2022-05-04 20:57:38,415 - mmseg - INFO - Environment info: ------------------------------------------------------------ sys.platform: linux Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] CUDA available: True GPU 0,1,2,3,4,5,6,7: A100-SXM-80GB CUDA_HOME: /mnt/lustre/share/cuda-11.3 NVCC: Build cuda_11.3.r11.3/compiler.29920130_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: 4.5.5 MMCV: 1.4.2 MMCV Compiler: GCC 7.3 MMCV CUDA Compiler: 11.1 MMSegmentation: 0.20.2+ ------------------------------------------------------------ 2022-05-04 20:57:38,416 - mmseg - INFO - Distributed training: True 2022-05-04 20:57:39,189 - mmseg - INFO - Config: num_things_classes = 80 num_stuff_classes = 91 num_classes = 171 norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( type='EncoderDecoderMask2Former', pretrained='pretrained/beit_large_patch16_224_pt22k_ft22k.pth', backbone=dict( type='BEiTDenseAdaptor', patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True, use_abs_pos_emb=False, use_rel_pos_bias=True, img_size=512, init_values=1e-06, drop_path_rate=0.3, conv_inplane=64, n_points=4, deform_num_heads=16, interact_with_ffn=True, interact_ffn_ratio=0.25, interact_deform_ratio=0.5, extract_with_ffn=True, extract_ffn_ratio=0.25, extract_deform_ratio=0.5, num_extract_block=2, add_vit_feature=True, interact_indexes=[[0, 5], [6, 11], [12, 17], [18, 23]]), decode_head=dict( type='Mask2FormerHead', in_channels=[1024, 1024, 1024, 1024], feat_channels=1024, out_channels=1024, in_index=[0, 1, 2, 3], num_things_classes=80, num_stuff_classes=91, num_queries=100, num_transformer_feat_level=3, pixel_decoder=dict( type='MSDeformAttnPixelDecoder', num_outs=3, norm_cfg=dict(type='GN', num_groups=32), act_cfg=dict(type='ReLU'), encoder=dict( type='DetrTransformerEncoder', num_layers=6, transformerlayers=dict( type='BaseTransformerLayer', attn_cfgs=dict( type='MultiScaleDeformableAttention', embed_dims=1024, num_heads=32, num_levels=3, num_points=4, im2col_step=64, dropout=0.0, batch_first=False, norm_cfg=None, init_cfg=None), ffn_cfgs=dict( type='FFN', embed_dims=1024, feedforward_channels=4096, num_fcs=2, ffn_drop=0.0, act_cfg=dict(type='ReLU', inplace=True)), operation_order=('self_attn', 'norm', 'ffn', 'norm')), init_cfg=None), positional_encoding=dict( type='SinePositionalEncoding', num_feats=512, normalize=True), init_cfg=None), enforce_decoder_input_project=False, positional_encoding=dict( type='SinePositionalEncoding', num_feats=512, normalize=True), transformer_decoder=dict( type='DetrTransformerDecoder', return_intermediate=True, num_layers=9, transformerlayers=dict( type='DetrTransformerDecoderLayer', attn_cfgs=dict( type='MultiheadAttention', embed_dims=1024, num_heads=32, attn_drop=0.0, proj_drop=0.0, dropout_layer=None, batch_first=False), ffn_cfgs=dict( embed_dims=1024, feedforward_channels=4096, num_fcs=2, act_cfg=dict(type='ReLU', inplace=True), ffn_drop=0.0, dropout_layer=None, add_identity=True), feedforward_channels=4096, operation_order=('cross_attn', 'norm', 'self_attn', 'norm', 'ffn', 'norm')), init_cfg=None), loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=2.0, reduction='mean', class_weight=[ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.1 ]), loss_mask=dict( type='CrossEntropyLoss', use_sigmoid=True, reduction='mean', loss_weight=5.0), loss_dice=dict( type='DiceLoss', use_sigmoid=True, activate=True, reduction='mean', naive_dice=True, eps=1.0, loss_weight=5.0)), train_cfg=dict( num_points=12544, oversample_ratio=3.0, importance_sample_ratio=0.75, assigner=dict( type='MaskHungarianAssigner', cls_cost=dict(type='ClassificationCost', weight=2.0), mask_cost=dict( type='CrossEntropyLossCost', weight=5.0, use_sigmoid=True), dice_cost=dict( type='DiceCost', weight=5.0, pred_act=True, eps=1.0)), sampler=dict(type='MaskPseudoSampler')), test_cfg=dict( panoptic_on=True, semantic_on=False, instance_on=True, max_per_image=100, iou_thr=0.8, filter_low_score=True, mode='slide', crop_size=(512, 512), stride=(341, 341)), init_cfg=None) find_unused_parameters = True dataset_type = 'COCOStuffDataset' data_root = 'data/coco_stuff10k' 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='ToMask'), dict(type='DefaultFormatBundle'), dict( type='Collect', keys=['img', 'gt_semantic_seg', 'gt_masks', 'gt_labels']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 512), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='ResizeToMultiple', size_divisor=32), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ] data = dict( samples_per_gpu=2, workers_per_gpu=4, train=dict( type='COCOStuffDataset', data_root='data/coco_stuff10k', reduce_zero_label=True, img_dir='images/train2014', ann_dir='annotations/train2014', 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='ToMask'), dict(type='DefaultFormatBundle'), dict( type='Collect', keys=['img', 'gt_semantic_seg', 'gt_masks', 'gt_labels']) ]), val=dict( type='COCOStuffDataset', data_root='data/coco_stuff10k', reduce_zero_label=True, img_dir='images/test2014', ann_dir='annotations/test2014', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 512), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='ResizeToMultiple', size_divisor=32), 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='COCOStuffDataset', data_root='data/coco_stuff10k', reduce_zero_label=True, img_dir='images/test2014', ann_dir='annotations/test2014', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 512), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='ResizeToMultiple', size_divisor=32), 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=2e-05, betas=(0.9, 0.999), weight_decay=0.05, constructor='LayerDecayOptimizerConstructor', paramwise_cfg=dict(num_layers=24, layer_decay_rate=0.9)) optimizer_config = dict() lr_config = dict( policy='poly', warmup='linear', warmup_iters=1500, warmup_ratio=1e-06, power=1.0, min_lr=0.0, by_epoch=False) runner = dict(type='IterBasedRunner', max_iters=40000) checkpoint_config = dict(by_epoch=False, interval=1000, max_keep_ckpts=1) evaluation = dict( interval=4000, metric='mIoU', pre_eval=True, save_best='mIoU') work_dir = './work_dirs/mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss' gpu_ids = range(0, 8) auto_resume = False 2022-05-04 20:57:47,774 - mmseg - INFO - Set random seed to 1719464028, deterministic: False 2022-05-04 20:57:55,198 - mmseg - WARNING - The model and loaded state dict do not match exactly unexpected key in source state_dict: fc_norm.weight, fc_norm.bias, head.weight, head.bias missing keys in source state_dict: blocks.0.attn.relative_position_index, blocks.1.attn.relative_position_index, blocks.2.attn.relative_position_index, blocks.3.attn.relative_position_index, blocks.4.attn.relative_position_index, blocks.5.attn.relative_position_index, blocks.6.attn.relative_position_index, blocks.7.attn.relative_position_index, blocks.8.attn.relative_position_index, blocks.9.attn.relative_position_index, blocks.10.attn.relative_position_index, blocks.11.attn.relative_position_index, blocks.12.attn.relative_position_index, blocks.13.attn.relative_position_index, blocks.14.attn.relative_position_index, blocks.15.attn.relative_position_index, blocks.16.attn.relative_position_index, blocks.17.attn.relative_position_index, blocks.18.attn.relative_position_index, blocks.19.attn.relative_position_index, blocks.20.attn.relative_position_index, blocks.21.attn.relative_position_index, blocks.22.attn.relative_position_index, blocks.23.attn.relative_position_index Name of parameter - Initialization information backbone.cls_token - torch.Size([1, 1, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.level_embed - torch.Size([3, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.patch_embed.proj.weight - torch.Size([1024, 3, 16, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.patch_embed.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.0.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.0.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.0.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.0.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.0.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.0.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.0.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.0.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.0.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.0.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.0.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.0.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.0.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.0.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.0.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.0.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.1.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.1.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.1.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.1.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.1.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.1.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.1.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.1.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.1.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.1.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.1.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.1.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.1.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.1.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.1.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.1.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.2.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.2.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.2.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.2.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.2.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.2.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.2.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.2.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.2.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.2.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.2.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.2.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.2.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.2.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.2.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.2.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.3.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.3.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.3.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.3.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.3.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.3.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.3.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.3.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.3.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.3.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.3.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.3.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.3.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.3.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.3.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.3.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.4.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.4.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.4.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.4.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.4.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.4.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.4.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.4.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.4.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.4.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.4.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.4.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.4.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.4.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.4.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.4.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.5.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.5.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.5.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.5.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.5.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.5.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.5.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.5.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.5.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.5.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.5.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.5.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.5.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.5.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.5.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.5.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.6.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.6.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.6.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.6.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.6.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.6.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.6.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.6.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.6.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.6.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.6.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.6.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.6.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.6.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.6.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.6.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.7.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.7.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.7.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.7.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.7.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.7.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.7.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.7.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.7.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.7.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.7.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.7.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.7.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.7.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.7.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.7.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.8.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.8.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.8.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.8.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.8.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.8.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.8.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.8.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.8.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.8.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.8.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.8.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.8.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.8.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.8.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.8.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.9.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.9.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.9.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.9.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.9.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.9.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.9.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.9.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.9.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.9.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.9.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.9.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.9.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.9.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.9.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.9.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.10.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.10.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.10.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.10.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.10.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.10.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.10.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.10.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.10.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.10.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.10.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.10.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.10.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.10.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.10.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.10.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.11.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.11.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.11.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.11.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.11.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.11.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.11.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.11.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.11.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.11.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.11.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.11.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.11.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.11.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.11.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.11.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.12.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.12.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.12.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.12.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.12.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.12.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.12.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.12.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.12.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.12.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.12.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.12.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.12.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.12.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.12.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.12.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.13.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.13.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.13.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.13.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.13.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.13.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.13.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.13.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.13.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.13.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.13.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.13.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.13.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.13.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.13.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.13.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.14.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.14.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.14.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.14.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.14.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.14.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.14.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.14.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.14.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.14.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.14.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.14.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.14.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.14.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.14.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.14.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.15.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.15.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.15.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.15.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.15.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.15.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.15.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.15.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.15.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.15.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.15.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.15.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.15.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.15.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.15.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.15.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.16.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.16.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.16.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.16.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.16.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.16.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.16.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.16.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.16.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.16.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.16.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.16.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.16.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.16.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.16.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.16.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.17.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.17.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.17.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.17.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.17.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.17.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.17.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.17.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.17.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.17.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.17.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.17.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.17.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.17.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.17.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.17.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.18.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.18.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.18.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.18.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.18.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.18.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.18.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.18.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.18.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.18.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.18.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.18.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.18.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.18.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.18.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.18.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.19.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.19.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.19.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.19.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.19.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.19.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.19.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.19.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.19.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.19.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.19.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.19.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.19.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.19.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.19.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.19.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.20.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.20.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.20.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.20.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.20.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.20.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.20.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.20.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.20.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.20.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.20.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.20.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.20.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.20.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.20.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.20.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.21.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.21.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.21.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.21.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.21.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.21.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.21.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.21.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.21.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.21.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.21.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.21.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.21.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.21.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.21.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.21.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.22.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.22.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.22.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.22.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.22.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.22.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.22.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.22.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.22.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.22.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.22.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.22.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.22.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.22.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.22.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.22.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.23.gamma_1 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.23.gamma_2 - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.23.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.23.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.23.attn.q_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.23.attn.v_bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.23.attn.relative_position_bias_table - torch.Size([3972, 16]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.23.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.23.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.23.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.23.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.23.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.23.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.23.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.23.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.blocks.23.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.stem.0.weight - torch.Size([64, 3, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.stem.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.stem.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.stem.3.weight - torch.Size([64, 64, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.stem.4.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.stem.4.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.stem.6.weight - torch.Size([64, 64, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.stem.7.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.stem.7.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.conv2.0.weight - torch.Size([128, 64, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.conv2.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.conv2.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.conv3.0.weight - torch.Size([256, 128, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.conv3.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.conv3.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.conv4.0.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.conv4.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.conv4.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.fc1.weight - torch.Size([1024, 64, 1, 1]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.fc1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.fc2.weight - torch.Size([1024, 128, 1, 1]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.fc3.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.fc3.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.fc4.weight - torch.Size([1024, 256, 1, 1]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.conv_branch.fc4.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.extract.query_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.extract.query_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.extract.feat_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.extract.feat_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.extract.attn.sampling_offsets.weight - torch.Size([128, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.extract.attn.sampling_offsets.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.extract.attn.attention_weights.weight - torch.Size([64, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.extract.attn.attention_weights.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.extract.attn.value_proj.weight - torch.Size([512, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.extract.attn.value_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.extract.attn.output_proj.weight - torch.Size([1024, 512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.extract.attn.output_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.insert.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.insert.query_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.insert.query_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.insert.feat_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.insert.feat_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.insert.attn.sampling_offsets.weight - torch.Size([384, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.insert.attn.sampling_offsets.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.insert.attn.attention_weights.weight - torch.Size([192, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.insert.attn.attention_weights.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.insert.attn.value_proj.weight - torch.Size([512, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.insert.attn.value_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.insert.attn.output_proj.weight - torch.Size([1024, 512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.insert.attn.output_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.ffn.fc1.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.ffn.fc1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.ffn.dwconv.dwconv.weight - torch.Size([256, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.ffn.dwconv.dwconv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.ffn.fc2.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.ffn.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.ffn_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.0.ffn_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.extract.query_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.extract.query_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.extract.feat_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.extract.feat_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.extract.attn.sampling_offsets.weight - torch.Size([128, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.extract.attn.sampling_offsets.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.extract.attn.attention_weights.weight - torch.Size([64, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.extract.attn.attention_weights.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.extract.attn.value_proj.weight - torch.Size([512, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.extract.attn.value_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.extract.attn.output_proj.weight - torch.Size([1024, 512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.extract.attn.output_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.insert.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.insert.query_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.insert.query_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.insert.feat_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.insert.feat_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.insert.attn.sampling_offsets.weight - torch.Size([384, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.insert.attn.sampling_offsets.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.insert.attn.attention_weights.weight - torch.Size([192, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.insert.attn.attention_weights.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.insert.attn.value_proj.weight - torch.Size([512, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.insert.attn.value_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.insert.attn.output_proj.weight - torch.Size([1024, 512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.insert.attn.output_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.ffn.fc1.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.ffn.fc1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.ffn.dwconv.dwconv.weight - torch.Size([256, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.ffn.dwconv.dwconv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.ffn.fc2.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.ffn.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.ffn_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.1.ffn_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.extract.query_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.extract.query_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.extract.feat_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.extract.feat_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.extract.attn.sampling_offsets.weight - torch.Size([128, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.extract.attn.sampling_offsets.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.extract.attn.attention_weights.weight - torch.Size([64, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.extract.attn.attention_weights.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.extract.attn.value_proj.weight - torch.Size([512, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.extract.attn.value_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.extract.attn.output_proj.weight - torch.Size([1024, 512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.extract.attn.output_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.insert.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.insert.query_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.insert.query_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.insert.feat_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.insert.feat_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.insert.attn.sampling_offsets.weight - torch.Size([384, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.insert.attn.sampling_offsets.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.insert.attn.attention_weights.weight - torch.Size([192, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.insert.attn.attention_weights.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.insert.attn.value_proj.weight - torch.Size([512, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.insert.attn.value_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.insert.attn.output_proj.weight - torch.Size([1024, 512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.insert.attn.output_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.ffn.fc1.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.ffn.fc1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.ffn.dwconv.dwconv.weight - torch.Size([256, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.ffn.dwconv.dwconv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.ffn.fc2.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.ffn.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.ffn_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.2.ffn_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.extract.query_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.extract.query_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.extract.feat_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.extract.feat_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.extract.attn.sampling_offsets.weight - torch.Size([128, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.extract.attn.sampling_offsets.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.extract.attn.attention_weights.weight - torch.Size([64, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.extract.attn.attention_weights.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.extract.attn.value_proj.weight - torch.Size([512, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.extract.attn.value_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.extract.attn.output_proj.weight - torch.Size([1024, 512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.extract.attn.output_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.insert.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.insert.query_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.insert.query_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.insert.feat_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.insert.feat_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.insert.attn.sampling_offsets.weight - torch.Size([384, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.insert.attn.sampling_offsets.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.insert.attn.attention_weights.weight - torch.Size([192, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.insert.attn.attention_weights.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.insert.attn.value_proj.weight - torch.Size([512, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.insert.attn.value_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.insert.attn.output_proj.weight - torch.Size([1024, 512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.insert.attn.output_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.ffn.fc1.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.ffn.fc1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.ffn.dwconv.dwconv.weight - torch.Size([256, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.ffn.dwconv.dwconv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.ffn.fc2.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.ffn.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.ffn_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.interact_blocks.3.ffn_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.extract.query_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.extract.query_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.extract.feat_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.extract.feat_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.extract.attn.sampling_offsets.weight - torch.Size([128, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.extract.attn.sampling_offsets.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.extract.attn.attention_weights.weight - torch.Size([64, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.extract.attn.attention_weights.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.extract.attn.value_proj.weight - torch.Size([512, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.extract.attn.value_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.extract.attn.output_proj.weight - torch.Size([1024, 512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.extract.attn.output_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.ffn.fc1.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.ffn.fc1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.ffn.dwconv.dwconv.weight - torch.Size([256, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.ffn.dwconv.dwconv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.ffn.fc2.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.ffn.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.ffn_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.0.ffn_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.extract.query_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.extract.query_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.extract.feat_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.extract.feat_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.extract.attn.sampling_offsets.weight - torch.Size([128, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.extract.attn.sampling_offsets.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.extract.attn.attention_weights.weight - torch.Size([64, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.extract.attn.attention_weights.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.extract.attn.value_proj.weight - torch.Size([512, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.extract.attn.value_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.extract.attn.output_proj.weight - torch.Size([1024, 512]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.extract.attn.output_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.ffn.fc1.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.ffn.fc1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.ffn.dwconv.dwconv.weight - torch.Size([256, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.ffn.dwconv.dwconv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.ffn.fc2.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.ffn.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.ffn_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.extract_blocks.1.ffn_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.up.weight - torch.Size([1024, 1024, 2, 2]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.up.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.norm3.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.norm3.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.norm4.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former backbone.norm4.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.conv_seg.weight - torch.Size([171, 1024, 1, 1]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.conv_seg.bias - torch.Size([171]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.input_convs.0.conv.weight - torch.Size([1024, 1024, 1, 1]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.input_convs.0.conv.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.input_convs.0.gn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.input_convs.0.gn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.input_convs.1.conv.weight - torch.Size([1024, 1024, 1, 1]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.input_convs.1.conv.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.input_convs.1.gn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.input_convs.1.gn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.input_convs.2.conv.weight - torch.Size([1024, 1024, 1, 1]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.input_convs.2.conv.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.input_convs.2.gn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.input_convs.2.gn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.0.attentions.0.sampling_offsets.weight - torch.Size([768, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.0.attentions.0.sampling_offsets.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.0.attentions.0.attention_weights.weight - torch.Size([384, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.0.attentions.0.attention_weights.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.0.attentions.0.value_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.0.attentions.0.value_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.0.attentions.0.output_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.0.attentions.0.output_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.0.ffns.0.layers.0.0.weight - torch.Size([4096, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.0.ffns.0.layers.0.0.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.0.ffns.0.layers.1.weight - torch.Size([1024, 4096]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.0.ffns.0.layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.0.norms.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.0.norms.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.0.norms.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.0.norms.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.1.attentions.0.sampling_offsets.weight - torch.Size([768, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.1.attentions.0.sampling_offsets.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.1.attentions.0.attention_weights.weight - torch.Size([384, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.1.attentions.0.attention_weights.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.1.attentions.0.value_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.1.attentions.0.value_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.1.attentions.0.output_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.1.attentions.0.output_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.1.ffns.0.layers.0.0.weight - torch.Size([4096, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.1.ffns.0.layers.0.0.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.1.ffns.0.layers.1.weight - torch.Size([1024, 4096]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.1.ffns.0.layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.1.norms.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.1.norms.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.1.norms.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.1.norms.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.2.attentions.0.sampling_offsets.weight - torch.Size([768, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.2.attentions.0.sampling_offsets.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.2.attentions.0.attention_weights.weight - torch.Size([384, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.2.attentions.0.attention_weights.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.2.attentions.0.value_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.2.attentions.0.value_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.2.attentions.0.output_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.2.attentions.0.output_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.2.ffns.0.layers.0.0.weight - torch.Size([4096, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.2.ffns.0.layers.0.0.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.2.ffns.0.layers.1.weight - torch.Size([1024, 4096]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.2.ffns.0.layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.2.norms.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.2.norms.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.2.norms.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.2.norms.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.3.attentions.0.sampling_offsets.weight - torch.Size([768, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.3.attentions.0.sampling_offsets.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.3.attentions.0.attention_weights.weight - torch.Size([384, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.3.attentions.0.attention_weights.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.3.attentions.0.value_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.3.attentions.0.value_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.3.attentions.0.output_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.3.attentions.0.output_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.3.ffns.0.layers.0.0.weight - torch.Size([4096, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.3.ffns.0.layers.0.0.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.3.ffns.0.layers.1.weight - torch.Size([1024, 4096]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.3.ffns.0.layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.3.norms.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.3.norms.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.3.norms.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.3.norms.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.4.attentions.0.sampling_offsets.weight - torch.Size([768, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.4.attentions.0.sampling_offsets.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.4.attentions.0.attention_weights.weight - torch.Size([384, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.4.attentions.0.attention_weights.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.4.attentions.0.value_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.4.attentions.0.value_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.4.attentions.0.output_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.4.attentions.0.output_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.4.ffns.0.layers.0.0.weight - torch.Size([4096, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.4.ffns.0.layers.0.0.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.4.ffns.0.layers.1.weight - torch.Size([1024, 4096]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.4.ffns.0.layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.4.norms.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.4.norms.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.4.norms.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.4.norms.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.5.attentions.0.sampling_offsets.weight - torch.Size([768, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.5.attentions.0.sampling_offsets.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.5.attentions.0.attention_weights.weight - torch.Size([384, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.5.attentions.0.attention_weights.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.5.attentions.0.value_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.5.attentions.0.value_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.5.attentions.0.output_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.5.attentions.0.output_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.5.ffns.0.layers.0.0.weight - torch.Size([4096, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.5.ffns.0.layers.0.0.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.5.ffns.0.layers.1.weight - torch.Size([1024, 4096]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.encoder.layers.5.ffns.0.layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.5.norms.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.5.norms.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.5.norms.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.encoder.layers.5.norms.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.level_encoding.weight - torch.Size([3, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.lateral_convs.0.conv.weight - torch.Size([1024, 1024, 1, 1]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.lateral_convs.0.gn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.lateral_convs.0.gn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.output_convs.0.conv.weight - torch.Size([1024, 1024, 3, 3]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.output_convs.0.gn.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.output_convs.0.gn.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.pixel_decoder.mask_feature.weight - torch.Size([1024, 1024, 1, 1]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.pixel_decoder.mask_feature.bias - torch.Size([1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.0.attentions.0.attn.in_proj_weight - torch.Size([3072, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.0.attentions.0.attn.in_proj_bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.0.attentions.0.attn.out_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.0.attentions.0.attn.out_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.0.attentions.1.attn.in_proj_weight - torch.Size([3072, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.0.attentions.1.attn.in_proj_bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.0.attentions.1.attn.out_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.0.attentions.1.attn.out_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.0.ffns.0.layers.0.0.weight - torch.Size([4096, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.0.ffns.0.layers.0.0.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.0.ffns.0.layers.1.weight - torch.Size([1024, 4096]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.0.ffns.0.layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.0.norms.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.0.norms.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.0.norms.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.0.norms.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.0.norms.2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.0.norms.2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.1.attentions.0.attn.in_proj_weight - torch.Size([3072, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.1.attentions.0.attn.in_proj_bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.1.attentions.0.attn.out_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.1.attentions.0.attn.out_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.1.attentions.1.attn.in_proj_weight - torch.Size([3072, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.1.attentions.1.attn.in_proj_bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.1.attentions.1.attn.out_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.1.attentions.1.attn.out_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.1.ffns.0.layers.0.0.weight - torch.Size([4096, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.1.ffns.0.layers.0.0.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.1.ffns.0.layers.1.weight - torch.Size([1024, 4096]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.1.ffns.0.layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.1.norms.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.1.norms.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.1.norms.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.1.norms.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.1.norms.2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.1.norms.2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.2.attentions.0.attn.in_proj_weight - torch.Size([3072, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.2.attentions.0.attn.in_proj_bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.2.attentions.0.attn.out_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.2.attentions.0.attn.out_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.2.attentions.1.attn.in_proj_weight - torch.Size([3072, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.2.attentions.1.attn.in_proj_bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.2.attentions.1.attn.out_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.2.attentions.1.attn.out_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.2.ffns.0.layers.0.0.weight - torch.Size([4096, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.2.ffns.0.layers.0.0.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.2.ffns.0.layers.1.weight - torch.Size([1024, 4096]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.2.ffns.0.layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.2.norms.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.2.norms.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.2.norms.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.2.norms.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.2.norms.2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.2.norms.2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.3.attentions.0.attn.in_proj_weight - torch.Size([3072, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.3.attentions.0.attn.in_proj_bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.3.attentions.0.attn.out_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.3.attentions.0.attn.out_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.3.attentions.1.attn.in_proj_weight - torch.Size([3072, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.3.attentions.1.attn.in_proj_bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.3.attentions.1.attn.out_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.3.attentions.1.attn.out_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.3.ffns.0.layers.0.0.weight - torch.Size([4096, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.3.ffns.0.layers.0.0.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.3.ffns.0.layers.1.weight - torch.Size([1024, 4096]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.3.ffns.0.layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.3.norms.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.3.norms.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.3.norms.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.3.norms.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.3.norms.2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.3.norms.2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.4.attentions.0.attn.in_proj_weight - torch.Size([3072, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.4.attentions.0.attn.in_proj_bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.4.attentions.0.attn.out_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.4.attentions.0.attn.out_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.4.attentions.1.attn.in_proj_weight - torch.Size([3072, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.4.attentions.1.attn.in_proj_bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.4.attentions.1.attn.out_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.4.attentions.1.attn.out_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.4.ffns.0.layers.0.0.weight - torch.Size([4096, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.4.ffns.0.layers.0.0.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.4.ffns.0.layers.1.weight - torch.Size([1024, 4096]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.4.ffns.0.layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.4.norms.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.4.norms.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.4.norms.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.4.norms.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.4.norms.2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.4.norms.2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.5.attentions.0.attn.in_proj_weight - torch.Size([3072, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.5.attentions.0.attn.in_proj_bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.5.attentions.0.attn.out_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.5.attentions.0.attn.out_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.5.attentions.1.attn.in_proj_weight - torch.Size([3072, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.5.attentions.1.attn.in_proj_bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.5.attentions.1.attn.out_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.5.attentions.1.attn.out_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.5.ffns.0.layers.0.0.weight - torch.Size([4096, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.5.ffns.0.layers.0.0.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.5.ffns.0.layers.1.weight - torch.Size([1024, 4096]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.5.ffns.0.layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.5.norms.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.5.norms.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.5.norms.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.5.norms.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.5.norms.2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.5.norms.2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.6.attentions.0.attn.in_proj_weight - torch.Size([3072, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.6.attentions.0.attn.in_proj_bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.6.attentions.0.attn.out_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.6.attentions.0.attn.out_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.6.attentions.1.attn.in_proj_weight - torch.Size([3072, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.6.attentions.1.attn.in_proj_bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.6.attentions.1.attn.out_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.6.attentions.1.attn.out_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.6.ffns.0.layers.0.0.weight - torch.Size([4096, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.6.ffns.0.layers.0.0.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.6.ffns.0.layers.1.weight - torch.Size([1024, 4096]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.6.ffns.0.layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.6.norms.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.6.norms.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.6.norms.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.6.norms.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.6.norms.2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.6.norms.2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.7.attentions.0.attn.in_proj_weight - torch.Size([3072, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.7.attentions.0.attn.in_proj_bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.7.attentions.0.attn.out_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.7.attentions.0.attn.out_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.7.attentions.1.attn.in_proj_weight - torch.Size([3072, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.7.attentions.1.attn.in_proj_bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.7.attentions.1.attn.out_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.7.attentions.1.attn.out_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.7.ffns.0.layers.0.0.weight - torch.Size([4096, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.7.ffns.0.layers.0.0.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.7.ffns.0.layers.1.weight - torch.Size([1024, 4096]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.7.ffns.0.layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.7.norms.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.7.norms.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.7.norms.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.7.norms.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.7.norms.2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.7.norms.2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.8.attentions.0.attn.in_proj_weight - torch.Size([3072, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.8.attentions.0.attn.in_proj_bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.8.attentions.0.attn.out_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.8.attentions.0.attn.out_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.8.attentions.1.attn.in_proj_weight - torch.Size([3072, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.8.attentions.1.attn.in_proj_bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.8.attentions.1.attn.out_proj.weight - torch.Size([1024, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.8.attentions.1.attn.out_proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.8.ffns.0.layers.0.0.weight - torch.Size([4096, 1024]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.8.ffns.0.layers.0.0.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.8.ffns.0.layers.1.weight - torch.Size([1024, 4096]): Initialized by user-defined `init_weights` in Mask2FormerHead decode_head.transformer_decoder.layers.8.ffns.0.layers.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.8.norms.0.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.8.norms.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.8.norms.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.8.norms.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.8.norms.2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.layers.8.norms.2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.post_norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.transformer_decoder.post_norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.query_embed.weight - torch.Size([100, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.query_feat.weight - torch.Size([100, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.level_embed.weight - torch.Size([3, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.cls_embed.weight - torch.Size([172, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.cls_embed.bias - torch.Size([172]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.mask_embed.0.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.mask_embed.0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.mask_embed.2.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.mask_embed.2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.mask_embed.4.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former decode_head.mask_embed.4.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of EncoderDecoderMask2Former 2022-05-04 20:57:58,336 - mmseg - INFO - EncoderDecoderMask2Former( (backbone): BEiTDenseAdaptor( (patch_embed): PatchEmbed( (proj): Conv2d(3, 1024, kernel_size=(16, 16), stride=(16, 16)) ) (pos_drop): Dropout(p=0.0, inplace=False) (blocks): ModuleList( (0): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): Identity() (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (1): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.013043479062616825) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (2): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.02608695812523365) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (3): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.03913043811917305) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (4): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.0521739162504673) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (5): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.06521739810705185) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (6): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.0782608762383461) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (7): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.09130435436964035) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (8): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.1043478325009346) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (9): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.11739131063222885) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (10): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.1304347962141037) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (11): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.14347827434539795) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (12): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.1565217524766922) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (13): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.16956523060798645) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (14): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.1826087087392807) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (15): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.19565218687057495) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (16): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.2086956650018692) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (17): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.22173914313316345) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (18): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.2347826212644577) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (19): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.24782609939575195) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (20): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.260869562625885) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (21): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.27391305565834045) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (22): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.2869565188884735) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (23): Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.30000001192092896) (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) (conv_branch): ConvBranch( (stem): Sequential( (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (4): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU(inplace=True) (6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (7): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (8): ReLU(inplace=True) (9): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) ) (conv2): Sequential( (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (conv3): Sequential( (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (conv4): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (fc1): Conv2d(64, 1024, kernel_size=(1, 1), stride=(1, 1)) (fc2): Conv2d(128, 1024, kernel_size=(1, 1), stride=(1, 1)) (fc3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1)) (fc4): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1)) ) (interact_blocks): Sequential( (0): InteractBlock( (extract): ExtractLayer( (query_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (feat_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): MSDeformAttn( (sampling_offsets): Linear(in_features=1024, out_features=128, bias=True) (attention_weights): Linear(in_features=1024, out_features=64, bias=True) (value_proj): Linear(in_features=1024, out_features=512, bias=True) (output_proj): Linear(in_features=512, out_features=1024, bias=True) ) ) (insert): InsertLayer( (query_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (feat_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): MSDeformAttn( (sampling_offsets): Linear(in_features=1024, out_features=384, bias=True) (attention_weights): Linear(in_features=1024, out_features=192, bias=True) (value_proj): Linear(in_features=1024, out_features=512, bias=True) (output_proj): Linear(in_features=512, out_features=1024, bias=True) ) ) (ffn): ConvFFN( (fc1): Linear(in_features=1024, out_features=256, bias=True) (dwconv): DWConv( (dwconv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256) ) (act): GELU() (fc2): Linear(in_features=256, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) (ffn_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (drop_path): DropPath() ) (1): InteractBlock( (extract): ExtractLayer( (query_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (feat_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): MSDeformAttn( (sampling_offsets): Linear(in_features=1024, out_features=128, bias=True) (attention_weights): Linear(in_features=1024, out_features=64, bias=True) (value_proj): Linear(in_features=1024, out_features=512, bias=True) (output_proj): Linear(in_features=512, out_features=1024, bias=True) ) ) (insert): InsertLayer( (query_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (feat_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): MSDeformAttn( (sampling_offsets): Linear(in_features=1024, out_features=384, bias=True) (attention_weights): Linear(in_features=1024, out_features=192, bias=True) (value_proj): Linear(in_features=1024, out_features=512, bias=True) (output_proj): Linear(in_features=512, out_features=1024, bias=True) ) ) (ffn): ConvFFN( (fc1): Linear(in_features=1024, out_features=256, bias=True) (dwconv): DWConv( (dwconv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256) ) (act): GELU() (fc2): Linear(in_features=256, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) (ffn_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (drop_path): DropPath() ) (2): InteractBlock( (extract): ExtractLayer( (query_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (feat_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): MSDeformAttn( (sampling_offsets): Linear(in_features=1024, out_features=128, bias=True) (attention_weights): Linear(in_features=1024, out_features=64, bias=True) (value_proj): Linear(in_features=1024, out_features=512, bias=True) (output_proj): Linear(in_features=512, out_features=1024, bias=True) ) ) (insert): InsertLayer( (query_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (feat_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): MSDeformAttn( (sampling_offsets): Linear(in_features=1024, out_features=384, bias=True) (attention_weights): Linear(in_features=1024, out_features=192, bias=True) (value_proj): Linear(in_features=1024, out_features=512, bias=True) (output_proj): Linear(in_features=512, out_features=1024, bias=True) ) ) (ffn): ConvFFN( (fc1): Linear(in_features=1024, out_features=256, bias=True) (dwconv): DWConv( (dwconv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256) ) (act): GELU() (fc2): Linear(in_features=256, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) (ffn_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (drop_path): DropPath() ) (3): InteractBlock( (extract): ExtractLayer( (query_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (feat_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): MSDeformAttn( (sampling_offsets): Linear(in_features=1024, out_features=128, bias=True) (attention_weights): Linear(in_features=1024, out_features=64, bias=True) (value_proj): Linear(in_features=1024, out_features=512, bias=True) (output_proj): Linear(in_features=512, out_features=1024, bias=True) ) ) (insert): InsertLayer( (query_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (feat_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): MSDeformAttn( (sampling_offsets): Linear(in_features=1024, out_features=384, bias=True) (attention_weights): Linear(in_features=1024, out_features=192, bias=True) (value_proj): Linear(in_features=1024, out_features=512, bias=True) (output_proj): Linear(in_features=512, out_features=1024, bias=True) ) ) (ffn): ConvFFN( (fc1): Linear(in_features=1024, out_features=256, bias=True) (dwconv): DWConv( (dwconv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256) ) (act): GELU() (fc2): Linear(in_features=256, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) (ffn_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (drop_path): DropPath() ) ) (extract_blocks): Sequential( (0): ExtractBlock( (extract): ExtractLayer( (query_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (feat_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): MSDeformAttn( (sampling_offsets): Linear(in_features=1024, out_features=128, bias=True) (attention_weights): Linear(in_features=1024, out_features=64, bias=True) (value_proj): Linear(in_features=1024, out_features=512, bias=True) (output_proj): Linear(in_features=512, out_features=1024, bias=True) ) ) (ffn): ConvFFN( (fc1): Linear(in_features=1024, out_features=256, bias=True) (dwconv): DWConv( (dwconv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256) ) (act): GELU() (fc2): Linear(in_features=256, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) (ffn_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (drop_path): Identity() ) (1): ExtractBlock( (extract): ExtractLayer( (query_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (feat_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): MSDeformAttn( (sampling_offsets): Linear(in_features=1024, out_features=128, bias=True) (attention_weights): Linear(in_features=1024, out_features=64, bias=True) (value_proj): Linear(in_features=1024, out_features=512, bias=True) (output_proj): Linear(in_features=512, out_features=1024, bias=True) ) ) (ffn): ConvFFN( (fc1): Linear(in_features=1024, out_features=256, bias=True) (dwconv): DWConv( (dwconv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256) ) (act): GELU() (fc2): Linear(in_features=256, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) (ffn_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (drop_path): Identity() ) ) (up): ConvTranspose2d(1024, 1024, kernel_size=(2, 2), stride=(2, 2)) (norm1): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (norm2): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (norm3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (norm4): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (decode_head): Mask2FormerHead( input_transform=multiple_select, ignore_index=255, align_corners=False (loss_decode): CrossEntropyLoss() (conv_seg): Conv2d(1024, 171, kernel_size=(1, 1), stride=(1, 1)) (dropout): Dropout2d(p=0.1, inplace=False) (pixel_decoder): MSDeformAttnPixelDecoder( (input_convs): ModuleList( (0): ConvModule( (conv): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1)) (gn): GroupNorm(32, 1024, eps=1e-05, affine=True) ) (1): ConvModule( (conv): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1)) (gn): GroupNorm(32, 1024, eps=1e-05, affine=True) ) (2): ConvModule( (conv): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1)) (gn): GroupNorm(32, 1024, eps=1e-05, affine=True) ) ) (encoder): DetrTransformerEncoder( (layers): ModuleList( (0): BaseTransformerLayer( (attentions): ModuleList( (0): MultiScaleDeformableAttention( (dropout): Dropout(p=0.0, inplace=False) (sampling_offsets): Linear(in_features=1024, out_features=768, bias=True) (attention_weights): Linear(in_features=1024, out_features=384, bias=True) (value_proj): Linear(in_features=1024, out_features=1024, bias=True) (output_proj): Linear(in_features=1024, out_features=1024, bias=True) ) ) (ffns): ModuleList( (0): FFN( (activate): ReLU(inplace=True) (layers): Sequential( (0): Sequential( (0): Linear(in_features=1024, out_features=4096, bias=True) (1): ReLU(inplace=True) (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=4096, out_features=1024, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) ) ) (1): BaseTransformerLayer( (attentions): ModuleList( (0): MultiScaleDeformableAttention( (dropout): Dropout(p=0.0, inplace=False) (sampling_offsets): Linear(in_features=1024, out_features=768, bias=True) (attention_weights): Linear(in_features=1024, out_features=384, bias=True) (value_proj): Linear(in_features=1024, out_features=1024, bias=True) (output_proj): Linear(in_features=1024, out_features=1024, bias=True) ) ) (ffns): ModuleList( (0): FFN( (activate): ReLU(inplace=True) (layers): Sequential( (0): Sequential( (0): Linear(in_features=1024, out_features=4096, bias=True) (1): ReLU(inplace=True) (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=4096, out_features=1024, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) ) ) (2): BaseTransformerLayer( (attentions): ModuleList( (0): MultiScaleDeformableAttention( (dropout): Dropout(p=0.0, inplace=False) (sampling_offsets): Linear(in_features=1024, out_features=768, bias=True) (attention_weights): Linear(in_features=1024, out_features=384, bias=True) (value_proj): Linear(in_features=1024, out_features=1024, bias=True) (output_proj): Linear(in_features=1024, out_features=1024, bias=True) ) ) (ffns): ModuleList( (0): FFN( (activate): ReLU(inplace=True) (layers): Sequential( (0): Sequential( (0): Linear(in_features=1024, out_features=4096, bias=True) (1): ReLU(inplace=True) (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=4096, out_features=1024, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) ) ) (3): BaseTransformerLayer( (attentions): ModuleList( (0): MultiScaleDeformableAttention( (dropout): Dropout(p=0.0, inplace=False) (sampling_offsets): Linear(in_features=1024, out_features=768, bias=True) (attention_weights): Linear(in_features=1024, out_features=384, bias=True) (value_proj): Linear(in_features=1024, out_features=1024, bias=True) (output_proj): Linear(in_features=1024, out_features=1024, bias=True) ) ) (ffns): ModuleList( (0): FFN( (activate): ReLU(inplace=True) (layers): Sequential( (0): Sequential( (0): Linear(in_features=1024, out_features=4096, bias=True) (1): ReLU(inplace=True) (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=4096, out_features=1024, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) ) ) (4): BaseTransformerLayer( (attentions): ModuleList( (0): MultiScaleDeformableAttention( (dropout): Dropout(p=0.0, inplace=False) (sampling_offsets): Linear(in_features=1024, out_features=768, bias=True) (attention_weights): Linear(in_features=1024, out_features=384, bias=True) (value_proj): Linear(in_features=1024, out_features=1024, bias=True) (output_proj): Linear(in_features=1024, out_features=1024, bias=True) ) ) (ffns): ModuleList( (0): FFN( (activate): ReLU(inplace=True) (layers): Sequential( (0): Sequential( (0): Linear(in_features=1024, out_features=4096, bias=True) (1): ReLU(inplace=True) (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=4096, out_features=1024, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) ) ) (5): BaseTransformerLayer( (attentions): ModuleList( (0): MultiScaleDeformableAttention( (dropout): Dropout(p=0.0, inplace=False) (sampling_offsets): Linear(in_features=1024, out_features=768, bias=True) (attention_weights): Linear(in_features=1024, out_features=384, bias=True) (value_proj): Linear(in_features=1024, out_features=1024, bias=True) (output_proj): Linear(in_features=1024, out_features=1024, bias=True) ) ) (ffns): ModuleList( (0): FFN( (activate): ReLU(inplace=True) (layers): Sequential( (0): Sequential( (0): Linear(in_features=1024, out_features=4096, bias=True) (1): ReLU(inplace=True) (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=4096, out_features=1024, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) ) ) ) ) (postional_encoding): SinePositionalEncoding(num_feats=512, temperature=10000, normalize=True, scale=6.283185307179586, eps=1e-06) (level_encoding): Embedding(3, 1024) (lateral_convs): ModuleList( (0): ConvModule( (conv): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (gn): GroupNorm(32, 1024, eps=1e-05, affine=True) ) ) (output_convs): ModuleList( (0): ConvModule( (conv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (gn): GroupNorm(32, 1024, eps=1e-05, affine=True) (activate): ReLU(inplace=True) ) ) (mask_feature): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1)) ) (transformer_decoder): DetrTransformerDecoder( (layers): ModuleList( (0): DetrTransformerDecoderLayer( (attentions): ModuleList( (0): MultiheadAttention( (attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) ) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): Identity() ) (1): MultiheadAttention( (attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) ) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): Identity() ) ) (ffns): ModuleList( (0): FFN( (activate): ReLU(inplace=True) (layers): Sequential( (0): Sequential( (0): Linear(in_features=1024, out_features=4096, bias=True) (1): ReLU(inplace=True) (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=4096, out_features=1024, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) ) ) (1): DetrTransformerDecoderLayer( (attentions): ModuleList( (0): MultiheadAttention( (attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) ) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): Identity() ) (1): MultiheadAttention( (attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) ) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): Identity() ) ) (ffns): ModuleList( (0): FFN( (activate): ReLU(inplace=True) (layers): Sequential( (0): Sequential( (0): Linear(in_features=1024, out_features=4096, bias=True) (1): ReLU(inplace=True) (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=4096, out_features=1024, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) ) ) (2): DetrTransformerDecoderLayer( (attentions): ModuleList( (0): MultiheadAttention( (attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) ) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): Identity() ) (1): MultiheadAttention( (attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) ) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): Identity() ) ) (ffns): ModuleList( (0): FFN( (activate): ReLU(inplace=True) (layers): Sequential( (0): Sequential( (0): Linear(in_features=1024, out_features=4096, bias=True) (1): ReLU(inplace=True) (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=4096, out_features=1024, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) ) ) (3): DetrTransformerDecoderLayer( (attentions): ModuleList( (0): MultiheadAttention( (attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) ) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): Identity() ) (1): MultiheadAttention( (attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) ) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): Identity() ) ) (ffns): ModuleList( (0): FFN( (activate): ReLU(inplace=True) (layers): Sequential( (0): Sequential( (0): Linear(in_features=1024, out_features=4096, bias=True) (1): ReLU(inplace=True) (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=4096, out_features=1024, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) ) ) (4): DetrTransformerDecoderLayer( (attentions): ModuleList( (0): MultiheadAttention( (attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) ) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): Identity() ) (1): MultiheadAttention( (attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) ) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): Identity() ) ) (ffns): ModuleList( (0): FFN( (activate): ReLU(inplace=True) (layers): Sequential( (0): Sequential( (0): Linear(in_features=1024, out_features=4096, bias=True) (1): ReLU(inplace=True) (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=4096, out_features=1024, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) ) ) (5): DetrTransformerDecoderLayer( (attentions): ModuleList( (0): MultiheadAttention( (attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) ) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): Identity() ) (1): MultiheadAttention( (attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) ) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): Identity() ) ) (ffns): ModuleList( (0): FFN( (activate): ReLU(inplace=True) (layers): Sequential( (0): Sequential( (0): Linear(in_features=1024, out_features=4096, bias=True) (1): ReLU(inplace=True) (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=4096, out_features=1024, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) ) ) (6): DetrTransformerDecoderLayer( (attentions): ModuleList( (0): MultiheadAttention( (attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) ) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): Identity() ) (1): MultiheadAttention( (attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) ) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): Identity() ) ) (ffns): ModuleList( (0): FFN( (activate): ReLU(inplace=True) (layers): Sequential( (0): Sequential( (0): Linear(in_features=1024, out_features=4096, bias=True) (1): ReLU(inplace=True) (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=4096, out_features=1024, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) ) ) (7): DetrTransformerDecoderLayer( (attentions): ModuleList( (0): MultiheadAttention( (attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) ) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): Identity() ) (1): MultiheadAttention( (attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) ) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): Identity() ) ) (ffns): ModuleList( (0): FFN( (activate): ReLU(inplace=True) (layers): Sequential( (0): Sequential( (0): Linear(in_features=1024, out_features=4096, bias=True) (1): ReLU(inplace=True) (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=4096, out_features=1024, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) ) ) (8): DetrTransformerDecoderLayer( (attentions): ModuleList( (0): MultiheadAttention( (attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) ) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): Identity() ) (1): MultiheadAttention( (attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) ) (proj_drop): Dropout(p=0.0, inplace=False) (dropout_layer): Identity() ) ) (ffns): ModuleList( (0): FFN( (activate): ReLU(inplace=True) (layers): Sequential( (0): Sequential( (0): Linear(in_features=1024, out_features=4096, bias=True) (1): ReLU(inplace=True) (2): Dropout(p=0.0, inplace=False) ) (1): Linear(in_features=4096, out_features=1024, bias=True) (2): Dropout(p=0.0, inplace=False) ) (dropout_layer): Identity() ) ) (norms): ModuleList( (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) ) ) ) (post_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) ) (decoder_input_projs): ModuleList( (0): Identity() (1): Identity() (2): Identity() ) (decoder_positional_encoding): SinePositionalEncoding(num_feats=512, temperature=10000, normalize=True, scale=6.283185307179586, eps=1e-06) (query_embed): Embedding(100, 1024) (query_feat): Embedding(100, 1024) (level_embed): Embedding(3, 1024) (cls_embed): Linear(in_features=1024, out_features=172, bias=True) (mask_embed): Sequential( (0): Linear(in_features=1024, out_features=1024, bias=True) (1): ReLU(inplace=True) (2): Linear(in_features=1024, out_features=1024, bias=True) (3): ReLU(inplace=True) (4): Linear(in_features=1024, out_features=1024, bias=True) ) (loss_cls): CrossEntropyLoss(avg_non_ignore=False) (loss_mask): CrossEntropyLoss(avg_non_ignore=False) (loss_dice): DiceLoss() ) ) 2022-05-04 20:57:59,824 - mmseg - INFO - Loaded 9000 images 2022-05-04 20:58:01,368 - mmseg - INFO - Loaded 1000 images 2022-05-04 20:58:01,368 - mmseg - INFO - Start running, host: chenzhe.vendor@SH-IDC1-10-140-1-137, work_dir: /mnt/lustre/chenzhe.vendor/workspace/DenseAdaptor/segmentation/work_dirs/mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss 2022-05-04 20:58:01,369 - 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 -------------------- 2022-05-04 20:58:01,369 - mmseg - INFO - workflow: [('train', 1)], max: 40000 iters 2022-05-04 20:58:01,369 - mmseg - INFO - Checkpoints will be saved to /mnt/lustre/chenzhe.vendor/workspace/DenseAdaptor/segmentation/work_dirs/mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss by HardDiskBackend. 2022-05-04 20:59:30,192 - mmseg - INFO - Iter [50/40000] lr: 4.685e-08, eta: 10:07:57, time: 0.913, data_time: 0.016, memory: 51557, decode.loss_cls: 9.0639, decode.loss_mask: 2.9845, decode.loss_dice: 4.2225, decode.d0.loss_cls: 10.4912, decode.d0.loss_mask: 2.1340, decode.d0.loss_dice: 3.8089, decode.d1.loss_cls: 10.7395, decode.d1.loss_mask: 2.1799, decode.d1.loss_dice: 3.8678, decode.d2.loss_cls: 10.9656, decode.d2.loss_mask: 2.5550, decode.d2.loss_dice: 3.9749, decode.d3.loss_cls: 10.0147, decode.d3.loss_mask: 2.4892, decode.d3.loss_dice: 4.0482, decode.d4.loss_cls: 10.6046, decode.d4.loss_mask: 2.4145, decode.d4.loss_dice: 4.1766, decode.d5.loss_cls: 10.4626, decode.d5.loss_mask: 2.3375, decode.d5.loss_dice: 4.3667, decode.d6.loss_cls: 10.5751, decode.d6.loss_mask: 2.5100, decode.d6.loss_dice: 4.2553, decode.d7.loss_cls: 10.5654, decode.d7.loss_mask: 2.3824, decode.d7.loss_dice: 4.3918, decode.d8.loss_cls: 9.5540, decode.d8.loss_mask: 2.4359, decode.d8.loss_dice: 4.3242, loss: 168.8964 2022-05-04 21:00:07,866 - mmseg - INFO - Iter [100/40000] lr: 9.453e-08, eta: 9:14:07, time: 0.753, data_time: 0.008, memory: 51557, decode.loss_cls: 5.5078, decode.loss_mask: 2.3842, decode.loss_dice: 4.2586, decode.d0.loss_cls: 10.4948, decode.d0.loss_mask: 2.0218, decode.d0.loss_dice: 3.6827, decode.d1.loss_cls: 8.2827, decode.d1.loss_mask: 2.0412, decode.d1.loss_dice: 3.7706, decode.d2.loss_cls: 7.0758, decode.d2.loss_mask: 2.1216, decode.d2.loss_dice: 3.8788, decode.d3.loss_cls: 6.0538, decode.d3.loss_mask: 2.1641, decode.d3.loss_dice: 3.9847, decode.d4.loss_cls: 5.9704, decode.d4.loss_mask: 2.2409, decode.d4.loss_dice: 4.0961, decode.d5.loss_cls: 5.8164, decode.d5.loss_mask: 2.3217, decode.d5.loss_dice: 4.1637, decode.d6.loss_cls: 5.9249, decode.d6.loss_mask: 2.3586, decode.d6.loss_dice: 4.1994, decode.d7.loss_cls: 5.8447, decode.d7.loss_mask: 2.3392, decode.d7.loss_dice: 4.2434, decode.d8.loss_cls: 5.5961, decode.d8.loss_mask: 2.3593, decode.d8.loss_dice: 4.2365, loss: 129.4343 2022-05-04 21:00:45,754 - mmseg - INFO - Iter [150/40000] lr: 1.421e-07, eta: 8:56:36, time: 0.757, data_time: 0.009, memory: 51557, decode.loss_cls: 4.8859, decode.loss_mask: 2.2901, decode.loss_dice: 4.2583, decode.d0.loss_cls: 10.4972, decode.d0.loss_mask: 1.9612, decode.d0.loss_dice: 3.6981, decode.d1.loss_cls: 5.5493, decode.d1.loss_mask: 1.9534, decode.d1.loss_dice: 3.7740, decode.d2.loss_cls: 5.0141, decode.d2.loss_mask: 1.9868, decode.d2.loss_dice: 3.8652, decode.d3.loss_cls: 4.9860, decode.d3.loss_mask: 2.0114, decode.d3.loss_dice: 3.9592, decode.d4.loss_cls: 4.9310, decode.d4.loss_mask: 2.0790, decode.d4.loss_dice: 4.0608, decode.d5.loss_cls: 4.9325, decode.d5.loss_mask: 2.1981, decode.d5.loss_dice: 4.1395, decode.d6.loss_cls: 4.9484, decode.d6.loss_mask: 2.2577, decode.d6.loss_dice: 4.2054, decode.d7.loss_cls: 4.9143, decode.d7.loss_mask: 2.2672, decode.d7.loss_dice: 4.2239, decode.d8.loss_cls: 4.9355, decode.d8.loss_mask: 2.2798, decode.d8.loss_dice: 4.2395, loss: 117.3030 2022-05-04 21:01:23,771 - mmseg - INFO - Iter [200/40000] lr: 1.895e-07, eta: 8:48:00, time: 0.760, data_time: 0.009, memory: 51557, decode.loss_cls: 4.8356, decode.loss_mask: 2.1885, decode.loss_dice: 4.2542, decode.d0.loss_cls: 10.4761, decode.d0.loss_mask: 1.8835, decode.d0.loss_dice: 3.7063, decode.d1.loss_cls: 5.0313, decode.d1.loss_mask: 1.8630, decode.d1.loss_dice: 3.7658, decode.d2.loss_cls: 4.9663, decode.d2.loss_mask: 1.8633, decode.d2.loss_dice: 3.8336, decode.d3.loss_cls: 4.9013, decode.d3.loss_mask: 1.8711, decode.d3.loss_dice: 3.9155, decode.d4.loss_cls: 4.8654, decode.d4.loss_mask: 1.8768, decode.d4.loss_dice: 4.0064, decode.d5.loss_cls: 4.8670, decode.d5.loss_mask: 1.9635, decode.d5.loss_dice: 4.0950, decode.d6.loss_cls: 4.8716, decode.d6.loss_mask: 2.0567, decode.d6.loss_dice: 4.1574, decode.d7.loss_cls: 4.8387, decode.d7.loss_mask: 2.1286, decode.d7.loss_dice: 4.2033, decode.d8.loss_cls: 4.8575, decode.d8.loss_mask: 2.1758, decode.d8.loss_dice: 4.2409, loss: 114.5597 2022-05-04 21:02:01,044 - mmseg - INFO - Iter [250/40000] lr: 2.369e-07, eta: 8:40:37, time: 0.745, data_time: 0.009, memory: 51557, decode.loss_cls: 4.7534, decode.loss_mask: 2.1196, decode.loss_dice: 4.1488, decode.d0.loss_cls: 10.4856, decode.d0.loss_mask: 1.8858, decode.d0.loss_dice: 3.6467, decode.d1.loss_cls: 4.9211, decode.d1.loss_mask: 1.8678, decode.d1.loss_dice: 3.6819, decode.d2.loss_cls: 4.8265, decode.d2.loss_mask: 1.8685, decode.d2.loss_dice: 3.7208, decode.d3.loss_cls: 4.7765, decode.d3.loss_mask: 1.8705, decode.d3.loss_dice: 3.7771, decode.d4.loss_cls: 4.7439, decode.d4.loss_mask: 1.8709, decode.d4.loss_dice: 3.8503, decode.d5.loss_cls: 4.7541, decode.d5.loss_mask: 1.8826, decode.d5.loss_dice: 3.9244, decode.d6.loss_cls: 4.7613, decode.d6.loss_mask: 1.9203, decode.d6.loss_dice: 3.9901, decode.d7.loss_cls: 4.7373, decode.d7.loss_mask: 1.9880, decode.d7.loss_dice: 4.0620, decode.d8.loss_cls: 4.7564, decode.d8.loss_mask: 2.0525, decode.d8.loss_dice: 4.1209, loss: 111.7653 2022-05-04 21:02:38,920 - mmseg - INFO - Iter [300/40000] lr: 2.841e-07, eta: 8:36:56, time: 0.758, data_time: 0.010, memory: 51557, decode.loss_cls: 4.6474, decode.loss_mask: 2.0069, decode.loss_dice: 3.9761, decode.d0.loss_cls: 10.4703, decode.d0.loss_mask: 1.8833, decode.d0.loss_dice: 3.5726, decode.d1.loss_cls: 4.8126, decode.d1.loss_mask: 1.8804, decode.d1.loss_dice: 3.5839, decode.d2.loss_cls: 4.7079, decode.d2.loss_mask: 1.8865, decode.d2.loss_dice: 3.5842, decode.d3.loss_cls: 4.6529, decode.d3.loss_mask: 1.8819, decode.d3.loss_dice: 3.6210, decode.d4.loss_cls: 4.6445, decode.d4.loss_mask: 1.8735, decode.d4.loss_dice: 3.6692, decode.d5.loss_cls: 4.6337, decode.d5.loss_mask: 1.8891, decode.d5.loss_dice: 3.7225, decode.d6.loss_cls: 4.6576, decode.d6.loss_mask: 1.8886, decode.d6.loss_dice: 3.8279, decode.d7.loss_cls: 4.6255, decode.d7.loss_mask: 1.9058, decode.d7.loss_dice: 3.8733, decode.d8.loss_cls: 4.6525, decode.d8.loss_mask: 1.9332, decode.d8.loss_dice: 3.9286, loss: 108.8932 2022-05-04 21:03:16,002 - mmseg - INFO - Iter [350/40000] lr: 3.311e-07, eta: 8:32:32, time: 0.742, data_time: 0.011, memory: 51557, decode.loss_cls: 4.6335, decode.loss_mask: 1.8892, decode.loss_dice: 3.8023, decode.d0.loss_cls: 10.4640, decode.d0.loss_mask: 1.8469, decode.d0.loss_dice: 3.5082, decode.d1.loss_cls: 4.7979, decode.d1.loss_mask: 1.8513, decode.d1.loss_dice: 3.5054, decode.d2.loss_cls: 4.6708, decode.d2.loss_mask: 1.8516, decode.d2.loss_dice: 3.4826, decode.d3.loss_cls: 4.6187, decode.d3.loss_mask: 1.8505, decode.d3.loss_dice: 3.5066, decode.d4.loss_cls: 4.6143, decode.d4.loss_mask: 1.8601, decode.d4.loss_dice: 3.5211, decode.d5.loss_cls: 4.6068, decode.d5.loss_mask: 1.8644, decode.d5.loss_dice: 3.5664, decode.d6.loss_cls: 4.6036, decode.d6.loss_mask: 1.8566, decode.d6.loss_dice: 3.6405, decode.d7.loss_cls: 4.6131, decode.d7.loss_mask: 1.8450, decode.d7.loss_dice: 3.6915, decode.d8.loss_cls: 4.6379, decode.d8.loss_mask: 1.8476, decode.d8.loss_dice: 3.7454, loss: 106.7936 2022-05-04 21:03:54,950 - mmseg - INFO - Iter [400/40000] lr: 3.781e-07, eta: 8:32:10, time: 0.779, data_time: 0.009, memory: 51557, decode.loss_cls: 4.6011, decode.loss_mask: 1.8445, decode.loss_dice: 3.6277, decode.d0.loss_cls: 10.4678, decode.d0.loss_mask: 1.8358, decode.d0.loss_dice: 3.4430, decode.d1.loss_cls: 4.7507, decode.d1.loss_mask: 1.8288, decode.d1.loss_dice: 3.4200, decode.d2.loss_cls: 4.6128, decode.d2.loss_mask: 1.8290, decode.d2.loss_dice: 3.3860, decode.d3.loss_cls: 4.5781, decode.d3.loss_mask: 1.8402, decode.d3.loss_dice: 3.3684, decode.d4.loss_cls: 4.5757, decode.d4.loss_mask: 1.8425, decode.d4.loss_dice: 3.3873, decode.d5.loss_cls: 4.5690, decode.d5.loss_mask: 1.8641, decode.d5.loss_dice: 3.4308, decode.d6.loss_cls: 4.5463, decode.d6.loss_mask: 1.8592, decode.d6.loss_dice: 3.4772, decode.d7.loss_cls: 4.5557, decode.d7.loss_mask: 1.8578, decode.d7.loss_dice: 3.5294, decode.d8.loss_cls: 4.5793, decode.d8.loss_mask: 1.8463, decode.d8.loss_dice: 3.5872, loss: 104.9415 2022-05-04 21:04:32,495 - mmseg - INFO - Iter [450/40000] lr: 4.250e-07, eta: 8:29:41, time: 0.751, data_time: 0.009, memory: 51557, decode.loss_cls: 4.5520, decode.loss_mask: 1.8382, decode.loss_dice: 3.4751, decode.d0.loss_cls: 10.4665, decode.d0.loss_mask: 1.8045, decode.d0.loss_dice: 3.3891, decode.d1.loss_cls: 4.7658, decode.d1.loss_mask: 1.7951, decode.d1.loss_dice: 3.3411, decode.d2.loss_cls: 4.6260, decode.d2.loss_mask: 1.7986, decode.d2.loss_dice: 3.2774, decode.d3.loss_cls: 4.5657, decode.d3.loss_mask: 1.8151, decode.d3.loss_dice: 3.2433, decode.d4.loss_cls: 4.5302, decode.d4.loss_mask: 1.8357, decode.d4.loss_dice: 3.2611, decode.d5.loss_cls: 4.5164, decode.d5.loss_mask: 1.8528, decode.d5.loss_dice: 3.2884, decode.d6.loss_cls: 4.5061, decode.d6.loss_mask: 1.8520, decode.d6.loss_dice: 3.3286, decode.d7.loss_cls: 4.5191, decode.d7.loss_mask: 1.8461, decode.d7.loss_dice: 3.3713, decode.d8.loss_cls: 4.5322, decode.d8.loss_mask: 1.8433, decode.d8.loss_dice: 3.4249, loss: 103.2615 2022-05-04 21:05:10,186 - mmseg - INFO - Iter [500/40000] lr: 4.717e-07, eta: 8:27:44, time: 0.753, data_time: 0.009, memory: 51557, decode.loss_cls: 4.3709, decode.loss_mask: 1.8338, decode.loss_dice: 3.3035, decode.d0.loss_cls: 10.4708, decode.d0.loss_mask: 1.8060, decode.d0.loss_dice: 3.2744, decode.d1.loss_cls: 4.5986, decode.d1.loss_mask: 1.7883, decode.d1.loss_dice: 3.1965, decode.d2.loss_cls: 4.4524, decode.d2.loss_mask: 1.7996, decode.d2.loss_dice: 3.1348, decode.d3.loss_cls: 4.3870, decode.d3.loss_mask: 1.8162, decode.d3.loss_dice: 3.0915, decode.d4.loss_cls: 4.3543, decode.d4.loss_mask: 1.8285, decode.d4.loss_dice: 3.1090, decode.d5.loss_cls: 4.3085, decode.d5.loss_mask: 1.8596, decode.d5.loss_dice: 3.1543, decode.d6.loss_cls: 4.2867, decode.d6.loss_mask: 1.8669, decode.d6.loss_dice: 3.1833, decode.d7.loss_cls: 4.3109, decode.d7.loss_mask: 1.8589, decode.d7.loss_dice: 3.2006, decode.d8.loss_cls: 4.3265, decode.d8.loss_mask: 1.8564, decode.d8.loss_dice: 3.2532, loss: 100.0817 2022-05-04 21:05:47,699 - mmseg - INFO - Iter [550/40000] lr: 5.183e-07, eta: 8:25:49, time: 0.750, data_time: 0.010, memory: 51557, decode.loss_cls: 4.3908, decode.loss_mask: 1.8352, decode.loss_dice: 3.2286, decode.d0.loss_cls: 10.4722, decode.d0.loss_mask: 1.7695, decode.d0.loss_dice: 3.2442, decode.d1.loss_cls: 4.6758, decode.d1.loss_mask: 1.7450, decode.d1.loss_dice: 3.1196, decode.d2.loss_cls: 4.4949, decode.d2.loss_mask: 1.7618, decode.d2.loss_dice: 3.0586, decode.d3.loss_cls: 4.4000, decode.d3.loss_mask: 1.8006, decode.d3.loss_dice: 3.0463, decode.d4.loss_cls: 4.3578, decode.d4.loss_mask: 1.8153, decode.d4.loss_dice: 3.0473, decode.d5.loss_cls: 4.3056, decode.d5.loss_mask: 1.8405, decode.d5.loss_dice: 3.0842, decode.d6.loss_cls: 4.3153, decode.d6.loss_mask: 1.8357, decode.d6.loss_dice: 3.1056, decode.d7.loss_cls: 4.3065, decode.d7.loss_mask: 1.8514, decode.d7.loss_dice: 3.1491, decode.d8.loss_cls: 4.3385, decode.d8.loss_mask: 1.8449, decode.d8.loss_dice: 3.1777, loss: 99.4184 2022-05-04 21:06:27,676 - mmseg - INFO - Iter [600/40000] lr: 5.648e-07, eta: 8:26:52, time: 0.800, data_time: 0.063, memory: 51557, decode.loss_cls: 4.2219, decode.loss_mask: 1.8138, decode.loss_dice: 3.0605, decode.d0.loss_cls: 10.4735, decode.d0.loss_mask: 1.7733, decode.d0.loss_dice: 3.1282, decode.d1.loss_cls: 4.5305, decode.d1.loss_mask: 1.7409, decode.d1.loss_dice: 2.9860, decode.d2.loss_cls: 4.3365, decode.d2.loss_mask: 1.7569, decode.d2.loss_dice: 2.9212, decode.d3.loss_cls: 4.2278, decode.d3.loss_mask: 1.7791, decode.d3.loss_dice: 2.9095, decode.d4.loss_cls: 4.1743, decode.d4.loss_mask: 1.8016, decode.d4.loss_dice: 2.9176, decode.d5.loss_cls: 4.1608, decode.d5.loss_mask: 1.8159, decode.d5.loss_dice: 2.9406, decode.d6.loss_cls: 4.1736, decode.d6.loss_mask: 1.8090, decode.d6.loss_dice: 2.9662, decode.d7.loss_cls: 4.1854, decode.d7.loss_mask: 1.8244, decode.d7.loss_dice: 2.9852, decode.d8.loss_cls: 4.1975, decode.d8.loss_mask: 1.8141, decode.d8.loss_dice: 3.0214, loss: 96.4471 2022-05-04 21:07:04,828 - mmseg - INFO - Iter [650/40000] lr: 6.111e-07, eta: 8:24:46, time: 0.743, data_time: 0.010, memory: 51557, decode.loss_cls: 4.2378, decode.loss_mask: 1.7428, decode.loss_dice: 2.9988, decode.d0.loss_cls: 10.4799, decode.d0.loss_mask: 1.6993, decode.d0.loss_dice: 3.1033, decode.d1.loss_cls: 4.5414, decode.d1.loss_mask: 1.6747, decode.d1.loss_dice: 2.9467, decode.d2.loss_cls: 4.3230, decode.d2.loss_mask: 1.7110, decode.d2.loss_dice: 2.8853, decode.d3.loss_cls: 4.2293, decode.d3.loss_mask: 1.7255, decode.d3.loss_dice: 2.8645, decode.d4.loss_cls: 4.1820, decode.d4.loss_mask: 1.7418, decode.d4.loss_dice: 2.8703, decode.d5.loss_cls: 4.1624, decode.d5.loss_mask: 1.7433, decode.d5.loss_dice: 2.9023, decode.d6.loss_cls: 4.1765, decode.d6.loss_mask: 1.7257, decode.d6.loss_dice: 2.9151, decode.d7.loss_cls: 4.1666, decode.d7.loss_mask: 1.7392, decode.d7.loss_dice: 2.9353, decode.d8.loss_cls: 4.1952, decode.d8.loss_mask: 1.7528, decode.d8.loss_dice: 2.9722, loss: 95.3439 2022-05-04 21:07:42,180 - mmseg - INFO - Iter [700/40000] lr: 6.574e-07, eta: 8:23:04, time: 0.747, data_time: 0.009, memory: 51557, decode.loss_cls: 4.1907, decode.loss_mask: 1.7341, decode.loss_dice: 2.9086, decode.d0.loss_cls: 10.4612, decode.d0.loss_mask: 1.6986, decode.d0.loss_dice: 3.0263, decode.d1.loss_cls: 4.4798, decode.d1.loss_mask: 1.6731, decode.d1.loss_dice: 2.8579, decode.d2.loss_cls: 4.2816, decode.d2.loss_mask: 1.6834, decode.d2.loss_dice: 2.7894, decode.d3.loss_cls: 4.2106, decode.d3.loss_mask: 1.6987, decode.d3.loss_dice: 2.7648, decode.d4.loss_cls: 4.1576, decode.d4.loss_mask: 1.7133, decode.d4.loss_dice: 2.7613, decode.d5.loss_cls: 4.1313, decode.d5.loss_mask: 1.7309, decode.d5.loss_dice: 2.7889, decode.d6.loss_cls: 4.1400, decode.d6.loss_mask: 1.7266, decode.d6.loss_dice: 2.7927, decode.d7.loss_cls: 4.1246, decode.d7.loss_mask: 1.7467, decode.d7.loss_dice: 2.8244, decode.d8.loss_cls: 4.1571, decode.d8.loss_mask: 1.7383, decode.d8.loss_dice: 2.8540, loss: 93.8467 2022-05-04 21:08:20,344 - mmseg - INFO - Iter [750/40000] lr: 7.035e-07, eta: 8:22:13, time: 0.763, data_time: 0.009, memory: 51557, decode.loss_cls: 4.1545, decode.loss_mask: 1.7227, decode.loss_dice: 2.7783, decode.d0.loss_cls: 10.4633, decode.d0.loss_mask: 1.6943, decode.d0.loss_dice: 2.9441, decode.d1.loss_cls: 4.4470, decode.d1.loss_mask: 1.6704, decode.d1.loss_dice: 2.7605, decode.d2.loss_cls: 4.2440, decode.d2.loss_mask: 1.6763, decode.d2.loss_dice: 2.6857, decode.d3.loss_cls: 4.1552, decode.d3.loss_mask: 1.6993, decode.d3.loss_dice: 2.6602, decode.d4.loss_cls: 4.0999, decode.d4.loss_mask: 1.7225, decode.d4.loss_dice: 2.6761, decode.d5.loss_cls: 4.0807, decode.d5.loss_mask: 1.7147, decode.d5.loss_dice: 2.6825, decode.d6.loss_cls: 4.0958, decode.d6.loss_mask: 1.7025, decode.d6.loss_dice: 2.6873, decode.d7.loss_cls: 4.0897, decode.d7.loss_mask: 1.7166, decode.d7.loss_dice: 2.7130, decode.d8.loss_cls: 4.1397, decode.d8.loss_mask: 1.7152, decode.d8.loss_dice: 2.7408, loss: 92.3328 2022-05-04 21:08:57,473 - mmseg - INFO - Iter [800/40000] lr: 7.495e-07, eta: 8:20:33, time: 0.743, data_time: 0.009, memory: 51557, decode.loss_cls: 4.1192, decode.loss_mask: 1.6590, decode.loss_dice: 2.7743, decode.d0.loss_cls: 10.4710, decode.d0.loss_mask: 1.6311, decode.d0.loss_dice: 2.9458, decode.d1.loss_cls: 4.4332, decode.d1.loss_mask: 1.5979, decode.d1.loss_dice: 2.7560, decode.d2.loss_cls: 4.2189, decode.d2.loss_mask: 1.6238, decode.d2.loss_dice: 2.7058, decode.d3.loss_cls: 4.1537, decode.d3.loss_mask: 1.6277, decode.d3.loss_dice: 2.6619, decode.d4.loss_cls: 4.1103, decode.d4.loss_mask: 1.6400, decode.d4.loss_dice: 2.6758, decode.d5.loss_cls: 4.0881, decode.d5.loss_mask: 1.6437, decode.d5.loss_dice: 2.6912, decode.d6.loss_cls: 4.0819, decode.d6.loss_mask: 1.6565, decode.d6.loss_dice: 2.7009, decode.d7.loss_cls: 4.0886, decode.d7.loss_mask: 1.6527, decode.d7.loss_dice: 2.7152, decode.d8.loss_cls: 4.1003, decode.d8.loss_mask: 1.6707, decode.d8.loss_dice: 2.7370, loss: 91.6322 2022-05-04 21:09:34,834 - mmseg - INFO - Iter [850/40000] lr: 7.954e-07, eta: 8:19:09, time: 0.746, data_time: 0.011, memory: 51557, decode.loss_cls: 4.0383, decode.loss_mask: 1.7070, decode.loss_dice: 2.7096, decode.d0.loss_cls: 10.4530, decode.d0.loss_mask: 1.6637, decode.d0.loss_dice: 2.8852, decode.d1.loss_cls: 4.3227, decode.d1.loss_mask: 1.6503, decode.d1.loss_dice: 2.6860, decode.d2.loss_cls: 4.1153, decode.d2.loss_mask: 1.6602, decode.d2.loss_dice: 2.6270, decode.d3.loss_cls: 4.0490, decode.d3.loss_mask: 1.6760, decode.d3.loss_dice: 2.6191, decode.d4.loss_cls: 4.0001, decode.d4.loss_mask: 1.6946, decode.d4.loss_dice: 2.6258, decode.d5.loss_cls: 3.9910, decode.d5.loss_mask: 1.6953, decode.d5.loss_dice: 2.6382, decode.d6.loss_cls: 3.9926, decode.d6.loss_mask: 1.7016, decode.d6.loss_dice: 2.6443, decode.d7.loss_cls: 4.0008, decode.d7.loss_mask: 1.6923, decode.d7.loss_dice: 2.6369, decode.d8.loss_cls: 4.0122, decode.d8.loss_mask: 1.7060, decode.d8.loss_dice: 2.6788, loss: 90.5730 2022-05-04 21:10:12,241 - mmseg - INFO - Iter [900/40000] lr: 8.412e-07, eta: 8:17:54, time: 0.748, data_time: 0.009, memory: 51557, decode.loss_cls: 3.9414, decode.loss_mask: 1.6331, decode.loss_dice: 2.5824, decode.d0.loss_cls: 10.4548, decode.d0.loss_mask: 1.6382, decode.d0.loss_dice: 2.8270, decode.d1.loss_cls: 4.2342, decode.d1.loss_mask: 1.6125, decode.d1.loss_dice: 2.6145, decode.d2.loss_cls: 4.0364, decode.d2.loss_mask: 1.6184, decode.d2.loss_dice: 2.5602, decode.d3.loss_cls: 3.9686, decode.d3.loss_mask: 1.6324, decode.d3.loss_dice: 2.5143, decode.d4.loss_cls: 3.9299, decode.d4.loss_mask: 1.6352, decode.d4.loss_dice: 