02,320 - mmseg - INFO - Iter [84150/160000] lr: 2.844e-05, eta: 7:40:59, time: 0.359, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2118, decode.acc_seg: 91.5629, aux.loss_ce: 0.1326, aux.acc_seg: 86.9242, loss: 0.3444, grad_norm: 3.0867 2023-02-17 00:02:20,257 - mmseg - INFO - Iter [84200/160000] lr: 2.843e-05, eta: 7:40:41, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2037, decode.acc_seg: 91.6822, aux.loss_ce: 0.1294, aux.acc_seg: 87.2025, loss: 0.3330, grad_norm: 3.1093 2023-02-17 00:02:38,394 - mmseg - INFO - Iter [84250/160000] lr: 2.841e-05, eta: 7:40:22, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2127, decode.acc_seg: 91.4682, aux.loss_ce: 0.1343, aux.acc_seg: 86.8838, loss: 0.3470, grad_norm: 3.0560 2023-02-17 00:02:56,535 - mmseg - INFO - Iter [84300/160000] lr: 2.839e-05, eta: 7:40:04, time: 0.363, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2074, decode.acc_seg: 91.9060, aux.loss_ce: 0.1292, aux.acc_seg: 87.4384, loss: 0.3365, grad_norm: 3.4256 2023-02-17 00:03:14,359 - mmseg - INFO - Iter [84350/160000] lr: 2.837e-05, eta: 7:39:46, time: 0.356, data_time: 0.007, memory: 16600, decode.loss_ce: 0.1957, decode.acc_seg: 92.0814, aux.loss_ce: 0.1218, aux.acc_seg: 87.9160, loss: 0.3175, grad_norm: 2.9218 2023-02-17 00:03:32,571 - mmseg - INFO - Iter [84400/160000] lr: 2.835e-05, eta: 7:39:27, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2125, decode.acc_seg: 91.4385, aux.loss_ce: 0.1317, aux.acc_seg: 86.9263, loss: 0.3442, grad_norm: 3.4442 2023-02-17 00:03:50,615 - mmseg - INFO - Iter [84450/160000] lr: 2.833e-05, eta: 7:39:09, time: 0.361, data_time: 0.007, memory: 16600, decode.loss_ce: 0.1971, decode.acc_seg: 92.0437, aux.loss_ce: 0.1267, aux.acc_seg: 87.5695, loss: 0.3238, grad_norm: 3.5858 2023-02-17 00:04:08,597 - mmseg - INFO - Iter [84500/160000] lr: 2.831e-05, eta: 7:38:50, time: 0.360, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2032, decode.acc_seg: 91.7913, aux.loss_ce: 0.1312, aux.acc_seg: 86.8270, loss: 0.3345, grad_norm: 3.4119 2023-02-17 00:04:26,560 - mmseg - INFO - Iter [84550/160000] lr: 2.829e-05, eta: 7:38:32, time: 0.359, data_time: 0.006, memory: 16600, decode.loss_ce: 0.2053, decode.acc_seg: 91.9925, aux.loss_ce: 0.1304, aux.acc_seg: 87.4292, loss: 0.3356, grad_norm: 3.6580 2023-02-17 00:04:44,914 - mmseg - INFO - Iter [84600/160000] lr: 2.828e-05, eta: 7:38:14, time: 0.367, data_time: 0.007, memory: 16600, decode.loss_ce: 0.2083, decode.acc_seg: 91.7863, aux.loss_ce: 0.1302, aux.acc_seg: 87.1584, loss: 0.3384, grad_norm: 3.3138 2023-02-17 00:05:05,258 - mmseg - INFO - Iter [84650/160000] lr: 2.826e-05, eta: 7:37:57, time: 0.407, data_time: 0.051, memory: 16600, decode.loss_ce: 0.1909, decode.acc_seg: 92.4097, aux.loss_ce: 0.1218, aux.acc_seg: 87.8228, loss: 0.3127, grad_norm: 2.9360 2023-02-17 00:05:23,095 - mmseg - INFO - Iter [84700/160000] lr: 2.824e-05, eta: 7:37:39, time: 0.357, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1916, decode.acc_seg: 92.1192, aux.loss_ce: 0.1201, aux.acc_seg: 87.7635, loss: 0.3117, grad_norm: 2.6599 2023-02-17 00:05:41,298 - mmseg - INFO - Iter [84750/160000] lr: 2.822e-05, eta: 7:37:21, time: 0.364, data_time: 0.006, memory: 16600, decode.loss_ce: 0.1882, decode.acc_seg: 92.4261, aux.loss_ce: 0.1168, aux.acc_seg: 88.2158, loss: 0.3050, grad_norm: 2.4760 2023-02-17 01:31:50,291 - mmseg - INFO - Multi-processing start method is `None` 2023-02-17 01:31:50,291 - mmseg - INFO - OpenCV num_threads is `112 2023-02-17 01:31:50,291 - mmseg - INFO - OMP num threads is 1 2023-02-17 01:31:50,348 - mmseg - INFO - Environment info: ------------------------------------------------------------ sys.platform: linux Python: 3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0] CUDA available: True GPU 0,1,2,3: A100-SXM-80GB CUDA_HOME: /usr/local/cuda NVCC: Build cuda_11.2.r11.2/compiler.29618528_0 GCC: gcc (GCC) 5.4.0 PyTorch: 1.9.0+cu111 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb) - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.1 - 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_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 - CuDNN 8.0.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, 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 -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-variable -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=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.10.0+cu111 OpenCV: 4.6.0 MMCV: 1.4.2 MMCV Compiler: GCC 7.3 MMCV CUDA Compiler: 11.1 MMSegmentation: 0.29.0+ ------------------------------------------------------------ 2023-02-17 01:31:50,348 - mmseg - INFO - Distributed training: True 2023-02-17 01:31:50,827 - mmseg - INFO - Config: dataset_type = 'ADE20KDataset' data_root = 'data/ade/ADEChallengeData2016' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) crop_size = (512, 512) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', reduce_zero_label=True), dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)), dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75), dict(type='RandomFlip', prob=0.5), dict(type='PhotoMetricDistortion'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_semantic_seg']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 512), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ] data = dict( samples_per_gpu=4, workers_per_gpu=4, train=dict( type='ADE20KDataset', data_root='data/ade/ADEChallengeData2016', img_dir='images/training', ann_dir='annotations/training', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', reduce_zero_label=True), dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)), dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75), dict(type='RandomFlip', prob=0.5), dict(type='PhotoMetricDistortion'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_semantic_seg']) ]), val=dict( type='ADE20KDataset', data_root='data/ade/ADEChallengeData2016', img_dir='images/validation', ann_dir='annotations/validation', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 512), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ]), test=dict( type='ADE20KDataset', data_root='data/ade/ADEChallengeData2016', img_dir='images/validation', ann_dir='annotations/validation', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 512), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='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)))) optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2)) 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=1000, max_keep_ckpts=1) evaluation = dict( interval=16000, metric='mIoU', pre_eval=True, save_best='mIoU') checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth' norm_cfg = dict(type='SyncBN', requires_grad=True) backbone_norm_cfg = dict(type='LN', requires_grad=True) model = dict( type='DiffSegV20', bit_scale=0.01, pretrained=None, backbone=dict( type='SwinTransformer', init_cfg=dict( type='Pretrained', checkpoint= 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth' ), pretrain_img_size=224, in_channels=3, embed_dims=96, patch_size=4, window_size=7, mlp_ratio=4, depths=[2, 2, 18, 2], num_heads=[3, 6, 12, 24], strides=(4, 2, 2, 2), out_indices=(0, 1, 2, 3), qkv_bias=True, qk_scale=None, patch_norm=True, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.3, use_abs_pos_embed=False, act_cfg=dict(type='GELU'), norm_cfg=dict(type='LN', requires_grad=True)), neck=[ dict( type='FPN', in_channels=[96, 192, 384, 768], out_channels=256, act_cfg=None, norm_cfg=dict(type='GN', num_groups=32), num_outs=4), dict( type='MultiStageMerging', in_channels=[256, 256, 256, 256], out_channels=256, kernel_size=1, norm_cfg=dict(type='GN', num_groups=32), act_cfg=None) ], auxiliary_head=dict( type='FCNHead', in_channels=256, in_index=0, channels=256, num_convs=1, concat_input=False, dropout_ratio=0.1, num_classes=150, norm_cfg=dict(type='SyncBN', requires_grad=True), align_corners=False, loss_decode=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), decode_head=dict( type='DeformableHeadWithTime', in_channels=[256], channels=256, in_index=[0], dropout_ratio=0.0, num_classes=150, norm_cfg=dict(type='SyncBN', requires_grad=True), align_corners=False, num_feature_levels=1, encoder=dict( type='DetrTransformerEncoder', num_layers=6, transformerlayers=dict( type='BaseTransformerLayer', use_time_mlp=True, attn_cfgs=dict( type='MultiScaleDeformableAttention', embed_dims=256, num_levels=1, num_heads=8, dropout=0.0), ffn_cfgs=dict( type='FFN', embed_dims=256, feedforward_channels=1024, ffn_drop=0.0, act_cfg=dict(type='GELU')), operation_order=('self_attn', 'norm', 'ffn', 'norm'))), positional_encoding=dict( type='SinePositionalEncoding', num_feats=128, normalize=True, offset=-0.5), loss_decode=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), train_cfg=dict(), test_cfg=dict(mode='whole')) work_dir = './work_dirs/diffseg_swin_s_4x4_512x512_160k_ade20k_v20' gpu_ids = range(0, 4) auto_resume = True 2023-02-17 01:31:55,212 - mmseg - INFO - Set random seed to 581460912, deterministic: True 2023-02-17 01:31:55,669 - mmseg - INFO - load checkpoint from http path: https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth 2023-02-17 01:31:55,866 - mmseg - WARNING - The model and loaded state dict do not match exactly unexpected key in source state_dict: norm.weight, norm.bias, stages.0.blocks.1.attn_mask, stages.1.blocks.1.attn_mask, stages.2.blocks.1.attn_mask, stages.2.blocks.3.attn_mask, stages.2.blocks.5.attn_mask, stages.2.blocks.7.attn_mask, stages.2.blocks.9.attn_mask, stages.2.blocks.11.attn_mask, stages.2.blocks.13.attn_mask, stages.2.blocks.15.attn_mask, stages.2.blocks.17.attn_mask missing keys in source state_dict: norm0.weight, norm0.bias, norm1.weight, norm1.bias, norm2.weight, norm2.bias, norm3.weight, norm3.bias 2023-02-17 01:31:55,895 - mmseg - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'} 2023-02-17 01:31:55,911 - mmseg - INFO - initialize MultiStageMerging with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'} 2023-02-17 01:31:55,961 - mmseg - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} Name of parameter - Initialization information backbone.patch_embed.projection.weight - torch.Size([96, 3, 4, 4]): Initialized by user-defined `init_weights` in SwinTransformer backbone.patch_embed.projection.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.patch_embed.norm.weight - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.patch_embed.norm.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.norm1.weight - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.norm1.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.attn.w_msa.relative_position_bias_table - torch.Size([169, 3]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.attn.w_msa.qkv.weight - torch.Size([288, 96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.attn.w_msa.qkv.bias - torch.Size([288]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.attn.w_msa.proj.weight - torch.Size([96, 96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.attn.w_msa.proj.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.norm2.weight - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.norm2.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.ffn.layers.0.0.weight - torch.Size([384, 96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.ffn.layers.0.0.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.ffn.layers.1.weight - torch.Size([96, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.0.ffn.layers.1.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.norm1.weight - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.norm1.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.attn.w_msa.relative_position_bias_table - torch.Size([169, 3]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.attn.w_msa.qkv.weight - torch.Size([288, 96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.attn.w_msa.qkv.bias - torch.Size([288]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.attn.w_msa.proj.weight - torch.Size([96, 96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.attn.w_msa.proj.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.norm2.weight - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.norm2.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.ffn.layers.0.0.weight - torch.Size([384, 96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.ffn.layers.0.0.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.ffn.layers.1.weight - torch.Size([96, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.blocks.1.ffn.layers.1.bias - torch.Size([96]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.downsample.norm.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.downsample.norm.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.0.downsample.reduction.weight - torch.Size([192, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.norm1.weight - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.norm1.bias - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.attn.w_msa.relative_position_bias_table - torch.Size([169, 6]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.attn.w_msa.qkv.weight - torch.Size([576, 192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.attn.w_msa.qkv.bias - torch.Size([576]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.attn.w_msa.proj.weight - torch.Size([192, 192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.attn.w_msa.proj.bias - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.norm2.weight - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.norm2.bias - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.ffn.layers.0.0.weight - torch.Size([768, 192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.ffn.layers.0.0.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.ffn.layers.1.weight - torch.Size([192, 768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.0.ffn.layers.1.bias - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.norm1.weight - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.norm1.bias - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.attn.w_msa.relative_position_bias_table - torch.Size([169, 6]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.attn.w_msa.qkv.weight - torch.Size([576, 192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.attn.w_msa.qkv.bias - torch.Size([576]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.attn.w_msa.proj.weight - torch.Size([192, 192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.attn.w_msa.proj.bias - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.norm2.weight - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.norm2.bias - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.ffn.layers.0.0.weight - torch.Size([768, 192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.ffn.layers.0.0.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.ffn.layers.1.weight - torch.Size([192, 768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.blocks.1.ffn.layers.1.bias - torch.Size([192]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.downsample.norm.weight - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.downsample.norm.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.1.downsample.reduction.weight - torch.Size([384, 768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.0.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.1.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.2.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.3.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.4.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.5.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.6.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.7.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.8.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.9.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.10.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.11.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.12.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.13.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.14.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.15.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.16.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.norm1.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.norm1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.attn.w_msa.qkv.weight - torch.Size([1152, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.attn.w_msa.qkv.bias - torch.Size([1152]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.attn.w_msa.proj.weight - torch.Size([384, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.attn.w_msa.proj.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.norm2.weight - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.norm2.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.ffn.layers.0.0.weight - torch.Size([1536, 384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.ffn.layers.0.0.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.ffn.layers.1.weight - torch.Size([384, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.blocks.17.ffn.layers.1.bias - torch.Size([384]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.downsample.norm.weight - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.downsample.norm.bias - torch.Size([1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.2.downsample.reduction.weight - torch.Size([768, 1536]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.norm1.weight - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.norm1.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.attn.w_msa.relative_position_bias_table - torch.Size([169, 24]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.attn.w_msa.qkv.weight - torch.Size([2304, 768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.attn.w_msa.qkv.bias - torch.Size([2304]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.attn.w_msa.proj.weight - torch.Size([768, 768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.attn.w_msa.proj.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.norm2.weight - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.norm2.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.ffn.layers.0.0.weight - torch.Size([3072, 768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.ffn.layers.0.0.bias - torch.Size([3072]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.ffn.layers.1.weight - torch.Size([768, 3072]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.0.ffn.layers.1.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.norm1.weight - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.norm1.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.attn.w_msa.relative_position_bias_table - torch.Size([169, 24]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.attn.w_msa.qkv.weight - torch.Size([2304, 768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.attn.w_msa.qkv.bias - torch.Size([2304]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.attn.w_msa.proj.weight - torch.Size([768, 768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.attn.w_msa.proj.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.norm2.weight - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.norm2.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.ffn.layers.0.0.weight - torch.Size([3072, 768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.ffn.layers.0.0.bias - torch.Size([3072]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.ffn.layers.1.weight - torch.Size([768, 3072]): Initialized by user-defined `init_weights` in SwinTransformer backbone.stages.3.blocks.1.ffn.layers.1.bias - torch.Size([768]): Initialized by user-defined `init_weights` in SwinTransformer backbone.norm0.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of DiffSegV20 backbone.norm0.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of DiffSegV20 backbone.norm1.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DiffSegV20 backbone.norm1.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DiffSegV20 backbone.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of DiffSegV20 backbone.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of DiffSegV20 backbone.norm3.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of DiffSegV20 backbone.norm3.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.lateral_convs.0.conv.weight - torch.Size([256, 96, 1, 1]): XavierInit: gain=1, distribution=uniform, bias=0 neck.0.lateral_convs.0.gn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.lateral_convs.0.gn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.lateral_convs.1.conv.weight - torch.Size([256, 192, 1, 1]): XavierInit: gain=1, distribution=uniform, bias=0 neck.0.lateral_convs.1.gn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.lateral_convs.1.gn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.lateral_convs.2.conv.weight - torch.Size([256, 384, 1, 1]): XavierInit: gain=1, distribution=uniform, bias=0 neck.0.lateral_convs.2.gn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.lateral_convs.2.gn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.lateral_convs.3.conv.weight - torch.Size([256, 768, 1, 1]): XavierInit: gain=1, distribution=uniform, bias=0 neck.0.lateral_convs.3.gn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.lateral_convs.3.gn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.fpn_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): XavierInit: gain=1, distribution=uniform, bias=0 neck.0.fpn_convs.0.gn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.fpn_convs.0.gn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.fpn_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): XavierInit: gain=1, distribution=uniform, bias=0 neck.0.fpn_convs.1.gn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.fpn_convs.1.gn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.fpn_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): XavierInit: gain=1, distribution=uniform, bias=0 neck.0.fpn_convs.2.gn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.fpn_convs.2.gn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.fpn_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): XavierInit: gain=1, distribution=uniform, bias=0 neck.0.fpn_convs.3.gn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.0.fpn_convs.3.gn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.1.down.conv.weight - torch.Size([256, 1024, 1, 1]): Initialized by user-defined `init_weights` in ConvModule neck.1.down.gn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 neck.1.down.gn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.conv_seg.weight - torch.Size([150, 256, 1, 1]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.conv_seg.bias - torch.Size([150]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.attentions.0.sampling_offsets.weight - torch.Size([64, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.attentions.0.sampling_offsets.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.attentions.0.attention_weights.weight - torch.Size([32, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.attentions.0.attention_weights.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.attentions.0.value_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.0.attentions.0.value_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.attentions.0.output_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.0.attentions.0.output_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.time_mlp.1.weight - torch.Size([512, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.0.time_mlp.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.ffns.0.layers.0.0.weight - torch.Size([1024, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.0.ffns.0.layers.0.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.ffns.0.layers.1.weight - torch.Size([256, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.0.ffns.0.layers.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.norms.0.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.norms.0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.norms.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.0.norms.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.attentions.0.sampling_offsets.weight - torch.Size([64, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.attentions.0.sampling_offsets.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.attentions.0.attention_weights.weight - torch.Size([32, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.attentions.0.attention_weights.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.attentions.0.value_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.1.attentions.0.value_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.attentions.0.output_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.1.attentions.0.output_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.time_mlp.1.weight - torch.Size([512, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.1.time_mlp.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.ffns.0.layers.0.0.weight - torch.Size([1024, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.1.ffns.0.layers.0.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.ffns.0.layers.1.weight - torch.Size([256, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.1.ffns.0.layers.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.norms.0.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.norms.0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.norms.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.1.norms.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.attentions.0.sampling_offsets.weight - torch.Size([64, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.attentions.0.sampling_offsets.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.attentions.0.attention_weights.weight - torch.Size([32, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.attentions.0.attention_weights.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.attentions.0.value_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.2.attentions.0.value_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.attentions.0.output_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.2.attentions.0.output_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.time_mlp.1.weight - torch.Size([512, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.2.time_mlp.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.ffns.0.layers.0.0.weight - torch.Size([1024, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.2.ffns.0.layers.0.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.ffns.0.layers.1.weight - torch.Size([256, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.2.ffns.0.layers.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.norms.0.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.norms.0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.norms.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.2.norms.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.attentions.0.sampling_offsets.weight - torch.Size([64, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.attentions.0.sampling_offsets.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.attentions.0.attention_weights.weight - torch.Size([32, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.attentions.0.attention_weights.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.attentions.0.value_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.3.attentions.0.value_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.attentions.0.output_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.3.attentions.0.output_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.time_mlp.1.weight - torch.Size([512, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.3.time_mlp.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.ffns.0.layers.0.0.weight - torch.Size([1024, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.3.ffns.0.layers.0.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.ffns.0.layers.1.weight - torch.Size([256, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.3.ffns.0.layers.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.norms.0.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.norms.0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.norms.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.3.norms.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.attentions.0.sampling_offsets.weight - torch.Size([64, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.attentions.0.sampling_offsets.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.attentions.0.attention_weights.weight - torch.Size([32, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.attentions.0.attention_weights.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.attentions.0.value_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.4.attentions.0.value_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.attentions.0.output_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.4.attentions.0.output_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.time_mlp.1.weight - torch.Size([512, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.4.time_mlp.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.ffns.0.layers.0.0.weight - torch.Size([1024, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.4.ffns.0.layers.0.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.ffns.0.layers.1.weight - torch.Size([256, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.4.ffns.0.layers.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.norms.0.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.norms.0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.norms.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.4.norms.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.attentions.0.sampling_offsets.weight - torch.Size([64, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.attentions.0.sampling_offsets.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.attentions.0.attention_weights.weight - torch.Size([32, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.attentions.0.attention_weights.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.attentions.0.value_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.5.attentions.0.value_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.attentions.0.output_proj.weight - torch.Size([256, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.5.attentions.0.output_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.time_mlp.1.weight - torch.Size([512, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.5.time_mlp.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.ffns.0.layers.0.0.weight - torch.Size([1024, 256]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.5.ffns.0.layers.0.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.ffns.0.layers.1.weight - torch.Size([256, 1024]): Initialized by user-defined `init_weights` in DeformableHeadWithTime decode_head.encoder.layers.5.ffns.0.layers.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.norms.0.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.norms.0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.norms.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 decode_head.encoder.layers.5.norms.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 auxiliary_head.conv_seg.weight - torch.Size([150, 256, 1, 1]): NormalInit: mean=0, std=0.01, bias=0 auxiliary_head.conv_seg.bias - torch.Size([150]): NormalInit: mean=0, std=0.01, bias=0 auxiliary_head.convs.0.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of DiffSegV20 auxiliary_head.convs.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 auxiliary_head.convs.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 embedding_table.weight - torch.Size([151, 256]): The value is the same before and after calling `init_weights` of DiffSegV20 transform.conv.weight - torch.Size([256, 512, 1, 1]): Initialized by user-defined `init_weights` in ConvModule transform.conv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DiffSegV20 time_mlp.0.weights - torch.Size([8]): The value is the same before and after calling `init_weights` of DiffSegV20 time_mlp.1.weight - torch.Size([1024, 17]): The value is the same before and after calling `init_weights` of DiffSegV20 time_mlp.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of DiffSegV20 time_mlp.3.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of DiffSegV20 time_mlp.3.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of DiffSegV20 2023-02-17 01:31:55,968 - mmseg - INFO - DiffSegV20( (backbone): SwinTransformer( (patch_embed): PatchEmbed( (adap_padding): AdaptivePadding() (projection): Conv2d(3, 96, kernel_size=(4, 4), stride=(4, 4)) (norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True) ) (drop_after_pos): Dropout(p=0.0, inplace=False) (stages): ModuleList( (0): SwinBlockSequence( (blocks): ModuleList( (0): SwinBlock( (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=96, out_features=288, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=96, out_features=96, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=96, out_features=384, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=384, out_features=96, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (1): SwinBlock( (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=96, out_features=288, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=96, out_features=96, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=96, out_features=384, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=384, out_features=96, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) ) (downsample): PatchMerging( (adap_padding): AdaptivePadding() (sampler): Unfold(kernel_size=(2, 2), dilation=(1, 1), padding=(0, 0), stride=(2, 2)) (norm): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (reduction): Linear(in_features=384, out_features=192, bias=False) ) ) (1): SwinBlockSequence( (blocks): ModuleList( (0): SwinBlock( (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=192, out_features=576, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=192, out_features=192, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=192, out_features=768, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=768, out_features=192, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (1): SwinBlock( (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=192, out_features=576, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=192, out_features=192, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=192, out_features=768, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=768, out_features=192, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) ) (downsample): PatchMerging( (adap_padding): AdaptivePadding() (sampler): Unfold(kernel_size=(2, 2), dilation=(1, 1), padding=(0, 0), stride=(2, 2)) (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (reduction): Linear(in_features=768, out_features=384, bias=False) ) ) (2): SwinBlockSequence( (blocks): ModuleList( (0): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (1): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (2): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (3): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (4): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (5): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (6): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (7): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (8): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (9): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (10): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (11): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (12): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (13): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (14): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (15): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (16): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (17): SwinBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=384, out_features=1536, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1536, out_features=384, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) ) (downsample): PatchMerging( (adap_padding): AdaptivePadding() (sampler): Unfold(kernel_size=(2, 2), dilation=(1, 1), padding=(0, 0), stride=(2, 2)) (norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True) (reduction): Linear(in_features=1536, out_features=768, bias=False) ) ) (3): SwinBlockSequence( (blocks): ModuleList( (0): SwinBlock( (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=768, out_features=2304, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=768, out_features=768, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=768, out_features=3072, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=3072, out_features=768, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) (1): SwinBlock( (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (attn): ShiftWindowMSA( (w_msa): WindowMSA( (qkv): Linear(in_features=768, out_features=2304, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=768, out_features=768, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop): DropPath() ) (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (ffn): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=768, out_features=3072, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=3072, out_features=768, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): DropPath() ) ) ) ) ) (norm0): LayerNorm((96,), eps=1e-05, elementwise_affine=True) (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (norm3): LayerNorm((768,), eps=1e-05, elementwise_affine=True) ) init_cfg={'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth'} (neck): Sequential( (0): FPN( (lateral_convs): ModuleList( (0): ConvModule( (conv): Conv2d(96, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (gn): GroupNorm(32, 256, eps=1e-05, affine=True) ) (1): ConvModule( (conv): Conv2d(192, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (gn): GroupNorm(32, 256, eps=1e-05, affine=True) ) (2): ConvModule( (conv): Conv2d(384, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (gn): GroupNorm(32, 256, eps=1e-05, affine=True) ) (3): ConvModule( (conv): Conv2d(768, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (gn): GroupNorm(32, 256, eps=1e-05, affine=True) ) ) (fpn_convs): ModuleList( (0): ConvModule( (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (gn): GroupNorm(32, 256, eps=1e-05, affine=True) ) (1): ConvModule( (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (gn): GroupNorm(32, 256, eps=1e-05, affine=True) ) (2): ConvModule( (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (gn): GroupNorm(32, 256, eps=1e-05, affine=True) ) (3): ConvModule( (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (gn): GroupNorm(32, 256, eps=1e-05, affine=True) ) ) ) init_cfg={'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'} (1): MultiStageMerging( (down): ConvModule( (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (gn): GroupNorm(32, 256, eps=1e-05, affine=True) ) ) init_cfg={'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'} ) (decode_head): DeformableHeadWithTime( input_transform=multiple_select, ignore_index=255, align_corners=False (loss_decode): CrossEntropyLoss(avg_non_ignore=False) (conv_seg): Conv2d(256, 150, kernel_size=(1, 1), stride=(1, 1)) (encoder): DetrTransformerEncoder( (layers): ModuleList( (0): BaseTransformerLayer( (attentions): ModuleList( (0): MultiScaleDeformableAttention( (dropout): Dropout(p=0.0, inplace=False) (sampling_offsets): Linear(in_features=256, out_features=64, bias=True) (attention_weights): Linear(in_features=256, out_features=32, bias=True) (value_proj): Linear(in_features=256, out_features=256, bias=True) (output_proj): Linear(in_features=256, out_features=256, bias=True) ) ) (time_mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (ffns): ModuleList( (0): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=256, out_features=1024, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1024, out_features=256, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) ) ) (1): BaseTransformerLayer( (attentions): ModuleList( (0): MultiScaleDeformableAttention( (dropout): Dropout(p=0.0, inplace=False) (sampling_offsets): Linear(in_features=256, out_features=64, bias=True) (attention_weights): Linear(in_features=256, out_features=32, bias=True) (value_proj): Linear(in_features=256, out_features=256, bias=True) (output_proj): Linear(in_features=256, out_features=256, bias=True) ) ) (time_mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (ffns): ModuleList( (0): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=256, out_features=1024, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1024, out_features=256, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) ) ) (2): BaseTransformerLayer( (attentions): ModuleList( (0): MultiScaleDeformableAttention( (dropout): Dropout(p=0.0, inplace=False) (sampling_offsets): Linear(in_features=256, out_features=64, bias=True) (attention_weights): Linear(in_features=256, out_features=32, bias=True) (value_proj): Linear(in_features=256, out_features=256, bias=True) (output_proj): Linear(in_features=256, out_features=256, bias=True) ) ) (time_mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (ffns): ModuleList( (0): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=256, out_features=1024, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1024, out_features=256, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) ) ) (3): BaseTransformerLayer( (attentions): ModuleList( (0): MultiScaleDeformableAttention( (dropout): Dropout(p=0.0, inplace=False) (sampling_offsets): Linear(in_features=256, out_features=64, bias=True) (attention_weights): Linear(in_features=256, out_features=32, bias=True) (value_proj): Linear(in_features=256, out_features=256, bias=True) (output_proj): Linear(in_features=256, out_features=256, bias=True) ) ) (time_mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (ffns): ModuleList( (0): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=256, out_features=1024, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1024, out_features=256, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) ) ) (4): BaseTransformerLayer( (attentions): ModuleList( (0): MultiScaleDeformableAttention( (dropout): Dropout(p=0.0, inplace=False) (sampling_offsets): Linear(in_features=256, out_features=64, bias=True) (attention_weights): Linear(in_features=256, out_features=32, bias=True) (value_proj): Linear(in_features=256, out_features=256, bias=True) (output_proj): Linear(in_features=256, out_features=256, bias=True) ) ) (time_mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (ffns): ModuleList( (0): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=256, out_features=1024, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1024, out_features=256, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) ) ) (5): BaseTransformerLayer( (attentions): ModuleList( (0): MultiScaleDeformableAttention( (dropout): Dropout(p=0.0, inplace=False) (sampling_offsets): Linear(in_features=256, out_features=64, bias=True) (attention_weights): Linear(in_features=256, out_features=32, bias=True) (value_proj): Linear(in_features=256, out_features=256, bias=True) (output_proj): Linear(in_features=256, out_features=256, bias=True) ) ) (time_mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (ffns): ModuleList( (0): FFN( (activate): GELU() (layers): Sequential( (0): Sequential( (0): Linear(in_features=256, out_features=1024, bias=True) (1): GELU() (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=1024, out_features=256, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) ) ) ) ) (positional_encoding): SinePositionalEncoding(num_feats=128, temperature=10000, normalize=True, scale=6.283185307179586, eps=1e-06) ) init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} (auxiliary_head): FCNHead( input_transform=None, ignore_index=255, align_corners=False (loss_decode): CrossEntropyLoss(avg_non_ignore=False) (conv_seg): Conv2d(256, 150, kernel_size=(1, 1), stride=(1, 1)) (dropout): Dropout2d(p=0.1, inplace=False) (convs): Sequential( (0): ConvModule( (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) ) init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} (embedding_table): Embedding(151, 256) (transform): ConvModule( (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) ) (time_mlp): Sequential( (0): LearnedSinusoidalPosEmb() (1): Linear(in_features=17, out_features=1024, bias=True) (2): GELU() (3): Linear(in_features=1024, out_features=1024, bias=True) ) ) 2023-02-17 01:31:55,979 - mmseg - INFO - Model size:216.93 2023-02-17 01:31:56,296 - mmseg - INFO - Loaded 20210 images 2023-02-17 01:31:57,094 - mmseg - INFO - Loaded 2000 images 2023-02-17 01:31:57,095 - mmseg - INFO - load checkpoint from local path: ./work_dirs/diffseg_swin_s_4x4_512x512_160k_ade20k_v20/latest.pth 2023-02-17 01:31:57,561 - mmseg - INFO - resumed from epoch: 67, iter 83999 2023-02-17 01:31:57,562 - 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 -------------------- 2023-02-17 01:31:57,563 - mmseg - INFO - workflow: [('train', 1)], max: 160000 iters 2023-02-17 01:32:12,271 - mmseg - INFO - Saving checkpoint at 84000 iterations 2023-02-17 01:32:13,712 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 01:32:13,712 - mmseg - INFO - Iter [84000/160000] lr: 2.850e-05, eta: 131 days, 16:30:39, time: 2.994, data_time: 0.135, memory: 16364, decode.loss_ce: 0.2311, decode.acc_seg: 91.3864, aux.loss_ce: 0.1342, aux.acc_seg: 87.3523, loss: 0.3653, grad_norm: 1.9950 2023-02-17 01:32:31,798 - mmseg - INFO - Iter [84050/160000] lr: 2.848e-05, eta: 2 days, 21:24:12, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2009, decode.acc_seg: 92.0626, aux.loss_ce: 0.1255, aux.acc_seg: 87.5868, loss: 0.3264, grad_norm: 2.6448 2023-02-17 01:32:50,133 - mmseg - INFO - Iter [84100/160000] lr: 2.846e-05, eta: 1 day, 14:51:09, time: 0.367, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1943, decode.acc_seg: 92.0012, aux.loss_ce: 0.1188, aux.acc_seg: 87.9070, loss: 0.3130, grad_norm: 3.2032 2023-02-17 01:33:08,275 - mmseg - INFO - Iter [84150/160000] lr: 2.844e-05, eta: 1 day, 4:30:07, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2092, decode.acc_seg: 91.6079, aux.loss_ce: 0.1330, aux.acc_seg: 86.6547, loss: 0.3422, grad_norm: 3.5814 2023-02-17 01:33:26,351 - mmseg - INFO - Iter [84200/160000] lr: 2.843e-05, eta: 23:17:24, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1876, decode.acc_seg: 92.7022, aux.loss_ce: 0.1206, aux.acc_seg: 88.3132, loss: 0.3082, grad_norm: 2.9552 2023-02-17 01:33:44,381 - mmseg - INFO - Iter [84250/160000] lr: 2.841e-05, eta: 20:09:03, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1852, decode.acc_seg: 92.5840, aux.loss_ce: 0.1208, aux.acc_seg: 88.1663, loss: 0.3060, grad_norm: 2.8081 2023-02-17 01:34:02,286 - mmseg - INFO - Iter [84300/160000] lr: 2.839e-05, eta: 18:02:35, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2162, decode.acc_seg: 91.2268, aux.loss_ce: 0.1297, aux.acc_seg: 86.9571, loss: 0.3459, grad_norm: 3.3063 2023-02-17 01:34:20,174 - mmseg - INFO - Iter [84350/160000] lr: 2.837e-05, eta: 16:32:00, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2032, decode.acc_seg: 91.8542, aux.loss_ce: 0.1279, aux.acc_seg: 87.3401, loss: 0.3312, grad_norm: 3.4690 2023-02-17 01:34:38,453 - mmseg - INFO - Iter [84400/160000] lr: 2.835e-05, eta: 15:25:10, time: 0.366, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1999, decode.acc_seg: 91.8516, aux.loss_ce: 0.1231, aux.acc_seg: 87.6829, loss: 0.3230, grad_norm: 3.2850 2023-02-17 01:34:56,521 - mmseg - INFO - Iter [84450/160000] lr: 2.833e-05, eta: 14:32:31, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1969, decode.acc_seg: 92.2219, aux.loss_ce: 0.1255, aux.acc_seg: 87.6937, loss: 0.3224, grad_norm: 2.9957 2023-02-17 01:35:14,538 - mmseg - INFO - Iter [84500/160000] lr: 2.831e-05, eta: 13:50:09, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1908, decode.acc_seg: 92.3181, aux.loss_ce: 0.1222, aux.acc_seg: 87.7436, loss: 0.3130, grad_norm: 2.8012 2023-02-17 01:35:32,786 - mmseg - INFO - Iter [84550/160000] lr: 2.829e-05, eta: 13:15:58, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2004, decode.acc_seg: 92.1487, aux.loss_ce: 0.1283, aux.acc_seg: 87.3520, loss: 0.3287, grad_norm: 3.0165 2023-02-17 01:35:50,708 - mmseg - INFO - Iter [84600/160000] lr: 2.828e-05, eta: 12:46:44, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1958, decode.acc_seg: 91.9862, aux.loss_ce: 0.1237, aux.acc_seg: 87.7084, loss: 0.3195, grad_norm: 2.7125 2023-02-17 01:36:08,506 - mmseg - INFO - Iter [84650/160000] lr: 2.826e-05, eta: 12:21:43, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2027, decode.acc_seg: 91.8835, aux.loss_ce: 0.1262, aux.acc_seg: 87.3728, loss: 0.3289, grad_norm: 3.6980 2023-02-17 01:36:26,387 - mmseg - INFO - Iter [84700/160000] lr: 2.824e-05, eta: 12:00:21, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1983, decode.acc_seg: 91.8242, aux.loss_ce: 0.1240, aux.acc_seg: 87.5551, loss: 0.3223, grad_norm: 2.8624 2023-02-17 01:36:44,788 - mmseg - INFO - Iter [84750/160000] lr: 2.822e-05, eta: 11:42:41, time: 0.368, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1916, decode.acc_seg: 92.1721, aux.loss_ce: 0.1234, aux.acc_seg: 87.5577, loss: 0.3150, grad_norm: 3.1178 2023-02-17 01:37:02,884 - mmseg - INFO - Iter [84800/160000] lr: 2.820e-05, eta: 11:26:42, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1972, decode.acc_seg: 91.9351, aux.loss_ce: 0.1201, aux.acc_seg: 87.6969, loss: 0.3173, grad_norm: 3.5900 2023-02-17 01:37:20,912 - mmseg - INFO - Iter [84850/160000] lr: 2.818e-05, eta: 11:12:26, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2212, decode.acc_seg: 91.1846, aux.loss_ce: 0.1357, aux.acc_seg: 86.7056, loss: 0.3569, grad_norm: 3.2750 2023-02-17 01:37:38,775 - mmseg - INFO - Iter [84900/160000] lr: 2.816e-05, eta: 10:59:32, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2051, decode.acc_seg: 92.0185, aux.loss_ce: 0.1326, aux.acc_seg: 87.0882, loss: 0.3377, grad_norm: 3.3720 2023-02-17 01:37:56,905 - mmseg - INFO - Iter [84950/160000] lr: 2.814e-05, eta: 10:48:17, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1962, decode.acc_seg: 91.9963, aux.loss_ce: 0.1245, aux.acc_seg: 87.4014, loss: 0.3207, grad_norm: 2.9951 2023-02-17 01:38:15,043 - mmseg - INFO - Saving checkpoint at 85000 iterations 2023-02-17 01:38:16,255 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 01:38:16,256 - mmseg - INFO - Iter [85000/160000] lr: 2.813e-05, eta: 10:39:39, time: 0.387, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2130, decode.acc_seg: 91.6451, aux.loss_ce: 0.1333, aux.acc_seg: 87.2326, loss: 0.3463, grad_norm: 3.5112 2023-02-17 01:38:34,107 - mmseg - INFO - Iter [85050/160000] lr: 2.811e-05, eta: 10:30:02, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2072, decode.acc_seg: 92.0566, aux.loss_ce: 0.1351, aux.acc_seg: 87.0533, loss: 0.3423, grad_norm: 3.9132 2023-02-17 01:38:52,025 - mmseg - INFO - Iter [85100/160000] lr: 2.809e-05, eta: 10:21:20, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1882, decode.acc_seg: 92.4977, aux.loss_ce: 0.1224, aux.acc_seg: 87.7893, loss: 0.3106, grad_norm: 2.8050 2023-02-17 01:39:09,968 - mmseg - INFO - Iter [85150/160000] lr: 2.807e-05, eta: 10:13:24, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2125, decode.acc_seg: 91.3752, aux.loss_ce: 0.1315, aux.acc_seg: 86.9595, loss: 0.3440, grad_norm: 3.0658 2023-02-17 01:39:27,788 - mmseg - INFO - Iter [85200/160000] lr: 2.805e-05, eta: 10:05:58, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1945, decode.acc_seg: 92.2251, aux.loss_ce: 0.1227, aux.acc_seg: 87.9825, loss: 0.3172, grad_norm: 3.2983 2023-02-17 01:39:46,043 - mmseg - INFO - Iter [85250/160000] lr: 2.803e-05, eta: 9:59:32, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1930, decode.acc_seg: 92.0766, aux.loss_ce: 0.1215, aux.acc_seg: 87.9197, loss: 0.3145, grad_norm: 3.1133 2023-02-17 01:40:06,224 - mmseg - INFO - Iter [85300/160000] lr: 2.801e-05, eta: 9:55:25, time: 0.404, data_time: 0.051, memory: 16596, decode.loss_ce: 0.1790, decode.acc_seg: 92.5255, aux.loss_ce: 0.1163, aux.acc_seg: 88.0146, loss: 0.2953, grad_norm: 2.9684 2023-02-17 01:40:24,146 - mmseg - INFO - Iter [85350/160000] lr: 2.799e-05, eta: 9:49:30, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2129, decode.acc_seg: 91.2893, aux.loss_ce: 0.1326, aux.acc_seg: 86.6491, loss: 0.3455, grad_norm: 3.5982 2023-02-17 01:40:41,884 - mmseg - INFO - Iter [85400/160000] lr: 2.798e-05, eta: 9:43:48, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1980, decode.acc_seg: 91.9151, aux.loss_ce: 0.1253, aux.acc_seg: 87.4205, loss: 0.3233, grad_norm: 3.7948 2023-02-17 01:40:59,853 - mmseg - INFO - Iter [85450/160000] lr: 2.796e-05, eta: 9:38:43, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1961, decode.acc_seg: 91.9947, aux.loss_ce: 0.1229, aux.acc_seg: 87.4955, loss: 0.3190, grad_norm: 3.2653 2023-02-17 01:41:17,880 - mmseg - INFO - Iter [85500/160000] lr: 2.794e-05, eta: 9:33:58, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1947, decode.acc_seg: 92.1668, aux.loss_ce: 0.1217, aux.acc_seg: 87.9801, loss: 0.3165, grad_norm: 3.3266 2023-02-17 01:41:36,087 - mmseg - INFO - Iter [85550/160000] lr: 2.792e-05, eta: 9:29:40, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1967, decode.acc_seg: 92.0718, aux.loss_ce: 0.1226, aux.acc_seg: 88.0069, loss: 0.3193, grad_norm: 2.9694 2023-02-17 01:41:54,293 - mmseg - INFO - Iter [85600/160000] lr: 2.790e-05, eta: 9:25:36, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1962, decode.acc_seg: 92.0501, aux.loss_ce: 0.1261, aux.acc_seg: 87.0443, loss: 0.3223, grad_norm: 3.2597 2023-02-17 01:42:12,139 - mmseg - INFO - Iter [85650/160000] lr: 2.788e-05, eta: 9:21:30, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1952, decode.acc_seg: 92.1196, aux.loss_ce: 0.1238, aux.acc_seg: 87.6524, loss: 0.3190, grad_norm: 3.1021 2023-02-17 01:42:30,205 - mmseg - INFO - Iter [85700/160000] lr: 2.786e-05, eta: 9:17:47, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2030, decode.acc_seg: 91.8318, aux.loss_ce: 0.1275, aux.acc_seg: 87.3982, loss: 0.3305, grad_norm: 3.1200 2023-02-17 01:42:48,342 - mmseg - INFO - Iter [85750/160000] lr: 2.784e-05, eta: 9:14:18, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2105, decode.acc_seg: 91.7689, aux.loss_ce: 0.1293, aux.acc_seg: 87.4898, loss: 0.3398, grad_norm: 3.2653 2023-02-17 01:43:06,524 - mmseg - INFO - Iter [85800/160000] lr: 2.783e-05, eta: 9:11:02, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1941, decode.acc_seg: 92.2454, aux.loss_ce: 0.1239, aux.acc_seg: 87.6593, loss: 0.3180, grad_norm: 2.8165 2023-02-17 01:43:24,658 - mmseg - INFO - Iter [85850/160000] lr: 2.781e-05, eta: 9:07:52, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2072, decode.acc_seg: 91.7355, aux.loss_ce: 0.1258, aux.acc_seg: 87.5631, loss: 0.3330, grad_norm: 3.1103 2023-02-17 01:43:42,902 - mmseg - INFO - Iter [85900/160000] lr: 2.779e-05, eta: 9:04:59, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1999, decode.acc_seg: 91.9678, aux.loss_ce: 0.1244, aux.acc_seg: 87.5348, loss: 0.3243, grad_norm: 2.6858 2023-02-17 01:44:00,889 - mmseg - INFO - Iter [85950/160000] lr: 2.777e-05, eta: 9:02:01, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2171, decode.acc_seg: 91.5603, aux.loss_ce: 0.1353, aux.acc_seg: 87.0862, loss: 0.3524, grad_norm: 3.2812 2023-02-17 01:44:19,064 - mmseg - INFO - Saving checkpoint at 86000 iterations 2023-02-17 01:44:20,192 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 01:44:20,192 - mmseg - INFO - Iter [86000/160000] lr: 2.775e-05, eta: 9:00:02, time: 0.386, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2014, decode.acc_seg: 91.9100, aux.loss_ce: 0.1262, aux.acc_seg: 87.4041, loss: 0.3275, grad_norm: 2.9609 2023-02-17 01:44:38,180 - mmseg - INFO - Iter [86050/160000] lr: 2.773e-05, eta: 8:57:19, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1982, decode.acc_seg: 91.9702, aux.loss_ce: 0.1210, aux.acc_seg: 87.8150, loss: 0.3192, grad_norm: 2.8774 2023-02-17 01:44:56,255 - mmseg - INFO - Iter [86100/160000] lr: 2.771e-05, eta: 8:54:46, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1981, decode.acc_seg: 91.9016, aux.loss_ce: 0.1256, aux.acc_seg: 87.2545, loss: 0.3237, grad_norm: 3.0786 2023-02-17 01:45:14,335 - mmseg - INFO - Iter [86150/160000] lr: 2.769e-05, eta: 8:52:19, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1973, decode.acc_seg: 92.0918, aux.loss_ce: 0.1253, aux.acc_seg: 87.3998, loss: 0.3226, grad_norm: 2.9682 2023-02-17 01:45:32,351 - mmseg - INFO - Iter [86200/160000] lr: 2.768e-05, eta: 8:49:57, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1969, decode.acc_seg: 92.1864, aux.loss_ce: 0.1214, aux.acc_seg: 87.9674, loss: 0.3183, grad_norm: 3.0188 2023-02-17 01:45:50,219 - mmseg - INFO - Iter [86250/160000] lr: 2.766e-05, eta: 8:47:35, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1858, decode.acc_seg: 92.4745, aux.loss_ce: 0.1186, aux.acc_seg: 88.0366, loss: 0.3044, grad_norm: 3.0285 2023-02-17 01:46:08,105 - mmseg - INFO - Iter [86300/160000] lr: 2.764e-05, eta: 8:45:19, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1852, decode.acc_seg: 92.5025, aux.loss_ce: 0.1142, aux.acc_seg: 88.4791, loss: 0.2994, grad_norm: 2.7583 2023-02-17 01:46:26,171 - mmseg - INFO - Iter [86350/160000] lr: 2.762e-05, eta: 8:43:14, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2027, decode.acc_seg: 91.8570, aux.loss_ce: 0.1240, aux.acc_seg: 87.6552, loss: 0.3268, grad_norm: 3.8366 2023-02-17 01:46:44,080 - mmseg - INFO - Iter [86400/160000] lr: 2.760e-05, eta: 8:41:08, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1876, decode.acc_seg: 92.3508, aux.loss_ce: 0.1235, aux.acc_seg: 87.3812, loss: 0.3110, grad_norm: 2.8090 2023-02-17 01:47:01,899 - mmseg - INFO - Iter [86450/160000] lr: 2.758e-05, eta: 8:39:04, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1999, decode.acc_seg: 91.9007, aux.loss_ce: 0.1249, aux.acc_seg: 87.5933, loss: 0.3249, grad_norm: 3.0179 2023-02-17 01:47:19,947 - mmseg - INFO - Iter [86500/160000] lr: 2.756e-05, eta: 8:37:11, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2021, decode.acc_seg: 91.9828, aux.loss_ce: 0.1253, aux.acc_seg: 87.6499, loss: 0.3274, grad_norm: 2.9953 2023-02-17 01:47:40,470 - mmseg - INFO - Iter [86550/160000] lr: 2.754e-05, eta: 8:36:33, time: 0.411, data_time: 0.055, memory: 16596, decode.loss_ce: 0.1769, decode.acc_seg: 92.7166, aux.loss_ce: 0.1139, aux.acc_seg: 88.4087, loss: 0.2909, grad_norm: 2.6069 2023-02-17 01:47:58,545 - mmseg - INFO - Iter [86600/160000] lr: 2.753e-05, eta: 8:34:47, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1947, decode.acc_seg: 92.1279, aux.loss_ce: 0.1267, aux.acc_seg: 87.3593, loss: 0.3215, grad_norm: 2.9020 2023-02-17 01:48:16,736 - mmseg - INFO - Iter [86650/160000] lr: 2.751e-05, eta: 8:33:07, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1931, decode.acc_seg: 92.1307, aux.loss_ce: 0.1237, aux.acc_seg: 87.6045, loss: 0.3168, grad_norm: 2.9541 2023-02-17 01:48:34,709 - mmseg - INFO - Iter [86700/160000] lr: 2.749e-05, eta: 8:31:24, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1938, decode.acc_seg: 92.2703, aux.loss_ce: 0.1214, aux.acc_seg: 88.2266, loss: 0.3153, grad_norm: 2.7385 2023-02-17 01:48:52,494 - mmseg - INFO - Iter [86750/160000] lr: 2.747e-05, eta: 8:29:39, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1992, decode.acc_seg: 91.9676, aux.loss_ce: 0.1247, aux.acc_seg: 87.4985, loss: 0.3239, grad_norm: 3.4934 2023-02-17 01:49:10,668 - mmseg - INFO - Iter [86800/160000] lr: 2.745e-05, eta: 8:28:08, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1943, decode.acc_seg: 92.2217, aux.loss_ce: 0.1228, aux.acc_seg: 87.9340, loss: 0.3171, grad_norm: 2.6424 2023-02-17 01:49:29,074 - mmseg - INFO - Iter [86850/160000] lr: 2.743e-05, eta: 8:26:45, time: 0.368, data_time: 0.007, memory: 16596, decode.loss_ce: 0.2006, decode.acc_seg: 91.9970, aux.loss_ce: 0.1224, aux.acc_seg: 87.8215, loss: 0.3230, grad_norm: 2.8469 2023-02-17 01:49:46,936 - mmseg - INFO - Iter [86900/160000] lr: 2.741e-05, eta: 8:25:11, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1996, decode.acc_seg: 91.9852, aux.loss_ce: 0.1218, aux.acc_seg: 87.7375, loss: 0.3214, grad_norm: 3.0107 2023-02-17 01:50:04,716 - mmseg - INFO - Iter [86950/160000] lr: 2.739e-05, eta: 8:23:37, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2025, decode.acc_seg: 91.8559, aux.loss_ce: 0.1292, aux.acc_seg: 86.9590, loss: 0.3316, grad_norm: 3.1571 2023-02-17 01:50:22,672 - mmseg - INFO - Saving checkpoint at 87000 iterations 2023-02-17 01:50:23,740 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 01:50:23,740 - mmseg - INFO - Iter [87000/160000] lr: 2.738e-05, eta: 8:22:36, time: 0.381, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2009, decode.acc_seg: 91.9023, aux.loss_ce: 0.1229, aux.acc_seg: 87.4762, loss: 0.3238, grad_norm: 3.5267 2023-02-17 01:50:41,688 - mmseg - INFO - Iter [87050/160000] lr: 2.736e-05, eta: 8:21:10, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1974, decode.acc_seg: 92.0215, aux.loss_ce: 0.1275, aux.acc_seg: 87.1233, loss: 0.3249, grad_norm: 2.8525 