2.5260, decode.d5.loss_cls: 3.9205, decode.d5.loss_mask: 1.6394, decode.d5.loss_dice: 2.5147, decode.d6.loss_cls: 3.8981, decode.d6.loss_mask: 1.6455, decode.d6.loss_dice: 2.5290, decode.d7.loss_cls: 3.9035, decode.d7.loss_mask: 1.6388, decode.d7.loss_dice: 2.5394, decode.d8.loss_cls: 3.9156, decode.d8.loss_mask: 1.6399, decode.d8.loss_dice: 2.5563, loss: 88.3001 2022-05-04 21:10:50,216 - mmseg - INFO - Iter [950/40000] lr: 8.868e-07, eta: 8:17:08, time: 0.760, data_time: 0.010, memory: 51557, decode.loss_cls: 3.9275, decode.loss_mask: 1.6393, decode.loss_dice: 2.5536, decode.d0.loss_cls: 10.4524, decode.d0.loss_mask: 1.6227, decode.d0.loss_dice: 2.7936, decode.d1.loss_cls: 4.2447, decode.d1.loss_mask: 1.6010, decode.d1.loss_dice: 2.5736, decode.d2.loss_cls: 4.0401, decode.d2.loss_mask: 1.6073, decode.d2.loss_dice: 2.5186, decode.d3.loss_cls: 3.9578, decode.d3.loss_mask: 1.6214, decode.d3.loss_dice: 2.4841, decode.d4.loss_cls: 3.9270, decode.d4.loss_mask: 1.6118, decode.d4.loss_dice: 2.4722, decode.d5.loss_cls: 3.9052, decode.d5.loss_mask: 1.6202, decode.d5.loss_dice: 2.4698, decode.d6.loss_cls: 3.8957, decode.d6.loss_mask: 1.6191, decode.d6.loss_dice: 2.4768, decode.d7.loss_cls: 3.9040, decode.d7.loss_mask: 1.6183, decode.d7.loss_dice: 2.4925, decode.d8.loss_cls: 3.9072, decode.d8.loss_mask: 1.6361, decode.d8.loss_dice: 2.5103, loss: 87.7039 2022-05-04 21:11:28,206 - mmseg - INFO - Saving checkpoint at 1000 iterations 2022-05-04 21:11:53,458 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-04 21:11:53,469 - mmseg - INFO - Iter [1000/40000] lr: 9.324e-07, eta: 8:32:40, time: 1.261, data_time: 0.009, memory: 51557, decode.loss_cls: 3.9078, decode.loss_mask: 1.5817, decode.loss_dice: 2.5237, decode.d0.loss_cls: 10.4397, decode.d0.loss_mask: 1.5736, decode.d0.loss_dice: 2.7786, decode.d1.loss_cls: 4.2396, decode.d1.loss_mask: 1.5440, decode.d1.loss_dice: 2.5514, decode.d2.loss_cls: 4.0365, decode.d2.loss_mask: 1.5606, decode.d2.loss_dice: 2.4834, decode.d3.loss_cls: 3.9453, decode.d3.loss_mask: 1.5719, decode.d3.loss_dice: 2.4402, decode.d4.loss_cls: 3.9162, decode.d4.loss_mask: 1.5827, decode.d4.loss_dice: 2.4569, decode.d5.loss_cls: 3.8958, decode.d5.loss_mask: 1.5934, decode.d5.loss_dice: 2.4522, decode.d6.loss_cls: 3.8788, decode.d6.loss_mask: 1.5929, decode.d6.loss_dice: 2.4694, decode.d7.loss_cls: 3.8829, decode.d7.loss_mask: 1.5901, decode.d7.loss_dice: 2.4878, decode.d8.loss_cls: 3.8785, decode.d8.loss_mask: 1.5920, decode.d8.loss_dice: 2.4959, loss: 86.9432 2022-05-04 21:12:34,332 - mmseg - INFO - Iter [1050/40000] lr: 9.778e-07, eta: 8:33:00, time: 0.821, data_time: 0.013, memory: 51557, decode.loss_cls: 3.8368, decode.loss_mask: 1.5894, decode.loss_dice: 2.4744, decode.d0.loss_cls: 10.4377, decode.d0.loss_mask: 1.5766, decode.d0.loss_dice: 2.7477, decode.d1.loss_cls: 4.1363, decode.d1.loss_mask: 1.5600, decode.d1.loss_dice: 2.5230, decode.d2.loss_cls: 3.9272, decode.d2.loss_mask: 1.5829, decode.d2.loss_dice: 2.4502, decode.d3.loss_cls: 3.8464, decode.d3.loss_mask: 1.5883, decode.d3.loss_dice: 2.4142, decode.d4.loss_cls: 3.8164, decode.d4.loss_mask: 1.5882, decode.d4.loss_dice: 2.4208, decode.d5.loss_cls: 3.8057, decode.d5.loss_mask: 1.5862, decode.d5.loss_dice: 2.4275, decode.d6.loss_cls: 3.7897, decode.d6.loss_mask: 1.6079, decode.d6.loss_dice: 2.4416, decode.d7.loss_cls: 3.8021, decode.d7.loss_mask: 1.5886, decode.d7.loss_dice: 2.4473, decode.d8.loss_cls: 3.8148, decode.d8.loss_mask: 1.5920, decode.d8.loss_dice: 2.4625, loss: 85.8826 2022-05-04 21:13:13,636 - mmseg - INFO - Iter [1100/40000] lr: 1.023e-06, eta: 8:32:13, time: 0.786, data_time: 0.009, memory: 51557, decode.loss_cls: 3.8226, decode.loss_mask: 1.5812, decode.loss_dice: 2.4433, decode.d0.loss_cls: 10.4323, decode.d0.loss_mask: 1.5683, decode.d0.loss_dice: 2.7149, decode.d1.loss_cls: 4.1632, decode.d1.loss_mask: 1.5479, decode.d1.loss_dice: 2.4808, decode.d2.loss_cls: 3.9338, decode.d2.loss_mask: 1.5629, decode.d2.loss_dice: 2.4157, decode.d3.loss_cls: 3.8530, decode.d3.loss_mask: 1.5683, decode.d3.loss_dice: 2.3956, decode.d4.loss_cls: 3.8177, decode.d4.loss_mask: 1.5672, decode.d4.loss_dice: 2.3918, decode.d5.loss_cls: 3.7869, decode.d5.loss_mask: 1.5741, decode.d5.loss_dice: 2.3860, decode.d6.loss_cls: 3.7864, decode.d6.loss_mask: 1.5714, decode.d6.loss_dice: 2.3720, decode.d7.loss_cls: 3.8039, decode.d7.loss_mask: 1.5688, decode.d7.loss_dice: 2.4041, decode.d8.loss_cls: 3.7861, decode.d8.loss_mask: 1.5777, decode.d8.loss_dice: 2.4167, loss: 85.2946 2022-05-04 21:13:56,376 - mmseg - INFO - Iter [1150/40000] lr: 1.068e-06, eta: 8:33:23, time: 0.855, data_time: 0.057, memory: 51557, decode.loss_cls: 3.6514, decode.loss_mask: 1.5413, decode.loss_dice: 2.3904, decode.d0.loss_cls: 10.4255, decode.d0.loss_mask: 1.5264, decode.d0.loss_dice: 2.6864, decode.d1.loss_cls: 4.0137, decode.d1.loss_mask: 1.5009, decode.d1.loss_dice: 2.4358, decode.d2.loss_cls: 3.7839, decode.d2.loss_mask: 1.5133, decode.d2.loss_dice: 2.3576, decode.d3.loss_cls: 3.6681, decode.d3.loss_mask: 1.5343, decode.d3.loss_dice: 2.3486, decode.d4.loss_cls: 3.6379, decode.d4.loss_mask: 1.5344, decode.d4.loss_dice: 2.3495, decode.d5.loss_cls: 3.6316, decode.d5.loss_mask: 1.5264, decode.d5.loss_dice: 2.3515, decode.d6.loss_cls: 3.6172, decode.d6.loss_mask: 1.5255, decode.d6.loss_dice: 2.3582, decode.d7.loss_cls: 3.6238, decode.d7.loss_mask: 1.5344, decode.d7.loss_dice: 2.3690, decode.d8.loss_cls: 3.6176, decode.d8.loss_mask: 1.5419, decode.d8.loss_dice: 2.3807, loss: 82.9770 2022-05-04 21:14:35,753 - mmseg - INFO - Iter [1200/40000] lr: 1.113e-06, eta: 8:32:35, time: 0.787, data_time: 0.009, memory: 51557, decode.loss_cls: 3.7242, decode.loss_mask: 1.4930, decode.loss_dice: 2.3359, decode.d0.loss_cls: 10.4149, decode.d0.loss_mask: 1.5035, decode.d0.loss_dice: 2.6483, decode.d1.loss_cls: 4.1216, decode.d1.loss_mask: 1.4897, decode.d1.loss_dice: 2.3983, decode.d2.loss_cls: 3.8528, decode.d2.loss_mask: 1.5059, decode.d2.loss_dice: 2.3228, decode.d3.loss_cls: 3.7379, decode.d3.loss_mask: 1.5198, decode.d3.loss_dice: 2.2924, decode.d4.loss_cls: 3.7262, decode.d4.loss_mask: 1.5106, decode.d4.loss_dice: 2.2984, decode.d5.loss_cls: 3.7108, decode.d5.loss_mask: 1.5127, decode.d5.loss_dice: 2.2935, decode.d6.loss_cls: 3.7093, decode.d6.loss_mask: 1.5070, decode.d6.loss_dice: 2.2936, decode.d7.loss_cls: 3.7060, decode.d7.loss_mask: 1.4981, decode.d7.loss_dice: 2.3105, decode.d8.loss_cls: 3.6975, decode.d8.loss_mask: 1.5006, decode.d8.loss_dice: 2.3211, loss: 82.9567 2022-05-04 21:15:15,831 - mmseg - INFO - Iter [1250/40000] lr: 1.158e-06, eta: 8:32:09, time: 0.801, data_time: 0.009, memory: 51557, decode.loss_cls: 3.6439, decode.loss_mask: 1.5025, decode.loss_dice: 2.3001, decode.d0.loss_cls: 10.4117, decode.d0.loss_mask: 1.4880, decode.d0.loss_dice: 2.6167, decode.d1.loss_cls: 4.0350, decode.d1.loss_mask: 1.4606, decode.d1.loss_dice: 2.3637, decode.d2.loss_cls: 3.7802, decode.d2.loss_mask: 1.4743, decode.d2.loss_dice: 2.2845, decode.d3.loss_cls: 3.6723, decode.d3.loss_mask: 1.4767, decode.d3.loss_dice: 2.2585, decode.d4.loss_cls: 3.6387, decode.d4.loss_mask: 1.4774, decode.d4.loss_dice: 2.2654, decode.d5.loss_cls: 3.6290, decode.d5.loss_mask: 1.4760, decode.d5.loss_dice: 2.2493, decode.d6.loss_cls: 3.6117, decode.d6.loss_mask: 1.4771, decode.d6.loss_dice: 2.2475, decode.d7.loss_cls: 3.6065, decode.d7.loss_mask: 1.4853, decode.d7.loss_dice: 2.2700, decode.d8.loss_cls: 3.6069, decode.d8.loss_mask: 1.4955, decode.d8.loss_dice: 2.2800, loss: 81.5851 2022-05-04 21:15:56,383 - mmseg - INFO - Iter [1300/40000] lr: 1.203e-06, eta: 8:31:56, time: 0.811, data_time: 0.010, memory: 51557, decode.loss_cls: 3.5292, decode.loss_mask: 1.4848, decode.loss_dice: 2.2475, decode.d0.loss_cls: 10.3982, decode.d0.loss_mask: 1.4711, decode.d0.loss_dice: 2.5684, decode.d1.loss_cls: 3.9589, decode.d1.loss_mask: 1.4490, decode.d1.loss_dice: 2.3033, decode.d2.loss_cls: 3.6907, decode.d2.loss_mask: 1.4692, decode.d2.loss_dice: 2.2409, decode.d3.loss_cls: 3.5776, decode.d3.loss_mask: 1.4850, decode.d3.loss_dice: 2.2018, decode.d4.loss_cls: 3.5556, decode.d4.loss_mask: 1.4725, decode.d4.loss_dice: 2.2208, decode.d5.loss_cls: 3.5425, decode.d5.loss_mask: 1.4695, decode.d5.loss_dice: 2.2112, decode.d6.loss_cls: 3.5184, decode.d6.loss_mask: 1.4730, decode.d6.loss_dice: 2.2126, decode.d7.loss_cls: 3.5191, decode.d7.loss_mask: 1.4753, decode.d7.loss_dice: 2.2236, decode.d8.loss_cls: 3.5088, decode.d8.loss_mask: 1.4763, decode.d8.loss_dice: 2.2244, loss: 80.1792 2022-05-04 21:16:36,457 - mmseg - INFO - Iter [1350/40000] lr: 1.248e-06, eta: 8:31:28, time: 0.801, data_time: 0.009, memory: 51557, decode.loss_cls: 3.4948, decode.loss_mask: 1.4461, decode.loss_dice: 2.2311, decode.d0.loss_cls: 10.3901, decode.d0.loss_mask: 1.4187, decode.d0.loss_dice: 2.5465, decode.d1.loss_cls: 3.9010, decode.d1.loss_mask: 1.4010, decode.d1.loss_dice: 2.2762, decode.d2.loss_cls: 3.6298, decode.d2.loss_mask: 1.4120, decode.d2.loss_dice: 2.2169, decode.d3.loss_cls: 3.5255, decode.d3.loss_mask: 1.4160, decode.d3.loss_dice: 2.1805, decode.d4.loss_cls: 3.4972, decode.d4.loss_mask: 1.4132, decode.d4.loss_dice: 2.1809, decode.d5.loss_cls: 3.4785, decode.d5.loss_mask: 1.4244, decode.d5.loss_dice: 2.1889, decode.d6.loss_cls: 3.4687, decode.d6.loss_mask: 1.4211, decode.d6.loss_dice: 2.1753, decode.d7.loss_cls: 3.4875, decode.d7.loss_mask: 1.4205, decode.d7.loss_dice: 2.1877, decode.d8.loss_cls: 3.4759, decode.d8.loss_mask: 1.4256, decode.d8.loss_dice: 2.2022, loss: 78.9336 2022-05-04 21:17:16,618 - mmseg - INFO - Iter [1400/40000] lr: 1.292e-06, eta: 8:31:00, time: 0.803, data_time: 0.011, memory: 51557, decode.loss_cls: 3.4416, decode.loss_mask: 1.4355, decode.loss_dice: 2.1509, decode.d0.loss_cls: 10.3784, decode.d0.loss_mask: 1.4310, decode.d0.loss_dice: 2.4955, decode.d1.loss_cls: 3.8592, decode.d1.loss_mask: 1.4250, decode.d1.loss_dice: 2.2255, decode.d2.loss_cls: 3.5922, decode.d2.loss_mask: 1.4217, decode.d2.loss_dice: 2.1527, decode.d3.loss_cls: 3.4861, decode.d3.loss_mask: 1.4275, decode.d3.loss_dice: 2.1240, decode.d4.loss_cls: 3.4577, decode.d4.loss_mask: 1.4160, decode.d4.loss_dice: 2.1229, decode.d5.loss_cls: 3.4540, decode.d5.loss_mask: 1.4242, decode.d5.loss_dice: 2.1212, decode.d6.loss_cls: 3.4319, decode.d6.loss_mask: 1.4200, decode.d6.loss_dice: 2.1156, decode.d7.loss_cls: 3.4310, decode.d7.loss_mask: 1.4261, decode.d7.loss_dice: 2.1375, decode.d8.loss_cls: 3.4198, decode.d8.loss_mask: 1.4354, decode.d8.loss_dice: 2.1315, loss: 77.9915 2022-05-04 21:17:56,237 - mmseg - INFO - Iter [1450/40000] lr: 1.337e-06, eta: 8:30:18, time: 0.792, data_time: 0.010, memory: 51557, decode.loss_cls: 3.2888, decode.loss_mask: 1.3882, decode.loss_dice: 2.0930, decode.d0.loss_cls: 10.3660, decode.d0.loss_mask: 1.3859, decode.d0.loss_dice: 2.4230, decode.d1.loss_cls: 3.7344, decode.d1.loss_mask: 1.3874, decode.d1.loss_dice: 2.1495, decode.d2.loss_cls: 3.4602, decode.d2.loss_mask: 1.3951, decode.d2.loss_dice: 2.0837, decode.d3.loss_cls: 3.3434, decode.d3.loss_mask: 1.3944, decode.d3.loss_dice: 2.0488, decode.d4.loss_cls: 3.2980, decode.d4.loss_mask: 1.3915, decode.d4.loss_dice: 2.0643, decode.d5.loss_cls: 3.2776, decode.d5.loss_mask: 1.3926, decode.d5.loss_dice: 2.0510, decode.d6.loss_cls: 3.2644, decode.d6.loss_mask: 1.3883, decode.d6.loss_dice: 2.0435, decode.d7.loss_cls: 3.2640, decode.d7.loss_mask: 1.3896, decode.d7.loss_dice: 2.0705, decode.d8.loss_cls: 3.2634, decode.d8.loss_mask: 1.3944, decode.d8.loss_dice: 2.0639, loss: 75.5590 2022-05-04 21:18:35,604 - mmseg - INFO - Iter [1500/40000] lr: 1.381e-06, eta: 8:29:30, time: 0.787, data_time: 0.009, memory: 51557, decode.loss_cls: 3.2787, decode.loss_mask: 1.3534, decode.loss_dice: 2.0481, decode.d0.loss_cls: 10.3482, decode.d0.loss_mask: 1.3435, decode.d0.loss_dice: 2.3983, decode.d1.loss_cls: 3.7237, decode.d1.loss_mask: 1.3444, decode.d1.loss_dice: 2.1464, decode.d2.loss_cls: 3.4358, decode.d2.loss_mask: 1.3432, decode.d2.loss_dice: 2.0621, decode.d3.loss_cls: 3.3257, decode.d3.loss_mask: 1.3379, decode.d3.loss_dice: 2.0194, decode.d4.loss_cls: 3.2897, decode.d4.loss_mask: 1.3429, decode.d4.loss_dice: 2.0234, decode.d5.loss_cls: 3.2687, decode.d5.loss_mask: 1.3324, decode.d5.loss_dice: 2.0215, decode.d6.loss_cls: 3.2487, decode.d6.loss_mask: 1.3338, decode.d6.loss_dice: 2.0094, decode.d7.loss_cls: 3.2508, decode.d7.loss_mask: 1.3310, decode.d7.loss_dice: 2.0162, decode.d8.loss_cls: 3.2514, decode.d8.loss_mask: 1.3417, decode.d8.loss_dice: 2.0303, loss: 74.6005 2022-05-04 21:19:15,827 - mmseg - INFO - Iter [1550/40000] lr: 1.380e-06, eta: 8:29:03, time: 0.804, data_time: 0.009, memory: 51557, decode.loss_cls: 3.1681, decode.loss_mask: 1.3636, decode.loss_dice: 1.9810, decode.d0.loss_cls: 10.3344, decode.d0.loss_mask: 1.3505, decode.d0.loss_dice: 2.3384, decode.d1.loss_cls: 3.6077, decode.d1.loss_mask: 1.3601, decode.d1.loss_dice: 2.0676, decode.d2.loss_cls: 3.3142, decode.d2.loss_mask: 1.3699, decode.d2.loss_dice: 1.9883, decode.d3.loss_cls: 3.2169, decode.d3.loss_mask: 1.3538, decode.d3.loss_dice: 1.9650, decode.d4.loss_cls: 3.1780, decode.d4.loss_mask: 1.3559, decode.d4.loss_dice: 1.9634, decode.d5.loss_cls: 3.1627, decode.d5.loss_mask: 1.3532, decode.d5.loss_dice: 1.9614, decode.d6.loss_cls: 3.1468, decode.d6.loss_mask: 1.3624, decode.d6.loss_dice: 1.9543, decode.d7.loss_cls: 3.1523, decode.d7.loss_mask: 1.3576, decode.d7.loss_dice: 1.9681, decode.d8.loss_cls: 3.1399, decode.d8.loss_mask: 1.3625, decode.d8.loss_dice: 1.9721, loss: 73.1700 2022-05-04 21:19:55,696 - mmseg - INFO - Iter [1600/40000] lr: 1.378e-06, eta: 8:28:27, time: 0.797, data_time: 0.010, memory: 51557, decode.loss_cls: 3.0781, decode.loss_mask: 1.3245, decode.loss_dice: 1.9160, decode.d0.loss_cls: 10.3198, decode.d0.loss_mask: 1.3019, decode.d0.loss_dice: 2.2834, decode.d1.loss_cls: 3.5349, decode.d1.loss_mask: 1.3182, decode.d1.loss_dice: 2.0102, decode.d2.loss_cls: 3.2351, decode.d2.loss_mask: 1.3121, decode.d2.loss_dice: 1.9295, decode.d3.loss_cls: 3.1177, decode.d3.loss_mask: 1.3110, decode.d3.loss_dice: 1.8899, decode.d4.loss_cls: 3.0889, decode.d4.loss_mask: 1.2952, decode.d4.loss_dice: 1.8922, decode.d5.loss_cls: 3.0892, decode.d5.loss_mask: 1.2968, decode.d5.loss_dice: 1.8862, decode.d6.loss_cls: 3.0743, decode.d6.loss_mask: 1.3039, decode.d6.loss_dice: 1.8817, decode.d7.loss_cls: 3.0715, decode.d7.loss_mask: 1.3022, decode.d7.loss_dice: 1.9024, decode.d8.loss_cls: 3.0614, decode.d8.loss_mask: 1.3006, decode.d8.loss_dice: 1.9019, loss: 71.2307 2022-05-04 21:20:36,321 - mmseg - INFO - Iter [1650/40000] lr: 1.377e-06, eta: 8:28:08, time: 0.812, data_time: 0.009, memory: 51557, decode.loss_cls: 3.0334, decode.loss_mask: 1.2816, decode.loss_dice: 1.8496, decode.d0.loss_cls: 10.3049, decode.d0.loss_mask: 1.3015, decode.d0.loss_dice: 2.2387, decode.d1.loss_cls: 3.5322, decode.d1.loss_mask: 1.3050, decode.d1.loss_dice: 1.9654, decode.d2.loss_cls: 3.2296, decode.d2.loss_mask: 1.2846, decode.d2.loss_dice: 1.8791, decode.d3.loss_cls: 3.1095, decode.d3.loss_mask: 1.2840, decode.d3.loss_dice: 1.8434, decode.d4.loss_cls: 3.0681, decode.d4.loss_mask: 1.2719, decode.d4.loss_dice: 1.8414, decode.d5.loss_cls: 3.0471, decode.d5.loss_mask: 1.2790, decode.d5.loss_dice: 1.8331, decode.d6.loss_cls: 3.0313, decode.d6.loss_mask: 1.2727, decode.d6.loss_dice: 1.8273, decode.d7.loss_cls: 3.0224, decode.d7.loss_mask: 1.2761, decode.d7.loss_dice: 1.8385, decode.d8.loss_cls: 3.0061, decode.d8.loss_mask: 1.2901, decode.d8.loss_dice: 1.8465, loss: 70.1942 2022-05-04 21:21:19,124 - mmseg - INFO - Iter [1700/40000] lr: 1.375e-06, eta: 8:28:37, time: 0.856, data_time: 0.058, memory: 51557, decode.loss_cls: 2.9909, decode.loss_mask: 1.2521, decode.loss_dice: 1.8976, decode.d0.loss_cls: 10.2886, decode.d0.loss_mask: 1.2504, decode.d0.loss_dice: 2.2561, decode.d1.loss_cls: 3.4815, decode.d1.loss_mask: 1.2468, decode.d1.loss_dice: 1.9845, decode.d2.loss_cls: 3.1862, decode.d2.loss_mask: 1.2400, decode.d2.loss_dice: 1.9050, decode.d3.loss_cls: 3.0675, decode.d3.loss_mask: 1.2433, decode.d3.loss_dice: 1.8753, decode.d4.loss_cls: 3.0218, decode.d4.loss_mask: 1.2432, decode.d4.loss_dice: 1.8814, decode.d5.loss_cls: 2.9975, decode.d5.loss_mask: 1.2507, decode.d5.loss_dice: 1.8727, decode.d6.loss_cls: 2.9902, decode.d6.loss_mask: 1.2486, decode.d6.loss_dice: 1.8677, decode.d7.loss_cls: 2.9754, decode.d7.loss_mask: 1.2570, decode.d7.loss_dice: 1.8842, decode.d8.loss_cls: 2.9637, decode.d8.loss_mask: 1.2529, decode.d8.loss_dice: 1.8893, loss: 69.7620 2022-05-04 21:21:58,781 - mmseg - INFO - Iter [1750/40000] lr: 1.373e-06, eta: 8:27:53, time: 0.793, data_time: 0.009, memory: 51557, decode.loss_cls: 2.9768, decode.loss_mask: 1.2457, decode.loss_dice: 1.8524, decode.d0.loss_cls: 10.2767, decode.d0.loss_mask: 1.2283, decode.d0.loss_dice: 2.2282, decode.d1.loss_cls: 3.4657, decode.d1.loss_mask: 1.2430, decode.d1.loss_dice: 1.9475, decode.d2.loss_cls: 3.1562, decode.d2.loss_mask: 1.2280, decode.d2.loss_dice: 1.8643, decode.d3.loss_cls: 3.0466, decode.d3.loss_mask: 1.2312, decode.d3.loss_dice: 1.8341, decode.d4.loss_cls: 3.0149, decode.d4.loss_mask: 1.2226, decode.d4.loss_dice: 1.8331, decode.d5.loss_cls: 2.9842, decode.d5.loss_mask: 1.2314, decode.d5.loss_dice: 1.8300, decode.d6.loss_cls: 2.9705, decode.d6.loss_mask: 1.2308, decode.d6.loss_dice: 1.8290, decode.d7.loss_cls: 2.9574, decode.d7.loss_mask: 1.2342, decode.d7.loss_dice: 1.8437, decode.d8.loss_cls: 2.9533, decode.d8.loss_mask: 1.2379, decode.d8.loss_dice: 1.8475, loss: 69.0453 2022-05-04 21:22:38,797 - mmseg - INFO - Iter [1800/40000] lr: 1.371e-06, eta: 8:27:17, time: 0.800, data_time: 0.009, memory: 51557, decode.loss_cls: 2.9118, decode.loss_mask: 1.2510, decode.loss_dice: 1.8132, decode.d0.loss_cls: 10.2512, decode.d0.loss_mask: 1.2358, decode.d0.loss_dice: 2.1698, decode.d1.loss_cls: 3.4235, decode.d1.loss_mask: 1.2354, decode.d1.loss_dice: 1.8904, decode.d2.loss_cls: 3.0844, decode.d2.loss_mask: 1.2374, decode.d2.loss_dice: 1.8185, decode.d3.loss_cls: 2.9667, decode.d3.loss_mask: 1.2315, decode.d3.loss_dice: 1.8010, decode.d4.loss_cls: 2.9368, decode.d4.loss_mask: 1.2358, decode.d4.loss_dice: 1.8048, decode.d5.loss_cls: 2.9161, decode.d5.loss_mask: 1.2383, decode.d5.loss_dice: 1.8080, decode.d6.loss_cls: 2.9078, decode.d6.loss_mask: 1.2422, decode.d6.loss_dice: 1.7989, decode.d7.loss_cls: 2.8837, decode.d7.loss_mask: 1.2494, decode.d7.loss_dice: 1.8138, decode.d8.loss_cls: 2.8861, decode.d8.loss_mask: 1.2463, decode.d8.loss_dice: 1.8105, loss: 68.1000 2022-05-04 21:23:18,600 - mmseg - INFO - Iter [1850/40000] lr: 1.369e-06, eta: 8:26:37, time: 0.796, data_time: 0.009, memory: 51557, decode.loss_cls: 2.8093, decode.loss_mask: 1.2006, decode.loss_dice: 1.7589, decode.d0.loss_cls: 10.2395, decode.d0.loss_mask: 1.2106, decode.d0.loss_dice: 2.1284, decode.d1.loss_cls: 3.3091, decode.d1.loss_mask: 1.2088, decode.d1.loss_dice: 1.8538, decode.d2.loss_cls: 2.9817, decode.d2.loss_mask: 1.1933, decode.d2.loss_dice: 1.7728, decode.d3.loss_cls: 2.8774, decode.d3.loss_mask: 1.2003, decode.d3.loss_dice: 1.7543, decode.d4.loss_cls: 2.8251, decode.d4.loss_mask: 1.1935, decode.d4.loss_dice: 1.7527, decode.d5.loss_cls: 2.8092, decode.d5.loss_mask: 1.1948, decode.d5.loss_dice: 1.7563, decode.d6.loss_cls: 2.7976, decode.d6.loss_mask: 1.1942, decode.d6.loss_dice: 1.7411, decode.d7.loss_cls: 2.7873, decode.d7.loss_mask: 1.1943, decode.d7.loss_dice: 1.7527, decode.d8.loss_cls: 2.7814, decode.d8.loss_mask: 1.1961, decode.d8.loss_dice: 1.7496, loss: 66.2249 2022-05-04 21:23:58,472 - mmseg - INFO - Iter [1900/40000] lr: 1.368e-06, eta: 8:25:57, time: 0.797, data_time: 0.011, memory: 51557, decode.loss_cls: 2.8526, decode.loss_mask: 1.1857, decode.loss_dice: 1.7749, decode.d0.loss_cls: 10.2255, decode.d0.loss_mask: 1.1961, decode.d0.loss_dice: 2.1509, decode.d1.loss_cls: 3.4180, decode.d1.loss_mask: 1.2034, decode.d1.loss_dice: 1.8852, decode.d2.loss_cls: 3.0543, decode.d2.loss_mask: 1.1943, decode.d2.loss_dice: 1.7981, decode.d3.loss_cls: 2.9309, decode.d3.loss_mask: 1.1831, decode.d3.loss_dice: 1.7591, decode.d4.loss_cls: 2.8767, decode.d4.loss_mask: 1.1841, decode.d4.loss_dice: 1.7766, decode.d5.loss_cls: 2.8552, decode.d5.loss_mask: 1.1877, decode.d5.loss_dice: 1.7717, decode.d6.loss_cls: 2.8396, decode.d6.loss_mask: 1.1910, decode.d6.loss_dice: 1.7643, decode.d7.loss_cls: 2.8327, decode.d7.loss_mask: 1.1896, decode.d7.loss_dice: 1.7819, decode.d8.loss_cls: 2.8272, decode.d8.loss_mask: 1.1915, decode.d8.loss_dice: 1.7738, loss: 66.8556 2022-05-04 21:24:38,137 - mmseg - INFO - Iter [1950/40000] lr: 1.366e-06, eta: 8:25:14, time: 0.794, data_time: 0.010, memory: 51557, decode.loss_cls: 2.7169, decode.loss_mask: 1.2180, decode.loss_dice: 1.7091, decode.d0.loss_cls: 10.2070, decode.d0.loss_mask: 1.2259, decode.d0.loss_dice: 2.0823, decode.d1.loss_cls: 3.2417, decode.d1.loss_mask: 1.2251, decode.d1.loss_dice: 1.7932, decode.d2.loss_cls: 2.8979, decode.d2.loss_mask: 1.2227, decode.d2.loss_dice: 1.7263, decode.d3.loss_cls: 2.8035, decode.d3.loss_mask: 1.2128, decode.d3.loss_dice: 1.7076, decode.d4.loss_cls: 2.7553, decode.d4.loss_mask: 1.2065, decode.d4.loss_dice: 1.7044, decode.d5.loss_cls: 2.7303, decode.d5.loss_mask: 1.2147, decode.d5.loss_dice: 1.6960, decode.d6.loss_cls: 2.7215, decode.d6.loss_mask: 1.2123, decode.d6.loss_dice: 1.6944, decode.d7.loss_cls: 2.7058, decode.d7.loss_mask: 1.2164, decode.d7.loss_dice: 1.7065, decode.d8.loss_cls: 2.6931, decode.d8.loss_mask: 1.2186, decode.d8.loss_dice: 1.7098, loss: 65.1756 2022-05-04 21:25:18,225 - mmseg - INFO - Saving checkpoint at 2000 iterations 2022-05-04 21:25:43,545 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-04 21:25:43,553 - mmseg - INFO - Iter [2000/40000] lr: 1.364e-06, eta: 8:32:38, time: 1.306, data_time: 0.009, memory: 51557, decode.loss_cls: 2.6742, decode.loss_mask: 1.1722, decode.loss_dice: 1.7140, decode.d0.loss_cls: 10.1916, decode.d0.loss_mask: 1.1789, decode.d0.loss_dice: 2.0597, decode.d1.loss_cls: 3.2014, decode.d1.loss_mask: 1.1792, decode.d1.loss_dice: 1.8007, decode.d2.loss_cls: 2.8551, decode.d2.loss_mask: 1.1793, decode.d2.loss_dice: 1.7253, decode.d3.loss_cls: 2.7339, decode.d3.loss_mask: 1.1738, decode.d3.loss_dice: 1.7016, decode.d4.loss_cls: 2.6819, decode.d4.loss_mask: 1.1720, decode.d4.loss_dice: 1.7060, decode.d5.loss_cls: 2.6637, decode.d5.loss_mask: 1.1726, decode.d5.loss_dice: 1.6962, decode.d6.loss_cls: 2.6540, decode.d6.loss_mask: 1.1728, decode.d6.loss_dice: 1.6888, decode.d7.loss_cls: 2.6438, decode.d7.loss_mask: 1.1665, decode.d7.loss_dice: 1.7013, decode.d8.loss_cls: 2.6427, decode.d8.loss_mask: 1.1648, decode.d8.loss_dice: 1.7009, loss: 64.1688 2022-05-04 21:26:23,920 - mmseg - INFO - Iter [2050/40000] lr: 1.362e-06, eta: 8:31:58, time: 0.810, data_time: 0.012, memory: 51557, decode.loss_cls: 2.6219, decode.loss_mask: 1.1733, decode.loss_dice: 1.7140, decode.d0.loss_cls: 10.1760, decode.d0.loss_mask: 1.1886, decode.d0.loss_dice: 2.0582, decode.d1.loss_cls: 3.1712, decode.d1.loss_mask: 1.1857, decode.d1.loss_dice: 1.8032, decode.d2.loss_cls: 2.8109, decode.d2.loss_mask: 1.1747, decode.d2.loss_dice: 1.7285, decode.d3.loss_cls: 2.6935, decode.d3.loss_mask: 1.1728, decode.d3.loss_dice: 1.7069, decode.d4.loss_cls: 2.6462, decode.d4.loss_mask: 1.1766, decode.d4.loss_dice: 1.7147, decode.d5.loss_cls: 2.6181, decode.d5.loss_mask: 1.1792, decode.d5.loss_dice: 1.7142, decode.d6.loss_cls: 2.5957, decode.d6.loss_mask: 1.1884, decode.d6.loss_dice: 1.7020, decode.d7.loss_cls: 2.5956, decode.d7.loss_mask: 1.1805, decode.d7.loss_dice: 1.7069, decode.d8.loss_cls: 2.5815, decode.d8.loss_mask: 1.1806, decode.d8.loss_dice: 1.7154, loss: 63.8753 2022-05-04 21:27:03,529 - mmseg - INFO - Iter [2100/40000] lr: 1.360e-06, eta: 8:31:01, time: 0.792, data_time: 0.009, memory: 51557, decode.loss_cls: 2.5520, decode.loss_mask: 1.1305, decode.loss_dice: 1.6627, decode.d0.loss_cls: 10.1695, decode.d0.loss_mask: 1.1338, decode.d0.loss_dice: 2.0243, decode.d1.loss_cls: 3.1438, decode.d1.loss_mask: 1.1397, decode.d1.loss_dice: 1.7536, decode.d2.loss_cls: 2.7685, decode.d2.loss_mask: 1.1227, decode.d2.loss_dice: 1.6760, decode.d3.loss_cls: 2.6460, decode.d3.loss_mask: 1.1286, decode.d3.loss_dice: 1.6509, decode.d4.loss_cls: 2.5922, decode.d4.loss_mask: 1.1244, decode.d4.loss_dice: 1.6658, decode.d5.loss_cls: 2.5635, decode.d5.loss_mask: 1.1260, decode.d5.loss_dice: 1.6648, decode.d6.loss_cls: 2.5464, decode.d6.loss_mask: 1.1319, decode.d6.loss_dice: 1.6548, decode.d7.loss_cls: 2.5342, decode.d7.loss_mask: 1.1282, decode.d7.loss_dice: 1.6543, decode.d8.loss_cls: 2.5261, decode.d8.loss_mask: 1.1270, decode.d8.loss_dice: 1.6565, loss: 62.3987 2022-05-04 21:27:43,289 - mmseg - INFO - Iter [2150/40000] lr: 1.359e-06, eta: 8:30:08, time: 0.795, data_time: 0.009, memory: 51557, decode.loss_cls: 2.5602, decode.loss_mask: 1.1203, decode.loss_dice: 1.6914, decode.d0.loss_cls: 10.1480, decode.d0.loss_mask: 1.1193, decode.d0.loss_dice: 2.0440, decode.d1.loss_cls: 3.1308, decode.d1.loss_mask: 1.1295, decode.d1.loss_dice: 1.7906, decode.d2.loss_cls: 2.7741, decode.d2.loss_mask: 1.1132, decode.d2.loss_dice: 1.7124, decode.d3.loss_cls: 2.6405, decode.d3.loss_mask: 1.1209, decode.d3.loss_dice: 1.6891, decode.d4.loss_cls: 2.5871, decode.d4.loss_mask: 1.1203, decode.d4.loss_dice: 1.6882, decode.d5.loss_cls: 2.5647, decode.d5.loss_mask: 1.1290, decode.d5.loss_dice: 1.6871, decode.d6.loss_cls: 2.5517, decode.d6.loss_mask: 1.1285, decode.d6.loss_dice: 1.6831, decode.d7.loss_cls: 2.5422, decode.d7.loss_mask: 1.1283, decode.d7.loss_dice: 1.6928, decode.d8.loss_cls: 2.5290, decode.d8.loss_mask: 1.1228, decode.d8.loss_dice: 1.6901, loss: 62.6292 2022-05-04 21:28:23,236 - mmseg - INFO - Iter [2200/40000] lr: 1.357e-06, eta: 8:29:20, time: 0.799, data_time: 0.010, memory: 51557, decode.loss_cls: 2.5174, decode.loss_mask: 1.1278, decode.loss_dice: 1.6638, decode.d0.loss_cls: 10.1256, decode.d0.loss_mask: 1.1168, decode.d0.loss_dice: 1.9940, decode.d1.loss_cls: 3.1007, decode.d1.loss_mask: 1.1363, decode.d1.loss_dice: 1.7456, decode.d2.loss_cls: 2.7230, decode.d2.loss_mask: 1.1305, decode.d2.loss_dice: 1.6736, decode.d3.loss_cls: 2.6034, decode.d3.loss_mask: 1.1252, decode.d3.loss_dice: 1.6457, decode.d4.loss_cls: 2.5465, decode.d4.loss_mask: 1.1182, decode.d4.loss_dice: 1.6553, decode.d5.loss_cls: 2.5230, decode.d5.loss_mask: 1.1255, decode.d5.loss_dice: 1.6545, decode.d6.loss_cls: 2.4941, decode.d6.loss_mask: 1.1284, decode.d6.loss_dice: 1.6483, decode.d7.loss_cls: 2.4797, decode.d7.loss_mask: 1.1338, decode.d7.loss_dice: 1.6637, decode.d8.loss_cls: 2.4709, decode.d8.loss_mask: 1.1360, decode.d8.loss_dice: 1.6672, loss: 61.8742 2022-05-04 21:29:06,067 - mmseg - INFO - Iter [2250/40000] lr: 1.355e-06, eta: 8:29:20, time: 0.857, data_time: 0.061, memory: 51557, decode.loss_cls: 2.4476, decode.loss_mask: 1.1221, decode.loss_dice: 1.6068, decode.d0.loss_cls: 10.1182, decode.d0.loss_mask: 1.1204, decode.d0.loss_dice: 1.9519, decode.d1.loss_cls: 3.0520, decode.d1.loss_mask: 1.1311, decode.d1.loss_dice: 1.6961, decode.d2.loss_cls: 2.6640, decode.d2.loss_mask: 1.1089, decode.d2.loss_dice: 1.6166, decode.d3.loss_cls: 2.5391, decode.d3.loss_mask: 1.1151, decode.d3.loss_dice: 1.5982, decode.d4.loss_cls: 2.4806, decode.d4.loss_mask: 1.1210, decode.d4.loss_dice: 1.6096, decode.d5.loss_cls: 2.4479, decode.d5.loss_mask: 1.1189, decode.d5.loss_dice: 1.5928, decode.d6.loss_cls: 2.4270, decode.d6.loss_mask: 1.1137, decode.d6.loss_dice: 1.5902, decode.d7.loss_cls: 2.4190, decode.d7.loss_mask: 1.1152, decode.d7.loss_dice: 1.6040, decode.d8.loss_cls: 2.4133, decode.d8.loss_mask: 1.1220, decode.d8.loss_dice: 1.6037, loss: 60.6672 2022-05-04 21:29:45,851 - mmseg - INFO - Iter [2300/40000] lr: 1.353e-06, eta: 8:28:28, time: 0.796, data_time: 0.009, memory: 51557, decode.loss_cls: 2.3894, decode.loss_mask: 1.1161, decode.loss_dice: 1.6050, decode.d0.loss_cls: 10.0991, decode.d0.loss_mask: 1.1048, decode.d0.loss_dice: 1.9530, decode.d1.loss_cls: 2.9916, decode.d1.loss_mask: 1.1126, decode.d1.loss_dice: 1.6909, decode.d2.loss_cls: 2.6222, decode.d2.loss_mask: 1.1015, decode.d2.loss_dice: 1.6181, decode.d3.loss_cls: 2.4892, decode.d3.loss_mask: 1.1068, decode.d3.loss_dice: 1.6001, decode.d4.loss_cls: 2.4256, decode.d4.loss_mask: 1.1058, decode.d4.loss_dice: 1.6060, decode.d5.loss_cls: 2.4042, decode.d5.loss_mask: 1.0990, decode.d5.loss_dice: 1.5857, decode.d6.loss_cls: 2.3855, decode.d6.loss_mask: 1.1077, decode.d6.loss_dice: 1.5850, decode.d7.loss_cls: 2.3684, decode.d7.loss_mask: 1.1055, decode.d7.loss_dice: 1.5937, decode.d8.loss_cls: 2.3613, decode.d8.loss_mask: 1.1017, decode.d8.loss_dice: 1.5983, loss: 60.0339 2022-05-04 21:30:25,452 - mmseg - INFO - Iter [2350/40000] lr: 1.351e-06, eta: 8:27:34, time: 0.792, data_time: 0.009, memory: 51557, decode.loss_cls: 2.2984, decode.loss_mask: 1.0651, decode.loss_dice: 1.5701, decode.d0.loss_cls: 10.0733, decode.d0.loss_mask: 1.0656, decode.d0.loss_dice: 1.8936, decode.d1.loss_cls: 2.8896, decode.d1.loss_mask: 1.0689, decode.d1.loss_dice: 1.6484, decode.d2.loss_cls: 2.5138, decode.d2.loss_mask: 1.0520, decode.d2.loss_dice: 1.5774, decode.d3.loss_cls: 2.3925, decode.d3.loss_mask: 1.0622, decode.d3.loss_dice: 1.5601, decode.d4.loss_cls: 2.3309, decode.d4.loss_mask: 1.0701, decode.d4.loss_dice: 1.5780, decode.d5.loss_cls: 2.3124, decode.d5.loss_mask: 1.0625, decode.d5.loss_dice: 1.5722, decode.d6.loss_cls: 2.2840, decode.d6.loss_mask: 1.0594, decode.d6.loss_dice: 1.5562, decode.d7.loss_cls: 2.2609, decode.d7.loss_mask: 1.0647, decode.d7.loss_dice: 1.5712, decode.d8.loss_cls: 2.2547, decode.d8.loss_mask: 1.0691, decode.d8.loss_dice: 1.5668, loss: 58.3441 2022-05-04 21:31:04,814 - mmseg - INFO - Iter [2400/40000] lr: 1.350e-06, eta: 8:26:37, time: 0.788, data_time: 0.009, memory: 51557, decode.loss_cls: 2.3222, decode.loss_mask: 1.1026, decode.loss_dice: 1.5294, decode.d0.loss_cls: 10.0533, decode.d0.loss_mask: 1.0980, decode.d0.loss_dice: 1.8855, decode.d1.loss_cls: 2.9375, decode.d1.loss_mask: 1.0913, decode.d1.loss_dice: 1.6119, decode.d2.loss_cls: 2.5507, decode.d2.loss_mask: 1.0802, decode.d2.loss_dice: 1.5489, decode.d3.loss_cls: 2.4147, decode.d3.loss_mask: 1.0940, decode.d3.loss_dice: 1.5281, decode.d4.loss_cls: 2.3540, decode.d4.loss_mask: 1.0913, decode.d4.loss_dice: 1.5237, decode.d5.loss_cls: 2.3253, decode.d5.loss_mask: 1.0952, decode.d5.loss_dice: 1.5268, decode.d6.loss_cls: 2.3022, decode.d6.loss_mask: 1.0943, decode.d6.loss_dice: 1.5149, decode.d7.loss_cls: 2.2955, decode.d7.loss_mask: 1.0958, decode.d7.loss_dice: 1.5258, decode.d8.loss_cls: 2.2782, decode.d8.loss_mask: 1.1065, decode.d8.loss_dice: 1.5316, loss: 58.5092 2022-05-04 21:31:44,781 - mmseg - INFO - Iter [2450/40000] lr: 1.348e-06, eta: 8:25:49, time: 0.799, data_time: 0.009, memory: 51557, decode.loss_cls: 2.2289, decode.loss_mask: 1.0867, decode.loss_dice: 1.5673, decode.d0.loss_cls: 10.0319, decode.d0.loss_mask: 1.0655, decode.d0.loss_dice: 1.8812, decode.d1.loss_cls: 2.8480, decode.d1.loss_mask: 1.0826, decode.d1.loss_dice: 1.6430, decode.d2.loss_cls: 2.4483, decode.d2.loss_mask: 1.0743, decode.d2.loss_dice: 1.5719, decode.d3.loss_cls: 2.3336, decode.d3.loss_mask: 1.0770, decode.d3.loss_dice: 1.5578, decode.d4.loss_cls: 2.2689, decode.d4.loss_mask: 1.0806, decode.d4.loss_dice: 1.5652, decode.d5.loss_cls: 2.2284, decode.d5.loss_mask: 1.0902, decode.d5.loss_dice: 1.5571, decode.d6.loss_cls: 2.2242, decode.d6.loss_mask: 1.0830, decode.d6.loss_dice: 1.5507, decode.d7.loss_cls: 2.2131, decode.d7.loss_mask: 1.0929, decode.d7.loss_dice: 1.5578, decode.d8.loss_cls: 2.2044, decode.d8.loss_mask: 1.0810, decode.d8.loss_dice: 1.5570, loss: 57.8525 2022-05-04 21:32:24,501 - mmseg - INFO - Iter [2500/40000] lr: 1.346e-06, eta: 8:24:58, time: 0.794, data_time: 0.009, memory: 51557, decode.loss_cls: 2.2871, decode.loss_mask: 1.0680, decode.loss_dice: 1.5899, decode.d0.loss_cls: 10.0470, decode.d0.loss_mask: 1.0608, decode.d0.loss_dice: 1.9154, decode.d1.loss_cls: 2.8937, decode.d1.loss_mask: 1.0731, decode.d1.loss_dice: 1.6710, decode.d2.loss_cls: 2.4965, decode.d2.loss_mask: 1.0739, decode.d2.loss_dice: 1.6202, decode.d3.loss_cls: 2.3637, decode.d3.loss_mask: 1.0802, decode.d3.loss_dice: 1.6030, decode.d4.loss_cls: 2.3238, decode.d4.loss_mask: 1.0732, decode.d4.loss_dice: 1.5968, decode.d5.loss_cls: 2.2825, decode.d5.loss_mask: 1.0776, decode.d5.loss_dice: 1.6042, decode.d6.loss_cls: 2.2774, decode.d6.loss_mask: 1.0819, decode.d6.loss_dice: 1.5856, decode.d7.loss_cls: 2.2707, decode.d7.loss_mask: 1.0737, decode.d7.loss_dice: 1.5862, decode.d8.loss_cls: 2.2674, decode.d8.loss_mask: 1.0690, decode.d8.loss_dice: 1.5843, loss: 58.5978 2022-05-04 21:33:03,486 - mmseg - INFO - Iter [2550/40000] lr: 1.344e-06, eta: 8:23:57, time: 0.779, data_time: 0.009, memory: 51557, decode.loss_cls: 2.2740, decode.loss_mask: 1.0889, decode.loss_dice: 1.6070, decode.d0.loss_cls: 10.0111, decode.d0.loss_mask: 1.0736, decode.d0.loss_dice: 1.9157, decode.d1.loss_cls: 2.8835, decode.d1.loss_mask: 1.0887, decode.d1.loss_dice: 1.6758, decode.d2.loss_cls: 2.5011, decode.d2.loss_mask: 1.0774, decode.d2.loss_dice: 1.6181, decode.d3.loss_cls: 2.3713, decode.d3.loss_mask: 1.0859, decode.d3.loss_dice: 1.5941, decode.d4.loss_cls: 2.3202, decode.d4.loss_mask: 1.0828, decode.d4.loss_dice: 1.6024, decode.d5.loss_cls: 2.2893, decode.d5.loss_mask: 1.0873, decode.d5.loss_dice: 1.5989, decode.d6.loss_cls: 2.2720, decode.d6.loss_mask: 1.0905, decode.d6.loss_dice: 1.5921, decode.d7.loss_cls: 2.2629, decode.d7.loss_mask: 1.0881, decode.d7.loss_dice: 1.6006, decode.d8.loss_cls: 2.2572, decode.d8.loss_mask: 1.0905, decode.d8.loss_dice: 1.6094, loss: 58.7104 2022-05-04 21:33:42,468 - mmseg - INFO - Iter [2600/40000] lr: 1.343e-06, eta: 8:22:57, time: 0.780, data_time: 0.009, memory: 51557, decode.loss_cls: 2.1873, decode.loss_mask: 1.0849, decode.loss_dice: 1.5648, decode.d0.loss_cls: 9.9901, decode.d0.loss_mask: 1.0821, decode.d0.loss_dice: 1.8486, decode.d1.loss_cls: 2.7762, decode.d1.loss_mask: 1.1000, decode.d1.loss_dice: 1.6191, decode.d2.loss_cls: 2.3974, decode.d2.loss_mask: 1.0846, decode.d2.loss_dice: 1.5680, decode.d3.loss_cls: 2.2636, decode.d3.loss_mask: 1.0814, decode.d3.loss_dice: 1.5435, decode.d4.loss_cls: 2.2194, decode.d4.loss_mask: 1.0771, decode.d4.loss_dice: 1.5469, decode.d5.loss_cls: 2.1946, decode.d5.loss_mask: 1.0814, decode.d5.loss_dice: 1.5475, decode.d6.loss_cls: 2.1839, decode.d6.loss_mask: 1.0805, decode.d6.loss_dice: 1.5390, decode.d7.loss_cls: 2.1827, decode.d7.loss_mask: 1.0771, decode.d7.loss_dice: 1.5496, decode.d8.loss_cls: 2.1621, decode.d8.loss_mask: 1.0807, decode.d8.loss_dice: 1.5560, loss: 57.2700 2022-05-04 21:34:22,571 - mmseg - INFO - Iter [2650/40000] lr: 1.341e-06, eta: 8:22:13, time: 0.801, data_time: 0.009, memory: 51557, decode.loss_cls: 2.1601, decode.loss_mask: 1.0932, decode.loss_dice: 1.5829, decode.d0.loss_cls: 9.9592, decode.d0.loss_mask: 1.0553, decode.d0.loss_dice: 1.8752, decode.d1.loss_cls: 2.7530, decode.d1.loss_mask: 1.0770, decode.d1.loss_dice: 1.6483, decode.d2.loss_cls: 2.3602, decode.d2.loss_mask: 1.0708, decode.d2.loss_dice: 1.5938, decode.d3.loss_cls: 2.2384, decode.d3.loss_mask: 1.0743, decode.d3.loss_dice: 1.5852, decode.d4.loss_cls: 2.1920, decode.d4.loss_mask: 1.0800, decode.d4.loss_dice: 1.5885, decode.d5.loss_cls: 2.1671, decode.d5.loss_mask: 1.0862, decode.d5.loss_dice: 1.5887, decode.d6.loss_cls: 2.1506, decode.d6.loss_mask: 1.0899, decode.d6.loss_dice: 1.5771, decode.d7.loss_cls: 2.1424, decode.d7.loss_mask: 1.0865, decode.d7.loss_dice: 1.5863, decode.d8.loss_cls: 2.1411, decode.d8.loss_mask: 1.0907, decode.d8.loss_dice: 1.5866, loss: 57.2810 2022-05-04 21:35:01,965 - mmseg - INFO - Iter [2700/40000] lr: 1.339e-06, eta: 8:21:20, time: 0.789, data_time: 0.009, memory: 51557, decode.loss_cls: 2.0628, decode.loss_mask: 1.0513, decode.loss_dice: 1.4832, decode.d0.loss_cls: 9.9471, decode.d0.loss_mask: 1.0410, decode.d0.loss_dice: 1.7905, decode.d1.loss_cls: 2.6438, decode.d1.loss_mask: 1.0455, decode.d1.loss_dice: 1.5577, decode.d2.loss_cls: 2.2503, decode.d2.loss_mask: 1.0449, decode.d2.loss_dice: 1.5132, decode.d3.loss_cls: 2.1416, decode.d3.loss_mask: 1.0481, decode.d3.loss_dice: 1.4836, decode.d4.loss_cls: 2.0754, decode.d4.loss_mask: 1.0574, decode.d4.loss_dice: 1.4902, decode.d5.loss_cls: 2.0574, decode.d5.loss_mask: 1.0530, decode.d5.loss_dice: 1.4779, decode.d6.loss_cls: 2.0428, decode.d6.loss_mask: 1.0508, decode.d6.loss_dice: 1.4736, decode.d7.loss_cls: 2.0353, decode.d7.loss_mask: 1.0561, decode.d7.loss_dice: 1.4810, decode.d8.loss_cls: 2.0277, decode.d8.loss_mask: 1.0583, decode.d8.loss_dice: 1.4828, loss: 55.0245 2022-05-04 21:35:40,970 - mmseg - INFO - Iter [2750/40000] lr: 1.337e-06, eta: 8:20:22, time: 0.780, data_time: 0.009, memory: 51557, decode.loss_cls: 2.1548, decode.loss_mask: 1.0698, decode.loss_dice: 1.5786, decode.d0.loss_cls: 9.9314, decode.d0.loss_mask: 1.0430, decode.d0.loss_dice: 1.8625, decode.d1.loss_cls: 2.7911, decode.d1.loss_mask: 1.0559, decode.d1.loss_dice: 1.6384, decode.d2.loss_cls: 2.3727, decode.d2.loss_mask: 1.0457, decode.d2.loss_dice: 1.5801, decode.d3.loss_cls: 2.2482, decode.d3.loss_mask: 1.0505, decode.d3.loss_dice: 1.5549, decode.d4.loss_cls: 2.1959, decode.d4.loss_mask: 1.0623, decode.d4.loss_dice: 1.5623, decode.d5.loss_cls: 2.1631, decode.d5.loss_mask: 1.0649, decode.d5.loss_dice: 1.5662, decode.d6.loss_cls: 2.1468, decode.d6.loss_mask: 1.0691, decode.d6.loss_dice: 1.5572, decode.d7.loss_cls: 2.1386, decode.d7.loss_mask: 1.0690, decode.d7.loss_dice: 1.5711, decode.d8.loss_cls: 2.1377, decode.d8.loss_mask: 1.0745, decode.d8.loss_dice: 1.5705, loss: 56.9268 2022-05-04 21:36:20,666 - mmseg - INFO - Iter [2800/40000] lr: 1.335e-06, eta: 8:19:33, time: 0.794, data_time: 0.009, memory: 51557, decode.loss_cls: 2.0852, decode.loss_mask: 1.0712, decode.loss_dice: 1.4972, decode.d0.loss_cls: 9.8964, decode.d0.loss_mask: 1.0562, decode.d0.loss_dice: 1.7885, decode.d1.loss_cls: 2.6625, decode.d1.loss_mask: 1.0672, decode.d1.loss_dice: 1.5677, decode.d2.loss_cls: 2.2753, decode.d2.loss_mask: 1.0627, decode.d2.loss_dice: 1.5082, decode.d3.loss_cls: 2.1683, decode.d3.loss_mask: 1.0629, decode.d3.loss_dice: 1.4901, decode.d4.loss_cls: 2.1193, decode.d4.loss_mask: 1.0672, decode.d4.loss_dice: 1.4952, decode.d5.loss_cls: 2.0956, decode.d5.loss_mask: 1.0675, decode.d5.loss_dice: 1.4915, decode.d6.loss_cls: 2.0822, decode.d6.loss_mask: 1.0672, decode.d6.loss_dice: 1.4877, decode.d7.loss_cls: 2.0859, decode.d7.loss_mask: 1.0697, decode.d7.loss_dice: 1.4955, decode.d8.loss_cls: 2.0702, decode.d8.loss_mask: 1.0729, decode.d8.loss_dice: 1.4999, loss: 55.5272 2022-05-04 21:37:02,883 - mmseg - INFO - Iter [2850/40000] lr: 1.334e-06, eta: 8:19:18, time: 0.844, data_time: 0.062, memory: 51557, decode.loss_cls: 2.0501, decode.loss_mask: 1.0459, decode.loss_dice: 1.5156, decode.d0.loss_cls: 9.8893, decode.d0.loss_mask: 1.0274, decode.d0.loss_dice: 1.8211, decode.d1.loss_cls: 2.6456, decode.d1.loss_mask: 1.0413, decode.d1.loss_dice: 1.5759, decode.d2.loss_cls: 2.2451, decode.d2.loss_mask: 1.0265, decode.d2.loss_dice: 1.5186, decode.d3.loss_cls: 2.1455, decode.d3.loss_mask: 1.0250, decode.d3.loss_dice: 1.4919, decode.d4.loss_cls: 2.0807, decode.d4.loss_mask: 1.0315, decode.d4.loss_dice: 1.5015, decode.d5.loss_cls: 2.0478, decode.d5.loss_mask: 1.0282, decode.d5.loss_dice: 1.4912, decode.d6.loss_cls: 2.0396, decode.d6.loss_mask: 1.0344, decode.d6.loss_dice: 1.4836, decode.d7.loss_cls: 2.0340, decode.d7.loss_mask: 1.0381, decode.d7.loss_dice: 1.4991, decode.d8.loss_cls: 2.0316, decode.d8.loss_mask: 1.0404, decode.d8.loss_dice: 1.5013, loss: 54.9477 2022-05-04 21:37:43,089 - mmseg - INFO - Iter [2900/40000] lr: 1.332e-06, eta: 8:18:36, time: 0.804, data_time: 0.009, memory: 51557, decode.loss_cls: 2.0321, decode.loss_mask: 1.0719, decode.loss_dice: 1.4483, decode.d0.loss_cls: 9.8800, decode.d0.loss_mask: 1.0588, decode.d0.loss_dice: 1.7599, decode.d1.loss_cls: 2.5757, decode.d1.loss_mask: 1.0671, decode.d1.loss_dice: 1.5114, decode.d2.loss_cls: 2.2091, decode.d2.loss_mask: 1.0609, decode.d2.loss_dice: 1.4579, decode.d3.loss_cls: 2.1019, decode.d3.loss_mask: 1.0660, decode.d3.loss_dice: 1.4428, decode.d4.loss_cls: 2.0463, decode.d4.loss_mask: 1.0693, decode.d4.loss_dice: 1.4514, decode.d5.loss_cls: 2.0247, decode.d5.loss_mask: 1.0692, decode.d5.loss_dice: 1.4433, decode.d6.loss_cls: 2.0152, decode.d6.loss_mask: 1.0759, decode.d6.loss_dice: 1.4390, decode.d7.loss_cls: 2.0124, decode.d7.loss_mask: 1.0781, decode.d7.loss_dice: 1.4486, decode.d8.loss_cls: 2.0054, decode.d8.loss_mask: 1.0734, decode.d8.loss_dice: 1.4511, loss: 54.4472 2022-05-04 21:38:23,592 - mmseg - INFO - Iter [2950/40000] lr: 1.330e-06, eta: 8:17:58, time: 0.810, data_time: 0.010, memory: 51557, decode.loss_cls: 1.9409, decode.loss_mask: 1.0482, decode.loss_dice: 1.4623, decode.d0.loss_cls: 9.8429, decode.d0.loss_mask: 1.0334, decode.d0.loss_dice: 1.7737, decode.d1.loss_cls: 2.5423, decode.d1.loss_mask: 1.0390, decode.d1.loss_dice: 1.5252, decode.d2.loss_cls: 2.1519, decode.d2.loss_mask: 1.0310, decode.d2.loss_dice: 1.4693, decode.d3.loss_cls: 2.0345, decode.d3.loss_mask: 1.0339, decode.d3.loss_dice: 1.4558, decode.d4.loss_cls: 1.9769, decode.d4.loss_mask: 1.0336, decode.d4.loss_dice: 1.4638, decode.d5.loss_cls: 1.9520, decode.d5.loss_mask: 1.0402, decode.d5.loss_dice: 1.4526, decode.d6.loss_cls: 1.9363, decode.d6.loss_mask: 1.0351, decode.d6.loss_dice: 1.4419, decode.d7.loss_cls: 1.9237, decode.d7.loss_mask: 1.0478, decode.d7.loss_dice: 1.4527, decode.d8.loss_cls: 1.9176, decode.d8.loss_mask: 1.0496, decode.d8.loss_dice: 1.4475, loss: 53.5558 2022-05-04 21:39:03,379 - mmseg - INFO - Saving checkpoint at 3000 iterations 2022-05-04 21:39:31,950 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-04 21:39:31,953 - mmseg - INFO - Iter [3000/40000] lr: 1.328e-06, eta: 8:23:02, time: 1.365, data_time: 0.010, memory: 51557, decode.loss_cls: 1.9200, decode.loss_mask: 1.0096, decode.loss_dice: 1.4586, decode.d0.loss_cls: 9.8386, decode.d0.loss_mask: 0.9951, decode.d0.loss_dice: 1.7499, decode.d1.loss_cls: 2.4919, decode.d1.loss_mask: 1.0092, decode.d1.loss_dice: 1.5329, decode.d2.loss_cls: 2.1066, decode.d2.loss_mask: 1.0005, decode.d2.loss_dice: 1.4752, decode.d3.loss_cls: 2.0041, decode.d3.loss_mask: 1.0007, decode.d3.loss_dice: 1.4599, decode.d4.loss_cls: 1.9445, decode.d4.loss_mask: 1.0082, decode.d4.loss_dice: 1.4672, decode.d5.loss_cls: 1.9211, decode.d5.loss_mask: 1.0098, decode.d5.loss_dice: 1.4721, decode.d6.loss_cls: 1.9137, decode.d6.loss_mask: 1.0020, decode.d6.loss_dice: 1.4588, decode.d7.loss_cls: 1.8942, decode.d7.loss_mask: 1.0088, decode.d7.loss_dice: 1.4674, decode.d8.loss_cls: 1.8761, decode.d8.loss_mask: 1.0030, decode.d8.loss_dice: 1.4597, loss: 52.9594 2022-05-04 21:40:12,249 - mmseg - INFO - Iter [3050/40000] lr: 1.326e-06, eta: 8:22:17, time: 0.808, data_time: 0.013, memory: 51557, decode.loss_cls: 1.9222, decode.loss_mask: 1.0462, decode.loss_dice: 1.4536, decode.d0.loss_cls: 9.8051, decode.d0.loss_mask: 1.0120, decode.d0.loss_dice: 1.7349, decode.d1.loss_cls: 2.5012, decode.d1.loss_mask: 1.0343, decode.d1.loss_dice: 1.5174, decode.d2.loss_cls: 2.1316, decode.d2.loss_mask: 1.0205, decode.d2.loss_dice: 1.4543, decode.d3.loss_cls: 2.0351, decode.d3.loss_mask: 1.0173, decode.d3.loss_dice: 1.4436, decode.d4.loss_cls: 1.9664, decode.d4.loss_mask: 1.0190, decode.d4.loss_dice: 1.4448, decode.d5.loss_cls: 1.9342, decode.d5.loss_mask: 1.0191, decode.d5.loss_dice: 1.4421, decode.d6.loss_cls: 1.9330, decode.d6.loss_mask: 1.0223, decode.d6.loss_dice: 1.4315, decode.d7.loss_cls: 1.9214, decode.d7.loss_mask: 1.0296, decode.d7.loss_dice: 1.4434, decode.d8.loss_cls: 1.9133, decode.d8.loss_mask: 1.0310, decode.d8.loss_dice: 1.4419, loss: 53.1223 2022-05-04 21:40:52,139 - mmseg - INFO - Iter [3100/40000] lr: 1.325e-06, eta: 8:21:26, time: 0.798, data_time: 0.009, memory: 51557, decode.loss_cls: 1.9661, decode.loss_mask: 1.0481, decode.loss_dice: 1.4859, decode.d0.loss_cls: 9.8084, decode.d0.loss_mask: 1.0348, decode.d0.loss_dice: 1.7873, decode.d1.loss_cls: 2.5426, decode.d1.loss_mask: 1.0465, decode.d1.loss_dice: 1.5619, decode.d2.loss_cls: 2.1725, decode.d2.loss_mask: 1.0302, decode.d2.loss_dice: 1.4996, decode.d3.loss_cls: 2.0613, decode.d3.loss_mask: 1.0333, decode.d3.loss_dice: 1.4678, decode.d4.loss_cls: 2.0172, decode.d4.loss_mask: 1.0340, decode.d4.loss_dice: 1.4749, decode.d5.loss_cls: 1.9798, decode.d5.loss_mask: 1.0381, decode.d5.loss_dice: 1.4809, decode.d6.loss_cls: 1.9775, decode.d6.loss_mask: 1.0352, decode.d6.loss_dice: 1.4728, decode.d7.loss_cls: 1.9585, decode.d7.loss_mask: 1.0443, decode.d7.loss_dice: 1.4722, decode.d8.loss_cls: 1.9554, decode.d8.loss_mask: 1.0367, decode.d8.loss_dice: 1.4746, loss: 53.9985 2022-05-04 21:41:33,249 - mmseg - INFO - Iter [3150/40000] lr: 1.323e-06, eta: 8:20:49, time: 0.822, data_time: 0.010, memory: 51557, decode.loss_cls: 1.8594, decode.loss_mask: 1.0050, decode.loss_dice: 1.4407, decode.d0.loss_cls: 9.7934, decode.d0.loss_mask: 0.9838, decode.d0.loss_dice: 1.7290, decode.d1.loss_cls: 2.4397, decode.d1.loss_mask: 1.0018, decode.d1.loss_dice: 1.5136, decode.d2.loss_cls: 2.0545, decode.d2.loss_mask: 0.9988, decode.d2.loss_dice: 1.4545, decode.d3.loss_cls: 1.9480, decode.d3.loss_mask: 0.9981, decode.d3.loss_dice: 1.4388, decode.d4.loss_cls: 1.9037, decode.d4.loss_mask: 0.9953, decode.d4.loss_dice: 1.4500, decode.d5.loss_cls: 1.8725, decode.d5.loss_mask: 1.0078, decode.d5.loss_dice: 1.4433, decode.d6.loss_cls: 1.8608, decode.d6.loss_mask: 1.0079, decode.d6.loss_dice: 1.4346, decode.d7.loss_cls: 1.8397, decode.d7.loss_mask: 1.0074, decode.d7.loss_dice: 1.4450, decode.d8.loss_cls: 1.8342, decode.d8.loss_mask: 1.0004, decode.d8.loss_dice: 1.4393, loss: 52.2010 2022-05-04 21:42:12,924 - mmseg - INFO - Iter [3200/40000] lr: 1.321e-06, eta: 8:19:55, time: 0.794, data_time: 0.009, memory: 51557, decode.loss_cls: 1.8235, decode.loss_mask: 1.0176, decode.loss_dice: 1.4322, decode.d0.loss_cls: 9.7417, decode.d0.loss_mask: 0.9932, decode.d0.loss_dice: 1.7011, decode.d1.loss_cls: 2.3625, decode.d1.loss_mask: 1.0164, decode.d1.loss_dice: 1.5024, decode.d2.loss_cls: 1.9787, decode.d2.loss_mask: 1.0091, decode.d2.loss_dice: 1.4435, decode.d3.loss_cls: 1.8915, decode.d3.loss_mask: 1.0061, decode.d3.loss_dice: 1.4194, decode.d4.loss_cls: 1.8506, decode.d4.loss_mask: 1.0101, decode.d4.loss_dice: 1.4418, decode.d5.loss_cls: 1.8244, decode.d5.loss_mask: 1.0181, decode.d5.loss_dice: 1.4319, decode.d6.loss_cls: 1.8143, decode.d6.loss_mask: 1.0162, decode.d6.loss_dice: 1.4183, decode.d7.loss_cls: 1.8028, decode.d7.loss_mask: 1.0266, decode.d7.loss_dice: 1.4314, decode.d8.loss_cls: 1.8034, decode.d8.loss_mask: 1.0197, decode.d8.loss_dice: 1.4311, loss: 51.6796 2022-05-04 21:42:53,768 - mmseg - INFO - Iter [3250/40000] lr: 1.319e-06, eta: 8:19:15, time: 0.816, data_time: 0.009, memory: 51557, decode.loss_cls: 1.8853, decode.loss_mask: 1.0315, decode.loss_dice: 1.4644, decode.d0.loss_cls: 9.7422, decode.d0.loss_mask: 1.0184, decode.d0.loss_dice: 1.7536, decode.d1.loss_cls: 2.4392, decode.d1.loss_mask: 1.0267, decode.d1.loss_dice: 1.5336, decode.d2.loss_cls: 2.0617, decode.d2.loss_mask: 1.0161, decode.d2.loss_dice: 1.4746, decode.d3.loss_cls: 1.9669, decode.d3.loss_mask: 1.0223, decode.d3.loss_dice: 1.4487, decode.d4.loss_cls: 1.9281, decode.d4.loss_mask: 1.0229, decode.d4.loss_dice: 1.4559, decode.d5.loss_cls: 1.8978, decode.d5.loss_mask: 1.0247, decode.d5.loss_dice: 1.4545, decode.d6.loss_cls: 1.8918, decode.d6.loss_mask: 1.0237, decode.d6.loss_dice: 1.4389, decode.d7.loss_cls: 1.8781, decode.d7.loss_mask: 1.0313, decode.d7.loss_dice: 1.4615, decode.d8.loss_cls: 1.8652, decode.d8.loss_mask: 1.0227, decode.d8.loss_dice: 1.4570, loss: 52.7393 2022-05-04 21:43:33,820 - mmseg - INFO - Iter [3300/40000] lr: 1.317e-06, eta: 8:18:27, time: 0.802, data_time: 0.010, memory: 51557, decode.loss_cls: 1.8516, decode.loss_mask: 1.0139, decode.loss_dice: 1.4638, decode.d0.loss_cls: 9.7185, decode.d0.loss_mask: 1.0058, decode.d0.loss_dice: 1.7489, decode.d1.loss_cls: 2.4045, decode.d1.loss_mask: 1.0184, decode.d1.loss_dice: 1.5242, decode.d2.loss_cls: 2.0126, decode.d2.loss_mask: 1.0132, decode.d2.loss_dice: 1.4633, decode.d3.loss_cls: 1.9137, decode.d3.loss_mask: 1.0101, decode.d3.loss_dice: 1.4399, decode.d4.loss_cls: 1.8790, decode.d4.loss_mask: 1.0093, decode.d4.loss_dice: 1.4471, decode.d5.loss_cls: 1.8520, decode.d5.loss_mask: 1.0136, decode.d5.loss_dice: 1.4514, decode.d6.loss_cls: 1.8304, decode.d6.loss_mask: 1.0141, decode.d6.loss_dice: 1.4502, decode.d7.loss_cls: 1.8308, decode.d7.loss_mask: 1.0183, decode.d7.loss_dice: 1.4486, decode.d8.loss_cls: 1.8247, decode.d8.loss_mask: 1.0150, decode.d8.loss_dice: 1.4488, loss: 52.1356 2022-05-04 21:44:14,910 - mmseg - INFO - Iter [3350/40000] lr: 1.316e-06, eta: 8:17:49, time: 0.821, data_time: 0.010, memory: 51557, decode.loss_cls: 1.8125, decode.loss_mask: 1.0257, decode.loss_dice: 1.4397, decode.d0.loss_cls: 9.6917, decode.d0.loss_mask: 1.0086, decode.d0.loss_dice: 1.7212, decode.d1.loss_cls: 2.3621, decode.d1.loss_mask: 1.0260, decode.d1.loss_dice: 1.5030, decode.d2.loss_cls: 1.9908, decode.d2.loss_mask: 1.0176, decode.d2.loss_dice: 1.4482, decode.d3.loss_cls: 1.8912, decode.d3.loss_mask: 1.0248, decode.d3.loss_dice: 1.4298, decode.d4.loss_cls: 1.8494, decode.d4.loss_mask: 1.0279, decode.d4.loss_dice: 1.4373, decode.d5.loss_cls: 1.8246, decode.d5.loss_mask: 1.0199, decode.d5.loss_dice: 1.4263, decode.d6.loss_cls: 1.8100, decode.d6.loss_mask: 1.0167, decode.d6.loss_dice: 1.4226, decode.d7.loss_cls: 1.8008, decode.d7.loss_mask: 1.0239, decode.d7.loss_dice: 1.4271, decode.d8.loss_cls: 1.7947, decode.d8.loss_mask: 1.0195, decode.d8.loss_dice: 1.4368, loss: 51.7304 2022-05-04 21:44:58,090 - mmseg - INFO - Iter [3400/40000] lr: 1.314e-06, eta: 8:17:36, time: 0.865, data_time: 0.063, memory: 51557, decode.loss_cls: 1.7458, decode.loss_mask: 0.9988, decode.loss_dice: 1.4627, decode.d0.loss_cls: 9.6551, decode.d0.loss_mask: 0.9917, decode.d0.loss_dice: 1.7413, decode.d1.loss_cls: 2.2921, decode.d1.loss_mask: 1.0027, decode.d1.loss_dice: 1.5251, decode.d2.loss_cls: 1.9228, decode.d2.loss_mask: 0.9943, decode.d2.loss_dice: 1.4763, decode.d3.loss_cls: 1.8224, decode.d3.loss_mask: 0.9981, decode.d3.loss_dice: 1.4481, decode.d4.loss_cls: 1.7988, decode.d4.loss_mask: 1.0010, decode.d4.loss_dice: 1.4649, decode.d5.loss_cls: 1.7599, decode.d5.loss_mask: 0.9964, decode.d5.loss_dice: 1.4637, decode.d6.loss_cls: 1.7504, decode.d6.loss_mask: 1.0075, decode.d6.loss_dice: 1.4559, decode.d7.loss_cls: 1.7377, decode.d7.loss_mask: 1.0089, decode.d7.loss_dice: 1.4588, decode.d8.loss_cls: 1.7273, decode.d8.loss_mask: 1.0075, decode.d8.loss_dice: 1.4606, loss: 51.1765 2022-05-04 21:45:37,474 - mmseg - INFO - Iter [3450/40000] lr: 1.312e-06, eta: 8:16:40, time: 0.788, data_time: 0.009, memory: 51557, decode.loss_cls: 1.7454, decode.loss_mask: 1.0139, decode.loss_dice: 1.4340, decode.d0.loss_cls: 9.6471, decode.d0.loss_mask: 0.9944, decode.d0.loss_dice: 1.6946, decode.d1.loss_cls: 2.2926, decode.d1.loss_mask: 1.0077, decode.d1.loss_dice: 1.4874, decode.d2.loss_cls: 1.9276, decode.d2.loss_mask: 1.0017, decode.d2.loss_dice: 1.4327, decode.d3.loss_cls: 1.8204, decode.d3.loss_mask: 0.9998, decode.d3.loss_dice: 1.4199, decode.d4.loss_cls: 1.7708, decode.d4.loss_mask: 1.0078, decode.d4.loss_dice: 1.4219, decode.d5.loss_cls: 1.7505, decode.d5.loss_mask: 1.0021, decode.d5.loss_dice: 1.4320, decode.d6.loss_cls: 1.7360, decode.d6.loss_mask: 1.0084, decode.d6.loss_dice: 1.4175, decode.d7.loss_cls: 1.7347, decode.d7.loss_mask: 1.0096, decode.d7.loss_dice: 1.4262, decode.d8.loss_cls: 1.7147, decode.d8.loss_mask: 1.0205, decode.d8.loss_dice: 1.4302, loss: 50.8022 2022-05-04 21:46:16,917 - mmseg - INFO - Iter [3500/40000] lr: 1.310e-06, eta: 8:15:45, time: 0.789, data_time: 0.009, memory: 51557, decode.loss_cls: 1.7610, decode.loss_mask: 1.0002, decode.loss_dice: 1.4194, decode.d0.loss_cls: 9.6387, decode.d0.loss_mask: 0.9789, decode.d0.loss_dice: 1.6991, decode.d1.loss_cls: 2.3072, decode.d1.loss_mask: 0.9894, decode.d1.loss_dice: 1.4850, decode.d2.loss_cls: 1.9271, decode.d2.loss_mask: 1.0009, decode.d2.loss_dice: 1.4368, decode.d3.loss_cls: 1.8473, decode.d3.loss_mask: 0.9945, decode.d3.loss_dice: 1.4085, decode.d4.loss_cls: 1.8017, decode.d4.loss_mask: 0.9896, decode.d4.loss_dice: 1.4186, decode.d5.loss_cls: 1.7876, decode.d5.loss_mask: 0.9920, decode.d5.loss_dice: 1.4128, decode.d6.loss_cls: 1.7601, decode.d6.loss_mask: 0.9980, decode.d6.loss_dice: 1.4135, decode.d7.loss_cls: 1.7562, decode.d7.loss_mask: 1.0029, decode.d7.loss_dice: 1.4206, decode.d8.loss_cls: 1.7447, decode.d8.loss_mask: 1.0022, decode.d8.loss_dice: 1.4225, loss: 50.8169 2022-05-04 21:46:55,800 - mmseg - INFO - Iter [3550/40000] lr: 1.308e-06, eta: 8:14:45, time: 0.778, data_time: 0.009, memory: 51557, decode.loss_cls: 1.7273, decode.loss_mask: 1.0117, decode.loss_dice: 1.4370, decode.d0.loss_cls: 9.6179, decode.d0.loss_mask: 0.9943, decode.d0.loss_dice: 1.7083, decode.d1.loss_cls: 2.2636, decode.d1.loss_mask: 1.0126, decode.d1.loss_dice: 1.4939, decode.d2.loss_cls: 1.8882, decode.d2.loss_mask: 1.0151, decode.d2.loss_dice: 1.4395, decode.d3.loss_cls: 1.7901, decode.d3.loss_mask: 1.0053, decode.d3.loss_dice: 1.4178, decode.d4.loss_cls: 1.7562, decode.d4.loss_mask: 1.0081, decode.d4.loss_dice: 1.4292, decode.d5.loss_cls: 1.7204, decode.d5.loss_mask: 1.0111, decode.d5.loss_dice: 1.4170, decode.d6.loss_cls: 1.7123, decode.d6.loss_mask: 1.0111, decode.d6.loss_dice: 1.4185, decode.d7.loss_cls: 1.7028, decode.d7.loss_mask: 1.0114, decode.d7.loss_dice: 1.4326, decode.d8.loss_cls: 1.6979, decode.d8.loss_mask: 1.0128, decode.d8.loss_dice: 1.4334, loss: 50.5973 2022-05-04 21:47:35,135 - mmseg - INFO - Iter [3600/40000] lr: 1.307e-06, eta: 8:13:50, time: 0.786, data_time: 0.011, memory: 51557, decode.loss_cls: 1.7216, decode.loss_mask: 0.9630, decode.loss_dice: 1.4248, decode.d0.loss_cls: 9.5869, decode.d0.loss_mask: 0.9548, decode.d0.loss_dice: 1.7201, decode.d1.loss_cls: 2.2489, decode.d1.loss_mask: 0.9719, decode.d1.loss_dice: 1.5067, decode.d2.loss_cls: 1.8891, decode.d2.loss_mask: 0.9637, decode.d2.loss_dice: 1.4459, decode.d3.loss_cls: 1.8054, decode.d3.loss_mask: 0.9575, decode.d3.loss_dice: 1.4288, decode.d4.loss_cls: 1.7559, decode.d4.loss_mask: 0.9671, decode.d4.loss_dice: 1.4331, decode.d5.loss_cls: 1.7420, decode.d5.loss_mask: 0.9575, decode.d5.loss_dice: 1.4219, decode.d6.loss_cls: 1.7207, decode.d6.loss_mask: 0.9619, decode.d6.loss_dice: 1.4184, decode.d7.loss_cls: 1.7205, decode.d7.loss_mask: 0.9668, decode.d7.loss_dice: 1.4174, decode.d8.loss_cls: 1.7111, decode.d8.loss_mask: 0.9649, decode.d8.loss_dice: 1.4175, loss: 50.1661 2022-05-04 21:48:15,095 - mmseg - INFO - Iter [3650/40000] lr: 1.305e-06, eta: 8:13:02, time: 0.799, data_time: 0.012, memory: 51557, decode.loss_cls: 1.7564, decode.loss_mask: 0.9768, decode.loss_dice: 1.4197, decode.d0.loss_cls: 9.5738, decode.d0.loss_mask: 0.9665, decode.d0.loss_dice: 1.6723, decode.d1.loss_cls: 2.2533, decode.d1.loss_mask: 0.9806, decode.d1.loss_dice: 1.4744, decode.d2.loss_cls: 1.9002, decode.d2.loss_mask: 0.9824, decode.d2.loss_dice: 1.4319, decode.d3.loss_cls: 1.8188, decode.d3.loss_mask: 0.9858, decode.d3.loss_dice: 1.4087, decode.d4.loss_cls: 1.7725, decode.d4.loss_mask: 0.9836, decode.d4.loss_dice: 1.4211, decode.d5.loss_cls: 1.7586, decode.d5.loss_mask: 0.9803, decode.d5.loss_dice: 1.4108, decode.d6.loss_cls: 1.7441, decode.d6.loss_mask: 0.9771, decode.d6.loss_dice: 1.4047, decode.d7.loss_cls: 1.7415, decode.d7.loss_mask: 0.9698, decode.d7.loss_dice: 1.4016, decode.d8.loss_cls: 1.7298, decode.d8.loss_mask: 0.9773, decode.d8.loss_dice: 1.4182, loss: 50.2927 2022-05-04 21:48:54,139 - mmseg - INFO - Iter [3700/40000] lr: 1.303e-06, eta: 8:12:06, time: 0.781, data_time: 0.009, memory: 51557, decode.loss_cls: 1.7342, decode.loss_mask: 0.9867, decode.loss_dice: 1.4225, decode.d0.loss_cls: 9.5533, decode.d0.loss_mask: 0.9711, decode.d0.loss_dice: 1.6996, decode.d1.loss_cls: 2.2194, decode.d1.loss_mask: 0.9920, decode.d1.loss_dice: 1.4965, decode.d2.loss_cls: 1.8693, decode.d2.loss_mask: 0.9842, decode.d2.loss_dice: 1.4356, decode.d3.loss_cls: 1.7889, decode.d3.loss_mask: 0.9780, decode.d3.loss_dice: 1.4138, decode.d4.loss_cls: 1.7616, decode.d4.loss_mask: 0.9794, decode.d4.loss_dice: 1.4203, decode.d5.loss_cls: 1.7427, decode.d5.loss_mask: 0.9785, decode.d5.loss_dice: 1.4124, decode.d6.loss_cls: 1.7344, decode.d6.loss_mask: 0.9746, decode.d6.loss_dice: 1.4122, decode.d7.loss_cls: 1.7309, decode.d7.loss_mask: 0.9859, decode.d7.loss_dice: 1.4161, decode.d8.loss_cls: 1.7188, decode.d8.loss_mask: 0.9867, decode.d8.loss_dice: 1.4223, loss: 50.2220 2022-05-04 21:49:33,376 - mmseg - INFO - Iter [3750/40000] lr: 1.301e-06, eta: 8:11:11, time: 0.785, data_time: 0.009, memory: 51557, decode.loss_cls: 1.6701, decode.loss_mask: 0.9999, decode.loss_dice: 1.4051, decode.d0.loss_cls: 9.5251, decode.d0.loss_mask: 0.9794, decode.d0.loss_dice: 1.6594, decode.d1.loss_cls: 2.1447, decode.d1.loss_mask: 1.0056, decode.d1.loss_dice: 1.4793, decode.d2.loss_cls: 1.8157, decode.d2.loss_mask: 0.9918, decode.d2.loss_dice: 1.4262, decode.d3.loss_cls: 1.7278, decode.d3.loss_mask: 0.9952, decode.d3.loss_dice: 1.4012, decode.d4.loss_cls: 1.6886, decode.d4.loss_mask: 0.9873, decode.d4.loss_dice: 1.3983, decode.d5.loss_cls: 1.6652, decode.d5.loss_mask: 0.9943, decode.d5.loss_dice: 1.3978, decode.d6.loss_cls: 1.6655, decode.d6.loss_mask: 1.0017, decode.d6.loss_dice: 1.4038, decode.d7.loss_cls: 1.6525, decode.d7.loss_mask: 1.0077, decode.d7.loss_dice: 1.4103, decode.d8.loss_cls: 1.6491, decode.d8.loss_mask: 0.9987, decode.d8.loss_dice: 1.4043, loss: 49.5516 2022-05-04 21:50:12,987 - mmseg - INFO - Iter [3800/40000] lr: 1.299e-06, eta: 8:10:21, time: 0.792, data_time: 0.009, memory: 51557, decode.loss_cls: 1.6637, decode.loss_mask: 1.0080, decode.loss_dice: 1.4040, decode.d0.loss_cls: 9.4984, decode.d0.loss_mask: 0.9913, decode.d0.loss_dice: 1.6952, decode.d1.loss_cls: 2.1566, decode.d1.loss_mask: 1.0233, decode.d1.loss_dice: 1.4892, decode.d2.loss_cls: 1.8149, decode.d2.loss_mask: 1.0163, decode.d2.loss_dice: 1.4317, decode.d3.loss_cls: 1.7385, decode.d3.loss_mask: 1.0122, decode.d3.loss_dice: 1.4051, decode.d4.loss_cls: 1.7035, decode.d4.loss_mask: 1.0129, decode.d4.loss_dice: 1.4115, decode.d5.loss_cls: 1.6825, decode.d5.loss_mask: 1.0098, decode.d5.loss_dice: 1.4035, decode.d6.loss_cls: 1.6700, decode.d6.loss_mask: 1.0106, decode.d6.loss_dice: 1.3900, decode.d7.loss_cls: 1.6678, decode.d7.loss_mask: 1.0060, decode.d7.loss_dice: 1.3968, decode.d8.loss_cls: 1.6467, decode.d8.loss_mask: 1.0111, decode.d8.loss_dice: 1.4087, loss: 49.7798 2022-05-04 21:50:52,044 - mmseg - INFO - Iter [3850/40000] lr: 1.298e-06, eta: 8:09:25, time: 0.781, data_time: 0.009, memory: 51557, decode.loss_cls: 1.6500, decode.loss_mask: 1.0284, decode.loss_dice: 1.3993, decode.d0.loss_cls: 9.4845, decode.d0.loss_mask: 1.0000, decode.d0.loss_dice: 1.6568, decode.d1.loss_cls: 2.1200, decode.d1.loss_mask: 1.0263, decode.d1.loss_dice: 1.4777, decode.d2.loss_cls: 1.7874, decode.d2.loss_mask: 1.0229, decode.d2.loss_dice: 1.4176, decode.d3.loss_cls: 1.7209, decode.d3.loss_mask: 1.0326, decode.d3.loss_dice: 1.4005, decode.d4.loss_cls: 1.6864, decode.d4.loss_mask: 1.0232, decode.d4.loss_dice: 1.4019, decode.d5.loss_cls: 1.6585, decode.d5.loss_mask: 1.0208, decode.d5.loss_dice: 1.3968, decode.d6.loss_cls: 1.6484, decode.d6.loss_mask: 1.0191, decode.d6.loss_dice: 1.3905, decode.d7.loss_cls: 1.6493, decode.d7.loss_mask: 1.0269, decode.d7.loss_dice: 1.4009, decode.d8.loss_cls: 1.6365, decode.d8.loss_mask: 1.0252, decode.d8.loss_dice: 1.3935, loss: 49.6029 2022-05-04 21:51:32,003 - mmseg - INFO - Iter [3900/40000] lr: 1.296e-06, eta: 8:08:38, time: 0.799, data_time: 0.009, memory: 51557, decode.loss_cls: 1.6628, decode.loss_mask: 0.9934, decode.loss_dice: 1.4150, decode.d0.loss_cls: 9.4536, decode.d0.loss_mask: 0.9771, decode.d0.loss_dice: 1.6837, decode.d1.loss_cls: 2.1229, decode.d1.loss_mask: 1.0004, decode.d1.loss_dice: 1.4722, decode.d2.loss_cls: 1.7830, decode.d2.loss_mask: 0.9968, decode.d2.loss_dice: 1.4277, decode.d3.loss_cls: 1.6976, decode.d3.loss_mask: 1.0018, decode.d3.loss_dice: 1.4067, decode.d4.loss_cls: 1.6748, decode.d4.loss_mask: 0.9993, decode.d4.loss_dice: 1.4120, decode.d5.loss_cls: 1.6558, decode.d5.loss_mask: 0.9998, decode.d5.loss_dice: 1.4115, decode.d6.loss_cls: 1.6523, decode.d6.loss_mask: 1.0005, decode.d6.loss_dice: 1.4002, decode.d7.loss_cls: 1.6554, decode.d7.loss_mask: 0.9994, decode.d7.loss_dice: 1.4163, decode.d8.loss_cls: 1.6504, decode.d8.loss_mask: 0.9992, decode.d8.loss_dice: 1.4161, loss: 49.4378 2022-05-04 21:52:13,956 - mmseg - INFO - Iter [3950/40000] lr: 1.294e-06, eta: 8:08:10, time: 0.839, data_time: 0.059, memory: 51557, decode.loss_cls: 1.6357, decode.loss_mask: 0.9944, decode.loss_dice: 1.4047, decode.d0.loss_cls: 9.4289, decode.d0.loss_mask: 0.9779, decode.d0.loss_dice: 1.6678, decode.d1.loss_cls: 2.0822, decode.d1.loss_mask: 0.9959, decode.d1.loss_dice: 1.4690, decode.d2.loss_cls: 1.7520, decode.d2.loss_mask: 0.9888, decode.d2.loss_dice: 1.4173, decode.d3.loss_cls: 1.6704, decode.d3.loss_mask: 0.9882, decode.d3.loss_dice: 1.4000, decode.d4.loss_cls: 1.6341, decode.d4.loss_mask: 0.9870, decode.d4.loss_dice: 1.4010, decode.d5.loss_cls: 1.6317, decode.d5.loss_mask: 0.9917, decode.d5.loss_dice: 1.3927, decode.d6.loss_cls: 1.6150, decode.d6.loss_mask: 0.9949, decode.d6.loss_dice: 1.3884, decode.d7.loss_cls: 1.6159, decode.d7.loss_mask: 0.9977, decode.d7.loss_dice: 1.4019, decode.d8.loss_cls: 1.6168, decode.d8.loss_mask: 0.9982, decode.d8.loss_dice: 1.4031, loss: 48.9435 2022-05-04 21:52:52,867 - mmseg - INFO - Saving checkpoint at 4000 iterations 2022-05-04 21:53:17,793 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-04 21:53:17,799 - mmseg - INFO - Iter [4000/40000] lr: 1.292e-06, eta: 8:10:58, time: 1.275, data_time: 0.010, memory: 51557, decode.loss_cls: 1.6393, decode.loss_mask: 0.9860, decode.loss_dice: 1.3862, decode.d0.loss_cls: 9.4206, decode.d0.loss_mask: 0.9755, decode.d0.loss_dice: 1.6598, decode.d1.loss_cls: 2.1058, decode.d1.loss_mask: 0.9876, decode.d1.loss_dice: 1.4536, decode.d2.loss_cls: 1.7852, decode.d2.loss_mask: 0.9711, decode.d2.loss_dice: 1.4008, decode.d3.loss_cls: 1.6852, decode.d3.loss_mask: 0.9828, decode.d3.loss_dice: 1.3890, decode.d4.loss_cls: 1.6578, decode.d4.loss_mask: 0.9852, decode.d4.loss_dice: 1.3902, decode.d5.loss_cls: 1.6384, decode.d5.loss_mask: 0.9798, decode.d5.loss_dice: 1.3840, decode.d6.loss_cls: 1.6347, decode.d6.loss_mask: 0.9790, decode.d6.loss_dice: 1.3713, decode.d7.loss_cls: 1.6258, decode.d7.loss_mask: 0.9819, decode.d7.loss_dice: 1.3802, decode.d8.loss_cls: 1.6262, decode.d8.loss_mask: 0.9811, decode.d8.loss_dice: 1.3778, loss: 48.8220 2022-05-04 21:54:26,740 - mmseg - INFO - per class results: 2022-05-04 21:54:26,751 - mmseg - INFO - +------------------+-------+-------+ | Class | IoU | Acc | +------------------+-------+-------+ | person | 86.65 | 92.38 | | bicycle | 66.91 | 89.55 | | car | 57.4 | 72.87 | | motorcycle | 88.36 | 94.39 | | airplane | 89.18 | 93.11 | | bus | 87.23 | 92.03 | | train | 85.79 | 94.54 | | truck | 61.0 | 79.81 | | boat | 75.22 | 83.86 | | traffic light | 55.35 | 92.94 | | fire hydrant | 86.0 | 97.25 | | stop sign | 81.77 | 93.17 | | parking meter | 65.57 | 66.48 | | bench | 41.97 | 61.91 | | bird | 68.57 | 81.51 | | cat | 92.57 | 95.44 | | dog | 91.22 | 95.93 | | horse | 90.58 | 95.16 | | sheep | 82.8 | 90.33 | | cow | 93.49 | 95.0 | | elephant | 91.62 | 94.44 | | bear | 91.73 | 92.95 | | zebra | 89.77 | 92.89 | | giraffe | 85.94 | 90.25 | | backpack | 14.9 | 52.41 | | umbrella | 63.51 | 93.14 | | handbag | 14.01 | 21.76 | | tie | 0.0 | 0.0 | | suitcase | 72.83 | 92.5 | | frisbee | 86.38 | 96.75 | | skis | 31.46 | 52.95 | | snowboard | 60.65 | 73.77 | | sports ball | 76.63 | 94.61 | | kite | 66.0 | 82.71 | | baseball bat | 58.45 | 86.69 | | baseball glove | 1.28 | 1.36 | | skateboard | 63.14 | 84.38 | | surfboard | 87.04 | 92.71 | | tennis racket | 84.52 | 94.48 | | bottle | 55.98 | 88.62 | | wine glass | 79.64 | 91.65 | | cup | 56.48 | 70.04 | | fork | 12.34 | 15.75 | | knife | 42.59 | 53.09 | | spoon | 22.09 | 33.92 | | bowl | 53.1 | 69.75 | | banana | 44.94 | 67.06 | | apple | 55.03 | 67.88 | | sandwich | 89.09 | 95.44 | | orange | 45.68 | 77.18 | | broccoli | 64.59 | 98.23 | | carrot | 12.65 | 12.77 | | hot dog | 73.25 | 84.59 | | pizza | 91.76 | 95.12 | | donut | 22.59 | 23.82 | | cake | 46.85 | 76.06 | | chair | 52.76 | 75.02 | | couch | 68.84 | 93.98 | | potted plant | 36.83 | 45.74 | | bed | 75.2 | 91.51 | | dining table | 51.35 | 80.53 | | toilet | 88.62 | 96.82 | | tv | 71.65 | 91.92 | | laptop | 74.61 | 91.95 | | mouse | 32.32 | 85.53 | | remote | 22.09 | 31.3 | | keyboard | 79.84 | 93.03 | | cell phone | 77.02 | 92.07 | | microwave | 37.14 | 49.71 | | oven | 49.55 | 84.11 | | toaster | 0.0 | 0.0 | | sink | 52.12 | 62.89 | | refrigerator | 59.81 | 63.26 | | book | 67.42 | 94.4 | | clock | 75.79 | 85.15 | | vase | 44.26 | 94.51 | | scissors | 0.04 | 0.04 | | teddy bear | 84.96 | 91.4 | | hair drier | 0.0 | 0.0 | | toothbrush | 0.0 | 0.0 | | banner | 33.73 | 52.2 | | blanket | 0.0 | 0.0 | | branch | 0.0 | 0.0 | | bridge | 0.0 | 0.0 | | building-other | 52.26 | 71.87 | | bush | 29.07 | 55.96 | | cabinet | 30.18 | 57.06 | | cage | 0.0 | 0.0 | | cardboard | 0.0 | 0.0 | | carpet | 54.74 | 84.83 | | ceiling-other | 62.41 | 86.41 | | ceiling-tile | 0.0 | 0.0 | | cloth | 0.0 | 0.0 | | clothes | 3.57 | 3.57 | | clouds | 58.66 | 89.2 | | counter | 38.86 | 45.33 | | cupboard | 52.76 | 75.51 | | curtain | 57.09 | 87.93 | | desk-stuff | 21.69 | 24.14 | | dirt | 36.5 | 65.28 | | door-stuff | 42.84 | 55.29 | | fence | 37.47 | 76.12 | | floor-marble | 0.0 | 0.0 | | floor-other | 38.18 | 50.44 | | floor-stone | 0.0 | 0.0 | | floor-tile | 59.6 | 69.84 | | floor-wood | 66.6 | 79.12 | | flower | 17.42 | 29.15 | | fog | 0.0 | 0.0 | | food-other | 9.46 | 19.04 | | fruit | 0.0 | 0.0 | | furniture-other | 11.56 | 15.25 | | grass | 72.8 | 85.31 | | gravel | 2.88 | 2.92 | | ground-other | 8.69 | 18.98 | | hill | 31.06 | 43.88 | | house | 31.42 | 47.0 | | leaves | 0.0 | 0.0 | | light | 36.31 | 56.93 | | mat | 0.0 | 0.0 | | metal | 0.0 | 0.0 | | mirror-stuff | 23.1 | 26.67 | | moss | 0.0 | 0.0 | | mountain | 37.06 | 62.38 | | mud | 0.0 | 0.0 | | napkin | 0.0 | 0.0 | | net | 15.8 | 15.96 | | paper | 28.06 | 43.44 | | pavement | 45.18 | 66.69 | | pillow | 0.0 | 0.0 | | plant-other | 11.99 | 13.47 | | plastic | 0.0 | 0.0 | | platform | 34.09 | 34.25 | | playingfield | 69.16 | 80.51 | | railing | 14.61 | 21.53 | | railroad | 54.94 | 94.31 | | river | 0.0 | 0.0 | | road | 66.36 | 80.7 | | rock | 53.1 | 70.17 | | roof | 0.38 | 0.39 | | rug | 48.58 | 53.34 | | salad | 0.0 | 0.0 | | sand | 72.97 | 88.57 | | sea | 69.35 | 95.94 | | shelf | 5.2 | 6.17 | | sky-other | 58.22 | 62.95 | | skyscraper | 0.0 | 0.0 | | snow | 90.98 | 94.64 | | solid-other | nan | nan | | stairs | 27.04 | 27.67 | | stone | 0.54 | 0.58 | | straw | 0.0 | 0.0 | | structural-other | 16.45 | 28.25 | | table | 15.01 | 24.29 | | tent | 0.0 | 0.0 | | textile-other | 6.13 | 7.93 | | towel | 22.26 | 37.42 | | tree | 78.01 | 88.53 | | vegetable | 9.02 | 11.73 | | wall-brick | 26.63 | 49.21 | | wall-concrete | 0.01 | 0.01 | | wall-other | 58.99 | 83.15 | | wall-panel | 0.0 | 0.0 | | wall-stone | 15.4 | 15.47 | | wall-tile | 53.2 | 85.68 | | wall-wood | 32.33 | 36.94 | | water-other | 20.99 | 30.3 | | waterdrops | nan | nan | | window-blind | 48.32 | 60.6 | | window-other | 42.9 | 54.46 | | wood | 15.65 | 24.79 | +------------------+-------+-------+ 2022-05-04 21:54:26,753 - mmseg - INFO - Summary: 2022-05-04 21:54:26,753 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 72.69 | 41.68 | 53.21 | +-------+-------+-------+ 2022-05-04 21:54:51,397 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_4000.pth. 2022-05-04 21:54:51,402 - mmseg - INFO - Best mIoU is 0.4168 at 4000 iter. 2022-05-04 21:54:51,411 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-04 21:54:51,412 - mmseg - INFO - Iter(val) [125] aAcc: 0.7269, mIoU: 0.4168, mAcc: 0.5321, IoU.person: 0.8665, IoU.bicycle: 0.6691, IoU.car: 0.5740, IoU.motorcycle: 0.8836, IoU.airplane: 0.8918, IoU.bus: 0.8723, IoU.train: 0.8579, IoU.truck: 0.6100, IoU.boat: 0.7522, IoU.traffic light: 0.5535, IoU.fire hydrant: 0.8600, IoU.stop sign: 0.8177, IoU.parking meter: 0.6557, IoU.bench: 0.4197, IoU.bird: 0.6857, IoU.cat: 0.9257, IoU.dog: 0.9122, IoU.horse: 0.9058, IoU.sheep: 0.8280, IoU.cow: 0.9349, IoU.elephant: 0.9162, IoU.bear: 0.9173, IoU.zebra: 0.8977, IoU.giraffe: 0.8594, IoU.backpack: 0.1490, IoU.umbrella: 0.6351, IoU.handbag: 0.1401, IoU.tie: 0.0000, IoU.suitcase: 0.7283, IoU.frisbee: 0.8638, IoU.skis: 0.3146, IoU.snowboard: 0.6065, IoU.sports ball: 0.7663, IoU.kite: 0.6600, IoU.baseball bat: 0.5845, IoU.baseball glove: 0.0128, IoU.skateboard: 0.6314, IoU.surfboard: 0.8704, IoU.tennis racket: 0.8452, IoU.bottle: 0.5598, IoU.wine glass: 0.7964, IoU.cup: 0.5648, IoU.fork: 0.1234, IoU.knife: 0.4259, IoU.spoon: 0.2209, IoU.bowl: 0.5310, IoU.banana: 0.4494, IoU.apple: 0.5503, IoU.sandwich: 0.8909, IoU.orange: 0.4568, IoU.broccoli: 0.6459, IoU.carrot: 0.1265, IoU.hot dog: 0.7325, IoU.pizza: 0.9176, IoU.donut: 0.2259, IoU.cake: 0.4685, IoU.chair: 0.5276, IoU.couch: 0.6884, IoU.potted plant: 0.3683, IoU.bed: 0.7520, IoU.dining table: 0.5135, IoU.toilet: 0.8862, IoU.tv: 0.7165, IoU.laptop: 0.7461, IoU.mouse: 0.3232, IoU.remote: 0.2209, IoU.keyboard: 0.7984, IoU.cell phone: 0.7702, IoU.microwave: 0.3714, IoU.oven: 0.4955, IoU.toaster: 0.0000, IoU.sink: 0.5212, IoU.refrigerator: 0.5981, IoU.book: 0.6742, IoU.clock: 0.7579, IoU.vase: 0.4426, IoU.scissors: 0.0004, IoU.teddy bear: 0.8496, IoU.hair drier: 0.0000, IoU.toothbrush: 0.0000, IoU.banner: 0.3373, IoU.blanket: 0.0000, IoU.branch: 0.0000, IoU.bridge: 0.0000, IoU.building-other: 0.5226, IoU.bush: 0.2907, IoU.cabinet: 0.3018, IoU.cage: 0.0000, IoU.cardboard: 0.0000, IoU.carpet: 0.5474, IoU.ceiling-other: 0.6241, IoU.ceiling-tile: 0.0000, IoU.cloth: 0.0000, IoU.clothes: 0.0357, IoU.clouds: 0.5866, IoU.counter: 0.3886, IoU.cupboard: 0.5276, IoU.curtain: 0.5709, IoU.desk-stuff: 0.2169, IoU.dirt: 0.3650, IoU.door-stuff: 0.4284, IoU.fence: 0.3747, IoU.floor-marble: 0.0000, IoU.floor-other: 0.3818, IoU.floor-stone: 0.0000, IoU.floor-tile: 0.5960, IoU.floor-wood: 0.6660, IoU.flower: 0.1742, IoU.fog: 0.0000, IoU.food-other: 0.0946, IoU.fruit: 0.0000, IoU.furniture-other: 0.1156, IoU.grass: 0.7280, IoU.gravel: 0.0288, IoU.ground-other: 0.0869, IoU.hill: 0.3106, IoU.house: 0.3142, IoU.leaves: 0.0000, IoU.light: 0.3631, IoU.mat: 0.0000, IoU.metal: 0.0000, IoU.mirror-stuff: 0.2310, IoU.moss: 0.0000, IoU.mountain: 0.3706, IoU.mud: 0.0000, IoU.napkin: 0.0000, IoU.net: 0.1580, IoU.paper: 0.2806, IoU.pavement: 0.4518, IoU.pillow: 0.0000, IoU.plant-other: 0.1199, IoU.plastic: 0.0000, IoU.platform: 0.3409, IoU.playingfield: 0.6916, IoU.railing: 0.1461, IoU.railroad: 0.5494, IoU.river: 0.0000, IoU.road: 0.6636, IoU.rock: 0.5310, IoU.roof: 0.0038, IoU.rug: 0.4858, IoU.salad: 0.0000, IoU.sand: 0.7297, IoU.sea: 0.6935, IoU.shelf: 0.0520, IoU.sky-other: 0.5822, IoU.skyscraper: 0.0000, IoU.snow: 0.9098, IoU.solid-other: nan, IoU.stairs: 0.2704, IoU.stone: 0.0054, IoU.straw: 0.0000, IoU.structural-other: 0.1645, IoU.table: 0.1501, IoU.tent: 0.0000, IoU.textile-other: 0.0613, IoU.towel: 0.2226, IoU.tree: 0.7801, IoU.vegetable: 0.0902, IoU.wall-brick: 0.2663, IoU.wall-concrete: 0.0001, IoU.wall-other: 0.5899, IoU.wall-panel: 0.0000, IoU.wall-stone: 0.1540, IoU.wall-tile: 0.5320, IoU.wall-wood: 0.3233, IoU.water-other: 0.2099, IoU.waterdrops: nan, IoU.window-blind: 0.4832, IoU.window-other: 0.4290, IoU.wood: 0.1565, Acc.person: 0.9238, Acc.bicycle: 0.8955, Acc.car: 0.7287, Acc.motorcycle: 0.9439, Acc.airplane: 0.9311, Acc.bus: 0.9203, Acc.train: 0.9454, Acc.truck: 0.7981, Acc.boat: 0.8386, Acc.traffic light: 0.9294, Acc.fire hydrant: 0.9725, Acc.stop sign: 0.9317, Acc.parking meter: 0.6648, Acc.bench: 0.6191, Acc.bird: 0.8151, Acc.cat: 0.9544, Acc.dog: 0.9593, Acc.horse: 0.9516, Acc.sheep: 0.9033, Acc.cow: 0.9500, Acc.elephant: 0.9444, Acc.bear: 0.9295, Acc.zebra: 0.9289, Acc.giraffe: 0.9025, Acc.backpack: 0.5241, Acc.umbrella: 0.9314, Acc.handbag: 0.2176, Acc.tie: 0.0000, Acc.suitcase: 0.9250, Acc.frisbee: 0.9675, Acc.skis: 0.5295, Acc.snowboard: 0.7377, Acc.sports ball: 0.9461, Acc.kite: 0.8271, Acc.baseball bat: 0.8669, Acc.baseball glove: 0.0136, Acc.skateboard: 0.8438, Acc.surfboard: 0.9271, Acc.tennis racket: 0.9448, Acc.bottle: 0.8862, Acc.wine glass: 0.9165, Acc.cup: 0.7004, Acc.fork: 0.1575, Acc.knife: 0.5309, Acc.spoon: 0.3392, Acc.bowl: 0.6975, Acc.banana: 0.6706, Acc.apple: 0.6788, Acc.sandwich: 0.9544, Acc.orange: 0.7718, Acc.broccoli: 0.9823, Acc.carrot: 0.1277, Acc.hot dog: 0.8459, Acc.pizza: 0.9512, Acc.donut: 0.2382, Acc.cake: 0.7606, Acc.chair: 0.7502, Acc.couch: 0.9398, Acc.potted plant: 0.4574, Acc.bed: 0.9151, Acc.dining table: 0.8053, Acc.toilet: 0.9682, Acc.tv: 0.9192, Acc.laptop: 0.9195, Acc.mouse: 0.8553, Acc.remote: 0.3130, Acc.keyboard: 0.9303, Acc.cell phone: 0.9207, Acc.microwave: 0.4971, Acc.oven: 0.8411, Acc.toaster: 0.0000, Acc.sink: 0.6289, Acc.refrigerator: 0.6326, Acc.book: 0.9440, Acc.clock: 0.8515, Acc.vase: 0.9451, Acc.scissors: 0.0004, Acc.teddy bear: 0.9140, Acc.hair drier: 0.0000, Acc.toothbrush: 0.0000, Acc.banner: 0.5220, Acc.blanket: 0.0000, Acc.branch: 0.0000, Acc.bridge: 0.0000, Acc.building-other: 0.7187, Acc.bush: 0.5596, Acc.cabinet: 0.5706, Acc.cage: 0.0000, Acc.cardboard: 0.0000, Acc.carpet: 0.8483, Acc.ceiling-other: 0.8641, Acc.ceiling-tile: 0.0000, Acc.cloth: 0.0000, Acc.clothes: 0.0357, Acc.clouds: 0.8920, Acc.counter: 0.4533, Acc.cupboard: 0.7551, Acc.curtain: 0.8793, Acc.desk-stuff: 0.2414, Acc.dirt: 0.6528, Acc.door-stuff: 0.5529, Acc.fence: 0.7612, Acc.floor-marble: 0.0000, Acc.floor-other: 0.5044, Acc.floor-stone: 0.0000, Acc.floor-tile: 0.6984, Acc.floor-wood: 0.7912, Acc.flower: 0.2915, Acc.fog: 0.0000, Acc.food-other: 0.1904, Acc.fruit: 0.0000, Acc.furniture-other: 0.1525, Acc.grass: 0.8531, Acc.gravel: 0.0292, Acc.ground-other: 0.1898, Acc.hill: 0.4388, Acc.house: 0.4700, Acc.leaves: 0.0000, Acc.light: 0.5693, Acc.mat: 0.0000, Acc.metal: 0.0000, Acc.mirror-stuff: 0.2667, Acc.moss: 0.0000, Acc.mountain: 0.6238, Acc.mud: 0.0000, Acc.napkin: 0.0000, Acc.net: 0.1596, Acc.paper: 0.4344, Acc.pavement: 0.6669, Acc.pillow: 0.0000, Acc.plant-other: 0.1347, Acc.plastic: 0.0000, Acc.platform: 0.3425, Acc.playingfield: 0.8051, Acc.railing: 0.2153, Acc.railroad: 0.9431, Acc.river: 0.0000, Acc.road: 0.8070, Acc.rock: 0.7017, Acc.roof: 0.0039, Acc.rug: 0.5334, Acc.salad: 0.0000, Acc.sand: 0.8857, Acc.sea: 0.9594, Acc.shelf: 0.0617, Acc.sky-other: 0.6295, Acc.skyscraper: 0.0000, Acc.snow: 0.9464, Acc.solid-other: nan, Acc.stairs: 0.2767, Acc.stone: 0.0058, Acc.straw: 0.0000, Acc.structural-other: 0.2825, Acc.table: 0.2429, Acc.tent: 0.0000, Acc.textile-other: 0.0793, Acc.towel: 0.3742, Acc.tree: 0.8853, Acc.vegetable: 0.1173, Acc.wall-brick: 0.4921, Acc.wall-concrete: 0.0001, Acc.wall-other: 0.8315, Acc.wall-panel: 0.0000, Acc.wall-stone: 0.1547, Acc.wall-tile: 0.8568, Acc.wall-wood: 0.3694, Acc.water-other: 0.3030, Acc.waterdrops: nan, Acc.window-blind: 0.6060, Acc.window-other: 0.5446, Acc.wood: 0.2479 2022-05-04 21:55:32,035 - mmseg - INFO - Iter [4050/40000] lr: 1.290e-06, eta: 8:24:06, time: 2.686, data_time: 1.885, memory: 51557, decode.loss_cls: 1.5863, decode.loss_mask: 0.9835, decode.loss_dice: 1.3596, decode.d0.loss_cls: 9.3776, decode.d0.loss_mask: 0.9722, decode.d0.loss_dice: 1.6471, decode.d1.loss_cls: 2.0291, decode.d1.loss_mask: 0.9981, decode.d1.loss_dice: 1.4470, decode.d2.loss_cls: 1.7047, decode.d2.loss_mask: 0.9912, decode.d2.loss_dice: 1.3943, decode.d3.loss_cls: 1.6392, decode.d3.loss_mask: 0.9900, decode.d3.loss_dice: 1.3617, decode.d4.loss_cls: 1.6073, decode.d4.loss_mask: 0.9888, decode.d4.loss_dice: 1.3659, decode.d5.loss_cls: 1.5901, decode.d5.loss_mask: 0.9810, decode.d5.loss_dice: 1.3615, decode.d6.loss_cls: 1.5865, decode.d6.loss_mask: 0.9814, decode.d6.loss_dice: 1.3547, decode.d7.loss_cls: 1.5763, decode.d7.loss_mask: 0.9783, decode.d7.loss_dice: 1.3614, decode.d8.loss_cls: 1.5669, decode.d8.loss_mask: 0.9835, decode.d8.loss_dice: 1.3600, loss: 48.1250 2022-05-04 21:56:11,369 - mmseg - INFO - Iter [4100/40000] lr: 1.289e-06, eta: 8:23:00, time: 0.787, data_time: 0.009, memory: 51557, decode.loss_cls: 1.6116, decode.loss_mask: 0.9816, decode.loss_dice: 1.4185, decode.d0.loss_cls: 9.3633, decode.d0.loss_mask: 0.9506, decode.d0.loss_dice: 1.6530, decode.d1.loss_cls: 2.0277, decode.d1.loss_mask: 0.9878, decode.d1.loss_dice: 1.4778, decode.d2.loss_cls: 1.7087, decode.d2.loss_mask: 0.9788, decode.d2.loss_dice: 1.4336, decode.d3.loss_cls: 1.6490, decode.d3.loss_mask: 0.9784, decode.d3.loss_dice: 1.4105, decode.d4.loss_cls: 1.6160, decode.d4.loss_mask: 0.9846, decode.d4.loss_dice: 1.4276, decode.d5.loss_cls: 1.6029, decode.d5.loss_mask: 0.9811, decode.d5.loss_dice: 1.4234, decode.d6.loss_cls: 1.5955, decode.d6.loss_mask: 0.9778, decode.d6.loss_dice: 1.4055, decode.d7.loss_cls: 1.6030, decode.d7.loss_mask: 0.9797, decode.d7.loss_dice: 1.4114, decode.d8.loss_cls: 1.5875, decode.d8.loss_mask: 0.9853, decode.d8.loss_dice: 1.4160, loss: 48.6282 2022-05-04 21:56:50,334 - mmseg - INFO - Iter [4150/40000] lr: 1.287e-06, eta: 8:21:51, time: 0.779, data_time: 0.009, memory: 51557, decode.loss_cls: 1.5970, decode.loss_mask: 0.9752, decode.loss_dice: 1.3522, decode.d0.loss_cls: 9.3471, decode.d0.loss_mask: 0.9710, decode.d0.loss_dice: 1.6151, decode.d1.loss_cls: 2.0222, decode.d1.loss_mask: 0.9825, decode.d1.loss_dice: 1.4237, decode.d2.loss_cls: 1.7156, decode.d2.loss_mask: 0.9671, decode.d2.loss_dice: 1.3678, decode.d3.loss_cls: 1.6489, decode.d3.loss_mask: 0.9645, decode.d3.loss_dice: 1.3458, decode.d4.loss_cls: 1.6164, decode.d4.loss_mask: 0.9610, decode.d4.loss_dice: 1.3497, decode.d5.loss_cls: 1.5984, decode.d5.loss_mask: 0.9673, decode.d5.loss_dice: 1.3433, decode.d6.loss_cls: 1.5917, decode.d6.loss_mask: 0.9667, decode.d6.loss_dice: 1.3418, decode.d7.loss_cls: 1.5806, decode.d7.loss_mask: 0.9731, decode.d7.loss_dice: 1.3518, decode.d8.loss_cls: 1.5816, decode.d8.loss_mask: 0.9684, decode.d8.loss_dice: 1.3516, loss: 47.8390 2022-05-04 21:57:29,411 - mmseg - INFO - Iter [4200/40000] lr: 1.285e-06, eta: 8:20:44, time: 0.782, data_time: 0.010, memory: 51557, decode.loss_cls: 1.5868, decode.loss_mask: 0.9673, decode.loss_dice: 1.3563, decode.d0.loss_cls: 9.3237, decode.d0.loss_mask: 0.9457, decode.d0.loss_dice: 1.6198, decode.d1.loss_cls: 2.0106, decode.d1.loss_mask: 0.9770, decode.d1.loss_dice: 1.4398, decode.d2.loss_cls: 1.7009, decode.d2.loss_mask: 0.9616, decode.d2.loss_dice: 1.3931, decode.d3.loss_cls: 1.6299, decode.d3.loss_mask: 0.9693, decode.d3.loss_dice: 1.3597, decode.d4.loss_cls: 1.6098, decode.d4.loss_mask: 0.9692, decode.d4.loss_dice: 1.3693, decode.d5.loss_cls: 1.5840, decode.d5.loss_mask: 0.9592, decode.d5.loss_dice: 1.3617, decode.d6.loss_cls: 1.5732, decode.d6.loss_mask: 0.9641, decode.d6.loss_dice: 1.3634, decode.d7.loss_cls: 1.5749, decode.d7.loss_mask: 0.9640, decode.d7.loss_dice: 1.3668, decode.d8.loss_cls: 1.5685, decode.d8.loss_mask: 0.9678, decode.d8.loss_dice: 1.3581, loss: 47.7957 2022-05-04 21:58:09,465 - mmseg - INFO - Iter [4250/40000] lr: 1.283e-06, eta: 8:19:46, time: 0.801, data_time: 0.009, memory: 51557, decode.loss_cls: 1.6116, decode.loss_mask: 0.9906, decode.loss_dice: 1.3738, decode.d0.loss_cls: 9.3135, decode.d0.loss_mask: 0.9588, decode.d0.loss_dice: 1.6353, decode.d1.loss_cls: 2.0263, decode.d1.loss_mask: 0.9942, decode.d1.loss_dice: 1.4515, decode.d2.loss_cls: 1.7319, decode.d2.loss_mask: 0.9904, decode.d2.loss_dice: 1.3994, decode.d3.loss_cls: 1.6644, decode.d3.loss_mask: 0.9812, decode.d3.loss_dice: 1.3738, decode.d4.loss_cls: 1.6462, decode.d4.loss_mask: 0.9780, decode.d4.loss_dice: 1.3818, decode.d5.loss_cls: 1.6049, decode.d5.loss_mask: 0.9861, decode.d5.loss_dice: 1.3755, decode.d6.loss_cls: 1.6018, decode.d6.loss_mask: 0.9861, decode.d6.loss_dice: 1.3563, decode.d7.loss_cls: 1.5974, decode.d7.loss_mask: 0.9880, decode.d7.loss_dice: 1.3696, decode.d8.loss_cls: 1.5892, decode.d8.loss_mask: 0.9862, decode.d8.loss_dice: 1.3815, loss: 48.3255 2022-05-04 21:58:49,041 - mmseg - INFO - Iter [4300/40000] lr: 1.281e-06, eta: 8:18:45, time: 0.792, data_time: 0.009, memory: 51557, decode.loss_cls: 1.5656, decode.loss_mask: 0.9815, decode.loss_dice: 1.4031, decode.d0.loss_cls: 9.2732, decode.d0.loss_mask: 0.9531, decode.d0.loss_dice: 1.6309, decode.d1.loss_cls: 1.9754, decode.d1.loss_mask: 0.9876, decode.d1.loss_dice: 1.4692, decode.d2.loss_cls: 1.6691, decode.d2.loss_mask: 0.9710, decode.d2.loss_dice: 1.4202, decode.d3.loss_cls: 1.5980, decode.d3.loss_mask: 0.9820, decode.d3.loss_dice: 1.3979, decode.d4.loss_cls: 1.5753, decode.d4.loss_mask: 0.9763, decode.d4.loss_dice: 1.3982, decode.d5.loss_cls: 1.5582, decode.d5.loss_mask: 0.9734, decode.d5.loss_dice: 1.4010, decode.d6.loss_cls: 1.5582, decode.d6.loss_mask: 0.9746, decode.d6.loss_dice: 1.3899, decode.d7.loss_cls: 1.5526, decode.d7.loss_mask: 0.9780, decode.d7.loss_dice: 1.3973, decode.d8.loss_cls: 1.5477, decode.d8.loss_mask: 0.9784, decode.d8.loss_dice: 1.3965, loss: 47.9337 2022-05-04 21:59:27,888 - mmseg - INFO - Iter [4350/40000] lr: 1.280e-06, eta: 8:17:38, time: 0.777, data_time: 0.010, memory: 51557, decode.loss_cls: 1.5258, decode.loss_mask: 0.9556, decode.loss_dice: 1.3658, decode.d0.loss_cls: 9.2473, decode.d0.loss_mask: 0.9517, decode.d0.loss_dice: 1.6086, decode.d1.loss_cls: 1.9391, decode.d1.loss_mask: 0.9667, decode.d1.loss_dice: 1.4417, decode.d2.loss_cls: 1.6435, decode.d2.loss_mask: 0.9642, decode.d2.loss_dice: 1.3853, decode.d3.loss_cls: 1.5818, decode.d3.loss_mask: 0.9619, decode.d3.loss_dice: 1.3665, decode.d4.loss_cls: 1.5513, decode.d4.loss_mask: 0.9569, decode.d4.loss_dice: 1.3624, decode.d5.loss_cls: 1.5365, decode.d5.loss_mask: 0.9575, decode.d5.loss_dice: 1.3608, decode.d6.loss_cls: 1.5232, decode.d6.loss_mask: 0.9627, decode.d6.loss_dice: 1.3495, decode.d7.loss_cls: 1.5155, decode.d7.loss_mask: 0.9678, decode.d7.loss_dice: 1.3615, decode.d8.loss_cls: 1.5160, decode.d8.loss_mask: 0.9602, decode.d8.loss_dice: 1.3647, loss: 47.1518 2022-05-04 22:00:07,227 - mmseg - INFO - Iter [4400/40000] lr: 1.278e-06, eta: 8:16:35, time: 0.787, data_time: 0.010, memory: 51557, decode.loss_cls: 1.5541, decode.loss_mask: 0.9710, decode.loss_dice: 1.3678, decode.d0.loss_cls: 9.2204, decode.d0.loss_mask: 0.9423, decode.d0.loss_dice: 1.6094, decode.d1.loss_cls: 1.9544, decode.d1.loss_mask: 0.9750, decode.d1.loss_dice: 1.4358, decode.d2.loss_cls: 1.6555, decode.d2.loss_mask: 0.9652, decode.d2.loss_dice: 1.3820, decode.d3.loss_cls: 1.5843, decode.d3.loss_mask: 0.9619, decode.d3.loss_dice: 1.3750, decode.d4.loss_cls: 1.5636, decode.d4.loss_mask: 0.9653, decode.d4.loss_dice: 1.3718, decode.d5.loss_cls: 1.5495, decode.d5.loss_mask: 0.9664, decode.d5.loss_dice: 1.3620, decode.d6.loss_cls: 1.5409, decode.d6.loss_mask: 0.9641, decode.d6.loss_dice: 1.3602, decode.d7.loss_cls: 1.5387, decode.d7.loss_mask: 0.9694, decode.d7.loss_dice: 1.3672, decode.d8.loss_cls: 1.5265, decode.d8.loss_mask: 0.9706, decode.d8.loss_dice: 1.3690, loss: 47.3392 2022-05-04 22:00:46,277 - mmseg - INFO - Iter [4450/40000] lr: 1.276e-06, eta: 8:15:31, time: 0.780, data_time: 0.009, memory: 51557, decode.loss_cls: 1.5036, decode.loss_mask: 0.9738, decode.loss_dice: 1.3603, decode.d0.loss_cls: 9.2213, decode.d0.loss_mask: 0.9496, decode.d0.loss_dice: 1.6156, decode.d1.loss_cls: 1.9145, decode.d1.loss_mask: 0.9709, decode.d1.loss_dice: 1.4309, decode.d2.loss_cls: 1.6183, decode.d2.loss_mask: 0.9544, decode.d2.loss_dice: 1.3753, decode.d3.loss_cls: 1.5396, decode.d3.loss_mask: 0.9616, decode.d3.loss_dice: 1.3618, decode.d4.loss_cls: 1.5349, decode.d4.loss_mask: 0.9520, decode.d4.loss_dice: 1.3640, decode.d5.loss_cls: 1.5116, decode.d5.loss_mask: 0.9618, decode.d5.loss_dice: 1.3601, decode.d6.loss_cls: 1.4920, decode.d6.loss_mask: 0.9712, decode.d6.loss_dice: 1.3544, decode.d7.loss_cls: 1.4973, decode.d7.loss_mask: 0.9778, decode.d7.loss_dice: 1.3600, decode.d8.loss_cls: 1.4852, decode.d8.loss_mask: 0.9699, decode.d8.loss_dice: 1.3681, loss: 46.9117 2022-05-04 22:01:29,137 - mmseg - INFO - Iter [4500/40000] lr: 1.274e-06, eta: 8:14:58, time: 0.858, data_time: 0.061, memory: 51557, decode.loss_cls: 1.5516, decode.loss_mask: 0.9916, decode.loss_dice: 1.3819, decode.d0.loss_cls: 9.1924, decode.d0.loss_mask: 0.9591, decode.d0.loss_dice: 1.6451, decode.d1.loss_cls: 1.9603, decode.d1.loss_mask: 0.9902, decode.d1.loss_dice: 1.4641, decode.d2.loss_cls: 1.6740, decode.d2.loss_mask: 0.9818, decode.d2.loss_dice: 1.4025, decode.d3.loss_cls: 1.6013, decode.d3.loss_mask: 0.9856, decode.d3.loss_dice: 1.3821, decode.d4.loss_cls: 1.5816, decode.d4.loss_mask: 0.9823, decode.d4.loss_dice: 1.3904, decode.d5.loss_cls: 1.5580, decode.d5.loss_mask: 0.9770, decode.d5.loss_dice: 1.3835, decode.d6.loss_cls: 1.5540, decode.d6.loss_mask: 0.9740, decode.d6.loss_dice: 1.3631, decode.d7.loss_cls: 1.5480, decode.d7.loss_mask: 0.9820, decode.d7.loss_dice: 1.3845, decode.d8.loss_cls: 1.5420, decode.d8.loss_mask: 0.9877, decode.d8.loss_dice: 1.3805, loss: 47.7523 2022-05-04 22:02:08,638 - mmseg - INFO - Iter [4550/40000] lr: 1.273e-06, eta: 8:13:58, time: 0.790, data_time: 0.009, memory: 51557, decode.loss_cls: 1.5456, decode.loss_mask: 0.9506, decode.loss_dice: 1.3639, decode.d0.loss_cls: 9.1703, decode.d0.loss_mask: 0.9268, decode.d0.loss_dice: 1.6112, decode.d1.loss_cls: 1.9323, decode.d1.loss_mask: 0.9437, decode.d1.loss_dice: 1.4371, decode.d2.loss_cls: 1.6405, decode.d2.loss_mask: 0.9429, decode.d2.loss_dice: 1.3957, decode.d3.loss_cls: 1.5749, decode.d3.loss_mask: 0.9490, decode.d3.loss_dice: 1.3734, decode.d4.loss_cls: 1.5467, decode.d4.loss_mask: 0.9507, decode.d4.loss_dice: 1.3789, decode.d5.loss_cls: 1.5320, decode.d5.loss_mask: 0.9480, decode.d5.loss_dice: 1.3670, decode.d6.loss_cls: 1.5213, decode.d6.loss_mask: 0.9478, decode.d6.loss_dice: 1.3584, decode.d7.loss_cls: 1.5311, decode.d7.loss_mask: 0.9462, decode.d7.loss_dice: 1.3692, decode.d8.loss_cls: 1.5198, decode.d8.loss_mask: 0.9496, decode.d8.loss_dice: 1.3719, loss: 46.9965 2022-05-04 22:02:48,477 - mmseg - INFO - Iter [4600/40000] lr: 1.271e-06, eta: 8:13:01, time: 0.797, data_time: 0.009, memory: 51557, decode.loss_cls: 1.4166, decode.loss_mask: 0.9514, decode.loss_dice: 1.3072, decode.d0.loss_cls: 9.1099, decode.d0.loss_mask: 0.9301, decode.d0.loss_dice: 1.5436, decode.d1.loss_cls: 1.7788, decode.d1.loss_mask: 0.9490, decode.d1.loss_dice: 1.3740, decode.d2.loss_cls: 1.5060, decode.d2.loss_mask: 0.9510, decode.d2.loss_dice: 1.3253, decode.d3.loss_cls: 1.4434, decode.d3.loss_mask: 0.9507, decode.d3.loss_dice: 1.3131, decode.d4.loss_cls: 1.4158, decode.d4.loss_mask: 0.9446, decode.d4.loss_dice: 1.3255, decode.d5.loss_cls: 1.4143, decode.d5.loss_mask: 0.9490, decode.d5.loss_dice: 1.3082, decode.d6.loss_cls: 1.4017, decode.d6.loss_mask: 0.9554, decode.d6.loss_dice: 1.3022, decode.d7.loss_cls: 1.4102, decode.d7.loss_mask: 0.9495, decode.d7.loss_dice: 1.3133, decode.d8.loss_cls: 1.4020, decode.d8.loss_mask: 0.9514, decode.d8.loss_dice: 1.3096, loss: 45.2030 2022-05-04 22:03:27,516 - mmseg - INFO - Iter [4650/40000] lr: 1.269e-06, eta: 8:11:58, time: 0.781, data_time: 0.010, memory: 51557, decode.loss_cls: 1.4944, decode.loss_mask: 0.9733, decode.loss_dice: 1.3472, decode.d0.loss_cls: 9.1198, decode.d0.loss_mask: 0.9478, decode.d0.loss_dice: 1.5951, decode.d1.loss_cls: 1.8841, decode.d1.loss_mask: 0.9676, decode.d1.loss_dice: 1.4009, decode.d2.loss_cls: 1.5776, decode.d2.loss_mask: 0.9661, decode.d2.loss_dice: 1.3677, decode.d3.loss_cls: 1.5341, decode.d3.loss_mask: 0.9670, decode.d3.loss_dice: 1.3527, decode.d4.loss_cls: 1.5093, decode.d4.loss_mask: 0.9677, decode.d4.loss_dice: 1.3473, decode.d5.loss_cls: 1.4949, decode.d5.loss_mask: 0.9723, decode.d5.loss_dice: 1.3406, decode.d6.loss_cls: 1.4894, decode.d6.loss_mask: 0.9653, decode.d6.loss_dice: 1.3335, decode.d7.loss_cls: 1.4881, decode.d7.loss_mask: 0.9663, decode.d7.loss_dice: 1.3446, decode.d8.loss_cls: 1.4826, decode.d8.loss_mask: 0.9681, decode.d8.loss_dice: 1.3443, loss: 46.5098 2022-05-04 22:04:07,241 - mmseg - INFO - Iter [4700/40000] lr: 1.267e-06, eta: 8:11:01, time: 0.795, data_time: 0.009, memory: 51557, decode.loss_cls: 1.5164, decode.loss_mask: 0.9407, decode.loss_dice: 1.3151, decode.d0.loss_cls: 9.0943, decode.d0.loss_mask: 0.9263, decode.d0.loss_dice: 1.5587, decode.d1.loss_cls: 1.8908, decode.d1.loss_mask: 0.9545, decode.d1.loss_dice: 1.3909, decode.d2.loss_cls: 1.6186, decode.d2.loss_mask: 0.9462, decode.d2.loss_dice: 1.3414, decode.d3.loss_cls: 1.5633, decode.d3.loss_mask: 0.9460, decode.d3.loss_dice: 1.3104, decode.d4.loss_cls: 1.5417, decode.d4.loss_mask: 0.9418, decode.d4.loss_dice: 1.3121, decode.d5.loss_cls: 1.5273, decode.d5.loss_mask: 0.9425, decode.d5.loss_dice: 1.3157, decode.d6.loss_cls: 1.5145, decode.d6.loss_mask: 0.9382, decode.d6.loss_dice: 1.3078, decode.d7.loss_cls: 1.5094, decode.d7.loss_mask: 0.9378, decode.d7.loss_dice: 1.3096, decode.d8.loss_cls: 1.5033, decode.d8.loss_mask: 0.9364, decode.d8.loss_dice: 1.3203, loss: 46.1720 2022-05-04 22:04:46,822 - mmseg - INFO - Iter [4750/40000] lr: 1.265e-06, eta: 8:10:03, time: 0.791, data_time: 0.009, memory: 51557, decode.loss_cls: 1.4479, decode.loss_mask: 0.9808, decode.loss_dice: 1.3293, decode.d0.loss_cls: 9.0540, decode.d0.loss_mask: 0.9576, decode.d0.loss_dice: 1.5727, decode.d1.loss_cls: 1.8163, decode.d1.loss_mask: 0.9811, decode.d1.loss_dice: 1.4041, decode.d2.loss_cls: 1.5384, decode.d2.loss_mask: 0.9798, decode.d2.loss_dice: 1.3529, decode.d3.loss_cls: 1.4880, decode.d3.loss_mask: 0.9738, decode.d3.loss_dice: 1.3300, decode.d4.loss_cls: 1.4621, decode.d4.loss_mask: 0.9689, decode.d4.loss_dice: 1.3276, decode.d5.loss_cls: 1.4408, decode.d5.loss_mask: 0.9769, decode.d5.loss_dice: 1.3263, decode.d6.loss_cls: 1.4321, decode.d6.loss_mask: 0.9834, decode.d6.loss_dice: 1.3182, decode.d7.loss_cls: 1.4385, decode.d7.loss_mask: 0.9879, decode.d7.loss_dice: 1.3259, decode.d8.loss_cls: 1.4363, decode.d8.loss_mask: 0.9877, decode.d8.loss_dice: 1.3268, loss: 45.9461 2022-05-04 22:05:25,965 - mmseg - INFO - Iter [4800/40000] lr: 1.264e-06, eta: 8:09:03, time: 0.783, data_time: 0.010, memory: 51557, decode.loss_cls: 1.4008, decode.loss_mask: 0.9820, decode.loss_dice: 1.3490, decode.d0.loss_cls: 9.0423, decode.d0.loss_mask: 0.9434, decode.d0.loss_dice: 1.5622, decode.d1.loss_cls: 1.7303, decode.d1.loss_mask: 0.9828, decode.d1.loss_dice: 1.4062, decode.d2.loss_cls: 1.4751, decode.d2.loss_mask: 0.9789, decode.d2.loss_dice: 1.3583, decode.d3.loss_cls: 1.4182, decode.d3.loss_mask: 0.9736, decode.d3.loss_dice: 1.3457, decode.d4.loss_cls: 1.4066, decode.d4.loss_mask: 0.9736, decode.d4.loss_dice: 1.3440, decode.d5.loss_cls: 1.3969, decode.d5.loss_mask: 0.9707, decode.d5.loss_dice: 1.3397, decode.d6.loss_cls: 1.3838, decode.d6.loss_mask: 0.9812, decode.d6.loss_dice: 1.3362, decode.d7.loss_cls: 1.3751, decode.d7.loss_mask: 0.9780, decode.d7.loss_dice: 1.3432, decode.d8.loss_cls: 1.3880, decode.d8.loss_mask: 0.9815, decode.d8.loss_dice: 1.3501, loss: 45.4974 2022-05-04 22:06:05,471 - mmseg - INFO - Iter [4850/40000] lr: 1.262e-06, eta: 8:08:05, time: 0.790, data_time: 0.009, memory: 51557, decode.loss_cls: 1.4295, decode.loss_mask: 1.0047, decode.loss_dice: 1.3657, decode.d0.loss_cls: 9.0132, decode.d0.loss_mask: 0.9726, decode.d0.loss_dice: 1.5991, decode.d1.loss_cls: 1.8039, decode.d1.loss_mask: 0.9974, decode.d1.loss_dice: 1.4256, decode.d2.loss_cls: 1.5384, decode.d2.loss_mask: 0.9883, decode.d2.loss_dice: 1.3795, decode.d3.loss_cls: 1.4807, decode.d3.loss_mask: 0.9959, decode.d3.loss_dice: 1.3632, decode.d4.loss_cls: 1.4517, decode.d4.loss_mask: 0.9915, decode.d4.loss_dice: 1.3629, decode.d5.loss_cls: 1.4365, decode.d5.loss_mask: 0.9927, decode.d5.loss_dice: 1.3608, decode.d6.loss_cls: 1.4240, decode.d6.loss_mask: 0.9995, decode.d6.loss_dice: 1.3516, decode.d7.loss_cls: 1.4284, decode.d7.loss_mask: 0.9971, decode.d7.loss_dice: 1.3649, decode.d8.loss_cls: 1.4164, decode.d8.loss_mask: 1.0037, decode.d8.loss_dice: 1.3581, loss: 46.2976 2022-05-04 22:06:44,725 - mmseg - INFO - Iter [4900/40000] lr: 1.260e-06, eta: 8:07:07, time: 0.785, data_time: 0.009, memory: 51557, decode.loss_cls: 1.4946, decode.loss_mask: 0.9939, decode.loss_dice: 1.3789, decode.d0.loss_cls: 8.9950, decode.d0.loss_mask: 0.9551, decode.d0.loss_dice: 1.6113, decode.d1.loss_cls: 1.8389, decode.d1.loss_mask: 0.9962, decode.d1.loss_dice: 1.4455, decode.d2.loss_cls: 1.5644, decode.d2.loss_mask: 0.9816, decode.d2.loss_dice: 1.4023, decode.d3.loss_cls: 1.5132, decode.d3.loss_mask: 0.9831, decode.d3.loss_dice: 1.3783, decode.d4.loss_cls: 1.5051, decode.d4.loss_mask: 0.9756, decode.d4.loss_dice: 1.3809, decode.d5.loss_cls: 1.4961, decode.d5.loss_mask: 0.9774, decode.d5.loss_dice: 1.3756, decode.d6.loss_cls: 1.4906, decode.d6.loss_mask: 0.9864, decode.d6.loss_dice: 1.3677, decode.d7.loss_cls: 1.4796, decode.d7.loss_mask: 0.9886, decode.d7.loss_dice: 1.3792, decode.d8.loss_cls: 1.4874, decode.d8.loss_mask: 0.9931, decode.d8.loss_dice: 1.3753, loss: 46.7909 2022-05-04 22:07:23,867 - mmseg - INFO - Iter [4950/40000] lr: 1.258e-06, eta: 8:06:07, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 1.4895, decode.loss_mask: 0.9706, decode.loss_dice: 1.3518, decode.d0.loss_cls: 8.9839, decode.d0.loss_mask: 0.9436, decode.d0.loss_dice: 1.5998, decode.d1.loss_cls: 1.8470, decode.d1.loss_mask: 0.9722, decode.d1.loss_dice: 1.4126, decode.d2.loss_cls: 1.5777, decode.d2.loss_mask: 0.9557, decode.d2.loss_dice: 1.3707, decode.d3.loss_cls: 1.5175, decode.d3.loss_mask: 0.9618, decode.d3.loss_dice: 1.3439, decode.d4.loss_cls: 1.5083, decode.d4.loss_mask: 0.9580, decode.d4.loss_dice: 1.3501, decode.d5.loss_cls: 1.4946, decode.d5.loss_mask: 0.9594, decode.d5.loss_dice: 1.3438, decode.d6.loss_cls: 1.4891, decode.d6.loss_mask: 0.9568, decode.d6.loss_dice: 1.3382, decode.d7.loss_cls: 1.4781, decode.d7.loss_mask: 0.9683, decode.d7.loss_dice: 1.3444, decode.d8.loss_cls: 1.4844, decode.d8.loss_mask: 0.9632, decode.d8.loss_dice: 1.3399, loss: 46.2750 2022-05-04 22:08:02,871 - mmseg - INFO - Saving checkpoint at 5000 iterations 2022-05-04 22:08:27,715 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-04 22:08:27,726 - mmseg - INFO - Iter [5000/40000] lr: 1.256e-06, eta: 8:08:00, time: 1.274, data_time: 0.010, memory: 51557, decode.loss_cls: 1.4033, decode.loss_mask: 0.9801, decode.loss_dice: 1.3284, decode.d0.loss_cls: 8.9450, decode.d0.loss_mask: 0.9493, decode.d0.loss_dice: 1.5724, decode.d1.loss_cls: 1.7723, decode.d1.loss_mask: 0.9867, decode.d1.loss_dice: 1.4026, decode.d2.loss_cls: 1.5073, decode.d2.loss_mask: 0.9717, decode.d2.loss_dice: 1.3509, decode.d3.loss_cls: 1.4348, decode.d3.loss_mask: 0.9801, decode.d3.loss_dice: 1.3336, decode.d4.loss_cls: 1.4232, decode.d4.loss_mask: 0.9748, decode.d4.loss_dice: 1.3378, decode.d5.loss_cls: 1.4187, decode.d5.loss_mask: 0.9736, decode.d5.loss_dice: 1.3261, decode.d6.loss_cls: 1.4088, decode.d6.loss_mask: 0.9776, decode.d6.loss_dice: 1.3179, decode.d7.loss_cls: 1.4017, decode.d7.loss_mask: 0.9869, decode.d7.loss_dice: 1.3270, decode.d8.loss_cls: 1.3934, decode.d8.loss_mask: 0.9806, decode.d8.loss_dice: 1.3255, loss: 45.4921 2022-05-04 22:09:07,838 - mmseg - INFO - Iter [5050/40000] lr: 1.255e-06, eta: 8:07:08, time: 0.806, data_time: 0.013, memory: 51557, decode.loss_cls: 1.4097, decode.loss_mask: 0.9848, decode.loss_dice: 1.3573, decode.d0.loss_cls: 8.9193, decode.d0.loss_mask: 0.9489, decode.d0.loss_dice: 1.5839, decode.d1.loss_cls: 1.7350, decode.d1.loss_mask: 0.9961, decode.d1.loss_dice: 1.4215, decode.d2.loss_cls: 1.4866, decode.d2.loss_mask: 0.9703, decode.d2.loss_dice: 1.3812, decode.d3.loss_cls: 1.4370, decode.d3.loss_mask: 0.9691, decode.d3.loss_dice: 1.3542, decode.d4.loss_cls: 1.4168, decode.d4.loss_mask: 0.9769, decode.d4.loss_dice: 1.3591, decode.d5.loss_cls: 1.4061, decode.d5.loss_mask: 0.9733, decode.d5.loss_dice: 1.3669, decode.d6.loss_cls: 1.4085, decode.d6.loss_mask: 0.9801, decode.d6.loss_dice: 1.3529, decode.d7.loss_cls: 1.4059, decode.d7.loss_mask: 0.9840, decode.d7.loss_dice: 1.3653, decode.d8.loss_cls: 1.4080, decode.d8.loss_mask: 0.9819, decode.d8.loss_dice: 1.3600, loss: 45.7006 2022-05-04 22:09:49,701 - mmseg - INFO - Iter [5100/40000] lr: 1.253e-06, eta: 8:06:26, time: 0.837, data_time: 0.058, memory: 51557, decode.loss_cls: 1.3524, decode.loss_mask: 0.9429, decode.loss_dice: 1.3308, decode.d0.loss_cls: 8.8916, decode.d0.loss_mask: 0.9125, decode.d0.loss_dice: 1.5437, decode.d1.loss_cls: 1.7103, decode.d1.loss_mask: 0.9448, decode.d1.loss_dice: 1.3872, decode.d2.loss_cls: 1.4500, decode.d2.loss_mask: 0.9382, decode.d2.loss_dice: 1.3357, decode.d3.loss_cls: 1.3911, decode.d3.loss_mask: 0.9360, decode.d3.loss_dice: 1.3238, decode.d4.loss_cls: 1.3562, decode.d4.loss_mask: 0.9321, decode.d4.loss_dice: 1.3300, decode.d5.loss_cls: 1.3460, decode.d5.loss_mask: 0.9250, decode.d5.loss_dice: 1.3212, decode.d6.loss_cls: 1.3476, decode.d6.loss_mask: 0.9291, decode.d6.loss_dice: 1.3137, decode.d7.loss_cls: 1.3392, decode.d7.loss_mask: 0.9406, decode.d7.loss_dice: 1.3181, decode.d8.loss_cls: 1.3338, decode.d8.loss_mask: 0.9414, decode.d8.loss_dice: 1.3213, loss: 44.3860 2022-05-04 22:10:29,001 - mmseg - INFO - Iter [5150/40000] lr: 1.251e-06, eta: 8:05:27, time: 0.786, data_time: 0.009, memory: 51557, decode.loss_cls: 1.3817, decode.loss_mask: 0.9627, decode.loss_dice: 1.3292, decode.d0.loss_cls: 8.8610, decode.d0.loss_mask: 0.9389, decode.d0.loss_dice: 1.5599, decode.d1.loss_cls: 1.7296, decode.d1.loss_mask: 0.9796, decode.d1.loss_dice: 1.4042, decode.d2.loss_cls: 1.4725, decode.d2.loss_mask: 0.9629, decode.d2.loss_dice: 1.3461, decode.d3.loss_cls: 1.4211, decode.d3.loss_mask: 0.9579, decode.d3.loss_dice: 1.3253, decode.d4.loss_cls: 1.4007, decode.d4.loss_mask: 0.9602, decode.d4.loss_dice: 1.3357, decode.d5.loss_cls: 1.3887, decode.d5.loss_mask: 0.9568, decode.d5.loss_dice: 1.3299, decode.d6.loss_cls: 1.3766, decode.d6.loss_mask: 0.9649, decode.d6.loss_dice: 1.3255, decode.d7.loss_cls: 1.3646, decode.d7.loss_mask: 0.9663, decode.d7.loss_dice: 1.3361, decode.d8.loss_cls: 1.3736, decode.d8.loss_mask: 0.9613, decode.d8.loss_dice: 1.3273, loss: 45.0008 2022-05-04 22:11:08,006 - mmseg - INFO - Iter [5200/40000] lr: 1.249e-06, eta: 8:04:27, time: 0.780, data_time: 0.010, memory: 51557, decode.loss_cls: 1.4314, decode.loss_mask: 0.9428, decode.loss_dice: 1.3036, decode.d0.loss_cls: 8.8566, decode.d0.loss_mask: 0.9329, decode.d0.loss_dice: 1.5646, decode.d1.loss_cls: 1.7599, decode.d1.loss_mask: 0.9639, decode.d1.loss_dice: 1.3858, decode.d2.loss_cls: 1.5070, decode.d2.loss_mask: 0.9507, decode.d2.loss_dice: 1.3402, decode.d3.loss_cls: 1.4621, decode.d3.loss_mask: 0.9407, decode.d3.loss_dice: 1.3140, decode.d4.loss_cls: 1.4584, decode.d4.loss_mask: 0.9312, decode.d4.loss_dice: 1.3207, decode.d5.loss_cls: 1.4322, decode.d5.loss_mask: 0.9352, decode.d5.loss_dice: 1.3113, decode.d6.loss_cls: 1.4281, decode.d6.loss_mask: 0.9387, decode.d6.loss_dice: 1.3037, decode.d7.loss_cls: 1.4272, decode.d7.loss_mask: 0.9425, decode.d7.loss_dice: 1.3110, decode.d8.loss_cls: 1.4160, decode.d8.loss_mask: 0.9401, decode.d8.loss_dice: 1.3019, loss: 45.0543 2022-05-04 22:11:47,324 - mmseg - INFO - Iter [5250/40000] lr: 1.247e-06, eta: 8:03:29, time: 0.786, data_time: 0.009, memory: 51557, decode.loss_cls: 1.3405, decode.loss_mask: 0.9472, decode.loss_dice: 1.2540, decode.d0.loss_cls: 8.8298, decode.d0.loss_mask: 0.9403, decode.d0.loss_dice: 1.4910, decode.d1.loss_cls: 1.6625, decode.d1.loss_mask: 0.9692, decode.d1.loss_dice: 1.3265, decode.d2.loss_cls: 1.4082, decode.d2.loss_mask: 0.9582, decode.d2.loss_dice: 1.2720, decode.d3.loss_cls: 1.3640, decode.d3.loss_mask: 0.9433, decode.d3.loss_dice: 1.2474, decode.d4.loss_cls: 1.3331, decode.d4.loss_mask: 0.9491, decode.d4.loss_dice: 1.2564, decode.d5.loss_cls: 1.3327, decode.d5.loss_mask: 0.9455, decode.d5.loss_dice: 1.2465, decode.d6.loss_cls: 1.3242, decode.d6.loss_mask: 0.9497, decode.d6.loss_dice: 1.2379, decode.d7.loss_cls: 1.3251, decode.d7.loss_mask: 0.9457, decode.d7.loss_dice: 1.2565, decode.d8.loss_cls: 1.3239, decode.d8.loss_mask: 0.9444, decode.d8.loss_dice: 1.2538, loss: 43.5788 2022-05-04 22:12:26,661 - mmseg - INFO - Iter [5300/40000] lr: 1.246e-06, eta: 8:02:31, time: 0.787, data_time: 0.010, memory: 51557, decode.loss_cls: 1.4447, decode.loss_mask: 0.9268, decode.loss_dice: 1.3452, decode.d0.loss_cls: 8.8293, decode.d0.loss_mask: 0.9147, decode.d0.loss_dice: 1.5837, decode.d1.loss_cls: 1.7894, decode.d1.loss_mask: 0.9451, decode.d1.loss_dice: 1.4262, decode.d2.loss_cls: 1.5159, decode.d2.loss_mask: 0.9320, decode.d2.loss_dice: 1.3667, decode.d3.loss_cls: 1.4688, decode.d3.loss_mask: 0.9278, decode.d3.loss_dice: 1.3457, decode.d4.loss_cls: 1.4624, decode.d4.loss_mask: 0.9267, decode.d4.loss_dice: 1.3503, decode.d5.loss_cls: 1.4381, decode.d5.loss_mask: 0.9273, decode.d5.loss_dice: 1.3448, decode.d6.loss_cls: 1.4434, decode.d6.loss_mask: 0.9213, decode.d6.loss_dice: 1.3389, decode.d7.loss_cls: 1.4385, decode.d7.loss_mask: 0.9224, decode.d7.loss_dice: 1.3490, decode.d8.loss_cls: 1.4293, decode.d8.loss_mask: 0.9227, decode.d8.loss_dice: 1.3435, loss: 45.3206 2022-05-04 22:13:06,476 - mmseg - INFO - Iter [5350/40000] lr: 1.244e-06, eta: 8:01:37, time: 0.796, data_time: 0.011, memory: 51557, decode.loss_cls: 1.4144, decode.loss_mask: 0.9581, decode.loss_dice: 1.3238, decode.d0.loss_cls: 8.7806, decode.d0.loss_mask: 0.9327, decode.d0.loss_dice: 1.5546, decode.d1.loss_cls: 1.7191, decode.d1.loss_mask: 0.9637, decode.d1.loss_dice: 1.3966, decode.d2.loss_cls: 1.4778, decode.d2.loss_mask: 0.9590, decode.d2.loss_dice: 1.3432, decode.d3.loss_cls: 1.4276, decode.d3.loss_mask: 0.9528, decode.d3.loss_dice: 1.3301, decode.d4.loss_cls: 1.4215, decode.d4.loss_mask: 0.9453, decode.d4.loss_dice: 1.3254, decode.d5.loss_cls: 1.4142, decode.d5.loss_mask: 0.9456, decode.d5.loss_dice: 1.3119, decode.d6.loss_cls: 1.4175, decode.d6.loss_mask: 0.9442, decode.d6.loss_dice: 1.3098, decode.d7.loss_cls: 1.4059, decode.d7.loss_mask: 0.9484, decode.d7.loss_dice: 1.3186, decode.d8.loss_cls: 1.4000, decode.d8.loss_mask: 0.9543, decode.d8.loss_dice: 1.3179, loss: 44.9147 2022-05-04 22:13:46,199 - mmseg - INFO - Iter [5400/40000] lr: 1.242e-06, eta: 8:00:43, time: 0.794, data_time: 0.009, memory: 51557, decode.loss_cls: 1.3843, decode.loss_mask: 0.9312, decode.loss_dice: 1.3246, decode.d0.loss_cls: 8.7526, decode.d0.loss_mask: 0.9108, decode.d0.loss_dice: 1.5641, decode.d1.loss_cls: 1.7130, decode.d1.loss_mask: 0.9452, decode.d1.loss_dice: 1.3993, decode.d2.loss_cls: 1.4563, decode.d2.loss_mask: 0.9334, decode.d2.loss_dice: 1.3579, decode.d3.loss_cls: 1.3986, decode.d3.loss_mask: 0.9279, decode.d3.loss_dice: 1.3287, decode.d4.loss_cls: 1.3879, decode.d4.loss_mask: 0.9282, decode.d4.loss_dice: 1.3315, decode.d5.loss_cls: 1.3757, decode.d5.loss_mask: 0.9295, decode.d5.loss_dice: 1.3250, decode.d6.loss_cls: 1.3688, decode.d6.loss_mask: 0.9300, decode.d6.loss_dice: 1.3132, decode.d7.loss_cls: 1.3778, decode.d7.loss_mask: 0.9292, decode.d7.loss_dice: 1.3129, decode.d8.loss_cls: 1.3782, decode.d8.loss_mask: 0.9329, decode.d8.loss_dice: 1.3145, loss: 44.4631 2022-05-04 22:14:26,139 - mmseg - INFO - Iter [5450/40000] lr: 1.240e-06, eta: 7:59:50, time: 0.799, data_time: 0.010, memory: 51557, decode.loss_cls: 1.3683, decode.loss_mask: 0.9450, decode.loss_dice: 1.3329, decode.d0.loss_cls: 8.7348, decode.d0.loss_mask: 0.9347, decode.d0.loss_dice: 1.5603, decode.d1.loss_cls: 1.6597, decode.d1.loss_mask: 0.9647, decode.d1.loss_dice: 1.4187, decode.d2.loss_cls: 1.4347, decode.d2.loss_mask: 0.9472, decode.d2.loss_dice: 1.3663, decode.d3.loss_cls: 1.3925, decode.d3.loss_mask: 0.9478, decode.d3.loss_dice: 1.3409, decode.d4.loss_cls: 1.3816, decode.d4.loss_mask: 0.9454, decode.d4.loss_dice: 1.3420, decode.d5.loss_cls: 1.3622, decode.d5.loss_mask: 0.9493, decode.d5.loss_dice: 1.3427, decode.d6.loss_cls: 1.3513, decode.d6.loss_mask: 0.9530, decode.d6.loss_dice: 1.3313, decode.d7.loss_cls: 1.3524, decode.d7.loss_mask: 0.9529, decode.d7.loss_dice: 1.3320, decode.d8.loss_cls: 1.3618, decode.d8.loss_mask: 0.9463, decode.d8.loss_dice: 1.3319, loss: 44.5848 2022-05-04 22:15:05,664 - mmseg - INFO - Iter [5500/40000] lr: 1.238e-06, eta: 7:58:55, time: 0.791, data_time: 0.009, memory: 51557, decode.loss_cls: 1.3811, decode.loss_mask: 0.9557, decode.loss_dice: 1.2931, decode.d0.loss_cls: 8.7065, decode.d0.loss_mask: 0.9282, decode.d0.loss_dice: 1.5122, decode.d1.loss_cls: 1.6739, decode.d1.loss_mask: 0.9633, decode.d1.loss_dice: 1.3604, decode.d2.loss_cls: 1.4451, decode.d2.loss_mask: 0.9445, decode.d2.loss_dice: 1.3109, decode.d3.loss_cls: 1.3999, decode.d3.loss_mask: 0.9459, decode.d3.loss_dice: 1.2904, decode.d4.loss_cls: 1.3938, decode.d4.loss_mask: 0.9510, decode.d4.loss_dice: 1.2894, decode.d5.loss_cls: 1.3718, decode.d5.loss_mask: 0.9431, decode.d5.loss_dice: 1.2847, decode.d6.loss_cls: 1.3647, decode.d6.loss_mask: 0.9462, decode.d6.loss_dice: 1.2806, decode.d7.loss_cls: 1.3724, decode.d7.loss_mask: 0.9448, decode.d7.loss_dice: 1.2849, decode.d8.loss_cls: 1.3619, decode.d8.loss_mask: 0.9474, decode.d8.loss_dice: 1.2857, loss: 44.1336 2022-05-04 22:15:45,993 - mmseg - INFO - Iter [5550/40000] lr: 1.237e-06, eta: 7:58:05, time: 0.806, data_time: 0.011, memory: 51557, decode.loss_cls: 1.3826, decode.loss_mask: 0.9489, decode.loss_dice: 1.3547, decode.d0.loss_cls: 8.6823, decode.d0.loss_mask: 0.9246, decode.d0.loss_dice: 1.5709, decode.d1.loss_cls: 1.6703, decode.d1.loss_mask: 0.9582, decode.d1.loss_dice: 1.4148, decode.d2.loss_cls: 1.4356, decode.d2.loss_mask: 0.9501, decode.d2.loss_dice: 1.3667, decode.d3.loss_cls: 1.3965, decode.d3.loss_mask: 0.9496, decode.d3.loss_dice: 1.3552, decode.d4.loss_cls: 1.3853, decode.d4.loss_mask: 0.9485, decode.d4.loss_dice: 1.3481, decode.d5.loss_cls: 1.3765, decode.d5.loss_mask: 0.9533, decode.d5.loss_dice: 1.3509, decode.d6.loss_cls: 1.3665, decode.d6.loss_mask: 0.9455, decode.d6.loss_dice: 1.3471, decode.d7.loss_cls: 1.3699, decode.d7.loss_mask: 0.9503, decode.d7.loss_dice: 1.3504, decode.d8.loss_cls: 1.3746, decode.d8.loss_mask: 0.9523, decode.d8.loss_dice: 1.3502, loss: 44.7305 2022-05-04 22:16:24,926 - mmseg - INFO - Iter [5600/40000] lr: 1.235e-06, eta: 7:57:07, time: 0.779, data_time: 0.009, memory: 51557, decode.loss_cls: 1.3594, decode.loss_mask: 0.9351, decode.loss_dice: 1.3266, decode.d0.loss_cls: 8.6515, decode.d0.loss_mask: 0.9096, decode.d0.loss_dice: 1.5491, decode.d1.loss_cls: 1.6546, decode.d1.loss_mask: 0.9492, decode.d1.loss_dice: 1.4089, decode.d2.loss_cls: 1.4122, decode.d2.loss_mask: 0.9359, decode.d2.loss_dice: 1.3501, decode.d3.loss_cls: 1.3820, decode.d3.loss_mask: 0.9319, decode.d3.loss_dice: 1.3222, decode.d4.loss_cls: 1.3579, decode.d4.loss_mask: 0.9303, decode.d4.loss_dice: 1.3216, decode.d5.loss_cls: 1.3610, decode.d5.loss_mask: 0.9265, decode.d5.loss_dice: 1.3232, decode.d6.loss_cls: 1.3566, decode.d6.loss_mask: 0.9311, decode.d6.loss_dice: 1.3197, decode.d7.loss_cls: 1.3499, decode.d7.loss_mask: 0.9365, decode.d7.loss_dice: 1.3319, decode.d8.loss_cls: 1.3458, decode.d8.loss_mask: 0.9416, decode.d8.loss_dice: 1.3274, loss: 44.1393 2022-05-04 22:17:06,993 - mmseg - INFO - Iter [5650/40000] lr: 1.233e-06, eta: 7:56:28, time: 0.841, data_time: 0.060, memory: 51557, decode.loss_cls: 1.3299, decode.loss_mask: 0.9409, decode.loss_dice: 1.3018, decode.d0.loss_cls: 8.6263, decode.d0.loss_mask: 0.9074, decode.d0.loss_dice: 1.5122, decode.d1.loss_cls: 1.6196, decode.d1.loss_mask: 0.9535, decode.d1.loss_dice: 1.3712, decode.d2.loss_cls: 1.3913, decode.d2.loss_mask: 0.9431, decode.d2.loss_dice: 1.3209, decode.d3.loss_cls: 1.3515, decode.d3.loss_mask: 0.9325, decode.d3.loss_dice: 1.3055, decode.d4.loss_cls: 1.3292, decode.d4.loss_mask: 0.9394, decode.d4.loss_dice: 1.3070, decode.d5.loss_cls: 1.3322, decode.d5.loss_mask: 0.9344, decode.d5.loss_dice: 1.3074, decode.d6.loss_cls: 1.3225, decode.d6.loss_mask: 0.9372, decode.d6.loss_dice: 1.2998, decode.d7.loss_cls: 1.3220, decode.d7.loss_mask: 0.9348, decode.d7.loss_dice: 1.3031, decode.d8.loss_cls: 1.3167, decode.d8.loss_mask: 0.9342, decode.d8.loss_dice: 1.2990, loss: 43.6266 2022-05-04 22:17:46,091 - mmseg - INFO - Iter [5700/40000] lr: 1.231e-06, eta: 7:55:32, time: 0.782, data_time: 0.010, memory: 51557, decode.loss_cls: 1.2820, decode.loss_mask: 0.9263, decode.loss_dice: 1.2924, decode.d0.loss_cls: 8.6034, decode.d0.loss_mask: 0.9065, decode.d0.loss_dice: 1.5244, decode.d1.loss_cls: 1.5672, decode.d1.loss_mask: 0.9460, decode.d1.loss_dice: 1.3854, decode.d2.loss_cls: 1.3493, decode.d2.loss_mask: 0.9298, decode.d2.loss_dice: 1.3192, decode.d3.loss_cls: 1.3078, decode.d3.loss_mask: 0.9283, decode.d3.loss_dice: 1.2981, decode.d4.loss_cls: 1.2948, decode.d4.loss_mask: 0.9238, decode.d4.loss_dice: 1.3026, decode.d5.loss_cls: 1.2939, decode.d5.loss_mask: 0.9214, decode.d5.loss_dice: 1.3012, decode.d6.loss_cls: 1.2807, decode.d6.loss_mask: 0.9132, decode.d6.loss_dice: 1.2911, decode.d7.loss_cls: 1.2825, decode.d7.loss_mask: 0.9176, decode.d7.loss_dice: 1.2949, decode.d8.loss_cls: 1.2782, decode.d8.loss_mask: 0.9234, decode.d8.loss_dice: 1.2900, loss: 43.0753 2022-05-04 22:18:25,658 - mmseg - INFO - Iter [5750/40000] lr: 1.229e-06, eta: 7:54:38, time: 0.791, data_time: 0.009, memory: 51557, decode.loss_cls: 1.3142, decode.loss_mask: 0.9445, decode.loss_dice: 1.2768, decode.d0.loss_cls: 8.5920, decode.d0.loss_mask: 0.9355, decode.d0.loss_dice: 1.5036, decode.d1.loss_cls: 1.6219, decode.d1.loss_mask: 0.9575, decode.d1.loss_dice: 1.3547, decode.d2.loss_cls: 1.3896, decode.d2.loss_mask: 0.9521, decode.d2.loss_dice: 1.3013, decode.d3.loss_cls: 1.3446, decode.d3.loss_mask: 0.9426, decode.d3.loss_dice: 1.2875, decode.d4.loss_cls: 1.3339, decode.d4.loss_mask: 0.9375, decode.d4.loss_dice: 1.2874, decode.d5.loss_cls: 1.3155, decode.d5.loss_mask: 0.9379, decode.d5.loss_dice: 1.2767, decode.d6.loss_cls: 1.3151, decode.d6.loss_mask: 0.9426, decode.d6.loss_dice: 1.2779, decode.d7.loss_cls: 1.3085, decode.d7.loss_mask: 0.9424, decode.d7.loss_dice: 1.2774, decode.d8.loss_cls: 1.3035, decode.d8.loss_mask: 0.9406, decode.d8.loss_dice: 1.2738, loss: 43.3890 2022-05-04 22:19:04,614 - mmseg - INFO - Iter [5800/40000] lr: 1.228e-06, eta: 7:53:41, time: 0.780, data_time: 0.011, memory: 51557, decode.loss_cls: 1.3050, decode.loss_mask: 0.9317, decode.loss_dice: 1.3014, decode.d0.loss_cls: 8.5517, decode.d0.loss_mask: 0.9101, decode.d0.loss_dice: 1.5072, decode.d1.loss_cls: 1.5990, decode.d1.loss_mask: 0.9434, decode.d1.loss_dice: 1.3509, decode.d2.loss_cls: 1.3708, decode.d2.loss_mask: 0.9336, decode.d2.loss_dice: 1.3151, decode.d3.loss_cls: 1.3199, decode.d3.loss_mask: 0.9281, decode.d3.loss_dice: 1.2992, decode.d4.loss_cls: 1.3158, decode.d4.loss_mask: 0.9285, decode.d4.loss_dice: 1.3075, decode.d5.loss_cls: 1.3037, decode.d5.loss_mask: 0.9335, decode.d5.loss_dice: 1.3020, decode.d6.loss_cls: 1.3022, decode.d6.loss_mask: 0.9264, decode.d6.loss_dice: 1.2897, decode.d7.loss_cls: 1.2981, decode.d7.loss_mask: 0.9290, decode.d7.loss_dice: 1.2893, decode.d8.loss_cls: 1.2947, decode.d8.loss_mask: 0.9302, decode.d8.loss_dice: 1.2971, loss: 43.2146 2022-05-04 22:19:43,940 - mmseg - INFO - Iter [5850/40000] lr: 1.226e-06, eta: 7:52:46, time: 0.787, data_time: 0.009, memory: 51557, decode.loss_cls: 1.3539, decode.loss_mask: 0.9283, decode.loss_dice: 1.2826, decode.d0.loss_cls: 8.5433, decode.d0.loss_mask: 0.9129, decode.d0.loss_dice: 1.5194, decode.d1.loss_cls: 1.6341, decode.d1.loss_mask: 0.9435, decode.d1.loss_dice: 1.3746, decode.d2.loss_cls: 1.4186, decode.d2.loss_mask: 0.9321, decode.d2.loss_dice: 1.3200, decode.d3.loss_cls: 1.3699, decode.d3.loss_mask: 0.9293, decode.d3.loss_dice: 1.2908, decode.d4.loss_cls: 1.3603, decode.d4.loss_mask: 0.9251, decode.d4.loss_dice: 1.2929, decode.d5.loss_cls: 1.3502, decode.d5.loss_mask: 0.9241, decode.d5.loss_dice: 1.2904, decode.d6.loss_cls: 1.3437, decode.d6.loss_mask: 0.9200, decode.d6.loss_dice: 1.2852, decode.d7.loss_cls: 1.3522, decode.d7.loss_mask: 0.9247, decode.d7.loss_dice: 1.2856, decode.d8.loss_cls: 1.3426, decode.d8.loss_mask: 0.9209, decode.d8.loss_dice: 1.2828, loss: 43.5538 2022-05-04 22:20:23,846 - mmseg - INFO - Iter [5900/40000] lr: 1.224e-06, eta: 7:51:55, time: 0.798, data_time: 0.009, memory: 51557, decode.loss_cls: 1.3255, decode.loss_mask: 0.9366, decode.loss_dice: 1.3236, decode.d0.loss_cls: 8.5324, decode.d0.loss_mask: 0.9072, decode.d0.loss_dice: 1.5412, decode.d1.loss_cls: 1.6060, decode.d1.loss_mask: 0.9573, decode.d1.loss_dice: 1.4046, decode.d2.loss_cls: 1.3839, decode.d2.loss_mask: 0.9386, decode.d2.loss_dice: 1.3432, decode.d3.loss_cls: 1.3421, decode.d3.loss_mask: 0.9379, decode.d3.loss_dice: 1.3267, decode.d4.loss_cls: 1.3302, decode.d4.loss_mask: 0.9374, decode.d4.loss_dice: 1.3306, decode.d5.loss_cls: 1.3308, decode.d5.loss_mask: 0.9316, decode.d5.loss_dice: 1.3298, decode.d6.loss_cls: 1.3220, decode.d6.loss_mask: 0.9341, decode.d6.loss_dice: 1.3235, decode.d7.loss_cls: 1.3201, decode.d7.loss_mask: 0.9320, decode.d7.loss_dice: 1.3175, decode.d8.loss_cls: 1.3136, decode.d8.loss_mask: 0.9336, decode.d8.loss_dice: 1.3287, loss: 43.7225 2022-05-04 22:21:03,672 - mmseg - INFO - Iter [5950/40000] lr: 1.222e-06, eta: 7:51:04, time: 0.796, data_time: 0.009, memory: 51557, decode.loss_cls: 1.3442, decode.loss_mask: 0.9783, decode.loss_dice: 1.3331, decode.d0.loss_cls: 8.5135, decode.d0.loss_mask: 0.9551, decode.d0.loss_dice: 1.5627, decode.d1.loss_cls: 1.6406, decode.d1.loss_mask: 0.9974, decode.d1.loss_dice: 1.4000, decode.d2.loss_cls: 1.4262, decode.d2.loss_mask: 0.9816, decode.d2.loss_dice: 1.3478, decode.d3.loss_cls: 1.3810, decode.d3.loss_mask: 0.9764, decode.d3.loss_dice: 1.3328, decode.d4.loss_cls: 1.3647, decode.d4.loss_mask: 0.9782, decode.d4.loss_dice: 1.3413, decode.d5.loss_cls: 1.3574, decode.d5.loss_mask: 0.9776, decode.d5.loss_dice: 1.3430, decode.d6.loss_cls: 1.3480, decode.d6.loss_mask: 0.9749, decode.d6.loss_dice: 1.3311, decode.d7.loss_cls: 1.3379, decode.d7.loss_mask: 0.9782, decode.d7.loss_dice: 1.3289, decode.d8.loss_cls: 1.3352, decode.d8.loss_mask: 0.9753, decode.d8.loss_dice: 1.3263, loss: 44.4690 2022-05-04 22:21:43,696 - mmseg - INFO - Saving checkpoint at 6000 iterations 2022-05-04 22:22:09,126 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-04 22:22:09,137 - mmseg - INFO - Iter [6000/40000] lr: 1.220e-06, eta: 7:52:38, time: 1.307, data_time: 0.010, memory: 51557, decode.loss_cls: 1.3225, decode.loss_mask: 0.9405, decode.loss_dice: 1.2731, decode.d0.loss_cls: 8.4367, decode.d0.loss_mask: 0.9220, decode.d0.loss_dice: 1.4952, decode.d1.loss_cls: 1.5950, decode.d1.loss_mask: 0.9616, decode.d1.loss_dice: 1.3546, decode.d2.loss_cls: 1.3797, decode.d2.loss_mask: 0.9466, decode.d2.loss_dice: 1.3055, decode.d3.loss_cls: 1.3432, decode.d3.loss_mask: 0.9412, decode.d3.loss_dice: 1.2873, decode.d4.loss_cls: 1.3324, decode.d4.loss_mask: 0.9365, decode.d4.loss_dice: 1.2814, decode.d5.loss_cls: 1.3237, decode.d5.loss_mask: 0.9300, decode.d5.loss_dice: 1.2764, decode.d6.loss_cls: 1.3119, decode.d6.loss_mask: 0.9417, decode.d6.loss_dice: 1.2730, decode.d7.loss_cls: 1.3143, decode.d7.loss_mask: 0.9372, decode.d7.loss_dice: 1.2793, decode.d8.loss_cls: 1.3127, decode.d8.loss_mask: 0.9399, decode.d8.loss_dice: 1.2784, loss: 43.1732 2022-05-04 22:22:49,906 - mmseg - INFO - Iter [6050/40000] lr: 1.219e-06, eta: 7:51:51, time: 0.818, data_time: 0.012, memory: 51557, decode.loss_cls: 1.2809, decode.loss_mask: 0.9526, decode.loss_dice: 1.2956, decode.d0.loss_cls: 8.4367, decode.d0.loss_mask: 0.9207, decode.d0.loss_dice: 1.4965, decode.d1.loss_cls: 1.5669, decode.d1.loss_mask: 0.9593, decode.d1.loss_dice: 1.3581, decode.d2.loss_cls: 1.3429, decode.d2.loss_mask: 0.9487, decode.d2.loss_dice: 1.3129, decode.d3.loss_cls: 1.2988, decode.d3.loss_mask: 0.9503, decode.d3.loss_dice: 1.3009, decode.d4.loss_cls: 1.2960, decode.d4.loss_mask: 0.9531, decode.d4.loss_dice: 1.2978, decode.d5.loss_cls: 1.2905, decode.d5.loss_mask: 0.9536, decode.d5.loss_dice: 1.3008, decode.d6.loss_cls: 1.2840, decode.d6.loss_mask: 0.9522, decode.d6.loss_dice: 1.2890, decode.d7.loss_cls: 1.2775, decode.d7.loss_mask: 0.9522, decode.d7.loss_dice: 1.2901, decode.d8.loss_cls: 1.2715, decode.d8.loss_mask: 0.9429, decode.d8.loss_dice: 1.2854, loss: 43.0586 2022-05-04 22:23:29,268 - mmseg - INFO - Iter [6100/40000] lr: 1.217e-06, eta: 7:50:57, time: 0.787, data_time: 0.010, memory: 51557, decode.loss_cls: 1.3596, decode.loss_mask: 0.9520, decode.loss_dice: 1.3202, decode.d0.loss_cls: 8.4355, decode.d0.loss_mask: 0.9312, decode.d0.loss_dice: 1.5423, decode.d1.loss_cls: 1.6366, decode.d1.loss_mask: 0.9680, decode.d1.loss_dice: 1.3917, decode.d2.loss_cls: 1.4166, decode.d2.loss_mask: 0.9475, decode.d2.loss_dice: 1.3352, decode.d3.loss_cls: 1.3774, decode.d3.loss_mask: 0.9449, decode.d3.loss_dice: 1.3169, decode.d4.loss_cls: 1.3675, decode.d4.loss_mask: 0.9444, decode.d4.loss_dice: 1.3174, decode.d5.loss_cls: 1.3676, decode.d5.loss_mask: 0.9447, decode.d5.loss_dice: 1.3151, decode.d6.loss_cls: 1.3421, decode.d6.loss_mask: 0.9555, decode.d6.loss_dice: 1.3060, decode.d7.loss_cls: 1.3465, decode.d7.loss_mask: 0.9526, decode.d7.loss_dice: 1.3139, decode.d8.loss_cls: 1.3408, decode.d8.loss_mask: 0.9557, decode.d8.loss_dice: 1.3128, loss: 43.9582 2022-05-04 22:24:08,714 - mmseg - INFO - Iter [6150/40000] lr: 1.215e-06, eta: 7:50:03, time: 0.788, data_time: 0.009, memory: 51557, decode.loss_cls: 1.3574, decode.loss_mask: 0.9426, decode.loss_dice: 1.3156, decode.d0.loss_cls: 8.4135, decode.d0.loss_mask: 0.9150, decode.d0.loss_dice: 1.5374, decode.d1.loss_cls: 1.6301, decode.d1.loss_mask: 0.9550, decode.d1.loss_dice: 1.3905, decode.d2.loss_cls: 1.4211, decode.d2.loss_mask: 0.9472, decode.d2.loss_dice: 1.3289, decode.d3.loss_cls: 1.3768, decode.d3.loss_mask: 0.9456, decode.d3.loss_dice: 1.3186, decode.d4.loss_cls: 1.3651, decode.d4.loss_mask: 0.9383, decode.d4.loss_dice: 1.3171, decode.d5.loss_cls: 1.3453, decode.d5.loss_mask: 0.9424, decode.d5.loss_dice: 1.3163, decode.d6.loss_cls: 1.3421, decode.d6.loss_mask: 0.9412, decode.d6.loss_dice: 1.3087, decode.d7.loss_cls: 1.3529, decode.d7.loss_mask: 0.9408, decode.d7.loss_dice: 1.3128, decode.d8.loss_cls: 1.3446, decode.d8.loss_mask: 0.9466, decode.d8.loss_dice: 1.3142, loss: 43.8236 2022-05-04 22:24:51,116 - mmseg - INFO - Iter [6200/40000] lr: 1.213e-06, eta: 7:49:25, time: 0.849, data_time: 0.063, memory: 51557, decode.loss_cls: 1.3262, decode.loss_mask: 0.9079, decode.loss_dice: 1.2902, decode.d0.loss_cls: 8.3771, decode.d0.loss_mask: 0.8843, decode.d0.loss_dice: 1.5036, decode.d1.loss_cls: 1.5836, decode.d1.loss_mask: 0.9262, decode.d1.loss_dice: 1.3600, decode.d2.loss_cls: 1.3782, decode.d2.loss_mask: 0.9160, decode.d2.loss_dice: 1.3109, decode.d3.loss_cls: 1.3404, decode.d3.loss_mask: 0.9074, decode.d3.loss_dice: 1.2861, decode.d4.loss_cls: 1.3300, decode.d4.loss_mask: 0.9123, decode.d4.loss_dice: 1.2960, decode.d5.loss_cls: 1.3176, decode.d5.loss_mask: 0.9071, decode.d5.loss_dice: 1.2869, decode.d6.loss_cls: 1.3197, decode.d6.loss_mask: 0.9069, decode.d6.loss_dice: 1.2768, decode.d7.loss_cls: 1.3191, decode.d7.loss_mask: 0.9060, decode.d7.loss_dice: 1.2866, decode.d8.loss_cls: 1.3107, decode.d8.loss_mask: 0.9060, decode.d8.loss_dice: 1.2808, loss: 42.8606 2022-05-04 22:25:30,083 - mmseg - INFO - Iter [6250/40000] lr: 1.211e-06, eta: 7:48:29, time: 0.779, data_time: 0.009, memory: 51557, decode.loss_cls: 1.2737, decode.loss_mask: 0.9364, decode.loss_dice: 1.2689, decode.d0.loss_cls: 8.3429, decode.d0.loss_mask: 0.9149, decode.d0.loss_dice: 1.4722, decode.d1.loss_cls: 1.5563, decode.d1.loss_mask: 0.9572, decode.d1.loss_dice: 1.3307, decode.d2.loss_cls: 1.3540, decode.d2.loss_mask: 0.9434, decode.d2.loss_dice: 1.2822, decode.d3.loss_cls: 1.3139, decode.d3.loss_mask: 0.9340, decode.d3.loss_dice: 1.2707, decode.d4.loss_cls: 1.3017, decode.d4.loss_mask: 0.9313, decode.d4.loss_dice: 1.2783, decode.d5.loss_cls: 1.2837, decode.d5.loss_mask: 0.9320, decode.d5.loss_dice: 1.2700, decode.d6.loss_cls: 1.2703, decode.d6.loss_mask: 0.9330, decode.d6.loss_dice: 1.2568, decode.d7.loss_cls: 1.2749, decode.d7.loss_mask: 0.9283, decode.d7.loss_dice: 1.2638, decode.d8.loss_cls: 1.2758, decode.d8.loss_mask: 0.9342, decode.d8.loss_dice: 1.2677, loss: 42.5533 2022-05-04 22:26:09,590 - mmseg - INFO - Iter [6300/40000] lr: 1.210e-06, eta: 7:47:36, time: 0.790, data_time: 0.009, memory: 51557, decode.loss_cls: 1.2730, decode.loss_mask: 0.9289, decode.loss_dice: 1.2653, decode.d0.loss_cls: 8.3202, decode.d0.loss_mask: 0.9195, decode.d0.loss_dice: 1.4755, decode.d1.loss_cls: 1.5549, decode.d1.loss_mask: 0.9511, decode.d1.loss_dice: 1.3487, decode.d2.loss_cls: 1.3251, decode.d2.loss_mask: 0.9369, decode.d2.loss_dice: 1.2947, decode.d3.loss_cls: 1.2934, decode.d3.loss_mask: 0.9348, decode.d3.loss_dice: 1.2660, decode.d4.loss_cls: 1.2845, decode.d4.loss_mask: 0.9348, decode.d4.loss_dice: 1.2724, decode.d5.loss_cls: 1.2775, decode.d5.loss_mask: 0.9253, decode.d5.loss_dice: 1.2696, decode.d6.loss_cls: 1.2588, decode.d6.loss_mask: 0.9322, decode.d6.loss_dice: 1.2597, decode.d7.loss_cls: 1.2651, decode.d7.loss_mask: 0.9232, decode.d7.loss_dice: 1.2701, decode.d8.loss_cls: 1.2610, decode.d8.loss_mask: 0.9317, decode.d8.loss_dice: 1.2661, loss: 42.4199 2022-05-04 22:26:50,178 - mmseg - INFO - Iter [6350/40000] lr: 1.208e-06, eta: 7:46:49, time: 0.812, data_time: 0.009, memory: 51557, decode.loss_cls: 1.2765, decode.loss_mask: 0.9053, decode.loss_dice: 1.2871, decode.d0.loss_cls: 8.3140, decode.d0.loss_mask: 0.9003, decode.d0.loss_dice: 1.5149, decode.d1.loss_cls: 1.5708, decode.d1.loss_mask: 0.9294, decode.d1.loss_dice: 1.3630, decode.d2.loss_cls: 1.3366, decode.d2.loss_mask: 0.9160, decode.d2.loss_dice: 1.3117, decode.d3.loss_cls: 1.3136, decode.d3.loss_mask: 0.9096, decode.d3.loss_dice: 1.2888, decode.d4.loss_cls: 1.3005, decode.d4.loss_mask: 0.9100, decode.d4.loss_dice: 1.2858, decode.d5.loss_cls: 1.2882, decode.d5.loss_mask: 0.9100, decode.d5.loss_dice: 1.2842, decode.d6.loss_cls: 1.2732, decode.d6.loss_mask: 0.9128, decode.d6.loss_dice: 1.2717, decode.d7.loss_cls: 1.2720, decode.d7.loss_mask: 0.9086, decode.d7.loss_dice: 1.2815, decode.d8.loss_cls: 1.2682, decode.d8.loss_mask: 0.9090, decode.d8.loss_dice: 1.2853, loss: 42.4984 2022-05-04 22:27:29,619 - mmseg - INFO - Iter [6400/40000] lr: 1.206e-06, eta: 7:45:56, time: 0.789, data_time: 0.009, memory: 51557, decode.loss_cls: 1.2220, decode.loss_mask: 0.9293, decode.loss_dice: 1.2723, decode.d0.loss_cls: 8.2531, decode.d0.loss_mask: 0.9078, decode.d0.loss_dice: 1.4627, decode.d1.loss_cls: 1.4914, decode.d1.loss_mask: 0.9408, decode.d1.loss_dice: 1.3443, decode.d2.loss_cls: 1.2766, decode.d2.loss_mask: 0.9316, decode.d2.loss_dice: 1.2966, decode.d3.loss_cls: 1.2349, decode.d3.loss_mask: 0.9245, decode.d3.loss_dice: 1.2673, decode.d4.loss_cls: 1.2247, decode.d4.loss_mask: 0.9233, decode.d4.loss_dice: 1.2626, decode.d5.loss_cls: 1.2102, decode.d5.loss_mask: 0.9264, decode.d5.loss_dice: 1.2652, decode.d6.loss_cls: 1.2081, decode.d6.loss_mask: 0.9204, decode.d6.loss_dice: 1.2635, decode.d7.loss_cls: 1.2144, decode.d7.loss_mask: 0.9250, decode.d7.loss_dice: 1.2635, decode.d8.loss_cls: 1.2133, decode.d8.loss_mask: 0.9272, decode.d8.loss_dice: 1.2678, loss: 41.7704 2022-05-04 22:28:09,265 - mmseg - INFO - Iter [6450/40000] lr: 1.204e-06, eta: 7:45:04, time: 0.792, data_time: 0.009, memory: 51557, decode.loss_cls: 1.2564, decode.loss_mask: 0.9286, decode.loss_dice: 1.3063, decode.d0.loss_cls: 8.2495, decode.d0.loss_mask: 0.9079, decode.d0.loss_dice: 1.5359, decode.d1.loss_cls: 1.5329, decode.d1.loss_mask: 0.9435, decode.d1.loss_dice: 1.3858, decode.d2.loss_cls: 1.3204, decode.d2.loss_mask: 0.9282, decode.d2.loss_dice: 1.3343, decode.d3.loss_cls: 1.2769, decode.d3.loss_mask: 0.9223, decode.d3.loss_dice: 1.3134, decode.d4.loss_cls: 1.2601, decode.d4.loss_mask: 0.9226, decode.d4.loss_dice: 1.3229, decode.d5.loss_cls: 1.2581, decode.d5.loss_mask: 0.9237, decode.d5.loss_dice: 1.3090, decode.d6.loss_cls: 1.2481, decode.d6.loss_mask: 0.9192, decode.d6.loss_dice: 1.3040, decode.d7.loss_cls: 1.2488, decode.d7.loss_mask: 0.9261, decode.d7.loss_dice: 1.3078, decode.d8.loss_cls: 1.2429, decode.d8.loss_mask: 0.9251, decode.d8.loss_dice: 1.3138, loss: 42.5748 2022-05-04 22:28:50,667 - mmseg - INFO - Iter [6500/40000] lr: 1.203e-06, eta: 7:44:21, time: 0.829, data_time: 0.010, memory: 51557, decode.loss_cls: 1.2293, decode.loss_mask: 0.9460, decode.loss_dice: 1.2847, decode.d0.loss_cls: 8.2133, decode.d0.loss_mask: 0.9205, decode.d0.loss_dice: 1.4949, decode.d1.loss_cls: 1.4844, decode.d1.loss_mask: 0.9538, decode.d1.loss_dice: 1.3591, decode.d2.loss_cls: 1.2870, decode.d2.loss_mask: 0.9395, decode.d2.loss_dice: 1.3024, decode.d3.loss_cls: 1.2587, decode.d3.loss_mask: 0.9330, decode.d3.loss_dice: 1.2825, decode.d4.loss_cls: 1.2353, decode.d4.loss_mask: 0.9304, decode.d4.loss_dice: 1.2831, decode.d5.loss_cls: 1.2320, decode.d5.loss_mask: 0.9359, decode.d5.loss_dice: 1.2817, decode.d6.loss_cls: 1.2137, decode.d6.loss_mask: 0.9452, decode.d6.loss_dice: 1.2771, decode.d7.loss_cls: 1.2215, decode.d7.loss_mask: 0.9418, decode.d7.loss_dice: 1.2773, decode.d8.loss_cls: 1.2211, decode.d8.loss_mask: 0.9427, decode.d8.loss_dice: 1.2729, loss: 42.1005 2022-05-04 22:29:32,223 - mmseg - INFO - Iter [6550/40000] lr: 1.201e-06, eta: 7:43:40, time: 0.831, data_time: 0.010, memory: 51557, decode.loss_cls: 1.2681, decode.loss_mask: 0.9492, decode.loss_dice: 1.2927, decode.d0.loss_cls: 8.1981, decode.d0.loss_mask: 0.9270, decode.d0.loss_dice: 1.5162, decode.d1.loss_cls: 1.5274, decode.d1.loss_mask: 0.9642, decode.d1.loss_dice: 1.3696, decode.d2.loss_cls: 1.3354, decode.d2.loss_mask: 0.9444, decode.d2.loss_dice: 1.3130, decode.d3.loss_cls: 1.2824, decode.d3.loss_mask: 0.9482, decode.d3.loss_dice: 1.2875, decode.d4.loss_cls: 1.2800, decode.d4.loss_mask: 0.9431, decode.d4.loss_dice: 1.2954, decode.d5.loss_cls: 1.2801, decode.d5.loss_mask: 0.9312, decode.d5.loss_dice: 1.2874, decode.d6.loss_cls: 1.2586, decode.d6.loss_mask: 0.9380, decode.d6.loss_dice: 1.2744, decode.d7.loss_cls: 1.2660, decode.d7.loss_mask: 0.9415, decode.d7.loss_dice: 1.2859, decode.d8.loss_cls: 1.2605, decode.d8.loss_mask: 0.9433, decode.d8.loss_dice: 1.2860, loss: 42.5947 2022-05-04 22:30:13,136 - mmseg - INFO - Iter [6600/40000] lr: 1.199e-06, eta: 7:42:55, time: 0.819, data_time: 0.010, memory: 51557, decode.loss_cls: 1.2804, decode.loss_mask: 0.9376, decode.loss_dice: 1.2618, decode.d0.loss_cls: 8.1688, decode.d0.loss_mask: 0.9163, decode.d0.loss_dice: 1.4857, decode.d1.loss_cls: 1.5191, decode.d1.loss_mask: 0.9556, decode.d1.loss_dice: 1.3525, decode.d2.loss_cls: 1.3317, decode.d2.loss_mask: 0.9432, decode.d2.loss_dice: 1.3046, decode.d3.loss_cls: 1.2914, decode.d3.loss_mask: 0.9340, decode.d3.loss_dice: 1.2716, decode.d4.loss_cls: 1.2834, decode.d4.loss_mask: 0.9324, decode.d4.loss_dice: 1.2680, decode.d5.loss_cls: 1.2767, decode.d5.loss_mask: 0.9331, decode.d5.loss_dice: 1.2596, decode.d6.loss_cls: 1.2622, decode.d6.loss_mask: 0.9362, decode.d6.loss_dice: 1.2604, decode.d7.loss_cls: 1.2753, decode.d7.loss_mask: 0.9391, decode.d7.loss_dice: 1.2599, decode.d8.loss_cls: 1.2626, decode.d8.loss_mask: 0.9477, decode.d8.loss_dice: 1.2687, loss: 42.3197 2022-05-04 22:30:54,966 - mmseg - INFO - Iter [6650/40000] lr: 1.197e-06, eta: 7:42:14, time: 0.837, data_time: 0.009, memory: 51557, decode.loss_cls: 1.2354, decode.loss_mask: 0.9246, decode.loss_dice: 1.2845, decode.d0.loss_cls: 8.1354, decode.d0.loss_mask: 0.9167, decode.d0.loss_dice: 1.4977, decode.d1.loss_cls: 1.5031, decode.d1.loss_mask: 0.9467, decode.d1.loss_dice: 1.3682, decode.d2.loss_cls: 1.3011, decode.d2.loss_mask: 0.9272, decode.d2.loss_dice: 1.3214, decode.d3.loss_cls: 1.2554, decode.d3.loss_mask: 0.9346, decode.d3.loss_dice: 1.2985, decode.d4.loss_cls: 1.2478, decode.d4.loss_mask: 0.9210, decode.d4.loss_dice: 1.3004, decode.d5.loss_cls: 1.2376, decode.d5.loss_mask: 0.9238, decode.d5.loss_dice: 1.2909, decode.d6.loss_cls: 1.2287, decode.d6.loss_mask: 0.9296, decode.d6.loss_dice: 1.2807, decode.d7.loss_cls: 1.2298, decode.d7.loss_mask: 0.9280, decode.d7.loss_dice: 1.2873, decode.d8.loss_cls: 1.2241, decode.d8.loss_mask: 0.9338, decode.d8.loss_dice: 1.2898, loss: 42.1039 2022-05-04 22:31:36,043 - mmseg - INFO - Iter [6700/40000] lr: 1.195e-06, eta: 7:41:30, time: 0.821, data_time: 0.010, memory: 51557, decode.loss_cls: 1.1982, decode.loss_mask: 0.8961, decode.loss_dice: 1.2633, decode.d0.loss_cls: 8.1173, decode.d0.loss_mask: 0.8733, decode.d0.loss_dice: 1.4810, decode.d1.loss_cls: 1.4487, decode.d1.loss_mask: 0.9124, decode.d1.loss_dice: 1.3391, decode.d2.loss_cls: 1.2528, decode.d2.loss_mask: 0.8962, decode.d2.loss_dice: 1.2847, decode.d3.loss_cls: 1.2226, decode.d3.loss_mask: 0.8918, decode.d3.loss_dice: 1.2576, decode.d4.loss_cls: 1.2098, decode.d4.loss_mask: 0.8944, decode.d4.loss_dice: 1.2657, decode.d5.loss_cls: 1.2006, decode.d5.loss_mask: 0.8997, decode.d5.loss_dice: 1.2613, decode.d6.loss_cls: 1.1912, decode.d6.loss_mask: 0.9033, decode.d6.loss_dice: 1.2561, decode.d7.loss_cls: 1.1851, decode.d7.loss_mask: 0.8995, decode.d7.loss_dice: 1.2685, decode.d8.loss_cls: 1.1860, decode.d8.loss_mask: 0.9019, decode.d8.loss_dice: 1.2635, loss: 41.1216 2022-05-04 22:32:19,292 - mmseg - INFO - Iter [6750/40000] lr: 1.194e-06, eta: 7:40:57, time: 0.866, data_time: 0.061, memory: 51557, decode.loss_cls: 1.2953, decode.loss_mask: 0.8946, decode.loss_dice: 1.2637, decode.d0.loss_cls: 8.1085, decode.d0.loss_mask: 0.8889, decode.d0.loss_dice: 1.4889, decode.d1.loss_cls: 1.5415, decode.d1.loss_mask: 0.9228, decode.d1.loss_dice: 1.3579, decode.d2.loss_cls: 1.3544, decode.d2.loss_mask: 0.9048, decode.d2.loss_dice: 1.2987, decode.d3.loss_cls: 1.3117, decode.d3.loss_mask: 0.9052, decode.d3.loss_dice: 1.2722, decode.d4.loss_cls: 1.3113, decode.d4.loss_mask: 0.8990, decode.d4.loss_dice: 1.2710, decode.d5.loss_cls: 1.3025, decode.d5.loss_mask: 0.8975, decode.d5.loss_dice: 1.2586, decode.d6.loss_cls: 1.2832, decode.d6.loss_mask: 0.9003, decode.d6.loss_dice: 1.2584, decode.d7.loss_cls: 1.2921, decode.d7.loss_mask: 0.8986, decode.d7.loss_dice: 1.2609, decode.d8.loss_cls: 1.2899, decode.d8.loss_mask: 0.8953, decode.d8.loss_dice: 1.2650, loss: 42.0927 2022-05-04 22:32:58,744 - mmseg - INFO - Iter [6800/40000] lr: 1.192e-06, eta: 7:40:05, time: 0.789, data_time: 0.009, memory: 51557, decode.loss_cls: 1.2243, decode.loss_mask: 0.9062, decode.loss_dice: 1.2520, decode.d0.loss_cls: 8.0526, decode.d0.loss_mask: 0.8926, decode.d0.loss_dice: 1.4737, decode.d1.loss_cls: 1.5049, decode.d1.loss_mask: 0.9258, decode.d1.loss_dice: 1.3252, decode.d2.loss_cls: 1.2901, decode.d2.loss_mask: 0.9216, decode.d2.loss_dice: 1.2781, decode.d3.loss_cls: 1.2552, decode.d3.loss_mask: 0.9095, decode.d3.loss_dice: 1.2559, decode.d4.loss_cls: 1.2342, decode.d4.loss_mask: 0.9161, decode.d4.loss_dice: 1.2677, decode.d5.loss_cls: 1.2320, decode.d5.loss_mask: 0.9052, decode.d5.loss_dice: 1.2576, decode.d6.loss_cls: 1.2285, decode.d6.loss_mask: 0.9171, decode.d6.loss_dice: 1.2556, decode.d7.loss_cls: 1.2228, decode.d7.loss_mask: 0.9074, decode.d7.loss_dice: 1.2626, decode.d8.loss_cls: 1.2118, decode.d8.loss_mask: 0.9087, decode.d8.loss_dice: 1.2586, loss: 41.4536 2022-05-04 22:33:38,541 - mmseg - INFO - Iter [6850/40000] lr: 1.190e-06, eta: 7:39:15, time: 0.796, data_time: 0.009, memory: 51557, decode.loss_cls: 1.2269, decode.loss_mask: 0.9169, decode.loss_dice: 1.2812, decode.d0.loss_cls: 8.0465, decode.d0.loss_mask: 0.8910, decode.d0.loss_dice: 1.4845, decode.d1.loss_cls: 1.4719, decode.d1.loss_mask: 0.9206, decode.d1.loss_dice: 1.3465, decode.d2.loss_cls: 1.2849, decode.d2.loss_mask: 0.9107, decode.d2.loss_dice: 1.3002, decode.d3.loss_cls: 1.2308, decode.d3.loss_mask: 0.9187, decode.d3.loss_dice: 1.2813, decode.d4.loss_cls: 1.2461, decode.d4.loss_mask: 0.9069, decode.d4.loss_dice: 1.2791, decode.d5.loss_cls: 1.2171, decode.d5.loss_mask: 0.9133, decode.d5.loss_dice: 1.2729, decode.d6.loss_cls: 1.2250, decode.d6.loss_mask: 0.9068, decode.d6.loss_dice: 1.2733, decode.d7.loss_cls: 1.2177, decode.d7.loss_mask: 0.9114, decode.d7.loss_dice: 1.2820, decode.d8.loss_cls: 1.2118, decode.d8.loss_mask: 0.9149, decode.d8.loss_dice: 1.2799, loss: 41.5703 2022-05-04 22:34:18,998 - mmseg - INFO - Iter [6900/40000] lr: 1.188e-06, eta: 7:38:28, time: 0.809, data_time: 0.010, memory: 51557, decode.loss_cls: 1.2185, decode.loss_mask: 0.9055, decode.loss_dice: 1.2707, decode.d0.loss_cls: 8.0353, decode.d0.loss_mask: 0.8936, decode.d0.loss_dice: 1.4869, decode.d1.loss_cls: 1.4916, decode.d1.loss_mask: 0.9297, decode.d1.loss_dice: 1.3454, decode.d2.loss_cls: 1.2882, decode.d2.loss_mask: 0.9131, decode.d2.loss_dice: 1.2940, decode.d3.loss_cls: 1.2427, decode.d3.loss_mask: 0.9038, decode.d3.loss_dice: 1.2862, decode.d4.loss_cls: 1.2271, decode.d4.loss_mask: 0.9036, decode.d4.loss_dice: 1.2804, decode.d5.loss_cls: 1.2212, decode.d5.loss_mask: 0.9021, decode.d5.loss_dice: 1.2705, decode.d6.loss_cls: 1.2215, decode.d6.loss_mask: 0.9019, decode.d6.loss_dice: 1.2641, decode.d7.loss_cls: 1.2095, decode.d7.loss_mask: 0.9062, decode.d7.loss_dice: 1.2725, decode.d8.loss_cls: 1.2097, decode.d8.loss_mask: 0.9084, decode.d8.loss_dice: 1.2677, loss: 41.4716 2022-05-04 22:34:59,876 - mmseg - INFO - Iter [6950/40000] lr: 1.186e-06, eta: 7:37:43, time: 0.816, data_time: 0.009, memory: 51557, decode.loss_cls: 1.1980, decode.loss_mask: 0.8967, decode.loss_dice: 1.2639, decode.d0.loss_cls: 8.0209, decode.d0.loss_mask: 0.8760, decode.d0.loss_dice: 1.4754, decode.d1.loss_cls: 1.4693, decode.d1.loss_mask: 0.9174, decode.d1.loss_dice: 1.3424, decode.d2.loss_cls: 1.2705, decode.d2.loss_mask: 0.8961, decode.d2.loss_dice: 1.2967, decode.d3.loss_cls: 1.2298, decode.d3.loss_mask: 0.9020, decode.d3.loss_dice: 1.2765, decode.d4.loss_cls: 1.2305, decode.d4.loss_mask: 0.8923, decode.d4.loss_dice: 1.2767, decode.d5.loss_cls: 1.2240, decode.d5.loss_mask: 0.8868, decode.d5.loss_dice: 1.2694, decode.d6.loss_cls: 1.2065, decode.d6.loss_mask: 0.8900, decode.d6.loss_dice: 1.2713, decode.d7.loss_cls: 1.2066, decode.d7.loss_mask: 0.8903, decode.d7.loss_dice: 1.2693, decode.d8.loss_cls: 1.2007, decode.d8.loss_mask: 0.8936, decode.d8.loss_dice: 1.2753, loss: 41.2148 2022-05-04 22:35:39,520 - mmseg - INFO - Saving checkpoint at 7000 iterations 2022-05-04 22:36:04,447 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-04 22:36:04,450 - mmseg - INFO - Iter [7000/40000] lr: 1.185e-06, eta: 7:38:49, time: 1.290, data_time: 0.011, memory: 51557, decode.loss_cls: 1.2114, decode.loss_mask: 0.9294, decode.loss_dice: 1.2554, decode.d0.loss_cls: 7.9996, decode.d0.loss_mask: 0.9113, decode.d0.loss_dice: 1.4636, decode.d1.loss_cls: 1.4680, decode.d1.loss_mask: 0.9446, decode.d1.loss_dice: 1.3217, decode.d2.loss_cls: 1.2771, decode.d2.loss_mask: 0.9334, decode.d2.loss_dice: 1.2761, decode.d3.loss_cls: 1.2413, decode.d3.loss_mask: 0.9282, decode.d3.loss_dice: 1.2596, decode.d4.loss_cls: 1.2332, decode.d4.loss_mask: 0.9327, decode.d4.loss_dice: 1.2539, decode.d5.loss_cls: 1.2310, decode.d5.loss_mask: 0.9297, decode.d5.loss_dice: 1.2491, decode.d6.loss_cls: 1.2012, decode.d6.loss_mask: 0.9279, decode.d6.loss_dice: 1.2475, decode.d7.loss_cls: 1.2089, decode.d7.loss_mask: 0.9297, decode.d7.loss_dice: 1.2505, decode.d8.loss_cls: 1.1970, decode.d8.loss_mask: 0.9333, decode.d8.loss_dice: 1.2438, loss: 41.3900 2022-05-04 22:36:45,964 - mmseg - INFO - Iter [7050/40000] lr: 1.183e-06, eta: 7:38:07, time: 0.833, data_time: 0.013, memory: 51557, decode.loss_cls: 1.2654, decode.loss_mask: 0.9011, decode.loss_dice: 1.2554, decode.d0.loss_cls: 7.9850, decode.d0.loss_mask: 0.8880, decode.d0.loss_dice: 1.4766, decode.d1.loss_cls: 1.5103, decode.d1.loss_mask: 0.9218, decode.d1.loss_dice: 1.3364, decode.d2.loss_cls: 1.3136, decode.d2.loss_mask: 0.8978, decode.d2.loss_dice: 1.2879, decode.d3.loss_cls: 1.2675, decode.d3.loss_mask: 0.9029, decode.d3.loss_dice: 1.2652, decode.d4.loss_cls: 1.2755, decode.d4.loss_mask: 0.8958, decode.d4.loss_dice: 1.2677, decode.d5.loss_cls: 1.2684, decode.d5.loss_mask: 0.8928, decode.d5.loss_dice: 1.2553, decode.d6.loss_cls: 1.2586, decode.d6.loss_mask: 0.8972, decode.d6.loss_dice: 1.2424, decode.d7.loss_cls: 1.2573, decode.d7.loss_mask: 0.8965, decode.d7.loss_dice: 1.2567, decode.d8.loss_cls: 1.2520, decode.d8.loss_mask: 0.9007, decode.d8.loss_dice: 1.2560, loss: 41.5479 2022-05-04 22:37:28,251 - mmseg - INFO - Iter [7100/40000] lr: 1.181e-06, eta: 7:37:28, time: 0.846, data_time: 0.009, memory: 51557, decode.loss_cls: 1.1563, decode.loss_mask: 0.8888, decode.loss_dice: 1.2256, decode.d0.loss_cls: 7.9018, decode.d0.loss_mask: 0.8760, decode.d0.loss_dice: 1.4393, decode.d1.loss_cls: 1.4128, decode.d1.loss_mask: 0.9119, decode.d1.loss_dice: 1.3218, decode.d2.loss_cls: 1.2331, decode.d2.loss_mask: 0.9000, decode.d2.loss_dice: 1.2658, decode.d3.loss_cls: 1.1838, decode.d3.loss_mask: 0.8967, decode.d3.loss_dice: 1.2498, decode.d4.loss_cls: 1.1733, decode.d4.loss_mask: 0.8928, decode.d4.loss_dice: 1.2513, decode.d5.loss_cls: 1.1723, decode.d5.loss_mask: 0.8900, decode.d5.loss_dice: 1.2419, decode.d6.loss_cls: 1.1543, decode.d6.loss_mask: 0.8836, decode.d6.loss_dice: 1.2294, decode.d7.loss_cls: 1.1593, decode.d7.loss_mask: 0.8819, decode.d7.loss_dice: 1.2304, decode.d8.loss_cls: 1.1500, decode.d8.loss_mask: 0.8878, decode.d8.loss_dice: 1.2307, loss: 40.2924 2022-05-04 22:38:08,934 - mmseg - INFO - Iter [7150/40000] lr: 1.179e-06, eta: 7:36:42, time: 0.814, data_time: 0.009, memory: 51557, decode.loss_cls: 1.2403, decode.loss_mask: 0.9203, decode.loss_dice: 1.2808, decode.d0.loss_cls: 7.9157, decode.d0.loss_mask: 0.9098, decode.d0.loss_dice: 1.4934, decode.d1.loss_cls: 1.4708, decode.d1.loss_mask: 0.9481, decode.d1.loss_dice: 1.3556, decode.d2.loss_cls: 1.2894, decode.d2.loss_mask: 0.9303, decode.d2.loss_dice: 1.3060, decode.d3.loss_cls: 1.2561, decode.d3.loss_mask: 0.9248, decode.d3.loss_dice: 1.2788, decode.d4.loss_cls: 1.2532, decode.d4.loss_mask: 0.9215, decode.d4.loss_dice: 1.2900, decode.d5.loss_cls: 1.2396, decode.d5.loss_mask: 0.9201, decode.d5.loss_dice: 1.2693, decode.d6.loss_cls: 1.2276, decode.d6.loss_mask: 0.9274, decode.d6.loss_dice: 1.2646, decode.d7.loss_cls: 1.2285, decode.d7.loss_mask: 0.9252, decode.d7.loss_dice: 1.2714, decode.d8.loss_cls: 1.2266, decode.d8.loss_mask: 0.9226, decode.d8.loss_dice: 1.2780, loss: 41.6857 2022-05-04 22:38:50,544 - mmseg - INFO - Iter [7200/40000] lr: 1.177e-06, eta: 7:36:00, time: 0.832, data_time: 0.009, memory: 51557, decode.loss_cls: 1.2036, decode.loss_mask: 0.9534, decode.loss_dice: 1.3030, decode.d0.loss_cls: 7.8967, decode.d0.loss_mask: 0.9128, decode.d0.loss_dice: 1.4962, decode.d1.loss_cls: 1.4615, decode.d1.loss_mask: 0.9530, decode.d1.loss_dice: 1.3801, decode.d2.loss_cls: 1.2779, decode.d2.loss_mask: 0.9451, decode.d2.loss_dice: 1.3279, decode.d3.loss_cls: 1.2393, decode.d3.loss_mask: 0.9447, decode.d3.loss_dice: 1.3037, decode.d4.loss_cls: 1.2322, decode.d4.loss_mask: 0.9474, decode.d4.loss_dice: 1.3036, decode.d5.loss_cls: 1.2263, decode.d5.loss_mask: 0.9509, decode.d5.loss_dice: 1.3013, decode.d6.loss_cls: 1.2143, decode.d6.loss_mask: 0.9439, decode.d6.loss_dice: 1.2977, decode.d7.loss_cls: 1.2107, decode.d7.loss_mask: 0.9451, decode.d7.loss_dice: 1.3000, decode.d8.loss_cls: 1.2050, decode.d8.loss_mask: 0.9445, decode.d8.loss_dice: 1.2995, loss: 41.9213 2022-05-04 22:39:31,704 - mmseg - INFO - Iter [7250/40000] lr: 1.176e-06, eta: 7:35:16, time: 0.823, data_time: 0.010, memory: 51557, decode.loss_cls: 1.2305, decode.loss_mask: 0.9288, decode.loss_dice: 1.2573, decode.d0.loss_cls: 7.8720, decode.d0.loss_mask: 0.9171, decode.d0.loss_dice: 1.4780, decode.d1.loss_cls: 1.4734, decode.d1.loss_mask: 0.9508, decode.d1.loss_dice: 1.3421, decode.d2.loss_cls: 1.2769, decode.d2.loss_mask: 0.9371, decode.d2.loss_dice: 1.2879, decode.d3.loss_cls: 1.2454, decode.d3.loss_mask: 0.9322, decode.d3.loss_dice: 1.2650, decode.d4.loss_cls: 1.2409, decode.d4.loss_mask: 0.9301, decode.d4.loss_dice: 1.2561, decode.d5.loss_cls: 1.2248, decode.d5.loss_mask: 0.9270, decode.d5.loss_dice: 1.2582, decode.d6.loss_cls: 1.2260, decode.d6.loss_mask: 0.9225, decode.d6.loss_dice: 1.2545, decode.d7.loss_cls: 1.2211, decode.d7.loss_mask: 0.9232, decode.d7.loss_dice: 1.2656, decode.d8.loss_cls: 1.2226, decode.d8.loss_mask: 0.9221, decode.d8.loss_dice: 1.2611, loss: 41.4503 2022-05-04 22:40:12,490 - mmseg - INFO - Iter [7300/40000] lr: 1.174e-06, eta: 7:34:30, time: 0.816, data_time: 0.009, memory: 51557, decode.loss_cls: 1.2207, decode.loss_mask: 0.8863, decode.loss_dice: 1.2738, decode.d0.loss_cls: 7.8457, decode.d0.loss_mask: 0.8694, decode.d0.loss_dice: 1.4851, decode.d1.loss_cls: 1.4717, decode.d1.loss_mask: 0.9006, decode.d1.loss_dice: 1.3528, decode.d2.loss_cls: 1.2879, decode.d2.loss_mask: 0.8814, decode.d2.loss_dice: 1.2937, decode.d3.loss_cls: 1.2600, decode.d3.loss_mask: 0.8838, decode.d3.loss_dice: 1.2784, decode.d4.loss_cls: 1.2444, decode.d4.loss_mask: 0.8801, decode.d4.loss_dice: 1.2880, decode.d5.loss_cls: 1.2281, decode.d5.loss_mask: 0.8833, decode.d5.loss_dice: 1.2768, decode.d6.loss_cls: 1.2241, decode.d6.loss_mask: 0.8846, decode.d6.loss_dice: 1.2670, decode.d7.loss_cls: 1.2208, decode.d7.loss_mask: 0.8820, decode.d7.loss_dice: 1.2715, decode.d8.loss_cls: 1.2174, decode.d8.loss_mask: 0.8855, decode.d8.loss_dice: 1.2683, loss: 41.1131 2022-05-04 22:40:55,865 - mmseg - INFO - Iter [7350/40000] lr: 1.172e-06, eta: 7:33:55, time: 0.868, data_time: 0.058, memory: 51557, decode.loss_cls: 1.1883, decode.loss_mask: 0.8892, decode.loss_dice: 1.2782, decode.d0.loss_cls: 7.8149, decode.d0.loss_mask: 0.8891, decode.d0.loss_dice: 1.4885, decode.d1.loss_cls: 1.4428, decode.d1.loss_mask: 0.9153, decode.d1.loss_dice: 1.3667, decode.d2.loss_cls: 1.2494, decode.d2.loss_mask: 0.9050, decode.d2.loss_dice: 1.3150, decode.d3.loss_cls: 1.1985, decode.d3.loss_mask: 0.8975, decode.d3.loss_dice: 1.2833, decode.d4.loss_cls: 1.1952, decode.d4.loss_mask: 0.8926, decode.d4.loss_dice: 1.2874, decode.d5.loss_cls: 1.1859, decode.d5.loss_mask: 0.8947, decode.d5.loss_dice: 1.2765, decode.d6.loss_cls: 1.1850, decode.d6.loss_mask: 0.8943, decode.d6.loss_dice: 1.2686, decode.d7.loss_cls: 1.1879, decode.d7.loss_mask: 0.8871, decode.d7.loss_dice: 1.2862, decode.d8.loss_cls: 1.1847, decode.d8.loss_mask: 0.8860, decode.d8.loss_dice: 1.2775, loss: 40.9114 2022-05-04 22:41:37,465 - mmseg - INFO - Iter [7400/40000] lr: 1.170e-06, eta: 7:33:13, time: 0.832, data_time: 0.010, memory: 51557, decode.loss_cls: 1.1919, decode.loss_mask: 0.8844, decode.loss_dice: 1.2385, decode.d0.loss_cls: 7.7898, decode.d0.loss_mask: 0.8848, decode.d0.loss_dice: 1.4658, decode.d1.loss_cls: 1.4680, decode.d1.loss_mask: 0.9038, decode.d1.loss_dice: 1.3258, decode.d2.loss_cls: 1.2775, decode.d2.loss_mask: 0.8840, decode.d2.loss_dice: 1.2662, decode.d3.loss_cls: 1.2247, decode.d3.loss_mask: 0.8826, decode.d3.loss_dice: 1.2418, decode.d4.loss_cls: 1.2066, decode.d4.loss_mask: 0.8810, decode.d4.loss_dice: 1.2500, decode.d5.loss_cls: 1.2087, decode.d5.loss_mask: 0.8797, decode.d5.loss_dice: 1.2457, decode.d6.loss_cls: 1.1923, decode.d6.loss_mask: 0.8790, decode.d6.loss_dice: 1.2319, decode.d7.loss_cls: 1.1878, decode.d7.loss_mask: 0.8833, decode.d7.loss_dice: 1.2411, decode.d8.loss_cls: 1.1874, decode.d8.loss_mask: 0.8807, decode.d8.loss_dice: 1.2381, loss: 40.5229 2022-05-04 22:42:18,311 - mmseg - INFO - Iter [7450/40000] lr: 1.168e-06, eta: 7:32:28, time: 0.817, data_time: 0.010, memory: 51557, decode.loss_cls: 1.1284, decode.loss_mask: 0.9394, decode.loss_dice: 1.2138, decode.d0.loss_cls: 7.7330, decode.d0.loss_mask: 0.9085, decode.d0.loss_dice: 1.3824, decode.d1.loss_cls: 1.3285, decode.d1.loss_mask: 0.9538, decode.d1.loss_dice: 1.2725, decode.d2.loss_cls: 1.1781, decode.d2.loss_mask: 0.9377, decode.d2.loss_dice: 1.2241, decode.d3.loss_cls: 1.1387, decode.d3.loss_mask: 0.9380, decode.d3.loss_dice: 1.2101, decode.d4.loss_cls: 1.1426, decode.d4.loss_mask: 0.9350, decode.d4.loss_dice: 1.2118, decode.d5.loss_cls: 1.1304, decode.d5.loss_mask: 0.9288, decode.d5.loss_dice: 1.2129, decode.d6.loss_cls: 1.1253, decode.d6.loss_mask: 0.9272, decode.d6.loss_dice: 1.1993, decode.d7.loss_cls: 1.1137, decode.d7.loss_mask: 0.9311, decode.d7.loss_dice: 1.2154, decode.d8.loss_cls: 1.1120, decode.d8.loss_mask: 0.9319, decode.d8.loss_dice: 1.2091, loss: 39.8135 2022-05-04 22:42:58,457 - mmseg - INFO - Iter [7500/40000] lr: 1.167e-06, eta: 7:31:39, time: 0.803, data_time: 0.009, memory: 51557, decode.loss_cls: 1.1615, decode.loss_mask: 0.9014, decode.loss_dice: 1.2393, decode.d0.loss_cls: 7.7284, decode.d0.loss_mask: 0.8873, decode.d0.loss_dice: 1.4423, decode.d1.loss_cls: 1.4054, decode.d1.loss_mask: 0.9218, decode.d1.loss_dice: 1.3136, decode.d2.loss_cls: 1.2128, decode.d2.loss_mask: 0.9012, decode.d2.loss_dice: 1.2516, decode.d3.loss_cls: 1.1896, decode.d3.loss_mask: 0.9026, decode.d3.loss_dice: 1.2334, decode.d4.loss_cls: 1.1761, decode.d4.loss_mask: 0.8960, decode.d4.loss_dice: 1.2427, decode.d5.loss_cls: 1.1685, decode.d5.loss_mask: 0.8988, decode.d5.loss_dice: 1.2301, decode.d6.loss_cls: 1.1467, decode.d6.loss_mask: 0.8956, decode.d6.loss_dice: 1.2240, decode.d7.loss_cls: 1.1503, decode.d7.loss_mask: 0.8959, decode.d7.loss_dice: 1.2325, decode.d8.loss_cls: 1.1469, decode.d8.loss_mask: 0.9024, decode.d8.loss_dice: 1.2346, loss: 40.1335 2022-05-04 22:43:38,573 - mmseg - INFO - Iter [7550/40000] lr: 1.165e-06, eta: 7:30:51, time: 0.802, data_time: 0.010, memory: 51557, decode.loss_cls: 1.1641, decode.loss_mask: 0.8866, decode.loss_dice: 1.2223, decode.d0.loss_cls: 7.7330, decode.d0.loss_mask: 0.8767, decode.d0.loss_dice: 1.4480, decode.d1.loss_cls: 1.4302, decode.d1.loss_mask: 0.9045, decode.d1.loss_dice: 1.3121, decode.d2.loss_cls: 1.2405, decode.d2.loss_mask: 0.8986, decode.d2.loss_dice: 1.2612, decode.d3.loss_cls: 1.1994, decode.d3.loss_mask: 0.8871, decode.d3.loss_dice: 1.2346, decode.d4.loss_cls: 1.1878, decode.d4.loss_mask: 0.8870, decode.d4.loss_dice: 1.2393, decode.d5.loss_cls: 1.1708, decode.d5.loss_mask: 0.8835, decode.d5.loss_dice: 1.2346, decode.d6.loss_cls: 1.1538, decode.d6.loss_mask: 0.8856, decode.d6.loss_dice: 1.2261, decode.d7.loss_cls: 1.1606, decode.d7.loss_mask: 0.8857, decode.d7.loss_dice: 1.2314, decode.d8.loss_cls: 1.1639, decode.d8.loss_mask: 0.8846, decode.d8.loss_dice: 1.2241, loss: 40.1177 2022-05-04 22:44:19,362 - mmseg - INFO - Iter [7600/40000] lr: 1.163e-06, eta: 7:30:05, time: 0.814, data_time: 0.010, memory: 51557, decode.loss_cls: 1.2077, decode.loss_mask: 0.8966, decode.loss_dice: 1.2416, decode.d0.loss_cls: 7.6870, decode.d0.loss_mask: 0.8799, decode.d0.loss_dice: 1.4542, decode.d1.loss_cls: 1.4302, decode.d1.loss_mask: 0.9077, decode.d1.loss_dice: 1.3206, decode.d2.loss_cls: 1.2509, decode.d2.loss_mask: 0.9052, decode.d2.loss_dice: 1.2787, decode.d3.loss_cls: 1.2254, decode.d3.loss_mask: 0.8911, decode.d3.loss_dice: 1.2442, decode.d4.loss_cls: 1.2066, decode.d4.loss_mask: 0.8965, decode.d4.loss_dice: 1.2488, decode.d5.loss_cls: 1.1953, decode.d5.loss_mask: 0.8959, decode.d5.loss_dice: 1.2411, decode.d6.loss_cls: 1.1965, decode.d6.loss_mask: 0.8913, decode.d6.loss_dice: 1.2367, decode.d7.loss_cls: 1.1994, decode.d7.loss_mask: 0.8876, decode.d7.loss_dice: 1.2390, decode.d8.loss_cls: 1.1937, decode.d8.loss_mask: 0.8941, decode.d8.loss_dice: 1.2418, loss: 40.4856 2022-05-04 22:44:59,552 - mmseg - INFO - Iter [7650/40000] lr: 1.161e-06, eta: 7:29:17, time: 0.805, data_time: 0.011, memory: 51557, decode.loss_cls: 1.1372, decode.loss_mask: 0.9177, decode.loss_dice: 1.2568, decode.d0.loss_cls: 7.6743, decode.d0.loss_mask: 0.9071, decode.d0.loss_dice: 1.4544, decode.d1.loss_cls: 1.3778, decode.d1.loss_mask: 0.9436, decode.d1.loss_dice: 1.3290, decode.d2.loss_cls: 1.1989, decode.d2.loss_mask: 0.9270, decode.d2.loss_dice: 1.2747, decode.d3.loss_cls: 1.1620, decode.d3.loss_mask: 0.9220, decode.d3.loss_dice: 1.2660, decode.d4.loss_cls: 1.1558, decode.d4.loss_mask: 0.9210, decode.d4.loss_dice: 1.2678, decode.d5.loss_cls: 1.1382, decode.d5.loss_mask: 0.9207, decode.d5.loss_dice: 1.2711, decode.d6.loss_cls: 1.1366, decode.d6.loss_mask: 0.9234, decode.d6.loss_dice: 1.2543, decode.d7.loss_cls: 1.1416, decode.d7.loss_mask: 0.9218, decode.d7.loss_dice: 1.2544, decode.d8.loss_cls: 1.1299, decode.d8.loss_mask: 0.9247, decode.d8.loss_dice: 1.2600, loss: 40.3699 2022-05-04 22:45:40,402 - mmseg - INFO - Iter [7700/40000] lr: 1.159e-06, eta: 7:28:32, time: 0.816, data_time: 0.009, memory: 51557, decode.loss_cls: 1.1452, decode.loss_mask: 0.8960, decode.loss_dice: 1.2623, decode.d0.loss_cls: 7.6463, decode.d0.loss_mask: 0.8752, decode.d0.loss_dice: 1.4691, decode.d1.loss_cls: 1.3621, decode.d1.loss_mask: 0.9086, decode.d1.loss_dice: 1.3418, decode.d2.loss_cls: 1.1883, decode.d2.loss_mask: 0.8978, decode.d2.loss_dice: 1.2886, decode.d3.loss_cls: 1.1569, decode.d3.loss_mask: 0.9008, decode.d3.loss_dice: 1.2638, decode.d4.loss_cls: 1.1496, decode.d4.loss_mask: 0.8957, decode.d4.loss_dice: 1.2660, decode.d5.loss_cls: 1.1358, decode.d5.loss_mask: 0.8980, decode.d5.loss_dice: 1.2678, decode.d6.loss_cls: 1.1292, decode.d6.loss_mask: 0.8997, decode.d6.loss_dice: 1.2636, decode.d7.loss_cls: 1.1331, decode.d7.loss_mask: 0.8974, decode.d7.loss_dice: 1.2708, decode.d8.loss_cls: 1.1303, decode.d8.loss_mask: 0.8997, decode.d8.loss_dice: 1.2747, loss: 40.1143 2022-05-04 22:46:21,508 - mmseg - INFO - Iter [7750/40000] lr: 1.158e-06, eta: 7:27:48, time: 0.823, data_time: 0.010, memory: 51557, decode.loss_cls: 1.1491, decode.loss_mask: 0.9254, decode.loss_dice: 1.2790, decode.d0.loss_cls: 7.5852, decode.d0.loss_mask: 0.9077, decode.d0.loss_dice: 1.4775, decode.d1.loss_cls: 1.3749, decode.d1.loss_mask: 0.9400, decode.d1.loss_dice: 1.3443, decode.d2.loss_cls: 1.1921, decode.d2.loss_mask: 0.9273, decode.d2.loss_dice: 1.3065, decode.d3.loss_cls: 1.1697, decode.d3.loss_mask: 0.9292, decode.d3.loss_dice: 1.2836, decode.d4.loss_cls: 1.1588, decode.d4.loss_mask: 0.9281, decode.d4.loss_dice: 1.2858, decode.d5.loss_cls: 1.1532, decode.d5.loss_mask: 0.9203, decode.d5.loss_dice: 1.2860, decode.d6.loss_cls: 1.1456, decode.d6.loss_mask: 0.9247, decode.d6.loss_dice: 1.2815, decode.d7.loss_cls: 1.1539, decode.d7.loss_mask: 0.9159, decode.d7.loss_dice: 1.2849, decode.d8.loss_cls: 1.1360, decode.d8.loss_mask: 0.9267, decode.d8.loss_dice: 1.2835, loss: 40.5767 2022-05-04 22:47:04,149 - mmseg - INFO - Iter [7800/40000] lr: 1.156e-06, eta: 7:27:11, time: 0.853, data_time: 0.010, memory: 51557, decode.loss_cls: 1.1918, decode.loss_mask: 0.9021, decode.loss_dice: 1.2509, decode.d0.loss_cls: 7.6225, decode.d0.loss_mask: 0.8885, decode.d0.loss_dice: 1.4579, decode.d1.loss_cls: 1.4394, decode.d1.loss_mask: 0.9173, decode.d1.loss_dice: 1.3446, decode.d2.loss_cls: 1.2599, decode.d2.loss_mask: 0.9035, decode.d2.loss_dice: 1.2910, decode.d3.loss_cls: 1.2213, decode.d3.loss_mask: 0.9000, decode.d3.loss_dice: 1.2647, decode.d4.loss_cls: 1.2093, decode.d4.loss_mask: 0.8969, decode.d4.loss_dice: 1.2613, decode.d5.loss_cls: 1.1927, decode.d5.loss_mask: 0.9022, decode.d5.loss_dice: 1.2641, decode.d6.loss_cls: 1.1887, decode.d6.loss_mask: 0.8948, decode.d6.loss_dice: 1.2580, decode.d7.loss_cls: 1.1828, decode.d7.loss_mask: 0.8991, decode.d7.loss_dice: 1.2632, decode.d8.loss_cls: 1.1885, decode.d8.loss_mask: 0.8979, decode.d8.loss_dice: 1.2520, loss: 40.6068 2022-05-04 22:47:45,331 - mmseg - INFO - Iter [7850/40000] lr: 1.154e-06, eta: 7:26:27, time: 0.824, data_time: 0.010, memory: 51557, decode.loss_cls: 1.1695, decode.loss_mask: 0.8982, decode.loss_dice: 1.2362, decode.d0.loss_cls: 7.5810, decode.d0.loss_mask: 0.8873, decode.d0.loss_dice: 1.4560, decode.d1.loss_cls: 1.4051, decode.d1.loss_mask: 0.9136, decode.d1.loss_dice: 1.3184, decode.d2.loss_cls: 1.2387, decode.d2.loss_mask: 0.9005, decode.d2.loss_dice: 1.2689, decode.d3.loss_cls: 1.1987, decode.d3.loss_mask: 0.8988, decode.d3.loss_dice: 1.2446, decode.d4.loss_cls: 1.1867, decode.d4.loss_mask: 0.8940, decode.d4.loss_dice: 1.2502, decode.d5.loss_cls: 1.1820, decode.d5.loss_mask: 0.8953, decode.d5.loss_dice: 1.2473, decode.d6.loss_cls: 1.1743, decode.d6.loss_mask: 0.8913, decode.d6.loss_dice: 1.2274, decode.d7.loss_cls: 1.1680, decode.d7.loss_mask: 0.8927, decode.d7.loss_dice: 1.2345, decode.d8.loss_cls: 1.1686, decode.d8.loss_mask: 0.8934, decode.d8.loss_dice: 1.2460, loss: 40.1671 2022-05-04 22:48:28,773 - mmseg - INFO - Iter [7900/40000] lr: 1.152e-06, eta: 7:25:53, time: 0.869, data_time: 0.062, memory: 51557, decode.loss_cls: 1.1937, decode.loss_mask: 0.8902, decode.loss_dice: 1.2437, decode.d0.loss_cls: 7.5701, decode.d0.loss_mask: 0.8794, decode.d0.loss_dice: 1.4496, decode.d1.loss_cls: 1.4170, decode.d1.loss_mask: 0.9104, decode.d1.loss_dice: 1.3149, decode.d2.loss_cls: 1.2461, decode.d2.loss_mask: 0.8920, decode.d2.loss_dice: 1.2622, decode.d3.loss_cls: 1.2086, decode.d3.loss_mask: 0.8886, decode.d3.loss_dice: 1.2411, decode.d4.loss_cls: 1.2011, decode.d4.loss_mask: 0.8872, decode.d4.loss_dice: 1.2477, decode.d5.loss_cls: 1.1986, decode.d5.loss_mask: 0.8881, decode.d5.loss_dice: 1.2449, decode.d6.loss_cls: 1.1961, decode.d6.loss_mask: 0.8809, decode.d6.loss_dice: 1.2391, decode.d7.loss_cls: 1.1871, decode.d7.loss_mask: 0.8829, decode.d7.loss_dice: 1.2345, decode.d8.loss_cls: 1.1846, decode.d8.loss_mask: 0.8859, decode.d8.loss_dice: 1.2334, loss: 40.2000 2022-05-04 22:49:09,273 - mmseg - INFO - Iter [7950/40000] lr: 1.150e-06, eta: 7:25:06, time: 0.810, data_time: 0.010, memory: 51557, decode.loss_cls: 1.1374, decode.loss_mask: 0.9092, decode.loss_dice: 1.2656, decode.d0.loss_cls: 7.5487, decode.d0.loss_mask: 0.8911, decode.d0.loss_dice: 1.4530, decode.d1.loss_cls: 1.3950, decode.d1.loss_mask: 0.9245, decode.d1.loss_dice: 1.3422, decode.d2.loss_cls: 1.2032, decode.d2.loss_mask: 0.9091, decode.d2.loss_dice: 1.2920, decode.d3.loss_cls: 1.1568, decode.d3.loss_mask: 0.9136, decode.d3.loss_dice: 1.2739, decode.d4.loss_cls: 1.1591, decode.d4.loss_mask: 0.9064, decode.d4.loss_dice: 1.2662, decode.d5.loss_cls: 1.1463, decode.d5.loss_mask: 0.9077, decode.d5.loss_dice: 1.2671, decode.d6.loss_cls: 1.1358, decode.d6.loss_mask: 0.9086, decode.d6.loss_dice: 1.2676, decode.d7.loss_cls: 1.1394, decode.d7.loss_mask: 0.9033, decode.d7.loss_dice: 1.2666, decode.d8.loss_cls: 1.1379, decode.d8.loss_mask: 0.9053, decode.d8.loss_dice: 1.2677, loss: 40.2004 2022-05-04 22:49:49,444 - mmseg - INFO - Saving checkpoint at 8000 iterations 2022-05-04 22:50:17,944 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-04 22:50:17,958 - mmseg - INFO - Iter [8000/40000] lr: 1.149e-06, eta: 7:26:12, time: 1.370, data_time: 0.008, memory: 51557, decode.loss_cls: 1.1420, decode.loss_mask: 0.9083, decode.loss_dice: 1.2568, decode.d0.loss_cls: 7.5092, decode.d0.loss_mask: 0.8933, decode.d0.loss_dice: 1.4510, decode.d1.loss_cls: 1.3928, decode.d1.loss_mask: 0.9195, decode.d1.loss_dice: 1.3291, decode.d2.loss_cls: 1.1977, decode.d2.loss_mask: 0.9008, decode.d2.loss_dice: 1.2859, decode.d3.loss_cls: 1.1650, decode.d3.loss_mask: 0.8987, decode.d3.loss_dice: 1.2646, decode.d4.loss_cls: 1.1535, decode.d4.loss_mask: 0.8980, decode.d4.loss_dice: 1.2683, decode.d5.loss_cls: 1.1484, decode.d5.loss_mask: 0.8985, decode.d5.loss_dice: 1.2606, decode.d6.loss_cls: 1.1329, decode.d6.loss_mask: 0.8996, decode.d6.loss_dice: 1.2557, decode.d7.loss_cls: 1.1433, decode.d7.loss_mask: 0.8959, decode.d7.loss_dice: 1.2568, decode.d8.loss_cls: 1.1347, decode.d8.loss_mask: 0.9058, decode.d8.loss_dice: 1.2614, loss: 40.0279 2022-05-04 22:50:50,665 - mmseg - INFO - per class results: 2022-05-04 22:50:50,678 - mmseg - INFO - +------------------+-------+-------+ | Class | IoU | Acc | +------------------+-------+-------+ | person | 87.1 | 93.83 | | bicycle | 74.09 | 90.39 | | car | 45.97 | 53.94 | | motorcycle | 89.22 | 95.3 | | airplane | 89.97 | 94.66 | | bus | 85.97 | 90.82 | | train | 81.88 | 96.66 | | truck | 62.44 | 89.28 | | boat | 78.98 | 85.32 | | traffic light | 81.01 | 93.11 | | fire hydrant | 86.44 | 96.47 | | stop sign | 93.86 | 97.58 | | parking meter | 72.92 | 75.51 | | bench | 52.47 | 71.85 | | bird | 77.08 | 82.82 | | cat | 92.48 | 95.0 | | dog | 91.29 | 95.83 | | horse | 90.83 | 95.39 | | sheep | 79.71 | 91.97 | | cow | 93.74 | 96.0 | | elephant | 92.21 | 95.32 | | bear | 92.62 | 94.32 | | zebra | 91.08 | 94.5 | | giraffe | 88.43 | 94.07 | | backpack | 30.38 | 66.37 | | umbrella | 75.92 | 88.66 | | handbag | 15.27 | 47.73 | | tie | 22.2 | 22.2 | | suitcase | 74.48 | 80.79 | | frisbee | 93.78 | 97.49 | | skis | 34.85 | 58.75 | | snowboard | 64.15 | 72.48 | | sports ball | 82.68 | 94.41 | | kite | 66.21 | 82.3 | | baseball bat | 63.9 | 78.24 | | baseball glove | 0.72 | 0.75 | | skateboard | 67.56 | 85.42 | | surfboard | 89.34 | 94.16 | | tennis racket | 27.66 | 30.97 | | bottle | 66.04 | 83.19 | | wine glass | 83.93 | 91.08 | | cup | 62.49 | 77.92 | | fork | 48.74 | 73.18 | | knife | 73.83 | 84.8 | | spoon | 49.36 | 61.36 | | bowl | 58.2 | 68.26 | | banana | 63.62 | 69.95 | | apple | 77.54 | 87.52 | | sandwich | 85.12 | 96.37 | | orange | 60.47 | 93.72 | | broccoli | 81.26 | 93.48 | | carrot | 46.87 | 69.01 | | hot dog | 59.46 | 96.55 | | pizza | 94.8 | 96.4 | | donut | 75.64 | 88.55 | | cake | 81.96 | 87.4 | | chair | 57.35 | 71.05 | | couch | 72.47 | 92.75 | | potted plant | 31.8 | 37.03 | | bed | 75.62 | 89.31 | | dining table | 59.51 | 78.41 | | toilet | 89.61 | 95.18 | | tv | 80.96 | 94.94 | | laptop | 86.74 | 96.59 | | mouse | 50.66 | 65.28 | | remote | 68.22 | 82.82 | | keyboard | 86.27 | 94.61 | | cell phone | 64.56 | 96.29 | | microwave | 48.18 | 74.18 | | oven | 60.42 | 79.37 | | toaster | 0.0 | 0.0 | | sink | 71.85 | 79.36 | | refrigerator | 85.19 | 96.1 | | book | 59.57 | 67.29 | | clock | 76.84 | 79.18 | | vase | 57.44 | 82.41 | | scissors | 81.29 | 88.58 | | teddy bear | 85.71 | 90.67 | | hair drier | 0.0 | 0.0 | | toothbrush | 5.04 | 6.04 | | banner | 35.71 | 64.08 | | blanket | 0.0 | 0.0 | | branch | 0.0 | 0.0 | | bridge | 0.68 | 0.94 | | building-other | 50.11 | 70.69 | | bush | 26.97 | 32.6 | | cabinet | 22.27 | 26.01 | | cage | 0.0 | 0.0 | | cardboard | 26.36 | 27.4 | | carpet | 61.92 | 69.75 | | ceiling-other | 57.44 | 62.35 | | ceiling-tile | 0.0 | 0.0 | | cloth | 0.0 | 0.0 | | clothes | 19.7 | 31.35 | | clouds | 54.0 | 71.06 | | counter | 42.41 | 48.72 | | cupboard | 59.96 | 93.52 | | curtain | 71.43 | 87.3 | | desk-stuff | 34.67 | 39.95 | | dirt | 44.8 | 75.52 | | door-stuff | 45.56 | 66.49 | | fence | 40.76 | 71.99 | | floor-marble | 0.0 | 0.0 | | floor-other | 39.5 | 59.84 | | floor-stone | 0.0 | 0.0 | | floor-tile | 58.11 | 73.46 | | floor-wood | 76.07 | 86.05 | | flower | 17.56 | 39.6 | | fog | 0.0 | 0.0 | | food-other | 34.14 | 75.12 | | fruit | 75.97 | 82.07 | | furniture-other | 16.06 | 21.31 | | grass | 73.11 | 82.49 | | gravel | 15.37 | 15.91 | | ground-other | 11.26 | 19.39 | | hill | 20.18 | 25.37 | | house | 23.68 | 26.92 | | leaves | 15.6 | 17.91 | | light | 36.87 | 50.35 | | mat | 9.87 | 11.02 | | metal | 18.43 | 27.66 | | mirror-stuff | 33.84 | 46.89 | | moss | 0.0 | 0.0 | | mountain | 33.84 | 67.26 | | mud | 0.0 | 0.0 | | napkin | 0.0 | 0.0 | | net | 26.77 | 33.2 | | paper | 32.06 | 75.19 | | pavement | 49.22 | 65.62 | | pillow | 0.0 | 0.0 | | plant-other | 28.93 | 49.96 | | plastic | 4.78 | 4.91 | | platform | 55.01 | 55.81 | | playingfield | 67.76 | 84.37 | | railing | 10.23 | 13.59 | | railroad | 59.99 | 92.1 | | river | 0.0 | 0.0 | | road | 69.97 | 77.53 | | rock | 44.55 | 64.07 | | roof | 5.66 | 12.52 | | rug | 52.88 | 68.87 | | salad | 0.0 | 0.0 | | sand | 74.41 | 86.48 | | sea | 68.93 | 90.05 | | shelf | 25.72 | 43.79 | | sky-other | 60.96 | 74.47 | | skyscraper | 8.27 | 15.16 | | snow | 92.53 | 95.68 | | solid-other | nan | nan | | stairs | 26.29 | 37.01 | | stone | 5.31 | 11.85 | | straw | 25.83 | 30.51 | | structural-other | 19.05 | 48.58 | | table | 17.44 | 28.77 | | tent | 4.72 | 4.86 | | textile-other | 13.64 | 27.36 | | towel | 29.6 | 49.9 | | tree | 78.06 | 89.11 | | vegetable | 31.07 | 49.38 | | wall-brick | 34.15 | 51.75 | | wall-concrete | 26.85 | 28.09 | | wall-other | 64.12 | 82.95 | | wall-panel | 0.0 | 0.0 | | wall-stone | 19.43 | 22.63 | | wall-tile | 53.01 | 80.72 | | wall-wood | 33.15 | 53.37 | | water-other | 24.47 | 36.97 | | waterdrops | nan | nan | | window-blind | 50.71 | 73.42 | | window-other | 49.02 | 61.39 | | wood | 14.12 | 31.55 | +------------------+-------+-------+ 2022-05-04 22:50:50,678 - mmseg - INFO - Summary: 2022-05-04 22:50:50,678 - mmseg - INFO - +-------+-------+------+ | aAcc | mIoU | mAcc | +-------+-------+------+ | 74.55 | 48.33 | 59.7 | +-------+-------+------+ 2022-05-04 22:50:50,681 - mmseg - INFO - The previous best checkpoint /mnt/lustre/chenzhe.vendor/workspace/DenseAdaptor/segmentation/work_dirs/mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss/best_mIoU_iter_4000.pth was removed 2022-05-04 22:51:18,038 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_8000.pth. 2022-05-04 22:51:18,050 - mmseg - INFO - Best mIoU is 0.4833 at 8000 iter. 2022-05-04 22:51:18,062 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-04 22:51:18,063 - mmseg - INFO - Iter(val) [125] aAcc: 0.7455, mIoU: 0.4833, mAcc: 0.5970, IoU.person: 0.8710, IoU.bicycle: 0.7409, IoU.car: 0.4597, IoU.motorcycle: 0.8922, IoU.airplane: 0.8997, IoU.bus: 0.8597, IoU.train: 0.8188, IoU.truck: 0.6244, IoU.boat: 0.7898, IoU.traffic light: 0.8101, IoU.fire hydrant: 0.8644, IoU.stop sign: 0.9386, IoU.parking meter: 0.7292, IoU.bench: 0.5247, IoU.bird: 0.7708, IoU.cat: 0.9248, IoU.dog: 0.9129, IoU.horse: 0.9083, IoU.sheep: 0.7971, IoU.cow: 0.9374, IoU.elephant: 0.9221, IoU.bear: 0.9262, IoU.zebra: 0.9108, IoU.giraffe: 0.8843, IoU.backpack: 0.3038, IoU.umbrella: 0.7592, IoU.handbag: 0.1527, IoU.tie: 0.2220, IoU.suitcase: 0.7448, IoU.frisbee: 0.9378, IoU.skis: 0.3485, IoU.snowboard: 0.6415, IoU.sports ball: 0.8268, IoU.kite: 0.6621, IoU.baseball bat: 0.6390, IoU.baseball glove: 0.0072, IoU.skateboard: 0.6756, IoU.surfboard: 0.8934, IoU.tennis racket: 0.2766, IoU.bottle: 0.6604, IoU.wine glass: 0.8393, IoU.cup: 0.6249, IoU.fork: 0.4874, IoU.knife: 0.7383, IoU.spoon: 0.4936, IoU.bowl: 0.5820, IoU.banana: 0.6362, IoU.apple: 0.7754, IoU.sandwich: 0.8512, IoU.orange: 0.6047, IoU.broccoli: 0.8126, IoU.carrot: 0.4687, IoU.hot dog: 0.5946, IoU.pizza: 0.9480, IoU.donut: 0.7564, IoU.cake: 0.8196, IoU.chair: 0.5735, IoU.couch: 0.7247, IoU.potted plant: 0.3180, IoU.bed: 0.7562, IoU.dining table: 0.5951, IoU.toilet: 0.8961, IoU.tv: 0.8096, IoU.laptop: 0.8674, IoU.mouse: 0.5066, IoU.remote: 0.6822, IoU.keyboard: 0.8627, IoU.cell phone: 0.6456, IoU.microwave: 0.4818, IoU.oven: 0.6042, IoU.toaster: 0.0000, IoU.sink: 0.7185, IoU.refrigerator: 0.8519, IoU.book: 0.5957, IoU.clock: 0.7684, IoU.vase: 0.5744, IoU.scissors: 0.8129, IoU.teddy bear: 0.8571, IoU.hair drier: 0.0000, IoU.toothbrush: 0.0504, IoU.banner: 0.3571, IoU.blanket: 0.0000, IoU.branch: 0.0000, IoU.bridge: 0.0068, IoU.building-other: 0.5011, IoU.bush: 0.2697, IoU.cabinet: 0.2227, IoU.cage: 0.0000, IoU.cardboard: 0.2636, IoU.carpet: 0.6192, IoU.ceiling-other: 0.5744, IoU.ceiling-tile: 0.0000, IoU.cloth: 0.0000, IoU.clothes: 0.1970, IoU.clouds: 0.5400, IoU.counter: 0.4241, IoU.cupboard: 0.5996, IoU.curtain: 0.7143, IoU.desk-stuff: 0.3467, IoU.dirt: 0.4480, IoU.door-stuff: 0.4556, IoU.fence: 0.4076, IoU.floor-marble: 0.0000, IoU.floor-other: 0.3950, IoU.floor-stone: 0.0000, IoU.floor-tile: 0.5811, IoU.floor-wood: 0.7607, IoU.flower: 0.1756, IoU.fog: 0.0000, IoU.food-other: 0.3414, IoU.fruit: 0.7597, IoU.furniture-other: 0.1606, IoU.grass: 0.7311, IoU.gravel: 0.1537, IoU.ground-other: 0.1126, IoU.hill: 0.2018, IoU.house: 0.2368, IoU.leaves: 0.1560, IoU.light: 0.3687, IoU.mat: 0.0987, IoU.metal: 0.1843, IoU.mirror-stuff: 0.3384, IoU.moss: 0.0000, IoU.mountain: 0.3384, IoU.mud: 0.0000, IoU.napkin: 0.0000, IoU.net: 0.2677, IoU.paper: 0.3206, IoU.pavement: 0.4922, IoU.pillow: 0.0000, IoU.plant-other: 0.2893, IoU.plastic: 0.0478, IoU.platform: 0.5501, IoU.playingfield: 0.6776, IoU.railing: 0.1023, IoU.railroad: 0.5999, IoU.river: 0.0000, IoU.road: 0.6997, IoU.rock: 0.4455, IoU.roof: 0.0566, IoU.rug: 0.5288, IoU.salad: 0.0000, IoU.sand: 0.7441, IoU.sea: 0.6893, IoU.shelf: 0.2572, IoU.sky-other: 0.6096, IoU.skyscraper: 0.0827, IoU.snow: 0.9253, IoU.solid-other: nan, IoU.stairs: 0.2629, IoU.stone: 0.0531, IoU.straw: 0.2583, IoU.structural-other: 0.1905, IoU.table: 0.1744, IoU.tent: 0.0472, IoU.textile-other: 0.1364, IoU.towel: 0.2960, IoU.tree: 0.7806, IoU.vegetable: 0.3107, IoU.wall-brick: 0.3415, IoU.wall-concrete: 0.2685, IoU.wall-other: 0.6412, IoU.wall-panel: 0.0000, IoU.wall-stone: 0.1943, IoU.wall-tile: 0.5301, IoU.wall-wood: 0.3315, IoU.water-other: 0.2447, IoU.waterdrops: nan, IoU.window-blind: 0.5071, IoU.window-other: 0.4902, IoU.wood: 0.1412, Acc.person: 0.9383, Acc.bicycle: 0.9039, Acc.car: 0.5394, Acc.motorcycle: 0.9530, Acc.airplane: 0.9466, Acc.bus: 0.9082, Acc.train: 0.9666, Acc.truck: 0.8928, Acc.boat: 0.8532, Acc.traffic light: 0.9311, Acc.fire hydrant: 0.9647, Acc.stop sign: 0.9758, Acc.parking meter: 0.7551, Acc.bench: 0.7185, Acc.bird: 0.8282, Acc.cat: 0.9500, Acc.dog: 0.9583, Acc.horse: 0.9539, Acc.sheep: 0.9197, Acc.cow: 0.9600, Acc.elephant: 0.9532, Acc.bear: 0.9432, Acc.zebra: 0.9450, Acc.giraffe: 0.9407, Acc.backpack: 0.6637, Acc.umbrella: 0.8866, Acc.handbag: 0.4773, Acc.tie: 0.2220, Acc.suitcase: 0.8079, Acc.frisbee: 0.9749, Acc.skis: 0.5875, Acc.snowboard: 0.7248, Acc.sports ball: 0.9441, Acc.kite: 0.8230, Acc.baseball bat: 0.7824, Acc.baseball glove: 0.0075, Acc.skateboard: 0.8542, Acc.surfboard: 0.9416, Acc.tennis racket: 0.3097, Acc.bottle: 0.8319, Acc.wine glass: 0.9108, Acc.cup: 0.7792, Acc.fork: 0.7318, Acc.knife: 0.8480, Acc.spoon: 0.6136, Acc.bowl: 0.6826, Acc.banana: 0.6995, Acc.apple: 0.8752, Acc.sandwich: 0.9637, Acc.orange: 0.9372, Acc.broccoli: 0.9348, Acc.carrot: 0.6901, Acc.hot dog: 0.9655, Acc.pizza: 0.9640, Acc.donut: 0.8855, Acc.cake: 0.8740, Acc.chair: 0.7105, Acc.couch: 0.9275, Acc.potted plant: 0.3703, Acc.bed: 0.8931, Acc.dining table: 0.7841, Acc.toilet: 0.9518, Acc.tv: 0.9494, Acc.laptop: 0.9659, Acc.mouse: 0.6528, Acc.remote: 0.8282, Acc.keyboard: 0.9461, Acc.cell phone: 0.9629, Acc.microwave: 0.7418, Acc.oven: 0.7937, Acc.toaster: 0.0000, Acc.sink: 0.7936, Acc.refrigerator: 0.9610, Acc.book: 0.6729, Acc.clock: 0.7918, Acc.vase: 0.8241, Acc.scissors: 0.8858, Acc.teddy bear: 0.9067, Acc.hair drier: 0.0000, Acc.toothbrush: 0.0604, Acc.banner: 0.6408, Acc.blanket: 0.0000, Acc.branch: 0.0000, Acc.bridge: 0.0094, Acc.building-other: 0.7069, Acc.bush: 0.3260, Acc.cabinet: 0.2601, Acc.cage: 0.0000, Acc.cardboard: 0.2740, Acc.carpet: 0.6975, Acc.ceiling-other: 0.6235, Acc.ceiling-tile: 0.0000, Acc.cloth: 0.0000, Acc.clothes: 0.3135, Acc.clouds: 0.7106, Acc.counter: 0.4872, Acc.cupboard: 0.9352, Acc.curtain: 0.8730, Acc.desk-stuff: 0.3995, Acc.dirt: 0.7552, Acc.door-stuff: 0.6649, Acc.fence: 0.7199, Acc.floor-marble: 0.0000, Acc.floor-other: 0.5984, Acc.floor-stone: 0.0000, Acc.floor-tile: 0.7346, Acc.floor-wood: 0.8605, Acc.flower: 0.3960, Acc.fog: 0.0000, Acc.food-other: 0.7512, Acc.fruit: 0.8207, Acc.furniture-other: 0.2131, Acc.grass: 0.8249, Acc.gravel: 0.1591, Acc.ground-other: 0.1939, Acc.hill: 0.2537, Acc.house: 0.2692, Acc.leaves: 0.1791, Acc.light: 0.5035, Acc.mat: 0.1102, Acc.metal: 0.2766, Acc.mirror-stuff: 0.4689, Acc.moss: 0.0000, Acc.mountain: 0.6726, Acc.mud: 0.0000, Acc.napkin: 0.0000, Acc.net: 0.3320, Acc.paper: 0.7519, Acc.pavement: 0.6562, Acc.pillow: 0.0000, Acc.plant-other: 0.4996, Acc.plastic: 0.0491, Acc.platform: 0.5581, Acc.playingfield: 0.8437, Acc.railing: 0.1359, Acc.railroad: 0.9210, Acc.river: 0.0000, Acc.road: 0.7753, Acc.rock: 0.6407, Acc.roof: 0.1252, Acc.rug: 0.6887, Acc.salad: 0.0000, Acc.sand: 0.8648, Acc.sea: 0.9005, Acc.shelf: 0.4379, Acc.sky-other: 0.7447, Acc.skyscraper: 0.1516, Acc.snow: 0.9568, Acc.solid-other: nan, Acc.stairs: 0.3701, Acc.stone: 0.1185, Acc.straw: 0.3051, Acc.structural-other: 0.4858, Acc.table: 0.2877, Acc.tent: 0.0486, Acc.textile-other: 0.2736, Acc.towel: 0.4990, Acc.tree: 0.8911, Acc.vegetable: 0.4938, Acc.wall-brick: 0.5175, Acc.wall-concrete: 0.2809, Acc.wall-other: 0.8295, Acc.wall-panel: 0.0000, Acc.wall-stone: 0.2263, Acc.wall-tile: 0.8072, Acc.wall-wood: 0.5337, Acc.water-other: 0.3697, Acc.waterdrops: nan, Acc.window-blind: 0.7342, Acc.window-other: 0.6139, Acc.wood: 0.3155 2022-05-04 22:51:59,880 - mmseg - INFO - Iter [8050/40000] lr: 1.147e-06, eta: 7:29:29, time: 2.042, data_time: 1.217, memory: 51557, decode.loss_cls: 1.1625, decode.loss_mask: 0.8932, decode.loss_dice: 1.2454, decode.d0.loss_cls: 7.4916, decode.d0.loss_mask: 0.8811, decode.d0.loss_dice: 1.4665, decode.d1.loss_cls: 1.4060, decode.d1.loss_mask: 0.9065, decode.d1.loss_dice: 1.3290, decode.d2.loss_cls: 1.2409, decode.d2.loss_mask: 0.8989, decode.d2.loss_dice: 1.2729, decode.d3.loss_cls: 1.1957, decode.d3.loss_mask: 0.8913, decode.d3.loss_dice: 1.2461, decode.d4.loss_cls: 1.1788, decode.d4.loss_mask: 0.8928, decode.d4.loss_dice: 1.2521, decode.d5.loss_cls: 1.1760, decode.d5.loss_mask: 0.8921, decode.d5.loss_dice: 1.2524, decode.d6.loss_cls: 1.1686, decode.d6.loss_mask: 0.8879, decode.d6.loss_dice: 1.2414, decode.d7.loss_cls: 1.1694, decode.d7.loss_mask: 0.8887, decode.d7.loss_dice: 1.2447, decode.d8.loss_cls: 1.1582, decode.d8.loss_mask: 0.8910, decode.d8.loss_dice: 1.2488, loss: 40.0703 2022-05-04 22:52:40,942 - mmseg - INFO - Iter [8100/40000] lr: 1.145e-06, eta: 7:28:43, time: 0.821, data_time: 0.010, memory: 51557, decode.loss_cls: 1.1590, decode.loss_mask: 0.8904, decode.loss_dice: 1.2389, decode.d0.loss_cls: 7.4552, decode.d0.loss_mask: 0.8829, decode.d0.loss_dice: 1.4354, decode.d1.loss_cls: 1.3640, decode.d1.loss_mask: 0.9182, decode.d1.loss_dice: 1.3082, decode.d2.loss_cls: 1.2087, decode.d2.loss_mask: 0.9042, decode.d2.loss_dice: 1.2628, decode.d3.loss_cls: 1.1740, decode.d3.loss_mask: 0.8974, decode.d3.loss_dice: 1.2391, decode.d4.loss_cls: 1.1654, decode.d4.loss_mask: 0.8965, decode.d4.loss_dice: 1.2477, decode.d5.loss_cls: 1.1499, decode.d5.loss_mask: 0.8985, decode.d5.loss_dice: 1.2443, decode.d6.loss_cls: 1.1407, decode.d6.loss_mask: 0.8976, decode.d6.loss_dice: 1.2356, decode.d7.loss_cls: 1.1465, decode.d7.loss_mask: 0.8986, decode.d7.loss_dice: 1.2399, decode.d8.loss_cls: 1.1422, decode.d8.loss_mask: 0.8947, decode.d8.loss_dice: 1.2396, loss: 39.7757 2022-05-04 22:53:21,071 - mmseg - INFO - Iter [8150/40000] lr: 1.143e-06, eta: 7:27:52, time: 0.803, data_time: 0.009, memory: 51557, decode.loss_cls: 1.1341, decode.loss_mask: 0.8602, decode.loss_dice: 1.2108, decode.d0.loss_cls: 7.4219, decode.d0.loss_mask: 0.8574, decode.d0.loss_dice: 1.4267, decode.d1.loss_cls: 1.3825, decode.d1.loss_mask: 0.8861, decode.d1.loss_dice: 1.3006, decode.d2.loss_cls: 1.2015, decode.d2.loss_mask: 0.8653, decode.d2.loss_dice: 1.2457, decode.d3.loss_cls: 1.1500, decode.d3.loss_mask: 0.8654, decode.d3.loss_dice: 1.2244, decode.d4.loss_cls: 1.1297, decode.d4.loss_mask: 0.8621, decode.d4.loss_dice: 1.2292, decode.d5.loss_cls: 1.1259, decode.d5.loss_mask: 0.8607, decode.d5.loss_dice: 1.2277, decode.d6.loss_cls: 1.1268, decode.d6.loss_mask: 0.8618, decode.d6.loss_dice: 1.2180, decode.d7.loss_cls: 1.1270, decode.d7.loss_mask: 0.8584, decode.d7.loss_dice: 1.2217, decode.d8.loss_cls: 1.1224, decode.d8.loss_mask: 0.8611, decode.d8.loss_dice: 1.2174, loss: 39.0827 2022-05-04 22:54:01,451 - mmseg - INFO - Iter [8200/40000] lr: 1.141e-06, eta: 7:27:03, time: 0.808, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0913, decode.loss_mask: 0.8718, decode.loss_dice: 1.2249, decode.d0.loss_cls: 7.4088, decode.d0.loss_mask: 0.8611, decode.d0.loss_dice: 1.4206, decode.d1.loss_cls: 1.3108, decode.d1.loss_mask: 0.8849, decode.d1.loss_dice: 1.3005, decode.d2.loss_cls: 1.1442, decode.d2.loss_mask: 0.8675, decode.d2.loss_dice: 1.2511, decode.d3.loss_cls: 1.1165, decode.d3.loss_mask: 0.8657, decode.d3.loss_dice: 1.2244, decode.d4.loss_cls: 1.1044, decode.d4.loss_mask: 0.8681, decode.d4.loss_dice: 1.2264, decode.d5.loss_cls: 1.0992, decode.d5.loss_mask: 0.8661, decode.d5.loss_dice: 1.2224, decode.d6.loss_cls: 1.0924, decode.d6.loss_mask: 0.8621, decode.d6.loss_dice: 1.2184, decode.d7.loss_cls: 1.0894, decode.d7.loss_mask: 0.8734, decode.d7.loss_dice: 1.2229, decode.d8.loss_cls: 1.0929, decode.d8.loss_mask: 0.8720, decode.d8.loss_dice: 1.2185, loss: 38.7727 2022-05-04 22:54:41,314 - mmseg - INFO - Iter [8250/40000] lr: 1.140e-06, eta: 7:26:12, time: 0.797, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0488, decode.loss_mask: 0.8595, decode.loss_dice: 1.2126, decode.d0.loss_cls: 7.3711, decode.d0.loss_mask: 0.8527, decode.d0.loss_dice: 1.3972, decode.d1.loss_cls: 1.2770, decode.d1.loss_mask: 0.8779, decode.d1.loss_dice: 1.2789, decode.d2.loss_cls: 1.1157, decode.d2.loss_mask: 0.8606, decode.d2.loss_dice: 1.2326, decode.d3.loss_cls: 1.0736, decode.d3.loss_mask: 0.8652, decode.d3.loss_dice: 1.2108, decode.d4.loss_cls: 1.0642, decode.d4.loss_mask: 0.8607, decode.d4.loss_dice: 1.2100, decode.d5.loss_cls: 1.0509, decode.d5.loss_mask: 0.8628, decode.d5.loss_dice: 1.2071, decode.d6.loss_cls: 1.0476, decode.d6.loss_mask: 0.8574, decode.d6.loss_dice: 1.2043, decode.d7.loss_cls: 1.0372, decode.d7.loss_mask: 0.8614, decode.d7.loss_dice: 1.2085, decode.d8.loss_cls: 1.0332, decode.d8.loss_mask: 0.8653, decode.d8.loss_dice: 1.2112, loss: 38.1161 2022-05-04 22:55:22,225 - mmseg - INFO - Iter [8300/40000] lr: 1.138e-06, eta: 7:25:25, time: 0.818, data_time: 0.009, memory: 51557, decode.loss_cls: 1.1029, decode.loss_mask: 0.8952, decode.loss_dice: 1.2417, decode.d0.loss_cls: 7.3424, decode.d0.loss_mask: 0.8808, decode.d0.loss_dice: 1.4402, decode.d1.loss_cls: 1.3148, decode.d1.loss_mask: 0.9066, decode.d1.loss_dice: 1.3151, decode.d2.loss_cls: 1.1540, decode.d2.loss_mask: 0.8957, decode.d2.loss_dice: 1.2678, decode.d3.loss_cls: 1.1176, decode.d3.loss_mask: 0.8958, decode.d3.loss_dice: 1.2487, decode.d4.loss_cls: 1.1144, decode.d4.loss_mask: 0.8974, decode.d4.loss_dice: 1.2512, decode.d5.loss_cls: 1.1051, decode.d5.loss_mask: 0.8999, decode.d5.loss_dice: 1.2441, decode.d6.loss_cls: 1.0954, decode.d6.loss_mask: 0.8966, decode.d6.loss_dice: 1.2387, decode.d7.loss_cls: 1.1018, decode.d7.loss_mask: 0.8924, decode.d7.loss_dice: 1.2409, decode.d8.loss_cls: 1.0990, decode.d8.loss_mask: 0.8963, decode.d8.loss_dice: 1.2330, loss: 39.2256 2022-05-04 22:56:03,131 - mmseg - INFO - Iter [8350/40000] lr: 1.136e-06, eta: 7:24:38, time: 0.818, data_time: 0.010, memory: 51557, decode.loss_cls: 1.0852, decode.loss_mask: 0.8895, decode.loss_dice: 1.2261, decode.d0.loss_cls: 7.3169, decode.d0.loss_mask: 0.8814, decode.d0.loss_dice: 1.4408, decode.d1.loss_cls: 1.3180, decode.d1.loss_mask: 0.9116, decode.d1.loss_dice: 1.3185, decode.d2.loss_cls: 1.1569, decode.d2.loss_mask: 0.8923, decode.d2.loss_dice: 1.2628, decode.d3.loss_cls: 1.1097, decode.d3.loss_mask: 0.8931, decode.d3.loss_dice: 1.2407, decode.d4.loss_cls: 1.0947, decode.d4.loss_mask: 0.8922, decode.d4.loss_dice: 1.2471, decode.d5.loss_cls: 1.0939, decode.d5.loss_mask: 0.8906, decode.d5.loss_dice: 1.2453, decode.d6.loss_cls: 1.0768, decode.d6.loss_mask: 0.8870, decode.d6.loss_dice: 1.2311, decode.d7.loss_cls: 1.0753, decode.d7.loss_mask: 0.8918, decode.d7.loss_dice: 1.2350, decode.d8.loss_cls: 1.0676, decode.d8.loss_mask: 0.8912, decode.d8.loss_dice: 1.2300, loss: 38.9931 2022-05-04 22:56:43,098 - mmseg - INFO - Iter [8400/40000] lr: 1.134e-06, eta: 7:23:48, time: 0.799, data_time: 0.009, memory: 51557, decode.loss_cls: 1.1280, decode.loss_mask: 0.9140, decode.loss_dice: 1.2285, decode.d0.loss_cls: 7.3178, decode.d0.loss_mask: 0.9145, decode.d0.loss_dice: 1.4392, decode.d1.loss_cls: 1.3273, decode.d1.loss_mask: 0.9424, decode.d1.loss_dice: 1.3075, decode.d2.loss_cls: 1.1647, decode.d2.loss_mask: 0.9287, decode.d2.loss_dice: 1.2510, decode.d3.loss_cls: 1.1410, decode.d3.loss_mask: 0.9187, decode.d3.loss_dice: 1.2286, decode.d4.loss_cls: 1.1312, decode.d4.loss_mask: 0.9172, decode.d4.loss_dice: 1.2329, decode.d5.loss_cls: 1.1241, decode.d5.loss_mask: 0.9190, decode.d5.loss_dice: 1.2380, decode.d6.loss_cls: 1.1108, decode.d6.loss_mask: 0.9256, decode.d6.loss_dice: 1.2243, decode.d7.loss_cls: 1.1168, decode.d7.loss_mask: 0.9208, decode.d7.loss_dice: 1.2330, decode.d8.loss_cls: 1.1237, decode.d8.loss_mask: 0.9196, decode.d8.loss_dice: 1.2334, loss: 39.5224 2022-05-04 22:57:26,282 - mmseg - INFO - Iter [8450/40000] lr: 1.133e-06, eta: 7:23:10, time: 0.864, data_time: 0.059, memory: 51557, decode.loss_cls: 1.1307, decode.loss_mask: 0.9016, decode.loss_dice: 1.2385, decode.d0.loss_cls: 7.3161, decode.d0.loss_mask: 0.8776, decode.d0.loss_dice: 1.4424, decode.d1.loss_cls: 1.3602, decode.d1.loss_mask: 0.9080, decode.d1.loss_dice: 1.3131, decode.d2.loss_cls: 1.1990, decode.d2.loss_mask: 0.8962, decode.d2.loss_dice: 1.2662, decode.d3.loss_cls: 1.1539, decode.d3.loss_mask: 0.9007, decode.d3.loss_dice: 1.2417, decode.d4.loss_cls: 1.1435, decode.d4.loss_mask: 0.9031, decode.d4.loss_dice: 1.2463, decode.d5.loss_cls: 1.1325, decode.d5.loss_mask: 0.9006, decode.d5.loss_dice: 1.2526, decode.d6.loss_cls: 1.1246, decode.d6.loss_mask: 0.8971, decode.d6.loss_dice: 1.2399, decode.d7.loss_cls: 1.1332, decode.d7.loss_mask: 0.8978, decode.d7.loss_dice: 1.2390, decode.d8.loss_cls: 1.1224, decode.d8.loss_mask: 0.9038, decode.d8.loss_dice: 1.2433, loss: 39.5253 2022-05-04 22:58:06,738 - mmseg - INFO - Iter [8500/40000] lr: 1.131e-06, eta: 7:22:21, time: 0.809, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0974, decode.loss_mask: 0.8904, decode.loss_dice: 1.2482, decode.d0.loss_cls: 7.2608, decode.d0.loss_mask: 0.8761, decode.d0.loss_dice: 1.4334, decode.d1.loss_cls: 1.3221, decode.d1.loss_mask: 0.9029, decode.d1.loss_dice: 1.3094, decode.d2.loss_cls: 1.1555, decode.d2.loss_mask: 0.8863, decode.d2.loss_dice: 1.2677, decode.d3.loss_cls: 1.1080, decode.d3.loss_mask: 0.8861, decode.d3.loss_dice: 1.2532, decode.d4.loss_cls: 1.0989, decode.d4.loss_mask: 0.8892, decode.d4.loss_dice: 1.2553, decode.d5.loss_cls: 1.0975, decode.d5.loss_mask: 0.8858, decode.d5.loss_dice: 1.2563, decode.d6.loss_cls: 1.0804, decode.d6.loss_mask: 0.8913, decode.d6.loss_dice: 1.2401, decode.d7.loss_cls: 1.0807, decode.d7.loss_mask: 0.8917, decode.d7.loss_dice: 1.2562, decode.d8.loss_cls: 1.0922, decode.d8.loss_mask: 0.8884, decode.d8.loss_dice: 1.2440, loss: 39.0456 2022-05-04 22:58:47,934 - mmseg - INFO - Iter [8550/40000] lr: 1.129e-06, eta: 7:21:36, time: 0.824, data_time: 0.009, memory: 51557, decode.loss_cls: 1.1032, decode.loss_mask: 0.9002, decode.loss_dice: 1.2527, decode.d0.loss_cls: 7.2781, decode.d0.loss_mask: 0.8833, decode.d0.loss_dice: 1.4631, decode.d1.loss_cls: 1.3453, decode.d1.loss_mask: 0.9181, decode.d1.loss_dice: 1.3373, decode.d2.loss_cls: 1.1714, decode.d2.loss_mask: 0.9037, decode.d2.loss_dice: 1.2857, decode.d3.loss_cls: 1.1442, decode.d3.loss_mask: 0.8981, decode.d3.loss_dice: 1.2598, decode.d4.loss_cls: 1.1293, decode.d4.loss_mask: 0.8960, decode.d4.loss_dice: 1.2603, decode.d5.loss_cls: 1.1127, decode.d5.loss_mask: 0.8991, decode.d5.loss_dice: 1.2622, decode.d6.loss_cls: 1.1031, decode.d6.loss_mask: 0.8981, decode.d6.loss_dice: 1.2521, decode.d7.loss_cls: 1.1085, decode.d7.loss_mask: 0.8988, decode.d7.loss_dice: 1.2590, decode.d8.loss_cls: 1.1057, decode.d8.loss_mask: 0.8956, decode.d8.loss_dice: 1.2562, loss: 39.4812 2022-05-04 22:59:28,332 - mmseg - INFO - Iter [8600/40000] lr: 1.127e-06, eta: 7:20:47, time: 0.808, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0998, decode.loss_mask: 0.8716, decode.loss_dice: 1.2065, decode.d0.loss_cls: 7.2111, decode.d0.loss_mask: 0.8650, decode.d0.loss_dice: 1.4048, decode.d1.loss_cls: 1.3351, decode.d1.loss_mask: 0.8943, decode.d1.loss_dice: 1.2793, decode.d2.loss_cls: 1.1588, decode.d2.loss_mask: 0.8752, decode.d2.loss_dice: 1.2364, decode.d3.loss_cls: 1.1284, decode.d3.loss_mask: 0.8755, decode.d3.loss_dice: 1.2090, decode.d4.loss_cls: 1.1164, decode.d4.loss_mask: 0.8743, decode.d4.loss_dice: 1.2096, decode.d5.loss_cls: 1.1043, decode.d5.loss_mask: 0.8757, decode.d5.loss_dice: 1.2121, decode.d6.loss_cls: 1.1006, decode.d6.loss_mask: 0.8692, decode.d6.loss_dice: 1.1991, decode.d7.loss_cls: 1.0987, decode.d7.loss_mask: 0.8723, decode.d7.loss_dice: 1.2018, decode.d8.loss_cls: 1.0874, decode.d8.loss_mask: 0.8728, decode.d8.loss_dice: 1.2064, loss: 38.5518 2022-05-04 23:00:08,390 - mmseg - INFO - Iter [8650/40000] lr: 1.125e-06, eta: 7:19:58, time: 0.801, data_time: 0.009, memory: 51557, decode.loss_cls: 1.1211, decode.loss_mask: 0.8583, decode.loss_dice: 1.2313, decode.d0.loss_cls: 7.2104, decode.d0.loss_mask: 0.8649, decode.d0.loss_dice: 1.4396, decode.d1.loss_cls: 1.3575, decode.d1.loss_mask: 0.8851, decode.d1.loss_dice: 1.3079, decode.d2.loss_cls: 1.1839, decode.d2.loss_mask: 0.8708, decode.d2.loss_dice: 1.2495, decode.d3.loss_cls: 1.1603, decode.d3.loss_mask: 0.8597, decode.d3.loss_dice: 1.2332, decode.d4.loss_cls: 1.1444, decode.d4.loss_mask: 0.8590, decode.d4.loss_dice: 1.2379, decode.d5.loss_cls: 1.1267, decode.d5.loss_mask: 0.8577, decode.d5.loss_dice: 1.2316, decode.d6.loss_cls: 1.1182, decode.d6.loss_mask: 0.8571, decode.d6.loss_dice: 1.2282, decode.d7.loss_cls: 1.1102, decode.d7.loss_mask: 0.8558, decode.d7.loss_dice: 1.2323, decode.d8.loss_cls: 1.1133, decode.d8.loss_mask: 0.8556, decode.d8.loss_dice: 1.2335, loss: 38.8949 2022-05-04 23:00:48,435 - mmseg - INFO - Iter [8700/40000] lr: 1.124e-06, eta: 7:19:08, time: 0.801, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0936, decode.loss_mask: 0.8944, decode.loss_dice: 1.2154, decode.d0.loss_cls: 7.1633, decode.d0.loss_mask: 0.8700, decode.d0.loss_dice: 1.4267, decode.d1.loss_cls: 1.3065, decode.d1.loss_mask: 0.9047, decode.d1.loss_dice: 1.2961, decode.d2.loss_cls: 1.1386, decode.d2.loss_mask: 0.8976, decode.d2.loss_dice: 1.2521, decode.d3.loss_cls: 1.1168, decode.d3.loss_mask: 0.8881, decode.d3.loss_dice: 1.2265, decode.d4.loss_cls: 1.1098, decode.d4.loss_mask: 0.8871, decode.d4.loss_dice: 1.2258, decode.d5.loss_cls: 1.1003, decode.d5.loss_mask: 0.8927, decode.d5.loss_dice: 1.2214, decode.d6.loss_cls: 1.0977, decode.d6.loss_mask: 0.8878, decode.d6.loss_dice: 1.2164, decode.d7.loss_cls: 1.0873, decode.d7.loss_mask: 0.8928, decode.d7.loss_dice: 1.2236, decode.d8.loss_cls: 1.0854, decode.d8.loss_mask: 0.9001, decode.d8.loss_dice: 1.2202, loss: 38.7387 2022-05-04 23:01:28,600 - mmseg - INFO - Iter [8750/40000] lr: 1.122e-06, eta: 7:18:19, time: 0.803, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0932, decode.loss_mask: 0.8835, decode.loss_dice: 1.2399, decode.d0.loss_cls: 7.1462, decode.d0.loss_mask: 0.8712, decode.d0.loss_dice: 1.4172, decode.d1.loss_cls: 1.3126, decode.d1.loss_mask: 0.9003, decode.d1.loss_dice: 1.3030, decode.d2.loss_cls: 1.1516, decode.d2.loss_mask: 0.8917, decode.d2.loss_dice: 1.2630, decode.d3.loss_cls: 1.1162, decode.d3.loss_mask: 0.8874, decode.d3.loss_dice: 1.2437, decode.d4.loss_cls: 1.1073, decode.d4.loss_mask: 0.8913, decode.d4.loss_dice: 1.2458, decode.d5.loss_cls: 1.0931, decode.d5.loss_mask: 0.8962, decode.d5.loss_dice: 1.2341, decode.d6.loss_cls: 1.0849, decode.d6.loss_mask: 0.8856, decode.d6.loss_dice: 1.2239, decode.d7.loss_cls: 1.0867, decode.d7.loss_mask: 0.8877, decode.d7.loss_dice: 1.2383, decode.d8.loss_cls: 1.0819, decode.d8.loss_mask: 0.8857, decode.d8.loss_dice: 1.2358, loss: 38.7988 2022-05-04 23:02:10,502 - mmseg - INFO - Iter [8800/40000] lr: 1.120e-06, eta: 7:17:37, time: 0.838, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0675, decode.loss_mask: 0.8530, decode.loss_dice: 1.2201, decode.d0.loss_cls: 7.1331, decode.d0.loss_mask: 0.8424, decode.d0.loss_dice: 1.4200, decode.d1.loss_cls: 1.2894, decode.d1.loss_mask: 0.8637, decode.d1.loss_dice: 1.2896, decode.d2.loss_cls: 1.1260, decode.d2.loss_mask: 0.8561, decode.d2.loss_dice: 1.2445, decode.d3.loss_cls: 1.0987, decode.d3.loss_mask: 0.8459, decode.d3.loss_dice: 1.2244, decode.d4.loss_cls: 1.0774, decode.d4.loss_mask: 0.8484, decode.d4.loss_dice: 1.2240, decode.d5.loss_cls: 1.0762, decode.d5.loss_mask: 0.8408, decode.d5.loss_dice: 1.2152, decode.d6.loss_cls: 1.0722, decode.d6.loss_mask: 0.8426, decode.d6.loss_dice: 1.2028, decode.d7.loss_cls: 1.0649, decode.d7.loss_mask: 0.8499, decode.d7.loss_dice: 1.2102, decode.d8.loss_cls: 1.0627, decode.d8.loss_mask: 0.8438, decode.d8.loss_dice: 1.2173, loss: 38.0229 2022-05-04 23:02:52,336 - mmseg - INFO - Iter [8850/40000] lr: 1.118e-06, eta: 7:16:53, time: 0.835, data_time: 0.010, memory: 51557, decode.loss_cls: 1.0835, decode.loss_mask: 0.8548, decode.loss_dice: 1.2329, decode.d0.loss_cls: 7.0868, decode.d0.loss_mask: 0.8482, decode.d0.loss_dice: 1.4097, decode.d1.loss_cls: 1.3009, decode.d1.loss_mask: 0.8750, decode.d1.loss_dice: 1.2871, decode.d2.loss_cls: 1.1394, decode.d2.loss_mask: 0.8600, decode.d2.loss_dice: 1.2524, decode.d3.loss_cls: 1.0979, decode.d3.loss_mask: 0.8547, decode.d3.loss_dice: 1.2280, decode.d4.loss_cls: 1.0938, decode.d4.loss_mask: 0.8557, decode.d4.loss_dice: 1.2287, decode.d5.loss_cls: 1.0935, decode.d5.loss_mask: 0.8532, decode.d5.loss_dice: 1.2277, decode.d6.loss_cls: 1.0873, decode.d6.loss_mask: 0.8549, decode.d6.loss_dice: 1.2180, decode.d7.loss_cls: 1.0828, decode.d7.loss_mask: 0.8524, decode.d7.loss_dice: 1.2232, decode.d8.loss_cls: 1.0766, decode.d8.loss_mask: 0.8554, decode.d8.loss_dice: 1.2216, loss: 38.2362 2022-05-04 23:03:34,080 - mmseg - INFO - Iter [8900/40000] lr: 1.116e-06, eta: 7:16:10, time: 0.837, data_time: 0.011, memory: 51557, decode.loss_cls: 1.1064, decode.loss_mask: 0.8873, decode.loss_dice: 1.2235, decode.d0.loss_cls: 7.0717, decode.d0.loss_mask: 0.8742, decode.d0.loss_dice: 1.4258, decode.d1.loss_cls: 1.3158, decode.d1.loss_mask: 0.8926, decode.d1.loss_dice: 1.2968, decode.d2.loss_cls: 1.1670, decode.d2.loss_mask: 0.8896, decode.d2.loss_dice: 1.2525, decode.d3.loss_cls: 1.1410, decode.d3.loss_mask: 0.8821, decode.d3.loss_dice: 1.2263, decode.d4.loss_cls: 1.1186, decode.d4.loss_mask: 0.8864, decode.d4.loss_dice: 1.2293, decode.d5.loss_cls: 1.1080, decode.d5.loss_mask: 0.8794, decode.d5.loss_dice: 1.2255, decode.d6.loss_cls: 1.0998, decode.d6.loss_mask: 0.8834, decode.d6.loss_dice: 1.2215, decode.d7.loss_cls: 1.1026, decode.d7.loss_mask: 0.8875, decode.d7.loss_dice: 1.2232, decode.d8.loss_cls: 1.0911, decode.d8.loss_mask: 0.8901, decode.d8.loss_dice: 1.2284, loss: 38.7272 2022-05-04 23:04:14,915 - mmseg - INFO - Iter [8950/40000] lr: 1.115e-06, eta: 7:15:24, time: 0.817, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0847, decode.loss_mask: 0.8729, decode.loss_dice: 1.1930, decode.d0.loss_cls: 7.0578, decode.d0.loss_mask: 0.8760, decode.d0.loss_dice: 1.3847, decode.d1.loss_cls: 1.2946, decode.d1.loss_mask: 0.9082, decode.d1.loss_dice: 1.2649, decode.d2.loss_cls: 1.1364, decode.d2.loss_mask: 0.8849, decode.d2.loss_dice: 1.2158, decode.d3.loss_cls: 1.1031, decode.d3.loss_mask: 0.8808, decode.d3.loss_dice: 1.1963, decode.d4.loss_cls: 1.0931, decode.d4.loss_mask: 0.8779, decode.d4.loss_dice: 1.1953, decode.d5.loss_cls: 1.0887, decode.d5.loss_mask: 0.8792, decode.d5.loss_dice: 1.1924, decode.d6.loss_cls: 1.0853, decode.d6.loss_mask: 0.8814, decode.d6.loss_dice: 1.1881, decode.d7.loss_cls: 1.0832, decode.d7.loss_mask: 0.8801, decode.d7.loss_dice: 1.1864, decode.d8.loss_cls: 1.0787, decode.d8.loss_mask: 0.8783, decode.d8.loss_dice: 1.1863, loss: 38.1284 2022-05-04 23:04:57,005 - mmseg - INFO - Saving checkpoint at 9000 iterations 2022-05-04 23:05:22,595 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-04 23:05:22,598 - mmseg - INFO - Iter [9000/40000] lr: 1.113e-06, eta: 7:16:10, time: 1.351, data_time: 0.057, memory: 51557, decode.loss_cls: 1.1147, decode.loss_mask: 0.8667, decode.loss_dice: 1.2507, decode.d0.loss_cls: 7.0645, decode.d0.loss_mask: 0.8493, decode.d0.loss_dice: 1.4478, decode.d1.loss_cls: 1.3408, decode.d1.loss_mask: 0.8836, decode.d1.loss_dice: 1.3328, decode.d2.loss_cls: 1.1872, decode.d2.loss_mask: 0.8639, decode.d2.loss_dice: 1.2722, decode.d3.loss_cls: 1.1512, decode.d3.loss_mask: 0.8603, decode.d3.loss_dice: 1.2560, decode.d4.loss_cls: 1.1422, decode.d4.loss_mask: 0.8586, decode.d4.loss_dice: 1.2593, decode.d5.loss_cls: 1.1321, decode.d5.loss_mask: 0.8572, decode.d5.loss_dice: 1.2472, decode.d6.loss_cls: 1.1221, decode.d6.loss_mask: 0.8575, decode.d6.loss_dice: 1.2412, decode.d7.loss_cls: 1.1091, decode.d7.loss_mask: 0.8601, decode.d7.loss_dice: 1.2513, decode.d8.loss_cls: 1.1159, decode.d8.loss_mask: 0.8616, decode.d8.loss_dice: 1.2554, loss: 38.9125 2022-05-04 23:06:03,357 - mmseg - INFO - Iter [9050/40000] lr: 1.111e-06, eta: 7:15:23, time: 0.817, data_time: 0.012, memory: 51557, decode.loss_cls: 1.1116, decode.loss_mask: 0.8676, decode.loss_dice: 1.2191, decode.d0.loss_cls: 7.0141, decode.d0.loss_mask: 0.8574, decode.d0.loss_dice: 1.4198, decode.d1.loss_cls: 1.3478, decode.d1.loss_mask: 0.8868, decode.d1.loss_dice: 1.3023, decode.d2.loss_cls: 1.1752, decode.d2.loss_mask: 0.8708, decode.d2.loss_dice: 1.2521, decode.d3.loss_cls: 1.1272, decode.d3.loss_mask: 0.8735, decode.d3.loss_dice: 1.2289, decode.d4.loss_cls: 1.1264, decode.d4.loss_mask: 0.8670, decode.d4.loss_dice: 1.2323, decode.d5.loss_cls: 1.1180, decode.d5.loss_mask: 0.8672, decode.d5.loss_dice: 1.2336, decode.d6.loss_cls: 1.1056, decode.d6.loss_mask: 0.8672, decode.d6.loss_dice: 1.2157, decode.d7.loss_cls: 1.1080, decode.d7.loss_mask: 0.8678, decode.d7.loss_dice: 1.2247, decode.d8.loss_cls: 1.1044, decode.d8.loss_mask: 0.8663, decode.d8.loss_dice: 1.2230, loss: 38.5815 2022-05-04 23:06:43,873 - mmseg - INFO - Iter [9100/40000] lr: 1.109e-06, eta: 7:14:35, time: 0.811, data_time: 0.010, memory: 51557, decode.loss_cls: 1.0460, decode.loss_mask: 0.8690, decode.loss_dice: 1.1671, decode.d0.loss_cls: 6.9675, decode.d0.loss_mask: 0.8698, decode.d0.loss_dice: 1.3695, decode.d1.loss_cls: 1.2749, decode.d1.loss_mask: 0.8873, decode.d1.loss_dice: 1.2419, decode.d2.loss_cls: 1.1109, decode.d2.loss_mask: 0.8735, decode.d2.loss_dice: 1.1882, decode.d3.loss_cls: 1.0753, decode.d3.loss_mask: 0.8642, decode.d3.loss_dice: 1.1652, decode.d4.loss_cls: 1.0536, decode.d4.loss_mask: 0.8658, decode.d4.loss_dice: 1.1720, decode.d5.loss_cls: 1.0545, decode.d5.loss_mask: 0.8649, decode.d5.loss_dice: 1.1707, decode.d6.loss_cls: 1.0455, decode.d6.loss_mask: 0.8612, decode.d6.loss_dice: 1.1615, decode.d7.loss_cls: 1.0372, decode.d7.loss_mask: 0.8624, decode.d7.loss_dice: 1.1700, decode.d8.loss_cls: 1.0369, decode.d8.loss_mask: 0.8695, decode.d8.loss_dice: 1.1701, loss: 37.3661 2022-05-04 23:07:26,820 - mmseg - INFO - Iter [9150/40000] lr: 1.107e-06, eta: 7:13:55, time: 0.859, data_time: 0.011, memory: 51557, decode.loss_cls: 1.0905, decode.loss_mask: 0.8783, decode.loss_dice: 1.2274, decode.d0.loss_cls: 6.9623, decode.d0.loss_mask: 0.8689, decode.d0.loss_dice: 1.4121, decode.d1.loss_cls: 1.3135, decode.d1.loss_mask: 0.8992, decode.d1.loss_dice: 1.3053, decode.d2.loss_cls: 1.1448, decode.d2.loss_mask: 0.8866, decode.d2.loss_dice: 1.2485, decode.d3.loss_cls: 1.1211, decode.d3.loss_mask: 0.8756, decode.d3.loss_dice: 1.2282, decode.d4.loss_cls: 1.1088, decode.d4.loss_mask: 0.8736, decode.d4.loss_dice: 1.2293, decode.d5.loss_cls: 1.0962, decode.d5.loss_mask: 0.8746, decode.d5.loss_dice: 1.2260, decode.d6.loss_cls: 1.0915, decode.d6.loss_mask: 0.8768, decode.d6.loss_dice: 1.2159, decode.d7.loss_cls: 1.0826, decode.d7.loss_mask: 0.8770, decode.d7.loss_dice: 1.2190, decode.d8.loss_cls: 1.0881, decode.d8.loss_mask: 0.8763, decode.d8.loss_dice: 1.2208, loss: 38.4189 2022-05-04 23:08:07,128 - mmseg - INFO - Iter [9200/40000] lr: 1.106e-06, eta: 7:13:07, time: 0.806, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0317, decode.loss_mask: 0.8896, decode.loss_dice: 1.2257, decode.d0.loss_cls: 6.9440, decode.d0.loss_mask: 0.8906, decode.d0.loss_dice: 1.4081, decode.d1.loss_cls: 1.2750, decode.d1.loss_mask: 0.9102, decode.d1.loss_dice: 1.2977, decode.d2.loss_cls: 1.0944, decode.d2.loss_mask: 0.8944, decode.d2.loss_dice: 1.2493, decode.d3.loss_cls: 1.0521, decode.d3.loss_mask: 0.8937, decode.d3.loss_dice: 1.2374, decode.d4.loss_cls: 1.0469, decode.d4.loss_mask: 0.8897, decode.d4.loss_dice: 1.2330, decode.d5.loss_cls: 1.0439, decode.d5.loss_mask: 0.8876, decode.d5.loss_dice: 1.2245, decode.d6.loss_cls: 1.0306, decode.d6.loss_mask: 0.8858, decode.d6.loss_dice: 1.2209, decode.d7.loss_cls: 1.0284, decode.d7.loss_mask: 0.8915, decode.d7.loss_dice: 1.2249, decode.d8.loss_cls: 1.0220, decode.d8.loss_mask: 0.8862, decode.d8.loss_dice: 1.2232, loss: 38.0330 2022-05-04 23:08:48,195 - mmseg - INFO - Iter [9250/40000] lr: 1.104e-06, eta: 7:12:21, time: 0.821, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0366, decode.loss_mask: 0.8610, decode.loss_dice: 1.2127, decode.d0.loss_cls: 6.9015, decode.d0.loss_mask: 0.8505, decode.d0.loss_dice: 1.4000, decode.d1.loss_cls: 1.2622, decode.d1.loss_mask: 0.8936, decode.d1.loss_dice: 1.2820, decode.d2.loss_cls: 1.1177, decode.d2.loss_mask: 0.8725, decode.d2.loss_dice: 1.2356, decode.d3.loss_cls: 1.0709, decode.d3.loss_mask: 0.8634, decode.d3.loss_dice: 1.2252, decode.d4.loss_cls: 1.0578, decode.d4.loss_mask: 0.8638, decode.d4.loss_dice: 1.2178, decode.d5.loss_cls: 1.0474, decode.d5.loss_mask: 0.8641, decode.d5.loss_dice: 1.2178, decode.d6.loss_cls: 1.0317, decode.d6.loss_mask: 0.8649, decode.d6.loss_dice: 1.2021, decode.d7.loss_cls: 1.0273, decode.d7.loss_mask: 0.8679, decode.d7.loss_dice: 1.2133, decode.d8.loss_cls: 1.0383, decode.d8.loss_mask: 0.8607, decode.d8.loss_dice: 1.2185, loss: 37.6789 2022-05-04 23:09:28,975 - mmseg - INFO - Iter [9300/40000] lr: 1.102e-06, eta: 7:11:34, time: 0.816, data_time: 0.011, memory: 51557, decode.loss_cls: 1.0370, decode.loss_mask: 0.8947, decode.loss_dice: 1.2335, decode.d0.loss_cls: 6.8704, decode.d0.loss_mask: 0.8809, decode.d0.loss_dice: 1.4135, decode.d1.loss_cls: 1.2818, decode.d1.loss_mask: 0.9120, decode.d1.loss_dice: 1.2977, decode.d2.loss_cls: 1.1180, decode.d2.loss_mask: 0.8959, decode.d2.loss_dice: 1.2599, decode.d3.loss_cls: 1.0782, decode.d3.loss_mask: 0.8936, decode.d3.loss_dice: 1.2394, decode.d4.loss_cls: 1.0708, decode.d4.loss_mask: 0.8980, decode.d4.loss_dice: 1.2412, decode.d5.loss_cls: 1.0651, decode.d5.loss_mask: 0.8978, decode.d5.loss_dice: 1.2355, decode.d6.loss_cls: 1.0573, decode.d6.loss_mask: 0.8897, decode.d6.loss_dice: 1.2234, decode.d7.loss_cls: 1.0491, decode.d7.loss_mask: 0.8958, decode.d7.loss_dice: 1.2301, decode.d8.loss_cls: 1.0370, decode.d8.loss_mask: 0.8897, decode.d8.loss_dice: 1.2360, loss: 38.2231 2022-05-04 23:10:09,667 - mmseg - INFO - Iter [9350/40000] lr: 1.100e-06, eta: 7:10:47, time: 0.814, data_time: 0.011, memory: 51557, decode.loss_cls: 1.0399, decode.loss_mask: 0.8692, decode.loss_dice: 1.2115, decode.d0.loss_cls: 6.8648, decode.d0.loss_mask: 0.8588, decode.d0.loss_dice: 1.4068, decode.d1.loss_cls: 1.2559, decode.d1.loss_mask: 0.8844, decode.d1.loss_dice: 1.2750, decode.d2.loss_cls: 1.0981, decode.d2.loss_mask: 0.8677, decode.d2.loss_dice: 1.2281, decode.d3.loss_cls: 1.0612, decode.d3.loss_mask: 0.8650, decode.d3.loss_dice: 1.2121, decode.d4.loss_cls: 1.0467, decode.d4.loss_mask: 0.8675, decode.d4.loss_dice: 1.2129, decode.d5.loss_cls: 1.0462, decode.d5.loss_mask: 0.8632, decode.d5.loss_dice: 1.2123, decode.d6.loss_cls: 1.0377, decode.d6.loss_mask: 0.8637, decode.d6.loss_dice: 1.2053, decode.d7.loss_cls: 1.0372, decode.d7.loss_mask: 0.8672, decode.d7.loss_dice: 1.2172, decode.d8.loss_cls: 1.0304, decode.d8.loss_mask: 0.8664, decode.d8.loss_dice: 1.2082, loss: 37.5808 2022-05-04 23:10:49,760 - mmseg - INFO - Iter [9400/40000] lr: 1.098e-06, eta: 7:09:58, time: 0.802, data_time: 0.009, memory: 51557, decode.loss_cls: 1.1069, decode.loss_mask: 0.8664, decode.loss_dice: 1.2487, decode.d0.loss_cls: 6.8843, decode.d0.loss_mask: 0.8640, decode.d0.loss_dice: 1.4534, decode.d1.loss_cls: 1.3500, decode.d1.loss_mask: 0.8904, decode.d1.loss_dice: 1.3360, decode.d2.loss_cls: 1.1761, decode.d2.loss_mask: 0.8772, decode.d2.loss_dice: 1.2847, decode.d3.loss_cls: 1.1430, decode.d3.loss_mask: 0.8728, decode.d3.loss_dice: 1.2640, decode.d4.loss_cls: 1.1327, decode.d4.loss_mask: 0.8757, decode.d4.loss_dice: 1.2625, decode.d5.loss_cls: 1.1176, decode.d5.loss_mask: 0.8666, decode.d5.loss_dice: 1.2569, decode.d6.loss_cls: 1.1097, decode.d6.loss_mask: 0.8711, decode.d6.loss_dice: 1.2482, decode.d7.loss_cls: 1.0970, decode.d7.loss_mask: 0.8668, decode.d7.loss_dice: 1.2512, decode.d8.loss_cls: 1.1016, decode.d8.loss_mask: 0.8678, decode.d8.loss_dice: 1.2515, loss: 38.7947 2022-05-04 23:11:30,438 - mmseg - INFO - Iter [9450/40000] lr: 1.097e-06, eta: 7:09:11, time: 0.813, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0915, decode.loss_mask: 0.9045, decode.loss_dice: 1.2568, decode.d0.loss_cls: 6.8433, decode.d0.loss_mask: 0.8901, decode.d0.loss_dice: 1.4529, decode.d1.loss_cls: 1.3063, decode.d1.loss_mask: 0.9229, decode.d1.loss_dice: 1.3309, decode.d2.loss_cls: 1.1511, decode.d2.loss_mask: 0.9100, decode.d2.loss_dice: 1.2848, decode.d3.loss_cls: 1.1116, decode.d3.loss_mask: 0.8984, decode.d3.loss_dice: 1.2630, decode.d4.loss_cls: 1.0943, decode.d4.loss_mask: 0.9024, decode.d4.loss_dice: 1.2654, decode.d5.loss_cls: 1.0869, decode.d5.loss_mask: 0.9003, decode.d5.loss_dice: 1.2730, decode.d6.loss_cls: 1.0848, decode.d6.loss_mask: 0.8993, decode.d6.loss_dice: 1.2509, decode.d7.loss_cls: 1.0822, decode.d7.loss_mask: 0.8975, decode.d7.loss_dice: 1.2555, decode.d8.loss_cls: 1.0828, decode.d8.loss_mask: 0.9011, decode.d8.loss_dice: 1.2573, loss: 38.8517 2022-05-04 23:12:11,619 - mmseg - INFO - Iter [9500/40000] lr: 1.095e-06, eta: 7:08:26, time: 0.824, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0514, decode.loss_mask: 0.8667, decode.loss_dice: 1.1746, decode.d0.loss_cls: 6.8169, decode.d0.loss_mask: 0.8653, decode.d0.loss_dice: 1.3719, decode.d1.loss_cls: 1.2676, decode.d1.loss_mask: 0.8848, decode.d1.loss_dice: 1.2525, decode.d2.loss_cls: 1.1189, decode.d2.loss_mask: 0.8670, decode.d2.loss_dice: 1.2028, decode.d3.loss_cls: 1.0837, decode.d3.loss_mask: 0.8682, decode.d3.loss_dice: 1.1750, decode.d4.loss_cls: 1.0699, decode.d4.loss_mask: 0.8620, decode.d4.loss_dice: 1.1787, decode.d5.loss_cls: 1.0658, decode.d5.loss_mask: 0.8621, decode.d5.loss_dice: 1.1735, decode.d6.loss_cls: 1.0511, decode.d6.loss_mask: 0.8659, decode.d6.loss_dice: 1.1698, decode.d7.loss_cls: 1.0535, decode.d7.loss_mask: 0.8633, decode.d7.loss_dice: 1.1747, decode.d8.loss_cls: 1.0415, decode.d8.loss_mask: 0.8651, decode.d8.loss_dice: 1.1827, loss: 37.3469 2022-05-04 23:12:52,540 - mmseg - INFO - Iter [9550/40000] lr: 1.093e-06, eta: 7:07:40, time: 0.818, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0829, decode.loss_mask: 0.8463, decode.loss_dice: 1.1831, decode.d0.loss_cls: 6.7856, decode.d0.loss_mask: 0.8469, decode.d0.loss_dice: 1.3784, decode.d1.loss_cls: 1.2831, decode.d1.loss_mask: 0.8765, decode.d1.loss_dice: 1.2663, decode.d2.loss_cls: 1.1349, decode.d2.loss_mask: 0.8639, decode.d2.loss_dice: 1.2164, decode.d3.loss_cls: 1.0927, decode.d3.loss_mask: 0.8583, decode.d3.loss_dice: 1.1961, decode.d4.loss_cls: 1.0901, decode.d4.loss_mask: 0.8555, decode.d4.loss_dice: 1.2012, decode.d5.loss_cls: 1.0892, decode.d5.loss_mask: 0.8554, decode.d5.loss_dice: 1.1920, decode.d6.loss_cls: 1.0775, decode.d6.loss_mask: 0.8544, decode.d6.loss_dice: 1.1832, decode.d7.loss_cls: 1.0680, decode.d7.loss_mask: 0.8612, decode.d7.loss_dice: 1.1866, decode.d8.loss_cls: 1.0601, decode.d8.loss_mask: 0.8628, decode.d8.loss_dice: 1.1893, loss: 37.5379 2022-05-04 23:13:36,412 - mmseg - INFO - Iter [9600/40000] lr: 1.091e-06, eta: 7:07:03, time: 0.877, data_time: 0.060, memory: 51557, decode.loss_cls: 1.0106, decode.loss_mask: 0.8826, decode.loss_dice: 1.2264, decode.d0.loss_cls: 6.7743, decode.d0.loss_mask: 0.8758, decode.d0.loss_dice: 1.4162, decode.d1.loss_cls: 1.2607, decode.d1.loss_mask: 0.9083, decode.d1.loss_dice: 1.3113, decode.d2.loss_cls: 1.0928, decode.d2.loss_mask: 0.8870, decode.d2.loss_dice: 1.2577, decode.d3.loss_cls: 1.0597, decode.d3.loss_mask: 0.8816, decode.d3.loss_dice: 1.2329, decode.d4.loss_cls: 1.0568, decode.d4.loss_mask: 0.8796, decode.d4.loss_dice: 1.2310, decode.d5.loss_cls: 1.0340, decode.d5.loss_mask: 0.8792, decode.d5.loss_dice: 1.2255, decode.d6.loss_cls: 1.0284, decode.d6.loss_mask: 0.8748, decode.d6.loss_dice: 1.2182, decode.d7.loss_cls: 1.0107, decode.d7.loss_mask: 0.8802, decode.d7.loss_dice: 1.2290, decode.d8.loss_cls: 1.0135, decode.d8.loss_mask: 0.8832, decode.d8.loss_dice: 1.2311, loss: 37.7532 2022-05-04 23:14:17,416 - mmseg - INFO - Iter [9650/40000] lr: 1.089e-06, eta: 7:06:18, time: 0.820, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0661, decode.loss_mask: 0.8637, decode.loss_dice: 1.2083, decode.d0.loss_cls: 6.7313, decode.d0.loss_mask: 0.8666, decode.d0.loss_dice: 1.4080, decode.d1.loss_cls: 1.2619, decode.d1.loss_mask: 0.8883, decode.d1.loss_dice: 1.2864, decode.d2.loss_cls: 1.1173, decode.d2.loss_mask: 0.8729, decode.d2.loss_dice: 1.2257, decode.d3.loss_cls: 1.0940, decode.d3.loss_mask: 0.8643, decode.d3.loss_dice: 1.2099, decode.d4.loss_cls: 1.0793, decode.d4.loss_mask: 0.8606, decode.d4.loss_dice: 1.2043, decode.d5.loss_cls: 1.0761, decode.d5.loss_mask: 0.8617, decode.d5.loss_dice: 1.2054, decode.d6.loss_cls: 1.0525, decode.d6.loss_mask: 0.8675, decode.d6.loss_dice: 1.2098, decode.d7.loss_cls: 1.0599, decode.d7.loss_mask: 0.8648, decode.d7.loss_dice: 1.2044, decode.d8.loss_cls: 1.0591, decode.d8.loss_mask: 0.8657, decode.d8.loss_dice: 1.2115, loss: 37.6474 2022-05-04 23:14:58,767 - mmseg - INFO - Iter [9700/40000] lr: 1.088e-06, eta: 7:05:33, time: 0.827, data_time: 0.010, memory: 51557, decode.loss_cls: 1.0339, decode.loss_mask: 0.8661, decode.loss_dice: 1.2075, decode.d0.loss_cls: 6.7591, decode.d0.loss_mask: 0.8612, decode.d0.loss_dice: 1.4022, decode.d1.loss_cls: 1.2709, decode.d1.loss_mask: 0.8847, decode.d1.loss_dice: 1.2816, decode.d2.loss_cls: 1.1038, decode.d2.loss_mask: 0.8749, decode.d2.loss_dice: 1.2336, decode.d3.loss_cls: 1.0604, decode.d3.loss_mask: 0.8683, decode.d3.loss_dice: 1.2180, decode.d4.loss_cls: 1.0571, decode.d4.loss_mask: 0.8706, decode.d4.loss_dice: 1.2181, decode.d5.loss_cls: 1.0423, decode.d5.loss_mask: 0.8759, decode.d5.loss_dice: 1.2091, decode.d6.loss_cls: 1.0276, decode.d6.loss_mask: 0.8712, decode.d6.loss_dice: 1.2070, decode.d7.loss_cls: 1.0332, decode.d7.loss_mask: 0.8682, decode.d7.loss_dice: 1.2148, decode.d8.loss_cls: 1.0248, decode.d8.loss_mask: 0.8715, decode.d8.loss_dice: 1.2134, loss: 37.5309 2022-05-04 23:15:40,654 - mmseg - INFO - Iter [9750/40000] lr: 1.086e-06, eta: 7:04:50, time: 0.838, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0334, decode.loss_mask: 0.8577, decode.loss_dice: 1.2078, decode.d0.loss_cls: 6.6890, decode.d0.loss_mask: 0.8583, decode.d0.loss_dice: 1.3983, decode.d1.loss_cls: 1.2567, decode.d1.loss_mask: 0.8741, decode.d1.loss_dice: 1.2842, decode.d2.loss_cls: 1.0993, decode.d2.loss_mask: 0.8593, decode.d2.loss_dice: 1.2270, decode.d3.loss_cls: 1.0491, decode.d3.loss_mask: 0.8581, decode.d3.loss_dice: 1.2124, decode.d4.loss_cls: 1.0400, decode.d4.loss_mask: 0.8616, decode.d4.loss_dice: 1.2062, decode.d5.loss_cls: 1.0291, decode.d5.loss_mask: 0.8591, decode.d5.loss_dice: 1.2047, decode.d6.loss_cls: 1.0228, decode.d6.loss_mask: 0.8569, decode.d6.loss_dice: 1.1962, decode.d7.loss_cls: 1.0222, decode.d7.loss_mask: 0.8602, decode.d7.loss_dice: 1.2118, decode.d8.loss_cls: 1.0295, decode.d8.loss_mask: 0.8603, decode.d8.loss_dice: 1.2033, loss: 37.2289 2022-05-04 23:16:21,250 - mmseg - INFO - Iter [9800/40000] lr: 1.084e-06, eta: 7:04:03, time: 0.812, data_time: 0.010, memory: 51557, decode.loss_cls: 1.0417, decode.loss_mask: 0.8395, decode.loss_dice: 1.2003, decode.d0.loss_cls: 6.6698, decode.d0.loss_mask: 0.8420, decode.d0.loss_dice: 1.3820, decode.d1.loss_cls: 1.2689, decode.d1.loss_mask: 0.8637, decode.d1.loss_dice: 1.2669, decode.d2.loss_cls: 1.0956, decode.d2.loss_mask: 0.8478, decode.d2.loss_dice: 1.2173, decode.d3.loss_cls: 1.0610, decode.d3.loss_mask: 0.8377, decode.d3.loss_dice: 1.1937, decode.d4.loss_cls: 1.0501, decode.d4.loss_mask: 0.8335, decode.d4.loss_dice: 1.1961, decode.d5.loss_cls: 1.0411, decode.d5.loss_mask: 0.8363, decode.d5.loss_dice: 1.2017, decode.d6.loss_cls: 1.0349, decode.d6.loss_mask: 0.8341, decode.d6.loss_dice: 1.1876, decode.d7.loss_cls: 1.0296, decode.d7.loss_mask: 0.8368, decode.d7.loss_dice: 1.1904, decode.d8.loss_cls: 1.0338, decode.d8.loss_mask: 0.8386, decode.d8.loss_dice: 1.1961, loss: 36.9686 2022-05-04 23:17:02,330 - mmseg - INFO - Iter [9850/40000] lr: 1.082e-06, eta: 7:03:18, time: 0.822, data_time: 0.010, memory: 51557, decode.loss_cls: 1.0089, decode.loss_mask: 0.8481, decode.loss_dice: 1.1629, decode.d0.loss_cls: 6.6455, decode.d0.loss_mask: 0.8428, decode.d0.loss_dice: 1.3569, decode.d1.loss_cls: 1.2205, decode.d1.loss_mask: 0.8700, decode.d1.loss_dice: 1.2507, decode.d2.loss_cls: 1.0756, decode.d2.loss_mask: 0.8480, decode.d2.loss_dice: 1.1971, decode.d3.loss_cls: 1.0415, decode.d3.loss_mask: 0.8422, decode.d3.loss_dice: 1.1768, decode.d4.loss_cls: 1.0284, decode.d4.loss_mask: 0.8424, decode.d4.loss_dice: 1.1723, decode.d5.loss_cls: 1.0205, decode.d5.loss_mask: 0.8445, decode.d5.loss_dice: 1.1696, decode.d6.loss_cls: 1.0120, decode.d6.loss_mask: 0.8405, decode.d6.loss_dice: 1.1607, decode.d7.loss_cls: 1.0014, decode.d7.loss_mask: 0.8429, decode.d7.loss_dice: 1.1645, decode.d8.loss_cls: 1.0042, decode.d8.loss_mask: 0.8491, decode.d8.loss_dice: 1.1666, loss: 36.5069 2022-05-04 23:17:43,867 - mmseg - INFO - Iter [9900/40000] lr: 1.080e-06, eta: 7:02:34, time: 0.831, data_time: 0.010, memory: 51557, decode.loss_cls: 1.0438, decode.loss_mask: 0.8585, decode.loss_dice: 1.1856, decode.d0.loss_cls: 6.6213, decode.d0.loss_mask: 0.8575, decode.d0.loss_dice: 1.3845, decode.d1.loss_cls: 1.3030, decode.d1.loss_mask: 0.8861, decode.d1.loss_dice: 1.2758, decode.d2.loss_cls: 1.1221, decode.d2.loss_mask: 0.8672, decode.d2.loss_dice: 1.2189, decode.d3.loss_cls: 1.0771, decode.d3.loss_mask: 0.8627, decode.d3.loss_dice: 1.1937, decode.d4.loss_cls: 1.0671, decode.d4.loss_mask: 0.8564, decode.d4.loss_dice: 1.1930, decode.d5.loss_cls: 1.0604, decode.d5.loss_mask: 0.8586, decode.d5.loss_dice: 1.1896, decode.d6.loss_cls: 1.0505, decode.d6.loss_mask: 0.8550, decode.d6.loss_dice: 1.1858, decode.d7.loss_cls: 1.0474, decode.d7.loss_mask: 0.8576, decode.d7.loss_dice: 1.1928, decode.d8.loss_cls: 1.0428, decode.d8.loss_mask: 0.8524, decode.d8.loss_dice: 1.1877, loss: 37.2551 2022-05-04 23:18:24,906 - mmseg - INFO - Iter [9950/40000] lr: 1.079e-06, eta: 7:01:49, time: 0.821, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9814, decode.loss_mask: 0.8766, decode.loss_dice: 1.2148, decode.d0.loss_cls: 6.5919, decode.d0.loss_mask: 0.8762, decode.d0.loss_dice: 1.3983, decode.d1.loss_cls: 1.2139, decode.d1.loss_mask: 0.8974, decode.d1.loss_dice: 1.2777, decode.d2.loss_cls: 1.0410, decode.d2.loss_mask: 0.8828, decode.d2.loss_dice: 1.2341, decode.d3.loss_cls: 1.0108, decode.d3.loss_mask: 0.8763, decode.d3.loss_dice: 1.2089, decode.d4.loss_cls: 0.9988, decode.d4.loss_mask: 0.8730, decode.d4.loss_dice: 1.2213, decode.d5.loss_cls: 0.9885, decode.d5.loss_mask: 0.8770, decode.d5.loss_dice: 1.2160, decode.d6.loss_cls: 0.9718, decode.d6.loss_mask: 0.8763, decode.d6.loss_dice: 1.2086, decode.d7.loss_cls: 0.9728, decode.d7.loss_mask: 0.8758, decode.d7.loss_dice: 1.2123, decode.d8.loss_cls: 0.9763, decode.d8.loss_mask: 0.8840, decode.d8.loss_dice: 1.2118, loss: 36.9465 2022-05-04 23:19:06,260 - mmseg - INFO - Saving checkpoint at 10000 iterations 2022-05-04 23:19:30,889 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-04 23:19:30,892 - mmseg - INFO - Iter [10000/40000] lr: 1.077e-06, eta: 7:02:18, time: 1.317, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0463, decode.loss_mask: 0.8699, decode.loss_dice: 1.2134, decode.d0.loss_cls: 6.5810, decode.d0.loss_mask: 0.8572, decode.d0.loss_dice: 1.4058, decode.d1.loss_cls: 1.2572, decode.d1.loss_mask: 0.8802, decode.d1.loss_dice: 1.2826, decode.d2.loss_cls: 1.0994, decode.d2.loss_mask: 0.8747, decode.d2.loss_dice: 1.2337, decode.d3.loss_cls: 1.0797, decode.d3.loss_mask: 0.8707, decode.d3.loss_dice: 1.2132, decode.d4.loss_cls: 1.0627, decode.d4.loss_mask: 0.8739, decode.d4.loss_dice: 1.2214, decode.d5.loss_cls: 1.0539, decode.d5.loss_mask: 0.8738, decode.d5.loss_dice: 1.2187, decode.d6.loss_cls: 1.0485, decode.d6.loss_mask: 0.8633, decode.d6.loss_dice: 1.2055, decode.d7.loss_cls: 1.0417, decode.d7.loss_mask: 0.8702, decode.d7.loss_dice: 1.2168, decode.d8.loss_cls: 1.0462, decode.d8.loss_mask: 0.8711, decode.d8.loss_dice: 1.2159, loss: 37.4488 2022-05-04 23:20:12,534 - mmseg - INFO - Iter [10050/40000] lr: 1.075e-06, eta: 7:01:34, time: 0.835, data_time: 0.012, memory: 51557, decode.loss_cls: 1.0154, decode.loss_mask: 0.8723, decode.loss_dice: 1.1913, decode.d0.loss_cls: 6.5497, decode.d0.loss_mask: 0.8688, decode.d0.loss_dice: 1.3878, decode.d1.loss_cls: 1.2298, decode.d1.loss_mask: 0.8981, decode.d1.loss_dice: 1.2616, decode.d2.loss_cls: 1.0781, decode.d2.loss_mask: 0.8823, decode.d2.loss_dice: 1.2136, decode.d3.loss_cls: 1.0381, decode.d3.loss_mask: 0.8801, decode.d3.loss_dice: 1.1951, decode.d4.loss_cls: 1.0270, decode.d4.loss_mask: 0.8847, decode.d4.loss_dice: 1.2071, decode.d5.loss_cls: 1.0234, decode.d5.loss_mask: 0.8803, decode.d5.loss_dice: 1.1986, decode.d6.loss_cls: 1.0096, decode.d6.loss_mask: 0.8747, decode.d6.loss_dice: 1.1863, decode.d7.loss_cls: 1.0046, decode.d7.loss_mask: 0.8769, decode.d7.loss_dice: 1.1961, decode.d8.loss_cls: 1.0131, decode.d8.loss_mask: 0.8749, decode.d8.loss_dice: 1.1922, loss: 37.0114 2022-05-04 23:20:53,143 - mmseg - INFO - Iter [10100/40000] lr: 1.073e-06, eta: 7:00:47, time: 0.811, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0303, decode.loss_mask: 0.8945, decode.loss_dice: 1.2105, decode.d0.loss_cls: 6.5591, decode.d0.loss_mask: 0.8781, decode.d0.loss_dice: 1.3981, decode.d1.loss_cls: 1.2565, decode.d1.loss_mask: 0.9052, decode.d1.loss_dice: 1.2840, decode.d2.loss_cls: 1.0953, decode.d2.loss_mask: 0.8912, decode.d2.loss_dice: 1.2339, decode.d3.loss_cls: 1.0620, decode.d3.loss_mask: 0.8779, decode.d3.loss_dice: 1.2075, decode.d4.loss_cls: 1.0542, decode.d4.loss_mask: 0.8839, decode.d4.loss_dice: 1.2146, decode.d5.loss_cls: 1.0451, decode.d5.loss_mask: 0.8843, decode.d5.loss_dice: 1.2018, decode.d6.loss_cls: 1.0372, decode.d6.loss_mask: 0.8891, decode.d6.loss_dice: 1.2037, decode.d7.loss_cls: 1.0217, decode.d7.loss_mask: 0.8932, decode.d7.loss_dice: 1.2073, decode.d8.loss_cls: 1.0202, decode.d8.loss_mask: 0.8909, decode.d8.loss_dice: 1.2091, loss: 37.4401 2022-05-04 23:21:38,618 - mmseg - INFO - Iter [10150/40000] lr: 1.071e-06, eta: 7:00:14, time: 0.910, data_time: 0.059, memory: 51557, decode.loss_cls: 0.9832, decode.loss_mask: 0.8473, decode.loss_dice: 1.1721, decode.d0.loss_cls: 6.4800, decode.d0.loss_mask: 0.8588, decode.d0.loss_dice: 1.3727, decode.d1.loss_cls: 1.2013, decode.d1.loss_mask: 0.8681, decode.d1.loss_dice: 1.2444, decode.d2.loss_cls: 1.0543, decode.d2.loss_mask: 0.8552, decode.d2.loss_dice: 1.2043, decode.d3.loss_cls: 1.0162, decode.d3.loss_mask: 0.8528, decode.d3.loss_dice: 1.1837, decode.d4.loss_cls: 1.0075, decode.d4.loss_mask: 0.8514, decode.d4.loss_dice: 1.1800, decode.d5.loss_cls: 0.9881, decode.d5.loss_mask: 0.8504, decode.d5.loss_dice: 1.1793, decode.d6.loss_cls: 0.9867, decode.d6.loss_mask: 0.8498, decode.d6.loss_dice: 1.1733, decode.d7.loss_cls: 0.9846, decode.d7.loss_mask: 0.8528, decode.d7.loss_dice: 1.1784, decode.d8.loss_cls: 0.9760, decode.d8.loss_mask: 0.8492, decode.d8.loss_dice: 1.1734, loss: 36.2752 2022-05-04 23:22:19,477 - mmseg - INFO - Iter [10200/40000] lr: 1.070e-06, eta: 6:59:28, time: 0.817, data_time: 0.011, memory: 51557, decode.loss_cls: 1.0086, decode.loss_mask: 0.8383, decode.loss_dice: 1.1900, decode.d0.loss_cls: 6.4846, decode.d0.loss_mask: 0.8436, decode.d0.loss_dice: 1.3947, decode.d1.loss_cls: 1.2472, decode.d1.loss_mask: 0.8633, decode.d1.loss_dice: 1.2664, decode.d2.loss_cls: 1.0857, decode.d2.loss_mask: 0.8460, decode.d2.loss_dice: 1.2214, decode.d3.loss_cls: 1.0400, decode.d3.loss_mask: 0.8416, decode.d3.loss_dice: 1.2001, decode.d4.loss_cls: 1.0304, decode.d4.loss_mask: 0.8395, decode.d4.loss_dice: 1.1907, decode.d5.loss_cls: 1.0234, decode.d5.loss_mask: 0.8344, decode.d5.loss_dice: 1.1904, decode.d6.loss_cls: 1.0118, decode.d6.loss_mask: 0.8395, decode.d6.loss_dice: 1.1856, decode.d7.loss_cls: 1.0118, decode.d7.loss_mask: 0.8387, decode.d7.loss_dice: 1.1889, decode.d8.loss_cls: 1.0053, decode.d8.loss_mask: 0.8366, decode.d8.loss_dice: 1.1847, loss: 36.5833 2022-05-04 23:22:59,909 - mmseg - INFO - Iter [10250/40000] lr: 1.068e-06, eta: 6:58:41, time: 0.808, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0384, decode.loss_mask: 0.8853, decode.loss_dice: 1.1908, decode.d0.loss_cls: 6.4662, decode.d0.loss_mask: 0.8805, decode.d0.loss_dice: 1.3836, decode.d1.loss_cls: 1.2601, decode.d1.loss_mask: 0.9007, decode.d1.loss_dice: 1.2641, decode.d2.loss_cls: 1.1090, decode.d2.loss_mask: 0.8794, decode.d2.loss_dice: 1.2138, decode.d3.loss_cls: 1.0749, decode.d3.loss_mask: 0.8782, decode.d3.loss_dice: 1.1852, decode.d4.loss_cls: 1.0574, decode.d4.loss_mask: 0.8779, decode.d4.loss_dice: 1.1839, decode.d5.loss_cls: 1.0488, decode.d5.loss_mask: 0.8767, decode.d5.loss_dice: 1.1834, decode.d6.loss_cls: 1.0420, decode.d6.loss_mask: 0.8799, decode.d6.loss_dice: 1.1864, decode.d7.loss_cls: 1.0282, decode.d7.loss_mask: 0.8809, decode.d7.loss_dice: 1.1827, decode.d8.loss_cls: 1.0307, decode.d8.loss_mask: 0.8860, decode.d8.loss_dice: 1.1878, loss: 37.1426 2022-05-04 23:23:40,597 - mmseg - INFO - Iter [10300/40000] lr: 1.066e-06, eta: 6:57:54, time: 0.815, data_time: 0.010, memory: 51557, decode.loss_cls: 1.0154, decode.loss_mask: 0.8354, decode.loss_dice: 1.2202, decode.d0.loss_cls: 6.4578, decode.d0.loss_mask: 0.8386, decode.d0.loss_dice: 1.4203, decode.d1.loss_cls: 1.2498, decode.d1.loss_mask: 0.8582, decode.d1.loss_dice: 1.2843, decode.d2.loss_cls: 1.0855, decode.d2.loss_mask: 0.8544, decode.d2.loss_dice: 1.2455, decode.d3.loss_cls: 1.0431, decode.d3.loss_mask: 0.8443, decode.d3.loss_dice: 1.2302, decode.d4.loss_cls: 1.0351, decode.d4.loss_mask: 0.8440, decode.d4.loss_dice: 1.2237, decode.d5.loss_cls: 1.0252, decode.d5.loss_mask: 0.8410, decode.d5.loss_dice: 1.2220, decode.d6.loss_cls: 1.0159, decode.d6.loss_mask: 0.8373, decode.d6.loss_dice: 1.2067, decode.d7.loss_cls: 1.0111, decode.d7.loss_mask: 0.8351, decode.d7.loss_dice: 1.2167, decode.d8.loss_cls: 1.0129, decode.d8.loss_mask: 0.8335, decode.d8.loss_dice: 1.2196, loss: 36.8627 2022-05-04 23:24:22,079 - mmseg - INFO - Iter [10350/40000] lr: 1.064e-06, eta: 6:57:10, time: 0.830, data_time: 0.009, memory: 51557, decode.loss_cls: 0.9872, decode.loss_mask: 0.8422, decode.loss_dice: 1.1941, decode.d0.loss_cls: 6.4165, decode.d0.loss_mask: 0.8439, decode.d0.loss_dice: 1.3855, decode.d1.loss_cls: 1.2134, decode.d1.loss_mask: 0.8604, decode.d1.loss_dice: 1.2735, decode.d2.loss_cls: 1.0487, decode.d2.loss_mask: 0.8485, decode.d2.loss_dice: 1.2255, decode.d3.loss_cls: 1.0217, decode.d3.loss_mask: 0.8419, decode.d3.loss_dice: 1.1988, decode.d4.loss_cls: 1.0009, decode.d4.loss_mask: 0.8484, decode.d4.loss_dice: 1.2045, decode.d5.loss_cls: 0.9972, decode.d5.loss_mask: 0.8434, decode.d5.loss_dice: 1.2034, decode.d6.loss_cls: 0.9847, decode.d6.loss_mask: 0.8445, decode.d6.loss_dice: 1.1895, decode.d7.loss_cls: 0.9762, decode.d7.loss_mask: 0.8468, decode.d7.loss_dice: 1.2052, decode.d8.loss_cls: 0.9777, decode.d8.loss_mask: 0.8435, decode.d8.loss_dice: 1.2042, loss: 36.3718 2022-05-04 23:25:02,871 - mmseg - INFO - Iter [10400/40000] lr: 1.063e-06, eta: 6:56:24, time: 0.816, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9879, decode.loss_mask: 0.8677, decode.loss_dice: 1.2050, decode.d0.loss_cls: 6.4064, decode.d0.loss_mask: 0.8651, decode.d0.loss_dice: 1.3822, decode.d1.loss_cls: 1.1966, decode.d1.loss_mask: 0.8810, decode.d1.loss_dice: 1.2709, decode.d2.loss_cls: 1.0514, decode.d2.loss_mask: 0.8593, decode.d2.loss_dice: 1.2262, decode.d3.loss_cls: 1.0239, decode.d3.loss_mask: 0.8643, decode.d3.loss_dice: 1.2099, decode.d4.loss_cls: 1.0131, decode.d4.loss_mask: 0.8645, decode.d4.loss_dice: 1.2081, decode.d5.loss_cls: 0.9996, decode.d5.loss_mask: 0.8648, decode.d5.loss_dice: 1.2067, decode.d6.loss_cls: 0.9963, decode.d6.loss_mask: 0.8644, decode.d6.loss_dice: 1.1979, decode.d7.loss_cls: 0.9800, decode.d7.loss_mask: 0.8726, decode.d7.loss_dice: 1.2046, decode.d8.loss_cls: 0.9979, decode.d8.loss_mask: 0.8646, decode.d8.loss_dice: 1.1978, loss: 36.6310 2022-05-04 23:25:43,150 - mmseg - INFO - Iter [10450/40000] lr: 1.061e-06, eta: 6:55:36, time: 0.805, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0041, decode.loss_mask: 0.8527, decode.loss_dice: 1.1889, decode.d0.loss_cls: 6.3830, decode.d0.loss_mask: 0.8476, decode.d0.loss_dice: 1.3862, decode.d1.loss_cls: 1.2115, decode.d1.loss_mask: 0.8708, decode.d1.loss_dice: 1.2673, decode.d2.loss_cls: 1.0744, decode.d2.loss_mask: 0.8514, decode.d2.loss_dice: 1.2250, decode.d3.loss_cls: 1.0294, decode.d3.loss_mask: 0.8499, decode.d3.loss_dice: 1.1988, decode.d4.loss_cls: 1.0163, decode.d4.loss_mask: 0.8481, decode.d4.loss_dice: 1.2047, decode.d5.loss_cls: 1.0102, decode.d5.loss_mask: 0.8519, decode.d5.loss_dice: 1.1942, decode.d6.loss_cls: 0.9883, decode.d6.loss_mask: 0.8535, decode.d6.loss_dice: 1.1842, decode.d7.loss_cls: 0.9904, decode.d7.loss_mask: 0.8473, decode.d7.loss_dice: 1.1952, decode.d8.loss_cls: 0.9912, decode.d8.loss_mask: 0.8550, decode.d8.loss_dice: 1.1908, loss: 36.4627 2022-05-04 23:26:24,571 - mmseg - INFO - Iter [10500/40000] lr: 1.059e-06, eta: 6:54:52, time: 0.828, data_time: 0.010, memory: 51557, decode.loss_cls: 1.0127, decode.loss_mask: 0.8722, decode.loss_dice: 1.2168, decode.d0.loss_cls: 6.3578, decode.d0.loss_mask: 0.8567, decode.d0.loss_dice: 1.3888, decode.d1.loss_cls: 1.2168, decode.d1.loss_mask: 0.8922, decode.d1.loss_dice: 1.2852, decode.d2.loss_cls: 1.0797, decode.d2.loss_mask: 0.8747, decode.d2.loss_dice: 1.2353, decode.d3.loss_cls: 1.0399, decode.d3.loss_mask: 0.8729, decode.d3.loss_dice: 1.2186, decode.d4.loss_cls: 1.0322, decode.d4.loss_mask: 0.8735, decode.d4.loss_dice: 1.2194, decode.d5.loss_cls: 1.0166, decode.d5.loss_mask: 0.8727, decode.d5.loss_dice: 1.2154, decode.d6.loss_cls: 1.0056, decode.d6.loss_mask: 0.8734, decode.d6.loss_dice: 1.2068, decode.d7.loss_cls: 1.0110, decode.d7.loss_mask: 0.8692, decode.d7.loss_dice: 1.2226, decode.d8.loss_cls: 0.9999, decode.d8.loss_mask: 0.8793, decode.d8.loss_dice: 1.2142, loss: 36.9319 2022-05-04 23:27:04,781 - mmseg - INFO - Iter [10550/40000] lr: 1.057e-06, eta: 6:54:04, time: 0.803, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9850, decode.loss_mask: 0.8810, decode.loss_dice: 1.1547, decode.d0.loss_cls: 6.3388, decode.d0.loss_mask: 0.8775, decode.d0.loss_dice: 1.3381, decode.d1.loss_cls: 1.1827, decode.d1.loss_mask: 0.9096, decode.d1.loss_dice: 1.2315, decode.d2.loss_cls: 1.0488, decode.d2.loss_mask: 0.8929, decode.d2.loss_dice: 1.1868, decode.d3.loss_cls: 1.0047, decode.d3.loss_mask: 0.8891, decode.d3.loss_dice: 1.1729, decode.d4.loss_cls: 0.9882, decode.d4.loss_mask: 0.8837, decode.d4.loss_dice: 1.1745, decode.d5.loss_cls: 0.9841, decode.d5.loss_mask: 0.8817, decode.d5.loss_dice: 1.1677, decode.d6.loss_cls: 0.9812, decode.d6.loss_mask: 0.8774, decode.d6.loss_dice: 1.1597, decode.d7.loss_cls: 0.9706, decode.d7.loss_mask: 0.8834, decode.d7.loss_dice: 1.1713, decode.d8.loss_cls: 0.9754, decode.d8.loss_mask: 0.8827, decode.d8.loss_dice: 1.1647, loss: 36.2405 2022-05-04 23:27:45,490 - mmseg - INFO - Iter [10600/40000] lr: 1.055e-06, eta: 6:53:18, time: 0.815, data_time: 0.010, memory: 51557, decode.loss_cls: 1.0112, decode.loss_mask: 0.8664, decode.loss_dice: 1.1892, decode.d0.loss_cls: 6.3139, decode.d0.loss_mask: 0.8612, decode.d0.loss_dice: 1.3986, decode.d1.loss_cls: 1.2300, decode.d1.loss_mask: 0.8878, decode.d1.loss_dice: 1.2716, decode.d2.loss_cls: 1.0861, decode.d2.loss_mask: 0.8728, decode.d2.loss_dice: 1.2118, decode.d3.loss_cls: 1.0382, decode.d3.loss_mask: 0.8663, decode.d3.loss_dice: 1.1956, decode.d4.loss_cls: 1.0240, decode.d4.loss_mask: 0.8700, decode.d4.loss_dice: 1.2000, decode.d5.loss_cls: 1.0079, decode.d5.loss_mask: 0.8692, decode.d5.loss_dice: 1.1945, decode.d6.loss_cls: 1.0002, decode.d6.loss_mask: 0.8663, decode.d6.loss_dice: 1.1863, decode.d7.loss_cls: 1.0003, decode.d7.loss_mask: 0.8661, decode.d7.loss_dice: 1.1925, decode.d8.loss_cls: 0.9984, decode.d8.loss_mask: 0.8672, decode.d8.loss_dice: 1.1908, loss: 36.6346 2022-05-04 23:28:27,079 - mmseg - INFO - Iter [10650/40000] lr: 1.054e-06, eta: 6:52:34, time: 0.832, data_time: 0.009, memory: 51557, decode.loss_cls: 0.9738, decode.loss_mask: 0.8702, decode.loss_dice: 1.1763, decode.d0.loss_cls: 6.2830, decode.d0.loss_mask: 0.8814, decode.d0.loss_dice: 1.3561, decode.d1.loss_cls: 1.1836, decode.d1.loss_mask: 0.8925, decode.d1.loss_dice: 1.2429, decode.d2.loss_cls: 1.0278, decode.d2.loss_mask: 0.8771, decode.d2.loss_dice: 1.2009, decode.d3.loss_cls: 0.9963, decode.d3.loss_mask: 0.8766, decode.d3.loss_dice: 1.1830, decode.d4.loss_cls: 0.9898, decode.d4.loss_mask: 0.8723, decode.d4.loss_dice: 1.1846, decode.d5.loss_cls: 0.9815, decode.d5.loss_mask: 0.8668, decode.d5.loss_dice: 1.1751, decode.d6.loss_cls: 0.9733, decode.d6.loss_mask: 0.8678, decode.d6.loss_dice: 1.1717, decode.d7.loss_cls: 0.9666, decode.d7.loss_mask: 0.8725, decode.d7.loss_dice: 1.1775, decode.d8.loss_cls: 0.9724, decode.d8.loss_mask: 0.8695, decode.d8.loss_dice: 1.1754, loss: 36.1382 2022-05-04 23:29:11,682 - mmseg - INFO - Iter [10700/40000] lr: 1.052e-06, eta: 6:51:58, time: 0.891, data_time: 0.060, memory: 51557, decode.loss_cls: 0.9953, decode.loss_mask: 0.8597, decode.loss_dice: 1.1852, decode.d0.loss_cls: 6.2758, decode.d0.loss_mask: 0.8563, decode.d0.loss_dice: 1.3722, decode.d1.loss_cls: 1.2337, decode.d1.loss_mask: 0.8923, decode.d1.loss_dice: 1.2652, decode.d2.loss_cls: 1.0751, decode.d2.loss_mask: 0.8708, decode.d2.loss_dice: 1.2097, decode.d3.loss_cls: 1.0198, decode.d3.loss_mask: 0.8641, decode.d3.loss_dice: 1.1962, decode.d4.loss_cls: 1.0132, decode.d4.loss_mask: 0.8625, decode.d4.loss_dice: 1.1959, decode.d5.loss_cls: 1.0068, decode.d5.loss_mask: 0.8529, decode.d5.loss_dice: 1.1853, decode.d6.loss_cls: 0.9937, decode.d6.loss_mask: 0.8598, decode.d6.loss_dice: 1.1873, decode.d7.loss_cls: 0.9974, decode.d7.loss_mask: 0.8626, decode.d7.loss_dice: 1.1899, decode.d8.loss_cls: 0.9982, decode.d8.loss_mask: 0.8576, decode.d8.loss_dice: 1.1822, loss: 36.4168 2022-05-04 23:29:52,248 - mmseg - INFO - Iter [10750/40000] lr: 1.050e-06, eta: 6:51:12, time: 0.812, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9595, decode.loss_mask: 0.8819, decode.loss_dice: 1.2038, decode.d0.loss_cls: 6.2424, decode.d0.loss_mask: 0.8879, decode.d0.loss_dice: 1.3838, decode.d1.loss_cls: 1.1783, decode.d1.loss_mask: 