2023-02-17 01:51:00,015 - mmseg - INFO - Iter [87100/160000] lr: 2.734e-05, eta: 8:19:56, time: 0.367, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1898, decode.acc_seg: 92.0931, aux.loss_ce: 0.1248, aux.acc_seg: 87.3962, loss: 0.3146, grad_norm: 3.0635 2023-02-17 01:51:17,832 - mmseg - INFO - Iter [87150/160000] lr: 2.732e-05, eta: 8:18:32, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1974, decode.acc_seg: 92.2723, aux.loss_ce: 0.1269, aux.acc_seg: 87.5856, loss: 0.3243, grad_norm: 3.0814 2023-02-17 01:51:35,799 - mmseg - INFO - Iter [87200/160000] lr: 2.730e-05, eta: 8:17:13, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1969, decode.acc_seg: 92.1610, aux.loss_ce: 0.1249, aux.acc_seg: 87.5518, loss: 0.3218, grad_norm: 3.3691 2023-02-17 01:51:54,082 - mmseg - INFO - Iter [87250/160000] lr: 2.728e-05, eta: 8:16:03, time: 0.366, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1927, decode.acc_seg: 92.0879, aux.loss_ce: 0.1261, aux.acc_seg: 87.2969, loss: 0.3189, grad_norm: 3.2296 2023-02-17 01:52:12,092 - mmseg - INFO - Iter [87300/160000] lr: 2.726e-05, eta: 8:14:49, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1981, decode.acc_seg: 92.2383, aux.loss_ce: 0.1221, aux.acc_seg: 88.0810, loss: 0.3202, grad_norm: 2.8608 2023-02-17 01:52:29,870 - mmseg - INFO - Iter [87350/160000] lr: 2.724e-05, eta: 8:13:31, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1805, decode.acc_seg: 92.8175, aux.loss_ce: 0.1219, aux.acc_seg: 88.2225, loss: 0.3023, grad_norm: 2.6469 2023-02-17 01:52:47,757 - mmseg - INFO - Iter [87400/160000] lr: 2.723e-05, eta: 8:12:17, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1848, decode.acc_seg: 92.6353, aux.loss_ce: 0.1175, aux.acc_seg: 88.3930, loss: 0.3022, grad_norm: 2.9706 2023-02-17 01:53:05,789 - mmseg - INFO - Iter [87450/160000] lr: 2.721e-05, eta: 8:11:09, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2060, decode.acc_seg: 91.8848, aux.loss_ce: 0.1284, aux.acc_seg: 87.5166, loss: 0.3344, grad_norm: 3.1257 2023-02-17 01:53:23,603 - mmseg - INFO - Iter [87500/160000] lr: 2.719e-05, eta: 8:09:56, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1964, decode.acc_seg: 92.3120, aux.loss_ce: 0.1302, aux.acc_seg: 87.3328, loss: 0.3266, grad_norm: 3.2109 2023-02-17 01:53:41,779 - mmseg - INFO - Iter [87550/160000] lr: 2.717e-05, eta: 8:08:54, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1882, decode.acc_seg: 92.5477, aux.loss_ce: 0.1269, aux.acc_seg: 87.5947, loss: 0.3151, grad_norm: 2.8414 2023-02-17 01:53:59,879 - mmseg - INFO - Iter [87600/160000] lr: 2.715e-05, eta: 8:07:50, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1888, decode.acc_seg: 92.3011, aux.loss_ce: 0.1199, aux.acc_seg: 87.8646, loss: 0.3087, grad_norm: 2.7022 2023-02-17 01:54:17,753 - mmseg - INFO - Iter [87650/160000] lr: 2.713e-05, eta: 8:06:43, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1847, decode.acc_seg: 92.6827, aux.loss_ce: 0.1215, aux.acc_seg: 87.6750, loss: 0.3062, grad_norm: 2.4627 2023-02-17 01:54:35,708 - mmseg - INFO - Iter [87700/160000] lr: 2.711e-05, eta: 8:05:40, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1995, decode.acc_seg: 92.0391, aux.loss_ce: 0.1257, aux.acc_seg: 87.5089, loss: 0.3252, grad_norm: 2.9719 2023-02-17 01:54:53,622 - mmseg - INFO - Iter [87750/160000] lr: 2.709e-05, eta: 8:04:37, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1873, decode.acc_seg: 92.0875, aux.loss_ce: 0.1132, aux.acc_seg: 88.1069, loss: 0.3004, grad_norm: 2.9186 2023-02-17 01:55:13,532 - mmseg - INFO - Iter [87800/160000] lr: 2.708e-05, eta: 8:04:12, time: 0.398, data_time: 0.051, memory: 16596, decode.loss_ce: 0.2004, decode.acc_seg: 91.9618, aux.loss_ce: 0.1257, aux.acc_seg: 87.4035, loss: 0.3261, grad_norm: 2.9810 2023-02-17 01:55:31,382 - mmseg - INFO - Iter [87850/160000] lr: 2.706e-05, eta: 8:03:10, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1910, decode.acc_seg: 92.3328, aux.loss_ce: 0.1191, aux.acc_seg: 88.0026, loss: 0.3101, grad_norm: 3.3712 2023-02-17 01:55:49,553 - mmseg - INFO - Iter [87900/160000] lr: 2.704e-05, eta: 8:02:14, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1898, decode.acc_seg: 92.3187, aux.loss_ce: 0.1214, aux.acc_seg: 87.9182, loss: 0.3113, grad_norm: 2.8050 2023-02-17 01:56:07,495 - mmseg - INFO - Iter [87950/160000] lr: 2.702e-05, eta: 8:01:15, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1958, decode.acc_seg: 92.2283, aux.loss_ce: 0.1241, aux.acc_seg: 87.7705, loss: 0.3199, grad_norm: 3.0201 2023-02-17 01:56:25,371 - mmseg - INFO - Saving checkpoint at 88000 iterations 2023-02-17 01:56:26,434 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 01:56:26,434 - mmseg - INFO - Iter [88000/160000] lr: 2.700e-05, eta: 8:00:36, time: 0.379, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1946, decode.acc_seg: 92.1570, aux.loss_ce: 0.1223, aux.acc_seg: 87.8557, loss: 0.3170, grad_norm: 2.8259 2023-02-17 01:56:44,699 - mmseg - INFO - Iter [88050/160000] lr: 2.698e-05, eta: 7:59:44, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1882, decode.acc_seg: 92.2545, aux.loss_ce: 0.1196, aux.acc_seg: 87.7563, loss: 0.3077, grad_norm: 3.0497 2023-02-17 01:57:02,336 - mmseg - INFO - Iter [88100/160000] lr: 2.696e-05, eta: 7:58:43, time: 0.353, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1994, decode.acc_seg: 91.8985, aux.loss_ce: 0.1252, aux.acc_seg: 87.6164, loss: 0.3246, grad_norm: 3.5397 2023-02-17 01:57:20,053 - mmseg - INFO - Iter [88150/160000] lr: 2.694e-05, eta: 7:57:44, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1896, decode.acc_seg: 92.3080, aux.loss_ce: 0.1239, aux.acc_seg: 87.6625, loss: 0.3134, grad_norm: 3.4637 2023-02-17 01:57:37,947 - mmseg - INFO - Iter [88200/160000] lr: 2.693e-05, eta: 7:56:49, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1896, decode.acc_seg: 92.3923, aux.loss_ce: 0.1218, aux.acc_seg: 87.9251, loss: 0.3114, grad_norm: 3.1570 2023-02-17 01:57:55,893 - mmseg - INFO - Iter [88250/160000] lr: 2.691e-05, eta: 7:55:55, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1990, decode.acc_seg: 92.1865, aux.loss_ce: 0.1277, aux.acc_seg: 87.7142, loss: 0.3267, grad_norm: 3.2401 2023-02-17 01:58:13,902 - mmseg - INFO - Iter [88300/160000] lr: 2.689e-05, eta: 7:55:03, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1996, decode.acc_seg: 91.9484, aux.loss_ce: 0.1253, aux.acc_seg: 87.5247, loss: 0.3249, grad_norm: 3.4464 2023-02-17 01:58:31,752 - mmseg - INFO - Iter [88350/160000] lr: 2.687e-05, eta: 7:54:11, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1774, decode.acc_seg: 92.7806, aux.loss_ce: 0.1169, aux.acc_seg: 88.2689, loss: 0.2943, grad_norm: 2.4427 2023-02-17 01:58:49,996 - mmseg - INFO - Iter [88400/160000] lr: 2.685e-05, eta: 7:53:25, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1945, decode.acc_seg: 92.3934, aux.loss_ce: 0.1201, aux.acc_seg: 88.1374, loss: 0.3145, grad_norm: 2.9197 2023-02-17 01:59:07,816 - mmseg - INFO - Iter [88450/160000] lr: 2.683e-05, eta: 7:52:32, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1931, decode.acc_seg: 92.2341, aux.loss_ce: 0.1228, aux.acc_seg: 87.8157, loss: 0.3159, grad_norm: 3.3543 2023-02-17 01:59:25,842 - mmseg - INFO - Iter [88500/160000] lr: 2.681e-05, eta: 7:51:44, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1876, decode.acc_seg: 92.4329, aux.loss_ce: 0.1168, aux.acc_seg: 88.3172, loss: 0.3045, grad_norm: 2.5651 2023-02-17 01:59:43,570 - mmseg - INFO - Iter [88550/160000] lr: 2.679e-05, eta: 7:50:52, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1858, decode.acc_seg: 92.5739, aux.loss_ce: 0.1213, aux.acc_seg: 87.9333, loss: 0.3071, grad_norm: 3.1056 2023-02-17 02:00:01,509 - mmseg - INFO - Iter [88600/160000] lr: 2.678e-05, eta: 7:50:04, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2085, decode.acc_seg: 91.9141, aux.loss_ce: 0.1369, aux.acc_seg: 86.7958, loss: 0.3454, grad_norm: 3.0726 2023-02-17 02:00:19,285 - mmseg - INFO - Iter [88650/160000] lr: 2.676e-05, eta: 7:49:14, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2028, decode.acc_seg: 91.6670, aux.loss_ce: 0.1231, aux.acc_seg: 87.5583, loss: 0.3259, grad_norm: 3.2136 2023-02-17 02:00:37,103 - mmseg - INFO - Iter [88700/160000] lr: 2.674e-05, eta: 7:48:25, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1849, decode.acc_seg: 92.5766, aux.loss_ce: 0.1243, aux.acc_seg: 87.5918, loss: 0.3092, grad_norm: 2.9399 2023-02-17 02:00:55,530 - mmseg - INFO - Iter [88750/160000] lr: 2.672e-05, eta: 7:47:46, time: 0.369, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1968, decode.acc_seg: 92.0490, aux.loss_ce: 0.1240, aux.acc_seg: 87.5832, loss: 0.3208, grad_norm: 2.8966 2023-02-17 02:01:13,630 - mmseg - INFO - Iter [88800/160000] lr: 2.670e-05, eta: 7:47:02, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1909, decode.acc_seg: 92.1721, aux.loss_ce: 0.1211, aux.acc_seg: 87.9350, loss: 0.3120, grad_norm: 2.8605 2023-02-17 02:01:31,281 - mmseg - INFO - Iter [88850/160000] lr: 2.668e-05, eta: 7:46:13, time: 0.354, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1887, decode.acc_seg: 92.2375, aux.loss_ce: 0.1206, aux.acc_seg: 87.7874, loss: 0.3093, grad_norm: 3.0264 2023-02-17 02:01:49,303 - mmseg - INFO - Iter [88900/160000] lr: 2.666e-05, eta: 7:45:29, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2001, decode.acc_seg: 92.1279, aux.loss_ce: 0.1236, aux.acc_seg: 87.9265, loss: 0.3237, grad_norm: 3.5061 2023-02-17 02:02:07,300 - mmseg - INFO - Iter [88950/160000] lr: 2.664e-05, eta: 7:44:46, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1929, decode.acc_seg: 92.3255, aux.loss_ce: 0.1266, aux.acc_seg: 87.4436, loss: 0.3195, grad_norm: 2.8529 2023-02-17 02:02:25,321 - mmseg - INFO - Saving checkpoint at 89000 iterations 2023-02-17 02:02:26,378 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 02:02:26,378 - mmseg - INFO - Iter [89000/160000] lr: 2.663e-05, eta: 7:44:19, time: 0.382, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1933, decode.acc_seg: 92.2904, aux.loss_ce: 0.1207, aux.acc_seg: 88.0059, loss: 0.3140, grad_norm: 2.6916 2023-02-17 02:02:44,186 - mmseg - INFO - Iter [89050/160000] lr: 2.661e-05, eta: 7:43:34, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1999, decode.acc_seg: 91.8530, aux.loss_ce: 0.1240, aux.acc_seg: 87.6905, loss: 0.3239, grad_norm: 3.5723 2023-02-17 02:03:04,813 - mmseg - INFO - Iter [89100/160000] lr: 2.659e-05, eta: 7:43:29, time: 0.413, data_time: 0.053, memory: 16596, decode.loss_ce: 0.1848, decode.acc_seg: 92.4929, aux.loss_ce: 0.1152, aux.acc_seg: 88.4877, loss: 0.3000, grad_norm: 2.5322 2023-02-17 02:03:22,471 - mmseg - INFO - Iter [89150/160000] lr: 2.657e-05, eta: 7:42:42, time: 0.352, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1831, decode.acc_seg: 92.6837, aux.loss_ce: 0.1196, aux.acc_seg: 88.2274, loss: 0.3027, grad_norm: 2.8043 2023-02-17 02:03:40,752 - mmseg - INFO - Iter [89200/160000] lr: 2.655e-05, eta: 7:42:05, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1795, decode.acc_seg: 92.7865, aux.loss_ce: 0.1187, aux.acc_seg: 88.1035, loss: 0.2982, grad_norm: 3.1923 2023-02-17 02:03:58,539 - mmseg - INFO - Iter [89250/160000] lr: 2.653e-05, eta: 7:41:21, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1933, decode.acc_seg: 92.1475, aux.loss_ce: 0.1222, aux.acc_seg: 87.8377, loss: 0.3155, grad_norm: 3.2750 2023-02-17 02:04:16,264 - mmseg - INFO - Iter [89300/160000] lr: 2.651e-05, eta: 7:40:37, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1869, decode.acc_seg: 92.3940, aux.loss_ce: 0.1199, aux.acc_seg: 88.0653, loss: 0.3068, grad_norm: 2.7933 2023-02-17 02:04:34,274 - mmseg - INFO - Iter [89350/160000] lr: 2.649e-05, eta: 7:39:57, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1963, decode.acc_seg: 92.0459, aux.loss_ce: 0.1270, aux.acc_seg: 87.2572, loss: 0.3232, grad_norm: 3.2540 2023-02-17 02:04:52,069 - mmseg - INFO - Iter [89400/160000] lr: 2.648e-05, eta: 7:39:15, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1686, decode.acc_seg: 93.0774, aux.loss_ce: 0.1114, aux.acc_seg: 88.6388, loss: 0.2799, grad_norm: 2.8090 2023-02-17 02:05:09,717 - mmseg - INFO - Iter [89450/160000] lr: 2.646e-05, eta: 7:38:31, time: 0.353, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1878, decode.acc_seg: 92.4217, aux.loss_ce: 0.1195, aux.acc_seg: 87.8431, loss: 0.3073, grad_norm: 2.7537 2023-02-17 02:05:28,185 - mmseg - INFO - Iter [89500/160000] lr: 2.644e-05, eta: 7:37:59, time: 0.369, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1850, decode.acc_seg: 92.5163, aux.loss_ce: 0.1195, aux.acc_seg: 87.8670, loss: 0.3045, grad_norm: 2.4960 2023-02-17 02:05:46,089 - mmseg - INFO - Iter [89550/160000] lr: 2.642e-05, eta: 7:37:19, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1957, decode.acc_seg: 92.1543, aux.loss_ce: 0.1227, aux.acc_seg: 87.7014, loss: 0.3184, grad_norm: 3.2694 2023-02-17 02:06:04,067 - mmseg - INFO - Iter [89600/160000] lr: 2.640e-05, eta: 7:36:41, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1777, decode.acc_seg: 92.5770, aux.loss_ce: 0.1176, aux.acc_seg: 87.7597, loss: 0.2953, grad_norm: 2.4246 2023-02-17 02:06:21,956 - mmseg - INFO - Iter [89650/160000] lr: 2.638e-05, eta: 7:36:02, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1852, decode.acc_seg: 92.3223, aux.loss_ce: 0.1166, aux.acc_seg: 88.1362, loss: 0.3017, grad_norm: 2.6518 2023-02-17 02:06:39,853 - mmseg - INFO - Iter [89700/160000] lr: 2.636e-05, eta: 7:35:23, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1852, decode.acc_seg: 92.3946, aux.loss_ce: 0.1149, aux.acc_seg: 88.3520, loss: 0.3001, grad_norm: 2.8943 2023-02-17 02:06:57,631 - mmseg - INFO - Iter [89750/160000] lr: 2.634e-05, eta: 7:34:43, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1901, decode.acc_seg: 92.5033, aux.loss_ce: 0.1204, aux.acc_seg: 88.1185, loss: 0.3105, grad_norm: 2.7573 2023-02-17 02:07:15,532 - mmseg - INFO - Iter [89800/160000] lr: 2.633e-05, eta: 7:34:06, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1916, decode.acc_seg: 92.0652, aux.loss_ce: 0.1214, aux.acc_seg: 87.7621, loss: 0.3131, grad_norm: 3.8455 2023-02-17 02:07:33,583 - mmseg - INFO - Iter [89850/160000] lr: 2.631e-05, eta: 7:33:30, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1914, decode.acc_seg: 92.2145, aux.loss_ce: 0.1178, aux.acc_seg: 88.0804, loss: 0.3092, grad_norm: 2.5980 2023-02-17 02:07:51,368 - mmseg - INFO - Iter [89900/160000] lr: 2.629e-05, eta: 7:32:51, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1985, decode.acc_seg: 92.1652, aux.loss_ce: 0.1284, aux.acc_seg: 87.5414, loss: 0.3269, grad_norm: 3.8504 2023-02-17 02:08:09,246 - mmseg - INFO - Iter [89950/160000] lr: 2.627e-05, eta: 7:32:14, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1979, decode.acc_seg: 92.0244, aux.loss_ce: 0.1277, aux.acc_seg: 87.3289, loss: 0.3256, grad_norm: 3.3217 2023-02-17 02:08:27,338 - mmseg - INFO - Saving checkpoint at 90000 iterations 2023-02-17 02:08:28,499 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 02:08:28,499 - mmseg - INFO - Iter [90000/160000] lr: 2.625e-05, eta: 7:31:54, time: 0.385, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1887, decode.acc_seg: 92.3304, aux.loss_ce: 0.1236, aux.acc_seg: 87.5652, loss: 0.3123, grad_norm: 2.6682 2023-02-17 02:08:46,884 - mmseg - INFO - Iter [90050/160000] lr: 2.623e-05, eta: 7:31:23, time: 0.368, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1902, decode.acc_seg: 92.1987, aux.loss_ce: 0.1242, aux.acc_seg: 87.4561, loss: 0.3144, grad_norm: 2.9101 2023-02-17 02:09:04,785 - mmseg - INFO - Iter [90100/160000] lr: 2.621e-05, eta: 7:30:47, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1877, decode.acc_seg: 92.5668, aux.loss_ce: 0.1270, aux.acc_seg: 87.6734, loss: 0.3148, grad_norm: 3.3302 2023-02-17 02:09:22,682 - mmseg - INFO - Iter [90150/160000] lr: 2.619e-05, eta: 7:30:11, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1868, decode.acc_seg: 92.4923, aux.loss_ce: 0.1156, aux.acc_seg: 88.6193, loss: 0.3024, grad_norm: 2.9189 2023-02-17 02:09:40,890 - mmseg - INFO - Iter [90200/160000] lr: 2.618e-05, eta: 7:29:39, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1992, decode.acc_seg: 91.7690, aux.loss_ce: 0.1284, aux.acc_seg: 87.1589, loss: 0.3276, grad_norm: 3.3358 2023-02-17 02:09:58,830 - mmseg - INFO - Iter [90250/160000] lr: 2.616e-05, eta: 7:29:04, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1945, decode.acc_seg: 92.0398, aux.loss_ce: 0.1235, aux.acc_seg: 87.5843, loss: 0.3180, grad_norm: 2.9227 2023-02-17 02:10:16,602 - mmseg - INFO - Iter [90300/160000] lr: 2.614e-05, eta: 7:28:28, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1987, decode.acc_seg: 91.8004, aux.loss_ce: 0.1269, aux.acc_seg: 87.1594, loss: 0.3256, grad_norm: 3.2868 2023-02-17 02:10:36,566 - mmseg - INFO - Iter [90350/160000] lr: 2.612e-05, eta: 7:28:16, time: 0.399, data_time: 0.052, memory: 16596, decode.loss_ce: 0.1891, decode.acc_seg: 92.4477, aux.loss_ce: 0.1178, aux.acc_seg: 88.5702, loss: 0.3069, grad_norm: 3.1131 2023-02-17 02:10:54,966 - mmseg - INFO - Iter [90400/160000] lr: 2.610e-05, eta: 7:27:47, time: 0.368, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1850, decode.acc_seg: 92.4639, aux.loss_ce: 0.1142, aux.acc_seg: 88.3608, loss: 0.2992, grad_norm: 2.7002 2023-02-17 02:11:12,910 - mmseg - INFO - Iter [90450/160000] lr: 2.608e-05, eta: 7:27:13, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1874, decode.acc_seg: 92.4693, aux.loss_ce: 0.1217, aux.acc_seg: 88.1040, loss: 0.3091, grad_norm: 2.6753 2023-02-17 02:11:31,003 - mmseg - INFO - Iter [90500/160000] lr: 2.606e-05, eta: 7:26:40, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1998, decode.acc_seg: 91.9394, aux.loss_ce: 0.1295, aux.acc_seg: 87.1594, loss: 0.3293, grad_norm: 3.3266 2023-02-17 02:11:49,076 - mmseg - INFO - Iter [90550/160000] lr: 2.604e-05, eta: 7:26:08, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1864, decode.acc_seg: 92.5311, aux.loss_ce: 0.1173, aux.acc_seg: 88.2918, loss: 0.3036, grad_norm: 2.5099 2023-02-17 02:12:07,249 - mmseg - INFO - Iter [90600/160000] lr: 2.603e-05, eta: 7:25:38, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1863, decode.acc_seg: 92.4142, aux.loss_ce: 0.1189, aux.acc_seg: 88.1241, loss: 0.3052, grad_norm: 2.8699 2023-02-17 02:12:25,057 - mmseg - INFO - Iter [90650/160000] lr: 2.601e-05, eta: 7:25:03, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1853, decode.acc_seg: 92.3719, aux.loss_ce: 0.1161, aux.acc_seg: 88.3569, loss: 0.3014, grad_norm: 2.8691 2023-02-17 02:12:43,189 - mmseg - INFO - Iter [90700/160000] lr: 2.599e-05, eta: 7:24:32, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1888, decode.acc_seg: 92.4265, aux.loss_ce: 0.1242, aux.acc_seg: 87.6471, loss: 0.3130, grad_norm: 2.9622 2023-02-17 02:13:01,091 - mmseg - INFO - Iter [90750/160000] lr: 2.597e-05, eta: 7:23:59, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1970, decode.acc_seg: 92.1596, aux.loss_ce: 0.1265, aux.acc_seg: 87.4355, loss: 0.3234, grad_norm: 3.0894 2023-02-17 02:13:18,985 - mmseg - INFO - Iter [90800/160000] lr: 2.595e-05, eta: 7:23:26, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1865, decode.acc_seg: 92.4203, aux.loss_ce: 0.1206, aux.acc_seg: 87.7149, loss: 0.3072, grad_norm: 2.8182 2023-02-17 02:13:37,112 - mmseg - INFO - Iter [90850/160000] lr: 2.593e-05, eta: 7:22:56, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.2080, decode.acc_seg: 91.7436, aux.loss_ce: 0.1273, aux.acc_seg: 87.2399, loss: 0.3353, grad_norm: 3.3945 2023-02-17 02:13:55,279 - mmseg - INFO - Iter [90900/160000] lr: 2.591e-05, eta: 7:22:26, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2011, decode.acc_seg: 91.9455, aux.loss_ce: 0.1269, aux.acc_seg: 87.2794, loss: 0.3280, grad_norm: 3.6014 2023-02-17 02:14:13,572 - mmseg - INFO - Iter [90950/160000] lr: 2.589e-05, eta: 7:21:58, time: 0.366, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1925, decode.acc_seg: 92.4341, aux.loss_ce: 0.1251, aux.acc_seg: 87.7988, loss: 0.3176, grad_norm: 2.9979 2023-02-17 02:14:31,476 - mmseg - INFO - Saving checkpoint at 91000 iterations 2023-02-17 02:14:32,536 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 02:14:32,536 - mmseg - INFO - Iter [91000/160000] lr: 2.588e-05, eta: 7:21:37, time: 0.379, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2025, decode.acc_seg: 91.9172, aux.loss_ce: 0.1256, aux.acc_seg: 87.4176, loss: 0.3280, grad_norm: 3.1733 2023-02-17 02:14:50,097 - mmseg - INFO - Iter [91050/160000] lr: 2.586e-05, eta: 7:21:01, time: 0.351, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1788, decode.acc_seg: 92.8228, aux.loss_ce: 0.1174, aux.acc_seg: 88.3137, loss: 0.2962, grad_norm: 2.9452 2023-02-17 02:15:08,149 - mmseg - INFO - Iter [91100/160000] lr: 2.584e-05, eta: 7:20:31, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1848, decode.acc_seg: 92.6080, aux.loss_ce: 0.1207, aux.acc_seg: 88.0657, loss: 0.3055, grad_norm: 2.8902 2023-02-17 02:15:26,364 - mmseg - INFO - Iter [91150/160000] lr: 2.582e-05, eta: 7:20:03, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1907, decode.acc_seg: 92.3216, aux.loss_ce: 0.1188, aux.acc_seg: 88.3071, loss: 0.3095, grad_norm: 2.6743 2023-02-17 02:15:44,258 - mmseg - INFO - Iter [91200/160000] lr: 2.580e-05, eta: 7:19:31, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1751, decode.acc_seg: 92.8312, aux.loss_ce: 0.1131, aux.acc_seg: 88.7378, loss: 0.2882, grad_norm: 2.5050 2023-02-17 02:16:02,354 - mmseg - INFO - Iter [91250/160000] lr: 2.578e-05, eta: 7:19:02, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1939, decode.acc_seg: 92.3251, aux.loss_ce: 0.1254, aux.acc_seg: 87.4980, loss: 0.3193, grad_norm: 3.1141 2023-02-17 02:16:20,403 - mmseg - INFO - Iter [91300/160000] lr: 2.576e-05, eta: 7:18:32, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1890, decode.acc_seg: 92.0492, aux.loss_ce: 0.1252, aux.acc_seg: 87.6244, loss: 0.3141, grad_norm: 3.5264 2023-02-17 02:16:38,279 - mmseg - INFO - Iter [91350/160000] lr: 2.574e-05, eta: 7:18:01, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1943, decode.acc_seg: 92.1398, aux.loss_ce: 0.1219, aux.acc_seg: 88.0232, loss: 0.3162, grad_norm: 3.3633 2023-02-17 02:16:56,399 - mmseg - INFO - Iter [91400/160000] lr: 2.573e-05, eta: 7:17:33, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1880, decode.acc_seg: 92.4281, aux.loss_ce: 0.1199, aux.acc_seg: 88.1971, loss: 0.3079, grad_norm: 3.5686 2023-02-17 02:17:14,198 - mmseg - INFO - Iter [91450/160000] lr: 2.571e-05, eta: 7:17:01, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1792, decode.acc_seg: 92.7622, aux.loss_ce: 0.1189, aux.acc_seg: 88.1146, loss: 0.2981, grad_norm: 2.8158 2023-02-17 02:17:32,078 - mmseg - INFO - Iter [91500/160000] lr: 2.569e-05, eta: 7:16:31, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1871, decode.acc_seg: 92.3934, aux.loss_ce: 0.1205, aux.acc_seg: 87.9259, loss: 0.3077, grad_norm: 3.0226 2023-02-17 02:17:50,037 - mmseg - INFO - Iter [91550/160000] lr: 2.567e-05, eta: 7:16:01, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1754, decode.acc_seg: 92.8102, aux.loss_ce: 0.1112, aux.acc_seg: 88.7092, loss: 0.2866, grad_norm: 2.6446 2023-02-17 02:18:10,434 - mmseg - INFO - Iter [91600/160000] lr: 2.565e-05, eta: 7:15:54, time: 0.408, data_time: 0.052, memory: 16596, decode.loss_ce: 0.1807, decode.acc_seg: 92.8161, aux.loss_ce: 0.1147, aux.acc_seg: 88.8950, loss: 0.2953, grad_norm: 2.8082 2023-02-17 02:18:28,409 - mmseg - INFO - Iter [91650/160000] lr: 2.563e-05, eta: 7:15:24, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1800, decode.acc_seg: 92.8535, aux.loss_ce: 0.1144, aux.acc_seg: 88.6823, loss: 0.2944, grad_norm: 2.9059 2023-02-17 02:18:46,352 - mmseg - INFO - Iter [91700/160000] lr: 2.561e-05, eta: 7:14:54, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1760, decode.acc_seg: 92.8015, aux.loss_ce: 0.1159, aux.acc_seg: 88.3078, loss: 0.2919, grad_norm: 2.6038 2023-02-17 02:19:04,200 - mmseg - INFO - Iter [91750/160000] lr: 2.559e-05, eta: 7:14:25, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1958, decode.acc_seg: 92.0912, aux.loss_ce: 0.1250, aux.acc_seg: 87.4893, loss: 0.3208, grad_norm: 3.1277 2023-02-17 02:19:22,185 - mmseg - INFO - Iter [91800/160000] lr: 2.558e-05, eta: 7:13:56, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1820, decode.acc_seg: 92.5918, aux.loss_ce: 0.1181, aux.acc_seg: 88.2184, loss: 0.3000, grad_norm: 2.6418 2023-02-17 02:19:40,257 - mmseg - INFO - Iter [91850/160000] lr: 2.556e-05, eta: 7:13:28, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1807, decode.acc_seg: 92.8157, aux.loss_ce: 0.1151, aux.acc_seg: 88.7376, loss: 0.2958, grad_norm: 3.0266 2023-02-17 02:19:58,496 - mmseg - INFO - Iter [91900/160000] lr: 2.554e-05, eta: 7:13:01, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2004, decode.acc_seg: 91.9391, aux.loss_ce: 0.1270, aux.acc_seg: 87.4216, loss: 0.3274, grad_norm: 3.1556 2023-02-17 02:20:16,731 - mmseg - INFO - Iter [91950/160000] lr: 2.552e-05, eta: 7:12:35, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1847, decode.acc_seg: 92.4611, aux.loss_ce: 0.1144, aux.acc_seg: 88.4307, loss: 0.2991, grad_norm: 2.9669 2023-02-17 02:20:34,454 - mmseg - INFO - Saving checkpoint at 92000 iterations 2023-02-17 02:20:35,502 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 02:20:35,502 - mmseg - INFO - Iter [92000/160000] lr: 2.550e-05, eta: 7:12:14, time: 0.375, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1843, decode.acc_seg: 92.5423, aux.loss_ce: 0.1219, aux.acc_seg: 87.9788, loss: 0.3062, grad_norm: 3.2235 2023-02-17 02:20:53,664 - mmseg - INFO - Iter [92050/160000] lr: 2.548e-05, eta: 7:11:47, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1875, decode.acc_seg: 92.3488, aux.loss_ce: 0.1162, aux.acc_seg: 88.1633, loss: 0.3037, grad_norm: 2.9813 2023-02-17 02:21:11,622 - mmseg - INFO - Iter [92100/160000] lr: 2.546e-05, eta: 7:11:18, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1862, decode.acc_seg: 92.5096, aux.loss_ce: 0.1182, aux.acc_seg: 88.0558, loss: 0.3044, grad_norm: 2.5191 2023-02-17 02:21:29,310 - mmseg - INFO - Iter [92150/160000] lr: 2.544e-05, eta: 7:10:48, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1969, decode.acc_seg: 91.9294, aux.loss_ce: 0.1216, aux.acc_seg: 87.5912, loss: 0.3185, grad_norm: 2.9844 2023-02-17 02:21:47,389 - mmseg - INFO - Iter [92200/160000] lr: 2.543e-05, eta: 7:10:21, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1826, decode.acc_seg: 92.7113, aux.loss_ce: 0.1133, aux.acc_seg: 88.9180, loss: 0.2959, grad_norm: 3.2317 2023-02-17 02:22:05,449 - mmseg - INFO - Iter [92250/160000] lr: 2.541e-05, eta: 7:09:54, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1816, decode.acc_seg: 92.5392, aux.loss_ce: 0.1199, aux.acc_seg: 87.7939, loss: 0.3015, grad_norm: 2.8365 2023-02-17 02:22:23,434 - mmseg - INFO - Iter [92300/160000] lr: 2.539e-05, eta: 7:09:26, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1966, decode.acc_seg: 91.9886, aux.loss_ce: 0.1234, aux.acc_seg: 87.5483, loss: 0.3200, grad_norm: 3.0311 2023-02-17 02:22:41,863 - mmseg - INFO - Iter [92350/160000] lr: 2.537e-05, eta: 7:09:02, time: 0.368, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1853, decode.acc_seg: 92.3312, aux.loss_ce: 0.1174, aux.acc_seg: 88.0544, loss: 0.3027, grad_norm: 2.7143 2023-02-17 02:22:59,999 - mmseg - INFO - Iter [92400/160000] lr: 2.535e-05, eta: 7:08:36, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1875, decode.acc_seg: 92.4062, aux.loss_ce: 0.1202, aux.acc_seg: 87.8709, loss: 0.3077, grad_norm: 2.9084 2023-02-17 02:23:17,768 - mmseg - INFO - Iter [92450/160000] lr: 2.533e-05, eta: 7:08:07, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1965, decode.acc_seg: 92.0835, aux.loss_ce: 0.1211, aux.acc_seg: 87.9381, loss: 0.3176, grad_norm: 2.9476 2023-02-17 02:23:35,817 - mmseg - INFO - Iter [92500/160000] lr: 2.531e-05, eta: 7:07:40, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1884, decode.acc_seg: 92.4066, aux.loss_ce: 0.1247, aux.acc_seg: 87.5557, loss: 0.3131, grad_norm: 2.8827 2023-02-17 02:23:53,907 - mmseg - INFO - Iter [92550/160000] lr: 2.529e-05, eta: 7:07:14, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1779, decode.acc_seg: 92.8641, aux.loss_ce: 0.1159, aux.acc_seg: 88.3552, loss: 0.2938, grad_norm: 2.4683 2023-02-17 02:24:11,735 - mmseg - INFO - Iter [92600/160000] lr: 2.528e-05, eta: 7:06:46, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1810, decode.acc_seg: 92.6991, aux.loss_ce: 0.1211, aux.acc_seg: 88.1096, loss: 0.3021, grad_norm: 3.6659 2023-02-17 02:24:29,730 - mmseg - INFO - Iter [92650/160000] lr: 2.526e-05, eta: 7:06:19, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1843, decode.acc_seg: 92.5823, aux.loss_ce: 0.1201, aux.acc_seg: 87.9579, loss: 0.3044, grad_norm: 2.7948 2023-02-17 02:24:48,067 - mmseg - INFO - Iter [92700/160000] lr: 2.524e-05, eta: 7:05:55, time: 0.367, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1884, decode.acc_seg: 92.5188, aux.loss_ce: 0.1200, aux.acc_seg: 88.2000, loss: 0.3083, grad_norm: 3.0001 2023-02-17 02:25:05,599 - mmseg - INFO - Iter [92750/160000] lr: 2.522e-05, eta: 7:05:25, time: 0.351, data_time: 0.006, memory: 16596, decode.loss_ce: 0.2044, decode.acc_seg: 91.7717, aux.loss_ce: 0.1270, aux.acc_seg: 87.4212, loss: 0.3313, grad_norm: 3.5434 2023-02-17 02:25:23,632 - mmseg - INFO - Iter [92800/160000] lr: 2.520e-05, eta: 7:04:59, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1880, decode.acc_seg: 92.2788, aux.loss_ce: 0.1233, aux.acc_seg: 87.6766, loss: 0.3113, grad_norm: 3.1900 2023-02-17 02:25:43,529 - mmseg - INFO - Iter [92850/160000] lr: 2.518e-05, eta: 7:04:47, time: 0.398, data_time: 0.051, memory: 16596, decode.loss_ce: 0.1874, decode.acc_seg: 92.2009, aux.loss_ce: 0.1170, aux.acc_seg: 88.0327, loss: 0.3044, grad_norm: 3.0230 2023-02-17 02:26:01,426 - mmseg - INFO - Iter [92900/160000] lr: 2.516e-05, eta: 7:04:20, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1876, decode.acc_seg: 92.3810, aux.loss_ce: 0.1212, aux.acc_seg: 87.6366, loss: 0.3088, grad_norm: 2.8950 2023-02-17 02:26:19,284 - mmseg - INFO - Iter [92950/160000] lr: 2.514e-05, eta: 7:03:52, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1784, decode.acc_seg: 92.8533, aux.loss_ce: 0.1121, aux.acc_seg: 88.9122, loss: 0.2905, grad_norm: 3.2196 2023-02-17 02:26:37,404 - mmseg - INFO - Saving checkpoint at 93000 iterations 2023-02-17 02:26:38,484 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 02:26:38,485 - mmseg - INFO - Iter [93000/160000] lr: 2.513e-05, eta: 7:03:35, time: 0.384, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1926, decode.acc_seg: 92.4128, aux.loss_ce: 0.1159, aux.acc_seg: 88.6987, loss: 0.3085, grad_norm: 2.9009 2023-02-17 02:26:56,598 - mmseg - INFO - Iter [93050/160000] lr: 2.511e-05, eta: 7:03:10, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1944, decode.acc_seg: 92.2556, aux.loss_ce: 0.1238, aux.acc_seg: 87.8684, loss: 0.3181, grad_norm: 3.1175 2023-02-17 02:27:14,593 - mmseg - INFO - Iter [93100/160000] lr: 2.509e-05, eta: 7:02:44, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1884, decode.acc_seg: 92.5353, aux.loss_ce: 0.1244, aux.acc_seg: 87.8143, loss: 0.3128, grad_norm: 3.1711 2023-02-17 02:27:32,768 - mmseg - INFO - Iter [93150/160000] lr: 2.507e-05, eta: 7:02:19, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1815, decode.acc_seg: 92.4505, aux.loss_ce: 0.1187, aux.acc_seg: 87.8350, loss: 0.3003, grad_norm: 2.8188 2023-02-17 02:27:51,005 - mmseg - INFO - Iter [93200/160000] lr: 2.505e-05, eta: 7:01:55, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1747, decode.acc_seg: 92.9528, aux.loss_ce: 0.1224, aux.acc_seg: 87.8678, loss: 0.2971, grad_norm: 3.1644 2023-02-17 02:28:09,674 - mmseg - INFO - Iter [93250/160000] lr: 2.503e-05, eta: 7:01:34, time: 0.373, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1769, decode.acc_seg: 92.6608, aux.loss_ce: 0.1145, aux.acc_seg: 88.4398, loss: 0.2914, grad_norm: 2.4650 2023-02-17 02:28:27,729 - mmseg - INFO - Iter [93300/160000] lr: 2.501e-05, eta: 7:01:09, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1820, decode.acc_seg: 92.8081, aux.loss_ce: 0.1150, aux.acc_seg: 88.7043, loss: 0.2970, grad_norm: 2.6776 2023-02-17 02:28:45,681 - mmseg - INFO - Iter [93350/160000] lr: 2.499e-05, eta: 7:00:43, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1752, decode.acc_seg: 92.8329, aux.loss_ce: 0.1179, aux.acc_seg: 87.9963, loss: 0.2931, grad_norm: 3.0927 2023-02-17 02:29:03,517 - mmseg - INFO - Iter [93400/160000] lr: 2.498e-05, eta: 7:00:16, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1821, decode.acc_seg: 92.5809, aux.loss_ce: 0.1192, aux.acc_seg: 88.1814, loss: 0.3013, grad_norm: 3.0584 2023-02-17 02:29:21,513 - mmseg - INFO - Iter [93450/160000] lr: 2.496e-05, eta: 6:59:50, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1742, decode.acc_seg: 92.7413, aux.loss_ce: 0.1151, aux.acc_seg: 88.0600, loss: 0.2893, grad_norm: 2.4648 2023-02-17 02:29:39,502 - mmseg - INFO - Iter [93500/160000] lr: 2.494e-05, eta: 6:59:25, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1806, decode.acc_seg: 92.7385, aux.loss_ce: 0.1139, aux.acc_seg: 88.7051, loss: 0.2944, grad_norm: 2.7571 2023-02-17 02:29:57,293 - mmseg - INFO - Iter [93550/160000] lr: 2.492e-05, eta: 6:58:58, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1787, decode.acc_seg: 92.7160, aux.loss_ce: 0.1156, aux.acc_seg: 88.1900, loss: 0.2943, grad_norm: 2.7573 2023-02-17 02:30:15,293 - mmseg - INFO - Iter [93600/160000] lr: 2.490e-05, eta: 6:58:33, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1930, decode.acc_seg: 92.2377, aux.loss_ce: 0.1237, aux.acc_seg: 87.8286, loss: 0.3167, grad_norm: 2.9005 2023-02-17 02:30:32,931 - mmseg - INFO - Iter [93650/160000] lr: 2.488e-05, eta: 6:58:05, time: 0.353, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1933, decode.acc_seg: 92.2405, aux.loss_ce: 0.1248, aux.acc_seg: 87.2981, loss: 0.3181, grad_norm: 2.9638 2023-02-17 02:30:51,059 - mmseg - INFO - Iter [93700/160000] lr: 2.486e-05, eta: 6:57:41, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1764, decode.acc_seg: 92.7037, aux.loss_ce: 0.1156, aux.acc_seg: 88.2360, loss: 0.2921, grad_norm: 2.8269 2023-02-17 02:31:09,163 - mmseg - INFO - Iter [93750/160000] lr: 2.484e-05, eta: 6:57:17, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1817, decode.acc_seg: 92.5592, aux.loss_ce: 0.1173, aux.acc_seg: 88.2322, loss: 0.2990, grad_norm: 2.7662 2023-02-17 02:31:27,156 - mmseg - INFO - Iter [93800/160000] lr: 2.483e-05, eta: 6:56:52, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1841, decode.acc_seg: 92.5075, aux.loss_ce: 0.1178, aux.acc_seg: 88.0662, loss: 0.3020, grad_norm: 2.9094 2023-02-17 02:31:45,153 - mmseg - INFO - Iter [93850/160000] lr: 2.481e-05, eta: 6:56:27, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1886, decode.acc_seg: 92.5091, aux.loss_ce: 0.1211, aux.acc_seg: 88.0033, loss: 0.3097, grad_norm: 3.5881 2023-02-17 02:32:03,324 - mmseg - INFO - Iter [93900/160000] lr: 2.479e-05, eta: 6:56:03, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1833, decode.acc_seg: 92.4535, aux.loss_ce: 0.1160, aux.acc_seg: 88.4396, loss: 0.2993, grad_norm: 2.9561 2023-02-17 02:32:20,913 - mmseg - INFO - Iter [93950/160000] lr: 2.477e-05, eta: 6:55:36, time: 0.352, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1766, decode.acc_seg: 92.7782, aux.loss_ce: 0.1115, aux.acc_seg: 88.7883, loss: 0.2880, grad_norm: 2.6618 2023-02-17 02:32:38,944 - mmseg - INFO - Saving checkpoint at 94000 iterations 2023-02-17 02:32:40,098 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 02:32:40,098 - mmseg - INFO - Iter [94000/160000] lr: 2.475e-05, eta: 6:55:19, time: 0.384, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1958, decode.acc_seg: 92.1215, aux.loss_ce: 0.1218, aux.acc_seg: 87.8962, loss: 0.3176, grad_norm: 2.9982 2023-02-17 02:32:57,907 - mmseg - INFO - Iter [94050/160000] lr: 2.473e-05, eta: 6:54:53, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1781, decode.acc_seg: 92.8172, aux.loss_ce: 0.1175, aux.acc_seg: 88.4095, loss: 0.2957, grad_norm: 3.2667 2023-02-17 02:33:16,100 - mmseg - INFO - Iter [94100/160000] lr: 2.471e-05, eta: 6:54:30, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1702, decode.acc_seg: 93.0396, aux.loss_ce: 0.1109, aux.acc_seg: 88.6405, loss: 0.2811, grad_norm: 2.5862 2023-02-17 02:33:36,408 - mmseg - INFO - Iter [94150/160000] lr: 2.469e-05, eta: 6:54:20, time: 0.406, data_time: 0.054, memory: 16596, decode.loss_ce: 0.1926, decode.acc_seg: 92.2740, aux.loss_ce: 0.1221, aux.acc_seg: 87.7902, loss: 0.3146, grad_norm: 3.3274 2023-02-17 02:33:54,501 - mmseg - INFO - Iter [94200/160000] lr: 2.468e-05, eta: 6:53:56, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1849, decode.acc_seg: 92.7213, aux.loss_ce: 0.1203, aux.acc_seg: 87.9937, loss: 0.3052, grad_norm: 3.1147 2023-02-17 02:34:12,644 - mmseg - INFO - Iter [94250/160000] lr: 2.466e-05, eta: 6:53:33, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1743, decode.acc_seg: 92.7956, aux.loss_ce: 0.1084, aux.acc_seg: 88.8764, loss: 0.2827, grad_norm: 2.7417 2023-02-17 02:34:30,755 - mmseg - INFO - Iter [94300/160000] lr: 2.464e-05, eta: 6:53:09, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1908, decode.acc_seg: 92.5218, aux.loss_ce: 0.1232, aux.acc_seg: 87.8329, loss: 0.3140, grad_norm: 3.0915 2023-02-17 02:34:48,853 - mmseg - INFO - Iter [94350/160000] lr: 2.462e-05, eta: 6:52:45, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1798, decode.acc_seg: 92.6803, aux.loss_ce: 0.1138, aux.acc_seg: 88.6500, loss: 0.2936, grad_norm: 2.6116 2023-02-17 02:35:06,766 - mmseg - INFO - Iter [94400/160000] lr: 2.460e-05, eta: 6:52:20, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1843, decode.acc_seg: 92.6615, aux.loss_ce: 0.1145, aux.acc_seg: 88.5489, loss: 0.2988, grad_norm: 2.8789 2023-02-17 02:35:24,396 - mmseg - INFO - Iter [94450/160000] lr: 2.458e-05, eta: 6:51:54, time: 0.352, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1894, decode.acc_seg: 92.3208, aux.loss_ce: 0.1186, aux.acc_seg: 88.3258, loss: 0.3080, grad_norm: 2.8158 2023-02-17 02:35:42,389 - mmseg - INFO - Iter [94500/160000] lr: 2.456e-05, eta: 6:51:30, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1894, decode.acc_seg: 92.4047, aux.loss_ce: 0.1149, aux.acc_seg: 88.7572, loss: 0.3043, grad_norm: 3.1728 2023-02-17 02:36:00,384 - mmseg - INFO - Iter [94550/160000] lr: 2.454e-05, eta: 6:51:05, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1794, decode.acc_seg: 92.7999, aux.loss_ce: 0.1189, aux.acc_seg: 88.3371, loss: 0.2983, grad_norm: 3.2254 2023-02-17 02:36:18,431 - mmseg - INFO - Iter [94600/160000] lr: 2.453e-05, eta: 6:50:41, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1916, decode.acc_seg: 92.1095, aux.loss_ce: 0.1127, aux.acc_seg: 88.4985, loss: 0.3043, grad_norm: 2.5938 2023-02-17 02:36:36,244 - mmseg - INFO - Iter [94650/160000] lr: 2.451e-05, eta: 6:50:16, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1841, decode.acc_seg: 92.3962, aux.loss_ce: 0.1175, aux.acc_seg: 88.3196, loss: 0.3016, grad_norm: 3.3474 2023-02-17 02:36:54,736 - mmseg - INFO - Iter [94700/160000] lr: 2.449e-05, eta: 6:49:56, time: 0.370, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1815, decode.acc_seg: 92.5252, aux.loss_ce: 0.1176, aux.acc_seg: 88.1196, loss: 0.2991, grad_norm: 2.7071 2023-02-17 02:37:12,994 - mmseg - INFO - Iter [94750/160000] lr: 2.447e-05, eta: 6:49:33, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1834, decode.acc_seg: 92.5854, aux.loss_ce: 0.1167, aux.acc_seg: 88.2506, loss: 0.3001, grad_norm: 2.8527 2023-02-17 02:37:30,520 - mmseg - INFO - Iter [94800/160000] lr: 2.445e-05, eta: 6:49:07, time: 0.350, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1721, decode.acc_seg: 93.2247, aux.loss_ce: 0.1159, aux.acc_seg: 88.7403, loss: 0.2880, grad_norm: 2.7502 2023-02-17 02:37:48,515 - mmseg - INFO - Iter [94850/160000] lr: 2.443e-05, eta: 6:48:43, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1823, decode.acc_seg: 92.5805, aux.loss_ce: 0.1175, aux.acc_seg: 88.1168, loss: 0.2999, grad_norm: 2.8728 2023-02-17 02:38:06,424 - mmseg - INFO - Iter [94900/160000] lr: 2.441e-05, eta: 6:48:18, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.2194, decode.acc_seg: 91.2221, aux.loss_ce: 0.1336, aux.acc_seg: 87.0595, loss: 0.3530, grad_norm: 3.8167 2023-02-17 02:38:24,333 - mmseg - INFO - Iter [94950/160000] lr: 2.439e-05, eta: 6:47:54, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1816, decode.acc_seg: 92.6858, aux.loss_ce: 0.1134, aux.acc_seg: 88.8403, loss: 0.2950, grad_norm: 2.8643 2023-02-17 02:38:42,587 - mmseg - INFO - Saving checkpoint at 95000 iterations 2023-02-17 02:38:43,655 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 02:38:43,656 - mmseg - INFO - Iter [95000/160000] lr: 2.438e-05, eta: 6:47:38, time: 0.386, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1860, decode.acc_seg: 92.6082, aux.loss_ce: 0.1220, aux.acc_seg: 87.8281, loss: 0.3080, grad_norm: 2.8752 2023-02-17 02:39:02,082 - mmseg - INFO - Iter [95050/160000] lr: 2.436e-05, eta: 6:47:17, time: 0.368, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1933, decode.acc_seg: 92.1287, aux.loss_ce: 0.1222, aux.acc_seg: 87.8248, loss: 0.3155, grad_norm: 3.2719 2023-02-17 02:39:20,339 - mmseg - INFO - Iter [95100/160000] lr: 2.434e-05, eta: 6:46:55, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1902, decode.acc_seg: 92.1767, aux.loss_ce: 0.1169, aux.acc_seg: 88.0695, loss: 0.3071, grad_norm: 2.9667 2023-02-17 02:39:38,284 - mmseg - INFO - Iter [95150/160000] lr: 2.432e-05, eta: 6:46:31, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1859, decode.acc_seg: 92.3953, aux.loss_ce: 0.1184, aux.acc_seg: 88.0291, loss: 0.3043, grad_norm: 3.1497 2023-02-17 02:39:56,666 - mmseg - INFO - Iter [95200/160000] lr: 2.430e-05, eta: 6:46:10, time: 0.368, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1907, decode.acc_seg: 92.2603, aux.loss_ce: 0.1226, aux.acc_seg: 87.6679, loss: 0.3133, grad_norm: 3.3186 2023-02-17 02:40:15,037 - mmseg - INFO - Iter [95250/160000] lr: 2.428e-05, eta: 6:45:49, time: 0.367, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1932, decode.acc_seg: 92.3639, aux.loss_ce: 0.1200, aux.acc_seg: 88.0962, loss: 0.3132, grad_norm: 3.2362 2023-02-17 02:40:33,068 - mmseg - INFO - Iter [95300/160000] lr: 2.426e-05, eta: 6:45:26, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1841, decode.acc_seg: 92.5423, aux.loss_ce: 0.1206, aux.acc_seg: 87.9176, loss: 0.3047, grad_norm: 3.0018 2023-02-17 02:40:50,873 - mmseg - INFO - Iter [95350/160000] lr: 2.424e-05, eta: 6:45:01, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1850, decode.acc_seg: 92.4590, aux.loss_ce: 0.1134, aux.acc_seg: 88.6390, loss: 0.2984, grad_norm: 2.8331 2023-02-17 02:41:10,891 - mmseg - INFO - Iter [95400/160000] lr: 2.423e-05, eta: 6:44:49, time: 0.400, data_time: 0.053, memory: 16596, decode.loss_ce: 0.1912, decode.acc_seg: 92.3268, aux.loss_ce: 0.1217, aux.acc_seg: 87.8910, loss: 0.3129, grad_norm: 3.4481 2023-02-17 02:41:28,612 - mmseg - INFO - Iter [95450/160000] lr: 2.421e-05, eta: 6:44:24, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1789, decode.acc_seg: 92.6699, aux.loss_ce: 0.1243, aux.acc_seg: 87.4955, loss: 0.3033, grad_norm: 2.7151 2023-02-17 02:41:46,675 - mmseg - INFO - Iter [95500/160000] lr: 2.419e-05, eta: 6:44:02, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1888, decode.acc_seg: 92.2097, aux.loss_ce: 0.1253, aux.acc_seg: 87.5279, loss: 0.3141, grad_norm: 3.3738 2023-02-17 02:42:04,529 - mmseg - INFO - Iter [95550/160000] lr: 2.417e-05, eta: 6:43:38, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1867, decode.acc_seg: 92.3954, aux.loss_ce: 0.1149, aux.acc_seg: 88.2230, loss: 0.3016, grad_norm: 2.7627 2023-02-17 02:42:22,890 - mmseg - INFO - Iter [95600/160000] lr: 2.415e-05, eta: 6:43:16, time: 0.367, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1794, decode.acc_seg: 92.6906, aux.loss_ce: 0.1181, aux.acc_seg: 87.8816, loss: 0.2975, grad_norm: 2.5171 2023-02-17 02:42:41,106 - mmseg - INFO - Iter [95650/160000] lr: 2.413e-05, eta: 6:42:54, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1830, decode.acc_seg: 92.6980, aux.loss_ce: 0.1213, aux.acc_seg: 88.1681, loss: 0.3043, grad_norm: 2.6574 2023-02-17 02:42:59,141 - mmseg - INFO - Iter [95700/160000] lr: 2.411e-05, eta: 6:42:32, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1732, decode.acc_seg: 92.7975, aux.loss_ce: 0.1092, aux.acc_seg: 88.8129, loss: 0.2825, grad_norm: 2.7837 2023-02-17 02:43:16,977 - mmseg - INFO - Iter [95750/160000] lr: 2.409e-05, eta: 6:42:08, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1803, decode.acc_seg: 92.8159, aux.loss_ce: 0.1170, aux.acc_seg: 88.4815, loss: 0.2973, grad_norm: 3.1112 2023-02-17 02:43:34,900 - mmseg - INFO - Iter [95800/160000] lr: 2.408e-05, eta: 6:41:44, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1814, decode.acc_seg: 92.6157, aux.loss_ce: 0.1206, aux.acc_seg: 87.9762, loss: 0.3019, grad_norm: 3.1311 2023-02-17 02:43:53,199 - mmseg - INFO - Iter [95850/160000] lr: 2.406e-05, eta: 6:41:23, time: 0.366, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1842, decode.acc_seg: 92.7859, aux.loss_ce: 0.1143, aux.acc_seg: 88.8077, loss: 0.2984, grad_norm: 3.1136 2023-02-17 02:44:11,603 - mmseg - INFO - Iter [95900/160000] lr: 2.404e-05, eta: 6:41:02, time: 0.368, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1842, decode.acc_seg: 92.5582, aux.loss_ce: 0.1158, aux.acc_seg: 88.5629, loss: 0.3000, grad_norm: 2.8544 2023-02-17 02:44:29,376 - mmseg - INFO - Iter [95950/160000] lr: 2.402e-05, eta: 6:40:38, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1678, decode.acc_seg: 93.2204, aux.loss_ce: 0.1096, aux.acc_seg: 88.8920, loss: 0.2774, grad_norm: 2.4827 2023-02-17 02:44:47,712 - mmseg - INFO - Saving checkpoint at 96000 iterations 2023-02-17 02:44:48,782 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 02:44:48,782 - mmseg - INFO - Iter [96000/160000] lr: 2.400e-05, eta: 6:40:23, time: 0.388, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1718, decode.acc_seg: 93.1690, aux.loss_ce: 0.1113, aux.acc_seg: 88.8010, loss: 0.2831, grad_norm: 2.4436 2023-02-17 02:45:30,129 - mmseg - INFO - per class results: 2023-02-17 02:45:30,135 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 75.85 | 89.53 | | building | 81.75 | 92.07 | | sky | 94.16 | 97.12 | | floor | 80.25 | 90.32 | | tree | 73.84 | 84.73 | | ceiling | 82.76 | 87.88 | | road | 82.59 | 87.85 | | bed | 86.76 | 96.53 | | windowpane | 62.53 | 76.9 | | grass | 64.79 | 79.3 | | cabinet | 59.06 | 77.54 | | sidewalk | 66.14 | 84.16 | | person | 79.98 | 90.6 | | earth | 34.12 | 48.82 | | door | 47.56 | 68.19 | | table | 59.67 | 74.86 | | mountain | 58.27 | 75.25 | | plant | 50.41 | 61.71 | | curtain | 71.85 | 82.55 | | chair | 55.97 | 69.13 | | car | 81.38 | 88.06 | | water | 54.38 | 69.58 | | painting | 74.34 | 85.26 | | sofa | 62.8 | 81.94 | | shelf | 42.59 | 56.5 | | house | 48.98 | 66.85 | | sea | 56.83 | 75.44 | | mirror | 62.49 | 68.39 | | rug | 62.64 | 72.06 | | field | 24.85 | 41.07 | | armchair | 39.97 | 56.38 | | seat | 58.13 | 76.97 | | fence | 39.27 | 57.53 | | desk | 46.51 | 54.36 | | rock | 44.86 | 64.74 | | wardrobe | 43.92 | 55.56 | | lamp | 61.32 | 71.07 | | bathtub | 71.55 | 76.53 | | railing | 33.99 | 54.55 | | cushion | 52.3 | 57.21 | | base | 27.93 | 36.7 | | box | 21.49 | 25.0 | | column | 42.95 | 56.49 | | signboard | 34.87 | 43.12 | | chest of drawers | 41.63 | 53.07 | | counter | 24.21 | 32.74 | | sand | 40.92 | 70.42 | | sink | 71.38 | 79.6 | | skyscraper | 49.09 | 71.62 | | fireplace | 73.19 | 85.46 | | refrigerator | 73.33 | 86.35 | | grandstand | 42.25 | 58.21 | | path | 26.0 | 37.05 | | stairs | 30.98 | 40.24 | | runway | 63.95 | 85.02 | | case | 39.5 | 54.01 | | pool table | 93.59 | 96.01 | | pillow | 57.37 | 69.6 | | screen door | 46.61 | 51.51 | | stairway | 28.43 | 39.35 | | river | 8.35 | 17.3 | | bridge | 65.76 | 77.71 | | bookcase | 32.47 | 44.81 | | blind | 45.12 | 50.48 | | coffee table | 57.28 | 80.65 | | toilet | 82.91 | 91.49 | | flower | 38.64 | 60.46 | | book | 44.33 | 66.55 | | hill | 5.1 | 8.62 | | bench | 39.23 | 49.7 | | countertop | 60.25 | 77.72 | | stove | 76.13 | 78.77 | | palm | 48.76 | 82.47 | | kitchen island | 37.75 | 62.18 | | computer | 63.02 | 77.65 | | swivel chair | 47.57 | 75.29 | | boat | 42.78 | 52.95 | | bar | 15.45 | 18.61 | | arcade machine | 32.38 | 35.5 | | hovel | 7.61 | 8.06 | | bus | 86.89 | 97.08 | | towel | 61.45 | 69.18 | | light | 54.47 | 60.5 | | truck | 34.11 | 47.31 | | tower | 17.26 | 24.15 | | chandelier | 65.41 | 77.73 | | awning | 27.72 | 31.4 | | streetlight | 25.49 | 32.69 | | booth | 38.25 | 39.07 | | television receiver | 66.97 | 76.48 | | airplane | 56.32 | 61.89 | | dirt track | 4.63 | 24.19 | | apparel | 35.86 | 42.93 | | pole | 21.86 | 30.5 | | land | 4.61 | 5.64 | | bannister | 12.66 | 17.63 | | escalator | 17.49 | 18.36 | | ottoman | 42.51 | 58.52 | | bottle | 38.53 | 53.7 | | buffet | 41.02 | 45.21 | | poster | 28.53 | 37.71 | | stage | 14.35 | 21.1 | | van | 49.23 | 63.0 | | ship | 21.41 | 30.48 | | fountain | 26.13 | 27.46 | | conveyer belt | 71.35 | 87.62 | | canopy | 14.74 | 19.46 | | washer | 68.7 | 70.95 | | plaything | 22.18 | 25.51 | | swimming pool | 50.21 | 62.6 | | stool | 36.32 | 56.06 | | barrel | 17.29 | 64.13 | | basket | 34.17 | 46.4 | | waterfall | 48.82 | 65.24 | | tent | 96.27 | 97.93 | | bag | 10.87 | 12.11 | | minibike | 55.9 | 75.15 | | cradle | 76.8 | 92.02 | | oven | 50.45 | 60.47 | | ball | 46.42 | 63.24 | | food | 61.17 | 71.93 | | step | 13.7 | 21.29 | | tank | 31.58 | 35.79 | | trade name | 31.18 | 42.31 | | microwave | 82.31 | 90.7 | | pot | 43.66 | 53.99 | | animal | 57.4 | 60.04 | | bicycle | 54.05 | 78.99 | | lake | 54.87 | 62.84 | | dishwasher | 70.35 | 82.28 | | screen | 67.69 | 87.35 | | blanket | 8.79 | 10.49 | | sculpture | 48.65 | 69.52 | | hood | 57.78 | 65.03 | | sconce | 39.28 | 43.07 | | vase | 35.36 | 62.82 | | traffic light | 33.08 | 46.13 | | tray | 8.03 | 13.23 | | ashcan | 39.38 | 57.04 | | fan | 60.63 | 77.4 | | pier | 24.44 | 42.59 | | crt screen | 0.03 | 0.06 | | plate | 55.88 | 74.31 | | monitor | 9.16 | 13.72 | | bulletin board | 39.67 | 49.91 | | shower | 0.75 | 2.25 | | radiator | 61.78 | 68.52 | | glass | 9.84 | 10.21 | | clock | 34.09 | 41.58 | | flag | 32.75 | 35.45 | +---------------------+-------+-------+ 2023-02-17 02:45:30,136 - mmseg - INFO - Summary: 2023-02-17 02:45:30,136 - mmseg - INFO - +------+-------+-------+ | aAcc | mIoU | mAcc | +------+-------+-------+ | 82.1 | 46.49 | 57.98 | +------+-------+-------+ 2023-02-17 02:45:31,098 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_96000.pth. 2023-02-17 02:45:31,098 - mmseg - INFO - Best mIoU is 0.4649 at 96000 iter. 2023-02-17 02:45:31,098 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 02:45:31,098 - mmseg - INFO - Iter(val) [500] aAcc: 0.8210, mIoU: 0.4649, mAcc: 0.5798, IoU.wall: 0.7585, IoU.building: 0.8175, IoU.sky: 0.9416, IoU.floor: 0.8025, IoU.tree: 0.7384, IoU.ceiling: 0.8276, IoU.road: 0.8259, IoU.bed : 0.8676, IoU.windowpane: 0.6253, IoU.grass: 0.6479, IoU.cabinet: 0.5906, IoU.sidewalk: 0.6614, IoU.person: 0.7998, IoU.earth: 0.3412, IoU.door: 0.4756, IoU.table: 0.5967, IoU.mountain: 0.5827, IoU.plant: 0.5041, IoU.curtain: 0.7185, IoU.chair: 0.5597, IoU.car: 0.8138, IoU.water: 0.5438, IoU.painting: 0.7434, IoU.sofa: 0.6280, IoU.shelf: 0.4259, IoU.house: 0.4898, IoU.sea: 0.5683, IoU.mirror: 0.6249, IoU.rug: 0.6264, IoU.field: 0.2485, IoU.armchair: 0.3997, IoU.seat: 0.5813, IoU.fence: 0.3927, IoU.desk: 0.4651, IoU.rock: 0.4486, IoU.wardrobe: 0.4392, IoU.lamp: 0.6132, IoU.bathtub: 0.7155, IoU.railing: 0.3399, IoU.cushion: 0.5230, IoU.base: 0.2793, IoU.box: 0.2149, IoU.column: 0.4295, IoU.signboard: 0.3487, IoU.chest of drawers: 0.4163, IoU.counter: 0.2421, IoU.sand: 0.4092, IoU.sink: 0.7138, IoU.skyscraper: 0.4909, IoU.fireplace: 0.7319, IoU.refrigerator: 0.7333, IoU.grandstand: 0.4225, IoU.path: 0.2600, IoU.stairs: 0.3098, IoU.runway: 0.6395, IoU.case: 0.3950, IoU.pool table: 0.9359, IoU.pillow: 0.5737, IoU.screen door: 0.4661, IoU.stairway: 0.2843, IoU.river: 0.0835, IoU.bridge: 0.6576, IoU.bookcase: 0.3247, IoU.blind: 0.4512, IoU.coffee table: 0.5728, IoU.toilet: 0.8291, IoU.flower: 0.3864, IoU.book: 0.4433, IoU.hill: 0.0510, IoU.bench: 0.3923, IoU.countertop: 0.6025, IoU.stove: 0.7613, IoU.palm: 0.4876, IoU.kitchen island: 0.3775, IoU.computer: 0.6302, IoU.swivel chair: 0.4757, IoU.boat: 0.4278, IoU.bar: 0.1545, IoU.arcade machine: 0.3238, IoU.hovel: 0.0761, IoU.bus: 0.8689, IoU.towel: 0.6145, IoU.light: 0.5447, IoU.truck: 0.3411, IoU.tower: 0.1726, IoU.chandelier: 0.6541, IoU.awning: 0.2772, IoU.streetlight: 0.2549, IoU.booth: 0.3825, IoU.television receiver: 0.6697, IoU.airplane: 0.5632, IoU.dirt track: 0.0463, IoU.apparel: 0.3586, IoU.pole: 0.2186, IoU.land: 0.0461, IoU.bannister: 0.1266, IoU.escalator: 0.1749, IoU.ottoman: 0.4251, IoU.bottle: 0.3853, IoU.buffet: 0.4102, IoU.poster: 0.2853, IoU.stage: 0.1435, IoU.van: 0.4923, IoU.ship: 0.2141, IoU.fountain: 0.2613, IoU.conveyer belt: 0.7135, IoU.canopy: 0.1474, IoU.washer: 0.6870, IoU.plaything: 0.2218, IoU.swimming pool: 0.5021, IoU.stool: 0.3632, IoU.barrel: 0.1729, IoU.basket: 0.3417, IoU.waterfall: 0.4882, IoU.tent: 0.9627, IoU.bag: 0.1087, IoU.minibike: 0.5590, IoU.cradle: 0.7680, IoU.oven: 0.5045, IoU.ball: 0.4642, IoU.food: 0.6117, IoU.step: 0.1370, IoU.tank: 0.3158, IoU.trade name: 0.3118, IoU.microwave: 0.8231, IoU.pot: 0.4366, IoU.animal: 0.5740, IoU.bicycle: 0.5405, IoU.lake: 0.5487, IoU.dishwasher: 0.7035, IoU.screen: 0.6769, IoU.blanket: 0.0879, IoU.sculpture: 0.4865, IoU.hood: 0.5778, IoU.sconce: 0.3928, IoU.vase: 0.3536, IoU.traffic light: 0.3308, IoU.tray: 0.0803, IoU.ashcan: 0.3938, IoU.fan: 0.6063, IoU.pier: 0.2444, IoU.crt screen: 0.0003, IoU.plate: 0.5588, IoU.monitor: 0.0916, IoU.bulletin board: 0.3967, IoU.shower: 0.0075, IoU.radiator: 0.6178, IoU.glass: 0.0984, IoU.clock: 0.3409, IoU.flag: 0.3275, Acc.wall: 0.8953, Acc.building: 0.9207, Acc.sky: 0.9712, Acc.floor: 0.9032, Acc.tree: 0.8473, Acc.ceiling: 0.8788, Acc.road: 0.8785, Acc.bed : 0.9653, Acc.windowpane: 0.7690, Acc.grass: 0.7930, Acc.cabinet: 0.7754, Acc.sidewalk: 0.8416, Acc.person: 0.9060, Acc.earth: 0.4882, Acc.door: 0.6819, Acc.table: 0.7486, Acc.mountain: 0.7525, Acc.plant: 0.6171, Acc.curtain: 0.8255, Acc.chair: 0.6913, Acc.car: 0.8806, Acc.water: 0.6958, Acc.painting: 0.8526, Acc.sofa: 0.8194, Acc.shelf: 0.5650, Acc.house: 0.6685, Acc.sea: 0.7544, Acc.mirror: 0.6839, Acc.rug: 0.7206, Acc.field: 0.4107, Acc.armchair: 0.5638, Acc.seat: 0.7697, Acc.fence: 0.5753, Acc.desk: 0.5436, Acc.rock: 0.6474, Acc.wardrobe: 0.5556, Acc.lamp: 0.7107, Acc.bathtub: 0.7653, Acc.railing: 0.5455, Acc.cushion: 0.5721, Acc.base: 0.3670, Acc.box: 0.2500, Acc.column: 0.5649, Acc.signboard: 0.4312, Acc.chest of drawers: 0.5307, Acc.counter: 0.3274, Acc.sand: 0.7042, Acc.sink: 0.7960, Acc.skyscraper: 0.7162, Acc.fireplace: 0.8546, Acc.refrigerator: 0.8635, Acc.grandstand: 0.5821, Acc.path: 0.3705, Acc.stairs: 0.4024, Acc.runway: 0.8502, Acc.case: 0.5401, Acc.pool table: 0.9601, Acc.pillow: 0.6960, Acc.screen door: 0.5151, Acc.stairway: 0.3935, Acc.river: 0.1730, Acc.bridge: 0.7771, Acc.bookcase: 0.4481, Acc.blind: 0.5048, Acc.coffee table: 0.8065, Acc.toilet: 0.9149, Acc.flower: 0.6046, Acc.book: 0.6655, Acc.hill: 0.0862, Acc.bench: 0.4970, Acc.countertop: 0.7772, Acc.stove: 0.7877, Acc.palm: 0.8247, Acc.kitchen island: 0.6218, Acc.computer: 0.7765, Acc.swivel chair: 0.7529, Acc.boat: 0.5295, Acc.bar: 0.1861, Acc.arcade machine: 0.3550, Acc.hovel: 0.0806, Acc.bus: 0.9708, Acc.towel: 0.6918, Acc.light: 0.6050, Acc.truck: 0.4731, Acc.tower: 0.2415, Acc.chandelier: 0.7773, Acc.awning: 0.3140, Acc.streetlight: 0.3269, Acc.booth: 0.3907, Acc.television receiver: 0.7648, Acc.airplane: 0.6189, Acc.dirt track: 0.2419, Acc.apparel: 0.4293, Acc.pole: 0.3050, Acc.land: 0.0564, Acc.bannister: 0.1763, Acc.escalator: 0.1836, Acc.ottoman: 0.5852, Acc.bottle: 0.5370, Acc.buffet: 0.4521, Acc.poster: 0.3771, Acc.stage: 0.2110, Acc.van: 0.6300, Acc.ship: 0.3048, Acc.fountain: 0.2746, Acc.conveyer belt: 0.8762, Acc.canopy: 0.1946, Acc.washer: 0.7095, Acc.plaything: 0.2551, Acc.swimming pool: 0.6260, Acc.stool: 0.5606, Acc.barrel: 0.6413, Acc.basket: 0.4640, Acc.waterfall: 0.6524, Acc.tent: 0.9793, Acc.bag: 0.1211, Acc.minibike: 0.7515, Acc.cradle: 0.9202, Acc.oven: 0.6047, Acc.ball: 0.6324, Acc.food: 0.7193, Acc.step: 0.2129, Acc.tank: 0.3579, Acc.trade name: 0.4231, Acc.microwave: 0.9070, Acc.pot: 0.5399, Acc.animal: 0.6004, Acc.bicycle: 0.7899, Acc.lake: 0.6284, Acc.dishwasher: 0.8228, Acc.screen: 0.8735, Acc.blanket: 0.1049, Acc.sculpture: 0.6952, Acc.hood: 0.6503, Acc.sconce: 0.4307, Acc.vase: 0.6282, Acc.traffic light: 0.4613, Acc.tray: 0.1323, Acc.ashcan: 0.5704, Acc.fan: 0.7740, Acc.pier: 0.4259, Acc.crt screen: 0.0006, Acc.plate: 0.7431, Acc.monitor: 0.1372, Acc.bulletin board: 0.4991, Acc.shower: 0.0225, Acc.radiator: 0.6852, Acc.glass: 0.1021, Acc.clock: 0.4158, Acc.flag: 0.3545 2023-02-17 02:45:49,520 - mmseg - INFO - Iter [96050/160000] lr: 2.398e-05, eta: 6:43:46, time: 1.214, data_time: 0.853, memory: 16596, decode.loss_ce: 0.1769, decode.acc_seg: 92.7589, aux.loss_ce: 0.1134, aux.acc_seg: 88.6258, loss: 0.2902, grad_norm: 2.6491 2023-02-17 02:46:07,635 - mmseg - INFO - Iter [96100/160000] lr: 2.396e-05, eta: 6:43:23, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1818, decode.acc_seg: 92.6614, aux.loss_ce: 0.1165, aux.acc_seg: 88.2211, loss: 0.2983, grad_norm: 3.1381 2023-02-17 02:46:25,290 - mmseg - INFO - Iter [96150/160000] lr: 2.394e-05, eta: 6:42:58, time: 0.353, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1785, decode.acc_seg: 92.8326, aux.loss_ce: 0.1107, aux.acc_seg: 88.9247, loss: 0.2893, grad_norm: 2.6453 2023-02-17 02:46:43,522 - mmseg - INFO - Iter [96200/160000] lr: 2.393e-05, eta: 6:42:35, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1747, decode.acc_seg: 92.5130, aux.loss_ce: 0.1168, aux.acc_seg: 87.9802, loss: 0.2915, grad_norm: 2.5665 2023-02-17 02:47:01,455 - mmseg - INFO - Iter [96250/160000] lr: 2.391e-05, eta: 6:42:11, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1760, decode.acc_seg: 92.8480, aux.loss_ce: 0.1173, aux.acc_seg: 88.0932, loss: 0.2934, grad_norm: 3.1037 2023-02-17 02:47:19,198 - mmseg - INFO - Iter [96300/160000] lr: 2.389e-05, eta: 6:41:46, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1746, decode.acc_seg: 93.0417, aux.loss_ce: 0.1119, aux.acc_seg: 88.9956, loss: 0.2865, grad_norm: 2.6434 2023-02-17 02:47:37,108 - mmseg - INFO - Iter [96350/160000] lr: 2.387e-05, eta: 6:41:22, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1706, decode.acc_seg: 92.9024, aux.loss_ce: 0.1140, aux.acc_seg: 88.4623, loss: 0.2846, grad_norm: 2.6852 2023-02-17 02:47:54,809 - mmseg - INFO - Iter [96400/160000] lr: 2.385e-05, eta: 6:40:56, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1828, decode.acc_seg: 92.5130, aux.loss_ce: 0.1190, aux.acc_seg: 88.0808, loss: 0.3018, grad_norm: 2.7006 2023-02-17 02:48:12,812 - mmseg - INFO - Iter [96450/160000] lr: 2.383e-05, eta: 6:40:33, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1780, decode.acc_seg: 92.6568, aux.loss_ce: 0.1165, aux.acc_seg: 88.1700, loss: 0.2945, grad_norm: 3.1947 2023-02-17 02:48:30,701 - mmseg - INFO - Iter [96500/160000] lr: 2.381e-05, eta: 6:40:09, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1908, decode.acc_seg: 92.3956, aux.loss_ce: 0.1191, aux.acc_seg: 88.4075, loss: 0.3099, grad_norm: 2.9928 2023-02-17 02:48:48,732 - mmseg - INFO - Iter [96550/160000] lr: 2.379e-05, eta: 6:39:45, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1799, decode.acc_seg: 92.5540, aux.loss_ce: 0.1112, aux.acc_seg: 88.8060, loss: 0.2912, grad_norm: 2.4112 2023-02-17 02:49:06,600 - mmseg - INFO - Iter [96600/160000] lr: 2.378e-05, eta: 6:39:21, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1794, decode.acc_seg: 92.6466, aux.loss_ce: 0.1181, aux.acc_seg: 88.0661, loss: 0.2975, grad_norm: 2.6659 2023-02-17 02:49:27,394 - mmseg - INFO - Iter [96650/160000] lr: 2.376e-05, eta: 6:39:12, time: 0.416, data_time: 0.054, memory: 16596, decode.loss_ce: 0.1824, decode.acc_seg: 92.7487, aux.loss_ce: 0.1175, aux.acc_seg: 88.4830, loss: 0.2999, grad_norm: 2.9240 2023-02-17 02:49:45,608 - mmseg - INFO - Iter [96700/160000] lr: 2.374e-05, eta: 6:38:50, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1712, decode.acc_seg: 92.9371, aux.loss_ce: 0.1102, aux.acc_seg: 88.7229, loss: 0.2814, grad_norm: 2.4800 2023-02-17 02:50:03,508 - mmseg - INFO - Iter [96750/160000] lr: 2.372e-05, eta: 6:38:26, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1805, decode.acc_seg: 92.4871, aux.loss_ce: 0.1128, aux.acc_seg: 88.3735, loss: 0.2932, grad_norm: 2.8850 2023-02-17 02:50:21,555 - mmseg - INFO - Iter [96800/160000] lr: 2.370e-05, eta: 6:38:03, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1866, decode.acc_seg: 92.2660, aux.loss_ce: 0.1203, aux.acc_seg: 87.9688, loss: 0.3069, grad_norm: 3.0511 2023-02-17 02:50:39,461 - mmseg - INFO - Iter [96850/160000] lr: 2.368e-05, eta: 6:37:39, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1745, decode.acc_seg: 93.1793, aux.loss_ce: 0.1171, aux.acc_seg: 88.4491, loss: 0.2916, grad_norm: 2.4384 2023-02-17 02:50:57,530 - mmseg - INFO - Iter [96900/160000] lr: 2.366e-05, eta: 6:37:16, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1729, decode.acc_seg: 93.0923, aux.loss_ce: 0.1117, aux.acc_seg: 88.9662, loss: 0.2846, grad_norm: 