0.8989, decode.d1.loss_dice: 1.2730, decode.d2.loss_cls: 1.0239, decode.d2.loss_mask: 0.8863, decode.d2.loss_dice: 1.2300, decode.d3.loss_cls: 0.9823, decode.d3.loss_mask: 0.8864, decode.d3.loss_dice: 1.2120, decode.d4.loss_cls: 0.9768, decode.d4.loss_mask: 0.8808, decode.d4.loss_dice: 1.2122, decode.d5.loss_cls: 0.9579, decode.d5.loss_mask: 0.8829, decode.d5.loss_dice: 1.2236, decode.d6.loss_cls: 0.9540, decode.d6.loss_mask: 0.8822, decode.d6.loss_dice: 1.2066, decode.d7.loss_cls: 0.9447, decode.d7.loss_mask: 0.8894, decode.d7.loss_dice: 1.2031, decode.d8.loss_cls: 0.9442, decode.d8.loss_mask: 0.8826, decode.d8.loss_dice: 1.2026, loss: 36.3741 2022-05-04 23:30:31,695 - mmseg - INFO - Iter [10800/40000] lr: 1.048e-06, eta: 6:50:22, time: 0.789, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9914, decode.loss_mask: 0.8231, decode.loss_dice: 1.1725, decode.d0.loss_cls: 6.2887, decode.d0.loss_mask: 0.8276, decode.d0.loss_dice: 1.3784, decode.d1.loss_cls: 1.2031, decode.d1.loss_mask: 0.8441, decode.d1.loss_dice: 1.2549, decode.d2.loss_cls: 1.0426, decode.d2.loss_mask: 0.8361, decode.d2.loss_dice: 1.1995, decode.d3.loss_cls: 1.0038, decode.d3.loss_mask: 0.8244, decode.d3.loss_dice: 1.1834, decode.d4.loss_cls: 1.0021, decode.d4.loss_mask: 0.8267, decode.d4.loss_dice: 1.1866, decode.d5.loss_cls: 0.9931, decode.d5.loss_mask: 0.8203, decode.d5.loss_dice: 1.1829, decode.d6.loss_cls: 0.9944, decode.d6.loss_mask: 0.8205, decode.d6.loss_dice: 1.1718, decode.d7.loss_cls: 0.9770, decode.d7.loss_mask: 0.8202, decode.d7.loss_dice: 1.1711, decode.d8.loss_cls: 0.9846, decode.d8.loss_mask: 0.8231, decode.d8.loss_dice: 1.1658, loss: 35.8138 2022-05-04 23:31:12,324 - mmseg - INFO - Iter [10850/40000] lr: 1.046e-06, eta: 6:49:36, time: 0.813, data_time: 0.009, memory: 51557, decode.loss_cls: 0.9904, decode.loss_mask: 0.8631, decode.loss_dice: 1.1826, decode.d0.loss_cls: 6.2085, decode.d0.loss_mask: 0.8668, decode.d0.loss_dice: 1.3704, decode.d1.loss_cls: 1.1998, decode.d1.loss_mask: 0.8844, decode.d1.loss_dice: 1.2465, decode.d2.loss_cls: 1.0485, decode.d2.loss_mask: 0.8672, decode.d2.loss_dice: 1.2044, decode.d3.loss_cls: 1.0151, decode.d3.loss_mask: 0.8608, decode.d3.loss_dice: 1.1878, decode.d4.loss_cls: 0.9918, decode.d4.loss_mask: 0.8681, decode.d4.loss_dice: 1.2003, decode.d5.loss_cls: 0.9879, decode.d5.loss_mask: 0.8622, decode.d5.loss_dice: 1.1907, decode.d6.loss_cls: 0.9941, decode.d6.loss_mask: 0.8610, decode.d6.loss_dice: 1.1803, decode.d7.loss_cls: 0.9823, decode.d7.loss_mask: 0.8670, decode.d7.loss_dice: 1.1848, decode.d8.loss_cls: 0.9783, decode.d8.loss_mask: 0.8611, decode.d8.loss_dice: 1.1849, loss: 36.1911 2022-05-04 23:31:52,725 - mmseg - INFO - Iter [10900/40000] lr: 1.045e-06, eta: 6:48:49, time: 0.808, data_time: 0.009, memory: 51557, decode.loss_cls: 1.0160, decode.loss_mask: 0.8390, decode.loss_dice: 1.1907, decode.d0.loss_cls: 6.2254, decode.d0.loss_mask: 0.8518, decode.d0.loss_dice: 1.3842, decode.d1.loss_cls: 1.2418, decode.d1.loss_mask: 0.8604, decode.d1.loss_dice: 1.2613, decode.d2.loss_cls: 1.0804, decode.d2.loss_mask: 0.8459, decode.d2.loss_dice: 1.2157, decode.d3.loss_cls: 1.0438, decode.d3.loss_mask: 0.8385, decode.d3.loss_dice: 1.2025, decode.d4.loss_cls: 1.0297, decode.d4.loss_mask: 0.8376, decode.d4.loss_dice: 1.1979, decode.d5.loss_cls: 1.0207, decode.d5.loss_mask: 0.8360, decode.d5.loss_dice: 1.1870, decode.d6.loss_cls: 1.0136, decode.d6.loss_mask: 0.8348, decode.d6.loss_dice: 1.1756, decode.d7.loss_cls: 1.0084, decode.d7.loss_mask: 0.8392, decode.d7.loss_dice: 1.1883, decode.d8.loss_cls: 1.0036, decode.d8.loss_mask: 0.8382, decode.d8.loss_dice: 1.1892, loss: 36.2971 2022-05-04 23:32:32,872 - mmseg - INFO - Iter [10950/40000] lr: 1.043e-06, eta: 6:48:02, time: 0.803, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9787, decode.loss_mask: 0.8439, decode.loss_dice: 1.1766, decode.d0.loss_cls: 6.1519, decode.d0.loss_mask: 0.8588, decode.d0.loss_dice: 1.3777, decode.d1.loss_cls: 1.1965, decode.d1.loss_mask: 0.8696, decode.d1.loss_dice: 1.2475, decode.d2.loss_cls: 1.0363, decode.d2.loss_mask: 0.8573, decode.d2.loss_dice: 1.2035, decode.d3.loss_cls: 1.0055, decode.d3.loss_mask: 0.8511, decode.d3.loss_dice: 1.1832, decode.d4.loss_cls: 0.9965, decode.d4.loss_mask: 0.8463, decode.d4.loss_dice: 1.1856, decode.d5.loss_cls: 0.9922, decode.d5.loss_mask: 0.8439, decode.d5.loss_dice: 1.1824, decode.d6.loss_cls: 0.9902, decode.d6.loss_mask: 0.8394, decode.d6.loss_dice: 1.1647, decode.d7.loss_cls: 0.9865, decode.d7.loss_mask: 0.8423, decode.d7.loss_dice: 1.1754, decode.d8.loss_cls: 0.9756, decode.d8.loss_mask: 0.8476, decode.d8.loss_dice: 1.1734, loss: 35.8800 2022-05-04 23:33:13,668 - mmseg - INFO - Saving checkpoint at 11000 iterations 2022-05-04 23:33:38,863 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-04 23:33:38,874 - mmseg - INFO - Iter [11000/40000] lr: 1.041e-06, eta: 6:48:22, time: 1.316, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9037, decode.loss_mask: 0.8545, decode.loss_dice: 1.1554, decode.d0.loss_cls: 6.1020, decode.d0.loss_mask: 0.8450, decode.d0.loss_dice: 1.3441, decode.d1.loss_cls: 1.1046, decode.d1.loss_mask: 0.8796, decode.d1.loss_dice: 1.2417, decode.d2.loss_cls: 0.9533, decode.d2.loss_mask: 0.8706, decode.d2.loss_dice: 1.1999, decode.d3.loss_cls: 0.9158, decode.d3.loss_mask: 0.8620, decode.d3.loss_dice: 1.1773, decode.d4.loss_cls: 0.9083, decode.d4.loss_mask: 0.8589, decode.d4.loss_dice: 1.1782, decode.d5.loss_cls: 0.8969, decode.d5.loss_mask: 0.8554, decode.d5.loss_dice: 1.1681, decode.d6.loss_cls: 0.8947, decode.d6.loss_mask: 0.8550, decode.d6.loss_dice: 1.1575, decode.d7.loss_cls: 0.8979, decode.d7.loss_mask: 0.8517, decode.d7.loss_dice: 1.1579, decode.d8.loss_cls: 0.8963, decode.d8.loss_mask: 0.8586, decode.d8.loss_dice: 1.1650, loss: 35.0099 2022-05-04 23:34:20,227 - mmseg - INFO - Iter [11050/40000] lr: 1.039e-06, eta: 6:47:38, time: 0.831, data_time: 0.014, memory: 51557, decode.loss_cls: 0.9636, decode.loss_mask: 0.8360, decode.loss_dice: 1.1610, decode.d0.loss_cls: 6.0845, decode.d0.loss_mask: 0.8329, decode.d0.loss_dice: 1.3522, decode.d1.loss_cls: 1.1828, decode.d1.loss_mask: 0.8544, decode.d1.loss_dice: 1.2342, decode.d2.loss_cls: 1.0358, decode.d2.loss_mask: 0.8401, decode.d2.loss_dice: 1.1809, decode.d3.loss_cls: 0.9997, decode.d3.loss_mask: 0.8317, decode.d3.loss_dice: 1.1583, decode.d4.loss_cls: 0.9910, decode.d4.loss_mask: 0.8340, decode.d4.loss_dice: 1.1602, decode.d5.loss_cls: 0.9729, decode.d5.loss_mask: 0.8369, decode.d5.loss_dice: 1.1626, decode.d6.loss_cls: 0.9565, decode.d6.loss_mask: 0.8390, decode.d6.loss_dice: 1.1545, decode.d7.loss_cls: 0.9583, decode.d7.loss_mask: 0.8353, decode.d7.loss_dice: 1.1630, decode.d8.loss_cls: 0.9636, decode.d8.loss_mask: 0.8357, decode.d8.loss_dice: 1.1603, loss: 35.3719 2022-05-04 23:35:00,825 - mmseg - INFO - Iter [11100/40000] lr: 1.037e-06, eta: 6:46:51, time: 0.812, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9634, decode.loss_mask: 0.8615, decode.loss_dice: 1.1902, decode.d0.loss_cls: 6.0823, decode.d0.loss_mask: 0.8611, decode.d0.loss_dice: 1.3525, decode.d1.loss_cls: 1.1702, decode.d1.loss_mask: 0.8820, decode.d1.loss_dice: 1.2680, decode.d2.loss_cls: 1.0210, decode.d2.loss_mask: 0.8656, decode.d2.loss_dice: 1.2050, decode.d3.loss_cls: 0.9850, decode.d3.loss_mask: 0.8653, decode.d3.loss_dice: 1.1926, decode.d4.loss_cls: 0.9795, decode.d4.loss_mask: 0.8647, decode.d4.loss_dice: 1.1900, decode.d5.loss_cls: 0.9813, decode.d5.loss_mask: 0.8636, decode.d5.loss_dice: 1.1915, decode.d6.loss_cls: 0.9769, decode.d6.loss_mask: 0.8605, decode.d6.loss_dice: 1.1836, decode.d7.loss_cls: 0.9715, decode.d7.loss_mask: 0.8633, decode.d7.loss_dice: 1.1887, decode.d8.loss_cls: 0.9707, decode.d8.loss_mask: 0.8624, decode.d8.loss_dice: 1.1844, loss: 35.8984 2022-05-04 23:35:42,282 - mmseg - INFO - Iter [11150/40000] lr: 1.036e-06, eta: 6:46:07, time: 0.829, data_time: 0.010, memory: 51557, decode.loss_cls: 1.0190, decode.loss_mask: 0.8585, decode.loss_dice: 1.1574, decode.d0.loss_cls: 6.0935, decode.d0.loss_mask: 0.8757, decode.d0.loss_dice: 1.3600, decode.d1.loss_cls: 1.2428, decode.d1.loss_mask: 0.8804, decode.d1.loss_dice: 1.2443, decode.d2.loss_cls: 1.1058, decode.d2.loss_mask: 0.8704, decode.d2.loss_dice: 1.1864, decode.d3.loss_cls: 1.0577, decode.d3.loss_mask: 0.8584, decode.d3.loss_dice: 1.1622, decode.d4.loss_cls: 1.0366, decode.d4.loss_mask: 0.8612, decode.d4.loss_dice: 1.1675, decode.d5.loss_cls: 1.0316, decode.d5.loss_mask: 0.8611, decode.d5.loss_dice: 1.1665, decode.d6.loss_cls: 1.0219, decode.d6.loss_mask: 0.8641, decode.d6.loss_dice: 1.1570, decode.d7.loss_cls: 1.0114, decode.d7.loss_mask: 0.8660, decode.d7.loss_dice: 1.1595, decode.d8.loss_cls: 1.0135, decode.d8.loss_mask: 0.8639, decode.d8.loss_dice: 1.1571, loss: 36.2113 2022-05-04 23:36:22,687 - mmseg - INFO - Iter [11200/40000] lr: 1.034e-06, eta: 6:45:20, time: 0.808, data_time: 0.011, memory: 51557, decode.loss_cls: 0.9957, decode.loss_mask: 0.8340, decode.loss_dice: 1.1686, decode.d0.loss_cls: 6.0543, decode.d0.loss_mask: 0.8416, decode.d0.loss_dice: 1.3703, decode.d1.loss_cls: 1.2174, decode.d1.loss_mask: 0.8734, decode.d1.loss_dice: 1.2671, decode.d2.loss_cls: 1.0727, decode.d2.loss_mask: 0.8496, decode.d2.loss_dice: 1.2019, decode.d3.loss_cls: 1.0291, decode.d3.loss_mask: 0.8448, decode.d3.loss_dice: 1.1805, decode.d4.loss_cls: 1.0145, decode.d4.loss_mask: 0.8417, decode.d4.loss_dice: 1.1812, decode.d5.loss_cls: 1.0012, decode.d5.loss_mask: 0.8427, decode.d5.loss_dice: 1.1804, decode.d6.loss_cls: 0.9939, decode.d6.loss_mask: 0.8389, decode.d6.loss_dice: 1.1754, decode.d7.loss_cls: 0.9889, decode.d7.loss_mask: 0.8398, decode.d7.loss_dice: 1.1742, decode.d8.loss_cls: 0.9913, decode.d8.loss_mask: 0.8338, decode.d8.loss_dice: 1.1700, loss: 35.8690 2022-05-04 23:37:07,027 - mmseg - INFO - Iter [11250/40000] lr: 1.032e-06, eta: 6:44:43, time: 0.887, data_time: 0.062, memory: 51557, decode.loss_cls: 0.9637, decode.loss_mask: 0.8503, decode.loss_dice: 1.1765, decode.d0.loss_cls: 6.0446, decode.d0.loss_mask: 0.8538, decode.d0.loss_dice: 1.3702, decode.d1.loss_cls: 1.1590, decode.d1.loss_mask: 0.8797, decode.d1.loss_dice: 1.2619, decode.d2.loss_cls: 1.0363, decode.d2.loss_mask: 0.8538, decode.d2.loss_dice: 1.2047, decode.d3.loss_cls: 0.9921, decode.d3.loss_mask: 0.8488, decode.d3.loss_dice: 1.1859, decode.d4.loss_cls: 0.9803, decode.d4.loss_mask: 0.8462, decode.d4.loss_dice: 1.1847, decode.d5.loss_cls: 0.9727, decode.d5.loss_mask: 0.8462, decode.d5.loss_dice: 1.1788, decode.d6.loss_cls: 0.9635, decode.d6.loss_mask: 0.8475, decode.d6.loss_dice: 1.1704, decode.d7.loss_cls: 0.9605, decode.d7.loss_mask: 0.8502, decode.d7.loss_dice: 1.1747, decode.d8.loss_cls: 0.9646, decode.d8.loss_mask: 0.8576, decode.d8.loss_dice: 1.1757, loss: 35.6550 2022-05-04 23:37:49,160 - mmseg - INFO - Iter [11300/40000] lr: 1.030e-06, eta: 6:44:01, time: 0.843, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9842, decode.loss_mask: 0.8285, decode.loss_dice: 1.1842, decode.d0.loss_cls: 6.0395, decode.d0.loss_mask: 0.8475, decode.d0.loss_dice: 1.3838, decode.d1.loss_cls: 1.2163, decode.d1.loss_mask: 0.8527, decode.d1.loss_dice: 1.2711, decode.d2.loss_cls: 1.0575, decode.d2.loss_mask: 0.8276, decode.d2.loss_dice: 1.2165, decode.d3.loss_cls: 1.0152, decode.d3.loss_mask: 0.8326, decode.d3.loss_dice: 1.1976, decode.d4.loss_cls: 0.9984, decode.d4.loss_mask: 0.8341, decode.d4.loss_dice: 1.1951, decode.d5.loss_cls: 0.9918, decode.d5.loss_mask: 0.8320, decode.d5.loss_dice: 1.1918, decode.d6.loss_cls: 0.9821, decode.d6.loss_mask: 0.8268, decode.d6.loss_dice: 1.1852, decode.d7.loss_cls: 0.9837, decode.d7.loss_mask: 0.8303, decode.d7.loss_dice: 1.1825, decode.d8.loss_cls: 0.9774, decode.d8.loss_mask: 0.8297, decode.d8.loss_dice: 1.1836, loss: 35.7796 2022-05-04 23:38:29,770 - mmseg - INFO - Iter [11350/40000] lr: 1.028e-06, eta: 6:43:14, time: 0.812, data_time: 0.011, memory: 51557, decode.loss_cls: 0.9500, decode.loss_mask: 0.8323, decode.loss_dice: 1.1564, decode.d0.loss_cls: 5.9969, decode.d0.loss_mask: 0.8424, decode.d0.loss_dice: 1.3462, decode.d1.loss_cls: 1.1699, decode.d1.loss_mask: 0.8543, decode.d1.loss_dice: 1.2398, decode.d2.loss_cls: 1.0100, decode.d2.loss_mask: 0.8337, decode.d2.loss_dice: 1.1907, decode.d3.loss_cls: 0.9722, decode.d3.loss_mask: 0.8298, decode.d3.loss_dice: 1.1687, decode.d4.loss_cls: 0.9564, decode.d4.loss_mask: 0.8302, decode.d4.loss_dice: 1.1695, decode.d5.loss_cls: 0.9474, decode.d5.loss_mask: 0.8248, decode.d5.loss_dice: 1.1679, decode.d6.loss_cls: 0.9427, decode.d6.loss_mask: 0.8297, decode.d6.loss_dice: 1.1573, decode.d7.loss_cls: 0.9296, decode.d7.loss_mask: 0.8330, decode.d7.loss_dice: 1.1646, decode.d8.loss_cls: 0.9380, decode.d8.loss_mask: 0.8286, decode.d8.loss_dice: 1.1664, loss: 35.0793 2022-05-04 23:39:10,362 - mmseg - INFO - Iter [11400/40000] lr: 1.027e-06, eta: 6:42:28, time: 0.811, data_time: 0.009, memory: 51557, decode.loss_cls: 0.9563, decode.loss_mask: 0.8518, decode.loss_dice: 1.2240, decode.d0.loss_cls: 6.0007, decode.d0.loss_mask: 0.8500, decode.d0.loss_dice: 1.4172, decode.d1.loss_cls: 1.1959, decode.d1.loss_mask: 0.8746, decode.d1.loss_dice: 1.2971, decode.d2.loss_cls: 1.0433, decode.d2.loss_mask: 0.8561, decode.d2.loss_dice: 1.2411, decode.d3.loss_cls: 1.0005, decode.d3.loss_mask: 0.8524, decode.d3.loss_dice: 1.2199, decode.d4.loss_cls: 0.9897, decode.d4.loss_mask: 0.8511, decode.d4.loss_dice: 1.2170, decode.d5.loss_cls: 0.9743, decode.d5.loss_mask: 0.8477, decode.d5.loss_dice: 1.2193, decode.d6.loss_cls: 0.9669, decode.d6.loss_mask: 0.8494, decode.d6.loss_dice: 1.2099, decode.d7.loss_cls: 0.9525, decode.d7.loss_mask: 0.8521, decode.d7.loss_dice: 1.2209, decode.d8.loss_cls: 0.9531, decode.d8.loss_mask: 0.8525, decode.d8.loss_dice: 1.2185, loss: 36.0558 2022-05-04 23:39:51,333 - mmseg - INFO - Iter [11450/40000] lr: 1.025e-06, eta: 6:41:43, time: 0.820, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9560, decode.loss_mask: 0.8394, decode.loss_dice: 1.1641, decode.d0.loss_cls: 5.9123, decode.d0.loss_mask: 0.8458, decode.d0.loss_dice: 1.3444, decode.d1.loss_cls: 1.1472, decode.d1.loss_mask: 0.8634, decode.d1.loss_dice: 1.2296, decode.d2.loss_cls: 1.0086, decode.d2.loss_mask: 0.8445, decode.d2.loss_dice: 1.1798, decode.d3.loss_cls: 0.9791, decode.d3.loss_mask: 0.8408, decode.d3.loss_dice: 1.1660, decode.d4.loss_cls: 0.9637, decode.d4.loss_mask: 0.8367, decode.d4.loss_dice: 1.1722, decode.d5.loss_cls: 0.9648, decode.d5.loss_mask: 0.8398, decode.d5.loss_dice: 1.1683, decode.d6.loss_cls: 0.9523, decode.d6.loss_mask: 0.8353, decode.d6.loss_dice: 1.1664, decode.d7.loss_cls: 0.9529, decode.d7.loss_mask: 0.8351, decode.d7.loss_dice: 1.1673, decode.d8.loss_cls: 0.9607, decode.d8.loss_mask: 0.8342, decode.d8.loss_dice: 1.1630, loss: 35.1338 2022-05-04 23:40:31,829 - mmseg - INFO - Iter [11500/40000] lr: 1.023e-06, eta: 6:40:56, time: 0.810, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9721, decode.loss_mask: 0.8305, decode.loss_dice: 1.1537, decode.d0.loss_cls: 5.9303, decode.d0.loss_mask: 0.8416, decode.d0.loss_dice: 1.3568, decode.d1.loss_cls: 1.1990, decode.d1.loss_mask: 0.8521, decode.d1.loss_dice: 1.2340, decode.d2.loss_cls: 1.0407, decode.d2.loss_mask: 0.8415, decode.d2.loss_dice: 1.1789, decode.d3.loss_cls: 1.0041, decode.d3.loss_mask: 0.8384, decode.d3.loss_dice: 1.1650, decode.d4.loss_cls: 0.9947, decode.d4.loss_mask: 0.8465, decode.d4.loss_dice: 1.1644, decode.d5.loss_cls: 0.9797, decode.d5.loss_mask: 0.8419, decode.d5.loss_dice: 1.1574, decode.d6.loss_cls: 0.9812, decode.d6.loss_mask: 0.8383, decode.d6.loss_dice: 1.1541, decode.d7.loss_cls: 0.9758, decode.d7.loss_mask: 0.8365, decode.d7.loss_dice: 1.1546, decode.d8.loss_cls: 0.9691, decode.d8.loss_mask: 0.8345, decode.d8.loss_dice: 1.1518, loss: 35.3192 2022-05-04 23:41:13,941 - mmseg - INFO - Iter [11550/40000] lr: 1.021e-06, eta: 6:40:14, time: 0.842, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9566, decode.loss_mask: 0.8391, decode.loss_dice: 1.1637, decode.d0.loss_cls: 5.9593, decode.d0.loss_mask: 0.8300, decode.d0.loss_dice: 1.3542, decode.d1.loss_cls: 1.1629, decode.d1.loss_mask: 0.8604, decode.d1.loss_dice: 1.2411, decode.d2.loss_cls: 1.0240, decode.d2.loss_mask: 0.8397, decode.d2.loss_dice: 1.1876, decode.d3.loss_cls: 0.9861, decode.d3.loss_mask: 0.8390, decode.d3.loss_dice: 1.1727, decode.d4.loss_cls: 0.9713, decode.d4.loss_mask: 0.8399, decode.d4.loss_dice: 1.1766, decode.d5.loss_cls: 0.9709, decode.d5.loss_mask: 0.8365, decode.d5.loss_dice: 1.1711, decode.d6.loss_cls: 0.9519, decode.d6.loss_mask: 0.8349, decode.d6.loss_dice: 1.1662, decode.d7.loss_cls: 0.9544, decode.d7.loss_mask: 0.8363, decode.d7.loss_dice: 1.1626, decode.d8.loss_cls: 0.9477, decode.d8.loss_mask: 0.8395, decode.d8.loss_dice: 1.1679, loss: 35.2440 2022-05-04 23:41:56,118 - mmseg - INFO - Iter [11600/40000] lr: 1.019e-06, eta: 6:39:32, time: 0.843, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9513, decode.loss_mask: 0.8372, decode.loss_dice: 1.1907, decode.d0.loss_cls: 5.9187, decode.d0.loss_mask: 0.8401, decode.d0.loss_dice: 1.3744, decode.d1.loss_cls: 1.1611, decode.d1.loss_mask: 0.8558, decode.d1.loss_dice: 1.2601, decode.d2.loss_cls: 1.0076, decode.d2.loss_mask: 0.8461, decode.d2.loss_dice: 1.2184, decode.d3.loss_cls: 0.9719, decode.d3.loss_mask: 0.8401, decode.d3.loss_dice: 1.2028, decode.d4.loss_cls: 0.9597, decode.d4.loss_mask: 0.8392, decode.d4.loss_dice: 1.2025, decode.d5.loss_cls: 0.9592, decode.d5.loss_mask: 0.8321, decode.d5.loss_dice: 1.1918, decode.d6.loss_cls: 0.9504, decode.d6.loss_mask: 0.8355, decode.d6.loss_dice: 1.1915, decode.d7.loss_cls: 0.9513, decode.d7.loss_mask: 0.8296, decode.d7.loss_dice: 1.1934, decode.d8.loss_cls: 0.9497, decode.d8.loss_mask: 0.8349, decode.d8.loss_dice: 1.1980, loss: 35.3952 2022-05-04 23:42:37,258 - mmseg - INFO - Iter [11650/40000] lr: 1.018e-06, eta: 6:38:47, time: 0.822, data_time: 0.009, memory: 51557, decode.loss_cls: 0.9334, decode.loss_mask: 0.8224, decode.loss_dice: 1.1444, decode.d0.loss_cls: 5.8454, decode.d0.loss_mask: 0.8294, decode.d0.loss_dice: 1.3257, decode.d1.loss_cls: 1.1411, decode.d1.loss_mask: 0.8445, decode.d1.loss_dice: 1.2282, decode.d2.loss_cls: 1.0141, decode.d2.loss_mask: 0.8278, decode.d2.loss_dice: 1.1691, decode.d3.loss_cls: 0.9707, decode.d3.loss_mask: 0.8270, decode.d3.loss_dice: 1.1502, decode.d4.loss_cls: 0.9477, decode.d4.loss_mask: 0.8294, decode.d4.loss_dice: 1.1490, decode.d5.loss_cls: 0.9431, decode.d5.loss_mask: 0.8249, decode.d5.loss_dice: 1.1422, decode.d6.loss_cls: 0.9346, decode.d6.loss_mask: 0.8216, decode.d6.loss_dice: 1.1356, decode.d7.loss_cls: 0.9282, decode.d7.loss_mask: 0.8247, decode.d7.loss_dice: 1.1371, decode.d8.loss_cls: 0.9231, decode.d8.loss_mask: 0.8216, decode.d8.loss_dice: 1.1443, loss: 34.5806 2022-05-04 23:43:18,458 - mmseg - INFO - Iter [11700/40000] lr: 1.016e-06, eta: 6:38:02, time: 0.825, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9457, decode.loss_mask: 0.8623, decode.loss_dice: 1.1837, decode.d0.loss_cls: 5.8553, decode.d0.loss_mask: 0.8763, decode.d0.loss_dice: 1.3842, decode.d1.loss_cls: 1.1519, decode.d1.loss_mask: 0.8930, decode.d1.loss_dice: 1.2613, decode.d2.loss_cls: 1.0137, decode.d2.loss_mask: 0.8730, decode.d2.loss_dice: 1.2129, decode.d3.loss_cls: 0.9671, decode.d3.loss_mask: 0.8655, decode.d3.loss_dice: 1.1913, decode.d4.loss_cls: 0.9543, decode.d4.loss_mask: 0.8636, decode.d4.loss_dice: 1.1907, decode.d5.loss_cls: 0.9486, decode.d5.loss_mask: 0.8589, decode.d5.loss_dice: 1.1930, decode.d6.loss_cls: 0.9522, decode.d6.loss_mask: 0.8572, decode.d6.loss_dice: 1.1778, decode.d7.loss_cls: 0.9423, decode.d7.loss_mask: 0.8627, decode.d7.loss_dice: 1.1796, decode.d8.loss_cls: 0.9388, decode.d8.loss_mask: 0.8657, decode.d8.loss_dice: 1.1818, loss: 35.5048 2022-05-04 23:44:00,370 - mmseg - INFO - Iter [11750/40000] lr: 1.014e-06, eta: 6:37:19, time: 0.838, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9390, decode.loss_mask: 0.8648, decode.loss_dice: 1.1770, decode.d0.loss_cls: 5.8101, decode.d0.loss_mask: 0.8544, decode.d0.loss_dice: 1.3432, decode.d1.loss_cls: 1.1212, decode.d1.loss_mask: 0.8800, decode.d1.loss_dice: 1.2492, decode.d2.loss_cls: 0.9958, decode.d2.loss_mask: 0.8674, decode.d2.loss_dice: 1.2021, decode.d3.loss_cls: 0.9650, decode.d3.loss_mask: 0.8593, decode.d3.loss_dice: 1.1851, decode.d4.loss_cls: 0.9491, decode.d4.loss_mask: 0.8664, decode.d4.loss_dice: 1.1885, decode.d5.loss_cls: 0.9449, decode.d5.loss_mask: 0.8619, decode.d5.loss_dice: 1.1865, decode.d6.loss_cls: 0.9340, decode.d6.loss_mask: 0.8599, decode.d6.loss_dice: 1.1698, decode.d7.loss_cls: 0.9340, decode.d7.loss_mask: 0.8599, decode.d7.loss_dice: 1.1788, decode.d8.loss_cls: 0.9239, decode.d8.loss_mask: 0.8646, decode.d8.loss_dice: 1.1755, loss: 35.2111 2022-05-04 23:44:42,245 - mmseg - INFO - Iter [11800/40000] lr: 1.012e-06, eta: 6:36:36, time: 0.837, data_time: 0.009, memory: 51557, decode.loss_cls: 0.9449, decode.loss_mask: 0.8433, decode.loss_dice: 1.1435, decode.d0.loss_cls: 5.7901, decode.d0.loss_mask: 0.8514, decode.d0.loss_dice: 1.3211, decode.d1.loss_cls: 1.1380, decode.d1.loss_mask: 0.8605, decode.d1.loss_dice: 1.2089, decode.d2.loss_cls: 1.0085, decode.d2.loss_mask: 0.8469, decode.d2.loss_dice: 1.1598, decode.d3.loss_cls: 0.9744, decode.d3.loss_mask: 0.8396, decode.d3.loss_dice: 1.1453, decode.d4.loss_cls: 0.9621, decode.d4.loss_mask: 0.8448, decode.d4.loss_dice: 1.1534, decode.d5.loss_cls: 0.9444, decode.d5.loss_mask: 0.8421, decode.d5.loss_dice: 1.1542, decode.d6.loss_cls: 0.9426, decode.d6.loss_mask: 0.8435, decode.d6.loss_dice: 1.1357, decode.d7.loss_cls: 0.9392, decode.d7.loss_mask: 0.8456, decode.d7.loss_dice: 1.1371, decode.d8.loss_cls: 0.9450, decode.d8.loss_mask: 0.8428, decode.d8.loss_dice: 1.1413, loss: 34.7499 2022-05-04 23:45:26,320 - mmseg - INFO - Iter [11850/40000] lr: 1.010e-06, eta: 6:35:58, time: 0.881, data_time: 0.060, memory: 51557, decode.loss_cls: 0.9346, decode.loss_mask: 0.8287, decode.loss_dice: 1.1552, decode.d0.loss_cls: 5.7842, decode.d0.loss_mask: 0.8417, decode.d0.loss_dice: 1.3640, decode.d1.loss_cls: 1.1273, decode.d1.loss_mask: 0.8524, decode.d1.loss_dice: 1.2359, decode.d2.loss_cls: 0.9997, decode.d2.loss_mask: 0.8399, decode.d2.loss_dice: 1.1856, decode.d3.loss_cls: 0.9632, decode.d3.loss_mask: 0.8324, decode.d3.loss_dice: 1.1636, decode.d4.loss_cls: 0.9446, decode.d4.loss_mask: 0.8296, decode.d4.loss_dice: 1.1687, decode.d5.loss_cls: 0.9398, decode.d5.loss_mask: 0.8336, decode.d5.loss_dice: 1.1583, decode.d6.loss_cls: 0.9386, decode.d6.loss_mask: 0.8313, decode.d6.loss_dice: 1.1464, decode.d7.loss_cls: 0.9277, decode.d7.loss_mask: 0.8323, decode.d7.loss_dice: 1.1574, decode.d8.loss_cls: 0.9275, decode.d8.loss_mask: 0.8333, decode.d8.loss_dice: 1.1617, loss: 34.7393 2022-05-04 23:46:07,113 - mmseg - INFO - Iter [11900/40000] lr: 1.009e-06, eta: 6:35:13, time: 0.817, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9266, decode.loss_mask: 0.8083, decode.loss_dice: 1.1547, decode.d0.loss_cls: 5.8017, decode.d0.loss_mask: 0.8147, decode.d0.loss_dice: 1.3489, decode.d1.loss_cls: 1.1424, decode.d1.loss_mask: 0.8325, decode.d1.loss_dice: 1.2243, decode.d2.loss_cls: 1.0032, decode.d2.loss_mask: 0.8169, decode.d2.loss_dice: 1.1734, decode.d3.loss_cls: 0.9581, decode.d3.loss_mask: 0.8087, decode.d3.loss_dice: 1.1550, decode.d4.loss_cls: 0.9353, decode.d4.loss_mask: 0.8079, decode.d4.loss_dice: 1.1616, decode.d5.loss_cls: 0.9176, decode.d5.loss_mask: 0.8099, decode.d5.loss_dice: 1.1619, decode.d6.loss_cls: 0.9263, decode.d6.loss_mask: 0.8042, decode.d6.loss_dice: 1.1504, decode.d7.loss_cls: 0.9129, decode.d7.loss_mask: 0.8066, decode.d7.loss_dice: 1.1526, decode.d8.loss_cls: 0.9139, decode.d8.loss_mask: 0.8112, decode.d8.loss_dice: 1.1543, loss: 34.3961 2022-05-04 23:46:47,135 - mmseg - INFO - Iter [11950/40000] lr: 1.007e-06, eta: 6:34:26, time: 0.801, data_time: 0.009, memory: 51557, decode.loss_cls: 0.9473, decode.loss_mask: 0.8342, decode.loss_dice: 1.1618, decode.d0.loss_cls: 5.7737, decode.d0.loss_mask: 0.8361, decode.d0.loss_dice: 1.3457, decode.d1.loss_cls: 1.1771, decode.d1.loss_mask: 0.8563, decode.d1.loss_dice: 1.2351, decode.d2.loss_cls: 1.0283, decode.d2.loss_mask: 0.8397, decode.d2.loss_dice: 1.1904, decode.d3.loss_cls: 0.9800, decode.d3.loss_mask: 0.8328, decode.d3.loss_dice: 1.1726, decode.d4.loss_cls: 0.9636, decode.d4.loss_mask: 0.8331, decode.d4.loss_dice: 1.1745, decode.d5.loss_cls: 0.9628, decode.d5.loss_mask: 0.8368, decode.d5.loss_dice: 1.1654, decode.d6.loss_cls: 0.9468, decode.d6.loss_mask: 0.8363, decode.d6.loss_dice: 1.1558, decode.d7.loss_cls: 0.9532, decode.d7.loss_mask: 0.8277, decode.d7.loss_dice: 1.1622, decode.d8.loss_cls: 0.9458, decode.d8.loss_mask: 0.8309, decode.d8.loss_dice: 1.1633, loss: 34.9696 2022-05-04 23:47:28,066 - mmseg - INFO - Saving checkpoint at 12000 iterations 2022-05-04 23:47:52,814 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-04 23:47:52,817 - mmseg - INFO - Iter [12000/40000] lr: 1.005e-06, eta: 6:34:38, time: 1.311, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9051, decode.loss_mask: 0.8432, decode.loss_dice: 1.1406, decode.d0.loss_cls: 5.7453, decode.d0.loss_mask: 0.8461, decode.d0.loss_dice: 1.3197, decode.d1.loss_cls: 1.1186, decode.d1.loss_mask: 0.8690, decode.d1.loss_dice: 1.2043, decode.d2.loss_cls: 0.9789, decode.d2.loss_mask: 0.8496, decode.d2.loss_dice: 1.1581, decode.d3.loss_cls: 0.9413, decode.d3.loss_mask: 0.8472, decode.d3.loss_dice: 1.1430, decode.d4.loss_cls: 0.9264, decode.d4.loss_mask: 0.8506, decode.d4.loss_dice: 1.1448, decode.d5.loss_cls: 0.9174, decode.d5.loss_mask: 0.8480, decode.d5.loss_dice: 1.1461, decode.d6.loss_cls: 0.9091, decode.d6.loss_mask: 0.8449, decode.d6.loss_dice: 1.1410, decode.d7.loss_cls: 0.9042, decode.d7.loss_mask: 0.8460, decode.d7.loss_dice: 1.1468, decode.d8.loss_cls: 0.9035, decode.d8.loss_mask: 0.8463, decode.d8.loss_dice: 1.1357, loss: 34.4210 2022-05-04 23:48:24,809 - mmseg - INFO - per class results: 2022-05-04 23:48:24,819 - mmseg - INFO - +------------------+-------+-------+ | Class | IoU | Acc | +------------------+-------+-------+ | person | 87.98 | 93.1 | | bicycle | 74.02 | 90.22 | | car | 60.79 | 68.02 | | motorcycle | 90.02 | 95.4 | | airplane | 83.64 | 94.08 | | bus | 76.59 | 80.13 | | train | 73.99 | 96.54 | | truck | 76.36 | 91.32 | | boat | 81.59 | 88.25 | | traffic light | 78.5 | 89.22 | | fire hydrant | 85.75 | 96.48 | | stop sign | 94.87 | 97.0 | | parking meter | 73.2 | 75.76 | | bench | 48.01 | 62.09 | | bird | 76.59 | 84.19 | | cat | 93.07 | 95.64 | | dog | 92.0 | 96.31 | | horse | 90.91 | 95.7 | | sheep | 83.88 | 91.08 | | cow | 93.52 | 96.21 | | elephant | 92.44 | 95.86 | | bear | 92.61 | 94.25 | | zebra | 91.57 | 94.64 | | giraffe | 88.28 | 93.69 | | backpack | 19.57 | 58.34 | | umbrella | 82.96 | 86.0 | | handbag | 17.26 | 28.12 | | tie | 54.57 | 65.26 | | suitcase | 80.74 | 91.97 | | frisbee | 91.44 | 97.41 | | skis | 38.49 | 66.67 | | snowboard | 65.73 | 80.51 | | sports ball | 82.22 | 94.9 | | kite | 68.4 | 82.13 | | baseball bat | 61.48 | 74.95 | | baseball glove | 56.05 | 60.4 | | skateboard | 68.63 | 84.14 | | surfboard | 89.95 | 94.27 | | tennis racket | 67.08 | 74.16 | | bottle | 65.31 | 81.75 | | wine glass | 84.19 | 92.31 | | cup | 67.33 | 75.68 | | fork | 55.36 | 67.0 | | knife | 74.75 | 85.9 | | spoon | 47.55 | 70.4 | | bowl | 59.94 | 73.35 | | banana | 83.95 | 91.05 | | apple | 76.67 | 85.07 | | sandwich | 83.46 | 93.3 | | orange | 68.29 | 90.13 | | broccoli | 83.37 | 96.84 | | carrot | 40.8 | 64.22 | | hot dog | 58.95 | 96.67 | | pizza | 94.52 | 96.08 | | donut | 78.84 | 94.15 | | cake | 82.28 | 89.66 | | chair | 60.85 | 73.81 | | couch | 68.6 | 92.01 | | potted plant | 34.06 | 40.02 | | bed | 71.45 | 84.73 | | dining table | 60.94 | 76.63 | | toilet | 87.32 | 92.57 | | tv | 61.86 | 96.41 | | laptop | 84.75 | 95.45 | | mouse | 80.18 | 90.18 | | remote | 69.91 | 96.06 | | keyboard | 86.5 | 97.33 | | cell phone | 78.65 | 90.28 | | microwave | 58.05 | 71.2 | | oven | 63.23 | 85.03 | | toaster | 0.0 | 0.0 | | sink | 73.09 | 80.51 | | refrigerator | 85.18 | 95.47 | | book | 75.24 | 90.06 | | clock | 75.93 | 79.99 | | vase | 63.25 | 93.68 | | scissors | 81.95 | 91.19 | | teddy bear | 84.26 | 91.73 | | hair drier | 0.0 | 0.0 | | toothbrush | 16.37 | 21.23 | | banner | 30.65 | 65.78 | | blanket | 0.12 | 0.14 | | branch | 50.41 | 53.49 | | bridge | 1.36 | 2.01 | | building-other | 51.22 | 64.94 | | bush | 24.89 | 30.48 | | cabinet | 24.95 | 35.31 | | cage | 0.0 | 0.0 | | cardboard | 28.24 | 28.89 | | carpet | 59.28 | 84.14 | | ceiling-other | 72.1 | 87.99 | | ceiling-tile | 0.0 | 0.0 | | cloth | 0.31 | 0.31 | | clothes | 21.94 | 32.89 | | clouds | 54.21 | 74.29 | | counter | 42.96 | 49.08 | | cupboard | 61.29 | 79.31 | | curtain | 66.12 | 81.37 | | desk-stuff | 33.04 | 45.64 | | dirt | 44.85 | 72.57 | | door-stuff | 46.14 | 56.75 | | fence | 43.65 | 69.66 | | floor-marble | 0.0 | 0.0 | | floor-other | 36.55 | 59.45 | | floor-stone | 21.37 | 21.76 | | floor-tile | 64.62 | 78.2 | | floor-wood | 76.1 | 85.72 | | flower | 21.27 | 51.21 | | fog | 0.0 | 0.0 | | food-other | 33.42 | 63.0 | | fruit | 61.74 | 87.16 | | furniture-other | 18.12 | 35.78 | | grass | 75.32 | 85.56 | | gravel | 22.89 | 27.19 | | ground-other | 8.82 | 17.88 | | hill | 27.4 | 39.17 | | house | 29.74 | 52.53 | | leaves | 16.55 | 18.51 | | light | 39.15 | 58.73 | | mat | 43.41 | 44.86 | | metal | 20.26 | 29.4 | | mirror-stuff | 41.61 | 55.78 | | moss | 0.0 | 0.0 | | mountain | 32.74 | 50.77 | | mud | 0.0 | 0.0 | | napkin | 57.07 | 61.13 | | net | 34.69 | 38.28 | | paper | 50.6 | 68.24 | | pavement | 53.24 | 70.44 | | pillow | 0.0 | 0.0 | | plant-other | 25.72 | 45.14 | | plastic | 29.23 | 38.96 | | platform | 58.11 | 58.94 | | playingfield | 71.23 | 83.26 | | railing | 23.75 | 39.64 | | railroad | 59.09 | 90.92 | | river | 25.02 | 30.47 | | road | 71.0 | 79.08 | | rock | 45.98 | 67.0 | | roof | 6.6 | 14.3 | | rug | 51.07 | 69.15 | | salad | 26.88 | 27.54 | | sand | 73.67 | 87.94 | | sea | 71.09 | 89.9 | | shelf | 25.78 | 51.62 | | sky-other | 59.87 | 72.67 | | skyscraper | 12.68 | 21.13 | | snow | 91.7 | 94.34 | | solid-other | nan | nan | | stairs | 44.35 | 75.24 | | stone | 7.1 | 11.27 | | straw | 17.12 | 33.68 | | structural-other | 18.5 | 30.81 | | table | 14.26 | 20.4 | | tent | 59.99 | 73.13 | | textile-other | 17.38 | 26.58 | | towel | 32.8 | 50.02 | | tree | 77.71 | 87.91 | | vegetable | 43.51 | 57.63 | | wall-brick | 38.59 | 59.5 | | wall-concrete | 30.38 | 38.76 | | wall-other | 63.31 | 78.51 | | wall-panel | 0.0 | 0.0 | | wall-stone | 29.43 | 37.04 | | wall-tile | 52.77 | 77.51 | | wall-wood | 40.63 | 70.42 | | water-other | 25.93 | 37.18 | | waterdrops | nan | nan | | window-blind | 40.33 | 76.55 | | window-other | 46.16 | 63.82 | | wood | 13.86 | 32.72 | +------------------+-------+-------+ 2022-05-04 23:48:24,819 - mmseg - INFO - Summary: 2022-05-04 23:48:24,820 - mmseg - INFO - +-------+------+-------+ | aAcc | mIoU | mAcc | +-------+------+-------+ | 75.41 | 52.3 | 64.38 | +-------+------+-------+ 2022-05-04 23:48:24,823 - mmseg - INFO - The previous best checkpoint /mnt/lustre/chenzhe.vendor/workspace/DenseAdaptor/segmentation/work_dirs/mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss/best_mIoU_iter_8000.pth was removed 2022-05-04 23:48:50,009 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_12000.pth. 2022-05-04 23:48:50,025 - mmseg - INFO - Best mIoU is 0.5230 at 12000 iter. 2022-05-04 23:48:50,040 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-04 23:48:50,041 - mmseg - INFO - Iter(val) [125] aAcc: 0.7541, mIoU: 0.5230, mAcc: 0.6438, IoU.person: 0.8798, IoU.bicycle: 0.7402, IoU.car: 0.6079, IoU.motorcycle: 0.9002, IoU.airplane: 0.8364, IoU.bus: 0.7659, IoU.train: 0.7399, IoU.truck: 0.7636, IoU.boat: 0.8159, IoU.traffic light: 0.7850, IoU.fire hydrant: 0.8575, IoU.stop sign: 0.9487, IoU.parking meter: 0.7320, IoU.bench: 0.4801, IoU.bird: 0.7659, IoU.cat: 0.9307, IoU.dog: 0.9200, IoU.horse: 0.9091, IoU.sheep: 0.8388, IoU.cow: 0.9352, IoU.elephant: 0.9244, IoU.bear: 0.9261, IoU.zebra: 0.9157, IoU.giraffe: 0.8828, IoU.backpack: 0.1957, IoU.umbrella: 0.8296, IoU.handbag: 0.1726, IoU.tie: 0.5457, IoU.suitcase: 0.8074, IoU.frisbee: 0.9144, IoU.skis: 0.3849, IoU.snowboard: 0.6573, IoU.sports ball: 0.8222, IoU.kite: 0.6840, IoU.baseball bat: 0.6148, IoU.baseball glove: 0.5605, IoU.skateboard: 0.6863, IoU.surfboard: 0.8995, IoU.tennis racket: 0.6708, IoU.bottle: 0.6531, IoU.wine glass: 0.8419, IoU.cup: 0.6733, IoU.fork: 0.5536, IoU.knife: 0.7475, IoU.spoon: 0.4755, IoU.bowl: 0.5994, IoU.banana: 0.8395, IoU.apple: 0.7667, IoU.sandwich: 0.8346, IoU.orange: 0.6829, IoU.broccoli: 0.8337, IoU.carrot: 0.4080, IoU.hot dog: 0.5895, IoU.pizza: 0.9452, IoU.donut: 0.7884, IoU.cake: 0.8228, IoU.chair: 0.6085, IoU.couch: 0.6860, IoU.potted plant: 0.3406, IoU.bed: 0.7145, IoU.dining table: 0.6094, IoU.toilet: 0.8732, IoU.tv: 0.6186, IoU.laptop: 0.8475, IoU.mouse: 0.8018, IoU.remote: 0.6991, IoU.keyboard: 0.8650, IoU.cell phone: 0.7865, IoU.microwave: 0.5805, IoU.oven: 0.6323, IoU.toaster: 0.0000, IoU.sink: 0.7309, IoU.refrigerator: 0.8518, IoU.book: 0.7524, IoU.clock: 0.7593, IoU.vase: 0.6325, IoU.scissors: 0.8195, IoU.teddy bear: 0.8426, IoU.hair drier: 0.0000, IoU.toothbrush: 0.1637, IoU.banner: 0.3065, IoU.blanket: 0.0012, IoU.branch: 0.5041, IoU.bridge: 0.0136, IoU.building-other: 0.5122, IoU.bush: 0.2489, IoU.cabinet: 0.2495, IoU.cage: 0.0000, IoU.cardboard: 0.2824, IoU.carpet: 0.5928, IoU.ceiling-other: 0.7210, IoU.ceiling-tile: 0.0000, IoU.cloth: 0.0031, IoU.clothes: 0.2194, IoU.clouds: 0.5421, IoU.counter: 0.4296, IoU.cupboard: 0.6129, IoU.curtain: 0.6612, IoU.desk-stuff: 0.3304, IoU.dirt: 0.4485, IoU.door-stuff: 0.4614, IoU.fence: 0.4365, IoU.floor-marble: 0.0000, IoU.floor-other: 0.3655, IoU.floor-stone: 0.2137, IoU.floor-tile: 0.6462, IoU.floor-wood: 0.7610, IoU.flower: 0.2127, IoU.fog: 0.0000, IoU.food-other: 0.3342, IoU.fruit: 0.6174, IoU.furniture-other: 0.1812, IoU.grass: 0.7532, IoU.gravel: 0.2289, IoU.ground-other: 0.0882, IoU.hill: 0.2740, IoU.house: 0.2974, IoU.leaves: 0.1655, IoU.light: 0.3915, IoU.mat: 0.4341, IoU.metal: 0.2026, IoU.mirror-stuff: 0.4161, IoU.moss: 0.0000, IoU.mountain: 0.3274, IoU.mud: 0.0000, IoU.napkin: 0.5707, IoU.net: 0.3469, IoU.paper: 0.5060, IoU.pavement: 0.5324, IoU.pillow: 0.0000, IoU.plant-other: 0.2572, IoU.plastic: 0.2923, IoU.platform: 0.5811, IoU.playingfield: 0.7123, IoU.railing: 0.2375, IoU.railroad: 0.5909, IoU.river: 0.2502, IoU.road: 0.7100, IoU.rock: 0.4598, IoU.roof: 0.0660, IoU.rug: 0.5107, IoU.salad: 0.2688, IoU.sand: 0.7367, IoU.sea: 0.7109, IoU.shelf: 0.2578, IoU.sky-other: 0.5987, IoU.skyscraper: 0.1268, IoU.snow: 0.9170, IoU.solid-other: nan, IoU.stairs: 0.4435, IoU.stone: 0.0710, IoU.straw: 0.1712, IoU.structural-other: 0.1850, IoU.table: 0.1426, IoU.tent: 0.5999, IoU.textile-other: 0.1738, IoU.towel: 0.3280, IoU.tree: 0.7771, IoU.vegetable: 0.4351, IoU.wall-brick: 0.3859, IoU.wall-concrete: 0.3038, IoU.wall-other: 0.6331, IoU.wall-panel: 0.0000, IoU.wall-stone: 0.2943, IoU.wall-tile: 0.5277, IoU.wall-wood: 0.4063, IoU.water-other: 0.2593, IoU.waterdrops: nan, IoU.window-blind: 0.4033, IoU.window-other: 0.4616, IoU.wood: 0.1386, Acc.person: 0.9310, Acc.bicycle: 0.9022, Acc.car: 0.6802, Acc.motorcycle: 0.9540, Acc.airplane: 0.9408, Acc.bus: 0.8013, Acc.train: 0.9654, Acc.truck: 0.9132, Acc.boat: 0.8825, Acc.traffic light: 0.8922, Acc.fire hydrant: 0.9648, Acc.stop sign: 0.9700, Acc.parking meter: 0.7576, Acc.bench: 0.6209, Acc.bird: 0.8419, Acc.cat: 0.9564, Acc.dog: 0.9631, Acc.horse: 0.9570, Acc.sheep: 0.9108, Acc.cow: 0.9621, Acc.elephant: 0.9586, Acc.bear: 0.9425, Acc.zebra: 0.9464, Acc.giraffe: 0.9369, Acc.backpack: 0.5834, Acc.umbrella: 0.8600, Acc.handbag: 0.2812, Acc.tie: 0.6526, Acc.suitcase: 0.9197, Acc.frisbee: 0.9741, Acc.skis: 0.6667, Acc.snowboard: 0.8051, Acc.sports ball: 0.9490, Acc.kite: 0.8213, Acc.baseball bat: 0.7495, Acc.baseball glove: 0.6040, Acc.skateboard: 0.8414, Acc.surfboard: 0.9427, Acc.tennis racket: 0.7416, Acc.bottle: 0.8175, Acc.wine glass: 0.9231, Acc.cup: 0.7568, Acc.fork: 0.6700, Acc.knife: 0.8590, Acc.spoon: 0.7040, Acc.bowl: 0.7335, Acc.banana: 0.9105, Acc.apple: 0.8507, Acc.sandwich: 0.9330, Acc.orange: 0.9013, Acc.broccoli: 0.9684, Acc.carrot: 0.6422, Acc.hot dog: 0.9667, Acc.pizza: 0.9608, Acc.donut: 0.9415, Acc.cake: 0.8966, Acc.chair: 0.7381, Acc.couch: 0.9201, Acc.potted plant: 0.4002, Acc.bed: 0.8473, Acc.dining table: 0.7663, Acc.toilet: 0.9257, Acc.tv: 0.9641, Acc.laptop: 0.9545, Acc.mouse: 0.9018, Acc.remote: 0.9606, Acc.keyboard: 0.9733, Acc.cell phone: 0.9028, Acc.microwave: 0.7120, Acc.oven: 0.8503, Acc.toaster: 0.0000, Acc.sink: 0.8051, Acc.refrigerator: 0.9547, Acc.book: 0.9006, Acc.clock: 0.7999, Acc.vase: 0.9368, Acc.scissors: 0.9119, Acc.teddy bear: 0.9173, Acc.hair drier: 0.0000, Acc.toothbrush: 0.2123, Acc.banner: 0.6578, Acc.blanket: 0.0014, Acc.branch: 0.5349, Acc.bridge: 0.0201, Acc.building-other: 0.6494, Acc.bush: 0.3048, Acc.cabinet: 0.3531, Acc.cage: 0.0000, Acc.cardboard: 0.2889, Acc.carpet: 0.8414, Acc.ceiling-other: 0.8799, Acc.ceiling-tile: 0.0000, Acc.cloth: 0.0031, Acc.clothes: 0.3289, Acc.clouds: 0.7429, Acc.counter: 0.4908, Acc.cupboard: 0.7931, Acc.curtain: 0.8137, Acc.desk-stuff: 0.4564, Acc.dirt: 0.7257, Acc.door-stuff: 0.5675, Acc.fence: 0.6966, Acc.floor-marble: 0.0000, Acc.floor-other: 0.5945, Acc.floor-stone: 0.2176, Acc.floor-tile: 0.7820, Acc.floor-wood: 0.8572, Acc.flower: 0.5121, Acc.fog: 0.0000, Acc.food-other: 0.6300, Acc.fruit: 0.8716, Acc.furniture-other: 0.3578, Acc.grass: 0.8556, Acc.gravel: 0.2719, Acc.ground-other: 0.1788, Acc.hill: 0.3917, Acc.house: 0.5253, Acc.leaves: 0.1851, Acc.light: 0.5873, Acc.mat: 0.4486, Acc.metal: 0.2940, Acc.mirror-stuff: 0.5578, Acc.moss: 0.0000, Acc.mountain: 0.5077, Acc.mud: 0.0000, Acc.napkin: 0.6113, Acc.net: 0.3828, Acc.paper: 0.6824, Acc.pavement: 0.7044, Acc.pillow: 0.0000, Acc.plant-other: 0.4514, Acc.plastic: 0.3896, Acc.platform: 0.5894, Acc.playingfield: 0.8326, Acc.railing: 0.3964, Acc.railroad: 0.9092, Acc.river: 0.3047, Acc.road: 0.7908, Acc.rock: 0.6700, Acc.roof: 0.1430, Acc.rug: 0.6915, Acc.salad: 0.2754, Acc.sand: 0.8794, Acc.sea: 0.8990, Acc.shelf: 0.5162, Acc.sky-other: 0.7267, Acc.skyscraper: 0.2113, Acc.snow: 0.9434, Acc.solid-other: nan, Acc.stairs: 0.7524, Acc.stone: 0.1127, Acc.straw: 0.3368, Acc.structural-other: 0.3081, Acc.table: 0.2040, Acc.tent: 0.7313, Acc.textile-other: 0.2658, Acc.towel: 0.5002, Acc.tree: 0.8791, Acc.vegetable: 0.5763, Acc.wall-brick: 0.5950, Acc.wall-concrete: 0.3876, Acc.wall-other: 0.7851, Acc.wall-panel: 0.0000, Acc.wall-stone: 0.3704, Acc.wall-tile: 0.7751, Acc.wall-wood: 0.7042, Acc.water-other: 0.3718, Acc.waterdrops: nan, Acc.window-blind: 0.7655, Acc.window-other: 0.6382, Acc.wood: 0.3272 2022-05-04 23:49:32,919 - mmseg - INFO - Iter [12050/40000] lr: 1.003e-06, eta: 6:36:10, time: 2.005, data_time: 1.159, memory: 51557, decode.loss_cls: 0.9174, decode.loss_mask: 0.8341, decode.loss_dice: 1.1695, decode.d0.loss_cls: 5.7249, decode.d0.loss_mask: 0.8383, decode.d0.loss_dice: 1.3481, decode.d1.loss_cls: 1.1258, decode.d1.loss_mask: 0.8651, decode.d1.loss_dice: 1.2544, decode.d2.loss_cls: 0.9910, decode.d2.loss_mask: 0.8416, decode.d2.loss_dice: 1.1987, decode.d3.loss_cls: 0.9527, decode.d3.loss_mask: 0.8406, decode.d3.loss_dice: 1.1757, decode.d4.loss_cls: 0.9342, decode.d4.loss_mask: 0.8394, decode.d4.loss_dice: 1.1705, decode.d5.loss_cls: 0.9334, decode.d5.loss_mask: 0.8406, decode.d5.loss_dice: 1.1704, decode.d6.loss_cls: 0.9265, decode.d6.loss_mask: 0.8362, decode.d6.loss_dice: 1.1608, decode.d7.loss_cls: 0.9194, decode.d7.loss_mask: 0.8386, decode.d7.loss_dice: 1.1667, decode.d8.loss_cls: 0.9134, decode.d8.loss_mask: 0.8376, decode.d8.loss_dice: 1.1691, loss: 34.7349 2022-05-04 23:50:15,070 - mmseg - INFO - Iter [12100/40000] lr: 1.002e-06, eta: 6:35:27, time: 0.843, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9389, decode.loss_mask: 0.8216, decode.loss_dice: 1.1933, decode.d0.loss_cls: 5.7241, decode.d0.loss_mask: 0.8261, decode.d0.loss_dice: 1.3770, decode.d1.loss_cls: 1.1730, decode.d1.loss_mask: 0.8373, decode.d1.loss_dice: 1.2779, decode.d2.loss_cls: 1.0082, decode.d2.loss_mask: 0.8180, decode.d2.loss_dice: 1.2323, decode.d3.loss_cls: 0.9724, decode.d3.loss_mask: 0.8159, decode.d3.loss_dice: 1.2039, decode.d4.loss_cls: 0.9620, decode.d4.loss_mask: 0.8204, decode.d4.loss_dice: 1.2034, decode.d5.loss_cls: 0.9548, decode.d5.loss_mask: 0.8133, decode.d5.loss_dice: 1.1980, decode.d6.loss_cls: 0.9472, decode.d6.loss_mask: 0.8059, decode.d6.loss_dice: 1.1913, decode.d7.loss_cls: 0.9405, decode.d7.loss_mask: 0.8120, decode.d7.loss_dice: 1.1923, decode.d8.loss_cls: 0.9302, decode.d8.loss_mask: 0.8161, decode.d8.loss_dice: 1.2007, loss: 35.0080 2022-05-04 23:50:57,263 - mmseg - INFO - Iter [12150/40000] lr: 9.997e-07, eta: 6:34:43, time: 0.843, data_time: 0.009, memory: 51557, decode.loss_cls: 0.9032, decode.loss_mask: 0.8409, decode.loss_dice: 1.1361, decode.d0.loss_cls: 5.6014, decode.d0.loss_mask: 0.8452, decode.d0.loss_dice: 1.3165, decode.d1.loss_cls: 1.1023, decode.d1.loss_mask: 0.8631, decode.d1.loss_dice: 1.2169, decode.d2.loss_cls: 0.9718, decode.d2.loss_mask: 0.8441, decode.d2.loss_dice: 1.1636, decode.d3.loss_cls: 0.9220, decode.d3.loss_mask: 0.8385, decode.d3.loss_dice: 1.1466, decode.d4.loss_cls: 0.9112, decode.d4.loss_mask: 0.8372, decode.d4.loss_dice: 1.1522, decode.d5.loss_cls: 0.8975, decode.d5.loss_mask: 0.8359, decode.d5.loss_dice: 1.1495, decode.d6.loss_cls: 0.8962, decode.d6.loss_mask: 0.8357, decode.d6.loss_dice: 1.1424, decode.d7.loss_cls: 0.8958, decode.d7.loss_mask: 0.8371, decode.d7.loss_dice: 1.1409, decode.d8.loss_cls: 0.8912, decode.d8.loss_mask: 0.8395, decode.d8.loss_dice: 1.1489, loss: 34.1232 2022-05-04 23:51:38,352 - mmseg - INFO - Iter [12200/40000] lr: 9.979e-07, eta: 6:33:58, time: 0.823, data_time: 0.011, memory: 51557, decode.loss_cls: 0.9446, decode.loss_mask: 0.8282, decode.loss_dice: 1.1555, decode.d0.loss_cls: 5.6688, decode.d0.loss_mask: 0.8409, decode.d0.loss_dice: 1.3468, decode.d1.loss_cls: 1.1347, decode.d1.loss_mask: 0.8584, decode.d1.loss_dice: 1.2289, decode.d2.loss_cls: 0.9936, decode.d2.loss_mask: 0.8371, decode.d2.loss_dice: 1.1846, decode.d3.loss_cls: 0.9727, decode.d3.loss_mask: 0.8249, decode.d3.loss_dice: 1.1627, decode.d4.loss_cls: 0.9520, decode.d4.loss_mask: 0.8293, decode.d4.loss_dice: 1.1616, decode.d5.loss_cls: 0.9446, decode.d5.loss_mask: 0.8267, decode.d5.loss_dice: 1.1607, decode.d6.loss_cls: 0.9375, decode.d6.loss_mask: 0.8254, decode.d6.loss_dice: 1.1517, decode.d7.loss_cls: 0.9429, decode.d7.loss_mask: 0.8284, decode.d7.loss_dice: 1.1496, decode.d8.loss_cls: 0.9423, decode.d8.loss_mask: 0.8262, decode.d8.loss_dice: 1.1576, loss: 34.6189 2022-05-04 23:52:20,173 - mmseg - INFO - Iter [12250/40000] lr: 9.961e-07, eta: 6:33:14, time: 0.836, data_time: 0.009, memory: 51557, decode.loss_cls: 0.9181, decode.loss_mask: 0.8446, decode.loss_dice: 1.1547, decode.d0.loss_cls: 5.5799, decode.d0.loss_mask: 0.8441, decode.d0.loss_dice: 1.3390, decode.d1.loss_cls: 1.1224, decode.d1.loss_mask: 0.8626, decode.d1.loss_dice: 1.2304, decode.d2.loss_cls: 0.9719, decode.d2.loss_mask: 0.8563, decode.d2.loss_dice: 1.1950, decode.d3.loss_cls: 0.9335, decode.d3.loss_mask: 0.8471, decode.d3.loss_dice: 1.1731, decode.d4.loss_cls: 0.9181, decode.d4.loss_mask: 0.8571, decode.d4.loss_dice: 1.1742, decode.d5.loss_cls: 0.9191, decode.d5.loss_mask: 0.8505, decode.d5.loss_dice: 1.1654, decode.d6.loss_cls: 0.9128, decode.d6.loss_mask: 0.8449, decode.d6.loss_dice: 1.1604, decode.d7.loss_cls: 0.9202, decode.d7.loss_mask: 0.8465, decode.d7.loss_dice: 1.1632, decode.d8.loss_cls: 0.9139, decode.d8.loss_mask: 0.8434, decode.d8.loss_dice: 1.1598, loss: 34.5224 2022-05-04 23:53:01,460 - mmseg - INFO - Iter [12300/40000] lr: 9.943e-07, eta: 6:32:28, time: 0.826, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9432, decode.loss_mask: 0.8486, decode.loss_dice: 1.1920, decode.d0.loss_cls: 5.6297, decode.d0.loss_mask: 0.8628, decode.d0.loss_dice: 1.3811, decode.d1.loss_cls: 1.1481, decode.d1.loss_mask: 0.8771, decode.d1.loss_dice: 1.2710, decode.d2.loss_cls: 1.0141, decode.d2.loss_mask: 0.8559, decode.d2.loss_dice: 1.2153, decode.d3.loss_cls: 0.9732, decode.d3.loss_mask: 0.8529, decode.d3.loss_dice: 1.1980, decode.d4.loss_cls: 0.9525, decode.d4.loss_mask: 0.8535, decode.d4.loss_dice: 1.1966, decode.d5.loss_cls: 0.9521, decode.d5.loss_mask: 0.8479, decode.d5.loss_dice: 1.1889, decode.d6.loss_cls: 0.9468, decode.d6.loss_mask: 0.8454, decode.d6.loss_dice: 1.1875, decode.d7.loss_cls: 0.9424, decode.d7.loss_mask: 0.8491, decode.d7.loss_dice: 1.1881, decode.d8.loss_cls: 0.9429, decode.d8.loss_mask: 0.8486, decode.d8.loss_dice: 1.1872, loss: 35.1923 2022-05-04 23:53:42,924 - mmseg - INFO - Iter [12350/40000] lr: 9.925e-07, eta: 6:31:44, time: 0.829, data_time: 0.011, memory: 51557, decode.loss_cls: 0.9013, decode.loss_mask: 0.8416, decode.loss_dice: 1.1738, decode.d0.loss_cls: 5.5621, decode.d0.loss_mask: 0.8383, decode.d0.loss_dice: 1.3501, decode.d1.loss_cls: 1.1064, decode.d1.loss_mask: 0.8558, decode.d1.loss_dice: 1.2430, decode.d2.loss_cls: 0.9648, decode.d2.loss_mask: 0.8409, decode.d2.loss_dice: 1.1956, decode.d3.loss_cls: 0.9323, decode.d3.loss_mask: 0.8345, decode.d3.loss_dice: 1.1758, decode.d4.loss_cls: 0.9191, decode.d4.loss_mask: 0.8354, decode.d4.loss_dice: 1.1775, decode.d5.loss_cls: 0.9122, decode.d5.loss_mask: 0.8391, decode.d5.loss_dice: 1.1746, decode.d6.loss_cls: 0.9079, decode.d6.loss_mask: 0.8366, decode.d6.loss_dice: 1.1612, decode.d7.loss_cls: 0.9034, decode.d7.loss_mask: 0.8335, decode.d7.loss_dice: 1.1671, decode.d8.loss_cls: 0.8979, decode.d8.loss_mask: 0.8363, decode.d8.loss_dice: 1.1643, loss: 34.3823 2022-05-04 23:54:26,452 - mmseg - INFO - Iter [12400/40000] lr: 9.907e-07, eta: 6:31:03, time: 0.871, data_time: 0.059, memory: 51557, decode.loss_cls: 0.9051, decode.loss_mask: 0.8131, decode.loss_dice: 1.1770, decode.d0.loss_cls: 5.5971, decode.d0.loss_mask: 0.8174, decode.d0.loss_dice: 1.3573, decode.d1.loss_cls: 1.1266, decode.d1.loss_mask: 0.8343, decode.d1.loss_dice: 1.2473, decode.d2.loss_cls: 0.9730, decode.d2.loss_mask: 0.8192, decode.d2.loss_dice: 1.2053, decode.d3.loss_cls: 0.9398, decode.d3.loss_mask: 0.8149, decode.d3.loss_dice: 1.1837, decode.d4.loss_cls: 0.9230, decode.d4.loss_mask: 0.8159, decode.d4.loss_dice: 1.1860, decode.d5.loss_cls: 0.9140, decode.d5.loss_mask: 0.8099, decode.d5.loss_dice: 1.1774, decode.d6.loss_cls: 0.9127, decode.d6.loss_mask: 0.8121, decode.d6.loss_dice: 1.1652, decode.d7.loss_cls: 0.9128, decode.d7.loss_mask: 0.8132, decode.d7.loss_dice: 1.1778, decode.d8.loss_cls: 0.9106, decode.d8.loss_mask: 0.8108, decode.d8.loss_dice: 1.1701, loss: 34.3230 2022-05-04 23:55:08,901 - mmseg - INFO - Iter [12450/40000] lr: 9.889e-07, eta: 6:30:21, time: 0.849, data_time: 0.010, memory: 51557, decode.loss_cls: 0.8996, decode.loss_mask: 0.8342, decode.loss_dice: 1.1784, decode.d0.loss_cls: 5.5552, decode.d0.loss_mask: 0.8586, decode.d0.loss_dice: 1.3705, decode.d1.loss_cls: 1.1190, decode.d1.loss_mask: 0.8614, decode.d1.loss_dice: 1.2579, decode.d2.loss_cls: 0.9753, decode.d2.loss_mask: 0.8452, decode.d2.loss_dice: 1.2149, decode.d3.loss_cls: 0.9268, decode.d3.loss_mask: 0.8386, decode.d3.loss_dice: 1.1869, decode.d4.loss_cls: 0.9181, decode.d4.loss_mask: 0.8366, decode.d4.loss_dice: 1.1855, decode.d5.loss_cls: 0.9062, decode.d5.loss_mask: 0.8386, decode.d5.loss_dice: 1.1767, decode.d6.loss_cls: 0.8944, decode.d6.loss_mask: 0.8382, decode.d6.loss_dice: 1.1724, decode.d7.loss_cls: 0.8916, decode.d7.loss_mask: 0.8380, decode.d7.loss_dice: 1.1798, decode.d8.loss_cls: 0.8914, decode.d8.loss_mask: 0.8367, decode.d8.loss_dice: 1.1708, loss: 34.4977 2022-05-04 23:55:49,146 - mmseg - INFO - Iter [12500/40000] lr: 9.871e-07, eta: 6:29:33, time: 0.805, data_time: 0.010, memory: 51557, decode.loss_cls: 0.8971, decode.loss_mask: 0.8071, decode.loss_dice: 1.1371, decode.d0.loss_cls: 5.5286, decode.d0.loss_mask: 0.8208, decode.d0.loss_dice: 1.3202, decode.d1.loss_cls: 1.1250, decode.d1.loss_mask: 0.8305, decode.d1.loss_dice: 1.2080, decode.d2.loss_cls: 0.9783, decode.d2.loss_mask: 0.8152, decode.d2.loss_dice: 1.1610, decode.d3.loss_cls: 0.9373, decode.d3.loss_mask: 0.8089, decode.d3.loss_dice: 1.1413, decode.d4.loss_cls: 0.9224, decode.d4.loss_mask: 0.8075, decode.d4.loss_dice: 1.1440, decode.d5.loss_cls: 0.9140, decode.d5.loss_mask: 0.8083, decode.d5.loss_dice: 1.1364, decode.d6.loss_cls: 0.9064, decode.d6.loss_mask: 0.8075, decode.d6.loss_dice: 1.1289, decode.d7.loss_cls: 0.9013, decode.d7.loss_mask: 0.8016, decode.d7.loss_dice: 1.1343, decode.d8.loss_cls: 0.8945, decode.d8.loss_mask: 0.8033, decode.d8.loss_dice: 1.1337, loss: 33.7604 2022-05-04 23:56:30,754 - mmseg - INFO - Iter [12550/40000] lr: 9.854e-07, eta: 6:28:49, time: 0.832, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9023, decode.loss_mask: 0.8620, decode.loss_dice: 1.1830, decode.d0.loss_cls: 5.5049, decode.d0.loss_mask: 0.8706, decode.d0.loss_dice: 1.3554, decode.d1.loss_cls: 1.1097, decode.d1.loss_mask: 0.8809, decode.d1.loss_dice: 1.2441, decode.d2.loss_cls: 0.9614, decode.d2.loss_mask: 0.8677, decode.d2.loss_dice: 1.2015, decode.d3.loss_cls: 0.9292, decode.d3.loss_mask: 0.8589, decode.d3.loss_dice: 1.1801, decode.d4.loss_cls: 0.9150, decode.d4.loss_mask: 0.8586, decode.d4.loss_dice: 1.1803, decode.d5.loss_cls: 0.9111, decode.d5.loss_mask: 0.8550, decode.d5.loss_dice: 1.1699, decode.d6.loss_cls: 0.9006, decode.d6.loss_mask: 0.8590, decode.d6.loss_dice: 1.1699, decode.d7.loss_cls: 0.9013, decode.d7.loss_mask: 0.8601, decode.d7.loss_dice: 1.1811, decode.d8.loss_cls: 0.8975, decode.d8.loss_mask: 0.8620, decode.d8.loss_dice: 1.1761, loss: 34.6090 2022-05-04 23:57:11,766 - mmseg - INFO - Iter [12600/40000] lr: 9.836e-07, eta: 6:28:03, time: 0.820, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8533, decode.loss_mask: 0.8224, decode.loss_dice: 1.1389, decode.d0.loss_cls: 5.4454, decode.d0.loss_mask: 0.8328, decode.d0.loss_dice: 1.3157, decode.d1.loss_cls: 1.0388, decode.d1.loss_mask: 0.8501, decode.d1.loss_dice: 1.2085, decode.d2.loss_cls: 0.9250, decode.d2.loss_mask: 0.8281, decode.d2.loss_dice: 1.1627, decode.d3.loss_cls: 0.8813, decode.d3.loss_mask: 0.8192, decode.d3.loss_dice: 1.1476, decode.d4.loss_cls: 0.8599, decode.d4.loss_mask: 0.8185, decode.d4.loss_dice: 1.1413, decode.d5.loss_cls: 0.8588, decode.d5.loss_mask: 0.8198, decode.d5.loss_dice: 1.1440, decode.d6.loss_cls: 0.8488, decode.d6.loss_mask: 0.8178, decode.d6.loss_dice: 1.1433, decode.d7.loss_cls: 0.8512, decode.d7.loss_mask: 0.8194, decode.d7.loss_dice: 1.1415, decode.d8.loss_cls: 0.8480, decode.d8.loss_mask: 0.8288, decode.d8.loss_dice: 1.1427, loss: 33.3539 2022-05-04 23:57:53,241 - mmseg - INFO - Iter [12650/40000] lr: 9.818e-07, eta: 6:27:18, time: 0.830, data_time: 0.009, memory: 51557, decode.loss_cls: 0.9168, decode.loss_mask: 0.8110, decode.loss_dice: 1.1331, decode.d0.loss_cls: 5.4989, decode.d0.loss_mask: 0.8242, decode.d0.loss_dice: 1.3191, decode.d1.loss_cls: 1.1298, decode.d1.loss_mask: 0.8346, decode.d1.loss_dice: 1.2067, decode.d2.loss_cls: 0.9801, decode.d2.loss_mask: 0.8192, decode.d2.loss_dice: 1.1586, decode.d3.loss_cls: 0.9385, decode.d3.loss_mask: 0.8100, decode.d3.loss_dice: 1.1393, decode.d4.loss_cls: 0.9246, decode.d4.loss_mask: 0.8100, decode.d4.loss_dice: 1.1409, decode.d5.loss_cls: 0.9189, decode.d5.loss_mask: 0.8062, decode.d5.loss_dice: 1.1385, decode.d6.loss_cls: 0.9106, decode.d6.loss_mask: 0.8091, decode.d6.loss_dice: 1.1298, decode.d7.loss_cls: 0.9090, decode.d7.loss_mask: 0.8140, decode.d7.loss_dice: 1.1332, decode.d8.loss_cls: 0.9104, decode.d8.loss_mask: 0.8114, decode.d8.loss_dice: 1.1311, loss: 33.8176 2022-05-04 23:58:35,187 - mmseg - INFO - Iter [12700/40000] lr: 9.800e-07, eta: 6:26:35, time: 0.839, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9158, decode.loss_mask: 0.8260, decode.loss_dice: 1.1339, decode.d0.loss_cls: 5.4580, decode.d0.loss_mask: 0.8235, decode.d0.loss_dice: 1.3204, decode.d1.loss_cls: 1.1177, decode.d1.loss_mask: 0.8465, decode.d1.loss_dice: 1.2072, decode.d2.loss_cls: 0.9912, decode.d2.loss_mask: 0.8331, decode.d2.loss_dice: 1.1602, decode.d3.loss_cls: 0.9622, decode.d3.loss_mask: 0.8219, decode.d3.loss_dice: 1.1394, decode.d4.loss_cls: 0.9402, decode.d4.loss_mask: 0.8288, decode.d4.loss_dice: 1.1417, decode.d5.loss_cls: 0.9274, decode.d5.loss_mask: 0.8252, decode.d5.loss_dice: 1.1380, decode.d6.loss_cls: 0.9269, decode.d6.loss_mask: 0.8239, decode.d6.loss_dice: 1.1281, decode.d7.loss_cls: 0.9206, decode.d7.loss_mask: 0.8255, decode.d7.loss_dice: 1.1410, decode.d8.loss_cls: 0.9199, decode.d8.loss_mask: 0.8227, decode.d8.loss_dice: 1.1325, loss: 33.9995 2022-05-04 23:59:16,362 - mmseg - INFO - Iter [12750/40000] lr: 9.782e-07, eta: 6:25:49, time: 0.824, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9360, decode.loss_mask: 0.8455, decode.loss_dice: 1.1609, decode.d0.loss_cls: 5.4759, decode.d0.loss_mask: 0.8509, decode.d0.loss_dice: 1.3476, decode.d1.loss_cls: 1.1482, decode.d1.loss_mask: 0.8613, decode.d1.loss_dice: 1.2338, decode.d2.loss_cls: 0.9996, decode.d2.loss_mask: 0.8476, decode.d2.loss_dice: 1.1733, decode.d3.loss_cls: 0.9611, decode.d3.loss_mask: 0.8419, decode.d3.loss_dice: 1.1636, decode.d4.loss_cls: 0.9485, decode.d4.loss_mask: 0.8416, decode.d4.loss_dice: 1.1676, decode.d5.loss_cls: 0.9415, decode.d5.loss_mask: 0.8401, decode.d5.loss_dice: 1.1644, decode.d6.loss_cls: 0.9376, decode.d6.loss_mask: 0.8368, decode.d6.loss_dice: 1.1582, decode.d7.loss_cls: 0.9390, decode.d7.loss_mask: 0.8341, decode.d7.loss_dice: 1.1638, decode.d8.loss_cls: 0.9435, decode.d8.loss_mask: 0.8385, decode.d8.loss_dice: 1.1524, loss: 34.5549 2022-05-04 23:59:57,135 - mmseg - INFO - Iter [12800/40000] lr: 9.764e-07, eta: 6:25:03, time: 0.815, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8420, decode.loss_mask: 0.8352, decode.loss_dice: 1.1552, decode.d0.loss_cls: 5.3628, decode.d0.loss_mask: 0.8406, decode.d0.loss_dice: 1.3367, decode.d1.loss_cls: 1.0603, decode.d1.loss_mask: 0.8525, decode.d1.loss_dice: 1.2226, decode.d2.loss_cls: 0.9171, decode.d2.loss_mask: 0.8326, decode.d2.loss_dice: 1.1760, decode.d3.loss_cls: 0.8864, decode.d3.loss_mask: 0.8304, decode.d3.loss_dice: 1.1567, decode.d4.loss_cls: 0.8636, decode.d4.loss_mask: 0.8315, decode.d4.loss_dice: 1.1585, decode.d5.loss_cls: 0.8574, decode.d5.loss_mask: 0.8308, decode.d5.loss_dice: 1.1565, decode.d6.loss_cls: 0.8511, decode.d6.loss_mask: 0.8335, decode.d6.loss_dice: 1.1473, decode.d7.loss_cls: 0.8433, decode.d7.loss_mask: 0.8362, decode.d7.loss_dice: 1.1521, decode.d8.loss_cls: 0.8411, decode.d8.loss_mask: 0.8372, decode.d8.loss_dice: 1.1488, loss: 33.4958 2022-05-05 00:00:39,123 - mmseg - INFO - Iter [12850/40000] lr: 9.746e-07, eta: 6:24:20, time: 0.839, data_time: 0.010, memory: 51557, decode.loss_cls: 0.8899, decode.loss_mask: 0.8422, decode.loss_dice: 1.1485, decode.d0.loss_cls: 5.3960, decode.d0.loss_mask: 0.8520, decode.d0.loss_dice: 1.3374, decode.d1.loss_cls: 1.1039, decode.d1.loss_mask: 0.8675, decode.d1.loss_dice: 1.2227, decode.d2.loss_cls: 0.9663, decode.d2.loss_mask: 0.8429, decode.d2.loss_dice: 1.1773, decode.d3.loss_cls: 0.9265, decode.d3.loss_mask: 0.8394, decode.d3.loss_dice: 1.1590, decode.d4.loss_cls: 0.9056, decode.d4.loss_mask: 0.8433, decode.d4.loss_dice: 1.1571, decode.d5.loss_cls: 0.9010, decode.d5.loss_mask: 0.8355, decode.d5.loss_dice: 1.1539, decode.d6.loss_cls: 0.9046, decode.d6.loss_mask: 0.8319, decode.d6.loss_dice: 1.1492, decode.d7.loss_cls: 0.8964, decode.d7.loss_mask: 0.8425, decode.d7.loss_dice: 1.1523, decode.d8.loss_cls: 0.8850, decode.d8.loss_mask: 0.8342, decode.d8.loss_dice: 1.1544, loss: 34.0183 2022-05-05 00:01:19,520 - mmseg - INFO - Iter [12900/40000] lr: 9.728e-07, eta: 6:23:33, time: 0.809, data_time: 0.011, memory: 51557, decode.loss_cls: 0.9274, decode.loss_mask: 0.8333, decode.loss_dice: 1.1793, decode.d0.loss_cls: 5.4475, decode.d0.loss_mask: 0.8330, decode.d0.loss_dice: 1.3734, decode.d1.loss_cls: 1.1224, decode.d1.loss_mask: 0.8569, decode.d1.loss_dice: 1.2538, decode.d2.loss_cls: 0.9921, decode.d2.loss_mask: 0.8477, decode.d2.loss_dice: 1.2148, decode.d3.loss_cls: 0.9531, decode.d3.loss_mask: 0.8399, decode.d3.loss_dice: 1.1900, decode.d4.loss_cls: 0.9467, decode.d4.loss_mask: 0.8317, decode.d4.loss_dice: 1.1867, decode.d5.loss_cls: 0.9288, decode.d5.loss_mask: 0.8348, decode.d5.loss_dice: 1.1822, decode.d6.loss_cls: 0.9258, decode.d6.loss_mask: 0.8325, decode.d6.loss_dice: 1.1795, decode.d7.loss_cls: 0.9192, decode.d7.loss_mask: 0.8305, decode.d7.loss_dice: 1.1804, decode.d8.loss_cls: 0.9246, decode.d8.loss_mask: 0.8332, decode.d8.loss_dice: 1.1793, loss: 34.5804 2022-05-05 00:02:03,335 - mmseg - INFO - Iter [12950/40000] lr: 9.710e-07, eta: 6:22:53, time: 0.876, data_time: 0.058, memory: 51557, decode.loss_cls: 0.8819, decode.loss_mask: 0.8231, decode.loss_dice: 1.1267, decode.d0.loss_cls: 5.3203, decode.d0.loss_mask: 0.8340, decode.d0.loss_dice: 1.3090, decode.d1.loss_cls: 1.0636, decode.d1.loss_mask: 0.8505, decode.d1.loss_dice: 1.2033, decode.d2.loss_cls: 0.9559, decode.d2.loss_mask: 0.8332, decode.d2.loss_dice: 1.1531, decode.d3.loss_cls: 0.9147, decode.d3.loss_mask: 0.8300, decode.d3.loss_dice: 1.1346, decode.d4.loss_cls: 0.9110, decode.d4.loss_mask: 0.8215, decode.d4.loss_dice: 1.1392, decode.d5.loss_cls: 0.8976, decode.d5.loss_mask: 0.8269, decode.d5.loss_dice: 1.1278, decode.d6.loss_cls: 0.8883, decode.d6.loss_mask: 0.8263, decode.d6.loss_dice: 1.1186, decode.d7.loss_cls: 0.8889, decode.d7.loss_mask: 0.8257, decode.d7.loss_dice: 1.1247, decode.d8.loss_cls: 0.8886, decode.d8.loss_mask: 0.8270, decode.d8.loss_dice: 1.1254, loss: 33.4715 2022-05-05 00:02:45,669 - mmseg - INFO - Saving checkpoint at 13000 iterations 2022-05-05 00:03:11,033 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 00:03:11,070 - mmseg - INFO - Iter [13000/40000] lr: 9.692e-07, eta: 6:23:03, time: 1.350, data_time: 0.010, memory: 51557, decode.loss_cls: 0.8574, decode.loss_mask: 0.8270, decode.loss_dice: 1.1646, decode.d0.loss_cls: 5.3477, decode.d0.loss_mask: 0.8382, decode.d0.loss_dice: 1.3414, decode.d1.loss_cls: 1.0767, decode.d1.loss_mask: 0.8570, decode.d1.loss_dice: 1.2368, decode.d2.loss_cls: 0.9352, decode.d2.loss_mask: 0.8329, decode.d2.loss_dice: 1.1902, decode.d3.loss_cls: 0.8851, decode.d3.loss_mask: 0.8309, decode.d3.loss_dice: 1.1684, decode.d4.loss_cls: 0.8710, decode.d4.loss_mask: 0.8311, decode.d4.loss_dice: 1.1667, decode.d5.loss_cls: 0.8558, decode.d5.loss_mask: 0.8278, decode.d5.loss_dice: 1.1667, decode.d6.loss_cls: 0.8506, decode.d6.loss_mask: 0.8240, decode.d6.loss_dice: 1.1595, decode.d7.loss_cls: 0.8584, decode.d7.loss_mask: 0.8262, decode.d7.loss_dice: 1.1653, decode.d8.loss_cls: 0.8572, decode.d8.loss_mask: 0.8233, decode.d8.loss_dice: 1.1725, loss: 33.6459 2022-05-05 00:03:52,999 - mmseg - INFO - Iter [13050/40000] lr: 9.674e-07, eta: 6:22:20, time: 0.843, data_time: 0.014, memory: 51557, decode.loss_cls: 0.8645, decode.loss_mask: 0.8172, decode.loss_dice: 1.1456, decode.d0.loss_cls: 5.3188, decode.d0.loss_mask: 0.8332, decode.d0.loss_dice: 1.3225, decode.d1.loss_cls: 1.0773, decode.d1.loss_mask: 0.8460, decode.d1.loss_dice: 1.2150, decode.d2.loss_cls: 0.9463, decode.d2.loss_mask: 0.8209, decode.d2.loss_dice: 1.1709, decode.d3.loss_cls: 0.8919, decode.d3.loss_mask: 0.8218, decode.d3.loss_dice: 1.1505, decode.d4.loss_cls: 0.8831, decode.d4.loss_mask: 0.8157, decode.d4.loss_dice: 1.1543, decode.d5.loss_cls: 0.8675, decode.d5.loss_mask: 0.8186, decode.d5.loss_dice: 1.1500, decode.d6.loss_cls: 0.8598, decode.d6.loss_mask: 0.8170, decode.d6.loss_dice: 1.1421, decode.d7.loss_cls: 0.8596, decode.d7.loss_mask: 0.8129, decode.d7.loss_dice: 1.1431, decode.d8.loss_cls: 0.8514, decode.d8.loss_mask: 0.8166, decode.d8.loss_dice: 1.1471, loss: 33.3812 2022-05-05 00:04:33,847 - mmseg - INFO - Iter [13100/40000] lr: 9.656e-07, eta: 6:21:33, time: 0.817, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9380, decode.loss_mask: 0.8122, decode.loss_dice: 1.1637, decode.d0.loss_cls: 5.3611, decode.d0.loss_mask: 0.8407, decode.d0.loss_dice: 1.3655, decode.d1.loss_cls: 1.1419, decode.d1.loss_mask: 0.8398, decode.d1.loss_dice: 1.2421, decode.d2.loss_cls: 1.0095, decode.d2.loss_mask: 0.8234, decode.d2.loss_dice: 1.1958, decode.d3.loss_cls: 0.9675, decode.d3.loss_mask: 0.8190, decode.d3.loss_dice: 1.1643, decode.d4.loss_cls: 0.9512, decode.d4.loss_mask: 0.8176, decode.d4.loss_dice: 1.1730, decode.d5.loss_cls: 0.9374, decode.d5.loss_mask: 0.8196, decode.d5.loss_dice: 1.1686, decode.d6.loss_cls: 0.9382, decode.d6.loss_mask: 0.8156, decode.d6.loss_dice: 1.1628, decode.d7.loss_cls: 0.9409, decode.d7.loss_mask: 0.8154, decode.d7.loss_dice: 1.1652, decode.d8.loss_cls: 0.9376, decode.d8.loss_mask: 0.8181, decode.d8.loss_dice: 1.1626, loss: 34.3087 2022-05-05 00:05:14,934 - mmseg - INFO - Iter [13150/40000] lr: 9.638e-07, eta: 6:20:48, time: 0.821, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8825, decode.loss_mask: 0.8196, decode.loss_dice: 1.1393, decode.d0.loss_cls: 5.2569, decode.d0.loss_mask: 0.8356, decode.d0.loss_dice: 1.3297, decode.d1.loss_cls: 1.0829, decode.d1.loss_mask: 0.8483, decode.d1.loss_dice: 1.2284, decode.d2.loss_cls: 0.9364, decode.d2.loss_mask: 0.8283, decode.d2.loss_dice: 1.1737, decode.d3.loss_cls: 0.8940, decode.d3.loss_mask: 0.8201, decode.d3.loss_dice: 1.1593, decode.d4.loss_cls: 0.8834, decode.d4.loss_mask: 0.8180, decode.d4.loss_dice: 1.1557, decode.d5.loss_cls: 0.8712, decode.d5.loss_mask: 0.8189, decode.d5.loss_dice: 1.1482, decode.d6.loss_cls: 0.8619, decode.d6.loss_mask: 0.8184, decode.d6.loss_dice: 1.1419, decode.d7.loss_cls: 0.8639, decode.d7.loss_mask: 0.8217, decode.d7.loss_dice: 1.1431, decode.d8.loss_cls: 0.8661, decode.d8.loss_mask: 0.8204, decode.d8.loss_dice: 1.1477, loss: 33.4155 2022-05-05 00:05:56,518 - mmseg - INFO - Iter [13200/40000] lr: 9.620e-07, eta: 6:20:03, time: 0.832, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8506, decode.loss_mask: 0.7967, decode.loss_dice: 1.1215, decode.d0.loss_cls: 5.2480, decode.d0.loss_mask: 0.8098, decode.d0.loss_dice: 1.3048, decode.d1.loss_cls: 1.0699, decode.d1.loss_mask: 0.8206, decode.d1.loss_dice: 1.2027, decode.d2.loss_cls: 0.9095, decode.d2.loss_mask: 0.8080, decode.d2.loss_dice: 1.1475, decode.d3.loss_cls: 0.8821, decode.d3.loss_mask: 0.8020, decode.d3.loss_dice: 1.1282, decode.d4.loss_cls: 0.8630, decode.d4.loss_mask: 0.7996, decode.d4.loss_dice: 1.1353, decode.d5.loss_cls: 0.8599, decode.d5.loss_mask: 0.7989, decode.d5.loss_dice: 1.1279, decode.d6.loss_cls: 0.8467, decode.d6.loss_mask: 0.8019, decode.d6.loss_dice: 1.1225, decode.d7.loss_cls: 0.8426, decode.d7.loss_mask: 0.7991, decode.d7.loss_dice: 1.1228, decode.d8.loss_cls: 0.8453, decode.d8.loss_mask: 0.7965, decode.d8.loss_dice: 1.1257, loss: 32.7898 2022-05-05 00:06:38,946 - mmseg - INFO - Iter [13250/40000] lr: 9.602e-07, eta: 6:19:21, time: 0.848, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9234, decode.loss_mask: 0.8310, decode.loss_dice: 1.1672, decode.d0.loss_cls: 5.3144, decode.d0.loss_mask: 0.8411, decode.d0.loss_dice: 1.3642, decode.d1.loss_cls: 1.1388, decode.d1.loss_mask: 0.8632, decode.d1.loss_dice: 1.2594, decode.d2.loss_cls: 0.9996, decode.d2.loss_mask: 0.8390, decode.d2.loss_dice: 1.2079, decode.d3.loss_cls: 0.9547, decode.d3.loss_mask: 0.8326, decode.d3.loss_dice: 1.1836, decode.d4.loss_cls: 0.9283, decode.d4.loss_mask: 0.8312, decode.d4.loss_dice: 1.1894, decode.d5.loss_cls: 0.9218, decode.d5.loss_mask: 0.8315, decode.d5.loss_dice: 1.1865, decode.d6.loss_cls: 0.9241, decode.d6.loss_mask: 0.8276, decode.d6.loss_dice: 1.1744, decode.d7.loss_cls: 0.9246, decode.d7.loss_mask: 0.8297, decode.d7.loss_dice: 1.1761, decode.d8.loss_cls: 0.9231, decode.d8.loss_mask: 0.8281, decode.d8.loss_dice: 1.1721, loss: 34.3887 2022-05-05 00:07:20,022 - mmseg - INFO - Iter [13300/40000] lr: 9.584e-07, eta: 6:18:35, time: 0.822, data_time: 0.009, memory: 51557, decode.loss_cls: 0.9162, decode.loss_mask: 0.8299, decode.loss_dice: 1.1361, decode.d0.loss_cls: 5.2486, decode.d0.loss_mask: 0.8394, decode.d0.loss_dice: 1.3295, decode.d1.loss_cls: 1.1354, decode.d1.loss_mask: 0.8488, decode.d1.loss_dice: 1.2124, decode.d2.loss_cls: 0.9916, decode.d2.loss_mask: 0.8374, decode.d2.loss_dice: 1.1640, decode.d3.loss_cls: 0.9508, decode.d3.loss_mask: 0.8350, decode.d3.loss_dice: 1.1437, decode.d4.loss_cls: 0.9359, decode.d4.loss_mask: 0.8324, decode.d4.loss_dice: 1.1461, decode.d5.loss_cls: 0.9237, decode.d5.loss_mask: 0.8334, decode.d5.loss_dice: 1.1455, decode.d6.loss_cls: 0.9107, decode.d6.loss_mask: 0.8350, decode.d6.loss_dice: 1.1415, decode.d7.loss_cls: 0.9106, decode.d7.loss_mask: 0.8362, decode.d7.loss_dice: 1.1448, decode.d8.loss_cls: 0.9162, decode.d8.loss_mask: 0.8352, decode.d8.loss_dice: 1.1393, loss: 33.9053 2022-05-05 00:08:01,873 - mmseg - INFO - Iter [13350/40000] lr: 9.566e-07, eta: 6:17:51, time: 0.837, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9327, decode.loss_mask: 0.8292, decode.loss_dice: 1.1279, decode.d0.loss_cls: 5.2550, decode.d0.loss_mask: 0.8462, decode.d0.loss_dice: 1.3251, decode.d1.loss_cls: 1.1363, decode.d1.loss_mask: 0.8608, decode.d1.loss_dice: 1.2143, decode.d2.loss_cls: 1.0039, decode.d2.loss_mask: 0.8324, decode.d2.loss_dice: 1.1572, decode.d3.loss_cls: 0.9635, decode.d3.loss_mask: 0.8282, decode.d3.loss_dice: 1.1392, decode.d4.loss_cls: 0.9459, decode.d4.loss_mask: 0.8307, decode.d4.loss_dice: 1.1419, decode.d5.loss_cls: 0.9441, decode.d5.loss_mask: 0.8280, decode.d5.loss_dice: 1.1407, decode.d6.loss_cls: 0.9348, decode.d6.loss_mask: 0.8222, decode.d6.loss_dice: 1.1299, decode.d7.loss_cls: 0.9277, decode.d7.loss_mask: 0.8303, decode.d7.loss_dice: 1.1327, decode.d8.loss_cls: 0.9287, decode.d8.loss_mask: 0.8296, decode.d8.loss_dice: 1.1333, loss: 33.9525 2022-05-05 00:08:42,880 - mmseg - INFO - Iter [13400/40000] lr: 9.548e-07, eta: 6:17:06, time: 0.820, data_time: 0.010, memory: 51557, decode.loss_cls: 0.8557, decode.loss_mask: 0.8103, decode.loss_dice: 1.1410, decode.d0.loss_cls: 5.1888, decode.d0.loss_mask: 0.8292, decode.d0.loss_dice: 1.3190, decode.d1.loss_cls: 1.0722, decode.d1.loss_mask: 0.8329, decode.d1.loss_dice: 1.2177, decode.d2.loss_cls: 0.9367, decode.d2.loss_mask: 0.8154, decode.d2.loss_dice: 1.1676, decode.d3.loss_cls: 0.8994, decode.d3.loss_mask: 0.8164, decode.d3.loss_dice: 1.1479, decode.d4.loss_cls: 0.8816, decode.d4.loss_mask: 0.8122, decode.d4.loss_dice: 1.1504, decode.d5.loss_cls: 0.8678, decode.d5.loss_mask: 0.8124, decode.d5.loss_dice: 1.1452, decode.d6.loss_cls: 0.8702, decode.d6.loss_mask: 0.8103, decode.d6.loss_dice: 1.1418, decode.d7.loss_cls: 0.8647, decode.d7.loss_mask: 0.8089, decode.d7.loss_dice: 1.1453, decode.d8.loss_cls: 0.8612, decode.d8.loss_mask: 0.8066, decode.d8.loss_dice: 1.1472, loss: 33.1761 2022-05-05 00:09:24,349 - mmseg - INFO - Iter [13450/40000] lr: 9.530e-07, eta: 6:16:21, time: 0.829, data_time: 0.010, memory: 51557, decode.loss_cls: 0.9071, decode.loss_mask: 0.8056, decode.loss_dice: 1.1554, decode.d0.loss_cls: 5.1722, decode.d0.loss_mask: 0.8149, decode.d0.loss_dice: 1.3444, decode.d1.loss_cls: 1.1023, decode.d1.loss_mask: 0.8421, decode.d1.loss_dice: 1.2421, decode.d2.loss_cls: 0.9602, decode.d2.loss_mask: 0.8165, decode.d2.loss_dice: 1.1904, decode.d3.loss_cls: 0.9229, decode.d3.loss_mask: 0.8070, decode.d3.loss_dice: 1.1688, decode.d4.loss_cls: 0.9091, decode.d4.loss_mask: 0.8090, decode.d4.loss_dice: 1.1695, decode.d5.loss_cls: 0.9010, decode.d5.loss_mask: 0.8099, decode.d5.loss_dice: 1.1633, decode.d6.loss_cls: 0.8971, decode.d6.loss_mask: 0.8056, decode.d6.loss_dice: 1.1474, decode.d7.loss_cls: 0.9046, decode.d7.loss_mask: 0.8072, decode.d7.loss_dice: 1.1519, decode.d8.loss_cls: 0.8969, decode.d8.loss_mask: 0.8086, decode.d8.loss_dice: 1.1598, loss: 33.5929 2022-05-05 00:10:07,947 - mmseg - INFO - Iter [13500/40000] lr: 9.513e-07, eta: 6:15:41, time: 0.872, data_time: 0.059, memory: 51557, decode.loss_cls: 0.8523, decode.loss_mask: 0.8360, decode.loss_dice: 1.1347, decode.d0.loss_cls: 5.1278, decode.d0.loss_mask: 0.8425, decode.d0.loss_dice: 1.3068, decode.d1.loss_cls: 1.0484, decode.d1.loss_mask: 0.8550, decode.d1.loss_dice: 1.2017, decode.d2.loss_cls: 0.9031, decode.d2.loss_mask: 0.8355, decode.d2.loss_dice: 1.1576, decode.d3.loss_cls: 0.8889, decode.d3.loss_mask: 0.8310, decode.d3.loss_dice: 1.1361, decode.d4.loss_cls: 0.8703, decode.d4.loss_mask: 0.8359, decode.d4.loss_dice: 1.1384, decode.d5.loss_cls: 0.8753, decode.d5.loss_mask: 0.8331, decode.d5.loss_dice: 1.1354, decode.d6.loss_cls: 0.8603, decode.d6.loss_mask: 0.8308, decode.d6.loss_dice: 1.1281, decode.d7.loss_cls: 0.8540, decode.d7.loss_mask: 0.8321, decode.d7.loss_dice: 1.1296, decode.d8.loss_cls: 0.8505, decode.d8.loss_mask: 0.8349, decode.d8.loss_dice: 1.1361, loss: 33.1026 2022-05-05 00:10:49,868 - mmseg - INFO - Iter [13550/40000] lr: 9.495e-07, eta: 6:14:57, time: 0.838, data_time: 0.010, memory: 51557, decode.loss_cls: 0.8548, decode.loss_mask: 0.8093, decode.loss_dice: 1.1307, decode.d0.loss_cls: 5.1301, decode.d0.loss_mask: 0.8246, decode.d0.loss_dice: 1.3235, decode.d1.loss_cls: 1.0448, decode.d1.loss_mask: 0.8319, decode.d1.loss_dice: 1.2056, decode.d2.loss_cls: 0.9290, decode.d2.loss_mask: 0.8155, decode.d2.loss_dice: 1.1571, decode.d3.loss_cls: 0.8935, decode.d3.loss_mask: 0.8082, decode.d3.loss_dice: 1.1295, decode.d4.loss_cls: 0.8895, decode.d4.loss_mask: 0.8116, decode.d4.loss_dice: 1.1377, decode.d5.loss_cls: 0.8612, decode.d5.loss_mask: 0.8125, decode.d5.loss_dice: 1.1365, decode.d6.loss_cls: 0.8626, decode.d6.loss_mask: 0.8094, decode.d6.loss_dice: 1.1275, decode.d7.loss_cls: 0.8490, decode.d7.loss_mask: 0.8095, decode.d7.loss_dice: 1.1260, decode.d8.loss_cls: 0.8522, decode.d8.loss_mask: 0.8074, decode.d8.loss_dice: 1.1305, loss: 32.9113 2022-05-05 00:11:30,261 - mmseg - INFO - Iter [13600/40000] lr: 9.477e-07, eta: 6:14:10, time: 0.808, data_time: 0.010, memory: 51557, decode.loss_cls: 0.8580, decode.loss_mask: 0.8179, decode.loss_dice: 1.1237, decode.d0.loss_cls: 5.1388, decode.d0.loss_mask: 0.8454, decode.d0.loss_dice: 1.3307, decode.d1.loss_cls: 1.0716, decode.d1.loss_mask: 0.8516, decode.d1.loss_dice: 1.2056, decode.d2.loss_cls: 0.9279, decode.d2.loss_mask: 0.8370, decode.d2.loss_dice: 1.1611, decode.d3.loss_cls: 0.8811, decode.d3.loss_mask: 0.8316, decode.d3.loss_dice: 1.1424, decode.d4.loss_cls: 0.8709, decode.d4.loss_mask: 0.8265, decode.d4.loss_dice: 1.1483, decode.d5.loss_cls: 0.8644, decode.d5.loss_mask: 0.8193, decode.d5.loss_dice: 1.1415, decode.d6.loss_cls: 0.8533, decode.d6.loss_mask: 0.8170, decode.d6.loss_dice: 1.1299, decode.d7.loss_cls: 0.8488, decode.d7.loss_mask: 0.8183, decode.d7.loss_dice: 1.1304, decode.d8.loss_cls: 0.8575, decode.d8.loss_mask: 0.8203, decode.d8.loss_dice: 1.1301, loss: 33.1011 2022-05-05 00:12:11,174 - mmseg - INFO - Iter [13650/40000] lr: 9.459e-07, eta: 6:13:25, time: 0.818, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8649, decode.loss_mask: 0.8327, decode.loss_dice: 1.1098, decode.d0.loss_cls: 5.1338, decode.d0.loss_mask: 0.8556, decode.d0.loss_dice: 1.2985, decode.d1.loss_cls: 1.0918, decode.d1.loss_mask: 0.8622, decode.d1.loss_dice: 1.1925, decode.d2.loss_cls: 0.9288, decode.d2.loss_mask: 0.8397, decode.d2.loss_dice: 1.1412, decode.d3.loss_cls: 0.8983, decode.d3.loss_mask: 0.8372, decode.d3.loss_dice: 1.1251, decode.d4.loss_cls: 0.8859, decode.d4.loss_mask: 0.8383, decode.d4.loss_dice: 1.1191, decode.d5.loss_cls: 0.8863, decode.d5.loss_mask: 0.8296, decode.d5.loss_dice: 1.1124, decode.d6.loss_cls: 0.8710, decode.d6.loss_mask: 0.8306, decode.d6.loss_dice: 1.1070, decode.d7.loss_cls: 0.8626, decode.d7.loss_mask: 0.8306, decode.d7.loss_dice: 1.1195, decode.d8.loss_cls: 0.8655, decode.d8.loss_mask: 0.8301, decode.d8.loss_dice: 1.1147, loss: 33.1155 2022-05-05 00:12:53,071 - mmseg - INFO - Iter [13700/40000] lr: 9.441e-07, eta: 6:12:41, time: 0.838, data_time: 0.010, memory: 51557, decode.loss_cls: 0.8434, decode.loss_mask: 0.8164, decode.loss_dice: 1.1267, decode.d0.loss_cls: 5.0914, decode.d0.loss_mask: 0.8403, decode.d0.loss_dice: 1.3179, decode.d1.loss_cls: 1.0445, decode.d1.loss_mask: 0.8364, decode.d1.loss_dice: 1.2126, decode.d2.loss_cls: 0.9125, decode.d2.loss_mask: 0.8220, decode.d2.loss_dice: 1.1567, decode.d3.loss_cls: 0.8653, decode.d3.loss_mask: 0.8166, decode.d3.loss_dice: 1.1328, decode.d4.loss_cls: 0.8565, decode.d4.loss_mask: 0.8193, decode.d4.loss_dice: 1.1303, decode.d5.loss_cls: 0.8396, decode.d5.loss_mask: 0.8163, decode.d5.loss_dice: 1.1305, decode.d6.loss_cls: 0.8423, decode.d6.loss_mask: 0.8116, decode.d6.loss_dice: 1.1241, decode.d7.loss_cls: 0.8356, decode.d7.loss_mask: 0.8162, decode.d7.loss_dice: 1.1265, decode.d8.loss_cls: 0.8337, decode.d8.loss_mask: 0.8179, decode.d8.loss_dice: 1.1321, loss: 32.7681 2022-05-05 00:13:34,705 - mmseg - INFO - Iter [13750/40000] lr: 9.423e-07, eta: 6:11:57, time: 0.833, data_time: 0.010, memory: 51557, decode.loss_cls: 0.8773, decode.loss_mask: 0.7930, decode.loss_dice: 1.1551, decode.d0.loss_cls: 5.1259, decode.d0.loss_mask: 0.8045, decode.d0.loss_dice: 1.3489, decode.d1.loss_cls: 1.1050, decode.d1.loss_mask: 0.8130, decode.d1.loss_dice: 1.2330, decode.d2.loss_cls: 0.9562, decode.d2.loss_mask: 0.7984, decode.d2.loss_dice: 1.1802, decode.d3.loss_cls: 0.9194, decode.d3.loss_mask: 0.7942, decode.d3.loss_dice: 1.1617, decode.d4.loss_cls: 0.9074, decode.d4.loss_mask: 0.7931, decode.d4.loss_dice: 1.1633, decode.d5.loss_cls: 0.8941, decode.d5.loss_mask: 0.7930, decode.d5.loss_dice: 1.1590, decode.d6.loss_cls: 0.8791, decode.d6.loss_mask: 0.7918, decode.d6.loss_dice: 1.1499, decode.d7.loss_cls: 0.8664, decode.d7.loss_mask: 0.7874, decode.d7.loss_dice: 1.1606, decode.d8.loss_cls: 0.8759, decode.d8.loss_mask: 0.7900, decode.d8.loss_dice: 1.1585, loss: 33.2353 2022-05-05 00:14:15,873 - mmseg - INFO - Iter [13800/40000] lr: 9.405e-07, eta: 6:11:12, time: 0.823, data_time: 0.010, memory: 51557, decode.loss_cls: 0.8181, decode.loss_mask: 0.8252, decode.loss_dice: 1.0873, decode.d0.loss_cls: 5.0139, decode.d0.loss_mask: 0.8317, decode.d0.loss_dice: 1.2573, decode.d1.loss_cls: 1.0250, decode.d1.loss_mask: 0.8430, decode.d1.loss_dice: 1.1627, decode.d2.loss_cls: 0.9039, decode.d2.loss_mask: 0.8284, decode.d2.loss_dice: 1.1167, decode.d3.loss_cls: 0.8528, decode.d3.loss_mask: 0.8202, decode.d3.loss_dice: 1.0966, decode.d4.loss_cls: 0.8348, decode.d4.loss_mask: 0.8191, decode.d4.loss_dice: 1.1025, decode.d5.loss_cls: 0.8382, decode.d5.loss_mask: 0.8182, decode.d5.loss_dice: 1.0925, decode.d6.loss_cls: 0.8238, decode.d6.loss_mask: 0.8148, decode.d6.loss_dice: 1.0832, decode.d7.loss_cls: 0.8251, decode.d7.loss_mask: 0.8106, decode.d7.loss_dice: 1.0840, decode.d8.loss_cls: 0.8124, decode.d8.loss_mask: 0.8230, decode.d8.loss_dice: 1.0901, loss: 32.1551 2022-05-05 00:14:57,064 - mmseg - INFO - Iter [13850/40000] lr: 9.387e-07, eta: 6:10:27, time: 0.823, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8373, decode.loss_mask: 0.8285, decode.loss_dice: 1.1172, decode.d0.loss_cls: 4.9881, decode.d0.loss_mask: 0.8401, decode.d0.loss_dice: 1.2931, decode.d1.loss_cls: 1.0324, decode.d1.loss_mask: 0.8519, decode.d1.loss_dice: 1.1954, decode.d2.loss_cls: 0.9001, decode.d2.loss_mask: 0.8382, decode.d2.loss_dice: 1.1443, decode.d3.loss_cls: 0.8657, decode.d3.loss_mask: 0.8266, decode.d3.loss_dice: 1.1222, decode.d4.loss_cls: 0.8510, decode.d4.loss_mask: 0.8288, decode.d4.loss_dice: 1.1285, decode.d5.loss_cls: 0.8403, decode.d5.loss_mask: 0.8327, decode.d5.loss_dice: 1.1252, decode.d6.loss_cls: 0.8385, decode.d6.loss_mask: 0.8335, decode.d6.loss_dice: 1.1190, decode.d7.loss_cls: 0.8341, decode.d7.loss_mask: 0.8284, decode.d7.loss_dice: 1.1206, decode.d8.loss_cls: 0.8341, decode.d8.loss_mask: 0.8285, decode.d8.loss_dice: 1.1179, loss: 32.6423 2022-05-05 00:15:38,510 - mmseg - INFO - Iter [13900/40000] lr: 9.369e-07, eta: 6:09:42, time: 0.830, data_time: 0.010, memory: 51557, decode.loss_cls: 0.8941, decode.loss_mask: 0.8089, decode.loss_dice: 1.1558, decode.d0.loss_cls: 5.1093, decode.d0.loss_mask: 0.8268, decode.d0.loss_dice: 1.3555, decode.d1.loss_cls: 1.1159, decode.d1.loss_mask: 0.8398, decode.d1.loss_dice: 1.2479, decode.d2.loss_cls: 0.9656, decode.d2.loss_mask: 0.8228, decode.d2.loss_dice: 1.1999, decode.d3.loss_cls: 0.9299, decode.d3.loss_mask: 0.8175, decode.d3.loss_dice: 1.1748, decode.d4.loss_cls: 0.9174, decode.d4.loss_mask: 0.8138, decode.d4.loss_dice: 1.1736, decode.d5.loss_cls: 0.9106, decode.d5.loss_mask: 0.8060, decode.d5.loss_dice: 1.1696, decode.d6.loss_cls: 0.8981, decode.d6.loss_mask: 0.8062, decode.d6.loss_dice: 1.1542, decode.d7.loss_cls: 0.8842, decode.d7.loss_mask: 0.8069, decode.d7.loss_dice: 1.1599, decode.d8.loss_cls: 0.8977, decode.d8.loss_mask: 0.8051, decode.d8.loss_dice: 1.1597, loss: 33.6275 2022-05-05 00:16:20,030 - mmseg - INFO - Iter [13950/40000] lr: 9.351e-07, eta: 6:08:58, time: 0.830, data_time: 0.013, memory: 51557, decode.loss_cls: 0.8248, decode.loss_mask: 0.8327, decode.loss_dice: 1.1393, decode.d0.loss_cls: 5.0126, decode.d0.loss_mask: 0.8505, decode.d0.loss_dice: 1.3156, decode.d1.loss_cls: 1.0361, decode.d1.loss_mask: 0.8560, decode.d1.loss_dice: 1.2161, decode.d2.loss_cls: 0.9054, decode.d2.loss_mask: 0.8362, decode.d2.loss_dice: 1.1674, decode.d3.loss_cls: 0.8746, decode.d3.loss_mask: 0.8302, decode.d3.loss_dice: 1.1436, decode.d4.loss_cls: 0.8591, decode.d4.loss_mask: 0.8273, decode.d4.loss_dice: 1.1497, decode.d5.loss_cls: 0.8466, decode.d5.loss_mask: 0.8288, decode.d5.loss_dice: 1.1448, decode.d6.loss_cls: 0.8395, decode.d6.loss_mask: 0.8308, decode.d6.loss_dice: 1.1323, decode.d7.loss_cls: 0.8319, decode.d7.loss_mask: 0.8371, decode.d7.loss_dice: 1.1402, decode.d8.loss_cls: 0.8268, decode.d8.loss_mask: 0.8347, decode.d8.loss_dice: 1.1398, loss: 32.9102 2022-05-05 00:17:00,807 - mmseg - INFO - Saving checkpoint at 14000 iterations 2022-05-05 00:17:28,357 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 00:17:28,361 - mmseg - INFO - Iter [14000/40000] lr: 9.333e-07, eta: 6:09:03, time: 1.363, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8717, decode.loss_mask: 0.8066, decode.loss_dice: 1.1529, decode.d0.loss_cls: 4.9609, decode.d0.loss_mask: 0.8083, decode.d0.loss_dice: 1.3164, decode.d1.loss_cls: 1.0895, decode.d1.loss_mask: 0.8241, decode.d1.loss_dice: 1.2128, decode.d2.loss_cls: 0.9457, decode.d2.loss_mask: 0.8087, decode.d2.loss_dice: 1.1702, decode.d3.loss_cls: 0.9055, decode.d3.loss_mask: 0.7989, decode.d3.loss_dice: 1.1563, decode.d4.loss_cls: 0.8821, decode.d4.loss_mask: 0.8009, decode.d4.loss_dice: 1.1543, decode.d5.loss_cls: 0.8771, decode.d5.loss_mask: 0.8004, decode.d5.loss_dice: 1.1486, decode.d6.loss_cls: 0.8702, decode.d6.loss_mask: 0.7993, decode.d6.loss_dice: 1.1430, decode.d7.loss_cls: 0.8717, decode.d7.loss_mask: 0.8025, decode.d7.loss_dice: 1.1472, decode.d8.loss_cls: 0.8682, decode.d8.loss_mask: 0.8083, decode.d8.loss_dice: 1.1505, loss: 32.9529 2022-05-05 00:18:10,951 - mmseg - INFO - Iter [14050/40000] lr: 9.315e-07, eta: 6:08:21, time: 0.855, data_time: 0.013, memory: 51557, decode.loss_cls: 0.8716, decode.loss_mask: 0.8238, decode.loss_dice: 1.1395, decode.d0.loss_cls: 4.9818, decode.d0.loss_mask: 0.8331, decode.d0.loss_dice: 1.3057, decode.d1.loss_cls: 1.0647, decode.d1.loss_mask: 0.8543, decode.d1.loss_dice: 1.2173, decode.d2.loss_cls: 0.9354, decode.d2.loss_mask: 0.8339, decode.d2.loss_dice: 1.1680, decode.d3.loss_cls: 0.9039, decode.d3.loss_mask: 0.8253, decode.d3.loss_dice: 1.1468, decode.d4.loss_cls: 0.8843, decode.d4.loss_mask: 0.8230, decode.d4.loss_dice: 1.1463, decode.d5.loss_cls: 0.8696, decode.d5.loss_mask: 0.8214, decode.d5.loss_dice: 1.1422, decode.d6.loss_cls: 0.8612, decode.d6.loss_mask: 0.8230, decode.d6.loss_dice: 1.1352, decode.d7.loss_cls: 0.8654, decode.d7.loss_mask: 0.8244, decode.d7.loss_dice: 1.1398, decode.d8.loss_cls: 0.8577, decode.d8.loss_mask: 0.8218, decode.d8.loss_dice: 1.1433, loss: 33.0636 2022-05-05 00:18:55,232 - mmseg - INFO - Iter [14100/40000] lr: 9.297e-07, eta: 6:07:41, time: 0.885, data_time: 0.063, memory: 51557, decode.loss_cls: 0.8552, decode.loss_mask: 0.8039, decode.loss_dice: 1.1194, decode.d0.loss_cls: 4.9569, decode.d0.loss_mask: 0.8229, decode.d0.loss_dice: 1.2969, decode.d1.loss_cls: 1.0741, decode.d1.loss_mask: 0.8267, decode.d1.loss_dice: 1.1990, decode.d2.loss_cls: 0.9237, decode.d2.loss_mask: 0.8071, decode.d2.loss_dice: 1.1510, decode.d3.loss_cls: 0.8799, decode.d3.loss_mask: 0.8013, decode.d3.loss_dice: 1.1318, decode.d4.loss_cls: 0.8645, decode.d4.loss_mask: 0.8067, decode.d4.loss_dice: 1.1271, decode.d5.loss_cls: 0.8514, decode.d5.loss_mask: 0.8065, decode.d5.loss_dice: 1.1317, decode.d6.loss_cls: 0.8417, decode.d6.loss_mask: 0.8046, decode.d6.loss_dice: 1.1223, decode.d7.loss_cls: 0.8473, decode.d7.loss_mask: 0.8032, decode.d7.loss_dice: 1.1200, decode.d8.loss_cls: 0.8410, decode.d8.loss_mask: 0.8047, decode.d8.loss_dice: 1.1263, loss: 32.5489 2022-05-05 00:19:37,116 - mmseg - INFO - Iter [14150/40000] lr: 9.279e-07, eta: 6:06:57, time: 0.838, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7997, decode.loss_mask: 0.8046, decode.loss_dice: 1.1219, decode.d0.loss_cls: 4.9277, decode.d0.loss_mask: 0.8159, decode.d0.loss_dice: 1.2904, decode.d1.loss_cls: 1.0169, decode.d1.loss_mask: 0.8277, decode.d1.loss_dice: 1.2025, decode.d2.loss_cls: 0.8697, decode.d2.loss_mask: 0.8122, decode.d2.loss_dice: 1.1517, decode.d3.loss_cls: 0.8348, decode.d3.loss_mask: 0.8052, decode.d3.loss_dice: 1.1274, decode.d4.loss_cls: 0.8185, decode.d4.loss_mask: 0.8048, decode.d4.loss_dice: 1.1288, decode.d5.loss_cls: 0.7955, decode.d5.loss_mask: 0.8003, decode.d5.loss_dice: 1.1314, decode.d6.loss_cls: 0.7922, decode.d6.loss_mask: 0.8046, decode.d6.loss_dice: 1.1248, decode.d7.loss_cls: 0.7846, decode.d7.loss_mask: 0.8012, decode.d7.loss_dice: 1.1288, decode.d8.loss_cls: 0.7989, decode.d8.loss_mask: 0.8015, decode.d8.loss_dice: 1.1249, loss: 32.0489 2022-05-05 00:20:19,032 - mmseg - INFO - Iter [14200/40000] lr: 9.261e-07, eta: 6:06:14, time: 0.838, data_time: 0.010, memory: 51557, decode.loss_cls: 0.8119, decode.loss_mask: 0.8226, decode.loss_dice: 1.1042, decode.d0.loss_cls: 4.8668, decode.d0.loss_mask: 0.8401, decode.d0.loss_dice: 1.2725, decode.d1.loss_cls: 0.9951, decode.d1.loss_mask: 0.8523, decode.d1.loss_dice: 1.1683, decode.d2.loss_cls: 0.8759, decode.d2.loss_mask: 0.8290, decode.d2.loss_dice: 1.1215, decode.d3.loss_cls: 0.8425, decode.d3.loss_mask: 0.8306, decode.d3.loss_dice: 1.0998, decode.d4.loss_cls: 0.8297, decode.d4.loss_mask: 0.8284, decode.d4.loss_dice: 1.1105, decode.d5.loss_cls: 0.8116, decode.d5.loss_mask: 0.8283, decode.d5.loss_dice: 1.1049, decode.d6.loss_cls: 0.8080, decode.d6.loss_mask: 0.8217, decode.d6.loss_dice: 1.1011, decode.d7.loss_cls: 0.8092, decode.d7.loss_mask: 0.8237, decode.d7.loss_dice: 1.1062, decode.d8.loss_cls: 0.8066, decode.d8.loss_mask: 0.8273, decode.d8.loss_dice: 1.1025, loss: 32.0527 2022-05-05 00:21:00,223 - mmseg - INFO - Iter [14250/40000] lr: 9.243e-07, eta: 6:05:29, time: 0.824, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8587, decode.loss_mask: 0.8024, decode.loss_dice: 1.1397, decode.d0.loss_cls: 4.8503, decode.d0.loss_mask: 0.8257, decode.d0.loss_dice: 1.3218, decode.d1.loss_cls: 1.0568, decode.d1.loss_mask: 0.8291, decode.d1.loss_dice: 1.2178, decode.d2.loss_cls: 0.9387, decode.d2.loss_mask: 0.8124, decode.d2.loss_dice: 1.1722, decode.d3.loss_cls: 0.8935, decode.d3.loss_mask: 0.8066, decode.d3.loss_dice: 1.1480, decode.d4.loss_cls: 0.8876, decode.d4.loss_mask: 0.8048, decode.d4.loss_dice: 1.1472, decode.d5.loss_cls: 0.8672, decode.d5.loss_mask: 0.8044, decode.d5.loss_dice: 1.1440, decode.d6.loss_cls: 0.8574, decode.d6.loss_mask: 0.8039, decode.d6.loss_dice: 1.1402, decode.d7.loss_cls: 0.8531, decode.d7.loss_mask: 0.8050, decode.d7.loss_dice: 1.1489, decode.d8.loss_cls: 0.8532, decode.d8.loss_mask: 0.8064, decode.d8.loss_dice: 1.1498, loss: 32.7469 2022-05-05 00:21:42,085 - mmseg - INFO - Iter [14300/40000] lr: 9.225e-07, eta: 6:04:45, time: 0.837, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8446, decode.loss_mask: 0.8049, decode.loss_dice: 1.1253, decode.d0.loss_cls: 4.9235, decode.d0.loss_mask: 0.8196, decode.d0.loss_dice: 1.3041, decode.d1.loss_cls: 1.0612, decode.d1.loss_mask: 0.8313, decode.d1.loss_dice: 1.1984, decode.d2.loss_cls: 0.9231, decode.d2.loss_mask: 0.8085, decode.d2.loss_dice: 1.1539, decode.d3.loss_cls: 0.8710, decode.d3.loss_mask: 0.8044, decode.d3.loss_dice: 1.1322, decode.d4.loss_cls: 0.8689, decode.d4.loss_mask: 0.8059, decode.d4.loss_dice: 1.1438, decode.d5.loss_cls: 0.8549, decode.d5.loss_mask: 0.8037, decode.d5.loss_dice: 1.1392, decode.d6.loss_cls: 0.8427, decode.d6.loss_mask: 0.8046, decode.d6.loss_dice: 1.1338, decode.d7.loss_cls: 0.8432, decode.d7.loss_mask: 0.8054, decode.d7.loss_dice: 1.1294, decode.d8.loss_cls: 0.8361, decode.d8.loss_mask: 0.8041, decode.d8.loss_dice: 1.1293, loss: 32.5508 2022-05-05 00:22:22,981 - mmseg - INFO - Iter [14350/40000] lr: 9.207e-07, eta: 6:03:59, time: 0.818, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8114, decode.loss_mask: 0.8056, decode.loss_dice: 1.1242, decode.d0.loss_cls: 4.9018, decode.d0.loss_mask: 0.8218, decode.d0.loss_dice: 1.3094, decode.d1.loss_cls: 1.0107, decode.d1.loss_mask: 0.8348, decode.d1.loss_dice: 1.2056, decode.d2.loss_cls: 0.8758, decode.d2.loss_mask: 0.8228, decode.d2.loss_dice: 1.1551, decode.d3.loss_cls: 0.8338, decode.d3.loss_mask: 0.8176, decode.d3.loss_dice: 1.1433, decode.d4.loss_cls: 0.8321, decode.d4.loss_mask: 0.8115, decode.d4.loss_dice: 1.1404, decode.d5.loss_cls: 0.8219, decode.d5.loss_mask: 0.8092, decode.d5.loss_dice: 1.1363, decode.d6.loss_cls: 0.8092, decode.d6.loss_mask: 0.8063, decode.d6.loss_dice: 1.1280, decode.d7.loss_cls: 0.8087, decode.d7.loss_mask: 0.8059, decode.d7.loss_dice: 1.1331, decode.d8.loss_cls: 0.8019, decode.d8.loss_mask: 0.8074, decode.d8.loss_dice: 1.1318, loss: 32.2577 2022-05-05 00:23:05,650 - mmseg - INFO - Iter [14400/40000] lr: 9.189e-07, eta: 6:03:17, time: 0.853, data_time: 0.010, memory: 51557, decode.loss_cls: 0.8596, decode.loss_mask: 0.8085, decode.loss_dice: 1.1356, decode.d0.loss_cls: 4.8772, decode.d0.loss_mask: 0.8181, decode.d0.loss_dice: 1.2987, decode.d1.loss_cls: 1.0549, decode.d1.loss_mask: 0.8277, decode.d1.loss_dice: 1.2041, decode.d2.loss_cls: 0.9261, decode.d2.loss_mask: 0.8125, decode.d2.loss_dice: 1.1590, decode.d3.loss_cls: 0.8899, decode.d3.loss_mask: 0.8095, decode.d3.loss_dice: 1.1332, decode.d4.loss_cls: 0.8686, decode.d4.loss_mask: 0.8111, decode.d4.loss_dice: 1.1422, decode.d5.loss_cls: 0.8683, decode.d5.loss_mask: 0.8077, decode.d5.loss_dice: 1.1329, decode.d6.loss_cls: 0.8545, decode.d6.loss_mask: 0.8078, decode.d6.loss_dice: 1.1380, decode.d7.loss_cls: 0.8514, decode.d7.loss_mask: 0.8069, decode.d7.loss_dice: 1.1339, decode.d8.loss_cls: 0.8451, decode.d8.loss_mask: 0.8112, decode.d8.loss_dice: 1.1335, loss: 32.6277 2022-05-05 00:23:46,012 - mmseg - INFO - Iter [14450/40000] lr: 9.172e-07, eta: 6:02:30, time: 0.807, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8615, decode.loss_mask: 0.8113, decode.loss_dice: 1.1259, decode.d0.loss_cls: 4.8803, decode.d0.loss_mask: 0.8324, decode.d0.loss_dice: 1.3228, decode.d1.loss_cls: 1.0616, decode.d1.loss_mask: 0.8361, decode.d1.loss_dice: 1.2059, decode.d2.loss_cls: 0.9307, decode.d2.loss_mask: 0.8124, decode.d2.loss_dice: 1.1593, decode.d3.loss_cls: 0.8931, decode.d3.loss_mask: 0.8109, decode.d3.loss_dice: 1.1356, decode.d4.loss_cls: 0.8700, decode.d4.loss_mask: 0.8127, decode.d4.loss_dice: 1.1347, decode.d5.loss_cls: 0.8614, decode.d5.loss_mask: 0.8088, decode.d5.loss_dice: 1.1338, decode.d6.loss_cls: 0.8575, decode.d6.loss_mask: 0.8031, decode.d6.loss_dice: 1.1228, decode.d7.loss_cls: 0.8550, decode.d7.loss_mask: 0.8070, decode.d7.loss_dice: 1.1264, decode.d8.loss_cls: 0.8571, decode.d8.loss_mask: 0.8079, decode.d8.loss_dice: 1.1238, loss: 32.6617 2022-05-05 00:24:26,694 - mmseg - INFO - Iter [14500/40000] lr: 9.154e-07, eta: 6:01:44, time: 0.814, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8693, decode.loss_mask: 0.8012, decode.loss_dice: 1.1390, decode.d0.loss_cls: 4.8875, decode.d0.loss_mask: 0.8166, decode.d0.loss_dice: 1.3164, decode.d1.loss_cls: 1.0555, decode.d1.loss_mask: 0.8241, decode.d1.loss_dice: 1.2126, decode.d2.loss_cls: 0.9396, decode.d2.loss_mask: 0.8119, decode.d2.loss_dice: 1.1651, decode.d3.loss_cls: 0.8960, decode.d3.loss_mask: 0.8033, decode.d3.loss_dice: 1.1480, decode.d4.loss_cls: 0.8793, decode.d4.loss_mask: 0.8042, decode.d4.loss_dice: 1.1467, decode.d5.loss_cls: 0.8754, decode.d5.loss_mask: 0.8025, decode.d5.loss_dice: 1.1428, decode.d6.loss_cls: 0.8608, decode.d6.loss_mask: 0.8025, decode.d6.loss_dice: 1.1326, decode.d7.loss_cls: 0.8639, decode.d7.loss_mask: 0.8027, decode.d7.loss_dice: 1.1361, decode.d8.loss_cls: 0.8604, decode.d8.loss_mask: 0.8016, decode.d8.loss_dice: 1.1441, loss: 32.7418 2022-05-05 00:25:06,366 - mmseg - INFO - Iter [14550/40000] lr: 9.136e-07, eta: 6:00:57, time: 0.793, data_time: 0.010, memory: 51557, decode.loss_cls: 0.8832, decode.loss_mask: 0.8234, decode.loss_dice: 1.1229, decode.d0.loss_cls: 4.8829, decode.d0.loss_mask: 0.8344, decode.d0.loss_dice: 1.3192, decode.d1.loss_cls: 1.0897, decode.d1.loss_mask: 0.8466, decode.d1.loss_dice: 1.1945, decode.d2.loss_cls: 0.9443, decode.d2.loss_mask: 0.8255, decode.d2.loss_dice: 1.1580, decode.d3.loss_cls: 0.8998, decode.d3.loss_mask: 0.8204, decode.d3.loss_dice: 1.1316, decode.d4.loss_cls: 0.8910, decode.d4.loss_mask: 0.8206, decode.d4.loss_dice: 1.1321, decode.d5.loss_cls: 0.8927, decode.d5.loss_mask: 0.8200, decode.d5.loss_dice: 1.1311, decode.d6.loss_cls: 0.8721, decode.d6.loss_mask: 0.8155, decode.d6.loss_dice: 1.1195, decode.d7.loss_cls: 0.8756, decode.d7.loss_mask: 0.8212, decode.d7.loss_dice: 1.1255, decode.d8.loss_cls: 0.8737, decode.d8.loss_mask: 0.8223, decode.d8.loss_dice: 1.1243, loss: 32.9134 2022-05-05 00:25:45,988 - mmseg - INFO - Iter [14600/40000] lr: 9.118e-07, eta: 6:00:09, time: 0.793, data_time: 0.011, memory: 51557, decode.loss_cls: 0.8499, decode.loss_mask: 0.8052, decode.loss_dice: 1.1394, decode.d0.loss_cls: 4.8743, decode.d0.loss_mask: 0.8165, decode.d0.loss_dice: 1.3249, decode.d1.loss_cls: 1.0505, decode.d1.loss_mask: 0.8373, decode.d1.loss_dice: 1.2193, decode.d2.loss_cls: 0.9174, decode.d2.loss_mask: 0.8179, decode.d2.loss_dice: 1.1650, decode.d3.loss_cls: 0.8811, decode.d3.loss_mask: 0.8130, decode.d3.loss_dice: 1.1509, decode.d4.loss_cls: 0.8716, decode.d4.loss_mask: 0.8118, decode.d4.loss_dice: 1.1407, decode.d5.loss_cls: 0.8486, decode.d5.loss_mask: 0.8112, decode.d5.loss_dice: 1.1479, decode.d6.loss_cls: 0.8429, decode.d6.loss_mask: 0.8079, decode.d6.loss_dice: 1.1408, decode.d7.loss_cls: 0.8380, decode.d7.loss_mask: 0.8087, decode.d7.loss_dice: 1.1417, decode.d8.loss_cls: 0.8449, decode.d8.loss_mask: 0.8090, decode.d8.loss_dice: 1.1387, loss: 32.6670 2022-05-05 00:26:28,781 - mmseg - INFO - Iter [14650/40000] lr: 9.100e-07, eta: 5:59:27, time: 0.856, data_time: 0.056, memory: 51557, decode.loss_cls: 0.8180, decode.loss_mask: 0.8190, decode.loss_dice: 1.1310, decode.d0.loss_cls: 4.7835, decode.d0.loss_mask: 0.8370, decode.d0.loss_dice: 1.3220, decode.d1.loss_cls: 1.0319, decode.d1.loss_mask: 0.8398, decode.d1.loss_dice: 1.2088, decode.d2.loss_cls: 0.8908, decode.d2.loss_mask: 0.8176, decode.d2.loss_dice: 1.1704, decode.d3.loss_cls: 0.8506, decode.d3.loss_mask: 0.8138, decode.d3.loss_dice: 1.1500, decode.d4.loss_cls: 0.8310, decode.d4.loss_mask: 0.8089, decode.d4.loss_dice: 1.1435, decode.d5.loss_cls: 0.8237, decode.d5.loss_mask: 0.8162, decode.d5.loss_dice: 1.1394, decode.d6.loss_cls: 0.8219, decode.d6.loss_mask: 0.8128, decode.d6.loss_dice: 1.1308, decode.d7.loss_cls: 0.8072, decode.d7.loss_mask: 0.8155, decode.d7.loss_dice: 1.1434, decode.d8.loss_cls: 0.8204, decode.d8.loss_mask: 0.8164, decode.d8.loss_dice: 1.1415, loss: 32.3570 2022-05-05 00:27:08,002 - mmseg - INFO - Iter [14700/40000] lr: 9.082e-07, eta: 5:58:39, time: 0.784, data_time: 0.010, memory: 51557, decode.loss_cls: 0.8412, decode.loss_mask: 0.8200, decode.loss_dice: 1.1283, decode.d0.loss_cls: 4.7939, decode.d0.loss_mask: 0.8365, decode.d0.loss_dice: 1.3089, decode.d1.loss_cls: 1.0487, decode.d1.loss_mask: 0.8483, decode.d1.loss_dice: 1.2107, decode.d2.loss_cls: 0.9095, decode.d2.loss_mask: 0.8319, decode.d2.loss_dice: 1.1624, decode.d3.loss_cls: 0.8751, decode.d3.loss_mask: 0.8276, decode.d3.loss_dice: 1.1395, decode.d4.loss_cls: 0.8622, decode.d4.loss_mask: 0.8215, decode.d4.loss_dice: 1.1370, decode.d5.loss_cls: 0.8496, decode.d5.loss_mask: 0.8209, decode.d5.loss_dice: 1.1341, decode.d6.loss_cls: 0.8462, decode.d6.loss_mask: 0.8200, decode.d6.loss_dice: 1.1273, decode.d7.loss_cls: 0.8380, decode.d7.loss_mask: 0.8228, decode.d7.loss_dice: 1.1342, decode.d8.loss_cls: 0.8363, decode.d8.loss_mask: 0.8227, decode.d8.loss_dice: 1.1283, loss: 32.5838 2022-05-05 00:27:48,150 - mmseg - INFO - Iter [14750/40000] lr: 9.064e-07, eta: 5:57:52, time: 0.803, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8292, decode.loss_mask: 0.7930, decode.loss_dice: 1.0853, decode.d0.loss_cls: 4.7803, decode.d0.loss_mask: 0.8031, decode.d0.loss_dice: 1.2708, decode.d1.loss_cls: 1.0437, decode.d1.loss_mask: 0.8040, decode.d1.loss_dice: 1.1655, decode.d2.loss_cls: 0.9018, decode.d2.loss_mask: 0.7943, decode.d2.loss_dice: 1.1245, decode.d3.loss_cls: 0.8583, decode.d3.loss_mask: 0.7860, decode.d3.loss_dice: 1.0976, decode.d4.loss_cls: 0.8452, decode.d4.loss_mask: 0.7899, decode.d4.loss_dice: 1.1082, decode.d5.loss_cls: 0.8367, decode.d5.loss_mask: 0.7878, decode.d5.loss_dice: 1.0961, decode.d6.loss_cls: 0.8243, decode.d6.loss_mask: 0.7889, decode.d6.loss_dice: 1.0910, decode.d7.loss_cls: 0.8195, decode.d7.loss_mask: 0.7919, decode.d7.loss_dice: 1.0975, decode.d8.loss_cls: 0.8205, decode.d8.loss_mask: 0.7926, decode.d8.loss_dice: 1.0934, loss: 31.7209 2022-05-05 00:28:28,183 - mmseg - INFO - Iter [14800/40000] lr: 9.046e-07, eta: 5:57:05, time: 0.801, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8530, decode.loss_mask: 0.8006, decode.loss_dice: 1.1302, decode.d0.loss_cls: 4.7954, decode.d0.loss_mask: 0.8201, decode.d0.loss_dice: 1.3162, decode.d1.loss_cls: 1.0647, decode.d1.loss_mask: 0.8307, decode.d1.loss_dice: 1.2094, decode.d2.loss_cls: 0.9326, decode.d2.loss_mask: 0.8047, decode.d2.loss_dice: 1.1550, decode.d3.loss_cls: 0.8985, decode.d3.loss_mask: 0.8019, decode.d3.loss_dice: 1.1399, decode.d4.loss_cls: 0.8721, decode.d4.loss_mask: 0.8076, decode.d4.loss_dice: 1.1427, decode.d5.loss_cls: 0.8670, decode.d5.loss_mask: 0.8042, decode.d5.loss_dice: 1.1342, decode.d6.loss_cls: 0.8524, decode.d6.loss_mask: 0.8011, decode.d6.loss_dice: 1.1249, decode.d7.loss_cls: 0.8570, decode.d7.loss_mask: 0.8034, decode.d7.loss_dice: 1.1316, decode.d8.loss_cls: 0.8488, decode.d8.loss_mask: 0.8060, decode.d8.loss_dice: 1.1310, loss: 32.5370 2022-05-05 00:29:07,638 - mmseg - INFO - Iter [14850/40000] lr: 9.028e-07, eta: 5:56:18, time: 0.789, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8160, decode.loss_mask: 0.8002, decode.loss_dice: 1.1051, decode.d0.loss_cls: 4.6770, decode.d0.loss_mask: 0.8219, decode.d0.loss_dice: 1.2868, decode.d1.loss_cls: 1.0344, decode.d1.loss_mask: 0.8312, decode.d1.loss_dice: 1.1894, decode.d2.loss_cls: 0.9022, decode.d2.loss_mask: 0.8130, decode.d2.loss_dice: 1.1369, decode.d3.loss_cls: 0.8551, decode.d3.loss_mask: 0.8058, decode.d3.loss_dice: 1.1154, decode.d4.loss_cls: 0.8463, decode.d4.loss_mask: 0.8023, decode.d4.loss_dice: 1.1174, decode.d5.loss_cls: 0.8284, decode.d5.loss_mask: 0.8014, decode.d5.loss_dice: 1.1175, decode.d6.loss_cls: 0.8213, decode.d6.loss_mask: 0.7999, decode.d6.loss_dice: 1.1013, decode.d7.loss_cls: 0.8182, decode.d7.loss_mask: 0.8015, decode.d7.loss_dice: 1.1075, decode.d8.loss_cls: 0.8176, decode.d8.loss_mask: 0.8010, decode.d8.loss_dice: 1.1089, loss: 31.8808 2022-05-05 00:29:47,593 - mmseg - INFO - Iter [14900/40000] lr: 9.010e-07, eta: 5:55:31, time: 0.799, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8203, decode.loss_mask: 0.8229, decode.loss_dice: 1.1078, decode.d0.loss_cls: 4.7650, decode.d0.loss_mask: 0.8418, decode.d0.loss_dice: 1.2894, decode.d1.loss_cls: 1.0377, decode.d1.loss_mask: 0.8370, decode.d1.loss_dice: 1.1703, decode.d2.loss_cls: 0.9013, decode.d2.loss_mask: 0.8262, decode.d2.loss_dice: 1.1256, decode.d3.loss_cls: 0.8574, decode.d3.loss_mask: 0.8191, decode.d3.loss_dice: 1.1118, decode.d4.loss_cls: 0.8330, decode.d4.loss_mask: 0.8228, decode.d4.loss_dice: 1.1157, decode.d5.loss_cls: 0.8397, decode.d5.loss_mask: 0.8202, decode.d5.loss_dice: 1.1120, decode.d6.loss_cls: 0.8301, decode.d6.loss_mask: 0.8219, decode.d6.loss_dice: 1.1044, decode.d7.loss_cls: 0.8254, decode.d7.loss_mask: 0.8200, decode.d7.loss_dice: 1.1039, decode.d8.loss_cls: 0.8301, decode.d8.loss_mask: 0.8180, decode.d8.loss_dice: 1.1009, loss: 32.1313 2022-05-05 00:30:27,804 - mmseg - INFO - Iter [14950/40000] lr: 8.992e-07, eta: 5:54:44, time: 0.803, data_time: 0.011, memory: 51557, decode.loss_cls: 0.7859, decode.loss_mask: 0.7837, decode.loss_dice: 1.1037, decode.d0.loss_cls: 4.7323, decode.d0.loss_mask: 0.8087, decode.d0.loss_dice: 1.2951, decode.d1.loss_cls: 0.9959, decode.d1.loss_mask: 0.8077, decode.d1.loss_dice: 1.1886, decode.d2.loss_cls: 0.8770, decode.d2.loss_mask: 0.7940, decode.d2.loss_dice: 1.1276, decode.d3.loss_cls: 0.8256, decode.d3.loss_mask: 0.7945, decode.d3.loss_dice: 1.1094, decode.d4.loss_cls: 0.8160, decode.d4.loss_mask: 0.7904, decode.d4.loss_dice: 1.1085, decode.d5.loss_cls: 0.8039, decode.d5.loss_mask: 0.7886, decode.d5.loss_dice: 1.1057, decode.d6.loss_cls: 0.7877, decode.d6.loss_mask: 0.7920, decode.d6.loss_dice: 1.1009, decode.d7.loss_cls: 0.7856, decode.d7.loss_mask: 0.7874, decode.d7.loss_dice: 1.1016, decode.d8.loss_cls: 0.7867, decode.d8.loss_mask: 0.7870, decode.d8.loss_dice: 1.1009, loss: 31.4728 2022-05-05 00:31:07,726 - mmseg - INFO - Saving checkpoint at 15000 iterations 2022-05-05 00:31:34,762 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 00:31:34,774 - mmseg - INFO - Iter [15000/40000] lr: 8.974e-07, eta: 5:54:43, time: 1.337, data_time: 0.010, memory: 51557, decode.loss_cls: 0.8003, decode.loss_mask: 0.8003, decode.loss_dice: 1.1196, decode.d0.loss_cls: 4.6848, decode.d0.loss_mask: 0.8194, decode.d0.loss_dice: 1.2999, decode.d1.loss_cls: 1.0380, decode.d1.loss_mask: 0.8224, decode.d1.loss_dice: 1.1944, decode.d2.loss_cls: 0.8883, decode.d2.loss_mask: 0.8063, decode.d2.loss_dice: 1.1452, decode.d3.loss_cls: 0.8380, decode.d3.loss_mask: 0.8006, decode.d3.loss_dice: 1.1346, decode.d4.loss_cls: 0.8239, decode.d4.loss_mask: 0.7976, decode.d4.loss_dice: 1.1312, decode.d5.loss_cls: 0.8047, decode.d5.loss_mask: 0.8025, decode.d5.loss_dice: 1.1286, decode.d6.loss_cls: 0.8096, decode.d6.loss_mask: 0.8009, decode.d6.loss_dice: 1.1187, decode.d7.loss_cls: 0.8035, decode.d7.loss_mask: 0.8005, decode.d7.loss_dice: 1.1218, decode.d8.loss_cls: 0.7977, decode.d8.loss_mask: 0.8012, decode.d8.loss_dice: 1.1217, loss: 31.8559 2022-05-05 00:32:15,341 - mmseg - INFO - Iter [15050/40000] lr: 8.956e-07, eta: 5:53:57, time: 0.815, data_time: 0.013, memory: 51557, decode.loss_cls: 0.8396, decode.loss_mask: 0.7803, decode.loss_dice: 1.0942, decode.d0.loss_cls: 4.6934, decode.d0.loss_mask: 0.7972, decode.d0.loss_dice: 1.2729, decode.d1.loss_cls: 1.0454, decode.d1.loss_mask: 0.8039, decode.d1.loss_dice: 1.1520, decode.d2.loss_cls: 0.9150, decode.d2.loss_mask: 0.7864, decode.d2.loss_dice: 1.1137, decode.d3.loss_cls: 0.8824, decode.d3.loss_mask: 0.7818, decode.d3.loss_dice: 1.1029, decode.d4.loss_cls: 0.8653, decode.d4.loss_mask: 0.7853, decode.d4.loss_dice: 1.1000, decode.d5.loss_cls: 0.8490, decode.d5.loss_mask: 0.7810, decode.d5.loss_dice: 1.0950, decode.d6.loss_cls: 0.8412, decode.d6.loss_mask: 0.7763, decode.d6.loss_dice: 1.0918, decode.d7.loss_cls: 0.8370, decode.d7.loss_mask: 0.7801, decode.d7.loss_dice: 1.0900, decode.d8.loss_cls: 0.8388, decode.d8.loss_mask: 0.7805, decode.d8.loss_dice: 1.0880, loss: 31.6603 2022-05-05 00:32:55,284 - mmseg - INFO - Iter [15100/40000] lr: 8.938e-07, eta: 5:53:10, time: 0.799, data_time: 0.010, memory: 51557, decode.loss_cls: 0.8351, decode.loss_mask: 0.7947, decode.loss_dice: 1.1271, decode.d0.loss_cls: 4.7094, decode.d0.loss_mask: 0.8203, decode.d0.loss_dice: 1.3241, decode.d1.loss_cls: 1.0625, decode.d1.loss_mask: 0.8195, decode.d1.loss_dice: 1.2159, decode.d2.loss_cls: 0.9273, decode.d2.loss_mask: 0.8027, decode.d2.loss_dice: 1.1543, decode.d3.loss_cls: 0.8771, decode.d3.loss_mask: 0.7989, decode.d3.loss_dice: 1.1333, decode.d4.loss_cls: 0.8601, decode.d4.loss_mask: 0.7966, decode.d4.loss_dice: 1.1357, decode.d5.loss_cls: 0.8492, decode.d5.loss_mask: 0.7951, decode.d5.loss_dice: 1.1290, decode.d6.loss_cls: 0.8338, decode.d6.loss_mask: 0.7937, decode.d6.loss_dice: 1.1195, decode.d7.loss_cls: 0.8290, decode.d7.loss_mask: 0.7956, decode.d7.loss_dice: 1.1227, decode.d8.loss_cls: 0.8281, decode.d8.loss_mask: 0.7940, decode.d8.loss_dice: 1.1209, loss: 32.2052 2022-05-05 00:33:35,152 - mmseg - INFO - Iter [15150/40000] lr: 8.920e-07, eta: 5:52:23, time: 0.797, data_time: 0.011, memory: 51557, decode.loss_cls: 0.8639, decode.loss_mask: 0.7939, decode.loss_dice: 1.1303, decode.d0.loss_cls: 4.7313, decode.d0.loss_mask: 0.8138, decode.d0.loss_dice: 1.3172, decode.d1.loss_cls: 1.0619, decode.d1.loss_mask: 0.8219, decode.d1.loss_dice: 1.2172, decode.d2.loss_cls: 0.9438, decode.d2.loss_mask: 0.8042, decode.d2.loss_dice: 1.1700, decode.d3.loss_cls: 0.9020, decode.d3.loss_mask: 0.8019, decode.d3.loss_dice: 1.1496, decode.d4.loss_cls: 0.8856, decode.d4.loss_mask: 0.7931, decode.d4.loss_dice: 1.1504, decode.d5.loss_cls: 0.8738, decode.d5.loss_mask: 0.7956, decode.d5.loss_dice: 1.1416, decode.d6.loss_cls: 0.8627, decode.d6.loss_mask: 0.7933, decode.d6.loss_dice: 1.1360, decode.d7.loss_cls: 0.8621, decode.d7.loss_mask: 0.7936, decode.d7.loss_dice: 1.1317, decode.d8.loss_cls: 0.8611, decode.d8.loss_mask: 0.7958, decode.d8.loss_dice: 1.1380, loss: 32.5373 2022-05-05 00:34:18,254 - mmseg - INFO - Iter [15200/40000] lr: 8.902e-07, eta: 5:51:42, time: 0.862, data_time: 0.059, memory: 51557, decode.loss_cls: 0.7884, decode.loss_mask: 0.7890, decode.loss_dice: 1.1393, decode.d0.loss_cls: 4.7007, decode.d0.loss_mask: 0.8114, decode.d0.loss_dice: 1.3230, decode.d1.loss_cls: 0.9952, decode.d1.loss_mask: 0.8201, decode.d1.loss_dice: 1.2157, decode.d2.loss_cls: 0.8671, decode.d2.loss_mask: 0.7959, decode.d2.loss_dice: 1.1693, decode.d3.loss_cls: 0.8247, decode.d3.loss_mask: 0.7909, decode.d3.loss_dice: 1.1466, decode.d4.loss_cls: 0.8139, decode.d4.loss_mask: 0.7896, decode.d4.loss_dice: 1.1463, decode.d5.loss_cls: 0.8027, decode.d5.loss_mask: 0.7839, decode.d5.loss_dice: 1.1406, decode.d6.loss_cls: 0.7804, decode.d6.loss_mask: 0.7880, decode.d6.loss_dice: 1.1382, decode.d7.loss_cls: 0.7782, decode.d7.loss_mask: 0.7864, decode.d7.loss_dice: 1.1434, decode.d8.loss_cls: 0.7707, decode.d8.loss_mask: 0.7887, decode.d8.loss_dice: 1.1426, loss: 31.7709 2022-05-05 00:34:58,576 - mmseg - INFO - Iter [15250/40000] lr: 8.884e-07, eta: 5:50:55, time: 0.806, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7865, decode.loss_mask: 0.7987, decode.loss_dice: 1.0934, decode.d0.loss_cls: 4.6616, decode.d0.loss_mask: 0.8210, decode.d0.loss_dice: 1.2872, decode.d1.loss_cls: 1.0003, decode.d1.loss_mask: 0.8313, decode.d1.loss_dice: 1.1769, decode.d2.loss_cls: 0.8594, decode.d2.loss_mask: 0.8148, decode.d2.loss_dice: 1.1297, decode.d3.loss_cls: 0.8255, decode.d3.loss_mask: 0.8055, decode.d3.loss_dice: 1.1097, decode.d4.loss_cls: 0.8053, decode.d4.loss_mask: 0.8016, decode.d4.loss_dice: 1.1155, decode.d5.loss_cls: 0.8012, decode.d5.loss_mask: 0.8034, decode.d5.loss_dice: 1.1013, decode.d6.loss_cls: 0.7883, decode.d6.loss_mask: 0.7992, decode.d6.loss_dice: 1.1003, decode.d7.loss_cls: 0.7900, decode.d7.loss_mask: 0.7971, decode.d7.loss_dice: 1.0942, decode.d8.loss_cls: 0.7832, decode.d8.loss_mask: 0.7988, decode.d8.loss_dice: 1.0932, loss: 31.4741 2022-05-05 00:35:39,182 - mmseg - INFO - Iter [15300/40000] lr: 8.866e-07, eta: 5:50:10, time: 0.812, data_time: 0.010, memory: 51557, decode.loss_cls: 0.8358, decode.loss_mask: 0.8140, decode.loss_dice: 1.1230, decode.d0.loss_cls: 4.6465, decode.d0.loss_mask: 0.8286, decode.d0.loss_dice: 1.3080, decode.d1.loss_cls: 1.0376, decode.d1.loss_mask: 0.8377, decode.d1.loss_dice: 1.2095, decode.d2.loss_cls: 0.9102, decode.d2.loss_mask: 0.8196, decode.d2.loss_dice: 1.1591, decode.d3.loss_cls: 0.8777, decode.d3.loss_mask: 0.8123, decode.d3.loss_dice: 1.1317, decode.d4.loss_cls: 0.8618, decode.d4.loss_mask: 0.8036, decode.d4.loss_dice: 1.1315, decode.d5.loss_cls: 0.8440, decode.d5.loss_mask: 0.8088, decode.d5.loss_dice: 1.1252, decode.d6.loss_cls: 0.8292, decode.d6.loss_mask: 0.8085, decode.d6.loss_dice: 1.1217, decode.d7.loss_cls: 0.8239, decode.d7.loss_mask: 0.8130, decode.d7.loss_dice: 1.1273, decode.d8.loss_cls: 0.8414, decode.d8.loss_mask: 0.8079, decode.d8.loss_dice: 1.1284, loss: 32.2275 2022-05-05 00:36:19,104 - mmseg - INFO - Iter [15350/40000] lr: 8.848e-07, eta: 5:49:23, time: 0.798, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7673, decode.loss_mask: 0.7952, decode.loss_dice: 1.1116, decode.d0.loss_cls: 4.6157, decode.d0.loss_mask: 0.8095, decode.d0.loss_dice: 1.2671, decode.d1.loss_cls: 0.9769, decode.d1.loss_mask: 0.8145, decode.d1.loss_dice: 1.1684, decode.d2.loss_cls: 0.8468, decode.d2.loss_mask: 0.8027, decode.d2.loss_dice: 1.1252, decode.d3.loss_cls: 0.7991, decode.d3.loss_mask: 0.7971, decode.d3.loss_dice: 1.1123, decode.d4.loss_cls: 0.7934, decode.d4.loss_mask: 0.7985, decode.d4.loss_dice: 1.1139, decode.d5.loss_cls: 0.7745, decode.d5.loss_mask: 0.7957, decode.d5.loss_dice: 1.1145, decode.d6.loss_cls: 0.7764, decode.d6.loss_mask: 0.7952, decode.d6.loss_dice: 1.1042, decode.d7.loss_cls: 0.7625, decode.d7.loss_mask: 0.7953, decode.d7.loss_dice: 1.1107, decode.d8.loss_cls: 0.7565, decode.d8.loss_mask: 0.7992, decode.d8.loss_dice: 1.1167, loss: 31.2165 2022-05-05 00:36:59,363 - mmseg - INFO - Iter [15400/40000] lr: 8.831e-07, eta: 5:48:37, time: 0.805, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8058, decode.loss_mask: 0.8093, decode.loss_dice: 1.1140, decode.d0.loss_cls: 4.6134, decode.d0.loss_mask: 0.8167, decode.d0.loss_dice: 1.2954, decode.d1.loss_cls: 1.0216, decode.d1.loss_mask: 0.8281, decode.d1.loss_dice: 1.1959, decode.d2.loss_cls: 0.8960, decode.d2.loss_mask: 0.8129, decode.d2.loss_dice: 1.1528, decode.d3.loss_cls: 0.8490, decode.d3.loss_mask: 0.8070, decode.d3.loss_dice: 1.1277, decode.d4.loss_cls: 0.8311, decode.d4.loss_mask: 0.8084, decode.d4.loss_dice: 1.1271, decode.d5.loss_cls: 0.8209, decode.d5.loss_mask: 0.8023, decode.d5.loss_dice: 1.1233, decode.d6.loss_cls: 0.8064, decode.d6.loss_mask: 0.8012, decode.d6.loss_dice: 1.1154, decode.d7.loss_cls: 0.7999, decode.d7.loss_mask: 0.8050, decode.d7.loss_dice: 1.1193, decode.d8.loss_cls: 0.7940, decode.d8.loss_mask: 0.8052, decode.d8.loss_dice: 1.1236, loss: 31.8284 2022-05-05 00:37:39,424 - mmseg - INFO - Iter [15450/40000] lr: 8.813e-07, eta: 5:47:50, time: 0.801, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8216, decode.loss_mask: 0.7870, decode.loss_dice: 1.1047, decode.d0.loss_cls: 4.6160, decode.d0.loss_mask: 0.8118, decode.d0.loss_dice: 1.2968, decode.d1.loss_cls: 1.0343, decode.d1.loss_mask: 0.8121, decode.d1.loss_dice: 1.1796, decode.d2.loss_cls: 0.8979, decode.d2.loss_mask: 0.7954, decode.d2.loss_dice: 1.1315, decode.d3.loss_cls: 0.8601, decode.d3.loss_mask: 0.7864, decode.d3.loss_dice: 1.1158, decode.d4.loss_cls: 0.8406, decode.d4.loss_mask: 0.7829, decode.d4.loss_dice: 1.1122, decode.d5.loss_cls: 0.8323, decode.d5.loss_mask: 0.7892, decode.d5.loss_dice: 1.1111, decode.d6.loss_cls: 0.8289, decode.d6.loss_mask: 0.7833, decode.d6.loss_dice: 1.1047, decode.d7.loss_cls: 0.8255, decode.d7.loss_mask: 0.7856, decode.d7.loss_dice: 1.1041, decode.d8.loss_cls: 0.8164, decode.d8.loss_mask: 0.7852, decode.d8.loss_dice: 1.1086, loss: 31.6617 2022-05-05 00:38:19,442 - mmseg - INFO - Iter [15500/40000] lr: 8.795e-07, eta: 5:47:04, time: 0.800, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8086, decode.loss_mask: 0.8175, decode.loss_dice: 1.1586, decode.d0.loss_cls: 4.5890, decode.d0.loss_mask: 0.8330, decode.d0.loss_dice: 1.3488, decode.d1.loss_cls: 0.9983, decode.d1.loss_mask: 0.8485, decode.d1.loss_dice: 1.2346, decode.d2.loss_cls: 0.8855, decode.d2.loss_mask: 0.8279, decode.d2.loss_dice: 1.1793, decode.d3.loss_cls: 0.8446, decode.d3.loss_mask: 0.8207, decode.d3.loss_dice: 1.1617, decode.d4.loss_cls: 0.8306, decode.d4.loss_mask: 0.8173, decode.d4.loss_dice: 1.1637, decode.d5.loss_cls: 0.8097, decode.d5.loss_mask: 0.8216, decode.d5.loss_dice: 1.1632, decode.d6.loss_cls: 0.8168, decode.d6.loss_mask: 0.8158, decode.d6.loss_dice: 1.1497, decode.d7.loss_cls: 0.8036, decode.d7.loss_mask: 0.8211, decode.d7.loss_dice: 1.1581, decode.d8.loss_cls: 0.8044, decode.d8.loss_mask: 0.8173, decode.d8.loss_dice: 1.1527, loss: 32.3022 2022-05-05 00:38:59,144 - mmseg - INFO - Iter [15550/40000] lr: 8.777e-07, eta: 5:46:17, time: 0.794, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8065, decode.loss_mask: 0.8099, decode.loss_dice: 1.1238, decode.d0.loss_cls: 4.5431, decode.d0.loss_mask: 0.8269, decode.d0.loss_dice: 1.2997, decode.d1.loss_cls: 1.0147, decode.d1.loss_mask: 0.8391, decode.d1.loss_dice: 1.2005, decode.d2.loss_cls: 0.8758, decode.d2.loss_mask: 0.8184, decode.d2.loss_dice: 1.1580, decode.d3.loss_cls: 0.8420, decode.d3.loss_mask: 0.8121, decode.d3.loss_dice: 1.1365, decode.d4.loss_cls: 0.8241, decode.d4.loss_mask: 0.8129, decode.d4.loss_dice: 1.1416, decode.d5.loss_cls: 0.8130, decode.d5.loss_mask: 0.8142, decode.d5.loss_dice: 1.1363, decode.d6.loss_cls: 0.8050, decode.d6.loss_mask: 0.8154, decode.d6.loss_dice: 1.1273, decode.d7.loss_cls: 0.8031, decode.d7.loss_mask: 0.8118, decode.d7.loss_dice: 1.1252, decode.d8.loss_cls: 0.8037, decode.d8.loss_mask: 0.8147, decode.d8.loss_dice: 1.1321, loss: 31.8875 2022-05-05 00:39:38,299 - mmseg - INFO - Iter [15600/40000] lr: 8.759e-07, eta: 5:45:29, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7834, decode.loss_mask: 0.8054, decode.loss_dice: 1.1148, decode.d0.loss_cls: 4.4811, decode.d0.loss_mask: 0.8313, decode.d0.loss_dice: 1.3077, decode.d1.loss_cls: 0.9904, decode.d1.loss_mask: 0.8377, decode.d1.loss_dice: 1.1935, decode.d2.loss_cls: 0.8729, decode.d2.loss_mask: 0.8173, decode.d2.loss_dice: 1.1422, decode.d3.loss_cls: 0.8216, decode.d3.loss_mask: 0.8198, decode.d3.loss_dice: 1.1315, decode.d4.loss_cls: 0.8097, decode.d4.loss_mask: 0.8161, decode.d4.loss_dice: 1.1329, decode.d5.loss_cls: 0.7958, decode.d5.loss_mask: 0.8121, decode.d5.loss_dice: 1.1247, decode.d6.loss_cls: 0.7816, decode.d6.loss_mask: 0.8110, decode.d6.loss_dice: 1.1188, decode.d7.loss_cls: 0.7790, decode.d7.loss_mask: 0.8091, decode.d7.loss_dice: 1.1141, decode.d8.loss_cls: 0.7790, decode.d8.loss_mask: 0.8117, decode.d8.loss_dice: 1.1194, loss: 31.5655 2022-05-05 00:40:17,824 - mmseg - INFO - Iter [15650/40000] lr: 8.741e-07, eta: 5:44:42, time: 0.790, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7828, decode.loss_mask: 0.7675, decode.loss_dice: 1.1148, decode.d0.loss_cls: 4.5167, decode.d0.loss_mask: 0.7885, decode.d0.loss_dice: 1.3070, decode.d1.loss_cls: 0.9968, decode.d1.loss_mask: 0.7906, decode.d1.loss_dice: 1.1975, decode.d2.loss_cls: 0.8652, decode.d2.loss_mask: 0.7762, decode.d2.loss_dice: 1.1520, decode.d3.loss_cls: 0.8143, decode.d3.loss_mask: 0.7730, decode.d3.loss_dice: 1.1314, decode.d4.loss_cls: 0.8048, decode.d4.loss_mask: 0.7749, decode.d4.loss_dice: 1.1312, decode.d5.loss_cls: 0.7932, decode.d5.loss_mask: 0.7698, decode.d5.loss_dice: 1.1272, decode.d6.loss_cls: 0.7841, decode.d6.loss_mask: 0.7703, decode.d6.loss_dice: 1.1166, decode.d7.loss_cls: 0.7798, decode.d7.loss_mask: 0.7680, decode.d7.loss_dice: 1.1162, decode.d8.loss_cls: 0.7863, decode.d8.loss_mask: 0.7647, decode.d8.loss_dice: 1.1121, loss: 31.1733 2022-05-05 00:40:57,412 - mmseg - INFO - Iter [15700/40000] lr: 8.723e-07, eta: 5:43:55, time: 0.792, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8520, decode.loss_mask: 0.8098, decode.loss_dice: 1.1237, decode.d0.loss_cls: 4.5491, decode.d0.loss_mask: 0.8383, decode.d0.loss_dice: 1.3106, decode.d1.loss_cls: 1.0494, decode.d1.loss_mask: 0.8446, decode.d1.loss_dice: 1.2029, decode.d2.loss_cls: 0.9269, decode.d2.loss_mask: 0.8231, decode.d2.loss_dice: 1.1541, decode.d3.loss_cls: 0.8981, decode.d3.loss_mask: 0.8141, decode.d3.loss_dice: 1.1291, decode.d4.loss_cls: 0.8790, decode.d4.loss_mask: 0.8160, decode.d4.loss_dice: 1.1307, decode.d5.loss_cls: 0.8620, decode.d5.loss_mask: 0.8137, decode.d5.loss_dice: 1.1267, decode.d6.loss_cls: 0.8564, decode.d6.loss_mask: 0.8128, decode.d6.loss_dice: 1.1240, decode.d7.loss_cls: 0.8563, decode.d7.loss_mask: 0.8101, decode.d7.loss_dice: 1.1247, decode.d8.loss_cls: 0.8515, decode.d8.loss_mask: 0.8063, decode.d8.loss_dice: 1.1195, loss: 32.3154 2022-05-05 00:41:40,177 - mmseg - INFO - Iter [15750/40000] lr: 8.705e-07, eta: 5:43:13, time: 0.855, data_time: 0.059, memory: 51557, decode.loss_cls: 0.7983, decode.loss_mask: 0.7954, decode.loss_dice: 1.0983, decode.d0.loss_cls: 4.5171, decode.d0.loss_mask: 0.8173, decode.d0.loss_dice: 1.2934, decode.d1.loss_cls: 1.0116, decode.d1.loss_mask: 0.8215, decode.d1.loss_dice: 1.1722, decode.d2.loss_cls: 0.8913, decode.d2.loss_mask: 0.8004, decode.d2.loss_dice: 1.1227, decode.d3.loss_cls: 0.8279, decode.d3.loss_mask: 0.7955, decode.d3.loss_dice: 1.1077, decode.d4.loss_cls: 0.8162, decode.d4.loss_mask: 0.7981, decode.d4.loss_dice: 1.1031, decode.d5.loss_cls: 0.8029, decode.d5.loss_mask: 0.7935, decode.d5.loss_dice: 1.1014, decode.d6.loss_cls: 0.7940, decode.d6.loss_mask: 0.7917, decode.d6.loss_dice: 1.0887, decode.d7.loss_cls: 0.7996, decode.d7.loss_mask: 0.7958, decode.d7.loss_dice: 1.0931, decode.d8.loss_cls: 0.7946, decode.d8.loss_mask: 0.7912, decode.d8.loss_dice: 1.0931, loss: 31.3272 2022-05-05 00:42:20,110 - mmseg - INFO - Iter [15800/40000] lr: 8.687e-07, eta: 5:42:27, time: 0.799, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7959, decode.loss_mask: 0.7889, decode.loss_dice: 1.0931, decode.d0.loss_cls: 4.5383, decode.d0.loss_mask: 0.8097, decode.d0.loss_dice: 1.2734, decode.d1.loss_cls: 1.0056, decode.d1.loss_mask: 0.8072, decode.d1.loss_dice: 1.1643, decode.d2.loss_cls: 0.8819, decode.d2.loss_mask: 0.7963, decode.d2.loss_dice: 1.1142, decode.d3.loss_cls: 0.8445, decode.d3.loss_mask: 0.7937, decode.d3.loss_dice: 1.0933, decode.d4.loss_cls: 0.8208, decode.d4.loss_mask: 0.7898, decode.d4.loss_dice: 1.0906, decode.d5.loss_cls: 0.8025, decode.d5.loss_mask: 0.7878, decode.d5.loss_dice: 1.0943, decode.d6.loss_cls: 0.7998, decode.d6.loss_mask: 0.7882, decode.d6.loss_dice: 1.0899, decode.d7.loss_cls: 0.7908, decode.d7.loss_mask: 0.7878, decode.d7.loss_dice: 1.0867, decode.d8.loss_cls: 0.7924, decode.d8.loss_mask: 0.7868, decode.d8.loss_dice: 1.0934, loss: 31.2019 2022-05-05 00:43:00,412 - mmseg - INFO - Iter [15850/40000] lr: 8.669e-07, eta: 5:41:41, time: 0.806, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7721, decode.loss_mask: 0.8096, decode.loss_dice: 1.0916, decode.d0.loss_cls: 4.4282, decode.d0.loss_mask: 0.8259, decode.d0.loss_dice: 1.2684, decode.d1.loss_cls: 0.9900, decode.d1.loss_mask: 0.8325, decode.d1.loss_dice: 1.1686, decode.d2.loss_cls: 0.8700, decode.d2.loss_mask: 0.8190, decode.d2.loss_dice: 1.1236, decode.d3.loss_cls: 0.8167, decode.d3.loss_mask: 0.8144, decode.d3.loss_dice: 1.1032, decode.d4.loss_cls: 0.8003, decode.d4.loss_mask: 0.8073, decode.d4.loss_dice: 1.1042, decode.d5.loss_cls: 0.7829, decode.d5.loss_mask: 0.8074, decode.d5.loss_dice: 1.1058, decode.d6.loss_cls: 0.7697, decode.d6.loss_mask: 0.8114, decode.d6.loss_dice: 1.0995, decode.d7.loss_cls: 0.7814, decode.d7.loss_mask: 0.8057, decode.d7.loss_dice: 1.0968, decode.d8.loss_cls: 0.7811, decode.d8.loss_mask: 0.8066, decode.d8.loss_dice: 1.0985, loss: 31.1924 2022-05-05 00:43:40,822 - mmseg - INFO - Iter [15900/40000] lr: 8.651e-07, eta: 5:40:56, time: 0.808, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7921, decode.loss_mask: 0.7941, decode.loss_dice: 1.1047, decode.d0.loss_cls: 4.4817, decode.d0.loss_mask: 0.8141, decode.d0.loss_dice: 1.2774, decode.d1.loss_cls: 1.0055, decode.d1.loss_mask: 0.8183, decode.d1.loss_dice: 1.1852, decode.d2.loss_cls: 0.8764, decode.d2.loss_mask: 0.7976, decode.d2.loss_dice: 1.1292, decode.d3.loss_cls: 0.8352, decode.d3.loss_mask: 0.7934, decode.d3.loss_dice: 1.1145, decode.d4.loss_cls: 0.8096, decode.d4.loss_mask: 0.7949, decode.d4.loss_dice: 1.1196, decode.d5.loss_cls: 0.8048, decode.d5.loss_mask: 0.7928, decode.d5.loss_dice: 1.1151, decode.d6.loss_cls: 0.7949, decode.d6.loss_mask: 0.7966, decode.d6.loss_dice: 1.1028, decode.d7.loss_cls: 0.7911, decode.d7.loss_mask: 0.7948, decode.d7.loss_dice: 1.1031, decode.d8.loss_cls: 0.7882, decode.d8.loss_mask: 0.7933, decode.d8.loss_dice: 1.1094, loss: 31.3304 2022-05-05 00:44:21,037 - mmseg - INFO - Iter [15950/40000] lr: 8.633e-07, eta: 5:40:10, time: 0.804, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8029, decode.loss_mask: 0.7770, decode.loss_dice: 1.1206, decode.d0.loss_cls: 4.5163, decode.d0.loss_mask: 0.7997, decode.d0.loss_dice: 1.3105, decode.d1.loss_cls: 1.0348, decode.d1.loss_mask: 0.8034, decode.d1.loss_dice: 1.2120, decode.d2.loss_cls: 0.8849, decode.d2.loss_mask: 0.7932, decode.d2.loss_dice: 1.1595, decode.d3.loss_cls: 0.8546, decode.d3.loss_mask: 0.7881, decode.d3.loss_dice: 1.1419, decode.d4.loss_cls: 0.8314, decode.d4.loss_mask: 0.7808, decode.d4.loss_dice: 1.1379, decode.d5.loss_cls: 0.8244, decode.d5.loss_mask: 0.7804, decode.d5.loss_dice: 1.1355, decode.d6.loss_cls: 0.8046, decode.d6.loss_mask: 0.7789, decode.d6.loss_dice: 1.1342, decode.d7.loss_cls: 0.7975, decode.d7.loss_mask: 0.7770, decode.d7.loss_dice: 1.1358, decode.d8.loss_cls: 0.7936, decode.d8.loss_mask: 0.7763, decode.d8.loss_dice: 1.1361, loss: 31.6237 2022-05-05 00:45:01,013 - mmseg - INFO - Saving checkpoint at 16000 iterations 2022-05-05 00:45:28,022 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 00:45:28,026 - mmseg - INFO - Iter [16000/40000] lr: 8.615e-07, eta: 5:40:04, time: 1.337, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7499, decode.loss_mask: 0.7842, decode.loss_dice: 1.1046, decode.d0.loss_cls: 4.4144, decode.d0.loss_mask: 0.7978, decode.d0.loss_dice: 1.2652, decode.d1.loss_cls: 0.9615, decode.d1.loss_mask: 0.8078, decode.d1.loss_dice: 1.1680, decode.d2.loss_cls: 0.8497, decode.d2.loss_mask: 0.7888, decode.d2.loss_dice: 1.1253, decode.d3.loss_cls: 0.8033, decode.d3.loss_mask: 0.7856, decode.d3.loss_dice: 1.1031, decode.d4.loss_cls: 0.7817, decode.d4.loss_mask: 0.7838, decode.d4.loss_dice: 1.1089, decode.d5.loss_cls: 0.7620, decode.d5.loss_mask: 0.7789, decode.d5.loss_dice: 1.1011, decode.d6.loss_cls: 0.7488, decode.d6.loss_mask: 0.7770, decode.d6.loss_dice: 1.0889, decode.d7.loss_cls: 0.7543, decode.d7.loss_mask: 0.7811, decode.d7.loss_dice: 1.0961, decode.d8.loss_cls: 0.7499, decode.d8.loss_mask: 0.7857, decode.d8.loss_dice: 1.1015, loss: 30.7088 2022-05-05 00:46:00,426 - mmseg - INFO - per class results: 2022-05-05 00:46:00,437 - mmseg - INFO - +------------------+-------+-------+ | Class | IoU | Acc | +------------------+-------+-------+ | person | 88.57 | 93.7 | | bicycle | 75.67 | 91.06 | | car | 54.63 | 63.29 | | motorcycle | 90.28 | 95.4 | | airplane | 89.3 | 94.91 | | bus | 78.65 | 82.66 | | train | 73.56 | 97.17 | | truck | 65.18 | 89.74 | | boat | 80.03 | 88.7 | | traffic light | 79.74 | 92.17 | | fire hydrant | 85.68 | 96.92 | | stop sign | 94.96 | 97.55 | | parking meter | 72.83 | 75.92 | | bench | 54.28 | 71.54 | | bird | 78.66 | 85.89 | | cat | 92.91 | 95.77 | | dog | 92.32 | 95.49 | | horse | 91.15 | 95.86 | | sheep | 82.82 | 91.1 | | cow | 93.43 | 96.32 | | elephant | 92.47 | 96.02 | | bear | 95.48 | 97.38 | | zebra | 91.75 | 95.52 | | giraffe | 88.95 | 94.39 | | backpack | 27.1 | 60.67 | | umbrella | 75.97 | 78.63 | | handbag | 17.03 | 34.62 | | tie | 65.89 | 65.89 | | suitcase | 75.69 | 95.81 | | frisbee | 95.09 | 97.81 | | skis | 40.67 | 67.01 | | snowboard | 68.48 | 76.83 | | sports ball | 83.11 | 94.66 | | kite | 72.21 | 90.47 | | baseball bat | 63.9 | 79.4 | | baseball glove | 2.29 | 2.39 | | skateboard | 65.21 | 86.33 | | surfboard | 89.75 | 94.51 | | tennis racket | 29.19 | 30.89 | | bottle | 68.7 | 89.0 | | wine glass | 85.55 | 90.99 | | cup | 67.42 | 74.81 | | fork | 56.13 | 67.68 | | knife | 78.17 | 90.39 | | spoon | 51.59 | 64.13 | | bowl | 61.68 | 73.85 | | banana | 84.84 | 93.49 | | apple | 80.19 | 88.22 | | sandwich | 93.4 | 98.06 | | orange | 83.31 | 90.88 | | broccoli | 91.3 | 96.89 | | carrot | 51.68 | 66.36 | | hot dog | 53.06 | 97.96 | | pizza | 95.09 | 96.78 | | donut | 76.25 | 94.79 | | cake | 81.53 | 87.88 | | chair | 56.21 | 70.52 | | couch | 66.53 | 92.41 | | potted plant | 33.33 | 42.32 | | bed | 71.6 | 86.63 | | dining table | 62.67 | 80.92 | | toilet | 89.3 | 96.25 | | tv | 81.35 | 92.94 | | laptop | 88.88 | 98.38 | | mouse | 85.37 | 90.28 | | remote | 68.53 | 96.44 | | keyboard | 84.31 | 98.05 | | cell phone | 83.13 | 94.73 | | microwave | 66.56 | 69.6 | | oven | 66.41 | 84.69 | | toaster | 38.54 | 39.77 | | sink | 74.68 | 79.59 | | refrigerator | 86.66 | 96.45 | | book | 80.31 | 90.6 | | clock | 75.35 | 78.03 | | vase | 61.53 | 89.55 | | scissors | 82.68 | 94.95 | | teddy bear | 86.41 | 95.93 | | hair drier | 0.0 | 0.0 | | toothbrush | 19.56 | 23.69 | | banner | 37.08 | 65.07 | | blanket | 0.15 | 0.22 | | branch | 54.43 | 59.58 | | bridge | 6.2 | 9.6 | | building-other | 54.76 | 72.09 | | bush | 29.36 | 42.4 | | cabinet | 30.28 | 46.63 | | cage | 38.37 | 76.01 | | cardboard | 24.0 | 29.13 | | carpet | 61.86 | 81.91 | | ceiling-other | 74.47 | 85.66 | | ceiling-tile | 0.0 | 0.0 | | cloth | 0.24 | 0.3 | | clothes | 24.55 | 41.44 | | clouds | 54.98 | 71.03 | | counter | 47.9 | 55.92 | | cupboard | 61.4 | 86.45 | | curtain | 68.25 | 80.99 | | desk-stuff | 21.19 | 23.44 | | dirt | 45.45 | 69.85 | | door-stuff | 47.32 | 64.95 | | fence | 41.96 | 72.79 | | floor-marble | 0.0 | 0.0 | | floor-other | 37.91 | 59.54 | | floor-stone | 10.8 | 10.85 | | floor-tile | 63.82 | 73.88 | | floor-wood | 75.83 | 85.24 | | flower | 15.59 | 34.76 | | fog | 0.0 | 0.0 | | food-other | 28.91 | 48.26 | | fruit | 61.73 | 87.08 | | furniture-other | 15.53 | 21.6 | | grass | 76.64 | 85.37 | | gravel | 33.13 | 39.74 | | ground-other | 16.04 | 30.53 | | hill | 27.77 | 36.84 | | house | 33.77 | 53.87 | | leaves | 40.29 | 46.42 | | light | 38.7 | 52.63 | | mat | 25.04 | 28.77 | | metal | 11.58 | 19.3 | | mirror-stuff | 36.91 | 44.07 | | moss | 0.0 | 0.0 | | mountain | 34.54 | 60.34 | | mud | 0.0 | 0.0 | | napkin | 44.7 | 79.42 | | net | 45.61 | 60.59 | | paper | 52.83 | 71.38 | | pavement | 50.65 | 69.85 | | pillow | 0.0 | 0.0 | | plant-other | 34.2 | 58.04 | | plastic | 22.11 | 26.9 | | platform | 51.62 | 65.44 | | playingfield | 72.03 | 84.12 | | railing | 14.94 | 23.06 | | railroad | 58.25 | 76.93 | | river | 24.48 | 29.98 | | road | 71.95 | 81.75 | | rock | 40.86 | 57.43 | | roof | 7.12 | 11.51 | | rug | 55.69 | 68.55 | | salad | 3.14 | 3.14 | | sand | 69.51 | 86.05 | | sea | 73.04 | 91.31 | | shelf | 23.52 | 46.33 | | sky-other | 62.6 | 76.94 | | skyscraper | 7.95 | 12.28 | | snow | 91.69 | 94.46 | | solid-other | nan | nan | | stairs | 44.17 | 70.16 | | stone | 6.75 | 17.56 | | straw | 22.41 | 33.77 | | structural-other | 18.58 | 29.17 | | table | 23.29 | 35.96 | | tent | 66.03 | 90.39 | | textile-other | 15.56 | 21.6 | | towel | 37.04 | 47.95 | | tree | 77.79 | 86.8 | | vegetable | 45.39 | 65.73 | | wall-brick | 46.07 | 59.74 | | wall-concrete | 26.06 | 33.87 | | wall-other | 63.73 | 81.81 | | wall-panel | 6.11 | 6.61 | | wall-stone | 32.45 | 36.93 | | wall-tile | 53.94 | 76.39 | | wall-wood | 41.57 | 63.32 | | water-other | 25.54 | 34.4 | | waterdrops | 0.0 | nan | | window-blind | 39.33 | 71.39 | | window-other | 51.33 | 70.66 | | wood | 15.58 | 32.07 | +------------------+-------+-------+ 2022-05-05 00:46:00,437 - mmseg - INFO - Summary: 2022-05-05 00:46:00,437 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 76.39 | 52.77 | 64.92 | +-------+-------+-------+ 2022-05-05 00:46:00,441 - mmseg - INFO - The previous best checkpoint /mnt/lustre/chenzhe.vendor/workspace/DenseAdaptor/segmentation/work_dirs/mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss/best_mIoU_iter_12000.pth was removed 2022-05-05 00:46:27,446 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_16000.pth. 2022-05-05 00:46:27,463 - mmseg - INFO - Best mIoU is 0.5277 at 16000 iter. 2022-05-05 00:46:27,476 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 00:46:27,477 - mmseg - INFO - Iter(val) [125] aAcc: 0.7639, mIoU: 0.5277, mAcc: 0.6492, IoU.person: 0.8857, IoU.bicycle: 0.7567, IoU.car: 0.5463, IoU.motorcycle: 0.9028, IoU.airplane: 0.8930, IoU.bus: 0.7865, IoU.train: 0.7356, IoU.truck: 0.6518, IoU.boat: 0.8003, IoU.traffic light: 0.7974, IoU.fire hydrant: 0.8568, IoU.stop sign: 0.9496, IoU.parking meter: 0.7283, IoU.bench: 0.5428, IoU.bird: 0.7866, IoU.cat: 0.9291, IoU.dog: 0.9232, IoU.horse: 0.9115, IoU.sheep: 0.8282, IoU.cow: 0.9343, IoU.elephant: 0.9247, IoU.bear: 0.9548, IoU.zebra: 0.9175, IoU.giraffe: 0.8895, IoU.backpack: 0.2710, IoU.umbrella: 0.7597, IoU.handbag: 0.1703, IoU.tie: 0.6589, IoU.suitcase: 0.7569, IoU.frisbee: 0.9509, IoU.skis: 0.4067, IoU.snowboard: 0.6848, IoU.sports ball: 0.8311, IoU.kite: 0.7221, IoU.baseball bat: 0.6390, IoU.baseball glove: 0.0229, IoU.skateboard: 0.6521, IoU.surfboard: 0.8975, IoU.tennis racket: 0.2919, IoU.bottle: 0.6870, IoU.wine glass: 0.8555, IoU.cup: 0.6742, IoU.fork: 0.5613, IoU.knife: 0.7817, IoU.spoon: 0.5159, IoU.bowl: 0.6168, IoU.banana: 0.8484, IoU.apple: 0.8019, IoU.sandwich: 0.9340, IoU.orange: 0.8331, IoU.broccoli: 0.9130, IoU.carrot: 0.5168, IoU.hot dog: 0.5306, IoU.pizza: 0.9509, IoU.donut: 0.7625, IoU.cake: 0.8153, IoU.chair: 0.5621, IoU.couch: 0.6653, IoU.potted plant: 0.3333, IoU.bed: 0.7160, IoU.dining table: 0.6267, IoU.toilet: 0.8930, IoU.tv: 0.8135, IoU.laptop: 0.8888, IoU.mouse: 0.8537, IoU.remote: 0.6853, IoU.keyboard: 0.8431, IoU.cell phone: 0.8313, IoU.microwave: 0.6656, IoU.oven: 0.6641, IoU.toaster: 0.3854, IoU.sink: 0.7468, IoU.refrigerator: 0.8666, IoU.book: 0.8031, IoU.clock: 0.7535, IoU.vase: 0.6153, IoU.scissors: 0.8268, IoU.teddy bear: 0.8641, IoU.hair drier: 0.0000, IoU.toothbrush: 0.1956, IoU.banner: 0.3708, IoU.blanket: 0.0015, IoU.branch: 0.5443, IoU.bridge: 0.0620, IoU.building-other: 0.5476, IoU.bush: 0.2936, IoU.cabinet: 0.3028, IoU.cage: 0.3837, IoU.cardboard: 0.2400, IoU.carpet: 0.6186, IoU.ceiling-other: 0.7447, IoU.ceiling-tile: 0.0000, IoU.cloth: 0.0024, IoU.clothes: 0.2455, IoU.clouds: 0.5498, IoU.counter: 0.4790, IoU.cupboard: 0.6140, IoU.curtain: 0.6825, IoU.desk-stuff: 0.2119, IoU.dirt: 0.4545, IoU.door-stuff: 0.4732, IoU.fence: 0.4196, IoU.floor-marble: 0.0000, IoU.floor-other: 0.3791, IoU.floor-stone: 0.1080, IoU.floor-tile: 0.6382, IoU.floor-wood: 0.7583, IoU.flower: 0.1559, IoU.fog: 0.0000, IoU.food-other: 0.2891, IoU.fruit: 0.6173, IoU.furniture-other: 0.1553, IoU.grass: 0.7664, IoU.gravel: 0.3313, IoU.ground-other: 0.1604, IoU.hill: 0.2777, IoU.house: 0.3377, IoU.leaves: 0.4029, IoU.light: 0.3870, IoU.mat: 0.2504, IoU.metal: 0.1158, IoU.mirror-stuff: 0.3691, IoU.moss: 0.0000, IoU.mountain: 0.3454, IoU.mud: 0.0000, IoU.napkin: 0.4470, IoU.net: 0.4561, IoU.paper: 0.5283, IoU.pavement: 0.5065, IoU.pillow: 0.0000, IoU.plant-other: 0.3420, IoU.plastic: 0.2211, IoU.platform: 0.5162, IoU.playingfield: 0.7203, IoU.railing: 0.1494, IoU.railroad: 0.5825, IoU.river: 0.2448, IoU.road: 0.7195, IoU.rock: 0.4086, IoU.roof: 0.0712, IoU.rug: 0.5569, IoU.salad: 0.0314, IoU.sand: 0.6951, IoU.sea: 0.7304, IoU.shelf: 0.2352, IoU.sky-other: 0.6260, IoU.skyscraper: 0.0795, IoU.snow: 0.9169, IoU.solid-other: nan, IoU.stairs: 0.4417, IoU.stone: 0.0675, IoU.straw: 0.2241, IoU.structural-other: 0.1858, IoU.table: 0.2329, IoU.tent: 0.6603, IoU.textile-other: 0.1556, IoU.towel: 0.3704, IoU.tree: 0.7779, IoU.vegetable: 0.4539, IoU.wall-brick: 0.4607, IoU.wall-concrete: 0.2606, IoU.wall-other: 0.6373, IoU.wall-panel: 0.0611, IoU.wall-stone: 0.3245, IoU.wall-tile: 0.5394, IoU.wall-wood: 0.4157, IoU.water-other: 0.2554, IoU.waterdrops: 0.0000, IoU.window-blind: 0.3933, IoU.window-other: 0.5133, IoU.wood: 0.1558, Acc.person: 0.9370, Acc.bicycle: 0.9106, Acc.car: 0.6329, Acc.motorcycle: 0.9540, Acc.airplane: 0.9491, Acc.bus: 0.8266, Acc.train: 0.9717, Acc.truck: 0.8974, Acc.boat: 0.8870, Acc.traffic light: 0.9217, Acc.fire hydrant: 0.9692, Acc.stop sign: 0.9755, Acc.parking meter: 0.7592, Acc.bench: 0.7154, Acc.bird: 0.8589, Acc.cat: 0.9577, Acc.dog: 0.9549, Acc.horse: 0.9586, Acc.sheep: 0.9110, Acc.cow: 0.9632, Acc.elephant: 0.9602, Acc.bear: 0.9738, Acc.zebra: 0.9552, Acc.giraffe: 0.9439, Acc.backpack: 0.6067, Acc.umbrella: 0.7863, Acc.handbag: 0.3462, Acc.tie: 0.6589, Acc.suitcase: 0.9581, Acc.frisbee: 0.9781, Acc.skis: 0.6701, Acc.snowboard: 0.7683, Acc.sports ball: 0.9466, Acc.kite: 0.9047, Acc.baseball bat: 0.7940, Acc.baseball glove: 0.0239, Acc.skateboard: 0.8633, Acc.surfboard: 0.9451, Acc.tennis racket: 0.3089, Acc.bottle: 0.8900, Acc.wine glass: 0.9099, Acc.cup: 0.7481, Acc.fork: 0.6768, Acc.knife: 0.9039, Acc.spoon: 0.6413, Acc.bowl: 0.7385, Acc.banana: 0.9349, Acc.apple: 0.8822, Acc.sandwich: 0.9806, Acc.orange: 0.9088, Acc.broccoli: 0.9689, Acc.carrot: 0.6636, Acc.hot dog: 0.9796, Acc.pizza: 0.9678, Acc.donut: 0.9479, Acc.cake: 0.8788, Acc.chair: 0.7052, Acc.couch: 0.9241, Acc.potted plant: 0.4232, Acc.bed: 0.8663, Acc.dining table: 0.8092, Acc.toilet: 0.9625, Acc.tv: 0.9294, Acc.laptop: 0.9838, Acc.mouse: 0.9028, Acc.remote: 0.9644, Acc.keyboard: 0.9805, Acc.cell phone: 0.9473, Acc.microwave: 0.6960, Acc.oven: 0.8469, Acc.toaster: 0.3977, Acc.sink: 0.7959, Acc.refrigerator: 0.9645, Acc.book: 0.9060, Acc.clock: 0.7803, Acc.vase: 0.8955, Acc.scissors: 0.9495, Acc.teddy bear: 0.9593, Acc.hair drier: 0.0000, Acc.toothbrush: 0.2369, Acc.banner: 0.6507, Acc.blanket: 0.0022, Acc.branch: 0.5958, Acc.bridge: 0.0960, Acc.building-other: 0.7209, Acc.bush: 0.4240, Acc.cabinet: 0.4663, Acc.cage: 0.7601, Acc.cardboard: 0.2913, Acc.carpet: 0.8191, Acc.ceiling-other: 0.8566, Acc.ceiling-tile: 0.0000, Acc.cloth: 0.0030, Acc.clothes: 0.4144, Acc.clouds: 0.7103, Acc.counter: 0.5592, Acc.cupboard: 0.8645, Acc.curtain: 0.8099, Acc.desk-stuff: 0.2344, Acc.dirt: 0.6985, Acc.door-stuff: 0.6495, Acc.fence: 0.7279, Acc.floor-marble: 0.0000, Acc.floor-other: 0.5954, Acc.floor-stone: 0.1085, Acc.floor-tile: 0.7388, Acc.floor-wood: 0.8524, Acc.flower: 0.3476, Acc.fog: 0.0000, Acc.food-other: 0.4826, Acc.fruit: 0.8708, Acc.furniture-other: 0.2160, Acc.grass: 0.8537, Acc.gravel: 0.3974, Acc.ground-other: 0.3053, Acc.hill: 0.3684, Acc.house: 0.5387, Acc.leaves: 0.4642, Acc.light: 0.5263, Acc.mat: 0.2877, Acc.metal: 0.1930, Acc.mirror-stuff: 0.4407, Acc.moss: 0.0000, Acc.mountain: 0.6034, Acc.mud: 0.0000, Acc.napkin: 0.7942, Acc.net: 0.6059, Acc.paper: 0.7138, Acc.pavement: 0.6985, Acc.pillow: 0.0000, Acc.plant-other: 0.5804, Acc.plastic: 0.2690, Acc.platform: 0.6544, Acc.playingfield: 0.8412, Acc.railing: 0.2306, Acc.railroad: 0.7693, Acc.river: 0.2998, Acc.road: 0.8175, Acc.rock: 0.5743, Acc.roof: 0.1151, Acc.rug: 0.6855, Acc.salad: 0.0314, Acc.sand: 0.8605, Acc.sea: 0.9131, Acc.shelf: 0.4633, Acc.sky-other: 0.7694, Acc.skyscraper: 0.1228, Acc.snow: 0.9446, Acc.solid-other: nan, Acc.stairs: 0.7016, Acc.stone: 0.1756, Acc.straw: 0.3377, Acc.structural-other: 0.2917, Acc.table: 0.3596, Acc.tent: 0.9039, Acc.textile-other: 0.2160, Acc.towel: 0.4795, Acc.tree: 0.8680, Acc.vegetable: 0.6573, Acc.wall-brick: 0.5974, Acc.wall-concrete: 0.3387, Acc.wall-other: 0.8181, Acc.wall-panel: 0.0661, Acc.wall-stone: 0.3693, Acc.wall-tile: 0.7639, Acc.wall-wood: 0.6332, Acc.water-other: 0.3440, Acc.waterdrops: nan, Acc.window-blind: 0.7139, Acc.window-other: 0.7066, Acc.wood: 0.3207 2022-05-05 00:47:08,685 - mmseg - INFO - Iter [16050/40000] lr: 8.597e-07, eta: 5:40:49, time: 2.016, data_time: 1.203, memory: 51557, decode.loss_cls: 0.7925, decode.loss_mask: 0.7635, decode.loss_dice: 1.0922, decode.d0.loss_cls: 4.4543, decode.d0.loss_mask: 0.7919, decode.d0.loss_dice: 1.2657, decode.d1.loss_cls: 1.0070, decode.d1.loss_mask: 0.7927, decode.d1.loss_dice: 1.1669, decode.d2.loss_cls: 0.8798, decode.d2.loss_mask: 0.7759, decode.d2.loss_dice: 1.1247, decode.d3.loss_cls: 0.8231, decode.d3.loss_mask: 0.7686, decode.d3.loss_dice: 1.1027, decode.d4.loss_cls: 0.7998, decode.d4.loss_mask: 0.7674, decode.d4.loss_dice: 1.1009, decode.d5.loss_cls: 0.7915, decode.d5.loss_mask: 0.7673, decode.d5.loss_dice: 1.0984, decode.d6.loss_cls: 0.7851, decode.d6.loss_mask: 0.7661, decode.d6.loss_dice: 1.0905, decode.d7.loss_cls: 0.7879, decode.d7.loss_mask: 0.7638, decode.d7.loss_dice: 1.0905, decode.d8.loss_cls: 0.7883, decode.d8.loss_mask: 0.7706, decode.d8.loss_dice: 1.0918, loss: 30.8615 2022-05-05 00:47:49,039 - mmseg - INFO - Iter [16100/40000] lr: 8.579e-07, eta: 5:40:02, time: 0.807, data_time: 0.011, memory: 51557, decode.loss_cls: 0.8019, decode.loss_mask: 0.7786, decode.loss_dice: 1.0911, decode.d0.loss_cls: 4.4223, decode.d0.loss_mask: 0.8019, decode.d0.loss_dice: 1.2759, decode.d1.loss_cls: 0.9783, decode.d1.loss_mask: 0.8039, decode.d1.loss_dice: 1.1641, decode.d2.loss_cls: 0.8627, decode.d2.loss_mask: 0.7841, decode.d2.loss_dice: 1.1236, decode.d3.loss_cls: 0.8270, decode.d3.loss_mask: 0.7805, decode.d3.loss_dice: 1.0971, decode.d4.loss_cls: 0.8025, decode.d4.loss_mask: 0.7812, decode.d4.loss_dice: 1.0997, decode.d5.loss_cls: 0.7964, decode.d5.loss_mask: 0.7788, decode.d5.loss_dice: 1.0916, decode.d6.loss_cls: 0.7883, decode.d6.loss_mask: 0.7790, decode.d6.loss_dice: 1.0906, decode.d7.loss_cls: 0.7860, decode.d7.loss_mask: 0.7786, decode.d7.loss_dice: 1.0918, decode.d8.loss_cls: 0.7851, decode.d8.loss_mask: 0.7811, decode.d8.loss_dice: 1.0887, loss: 30.9124 2022-05-05 00:48:29,910 - mmseg - INFO - Iter [16150/40000] lr: 8.561e-07, eta: 5:39:17, time: 0.817, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7859, decode.loss_mask: 0.8139, decode.loss_dice: 1.1128, decode.d0.loss_cls: 4.4364, decode.d0.loss_mask: 0.8368, decode.d0.loss_dice: 1.2878, decode.d1.loss_cls: 0.9843, decode.d1.loss_mask: 0.8362, decode.d1.loss_dice: 1.1707, decode.d2.loss_cls: 0.8562, decode.d2.loss_mask: 0.8279, decode.d2.loss_dice: 1.1348, decode.d3.loss_cls: 0.8078, decode.d3.loss_mask: 0.8242, decode.d3.loss_dice: 1.1211, decode.d4.loss_cls: 0.7947, decode.d4.loss_mask: 0.8183, decode.d4.loss_dice: 1.1171, decode.d5.loss_cls: 0.7900, decode.d5.loss_mask: 0.8156, decode.d5.loss_dice: 1.1165, decode.d6.loss_cls: 0.7807, decode.d6.loss_mask: 0.8096, decode.d6.loss_dice: 1.1079, decode.d7.loss_cls: 0.7801, decode.d7.loss_mask: 0.8119, decode.d7.loss_dice: 1.1167, decode.d8.loss_cls: 0.7751, decode.d8.loss_mask: 0.8163, decode.d8.loss_dice: 1.1207, loss: 31.4080 2022-05-05 00:49:10,359 - mmseg - INFO - Iter [16200/40000] lr: 8.543e-07, eta: 5:38:31, time: 0.809, data_time: 0.010, memory: 51557, decode.loss_cls: 0.8081, decode.loss_mask: 0.8150, decode.loss_dice: 1.1102, decode.d0.loss_cls: 4.4546, decode.d0.loss_mask: 0.8343, decode.d0.loss_dice: 1.3027, decode.d1.loss_cls: 1.0271, decode.d1.loss_mask: 0.8350, decode.d1.loss_dice: 1.1821, decode.d2.loss_cls: 0.8968, decode.d2.loss_mask: 0.8172, decode.d2.loss_dice: 1.1386, decode.d3.loss_cls: 0.8570, decode.d3.loss_mask: 0.8170, decode.d3.loss_dice: 1.1192, decode.d4.loss_cls: 0.8342, decode.d4.loss_mask: 0.8161, decode.d4.loss_dice: 1.1212, decode.d5.loss_cls: 0.8294, decode.d5.loss_mask: 0.8125, decode.d5.loss_dice: 1.1111, decode.d6.loss_cls: 0.8150, decode.d6.loss_mask: 0.8139, decode.d6.loss_dice: 1.1094, decode.d7.loss_cls: 0.8094, decode.d7.loss_mask: 0.8124, decode.d7.loss_dice: 1.1121, decode.d8.loss_cls: 0.8135, decode.d8.loss_mask: 0.8117, decode.d8.loss_dice: 1.1132, loss: 31.7500 2022-05-05 00:49:49,852 - mmseg - INFO - Iter [16250/40000] lr: 8.525e-07, eta: 5:37:44, time: 0.790, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7795, decode.loss_mask: 0.8078, decode.loss_dice: 1.1218, decode.d0.loss_cls: 4.3406, decode.d0.loss_mask: 0.8227, decode.d0.loss_dice: 1.2871, decode.d1.loss_cls: 0.9953, decode.d1.loss_mask: 0.8418, decode.d1.loss_dice: 1.1931, decode.d2.loss_cls: 0.8581, decode.d2.loss_mask: 0.8218, decode.d2.loss_dice: 1.1546, decode.d3.loss_cls: 0.8143, decode.d3.loss_mask: 0.8187, decode.d3.loss_dice: 1.1330, decode.d4.loss_cls: 0.8052, decode.d4.loss_mask: 0.8110, decode.d4.loss_dice: 1.1360, decode.d5.loss_cls: 0.7849, decode.d5.loss_mask: 0.8107, decode.d5.loss_dice: 1.1273, decode.d6.loss_cls: 0.7898, decode.d6.loss_mask: 0.8076, decode.d6.loss_dice: 1.1165, decode.d7.loss_cls: 0.7735, decode.d7.loss_mask: 0.8082, decode.d7.loss_dice: 1.1249, decode.d8.loss_cls: 0.7773, decode.d8.loss_mask: 0.8065, decode.d8.loss_dice: 1.1138, loss: 31.3834 2022-05-05 00:50:32,706 - mmseg - INFO - Iter [16300/40000] lr: 8.507e-07, eta: 5:37:01, time: 0.857, data_time: 0.060, memory: 51557, decode.loss_cls: 0.7950, decode.loss_mask: 0.8000, decode.loss_dice: 1.1114, decode.d0.loss_cls: 4.4114, decode.d0.loss_mask: 0.8201, decode.d0.loss_dice: 1.3004, decode.d1.loss_cls: 1.0097, decode.d1.loss_mask: 0.8266, decode.d1.loss_dice: 1.1908, decode.d2.loss_cls: 0.8836, decode.d2.loss_mask: 0.8074, decode.d2.loss_dice: 1.1346, decode.d3.loss_cls: 0.8384, decode.d3.loss_mask: 0.8045, decode.d3.loss_dice: 1.1138, decode.d4.loss_cls: 0.8179, decode.d4.loss_mask: 0.8068, decode.d4.loss_dice: 1.1177, decode.d5.loss_cls: 0.8003, decode.d5.loss_mask: 0.8049, decode.d5.loss_dice: 1.1188, decode.d6.loss_cls: 0.7981, decode.d6.loss_mask: 0.7978, decode.d6.loss_dice: 1.1063, decode.d7.loss_cls: 0.7873, decode.d7.loss_mask: 0.8034, decode.d7.loss_dice: 1.1131, decode.d8.loss_cls: 0.7957, decode.d8.loss_mask: 0.8042, decode.d8.loss_dice: 1.1120, loss: 31.4320 2022-05-05 00:51:12,672 - mmseg - INFO - Iter [16350/40000] lr: 8.490e-07, eta: 5:36:15, time: 0.799, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7773, decode.loss_mask: 0.7880, decode.loss_dice: 1.0922, decode.d0.loss_cls: 4.3611, decode.d0.loss_mask: 0.8075, decode.d0.loss_dice: 1.2734, decode.d1.loss_cls: 1.0067, decode.d1.loss_mask: 0.8079, decode.d1.loss_dice: 1.1674, decode.d2.loss_cls: 0.8553, decode.d2.loss_mask: 0.7958, decode.d2.loss_dice: 1.1206, decode.d3.loss_cls: 0.8158, decode.d3.loss_mask: 0.7871, decode.d3.loss_dice: 1.1036, decode.d4.loss_cls: 0.7999, decode.d4.loss_mask: 0.7853, decode.d4.loss_dice: 1.1054, decode.d5.loss_cls: 0.7806, decode.d5.loss_mask: 0.7853, decode.d5.loss_dice: 1.1010, decode.d6.loss_cls: 0.7821, decode.d6.loss_mask: 0.7828, decode.d6.loss_dice: 1.0876, decode.d7.loss_cls: 0.7753, decode.d7.loss_mask: 0.7857, decode.d7.loss_dice: 1.0976, decode.d8.loss_cls: 0.7680, decode.d8.loss_mask: 0.7874, decode.d8.loss_dice: 1.0998, loss: 30.8834 2022-05-05 00:51:52,842 - mmseg - INFO - Iter [16400/40000] lr: 8.472e-07, eta: 5:35:29, time: 0.803, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7834, decode.loss_mask: 0.7768, decode.loss_dice: 1.1310, decode.d0.loss_cls: 4.3877, decode.d0.loss_mask: 0.8011, decode.d0.loss_dice: 1.3087, decode.d1.loss_cls: 0.9955, decode.d1.loss_mask: 0.8028, decode.d1.loss_dice: 1.2094, decode.d2.loss_cls: 0.8603, decode.d2.loss_mask: 0.7881, decode.d2.loss_dice: 1.1591, decode.d3.loss_cls: 0.8082, decode.d3.loss_mask: 0.7847, decode.d3.loss_dice: 1.1376, decode.d4.loss_cls: 0.7955, decode.d4.loss_mask: 0.7791, decode.d4.loss_dice: 1.1369, decode.d5.loss_cls: 0.7898, decode.d5.loss_mask: 0.7768, decode.d5.loss_dice: 1.1436, decode.d6.loss_cls: 0.7800, decode.d6.loss_mask: 0.7793, decode.d6.loss_dice: 1.1351, decode.d7.loss_cls: 0.7739, decode.d7.loss_mask: 0.7818, decode.d7.loss_dice: 1.1411, decode.d8.loss_cls: 0.7821, decode.d8.loss_mask: 0.7789, decode.d8.loss_dice: 1.1351, loss: 31.2435 2022-05-05 00:52:33,593 - mmseg - INFO - Iter [16450/40000] lr: 8.454e-07, eta: 5:34:43, time: 0.816, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7680, decode.loss_mask: 0.7918, decode.loss_dice: 1.0979, decode.d0.loss_cls: 4.3775, decode.d0.loss_mask: 0.8086, decode.d0.loss_dice: 1.2599, decode.d1.loss_cls: 0.9717, decode.d1.loss_mask: 0.8128, decode.d1.loss_dice: 1.1668, decode.d2.loss_cls: 0.8492, decode.d2.loss_mask: 0.7952, decode.d2.loss_dice: 1.1252, decode.d3.loss_cls: 0.8017, decode.d3.loss_mask: 0.7922, decode.d3.loss_dice: 1.1067, decode.d4.loss_cls: 0.7890, decode.d4.loss_mask: 0.7901, decode.d4.loss_dice: 1.1074, decode.d5.loss_cls: 0.7664, decode.d5.loss_mask: 0.7931, decode.d5.loss_dice: 1.1100, decode.d6.loss_cls: 0.7578, decode.d6.loss_mask: 0.7926, decode.d6.loss_dice: 1.0954, decode.d7.loss_cls: 0.7501, decode.d7.loss_mask: 0.7948, decode.d7.loss_dice: 1.1021, decode.d8.loss_cls: 0.7572, decode.d8.loss_mask: 0.7922, decode.d8.loss_dice: 1.0993, loss: 30.8228 2022-05-05 00:53:14,348 - mmseg - INFO - Iter [16500/40000] lr: 8.436e-07, eta: 5:33:58, time: 0.815, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7572, decode.loss_mask: 0.7810, decode.loss_dice: 1.0930, decode.d0.loss_cls: 4.3848, decode.d0.loss_mask: 0.8162, decode.d0.loss_dice: 1.2852, decode.d1.loss_cls: 0.9566, decode.d1.loss_mask: 0.8153, decode.d1.loss_dice: 1.1608, decode.d2.loss_cls: 0.8356, decode.d2.loss_mask: 0.7898, decode.d2.loss_dice: 1.1238, decode.d3.loss_cls: 0.7840, decode.d3.loss_mask: 0.7875, decode.d3.loss_dice: 1.1019, decode.d4.loss_cls: 0.7717, decode.d4.loss_mask: 0.7839, decode.d4.loss_dice: 1.0987, decode.d5.loss_cls: 0.7673, decode.d5.loss_mask: 0.7843, decode.d5.loss_dice: 1.0914, decode.d6.loss_cls: 0.7493, decode.d6.loss_mask: 0.7835, decode.d6.loss_dice: 1.0944, decode.d7.loss_cls: 0.7493, decode.d7.loss_mask: 0.7861, decode.d7.loss_dice: 1.0980, decode.d8.loss_cls: 0.7496, decode.d8.loss_mask: 0.7844, decode.d8.loss_dice: 1.0921, loss: 30.6565 2022-05-05 00:53:55,567 - mmseg - INFO - Iter [16550/40000] lr: 8.418e-07, eta: 5:33:13, time: 0.824, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8019, decode.loss_mask: 0.7782, decode.loss_dice: 1.1343, decode.d0.loss_cls: 4.3438, decode.d0.loss_mask: 0.7929, decode.d0.loss_dice: 1.3168, decode.d1.loss_cls: 1.0217, decode.d1.loss_mask: 0.7891, decode.d1.loss_dice: 1.2080, decode.d2.loss_cls: 0.8742, decode.d2.loss_mask: 0.7768, decode.d2.loss_dice: 1.1638, decode.d3.loss_cls: 0.8329, decode.d3.loss_mask: 0.7778, decode.d3.loss_dice: 1.1488, decode.d4.loss_cls: 0.8148, decode.d4.loss_mask: 0.7793, decode.d4.loss_dice: 1.1509, decode.d5.loss_cls: 0.8096, decode.d5.loss_mask: 0.7726, decode.d5.loss_dice: 1.1431, decode.d6.loss_cls: 0.8022, decode.d6.loss_mask: 0.7726, decode.d6.loss_dice: 1.1380, decode.d7.loss_cls: 0.7985, decode.d7.loss_mask: 0.7759, decode.d7.loss_dice: 1.1411, decode.d8.loss_cls: 0.7996, decode.d8.loss_mask: 0.7740, decode.d8.loss_dice: 1.1344, loss: 31.3677 2022-05-05 00:54:36,877 - mmseg - INFO - Iter [16600/40000] lr: 8.400e-07, eta: 5:32:29, time: 0.826, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7830, decode.loss_mask: 0.7940, decode.loss_dice: 1.1371, decode.d0.loss_cls: 4.3796, decode.d0.loss_mask: 0.8112, decode.d0.loss_dice: 1.3125, decode.d1.loss_cls: 0.9939, decode.d1.loss_mask: 0.8127, decode.d1.loss_dice: 1.2043, decode.d2.loss_cls: 0.8575, decode.d2.loss_mask: 0.7895, decode.d2.loss_dice: 1.1668, decode.d3.loss_cls: 0.8276, decode.d3.loss_mask: 0.7852, decode.d3.loss_dice: 1.1424, decode.d4.loss_cls: 0.8169, decode.d4.loss_mask: 0.7832, decode.d4.loss_dice: 1.1321, decode.d5.loss_cls: 0.8049, decode.d5.loss_mask: 0.7945, decode.d5.loss_dice: 1.1355, decode.d6.loss_cls: 0.7817, decode.d6.loss_mask: 0.7953, decode.d6.loss_dice: 1.1324, decode.d7.loss_cls: 0.7793, decode.d7.loss_mask: 0.7985, decode.d7.loss_dice: 1.1355, decode.d8.loss_cls: 0.7780, decode.d8.loss_mask: 0.7961, decode.d8.loss_dice: 1.1350, loss: 31.3964 2022-05-05 00:55:16,937 - mmseg - INFO - Iter [16650/40000] lr: 8.382e-07, eta: 5:31:43, time: 0.801, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7827, decode.loss_mask: 0.8124, decode.loss_dice: 1.1287, decode.d0.loss_cls: 4.4176, decode.d0.loss_mask: 0.8329, decode.d0.loss_dice: 1.3156, decode.d1.loss_cls: 1.0069, decode.d1.loss_mask: 0.8355, decode.d1.loss_dice: 1.2137, decode.d2.loss_cls: 0.8820, decode.d2.loss_mask: 0.8180, decode.d2.loss_dice: 1.1608, decode.d3.loss_cls: 0.8298, decode.d3.loss_mask: 0.8144, decode.d3.loss_dice: 1.1401, decode.d4.loss_cls: 0.8193, decode.d4.loss_mask: 0.8136, decode.d4.loss_dice: 1.1419, decode.d5.loss_cls: 0.7927, decode.d5.loss_mask: 0.8104, decode.d5.loss_dice: 1.1342, decode.d6.loss_cls: 0.7903, decode.d6.loss_mask: 0.8073, decode.d6.loss_dice: 1.1312, decode.d7.loss_cls: 0.7810, decode.d7.loss_mask: 0.8105, decode.d7.loss_dice: 1.1352, decode.d8.loss_cls: 0.7811, decode.d8.loss_mask: 0.8132, decode.d8.loss_dice: 1.1337, loss: 31.6866 2022-05-05 00:55:58,224 - mmseg - INFO - Iter [16700/40000] lr: 8.364e-07, eta: 5:30:58, time: 0.826, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7727, decode.loss_mask: 0.7572, decode.loss_dice: 1.0929, decode.d0.loss_cls: 4.3451, decode.d0.loss_mask: 0.7811, decode.d0.loss_dice: 1.2839, decode.d1.loss_cls: 0.9947, decode.d1.loss_mask: 0.7882, decode.d1.loss_dice: 1.1716, decode.d2.loss_cls: 0.8564, decode.d2.loss_mask: 0.7643, decode.d2.loss_dice: 1.1296, decode.d3.loss_cls: 0.8144, decode.d3.loss_mask: 0.7590, decode.d3.loss_dice: 1.1113, decode.d4.loss_cls: 0.7994, decode.d4.loss_mask: 0.7589, decode.d4.loss_dice: 1.1138, decode.d5.loss_cls: 0.7785, decode.d5.loss_mask: 0.7639, decode.d5.loss_dice: 1.1072, decode.d6.loss_cls: 0.7771, decode.d6.loss_mask: 0.7606, decode.d6.loss_dice: 1.0948, decode.d7.loss_cls: 0.7623, decode.d7.loss_mask: 0.7630, decode.d7.loss_dice: 1.0955, decode.d8.loss_cls: 0.7652, decode.d8.loss_mask: 0.7606, decode.d8.loss_dice: 1.0966, loss: 30.6198 2022-05-05 00:56:39,115 - mmseg - INFO - Iter [16750/40000] lr: 8.346e-07, eta: 5:30:13, time: 0.818, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7872, decode.loss_mask: 0.8005, decode.loss_dice: 1.1279, decode.d0.loss_cls: 4.2711, decode.d0.loss_mask: 0.8151, decode.d0.loss_dice: 1.3111, decode.d1.loss_cls: 0.9783, decode.d1.loss_mask: 0.8237, decode.d1.loss_dice: 1.2128, decode.d2.loss_cls: 0.8620, decode.d2.loss_mask: 0.8095, decode.d2.loss_dice: 1.1589, decode.d3.loss_cls: 0.8190, decode.d3.loss_mask: 0.8043, decode.d3.loss_dice: 1.1414, decode.d4.loss_cls: 0.7981, decode.d4.loss_mask: 0.8030, decode.d4.loss_dice: 1.1412, decode.d5.loss_cls: 0.7954, decode.d5.loss_mask: 0.8034, decode.d5.loss_dice: 1.1375, decode.d6.loss_cls: 0.7933, decode.d6.loss_mask: 0.8007, decode.d6.loss_dice: 1.1287, decode.d7.loss_cls: 0.7851, decode.d7.loss_mask: 0.7985, decode.d7.loss_dice: 1.1303, decode.d8.loss_cls: 0.7863, decode.d8.loss_mask: 0.7972, decode.d8.loss_dice: 1.1306, loss: 31.3520 2022-05-05 00:57:19,950 - mmseg - INFO - Iter [16800/40000] lr: 8.328e-07, eta: 5:29:28, time: 0.816, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7404, decode.loss_mask: 0.7922, decode.loss_dice: 1.0536, decode.d0.loss_cls: 4.2315, decode.d0.loss_mask: 0.8129, decode.d0.loss_dice: 1.2308, decode.d1.loss_cls: 0.9362, decode.d1.loss_mask: 0.8220, decode.d1.loss_dice: 1.1294, decode.d2.loss_cls: 0.8139, decode.d2.loss_mask: 0.8045, decode.d2.loss_dice: 1.0888, decode.d3.loss_cls: 0.7791, decode.d3.loss_mask: 0.7910, decode.d3.loss_dice: 1.0620, decode.d4.loss_cls: 0.7655, decode.d4.loss_mask: 0.7858, decode.d4.loss_dice: 1.0603, decode.d5.loss_cls: 0.7419, decode.d5.loss_mask: 0.7877, decode.d5.loss_dice: 1.0583, decode.d6.loss_cls: 0.7331, decode.d6.loss_mask: 0.7861, decode.d6.loss_dice: 1.0560, decode.d7.loss_cls: 0.7356, decode.d7.loss_mask: 0.7860, decode.d7.loss_dice: 1.0648, decode.d8.loss_cls: 0.7315, decode.d8.loss_mask: 0.7887, decode.d8.loss_dice: 1.0637, loss: 30.0330 2022-05-05 00:57:59,764 - mmseg - INFO - Iter [16850/40000] lr: 8.310e-07, eta: 5:28:42, time: 0.797, data_time: 0.011, memory: 51557, decode.loss_cls: 0.7803, decode.loss_mask: 0.7911, decode.loss_dice: 1.0818, decode.d0.loss_cls: 4.3216, decode.d0.loss_mask: 0.8108, decode.d0.loss_dice: 1.2737, decode.d1.loss_cls: 1.0097, decode.d1.loss_mask: 0.8142, decode.d1.loss_dice: 1.1646, decode.d2.loss_cls: 0.8787, decode.d2.loss_mask: 0.7973, decode.d2.loss_dice: 1.1101, decode.d3.loss_cls: 0.8187, decode.d3.loss_mask: 0.7983, decode.d3.loss_dice: 1.0918, decode.d4.loss_cls: 0.8107, decode.d4.loss_mask: 0.7930, decode.d4.loss_dice: 1.0891, decode.d5.loss_cls: 0.7945, decode.d5.loss_mask: 0.7913, decode.d5.loss_dice: 1.0866, decode.d6.loss_cls: 0.7808, decode.d6.loss_mask: 0.7912, decode.d6.loss_dice: 1.0862, decode.d7.loss_cls: 0.7759, decode.d7.loss_mask: 0.7949, decode.d7.loss_dice: 1.0891, decode.d8.loss_cls: 0.7875, decode.d8.loss_mask: 0.7916, decode.d8.loss_dice: 1.0809, loss: 30.8857 2022-05-05 00:58:42,941 - mmseg - INFO - Iter [16900/40000] lr: 8.292e-07, eta: 5:28:00, time: 0.863, data_time: 0.058, memory: 51557, decode.loss_cls: 0.7396, decode.loss_mask: 0.7642, decode.loss_dice: 1.0886, decode.d0.loss_cls: 4.2648, decode.d0.loss_mask: 0.7876, decode.d0.loss_dice: 1.2543, decode.d1.loss_cls: 0.9563, decode.d1.loss_mask: 0.7891, decode.d1.loss_dice: 1.1603, decode.d2.loss_cls: 0.8156, decode.d2.loss_mask: 0.7759, decode.d2.loss_dice: 1.1144, decode.d3.loss_cls: 0.7792, decode.d3.loss_mask: 0.7684, decode.d3.loss_dice: 1.0976, decode.d4.loss_cls: 0.7579, decode.d4.loss_mask: 0.7639, decode.d4.loss_dice: 1.0954, decode.d5.loss_cls: 0.7494, decode.d5.loss_mask: 0.7654, decode.d5.loss_dice: 1.0917, decode.d6.loss_cls: 0.7350, decode.d6.loss_mask: 0.7649, decode.d6.loss_dice: 1.0883, decode.d7.loss_cls: 0.7338, decode.d7.loss_mask: 0.7625, decode.d7.loss_dice: 1.0896, decode.d8.loss_cls: 0.7384, decode.d8.loss_mask: 0.7630, decode.d8.loss_dice: 1.0807, loss: 30.1359 2022-05-05 00:59:24,063 - mmseg - INFO - Iter [16950/40000] lr: 8.274e-07, eta: 5:27:15, time: 0.823, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7121, decode.loss_mask: 0.8000, decode.loss_dice: 1.0965, decode.d0.loss_cls: 4.2290, decode.d0.loss_mask: 0.8190, decode.d0.loss_dice: 1.2574, decode.d1.loss_cls: 0.9392, decode.d1.loss_mask: 0.8203, decode.d1.loss_dice: 1.1631, decode.d2.loss_cls: 0.8087, decode.d2.loss_mask: 0.8045, decode.d2.loss_dice: 1.1190, decode.d3.loss_cls: 0.7563, decode.d3.loss_mask: 0.8010, decode.d3.loss_dice: 1.1047, decode.d4.loss_cls: 0.7436, decode.d4.loss_mask: 0.8001, decode.d4.loss_dice: 1.1063, decode.d5.loss_cls: 0.7350, decode.d5.loss_mask: 0.7974, decode.d5.loss_dice: 1.0989, decode.d6.loss_cls: 0.7158, decode.d6.loss_mask: 0.7991, decode.d6.loss_dice: 1.0879, decode.d7.loss_cls: 0.7021, decode.d7.loss_mask: 0.7992, decode.d7.loss_dice: 1.0954, decode.d8.loss_cls: 0.7098, decode.d8.loss_mask: 0.7979, decode.d8.loss_dice: 1.0958, loss: 30.3149 2022-05-05 01:00:04,835 - mmseg - INFO - Saving checkpoint at 17000 iterations 2022-05-05 01:00:30,065 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 01:00:30,068 - mmseg - INFO - Iter [17000/40000] lr: 8.256e-07, eta: 5:27:04, time: 1.317, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7492, decode.loss_mask: 0.7956, decode.loss_dice: 1.1217, decode.d0.loss_cls: 4.2329, decode.d0.loss_mask: 0.8123, decode.d0.loss_dice: 1.2840, decode.d1.loss_cls: 0.9583, decode.d1.loss_mask: 0.8150, decode.d1.loss_dice: 1.1860, decode.d2.loss_cls: 0.8252, decode.d2.loss_mask: 0.7990, decode.d2.loss_dice: 1.1392, decode.d3.loss_cls: 0.7782, decode.d3.loss_mask: 0.7987, decode.d3.loss_dice: 1.1260, decode.d4.loss_cls: 0.7694, decode.d4.loss_mask: 0.7964, decode.d4.loss_dice: 1.1255, decode.d5.loss_cls: 0.7602, decode.d5.loss_mask: 0.7999, decode.d5.loss_dice: 1.1159, decode.d6.loss_cls: 0.7598, decode.d6.loss_mask: 0.7949, decode.d6.loss_dice: 1.1155, decode.d7.loss_cls: 0.7520, decode.d7.loss_mask: 0.7907, decode.d7.loss_dice: 1.1172, decode.d8.loss_cls: 0.7523, decode.d8.loss_mask: 0.7951, decode.d8.loss_dice: 1.1126, loss: 30.7788 2022-05-05 01:01:10,310 - mmseg - INFO - Iter [17050/40000] lr: 8.238e-07, eta: 5:26:18, time: 0.808, data_time: 0.012, memory: 51557, decode.loss_cls: 0.7226, decode.loss_mask: 0.7969, decode.loss_dice: 1.1141, decode.d0.loss_cls: 4.2160, decode.d0.loss_mask: 0.8199, decode.d0.loss_dice: 1.2704, decode.d1.loss_cls: 0.9372, decode.d1.loss_mask: 0.8199, decode.d1.loss_dice: 1.1815, decode.d2.loss_cls: 0.7964, decode.d2.loss_mask: 0.8045, decode.d2.loss_dice: 1.1367, decode.d3.loss_cls: 0.7484, decode.d3.loss_mask: 0.7967, decode.d3.loss_dice: 1.1182, decode.d4.loss_cls: 0.7477, decode.d4.loss_mask: 0.7986, decode.d4.loss_dice: 1.1168, decode.d5.loss_cls: 0.7303, decode.d5.loss_mask: 0.7966, decode.d5.loss_dice: 1.1158, decode.d6.loss_cls: 0.7300, decode.d6.loss_mask: 0.7993, decode.d6.loss_dice: 1.1132, decode.d7.loss_cls: 0.7171, decode.d7.loss_mask: 0.8011, decode.d7.loss_dice: 1.1156, decode.d8.loss_cls: 0.7224, decode.d8.loss_mask: 0.7986, decode.d8.loss_dice: 1.1153, loss: 30.4978 2022-05-05 01:01:50,861 - mmseg - INFO - Iter [17100/40000] lr: 8.220e-07, eta: 5:25:33, time: 0.811, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7131, decode.loss_mask: 0.8049, decode.loss_dice: 1.0980, decode.d0.loss_cls: 4.2210, decode.d0.loss_mask: 0.8259, decode.d0.loss_dice: 1.2660, decode.d1.loss_cls: 0.9263, decode.d1.loss_mask: 0.8349, decode.d1.loss_dice: 1.1709, decode.d2.loss_cls: 0.7988, decode.d2.loss_mask: 0.8161, decode.d2.loss_dice: 1.1209, decode.d3.loss_cls: 0.7562, decode.d3.loss_mask: 0.8022, decode.d3.loss_dice: 1.1072, decode.d4.loss_cls: 0.7348, decode.d4.loss_mask: 0.8088, decode.d4.loss_dice: 1.1055, decode.d5.loss_cls: 0.7153, decode.d5.loss_mask: 0.8074, decode.d5.loss_dice: 1.1084, decode.d6.loss_cls: 0.7161, decode.d6.loss_mask: 0.8085, decode.d6.loss_dice: 1.0964, decode.d7.loss_cls: 0.7101, decode.d7.loss_mask: 0.8119, decode.d7.loss_dice: 1.1033, decode.d8.loss_cls: 0.7101, decode.d8.loss_mask: 0.8070, decode.d8.loss_dice: 1.0992, loss: 30.4052 2022-05-05 01:02:31,769 - mmseg - INFO - Iter [17150/40000] lr: 8.202e-07, eta: 5:24:48, time: 0.818, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7298, decode.loss_mask: 0.7920, decode.loss_dice: 1.0693, decode.d0.loss_cls: 4.1690, decode.d0.loss_mask: 0.8185, decode.d0.loss_dice: 1.2472, decode.d1.loss_cls: 0.9242, decode.d1.loss_mask: 0.8148, decode.d1.loss_dice: 1.1450, decode.d2.loss_cls: 0.8136, decode.d2.loss_mask: 0.8022, decode.d2.loss_dice: 1.1043, decode.d3.loss_cls: 0.7675, decode.d3.loss_mask: 0.7966, decode.d3.loss_dice: 1.0873, decode.d4.loss_cls: 0.7625, decode.d4.loss_mask: 0.7956, decode.d4.loss_dice: 1.0823, decode.d5.loss_cls: 0.7420, decode.d5.loss_mask: 0.7936, decode.d5.loss_dice: 1.0756, decode.d6.loss_cls: 0.7332, decode.d6.loss_mask: 0.7959, decode.d6.loss_dice: 1.0645, decode.d7.loss_cls: 0.7329, decode.d7.loss_mask: 0.7869, decode.d7.loss_dice: 1.0679, decode.d8.loss_cls: 0.7288, decode.d8.loss_mask: 0.7914, decode.d8.loss_dice: 1.0697, loss: 30.1040 2022-05-05 01:03:12,893 - mmseg - INFO - Iter [17200/40000] lr: 8.184e-07, eta: 5:24:03, time: 0.823, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7522, decode.loss_mask: 0.7938, decode.loss_dice: 1.0984, decode.d0.loss_cls: 4.2060, decode.d0.loss_mask: 0.8136, decode.d0.loss_dice: 1.2587, decode.d1.loss_cls: 0.9530, decode.d1.loss_mask: 0.8236, decode.d1.loss_dice: 1.1623, decode.d2.loss_cls: 0.8214, decode.d2.loss_mask: 0.8075, decode.d2.loss_dice: 1.1209, decode.d3.loss_cls: 0.7849, decode.d3.loss_mask: 0.8008, decode.d3.loss_dice: 1.1014, decode.d4.loss_cls: 0.7655, decode.d4.loss_mask: 0.7989, decode.d4.loss_dice: 1.1074, decode.d5.loss_cls: 0.7696, decode.d5.loss_mask: 0.7986, decode.d5.loss_dice: 1.0956, decode.d6.loss_cls: 0.7573, decode.d6.loss_mask: 0.7935, decode.d6.loss_dice: 1.0881, decode.d7.loss_cls: 0.7508, decode.d7.loss_mask: 0.7951, decode.d7.loss_dice: 1.0922, decode.d8.loss_cls: 0.7497, decode.d8.loss_mask: 0.7981, decode.d8.loss_dice: 1.0959, loss: 30.5552 2022-05-05 01:03:52,716 - mmseg - INFO - Iter [17250/40000] lr: 8.166e-07, eta: 5:23:17, time: 0.796, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7101, decode.loss_mask: 0.7869, decode.loss_dice: 1.1007, decode.d0.loss_cls: 4.1786, decode.d0.loss_mask: 0.8186, decode.d0.loss_dice: 1.2794, decode.d1.loss_cls: 0.9067, decode.d1.loss_mask: 0.8200, decode.d1.loss_dice: 1.1845, decode.d2.loss_cls: 0.7710, decode.d2.loss_mask: 0.7990, decode.d2.loss_dice: 1.1314, decode.d3.loss_cls: 0.7493, decode.d3.loss_mask: 0.7923, decode.d3.loss_dice: 1.1113, decode.d4.loss_cls: 0.7362, decode.d4.loss_mask: 0.7914, decode.d4.loss_dice: 1.1089, decode.d5.loss_cls: 0.7045, decode.d5.loss_mask: 0.7940, decode.d5.loss_dice: 1.1110, decode.d6.loss_cls: 0.7096, decode.d6.loss_mask: 0.7882, decode.d6.loss_dice: 1.0945, decode.d7.loss_cls: 0.7013, decode.d7.loss_mask: 0.7906, decode.d7.loss_dice: 1.1028, decode.d8.loss_cls: 0.7122, decode.d8.loss_mask: 0.7880, decode.d8.loss_dice: 1.0936, loss: 30.1667 2022-05-05 01:04:32,780 - mmseg - INFO - Iter [17300/40000] lr: 8.149e-07, eta: 5:22:31, time: 0.801, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7630, decode.loss_mask: 0.7895, decode.loss_dice: 1.0991, decode.d0.loss_cls: 4.2424, decode.d0.loss_mask: 0.8033, decode.d0.loss_dice: 1.2754, decode.d1.loss_cls: 0.9716, decode.d1.loss_mask: 0.8124, decode.d1.loss_dice: 1.1767, decode.d2.loss_cls: 0.8454, decode.d2.loss_mask: 0.7959, decode.d2.loss_dice: 1.1224, decode.d3.loss_cls: 0.7912, decode.d3.loss_mask: 0.7933, decode.d3.loss_dice: 1.1091, decode.d4.loss_cls: 0.7736, decode.d4.loss_mask: 0.7984, decode.d4.loss_dice: 1.1153, decode.d5.loss_cls: 0.7704, decode.d5.loss_mask: 0.7893, decode.d5.loss_dice: 1.1108, decode.d6.loss_cls: 0.7554, decode.d6.loss_mask: 0.7893, decode.d6.loss_dice: 1.1048, decode.d7.loss_cls: 0.7571, decode.d7.loss_mask: 0.7868, decode.d7.loss_dice: 1.1017, decode.d8.loss_cls: 0.7523, decode.d8.loss_mask: 0.7907, decode.d8.loss_dice: 1.1057, loss: 30.6923 2022-05-05 01:05:13,239 - mmseg - INFO - Iter [17350/40000] lr: 8.131e-07, eta: 5:21:45, time: 0.809, data_time: 0.009, memory: 51557, decode.loss_cls: 0.8054, decode.loss_mask: 0.7758, decode.loss_dice: 1.1091, decode.d0.loss_cls: 4.1997, decode.d0.loss_mask: 0.7974, decode.d0.loss_dice: 1.2851, decode.d1.loss_cls: 1.0382, decode.d1.loss_mask: 0.7956, decode.d1.loss_dice: 1.1855, decode.d2.loss_cls: 0.8880, decode.d2.loss_mask: 0.7813, decode.d2.loss_dice: 1.1371, decode.d3.loss_cls: 0.8404, decode.d3.loss_mask: 0.7733, decode.d3.loss_dice: 1.1235, decode.d4.loss_cls: 0.8281, decode.d4.loss_mask: 0.7757, decode.d4.loss_dice: 1.1189, decode.d5.loss_cls: 0.8197, decode.d5.loss_mask: 0.7754, decode.d5.loss_dice: 1.1131, decode.d6.loss_cls: 0.8029, decode.d6.loss_mask: 0.7744, decode.d6.loss_dice: 1.1144, decode.d7.loss_cls: 0.8029, decode.d7.loss_mask: 0.7772, decode.d7.loss_dice: 1.1188, decode.d8.loss_cls: 0.7985, decode.d8.loss_mask: 0.7801, decode.d8.loss_dice: 1.1148, loss: 31.0501 2022-05-05 01:05:53,664 - mmseg - INFO - Iter [17400/40000] lr: 8.113e-07, eta: 5:21:00, time: 0.809, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7778, decode.loss_mask: 0.7587, decode.loss_dice: 1.0845, decode.d0.loss_cls: 4.1976, decode.d0.loss_mask: 0.7915, decode.d0.loss_dice: 1.2673, decode.d1.loss_cls: 0.9961, decode.d1.loss_mask: 0.7911, decode.d1.loss_dice: 1.1591, decode.d2.loss_cls: 0.8631, decode.d2.loss_mask: 0.7713, decode.d2.loss_dice: 1.1119, decode.d3.loss_cls: 0.8130, decode.d3.loss_mask: 0.7643, decode.d3.loss_dice: 1.0984, decode.d4.loss_cls: 0.7958, decode.d4.loss_mask: 0.7643, decode.d4.loss_dice: 1.0994, decode.d5.loss_cls: 0.7879, decode.d5.loss_mask: 0.7641, decode.d5.loss_dice: 1.0888, decode.d6.loss_cls: 0.7687, decode.d6.loss_mask: 0.7621, decode.d6.loss_dice: 1.0786, decode.d7.loss_cls: 0.7699, decode.d7.loss_mask: 0.7631, decode.d7.loss_dice: 1.0853, decode.d8.loss_cls: 0.7704, decode.d8.loss_mask: 0.7640, decode.d8.loss_dice: 1.0850, loss: 30.3931 2022-05-05 01:06:37,345 - mmseg - INFO - Iter [17450/40000] lr: 8.095e-07, eta: 5:20:19, time: 0.874, data_time: 0.059, memory: 51557, decode.loss_cls: 0.7551, decode.loss_mask: 0.8003, decode.loss_dice: 1.1136, decode.d0.loss_cls: 4.1549, decode.d0.loss_mask: 0.8312, decode.d0.loss_dice: 1.3018, decode.d1.loss_cls: 0.9634, decode.d1.loss_mask: 0.8243, decode.d1.loss_dice: 1.1926, decode.d2.loss_cls: 0.8453, decode.d2.loss_mask: 0.8091, decode.d2.loss_dice: 1.1445, decode.d3.loss_cls: 0.7981, decode.d3.loss_mask: 0.8022, decode.d3.loss_dice: 1.1232, decode.d4.loss_cls: 0.7816, decode.d4.loss_mask: 0.7992, decode.d4.loss_dice: 1.1293, decode.d5.loss_cls: 0.7707, decode.d5.loss_mask: 0.8004, decode.d5.loss_dice: 1.1290, decode.d6.loss_cls: 0.7610, decode.d6.loss_mask: 0.7965, decode.d6.loss_dice: 1.1138, decode.d7.loss_cls: 0.7462, decode.d7.loss_mask: 0.8001, decode.d7.loss_dice: 1.1183, decode.d8.loss_cls: 0.7448, decode.d8.loss_mask: 0.7994, decode.d8.loss_dice: 1.1149, loss: 30.8646 2022-05-05 01:07:17,268 - mmseg - INFO - Iter [17500/40000] lr: 8.077e-07, eta: 5:19:33, time: 0.798, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7708, decode.loss_mask: 0.7612, decode.loss_dice: 1.0829, decode.d0.loss_cls: 4.2175, decode.d0.loss_mask: 0.7991, decode.d0.loss_dice: 1.2631, decode.d1.loss_cls: 1.0059, decode.d1.loss_mask: 0.7933, decode.d1.loss_dice: 1.1671, decode.d2.loss_cls: 0.8681, decode.d2.loss_mask: 0.7723, decode.d2.loss_dice: 1.1160, decode.d3.loss_cls: 0.8086, decode.d3.loss_mask: 0.7673, decode.d3.loss_dice: 1.0949, decode.d4.loss_cls: 0.7983, decode.d4.loss_mask: 0.7625, decode.d4.loss_dice: 1.0958, decode.d5.loss_cls: 0.7804, decode.d5.loss_mask: 0.7668, decode.d5.loss_dice: 1.0933, decode.d6.loss_cls: 0.7695, decode.d6.loss_mask: 0.7623, decode.d6.loss_dice: 1.0807, decode.d7.loss_cls: 0.7745, decode.d7.loss_mask: 0.7618, decode.d7.loss_dice: 1.0831, decode.d8.loss_cls: 0.7664, decode.d8.loss_mask: 0.7622, decode.d8.loss_dice: 1.0798, loss: 30.4255 2022-05-05 01:07:58,302 - mmseg - INFO - Iter [17550/40000] lr: 8.059e-07, eta: 5:18:48, time: 0.820, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7328, decode.loss_mask: 0.7614, decode.loss_dice: 1.0903, decode.d0.loss_cls: 4.1262, decode.d0.loss_mask: 0.7824, decode.d0.loss_dice: 1.2614, decode.d1.loss_cls: 0.9346, decode.d1.loss_mask: 0.7781, decode.d1.loss_dice: 1.1521, decode.d2.loss_cls: 0.8069, decode.d2.loss_mask: 0.7649, decode.d2.loss_dice: 1.1180, decode.d3.loss_cls: 0.7727, decode.d3.loss_mask: 0.7625, decode.d3.loss_dice: 1.0929, decode.d4.loss_cls: 0.7509, decode.d4.loss_mask: 0.7609, decode.d4.loss_dice: 1.0903, decode.d5.loss_cls: 0.7436, decode.d5.loss_mask: 0.7587, decode.d5.loss_dice: 1.0838, decode.d6.loss_cls: 0.7416, decode.d6.loss_mask: 0.7599, decode.d6.loss_dice: 1.0863, decode.d7.loss_cls: 0.7322, decode.d7.loss_mask: 0.7551, decode.d7.loss_dice: 1.0909, decode.d8.loss_cls: 0.7308, decode.d8.loss_mask: 0.7547, decode.d8.loss_dice: 1.0908, loss: 29.8674 2022-05-05 01:08:39,621 - mmseg - INFO - Iter [17600/40000] lr: 8.041e-07, eta: 5:18:04, time: 0.827, data_time: 0.011, memory: 51557, decode.loss_cls: 0.7812, decode.loss_mask: 0.7923, decode.loss_dice: 1.0940, decode.d0.loss_cls: 4.1450, decode.d0.loss_mask: 0.8190, decode.d0.loss_dice: 1.2638, decode.d1.loss_cls: 0.9763, decode.d1.loss_mask: 0.8155, decode.d1.loss_dice: 1.1702, decode.d2.loss_cls: 0.8573, decode.d2.loss_mask: 0.8013, decode.d2.loss_dice: 1.1260, decode.d3.loss_cls: 0.8126, decode.d3.loss_mask: 0.7977, decode.d3.loss_dice: 1.0976, decode.d4.loss_cls: 0.7991, decode.d4.loss_mask: 0.7910, decode.d4.loss_dice: 1.0975, decode.d5.loss_cls: 0.7840, decode.d5.loss_mask: 0.7902, decode.d5.loss_dice: 1.0931, decode.d6.loss_cls: 0.7924, decode.d6.loss_mask: 0.7879, decode.d6.loss_dice: 1.0857, decode.d7.loss_cls: 0.7699, decode.d7.loss_mask: 0.7890, decode.d7.loss_dice: 1.0933, decode.d8.loss_cls: 0.7698, decode.d8.loss_mask: 0.7908, decode.d8.loss_dice: 1.0964, loss: 30.6801 2022-05-05 01:09:20,488 - mmseg - INFO - Iter [17650/40000] lr: 8.023e-07, eta: 5:17:19, time: 0.816, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7202, decode.loss_mask: 0.7635, decode.loss_dice: 1.1048, decode.d0.loss_cls: 4.1842, decode.d0.loss_mask: 0.7898, decode.d0.loss_dice: 1.2896, decode.d1.loss_cls: 0.9412, decode.d1.loss_mask: 0.7934, decode.d1.loss_dice: 1.1846, decode.d2.loss_cls: 0.8024, decode.d2.loss_mask: 0.7760, decode.d2.loss_dice: 1.1335, decode.d3.loss_cls: 0.7537, decode.d3.loss_mask: 0.7659, decode.d3.loss_dice: 1.1134, decode.d4.loss_cls: 0.7394, decode.d4.loss_mask: 0.7674, decode.d4.loss_dice: 1.1142, decode.d5.loss_cls: 0.7205, decode.d5.loss_mask: 0.7669, decode.d5.loss_dice: 1.1024, decode.d6.loss_cls: 0.7206, decode.d6.loss_mask: 0.7626, decode.d6.loss_dice: 1.1007, decode.d7.loss_cls: 0.7172, decode.d7.loss_mask: 0.7648, decode.d7.loss_dice: 1.1031, decode.d8.loss_cls: 0.7146, decode.d8.loss_mask: 0.7625, decode.d8.loss_dice: 1.1045, loss: 30.0776 2022-05-05 01:10:00,508 - mmseg - INFO - Iter [17700/40000] lr: 8.005e-07, eta: 5:16:33, time: 0.801, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7433, decode.loss_mask: 0.7883, decode.loss_dice: 1.0889, decode.d0.loss_cls: 4.1637, decode.d0.loss_mask: 0.8072, decode.d0.loss_dice: 1.2792, decode.d1.loss_cls: 0.9499, decode.d1.loss_mask: 0.8069, decode.d1.loss_dice: 1.1705, decode.d2.loss_cls: 0.8232, decode.d2.loss_mask: 0.7913, decode.d2.loss_dice: 1.1123, decode.d3.loss_cls: 0.7795, decode.d3.loss_mask: 0.7880, decode.d3.loss_dice: 1.0991, decode.d4.loss_cls: 0.7642, decode.d4.loss_mask: 0.7861, decode.d4.loss_dice: 1.0931, decode.d5.loss_cls: 0.7497, decode.d5.loss_mask: 0.7836, decode.d5.loss_dice: 1.0918, decode.d6.loss_cls: 0.7402, decode.d6.loss_mask: 0.7847, decode.d6.loss_dice: 1.0820, decode.d7.loss_cls: 0.7414, decode.d7.loss_mask: 0.7883, decode.d7.loss_dice: 1.0902, decode.d8.loss_cls: 0.7396, decode.d8.loss_mask: 0.7846, decode.d8.loss_dice: 1.0886, loss: 30.2993 2022-05-05 01:10:40,771 - mmseg - INFO - Iter [17750/40000] lr: 7.987e-07, eta: 5:15:48, time: 0.805, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7398, decode.loss_mask: 0.7759, decode.loss_dice: 1.1158, decode.d0.loss_cls: 4.1258, decode.d0.loss_mask: 0.8088, decode.d0.loss_dice: 1.2770, decode.d1.loss_cls: 0.9633, decode.d1.loss_mask: 0.7956, decode.d1.loss_dice: 1.1872, decode.d2.loss_cls: 0.8392, decode.d2.loss_mask: 0.7839, decode.d2.loss_dice: 1.1333, decode.d3.loss_cls: 0.7886, decode.d3.loss_mask: 0.7813, decode.d3.loss_dice: 1.1197, decode.d4.loss_cls: 0.7776, decode.d4.loss_mask: 0.7828, decode.d4.loss_dice: 1.1245, decode.d5.loss_cls: 0.7706, decode.d5.loss_mask: 0.7785, decode.d5.loss_dice: 1.1181, decode.d6.loss_cls: 0.7628, decode.d6.loss_mask: 0.7750, decode.d6.loss_dice: 1.1036, decode.d7.loss_cls: 0.7556, decode.d7.loss_mask: 0.7754, decode.d7.loss_dice: 1.1115, decode.d8.loss_cls: 0.7414, decode.d8.loss_mask: 0.7745, decode.d8.loss_dice: 1.1164, loss: 30.5035 2022-05-05 01:11:20,541 - mmseg - INFO - Iter [17800/40000] lr: 7.969e-07, eta: 5:15:02, time: 0.795, data_time: 0.011, memory: 51557, decode.loss_cls: 0.7515, decode.loss_mask: 0.7736, decode.loss_dice: 1.1142, decode.d0.loss_cls: 4.1008, decode.d0.loss_mask: 0.7919, decode.d0.loss_dice: 1.2792, decode.d1.loss_cls: 0.9434, decode.d1.loss_mask: 0.7957, decode.d1.loss_dice: 1.1882, decode.d2.loss_cls: 0.8176, decode.d2.loss_mask: 0.7770, decode.d2.loss_dice: 1.1407, decode.d3.loss_cls: 0.7846, decode.d3.loss_mask: 0.7761, decode.d3.loss_dice: 1.1185, decode.d4.loss_cls: 0.7702, decode.d4.loss_mask: 0.7781, decode.d4.loss_dice: 1.1165, decode.d5.loss_cls: 0.7498, decode.d5.loss_mask: 0.7775, decode.d5.loss_dice: 1.1203, decode.d6.loss_cls: 0.7536, decode.d6.loss_mask: 0.7737, decode.d6.loss_dice: 1.1117, decode.d7.loss_cls: 0.7475, decode.d7.loss_mask: 0.7715, decode.d7.loss_dice: 1.1164, decode.d8.loss_cls: 0.7406, decode.d8.loss_mask: 0.7727, decode.d8.loss_dice: 1.1154, loss: 30.3686 2022-05-05 01:12:00,182 - mmseg - INFO - Iter [17850/40000] lr: 7.951e-07, eta: 5:14:15, time: 0.793, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7178, decode.loss_mask: 0.7715, decode.loss_dice: 1.0710, decode.d0.loss_cls: 4.0801, decode.d0.loss_mask: 0.7923, decode.d0.loss_dice: 1.2463, decode.d1.loss_cls: 0.9249, decode.d1.loss_mask: 0.8028, decode.d1.loss_dice: 1.1510, decode.d2.loss_cls: 0.8018, decode.d2.loss_mask: 0.7869, decode.d2.loss_dice: 1.1109, decode.d3.loss_cls: 0.7590, decode.d3.loss_mask: 0.7730, decode.d3.loss_dice: 1.0887, decode.d4.loss_cls: 0.7374, decode.d4.loss_mask: 0.7731, decode.d4.loss_dice: 1.0902, decode.d5.loss_cls: 0.7243, decode.d5.loss_mask: 0.7764, decode.d5.loss_dice: 1.0836, decode.d6.loss_cls: 0.7182, decode.d6.loss_mask: 0.7708, decode.d6.loss_dice: 1.0734, decode.d7.loss_cls: 0.7157, decode.d7.loss_mask: 0.7714, decode.d7.loss_dice: 1.0779, decode.d8.loss_cls: 0.7147, decode.d8.loss_mask: 0.7679, decode.d8.loss_dice: 1.0703, loss: 29.7434 2022-05-05 01:12:39,829 - mmseg - INFO - Iter [17900/40000] lr: 7.933e-07, eta: 5:13:29, time: 0.793, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7265, decode.loss_mask: 0.7921, decode.loss_dice: 1.0916, decode.d0.loss_cls: 4.0849, decode.d0.loss_mask: 0.8239, decode.d0.loss_dice: 1.2595, decode.d1.loss_cls: 0.9514, decode.d1.loss_mask: 0.8161, decode.d1.loss_dice: 1.1652, decode.d2.loss_cls: 0.8122, decode.d2.loss_mask: 0.8049, decode.d2.loss_dice: 1.1216, decode.d3.loss_cls: 0.7585, decode.d3.loss_mask: 0.7992, decode.d3.loss_dice: 1.1047, decode.d4.loss_cls: 0.7422, decode.d4.loss_mask: 0.7989, decode.d4.loss_dice: 1.1068, decode.d5.loss_cls: 0.7335, decode.d5.loss_mask: 0.7992, decode.d5.loss_dice: 1.1000, decode.d6.loss_cls: 0.7290, decode.d6.loss_mask: 0.7901, decode.d6.loss_dice: 1.0926, decode.d7.loss_cls: 0.7345, decode.d7.loss_mask: 0.7850, decode.d7.loss_dice: 1.0912, decode.d8.loss_cls: 0.7212, decode.d8.loss_mask: 0.7898, decode.d8.loss_dice: 1.0954, loss: 30.2217 2022-05-05 01:13:20,588 - mmseg - INFO - Iter [17950/40000] lr: 7.915e-07, eta: 5:12:44, time: 0.815, data_time: 0.012, memory: 51557, decode.loss_cls: 0.7461, decode.loss_mask: 0.8013, decode.loss_dice: 1.1034, decode.d0.loss_cls: 4.0859, decode.d0.loss_mask: 0.8171, decode.d0.loss_dice: 1.2705, decode.d1.loss_cls: 0.9680, decode.d1.loss_mask: 0.8210, decode.d1.loss_dice: 1.1761, decode.d2.loss_cls: 0.8409, decode.d2.loss_mask: 0.8047, decode.d2.loss_dice: 1.1389, decode.d3.loss_cls: 0.8030, decode.d3.loss_mask: 0.7960, decode.d3.loss_dice: 1.1180, decode.d4.loss_cls: 0.7717, decode.d4.loss_mask: 0.7957, decode.d4.loss_dice: 1.1201, decode.d5.loss_cls: 0.7598, decode.d5.loss_mask: 0.8001, decode.d5.loss_dice: 1.1105, decode.d6.loss_cls: 0.7498, decode.d6.loss_mask: 0.8011, decode.d6.loss_dice: 1.1064, decode.d7.loss_cls: 0.7445, decode.d7.loss_mask: 0.8015, decode.d7.loss_dice: 1.1115, decode.d8.loss_cls: 0.7502, decode.d8.loss_mask: 0.8010, decode.d8.loss_dice: 1.1025, loss: 30.6173 2022-05-05 01:14:03,130 - mmseg - INFO - Saving checkpoint at 18000 iterations 2022-05-05 01:14:29,504 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 01:14:29,510 - mmseg - INFO - Iter [18000/40000] lr: 7.897e-07, eta: 5:12:34, time: 1.376, data_time: 0.062, memory: 51557, decode.loss_cls: 0.7619, decode.loss_mask: 0.7950, decode.loss_dice: 1.1247, decode.d0.loss_cls: 4.1743, decode.d0.loss_mask: 0.8205, decode.d0.loss_dice: 1.2996, decode.d1.loss_cls: 0.9693, decode.d1.loss_mask: 0.8147, decode.d1.loss_dice: 1.1944, decode.d2.loss_cls: 0.8313, decode.d2.loss_mask: 0.8040, decode.d2.loss_dice: 1.1525, decode.d3.loss_cls: 0.7929, decode.d3.loss_mask: 0.7969, decode.d3.loss_dice: 1.1288, decode.d4.loss_cls: 0.7819, decode.d4.loss_mask: 0.7963, decode.d4.loss_dice: 1.1292, decode.d5.loss_cls: 0.7754, decode.d5.loss_mask: 0.7935, decode.d5.loss_dice: 1.1239, decode.d6.loss_cls: 0.7673, decode.d6.loss_mask: 0.7947, decode.d6.loss_dice: 1.1201, decode.d7.loss_cls: 0.7588, decode.d7.loss_mask: 0.7951, decode.d7.loss_dice: 1.1263, decode.d8.loss_cls: 0.7588, decode.d8.loss_mask: 0.7936, decode.d8.loss_dice: 1.1240, loss: 30.8997 2022-05-05 01:15:09,832 - mmseg - INFO - Iter [18050/40000] lr: 7.879e-07, eta: 5:11:49, time: 0.809, data_time: 0.012, memory: 51557, decode.loss_cls: 0.6934, decode.loss_mask: 0.7732, decode.loss_dice: 1.0905, decode.d0.loss_cls: 4.0386, decode.d0.loss_mask: 0.7974, decode.d0.loss_dice: 1.2551, decode.d1.loss_cls: 0.9439, decode.d1.loss_mask: 0.7985, decode.d1.loss_dice: 1.1640, decode.d2.loss_cls: 0.7901, decode.d2.loss_mask: 0.7821, decode.d2.loss_dice: 1.1217, decode.d3.loss_cls: 0.7434, decode.d3.loss_mask: 0.7765, decode.d3.loss_dice: 1.0937, decode.d4.loss_cls: 0.7271, decode.d4.loss_mask: 0.7746, decode.d4.loss_dice: 1.0953, decode.d5.loss_cls: 0.7143, decode.d5.loss_mask: 0.7738, decode.d5.loss_dice: 1.0878, decode.d6.loss_cls: 0.7005, decode.d6.loss_mask: 0.7703, decode.d6.loss_dice: 1.0867, decode.d7.loss_cls: 0.6980, decode.d7.loss_mask: 0.7712, decode.d7.loss_dice: 1.0912, decode.d8.loss_cls: 0.6995, decode.d8.loss_mask: 0.7710, decode.d8.loss_dice: 1.0877, loss: 29.7111 2022-05-05 01:15:49,337 - mmseg - INFO - Iter [18100/40000] lr: 7.861e-07, eta: 5:11:02, time: 0.790, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7223, decode.loss_mask: 0.7895, decode.loss_dice: 1.0936, decode.d0.loss_cls: 4.0667, decode.d0.loss_mask: 0.8157, decode.d0.loss_dice: 1.2660, decode.d1.loss_cls: 0.9515, decode.d1.loss_mask: 0.8189, decode.d1.loss_dice: 1.1695, decode.d2.loss_cls: 0.8098, decode.d2.loss_mask: 0.8055, decode.d2.loss_dice: 1.1251, decode.d3.loss_cls: 0.7545, decode.d3.loss_mask: 0.7966, decode.d3.loss_dice: 1.1066, decode.d4.loss_cls: 0.7429, decode.d4.loss_mask: 0.8014, decode.d4.loss_dice: 1.1058, decode.d5.loss_cls: 0.7411, decode.d5.loss_mask: 0.7926, decode.d5.loss_dice: 1.0993, decode.d6.loss_cls: 0.7290, decode.d6.loss_mask: 0.7866, decode.d6.loss_dice: 1.0940, decode.d7.loss_cls: 0.7168, decode.d7.loss_mask: 0.7885, decode.d7.loss_dice: 1.0975, decode.d8.loss_cls: 0.7144, decode.d8.loss_mask: 0.7906, decode.d8.loss_dice: 1.0946, loss: 30.1869 2022-05-05 01:16:29,279 - mmseg - INFO - Iter [18150/40000] lr: 7.843e-07, eta: 5:10:17, time: 0.799, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7411, decode.loss_mask: 0.7659, decode.loss_dice: 1.0732, decode.d0.loss_cls: 4.1182, decode.d0.loss_mask: 0.8029, decode.d0.loss_dice: 1.2579, decode.d1.loss_cls: 0.9396, decode.d1.loss_mask: 0.7937, decode.d1.loss_dice: 1.1505, decode.d2.loss_cls: 0.8193, decode.d2.loss_mask: 0.7725, decode.d2.loss_dice: 1.1015, decode.d3.loss_cls: 0.7824, decode.d3.loss_mask: 0.7680, decode.d3.loss_dice: 1.0786, decode.d4.loss_cls: 0.7571, decode.d4.loss_mask: 0.7669, decode.d4.loss_dice: 1.0814, decode.d5.loss_cls: 0.7534, decode.d5.loss_mask: 0.7699, decode.d5.loss_dice: 1.0775, decode.d6.loss_cls: 0.7455, decode.d6.loss_mask: 0.7688, decode.d6.loss_dice: 1.0762, decode.d7.loss_cls: 0.7439, decode.d7.loss_mask: 0.7703, decode.d7.loss_dice: 1.0759, decode.d8.loss_cls: 0.7415, decode.d8.loss_mask: 0.7674, decode.d8.loss_dice: 1.0731, loss: 29.9339 2022-05-05 01:17:08,645 - mmseg - INFO - Iter [18200/40000] lr: 7.825e-07, eta: 5:09:30, time: 0.787, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6870, decode.loss_mask: 0.8048, decode.loss_dice: 1.0629, decode.d0.loss_cls: 3.9955, decode.d0.loss_mask: 0.8314, decode.d0.loss_dice: 1.2315, decode.d1.loss_cls: 0.9053, decode.d1.loss_mask: 0.8328, decode.d1.loss_dice: 1.1428, decode.d2.loss_cls: 0.7703, decode.d2.loss_mask: 0.8146, decode.d2.loss_dice: 1.0944, decode.d3.loss_cls: 0.7238, decode.d3.loss_mask: 0.8084, decode.d3.loss_dice: 1.0809, decode.d4.loss_cls: 0.6998, decode.d4.loss_mask: 0.8077, decode.d4.loss_dice: 1.0694, decode.d5.loss_cls: 0.6974, decode.d5.loss_mask: 0.8085, decode.d5.loss_dice: 1.0663, decode.d6.loss_cls: 0.6897, decode.d6.loss_mask: 0.8006, decode.d6.loss_dice: 1.0619, decode.d7.loss_cls: 0.6877, decode.d7.loss_mask: 0.8024, decode.d7.loss_dice: 1.0610, decode.d8.loss_cls: 0.6746, decode.d8.loss_mask: 0.8021, decode.d8.loss_dice: 1.0638, loss: 29.5792 2022-05-05 01:17:48,416 - mmseg - INFO - Iter [18250/40000] lr: 7.807e-07, eta: 5:08:44, time: 0.795, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7509, decode.loss_mask: 0.7742, decode.loss_dice: 1.0606, decode.d0.loss_cls: 4.0654, decode.d0.loss_mask: 0.8034, decode.d0.loss_dice: 1.2552, decode.d1.loss_cls: 0.9631, decode.d1.loss_mask: 0.7961, decode.d1.loss_dice: 1.1416, decode.d2.loss_cls: 0.8355, decode.d2.loss_mask: 0.7826, decode.d2.loss_dice: 1.0927, decode.d3.loss_cls: 0.7913, decode.d3.loss_mask: 0.7768, decode.d3.loss_dice: 1.0770, decode.d4.loss_cls: 0.7666, decode.d4.loss_mask: 0.7831, decode.d4.loss_dice: 1.0774, decode.d5.loss_cls: 0.7594, decode.d5.loss_mask: 0.7752, decode.d5.loss_dice: 1.0753, decode.d6.loss_cls: 0.7468, decode.d6.loss_mask: 0.7748, decode.d6.loss_dice: 1.0644, decode.d7.loss_cls: 0.7481, decode.d7.loss_mask: 0.7747, decode.d7.loss_dice: 1.0612, decode.d8.loss_cls: 0.7404, decode.d8.loss_mask: 0.7775, decode.d8.loss_dice: 1.0622, loss: 29.9534 2022-05-05 01:18:28,627 - mmseg - INFO - Iter [18300/40000] lr: 7.790e-07, eta: 5:07:59, time: 0.804, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7284, decode.loss_mask: 0.7800, decode.loss_dice: 1.1056, decode.d0.loss_cls: 4.0791, decode.d0.loss_mask: 0.7977, decode.d0.loss_dice: 1.2733, decode.d1.loss_cls: 0.9361, decode.d1.loss_mask: 0.8077, decode.d1.loss_dice: 1.1872, decode.d2.loss_cls: 0.8085, decode.d2.loss_mask: 0.7968, decode.d2.loss_dice: 1.1415, decode.d3.loss_cls: 0.7773, decode.d3.loss_mask: 0.7858, decode.d3.loss_dice: 1.1202, decode.d4.loss_cls: 0.7516, decode.d4.loss_mask: 0.7853, decode.d4.loss_dice: 1.1161, decode.d5.loss_cls: 0.7364, decode.d5.loss_mask: 0.7855, decode.d5.loss_dice: 1.1106, decode.d6.loss_cls: 0.7313, decode.d6.loss_mask: 0.7811, decode.d6.loss_dice: 1.1011, decode.d7.loss_cls: 0.7241, decode.d7.loss_mask: 0.7791, decode.d7.loss_dice: 1.1068, decode.d8.loss_cls: 0.7191, decode.d8.loss_mask: 0.7805, decode.d8.loss_dice: 1.1125, loss: 30.2463 2022-05-05 01:19:08,254 - mmseg - INFO - Iter [18350/40000] lr: 7.772e-07, eta: 5:07:13, time: 0.793, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6982, decode.loss_mask: 0.7900, decode.loss_dice: 1.1118, decode.d0.loss_cls: 3.9734, decode.d0.loss_mask: 0.8146, decode.d0.loss_dice: 1.2817, decode.d1.loss_cls: 0.9136, decode.d1.loss_mask: 0.8166, decode.d1.loss_dice: 1.1782, decode.d2.loss_cls: 0.7922, decode.d2.loss_mask: 0.7971, decode.d2.loss_dice: 1.1408, decode.d3.loss_cls: 0.7369, decode.d3.loss_mask: 0.7956, decode.d3.loss_dice: 1.1113, decode.d4.loss_cls: 0.7250, decode.d4.loss_mask: 0.7914, decode.d4.loss_dice: 1.1214, decode.d5.loss_cls: 0.7025, decode.d5.loss_mask: 0.7919, decode.d5.loss_dice: 1.1121, decode.d6.loss_cls: 0.6973, decode.d6.loss_mask: 0.7895, decode.d6.loss_dice: 1.1141, decode.d7.loss_cls: 0.6945, decode.d7.loss_mask: 0.7898, decode.d7.loss_dice: 1.1147, decode.d8.loss_cls: 0.6936, decode.d8.loss_mask: 0.7892, decode.d8.loss_dice: 1.1081, loss: 29.9868 2022-05-05 01:19:47,711 - mmseg - INFO - Iter [18400/40000] lr: 7.754e-07, eta: 5:06:26, time: 0.790, data_time: 0.011, memory: 51557, decode.loss_cls: 0.7146, decode.loss_mask: 0.7916, decode.loss_dice: 1.1016, decode.d0.loss_cls: 4.0238, decode.d0.loss_mask: 0.8095, decode.d0.loss_dice: 1.2739, decode.d1.loss_cls: 0.9263, decode.d1.loss_mask: 0.8121, decode.d1.loss_dice: 1.1781, decode.d2.loss_cls: 0.7921, decode.d2.loss_mask: 0.7991, decode.d2.loss_dice: 1.1313, decode.d3.loss_cls: 0.7561, decode.d3.loss_mask: 0.7938, decode.d3.loss_dice: 1.1119, decode.d4.loss_cls: 0.7398, decode.d4.loss_mask: 0.7925, decode.d4.loss_dice: 1.1086, decode.d5.loss_cls: 0.7313, decode.d5.loss_mask: 0.7892, decode.d5.loss_dice: 1.1090, decode.d6.loss_cls: 0.7183, decode.d6.loss_mask: 0.7874, decode.d6.loss_dice: 1.1028, decode.d7.loss_cls: 0.7148, decode.d7.loss_mask: 0.7921, decode.d7.loss_dice: 1.1048, decode.d8.loss_cls: 0.7085, decode.d8.loss_mask: 0.7924, decode.d8.loss_dice: 1.1058, loss: 30.1131 2022-05-05 01:20:27,346 - mmseg - INFO - Iter [18450/40000] lr: 7.736e-07, eta: 5:05:40, time: 0.793, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7539, decode.loss_mask: 0.7946, decode.loss_dice: 1.1007, decode.d0.loss_cls: 4.0212, decode.d0.loss_mask: 0.8147, decode.d0.loss_dice: 1.2837, decode.d1.loss_cls: 0.9386, decode.d1.loss_mask: 0.8159, decode.d1.loss_dice: 1.1773, decode.d2.loss_cls: 0.8255, decode.d2.loss_mask: 0.8021, decode.d2.loss_dice: 1.1327, decode.d3.loss_cls: 0.7804, decode.d3.loss_mask: 0.7966, decode.d3.loss_dice: 1.1098, decode.d4.loss_cls: 0.7711, decode.d4.loss_mask: 0.7974, decode.d4.loss_dice: 1.1104, decode.d5.loss_cls: 0.7534, decode.d5.loss_mask: 0.7977, decode.d5.loss_dice: 1.1124, decode.d6.loss_cls: 0.7419, decode.d6.loss_mask: 0.7914, decode.d6.loss_dice: 1.1019, decode.d7.loss_cls: 0.7482, decode.d7.loss_mask: 0.7913, decode.d7.loss_dice: 1.0992, decode.d8.loss_cls: 0.7396, decode.d8.loss_mask: 0.7983, decode.d8.loss_dice: 1.1026, loss: 30.4046 2022-05-05 01:21:06,909 - mmseg - INFO - Iter [18500/40000] lr: 7.718e-07, eta: 5:04:54, time: 0.791, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7305, decode.loss_mask: 0.7809, decode.loss_dice: 1.0794, decode.d0.loss_cls: 4.0179, decode.d0.loss_mask: 0.7942, decode.d0.loss_dice: 1.2407, decode.d1.loss_cls: 0.9418, decode.d1.loss_mask: 0.8047, decode.d1.loss_dice: 1.1521, decode.d2.loss_cls: 0.8010, decode.d2.loss_mask: 0.7940, decode.d2.loss_dice: 1.1103, decode.d3.loss_cls: 0.7644, decode.d3.loss_mask: 0.7911, decode.d3.loss_dice: 1.0896, decode.d4.loss_cls: 0.7517, decode.d4.loss_mask: 0.7911, decode.d4.loss_dice: 1.0853, decode.d5.loss_cls: 0.7426, decode.d5.loss_mask: 0.7843, decode.d5.loss_dice: 1.0845, decode.d6.loss_cls: 0.7239, decode.d6.loss_mask: 0.7839, decode.d6.loss_dice: 1.0744, decode.d7.loss_cls: 0.7259, decode.d7.loss_mask: 0.7883, decode.d7.loss_dice: 1.0777, decode.d8.loss_cls: 0.7329, decode.d8.loss_mask: 0.7839, decode.d8.loss_dice: 1.0766, loss: 29.8997 2022-05-05 01:21:48,707 - mmseg - INFO - Iter [18550/40000] lr: 7.700e-07, eta: 5:04:11, time: 0.836, data_time: 0.060, memory: 51557, decode.loss_cls: 0.7578, decode.loss_mask: 0.7593, decode.loss_dice: 1.0548, decode.d0.loss_cls: 4.0513, decode.d0.loss_mask: 0.7924, decode.d0.loss_dice: 1.2500, decode.d1.loss_cls: 0.9744, decode.d1.loss_mask: 0.7853, decode.d1.loss_dice: 1.1427, decode.d2.loss_cls: 0.8511, decode.d2.loss_mask: 0.7600, decode.d2.loss_dice: 1.0848, decode.d3.loss_cls: 0.8054, decode.d3.loss_mask: 0.7574, decode.d3.loss_dice: 1.0616, decode.d4.loss_cls: 0.7919, decode.d4.loss_mask: 0.7571, decode.d4.loss_dice: 1.0647, decode.d5.loss_cls: 0.7761, decode.d5.loss_mask: 0.7554, decode.d5.loss_dice: 1.0581, decode.d6.loss_cls: 0.7655, decode.d6.loss_mask: 0.7558, decode.d6.loss_dice: 1.0502, decode.d7.loss_cls: 0.7605, decode.d7.loss_mask: 0.7592, decode.d7.loss_dice: 1.0517, decode.d8.loss_cls: 0.7634, decode.d8.loss_mask: 0.7550, decode.d8.loss_dice: 1.0535, loss: 29.8063 2022-05-05 01:22:28,446 - mmseg - INFO - Iter [18600/40000] lr: 7.682e-07, eta: 5:03:25, time: 0.795, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7164, decode.loss_mask: 0.7783, decode.loss_dice: 1.0910, decode.d0.loss_cls: 4.0051, decode.d0.loss_mask: 0.8043, decode.d0.loss_dice: 1.2728, decode.d1.loss_cls: 0.9326, decode.d1.loss_mask: 0.8039, decode.d1.loss_dice: 1.1710, decode.d2.loss_cls: 0.7994, decode.d2.loss_mask: 0.7891, decode.d2.loss_dice: 1.1209, decode.d3.loss_cls: 0.7524, decode.d3.loss_mask: 0.7853, decode.d3.loss_dice: 1.1013, decode.d4.loss_cls: 0.7335, decode.d4.loss_mask: 0.7807, decode.d4.loss_dice: 1.1008, decode.d5.loss_cls: 0.7333, decode.d5.loss_mask: 0.7790, decode.d5.loss_dice: 1.0990, decode.d6.loss_cls: 0.7266, decode.d6.loss_mask: 0.7755, decode.d6.loss_dice: 1.0939, decode.d7.loss_cls: 0.7152, decode.d7.loss_mask: 0.7811, decode.d7.loss_dice: 1.0939, decode.d8.loss_cls: 0.7156, decode.d8.loss_mask: 0.7757, decode.d8.loss_dice: 1.0903, loss: 29.9177 2022-05-05 01:23:07,884 - mmseg - INFO - Iter [18650/40000] lr: 7.664e-07, eta: 5:02:39, time: 0.789, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6827, decode.loss_mask: 0.7622, decode.loss_dice: 1.0899, decode.d0.loss_cls: 3.9759, decode.d0.loss_mask: 0.7958, decode.d0.loss_dice: 1.2637, decode.d1.loss_cls: 0.9086, decode.d1.loss_mask: 0.7883, decode.d1.loss_dice: 1.1581, decode.d2.loss_cls: 0.7675, decode.d2.loss_mask: 0.7676, decode.d2.loss_dice: 1.1199, decode.d3.loss_cls: 0.7358, decode.d3.loss_mask: 0.7619, decode.d3.loss_dice: 1.0955, decode.d4.loss_cls: 0.7104, decode.d4.loss_mask: 0.7577, decode.d4.loss_dice: 1.0955, decode.d5.loss_cls: 0.7039, decode.d5.loss_mask: 0.7569, decode.d5.loss_dice: 1.0839, decode.d6.loss_cls: 0.6852, decode.d6.loss_mask: 0.7587, decode.d6.loss_dice: 1.0837, decode.d7.loss_cls: 0.6813, decode.d7.loss_mask: 0.7578, decode.d7.loss_dice: 1.0866, decode.d8.loss_cls: 0.6811, decode.d8.loss_mask: 0.7602, decode.d8.loss_dice: 1.0867, loss: 29.3629 2022-05-05 01:23:47,248 - mmseg - INFO - Iter [18700/40000] lr: 7.646e-07, eta: 5:01:53, time: 0.787, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7155, decode.loss_mask: 0.7298, decode.loss_dice: 1.0392, decode.d0.loss_cls: 4.0432, decode.d0.loss_mask: 0.7591, decode.d0.loss_dice: 1.2284, decode.d1.loss_cls: 0.9673, decode.d1.loss_mask: 0.7518, decode.d1.loss_dice: 1.1147, decode.d2.loss_cls: 0.8305, decode.d2.loss_mask: 0.7322, decode.d2.loss_dice: 1.0679, decode.d3.loss_cls: 0.7692, decode.d3.loss_mask: 0.7306, decode.d3.loss_dice: 1.0483, decode.d4.loss_cls: 0.7496, decode.d4.loss_mask: 0.7251, decode.d4.loss_dice: 1.0505, decode.d5.loss_cls: 0.7278, decode.d5.loss_mask: 0.7288, decode.d5.loss_dice: 1.0490, decode.d6.loss_cls: 0.7140, decode.d6.loss_mask: 0.7298, decode.d6.loss_dice: 1.0439, decode.d7.loss_cls: 0.7091, decode.d7.loss_mask: 0.7291, decode.d7.loss_dice: 1.0429, decode.d8.loss_cls: 0.7195, decode.d8.loss_mask: 0.7279, decode.d8.loss_dice: 1.0395, loss: 29.0144 2022-05-05 01:24:27,702 - mmseg - INFO - Iter [18750/40000] lr: 7.628e-07, eta: 5:01:08, time: 0.809, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6901, decode.loss_mask: 0.7531, decode.loss_dice: 1.0933, decode.d0.loss_cls: 3.9239, decode.d0.loss_mask: 0.7802, decode.d0.loss_dice: 1.2561, decode.d1.loss_cls: 0.9038, decode.d1.loss_mask: 0.7803, decode.d1.loss_dice: 1.1587, decode.d2.loss_cls: 0.7804, decode.d2.loss_mask: 0.7653, decode.d2.loss_dice: 1.1161, decode.d3.loss_cls: 0.7291, decode.d3.loss_mask: 0.7586, decode.d3.loss_dice: 1.1011, decode.d4.loss_cls: 0.7081, decode.d4.loss_mask: 0.7586, decode.d4.loss_dice: 1.1007, decode.d5.loss_cls: 0.6948, decode.d5.loss_mask: 0.7571, decode.d5.loss_dice: 1.0953, decode.d6.loss_cls: 0.6844, decode.d6.loss_mask: 0.7563, decode.d6.loss_dice: 1.0878, decode.d7.loss_cls: 0.6824, decode.d7.loss_mask: 0.7534, decode.d7.loss_dice: 1.0865, decode.d8.loss_cls: 0.6867, decode.d8.loss_mask: 0.7533, decode.d8.loss_dice: 1.0876, loss: 29.2833 2022-05-05 01:25:07,976 - mmseg - INFO - Iter [18800/40000] lr: 7.610e-07, eta: 5:00:23, time: 0.805, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7261, decode.loss_mask: 0.7874, decode.loss_dice: 1.0707, decode.d0.loss_cls: 3.9725, decode.d0.loss_mask: 0.8138, decode.d0.loss_dice: 1.2672, decode.d1.loss_cls: 0.9573, decode.d1.loss_mask: 0.8067, decode.d1.loss_dice: 1.1497, decode.d2.loss_cls: 0.8229, decode.d2.loss_mask: 0.7876, decode.d2.loss_dice: 1.0950, decode.d3.loss_cls: 0.7777, decode.d3.loss_mask: 0.7834, decode.d3.loss_dice: 1.0771, decode.d4.loss_cls: 0.7557, decode.d4.loss_mask: 0.7843, decode.d4.loss_dice: 1.0811, decode.d5.loss_cls: 0.7444, decode.d5.loss_mask: 0.7871, decode.d5.loss_dice: 1.0820, decode.d6.loss_cls: 0.7272, decode.d6.loss_mask: 0.7858, decode.d6.loss_dice: 1.0745, decode.d7.loss_cls: 0.7164, decode.d7.loss_mask: 0.7835, decode.d7.loss_dice: 1.0781, decode.d8.loss_cls: 0.7159, decode.d8.loss_mask: 0.7847, decode.d8.loss_dice: 1.0751, loss: 29.8711 2022-05-05 01:25:47,001 - mmseg - INFO - Iter [18850/40000] lr: 7.592e-07, eta: 4:59:37, time: 0.781, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7293, decode.loss_mask: 0.7814, decode.loss_dice: 1.0980, decode.d0.loss_cls: 4.0172, decode.d0.loss_mask: 0.8043, decode.d0.loss_dice: 1.2682, decode.d1.loss_cls: 0.9488, decode.d1.loss_mask: 0.8050, decode.d1.loss_dice: 1.1659, decode.d2.loss_cls: 0.8238, decode.d2.loss_mask: 0.7823, decode.d2.loss_dice: 1.1163, decode.d3.loss_cls: 0.7654, decode.d3.loss_mask: 0.7820, decode.d3.loss_dice: 1.1023, decode.d4.loss_cls: 0.7544, decode.d4.loss_mask: 0.7789, decode.d4.loss_dice: 1.0976, decode.d5.loss_cls: 0.7311, decode.d5.loss_mask: 0.7830, decode.d5.loss_dice: 1.0989, decode.d6.loss_cls: 0.7232, decode.d6.loss_mask: 0.7834, decode.d6.loss_dice: 1.0866, decode.d7.loss_cls: 0.7201, decode.d7.loss_mask: 0.7842, decode.d7.loss_dice: 1.0946, decode.d8.loss_cls: 0.7201, decode.d8.loss_mask: 0.7832, decode.d8.loss_dice: 1.0987, loss: 30.0281 2022-05-05 01:26:26,907 - mmseg - INFO - Iter [18900/40000] lr: 7.574e-07, eta: 4:58:51, time: 0.797, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7174, decode.loss_mask: 0.7781, decode.loss_dice: 1.0724, decode.d0.loss_cls: 4.0060, decode.d0.loss_mask: 0.8041, decode.d0.loss_dice: 1.2499, decode.d1.loss_cls: 0.9461, decode.d1.loss_mask: 0.8001, decode.d1.loss_dice: 1.1558, decode.d2.loss_cls: 0.8204, decode.d2.loss_mask: 0.7836, decode.d2.loss_dice: 1.1052, decode.d3.loss_cls: 0.7789, decode.d3.loss_mask: 0.7821, decode.d3.loss_dice: 1.0839, decode.d4.loss_cls: 0.7536, decode.d4.loss_mask: 0.7809, decode.d4.loss_dice: 1.0783, decode.d5.loss_cls: 0.7409, decode.d5.loss_mask: 0.7794, decode.d5.loss_dice: 1.0817, decode.d6.loss_cls: 0.7280, decode.d6.loss_mask: 0.7731, decode.d6.loss_dice: 1.0709, decode.d7.loss_cls: 0.7156, decode.d7.loss_mask: 0.7772, decode.d7.loss_dice: 1.0736, decode.d8.loss_cls: 0.7134, decode.d8.loss_mask: 0.7812, decode.d8.loss_dice: 1.0727, loss: 29.8044 2022-05-05 01:27:07,040 - mmseg - INFO - Iter [18950/40000] lr: 7.556e-07, eta: 4:58:06, time: 0.803, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6616, decode.loss_mask: 0.7923, decode.loss_dice: 1.0702, decode.d0.loss_cls: 3.9139, decode.d0.loss_mask: 0.8137, decode.d0.loss_dice: 1.2311, decode.d1.loss_cls: 0.8512, decode.d1.loss_mask: 0.8164, decode.d1.loss_dice: 1.1358, decode.d2.loss_cls: 0.7363, decode.d2.loss_mask: 0.7938, decode.d2.loss_dice: 1.0918, decode.d3.loss_cls: 0.6999, decode.d3.loss_mask: 0.7867, decode.d3.loss_dice: 1.0740, decode.d4.loss_cls: 0.6855, decode.d4.loss_mask: 0.7871, decode.d4.loss_dice: 1.0765, decode.d5.loss_cls: 0.6827, decode.d5.loss_mask: 0.7885, decode.d5.loss_dice: 1.0666, decode.d6.loss_cls: 0.6677, decode.d6.loss_mask: 0.7876, decode.d6.loss_dice: 1.0643, decode.d7.loss_cls: 0.6541, decode.d7.loss_mask: 0.7902, decode.d7.loss_dice: 1.0670, decode.d8.loss_cls: 0.6559, decode.d8.loss_mask: 0.7914, decode.d8.loss_dice: 1.0682, loss: 29.1020 2022-05-05 01:27:46,925 - mmseg - INFO - Saving checkpoint at 19000 iterations 2022-05-05 01:28:15,102 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 01:28:15,104 - mmseg - INFO - Iter [19000/40000] lr: 7.538e-07, eta: 4:57:52, time: 1.359, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6961, decode.loss_mask: 0.7805, decode.loss_dice: 1.0998, decode.d0.loss_cls: 3.9483, decode.d0.loss_mask: 0.8212, decode.d0.loss_dice: 1.2793, decode.d1.loss_cls: 0.9019, decode.d1.loss_mask: 0.8084, decode.d1.loss_dice: 1.1836, decode.d2.loss_cls: 0.7715, decode.d2.loss_mask: 0.7922, decode.d2.loss_dice: 1.1358, decode.d3.loss_cls: 0.7274, decode.d3.loss_mask: 0.7888, decode.d3.loss_dice: 1.1151, decode.d4.loss_cls: 0.7117, decode.d4.loss_mask: 0.7850, decode.d4.loss_dice: 1.1160, decode.d5.loss_cls: 0.6930, decode.d5.loss_mask: 0.7846, decode.d5.loss_dice: 1.1147, decode.d6.loss_cls: 0.6894, decode.d6.loss_mask: 0.7844, decode.d6.loss_dice: 1.0986, decode.d7.loss_cls: 0.6902, decode.d7.loss_mask: 0.7839, decode.d7.loss_dice: 1.1006, decode.d8.loss_cls: 0.6861, decode.d8.loss_mask: 0.7822, decode.d8.loss_dice: 1.1050, loss: 29.7754 2022-05-05 01:28:54,897 - mmseg - INFO - Iter [19050/40000] lr: 7.520e-07, eta: 4:57:06, time: 0.798, data_time: 0.011, memory: 51557, decode.loss_cls: 0.6996, decode.loss_mask: 0.7972, decode.loss_dice: 1.0963, decode.d0.loss_cls: 3.9491, decode.d0.loss_mask: 0.8224, decode.d0.loss_dice: 1.2680, decode.d1.loss_cls: 0.9176, decode.d1.loss_mask: 0.8243, decode.d1.loss_dice: 1.1701, decode.d2.loss_cls: 0.7846, decode.d2.loss_mask: 0.8065, decode.d2.loss_dice: 1.1272, decode.d3.loss_cls: 0.7424, decode.d3.loss_mask: 0.8016, decode.d3.loss_dice: 1.1078, decode.d4.loss_cls: 0.7249, decode.d4.loss_mask: 0.8038, decode.d4.loss_dice: 1.1074, decode.d5.loss_cls: 0.7206, decode.d5.loss_mask: 0.7962, decode.d5.loss_dice: 1.1029, decode.d6.loss_cls: 0.7008, decode.d6.loss_mask: 0.7922, decode.d6.loss_dice: 1.0947, decode.d7.loss_cls: 0.6920, decode.d7.loss_mask: 0.7962, decode.d7.loss_dice: 1.0982, decode.d8.loss_cls: 0.6946, decode.d8.loss_mask: 0.7996, decode.d8.loss_dice: 1.0947, loss: 29.9335 2022-05-05 01:29:34,780 - mmseg - INFO - Iter [19100/40000] lr: 7.502e-07, eta: 4:56:21, time: 0.797, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6943, decode.loss_mask: 0.7577, decode.loss_dice: 1.0586, decode.d0.loss_cls: 3.9163, decode.d0.loss_mask: 0.7890, decode.d0.loss_dice: 1.2344, decode.d1.loss_cls: 0.8971, decode.d1.loss_mask: 0.7833, decode.d1.loss_dice: 1.1247, decode.d2.loss_cls: 0.7776, decode.d2.loss_mask: 0.7588, decode.d2.loss_dice: 1.0798, decode.d3.loss_cls: 0.7404, decode.d3.loss_mask: 0.7587, decode.d3.loss_dice: 1.0708, decode.d4.loss_cls: 0.7237, decode.d4.loss_mask: 0.7585, decode.d4.loss_dice: 1.0678, decode.d5.loss_cls: 0.7045, decode.d5.loss_mask: 0.7626, decode.d5.loss_dice: 1.0626, decode.d6.loss_cls: 0.6961, decode.d6.loss_mask: 0.7590, decode.d6.loss_dice: 1.0545, decode.d7.loss_cls: 0.6837, decode.d7.loss_mask: 0.7624, decode.d7.loss_dice: 1.0648, decode.d8.loss_cls: 0.6856, decode.d8.loss_mask: 0.7621, decode.d8.loss_dice: 1.0560, loss: 29.0456 2022-05-05 01:30:17,761 - mmseg - INFO - Iter [19150/40000] lr: 7.484e-07, eta: 4:55:39, time: 0.860, data_time: 0.058, memory: 51557, decode.loss_cls: 0.6622, decode.loss_mask: 0.7963, decode.loss_dice: 1.0664, decode.d0.loss_cls: 3.8735, decode.d0.loss_mask: 0.8203, decode.d0.loss_dice: 1.2338, decode.d1.loss_cls: 0.8649, decode.d1.loss_mask: 0.8312, decode.d1.loss_dice: 1.1444, decode.d2.loss_cls: 0.7504, decode.d2.loss_mask: 0.8083, decode.d2.loss_dice: 1.0962, decode.d3.loss_cls: 0.7151, decode.d3.loss_mask: 0.8005, decode.d3.loss_dice: 1.0701, decode.d4.loss_cls: 0.6941, decode.d4.loss_mask: 0.7939, decode.d4.loss_dice: 1.0756, decode.d5.loss_cls: 0.6792, decode.d5.loss_mask: 0.7969, decode.d5.loss_dice: 1.0682, decode.d6.loss_cls: 0.6707, decode.d6.loss_mask: 0.7925, decode.d6.loss_dice: 1.0575, decode.d7.loss_cls: 0.6556, decode.d7.loss_mask: 0.7941, decode.d7.loss_dice: 1.0680, decode.d8.loss_cls: 0.6625, decode.d8.loss_mask: 0.7962, decode.d8.loss_dice: 1.0694, loss: 29.2081 2022-05-05 01:30:57,448 - mmseg - INFO - Iter [19200/40000] lr: 7.466e-07, eta: 4:54:53, time: 0.794, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7080, decode.loss_mask: 0.7891, decode.loss_dice: 1.0973, decode.d0.loss_cls: 3.9804, decode.d0.loss_mask: 0.8122, decode.d0.loss_dice: 1.2811, decode.d1.loss_cls: 0.9303, decode.d1.loss_mask: 0.8168, decode.d1.loss_dice: 1.1868, decode.d2.loss_cls: 0.8116, decode.d2.loss_mask: 0.7962, decode.d2.loss_dice: 1.1313, decode.d3.loss_cls: 0.7591, decode.d3.loss_mask: 0.7923, decode.d3.loss_dice: 1.1111, decode.d4.loss_cls: 0.7406, decode.d4.loss_mask: 0.7903, decode.d4.loss_dice: 1.1133, decode.d5.loss_cls: 0.7310, decode.d5.loss_mask: 0.7872, decode.d5.loss_dice: 1.1094, decode.d6.loss_cls: 0.7219, decode.d6.loss_mask: 0.7862, decode.d6.loss_dice: 1.1031, decode.d7.loss_cls: 0.7144, decode.d7.loss_mask: 0.7862, decode.d7.loss_dice: 1.1070, decode.d8.loss_cls: 0.7114, decode.d8.loss_mask: 0.7884, decode.d8.loss_dice: 1.1051, loss: 30.0988 2022-05-05 01:31:36,819 - mmseg - INFO - Iter [19250/40000] lr: 7.449e-07, eta: 4:54:07, time: 0.787, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6941, decode.loss_mask: 0.7604, decode.loss_dice: 1.0663, decode.d0.loss_cls: 3.9446, decode.d0.loss_mask: 0.7933, decode.d0.loss_dice: 1.2442, decode.d1.loss_cls: 0.9009, decode.d1.loss_mask: 0.7947, decode.d1.loss_dice: 1.1413, decode.d2.loss_cls: 0.7700, decode.d2.loss_mask: 0.7732, decode.d2.loss_dice: 1.0999, decode.d3.loss_cls: 0.7285, decode.d3.loss_mask: 0.7675, decode.d3.loss_dice: 1.0778, decode.d4.loss_cls: 0.7062, decode.d4.loss_mask: 0.7684, decode.d4.loss_dice: 1.0737, decode.d5.loss_cls: 0.7001, decode.d5.loss_mask: 0.7636, decode.d5.loss_dice: 1.0711, decode.d6.loss_cls: 0.6955, decode.d6.loss_mask: 0.7628, decode.d6.loss_dice: 1.0587, decode.d7.loss_cls: 0.6895, decode.d7.loss_mask: 0.7619, decode.d7.loss_dice: 1.0673, decode.d8.loss_cls: 0.6762, decode.d8.loss_mask: 0.7622, decode.d8.loss_dice: 1.0678, loss: 29.1819 2022-05-05 01:32:16,624 - mmseg - INFO - Iter [19300/40000] lr: 7.431e-07, eta: 4:53:22, time: 0.795, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6890, decode.loss_mask: 0.7846, decode.loss_dice: 1.0794, decode.d0.loss_cls: 3.9048, decode.d0.loss_mask: 0.8144, decode.d0.loss_dice: 1.2439, decode.d1.loss_cls: 0.9001, decode.d1.loss_mask: 0.8111, decode.d1.loss_dice: 1.1533, decode.d2.loss_cls: 0.7763, decode.d2.loss_mask: 0.8030, decode.d2.loss_dice: 1.1129, decode.d3.loss_cls: 0.7322, decode.d3.loss_mask: 0.7913, decode.d3.loss_dice: 1.0911, decode.d4.loss_cls: 0.7158, decode.d4.loss_mask: 0.7872, decode.d4.loss_dice: 1.0939, decode.d5.loss_cls: 0.7100, decode.d5.loss_mask: 0.7845, decode.d5.loss_dice: 1.0832, decode.d6.loss_cls: 0.6963, decode.d6.loss_mask: 0.7872, decode.d6.loss_dice: 1.0725, decode.d7.loss_cls: 0.6928, decode.d7.loss_mask: 0.7879, decode.d7.loss_dice: 1.0814, decode.d8.loss_cls: 0.6862, decode.d8.loss_mask: 0.7846, decode.d8.loss_dice: 1.0793, loss: 29.5303 2022-05-05 01:32:56,387 - mmseg - INFO - Iter [19350/40000] lr: 7.413e-07, eta: 4:52:36, time: 0.796, data_time: 0.011, memory: 51557, decode.loss_cls: 0.6683, decode.loss_mask: 0.7490, decode.loss_dice: 1.0737, decode.d0.loss_cls: 3.8913, decode.d0.loss_mask: 0.7791, decode.d0.loss_dice: 1.2400, decode.d1.loss_cls: 0.8925, decode.d1.loss_mask: 0.7714, decode.d1.loss_dice: 1.1429, decode.d2.loss_cls: 0.7531, decode.d2.loss_mask: 0.7574, decode.d2.loss_dice: 1.0940, decode.d3.loss_cls: 0.7210, decode.d3.loss_mask: 0.7494, decode.d3.loss_dice: 1.0693, decode.d4.loss_cls: 0.7020, decode.d4.loss_mask: 0.7512, decode.d4.loss_dice: 1.0688, decode.d5.loss_cls: 0.6910, decode.d5.loss_mask: 0.7452, decode.d5.loss_dice: 1.0712, decode.d6.loss_cls: 0.6768, decode.d6.loss_mask: 0.7442, decode.d6.loss_dice: 1.0613, decode.d7.loss_cls: 0.6745, decode.d7.loss_mask: 0.7463, decode.d7.loss_dice: 1.0662, decode.d8.loss_cls: 0.6646, decode.d8.loss_mask: 0.7490, decode.d8.loss_dice: 1.0670, loss: 28.8318 2022-05-05 01:33:35,853 - mmseg - INFO - Iter [19400/40000] lr: 7.395e-07, eta: 4:51:51, time: 0.790, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7056, decode.loss_mask: 0.7735, decode.loss_dice: 1.0898, decode.d0.loss_cls: 3.9223, decode.d0.loss_mask: 0.7994, decode.d0.loss_dice: 1.2521, decode.d1.loss_cls: 0.9231, decode.d1.loss_mask: 0.7937, decode.d1.loss_dice: 1.1611, decode.d2.loss_cls: 0.8000, decode.d2.loss_mask: 0.7744, decode.d2.loss_dice: 1.1205, decode.d3.loss_cls: 0.7514, decode.d3.loss_mask: 0.7709, decode.d3.loss_dice: 1.0989, decode.d4.loss_cls: 0.7210, decode.d4.loss_mask: 0.7701, decode.d4.loss_dice: 1.1018, decode.d5.loss_cls: 0.7160, decode.d5.loss_mask: 0.7703, decode.d5.loss_dice: 1.0990, decode.d6.loss_cls: 0.6978, decode.d6.loss_mask: 0.7697, decode.d6.loss_dice: 1.0847, decode.d7.loss_cls: 0.6969, decode.d7.loss_mask: 0.7743, decode.d7.loss_dice: 1.0852, decode.d8.loss_cls: 0.7006, decode.d8.loss_mask: 0.7709, decode.d8.loss_dice: 1.0947, loss: 29.5898 2022-05-05 01:34:15,216 - mmseg - INFO - Iter [19450/40000] lr: 7.377e-07, eta: 4:51:05, time: 0.787, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6667, decode.loss_mask: 0.7794, decode.loss_dice: 1.0542, decode.d0.loss_cls: 3.8435, decode.d0.loss_mask: 0.8095, decode.d0.loss_dice: 1.2237, decode.d1.loss_cls: 0.8773, decode.d1.loss_mask: 0.8071, decode.d1.loss_dice: 1.1276, decode.d2.loss_cls: 0.7482, decode.d2.loss_mask: 0.7917, decode.d2.loss_dice: 1.0874, decode.d3.loss_cls: 0.7101, decode.d3.loss_mask: 0.7817, decode.d3.loss_dice: 1.0633, decode.d4.loss_cls: 0.6871, decode.d4.loss_mask: 0.7866, decode.d4.loss_dice: 1.0641, decode.d5.loss_cls: 0.6874, decode.d5.loss_mask: 0.7857, decode.d5.loss_dice: 1.0565, decode.d6.loss_cls: 0.6669, decode.d6.loss_mask: 0.7797, decode.d6.loss_dice: 1.0561, decode.d7.loss_cls: 0.6699, decode.d7.loss_mask: 0.7798, decode.d7.loss_dice: 1.0602, decode.d8.loss_cls: 0.6699, decode.d8.loss_mask: 0.7822, decode.d8.loss_dice: 1.0512, loss: 28.9545 2022-05-05 01:34:54,498 - mmseg - INFO - Iter [19500/40000] lr: 7.359e-07, eta: 4:50:19, time: 0.786, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6710, decode.loss_mask: 0.7533, decode.loss_dice: 1.0671, decode.d0.loss_cls: 3.9283, decode.d0.loss_mask: 0.7814, decode.d0.loss_dice: 1.2426, decode.d1.loss_cls: 0.8845, decode.d1.loss_mask: 0.7773, decode.d1.loss_dice: 1.1372, decode.d2.loss_cls: 0.7558, decode.d2.loss_mask: 0.7604, decode.d2.loss_dice: 1.0993, decode.d3.loss_cls: 0.7197, decode.d3.loss_mask: 0.7568, decode.d3.loss_dice: 1.0818, decode.d4.loss_cls: 0.7007, decode.d4.loss_mask: 0.7583, decode.d4.loss_dice: 1.0781, decode.d5.loss_cls: 0.6833, decode.d5.loss_mask: 0.7529, decode.d5.loss_dice: 1.0776, decode.d6.loss_cls: 0.6766, decode.d6.loss_mask: 0.7542, decode.d6.loss_dice: 1.0649, decode.d7.loss_cls: 0.6728, decode.d7.loss_mask: 0.7534, decode.d7.loss_dice: 1.0710, decode.d8.loss_cls: 0.6634, decode.d8.loss_mask: 0.7554, decode.d8.loss_dice: 1.0639, loss: 28.9427 2022-05-05 01:35:34,926 - mmseg - INFO - Iter [19550/40000] lr: 7.341e-07, eta: 4:49:34, time: 0.809, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7106, decode.loss_mask: 0.7758, decode.loss_dice: 1.0908, decode.d0.loss_cls: 3.9097, decode.d0.loss_mask: 0.7985, decode.d0.loss_dice: 1.2770, decode.d1.loss_cls: 0.9177, decode.d1.loss_mask: 0.8028, decode.d1.loss_dice: 1.1706, decode.d2.loss_cls: 0.7937, decode.d2.loss_mask: 0.7857, decode.d2.loss_dice: 1.1199, decode.d3.loss_cls: 0.7434, decode.d3.loss_mask: 0.7787, decode.d3.loss_dice: 1.1036, decode.d4.loss_cls: 0.7310, decode.d4.loss_mask: 0.7789, decode.d4.loss_dice: 1.0955, decode.d5.loss_cls: 0.7068, decode.d5.loss_mask: 0.7783, decode.d5.loss_dice: 1.1002, decode.d6.loss_cls: 0.7067, decode.d6.loss_mask: 0.7760, decode.d6.loss_dice: 1.0911, decode.d7.loss_cls: 0.7121, decode.d7.loss_mask: 0.7806, decode.d7.loss_dice: 1.0879, decode.d8.loss_cls: 0.7046, decode.d8.loss_mask: 0.7783, decode.d8.loss_dice: 1.0873, loss: 29.6938 2022-05-05 01:36:15,209 - mmseg - INFO - Iter [19600/40000] lr: 7.323e-07, eta: 4:48:49, time: 0.805, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7149, decode.loss_mask: 0.7563, decode.loss_dice: 1.0661, decode.d0.loss_cls: 3.8593, decode.d0.loss_mask: 0.7949, decode.d0.loss_dice: 1.2430, decode.d1.loss_cls: 0.9184, decode.d1.loss_mask: 0.7840, decode.d1.loss_dice: 1.1431, decode.d2.loss_cls: 0.7964, decode.d2.loss_mask: 0.7661, decode.d2.loss_dice: 1.0997, decode.d3.loss_cls: 0.7509, decode.d3.loss_mask: 0.7633, decode.d3.loss_dice: 1.0811, decode.d4.loss_cls: 0.7397, decode.d4.loss_mask: 0.7585, decode.d4.loss_dice: 1.0744, decode.d5.loss_cls: 0.7192, decode.d5.loss_mask: 0.7581, decode.d5.loss_dice: 1.0714, decode.d6.loss_cls: 0.7115, decode.d6.loss_mask: 0.7579, decode.d6.loss_dice: 1.0625, decode.d7.loss_cls: 0.7044, decode.d7.loss_mask: 0.7627, decode.d7.loss_dice: 1.0640, decode.d8.loss_cls: 0.7070, decode.d8.loss_mask: 0.7555, decode.d8.loss_dice: 1.0650, loss: 29.2493 2022-05-05 01:36:55,146 - mmseg - INFO - Iter [19650/40000] lr: 7.305e-07, eta: 4:48:04, time: 0.799, data_time: 0.010, memory: 51557, decode.loss_cls: 0.7010, decode.loss_mask: 0.7503, decode.loss_dice: 1.0761, decode.d0.loss_cls: 3.9058, decode.d0.loss_mask: 0.7822, decode.d0.loss_dice: 1.2482, decode.d1.loss_cls: 0.9212, decode.d1.loss_mask: 0.7823, decode.d1.loss_dice: 1.1555, decode.d2.loss_cls: 0.7995, decode.d2.loss_mask: 0.7612, decode.d2.loss_dice: 1.1055, decode.d3.loss_cls: 0.7453, decode.d3.loss_mask: 0.7558, decode.d3.loss_dice: 1.0879, decode.d4.loss_cls: 0.7401, decode.d4.loss_mask: 0.7502, decode.d4.loss_dice: 1.0872, decode.d5.loss_cls: 0.7220, decode.d5.loss_mask: 0.7530, decode.d5.loss_dice: 1.0796, decode.d6.loss_cls: 0.7131, decode.d6.loss_mask: 0.7507, decode.d6.loss_dice: 1.0749, decode.d7.loss_cls: 0.7066, decode.d7.loss_mask: 0.7525, decode.d7.loss_dice: 1.0768, decode.d8.loss_cls: 0.7039, decode.d8.loss_mask: 0.7509, decode.d8.loss_dice: 1.0809, loss: 29.3203 2022-05-05 01:37:36,382 - mmseg - INFO - Iter [19700/40000] lr: 7.287e-07, eta: 4:47:21, time: 0.826, data_time: 0.059, memory: 51557, decode.loss_cls: 0.7070, decode.loss_mask: 0.7550, decode.loss_dice: 1.0424, decode.d0.loss_cls: 3.8986, decode.d0.loss_mask: 0.7870, decode.d0.loss_dice: 1.2271, decode.d1.loss_cls: 0.9284, decode.d1.loss_mask: 0.7810, decode.d1.loss_dice: 1.1228, decode.d2.loss_cls: 0.7972, decode.d2.loss_mask: 0.7639, decode.d2.loss_dice: 1.0724, decode.d3.loss_cls: 0.7487, decode.d3.loss_mask: 0.7585, decode.d3.loss_dice: 1.0589, decode.d4.loss_cls: 0.7295, decode.d4.loss_mask: 0.7565, decode.d4.loss_dice: 1.0615, decode.d5.loss_cls: 0.7089, decode.d5.loss_mask: 0.7569, decode.d5.loss_dice: 1.0605, decode.d6.loss_cls: 0.7136, decode.d6.loss_mask: 0.7555, decode.d6.loss_dice: 1.0458, decode.d7.loss_cls: 0.7016, decode.d7.loss_mask: 0.7589, decode.d7.loss_dice: 1.0552, decode.d8.loss_cls: 0.6999, decode.d8.loss_mask: 0.7586, decode.d8.loss_dice: 1.0466, loss: 29.0585 2022-05-05 01:38:15,671 - mmseg - INFO - Iter [19750/40000] lr: 7.269e-07, eta: 4:46:35, time: 0.786, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7007, decode.loss_mask: 0.7748, decode.loss_dice: 1.1005, decode.d0.loss_cls: 3.9109, decode.d0.loss_mask: 0.8138, decode.d0.loss_dice: 1.2768, decode.d1.loss_cls: 0.9286, decode.d1.loss_mask: 0.8012, decode.d1.loss_dice: 1.1707, decode.d2.loss_cls: 0.8023, decode.d2.loss_mask: 0.7860, decode.d2.loss_dice: 1.1266, decode.d3.loss_cls: 0.7576, decode.d3.loss_mask: 0.7791, decode.d3.loss_dice: 1.1103, decode.d4.loss_cls: 0.7343, decode.d4.loss_mask: 0.7772, decode.d4.loss_dice: 1.1070, decode.d5.loss_cls: 0.7106, decode.d5.loss_mask: 0.7778, decode.d5.loss_dice: 1.1009, decode.d6.loss_cls: 0.7033, decode.d6.loss_mask: 0.7764, decode.d6.loss_dice: 1.0968, decode.d7.loss_cls: 0.7003, decode.d7.loss_mask: 0.7729, decode.d7.loss_dice: 1.1022, decode.d8.loss_cls: 0.7002, decode.d8.loss_mask: 0.7738, decode.d8.loss_dice: 1.0960, loss: 29.7695 2022-05-05 01:38:54,856 - mmseg - INFO - Iter [19800/40000] lr: 7.251e-07, eta: 4:45:49, time: 0.784, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6532, decode.loss_mask: 0.7581, decode.loss_dice: 1.0276, decode.d0.loss_cls: 3.8680, decode.d0.loss_mask: 0.7879, decode.d0.loss_dice: 1.2017, decode.d1.loss_cls: 0.8784, decode.d1.loss_mask: 0.7811, decode.d1.loss_dice: 1.1130, decode.d2.loss_cls: 0.7514, decode.d2.loss_mask: 0.7682, decode.d2.loss_dice: 1.0628, decode.d3.loss_cls: 0.7009, decode.d3.loss_mask: 0.7641, decode.d3.loss_dice: 1.0387, decode.d4.loss_cls: 0.6750, decode.d4.loss_mask: 0.7625, decode.d4.loss_dice: 1.0404, decode.d5.loss_cls: 0.6692, decode.d5.loss_mask: 0.7602, decode.d5.loss_dice: 1.0367, decode.d6.loss_cls: 0.6566, decode.d6.loss_mask: 0.7572, decode.d6.loss_dice: 1.0321, decode.d7.loss_cls: 0.6496, decode.d7.loss_mask: 0.7598, decode.d7.loss_dice: 1.0318, decode.d8.loss_cls: 0.6476, decode.d8.loss_mask: 0.7575, decode.d8.loss_dice: 1.0311, loss: 28.4222 2022-05-05 01:39:33,923 - mmseg - INFO - Iter [19850/40000] lr: 7.233e-07, eta: 4:45:03, time: 0.781, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6675, decode.loss_mask: 0.7464, decode.loss_dice: 1.0866, decode.d0.loss_cls: 3.9111, decode.d0.loss_mask: 0.7806, decode.d0.loss_dice: 1.2695, decode.d1.loss_cls: 0.8899, decode.d1.loss_mask: 0.7758, decode.d1.loss_dice: 1.1632, decode.d2.loss_cls: 0.7639, decode.d2.loss_mask: 0.7566, decode.d2.loss_dice: 1.1189, decode.d3.loss_cls: 0.7228, decode.d3.loss_mask: 0.7467, decode.d3.loss_dice: 1.0992, decode.d4.loss_cls: 0.6992, decode.d4.loss_mask: 0.7453, decode.d4.loss_dice: 1.0982, decode.d5.loss_cls: 0.6894, decode.d5.loss_mask: 0.7436, decode.d5.loss_dice: 1.0930, decode.d6.loss_cls: 0.6748, decode.d6.loss_mask: 0.7418, decode.d6.loss_dice: 1.0819, decode.d7.loss_cls: 0.6671, decode.d7.loss_mask: 0.7487, decode.d7.loss_dice: 1.0876, decode.d8.loss_cls: 0.6666, decode.d8.loss_mask: 0.7474, decode.d8.loss_dice: 1.0831, loss: 29.0669 2022-05-05 01:40:12,764 - mmseg - INFO - Iter [19900/40000] lr: 7.215e-07, eta: 4:44:17, time: 0.777, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6782, decode.loss_mask: 0.7802, decode.loss_dice: 1.0732, decode.d0.loss_cls: 3.8507, decode.d0.loss_mask: 0.8215, decode.d0.loss_dice: 1.2499, decode.d1.loss_cls: 0.9108, decode.d1.loss_mask: 0.8056, decode.d1.loss_dice: 1.1430, decode.d2.loss_cls: 0.7656, decode.d2.loss_mask: 0.7875, decode.d2.loss_dice: 1.0990, decode.d3.loss_cls: 0.7214, decode.d3.loss_mask: 0.7856, decode.d3.loss_dice: 1.0828, decode.d4.loss_cls: 0.7055, decode.d4.loss_mask: 0.7821, decode.d4.loss_dice: 1.0814, decode.d5.loss_cls: 0.6866, decode.d5.loss_mask: 0.7796, decode.d5.loss_dice: 1.0778, decode.d6.loss_cls: 0.6735, decode.d6.loss_mask: 0.7819, decode.d6.loss_dice: 1.0744, decode.d7.loss_cls: 0.6777, decode.d7.loss_mask: 0.7833, decode.d7.loss_dice: 1.0742, decode.d8.loss_cls: 0.6748, decode.d8.loss_mask: 0.7805, decode.d8.loss_dice: 1.0737, loss: 29.2621 2022-05-05 01:40:52,418 - mmseg - INFO - Iter [19950/40000] lr: 7.197e-07, eta: 4:43:32, time: 0.793, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6543, decode.loss_mask: 0.7471, decode.loss_dice: 1.0584, decode.d0.loss_cls: 3.8052, decode.d0.loss_mask: 0.7737, decode.d0.loss_dice: 1.2227, decode.d1.loss_cls: 0.8656, decode.d1.loss_mask: 0.7765, decode.d1.loss_dice: 1.1271, decode.d2.loss_cls: 0.7349, decode.d2.loss_mask: 0.7488, decode.d2.loss_dice: 1.0855, decode.d3.loss_cls: 0.6966, decode.d3.loss_mask: 0.7458, decode.d3.loss_dice: 1.0653, decode.d4.loss_cls: 0.6780, decode.d4.loss_mask: 0.7467, decode.d4.loss_dice: 1.0662, decode.d5.loss_cls: 0.6732, decode.d5.loss_mask: 0.7433, decode.d5.loss_dice: 1.0647, decode.d6.loss_cls: 0.6523, decode.d6.loss_mask: 0.7448, decode.d6.loss_dice: 1.0569, decode.d7.loss_cls: 0.6501, decode.d7.loss_mask: 0.7464, decode.d7.loss_dice: 1.0607, decode.d8.loss_cls: 0.6518, decode.d8.loss_mask: 0.7467, decode.d8.loss_dice: 1.0595, loss: 28.4489 2022-05-05 01:41:32,022 - mmseg - INFO - Saving checkpoint at 20000 iterations 2022-05-05 01:41:56,426 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 01:41:56,429 - mmseg - INFO - Iter [20000/40000] lr: 7.179e-07, eta: 4:43:11, time: 1.278, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6818, decode.loss_mask: 0.7480, decode.loss_dice: 1.0896, decode.d0.loss_cls: 3.8416, decode.d0.loss_mask: 0.7798, decode.d0.loss_dice: 1.2556, decode.d1.loss_cls: 0.9009, decode.d1.loss_mask: 0.7696, decode.d1.loss_dice: 1.1519, decode.d2.loss_cls: 0.7703, decode.d2.loss_mask: 0.7587, decode.d2.loss_dice: 1.1134, decode.d3.loss_cls: 0.7366, decode.d3.loss_mask: 0.7510, decode.d3.loss_dice: 1.0945, decode.d4.loss_cls: 0.7203, decode.d4.loss_mask: 0.7481, decode.d4.loss_dice: 1.1001, decode.d5.loss_cls: 0.6996, decode.d5.loss_mask: 0.7506, decode.d5.loss_dice: 1.0924, decode.d6.loss_cls: 0.6903, decode.d6.loss_mask: 0.7482, decode.d6.loss_dice: 1.0855, decode.d7.loss_cls: 0.6750, decode.d7.loss_mask: 0.7459, decode.d7.loss_dice: 1.0901, decode.d8.loss_cls: 0.6770, decode.d8.loss_mask: 0.7442, decode.d8.loss_dice: 1.0913, loss: 29.1021 2022-05-05 01:42:28,259 - mmseg - INFO - per class results: 2022-05-05 01:42:28,274 - mmseg - INFO - +------------------+-------+-------+ | Class | IoU | Acc | +------------------+-------+-------+ | person | 88.52 | 94.31 | | bicycle | 77.9 | 92.04 | | car | 49.07 | 53.43 | | motorcycle | 90.72 | 95.22 | | airplane | 89.46 | 95.03 | | bus | 76.69 | 80.4 | | train | 75.57 | 96.68 | | truck | 62.47 | 90.52 | | boat | 80.33 | 85.17 | | traffic light | 82.99 | 92.74 | | fire hydrant | 85.74 | 96.98 | | stop sign | 95.12 | 97.77 | | parking meter | 72.86 | 75.88 | | bench | 56.37 | 71.38 | | bird | 70.54 | 84.39 | | cat | 93.04 | 95.7 | | dog | 91.55 | 96.11 | | horse | 91.29 | 95.78 | | sheep | 83.17 | 90.75 | | cow | 93.57 | 95.92 | | elephant | 92.68 | 96.05 | | bear | 92.53 | 93.9 | | zebra | 91.82 | 95.02 | | giraffe | 88.42 | 93.26 | | backpack | 23.77 | 55.32 | | umbrella | 84.47 | 93.4 | | handbag | 24.2 | 41.33 | | tie | 65.69 | 65.7 | | suitcase | 77.1 | 88.61 | | frisbee | 91.69 | 98.12 | | skis | 41.69 | 66.48 | | snowboard | 64.15 | 80.74 | | sports ball | 83.78 | 94.5 | | kite | 61.31 | 68.95 | | baseball bat | 60.59 | 78.27 | | baseball glove | 2.5 | 2.65 | | skateboard | 70.16 | 86.95 | | surfboard | 90.21 | 94.69 | | tennis racket | 29.29 | 31.01 | | bottle | 68.36 | 87.29 | | wine glass | 85.95 | 93.67 | | cup | 70.25 | 80.26 | | fork | 56.54 | 70.68 | | knife | 74.63 | 89.68 | | spoon | 50.08 | 70.72 | | bowl | 62.77 | 76.0 | | banana | 85.71 | 93.88 | | apple | 70.89 | 83.13 | | sandwich | 85.14 | 94.42 | | orange | 67.43 | 88.16 | | broccoli | 93.05 | 96.75 | | carrot | 62.42 | 84.16 | | hot dog | 58.46 | 96.78 | | pizza | 94.74 | 96.31 | | donut | 78.65 | 93.98 | | cake | 84.79 | 87.61 | | chair | 61.87 | 76.45 | | couch | 78.79 | 95.12 | | potted plant | 33.44 | 39.91 | | bed | 71.1 | 83.5 | | dining table | 62.52 | 83.44 | | toilet | 89.37 | 96.32 | | tv | 79.83 | 92.41 | | laptop | 88.01 | 97.01 | | mouse | 83.24 | 88.2 | | remote | 73.99 | 94.82 | | keyboard | 87.15 | 97.75 | | cell phone | 83.17 | 95.66 | | microwave | 69.59 | 75.02 | | oven | 63.6 | 80.59 | | toaster | 55.55 | 57.21 | | sink | 75.85 | 80.74 | | refrigerator | 85.96 | 95.79 | | book | 80.43 | 90.78 | | clock | 80.48 | 85.0 | | vase | 56.8 | 84.61 | | scissors | 81.66 | 91.59 | | teddy bear | 87.8 | 96.04 | | hair drier | 0.0 | 0.0 | | toothbrush | 25.63 | 49.23 | | banner | 33.51 | 64.69 | | blanket | 0.0 | 0.0 | | branch | 43.05 | 52.0 | | bridge | 6.42 | 9.92 | | building-other | 57.14 | 74.64 | | bush | 33.65 | 56.83 | | cabinet | 25.65 | 36.93 | | cage | 27.29 | 80.47 | | cardboard | 24.72 | 29.2 | | carpet | 59.93 | 77.6 | | ceiling-other | 73.76 | 83.71 | | ceiling-tile | 12.72 | 14.03 | | cloth | 3.17 | 3.75 | | clothes | 26.78 | 43.98 | | clouds | 53.23 | 65.11 | | counter | 48.45 | 54.78 | | cupboard | 64.26 | 81.73 | | curtain | 64.75 | 81.59 | | desk-stuff | 24.99 | 26.31 | | dirt | 39.59 | 65.11 | | door-stuff | 43.7 | 60.76 | | fence | 42.39 | 72.17 | | floor-marble | 0.0 | 0.0 | | floor-other | 32.02 | 53.38 | | floor-stone | 21.39 | 22.27 | | floor-tile | 64.5 | 79.36 | | floor-wood | 69.86 | 78.49 | | flower | 23.64 | 53.52 | | fog | 0.0 | 0.0 | | food-other | 45.45 | 55.33 | | fruit | 64.51 | 83.81 | | furniture-other | 18.99 | 28.48 | | grass | 74.43 | 82.8 | | gravel | 30.56 | 37.43 | | ground-other | 14.93 | 30.38 | | hill | 23.66 | 30.74 | | house | 30.57 | 41.97 | | leaves | 40.06 | 45.13 | | light | 40.78 | 59.28 | | mat | 36.7 | 61.32 | | metal | 14.79 | 26.43 | | mirror-stuff | 52.55 | 72.94 | | moss | 0.0 | 0.0 | | mountain | 32.87 | 60.02 | | mud | 0.0 | 0.0 | | napkin | 76.96 | 89.19 | | net | 30.07 | 38.45 | | paper | 50.42 | 67.55 | | pavement | 49.97 | 69.49 | | pillow | 0.0 | 0.0 | | plant-other | 26.39 | 45.62 | | plastic | 27.01 | 43.2 | | platform | 52.62 | 61.01 | | playingfield | 72.54 | 84.51 | | railing | 20.34 | 38.39 | | railroad | 59.76 | 80.0 | | river | 32.28 | 42.75 | | road | 72.48 | 79.98 | | rock | 46.44 | 66.3 | | roof | 6.98 | 11.47 | | rug | 44.33 | 57.09 | | salad | 0.26 | 0.26 | | sand | 69.98 | 85.4 | | sea | 77.68 | 88.33 | | shelf | 28.58 | 42.21 | | sky-other | 63.6 | 81.3 | | skyscraper | 9.04 | 13.56 | | snow | 91.57 | 94.15 | | solid-other | nan | nan | | stairs | 46.73 | 75.34 | | stone | 7.33 | 14.65 | | straw | 9.73 | 33.75 | | structural-other | 19.07 | 30.68 | | table | 22.44 | 38.73 | | tent | 41.53 | 46.57 | | textile-other | 15.68 | 24.49 | | towel | 41.4 | 48.7 | | tree | 77.82 | 85.77 | | vegetable | 56.63 | 62.1 | | wall-brick | 50.07 | 59.73 | | wall-concrete | 23.45 | 30.01 | | wall-other | 62.63 | 82.14 | | wall-panel | 5.86 | 6.57 | | wall-stone | 39.79 | 41.99 | | wall-tile | 50.84 | 76.31 | | wall-wood | 42.06 | 52.5 | | water-other | 41.04 | 56.15 | | waterdrops | 0.0 | nan | | window-blind | 27.51 | 47.54 | | window-other | 52.35 | 67.73 | | wood | 15.2 | 30.65 | +------------------+-------+-------+ 2022-05-05 01:42:28,276 - mmseg - INFO - Summary: 2022-05-05 01:42:28,276 - mmseg - INFO - +------+-------+-------+ | aAcc | mIoU | mAcc | +------+-------+-------+ | 76.3 | 53.23 | 65.34 | +------+-------+-------+ 2022-05-05 01:42:28,279 - mmseg - INFO - The previous best checkpoint /mnt/lustre/chenzhe.vendor/workspace/DenseAdaptor/segmentation/work_dirs/mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss/best_mIoU_iter_16000.pth was removed 2022-05-05 01:42:53,185 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_20000.pth. 2022-05-05 01:42:53,197 - mmseg - INFO - Best mIoU is 0.5323 at 20000 iter. 2022-05-05 01:42:53,208 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 01:42:53,209 - mmseg - INFO - Iter(val) [125] aAcc: 0.7630, mIoU: 0.5323, mAcc: 0.6534, IoU.person: 0.8852, IoU.bicycle: 0.7790, IoU.car: 0.4907, IoU.motorcycle: 0.9072, IoU.airplane: 0.8946, IoU.bus: 0.7669, IoU.train: 0.7557, IoU.truck: 0.6247, IoU.boat: 0.8033, IoU.traffic light: 0.8299, IoU.fire hydrant: 0.8574, IoU.stop sign: 0.9512, IoU.parking meter: 0.7286, IoU.bench: 0.5637, IoU.bird: 0.7054, IoU.cat: 0.9304, IoU.dog: 0.9155, IoU.horse: 0.9129, IoU.sheep: 0.8317, IoU.cow: 0.9357, IoU.elephant: 0.9268, IoU.bear: 0.9253, IoU.zebra: 0.9182, IoU.giraffe: 0.8842, IoU.backpack: 0.2377, IoU.umbrella: 0.8447, IoU.handbag: 0.2420, IoU.tie: 0.6569, IoU.suitcase: 0.7710, IoU.frisbee: 0.9169, IoU.skis: 0.4169, IoU.snowboard: 0.6415, IoU.sports ball: 0.8378, IoU.kite: 0.6131, IoU.baseball bat: 0.6059, IoU.baseball glove: 0.0250, IoU.skateboard: 0.7016, IoU.surfboard: 0.9021, IoU.tennis racket: 0.2929, IoU.bottle: 0.6836, IoU.wine glass: 0.8595, IoU.cup: 0.7025, IoU.fork: 0.5654, IoU.knife: 0.7463, IoU.spoon: 0.5008, IoU.bowl: 0.6277, IoU.banana: 0.8571, IoU.apple: 0.7089, IoU.sandwich: 0.8514, IoU.orange: 0.6743, IoU.broccoli: 0.9305, IoU.carrot: 0.6242, IoU.hot dog: 0.5846, IoU.pizza: 0.9474, IoU.donut: 0.7865, IoU.cake: 0.8479, IoU.chair: 0.6187, IoU.couch: 0.7879, IoU.potted plant: 0.3344, IoU.bed: 0.7110, IoU.dining table: 0.6252, IoU.toilet: 0.8937, IoU.tv: 0.7983, IoU.laptop: 0.8801, IoU.mouse: 0.8324, IoU.remote: 0.7399, IoU.keyboard: 0.8715, IoU.cell phone: 0.8317, IoU.microwave: 0.6959, IoU.oven: 0.6360, IoU.toaster: 0.5555, IoU.sink: 0.7585, IoU.refrigerator: 0.8596, IoU.book: 0.8043, IoU.clock: 0.8048, IoU.vase: 0.5680, IoU.scissors: 0.8166, IoU.teddy bear: 0.8780, IoU.hair drier: 0.0000, IoU.toothbrush: 0.2563, IoU.banner: 0.3351, IoU.blanket: 0.0000, IoU.branch: 0.4305, IoU.bridge: 0.0642, IoU.building-other: 0.5714, IoU.bush: 0.3365, IoU.cabinet: 0.2565, IoU.cage: 0.2729, IoU.cardboard: 0.2472, IoU.carpet: 0.5993, IoU.ceiling-other: 0.7376, IoU.ceiling-tile: 0.1272, IoU.cloth: 0.0317, IoU.clothes: 0.2678, IoU.clouds: 0.5323, IoU.counter: 0.4845, IoU.cupboard: 0.6426, IoU.curtain: 0.6475, IoU.desk-stuff: 0.2499, IoU.dirt: 0.3959, IoU.door-stuff: 0.4370, IoU.fence: 0.4239, IoU.floor-marble: 0.0000, IoU.floor-other: 0.3202, IoU.floor-stone: 0.2139, IoU.floor-tile: 0.6450, IoU.floor-wood: 0.6986, IoU.flower: 0.2364, IoU.fog: 0.0000, IoU.food-other: 0.4545, IoU.fruit: 0.6451, IoU.furniture-other: 0.1899, IoU.grass: 0.7443, IoU.gravel: 0.3056, IoU.ground-other: 0.1493, IoU.hill: 0.2366, IoU.house: 0.3057, IoU.leaves: 0.4006, IoU.light: 0.4078, IoU.mat: 0.3670, IoU.metal: 0.1479, IoU.mirror-stuff: 0.5255, IoU.moss: 0.0000, IoU.mountain: 0.3287, IoU.mud: 0.0000, IoU.napkin: 0.7696, IoU.net: 0.3007, IoU.paper: 0.5042, IoU.pavement: 0.4997, IoU.pillow: 0.0000, IoU.plant-other: 0.2639, IoU.plastic: 0.2701, IoU.platform: 0.5262, IoU.playingfield: 0.7254, IoU.railing: 0.2034, IoU.railroad: 0.5976, IoU.river: 0.3228, IoU.road: 0.7248, IoU.rock: 0.4644, IoU.roof: 0.0698, IoU.rug: 0.4433, IoU.salad: 0.0026, IoU.sand: 0.6998, IoU.sea: 0.7768, IoU.shelf: 0.2858, IoU.sky-other: 0.6360, IoU.skyscraper: 0.0904, IoU.snow: 0.9157, IoU.solid-other: nan, IoU.stairs: 0.4673, IoU.stone: 0.0733, IoU.straw: 0.0973, IoU.structural-other: 0.1907, IoU.table: 0.2244, IoU.tent: 0.4153, IoU.textile-other: 0.1568, IoU.towel: 0.4140, IoU.tree: 0.7782, IoU.vegetable: 0.5663, IoU.wall-brick: 0.5007, IoU.wall-concrete: 0.2345, IoU.wall-other: 0.6263, IoU.wall-panel: 0.0586, IoU.wall-stone: 0.3979, IoU.wall-tile: 0.5084, IoU.wall-wood: 0.4206, IoU.water-other: 0.4104, IoU.waterdrops: 0.0000, IoU.window-blind: 0.2751, IoU.window-other: 0.5235, IoU.wood: 0.1520, Acc.person: 0.9431, Acc.bicycle: 0.9204, Acc.car: 0.5343, Acc.motorcycle: 0.9522, Acc.airplane: 0.9503, Acc.bus: 0.8040, Acc.train: 0.9668, Acc.truck: 0.9052, Acc.boat: 0.8517, Acc.traffic light: 0.9274, Acc.fire hydrant: 0.9698, Acc.stop sign: 0.9777, Acc.parking meter: 0.7588, Acc.bench: 0.7138, Acc.bird: 0.8439, Acc.cat: 0.9570, Acc.dog: 0.9611, Acc.horse: 0.9578, Acc.sheep: 0.9075, Acc.cow: 0.9592, Acc.elephant: 0.9605, Acc.bear: 0.9390, Acc.zebra: 0.9502, Acc.giraffe: 0.9326, Acc.backpack: 0.5532, Acc.umbrella: 0.9340, Acc.handbag: 0.4133, Acc.tie: 0.6570, Acc.suitcase: 0.8861, Acc.frisbee: 0.9812, Acc.skis: 0.6648, Acc.snowboard: 0.8074, Acc.sports ball: 0.9450, Acc.kite: 0.6895, Acc.baseball bat: 0.7827, Acc.baseball glove: 0.0265, Acc.skateboard: 0.8695, Acc.surfboard: 0.9469, Acc.tennis racket: 0.3101, Acc.bottle: 0.8729, Acc.wine glass: 0.9367, Acc.cup: 0.8026, Acc.fork: 0.7068, Acc.knife: 0.8968, Acc.spoon: 0.7072, Acc.bowl: 0.7600, Acc.banana: 0.9388, Acc.apple: 0.8313, Acc.sandwich: 0.9442, Acc.orange: 0.8816, Acc.broccoli: 0.9675, Acc.carrot: 0.8416, Acc.hot dog: 0.9678, Acc.pizza: 0.9631, Acc.donut: 0.9398, Acc.cake: 0.8761, Acc.chair: 0.7645, Acc.couch: 0.9512, Acc.potted plant: 