2.6587 2023-02-17 02:51:15,374 - mmseg - INFO - Iter [96950/160000] lr: 2.364e-05, eta: 6:36:52, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1766, decode.acc_seg: 92.8404, aux.loss_ce: 0.1107, aux.acc_seg: 88.7768, loss: 0.2873, grad_norm: 2.7352 2023-02-17 02:51:33,351 - mmseg - INFO - Saving checkpoint at 97000 iterations 2023-02-17 02:51:34,385 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 02:51:34,385 - mmseg - INFO - Iter [97000/160000] lr: 2.363e-05, eta: 6:36:34, time: 0.380, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1772, decode.acc_seg: 92.6920, aux.loss_ce: 0.1153, aux.acc_seg: 88.3411, loss: 0.2925, grad_norm: 2.6375 2023-02-17 02:51:52,220 - mmseg - INFO - Iter [97050/160000] lr: 2.361e-05, eta: 6:36:10, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1867, decode.acc_seg: 92.5319, aux.loss_ce: 0.1191, aux.acc_seg: 88.1083, loss: 0.3058, grad_norm: 2.7741 2023-02-17 02:52:10,454 - mmseg - INFO - Iter [97100/160000] lr: 2.359e-05, eta: 6:35:48, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1735, decode.acc_seg: 92.8370, aux.loss_ce: 0.1118, aux.acc_seg: 88.6195, loss: 0.2853, grad_norm: 2.6176 2023-02-17 02:52:28,476 - mmseg - INFO - Iter [97150/160000] lr: 2.357e-05, eta: 6:35:25, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1767, decode.acc_seg: 92.8186, aux.loss_ce: 0.1117, aux.acc_seg: 88.5194, loss: 0.2885, grad_norm: 3.0171 2023-02-17 02:52:46,606 - mmseg - INFO - Iter [97200/160000] lr: 2.355e-05, eta: 6:35:02, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1656, decode.acc_seg: 93.2537, aux.loss_ce: 0.1032, aux.acc_seg: 89.3546, loss: 0.2688, grad_norm: 2.4238 2023-02-17 02:53:04,706 - mmseg - INFO - Iter [97250/160000] lr: 2.353e-05, eta: 6:34:40, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1793, decode.acc_seg: 92.8574, aux.loss_ce: 0.1173, aux.acc_seg: 88.5105, loss: 0.2966, grad_norm: 2.6909 2023-02-17 02:53:22,678 - mmseg - INFO - Iter [97300/160000] lr: 2.351e-05, eta: 6:34:17, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1781, decode.acc_seg: 92.6323, aux.loss_ce: 0.1165, aux.acc_seg: 88.0118, loss: 0.2946, grad_norm: 2.7394 2023-02-17 02:53:40,532 - mmseg - INFO - Iter [97350/160000] lr: 2.349e-05, eta: 6:33:53, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1866, decode.acc_seg: 92.4325, aux.loss_ce: 0.1194, aux.acc_seg: 88.0615, loss: 0.3059, grad_norm: 3.0371 2023-02-17 02:53:58,588 - mmseg - INFO - Iter [97400/160000] lr: 2.348e-05, eta: 6:33:31, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1789, decode.acc_seg: 92.7829, aux.loss_ce: 0.1142, aux.acc_seg: 88.4850, loss: 0.2931, grad_norm: 2.4658 2023-02-17 02:54:16,842 - mmseg - INFO - Iter [97450/160000] lr: 2.346e-05, eta: 6:33:09, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1721, decode.acc_seg: 92.9603, aux.loss_ce: 0.1093, aux.acc_seg: 89.0994, loss: 0.2814, grad_norm: 2.4651 2023-02-17 02:54:35,105 - mmseg - INFO - Iter [97500/160000] lr: 2.344e-05, eta: 6:32:47, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1753, decode.acc_seg: 92.8205, aux.loss_ce: 0.1103, aux.acc_seg: 88.7807, loss: 0.2857, grad_norm: 2.8417 2023-02-17 02:54:52,974 - mmseg - INFO - Iter [97550/160000] lr: 2.342e-05, eta: 6:32:24, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1821, decode.acc_seg: 92.7412, aux.loss_ce: 0.1175, aux.acc_seg: 88.3386, loss: 0.2996, grad_norm: 3.0060 2023-02-17 02:55:11,008 - mmseg - INFO - Iter [97600/160000] lr: 2.340e-05, eta: 6:32:01, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1873, decode.acc_seg: 92.6077, aux.loss_ce: 0.1176, aux.acc_seg: 88.5069, loss: 0.3049, grad_norm: 2.6940 2023-02-17 02:55:28,984 - mmseg - INFO - Iter [97650/160000] lr: 2.338e-05, eta: 6:31:38, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1836, decode.acc_seg: 92.3333, aux.loss_ce: 0.1200, aux.acc_seg: 87.7656, loss: 0.3035, grad_norm: 2.8925 2023-02-17 02:55:46,841 - mmseg - INFO - Iter [97700/160000] lr: 2.336e-05, eta: 6:31:15, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1789, decode.acc_seg: 92.6956, aux.loss_ce: 0.1133, aux.acc_seg: 88.7191, loss: 0.2923, grad_norm: 2.8741 2023-02-17 02:56:05,036 - mmseg - INFO - Iter [97750/160000] lr: 2.334e-05, eta: 6:30:53, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1991, decode.acc_seg: 91.9998, aux.loss_ce: 0.1263, aux.acc_seg: 87.4930, loss: 0.3253, grad_norm: 3.6328 2023-02-17 02:56:23,322 - mmseg - INFO - Iter [97800/160000] lr: 2.333e-05, eta: 6:30:32, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1772, decode.acc_seg: 92.7791, aux.loss_ce: 0.1130, aux.acc_seg: 88.7788, loss: 0.2902, grad_norm: 2.6892 2023-02-17 02:56:41,223 - mmseg - INFO - Iter [97850/160000] lr: 2.331e-05, eta: 6:30:09, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1801, decode.acc_seg: 92.6292, aux.loss_ce: 0.1146, aux.acc_seg: 88.4662, loss: 0.2947, grad_norm: 2.4932 2023-02-17 02:57:01,932 - mmseg - INFO - Iter [97900/160000] lr: 2.329e-05, eta: 6:29:59, time: 0.414, data_time: 0.053, memory: 16596, decode.loss_ce: 0.1727, decode.acc_seg: 92.8894, aux.loss_ce: 0.1113, aux.acc_seg: 88.7191, loss: 0.2840, grad_norm: 2.8775 2023-02-17 02:57:19,757 - mmseg - INFO - Iter [97950/160000] lr: 2.327e-05, eta: 6:29:35, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1897, decode.acc_seg: 92.5071, aux.loss_ce: 0.1214, aux.acc_seg: 87.8636, loss: 0.3110, grad_norm: 3.1950 2023-02-17 02:57:37,682 - mmseg - INFO - Saving checkpoint at 98000 iterations 2023-02-17 02:57:38,731 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 02:57:38,731 - mmseg - INFO - Iter [98000/160000] lr: 2.325e-05, eta: 6:29:17, time: 0.379, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1748, decode.acc_seg: 93.0470, aux.loss_ce: 0.1129, aux.acc_seg: 88.8171, loss: 0.2877, grad_norm: 2.8225 2023-02-17 02:57:56,771 - mmseg - INFO - Iter [98050/160000] lr: 2.323e-05, eta: 6:28:55, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1721, decode.acc_seg: 92.8652, aux.loss_ce: 0.1125, aux.acc_seg: 88.4966, loss: 0.2847, grad_norm: 2.8819 2023-02-17 02:58:14,814 - mmseg - INFO - Iter [98100/160000] lr: 2.321e-05, eta: 6:28:32, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1831, decode.acc_seg: 92.5963, aux.loss_ce: 0.1191, aux.acc_seg: 88.1037, loss: 0.3022, grad_norm: 3.1517 2023-02-17 02:58:33,043 - mmseg - INFO - Iter [98150/160000] lr: 2.319e-05, eta: 6:28:11, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1645, decode.acc_seg: 93.0897, aux.loss_ce: 0.1052, aux.acc_seg: 89.0181, loss: 0.2697, grad_norm: 2.5198 2023-02-17 02:58:50,996 - mmseg - INFO - Iter [98200/160000] lr: 2.318e-05, eta: 6:27:48, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1697, decode.acc_seg: 93.0974, aux.loss_ce: 0.1084, aux.acc_seg: 89.0146, loss: 0.2781, grad_norm: 2.6913 2023-02-17 02:59:09,039 - mmseg - INFO - Iter [98250/160000] lr: 2.316e-05, eta: 6:27:26, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1833, decode.acc_seg: 92.6696, aux.loss_ce: 0.1160, aux.acc_seg: 88.4303, loss: 0.2992, grad_norm: 3.3031 2023-02-17 02:59:26,891 - mmseg - INFO - Iter [98300/160000] lr: 2.314e-05, eta: 6:27:03, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1808, decode.acc_seg: 92.6429, aux.loss_ce: 0.1137, aux.acc_seg: 88.5766, loss: 0.2945, grad_norm: 2.8073 2023-02-17 02:59:44,850 - mmseg - INFO - Iter [98350/160000] lr: 2.312e-05, eta: 6:26:40, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1717, decode.acc_seg: 93.1418, aux.loss_ce: 0.1119, aux.acc_seg: 88.7637, loss: 0.2837, grad_norm: 2.5852 2023-02-17 03:00:02,890 - mmseg - INFO - Iter [98400/160000] lr: 2.310e-05, eta: 6:26:18, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1881, decode.acc_seg: 92.6943, aux.loss_ce: 0.1201, aux.acc_seg: 88.3669, loss: 0.3082, grad_norm: 3.2430 2023-02-17 03:00:21,063 - mmseg - INFO - Iter [98450/160000] lr: 2.308e-05, eta: 6:25:57, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1755, decode.acc_seg: 92.8702, aux.loss_ce: 0.1102, aux.acc_seg: 89.0533, loss: 0.2857, grad_norm: 3.2787 2023-02-17 03:00:38,775 - mmseg - INFO - Iter [98500/160000] lr: 2.306e-05, eta: 6:25:33, time: 0.355, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1757, decode.acc_seg: 92.8496, aux.loss_ce: 0.1132, aux.acc_seg: 88.3931, loss: 0.2889, grad_norm: 2.6638 2023-02-17 03:00:56,897 - mmseg - INFO - Iter [98550/160000] lr: 2.304e-05, eta: 6:25:12, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1844, decode.acc_seg: 92.6347, aux.loss_ce: 0.1185, aux.acc_seg: 88.3046, loss: 0.3029, grad_norm: 2.6315 2023-02-17 03:01:14,686 - mmseg - INFO - Iter [98600/160000] lr: 2.303e-05, eta: 6:24:48, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1798, decode.acc_seg: 92.5148, aux.loss_ce: 0.1159, aux.acc_seg: 88.3611, loss: 0.2957, grad_norm: 2.8347 2023-02-17 03:01:32,868 - mmseg - INFO - Iter [98650/160000] lr: 2.301e-05, eta: 6:24:27, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1764, decode.acc_seg: 92.8568, aux.loss_ce: 0.1119, aux.acc_seg: 88.6997, loss: 0.2883, grad_norm: 2.6979 2023-02-17 03:01:50,822 - mmseg - INFO - Iter [98700/160000] lr: 2.299e-05, eta: 6:24:05, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1910, decode.acc_seg: 92.1549, aux.loss_ce: 0.1253, aux.acc_seg: 87.4014, loss: 0.3162, grad_norm: 3.3866 2023-02-17 03:02:08,727 - mmseg - INFO - Iter [98750/160000] lr: 2.297e-05, eta: 6:23:42, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1775, decode.acc_seg: 92.7859, aux.loss_ce: 0.1133, aux.acc_seg: 88.5671, loss: 0.2908, grad_norm: 2.9046 2023-02-17 03:02:26,678 - mmseg - INFO - Iter [98800/160000] lr: 2.295e-05, eta: 6:23:20, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1746, decode.acc_seg: 92.6164, aux.loss_ce: 0.1107, aux.acc_seg: 88.3420, loss: 0.2853, grad_norm: 2.7937 2023-02-17 03:02:44,744 - mmseg - INFO - Iter [98850/160000] lr: 2.293e-05, eta: 6:22:58, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1741, decode.acc_seg: 92.8625, aux.loss_ce: 0.1142, aux.acc_seg: 88.4932, loss: 0.2882, grad_norm: 2.5264 2023-02-17 03:03:02,791 - mmseg - INFO - Iter [98900/160000] lr: 2.291e-05, eta: 6:22:36, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1722, decode.acc_seg: 92.9699, aux.loss_ce: 0.1093, aux.acc_seg: 88.8208, loss: 0.2815, grad_norm: 2.7479 2023-02-17 03:03:20,514 - mmseg - INFO - Iter [98950/160000] lr: 2.289e-05, eta: 6:22:13, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1825, decode.acc_seg: 92.5241, aux.loss_ce: 0.1227, aux.acc_seg: 87.6861, loss: 0.3051, grad_norm: 3.1274 2023-02-17 03:03:38,419 - mmseg - INFO - Saving checkpoint at 99000 iterations 2023-02-17 03:03:39,458 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 03:03:39,458 - mmseg - INFO - Iter [99000/160000] lr: 2.288e-05, eta: 6:21:55, time: 0.379, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1828, decode.acc_seg: 92.6641, aux.loss_ce: 0.1144, aux.acc_seg: 88.6968, loss: 0.2972, grad_norm: 3.3766 2023-02-17 03:03:57,587 - mmseg - INFO - Iter [99050/160000] lr: 2.286e-05, eta: 6:21:34, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1779, decode.acc_seg: 92.6654, aux.loss_ce: 0.1156, aux.acc_seg: 88.4196, loss: 0.2935, grad_norm: 2.4260 2023-02-17 03:04:15,546 - mmseg - INFO - Iter [99100/160000] lr: 2.284e-05, eta: 6:21:12, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1780, decode.acc_seg: 92.7259, aux.loss_ce: 0.1189, aux.acc_seg: 88.0861, loss: 0.2969, grad_norm: 3.0622 2023-02-17 03:04:33,426 - mmseg - INFO - Iter [99150/160000] lr: 2.282e-05, eta: 6:20:49, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1723, decode.acc_seg: 92.9286, aux.loss_ce: 0.1145, aux.acc_seg: 88.2856, loss: 0.2869, grad_norm: 2.8449 2023-02-17 03:04:53,833 - mmseg - INFO - Iter [99200/160000] lr: 2.280e-05, eta: 6:20:37, time: 0.408, data_time: 0.052, memory: 16596, decode.loss_ce: 0.1831, decode.acc_seg: 92.5585, aux.loss_ce: 0.1145, aux.acc_seg: 88.3177, loss: 0.2976, grad_norm: 2.9831 2023-02-17 03:05:12,168 - mmseg - INFO - Iter [99250/160000] lr: 2.278e-05, eta: 6:20:16, time: 0.367, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1753, decode.acc_seg: 92.8567, aux.loss_ce: 0.1135, aux.acc_seg: 88.7298, loss: 0.2888, grad_norm: 2.9303 2023-02-17 03:05:30,369 - mmseg - INFO - Iter [99300/160000] lr: 2.276e-05, eta: 6:19:55, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1864, decode.acc_seg: 92.5109, aux.loss_ce: 0.1180, aux.acc_seg: 88.1623, loss: 0.3044, grad_norm: 3.0709 2023-02-17 03:05:48,687 - mmseg - INFO - Iter [99350/160000] lr: 2.274e-05, eta: 6:19:35, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1852, decode.acc_seg: 92.4125, aux.loss_ce: 0.1126, aux.acc_seg: 88.5571, loss: 0.2978, grad_norm: 3.7187 2023-02-17 03:06:07,053 - mmseg - INFO - Iter [99400/160000] lr: 2.273e-05, eta: 6:19:14, time: 0.367, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1742, decode.acc_seg: 93.0067, aux.loss_ce: 0.1128, aux.acc_seg: 88.8480, loss: 0.2870, grad_norm: 3.0328 2023-02-17 03:06:24,967 - mmseg - INFO - Iter [99450/160000] lr: 2.271e-05, eta: 6:18:52, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1840, decode.acc_seg: 92.5529, aux.loss_ce: 0.1180, aux.acc_seg: 88.4328, loss: 0.3020, grad_norm: 3.0718 2023-02-17 03:06:42,879 - mmseg - INFO - Iter [99500/160000] lr: 2.269e-05, eta: 6:18:30, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1659, decode.acc_seg: 93.2518, aux.loss_ce: 0.1105, aux.acc_seg: 89.0271, loss: 0.2764, grad_norm: 2.5158 2023-02-17 03:07:00,584 - mmseg - INFO - Iter [99550/160000] lr: 2.267e-05, eta: 6:18:07, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1760, decode.acc_seg: 92.9190, aux.loss_ce: 0.1148, aux.acc_seg: 88.5575, loss: 0.2908, grad_norm: 2.6839 2023-02-17 03:07:18,283 - mmseg - INFO - Iter [99600/160000] lr: 2.265e-05, eta: 6:17:44, time: 0.353, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1774, decode.acc_seg: 92.6248, aux.loss_ce: 0.1153, aux.acc_seg: 88.2532, loss: 0.2927, grad_norm: 2.9691 2023-02-17 03:07:36,173 - mmseg - INFO - Iter [99650/160000] lr: 2.263e-05, eta: 6:17:22, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1817, decode.acc_seg: 92.5597, aux.loss_ce: 0.1190, aux.acc_seg: 88.0246, loss: 0.3007, grad_norm: 3.1250 2023-02-17 03:07:54,128 - mmseg - INFO - Iter [99700/160000] lr: 2.261e-05, eta: 6:17:00, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1812, decode.acc_seg: 92.5681, aux.loss_ce: 0.1169, aux.acc_seg: 87.9819, loss: 0.2981, grad_norm: 2.7809 2023-02-17 03:08:12,163 - mmseg - INFO - Iter [99750/160000] lr: 2.259e-05, eta: 6:16:39, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1659, decode.acc_seg: 93.1988, aux.loss_ce: 0.1099, aux.acc_seg: 88.7560, loss: 0.2759, grad_norm: 2.5319 2023-02-17 03:08:30,395 - mmseg - INFO - Iter [99800/160000] lr: 2.258e-05, eta: 6:16:18, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1787, decode.acc_seg: 92.6534, aux.loss_ce: 0.1147, aux.acc_seg: 88.3407, loss: 0.2934, grad_norm: 3.1865 2023-02-17 03:08:48,518 - mmseg - INFO - Iter [99850/160000] lr: 2.256e-05, eta: 6:15:57, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1845, decode.acc_seg: 92.6048, aux.loss_ce: 0.1167, aux.acc_seg: 88.5242, loss: 0.3012, grad_norm: 3.1118 2023-02-17 03:09:06,323 - mmseg - INFO - Iter [99900/160000] lr: 2.254e-05, eta: 6:15:35, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1748, decode.acc_seg: 92.6239, aux.loss_ce: 0.1154, aux.acc_seg: 88.1823, loss: 0.2902, grad_norm: 2.9320 2023-02-17 03:09:24,204 - mmseg - INFO - Iter [99950/160000] lr: 2.252e-05, eta: 6:15:13, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1735, decode.acc_seg: 93.0621, aux.loss_ce: 0.1146, aux.acc_seg: 88.7907, loss: 0.2881, grad_norm: 2.3517 2023-02-17 03:09:42,092 - mmseg - INFO - Saving checkpoint at 100000 iterations 2023-02-17 03:09:43,156 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 03:09:43,157 - mmseg - INFO - Iter [100000/160000] lr: 2.250e-05, eta: 6:14:55, time: 0.379, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1796, decode.acc_seg: 92.5822, aux.loss_ce: 0.1152, aux.acc_seg: 88.3954, loss: 0.2947, grad_norm: 3.0723 2023-02-17 03:10:01,643 - mmseg - INFO - Iter [100050/160000] lr: 2.248e-05, eta: 6:14:35, time: 0.370, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1772, decode.acc_seg: 92.7706, aux.loss_ce: 0.1180, aux.acc_seg: 88.1167, loss: 0.2953, grad_norm: 3.3882 2023-02-17 03:10:19,530 - mmseg - INFO - Iter [100100/160000] lr: 2.246e-05, eta: 6:14:13, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1873, decode.acc_seg: 92.6073, aux.loss_ce: 0.1231, aux.acc_seg: 87.9994, loss: 0.3104, grad_norm: 3.4354 2023-02-17 03:10:37,628 - mmseg - INFO - Iter [100150/160000] lr: 2.244e-05, eta: 6:13:52, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1774, decode.acc_seg: 92.7251, aux.loss_ce: 0.1117, aux.acc_seg: 88.5472, loss: 0.2891, grad_norm: 3.2810 2023-02-17 03:10:55,839 - mmseg - INFO - Iter [100200/160000] lr: 2.243e-05, eta: 6:13:31, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1792, decode.acc_seg: 92.6319, aux.loss_ce: 0.1167, aux.acc_seg: 87.9036, loss: 0.2959, grad_norm: 2.8605 2023-02-17 03:11:14,076 - mmseg - INFO - Iter [100250/160000] lr: 2.241e-05, eta: 6:13:10, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1781, decode.acc_seg: 92.8309, aux.loss_ce: 0.1156, aux.acc_seg: 88.4421, loss: 0.2937, grad_norm: 2.7528 2023-02-17 03:11:31,954 - mmseg - INFO - Iter [100300/160000] lr: 2.239e-05, eta: 6:12:49, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1876, decode.acc_seg: 92.5786, aux.loss_ce: 0.1209, aux.acc_seg: 88.0870, loss: 0.3085, grad_norm: 2.8632 2023-02-17 03:11:49,783 - mmseg - INFO - Iter [100350/160000] lr: 2.237e-05, eta: 6:12:27, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1675, decode.acc_seg: 93.0725, aux.loss_ce: 0.1107, aux.acc_seg: 88.5282, loss: 0.2782, grad_norm: 2.6143 2023-02-17 03:12:07,865 - mmseg - INFO - Iter [100400/160000] lr: 2.235e-05, eta: 6:12:05, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1750, decode.acc_seg: 92.8597, aux.loss_ce: 0.1129, aux.acc_seg: 88.5517, loss: 0.2879, grad_norm: 2.7936 2023-02-17 03:12:27,971 - mmseg - INFO - Iter [100450/160000] lr: 2.233e-05, eta: 6:11:52, time: 0.402, data_time: 0.052, memory: 16596, decode.loss_ce: 0.1714, decode.acc_seg: 92.8584, aux.loss_ce: 0.1106, aux.acc_seg: 88.6427, loss: 0.2820, grad_norm: 3.2759 2023-02-17 03:12:46,250 - mmseg - INFO - Iter [100500/160000] lr: 2.231e-05, eta: 6:11:31, time: 0.366, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1778, decode.acc_seg: 92.7752, aux.loss_ce: 0.1121, aux.acc_seg: 88.7360, loss: 0.2899, grad_norm: 2.6377 2023-02-17 03:13:04,225 - mmseg - INFO - Iter [100550/160000] lr: 2.229e-05, eta: 6:11:10, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1676, decode.acc_seg: 93.1847, aux.loss_ce: 0.1070, aux.acc_seg: 89.1863, loss: 0.2746, grad_norm: 2.5129 2023-02-17 03:13:22,475 - mmseg - INFO - Iter [100600/160000] lr: 2.228e-05, eta: 6:10:49, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1795, decode.acc_seg: 92.7596, aux.loss_ce: 0.1116, aux.acc_seg: 88.7841, loss: 0.2911, grad_norm: 3.1312 2023-02-17 03:13:40,496 - mmseg - INFO - Iter [100650/160000] lr: 2.226e-05, eta: 6:10:28, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1795, decode.acc_seg: 92.6803, aux.loss_ce: 0.1176, aux.acc_seg: 88.2702, loss: 0.2971, grad_norm: 3.0054 2023-02-17 03:13:58,423 - mmseg - INFO - Iter [100700/160000] lr: 2.224e-05, eta: 6:10:07, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1679, decode.acc_seg: 93.1491, aux.loss_ce: 0.1050, aux.acc_seg: 89.6265, loss: 0.2728, grad_norm: 3.1018 2023-02-17 03:14:16,957 - mmseg - INFO - Iter [100750/160000] lr: 2.222e-05, eta: 6:09:47, time: 0.371, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1844, decode.acc_seg: 92.3638, aux.loss_ce: 0.1151, aux.acc_seg: 88.4435, loss: 0.2995, grad_norm: 3.7358 2023-02-17 03:14:35,018 - mmseg - INFO - Iter [100800/160000] lr: 2.220e-05, eta: 6:09:26, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1724, decode.acc_seg: 92.9998, aux.loss_ce: 0.1107, aux.acc_seg: 88.8997, loss: 0.2831, grad_norm: 2.4170 2023-02-17 03:14:53,032 - mmseg - INFO - Iter [100850/160000] lr: 2.218e-05, eta: 6:09:05, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1741, decode.acc_seg: 92.9196, aux.loss_ce: 0.1116, aux.acc_seg: 88.8446, loss: 0.2857, grad_norm: 2.4928 2023-02-17 03:15:10,932 - mmseg - INFO - Iter [100900/160000] lr: 2.216e-05, eta: 6:08:43, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1682, decode.acc_seg: 93.2243, aux.loss_ce: 0.1089, aux.acc_seg: 89.0808, loss: 0.2771, grad_norm: 2.2940 2023-02-17 03:15:28,780 - mmseg - INFO - Iter [100950/160000] lr: 2.214e-05, eta: 6:08:22, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1842, decode.acc_seg: 92.5088, aux.loss_ce: 0.1167, aux.acc_seg: 88.1892, loss: 0.3009, grad_norm: 3.1703 2023-02-17 03:15:46,966 - mmseg - INFO - Saving checkpoint at 101000 iterations 2023-02-17 03:15:48,055 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 03:15:48,055 - mmseg - INFO - Iter [101000/160000] lr: 2.213e-05, eta: 6:08:05, time: 0.386, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1804, decode.acc_seg: 92.7289, aux.loss_ce: 0.1125, aux.acc_seg: 88.6889, loss: 0.2929, grad_norm: 3.0094 2023-02-17 03:16:05,897 - mmseg - INFO - Iter [101050/160000] lr: 2.211e-05, eta: 6:07:43, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1722, decode.acc_seg: 92.9450, aux.loss_ce: 0.1116, aux.acc_seg: 88.7019, loss: 0.2838, grad_norm: 2.8010 2023-02-17 03:16:23,964 - mmseg - INFO - Iter [101100/160000] lr: 2.209e-05, eta: 6:07:22, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1743, decode.acc_seg: 92.9741, aux.loss_ce: 0.1139, aux.acc_seg: 88.5279, loss: 0.2882, grad_norm: 2.5811 2023-02-17 03:16:41,988 - mmseg - INFO - Iter [101150/160000] lr: 2.207e-05, eta: 6:07:01, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1774, decode.acc_seg: 92.6593, aux.loss_ce: 0.1114, aux.acc_seg: 88.6678, loss: 0.2888, grad_norm: 2.3875 2023-02-17 03:17:00,262 - mmseg - INFO - Iter [101200/160000] lr: 2.205e-05, eta: 6:06:41, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1835, decode.acc_seg: 92.5828, aux.loss_ce: 0.1181, aux.acc_seg: 88.3131, loss: 0.3016, grad_norm: 2.5472 2023-02-17 03:17:18,278 - mmseg - INFO - Iter [101250/160000] lr: 2.203e-05, eta: 6:06:20, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1742, decode.acc_seg: 92.9560, aux.loss_ce: 0.1095, aux.acc_seg: 89.1621, loss: 0.2837, grad_norm: 2.6750 2023-02-17 03:17:36,296 - mmseg - INFO - Iter [101300/160000] lr: 2.201e-05, eta: 6:05:59, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1780, decode.acc_seg: 92.8392, aux.loss_ce: 0.1180, aux.acc_seg: 88.2286, loss: 0.2960, grad_norm: 2.7732 2023-02-17 03:17:54,283 - mmseg - INFO - Iter [101350/160000] lr: 2.199e-05, eta: 6:05:38, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1722, decode.acc_seg: 92.8500, aux.loss_ce: 0.1102, aux.acc_seg: 88.8540, loss: 0.2824, grad_norm: 2.8009 2023-02-17 03:18:12,172 - mmseg - INFO - Iter [101400/160000] lr: 2.198e-05, eta: 6:05:16, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1874, decode.acc_seg: 92.5600, aux.loss_ce: 0.1190, aux.acc_seg: 88.2970, loss: 0.3064, grad_norm: 3.3708 2023-02-17 03:18:30,167 - mmseg - INFO - Iter [101450/160000] lr: 2.196e-05, eta: 6:04:55, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1732, decode.acc_seg: 93.0022, aux.loss_ce: 0.1140, aux.acc_seg: 88.6379, loss: 0.2872, grad_norm: 2.7958 2023-02-17 03:18:48,206 - mmseg - INFO - Iter [101500/160000] lr: 2.194e-05, eta: 6:04:34, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1776, decode.acc_seg: 92.8540, aux.loss_ce: 0.1170, aux.acc_seg: 88.3031, loss: 0.2946, grad_norm: 2.6128 2023-02-17 03:19:06,005 - mmseg - INFO - Iter [101550/160000] lr: 2.192e-05, eta: 6:04:13, time: 0.356, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1728, decode.acc_seg: 93.0070, aux.loss_ce: 0.1124, aux.acc_seg: 88.8019, loss: 0.2852, grad_norm: 2.9888 2023-02-17 03:19:24,059 - mmseg - INFO - Iter [101600/160000] lr: 2.190e-05, eta: 6:03:52, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1639, decode.acc_seg: 93.1737, aux.loss_ce: 0.1096, aux.acc_seg: 88.4852, loss: 0.2735, grad_norm: 2.5992 2023-02-17 03:19:42,167 - mmseg - INFO - Iter [101650/160000] lr: 2.188e-05, eta: 6:03:31, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1744, decode.acc_seg: 92.8181, aux.loss_ce: 0.1101, aux.acc_seg: 88.9030, loss: 0.2845, grad_norm: 2.7662 2023-02-17 03:20:02,595 - mmseg - INFO - Iter [101700/160000] lr: 2.186e-05, eta: 6:03:18, time: 0.409, data_time: 0.054, memory: 16596, decode.loss_ce: 0.1894, decode.acc_seg: 92.3958, aux.loss_ce: 0.1197, aux.acc_seg: 88.1232, loss: 0.3091, grad_norm: 3.5311 2023-02-17 03:20:20,404 - mmseg - INFO - Iter [101750/160000] lr: 2.184e-05, eta: 6:02:57, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1746, decode.acc_seg: 92.7972, aux.loss_ce: 0.1120, aux.acc_seg: 88.4496, loss: 0.2866, grad_norm: 2.8195 2023-02-17 03:20:38,327 - mmseg - INFO - Iter [101800/160000] lr: 2.183e-05, eta: 6:02:35, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1659, decode.acc_seg: 93.0485, aux.loss_ce: 0.1057, aux.acc_seg: 89.2008, loss: 0.2716, grad_norm: 2.2467 2023-02-17 03:20:56,075 - mmseg - INFO - Iter [101850/160000] lr: 2.181e-05, eta: 6:02:14, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1737, decode.acc_seg: 92.9324, aux.loss_ce: 0.1112, aux.acc_seg: 88.8933, loss: 0.2849, grad_norm: 2.4090 2023-02-17 03:21:13,761 - mmseg - INFO - Iter [101900/160000] lr: 2.179e-05, eta: 6:01:52, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1743, decode.acc_seg: 93.0926, aux.loss_ce: 0.1134, aux.acc_seg: 88.6749, loss: 0.2877, grad_norm: 2.7157 2023-02-17 03:21:31,675 - mmseg - INFO - Iter [101950/160000] lr: 2.177e-05, eta: 6:01:30, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1935, decode.acc_seg: 92.2702, aux.loss_ce: 0.1209, aux.acc_seg: 87.8613, loss: 0.3144, grad_norm: 3.1803 2023-02-17 03:21:50,030 - mmseg - INFO - Saving checkpoint at 102000 iterations 2023-02-17 03:21:51,129 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 03:21:51,130 - mmseg - INFO - Iter [102000/160000] lr: 2.175e-05, eta: 6:01:14, time: 0.389, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1780, decode.acc_seg: 92.7976, aux.loss_ce: 0.1154, aux.acc_seg: 88.3529, loss: 0.2935, grad_norm: 2.9479 2023-02-17 03:22:09,092 - mmseg - INFO - Iter [102050/160000] lr: 2.173e-05, eta: 6:00:53, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1690, decode.acc_seg: 92.9091, aux.loss_ce: 0.1094, aux.acc_seg: 88.8248, loss: 0.2784, grad_norm: 2.7700 2023-02-17 03:22:27,187 - mmseg - INFO - Iter [102100/160000] lr: 2.171e-05, eta: 6:00:33, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1788, decode.acc_seg: 92.8621, aux.loss_ce: 0.1176, aux.acc_seg: 88.3114, loss: 0.2964, grad_norm: 2.9998 2023-02-17 03:22:45,081 - mmseg - INFO - Iter [102150/160000] lr: 2.169e-05, eta: 6:00:11, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1759, decode.acc_seg: 93.0012, aux.loss_ce: 0.1108, aux.acc_seg: 88.9893, loss: 0.2867, grad_norm: 3.2498 2023-02-17 03:23:02,913 - mmseg - INFO - Iter [102200/160000] lr: 2.168e-05, eta: 5:59:50, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1829, decode.acc_seg: 92.6058, aux.loss_ce: 0.1171, aux.acc_seg: 88.3036, loss: 0.3000, grad_norm: 3.0387 2023-02-17 03:23:20,806 - mmseg - INFO - Iter [102250/160000] lr: 2.166e-05, eta: 5:59:29, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1786, decode.acc_seg: 92.6435, aux.loss_ce: 0.1171, aux.acc_seg: 88.0385, loss: 0.2957, grad_norm: 2.8721 2023-02-17 03:23:38,629 - mmseg - INFO - Iter [102300/160000] lr: 2.164e-05, eta: 5:59:08, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1800, decode.acc_seg: 92.6973, aux.loss_ce: 0.1132, aux.acc_seg: 88.6200, loss: 0.2932, grad_norm: 2.8887 2023-02-17 03:23:56,779 - mmseg - INFO - Iter [102350/160000] lr: 2.162e-05, eta: 5:58:47, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1784, decode.acc_seg: 92.8750, aux.loss_ce: 0.1105, aux.acc_seg: 89.0410, loss: 0.2888, grad_norm: 2.9876 2023-02-17 03:24:14,711 - mmseg - INFO - Iter [102400/160000] lr: 2.160e-05, eta: 5:58:26, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1715, decode.acc_seg: 92.9374, aux.loss_ce: 0.1095, aux.acc_seg: 89.0757, loss: 0.2809, grad_norm: 2.4316 2023-02-17 03:24:32,586 - mmseg - INFO - Iter [102450/160000] lr: 2.158e-05, eta: 5:58:05, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1655, decode.acc_seg: 93.1558, aux.loss_ce: 0.1066, aux.acc_seg: 89.1113, loss: 0.2721, grad_norm: 2.4578 2023-02-17 03:24:50,571 - mmseg - INFO - Iter [102500/160000] lr: 2.156e-05, eta: 5:57:44, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1665, decode.acc_seg: 93.2698, aux.loss_ce: 0.1096, aux.acc_seg: 88.9205, loss: 0.2761, grad_norm: 2.7589 2023-02-17 03:25:08,582 - mmseg - INFO - Iter [102550/160000] lr: 2.154e-05, eta: 5:57:24, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1812, decode.acc_seg: 92.7051, aux.loss_ce: 0.1162, aux.acc_seg: 88.3604, loss: 0.2974, grad_norm: 2.7776 2023-02-17 03:25:26,791 - mmseg - INFO - Iter [102600/160000] lr: 2.153e-05, eta: 5:57:04, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1766, decode.acc_seg: 92.9287, aux.loss_ce: 0.1145, aux.acc_seg: 88.4998, loss: 0.2910, grad_norm: 3.4921 2023-02-17 03:25:44,952 - mmseg - INFO - Iter [102650/160000] lr: 2.151e-05, eta: 5:56:43, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1742, decode.acc_seg: 92.8130, aux.loss_ce: 0.1132, aux.acc_seg: 88.6647, loss: 0.2874, grad_norm: 2.8254 2023-02-17 03:26:02,959 - mmseg - INFO - Iter [102700/160000] lr: 2.149e-05, eta: 5:56:23, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1626, decode.acc_seg: 93.2407, aux.loss_ce: 0.1065, aux.acc_seg: 89.0986, loss: 0.2691, grad_norm: 2.5821 2023-02-17 03:26:21,062 - mmseg - INFO - Iter [102750/160000] lr: 2.147e-05, eta: 5:56:02, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1838, decode.acc_seg: 92.7150, aux.loss_ce: 0.1232, aux.acc_seg: 87.9846, loss: 0.3070, grad_norm: 2.9992 2023-02-17 03:26:39,118 - mmseg - INFO - Iter [102800/160000] lr: 2.145e-05, eta: 5:55:42, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1769, decode.acc_seg: 92.7989, aux.loss_ce: 0.1176, aux.acc_seg: 88.3030, loss: 0.2945, grad_norm: 3.8754 2023-02-17 03:26:57,305 - mmseg - INFO - Iter [102850/160000] lr: 2.143e-05, eta: 5:55:22, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1730, decode.acc_seg: 92.9171, aux.loss_ce: 0.1110, aux.acc_seg: 88.7472, loss: 0.2840, grad_norm: 2.6198 2023-02-17 03:27:15,405 - mmseg - INFO - Iter [102900/160000] lr: 2.141e-05, eta: 5:55:01, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1703, decode.acc_seg: 92.8121, aux.loss_ce: 0.1110, aux.acc_seg: 88.7695, loss: 0.2813, grad_norm: 2.8469 2023-02-17 03:27:35,472 - mmseg - INFO - Iter [102950/160000] lr: 2.139e-05, eta: 5:54:47, time: 0.402, data_time: 0.051, memory: 16596, decode.loss_ce: 0.1736, decode.acc_seg: 92.9234, aux.loss_ce: 0.1076, aux.acc_seg: 89.2979, loss: 0.2811, grad_norm: 3.2125 2023-02-17 03:27:53,166 - mmseg - INFO - Saving checkpoint at 103000 iterations 2023-02-17 03:27:54,219 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 03:27:54,219 - mmseg - INFO - Iter [103000/160000] lr: 2.138e-05, eta: 5:54:29, time: 0.375, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1758, decode.acc_seg: 92.8513, aux.loss_ce: 0.1126, aux.acc_seg: 88.8275, loss: 0.2884, grad_norm: 3.3877 2023-02-17 03:28:11,972 - mmseg - INFO - Iter [103050/160000] lr: 2.136e-05, eta: 5:54:07, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1791, decode.acc_seg: 92.8004, aux.loss_ce: 0.1168, aux.acc_seg: 88.5022, loss: 0.2960, grad_norm: 2.7868 2023-02-17 03:28:29,914 - mmseg - INFO - Iter [103100/160000] lr: 2.134e-05, eta: 5:53:46, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1687, decode.acc_seg: 93.0880, aux.loss_ce: 0.1105, aux.acc_seg: 88.8286, loss: 0.2791, grad_norm: 2.6611 2023-02-17 03:28:47,931 - mmseg - INFO - Iter [103150/160000] lr: 2.132e-05, eta: 5:53:26, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1756, decode.acc_seg: 92.9656, aux.loss_ce: 0.1106, aux.acc_seg: 88.7884, loss: 0.2861, grad_norm: 2.7524 2023-02-17 03:29:05,986 - mmseg - INFO - Iter [103200/160000] lr: 2.130e-05, eta: 5:53:06, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1755, decode.acc_seg: 92.8252, aux.loss_ce: 0.1138, aux.acc_seg: 88.4935, loss: 0.2893, grad_norm: 3.2572 2023-02-17 03:29:24,283 - mmseg - INFO - Iter [103250/160000] lr: 2.128e-05, eta: 5:52:46, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1720, decode.acc_seg: 92.9079, aux.loss_ce: 0.1141, aux.acc_seg: 88.3903, loss: 0.2861, grad_norm: 2.9248 2023-02-17 03:29:42,308 - mmseg - INFO - Iter [103300/160000] lr: 2.126e-05, eta: 5:52:25, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1682, decode.acc_seg: 93.2562, aux.loss_ce: 0.1077, aux.acc_seg: 89.1900, loss: 0.2759, grad_norm: 2.4204 2023-02-17 03:30:00,312 - mmseg - INFO - Iter [103350/160000] lr: 2.124e-05, eta: 5:52:05, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1681, decode.acc_seg: 93.2984, aux.loss_ce: 0.1101, aux.acc_seg: 88.9676, loss: 0.2782, grad_norm: 3.3551 2023-02-17 03:30:18,603 - mmseg - INFO - Iter [103400/160000] lr: 2.123e-05, eta: 5:51:45, time: 0.366, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1664, decode.acc_seg: 93.1763, aux.loss_ce: 0.1078, aux.acc_seg: 89.0790, loss: 0.2742, grad_norm: 2.7439 2023-02-17 03:30:36,394 - mmseg - INFO - Iter [103450/160000] lr: 2.121e-05, eta: 5:51:24, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1757, decode.acc_seg: 93.0675, aux.loss_ce: 0.1130, aux.acc_seg: 88.6617, loss: 0.2887, grad_norm: 3.0457 2023-02-17 03:30:54,397 - mmseg - INFO - Iter [103500/160000] lr: 2.119e-05, eta: 5:51:04, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1716, decode.acc_seg: 93.0359, aux.loss_ce: 0.1103, aux.acc_seg: 88.8419, loss: 0.2819, grad_norm: 2.7188 2023-02-17 03:31:12,308 - mmseg - INFO - Iter [103550/160000] lr: 2.117e-05, eta: 5:50:43, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1703, decode.acc_seg: 92.9323, aux.loss_ce: 0.1114, aux.acc_seg: 88.6586, loss: 0.2817, grad_norm: 2.4983 2023-02-17 03:31:30,286 - mmseg - INFO - Iter [103600/160000] lr: 2.115e-05, eta: 5:50:22, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1697, decode.acc_seg: 93.0624, aux.loss_ce: 0.1125, aux.acc_seg: 88.6227, loss: 0.2823, grad_norm: 2.5720 2023-02-17 03:31:48,244 - mmseg - INFO - Iter [103650/160000] lr: 2.113e-05, eta: 5:50:02, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1776, decode.acc_seg: 92.6698, aux.loss_ce: 0.1109, aux.acc_seg: 88.8341, loss: 0.2885, grad_norm: 2.5785 2023-02-17 03:32:06,578 - mmseg - INFO - Iter [103700/160000] lr: 2.111e-05, eta: 5:49:42, time: 0.367, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1770, decode.acc_seg: 92.8749, aux.loss_ce: 0.1108, aux.acc_seg: 88.9410, loss: 0.2878, grad_norm: 3.2059 2023-02-17 03:32:24,675 - mmseg - INFO - Iter [103750/160000] lr: 2.109e-05, eta: 5:49:22, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1799, decode.acc_seg: 92.6450, aux.loss_ce: 0.1158, aux.acc_seg: 88.2117, loss: 0.2957, grad_norm: 2.9894 2023-02-17 03:32:42,761 - mmseg - INFO - Iter [103800/160000] lr: 2.108e-05, eta: 5:49:02, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1749, decode.acc_seg: 92.9544, aux.loss_ce: 0.1120, aux.acc_seg: 88.8555, loss: 0.2869, grad_norm: 2.5662 2023-02-17 03:33:00,600 - mmseg - INFO - Iter [103850/160000] lr: 2.106e-05, eta: 5:48:41, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1703, decode.acc_seg: 92.6542, aux.loss_ce: 0.1097, aux.acc_seg: 88.5263, loss: 0.2801, grad_norm: 3.1464 2023-02-17 03:33:18,769 - mmseg - INFO - Iter [103900/160000] lr: 2.104e-05, eta: 5:48:21, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1724, decode.acc_seg: 93.0618, aux.loss_ce: 0.1139, aux.acc_seg: 88.6840, loss: 0.2864, grad_norm: 2.5540 2023-02-17 03:33:36,656 - mmseg - INFO - Iter [103950/160000] lr: 2.102e-05, eta: 5:48:00, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1643, decode.acc_seg: 93.2146, aux.loss_ce: 0.1092, aux.acc_seg: 88.8364, loss: 0.2735, grad_norm: 2.4787 2023-02-17 03:33:54,534 - mmseg - INFO - Saving checkpoint at 104000 iterations 2023-02-17 03:33:55,571 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 03:33:55,571 - mmseg - INFO - Iter [104000/160000] lr: 2.100e-05, eta: 5:47:42, time: 0.378, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1708, decode.acc_seg: 92.8244, aux.loss_ce: 0.1160, aux.acc_seg: 88.4427, loss: 0.2867, grad_norm: 2.8464 2023-02-17 03:34:13,495 - mmseg - INFO - Iter [104050/160000] lr: 2.098e-05, eta: 5:47:22, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1733, decode.acc_seg: 92.7996, aux.loss_ce: 0.1137, aux.acc_seg: 88.2782, loss: 0.2870, grad_norm: 2.6334 2023-02-17 03:34:31,642 - mmseg - INFO - Iter [104100/160000] lr: 2.096e-05, eta: 5:47:02, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1677, decode.acc_seg: 93.2244, aux.loss_ce: 0.1082, aux.acc_seg: 89.1909, loss: 0.2759, grad_norm: 2.4701 2023-02-17 03:34:49,495 - mmseg - INFO - Iter [104150/160000] lr: 2.094e-05, eta: 5:46:41, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1767, decode.acc_seg: 93.0077, aux.loss_ce: 0.1139, aux.acc_seg: 88.8178, loss: 0.2906, grad_norm: 2.7932 2023-02-17 03:35:07,927 - mmseg - INFO - Iter [104200/160000] lr: 2.093e-05, eta: 5:46:22, time: 0.369, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1726, decode.acc_seg: 92.7938, aux.loss_ce: 0.1109, aux.acc_seg: 88.6563, loss: 0.2835, grad_norm: 2.7819 2023-02-17 03:35:28,362 - mmseg - INFO - Iter [104250/160000] lr: 2.091e-05, eta: 5:46:08, time: 0.409, data_time: 0.052, memory: 16596, decode.loss_ce: 0.1695, decode.acc_seg: 93.1465, aux.loss_ce: 0.1112, aux.acc_seg: 88.7280, loss: 0.2807, grad_norm: 2.9187 2023-02-17 03:35:46,181 - mmseg - INFO - Iter [104300/160000] lr: 2.089e-05, eta: 5:45:48, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1612, decode.acc_seg: 93.2496, aux.loss_ce: 0.1065, aux.acc_seg: 88.9834, loss: 0.2677, grad_norm: 2.5310 2023-02-17 03:36:04,145 - mmseg - INFO - Iter [104350/160000] lr: 2.087e-05, eta: 5:45:27, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1787, decode.acc_seg: 92.8522, aux.loss_ce: 0.1098, aux.acc_seg: 88.9922, loss: 0.2886, grad_norm: 2.8399 2023-02-17 03:36:22,115 - mmseg - INFO - Iter [104400/160000] lr: 2.085e-05, eta: 5:45:07, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1617, decode.acc_seg: 93.3940, aux.loss_ce: 0.1074, aux.acc_seg: 89.0818, loss: 0.2691, grad_norm: 2.4036 2023-02-17 03:36:39,989 - mmseg - INFO - Iter [104450/160000] lr: 2.083e-05, eta: 5:44:46, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1766, decode.acc_seg: 92.9005, aux.loss_ce: 0.1171, aux.acc_seg: 88.2640, loss: 0.2936, grad_norm: 2.8016 2023-02-17 03:36:57,964 - mmseg - INFO - Iter [104500/160000] lr: 2.081e-05, eta: 5:44:26, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1863, decode.acc_seg: 92.4854, aux.loss_ce: 0.1190, aux.acc_seg: 88.3123, loss: 0.3053, grad_norm: 3.2265 2023-02-17 03:37:16,138 - mmseg - INFO - Iter [104550/160000] lr: 2.079e-05, eta: 5:44:06, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1769, decode.acc_seg: 92.5642, aux.loss_ce: 0.1101, aux.acc_seg: 88.6247, loss: 0.2871, grad_norm: 3.2598 2023-02-17 03:37:34,320 - mmseg - INFO - Iter [104600/160000] lr: 2.078e-05, eta: 5:43:46, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1719, decode.acc_seg: 92.8967, aux.loss_ce: 0.1128, aux.acc_seg: 88.3900, loss: 0.2846, grad_norm: 2.9800 2023-02-17 03:37:52,201 - mmseg - INFO - Iter [104650/160000] lr: 2.076e-05, eta: 5:43:26, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1719, decode.acc_seg: 93.1864, aux.loss_ce: 0.1139, aux.acc_seg: 88.6977, loss: 0.2857, grad_norm: 3.2893 2023-02-17 03:38:10,106 - mmseg - INFO - Iter [104700/160000] lr: 2.074e-05, eta: 5:43:05, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1690, decode.acc_seg: 93.2427, aux.loss_ce: 0.1099, aux.acc_seg: 89.0843, loss: 0.2788, grad_norm: 2.2509 2023-02-17 03:38:27,821 - mmseg - INFO - Iter [104750/160000] lr: 2.072e-05, eta: 5:42:44, time: 0.354, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1739, decode.acc_seg: 93.0407, aux.loss_ce: 0.1130, aux.acc_seg: 88.9382, loss: 0.2870, grad_norm: 2.8354 2023-02-17 03:38:45,789 - mmseg - INFO - Iter [104800/160000] lr: 2.070e-05, eta: 5:42:24, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1619, decode.acc_seg: 93.2911, aux.loss_ce: 0.1050, aux.acc_seg: 89.3563, loss: 0.2669, grad_norm: 2.5385 2023-02-17 03:39:03,810 - mmseg - INFO - Iter [104850/160000] lr: 2.068e-05, eta: 5:42:04, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1732, decode.acc_seg: 92.7607, aux.loss_ce: 0.1122, aux.acc_seg: 88.4026, loss: 0.2855, grad_norm: 2.9268 2023-02-17 03:39:21,963 - mmseg - INFO - Iter [104900/160000] lr: 2.066e-05, eta: 5:41:44, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1781, decode.acc_seg: 92.8105, aux.loss_ce: 0.1178, aux.acc_seg: 88.2381, loss: 0.2959, grad_norm: 2.6663 2023-02-17 03:39:40,180 - mmseg - INFO - Iter [104950/160000] lr: 2.064e-05, eta: 5:41:24, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1668, decode.acc_seg: 93.2317, aux.loss_ce: 0.1077, aux.acc_seg: 89.1489, loss: 0.2746, grad_norm: 2.4394 2023-02-17 03:39:58,433 - mmseg - INFO - Saving checkpoint at 105000 iterations 2023-02-17 03:39:59,502 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 03:39:59,502 - mmseg - INFO - Iter [105000/160000] lr: 2.063e-05, eta: 5:41:07, time: 0.387, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1770, decode.acc_seg: 92.4987, aux.loss_ce: 0.1143, aux.acc_seg: 88.1451, loss: 0.2913, grad_norm: 3.0939 2023-02-17 03:40:17,796 - mmseg - INFO - Iter [105050/160000] lr: 2.061e-05, eta: 5:40:48, time: 0.366, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1680, decode.acc_seg: 93.1263, aux.loss_ce: 0.1080, aux.acc_seg: 89.0880, loss: 0.2760, grad_norm: 2.6377 2023-02-17 03:40:35,864 - mmseg - INFO - Iter [105100/160000] lr: 2.059e-05, eta: 5:40:28, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1801, decode.acc_seg: 92.4933, aux.loss_ce: 0.1158, aux.acc_seg: 88.2210, loss: 0.2959, grad_norm: 3.0208 2023-02-17 03:40:53,964 - mmseg - INFO - Iter [105150/160000] lr: 2.057e-05, eta: 5:40:08, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1710, decode.acc_seg: 92.8990, aux.loss_ce: 0.1117, aux.acc_seg: 88.5677, loss: 0.2827, grad_norm: 2.8388 2023-02-17 03:41:11,965 - mmseg - INFO - Iter [105200/160000] lr: 2.055e-05, eta: 5:39:48, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1564, decode.acc_seg: 93.5379, aux.loss_ce: 0.1011, aux.acc_seg: 89.6865, loss: 0.2575, grad_norm: 2.0741 2023-02-17 03:41:29,877 - mmseg - INFO - Iter [105250/160000] lr: 2.053e-05, eta: 5:39:27, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1729, decode.acc_seg: 92.8338, aux.loss_ce: 0.1133, aux.acc_seg: 88.6321, loss: 0.2862, grad_norm: 2.6585 2023-02-17 03:41:48,044 - mmseg - INFO - Iter [105300/160000] lr: 2.051e-05, eta: 5:39:08, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1758, decode.acc_seg: 92.9161, aux.loss_ce: 0.1161, aux.acc_seg: 88.3078, loss: 0.2919, grad_norm: 2.9071 2023-02-17 03:42:06,062 - mmseg - INFO - Iter [105350/160000] lr: 2.049e-05, eta: 5:38:48, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1652, decode.acc_seg: 93.1648, aux.loss_ce: 0.1077, aux.acc_seg: 89.1056, loss: 0.2729, grad_norm: 2.4114 2023-02-17 03:42:23,842 - mmseg - INFO - Iter [105400/160000] lr: 2.048e-05, eta: 5:38:27, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1702, decode.acc_seg: 93.0446, aux.loss_ce: 0.1118, aux.acc_seg: 88.7202, loss: 0.2820, grad_norm: 2.6319 2023-02-17 03:42:41,775 - mmseg - INFO - Iter [105450/160000] lr: 2.046e-05, eta: 5:38:07, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1738, decode.acc_seg: 92.9895, aux.loss_ce: 0.1115, aux.acc_seg: 88.7493, loss: 0.2853, grad_norm: 2.7569 2023-02-17 03:43:01,913 - mmseg - INFO - Iter [105500/160000] lr: 2.044e-05, eta: 5:37:52, time: 0.402, data_time: 0.052, memory: 16596, decode.loss_ce: 0.1794, decode.acc_seg: 92.7418, aux.loss_ce: 0.1169, aux.acc_seg: 88.4920, loss: 0.2963, grad_norm: 3.2378 2023-02-17 03:43:19,980 - mmseg - INFO - Iter [105550/160000] lr: 2.042e-05, eta: 5:37:32, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1601, decode.acc_seg: 93.4255, aux.loss_ce: 0.1077, aux.acc_seg: 89.0231, loss: 0.2678, grad_norm: 2.4198 2023-02-17 03:43:38,154 - mmseg - INFO - Iter [105600/160000] lr: 2.040e-05, eta: 5:37:12, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1629, decode.acc_seg: 93.4893, aux.loss_ce: 0.1053, aux.acc_seg: 89.5103, loss: 0.2683, grad_norm: 2.4719 2023-02-17 03:43:56,427 - mmseg - INFO - Iter [105650/160000] lr: 2.038e-05, eta: 5:36:53, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1750, decode.acc_seg: 92.6432, aux.loss_ce: 0.1123, aux.acc_seg: 88.5015, loss: 0.2874, grad_norm: 3.0611 2023-02-17 03:44:14,270 - mmseg - INFO - Iter [105700/160000] lr: 2.036e-05, eta: 5:36:32, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1660, decode.acc_seg: 93.1930, aux.loss_ce: 0.1044, aux.acc_seg: 89.4613, loss: 0.2704, grad_norm: 2.6359 2023-02-17 03:44:32,273 - mmseg - INFO - Iter [105750/160000] lr: 2.034e-05, eta: 5:36:12, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1619, decode.acc_seg: 93.2672, aux.loss_ce: 0.1087, aux.acc_seg: 88.9137, loss: 0.2706, grad_norm: 2.6797 2023-02-17 03:44:50,359 - mmseg - INFO - Iter [105800/160000] lr: 2.033e-05, eta: 5:35:52, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1706, decode.acc_seg: 92.9961, aux.loss_ce: 0.1099, aux.acc_seg: 88.9289, loss: 0.2806, grad_norm: 3.2382 2023-02-17 03:45:08,382 - mmseg - INFO - Iter [105850/160000] lr: 2.031e-05, eta: 5:35:33, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1613, decode.acc_seg: 93.4161, aux.loss_ce: 0.1068, aux.acc_seg: 89.1470, loss: 0.2681, grad_norm: 2.6407 2023-02-17 03:45:26,396 - mmseg - INFO - Iter [105900/160000] lr: 2.029e-05, eta: 5:35:13, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1686, decode.acc_seg: 92.9565, aux.loss_ce: 0.1061, aux.acc_seg: 89.1707, loss: 0.2746, grad_norm: 2.7699 2023-02-17 03:45:44,354 - mmseg - INFO - Iter [105950/160000] lr: 2.027e-05, eta: 5:34:52, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1744, decode.acc_seg: 93.0390, aux.loss_ce: 0.1181, aux.acc_seg: 88.4999, loss: 0.2925, grad_norm: 2.6617 2023-02-17 03:46:02,295 - mmseg - INFO - Saving checkpoint at 106000 iterations 2023-02-17 03:46:03,353 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 03:46:03,353 - mmseg - INFO - Iter [106000/160000] lr: 2.025e-05, eta: 5:34:35, time: 0.380, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1723, decode.acc_seg: 92.9672, aux.loss_ce: 0.1118, aux.acc_seg: 88.7563, loss: 0.2841, grad_norm: 2.6318 2023-02-17 03:46:21,307 - mmseg - INFO - Iter [106050/160000] lr: 2.023e-05, eta: 5:34:15, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1667, decode.acc_seg: 93.0758, aux.loss_ce: 0.1076, aux.acc_seg: 89.1442, loss: 0.2743, grad_norm: 2.7712 2023-02-17 03:46:39,505 - mmseg - INFO - Iter [106100/160000] lr: 2.021e-05, eta: 5:33:55, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1728, decode.acc_seg: 92.8278, aux.loss_ce: 0.1097, aux.acc_seg: 88.8668, loss: 0.2825, grad_norm: 2.7509 2023-02-17 03:46:57,253 - mmseg - INFO - Iter [106150/160000] lr: 2.019e-05, eta: 5:33:35, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1699, decode.acc_seg: 92.9509, aux.loss_ce: 0.1116, aux.acc_seg: 88.7621, loss: 0.2815, grad_norm: 2.6615 2023-02-17 03:47:15,280 - mmseg - INFO - Iter [106200/160000] lr: 2.018e-05, eta: 5:33:15, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1782, decode.acc_seg: 92.5455, aux.loss_ce: 0.1147, aux.acc_seg: 88.3015, loss: 0.2929, grad_norm: 2.7199 2023-02-17 03:47:33,197 - mmseg - INFO - Iter [106250/160000] lr: 2.016e-05, eta: 5:32:54, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1771, decode.acc_seg: 92.9270, aux.loss_ce: 0.1128, aux.acc_seg: 88.8220, loss: 0.2899, grad_norm: 2.7729 2023-02-17 03:47:50,935 - mmseg - INFO - Iter [106300/160000] lr: 2.014e-05, eta: 5:32:34, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1748, decode.acc_seg: 93.0958, aux.loss_ce: 0.1127, aux.acc_seg: 88.9422, loss: 0.2875, grad_norm: 3.1162 2023-02-17 03:48:08,733 - mmseg - INFO - Iter [106350/160000] lr: 2.012e-05, eta: 5:32:13, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1698, decode.acc_seg: 93.1382, aux.loss_ce: 0.1159, aux.acc_seg: 88.5302, loss: 0.2857, grad_norm: 2.8566 2023-02-17 03:48:26,892 - mmseg - INFO - Iter [106400/160000] lr: 2.010e-05, eta: 5:31:54, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1694, decode.acc_seg: 93.1044, aux.loss_ce: 0.1100, aux.acc_seg: 88.7494, loss: 0.2794, grad_norm: 2.5888 2023-02-17 03:48:45,046 - mmseg - INFO - Iter [106450/160000] lr: 2.008e-05, eta: 5:31:34, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1686, decode.acc_seg: 93.1328, aux.loss_ce: 0.1115, aux.acc_seg: 89.0373, loss: 0.2802, grad_norm: 2.7930 2023-02-17 03:49:02,885 - mmseg - INFO - Iter [106500/160000] lr: 2.006e-05, eta: 5:31:14, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1650, decode.acc_seg: 93.3852, aux.loss_ce: 0.1055, aux.acc_seg: 89.5067, loss: 0.2705, grad_norm: 2.5569 2023-02-17 03:49:21,038 - mmseg - INFO - Iter [106550/160000] lr: 2.004e-05, eta: 5:30:54, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1769, decode.acc_seg: 92.7224, aux.loss_ce: 0.1152, aux.acc_seg: 88.3233, loss: 0.2920, grad_norm: 2.9883 2023-02-17 03:49:38,915 - mmseg - INFO - Iter [106600/160000] lr: 2.003e-05, eta: 5:30:34, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1727, decode.acc_seg: 92.9966, aux.loss_ce: 0.1170, aux.acc_seg: 88.2038, loss: 0.2896, grad_norm: 2.8101 2023-02-17 03:49:56,924 - mmseg - INFO - Iter [106650/160000] lr: 2.001e-05, eta: 5:30:14, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1672, decode.acc_seg: 93.2028, aux.loss_ce: 0.1103, aux.acc_seg: 88.6568, loss: 0.2774, grad_norm: 2.9071 2023-02-17 03:50:15,138 - mmseg - INFO - Iter [106700/160000] lr: 1.999e-05, eta: 5:29:55, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1716, decode.acc_seg: 92.7881, aux.loss_ce: 0.1099, aux.acc_seg: 88.7992, loss: 0.2815, grad_norm: 2.7675 2023-02-17 03:50:35,687 - mmseg - INFO - Iter [106750/160000] lr: 1.997e-05, eta: 5:29:41, time: 0.411, data_time: 0.051, memory: 16596, decode.loss_ce: 0.1645, decode.acc_seg: 93.3072, aux.loss_ce: 0.1060, aux.acc_seg: 89.2111, loss: 0.2705, grad_norm: 2.7492 2023-02-17 03:50:53,729 - mmseg - INFO - Iter [106800/160000] lr: 1.995e-05, eta: 5:29:21, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1711, decode.acc_seg: 92.7855, aux.loss_ce: 0.1088, aux.acc_seg: 88.7863, loss: 0.2799, grad_norm: 2.7547 2023-02-17 03:51:11,683 - mmseg - INFO - Iter [106850/160000] lr: 1.993e-05, eta: 5:29:01, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1705, decode.acc_seg: 92.9115, aux.loss_ce: 0.1109, aux.acc_seg: 88.6944, loss: 0.2813, grad_norm: 2.7940 2023-02-17 03:51:29,848 - mmseg - INFO - Iter [106900/160000] lr: 1.991e-05, eta: 5:28:41, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1770, decode.acc_seg: 92.6978, aux.loss_ce: 0.1114, aux.acc_seg: 88.6557, loss: 0.2884, grad_norm: 3.1160 2023-02-17 03:51:47,793 - mmseg - INFO - Iter [106950/160000] lr: 1.989e-05, eta: 5:28:21, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1641, decode.acc_seg: 93.0707, aux.loss_ce: 0.1097, aux.acc_seg: 88.9111, loss: 0.2738, grad_norm: 3.0115 2023-02-17 03:52:05,615 - mmseg - INFO - Saving checkpoint at 107000 iterations 2023-02-17 03:52:06,708 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 03:52:06,708 - mmseg - INFO - Iter [107000/160000] lr: 1.988e-05, eta: 5:28:04, time: 0.378, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1631, decode.acc_seg: 93.2787, aux.loss_ce: 0.1072, aux.acc_seg: 89.0696, loss: 0.2703, grad_norm: 2.5620 2023-02-17 03:52:24,543 - mmseg - INFO - Iter [107050/160000] lr: 1.986e-05, eta: 5:27:43, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1681, decode.acc_seg: 93.0349, aux.loss_ce: 0.1100, aux.acc_seg: 88.7438, loss: 0.2781, grad_norm: 2.6496 2023-02-17 03:52:42,797 - mmseg - INFO - Iter [107100/160000] lr: 1.984e-05, eta: 5:27:24, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1765, decode.acc_seg: 92.9496, aux.loss_ce: 0.1140, aux.acc_seg: 88.6999, loss: 0.2905, grad_norm: 2.9589 2023-02-17 03:53:01,014 - mmseg - INFO - Iter [107150/160000] lr: 1.982e-05, eta: 5:27:05, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1764, decode.acc_seg: 93.0063, aux.loss_ce: 0.1136, aux.acc_seg: 88.5456, loss: 0.2900, grad_norm: 2.9323 2023-02-17 03:53:19,383 - mmseg - INFO - Iter [107200/160000] lr: 1.980e-05, eta: 5:26:46, time: 0.367, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1642, decode.acc_seg: 93.2122, aux.loss_ce: 0.1080, aux.acc_seg: 89.1756, loss: 0.2722, grad_norm: 2.7849 2023-02-17 03:53:37,113 - mmseg - INFO - Iter [107250/160000] lr: 1.978e-05, eta: 5:26:25, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1537, decode.acc_seg: 93.6501, aux.loss_ce: 0.0999, aux.acc_seg: 89.8759, loss: 0.2537, grad_norm: 2.1834 2023-02-17 03:53:55,002 - mmseg - INFO - Iter [107300/160000] lr: 1.976e-05, eta: 5:26:05, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1713, decode.acc_seg: 93.0157, aux.loss_ce: 0.1097, aux.acc_seg: 89.0668, loss: 0.2810, grad_norm: 2.7971 2023-02-17 03:54:12,907 - mmseg - INFO - Iter [107350/160000] lr: 1.974e-05, eta: 5:25:45, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1655, decode.acc_seg: 93.1734, aux.loss_ce: 0.1093, aux.acc_seg: 89.0139, loss: 0.2749, grad_norm: 2.3991 2023-02-17 03:54:30,796 - mmseg - INFO - Iter [107400/160000] lr: 1.973e-05, eta: 5:25:25, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1712, decode.acc_seg: 93.0905, aux.loss_ce: 0.1139, aux.acc_seg: 88.6989, loss: 0.2851, grad_norm: 2.7663 2023-02-17 03:54:48,687 - mmseg - INFO - Iter [107450/160000] lr: 1.971e-05, eta: 5:25:05, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1681, decode.acc_seg: 92.9841, aux.loss_ce: 0.1073, aux.acc_seg: 89.0606, loss: 0.2754, grad_norm: 2.9041 2023-02-17 03:55:06,785 - mmseg - INFO - Iter [107500/160000] lr: 1.969e-05, eta: 5:24:45, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1740, decode.acc_seg: 92.9884, aux.loss_ce: 0.1121, aux.acc_seg: 88.7437, loss: 0.2861, grad_norm: 2.7347 2023-02-17 03:55:24,865 - mmseg - INFO - Iter [107550/160000] lr: 1.967e-05, eta: 5:24:26, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1656, decode.acc_seg: 93.2822, aux.loss_ce: 0.1081, aux.acc_seg: 89.3157, loss: 0.2737, grad_norm: 2.5158 2023-02-17 03:55:42,924 - mmseg - INFO - Iter [107600/160000] lr: 1.965e-05, eta: 5:24:06, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1667, decode.acc_seg: 93.2102, aux.loss_ce: 0.1091, aux.acc_seg: 88.8606, loss: 0.2759, grad_norm: 2.4740 2023-02-17 03:56:01,387 - mmseg - INFO - Iter [107650/160000] lr: 1.963e-05, eta: 5:23:47, time: 0.370, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1677, decode.acc_seg: 93.2997, aux.loss_ce: 0.1109, aux.acc_seg: 88.9755, loss: 0.2787, grad_norm: 2.5270 2023-02-17 03:56:19,498 - mmseg - INFO - Iter [107700/160000] lr: 1.961e-05, eta: 5:23:28, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1695, decode.acc_seg: 93.1260, aux.loss_ce: 0.1096, aux.acc_seg: 89.0071, loss: 0.2792, grad_norm: 2.6588 2023-02-17 03:56:37,356 - mmseg - INFO - Iter [107750/160000] lr: 1.959e-05, eta: 5:23:08, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1739, decode.acc_seg: 92.9669, aux.loss_ce: 0.1176, aux.acc_seg: 88.4344, loss: 0.2916, grad_norm: 2.7591 2023-02-17 03:56:55,438 - mmseg - INFO - Iter [107800/160000] lr: 1.958e-05, eta: 5:22:48, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1714, decode.acc_seg: 93.2175, aux.loss_ce: 0.1141, aux.acc_seg: 88.6416, loss: 0.2855, grad_norm: 2.5163 2023-02-17 03:57:13,203 - mmseg - INFO - Iter [107850/160000] lr: 1.956e-05, eta: 5:22:28, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1606, decode.acc_seg: 93.0813, aux.loss_ce: 0.1013, aux.acc_seg: 89.2730, loss: 0.2619, grad_norm: 2.5345 2023-02-17 03:57:31,050 - mmseg - INFO - Iter [107900/160000] lr: 1.954e-05, eta: 5:22:08, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1651, decode.acc_seg: 93.2547, aux.loss_ce: 0.1069, aux.acc_seg: 89.1246, loss: 0.2720, grad_norm: 2.4983 2023-02-17 03:57:48,996 - mmseg - INFO - Iter [107950/160000] lr: 1.952e-05, eta: 5:21:48, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1710, decode.acc_seg: 92.8703, aux.loss_ce: 0.1098, aux.acc_seg: 88.7500, loss: 0.2808, grad_norm: 2.9954 2023-02-17 03:58:09,520 - mmseg - INFO - Saving checkpoint at 108000 iterations 2023-02-17 03:58:10,571 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 03:58:10,571 - mmseg - INFO - Iter [108000/160000] lr: 1.950e-05, eta: 5:21:36, time: 0.432, data_time: 0.053, memory: 16596, decode.loss_ce: 0.1736, decode.acc_seg: 92.8892, aux.loss_ce: 0.1182, aux.acc_seg: 88.3078, loss: 0.2918, grad_norm: 3.0777 2023-02-17 03:58:28,687 - mmseg - INFO - Iter [108050/160000] lr: 1.948e-05, eta: 5:21:16, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1679, decode.acc_seg: 93.1644, aux.loss_ce: 0.1087, aux.acc_seg: 89.2645, loss: 0.2766, grad_norm: 3.2488 2023-02-17 03:58:46,406 - mmseg - INFO - Iter [108100/160000] lr: 1.946e-05, eta: 5:20:56, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1697, decode.acc_seg: 93.0142, aux.loss_ce: 0.1063, aux.acc_seg: 89.2124, loss: 0.2760, grad_norm: 2.6808 2023-02-17 03:59:04,397 - mmseg - INFO - Iter [108150/160000] lr: 1.944e-05, eta: 5:20:36, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1654, decode.acc_seg: 93.3290, aux.loss_ce: 0.1110, aux.acc_seg: 89.0252, loss: 0.2764, grad_norm: 2.7313 2023-02-17 03:59:22,558 - mmseg - INFO - Iter [108200/160000] lr: 1.943e-05, eta: 5:20:17, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1668, decode.acc_seg: 93.2839, aux.loss_ce: 0.1117, aux.acc_seg: 88.6795, loss: 0.2785, grad_norm: 2.5566 2023-02-17 03:59:40,343 - mmseg - INFO - Iter [108250/160000] lr: 1.941e-05, eta: 5:19:57, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1645, decode.acc_seg: 93.1544, aux.loss_ce: 0.1081, aux.acc_seg: 88.9000, loss: 0.2726, grad_norm: 2.4596 2023-02-17 03:59:58,219 - mmseg - INFO - Iter [108300/160000] lr: 1.939e-05, eta: 5:19:37, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1645, decode.acc_seg: 93.3283, aux.loss_ce: 0.1083, aux.acc_seg: 88.9837, loss: 0.2728, grad_norm: 2.7707 2023-02-17 04:00:16,527 - mmseg - INFO - Iter [108350/160000] lr: 1.937e-05, eta: 5:19:18, time: 0.366, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1694, decode.acc_seg: 93.0316, aux.loss_ce: 0.1087, aux.acc_seg: 89.1500, loss: 0.2781, grad_norm: 2.9954 2023-02-17 04:00:34,530 - mmseg - INFO - Iter [108400/160000] lr: 1.935e-05, eta: 5:18:58, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1767, decode.acc_seg: 92.8221, aux.loss_ce: 0.1116, aux.acc_seg: 88.8974, loss: 0.2883, grad_norm: 2.6395 2023-02-17 04:00:52,844 - mmseg - INFO - Iter [108450/160000] lr: 1.933e-05, eta: 5:18:39, time: 0.367, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1716, decode.acc_seg: 92.8755, aux.loss_ce: 0.1134, aux.acc_seg: 88.7399, loss: 0.2850, grad_norm: 2.6697 2023-02-17 04:01:10,976 - mmseg - INFO - Iter [108500/160000] lr: 1.931e-05, eta: 5:18:20, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1699, decode.acc_seg: 92.8416, aux.loss_ce: 0.1048, aux.acc_seg: 89.1576, loss: 0.2746, grad_norm: 2.3872 2023-02-17 04:01:29,263 - mmseg - INFO - Iter [108550/160000] lr: 1.929e-05, eta: 5:18:00, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1645, decode.acc_seg: 93.1996, aux.loss_ce: 0.1064, aux.acc_seg: 89.1104, loss: 0.2709, grad_norm: 2.4186 2023-02-17 04:01:47,327 - mmseg - INFO - Iter [108600/160000] lr: 1.928e-05, eta: 5:17:41, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1606, decode.acc_seg: 93.0935, aux.loss_ce: 0.1071, aux.acc_seg: 88.8391, loss: 0.2678, grad_norm: 2.8939 2023-02-17 04:02:05,587 - mmseg - INFO - Iter [108650/160000] lr: 1.926e-05, eta: 5:17:22, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1590, decode.acc_seg: 93.5909, aux.loss_ce: 0.1038, aux.acc_seg: 89.5571, loss: 0.2628, grad_norm: 2.4977 2023-02-17 04:02:23,500 - mmseg - INFO - Iter [108700/160000] lr: 1.924e-05, eta: 5:17:02, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1771, decode.acc_seg: 92.9257, aux.loss_ce: 0.1147, aux.acc_seg: 88.5193, loss: 0.2918, grad_norm: 3.3290 2023-02-17 04:02:41,303 - mmseg - INFO - Iter [108750/160000] lr: 1.922e-05, eta: 5:16:42, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1708, decode.acc_seg: 92.9022, aux.loss_ce: 0.1129, aux.acc_seg: 88.4366, loss: 0.2837, grad_norm: 2.7147 2023-02-17 04:02:59,371 - mmseg - INFO - Iter [108800/160000] lr: 1.920e-05, eta: 5:16:22, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1719, decode.acc_seg: 93.1554, aux.loss_ce: 0.1164, aux.acc_seg: 88.5606, loss: 0.2883, grad_norm: 3.4530 2023-02-17 04:03:17,122 - mmseg - INFO - Iter [108850/160000] lr: 1.918e-05, eta: 5:16:02, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1787, decode.acc_seg: 92.9587, aux.loss_ce: 0.1176, aux.acc_seg: 88.3201, loss: 