0.3991, Acc.bed: 0.8350, Acc.dining table: 0.8344, Acc.toilet: 0.9632, Acc.tv: 0.9241, Acc.laptop: 0.9701, Acc.mouse: 0.8820, Acc.remote: 0.9482, Acc.keyboard: 0.9775, Acc.cell phone: 0.9566, Acc.microwave: 0.7502, Acc.oven: 0.8059, Acc.toaster: 0.5721, Acc.sink: 0.8074, Acc.refrigerator: 0.9579, Acc.book: 0.9078, Acc.clock: 0.8500, Acc.vase: 0.8461, Acc.scissors: 0.9159, Acc.teddy bear: 0.9604, Acc.hair drier: 0.0000, Acc.toothbrush: 0.4923, Acc.banner: 0.6469, Acc.blanket: 0.0000, Acc.branch: 0.5200, Acc.bridge: 0.0992, Acc.building-other: 0.7464, Acc.bush: 0.5683, Acc.cabinet: 0.3693, Acc.cage: 0.8047, Acc.cardboard: 0.2920, Acc.carpet: 0.7760, Acc.ceiling-other: 0.8371, Acc.ceiling-tile: 0.1403, Acc.cloth: 0.0375, Acc.clothes: 0.4398, Acc.clouds: 0.6511, Acc.counter: 0.5478, Acc.cupboard: 0.8173, Acc.curtain: 0.8159, Acc.desk-stuff: 0.2631, Acc.dirt: 0.6511, Acc.door-stuff: 0.6076, Acc.fence: 0.7217, Acc.floor-marble: 0.0000, Acc.floor-other: 0.5338, Acc.floor-stone: 0.2227, Acc.floor-tile: 0.7936, Acc.floor-wood: 0.7849, Acc.flower: 0.5352, Acc.fog: 0.0000, Acc.food-other: 0.5533, Acc.fruit: 0.8381, Acc.furniture-other: 0.2848, Acc.grass: 0.8280, Acc.gravel: 0.3743, Acc.ground-other: 0.3038, Acc.hill: 0.3074, Acc.house: 0.4197, Acc.leaves: 0.4513, Acc.light: 0.5928, Acc.mat: 0.6132, Acc.metal: 0.2643, Acc.mirror-stuff: 0.7294, Acc.moss: 0.0000, Acc.mountain: 0.6002, Acc.mud: 0.0000, Acc.napkin: 0.8919, Acc.net: 0.3845, Acc.paper: 0.6755, Acc.pavement: 0.6949, Acc.pillow: 0.0000, Acc.plant-other: 0.4562, Acc.plastic: 0.4320, Acc.platform: 0.6101, Acc.playingfield: 0.8451, Acc.railing: 0.3839, Acc.railroad: 0.8000, Acc.river: 0.4275, Acc.road: 0.7998, Acc.rock: 0.6630, Acc.roof: 0.1147, Acc.rug: 0.5709, Acc.salad: 0.0026, Acc.sand: 0.8540, Acc.sea: 0.8833, Acc.shelf: 0.4221, Acc.sky-other: 0.8130, Acc.skyscraper: 0.1356, Acc.snow: 0.9415, Acc.solid-other: nan, Acc.stairs: 0.7534, Acc.stone: 0.1465, Acc.straw: 0.3375, Acc.structural-other: 0.3068, Acc.table: 0.3873, Acc.tent: 0.4657, Acc.textile-other: 0.2449, Acc.towel: 0.4870, Acc.tree: 0.8577, Acc.vegetable: 0.6210, Acc.wall-brick: 0.5973, Acc.wall-concrete: 0.3001, Acc.wall-other: 0.8214, Acc.wall-panel: 0.0657, Acc.wall-stone: 0.4199, Acc.wall-tile: 0.7631, Acc.wall-wood: 0.5250, Acc.water-other: 0.5615, Acc.waterdrops: nan, Acc.window-blind: 0.4754, Acc.window-other: 0.6773, Acc.wood: 0.3065 2022-05-05 01:43:33,213 - mmseg - INFO - Iter [20050/40000] lr: 7.161e-07, eta: 4:43:23, time: 1.937, data_time: 1.148, memory: 51557, decode.loss_cls: 0.6714, decode.loss_mask: 0.7481, decode.loss_dice: 1.0573, decode.d0.loss_cls: 3.8706, decode.d0.loss_mask: 0.7840, decode.d0.loss_dice: 1.2386, decode.d1.loss_cls: 0.9018, decode.d1.loss_mask: 0.7712, decode.d1.loss_dice: 1.1404, decode.d2.loss_cls: 0.7695, decode.d2.loss_mask: 0.7563, decode.d2.loss_dice: 1.0908, decode.d3.loss_cls: 0.7270, decode.d3.loss_mask: 0.7452, decode.d3.loss_dice: 1.0654, decode.d4.loss_cls: 0.7154, decode.d4.loss_mask: 0.7431, decode.d4.loss_dice: 1.0638, decode.d5.loss_cls: 0.6944, decode.d5.loss_mask: 0.7461, decode.d5.loss_dice: 1.0611, decode.d6.loss_cls: 0.6793, decode.d6.loss_mask: 0.7486, decode.d6.loss_dice: 1.0554, decode.d7.loss_cls: 0.6729, decode.d7.loss_mask: 0.7477, decode.d7.loss_dice: 1.0559, decode.d8.loss_cls: 0.6702, decode.d8.loss_mask: 0.7502, decode.d8.loss_dice: 1.0641, loss: 28.8060 2022-05-05 01:44:12,521 - mmseg - INFO - Iter [20100/40000] lr: 7.143e-07, eta: 4:42:37, time: 0.786, data_time: 0.011, memory: 51557, decode.loss_cls: 0.6823, decode.loss_mask: 0.7618, decode.loss_dice: 1.0746, decode.d0.loss_cls: 3.8104, decode.d0.loss_mask: 0.7924, decode.d0.loss_dice: 1.2454, decode.d1.loss_cls: 0.8827, decode.d1.loss_mask: 0.7911, decode.d1.loss_dice: 1.1567, decode.d2.loss_cls: 0.7556, decode.d2.loss_mask: 0.7719, decode.d2.loss_dice: 1.1090, decode.d3.loss_cls: 0.7191, decode.d3.loss_mask: 0.7666, decode.d3.loss_dice: 1.0823, decode.d4.loss_cls: 0.6978, decode.d4.loss_mask: 0.7624, decode.d4.loss_dice: 1.0820, decode.d5.loss_cls: 0.6955, decode.d5.loss_mask: 0.7629, decode.d5.loss_dice: 1.0818, decode.d6.loss_cls: 0.6792, decode.d6.loss_mask: 0.7639, decode.d6.loss_dice: 1.0759, decode.d7.loss_cls: 0.6797, decode.d7.loss_mask: 0.7633, decode.d7.loss_dice: 1.0728, decode.d8.loss_cls: 0.6804, decode.d8.loss_mask: 0.7573, decode.d8.loss_dice: 1.0767, loss: 29.0334 2022-05-05 01:44:52,308 - mmseg - INFO - Iter [20150/40000] lr: 7.125e-07, eta: 4:41:51, time: 0.796, data_time: 0.011, memory: 51557, decode.loss_cls: 0.6800, decode.loss_mask: 0.7895, decode.loss_dice: 1.0672, decode.d0.loss_cls: 3.8652, decode.d0.loss_mask: 0.8249, decode.d0.loss_dice: 1.2354, decode.d1.loss_cls: 0.9022, decode.d1.loss_mask: 0.8167, decode.d1.loss_dice: 1.1401, decode.d2.loss_cls: 0.7764, decode.d2.loss_mask: 0.7941, decode.d2.loss_dice: 1.0911, decode.d3.loss_cls: 0.7228, decode.d3.loss_mask: 0.7922, decode.d3.loss_dice: 1.0754, decode.d4.loss_cls: 0.7079, decode.d4.loss_mask: 0.7922, decode.d4.loss_dice: 1.0683, decode.d5.loss_cls: 0.6851, decode.d5.loss_mask: 0.7921, decode.d5.loss_dice: 1.0706, decode.d6.loss_cls: 0.6758, decode.d6.loss_mask: 0.7908, decode.d6.loss_dice: 1.0694, decode.d7.loss_cls: 0.6733, decode.d7.loss_mask: 0.7931, decode.d7.loss_dice: 1.0667, decode.d8.loss_cls: 0.6697, decode.d8.loss_mask: 0.7925, decode.d8.loss_dice: 1.0633, loss: 29.2842 2022-05-05 01:45:32,176 - mmseg - INFO - Iter [20200/40000] lr: 7.108e-07, eta: 4:41:06, time: 0.797, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6763, decode.loss_mask: 0.7686, decode.loss_dice: 1.0546, decode.d0.loss_cls: 3.8220, decode.d0.loss_mask: 0.8045, decode.d0.loss_dice: 1.2270, decode.d1.loss_cls: 0.8910, decode.d1.loss_mask: 0.8043, decode.d1.loss_dice: 1.1289, decode.d2.loss_cls: 0.7562, decode.d2.loss_mask: 0.7771, decode.d2.loss_dice: 1.0835, decode.d3.loss_cls: 0.7034, decode.d3.loss_mask: 0.7721, decode.d3.loss_dice: 1.0631, decode.d4.loss_cls: 0.6874, decode.d4.loss_mask: 0.7711, decode.d4.loss_dice: 1.0636, decode.d5.loss_cls: 0.6711, decode.d5.loss_mask: 0.7744, decode.d5.loss_dice: 1.0570, decode.d6.loss_cls: 0.6721, decode.d6.loss_mask: 0.7689, decode.d6.loss_dice: 1.0580, decode.d7.loss_cls: 0.6640, decode.d7.loss_mask: 0.7725, decode.d7.loss_dice: 1.0607, decode.d8.loss_cls: 0.6673, decode.d8.loss_mask: 0.7691, decode.d8.loss_dice: 1.0599, loss: 28.8499 2022-05-05 01:46:13,984 - mmseg - INFO - Iter [20250/40000] lr: 7.090e-07, eta: 4:40:23, time: 0.836, data_time: 0.058, memory: 51557, decode.loss_cls: 0.6974, decode.loss_mask: 0.7739, decode.loss_dice: 1.0788, decode.d0.loss_cls: 3.8744, decode.d0.loss_mask: 0.8057, decode.d0.loss_dice: 1.2449, decode.d1.loss_cls: 0.9191, decode.d1.loss_mask: 0.8031, decode.d1.loss_dice: 1.1510, decode.d2.loss_cls: 0.7906, decode.d2.loss_mask: 0.7818, decode.d2.loss_dice: 1.1079, decode.d3.loss_cls: 0.7368, decode.d3.loss_mask: 0.7778, decode.d3.loss_dice: 1.0922, decode.d4.loss_cls: 0.7326, decode.d4.loss_mask: 0.7713, decode.d4.loss_dice: 1.0893, decode.d5.loss_cls: 0.7061, decode.d5.loss_mask: 0.7743, decode.d5.loss_dice: 1.0838, decode.d6.loss_cls: 0.7040, decode.d6.loss_mask: 0.7732, decode.d6.loss_dice: 1.0765, decode.d7.loss_cls: 0.7023, decode.d7.loss_mask: 0.7728, decode.d7.loss_dice: 1.0750, decode.d8.loss_cls: 0.7033, decode.d8.loss_mask: 0.7711, decode.d8.loss_dice: 1.0760, loss: 29.4470 2022-05-05 01:46:53,600 - mmseg - INFO - Iter [20300/40000] lr: 7.072e-07, eta: 4:39:37, time: 0.792, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6555, decode.loss_mask: 0.7512, decode.loss_dice: 1.0785, decode.d0.loss_cls: 3.8491, decode.d0.loss_mask: 0.7869, decode.d0.loss_dice: 1.2508, decode.d1.loss_cls: 0.8876, decode.d1.loss_mask: 0.7763, decode.d1.loss_dice: 1.1492, decode.d2.loss_cls: 0.7601, decode.d2.loss_mask: 0.7630, decode.d2.loss_dice: 1.1061, decode.d3.loss_cls: 0.7175, decode.d3.loss_mask: 0.7542, decode.d3.loss_dice: 1.0826, decode.d4.loss_cls: 0.6937, decode.d4.loss_mask: 0.7553, decode.d4.loss_dice: 1.0772, decode.d5.loss_cls: 0.6847, decode.d5.loss_mask: 0.7538, decode.d5.loss_dice: 1.0793, decode.d6.loss_cls: 0.6730, decode.d6.loss_mask: 0.7501, decode.d6.loss_dice: 1.0717, decode.d7.loss_cls: 0.6677, decode.d7.loss_mask: 0.7506, decode.d7.loss_dice: 1.0773, decode.d8.loss_cls: 0.6618, decode.d8.loss_mask: 0.7518, decode.d8.loss_dice: 1.0801, loss: 28.8967 2022-05-05 01:47:32,827 - mmseg - INFO - Iter [20350/40000] lr: 7.054e-07, eta: 4:38:51, time: 0.785, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6375, decode.loss_mask: 0.7791, decode.loss_dice: 1.1033, decode.d0.loss_cls: 3.8215, decode.d0.loss_mask: 0.8137, decode.d0.loss_dice: 1.2669, decode.d1.loss_cls: 0.8505, decode.d1.loss_mask: 0.8075, decode.d1.loss_dice: 1.1693, decode.d2.loss_cls: 0.7441, decode.d2.loss_mask: 0.7893, decode.d2.loss_dice: 1.1307, decode.d3.loss_cls: 0.6890, decode.d3.loss_mask: 0.7845, decode.d3.loss_dice: 1.1145, decode.d4.loss_cls: 0.6644, decode.d4.loss_mask: 0.7804, decode.d4.loss_dice: 1.1141, decode.d5.loss_cls: 0.6497, decode.d5.loss_mask: 0.7830, decode.d5.loss_dice: 1.1102, decode.d6.loss_cls: 0.6413, decode.d6.loss_mask: 0.7807, decode.d6.loss_dice: 1.1018, decode.d7.loss_cls: 0.6315, decode.d7.loss_mask: 0.7771, decode.d7.loss_dice: 1.1043, decode.d8.loss_cls: 0.6469, decode.d8.loss_mask: 0.7779, decode.d8.loss_dice: 1.1067, loss: 29.1715 2022-05-05 01:48:13,003 - mmseg - INFO - Iter [20400/40000] lr: 7.036e-07, eta: 4:38:07, time: 0.803, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6797, decode.loss_mask: 0.7519, decode.loss_dice: 1.0607, decode.d0.loss_cls: 3.8188, decode.d0.loss_mask: 0.7911, decode.d0.loss_dice: 1.2399, decode.d1.loss_cls: 0.9123, decode.d1.loss_mask: 0.7824, decode.d1.loss_dice: 1.1448, decode.d2.loss_cls: 0.7899, decode.d2.loss_mask: 0.7631, decode.d2.loss_dice: 1.1011, decode.d3.loss_cls: 0.7383, decode.d3.loss_mask: 0.7581, decode.d3.loss_dice: 1.0783, decode.d4.loss_cls: 0.7184, decode.d4.loss_mask: 0.7552, decode.d4.loss_dice: 1.0763, decode.d5.loss_cls: 0.7044, decode.d5.loss_mask: 0.7540, decode.d5.loss_dice: 1.0671, decode.d6.loss_cls: 0.6904, decode.d6.loss_mask: 0.7561, decode.d6.loss_dice: 1.0633, decode.d7.loss_cls: 0.6849, decode.d7.loss_mask: 0.7558, decode.d7.loss_dice: 1.0623, decode.d8.loss_cls: 0.6759, decode.d8.loss_mask: 0.7554, decode.d8.loss_dice: 1.0631, loss: 28.9929 2022-05-05 01:48:52,239 - mmseg - INFO - Iter [20450/40000] lr: 7.018e-07, eta: 4:37:21, time: 0.784, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6491, decode.loss_mask: 0.7830, decode.loss_dice: 1.0773, decode.d0.loss_cls: 3.7791, decode.d0.loss_mask: 0.8090, decode.d0.loss_dice: 1.2464, decode.d1.loss_cls: 0.8746, decode.d1.loss_mask: 0.8064, decode.d1.loss_dice: 1.1495, decode.d2.loss_cls: 0.7389, decode.d2.loss_mask: 0.7846, decode.d2.loss_dice: 1.1029, decode.d3.loss_cls: 0.6884, decode.d3.loss_mask: 0.7782, decode.d3.loss_dice: 1.0861, decode.d4.loss_cls: 0.6662, decode.d4.loss_mask: 0.7817, decode.d4.loss_dice: 1.0876, decode.d5.loss_cls: 0.6589, decode.d5.loss_mask: 0.7764, decode.d5.loss_dice: 1.0823, decode.d6.loss_cls: 0.6503, decode.d6.loss_mask: 0.7750, decode.d6.loss_dice: 1.0765, decode.d7.loss_cls: 0.6545, decode.d7.loss_mask: 0.7727, decode.d7.loss_dice: 1.0809, decode.d8.loss_cls: 0.6479, decode.d8.loss_mask: 0.7763, decode.d8.loss_dice: 1.0786, loss: 28.9193 2022-05-05 01:49:31,506 - mmseg - INFO - Iter [20500/40000] lr: 7.000e-07, eta: 4:36:35, time: 0.786, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6397, decode.loss_mask: 0.7772, decode.loss_dice: 1.0577, decode.d0.loss_cls: 3.7343, decode.d0.loss_mask: 0.8188, decode.d0.loss_dice: 1.2450, decode.d1.loss_cls: 0.8405, decode.d1.loss_mask: 0.8133, decode.d1.loss_dice: 1.1337, decode.d2.loss_cls: 0.7111, decode.d2.loss_mask: 0.7899, decode.d2.loss_dice: 1.0864, decode.d3.loss_cls: 0.6678, decode.d3.loss_mask: 0.7847, decode.d3.loss_dice: 1.0664, decode.d4.loss_cls: 0.6573, decode.d4.loss_mask: 0.7821, decode.d4.loss_dice: 1.0688, decode.d5.loss_cls: 0.6469, decode.d5.loss_mask: 0.7816, decode.d5.loss_dice: 1.0610, decode.d6.loss_cls: 0.6446, decode.d6.loss_mask: 0.7776, decode.d6.loss_dice: 1.0585, decode.d7.loss_cls: 0.6305, decode.d7.loss_mask: 0.7795, decode.d7.loss_dice: 1.0587, decode.d8.loss_cls: 0.6406, decode.d8.loss_mask: 0.7752, decode.d8.loss_dice: 1.0497, loss: 28.5790 2022-05-05 01:50:10,840 - mmseg - INFO - Iter [20550/40000] lr: 6.982e-07, eta: 4:35:50, time: 0.787, data_time: 0.009, memory: 51557, decode.loss_cls: 0.7038, decode.loss_mask: 0.7460, decode.loss_dice: 1.0500, decode.d0.loss_cls: 3.8516, decode.d0.loss_mask: 0.7865, decode.d0.loss_dice: 1.2247, decode.d1.loss_cls: 0.9145, decode.d1.loss_mask: 0.7750, decode.d1.loss_dice: 1.1375, decode.d2.loss_cls: 0.7900, decode.d2.loss_mask: 0.7634, decode.d2.loss_dice: 1.0913, decode.d3.loss_cls: 0.7473, decode.d3.loss_mask: 0.7564, decode.d3.loss_dice: 1.0677, decode.d4.loss_cls: 0.7289, decode.d4.loss_mask: 0.7495, decode.d4.loss_dice: 1.0707, decode.d5.loss_cls: 0.7205, decode.d5.loss_mask: 0.7466, decode.d5.loss_dice: 1.0613, decode.d6.loss_cls: 0.7012, decode.d6.loss_mask: 0.7436, decode.d6.loss_dice: 1.0517, decode.d7.loss_cls: 0.7003, decode.d7.loss_mask: 0.7452, decode.d7.loss_dice: 1.0524, decode.d8.loss_cls: 0.7012, decode.d8.loss_mask: 0.7429, decode.d8.loss_dice: 1.0486, loss: 28.9706 2022-05-05 01:50:50,590 - mmseg - INFO - Iter [20600/40000] lr: 6.964e-07, eta: 4:35:04, time: 0.795, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6498, decode.loss_mask: 0.7474, decode.loss_dice: 1.0318, decode.d0.loss_cls: 3.8202, decode.d0.loss_mask: 0.7766, decode.d0.loss_dice: 1.1825, decode.d1.loss_cls: 0.8488, decode.d1.loss_mask: 0.7790, decode.d1.loss_dice: 1.0972, decode.d2.loss_cls: 0.7313, decode.d2.loss_mask: 0.7580, decode.d2.loss_dice: 1.0584, decode.d3.loss_cls: 0.6952, decode.d3.loss_mask: 0.7556, decode.d3.loss_dice: 1.0427, decode.d4.loss_cls: 0.6753, decode.d4.loss_mask: 0.7545, decode.d4.loss_dice: 1.0442, decode.d5.loss_cls: 0.6613, decode.d5.loss_mask: 0.7536, decode.d5.loss_dice: 1.0459, decode.d6.loss_cls: 0.6461, decode.d6.loss_mask: 0.7496, decode.d6.loss_dice: 1.0382, decode.d7.loss_cls: 0.6365, decode.d7.loss_mask: 0.7515, decode.d7.loss_dice: 1.0389, decode.d8.loss_cls: 0.6473, decode.d8.loss_mask: 0.7495, decode.d8.loss_dice: 1.0357, loss: 28.2025 2022-05-05 01:51:29,714 - mmseg - INFO - Iter [20650/40000] lr: 6.946e-07, eta: 4:34:19, time: 0.782, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6817, decode.loss_mask: 0.7278, decode.loss_dice: 1.0537, decode.d0.loss_cls: 3.8105, decode.d0.loss_mask: 0.7676, decode.d0.loss_dice: 1.2318, decode.d1.loss_cls: 0.9154, decode.d1.loss_mask: 0.7540, decode.d1.loss_dice: 1.1283, decode.d2.loss_cls: 0.7787, decode.d2.loss_mask: 0.7425, decode.d2.loss_dice: 1.0824, decode.d3.loss_cls: 0.7190, decode.d3.loss_mask: 0.7347, decode.d3.loss_dice: 1.0697, decode.d4.loss_cls: 0.7027, decode.d4.loss_mask: 0.7364, decode.d4.loss_dice: 1.0638, decode.d5.loss_cls: 0.6869, decode.d5.loss_mask: 0.7297, decode.d5.loss_dice: 1.0615, decode.d6.loss_cls: 0.6802, decode.d6.loss_mask: 0.7308, decode.d6.loss_dice: 1.0556, decode.d7.loss_cls: 0.6774, decode.d7.loss_mask: 0.7280, decode.d7.loss_dice: 1.0518, decode.d8.loss_cls: 0.6714, decode.d8.loss_mask: 0.7315, decode.d8.loss_dice: 1.0556, loss: 28.5613 2022-05-05 01:52:09,416 - mmseg - INFO - Iter [20700/40000] lr: 6.928e-07, eta: 4:33:33, time: 0.794, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6452, decode.loss_mask: 0.7499, decode.loss_dice: 1.0624, decode.d0.loss_cls: 3.7581, decode.d0.loss_mask: 0.7803, decode.d0.loss_dice: 1.2442, decode.d1.loss_cls: 0.8728, decode.d1.loss_mask: 0.7730, decode.d1.loss_dice: 1.1385, decode.d2.loss_cls: 0.7415, decode.d2.loss_mask: 0.7629, decode.d2.loss_dice: 1.1031, decode.d3.loss_cls: 0.7034, decode.d3.loss_mask: 0.7549, decode.d3.loss_dice: 1.0734, decode.d4.loss_cls: 0.6830, decode.d4.loss_mask: 0.7555, decode.d4.loss_dice: 1.0720, decode.d5.loss_cls: 0.6721, decode.d5.loss_mask: 0.7512, decode.d5.loss_dice: 1.0696, decode.d6.loss_cls: 0.6494, decode.d6.loss_mask: 0.7537, decode.d6.loss_dice: 1.0635, decode.d7.loss_cls: 0.6523, decode.d7.loss_mask: 0.7523, decode.d7.loss_dice: 1.0698, decode.d8.loss_cls: 0.6413, decode.d8.loss_mask: 0.7527, decode.d8.loss_dice: 1.0606, loss: 28.5627 2022-05-05 01:52:49,738 - mmseg - INFO - Iter [20750/40000] lr: 6.910e-07, eta: 4:32:49, time: 0.806, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6535, decode.loss_mask: 0.7656, decode.loss_dice: 1.0918, decode.d0.loss_cls: 3.7956, decode.d0.loss_mask: 0.8000, decode.d0.loss_dice: 1.2573, decode.d1.loss_cls: 0.8783, decode.d1.loss_mask: 0.7931, decode.d1.loss_dice: 1.1606, decode.d2.loss_cls: 0.7519, decode.d2.loss_mask: 0.7800, decode.d2.loss_dice: 1.1155, decode.d3.loss_cls: 0.7050, decode.d3.loss_mask: 0.7747, decode.d3.loss_dice: 1.0929, decode.d4.loss_cls: 0.6765, decode.d4.loss_mask: 0.7730, decode.d4.loss_dice: 1.0980, decode.d5.loss_cls: 0.6653, decode.d5.loss_mask: 0.7713, decode.d5.loss_dice: 1.0918, decode.d6.loss_cls: 0.6514, decode.d6.loss_mask: 0.7701, decode.d6.loss_dice: 1.0881, decode.d7.loss_cls: 0.6481, decode.d7.loss_mask: 0.7703, decode.d7.loss_dice: 1.0871, decode.d8.loss_cls: 0.6548, decode.d8.loss_mask: 0.7670, decode.d8.loss_dice: 1.0908, loss: 29.0191 2022-05-05 01:53:31,456 - mmseg - INFO - Iter [20800/40000] lr: 6.892e-07, eta: 4:32:06, time: 0.834, data_time: 0.059, memory: 51557, decode.loss_cls: 0.6739, decode.loss_mask: 0.7770, decode.loss_dice: 1.0658, decode.d0.loss_cls: 3.7800, decode.d0.loss_mask: 0.8205, decode.d0.loss_dice: 1.2477, decode.d1.loss_cls: 0.9027, decode.d1.loss_mask: 0.8000, decode.d1.loss_dice: 1.1401, decode.d2.loss_cls: 0.7763, decode.d2.loss_mask: 0.7865, decode.d2.loss_dice: 1.0941, decode.d3.loss_cls: 0.7239, decode.d3.loss_mask: 0.7814, decode.d3.loss_dice: 1.0692, decode.d4.loss_cls: 0.7048, decode.d4.loss_mask: 0.7821, decode.d4.loss_dice: 1.0785, decode.d5.loss_cls: 0.6932, decode.d5.loss_mask: 0.7772, decode.d5.loss_dice: 1.0720, decode.d6.loss_cls: 0.6793, decode.d6.loss_mask: 0.7719, decode.d6.loss_dice: 1.0672, decode.d7.loss_cls: 0.6745, decode.d7.loss_mask: 0.7785, decode.d7.loss_dice: 1.0689, decode.d8.loss_cls: 0.6753, decode.d8.loss_mask: 0.7763, decode.d8.loss_dice: 1.0685, loss: 29.1071 2022-05-05 01:54:10,691 - mmseg - INFO - Iter [20850/40000] lr: 6.874e-07, eta: 4:31:20, time: 0.785, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6561, decode.loss_mask: 0.7533, decode.loss_dice: 1.0723, decode.d0.loss_cls: 3.8051, decode.d0.loss_mask: 0.7881, decode.d0.loss_dice: 1.2412, decode.d1.loss_cls: 0.8674, decode.d1.loss_mask: 0.7734, decode.d1.loss_dice: 1.1408, decode.d2.loss_cls: 0.7377, decode.d2.loss_mask: 0.7641, decode.d2.loss_dice: 1.0953, decode.d3.loss_cls: 0.6970, decode.d3.loss_mask: 0.7562, decode.d3.loss_dice: 1.0826, decode.d4.loss_cls: 0.6709, decode.d4.loss_mask: 0.7571, decode.d4.loss_dice: 1.0787, decode.d5.loss_cls: 0.6607, decode.d5.loss_mask: 0.7557, decode.d5.loss_dice: 1.0785, decode.d6.loss_cls: 0.6580, decode.d6.loss_mask: 0.7566, decode.d6.loss_dice: 1.0680, decode.d7.loss_cls: 0.6509, decode.d7.loss_mask: 0.7553, decode.d7.loss_dice: 1.0735, decode.d8.loss_cls: 0.6545, decode.d8.loss_mask: 0.7535, decode.d8.loss_dice: 1.0765, loss: 28.6790 2022-05-05 01:54:49,764 - mmseg - INFO - Iter [20900/40000] lr: 6.856e-07, eta: 4:30:34, time: 0.781, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6321, decode.loss_mask: 0.7504, decode.loss_dice: 1.0570, decode.d0.loss_cls: 3.7551, decode.d0.loss_mask: 0.7871, decode.d0.loss_dice: 1.2246, decode.d1.loss_cls: 0.8700, decode.d1.loss_mask: 0.7814, decode.d1.loss_dice: 1.1280, decode.d2.loss_cls: 0.7380, decode.d2.loss_mask: 0.7630, decode.d2.loss_dice: 1.0891, decode.d3.loss_cls: 0.6940, decode.d3.loss_mask: 0.7582, decode.d3.loss_dice: 1.0693, decode.d4.loss_cls: 0.6588, decode.d4.loss_mask: 0.7591, decode.d4.loss_dice: 1.0678, decode.d5.loss_cls: 0.6499, decode.d5.loss_mask: 0.7514, decode.d5.loss_dice: 1.0669, decode.d6.loss_cls: 0.6358, decode.d6.loss_mask: 0.7549, decode.d6.loss_dice: 1.0585, decode.d7.loss_cls: 0.6369, decode.d7.loss_mask: 0.7526, decode.d7.loss_dice: 1.0558, decode.d8.loss_cls: 0.6330, decode.d8.loss_mask: 0.7513, decode.d8.loss_dice: 1.0519, loss: 28.3819 2022-05-05 01:55:28,668 - mmseg - INFO - Iter [20950/40000] lr: 6.838e-07, eta: 4:29:49, time: 0.777, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6637, decode.loss_mask: 0.7487, decode.loss_dice: 1.0769, decode.d0.loss_cls: 3.7846, decode.d0.loss_mask: 0.7877, decode.d0.loss_dice: 1.2469, decode.d1.loss_cls: 0.9135, decode.d1.loss_mask: 0.7823, decode.d1.loss_dice: 1.1530, decode.d2.loss_cls: 0.7789, decode.d2.loss_mask: 0.7572, decode.d2.loss_dice: 1.1122, decode.d3.loss_cls: 0.7196, decode.d3.loss_mask: 0.7535, decode.d3.loss_dice: 1.0842, decode.d4.loss_cls: 0.7001, decode.d4.loss_mask: 0.7518, decode.d4.loss_dice: 1.0949, decode.d5.loss_cls: 0.6783, decode.d5.loss_mask: 0.7466, decode.d5.loss_dice: 1.0893, decode.d6.loss_cls: 0.6660, decode.d6.loss_mask: 0.7466, decode.d6.loss_dice: 1.0730, decode.d7.loss_cls: 0.6618, decode.d7.loss_mask: 0.7474, decode.d7.loss_dice: 1.0822, decode.d8.loss_cls: 0.6637, decode.d8.loss_mask: 0.7476, decode.d8.loss_dice: 1.0791, loss: 28.8913 2022-05-05 01:56:07,525 - mmseg - INFO - Saving checkpoint at 21000 iterations 2022-05-05 01:56:32,816 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 01:56:32,823 - mmseg - INFO - Iter [21000/40000] lr: 6.820e-07, eta: 4:29:26, time: 1.281, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6272, decode.loss_mask: 0.7651, decode.loss_dice: 1.0599, decode.d0.loss_cls: 3.7786, decode.d0.loss_mask: 0.7963, decode.d0.loss_dice: 1.2311, decode.d1.loss_cls: 0.8412, decode.d1.loss_mask: 0.7859, decode.d1.loss_dice: 1.1467, decode.d2.loss_cls: 0.7172, decode.d2.loss_mask: 0.7701, decode.d2.loss_dice: 1.0919, decode.d3.loss_cls: 0.6698, decode.d3.loss_mask: 0.7694, decode.d3.loss_dice: 1.0697, decode.d4.loss_cls: 0.6507, decode.d4.loss_mask: 0.7669, decode.d4.loss_dice: 1.0760, decode.d5.loss_cls: 0.6350, decode.d5.loss_mask: 0.7658, decode.d5.loss_dice: 1.0678, decode.d6.loss_cls: 0.6352, decode.d6.loss_mask: 0.7631, decode.d6.loss_dice: 1.0552, decode.d7.loss_cls: 0.6318, decode.d7.loss_mask: 0.7632, decode.d7.loss_dice: 1.0625, decode.d8.loss_cls: 0.6272, decode.d8.loss_mask: 0.7648, decode.d8.loss_dice: 1.0574, loss: 28.4428 2022-05-05 01:57:12,971 - mmseg - INFO - Iter [21050/40000] lr: 6.802e-07, eta: 4:28:41, time: 0.806, data_time: 0.012, memory: 51557, decode.loss_cls: 0.6467, decode.loss_mask: 0.7407, decode.loss_dice: 1.0417, decode.d0.loss_cls: 3.7862, decode.d0.loss_mask: 0.7900, decode.d0.loss_dice: 1.2272, decode.d1.loss_cls: 0.8499, decode.d1.loss_mask: 0.7791, decode.d1.loss_dice: 1.1260, decode.d2.loss_cls: 0.7246, decode.d2.loss_mask: 0.7637, decode.d2.loss_dice: 1.0836, decode.d3.loss_cls: 0.6917, decode.d3.loss_mask: 0.7510, decode.d3.loss_dice: 1.0596, decode.d4.loss_cls: 0.6719, decode.d4.loss_mask: 0.7463, decode.d4.loss_dice: 1.0633, decode.d5.loss_cls: 0.6682, decode.d5.loss_mask: 0.7423, decode.d5.loss_dice: 1.0546, decode.d6.loss_cls: 0.6556, decode.d6.loss_mask: 0.7461, decode.d6.loss_dice: 1.0449, decode.d7.loss_cls: 0.6478, decode.d7.loss_mask: 0.7470, decode.d7.loss_dice: 1.0502, decode.d8.loss_cls: 0.6393, decode.d8.loss_mask: 0.7413, decode.d8.loss_dice: 1.0460, loss: 28.3265 2022-05-05 01:57:52,431 - mmseg - INFO - Iter [21100/40000] lr: 6.784e-07, eta: 4:27:56, time: 0.789, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6447, decode.loss_mask: 0.7417, decode.loss_dice: 1.0397, decode.d0.loss_cls: 3.7357, decode.d0.loss_mask: 0.7807, decode.d0.loss_dice: 1.2093, decode.d1.loss_cls: 0.8560, decode.d1.loss_mask: 0.7721, decode.d1.loss_dice: 1.1072, decode.d2.loss_cls: 0.7261, decode.d2.loss_mask: 0.7540, decode.d2.loss_dice: 1.0685, decode.d3.loss_cls: 0.6837, decode.d3.loss_mask: 0.7496, decode.d3.loss_dice: 1.0405, decode.d4.loss_cls: 0.6611, decode.d4.loss_mask: 0.7452, decode.d4.loss_dice: 1.0464, decode.d5.loss_cls: 0.6509, decode.d5.loss_mask: 0.7430, decode.d5.loss_dice: 1.0391, decode.d6.loss_cls: 0.6395, decode.d6.loss_mask: 0.7430, decode.d6.loss_dice: 1.0337, decode.d7.loss_cls: 0.6374, decode.d7.loss_mask: 0.7462, decode.d7.loss_dice: 1.0430, decode.d8.loss_cls: 0.6445, decode.d8.loss_mask: 0.7425, decode.d8.loss_dice: 1.0432, loss: 28.0681 2022-05-05 01:58:31,230 - mmseg - INFO - Iter [21150/40000] lr: 6.767e-07, eta: 4:27:10, time: 0.776, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6697, decode.loss_mask: 0.7675, decode.loss_dice: 1.0486, decode.d0.loss_cls: 3.7717, decode.d0.loss_mask: 0.8025, decode.d0.loss_dice: 1.2452, decode.d1.loss_cls: 0.8797, decode.d1.loss_mask: 0.7888, decode.d1.loss_dice: 1.1395, decode.d2.loss_cls: 0.7571, decode.d2.loss_mask: 0.7737, decode.d2.loss_dice: 1.0865, decode.d3.loss_cls: 0.7126, decode.d3.loss_mask: 0.7711, decode.d3.loss_dice: 1.0592, decode.d4.loss_cls: 0.6966, decode.d4.loss_mask: 0.7653, decode.d4.loss_dice: 1.0595, decode.d5.loss_cls: 0.6840, decode.d5.loss_mask: 0.7621, decode.d5.loss_dice: 1.0570, decode.d6.loss_cls: 0.6741, decode.d6.loss_mask: 0.7591, decode.d6.loss_dice: 1.0545, decode.d7.loss_cls: 0.6717, decode.d7.loss_mask: 0.7602, decode.d7.loss_dice: 1.0470, decode.d8.loss_cls: 0.6715, decode.d8.loss_mask: 0.7602, decode.d8.loss_dice: 1.0525, loss: 28.7487 2022-05-05 01:59:10,356 - mmseg - INFO - Iter [21200/40000] lr: 6.749e-07, eta: 4:26:24, time: 0.782, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6468, decode.loss_mask: 0.7555, decode.loss_dice: 1.0228, decode.d0.loss_cls: 3.7857, decode.d0.loss_mask: 0.7944, decode.d0.loss_dice: 1.2059, decode.d1.loss_cls: 0.8738, decode.d1.loss_mask: 0.7810, decode.d1.loss_dice: 1.0953, decode.d2.loss_cls: 0.7439, decode.d2.loss_mask: 0.7622, decode.d2.loss_dice: 1.0554, decode.d3.loss_cls: 0.6903, decode.d3.loss_mask: 0.7628, decode.d3.loss_dice: 1.0330, decode.d4.loss_cls: 0.6716, decode.d4.loss_mask: 0.7605, decode.d4.loss_dice: 1.0318, decode.d5.loss_cls: 0.6602, decode.d5.loss_mask: 0.7595, decode.d5.loss_dice: 1.0278, decode.d6.loss_cls: 0.6499, decode.d6.loss_mask: 0.7564, decode.d6.loss_dice: 1.0177, decode.d7.loss_cls: 0.6399, decode.d7.loss_mask: 0.7542, decode.d7.loss_dice: 1.0215, decode.d8.loss_cls: 0.6418, decode.d8.loss_mask: 0.7568, decode.d8.loss_dice: 1.0207, loss: 28.1792 2022-05-05 01:59:49,365 - mmseg - INFO - Iter [21250/40000] lr: 6.731e-07, eta: 4:25:39, time: 0.780, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6705, decode.loss_mask: 0.7701, decode.loss_dice: 1.0480, decode.d0.loss_cls: 3.7379, decode.d0.loss_mask: 0.8063, decode.d0.loss_dice: 1.2263, decode.d1.loss_cls: 0.9040, decode.d1.loss_mask: 0.7969, decode.d1.loss_dice: 1.1175, decode.d2.loss_cls: 0.7629, decode.d2.loss_mask: 0.7837, decode.d2.loss_dice: 1.0789, decode.d3.loss_cls: 0.7180, decode.d3.loss_mask: 0.7758, decode.d3.loss_dice: 1.0584, decode.d4.loss_cls: 0.7015, decode.d4.loss_mask: 0.7741, decode.d4.loss_dice: 1.0601, decode.d5.loss_cls: 0.6918, decode.d5.loss_mask: 0.7733, decode.d5.loss_dice: 1.0590, decode.d6.loss_cls: 0.6711, decode.d6.loss_mask: 0.7784, decode.d6.loss_dice: 1.0561, decode.d7.loss_cls: 0.6636, decode.d7.loss_mask: 0.7712, decode.d7.loss_dice: 1.0571, decode.d8.loss_cls: 0.6656, decode.d8.loss_mask: 0.7726, decode.d8.loss_dice: 1.0565, loss: 28.8072 2022-05-05 02:00:31,795 - mmseg - INFO - Iter [21300/40000] lr: 6.713e-07, eta: 4:24:56, time: 0.849, data_time: 0.011, memory: 51557, decode.loss_cls: 0.6704, decode.loss_mask: 0.7598, decode.loss_dice: 1.0798, decode.d0.loss_cls: 3.7733, decode.d0.loss_mask: 0.7919, decode.d0.loss_dice: 1.2332, decode.d1.loss_cls: 0.8977, decode.d1.loss_mask: 0.7872, decode.d1.loss_dice: 1.1440, decode.d2.loss_cls: 0.7671, decode.d2.loss_mask: 0.7755, decode.d2.loss_dice: 1.1073, decode.d3.loss_cls: 0.7150, decode.d3.loss_mask: 0.7727, decode.d3.loss_dice: 1.0901, decode.d4.loss_cls: 0.6980, decode.d4.loss_mask: 0.7710, decode.d4.loss_dice: 1.0861, decode.d5.loss_cls: 0.6818, decode.d5.loss_mask: 0.7650, decode.d5.loss_dice: 1.0825, decode.d6.loss_cls: 0.6720, decode.d6.loss_mask: 0.7612, decode.d6.loss_dice: 1.0762, decode.d7.loss_cls: 0.6702, decode.d7.loss_mask: 0.7609, decode.d7.loss_dice: 1.0770, decode.d8.loss_cls: 0.6646, decode.d8.loss_mask: 0.7574, decode.d8.loss_dice: 1.0759, loss: 28.9646 2022-05-05 02:01:10,599 - mmseg - INFO - Iter [21350/40000] lr: 6.695e-07, eta: 4:24:10, time: 0.777, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6668, decode.loss_mask: 0.7726, decode.loss_dice: 1.0490, decode.d0.loss_cls: 3.6989, decode.d0.loss_mask: 0.8107, decode.d0.loss_dice: 1.2138, decode.d1.loss_cls: 0.8657, decode.d1.loss_mask: 0.7946, decode.d1.loss_dice: 1.1192, decode.d2.loss_cls: 0.7505, decode.d2.loss_mask: 0.7753, decode.d2.loss_dice: 1.0759, decode.d3.loss_cls: 0.7024, decode.d3.loss_mask: 0.7718, decode.d3.loss_dice: 1.0589, decode.d4.loss_cls: 0.6836, decode.d4.loss_mask: 0.7756, decode.d4.loss_dice: 1.0636, decode.d5.loss_cls: 0.6731, decode.d5.loss_mask: 0.7755, decode.d5.loss_dice: 1.0581, decode.d6.loss_cls: 0.6688, decode.d6.loss_mask: 0.7756, decode.d6.loss_dice: 1.0547, decode.d7.loss_cls: 0.6574, decode.d7.loss_mask: 0.7730, decode.d7.loss_dice: 1.0605, decode.d8.loss_cls: 0.6564, decode.d8.loss_mask: 0.7729, decode.d8.loss_dice: 1.0542, loss: 28.6290 2022-05-05 02:01:53,078 - mmseg - INFO - Iter [21400/40000] lr: 6.677e-07, eta: 4:23:28, time: 0.849, data_time: 0.058, memory: 51557, decode.loss_cls: 0.6313, decode.loss_mask: 0.7507, decode.loss_dice: 1.0297, decode.d0.loss_cls: 3.7051, decode.d0.loss_mask: 0.7867, decode.d0.loss_dice: 1.1936, decode.d1.loss_cls: 0.8790, decode.d1.loss_mask: 0.7787, decode.d1.loss_dice: 1.1080, decode.d2.loss_cls: 0.7245, decode.d2.loss_mask: 0.7643, decode.d2.loss_dice: 1.0582, decode.d3.loss_cls: 0.6853, decode.d3.loss_mask: 0.7574, decode.d3.loss_dice: 1.0435, decode.d4.loss_cls: 0.6752, decode.d4.loss_mask: 0.7516, decode.d4.loss_dice: 1.0419, decode.d5.loss_cls: 0.6481, decode.d5.loss_mask: 0.7481, decode.d5.loss_dice: 1.0396, decode.d6.loss_cls: 0.6379, decode.d6.loss_mask: 0.7459, decode.d6.loss_dice: 1.0287, decode.d7.loss_cls: 0.6362, decode.d7.loss_mask: 0.7466, decode.d7.loss_dice: 1.0264, decode.d8.loss_cls: 0.6273, decode.d8.loss_mask: 0.7473, decode.d8.loss_dice: 1.0270, loss: 28.0240 2022-05-05 02:02:33,331 - mmseg - INFO - Iter [21450/40000] lr: 6.659e-07, eta: 4:22:44, time: 0.805, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6097, decode.loss_mask: 0.7302, decode.loss_dice: 1.0278, decode.d0.loss_cls: 3.7444, decode.d0.loss_mask: 0.7658, decode.d0.loss_dice: 1.2054, decode.d1.loss_cls: 0.8445, decode.d1.loss_mask: 0.7581, decode.d1.loss_dice: 1.1026, decode.d2.loss_cls: 0.7090, decode.d2.loss_mask: 0.7406, decode.d2.loss_dice: 1.0539, decode.d3.loss_cls: 0.6555, decode.d3.loss_mask: 0.7398, decode.d3.loss_dice: 1.0350, decode.d4.loss_cls: 0.6377, decode.d4.loss_mask: 0.7378, decode.d4.loss_dice: 1.0323, decode.d5.loss_cls: 0.6235, decode.d5.loss_mask: 0.7337, decode.d5.loss_dice: 1.0351, decode.d6.loss_cls: 0.6091, decode.d6.loss_mask: 0.7329, decode.d6.loss_dice: 1.0318, decode.d7.loss_cls: 0.6097, decode.d7.loss_mask: 0.7333, decode.d7.loss_dice: 1.0356, decode.d8.loss_cls: 0.6038, decode.d8.loss_mask: 0.7330, decode.d8.loss_dice: 1.0330, loss: 27.6445 2022-05-05 02:03:13,233 - mmseg - INFO - Iter [21500/40000] lr: 6.641e-07, eta: 4:21:59, time: 0.798, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6506, decode.loss_mask: 0.7664, decode.loss_dice: 1.0414, decode.d0.loss_cls: 3.7089, decode.d0.loss_mask: 0.7974, decode.d0.loss_dice: 1.2106, decode.d1.loss_cls: 0.8643, decode.d1.loss_mask: 0.7991, decode.d1.loss_dice: 1.1148, decode.d2.loss_cls: 0.7332, decode.d2.loss_mask: 0.7822, decode.d2.loss_dice: 1.0804, decode.d3.loss_cls: 0.6954, decode.d3.loss_mask: 0.7704, decode.d3.loss_dice: 1.0566, decode.d4.loss_cls: 0.6751, decode.d4.loss_mask: 0.7720, decode.d4.loss_dice: 1.0557, decode.d5.loss_cls: 0.6598, decode.d5.loss_mask: 0.7653, decode.d5.loss_dice: 1.0479, decode.d6.loss_cls: 0.6446, decode.d6.loss_mask: 0.7651, decode.d6.loss_dice: 1.0417, decode.d7.loss_cls: 0.6391, decode.d7.loss_mask: 0.7681, decode.d7.loss_dice: 1.0458, decode.d8.loss_cls: 0.6454, decode.d8.loss_mask: 0.7706, decode.d8.loss_dice: 1.0442, loss: 28.4121 2022-05-05 02:03:52,112 - mmseg - INFO - Iter [21550/40000] lr: 6.623e-07, eta: 4:21:13, time: 0.778, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6310, decode.loss_mask: 0.7928, decode.loss_dice: 1.0930, decode.d0.loss_cls: 3.7362, decode.d0.loss_mask: 0.8274, decode.d0.loss_dice: 1.2542, decode.d1.loss_cls: 0.8433, decode.d1.loss_mask: 0.8161, decode.d1.loss_dice: 1.1631, decode.d2.loss_cls: 0.7345, decode.d2.loss_mask: 0.8023, decode.d2.loss_dice: 1.1175, decode.d3.loss_cls: 0.6935, decode.d3.loss_mask: 0.7918, decode.d3.loss_dice: 1.0976, decode.d4.loss_cls: 0.6685, decode.d4.loss_mask: 0.7946, decode.d4.loss_dice: 1.0928, decode.d5.loss_cls: 0.6599, decode.d5.loss_mask: 0.7930, decode.d5.loss_dice: 1.0899, decode.d6.loss_cls: 0.6491, decode.d6.loss_mask: 0.7930, decode.d6.loss_dice: 1.0879, decode.d7.loss_cls: 0.6382, decode.d7.loss_mask: 0.7948, decode.d7.loss_dice: 1.0916, decode.d8.loss_cls: 0.6354, decode.d8.loss_mask: 0.7908, decode.d8.loss_dice: 1.0936, loss: 29.0675 2022-05-05 02:04:31,392 - mmseg - INFO - Iter [21600/40000] lr: 6.605e-07, eta: 4:20:28, time: 0.786, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6467, decode.loss_mask: 0.7797, decode.loss_dice: 1.0942, decode.d0.loss_cls: 3.7460, decode.d0.loss_mask: 0.8106, decode.d0.loss_dice: 1.2600, decode.d1.loss_cls: 0.8821, decode.d1.loss_mask: 0.8007, decode.d1.loss_dice: 1.1658, decode.d2.loss_cls: 0.7463, decode.d2.loss_mask: 0.7867, decode.d2.loss_dice: 1.1181, decode.d3.loss_cls: 0.6948, decode.d3.loss_mask: 0.7813, decode.d3.loss_dice: 1.1069, decode.d4.loss_cls: 0.6741, decode.d4.loss_mask: 0.7812, decode.d4.loss_dice: 1.1020, decode.d5.loss_cls: 0.6571, decode.d5.loss_mask: 0.7843, decode.d5.loss_dice: 1.1058, decode.d6.loss_cls: 0.6518, decode.d6.loss_mask: 0.7816, decode.d6.loss_dice: 1.0930, decode.d7.loss_cls: 0.6579, decode.d7.loss_mask: 0.7812, decode.d7.loss_dice: 1.0935, decode.d8.loss_cls: 0.6482, decode.d8.loss_mask: 0.7801, decode.d8.loss_dice: 1.0900, loss: 29.1018 2022-05-05 02:05:09,950 - mmseg - INFO - Iter [21650/40000] lr: 6.587e-07, eta: 4:19:42, time: 0.771, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6376, decode.loss_mask: 0.7357, decode.loss_dice: 1.0511, decode.d0.loss_cls: 3.7568, decode.d0.loss_mask: 0.7721, decode.d0.loss_dice: 1.2249, decode.d1.loss_cls: 0.8489, decode.d1.loss_mask: 0.7701, decode.d1.loss_dice: 1.1158, decode.d2.loss_cls: 0.7358, decode.d2.loss_mask: 0.7485, decode.d2.loss_dice: 1.0720, decode.d3.loss_cls: 0.6842, decode.d3.loss_mask: 0.7440, decode.d3.loss_dice: 1.0529, decode.d4.loss_cls: 0.6704, decode.d4.loss_mask: 0.7459, decode.d4.loss_dice: 1.0591, decode.d5.loss_cls: 0.6572, decode.d5.loss_mask: 0.7368, decode.d5.loss_dice: 1.0486, decode.d6.loss_cls: 0.6415, decode.d6.loss_mask: 0.7398, decode.d6.loss_dice: 1.0495, decode.d7.loss_cls: 0.6388, decode.d7.loss_mask: 0.7380, decode.d7.loss_dice: 1.0477, decode.d8.loss_cls: 0.6297, decode.d8.loss_mask: 0.7419, decode.d8.loss_dice: 1.0518, loss: 28.1470 2022-05-05 02:05:48,919 - mmseg - INFO - Iter [21700/40000] lr: 6.569e-07, eta: 4:18:57, time: 0.780, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6489, decode.loss_mask: 0.7266, decode.loss_dice: 1.0355, decode.d0.loss_cls: 3.7226, decode.d0.loss_mask: 0.7543, decode.d0.loss_dice: 1.2047, decode.d1.loss_cls: 0.8601, decode.d1.loss_mask: 0.7469, decode.d1.loss_dice: 1.1101, decode.d2.loss_cls: 0.7514, decode.d2.loss_mask: 0.7304, decode.d2.loss_dice: 1.0665, decode.d3.loss_cls: 0.7016, decode.d3.loss_mask: 0.7249, decode.d3.loss_dice: 1.0455, decode.d4.loss_cls: 0.6825, decode.d4.loss_mask: 0.7295, decode.d4.loss_dice: 1.0471, decode.d5.loss_cls: 0.6677, decode.d5.loss_mask: 0.7297, decode.d5.loss_dice: 1.0452, decode.d6.loss_cls: 0.6546, decode.d6.loss_mask: 0.7280, decode.d6.loss_dice: 1.0367, decode.d7.loss_cls: 0.6377, decode.d7.loss_mask: 0.7262, decode.d7.loss_dice: 1.0363, decode.d8.loss_cls: 0.6438, decode.d8.loss_mask: 0.7274, decode.d8.loss_dice: 1.0366, loss: 27.9593 2022-05-05 02:06:27,719 - mmseg - INFO - Iter [21750/40000] lr: 6.551e-07, eta: 4:18:11, time: 0.776, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6612, decode.loss_mask: 0.7636, decode.loss_dice: 1.0640, decode.d0.loss_cls: 3.7135, decode.d0.loss_mask: 0.8064, decode.d0.loss_dice: 1.2513, decode.d1.loss_cls: 0.8927, decode.d1.loss_mask: 0.7950, decode.d1.loss_dice: 1.1475, decode.d2.loss_cls: 0.7489, decode.d2.loss_mask: 0.7812, decode.d2.loss_dice: 1.1031, decode.d3.loss_cls: 0.6994, decode.d3.loss_mask: 0.7758, decode.d3.loss_dice: 1.0810, decode.d4.loss_cls: 0.6948, decode.d4.loss_mask: 0.7699, decode.d4.loss_dice: 1.0744, decode.d5.loss_cls: 0.6812, decode.d5.loss_mask: 0.7671, decode.d5.loss_dice: 1.0695, decode.d6.loss_cls: 0.6777, decode.d6.loss_mask: 0.7624, decode.d6.loss_dice: 1.0639, decode.d7.loss_cls: 0.6640, decode.d7.loss_mask: 0.7668, decode.d7.loss_dice: 1.0698, decode.d8.loss_cls: 0.6643, decode.d8.loss_mask: 0.7632, decode.d8.loss_dice: 1.0650, loss: 28.8385 2022-05-05 02:07:06,852 - mmseg - INFO - Iter [21800/40000] lr: 6.533e-07, eta: 4:17:26, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6496, decode.loss_mask: 0.7470, decode.loss_dice: 1.0410, decode.d0.loss_cls: 3.7929, decode.d0.loss_mask: 0.7767, decode.d0.loss_dice: 1.2153, decode.d1.loss_cls: 0.8875, decode.d1.loss_mask: 0.7610, decode.d1.loss_dice: 1.1122, decode.d2.loss_cls: 0.7445, decode.d2.loss_mask: 0.7515, decode.d2.loss_dice: 1.0741, decode.d3.loss_cls: 0.7074, decode.d3.loss_mask: 0.7494, decode.d3.loss_dice: 1.0576, decode.d4.loss_cls: 0.6855, decode.d4.loss_mask: 0.7507, decode.d4.loss_dice: 1.0553, decode.d5.loss_cls: 0.6799, decode.d5.loss_mask: 0.7435, decode.d5.loss_dice: 1.0471, decode.d6.loss_cls: 0.6671, decode.d6.loss_mask: 0.7414, decode.d6.loss_dice: 1.0414, decode.d7.loss_cls: 0.6621, decode.d7.loss_mask: 0.7430, decode.d7.loss_dice: 1.0423, decode.d8.loss_cls: 0.6500, decode.d8.loss_mask: 0.7485, decode.d8.loss_dice: 1.0466, loss: 28.3721 2022-05-05 02:07:46,534 - mmseg - INFO - Iter [21850/40000] lr: 6.515e-07, eta: 4:16:41, time: 0.794, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6176, decode.loss_mask: 0.7355, decode.loss_dice: 1.0581, decode.d0.loss_cls: 3.7378, decode.d0.loss_mask: 0.7770, decode.d0.loss_dice: 1.2325, decode.d1.loss_cls: 0.8607, decode.d1.loss_mask: 0.7534, decode.d1.loss_dice: 1.1221, decode.d2.loss_cls: 0.7153, decode.d2.loss_mask: 0.7464, decode.d2.loss_dice: 1.0875, decode.d3.loss_cls: 0.6688, decode.d3.loss_mask: 0.7406, decode.d3.loss_dice: 1.0714, decode.d4.loss_cls: 0.6524, decode.d4.loss_mask: 0.7394, decode.d4.loss_dice: 1.0672, decode.d5.loss_cls: 0.6392, decode.d5.loss_mask: 0.7343, decode.d5.loss_dice: 1.0574, decode.d6.loss_cls: 0.6306, decode.d6.loss_mask: 0.7367, decode.d6.loss_dice: 1.0554, decode.d7.loss_cls: 0.6239, decode.d7.loss_mask: 0.7368, decode.d7.loss_dice: 1.0587, decode.d8.loss_cls: 0.6182, decode.d8.loss_mask: 0.7381, decode.d8.loss_dice: 1.0549, loss: 28.0678 2022-05-05 02:08:26,786 - mmseg - INFO - Iter [21900/40000] lr: 6.497e-07, eta: 4:15:57, time: 0.804, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6541, decode.loss_mask: 0.7671, decode.loss_dice: 1.0362, decode.d0.loss_cls: 3.7061, decode.d0.loss_mask: 0.8043, decode.d0.loss_dice: 1.2144, decode.d1.loss_cls: 0.8935, decode.d1.loss_mask: 0.7946, decode.d1.loss_dice: 1.1113, decode.d2.loss_cls: 0.7615, decode.d2.loss_mask: 0.7801, decode.d2.loss_dice: 1.0621, decode.d3.loss_cls: 0.6981, decode.d3.loss_mask: 0.7726, decode.d3.loss_dice: 1.0445, decode.d4.loss_cls: 0.6850, decode.d4.loss_mask: 0.7732, decode.d4.loss_dice: 1.0437, decode.d5.loss_cls: 0.6651, decode.d5.loss_mask: 0.7714, decode.d5.loss_dice: 1.0385, decode.d6.loss_cls: 0.6551, decode.d6.loss_mask: 0.7684, decode.d6.loss_dice: 1.0332, decode.d7.loss_cls: 0.6431, decode.d7.loss_mask: 0.7672, decode.d7.loss_dice: 1.0373, decode.d8.loss_cls: 0.6494, decode.d8.loss_mask: 0.7649, decode.d8.loss_dice: 1.0280, loss: 28.4237 2022-05-05 02:09:09,535 - mmseg - INFO - Iter [21950/40000] lr: 6.479e-07, eta: 4:15:15, time: 0.856, data_time: 0.058, memory: 51557, decode.loss_cls: 0.6177, decode.loss_mask: 0.7239, decode.loss_dice: 1.0509, decode.d0.loss_cls: 3.6828, decode.d0.loss_mask: 0.7574, decode.d0.loss_dice: 1.2246, decode.d1.loss_cls: 0.8503, decode.d1.loss_mask: 0.7437, decode.d1.loss_dice: 1.1238, decode.d2.loss_cls: 0.7108, decode.d2.loss_mask: 0.7332, decode.d2.loss_dice: 1.0808, decode.d3.loss_cls: 0.6556, decode.d3.loss_mask: 0.7296, decode.d3.loss_dice: 1.0633, decode.d4.loss_cls: 0.6425, decode.d4.loss_mask: 0.7306, decode.d4.loss_dice: 1.0663, decode.d5.loss_cls: 0.6255, decode.d5.loss_mask: 0.7260, decode.d5.loss_dice: 1.0600, decode.d6.loss_cls: 0.6168, decode.d6.loss_mask: 0.7222, decode.d6.loss_dice: 1.0574, decode.d7.loss_cls: 0.6247, decode.d7.loss_mask: 0.7241, decode.d7.loss_dice: 1.0556, decode.d8.loss_cls: 0.6206, decode.d8.loss_mask: 0.7241, decode.d8.loss_dice: 1.0554, loss: 27.8001 2022-05-05 02:09:48,937 - mmseg - INFO - Saving checkpoint at 22000 iterations 2022-05-05 02:10:16,396 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 02:10:16,405 - mmseg - INFO - Iter [22000/40000] lr: 6.461e-07, eta: 4:14:52, time: 1.335, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6498, decode.loss_mask: 0.7573, decode.loss_dice: 1.0570, decode.d0.loss_cls: 3.7443, decode.d0.loss_mask: 0.7941, decode.d0.loss_dice: 1.2304, decode.d1.loss_cls: 0.8751, decode.d1.loss_mask: 0.7786, decode.d1.loss_dice: 1.1219, decode.d2.loss_cls: 0.7644, decode.d2.loss_mask: 0.7640, decode.d2.loss_dice: 1.0824, decode.d3.loss_cls: 0.7026, decode.d3.loss_mask: 0.7568, decode.d3.loss_dice: 1.0609, decode.d4.loss_cls: 0.6799, decode.d4.loss_mask: 0.7552, decode.d4.loss_dice: 1.0675, decode.d5.loss_cls: 0.6710, decode.d5.loss_mask: 0.7566, decode.d5.loss_dice: 1.0584, decode.d6.loss_cls: 0.6607, decode.d6.loss_mask: 0.7573, decode.d6.loss_dice: 1.0528, decode.d7.loss_cls: 0.6493, decode.d7.loss_mask: 0.7565, decode.d7.loss_dice: 1.0509, decode.d8.loss_cls: 0.6423, decode.d8.loss_mask: 0.7572, decode.d8.loss_dice: 1.0502, loss: 28.5052 2022-05-05 02:10:56,367 - mmseg - INFO - Iter [22050/40000] lr: 6.443e-07, eta: 4:14:08, time: 0.802, data_time: 0.013, memory: 51557, decode.loss_cls: 0.6136, decode.loss_mask: 0.7724, decode.loss_dice: 1.0541, decode.d0.loss_cls: 3.6659, decode.d0.loss_mask: 0.8044, decode.d0.loss_dice: 1.1986, decode.d1.loss_cls: 0.8336, decode.d1.loss_mask: 0.7998, decode.d1.loss_dice: 1.1202, decode.d2.loss_cls: 0.7122, decode.d2.loss_mask: 0.7787, decode.d2.loss_dice: 1.0824, decode.d3.loss_cls: 0.6679, decode.d3.loss_mask: 0.7756, decode.d3.loss_dice: 1.0628, decode.d4.loss_cls: 0.6445, decode.d4.loss_mask: 0.7763, decode.d4.loss_dice: 1.0671, decode.d5.loss_cls: 0.6318, decode.d5.loss_mask: 0.7746, decode.d5.loss_dice: 1.0644, decode.d6.loss_cls: 0.6176, decode.d6.loss_mask: 0.7689, decode.d6.loss_dice: 1.0591, decode.d7.loss_cls: 0.6160, decode.d7.loss_mask: 0.7686, decode.d7.loss_dice: 1.0603, decode.d8.loss_cls: 0.6098, decode.d8.loss_mask: 0.7723, decode.d8.loss_dice: 1.0591, loss: 28.2324 2022-05-05 02:11:35,757 - mmseg - INFO - Iter [22100/40000] lr: 6.426e-07, eta: 4:13:23, time: 0.787, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6336, decode.loss_mask: 0.7574, decode.loss_dice: 1.0515, decode.d0.loss_cls: 3.7102, decode.d0.loss_mask: 0.7919, decode.d0.loss_dice: 1.2265, decode.d1.loss_cls: 0.8426, decode.d1.loss_mask: 0.7837, decode.d1.loss_dice: 1.1267, decode.d2.loss_cls: 0.7236, decode.d2.loss_mask: 0.7631, decode.d2.loss_dice: 1.0812, decode.d3.loss_cls: 0.6740, decode.d3.loss_mask: 0.7630, decode.d3.loss_dice: 1.0620, decode.d4.loss_cls: 0.6567, decode.d4.loss_mask: 0.7556, decode.d4.loss_dice: 1.0615, decode.d5.loss_cls: 0.6449, decode.d5.loss_mask: 0.7554, decode.d5.loss_dice: 1.0483, decode.d6.loss_cls: 0.6364, decode.d6.loss_mask: 0.7534, decode.d6.loss_dice: 1.0486, decode.d7.loss_cls: 0.6369, decode.d7.loss_mask: 0.7527, decode.d7.loss_dice: 1.0531, decode.d8.loss_cls: 0.6307, decode.d8.loss_mask: 0.7567, decode.d8.loss_dice: 1.0505, loss: 28.2324 2022-05-05 02:12:16,019 - mmseg - INFO - Iter [22150/40000] lr: 6.408e-07, eta: 4:12:39, time: 0.806, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6234, decode.loss_mask: 0.7342, decode.loss_dice: 1.0244, decode.d0.loss_cls: 3.6604, decode.d0.loss_mask: 0.7705, decode.d0.loss_dice: 1.1861, decode.d1.loss_cls: 0.8440, decode.d1.loss_mask: 0.7615, decode.d1.loss_dice: 1.0904, decode.d2.loss_cls: 0.7134, decode.d2.loss_mask: 0.7465, decode.d2.loss_dice: 1.0499, decode.d3.loss_cls: 0.6741, decode.d3.loss_mask: 0.7370, decode.d3.loss_dice: 1.0305, decode.d4.loss_cls: 0.6603, decode.d4.loss_mask: 0.7371, decode.d4.loss_dice: 1.0317, decode.d5.loss_cls: 0.6410, decode.d5.loss_mask: 0.7398, decode.d5.loss_dice: 1.0299, decode.d6.loss_cls: 0.6325, decode.d6.loss_mask: 0.7362, decode.d6.loss_dice: 1.0215, decode.d7.loss_cls: 0.6240, decode.d7.loss_mask: 0.7351, decode.d7.loss_dice: 1.0212, decode.d8.loss_cls: 0.6209, decode.d8.loss_mask: 0.7355, decode.d8.loss_dice: 1.0212, loss: 27.6342 2022-05-05 02:12:55,315 - mmseg - INFO - Iter [22200/40000] lr: 6.390e-07, eta: 4:11:54, time: 0.786, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6331, decode.loss_mask: 0.7465, decode.loss_dice: 1.0235, decode.d0.loss_cls: 3.6430, decode.d0.loss_mask: 0.7814, decode.d0.loss_dice: 1.2061, decode.d1.loss_cls: 0.8524, decode.d1.loss_mask: 0.7727, decode.d1.loss_dice: 1.0906, decode.d2.loss_cls: 0.7176, decode.d2.loss_mask: 0.7539, decode.d2.loss_dice: 1.0599, decode.d3.loss_cls: 0.6828, decode.d3.loss_mask: 0.7464, decode.d3.loss_dice: 1.0360, decode.d4.loss_cls: 0.6648, decode.d4.loss_mask: 0.7450, decode.d4.loss_dice: 1.0338, decode.d5.loss_cls: 0.6456, decode.d5.loss_mask: 0.7459, decode.d5.loss_dice: 1.0268, decode.d6.loss_cls: 0.6385, decode.d6.loss_mask: 0.7431, decode.d6.loss_dice: 1.0212, decode.d7.loss_cls: 0.6326, decode.d7.loss_mask: 0.7498, decode.d7.loss_dice: 1.0236, decode.d8.loss_cls: 0.6308, decode.d8.loss_mask: 0.7476, decode.d8.loss_dice: 1.0261, loss: 27.8209 2022-05-05 02:13:34,458 - mmseg - INFO - Iter [22250/40000] lr: 6.372e-07, eta: 4:11:09, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6287, decode.loss_mask: 0.7351, decode.loss_dice: 1.0461, decode.d0.loss_cls: 3.7277, decode.d0.loss_mask: 0.7646, decode.d0.loss_dice: 1.2231, decode.d1.loss_cls: 0.8473, decode.d1.loss_mask: 0.7473, decode.d1.loss_dice: 1.1137, decode.d2.loss_cls: 0.7228, decode.d2.loss_mask: 0.7450, decode.d2.loss_dice: 1.0705, decode.d3.loss_cls: 0.6801, decode.d3.loss_mask: 0.7373, decode.d3.loss_dice: 1.0520, decode.d4.loss_cls: 0.6519, decode.d4.loss_mask: 0.7382, decode.d4.loss_dice: 1.0565, decode.d5.loss_cls: 0.6443, decode.d5.loss_mask: 0.7350, decode.d5.loss_dice: 1.0500, decode.d6.loss_cls: 0.6363, decode.d6.loss_mask: 0.7368, decode.d6.loss_dice: 1.0445, decode.d7.loss_cls: 0.6360, decode.d7.loss_mask: 0.7349, decode.d7.loss_dice: 1.0437, decode.d8.loss_cls: 0.6234, decode.d8.loss_mask: 0.7370, decode.d8.loss_dice: 1.0444, loss: 27.9539 2022-05-05 02:14:13,449 - mmseg - INFO - Iter [22300/40000] lr: 6.354e-07, eta: 4:10:24, time: 0.780, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6616, decode.loss_mask: 0.7287, decode.loss_dice: 1.0563, decode.d0.loss_cls: 3.6926, decode.d0.loss_mask: 0.7798, decode.d0.loss_dice: 1.2406, decode.d1.loss_cls: 0.8989, decode.d1.loss_mask: 0.7608, decode.d1.loss_dice: 1.1373, decode.d2.loss_cls: 0.7544, decode.d2.loss_mask: 0.7425, decode.d2.loss_dice: 1.0893, decode.d3.loss_cls: 0.6980, decode.d3.loss_mask: 0.7361, decode.d3.loss_dice: 1.0660, decode.d4.loss_cls: 0.6816, decode.d4.loss_mask: 0.7348, decode.d4.loss_dice: 1.0644, decode.d5.loss_cls: 0.6719, decode.d5.loss_mask: 0.7336, decode.d5.loss_dice: 1.0590, decode.d6.loss_cls: 0.6566, decode.d6.loss_mask: 0.7315, decode.d6.loss_dice: 1.0543, decode.d7.loss_cls: 0.6562, decode.d7.loss_mask: 0.7304, decode.d7.loss_dice: 1.0570, decode.d8.loss_cls: 0.6602, decode.d8.loss_mask: 0.7287, decode.d8.loss_dice: 1.0606, loss: 28.3236 2022-05-05 02:14:52,795 - mmseg - INFO - Iter [22350/40000] lr: 6.336e-07, eta: 4:09:39, time: 0.787, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6558, decode.loss_mask: 0.7316, decode.loss_dice: 1.0597, decode.d0.loss_cls: 3.7383, decode.d0.loss_mask: 0.7737, decode.d0.loss_dice: 1.2460, decode.d1.loss_cls: 0.8805, decode.d1.loss_mask: 0.7576, decode.d1.loss_dice: 1.1454, decode.d2.loss_cls: 0.7494, decode.d2.loss_mask: 0.7455, decode.d2.loss_dice: 1.0970, decode.d3.loss_cls: 0.7141, decode.d3.loss_mask: 0.7418, decode.d3.loss_dice: 1.0796, decode.d4.loss_cls: 0.6947, decode.d4.loss_mask: 0.7376, decode.d4.loss_dice: 1.0826, decode.d5.loss_cls: 0.6677, decode.d5.loss_mask: 0.7382, decode.d5.loss_dice: 1.0748, decode.d6.loss_cls: 0.6517, decode.d6.loss_mask: 0.7385, decode.d6.loss_dice: 1.0682, decode.d7.loss_cls: 0.6561, decode.d7.loss_mask: 0.7325, decode.d7.loss_dice: 1.0681, decode.d8.loss_cls: 0.6475, decode.d8.loss_mask: 0.7331, decode.d8.loss_dice: 1.0692, loss: 28.4764 2022-05-05 02:15:32,859 - mmseg - INFO - Iter [22400/40000] lr: 6.318e-07, eta: 4:08:54, time: 0.801, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6092, decode.loss_mask: 0.7076, decode.loss_dice: 1.0288, decode.d0.loss_cls: 3.7095, decode.d0.loss_mask: 0.7586, decode.d0.loss_dice: 1.2131, decode.d1.loss_cls: 0.8501, decode.d1.loss_mask: 0.7412, decode.d1.loss_dice: 1.1160, decode.d2.loss_cls: 0.7019, decode.d2.loss_mask: 0.7227, decode.d2.loss_dice: 1.0663, decode.d3.loss_cls: 0.6594, decode.d3.loss_mask: 0.7192, decode.d3.loss_dice: 1.0563, decode.d4.loss_cls: 0.6433, decode.d4.loss_mask: 0.7147, decode.d4.loss_dice: 1.0456, decode.d5.loss_cls: 0.6155, decode.d5.loss_mask: 0.7195, decode.d5.loss_dice: 1.0420, decode.d6.loss_cls: 0.6132, decode.d6.loss_mask: 0.7131, decode.d6.loss_dice: 1.0329, decode.d7.loss_cls: 0.6051, decode.d7.loss_mask: 0.7124, decode.d7.loss_dice: 1.0335, decode.d8.loss_cls: 0.6065, decode.d8.loss_mask: 0.7089, decode.d8.loss_dice: 1.0312, loss: 27.4974 2022-05-05 02:16:12,519 - mmseg - INFO - Iter [22450/40000] lr: 6.300e-07, eta: 4:08:10, time: 0.792, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6296, decode.loss_mask: 0.7358, decode.loss_dice: 1.0524, decode.d0.loss_cls: 3.7271, decode.d0.loss_mask: 0.7735, decode.d0.loss_dice: 1.2228, decode.d1.loss_cls: 0.8314, decode.d1.loss_mask: 0.7632, decode.d1.loss_dice: 1.1329, decode.d2.loss_cls: 0.7170, decode.d2.loss_mask: 0.7407, decode.d2.loss_dice: 1.0808, decode.d3.loss_cls: 0.6625, decode.d3.loss_mask: 0.7389, decode.d3.loss_dice: 1.0689, decode.d4.loss_cls: 0.6501, decode.d4.loss_mask: 0.7348, decode.d4.loss_dice: 1.0694, decode.d5.loss_cls: 0.6389, decode.d5.loss_mask: 0.7346, decode.d5.loss_dice: 1.0640, decode.d6.loss_cls: 0.6245, decode.d6.loss_mask: 0.7368, decode.d6.loss_dice: 1.0619, decode.d7.loss_cls: 0.6237, decode.d7.loss_mask: 0.7341, decode.d7.loss_dice: 1.0559, decode.d8.loss_cls: 0.6206, decode.d8.loss_mask: 0.7353, decode.d8.loss_dice: 1.0583, loss: 28.0204 2022-05-05 02:16:53,752 - mmseg - INFO - Iter [22500/40000] lr: 6.282e-07, eta: 4:07:26, time: 0.825, data_time: 0.058, memory: 51557, decode.loss_cls: 0.6132, decode.loss_mask: 0.7464, decode.loss_dice: 1.0491, decode.d0.loss_cls: 3.6427, decode.d0.loss_mask: 0.7771, decode.d0.loss_dice: 1.2118, decode.d1.loss_cls: 0.8350, decode.d1.loss_mask: 0.7631, decode.d1.loss_dice: 1.1220, decode.d2.loss_cls: 0.7079, decode.d2.loss_mask: 0.7521, decode.d2.loss_dice: 1.0789, decode.d3.loss_cls: 0.6636, decode.d3.loss_mask: 0.7457, decode.d3.loss_dice: 1.0625, decode.d4.loss_cls: 0.6398, decode.d4.loss_mask: 0.7473, decode.d4.loss_dice: 1.0561, decode.d5.loss_cls: 0.6276, decode.d5.loss_mask: 0.7450, decode.d5.loss_dice: 1.0585, decode.d6.loss_cls: 0.6165, decode.d6.loss_mask: 0.7465, decode.d6.loss_dice: 1.0530, decode.d7.loss_cls: 0.6137, decode.d7.loss_mask: 0.7456, decode.d7.loss_dice: 1.0559, decode.d8.loss_cls: 0.6129, decode.d8.loss_mask: 0.7466, decode.d8.loss_dice: 1.0539, loss: 27.8902 2022-05-05 02:17:32,846 - mmseg - INFO - Iter [22550/40000] lr: 6.264e-07, eta: 4:06:41, time: 0.782, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6352, decode.loss_mask: 0.7431, decode.loss_dice: 1.0649, decode.d0.loss_cls: 3.6807, decode.d0.loss_mask: 0.7830, decode.d0.loss_dice: 1.2427, decode.d1.loss_cls: 0.8627, decode.d1.loss_mask: 0.7753, decode.d1.loss_dice: 1.1436, decode.d2.loss_cls: 0.7240, decode.d2.loss_mask: 0.7621, decode.d2.loss_dice: 1.0979, decode.d3.loss_cls: 0.6746, decode.d3.loss_mask: 0.7537, decode.d3.loss_dice: 1.0787, decode.d4.loss_cls: 0.6565, decode.d4.loss_mask: 0.7551, decode.d4.loss_dice: 1.0801, decode.d5.loss_cls: 0.6445, decode.d5.loss_mask: 0.7549, decode.d5.loss_dice: 1.0791, decode.d6.loss_cls: 0.6431, decode.d6.loss_mask: 0.7491, decode.d6.loss_dice: 1.0688, decode.d7.loss_cls: 0.6283, decode.d7.loss_mask: 0.7489, decode.d7.loss_dice: 1.0677, decode.d8.loss_cls: 0.6339, decode.d8.loss_mask: 0.7472, decode.d8.loss_dice: 1.0709, loss: 28.3505 2022-05-05 02:18:12,555 - mmseg - INFO - Iter [22600/40000] lr: 6.246e-07, eta: 4:05:57, time: 0.795, data_time: 0.011, memory: 51557, decode.loss_cls: 0.6289, decode.loss_mask: 0.7159, decode.loss_dice: 1.0227, decode.d0.loss_cls: 3.7049, decode.d0.loss_mask: 0.7464, decode.d0.loss_dice: 1.2072, decode.d1.loss_cls: 0.8380, decode.d1.loss_mask: 0.7381, decode.d1.loss_dice: 1.1030, decode.d2.loss_cls: 0.7077, decode.d2.loss_mask: 0.7246, decode.d2.loss_dice: 1.0570, decode.d3.loss_cls: 0.6814, decode.d3.loss_mask: 0.7131, decode.d3.loss_dice: 1.0327, decode.d4.loss_cls: 0.6545, decode.d4.loss_mask: 0.7140, decode.d4.loss_dice: 1.0299, decode.d5.loss_cls: 0.6489, decode.d5.loss_mask: 0.7119, decode.d5.loss_dice: 1.0240, decode.d6.loss_cls: 0.6355, decode.d6.loss_mask: 0.7086, decode.d6.loss_dice: 1.0230, decode.d7.loss_cls: 0.6308, decode.d7.loss_mask: 0.7126, decode.d7.loss_dice: 1.0234, decode.d8.loss_cls: 0.6245, decode.d8.loss_mask: 0.7138, decode.d8.loss_dice: 1.0259, loss: 27.5029 2022-05-05 02:18:51,961 - mmseg - INFO - Iter [22650/40000] lr: 6.228e-07, eta: 4:05:12, time: 0.788, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5744, decode.loss_mask: 0.7345, decode.loss_dice: 1.0187, decode.d0.loss_cls: 3.6169, decode.d0.loss_mask: 0.7742, decode.d0.loss_dice: 1.1910, decode.d1.loss_cls: 0.8058, decode.d1.loss_mask: 0.7650, decode.d1.loss_dice: 1.0926, decode.d2.loss_cls: 0.6679, decode.d2.loss_mask: 0.7454, decode.d2.loss_dice: 1.0576, decode.d3.loss_cls: 0.6230, decode.d3.loss_mask: 0.7415, decode.d3.loss_dice: 1.0326, decode.d4.loss_cls: 0.6086, decode.d4.loss_mask: 0.7378, decode.d4.loss_dice: 1.0261, decode.d5.loss_cls: 0.5962, decode.d5.loss_mask: 0.7344, decode.d5.loss_dice: 1.0202, decode.d6.loss_cls: 0.5811, decode.d6.loss_mask: 0.7348, decode.d6.loss_dice: 1.0170, decode.d7.loss_cls: 0.5755, decode.d7.loss_mask: 0.7365, decode.d7.loss_dice: 1.0209, decode.d8.loss_cls: 0.5752, decode.d8.loss_mask: 0.7354, decode.d8.loss_dice: 1.0179, loss: 27.1585 2022-05-05 02:19:31,244 - mmseg - INFO - Iter [22700/40000] lr: 6.210e-07, eta: 4:04:27, time: 0.785, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6295, decode.loss_mask: 0.7445, decode.loss_dice: 1.0324, decode.d0.loss_cls: 3.6995, decode.d0.loss_mask: 0.7829, decode.d0.loss_dice: 1.1980, decode.d1.loss_cls: 0.8458, decode.d1.loss_mask: 0.7699, decode.d1.loss_dice: 1.1116, decode.d2.loss_cls: 0.7214, decode.d2.loss_mask: 0.7505, decode.d2.loss_dice: 1.0638, decode.d3.loss_cls: 0.6775, decode.d3.loss_mask: 0.7415, decode.d3.loss_dice: 1.0450, decode.d4.loss_cls: 0.6508, decode.d4.loss_mask: 0.7414, decode.d4.loss_dice: 1.0431, decode.d5.loss_cls: 0.6336, decode.d5.loss_mask: 0.7416, decode.d5.loss_dice: 1.0350, decode.d6.loss_cls: 0.6259, decode.d6.loss_mask: 0.7400, decode.d6.loss_dice: 1.0294, decode.d7.loss_cls: 0.6231, decode.d7.loss_mask: 0.7439, decode.d7.loss_dice: 1.0320, decode.d8.loss_cls: 0.6236, decode.d8.loss_mask: 0.7415, decode.d8.loss_dice: 1.0291, loss: 27.8476 2022-05-05 02:20:10,212 - mmseg - INFO - Iter [22750/40000] lr: 6.192e-07, eta: 4:03:42, time: 0.780, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6260, decode.loss_mask: 0.7264, decode.loss_dice: 1.0428, decode.d0.loss_cls: 3.6996, decode.d0.loss_mask: 0.7663, decode.d0.loss_dice: 1.2261, decode.d1.loss_cls: 0.8545, decode.d1.loss_mask: 0.7526, decode.d1.loss_dice: 1.1219, decode.d2.loss_cls: 0.7285, decode.d2.loss_mask: 0.7394, decode.d2.loss_dice: 1.0743, decode.d3.loss_cls: 0.6701, decode.d3.loss_mask: 0.7340, decode.d3.loss_dice: 1.0565, decode.d4.loss_cls: 0.6559, decode.d4.loss_mask: 0.7306, decode.d4.loss_dice: 1.0511, decode.d5.loss_cls: 0.6516, decode.d5.loss_mask: 0.7266, decode.d5.loss_dice: 1.0507, decode.d6.loss_cls: 0.6285, decode.d6.loss_mask: 0.7261, decode.d6.loss_dice: 1.0446, decode.d7.loss_cls: 0.6334, decode.d7.loss_mask: 0.7253, decode.d7.loss_dice: 1.0455, decode.d8.loss_cls: 0.6295, decode.d8.loss_mask: 0.7272, decode.d8.loss_dice: 1.0391, loss: 27.8849 2022-05-05 02:20:49,974 - mmseg - INFO - Iter [22800/40000] lr: 6.174e-07, eta: 4:02:58, time: 0.794, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6209, decode.loss_mask: 0.7344, decode.loss_dice: 1.0266, decode.d0.loss_cls: 3.6835, decode.d0.loss_mask: 0.7647, decode.d0.loss_dice: 1.1978, decode.d1.loss_cls: 0.8411, decode.d1.loss_mask: 0.7546, decode.d1.loss_dice: 1.1017, decode.d2.loss_cls: 0.7174, decode.d2.loss_mask: 0.7409, decode.d2.loss_dice: 1.0610, decode.d3.loss_cls: 0.6650, decode.d3.loss_mask: 0.7364, decode.d3.loss_dice: 1.0440, decode.d4.loss_cls: 0.6546, decode.d4.loss_mask: 0.7369, decode.d4.loss_dice: 1.0389, decode.d5.loss_cls: 0.6455, decode.d5.loss_mask: 0.7349, decode.d5.loss_dice: 1.0296, decode.d6.loss_cls: 0.6293, decode.d6.loss_mask: 0.7330, decode.d6.loss_dice: 1.0273, decode.d7.loss_cls: 0.6258, decode.d7.loss_mask: 0.7317, decode.d7.loss_dice: 1.0269, decode.d8.loss_cls: 0.6220, decode.d8.loss_mask: 0.7288, decode.d8.loss_dice: 1.0273, loss: 27.6827 2022-05-05 02:21:29,868 - mmseg - INFO - Iter [22850/40000] lr: 6.156e-07, eta: 4:02:14, time: 0.799, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6464, decode.loss_mask: 0.7572, decode.loss_dice: 1.0680, decode.d0.loss_cls: 3.6922, decode.d0.loss_mask: 0.7960, decode.d0.loss_dice: 1.2380, decode.d1.loss_cls: 0.8559, decode.d1.loss_mask: 0.7883, decode.d1.loss_dice: 1.1487, decode.d2.loss_cls: 0.7418, decode.d2.loss_mask: 0.7693, decode.d2.loss_dice: 1.1019, decode.d3.loss_cls: 0.6922, decode.d3.loss_mask: 0.7671, decode.d3.loss_dice: 1.0816, decode.d4.loss_cls: 0.6780, decode.d4.loss_mask: 0.7623, decode.d4.loss_dice: 1.0811, decode.d5.loss_cls: 0.6577, decode.d5.loss_mask: 0.7576, decode.d5.loss_dice: 1.0778, decode.d6.loss_cls: 0.6462, decode.d6.loss_mask: 0.7604, decode.d6.loss_dice: 1.0748, decode.d7.loss_cls: 0.6488, decode.d7.loss_mask: 0.7575, decode.d7.loss_dice: 1.0645, decode.d8.loss_cls: 0.6418, decode.d8.loss_mask: 0.7574, decode.d8.loss_dice: 1.0696, loss: 28.5800 2022-05-05 02:22:08,723 - mmseg - INFO - Iter [22900/40000] lr: 6.138e-07, eta: 4:01:29, time: 0.777, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6418, decode.loss_mask: 0.7458, decode.loss_dice: 1.0538, decode.d0.loss_cls: 3.6740, decode.d0.loss_mask: 0.7886, decode.d0.loss_dice: 1.2403, decode.d1.loss_cls: 0.8259, decode.d1.loss_mask: 0.7732, decode.d1.loss_dice: 1.1298, decode.d2.loss_cls: 0.7293, decode.d2.loss_mask: 0.7573, decode.d2.loss_dice: 1.0816, decode.d3.loss_cls: 0.6805, decode.d3.loss_mask: 0.7524, decode.d3.loss_dice: 1.0685, decode.d4.loss_cls: 0.6658, decode.d4.loss_mask: 0.7497, decode.d4.loss_dice: 1.0590, decode.d5.loss_cls: 0.6620, decode.d5.loss_mask: 0.7477, decode.d5.loss_dice: 1.0579, decode.d6.loss_cls: 0.6521, decode.d6.loss_mask: 0.7472, decode.d6.loss_dice: 1.0534, decode.d7.loss_cls: 0.6476, decode.d7.loss_mask: 0.7459, decode.d7.loss_dice: 1.0556, decode.d8.loss_cls: 0.6462, decode.d8.loss_mask: 0.7455, decode.d8.loss_dice: 1.0529, loss: 28.2314 2022-05-05 02:22:47,895 - mmseg - INFO - Iter [22950/40000] lr: 6.120e-07, eta: 4:00:44, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6345, decode.loss_mask: 0.7489, decode.loss_dice: 1.1060, decode.d0.loss_cls: 3.6558, decode.d0.loss_mask: 0.7854, decode.d0.loss_dice: 1.2670, decode.d1.loss_cls: 0.8665, decode.d1.loss_mask: 0.7687, decode.d1.loss_dice: 1.1769, decode.d2.loss_cls: 0.7480, decode.d2.loss_mask: 0.7628, decode.d2.loss_dice: 1.1347, decode.d3.loss_cls: 0.6894, decode.d3.loss_mask: 0.7570, decode.d3.loss_dice: 1.1055, decode.d4.loss_cls: 0.6747, decode.d4.loss_mask: 0.7534, decode.d4.loss_dice: 1.1127, decode.d5.loss_cls: 0.6595, decode.d5.loss_mask: 0.7522, decode.d5.loss_dice: 1.1068, decode.d6.loss_cls: 0.6550, decode.d6.loss_mask: 0.7489, decode.d6.loss_dice: 1.0952, decode.d7.loss_cls: 0.6342, decode.d7.loss_mask: 0.7503, decode.d7.loss_dice: 1.1052, decode.d8.loss_cls: 0.6323, decode.d8.loss_mask: 0.7484, decode.d8.loss_dice: 1.1033, loss: 28.7390 2022-05-05 02:23:27,017 - mmseg - INFO - Saving checkpoint at 23000 iterations 2022-05-05 02:23:51,582 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 02:23:51,591 - mmseg - INFO - Iter [23000/40000] lr: 6.102e-07, eta: 4:00:17, time: 1.271, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6386, decode.loss_mask: 0.7501, decode.loss_dice: 1.0276, decode.d0.loss_cls: 3.6129, decode.d0.loss_mask: 0.7875, decode.d0.loss_dice: 1.1982, decode.d1.loss_cls: 0.8322, decode.d1.loss_mask: 0.7757, decode.d1.loss_dice: 1.0981, decode.d2.loss_cls: 0.7116, decode.d2.loss_mask: 0.7621, decode.d2.loss_dice: 1.0585, decode.d3.loss_cls: 0.6827, decode.d3.loss_mask: 0.7535, decode.d3.loss_dice: 1.0320, decode.d4.loss_cls: 0.6593, decode.d4.loss_mask: 0.7517, decode.d4.loss_dice: 1.0355, decode.d5.loss_cls: 0.6469, decode.d5.loss_mask: 0.7547, decode.d5.loss_dice: 1.0344, decode.d6.loss_cls: 0.6389, decode.d6.loss_mask: 0.7515, decode.d6.loss_dice: 1.0186, decode.d7.loss_cls: 0.6293, decode.d7.loss_mask: 0.7524, decode.d7.loss_dice: 1.0300, decode.d8.loss_cls: 0.6332, decode.d8.loss_mask: 0.7464, decode.d8.loss_dice: 1.0336, loss: 27.8380 2022-05-05 02:24:33,778 - mmseg - INFO - Iter [23050/40000] lr: 6.085e-07, eta: 3:59:35, time: 0.847, data_time: 0.061, memory: 51557, decode.loss_cls: 0.6058, decode.loss_mask: 0.7567, decode.loss_dice: 1.0547, decode.d0.loss_cls: 3.6853, decode.d0.loss_mask: 0.7912, decode.d0.loss_dice: 1.2296, decode.d1.loss_cls: 0.8295, decode.d1.loss_mask: 0.7803, decode.d1.loss_dice: 1.1339, decode.d2.loss_cls: 0.6909, decode.d2.loss_mask: 0.7551, decode.d2.loss_dice: 1.0882, decode.d3.loss_cls: 0.6447, decode.d3.loss_mask: 0.7549, decode.d3.loss_dice: 1.0702, decode.d4.loss_cls: 0.6286, decode.d4.loss_mask: 0.7564, decode.d4.loss_dice: 1.0638, decode.d5.loss_cls: 0.6247, decode.d5.loss_mask: 0.7517, decode.d5.loss_dice: 1.0604, decode.d6.loss_cls: 0.6161, decode.d6.loss_mask: 0.7501, decode.d6.loss_dice: 1.0522, decode.d7.loss_cls: 0.6102, decode.d7.loss_mask: 0.7553, decode.d7.loss_dice: 1.0552, decode.d8.loss_cls: 0.6002, decode.d8.loss_mask: 0.7552, decode.d8.loss_dice: 1.0561, loss: 28.0073 2022-05-05 02:25:13,202 - mmseg - INFO - Iter [23100/40000] lr: 6.067e-07, eta: 3:58:50, time: 0.788, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5923, decode.loss_mask: 0.7335, decode.loss_dice: 1.0427, decode.d0.loss_cls: 3.6991, decode.d0.loss_mask: 0.7774, decode.d0.loss_dice: 1.2113, decode.d1.loss_cls: 0.8429, decode.d1.loss_mask: 0.7592, decode.d1.loss_dice: 1.1075, decode.d2.loss_cls: 0.6951, decode.d2.loss_mask: 0.7442, decode.d2.loss_dice: 1.0661, decode.d3.loss_cls: 0.6491, decode.d3.loss_mask: 0.7436, decode.d3.loss_dice: 1.0485, decode.d4.loss_cls: 0.6272, decode.d4.loss_mask: 0.7383, decode.d4.loss_dice: 1.0487, decode.d5.loss_cls: 0.6118, decode.d5.loss_mask: 0.7347, decode.d5.loss_dice: 1.0425, decode.d6.loss_cls: 0.5982, decode.d6.loss_mask: 0.7338, decode.d6.loss_dice: 1.0363, decode.d7.loss_cls: 0.5924, decode.d7.loss_mask: 0.7335, decode.d7.loss_dice: 1.0409, decode.d8.loss_cls: 0.5853, decode.d8.loss_mask: 0.7360, decode.d8.loss_dice: 1.0414, loss: 27.6134 2022-05-05 02:25:53,063 - mmseg - INFO - Iter [23150/40000] lr: 6.049e-07, eta: 3:58:06, time: 0.797, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5776, decode.loss_mask: 0.7396, decode.loss_dice: 1.0402, decode.d0.loss_cls: 3.5975, decode.d0.loss_mask: 0.7778, decode.d0.loss_dice: 1.2080, decode.d1.loss_cls: 0.8116, decode.d1.loss_mask: 0.7673, decode.d1.loss_dice: 1.1139, decode.d2.loss_cls: 0.6877, decode.d2.loss_mask: 0.7513, decode.d2.loss_dice: 1.0735, decode.d3.loss_cls: 0.6378, decode.d3.loss_mask: 0.7488, decode.d3.loss_dice: 1.0520, decode.d4.loss_cls: 0.6186, decode.d4.loss_mask: 0.7424, decode.d4.loss_dice: 1.0523, decode.d5.loss_cls: 0.6030, decode.d5.loss_mask: 0.7413, decode.d5.loss_dice: 1.0522, decode.d6.loss_cls: 0.5894, decode.d6.loss_mask: 0.7367, decode.d6.loss_dice: 1.0471, decode.d7.loss_cls: 0.5851, decode.d7.loss_mask: 0.7363, decode.d7.loss_dice: 1.0446, decode.d8.loss_cls: 0.5755, decode.d8.loss_mask: 0.7370, decode.d8.loss_dice: 1.0404, loss: 27.4863 2022-05-05 02:26:32,228 - mmseg - INFO - Iter [23200/40000] lr: 6.031e-07, eta: 3:57:21, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6219, decode.loss_mask: 0.7468, decode.loss_dice: 1.0545, decode.d0.loss_cls: 3.6705, decode.d0.loss_mask: 0.7889, decode.d0.loss_dice: 1.2351, decode.d1.loss_cls: 0.8598, decode.d1.loss_mask: 0.7806, decode.d1.loss_dice: 1.1288, decode.d2.loss_cls: 0.7244, decode.d2.loss_mask: 0.7603, decode.d2.loss_dice: 1.0913, decode.d3.loss_cls: 0.6756, decode.d3.loss_mask: 0.7542, decode.d3.loss_dice: 1.0742, decode.d4.loss_cls: 0.6454, decode.d4.loss_mask: 0.7507, decode.d4.loss_dice: 1.0777, decode.d5.loss_cls: 0.6380, decode.d5.loss_mask: 0.7459, decode.d5.loss_dice: 1.0655, decode.d6.loss_cls: 0.6262, decode.d6.loss_mask: 0.7463, decode.d6.loss_dice: 1.0615, decode.d7.loss_cls: 0.6281, decode.d7.loss_mask: 0.7461, decode.d7.loss_dice: 1.0645, decode.d8.loss_cls: 0.6181, decode.d8.loss_mask: 0.7489, decode.d8.loss_dice: 1.0605, loss: 28.1900 2022-05-05 02:27:11,470 - mmseg - INFO - Iter [23250/40000] lr: 6.013e-07, eta: 3:56:37, time: 0.785, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6246, decode.loss_mask: 0.7437, decode.loss_dice: 1.0404, decode.d0.loss_cls: 3.6330, decode.d0.loss_mask: 0.7825, decode.d0.loss_dice: 1.2132, decode.d1.loss_cls: 0.8675, decode.d1.loss_mask: 0.7681, decode.d1.loss_dice: 1.1168, decode.d2.loss_cls: 0.7194, decode.d2.loss_mask: 0.7551, decode.d2.loss_dice: 1.0716, decode.d3.loss_cls: 0.6772, decode.d3.loss_mask: 0.7517, decode.d3.loss_dice: 1.0524, decode.d4.loss_cls: 0.6558, decode.d4.loss_mask: 0.7509, decode.d4.loss_dice: 1.0522, decode.d5.loss_cls: 0.6435, decode.d5.loss_mask: 0.7496, decode.d5.loss_dice: 1.0496, decode.d6.loss_cls: 0.6310, decode.d6.loss_mask: 0.7470, decode.d6.loss_dice: 1.0493, decode.d7.loss_cls: 0.6252, decode.d7.loss_mask: 0.7493, decode.d7.loss_dice: 1.0458, decode.d8.loss_cls: 0.6205, decode.d8.loss_mask: 0.7472, decode.d8.loss_dice: 1.0451, loss: 27.9791 2022-05-05 02:27:50,496 - mmseg - INFO - Iter [23300/40000] lr: 5.995e-07, eta: 3:55:52, time: 0.780, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6135, decode.loss_mask: 0.7162, decode.loss_dice: 1.0168, decode.d0.loss_cls: 3.6198, decode.d0.loss_mask: 0.7502, decode.d0.loss_dice: 1.1921, decode.d1.loss_cls: 0.8567, decode.d1.loss_mask: 0.7431, decode.d1.loss_dice: 1.0977, decode.d2.loss_cls: 0.7125, decode.d2.loss_mask: 0.7298, decode.d2.loss_dice: 1.0510, decode.d3.loss_cls: 0.6660, decode.d3.loss_mask: 0.7228, decode.d3.loss_dice: 1.0340, decode.d4.loss_cls: 0.6465, decode.d4.loss_mask: 0.7188, decode.d4.loss_dice: 1.0311, decode.d5.loss_cls: 0.6329, decode.d5.loss_mask: 0.7137, decode.d5.loss_dice: 1.0303, decode.d6.loss_cls: 0.6212, decode.d6.loss_mask: 0.7194, decode.d6.loss_dice: 1.0156, decode.d7.loss_cls: 0.6139, decode.d7.loss_mask: 0.7157, decode.d7.loss_dice: 1.0228, decode.d8.loss_cls: 0.6144, decode.d8.loss_mask: 0.7155, decode.d8.loss_dice: 1.0201, loss: 27.3542 2022-05-05 02:28:29,746 - mmseg - INFO - Iter [23350/40000] lr: 5.977e-07, eta: 3:55:07, time: 0.784, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6052, decode.loss_mask: 0.7302, decode.loss_dice: 1.0157, decode.d0.loss_cls: 3.6346, decode.d0.loss_mask: 0.7663, decode.d0.loss_dice: 1.1833, decode.d1.loss_cls: 0.8149, decode.d1.loss_mask: 0.7528, decode.d1.loss_dice: 1.0870, decode.d2.loss_cls: 0.6905, decode.d2.loss_mask: 0.7401, decode.d2.loss_dice: 1.0483, decode.d3.loss_cls: 0.6465, decode.d3.loss_mask: 0.7360, decode.d3.loss_dice: 1.0286, decode.d4.loss_cls: 0.6203, decode.d4.loss_mask: 0.7380, decode.d4.loss_dice: 1.0263, decode.d5.loss_cls: 0.6116, decode.d5.loss_mask: 0.7363, decode.d5.loss_dice: 1.0261, decode.d6.loss_cls: 0.6081, decode.d6.loss_mask: 0.7323, decode.d6.loss_dice: 1.0179, decode.d7.loss_cls: 0.5959, decode.d7.loss_mask: 0.7331, decode.d7.loss_dice: 1.0272, decode.d8.loss_cls: 0.5954, decode.d8.loss_mask: 0.7322, decode.d8.loss_dice: 1.0162, loss: 27.2967 2022-05-05 02:29:09,133 - mmseg - INFO - Iter [23400/40000] lr: 5.959e-07, eta: 3:54:23, time: 0.789, data_time: 0.010, memory: 51557, decode.loss_cls: 0.6218, decode.loss_mask: 0.7254, decode.loss_dice: 1.0244, decode.d0.loss_cls: 3.6419, decode.d0.loss_mask: 0.7654, decode.d0.loss_dice: 1.2069, decode.d1.loss_cls: 0.8560, decode.d1.loss_mask: 0.7598, decode.d1.loss_dice: 1.1017, decode.d2.loss_cls: 0.7210, decode.d2.loss_mask: 0.7390, decode.d2.loss_dice: 1.0608, decode.d3.loss_cls: 0.6668, decode.d3.loss_mask: 0.7357, decode.d3.loss_dice: 1.0387, decode.d4.loss_cls: 0.6495, decode.d4.loss_mask: 0.7294, decode.d4.loss_dice: 1.0395, decode.d5.loss_cls: 0.6365, decode.d5.loss_mask: 0.7287, decode.d5.loss_dice: 1.0370, decode.d6.loss_cls: 0.6331, decode.d6.loss_mask: 0.7254, decode.d6.loss_dice: 1.0336, decode.d7.loss_cls: 0.6179, decode.d7.loss_mask: 0.7274, decode.d7.loss_dice: 1.0300, decode.d8.loss_cls: 0.6194, decode.d8.loss_mask: 0.7264, decode.d8.loss_dice: 1.0271, loss: 27.6263 2022-05-05 02:29:48,020 - mmseg - INFO - Iter [23450/40000] lr: 5.941e-07, eta: 3:53:38, time: 0.778, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6148, decode.loss_mask: 0.7346, decode.loss_dice: 1.0390, decode.d0.loss_cls: 3.6805, decode.d0.loss_mask: 0.7742, decode.d0.loss_dice: 1.2184, decode.d1.loss_cls: 0.8515, decode.d1.loss_mask: 0.7640, decode.d1.loss_dice: 1.1227, decode.d2.loss_cls: 0.7203, decode.d2.loss_mask: 0.7483, decode.d2.loss_dice: 1.0763, decode.d3.loss_cls: 0.6649, decode.d3.loss_mask: 0.7388, decode.d3.loss_dice: 1.0546, decode.d4.loss_cls: 0.6365, decode.d4.loss_mask: 0.7387, decode.d4.loss_dice: 1.0523, decode.d5.loss_cls: 0.6281, decode.d5.loss_mask: 0.7376, decode.d5.loss_dice: 1.0506, decode.d6.loss_cls: 0.6187, decode.d6.loss_mask: 0.7358, decode.d6.loss_dice: 1.0397, decode.d7.loss_cls: 0.6173, decode.d7.loss_mask: 0.7330, decode.d7.loss_dice: 1.0424, decode.d8.loss_cls: 0.6074, decode.d8.loss_mask: 0.7336, decode.d8.loss_dice: 1.0411, loss: 27.8157 2022-05-05 02:30:27,160 - mmseg - INFO - Iter [23500/40000] lr: 5.923e-07, eta: 3:52:53, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6278, decode.loss_mask: 0.7332, decode.loss_dice: 1.0601, decode.d0.loss_cls: 3.6692, decode.d0.loss_mask: 0.7630, decode.d0.loss_dice: 1.2145, decode.d1.loss_cls: 0.8649, decode.d1.loss_mask: 0.7659, decode.d1.loss_dice: 1.1353, decode.d2.loss_cls: 0.7379, decode.d2.loss_mask: 0.7414, decode.d2.loss_dice: 1.0933, decode.d3.loss_cls: 0.6892, decode.d3.loss_mask: 0.7344, decode.d3.loss_dice: 1.0748, decode.d4.loss_cls: 0.6607, decode.d4.loss_mask: 0.7364, decode.d4.loss_dice: 1.0756, decode.d5.loss_cls: 0.6507, decode.d5.loss_mask: 0.7324, decode.d5.loss_dice: 1.0680, decode.d6.loss_cls: 0.6419, decode.d6.loss_mask: 0.7324, decode.d6.loss_dice: 1.0615, decode.d7.loss_cls: 0.6288, decode.d7.loss_mask: 0.7305, decode.d7.loss_dice: 1.0622, decode.d8.loss_cls: 0.6267, decode.d8.loss_mask: 0.7316, decode.d8.loss_dice: 1.0649, loss: 28.1090 2022-05-05 02:31:06,801 - mmseg - INFO - Iter [23550/40000] lr: 5.905e-07, eta: 3:52:09, time: 0.793, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5970, decode.loss_mask: 0.7208, decode.loss_dice: 1.0181, decode.d0.loss_cls: 3.6104, decode.d0.loss_mask: 0.7659, decode.d0.loss_dice: 1.1999, decode.d1.loss_cls: 0.8325, decode.d1.loss_mask: 0.7503, decode.d1.loss_dice: 1.0944, decode.d2.loss_cls: 0.7073, decode.d2.loss_mask: 0.7339, decode.d2.loss_dice: 1.0496, decode.d3.loss_cls: 0.6537, decode.d3.loss_mask: 0.7230, decode.d3.loss_dice: 1.0306, decode.d4.loss_cls: 0.6307, decode.d4.loss_mask: 0.7220, decode.d4.loss_dice: 1.0281, decode.d5.loss_cls: 0.6096, decode.d5.loss_mask: 0.7204, decode.d5.loss_dice: 1.0266, decode.d6.loss_cls: 0.6017, decode.d6.loss_mask: 0.7204, decode.d6.loss_dice: 1.0164, decode.d7.loss_cls: 0.5937, decode.d7.loss_mask: 0.7183, decode.d7.loss_dice: 1.0190, decode.d8.loss_cls: 0.5921, decode.d8.loss_mask: 0.7191, decode.d8.loss_dice: 1.0205, loss: 27.2261 2022-05-05 02:31:45,750 - mmseg - INFO - Iter [23600/40000] lr: 5.887e-07, eta: 3:51:25, time: 0.779, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5969, decode.loss_mask: 0.7443, decode.loss_dice: 1.0401, decode.d0.loss_cls: 3.6741, decode.d0.loss_mask: 0.7759, decode.d0.loss_dice: 1.2017, decode.d1.loss_cls: 0.8230, decode.d1.loss_mask: 0.7682, decode.d1.loss_dice: 1.1121, decode.d2.loss_cls: 0.6995, decode.d2.loss_mask: 0.7509, decode.d2.loss_dice: 1.0664, decode.d3.loss_cls: 0.6491, decode.d3.loss_mask: 0.7405, decode.d3.loss_dice: 1.0495, decode.d4.loss_cls: 0.6344, decode.d4.loss_mask: 0.7455, decode.d4.loss_dice: 1.0493, decode.d5.loss_cls: 0.6166, decode.d5.loss_mask: 0.7406, decode.d5.loss_dice: 1.0461, decode.d6.loss_cls: 0.6072, decode.d6.loss_mask: 0.7469, decode.d6.loss_dice: 1.0405, decode.d7.loss_cls: 0.6011, decode.d7.loss_mask: 0.7446, decode.d7.loss_dice: 1.0415, decode.d8.loss_cls: 0.5930, decode.d8.loss_mask: 0.7469, decode.d8.loss_dice: 1.0399, loss: 27.6862 2022-05-05 02:32:28,352 - mmseg - INFO - Iter [23650/40000] lr: 5.869e-07, eta: 3:50:42, time: 0.852, data_time: 0.057, memory: 51557, decode.loss_cls: 0.5656, decode.loss_mask: 0.7470, decode.loss_dice: 1.0391, decode.d0.loss_cls: 3.6043, decode.d0.loss_mask: 0.7963, decode.d0.loss_dice: 1.1953, decode.d1.loss_cls: 0.7917, decode.d1.loss_mask: 0.7770, decode.d1.loss_dice: 1.1033, decode.d2.loss_cls: 0.6672, decode.d2.loss_mask: 0.7629, decode.d2.loss_dice: 1.0714, decode.d3.loss_cls: 0.6143, decode.d3.loss_mask: 0.7568, decode.d3.loss_dice: 1.0473, decode.d4.loss_cls: 0.5925, decode.d4.loss_mask: 0.7499, decode.d4.loss_dice: 1.0453, decode.d5.loss_cls: 0.5740, decode.d5.loss_mask: 0.7502, decode.d5.loss_dice: 1.0459, decode.d6.loss_cls: 0.5736, decode.d6.loss_mask: 0.7485, decode.d6.loss_dice: 1.0383, decode.d7.loss_cls: 0.5566, decode.d7.loss_mask: 0.7474, decode.d7.loss_dice: 1.0382, decode.d8.loss_cls: 0.5540, decode.d8.loss_mask: 0.7520, decode.d8.loss_dice: 1.0402, loss: 27.3461 2022-05-05 02:33:08,267 - mmseg - INFO - Iter [23700/40000] lr: 5.851e-07, eta: 3:49:58, time: 0.798, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5742, decode.loss_mask: 0.7283, decode.loss_dice: 1.0432, decode.d0.loss_cls: 3.5961, decode.d0.loss_mask: 0.7636, decode.d0.loss_dice: 1.2160, decode.d1.loss_cls: 0.8206, decode.d1.loss_mask: 0.7557, decode.d1.loss_dice: 1.1253, decode.d2.loss_cls: 0.6823, decode.d2.loss_mask: 0.7378, decode.d2.loss_dice: 1.0811, decode.d3.loss_cls: 0.6358, decode.d3.loss_mask: 0.7361, decode.d3.loss_dice: 1.0591, decode.d4.loss_cls: 0.6190, decode.d4.loss_mask: 0.7319, decode.d4.loss_dice: 1.0575, decode.d5.loss_cls: 0.6021, decode.d5.loss_mask: 0.7340, decode.d5.loss_dice: 1.0496, decode.d6.loss_cls: 0.5768, decode.d6.loss_mask: 0.7312, decode.d6.loss_dice: 1.0490, decode.d7.loss_cls: 0.5741, decode.d7.loss_mask: 0.7315, decode.d7.loss_dice: 1.0485, decode.d8.loss_cls: 0.5751, decode.d8.loss_mask: 0.7290, decode.d8.loss_dice: 1.0440, loss: 27.4082 2022-05-05 02:33:47,849 - mmseg - INFO - Iter [23750/40000] lr: 5.833e-07, eta: 3:49:14, time: 0.792, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5867, decode.loss_mask: 0.7247, decode.loss_dice: 1.0189, decode.d0.loss_cls: 3.6270, decode.d0.loss_mask: 0.7761, decode.d0.loss_dice: 1.2057, decode.d1.loss_cls: 0.8247, decode.d1.loss_mask: 0.7517, decode.d1.loss_dice: 1.1104, decode.d2.loss_cls: 0.6942, decode.d2.loss_mask: 0.7395, decode.d2.loss_dice: 1.0513, decode.d3.loss_cls: 0.6358, decode.d3.loss_mask: 0.7345, decode.d3.loss_dice: 1.0324, decode.d4.loss_cls: 0.6230, decode.d4.loss_mask: 0.7285, decode.d4.loss_dice: 1.0319, decode.d5.loss_cls: 0.6057, decode.d5.loss_mask: 0.7238, decode.d5.loss_dice: 1.0290, decode.d6.loss_cls: 0.5875, decode.d6.loss_mask: 0.7293, decode.d6.loss_dice: 1.0228, decode.d7.loss_cls: 0.5808, decode.d7.loss_mask: 0.7259, decode.d7.loss_dice: 1.0237, decode.d8.loss_cls: 0.5821, decode.d8.loss_mask: 0.7270, decode.d8.loss_dice: 1.0205, loss: 27.2552 2022-05-05 02:34:26,818 - mmseg - INFO - Iter [23800/40000] lr: 5.815e-07, eta: 3:48:30, time: 0.779, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5892, decode.loss_mask: 0.7437, decode.loss_dice: 1.0213, decode.d0.loss_cls: 3.5756, decode.d0.loss_mask: 0.7743, decode.d0.loss_dice: 1.1739, decode.d1.loss_cls: 0.7931, decode.d1.loss_mask: 0.7666, decode.d1.loss_dice: 1.0887, decode.d2.loss_cls: 0.6721, decode.d2.loss_mask: 0.7507, decode.d2.loss_dice: 1.0484, decode.d3.loss_cls: 0.6269, decode.d3.loss_mask: 0.7440, decode.d3.loss_dice: 1.0259, decode.d4.loss_cls: 0.6144, decode.d4.loss_mask: 0.7415, decode.d4.loss_dice: 1.0276, decode.d5.loss_cls: 0.5965, decode.d5.loss_mask: 0.7422, decode.d5.loss_dice: 1.0257, decode.d6.loss_cls: 0.5957, decode.d6.loss_mask: 0.7398, decode.d6.loss_dice: 1.0158, decode.d7.loss_cls: 0.5862, decode.d7.loss_mask: 0.7419, decode.d7.loss_dice: 1.0155, decode.d8.loss_cls: 0.5834, decode.d8.loss_mask: 0.7403, decode.d8.loss_dice: 1.0187, loss: 27.1796 2022-05-05 02:35:06,433 - mmseg - INFO - Iter [23850/40000] lr: 5.797e-07, eta: 3:47:45, time: 0.792, data_time: 0.008, memory: 51557, decode.loss_cls: 0.6011, decode.loss_mask: 0.7320, decode.loss_dice: 1.0403, decode.d0.loss_cls: 3.6684, decode.d0.loss_mask: 0.7663, decode.d0.loss_dice: 1.1984, decode.d1.loss_cls: 0.8520, decode.d1.loss_mask: 0.7505, decode.d1.loss_dice: 1.1024, decode.d2.loss_cls: 0.7044, decode.d2.loss_mask: 0.7457, decode.d2.loss_dice: 1.0734, decode.d3.loss_cls: 0.6682, decode.d3.loss_mask: 0.7374, decode.d3.loss_dice: 1.0490, decode.d4.loss_cls: 0.6412, decode.d4.loss_mask: 0.7355, decode.d4.loss_dice: 1.0458, decode.d5.loss_cls: 0.6264, decode.d5.loss_mask: 0.7357, decode.d5.loss_dice: 1.0422, decode.d6.loss_cls: 0.6060, decode.d6.loss_mask: 0.7323, decode.d6.loss_dice: 1.0284, decode.d7.loss_cls: 0.5999, decode.d7.loss_mask: 0.7334, decode.d7.loss_dice: 1.0415, decode.d8.loss_cls: 0.5965, decode.d8.loss_mask: 0.7309, decode.d8.loss_dice: 1.0395, loss: 27.6248 2022-05-05 02:35:45,892 - mmseg - INFO - Iter [23900/40000] lr: 5.779e-07, eta: 3:47:01, time: 0.789, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5727, decode.loss_mask: 0.7297, decode.loss_dice: 1.0023, decode.d0.loss_cls: 3.6043, decode.d0.loss_mask: 0.7756, decode.d0.loss_dice: 1.1783, decode.d1.loss_cls: 0.8063, decode.d1.loss_mask: 0.7647, decode.d1.loss_dice: 1.0757, decode.d2.loss_cls: 0.6845, decode.d2.loss_mask: 0.7435, decode.d2.loss_dice: 1.0361, decode.d3.loss_cls: 0.6388, decode.d3.loss_mask: 0.7334, decode.d3.loss_dice: 1.0137, decode.d4.loss_cls: 0.6096, decode.d4.loss_mask: 0.7313, decode.d4.loss_dice: 1.0112, decode.d5.loss_cls: 0.5831, decode.d5.loss_mask: 0.7277, decode.d5.loss_dice: 1.0123, decode.d6.loss_cls: 0.5814, decode.d6.loss_mask: 0.7257, decode.d6.loss_dice: 1.0020, decode.d7.loss_cls: 0.5693, decode.d7.loss_mask: 0.7271, decode.d7.loss_dice: 1.0063, decode.d8.loss_cls: 0.5679, decode.d8.loss_mask: 0.7297, decode.d8.loss_dice: 0.9961, loss: 26.9405 2022-05-05 02:36:25,270 - mmseg - INFO - Iter [23950/40000] lr: 5.761e-07, eta: 3:46:17, time: 0.788, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6388, decode.loss_mask: 0.7422, decode.loss_dice: 1.0315, decode.d0.loss_cls: 3.6975, decode.d0.loss_mask: 0.7896, decode.d0.loss_dice: 1.2054, decode.d1.loss_cls: 0.8725, decode.d1.loss_mask: 0.7731, decode.d1.loss_dice: 1.1151, decode.d2.loss_cls: 0.7420, decode.d2.loss_mask: 0.7517, decode.d2.loss_dice: 1.0686, decode.d3.loss_cls: 0.6882, decode.d3.loss_mask: 0.7471, decode.d3.loss_dice: 1.0407, decode.d4.loss_cls: 0.6586, decode.d4.loss_mask: 0.7502, decode.d4.loss_dice: 1.0429, decode.d5.loss_cls: 0.6445, decode.d5.loss_mask: 0.7484, decode.d5.loss_dice: 1.0344, decode.d6.loss_cls: 0.6328, decode.d6.loss_mask: 0.7418, decode.d6.loss_dice: 1.0334, decode.d7.loss_cls: 0.6355, decode.d7.loss_mask: 0.7419, decode.d7.loss_dice: 1.0340, decode.d8.loss_cls: 0.6279, decode.d8.loss_mask: 0.7420, decode.d8.loss_dice: 1.0268, loss: 27.9991 2022-05-05 02:37:04,374 - mmseg - INFO - Saving checkpoint at 24000 iterations 2022-05-05 02:37:29,388 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 02:37:29,391 - mmseg - INFO - Iter [24000/40000] lr: 5.744e-07, eta: 3:45:49, time: 1.279, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5801, decode.loss_mask: 0.7492, decode.loss_dice: 1.0396, decode.d0.loss_cls: 3.5461, decode.d0.loss_mask: 0.7901, decode.d0.loss_dice: 1.1899, decode.d1.loss_cls: 0.8114, decode.d1.loss_mask: 0.7709, decode.d1.loss_dice: 1.1014, decode.d2.loss_cls: 0.6722, decode.d2.loss_mask: 0.7577, decode.d2.loss_dice: 1.0640, decode.d3.loss_cls: 0.6276, decode.d3.loss_mask: 0.7526, decode.d3.loss_dice: 1.0452, decode.d4.loss_cls: 0.6090, decode.d4.loss_mask: 0.7534, decode.d4.loss_dice: 1.0446, decode.d5.loss_cls: 0.5940, decode.d5.loss_mask: 0.7472, decode.d5.loss_dice: 1.0386, decode.d6.loss_cls: 0.5810, decode.d6.loss_mask: 0.7479, decode.d6.loss_dice: 1.0357, decode.d7.loss_cls: 0.5738, decode.d7.loss_mask: 0.7478, decode.d7.loss_dice: 1.0378, decode.d8.loss_cls: 0.5741, decode.d8.loss_mask: 0.7496, decode.d8.loss_dice: 1.0404, loss: 27.3727 2022-05-05 02:38:01,464 - mmseg - INFO - per class results: 2022-05-05 02:38:01,475 - mmseg - INFO - +------------------+-------+-------+ | Class | IoU | Acc | +------------------+-------+-------+ | person | 88.73 | 94.48 | | bicycle | 76.67 | 91.76 | | car | 48.18 | 54.41 | | motorcycle | 90.53 | 95.42 | | airplane | 88.2 | 93.26 | | bus | 78.66 | 83.8 | | train | 74.83 | 97.11 | | truck | 64.02 | 90.67 | | boat | 80.09 | 86.89 | | traffic light | 84.42 | 93.79 | | fire hydrant | 85.98 | 97.33 | | stop sign | 95.25 | 97.85 | | parking meter | 73.39 | 75.65 | | bench | 47.8 | 68.59 | | bird | 80.82 | 86.47 | | cat | 93.03 | 96.13 | | dog | 91.85 | 96.19 | | horse | 91.26 | 96.01 | | sheep | 82.91 | 90.58 | | cow | 93.59 | 96.04 | | elephant | 92.92 | 96.23 | | bear | 92.76 | 94.72 | | zebra | 91.93 | 96.08 | | giraffe | 89.27 | 94.57 | | backpack | 23.16 | 56.68 | | umbrella | 81.17 | 86.1 | | handbag | 19.91 | 26.35 | | tie | 65.42 | 65.48 | | suitcase | 78.44 | 93.11 | | frisbee | 92.02 | 97.76 | | skis | 41.27 | 78.59 | | snowboard | 68.24 | 76.58 | | sports ball | 86.57 | 94.39 | | kite | 66.78 | 82.59 | | baseball bat | 59.54 | 74.04 | | baseball glove | 2.98 | 3.14 | | skateboard | 70.49 | 88.6 | | surfboard | 90.53 | 95.68 | | tennis racket | 29.41 | 31.41 | | bottle | 73.59 | 85.82 | | wine glass | 85.4 | 92.57 | | cup | 77.39 | 86.3 | | fork | 55.86 | 76.35 | | knife | 78.22 | 88.22 | | spoon | 53.13 | 68.91 | | bowl | 66.48 | 80.21 | | banana | 83.41 | 92.62 | | apple | 75.83 | 87.79 | | sandwich | 88.64 | 97.74 | | orange | 83.95 | 88.65 | | broccoli | 90.45 | 96.6 | | carrot | 52.88 | 72.56 | | hot dog | 53.87 | 97.92 | | pizza | 95.41 | 97.34 | | donut | 80.05 | 94.99 | | cake | 71.02 | 78.15 | | chair | 61.69 | 73.71 | | couch | 73.81 | 94.41 | | potted plant | 36.96 | 49.54 | | bed | 71.16 | 82.39 | | dining table | 62.39 | 78.05 | | toilet | 88.94 | 95.55 | | tv | 66.57 | 95.12 | | laptop | 86.33 | 97.51 | | mouse | 84.84 | 89.0 | | remote | 70.35 | 88.66 | | keyboard | 86.33 | 97.56 | | cell phone | 85.8 | 96.39 | | microwave | 49.04 | 79.78 | | oven | 56.77 | 72.05 | | toaster | 43.22 | 44.98 | | sink | 74.88 | 80.08 | | refrigerator | 87.15 | 95.76 | | book | 79.89 | 91.38 | | clock | 77.88 | 82.11 | | vase | 59.31 | 88.49 | | scissors | 79.94 | 92.36 | | teddy bear | 85.2 | 91.96 | | hair drier | 0.0 | 0.0 | | toothbrush | 7.72 | 11.34 | | banner | 36.77 | 63.33 | | blanket | 14.76 | 22.21 | | branch | 36.68 | 40.51 | | bridge | 3.74 | 5.6 | | building-other | 57.8 | 78.42 | | bush | 24.65 | 38.98 | | cabinet | 27.56 | 43.73 | | cage | 19.79 | 81.97 | | cardboard | 25.32 | 30.05 | | carpet | 55.42 | 74.78 | | ceiling-other | 73.2 | 81.75 | | ceiling-tile | 12.63 | 14.18 | | cloth | 0.0 | 0.0 | | clothes | 25.85 | 33.15 | | clouds | 51.35 | 64.24 | | counter | 44.67 | 50.95 | | cupboard | 61.92 | 78.95 | | curtain | 70.78 | 85.73 | | desk-stuff | 28.21 | 30.67 | | dirt | 36.26 | 59.91 | | door-stuff | 39.14 | 52.81 | | fence | 43.41 | 73.32 | | floor-marble | 0.0 | 0.0 | | floor-other | 36.4 | 52.59 | | floor-stone | 30.25 | 51.93 | | floor-tile | 64.23 | 79.6 | | floor-wood | 77.31 | 86.69 | | flower | 19.98 | 39.07 | | fog | 0.0 | 0.0 | | food-other | 37.47 | 50.65 | | fruit | 64.27 | 86.54 | | furniture-other | 18.49 | 30.18 | | grass | 75.12 | 84.61 | | gravel | 30.12 | 40.84 | | ground-other | 9.83 | 16.79 | | hill | 20.34 | 26.06 | | house | 33.75 | 47.2 | | leaves | 17.84 | 18.16 | | light | 41.49 | 54.66 | | mat | 26.45 | 38.04 | | metal | 19.22 | 27.87 | | mirror-stuff | 54.55 | 76.47 | | moss | 0.0 | 0.0 | | mountain | 31.34 | 61.38 | | mud | 0.0 | 0.0 | | napkin | 32.41 | 32.7 | | net | 45.14 | 57.83 | | paper | 50.72 | 66.78 | | pavement | 55.02 | 69.54 | | pillow | 0.0 | 0.0 | | plant-other | 29.77 | 45.21 | | plastic | 29.23 | 43.05 | | platform | 47.92 | 60.95 | | playingfield | 71.17 | 84.11 | | railing | 15.8 | 25.52 | | railroad | 57.81 | 77.68 | | river | 19.2 | 22.6 | | road | 73.76 | 81.88 | | rock | 46.46 | 66.82 | | roof | 1.66 | 2.27 | | rug | 47.09 | 62.24 | | salad | 27.75 | 27.85 | | sand | 68.04 | 87.52 | | sea | 75.87 | 91.15 | | shelf | 28.68 | 49.35 | | sky-other | 61.95 | 79.53 | | skyscraper | 11.48 | 12.85 | | snow | 91.53 | 93.94 | | solid-other | nan | nan | | stairs | 45.45 | 72.89 | | stone | 8.25 | 13.7 | | straw | 21.11 | 33.59 | | structural-other | 17.39 | 31.46 | | table | 26.09 | 45.76 | | tent | 74.3 | 92.5 | | textile-other | 18.52 | 25.92 | | towel | 39.62 | 47.6 | | tree | 77.85 | 87.33 | | vegetable | 44.07 | 62.69 | | wall-brick | 46.05 | 59.66 | | wall-concrete | 16.77 | 19.5 | | wall-other | 62.93 | 82.7 | | wall-panel | 5.9 | 6.62 | | wall-stone | 39.14 | 43.02 | | wall-tile | 56.41 | 79.5 | | wall-wood | 38.97 | 62.47 | | water-other | 31.96 | 44.78 | | waterdrops | nan | nan | | window-blind | 37.95 | 65.66 | | window-other | 50.94 | 63.3 | | wood | 14.58 | 32.01 | +------------------+-------+-------+ 2022-05-05 02:38:01,477 - mmseg - INFO - Summary: 2022-05-05 02:38:01,478 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 76.25 | 52.99 | 64.82 | +-------+-------+-------+ 2022-05-05 02:38:01,483 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 02:38:01,483 - mmseg - INFO - Iter(val) [125] aAcc: 0.7625, mIoU: 0.5299, mAcc: 0.6482, IoU.person: 0.8873, IoU.bicycle: 0.7667, IoU.car: 0.4818, IoU.motorcycle: 0.9053, IoU.airplane: 0.8820, IoU.bus: 0.7866, IoU.train: 0.7483, IoU.truck: 0.6402, IoU.boat: 0.8009, IoU.traffic light: 0.8442, IoU.fire hydrant: 0.8598, IoU.stop sign: 0.9525, IoU.parking meter: 0.7339, IoU.bench: 0.4780, IoU.bird: 0.8082, IoU.cat: 0.9303, IoU.dog: 0.9185, IoU.horse: 0.9126, IoU.sheep: 0.8291, IoU.cow: 0.9359, IoU.elephant: 0.9292, IoU.bear: 0.9276, IoU.zebra: 0.9193, IoU.giraffe: 0.8927, IoU.backpack: 0.2316, IoU.umbrella: 0.8117, IoU.handbag: 0.1991, IoU.tie: 0.6542, IoU.suitcase: 0.7844, IoU.frisbee: 0.9202, IoU.skis: 0.4127, IoU.snowboard: 0.6824, IoU.sports ball: 0.8657, IoU.kite: 0.6678, IoU.baseball bat: 0.5954, IoU.baseball glove: 0.0298, IoU.skateboard: 0.7049, IoU.surfboard: 0.9053, IoU.tennis racket: 0.2941, IoU.bottle: 0.7359, IoU.wine glass: 0.8540, IoU.cup: 0.7739, IoU.fork: 0.5586, IoU.knife: 0.7822, IoU.spoon: 0.5313, IoU.bowl: 0.6648, IoU.banana: 0.8341, IoU.apple: 0.7583, IoU.sandwich: 0.8864, IoU.orange: 0.8395, IoU.broccoli: 0.9045, IoU.carrot: 0.5288, IoU.hot dog: 0.5387, IoU.pizza: 0.9541, IoU.donut: 0.8005, IoU.cake: 0.7102, IoU.chair: 0.6169, IoU.couch: 0.7381, IoU.potted plant: 0.3696, IoU.bed: 0.7116, IoU.dining table: 0.6239, IoU.toilet: 0.8894, IoU.tv: 0.6657, IoU.laptop: 0.8633, IoU.mouse: 0.8484, IoU.remote: 0.7035, IoU.keyboard: 0.8633, IoU.cell phone: 0.8580, IoU.microwave: 0.4904, IoU.oven: 0.5677, IoU.toaster: 0.4322, IoU.sink: 0.7488, IoU.refrigerator: 0.8715, IoU.book: 0.7989, IoU.clock: 0.7788, IoU.vase: 0.5931, IoU.scissors: 0.7994, IoU.teddy bear: 0.8520, IoU.hair drier: 0.0000, IoU.toothbrush: 0.0772, IoU.banner: 0.3677, IoU.blanket: 0.1476, IoU.branch: 0.3668, IoU.bridge: 0.0374, IoU.building-other: 0.5780, IoU.bush: 0.2465, IoU.cabinet: 0.2756, IoU.cage: 0.1979, IoU.cardboard: 0.2532, IoU.carpet: 0.5542, IoU.ceiling-other: 0.7320, IoU.ceiling-tile: 0.1263, IoU.cloth: 0.0000, IoU.clothes: 0.2585, IoU.clouds: 0.5135, IoU.counter: 0.4467, IoU.cupboard: 0.6192, IoU.curtain: 0.7078, IoU.desk-stuff: 0.2821, IoU.dirt: 0.3626, IoU.door-stuff: 0.3914, IoU.fence: 0.4341, IoU.floor-marble: 0.0000, IoU.floor-other: 0.3640, IoU.floor-stone: 0.3025, IoU.floor-tile: 0.6423, IoU.floor-wood: 0.7731, IoU.flower: 0.1998, IoU.fog: 0.0000, IoU.food-other: 0.3747, IoU.fruit: 0.6427, IoU.furniture-other: 0.1849, IoU.grass: 0.7512, IoU.gravel: 0.3012, IoU.ground-other: 0.0983, IoU.hill: 0.2034, IoU.house: 0.3375, IoU.leaves: 0.1784, IoU.light: 0.4149, IoU.mat: 0.2645, IoU.metal: 0.1922, IoU.mirror-stuff: 0.5455, IoU.moss: 0.0000, IoU.mountain: 0.3134, IoU.mud: 0.0000, IoU.napkin: 0.3241, IoU.net: 0.4514, IoU.paper: 0.5072, IoU.pavement: 0.5502, IoU.pillow: 0.0000, IoU.plant-other: 0.2977, IoU.plastic: 0.2923, IoU.platform: 0.4792, IoU.playingfield: 0.7117, IoU.railing: 0.1580, IoU.railroad: 0.5781, IoU.river: 0.1920, IoU.road: 0.7376, IoU.rock: 0.4646, IoU.roof: 0.0166, IoU.rug: 0.4709, IoU.salad: 0.2775, IoU.sand: 0.6804, IoU.sea: 0.7587, IoU.shelf: 0.2868, IoU.sky-other: 0.6195, IoU.skyscraper: 0.1148, IoU.snow: 0.9153, IoU.solid-other: nan, IoU.stairs: 0.4545, IoU.stone: 0.0825, IoU.straw: 0.2111, IoU.structural-other: 0.1739, IoU.table: 0.2609, IoU.tent: 0.7430, IoU.textile-other: 0.1852, IoU.towel: 0.3962, IoU.tree: 0.7785, IoU.vegetable: 0.4407, IoU.wall-brick: 0.4605, IoU.wall-concrete: 0.1677, IoU.wall-other: 0.6293, IoU.wall-panel: 0.0590, IoU.wall-stone: 0.3914, IoU.wall-tile: 0.5641, IoU.wall-wood: 0.3897, IoU.water-other: 0.3196, IoU.waterdrops: nan, IoU.window-blind: 0.3795, IoU.window-other: 0.5094, IoU.wood: 0.1458, Acc.person: 0.9448, Acc.bicycle: 0.9176, Acc.car: 0.5441, Acc.motorcycle: 0.9542, Acc.airplane: 0.9326, Acc.bus: 0.8380, Acc.train: 0.9711, Acc.truck: 0.9067, Acc.boat: 0.8689, Acc.traffic light: 0.9379, Acc.fire hydrant: 0.9733, Acc.stop sign: 0.9785, Acc.parking meter: 0.7565, Acc.bench: 0.6859, Acc.bird: 0.8647, Acc.cat: 0.9613, Acc.dog: 0.9619, Acc.horse: 0.9601, Acc.sheep: 0.9058, Acc.cow: 0.9604, Acc.elephant: 0.9623, Acc.bear: 0.9472, Acc.zebra: 0.9608, Acc.giraffe: 0.9457, Acc.backpack: 0.5668, Acc.umbrella: 0.8610, Acc.handbag: 0.2635, Acc.tie: 0.6548, Acc.suitcase: 0.9311, Acc.frisbee: 0.9776, Acc.skis: 0.7859, Acc.snowboard: 0.7658, Acc.sports ball: 0.9439, Acc.kite: 0.8259, Acc.baseball bat: 0.7404, Acc.baseball glove: 0.0314, Acc.skateboard: 0.8860, Acc.surfboard: 0.9568, Acc.tennis racket: 0.3141, Acc.bottle: 0.8582, Acc.wine glass: 0.9257, Acc.cup: 0.8630, Acc.fork: 0.7635, Acc.knife: 0.8822, Acc.spoon: 0.6891, Acc.bowl: 0.8021, Acc.banana: 0.9262, Acc.apple: 0.8779, Acc.sandwich: 0.9774, Acc.orange: 0.8865, Acc.broccoli: 0.9660, Acc.carrot: 0.7256, Acc.hot dog: 0.9792, Acc.pizza: 0.9734, Acc.donut: 0.9499, Acc.cake: 0.7815, Acc.chair: 0.7371, Acc.couch: 0.9441, Acc.potted plant: 0.4954, Acc.bed: 0.8239, Acc.dining table: 0.7805, Acc.toilet: 0.9555, Acc.tv: 0.9512, Acc.laptop: 0.9751, Acc.mouse: 0.8900, Acc.remote: 0.8866, Acc.keyboard: 0.9756, Acc.cell phone: 0.9639, Acc.microwave: 0.7978, Acc.oven: 0.7205, Acc.toaster: 0.4498, Acc.sink: 0.8008, Acc.refrigerator: 0.9576, Acc.book: 0.9138, Acc.clock: 0.8211, Acc.vase: 0.8849, Acc.scissors: 0.9236, Acc.teddy bear: 0.9196, Acc.hair drier: 0.0000, Acc.toothbrush: 0.1134, Acc.banner: 0.6333, Acc.blanket: 0.2221, Acc.branch: 0.4051, Acc.bridge: 0.0560, Acc.building-other: 0.7842, Acc.bush: 0.3898, Acc.cabinet: 0.4373, Acc.cage: 0.8197, Acc.cardboard: 0.3005, Acc.carpet: 0.7478, Acc.ceiling-other: 0.8175, Acc.ceiling-tile: 0.1418, Acc.cloth: 0.0000, Acc.clothes: 0.3315, Acc.clouds: 0.6424, Acc.counter: 0.5095, Acc.cupboard: 0.7895, Acc.curtain: 0.8573, Acc.desk-stuff: 0.3067, Acc.dirt: 0.5991, Acc.door-stuff: 0.5281, Acc.fence: 0.7332, Acc.floor-marble: 0.0000, Acc.floor-other: 0.5259, Acc.floor-stone: 0.5193, Acc.floor-tile: 0.7960, Acc.floor-wood: 0.8669, Acc.flower: 0.3907, Acc.fog: 0.0000, Acc.food-other: 0.5065, Acc.fruit: 0.8654, Acc.furniture-other: 0.3018, Acc.grass: 0.8461, Acc.gravel: 0.4084, Acc.ground-other: 0.1679, Acc.hill: 0.2606, Acc.house: 0.4720, Acc.leaves: 0.1816, Acc.light: 0.5466, Acc.mat: 0.3804, Acc.metal: 0.2787, Acc.mirror-stuff: 0.7647, Acc.moss: 0.0000, Acc.mountain: 0.6138, Acc.mud: 0.0000, Acc.napkin: 0.3270, Acc.net: 0.5783, Acc.paper: 0.6678, Acc.pavement: 0.6954, Acc.pillow: 0.0000, Acc.plant-other: 0.4521, Acc.plastic: 0.4305, Acc.platform: 0.6095, Acc.playingfield: 0.8411, Acc.railing: 0.2552, Acc.railroad: 0.7768, Acc.river: 0.2260, Acc.road: 0.8188, Acc.rock: 0.6682, Acc.roof: 0.0227, Acc.rug: 0.6224, Acc.salad: 0.2785, Acc.sand: 0.8752, Acc.sea: 0.9115, Acc.shelf: 0.4935, Acc.sky-other: 0.7953, Acc.skyscraper: 0.1285, Acc.snow: 0.9394, Acc.solid-other: nan, Acc.stairs: 0.7289, Acc.stone: 0.1370, Acc.straw: 0.3359, Acc.structural-other: 0.3146, Acc.table: 0.4576, Acc.tent: 0.9250, Acc.textile-other: 0.2592, Acc.towel: 0.4760, Acc.tree: 0.8733, Acc.vegetable: 0.6269, Acc.wall-brick: 0.5966, Acc.wall-concrete: 0.1950, Acc.wall-other: 0.8270, Acc.wall-panel: 0.0662, Acc.wall-stone: 0.4302, Acc.wall-tile: 0.7950, Acc.wall-wood: 0.6247, Acc.water-other: 0.4478, Acc.waterdrops: nan, Acc.window-blind: 0.6566, Acc.window-other: 0.6330, Acc.wood: 0.3201 2022-05-05 02:38:40,903 - mmseg - INFO - Iter [24050/40000] lr: 5.726e-07, eta: 3:45:26, time: 1.433, data_time: 0.653, memory: 51557, decode.loss_cls: 0.5896, decode.loss_mask: 0.7284, decode.loss_dice: 1.0562, decode.d0.loss_cls: 3.5831, decode.d0.loss_mask: 0.7693, decode.d0.loss_dice: 1.2134, decode.d1.loss_cls: 0.8141, decode.d1.loss_mask: 0.7582, decode.d1.loss_dice: 1.1339, decode.d2.loss_cls: 0.6839, decode.d2.loss_mask: 0.7421, decode.d2.loss_dice: 1.0900, decode.d3.loss_cls: 0.6342, decode.d3.loss_mask: 0.7322, decode.d3.loss_dice: 1.0751, decode.d4.loss_cls: 0.6182, decode.d4.loss_mask: 0.7332, decode.d4.loss_dice: 1.0689, decode.d5.loss_cls: 0.6010, decode.d5.loss_mask: 0.7323, decode.d5.loss_dice: 1.0616, decode.d6.loss_cls: 0.5897, decode.d6.loss_mask: 0.7281, decode.d6.loss_dice: 1.0614, decode.d7.loss_cls: 0.5825, decode.d7.loss_mask: 0.7293, decode.d7.loss_dice: 1.0597, decode.d8.loss_cls: 0.5891, decode.d8.loss_mask: 0.7280, decode.d8.loss_dice: 1.0546, loss: 27.5411 2022-05-05 02:39:20,767 - mmseg - INFO - Iter [24100/40000] lr: 5.708e-07, eta: 3:44:42, time: 0.797, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5864, decode.loss_mask: 0.7501, decode.loss_dice: 1.0389, decode.d0.loss_cls: 3.5543, decode.d0.loss_mask: 0.7985, decode.d0.loss_dice: 1.2024, decode.d1.loss_cls: 0.8083, decode.d1.loss_mask: 0.7792, decode.d1.loss_dice: 1.1065, decode.d2.loss_cls: 0.6863, decode.d2.loss_mask: 0.7594, decode.d2.loss_dice: 1.0633, decode.d3.loss_cls: 0.6387, decode.d3.loss_mask: 0.7539, decode.d3.loss_dice: 1.0453, decode.d4.loss_cls: 0.6161, decode.d4.loss_mask: 0.7518, decode.d4.loss_dice: 1.0401, decode.d5.loss_cls: 0.6091, decode.d5.loss_mask: 0.7520, decode.d5.loss_dice: 1.0418, decode.d6.loss_cls: 0.5985, decode.d6.loss_mask: 0.7505, decode.d6.loss_dice: 1.0359, decode.d7.loss_cls: 0.5869, decode.d7.loss_mask: 0.7456, decode.d7.loss_dice: 1.0368, decode.d8.loss_cls: 0.5850, decode.d8.loss_mask: 0.7524, decode.d8.loss_dice: 1.0343, loss: 27.5082 2022-05-05 02:40:00,045 - mmseg - INFO - Iter [24150/40000] lr: 5.690e-07, eta: 3:43:58, time: 0.786, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5904, decode.loss_mask: 0.7375, decode.loss_dice: 1.0213, decode.d0.loss_cls: 3.6479, decode.d0.loss_mask: 0.7802, decode.d0.loss_dice: 1.1930, decode.d1.loss_cls: 0.8295, decode.d1.loss_mask: 0.7680, decode.d1.loss_dice: 1.1030, decode.d2.loss_cls: 0.6928, decode.d2.loss_mask: 0.7496, decode.d2.loss_dice: 1.0571, decode.d3.loss_cls: 0.6449, decode.d3.loss_mask: 0.7445, decode.d3.loss_dice: 1.0347, decode.d4.loss_cls: 0.6215, decode.d4.loss_mask: 0.7420, decode.d4.loss_dice: 1.0383, decode.d5.loss_cls: 0.6084, decode.d5.loss_mask: 0.7397, decode.d5.loss_dice: 1.0354, decode.d6.loss_cls: 0.6004, decode.d6.loss_mask: 0.7391, decode.d6.loss_dice: 1.0188, decode.d7.loss_cls: 0.6014, decode.d7.loss_mask: 0.7365, decode.d7.loss_dice: 1.0202, decode.d8.loss_cls: 0.5974, decode.d8.loss_mask: 0.7370, decode.d8.loss_dice: 1.0191, loss: 27.4496 2022-05-05 02:40:41,999 - mmseg - INFO - Iter [24200/40000] lr: 5.672e-07, eta: 3:43:15, time: 0.839, data_time: 0.062, memory: 51557, decode.loss_cls: 0.6211, decode.loss_mask: 0.7350, decode.loss_dice: 1.0456, decode.d0.loss_cls: 3.6283, decode.d0.loss_mask: 0.7857, decode.d0.loss_dice: 1.2318, decode.d1.loss_cls: 0.8307, decode.d1.loss_mask: 0.7749, decode.d1.loss_dice: 1.1349, decode.d2.loss_cls: 0.7169, decode.d2.loss_mask: 0.7539, decode.d2.loss_dice: 1.0875, decode.d3.loss_cls: 0.6685, decode.d3.loss_mask: 0.7454, decode.d3.loss_dice: 1.0644, decode.d4.loss_cls: 0.6439, decode.d4.loss_mask: 0.7446, decode.d4.loss_dice: 1.0693, decode.d5.loss_cls: 0.6362, decode.d5.loss_mask: 0.7431, decode.d5.loss_dice: 1.0633, decode.d6.loss_cls: 0.6266, decode.d6.loss_mask: 0.7392, decode.d6.loss_dice: 1.0574, decode.d7.loss_cls: 0.6175, decode.d7.loss_mask: 0.7354, decode.d7.loss_dice: 1.0490, decode.d8.loss_cls: 0.6247, decode.d8.loss_mask: 0.7374, decode.d8.loss_dice: 1.0445, loss: 27.9567 2022-05-05 02:41:21,006 - mmseg - INFO - Iter [24250/40000] lr: 5.654e-07, eta: 3:42:30, time: 0.780, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5619, decode.loss_mask: 0.7393, decode.loss_dice: 1.0166, decode.d0.loss_cls: 3.6111, decode.d0.loss_mask: 0.7858, decode.d0.loss_dice: 1.1923, decode.d1.loss_cls: 0.7836, decode.d1.loss_mask: 0.7663, decode.d1.loss_dice: 1.0934, decode.d2.loss_cls: 0.6690, decode.d2.loss_mask: 0.7520, decode.d2.loss_dice: 1.0511, decode.d3.loss_cls: 0.6280, decode.d3.loss_mask: 0.7449, decode.d3.loss_dice: 1.0338, decode.d4.loss_cls: 0.5997, decode.d4.loss_mask: 0.7419, decode.d4.loss_dice: 1.0310, decode.d5.loss_cls: 0.5823, decode.d5.loss_mask: 0.7393, decode.d5.loss_dice: 1.0284, decode.d6.loss_cls: 0.5692, decode.d6.loss_mask: 0.7383, decode.d6.loss_dice: 1.0208, decode.d7.loss_cls: 0.5648, decode.d7.loss_mask: 0.7365, decode.d7.loss_dice: 1.0206, decode.d8.loss_cls: 0.5571, decode.d8.loss_mask: 0.7421, decode.d8.loss_dice: 1.0215, loss: 27.1227 2022-05-05 02:42:00,653 - mmseg - INFO - Iter [24300/40000] lr: 5.636e-07, eta: 3:41:46, time: 0.793, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5778, decode.loss_mask: 0.7392, decode.loss_dice: 1.0527, decode.d0.loss_cls: 3.6741, decode.d0.loss_mask: 0.7840, decode.d0.loss_dice: 1.2292, decode.d1.loss_cls: 0.8174, decode.d1.loss_mask: 0.7655, decode.d1.loss_dice: 1.1296, decode.d2.loss_cls: 0.6827, decode.d2.loss_mask: 0.7516, decode.d2.loss_dice: 1.0860, decode.d3.loss_cls: 0.6335, decode.d3.loss_mask: 0.7429, decode.d3.loss_dice: 1.0676, decode.d4.loss_cls: 0.6074, decode.d4.loss_mask: 0.7459, decode.d4.loss_dice: 1.0665, decode.d5.loss_cls: 0.5973, decode.d5.loss_mask: 0.7406, decode.d5.loss_dice: 1.0521, decode.d6.loss_cls: 0.5826, decode.d6.loss_mask: 0.7413, decode.d6.loss_dice: 1.0523, decode.d7.loss_cls: 0.5776, decode.d7.loss_mask: 0.7397, decode.d7.loss_dice: 1.0532, decode.d8.loss_cls: 0.5777, decode.d8.loss_mask: 0.7396, decode.d8.loss_dice: 1.0565, loss: 27.6642 2022-05-05 02:42:40,401 - mmseg - INFO - Iter [24350/40000] lr: 5.618e-07, eta: 3:41:02, time: 0.795, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5595, decode.loss_mask: 0.7374, decode.loss_dice: 1.0369, decode.d0.loss_cls: 3.5909, decode.d0.loss_mask: 0.7783, decode.d0.loss_dice: 1.2103, decode.d1.loss_cls: 0.7790, decode.d1.loss_mask: 0.7661, decode.d1.loss_dice: 1.1143, decode.d2.loss_cls: 0.6515, decode.d2.loss_mask: 0.7498, decode.d2.loss_dice: 1.0715, decode.d3.loss_cls: 0.6092, decode.d3.loss_mask: 0.7437, decode.d3.loss_dice: 1.0540, decode.d4.loss_cls: 0.5913, decode.d4.loss_mask: 0.7397, decode.d4.loss_dice: 1.0476, decode.d5.loss_cls: 0.5708, decode.d5.loss_mask: 0.7399, decode.d5.loss_dice: 1.0477, decode.d6.loss_cls: 0.5693, decode.d6.loss_mask: 0.7370, decode.d6.loss_dice: 1.0377, decode.d7.loss_cls: 0.5566, decode.d7.loss_mask: 0.7396, decode.d7.loss_dice: 1.0381, decode.d8.loss_cls: 0.5548, decode.d8.loss_mask: 0.7392, decode.d8.loss_dice: 1.0459, loss: 27.2076 2022-05-05 02:43:19,493 - mmseg - INFO - Iter [24400/40000] lr: 5.600e-07, eta: 3:40:18, time: 0.782, data_time: 0.009, memory: 51557, decode.loss_cls: 0.6147, decode.loss_mask: 0.7179, decode.loss_dice: 1.0167, decode.d0.loss_cls: 3.6797, decode.d0.loss_mask: 0.7647, decode.d0.loss_dice: 1.1950, decode.d1.loss_cls: 0.8680, decode.d1.loss_mask: 0.7442, decode.d1.loss_dice: 1.0886, decode.d2.loss_cls: 0.7269, decode.d2.loss_mask: 0.7322, decode.d2.loss_dice: 1.0477, decode.d3.loss_cls: 0.6795, decode.d3.loss_mask: 0.7252, decode.d3.loss_dice: 1.0281, decode.d4.loss_cls: 0.6519, decode.d4.loss_mask: 0.7246, decode.d4.loss_dice: 1.0288, decode.d5.loss_cls: 0.6320, decode.d5.loss_mask: 0.7197, decode.d5.loss_dice: 1.0222, decode.d6.loss_cls: 0.6217, decode.d6.loss_mask: 0.7205, decode.d6.loss_dice: 1.0151, decode.d7.loss_cls: 0.6107, decode.d7.loss_mask: 0.7214, decode.d7.loss_dice: 1.0196, decode.d8.loss_cls: 0.6126, decode.d8.loss_mask: 0.7177, decode.d8.loss_dice: 1.0175, loss: 27.4652 2022-05-05 02:43:58,856 - mmseg - INFO - Iter [24450/40000] lr: 5.582e-07, eta: 3:39:33, time: 0.787, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5722, decode.loss_mask: 0.7303, decode.loss_dice: 1.0355, decode.d0.loss_cls: 3.6177, decode.d0.loss_mask: 0.7687, decode.d0.loss_dice: 1.1982, decode.d1.loss_cls: 0.7941, decode.d1.loss_mask: 0.7558, decode.d1.loss_dice: 1.1095, decode.d2.loss_cls: 0.6653, decode.d2.loss_mask: 0.7423, decode.d2.loss_dice: 1.0679, decode.d3.loss_cls: 0.6207, decode.d3.loss_mask: 0.7349, decode.d3.loss_dice: 1.0509, decode.d4.loss_cls: 0.6120, decode.d4.loss_mask: 0.7301, decode.d4.loss_dice: 1.0415, decode.d5.loss_cls: 0.5858, decode.d5.loss_mask: 0.7318, decode.d5.loss_dice: 1.0383, decode.d6.loss_cls: 0.5773, decode.d6.loss_mask: 0.7270, decode.d6.loss_dice: 1.0280, decode.d7.loss_cls: 0.5727, decode.d7.loss_mask: 0.7288, decode.d7.loss_dice: 1.0338, decode.d8.loss_cls: 0.5745, decode.d8.loss_mask: 0.7293, decode.d8.loss_dice: 1.0299, loss: 27.2047 2022-05-05 02:44:38,402 - mmseg - INFO - Iter [24500/40000] lr: 5.564e-07, eta: 3:38:49, time: 0.791, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5721, decode.loss_mask: 0.7580, decode.loss_dice: 1.0397, decode.d0.loss_cls: 3.5558, decode.d0.loss_mask: 0.7997, decode.d0.loss_dice: 1.2045, decode.d1.loss_cls: 0.8116, decode.d1.loss_mask: 0.7971, decode.d1.loss_dice: 1.1166, decode.d2.loss_cls: 0.6676, decode.d2.loss_mask: 0.7735, decode.d2.loss_dice: 1.0681, decode.d3.loss_cls: 0.6213, decode.d3.loss_mask: 0.7650, decode.d3.loss_dice: 1.0463, decode.d4.loss_cls: 0.6097, decode.d4.loss_mask: 0.7629, decode.d4.loss_dice: 1.0475, decode.d5.loss_cls: 0.5946, decode.d5.loss_mask: 0.7620, decode.d5.loss_dice: 1.0429, decode.d6.loss_cls: 0.5854, decode.d6.loss_mask: 0.7603, decode.d6.loss_dice: 1.0390, decode.d7.loss_cls: 0.5754, decode.d7.loss_mask: 0.7592, decode.d7.loss_dice: 1.0455, decode.d8.loss_cls: 0.5720, decode.d8.loss_mask: 0.7602, decode.d8.loss_dice: 1.0421, loss: 27.5555 2022-05-05 02:45:17,960 - mmseg - INFO - Iter [24550/40000] lr: 5.546e-07, eta: 3:38:05, time: 0.791, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5716, decode.loss_mask: 0.7569, decode.loss_dice: 1.0408, decode.d0.loss_cls: 3.5725, decode.d0.loss_mask: 0.7951, decode.d0.loss_dice: 1.2161, decode.d1.loss_cls: 0.8014, decode.d1.loss_mask: 0.7875, decode.d1.loss_dice: 1.1223, decode.d2.loss_cls: 0.6868, decode.d2.loss_mask: 0.7655, decode.d2.loss_dice: 1.0648, decode.d3.loss_cls: 0.6335, decode.d3.loss_mask: 0.7605, decode.d3.loss_dice: 1.0502, decode.d4.loss_cls: 0.6081, decode.d4.loss_mask: 0.7542, decode.d4.loss_dice: 1.0501, decode.d5.loss_cls: 0.5981, decode.d5.loss_mask: 0.7541, decode.d5.loss_dice: 1.0422, decode.d6.loss_cls: 0.5874, decode.d6.loss_mask: 0.7532, decode.d6.loss_dice: 1.0364, decode.d7.loss_cls: 0.5733, decode.d7.loss_mask: 0.7546, decode.d7.loss_dice: 1.0411, decode.d8.loss_cls: 0.5712, decode.d8.loss_mask: 0.7548, decode.d8.loss_dice: 1.0398, loss: 27.5442 2022-05-05 02:45:57,320 - mmseg - INFO - Iter [24600/40000] lr: 5.528e-07, eta: 3:37:21, time: 0.787, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5666, decode.loss_mask: 0.7209, decode.loss_dice: 1.0253, decode.d0.loss_cls: 3.5709, decode.d0.loss_mask: 0.7686, decode.d0.loss_dice: 1.1988, decode.d1.loss_cls: 0.8123, decode.d1.loss_mask: 0.7513, decode.d1.loss_dice: 1.1081, decode.d2.loss_cls: 0.6745, decode.d2.loss_mask: 0.7363, decode.d2.loss_dice: 1.0579, decode.d3.loss_cls: 0.6216, decode.d3.loss_mask: 0.7270, decode.d3.loss_dice: 1.0387, decode.d4.loss_cls: 0.6099, decode.d4.loss_mask: 0.7237, decode.d4.loss_dice: 1.0371, decode.d5.loss_cls: 0.5850, decode.d5.loss_mask: 0.7250, decode.d5.loss_dice: 1.0316, decode.d6.loss_cls: 0.5802, decode.d6.loss_mask: 0.7226, decode.d6.loss_dice: 1.0258, decode.d7.loss_cls: 0.5683, decode.d7.loss_mask: 0.7228, decode.d7.loss_dice: 1.0286, decode.d8.loss_cls: 0.5674, decode.d8.loss_mask: 0.7209, decode.d8.loss_dice: 1.0265, loss: 27.0539 2022-05-05 02:46:36,311 - mmseg - INFO - Iter [24650/40000] lr: 5.510e-07, eta: 3:36:37, time: 0.780, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5991, decode.loss_mask: 0.7362, decode.loss_dice: 1.0317, decode.d0.loss_cls: 3.5572, decode.d0.loss_mask: 0.7777, decode.d0.loss_dice: 1.1988, decode.d1.loss_cls: 0.8189, decode.d1.loss_mask: 0.7651, decode.d1.loss_dice: 1.1034, decode.d2.loss_cls: 0.7064, decode.d2.loss_mask: 0.7441, decode.d2.loss_dice: 1.0605, decode.d3.loss_cls: 0.6498, decode.d3.loss_mask: 0.7413, decode.d3.loss_dice: 1.0421, decode.d4.loss_cls: 0.6329, decode.d4.loss_mask: 0.7362, decode.d4.loss_dice: 1.0404, decode.d5.loss_cls: 0.6189, decode.d5.loss_mask: 0.7352, decode.d5.loss_dice: 1.0376, decode.d6.loss_cls: 0.6137, decode.d6.loss_mask: 0.7364, decode.d6.loss_dice: 1.0320, decode.d7.loss_cls: 0.6020, decode.d7.loss_mask: 0.7352, decode.d7.loss_dice: 1.0310, decode.d8.loss_cls: 0.6078, decode.d8.loss_mask: 0.7339, decode.d8.loss_dice: 1.0328, loss: 27.4582 2022-05-05 02:47:15,333 - mmseg - INFO - Iter [24700/40000] lr: 5.492e-07, eta: 3:35:52, time: 0.780, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5424, decode.loss_mask: 0.7227, decode.loss_dice: 1.0034, decode.d0.loss_cls: 3.5618, decode.d0.loss_mask: 0.7729, decode.d0.loss_dice: 1.1747, decode.d1.loss_cls: 0.7746, decode.d1.loss_mask: 0.7519, decode.d1.loss_dice: 1.0809, decode.d2.loss_cls: 0.6375, decode.d2.loss_mask: 0.7347, decode.d2.loss_dice: 1.0370, decode.d3.loss_cls: 0.5877, decode.d3.loss_mask: 0.7296, decode.d3.loss_dice: 1.0173, decode.d4.loss_cls: 0.5616, decode.d4.loss_mask: 0.7280, decode.d4.loss_dice: 1.0177, decode.d5.loss_cls: 0.5550, decode.d5.loss_mask: 0.7287, decode.d5.loss_dice: 1.0115, decode.d6.loss_cls: 0.5409, decode.d6.loss_mask: 0.7255, decode.d6.loss_dice: 1.0057, decode.d7.loss_cls: 0.5410, decode.d7.loss_mask: 0.7227, decode.d7.loss_dice: 1.0090, decode.d8.loss_cls: 0.5369, decode.d8.loss_mask: 0.7233, decode.d8.loss_dice: 1.0032, loss: 26.5399 2022-05-05 02:47:57,112 - mmseg - INFO - Iter [24750/40000] lr: 5.474e-07, eta: 3:35:09, time: 0.836, data_time: 0.058, memory: 51557, decode.loss_cls: 0.5798, decode.loss_mask: 0.7079, decode.loss_dice: 1.0091, decode.d0.loss_cls: 3.5162, decode.d0.loss_mask: 0.7415, decode.d0.loss_dice: 1.1691, decode.d1.loss_cls: 0.8102, decode.d1.loss_mask: 0.7353, decode.d1.loss_dice: 1.0841, decode.d2.loss_cls: 0.6762, decode.d2.loss_mask: 0.7180, decode.d2.loss_dice: 1.0294, decode.d3.loss_cls: 0.6285, decode.d3.loss_mask: 0.7106, decode.d3.loss_dice: 1.0162, decode.d4.loss_cls: 0.6027, decode.d4.loss_mask: 0.7100, decode.d4.loss_dice: 1.0173, decode.d5.loss_cls: 0.5835, decode.d5.loss_mask: 0.7151, decode.d5.loss_dice: 1.0122, decode.d6.loss_cls: 0.5827, decode.d6.loss_mask: 0.7085, decode.d6.loss_dice: 1.0041, decode.d7.loss_cls: 0.5748, decode.d7.loss_mask: 0.7062, decode.d7.loss_dice: 1.0055, decode.d8.loss_cls: 0.5802, decode.d8.loss_mask: 0.7085, decode.d8.loss_dice: 1.0046, loss: 26.6482 2022-05-05 02:48:36,268 - mmseg - INFO - Iter [24800/40000] lr: 5.456e-07, eta: 3:34:25, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5739, decode.loss_mask: 0.7335, decode.loss_dice: 1.0393, decode.d0.loss_cls: 3.5490, decode.d0.loss_mask: 0.7693, decode.d0.loss_dice: 1.1972, decode.d1.loss_cls: 0.8012, decode.d1.loss_mask: 0.7528, decode.d1.loss_dice: 1.1163, decode.d2.loss_cls: 0.6880, decode.d2.loss_mask: 0.7381, decode.d2.loss_dice: 1.0660, decode.d3.loss_cls: 0.6358, decode.d3.loss_mask: 0.7297, decode.d3.loss_dice: 1.0425, decode.d4.loss_cls: 0.6178, decode.d4.loss_mask: 0.7300, decode.d4.loss_dice: 1.0458, decode.d5.loss_cls: 0.6010, decode.d5.loss_mask: 0.7293, decode.d5.loss_dice: 1.0395, decode.d6.loss_cls: 0.5759, decode.d6.loss_mask: 0.7282, decode.d6.loss_dice: 1.0391, decode.d7.loss_cls: 0.5782, decode.d7.loss_mask: 0.7305, decode.d7.loss_dice: 1.0369, decode.d8.loss_cls: 0.5709, decode.d8.loss_mask: 0.7267, decode.d8.loss_dice: 1.0337, loss: 27.2159 2022-05-05 02:49:16,065 - mmseg - INFO - Iter [24850/40000] lr: 5.438e-07, eta: 3:33:41, time: 0.796, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5814, decode.loss_mask: 0.7326, decode.loss_dice: 1.0461, decode.d0.loss_cls: 3.6177, decode.d0.loss_mask: 0.7739, decode.d0.loss_dice: 1.2244, decode.d1.loss_cls: 0.8314, decode.d1.loss_mask: 0.7597, decode.d1.loss_dice: 1.1264, decode.d2.loss_cls: 0.6948, decode.d2.loss_mask: 0.7427, decode.d2.loss_dice: 1.0908, decode.d3.loss_cls: 0.6414, decode.d3.loss_mask: 0.7406, decode.d3.loss_dice: 1.0705, decode.d4.loss_cls: 0.6200, decode.d4.loss_mask: 0.7378, decode.d4.loss_dice: 1.0648, decode.d5.loss_cls: 0.6010, decode.d5.loss_mask: 0.7349, decode.d5.loss_dice: 1.0591, decode.d6.loss_cls: 0.5929, decode.d6.loss_mask: 0.7350, decode.d6.loss_dice: 1.0466, decode.d7.loss_cls: 0.5779, decode.d7.loss_mask: 0.7385, decode.d7.loss_dice: 1.0565, decode.d8.loss_cls: 0.5801, decode.d8.loss_mask: 0.7334, decode.d8.loss_dice: 1.0567, loss: 27.6096 2022-05-05 02:49:55,356 - mmseg - INFO - Iter [24900/40000] lr: 5.420e-07, eta: 3:32:57, time: 0.786, data_time: 0.011, memory: 51557, decode.loss_cls: 0.5829, decode.loss_mask: 0.7158, decode.loss_dice: 1.0423, decode.d0.loss_cls: 3.6451, decode.d0.loss_mask: 0.7583, decode.d0.loss_dice: 1.2116, decode.d1.loss_cls: 0.8211, decode.d1.loss_mask: 0.7402, decode.d1.loss_dice: 1.1202, decode.d2.loss_cls: 0.6882, decode.d2.loss_mask: 0.7278, decode.d2.loss_dice: 1.0767, decode.d3.loss_cls: 0.6436, decode.d3.loss_mask: 0.7212, decode.d3.loss_dice: 1.0603, decode.d4.loss_cls: 0.6100, decode.d4.loss_mask: 0.7200, decode.d4.loss_dice: 1.0508, decode.d5.loss_cls: 0.5980, decode.d5.loss_mask: 0.7213, decode.d5.loss_dice: 1.0426, decode.d6.loss_cls: 0.5901, decode.d6.loss_mask: 0.7133, decode.d6.loss_dice: 1.0408, decode.d7.loss_cls: 0.5892, decode.d7.loss_mask: 0.7162, decode.d7.loss_dice: 1.0384, decode.d8.loss_cls: 0.5825, decode.d8.loss_mask: 0.7175, decode.d8.loss_dice: 1.0425, loss: 27.3286 2022-05-05 02:50:34,107 - mmseg - INFO - Iter [24950/40000] lr: 5.403e-07, eta: 3:32:13, time: 0.774, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5540, decode.loss_mask: 0.7302, decode.loss_dice: 1.0230, decode.d0.loss_cls: 3.6029, decode.d0.loss_mask: 0.7755, decode.d0.loss_dice: 1.1857, decode.d1.loss_cls: 0.8090, decode.d1.loss_mask: 0.7518, decode.d1.loss_dice: 1.0992, decode.d2.loss_cls: 0.6727, decode.d2.loss_mask: 0.7359, decode.d2.loss_dice: 1.0564, decode.d3.loss_cls: 0.6240, decode.d3.loss_mask: 0.7316, decode.d3.loss_dice: 1.0376, decode.d4.loss_cls: 0.5936, decode.d4.loss_mask: 0.7302, decode.d4.loss_dice: 1.0443, decode.d5.loss_cls: 0.5795, decode.d5.loss_mask: 0.7275, decode.d5.loss_dice: 1.0316, decode.d6.loss_cls: 0.5673, decode.d6.loss_mask: 0.7271, decode.d6.loss_dice: 1.0297, decode.d7.loss_cls: 0.5646, decode.d7.loss_mask: 0.7280, decode.d7.loss_dice: 1.0271, decode.d8.loss_cls: 0.5526, decode.d8.loss_mask: 0.7305, decode.d8.loss_dice: 1.0332, loss: 27.0566 2022-05-05 02:51:12,806 - mmseg - INFO - Saving checkpoint at 25000 iterations 2022-05-05 02:51:39,321 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 02:51:39,329 - mmseg - INFO - Iter [25000/40000] lr: 5.385e-07, eta: 3:31:44, time: 1.303, data_time: 0.011, memory: 51557, decode.loss_cls: 0.5437, decode.loss_mask: 0.7147, decode.loss_dice: 1.0123, decode.d0.loss_cls: 3.5523, decode.d0.loss_mask: 0.7518, decode.d0.loss_dice: 1.1701, decode.d1.loss_cls: 0.7755, decode.d1.loss_mask: 0.7449, decode.d1.loss_dice: 1.0796, decode.d2.loss_cls: 0.6393, decode.d2.loss_mask: 0.7304, decode.d2.loss_dice: 1.0318, decode.d3.loss_cls: 0.5974, decode.d3.loss_mask: 0.7240, decode.d3.loss_dice: 1.0173, decode.d4.loss_cls: 0.5703, decode.d4.loss_mask: 0.7209, decode.d4.loss_dice: 1.0251, decode.d5.loss_cls: 0.5588, decode.d5.loss_mask: 0.7172, decode.d5.loss_dice: 1.0121, decode.d6.loss_cls: 0.5512, decode.d6.loss_mask: 0.7160, decode.d6.loss_dice: 1.0074, decode.d7.loss_cls: 0.5435, decode.d7.loss_mask: 0.7193, decode.d7.loss_dice: 1.0113, decode.d8.loss_cls: 0.5428, decode.d8.loss_mask: 0.7152, decode.d8.loss_dice: 1.0062, loss: 26.5025 2022-05-05 02:52:18,985 - mmseg - INFO - Iter [25050/40000] lr: 5.367e-07, eta: 3:31:00, time: 0.796, data_time: 0.012, memory: 51557, decode.loss_cls: 0.5711, decode.loss_mask: 0.7181, decode.loss_dice: 1.0038, decode.d0.loss_cls: 3.6096, decode.d0.loss_mask: 0.7676, decode.d0.loss_dice: 1.1900, decode.d1.loss_cls: 0.7979, decode.d1.loss_mask: 0.7489, decode.d1.loss_dice: 1.0772, decode.d2.loss_cls: 0.6672, decode.d2.loss_mask: 0.7277, decode.d2.loss_dice: 1.0293, decode.d3.loss_cls: 0.6274, decode.d3.loss_mask: 0.7209, decode.d3.loss_dice: 1.0113, decode.d4.loss_cls: 0.6035, decode.d4.loss_mask: 0.7215, decode.d4.loss_dice: 1.0086, decode.d5.loss_cls: 0.5863, decode.d5.loss_mask: 0.7217, decode.d5.loss_dice: 1.0080, decode.d6.loss_cls: 0.5760, decode.d6.loss_mask: 0.7189, decode.d6.loss_dice: 1.0020, decode.d7.loss_cls: 0.5707, decode.d7.loss_mask: 0.7200, decode.d7.loss_dice: 1.0055, decode.d8.loss_cls: 0.5646, decode.d8.loss_mask: 0.7210, decode.d8.loss_dice: 1.0040, loss: 26.8002 2022-05-05 02:52:58,890 - mmseg - INFO - Iter [25100/40000] lr: 5.349e-07, eta: 3:30:16, time: 0.797, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5702, decode.loss_mask: 0.7229, decode.loss_dice: 1.0238, decode.d0.loss_cls: 3.6101, decode.d0.loss_mask: 0.7625, decode.d0.loss_dice: 1.1865, decode.d1.loss_cls: 0.8004, decode.d1.loss_mask: 0.7419, decode.d1.loss_dice: 1.0938, decode.d2.loss_cls: 0.6648, decode.d2.loss_mask: 0.7347, decode.d2.loss_dice: 1.0539, decode.d3.loss_cls: 0.6120, decode.d3.loss_mask: 0.7306, decode.d3.loss_dice: 1.0307, decode.d4.loss_cls: 0.5950, decode.d4.loss_mask: 0.7323, decode.d4.loss_dice: 1.0313, decode.d5.loss_cls: 0.5816, decode.d5.loss_mask: 0.7304, decode.d5.loss_dice: 1.0317, decode.d6.loss_cls: 0.5792, decode.d6.loss_mask: 0.7280, decode.d6.loss_dice: 1.0256, decode.d7.loss_cls: 0.5714, decode.d7.loss_mask: 0.7265, decode.d7.loss_dice: 1.0218, decode.d8.loss_cls: 0.5695, decode.d8.loss_mask: 0.7262, decode.d8.loss_dice: 1.0241, loss: 27.0135 2022-05-05 02:53:38,028 - mmseg - INFO - Iter [25150/40000] lr: 5.331e-07, eta: 3:29:32, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5408, decode.loss_mask: 0.7199, decode.loss_dice: 1.0240, decode.d0.loss_cls: 3.5310, decode.d0.loss_mask: 0.7652, decode.d0.loss_dice: 1.1935, decode.d1.loss_cls: 0.7827, decode.d1.loss_mask: 0.7499, decode.d1.loss_dice: 1.0922, decode.d2.loss_cls: 0.6447, decode.d2.loss_mask: 0.7333, decode.d2.loss_dice: 1.0577, decode.d3.loss_cls: 0.5925, decode.d3.loss_mask: 0.7270, decode.d3.loss_dice: 1.0344, decode.d4.loss_cls: 0.5705, decode.d4.loss_mask: 0.7253, decode.d4.loss_dice: 1.0291, decode.d5.loss_cls: 0.5595, decode.d5.loss_mask: 0.7236, decode.d5.loss_dice: 1.0211, decode.d6.loss_cls: 0.5521, decode.d6.loss_mask: 0.7240, decode.d6.loss_dice: 1.0216, decode.d7.loss_cls: 0.5400, decode.d7.loss_mask: 0.7191, decode.d7.loss_dice: 1.0285, decode.d8.loss_cls: 0.5408, decode.d8.loss_mask: 0.7179, decode.d8.loss_dice: 1.0225, loss: 26.6845 2022-05-05 02:54:18,092 - mmseg - INFO - Iter [25200/40000] lr: 5.313e-07, eta: 3:28:49, time: 0.801, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5648, decode.loss_mask: 0.7239, decode.loss_dice: 1.0192, decode.d0.loss_cls: 3.5559, decode.d0.loss_mask: 0.7630, decode.d0.loss_dice: 1.1991, decode.d1.loss_cls: 0.8019, decode.d1.loss_mask: 0.7501, decode.d1.loss_dice: 1.0941, decode.d2.loss_cls: 0.6604, decode.d2.loss_mask: 0.7383, decode.d2.loss_dice: 1.0578, decode.d3.loss_cls: 0.6070, decode.d3.loss_mask: 0.7263, decode.d3.loss_dice: 1.0364, decode.d4.loss_cls: 0.5839, decode.d4.loss_mask: 0.7233, decode.d4.loss_dice: 1.0395, decode.d5.loss_cls: 0.5759, decode.d5.loss_mask: 0.7209, decode.d5.loss_dice: 1.0351, decode.d6.loss_cls: 0.5646, decode.d6.loss_mask: 0.7206, decode.d6.loss_dice: 1.0262, decode.d7.loss_cls: 0.5621, decode.d7.loss_mask: 0.7212, decode.d7.loss_dice: 1.0263, decode.d8.loss_cls: 0.5639, decode.d8.loss_mask: 0.7228, decode.d8.loss_dice: 1.0271, loss: 26.9116 2022-05-05 02:54:57,719 - mmseg - INFO - Iter [25250/40000] lr: 5.295e-07, eta: 3:28:05, time: 0.793, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5683, decode.loss_mask: 0.7406, decode.loss_dice: 1.0508, decode.d0.loss_cls: 3.6012, decode.d0.loss_mask: 0.7765, decode.d0.loss_dice: 1.2129, decode.d1.loss_cls: 0.8226, decode.d1.loss_mask: 0.7727, decode.d1.loss_dice: 1.1190, decode.d2.loss_cls: 0.6942, decode.d2.loss_mask: 0.7527, decode.d2.loss_dice: 1.0714, decode.d3.loss_cls: 0.6420, decode.d3.loss_mask: 0.7453, decode.d3.loss_dice: 1.0571, decode.d4.loss_cls: 0.6119, decode.d4.loss_mask: 0.7434, decode.d4.loss_dice: 1.0575, decode.d5.loss_cls: 0.5996, decode.d5.loss_mask: 0.7428, decode.d5.loss_dice: 1.0621, decode.d6.loss_cls: 0.5812, decode.d6.loss_mask: 0.7426, decode.d6.loss_dice: 1.0498, decode.d7.loss_cls: 0.5707, decode.d7.loss_mask: 0.7416, decode.d7.loss_dice: 1.0478, decode.d8.loss_cls: 0.5718, decode.d8.loss_mask: 0.7408, decode.d8.loss_dice: 1.0541, loss: 27.5451 2022-05-05 02:55:39,382 - mmseg - INFO - Iter [25300/40000] lr: 5.277e-07, eta: 3:27:22, time: 0.833, data_time: 0.059, memory: 51557, decode.loss_cls: 0.5915, decode.loss_mask: 0.7187, decode.loss_dice: 0.9977, decode.d0.loss_cls: 3.6489, decode.d0.loss_mask: 0.7657, decode.d0.loss_dice: 1.1723, decode.d1.loss_cls: 0.8053, decode.d1.loss_mask: 0.7478, decode.d1.loss_dice: 1.0635, decode.d2.loss_cls: 0.6965, decode.d2.loss_mask: 0.7297, decode.d2.loss_dice: 1.0272, decode.d3.loss_cls: 0.6513, decode.d3.loss_mask: 0.7239, decode.d3.loss_dice: 1.0070, decode.d4.loss_cls: 0.6286, decode.d4.loss_mask: 0.7226, decode.d4.loss_dice: 1.0079, decode.d5.loss_cls: 0.6126, decode.d5.loss_mask: 0.7197, decode.d5.loss_dice: 1.0044, decode.d6.loss_cls: 0.5955, decode.d6.loss_mask: 0.7177, decode.d6.loss_dice: 0.9979, decode.d7.loss_cls: 0.5960, decode.d7.loss_mask: 0.7237, decode.d7.loss_dice: 0.9977, decode.d8.loss_cls: 0.5948, decode.d8.loss_mask: 0.7190, decode.d8.loss_dice: 0.9983, loss: 26.9836 2022-05-05 02:56:18,559 - mmseg - INFO - Iter [25350/40000] lr: 5.259e-07, eta: 3:26:38, time: 0.784, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5461, decode.loss_mask: 0.7215, decode.loss_dice: 0.9868, decode.d0.loss_cls: 3.5135, decode.d0.loss_mask: 0.7653, decode.d0.loss_dice: 1.1570, decode.d1.loss_cls: 0.7911, decode.d1.loss_mask: 0.7444, decode.d1.loss_dice: 1.0513, decode.d2.loss_cls: 0.6558, decode.d2.loss_mask: 0.7312, decode.d2.loss_dice: 1.0215, decode.d3.loss_cls: 0.6012, decode.d3.loss_mask: 0.7267, decode.d3.loss_dice: 1.0024, decode.d4.loss_cls: 0.5848, decode.d4.loss_mask: 0.7203, decode.d4.loss_dice: 1.0017, decode.d5.loss_cls: 0.5727, decode.d5.loss_mask: 0.7222, decode.d5.loss_dice: 0.9935, decode.d6.loss_cls: 0.5611, decode.d6.loss_mask: 0.7210, decode.d6.loss_dice: 0.9903, decode.d7.loss_cls: 0.5548, decode.d7.loss_mask: 0.7197, decode.d7.loss_dice: 0.9878, decode.d8.loss_cls: 0.5511, decode.d8.loss_mask: 0.7209, decode.d8.loss_dice: 0.9887, loss: 26.4063 2022-05-05 02:56:58,180 - mmseg - INFO - Iter [25400/40000] lr: 5.241e-07, eta: 3:25:54, time: 0.792, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5643, decode.loss_mask: 0.7219, decode.loss_dice: 1.0276, decode.d0.loss_cls: 3.5750, decode.d0.loss_mask: 0.7592, decode.d0.loss_dice: 1.1938, decode.d1.loss_cls: 0.7880, decode.d1.loss_mask: 0.7500, decode.d1.loss_dice: 1.0980, decode.d2.loss_cls: 0.6473, decode.d2.loss_mask: 0.7373, decode.d2.loss_dice: 1.0616, decode.d3.loss_cls: 0.6212, decode.d3.loss_mask: 0.7288, decode.d3.loss_dice: 1.0337, decode.d4.loss_cls: 0.5859, decode.d4.loss_mask: 0.7294, decode.d4.loss_dice: 1.0390, decode.d5.loss_cls: 0.5756, decode.d5.loss_mask: 0.7261, decode.d5.loss_dice: 1.0354, decode.d6.loss_cls: 0.5735, decode.d6.loss_mask: 0.7263, decode.d6.loss_dice: 1.0265, decode.d7.loss_cls: 0.5611, decode.d7.loss_mask: 0.7236, decode.d7.loss_dice: 1.0296, decode.d8.loss_cls: 0.5595, decode.d8.loss_mask: 0.7224, decode.d8.loss_dice: 1.0276, loss: 26.9494 2022-05-05 02:57:37,073 - mmseg - INFO - Iter [25450/40000] lr: 5.223e-07, eta: 3:25:10, time: 0.778, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5824, decode.loss_mask: 0.7191, decode.loss_dice: 1.0311, decode.d0.loss_cls: 3.6621, decode.d0.loss_mask: 0.7688, decode.d0.loss_dice: 1.1978, decode.d1.loss_cls: 0.8169, decode.d1.loss_mask: 0.7504, decode.d1.loss_dice: 1.1044, decode.d2.loss_cls: 0.6884, decode.d2.loss_mask: 0.7332, decode.d2.loss_dice: 1.0564, decode.d3.loss_cls: 0.6323, decode.d3.loss_mask: 0.7253, decode.d3.loss_dice: 1.0354, decode.d4.loss_cls: 0.6114, decode.d4.loss_mask: 0.7244, decode.d4.loss_dice: 1.0351, decode.d5.loss_cls: 0.5950, decode.d5.loss_mask: 0.7261, decode.d5.loss_dice: 1.0318, decode.d6.loss_cls: 0.5888, decode.d6.loss_mask: 0.7242, decode.d6.loss_dice: 1.0228, decode.d7.loss_cls: 0.5763, decode.d7.loss_mask: 0.7246, decode.d7.loss_dice: 1.0298, decode.d8.loss_cls: 0.5833, decode.d8.loss_mask: 0.7180, decode.d8.loss_dice: 1.0246, loss: 27.2206 2022-05-05 02:58:15,750 - mmseg - INFO - Iter [25500/40000] lr: 5.205e-07, eta: 3:24:25, time: 0.772, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5449, decode.loss_mask: 0.7226, decode.loss_dice: 1.0268, decode.d0.loss_cls: 3.5928, decode.d0.loss_mask: 0.7670, decode.d0.loss_dice: 1.1932, decode.d1.loss_cls: 0.8011, decode.d1.loss_mask: 0.7525, decode.d1.loss_dice: 1.0970, decode.d2.loss_cls: 0.6493, decode.d2.loss_mask: 0.7380, decode.d2.loss_dice: 1.0546, decode.d3.loss_cls: 0.5906, decode.d3.loss_mask: 0.7297, decode.d3.loss_dice: 1.0337, decode.d4.loss_cls: 0.5817, decode.d4.loss_mask: 0.7255, decode.d4.loss_dice: 1.0342, decode.d5.loss_cls: 0.5669, decode.d5.loss_mask: 0.7212, decode.d5.loss_dice: 1.0314, decode.d6.loss_cls: 0.5481, decode.d6.loss_mask: 0.7234, decode.d6.loss_dice: 1.0259, decode.d7.loss_cls: 0.5438, decode.d7.loss_mask: 0.7243, decode.d7.loss_dice: 1.0245, decode.d8.loss_cls: 0.5390, decode.d8.loss_mask: 0.7232, decode.d8.loss_dice: 1.0252, loss: 26.8320 2022-05-05 02:58:54,403 - mmseg - INFO - Iter [25550/40000] lr: 5.187e-07, eta: 3:23:41, time: 0.774, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5634, decode.loss_mask: 0.7127, decode.loss_dice: 0.9832, decode.d0.loss_cls: 3.5936, decode.d0.loss_mask: 0.7645, decode.d0.loss_dice: 1.1521, decode.d1.loss_cls: 0.7839, decode.d1.loss_mask: 0.7436, decode.d1.loss_dice: 1.0656, decode.d2.loss_cls: 0.6655, decode.d2.loss_mask: 0.7225, decode.d2.loss_dice: 1.0181, decode.d3.loss_cls: 0.6123, decode.d3.loss_mask: 0.7147, decode.d3.loss_dice: 0.9965, decode.d4.loss_cls: 0.5888, decode.d4.loss_mask: 0.7173, decode.d4.loss_dice: 0.9954, decode.d5.loss_cls: 0.5670, decode.d5.loss_mask: 0.7155, decode.d5.loss_dice: 0.9974, decode.d6.loss_cls: 0.5658, decode.d6.loss_mask: 0.7150, decode.d6.loss_dice: 0.9884, decode.d7.loss_cls: 0.5563, decode.d7.loss_mask: 0.7136, decode.d7.loss_dice: 0.9919, decode.d8.loss_cls: 0.5508, decode.d8.loss_mask: 0.7122, decode.d8.loss_dice: 0.9906, loss: 26.4583 2022-05-05 02:59:33,113 - mmseg - INFO - Iter [25600/40000] lr: 5.169e-07, eta: 3:22:57, time: 0.774, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5758, decode.loss_mask: 0.7417, decode.loss_dice: 1.0560, decode.d0.loss_cls: 3.6506, decode.d0.loss_mask: 0.7901, decode.d0.loss_dice: 1.2134, decode.d1.loss_cls: 0.8216, decode.d1.loss_mask: 0.7671, decode.d1.loss_dice: 1.1291, decode.d2.loss_cls: 0.6971, decode.d2.loss_mask: 0.7521, decode.d2.loss_dice: 1.0782, decode.d3.loss_cls: 0.6316, decode.d3.loss_mask: 0.7486, decode.d3.loss_dice: 1.0613, decode.d4.loss_cls: 0.6203, decode.d4.loss_mask: 0.7467, decode.d4.loss_dice: 1.0566, decode.d5.loss_cls: 0.6029, decode.d5.loss_mask: 0.7431, decode.d5.loss_dice: 1.0526, decode.d6.loss_cls: 0.5915, decode.d6.loss_mask: 0.7438, decode.d6.loss_dice: 1.0524, decode.d7.loss_cls: 0.5789, decode.d7.loss_mask: 0.7422, decode.d7.loss_dice: 1.0490, decode.d8.loss_cls: 0.5728, decode.d8.loss_mask: 0.7411, decode.d8.loss_dice: 1.0518, loss: 27.6598 2022-05-05 03:00:11,777 - mmseg - INFO - Iter [25650/40000] lr: 5.151e-07, eta: 3:22:12, time: 0.773, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5748, decode.loss_mask: 0.7145, decode.loss_dice: 1.0185, decode.d0.loss_cls: 3.5849, decode.d0.loss_mask: 0.7630, decode.d0.loss_dice: 1.1961, decode.d1.loss_cls: 0.8161, decode.d1.loss_mask: 0.7441, decode.d1.loss_dice: 1.1035, decode.d2.loss_cls: 0.6822, decode.d2.loss_mask: 0.7244, decode.d2.loss_dice: 1.0554, decode.d3.loss_cls: 0.6286, decode.d3.loss_mask: 0.7206, decode.d3.loss_dice: 1.0380, decode.d4.loss_cls: 0.5997, decode.d4.loss_mask: 0.7191, decode.d4.loss_dice: 1.0357, decode.d5.loss_cls: 0.5927, decode.d5.loss_mask: 0.7170, decode.d5.loss_dice: 1.0313, decode.d6.loss_cls: 0.5752, decode.d6.loss_mask: 0.7153, decode.d6.loss_dice: 1.0223, decode.d7.loss_cls: 0.5752, decode.d7.loss_mask: 0.7176, decode.d7.loss_dice: 1.0268, decode.d8.loss_cls: 0.5644, decode.d8.loss_mask: 0.7148, decode.d8.loss_dice: 1.0262, loss: 26.9980 2022-05-05 03:00:50,765 - mmseg - INFO - Iter [25700/40000] lr: 5.133e-07, eta: 3:21:28, time: 0.779, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5835, decode.loss_mask: 0.7368, decode.loss_dice: 1.0158, decode.d0.loss_cls: 3.5743, decode.d0.loss_mask: 0.7806, decode.d0.loss_dice: 1.1809, decode.d1.loss_cls: 0.8148, decode.d1.loss_mask: 0.7644, decode.d1.loss_dice: 1.0894, decode.d2.loss_cls: 0.6801, decode.d2.loss_mask: 0.7476, decode.d2.loss_dice: 1.0485, decode.d3.loss_cls: 0.6413, decode.d3.loss_mask: 0.7400, decode.d3.loss_dice: 1.0255, decode.d4.loss_cls: 0.6136, decode.d4.loss_mask: 0.7381, decode.d4.loss_dice: 1.0235, decode.d5.loss_cls: 0.5994, decode.d5.loss_mask: 0.7341, decode.d5.loss_dice: 1.0207, decode.d6.loss_cls: 0.6006, decode.d6.loss_mask: 0.7297, decode.d6.loss_dice: 1.0190, decode.d7.loss_cls: 0.5836, decode.d7.loss_mask: 0.7323, decode.d7.loss_dice: 1.0154, decode.d8.loss_cls: 0.5819, decode.d8.loss_mask: 0.7335, decode.d8.loss_dice: 1.0178, loss: 27.1666 2022-05-05 03:01:30,312 - mmseg - INFO - Iter [25750/40000] lr: 5.115e-07, eta: 3:20:44, time: 0.791, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5416, decode.loss_mask: 0.7572, decode.loss_dice: 1.0176, decode.d0.loss_cls: 3.5113, decode.d0.loss_mask: 0.8042, decode.d0.loss_dice: 1.1737, decode.d1.loss_cls: 0.7714, decode.d1.loss_mask: 0.7798, decode.d1.loss_dice: 1.0864, decode.d2.loss_cls: 0.6580, decode.d2.loss_mask: 0.7627, decode.d2.loss_dice: 1.0483, decode.d3.loss_cls: 0.6034, decode.d3.loss_mask: 0.7604, decode.d3.loss_dice: 1.0250, decode.d4.loss_cls: 0.5820, decode.d4.loss_mask: 0.7546, decode.d4.loss_dice: 1.0238, decode.d5.loss_cls: 0.5622, decode.d5.loss_mask: 0.7604, decode.d5.loss_dice: 1.0186, decode.d6.loss_cls: 0.5474, decode.d6.loss_mask: 0.7569, decode.d6.loss_dice: 1.0179, decode.d7.loss_cls: 0.5397, decode.d7.loss_mask: 0.7548, decode.d7.loss_dice: 1.0140, decode.d8.loss_cls: 0.5378, decode.d8.loss_mask: 0.7532, decode.d8.loss_dice: 1.0180, loss: 26.9423 2022-05-05 03:02:09,523 - mmseg - INFO - Iter [25800/40000] lr: 5.097e-07, eta: 3:20:00, time: 0.784, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5642, decode.loss_mask: 0.7175, decode.loss_dice: 1.0224, decode.d0.loss_cls: 3.4874, decode.d0.loss_mask: 0.7648, decode.d0.loss_dice: 1.1867, decode.d1.loss_cls: 0.7977, decode.d1.loss_mask: 0.7498, decode.d1.loss_dice: 1.1026, decode.d2.loss_cls: 0.6646, decode.d2.loss_mask: 0.7343, decode.d2.loss_dice: 1.0600, decode.d3.loss_cls: 0.6102, decode.d3.loss_mask: 0.7292, decode.d3.loss_dice: 1.0388, decode.d4.loss_cls: 0.5983, decode.d4.loss_mask: 0.7247, decode.d4.loss_dice: 1.0407, decode.d5.loss_cls: 0.5806, decode.d5.loss_mask: 0.7251, decode.d5.loss_dice: 1.0324, decode.d6.loss_cls: 0.5649, decode.d6.loss_mask: 0.7200, decode.d6.loss_dice: 1.0279, decode.d7.loss_cls: 0.5631, decode.d7.loss_mask: 0.7190, decode.d7.loss_dice: 1.0306, decode.d8.loss_cls: 0.5630, decode.d8.loss_mask: 0.7138, decode.d8.loss_dice: 1.0205, loss: 26.8547 2022-05-05 03:02:48,238 - mmseg - INFO - Iter [25850/40000] lr: 5.079e-07, eta: 3:19:16, time: 0.774, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5562, decode.loss_mask: 0.7201, decode.loss_dice: 1.0309, decode.d0.loss_cls: 3.5436, decode.d0.loss_mask: 0.7488, decode.d0.loss_dice: 1.1837, decode.d1.loss_cls: 0.8051, decode.d1.loss_mask: 0.7432, decode.d1.loss_dice: 1.1010, decode.d2.loss_cls: 0.6713, decode.d2.loss_mask: 0.7263, decode.d2.loss_dice: 1.0602, decode.d3.loss_cls: 0.6109, decode.d3.loss_mask: 0.7266, decode.d3.loss_dice: 1.0386, decode.d4.loss_cls: 0.5877, decode.d4.loss_mask: 0.7250, decode.d4.loss_dice: 1.0404, decode.d5.loss_cls: 0.5775, decode.d5.loss_mask: 0.7193, decode.d5.loss_dice: 1.0339, decode.d6.loss_cls: 0.5588, decode.d6.loss_mask: 0.7213, decode.d6.loss_dice: 1.0251, decode.d7.loss_cls: 0.5516, decode.d7.loss_mask: 0.7205, decode.d7.loss_dice: 1.0276, decode.d8.loss_cls: 0.5514, decode.d8.loss_mask: 0.7206, decode.d8.loss_dice: 1.0286, loss: 26.8559 2022-05-05 03:03:30,302 - mmseg - INFO - Iter [25900/40000] lr: 5.062e-07, eta: 3:18:34, time: 0.842, data_time: 0.060, memory: 51557, decode.loss_cls: 0.5114, decode.loss_mask: 0.7466, decode.loss_dice: 1.0137, decode.d0.loss_cls: 3.4933, decode.d0.loss_mask: 0.7888, decode.d0.loss_dice: 1.1684, decode.d1.loss_cls: 0.7435, decode.d1.loss_mask: 0.7729, decode.d1.loss_dice: 1.0815, decode.d2.loss_cls: 0.6142, decode.d2.loss_mask: 0.7599, decode.d2.loss_dice: 1.0405, decode.d3.loss_cls: 0.5713, decode.d3.loss_mask: 0.7560, decode.d3.loss_dice: 1.0256, decode.d4.loss_cls: 0.5410, decode.d4.loss_mask: 0.7539, decode.d4.loss_dice: 1.0254, decode.d5.loss_cls: 0.5277, decode.d5.loss_mask: 0.7496, decode.d5.loss_dice: 1.0222, decode.d6.loss_cls: 0.5123, decode.d6.loss_mask: 0.7502, decode.d6.loss_dice: 1.0160, decode.d7.loss_cls: 0.5103, decode.d7.loss_mask: 0.7488, decode.d7.loss_dice: 1.0183, decode.d8.loss_cls: 0.5106, decode.d8.loss_mask: 0.7486, decode.d8.loss_dice: 1.0148, loss: 26.5375 2022-05-05 03:04:09,601 - mmseg - INFO - Iter [25950/40000] lr: 5.044e-07, eta: 3:17:50, time: 0.786, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5617, decode.loss_mask: 0.7150, decode.loss_dice: 1.0224, decode.d0.loss_cls: 3.5507, decode.d0.loss_mask: 0.7592, decode.d0.loss_dice: 1.2009, decode.d1.loss_cls: 0.7953, decode.d1.loss_mask: 0.7524, decode.d1.loss_dice: 1.1000, decode.d2.loss_cls: 0.6661, decode.d2.loss_mask: 0.7247, decode.d2.loss_dice: 1.0509, decode.d3.loss_cls: 0.6168, decode.d3.loss_mask: 0.7217, decode.d3.loss_dice: 1.0306, decode.d4.loss_cls: 0.5941, decode.d4.loss_mask: 0.7194, decode.d4.loss_dice: 1.0280, decode.d5.loss_cls: 0.5743, decode.d5.loss_mask: 0.7162, decode.d5.loss_dice: 1.0207, decode.d6.loss_cls: 0.5684, decode.d6.loss_mask: 0.7154, decode.d6.loss_dice: 1.0235, decode.d7.loss_cls: 0.5604, decode.d7.loss_mask: 0.7134, decode.d7.loss_dice: 1.0198, decode.d8.loss_cls: 0.5624, decode.d8.loss_mask: 0.7138, decode.d8.loss_dice: 1.0229, loss: 26.8210 2022-05-05 03:04:48,498 - mmseg - INFO - Saving checkpoint at 26000 iterations 2022-05-05 03:05:15,879 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 03:05:15,888 - mmseg - INFO - Iter [26000/40000] lr: 5.026e-07, eta: 3:17:21, time: 1.323, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5179, decode.loss_mask: 0.7328, decode.loss_dice: 1.0206, decode.d0.loss_cls: 3.4461, decode.d0.loss_mask: 0.7761, decode.d0.loss_dice: 1.1880, decode.d1.loss_cls: 0.7766, decode.d1.loss_mask: 0.7617, decode.d1.loss_dice: 1.0921, decode.d2.loss_cls: 0.6297, decode.d2.loss_mask: 0.7437, decode.d2.loss_dice: 1.0543, decode.d3.loss_cls: 0.5811, decode.d3.loss_mask: 0.7390, decode.d3.loss_dice: 1.0353, decode.d4.loss_cls: 0.5656, decode.d4.loss_mask: 0.7366, decode.d4.loss_dice: 1.0300, decode.d5.loss_cls: 0.5474, decode.d5.loss_mask: 0.7350, decode.d5.loss_dice: 1.0303, decode.d6.loss_cls: 0.5327, decode.d6.loss_mask: 0.7311, decode.d6.loss_dice: 1.0164, decode.d7.loss_cls: 0.5221, decode.d7.loss_mask: 0.7324, decode.d7.loss_dice: 1.0216, decode.d8.loss_cls: 0.5080, decode.d8.loss_mask: 0.7320, decode.d8.loss_dice: 1.0221, loss: 26.5583 2022-05-05 03:05:55,435 - mmseg - INFO - Iter [26050/40000] lr: 5.008e-07, eta: 3:16:37, time: 0.792, data_time: 0.012, memory: 51557, decode.loss_cls: 0.5695, decode.loss_mask: 0.7223, decode.loss_dice: 1.0403, decode.d0.loss_cls: 3.5659, decode.d0.loss_mask: 0.7731, decode.d0.loss_dice: 1.2210, decode.d1.loss_cls: 0.8284, decode.d1.loss_mask: 0.7513, decode.d1.loss_dice: 1.1191, decode.d2.loss_cls: 0.6768, decode.d2.loss_mask: 0.7312, decode.d2.loss_dice: 1.0708, decode.d3.loss_cls: 0.6129, decode.d3.loss_mask: 0.7302, decode.d3.loss_dice: 1.0493, decode.d4.loss_cls: 0.5969, decode.d4.loss_mask: 0.7297, decode.d4.loss_dice: 1.0482, decode.d5.loss_cls: 0.5830, decode.d5.loss_mask: 0.7265, decode.d5.loss_dice: 1.0479, decode.d6.loss_cls: 0.5662, decode.d6.loss_mask: 0.7264, decode.d6.loss_dice: 1.0431, decode.d7.loss_cls: 0.5644, decode.d7.loss_mask: 0.7254, decode.d7.loss_dice: 1.0448, decode.d8.loss_cls: 0.5714, decode.d8.loss_mask: 0.7249, decode.d8.loss_dice: 1.0381, loss: 27.1992 2022-05-05 03:06:34,638 - mmseg - INFO - Iter [26100/40000] lr: 4.990e-07, eta: 3:15:53, time: 0.785, data_time: 0.011, memory: 51557, decode.loss_cls: 0.5190, decode.loss_mask: 0.7322, decode.loss_dice: 1.0041, decode.d0.loss_cls: 3.5740, decode.d0.loss_mask: 0.7718, decode.d0.loss_dice: 1.1791, decode.d1.loss_cls: 0.7640, decode.d1.loss_mask: 0.7569, decode.d1.loss_dice: 1.0860, decode.d2.loss_cls: 0.6251, decode.d2.loss_mask: 0.7416, decode.d2.loss_dice: 1.0375, decode.d3.loss_cls: 0.5728, decode.d3.loss_mask: 0.7372, decode.d3.loss_dice: 1.0238, decode.d4.loss_cls: 0.5496, decode.d4.loss_mask: 0.7350, decode.d4.loss_dice: 1.0176, decode.d5.loss_cls: 0.5324, decode.d5.loss_mask: 0.7352, decode.d5.loss_dice: 1.0126, decode.d6.loss_cls: 0.5197, decode.d6.loss_mask: 0.7324, decode.d6.loss_dice: 1.0048, decode.d7.loss_cls: 0.5125, decode.d7.loss_mask: 0.7322, decode.d7.loss_dice: 1.0095, decode.d8.loss_cls: 0.5109, decode.d8.loss_mask: 0.7307, decode.d8.loss_dice: 1.0058, loss: 26.4661 2022-05-05 03:07:13,958 - mmseg - INFO - Iter [26150/40000] lr: 4.972e-07, eta: 3:15:09, time: 0.786, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5743, decode.loss_mask: 0.6995, decode.loss_dice: 1.0328, decode.d0.loss_cls: 3.6315, decode.d0.loss_mask: 0.7511, decode.d0.loss_dice: 1.2130, decode.d1.loss_cls: 0.8175, decode.d1.loss_mask: 0.7339, decode.d1.loss_dice: 1.1151, decode.d2.loss_cls: 0.6828, decode.d2.loss_mask: 0.7170, decode.d2.loss_dice: 1.0612, decode.d3.loss_cls: 0.6308, decode.d3.loss_mask: 0.7086, decode.d3.loss_dice: 1.0460, decode.d4.loss_cls: 0.6062, decode.d4.loss_mask: 0.7068, decode.d4.loss_dice: 1.0468, decode.d5.loss_cls: 0.5867, decode.d5.loss_mask: 0.7055, decode.d5.loss_dice: 1.0390, decode.d6.loss_cls: 0.5789, decode.d6.loss_mask: 0.7043, decode.d6.loss_dice: 1.0356, decode.d7.loss_cls: 0.5720, decode.d7.loss_mask: 0.7031, decode.d7.loss_dice: 1.0331, decode.d8.loss_cls: 0.5740, decode.d8.loss_mask: 0.7018, decode.d8.loss_dice: 1.0327, loss: 27.0418 2022-05-05 03:07:53,661 - mmseg - INFO - Iter [26200/40000] lr: 4.954e-07, eta: 3:14:26, time: 0.794, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5538, decode.loss_mask: 0.7025, decode.loss_dice: 1.0128, decode.d0.loss_cls: 3.5684, decode.d0.loss_mask: 0.7463, decode.d0.loss_dice: 1.1735, decode.d1.loss_cls: 0.7927, decode.d1.loss_mask: 0.7325, decode.d1.loss_dice: 1.0814, decode.d2.loss_cls: 0.6703, decode.d2.loss_mask: 0.7108, decode.d2.loss_dice: 1.0349, decode.d3.loss_cls: 0.6063, decode.d3.loss_mask: 0.7066, decode.d3.loss_dice: 1.0242, decode.d4.loss_cls: 0.5887, decode.d4.loss_mask: 0.7040, decode.d4.loss_dice: 1.0212, decode.d5.loss_cls: 0.5735, decode.d5.loss_mask: 0.7052, decode.d5.loss_dice: 1.0193, decode.d6.loss_cls: 0.5622, decode.d6.loss_mask: 0.7029, decode.d6.loss_dice: 1.0091, decode.d7.loss_cls: 0.5503, decode.d7.loss_mask: 0.7015, decode.d7.loss_dice: 1.0128, decode.d8.loss_cls: 0.5468, decode.d8.loss_mask: 0.7033, decode.d8.loss_dice: 1.0117, loss: 26.5296 2022-05-05 03:08:32,739 - mmseg - INFO - Iter [26250/40000] lr: 4.936e-07, eta: 3:13:42, time: 0.782, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5483, decode.loss_mask: 0.7195, decode.loss_dice: 1.0228, decode.d0.loss_cls: 3.5800, decode.d0.loss_mask: 0.7662, decode.d0.loss_dice: 1.1944, decode.d1.loss_cls: 0.7796, decode.d1.loss_mask: 0.7475, decode.d1.loss_dice: 1.0917, decode.d2.loss_cls: 0.6631, decode.d2.loss_mask: 0.7406, decode.d2.loss_dice: 1.0553, decode.d3.loss_cls: 0.6107, decode.d3.loss_mask: 0.7306, decode.d3.loss_dice: 1.0324, decode.d4.loss_cls: 0.5852, decode.d4.loss_mask: 0.7239, decode.d4.loss_dice: 1.0337, decode.d5.loss_cls: 0.5692, decode.d5.loss_mask: 0.7254, decode.d5.loss_dice: 1.0250, decode.d6.loss_cls: 0.5543, decode.d6.loss_mask: 0.7225, decode.d6.loss_dice: 1.0164, decode.d7.loss_cls: 0.5468, decode.d7.loss_mask: 0.7166, decode.d7.loss_dice: 1.0244, decode.d8.loss_cls: 0.5452, decode.d8.loss_mask: 0.7225, decode.d8.loss_dice: 1.0240, loss: 26.8179 2022-05-05 03:09:11,223 - mmseg - INFO - Iter [26300/40000] lr: 4.918e-07, eta: 3:12:57, time: 0.770, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5556, decode.loss_mask: 0.7204, decode.loss_dice: 1.0425, decode.d0.loss_cls: 3.5899, decode.d0.loss_mask: 0.7726, decode.d0.loss_dice: 1.2135, decode.d1.loss_cls: 0.7820, decode.d1.loss_mask: 0.7458, decode.d1.loss_dice: 1.1184, decode.d2.loss_cls: 0.6665, decode.d2.loss_mask: 0.7316, decode.d2.loss_dice: 1.0744, decode.d3.loss_cls: 0.6228, decode.d3.loss_mask: 0.7245, decode.d3.loss_dice: 1.0505, decode.d4.loss_cls: 0.5961, decode.d4.loss_mask: 0.7247, decode.d4.loss_dice: 1.0561, decode.d5.loss_cls: 0.5732, decode.d5.loss_mask: 0.7245, decode.d5.loss_dice: 1.0550, decode.d6.loss_cls: 0.5590, decode.d6.loss_mask: 0.7226, decode.d6.loss_dice: 1.0395, decode.d7.loss_cls: 0.5609, decode.d7.loss_mask: 0.7227, decode.d7.loss_dice: 1.0396, decode.d8.loss_cls: 0.5561, decode.d8.loss_mask: 0.7206, decode.d8.loss_dice: 1.0405, loss: 27.1022 2022-05-05 03:09:50,295 - mmseg - INFO - Iter [26350/40000] lr: 4.900e-07, eta: 3:12:14, time: 0.781, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5514, decode.loss_mask: 0.7310, decode.loss_dice: 1.0241, decode.d0.loss_cls: 3.5247, decode.d0.loss_mask: 0.7740, decode.d0.loss_dice: 1.1821, decode.d1.loss_cls: 0.7893, decode.d1.loss_mask: 0.7601, decode.d1.loss_dice: 1.0794, decode.d2.loss_cls: 0.6596, decode.d2.loss_mask: 0.7438, decode.d2.loss_dice: 1.0399, decode.d3.loss_cls: 0.5926, decode.d3.loss_mask: 0.7338, decode.d3.loss_dice: 1.0302, decode.d4.loss_cls: 0.5809, decode.d4.loss_mask: 0.7325, decode.d4.loss_dice: 1.0303, decode.d5.loss_cls: 0.5684, decode.d5.loss_mask: 0.7299, decode.d5.loss_dice: 1.0275, decode.d6.loss_cls: 0.5515, decode.d6.loss_mask: 0.7302, decode.d6.loss_dice: 1.0214, decode.d7.loss_cls: 0.5431, decode.d7.loss_mask: 0.7325, decode.d7.loss_dice: 1.0204, decode.d8.loss_cls: 0.5501, decode.d8.loss_mask: 0.7318, decode.d8.loss_dice: 1.0217, loss: 26.7882 2022-05-05 03:10:29,361 - mmseg - INFO - Iter [26400/40000] lr: 4.882e-07, eta: 3:11:30, time: 0.780, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5529, decode.loss_mask: 0.7172, decode.loss_dice: 1.0179, decode.d0.loss_cls: 3.5413, decode.d0.loss_mask: 0.7650, decode.d0.loss_dice: 1.1870, decode.d1.loss_cls: 0.7918, decode.d1.loss_mask: 0.7461, decode.d1.loss_dice: 1.0915, decode.d2.loss_cls: 0.6507, decode.d2.loss_mask: 0.7290, decode.d2.loss_dice: 1.0487, decode.d3.loss_cls: 0.6112, decode.d3.loss_mask: 0.7282, decode.d3.loss_dice: 1.0326, decode.d4.loss_cls: 0.5832, decode.d4.loss_mask: 0.7218, decode.d4.loss_dice: 1.0295, decode.d5.loss_cls: 0.5780, decode.d5.loss_mask: 0.7209, decode.d5.loss_dice: 1.0251, decode.d6.loss_cls: 0.5583, decode.d6.loss_mask: 0.7203, decode.d6.loss_dice: 1.0206, decode.d7.loss_cls: 0.5491, decode.d7.loss_mask: 0.7195, decode.d7.loss_dice: 1.0231, decode.d8.loss_cls: 0.5477, decode.d8.loss_mask: 0.7192, decode.d8.loss_dice: 1.0187, loss: 26.7461 2022-05-05 03:11:11,636 - mmseg - INFO - Iter [26450/40000] lr: 4.864e-07, eta: 3:10:47, time: 0.846, data_time: 0.060, memory: 51557, decode.loss_cls: 0.4917, decode.loss_mask: 0.7108, decode.loss_dice: 0.9584, decode.d0.loss_cls: 3.4872, decode.d0.loss_mask: 0.7586, decode.d0.loss_dice: 1.1333, decode.d1.loss_cls: 0.7139, decode.d1.loss_mask: 0.7343, decode.d1.loss_dice: 1.0336, decode.d2.loss_cls: 0.5923, decode.d2.loss_mask: 0.7226, decode.d2.loss_dice: 0.9921, decode.d3.loss_cls: 0.5458, decode.d3.loss_mask: 0.7150, decode.d3.loss_dice: 0.9660, decode.d4.loss_cls: 0.5301, decode.d4.loss_mask: 0.7097, decode.d4.loss_dice: 0.9705, decode.d5.loss_cls: 0.5083, decode.d5.loss_mask: 0.7124, decode.d5.loss_dice: 0.9713, decode.d6.loss_cls: 0.4987, decode.d6.loss_mask: 0.7108, decode.d6.loss_dice: 0.9589, decode.d7.loss_cls: 0.4867, decode.d7.loss_mask: 0.7124, decode.d7.loss_dice: 0.9571, decode.d8.loss_cls: 0.4880, decode.d8.loss_mask: 0.7122, decode.d8.loss_dice: 0.9581, loss: 25.4405 2022-05-05 03:11:51,047 - mmseg - INFO - Iter [26500/40000] lr: 4.846e-07, eta: 3:10:04, time: 0.788, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5338, decode.loss_mask: 0.7306, decode.loss_dice: 1.0076, decode.d0.loss_cls: 3.5539, decode.d0.loss_mask: 0.7717, decode.d0.loss_dice: 1.1778, decode.d1.loss_cls: 0.7588, decode.d1.loss_mask: 0.7563, decode.d1.loss_dice: 1.0836, decode.d2.loss_cls: 0.6442, decode.d2.loss_mask: 0.7416, decode.d2.loss_dice: 1.0379, decode.d3.loss_cls: 0.5821, decode.d3.loss_mask: 0.7355, decode.d3.loss_dice: 1.0178, decode.d4.loss_cls: 0.5680, decode.d4.loss_mask: 0.7347, decode.d4.loss_dice: 1.0135, decode.d5.loss_cls: 0.5535, decode.d5.loss_mask: 0.7297, decode.d5.loss_dice: 1.0194, decode.d6.loss_cls: 0.5450, decode.d6.loss_mask: 0.7309, decode.d6.loss_dice: 1.0102, decode.d7.loss_cls: 0.5375, decode.d7.loss_mask: 0.7284, decode.d7.loss_dice: 1.0080, decode.d8.loss_cls: 0.5356, decode.d8.loss_mask: 0.7272, decode.d8.loss_dice: 1.0099, loss: 26.5846 2022-05-05 03:12:30,247 - mmseg - INFO - Iter [26550/40000] lr: 4.828e-07, eta: 3:09:20, time: 0.784, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5361, decode.loss_mask: 0.7172, decode.loss_dice: 1.0303, decode.d0.loss_cls: 3.5756, decode.d0.loss_mask: 0.7611, decode.d0.loss_dice: 1.2065, decode.d1.loss_cls: 0.8019, decode.d1.loss_mask: 0.7402, decode.d1.loss_dice: 1.1048, decode.d2.loss_cls: 0.6607, decode.d2.loss_mask: 0.7247, decode.d2.loss_dice: 1.0632, decode.d3.loss_cls: 0.5964, decode.d3.loss_mask: 0.7188, decode.d3.loss_dice: 1.0407, decode.d4.loss_cls: 0.5785, decode.d4.loss_mask: 0.7171, decode.d4.loss_dice: 1.0459, decode.d5.loss_cls: 0.5612, decode.d5.loss_mask: 0.7199, decode.d5.loss_dice: 1.0405, decode.d6.loss_cls: 0.5445, decode.d6.loss_mask: 0.7184, decode.d6.loss_dice: 1.0302, decode.d7.loss_cls: 0.5399, decode.d7.loss_mask: 0.7181, decode.d7.loss_dice: 1.0302, decode.d8.loss_cls: 0.5395, decode.d8.loss_mask: 0.7161, decode.d8.loss_dice: 1.0304, loss: 26.8084 2022-05-05 03:13:09,472 - mmseg - INFO - Iter [26600/40000] lr: 4.810e-07, eta: 3:08:36, time: 0.785, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5086, decode.loss_mask: 0.7348, decode.loss_dice: 1.0116, decode.d0.loss_cls: 3.5025, decode.d0.loss_mask: 0.7737, decode.d0.loss_dice: 1.1699, decode.d1.loss_cls: 0.7523, decode.d1.loss_mask: 0.7617, decode.d1.loss_dice: 1.0849, decode.d2.loss_cls: 0.6189, decode.d2.loss_mask: 0.7437, decode.d2.loss_dice: 1.0417, decode.d3.loss_cls: 0.5643, decode.d3.loss_mask: 0.7372, decode.d3.loss_dice: 1.0200, decode.d4.loss_cls: 0.5405, decode.d4.loss_mask: 0.7370, decode.d4.loss_dice: 1.0202, decode.d5.loss_cls: 0.5244, decode.d5.loss_mask: 0.7357, decode.d5.loss_dice: 1.0157, decode.d6.loss_cls: 0.5092, decode.d6.loss_mask: 0.7349, decode.d6.loss_dice: 1.0136, decode.d7.loss_cls: 0.5067, decode.d7.loss_mask: 0.7363, decode.d7.loss_dice: 1.0176, decode.d8.loss_cls: 0.4989, decode.d8.loss_mask: 0.7346, decode.d8.loss_dice: 1.0156, loss: 26.3664 2022-05-05 03:13:48,891 - mmseg - INFO - Iter [26650/40000] lr: 4.792e-07, eta: 3:07:53, time: 0.788, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5668, decode.loss_mask: 0.7027, decode.loss_dice: 0.9968, decode.d0.loss_cls: 3.5074, decode.d0.loss_mask: 0.7457, decode.d0.loss_dice: 1.1728, decode.d1.loss_cls: 0.7952, decode.d1.loss_mask: 0.7283, decode.d1.loss_dice: 1.0830, decode.d2.loss_cls: 0.6650, decode.d2.loss_mask: 0.7176, decode.d2.loss_dice: 1.0424, decode.d3.loss_cls: 0.6139, decode.d3.loss_mask: 0.7102, decode.d3.loss_dice: 1.0193, decode.d4.loss_cls: 0.6018, decode.d4.loss_mask: 0.7086, decode.d4.loss_dice: 1.0157, decode.d5.loss_cls: 0.5779, decode.d5.loss_mask: 0.7092, decode.d5.loss_dice: 1.0086, decode.d6.loss_cls: 0.5686, decode.d6.loss_mask: 0.7042, decode.d6.loss_dice: 1.0040, decode.d7.loss_cls: 0.5656, decode.d7.loss_mask: 0.7040, decode.d7.loss_dice: 1.0038, decode.d8.loss_cls: 0.5593, decode.d8.loss_mask: 0.7054, decode.d8.loss_dice: 1.0027, loss: 26.5064 2022-05-05 03:14:27,992 - mmseg - INFO - Iter [26700/40000] lr: 4.774e-07, eta: 3:07:09, time: 0.782, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5408, decode.loss_mask: 0.7131, decode.loss_dice: 1.0110, decode.d0.loss_cls: 3.5329, decode.d0.loss_mask: 0.7627, decode.d0.loss_dice: 1.1863, decode.d1.loss_cls: 0.7787, decode.d1.loss_mask: 0.7438, decode.d1.loss_dice: 1.0930, decode.d2.loss_cls: 0.6483, decode.d2.loss_mask: 0.7291, decode.d2.loss_dice: 1.0482, decode.d3.loss_cls: 0.5992, decode.d3.loss_mask: 0.7206, decode.d3.loss_dice: 1.0310, decode.d4.loss_cls: 0.5759, decode.d4.loss_mask: 0.7175, decode.d4.loss_dice: 1.0269, decode.d5.loss_cls: 0.5506, decode.d5.loss_mask: 0.7157, decode.d5.loss_dice: 1.0208, decode.d6.loss_cls: 0.5435, decode.d6.loss_mask: 0.7145, decode.d6.loss_dice: 1.0159, decode.d7.loss_cls: 0.5334, decode.d7.loss_mask: 0.7159, decode.d7.loss_dice: 1.0122, decode.d8.loss_cls: 0.5249, decode.d8.loss_mask: 0.7152, decode.d8.loss_dice: 1.0115, loss: 26.5333 2022-05-05 03:15:07,788 - mmseg - INFO - Iter [26750/40000] lr: 4.756e-07, eta: 3:06:25, time: 0.796, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5421, decode.loss_mask: 0.7561, decode.loss_dice: 1.0301, decode.d0.loss_cls: 3.4901, decode.d0.loss_mask: 0.8057, decode.d0.loss_dice: 1.1967, decode.d1.loss_cls: 0.7827, decode.d1.loss_mask: 0.7920, decode.d1.loss_dice: 1.1094, decode.d2.loss_cls: 0.6571, decode.d2.loss_mask: 0.7725, decode.d2.loss_dice: 1.0621, decode.d3.loss_cls: 0.5980, decode.d3.loss_mask: 0.7682, decode.d3.loss_dice: 1.0504, decode.d4.loss_cls: 0.5703, decode.d4.loss_mask: 0.7647, decode.d4.loss_dice: 1.0490, decode.d5.loss_cls: 0.5650, decode.d5.loss_mask: 0.7619, decode.d5.loss_dice: 1.0411, decode.d6.loss_cls: 0.5499, decode.d6.loss_mask: 0.7603, decode.d6.loss_dice: 1.0384, decode.d7.loss_cls: 0.5472, decode.d7.loss_mask: 0.7565, decode.d7.loss_dice: 1.0346, decode.d8.loss_cls: 0.5386, decode.d8.loss_mask: 0.7564, decode.d8.loss_dice: 1.0363, loss: 27.1834 2022-05-05 03:15:47,041 - mmseg - INFO - Iter [26800/40000] lr: 4.738e-07, eta: 3:05:42, time: 0.785, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5723, decode.loss_mask: 0.7387, decode.loss_dice: 1.0394, decode.d0.loss_cls: 3.5528, decode.d0.loss_mask: 0.7894, decode.d0.loss_dice: 1.2066, decode.d1.loss_cls: 0.7783, decode.d1.loss_mask: 0.7724, decode.d1.loss_dice: 1.1098, decode.d2.loss_cls: 0.6699, decode.d2.loss_mask: 0.7520, decode.d2.loss_dice: 1.0711, decode.d3.loss_cls: 0.6221, decode.d3.loss_mask: 0.7432, decode.d3.loss_dice: 1.0478, decode.d4.loss_cls: 0.6027, decode.d4.loss_mask: 0.7393, decode.d4.loss_dice: 1.0479, decode.d5.loss_cls: 0.5952, decode.d5.loss_mask: 0.7371, decode.d5.loss_dice: 1.0493, decode.d6.loss_cls: 0.5791, decode.d6.loss_mask: 0.7370, decode.d6.loss_dice: 1.0373, decode.d7.loss_cls: 0.5793, decode.d7.loss_mask: 0.7385, decode.d7.loss_dice: 1.0421, decode.d8.loss_cls: 0.5728, decode.d8.loss_mask: 0.7391, decode.d8.loss_dice: 1.0418, loss: 27.3045 2022-05-05 03:16:26,433 - mmseg - INFO - Iter [26850/40000] lr: 4.721e-07, eta: 3:04:58, time: 0.787, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5574, decode.loss_mask: 0.7204, decode.loss_dice: 0.9895, decode.d0.loss_cls: 3.5573, decode.d0.loss_mask: 0.7638, decode.d0.loss_dice: 1.1645, decode.d1.loss_cls: 0.7883, decode.d1.loss_mask: 0.7466, decode.d1.loss_dice: 1.0695, decode.d2.loss_cls: 0.6568, decode.d2.loss_mask: 0.7349, decode.d2.loss_dice: 1.0248, decode.d3.loss_cls: 0.6030, decode.d3.loss_mask: 0.7284, decode.d3.loss_dice: 0.9984, decode.d4.loss_cls: 0.5803, decode.d4.loss_mask: 0.7263, decode.d4.loss_dice: 1.0019, decode.d5.loss_cls: 0.5675, decode.d5.loss_mask: 0.7223, decode.d5.loss_dice: 1.0047, decode.d6.loss_cls: 0.5635, decode.d6.loss_mask: 0.7187, decode.d6.loss_dice: 0.9915, decode.d7.loss_cls: 0.5487, decode.d7.loss_mask: 0.7183, decode.d7.loss_dice: 0.9918, decode.d8.loss_cls: 0.5475, decode.d8.loss_mask: 0.7193, decode.d8.loss_dice: 0.9922, loss: 26.4980 2022-05-05 03:17:05,022 - mmseg - INFO - Iter [26900/40000] lr: 4.703e-07, eta: 3:04:14, time: 0.772, data_time: 0.011, memory: 51557, decode.loss_cls: 0.5511, decode.loss_mask: 0.7113, decode.loss_dice: 1.0122, decode.d0.loss_cls: 3.5497, decode.d0.loss_mask: 0.7518, decode.d0.loss_dice: 1.1738, decode.d1.loss_cls: 0.8004, decode.d1.loss_mask: 0.7355, decode.d1.loss_dice: 1.0773, decode.d2.loss_cls: 0.6537, decode.d2.loss_mask: 0.7196, decode.d2.loss_dice: 1.0406, decode.d3.loss_cls: 0.6214, decode.d3.loss_mask: 0.7161, decode.d3.loss_dice: 1.0234, decode.d4.loss_cls: 0.5968, decode.d4.loss_mask: 0.7115, decode.d4.loss_dice: 1.0160, decode.d5.loss_cls: 0.5731, decode.d5.loss_mask: 0.7116, decode.d5.loss_dice: 1.0141, decode.d6.loss_cls: 0.5604, decode.d6.loss_mask: 0.7104, decode.d6.loss_dice: 1.0086, decode.d7.loss_cls: 0.5524, decode.d7.loss_mask: 0.7084, decode.d7.loss_dice: 1.0120, decode.d8.loss_cls: 0.5455, decode.d8.loss_mask: 0.7093, decode.d8.loss_dice: 1.0156, loss: 26.5838 2022-05-05 03:17:44,073 - mmseg - INFO - Iter [26950/40000] lr: 4.685e-07, eta: 3:03:30, time: 0.781, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5725, decode.loss_mask: 0.7231, decode.loss_dice: 1.0204, decode.d0.loss_cls: 3.5307, decode.d0.loss_mask: 0.7677, decode.d0.loss_dice: 1.2053, decode.d1.loss_cls: 0.8124, decode.d1.loss_mask: 0.7480, decode.d1.loss_dice: 1.1011, decode.d2.loss_cls: 0.6794, decode.d2.loss_mask: 0.7358, decode.d2.loss_dice: 1.0583, decode.d3.loss_cls: 0.6266, decode.d3.loss_mask: 0.7292, decode.d3.loss_dice: 1.0354, decode.d4.loss_cls: 0.6074, decode.d4.loss_mask: 0.7263, decode.d4.loss_dice: 1.0334, decode.d5.loss_cls: 0.5924, decode.d5.loss_mask: 0.7221, decode.d5.loss_dice: 1.0281, decode.d6.loss_cls: 0.5779, decode.d6.loss_mask: 0.7238, decode.d6.loss_dice: 1.0217, decode.d7.loss_cls: 0.5675, decode.d7.loss_mask: 0.7246, decode.d7.loss_dice: 1.0300, decode.d8.loss_cls: 0.5703, decode.d8.loss_mask: 0.7221, decode.d8.loss_dice: 1.0265, loss: 27.0199 2022-05-05 03:18:26,218 - mmseg - INFO - Saving checkpoint at 27000 iterations 2022-05-05 03:18:50,723 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 03:18:50,725 - mmseg - INFO - Iter [27000/40000] lr: 4.667e-07, eta: 3:03:00, time: 1.331, data_time: 0.059, memory: 51557, decode.loss_cls: 0.5610, decode.loss_mask: 0.7208, decode.loss_dice: 1.0591, decode.d0.loss_cls: 3.5483, decode.d0.loss_mask: 0.7605, decode.d0.loss_dice: 1.2131, decode.d1.loss_cls: 0.7956, decode.d1.loss_mask: 0.7440, decode.d1.loss_dice: 1.1257, decode.d2.loss_cls: 0.6638, decode.d2.loss_mask: 0.7348, decode.d2.loss_dice: 1.0895, decode.d3.loss_cls: 0.6105, decode.d3.loss_mask: 0.7238, decode.d3.loss_dice: 1.0682, decode.d4.loss_cls: 0.5912, decode.d4.loss_mask: 0.7230, decode.d4.loss_dice: 1.0703, decode.d5.loss_cls: 0.5849, decode.d5.loss_mask: 0.7258, decode.d5.loss_dice: 1.0655, decode.d6.loss_cls: 0.5715, decode.d6.loss_mask: 0.7249, decode.d6.loss_dice: 1.0562, decode.d7.loss_cls: 0.5649, decode.d7.loss_mask: 0.7248, decode.d7.loss_dice: 1.0598, decode.d8.loss_cls: 0.5638, decode.d8.loss_mask: 0.7192, decode.d8.loss_dice: 1.0639, loss: 27.2285 2022-05-05 03:19:31,353 - mmseg - INFO - Iter [27050/40000] lr: 4.649e-07, eta: 3:02:17, time: 0.815, data_time: 0.012, memory: 51557, decode.loss_cls: 0.5729, decode.loss_mask: 0.7219, decode.loss_dice: 1.0433, decode.d0.loss_cls: 3.4964, decode.d0.loss_mask: 0.7758, decode.d0.loss_dice: 1.2098, decode.d1.loss_cls: 0.8177, decode.d1.loss_mask: 0.7556, decode.d1.loss_dice: 1.1167, decode.d2.loss_cls: 0.6818, decode.d2.loss_mask: 0.7375, decode.d2.loss_dice: 1.0678, decode.d3.loss_cls: 0.6352, decode.d3.loss_mask: 0.7282, decode.d3.loss_dice: 1.0511, decode.d4.loss_cls: 0.6103, decode.d4.loss_mask: 0.7271, decode.d4.loss_dice: 1.0517, decode.d5.loss_cls: 0.5885, decode.d5.loss_mask: 0.7285, decode.d5.loss_dice: 1.0531, decode.d6.loss_cls: 0.5828, decode.d6.loss_mask: 0.7226, decode.d6.loss_dice: 1.0495, decode.d7.loss_cls: 0.5708, decode.d7.loss_mask: 0.7242, decode.d7.loss_dice: 1.0491, decode.d8.loss_cls: 0.5701, decode.d8.loss_mask: 0.7213, decode.d8.loss_dice: 1.0479, loss: 27.2094 2022-05-05 03:20:11,043 - mmseg - INFO - Iter [27100/40000] lr: 4.631e-07, eta: 3:01:34, time: 0.794, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5085, decode.loss_mask: 0.7201, decode.loss_dice: 0.9928, decode.d0.loss_cls: 3.4903, decode.d0.loss_mask: 0.7664, decode.d0.loss_dice: 1.1760, decode.d1.loss_cls: 0.7685, decode.d1.loss_mask: 0.7432, decode.d1.loss_dice: 1.0659, decode.d2.loss_cls: 0.6284, decode.d2.loss_mask: 0.7287, decode.d2.loss_dice: 1.0229, decode.d3.loss_cls: 0.5695, decode.d3.loss_mask: 0.7208, decode.d3.loss_dice: 1.0101, decode.d4.loss_cls: 0.5491, decode.d4.loss_mask: 0.7215, decode.d4.loss_dice: 1.0037, decode.d5.loss_cls: 0.5352, decode.d5.loss_mask: 0.7215, decode.d5.loss_dice: 1.0026, decode.d6.loss_cls: 0.5124, decode.d6.loss_mask: 0.7178, decode.d6.loss_dice: 0.9972, decode.d7.loss_cls: 0.5084, decode.d7.loss_mask: 0.7232, decode.d7.loss_dice: 0.9945, decode.d8.loss_cls: 0.5082, decode.d8.loss_mask: 0.7193, decode.d8.loss_dice: 0.9945, loss: 26.1211 2022-05-05 03:20:49,901 - mmseg - INFO - Iter [27150/40000] lr: 4.613e-07, eta: 3:00:50, time: 0.777, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5146, decode.loss_mask: 0.6926, decode.loss_dice: 0.9981, decode.d0.loss_cls: 3.5529, decode.d0.loss_mask: 0.7504, decode.d0.loss_dice: 1.1808, decode.d1.loss_cls: 0.7572, decode.d1.loss_mask: 0.7252, decode.d1.loss_dice: 1.0753, decode.d2.loss_cls: 0.6175, decode.d2.loss_mask: 0.7078, decode.d2.loss_dice: 1.0409, decode.d3.loss_cls: 0.5677, decode.d3.loss_mask: 0.7061, decode.d3.loss_dice: 1.0232, decode.d4.loss_cls: 0.5429, decode.d4.loss_mask: 0.7049, decode.d4.loss_dice: 1.0180, decode.d5.loss_cls: 0.5359, decode.d5.loss_mask: 0.7012, decode.d5.loss_dice: 1.0132, decode.d6.loss_cls: 0.5209, decode.d6.loss_mask: 0.6962, decode.d6.loss_dice: 1.0071, decode.d7.loss_cls: 0.5134, decode.d7.loss_mask: 0.6942, decode.d7.loss_dice: 1.0026, decode.d8.loss_cls: 0.5079, decode.d8.loss_mask: 0.6926, decode.d8.loss_dice: 1.0053, loss: 26.0665 2022-05-05 03:21:29,748 - mmseg - INFO - Iter [27200/40000] lr: 4.595e-07, eta: 3:00:06, time: 0.796, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5234, decode.loss_mask: 0.7297, decode.loss_dice: 0.9972, decode.d0.loss_cls: 3.4803, decode.d0.loss_mask: 0.7771, decode.d0.loss_dice: 1.1531, decode.d1.loss_cls: 0.7531, decode.d1.loss_mask: 0.7655, decode.d1.loss_dice: 1.0641, decode.d2.loss_cls: 0.6317, decode.d2.loss_mask: 0.7398, decode.d2.loss_dice: 1.0141, decode.d3.loss_cls: 0.5835, decode.d3.loss_mask: 0.7347, decode.d3.loss_dice: 0.9991, decode.d4.loss_cls: 0.5549, decode.d4.loss_mask: 0.7318, decode.d4.loss_dice: 0.9956, decode.d5.loss_cls: 0.5406, decode.d5.loss_mask: 0.7275, decode.d5.loss_dice: 0.9969, decode.d6.loss_cls: 0.5281, decode.d6.loss_mask: 0.7282, decode.d6.loss_dice: 0.9926, decode.d7.loss_cls: 0.5215, decode.d7.loss_mask: 0.7291, decode.d7.loss_dice: 0.9919, decode.d8.loss_cls: 0.5200, decode.d8.loss_mask: 0.7290, decode.d8.loss_dice: 0.9905, loss: 26.2246 2022-05-05 03:22:08,853 - mmseg - INFO - Iter [27250/40000] lr: 4.577e-07, eta: 2:59:23, time: 0.783, data_time: 0.011, memory: 51557, decode.loss_cls: 0.5426, decode.loss_mask: 0.7134, decode.loss_dice: 1.0118, decode.d0.loss_cls: 3.4918, decode.d0.loss_mask: 0.7529, decode.d0.loss_dice: 1.1757, decode.d1.loss_cls: 0.7662, decode.d1.loss_mask: 0.7426, decode.d1.loss_dice: 1.0844, decode.d2.loss_cls: 0.6361, decode.d2.loss_mask: 0.7305, decode.d2.loss_dice: 1.0431, decode.d3.loss_cls: 0.5974, decode.d3.loss_mask: 0.7203, decode.d3.loss_dice: 1.0219, decode.d4.loss_cls: 0.5848, decode.d4.loss_mask: 0.7177, decode.d4.loss_dice: 1.0188, decode.d5.loss_cls: 0.5645, decode.d5.loss_mask: 0.7145, decode.d5.loss_dice: 1.0223, decode.d6.loss_cls: 0.5504, decode.d6.loss_mask: 0.7093, decode.d6.loss_dice: 1.0135, decode.d7.loss_cls: 0.5395, decode.d7.loss_mask: 0.7139, decode.d7.loss_dice: 1.0139, decode.d8.loss_cls: 0.5384, decode.d8.loss_mask: 0.7118, decode.d8.loss_dice: 1.0118, loss: 26.4560 2022-05-05 03:22:47,906 - mmseg - INFO - Iter [27300/40000] lr: 4.559e-07, eta: 2:58:39, time: 0.781, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4918, decode.loss_mask: 0.7151, decode.loss_dice: 0.9819, decode.d0.loss_cls: 3.4290, decode.d0.loss_mask: 0.7618, decode.d0.loss_dice: 1.1331, decode.d1.loss_cls: 0.7214, decode.d1.loss_mask: 0.7451, decode.d1.loss_dice: 1.0496, decode.d2.loss_cls: 0.5939, decode.d2.loss_mask: 0.7269, decode.d2.loss_dice: 1.0084, decode.d3.loss_cls: 0.5427, decode.d3.loss_mask: 0.7261, decode.d3.loss_dice: 0.9987, decode.d4.loss_cls: 0.5297, decode.d4.loss_mask: 0.7215, decode.d4.loss_dice: 0.9917, decode.d5.loss_cls: 0.5112, decode.d5.loss_mask: 0.7180, decode.d5.loss_dice: 0.9850, decode.d6.loss_cls: 0.4996, decode.d6.loss_mask: 0.7182, decode.d6.loss_dice: 0.9857, decode.d7.loss_cls: 0.4933, decode.d7.loss_mask: 0.7193, decode.d7.loss_dice: 0.9838, decode.d8.loss_cls: 0.4910, decode.d8.loss_mask: 0.7172, decode.d8.loss_dice: 0.9820, loss: 25.6728 2022-05-05 03:23:27,196 - mmseg - INFO - Iter [27350/40000] lr: 4.541e-07, eta: 2:57:56, time: 0.785, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5316, decode.loss_mask: 0.7280, decode.loss_dice: 1.0319, decode.d0.loss_cls: 3.5223, decode.d0.loss_mask: 0.7688, decode.d0.loss_dice: 1.1939, decode.d1.loss_cls: 0.7658, decode.d1.loss_mask: 0.7557, decode.d1.loss_dice: 1.1002, decode.d2.loss_cls: 0.6294, decode.d2.loss_mask: 0.7407, decode.d2.loss_dice: 1.0668, decode.d3.loss_cls: 0.5878, decode.d3.loss_mask: 0.7391, decode.d3.loss_dice: 1.0452, decode.d4.loss_cls: 0.5643, decode.d4.loss_mask: 0.7387, decode.d4.loss_dice: 1.0418, decode.d5.loss_cls: 0.5411, decode.d5.loss_mask: 0.7356, decode.d5.loss_dice: 1.0427, decode.d6.loss_cls: 0.5291, decode.d6.loss_mask: 0.7340, decode.d6.loss_dice: 1.0344, decode.d7.loss_cls: 0.5268, decode.d7.loss_mask: 0.7341, decode.d7.loss_dice: 1.0372, decode.d8.loss_cls: 0.5262, decode.d8.loss_mask: 0.7320, decode.d8.loss_dice: 1.0327, loss: 26.7581 2022-05-05 03:24:06,241 - mmseg - INFO - Iter [27400/40000] lr: 4.523e-07, eta: 2:57:12, time: 0.781, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5879, decode.loss_mask: 0.7315, decode.loss_dice: 1.0632, decode.d0.loss_cls: 3.6365, decode.d0.loss_mask: 0.7843, decode.d0.loss_dice: 1.2388, decode.d1.loss_cls: 0.8389, decode.d1.loss_mask: 0.7625, decode.d1.loss_dice: 1.1328, decode.d2.loss_cls: 0.6954, decode.d2.loss_mask: 0.7459, decode.d2.loss_dice: 1.0944, decode.d3.loss_cls: 0.6455, decode.d3.loss_mask: 0.7363, decode.d3.loss_dice: 1.0753, decode.d4.loss_cls: 0.6320, decode.d4.loss_mask: 0.7369, decode.d4.loss_dice: 1.0783, decode.d5.loss_cls: 0.6037, decode.d5.loss_mask: 0.7395, decode.d5.loss_dice: 1.0751, decode.d6.loss_cls: 0.5938, decode.d6.loss_mask: 0.7340, decode.d6.loss_dice: 1.0657, decode.d7.loss_cls: 0.5881, decode.d7.loss_mask: 0.7313, decode.d7.loss_dice: 1.0651, decode.d8.loss_cls: 0.5762, decode.d8.loss_mask: 0.7296, decode.d8.loss_dice: 1.0657, loss: 27.7843 2022-05-05 03:24:45,174 - mmseg - INFO - Iter [27450/40000] lr: 4.505e-07, eta: 2:56:28, time: 0.779, data_time: 0.012, memory: 51557, decode.loss_cls: 0.5473, decode.loss_mask: 0.7197, decode.loss_dice: 0.9893, decode.d0.loss_cls: 3.5746, decode.d0.loss_mask: 0.7679, decode.d0.loss_dice: 1.1699, decode.d1.loss_cls: 0.7705, decode.d1.loss_mask: 0.7534, decode.d1.loss_dice: 1.0581, decode.d2.loss_cls: 0.6455, decode.d2.loss_mask: 0.7337, decode.d2.loss_dice: 1.0263, decode.d3.loss_cls: 0.6013, decode.d3.loss_mask: 0.7232, decode.d3.loss_dice: 1.0020, decode.d4.loss_cls: 0.5804, decode.d4.loss_mask: 0.7223, decode.d4.loss_dice: 1.0020, decode.d5.loss_cls: 0.5668, decode.d5.loss_mask: 0.7216, decode.d5.loss_dice: 0.9991, decode.d6.loss_cls: 0.5527, decode.d6.loss_mask: 0.7197, decode.d6.loss_dice: 0.9922, decode.d7.loss_cls: 0.5487, decode.d7.loss_mask: 0.7216, decode.d7.loss_dice: 0.9922, decode.d8.loss_cls: 0.5390, decode.d8.loss_mask: 0.7178, decode.d8.loss_dice: 0.9948, loss: 26.4536 2022-05-05 03:25:24,317 - mmseg - INFO - Iter [27500/40000] lr: 4.487e-07, eta: 2:55:45, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5310, decode.loss_mask: 0.6993, decode.loss_dice: 1.0308, decode.d0.loss_cls: 3.5188, decode.d0.loss_mask: 0.7476, decode.d0.loss_dice: 1.1954, decode.d1.loss_cls: 0.7631, decode.d1.loss_mask: 0.7311, decode.d1.loss_dice: 1.1015, decode.d2.loss_cls: 0.6194, decode.d2.loss_mask: 0.7130, decode.d2.loss_dice: 1.0622, decode.d3.loss_cls: 0.5766, decode.d3.loss_mask: 0.7082, decode.d3.loss_dice: 1.0449, decode.d4.loss_cls: 0.5552, decode.d4.loss_mask: 0.7088, decode.d4.loss_dice: 1.0408, decode.d5.loss_cls: 0.5472, decode.d5.loss_mask: 0.7050, decode.d5.loss_dice: 1.0382, decode.d6.loss_cls: 0.5379, decode.d6.loss_mask: 0.7029, decode.d6.loss_dice: 1.0375, decode.d7.loss_cls: 0.5394, decode.d7.loss_mask: 0.7027, decode.d7.loss_dice: 1.0363, decode.d8.loss_cls: 0.5261, decode.d8.loss_mask: 0.7031, decode.d8.loss_dice: 1.0366, loss: 26.4607 2022-05-05 03:26:05,669 - mmseg - INFO - Iter [27550/40000] lr: 4.469e-07, eta: 2:55:02, time: 0.827, data_time: 0.064, memory: 51557, decode.loss_cls: 0.5278, decode.loss_mask: 0.7283, decode.loss_dice: 1.0200, decode.d0.loss_cls: 3.5002, decode.d0.loss_mask: 0.7700, decode.d0.loss_dice: 1.1905, decode.d1.loss_cls: 0.7716, decode.d1.loss_mask: 0.7536, decode.d1.loss_dice: 1.0876, decode.d2.loss_cls: 0.6454, decode.d2.loss_mask: 0.7323, decode.d2.loss_dice: 1.0358, decode.d3.loss_cls: 0.5924, decode.d3.loss_mask: 0.7327, decode.d3.loss_dice: 1.0240, decode.d4.loss_cls: 0.5660, decode.d4.loss_mask: 0.7256, decode.d4.loss_dice: 1.0259, decode.d5.loss_cls: 0.5499, decode.d5.loss_mask: 0.7276, decode.d5.loss_dice: 1.0267, decode.d6.loss_cls: 0.5354, decode.d6.loss_mask: 0.7260, decode.d6.loss_dice: 1.0181, decode.d7.loss_cls: 0.5318, decode.d7.loss_mask: 0.7271, decode.d7.loss_dice: 1.0141, decode.d8.loss_cls: 0.5274, decode.d8.loss_mask: 0.7281, decode.d8.loss_dice: 1.0208, loss: 26.5631 2022-05-05 03:26:45,060 - mmseg - INFO - Iter [27600/40000] lr: 4.451e-07, eta: 2:54:19, time: 0.788, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5347, decode.loss_mask: 0.7336, decode.loss_dice: 1.0368, decode.d0.loss_cls: 3.4932, decode.d0.loss_mask: 0.7820, decode.d0.loss_dice: 1.1959, decode.d1.loss_cls: 0.7511, decode.d1.loss_mask: 0.7656, decode.d1.loss_dice: 1.1054, decode.d2.loss_cls: 0.6273, decode.d2.loss_mask: 0.7467, decode.d2.loss_dice: 1.0645, decode.d3.loss_cls: 0.5757, decode.d3.loss_mask: 0.7405, decode.d3.loss_dice: 1.0460, decode.d4.loss_cls: 0.5520, decode.d4.loss_mask: 0.7421, decode.d4.loss_dice: 1.0482, decode.d5.loss_cls: 0.5430, decode.d5.loss_mask: 0.7408, decode.d5.loss_dice: 1.0451, decode.d6.loss_cls: 0.5317, decode.d6.loss_mask: 0.7345, decode.d6.loss_dice: 1.0376, decode.d7.loss_cls: 0.5309, decode.d7.loss_mask: 0.7332, decode.d7.loss_dice: 1.0327, decode.d8.loss_cls: 0.5324, decode.d8.loss_mask: 0.7358, decode.d8.loss_dice: 1.0314, loss: 26.7705 2022-05-05 03:27:24,458 - mmseg - INFO - Iter [27650/40000] lr: 4.433e-07, eta: 2:53:35, time: 0.788, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5398, decode.loss_mask: 0.7040, decode.loss_dice: 1.0036, decode.d0.loss_cls: 3.5904, decode.d0.loss_mask: 0.7507, decode.d0.loss_dice: 1.1903, decode.d1.loss_cls: 0.7903, decode.d1.loss_mask: 0.7318, decode.d1.loss_dice: 1.0766, decode.d2.loss_cls: 0.6595, decode.d2.loss_mask: 0.7120, decode.d2.loss_dice: 1.0284, decode.d3.loss_cls: 0.6018, decode.d3.loss_mask: 0.7053, decode.d3.loss_dice: 1.0168, decode.d4.loss_cls: 0.5801, decode.d4.loss_mask: 0.7076, decode.d4.loss_dice: 1.0163, decode.d5.loss_cls: 0.5578, decode.d5.loss_mask: 0.7033, decode.d5.loss_dice: 1.0090, decode.d6.loss_cls: 0.5489, decode.d6.loss_mask: 0.7011, decode.d6.loss_dice: 1.0039, decode.d7.loss_cls: 0.5415, decode.d7.loss_mask: 0.6996, decode.d7.loss_dice: 1.0054, decode.d8.loss_cls: 0.5419, decode.d8.loss_mask: 0.7059, decode.d8.loss_dice: 1.0034, loss: 26.4270 2022-05-05 03:28:03,629 - mmseg - INFO - Iter [27700/40000] lr: 4.415e-07, eta: 2:52:52, time: 0.783, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5141, decode.loss_mask: 0.7218, decode.loss_dice: 1.0168, decode.d0.loss_cls: 3.4673, decode.d0.loss_mask: 0.7624, decode.d0.loss_dice: 1.1767, decode.d1.loss_cls: 0.7430, decode.d1.loss_mask: 0.7447, decode.d1.loss_dice: 1.0858, decode.d2.loss_cls: 0.6238, decode.d2.loss_mask: 0.7262, decode.d2.loss_dice: 1.0440, decode.d3.loss_cls: 0.5793, decode.d3.loss_mask: 0.7218, decode.d3.loss_dice: 1.0294, decode.d4.loss_cls: 0.5605, decode.d4.loss_mask: 0.7205, decode.d4.loss_dice: 1.0184, decode.d5.loss_cls: 0.5394, decode.d5.loss_mask: 0.7194, decode.d5.loss_dice: 1.0186, decode.d6.loss_cls: 0.5285, decode.d6.loss_mask: 0.7186, decode.d6.loss_dice: 1.0086, decode.d7.loss_cls: 0.5199, decode.d7.loss_mask: 0.7202, decode.d7.loss_dice: 1.0143, decode.d8.loss_cls: 0.5238, decode.d8.loss_mask: 0.7202, decode.d8.loss_dice: 1.0136, loss: 26.3016 2022-05-05 03:28:42,755 - mmseg - INFO - Iter [27750/40000] lr: 4.397e-07, eta: 2:52:08, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5305, decode.loss_mask: 0.7121, decode.loss_dice: 1.0050, decode.d0.loss_cls: 3.5039, decode.d0.loss_mask: 0.7596, decode.d0.loss_dice: 1.1648, decode.d1.loss_cls: 0.7742, decode.d1.loss_mask: 0.7428, decode.d1.loss_dice: 1.0669, decode.d2.loss_cls: 0.6453, decode.d2.loss_mask: 0.7265, decode.d2.loss_dice: 1.0287, decode.d3.loss_cls: 0.5851, decode.d3.loss_mask: 0.7186, decode.d3.loss_dice: 1.0137, decode.d4.loss_cls: 0.5654, decode.d4.loss_mask: 0.7162, decode.d4.loss_dice: 1.0103, decode.d5.loss_cls: 0.5506, decode.d5.loss_mask: 0.7133, decode.d5.loss_dice: 1.0105, decode.d6.loss_cls: 0.5391, decode.d6.loss_mask: 0.7115, decode.d6.loss_dice: 1.0048, decode.d7.loss_cls: 0.5327, decode.d7.loss_mask: 0.7104, decode.d7.loss_dice: 1.0024, decode.d8.loss_cls: 0.5189, decode.d8.loss_mask: 0.7101, decode.d8.loss_dice: 0.9986, loss: 26.2724 2022-05-05 03:29:21,457 - mmseg - INFO - Iter [27800/40000] lr: 4.380e-07, eta: 2:51:25, time: 0.773, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5172, decode.loss_mask: 0.7268, decode.loss_dice: 0.9978, decode.d0.loss_cls: 3.4732, decode.d0.loss_mask: 0.7774, decode.d0.loss_dice: 1.1636, decode.d1.loss_cls: 0.7519, decode.d1.loss_mask: 0.7554, decode.d1.loss_dice: 1.0721, decode.d2.loss_cls: 0.6268, decode.d2.loss_mask: 0.7348, decode.d2.loss_dice: 1.0242, decode.d3.loss_cls: 0.5747, decode.d3.loss_mask: 0.7301, decode.d3.loss_dice: 1.0069, decode.d4.loss_cls: 0.5475, decode.d4.loss_mask: 0.7296, decode.d4.loss_dice: 1.0014, decode.d5.loss_cls: 0.5346, decode.d5.loss_mask: 0.7292, decode.d5.loss_dice: 1.0045, decode.d6.loss_cls: 0.5251, decode.d6.loss_mask: 0.7257, decode.d6.loss_dice: 1.0007, decode.d7.loss_cls: 0.5185, decode.d7.loss_mask: 0.7272, decode.d7.loss_dice: 1.0006, decode.d8.loss_cls: 0.5158, decode.d8.loss_mask: 0.7253, decode.d8.loss_dice: 1.0006, loss: 26.2192 2022-05-05 03:30:00,601 - mmseg - INFO - Iter [27850/40000] lr: 4.362e-07, eta: 2:50:41, time: 0.784, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5226, decode.loss_mask: 0.6998, decode.loss_dice: 1.0318, decode.d0.loss_cls: 3.5613, decode.d0.loss_mask: 0.7417, decode.d0.loss_dice: 1.1857, decode.d1.loss_cls: 0.7516, decode.d1.loss_mask: 0.7301, decode.d1.loss_dice: 1.0984, decode.d2.loss_cls: 0.6230, decode.d2.loss_mask: 0.7170, decode.d2.loss_dice: 1.0531, decode.d3.loss_cls: 0.5764, decode.d3.loss_mask: 0.7042, decode.d3.loss_dice: 1.0324, decode.d4.loss_cls: 0.5535, decode.d4.loss_mask: 0.7017, decode.d4.loss_dice: 1.0364, decode.d5.loss_cls: 0.5456, decode.d5.loss_mask: 0.7025, decode.d5.loss_dice: 1.0307, decode.d6.loss_cls: 0.5372, decode.d6.loss_mask: 0.7005, decode.d6.loss_dice: 1.0208, decode.d7.loss_cls: 0.5317, decode.d7.loss_mask: 0.6991, decode.d7.loss_dice: 1.0271, decode.d8.loss_cls: 0.5175, decode.d8.loss_mask: 0.7021, decode.d8.loss_dice: 1.0274, loss: 26.3629 2022-05-05 03:30:39,645 - mmseg - INFO - Iter [27900/40000] lr: 4.344e-07, eta: 2:49:58, time: 0.781, data_time: 0.011, memory: 51557, decode.loss_cls: 0.5256, decode.loss_mask: 0.7197, decode.loss_dice: 1.0482, decode.d0.loss_cls: 3.4894, decode.d0.loss_mask: 0.7707, decode.d0.loss_dice: 1.2073, decode.d1.loss_cls: 0.7704, decode.d1.loss_mask: 0.7507, decode.d1.loss_dice: 1.1126, decode.d2.loss_cls: 0.6398, decode.d2.loss_mask: 0.7358, decode.d2.loss_dice: 1.0744, decode.d3.loss_cls: 0.5875, decode.d3.loss_mask: 0.7315, decode.d3.loss_dice: 1.0619, decode.d4.loss_cls: 0.5662, decode.d4.loss_mask: 0.7264, decode.d4.loss_dice: 1.0543, decode.d5.loss_cls: 0.5529, decode.d5.loss_mask: 0.7269, decode.d5.loss_dice: 1.0552, decode.d6.loss_cls: 0.5377, decode.d6.loss_mask: 0.7265, decode.d6.loss_dice: 1.0470, decode.d7.loss_cls: 0.5317, decode.d7.loss_mask: 0.7211, decode.d7.loss_dice: 1.0514, decode.d8.loss_cls: 0.5206, decode.d8.loss_mask: 0.7232, decode.d8.loss_dice: 1.0518, loss: 26.8184 2022-05-05 03:31:18,926 - mmseg - INFO - Iter [27950/40000] lr: 4.326e-07, eta: 2:49:14, time: 0.785, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5150, decode.loss_mask: 0.7210, decode.loss_dice: 0.9768, decode.d0.loss_cls: 3.4083, decode.d0.loss_mask: 0.7729, decode.d0.loss_dice: 1.1510, decode.d1.loss_cls: 0.7619, decode.d1.loss_mask: 0.7462, decode.d1.loss_dice: 1.0414, decode.d2.loss_cls: 0.6113, decode.d2.loss_mask: 0.7302, decode.d2.loss_dice: 1.0102, decode.d3.loss_cls: 0.5687, decode.d3.loss_mask: 0.7250, decode.d3.loss_dice: 0.9902, decode.d4.loss_cls: 0.5559, decode.d4.loss_mask: 0.7241, decode.d4.loss_dice: 0.9875, decode.d5.loss_cls: 0.5361, decode.d5.loss_mask: 0.7233, decode.d5.loss_dice: 0.9878, decode.d6.loss_cls: 0.5167, decode.d6.loss_mask: 0.7228, decode.d6.loss_dice: 0.9767, decode.d7.loss_cls: 0.5125, decode.d7.loss_mask: 0.7188, decode.d7.loss_dice: 0.9787, decode.d8.loss_cls: 0.5114, decode.d8.loss_mask: 0.7190, decode.d8.loss_dice: 0.9762, loss: 25.8773 2022-05-05 03:31:58,235 - mmseg - INFO - Saving checkpoint at 28000 iterations 2022-05-05 03:32:23,942 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 03:32:23,945 - mmseg - INFO - Iter [28000/40000] lr: 4.308e-07, eta: 2:48:42, time: 1.299, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5477, decode.loss_mask: 0.6970, decode.loss_dice: 0.9966, decode.d0.loss_cls: 3.4683, decode.d0.loss_mask: 0.7451, decode.d0.loss_dice: 1.1831, decode.d1.loss_cls: 0.7930, decode.d1.loss_mask: 0.7280, decode.d1.loss_dice: 1.0636, decode.d2.loss_cls: 0.6545, decode.d2.loss_mask: 0.7133, decode.d2.loss_dice: 1.0244, decode.d3.loss_cls: 0.6098, decode.d3.loss_mask: 0.7080, decode.d3.loss_dice: 1.0097, decode.d4.loss_cls: 0.5884, decode.d4.loss_mask: 0.7089, decode.d4.loss_dice: 1.0018, decode.d5.loss_cls: 0.5707, decode.d5.loss_mask: 0.7003, decode.d5.loss_dice: 1.0026, decode.d6.loss_cls: 0.5634, decode.d6.loss_mask: 0.6956, decode.d6.loss_dice: 0.9979, decode.d7.loss_cls: 0.5535, decode.d7.loss_mask: 0.6922, decode.d7.loss_dice: 0.9975, decode.d8.loss_cls: 0.5464, decode.d8.loss_mask: 0.6982, decode.d8.loss_dice: 0.9961, loss: 26.2557 2022-05-05 03:32:55,683 - mmseg - INFO - per class results: 2022-05-05 03:32:55,693 - mmseg - INFO - +------------------+-------+-------+ | Class | IoU | Acc | +------------------+-------+-------+ | person | 88.86 | 94.79 | | bicycle | 76.02 | 92.7 | | car | 64.08 | 72.97 | | motorcycle | 90.72 | 95.19 | | airplane | 90.68 | 94.49 | | bus | 86.84 | 92.08 | | train | 84.86 | 97.06 | | truck | 73.25 | 90.04 | | boat | 81.15 | 87.95 | | traffic light | 82.11 | 91.97 | | fire hydrant | 83.81 | 97.0 | | stop sign | 94.49 | 97.82 | | parking meter | 73.84 | 76.15 | | bench | 51.88 | 70.54 | | bird | 77.45 | 85.54 | | cat | 93.31 | 96.47 | | dog | 91.94 | 96.7 | | horse | 91.3 | 95.7 | | sheep | 82.93 | 89.9 | | cow | 93.64 | 96.45 | | elephant | 92.91 | 96.21 | | bear | 92.98 | 95.18 | | zebra | 92.15 | 95.78 | | giraffe | 88.8 | 94.37 | | backpack | 26.93 | 63.86 | | umbrella | 80.85 | 85.72 | | handbag | 20.69 | 29.68 | | tie | 65.55 | 65.96 | | suitcase | 79.09 | 91.75 | | frisbee | 93.61 | 98.2 | | skis | 37.17 | 60.22 | | snowboard | 60.71 | 76.1 | | sports ball | 85.24 | 94.48 | | kite | 65.52 | 79.65 | | baseball bat | 57.3 | 71.17 | | baseball glove | 1.38 | 1.44 | | skateboard | 69.67 | 88.96 | | surfboard | 90.4 | 95.07 | | tennis racket | 30.59 | 31.18 | | bottle | 71.67 | 84.26 | | wine glass | 84.57 | 91.09 | | cup | 69.67 | 84.35 | | fork | 55.69 | 70.3 | | knife | 78.93 | 90.15 | | spoon | 52.86 | 71.21 | | bowl | 52.64 | 59.75 | | banana | 78.32 | 86.21 | | apple | 73.73 | 84.3 | | sandwich | 87.38 | 96.51 | | orange | 66.68 | 88.48 | | broccoli | 92.65 | 97.78 | | carrot | 58.1 | 76.74 | | hot dog | 53.57 | 97.79 | | pizza | 95.25 | 97.19 | | donut | 79.9 | 94.87 | | cake | 79.89 | 88.61 | | chair | 59.43 | 73.35 | | couch | 76.3 | 95.38 | | potted plant | 34.33 | 41.27 | | bed | 70.98 | 82.75 | | dining table | 59.77 | 80.19 | | toilet | 89.92 | 96.19 | | tv | 79.09 | 93.58 | | laptop | 88.0 | 97.28 | | mouse | 86.44 | 89.67 | | remote | 69.2 | 90.47 | | keyboard | 86.67 | 97.59 | | cell phone | 84.65 | 95.88 | | microwave | 70.82 | 74.86 | | oven | 65.11 | 84.2 | | toaster | 77.78 | 80.75 | | sink | 77.15 | 80.97 | | refrigerator | 79.6 | 87.62 | | book | 79.23 | 90.8 | | clock | 76.61 | 80.73 | | vase | 60.42 | 88.25 | | scissors | 82.6 | 93.02 | | teddy bear | 86.93 | 93.82 | | hair drier | 0.0 | 0.0 | | toothbrush | 26.42 | 39.15 | | banner | 30.33 | 64.05 | | blanket | 0.0 | 0.01 | | branch | 31.92 | 34.58 | | bridge | 3.86 | 5.75 | | building-other | 54.9 | 71.51 | | bush | 20.1 | 26.53 | | cabinet | 19.43 | 40.22 | | cage | 18.52 | 84.5 | | cardboard | 23.65 | 29.06 | | carpet | 60.42 | 78.59 | | ceiling-other | 73.12 | 83.59 | | ceiling-tile | 12.66 | 14.23 | | cloth | 2.64 | 2.94 | | clothes | 26.32 | 43.65 | | clouds | 52.59 | 66.46 | | counter | 41.21 | 53.06 | | cupboard | 57.38 | 76.38 | | curtain | 66.87 | 86.52 | | desk-stuff | 28.6 | 39.75 | | dirt | 37.89 | 64.53 | | door-stuff | 41.19 | 55.75 | | fence | 43.47 | 71.42 | | floor-marble | 0.0 | 0.0 | | floor-other | 32.12 | 56.06 | | floor-stone | 24.16 | 29.99 | | floor-tile | 67.24 | 78.82 | | floor-wood | 75.87 | 85.9 | | flower | 23.2 | 54.55 | | fog | 0.0 | 0.0 | | food-other | 39.93 | 57.01 | | fruit | 65.72 | 82.83 | | furniture-other | 15.26 | 23.21 | | grass | 74.28 | 83.27 | | gravel | 30.01 | 38.34 | | ground-other | 7.93 | 16.44 | | hill | 26.78 | 34.49 | | house | 30.52 | 53.5 | | leaves | 17.3 | 18.05 | | light | 44.66 | 60.15 | | mat | 32.5 | 58.5 | | metal | 15.24 | 24.18 | | mirror-stuff | 42.38 | 55.67 | | moss | 0.0 | 0.0 | | mountain | 34.57 | 60.42 | | mud | 0.5 | 1.19 | | napkin | 12.86 | 88.42 | | net | 48.37 | 64.01 | | paper | 48.71 | 71.33 | | pavement | 49.65 | 68.47 | | pillow | 0.0 | 0.0 | | plant-other | 24.79 | 38.53 | | plastic | 32.09 | 43.73 | | platform | 34.72 | 61.67 | | playingfield | 65.08 | 76.95 | | railing | 13.69 | 21.77 | | railroad | 59.53 | 82.32 | | river | 30.31 | 43.17 | | road | 70.97 | 79.01 | | rock | 44.6 | 59.2 | | roof | 3.57 | 5.44 | | rug | 43.17 | 55.88 | | salad | 0.0 | 0.0 | | sand | 70.72 | 87.25 | | sea | 76.34 | 92.79 | | shelf | 30.9 | 49.76 | | sky-other | 62.16 | 78.81 | | skyscraper | 10.76 | 12.64 | | snow | 91.66 | 94.37 | | solid-other | nan | nan | | stairs | 46.58 | 74.39 | | stone | 6.76 | 14.63 | | straw | 15.23 | 34.86 | | structural-other | 13.79 | 21.56 | | table | 17.01 | 20.76 | | tent | 77.01 | 91.35 | | textile-other | 19.72 | 27.32 | | towel | 32.92 | 41.09 | | tree | 76.64 | 88.77 | | vegetable | 44.39 | 63.04 | | wall-brick | 43.73 | 59.74 | | wall-concrete | 22.93 | 26.59 | | wall-other | 63.37 | 82.79 | | wall-panel | 6.19 | 6.62 | | wall-stone | 33.85 | 43.19 | | wall-tile | 57.78 | 77.57 | | wall-wood | 40.18 | 63.43 | | water-other | 25.86 | 33.33 | | waterdrops | 0.0 | nan | | window-blind | 35.83 | 62.9 | | window-other | 50.52 | 63.64 | | wood | 18.7 | 39.76 | +------------------+-------+-------+ 2022-05-05 03:32:55,693 - mmseg - INFO - Summary: 2022-05-05 03:32:55,693 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 75.64 | 52.38 | 65.04 | +-------+-------+-------+ 2022-05-05 03:32:55,698 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 03:32:55,699 - mmseg - INFO - Iter(val) [125] aAcc: 0.7564, mIoU: 0.5238, mAcc: 0.6504, IoU.person: 0.8886, IoU.bicycle: 0.7602, IoU.car: 0.6408, IoU.motorcycle: 0.9072, IoU.airplane: 0.9068, IoU.bus: 0.8684, IoU.train: 0.8486, IoU.truck: 0.7325, IoU.boat: 0.8115, IoU.traffic light: 0.8211, IoU.fire hydrant: 0.8381, IoU.stop sign: 0.9449, IoU.parking meter: 0.7384, IoU.bench: 0.5188, IoU.bird: 0.7745, IoU.cat: 0.9331, IoU.dog: 0.9194, IoU.horse: 0.9130, IoU.sheep: 0.8293, IoU.cow: 0.9364, IoU.elephant: 0.9291, IoU.bear: 0.9298, IoU.zebra: 0.9215, IoU.giraffe: 0.8880, IoU.backpack: 0.2693, IoU.umbrella: 0.8085, IoU.handbag: 0.2069, IoU.tie: 0.6555, IoU.suitcase: 0.7909, IoU.frisbee: 0.9361, IoU.skis: 0.3717, IoU.snowboard: 0.6071, IoU.sports ball: 0.8524, IoU.kite: 0.6552, IoU.baseball bat: 0.5730, IoU.baseball glove: 0.0138, IoU.skateboard: 0.6967, IoU.surfboard: 0.9040, IoU.tennis racket: 0.3059, IoU.bottle: 0.7167, IoU.wine glass: 0.8457, IoU.cup: 0.6967, IoU.fork: 0.5569, IoU.knife: 0.7893, IoU.spoon: 0.5286, IoU.bowl: 0.5264, IoU.banana: 0.7832, IoU.apple: 0.7373, IoU.sandwich: 0.8738, IoU.orange: 0.6668, IoU.broccoli: 0.9265, IoU.carrot: 0.5810, IoU.hot dog: 0.5357, IoU.pizza: 0.9525, IoU.donut: 0.7990, IoU.cake: 0.7989, IoU.chair: 0.5943, IoU.couch: 0.7630, IoU.potted plant: 0.3433, IoU.bed: 0.7098, IoU.dining table: 0.5977, IoU.toilet: 0.8992, IoU.tv: 0.7909, IoU.laptop: 0.8800, IoU.mouse: 0.8644, IoU.remote: 0.6920, IoU.keyboard: 0.8667, IoU.cell phone: 0.8465, IoU.microwave: 0.7082, IoU.oven: 0.6511, IoU.toaster: 0.7778, IoU.sink: 0.7715, IoU.refrigerator: 0.7960, IoU.book: 0.7923, IoU.clock: 0.7661, IoU.vase: 0.6042, IoU.scissors: 0.8260, IoU.teddy bear: 0.8693, IoU.hair drier: 0.0000, IoU.toothbrush: 0.2642, IoU.banner: 0.3033, IoU.blanket: 0.0000, IoU.branch: 0.3192, IoU.bridge: 0.0386, IoU.building-other: 0.5490, IoU.bush: 0.2010, IoU.cabinet: 0.1943, IoU.cage: 0.1852, IoU.cardboard: 0.2365, IoU.carpet: 0.6042, IoU.ceiling-other: 0.7312, IoU.ceiling-tile: 0.1266, IoU.cloth: 0.0264, IoU.clothes: 0.2632, IoU.clouds: 0.5259, IoU.counter: 0.4121, IoU.cupboard: 0.5738, IoU.curtain: 0.6687, IoU.desk-stuff: 0.2860, IoU.dirt: 0.3789, IoU.door-stuff: 0.4119, IoU.fence: 0.4347, IoU.floor-marble: 0.0000, IoU.floor-other: 0.3212, IoU.floor-stone: 0.2416, IoU.floor-tile: 0.6724, IoU.floor-wood: 0.7587, IoU.flower: 0.2320, IoU.fog: 0.0000, IoU.food-other: 0.3993, IoU.fruit: 0.6572, IoU.furniture-other: 0.1526, IoU.grass: 0.7428, IoU.gravel: 0.3001, IoU.ground-other: 0.0793, IoU.hill: 0.2678, IoU.house: 0.3052, IoU.leaves: 0.1730, IoU.light: 0.4466, IoU.mat: 0.3250, IoU.metal: 0.1524, IoU.mirror-stuff: 0.4238, IoU.moss: 0.0000, IoU.mountain: 0.3457, IoU.mud: 0.0050, IoU.napkin: 0.1286, IoU.net: 0.4837, IoU.paper: 0.4871, IoU.pavement: 0.4965, IoU.pillow: 0.0000, IoU.plant-other: 0.2479, IoU.plastic: 0.3209, IoU.platform: 0.3472, IoU.playingfield: 0.6508, IoU.railing: 0.1369, IoU.railroad: 0.5953, IoU.river: 0.3031, IoU.road: 0.7097, IoU.rock: 0.4460, IoU.roof: 0.0357, IoU.rug: 0.4317, IoU.salad: 0.0000, IoU.sand: 0.7072, IoU.sea: 0.7634, IoU.shelf: 0.3090, IoU.sky-other: 0.6216, IoU.skyscraper: 0.1076, IoU.snow: 0.9166, IoU.solid-other: nan, IoU.stairs: 0.4658, IoU.stone: 0.0676, IoU.straw: 0.1523, IoU.structural-other: 0.1379, IoU.table: 0.1701, IoU.tent: 0.7701, IoU.textile-other: 0.1972, IoU.towel: 0.3292, IoU.tree: 0.7664, IoU.vegetable: 0.4439, IoU.wall-brick: 0.4373, IoU.wall-concrete: 0.2293, IoU.wall-other: 0.6337, IoU.wall-panel: 0.0619, IoU.wall-stone: 0.3385, IoU.wall-tile: 0.5778, IoU.wall-wood: 0.4018, IoU.water-other: 0.2586, IoU.waterdrops: 0.0000, IoU.window-blind: 0.3583, IoU.window-other: 0.5052, IoU.wood: 0.1870, Acc.person: 0.9479, Acc.bicycle: 0.9270, Acc.car: 0.7297, Acc.motorcycle: 0.9519, Acc.airplane: 0.9449, Acc.bus: 0.9208, Acc.train: 0.9706, Acc.truck: 0.9004, Acc.boat: 0.8795, Acc.traffic light: 0.9197, Acc.fire hydrant: 0.9700, Acc.stop sign: 0.9782, Acc.parking meter: 0.7615, Acc.bench: 0.7054, Acc.bird: 0.8554, Acc.cat: 0.9647, Acc.dog: 0.9670, Acc.horse: 0.9570, Acc.sheep: 0.8990, Acc.cow: 0.9645, Acc.elephant: 0.9621, Acc.bear: 0.9518, Acc.zebra: 0.9578, Acc.giraffe: 0.9437, Acc.backpack: 0.6386, Acc.umbrella: 0.8572, Acc.handbag: 0.2968, Acc.tie: 0.6596, Acc.suitcase: 0.9175, Acc.frisbee: 0.9820, Acc.skis: 0.6022, Acc.snowboard: 0.7610, Acc.sports ball: 0.9448, Acc.kite: 0.7965, Acc.baseball bat: 0.7117, Acc.baseball glove: 0.0144, Acc.skateboard: 0.8896, Acc.surfboard: 0.9507, Acc.tennis racket: 0.3118, Acc.bottle: 0.8426, Acc.wine glass: 0.9109, Acc.cup: 0.8435, Acc.fork: 0.7030, Acc.knife: 0.9015, Acc.spoon: 0.7121, Acc.bowl: 0.5975, Acc.banana: 0.8621, Acc.apple: 0.8430, Acc.sandwich: 0.9651, Acc.orange: 0.8848, Acc.broccoli: 0.9778, Acc.carrot: 0.7674, Acc.hot dog: 0.9779, Acc.pizza: 0.9719, Acc.donut: 0.9487, Acc.cake: 0.8861, Acc.chair: 0.7335, Acc.couch: 0.9538, Acc.potted plant: 0.4127, Acc.bed: 0.8275, Acc.dining table: 0.8019, Acc.toilet: 0.9619, Acc.tv: 0.9358, Acc.laptop: 0.9728, Acc.mouse: 0.8967, Acc.remote: 0.9047, Acc.keyboard: 0.9759, Acc.cell phone: 0.9588, Acc.microwave: 0.7486, Acc.oven: 0.8420, Acc.toaster: 0.8075, Acc.sink: 0.8097, Acc.refrigerator: 0.8762, Acc.book: 0.9080, Acc.clock: 0.8073, Acc.vase: 0.8825, Acc.scissors: 0.9302, Acc.teddy bear: 0.9382, Acc.hair drier: 0.0000, Acc.toothbrush: 0.3915, Acc.banner: 0.6405, Acc.blanket: 0.0001, Acc.branch: 0.3458, Acc.bridge: 0.0575, Acc.building-other: 0.7151, Acc.bush: 0.2653, Acc.cabinet: 0.4022, Acc.cage: 0.8450, Acc.cardboard: 0.2906, Acc.carpet: 0.7859, Acc.ceiling-other: 0.8359, Acc.ceiling-tile: 0.1423, Acc.cloth: 0.0294, Acc.clothes: 0.4365, Acc.clouds: 0.6646, Acc.counter: 0.5306, Acc.cupboard: 0.7638, Acc.curtain: 0.8652, Acc.desk-stuff: 0.3975, Acc.dirt: 0.6453, Acc.door-stuff: 0.5575, Acc.fence: 0.7142, Acc.floor-marble: 0.0000, Acc.floor-other: 0.5606, Acc.floor-stone: 0.2999, Acc.floor-tile: 0.7882, Acc.floor-wood: 0.8590, Acc.flower: 0.5455, Acc.fog: 0.0000, Acc.food-other: 0.5701, Acc.fruit: 0.8283, Acc.furniture-other: 0.2321, Acc.grass: 0.8327, Acc.gravel: 0.3834, Acc.ground-other: 0.1644, Acc.hill: 0.3449, Acc.house: 0.5350, Acc.leaves: 0.1805, Acc.light: 0.6015, Acc.mat: 0.5850, Acc.metal: 0.2418, Acc.mirror-stuff: 0.5567, Acc.moss: 0.0000, Acc.mountain: 0.6042, Acc.mud: 0.0119, Acc.napkin: 0.8842, Acc.net: 0.6401, Acc.paper: 0.7133, Acc.pavement: 0.6847, Acc.pillow: 0.0000, Acc.plant-other: 0.3853, Acc.plastic: 0.4373, Acc.platform: 0.6167, Acc.playingfield: 0.7695, Acc.railing: 0.2177, Acc.railroad: 0.8232, Acc.river: 0.4317, Acc.road: 0.7901, Acc.rock: 0.5920, Acc.roof: 0.0544, Acc.rug: 0.5588, Acc.salad: 0.0000, Acc.sand: 0.8725, Acc.sea: 0.9279, Acc.shelf: 0.4976, Acc.sky-other: 0.7881, Acc.skyscraper: 0.1264, Acc.snow: 0.9437, Acc.solid-other: nan, Acc.stairs: 0.7439, Acc.stone: 0.1463, Acc.straw: 0.3486, Acc.structural-other: 0.2156, Acc.table: 0.2076, Acc.tent: 0.9135, Acc.textile-other: 0.2732, Acc.towel: 0.4109, Acc.tree: 0.8877, Acc.vegetable: 0.6304, Acc.wall-brick: 0.5974, Acc.wall-concrete: 0.2659, Acc.wall-other: 0.8279, Acc.wall-panel: 0.0662, Acc.wall-stone: 0.4319, Acc.wall-tile: 0.7757, Acc.wall-wood: 0.6343, Acc.water-other: 0.3333, Acc.waterdrops: nan, Acc.window-blind: 0.6290, Acc.window-other: 0.6364, Acc.wood: 0.3976 2022-05-05 03:33:35,426 - mmseg - INFO - Iter [28050/40000] lr: 4.290e-07, eta: 2:48:12, time: 1.432, data_time: 0.646, memory: 51557, decode.loss_cls: 0.5223, decode.loss_mask: 0.7104, decode.loss_dice: 1.0068, decode.d0.loss_cls: 3.5555, decode.d0.loss_mask: 0.7526, decode.d0.loss_dice: 1.1705, decode.d1.loss_cls: 0.7844, decode.d1.loss_mask: 0.7388, decode.d1.loss_dice: 1.0776, decode.d2.loss_cls: 0.6461, decode.d2.loss_mask: 0.7166, decode.d2.loss_dice: 1.0310, decode.d3.loss_cls: 0.5917, decode.d3.loss_mask: 0.7133, decode.d3.loss_dice: 1.0131, decode.d4.loss_cls: 0.5676, decode.d4.loss_mask: 0.7130, decode.d4.loss_dice: 1.0172, decode.d5.loss_cls: 0.5526, decode.d5.loss_mask: 0.7068, decode.d5.loss_dice: 1.0159, decode.d6.loss_cls: 0.5377, decode.d6.loss_mask: 0.7095, decode.d6.loss_dice: 1.0091, decode.d7.loss_cls: 0.5361, decode.d7.loss_mask: 0.7123, decode.d7.loss_dice: 1.0105, decode.d8.loss_cls: 0.5241, decode.d8.loss_mask: 0.7080, decode.d8.loss_dice: 1.0098, loss: 26.3608 2022-05-05 03:34:14,598 - mmseg - INFO - Iter [28100/40000] lr: 4.272e-07, eta: 2:47:29, time: 0.783, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5198, decode.loss_mask: 0.6890, decode.loss_dice: 0.9972, decode.d0.loss_cls: 3.4879, decode.d0.loss_mask: 0.7458, decode.d0.loss_dice: 1.1635, decode.d1.loss_cls: 0.7682, decode.d1.loss_mask: 0.7194, decode.d1.loss_dice: 1.0678, decode.d2.loss_cls: 0.6178, decode.d2.loss_mask: 0.7079, decode.d2.loss_dice: 1.0254, decode.d3.loss_cls: 0.5764, decode.d3.loss_mask: 0.6993, decode.d3.loss_dice: 1.0106, decode.d4.loss_cls: 0.5471, decode.d4.loss_mask: 0.6963, decode.d4.loss_dice: 1.0094, decode.d5.loss_cls: 0.5374, decode.d5.loss_mask: 0.6958, decode.d5.loss_dice: 1.0078, decode.d6.loss_cls: 0.5255, decode.d6.loss_mask: 0.6963, decode.d6.loss_dice: 0.9982, decode.d7.loss_cls: 0.5130, decode.d7.loss_mask: 0.6948, decode.d7.loss_dice: 0.9968, decode.d8.loss_cls: 0.5114, decode.d8.loss_mask: 0.6908, decode.d8.loss_dice: 1.0015, loss: 25.9182 2022-05-05 03:34:56,258 - mmseg - INFO - Iter [28150/40000] lr: 4.254e-07, eta: 2:46:46, time: 0.834, data_time: 0.062, memory: 51557, decode.loss_cls: 0.4755, decode.loss_mask: 0.7093, decode.loss_dice: 1.0133, decode.d0.loss_cls: 3.4881, decode.d0.loss_mask: 0.7522, decode.d0.loss_dice: 1.1629, decode.d1.loss_cls: 0.7208, decode.d1.loss_mask: 0.7299, decode.d1.loss_dice: 1.0756, decode.d2.loss_cls: 0.5916, decode.d2.loss_mask: 0.7182, decode.d2.loss_dice: 1.0397, decode.d3.loss_cls: 0.5290, decode.d3.loss_mask: 0.7153, decode.d3.loss_dice: 1.0252, decode.d4.loss_cls: 0.5172, decode.d4.loss_mask: 0.7131, decode.d4.loss_dice: 1.0215, decode.d5.loss_cls: 0.4900, decode.d5.loss_mask: 0.7103, decode.d5.loss_dice: 1.0138, decode.d6.loss_cls: 0.4884, decode.d6.loss_mask: 0.7113, decode.d6.loss_dice: 1.0051, decode.d7.loss_cls: 0.4730, decode.d7.loss_mask: 0.7085, decode.d7.loss_dice: 1.0157, decode.d8.loss_cls: 0.4696, decode.d8.loss_mask: 0.7092, decode.d8.loss_dice: 1.0146, loss: 25.8080 2022-05-05 03:35:36,013 - mmseg - INFO - Iter [28200/40000] lr: 4.236e-07, eta: 2:46:03, time: 0.795, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4930, decode.loss_mask: 0.7180, decode.loss_dice: 0.9893, decode.d0.loss_cls: 3.4685, decode.d0.loss_mask: 0.7627, decode.d0.loss_dice: 1.1611, decode.d1.loss_cls: 0.7500, decode.d1.loss_mask: 0.7467, decode.d1.loss_dice: 1.0632, decode.d2.loss_cls: 0.6148, decode.d2.loss_mask: 0.7361, decode.d2.loss_dice: 1.0205, decode.d3.loss_cls: 0.5695, decode.d3.loss_mask: 0.7273, decode.d3.loss_dice: 1.0081, decode.d4.loss_cls: 0.5430, decode.d4.loss_mask: 0.7243, decode.d4.loss_dice: 1.0059, decode.d5.loss_cls: 0.5217, decode.d5.loss_mask: 0.7199, decode.d5.loss_dice: 0.9949, decode.d6.loss_cls: 0.5157, decode.d6.loss_mask: 0.7201, decode.d6.loss_dice: 0.9877, decode.d7.loss_cls: 0.5020, decode.d7.loss_mask: 0.7162, decode.d7.loss_dice: 0.9878, decode.d8.loss_cls: 0.4978, decode.d8.loss_mask: 0.7198, decode.d8.loss_dice: 0.9932, loss: 25.9789 2022-05-05 03:36:15,922 - mmseg - INFO - Iter [28250/40000] lr: 4.218e-07, eta: 2:45:20, time: 0.798, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5302, decode.loss_mask: 0.7101, decode.loss_dice: 1.0218, decode.d0.loss_cls: 3.4768, decode.d0.loss_mask: 0.7709, decode.d0.loss_dice: 1.1972, decode.d1.loss_cls: 0.7715, decode.d1.loss_mask: 0.7423, decode.d1.loss_dice: 1.0922, decode.d2.loss_cls: 0.6237, decode.d2.loss_mask: 0.7276, decode.d2.loss_dice: 1.0482, decode.d3.loss_cls: 0.5791, decode.d3.loss_mask: 0.7152, decode.d3.loss_dice: 1.0321, decode.d4.loss_cls: 0.5612, decode.d4.loss_mask: 0.7135, decode.d4.loss_dice: 1.0308, decode.d5.loss_cls: 0.5434, decode.d5.loss_mask: 0.7121, decode.d5.loss_dice: 1.0292, decode.d6.loss_cls: 0.5295, decode.d6.loss_mask: 0.7113, decode.d6.loss_dice: 1.0249, decode.d7.loss_cls: 0.5256, decode.d7.loss_mask: 0.7113, decode.d7.loss_dice: 1.0225, decode.d8.loss_cls: 0.5244, decode.d8.loss_mask: 0.7087, decode.d8.loss_dice: 1.0185, loss: 26.4053 2022-05-05 03:36:55,009 - mmseg - INFO - Iter [28300/40000] lr: 4.200e-07, eta: 2:44:36, time: 0.782, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5500, decode.loss_mask: 0.7189, decode.loss_dice: 1.0224, decode.d0.loss_cls: 3.5101, decode.d0.loss_mask: 0.7545, decode.d0.loss_dice: 1.1919, decode.d1.loss_cls: 0.7792, decode.d1.loss_mask: 0.7301, decode.d1.loss_dice: 1.0950, decode.d2.loss_cls: 0.6565, decode.d2.loss_mask: 0.7137, decode.d2.loss_dice: 1.0493, decode.d3.loss_cls: 0.6051, decode.d3.loss_mask: 0.7150, decode.d3.loss_dice: 1.0392, decode.d4.loss_cls: 0.5860, decode.d4.loss_mask: 0.7167, decode.d4.loss_dice: 1.0386, decode.d5.loss_cls: 0.5740, decode.d5.loss_mask: 0.7176, decode.d5.loss_dice: 1.0321, decode.d6.loss_cls: 0.5635, decode.d6.loss_mask: 0.7159, decode.d6.loss_dice: 1.0220, decode.d7.loss_cls: 0.5547, decode.d7.loss_mask: 0.7146, decode.d7.loss_dice: 1.0237, decode.d8.loss_cls: 0.5534, decode.d8.loss_mask: 0.7166, decode.d8.loss_dice: 1.0190, loss: 26.6794 2022-05-05 03:37:33,931 - mmseg - INFO - Iter [28350/40000] lr: 4.182e-07, eta: 2:43:53, time: 0.779, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5178, decode.loss_mask: 0.7139, decode.loss_dice: 0.9897, decode.d0.loss_cls: 3.5102, decode.d0.loss_mask: 0.7722, decode.d0.loss_dice: 1.1648, decode.d1.loss_cls: 0.7632, decode.d1.loss_mask: 0.7446, decode.d1.loss_dice: 1.0601, decode.d2.loss_cls: 0.6340, decode.d2.loss_mask: 0.7261, decode.d2.loss_dice: 1.0125, decode.d3.loss_cls: 0.5776, decode.d3.loss_mask: 0.7195, decode.d3.loss_dice: 0.9996, decode.d4.loss_cls: 0.5492, decode.d4.loss_mask: 0.7181, decode.d4.loss_dice: 0.9987, decode.d5.loss_cls: 0.5388, decode.d5.loss_mask: 0.7165, decode.d5.loss_dice: 0.9905, decode.d6.loss_cls: 0.5202, decode.d6.loss_mask: 0.7101, decode.d6.loss_dice: 0.9897, decode.d7.loss_cls: 0.5182, decode.d7.loss_mask: 0.7137, decode.d7.loss_dice: 0.9878, decode.d8.loss_cls: 0.5118, decode.d8.loss_mask: 0.7143, decode.d8.loss_dice: 0.9854, loss: 26.0688 2022-05-05 03:38:12,726 - mmseg - INFO - Iter [28400/40000] lr: 4.164e-07, eta: 2:43:09, time: 0.776, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4982, decode.loss_mask: 0.7069, decode.loss_dice: 1.0036, decode.d0.loss_cls: 3.4695, decode.d0.loss_mask: 0.7518, decode.d0.loss_dice: 1.1680, decode.d1.loss_cls: 0.7399, decode.d1.loss_mask: 0.7320, decode.d1.loss_dice: 1.0671, decode.d2.loss_cls: 0.6035, decode.d2.loss_mask: 0.7168, decode.d2.loss_dice: 1.0245, decode.d3.loss_cls: 0.5566, decode.d3.loss_mask: 0.7108, decode.d3.loss_dice: 1.0115, decode.d4.loss_cls: 0.5358, decode.d4.loss_mask: 0.7093, decode.d4.loss_dice: 1.0110, decode.d5.loss_cls: 0.5206, decode.d5.loss_mask: 0.7078, decode.d5.loss_dice: 1.0072, decode.d6.loss_cls: 0.5108, decode.d6.loss_mask: 0.7072, decode.d6.loss_dice: 1.0030, decode.d7.loss_cls: 0.5043, decode.d7.loss_mask: 0.7050, decode.d7.loss_dice: 0.9961, decode.d8.loss_cls: 0.4972, decode.d8.loss_mask: 0.7028, decode.d8.loss_dice: 0.9995, loss: 25.8780 2022-05-05 03:38:52,054 - mmseg - INFO - Iter [28450/40000] lr: 4.146e-07, eta: 2:42:26, time: 0.787, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5243, decode.loss_mask: 0.7217, decode.loss_dice: 1.0207, decode.d0.loss_cls: 3.4869, decode.d0.loss_mask: 0.7625, decode.d0.loss_dice: 1.1835, decode.d1.loss_cls: 0.7504, decode.d1.loss_mask: 0.7545, decode.d1.loss_dice: 1.0880, decode.d2.loss_cls: 0.6253, decode.d2.loss_mask: 0.7370, decode.d2.loss_dice: 1.0472, decode.d3.loss_cls: 0.5683, decode.d3.loss_mask: 0.7283, decode.d3.loss_dice: 1.0271, decode.d4.loss_cls: 0.5546, decode.d4.loss_mask: 0.7257, decode.d4.loss_dice: 1.0244, decode.d5.loss_cls: 0.5340, decode.d5.loss_mask: 0.7280, decode.d5.loss_dice: 1.0217, decode.d6.loss_cls: 0.5286, decode.d6.loss_mask: 0.7242, decode.d6.loss_dice: 1.0168, decode.d7.loss_cls: 0.5187, decode.d7.loss_mask: 0.7245, decode.d7.loss_dice: 1.0166, decode.d8.loss_cls: 0.5141, decode.d8.loss_mask: 0.7235, decode.d8.loss_dice: 1.0226, loss: 26.4036 2022-05-05 03:39:31,223 - mmseg - INFO - Iter [28500/40000] lr: 4.128e-07, eta: 2:41:42, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5369, decode.loss_mask: 0.7218, decode.loss_dice: 1.0135, decode.d0.loss_cls: 3.4949, decode.d0.loss_mask: 0.7690, decode.d0.loss_dice: 1.1735, decode.d1.loss_cls: 0.8022, decode.d1.loss_mask: 0.7522, decode.d1.loss_dice: 1.0866, decode.d2.loss_cls: 0.6637, decode.d2.loss_mask: 0.7367, decode.d2.loss_dice: 1.0533, decode.d3.loss_cls: 0.6134, decode.d3.loss_mask: 0.7300, decode.d3.loss_dice: 1.0365, decode.d4.loss_cls: 0.5824, decode.d4.loss_mask: 0.7284, decode.d4.loss_dice: 1.0348, decode.d5.loss_cls: 0.5671, decode.d5.loss_mask: 0.7299, decode.d5.loss_dice: 1.0262, decode.d6.loss_cls: 0.5549, decode.d6.loss_mask: 0.7254, decode.d6.loss_dice: 1.0161, decode.d7.loss_cls: 0.5547, decode.d7.loss_mask: 0.7222, decode.d7.loss_dice: 1.0137, decode.d8.loss_cls: 0.5363, decode.d8.loss_mask: 0.7231, decode.d8.loss_dice: 1.0188, loss: 26.7184 2022-05-05 03:40:10,168 - mmseg - INFO - Iter [28550/40000] lr: 4.110e-07, eta: 2:40:59, time: 0.779, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5242, decode.loss_mask: 0.7150, decode.loss_dice: 1.0044, decode.d0.loss_cls: 3.5069, decode.d0.loss_mask: 0.7751, decode.d0.loss_dice: 1.1756, decode.d1.loss_cls: 0.7567, decode.d1.loss_mask: 0.7465, decode.d1.loss_dice: 1.0712, decode.d2.loss_cls: 0.6329, decode.d2.loss_mask: 0.7333, decode.d2.loss_dice: 1.0280, decode.d3.loss_cls: 0.5852, decode.d3.loss_mask: 0.7260, decode.d3.loss_dice: 1.0063, decode.d4.loss_cls: 0.5714, decode.d4.loss_mask: 0.7255, decode.d4.loss_dice: 1.0133, decode.d5.loss_cls: 0.5491, decode.d5.loss_mask: 0.7224, decode.d5.loss_dice: 1.0068, decode.d6.loss_cls: 0.5352, decode.d6.loss_mask: 0.7169, decode.d6.loss_dice: 0.9991, decode.d7.loss_cls: 0.5260, decode.d7.loss_mask: 0.7166, decode.d7.loss_dice: 1.0030, decode.d8.loss_cls: 0.5212, decode.d8.loss_mask: 0.7158, decode.d8.loss_dice: 1.0071, loss: 26.3168 2022-05-05 03:40:49,675 - mmseg - INFO - Iter [28600/40000] lr: 4.092e-07, eta: 2:40:16, time: 0.790, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5330, decode.loss_mask: 0.6959, decode.loss_dice: 0.9937, decode.d0.loss_cls: 3.4509, decode.d0.loss_mask: 0.7459, decode.d0.loss_dice: 1.1617, decode.d1.loss_cls: 0.7736, decode.d1.loss_mask: 0.7216, decode.d1.loss_dice: 1.0713, decode.d2.loss_cls: 0.6399, decode.d2.loss_mask: 0.7098, decode.d2.loss_dice: 1.0272, decode.d3.loss_cls: 0.5865, decode.d3.loss_mask: 0.7050, decode.d3.loss_dice: 1.0113, decode.d4.loss_cls: 0.5651, decode.d4.loss_mask: 0.7039, decode.d4.loss_dice: 1.0128, decode.d5.loss_cls: 0.5425, decode.d5.loss_mask: 0.7003, decode.d5.loss_dice: 1.0137, decode.d6.loss_cls: 0.5365, decode.d6.loss_mask: 0.6988, decode.d6.loss_dice: 0.9989, decode.d7.loss_cls: 0.5262, decode.d7.loss_mask: 0.6991, decode.d7.loss_dice: 0.9975, decode.d8.loss_cls: 0.5347, decode.d8.loss_mask: 0.6944, decode.d8.loss_dice: 0.9963, loss: 26.0480 2022-05-05 03:41:29,594 - mmseg - INFO - Iter [28650/40000] lr: 4.074e-07, eta: 2:39:33, time: 0.798, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5104, decode.loss_mask: 0.7025, decode.loss_dice: 0.9995, decode.d0.loss_cls: 3.4990, decode.d0.loss_mask: 0.7477, decode.d0.loss_dice: 1.1683, decode.d1.loss_cls: 0.7312, decode.d1.loss_mask: 0.7314, decode.d1.loss_dice: 1.0723, decode.d2.loss_cls: 0.6166, decode.d2.loss_mask: 0.7113, decode.d2.loss_dice: 1.0223, decode.d3.loss_cls: 0.5749, decode.d3.loss_mask: 0.7069, decode.d3.loss_dice: 1.0117, decode.d4.loss_cls: 0.5469, decode.d4.loss_mask: 0.7061, decode.d4.loss_dice: 1.0124, decode.d5.loss_cls: 0.5327, decode.d5.loss_mask: 0.7042, decode.d5.loss_dice: 1.0136, decode.d6.loss_cls: 0.5281, decode.d6.loss_mask: 0.7015, decode.d6.loss_dice: 0.9969, decode.d7.loss_cls: 0.5176, decode.d7.loss_mask: 0.7008, decode.d7.loss_dice: 0.9991, decode.d8.loss_cls: 0.5130, decode.d8.loss_mask: 0.7009, decode.d8.loss_dice: 1.0028, loss: 25.9823 2022-05-05 03:42:11,446 - mmseg - INFO - Iter [28700/40000] lr: 4.056e-07, eta: 2:38:50, time: 0.837, data_time: 0.058, memory: 51557, decode.loss_cls: 0.5267, decode.loss_mask: 0.6961, decode.loss_dice: 1.0148, decode.d0.loss_cls: 3.5017, decode.d0.loss_mask: 0.7421, decode.d0.loss_dice: 1.1820, decode.d1.loss_cls: 0.7825, decode.d1.loss_mask: 0.7260, decode.d1.loss_dice: 1.0808, decode.d2.loss_cls: 0.6466, decode.d2.loss_mask: 0.7090, decode.d2.loss_dice: 1.0340, decode.d3.loss_cls: 0.5935, decode.d3.loss_mask: 0.6982, decode.d3.loss_dice: 1.0179, decode.d4.loss_cls: 0.5742, decode.d4.loss_mask: 0.6971, decode.d4.loss_dice: 1.0144, decode.d5.loss_cls: 0.5485, decode.d5.loss_mask: 0.6969, decode.d5.loss_dice: 1.0136, decode.d6.loss_cls: 0.5356, decode.d6.loss_mask: 0.6984, decode.d6.loss_dice: 1.0081, decode.d7.loss_cls: 0.5277, decode.d7.loss_mask: 0.6947, decode.d7.loss_dice: 1.0078, decode.d8.loss_cls: 0.5217, decode.d8.loss_mask: 0.6967, decode.d8.loss_dice: 1.0090, loss: 26.1961 2022-05-05 03:42:50,749 - mmseg - INFO - Iter [28750/40000] lr: 4.039e-07, eta: 2:38:07, time: 0.786, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4711, decode.loss_mask: 0.7159, decode.loss_dice: 0.9970, decode.d0.loss_cls: 3.4499, decode.d0.loss_mask: 0.7706, decode.d0.loss_dice: 1.1609, decode.d1.loss_cls: 0.7343, decode.d1.loss_mask: 0.7473, decode.d1.loss_dice: 1.0704, decode.d2.loss_cls: 0.5974, decode.d2.loss_mask: 0.7315, decode.d2.loss_dice: 1.0268, decode.d3.loss_cls: 0.5331, decode.d3.loss_mask: 0.7242, decode.d3.loss_dice: 1.0056, decode.d4.loss_cls: 0.5110, decode.d4.loss_mask: 0.7190, decode.d4.loss_dice: 1.0107, decode.d5.loss_cls: 0.4875, decode.d5.loss_mask: 0.7218, decode.d5.loss_dice: 1.0069, decode.d6.loss_cls: 0.4799, decode.d6.loss_mask: 0.7186, decode.d6.loss_dice: 0.9953, decode.d7.loss_cls: 0.4727, decode.d7.loss_mask: 0.7165, decode.d7.loss_dice: 0.9959, decode.d8.loss_cls: 0.4712, decode.d8.loss_mask: 0.7165, decode.d8.loss_dice: 0.9907, loss: 25.7501 2022-05-05 03:43:30,373 - mmseg - INFO - Iter [28800/40000] lr: 4.021e-07, eta: 2:37:24, time: 0.792, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5153, decode.loss_mask: 0.7267, decode.loss_dice: 1.0165, decode.d0.loss_cls: 3.5012, decode.d0.loss_mask: 0.7767, decode.d0.loss_dice: 1.1676, decode.d1.loss_cls: 0.7609, decode.d1.loss_mask: 0.7594, decode.d1.loss_dice: 1.0863, decode.d2.loss_cls: 0.6241, decode.d2.loss_mask: 0.7409, decode.d2.loss_dice: 1.0471, decode.d3.loss_cls: 0.5727, decode.d3.loss_mask: 0.7334, decode.d3.loss_dice: 1.0249, decode.d4.loss_cls: 0.5559, decode.d4.loss_mask: 0.7285, decode.d4.loss_dice: 1.0227, decode.d5.loss_cls: 0.5370, decode.d5.loss_mask: 0.7289, decode.d5.loss_dice: 1.0219, decode.d6.loss_cls: 0.5257, decode.d6.loss_mask: 0.7273, decode.d6.loss_dice: 1.0153, decode.d7.loss_cls: 0.5192, decode.d7.loss_mask: 0.7233, decode.d7.loss_dice: 1.0192, decode.d8.loss_cls: 0.5179, decode.d8.loss_mask: 0.7258, decode.d8.loss_dice: 1.0129, loss: 26.4354 2022-05-05 03:44:10,151 - mmseg - INFO - Iter [28850/40000] lr: 4.003e-07, eta: 2:36:41, time: 0.795, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4927, decode.loss_mask: 0.7070, decode.loss_dice: 1.0171, decode.d0.loss_cls: 3.5409, decode.d0.loss_mask: 0.7489, decode.d0.loss_dice: 1.1918, decode.d1.loss_cls: 0.7620, decode.d1.loss_mask: 0.7341, decode.d1.loss_dice: 1.0945, decode.d2.loss_cls: 0.6224, decode.d2.loss_mask: 0.7188, decode.d2.loss_dice: 1.0491, decode.d3.loss_cls: 0.5620, decode.d3.loss_mask: 0.7133, decode.d3.loss_dice: 1.0328, decode.d4.loss_cls: 0.5374, decode.d4.loss_mask: 0.7072, decode.d4.loss_dice: 1.0327, decode.d5.loss_cls: 0.5124, decode.d5.loss_mask: 0.7056, decode.d5.loss_dice: 1.0372, decode.d6.loss_cls: 0.5024, decode.d6.loss_mask: 0.7048, decode.d6.loss_dice: 1.0259, decode.d7.loss_cls: 0.4971, decode.d7.loss_mask: 0.7074, decode.d7.loss_dice: 1.0260, decode.d8.loss_cls: 0.4960, decode.d8.loss_mask: 0.7074, decode.d8.loss_dice: 1.0214, loss: 26.2085 2022-05-05 03:44:48,974 - mmseg - INFO - Iter [28900/40000] lr: 3.985e-07, eta: 2:35:57, time: 0.776, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5185, decode.loss_mask: 0.7048, decode.loss_dice: 1.0213, decode.d0.loss_cls: 3.5063, decode.d0.loss_mask: 0.7531, decode.d0.loss_dice: 1.1795, decode.d1.loss_cls: 0.7757, decode.d1.loss_mask: 0.7337, decode.d1.loss_dice: 1.0825, decode.d2.loss_cls: 0.6395, decode.d2.loss_mask: 0.7118, decode.d2.loss_dice: 1.0513, decode.d3.loss_cls: 0.5830, decode.d3.loss_mask: 0.7090, decode.d3.loss_dice: 1.0305, decode.d4.loss_cls: 0.5628, decode.d4.loss_mask: 0.7053, decode.d4.loss_dice: 1.0313, decode.d5.loss_cls: 0.5456, decode.d5.loss_mask: 0.7046, decode.d5.loss_dice: 1.0251, decode.d6.loss_cls: 0.5314, decode.d6.loss_mask: 0.7054, decode.d6.loss_dice: 1.0165, decode.d7.loss_cls: 0.5271, decode.d7.loss_mask: 0.7047, decode.d7.loss_dice: 1.0163, decode.d8.loss_cls: 0.5201, decode.d8.loss_mask: 0.7055, decode.d8.loss_dice: 1.0186, loss: 26.3207 2022-05-05 03:45:28,661 - mmseg - INFO - Iter [28950/40000] lr: 3.967e-07, eta: 2:35:14, time: 0.794, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4892, decode.loss_mask: 0.6938, decode.loss_dice: 0.9718, decode.d0.loss_cls: 3.3821, decode.d0.loss_mask: 0.7472, decode.d0.loss_dice: 1.1260, decode.d1.loss_cls: 0.7120, decode.d1.loss_mask: 0.7231, decode.d1.loss_dice: 1.0331, decode.d2.loss_cls: 0.5932, decode.d2.loss_mask: 0.7075, decode.d2.loss_dice: 0.9970, decode.d3.loss_cls: 0.5499, decode.d3.loss_mask: 0.6998, decode.d3.loss_dice: 0.9839, decode.d4.loss_cls: 0.5185, decode.d4.loss_mask: 0.6969, decode.d4.loss_dice: 0.9795, decode.d5.loss_cls: 0.5094, decode.d5.loss_mask: 0.6919, decode.d5.loss_dice: 0.9741, decode.d6.loss_cls: 0.5029, decode.d6.loss_mask: 0.6945, decode.d6.loss_dice: 0.9704, decode.d7.loss_cls: 0.4937, decode.d7.loss_mask: 0.6950, decode.d7.loss_dice: 0.9674, decode.d8.loss_cls: 0.4922, decode.d8.loss_mask: 0.6950, decode.d8.loss_dice: 0.9685, loss: 25.2594 2022-05-05 03:46:07,939 - mmseg - INFO - Saving checkpoint at 29000 iterations 2022-05-05 03:46:32,698 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 03:46:32,706 - mmseg - INFO - Iter [29000/40000] lr: 3.949e-07, eta: 2:34:40, time: 1.278, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4912, decode.loss_mask: 0.7065, decode.loss_dice: 0.9755, decode.d0.loss_cls: 3.5122, decode.d0.loss_mask: 0.7558, decode.d0.loss_dice: 1.1678, decode.d1.loss_cls: 0.7409, decode.d1.loss_mask: 0.7364, decode.d1.loss_dice: 1.0602, decode.d2.loss_cls: 0.6041, decode.d2.loss_mask: 0.7187, decode.d2.loss_dice: 1.0214, decode.d3.loss_cls: 0.5529, decode.d3.loss_mask: 0.7109, decode.d3.loss_dice: 0.9964, decode.d4.loss_cls: 0.5228, decode.d4.loss_mask: 0.7114, decode.d4.loss_dice: 0.9972, decode.d5.loss_cls: 0.5073, decode.d5.loss_mask: 0.7078, decode.d5.loss_dice: 0.9839, decode.d6.loss_cls: 0.4975, decode.d6.loss_mask: 0.7073, decode.d6.loss_dice: 0.9815, decode.d7.loss_cls: 0.4902, decode.d7.loss_mask: 0.7070, decode.d7.loss_dice: 0.9875, decode.d8.loss_cls: 0.4887, decode.d8.loss_mask: 0.7068, decode.d8.loss_dice: 0.9901, loss: 25.7377 2022-05-05 03:47:12,188 - mmseg - INFO - Iter [29050/40000] lr: 3.931e-07, eta: 2:33:57, time: 0.793, data_time: 0.012, memory: 51557, decode.loss_cls: 0.5191, decode.loss_mask: 0.7363, decode.loss_dice: 1.0249, decode.d0.loss_cls: 3.4975, decode.d0.loss_mask: 0.7795, decode.d0.loss_dice: 1.1990, decode.d1.loss_cls: 0.7686, decode.d1.loss_mask: 0.7678, decode.d1.loss_dice: 1.1073, decode.d2.loss_cls: 0.6389, decode.d2.loss_mask: 0.7475, decode.d2.loss_dice: 1.0575, decode.d3.loss_cls: 0.5854, decode.d3.loss_mask: 0.7372, decode.d3.loss_dice: 1.0339, decode.d4.loss_cls: 0.5575, decode.d4.loss_mask: 0.7345, decode.d4.loss_dice: 1.0383, decode.d5.loss_cls: 0.5437, decode.d5.loss_mask: 0.7381, decode.d5.loss_dice: 1.0308, decode.d6.loss_cls: 0.5309, decode.d6.loss_mask: 0.7344, decode.d6.loss_dice: 1.0296, decode.d7.loss_cls: 0.5197, decode.d7.loss_mask: 0.7354, decode.d7.loss_dice: 1.0318, decode.d8.loss_cls: 0.5180, decode.d8.loss_mask: 0.7334, decode.d8.loss_dice: 1.0295, loss: 26.7062 2022-05-05 03:47:51,107 - mmseg - INFO - Iter [29100/40000] lr: 3.913e-07, eta: 2:33:14, time: 0.778, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4925, decode.loss_mask: 0.6944, decode.loss_dice: 1.0067, decode.d0.loss_cls: 3.4761, decode.d0.loss_mask: 0.7486, decode.d0.loss_dice: 1.1723, decode.d1.loss_cls: 0.7356, decode.d1.loss_mask: 0.7255, decode.d1.loss_dice: 1.0837, decode.d2.loss_cls: 0.6171, decode.d2.loss_mask: 0.7040, decode.d2.loss_dice: 1.0312, decode.d3.loss_cls: 0.5557, decode.d3.loss_mask: 0.6988, decode.d3.loss_dice: 1.0154, decode.d4.loss_cls: 0.5283, decode.d4.loss_mask: 0.6974, decode.d4.loss_dice: 1.0115, decode.d5.loss_cls: 0.5134, decode.d5.loss_mask: 0.6976, decode.d5.loss_dice: 1.0095, decode.d6.loss_cls: 0.5024, decode.d6.loss_mask: 0.6949, decode.d6.loss_dice: 1.0069, decode.d7.loss_cls: 0.4939, decode.d7.loss_mask: 0.6958, decode.d7.loss_dice: 1.0032, decode.d8.loss_cls: 0.4953, decode.d8.loss_mask: 0.6966, decode.d8.loss_dice: 1.0108, loss: 25.8150 2022-05-05 03:48:30,282 - mmseg - INFO - Iter [29150/40000] lr: 3.895e-07, eta: 2:32:30, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5154, decode.loss_mask: 0.6977, decode.loss_dice: 0.9982, decode.d0.loss_cls: 3.4705, decode.d0.loss_mask: 0.7519, decode.d0.loss_dice: 1.1739, decode.d1.loss_cls: 0.7630, decode.d1.loss_mask: 0.7328, decode.d1.loss_dice: 1.0780, decode.d2.loss_cls: 0.6377, decode.d2.loss_mask: 0.7118, decode.d2.loss_dice: 1.0244, decode.d3.loss_cls: 0.5744, decode.d3.loss_mask: 0.7024, decode.d3.loss_dice: 1.0077, decode.d4.loss_cls: 0.5510, decode.d4.loss_mask: 0.6989, decode.d4.loss_dice: 1.0080, decode.d5.loss_cls: 0.5370, decode.d5.loss_mask: 0.7009, decode.d5.loss_dice: 1.0019, decode.d6.loss_cls: 0.5201, decode.d6.loss_mask: 0.6981, decode.d6.loss_dice: 0.9998, decode.d7.loss_cls: 0.5198, decode.d7.loss_mask: 0.6955, decode.d7.loss_dice: 1.0011, decode.d8.loss_cls: 0.5193, decode.d8.loss_mask: 0.6942, decode.d8.loss_dice: 1.0001, loss: 25.9852 2022-05-05 03:49:09,442 - mmseg - INFO - Iter [29200/40000] lr: 3.877e-07, eta: 2:31:47, time: 0.782, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5050, decode.loss_mask: 0.6982, decode.loss_dice: 1.0035, decode.d0.loss_cls: 3.4342, decode.d0.loss_mask: 0.7541, decode.d0.loss_dice: 1.1812, decode.d1.loss_cls: 0.7400, decode.d1.loss_mask: 0.7264, decode.d1.loss_dice: 1.0855, decode.d2.loss_cls: 0.6205, decode.d2.loss_mask: 0.7118, decode.d2.loss_dice: 1.0381, decode.d3.loss_cls: 0.5615, decode.d3.loss_mask: 0.7053, decode.d3.loss_dice: 1.0170, decode.d4.loss_cls: 0.5409, decode.d4.loss_mask: 0.7010, decode.d4.loss_dice: 1.0183, decode.d5.loss_cls: 0.5149, decode.d5.loss_mask: 0.7026, decode.d5.loss_dice: 1.0109, decode.d6.loss_cls: 0.5109, decode.d6.loss_mask: 0.7010, decode.d6.loss_dice: 1.0015, decode.d7.loss_cls: 0.4981, decode.d7.loss_mask: 0.7001, decode.d7.loss_dice: 1.0070, decode.d8.loss_cls: 0.4995, decode.d8.loss_mask: 0.7015, decode.d8.loss_dice: 1.0040, loss: 25.8946 2022-05-05 03:49:51,399 - mmseg - INFO - Iter [29250/40000] lr: 3.859e-07, eta: 2:31:05, time: 0.840, data_time: 0.061, memory: 51557, decode.loss_cls: 0.5154, decode.loss_mask: 0.7095, decode.loss_dice: 1.0046, decode.d0.loss_cls: 3.5063, decode.d0.loss_mask: 0.7604, decode.d0.loss_dice: 1.1825, decode.d1.loss_cls: 0.7879, decode.d1.loss_mask: 0.7351, decode.d1.loss_dice: 1.0805, decode.d2.loss_cls: 0.6336, decode.d2.loss_mask: 0.7182, decode.d2.loss_dice: 1.0421, decode.d3.loss_cls: 0.5847, decode.d3.loss_mask: 0.7132, decode.d3.loss_dice: 1.0198, decode.d4.loss_cls: 0.5615, decode.d4.loss_mask: 0.7113, decode.d4.loss_dice: 1.0204, decode.d5.loss_cls: 0.5396, decode.d5.loss_mask: 0.7095, decode.d5.loss_dice: 1.0149, decode.d6.loss_cls: 0.5267, decode.d6.loss_mask: 0.7085, decode.d6.loss_dice: 1.0129, decode.d7.loss_cls: 0.5147, decode.d7.loss_mask: 0.7088, decode.d7.loss_dice: 1.0118, decode.d8.loss_cls: 0.5087, decode.d8.loss_mask: 0.7092, decode.d8.loss_dice: 1.0113, loss: 26.2634 2022-05-05 03:50:31,040 - mmseg - INFO - Iter [29300/40000] lr: 3.841e-07, eta: 2:30:22, time: 0.793, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5167, decode.loss_mask: 0.7006, decode.loss_dice: 0.9995, decode.d0.loss_cls: 3.5064, decode.d0.loss_mask: 0.7442, decode.d0.loss_dice: 1.1687, decode.d1.loss_cls: 0.7524, decode.d1.loss_mask: 0.7252, decode.d1.loss_dice: 1.0797, decode.d2.loss_cls: 0.6405, decode.d2.loss_mask: 0.7128, decode.d2.loss_dice: 1.0266, decode.d3.loss_cls: 0.5915, decode.d3.loss_mask: 0.7032, decode.d3.loss_dice: 1.0055, decode.d4.loss_cls: 0.5623, decode.d4.loss_mask: 0.7027, decode.d4.loss_dice: 1.0035, decode.d5.loss_cls: 0.5471, decode.d5.loss_mask: 0.6998, decode.d5.loss_dice: 0.9960, decode.d6.loss_cls: 0.5295, decode.d6.loss_mask: 0.6955, decode.d6.loss_dice: 0.9953, decode.d7.loss_cls: 0.5173, decode.d7.loss_mask: 0.6976, decode.d7.loss_dice: 0.9958, decode.d8.loss_cls: 0.5200, decode.d8.loss_mask: 0.6942, decode.d8.loss_dice: 0.9938, loss: 26.0240 2022-05-05 03:51:10,519 - mmseg - INFO - Iter [29350/40000] lr: 3.823e-07, eta: 2:29:39, time: 0.790, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4920, decode.loss_mask: 0.6974, decode.loss_dice: 0.9928, decode.d0.loss_cls: 3.4652, decode.d0.loss_mask: 0.7496, decode.d0.loss_dice: 1.1594, decode.d1.loss_cls: 0.7424, decode.d1.loss_mask: 0.7251, decode.d1.loss_dice: 1.0696, decode.d2.loss_cls: 0.6009, decode.d2.loss_mask: 0.7044, decode.d2.loss_dice: 1.0225, decode.d3.loss_cls: 0.5547, decode.d3.loss_mask: 0.6991, decode.d3.loss_dice: 1.0097, decode.d4.loss_cls: 0.5263, decode.d4.loss_mask: 0.6973, decode.d4.loss_dice: 1.0070, decode.d5.loss_cls: 0.5151, decode.d5.loss_mask: 0.6977, decode.d5.loss_dice: 1.0045, decode.d6.loss_cls: 0.5069, decode.d6.loss_mask: 0.6981, decode.d6.loss_dice: 0.9987, decode.d7.loss_cls: 0.4962, decode.d7.loss_mask: 0.6969, decode.d7.loss_dice: 0.9996, decode.d8.loss_cls: 0.4978, decode.d8.loss_mask: 0.6984, decode.d8.loss_dice: 0.9934, loss: 25.7187 2022-05-05 03:51:49,936 - mmseg - INFO - Iter [29400/40000] lr: 3.805e-07, eta: 2:28:56, time: 0.787, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4956, decode.loss_mask: 0.6857, decode.loss_dice: 0.9976, decode.d0.loss_cls: 3.4755, decode.d0.loss_mask: 0.7391, decode.d0.loss_dice: 1.1663, decode.d1.loss_cls: 0.7420, decode.d1.loss_mask: 0.7153, decode.d1.loss_dice: 1.0756, decode.d2.loss_cls: 0.6037, decode.d2.loss_mask: 0.7000, decode.d2.loss_dice: 1.0266, decode.d3.loss_cls: 0.5584, decode.d3.loss_mask: 0.6953, decode.d3.loss_dice: 1.0036, decode.d4.loss_cls: 0.5353, decode.d4.loss_mask: 0.6914, decode.d4.loss_dice: 1.0036, decode.d5.loss_cls: 0.5139, decode.d5.loss_mask: 0.6927, decode.d5.loss_dice: 1.0036, decode.d6.loss_cls: 0.5020, decode.d6.loss_mask: 0.6906, decode.d6.loss_dice: 1.0003, decode.d7.loss_cls: 0.5046, decode.d7.loss_mask: 0.6852, decode.d7.loss_dice: 0.9993, decode.d8.loss_cls: 0.5024, decode.d8.loss_mask: 0.6864, decode.d8.loss_dice: 0.9978, loss: 25.6894 2022-05-05 03:52:29,301 - mmseg - INFO - Iter [29450/40000] lr: 3.787e-07, eta: 2:28:12, time: 0.788, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5066, decode.loss_mask: 0.7069, decode.loss_dice: 0.9959, decode.d0.loss_cls: 3.4722, decode.d0.loss_mask: 0.7452, decode.d0.loss_dice: 1.1687, decode.d1.loss_cls: 0.7728, decode.d1.loss_mask: 0.7330, decode.d1.loss_dice: 1.0615, decode.d2.loss_cls: 0.6294, decode.d2.loss_mask: 0.7153, decode.d2.loss_dice: 1.0166, decode.d3.loss_cls: 0.5725, decode.d3.loss_mask: 0.7081, decode.d3.loss_dice: 1.0054, decode.d4.loss_cls: 0.5473, decode.d4.loss_mask: 0.7053, decode.d4.loss_dice: 1.0051, decode.d5.loss_cls: 0.5304, decode.d5.loss_mask: 0.7089, decode.d5.loss_dice: 1.0008, decode.d6.loss_cls: 0.5229, decode.d6.loss_mask: 0.7031, decode.d6.loss_dice: 0.9906, decode.d7.loss_cls: 0.5151, decode.d7.loss_mask: 0.7016, decode.d7.loss_dice: 0.9873, decode.d8.loss_cls: 0.5090, decode.d8.loss_mask: 0.7046, decode.d8.loss_dice: 0.9933, loss: 25.9355 2022-05-05 03:53:08,234 - mmseg - INFO - Iter [29500/40000] lr: 3.769e-07, eta: 2:27:29, time: 0.779, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4832, decode.loss_mask: 0.7160, decode.loss_dice: 0.9893, decode.d0.loss_cls: 3.4439, decode.d0.loss_mask: 0.7602, decode.d0.loss_dice: 1.1512, decode.d1.loss_cls: 0.7399, decode.d1.loss_mask: 0.7426, decode.d1.loss_dice: 1.0582, decode.d2.loss_cls: 0.6170, decode.d2.loss_mask: 0.7240, decode.d2.loss_dice: 1.0139, decode.d3.loss_cls: 0.5628, decode.d3.loss_mask: 0.7190, decode.d3.loss_dice: 0.9976, decode.d4.loss_cls: 0.5350, decode.d4.loss_mask: 0.7187, decode.d4.loss_dice: 1.0048, decode.d5.loss_cls: 0.5240, decode.d5.loss_mask: 0.7162, decode.d5.loss_dice: 0.9955, decode.d6.loss_cls: 0.5054, decode.d6.loss_mask: 0.7146, decode.d6.loss_dice: 0.9852, decode.d7.loss_cls: 0.4936, decode.d7.loss_mask: 0.7152, decode.d7.loss_dice: 0.9875, decode.d8.loss_cls: 0.4863, decode.d8.loss_mask: 0.7177, decode.d8.loss_dice: 0.9972, loss: 25.8155 2022-05-05 03:53:47,065 - mmseg - INFO - Iter [29550/40000] lr: 3.751e-07, eta: 2:26:46, time: 0.777, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5211, decode.loss_mask: 0.7003, decode.loss_dice: 1.0112, decode.d0.loss_cls: 3.4780, decode.d0.loss_mask: 0.7630, decode.d0.loss_dice: 1.1825, decode.d1.loss_cls: 0.7569, decode.d1.loss_mask: 0.7344, decode.d1.loss_dice: 1.0893, decode.d2.loss_cls: 0.6344, decode.d2.loss_mask: 0.7175, decode.d2.loss_dice: 1.0449, decode.d3.loss_cls: 0.5768, decode.d3.loss_mask: 0.7100, decode.d3.loss_dice: 1.0250, decode.d4.loss_cls: 0.5538, decode.d4.loss_mask: 0.7108, decode.d4.loss_dice: 1.0211, decode.d5.loss_cls: 0.5451, decode.d5.loss_mask: 0.7045, decode.d5.loss_dice: 1.0168, decode.d6.loss_cls: 0.5309, decode.d6.loss_mask: 0.7032, decode.d6.loss_dice: 1.0100, decode.d7.loss_cls: 0.5208, decode.d7.loss_mask: 0.7006, decode.d7.loss_dice: 1.0123, decode.d8.loss_cls: 0.5193, decode.d8.loss_mask: 0.6984, decode.d8.loss_dice: 1.0126, loss: 26.2055 2022-05-05 03:54:26,939 - mmseg - INFO - Iter [29600/40000] lr: 3.733e-07, eta: 2:26:03, time: 0.797, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4432, decode.loss_mask: 0.6989, decode.loss_dice: 1.0022, decode.d0.loss_cls: 3.3743, decode.d0.loss_mask: 0.7395, decode.d0.loss_dice: 1.1554, decode.d1.loss_cls: 0.6784, decode.d1.loss_mask: 0.7226, decode.d1.loss_dice: 1.0672, decode.d2.loss_cls: 0.5582, decode.d2.loss_mask: 0.7072, decode.d2.loss_dice: 1.0245, decode.d3.loss_cls: 0.5029, decode.d3.loss_mask: 0.7017, decode.d3.loss_dice: 1.0114, decode.d4.loss_cls: 0.4734, decode.d4.loss_mask: 0.7007, decode.d4.loss_dice: 1.0104, decode.d5.loss_cls: 0.4586, decode.d5.loss_mask: 0.7032, decode.d5.loss_dice: 1.0097, decode.d6.loss_cls: 0.4492, decode.d6.loss_mask: 0.7017, decode.d6.loss_dice: 0.9970, decode.d7.loss_cls: 0.4452, decode.d7.loss_mask: 0.7003, decode.d7.loss_dice: 0.9997, decode.d8.loss_cls: 0.4358, decode.d8.loss_mask: 0.6992, decode.d8.loss_dice: 1.0019, loss: 25.1739 2022-05-05 03:55:06,291 - mmseg - INFO - Iter [29650/40000] lr: 3.715e-07, eta: 2:25:20, time: 0.787, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4781, decode.loss_mask: 0.7174, decode.loss_dice: 0.9898, decode.d0.loss_cls: 3.3942, decode.d0.loss_mask: 0.7680, decode.d0.loss_dice: 1.1499, decode.d1.loss_cls: 0.6919, decode.d1.loss_mask: 0.7436, decode.d1.loss_dice: 1.0581, decode.d2.loss_cls: 0.5818, decode.d2.loss_mask: 0.7310, decode.d2.loss_dice: 1.0122, decode.d3.loss_cls: 0.5267, decode.d3.loss_mask: 0.7231, decode.d3.loss_dice: 0.9991, decode.d4.loss_cls: 0.5096, decode.d4.loss_mask: 0.7179, decode.d4.loss_dice: 1.0000, decode.d5.loss_cls: 0.4963, decode.d5.loss_mask: 0.7169, decode.d5.loss_dice: 1.0001, decode.d6.loss_cls: 0.4820, decode.d6.loss_mask: 0.7148, decode.d6.loss_dice: 0.9941, decode.d7.loss_cls: 0.4738, decode.d7.loss_mask: 0.7138, decode.d7.loss_dice: 0.9942, decode.d8.loss_cls: 0.4779, decode.d8.loss_mask: 0.7158, decode.d8.loss_dice: 0.9929, loss: 25.5650 2022-05-05 03:55:45,164 - mmseg - INFO - Iter [29700/40000] lr: 3.698e-07, eta: 2:24:37, time: 0.777, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5139, decode.loss_mask: 0.7131, decode.loss_dice: 1.0090, decode.d0.loss_cls: 3.5123, decode.d0.loss_mask: 0.7596, decode.d0.loss_dice: 1.1604, decode.d1.loss_cls: 0.7580, decode.d1.loss_mask: 0.7428, decode.d1.loss_dice: 1.0851, decode.d2.loss_cls: 0.6334, decode.d2.loss_mask: 0.7313, decode.d2.loss_dice: 1.0326, decode.d3.loss_cls: 0.5779, decode.d3.loss_mask: 0.7269, decode.d3.loss_dice: 1.0193, decode.d4.loss_cls: 0.5559, decode.d4.loss_mask: 0.7204, decode.d4.loss_dice: 1.0194, decode.d5.loss_cls: 0.5389, decode.d5.loss_mask: 0.7156, decode.d5.loss_dice: 1.0167, decode.d6.loss_cls: 0.5247, decode.d6.loss_mask: 0.7179, decode.d6.loss_dice: 1.0057, decode.d7.loss_cls: 0.5180, decode.d7.loss_mask: 0.7136, decode.d7.loss_dice: 1.0070, decode.d8.loss_cls: 0.5153, decode.d8.loss_mask: 0.7129, decode.d8.loss_dice: 1.0060, loss: 26.2634 2022-05-05 03:56:24,367 - mmseg - INFO - Iter [29750/40000] lr: 3.680e-07, eta: 2:23:53, time: 0.784, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5008, decode.loss_mask: 0.7009, decode.loss_dice: 1.0119, decode.d0.loss_cls: 3.4715, decode.d0.loss_mask: 0.7561, decode.d0.loss_dice: 1.1763, decode.d1.loss_cls: 0.7471, decode.d1.loss_mask: 0.7388, decode.d1.loss_dice: 1.0938, decode.d2.loss_cls: 0.6224, decode.d2.loss_mask: 0.7137, decode.d2.loss_dice: 1.0359, decode.d3.loss_cls: 0.5679, decode.d3.loss_mask: 0.7081, decode.d3.loss_dice: 1.0224, decode.d4.loss_cls: 0.5527, decode.d4.loss_mask: 0.7048, decode.d4.loss_dice: 1.0191, decode.d5.loss_cls: 0.5213, decode.d5.loss_mask: 0.7050, decode.d5.loss_dice: 1.0144, decode.d6.loss_cls: 0.5057, decode.d6.loss_mask: 0.7041, decode.d6.loss_dice: 1.0133, decode.d7.loss_cls: 0.5049, decode.d7.loss_mask: 0.7012, decode.d7.loss_dice: 1.0164, decode.d8.loss_cls: 