0.2963, grad_norm: 3.2465 2023-02-17 04:03:35,047 - mmseg - INFO - Iter [108900/160000] lr: 1.916e-05, eta: 5:15:42, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1572, decode.acc_seg: 93.5772, aux.loss_ce: 0.1027, aux.acc_seg: 89.5789, loss: 0.2599, grad_norm: 3.0954 2023-02-17 04:03:53,141 - mmseg - INFO - Iter [108950/160000] lr: 1.914e-05, eta: 5:15:23, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1723, decode.acc_seg: 93.0608, aux.loss_ce: 0.1109, aux.acc_seg: 88.6836, loss: 0.2832, grad_norm: 3.1738 2023-02-17 04:04:11,350 - mmseg - INFO - Saving checkpoint at 109000 iterations 2023-02-17 04:04:12,422 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 04:04:12,422 - mmseg - INFO - Iter [109000/160000] lr: 1.913e-05, eta: 5:15:06, time: 0.386, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1613, decode.acc_seg: 93.2106, aux.loss_ce: 0.1065, aux.acc_seg: 89.0763, loss: 0.2677, grad_norm: 2.8368 2023-02-17 04:04:30,382 - mmseg - INFO - Iter [109050/160000] lr: 1.911e-05, eta: 5:14:46, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1773, decode.acc_seg: 92.7714, aux.loss_ce: 0.1170, aux.acc_seg: 88.4306, loss: 0.2943, grad_norm: 2.9122 2023-02-17 04:04:48,428 - mmseg - INFO - Iter [109100/160000] lr: 1.909e-05, eta: 5:14:27, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1605, decode.acc_seg: 93.4766, aux.loss_ce: 0.1020, aux.acc_seg: 89.6574, loss: 0.2625, grad_norm: 2.5864 2023-02-17 04:05:06,311 - mmseg - INFO - Iter [109150/160000] lr: 1.907e-05, eta: 5:14:07, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1705, decode.acc_seg: 93.1172, aux.loss_ce: 0.1101, aux.acc_seg: 89.0505, loss: 0.2807, grad_norm: 2.8419 2023-02-17 04:05:24,511 - mmseg - INFO - Iter [109200/160000] lr: 1.905e-05, eta: 5:13:48, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1625, decode.acc_seg: 93.1968, aux.loss_ce: 0.1067, aux.acc_seg: 89.0411, loss: 0.2692, grad_norm: 3.5015 2023-02-17 04:05:42,479 - mmseg - INFO - Iter [109250/160000] lr: 1.903e-05, eta: 5:13:28, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1709, decode.acc_seg: 92.9832, aux.loss_ce: 0.1119, aux.acc_seg: 88.8817, loss: 0.2828, grad_norm: 2.8675 2023-02-17 04:06:03,186 - mmseg - INFO - Iter [109300/160000] lr: 1.901e-05, eta: 5:13:14, time: 0.414, data_time: 0.054, memory: 16596, decode.loss_ce: 0.1699, decode.acc_seg: 92.8222, aux.loss_ce: 0.1038, aux.acc_seg: 89.2649, loss: 0.2737, grad_norm: 2.5397 2023-02-17 04:06:21,378 - mmseg - INFO - Iter [109350/160000] lr: 1.899e-05, eta: 5:12:55, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1694, decode.acc_seg: 93.0201, aux.loss_ce: 0.1083, aux.acc_seg: 89.1388, loss: 0.2777, grad_norm: 2.9499 2023-02-17 04:06:39,470 - mmseg - INFO - Iter [109400/160000] lr: 1.898e-05, eta: 5:12:35, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1625, decode.acc_seg: 93.3444, aux.loss_ce: 0.1085, aux.acc_seg: 88.8753, loss: 0.2710, grad_norm: 2.5920 2023-02-17 04:06:57,640 - mmseg - INFO - Iter [109450/160000] lr: 1.896e-05, eta: 5:12:16, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1662, decode.acc_seg: 93.0615, aux.loss_ce: 0.1064, aux.acc_seg: 89.2092, loss: 0.2726, grad_norm: 3.0186 2023-02-17 04:07:15,520 - mmseg - INFO - Iter [109500/160000] lr: 1.894e-05, eta: 5:11:56, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1645, decode.acc_seg: 93.3344, aux.loss_ce: 0.1074, aux.acc_seg: 89.1036, loss: 0.2719, grad_norm: 2.5703 2023-02-17 04:07:33,314 - mmseg - INFO - Iter [109550/160000] lr: 1.892e-05, eta: 5:11:36, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1617, decode.acc_seg: 93.1767, aux.loss_ce: 0.1079, aux.acc_seg: 88.8782, loss: 0.2696, grad_norm: 2.5979 2023-02-17 04:07:51,142 - mmseg - INFO - Iter [109600/160000] lr: 1.890e-05, eta: 5:11:16, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1673, decode.acc_seg: 93.1230, aux.loss_ce: 0.1107, aux.acc_seg: 88.9231, loss: 0.2780, grad_norm: 3.1054 2023-02-17 04:08:08,980 - mmseg - INFO - Iter [109650/160000] lr: 1.888e-05, eta: 5:10:56, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1623, decode.acc_seg: 93.4024, aux.loss_ce: 0.1122, aux.acc_seg: 88.8792, loss: 0.2744, grad_norm: 2.7116 2023-02-17 04:08:26,739 - mmseg - INFO - Iter [109700/160000] lr: 1.886e-05, eta: 5:10:36, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1632, decode.acc_seg: 93.4121, aux.loss_ce: 0.1072, aux.acc_seg: 89.1071, loss: 0.2704, grad_norm: 2.8333 2023-02-17 04:08:44,719 - mmseg - INFO - Iter [109750/160000] lr: 1.884e-05, eta: 5:10:17, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1640, decode.acc_seg: 93.1546, aux.loss_ce: 0.1074, aux.acc_seg: 89.0143, loss: 0.2714, grad_norm: 2.5616 2023-02-17 04:09:02,920 - mmseg - INFO - Iter [109800/160000] lr: 1.883e-05, eta: 5:09:58, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1685, decode.acc_seg: 93.1221, aux.loss_ce: 0.1083, aux.acc_seg: 89.0941, loss: 0.2768, grad_norm: 2.4316 2023-02-17 04:09:21,003 - mmseg - INFO - Iter [109850/160000] lr: 1.881e-05, eta: 5:09:38, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1578, decode.acc_seg: 93.4337, aux.loss_ce: 0.1051, aux.acc_seg: 89.3395, loss: 0.2629, grad_norm: 2.3417 2023-02-17 04:09:39,105 - mmseg - INFO - Iter [109900/160000] lr: 1.879e-05, eta: 5:09:19, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1676, decode.acc_seg: 93.1881, aux.loss_ce: 0.1112, aux.acc_seg: 88.7570, loss: 0.2788, grad_norm: 2.8785 2023-02-17 04:09:57,214 - mmseg - INFO - Iter [109950/160000] lr: 1.877e-05, eta: 5:09:00, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1624, decode.acc_seg: 93.5133, aux.loss_ce: 0.1052, aux.acc_seg: 89.5184, loss: 0.2676, grad_norm: 2.4590 2023-02-17 04:10:15,549 - mmseg - INFO - Saving checkpoint at 110000 iterations 2023-02-17 04:10:16,593 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 04:10:16,593 - mmseg - INFO - Iter [110000/160000] lr: 1.875e-05, eta: 5:08:43, time: 0.388, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1780, decode.acc_seg: 92.7330, aux.loss_ce: 0.1142, aux.acc_seg: 88.4698, loss: 0.2921, grad_norm: 2.7456 2023-02-17 04:10:34,472 - mmseg - INFO - Iter [110050/160000] lr: 1.873e-05, eta: 5:08:23, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1717, decode.acc_seg: 92.9502, aux.loss_ce: 0.1086, aux.acc_seg: 88.9017, loss: 0.2804, grad_norm: 2.9897 2023-02-17 04:10:52,692 - mmseg - INFO - Iter [110100/160000] lr: 1.871e-05, eta: 5:08:04, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1638, decode.acc_seg: 93.3402, aux.loss_ce: 0.1083, aux.acc_seg: 89.1347, loss: 0.2722, grad_norm: 2.8525 2023-02-17 04:11:10,711 - mmseg - INFO - Iter [110150/160000] lr: 1.869e-05, eta: 5:07:44, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1746, decode.acc_seg: 92.9544, aux.loss_ce: 0.1106, aux.acc_seg: 88.8722, loss: 0.2853, grad_norm: 3.3344 2023-02-17 04:11:28,800 - mmseg - INFO - Iter [110200/160000] lr: 1.868e-05, eta: 5:07:25, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1756, decode.acc_seg: 92.8915, aux.loss_ce: 0.1147, aux.acc_seg: 88.6401, loss: 0.2903, grad_norm: 2.6012 2023-02-17 04:11:46,457 - mmseg - INFO - Iter [110250/160000] lr: 1.866e-05, eta: 5:07:05, time: 0.353, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1590, decode.acc_seg: 93.4370, aux.loss_ce: 0.1032, aux.acc_seg: 89.4773, loss: 0.2623, grad_norm: 2.4930 2023-02-17 04:12:04,415 - mmseg - INFO - Iter [110300/160000] lr: 1.864e-05, eta: 5:06:45, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1769, decode.acc_seg: 92.8188, aux.loss_ce: 0.1132, aux.acc_seg: 88.8466, loss: 0.2901, grad_norm: 3.1567 2023-02-17 04:12:22,331 - mmseg - INFO - Iter [110350/160000] lr: 1.862e-05, eta: 5:06:26, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1638, decode.acc_seg: 93.2369, aux.loss_ce: 0.1091, aux.acc_seg: 88.8800, loss: 0.2728, grad_norm: 2.7084 2023-02-17 04:12:40,520 - mmseg - INFO - Iter [110400/160000] lr: 1.860e-05, eta: 5:06:07, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1684, decode.acc_seg: 93.2412, aux.loss_ce: 0.1108, aux.acc_seg: 89.0141, loss: 0.2792, grad_norm: 2.6638 2023-02-17 04:12:58,295 - mmseg - INFO - Iter [110450/160000] lr: 1.858e-05, eta: 5:05:47, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1726, decode.acc_seg: 92.8054, aux.loss_ce: 0.1129, aux.acc_seg: 88.5889, loss: 0.2855, grad_norm: 2.6778 2023-02-17 04:13:16,084 - mmseg - INFO - Iter [110500/160000] lr: 1.856e-05, eta: 5:05:27, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1623, decode.acc_seg: 93.1602, aux.loss_ce: 0.1054, aux.acc_seg: 89.0467, loss: 0.2677, grad_norm: 2.7432 2023-02-17 04:13:36,202 - mmseg - INFO - Iter [110550/160000] lr: 1.854e-05, eta: 5:05:11, time: 0.402, data_time: 0.055, memory: 16596, decode.loss_ce: 0.1668, decode.acc_seg: 93.1352, aux.loss_ce: 0.1081, aux.acc_seg: 88.9931, loss: 0.2748, grad_norm: 2.9330 2023-02-17 04:13:53,951 - mmseg - INFO - Iter [110600/160000] lr: 1.853e-05, eta: 5:04:51, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1572, decode.acc_seg: 93.3993, aux.loss_ce: 0.1038, aux.acc_seg: 89.3772, loss: 0.2611, grad_norm: 2.5414 2023-02-17 04:14:11,913 - mmseg - INFO - Iter [110650/160000] lr: 1.851e-05, eta: 5:04:32, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1688, decode.acc_seg: 93.1225, aux.loss_ce: 0.1112, aux.acc_seg: 88.9569, loss: 0.2800, grad_norm: 2.9241 2023-02-17 04:14:29,917 - mmseg - INFO - Iter [110700/160000] lr: 1.849e-05, eta: 5:04:12, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1651, decode.acc_seg: 93.2996, aux.loss_ce: 0.1060, aux.acc_seg: 89.3538, loss: 0.2711, grad_norm: 2.6563 2023-02-17 04:14:48,367 - mmseg - INFO - Iter [110750/160000] lr: 1.847e-05, eta: 5:03:54, time: 0.369, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1610, decode.acc_seg: 93.4447, aux.loss_ce: 0.1068, aux.acc_seg: 88.9340, loss: 0.2678, grad_norm: 2.5145 2023-02-17 04:15:06,209 - mmseg - INFO - Iter [110800/160000] lr: 1.845e-05, eta: 5:03:34, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1585, decode.acc_seg: 93.4419, aux.loss_ce: 0.1084, aux.acc_seg: 88.9493, loss: 0.2669, grad_norm: 2.4169 2023-02-17 04:15:24,085 - mmseg - INFO - Iter [110850/160000] lr: 1.843e-05, eta: 5:03:14, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1752, decode.acc_seg: 92.8967, aux.loss_ce: 0.1176, aux.acc_seg: 88.6039, loss: 0.2928, grad_norm: 2.8652 2023-02-17 04:15:42,498 - mmseg - INFO - Iter [110900/160000] lr: 1.841e-05, eta: 5:02:56, time: 0.368, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1605, decode.acc_seg: 93.2803, aux.loss_ce: 0.1091, aux.acc_seg: 88.8275, loss: 0.2696, grad_norm: 2.8689 2023-02-17 04:16:00,304 - mmseg - INFO - Iter [110950/160000] lr: 1.839e-05, eta: 5:02:36, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1669, decode.acc_seg: 92.9799, aux.loss_ce: 0.1094, aux.acc_seg: 88.7294, loss: 0.2764, grad_norm: 2.7637 2023-02-17 04:16:18,135 - mmseg - INFO - Saving checkpoint at 111000 iterations 2023-02-17 04:16:19,237 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 04:16:19,237 - mmseg - INFO - Iter [111000/160000] lr: 1.838e-05, eta: 5:02:18, time: 0.379, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1637, decode.acc_seg: 93.3285, aux.loss_ce: 0.1095, aux.acc_seg: 89.0576, loss: 0.2732, grad_norm: 2.4400 2023-02-17 04:16:37,231 - mmseg - INFO - Iter [111050/160000] lr: 1.836e-05, eta: 5:01:59, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1629, decode.acc_seg: 93.2069, aux.loss_ce: 0.1082, aux.acc_seg: 89.1497, loss: 0.2711, grad_norm: 2.6371 2023-02-17 04:16:55,283 - mmseg - INFO - Iter [111100/160000] lr: 1.834e-05, eta: 5:01:39, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1719, decode.acc_seg: 93.0010, aux.loss_ce: 0.1117, aux.acc_seg: 88.9904, loss: 0.2836, grad_norm: 2.9126 2023-02-17 04:17:13,074 - mmseg - INFO - Iter [111150/160000] lr: 1.832e-05, eta: 5:01:19, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1656, decode.acc_seg: 93.1015, aux.loss_ce: 0.1068, aux.acc_seg: 89.0532, loss: 0.2724, grad_norm: 2.4133 2023-02-17 04:17:30,811 - mmseg - INFO - Iter [111200/160000] lr: 1.830e-05, eta: 5:01:00, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1571, decode.acc_seg: 93.4790, aux.loss_ce: 0.1078, aux.acc_seg: 89.2142, loss: 0.2650, grad_norm: 2.6352 2023-02-17 04:17:48,906 - mmseg - INFO - Iter [111250/160000] lr: 1.828e-05, eta: 5:00:40, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1744, decode.acc_seg: 92.9430, aux.loss_ce: 0.1090, aux.acc_seg: 88.9675, loss: 0.2834, grad_norm: 3.1212 2023-02-17 04:18:06,898 - mmseg - INFO - Iter [111300/160000] lr: 1.826e-05, eta: 5:00:21, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1616, decode.acc_seg: 93.1989, aux.loss_ce: 0.1082, aux.acc_seg: 88.9036, loss: 0.2699, grad_norm: 2.7069 2023-02-17 04:18:25,368 - mmseg - INFO - Iter [111350/160000] lr: 1.824e-05, eta: 5:00:02, time: 0.369, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1721, decode.acc_seg: 93.0941, aux.loss_ce: 0.1123, aux.acc_seg: 88.7695, loss: 0.2844, grad_norm: 3.2494 2023-02-17 04:18:43,427 - mmseg - INFO - Iter [111400/160000] lr: 1.823e-05, eta: 4:59:43, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1690, decode.acc_seg: 92.9727, aux.loss_ce: 0.1114, aux.acc_seg: 88.6176, loss: 0.2804, grad_norm: 2.8595 2023-02-17 04:19:01,383 - mmseg - INFO - Iter [111450/160000] lr: 1.821e-05, eta: 4:59:24, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1528, decode.acc_seg: 93.6436, aux.loss_ce: 0.1035, aux.acc_seg: 89.3033, loss: 0.2563, grad_norm: 2.2456 2023-02-17 04:19:19,697 - mmseg - INFO - Iter [111500/160000] lr: 1.819e-05, eta: 4:59:05, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1654, decode.acc_seg: 93.2730, aux.loss_ce: 0.1098, aux.acc_seg: 89.0171, loss: 0.2752, grad_norm: 2.5386 2023-02-17 04:19:37,769 - mmseg - INFO - Iter [111550/160000] lr: 1.817e-05, eta: 4:58:46, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1620, decode.acc_seg: 93.4222, aux.loss_ce: 0.1062, aux.acc_seg: 89.3464, loss: 0.2682, grad_norm: 2.5820 2023-02-17 04:19:55,964 - mmseg - INFO - Iter [111600/160000] lr: 1.815e-05, eta: 4:58:26, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1552, decode.acc_seg: 93.6128, aux.loss_ce: 0.1026, aux.acc_seg: 89.5935, loss: 0.2578, grad_norm: 3.3801 2023-02-17 04:20:13,928 - mmseg - INFO - Iter [111650/160000] lr: 1.813e-05, eta: 4:58:07, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1653, decode.acc_seg: 93.2838, aux.loss_ce: 0.1106, aux.acc_seg: 88.9748, loss: 0.2760, grad_norm: 2.6420 2023-02-17 04:20:31,539 - mmseg - INFO - Iter [111700/160000] lr: 1.811e-05, eta: 4:57:47, time: 0.352, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1587, decode.acc_seg: 93.4269, aux.loss_ce: 0.1095, aux.acc_seg: 88.9629, loss: 0.2681, grad_norm: 2.9325 2023-02-17 04:20:49,510 - mmseg - INFO - Iter [111750/160000] lr: 1.809e-05, eta: 4:57:28, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1713, decode.acc_seg: 93.1603, aux.loss_ce: 0.1158, aux.acc_seg: 88.5445, loss: 0.2871, grad_norm: 2.8513 2023-02-17 04:21:10,143 - mmseg - INFO - Iter [111800/160000] lr: 1.808e-05, eta: 4:57:13, time: 0.413, data_time: 0.053, memory: 16596, decode.loss_ce: 0.1765, decode.acc_seg: 92.6851, aux.loss_ce: 0.1153, aux.acc_seg: 88.3232, loss: 0.2918, grad_norm: 3.2871 2023-02-17 04:21:27,926 - mmseg - INFO - Iter [111850/160000] lr: 1.806e-05, eta: 4:56:53, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1642, decode.acc_seg: 93.3010, aux.loss_ce: 0.1058, aux.acc_seg: 89.4751, loss: 0.2701, grad_norm: 2.5287 2023-02-17 04:21:45,812 - mmseg - INFO - Iter [111900/160000] lr: 1.804e-05, eta: 4:56:33, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1559, decode.acc_seg: 93.5818, aux.loss_ce: 0.1050, aux.acc_seg: 89.3200, loss: 0.2609, grad_norm: 2.2685 2023-02-17 04:22:03,830 - mmseg - INFO - Iter [111950/160000] lr: 1.802e-05, eta: 4:56:14, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1728, decode.acc_seg: 92.9977, aux.loss_ce: 0.1087, aux.acc_seg: 89.1314, loss: 0.2815, grad_norm: 2.9430 2023-02-17 04:22:21,776 - mmseg - INFO - Saving checkpoint at 112000 iterations 2023-02-17 04:22:22,848 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 04:22:22,849 - mmseg - INFO - Iter [112000/160000] lr: 1.800e-05, eta: 4:55:57, time: 0.380, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1644, decode.acc_seg: 93.1136, aux.loss_ce: 0.1051, aux.acc_seg: 89.1790, loss: 0.2695, grad_norm: 2.8412 2023-02-17 04:22:51,525 - mmseg - INFO - per class results: 2023-02-17 04:22:51,531 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 76.75 | 86.71 | | building | 81.92 | 91.83 | | sky | 94.31 | 97.12 | | floor | 80.12 | 91.52 | | tree | 73.97 | 86.38 | | ceiling | 84.52 | 92.74 | | road | 83.71 | 93.31 | | bed | 88.0 | 95.96 | | windowpane | 60.15 | 80.55 | | grass | 66.95 | 83.41 | | cabinet | 58.44 | 75.41 | | sidewalk | 65.02 | 74.45 | | person | 80.1 | 92.43 | | earth | 34.56 | 47.09 | | door | 49.66 | 64.98 | | table | 59.26 | 77.13 | | mountain | 57.6 | 77.18 | | plant | 50.29 | 61.16 | | curtain | 70.56 | 84.47 | | chair | 55.32 | 65.8 | | car | 82.03 | 89.35 | | water | 44.64 | 59.8 | | painting | 73.69 | 85.77 | | sofa | 63.42 | 76.56 | | shelf | 42.98 | 59.39 | | house | 44.41 | 55.36 | | sea | 54.86 | 86.27 | | mirror | 64.51 | 76.05 | | rug | 59.96 | 67.36 | | field | 26.55 | 37.48 | | armchair | 40.99 | 61.96 | | seat | 59.3 | 78.89 | | fence | 47.74 | 56.2 | | desk | 47.03 | 72.22 | | rock | 43.0 | 60.39 | | wardrobe | 47.9 | 71.45 | | lamp | 61.69 | 73.86 | | bathtub | 73.37 | 80.37 | | railing | 34.47 | 54.16 | | cushion | 57.86 | 76.97 | | base | 31.8 | 40.93 | | box | 24.5 | 29.02 | | column | 45.36 | 58.08 | | signboard | 37.37 | 50.58 | | chest of drawers | 39.59 | 47.5 | | counter | 23.73 | 29.01 | | sand | 43.89 | 73.46 | | sink | 73.02 | 80.01 | | skyscraper | 50.53 | 70.33 | | fireplace | 72.34 | 91.81 | | refrigerator | 77.8 | 86.32 | | grandstand | 46.76 | 63.29 | | path | 22.83 | 38.13 | | stairs | 30.91 | 37.8 | | runway | 64.79 | 85.91 | | case | 36.84 | 46.57 | | pool table | 93.17 | 94.44 | | pillow | 53.99 | 61.36 | | screen door | 57.43 | 79.65 | | stairway | 30.93 | 43.43 | | river | 9.86 | 21.85 | | bridge | 65.08 | 81.99 | | bookcase | 33.15 | 54.48 | | blind | 44.72 | 56.55 | | coffee table | 57.29 | 82.35 | | toilet | 85.13 | 90.87 | | flower | 41.31 | 68.42 | | book | 44.34 | 62.36 | | hill | 7.25 | 12.66 | | bench | 43.84 | 50.46 | | countertop | 57.21 | 77.65 | | stove | 75.9 | 81.05 | | palm | 51.13 | 77.67 | | kitchen island | 40.58 | 59.41 | | computer | 66.96 | 84.0 | | swivel chair | 40.1 | 54.47 | | boat | 34.27 | 49.8 | | bar | 22.24 | 25.39 | | arcade machine | 38.82 | 40.46 | | hovel | 19.8 | 21.82 | | bus | 82.71 | 97.19 | | towel | 64.54 | 69.77 | | light | 53.69 | 59.65 | | truck | 33.23 | 51.26 | | tower | 10.7 | 17.13 | | chandelier | 67.32 | 79.0 | | awning | 26.65 | 31.52 | | streetlight | 25.63 | 32.63 | | booth | 46.32 | 48.22 | | television receiver | 66.2 | 81.18 | | airplane | 47.56 | 66.09 | | dirt track | 8.0 | 20.4 | | apparel | 44.45 | 61.4 | | pole | 23.2 | 33.96 | | land | 2.84 | 3.46 | | bannister | 9.65 | 12.9 | | escalator | 32.04 | 35.25 | | ottoman | 47.96 | 70.8 | | bottle | 38.2 | 53.01 | | buffet | 35.89 | 38.73 | | poster | 26.16 | 38.62 | | stage | 15.36 | 22.38 | | van | 51.29 | 67.68 | | ship | 42.66 | 61.0 | | fountain | 25.25 | 26.74 | | conveyer belt | 74.28 | 84.25 | | canopy | 23.38 | 30.36 | | washer | 68.78 | 72.2 | | plaything | 25.42 | 36.72 | | swimming pool | 55.1 | 63.43 | | stool | 37.39 | 53.99 | | barrel | 55.26 | 64.21 | | basket | 35.56 | 50.33 | | waterfall | 41.45 | 54.1 | | tent | 95.25 | 98.06 | | bag | 18.32 | 22.67 | | minibike | 54.1 | 62.93 | | cradle | 78.62 | 95.66 | | oven | 40.7 | 50.86 | | ball | 48.76 | 62.15 | | food | 60.4 | 68.28 | | step | 9.83 | 15.84 | | tank | 34.82 | 40.27 | | trade name | 30.51 | 39.44 | | microwave | 80.62 | 90.68 | | pot | 44.26 | 54.94 | | animal | 60.73 | 64.58 | | bicycle | 54.57 | 73.79 | | lake | 42.28 | 46.46 | | dishwasher | 76.02 | 81.5 | | screen | 71.78 | 91.27 | | blanket | 9.51 | 10.5 | | sculpture | 50.99 | 77.26 | | hood | 54.04 | 62.05 | | sconce | 40.97 | 46.41 | | vase | 36.17 | 56.09 | | traffic light | 34.23 | 56.9 | | tray | 4.48 | 8.41 | | ashcan | 41.86 | 54.25 | | fan | 61.48 | 78.84 | | pier | 23.26 | 41.86 | | crt screen | 5.98 | 15.41 | | plate | 53.49 | 69.17 | | monitor | 13.36 | 17.99 | | bulletin board | 45.19 | 54.66 | | shower | 5.69 | 12.46 | | radiator | 63.1 | 72.23 | | glass | 11.05 | 11.64 | | clock | 33.51 | 44.53 | | flag | 36.25 | 39.99 | +---------------------+-------+-------+ 2023-02-17 04:22:51,531 - mmseg - INFO - Summary: 2023-02-17 04:22:51,531 - mmseg - INFO - +-------+-------+------+ | aAcc | mIoU | mAcc | +-------+-------+------+ | 82.39 | 47.66 | 59.6 | +-------+-------+------+ 2023-02-17 04:22:52,537 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_112000.pth. 2023-02-17 04:22:52,537 - mmseg - INFO - Best mIoU is 0.4766 at 112000 iter. 2023-02-17 04:22:52,537 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 04:22:52,537 - mmseg - INFO - Iter(val) [500] aAcc: 0.8239, mIoU: 0.4766, mAcc: 0.5960, IoU.wall: 0.7675, IoU.building: 0.8192, IoU.sky: 0.9431, IoU.floor: 0.8012, IoU.tree: 0.7397, IoU.ceiling: 0.8452, IoU.road: 0.8371, IoU.bed : 0.8800, IoU.windowpane: 0.6015, IoU.grass: 0.6695, IoU.cabinet: 0.5844, IoU.sidewalk: 0.6502, IoU.person: 0.8010, IoU.earth: 0.3456, IoU.door: 0.4966, IoU.table: 0.5926, IoU.mountain: 0.5760, IoU.plant: 0.5029, IoU.curtain: 0.7056, IoU.chair: 0.5532, IoU.car: 0.8203, IoU.water: 0.4464, IoU.painting: 0.7369, IoU.sofa: 0.6342, IoU.shelf: 0.4298, IoU.house: 0.4441, IoU.sea: 0.5486, IoU.mirror: 0.6451, IoU.rug: 0.5996, IoU.field: 0.2655, IoU.armchair: 0.4099, IoU.seat: 0.5930, IoU.fence: 0.4774, IoU.desk: 0.4703, IoU.rock: 0.4300, IoU.wardrobe: 0.4790, IoU.lamp: 0.6169, IoU.bathtub: 0.7337, IoU.railing: 0.3447, IoU.cushion: 0.5786, IoU.base: 0.3180, IoU.box: 0.2450, IoU.column: 0.4536, IoU.signboard: 0.3737, IoU.chest of drawers: 0.3959, IoU.counter: 0.2373, IoU.sand: 0.4389, IoU.sink: 0.7302, IoU.skyscraper: 0.5053, IoU.fireplace: 0.7234, IoU.refrigerator: 0.7780, IoU.grandstand: 0.4676, IoU.path: 0.2283, IoU.stairs: 0.3091, IoU.runway: 0.6479, IoU.case: 0.3684, IoU.pool table: 0.9317, IoU.pillow: 0.5399, IoU.screen door: 0.5743, IoU.stairway: 0.3093, IoU.river: 0.0986, IoU.bridge: 0.6508, IoU.bookcase: 0.3315, IoU.blind: 0.4472, IoU.coffee table: 0.5729, IoU.toilet: 0.8513, IoU.flower: 0.4131, IoU.book: 0.4434, IoU.hill: 0.0725, IoU.bench: 0.4384, IoU.countertop: 0.5721, IoU.stove: 0.7590, IoU.palm: 0.5113, IoU.kitchen island: 0.4058, IoU.computer: 0.6696, IoU.swivel chair: 0.4010, IoU.boat: 0.3427, IoU.bar: 0.2224, IoU.arcade machine: 0.3882, IoU.hovel: 0.1980, IoU.bus: 0.8271, IoU.towel: 0.6454, IoU.light: 0.5369, IoU.truck: 0.3323, IoU.tower: 0.1070, IoU.chandelier: 0.6732, IoU.awning: 0.2665, IoU.streetlight: 0.2563, IoU.booth: 0.4632, IoU.television receiver: 0.6620, IoU.airplane: 0.4756, IoU.dirt track: 0.0800, IoU.apparel: 0.4445, IoU.pole: 0.2320, IoU.land: 0.0284, IoU.bannister: 0.0965, IoU.escalator: 0.3204, IoU.ottoman: 0.4796, IoU.bottle: 0.3820, IoU.buffet: 0.3589, IoU.poster: 0.2616, IoU.stage: 0.1536, IoU.van: 0.5129, IoU.ship: 0.4266, IoU.fountain: 0.2525, IoU.conveyer belt: 0.7428, IoU.canopy: 0.2338, IoU.washer: 0.6878, IoU.plaything: 0.2542, IoU.swimming pool: 0.5510, IoU.stool: 0.3739, IoU.barrel: 0.5526, IoU.basket: 0.3556, IoU.waterfall: 0.4145, IoU.tent: 0.9525, IoU.bag: 0.1832, IoU.minibike: 0.5410, IoU.cradle: 0.7862, IoU.oven: 0.4070, IoU.ball: 0.4876, IoU.food: 0.6040, IoU.step: 0.0983, IoU.tank: 0.3482, IoU.trade name: 0.3051, IoU.microwave: 0.8062, IoU.pot: 0.4426, IoU.animal: 0.6073, IoU.bicycle: 0.5457, IoU.lake: 0.4228, IoU.dishwasher: 0.7602, IoU.screen: 0.7178, IoU.blanket: 0.0951, IoU.sculpture: 0.5099, IoU.hood: 0.5404, IoU.sconce: 0.4097, IoU.vase: 0.3617, IoU.traffic light: 0.3423, IoU.tray: 0.0448, IoU.ashcan: 0.4186, IoU.fan: 0.6148, IoU.pier: 0.2326, IoU.crt screen: 0.0598, IoU.plate: 0.5349, IoU.monitor: 0.1336, IoU.bulletin board: 0.4519, IoU.shower: 0.0569, IoU.radiator: 0.6310, IoU.glass: 0.1105, IoU.clock: 0.3351, IoU.flag: 0.3625, Acc.wall: 0.8671, Acc.building: 0.9183, Acc.sky: 0.9712, Acc.floor: 0.9152, Acc.tree: 0.8638, Acc.ceiling: 0.9274, Acc.road: 0.9331, Acc.bed : 0.9596, Acc.windowpane: 0.8055, Acc.grass: 0.8341, Acc.cabinet: 0.7541, Acc.sidewalk: 0.7445, Acc.person: 0.9243, Acc.earth: 0.4709, Acc.door: 0.6498, Acc.table: 0.7713, Acc.mountain: 0.7718, Acc.plant: 0.6116, Acc.curtain: 0.8447, Acc.chair: 0.6580, Acc.car: 0.8935, Acc.water: 0.5980, Acc.painting: 0.8577, Acc.sofa: 0.7656, Acc.shelf: 0.5939, Acc.house: 0.5536, Acc.sea: 0.8627, Acc.mirror: 0.7605, Acc.rug: 0.6736, Acc.field: 0.3748, Acc.armchair: 0.6196, Acc.seat: 0.7889, Acc.fence: 0.5620, Acc.desk: 0.7222, Acc.rock: 0.6039, Acc.wardrobe: 0.7145, Acc.lamp: 0.7386, Acc.bathtub: 0.8037, Acc.railing: 0.5416, Acc.cushion: 0.7697, Acc.base: 0.4093, Acc.box: 0.2902, Acc.column: 0.5808, Acc.signboard: 0.5058, Acc.chest of drawers: 0.4750, Acc.counter: 0.2901, Acc.sand: 0.7346, Acc.sink: 0.8001, Acc.skyscraper: 0.7033, Acc.fireplace: 0.9181, Acc.refrigerator: 0.8632, Acc.grandstand: 0.6329, Acc.path: 0.3813, Acc.stairs: 0.3780, Acc.runway: 0.8591, Acc.case: 0.4657, Acc.pool table: 0.9444, Acc.pillow: 0.6136, Acc.screen door: 0.7965, Acc.stairway: 0.4343, Acc.river: 0.2185, Acc.bridge: 0.8199, Acc.bookcase: 0.5448, Acc.blind: 0.5655, Acc.coffee table: 0.8235, Acc.toilet: 0.9087, Acc.flower: 0.6842, Acc.book: 0.6236, Acc.hill: 0.1266, Acc.bench: 0.5046, Acc.countertop: 0.7765, Acc.stove: 0.8105, Acc.palm: 0.7767, Acc.kitchen island: 0.5941, Acc.computer: 0.8400, Acc.swivel chair: 0.5447, Acc.boat: 0.4980, Acc.bar: 0.2539, Acc.arcade machine: 0.4046, Acc.hovel: 0.2182, Acc.bus: 0.9719, Acc.towel: 0.6977, Acc.light: 0.5965, Acc.truck: 0.5126, Acc.tower: 0.1713, Acc.chandelier: 0.7900, Acc.awning: 0.3152, Acc.streetlight: 0.3263, Acc.booth: 0.4822, Acc.television receiver: 0.8118, Acc.airplane: 0.6609, Acc.dirt track: 0.2040, Acc.apparel: 0.6140, Acc.pole: 0.3396, Acc.land: 0.0346, Acc.bannister: 0.1290, Acc.escalator: 0.3525, Acc.ottoman: 0.7080, Acc.bottle: 0.5301, Acc.buffet: 0.3873, Acc.poster: 0.3862, Acc.stage: 0.2238, Acc.van: 0.6768, Acc.ship: 0.6100, Acc.fountain: 0.2674, Acc.conveyer belt: 0.8425, Acc.canopy: 0.3036, Acc.washer: 0.7220, Acc.plaything: 0.3672, Acc.swimming pool: 0.6343, Acc.stool: 0.5399, Acc.barrel: 0.6421, Acc.basket: 0.5033, Acc.waterfall: 0.5410, Acc.tent: 0.9806, Acc.bag: 0.2267, Acc.minibike: 0.6293, Acc.cradle: 0.9566, Acc.oven: 0.5086, Acc.ball: 0.6215, Acc.food: 0.6828, Acc.step: 0.1584, Acc.tank: 0.4027, Acc.trade name: 0.3944, Acc.microwave: 0.9068, Acc.pot: 0.5494, Acc.animal: 0.6458, Acc.bicycle: 0.7379, Acc.lake: 0.4646, Acc.dishwasher: 0.8150, Acc.screen: 0.9127, Acc.blanket: 0.1050, Acc.sculpture: 0.7726, Acc.hood: 0.6205, Acc.sconce: 0.4641, Acc.vase: 0.5609, Acc.traffic light: 0.5690, Acc.tray: 0.0841, Acc.ashcan: 0.5425, Acc.fan: 0.7884, Acc.pier: 0.4186, Acc.crt screen: 0.1541, Acc.plate: 0.6917, Acc.monitor: 0.1799, Acc.bulletin board: 0.5466, Acc.shower: 0.1246, Acc.radiator: 0.7223, Acc.glass: 0.1164, Acc.clock: 0.4453, Acc.flag: 0.3999 2023-02-17 04:23:10,421 - mmseg - INFO - Iter [112050/160000] lr: 1.798e-05, eta: 4:56:28, time: 0.951, data_time: 0.600, memory: 16596, decode.loss_ce: 0.1603, decode.acc_seg: 93.4915, aux.loss_ce: 0.1054, aux.acc_seg: 89.3440, loss: 0.2656, grad_norm: 2.4552 2023-02-17 04:23:28,305 - mmseg - INFO - Iter [112100/160000] lr: 1.796e-05, eta: 4:56:08, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1656, decode.acc_seg: 93.2524, aux.loss_ce: 0.1069, aux.acc_seg: 89.0683, loss: 0.2724, grad_norm: 2.7069 2023-02-17 04:23:46,283 - mmseg - INFO - Iter [112150/160000] lr: 1.794e-05, eta: 4:55:49, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1665, decode.acc_seg: 93.2361, aux.loss_ce: 0.1059, aux.acc_seg: 89.3701, loss: 0.2724, grad_norm: 2.4710 2023-02-17 04:24:04,504 - mmseg - INFO - Iter [112200/160000] lr: 1.793e-05, eta: 4:55:30, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1645, decode.acc_seg: 93.2963, aux.loss_ce: 0.1046, aux.acc_seg: 89.3053, loss: 0.2690, grad_norm: 3.0671 2023-02-17 04:24:22,421 - mmseg - INFO - Iter [112250/160000] lr: 1.791e-05, eta: 4:55:10, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1623, decode.acc_seg: 93.3990, aux.loss_ce: 0.1054, aux.acc_seg: 89.4592, loss: 0.2677, grad_norm: 2.8929 2023-02-17 04:24:40,641 - mmseg - INFO - Iter [112300/160000] lr: 1.789e-05, eta: 4:54:51, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1759, decode.acc_seg: 92.6751, aux.loss_ce: 0.1147, aux.acc_seg: 88.2647, loss: 0.2905, grad_norm: 2.8023 2023-02-17 04:24:58,661 - mmseg - INFO - Iter [112350/160000] lr: 1.787e-05, eta: 4:54:31, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1738, decode.acc_seg: 92.8766, aux.loss_ce: 0.1138, aux.acc_seg: 88.4651, loss: 0.2876, grad_norm: 2.9509 2023-02-17 04:25:16,512 - mmseg - INFO - Iter [112400/160000] lr: 1.785e-05, eta: 4:54:12, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1600, decode.acc_seg: 93.1800, aux.loss_ce: 0.1022, aux.acc_seg: 89.1612, loss: 0.2622, grad_norm: 2.2276 2023-02-17 04:25:34,525 - mmseg - INFO - Iter [112450/160000] lr: 1.783e-05, eta: 4:53:52, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1647, decode.acc_seg: 93.3465, aux.loss_ce: 0.1088, aux.acc_seg: 89.0861, loss: 0.2735, grad_norm: 2.7507 2023-02-17 04:25:52,629 - mmseg - INFO - Iter [112500/160000] lr: 1.781e-05, eta: 4:53:33, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1616, decode.acc_seg: 93.2427, aux.loss_ce: 0.1043, aux.acc_seg: 89.3977, loss: 0.2659, grad_norm: 2.4723 2023-02-17 04:26:10,913 - mmseg - INFO - Iter [112550/160000] lr: 1.779e-05, eta: 4:53:14, time: 0.366, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1636, decode.acc_seg: 93.1328, aux.loss_ce: 0.1061, aux.acc_seg: 89.1555, loss: 0.2697, grad_norm: 2.3456 2023-02-17 04:26:28,926 - mmseg - INFO - Iter [112600/160000] lr: 1.778e-05, eta: 4:52:55, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1570, decode.acc_seg: 93.5074, aux.loss_ce: 0.1053, aux.acc_seg: 89.3294, loss: 0.2624, grad_norm: 2.2232 2023-02-17 04:26:46,758 - mmseg - INFO - Iter [112650/160000] lr: 1.776e-05, eta: 4:52:35, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1621, decode.acc_seg: 93.4112, aux.loss_ce: 0.1048, aux.acc_seg: 89.5077, loss: 0.2669, grad_norm: 2.6882 2023-02-17 04:27:04,952 - mmseg - INFO - Iter [112700/160000] lr: 1.774e-05, eta: 4:52:16, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1663, decode.acc_seg: 93.2014, aux.loss_ce: 0.1073, aux.acc_seg: 89.1846, loss: 0.2735, grad_norm: 2.6895 2023-02-17 04:27:22,977 - mmseg - INFO - Iter [112750/160000] lr: 1.772e-05, eta: 4:51:56, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1639, decode.acc_seg: 93.3089, aux.loss_ce: 0.1081, aux.acc_seg: 89.0650, loss: 0.2720, grad_norm: 2.5106 2023-02-17 04:27:41,059 - mmseg - INFO - Iter [112800/160000] lr: 1.770e-05, eta: 4:51:37, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1641, decode.acc_seg: 93.2912, aux.loss_ce: 0.1057, aux.acc_seg: 89.2863, loss: 0.2698, grad_norm: 2.5522 2023-02-17 04:27:59,040 - mmseg - INFO - Iter [112850/160000] lr: 1.768e-05, eta: 4:51:18, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1643, decode.acc_seg: 93.2803, aux.loss_ce: 0.1070, aux.acc_seg: 89.1961, loss: 0.2713, grad_norm: 2.7323 2023-02-17 04:28:17,100 - mmseg - INFO - Iter [112900/160000] lr: 1.766e-05, eta: 4:50:58, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1562, decode.acc_seg: 93.5481, aux.loss_ce: 0.1019, aux.acc_seg: 89.6990, loss: 0.2581, grad_norm: 2.3461 2023-02-17 04:28:35,312 - mmseg - INFO - Iter [112950/160000] lr: 1.764e-05, eta: 4:50:39, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1701, decode.acc_seg: 93.0074, aux.loss_ce: 0.1108, aux.acc_seg: 88.8131, loss: 0.2809, grad_norm: 3.1577 2023-02-17 04:28:53,521 - mmseg - INFO - Saving checkpoint at 113000 iterations 2023-02-17 04:28:54,559 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 04:28:54,559 - mmseg - INFO - Iter [113000/160000] lr: 1.763e-05, eta: 4:50:22, time: 0.385, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1628, decode.acc_seg: 93.2400, aux.loss_ce: 0.1034, aux.acc_seg: 89.4717, loss: 0.2662, grad_norm: 2.3854 2023-02-17 04:29:14,802 - mmseg - INFO - Iter [113050/160000] lr: 1.761e-05, eta: 4:50:06, time: 0.405, data_time: 0.052, memory: 16596, decode.loss_ce: 0.1636, decode.acc_seg: 93.3694, aux.loss_ce: 0.1090, aux.acc_seg: 89.1740, loss: 0.2726, grad_norm: 2.5977 2023-02-17 04:29:32,755 - mmseg - INFO - Iter [113100/160000] lr: 1.759e-05, eta: 4:49:47, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1542, decode.acc_seg: 93.5817, aux.loss_ce: 0.1045, aux.acc_seg: 89.3986, loss: 0.2587, grad_norm: 2.4786 2023-02-17 04:29:50,836 - mmseg - INFO - Iter [113150/160000] lr: 1.757e-05, eta: 4:49:27, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1619, decode.acc_seg: 93.3568, aux.loss_ce: 0.1056, aux.acc_seg: 89.3292, loss: 0.2674, grad_norm: 2.8349 2023-02-17 04:30:09,055 - mmseg - INFO - Iter [113200/160000] lr: 1.755e-05, eta: 4:49:08, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1652, decode.acc_seg: 92.9855, aux.loss_ce: 0.1048, aux.acc_seg: 89.0965, loss: 0.2699, grad_norm: 3.2154 2023-02-17 04:30:27,141 - mmseg - INFO - Iter [113250/160000] lr: 1.753e-05, eta: 4:48:49, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1633, decode.acc_seg: 93.4423, aux.loss_ce: 0.1091, aux.acc_seg: 89.2386, loss: 0.2724, grad_norm: 2.6928 2023-02-17 04:30:45,056 - mmseg - INFO - Iter [113300/160000] lr: 1.751e-05, eta: 4:48:30, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1597, decode.acc_seg: 93.4668, aux.loss_ce: 0.1074, aux.acc_seg: 89.1960, loss: 0.2671, grad_norm: 3.0830 2023-02-17 04:31:03,038 - mmseg - INFO - Iter [113350/160000] lr: 1.749e-05, eta: 4:48:10, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1696, decode.acc_seg: 93.1361, aux.loss_ce: 0.1101, aux.acc_seg: 89.0317, loss: 0.2798, grad_norm: 2.7999 2023-02-17 04:31:21,151 - mmseg - INFO - Iter [113400/160000] lr: 1.748e-05, eta: 4:47:51, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1567, decode.acc_seg: 93.3655, aux.loss_ce: 0.1037, aux.acc_seg: 89.2421, loss: 0.2604, grad_norm: 2.4590 2023-02-17 04:31:39,005 - mmseg - INFO - Iter [113450/160000] lr: 1.746e-05, eta: 4:47:31, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1569, decode.acc_seg: 93.5601, aux.loss_ce: 0.1079, aux.acc_seg: 89.3312, loss: 0.2648, grad_norm: 2.9583 2023-02-17 04:31:56,967 - mmseg - INFO - Iter [113500/160000] lr: 1.744e-05, eta: 4:47:12, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1680, decode.acc_seg: 93.0400, aux.loss_ce: 0.1098, aux.acc_seg: 88.9338, loss: 0.2778, grad_norm: 2.7651 2023-02-17 04:32:15,214 - mmseg - INFO - Iter [113550/160000] lr: 1.742e-05, eta: 4:46:53, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1647, decode.acc_seg: 93.3671, aux.loss_ce: 0.1046, aux.acc_seg: 89.5687, loss: 0.2693, grad_norm: 2.1562 2023-02-17 04:32:32,874 - mmseg - INFO - Iter [113600/160000] lr: 1.740e-05, eta: 4:46:33, time: 0.353, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1555, decode.acc_seg: 93.4637, aux.loss_ce: 0.1009, aux.acc_seg: 89.7215, loss: 0.2564, grad_norm: 2.5293 2023-02-17 04:32:51,327 - mmseg - INFO - Iter [113650/160000] lr: 1.738e-05, eta: 4:46:14, time: 0.369, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1623, decode.acc_seg: 93.5052, aux.loss_ce: 0.1063, aux.acc_seg: 89.4068, loss: 0.2686, grad_norm: 2.7212 2023-02-17 04:33:09,346 - mmseg - INFO - Iter [113700/160000] lr: 1.736e-05, eta: 4:45:55, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1600, decode.acc_seg: 93.4305, aux.loss_ce: 0.1051, aux.acc_seg: 89.4006, loss: 0.2651, grad_norm: 2.3570 2023-02-17 04:33:27,652 - mmseg - INFO - Iter [113750/160000] lr: 1.734e-05, eta: 4:45:36, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1714, decode.acc_seg: 93.0966, aux.loss_ce: 0.1117, aux.acc_seg: 88.7930, loss: 0.2832, grad_norm: 3.3054 2023-02-17 04:33:45,622 - mmseg - INFO - Iter [113800/160000] lr: 1.733e-05, eta: 4:45:17, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1703, decode.acc_seg: 93.1503, aux.loss_ce: 0.1122, aux.acc_seg: 88.8227, loss: 0.2825, grad_norm: 2.7661 2023-02-17 04:34:03,553 - mmseg - INFO - Iter [113850/160000] lr: 1.731e-05, eta: 4:44:57, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1618, decode.acc_seg: 93.2831, aux.loss_ce: 0.1065, aux.acc_seg: 89.0952, loss: 0.2683, grad_norm: 2.4981 2023-02-17 04:34:21,651 - mmseg - INFO - Iter [113900/160000] lr: 1.729e-05, eta: 4:44:38, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1609, decode.acc_seg: 93.2320, aux.loss_ce: 0.1025, aux.acc_seg: 89.4892, loss: 0.2634, grad_norm: 2.6518 2023-02-17 04:34:39,645 - mmseg - INFO - Iter [113950/160000] lr: 1.727e-05, eta: 4:44:19, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1582, decode.acc_seg: 93.4881, aux.loss_ce: 0.1044, aux.acc_seg: 89.4182, loss: 0.2625, grad_norm: 2.6394 2023-02-17 04:34:57,425 - mmseg - INFO - Saving checkpoint at 114000 iterations 2023-02-17 04:34:58,475 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 04:34:58,476 - mmseg - INFO - Iter [114000/160000] lr: 1.725e-05, eta: 4:44:01, time: 0.377, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1690, decode.acc_seg: 93.2418, aux.loss_ce: 0.1058, aux.acc_seg: 89.4464, loss: 0.2747, grad_norm: 2.5559 2023-02-17 04:35:16,448 - mmseg - INFO - Iter [114050/160000] lr: 1.723e-05, eta: 4:43:41, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1601, decode.acc_seg: 93.2916, aux.loss_ce: 0.1054, aux.acc_seg: 89.3305, loss: 0.2655, grad_norm: 3.0839 2023-02-17 04:35:34,435 - mmseg - INFO - Iter [114100/160000] lr: 1.721e-05, eta: 4:43:22, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1636, decode.acc_seg: 93.1827, aux.loss_ce: 0.1083, aux.acc_seg: 88.8966, loss: 0.2719, grad_norm: 2.2933 2023-02-17 04:35:52,612 - mmseg - INFO - Iter [114150/160000] lr: 1.719e-05, eta: 4:43:03, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1641, decode.acc_seg: 93.1895, aux.loss_ce: 0.1064, aux.acc_seg: 89.0841, loss: 0.2705, grad_norm: 2.5480 2023-02-17 04:36:10,463 - mmseg - INFO - Iter [114200/160000] lr: 1.718e-05, eta: 4:42:44, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1649, decode.acc_seg: 93.1146, aux.loss_ce: 0.1080, aux.acc_seg: 88.9995, loss: 0.2729, grad_norm: 2.7347 2023-02-17 04:36:28,674 - mmseg - INFO - Iter [114250/160000] lr: 1.716e-05, eta: 4:42:25, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1653, decode.acc_seg: 93.1731, aux.loss_ce: 0.1067, aux.acc_seg: 89.2594, loss: 0.2720, grad_norm: 2.7561 2023-02-17 04:36:46,683 - mmseg - INFO - Iter [114300/160000] lr: 1.714e-05, eta: 4:42:05, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1540, decode.acc_seg: 93.6974, aux.loss_ce: 0.1026, aux.acc_seg: 89.6711, loss: 0.2566, grad_norm: 2.2488 2023-02-17 04:37:07,017 - mmseg - INFO - Iter [114350/160000] lr: 1.712e-05, eta: 4:41:49, time: 0.407, data_time: 0.052, memory: 16596, decode.loss_ce: 0.1529, decode.acc_seg: 93.7672, aux.loss_ce: 0.1004, aux.acc_seg: 89.7840, loss: 0.2532, grad_norm: 2.2046 2023-02-17 04:37:25,270 - mmseg - INFO - Iter [114400/160000] lr: 1.710e-05, eta: 4:41:31, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1641, decode.acc_seg: 93.5595, aux.loss_ce: 0.1077, aux.acc_seg: 89.8057, loss: 0.2718, grad_norm: 2.5906 2023-02-17 04:37:43,700 - mmseg - INFO - Iter [114450/160000] lr: 1.708e-05, eta: 4:41:12, time: 0.368, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1586, decode.acc_seg: 93.6640, aux.loss_ce: 0.1025, aux.acc_seg: 89.7973, loss: 0.2612, grad_norm: 2.7303 2023-02-17 04:38:01,830 - mmseg - INFO - Iter [114500/160000] lr: 1.706e-05, eta: 4:40:53, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1638, decode.acc_seg: 93.2215, aux.loss_ce: 0.1088, aux.acc_seg: 88.9270, loss: 0.2726, grad_norm: 2.5319 2023-02-17 04:38:19,906 - mmseg - INFO - Iter [114550/160000] lr: 1.704e-05, eta: 4:40:34, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1672, decode.acc_seg: 93.1469, aux.loss_ce: 0.1108, aux.acc_seg: 88.9575, loss: 0.2780, grad_norm: 3.4260 2023-02-17 04:38:38,009 - mmseg - INFO - Iter [114600/160000] lr: 1.703e-05, eta: 4:40:14, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1597, decode.acc_seg: 93.3309, aux.loss_ce: 0.1024, aux.acc_seg: 89.6614, loss: 0.2621, grad_norm: 2.4220 2023-02-17 04:38:55,912 - mmseg - INFO - Iter [114650/160000] lr: 1.701e-05, eta: 4:39:55, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1535, decode.acc_seg: 93.6891, aux.loss_ce: 0.1023, aux.acc_seg: 89.6314, loss: 0.2558, grad_norm: 2.5795 2023-02-17 04:39:13,897 - mmseg - INFO - Iter [114700/160000] lr: 1.699e-05, eta: 4:39:36, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1558, decode.acc_seg: 93.6171, aux.loss_ce: 0.1009, aux.acc_seg: 89.5259, loss: 0.2567, grad_norm: 2.8203 2023-02-17 04:39:32,829 - mmseg - INFO - Iter [114750/160000] lr: 1.697e-05, eta: 4:39:18, time: 0.379, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1586, decode.acc_seg: 93.3021, aux.loss_ce: 0.1023, aux.acc_seg: 89.3532, loss: 0.2609, grad_norm: 2.4391 2023-02-17 04:39:50,784 - mmseg - INFO - Iter [114800/160000] lr: 1.695e-05, eta: 4:38:58, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1555, decode.acc_seg: 93.5555, aux.loss_ce: 0.1051, aux.acc_seg: 89.2980, loss: 0.2606, grad_norm: 2.7886 2023-02-17 04:40:08,911 - mmseg - INFO - Iter [114850/160000] lr: 1.693e-05, eta: 4:38:39, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1603, decode.acc_seg: 93.3426, aux.loss_ce: 0.1052, aux.acc_seg: 89.3175, loss: 0.2655, grad_norm: 2.8671 2023-02-17 04:40:26,942 - mmseg - INFO - Iter [114900/160000] lr: 1.691e-05, eta: 4:38:20, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1562, decode.acc_seg: 93.4468, aux.loss_ce: 0.1038, aux.acc_seg: 89.4981, loss: 0.2601, grad_norm: 2.7447 2023-02-17 04:40:44,909 - mmseg - INFO - Iter [114950/160000] lr: 1.689e-05, eta: 4:38:01, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1639, decode.acc_seg: 93.3096, aux.loss_ce: 0.1066, aux.acc_seg: 89.3474, loss: 0.2705, grad_norm: 2.4829 2023-02-17 04:41:02,909 - mmseg - INFO - Saving checkpoint at 115000 iterations 2023-02-17 04:41:03,934 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 04:41:03,934 - mmseg - INFO - Iter [115000/160000] lr: 1.688e-05, eta: 4:37:43, time: 0.380, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1584, decode.acc_seg: 93.4905, aux.loss_ce: 0.1042, aux.acc_seg: 89.3999, loss: 0.2626, grad_norm: 2.7492 2023-02-17 04:41:22,011 - mmseg - INFO - Iter [115050/160000] lr: 1.686e-05, eta: 4:37:24, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1671, decode.acc_seg: 92.9726, aux.loss_ce: 0.1071, aux.acc_seg: 88.8824, loss: 0.2742, grad_norm: 2.7940 2023-02-17 04:41:39,938 - mmseg - INFO - Iter [115100/160000] lr: 1.684e-05, eta: 4:37:05, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1580, decode.acc_seg: 93.3605, aux.loss_ce: 0.1033, aux.acc_seg: 89.4461, loss: 0.2613, grad_norm: 2.8328 2023-02-17 04:41:57,766 - mmseg - INFO - Iter [115150/160000] lr: 1.682e-05, eta: 4:36:45, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1686, decode.acc_seg: 93.2509, aux.loss_ce: 0.1112, aux.acc_seg: 88.9337, loss: 0.2798, grad_norm: 2.9570 2023-02-17 04:42:15,712 - mmseg - INFO - Iter [115200/160000] lr: 1.680e-05, eta: 4:36:26, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1650, decode.acc_seg: 93.0246, aux.loss_ce: 0.1096, aux.acc_seg: 88.8462, loss: 0.2746, grad_norm: 2.4746 2023-02-17 04:42:33,482 - mmseg - INFO - Iter [115250/160000] lr: 1.678e-05, eta: 4:36:06, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1573, decode.acc_seg: 93.3410, aux.loss_ce: 0.1020, aux.acc_seg: 89.5807, loss: 0.2593, grad_norm: 2.5573 2023-02-17 04:42:51,118 - mmseg - INFO - Iter [115300/160000] lr: 1.676e-05, eta: 4:35:46, time: 0.353, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1745, decode.acc_seg: 93.1083, aux.loss_ce: 0.1115, aux.acc_seg: 89.0154, loss: 0.2860, grad_norm: 2.5534 2023-02-17 04:43:09,240 - mmseg - INFO - Iter [115350/160000] lr: 1.674e-05, eta: 4:35:27, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1555, decode.acc_seg: 93.5763, aux.loss_ce: 0.1021, aux.acc_seg: 89.5589, loss: 0.2576, grad_norm: 2.3895 2023-02-17 04:43:27,500 - mmseg - INFO - Iter [115400/160000] lr: 1.673e-05, eta: 4:35:09, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1568, decode.acc_seg: 93.3768, aux.loss_ce: 0.1045, aux.acc_seg: 89.0751, loss: 0.2614, grad_norm: 2.1555 2023-02-17 04:43:45,424 - mmseg - INFO - Iter [115450/160000] lr: 1.671e-05, eta: 4:34:49, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1599, decode.acc_seg: 93.3412, aux.loss_ce: 0.1024, aux.acc_seg: 89.5306, loss: 0.2623, grad_norm: 2.5851 2023-02-17 04:44:03,199 - mmseg - INFO - Iter [115500/160000] lr: 1.669e-05, eta: 4:34:30, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1698, decode.acc_seg: 93.2105, aux.loss_ce: 0.1093, aux.acc_seg: 88.9077, loss: 0.2790, grad_norm: 2.6846 2023-02-17 04:44:21,359 - mmseg - INFO - Iter [115550/160000] lr: 1.667e-05, eta: 4:34:11, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1727, decode.acc_seg: 92.9492, aux.loss_ce: 0.1106, aux.acc_seg: 88.8963, loss: 0.2833, grad_norm: 2.9058 2023-02-17 04:44:41,903 - mmseg - INFO - Iter [115600/160000] lr: 1.665e-05, eta: 4:33:55, time: 0.411, data_time: 0.051, memory: 16596, decode.loss_ce: 0.1618, decode.acc_seg: 93.3399, aux.loss_ce: 0.1083, aux.acc_seg: 88.8805, loss: 0.2701, grad_norm: 2.4133 2023-02-17 04:44:59,878 - mmseg - INFO - Iter [115650/160000] lr: 1.663e-05, eta: 4:33:36, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1591, decode.acc_seg: 93.3922, aux.loss_ce: 0.1024, aux.acc_seg: 89.7147, loss: 0.2616, grad_norm: 2.9852 2023-02-17 04:45:17,782 - mmseg - INFO - Iter [115700/160000] lr: 1.661e-05, eta: 4:33:16, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1572, decode.acc_seg: 93.4561, aux.loss_ce: 0.1002, aux.acc_seg: 89.8074, loss: 0.2574, grad_norm: 2.1608 2023-02-17 04:45:35,727 - mmseg - INFO - Iter [115750/160000] lr: 1.659e-05, eta: 4:32:57, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1572, decode.acc_seg: 93.5232, aux.loss_ce: 0.1022, aux.acc_seg: 89.6121, loss: 0.2593, grad_norm: 2.3601 2023-02-17 04:45:53,794 - mmseg - INFO - Iter [115800/160000] lr: 1.658e-05, eta: 4:32:38, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1600, decode.acc_seg: 93.3401, aux.loss_ce: 0.1067, aux.acc_seg: 89.2894, loss: 0.2667, grad_norm: 2.9045 2023-02-17 04:46:11,671 - mmseg - INFO - Iter [115850/160000] lr: 1.656e-05, eta: 4:32:19, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1549, decode.acc_seg: 93.6182, aux.loss_ce: 0.1052, aux.acc_seg: 89.3968, loss: 0.2602, grad_norm: 2.3402 2023-02-17 04:46:30,045 - mmseg - INFO - Iter [115900/160000] lr: 1.654e-05, eta: 4:32:00, time: 0.368, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1597, decode.acc_seg: 93.3145, aux.loss_ce: 0.1069, aux.acc_seg: 89.0158, loss: 0.2666, grad_norm: 2.5565 2023-02-17 04:46:48,050 - mmseg - INFO - Iter [115950/160000] lr: 1.652e-05, eta: 4:31:41, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1665, decode.acc_seg: 93.1400, aux.loss_ce: 0.1107, aux.acc_seg: 88.8286, loss: 0.2771, grad_norm: 2.9921 2023-02-17 04:47:06,437 - mmseg - INFO - Saving checkpoint at 116000 iterations 2023-02-17 04:47:07,498 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 04:47:07,498 - mmseg - INFO - Iter [116000/160000] lr: 1.650e-05, eta: 4:31:24, time: 0.389, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1676, decode.acc_seg: 93.0525, aux.loss_ce: 0.1091, aux.acc_seg: 88.9916, loss: 0.2768, grad_norm: 3.1774 2023-02-17 04:47:25,361 - mmseg - INFO - Iter [116050/160000] lr: 1.648e-05, eta: 4:31:04, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1635, decode.acc_seg: 93.4000, aux.loss_ce: 0.1075, aux.acc_seg: 89.3151, loss: 0.2710, grad_norm: 2.4864 2023-02-17 04:47:43,239 - mmseg - INFO - Iter [116100/160000] lr: 1.646e-05, eta: 4:30:45, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1673, decode.acc_seg: 93.2207, aux.loss_ce: 0.1104, aux.acc_seg: 88.8523, loss: 0.2777, grad_norm: 2.7040 2023-02-17 04:48:01,247 - mmseg - INFO - Iter [116150/160000] lr: 1.644e-05, eta: 4:30:26, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1638, decode.acc_seg: 93.1940, aux.loss_ce: 0.1071, aux.acc_seg: 89.0368, loss: 0.2709, grad_norm: 2.7956 2023-02-17 04:48:19,310 - mmseg - INFO - Iter [116200/160000] lr: 1.643e-05, eta: 4:30:06, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1541, decode.acc_seg: 93.7521, aux.loss_ce: 0.1011, aux.acc_seg: 89.8321, loss: 0.2552, grad_norm: 2.4369 2023-02-17 04:48:37,561 - mmseg - INFO - Iter [116250/160000] lr: 1.641e-05, eta: 4:29:48, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1607, decode.acc_seg: 93.4855, aux.loss_ce: 0.1040, aux.acc_seg: 89.5710, loss: 0.2647, grad_norm: 2.9086 2023-02-17 04:48:55,615 - mmseg - INFO - Iter [116300/160000] lr: 1.639e-05, eta: 4:29:29, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1669, decode.acc_seg: 93.1386, aux.loss_ce: 0.1094, aux.acc_seg: 88.8255, loss: 0.2763, grad_norm: 2.4919 2023-02-17 04:49:13,841 - mmseg - INFO - Iter [116350/160000] lr: 1.637e-05, eta: 4:29:10, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1580, decode.acc_seg: 93.5685, aux.loss_ce: 0.1039, aux.acc_seg: 89.5121, loss: 0.2619, grad_norm: 2.1133 2023-02-17 04:49:31,777 - mmseg - INFO - Iter [116400/160000] lr: 1.635e-05, eta: 4:28:50, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1659, decode.acc_seg: 93.3249, aux.loss_ce: 0.1093, aux.acc_seg: 89.1539, loss: 0.2752, grad_norm: 3.7281 2023-02-17 04:49:49,648 - mmseg - INFO - Iter [116450/160000] lr: 1.633e-05, eta: 4:28:31, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1598, decode.acc_seg: 93.3135, aux.loss_ce: 0.1080, aux.acc_seg: 88.7622, loss: 0.2678, grad_norm: 2.7965 2023-02-17 04:50:07,805 - mmseg - INFO - Iter [116500/160000] lr: 1.631e-05, eta: 4:28:12, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1654, decode.acc_seg: 93.1108, aux.loss_ce: 0.1080, aux.acc_seg: 88.8208, loss: 0.2734, grad_norm: 2.8563 2023-02-17 04:50:25,689 - mmseg - INFO - Iter [116550/160000] lr: 1.629e-05, eta: 4:27:53, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1566, decode.acc_seg: 93.6101, aux.loss_ce: 0.1005, aux.acc_seg: 89.8199, loss: 0.2571, grad_norm: 2.3355 2023-02-17 04:50:43,387 - mmseg - INFO - Iter [116600/160000] lr: 1.628e-05, eta: 4:27:33, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1591, decode.acc_seg: 93.3810, aux.loss_ce: 0.1046, aux.acc_seg: 89.3197, loss: 0.2638, grad_norm: 2.5310 2023-02-17 04:51:01,626 - mmseg - INFO - Iter [116650/160000] lr: 1.626e-05, eta: 4:27:14, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1596, decode.acc_seg: 93.4316, aux.loss_ce: 0.1048, aux.acc_seg: 89.4068, loss: 0.2643, grad_norm: 2.7917 2023-02-17 04:51:19,415 - mmseg - INFO - Iter [116700/160000] lr: 1.624e-05, eta: 4:26:55, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1586, decode.acc_seg: 93.5338, aux.loss_ce: 0.1015, aux.acc_seg: 89.7200, loss: 0.2601, grad_norm: 2.5582 2023-02-17 04:51:37,525 - mmseg - INFO - Iter [116750/160000] lr: 1.622e-05, eta: 4:26:36, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1573, decode.acc_seg: 93.5416, aux.loss_ce: 0.1060, aux.acc_seg: 89.3363, loss: 0.2633, grad_norm: 2.5857 2023-02-17 04:51:55,495 - mmseg - INFO - Iter [116800/160000] lr: 1.620e-05, eta: 4:26:17, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1544, decode.acc_seg: 93.5683, aux.loss_ce: 0.1004, aux.acc_seg: 89.7683, loss: 0.2548, grad_norm: 2.8442 2023-02-17 04:52:15,702 - mmseg - INFO - Iter [116850/160000] lr: 1.618e-05, eta: 4:26:01, time: 0.405, data_time: 0.054, memory: 16596, decode.loss_ce: 0.1624, decode.acc_seg: 93.3416, aux.loss_ce: 0.1068, aux.acc_seg: 89.1664, loss: 0.2692, grad_norm: 2.1398 2023-02-17 04:52:33,607 - mmseg - INFO - Iter [116900/160000] lr: 1.616e-05, eta: 4:25:41, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1557, decode.acc_seg: 93.5776, aux.loss_ce: 0.1032, aux.acc_seg: 89.3420, loss: 0.2588, grad_norm: 2.3966 2023-02-17 04:52:51,516 - mmseg - INFO - Iter [116950/160000] lr: 1.614e-05, eta: 4:25:22, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1595, decode.acc_seg: 93.4598, aux.loss_ce: 0.1046, aux.acc_seg: 89.4132, loss: 0.2642, grad_norm: 2.6200 2023-02-17 04:53:09,530 - mmseg - INFO - Saving checkpoint at 117000 iterations 2023-02-17 04:53:10,654 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 04:53:10,655 - mmseg - INFO - Iter [117000/160000] lr: 1.613e-05, eta: 4:25:04, time: 0.383, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1615, decode.acc_seg: 93.4702, aux.loss_ce: 0.1039, aux.acc_seg: 89.4637, loss: 0.2654, grad_norm: 2.7078 2023-02-17 04:53:28,823 - mmseg - INFO - Iter [117050/160000] lr: 1.611e-05, eta: 4:24:45, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1655, decode.acc_seg: 93.3685, aux.loss_ce: 0.1054, aux.acc_seg: 89.4314, loss: 0.2709, grad_norm: 2.8482 2023-02-17 04:53:46,929 - mmseg - INFO - Iter [117100/160000] lr: 1.609e-05, eta: 4:24:26, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1583, decode.acc_seg: 93.4848, aux.loss_ce: 0.1062, aux.acc_seg: 89.4443, loss: 0.2645, grad_norm: 2.7266 2023-02-17 04:54:04,961 - mmseg - INFO - Iter [117150/160000] lr: 1.607e-05, eta: 4:24:07, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1604, decode.acc_seg: 93.3358, aux.loss_ce: 0.1098, aux.acc_seg: 88.9831, loss: 0.2702, grad_norm: 3.1319 2023-02-17 04:54:23,115 - mmseg - INFO - Iter [117200/160000] lr: 1.605e-05, eta: 4:23:48, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1642, decode.acc_seg: 93.2570, aux.loss_ce: 0.1067, aux.acc_seg: 88.9153, loss: 0.2710, grad_norm: 2.9358 2023-02-17 04:54:41,002 - mmseg - INFO - Iter [117250/160000] lr: 1.603e-05, eta: 4:23:29, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1594, decode.acc_seg: 93.4670, aux.loss_ce: 0.1057, aux.acc_seg: 89.4751, loss: 0.2651, grad_norm: 2.4380 2023-02-17 04:54:58,757 - mmseg - INFO - Iter [117300/160000] lr: 1.601e-05, eta: 4:23:10, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1649, decode.acc_seg: 93.3221, aux.loss_ce: 0.1039, aux.acc_seg: 89.5800, loss: 0.2687, grad_norm: 2.6574 2023-02-17 04:55:16,591 - mmseg - INFO - Iter [117350/160000] lr: 1.599e-05, eta: 4:22:50, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1609, decode.acc_seg: 93.2947, aux.loss_ce: 0.1037, aux.acc_seg: 89.2188, loss: 0.2645, grad_norm: 2.6963 2023-02-17 04:55:34,653 - mmseg - INFO - Iter [117400/160000] lr: 1.598e-05, eta: 4:22:31, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1605, decode.acc_seg: 93.4602, aux.loss_ce: 0.1071, aux.acc_seg: 89.2500, loss: 0.2676, grad_norm: 2.6747 2023-02-17 04:55:52,541 - mmseg - INFO - Iter [117450/160000] lr: 1.596e-05, eta: 4:22:12, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1611, decode.acc_seg: 93.5325, aux.loss_ce: 0.1059, aux.acc_seg: 89.7008, loss: 0.2671, grad_norm: 2.6290 2023-02-17 04:56:10,535 - mmseg - INFO - Iter [117500/160000] lr: 1.594e-05, eta: 4:21:53, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1631, decode.acc_seg: 93.5070, aux.loss_ce: 0.1089, aux.acc_seg: 89.4070, loss: 0.2721, grad_norm: 2.8077 2023-02-17 04:56:28,504 - mmseg - INFO - Iter [117550/160000] lr: 1.592e-05, eta: 4:21:34, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1604, decode.acc_seg: 93.3639, aux.loss_ce: 0.1089, aux.acc_seg: 89.1878, loss: 0.2694, grad_norm: 3.0541 2023-02-17 04:56:46,422 - mmseg - INFO - Iter [117600/160000] lr: 1.590e-05, eta: 4:21:15, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1583, decode.acc_seg: 93.4765, aux.loss_ce: 0.1036, aux.acc_seg: 89.5577, loss: 0.2619, grad_norm: 2.6149 2023-02-17 04:57:04,673 - mmseg - INFO - Iter [117650/160000] lr: 1.588e-05, eta: 4:20:56, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1632, decode.acc_seg: 93.2155, aux.loss_ce: 0.1060, aux.acc_seg: 89.2237, loss: 0.2692, grad_norm: 2.7778 2023-02-17 04:57:22,704 - mmseg - INFO - Iter [117700/160000] lr: 1.586e-05, eta: 4:20:37, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1594, decode.acc_seg: 93.4331, aux.loss_ce: 0.1065, aux.acc_seg: 89.2161, loss: 0.2659, grad_norm: 2.6470 2023-02-17 04:57:40,659 - mmseg - INFO - Iter [117750/160000] lr: 1.584e-05, eta: 4:20:18, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1550, decode.acc_seg: 93.5048, aux.loss_ce: 0.1026, aux.acc_seg: 89.2951, loss: 0.2577, grad_norm: 3.1217 2023-02-17 04:57:58,613 - mmseg - INFO - Iter [117800/160000] lr: 1.583e-05, eta: 4:19:59, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1586, decode.acc_seg: 93.5263, aux.loss_ce: 0.1051, aux.acc_seg: 89.4242, loss: 0.2637, grad_norm: 2.5176 2023-02-17 04:58:16,714 - mmseg - INFO - Iter [117850/160000] lr: 1.581e-05, eta: 4:19:40, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1645, decode.acc_seg: 93.1564, aux.loss_ce: 0.1069, aux.acc_seg: 88.9122, loss: 0.2715, grad_norm: 3.1168 2023-02-17 04:58:34,549 - mmseg - INFO - Iter [117900/160000] lr: 1.579e-05, eta: 4:19:20, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1488, decode.acc_seg: 93.9499, aux.loss_ce: 0.0979, aux.acc_seg: 90.1314, loss: 0.2466, grad_norm: 2.4998 2023-02-17 04:58:52,449 - mmseg - INFO - Iter [117950/160000] lr: 1.577e-05, eta: 4:19:01, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1546, decode.acc_seg: 93.6604, aux.loss_ce: 0.1056, aux.acc_seg: 89.3120, loss: 0.2602, grad_norm: 2.5911 2023-02-17 04:59:10,352 - mmseg - INFO - Saving checkpoint at 118000 iterations 2023-02-17 04:59:11,393 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 04:59:11,393 - mmseg - INFO - Iter [118000/160000] lr: 1.575e-05, eta: 4:18:43, time: 0.379, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1725, decode.acc_seg: 93.0338, aux.loss_ce: 0.1118, aux.acc_seg: 89.0060, loss: 0.2843, grad_norm: 2.6188 2023-02-17 04:59:29,291 - mmseg - INFO - Iter [118050/160000] lr: 1.573e-05, eta: 4:18:24, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1615, decode.acc_seg: 93.4262, aux.loss_ce: 0.1080, aux.acc_seg: 89.2206, loss: 0.2696, grad_norm: 3.0556 2023-02-17 04:59:47,639 - mmseg - INFO - Iter [118100/160000] lr: 1.571e-05, eta: 4:18:05, time: 0.367, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1621, decode.acc_seg: 93.3116, aux.loss_ce: 0.1058, aux.acc_seg: 89.3098, loss: 0.2679, grad_norm: 2.8208 2023-02-17 05:00:07,919 - mmseg - INFO - Iter [118150/160000] lr: 1.569e-05, eta: 4:17:49, time: 0.406, data_time: 0.052, memory: 16596, decode.loss_ce: 0.1545, decode.acc_seg: 93.6761, aux.loss_ce: 0.1031, aux.acc_seg: 89.5908, loss: 0.2576, grad_norm: 2.6823 2023-02-17 05:00:25,733 - mmseg - INFO - Iter [118200/160000] lr: 1.568e-05, eta: 4:17:30, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1507, decode.acc_seg: 93.7845, aux.loss_ce: 0.1008, aux.acc_seg: 89.7198, loss: 0.2515, grad_norm: 2.2619 2023-02-17 05:00:44,091 - mmseg - INFO - Iter [118250/160000] lr: 1.566e-05, eta: 4:17:11, time: 0.367, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1659, decode.acc_seg: 93.2505, aux.loss_ce: 0.1115, aux.acc_seg: 88.7145, loss: 0.2775, grad_norm: 2.8855 2023-02-17 05:01:02,114 - mmseg - INFO - Iter [118300/160000] lr: 1.564e-05, eta: 4:16:52, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1652, decode.acc_seg: 93.1402, aux.loss_ce: 0.1085, aux.acc_seg: 88.9966, loss: 0.2737, grad_norm: 2.6937 2023-02-17 05:01:20,357 - mmseg - INFO - Iter [118350/160000] lr: 1.562e-05, eta: 4:16:33, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1592, decode.acc_seg: 93.4659, aux.loss_ce: 0.1069, aux.acc_seg: 89.2386, loss: 0.2661, grad_norm: 2.9563 2023-02-17 05:01:38,245 - mmseg - INFO - Iter [118400/160000] lr: 1.560e-05, eta: 4:16:14, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1678, decode.acc_seg: 92.9264, aux.loss_ce: 0.1136, aux.acc_seg: 88.7045, loss: 0.2814, grad_norm: 2.9076 2023-02-17 05:01:55,985 - mmseg - INFO - Iter [118450/160000] lr: 1.558e-05, eta: 4:15:55, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1510, decode.acc_seg: 93.6242, aux.loss_ce: 0.1026, aux.acc_seg: 89.3251, loss: 0.2536, grad_norm: 3.0016 2023-02-17 05:02:13,995 - mmseg - INFO - Iter [118500/160000] lr: 1.556e-05, eta: 4:15:36, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1578, decode.acc_seg: 93.1391, aux.loss_ce: 0.1034, aux.acc_seg: 89.0416, loss: 0.2612, grad_norm: 2.7970 2023-02-17 05:02:31,959 - mmseg - INFO - Iter [118550/160000] lr: 1.554e-05, eta: 4:15:17, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1590, decode.acc_seg: 93.2350, aux.loss_ce: 0.0982, aux.acc_seg: 89.7726, loss: 0.2571, grad_norm: 2.6755 2023-02-17 05:02:50,032 - mmseg - INFO - Iter [118600/160000] lr: 1.553e-05, eta: 4:14:58, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1497, decode.acc_seg: 93.8885, aux.loss_ce: 0.0998, aux.acc_seg: 89.9075, loss: 0.2495, grad_norm: 2.3247 2023-02-17 05:03:08,014 - mmseg - INFO - Iter [118650/160000] lr: 1.551e-05, eta: 4:14:39, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1664, decode.acc_seg: 93.1468, aux.loss_ce: 0.1119, aux.acc_seg: 88.7285, loss: 0.2783, grad_norm: 3.1353 2023-02-17 05:03:25,831 - mmseg - INFO - Iter [118700/160000] lr: 1.549e-05, eta: 4:14:19, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1639, decode.acc_seg: 93.2880, aux.loss_ce: 0.1066, aux.acc_seg: 89.1239, loss: 0.2704, grad_norm: 2.5956 2023-02-17 05:03:43,973 - mmseg - INFO - Iter [118750/160000] lr: 1.547e-05, eta: 4:14:00, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1663, decode.acc_seg: 93.2203, aux.loss_ce: 0.1111, aux.acc_seg: 88.8674, loss: 0.2774, grad_norm: 2.7694 2023-02-17 05:04:01,911 - mmseg - INFO - Iter [118800/160000] lr: 1.545e-05, eta: 4:13:41, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1565, decode.acc_seg: 93.5823, aux.loss_ce: 0.1040, aux.acc_seg: 89.4135, loss: 0.2604, grad_norm: 2.6008 2023-02-17 05:04:19,914 - mmseg - INFO - Iter [118850/160000] lr: 1.543e-05, eta: 4:13:22, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1527, decode.acc_seg: 93.6933, aux.loss_ce: 0.1034, aux.acc_seg: 89.3333, loss: 0.2560, grad_norm: 2.4191 2023-02-17 05:04:37,929 - mmseg - INFO - Iter [118900/160000] lr: 1.541e-05, eta: 4:13:03, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1502, decode.acc_seg: 93.6074, aux.loss_ce: 0.0997, aux.acc_seg: 89.5844, loss: 0.2500, grad_norm: 2.6713 2023-02-17 05:04:56,190 - mmseg - INFO - Iter [118950/160000] lr: 1.539e-05, eta: 4:12:45, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1590, decode.acc_seg: 93.4717, aux.loss_ce: 0.1048, aux.acc_seg: 89.3819, loss: 0.2638, grad_norm: 2.4771 2023-02-17 05:05:14,427 - mmseg - INFO - Saving checkpoint at 119000 iterations 2023-02-17 05:05:15,473 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 05:05:15,473 - mmseg - INFO - Iter [119000/160000] lr: 1.538e-05, eta: 4:12:27, time: 0.386, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1534, decode.acc_seg: 93.6671, aux.loss_ce: 0.1004, aux.acc_seg: 89.8594, loss: 0.2538, grad_norm: 2.4179 2023-02-17 05:05:33,817 - mmseg - INFO - Iter [119050/160000] lr: 1.536e-05, eta: 4:12:08, time: 0.367, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1494, decode.acc_seg: 93.7659, aux.loss_ce: 0.1010, aux.acc_seg: 89.7521, loss: 0.2504, grad_norm: 2.5728 2023-02-17 05:05:52,002 - mmseg - INFO - Iter [119100/160000] lr: 1.534e-05, eta: 4:11:50, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1626, decode.acc_seg: 93.4220, aux.loss_ce: 0.1060, aux.acc_seg: 89.3427, loss: 0.2686, grad_norm: 2.2830 2023-02-17 05:06:10,113 - mmseg - INFO - Iter [119150/160000] lr: 1.532e-05, eta: 4:11:31, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1650, decode.acc_seg: 93.2394, aux.loss_ce: 0.1069, aux.acc_seg: 89.2722, loss: 0.2719, grad_norm: 2.9591 2023-02-17 05:06:27,891 - mmseg - INFO - Iter [119200/160000] lr: 1.530e-05, eta: 4:11:12, time: 0.356, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1626, decode.acc_seg: 93.1755, aux.loss_ce: 0.1038, aux.acc_seg: 89.3743, loss: 0.2665, grad_norm: 2.3309 2023-02-17 05:06:46,073 - mmseg - INFO - Iter [119250/160000] lr: 1.528e-05, eta: 4:10:53, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1583, decode.acc_seg: 93.4170, aux.loss_ce: 0.1066, aux.acc_seg: 89.1300, loss: 0.2649, grad_norm: 2.4893 2023-02-17 05:07:04,137 - mmseg - INFO - Iter [119300/160000] lr: 1.526e-05, eta: 4:10:34, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1545, decode.acc_seg: 93.4573, aux.loss_ce: 0.1037, aux.acc_seg: 89.3586, loss: 0.2583, grad_norm: 3.0401 2023-02-17 05:07:22,013 - mmseg - INFO - Iter [119350/160000] lr: 1.524e-05, eta: 4:10:15, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1574, decode.acc_seg: 93.5008, aux.loss_ce: 0.1051, aux.acc_seg: 89.1672, loss: 0.2625, grad_norm: 2.7382 2023-02-17 05:07:42,338 - mmseg - INFO - Iter [119400/160000] lr: 1.523e-05, eta: 4:09:58, time: 0.406, data_time: 0.051, memory: 16596, decode.loss_ce: 0.1736, decode.acc_seg: 92.9214, aux.loss_ce: 0.1091, aux.acc_seg: 89.0514, loss: 0.2827, grad_norm: 3.1926 2023-02-17 05:08:00,428 - mmseg - INFO - Iter [119450/160000] lr: 1.521e-05, eta: 4:09:39, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1472, decode.acc_seg: 93.9336, aux.loss_ce: 0.1015, aux.acc_seg: 89.6690, loss: 0.2487, grad_norm: 2.5539 2023-02-17 05:08:18,822 - mmseg - INFO - Iter [119500/160000] lr: 1.519e-05, eta: 4:09:21, time: 0.369, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1496, decode.acc_seg: 93.8413, aux.loss_ce: 0.0999, aux.acc_seg: 89.6622, loss: 0.2495, grad_norm: 2.5090 2023-02-17 05:08:36,835 - mmseg - INFO - Iter [119550/160000] lr: 1.517e-05, eta: 4:09:02, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1490, decode.acc_seg: 93.8289, aux.loss_ce: 0.1020, aux.acc_seg: 89.6970, loss: 0.2510, grad_norm: 2.5397 2023-02-17 05:08:54,961 - mmseg - INFO - Iter [119600/160000] lr: 1.515e-05, eta: 4:08:43, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1615, decode.acc_seg: 93.5047, aux.loss_ce: 0.1048, aux.acc_seg: 89.6616, loss: 0.2664, grad_norm: 2.1700 2023-02-17 05:09:13,131 - mmseg - INFO - Iter [119650/160000] lr: 1.513e-05, eta: 4:08:24, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1569, decode.acc_seg: 93.4349, aux.loss_ce: 0.1028, aux.acc_seg: 89.4180, loss: 0.2597, grad_norm: 2.5857 2023-02-17 05:09:31,041 - mmseg - INFO - Iter [119700/160000] lr: 1.511e-05, eta: 4:08:05, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1603, decode.acc_seg: 93.4717, aux.loss_ce: 0.1097, aux.acc_seg: 89.1469, loss: 0.2701, grad_norm: 3.0477 2023-02-17 05:09:48,925 - mmseg - INFO - Iter [119750/160000] lr: 1.509e-05, eta: 4:07:46, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1553, decode.acc_seg: 93.5837, aux.loss_ce: 0.0998, aux.acc_seg: 89.7294, loss: 0.2551, grad_norm: 2.5156 2023-02-17 05:10:06,752 - mmseg - INFO - Iter [119800/160000] lr: 1.508e-05, eta: 4:07:27, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1595, decode.acc_seg: 93.4657, aux.loss_ce: 0.1077, aux.acc_seg: 89.0513, loss: 0.2672, grad_norm: 2.7704 2023-02-17 05:10:25,039 - mmseg - INFO - Iter [119850/160000] lr: 1.506e-05, eta: 4:07:08, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1671, decode.acc_seg: 93.1828, aux.loss_ce: 0.1116, aux.acc_seg: 88.8068, loss: 0.2788, grad_norm: 2.6674 2023-02-17 05:10:43,015 - mmseg - INFO - Iter [119900/160000] lr: 1.504e-05, eta: 4:06:49, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1579, decode.acc_seg: 93.4525, aux.loss_ce: 0.1011, aux.acc_seg: 89.8382, loss: 0.2590, grad_norm: 2.6149 2023-02-17 05:11:00,910 - mmseg - INFO - Iter [119950/160000] lr: 1.502e-05, eta: 4:06:30, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1561, decode.acc_seg: 93.4075, aux.loss_ce: 0.1021, aux.acc_seg: 89.5305, loss: 0.2582, grad_norm: 2.5044 2023-02-17 05:11:18,898 - mmseg - INFO - Saving checkpoint at 120000 iterations 2023-02-17 05:11:19,944 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 05:11:19,944 - mmseg - INFO - Iter [120000/160000] lr: 1.500e-05, eta: 4:06:12, time: 0.381, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1584, decode.acc_seg: 93.3788, aux.loss_ce: 0.1077, aux.acc_seg: 88.9812, loss: 0.2661, grad_norm: 2.7187 2023-02-17 05:11:37,904 - mmseg - INFO - Iter [120050/160000] lr: 1.498e-05, eta: 4:05:53, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1420, decode.acc_seg: 93.8642, aux.loss_ce: 0.0942, aux.acc_seg: 90.1257, loss: 0.2362, grad_norm: 2.4796 2023-02-17 05:11:55,711 - mmseg - INFO - Iter [120100/160000] lr: 1.496e-05, eta: 4:05:34, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1601, decode.acc_seg: 93.2303, aux.loss_ce: 0.1035, aux.acc_seg: 89.3587, loss: 0.2637, grad_norm: 2.6063 2023-02-17 05:12:13,498 - mmseg - INFO - Iter [120150/160000] lr: 1.494e-05, eta: 4:05:15, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1488, decode.acc_seg: 93.8957, aux.loss_ce: 0.0974, aux.acc_seg: 89.9570, loss: 0.2461, grad_norm: 2.3176 2023-02-17 05:12:31,281 - mmseg - INFO - Iter [120200/160000] lr: 1.493e-05, eta: 4:04:55, time: 0.356, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1484, decode.acc_seg: 93.8807, aux.loss_ce: 0.0963, aux.acc_seg: 90.2395, loss: 0.2446, grad_norm: 2.1584 2023-02-17 05:12:48,990 - mmseg - INFO - Iter [120250/160000] lr: 1.491e-05, eta: 4:04:36, time: 0.354, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1527, decode.acc_seg: 93.5244, aux.loss_ce: 0.0992, aux.acc_seg: 89.6150, loss: 0.2520, grad_norm: 2.6867 2023-02-17 05:13:06,975 - mmseg - INFO - Iter [120300/160000] lr: 1.489e-05, eta: 4:04:17, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1653, decode.acc_seg: 93.1504, aux.loss_ce: 0.1084, aux.acc_seg: 88.8719, loss: 0.2737, grad_norm: 3.0121 2023-02-17 05:13:25,149 - mmseg - INFO - Iter [120350/160000] lr: 1.487e-05, eta: 4:03:58, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1475, decode.acc_seg: 93.9291, aux.loss_ce: 0.0968, aux.acc_seg: 90.0204, loss: 0.2442, grad_norm: 2.5842 2023-02-17 05:13:43,538 - mmseg - INFO - Iter [120400/160000] lr: 1.485e-05, eta: 4:03:40, time: 0.368, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1626, decode.acc_seg: 93.4985, aux.loss_ce: 0.1076, aux.acc_seg: 89.3907, loss: 0.2702, grad_norm: 2.9122 2023-02-17 05:14:01,637 - mmseg - INFO - Iter [120450/160000] lr: 1.483e-05, eta: 4:03:21, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1616, decode.acc_seg: 93.3787, aux.loss_ce: 0.1045, aux.acc_seg: 89.3658, loss: 0.2660, grad_norm: 2.7469 2023-02-17 05:14:19,670 - mmseg - INFO - Iter [120500/160000] lr: 1.481e-05, eta: 4:03:02, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1510, decode.acc_seg: 93.8704, aux.loss_ce: 0.1020, aux.acc_seg: 89.5443, loss: 0.2530, grad_norm: 2.6990 2023-02-17 05:14:37,756 - mmseg - INFO - Iter [120550/160000] lr: 1.479e-05, eta: 4:02:43, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1637, decode.acc_seg: 93.4213, aux.loss_ce: 0.1067, aux.acc_seg: 89.3767, loss: 0.2703, grad_norm: 2.4319 2023-02-17 05:14:55,751 - mmseg - INFO - Iter [120600/160000] lr: 1.478e-05, eta: 4:02:24, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1562, decode.acc_seg: 93.5205, aux.loss_ce: 0.1062, aux.acc_seg: 89.1953, loss: 0.2624, grad_norm: 2.5112 2023-02-17 05:15:16,154 - mmseg - INFO - Iter [120650/160000] lr: 1.476e-05, eta: 4:02:08, time: 0.408, data_time: 0.055, memory: 16596, decode.loss_ce: 0.1629, decode.acc_seg: 93.2669, aux.loss_ce: 0.1047, aux.acc_seg: 89.2977, loss: 0.2676, grad_norm: 2.4644 2023-02-17 05:15:34,084 - mmseg - INFO - Iter [120700/160000] lr: 1.474e-05, eta: 4:01:49, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1558, decode.acc_seg: 93.5851, aux.loss_ce: 0.1007, aux.acc_seg: 89.6540, loss: 0.2565, grad_norm: 2.4230 2023-02-17 05:15:52,476 - mmseg - INFO - Iter [120750/160000] lr: 1.472e-05, eta: 4:01:30, time: 0.368, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1577, decode.acc_seg: 93.5397, aux.loss_ce: 0.1076, aux.acc_seg: 89.1699, loss: 0.2653, grad_norm: 2.5657 2023-02-17 05:16:10,469 - mmseg - INFO - Iter [120800/160000] lr: 1.470e-05, eta: 4:01:11, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1568, decode.acc_seg: 93.5986, aux.loss_ce: 0.1027, aux.acc_seg: 89.6215, loss: 0.2595, grad_norm: 2.1513 2023-02-17 05:16:28,405 - mmseg - INFO - Iter [120850/160000] lr: 1.468e-05, eta: 4:00:52, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1548, decode.acc_seg: 93.6901, aux.loss_ce: 0.0992, aux.acc_seg: 89.9125, loss: 0.2540, grad_norm: 2.5067 2023-02-17 05:16:46,454 - mmseg - INFO - Iter [120900/160000] lr: 1.466e-05, eta: 4:00:33, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1579, decode.acc_seg: 93.4101, aux.loss_ce: 0.1037, aux.acc_seg: 89.4016, loss: 0.2616, grad_norm: 2.8572 2023-02-17 05:17:04,509 - mmseg - INFO - Iter [120950/160000] lr: 1.464e-05, eta: 4:00:15, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1566, decode.acc_seg: 93.6337, aux.loss_ce: 0.1054, aux.acc_seg: 89.3345, loss: 0.2621, grad_norm: 2.6008 2023-02-17 05:17:22,376 - mmseg - INFO - Saving checkpoint at 121000 iterations 2023-02-17 05:17:23,473 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 05:17:23,474 - mmseg - INFO - Iter [121000/160000] lr: 1.463e-05, eta: 3:59:57, time: 0.379, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1619, decode.acc_seg: 93.1982, aux.loss_ce: 0.1007, aux.acc_seg: 89.5419, loss: 0.2626, grad_norm: 2.5634 2023-02-17 05:17:41,180 - mmseg - INFO - Iter [121050/160000] lr: 1.461e-05, eta: 3:59:37, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1525, decode.acc_seg: 93.7085, aux.loss_ce: 0.1015, aux.acc_seg: 89.6587, loss: 0.2539, grad_norm: 2.9130 2023-02-17 05:17:59,061 - mmseg - INFO - Iter [121100/160000] lr: 1.459e-05, eta: 3:59:18, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1575, decode.acc_seg: 93.3857, aux.loss_ce: 0.1033, aux.acc_seg: 89.3162, loss: 0.2608, grad_norm: 2.5445 2023-02-17 05:18:17,225 - mmseg - INFO - Iter [121150/160000] lr: 1.457e-05, eta: 3:59:00, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1556, decode.acc_seg: 93.8044, aux.loss_ce: 0.1024, aux.acc_seg: 89.7892, loss: 0.2579, grad_norm: 2.1804 2023-02-17 05:18:35,642 - mmseg - INFO - Iter [121200/160000] lr: 1.455e-05, eta: 3:58:41, time: 0.369, data_time: 0.008, memory: 16596, decode.loss_ce: 0.1524, decode.acc_seg: 93.6605, aux.loss_ce: 0.1034, aux.acc_seg: 89.5374, loss: 0.2558, grad_norm: 3.0446 2023-02-17 05:18:53,629 - mmseg - INFO - Iter [121250/160000] lr: 1.453e-05, eta: 3:58:22, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1514, decode.acc_seg: 93.8813, aux.loss_ce: 0.1005, aux.acc_seg: 90.0605, loss: 0.2519, grad_norm: 2.3821 2023-02-17 05:19:11,862 - mmseg - INFO - Iter [121300/160000] lr: 1.451e-05, eta: 3:58:03, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1607, decode.acc_seg: 93.4077, aux.loss_ce: 0.1021, aux.acc_seg: 89.6321, loss: 0.2627, grad_norm: 2.5586 2023-02-17 05:19:29,893 - mmseg - INFO - Iter [121350/160000] lr: 1.449e-05, eta: 3:57:45, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1510, decode.acc_seg: 93.8650, aux.loss_ce: 0.1031, aux.acc_seg: 89.6480, loss: 0.2541, grad_norm: 2.6359 2023-02-17 05:19:47,895 - mmseg - INFO - Iter [121400/160000] lr: 1.448e-05, eta: 3:57:26, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1566, decode.acc_seg: 93.5334, aux.loss_ce: 0.1048, aux.acc_seg: 89.2895, loss: 0.2614, grad_norm: 2.4375 2023-02-17 05:20:05,786 - mmseg - INFO - Iter [121450/160000] lr: 1.446e-05, eta: 3:57:07, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1591, decode.acc_seg: 93.3757, aux.loss_ce: 0.1021, aux.acc_seg: 89.6174, loss: 0.2612, grad_norm: 2.5313 2023-02-17 05:20:23,734 - mmseg - INFO - Iter [121500/160000] lr: 1.444e-05, eta: 3:56:48, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1546, decode.acc_seg: 93.5729, aux.loss_ce: 0.1003, aux.acc_seg: 89.7468, loss: 0.2549, grad_norm: 2.9708 2023-02-17 05:20:41,468 - mmseg - INFO - Iter [121550/160000] lr: 1.442e-05, eta: 3:56:28, time: 0.355, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1518, decode.acc_seg: 93.6731, aux.loss_ce: 0.1002, aux.acc_seg: 89.7796, loss: 0.2521, grad_norm: 2.4934 2023-02-17 05:20:59,266 - mmseg - INFO - Iter [121600/160000] lr: 1.440e-05, eta: 3:56:09, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1645, decode.acc_seg: 93.3172, aux.loss_ce: 0.1072, aux.acc_seg: 89.1841, loss: 0.2717, grad_norm: 2.6214 2023-02-17 05:21:17,028 - mmseg - INFO - Iter [121650/160000] lr: 1.438e-05, eta: 3:55:50, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1542, decode.acc_seg: 93.6046, aux.loss_ce: 0.1023, aux.acc_seg: 89.6158, loss: 0.2565, grad_norm: 2.8283 2023-02-17 05:21:35,362 - mmseg - INFO - Iter [121700/160000] lr: 1.436e-05, eta: 3:55:32, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1542, decode.acc_seg: 93.6820, aux.loss_ce: 0.1027, aux.acc_seg: 89.3549, loss: 0.2569, grad_norm: 2.5325 2023-02-17 05:21:53,507 - mmseg - INFO - Iter [121750/160000] lr: 1.434e-05, eta: 3:55:13, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1480, decode.acc_seg: 93.6760, aux.loss_ce: 0.0984, aux.acc_seg: 89.8486, loss: 0.2463, grad_norm: 2.5194 2023-02-17 05:22:11,396 - mmseg - INFO - Iter [121800/160000] lr: 1.433e-05, eta: 3:54:54, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1470, decode.acc_seg: 93.7283, aux.loss_ce: 0.0985, aux.acc_seg: 89.7542, loss: 0.2454, grad_norm: 2.2257 2023-02-17 05:22:29,650 - mmseg - INFO - Iter [121850/160000] lr: 1.431e-05, eta: 3:54:35, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1550, decode.acc_seg: 93.5454, aux.loss_ce: 0.1031, aux.acc_seg: 89.5052, loss: 0.2581, grad_norm: 2.6143 2023-02-17 05:22:49,793 - mmseg - INFO - Iter [121900/160000] lr: 1.429e-05, eta: 3:54:18, time: 0.403, data_time: 0.053, memory: 16596, decode.loss_ce: 0.1638, decode.acc_seg: 93.3505, aux.loss_ce: 0.1060, aux.acc_seg: 89.5438, loss: 0.2698, grad_norm: 2.8743 2023-02-17 05:23:07,986 - mmseg - INFO - Iter [121950/160000] lr: 1.427e-05, eta: 3:54:00, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1652, decode.acc_seg: 93.4314, aux.loss_ce: 0.1110, aux.acc_seg: 89.2297, loss: 0.2762, grad_norm: 3.1180 2023-02-17 05:23:25,788 - mmseg - INFO - Saving checkpoint at 122000 iterations 2023-02-17 05:23:26,834 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 05:23:26,834 - mmseg - INFO - Iter [122000/160000] lr: 1.425e-05, eta: 3:53:42, time: 0.377, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1673, decode.acc_seg: 93.1075, aux.loss_ce: 0.1076, aux.acc_seg: 89.1490, loss: 0.2749, grad_norm: 3.1315 2023-02-17 05:23:44,785 - mmseg - INFO - Iter [122050/160000] lr: 1.423e-05, eta: 3:53:23, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1538, decode.acc_seg: 93.5669, aux.loss_ce: 0.0998, aux.acc_seg: 89.7710, loss: 0.2535, grad_norm: 2.3471 2023-02-17 05:24:03,242 - mmseg - INFO - Iter [122100/160000] lr: 1.421e-05, eta: 3:53:04, time: 0.369, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1461, decode.acc_seg: 93.8257, aux.loss_ce: 0.0949, aux.acc_seg: 90.0121, loss: 0.2410, grad_norm: 2.3375 2023-02-17 05:24:21,484 - mmseg - INFO - Iter [122150/160000] lr: 1.419e-05, eta: 3:52:46, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1498, decode.acc_seg: 93.8939, aux.loss_ce: 0.0955, aux.acc_seg: 90.3406, loss: 0.2452, grad_norm: 2.0918 2023-02-17 05:24:39,313 - mmseg - INFO - Iter [122200/160000] lr: 1.418e-05, eta: 3:52:27, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1444, decode.acc_seg: 93.9182, aux.loss_ce: 0.1006, aux.acc_seg: 89.7563, loss: 0.2450, grad_norm: 2.6654 2023-02-17 05:24:57,699 - mmseg - INFO - Iter [122250/160000] lr: 1.416e-05, eta: 3:52:08, time: 0.368, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1577, decode.acc_seg: 93.3944, aux.loss_ce: 0.1030, aux.acc_seg: 89.2958, loss: 0.2608, grad_norm: 2.4134 2023-02-17 05:25:15,524 - mmseg - INFO - Iter [122300/160000] lr: 1.414e-05, eta: 3:51:49, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1586, decode.acc_seg: 93.5279, aux.loss_ce: 0.1031, aux.acc_seg: 89.5242, loss: 0.2617, grad_norm: 2.5938 2023-02-17 05:25:33,463 - mmseg - INFO - Iter [122350/160000] lr: 1.412e-05, eta: 3:51:30, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1476, decode.acc_seg: 93.8485, aux.loss_ce: 0.1016, aux.acc_seg: 89.5232, loss: 0.2492, grad_norm: 2.5571 2023-02-17 05:25:51,727 - mmseg - INFO - Iter [122400/160000] lr: 1.410e-05, eta: 3:51:11, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1568, decode.acc_seg: 93.5779, aux.loss_ce: 0.1059, aux.acc_seg: 89.0973, loss: 0.2627, grad_norm: 2.4549 2023-02-17 05:26:09,884 - mmseg - INFO - Iter [122450/160000] lr: 1.408e-05, eta: 3:50:53, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1486, decode.acc_seg: 93.6620, aux.loss_ce: 0.0986, aux.acc_seg: 89.6921, loss: 0.2472, grad_norm: 2.0767 2023-02-17 05:26:27,850 - mmseg - INFO - Iter [122500/160000] lr: 1.406e-05, eta: 3:50:34, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1550, decode.acc_seg: 93.5259, aux.loss_ce: 0.1025, aux.acc_seg: 89.5984, loss: 0.2575, grad_norm: 2.4764 2023-02-17 05:26:45,789 - mmseg - INFO - Iter [122550/160000] lr: 1.404e-05, eta: 3:50:15, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1450, decode.acc_seg: 93.8585, aux.loss_ce: 0.0987, aux.acc_seg: 89.6759, loss: 0.2437, grad_norm: 2.8127 2023-02-17 05:27:04,040 - mmseg - INFO - Iter [122600/160000] lr: 1.403e-05, eta: 3:49:56, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1483, decode.acc_seg: 93.7894, aux.loss_ce: 0.1009, aux.acc_seg: 89.6893, loss: 0.2492, grad_norm: 2.7730 2023-02-17 05:27:21,824 - mmseg - INFO - Iter [122650/160000] lr: 1.401e-05, eta: 3:49:37, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1602, decode.acc_seg: 93.4227, aux.loss_ce: 0.1042, aux.acc_seg: 89.5977, loss: 0.2644, grad_norm: 2.3879 2023-02-17 05:27:39,572 - mmseg - INFO - Iter [122700/160000] lr: 1.399e-05, eta: 3:49:18, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1532, decode.acc_seg: 93.6116, aux.loss_ce: 0.1011, aux.acc_seg: 89.5886, loss: 0.2543, grad_norm: 2.4584 2023-02-17 05:27:57,384 - mmseg - INFO - Iter [122750/160000] lr: 1.397e-05, eta: 3:48:59, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1569, decode.acc_seg: 93.6353, aux.loss_ce: 0.1067, aux.acc_seg: 89.2635, loss: 0.2636, grad_norm: 2.5116 2023-02-17 05:28:15,817 - mmseg - INFO - Iter [122800/160000] lr: 1.395e-05, eta: 3:48:41, time: 0.369, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1513, decode.acc_seg: 93.6876, aux.loss_ce: 0.0980, aux.acc_seg: 89.9825, loss: 0.2494, grad_norm: 2.3811 2023-02-17 05:28:33,742 - mmseg - INFO - Iter [122850/160000] lr: 1.393e-05, eta: 3:48:22, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1546, decode.acc_seg: 93.4624, aux.loss_ce: 0.1026, aux.acc_seg: 89.3212, loss: 0.2573, grad_norm: 2.8232 2023-02-17 05:28:51,579 - mmseg - INFO - Iter [122900/160000] lr: 1.391e-05, eta: 3:48:03, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1578, decode.acc_seg: 93.4983, aux.loss_ce: 0.1035, aux.acc_seg: 89.5138, loss: 0.2613, grad_norm: 2.9334 2023-02-17 05:29:09,439 - mmseg - INFO - Iter [122950/160000] lr: 1.389e-05, eta: 3:47:44, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1598, decode.acc_seg: 93.3707, aux.loss_ce: 0.1035, aux.acc_seg: 89.4825, loss: 0.2634, grad_norm: 2.9678 2023-02-17 05:29:27,407 - mmseg - INFO - Saving checkpoint at 123000 iterations 2023-02-17 05:29:28,455 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 05:29:28,456 - mmseg - INFO - Iter [123000/160000] lr: 1.388e-05, eta: 3:47:26, time: 0.380, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1554, decode.acc_seg: 93.5361, aux.loss_ce: 0.1058, aux.acc_seg: 89.3245, loss: 0.2612, grad_norm: 3.1311 2023-02-17 05:29:46,288 - mmseg - INFO - Iter [123050/160000] lr: 1.386e-05, eta: 3:47:07, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1556, decode.acc_seg: 93.6434, aux.loss_ce: 0.1053, aux.acc_seg: 89.3833, loss: 0.2609, grad_norm: 2.3815 2023-02-17 05:30:04,093 - mmseg - INFO - Iter [123100/160000] lr: 1.384e-05, eta: 3:46:48, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1521, decode.acc_seg: 93.8904, aux.loss_ce: 0.1010, aux.acc_seg: 90.0477, loss: 0.2531, grad_norm: 2.3113 2023-02-17 05:30:22,168 - mmseg - INFO - Iter [123150/160000] lr: 1.382e-05, eta: 3:46:29, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1509, decode.acc_seg: 93.7215, aux.loss_ce: 0.1000, aux.acc_seg: 89.8149, loss: 0.2509, grad_norm: 2.6911 2023-02-17 05:30:42,601 - mmseg - INFO - Iter [123200/160000] lr: 1.380e-05, eta: 3:46:12, time: 0.408, data_time: 0.053, memory: 16596, decode.loss_ce: 0.1588, decode.acc_seg: 93.2643, aux.loss_ce: 0.1035, aux.acc_seg: 89.3561, loss: 0.2623, grad_norm: 3.0167 2023-02-17 05:31:00,718 - mmseg - INFO - Iter [123250/160000] lr: 1.378e-05, eta: 3:45:54, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1466, decode.acc_seg: 93.6947, aux.loss_ce: 0.0966, aux.acc_seg: 89.7238, loss: 0.2432, grad_norm: 2.7550 2023-02-17 05:31:18,514 - mmseg - INFO - Iter [123300/160000] lr: 1.376e-05, eta: 3:45:34, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1514, decode.acc_seg: 93.6761, aux.loss_ce: 0.1016, aux.acc_seg: 89.7520, loss: 0.2530, grad_norm: 2.3776 2023-02-17 05:31:36,645 - mmseg - INFO - Iter [123350/160000] lr: 1.374e-05, eta: 3:45:16, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1530, decode.acc_seg: 93.5931, aux.loss_ce: 0.1012, aux.acc_seg: 89.6156, loss: 0.2541, grad_norm: 2.3042 2023-02-17 05:31:55,101 - mmseg - INFO - Iter [123400/160000] lr: 1.373e-05, eta: 3:44:57, time: 0.369, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1483, decode.acc_seg: 93.8457, aux.loss_ce: 0.1029, aux.acc_seg: 89.6129, loss: 0.2512, grad_norm: 2.5681 2023-02-17 05:32:12,951 - mmseg - INFO - Iter [123450/160000] lr: 1.371e-05, eta: 3:44:38, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1587, decode.acc_seg: 93.4450, aux.loss_ce: 0.1053, aux.acc_seg: 89.2723, loss: 0.2640, grad_norm: 2.4284 2023-02-17 05:32:31,122 - mmseg - INFO - Iter [123500/160000] lr: 1.369e-05, eta: 3:44:20, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1539, decode.acc_seg: 93.5522, aux.loss_ce: 0.1024, aux.acc_seg: 89.5139, loss: 0.2564, grad_norm: 2.4331 2023-02-17 05:32:48,933 - mmseg - INFO - Iter [123550/160000] lr: 1.367e-05, eta: 3:44:01, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1589, decode.acc_seg: 93.4799, aux.loss_ce: 0.1049, aux.acc_seg: 89.2671, loss: 0.2638, grad_norm: 2.5441 2023-02-17 05:33:06,702 - mmseg - INFO - Iter [123600/160000] lr: 1.365e-05, eta: 3:43:42, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1562, decode.acc_seg: 93.4286, aux.loss_ce: 0.1030, aux.acc_seg: 89.5536, loss: 0.2592, grad_norm: 3.1797 2023-02-17 05:33:24,645 - mmseg - INFO - Iter [123650/160000] lr: 1.363e-05, eta: 3:43:23, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1631, decode.acc_seg: 93.2653, aux.loss_ce: 0.1036, aux.acc_seg: 89.5233, loss: 0.2667, grad_norm: 2.9536 2023-02-17 05:33:42,490 - mmseg - INFO - Iter [123700/160000] lr: 1.361e-05, eta: 3:43:04, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1468, decode.acc_seg: 93.7935, aux.loss_ce: 0.0975, aux.acc_seg: 89.9063, loss: 0.2442, grad_norm: 2.8723 2023-02-17 05:34:00,361 - mmseg - INFO - Iter [123750/160000] lr: 1.359e-05, eta: 3:42:45, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1502, decode.acc_seg: 93.8448, aux.loss_ce: 0.1004, aux.acc_seg: 89.9317, loss: 0.2506, grad_norm: 2.1456 2023-02-17 05:34:18,430 - mmseg - INFO - Iter [123800/160000] lr: 1.358e-05, eta: 3:42:26, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1437, decode.acc_seg: 94.0157, aux.loss_ce: 0.0969, aux.acc_seg: 89.9877, loss: 0.2407, grad_norm: 2.2768 2023-02-17 05:34:36,722 - mmseg - INFO - Iter [123850/160000] lr: 1.356e-05, eta: 3:42:07, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1551, decode.acc_seg: 93.5637, aux.loss_ce: 0.1021, aux.acc_seg: 89.8114, loss: 0.2572, grad_norm: 2.5391 2023-02-17 05:34:54,817 - mmseg - INFO - Iter [123900/160000] lr: 1.354e-05, eta: 3:41:49, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1546, decode.acc_seg: 93.6787, aux.loss_ce: 0.0990, aux.acc_seg: 89.9235, loss: 0.2536, grad_norm: 2.4087 2023-02-17 05:35:12,573 - mmseg - INFO - Iter [123950/160000] lr: 1.352e-05, eta: 3:41:30, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1568, decode.acc_seg: 93.7080, aux.loss_ce: 0.1045, aux.acc_seg: 89.6771, loss: 0.2613, grad_norm: 2.5261 2023-02-17 05:35:30,872 - mmseg - INFO - Saving checkpoint at 124000 iterations 2023-02-17 05:35:31,906 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 05:35:31,906 - mmseg - INFO - Iter [124000/160000] lr: 1.350e-05, eta: 3:41:12, time: 0.387, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1627, decode.acc_seg: 93.2914, aux.loss_ce: 0.1050, aux.acc_seg: 89.2059, loss: 0.2678, grad_norm: 3.1478 2023-02-17 05:35:49,451 - mmseg - INFO - Iter [124050/160000] lr: 1.348e-05, eta: 3:40:53, time: 0.351, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1521, decode.acc_seg: 93.6455, aux.loss_ce: 0.1007, aux.acc_seg: 89.6841, loss: 0.2528, grad_norm: 2.4401 2023-02-17 05:36:07,710 - mmseg - INFO - Iter [124100/160000] lr: 1.346e-05, eta: 3:40:34, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1541, decode.acc_seg: 93.5425, aux.loss_ce: 0.1031, aux.acc_seg: 89.2337, loss: 0.2573, grad_norm: 2.6252 2023-02-17 05:36:25,754 - mmseg - INFO - Iter [124150/160000] lr: 1.344e-05, eta: 3:40:15, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1613, decode.acc_seg: 93.3923, aux.loss_ce: 0.1056, aux.acc_seg: 89.4556, loss: 0.2669, grad_norm: 3.5252 2023-02-17 05:36:43,747 - mmseg - INFO - Iter [124200/160000] lr: 1.343e-05, eta: 3:39:57, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1526, decode.acc_seg: 93.5248, aux.loss_ce: 0.1051, aux.acc_seg: 89.3063, loss: 0.2577, grad_norm: 2.8012 2023-02-17 05:37:01,741 - mmseg - INFO - Iter [124250/160000] lr: 1.341e-05, eta: 3:39:38, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1532, decode.acc_seg: 93.6932, aux.loss_ce: 0.1052, aux.acc_seg: 89.4749, loss: 0.2585, grad_norm: 2.8193 2023-02-17 05:37:19,738 - mmseg - INFO - Iter [124300/160000] lr: 1.339e-05, eta: 3:39:19, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1544, decode.acc_seg: 93.5761, aux.loss_ce: 0.0997, aux.acc_seg: 89.8430, loss: 0.2540, grad_norm: 2.1300 2023-02-17 05:37:37,860 - mmseg - INFO - Iter [124350/160000] lr: 1.337e-05, eta: 3:39:00, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1590, decode.acc_seg: 93.4477, aux.loss_ce: 0.1053, aux.acc_seg: 89.3215, loss: 0.2643, grad_norm: 2.5219 2023-02-17 05:37:55,939 - mmseg - INFO - Iter [124400/160000] lr: 1.335e-05, eta: 3:38:42, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1504, decode.acc_seg: 93.6630, aux.loss_ce: 0.1001, aux.acc_seg: 89.6037, loss: 0.2504, grad_norm: 2.7007 2023-02-17 05:38:16,163 - mmseg - INFO - Iter [124450/160000] lr: 1.333e-05, eta: 3:38:25, time: 0.405, data_time: 0.051, memory: 16596, decode.loss_ce: 0.1481, decode.acc_seg: 93.7662, aux.loss_ce: 0.0996, aux.acc_seg: 89.7078, loss: 0.2477, grad_norm: 2.5245 2023-02-17 05:38:34,121 - mmseg - INFO - Iter [124500/160000] lr: 1.331e-05, eta: 3:38:06, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1481, decode.acc_seg: 93.8276, aux.loss_ce: 0.0998, aux.acc_seg: 89.7976, loss: 0.2479, grad_norm: 2.2062 2023-02-17 05:38:52,091 - mmseg - INFO - Iter [124550/160000] lr: 1.329e-05, eta: 3:37:47, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1554, decode.acc_seg: 93.5139, aux.loss_ce: 0.1051, aux.acc_seg: 89.4093, loss: 0.2605, grad_norm: 2.6080 2023-02-17 05:39:09,977 - mmseg - INFO - Iter [124600/160000] lr: 1.328e-05, eta: 3:37:28, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1499, decode.acc_seg: 93.6660, aux.loss_ce: 0.1033, aux.acc_seg: 89.2881, loss: 0.2533, grad_norm: 2.7576 2023-02-17 05:39:28,094 - mmseg - INFO - Iter [124650/160000] lr: 1.326e-05, eta: 3:37:09, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1493, decode.acc_seg: 93.7948, aux.loss_ce: 0.1013, aux.acc_seg: 89.6508, loss: 0.2506, grad_norm: 2.1964 2023-02-17 05:39:46,210 - mmseg - INFO - Iter [124700/160000] lr: 1.324e-05, eta: 3:36:51, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1541, decode.acc_seg: 93.7327, aux.loss_ce: 0.0991, aux.acc_seg: 90.1369, loss: 0.2531, grad_norm: 2.2310 2023-02-17 05:40:04,260 - mmseg - INFO - Iter [124750/160000] lr: 1.322e-05, eta: 3:36:32, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1547, decode.acc_seg: 93.5682, aux.loss_ce: 0.1002, aux.acc_seg: 89.7506, loss: 0.2548, grad_norm: 2.5489 2023-02-17 05:40:22,718 - mmseg - INFO - Iter [124800/160000] lr: 1.320e-05, eta: 3:36:14, time: 0.369, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1499, decode.acc_seg: 93.7322, aux.loss_ce: 0.1022, aux.acc_seg: 89.5606, loss: 0.2521, grad_norm: 2.8398 2023-02-17 05:40:40,582 - mmseg - INFO - Iter [124850/160000] lr: 1.318e-05, eta: 3:35:55, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1519, decode.acc_seg: 93.6113, aux.loss_ce: 0.1022, aux.acc_seg: 89.5425, loss: 0.2540, grad_norm: 2.3183 2023-02-17 05:40:58,600 - mmseg - INFO - Iter [124900/160000] lr: 1.316e-05, eta: 3:35:36, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1431, decode.acc_seg: 93.9283, aux.loss_ce: 0.0986, aux.acc_seg: 89.7301, loss: 0.2418, grad_norm: 2.7604 2023-02-17 05:41:16,835 - mmseg - INFO - Iter [124950/160000] lr: 1.314e-05, eta: 3:35:17, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1536, decode.acc_seg: 93.6245, aux.loss_ce: 0.1026, aux.acc_seg: 89.5460, loss: 0.2562, grad_norm: 2.7998 2023-02-17 05:41:35,002 - mmseg - INFO - Saving checkpoint at 125000 iterations 2023-02-17 05:41:36,030 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 05:41:36,030 - mmseg - INFO - Iter [125000/160000] lr: 1.313e-05, eta: 3:34:59, time: 0.384, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1505, decode.acc_seg: 93.8975, aux.loss_ce: 0.1006, aux.acc_seg: 89.9702, loss: 0.2511, grad_norm: 3.2284 2023-02-17 05:41:53,931 - mmseg - INFO - Iter [125050/160000] lr: 1.311e-05, eta: 3:34:41, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1546, decode.acc_seg: 93.6270, aux.loss_ce: 0.1020, aux.acc_seg: 89.7493, loss: 0.2566, grad_norm: 2.6481 2023-02-17 05:42:12,153 - mmseg - INFO - Iter [125100/160000] lr: 1.309e-05, eta: 3:34:22, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1505, decode.acc_seg: 93.7714, aux.loss_ce: 0.0995, aux.acc_seg: 89.9223, loss: 0.2500, grad_norm: 2.7700 2023-02-17 05:42:29,994 - mmseg - INFO - Iter [125150/160000] lr: 1.307e-05, eta: 3:34:03, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1549, decode.acc_seg: 93.5915, aux.loss_ce: 0.1030, aux.acc_seg: 89.6077, loss: 0.2579, grad_norm: 2.5035 2023-02-17 05:42:47,926 - mmseg - INFO - Iter [125200/160000] lr: 1.305e-05, eta: 3:33:44, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1451, decode.acc_seg: 93.9925, aux.loss_ce: 0.0963, aux.acc_seg: 90.2286, loss: 0.2414, grad_norm: 2.2258 2023-02-17 05:43:05,711 - mmseg - INFO - Iter [125250/160000] lr: 1.303e-05, eta: 3:33:25, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1487, decode.acc_seg: 93.9363, aux.loss_ce: 0.0984, aux.acc_seg: 90.1724, loss: 0.2471, grad_norm: 2.6301 2023-02-17 05:43:23,499 - mmseg - INFO - Iter [125300/160000] lr: 1.301e-05, eta: 3:33:06, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1544, decode.acc_seg: 93.5933, aux.loss_ce: 0.1000, aux.acc_seg: 90.0163, loss: 0.2545, grad_norm: 2.9645 2023-02-17 05:43:41,371 - mmseg - INFO - Iter [125350/160000] lr: 1.299e-05, eta: 3:32:47, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1559, decode.acc_seg: 93.5554, aux.loss_ce: 0.1005, aux.acc_seg: 89.7897, loss: 0.2564, grad_norm: 3.1679 2023-02-17 05:43:59,034 - mmseg - INFO - Iter [125400/160000] lr: 1.298e-05, eta: 3:32:28, time: 0.353, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1472, decode.acc_seg: 93.9095, aux.loss_ce: 0.0998, aux.acc_seg: 89.8824, loss: 0.2470, grad_norm: 2.3648 2023-02-17 05:44:17,254 - mmseg - INFO - Iter [125450/160000] lr: 1.296e-05, eta: 3:32:10, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1593, decode.acc_seg: 93.6054, aux.loss_ce: 0.1032, aux.acc_seg: 89.5988, loss: 0.2625, grad_norm: 3.1469 2023-02-17 05:44:35,535 - mmseg - INFO - Iter [125500/160000] lr: 1.294e-05, eta: 3:31:51, time: 0.366, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1596, decode.acc_seg: 93.3232, aux.loss_ce: 0.1058, aux.acc_seg: 89.3078, loss: 0.2654, grad_norm: 2.7917 2023-02-17 05:44:53,277 - mmseg - INFO - Iter [125550/160000] lr: 1.292e-05, eta: 3:31:32, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1501, decode.acc_seg: 93.7768, aux.loss_ce: 0.0997, aux.acc_seg: 89.8685, loss: 0.2498, grad_norm: 2.3650 2023-02-17 05:45:11,084 - mmseg - INFO - Iter [125600/160000] lr: 1.290e-05, eta: 3:31:13, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1529, decode.acc_seg: 93.6305, aux.loss_ce: 0.1019, aux.acc_seg: 89.8237, loss: 0.2547, grad_norm: 2.7208 2023-02-17 05:45:29,354 - mmseg - INFO - Iter [125650/160000] lr: 1.288e-05, eta: 3:30:55, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1564, decode.acc_seg: 93.5337, aux.loss_ce: 0.1016, aux.acc_seg: 89.5678, loss: 0.2580, grad_norm: 2.6691 2023-02-17 05:45:49,715 - mmseg - INFO - Iter [125700/160000] lr: 1.286e-05, eta: 3:30:38, time: 0.407, data_time: 0.053, memory: 16596, decode.loss_ce: 0.1584, decode.acc_seg: 93.2463, aux.loss_ce: 0.1042, aux.acc_seg: 89.2850, loss: 0.2625, grad_norm: 2.7332 2023-02-17 05:46:07,583 - mmseg - INFO - Iter [125750/160000] lr: 1.284e-05, eta: 3:30:19, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1504, decode.acc_seg: 93.6910, aux.loss_ce: 0.1007, aux.acc_seg: 89.7285, loss: 0.2512, grad_norm: 2.6055 2023-02-17 05:46:25,769 - mmseg - INFO - Iter [125800/160000] lr: 1.283e-05, eta: 3:30:00, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1510, decode.acc_seg: 93.7991, aux.loss_ce: 0.0982, aux.acc_seg: 89.8490, loss: 0.2491, grad_norm: 3.0013 2023-02-17 05:46:43,732 - mmseg - INFO - Iter [125850/160000] lr: 1.281e-05, eta: 3:29:42, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1540, decode.acc_seg: 93.6244, aux.loss_ce: 0.1003, aux.acc_seg: 89.8564, loss: 0.2543, grad_norm: 2.4421 2023-02-17 05:47:02,188 - mmseg - INFO - Iter [125900/160000] lr: 1.279e-05, eta: 3:29:23, time: 0.369, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1512, decode.acc_seg: 93.8464, aux.loss_ce: 0.1054, aux.acc_seg: 89.4469, loss: 0.2566, grad_norm: 2.6386 2023-02-17 05:47:20,517 - mmseg - INFO - Iter [125950/160000] lr: 1.277e-05, eta: 3:29:05, time: 0.367, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1515, decode.acc_seg: 93.7504, aux.loss_ce: 0.1026, aux.acc_seg: 89.4591, loss: 0.2542, grad_norm: 2.3023 2023-02-17 05:47:38,752 - mmseg - INFO - Saving checkpoint at 126000 iterations 2023-02-17 05:47:39,876 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 05:47:39,876 - mmseg - INFO - Iter [126000/160000] lr: 1.275e-05, eta: 3:28:47, time: 0.387, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1451, decode.acc_seg: 93.8916, aux.loss_ce: 0.0998, aux.acc_seg: 89.6462, loss: 0.2449, grad_norm: 2.4382 2023-02-17 05:47:57,948 - mmseg - INFO - Iter [126050/160000] lr: 1.273e-05, eta: 3:28:28, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1442, decode.acc_seg: 94.0828, aux.loss_ce: 0.1007, aux.acc_seg: 89.8371, loss: 0.2449, grad_norm: 2.3697 2023-02-17 05:48:15,907 - mmseg - INFO - Iter [126100/160000] lr: 1.271e-05, eta: 3:28:10, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1547, decode.acc_seg: 93.4624, aux.loss_ce: 0.0963, aux.acc_seg: 90.1112, loss: 0.2510, grad_norm: 2.3697 2023-02-17 05:48:33,844 - mmseg - INFO - Iter [126150/160000] lr: 1.269e-05, eta: 3:27:51, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1425, decode.acc_seg: 93.8341, aux.loss_ce: 0.0956, aux.acc_seg: 89.9828, loss: 0.2381, grad_norm: 2.1828 2023-02-17 05:48:52,033 - mmseg - INFO - Iter [126200/160000] lr: 1.268e-05, eta: 3:27:32, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1461, decode.acc_seg: 93.8762, aux.loss_ce: 0.0993, aux.acc_seg: 89.8396, loss: 0.2455, grad_norm: 2.4589 2023-02-17 05:49:10,358 - mmseg - INFO - Iter [126250/160000] lr: 1.266e-05, eta: 3:27:14, time: 0.367, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1532, decode.acc_seg: 93.8333, aux.loss_ce: 0.1047, aux.acc_seg: 89.4491, loss: 0.2579, grad_norm: 2.3733 2023-02-17 05:49:28,459 - mmseg - INFO - Iter [126300/160000] lr: 1.264e-05, eta: 3:26:55, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1512, decode.acc_seg: 93.5854, aux.loss_ce: 0.0978, aux.acc_seg: 89.7462, loss: 0.2490, grad_norm: 3.1802 2023-02-17 05:49:46,363 - mmseg - INFO - Iter [126350/160000] lr: 1.262e-05, eta: 3:26:36, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1461, decode.acc_seg: 93.8636, aux.loss_ce: 0.0978, aux.acc_seg: 89.8574, loss: 0.2439, grad_norm: 3.0299 2023-02-17 05:50:04,042 - mmseg - INFO - Iter [126400/160000] lr: 1.260e-05, eta: 3:26:17, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1546, decode.acc_seg: 93.5445, aux.loss_ce: 0.1001, aux.acc_seg: 89.7661, loss: 0.2548, grad_norm: 2.1253 2023-02-17 05:50:21,979 - mmseg - INFO - Iter [126450/160000] lr: 1.258e-05, eta: 3:25:58, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1500, decode.acc_seg: 93.6883, aux.loss_ce: 0.0978, aux.acc_seg: 89.9607, loss: 0.2478, grad_norm: 2.6252 2023-02-17 05:50:39,956 - mmseg - INFO - Iter [126500/160000] lr: 1.256e-05, eta: 3:25:40, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1509, decode.acc_seg: 93.6023, aux.loss_ce: 0.0999, aux.acc_seg: 89.6302, loss: 0.2508, grad_norm: 2.4966 2023-02-17 05:50:58,066 - mmseg - INFO - Iter [126550/160000] lr: 1.254e-05, eta: 3:25:21, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1439, decode.acc_seg: 93.9484, aux.loss_ce: 0.0948, aux.acc_seg: 90.2379, loss: 0.2387, grad_norm: 2.4789 2023-02-17 05:51:16,200 - mmseg - INFO - Iter [126600/160000] lr: 1.253e-05, eta: 3:25:02, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1493, decode.acc_seg: 93.6735, aux.loss_ce: 0.1005, aux.acc_seg: 89.7288, loss: 0.2498, grad_norm: 2.4383 2023-02-17 05:51:33,962 - mmseg - INFO - Iter [126650/160000] lr: 1.251e-05, eta: 3:24:43, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1575, decode.acc_seg: 93.5694, aux.loss_ce: 0.1024, aux.acc_seg: 89.7790, loss: 0.2599, grad_norm: 2.4973 2023-02-17 05:51:52,003 - mmseg - INFO - Iter [126700/160000] lr: 1.249e-05, eta: 3:24:25, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1614, decode.acc_seg: 93.4579, aux.loss_ce: 0.1050, aux.acc_seg: 89.4771, loss: 0.2664, grad_norm: 2.3334 2023-02-17 05:52:10,107 - mmseg - INFO - Iter [126750/160000] lr: 1.247e-05, eta: 3:24:06, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1579, decode.acc_seg: 93.6110, aux.loss_ce: 0.1033, aux.acc_seg: 89.7139, loss: 0.2612, grad_norm: 2.4815 2023-02-17 05:52:28,303 - mmseg - INFO - Iter [126800/160000] lr: 1.245e-05, eta: 3:23:47, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1491, decode.acc_seg: 93.8873, aux.loss_ce: 0.0991, aux.acc_seg: 89.8882, loss: 0.2483, grad_norm: 2.7543 2023-02-17 05:52:46,317 - mmseg - INFO - Iter [126850/160000] lr: 1.243e-05, eta: 3:23:29, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1496, decode.acc_seg: 93.7202, aux.loss_ce: 0.0965, aux.acc_seg: 90.0407, loss: 0.2462, grad_norm: 2.5685 2023-02-17 05:53:04,310 - mmseg - INFO - Iter [126900/160000] lr: 1.241e-05, eta: 3:23:10, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1602, decode.acc_seg: 93.4574, aux.loss_ce: 0.1045, aux.acc_seg: 89.4501, loss: 0.2646, grad_norm: 2.7947 2023-02-17 05:53:24,785 - mmseg - INFO - Iter [126950/160000] lr: 1.239e-05, eta: 3:22:53, time: 0.409, data_time: 0.052, memory: 16596, decode.loss_ce: 0.1438, decode.acc_seg: 93.9920, aux.loss_ce: 0.0978, aux.acc_seg: 89.8970, loss: 0.2417, grad_norm: 2.2863 2023-02-17 05:53:43,107 - mmseg - INFO - Saving checkpoint at 127000 iterations 2023-02-17 05:53:44,148 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 05:53:44,148 - mmseg - INFO - Iter [127000/160000] lr: 1.238e-05, eta: 3:22:35, time: 0.387, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1554, decode.acc_seg: 93.5753, aux.loss_ce: 0.1024, aux.acc_seg: 89.7641, loss: 0.2578, grad_norm: 2.9590 2023-02-17 05:54:02,437 - mmseg - INFO - Iter [127050/160000] lr: 1.236e-05, eta: 3:22:17, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1503, decode.acc_seg: 93.8216, aux.loss_ce: 0.1005, aux.acc_seg: 89.9483, loss: 0.2507, grad_norm: 2.4557 2023-02-17 05:54:20,616 - mmseg - INFO - Iter [127100/160000] lr: 1.234e-05, eta: 3:21:58, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1574, decode.acc_seg: 93.4721, aux.loss_ce: 0.1029, aux.acc_seg: 89.6215, loss: 0.2603, grad_norm: 2.5232 2023-02-17 05:54:38,732 - mmseg - INFO - Iter [127150/160000] lr: 1.232e-05, eta: 3:21:40, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1584, decode.acc_seg: 93.5383, aux.loss_ce: 0.1057, aux.acc_seg: 89.5444, loss: 0.2641, grad_norm: 2.9549 2023-02-17 05:54:56,465 - mmseg - INFO - Iter [127200/160000] lr: 1.230e-05, eta: 3:21:21, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1478, decode.acc_seg: 93.8900, aux.loss_ce: 0.0974, aux.acc_seg: 90.0614, loss: 0.2452, grad_norm: 2.3548 2023-02-17 05:55:14,319 - mmseg - INFO - Iter [127250/160000] lr: 1.228e-05, eta: 3:21:02, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1545, decode.acc_seg: 93.4893, aux.loss_ce: 0.1015, aux.acc_seg: 89.6941, loss: 0.2560, grad_norm: 2.2513 2023-02-17 05:55:32,477 - mmseg - INFO - Iter [127300/160000] lr: 1.226e-05, eta: 3:20:43, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1444, decode.acc_seg: 94.0912, aux.loss_ce: 0.0975, aux.acc_seg: 90.1625, loss: 0.2418, grad_norm: 2.3907 2023-02-17 05:55:50,401 - mmseg - INFO - Iter [127350/160000] lr: 1.224e-05, eta: 3:20:24, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1423, decode.acc_seg: 94.0949, aux.loss_ce: 0.0991, aux.acc_seg: 89.8544, loss: 0.2414, grad_norm: 2.3463 2023-02-17 05:56:08,180 - mmseg - INFO - Iter [127400/160000] lr: 1.223e-05, eta: 3:20:06, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1468, decode.acc_seg: 93.8776, aux.loss_ce: 0.0991, aux.acc_seg: 89.8717, loss: 0.2458, grad_norm: 2.3406 2023-02-17 05:56:26,392 - mmseg - INFO - Iter [127450/160000] lr: 1.221e-05, eta: 3:19:47, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1447, decode.acc_seg: 93.9338, aux.loss_ce: 0.0952, aux.acc_seg: 90.2026, loss: 0.2399, grad_norm: 2.1596 2023-02-17 05:56:44,418 - mmseg - INFO - Iter [127500/160000] lr: 1.219e-05, eta: 3:19:28, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1540, decode.acc_seg: 93.6567, aux.loss_ce: 0.0991, aux.acc_seg: 89.8045, loss: 0.2532, grad_norm: 2.2221 2023-02-17 05:57:02,707 - mmseg - INFO - Iter [127550/160000] lr: 1.217e-05, eta: 3:19:10, time: 0.366, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1552, decode.acc_seg: 93.5330, aux.loss_ce: 0.1045, aux.acc_seg: 89.4835, loss: 0.2597, grad_norm: 2.3654 2023-02-17 05:57:20,885 - mmseg - INFO - Iter [127600/160000] lr: 1.215e-05, eta: 3:18:51, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1500, decode.acc_seg: 93.8557, aux.loss_ce: 0.1013, aux.acc_seg: 89.7340, loss: 0.2513, grad_norm: 2.5233 2023-02-17 05:57:38,852 - mmseg - INFO - Iter [127650/160000] lr: 1.213e-05, eta: 3:18:32, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1447, decode.acc_seg: 94.0325, aux.loss_ce: 0.0951, aux.acc_seg: 90.2193, loss: 0.2398, grad_norm: 2.3672 2023-02-17 05:57:56,903 - mmseg - INFO - Iter [127700/160000] lr: 1.211e-05, eta: 3:18:14, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1510, decode.acc_seg: 93.6634, aux.loss_ce: 0.0977, aux.acc_seg: 89.8792, loss: 0.2487, grad_norm: 2.7349 2023-02-17 05:58:14,998 - mmseg - INFO - Iter [127750/160000] lr: 1.209e-05, eta: 3:17:55, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1481, decode.acc_seg: 93.8806, aux.loss_ce: 0.0988, aux.acc_seg: 89.9930, loss: 0.2469, grad_norm: 2.4573 2023-02-17 05:58:32,835 - mmseg - INFO - Iter [127800/160000] lr: 1.208e-05, eta: 3:17:36, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1413, decode.acc_seg: 94.1307, aux.loss_ce: 0.0929, aux.acc_seg: 90.4090, loss: 0.2343, grad_norm: 2.1851 2023-02-17 05:58:50,844 - mmseg - INFO - Iter [127850/160000] lr: 1.206e-05, eta: 3:17:18, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1513, decode.acc_seg: 93.6919, aux.loss_ce: 0.0965, aux.acc_seg: 90.0909, loss: 0.2478, grad_norm: 2.4322 2023-02-17 05:59:08,709 - mmseg - INFO - Iter [127900/160000] lr: 1.204e-05, eta: 3:16:59, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1486, decode.acc_seg: 93.9349, aux.loss_ce: 0.0962, aux.acc_seg: 90.2199, loss: 0.2448, grad_norm: 2.2117 2023-02-17 05:59:26,702 - mmseg - INFO - Iter [127950/160000] lr: 1.202e-05, eta: 3:16:40, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1524, decode.acc_seg: 93.5605, aux.loss_ce: 0.1010, aux.acc_seg: 89.5689, loss: 0.2534, grad_norm: 2.5237 2023-02-17 05:59:44,648 - mmseg - INFO - Saving checkpoint at 128000 iterations 2023-02-17 05:59:45,713 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 05:59:45,714 - mmseg - INFO - Iter [128000/160000] lr: 1.200e-05, eta: 3:16:22, time: 0.381, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1582, decode.acc_seg: 93.4375, aux.loss_ce: 0.1014, aux.acc_seg: 89.5553, loss: 0.2596, grad_norm: 2.7479 2023-02-17 06:00:13,984 - mmseg - INFO - per class results: 2023-02-17 06:00:13,990 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 76.84 | 88.88 | | building | 82.51 | 91.74 | | sky | 94.34 | 97.66 | | floor | 80.33 | 90.21 | | tree | 74.48 | 86.57 | | ceiling | 84.98 | 92.09 | | road | 84.22 | 90.53 | | bed | 87.84 | 94.8 | | windowpane | 60.9 | 82.87 | | grass | 67.55 | 83.12 | | cabinet | 58.71 | 77.41 | | sidewalk | 67.17 | 82.79 | | person | 78.64 | 93.3 | | earth | 36.21 | 51.83 | | door | 51.57 | 66.75 | | table | 60.7 | 76.52 | | mountain | 58.21 | 73.57 | | plant | 52.42 | 65.55 | | curtain | 70.19 | 79.79 | | chair | 55.7 | 65.5 | | car | 81.99 | 89.25 | | water | 52.14 | 68.13 | | painting | 73.65 | 83.18 | | sofa | 65.13 | 83.1 | | shelf | 42.91 | 60.24 | | house | 51.27 | 64.53 | | sea | 59.77 | 87.74 | | mirror | 63.0 | 66.87 | | rug | 59.93 | 67.06 | | field | 25.26 | 35.95 | | armchair | 45.08 | 63.69 | | seat | 59.92 | 78.41 | | fence | 43.78 | 55.61 | | desk | 50.47 | 65.22 | | rock | 43.71 | 58.24 | | wardrobe | 47.57 | 62.89 | | lamp | 62.12 | 75.28 | | bathtub | 72.57 | 78.13 | | railing | 33.5 | 46.67 | | cushion | 57.23 | 69.33 | | base | 29.15 | 40.64 | | box | 28.04 | 35.83 | | column | 44.21 | 57.8 | | signboard | 36.54 | 49.01 | | chest of drawers | 40.46 | 53.16 | | counter | 25.04 | 33.78 | | sand | 49.85 | 67.63 | | sink | 72.59 | 81.03 | | skyscraper | 57.1 | 81.48 | | fireplace | 74.56 | 87.24 | | refrigerator | 78.21 | 85.4 | | grandstand | 45.08 | 61.4 | | path | 26.37 | 41.48 | | stairs | 30.07 | 39.61 | | runway | 68.55 | 92.11 | | case | 36.21 | 50.79 | | pool table | 92.69 | 94.55 | | pillow | 60.31 | 75.22 | | screen door | 70.31 | 81.84 | | stairway | 31.05 | 45.04 | | river | 12.99 | 19.33 | | bridge | 75.41 | 84.8 | | bookcase | 34.97 | 53.54 | | blind | 45.92 | 52.07 | | coffee table | 60.03 | 80.08 | | toilet | 84.36 | 91.27 | | flower | 37.82 | 51.23 | | book | 45.93 | 64.67 | | hill | 12.45 | 24.8 | | bench | 41.37 | 53.44 | | countertop | 56.03 | 73.59 | | stove | 76.61 | 82.76 | | palm | 49.87 | 71.22 | | kitchen island | 38.11 | 48.52 | | computer | 64.63 | 72.54 | | swivel chair | 43.95 | 65.4 | | boat | 38.26 | 52.26 | | bar | 25.97 | 29.35 | | arcade machine | 23.7 | 28.22 | | hovel | 23.04 | 27.82 | | bus | 89.71 | 96.39 | | towel | 64.11 | 72.44 | | light | 56.05 | 63.0 | | truck | 38.87 | 53.94 | | tower | 7.03 | 8.73 | | chandelier | 64.4 | 75.78 | | awning | 27.87 | 30.86 | | streetlight | 25.42 | 31.67 | | booth | 52.86 | 54.73 | | television receiver | 67.01 | 77.69 | | airplane | 53.27 | 68.89 | | dirt track | 14.24 | 21.36 | | apparel | 38.57 | 53.98 | | pole | 25.54 | 42.25 | | land | 4.31 | 5.4 | | bannister | 9.17 | 10.87 | | escalator | 25.37 | 27.16 | | ottoman | 52.39 | 67.37 | | bottle | 38.38 | 48.59 | | buffet | 49.22 | 54.08 | | poster | 27.26 | 35.26 | | stage | 13.27 | 19.26 | | van | 50.99 | 64.19 | | ship | 19.15 | 25.82 | | fountain | 25.56 | 26.48 | | conveyer belt | 69.54 | 86.34 | | canopy | 19.09 | 22.83 | | washer | 66.83 | 68.77 | | plaything | 24.61 | 33.83 | | swimming pool | 51.44 | 64.45 | | stool | 40.65 | 59.49 | | barrel | 48.32 | 64.63 | | basket | 33.47 | 41.64 | | waterfall | 40.84 | 50.73 | | tent | 95.43 | 98.08 | | bag | 13.49 | 16.44 | | minibike | 55.66 | 67.01 | | cradle | 78.87 | 94.8 | | oven | 43.83 | 56.93 | | ball | 35.11 | 43.41 | | food | 57.63 | 67.86 | | step | 15.9 | 26.25 | | tank | 31.62 | 36.85 | | trade name | 30.3 | 35.77 | | microwave | 71.07 | 77.27 | | pot | 42.51 | 50.32 | | animal | 57.3 | 60.28 | | bicycle | 54.65 | 77.67 | | lake | 52.36 | 55.94 | | dishwasher | 69.3 | 73.31 | | screen | 75.62 | 87.27 | | blanket | 8.82 | 9.82 | | sculpture | 59.16 | 81.35 | | hood | 60.4 | 65.96 | | sconce | 40.98 | 46.51 | | vase | 36.25 | 51.53 | | traffic light | 35.55 | 50.05 | | tray | 8.6 | 13.73 | | ashcan | 43.62 | 56.5 | | fan | 58.57 | 64.87 | | pier | 28.19 | 44.95 | | crt screen | 4.23 | 10.88 | | plate | 54.3 | 69.01 | | monitor | 4.48 | 5.85 | | bulletin board | 39.74 | 45.15 | | shower | 0.33 | 0.38 | | radiator | 59.56 | 62.63 | | glass | 11.35 | 11.84 | | clock | 35.76 | 41.93 | | flag | 50.09 | 56.08 | +---------------------+-------+-------+ 2023-02-17 06:00:13,990 - mmseg - INFO - Summary: 2023-02-17 06:00:13,990 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 82.85 | 48.12 | 58.78 | +-------+-------+-------+ 2023-02-17 06:00:15,000 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_128000.pth. 2023-02-17 06:00:15,000 - mmseg - INFO - Best mIoU is 0.4812 at 128000 iter. 2023-02-17 06:00:15,001 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 06:00:15,001 - mmseg - INFO - Iter(val) [500] aAcc: 0.8285, mIoU: 0.4812, mAcc: 0.5878, IoU.wall: 0.7684, IoU.building: 0.8251, IoU.sky: 0.9434, IoU.floor: 0.8033, IoU.tree: 0.7448, IoU.ceiling: 0.8498, IoU.road: 0.8422, IoU.bed : 0.8784, IoU.windowpane: 0.6090, IoU.grass: 0.6755, IoU.cabinet: 0.5871, IoU.sidewalk: 0.6717, IoU.person: 0.7864, IoU.earth: 0.3621, IoU.door: 0.5157, IoU.table: 0.6070, IoU.mountain: 0.5821, IoU.plant: 0.5242, IoU.curtain: 0.7019, IoU.chair: 0.5570, IoU.car: 0.8199, IoU.water: 0.5214, IoU.painting: 0.7365, IoU.sofa: 0.6513, IoU.shelf: 0.4291, IoU.house: 0.5127, IoU.sea: 0.5977, IoU.mirror: 0.6300, IoU.rug: 0.5993, IoU.field: 0.2526, IoU.armchair: 0.4508, IoU.seat: 0.5992, IoU.fence: 0.4378, IoU.desk: 0.5047, IoU.rock: 0.4371, IoU.wardrobe: 0.4757, IoU.lamp: 0.6212, IoU.bathtub: 0.7257, IoU.railing: 0.3350, IoU.cushion: 0.5723, IoU.base: 0.2915, IoU.box: 0.2804, IoU.column: 0.4421, IoU.signboard: 0.3654, IoU.chest of drawers: 0.4046, IoU.counter: 0.2504, IoU.sand: 0.4985, IoU.sink: 0.7259, IoU.skyscraper: 0.5710, IoU.fireplace: 0.7456, IoU.refrigerator: 0.7821, IoU.grandstand: 0.4508, IoU.path: 0.2637, IoU.stairs: 0.3007, IoU.runway: 0.6855, IoU.case: 0.3621, IoU.pool table: 0.9269, IoU.pillow: 0.6031, IoU.screen door: 0.7031, IoU.stairway: 0.3105, IoU.river: 0.1299, IoU.bridge: 0.7541, IoU.bookcase: 0.3497, IoU.blind: 0.4592, IoU.coffee table: 0.6003, IoU.toilet: 0.8436, IoU.flower: 0.3782, IoU.book: 0.4593, IoU.hill: 0.1245, IoU.bench: 0.4137, IoU.countertop: 0.5603, IoU.stove: 0.7661, IoU.palm: 0.4987, IoU.kitchen island: 0.3811, IoU.computer: 0.6463, IoU.swivel chair: 0.4395, IoU.boat: 0.3826, IoU.bar: 0.2597, IoU.arcade machine: 0.2370, IoU.hovel: 0.2304, IoU.bus: 0.8971, IoU.towel: 0.6411, IoU.light: 0.5605, IoU.truck: 0.3887, IoU.tower: 0.0703, IoU.chandelier: 0.6440, IoU.awning: 0.2787, IoU.streetlight: 0.2542, IoU.booth: 0.5286, IoU.television receiver: 0.6701, IoU.airplane: 0.5327, IoU.dirt track: 0.1424, IoU.apparel: 0.3857, IoU.pole: 0.2554, IoU.land: 0.0431, IoU.bannister: 0.0917, IoU.escalator: 0.2537, IoU.ottoman: 0.5239, IoU.bottle: 0.3838, IoU.buffet: 0.4922, IoU.poster: 0.2726, IoU.stage: 0.1327, IoU.van: 0.5099, IoU.ship: 0.1915, IoU.fountain: 0.2556, IoU.conveyer belt: 0.6954, IoU.canopy: 0.1909, IoU.washer: 0.6683, IoU.plaything: 0.2461, IoU.swimming pool: 0.5144, IoU.stool: 0.4065, IoU.barrel: 0.4832, IoU.basket: 0.3347, IoU.waterfall: 0.4084, IoU.tent: 0.9543, IoU.bag: 0.1349, IoU.minibike: 0.5566, IoU.cradle: 0.7887, IoU.oven: 0.4383, IoU.ball: 0.3511, IoU.food: 0.5763, IoU.step: 0.1590, IoU.tank: 0.3162, IoU.trade name: 0.3030, IoU.microwave: 0.7107, IoU.pot: 0.4251, IoU.animal: 0.5730, IoU.bicycle: 0.5465, IoU.lake: 0.5236, IoU.dishwasher: 0.6930, IoU.screen: 0.7562, IoU.blanket: 0.0882, IoU.sculpture: 0.5916, IoU.hood: 0.6040, IoU.sconce: 0.4098, IoU.vase: 0.3625, IoU.traffic light: 0.3555, IoU.tray: 0.0860, IoU.ashcan: 0.4362, IoU.fan: 0.5857, IoU.pier: 0.2819, IoU.crt screen: 0.0423, IoU.plate: 0.5430, IoU.monitor: 0.0448, IoU.bulletin board: 0.3974, IoU.shower: 0.0033, IoU.radiator: 0.5956, IoU.glass: 0.1135, IoU.clock: 0.3576, IoU.flag: 0.5009, Acc.wall: 0.8888, Acc.building: 0.9174, Acc.sky: 0.9766, Acc.floor: 0.9021, Acc.tree: 0.8657, Acc.ceiling: 0.9209, Acc.road: 0.9053, Acc.bed : 0.9480, Acc.windowpane: 0.8287, Acc.grass: 0.8312, Acc.cabinet: 0.7741, Acc.sidewalk: 0.8279, Acc.person: 0.9330, Acc.earth: 0.5183, Acc.door: 0.6675, Acc.table: 0.7652, Acc.mountain: 0.7357, Acc.plant: 0.6555, Acc.curtain: 0.7979, Acc.chair: 0.6550, Acc.car: 0.8925, Acc.water: 0.6813, Acc.painting: 0.8318, Acc.sofa: 0.8310, Acc.shelf: 0.6024, Acc.house: 0.6453, Acc.sea: 0.8774, Acc.mirror: 0.6687, Acc.rug: 0.6706, Acc.field: 0.3595, Acc.armchair: 0.6369, Acc.seat: 0.7841, Acc.fence: 0.5561, Acc.desk: 0.6522, Acc.rock: 0.5824, Acc.wardrobe: 0.6289, Acc.lamp: 0.7528, Acc.bathtub: 0.7813, Acc.railing: 0.4667, Acc.cushion: 0.6933, Acc.base: 0.4064, Acc.box: 0.3583, Acc.column: 0.5780, Acc.signboard: 0.4901, Acc.chest of drawers: 0.5316, Acc.counter: 0.3378, Acc.sand: 0.6763, Acc.sink: 0.8103, Acc.skyscraper: 0.8148, Acc.fireplace: 0.8724, Acc.refrigerator: 0.8540, Acc.grandstand: 0.6140, Acc.path: 0.4148, Acc.stairs: 0.3961, Acc.runway: 0.9211, Acc.case: 0.5079, Acc.pool table: 0.9455, Acc.pillow: 0.7522, Acc.screen door: 0.8184, Acc.stairway: 0.4504, Acc.river: 0.1933, Acc.bridge: 0.8480, Acc.bookcase: 0.5354, Acc.blind: 0.5207, Acc.coffee table: 0.8008, Acc.toilet: 0.9127, Acc.flower: 0.5123, Acc.book: 0.6467, Acc.hill: 0.2480, Acc.bench: 0.5344, Acc.countertop: 0.7359, Acc.stove: 0.8276, Acc.palm: 0.7122, Acc.kitchen island: 0.4852, Acc.computer: 0.7254, Acc.swivel chair: 0.6540, Acc.boat: 0.5226, Acc.bar: 0.2935, Acc.arcade machine: 0.2822, Acc.hovel: 0.2782, Acc.bus: 0.9639, Acc.towel: 0.7244, Acc.light: 0.6300, Acc.truck: 0.5394, Acc.tower: 0.0873, Acc.chandelier: 0.7578, Acc.awning: 0.3086, Acc.streetlight: 0.3167, Acc.booth: 0.5473, Acc.television receiver: 0.7769, Acc.airplane: 0.6889, Acc.dirt track: 0.2136, Acc.apparel: 0.5398, Acc.pole: 0.4225, Acc.land: 0.0540, Acc.bannister: 0.1087, Acc.escalator: 0.2716, Acc.ottoman: 0.6737, Acc.bottle: 0.4859, Acc.buffet: 0.5408, Acc.poster: 0.3526, Acc.stage: 0.1926, Acc.van: 0.6419, Acc.ship: 0.2582, Acc.fountain: 0.2648, Acc.conveyer belt: 0.8634, Acc.canopy: 0.2283, Acc.washer: 0.6877, Acc.plaything: 0.3383, Acc.swimming pool: 0.6445, Acc.stool: 0.5949, Acc.barrel: 0.6463, Acc.basket: 0.4164, Acc.waterfall: 0.5073, Acc.tent: 0.9808, Acc.bag: 0.1644, Acc.minibike: 0.6701, Acc.cradle: 0.9480, Acc.oven: 0.5693, Acc.ball: 0.4341, Acc.food: 0.6786, Acc.step: 0.2625, Acc.tank: 0.3685, Acc.trade name: 0.3577, Acc.microwave: 0.7727, Acc.pot: 0.5032, Acc.animal: 0.6028, Acc.bicycle: 0.7767, Acc.lake: 0.5594, Acc.dishwasher: 0.7331, Acc.screen: 0.8727, Acc.blanket: 0.0982, Acc.sculpture: 0.8135, Acc.hood: 0.6596, Acc.sconce: 0.4651, Acc.vase: 0.5153, Acc.traffic light: 0.5005, Acc.tray: 0.1373, Acc.ashcan: 0.5650, Acc.fan: 0.6487, Acc.pier: 0.4495, Acc.crt screen: 0.1088, Acc.plate: 0.6901, Acc.monitor: 0.0585, Acc.bulletin board: 0.4515, Acc.shower: 0.0038, Acc.radiator: 0.6263, Acc.glass: 0.1184, Acc.clock: 0.4193, Acc.flag: 0.5608 2023-02-17 06:00:32,994 - mmseg - INFO - Iter [128050/160000] lr: 1.198e-05, eta: 3:16:25, time: 0.946, data_time: 0.592, memory: 16596, decode.loss_ce: 0.1536, decode.acc_seg: 93.7369, aux.loss_ce: 0.1029, aux.acc_seg: 89.7388, loss: 0.2565, grad_norm: 2.5962 2023-02-17 06:00:51,271 - mmseg - INFO - Iter [128100/160000] lr: 1.196e-05, eta: 3:16:06, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1511, decode.acc_seg: 93.7608, aux.loss_ce: 0.1034, aux.acc_seg: 89.4866, loss: 0.2545, grad_norm: 2.2350 2023-02-17 06:01:09,670 - mmseg - INFO - Iter [128150/160000] lr: 1.194e-05, eta: 3:15:48, time: 0.368, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1548, decode.acc_seg: 93.5417, aux.loss_ce: 0.1006, aux.acc_seg: 89.7177, loss: 0.2554, grad_norm: 3.3457 2023-02-17 06:01:27,528 - mmseg - INFO - Iter [128200/160000] lr: 1.193e-05, eta: 3:15:29, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1535, decode.acc_seg: 93.6527, aux.loss_ce: 0.1031, aux.acc_seg: 89.5064, loss: 0.2566, grad_norm: 2.4560 2023-02-17 06:01:47,624 - mmseg - INFO - Iter [128250/160000] lr: 1.191e-05, eta: 3:15:11, time: 0.402, data_time: 0.052, memory: 16596, decode.loss_ce: 0.1474, decode.acc_seg: 93.8292, aux.loss_ce: 0.0942, aux.acc_seg: 90.3547, loss: 0.2417, grad_norm: 2.1450 2023-02-17 06:02:05,424 - mmseg - INFO - Iter [128300/160000] lr: 1.189e-05, eta: 3:14:53, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1492, decode.acc_seg: 93.7495, aux.loss_ce: 0.1018, aux.acc_seg: 89.5996, loss: 0.2511, grad_norm: 2.6016 2023-02-17 06:02:23,498 - mmseg - INFO - Iter [128350/160000] lr: 1.187e-05, eta: 3:14:34, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1480, decode.acc_seg: 93.8553, aux.loss_ce: 0.0997, aux.acc_seg: 89.7902, loss: 0.2478, grad_norm: 2.7434 2023-02-17 06:02:41,695 - mmseg - INFO - Iter [128400/160000] lr: 1.185e-05, eta: 3:14:15, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1553, decode.acc_seg: 93.4517, aux.loss_ce: 0.1016, aux.acc_seg: 89.6644, loss: 0.2568, grad_norm: 3.3213 2023-02-17 06:02:59,495 - mmseg - INFO - Iter [128450/160000] lr: 1.183e-05, eta: 3:13:56, time: 0.356, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1420, decode.acc_seg: 93.9966, aux.loss_ce: 0.0962, aux.acc_seg: 90.1244, loss: 0.2382, grad_norm: 2.1106 2023-02-17 06:03:17,434 - mmseg - INFO - Iter [128500/160000] lr: 1.181e-05, eta: 3:13:38, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1508, decode.acc_seg: 93.7808, aux.loss_ce: 0.1010, aux.acc_seg: 89.8674, loss: 0.2518, grad_norm: 2.4004 2023-02-17 06:03:35,492 - mmseg - INFO - Iter [128550/160000] lr: 1.179e-05, eta: 3:13:19, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1473, decode.acc_seg: 93.9195, aux.loss_ce: 0.0985, aux.acc_seg: 90.1124, loss: 0.2457, grad_norm: 2.3042 2023-02-17 06:03:53,371 - mmseg - INFO - Iter [128600/160000] lr: 1.178e-05, eta: 3:13:00, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1394, decode.acc_seg: 94.1674, aux.loss_ce: 0.0929, aux.acc_seg: 90.3772, loss: 0.2323, grad_norm: 2.3456 2023-02-17 06:04:11,142 - mmseg - INFO - Iter [128650/160000] lr: 1.176e-05, eta: 3:12:41, time: 0.356, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1521, decode.acc_seg: 93.7555, aux.loss_ce: 0.1020, aux.acc_seg: 89.7084, loss: 0.2541, grad_norm: 2.3957 2023-02-17 06:04:29,570 - mmseg - INFO - Iter [128700/160000] lr: 1.174e-05, eta: 3:12:23, time: 0.369, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1468, decode.acc_seg: 93.9774, aux.loss_ce: 0.1014, aux.acc_seg: 89.9040, loss: 0.2482, grad_norm: 2.8709 2023-02-17 06:04:47,478 - mmseg - INFO - Iter [128750/160000] lr: 1.172e-05, eta: 3:12:04, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1541, decode.acc_seg: 93.5237, aux.loss_ce: 0.0992, aux.acc_seg: 89.7954, loss: 0.2533, grad_norm: 2.2846 2023-02-17 06:05:05,404 - mmseg - INFO - Iter [128800/160000] lr: 1.170e-05, eta: 3:11:45, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1391, decode.acc_seg: 94.0275, aux.loss_ce: 0.0922, aux.acc_seg: 90.3591, loss: 0.2313, grad_norm: 2.2886 2023-02-17 06:05:23,606 - mmseg - INFO - Iter [128850/160000] lr: 1.168e-05, eta: 3:11:26, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1442, decode.acc_seg: 94.0811, aux.loss_ce: 0.0969, aux.acc_seg: 90.2525, loss: 0.2411, grad_norm: 2.4145 2023-02-17 06:05:41,733 - mmseg - INFO - Iter [128900/160000] lr: 1.166e-05, eta: 3:11:08, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1534, decode.acc_seg: 93.7798, aux.loss_ce: 0.1018, aux.acc_seg: 89.7119, loss: 0.2553, grad_norm: 2.8331 2023-02-17 06:05:59,825 - mmseg - INFO - Iter [128950/160000] lr: 1.164e-05, eta: 3:10:49, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1555, decode.acc_seg: 93.6503, aux.loss_ce: 0.1077, aux.acc_seg: 89.3640, loss: 0.2632, grad_norm: 2.6816 2023-02-17 06:06:17,938 - mmseg - INFO - Saving checkpoint at 129000 iterations 2023-02-17 06:06:18,999 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 06:06:18,999 - mmseg - INFO - Iter [129000/160000] lr: 1.163e-05, eta: 3:10:31, time: 0.383, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1542, decode.acc_seg: 93.4122, aux.loss_ce: 0.1047, aux.acc_seg: 89.2297, loss: 0.2589, grad_norm: 2.2945 2023-02-17 06:06:37,151 - mmseg - INFO - Iter [129050/160000] lr: 1.161e-05, eta: 3:10:13, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1594, decode.acc_seg: 93.4826, aux.loss_ce: 0.1032, aux.acc_seg: 89.5850, loss: 0.2626, grad_norm: 2.7955 2023-02-17 06:06:55,132 - mmseg - INFO - Iter [129100/160000] lr: 1.159e-05, eta: 3:09:54, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1451, decode.acc_seg: 93.8368, aux.loss_ce: 0.0959, aux.acc_seg: 90.0630, loss: 0.2410, grad_norm: 2.6255 2023-02-17 06:07:13,455 - mmseg - INFO - Iter [129150/160000] lr: 1.157e-05, eta: 3:09:35, time: 0.367, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1484, decode.acc_seg: 93.7996, aux.loss_ce: 0.0964, aux.acc_seg: 89.9563, loss: 0.2449, grad_norm: 2.3502 2023-02-17 06:07:31,723 - mmseg - INFO - Iter [129200/160000] lr: 1.155e-05, eta: 3:09:17, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1491, decode.acc_seg: 93.7221, aux.loss_ce: 0.1002, aux.acc_seg: 89.6583, loss: 0.2493, grad_norm: 2.6009 2023-02-17 06:07:49,753 - mmseg - INFO - Iter [129250/160000] lr: 1.153e-05, eta: 3:08:58, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1488, decode.acc_seg: 93.8175, aux.loss_ce: 0.0983, aux.acc_seg: 89.9595, loss: 0.2471, grad_norm: 2.4745 2023-02-17 06:08:07,742 - mmseg - INFO - Iter [129300/160000] lr: 1.151e-05, eta: 3:08:39, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1435, decode.acc_seg: 94.0009, aux.loss_ce: 0.0954, aux.acc_seg: 90.2832, loss: 0.2389, grad_norm: 2.0330 2023-02-17 06:08:25,609 - mmseg - INFO - Iter [129350/160000] lr: 1.149e-05, eta: 3:08:20, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1567, decode.acc_seg: 93.4781, aux.loss_ce: 0.1032, aux.acc_seg: 89.3862, loss: 0.2599, grad_norm: 2.4296 2023-02-17 06:08:43,457 - mmseg - INFO - Iter [129400/160000] lr: 1.148e-05, eta: 3:08:02, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1456, decode.acc_seg: 93.9250, aux.loss_ce: 0.0964, aux.acc_seg: 90.1579, loss: 0.2420, grad_norm: 2.7019 2023-02-17 06:09:01,837 - mmseg - INFO - Iter [129450/160000] lr: 1.146e-05, eta: 3:07:43, time: 0.368, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1544, decode.acc_seg: 93.4602, aux.loss_ce: 0.1013, aux.acc_seg: 89.5436, loss: 0.2557, grad_norm: 2.1596 2023-02-17 06:09:22,266 - mmseg - INFO - Iter [129500/160000] lr: 1.144e-05, eta: 3:07:26, time: 0.409, data_time: 0.054, memory: 16596, decode.loss_ce: 0.1459, decode.acc_seg: 93.9035, aux.loss_ce: 0.0979, aux.acc_seg: 89.9863, loss: 0.2438, grad_norm: 2.5458 2023-02-17 06:09:40,541 - mmseg - INFO - Iter [129550/160000] lr: 1.142e-05, eta: 3:07:07, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1431, decode.acc_seg: 94.1203, aux.loss_ce: 0.0926, aux.acc_seg: 90.5829, loss: 0.2357, grad_norm: 2.3084 2023-02-17 06:09:58,548 - mmseg - INFO - Iter [129600/160000] lr: 1.140e-05, eta: 3:06:49, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1459, decode.acc_seg: 93.9729, aux.loss_ce: 0.0992, aux.acc_seg: 89.9653, loss: 0.2451, grad_norm: 2.5385 2023-02-17 06:10:16,506 - mmseg - INFO - Iter [129650/160000] lr: 1.138e-05, eta: 3:06:30, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1536, decode.acc_seg: 93.5562, aux.loss_ce: 0.1017, aux.acc_seg: 89.5324, loss: 0.2553, grad_norm: 2.6871 2023-02-17 06:10:34,718 - mmseg - INFO - Iter [129700/160000] lr: 1.136e-05, eta: 3:06:11, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1447, decode.acc_seg: 94.0531, aux.loss_ce: 0.0993, aux.acc_seg: 89.9131, loss: 0.2440, grad_norm: 2.3694 2023-02-17 06:10:52,935 - mmseg - INFO - Iter [129750/160000] lr: 1.134e-05, eta: 3:05:53, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1553, decode.acc_seg: 93.4691, aux.loss_ce: 0.1045, aux.acc_seg: 89.2058, loss: 0.2599, grad_norm: 3.6331 2023-02-17 06:11:10,847 - mmseg - INFO - Iter [129800/160000] lr: 1.133e-05, eta: 3:05:34, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1492, decode.acc_seg: 93.8034, aux.loss_ce: 0.1012, aux.acc_seg: 89.9364, loss: 0.2504, grad_norm: 2.5007 2023-02-17 06:11:29,039 - mmseg - INFO - Iter [129850/160000] lr: 1.131e-05, eta: 3:05:15, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1445, decode.acc_seg: 94.0831, aux.loss_ce: 0.0967, aux.acc_seg: 90.2167, loss: 0.2413, grad_norm: 2.4355 2023-02-17 06:11:47,210 - mmseg - INFO - Iter [129900/160000] lr: 1.129e-05, eta: 3:04:57, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1557, decode.acc_seg: 93.5252, aux.loss_ce: 0.1034, aux.acc_seg: 89.4925, loss: 0.2590, grad_norm: 2.9900 2023-02-17 06:12:05,328 - mmseg - INFO - Iter [129950/160000] lr: 1.127e-05, eta: 3:04:38, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1474, decode.acc_seg: 93.7836, aux.loss_ce: 0.0940, aux.acc_seg: 90.3729, loss: 0.2414, grad_norm: 2.2930 2023-02-17 06:12:23,224 - mmseg - INFO - Saving checkpoint at 130000 iterations 2023-02-17 06:12:24,273 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 06:12:24,273 - mmseg - INFO - Iter [130000/160000] lr: 1.125e-05, eta: 3:04:20, time: 0.379, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1467, decode.acc_seg: 93.8339, aux.loss_ce: 0.0969, aux.acc_seg: 90.1132, loss: 0.2437, grad_norm: 2.3949 2023-02-17 06:12:42,479 - mmseg - INFO - Iter [130050/160000] lr: 1.123e-05, eta: 3:04:02, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1526, decode.acc_seg: 93.8207, aux.loss_ce: 0.0999, aux.acc_seg: 90.0324, loss: 0.2525, grad_norm: 2.7332 2023-02-17 06:13:00,541 - mmseg - INFO - Iter [130100/160000] lr: 1.121e-05, eta: 3:03:43, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1455, decode.acc_seg: 93.9675, aux.loss_ce: 0.1031, aux.acc_seg: 89.6086, loss: 0.2487, grad_norm: 2.6047 2023-02-17 06:13:18,785 - mmseg - INFO - Iter [130150/160000] lr: 1.119e-05, eta: 3:03:24, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1522, decode.acc_seg: 93.7067, aux.loss_ce: 0.1031, aux.acc_seg: 89.6047, loss: 0.2553, grad_norm: 2.4081 2023-02-17 06:13:36,909 - mmseg - INFO - Iter [130200/160000] lr: 1.118e-05, eta: 3:03:06, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1520, decode.acc_seg: 93.6396, aux.loss_ce: 0.0998, aux.acc_seg: 89.8396, loss: 0.2517, grad_norm: 2.1539 2023-02-17 06:13:54,605 - mmseg - INFO - Iter [130250/160000] lr: 1.116e-05, eta: 3:02:47, time: 0.354, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1477, decode.acc_seg: 93.9453, aux.loss_ce: 0.0995, aux.acc_seg: 89.9603, loss: 0.2472, grad_norm: 2.3991 2023-02-17 06:14:12,860 - mmseg - INFO - Iter [130300/160000] lr: 1.114e-05, eta: 3:02:28, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1503, decode.acc_seg: 93.6632, aux.loss_ce: 0.0991, aux.acc_seg: 89.6712, loss: 0.2494, grad_norm: 2.8173 2023-02-17 06:14:30,688 - mmseg - INFO - Iter [130350/160000] lr: 1.112e-05, eta: 3:02:09, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1500, decode.acc_seg: 93.7708, aux.loss_ce: 0.0987, aux.acc_seg: 89.9001, loss: 0.2487, grad_norm: 2.3994 2023-02-17 06:14:48,611 - mmseg - INFO - Iter [130400/160000] lr: 1.110e-05, eta: 3:01:51, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1576, decode.acc_seg: 93.3288, aux.loss_ce: 0.1046, aux.acc_seg: 89.2413, loss: 0.2622, grad_norm: 3.0310 2023-02-17 06:15:06,617 - mmseg - INFO - Iter [130450/160000] lr: 1.108e-05, eta: 3:01:32, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1539, decode.acc_seg: 93.5515, aux.loss_ce: 0.1027, aux.acc_seg: 89.4510, loss: 0.2566, grad_norm: 2.2205 2023-02-17 06:15:24,336 - mmseg - INFO - Iter [130500/160000] lr: 1.106e-05, eta: 3:01:13, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1468, decode.acc_seg: 93.8363, aux.loss_ce: 0.1010, aux.acc_seg: 89.7292, loss: 0.2478, grad_norm: 2.4360 2023-02-17 06:15:42,252 - mmseg - INFO - Iter [130550/160000] lr: 1.104e-05, eta: 3:00:54, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1514, decode.acc_seg: 93.6945, aux.loss_ce: 0.0993, aux.acc_seg: 89.8826, loss: 0.2507, grad_norm: 2.2072 2023-02-17 06:16:00,251 - mmseg - INFO - Iter [130600/160000] lr: 1.103e-05, eta: 3:00:36, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1528, decode.acc_seg: 93.7443, aux.loss_ce: 0.1024, aux.acc_seg: 89.7114, loss: 0.2552, grad_norm: 2.4440 2023-02-17 06:16:18,448 - mmseg - INFO - Iter [130650/160000] lr: 1.101e-05, eta: 3:00:17, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1452, decode.acc_seg: 93.9284, aux.loss_ce: 0.0967, aux.acc_seg: 90.1079, loss: 0.2419, grad_norm: 2.4810 2023-02-17 06:16:36,706 - mmseg - INFO - Iter [130700/160000] lr: 1.099e-05, eta: 2:59:58, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1516, decode.acc_seg: 93.6086, aux.loss_ce: 0.0992, aux.acc_seg: 89.8077, loss: 0.2508, grad_norm: 2.5512 2023-02-17 06:16:56,822 - mmseg - INFO - Iter [130750/160000] lr: 1.097e-05, eta: 2:59:41, time: 0.403, data_time: 0.055, memory: 16596, decode.loss_ce: 0.1397, decode.acc_seg: 94.1874, aux.loss_ce: 0.0952, aux.acc_seg: 90.2561, loss: 0.2349, grad_norm: 2.2045 2023-02-17 06:17:14,663 - mmseg - INFO - Iter [130800/160000] lr: 1.095e-05, eta: 2:59:22, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1498, decode.acc_seg: 93.8696, aux.loss_ce: 0.0991, aux.acc_seg: 90.0412, loss: 0.2489, grad_norm: 2.4261 2023-02-17 06:17:32,653 - mmseg - INFO - Iter [130850/160000] lr: 1.093e-05, eta: 2:59:04, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1484, decode.acc_seg: 93.8051, aux.loss_ce: 0.0981, aux.acc_seg: 89.8091, loss: 0.2465, grad_norm: 2.4456 2023-02-17 06:17:50,807 - mmseg - INFO - Iter [130900/160000] lr: 1.091e-05, eta: 2:58:45, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1441, decode.acc_seg: 94.0944, aux.loss_ce: 0.0958, aux.acc_seg: 90.3525, loss: 0.2399, grad_norm: 2.6296 2023-02-17 06:18:08,846 - mmseg - INFO - Iter [130950/160000] lr: 1.089e-05, eta: 2:58:26, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1386, decode.acc_seg: 94.1792, aux.loss_ce: 0.0917, aux.acc_seg: 90.5333, loss: 0.2303, grad_norm: 2.1398 2023-02-17 06:18:26,892 - mmseg - INFO - Saving checkpoint at 131000 iterations 2023-02-17 06:18:27,941 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 06:18:27,942 - mmseg - INFO - Iter [131000/160000] lr: 1.088e-05, eta: 2:58:08, time: 0.382, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1626, decode.acc_seg: 93.3550, aux.loss_ce: 0.1033, aux.acc_seg: 89.6291, loss: 0.2659, grad_norm: 2.5127 2023-02-17 06:18:45,901 - mmseg - INFO - Iter [131050/160000] lr: 1.086e-05, eta: 2:57:50, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1449, decode.acc_seg: 93.8426, aux.loss_ce: 0.0926, aux.acc_seg: 90.2581, loss: 0.2374, grad_norm: 2.1713 2023-02-17 06:19:03,969 - mmseg - INFO - Iter [131100/160000] lr: 1.084e-05, eta: 2:57:31, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1453, decode.acc_seg: 93.9302, aux.loss_ce: 0.0993, aux.acc_seg: 89.8081, loss: 0.2446, grad_norm: 2.3056 2023-02-17 06:19:22,088 - mmseg - INFO - Iter [131150/160000] lr: 1.082e-05, eta: 2:57:12, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1499, decode.acc_seg: 93.6933, aux.loss_ce: 0.0980, aux.acc_seg: 89.8401, loss: 0.2479, grad_norm: 2.2556 2023-02-17 06:19:40,001 - mmseg - INFO - Iter [131200/160000] lr: 1.080e-05, eta: 2:56:54, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1454, decode.acc_seg: 93.9357, aux.loss_ce: 0.0966, aux.acc_seg: 89.9949, loss: 0.2420, grad_norm: 2.4512 2023-02-17 06:19:57,768 - mmseg - INFO - Iter [131250/160000] lr: 1.078e-05, eta: 2:56:35, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1537, decode.acc_seg: 93.6643, aux.loss_ce: 0.1038, aux.acc_seg: 89.6421, loss: 0.2575, grad_norm: 2.9642 2023-02-17 06:20:16,122 - mmseg - INFO - Iter [131300/160000] lr: 1.076e-05, eta: 2:56:16, time: 0.367, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1475, decode.acc_seg: 93.8839, aux.loss_ce: 0.0951, aux.acc_seg: 90.3348, loss: 0.2426, grad_norm: 2.2778 2023-02-17 06:20:33,900 - mmseg - INFO - Iter [131350/160000] lr: 1.074e-05, eta: 2:55:57, time: 0.356, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1435, decode.acc_seg: 94.0043, aux.loss_ce: 0.0973, aux.acc_seg: 90.0316, loss: 0.2408, grad_norm: 2.5974 2023-02-17 06:20:51,935 - mmseg - INFO - Iter [131400/160000] lr: 1.073e-05, eta: 2:55:39, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1501, decode.acc_seg: 93.8320, aux.loss_ce: 0.0970, aux.acc_seg: 90.2042, loss: 0.2471, grad_norm: 2.1767 2023-02-17 06:21:09,993 - mmseg - INFO - Iter [131450/160000] lr: 1.071e-05, eta: 2:55:20, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1409, decode.acc_seg: 94.0461, aux.loss_ce: 0.0964, aux.acc_seg: 90.1104, loss: 0.2373, grad_norm: 2.3468 2023-02-17 06:21:28,056 - mmseg - INFO - Iter [131500/160000] lr: 1.069e-05, eta: 2:55:02, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1520, decode.acc_seg: 93.6472, aux.loss_ce: 0.0988, aux.acc_seg: 89.9163, loss: 0.2508, grad_norm: 2.6021 2023-02-17 06:21:46,186 - mmseg - INFO - Iter [131550/160000] lr: 1.067e-05, eta: 2:54:43, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1521, decode.acc_seg: 93.5987, aux.loss_ce: 0.1040, aux.acc_seg: 89.4258, loss: 0.2561, grad_norm: 2.7516 2023-02-17 06:22:04,061 - mmseg - INFO - Iter [131600/160000] lr: 1.065e-05, eta: 2:54:24, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1510, decode.acc_seg: 93.6161, aux.loss_ce: 0.0995, aux.acc_seg: 89.8432, loss: 0.2505, grad_norm: 2.4026 2023-02-17 06:22:22,021 - mmseg - INFO - Iter [131650/160000] lr: 1.063e-05, eta: 2:54:05, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1490, decode.acc_seg: 93.8340, aux.loss_ce: 0.0982, aux.acc_seg: 90.0560, loss: 0.2472, grad_norm: 2.4757 2023-02-17 06:22:40,047 - mmseg - INFO - Iter [131700/160000] lr: 1.061e-05, eta: 2:53:47, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1462, decode.acc_seg: 93.8312, aux.loss_ce: 0.0962, aux.acc_seg: 90.1269, loss: 0.2424, grad_norm: 2.3943 2023-02-17 06:22:58,019 - mmseg - INFO - Iter [131750/160000] lr: 1.059e-05, eta: 2:53:28, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1460, decode.acc_seg: 93.9312, aux.loss_ce: 0.0987, aux.acc_seg: 89.9379, loss: 0.2447, grad_norm: 2.2715 2023-02-17 06:23:15,988 - mmseg - INFO - Iter [131800/160000] lr: 1.058e-05, eta: 2:53:09, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1548, decode.acc_seg: 93.6783, aux.loss_ce: 0.1036, aux.acc_seg: 89.5915, loss: 0.2585, grad_norm: 2.5037 2023-02-17 06:23:34,112 - mmseg - INFO - Iter [131850/160000] lr: 1.056e-05, eta: 2:52:51, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1517, decode.acc_seg: 93.6381, aux.loss_ce: 0.1052, aux.acc_seg: 89.2917, loss: 0.2568, grad_norm: 2.6518 2023-02-17 06:23:52,034 - mmseg - INFO - Iter [131900/160000] lr: 1.054e-05, eta: 2:52:32, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1540, decode.acc_seg: 93.7596, aux.loss_ce: 0.1016, aux.acc_seg: 89.7960, loss: 0.2556, grad_norm: 2.8626 2023-02-17 06:24:09,832 - mmseg - INFO - Iter [131950/160000] lr: 1.052e-05, eta: 2:52:13, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1526, decode.acc_seg: 93.5070, aux.loss_ce: 0.1025, aux.acc_seg: 89.5366, loss: 0.2552, grad_norm: 2.3392 2023-02-17 06:24:30,267 - mmseg - INFO - Saving checkpoint at 132000 iterations 2023-02-17 06:24:31,311 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 06:24:31,311 - mmseg - INFO - Iter [132000/160000] lr: 1.050e-05, eta: 2:51:57, time: 0.430, data_time: 0.053, memory: 16596, decode.loss_ce: 0.1484, decode.acc_seg: 93.8869, aux.loss_ce: 0.0979, aux.acc_seg: 90.1273, loss: 0.2463, grad_norm: 2.2923 2023-02-17 06:24:49,362 - mmseg - INFO - Iter [132050/160000] lr: 1.048e-05, eta: 2:51:38, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1477, decode.acc_seg: 93.8480, aux.loss_ce: 0.1008, aux.acc_seg: 89.7087, loss: 0.2486, grad_norm: 2.3852 2023-02-17 06:25:07,471 - mmseg - INFO - Iter [132100/160000] lr: 1.046e-05, eta: 2:51:19, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1565, decode.acc_seg: 93.6473, aux.loss_ce: 0.1050, aux.acc_seg: 89.5611, loss: 0.2615, grad_norm: 2.6824 2023-02-17 06:25:25,277 - mmseg - INFO - Iter [132150/160000] lr: 1.044e-05, eta: 2:51:01, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1458, decode.acc_seg: 93.8987, aux.loss_ce: 0.0984, aux.acc_seg: 90.0297, loss: 0.2442, grad_norm: 2.3801 2023-02-17 06:25:43,212 - mmseg - INFO - Iter [132200/160000] lr: 1.043e-05, eta: 2:50:42, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1426, decode.acc_seg: 93.9755, aux.loss_ce: 0.0963, aux.acc_seg: 89.9420, loss: 0.2388, grad_norm: 2.8327 2023-02-17 06:26:01,406 - mmseg - INFO - Iter [132250/160000] lr: 1.041e-05, eta: 2:50:23, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1457, decode.acc_seg: 93.8895, aux.loss_ce: 0.0958, aux.acc_seg: 90.0723, loss: 0.2415, grad_norm: 2.0802 2023-02-17 06:26:19,543 - mmseg - INFO - Iter [132300/160000] lr: 1.039e-05, eta: 2:50:05, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1461, decode.acc_seg: 93.8758, aux.loss_ce: 0.0989, aux.acc_seg: 89.8414, loss: 0.2450, grad_norm: 2.3600 2023-02-17 06:26:37,510 - mmseg - INFO - Iter [132350/160000] lr: 1.037e-05, eta: 2:49:46, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1473, decode.acc_seg: 93.8030, aux.loss_ce: 0.0981, aux.acc_seg: 90.0257, loss: 0.2454, grad_norm: 2.8433 2023-02-17 06:26:55,210 - mmseg - INFO - Iter [132400/160000] lr: 1.035e-05, eta: 2:49:27, time: 0.354, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1510, decode.acc_seg: 93.6352, aux.loss_ce: 0.0986, aux.acc_seg: 89.9320, loss: 0.2496, grad_norm: 2.8479 2023-02-17 06:27:13,455 - mmseg - INFO - Iter [132450/160000] lr: 1.033e-05, eta: 2:49:09, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1461, decode.acc_seg: 93.7687, aux.loss_ce: 0.0923, aux.acc_seg: 90.3400, loss: 0.2384, grad_norm: 2.0206 2023-02-17 06:27:31,207 - mmseg - INFO - Iter [132500/160000] lr: 1.031e-05, eta: 2:48:50, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1526, decode.acc_seg: 93.5996, aux.loss_ce: 0.0972, aux.acc_seg: 89.8642, loss: 0.2499, grad_norm: 2.5317 2023-02-17 06:27:49,158 - mmseg - INFO - Iter [132550/160000] lr: 1.029e-05, eta: 2:48:31, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1487, decode.acc_seg: 93.7861, aux.loss_ce: 0.1014, aux.acc_seg: 89.6501, loss: 0.2501, grad_norm: 2.9630 2023-02-17 06:28:07,127 - mmseg - INFO - Iter [132600/160000] lr: 1.028e-05, eta: 2:48:13, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1502, decode.acc_seg: 93.8362, aux.loss_ce: 0.0980, aux.acc_seg: 90.1626, loss: 0.2482, grad_norm: 2.2600 2023-02-17 06:28:25,030 - mmseg - INFO - Iter [132650/160000] lr: 1.026e-05, eta: 2:47:54, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1481, decode.acc_seg: 93.7835, aux.loss_ce: 0.1015, aux.acc_seg: 89.6870, loss: 0.2496, grad_norm: 2.8433 2023-02-17 06:28:42,937 - mmseg - INFO - Iter [132700/160000] lr: 1.024e-05, eta: 2:47:35, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1456, decode.acc_seg: 93.9474, aux.loss_ce: 0.0977, aux.acc_seg: 90.1383, loss: 0.2433, grad_norm: 2.3642 2023-02-17 06:29:01,191 - mmseg - INFO - Iter [132750/160000] lr: 1.022e-05, eta: 2:47:17, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1488, decode.acc_seg: 93.9243, aux.loss_ce: 0.0968, aux.acc_seg: 90.3024, loss: 0.2456, grad_norm: 2.3471 2023-02-17 06:29:19,153 - mmseg - INFO - Iter [132800/160000] lr: 1.020e-05, eta: 2:46:58, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1393, decode.acc_seg: 94.0717, aux.loss_ce: 0.0904, aux.acc_seg: 90.6154, loss: 0.2297, grad_norm: 1.8854 2023-02-17 06:29:37,150 - mmseg - INFO - Iter [132850/160000] lr: 1.018e-05, eta: 2:46:39, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1416, decode.acc_seg: 94.0278, aux.loss_ce: 0.0962, aux.acc_seg: 90.0406, loss: 0.2378, grad_norm: 2.0397 2023-02-17 06:29:55,118 - mmseg - INFO - Iter [132900/160000] lr: 1.016e-05, eta: 2:46:21, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1558, decode.acc_seg: 93.4293, aux.loss_ce: 0.1032, aux.acc_seg: 89.3787, loss: 0.2590, grad_norm: 2.5471 2023-02-17 06:30:13,147 - mmseg - INFO - Iter [132950/160000] lr: 1.014e-05, eta: 2:46:02, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1456, decode.acc_seg: 93.8415, aux.loss_ce: 0.0958, aux.acc_seg: 90.2722, loss: 0.2414, grad_norm: 2.3847 2023-02-17 06:30:31,180 - mmseg - INFO - Saving checkpoint at 133000 iterations 2023-02-17 06:30:32,265 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 06:30:32,265 - mmseg - INFO - Iter [133000/160000] lr: 1.013e-05, eta: 2:45:44, time: 0.382, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1486, decode.acc_seg: 93.7910, aux.loss_ce: 0.0951, aux.acc_seg: 90.3256, loss: 0.2437, grad_norm: 2.3268 2023-02-17 06:30:50,470 - mmseg - INFO - Iter [133050/160000] lr: 1.011e-05, eta: 2:45:26, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1451, decode.acc_seg: 94.0237, aux.loss_ce: 0.0950, aux.acc_seg: 90.4934, loss: 0.2400, grad_norm: 2.7000 2023-02-17 06:31:08,744 - mmseg - INFO - Iter [133100/160000] lr: 1.009e-05, eta: 2:45:07, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1587, decode.acc_seg: 93.2925, aux.loss_ce: 0.1038, aux.acc_seg: 89.2695, loss: 0.2625, grad_norm: 2.7572 2023-02-17 06:31:26,802 - mmseg - INFO - Iter [133150/160000] lr: 1.007e-05, eta: 2:44:48, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1554, decode.acc_seg: 93.5239, aux.loss_ce: 0.1013, aux.acc_seg: 89.6350, loss: 0.2567, grad_norm: 2.5953 2023-02-17 06:31:45,139 - mmseg - INFO - Iter [133200/160000] lr: 1.005e-05, eta: 2:44:30, time: 0.367, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1558, decode.acc_seg: 93.5597, aux.loss_ce: 0.1058, aux.acc_seg: 89.3650, loss: 0.2616, grad_norm: 2.9675 2023-02-17 06:32:02,896 - mmseg - INFO - Iter [133250/160000] lr: 1.003e-05, eta: 2:44:11, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1517, decode.acc_seg: 93.6782, aux.loss_ce: 0.1010, aux.acc_seg: 89.5892, loss: 0.2527, grad_norm: 2.2441 2023-02-17 06:32:23,562 - mmseg - INFO - Iter [133300/160000] lr: 1.001e-05, eta: 2:43:54, time: 0.413, data_time: 0.052, memory: 16596, decode.loss_ce: 0.1488, decode.acc_seg: 94.0020, aux.loss_ce: 0.0977, aux.acc_seg: 90.1664, loss: 0.2466, grad_norm: 2.2383 2023-02-17 06:32:41,547 - mmseg - INFO - Iter [133350/160000] lr: 9.994e-06, eta: 2:43:35, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1472, decode.acc_seg: 93.6898, aux.loss_ce: 0.0954, aux.acc_seg: 90.1568, loss: 0.2426, grad_norm: 2.4755 2023-02-17 06:32:59,553 - mmseg - INFO - Iter [133400/160000] lr: 9.975e-06, eta: 2:43:17, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1434, decode.acc_seg: 94.0436, aux.loss_ce: 0.0959, aux.acc_seg: 90.2142, loss: 0.2392, grad_norm: 2.3692 2023-02-17 06:33:17,304 - mmseg - INFO - Iter [133450/160000] lr: 9.957e-06, eta: 2:42:58, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1467, decode.acc_seg: 93.8459, aux.loss_ce: 0.1011, aux.acc_seg: 89.7415, loss: 0.2478, grad_norm: 2.7056 2023-02-17 06:33:35,344 - mmseg - INFO - Iter [133500/160000] lr: 9.938e-06, eta: 2:42:39, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1415, decode.acc_seg: 93.9952, aux.loss_ce: 0.0954, aux.acc_seg: 90.1638, loss: 0.2370, grad_norm: 2.2509 2023-02-17 06:33:53,085 - mmseg - INFO - Iter [133550/160000] lr: 9.919e-06, eta: 2:42:21, time: 0.355, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1446, decode.acc_seg: 93.8903, aux.loss_ce: 0.0959, aux.acc_seg: 90.0469, loss: 0.2405, grad_norm: 2.6551 2023-02-17 06:34:11,186 - mmseg - INFO - Iter [133600/160000] lr: 9.900e-06, eta: 2:42:02, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1417, decode.acc_seg: 94.0483, aux.loss_ce: 0.0961, aux.acc_seg: 90.1633, loss: 0.2378, grad_norm: 2.5685 2023-02-17 06:34:29,174 - mmseg - INFO - Iter [133650/160000] lr: 9.882e-06, eta: 2:41:43, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1406, decode.acc_seg: 94.1160, aux.loss_ce: 0.0935, aux.acc_seg: 90.4646, loss: 0.2341, grad_norm: 2.1896 2023-02-17 06:34:47,105 - mmseg - INFO - Iter [133700/160000] lr: 9.863e-06, eta: 2:41:25, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1471, decode.acc_seg: 93.8139, aux.loss_ce: 0.1001, aux.acc_seg: 89.7153, loss: 0.2472, grad_norm: 2.3684 2023-02-17 06:35:05,595 - mmseg - INFO - Iter [133750/160000] lr: 9.844e-06, eta: 2:41:06, time: 0.370, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1536, decode.acc_seg: 93.5234, aux.loss_ce: 0.1010, aux.acc_seg: 89.8282, loss: 0.2547, grad_norm: 2.9111 2023-02-17 06:35:23,543 - mmseg - INFO - Iter [133800/160000] lr: 9.825e-06, eta: 2:40:48, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1529, decode.acc_seg: 93.8541, aux.loss_ce: 0.0985, aux.acc_seg: 90.1123, loss: 0.2514, grad_norm: 2.2961 2023-02-17 06:35:41,354 - mmseg - INFO - Iter [133850/160000] lr: 9.807e-06, eta: 2:40:29, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1544, decode.acc_seg: 93.6892, aux.loss_ce: 0.1041, aux.acc_seg: 89.5663, loss: 0.2585, grad_norm: 3.2888 2023-02-17 06:35:59,552 - mmseg - INFO - Iter [133900/160000] lr: 9.788e-06, eta: 2:40:10, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1427, decode.acc_seg: 93.8405, aux.loss_ce: 0.0947, aux.acc_seg: 90.1447, loss: 0.2374, grad_norm: 2.2955 2023-02-17 06:36:17,922 - mmseg - INFO - Iter [133950/160000] lr: 9.769e-06, eta: 2:39:52, time: 0.367, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1497, decode.acc_seg: 93.7113, aux.loss_ce: 0.0966, aux.acc_seg: 90.1478, loss: 0.2463, grad_norm: 2.3182 2023-02-17 06:36:36,132 - mmseg - INFO - Saving checkpoint at 134000 iterations 2023-02-17 06:36:37,243 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 06:36:37,244 - mmseg - INFO - Iter [134000/160000] lr: 9.750e-06, eta: 2:39:34, time: 0.386, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1455, decode.acc_seg: 93.8270, aux.loss_ce: 0.0967, aux.acc_seg: 90.2167, loss: 0.2422, grad_norm: 2.3819 2023-02-17 06:36:55,122 - mmseg - INFO - Iter [134050/160000] lr: 9.732e-06, eta: 2:39:15, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1447, decode.acc_seg: 93.8353, aux.loss_ce: 0.0987, aux.acc_seg: 89.6560, loss: 0.2435, grad_norm: 2.4386 2023-02-17 06:37:13,201 - mmseg - INFO - Iter [134100/160000] lr: 9.713e-06, eta: 2:38:57, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1459, decode.acc_seg: 93.9213, aux.loss_ce: 0.0944, aux.acc_seg: 90.3162, loss: 0.2403, grad_norm: 2.4281 2023-02-17 06:37:31,196 - mmseg - INFO - Iter [134150/160000] lr: 9.694e-06, eta: 2:38:38, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1461, decode.acc_seg: 93.8957, aux.loss_ce: 0.0995, aux.acc_seg: 89.8451, loss: 0.2456, grad_norm: 2.2039 2023-02-17 06:37:48,972 - mmseg - INFO - Iter [134200/160000] lr: 9.675e-06, eta: 2:38:19, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1417, decode.acc_seg: 94.0004, aux.loss_ce: 0.0956, aux.acc_seg: 90.1691, loss: 0.2373, grad_norm: 2.6867 2023-02-17 06:38:07,202 - mmseg - INFO - Iter [134250/160000] lr: 9.657e-06, eta: 2:38:01, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1384, decode.acc_seg: 94.1726, aux.loss_ce: 0.0921, aux.acc_seg: 90.3558, loss: 0.2305, grad_norm: 1.9299 2023-02-17 06:38:25,076 - mmseg - INFO - Iter [134300/160000] lr: 9.638e-06, eta: 2:37:42, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1420, decode.acc_seg: 94.0705, aux.loss_ce: 0.0933, aux.acc_seg: 90.3547, loss: 0.2354, grad_norm: 2.3135 2023-02-17 06:38:43,041 - mmseg - INFO - Iter [134350/160000] lr: 9.619e-06, eta: 2:37:24, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1512, decode.acc_seg: 93.5768, aux.loss_ce: 0.1003, aux.acc_seg: 89.6987, loss: 0.2515, grad_norm: 2.3711 2023-02-17 06:39:01,191 - mmseg - INFO - Iter [134400/160000] lr: 9.600e-06, eta: 2:37:05, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1419, decode.acc_seg: 94.0318, aux.loss_ce: 0.0942, aux.acc_seg: 90.4060, loss: 0.2361, grad_norm: 2.4146 2023-02-17 06:39:19,334 - mmseg - INFO - Iter [134450/160000] lr: 9.582e-06, eta: 2:36:46, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1480, decode.acc_seg: 93.7360, aux.loss_ce: 0.0974, aux.acc_seg: 89.8962, loss: 0.2454, grad_norm: 2.4974 2023-02-17 06:39:37,214 - mmseg - INFO - Iter [134500/160000] lr: 9.563e-06, eta: 2:36:28, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1543, decode.acc_seg: 93.4880, aux.loss_ce: 0.1009, aux.acc_seg: 89.4910, loss: 0.2552, grad_norm: 2.5412 2023-02-17 06:39:57,416 - mmseg - INFO - Iter [134550/160000] lr: 9.544e-06, eta: 2:36:10, time: 0.404, data_time: 0.054, memory: 16596, decode.loss_ce: 0.1596, decode.acc_seg: 93.6022, aux.loss_ce: 0.1049, aux.acc_seg: 89.5575, loss: 0.2645, grad_norm: 2.8356 2023-02-17 06:40:15,381 - mmseg - INFO - Iter [134600/160000] lr: 9.525e-06, eta: 2:35:52, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1588, decode.acc_seg: 93.4957, aux.loss_ce: 0.1032, aux.acc_seg: 89.5874, loss: 0.2619, grad_norm: 2.7245 2023-02-17 06:40:33,554 - mmseg - INFO - Iter [134650/160000] lr: 9.507e-06, eta: 2:35:33, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1416, decode.acc_seg: 94.0346, aux.loss_ce: 0.0978, aux.acc_seg: 89.9653, loss: 0.2394, grad_norm: 2.1450 2023-02-17 06:40:51,788 - mmseg - INFO - Iter [134700/160000] lr: 9.488e-06, eta: 2:35:15, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1427, decode.acc_seg: 94.0288, aux.loss_ce: 0.0959, aux.acc_seg: 90.2263, loss: 0.2387, grad_norm: 2.2897 2023-02-17 06:41:09,977 - mmseg - INFO - Iter [134750/160000] lr: 9.469e-06, eta: 2:34:56, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1422, decode.acc_seg: 94.0810, aux.loss_ce: 0.0938, aux.acc_seg: 90.4075, loss: 0.2360, grad_norm: 2.3506 2023-02-17 06:41:27,754 - mmseg - INFO - Iter [134800/160000] lr: 9.450e-06, eta: 2:34:37, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1426, decode.acc_seg: 93.9773, aux.loss_ce: 0.0957, aux.acc_seg: 90.2195, loss: 0.2383, grad_norm: 2.3379 2023-02-17 06:41:45,628 - mmseg - INFO - Iter [134850/160000] lr: 9.432e-06, eta: 2:34:19, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1445, decode.acc_seg: 93.9040, aux.loss_ce: 0.0950, aux.acc_seg: 90.2642, loss: 0.2396, grad_norm: 2.3019 2023-02-17 06:42:03,320 - mmseg - INFO - Iter [134900/160000] lr: 9.413e-06, eta: 2:34:00, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1537, decode.acc_seg: 93.6605, aux.loss_ce: 0.1000, aux.acc_seg: 89.9542, loss: 0.2537, grad_norm: 2.4619 2023-02-17 06:42:21,278 - mmseg - INFO - Iter [134950/160000] lr: 9.394e-06, eta: 2:33:41, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1482, decode.acc_seg: 93.8291, aux.loss_ce: 0.0980, aux.acc_seg: 89.9771, loss: 0.2462, grad_norm: 2.5023 2023-02-17 06:42:39,451 - mmseg - INFO - Saving checkpoint at 135000 iterations 2023-02-17 06:42:40,505 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 06:42:40,505 - mmseg - INFO - Iter [135000/160000] lr: 9.375e-06, eta: 2:33:23, time: 0.385, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1526, decode.acc_seg: 93.6689, aux.loss_ce: 0.0995, aux.acc_seg: 89.7982, loss: 0.2521, grad_norm: 2.4869 2023-02-17 06:42:58,643 - mmseg - INFO - Iter [135050/160000] lr: 9.357e-06, eta: 2:33:05, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1437, decode.acc_seg: 94.1247, aux.loss_ce: 0.0957, aux.acc_seg: 90.3741, loss: 0.2394, grad_norm: 2.1650 2023-02-17 06:43:16,727 - mmseg - INFO - Iter [135100/160000] lr: 9.338e-06, eta: 2:32:46, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1451, decode.acc_seg: 93.9045, aux.loss_ce: 0.0956, aux.acc_seg: 90.2634, loss: 0.2407, grad_norm: 2.1818 2023-02-17 06:43:35,135 - mmseg - INFO - Iter [135150/160000] lr: 9.319e-06, eta: 2:32:28, time: 0.367, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1482, decode.acc_seg: 93.6993, aux.loss_ce: 0.1015, aux.acc_seg: 89.5373, loss: 0.2496, grad_norm: 2.8818 2023-02-17 06:43:53,261 - mmseg - INFO - Iter [135200/160000] lr: 9.300e-06, eta: 2:32:09, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1482, decode.acc_seg: 93.7729, aux.loss_ce: 0.0973, aux.acc_seg: 89.9427, loss: 0.2454, grad_norm: 2.6517 2023-02-17 06:44:10,998 - mmseg - INFO - Iter [135250/160000] lr: 9.282e-06, eta: 2:31:51, time: 0.355, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1480, decode.acc_seg: 93.7806, aux.loss_ce: 0.0988, aux.acc_seg: 89.7679, loss: 0.2468, grad_norm: 2.1858 2023-02-17 06:44:28,833 - mmseg - INFO - Iter [135300/160000] lr: 9.263e-06, eta: 2:31:32, time: 0.356, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1436, decode.acc_seg: 93.8085, aux.loss_ce: 0.0986, aux.acc_seg: 89.6977, loss: 0.2422, grad_norm: 2.7042 2023-02-17 06:44:46,960 - mmseg - INFO - Iter [135350/160000] lr: 9.244e-06, eta: 2:31:13, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1508, decode.acc_seg: 93.7777, aux.loss_ce: 0.0978, aux.acc_seg: 89.9699, loss: 0.2485, grad_norm: 2.2264 2023-02-17 06:45:04,869 - mmseg - INFO - Iter [135400/160000] lr: 9.225e-06, eta: 2:30:55, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1385, decode.acc_seg: 94.3335, aux.loss_ce: 0.0924, aux.acc_seg: 90.6574, loss: 0.2309, grad_norm: 2.1598 2023-02-17 06:45:22,987 - mmseg - INFO - Iter [135450/160000] lr: 9.207e-06, eta: 2:30:36, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1629, decode.acc_seg: 93.3520, aux.loss_ce: 0.1067, aux.acc_seg: 89.1723, loss: 0.2696, grad_norm: 2.9737 2023-02-17 06:45:41,048 - mmseg - INFO - Iter [135500/160000] lr: 9.188e-06, eta: 2:30:18, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1461, decode.acc_seg: 94.1118, aux.loss_ce: 0.1002, aux.acc_seg: 89.8896, loss: 0.2463, grad_norm: 2.6900 2023-02-17 06:45:59,214 - mmseg - INFO - Iter [135550/160000] lr: 9.169e-06, eta: 2:29:59, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1434, decode.acc_seg: 94.0702, aux.loss_ce: 0.0945, aux.acc_seg: 90.5051, loss: 0.2379, grad_norm: 2.2504 2023-02-17 06:46:17,309 - mmseg - INFO - Iter [135600/160000] lr: 9.150e-06, eta: 2:29:41, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1480, decode.acc_seg: 93.9683, aux.loss_ce: 0.1000, aux.acc_seg: 89.9811, loss: 0.2480, grad_norm: 2.7491 2023-02-17 06:46:35,166 - mmseg - INFO - Iter [135650/160000] lr: 9.132e-06, eta: 2:29:22, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1404, decode.acc_seg: 94.0346, aux.loss_ce: 0.0940, aux.acc_seg: 90.2307, loss: 0.2344, grad_norm: 2.3006 2023-02-17 06:46:53,346 - mmseg - INFO - Iter [135700/160000] lr: 9.113e-06, eta: 2:29:03, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1425, decode.acc_seg: 93.9719, aux.loss_ce: 0.0947, aux.acc_seg: 90.2294, loss: 0.2371, grad_norm: 2.3854 2023-02-17 06:47:11,302 - mmseg - INFO - Iter [135750/160000] lr: 9.094e-06, eta: 2:28:45, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1459, decode.acc_seg: 93.6915, aux.loss_ce: 0.0953, aux.acc_seg: 89.9887, loss: 0.2412, grad_norm: 2.5220 2023-02-17 06:47:31,658 - mmseg - INFO - Iter [135800/160000] lr: 9.075e-06, eta: 2:28:27, time: 0.407, data_time: 0.053, memory: 16596, decode.loss_ce: 0.1534, decode.acc_seg: 93.7984, aux.loss_ce: 0.1012, aux.acc_seg: 89.9541, loss: 0.2546, grad_norm: 3.0181 2023-02-17 06:47:49,771 - mmseg - INFO - Iter [135850/160000] lr: 9.057e-06, eta: 2:28:09, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1407, decode.acc_seg: 93.9575, aux.loss_ce: 0.0916, aux.acc_seg: 90.3782, loss: 0.2323, grad_norm: 2.0560 2023-02-17 06:48:07,836 - mmseg - INFO - Iter [135900/160000] lr: 9.038e-06, eta: 2:27:50, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1364, decode.acc_seg: 93.9875, aux.loss_ce: 0.0931, aux.acc_seg: 90.0213, loss: 0.2295, grad_norm: 2.7153 2023-02-17 06:48:25,768 - mmseg - INFO - Iter [135950/160000] lr: 9.019e-06, eta: 2:27:32, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1438, decode.acc_seg: 94.0262, aux.loss_ce: 0.0940, aux.acc_seg: 90.5093, loss: 0.2378, grad_norm: 2.2723 2023-02-17 06:48:43,903 - mmseg - INFO - Saving checkpoint at 136000 iterations 2023-02-17 06:48:44,945 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 06:48:44,946 - mmseg - INFO - Iter [136000/160000] lr: 9.000e-06, eta: 2:27:13, time: 0.384, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1445, decode.acc_seg: 94.0528, aux.loss_ce: 0.0943, aux.acc_seg: 90.5199, loss: 0.2388, grad_norm: 2.4097 2023-02-17 06:49:02,978 - mmseg - INFO - Iter [136050/160000] lr: 8.982e-06, eta: 2:26:55, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1536, decode.acc_seg: 93.7632, aux.loss_ce: 0.0987, aux.acc_seg: 90.0373, loss: 0.2523, grad_norm: 2.4194 2023-02-17 06:49:21,248 - mmseg - INFO - Iter [136100/160000] lr: 8.963e-06, eta: 2:26:36, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1400, decode.acc_seg: 94.1153, aux.loss_ce: 0.0956, aux.acc_seg: 90.3399, loss: 0.2356, grad_norm: 2.1656 2023-02-17 06:49:39,066 - mmseg - INFO - Iter [136150/160000] lr: 8.944e-06, eta: 2:26:18, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1461, decode.acc_seg: 93.8058, aux.loss_ce: 0.0960, aux.acc_seg: 90.1224, loss: 0.2421, grad_norm: 2.5625 2023-02-17 06:49:57,041 - mmseg - INFO - Iter [136200/160000] lr: 8.925e-06, eta: 2:25:59, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1443, decode.acc_seg: 93.9913, aux.loss_ce: 0.0979, aux.acc_seg: 89.8724, loss: 0.2423, grad_norm: 3.1578 2023-02-17 06:50:15,001 - mmseg - INFO - Iter [136250/160000] lr: 8.907e-06, eta: 2:25:41, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1460, decode.acc_seg: 94.0496, aux.loss_ce: 0.0975, aux.acc_seg: 90.1154, loss: 0.2435, grad_norm: 2.2807 2023-02-17 06:50:33,263 - mmseg - INFO - Iter [136300/160000] lr: 8.888e-06, eta: 2:25:22, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1558, decode.acc_seg: 93.6372, aux.loss_ce: 0.0981, aux.acc_seg: 90.0514, loss: 0.2539, grad_norm: 3.0260 2023-02-17 06:50:51,338 - mmseg - INFO - Iter [136350/160000] lr: 8.869e-06, eta: 2:25:04, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1504, decode.acc_seg: 93.7800, aux.loss_ce: 0.0980, aux.acc_seg: 89.9889, loss: 0.2485, grad_norm: 2.5907 2023-02-17 06:51:09,406 - mmseg - INFO - Iter [136400/160000] lr: 8.850e-06, eta: 2:24:45, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1513, decode.acc_seg: 93.5831, aux.loss_ce: 0.0983, aux.acc_seg: 89.8916, loss: 0.2496, grad_norm: 2.6814 2023-02-17 06:51:27,486 - mmseg - INFO - Iter [136450/160000] lr: 8.832e-06, eta: 2:24:26, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1543, decode.acc_seg: 93.7151, aux.loss_ce: 0.1001, aux.acc_seg: 89.8754, loss: 0.2544, grad_norm: 3.0506 2023-02-17 06:51:45,484 - mmseg - INFO - Iter [136500/160000] lr: 8.813e-06, eta: 2:24:08, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1448, decode.acc_seg: 93.9961, aux.loss_ce: 0.0990, aux.acc_seg: 89.9971, loss: 0.2438, grad_norm: 3.8625 2023-02-17 06:52:03,202 - mmseg - INFO - Iter [136550/160000] lr: 8.794e-06, eta: 2:23:49, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1446, decode.acc_seg: 94.0067, aux.loss_ce: 0.0970, aux.acc_seg: 90.1658, loss: 0.2416, grad_norm: 2.2338 2023-02-17 06:52:21,300 - mmseg - INFO - Iter [136600/160000] lr: 8.775e-06, eta: 2:23:31, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1491, decode.acc_seg: 93.9216, aux.loss_ce: 0.0969, aux.acc_seg: 90.1812, loss: 0.2461, grad_norm: 2.3329 2023-02-17 06:52:39,270 - mmseg - INFO - Iter [136650/160000] lr: 8.757e-06, eta: 2:23:12, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1453, decode.acc_seg: 94.0178, aux.loss_ce: 0.0972, aux.acc_seg: 90.1975, loss: 0.2425, grad_norm: 2.3835 2023-02-17 06:52:57,335 - mmseg - INFO - Iter [136700/160000] lr: 8.738e-06, eta: 2:22:53, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1417, decode.acc_seg: 94.0641, aux.loss_ce: 0.0942, aux.acc_seg: 90.3067, loss: 0.2359, grad_norm: 2.3421 2023-02-17 06:53:15,293 - mmseg - INFO - Iter [136750/160000] lr: 8.719e-06, eta: 2:22:35, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1496, decode.acc_seg: 93.6714, aux.loss_ce: 0.1027, aux.acc_seg: 89.4864, loss: 0.2524, grad_norm: 2.8697 2023-02-17 06:53:33,165 - mmseg - INFO - Iter [136800/160000] lr: 8.700e-06, eta: 2:22:16, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1380, decode.acc_seg: 94.1578, aux.loss_ce: 0.0951, aux.acc_seg: 90.0732, loss: 0.2330, grad_norm: 2.1479 2023-02-17 06:53:51,038 - mmseg - INFO - Iter [136850/160000] lr: 8.682e-06, eta: 2:21:58, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1505, decode.acc_seg: 93.7585, aux.loss_ce: 0.1008, aux.acc_seg: 89.9244, loss: 0.2513, grad_norm: 2.6905 2023-02-17 06:54:09,278 - mmseg - INFO - Iter [136900/160000] lr: 8.663e-06, eta: 2:21:39, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1441, decode.acc_seg: 93.8501, aux.loss_ce: 0.0944, aux.acc_seg: 90.1917, loss: 0.2385, grad_norm: 2.7933 2023-02-17 06:54:26,968 - mmseg - INFO - Iter [136950/160000] lr: 8.644e-06, eta: 2:21:20, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1535, decode.acc_seg: 93.5371, aux.loss_ce: 0.1030, aux.acc_seg: 89.4321, loss: 0.2565, grad_norm: 2.6428 2023-02-17 06:54:45,221 - mmseg - INFO - Saving checkpoint at 137000 iterations 2023-02-17 06:54:46,260 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 06:54:46,260 - mmseg - INFO - Iter [137000/160000] lr: 8.625e-06, eta: 2:21:02, time: 0.386, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1520, decode.acc_seg: 93.5835, aux.loss_ce: 0.1010, aux.acc_seg: 89.5818, loss: 0.2531, grad_norm: 2.4291 2023-02-17 06:55:06,682 - mmseg - INFO - Iter [137050/160000] lr: 8.607e-06, eta: 2:20:45, time: 0.408, data_time: 0.053, memory: 16596, decode.loss_ce: 0.1515, decode.acc_seg: 93.7815, aux.loss_ce: 0.1024, aux.acc_seg: 89.5697, loss: 0.2539, grad_norm: 2.5319 2023-02-17 06:55:24,757 - mmseg - INFO - Iter [137100/160000] lr: 8.588e-06, eta: 2:20:26, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1415, decode.acc_seg: 94.0288, aux.loss_ce: 0.0940, aux.acc_seg: 90.3330, loss: 0.2356, grad_norm: 2.1101 2023-02-17 06:55:42,835 - mmseg - INFO - Iter [137150/160000] lr: 8.569e-06, eta: 2:20:08, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1415, decode.acc_seg: 94.1199, aux.loss_ce: 0.0947, aux.acc_seg: 90.2272, loss: 0.2362, grad_norm: 2.3682 2023-02-17 06:56:01,016 - mmseg - INFO - Iter [137200/160000] lr: 8.550e-06, eta: 2:19:49, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1398, decode.acc_seg: 94.0200, aux.loss_ce: 0.0930, aux.acc_seg: 90.3459, loss: 0.2327, grad_norm: 2.4957 2023-02-17 06:56:18,932 - mmseg - INFO - Iter [137250/160000] lr: 8.532e-06, eta: 2:19:31, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1383, decode.acc_seg: 94.1712, aux.loss_ce: 0.0925, aux.acc_seg: 90.4733, loss: 0.2309, grad_norm: 2.3787 2023-02-17 06:56:36,906 - mmseg - INFO - Iter [137300/160000] lr: 8.513e-06, eta: 2:19:12, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1467, decode.acc_seg: 93.9123, aux.loss_ce: 0.1010, aux.acc_seg: 89.9136, loss: 0.2477, grad_norm: 2.5782 2023-02-17 06:56:55,153 - mmseg - INFO - Iter [137350/160000] lr: 8.494e-06, eta: 2:18:54, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1405, decode.acc_seg: 93.9746, aux.loss_ce: 0.0934, aux.acc_seg: 90.2789, loss: 0.2339, grad_norm: 2.8082 2023-02-17 06:57:13,031 - mmseg - INFO - Iter [137400/160000] lr: 8.475e-06, eta: 2:18:35, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1563, decode.acc_seg: 93.7708, aux.loss_ce: 0.1026, aux.acc_seg: 89.6968, loss: 0.2589, grad_norm: 3.3621 2023-02-17 06:57:31,036 - mmseg - INFO - Iter [137450/160000] lr: 8.457e-06, eta: 2:18:17, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1446, decode.acc_seg: 93.8125, aux.loss_ce: 0.0982, aux.acc_seg: 89.6215, loss: 0.2428, grad_norm: 2.6149 2023-02-17 06:57:49,033 - mmseg - INFO - Iter [137500/160000] lr: 8.438e-06, eta: 2:17:58, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1429, decode.acc_seg: 94.1181, aux.loss_ce: 0.0971, aux.acc_seg: 90.0276, loss: 0.2400, grad_norm: 2.3307 2023-02-17 06:58:06,713 - mmseg - INFO - Iter [137550/160000] lr: 8.419e-06, eta: 2:17:39, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1415, decode.acc_seg: 93.9646, aux.loss_ce: 0.0914, aux.acc_seg: 90.4143, loss: 0.2329, grad_norm: 2.1185 2023-02-17 06:58:24,865 - mmseg - INFO - Iter [137600/160000] lr: 8.400e-06, eta: 2:17:21, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1495, decode.acc_seg: 93.8549, aux.loss_ce: 0.0977, aux.acc_seg: 90.1640, loss: 0.2472, grad_norm: 2.9792 2023-02-17 06:58:43,034 - mmseg - INFO - Iter [137650/160000] lr: 8.382e-06, eta: 2:17:02, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1536, decode.acc_seg: 93.5883, aux.loss_ce: 0.1025, aux.acc_seg: 89.6493, loss: 0.2561, grad_norm: 2.3978 2023-02-17 06:59:00,776 - mmseg - INFO - Iter [137700/160000] lr: 8.363e-06, eta: 2:16:44, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1534, decode.acc_seg: 93.7371, aux.loss_ce: 0.1014, aux.acc_seg: 89.8807, loss: 0.2548, grad_norm: 2.7842 2023-02-17 06:59:19,097 - mmseg - INFO - Iter [137750/160000] lr: 8.344e-06, eta: 2:16:25, time: 0.366, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1413, decode.acc_seg: 94.0231, aux.loss_ce: 0.0953, aux.acc_seg: 90.2865, loss: 0.2367, grad_norm: 2.5220 2023-02-17 06:59:36,874 - mmseg - INFO - Iter [137800/160000] lr: 8.325e-06, eta: 2:16:07, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1463, decode.acc_seg: 93.9707, aux.loss_ce: 0.0993, aux.acc_seg: 89.8997, loss: 0.2455, grad_norm: 2.0250 2023-02-17 06:59:54,909 - mmseg - INFO - Iter [137850/160000] lr: 8.307e-06, eta: 2:15:48, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1462, decode.acc_seg: 93.9402, aux.loss_ce: 0.0982, aux.acc_seg: 90.0505, loss: 0.2445, grad_norm: 2.2698 2023-02-17 07:00:12,961 - mmseg - INFO - Iter [137900/160000] lr: 8.288e-06, eta: 2:15:29, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1406, decode.acc_seg: 94.0910, aux.loss_ce: 0.0961, aux.acc_seg: 90.1746, loss: 0.2367, grad_norm: 2.1903 2023-02-17 07:00:30,691 - mmseg - INFO - Iter [137950/160000] lr: 8.269e-06, eta: 2:15:11, time: 0.354, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1432, decode.acc_seg: 93.9307, aux.loss_ce: 0.0952, aux.acc_seg: 90.0857, loss: 0.2384, grad_norm: 2.3702 2023-02-17 07:00:49,094 - mmseg - INFO - Saving checkpoint at 138000 iterations 2023-02-17 07:00:50,138 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 07:00:50,139 - mmseg - INFO - Iter [138000/160000] lr: 8.250e-06, eta: 2:14:53, time: 0.389, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1378, decode.acc_seg: 94.2206, aux.loss_ce: 0.0906, aux.acc_seg: 90.6167, loss: 0.2284, grad_norm: 2.2146 2023-02-17 07:01:08,056 - mmseg - INFO - Iter [138050/160000] lr: 8.232e-06, eta: 2:14:34, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1567, decode.acc_seg: 93.6055, aux.loss_ce: 0.0987, aux.acc_seg: 90.3302, loss: 0.2554, grad_norm: 2.5075 2023-02-17 07:01:25,830 - mmseg - INFO - Iter [138100/160000] lr: 8.213e-06, eta: 2:14:16, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1473, decode.acc_seg: 93.9440, aux.loss_ce: 0.1013, aux.acc_seg: 89.6289, loss: 0.2486, grad_norm: 2.1415 2023-02-17 07:01:43,574 - mmseg - INFO - Iter [138150/160000] lr: 8.194e-06, eta: 2:13:57, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1517, decode.acc_seg: 93.5309, aux.loss_ce: 0.0988, aux.acc_seg: 89.5640, loss: 0.2505, grad_norm: 2.6867 2023-02-17 07:02:01,467 - mmseg - INFO - Iter [138200/160000] lr: 8.175e-06, eta: 2:13:38, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1415, decode.acc_seg: 94.0683, aux.loss_ce: 0.0914, aux.acc_seg: 90.6514, loss: 0.2329, grad_norm: 2.0860 2023-02-17 07:02:19,548 - mmseg - INFO - Iter [138250/160000] lr: 8.157e-06, eta: 2:13:20, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1430, decode.acc_seg: 94.0513, aux.loss_ce: 0.0939, aux.acc_seg: 90.4130, loss: 0.2370, grad_norm: 2.2100 2023-02-17 07:02:37,747 - mmseg - INFO - Iter [138300/160000] lr: 8.138e-06, eta: 2:13:01, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1440, decode.acc_seg: 93.9155, aux.loss_ce: 0.0970, aux.acc_seg: 90.0129, loss: 0.2411, grad_norm: 2.3754 2023-02-17 07:02:58,111 - mmseg - INFO - Iter [138350/160000] lr: 8.119e-06, eta: 2:12:44, time: 0.407, data_time: 0.051, memory: 16596, decode.loss_ce: 0.1539, decode.acc_seg: 93.7936, aux.loss_ce: 0.1032, aux.acc_seg: 89.7673, loss: 0.2571, grad_norm: 2.5437 2023-02-17 07:03:15,808 - mmseg - INFO - Iter [138400/160000] lr: 8.100e-06, eta: 2:12:25, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1463, decode.acc_seg: 93.6966, aux.loss_ce: 0.0988, aux.acc_seg: 89.7942, loss: 0.2451, grad_norm: 2.6115 2023-02-17 07:03:34,198 - mmseg - INFO - Iter [138450/160000] lr: 8.082e-06, eta: 2:12:07, time: 0.367, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1441, decode.acc_seg: 94.1645, aux.loss_ce: 0.0944, aux.acc_seg: 90.4612, loss: 0.2386, grad_norm: 2.0534 2023-02-17 07:03:52,368 - mmseg - INFO - Iter [138500/160000] lr: 8.063e-06, eta: 2:11:48, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1345, decode.acc_seg: 94.4074, aux.loss_ce: 0.0904, aux.acc_seg: 90.6937, loss: 0.2249, grad_norm: 2.1003 2023-02-17 07:04:10,435 - mmseg - INFO - Iter [138550/160000] lr: 8.044e-06, eta: 2:11:30, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1441, decode.acc_seg: 94.0528, aux.loss_ce: 0.0951, aux.acc_seg: 90.3515, loss: 0.2392, grad_norm: 2.4587 2023-02-17 07:04:28,804 - mmseg - INFO - Iter [138600/160000] lr: 8.025e-06, eta: 2:11:11, time: 0.368, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1389, decode.acc_seg: 94.2667, aux.loss_ce: 0.0950, aux.acc_seg: 90.3875, loss: 0.2340, grad_norm: 2.3723 2023-02-17 07:04:46,953 - mmseg - INFO - Iter [138650/160000] lr: 8.007e-06, eta: 2:10:53, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1443, decode.acc_seg: 93.8954, aux.loss_ce: 0.0960, aux.acc_seg: 90.1675, loss: 0.2403, grad_norm: 2.5850 2023-02-17 07:05:04,981 - mmseg - INFO - Iter [138700/160000] lr: 7.988e-06, eta: 2:10:34, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1474, decode.acc_seg: 93.8524, aux.loss_ce: 0.0965, aux.acc_seg: 90.1536, loss: 0.2439, grad_norm: 2.3998 2023-02-17 07:05:23,101 - mmseg - INFO - Iter [138750/160000] lr: 7.969e-06, eta: 2:10:16, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1431, decode.acc_seg: 94.0645, aux.loss_ce: 0.0946, aux.acc_seg: 90.4366, loss: 0.2377, grad_norm: 2.4631 2023-02-17 07:05:40,928 - mmseg - INFO - Iter [138800/160000] lr: 7.950e-06, eta: 2:09:57, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1417, decode.acc_seg: 94.0768, aux.loss_ce: 0.0951, aux.acc_seg: 90.3693, loss: 0.2368, grad_norm: 2.2152 2023-02-17 07:05:59,062 - mmseg - INFO - Iter [138850/160000] lr: 7.932e-06, eta: 2:09:39, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1335, decode.acc_seg: 94.3892, aux.loss_ce: 0.0926, aux.acc_seg: 90.4402, loss: 0.2261, grad_norm: 1.9697 2023-02-17 07:06:16,831 - mmseg - INFO - Iter [138900/160000] lr: 7.913e-06, eta: 2:09:20, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1480, decode.acc_seg: 93.8078, aux.loss_ce: 0.0960, aux.acc_seg: 90.1722, loss: 0.2440, grad_norm: 2.5191 2023-02-17 07:06:35,131 - mmseg - INFO - Iter [138950/160000] lr: 7.894e-06, eta: 2:09:02, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1400, decode.acc_seg: 94.0082, aux.loss_ce: 0.0966, aux.acc_seg: 89.9940, loss: 0.2365, grad_norm: 2.9575 2023-02-17 07:06:53,281 - mmseg - INFO - Saving checkpoint at 139000 iterations 2023-02-17 07:06:54,314 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 07:06:54,315 - mmseg - INFO - Iter [139000/160000] lr: 7.875e-06, eta: 2:08:44, time: 0.384, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1466, decode.acc_seg: 93.8149, aux.loss_ce: 0.0989, aux.acc_seg: 89.8295, loss: 0.2455, grad_norm: 2.5337 2023-02-17 07:07:12,180 - mmseg - INFO - Iter [139050/160000] lr: 7.857e-06, eta: 2:08:25, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1435, decode.acc_seg: 93.9586, aux.loss_ce: 0.0939, aux.acc_seg: 90.2929, loss: 0.2373, grad_norm: 2.3601 2023-02-17 07:07:30,028 - mmseg - INFO - Iter [139100/160000] lr: 7.838e-06, eta: 2:08:06, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1472, decode.acc_seg: 93.9598, aux.loss_ce: 0.0970, aux.acc_seg: 90.2652, loss: 0.2442, grad_norm: 2.4568 2023-02-17 07:07:48,157 - mmseg - INFO - Iter [139150/160000] lr: 7.819e-06, eta: 2:07:48, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1499, decode.acc_seg: 93.7048, aux.loss_ce: 0.0974, aux.acc_seg: 90.0436, loss: 0.2473, grad_norm: 2.5265 2023-02-17 07:08:05,999 - mmseg - INFO - Iter [139200/160000] lr: 7.800e-06, eta: 2:07:29, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1380, decode.acc_seg: 94.2037, aux.loss_ce: 0.0919, aux.acc_seg: 90.5500, loss: 0.2299, grad_norm: 2.1270 2023-02-17 07:08:23,871 - mmseg - INFO - Iter [139250/160000] lr: 7.782e-06, eta: 2:07:11, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1384, decode.acc_seg: 94.2241, aux.loss_ce: 0.0910, aux.acc_seg: 90.7009, loss: 0.2294, grad_norm: 2.0542 2023-02-17 07:08:41,693 - mmseg - INFO - Iter [139300/160000] lr: 7.763e-06, eta: 2:06:52, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1426, decode.acc_seg: 94.0116, aux.loss_ce: 0.0963, aux.acc_seg: 90.0874, loss: 0.2388, grad_norm: 2.2742 2023-02-17 07:08:59,901 - mmseg - INFO - Iter [139350/160000] lr: 7.744e-06, eta: 2:06:34, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1486, decode.acc_seg: 93.9593, aux.loss_ce: 0.0993, aux.acc_seg: 90.0744, loss: 0.2478, grad_norm: 2.3150 2023-02-17 07:09:17,710 - mmseg - INFO - Iter [139400/160000] lr: 7.725e-06, eta: 2:06:15, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1482, decode.acc_seg: 93.7353, aux.loss_ce: 0.1015, aux.acc_seg: 89.5358, loss: 0.2497, grad_norm: 2.3826 2023-02-17 07:09:35,825 - mmseg - INFO - Iter [139450/160000] lr: 7.707e-06, eta: 2:05:57, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1600, decode.acc_seg: 93.7547, aux.loss_ce: 0.0994, aux.acc_seg: 90.1306, loss: 0.2593, grad_norm: 3.1870 2023-02-17 07:09:53,857 - mmseg - INFO - Iter [139500/160000] lr: 7.688e-06, eta: 2:05:38, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1524, decode.acc_seg: 93.6478, aux.loss_ce: 0.1005, aux.acc_seg: 89.6862, loss: 0.2529, grad_norm: 2.5942 2023-02-17 07:10:12,119 - mmseg - INFO - Iter [139550/160000] lr: 7.669e-06, eta: 2:05:20, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1457, decode.acc_seg: 93.8957, aux.loss_ce: 0.0943, aux.acc_seg: 90.1973, loss: 0.2399, grad_norm: 2.4859 2023-02-17 07:10:33,075 - mmseg - INFO - Iter [139600/160000] lr: 7.650e-06, eta: 2:05:02, time: 0.419, data_time: 0.053, memory: 16596, decode.loss_ce: 0.1472, decode.acc_seg: 93.9043, aux.loss_ce: 0.0981, aux.acc_seg: 90.1050, loss: 0.2453, grad_norm: 3.1199 2023-02-17 07:10:51,112 - mmseg - INFO - Iter [139650/160000] lr: 7.632e-06, eta: 2:04:44, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1433, decode.acc_seg: 94.0924, aux.loss_ce: 0.0946, aux.acc_seg: 90.3910, loss: 0.2379, grad_norm: 2.1946 2023-02-17 07:11:09,050 - mmseg - INFO - Iter [139700/160000] lr: 7.613e-06, eta: 2:04:25, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1499, decode.acc_seg: 93.7257, aux.loss_ce: 0.1000, aux.acc_seg: 89.8914, loss: 0.2499, grad_norm: 2.5713 2023-02-17 07:11:26,827 - mmseg - INFO - Iter [139750/160000] lr: 7.594e-06, eta: 2:04:06, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1381, decode.acc_seg: 94.1679, aux.loss_ce: 0.0933, aux.acc_seg: 90.4628, loss: 0.2314, grad_norm: 2.1438 2023-02-17 07:11:44,546 - mmseg - INFO - Iter [139800/160000] lr: 7.575e-06, eta: 2:03:48, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1419, decode.acc_seg: 94.2004, aux.loss_ce: 0.0967, aux.acc_seg: 90.2836, loss: 0.2387, grad_norm: 2.4259 2023-02-17 07:12:02,499 - mmseg - INFO - Iter [139850/160000] lr: 7.557e-06, eta: 2:03:29, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1439, decode.acc_seg: 93.8394, aux.loss_ce: 0.1009, aux.acc_seg: 89.7183, loss: 0.2448, grad_norm: 3.3653 2023-02-17 07:12:20,344 - mmseg - INFO - Iter [139900/160000] lr: 7.538e-06, eta: 2:03:11, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1407, decode.acc_seg: 94.0855, aux.loss_ce: 0.0939, aux.acc_seg: 90.4870, loss: 0.2346, grad_norm: 2.5983 2023-02-17 07:12:38,186 - mmseg - INFO - Iter [139950/160000] lr: 7.519e-06, eta: 2:02:52, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1441, decode.acc_seg: 94.0738, aux.loss_ce: 0.0959, aux.acc_seg: 90.3235, loss: 0.2399, grad_norm: 2.3568 2023-02-17 07:12:56,248 - mmseg - INFO - Saving checkpoint at 140000 iterations 2023-02-17 07:12:57,308 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 07:12:57,308 - mmseg - INFO - Iter [140000/160000] lr: 7.500e-06, eta: 2:02:34, time: 0.383, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1392, decode.acc_seg: 94.1646, aux.loss_ce: 0.0931, aux.acc_seg: 90.5317, loss: 0.2323, grad_norm: 2.3389 2023-02-17 07:13:15,554 - mmseg - INFO - Iter [140050/160000] lr: 7.482e-06, eta: 2:02:16, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1380, decode.acc_seg: 93.9997, aux.loss_ce: 0.0953, aux.acc_seg: 90.2000, loss: 0.2334, grad_norm: 2.5405 2023-02-17 07:13:33,258 - mmseg - INFO - Iter [140100/160000] lr: 7.463e-06, eta: 2:01:57, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1397, decode.acc_seg: 94.1108, aux.loss_ce: 0.0954, aux.acc_seg: 90.2967, loss: 0.2351, grad_norm: 2.3853 2023-02-17 07:13:51,318 - mmseg - INFO - Iter [140150/160000] lr: 7.444e-06, eta: 2:01:38, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1364, decode.acc_seg: 94.3310, aux.loss_ce: 0.0917, aux.acc_seg: 90.6114, loss: 0.2281, grad_norm: 2.0412 2023-02-17 07:14:09,209 - mmseg - INFO - Iter [140200/160000] lr: 7.425e-06, eta: 2:01:20, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1401, decode.acc_seg: 94.1055, aux.loss_ce: 0.0947, aux.acc_seg: 90.2589, loss: 0.2348, grad_norm: 2.2999 2023-02-17 07:14:26,944 - mmseg - INFO - Iter [140250/160000] lr: 7.407e-06, eta: 2:01:01, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1457, decode.acc_seg: 93.6630, aux.loss_ce: 0.0975, aux.acc_seg: 89.6598, loss: 0.2432, grad_norm: 2.4268 2023-02-17 07:14:44,961 - mmseg - INFO - Iter [140300/160000] lr: 7.388e-06, eta: 2:00:43, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1345, decode.acc_seg: 94.3453, aux.loss_ce: 0.0905, aux.acc_seg: 90.7129, loss: 0.2250, grad_norm: 2.0949 2023-02-17 07:15:03,042 - mmseg - INFO - Iter [140350/160000] lr: 7.369e-06, eta: 2:00:24, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1369, decode.acc_seg: 94.2758, aux.loss_ce: 0.0906, aux.acc_seg: 90.6542, loss: 0.2275, grad_norm: 2.4968 2023-02-17 07:15:21,080 - mmseg - INFO - Iter [140400/160000] lr: 7.350e-06, eta: 2:00:06, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1515, decode.acc_seg: 93.6843, aux.loss_ce: 0.0980, aux.acc_seg: 90.0133, loss: 0.2495, grad_norm: 2.4617 2023-02-17 07:15:39,124 - mmseg - INFO - Iter [140450/160000] lr: 7.332e-06, eta: 1:59:47, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1464, decode.acc_seg: 93.7853, aux.loss_ce: 0.0978, aux.acc_seg: 89.9095, loss: 0.2443, grad_norm: 2.5737 2023-02-17 07:15:57,287 - mmseg - INFO - Iter [140500/160000] lr: 7.313e-06, eta: 1:59:29, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1454, decode.acc_seg: 93.9416, aux.loss_ce: 0.0936, aux.acc_seg: 90.4366, loss: 0.2390, grad_norm: 2.0499 2023-02-17 07:16:15,176 - mmseg - INFO - Iter [140550/160000] lr: 7.294e-06, eta: 1:59:10, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1447, decode.acc_seg: 93.9141, aux.loss_ce: 0.0952, aux.acc_seg: 90.2908, loss: 0.2398, grad_norm: 2.3824 2023-02-17 07:16:33,012 - mmseg - INFO - Iter [140600/160000] lr: 7.275e-06, eta: 1:58:52, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1407, decode.acc_seg: 94.0699, aux.loss_ce: 0.0936, aux.acc_seg: 90.2388, loss: 0.2343, grad_norm: 2.2542 2023-02-17 07:16:50,993 - mmseg - INFO - Iter [140650/160000] lr: 7.257e-06, eta: 1:58:33, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1442, decode.acc_seg: 93.7482, aux.loss_ce: 0.0962, aux.acc_seg: 89.8201, loss: 0.2404, grad_norm: 2.5659 2023-02-17 07:17:08,862 - mmseg - INFO - Iter [140700/160000] lr: 7.238e-06, eta: 1:58:15, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1482, decode.acc_seg: 93.8071, aux.loss_ce: 0.0982, aux.acc_seg: 89.9457, loss: 0.2464, grad_norm: 2.5576 2023-02-17 07:17:26,778 - mmseg - INFO - Iter [140750/160000] lr: 7.219e-06, eta: 1:57:56, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1517, decode.acc_seg: 93.7092, aux.loss_ce: 0.1000, aux.acc_seg: 89.9879, loss: 0.2517, grad_norm: 2.7309 2023-02-17 07:17:44,813 - mmseg - INFO - Iter [140800/160000] lr: 7.200e-06, eta: 1:57:38, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1381, decode.acc_seg: 94.2888, aux.loss_ce: 0.0942, aux.acc_seg: 90.4540, loss: 0.2323, grad_norm: 1.9250 2023-02-17 07:18:05,162 - mmseg - INFO - Iter [140850/160000] lr: 7.182e-06, eta: 1:57:20, time: 0.406, data_time: 0.053, memory: 16596, decode.loss_ce: 0.1441, decode.acc_seg: 93.7978, aux.loss_ce: 0.0975, aux.acc_seg: 89.9171, loss: 0.2416, grad_norm: 2.6184 2023-02-17 07:18:23,190 - mmseg - INFO - Iter [140900/160000] lr: 7.163e-06, eta: 1:57:01, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1401, decode.acc_seg: 94.0133, aux.loss_ce: 0.0916, aux.acc_seg: 90.5611, loss: 0.2316, grad_norm: 2.3447 2023-02-17 07:18:41,183 - mmseg - INFO - Iter [140950/160000] lr: 7.144e-06, eta: 1:56:43, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1473, decode.acc_seg: 93.7618, aux.loss_ce: 0.0945, aux.acc_seg: 90.3142, loss: 0.2417, grad_norm: 2.3630 2023-02-17 07:18:59,118 - mmseg - INFO - Saving checkpoint at 141000 iterations 2023-02-17 07:19:00,221 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 07:19:00,221 - mmseg - INFO - Iter [141000/160000] lr: 7.125e-06, eta: 1:56:25, time: 0.381, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1393, decode.acc_seg: 94.0149, aux.loss_ce: 0.0910, aux.acc_seg: 90.5021, loss: 0.2303, grad_norm: 2.0235 2023-02-17 07:19:18,230 - mmseg - INFO - Iter [141050/160000] lr: 7.107e-06, eta: 1:56:06, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1344, decode.acc_seg: 94.2706, aux.loss_ce: 0.0927, aux.acc_seg: 90.3105, loss: 0.2270, grad_norm: 2.0918 2023-02-17 07:19:36,210 - mmseg - INFO - Iter [141100/160000] lr: 7.088e-06, eta: 1:55:48, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1383, decode.acc_seg: 94.1570, aux.loss_ce: 0.0921, aux.acc_seg: 90.3784, loss: 0.2304, grad_norm: 2.1753 2023-02-17 07:19:54,201 - mmseg - INFO - Iter [141150/160000] lr: 7.069e-06, eta: 1:55:29, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1363, decode.acc_seg: 94.0980, aux.loss_ce: 0.0929, aux.acc_seg: 90.3255, loss: 0.2292, grad_norm: 2.0202 2023-02-17 07:20:12,073 - mmseg - INFO - Iter [141200/160000] lr: 7.050e-06, eta: 1:55:11, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1415, decode.acc_seg: 93.8439, aux.loss_ce: 0.0936, aux.acc_seg: 90.2274, loss: 0.2351, grad_norm: 2.0939 2023-02-17 07:20:29,775 - mmseg - INFO - Iter [141250/160000] lr: 7.032e-06, eta: 1:54:52, time: 0.355, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1341, decode.acc_seg: 94.4544, aux.loss_ce: 0.0946, aux.acc_seg: 90.3729, loss: 0.2288, grad_norm: 2.2975 2023-02-17 07:20:47,493 - mmseg - INFO - Iter [141300/160000] lr: 7.013e-06, eta: 1:54:33, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1330, decode.acc_seg: 94.5218, aux.loss_ce: 0.0895, aux.acc_seg: 90.8557, loss: 0.2224, grad_norm: 2.2364 2023-02-17 07:21:05,779 - mmseg - INFO - Iter [141350/160000] lr: 6.994e-06, eta: 1:54:15, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1435, decode.acc_seg: 94.0733, aux.loss_ce: 0.0927, aux.acc_seg: 90.5087, loss: 0.2362, grad_norm: 2.6646 2023-02-17 07:21:23,728 - mmseg - INFO - Iter [141400/160000] lr: 6.975e-06, eta: 1:53:56, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1421, decode.acc_seg: 94.0670, aux.loss_ce: 0.0971, aux.acc_seg: 90.0336, loss: 0.2391, grad_norm: 2.5524 2023-02-17 07:21:41,407 - mmseg - INFO - Iter [141450/160000] lr: 6.957e-06, eta: 1:53:38, time: 0.353, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1490, decode.acc_seg: 93.7444, aux.loss_ce: 0.0999, aux.acc_seg: 89.9711, loss: 0.2489, grad_norm: 2.7138 2023-02-17 07:21:59,598 - mmseg - INFO - Iter [141500/160000] lr: 6.938e-06, eta: 1:53:19, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1457, decode.acc_seg: 94.1032, aux.loss_ce: 0.0978, aux.acc_seg: 90.3695, loss: 0.2436, grad_norm: 2.7723 2023-02-17 07:22:17,669 - mmseg - INFO - Iter [141550/160000] lr: 6.919e-06, eta: 1:53:01, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1380, decode.acc_seg: 94.1134, aux.loss_ce: 0.0951, aux.acc_seg: 90.0988, loss: 0.2331, grad_norm: 2.1163 2023-02-17 07:22:35,867 - mmseg - INFO - Iter [141600/160000] lr: 6.900e-06, eta: 1:52:43, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1333, decode.acc_seg: 94.2904, aux.loss_ce: 0.0919, aux.acc_seg: 90.3632, loss: 0.2251, grad_norm: 2.2736 2023-02-17 07:22:54,040 - mmseg - INFO - Iter [141650/160000] lr: 6.882e-06, eta: 1:52:24, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1407, decode.acc_seg: 94.0262, aux.loss_ce: 0.0921, aux.acc_seg: 90.5196, loss: 0.2328, grad_norm: 2.1414 2023-02-17 07:23:12,124 - mmseg - INFO - Iter [141700/160000] lr: 6.863e-06, eta: 1:52:06, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1419, decode.acc_seg: 94.0173, aux.loss_ce: 0.0969, aux.acc_seg: 90.1839, loss: 0.2388, grad_norm: 2.2878 2023-02-17 07:23:30,471 - mmseg - INFO - Iter [141750/160000] lr: 6.844e-06, eta: 1:51:47, time: 0.367, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1472, decode.acc_seg: 93.7661, aux.loss_ce: 0.0970, aux.acc_seg: 89.9145, loss: 0.2442, grad_norm: 3.2877 2023-02-17 07:23:48,258 - mmseg - INFO - Iter [141800/160000] lr: 6.825e-06, eta: 1:51:29, time: 0.356, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1477, decode.acc_seg: 93.8256, aux.loss_ce: 0.0989, aux.acc_seg: 90.0213, loss: 0.2466, grad_norm: 2.7487 2023-02-17 07:24:06,200 - mmseg - INFO - Iter [141850/160000] lr: 6.807e-06, eta: 1:51:10, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1392, decode.acc_seg: 94.2126, aux.loss_ce: 0.0979, aux.acc_seg: 90.0666, loss: 0.2371, grad_norm: 2.3339 2023-02-17 07:24:24,466 - mmseg - INFO - Iter [141900/160000] lr: 6.788e-06, eta: 1:50:52, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1483, decode.acc_seg: 93.6623, aux.loss_ce: 0.0956, aux.acc_seg: 90.0668, loss: 0.2439, grad_norm: 2.1341 2023-02-17 07:24:42,301 - mmseg - INFO - Iter [141950/160000] lr: 6.769e-06, eta: 1:50:33, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1415, decode.acc_seg: 94.1846, aux.loss_ce: 0.0944, aux.acc_seg: 90.5554, loss: 0.2359, grad_norm: 2.5749 2023-02-17 07:25:00,197 - mmseg - INFO - Saving checkpoint at 142000 iterations 2023-02-17 07:25:01,457 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 07:25:01,457 - mmseg - INFO - Iter [142000/160000] lr: 6.750e-06, eta: 1:50:15, time: 0.383, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1508, decode.acc_seg: 93.9145, aux.loss_ce: 0.0996, aux.acc_seg: 90.0308, loss: 0.2505, grad_norm: 2.4894 2023-02-17 07:25:19,575 - mmseg - INFO - Iter [142050/160000] lr: 6.732e-06, eta: 1:49:57, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1435, decode.acc_seg: 93.9780, aux.loss_ce: 0.0956, aux.acc_seg: 90.1778, loss: 0.2390, grad_norm: 2.4787 2023-02-17 07:25:39,938 - mmseg - INFO - Iter [142100/160000] lr: 6.713e-06, eta: 1:49:39, time: 0.407, data_time: 0.053, memory: 16596, decode.loss_ce: 0.1368, decode.acc_seg: 94.2817, aux.loss_ce: 0.0936, aux.acc_seg: 90.4506, loss: 0.2303, grad_norm: 2.0548 2023-02-17 07:25:57,864 - mmseg - INFO - Iter [142150/160000] lr: 6.694e-06, eta: 1:49:20, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1444, decode.acc_seg: 93.9764, aux.loss_ce: 0.0983, aux.acc_seg: 89.9713, loss: 0.2427, grad_norm: 3.1645 2023-02-17 07:26:15,713 - mmseg - INFO - Iter [142200/160000] lr: 6.675e-06, eta: 1:49:02, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1437, decode.acc_seg: 93.9802, aux.loss_ce: 0.0964, aux.acc_seg: 89.9963, loss: 0.2401, grad_norm: 2.3918 2023-02-17 07:26:33,653 - mmseg - INFO - Iter [142250/160000] lr: 6.657e-06, eta: 1:48:43, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1414, decode.acc_seg: 94.0858, aux.loss_ce: 0.0935, aux.acc_seg: 90.4742, loss: 0.2349, grad_norm: 2.0419 2023-02-17 07:26:51,753 - mmseg - INFO - Iter [142300/160000] lr: 6.638e-06, eta: 1:48:25, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1381, decode.acc_seg: 94.1437, aux.loss_ce: 0.0932, aux.acc_seg: 90.4469, loss: 0.2313, grad_norm: 2.1610 2023-02-17 07:27:09,677 - mmseg - INFO - Iter [142350/160000] lr: 6.619e-06, eta: 1:48:06, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1458, decode.acc_seg: 93.8944, aux.loss_ce: 0.0954, aux.acc_seg: 90.2348, loss: 0.2412, grad_norm: 2.3742 2023-02-17 07:27:27,905 - mmseg - INFO - Iter [142400/160000] lr: 6.600e-06, eta: 1:47:48, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1390, decode.acc_seg: 94.1771, aux.loss_ce: 0.0901, aux.acc_seg: 90.7440, loss: 0.2291, grad_norm: 2.2860 2023-02-17 07:27:45,650 - mmseg - INFO - Iter [142450/160000] lr: 6.582e-06, eta: 1:47:29, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1378, decode.acc_seg: 94.2256, aux.loss_ce: 0.0938, aux.acc_seg: 90.4333, loss: 0.2316, grad_norm: 2.3061 2023-02-17 07:28:03,619 - mmseg - INFO - Iter [142500/160000] lr: 6.563e-06, eta: 1:47:11, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1387, decode.acc_seg: 94.3202, aux.loss_ce: 0.0945, aux.acc_seg: 90.4907, loss: 0.2332, grad_norm: 2.3827 2023-02-17 07:28:21,403 - mmseg - INFO - Iter [142550/160000] lr: 6.544e-06, eta: 1:46:52, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1460, decode.acc_seg: 93.8514, aux.loss_ce: 0.0959, aux.acc_seg: 90.1254, loss: 0.2419, grad_norm: 2.6265 2023-02-17 07:28:39,021 - mmseg - INFO - Iter [142600/160000] lr: 6.525e-06, eta: 1:46:34, time: 0.352, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1435, decode.acc_seg: 93.8638, aux.loss_ce: 0.0960, aux.acc_seg: 90.2202, loss: 0.2396, grad_norm: 2.3838 2023-02-17 07:28:56,903 - mmseg - INFO - Iter [142650/160000] lr: 6.507e-06, eta: 1:46:15, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1425, decode.acc_seg: 93.9225, aux.loss_ce: 0.0996, aux.acc_seg: 89.6146, loss: 0.2421, grad_norm: 2.5835 2023-02-17 07:29:14,641 - mmseg - INFO - Iter [142700/160000] lr: 6.488e-06, eta: 1:45:57, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1498, decode.acc_seg: 93.8279, aux.loss_ce: 0.0963, aux.acc_seg: 90.1976, loss: 0.2461, grad_norm: 2.2527 2023-02-17 07:29:32,408 - mmseg - INFO - Iter [142750/160000] lr: 6.469e-06, eta: 1:45:38, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1519, decode.acc_seg: 93.6717, aux.loss_ce: 0.1006, aux.acc_seg: 89.6331, loss: 0.2525, grad_norm: 2.5068 2023-02-17 07:29:50,307 - mmseg - INFO - Iter [142800/160000] lr: 6.450e-06, eta: 1:45:19, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1385, decode.acc_seg: 94.1263, aux.loss_ce: 0.0945, aux.acc_seg: 90.2406, loss: 0.2330, grad_norm: 2.2744 2023-02-17 07:30:08,463 - mmseg - INFO - Iter [142850/160000] lr: 6.432e-06, eta: 1:45:01, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1384, decode.acc_seg: 94.1174, aux.loss_ce: 0.0924, aux.acc_seg: 90.3963, loss: 0.2308, grad_norm: 2.0206 2023-02-17 07:30:26,821 - mmseg - INFO - Iter [142900/160000] lr: 6.413e-06, eta: 1:44:43, time: 0.367, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1394, decode.acc_seg: 94.1472, aux.loss_ce: 0.0943, aux.acc_seg: 90.3085, loss: 0.2338, grad_norm: 2.2090 2023-02-17 07:30:44,826 - mmseg - INFO - Iter [142950/160000] lr: 6.394e-06, eta: 1:44:24, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1452, decode.acc_seg: 94.0058, aux.loss_ce: 0.0973, aux.acc_seg: 90.2347, loss: 0.2425, grad_norm: 2.3675 2023-02-17 07:31:03,470 - mmseg - INFO - Saving checkpoint at 143000 iterations 2023-02-17 07:31:04,509 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 07:31:04,509 - mmseg - INFO - Iter [143000/160000] lr: 6.375e-06, eta: 1:44:06, time: 0.394, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1432, decode.acc_seg: 93.9335, aux.loss_ce: 0.0935, aux.acc_seg: 90.4451, loss: 0.2367, grad_norm: 2.4582 2023-02-17 07:31:22,401 - mmseg - INFO - Iter [143050/160000] lr: 6.357e-06, eta: 1:43:48, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1450, decode.acc_seg: 93.7867, aux.loss_ce: 0.0980, aux.acc_seg: 89.8284, loss: 0.2430, grad_norm: 2.5501 2023-02-17 07:31:40,363 - mmseg - INFO - Iter [143100/160000] lr: 6.338e-06, eta: 1:43:29, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1421, decode.acc_seg: 94.0915, aux.loss_ce: 0.0953, aux.acc_seg: 90.3804, loss: 0.2375, grad_norm: 2.1650 2023-02-17 07:31:58,125 - mmseg - INFO - Iter [143150/160000] lr: 6.319e-06, eta: 1:43:11, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1366, decode.acc_seg: 94.1747, aux.loss_ce: 0.0923, aux.acc_seg: 90.4758, loss: 0.2289, grad_norm: 2.5263 2023-02-17 07:32:16,093 - mmseg - INFO - Iter [143200/160000] lr: 6.300e-06, eta: 1:42:52, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1344, decode.acc_seg: 94.2771, aux.loss_ce: 0.0886, aux.acc_seg: 90.8138, loss: 0.2231, grad_norm: 2.1452 2023-02-17 07:32:34,368 - mmseg - INFO - Iter [143250/160000] lr: 6.282e-06, eta: 1:42:34, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1478, decode.acc_seg: 93.7211, aux.loss_ce: 0.0943, aux.acc_seg: 90.2589, loss: 0.2422, grad_norm: 2.6823 2023-02-17 07:32:52,288 - mmseg - INFO - Iter [143300/160000] lr: 6.263e-06, eta: 1:42:15, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1343, decode.acc_seg: 94.2952, aux.loss_ce: 0.0946, aux.acc_seg: 90.1966, loss: 0.2289, grad_norm: 2.3349 2023-02-17 07:33:10,410 - mmseg - INFO - Iter [143350/160000] lr: 6.244e-06, eta: 1:41:57, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1424, decode.acc_seg: 93.9866, aux.loss_ce: 0.0972, aux.acc_seg: 90.0695, loss: 0.2396, grad_norm: 2.2566 2023-02-17 07:33:30,645 - mmseg - INFO - Iter [143400/160000] lr: 6.225e-06, eta: 1:41:39, time: 0.405, data_time: 0.053, memory: 16596, decode.loss_ce: 0.1395, decode.acc_seg: 94.0511, aux.loss_ce: 0.0952, aux.acc_seg: 89.9532, loss: 0.2347, grad_norm: 2.3358 2023-02-17 07:33:48,605 - mmseg - INFO - Iter [143450/160000] lr: 6.207e-06, eta: 1:41:21, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1419, decode.acc_seg: 93.9653, aux.loss_ce: 0.0916, aux.acc_seg: 90.5580, loss: 0.2335, grad_norm: 2.1633 2023-02-17 07:34:06,896 - mmseg - INFO - Iter [143500/160000] lr: 6.188e-06, eta: 1:41:02, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1367, decode.acc_seg: 94.1694, aux.loss_ce: 0.0931, aux.acc_seg: 90.3077, loss: 0.2298, grad_norm: 2.4997 2023-02-17 07:34:25,084 - mmseg - INFO - Iter [143550/160000] lr: 6.169e-06, eta: 1:40:44, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1449, decode.acc_seg: 93.8934, aux.loss_ce: 0.0942, aux.acc_seg: 90.3449, loss: 0.2391, grad_norm: 2.7498 2023-02-17 07:34:42,978 - mmseg - INFO - Iter [143600/160000] lr: 6.150e-06, eta: 1:40:25, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1406, decode.acc_seg: 94.0766, aux.loss_ce: 0.0941, aux.acc_seg: 90.2191, loss: 0.2348, grad_norm: 2.5696 2023-02-17 07:35:01,218 - mmseg - INFO - Iter [143650/160000] lr: 6.132e-06, eta: 1:40:07, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1459, decode.acc_seg: 93.8988, aux.loss_ce: 0.0997, aux.acc_seg: 89.9364, loss: 0.2456, grad_norm: 2.7419 2023-02-17 07:35:19,016 - mmseg - INFO - Iter [143700/160000] lr: 6.113e-06, eta: 1:39:48, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1480, decode.acc_seg: 93.8581, aux.loss_ce: 0.0997, aux.acc_seg: 89.8701, loss: 0.2476, grad_norm: 2.6082 2023-02-17 07:35:37,070 - mmseg - INFO - Iter [143750/160000] lr: 6.094e-06, eta: 1:39:30, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1367, decode.acc_seg: 94.2214, aux.loss_ce: 0.0883, aux.acc_seg: 90.8921, loss: 0.2249, grad_norm: 2.0872 2023-02-17 07:35:55,255 - mmseg - INFO - Iter [143800/160000] lr: 6.075e-06, eta: 1:39:11, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1505, decode.acc_seg: 93.8010, aux.loss_ce: 0.1027, aux.acc_seg: 89.6424, loss: 0.2532, grad_norm: 2.8258 2023-02-17 07:36:13,258 - mmseg - INFO - Iter [143850/160000] lr: 6.057e-06, eta: 1:38:53, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1339, decode.acc_seg: 94.1780, aux.loss_ce: 0.0882, aux.acc_seg: 90.6824, loss: 0.2221, grad_norm: 2.3712 2023-02-17 07:36:31,226 - mmseg - INFO - Iter [143900/160000] lr: 6.038e-06, eta: 1:38:34, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1438, decode.acc_seg: 93.9792, aux.loss_ce: 0.0962, aux.acc_seg: 90.1183, loss: 0.2400, grad_norm: 2.2357 2023-02-17 07:36:49,273 - mmseg - INFO - Iter [143950/160000] lr: 6.019e-06, eta: 1:38:16, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1379, decode.acc_seg: 94.0275, aux.loss_ce: 0.0909, aux.acc_seg: 90.5657, loss: 0.2288, grad_norm: 1.7366 2023-02-17 07:37:07,052 - mmseg - INFO - Saving checkpoint at 144000 iterations 2023-02-17 07:37:08,130 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 07:37:08,130 - mmseg - INFO - Iter [144000/160000] lr: 6.000e-06, eta: 1:37:58, time: 0.377, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1434, decode.acc_seg: 94.0090, aux.loss_ce: 0.0964, aux.acc_seg: 90.1869, loss: 0.2398, grad_norm: 2.1527 2023-02-17 07:37:37,290 - mmseg - INFO - per class results: 2023-02-17 07:37:37,295 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 77.06 | 88.22 | | building | 82.56 | 92.11 | | sky | 94.49 | 97.38 | | floor | 80.43 | 90.32 | | tree | 74.4 | 88.31 | | ceiling | 84.27 | 92.81 | | road | 84.59 | 91.26 | | bed | 88.23 | 96.19 | | windowpane | 61.88 | 79.79 | | grass | 67.62 | 83.04 | | cabinet | 59.68 | 76.36 | | sidewalk | 67.83 | 82.47 | | person | 80.05 | 93.01 | | earth | 32.82 | 45.68 | | door | 50.26 | 71.34 | | table | 60.96 | 74.15 | | mountain | 59.23 | 74.93 | | plant | 51.02 | 60.5 | | curtain | 71.96 | 82.04 | | chair | 57.26 | 71.86 | | car | 81.57 | 90.67 | | water | 46.15 | 61.68 | | painting | 75.33 | 88.67 | | sofa | 65.12 | 78.5 | | shelf | 43.93 | 60.57 | | house | 49.39 | 60.09 | | sea | 53.92 | 83.7 | | mirror | 64.1 | 71.84 | | rug | 58.21 | 64.89 | | field | 27.34 | 40.85 | | armchair | 41.34 | 59.14 | | seat | 58.4 | 76.36 | | fence | 44.44 | 57.28 | | desk | 51.28 | 66.3 | | rock | 44.29 | 60.15 | | wardrobe | 45.39 | 61.79 | | lamp | 62.4 | 76.37 | | bathtub | 70.25 | 74.77 | | railing | 32.33 | 46.86 | | cushion | 58.58 | 72.83 | | base | 28.95 | 37.6 | | box | 28.63 | 35.17 | | column | 45.12 | 57.38 | | signboard | 36.46 | 52.38 | | chest of drawers | 39.07 | 51.96 | | counter | 21.76 | 28.95 | | sand | 42.49 | 72.29 | | sink | 70.95 | 82.59 | | skyscraper | 48.9 | 65.34 | | fireplace | 74.39 | 85.74 | | refrigerator | 77.76 | 86.46 | | grandstand | 45.59 | 62.94 | | path | 25.44 | 38.92 | | stairs | 31.27 | 36.48 | | runway | 70.03 | 93.46 | | case | 34.83 | 46.24 | | pool table | 93.02 | 94.69 | | pillow | 57.49 | 70.87 | | screen door | 48.11 | 52.81 | | stairway | 29.39 | 40.5 | | river | 9.6 | 17.22 | | bridge | 72.81 | 80.72 | | bookcase | 34.22 | 55.97 | | blind | 45.23 | 56.54 | | coffee table | 61.62 | 81.77 | | toilet | 83.88 | 91.28 | | flower | 39.22 | 58.64 | | book | 47.55 | 68.19 | | hill | 9.47 | 19.83 | | bench | 44.43 | 52.52 | | countertop | 56.24 | 69.37 | | stove | 76.8 | 83.95 | | palm | 52.78 | 78.1 | | kitchen island | 49.78 | 66.33 | | computer | 64.31 | 73.08 | | swivel chair | 42.69 | 64.16 | | boat | 37.69 | 50.41 | | bar | 28.59 | 36.39 | | arcade machine | 31.18 | 33.68 | | hovel | 15.5 | 21.46 | | bus | 88.9 | 96.37 | | towel | 65.38 | 71.52 | | light | 58.27 | 67.21 | | truck | 35.34 | 49.06 | | tower | 6.9 | 9.8 | | chandelier | 67.56 | 80.92 | | awning | 31.57 | 38.36 | | streetlight | 26.02 | 33.12 | | booth | 48.47 | 49.91 | | television receiver | 66.38 | 79.18 | | airplane | 56.01 | 63.56 | | dirt track | 15.41 | 21.95 | | apparel | 45.59 | 62.3 | | pole | 23.24 | 34.21 | | land | 3.43 | 4.71 | | bannister | 8.38 | 9.95 | | escalator | 19.41 | 19.81 | | ottoman | 47.79 | 70.03 | | bottle | 37.28 | 47.53 | | buffet | 40.54 | 44.38 | | poster | 24.37 | 32.03 | | stage | 19.9 | 27.84 | | van | 47.64 | 57.14 | | ship | 14.7 | 20.96 | | fountain | 26.31 | 27.25 | | conveyer belt | 72.53 | 85.44 | | canopy | 26.19 | 33.23 | | washer | 69.17 | 71.36 | | plaything | 25.8 | 38.9 | | swimming pool | 51.74 | 68.17 | | stool | 38.26 | 61.4 | | barrel | 58.94 | 67.71 | | basket | 32.84 | 44.51 | | waterfall | 37.23 | 47.15 | | tent | 94.08 | 98.78 | | bag | 17.06 | 22.49 | | minibike | 54.69 | 64.05 | | cradle | 80.92 | 94.96 | | oven | 43.19 | 58.75 | | ball | 40.5 | 50.16 | | food | 57.02 | 68.96 | | step | 18.38 | 26.37 | | tank | 35.18 | 41.17 | | trade name | 26.33 | 30.25 | | microwave | 78.77 | 86.46 | | pot | 44.72 | 53.43 | | animal | 53.52 | 55.65 | | bicycle | 52.52 | 79.57 | | lake | 55.3 | 60.51 | | dishwasher | 73.9 | 79.68 | | screen | 74.39 | 87.52 | | blanket | 11.23 | 12.91 | | sculpture | 63.06 | 78.58 | | hood | 57.35 | 71.41 | | sconce | 46.74 | 54.52 | | vase | 37.73 | 50.7 | | traffic light | 33.96 | 53.12 | | tray | 12.26 | 24.02 | | ashcan | 41.47 | 56.31 | | fan | 61.4 | 70.48 | | pier | 19.48 | 44.36 | | crt screen | 7.24 | 20.28 | | plate | 54.45 | 71.19 | | monitor | 6.24 | 9.89 | | bulletin board | 38.13 | 49.48 | | shower | 3.35 | 7.95 | | radiator | 64.15 | 68.63 | | glass | 12.74 | 13.78 | | clock | 33.01 | 39.8 | | flag | 49.74 | 54.75 | +---------------------+-------+-------+ 2023-02-17 07:37:37,296 - mmseg - INFO - Summary: 2023-02-17 07:37:37,296 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 82.73 | 48.04 | 59.24 | +-------+-------+-------+ 2023-02-17 07:37:37,297 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 07:37:37,297 - mmseg - INFO - Iter(val) [500] aAcc: 0.8273, mIoU: 0.4804, mAcc: 0.5924, IoU.wall: 0.7706, IoU.building: 0.8256, IoU.sky: 0.9449, IoU.floor: 0.8043, IoU.tree: 0.7440, IoU.ceiling: 0.8427, IoU.road: 0.8459, IoU.bed : 0.8823, IoU.windowpane: 0.6188, IoU.grass: 0.6762, IoU.cabinet: 0.5968, IoU.sidewalk: 0.6783, IoU.person: 0.8005, IoU.earth: 0.3282, IoU.door: 0.5026, IoU.table: 0.6096, IoU.mountain: 0.5923, IoU.plant: 0.5102, IoU.curtain: 0.7196, IoU.chair: 0.5726, IoU.car: 0.8157, IoU.water: 0.4615, IoU.painting: 0.7533, IoU.sofa: 0.6512, IoU.shelf: 0.4393, IoU.house: 0.4939, IoU.sea: 0.5392, IoU.mirror: 0.6410, IoU.rug: 0.5821, IoU.field: 0.2734, IoU.armchair: 0.4134, IoU.seat: 0.5840, IoU.fence: 0.4444, IoU.desk: 0.5128, IoU.rock: 0.4429, IoU.wardrobe: 0.4539, IoU.lamp: 0.6240, IoU.bathtub: 0.7025, IoU.railing: 0.3233, IoU.cushion: 0.5858, IoU.base: 0.2895, IoU.box: 0.2863, IoU.column: 0.4512, IoU.signboard: 0.3646, IoU.chest of drawers: 0.3907, IoU.counter: 0.2176, IoU.sand: 0.4249, IoU.sink: 0.7095, IoU.skyscraper: 0.4890, IoU.fireplace: 0.7439, IoU.refrigerator: 0.7776, IoU.grandstand: 0.4559, IoU.path: 0.2544, IoU.stairs: 0.3127, IoU.runway: 0.7003, IoU.case: 0.3483, IoU.pool table: 0.9302, IoU.pillow: 0.5749, IoU.screen door: 0.4811, IoU.stairway: 0.2939, IoU.river: 0.0960, IoU.bridge: 0.7281, IoU.bookcase: 0.3422, IoU.blind: 0.4523, IoU.coffee table: 0.6162, IoU.toilet: 0.8388, IoU.flower: 0.3922, IoU.book: 0.4755, IoU.hill: 0.0947, IoU.bench: 0.4443, IoU.countertop: 0.5624, IoU.stove: 0.7680, IoU.palm: 0.5278, IoU.kitchen island: 0.4978, IoU.computer: 0.6431, IoU.swivel chair: 0.4269, IoU.boat: 0.3769, IoU.bar: 0.2859, IoU.arcade machine: 0.3118, IoU.hovel: 0.1550, IoU.bus: 0.8890, IoU.towel: 0.6538, IoU.light: 0.5827, IoU.truck: 0.3534, IoU.tower: 0.0690, IoU.chandelier: 0.6756, IoU.awning: 0.3157, IoU.streetlight: 0.2602, IoU.booth: 0.4847, IoU.television receiver: 0.6638, IoU.airplane: 0.5601, IoU.dirt track: 0.1541, IoU.apparel: 0.4559, IoU.pole: 0.2324, IoU.land: 0.0343, IoU.bannister: 0.0838, IoU.escalator: 0.1941, IoU.ottoman: 0.4779, IoU.bottle: 0.3728, IoU.buffet: 0.4054, IoU.poster: 0.2437, IoU.stage: 0.1990, IoU.van: 0.4764, IoU.ship: 0.1470, IoU.fountain: 0.2631, IoU.conveyer belt: 0.7253, IoU.canopy: 0.2619, IoU.washer: 0.6917, IoU.plaything: 0.2580, IoU.swimming pool: 0.5174, IoU.stool: 0.3826, IoU.barrel: 0.5894, IoU.basket: 0.3284, IoU.waterfall: 0.3723, IoU.tent: 0.9408, IoU.bag: 0.1706, IoU.minibike: 0.5469, IoU.cradle: 0.8092, IoU.oven: 0.4319, IoU.ball: 0.4050, IoU.food: 0.5702, IoU.step: 0.1838, IoU.tank: 0.3518, IoU.trade name: 0.2633, IoU.microwave: 0.7877, IoU.pot: 0.4472, IoU.animal: 0.5352, IoU.bicycle: 0.5252, IoU.lake: 0.5530, IoU.dishwasher: 0.7390, IoU.screen: 0.7439, IoU.blanket: 0.1123, IoU.sculpture: 0.6306, IoU.hood: 0.5735, IoU.sconce: 0.4674, IoU.vase: 0.3773, IoU.traffic light: 0.3396, IoU.tray: 0.1226, IoU.ashcan: 0.4147, IoU.fan: 0.6140, IoU.pier: 0.1948, IoU.crt screen: 0.0724, IoU.plate: 0.5445, IoU.monitor: 0.0624, IoU.bulletin board: 0.3813, IoU.shower: 0.0335, IoU.radiator: 0.6415, IoU.glass: 0.1274, IoU.clock: 0.3301, IoU.flag: 0.4974, Acc.wall: 0.8822, Acc.building: 0.9211, Acc.sky: 0.9738, Acc.floor: 0.9032, Acc.tree: 0.8831, Acc.ceiling: 0.9281, Acc.road: 0.9126, Acc.bed : 0.9619, Acc.windowpane: 0.7979, Acc.grass: 0.8304, Acc.cabinet: 0.7636, Acc.sidewalk: 0.8247, Acc.person: 0.9301, Acc.earth: 0.4568, Acc.door: 0.7134, Acc.table: 0.7415, Acc.mountain: 0.7493, Acc.plant: 0.6050, Acc.curtain: 0.8204, Acc.chair: 0.7186, Acc.car: 0.9067, Acc.water: 0.6168, Acc.painting: 0.8867, Acc.sofa: 0.7850, Acc.shelf: 0.6057, Acc.house: 0.6009, Acc.sea: 0.8370, Acc.mirror: 0.7184, Acc.rug: 0.6489, Acc.field: 0.4085, Acc.armchair: 0.5914, Acc.seat: 0.7636, Acc.fence: 0.5728, Acc.desk: 0.6630, Acc.rock: 0.6015, Acc.wardrobe: 0.6179, Acc.lamp: 0.7637, Acc.bathtub: 0.7477, Acc.railing: 0.4686, Acc.cushion: 0.7283, Acc.base: 0.3760, Acc.box: 0.3517, Acc.column: 0.5738, Acc.signboard: 0.5238, Acc.chest of drawers: 0.5196, Acc.counter: 0.2895, Acc.sand: 0.7229, Acc.sink: 0.8259, Acc.skyscraper: 0.6534, Acc.fireplace: 0.8574, Acc.refrigerator: 0.8646, Acc.grandstand: 0.6294, Acc.path: 0.3892, Acc.stairs: 0.3648, Acc.runway: 0.9346, Acc.case: 0.4624, Acc.pool table: 0.9469, Acc.pillow: 0.7087, Acc.screen door: 0.5281, Acc.stairway: 0.4050, Acc.river: 0.1722, Acc.bridge: 0.8072, Acc.bookcase: 0.5597, Acc.blind: 0.5654, Acc.coffee table: 0.8177, Acc.toilet: 0.9128, Acc.flower: 0.5864, Acc.book: 0.6819, Acc.hill: 0.1983, Acc.bench: 0.5252, Acc.countertop: 0.6937, Acc.stove: 0.8395, Acc.palm: 0.7810, Acc.kitchen island: 0.6633, Acc.computer: 0.7308, Acc.swivel chair: 0.6416, Acc.boat: 0.5041, Acc.bar: 0.3639, Acc.arcade machine: 0.3368, Acc.hovel: 0.2146, Acc.bus: 0.9637, Acc.towel: 0.7152, Acc.light: 0.6721, Acc.truck: 0.4906, Acc.tower: 0.0980, Acc.chandelier: 0.8092, Acc.awning: 0.3836, Acc.streetlight: 0.3312, Acc.booth: 0.4991, Acc.television receiver: 0.7918, Acc.airplane: 0.6356, Acc.dirt track: 0.2195, Acc.apparel: 0.6230, Acc.pole: 0.3421, Acc.land: 0.0471, Acc.bannister: 0.0995, Acc.escalator: 0.1981, Acc.ottoman: 0.7003, Acc.bottle: 0.4753, Acc.buffet: 0.4438, Acc.poster: 0.3203, Acc.stage: 0.2784, Acc.van: 0.5714, Acc.ship: 0.2096, Acc.fountain: 0.2725, Acc.conveyer belt: 0.8544, Acc.canopy: 0.3323, Acc.washer: 0.7136, Acc.plaything: 0.3890, Acc.swimming pool: 0.6817, Acc.stool: 0.6140, Acc.barrel: 0.6771, Acc.basket: 0.4451, Acc.waterfall: 0.4715, Acc.tent: 0.9878, Acc.bag: 0.2249, Acc.minibike: 0.6405, Acc.cradle: 0.9496, Acc.oven: 0.5875, Acc.ball: 0.5016, Acc.food: 0.6896, Acc.step: 0.2637, Acc.tank: 0.4117, Acc.trade name: 0.3025, Acc.microwave: 0.8646, Acc.pot: 0.5343, Acc.animal: 0.5565, Acc.bicycle: 0.7957, Acc.lake: 0.6051, Acc.dishwasher: 0.7968, Acc.screen: 0.8752, Acc.blanket: 0.1291, Acc.sculpture: 0.7858, Acc.hood: 0.7141, Acc.sconce: 0.5452, Acc.vase: 0.5070, Acc.traffic light: 0.5312, Acc.tray: 0.2402, Acc.ashcan: 0.5631, Acc.fan: 0.7048, Acc.pier: 0.4436, Acc.crt screen: 0.2028, Acc.plate: 0.7119, Acc.monitor: 0.0989, Acc.bulletin board: 0.4948, Acc.shower: 0.0795, Acc.radiator: 0.6863, Acc.glass: 0.1378, Acc.clock: 0.3980, Acc.flag: 0.5475 2023-02-17 07:37:55,218 - mmseg - INFO - Iter [144050/160000] lr: 5.982e-06, eta: 1:37:47, time: 0.942, data_time: 0.590, memory: 16596, decode.loss_ce: 0.1438, decode.acc_seg: 93.9499, aux.loss_ce: 0.0958, aux.acc_seg: 90.2268, loss: 0.2396, grad_norm: 2.5419 2023-02-17 07:38:13,182 - mmseg - INFO - Iter [144100/160000] lr: 5.963e-06, eta: 1:37:28, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1457, decode.acc_seg: 94.0269, aux.loss_ce: 0.0983, aux.acc_seg: 90.0998, loss: 0.2440, grad_norm: 2.2477 2023-02-17 07:38:31,273 - mmseg - INFO - Iter [144150/160000] lr: 5.944e-06, eta: 1:37:10, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1435, decode.acc_seg: 94.0327, aux.loss_ce: 0.0984, aux.acc_seg: 90.0788, loss: 0.2419, grad_norm: 2.1478 2023-02-17 07:38:49,400 - mmseg - INFO - Iter [144200/160000] lr: 5.925e-06, eta: 1:36:52, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1462, decode.acc_seg: 93.8080, aux.loss_ce: 0.0969, aux.acc_seg: 90.0888, loss: 0.2430, grad_norm: 2.5779 2023-02-17 07:39:07,595 - mmseg - INFO - Iter [144250/160000] lr: 5.907e-06, eta: 1:36:33, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1354, decode.acc_seg: 94.1379, aux.loss_ce: 0.0927, aux.acc_seg: 90.3438, loss: 0.2281, grad_norm: 2.2543 2023-02-17 07:39:25,661 - mmseg - INFO - Iter [144300/160000] lr: 5.888e-06, eta: 1:36:15, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1371, decode.acc_seg: 94.2649, aux.loss_ce: 0.0911, aux.acc_seg: 90.7117, loss: 0.2281, grad_norm: 2.5292 2023-02-17 07:39:43,908 - mmseg - INFO - Iter [144350/160000] lr: 5.869e-06, eta: 1:35:56, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1360, decode.acc_seg: 94.2819, aux.loss_ce: 0.0934, aux.acc_seg: 90.3982, loss: 0.2294, grad_norm: 2.3876 2023-02-17 07:40:01,818 - mmseg - INFO - Iter [144400/160000] lr: 5.850e-06, eta: 1:35:38, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1517, decode.acc_seg: 93.7461, aux.loss_ce: 0.1008, aux.acc_seg: 89.6780, loss: 0.2525, grad_norm: 2.2972 2023-02-17 07:40:20,151 - mmseg - INFO - Iter [144450/160000] lr: 5.832e-06, eta: 1:35:19, time: 0.367, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1417, decode.acc_seg: 93.9888, aux.loss_ce: 0.0925, aux.acc_seg: 90.4105, loss: 0.2342, grad_norm: 2.6261 2023-02-17 07:40:38,219 - mmseg - INFO - Iter [144500/160000] lr: 5.813e-06, eta: 1:35:01, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1418, decode.acc_seg: 93.9532, aux.loss_ce: 0.0932, aux.acc_seg: 90.4112, loss: 0.2351, grad_norm: 2.5364 2023-02-17 07:40:56,234 - mmseg - INFO - Iter [144550/160000] lr: 5.794e-06, eta: 1:34:42, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1395, decode.acc_seg: 94.1696, aux.loss_ce: 0.0933, aux.acc_seg: 90.4167, loss: 0.2328, grad_norm: 2.2012 2023-02-17 07:41:14,187 - mmseg - INFO - Iter [144600/160000] lr: 5.775e-06, eta: 1:34:24, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1462, decode.acc_seg: 93.8882, aux.loss_ce: 0.0958, aux.acc_seg: 90.2011, loss: 0.2420, grad_norm: 2.4012 2023-02-17 07:41:34,408 - mmseg - INFO - Iter [144650/160000] lr: 5.757e-06, eta: 1:34:06, time: 0.404, data_time: 0.054, memory: 16596, decode.loss_ce: 0.1383, decode.acc_seg: 94.2923, aux.loss_ce: 0.0918, aux.acc_seg: 90.6380, loss: 0.2301, grad_norm: 2.1151 2023-02-17 07:41:52,205 - mmseg - INFO - Iter [144700/160000] lr: 5.738e-06, eta: 1:33:47, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1335, decode.acc_seg: 94.3479, aux.loss_ce: 0.0915, aux.acc_seg: 90.5934, loss: 0.2250, grad_norm: 2.1042 2023-02-17 07:42:10,417 - mmseg - INFO - Iter [144750/160000] lr: 5.719e-06, eta: 1:33:29, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1373, decode.acc_seg: 94.2803, aux.loss_ce: 0.0913, aux.acc_seg: 90.7372, loss: 0.2286, grad_norm: 2.2031 2023-02-17 07:42:28,622 - mmseg - INFO - Iter [144800/160000] lr: 5.700e-06, eta: 1:33:10, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1440, decode.acc_seg: 93.9657, aux.loss_ce: 0.0969, aux.acc_seg: 90.0732, loss: 0.2410, grad_norm: 2.4068 2023-02-17 07:42:46,698 - mmseg - INFO - Iter [144850/160000] lr: 5.682e-06, eta: 1:32:52, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1430, decode.acc_seg: 93.9899, aux.loss_ce: 0.0949, aux.acc_seg: 90.3287, loss: 0.2379, grad_norm: 2.2072 2023-02-17 07:43:04,758 - mmseg - INFO - Iter [144900/160000] lr: 5.663e-06, eta: 1:32:34, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1375, decode.acc_seg: 94.2409, aux.loss_ce: 0.0894, aux.acc_seg: 90.9559, loss: 0.2268, grad_norm: 2.0579 2023-02-17 07:43:22,893 - mmseg - INFO - Iter [144950/160000] lr: 5.644e-06, eta: 1:32:15, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1400, decode.acc_seg: 93.9859, aux.loss_ce: 0.0935, aux.acc_seg: 90.2549, loss: 0.2335, grad_norm: 2.4884 2023-02-17 07:43:41,364 - mmseg - INFO - Saving checkpoint at 145000 iterations 2023-02-17 07:43:42,541 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 07:43:42,541 - mmseg - INFO - Iter [145000/160000] lr: 5.625e-06, eta: 1:31:57, time: 0.393, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1461, decode.acc_seg: 93.9333, aux.loss_ce: 0.0966, aux.acc_seg: 90.1514, loss: 0.2428, grad_norm: 2.2409 2023-02-17 07:44:00,541 - mmseg - INFO - Iter [145050/160000] lr: 5.607e-06, eta: 1:31:39, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1430, decode.acc_seg: 93.9335, aux.loss_ce: 0.0965, aux.acc_seg: 89.7974, loss: 0.2395, grad_norm: 2.7975 2023-02-17 07:44:18,557 - mmseg - INFO - Iter [145100/160000] lr: 5.588e-06, eta: 1:31:20, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1426, decode.acc_seg: 94.0763, aux.loss_ce: 0.0934, aux.acc_seg: 90.4762, loss: 0.2361, grad_norm: 2.1992 2023-02-17 07:44:36,697 - mmseg - INFO - Iter [145150/160000] lr: 5.569e-06, eta: 1:31:02, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1436, decode.acc_seg: 93.9221, aux.loss_ce: 0.0975, aux.acc_seg: 89.8430, loss: 0.2411, grad_norm: 2.6337 2023-02-17 07:44:54,706 - mmseg - INFO - Iter [145200/160000] lr: 5.550e-06, eta: 1:30:43, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1432, decode.acc_seg: 93.9004, aux.loss_ce: 0.0926, aux.acc_seg: 90.4149, loss: 0.2358, grad_norm: 2.2777 2023-02-17 07:45:12,932 - mmseg - INFO - Iter [145250/160000] lr: 5.532e-06, eta: 1:30:25, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1520, decode.acc_seg: 93.7534, aux.loss_ce: 0.1008, aux.acc_seg: 90.0224, loss: 0.2527, grad_norm: 2.5848 2023-02-17 07:45:31,002 - mmseg - INFO - Iter [145300/160000] lr: 5.513e-06, eta: 1:30:06, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1462, decode.acc_seg: 93.7794, aux.loss_ce: 0.1011, aux.acc_seg: 89.4706, loss: 0.2473, grad_norm: 2.7839 2023-02-17 07:45:48,993 - mmseg - INFO - Iter [145350/160000] lr: 5.494e-06, eta: 1:29:48, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1320, decode.acc_seg: 94.4283, aux.loss_ce: 0.0942, aux.acc_seg: 90.3420, loss: 0.2262, grad_norm: 2.1748 2023-02-17 07:46:07,085 - mmseg - INFO - Iter [145400/160000] lr: 5.475e-06, eta: 1:29:29, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1416, decode.acc_seg: 94.1315, aux.loss_ce: 0.0971, aux.acc_seg: 90.1805, loss: 0.2387, grad_norm: 2.7007 2023-02-17 07:46:25,001 - mmseg - INFO - Iter [145450/160000] lr: 5.457e-06, eta: 1:29:11, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1416, decode.acc_seg: 94.0948, aux.loss_ce: 0.0960, aux.acc_seg: 90.1770, loss: 0.2376, grad_norm: 2.1107 2023-02-17 07:46:43,036 - mmseg - INFO - Iter [145500/160000] lr: 5.438e-06, eta: 1:28:52, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1455, decode.acc_seg: 93.8566, aux.loss_ce: 0.0940, aux.acc_seg: 90.3559, loss: 0.2395, grad_norm: 2.3859 2023-02-17 07:47:01,028 - mmseg - INFO - Iter [145550/160000] lr: 5.419e-06, eta: 1:28:34, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1371, decode.acc_seg: 94.1955, aux.loss_ce: 0.0932, aux.acc_seg: 90.3330, loss: 0.2302, grad_norm: 2.5579 2023-02-17 07:47:18,957 - mmseg - INFO - Iter [145600/160000] lr: 5.400e-06, eta: 1:28:15, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1472, decode.acc_seg: 93.7712, aux.loss_ce: 0.1009, aux.acc_seg: 89.7159, loss: 0.2482, grad_norm: 2.7124 2023-02-17 07:47:37,114 - mmseg - INFO - Iter [145650/160000] lr: 5.382e-06, eta: 1:27:57, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1486, decode.acc_seg: 93.7840, aux.loss_ce: 0.0958, aux.acc_seg: 90.3091, loss: 0.2444, grad_norm: 2.6222 2023-02-17 07:47:55,155 - mmseg - INFO - Iter [145700/160000] lr: 5.363e-06, eta: 1:27:38, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1460, decode.acc_seg: 93.8649, aux.loss_ce: 0.0950, aux.acc_seg: 90.1950, loss: 0.2411, grad_norm: 2.6406 2023-02-17 07:48:13,443 - mmseg - INFO - Iter [145750/160000] lr: 5.344e-06, eta: 1:27:20, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1443, decode.acc_seg: 93.9319, aux.loss_ce: 0.0974, aux.acc_seg: 90.0762, loss: 0.2416, grad_norm: 2.6733 2023-02-17 07:48:31,538 - mmseg - INFO - Iter [145800/160000] lr: 5.325e-06, eta: 1:27:02, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1457, decode.acc_seg: 94.0805, aux.loss_ce: 0.0961, aux.acc_seg: 90.4585, loss: 0.2417, grad_norm: 2.1233 2023-02-17 07:48:49,647 - mmseg - INFO - Iter [145850/160000] lr: 5.307e-06, eta: 1:26:43, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1400, decode.acc_seg: 94.1116, aux.loss_ce: 0.0955, aux.acc_seg: 90.3486, loss: 0.2355, grad_norm: 2.2415 2023-02-17 07:49:09,904 - mmseg - INFO - Iter [145900/160000] lr: 5.288e-06, eta: 1:26:25, time: 0.405, data_time: 0.052, memory: 16596, decode.loss_ce: 0.1376, decode.acc_seg: 94.1988, aux.loss_ce: 0.0910, aux.acc_seg: 90.6286, loss: 0.2286, grad_norm: 2.3512 2023-02-17 07:49:27,941 - mmseg - INFO - Iter [145950/160000] lr: 5.269e-06, eta: 1:26:07, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1488, decode.acc_seg: 93.8678, aux.loss_ce: 0.0962, aux.acc_seg: 90.2654, loss: 0.2450, grad_norm: 2.6471 2023-02-17 07:49:45,769 - mmseg - INFO - Saving checkpoint at 146000 iterations 2023-02-17 07:49:46,837 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 07:49:46,837 - mmseg - INFO - Iter [146000/160000] lr: 5.250e-06, eta: 1:25:48, time: 0.378, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1472, decode.acc_seg: 93.7728, aux.loss_ce: 0.0956, aux.acc_seg: 90.1380, loss: 0.2428, grad_norm: 2.4407 2023-02-17 07:50:04,472 - mmseg - INFO - Iter [146050/160000] lr: 5.232e-06, eta: 1:25:30, time: 0.353, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1370, decode.acc_seg: 94.3202, aux.loss_ce: 0.0908, aux.acc_seg: 90.8488, loss: 0.2277, grad_norm: 2.3207 2023-02-17 07:50:22,269 - mmseg - INFO - Iter [146100/160000] lr: 5.213e-06, eta: 1:25:11, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1395, decode.acc_seg: 94.0984, aux.loss_ce: 0.0941, aux.acc_seg: 90.3165, loss: 0.2336, grad_norm: 2.4601 2023-02-17 07:50:40,058 - mmseg - INFO - Iter [146150/160000] lr: 5.194e-06, eta: 1:24:53, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1381, decode.acc_seg: 94.2968, aux.loss_ce: 0.0901, aux.acc_seg: 90.8680, loss: 0.2282, grad_norm: 2.4379 2023-02-17 07:50:58,184 - mmseg - INFO - Iter [146200/160000] lr: 5.175e-06, eta: 1:24:34, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1474, decode.acc_seg: 93.8007, aux.loss_ce: 0.0964, aux.acc_seg: 90.0317, loss: 0.2438, grad_norm: 2.5504 2023-02-17 07:51:16,203 - mmseg - INFO - Iter [146250/160000] lr: 5.157e-06, eta: 1:24:16, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1381, decode.acc_seg: 94.2432, aux.loss_ce: 0.0913, aux.acc_seg: 90.5894, loss: 0.2294, grad_norm: 2.3151 2023-02-17 07:51:34,380 - mmseg - INFO - Iter [146300/160000] lr: 5.138e-06, eta: 1:23:57, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1418, decode.acc_seg: 93.9832, aux.loss_ce: 0.0941, aux.acc_seg: 90.3614, loss: 0.2359, grad_norm: 2.2219 2023-02-17 07:51:52,601 - mmseg - INFO - Iter [146350/160000] lr: 5.119e-06, eta: 1:23:39, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1383, decode.acc_seg: 94.1344, aux.loss_ce: 0.0868, aux.acc_seg: 90.9306, loss: 0.2251, grad_norm: 2.5019 2023-02-17 07:52:10,642 - mmseg - INFO - Iter [146400/160000] lr: 5.100e-06, eta: 1:23:21, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1430, decode.acc_seg: 93.9995, aux.loss_ce: 0.0950, aux.acc_seg: 90.1081, loss: 0.2380, grad_norm: 2.3680 2023-02-17 07:52:28,777 - mmseg - INFO - Iter [146450/160000] lr: 5.082e-06, eta: 1:23:02, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1460, decode.acc_seg: 93.7497, aux.loss_ce: 0.0981, aux.acc_seg: 89.8295, loss: 0.2441, grad_norm: 2.5279 2023-02-17 07:52:46,946 - mmseg - INFO - Iter [146500/160000] lr: 5.063e-06, eta: 1:22:44, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1451, decode.acc_seg: 94.0809, aux.loss_ce: 0.0965, aux.acc_seg: 90.3119, loss: 0.2417, grad_norm: 2.2401 2023-02-17 07:53:04,789 - mmseg - INFO - Iter [146550/160000] lr: 5.044e-06, eta: 1:22:25, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1432, decode.acc_seg: 93.9959, aux.loss_ce: 0.0940, aux.acc_seg: 90.3606, loss: 0.2372, grad_norm: 2.2358 2023-02-17 07:53:23,059 - mmseg - INFO - Iter [146600/160000] lr: 5.025e-06, eta: 1:22:07, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1434, decode.acc_seg: 93.9495, aux.loss_ce: 0.0959, aux.acc_seg: 90.2567, loss: 0.2393, grad_norm: 2.3879 2023-02-17 07:53:40,940 - mmseg - INFO - Iter [146650/160000] lr: 5.007e-06, eta: 1:21:48, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1398, decode.acc_seg: 94.0783, aux.loss_ce: 0.0915, aux.acc_seg: 90.6107, loss: 0.2313, grad_norm: 2.2047 2023-02-17 07:53:59,050 - mmseg - INFO - Iter [146700/160000] lr: 4.988e-06, eta: 1:21:30, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1383, decode.acc_seg: 94.1930, aux.loss_ce: 0.0915, aux.acc_seg: 90.7234, loss: 0.2298, grad_norm: 1.8760 2023-02-17 07:54:17,276 - mmseg - INFO - Iter [146750/160000] lr: 4.969e-06, eta: 1:21:11, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1331, decode.acc_seg: 94.3074, aux.loss_ce: 0.0890, aux.acc_seg: 90.8012, loss: 0.2221, grad_norm: 2.0592 2023-02-17 07:54:35,171 - mmseg - INFO - Iter [146800/160000] lr: 4.950e-06, eta: 1:20:53, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1483, decode.acc_seg: 93.7775, aux.loss_ce: 0.0995, aux.acc_seg: 89.8177, loss: 0.2478, grad_norm: 2.4052 2023-02-17 07:54:53,307 - mmseg - INFO - Iter [146850/160000] lr: 4.932e-06, eta: 1:20:34, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1387, decode.acc_seg: 94.2551, aux.loss_ce: 0.0980, aux.acc_seg: 90.1124, loss: 0.2366, grad_norm: 2.5765 2023-02-17 07:55:11,022 - mmseg - INFO - Iter [146900/160000] lr: 4.913e-06, eta: 1:20:16, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1462, decode.acc_seg: 93.8851, aux.loss_ce: 0.0966, aux.acc_seg: 90.2767, loss: 0.2428, grad_norm: 2.4714 2023-02-17 07:55:29,191 - mmseg - INFO - Iter [146950/160000] lr: 4.894e-06, eta: 1:19:58, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1328, decode.acc_seg: 94.3333, aux.loss_ce: 0.0905, aux.acc_seg: 90.6016, loss: 0.2233, grad_norm: 2.1878 2023-02-17 07:55:47,331 - mmseg - INFO - Saving checkpoint at 147000 iterations 2023-02-17 07:55:48,451 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 07:55:48,451 - mmseg - INFO - Iter [147000/160000] lr: 4.875e-06, eta: 1:19:39, time: 0.385, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1440, decode.acc_seg: 93.8332, aux.loss_ce: 0.0965, aux.acc_seg: 89.8969, loss: 0.2405, grad_norm: 2.3024 2023-02-17 07:56:06,384 - mmseg - INFO - Iter [147050/160000] lr: 4.857e-06, eta: 1:19:21, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1382, decode.acc_seg: 94.3123, aux.loss_ce: 0.0953, aux.acc_seg: 90.3834, loss: 0.2335, grad_norm: 2.1777 2023-02-17 07:56:24,638 - mmseg - INFO - Iter [147100/160000] lr: 4.838e-06, eta: 1:19:02, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1427, decode.acc_seg: 94.1291, aux.loss_ce: 0.0938, aux.acc_seg: 90.5320, loss: 0.2364, grad_norm: 2.2124 2023-02-17 07:56:45,128 - mmseg - INFO - Iter [147150/160000] lr: 4.819e-06, eta: 1:18:44, time: 0.410, data_time: 0.051, memory: 16596, decode.loss_ce: 0.1355, decode.acc_seg: 94.1740, aux.loss_ce: 0.0919, aux.acc_seg: 90.3817, loss: 0.2274, grad_norm: 2.2050 2023-02-17 07:57:03,060 - mmseg - INFO - Iter [147200/160000] lr: 4.800e-06, eta: 1:18:26, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1411, decode.acc_seg: 94.2625, aux.loss_ce: 0.0944, aux.acc_seg: 90.4210, loss: 0.2355, grad_norm: 2.2994 2023-02-17 07:57:21,026 - mmseg - INFO - Iter [147250/160000] lr: 4.782e-06, eta: 1:18:08, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1450, decode.acc_seg: 93.9736, aux.loss_ce: 0.0941, aux.acc_seg: 90.5647, loss: 0.2391, grad_norm: 2.5158 2023-02-17 07:57:39,053 - mmseg - INFO - Iter [147300/160000] lr: 4.763e-06, eta: 1:17:49, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1343, decode.acc_seg: 94.3728, aux.loss_ce: 0.0921, aux.acc_seg: 90.5256, loss: 0.2264, grad_norm: 2.0068 2023-02-17 07:57:57,340 - mmseg - INFO - Iter [147350/160000] lr: 4.744e-06, eta: 1:17:31, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1365, decode.acc_seg: 94.3254, aux.loss_ce: 0.0911, aux.acc_seg: 90.7446, loss: 0.2276, grad_norm: 2.0442 2023-02-17 07:58:15,301 - mmseg - INFO - Iter [147400/160000] lr: 4.725e-06, eta: 1:17:12, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1423, decode.acc_seg: 93.9316, aux.loss_ce: 0.0967, aux.acc_seg: 89.9426, loss: 0.2390, grad_norm: 2.0241 2023-02-17 07:58:33,200 - mmseg - INFO - Iter [147450/160000] lr: 4.707e-06, eta: 1:16:54, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1404, decode.acc_seg: 94.1012, aux.loss_ce: 0.0940, aux.acc_seg: 90.4572, loss: 0.2344, grad_norm: 2.1289 2023-02-17 07:58:51,162 - mmseg - INFO - Iter [147500/160000] lr: 4.688e-06, eta: 1:16:35, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1442, decode.acc_seg: 93.8933, aux.loss_ce: 0.0962, aux.acc_seg: 90.0869, loss: 0.2404, grad_norm: 2.4301 2023-02-17 07:59:09,182 - mmseg - INFO - Iter [147550/160000] lr: 4.669e-06, eta: 1:16:17, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1365, decode.acc_seg: 94.2225, aux.loss_ce: 0.0921, aux.acc_seg: 90.3298, loss: 0.2286, grad_norm: 2.1417 2023-02-17 07:59:27,194 - mmseg - INFO - Iter [147600/160000] lr: 4.650e-06, eta: 1:15:58, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1370, decode.acc_seg: 94.2109, aux.loss_ce: 0.0950, aux.acc_seg: 90.2271, loss: 0.2320, grad_norm: 2.4888 2023-02-17 07:59:45,368 - mmseg - INFO - Iter [147650/160000] lr: 4.632e-06, eta: 1:15:40, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1393, decode.acc_seg: 94.0389, aux.loss_ce: 0.0966, aux.acc_seg: 90.1610, loss: 0.2359, grad_norm: 2.5405 2023-02-17 08:00:03,621 - mmseg - INFO - Iter [147700/160000] lr: 4.613e-06, eta: 1:15:22, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1382, decode.acc_seg: 94.2331, aux.loss_ce: 0.0907, aux.acc_seg: 90.7716, loss: 0.2288, grad_norm: 2.0320 2023-02-17 08:00:21,535 - mmseg - INFO - Iter [147750/160000] lr: 4.594e-06, eta: 1:15:03, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1441, decode.acc_seg: 94.0173, aux.loss_ce: 0.0955, aux.acc_seg: 90.2306, loss: 0.2396, grad_norm: 2.2993 2023-02-17 08:00:39,517 - mmseg - INFO - Iter [147800/160000] lr: 4.575e-06, eta: 1:14:45, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1359, decode.acc_seg: 94.2678, aux.loss_ce: 0.0926, aux.acc_seg: 90.4549, loss: 0.2286, grad_norm: 2.3249 2023-02-17 08:00:57,527 - mmseg - INFO - Iter [147850/160000] lr: 4.557e-06, eta: 1:14:26, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1391, decode.acc_seg: 94.0649, aux.loss_ce: 0.0932, aux.acc_seg: 90.4082, loss: 0.2324, grad_norm: 2.9842 2023-02-17 08:01:15,501 - mmseg - INFO - Iter [147900/160000] lr: 4.538e-06, eta: 1:14:08, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1504, decode.acc_seg: 93.7376, aux.loss_ce: 0.0970, aux.acc_seg: 90.0978, loss: 0.2474, grad_norm: 2.8027 2023-02-17 08:01:33,516 - mmseg - INFO - Iter [147950/160000] lr: 4.519e-06, eta: 1:13:49, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1334, decode.acc_seg: 94.4755, aux.loss_ce: 0.0927, aux.acc_seg: 90.5312, loss: 0.2262, grad_norm: 2.1571 2023-02-17 08:01:51,579 - mmseg - INFO - Saving checkpoint at 148000 iterations 2023-02-17 08:01:52,658 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 08:01:52,659 - mmseg - INFO - Iter [148000/160000] lr: 4.500e-06, eta: 1:13:31, time: 0.384, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1316, decode.acc_seg: 94.5347, aux.loss_ce: 0.0903, aux.acc_seg: 90.7324, loss: 0.2219, grad_norm: 2.0111 2023-02-17 08:02:10,681 - mmseg - INFO - Iter [148050/160000] lr: 4.482e-06, eta: 1:13:13, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1386, decode.acc_seg: 94.1264, aux.loss_ce: 0.0904, aux.acc_seg: 90.7270, loss: 0.2290, grad_norm: 2.4473 2023-02-17 08:02:28,688 - mmseg - INFO - Iter [148100/160000] lr: 4.463e-06, eta: 1:12:54, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1450, decode.acc_seg: 93.9844, aux.loss_ce: 0.0961, aux.acc_seg: 90.2114, loss: 0.2411, grad_norm: 2.1458 2023-02-17 08:02:46,698 - mmseg - INFO - Iter [148150/160000] lr: 4.444e-06, eta: 1:12:36, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1375, decode.acc_seg: 94.2224, aux.loss_ce: 0.0939, aux.acc_seg: 90.5253, loss: 0.2314, grad_norm: 2.4455 2023-02-17 08:03:04,935 - mmseg - INFO - Iter [148200/160000] lr: 4.425e-06, eta: 1:12:17, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1417, decode.acc_seg: 94.1280, aux.loss_ce: 0.0930, aux.acc_seg: 90.4835, loss: 0.2347, grad_norm: 2.2909 2023-02-17 08:03:23,195 - mmseg - INFO - Iter [148250/160000] lr: 4.407e-06, eta: 1:11:59, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1472, decode.acc_seg: 93.8006, aux.loss_ce: 0.0999, aux.acc_seg: 90.1749, loss: 0.2471, grad_norm: 2.5132 2023-02-17 08:03:41,100 - mmseg - INFO - Iter [148300/160000] lr: 4.388e-06, eta: 1:11:40, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1425, decode.acc_seg: 94.0484, aux.loss_ce: 0.0938, aux.acc_seg: 90.4390, loss: 0.2363, grad_norm: 2.8181 2023-02-17 08:03:59,372 - mmseg - INFO - Iter [148350/160000] lr: 4.369e-06, eta: 1:11:22, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1442, decode.acc_seg: 93.9016, aux.loss_ce: 0.0928, aux.acc_seg: 90.3601, loss: 0.2370, grad_norm: 1.9017 2023-02-17 08:04:17,394 - mmseg - INFO - Iter [148400/160000] lr: 4.350e-06, eta: 1:11:04, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1400, decode.acc_seg: 94.1829, aux.loss_ce: 0.0942, aux.acc_seg: 90.3010, loss: 0.2342, grad_norm: 2.3863 2023-02-17 08:04:37,732 - mmseg - INFO - Iter [148450/160000] lr: 4.332e-06, eta: 1:10:45, time: 0.407, data_time: 0.052, memory: 16596, decode.loss_ce: 0.1440, decode.acc_seg: 93.8121, aux.loss_ce: 0.0940, aux.acc_seg: 90.2166, loss: 0.2380, grad_norm: 2.0972 2023-02-17 08:04:55,685 - mmseg - INFO - Iter [148500/160000] lr: 4.313e-06, eta: 1:10:27, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1342, decode.acc_seg: 94.2831, aux.loss_ce: 0.0886, aux.acc_seg: 90.7243, loss: 0.2228, grad_norm: 2.1092 2023-02-17 08:05:13,448 - mmseg - INFO - Iter [148550/160000] lr: 4.294e-06, eta: 1:10:09, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1410, decode.acc_seg: 94.1094, aux.loss_ce: 0.0969, aux.acc_seg: 90.1622, loss: 0.2379, grad_norm: 2.5785 2023-02-17 08:05:31,410 - mmseg - INFO - Iter [148600/160000] lr: 4.275e-06, eta: 1:09:50, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1319, decode.acc_seg: 94.4819, aux.loss_ce: 0.0914, aux.acc_seg: 90.7866, loss: 0.2233, grad_norm: 2.0080 2023-02-17 08:05:49,476 - mmseg - INFO - Iter [148650/160000] lr: 4.257e-06, eta: 1:09:32, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1353, decode.acc_seg: 94.2640, aux.loss_ce: 0.0938, aux.acc_seg: 90.2957, loss: 0.2292, grad_norm: 2.2453 2023-02-17 08:06:07,732 - mmseg - INFO - Iter [148700/160000] lr: 4.238e-06, eta: 1:09:13, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1433, decode.acc_seg: 93.7934, aux.loss_ce: 0.0954, aux.acc_seg: 89.9893, loss: 0.2387, grad_norm: 2.7730 2023-02-17 08:06:25,870 - mmseg - INFO - Iter [148750/160000] lr: 4.219e-06, eta: 1:08:55, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1390, decode.acc_seg: 94.2161, aux.loss_ce: 0.0940, aux.acc_seg: 90.3873, loss: 0.2330, grad_norm: 2.0685 2023-02-17 08:06:43,983 - mmseg - INFO - Iter [148800/160000] lr: 4.200e-06, eta: 1:08:36, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1413, decode.acc_seg: 94.1399, aux.loss_ce: 0.0955, aux.acc_seg: 90.3047, loss: 0.2368, grad_norm: 2.8667 2023-02-17 08:07:02,007 - mmseg - INFO - Iter [148850/160000] lr: 4.182e-06, eta: 1:08:18, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1426, decode.acc_seg: 93.9518, aux.loss_ce: 0.0945, aux.acc_seg: 90.3373, loss: 0.2371, grad_norm: 2.2891 2023-02-17 08:07:20,031 - mmseg - INFO - Iter [148900/160000] lr: 4.163e-06, eta: 1:08:00, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1354, decode.acc_seg: 94.3425, aux.loss_ce: 0.0927, aux.acc_seg: 90.4620, loss: 0.2281, grad_norm: 1.8495 2023-02-17 08:07:37,779 - mmseg - INFO - Iter [148950/160000] lr: 4.144e-06, eta: 1:07:41, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1328, decode.acc_seg: 94.3577, aux.loss_ce: 0.0888, aux.acc_seg: 90.8207, loss: 0.2216, grad_norm: 2.1201 2023-02-17 08:07:55,436 - mmseg - INFO - Saving checkpoint at 149000 iterations 2023-02-17 08:07:56,495 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 08:07:56,495 - mmseg - INFO - Iter [149000/160000] lr: 4.125e-06, eta: 1:07:23, time: 0.374, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1491, decode.acc_seg: 93.8201, aux.loss_ce: 0.0979, aux.acc_seg: 90.0924, loss: 0.2470, grad_norm: 2.7913 2023-02-17 08:08:14,738 - mmseg - INFO - Iter [149050/160000] lr: 4.107e-06, eta: 1:07:04, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1385, decode.acc_seg: 93.8854, aux.loss_ce: 0.0917, aux.acc_seg: 90.0912, loss: 0.2302, grad_norm: 2.0329 2023-02-17 08:08:32,815 - mmseg - INFO - Iter [149100/160000] lr: 4.088e-06, eta: 1:06:46, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1351, decode.acc_seg: 94.2107, aux.loss_ce: 0.0924, aux.acc_seg: 90.3371, loss: 0.2275, grad_norm: 2.0557 2023-02-17 08:08:50,678 - mmseg - INFO - Iter [149150/160000] lr: 4.069e-06, eta: 1:06:27, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1432, decode.acc_seg: 94.0320, aux.loss_ce: 0.0973, aux.acc_seg: 89.9201, loss: 0.2405, grad_norm: 2.7209 2023-02-17 08:09:08,503 - mmseg - INFO - Iter [149200/160000] lr: 4.050e-06, eta: 1:06:09, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1416, decode.acc_seg: 94.0985, aux.loss_ce: 0.0923, aux.acc_seg: 90.6257, loss: 0.2339, grad_norm: 2.3672 2023-02-17 08:09:26,321 - mmseg - INFO - Iter [149250/160000] lr: 4.032e-06, eta: 1:05:50, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1460, decode.acc_seg: 93.8328, aux.loss_ce: 0.0979, aux.acc_seg: 89.9863, loss: 0.2439, grad_norm: 2.3782 2023-02-17 08:09:44,541 - mmseg - INFO - Iter [149300/160000] lr: 4.013e-06, eta: 1:05:32, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1424, decode.acc_seg: 93.9672, aux.loss_ce: 0.0955, aux.acc_seg: 90.0512, loss: 0.2380, grad_norm: 2.3262 2023-02-17 08:10:02,479 - mmseg - INFO - Iter [149350/160000] lr: 3.994e-06, eta: 1:05:14, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1428, decode.acc_seg: 94.1337, aux.loss_ce: 0.0971, aux.acc_seg: 90.2712, loss: 0.2398, grad_norm: 2.7312 2023-02-17 08:10:20,266 - mmseg - INFO - Iter [149400/160000] lr: 3.975e-06, eta: 1:04:55, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1306, decode.acc_seg: 94.4500, aux.loss_ce: 0.0911, aux.acc_seg: 90.6246, loss: 0.2217, grad_norm: 2.2153 2023-02-17 08:10:38,334 - mmseg - INFO - Iter [149450/160000] lr: 3.957e-06, eta: 1:04:37, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1437, decode.acc_seg: 93.9355, aux.loss_ce: 0.0971, aux.acc_seg: 90.0307, loss: 0.2408, grad_norm: 2.7857 2023-02-17 08:10:56,483 - mmseg - INFO - Iter [149500/160000] lr: 3.938e-06, eta: 1:04:18, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1389, decode.acc_seg: 94.2925, aux.loss_ce: 0.0940, aux.acc_seg: 90.5058, loss: 0.2328, grad_norm: 2.2308 2023-02-17 08:11:14,598 - mmseg - INFO - Iter [149550/160000] lr: 3.919e-06, eta: 1:04:00, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1351, decode.acc_seg: 94.3013, aux.loss_ce: 0.0943, aux.acc_seg: 90.2482, loss: 0.2295, grad_norm: 2.0454 2023-02-17 08:11:32,524 - mmseg - INFO - Iter [149600/160000] lr: 3.900e-06, eta: 1:03:41, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1335, decode.acc_seg: 94.4330, aux.loss_ce: 0.0885, aux.acc_seg: 90.9726, loss: 0.2220, grad_norm: 1.8957 2023-02-17 08:11:50,675 - mmseg - INFO - Iter [149650/160000] lr: 3.882e-06, eta: 1:03:23, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1434, decode.acc_seg: 93.8712, aux.loss_ce: 0.0939, aux.acc_seg: 90.4254, loss: 0.2372, grad_norm: 2.0996 2023-02-17 08:12:11,248 - mmseg - INFO - Iter [149700/160000] lr: 3.863e-06, eta: 1:03:05, time: 0.411, data_time: 0.053, memory: 16596, decode.loss_ce: 0.1367, decode.acc_seg: 94.3630, aux.loss_ce: 0.0950, aux.acc_seg: 90.3757, loss: 0.2317, grad_norm: 2.1115 2023-02-17 08:12:29,036 - mmseg - INFO - Iter [149750/160000] lr: 3.844e-06, eta: 1:02:47, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1375, decode.acc_seg: 94.2621, aux.loss_ce: 0.0921, aux.acc_seg: 90.5221, loss: 0.2297, grad_norm: 2.5059 2023-02-17 08:12:47,082 - mmseg - INFO - Iter [149800/160000] lr: 3.825e-06, eta: 1:02:28, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1372, decode.acc_seg: 94.2794, aux.loss_ce: 0.0920, aux.acc_seg: 90.6819, loss: 0.2292, grad_norm: 1.9557 2023-02-17 08:13:04,878 - mmseg - INFO - Iter [149850/160000] lr: 3.807e-06, eta: 1:02:10, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1355, decode.acc_seg: 94.2202, aux.loss_ce: 0.0918, aux.acc_seg: 90.5060, loss: 0.2273, grad_norm: 2.0044 2023-02-17 08:13:23,001 - mmseg - INFO - Iter [149900/160000] lr: 3.788e-06, eta: 1:01:51, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1397, decode.acc_seg: 94.2897, aux.loss_ce: 0.0932, aux.acc_seg: 90.5499, loss: 0.2329, grad_norm: 2.1814 2023-02-17 08:13:41,028 - mmseg - INFO - Iter [149950/160000] lr: 3.769e-06, eta: 1:01:33, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1389, decode.acc_seg: 94.2133, aux.loss_ce: 0.0927, aux.acc_seg: 90.4888, loss: 0.2316, grad_norm: 1.9443 2023-02-17 08:13:59,243 - mmseg - INFO - Saving checkpoint at 150000 iterations 2023-02-17 08:14:00,297 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 08:14:00,297 - mmseg - INFO - Iter [150000/160000] lr: 3.750e-06, eta: 1:01:15, time: 0.386, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1398, decode.acc_seg: 94.0598, aux.loss_ce: 0.0927, aux.acc_seg: 90.4271, loss: 0.2325, grad_norm: 2.6813 2023-02-17 08:14:18,092 - mmseg - INFO - Iter [150050/160000] lr: 3.732e-06, eta: 1:00:56, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1406, decode.acc_seg: 94.1318, aux.loss_ce: 0.0977, aux.acc_seg: 90.2158, loss: 0.2383, grad_norm: 2.1598 2023-02-17 08:14:35,952 - mmseg - INFO - Iter [150100/160000] lr: 3.713e-06, eta: 1:00:38, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1314, decode.acc_seg: 94.4912, aux.loss_ce: 0.0883, aux.acc_seg: 91.0413, loss: 0.2197, grad_norm: 1.8143 2023-02-17 08:14:54,107 - mmseg - INFO - Iter [150150/160000] lr: 3.694e-06, eta: 1:00:19, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1393, decode.acc_seg: 94.1823, aux.loss_ce: 0.0925, aux.acc_seg: 90.6529, loss: 0.2318, grad_norm: 2.2640 2023-02-17 08:15:12,256 - mmseg - INFO - Iter [150200/160000] lr: 3.675e-06, eta: 1:00:01, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1309, decode.acc_seg: 94.3558, aux.loss_ce: 0.0896, aux.acc_seg: 90.8419, loss: 0.2205, grad_norm: 2.1336 2023-02-17 08:15:30,450 - mmseg - INFO - Iter [150250/160000] lr: 3.657e-06, eta: 0:59:42, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1300, decode.acc_seg: 94.4628, aux.loss_ce: 0.0889, aux.acc_seg: 90.8430, loss: 0.2189, grad_norm: 1.9338 2023-02-17 08:15:48,566 - mmseg - INFO - Iter [150300/160000] lr: 3.638e-06, eta: 0:59:24, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1394, decode.acc_seg: 94.1671, aux.loss_ce: 0.0932, aux.acc_seg: 90.3327, loss: 0.2326, grad_norm: 2.2148 2023-02-17 08:16:06,394 - mmseg - INFO - Iter [150350/160000] lr: 3.619e-06, eta: 0:59:06, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1375, decode.acc_seg: 94.2495, aux.loss_ce: 0.0949, aux.acc_seg: 90.2410, loss: 0.2324, grad_norm: 2.3129 2023-02-17 08:16:24,493 - mmseg - INFO - Iter [150400/160000] lr: 3.600e-06, eta: 0:58:47, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1395, decode.acc_seg: 94.0804, aux.loss_ce: 0.0947, aux.acc_seg: 90.3336, loss: 0.2341, grad_norm: 2.4180 2023-02-17 08:16:42,699 - mmseg - INFO - Iter [150450/160000] lr: 3.582e-06, eta: 0:58:29, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1400, decode.acc_seg: 94.2200, aux.loss_ce: 0.0915, aux.acc_seg: 90.7410, loss: 0.2316, grad_norm: 2.1958 2023-02-17 08:17:00,786 - mmseg - INFO - Iter [150500/160000] lr: 3.563e-06, eta: 0:58:10, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1396, decode.acc_seg: 94.0251, aux.loss_ce: 0.0951, aux.acc_seg: 90.1955, loss: 0.2347, grad_norm: 2.1818 2023-02-17 08:17:18,800 - mmseg - INFO - Iter [150550/160000] lr: 3.544e-06, eta: 0:57:52, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1386, decode.acc_seg: 94.2117, aux.loss_ce: 0.0914, aux.acc_seg: 90.7474, loss: 0.2300, grad_norm: 2.3980 2023-02-17 08:17:36,879 - mmseg - INFO - Iter [150600/160000] lr: 3.525e-06, eta: 0:57:34, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1417, decode.acc_seg: 93.7791, aux.loss_ce: 0.0940, aux.acc_seg: 90.0594, loss: 0.2357, grad_norm: 2.7375 2023-02-17 08:17:54,817 - mmseg - INFO - Iter [150650/160000] lr: 3.507e-06, eta: 0:57:15, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1426, decode.acc_seg: 93.9779, aux.loss_ce: 0.0967, aux.acc_seg: 90.1919, loss: 0.2393, grad_norm: 2.6438 2023-02-17 08:18:12,731 - mmseg - INFO - Iter [150700/160000] lr: 3.488e-06, eta: 0:56:57, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1402, decode.acc_seg: 94.1419, aux.loss_ce: 0.0930, aux.acc_seg: 90.4961, loss: 0.2331, grad_norm: 2.4226 2023-02-17 08:18:30,838 - mmseg - INFO - Iter [150750/160000] lr: 3.469e-06, eta: 0:56:38, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1404, decode.acc_seg: 94.2902, aux.loss_ce: 0.0950, aux.acc_seg: 90.3579, loss: 0.2354, grad_norm: 2.5527 2023-02-17 08:18:49,000 - mmseg - INFO - Iter [150800/160000] lr: 3.450e-06, eta: 0:56:20, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1403, decode.acc_seg: 93.9791, aux.loss_ce: 0.0955, aux.acc_seg: 90.1030, loss: 0.2359, grad_norm: 2.6808 2023-02-17 08:19:06,961 - mmseg - INFO - Iter [150850/160000] lr: 3.432e-06, eta: 0:56:01, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1395, decode.acc_seg: 94.1799, aux.loss_ce: 0.0953, aux.acc_seg: 90.2513, loss: 0.2348, grad_norm: 2.3620 2023-02-17 08:19:25,142 - mmseg - INFO - Iter [150900/160000] lr: 3.413e-06, eta: 0:55:43, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1399, decode.acc_seg: 94.1933, aux.loss_ce: 0.0932, aux.acc_seg: 90.5135, loss: 0.2331, grad_norm: 2.1361 2023-02-17 08:19:45,503 - mmseg - INFO - Iter [150950/160000] lr: 3.394e-06, eta: 0:55:25, time: 0.407, data_time: 0.052, memory: 16596, decode.loss_ce: 0.1338, decode.acc_seg: 94.3029, aux.loss_ce: 0.0901, aux.acc_seg: 90.6648, loss: 0.2239, grad_norm: 2.2557 2023-02-17 08:20:03,124 - mmseg - INFO - Saving checkpoint at 151000 iterations 2023-02-17 08:20:04,264 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 08:20:04,264 - mmseg - INFO - Iter [151000/160000] lr: 3.375e-06, eta: 0:55:07, time: 0.375, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1395, decode.acc_seg: 94.1951, aux.loss_ce: 0.0943, aux.acc_seg: 90.3437, loss: 0.2338, grad_norm: 2.5206 2023-02-17 08:20:22,221 - mmseg - INFO - Iter [151050/160000] lr: 3.357e-06, eta: 0:54:48, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1318, decode.acc_seg: 94.5065, aux.loss_ce: 0.0881, aux.acc_seg: 91.0532, loss: 0.2199, grad_norm: 1.8833 2023-02-17 08:20:40,234 - mmseg - INFO - Iter [151100/160000] lr: 3.338e-06, eta: 0:54:30, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1382, decode.acc_seg: 94.0148, aux.loss_ce: 0.0913, aux.acc_seg: 90.4838, loss: 0.2294, grad_norm: 2.3495 2023-02-17 08:20:58,256 - mmseg - INFO - Iter [151150/160000] lr: 3.319e-06, eta: 0:54:11, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1310, decode.acc_seg: 94.4647, aux.loss_ce: 0.0885, aux.acc_seg: 90.8753, loss: 0.2195, grad_norm: 2.1571 2023-02-17 08:21:16,095 - mmseg - INFO - Iter [151200/160000] lr: 3.300e-06, eta: 0:53:53, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1326, decode.acc_seg: 94.3733, aux.loss_ce: 0.0920, aux.acc_seg: 90.4765, loss: 0.2247, grad_norm: 2.1774 2023-02-17 08:21:33,868 - mmseg - INFO - Iter [151250/160000] lr: 3.282e-06, eta: 0:53:34, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1351, decode.acc_seg: 94.3646, aux.loss_ce: 0.0906, aux.acc_seg: 90.7871, loss: 0.2257, grad_norm: 2.2529 2023-02-17 08:21:51,885 - mmseg - INFO - Iter [151300/160000] lr: 3.263e-06, eta: 0:53:16, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1395, decode.acc_seg: 94.2556, aux.loss_ce: 0.0940, aux.acc_seg: 90.6763, loss: 0.2336, grad_norm: 2.2462 2023-02-17 08:22:09,842 - mmseg - INFO - Iter [151350/160000] lr: 3.244e-06, eta: 0:52:58, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1431, decode.acc_seg: 94.0630, aux.loss_ce: 0.0958, aux.acc_seg: 90.2349, loss: 0.2388, grad_norm: 2.5632 2023-02-17 08:22:27,623 - mmseg - INFO - Iter [151400/160000] lr: 3.225e-06, eta: 0:52:39, time: 0.356, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1336, decode.acc_seg: 94.3408, aux.loss_ce: 0.0929, aux.acc_seg: 90.3723, loss: 0.2265, grad_norm: 2.4385 2023-02-17 08:22:45,327 - mmseg - INFO - Iter [151450/160000] lr: 3.207e-06, eta: 0:52:21, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1350, decode.acc_seg: 94.3298, aux.loss_ce: 0.0870, aux.acc_seg: 91.0518, loss: 0.2220, grad_norm: 2.1006 2023-02-17 08:23:03,441 - mmseg - INFO - Iter [151500/160000] lr: 3.188e-06, eta: 0:52:02, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1269, decode.acc_seg: 94.6789, aux.loss_ce: 0.0873, aux.acc_seg: 91.0948, loss: 0.2142, grad_norm: 2.2073 2023-02-17 08:23:21,454 - mmseg - INFO - Iter [151550/160000] lr: 3.169e-06, eta: 0:51:44, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1402, decode.acc_seg: 94.1024, aux.loss_ce: 0.0986, aux.acc_seg: 90.0251, loss: 0.2388, grad_norm: 2.3354 2023-02-17 08:23:39,472 - mmseg - INFO - Iter [151600/160000] lr: 3.150e-06, eta: 0:51:25, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1362, decode.acc_seg: 94.2406, aux.loss_ce: 0.0909, aux.acc_seg: 90.6233, loss: 0.2271, grad_norm: 1.9271 2023-02-17 08:23:57,900 - mmseg - INFO - Iter [151650/160000] lr: 3.132e-06, eta: 0:51:07, time: 0.369, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1428, decode.acc_seg: 93.9270, aux.loss_ce: 0.0980, aux.acc_seg: 90.0567, loss: 0.2408, grad_norm: 2.3607 2023-02-17 08:24:16,296 - mmseg - INFO - Iter [151700/160000] lr: 3.113e-06, eta: 0:50:49, time: 0.367, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1418, decode.acc_seg: 94.1030, aux.loss_ce: 0.0927, aux.acc_seg: 90.7055, loss: 0.2345, grad_norm: 2.3703 2023-02-17 08:24:34,582 - mmseg - INFO - Iter [151750/160000] lr: 3.094e-06, eta: 0:50:30, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1349, decode.acc_seg: 94.1291, aux.loss_ce: 0.0931, aux.acc_seg: 90.1812, loss: 0.2280, grad_norm: 2.0454 2023-02-17 08:24:52,405 - mmseg - INFO - Iter [151800/160000] lr: 3.075e-06, eta: 0:50:12, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1445, decode.acc_seg: 93.9432, aux.loss_ce: 0.0952, aux.acc_seg: 90.2601, loss: 0.2397, grad_norm: 2.4063 2023-02-17 08:25:10,396 - mmseg - INFO - Iter [151850/160000] lr: 3.057e-06, eta: 0:49:54, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1382, decode.acc_seg: 94.2102, aux.loss_ce: 0.0899, aux.acc_seg: 90.7647, loss: 0.2281, grad_norm: 2.0679 2023-02-17 08:25:28,668 - mmseg - INFO - Iter [151900/160000] lr: 3.038e-06, eta: 0:49:35, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1472, decode.acc_seg: 93.8170, aux.loss_ce: 0.0968, aux.acc_seg: 90.0835, loss: 0.2440, grad_norm: 2.2871 2023-02-17 08:25:46,590 - mmseg - INFO - Iter [151950/160000] lr: 3.019e-06, eta: 0:49:17, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1410, decode.acc_seg: 94.1449, aux.loss_ce: 0.0903, aux.acc_seg: 90.8104, loss: 0.2313, grad_norm: 2.5811 2023-02-17 08:26:04,674 - mmseg - INFO - Saving checkpoint at 152000 iterations 2023-02-17 08:26:05,729 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 08:26:05,730 - mmseg - INFO - Iter [152000/160000] lr: 3.000e-06, eta: 0:48:58, time: 0.383, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1427, decode.acc_seg: 94.0497, aux.loss_ce: 0.0946, aux.acc_seg: 90.4463, loss: 0.2373, grad_norm: 2.3341 2023-02-17 08:26:23,435 - mmseg - INFO - Iter [152050/160000] lr: 2.982e-06, eta: 0:48:40, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1479, decode.acc_seg: 93.8315, aux.loss_ce: 0.0950, aux.acc_seg: 90.3275, loss: 0.2428, grad_norm: 2.6521 2023-02-17 08:26:41,527 - mmseg - INFO - Iter [152100/160000] lr: 2.963e-06, eta: 0:48:22, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1366, decode.acc_seg: 94.2766, aux.loss_ce: 0.0940, aux.acc_seg: 90.1630, loss: 0.2306, grad_norm: 2.2089 2023-02-17 08:26:59,256 - mmseg - INFO - Iter [152150/160000] lr: 2.944e-06, eta: 0:48:03, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1364, decode.acc_seg: 94.1733, aux.loss_ce: 0.0887, aux.acc_seg: 90.7325, loss: 0.2251, grad_norm: 2.1225 2023-02-17 08:27:17,004 - mmseg - INFO - Iter [152200/160000] lr: 2.925e-06, eta: 0:47:45, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1405, decode.acc_seg: 94.1328, aux.loss_ce: 0.0925, aux.acc_seg: 90.4305, loss: 0.2330, grad_norm: 2.3597 2023-02-17 08:27:37,434 - mmseg - INFO - Iter [152250/160000] lr: 2.907e-06, eta: 0:47:27, time: 0.409, data_time: 0.052, memory: 16596, decode.loss_ce: 0.1383, decode.acc_seg: 94.2109, aux.loss_ce: 0.0913, aux.acc_seg: 90.5924, loss: 0.2297, grad_norm: 2.4860 2023-02-17 08:27:55,528 - mmseg - INFO - Iter [152300/160000] lr: 2.888e-06, eta: 0:47:08, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1405, decode.acc_seg: 94.1746, aux.loss_ce: 0.0941, aux.acc_seg: 90.3914, loss: 0.2346, grad_norm: 2.2831 2023-02-17 08:28:13,565 - mmseg - INFO - Iter [152350/160000] lr: 2.869e-06, eta: 0:46:50, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1363, decode.acc_seg: 94.2764, aux.loss_ce: 0.0952, aux.acc_seg: 90.2641, loss: 0.2315, grad_norm: 2.4241 2023-02-17 08:28:31,930 - mmseg - INFO - Iter [152400/160000] lr: 2.850e-06, eta: 0:46:31, time: 0.368, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1381, decode.acc_seg: 94.2027, aux.loss_ce: 0.0910, aux.acc_seg: 90.8010, loss: 0.2290, grad_norm: 2.3040 2023-02-17 08:28:50,021 - mmseg - INFO - Iter [152450/160000] lr: 2.832e-06, eta: 0:46:13, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1334, decode.acc_seg: 94.4583, aux.loss_ce: 0.0893, aux.acc_seg: 90.7718, loss: 0.2228, grad_norm: 2.1682 2023-02-17 08:29:08,201 - mmseg - INFO - Iter [152500/160000] lr: 2.813e-06, eta: 0:45:55, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1318, decode.acc_seg: 94.5056, aux.loss_ce: 0.0917, aux.acc_seg: 90.6878, loss: 0.2235, grad_norm: 2.0823 2023-02-17 08:29:26,173 - mmseg - INFO - Iter [152550/160000] lr: 2.794e-06, eta: 0:45:36, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1359, decode.acc_seg: 94.2889, aux.loss_ce: 0.0896, aux.acc_seg: 90.8341, loss: 0.2255, grad_norm: 2.3578 2023-02-17 08:29:44,219 - mmseg - INFO - Iter [152600/160000] lr: 2.775e-06, eta: 0:45:18, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1341, decode.acc_seg: 94.3793, aux.loss_ce: 0.0937, aux.acc_seg: 90.2915, loss: 0.2278, grad_norm: 2.4049 2023-02-17 08:30:02,362 - mmseg - INFO - Iter [152650/160000] lr: 2.757e-06, eta: 0:44:59, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1440, decode.acc_seg: 93.8619, aux.loss_ce: 0.0947, aux.acc_seg: 90.3388, loss: 0.2387, grad_norm: 2.2032 2023-02-17 08:30:20,256 - mmseg - INFO - Iter [152700/160000] lr: 2.738e-06, eta: 0:44:41, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1339, decode.acc_seg: 94.3497, aux.loss_ce: 0.0912, aux.acc_seg: 90.5554, loss: 0.2251, grad_norm: 1.9904 2023-02-17 08:30:38,229 - mmseg - INFO - Iter [152750/160000] lr: 2.719e-06, eta: 0:44:23, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1467, decode.acc_seg: 93.7894, aux.loss_ce: 0.0987, aux.acc_seg: 89.9691, loss: 0.2454, grad_norm: 2.5964 2023-02-17 08:30:56,243 - mmseg - INFO - Iter [152800/160000] lr: 2.700e-06, eta: 0:44:04, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1490, decode.acc_seg: 94.1516, aux.loss_ce: 0.0962, aux.acc_seg: 90.2448, loss: 0.2452, grad_norm: 2.4377 2023-02-17 08:31:14,049 - mmseg - INFO - Iter [152850/160000] lr: 2.682e-06, eta: 0:43:46, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1364, decode.acc_seg: 94.1561, aux.loss_ce: 0.0899, aux.acc_seg: 90.5842, loss: 0.2263, grad_norm: 2.2646 2023-02-17 08:31:32,016 - mmseg - INFO - Iter [152900/160000] lr: 2.663e-06, eta: 0:43:27, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1385, decode.acc_seg: 94.1769, aux.loss_ce: 0.0962, aux.acc_seg: 90.2252, loss: 0.2347, grad_norm: 2.1675 2023-02-17 08:31:50,341 - mmseg - INFO - Iter [152950/160000] lr: 2.644e-06, eta: 0:43:09, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1381, decode.acc_seg: 94.0782, aux.loss_ce: 0.0925, aux.acc_seg: 90.4685, loss: 0.2305, grad_norm: 2.4247 2023-02-17 08:32:08,297 - mmseg - INFO - Saving checkpoint at 153000 iterations 2023-02-17 08:32:09,334 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 08:32:09,335 - mmseg - INFO - Iter [153000/160000] lr: 2.625e-06, eta: 0:42:51, time: 0.380, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1347, decode.acc_seg: 94.2411, aux.loss_ce: 0.0900, aux.acc_seg: 90.5921, loss: 0.2247, grad_norm: 1.9593 2023-02-17 08:32:27,206 - mmseg - INFO - Iter [153050/160000] lr: 2.607e-06, eta: 0:42:32, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1351, decode.acc_seg: 94.1651, aux.loss_ce: 0.0893, aux.acc_seg: 90.8100, loss: 0.2244, grad_norm: 2.0901 2023-02-17 08:32:45,135 - mmseg - INFO - Iter [153100/160000] lr: 2.588e-06, eta: 0:42:14, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1379, decode.acc_seg: 94.1158, aux.loss_ce: 0.0914, aux.acc_seg: 90.5950, loss: 0.2293, grad_norm: 2.1062 2023-02-17 08:33:03,041 - mmseg - INFO - Iter [153150/160000] lr: 2.569e-06, eta: 0:41:55, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1355, decode.acc_seg: 94.3593, aux.loss_ce: 0.0903, aux.acc_seg: 90.8791, loss: 0.2258, grad_norm: 2.0293 2023-02-17 08:33:21,162 - mmseg - INFO - Iter [153200/160000] lr: 2.550e-06, eta: 0:41:37, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1391, decode.acc_seg: 94.1062, aux.loss_ce: 0.0931, aux.acc_seg: 90.5362, loss: 0.2321, grad_norm: 2.2867 2023-02-17 08:33:39,122 - mmseg - INFO - Iter [153250/160000] lr: 2.532e-06, eta: 0:41:19, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1403, decode.acc_seg: 94.1661, aux.loss_ce: 0.0919, aux.acc_seg: 90.6658, loss: 0.2322, grad_norm: 2.1720 2023-02-17 08:33:57,011 - mmseg - INFO - Iter [153300/160000] lr: 2.513e-06, eta: 0:41:00, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1422, decode.acc_seg: 94.1053, aux.loss_ce: 0.0954, aux.acc_seg: 90.3881, loss: 0.2377, grad_norm: 2.3346 2023-02-17 08:34:14,935 - mmseg - INFO - Iter [153350/160000] lr: 2.494e-06, eta: 0:40:42, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1390, decode.acc_seg: 94.1699, aux.loss_ce: 0.0928, aux.acc_seg: 90.4776, loss: 0.2318, grad_norm: 2.3288 2023-02-17 08:34:32,965 - mmseg - INFO - Iter [153400/160000] lr: 2.475e-06, eta: 0:40:23, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1442, decode.acc_seg: 94.0577, aux.loss_ce: 0.0984, aux.acc_seg: 90.0375, loss: 0.2426, grad_norm: 2.4977 2023-02-17 08:34:51,039 - mmseg - INFO - Iter [153450/160000] lr: 2.457e-06, eta: 0:40:05, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1364, decode.acc_seg: 94.2613, aux.loss_ce: 0.0935, aux.acc_seg: 90.4509, loss: 0.2299, grad_norm: 2.1764 2023-02-17 08:35:11,069 - mmseg - INFO - Iter [153500/160000] lr: 2.438e-06, eta: 0:39:47, time: 0.401, data_time: 0.053, memory: 16596, decode.loss_ce: 0.1425, decode.acc_seg: 94.0566, aux.loss_ce: 0.0925, aux.acc_seg: 90.6385, loss: 0.2350, grad_norm: 2.2490 2023-02-17 08:35:28,928 - mmseg - INFO - Iter [153550/160000] lr: 2.419e-06, eta: 0:39:28, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1352, decode.acc_seg: 94.3667, aux.loss_ce: 0.0925, aux.acc_seg: 90.6215, loss: 0.2277, grad_norm: 2.3056 2023-02-17 08:35:47,120 - mmseg - INFO - Iter [153600/160000] lr: 2.400e-06, eta: 0:39:10, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1397, decode.acc_seg: 94.0675, aux.loss_ce: 0.0921, aux.acc_seg: 90.5343, loss: 0.2318, grad_norm: 2.6155 2023-02-17 08:36:05,142 - mmseg - INFO - Iter [153650/160000] lr: 2.382e-06, eta: 0:38:52, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1329, decode.acc_seg: 94.4113, aux.loss_ce: 0.0896, aux.acc_seg: 90.7424, loss: 0.2225, grad_norm: 2.0618 2023-02-17 08:36:23,228 - mmseg - INFO - Iter [153700/160000] lr: 2.363e-06, eta: 0:38:33, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1439, decode.acc_seg: 93.9426, aux.loss_ce: 0.0949, aux.acc_seg: 90.2022, loss: 0.2388, grad_norm: 2.2481 2023-02-17 08:36:41,072 - mmseg - INFO - Iter [153750/160000] lr: 2.344e-06, eta: 0:38:15, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1373, decode.acc_seg: 94.2633, aux.loss_ce: 0.0935, aux.acc_seg: 90.5666, loss: 0.2308, grad_norm: 2.2272 2023-02-17 08:36:59,232 - mmseg - INFO - Iter [153800/160000] lr: 2.325e-06, eta: 0:37:56, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1329, decode.acc_seg: 94.2632, aux.loss_ce: 0.0873, aux.acc_seg: 90.8032, loss: 0.2202, grad_norm: 2.0240 2023-02-17 08:37:17,333 - mmseg - INFO - Iter [153850/160000] lr: 2.307e-06, eta: 0:37:38, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1378, decode.acc_seg: 94.1836, aux.loss_ce: 0.0923, aux.acc_seg: 90.5049, loss: 0.2301, grad_norm: 2.0291 2023-02-17 08:37:35,472 - mmseg - INFO - Iter [153900/160000] lr: 2.288e-06, eta: 0:37:20, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1391, decode.acc_seg: 94.2282, aux.loss_ce: 0.0944, aux.acc_seg: 90.5601, loss: 0.2335, grad_norm: 2.3473 2023-02-17 08:37:53,372 - mmseg - INFO - Iter [153950/160000] lr: 2.269e-06, eta: 0:37:01, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1393, decode.acc_seg: 94.3034, aux.loss_ce: 0.0965, aux.acc_seg: 90.4492, loss: 0.2358, grad_norm: 2.5011 2023-02-17 08:38:11,163 - mmseg - INFO - Saving checkpoint at 154000 iterations 2023-02-17 08:38:12,215 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 08:38:12,215 - mmseg - INFO - Iter [154000/160000] lr: 2.250e-06, eta: 0:36:43, time: 0.377, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1353, decode.acc_seg: 94.2450, aux.loss_ce: 0.0926, aux.acc_seg: 90.4367, loss: 0.2279, grad_norm: 2.3815 2023-02-17 08:38:30,199 - mmseg - INFO - Iter [154050/160000] lr: 2.232e-06, eta: 0:36:25, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1418, decode.acc_seg: 93.9968, aux.loss_ce: 0.0965, aux.acc_seg: 89.9898, loss: 0.2384, grad_norm: 2.5752 2023-02-17 08:38:48,022 - mmseg - INFO - Iter [154100/160000] lr: 2.213e-06, eta: 0:36:06, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1501, decode.acc_seg: 93.9956, aux.loss_ce: 0.0960, aux.acc_seg: 90.4056, loss: 0.2461, grad_norm: 3.3233 2023-02-17 08:39:05,912 - mmseg - INFO - Iter [154150/160000] lr: 2.194e-06, eta: 0:35:48, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1350, decode.acc_seg: 94.1534, aux.loss_ce: 0.0916, aux.acc_seg: 90.4929, loss: 0.2266, grad_norm: 2.2734 2023-02-17 08:39:24,292 - mmseg - INFO - Iter [154200/160000] lr: 2.175e-06, eta: 0:35:29, time: 0.368, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1285, decode.acc_seg: 94.4877, aux.loss_ce: 0.0888, aux.acc_seg: 90.6849, loss: 0.2173, grad_norm: 1.8310 2023-02-17 08:39:42,262 - mmseg - INFO - Iter [154250/160000] lr: 2.157e-06, eta: 0:35:11, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1282, decode.acc_seg: 94.4613, aux.loss_ce: 0.0877, aux.acc_seg: 90.7912, loss: 0.2159, grad_norm: 2.0226 2023-02-17 08:40:00,340 - mmseg - INFO - Iter [154300/160000] lr: 2.138e-06, eta: 0:34:53, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1433, decode.acc_seg: 93.8487, aux.loss_ce: 0.0962, aux.acc_seg: 90.0435, loss: 0.2395, grad_norm: 2.2108 2023-02-17 08:40:18,125 - mmseg - INFO - Iter [154350/160000] lr: 2.119e-06, eta: 0:34:34, time: 0.356, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1391, decode.acc_seg: 94.1682, aux.loss_ce: 0.0952, aux.acc_seg: 90.2271, loss: 0.2343, grad_norm: 2.7596 2023-02-17 08:40:35,742 - mmseg - INFO - Iter [154400/160000] lr: 2.100e-06, eta: 0:34:16, time: 0.352, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1279, decode.acc_seg: 94.3766, aux.loss_ce: 0.0838, aux.acc_seg: 91.0708, loss: 0.2117, grad_norm: 2.0465 2023-02-17 08:40:53,806 - mmseg - INFO - Iter [154450/160000] lr: 2.082e-06, eta: 0:33:57, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1328, decode.acc_seg: 94.3753, aux.loss_ce: 0.0916, aux.acc_seg: 90.4295, loss: 0.2244, grad_norm: 2.5659 2023-02-17 08:41:11,908 - mmseg - INFO - Iter [154500/160000] lr: 2.063e-06, eta: 0:33:39, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1362, decode.acc_seg: 94.3042, aux.loss_ce: 0.0918, aux.acc_seg: 90.5339, loss: 0.2280, grad_norm: 1.8870 2023-02-17 08:41:29,679 - mmseg - INFO - Iter [154550/160000] lr: 2.044e-06, eta: 0:33:21, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1381, decode.acc_seg: 94.2688, aux.loss_ce: 0.0911, aux.acc_seg: 90.7135, loss: 0.2292, grad_norm: 2.0748 2023-02-17 08:41:47,680 - mmseg - INFO - Iter [154600/160000] lr: 2.025e-06, eta: 0:33:02, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1390, decode.acc_seg: 94.2353, aux.loss_ce: 0.0944, aux.acc_seg: 90.4270, loss: 0.2334, grad_norm: 2.0484 2023-02-17 08:42:05,725 - mmseg - INFO - Iter [154650/160000] lr: 2.007e-06, eta: 0:32:44, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1368, decode.acc_seg: 94.2668, aux.loss_ce: 0.0924, aux.acc_seg: 90.5984, loss: 0.2293, grad_norm: 2.3166 2023-02-17 08:42:23,847 - mmseg - INFO - Iter [154700/160000] lr: 1.988e-06, eta: 0:32:26, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1392, decode.acc_seg: 94.1659, aux.loss_ce: 0.0949, aux.acc_seg: 90.4678, loss: 0.2341, grad_norm: 2.2630 2023-02-17 08:42:44,363 - mmseg - INFO - Iter [154750/160000] lr: 1.969e-06, eta: 0:32:07, time: 0.410, data_time: 0.052, memory: 16596, decode.loss_ce: 0.1372, decode.acc_seg: 94.2842, aux.loss_ce: 0.0908, aux.acc_seg: 90.7692, loss: 0.2280, grad_norm: 2.0451 2023-02-17 08:43:02,464 - mmseg - INFO - Iter [154800/160000] lr: 1.950e-06, eta: 0:31:49, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1425, decode.acc_seg: 93.9972, aux.loss_ce: 0.0956, aux.acc_seg: 90.1015, loss: 0.2381, grad_norm: 2.6503 2023-02-17 08:43:20,542 - mmseg - INFO - Iter [154850/160000] lr: 1.932e-06, eta: 0:31:31, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1413, decode.acc_seg: 94.0499, aux.loss_ce: 0.0953, aux.acc_seg: 90.1631, loss: 0.2366, grad_norm: 2.4346 2023-02-17 08:43:38,589 - mmseg - INFO - Iter [154900/160000] lr: 1.913e-06, eta: 0:31:12, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1409, decode.acc_seg: 94.0883, aux.loss_ce: 0.0928, aux.acc_seg: 90.3683, loss: 0.2338, grad_norm: 2.0101 2023-02-17 08:43:56,573 - mmseg - INFO - Iter [154950/160000] lr: 1.894e-06, eta: 0:30:54, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1379, decode.acc_seg: 94.2072, aux.loss_ce: 0.0923, aux.acc_seg: 90.4928, loss: 0.2302, grad_norm: 2.1225 2023-02-17 08:44:14,599 - mmseg - INFO - Saving checkpoint at 155000 iterations 2023-02-17 08:44:15,655 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 08:44:15,655 - mmseg - INFO - Iter [155000/160000] lr: 1.875e-06, eta: 0:30:35, time: 0.382, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1316, decode.acc_seg: 94.3404, aux.loss_ce: 0.0869, aux.acc_seg: 90.7444, loss: 0.2185, grad_norm: 2.0219 2023-02-17 08:44:33,832 - mmseg - INFO - Iter [155050/160000] lr: 1.857e-06, eta: 0:30:17, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1434, decode.acc_seg: 93.9571, aux.loss_ce: 0.0955, aux.acc_seg: 90.3083, loss: 0.2389, grad_norm: 2.2929 2023-02-17 08:44:51,856 - mmseg - INFO - Iter [155100/160000] lr: 1.838e-06, eta: 0:29:59, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1306, decode.acc_seg: 94.4474, aux.loss_ce: 0.0897, aux.acc_seg: 90.6700, loss: 0.2202, grad_norm: 1.9390 2023-02-17 08:45:10,047 - mmseg - INFO - Iter [155150/160000] lr: 1.819e-06, eta: 0:29:40, time: 0.363, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1382, decode.acc_seg: 94.2040, aux.loss_ce: 0.0927, aux.acc_seg: 90.4660, loss: 0.2309, grad_norm: 2.1691 2023-02-17 08:45:28,150 - mmseg - INFO - Iter [155200/160000] lr: 1.800e-06, eta: 0:29:22, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1368, decode.acc_seg: 94.2106, aux.loss_ce: 0.0921, aux.acc_seg: 90.5890, loss: 0.2289, grad_norm: 2.0804 2023-02-17 08:45:46,262 - mmseg - INFO - Iter [155250/160000] lr: 1.782e-06, eta: 0:29:04, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1423, decode.acc_seg: 93.9895, aux.loss_ce: 0.0944, aux.acc_seg: 90.3399, loss: 0.2367, grad_norm: 2.1035 2023-02-17 08:46:04,345 - mmseg - INFO - Iter [155300/160000] lr: 1.763e-06, eta: 0:28:45, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1406, decode.acc_seg: 94.1022, aux.loss_ce: 0.0924, aux.acc_seg: 90.6433, loss: 0.2330, grad_norm: 2.8814 2023-02-17 08:46:22,392 - mmseg - INFO - Iter [155350/160000] lr: 1.744e-06, eta: 0:28:27, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1369, decode.acc_seg: 94.2715, aux.loss_ce: 0.0932, aux.acc_seg: 90.4494, loss: 0.2301, grad_norm: 2.3210 2023-02-17 08:46:40,306 - mmseg - INFO - Iter [155400/160000] lr: 1.725e-06, eta: 0:28:08, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1405, decode.acc_seg: 94.1060, aux.loss_ce: 0.0938, aux.acc_seg: 90.2065, loss: 0.2343, grad_norm: 2.4399 2023-02-17 08:46:58,606 - mmseg - INFO - Iter [155450/160000] lr: 1.707e-06, eta: 0:27:50, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1376, decode.acc_seg: 94.3294, aux.loss_ce: 0.0912, aux.acc_seg: 90.8754, loss: 0.2289, grad_norm: 1.9511 2023-02-17 08:47:16,762 - mmseg - INFO - Iter [155500/160000] lr: 1.688e-06, eta: 0:27:32, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1357, decode.acc_seg: 94.2728, aux.loss_ce: 0.0898, aux.acc_seg: 90.7430, loss: 0.2256, grad_norm: 2.3926 2023-02-17 08:47:34,622 - mmseg - INFO - Iter [155550/160000] lr: 1.669e-06, eta: 0:27:13, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1335, decode.acc_seg: 94.2442, aux.loss_ce: 0.0900, aux.acc_seg: 90.5463, loss: 0.2235, grad_norm: 1.9582 2023-02-17 08:47:52,614 - mmseg - INFO - Iter [155600/160000] lr: 1.650e-06, eta: 0:26:55, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1314, decode.acc_seg: 94.4302, aux.loss_ce: 0.0886, aux.acc_seg: 90.8165, loss: 0.2200, grad_norm: 2.4513 2023-02-17 08:48:10,619 - mmseg - INFO - Iter [155650/160000] lr: 1.632e-06, eta: 0:26:37, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1396, decode.acc_seg: 94.0432, aux.loss_ce: 0.0952, aux.acc_seg: 90.0856, loss: 0.2348, grad_norm: 2.4544 2023-02-17 08:48:28,926 - mmseg - INFO - Iter [155700/160000] lr: 1.613e-06, eta: 0:26:18, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1445, decode.acc_seg: 94.1270, aux.loss_ce: 0.0929, aux.acc_seg: 90.5879, loss: 0.2374, grad_norm: 3.2438 2023-02-17 08:48:46,999 - mmseg - INFO - Iter [155750/160000] lr: 1.594e-06, eta: 0:26:00, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1421, decode.acc_seg: 94.1495, aux.loss_ce: 0.0918, aux.acc_seg: 90.7062, loss: 0.2339, grad_norm: 2.3351 2023-02-17 08:49:04,885 - mmseg - INFO - Iter [155800/160000] lr: 1.575e-06, eta: 0:25:41, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1390, decode.acc_seg: 94.2807, aux.loss_ce: 0.0921, aux.acc_seg: 90.8537, loss: 0.2311, grad_norm: 2.2379 2023-02-17 08:49:23,022 - mmseg - INFO - Iter [155850/160000] lr: 1.557e-06, eta: 0:25:23, time: 0.362, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1423, decode.acc_seg: 93.9539, aux.loss_ce: 0.0930, aux.acc_seg: 90.4095, loss: 0.2354, grad_norm: 2.2177 2023-02-17 08:49:40,982 - mmseg - INFO - Iter [155900/160000] lr: 1.538e-06, eta: 0:25:05, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1371, decode.acc_seg: 94.2656, aux.loss_ce: 0.0922, aux.acc_seg: 90.6384, loss: 0.2293, grad_norm: 2.3996 2023-02-17 08:49:59,043 - mmseg - INFO - Iter [155950/160000] lr: 1.519e-06, eta: 0:24:46, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1355, decode.acc_seg: 94.2014, aux.loss_ce: 0.0952, aux.acc_seg: 90.2587, loss: 0.2307, grad_norm: 2.2916 2023-02-17 08:50:19,690 - mmseg - INFO - Saving checkpoint at 156000 iterations 2023-02-17 08:50:20,825 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 08:50:20,825 - mmseg - INFO - Iter [156000/160000] lr: 1.500e-06, eta: 0:24:28, time: 0.436, data_time: 0.052, memory: 16596, decode.loss_ce: 0.1380, decode.acc_seg: 94.2136, aux.loss_ce: 0.0922, aux.acc_seg: 90.6536, loss: 0.2303, grad_norm: 2.5258 2023-02-17 08:50:38,867 - mmseg - INFO - Iter [156050/160000] lr: 1.482e-06, eta: 0:24:10, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1317, decode.acc_seg: 94.4670, aux.loss_ce: 0.0906, aux.acc_seg: 90.7444, loss: 0.2223, grad_norm: 2.0638 2023-02-17 08:50:57,260 - mmseg - INFO - Iter [156100/160000] lr: 1.463e-06, eta: 0:23:51, time: 0.368, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1429, decode.acc_seg: 94.0009, aux.loss_ce: 0.0951, aux.acc_seg: 90.2703, loss: 0.2380, grad_norm: 2.8553 2023-02-17 08:51:14,956 - mmseg - INFO - Iter [156150/160000] lr: 1.444e-06, eta: 0:23:33, time: 0.354, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1364, decode.acc_seg: 94.1182, aux.loss_ce: 0.0914, aux.acc_seg: 90.6007, loss: 0.2278, grad_norm: 2.1551 2023-02-17 08:51:33,175 - mmseg - INFO - Iter [156200/160000] lr: 1.425e-06, eta: 0:23:15, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1338, decode.acc_seg: 94.2560, aux.loss_ce: 0.0905, aux.acc_seg: 90.6292, loss: 0.2243, grad_norm: 2.4127 2023-02-17 08:51:50,837 - mmseg - INFO - Iter [156250/160000] lr: 1.407e-06, eta: 0:22:56, time: 0.353, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1477, decode.acc_seg: 93.9096, aux.loss_ce: 0.0963, aux.acc_seg: 90.1183, loss: 0.2440, grad_norm: 3.0593 2023-02-17 08:52:08,447 - mmseg - INFO - Iter [156300/160000] lr: 1.388e-06, eta: 0:22:38, time: 0.352, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1398, decode.acc_seg: 93.9594, aux.loss_ce: 0.0938, aux.acc_seg: 90.3558, loss: 0.2336, grad_norm: 2.2795 2023-02-17 08:52:26,408 - mmseg - INFO - Iter [156350/160000] lr: 1.369e-06, eta: 0:22:20, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1315, decode.acc_seg: 94.3608, aux.loss_ce: 0.0883, aux.acc_seg: 90.8095, loss: 0.2198, grad_norm: 2.0935 2023-02-17 08:52:44,216 - mmseg - INFO - Iter [156400/160000] lr: 1.350e-06, eta: 0:22:01, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1322, decode.acc_seg: 94.4927, aux.loss_ce: 0.0895, aux.acc_seg: 90.8950, loss: 0.2218, grad_norm: 2.0059 2023-02-17 08:53:02,085 - mmseg - INFO - Iter [156450/160000] lr: 1.332e-06, eta: 0:21:43, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1363, decode.acc_seg: 94.4686, aux.loss_ce: 0.0899, aux.acc_seg: 90.8381, loss: 0.2262, grad_norm: 2.6795 2023-02-17 08:53:19,939 - mmseg - INFO - Iter [156500/160000] lr: 1.313e-06, eta: 0:21:24, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1438, decode.acc_seg: 94.1039, aux.loss_ce: 0.0955, aux.acc_seg: 90.4962, loss: 0.2394, grad_norm: 2.2843 2023-02-17 08:53:38,089 - mmseg - INFO - Iter [156550/160000] lr: 1.294e-06, eta: 0:21:06, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1375, decode.acc_seg: 94.1291, aux.loss_ce: 0.0905, aux.acc_seg: 90.6902, loss: 0.2280, grad_norm: 2.5112 2023-02-17 08:53:56,012 - mmseg - INFO - Iter [156600/160000] lr: 1.275e-06, eta: 0:20:48, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1442, decode.acc_seg: 94.0355, aux.loss_ce: 0.0971, aux.acc_seg: 90.1607, loss: 0.2413, grad_norm: 2.3115 2023-02-17 08:54:14,140 - mmseg - INFO - Iter [156650/160000] lr: 1.257e-06, eta: 0:20:29, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1420, decode.acc_seg: 94.1014, aux.loss_ce: 0.0963, aux.acc_seg: 90.3277, loss: 0.2383, grad_norm: 2.2784 2023-02-17 08:54:32,333 - mmseg - INFO - Iter [156700/160000] lr: 1.238e-06, eta: 0:20:11, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1358, decode.acc_seg: 94.2771, aux.loss_ce: 0.0931, aux.acc_seg: 90.4729, loss: 0.2289, grad_norm: 2.2251 2023-02-17 08:54:50,388 - mmseg - INFO - Iter [156750/160000] lr: 1.219e-06, eta: 0:19:53, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1349, decode.acc_seg: 94.2412, aux.loss_ce: 0.0909, aux.acc_seg: 90.5517, loss: 0.2258, grad_norm: 2.1983 2023-02-17 08:55:08,400 - mmseg - INFO - Iter [156800/160000] lr: 1.200e-06, eta: 0:19:34, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1348, decode.acc_seg: 94.2859, aux.loss_ce: 0.0928, aux.acc_seg: 90.2599, loss: 0.2276, grad_norm: 1.8833 2023-02-17 08:55:26,247 - mmseg - INFO - Iter [156850/160000] lr: 1.182e-06, eta: 0:19:16, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1324, decode.acc_seg: 94.4041, aux.loss_ce: 0.0874, aux.acc_seg: 90.9280, loss: 0.2198, grad_norm: 2.2217 2023-02-17 08:55:44,210 - mmseg - INFO - Iter [156900/160000] lr: 1.163e-06, eta: 0:18:57, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1361, decode.acc_seg: 94.1645, aux.loss_ce: 0.0931, aux.acc_seg: 90.4169, loss: 0.2292, grad_norm: 2.0349 2023-02-17 08:56:02,401 - mmseg - INFO - Iter [156950/160000] lr: 1.144e-06, eta: 0:18:39, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1466, decode.acc_seg: 93.8440, aux.loss_ce: 0.0965, aux.acc_seg: 90.0362, loss: 0.2431, grad_norm: 2.6007 2023-02-17 08:56:20,597 - mmseg - INFO - Saving checkpoint at 157000 iterations 2023-02-17 08:56:21,643 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 08:56:21,643 - mmseg - INFO - Iter [157000/160000] lr: 1.125e-06, eta: 0:18:21, time: 0.385, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1292, decode.acc_seg: 94.6168, aux.loss_ce: 0.0867, aux.acc_seg: 91.2731, loss: 0.2159, grad_norm: 1.9104 2023-02-17 08:56:39,831 - mmseg - INFO - Iter [157050/160000] lr: 1.107e-06, eta: 0:18:02, time: 0.364, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1356, decode.acc_seg: 94.3593, aux.loss_ce: 0.0897, aux.acc_seg: 90.8671, loss: 0.2254, grad_norm: 2.2718 2023-02-17 08:56:58,258 - mmseg - INFO - Iter [157100/160000] lr: 1.088e-06, eta: 0:17:44, time: 0.369, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1408, decode.acc_seg: 93.9050, aux.loss_ce: 0.0978, aux.acc_seg: 89.7726, loss: 0.2386, grad_norm: 2.4597 2023-02-17 08:57:16,771 - mmseg - INFO - Iter [157150/160000] lr: 1.069e-06, eta: 0:17:26, time: 0.370, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1386, decode.acc_seg: 94.1514, aux.loss_ce: 0.0942, aux.acc_seg: 90.3697, loss: 0.2327, grad_norm: 2.3324 2023-02-17 08:57:34,638 - mmseg - INFO - Iter [157200/160000] lr: 1.050e-06, eta: 0:17:07, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1389, decode.acc_seg: 94.1949, aux.loss_ce: 0.0907, aux.acc_seg: 90.7315, loss: 0.2296, grad_norm: 2.0282 2023-02-17 08:57:52,540 - mmseg - INFO - Iter [157250/160000] lr: 1.032e-06, eta: 0:16:49, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1349, decode.acc_seg: 94.1884, aux.loss_ce: 0.0898, aux.acc_seg: 90.6780, loss: 0.2247, grad_norm: 2.6559 2023-02-17 08:58:12,834 - mmseg - INFO - Iter [157300/160000] lr: 1.013e-06, eta: 0:16:31, time: 0.406, data_time: 0.050, memory: 16596, decode.loss_ce: 0.1363, decode.acc_seg: 94.2267, aux.loss_ce: 0.0930, aux.acc_seg: 90.4916, loss: 0.2293, grad_norm: 2.2814 2023-02-17 08:58:30,870 - mmseg - INFO - Iter [157350/160000] lr: 9.941e-07, eta: 0:16:12, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1296, decode.acc_seg: 94.4257, aux.loss_ce: 0.0892, aux.acc_seg: 90.9103, loss: 0.2188, grad_norm: 2.3176 2023-02-17 08:58:49,262 - mmseg - INFO - Iter [157400/160000] lr: 9.754e-07, eta: 0:15:54, time: 0.368, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1345, decode.acc_seg: 94.3130, aux.loss_ce: 0.0904, aux.acc_seg: 90.8442, loss: 0.2250, grad_norm: 2.1784 2023-02-17 08:59:07,461 - mmseg - INFO - Iter [157450/160000] lr: 9.566e-07, eta: 0:15:36, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1384, decode.acc_seg: 94.1698, aux.loss_ce: 0.0924, aux.acc_seg: 90.4606, loss: 0.2308, grad_norm: 2.2067 2023-02-17 08:59:25,475 - mmseg - INFO - Iter [157500/160000] lr: 9.379e-07, eta: 0:15:17, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1372, decode.acc_seg: 94.1119, aux.loss_ce: 0.0908, aux.acc_seg: 90.4927, loss: 0.2281, grad_norm: 2.1880 2023-02-17 08:59:43,454 - mmseg - INFO - Iter [157550/160000] lr: 9.191e-07, eta: 0:14:59, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1292, decode.acc_seg: 94.4738, aux.loss_ce: 0.0905, aux.acc_seg: 90.5626, loss: 0.2197, grad_norm: 2.4556 2023-02-17 09:00:01,285 - mmseg - INFO - Iter [157600/160000] lr: 9.004e-07, eta: 0:14:40, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1408, decode.acc_seg: 94.1327, aux.loss_ce: 0.0958, aux.acc_seg: 90.0151, loss: 0.2367, grad_norm: 2.2662 2023-02-17 09:00:19,353 - mmseg - INFO - Iter [157650/160000] lr: 8.816e-07, eta: 0:14:22, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1339, decode.acc_seg: 94.4915, aux.loss_ce: 0.0896, aux.acc_seg: 91.0227, loss: 0.2235, grad_norm: 2.0078 2023-02-17 09:00:37,258 - mmseg - INFO - Iter [157700/160000] lr: 8.629e-07, eta: 0:14:04, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1392, decode.acc_seg: 94.1588, aux.loss_ce: 0.0943, aux.acc_seg: 90.2077, loss: 0.2335, grad_norm: 1.9965 2023-02-17 09:00:55,187 - mmseg - INFO - Iter [157750/160000] lr: 8.441e-07, eta: 0:13:45, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1414, decode.acc_seg: 94.1312, aux.loss_ce: 0.0927, aux.acc_seg: 90.5425, loss: 0.2341, grad_norm: 2.3377 2023-02-17 09:01:13,470 - mmseg - INFO - Iter [157800/160000] lr: 8.254e-07, eta: 0:13:27, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1412, decode.acc_seg: 93.9600, aux.loss_ce: 0.0915, aux.acc_seg: 90.6280, loss: 0.2327, grad_norm: 2.7159 2023-02-17 09:01:31,697 - mmseg - INFO - Iter [157850/160000] lr: 8.066e-07, eta: 0:13:09, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1348, decode.acc_seg: 94.3160, aux.loss_ce: 0.0893, aux.acc_seg: 90.8176, loss: 0.2241, grad_norm: 2.2704 2023-02-17 09:01:49,497 - mmseg - INFO - Iter [157900/160000] lr: 7.879e-07, eta: 0:12:50, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1350, decode.acc_seg: 94.3285, aux.loss_ce: 0.0912, aux.acc_seg: 90.4932, loss: 0.2262, grad_norm: 2.0093 2023-02-17 09:02:07,699 - mmseg - INFO - Iter [157950/160000] lr: 7.691e-07, eta: 0:12:32, time: 0.364, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1334, decode.acc_seg: 94.4144, aux.loss_ce: 0.0883, aux.acc_seg: 90.8609, loss: 0.2217, grad_norm: 2.0204 2023-02-17 09:02:25,721 - mmseg - INFO - Saving checkpoint at 158000 iterations 2023-02-17 09:02:26,737 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 09:02:26,737 - mmseg - INFO - Iter [158000/160000] lr: 7.504e-07, eta: 0:12:14, time: 0.381, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1412, decode.acc_seg: 94.1262, aux.loss_ce: 0.0950, aux.acc_seg: 90.3022, loss: 0.2361, grad_norm: 2.1187 2023-02-17 09:02:44,698 - mmseg - INFO - Iter [158050/160000] lr: 7.316e-07, eta: 0:11:55, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1395, decode.acc_seg: 94.1948, aux.loss_ce: 0.0915, aux.acc_seg: 90.6489, loss: 0.2310, grad_norm: 3.0000 2023-02-17 09:03:02,572 - mmseg - INFO - Iter [158100/160000] lr: 7.129e-07, eta: 0:11:37, time: 0.358, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1316, decode.acc_seg: 94.3923, aux.loss_ce: 0.0895, aux.acc_seg: 90.8388, loss: 0.2210, grad_norm: 2.0384 2023-02-17 09:03:20,271 - mmseg - INFO - Iter [158150/160000] lr: 6.941e-07, eta: 0:11:19, time: 0.354, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1300, decode.acc_seg: 94.3410, aux.loss_ce: 0.0863, aux.acc_seg: 90.8874, loss: 0.2163, grad_norm: 2.2792 2023-02-17 09:03:38,209 - mmseg - INFO - Iter [158200/160000] lr: 6.754e-07, eta: 0:11:00, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1390, decode.acc_seg: 94.1728, aux.loss_ce: 0.0925, aux.acc_seg: 90.4726, loss: 0.2315, grad_norm: 2.7052 2023-02-17 09:03:56,029 - mmseg - INFO - Iter [158250/160000] lr: 6.566e-07, eta: 0:10:42, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1375, decode.acc_seg: 94.2461, aux.loss_ce: 0.0919, aux.acc_seg: 90.7045, loss: 0.2294, grad_norm: 2.1797 2023-02-17 09:04:14,280 - mmseg - INFO - Iter [158300/160000] lr: 6.379e-07, eta: 0:10:23, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1325, decode.acc_seg: 94.3402, aux.loss_ce: 0.0917, aux.acc_seg: 90.4073, loss: 0.2243, grad_norm: 2.1351 2023-02-17 09:04:32,332 - mmseg - INFO - Iter [158350/160000] lr: 6.191e-07, eta: 0:10:05, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1354, decode.acc_seg: 94.3593, aux.loss_ce: 0.0914, aux.acc_seg: 90.7520, loss: 0.2267, grad_norm: 2.2896 2023-02-17 09:04:50,358 - mmseg - INFO - Iter [158400/160000] lr: 6.004e-07, eta: 0:09:47, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1416, decode.acc_seg: 94.0975, aux.loss_ce: 0.0933, aux.acc_seg: 90.4538, loss: 0.2349, grad_norm: 2.3331 2023-02-17 09:05:08,503 - mmseg - INFO - Iter [158450/160000] lr: 5.816e-07, eta: 0:09:28, time: 0.362, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1436, decode.acc_seg: 93.8766, aux.loss_ce: 0.0950, aux.acc_seg: 89.9839, loss: 0.2386, grad_norm: 2.8460 2023-02-17 09:05:26,454 - mmseg - INFO - Iter [158500/160000] lr: 5.629e-07, eta: 0:09:10, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1364, decode.acc_seg: 94.3208, aux.loss_ce: 0.0916, aux.acc_seg: 90.5925, loss: 0.2280, grad_norm: 2.1504 2023-02-17 09:05:46,760 - mmseg - INFO - Iter [158550/160000] lr: 5.441e-07, eta: 0:08:52, time: 0.407, data_time: 0.054, memory: 16596, decode.loss_ce: 0.1391, decode.acc_seg: 94.1269, aux.loss_ce: 0.0944, aux.acc_seg: 90.2669, loss: 0.2335, grad_norm: 2.5342 2023-02-17 09:06:04,746 - mmseg - INFO - Iter [158600/160000] lr: 5.254e-07, eta: 0:08:33, time: 0.360, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1422, decode.acc_seg: 94.0348, aux.loss_ce: 0.0946, aux.acc_seg: 90.4906, loss: 0.2369, grad_norm: 2.5814 2023-02-17 09:06:22,572 - mmseg - INFO - Iter [158650/160000] lr: 5.066e-07, eta: 0:08:15, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1390, decode.acc_seg: 94.0579, aux.loss_ce: 0.0935, aux.acc_seg: 90.3726, loss: 0.2324, grad_norm: 2.3665 2023-02-17 09:06:40,341 - mmseg - INFO - Iter [158700/160000] lr: 4.879e-07, eta: 0:07:57, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1335, decode.acc_seg: 94.3793, aux.loss_ce: 0.0889, aux.acc_seg: 90.8889, loss: 0.2224, grad_norm: 1.9429 2023-02-17 09:06:58,169 - mmseg - INFO - Iter [158750/160000] lr: 4.691e-07, eta: 0:07:38, time: 0.357, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1349, decode.acc_seg: 94.3149, aux.loss_ce: 0.0913, aux.acc_seg: 90.4994, loss: 0.2261, grad_norm: 1.9164 2023-02-17 09:07:16,057 - mmseg - INFO - Iter [158800/160000] lr: 4.504e-07, eta: 0:07:20, time: 0.358, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1299, decode.acc_seg: 94.6259, aux.loss_ce: 0.0911, aux.acc_seg: 90.7278, loss: 0.2210, grad_norm: 2.3750 2023-02-17 09:07:34,085 - mmseg - INFO - Iter [158850/160000] lr: 4.316e-07, eta: 0:07:02, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1371, decode.acc_seg: 94.2651, aux.loss_ce: 0.0952, aux.acc_seg: 90.3133, loss: 0.2323, grad_norm: 2.5331 2023-02-17 09:07:52,351 - mmseg - INFO - Iter [158900/160000] lr: 4.129e-07, eta: 0:06:43, time: 0.365, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1319, decode.acc_seg: 94.3523, aux.loss_ce: 0.0930, aux.acc_seg: 90.4433, loss: 0.2249, grad_norm: 2.1248 2023-02-17 09:08:10,346 - mmseg - INFO - Iter [158950/160000] lr: 3.941e-07, eta: 0:06:25, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1417, decode.acc_seg: 94.1014, aux.loss_ce: 0.0929, aux.acc_seg: 90.3968, loss: 0.2346, grad_norm: 2.1831 2023-02-17 09:08:28,227 - mmseg - INFO - Saving checkpoint at 159000 iterations 2023-02-17 09:08:29,317 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 09:08:29,317 - mmseg - INFO - Iter [159000/160000] lr: 3.754e-07, eta: 0:06:06, time: 0.379, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1294, decode.acc_seg: 94.5346, aux.loss_ce: 0.0930, aux.acc_seg: 90.5945, loss: 0.2223, grad_norm: 2.3651 2023-02-17 09:08:47,384 - mmseg - INFO - Iter [159050/160000] lr: 3.566e-07, eta: 0:05:48, time: 0.361, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1387, decode.acc_seg: 94.2523, aux.loss_ce: 0.0936, aux.acc_seg: 90.5715, loss: 0.2323, grad_norm: 2.2219 2023-02-17 09:09:05,318 - mmseg - INFO - Iter [159100/160000] lr: 3.379e-07, eta: 0:05:30, time: 0.359, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1341, decode.acc_seg: 94.2923, aux.loss_ce: 0.0939, aux.acc_seg: 90.5480, loss: 0.2280, grad_norm: 2.1763 2023-02-17 09:09:23,935 - mmseg - INFO - Iter [159150/160000] lr: 3.191e-07, eta: 0:05:11, time: 0.372, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1397, decode.acc_seg: 94.1245, aux.loss_ce: 0.0957, aux.acc_seg: 90.0840, loss: 0.2355, grad_norm: 2.5674 2023-02-17 09:09:42,240 - mmseg - INFO - Iter [159200/160000] lr: 3.004e-07, eta: 0:04:53, time: 0.366, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1274, decode.acc_seg: 94.4963, aux.loss_ce: 0.0869, aux.acc_seg: 90.8554, loss: 0.2143, grad_norm: 1.9603 2023-02-17 09:10:00,065 - mmseg - INFO - Iter [159250/160000] lr: 2.816e-07, eta: 0:04:35, time: 0.357, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1294, decode.acc_seg: 94.5384, aux.loss_ce: 0.0878, aux.acc_seg: 91.0334, loss: 0.2172, grad_norm: 1.9552 2023-02-17 09:10:18,115 - mmseg - INFO - Iter [159300/160000] lr: 2.629e-07, eta: 0:04:16, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1409, decode.acc_seg: 93.9497, aux.loss_ce: 0.0937, aux.acc_seg: 90.2410, loss: 0.2345, grad_norm: 2.2927 2023-02-17 09:10:36,375 - mmseg - INFO - Iter [159350/160000] lr: 2.441e-07, eta: 0:03:58, time: 0.365, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1316, decode.acc_seg: 94.4302, aux.loss_ce: 0.0908, aux.acc_seg: 90.6653, loss: 0.2225, grad_norm: 1.9897 2023-02-17 09:10:54,433 - mmseg - INFO - Iter [159400/160000] lr: 2.254e-07, eta: 0:03:40, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1363, decode.acc_seg: 94.2239, aux.loss_ce: 0.0930, aux.acc_seg: 90.2729, loss: 0.2293, grad_norm: 2.0948 2023-02-17 09:11:12,451 - mmseg - INFO - Iter [159450/160000] lr: 2.066e-07, eta: 0:03:21, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1328, decode.acc_seg: 94.2809, aux.loss_ce: 0.0910, aux.acc_seg: 90.5612, loss: 0.2238, grad_norm: 2.4614 2023-02-17 09:11:30,476 - mmseg - INFO - Iter [159500/160000] lr: 1.879e-07, eta: 0:03:03, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1406, decode.acc_seg: 94.2751, aux.loss_ce: 0.0903, aux.acc_seg: 90.9984, loss: 0.2309, grad_norm: 2.4226 2023-02-17 09:11:48,275 - mmseg - INFO - Iter [159550/160000] lr: 1.691e-07, eta: 0:02:45, time: 0.356, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1440, decode.acc_seg: 94.0371, aux.loss_ce: 0.0944, aux.acc_seg: 90.4045, loss: 0.2384, grad_norm: 2.1468 2023-02-17 09:12:06,339 - mmseg - INFO - Iter [159600/160000] lr: 1.504e-07, eta: 0:02:26, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1318, decode.acc_seg: 94.5056, aux.loss_ce: 0.0945, aux.acc_seg: 90.5818, loss: 0.2263, grad_norm: 2.1746 2023-02-17 09:12:24,339 - mmseg - INFO - Iter [159650/160000] lr: 1.316e-07, eta: 0:02:08, time: 0.360, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1409, decode.acc_seg: 94.0521, aux.loss_ce: 0.0929, aux.acc_seg: 90.3111, loss: 0.2339, grad_norm: 1.9922 2023-02-17 09:12:42,293 - mmseg - INFO - Iter [159700/160000] lr: 1.129e-07, eta: 0:01:50, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1397, decode.acc_seg: 94.1529, aux.loss_ce: 0.0915, aux.acc_seg: 90.7657, loss: 0.2312, grad_norm: 1.9452 2023-02-17 09:13:00,250 - mmseg - INFO - Iter [159750/160000] lr: 9.413e-08, eta: 0:01:31, time: 0.359, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1303, decode.acc_seg: 94.5041, aux.loss_ce: 0.0856, aux.acc_seg: 91.2187, loss: 0.2159, grad_norm: 1.8934 2023-02-17 09:13:20,538 - mmseg - INFO - Iter [159800/160000] lr: 7.537e-08, eta: 0:01:13, time: 0.406, data_time: 0.056, memory: 16596, decode.loss_ce: 0.1326, decode.acc_seg: 94.5553, aux.loss_ce: 0.0917, aux.acc_seg: 90.7525, loss: 0.2243, grad_norm: 2.0535 2023-02-17 09:13:38,673 - mmseg - INFO - Iter [159850/160000] lr: 5.663e-08, eta: 0:00:55, time: 0.363, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1393, decode.acc_seg: 94.2069, aux.loss_ce: 0.0942, aux.acc_seg: 90.4889, loss: 0.2335, grad_norm: 2.4179 2023-02-17 09:13:56,412 - mmseg - INFO - Iter [159900/160000] lr: 3.787e-08, eta: 0:00:36, time: 0.355, data_time: 0.006, memory: 16596, decode.loss_ce: 0.1410, decode.acc_seg: 93.9136, aux.loss_ce: 0.0943, aux.acc_seg: 90.1158, loss: 0.2353, grad_norm: 2.5049 2023-02-17 09:14:14,461 - mmseg - INFO - Iter [159950/160000] lr: 1.913e-08, eta: 0:00:18, time: 0.361, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1356, decode.acc_seg: 94.3660, aux.loss_ce: 0.0912, aux.acc_seg: 90.8674, loss: 0.2268, grad_norm: 2.3428 2023-02-17 09:14:32,574 - mmseg - INFO - Saving checkpoint at 160000 iterations 2023-02-17 09:14:33,851 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 09:14:33,852 - mmseg - INFO - Iter [160000/160000] lr: 3.750e-10, eta: 0:00:00, time: 0.388, data_time: 0.007, memory: 16596, decode.loss_ce: 0.1299, decode.acc_seg: 94.3354, aux.loss_ce: 0.0854, aux.acc_seg: 91.0077, loss: 0.2153, grad_norm: 2.2990 2023-02-17 09:15:03,071 - mmseg - INFO - per class results: 2023-02-17 09:15:03,077 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 77.56 | 88.74 | | building | 82.81 | 91.83 | | sky | 94.47 | 97.37 | | floor | 80.13 | 91.22 | | tree | 74.68 | 88.33 | | ceiling | 84.85 | 93.12 | | road | 84.28 | 90.63 | | bed | 88.26 | 96.13 | | windowpane | 61.84 | 81.19 | | grass | 66.37 | 80.87 | | cabinet | 60.02 | 74.24 | | sidewalk | 67.43 | 84.36 | | person | 79.77 | 92.86 | | earth | 34.14 | 47.02 | | door | 50.64 | 65.47 | | table | 61.1 | 76.69 | | mountain | 58.78 | 75.27 | | plant | 52.69 | 64.25 | | curtain | 71.32 | 81.49 | | chair | 57.71 | 72.14 | | car | 81.97 | 90.86 | | water | 46.03 | 60.49 | | painting | 75.59 | 89.23 | | sofa | 64.44 | 79.39 | | shelf | 44.39 | 61.33 | | house | 52.63 | 66.11 | | sea | 54.4 | 83.12 | | mirror | 64.95 | 71.6 | | rug | 57.64 | 63.57 | | field | 26.12 | 41.59 | | armchair | 40.42 | 58.31 | | seat | 60.03 | 77.83 | | fence | 43.9 | 57.78 | | desk | 50.3 | 66.0 | | rock | 43.81 | 59.03 | | wardrobe | 47.18 | 63.41 | | lamp | 62.79 | 75.14 | | bathtub | 70.95 | 75.78 | | railing | 32.58 | 48.29 | | cushion | 59.12 | 76.28 | | base | 30.35 | 38.12 | | box | 29.15 | 35.18 | | column | 44.98 | 58.23 | | signboard | 36.36 | 51.57 | | chest of drawers | 39.96 | 54.68 | | counter | 22.5 | 31.21 | | sand | 45.87 | 72.29 | | sink | 72.4 | 82.34 | | skyscraper | 49.31 | 69.07 | | fireplace | 75.7 | 86.99 | | refrigerator | 78.69 | 86.31 | | grandstand | 45.65 | 63.11 | | path | 24.17 | 34.98 | | stairs | 31.07 | 37.7 | | runway | 66.29 | 87.73 | | case | 34.42 | 45.74 | | pool table | 92.84 | 94.41 | | pillow | 56.13 | 66.2 | | screen door | 62.03 | 68.83 | | stairway | 29.96 | 40.59 | | river | 9.52 | 18.86 | | bridge | 73.51 | 81.63 | | bookcase | 34.26 | 53.56 | | blind | 46.84 | 54.6 | | coffee table | 59.73 | 83.74 | | toilet | 84.51 | 91.32 | | flower | 38.76 | 53.94 | | book | 46.61 | 68.58 | | hill | 7.49 | 14.92 | | bench | 42.46 | 50.54 | | countertop | 55.72 | 71.4 | | stove | 76.53 | 83.94 | | palm | 52.46 | 79.75 | | kitchen island | 51.01 | 78.34 | | computer | 64.76 | 73.71 | | swivel chair | 46.53 | 64.16 | | boat | 40.6 | 50.76 | | bar | 27.8 | 32.32 | | arcade machine | 32.77 | 34.91 | | hovel | 13.91 | 16.41 | | bus | 88.5 | 96.61 | | towel | 63.55 | 70.24 | | light | 56.37 | 63.49 | | truck | 36.28 | 49.5 | | tower | 8.98 | 12.66 | | chandelier | 67.53 | 81.79 | | awning | 31.45 | 36.44 | | streetlight | 27.36 | 35.25 | | booth | 49.86 | 51.31 | | television receiver | 68.59 | 78.82 | | airplane | 46.25 | 66.17 | | dirt track | 15.29 | 21.36 | | apparel | 41.72 | 59.87 | | pole | 25.53 | 40.47 | | land | 3.34 | 4.34 | | bannister | 10.51 | 13.63 | | escalator | 21.9 | 22.69 | | ottoman | 51.74 | 70.28 | | bottle | 36.65 | 43.98 | | buffet | 45.89 | 52.28 | | poster | 28.14 | 36.9 | | stage | 19.83 | 26.46 | | van | 51.89 | 63.45 | | ship | 7.57 | 10.68 | | fountain | 25.93 | 26.69 | | conveyer belt | 73.1 | 86.33 | | canopy | 22.56 | 27.55 | | washer | 69.62 | 71.84 | | plaything | 26.32 | 37.47 | | swimming pool | 52.07 | 65.71 | | stool | 38.73 | 58.82 | | barrel | 57.4 | 65.9 | | basket | 33.45 | 42.41 | | waterfall | 39.28 | 49.4 | | tent | 95.94 | 98.34 | | bag | 18.11 | 25.2 | | minibike | 55.09 | 64.28 | | cradle | 80.88 | 94.09 | | oven | 46.22 | 57.58 | | ball | 39.95 | 49.74 | | food | 56.28 | 65.48 | | step | 19.15 | 26.04 | | tank | 34.39 | 40.64 | | trade name | 26.67 | 30.86 | | microwave | 80.71 | 88.22 | | pot | 44.53 | 52.34 | | animal | 54.13 | 56.62 | | bicycle | 55.29 | 76.99 | | lake | 53.68 | 60.57 | | dishwasher | 74.04 | 82.0 | | screen | 75.24 | 87.94 | | blanket | 11.23 | 12.43 | | sculpture | 62.01 | 82.13 | | hood | 59.37 | 71.29 | | sconce | 46.5 | 53.01 | | vase | 37.65 | 55.77 | | traffic light | 34.17 | 51.66 | | tray | 9.37 | 19.54 | | ashcan | 41.62 | 56.4 | | fan | 60.39 | 68.92 | | pier | 23.63 | 45.06 | | crt screen | 7.94 | 19.74 | | plate | 53.76 | 66.64 | | monitor | 11.03 | 16.64 | | bulletin board | 41.18 | 48.67 | | shower | 2.68 | 6.27 | | radiator | 61.39 | 64.99 | | glass | 13.32 | 14.32 | | clock | 34.19 | 42.25 | | flag | 48.43 | 54.08 | +---------------------+-------+-------+ 2023-02-17 09:15:03,077 - mmseg - INFO - Summary: 2023-02-17 09:15:03,078 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 82.89 | 48.37 | 59.39 | +-------+-------+-------+ 2023-02-17 09:15:04,133 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_160000.pth. 2023-02-17 09:15:04,134 - mmseg - INFO - Best mIoU is 0.4837 at 160000 iter. 2023-02-17 09:15:04,134 - mmseg - INFO - Exp name: diffseg_swin_s_4x4_512x512_160k_ade20k_v20.py 2023-02-17 09:15:04,134 - mmseg - INFO - Iter(val) [500] aAcc: 0.8289, mIoU: 0.4837, mAcc: 0.5939, IoU.wall: 0.7756, IoU.building: 0.8281, IoU.sky: 0.9447, IoU.floor: 0.8013, IoU.tree: 0.7468, IoU.ceiling: 0.8485, IoU.road: 0.8428, IoU.bed : 0.8826, IoU.windowpane: 0.6184, IoU.grass: 0.6637, IoU.cabinet: 0.6002, IoU.sidewalk: 0.6743, IoU.person: 0.7977, IoU.earth: 0.3414, IoU.door: 0.5064, IoU.table: 0.6110, IoU.mountain: 0.5878, IoU.plant: 0.5269, IoU.curtain: 0.7132, IoU.chair: 0.5771, IoU.car: 0.8197, IoU.water: 0.4603, IoU.painting: 0.7559, IoU.sofa: 0.6444, IoU.shelf: 0.4439, IoU.house: 0.5263, IoU.sea: 0.5440, IoU.mirror: 0.6495, IoU.rug: 0.5764, IoU.field: 0.2612, IoU.armchair: 0.4042, IoU.seat: 0.6003, IoU.fence: 0.4390, IoU.desk: 0.5030, IoU.rock: 0.4381, IoU.wardrobe: 0.4718, IoU.lamp: 0.6279, IoU.bathtub: 0.7095, IoU.railing: 0.3258, IoU.cushion: 0.5912, IoU.base: 0.3035, IoU.box: 0.2915, IoU.column: 0.4498, IoU.signboard: 0.3636, IoU.chest of drawers: 0.3996, IoU.counter: 0.2250, IoU.sand: 0.4587, IoU.sink: 0.7240, IoU.skyscraper: 0.4931, IoU.fireplace: 0.7570, IoU.refrigerator: 0.7869, IoU.grandstand: 0.4565, IoU.path: 0.2417, IoU.stairs: 0.3107, IoU.runway: 0.6629, IoU.case: 0.3442, IoU.pool table: 0.9284, IoU.pillow: 0.5613, IoU.screen door: 0.6203, IoU.stairway: 0.2996, IoU.river: 0.0952, IoU.bridge: 0.7351, IoU.bookcase: 0.3426, IoU.blind: 0.4684, IoU.coffee table: 0.5973, IoU.toilet: 0.8451, IoU.flower: 0.3876, IoU.book: 0.4661, IoU.hill: 0.0749, IoU.bench: 0.4246, IoU.countertop: 0.5572, IoU.stove: 0.7653, IoU.palm: 0.5246, IoU.kitchen island: 0.5101, IoU.computer: 0.6476, IoU.swivel chair: 0.4653, IoU.boat: 0.4060, IoU.bar: 0.2780, IoU.arcade machine: 0.3277, IoU.hovel: 0.1391, IoU.bus: 0.8850, IoU.towel: 0.6355, IoU.light: 0.5637, IoU.truck: 0.3628, IoU.tower: 0.0898, IoU.chandelier: 0.6753, IoU.awning: 0.3145, IoU.streetlight: 0.2736, IoU.booth: 0.4986, IoU.television receiver: 0.6859, IoU.airplane: 0.4625, IoU.dirt track: 0.1529, IoU.apparel: 0.4172, IoU.pole: 0.2553, IoU.land: 0.0334, IoU.bannister: 0.1051, IoU.escalator: 0.2190, IoU.ottoman: 0.5174, IoU.bottle: 0.3665, IoU.buffet: 0.4589, IoU.poster: 0.2814, IoU.stage: 0.1983, IoU.van: 0.5189, IoU.ship: 0.0757, IoU.fountain: 0.2593, IoU.conveyer belt: 0.7310, IoU.canopy: 0.2256, IoU.washer: 0.6962, IoU.plaything: 0.2632, IoU.swimming pool: 0.5207, IoU.stool: 0.3873, IoU.barrel: 0.5740, IoU.basket: 0.3345, IoU.waterfall: 0.3928, IoU.tent: 0.9594, IoU.bag: 0.1811, IoU.minibike: 0.5509, IoU.cradle: 0.8088, IoU.oven: 0.4622, IoU.ball: 0.3995, IoU.food: 0.5628, IoU.step: 0.1915, IoU.tank: 0.3439, IoU.trade name: 0.2667, IoU.microwave: 0.8071, IoU.pot: 0.4453, IoU.animal: 0.5413, IoU.bicycle: 0.5529, IoU.lake: 0.5368, IoU.dishwasher: 0.7404, IoU.screen: 0.7524, IoU.blanket: 0.1123, IoU.sculpture: 0.6201, IoU.hood: 0.5937, IoU.sconce: 0.4650, IoU.vase: 0.3765, IoU.traffic light: 0.3417, IoU.tray: 0.0937, IoU.ashcan: 0.4162, IoU.fan: 0.6039, IoU.pier: 0.2363, IoU.crt screen: 0.0794, IoU.plate: 0.5376, IoU.monitor: 0.1103, IoU.bulletin board: 0.4118, IoU.shower: 0.0268, IoU.radiator: 0.6139, IoU.glass: 0.1332, IoU.clock: 0.3419, IoU.flag: 0.4843, Acc.wall: 0.8874, Acc.building: 0.9183, Acc.sky: 0.9737, Acc.floor: 0.9122, Acc.tree: 0.8833, Acc.ceiling: 0.9312, Acc.road: 0.9063, Acc.bed : 0.9613, Acc.windowpane: 0.8119, Acc.grass: 0.8087, Acc.cabinet: 0.7424, Acc.sidewalk: 0.8436, Acc.person: 0.9286, Acc.earth: 0.4702, Acc.door: 0.6547, Acc.table: 0.7669, Acc.mountain: 0.7527, Acc.plant: 0.6425, Acc.curtain: 0.8149, Acc.chair: 0.7214, Acc.car: 0.9086, Acc.water: 0.6049, Acc.painting: 0.8923, Acc.sofa: 0.7939, Acc.shelf: 0.6133, Acc.house: 0.6611, Acc.sea: 0.8312, Acc.mirror: 0.7160, Acc.rug: 0.6357, Acc.field: 0.4159, Acc.armchair: 0.5831, Acc.seat: 0.7783, Acc.fence: 0.5778, Acc.desk: 0.6600, Acc.rock: 0.5903, Acc.wardrobe: 0.6341, Acc.lamp: 0.7514, Acc.bathtub: 0.7578, Acc.railing: 0.4829, Acc.cushion: 0.7628, Acc.base: 0.3812, Acc.box: 0.3518, Acc.column: 0.5823, Acc.signboard: 0.5157, Acc.chest of drawers: 0.5468, Acc.counter: 0.3121, Acc.sand: 0.7229, Acc.sink: 0.8234, Acc.skyscraper: 0.6907, Acc.fireplace: 0.8699, Acc.refrigerator: 0.8631, Acc.grandstand: 0.6311, Acc.path: 0.3498, Acc.stairs: 0.3770, Acc.runway: 0.8773, Acc.case: 0.4574, Acc.pool table: 0.9441, Acc.pillow: 0.6620, Acc.screen door: 0.6883, Acc.stairway: 0.4059, Acc.river: 0.1886, Acc.bridge: 0.8163, Acc.bookcase: 0.5356, Acc.blind: 0.5460, Acc.coffee table: 0.8374, Acc.toilet: 0.9132, Acc.flower: 0.5394, Acc.book: 0.6858, Acc.hill: 0.1492, Acc.bench: 0.5054, Acc.countertop: 0.7140, Acc.stove: 0.8394, Acc.palm: 0.7975, Acc.kitchen island: 0.7834, Acc.computer: 0.7371, Acc.swivel chair: 0.6416, Acc.boat: 0.5076, Acc.bar: 0.3232, Acc.arcade machine: 0.3491, Acc.hovel: 0.1641, Acc.bus: 0.9661, Acc.towel: 0.7024, Acc.light: 0.6349, Acc.truck: 0.4950, Acc.tower: 0.1266, Acc.chandelier: 0.8179, Acc.awning: 0.3644, Acc.streetlight: 0.3525, Acc.booth: 0.5131, Acc.television receiver: 0.7882, Acc.airplane: 0.6617, Acc.dirt track: 0.2136, Acc.apparel: 0.5987, Acc.pole: 0.4047, Acc.land: 0.0434, Acc.bannister: 0.1363, Acc.escalator: 0.2269, Acc.ottoman: 0.7028, Acc.bottle: 0.4398, Acc.buffet: 0.5228, Acc.poster: 0.3690, Acc.stage: 0.2646, Acc.van: 0.6345, Acc.ship: 0.1068, Acc.fountain: 0.2669, Acc.conveyer belt: 0.8633, Acc.canopy: 0.2755, Acc.washer: 0.7184, Acc.plaything: 0.3747, Acc.swimming pool: 0.6571, Acc.stool: 0.5882, Acc.barrel: 0.6590, Acc.basket: 0.4241, Acc.waterfall: 0.4940, Acc.tent: 0.9834, Acc.bag: 0.2520, Acc.minibike: 0.6428, Acc.cradle: 0.9409, Acc.oven: 0.5758, Acc.ball: 0.4974, Acc.food: 0.6548, Acc.step: 0.2604, Acc.tank: 0.4064, Acc.trade name: 0.3086, Acc.microwave: 0.8822, Acc.pot: 0.5234, Acc.animal: 0.5662, Acc.bicycle: 0.7699, Acc.lake: 0.6057, Acc.dishwasher: 0.8200, Acc.screen: 0.8794, Acc.blanket: 0.1243, Acc.sculpture: 0.8213, Acc.hood: 0.7129, Acc.sconce: 0.5301, Acc.vase: 0.5577, Acc.traffic light: 0.5166, Acc.tray: 0.1954, Acc.ashcan: 0.5640, Acc.fan: 0.6892, Acc.pier: 0.4506, Acc.crt screen: 0.1974, Acc.plate: 0.6664, Acc.monitor: 0.1664, Acc.bulletin board: 0.4867, Acc.shower: 0.0627, Acc.radiator: 0.6499, Acc.glass: 0.1432, Acc.clock: 0.4225, Acc.flag: 0.5408