0.5057, decode.d8.loss_mask: 0.6995, decode.d8.loss_dice: 1.0078, loss: 26.0436 2022-05-05 03:57:05,310 - mmseg - INFO - Iter [29800/40000] lr: 3.662e-07, eta: 2:23:11, time: 0.819, data_time: 0.059, memory: 51557, decode.loss_cls: 0.5190, decode.loss_mask: 0.7061, decode.loss_dice: 0.9869, decode.d0.loss_cls: 3.4808, decode.d0.loss_mask: 0.7556, decode.d0.loss_dice: 1.1728, decode.d1.loss_cls: 0.7636, decode.d1.loss_mask: 0.7390, decode.d1.loss_dice: 1.0649, decode.d2.loss_cls: 0.6366, decode.d2.loss_mask: 0.7261, decode.d2.loss_dice: 1.0244, decode.d3.loss_cls: 0.5854, decode.d3.loss_mask: 0.7176, decode.d3.loss_dice: 1.0013, decode.d4.loss_cls: 0.5561, decode.d4.loss_mask: 0.7161, decode.d4.loss_dice: 1.0028, decode.d5.loss_cls: 0.5410, decode.d5.loss_mask: 0.7111, decode.d5.loss_dice: 0.9929, decode.d6.loss_cls: 0.5304, decode.d6.loss_mask: 0.7086, decode.d6.loss_dice: 0.9817, decode.d7.loss_cls: 0.5265, decode.d7.loss_mask: 0.7090, decode.d7.loss_dice: 0.9834, decode.d8.loss_cls: 0.5146, decode.d8.loss_mask: 0.7090, decode.d8.loss_dice: 0.9903, loss: 26.0534 2022-05-05 03:57:43,420 - mmseg - INFO - Iter [29850/40000] lr: 3.644e-07, eta: 2:22:27, time: 0.762, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4822, decode.loss_mask: 0.6933, decode.loss_dice: 0.9720, decode.d0.loss_cls: 3.4426, decode.d0.loss_mask: 0.7519, decode.d0.loss_dice: 1.1256, decode.d1.loss_cls: 0.7274, decode.d1.loss_mask: 0.7288, decode.d1.loss_dice: 1.0384, decode.d2.loss_cls: 0.6031, decode.d2.loss_mask: 0.7081, decode.d2.loss_dice: 0.9956, decode.d3.loss_cls: 0.5453, decode.d3.loss_mask: 0.7032, decode.d3.loss_dice: 0.9820, decode.d4.loss_cls: 0.5250, decode.d4.loss_mask: 0.6983, decode.d4.loss_dice: 0.9800, decode.d5.loss_cls: 0.5097, decode.d5.loss_mask: 0.6981, decode.d5.loss_dice: 0.9757, decode.d6.loss_cls: 0.4939, decode.d6.loss_mask: 0.6921, decode.d6.loss_dice: 0.9720, decode.d7.loss_cls: 0.4890, decode.d7.loss_mask: 0.6914, decode.d7.loss_dice: 0.9733, decode.d8.loss_cls: 0.4803, decode.d8.loss_mask: 0.6922, decode.d8.loss_dice: 0.9729, loss: 25.3433 2022-05-05 03:58:22,206 - mmseg - INFO - Iter [29900/40000] lr: 3.626e-07, eta: 2:21:44, time: 0.775, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4914, decode.loss_mask: 0.7088, decode.loss_dice: 0.9597, decode.d0.loss_cls: 3.4246, decode.d0.loss_mask: 0.7520, decode.d0.loss_dice: 1.1333, decode.d1.loss_cls: 0.7084, decode.d1.loss_mask: 0.7381, decode.d1.loss_dice: 1.0342, decode.d2.loss_cls: 0.6049, decode.d2.loss_mask: 0.7193, decode.d2.loss_dice: 0.9861, decode.d3.loss_cls: 0.5510, decode.d3.loss_mask: 0.7121, decode.d3.loss_dice: 0.9685, decode.d4.loss_cls: 0.5220, decode.d4.loss_mask: 0.7108, decode.d4.loss_dice: 0.9710, decode.d5.loss_cls: 0.5032, decode.d5.loss_mask: 0.7129, decode.d5.loss_dice: 0.9657, decode.d6.loss_cls: 0.4915, decode.d6.loss_mask: 0.7142, decode.d6.loss_dice: 0.9609, decode.d7.loss_cls: 0.4911, decode.d7.loss_mask: 0.7103, decode.d7.loss_dice: 0.9627, decode.d8.loss_cls: 0.4824, decode.d8.loss_mask: 0.7078, decode.d8.loss_dice: 0.9610, loss: 25.3598 2022-05-05 03:59:00,768 - mmseg - INFO - Iter [29950/40000] lr: 3.608e-07, eta: 2:21:01, time: 0.772, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4956, decode.loss_mask: 0.6890, decode.loss_dice: 0.9950, decode.d0.loss_cls: 3.4208, decode.d0.loss_mask: 0.7366, decode.d0.loss_dice: 1.1559, decode.d1.loss_cls: 0.7579, decode.d1.loss_mask: 0.7163, decode.d1.loss_dice: 1.0658, decode.d2.loss_cls: 0.6146, decode.d2.loss_mask: 0.7003, decode.d2.loss_dice: 1.0242, decode.d3.loss_cls: 0.5548, decode.d3.loss_mask: 0.6934, decode.d3.loss_dice: 1.0029, decode.d4.loss_cls: 0.5223, decode.d4.loss_mask: 0.6932, decode.d4.loss_dice: 1.0073, decode.d5.loss_cls: 0.5238, decode.d5.loss_mask: 0.6930, decode.d5.loss_dice: 0.9978, decode.d6.loss_cls: 0.5005, decode.d6.loss_mask: 0.6937, decode.d6.loss_dice: 0.9981, decode.d7.loss_cls: 0.5020, decode.d7.loss_mask: 0.6864, decode.d7.loss_dice: 1.0006, decode.d8.loss_cls: 0.4972, decode.d8.loss_mask: 0.6897, decode.d8.loss_dice: 1.0016, loss: 25.6304 2022-05-05 03:59:39,638 - mmseg - INFO - Saving checkpoint at 30000 iterations 2022-05-05 04:00:04,682 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 04:00:04,684 - mmseg - INFO - Iter [30000/40000] lr: 3.590e-07, eta: 2:20:26, time: 1.276, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4982, decode.loss_mask: 0.6919, decode.loss_dice: 1.0039, decode.d0.loss_cls: 3.4431, decode.d0.loss_mask: 0.7446, decode.d0.loss_dice: 1.1771, decode.d1.loss_cls: 0.7479, decode.d1.loss_mask: 0.7246, decode.d1.loss_dice: 1.0822, decode.d2.loss_cls: 0.6087, decode.d2.loss_mask: 0.7063, decode.d2.loss_dice: 1.0395, decode.d3.loss_cls: 0.5581, decode.d3.loss_mask: 0.6985, decode.d3.loss_dice: 1.0220, decode.d4.loss_cls: 0.5326, decode.d4.loss_mask: 0.6957, decode.d4.loss_dice: 1.0196, decode.d5.loss_cls: 0.5132, decode.d5.loss_mask: 0.6926, decode.d5.loss_dice: 1.0181, decode.d6.loss_cls: 0.4984, decode.d6.loss_mask: 0.6935, decode.d6.loss_dice: 1.0055, decode.d7.loss_cls: 0.4928, decode.d7.loss_mask: 0.6938, decode.d7.loss_dice: 1.0099, decode.d8.loss_cls: 0.4891, decode.d8.loss_mask: 0.6912, decode.d8.loss_dice: 1.0109, loss: 25.8035 2022-05-05 04:00:44,596 - mmseg - INFO - Iter [30050/40000] lr: 3.572e-07, eta: 2:19:43, time: 0.800, data_time: 0.012, memory: 51557, decode.loss_cls: 0.5140, decode.loss_mask: 0.7310, decode.loss_dice: 1.0028, decode.d0.loss_cls: 3.4218, decode.d0.loss_mask: 0.7856, decode.d0.loss_dice: 1.1726, decode.d1.loss_cls: 0.7535, decode.d1.loss_mask: 0.7586, decode.d1.loss_dice: 1.0759, decode.d2.loss_cls: 0.6243, decode.d2.loss_mask: 0.7464, decode.d2.loss_dice: 1.0382, decode.d3.loss_cls: 0.5646, decode.d3.loss_mask: 0.7418, decode.d3.loss_dice: 1.0165, decode.d4.loss_cls: 0.5439, decode.d4.loss_mask: 0.7384, decode.d4.loss_dice: 1.0113, decode.d5.loss_cls: 0.5202, decode.d5.loss_mask: 0.7365, decode.d5.loss_dice: 1.0121, decode.d6.loss_cls: 0.5115, decode.d6.loss_mask: 0.7318, decode.d6.loss_dice: 1.0059, decode.d7.loss_cls: 0.5047, decode.d7.loss_mask: 0.7377, decode.d7.loss_dice: 1.0088, decode.d8.loss_cls: 0.5007, decode.d8.loss_mask: 0.7369, decode.d8.loss_dice: 1.0052, loss: 26.2532 2022-05-05 04:01:23,623 - mmseg - INFO - Iter [30100/40000] lr: 3.554e-07, eta: 2:19:00, time: 0.781, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4915, decode.loss_mask: 0.6912, decode.loss_dice: 0.9823, decode.d0.loss_cls: 3.4271, decode.d0.loss_mask: 0.7499, decode.d0.loss_dice: 1.1577, decode.d1.loss_cls: 0.7403, decode.d1.loss_mask: 0.7283, decode.d1.loss_dice: 1.0630, decode.d2.loss_cls: 0.6040, decode.d2.loss_mask: 0.7070, decode.d2.loss_dice: 1.0151, decode.d3.loss_cls: 0.5497, decode.d3.loss_mask: 0.7037, decode.d3.loss_dice: 0.9966, decode.d4.loss_cls: 0.5294, decode.d4.loss_mask: 0.7003, decode.d4.loss_dice: 0.9980, decode.d5.loss_cls: 0.5157, decode.d5.loss_mask: 0.6963, decode.d5.loss_dice: 0.9947, decode.d6.loss_cls: 0.4975, decode.d6.loss_mask: 0.6954, decode.d6.loss_dice: 0.9823, decode.d7.loss_cls: 0.4944, decode.d7.loss_mask: 0.6973, decode.d7.loss_dice: 0.9818, decode.d8.loss_cls: 0.4922, decode.d8.loss_mask: 0.6931, decode.d8.loss_dice: 0.9834, loss: 25.5592 2022-05-05 04:02:02,365 - mmseg - INFO - Iter [30150/40000] lr: 3.536e-07, eta: 2:18:17, time: 0.775, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4951, decode.loss_mask: 0.6774, decode.loss_dice: 0.9881, decode.d0.loss_cls: 3.5285, decode.d0.loss_mask: 0.7305, decode.d0.loss_dice: 1.1681, decode.d1.loss_cls: 0.7520, decode.d1.loss_mask: 0.7082, decode.d1.loss_dice: 1.0643, decode.d2.loss_cls: 0.6130, decode.d2.loss_mask: 0.6937, decode.d2.loss_dice: 1.0168, decode.d3.loss_cls: 0.5655, decode.d3.loss_mask: 0.6809, decode.d3.loss_dice: 1.0005, decode.d4.loss_cls: 0.5459, decode.d4.loss_mask: 0.6804, decode.d4.loss_dice: 0.9947, decode.d5.loss_cls: 0.5053, decode.d5.loss_mask: 0.6794, decode.d5.loss_dice: 0.9953, decode.d6.loss_cls: 0.5043, decode.d6.loss_mask: 0.6811, decode.d6.loss_dice: 0.9924, decode.d7.loss_cls: 0.4933, decode.d7.loss_mask: 0.6804, decode.d7.loss_dice: 0.9914, decode.d8.loss_cls: 0.4966, decode.d8.loss_mask: 0.6779, decode.d8.loss_dice: 0.9914, loss: 25.5922 2022-05-05 04:02:42,122 - mmseg - INFO - Iter [30200/40000] lr: 3.518e-07, eta: 2:17:34, time: 0.794, data_time: 0.011, memory: 51557, decode.loss_cls: 0.4909, decode.loss_mask: 0.7284, decode.loss_dice: 1.0061, decode.d0.loss_cls: 3.3796, decode.d0.loss_mask: 0.7789, decode.d0.loss_dice: 1.1694, decode.d1.loss_cls: 0.7447, decode.d1.loss_mask: 0.7580, decode.d1.loss_dice: 1.0726, decode.d2.loss_cls: 0.6185, decode.d2.loss_mask: 0.7441, decode.d2.loss_dice: 1.0320, decode.d3.loss_cls: 0.5612, decode.d3.loss_mask: 0.7369, decode.d3.loss_dice: 1.0129, decode.d4.loss_cls: 0.5308, decode.d4.loss_mask: 0.7353, decode.d4.loss_dice: 1.0149, decode.d5.loss_cls: 0.5181, decode.d5.loss_mask: 0.7322, decode.d5.loss_dice: 1.0138, decode.d6.loss_cls: 0.5086, decode.d6.loss_mask: 0.7247, decode.d6.loss_dice: 1.0061, decode.d7.loss_cls: 0.4981, decode.d7.loss_mask: 0.7257, decode.d7.loss_dice: 1.0066, decode.d8.loss_cls: 0.4931, decode.d8.loss_mask: 0.7268, decode.d8.loss_dice: 1.0051, loss: 26.0741 2022-05-05 04:03:21,316 - mmseg - INFO - Iter [30250/40000] lr: 3.500e-07, eta: 2:16:51, time: 0.784, data_time: 0.010, memory: 51557, decode.loss_cls: 0.5026, decode.loss_mask: 0.7046, decode.loss_dice: 0.9905, decode.d0.loss_cls: 3.5301, decode.d0.loss_mask: 0.7572, decode.d0.loss_dice: 1.1681, decode.d1.loss_cls: 0.7530, decode.d1.loss_mask: 0.7333, decode.d1.loss_dice: 1.0711, decode.d2.loss_cls: 0.6206, decode.d2.loss_mask: 0.7152, decode.d2.loss_dice: 1.0233, decode.d3.loss_cls: 0.5612, decode.d3.loss_mask: 0.7146, decode.d3.loss_dice: 1.0094, decode.d4.loss_cls: 0.5381, decode.d4.loss_mask: 0.7104, decode.d4.loss_dice: 1.0066, decode.d5.loss_cls: 0.5231, decode.d5.loss_mask: 0.7073, decode.d5.loss_dice: 1.0027, decode.d6.loss_cls: 0.5108, decode.d6.loss_mask: 0.7073, decode.d6.loss_dice: 0.9960, decode.d7.loss_cls: 0.5126, decode.d7.loss_mask: 0.7072, decode.d7.loss_dice: 0.9941, decode.d8.loss_cls: 0.5050, decode.d8.loss_mask: 0.7059, decode.d8.loss_dice: 0.9907, loss: 25.9726 2022-05-05 04:04:00,389 - mmseg - INFO - Iter [30300/40000] lr: 3.482e-07, eta: 2:16:08, time: 0.782, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4820, decode.loss_mask: 0.6747, decode.loss_dice: 0.9965, decode.d0.loss_cls: 3.5313, decode.d0.loss_mask: 0.7288, decode.d0.loss_dice: 1.1736, decode.d1.loss_cls: 0.7432, decode.d1.loss_mask: 0.7020, decode.d1.loss_dice: 1.0748, decode.d2.loss_cls: 0.6104, decode.d2.loss_mask: 0.6869, decode.d2.loss_dice: 1.0305, decode.d3.loss_cls: 0.5548, decode.d3.loss_mask: 0.6815, decode.d3.loss_dice: 1.0109, decode.d4.loss_cls: 0.5184, decode.d4.loss_mask: 0.6777, decode.d4.loss_dice: 1.0114, decode.d5.loss_cls: 0.5056, decode.d5.loss_mask: 0.6790, decode.d5.loss_dice: 1.0029, decode.d6.loss_cls: 0.4977, decode.d6.loss_mask: 0.6754, decode.d6.loss_dice: 0.9997, decode.d7.loss_cls: 0.4839, decode.d7.loss_mask: 0.6739, decode.d7.loss_dice: 1.0010, decode.d8.loss_cls: 0.4828, decode.d8.loss_mask: 0.6737, decode.d8.loss_dice: 0.9985, loss: 25.5636 2022-05-05 04:04:41,792 - mmseg - INFO - Iter [30350/40000] lr: 3.464e-07, eta: 2:15:25, time: 0.828, data_time: 0.059, memory: 51557, decode.loss_cls: 0.4669, decode.loss_mask: 0.7190, decode.loss_dice: 0.9907, decode.d0.loss_cls: 3.4098, decode.d0.loss_mask: 0.7651, decode.d0.loss_dice: 1.1451, decode.d1.loss_cls: 0.7026, decode.d1.loss_mask: 0.7444, decode.d1.loss_dice: 1.0577, decode.d2.loss_cls: 0.5890, decode.d2.loss_mask: 0.7311, decode.d2.loss_dice: 1.0205, decode.d3.loss_cls: 0.5243, decode.d3.loss_mask: 0.7278, decode.d3.loss_dice: 1.0003, decode.d4.loss_cls: 0.5062, decode.d4.loss_mask: 0.7264, decode.d4.loss_dice: 0.9994, decode.d5.loss_cls: 0.4893, decode.d5.loss_mask: 0.7246, decode.d5.loss_dice: 0.9986, decode.d6.loss_cls: 0.4772, decode.d6.loss_mask: 0.7210, decode.d6.loss_dice: 0.9877, decode.d7.loss_cls: 0.4730, decode.d7.loss_mask: 0.7201, decode.d7.loss_dice: 0.9891, decode.d8.loss_cls: 0.4656, decode.d8.loss_mask: 0.7226, decode.d8.loss_dice: 0.9905, loss: 25.5859 2022-05-05 04:05:20,573 - mmseg - INFO - Iter [30400/40000] lr: 3.446e-07, eta: 2:14:42, time: 0.776, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4692, decode.loss_mask: 0.7075, decode.loss_dice: 0.9891, decode.d0.loss_cls: 3.4715, decode.d0.loss_mask: 0.7554, decode.d0.loss_dice: 1.1557, decode.d1.loss_cls: 0.7274, decode.d1.loss_mask: 0.7389, decode.d1.loss_dice: 1.0602, decode.d2.loss_cls: 0.5917, decode.d2.loss_mask: 0.7180, decode.d2.loss_dice: 1.0158, decode.d3.loss_cls: 0.5261, decode.d3.loss_mask: 0.7123, decode.d3.loss_dice: 0.9958, decode.d4.loss_cls: 0.5062, decode.d4.loss_mask: 0.7116, decode.d4.loss_dice: 0.9965, decode.d5.loss_cls: 0.4870, decode.d5.loss_mask: 0.7061, decode.d5.loss_dice: 0.9908, decode.d6.loss_cls: 0.4702, decode.d6.loss_mask: 0.7058, decode.d6.loss_dice: 0.9870, decode.d7.loss_cls: 0.4687, decode.d7.loss_mask: 0.7052, decode.d7.loss_dice: 0.9848, decode.d8.loss_cls: 0.4669, decode.d8.loss_mask: 0.7045, decode.d8.loss_dice: 0.9886, loss: 25.5145 2022-05-05 04:05:58,825 - mmseg - INFO - Iter [30450/40000] lr: 3.428e-07, eta: 2:13:59, time: 0.765, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4512, decode.loss_mask: 0.6825, decode.loss_dice: 0.9713, decode.d0.loss_cls: 3.4224, decode.d0.loss_mask: 0.7307, decode.d0.loss_dice: 1.1395, decode.d1.loss_cls: 0.7104, decode.d1.loss_mask: 0.7122, decode.d1.loss_dice: 1.0352, decode.d2.loss_cls: 0.5768, decode.d2.loss_mask: 0.6996, decode.d2.loss_dice: 1.0014, decode.d3.loss_cls: 0.5187, decode.d3.loss_mask: 0.6912, decode.d3.loss_dice: 0.9835, decode.d4.loss_cls: 0.5073, decode.d4.loss_mask: 0.6880, decode.d4.loss_dice: 0.9814, decode.d5.loss_cls: 0.4888, decode.d5.loss_mask: 0.6854, decode.d5.loss_dice: 0.9822, decode.d6.loss_cls: 0.4673, decode.d6.loss_mask: 0.6845, decode.d6.loss_dice: 0.9732, decode.d7.loss_cls: 0.4569, decode.d7.loss_mask: 0.6832, decode.d7.loss_dice: 0.9762, decode.d8.loss_cls: 0.4584, decode.d8.loss_mask: 0.6837, decode.d8.loss_dice: 0.9748, loss: 25.0179 2022-05-05 04:06:37,572 - mmseg - INFO - Iter [30500/40000] lr: 3.410e-07, eta: 2:13:16, time: 0.775, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4610, decode.loss_mask: 0.7047, decode.loss_dice: 0.9757, decode.d0.loss_cls: 3.4163, decode.d0.loss_mask: 0.7581, decode.d0.loss_dice: 1.1496, decode.d1.loss_cls: 0.7203, decode.d1.loss_mask: 0.7380, decode.d1.loss_dice: 1.0470, decode.d2.loss_cls: 0.5694, decode.d2.loss_mask: 0.7222, decode.d2.loss_dice: 1.0114, decode.d3.loss_cls: 0.5238, decode.d3.loss_mask: 0.7110, decode.d3.loss_dice: 0.9936, decode.d4.loss_cls: 0.4998, decode.d4.loss_mask: 0.7123, decode.d4.loss_dice: 0.9909, decode.d5.loss_cls: 0.4782, decode.d5.loss_mask: 0.7076, decode.d5.loss_dice: 0.9885, decode.d6.loss_cls: 0.4710, decode.d6.loss_mask: 0.7071, decode.d6.loss_dice: 0.9786, decode.d7.loss_cls: 0.4611, decode.d7.loss_mask: 0.7045, decode.d7.loss_dice: 0.9800, decode.d8.loss_cls: 0.4702, decode.d8.loss_mask: 0.7050, decode.d8.loss_dice: 0.9767, loss: 25.3338 2022-05-05 04:07:15,808 - mmseg - INFO - Iter [30550/40000] lr: 3.392e-07, eta: 2:12:33, time: 0.765, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4780, decode.loss_mask: 0.6931, decode.loss_dice: 0.9693, decode.d0.loss_cls: 3.4140, decode.d0.loss_mask: 0.7434, decode.d0.loss_dice: 1.1459, decode.d1.loss_cls: 0.7256, decode.d1.loss_mask: 0.7206, decode.d1.loss_dice: 1.0420, decode.d2.loss_cls: 0.5839, decode.d2.loss_mask: 0.7076, decode.d2.loss_dice: 1.0031, decode.d3.loss_cls: 0.5363, decode.d3.loss_mask: 0.7027, decode.d3.loss_dice: 0.9857, decode.d4.loss_cls: 0.5112, decode.d4.loss_mask: 0.6979, decode.d4.loss_dice: 0.9813, decode.d5.loss_cls: 0.4935, decode.d5.loss_mask: 0.6962, decode.d5.loss_dice: 0.9811, decode.d6.loss_cls: 0.4846, decode.d6.loss_mask: 0.6942, decode.d6.loss_dice: 0.9741, decode.d7.loss_cls: 0.4817, decode.d7.loss_mask: 0.6941, decode.d7.loss_dice: 0.9762, decode.d8.loss_cls: 0.4681, decode.d8.loss_mask: 0.6940, decode.d8.loss_dice: 0.9782, loss: 25.2576 2022-05-05 04:07:54,835 - mmseg - INFO - Iter [30600/40000] lr: 3.374e-07, eta: 2:11:50, time: 0.781, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4762, decode.loss_mask: 0.7126, decode.loss_dice: 0.9759, decode.d0.loss_cls: 3.3974, decode.d0.loss_mask: 0.7660, decode.d0.loss_dice: 1.1415, decode.d1.loss_cls: 0.7323, decode.d1.loss_mask: 0.7407, decode.d1.loss_dice: 1.0560, decode.d2.loss_cls: 0.6056, decode.d2.loss_mask: 0.7251, decode.d2.loss_dice: 1.0147, decode.d3.loss_cls: 0.5415, decode.d3.loss_mask: 0.7176, decode.d3.loss_dice: 0.9867, decode.d4.loss_cls: 0.5190, decode.d4.loss_mask: 0.7149, decode.d4.loss_dice: 0.9851, decode.d5.loss_cls: 0.4973, decode.d5.loss_mask: 0.7130, decode.d5.loss_dice: 0.9894, decode.d6.loss_cls: 0.4874, decode.d6.loss_mask: 0.7100, decode.d6.loss_dice: 0.9773, decode.d7.loss_cls: 0.4809, decode.d7.loss_mask: 0.7147, decode.d7.loss_dice: 0.9781, decode.d8.loss_cls: 0.4821, decode.d8.loss_mask: 0.7137, decode.d8.loss_dice: 0.9779, loss: 25.5306 2022-05-05 04:08:33,698 - mmseg - INFO - Iter [30650/40000] lr: 3.357e-07, eta: 2:11:07, time: 0.777, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4596, decode.loss_mask: 0.6889, decode.loss_dice: 0.9857, decode.d0.loss_cls: 3.5179, decode.d0.loss_mask: 0.7395, decode.d0.loss_dice: 1.1546, decode.d1.loss_cls: 0.7073, decode.d1.loss_mask: 0.7217, decode.d1.loss_dice: 1.0565, decode.d2.loss_cls: 0.5755, decode.d2.loss_mask: 0.7122, decode.d2.loss_dice: 1.0197, decode.d3.loss_cls: 0.5218, decode.d3.loss_mask: 0.7006, decode.d3.loss_dice: 0.9994, decode.d4.loss_cls: 0.5043, decode.d4.loss_mask: 0.6961, decode.d4.loss_dice: 0.9973, decode.d5.loss_cls: 0.4880, decode.d5.loss_mask: 0.6931, decode.d5.loss_dice: 0.9980, decode.d6.loss_cls: 0.4646, decode.d6.loss_mask: 0.6912, decode.d6.loss_dice: 0.9887, decode.d7.loss_cls: 0.4599, decode.d7.loss_mask: 0.6911, decode.d7.loss_dice: 0.9896, decode.d8.loss_cls: 0.4557, decode.d8.loss_mask: 0.6892, decode.d8.loss_dice: 0.9870, loss: 25.3547 2022-05-05 04:09:12,626 - mmseg - INFO - Iter [30700/40000] lr: 3.339e-07, eta: 2:10:24, time: 0.778, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4942, decode.loss_mask: 0.7050, decode.loss_dice: 1.0030, decode.d0.loss_cls: 3.4428, decode.d0.loss_mask: 0.7597, decode.d0.loss_dice: 1.1730, decode.d1.loss_cls: 0.7628, decode.d1.loss_mask: 0.7320, decode.d1.loss_dice: 1.0747, decode.d2.loss_cls: 0.6213, decode.d2.loss_mask: 0.7211, decode.d2.loss_dice: 1.0380, decode.d3.loss_cls: 0.5651, decode.d3.loss_mask: 0.7095, decode.d3.loss_dice: 1.0131, decode.d4.loss_cls: 0.5355, decode.d4.loss_mask: 0.7095, decode.d4.loss_dice: 1.0111, decode.d5.loss_cls: 0.5131, decode.d5.loss_mask: 0.7102, decode.d5.loss_dice: 1.0121, decode.d6.loss_cls: 0.5001, decode.d6.loss_mask: 0.7075, decode.d6.loss_dice: 1.0073, decode.d7.loss_cls: 0.4920, decode.d7.loss_mask: 0.7043, decode.d7.loss_dice: 1.0080, decode.d8.loss_cls: 0.4909, decode.d8.loss_mask: 0.7067, decode.d8.loss_dice: 1.0097, loss: 25.9333 2022-05-05 04:09:51,284 - mmseg - INFO - Iter [30750/40000] lr: 3.321e-07, eta: 2:09:40, time: 0.773, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4924, decode.loss_mask: 0.7108, decode.loss_dice: 0.9799, decode.d0.loss_cls: 3.4432, decode.d0.loss_mask: 0.7545, decode.d0.loss_dice: 1.1542, decode.d1.loss_cls: 0.7370, decode.d1.loss_mask: 0.7330, decode.d1.loss_dice: 1.0533, decode.d2.loss_cls: 0.6024, decode.d2.loss_mask: 0.7168, decode.d2.loss_dice: 1.0073, decode.d3.loss_cls: 0.5610, decode.d3.loss_mask: 0.7126, decode.d3.loss_dice: 0.9902, decode.d4.loss_cls: 0.5346, decode.d4.loss_mask: 0.7103, decode.d4.loss_dice: 0.9914, decode.d5.loss_cls: 0.5130, decode.d5.loss_mask: 0.7075, decode.d5.loss_dice: 0.9863, decode.d6.loss_cls: 0.5001, decode.d6.loss_mask: 0.7054, decode.d6.loss_dice: 0.9869, decode.d7.loss_cls: 0.4878, decode.d7.loss_mask: 0.7071, decode.d7.loss_dice: 0.9837, decode.d8.loss_cls: 0.4873, decode.d8.loss_mask: 0.7084, decode.d8.loss_dice: 0.9799, loss: 25.6382 2022-05-05 04:10:30,538 - mmseg - INFO - Iter [30800/40000] lr: 3.303e-07, eta: 2:08:58, time: 0.785, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4695, decode.loss_mask: 0.7005, decode.loss_dice: 1.0008, decode.d0.loss_cls: 3.4474, decode.d0.loss_mask: 0.7573, decode.d0.loss_dice: 1.1690, decode.d1.loss_cls: 0.7171, decode.d1.loss_mask: 0.7254, decode.d1.loss_dice: 1.0757, decode.d2.loss_cls: 0.5895, decode.d2.loss_mask: 0.7101, decode.d2.loss_dice: 1.0275, decode.d3.loss_cls: 0.5327, decode.d3.loss_mask: 0.7093, decode.d3.loss_dice: 1.0144, decode.d4.loss_cls: 0.5030, decode.d4.loss_mask: 0.7067, decode.d4.loss_dice: 1.0091, decode.d5.loss_cls: 0.4868, decode.d5.loss_mask: 0.7020, decode.d5.loss_dice: 1.0044, decode.d6.loss_cls: 0.4709, decode.d6.loss_mask: 0.7043, decode.d6.loss_dice: 1.0006, decode.d7.loss_cls: 0.4733, decode.d7.loss_mask: 0.7040, decode.d7.loss_dice: 1.0006, decode.d8.loss_cls: 0.4673, decode.d8.loss_mask: 0.6987, decode.d8.loss_dice: 0.9990, loss: 25.5771 2022-05-05 04:11:10,029 - mmseg - INFO - Iter [30850/40000] lr: 3.285e-07, eta: 2:08:15, time: 0.790, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5104, decode.loss_mask: 0.6900, decode.loss_dice: 1.0328, decode.d0.loss_cls: 3.5009, decode.d0.loss_mask: 0.7408, decode.d0.loss_dice: 1.2064, decode.d1.loss_cls: 0.7653, decode.d1.loss_mask: 0.7180, decode.d1.loss_dice: 1.1086, decode.d2.loss_cls: 0.6296, decode.d2.loss_mask: 0.7025, decode.d2.loss_dice: 1.0597, decode.d3.loss_cls: 0.5801, decode.d3.loss_mask: 0.6983, decode.d3.loss_dice: 1.0467, decode.d4.loss_cls: 0.5553, decode.d4.loss_mask: 0.6935, decode.d4.loss_dice: 1.0439, decode.d5.loss_cls: 0.5392, decode.d5.loss_mask: 0.6947, decode.d5.loss_dice: 1.0430, decode.d6.loss_cls: 0.5224, decode.d6.loss_mask: 0.6922, decode.d6.loss_dice: 1.0356, decode.d7.loss_cls: 0.5234, decode.d7.loss_mask: 0.6895, decode.d7.loss_dice: 1.0377, decode.d8.loss_cls: 0.5158, decode.d8.loss_mask: 0.6918, decode.d8.loss_dice: 1.0349, loss: 26.3029 2022-05-05 04:11:48,926 - mmseg - INFO - Iter [30900/40000] lr: 3.267e-07, eta: 2:07:32, time: 0.778, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4795, decode.loss_mask: 0.7006, decode.loss_dice: 0.9711, decode.d0.loss_cls: 3.4723, decode.d0.loss_mask: 0.7561, decode.d0.loss_dice: 1.1379, decode.d1.loss_cls: 0.7206, decode.d1.loss_mask: 0.7308, decode.d1.loss_dice: 1.0450, decode.d2.loss_cls: 0.5938, decode.d2.loss_mask: 0.7145, decode.d2.loss_dice: 1.0056, decode.d3.loss_cls: 0.5410, decode.d3.loss_mask: 0.7089, decode.d3.loss_dice: 0.9860, decode.d4.loss_cls: 0.5158, decode.d4.loss_mask: 0.7085, decode.d4.loss_dice: 0.9888, decode.d5.loss_cls: 0.5080, decode.d5.loss_mask: 0.7057, decode.d5.loss_dice: 0.9816, decode.d6.loss_cls: 0.4905, decode.d6.loss_mask: 0.7028, decode.d6.loss_dice: 0.9684, decode.d7.loss_cls: 0.4807, decode.d7.loss_mask: 0.7016, decode.d7.loss_dice: 0.9694, decode.d8.loss_cls: 0.4876, decode.d8.loss_mask: 0.7002, decode.d8.loss_dice: 0.9686, loss: 25.4420 2022-05-05 04:12:30,241 - mmseg - INFO - Iter [30950/40000] lr: 3.249e-07, eta: 2:06:49, time: 0.826, data_time: 0.061, memory: 51557, decode.loss_cls: 0.4618, decode.loss_mask: 0.6941, decode.loss_dice: 0.9706, decode.d0.loss_cls: 3.4928, decode.d0.loss_mask: 0.7529, decode.d0.loss_dice: 1.1387, decode.d1.loss_cls: 0.7007, decode.d1.loss_mask: 0.7274, decode.d1.loss_dice: 1.0527, decode.d2.loss_cls: 0.5799, decode.d2.loss_mask: 0.7080, decode.d2.loss_dice: 1.0046, decode.d3.loss_cls: 0.5240, decode.d3.loss_mask: 0.6982, decode.d3.loss_dice: 0.9802, decode.d4.loss_cls: 0.4976, decode.d4.loss_mask: 0.7010, decode.d4.loss_dice: 0.9839, decode.d5.loss_cls: 0.4818, decode.d5.loss_mask: 0.6986, decode.d5.loss_dice: 0.9764, decode.d6.loss_cls: 0.4612, decode.d6.loss_mask: 0.6949, decode.d6.loss_dice: 0.9696, decode.d7.loss_cls: 0.4551, decode.d7.loss_mask: 0.6938, decode.d7.loss_dice: 0.9720, decode.d8.loss_cls: 0.4623, decode.d8.loss_mask: 0.6938, decode.d8.loss_dice: 0.9689, loss: 25.1973 2022-05-05 04:13:09,061 - mmseg - INFO - Saving checkpoint at 31000 iterations 2022-05-05 04:13:36,541 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 04:13:36,549 - mmseg - INFO - Iter [31000/40000] lr: 3.231e-07, eta: 2:06:14, time: 1.324, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4698, decode.loss_mask: 0.6992, decode.loss_dice: 0.9876, decode.d0.loss_cls: 3.4084, decode.d0.loss_mask: 0.7523, decode.d0.loss_dice: 1.1577, decode.d1.loss_cls: 0.7160, decode.d1.loss_mask: 0.7321, decode.d1.loss_dice: 1.0595, decode.d2.loss_cls: 0.5891, decode.d2.loss_mask: 0.7089, decode.d2.loss_dice: 1.0069, decode.d3.loss_cls: 0.5304, decode.d3.loss_mask: 0.7052, decode.d3.loss_dice: 0.9970, decode.d4.loss_cls: 0.5051, decode.d4.loss_mask: 0.7040, decode.d4.loss_dice: 0.9962, decode.d5.loss_cls: 0.4944, decode.d5.loss_mask: 0.7011, decode.d5.loss_dice: 0.9903, decode.d6.loss_cls: 0.4782, decode.d6.loss_mask: 0.7049, decode.d6.loss_dice: 0.9881, decode.d7.loss_cls: 0.4728, decode.d7.loss_mask: 0.6996, decode.d7.loss_dice: 0.9867, decode.d8.loss_cls: 0.4665, decode.d8.loss_mask: 0.7012, decode.d8.loss_dice: 0.9841, loss: 25.3932 2022-05-05 04:14:15,733 - mmseg - INFO - Iter [31050/40000] lr: 3.213e-07, eta: 2:05:32, time: 0.786, data_time: 0.012, memory: 51557, decode.loss_cls: 0.4785, decode.loss_mask: 0.7163, decode.loss_dice: 0.9910, decode.d0.loss_cls: 3.4597, decode.d0.loss_mask: 0.7680, decode.d0.loss_dice: 1.1566, decode.d1.loss_cls: 0.7315, decode.d1.loss_mask: 0.7436, decode.d1.loss_dice: 1.0607, decode.d2.loss_cls: 0.5885, decode.d2.loss_mask: 0.7297, decode.d2.loss_dice: 1.0219, decode.d3.loss_cls: 0.5441, decode.d3.loss_mask: 0.7244, decode.d3.loss_dice: 1.0051, decode.d4.loss_cls: 0.5177, decode.d4.loss_mask: 0.7187, decode.d4.loss_dice: 1.0034, decode.d5.loss_cls: 0.5063, decode.d5.loss_mask: 0.7178, decode.d5.loss_dice: 0.9950, decode.d6.loss_cls: 0.4936, decode.d6.loss_mask: 0.7196, decode.d6.loss_dice: 0.9941, decode.d7.loss_cls: 0.4802, decode.d7.loss_mask: 0.7184, decode.d7.loss_dice: 0.9947, decode.d8.loss_cls: 0.4859, decode.d8.loss_mask: 0.7134, decode.d8.loss_dice: 0.9924, loss: 25.7706 2022-05-05 04:14:55,233 - mmseg - INFO - Iter [31100/40000] lr: 3.195e-07, eta: 2:04:49, time: 0.790, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4624, decode.loss_mask: 0.7184, decode.loss_dice: 1.0050, decode.d0.loss_cls: 3.3937, decode.d0.loss_mask: 0.7671, decode.d0.loss_dice: 1.1585, decode.d1.loss_cls: 0.7211, decode.d1.loss_mask: 0.7508, decode.d1.loss_dice: 1.0713, decode.d2.loss_cls: 0.5836, decode.d2.loss_mask: 0.7368, decode.d2.loss_dice: 1.0390, decode.d3.loss_cls: 0.5321, decode.d3.loss_mask: 0.7257, decode.d3.loss_dice: 1.0165, decode.d4.loss_cls: 0.5057, decode.d4.loss_mask: 0.7222, decode.d4.loss_dice: 1.0133, decode.d5.loss_cls: 0.4918, decode.d5.loss_mask: 0.7194, decode.d5.loss_dice: 1.0146, decode.d6.loss_cls: 0.4797, decode.d6.loss_mask: 0.7185, decode.d6.loss_dice: 1.0071, decode.d7.loss_cls: 0.4685, decode.d7.loss_mask: 0.7195, decode.d7.loss_dice: 1.0051, decode.d8.loss_cls: 0.4660, decode.d8.loss_mask: 0.7195, decode.d8.loss_dice: 1.0015, loss: 25.7344 2022-05-05 04:15:34,584 - mmseg - INFO - Iter [31150/40000] lr: 3.177e-07, eta: 2:04:06, time: 0.787, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4970, decode.loss_mask: 0.6918, decode.loss_dice: 0.9977, decode.d0.loss_cls: 3.5290, decode.d0.loss_mask: 0.7364, decode.d0.loss_dice: 1.1689, decode.d1.loss_cls: 0.7546, decode.d1.loss_mask: 0.7208, decode.d1.loss_dice: 1.0791, decode.d2.loss_cls: 0.6126, decode.d2.loss_mask: 0.7033, decode.d2.loss_dice: 1.0349, decode.d3.loss_cls: 0.5612, decode.d3.loss_mask: 0.6969, decode.d3.loss_dice: 1.0140, decode.d4.loss_cls: 0.5356, decode.d4.loss_mask: 0.6924, decode.d4.loss_dice: 1.0137, decode.d5.loss_cls: 0.5131, decode.d5.loss_mask: 0.6917, decode.d5.loss_dice: 1.0075, decode.d6.loss_cls: 0.5052, decode.d6.loss_mask: 0.6895, decode.d6.loss_dice: 0.9988, decode.d7.loss_cls: 0.4899, decode.d7.loss_mask: 0.6898, decode.d7.loss_dice: 1.0032, decode.d8.loss_cls: 0.4947, decode.d8.loss_mask: 0.6902, decode.d8.loss_dice: 1.0002, loss: 25.8137 2022-05-05 04:16:13,129 - mmseg - INFO - Iter [31200/40000] lr: 3.159e-07, eta: 2:03:23, time: 0.771, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5074, decode.loss_mask: 0.6945, decode.loss_dice: 1.0059, decode.d0.loss_cls: 3.4822, decode.d0.loss_mask: 0.7490, decode.d0.loss_dice: 1.1827, decode.d1.loss_cls: 0.7508, decode.d1.loss_mask: 0.7212, decode.d1.loss_dice: 1.0779, decode.d2.loss_cls: 0.6232, decode.d2.loss_mask: 0.7051, decode.d2.loss_dice: 1.0354, decode.d3.loss_cls: 0.5691, decode.d3.loss_mask: 0.6988, decode.d3.loss_dice: 1.0181, decode.d4.loss_cls: 0.5435, decode.d4.loss_mask: 0.6986, decode.d4.loss_dice: 1.0182, decode.d5.loss_cls: 0.5254, decode.d5.loss_mask: 0.6984, decode.d5.loss_dice: 1.0100, decode.d6.loss_cls: 0.5154, decode.d6.loss_mask: 0.6983, decode.d6.loss_dice: 1.0043, decode.d7.loss_cls: 0.5077, decode.d7.loss_mask: 0.6960, decode.d7.loss_dice: 1.0029, decode.d8.loss_cls: 0.5035, decode.d8.loss_mask: 0.6942, decode.d8.loss_dice: 1.0017, loss: 25.9394 2022-05-05 04:16:51,800 - mmseg - INFO - Iter [31250/40000] lr: 3.141e-07, eta: 2:02:40, time: 0.773, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4578, decode.loss_mask: 0.6902, decode.loss_dice: 0.9717, decode.d0.loss_cls: 3.4162, decode.d0.loss_mask: 0.7395, decode.d0.loss_dice: 1.1400, decode.d1.loss_cls: 0.6995, decode.d1.loss_mask: 0.7162, decode.d1.loss_dice: 1.0488, decode.d2.loss_cls: 0.5756, decode.d2.loss_mask: 0.7034, decode.d2.loss_dice: 1.0067, decode.d3.loss_cls: 0.5313, decode.d3.loss_mask: 0.6959, decode.d3.loss_dice: 0.9879, decode.d4.loss_cls: 0.4967, decode.d4.loss_mask: 0.6959, decode.d4.loss_dice: 0.9872, decode.d5.loss_cls: 0.4791, decode.d5.loss_mask: 0.6916, decode.d5.loss_dice: 0.9796, decode.d6.loss_cls: 0.4784, decode.d6.loss_mask: 0.6896, decode.d6.loss_dice: 0.9723, decode.d7.loss_cls: 0.4668, decode.d7.loss_mask: 0.6909, decode.d7.loss_dice: 0.9772, decode.d8.loss_cls: 0.4733, decode.d8.loss_mask: 0.6911, decode.d8.loss_dice: 0.9697, loss: 25.1200 2022-05-05 04:17:30,063 - mmseg - INFO - Iter [31300/40000] lr: 3.123e-07, eta: 2:01:57, time: 0.765, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4845, decode.loss_mask: 0.6848, decode.loss_dice: 1.0048, decode.d0.loss_cls: 3.4960, decode.d0.loss_mask: 0.7326, decode.d0.loss_dice: 1.1716, decode.d1.loss_cls: 0.7365, decode.d1.loss_mask: 0.7109, decode.d1.loss_dice: 1.0805, decode.d2.loss_cls: 0.6097, decode.d2.loss_mask: 0.6958, decode.d2.loss_dice: 1.0454, decode.d3.loss_cls: 0.5495, decode.d3.loss_mask: 0.6926, decode.d3.loss_dice: 1.0249, decode.d4.loss_cls: 0.5252, decode.d4.loss_mask: 0.6913, decode.d4.loss_dice: 1.0219, decode.d5.loss_cls: 0.5103, decode.d5.loss_mask: 0.6892, decode.d5.loss_dice: 1.0113, decode.d6.loss_cls: 0.4972, decode.d6.loss_mask: 0.6831, decode.d6.loss_dice: 1.0059, decode.d7.loss_cls: 0.4829, decode.d7.loss_mask: 0.6838, decode.d7.loss_dice: 1.0084, decode.d8.loss_cls: 0.4910, decode.d8.loss_mask: 0.6840, decode.d8.loss_dice: 1.0061, loss: 25.7114 2022-05-05 04:18:09,088 - mmseg - INFO - Iter [31350/40000] lr: 3.105e-07, eta: 2:01:14, time: 0.780, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4605, decode.loss_mask: 0.6736, decode.loss_dice: 0.9705, decode.d0.loss_cls: 3.3728, decode.d0.loss_mask: 0.7228, decode.d0.loss_dice: 1.1413, decode.d1.loss_cls: 0.7113, decode.d1.loss_mask: 0.7063, decode.d1.loss_dice: 1.0439, decode.d2.loss_cls: 0.5718, decode.d2.loss_mask: 0.6884, decode.d2.loss_dice: 0.9979, decode.d3.loss_cls: 0.5263, decode.d3.loss_mask: 0.6774, decode.d3.loss_dice: 0.9794, decode.d4.loss_cls: 0.5009, decode.d4.loss_mask: 0.6773, decode.d4.loss_dice: 0.9832, decode.d5.loss_cls: 0.4836, decode.d5.loss_mask: 0.6749, decode.d5.loss_dice: 0.9785, decode.d6.loss_cls: 0.4686, decode.d6.loss_mask: 0.6726, decode.d6.loss_dice: 0.9675, decode.d7.loss_cls: 0.4547, decode.d7.loss_mask: 0.6754, decode.d7.loss_dice: 0.9718, decode.d8.loss_cls: 0.4567, decode.d8.loss_mask: 0.6716, decode.d8.loss_dice: 0.9692, loss: 24.8508 2022-05-05 04:18:48,538 - mmseg - INFO - Iter [31400/40000] lr: 3.087e-07, eta: 2:00:31, time: 0.789, data_time: 0.008, memory: 51557, decode.loss_cls: 0.4672, decode.loss_mask: 0.7066, decode.loss_dice: 0.9876, decode.d0.loss_cls: 3.3786, decode.d0.loss_mask: 0.7585, decode.d0.loss_dice: 1.1536, decode.d1.loss_cls: 0.7117, decode.d1.loss_mask: 0.7374, decode.d1.loss_dice: 1.0660, decode.d2.loss_cls: 0.5917, decode.d2.loss_mask: 0.7179, decode.d2.loss_dice: 1.0224, decode.d3.loss_cls: 0.5425, decode.d3.loss_mask: 0.7126, decode.d3.loss_dice: 1.0021, decode.d4.loss_cls: 0.5175, decode.d4.loss_mask: 0.7089, decode.d4.loss_dice: 0.9980, decode.d5.loss_cls: 0.4886, decode.d5.loss_mask: 0.7091, decode.d5.loss_dice: 0.9987, decode.d6.loss_cls: 0.4834, decode.d6.loss_mask: 0.7071, decode.d6.loss_dice: 0.9879, decode.d7.loss_cls: 0.4773, decode.d7.loss_mask: 0.7057, decode.d7.loss_dice: 0.9872, decode.d8.loss_cls: 0.4692, decode.d8.loss_mask: 0.7094, decode.d8.loss_dice: 0.9896, loss: 25.4937 2022-05-05 04:19:27,290 - mmseg - INFO - Iter [31450/40000] lr: 3.069e-07, eta: 1:59:48, time: 0.775, data_time: 0.009, memory: 51557, decode.loss_cls: 0.5051, decode.loss_mask: 0.7174, decode.loss_dice: 1.0314, decode.d0.loss_cls: 3.4580, decode.d0.loss_mask: 0.7618, decode.d0.loss_dice: 1.2000, decode.d1.loss_cls: 0.7804, decode.d1.loss_mask: 0.7421, decode.d1.loss_dice: 1.1052, decode.d2.loss_cls: 0.6336, decode.d2.loss_mask: 0.7285, decode.d2.loss_dice: 1.0593, decode.d3.loss_cls: 0.5778, decode.d3.loss_mask: 0.7217, decode.d3.loss_dice: 1.0438, decode.d4.loss_cls: 0.5491, decode.d4.loss_mask: 0.7220, decode.d4.loss_dice: 1.0411, decode.d5.loss_cls: 0.5281, decode.d5.loss_mask: 0.7166, decode.d5.loss_dice: 1.0362, decode.d6.loss_cls: 0.5189, decode.d6.loss_mask: 0.7146, decode.d6.loss_dice: 1.0308, decode.d7.loss_cls: 0.5119, decode.d7.loss_mask: 0.7133, decode.d7.loss_dice: 1.0296, decode.d8.loss_cls: 0.5013, decode.d8.loss_mask: 0.7158, decode.d8.loss_dice: 1.0326, loss: 26.4279 2022-05-05 04:20:08,359 - mmseg - INFO - Iter [31500/40000] lr: 3.051e-07, eta: 1:59:06, time: 0.821, data_time: 0.059, memory: 51557, decode.loss_cls: 0.4840, decode.loss_mask: 0.6974, decode.loss_dice: 1.0046, decode.d0.loss_cls: 3.4749, decode.d0.loss_mask: 0.7526, decode.d0.loss_dice: 1.1749, decode.d1.loss_cls: 0.7430, decode.d1.loss_mask: 0.7335, decode.d1.loss_dice: 1.0814, decode.d2.loss_cls: 0.6150, decode.d2.loss_mask: 0.7122, decode.d2.loss_dice: 1.0428, decode.d3.loss_cls: 0.5535, decode.d3.loss_mask: 0.7100, decode.d3.loss_dice: 1.0248, decode.d4.loss_cls: 0.5209, decode.d4.loss_mask: 0.7051, decode.d4.loss_dice: 1.0188, decode.d5.loss_cls: 0.5045, decode.d5.loss_mask: 0.7049, decode.d5.loss_dice: 1.0140, decode.d6.loss_cls: 0.4905, decode.d6.loss_mask: 0.7000, decode.d6.loss_dice: 1.0057, decode.d7.loss_cls: 0.4803, decode.d7.loss_mask: 0.7016, decode.d7.loss_dice: 1.0104, decode.d8.loss_cls: 0.4800, decode.d8.loss_mask: 0.6995, decode.d8.loss_dice: 1.0056, loss: 25.8463 2022-05-05 04:20:46,743 - mmseg - INFO - Iter [31550/40000] lr: 3.033e-07, eta: 1:58:23, time: 0.768, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4746, decode.loss_mask: 0.6915, decode.loss_dice: 0.9906, decode.d0.loss_cls: 3.4948, decode.d0.loss_mask: 0.7386, decode.d0.loss_dice: 1.1733, decode.d1.loss_cls: 0.7347, decode.d1.loss_mask: 0.7176, decode.d1.loss_dice: 1.0711, decode.d2.loss_cls: 0.5899, decode.d2.loss_mask: 0.7041, decode.d2.loss_dice: 1.0266, decode.d3.loss_cls: 0.5328, decode.d3.loss_mask: 0.7050, decode.d3.loss_dice: 0.9987, decode.d4.loss_cls: 0.5153, decode.d4.loss_mask: 0.6961, decode.d4.loss_dice: 1.0128, decode.d5.loss_cls: 0.4913, decode.d5.loss_mask: 0.6938, decode.d5.loss_dice: 1.0016, decode.d6.loss_cls: 0.4841, decode.d6.loss_mask: 0.6929, decode.d6.loss_dice: 0.9954, decode.d7.loss_cls: 0.4707, decode.d7.loss_mask: 0.6947, decode.d7.loss_dice: 0.9970, decode.d8.loss_cls: 0.4707, decode.d8.loss_mask: 0.6936, decode.d8.loss_dice: 0.9940, loss: 25.5480 2022-05-05 04:21:25,648 - mmseg - INFO - Iter [31600/40000] lr: 3.016e-07, eta: 1:57:40, time: 0.778, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4598, decode.loss_mask: 0.6908, decode.loss_dice: 0.9693, decode.d0.loss_cls: 3.4050, decode.d0.loss_mask: 0.7429, decode.d0.loss_dice: 1.1387, decode.d1.loss_cls: 0.7145, decode.d1.loss_mask: 0.7179, decode.d1.loss_dice: 1.0433, decode.d2.loss_cls: 0.5872, decode.d2.loss_mask: 0.7022, decode.d2.loss_dice: 1.0080, decode.d3.loss_cls: 0.5220, decode.d3.loss_mask: 0.6956, decode.d3.loss_dice: 0.9822, decode.d4.loss_cls: 0.5038, decode.d4.loss_mask: 0.6913, decode.d4.loss_dice: 0.9868, decode.d5.loss_cls: 0.4902, decode.d5.loss_mask: 0.6898, decode.d5.loss_dice: 0.9823, decode.d6.loss_cls: 0.4790, decode.d6.loss_mask: 0.6878, decode.d6.loss_dice: 0.9709, decode.d7.loss_cls: 0.4676, decode.d7.loss_mask: 0.6906, decode.d7.loss_dice: 0.9753, decode.d8.loss_cls: 0.4588, decode.d8.loss_mask: 0.6925, decode.d8.loss_dice: 0.9719, loss: 25.1180 2022-05-05 04:22:04,140 - mmseg - INFO - Iter [31650/40000] lr: 2.998e-07, eta: 1:56:57, time: 0.769, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4760, decode.loss_mask: 0.6965, decode.loss_dice: 1.0178, decode.d0.loss_cls: 3.5173, decode.d0.loss_mask: 0.7422, decode.d0.loss_dice: 1.1963, decode.d1.loss_cls: 0.7314, decode.d1.loss_mask: 0.7277, decode.d1.loss_dice: 1.0934, decode.d2.loss_cls: 0.6019, decode.d2.loss_mask: 0.7087, decode.d2.loss_dice: 1.0499, decode.d3.loss_cls: 0.5391, decode.d3.loss_mask: 0.7056, decode.d3.loss_dice: 1.0310, decode.d4.loss_cls: 0.5175, decode.d4.loss_mask: 0.7052, decode.d4.loss_dice: 1.0325, decode.d5.loss_cls: 0.5035, decode.d5.loss_mask: 0.7005, decode.d5.loss_dice: 1.0235, decode.d6.loss_cls: 0.4801, decode.d6.loss_mask: 0.6975, decode.d6.loss_dice: 1.0252, decode.d7.loss_cls: 0.4778, decode.d7.loss_mask: 0.6968, decode.d7.loss_dice: 1.0218, decode.d8.loss_cls: 0.4742, decode.d8.loss_mask: 0.6946, decode.d8.loss_dice: 1.0208, loss: 25.9062 2022-05-05 04:22:43,030 - mmseg - INFO - Iter [31700/40000] lr: 2.980e-07, eta: 1:56:14, time: 0.779, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4818, decode.loss_mask: 0.6768, decode.loss_dice: 0.9658, decode.d0.loss_cls: 3.4132, decode.d0.loss_mask: 0.7257, decode.d0.loss_dice: 1.1467, decode.d1.loss_cls: 0.7157, decode.d1.loss_mask: 0.7073, decode.d1.loss_dice: 1.0436, decode.d2.loss_cls: 0.5948, decode.d2.loss_mask: 0.6934, decode.d2.loss_dice: 1.0020, decode.d3.loss_cls: 0.5431, decode.d3.loss_mask: 0.6837, decode.d3.loss_dice: 0.9862, decode.d4.loss_cls: 0.5192, decode.d4.loss_mask: 0.6791, decode.d4.loss_dice: 0.9735, decode.d5.loss_cls: 0.5002, decode.d5.loss_mask: 0.6778, decode.d5.loss_dice: 0.9785, decode.d6.loss_cls: 0.4921, decode.d6.loss_mask: 0.6771, decode.d6.loss_dice: 0.9699, decode.d7.loss_cls: 0.4802, decode.d7.loss_mask: 0.6745, decode.d7.loss_dice: 0.9702, decode.d8.loss_cls: 0.4781, decode.d8.loss_mask: 0.6760, decode.d8.loss_dice: 0.9681, loss: 25.0944 2022-05-05 04:23:22,200 - mmseg - INFO - Iter [31750/40000] lr: 2.962e-07, eta: 1:55:31, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4685, decode.loss_mask: 0.6704, decode.loss_dice: 0.9525, decode.d0.loss_cls: 3.5039, decode.d0.loss_mask: 0.7234, decode.d0.loss_dice: 1.1287, decode.d1.loss_cls: 0.7270, decode.d1.loss_mask: 0.6958, decode.d1.loss_dice: 1.0249, decode.d2.loss_cls: 0.5769, decode.d2.loss_mask: 0.6855, decode.d2.loss_dice: 0.9841, decode.d3.loss_cls: 0.5262, decode.d3.loss_mask: 0.6781, decode.d3.loss_dice: 0.9702, decode.d4.loss_cls: 0.5010, decode.d4.loss_mask: 0.6770, decode.d4.loss_dice: 0.9603, decode.d5.loss_cls: 0.4867, decode.d5.loss_mask: 0.6761, decode.d5.loss_dice: 0.9630, decode.d6.loss_cls: 0.4701, decode.d6.loss_mask: 0.6734, decode.d6.loss_dice: 0.9542, decode.d7.loss_cls: 0.4668, decode.d7.loss_mask: 0.6700, decode.d7.loss_dice: 0.9557, decode.d8.loss_cls: 0.4668, decode.d8.loss_mask: 0.6716, decode.d8.loss_dice: 0.9563, loss: 24.8651 2022-05-05 04:24:01,151 - mmseg - INFO - Iter [31800/40000] lr: 2.944e-07, eta: 1:54:49, time: 0.778, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4821, decode.loss_mask: 0.7122, decode.loss_dice: 1.0068, decode.d0.loss_cls: 3.4723, decode.d0.loss_mask: 0.7652, decode.d0.loss_dice: 1.1864, decode.d1.loss_cls: 0.7384, decode.d1.loss_mask: 0.7405, decode.d1.loss_dice: 1.0838, decode.d2.loss_cls: 0.6147, decode.d2.loss_mask: 0.7257, decode.d2.loss_dice: 1.0360, decode.d3.loss_cls: 0.5538, decode.d3.loss_mask: 0.7198, decode.d3.loss_dice: 1.0201, decode.d4.loss_cls: 0.5227, decode.d4.loss_mask: 0.7196, decode.d4.loss_dice: 1.0229, decode.d5.loss_cls: 0.4992, decode.d5.loss_mask: 0.7156, decode.d5.loss_dice: 1.0223, decode.d6.loss_cls: 0.4833, decode.d6.loss_mask: 0.7169, decode.d6.loss_dice: 1.0153, decode.d7.loss_cls: 0.4827, decode.d7.loss_mask: 0.7107, decode.d7.loss_dice: 1.0154, decode.d8.loss_cls: 0.4758, decode.d8.loss_mask: 0.7132, decode.d8.loss_dice: 1.0132, loss: 25.9868 2022-05-05 04:24:39,842 - mmseg - INFO - Iter [31850/40000] lr: 2.926e-07, eta: 1:54:06, time: 0.775, data_time: 0.011, memory: 51557, decode.loss_cls: 0.4822, decode.loss_mask: 0.6854, decode.loss_dice: 0.9572, decode.d0.loss_cls: 3.4389, decode.d0.loss_mask: 0.7535, decode.d0.loss_dice: 1.1405, decode.d1.loss_cls: 0.7187, decode.d1.loss_mask: 0.7225, decode.d1.loss_dice: 1.0341, decode.d2.loss_cls: 0.5872, decode.d2.loss_mask: 0.7062, decode.d2.loss_dice: 0.9930, decode.d3.loss_cls: 0.5468, decode.d3.loss_mask: 0.6955, decode.d3.loss_dice: 0.9685, decode.d4.loss_cls: 0.5167, decode.d4.loss_mask: 0.6919, decode.d4.loss_dice: 0.9710, decode.d5.loss_cls: 0.5016, decode.d5.loss_mask: 0.6896, decode.d5.loss_dice: 0.9669, decode.d6.loss_cls: 0.4891, decode.d6.loss_mask: 0.6895, decode.d6.loss_dice: 0.9636, decode.d7.loss_cls: 0.4874, decode.d7.loss_mask: 0.6881, decode.d7.loss_dice: 0.9607, decode.d8.loss_cls: 0.4779, decode.d8.loss_mask: 0.6875, decode.d8.loss_dice: 0.9561, loss: 25.1678 2022-05-05 04:25:18,625 - mmseg - INFO - Iter [31900/40000] lr: 2.908e-07, eta: 1:53:23, time: 0.775, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4759, decode.loss_mask: 0.6889, decode.loss_dice: 0.9716, decode.d0.loss_cls: 3.4409, decode.d0.loss_mask: 0.7471, decode.d0.loss_dice: 1.1492, decode.d1.loss_cls: 0.7427, decode.d1.loss_mask: 0.7243, decode.d1.loss_dice: 1.0601, decode.d2.loss_cls: 0.5987, decode.d2.loss_mask: 0.7037, decode.d2.loss_dice: 1.0137, decode.d3.loss_cls: 0.5398, decode.d3.loss_mask: 0.6968, decode.d3.loss_dice: 0.9964, decode.d4.loss_cls: 0.5133, decode.d4.loss_mask: 0.6935, decode.d4.loss_dice: 0.9869, decode.d5.loss_cls: 0.5011, decode.d5.loss_mask: 0.6959, decode.d5.loss_dice: 0.9861, decode.d6.loss_cls: 0.4889, decode.d6.loss_mask: 0.6906, decode.d6.loss_dice: 0.9756, decode.d7.loss_cls: 0.4809, decode.d7.loss_mask: 0.6912, decode.d7.loss_dice: 0.9761, decode.d8.loss_cls: 0.4740, decode.d8.loss_mask: 0.6890, decode.d8.loss_dice: 0.9773, loss: 25.3701 2022-05-05 04:25:57,195 - mmseg - INFO - Iter [31950/40000] lr: 2.890e-07, eta: 1:52:40, time: 0.772, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4477, decode.loss_mask: 0.6939, decode.loss_dice: 0.9688, decode.d0.loss_cls: 3.3645, decode.d0.loss_mask: 0.7449, decode.d0.loss_dice: 1.1168, decode.d1.loss_cls: 0.6745, decode.d1.loss_mask: 0.7273, decode.d1.loss_dice: 1.0256, decode.d2.loss_cls: 0.5550, decode.d2.loss_mask: 0.7081, decode.d2.loss_dice: 0.9940, decode.d3.loss_cls: 0.5035, decode.d3.loss_mask: 0.7004, decode.d3.loss_dice: 0.9714, decode.d4.loss_cls: 0.4751, decode.d4.loss_mask: 0.7009, decode.d4.loss_dice: 0.9774, decode.d5.loss_cls: 0.4613, decode.d5.loss_mask: 0.6993, decode.d5.loss_dice: 0.9703, decode.d6.loss_cls: 0.4539, decode.d6.loss_mask: 0.6960, decode.d6.loss_dice: 0.9600, decode.d7.loss_cls: 0.4517, decode.d7.loss_mask: 0.6965, decode.d7.loss_dice: 0.9689, decode.d8.loss_cls: 0.4471, decode.d8.loss_mask: 0.6943, decode.d8.loss_dice: 0.9661, loss: 24.8154 2022-05-05 04:26:35,976 - mmseg - INFO - Saving checkpoint at 32000 iterations 2022-05-05 04:27:02,078 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 04:27:02,080 - mmseg - INFO - Iter [32000/40000] lr: 2.872e-07, eta: 1:52:04, time: 1.295, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4508, decode.loss_mask: 0.6901, decode.loss_dice: 0.9741, decode.d0.loss_cls: 3.3893, decode.d0.loss_mask: 0.7439, decode.d0.loss_dice: 1.1443, decode.d1.loss_cls: 0.7180, decode.d1.loss_mask: 0.7157, decode.d1.loss_dice: 1.0476, decode.d2.loss_cls: 0.5739, decode.d2.loss_mask: 0.7005, decode.d2.loss_dice: 1.0086, decode.d3.loss_cls: 0.5154, decode.d3.loss_mask: 0.6973, decode.d3.loss_dice: 0.9898, decode.d4.loss_cls: 0.4883, decode.d4.loss_mask: 0.6935, decode.d4.loss_dice: 0.9879, decode.d5.loss_cls: 0.4775, decode.d5.loss_mask: 0.6903, decode.d5.loss_dice: 0.9812, decode.d6.loss_cls: 0.4659, decode.d6.loss_mask: 0.6891, decode.d6.loss_dice: 0.9718, decode.d7.loss_cls: 0.4588, decode.d7.loss_mask: 0.6891, decode.d7.loss_dice: 0.9702, decode.d8.loss_cls: 0.4553, decode.d8.loss_mask: 0.6903, decode.d8.loss_dice: 0.9723, loss: 25.0409 2022-05-05 04:27:33,604 - mmseg - INFO - per class results: 2022-05-05 04:27:33,627 - mmseg - INFO - +------------------+-------+-------+ | Class | IoU | Acc | +------------------+-------+-------+ | person | 88.72 | 94.48 | | bicycle | 78.37 | 91.47 | | car | 49.89 | 54.86 | | motorcycle | 90.57 | 95.23 | | airplane | 88.53 | 93.58 | | bus | 79.26 | 83.16 | | train | 78.24 | 96.96 | | truck | 64.62 | 90.28 | | boat | 81.37 | 88.02 | | traffic light | 80.25 | 92.62 | | fire hydrant | 86.57 | 96.86 | | stop sign | 94.42 | 97.59 | | parking meter | 73.34 | 75.45 | | bench | 52.96 | 67.91 | | bird | 77.81 | 83.31 | | cat | 93.0 | 96.34 | | dog | 91.79 | 96.08 | | horse | 91.27 | 95.67 | | sheep | 87.66 | 90.73 | | cow | 95.51 | 98.01 | | elephant | 92.87 | 96.3 | | bear | 92.84 | 94.69 | | zebra | 92.08 | 95.71 | | giraffe | 88.73 | 94.6 | | backpack | 24.44 | 55.41 | | umbrella | 82.13 | 86.36 | | handbag | 23.38 | 41.29 | | tie | 65.32 | 65.55 | | suitcase | 77.7 | 91.83 | | frisbee | 95.35 | 98.2 | | skis | 37.51 | 56.7 | | snowboard | 60.49 | 76.41 | | sports ball | 86.45 | 94.88 | | kite | 68.17 | 81.95 | | baseball bat | 61.63 | 77.92 | | baseball glove | 2.6 | 2.75 | | skateboard | 71.28 | 88.44 | | surfboard | 90.15 | 94.44 | | tennis racket | 30.14 | 31.57 | | bottle | 72.35 | 84.74 | | wine glass | 85.59 | 91.82 | | cup | 70.06 | 84.38 | | fork | 55.64 | 72.63 | | knife | 78.05 | 88.37 | | spoon | 53.19 | 70.85 | | bowl | 60.32 | 71.9 | | banana | 80.38 | 91.09 | | apple | 72.1 | 78.95 | | sandwich | 87.23 | 96.38 | | orange | 66.96 | 88.53 | | broccoli | 91.78 | 97.12 | | carrot | 51.15 | 59.81 | | hot dog | 53.26 | 97.5 | | pizza | 94.95 | 96.69 | | donut | 80.54 | 94.35 | | cake | 83.93 | 88.44 | | chair | 59.96 | 75.35 | | couch | 76.31 | 93.94 | | potted plant | 35.33 | 45.35 | | bed | 70.66 | 83.44 | | dining table | 63.35 | 77.78 | | toilet | 90.28 | 96.21 | | tv | 80.46 | 91.61 | | laptop | 86.54 | 97.23 | | mouse | 83.15 | 90.0 | | remote | 71.0 | 88.18 | | keyboard | 86.0 | 97.66 | | cell phone | 85.28 | 95.89 | | microwave | 61.4 | 74.55 | | oven | 63.25 | 80.02 | | toaster | 61.28 | 63.1 | | sink | 76.92 | 80.26 | | refrigerator | 80.14 | 88.14 | | book | 79.69 | 91.89 | | clock | 76.96 | 79.44 | | vase | 61.6 | 90.03 | | scissors | 83.13 | 93.13 | | teddy bear | 84.79 | 91.91 | | hair drier | 0.0 | 0.0 | | toothbrush | 29.85 | 42.83 | | banner | 35.02 | 65.43 | | blanket | 0.0 | 0.0 | | branch | 35.56 | 39.07 | | bridge | 3.58 | 5.82 | | building-other | 57.7 | 74.66 | | bush | 23.24 | 33.9 | | cabinet | 24.21 | 47.61 | | cage | 16.26 | 79.44 | | cardboard | 24.26 | 30.77 | | carpet | 58.76 | 78.18 | | ceiling-other | 70.95 | 80.68 | | ceiling-tile | 12.65 | 14.2 | | cloth | 2.0 | 2.36 | | clothes | 25.1 | 32.5 | | clouds | 56.57 | 74.5 | | counter | 42.68 | 53.63 | | cupboard | 54.98 | 70.55 | | curtain | 69.87 | 85.44 | | desk-stuff | 31.17 | 37.81 | | dirt | 33.72 | 54.6 | | door-stuff | 40.27 | 52.35 | | fence | 45.88 | 72.23 | | floor-marble | 0.0 | 0.0 | | floor-other | 34.86 | 56.78 | | floor-stone | 25.25 | 33.59 | | floor-tile | 65.41 | 76.09 | | floor-wood | 76.16 | 86.45 | | flower | 22.22 | 52.72 | | fog | 0.0 | 0.0 | | food-other | 39.33 | 57.24 | | fruit | 63.94 | 84.01 | | furniture-other | 17.55 | 28.42 | | grass | 74.08 | 83.31 | | gravel | 30.59 | 37.04 | | ground-other | 7.77 | 15.1 | | hill | 25.44 | 33.15 | | house | 32.85 | 53.36 | | leaves | 17.69 | 18.55 | | light | 43.16 | 61.3 | | mat | 31.66 | 58.63 | | metal | 21.43 | 34.87 | | mirror-stuff | 43.78 | 57.92 | | moss | 0.0 | 0.0 | | mountain | 34.67 | 59.9 | | mud | 0.64 | 1.79 | | napkin | 14.49 | 92.67 | | net | 45.49 | 57.86 | | paper | 47.62 | 69.28 | | pavement | 52.55 | 74.11 | | pillow | 0.0 | 0.0 | | plant-other | 29.67 | 50.64 | | plastic | 29.1 | 40.46 | | platform | 43.0 | 60.96 | | playingfield | 65.71 | 76.65 | | railing | 15.38 | 23.82 | | railroad | 60.85 | 77.18 | | river | 46.56 | 64.32 | | road | 71.71 | 80.34 | | rock | 45.41 | 60.38 | | roof | 3.64 | 5.13 | | rug | 42.65 | 54.7 | | salad | 26.15 | 27.01 | | sand | 71.2 | 89.63 | | sea | 74.9 | 92.39 | | shelf | 26.26 | 50.01 | | sky-other | 62.69 | 75.64 | | skyscraper | 10.33 | 11.5 | | snow | 91.48 | 94.63 | | solid-other | nan | nan | | stairs | 44.04 | 70.02 | | stone | 5.96 | 15.36 | | straw | 13.41 | 33.62 | | structural-other | 18.58 | 29.37 | | table | 24.03 | 38.08 | | tent | 74.31 | 92.16 | | textile-other | 20.46 | 30.62 | | towel | 42.9 | 51.33 | | tree | 77.76 | 87.22 | | vegetable | 49.63 | 77.79 | | wall-brick | 49.44 | 65.96 | | wall-concrete | 25.72 | 35.21 | | wall-other | 63.27 | 83.52 | | wall-panel | 6.32 | 6.56 | | wall-stone | 32.38 | 37.13 | | wall-tile | 59.11 | 82.0 | | wall-wood | 43.26 | 65.58 | | water-other | 25.62 | 31.13 | | waterdrops | 0.0 | nan | | window-blind | 35.16 | 61.31 | | window-other | 54.22 | 67.28 | | wood | 14.77 | 33.13 | +------------------+-------+-------+ 2022-05-05 04:27:33,628 - mmseg - INFO - Summary: 2022-05-05 04:27:33,628 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 76.18 | 52.84 | 65.47 | +-------+-------+-------+ 2022-05-05 04:27:33,634 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 04:27:33,635 - mmseg - INFO - Iter(val) [125] aAcc: 0.7618, mIoU: 0.5284, mAcc: 0.6547, IoU.person: 0.8872, IoU.bicycle: 0.7837, IoU.car: 0.4989, IoU.motorcycle: 0.9057, IoU.airplane: 0.8853, IoU.bus: 0.7926, IoU.train: 0.7824, IoU.truck: 0.6462, IoU.boat: 0.8137, IoU.traffic light: 0.8025, IoU.fire hydrant: 0.8657, IoU.stop sign: 0.9442, IoU.parking meter: 0.7334, IoU.bench: 0.5296, IoU.bird: 0.7781, IoU.cat: 0.9300, IoU.dog: 0.9179, IoU.horse: 0.9127, IoU.sheep: 0.8766, IoU.cow: 0.9551, IoU.elephant: 0.9287, IoU.bear: 0.9284, IoU.zebra: 0.9208, IoU.giraffe: 0.8873, IoU.backpack: 0.2444, IoU.umbrella: 0.8213, IoU.handbag: 0.2338, IoU.tie: 0.6532, IoU.suitcase: 0.7770, IoU.frisbee: 0.9535, IoU.skis: 0.3751, IoU.snowboard: 0.6049, IoU.sports ball: 0.8645, IoU.kite: 0.6817, IoU.baseball bat: 0.6163, IoU.baseball glove: 0.0260, IoU.skateboard: 0.7128, IoU.surfboard: 0.9015, IoU.tennis racket: 0.3014, IoU.bottle: 0.7235, IoU.wine glass: 0.8559, IoU.cup: 0.7006, IoU.fork: 0.5564, IoU.knife: 0.7805, IoU.spoon: 0.5319, IoU.bowl: 0.6032, IoU.banana: 0.8038, IoU.apple: 0.7210, IoU.sandwich: 0.8723, IoU.orange: 0.6696, IoU.broccoli: 0.9178, IoU.carrot: 0.5115, IoU.hot dog: 0.5326, IoU.pizza: 0.9495, IoU.donut: 0.8054, IoU.cake: 0.8393, IoU.chair: 0.5996, IoU.couch: 0.7631, IoU.potted plant: 0.3533, IoU.bed: 0.7066, IoU.dining table: 0.6335, IoU.toilet: 0.9028, IoU.tv: 0.8046, IoU.laptop: 0.8654, IoU.mouse: 0.8315, IoU.remote: 0.7100, IoU.keyboard: 0.8600, IoU.cell phone: 0.8528, IoU.microwave: 0.6140, IoU.oven: 0.6325, IoU.toaster: 0.6128, IoU.sink: 0.7692, IoU.refrigerator: 0.8014, IoU.book: 0.7969, IoU.clock: 0.7696, IoU.vase: 0.6160, IoU.scissors: 0.8313, IoU.teddy bear: 0.8479, IoU.hair drier: 0.0000, IoU.toothbrush: 0.2985, IoU.banner: 0.3502, IoU.blanket: 0.0000, IoU.branch: 0.3556, IoU.bridge: 0.0358, IoU.building-other: 0.5770, IoU.bush: 0.2324, IoU.cabinet: 0.2421, IoU.cage: 0.1626, IoU.cardboard: 0.2426, IoU.carpet: 0.5876, IoU.ceiling-other: 0.7095, IoU.ceiling-tile: 0.1265, IoU.cloth: 0.0200, IoU.clothes: 0.2510, IoU.clouds: 0.5657, IoU.counter: 0.4268, IoU.cupboard: 0.5498, IoU.curtain: 0.6987, IoU.desk-stuff: 0.3117, IoU.dirt: 0.3372, IoU.door-stuff: 0.4027, IoU.fence: 0.4588, IoU.floor-marble: 0.0000, IoU.floor-other: 0.3486, IoU.floor-stone: 0.2525, IoU.floor-tile: 0.6541, IoU.floor-wood: 0.7616, IoU.flower: 0.2222, IoU.fog: 0.0000, IoU.food-other: 0.3933, IoU.fruit: 0.6394, IoU.furniture-other: 0.1755, IoU.grass: 0.7408, IoU.gravel: 0.3059, IoU.ground-other: 0.0777, IoU.hill: 0.2544, IoU.house: 0.3285, IoU.leaves: 0.1769, IoU.light: 0.4316, IoU.mat: 0.3166, IoU.metal: 0.2143, IoU.mirror-stuff: 0.4378, IoU.moss: 0.0000, IoU.mountain: 0.3467, IoU.mud: 0.0064, IoU.napkin: 0.1449, IoU.net: 0.4549, IoU.paper: 0.4762, IoU.pavement: 0.5255, IoU.pillow: 0.0000, IoU.plant-other: 0.2967, IoU.plastic: 0.2910, IoU.platform: 0.4300, IoU.playingfield: 0.6571, IoU.railing: 0.1538, IoU.railroad: 0.6085, IoU.river: 0.4656, IoU.road: 0.7171, IoU.rock: 0.4541, IoU.roof: 0.0364, IoU.rug: 0.4265, IoU.salad: 0.2615, IoU.sand: 0.7120, IoU.sea: 0.7490, IoU.shelf: 0.2626, IoU.sky-other: 0.6269, IoU.skyscraper: 0.1033, IoU.snow: 0.9148, IoU.solid-other: nan, IoU.stairs: 0.4404, IoU.stone: 0.0596, IoU.straw: 0.1341, IoU.structural-other: 0.1858, IoU.table: 0.2403, IoU.tent: 0.7431, IoU.textile-other: 0.2046, IoU.towel: 0.4290, IoU.tree: 0.7776, IoU.vegetable: 0.4963, IoU.wall-brick: 0.4944, IoU.wall-concrete: 0.2572, IoU.wall-other: 0.6327, IoU.wall-panel: 0.0632, IoU.wall-stone: 0.3238, IoU.wall-tile: 0.5911, IoU.wall-wood: 0.4326, IoU.water-other: 0.2562, IoU.waterdrops: 0.0000, IoU.window-blind: 0.3516, IoU.window-other: 0.5422, IoU.wood: 0.1477, Acc.person: 0.9448, Acc.bicycle: 0.9147, Acc.car: 0.5486, Acc.motorcycle: 0.9523, Acc.airplane: 0.9358, Acc.bus: 0.8316, Acc.train: 0.9696, Acc.truck: 0.9028, Acc.boat: 0.8802, Acc.traffic light: 0.9262, Acc.fire hydrant: 0.9686, Acc.stop sign: 0.9759, Acc.parking meter: 0.7545, Acc.bench: 0.6791, Acc.bird: 0.8331, Acc.cat: 0.9634, Acc.dog: 0.9608, Acc.horse: 0.9567, Acc.sheep: 0.9073, Acc.cow: 0.9801, Acc.elephant: 0.9630, Acc.bear: 0.9469, Acc.zebra: 0.9571, Acc.giraffe: 0.9460, Acc.backpack: 0.5541, Acc.umbrella: 0.8636, Acc.handbag: 0.4129, Acc.tie: 0.6555, Acc.suitcase: 0.9183, Acc.frisbee: 0.9820, Acc.skis: 0.5670, Acc.snowboard: 0.7641, Acc.sports ball: 0.9488, Acc.kite: 0.8195, Acc.baseball bat: 0.7792, Acc.baseball glove: 0.0275, Acc.skateboard: 0.8844, Acc.surfboard: 0.9444, Acc.tennis racket: 0.3157, Acc.bottle: 0.8474, Acc.wine glass: 0.9182, Acc.cup: 0.8438, Acc.fork: 0.7263, Acc.knife: 0.8837, Acc.spoon: 0.7085, Acc.bowl: 0.7190, Acc.banana: 0.9109, Acc.apple: 0.7895, Acc.sandwich: 0.9638, Acc.orange: 0.8853, Acc.broccoli: 0.9712, Acc.carrot: 0.5981, Acc.hot dog: 0.9750, Acc.pizza: 0.9669, Acc.donut: 0.9435, Acc.cake: 0.8844, Acc.chair: 0.7535, Acc.couch: 0.9394, Acc.potted plant: 0.4535, Acc.bed: 0.8344, Acc.dining table: 0.7778, Acc.toilet: 0.9621, Acc.tv: 0.9161, Acc.laptop: 0.9723, Acc.mouse: 0.9000, Acc.remote: 0.8818, Acc.keyboard: 0.9766, Acc.cell phone: 0.9589, Acc.microwave: 0.7455, Acc.oven: 0.8002, Acc.toaster: 0.6310, Acc.sink: 0.8026, Acc.refrigerator: 0.8814, Acc.book: 0.9189, Acc.clock: 0.7944, Acc.vase: 0.9003, Acc.scissors: 0.9313, Acc.teddy bear: 0.9191, Acc.hair drier: 0.0000, Acc.toothbrush: 0.4283, Acc.banner: 0.6543, Acc.blanket: 0.0000, Acc.branch: 0.3907, Acc.bridge: 0.0582, Acc.building-other: 0.7466, Acc.bush: 0.3390, Acc.cabinet: 0.4761, Acc.cage: 0.7944, Acc.cardboard: 0.3077, Acc.carpet: 0.7818, Acc.ceiling-other: 0.8068, Acc.ceiling-tile: 0.1420, Acc.cloth: 0.0236, Acc.clothes: 0.3250, Acc.clouds: 0.7450, Acc.counter: 0.5363, Acc.cupboard: 0.7055, Acc.curtain: 0.8544, Acc.desk-stuff: 0.3781, Acc.dirt: 0.5460, Acc.door-stuff: 0.5235, Acc.fence: 0.7223, Acc.floor-marble: 0.0000, Acc.floor-other: 0.5678, Acc.floor-stone: 0.3359, Acc.floor-tile: 0.7609, Acc.floor-wood: 0.8645, Acc.flower: 0.5272, Acc.fog: 0.0000, Acc.food-other: 0.5724, Acc.fruit: 0.8401, Acc.furniture-other: 0.2842, Acc.grass: 0.8331, Acc.gravel: 0.3704, Acc.ground-other: 0.1510, Acc.hill: 0.3315, Acc.house: 0.5336, Acc.leaves: 0.1855, Acc.light: 0.6130, Acc.mat: 0.5863, Acc.metal: 0.3487, Acc.mirror-stuff: 0.5792, Acc.moss: 0.0000, Acc.mountain: 0.5990, Acc.mud: 0.0179, Acc.napkin: 0.9267, Acc.net: 0.5786, Acc.paper: 0.6928, Acc.pavement: 0.7411, Acc.pillow: 0.0000, Acc.plant-other: 0.5064, Acc.plastic: 0.4046, Acc.platform: 0.6096, Acc.playingfield: 0.7665, Acc.railing: 0.2382, Acc.railroad: 0.7718, Acc.river: 0.6432, Acc.road: 0.8034, Acc.rock: 0.6038, Acc.roof: 0.0513, Acc.rug: 0.5470, Acc.salad: 0.2701, Acc.sand: 0.8963, Acc.sea: 0.9239, Acc.shelf: 0.5001, Acc.sky-other: 0.7564, Acc.skyscraper: 0.1150, Acc.snow: 0.9463, Acc.solid-other: nan, Acc.stairs: 0.7002, Acc.stone: 0.1536, Acc.straw: 0.3362, Acc.structural-other: 0.2937, Acc.table: 0.3808, Acc.tent: 0.9216, Acc.textile-other: 0.3062, Acc.towel: 0.5133, Acc.tree: 0.8722, Acc.vegetable: 0.7779, Acc.wall-brick: 0.6596, Acc.wall-concrete: 0.3521, Acc.wall-other: 0.8352, Acc.wall-panel: 0.0656, Acc.wall-stone: 0.3713, Acc.wall-tile: 0.8200, Acc.wall-wood: 0.6558, Acc.water-other: 0.3113, Acc.waterdrops: nan, Acc.window-blind: 0.6131, Acc.window-other: 0.6728, Acc.wood: 0.3313 2022-05-05 04:28:15,120 - mmseg - INFO - Iter [32050/40000] lr: 2.854e-07, eta: 1:51:29, time: 1.463, data_time: 0.696, memory: 51557, decode.loss_cls: 0.4412, decode.loss_mask: 0.6979, decode.loss_dice: 0.9622, decode.d0.loss_cls: 3.3053, decode.d0.loss_mask: 0.7529, decode.d0.loss_dice: 1.1163, decode.d1.loss_cls: 0.6766, decode.d1.loss_mask: 0.7274, decode.d1.loss_dice: 1.0244, decode.d2.loss_cls: 0.5460, decode.d2.loss_mask: 0.7162, decode.d2.loss_dice: 0.9896, decode.d3.loss_cls: 0.5020, decode.d3.loss_mask: 0.7074, decode.d3.loss_dice: 0.9630, decode.d4.loss_cls: 0.4864, decode.d4.loss_mask: 0.7043, decode.d4.loss_dice: 0.9656, decode.d5.loss_cls: 0.4669, decode.d5.loss_mask: 0.7002, decode.d5.loss_dice: 0.9612, decode.d6.loss_cls: 0.4544, decode.d6.loss_mask: 0.7012, decode.d6.loss_dice: 0.9550, decode.d7.loss_cls: 0.4434, decode.d7.loss_mask: 0.6962, decode.d7.loss_dice: 0.9606, decode.d8.loss_cls: 0.4431, decode.d8.loss_mask: 0.6942, decode.d8.loss_dice: 0.9596, loss: 24.7206 2022-05-05 04:28:53,614 - mmseg - INFO - Iter [32100/40000] lr: 2.836e-07, eta: 1:50:46, time: 0.769, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4626, decode.loss_mask: 0.6801, decode.loss_dice: 0.9564, decode.d0.loss_cls: 3.4469, decode.d0.loss_mask: 0.7437, decode.d0.loss_dice: 1.1388, decode.d1.loss_cls: 0.7188, decode.d1.loss_mask: 0.7123, decode.d1.loss_dice: 1.0323, decode.d2.loss_cls: 0.5908, decode.d2.loss_mask: 0.6991, decode.d2.loss_dice: 0.9854, decode.d3.loss_cls: 0.5341, decode.d3.loss_mask: 0.6946, decode.d3.loss_dice: 0.9694, decode.d4.loss_cls: 0.5072, decode.d4.loss_mask: 0.6900, decode.d4.loss_dice: 0.9699, decode.d5.loss_cls: 0.4934, decode.d5.loss_mask: 0.6846, decode.d5.loss_dice: 0.9639, decode.d6.loss_cls: 0.4691, decode.d6.loss_mask: 0.6863, decode.d6.loss_dice: 0.9588, decode.d7.loss_cls: 0.4583, decode.d7.loss_mask: 0.6836, decode.d7.loss_dice: 0.9628, decode.d8.loss_cls: 0.4560, decode.d8.loss_mask: 0.6858, decode.d8.loss_dice: 0.9569, loss: 24.9915 2022-05-05 04:29:32,102 - mmseg - INFO - Iter [32150/40000] lr: 2.818e-07, eta: 1:50:04, time: 0.770, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4870, decode.loss_mask: 0.6888, decode.loss_dice: 1.0057, decode.d0.loss_cls: 3.4759, decode.d0.loss_mask: 0.7456, decode.d0.loss_dice: 1.1818, decode.d1.loss_cls: 0.7435, decode.d1.loss_mask: 0.7227, decode.d1.loss_dice: 1.0842, decode.d2.loss_cls: 0.6133, decode.d2.loss_mask: 0.7027, decode.d2.loss_dice: 1.0428, decode.d3.loss_cls: 0.5453, decode.d3.loss_mask: 0.6947, decode.d3.loss_dice: 1.0256, decode.d4.loss_cls: 0.5252, decode.d4.loss_mask: 0.6900, decode.d4.loss_dice: 1.0191, decode.d5.loss_cls: 0.5109, decode.d5.loss_mask: 0.6909, decode.d5.loss_dice: 1.0133, decode.d6.loss_cls: 0.4981, decode.d6.loss_mask: 0.6879, decode.d6.loss_dice: 1.0064, decode.d7.loss_cls: 0.4877, decode.d7.loss_mask: 0.6902, decode.d7.loss_dice: 1.0097, decode.d8.loss_cls: 0.4858, decode.d8.loss_mask: 0.6917, decode.d8.loss_dice: 1.0096, loss: 25.7760 2022-05-05 04:30:10,822 - mmseg - INFO - Iter [32200/40000] lr: 2.800e-07, eta: 1:49:21, time: 0.774, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4361, decode.loss_mask: 0.6916, decode.loss_dice: 0.9558, decode.d0.loss_cls: 3.3877, decode.d0.loss_mask: 0.7391, decode.d0.loss_dice: 1.1196, decode.d1.loss_cls: 0.7151, decode.d1.loss_mask: 0.7184, decode.d1.loss_dice: 1.0276, decode.d2.loss_cls: 0.5690, decode.d2.loss_mask: 0.7053, decode.d2.loss_dice: 0.9856, decode.d3.loss_cls: 0.5126, decode.d3.loss_mask: 0.6956, decode.d3.loss_dice: 0.9631, decode.d4.loss_cls: 0.4841, decode.d4.loss_mask: 0.6955, decode.d4.loss_dice: 0.9649, decode.d5.loss_cls: 0.4606, decode.d5.loss_mask: 0.6920, decode.d5.loss_dice: 0.9631, decode.d6.loss_cls: 0.4520, decode.d6.loss_mask: 0.6906, decode.d6.loss_dice: 0.9550, decode.d7.loss_cls: 0.4478, decode.d7.loss_mask: 0.6912, decode.d7.loss_dice: 0.9540, decode.d8.loss_cls: 0.4415, decode.d8.loss_mask: 0.6913, decode.d8.loss_dice: 0.9529, loss: 24.7585 2022-05-05 04:30:49,354 - mmseg - INFO - Iter [32250/40000] lr: 2.782e-07, eta: 1:48:38, time: 0.771, data_time: 0.008, memory: 51557, decode.loss_cls: 0.4423, decode.loss_mask: 0.6900, decode.loss_dice: 0.9806, decode.d0.loss_cls: 3.4192, decode.d0.loss_mask: 0.7389, decode.d0.loss_dice: 1.1291, decode.d1.loss_cls: 0.7060, decode.d1.loss_mask: 0.7186, decode.d1.loss_dice: 1.0461, decode.d2.loss_cls: 0.5685, decode.d2.loss_mask: 0.7012, decode.d2.loss_dice: 1.0089, decode.d3.loss_cls: 0.5171, decode.d3.loss_mask: 0.6953, decode.d3.loss_dice: 0.9894, decode.d4.loss_cls: 0.4872, decode.d4.loss_mask: 0.6936, decode.d4.loss_dice: 0.9886, decode.d5.loss_cls: 0.4758, decode.d5.loss_mask: 0.6901, decode.d5.loss_dice: 0.9815, decode.d6.loss_cls: 0.4573, decode.d6.loss_mask: 0.6902, decode.d6.loss_dice: 0.9824, decode.d7.loss_cls: 0.4521, decode.d7.loss_mask: 0.6916, decode.d7.loss_dice: 0.9807, decode.d8.loss_cls: 0.4427, decode.d8.loss_mask: 0.6896, decode.d8.loss_dice: 0.9727, loss: 25.0275 2022-05-05 04:31:28,248 - mmseg - INFO - Iter [32300/40000] lr: 2.764e-07, eta: 1:47:55, time: 0.778, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4603, decode.loss_mask: 0.6973, decode.loss_dice: 1.0203, decode.d0.loss_cls: 3.4149, decode.d0.loss_mask: 0.7478, decode.d0.loss_dice: 1.1866, decode.d1.loss_cls: 0.6955, decode.d1.loss_mask: 0.7320, decode.d1.loss_dice: 1.1030, decode.d2.loss_cls: 0.5595, decode.d2.loss_mask: 0.7186, decode.d2.loss_dice: 1.0616, decode.d3.loss_cls: 0.5081, decode.d3.loss_mask: 0.7072, decode.d3.loss_dice: 1.0377, decode.d4.loss_cls: 0.5018, decode.d4.loss_mask: 0.7062, decode.d4.loss_dice: 1.0344, decode.d5.loss_cls: 0.4853, decode.d5.loss_mask: 0.7039, decode.d5.loss_dice: 1.0326, decode.d6.loss_cls: 0.4698, decode.d6.loss_mask: 0.7032, decode.d6.loss_dice: 1.0250, decode.d7.loss_cls: 0.4642, decode.d7.loss_mask: 0.7031, decode.d7.loss_dice: 1.0249, decode.d8.loss_cls: 0.4578, decode.d8.loss_mask: 0.7002, decode.d8.loss_dice: 1.0259, loss: 25.6885 2022-05-05 04:32:07,239 - mmseg - INFO - Iter [32350/40000] lr: 2.746e-07, eta: 1:47:12, time: 0.780, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4677, decode.loss_mask: 0.7144, decode.loss_dice: 0.9940, decode.d0.loss_cls: 3.4630, decode.d0.loss_mask: 0.7705, decode.d0.loss_dice: 1.1588, decode.d1.loss_cls: 0.7386, decode.d1.loss_mask: 0.7462, decode.d1.loss_dice: 1.0618, decode.d2.loss_cls: 0.5953, decode.d2.loss_mask: 0.7267, decode.d2.loss_dice: 1.0194, decode.d3.loss_cls: 0.5340, decode.d3.loss_mask: 0.7228, decode.d3.loss_dice: 1.0001, decode.d4.loss_cls: 0.5064, decode.d4.loss_mask: 0.7210, decode.d4.loss_dice: 1.0010, decode.d5.loss_cls: 0.4967, decode.d5.loss_mask: 0.7161, decode.d5.loss_dice: 0.9993, decode.d6.loss_cls: 0.4822, decode.d6.loss_mask: 0.7152, decode.d6.loss_dice: 0.9917, decode.d7.loss_cls: 0.4682, decode.d7.loss_mask: 0.7151, decode.d7.loss_dice: 0.9864, decode.d8.loss_cls: 0.4669, decode.d8.loss_mask: 0.7132, decode.d8.loss_dice: 0.9905, loss: 25.6831 2022-05-05 04:32:45,961 - mmseg - INFO - Iter [32400/40000] lr: 2.728e-07, eta: 1:46:29, time: 0.774, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4459, decode.loss_mask: 0.6831, decode.loss_dice: 0.9864, decode.d0.loss_cls: 3.3199, decode.d0.loss_mask: 0.7391, decode.d0.loss_dice: 1.1339, decode.d1.loss_cls: 0.6842, decode.d1.loss_mask: 0.7149, decode.d1.loss_dice: 1.0562, decode.d2.loss_cls: 0.5582, decode.d2.loss_mask: 0.6951, decode.d2.loss_dice: 1.0166, decode.d3.loss_cls: 0.5146, decode.d3.loss_mask: 0.6916, decode.d3.loss_dice: 0.9992, decode.d4.loss_cls: 0.4866, decode.d4.loss_mask: 0.6904, decode.d4.loss_dice: 0.9952, decode.d5.loss_cls: 0.4662, decode.d5.loss_mask: 0.6905, decode.d5.loss_dice: 0.9906, decode.d6.loss_cls: 0.4446, decode.d6.loss_mask: 0.6888, decode.d6.loss_dice: 0.9856, decode.d7.loss_cls: 0.4487, decode.d7.loss_mask: 0.6848, decode.d7.loss_dice: 0.9850, decode.d8.loss_cls: 0.4449, decode.d8.loss_mask: 0.6841, decode.d8.loss_dice: 0.9869, loss: 24.9118 2022-05-05 04:33:24,276 - mmseg - INFO - Iter [32450/40000] lr: 2.710e-07, eta: 1:45:46, time: 0.766, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4918, decode.loss_mask: 0.6921, decode.loss_dice: 0.9732, decode.d0.loss_cls: 3.4965, decode.d0.loss_mask: 0.7518, decode.d0.loss_dice: 1.1631, decode.d1.loss_cls: 0.7507, decode.d1.loss_mask: 0.7245, decode.d1.loss_dice: 1.0542, decode.d2.loss_cls: 0.6145, decode.d2.loss_mask: 0.7076, decode.d2.loss_dice: 1.0124, decode.d3.loss_cls: 0.5656, decode.d3.loss_mask: 0.6995, decode.d3.loss_dice: 0.9878, decode.d4.loss_cls: 0.5423, decode.d4.loss_mask: 0.6969, decode.d4.loss_dice: 0.9884, decode.d5.loss_cls: 0.5297, decode.d5.loss_mask: 0.6960, decode.d5.loss_dice: 0.9834, decode.d6.loss_cls: 0.5117, decode.d6.loss_mask: 0.6942, decode.d6.loss_dice: 0.9778, decode.d7.loss_cls: 0.4976, decode.d7.loss_mask: 0.6914, decode.d7.loss_dice: 0.9779, decode.d8.loss_cls: 0.4975, decode.d8.loss_mask: 0.6909, decode.d8.loss_dice: 0.9785, loss: 25.6394 2022-05-05 04:34:03,421 - mmseg - INFO - Iter [32500/40000] lr: 2.692e-07, eta: 1:45:04, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4225, decode.loss_mask: 0.6732, decode.loss_dice: 0.9756, decode.d0.loss_cls: 3.4235, decode.d0.loss_mask: 0.7310, decode.d0.loss_dice: 1.1430, decode.d1.loss_cls: 0.6833, decode.d1.loss_mask: 0.7046, decode.d1.loss_dice: 1.0480, decode.d2.loss_cls: 0.5337, decode.d2.loss_mask: 0.6869, decode.d2.loss_dice: 1.0076, decode.d3.loss_cls: 0.4945, decode.d3.loss_mask: 0.6814, decode.d3.loss_dice: 0.9823, decode.d4.loss_cls: 0.4662, decode.d4.loss_mask: 0.6741, decode.d4.loss_dice: 0.9827, decode.d5.loss_cls: 0.4471, decode.d5.loss_mask: 0.6746, decode.d5.loss_dice: 0.9801, decode.d6.loss_cls: 0.4347, decode.d6.loss_mask: 0.6737, decode.d6.loss_dice: 0.9721, decode.d7.loss_cls: 0.4287, decode.d7.loss_mask: 0.6755, decode.d7.loss_dice: 0.9760, decode.d8.loss_cls: 0.4256, decode.d8.loss_mask: 0.6717, decode.d8.loss_dice: 0.9722, loss: 24.6461 2022-05-05 04:34:42,451 - mmseg - INFO - Iter [32550/40000] lr: 2.675e-07, eta: 1:44:21, time: 0.781, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4735, decode.loss_mask: 0.7007, decode.loss_dice: 0.9936, decode.d0.loss_cls: 3.3705, decode.d0.loss_mask: 0.7545, decode.d0.loss_dice: 1.1584, decode.d1.loss_cls: 0.7255, decode.d1.loss_mask: 0.7286, decode.d1.loss_dice: 1.0698, decode.d2.loss_cls: 0.6082, decode.d2.loss_mask: 0.7088, decode.d2.loss_dice: 1.0222, decode.d3.loss_cls: 0.5433, decode.d3.loss_mask: 0.7043, decode.d3.loss_dice: 1.0069, decode.d4.loss_cls: 0.5251, decode.d4.loss_mask: 0.7033, decode.d4.loss_dice: 1.0052, decode.d5.loss_cls: 0.5082, decode.d5.loss_mask: 0.6983, decode.d5.loss_dice: 1.0017, decode.d6.loss_cls: 0.4853, decode.d6.loss_mask: 0.7013, decode.d6.loss_dice: 0.9958, decode.d7.loss_cls: 0.4787, decode.d7.loss_mask: 0.7028, decode.d7.loss_dice: 0.9925, decode.d8.loss_cls: 0.4765, decode.d8.loss_mask: 0.7009, decode.d8.loss_dice: 0.9914, loss: 25.5360 2022-05-05 04:35:24,610 - mmseg - INFO - Iter [32600/40000] lr: 2.657e-07, eta: 1:43:39, time: 0.842, data_time: 0.059, memory: 51557, decode.loss_cls: 0.4776, decode.loss_mask: 0.6898, decode.loss_dice: 0.9875, decode.d0.loss_cls: 3.4424, decode.d0.loss_mask: 0.7312, decode.d0.loss_dice: 1.1558, decode.d1.loss_cls: 0.7334, decode.d1.loss_mask: 0.7169, decode.d1.loss_dice: 1.0622, decode.d2.loss_cls: 0.5938, decode.d2.loss_mask: 0.6977, decode.d2.loss_dice: 1.0175, decode.d3.loss_cls: 0.5425, decode.d3.loss_mask: 0.6953, decode.d3.loss_dice: 1.0011, decode.d4.loss_cls: 0.5169, decode.d4.loss_mask: 0.6933, decode.d4.loss_dice: 1.0043, decode.d5.loss_cls: 0.4994, decode.d5.loss_mask: 0.6920, decode.d5.loss_dice: 0.9972, decode.d6.loss_cls: 0.4931, decode.d6.loss_mask: 0.6891, decode.d6.loss_dice: 0.9848, decode.d7.loss_cls: 0.4784, decode.d7.loss_mask: 0.6895, decode.d7.loss_dice: 0.9865, decode.d8.loss_cls: 0.4831, decode.d8.loss_mask: 0.6885, decode.d8.loss_dice: 0.9860, loss: 25.4268 2022-05-05 04:36:03,420 - mmseg - INFO - Iter [32650/40000] lr: 2.639e-07, eta: 1:42:56, time: 0.777, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4602, decode.loss_mask: 0.6753, decode.loss_dice: 0.9760, decode.d0.loss_cls: 3.4764, decode.d0.loss_mask: 0.7357, decode.d0.loss_dice: 1.1462, decode.d1.loss_cls: 0.7230, decode.d1.loss_mask: 0.7051, decode.d1.loss_dice: 1.0469, decode.d2.loss_cls: 0.6022, decode.d2.loss_mask: 0.6872, decode.d2.loss_dice: 1.0033, decode.d3.loss_cls: 0.5360, decode.d3.loss_mask: 0.6834, decode.d3.loss_dice: 0.9827, decode.d4.loss_cls: 0.5142, decode.d4.loss_mask: 0.6828, decode.d4.loss_dice: 0.9885, decode.d5.loss_cls: 0.4940, decode.d5.loss_mask: 0.6792, decode.d5.loss_dice: 0.9784, decode.d6.loss_cls: 0.4803, decode.d6.loss_mask: 0.6748, decode.d6.loss_dice: 0.9720, decode.d7.loss_cls: 0.4727, decode.d7.loss_mask: 0.6763, decode.d7.loss_dice: 0.9695, decode.d8.loss_cls: 0.4605, decode.d8.loss_mask: 0.6731, decode.d8.loss_dice: 0.9780, loss: 25.1337 2022-05-05 04:36:42,572 - mmseg - INFO - Iter [32700/40000] lr: 2.621e-07, eta: 1:42:14, time: 0.784, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4661, decode.loss_mask: 0.7116, decode.loss_dice: 1.0217, decode.d0.loss_cls: 3.3924, decode.d0.loss_mask: 0.7615, decode.d0.loss_dice: 1.1861, decode.d1.loss_cls: 0.7030, decode.d1.loss_mask: 0.7381, decode.d1.loss_dice: 1.0926, decode.d2.loss_cls: 0.5766, decode.d2.loss_mask: 0.7242, decode.d2.loss_dice: 1.0504, decode.d3.loss_cls: 0.5253, decode.d3.loss_mask: 0.7207, decode.d3.loss_dice: 1.0312, decode.d4.loss_cls: 0.5001, decode.d4.loss_mask: 0.7154, decode.d4.loss_dice: 1.0375, decode.d5.loss_cls: 0.4921, decode.d5.loss_mask: 0.7173, decode.d5.loss_dice: 1.0249, decode.d6.loss_cls: 0.4770, decode.d6.loss_mask: 0.7162, decode.d6.loss_dice: 1.0173, decode.d7.loss_cls: 0.4628, decode.d7.loss_mask: 0.7113, decode.d7.loss_dice: 1.0189, decode.d8.loss_cls: 0.4647, decode.d8.loss_mask: 0.7132, decode.d8.loss_dice: 1.0242, loss: 25.7945 2022-05-05 04:37:21,543 - mmseg - INFO - Iter [32750/40000] lr: 2.603e-07, eta: 1:41:31, time: 0.779, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4221, decode.loss_mask: 0.6833, decode.loss_dice: 0.9641, decode.d0.loss_cls: 3.4233, decode.d0.loss_mask: 0.7336, decode.d0.loss_dice: 1.1312, decode.d1.loss_cls: 0.6703, decode.d1.loss_mask: 0.7168, decode.d1.loss_dice: 1.0345, decode.d2.loss_cls: 0.5294, decode.d2.loss_mask: 0.6986, decode.d2.loss_dice: 0.9985, decode.d3.loss_cls: 0.4804, decode.d3.loss_mask: 0.6911, decode.d3.loss_dice: 0.9824, decode.d4.loss_cls: 0.4576, decode.d4.loss_mask: 0.6882, decode.d4.loss_dice: 0.9795, decode.d5.loss_cls: 0.4470, decode.d5.loss_mask: 0.6848, decode.d5.loss_dice: 0.9750, decode.d6.loss_cls: 0.4381, decode.d6.loss_mask: 0.6871, decode.d6.loss_dice: 0.9658, decode.d7.loss_cls: 0.4346, decode.d7.loss_mask: 0.6822, decode.d7.loss_dice: 0.9661, decode.d8.loss_cls: 0.4239, decode.d8.loss_mask: 0.6842, decode.d8.loss_dice: 0.9670, loss: 24.6407 2022-05-05 04:38:00,571 - mmseg - INFO - Iter [32800/40000] lr: 2.585e-07, eta: 1:40:48, time: 0.780, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4530, decode.loss_mask: 0.6818, decode.loss_dice: 0.9554, decode.d0.loss_cls: 3.4159, decode.d0.loss_mask: 0.7296, decode.d0.loss_dice: 1.1333, decode.d1.loss_cls: 0.6879, decode.d1.loss_mask: 0.7102, decode.d1.loss_dice: 1.0234, decode.d2.loss_cls: 0.5732, decode.d2.loss_mask: 0.6956, decode.d2.loss_dice: 0.9912, decode.d3.loss_cls: 0.5163, decode.d3.loss_mask: 0.6895, decode.d3.loss_dice: 0.9711, decode.d4.loss_cls: 0.4906, decode.d4.loss_mask: 0.6866, decode.d4.loss_dice: 0.9735, decode.d5.loss_cls: 0.4750, decode.d5.loss_mask: 0.6860, decode.d5.loss_dice: 0.9658, decode.d6.loss_cls: 0.4632, decode.d6.loss_mask: 0.6827, decode.d6.loss_dice: 0.9596, decode.d7.loss_cls: 0.4481, decode.d7.loss_mask: 0.6839, decode.d7.loss_dice: 0.9614, decode.d8.loss_cls: 0.4557, decode.d8.loss_mask: 0.6827, decode.d8.loss_dice: 0.9618, loss: 24.8041 2022-05-05 04:38:39,870 - mmseg - INFO - Iter [32850/40000] lr: 2.567e-07, eta: 1:40:06, time: 0.786, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4570, decode.loss_mask: 0.6744, decode.loss_dice: 0.9884, decode.d0.loss_cls: 3.4535, decode.d0.loss_mask: 0.7312, decode.d0.loss_dice: 1.1675, decode.d1.loss_cls: 0.7398, decode.d1.loss_mask: 0.7012, decode.d1.loss_dice: 1.0620, decode.d2.loss_cls: 0.5923, decode.d2.loss_mask: 0.6912, decode.d2.loss_dice: 1.0229, decode.d3.loss_cls: 0.5339, decode.d3.loss_mask: 0.6828, decode.d3.loss_dice: 0.9998, decode.d4.loss_cls: 0.5064, decode.d4.loss_mask: 0.6829, decode.d4.loss_dice: 1.0016, decode.d5.loss_cls: 0.4840, decode.d5.loss_mask: 0.6809, decode.d5.loss_dice: 0.9958, decode.d6.loss_cls: 0.4651, decode.d6.loss_mask: 0.6775, decode.d6.loss_dice: 0.9895, decode.d7.loss_cls: 0.4656, decode.d7.loss_mask: 0.6780, decode.d7.loss_dice: 0.9926, decode.d8.loss_cls: 0.4574, decode.d8.loss_mask: 0.6752, decode.d8.loss_dice: 0.9877, loss: 25.2382 2022-05-05 04:39:19,482 - mmseg - INFO - Iter [32900/40000] lr: 2.549e-07, eta: 1:39:23, time: 0.791, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4810, decode.loss_mask: 0.6814, decode.loss_dice: 0.9600, decode.d0.loss_cls: 3.4522, decode.d0.loss_mask: 0.7363, decode.d0.loss_dice: 1.1247, decode.d1.loss_cls: 0.7285, decode.d1.loss_mask: 0.7205, decode.d1.loss_dice: 1.0396, decode.d2.loss_cls: 0.5973, decode.d2.loss_mask: 0.7054, decode.d2.loss_dice: 1.0009, decode.d3.loss_cls: 0.5471, decode.d3.loss_mask: 0.6921, decode.d3.loss_dice: 0.9790, decode.d4.loss_cls: 0.5131, decode.d4.loss_mask: 0.6911, decode.d4.loss_dice: 0.9741, decode.d5.loss_cls: 0.4966, decode.d5.loss_mask: 0.6913, decode.d5.loss_dice: 0.9738, decode.d6.loss_cls: 0.4873, decode.d6.loss_mask: 0.6883, decode.d6.loss_dice: 0.9658, decode.d7.loss_cls: 0.4780, decode.d7.loss_mask: 0.6858, decode.d7.loss_dice: 0.9650, decode.d8.loss_cls: 0.4735, decode.d8.loss_mask: 0.6858, decode.d8.loss_dice: 0.9636, loss: 25.1791 2022-05-05 04:39:58,504 - mmseg - INFO - Iter [32950/40000] lr: 2.531e-07, eta: 1:38:41, time: 0.781, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4658, decode.loss_mask: 0.7130, decode.loss_dice: 1.0043, decode.d0.loss_cls: 3.4195, decode.d0.loss_mask: 0.7660, decode.d0.loss_dice: 1.1715, decode.d1.loss_cls: 0.7036, decode.d1.loss_mask: 0.7390, decode.d1.loss_dice: 1.0691, decode.d2.loss_cls: 0.5764, decode.d2.loss_mask: 0.7263, decode.d2.loss_dice: 1.0327, decode.d3.loss_cls: 0.5227, decode.d3.loss_mask: 0.7209, decode.d3.loss_dice: 1.0120, decode.d4.loss_cls: 0.5033, decode.d4.loss_mask: 0.7202, decode.d4.loss_dice: 1.0120, decode.d5.loss_cls: 0.4907, decode.d5.loss_mask: 0.7160, decode.d5.loss_dice: 1.0109, decode.d6.loss_cls: 0.4817, decode.d6.loss_mask: 0.7148, decode.d6.loss_dice: 1.0021, decode.d7.loss_cls: 0.4698, decode.d7.loss_mask: 0.7133, decode.d7.loss_dice: 1.0064, decode.d8.loss_cls: 0.4707, decode.d8.loss_mask: 0.7130, decode.d8.loss_dice: 1.0014, loss: 25.6691 2022-05-05 04:40:36,930 - mmseg - INFO - Saving checkpoint at 33000 iterations 2022-05-05 04:41:02,358 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 04:41:02,361 - mmseg - INFO - Iter [33000/40000] lr: 2.513e-07, eta: 1:38:03, time: 1.275, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4870, decode.loss_mask: 0.6905, decode.loss_dice: 0.9902, decode.d0.loss_cls: 3.4284, decode.d0.loss_mask: 0.7468, decode.d0.loss_dice: 1.1579, decode.d1.loss_cls: 0.7314, decode.d1.loss_mask: 0.7227, decode.d1.loss_dice: 1.0653, decode.d2.loss_cls: 0.6015, decode.d2.loss_mask: 0.7064, decode.d2.loss_dice: 1.0263, decode.d3.loss_cls: 0.5515, decode.d3.loss_mask: 0.7032, decode.d3.loss_dice: 1.0007, decode.d4.loss_cls: 0.5177, decode.d4.loss_mask: 0.6970, decode.d4.loss_dice: 0.9967, decode.d5.loss_cls: 0.5076, decode.d5.loss_mask: 0.6984, decode.d5.loss_dice: 0.9988, decode.d6.loss_cls: 0.4931, decode.d6.loss_mask: 0.6993, decode.d6.loss_dice: 0.9927, decode.d7.loss_cls: 0.4867, decode.d7.loss_mask: 0.6961, decode.d7.loss_dice: 0.9890, decode.d8.loss_cls: 0.4838, decode.d8.loss_mask: 0.6970, decode.d8.loss_dice: 0.9918, loss: 25.5559 2022-05-05 04:41:41,845 - mmseg - INFO - Iter [33050/40000] lr: 2.495e-07, eta: 1:37:21, time: 0.792, data_time: 0.011, memory: 51557, decode.loss_cls: 0.4507, decode.loss_mask: 0.6920, decode.loss_dice: 0.9761, decode.d0.loss_cls: 3.3739, decode.d0.loss_mask: 0.7501, decode.d0.loss_dice: 1.1338, decode.d1.loss_cls: 0.7045, decode.d1.loss_mask: 0.7238, decode.d1.loss_dice: 1.0460, decode.d2.loss_cls: 0.5667, decode.d2.loss_mask: 0.7052, decode.d2.loss_dice: 1.0004, decode.d3.loss_cls: 0.5125, decode.d3.loss_mask: 0.6982, decode.d3.loss_dice: 0.9820, decode.d4.loss_cls: 0.4939, decode.d4.loss_mask: 0.6961, decode.d4.loss_dice: 0.9824, decode.d5.loss_cls: 0.4761, decode.d5.loss_mask: 0.6970, decode.d5.loss_dice: 0.9806, decode.d6.loss_cls: 0.4604, decode.d6.loss_mask: 0.6947, decode.d6.loss_dice: 0.9758, decode.d7.loss_cls: 0.4570, decode.d7.loss_mask: 0.6922, decode.d7.loss_dice: 0.9756, decode.d8.loss_cls: 0.4560, decode.d8.loss_mask: 0.6920, decode.d8.loss_dice: 0.9781, loss: 25.0239 2022-05-05 04:42:20,852 - mmseg - INFO - Iter [33100/40000] lr: 2.477e-07, eta: 1:36:38, time: 0.780, data_time: 0.008, memory: 51557, decode.loss_cls: 0.4791, decode.loss_mask: 0.7135, decode.loss_dice: 0.9759, decode.d0.loss_cls: 3.3941, decode.d0.loss_mask: 0.7729, decode.d0.loss_dice: 1.1445, decode.d1.loss_cls: 0.7343, decode.d1.loss_mask: 0.7469, decode.d1.loss_dice: 1.0511, decode.d2.loss_cls: 0.6017, decode.d2.loss_mask: 0.7301, decode.d2.loss_dice: 1.0104, decode.d3.loss_cls: 0.5494, decode.d3.loss_mask: 0.7250, decode.d3.loss_dice: 0.9886, decode.d4.loss_cls: 0.5186, decode.d4.loss_mask: 0.7184, decode.d4.loss_dice: 0.9882, decode.d5.loss_cls: 0.4994, decode.d5.loss_mask: 0.7170, decode.d5.loss_dice: 0.9881, decode.d6.loss_cls: 0.4852, decode.d6.loss_mask: 0.7169, decode.d6.loss_dice: 0.9783, decode.d7.loss_cls: 0.4810, decode.d7.loss_mask: 0.7180, decode.d7.loss_dice: 0.9782, decode.d8.loss_cls: 0.4821, decode.d8.loss_mask: 0.7161, decode.d8.loss_dice: 0.9747, loss: 25.5777 2022-05-05 04:43:00,100 - mmseg - INFO - Iter [33150/40000] lr: 2.459e-07, eta: 1:35:55, time: 0.785, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4647, decode.loss_mask: 0.6796, decode.loss_dice: 0.9761, decode.d0.loss_cls: 3.4454, decode.d0.loss_mask: 0.7362, decode.d0.loss_dice: 1.1444, decode.d1.loss_cls: 0.7203, decode.d1.loss_mask: 0.7081, decode.d1.loss_dice: 1.0458, decode.d2.loss_cls: 0.5780, decode.d2.loss_mask: 0.6929, decode.d2.loss_dice: 1.0085, decode.d3.loss_cls: 0.5171, decode.d3.loss_mask: 0.6926, decode.d3.loss_dice: 0.9953, decode.d4.loss_cls: 0.4950, decode.d4.loss_mask: 0.6884, decode.d4.loss_dice: 0.9885, decode.d5.loss_cls: 0.4773, decode.d5.loss_mask: 0.6847, decode.d5.loss_dice: 0.9817, decode.d6.loss_cls: 0.4705, decode.d6.loss_mask: 0.6819, decode.d6.loss_dice: 0.9805, decode.d7.loss_cls: 0.4541, decode.d7.loss_mask: 0.6812, decode.d7.loss_dice: 0.9775, decode.d8.loss_cls: 0.4574, decode.d8.loss_mask: 0.6804, decode.d8.loss_dice: 0.9789, loss: 25.0831 2022-05-05 04:43:41,371 - mmseg - INFO - Iter [33200/40000] lr: 2.441e-07, eta: 1:35:13, time: 0.825, data_time: 0.056, memory: 51557, decode.loss_cls: 0.4590, decode.loss_mask: 0.6962, decode.loss_dice: 0.9845, decode.d0.loss_cls: 3.3942, decode.d0.loss_mask: 0.7542, decode.d0.loss_dice: 1.1494, decode.d1.loss_cls: 0.7165, decode.d1.loss_mask: 0.7263, decode.d1.loss_dice: 1.0564, decode.d2.loss_cls: 0.5956, decode.d2.loss_mask: 0.7129, decode.d2.loss_dice: 1.0089, decode.d3.loss_cls: 0.5391, decode.d3.loss_mask: 0.7060, decode.d3.loss_dice: 0.9914, decode.d4.loss_cls: 0.5149, decode.d4.loss_mask: 0.7054, decode.d4.loss_dice: 0.9892, decode.d5.loss_cls: 0.4980, decode.d5.loss_mask: 0.7005, decode.d5.loss_dice: 0.9878, decode.d6.loss_cls: 0.4719, decode.d6.loss_mask: 0.7002, decode.d6.loss_dice: 0.9825, decode.d7.loss_cls: 0.4634, decode.d7.loss_mask: 0.6985, decode.d7.loss_dice: 0.9828, decode.d8.loss_cls: 0.4586, decode.d8.loss_mask: 0.6951, decode.d8.loss_dice: 0.9863, loss: 25.3257 2022-05-05 04:44:20,598 - mmseg - INFO - Iter [33250/40000] lr: 2.423e-07, eta: 1:34:31, time: 0.785, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4728, decode.loss_mask: 0.6984, decode.loss_dice: 0.9746, decode.d0.loss_cls: 3.3747, decode.d0.loss_mask: 0.7474, decode.d0.loss_dice: 1.1427, decode.d1.loss_cls: 0.7117, decode.d1.loss_mask: 0.7306, decode.d1.loss_dice: 1.0466, decode.d2.loss_cls: 0.5840, decode.d2.loss_mask: 0.7166, decode.d2.loss_dice: 1.0080, decode.d3.loss_cls: 0.5276, decode.d3.loss_mask: 0.7085, decode.d3.loss_dice: 0.9844, decode.d4.loss_cls: 0.4988, decode.d4.loss_mask: 0.7047, decode.d4.loss_dice: 0.9815, decode.d5.loss_cls: 0.4901, decode.d5.loss_mask: 0.7021, decode.d5.loss_dice: 0.9824, decode.d6.loss_cls: 0.4715, decode.d6.loss_mask: 0.7027, decode.d6.loss_dice: 0.9741, decode.d7.loss_cls: 0.4653, decode.d7.loss_mask: 0.7026, decode.d7.loss_dice: 0.9828, decode.d8.loss_cls: 0.4669, decode.d8.loss_mask: 0.6990, decode.d8.loss_dice: 0.9794, loss: 25.2328 2022-05-05 04:44:58,997 - mmseg - INFO - Iter [33300/40000] lr: 2.405e-07, eta: 1:33:48, time: 0.768, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4408, decode.loss_mask: 0.7051, decode.loss_dice: 0.9821, decode.d0.loss_cls: 3.3024, decode.d0.loss_mask: 0.7578, decode.d0.loss_dice: 1.1353, decode.d1.loss_cls: 0.6662, decode.d1.loss_mask: 0.7387, decode.d1.loss_dice: 1.0650, decode.d2.loss_cls: 0.5397, decode.d2.loss_mask: 0.7213, decode.d2.loss_dice: 1.0184, decode.d3.loss_cls: 0.4966, decode.d3.loss_mask: 0.7110, decode.d3.loss_dice: 0.9940, decode.d4.loss_cls: 0.4773, decode.d4.loss_mask: 0.7093, decode.d4.loss_dice: 1.0010, decode.d5.loss_cls: 0.4606, decode.d5.loss_mask: 0.7059, decode.d5.loss_dice: 0.9932, decode.d6.loss_cls: 0.4456, decode.d6.loss_mask: 0.7087, decode.d6.loss_dice: 0.9880, decode.d7.loss_cls: 0.4392, decode.d7.loss_mask: 0.7054, decode.d7.loss_dice: 0.9904, decode.d8.loss_cls: 0.4394, decode.d8.loss_mask: 0.7060, decode.d8.loss_dice: 0.9862, loss: 25.0305 2022-05-05 04:45:37,279 - mmseg - INFO - Iter [33350/40000] lr: 2.387e-07, eta: 1:33:05, time: 0.766, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4546, decode.loss_mask: 0.7029, decode.loss_dice: 0.9865, decode.d0.loss_cls: 3.4409, decode.d0.loss_mask: 0.7556, decode.d0.loss_dice: 1.1639, decode.d1.loss_cls: 0.7001, decode.d1.loss_mask: 0.7308, decode.d1.loss_dice: 1.0678, decode.d2.loss_cls: 0.5758, decode.d2.loss_mask: 0.7152, decode.d2.loss_dice: 1.0127, decode.d3.loss_cls: 0.5186, decode.d3.loss_mask: 0.7101, decode.d3.loss_dice: 0.9964, decode.d4.loss_cls: 0.4986, decode.d4.loss_mask: 0.7077, decode.d4.loss_dice: 0.9992, decode.d5.loss_cls: 0.4728, decode.d5.loss_mask: 0.7089, decode.d5.loss_dice: 1.0023, decode.d6.loss_cls: 0.4670, decode.d6.loss_mask: 0.7079, decode.d6.loss_dice: 0.9944, decode.d7.loss_cls: 0.4509, decode.d7.loss_mask: 0.7066, decode.d7.loss_dice: 0.9916, decode.d8.loss_cls: 0.4506, decode.d8.loss_mask: 0.7017, decode.d8.loss_dice: 0.9902, loss: 25.3825 2022-05-05 04:46:16,660 - mmseg - INFO - Iter [33400/40000] lr: 2.369e-07, eta: 1:32:23, time: 0.788, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4496, decode.loss_mask: 0.6906, decode.loss_dice: 0.9718, decode.d0.loss_cls: 3.4140, decode.d0.loss_mask: 0.7481, decode.d0.loss_dice: 1.1630, decode.d1.loss_cls: 0.7127, decode.d1.loss_mask: 0.7195, decode.d1.loss_dice: 1.0651, decode.d2.loss_cls: 0.5762, decode.d2.loss_mask: 0.7083, decode.d2.loss_dice: 1.0175, decode.d3.loss_cls: 0.5203, decode.d3.loss_mask: 0.6983, decode.d3.loss_dice: 0.9892, decode.d4.loss_cls: 0.4958, decode.d4.loss_mask: 0.6961, decode.d4.loss_dice: 0.9847, decode.d5.loss_cls: 0.4679, decode.d5.loss_mask: 0.6951, decode.d5.loss_dice: 0.9819, decode.d6.loss_cls: 0.4636, decode.d6.loss_mask: 0.6906, decode.d6.loss_dice: 0.9799, decode.d7.loss_cls: 0.4502, decode.d7.loss_mask: 0.6940, decode.d7.loss_dice: 0.9816, decode.d8.loss_cls: 0.4510, decode.d8.loss_mask: 0.6924, decode.d8.loss_dice: 0.9740, loss: 25.1429 2022-05-05 04:46:55,320 - mmseg - INFO - Iter [33450/40000] lr: 2.351e-07, eta: 1:31:40, time: 0.773, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4779, decode.loss_mask: 0.6867, decode.loss_dice: 0.9929, decode.d0.loss_cls: 3.4624, decode.d0.loss_mask: 0.7431, decode.d0.loss_dice: 1.1580, decode.d1.loss_cls: 0.7385, decode.d1.loss_mask: 0.7204, decode.d1.loss_dice: 1.0676, decode.d2.loss_cls: 0.6097, decode.d2.loss_mask: 0.7023, decode.d2.loss_dice: 1.0257, decode.d3.loss_cls: 0.5555, decode.d3.loss_mask: 0.6950, decode.d3.loss_dice: 1.0016, decode.d4.loss_cls: 0.5265, decode.d4.loss_mask: 0.6914, decode.d4.loss_dice: 1.0014, decode.d5.loss_cls: 0.5040, decode.d5.loss_mask: 0.6898, decode.d5.loss_dice: 0.9984, decode.d6.loss_cls: 0.4856, decode.d6.loss_mask: 0.6854, decode.d6.loss_dice: 0.9957, decode.d7.loss_cls: 0.4821, decode.d7.loss_mask: 0.6841, decode.d7.loss_dice: 0.9955, decode.d8.loss_cls: 0.4764, decode.d8.loss_mask: 0.6863, decode.d8.loss_dice: 0.9972, loss: 25.5372 2022-05-05 04:47:33,632 - mmseg - INFO - Iter [33500/40000] lr: 2.334e-07, eta: 1:30:57, time: 0.766, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4713, decode.loss_mask: 0.6994, decode.loss_dice: 0.9789, decode.d0.loss_cls: 3.4407, decode.d0.loss_mask: 0.7570, decode.d0.loss_dice: 1.1570, decode.d1.loss_cls: 0.7244, decode.d1.loss_mask: 0.7315, decode.d1.loss_dice: 1.0586, decode.d2.loss_cls: 0.5907, decode.d2.loss_mask: 0.7166, decode.d2.loss_dice: 1.0133, decode.d3.loss_cls: 0.5257, decode.d3.loss_mask: 0.7115, decode.d3.loss_dice: 1.0028, decode.d4.loss_cls: 0.5097, decode.d4.loss_mask: 0.7064, decode.d4.loss_dice: 0.9964, decode.d5.loss_cls: 0.4826, decode.d5.loss_mask: 0.7047, decode.d5.loss_dice: 0.9930, decode.d6.loss_cls: 0.4717, decode.d6.loss_mask: 0.7029, decode.d6.loss_dice: 0.9880, decode.d7.loss_cls: 0.4701, decode.d7.loss_mask: 0.6994, decode.d7.loss_dice: 0.9848, decode.d8.loss_cls: 0.4679, decode.d8.loss_mask: 0.7029, decode.d8.loss_dice: 0.9863, loss: 25.4463 2022-05-05 04:48:12,148 - mmseg - INFO - Iter [33550/40000] lr: 2.316e-07, eta: 1:30:15, time: 0.771, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4544, decode.loss_mask: 0.6621, decode.loss_dice: 0.9919, decode.d0.loss_cls: 3.4638, decode.d0.loss_mask: 0.7227, decode.d0.loss_dice: 1.1778, decode.d1.loss_cls: 0.7322, decode.d1.loss_mask: 0.6949, decode.d1.loss_dice: 1.0706, decode.d2.loss_cls: 0.5885, decode.d2.loss_mask: 0.6783, decode.d2.loss_dice: 1.0207, decode.d3.loss_cls: 0.5260, decode.d3.loss_mask: 0.6720, decode.d3.loss_dice: 1.0012, decode.d4.loss_cls: 0.4976, decode.d4.loss_mask: 0.6705, decode.d4.loss_dice: 1.0032, decode.d5.loss_cls: 0.4799, decode.d5.loss_mask: 0.6641, decode.d5.loss_dice: 0.9965, decode.d6.loss_cls: 0.4627, decode.d6.loss_mask: 0.6645, decode.d6.loss_dice: 0.9963, decode.d7.loss_cls: 0.4634, decode.d7.loss_mask: 0.6630, decode.d7.loss_dice: 0.9949, decode.d8.loss_cls: 0.4551, decode.d8.loss_mask: 0.6625, decode.d8.loss_dice: 0.9916, loss: 25.1230 2022-05-05 04:48:51,115 - mmseg - INFO - Iter [33600/40000] lr: 2.298e-07, eta: 1:29:32, time: 0.779, data_time: 0.011, memory: 51557, decode.loss_cls: 0.4532, decode.loss_mask: 0.6859, decode.loss_dice: 0.9744, decode.d0.loss_cls: 3.3840, decode.d0.loss_mask: 0.7389, decode.d0.loss_dice: 1.1471, decode.d1.loss_cls: 0.6778, decode.d1.loss_mask: 0.7124, decode.d1.loss_dice: 1.0443, decode.d2.loss_cls: 0.5770, decode.d2.loss_mask: 0.6965, decode.d2.loss_dice: 1.0105, decode.d3.loss_cls: 0.5198, decode.d3.loss_mask: 0.6916, decode.d3.loss_dice: 0.9934, decode.d4.loss_cls: 0.4914, decode.d4.loss_mask: 0.6903, decode.d4.loss_dice: 0.9880, decode.d5.loss_cls: 0.4654, decode.d5.loss_mask: 0.6899, decode.d5.loss_dice: 0.9902, decode.d6.loss_cls: 0.4550, decode.d6.loss_mask: 0.6893, decode.d6.loss_dice: 0.9777, decode.d7.loss_cls: 0.4496, decode.d7.loss_mask: 0.6889, decode.d7.loss_dice: 0.9803, decode.d8.loss_cls: 0.4484, decode.d8.loss_mask: 0.6857, decode.d8.loss_dice: 0.9783, loss: 24.9756 2022-05-05 04:49:29,786 - mmseg - INFO - Iter [33650/40000] lr: 2.280e-07, eta: 1:28:49, time: 0.774, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4555, decode.loss_mask: 0.6802, decode.loss_dice: 0.9693, decode.d0.loss_cls: 3.3867, decode.d0.loss_mask: 0.7349, decode.d0.loss_dice: 1.1328, decode.d1.loss_cls: 0.6878, decode.d1.loss_mask: 0.7082, decode.d1.loss_dice: 1.0293, decode.d2.loss_cls: 0.5682, decode.d2.loss_mask: 0.6944, decode.d2.loss_dice: 0.9974, decode.d3.loss_cls: 0.5199, decode.d3.loss_mask: 0.6895, decode.d3.loss_dice: 0.9830, decode.d4.loss_cls: 0.4907, decode.d4.loss_mask: 0.6884, decode.d4.loss_dice: 0.9871, decode.d5.loss_cls: 0.4770, decode.d5.loss_mask: 0.6862, decode.d5.loss_dice: 0.9836, decode.d6.loss_cls: 0.4706, decode.d6.loss_mask: 0.6822, decode.d6.loss_dice: 0.9736, decode.d7.loss_cls: 0.4588, decode.d7.loss_mask: 0.6822, decode.d7.loss_dice: 0.9734, decode.d8.loss_cls: 0.4565, decode.d8.loss_mask: 0.6795, decode.d8.loss_dice: 0.9737, loss: 24.9005 2022-05-05 04:50:08,343 - mmseg - INFO - Iter [33700/40000] lr: 2.262e-07, eta: 1:28:07, time: 0.771, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4663, decode.loss_mask: 0.6613, decode.loss_dice: 0.9526, decode.d0.loss_cls: 3.4109, decode.d0.loss_mask: 0.7164, decode.d0.loss_dice: 1.1177, decode.d1.loss_cls: 0.7325, decode.d1.loss_mask: 0.6881, decode.d1.loss_dice: 1.0231, decode.d2.loss_cls: 0.5938, decode.d2.loss_mask: 0.6731, decode.d2.loss_dice: 0.9812, decode.d3.loss_cls: 0.5282, decode.d3.loss_mask: 0.6662, decode.d3.loss_dice: 0.9647, decode.d4.loss_cls: 0.5125, decode.d4.loss_mask: 0.6634, decode.d4.loss_dice: 0.9560, decode.d5.loss_cls: 0.4866, decode.d5.loss_mask: 0.6626, decode.d5.loss_dice: 0.9582, decode.d6.loss_cls: 0.4713, decode.d6.loss_mask: 0.6619, decode.d6.loss_dice: 0.9560, decode.d7.loss_cls: 0.4725, decode.d7.loss_mask: 0.6597, decode.d7.loss_dice: 0.9546, decode.d8.loss_cls: 0.4648, decode.d8.loss_mask: 0.6608, decode.d8.loss_dice: 0.9573, loss: 24.6745 2022-05-05 04:50:49,427 - mmseg - INFO - Iter [33750/40000] lr: 2.244e-07, eta: 1:27:25, time: 0.822, data_time: 0.057, memory: 51557, decode.loss_cls: 0.4641, decode.loss_mask: 0.6570, decode.loss_dice: 0.9508, decode.d0.loss_cls: 3.4546, decode.d0.loss_mask: 0.6967, decode.d0.loss_dice: 1.1114, decode.d1.loss_cls: 0.7210, decode.d1.loss_mask: 0.6821, decode.d1.loss_dice: 1.0197, decode.d2.loss_cls: 0.5988, decode.d2.loss_mask: 0.6730, decode.d2.loss_dice: 0.9857, decode.d3.loss_cls: 0.5393, decode.d3.loss_mask: 0.6665, decode.d3.loss_dice: 0.9650, decode.d4.loss_cls: 0.5098, decode.d4.loss_mask: 0.6630, decode.d4.loss_dice: 0.9595, decode.d5.loss_cls: 0.4881, decode.d5.loss_mask: 0.6578, decode.d5.loss_dice: 0.9530, decode.d6.loss_cls: 0.4700, decode.d6.loss_mask: 0.6606, decode.d6.loss_dice: 0.9552, decode.d7.loss_cls: 0.4632, decode.d7.loss_mask: 0.6605, decode.d7.loss_dice: 0.9540, decode.d8.loss_cls: 0.4657, decode.d8.loss_mask: 0.6543, decode.d8.loss_dice: 0.9520, loss: 24.6522 2022-05-05 04:51:27,867 - mmseg - INFO - Iter [33800/40000] lr: 2.226e-07, eta: 1:26:42, time: 0.769, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4410, decode.loss_mask: 0.7043, decode.loss_dice: 0.9691, decode.d0.loss_cls: 3.3920, decode.d0.loss_mask: 0.7594, decode.d0.loss_dice: 1.1205, decode.d1.loss_cls: 0.6779, decode.d1.loss_mask: 0.7330, decode.d1.loss_dice: 1.0318, decode.d2.loss_cls: 0.5477, decode.d2.loss_mask: 0.7188, decode.d2.loss_dice: 0.9966, decode.d3.loss_cls: 0.4924, decode.d3.loss_mask: 0.7114, decode.d3.loss_dice: 0.9786, decode.d4.loss_cls: 0.4624, decode.d4.loss_mask: 0.7126, decode.d4.loss_dice: 0.9825, decode.d5.loss_cls: 0.4555, decode.d5.loss_mask: 0.7071, decode.d5.loss_dice: 0.9813, decode.d6.loss_cls: 0.4394, decode.d6.loss_mask: 0.7059, decode.d6.loss_dice: 0.9707, decode.d7.loss_cls: 0.4360, decode.d7.loss_mask: 0.7038, decode.d7.loss_dice: 0.9697, decode.d8.loss_cls: 0.4260, decode.d8.loss_mask: 0.7034, decode.d8.loss_dice: 0.9746, loss: 24.9055 2022-05-05 04:52:07,238 - mmseg - INFO - Iter [33850/40000] lr: 2.208e-07, eta: 1:26:00, time: 0.787, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4444, decode.loss_mask: 0.6972, decode.loss_dice: 0.9712, decode.d0.loss_cls: 3.4050, decode.d0.loss_mask: 0.7601, decode.d0.loss_dice: 1.1548, decode.d1.loss_cls: 0.6884, decode.d1.loss_mask: 0.7301, decode.d1.loss_dice: 1.0492, decode.d2.loss_cls: 0.5623, decode.d2.loss_mask: 0.7074, decode.d2.loss_dice: 1.0049, decode.d3.loss_cls: 0.5091, decode.d3.loss_mask: 0.6989, decode.d3.loss_dice: 0.9843, decode.d4.loss_cls: 0.4743, decode.d4.loss_mask: 0.6988, decode.d4.loss_dice: 0.9828, decode.d5.loss_cls: 0.4688, decode.d5.loss_mask: 0.6955, decode.d5.loss_dice: 0.9829, decode.d6.loss_cls: 0.4534, decode.d6.loss_mask: 0.6944, decode.d6.loss_dice: 0.9754, decode.d7.loss_cls: 0.4471, decode.d7.loss_mask: 0.6981, decode.d7.loss_dice: 0.9805, decode.d8.loss_cls: 0.4379, decode.d8.loss_mask: 0.6963, decode.d8.loss_dice: 0.9763, loss: 25.0298 2022-05-05 04:52:45,729 - mmseg - INFO - Iter [33900/40000] lr: 2.190e-07, eta: 1:25:17, time: 0.769, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4578, decode.loss_mask: 0.7004, decode.loss_dice: 0.9839, decode.d0.loss_cls: 3.4194, decode.d0.loss_mask: 0.7522, decode.d0.loss_dice: 1.1443, decode.d1.loss_cls: 0.7195, decode.d1.loss_mask: 0.7291, decode.d1.loss_dice: 1.0543, decode.d2.loss_cls: 0.5819, decode.d2.loss_mask: 0.7105, decode.d2.loss_dice: 1.0137, decode.d3.loss_cls: 0.5229, decode.d3.loss_mask: 0.7064, decode.d3.loss_dice: 1.0008, decode.d4.loss_cls: 0.4982, decode.d4.loss_mask: 0.7026, decode.d4.loss_dice: 0.9963, decode.d5.loss_cls: 0.4812, decode.d5.loss_mask: 0.7001, decode.d5.loss_dice: 0.9933, decode.d6.loss_cls: 0.4685, decode.d6.loss_mask: 0.7009, decode.d6.loss_dice: 0.9863, decode.d7.loss_cls: 0.4543, decode.d7.loss_mask: 0.6971, decode.d7.loss_dice: 0.9883, decode.d8.loss_cls: 0.4484, decode.d8.loss_mask: 0.7005, decode.d8.loss_dice: 0.9855, loss: 25.2989 2022-05-05 04:53:24,067 - mmseg - INFO - Iter [33950/40000] lr: 2.172e-07, eta: 1:24:35, time: 0.767, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4655, decode.loss_mask: 0.7005, decode.loss_dice: 0.9953, decode.d0.loss_cls: 3.3332, decode.d0.loss_mask: 0.7531, decode.d0.loss_dice: 1.1521, decode.d1.loss_cls: 0.7058, decode.d1.loss_mask: 0.7347, decode.d1.loss_dice: 1.0648, decode.d2.loss_cls: 0.5894, decode.d2.loss_mask: 0.7137, decode.d2.loss_dice: 1.0243, decode.d3.loss_cls: 0.5292, decode.d3.loss_mask: 0.7061, decode.d3.loss_dice: 1.0088, decode.d4.loss_cls: 0.5024, decode.d4.loss_mask: 0.7079, decode.d4.loss_dice: 1.0088, decode.d5.loss_cls: 0.4952, decode.d5.loss_mask: 0.7023, decode.d5.loss_dice: 1.0062, decode.d6.loss_cls: 0.4786, decode.d6.loss_mask: 0.7008, decode.d6.loss_dice: 0.9999, decode.d7.loss_cls: 0.4632, decode.d7.loss_mask: 0.6979, decode.d7.loss_dice: 0.9974, decode.d8.loss_cls: 0.4577, decode.d8.loss_mask: 0.6995, decode.d8.loss_dice: 0.9988, loss: 25.3932 2022-05-05 04:54:02,852 - mmseg - INFO - Saving checkpoint at 34000 iterations 2022-05-05 04:54:28,251 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 04:54:28,259 - mmseg - INFO - Iter [34000/40000] lr: 2.154e-07, eta: 1:23:57, time: 1.281, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4700, decode.loss_mask: 0.6743, decode.loss_dice: 0.9458, decode.d0.loss_cls: 3.4324, decode.d0.loss_mask: 0.7303, decode.d0.loss_dice: 1.1045, decode.d1.loss_cls: 0.7263, decode.d1.loss_mask: 0.7090, decode.d1.loss_dice: 1.0174, decode.d2.loss_cls: 0.5896, decode.d2.loss_mask: 0.6867, decode.d2.loss_dice: 0.9718, decode.d3.loss_cls: 0.5266, decode.d3.loss_mask: 0.6771, decode.d3.loss_dice: 0.9545, decode.d4.loss_cls: 0.4968, decode.d4.loss_mask: 0.6770, decode.d4.loss_dice: 0.9631, decode.d5.loss_cls: 0.4884, decode.d5.loss_mask: 0.6796, decode.d5.loss_dice: 0.9539, decode.d6.loss_cls: 0.4707, decode.d6.loss_mask: 0.6775, decode.d6.loss_dice: 0.9542, decode.d7.loss_cls: 0.4627, decode.d7.loss_mask: 0.6772, decode.d7.loss_dice: 0.9565, decode.d8.loss_cls: 0.4643, decode.d8.loss_mask: 0.6730, decode.d8.loss_dice: 0.9454, loss: 24.7566 2022-05-05 04:55:07,748 - mmseg - INFO - Iter [34050/40000] lr: 2.136e-07, eta: 1:23:14, time: 0.793, data_time: 0.012, memory: 51557, decode.loss_cls: 0.4386, decode.loss_mask: 0.6953, decode.loss_dice: 0.9721, decode.d0.loss_cls: 3.4256, decode.d0.loss_mask: 0.7530, decode.d0.loss_dice: 1.1426, decode.d1.loss_cls: 0.7190, decode.d1.loss_mask: 0.7232, decode.d1.loss_dice: 1.0407, decode.d2.loss_cls: 0.5699, decode.d2.loss_mask: 0.7079, decode.d2.loss_dice: 1.0075, decode.d3.loss_cls: 0.5038, decode.d3.loss_mask: 0.7039, decode.d3.loss_dice: 0.9878, decode.d4.loss_cls: 0.4759, decode.d4.loss_mask: 0.7011, decode.d4.loss_dice: 0.9857, decode.d5.loss_cls: 0.4501, decode.d5.loss_mask: 0.6999, decode.d5.loss_dice: 0.9791, decode.d6.loss_cls: 0.4461, decode.d6.loss_mask: 0.6989, decode.d6.loss_dice: 0.9744, decode.d7.loss_cls: 0.4364, decode.d7.loss_mask: 0.6976, decode.d7.loss_dice: 0.9780, decode.d8.loss_cls: 0.4335, decode.d8.loss_mask: 0.6980, decode.d8.loss_dice: 0.9768, loss: 25.0223 2022-05-05 04:55:46,937 - mmseg - INFO - Iter [34100/40000] lr: 2.118e-07, eta: 1:22:32, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4637, decode.loss_mask: 0.6867, decode.loss_dice: 0.9977, decode.d0.loss_cls: 3.4592, decode.d0.loss_mask: 0.7398, decode.d0.loss_dice: 1.1654, decode.d1.loss_cls: 0.7377, decode.d1.loss_mask: 0.7203, decode.d1.loss_dice: 1.0707, decode.d2.loss_cls: 0.5881, decode.d2.loss_mask: 0.7069, decode.d2.loss_dice: 1.0318, decode.d3.loss_cls: 0.5344, decode.d3.loss_mask: 0.6978, decode.d3.loss_dice: 1.0176, decode.d4.loss_cls: 0.5080, decode.d4.loss_mask: 0.6940, decode.d4.loss_dice: 1.0096, decode.d5.loss_cls: 0.4895, decode.d5.loss_mask: 0.6893, decode.d5.loss_dice: 1.0049, decode.d6.loss_cls: 0.4771, decode.d6.loss_mask: 0.6884, decode.d6.loss_dice: 0.9980, decode.d7.loss_cls: 0.4705, decode.d7.loss_mask: 0.6885, decode.d7.loss_dice: 0.9977, decode.d8.loss_cls: 0.4675, decode.d8.loss_mask: 0.6887, decode.d8.loss_dice: 1.0012, loss: 25.4907 2022-05-05 04:56:26,229 - mmseg - INFO - Iter [34150/40000] lr: 2.100e-07, eta: 1:21:49, time: 0.786, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4430, decode.loss_mask: 0.6999, decode.loss_dice: 0.9830, decode.d0.loss_cls: 3.4106, decode.d0.loss_mask: 0.7603, decode.d0.loss_dice: 1.1573, decode.d1.loss_cls: 0.7034, decode.d1.loss_mask: 0.7360, decode.d1.loss_dice: 1.0662, decode.d2.loss_cls: 0.5696, decode.d2.loss_mask: 0.7130, decode.d2.loss_dice: 1.0136, decode.d3.loss_cls: 0.5198, decode.d3.loss_mask: 0.7029, decode.d3.loss_dice: 0.9972, decode.d4.loss_cls: 0.4880, decode.d4.loss_mask: 0.7043, decode.d4.loss_dice: 0.9969, decode.d5.loss_cls: 0.4709, decode.d5.loss_mask: 0.6999, decode.d5.loss_dice: 0.9921, decode.d6.loss_cls: 0.4539, decode.d6.loss_mask: 0.6969, decode.d6.loss_dice: 0.9874, decode.d7.loss_cls: 0.4490, decode.d7.loss_mask: 0.6997, decode.d7.loss_dice: 0.9915, decode.d8.loss_cls: 0.4421, decode.d8.loss_mask: 0.7006, decode.d8.loss_dice: 0.9847, loss: 25.2337 2022-05-05 04:57:04,579 - mmseg - INFO - Iter [34200/40000] lr: 2.082e-07, eta: 1:21:07, time: 0.767, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4468, decode.loss_mask: 0.6804, decode.loss_dice: 0.9706, decode.d0.loss_cls: 3.3429, decode.d0.loss_mask: 0.7356, decode.d0.loss_dice: 1.1212, decode.d1.loss_cls: 0.6822, decode.d1.loss_mask: 0.7126, decode.d1.loss_dice: 1.0355, decode.d2.loss_cls: 0.5604, decode.d2.loss_mask: 0.6978, decode.d2.loss_dice: 0.9953, decode.d3.loss_cls: 0.5156, decode.d3.loss_mask: 0.6889, decode.d3.loss_dice: 0.9789, decode.d4.loss_cls: 0.4823, decode.d4.loss_mask: 0.6860, decode.d4.loss_dice: 0.9760, decode.d5.loss_cls: 0.4753, decode.d5.loss_mask: 0.6845, decode.d5.loss_dice: 0.9787, decode.d6.loss_cls: 0.4539, decode.d6.loss_mask: 0.6821, decode.d6.loss_dice: 0.9674, decode.d7.loss_cls: 0.4492, decode.d7.loss_mask: 0.6794, decode.d7.loss_dice: 0.9645, decode.d8.loss_cls: 0.4476, decode.d8.loss_mask: 0.6817, decode.d8.loss_dice: 0.9684, loss: 24.7416 2022-05-05 04:57:43,041 - mmseg - INFO - Iter [34250/40000] lr: 2.064e-07, eta: 1:20:24, time: 0.769, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4430, decode.loss_mask: 0.6794, decode.loss_dice: 0.9853, decode.d0.loss_cls: 3.4085, decode.d0.loss_mask: 0.7245, decode.d0.loss_dice: 1.1543, decode.d1.loss_cls: 0.7149, decode.d1.loss_mask: 0.7106, decode.d1.loss_dice: 1.0508, decode.d2.loss_cls: 0.5714, decode.d2.loss_mask: 0.6943, decode.d2.loss_dice: 1.0065, decode.d3.loss_cls: 0.5154, decode.d3.loss_mask: 0.6846, decode.d3.loss_dice: 0.9983, decode.d4.loss_cls: 0.4832, decode.d4.loss_mask: 0.6827, decode.d4.loss_dice: 0.9901, decode.d5.loss_cls: 0.4675, decode.d5.loss_mask: 0.6799, decode.d5.loss_dice: 0.9917, decode.d6.loss_cls: 0.4508, decode.d6.loss_mask: 0.6802, decode.d6.loss_dice: 0.9841, decode.d7.loss_cls: 0.4447, decode.d7.loss_mask: 0.6828, decode.d7.loss_dice: 0.9833, decode.d8.loss_cls: 0.4430, decode.d8.loss_mask: 0.6799, decode.d8.loss_dice: 0.9851, loss: 24.9705 2022-05-05 04:58:23,951 - mmseg - INFO - Iter [34300/40000] lr: 2.046e-07, eta: 1:19:42, time: 0.818, data_time: 0.058, memory: 51557, decode.loss_cls: 0.4668, decode.loss_mask: 0.6964, decode.loss_dice: 1.0005, decode.d0.loss_cls: 3.4768, decode.d0.loss_mask: 0.7536, decode.d0.loss_dice: 1.1673, decode.d1.loss_cls: 0.7209, decode.d1.loss_mask: 0.7247, decode.d1.loss_dice: 1.0757, decode.d2.loss_cls: 0.5908, decode.d2.loss_mask: 0.7081, decode.d2.loss_dice: 1.0305, decode.d3.loss_cls: 0.5371, decode.d3.loss_mask: 0.7038, decode.d3.loss_dice: 1.0164, decode.d4.loss_cls: 0.5077, decode.d4.loss_mask: 0.7011, decode.d4.loss_dice: 1.0108, decode.d5.loss_cls: 0.5001, decode.d5.loss_mask: 0.6994, decode.d5.loss_dice: 1.0103, decode.d6.loss_cls: 0.4835, decode.d6.loss_mask: 0.6972, decode.d6.loss_dice: 1.0003, decode.d7.loss_cls: 0.4687, decode.d7.loss_mask: 0.6945, decode.d7.loss_dice: 1.0053, decode.d8.loss_cls: 0.4701, decode.d8.loss_mask: 0.6928, decode.d8.loss_dice: 1.0042, loss: 25.6153 2022-05-05 04:59:02,405 - mmseg - INFO - Iter [34350/40000] lr: 2.028e-07, eta: 1:18:59, time: 0.769, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4553, decode.loss_mask: 0.6900, decode.loss_dice: 0.9614, decode.d0.loss_cls: 3.3692, decode.d0.loss_mask: 0.7498, decode.d0.loss_dice: 1.1326, decode.d1.loss_cls: 0.7081, decode.d1.loss_mask: 0.7184, decode.d1.loss_dice: 1.0428, decode.d2.loss_cls: 0.5753, decode.d2.loss_mask: 0.7024, decode.d2.loss_dice: 1.0035, decode.d3.loss_cls: 0.5123, decode.d3.loss_mask: 0.6987, decode.d3.loss_dice: 0.9863, decode.d4.loss_cls: 0.4922, decode.d4.loss_mask: 0.6978, decode.d4.loss_dice: 0.9760, decode.d5.loss_cls: 0.4825, decode.d5.loss_mask: 0.6913, decode.d5.loss_dice: 0.9742, decode.d6.loss_cls: 0.4600, decode.d6.loss_mask: 0.6922, decode.d6.loss_dice: 0.9681, decode.d7.loss_cls: 0.4526, decode.d7.loss_mask: 0.6935, decode.d7.loss_dice: 0.9696, decode.d8.loss_cls: 0.4535, decode.d8.loss_mask: 0.6904, decode.d8.loss_dice: 0.9650, loss: 24.9649 2022-05-05 04:59:40,946 - mmseg - INFO - Iter [34400/40000] lr: 2.010e-07, eta: 1:18:17, time: 0.771, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4313, decode.loss_mask: 0.6924, decode.loss_dice: 0.9587, decode.d0.loss_cls: 3.3989, decode.d0.loss_mask: 0.7501, decode.d0.loss_dice: 1.1343, decode.d1.loss_cls: 0.6903, decode.d1.loss_mask: 0.7210, decode.d1.loss_dice: 1.0273, decode.d2.loss_cls: 0.5573, decode.d2.loss_mask: 0.7038, decode.d2.loss_dice: 0.9892, decode.d3.loss_cls: 0.5012, decode.d3.loss_mask: 0.7006, decode.d3.loss_dice: 0.9700, decode.d4.loss_cls: 0.4740, decode.d4.loss_mask: 0.6992, decode.d4.loss_dice: 0.9702, decode.d5.loss_cls: 0.4529, decode.d5.loss_mask: 0.6958, decode.d5.loss_dice: 0.9695, decode.d6.loss_cls: 0.4397, decode.d6.loss_mask: 0.6935, decode.d6.loss_dice: 0.9620, decode.d7.loss_cls: 0.4384, decode.d7.loss_mask: 0.6925, decode.d7.loss_dice: 0.9599, decode.d8.loss_cls: 0.4275, decode.d8.loss_mask: 0.6918, decode.d8.loss_dice: 0.9610, loss: 24.7543 2022-05-05 05:00:20,018 - mmseg - INFO - Iter [34450/40000] lr: 1.993e-07, eta: 1:17:35, time: 0.781, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4675, decode.loss_mask: 0.6673, decode.loss_dice: 0.9687, decode.d0.loss_cls: 3.4672, decode.d0.loss_mask: 0.7319, decode.d0.loss_dice: 1.1585, decode.d1.loss_cls: 0.7325, decode.d1.loss_mask: 0.7002, decode.d1.loss_dice: 1.0500, decode.d2.loss_cls: 0.5951, decode.d2.loss_mask: 0.6840, decode.d2.loss_dice: 1.0085, decode.d3.loss_cls: 0.5391, decode.d3.loss_mask: 0.6749, decode.d3.loss_dice: 0.9887, decode.d4.loss_cls: 0.5129, decode.d4.loss_mask: 0.6734, decode.d4.loss_dice: 0.9821, decode.d5.loss_cls: 0.4941, decode.d5.loss_mask: 0.6694, decode.d5.loss_dice: 0.9837, decode.d6.loss_cls: 0.4818, decode.d6.loss_mask: 0.6708, decode.d6.loss_dice: 0.9795, decode.d7.loss_cls: 0.4751, decode.d7.loss_mask: 0.6675, decode.d7.loss_dice: 0.9750, decode.d8.loss_cls: 0.4593, decode.d8.loss_mask: 0.6668, decode.d8.loss_dice: 0.9750, loss: 25.1008 2022-05-05 05:00:58,606 - mmseg - INFO - Iter [34500/40000] lr: 1.975e-07, eta: 1:16:52, time: 0.771, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4537, decode.loss_mask: 0.6798, decode.loss_dice: 0.9717, decode.d0.loss_cls: 3.4383, decode.d0.loss_mask: 0.7376, decode.d0.loss_dice: 1.1409, decode.d1.loss_cls: 0.7287, decode.d1.loss_mask: 0.7080, decode.d1.loss_dice: 1.0425, decode.d2.loss_cls: 0.5809, decode.d2.loss_mask: 0.6964, decode.d2.loss_dice: 1.0099, decode.d3.loss_cls: 0.5280, decode.d3.loss_mask: 0.6873, decode.d3.loss_dice: 0.9833, decode.d4.loss_cls: 0.4999, decode.d4.loss_mask: 0.6840, decode.d4.loss_dice: 0.9885, decode.d5.loss_cls: 0.4842, decode.d5.loss_mask: 0.6819, decode.d5.loss_dice: 0.9849, decode.d6.loss_cls: 0.4613, decode.d6.loss_mask: 0.6805, decode.d6.loss_dice: 0.9803, decode.d7.loss_cls: 0.4531, decode.d7.loss_mask: 0.6824, decode.d7.loss_dice: 0.9783, decode.d8.loss_cls: 0.4527, decode.d8.loss_mask: 0.6804, decode.d8.loss_dice: 0.9747, loss: 25.0540 2022-05-05 05:01:37,185 - mmseg - INFO - Iter [34550/40000] lr: 1.957e-07, eta: 1:16:10, time: 0.772, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4715, decode.loss_mask: 0.6626, decode.loss_dice: 0.9825, decode.d0.loss_cls: 3.4755, decode.d0.loss_mask: 0.7228, decode.d0.loss_dice: 1.1668, decode.d1.loss_cls: 0.7384, decode.d1.loss_mask: 0.6974, decode.d1.loss_dice: 1.0618, decode.d2.loss_cls: 0.6023, decode.d2.loss_mask: 0.6804, decode.d2.loss_dice: 1.0188, decode.d3.loss_cls: 0.5458, decode.d3.loss_mask: 0.6688, decode.d3.loss_dice: 0.9929, decode.d4.loss_cls: 0.5273, decode.d4.loss_mask: 0.6675, decode.d4.loss_dice: 0.9949, decode.d5.loss_cls: 0.5026, decode.d5.loss_mask: 0.6619, decode.d5.loss_dice: 0.9850, decode.d6.loss_cls: 0.4903, decode.d6.loss_mask: 0.6643, decode.d6.loss_dice: 0.9804, decode.d7.loss_cls: 0.4810, decode.d7.loss_mask: 0.6634, decode.d7.loss_dice: 0.9818, decode.d8.loss_cls: 0.4750, decode.d8.loss_mask: 0.6612, decode.d8.loss_dice: 0.9763, loss: 25.2009 2022-05-05 05:02:15,550 - mmseg - INFO - Iter [34600/40000] lr: 1.939e-07, eta: 1:15:27, time: 0.767, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4532, decode.loss_mask: 0.6804, decode.loss_dice: 1.0030, decode.d0.loss_cls: 3.3929, decode.d0.loss_mask: 0.7344, decode.d0.loss_dice: 1.1622, decode.d1.loss_cls: 0.6968, decode.d1.loss_mask: 0.7104, decode.d1.loss_dice: 1.0782, decode.d2.loss_cls: 0.5712, decode.d2.loss_mask: 0.6926, decode.d2.loss_dice: 1.0391, decode.d3.loss_cls: 0.5267, decode.d3.loss_mask: 0.6869, decode.d3.loss_dice: 1.0223, decode.d4.loss_cls: 0.5020, decode.d4.loss_mask: 0.6878, decode.d4.loss_dice: 1.0219, decode.d5.loss_cls: 0.4785, decode.d5.loss_mask: 0.6845, decode.d5.loss_dice: 1.0123, decode.d6.loss_cls: 0.4585, decode.d6.loss_mask: 0.6837, decode.d6.loss_dice: 1.0083, decode.d7.loss_cls: 0.4593, decode.d7.loss_mask: 0.6796, decode.d7.loss_dice: 1.0068, decode.d8.loss_cls: 0.4542, decode.d8.loss_mask: 0.6797, decode.d8.loss_dice: 1.0034, loss: 25.2707 2022-05-05 05:02:54,560 - mmseg - INFO - Iter [34650/40000] lr: 1.921e-07, eta: 1:14:45, time: 0.779, data_time: 0.008, memory: 51557, decode.loss_cls: 0.4500, decode.loss_mask: 0.6888, decode.loss_dice: 0.9750, decode.d0.loss_cls: 3.3687, decode.d0.loss_mask: 0.7374, decode.d0.loss_dice: 1.1370, decode.d1.loss_cls: 0.6924, decode.d1.loss_mask: 0.7203, decode.d1.loss_dice: 1.0407, decode.d2.loss_cls: 0.5673, decode.d2.loss_mask: 0.7025, decode.d2.loss_dice: 0.9995, decode.d3.loss_cls: 0.5139, decode.d3.loss_mask: 0.6954, decode.d3.loss_dice: 0.9845, decode.d4.loss_cls: 0.4876, decode.d4.loss_mask: 0.6914, decode.d4.loss_dice: 0.9843, decode.d5.loss_cls: 0.4740, decode.d5.loss_mask: 0.6902, decode.d5.loss_dice: 0.9845, decode.d6.loss_cls: 0.4608, decode.d6.loss_mask: 0.6884, decode.d6.loss_dice: 0.9757, decode.d7.loss_cls: 0.4421, decode.d7.loss_mask: 0.6879, decode.d7.loss_dice: 0.9758, decode.d8.loss_cls: 0.4478, decode.d8.loss_mask: 0.6891, decode.d8.loss_dice: 0.9736, loss: 24.9266 2022-05-05 05:03:32,990 - mmseg - INFO - Iter [34700/40000] lr: 1.903e-07, eta: 1:14:02, time: 0.769, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4512, decode.loss_mask: 0.7013, decode.loss_dice: 0.9617, decode.d0.loss_cls: 3.3423, decode.d0.loss_mask: 0.7646, decode.d0.loss_dice: 1.1341, decode.d1.loss_cls: 0.6871, decode.d1.loss_mask: 0.7364, decode.d1.loss_dice: 1.0363, decode.d2.loss_cls: 0.5733, decode.d2.loss_mask: 0.7156, decode.d2.loss_dice: 0.9990, decode.d3.loss_cls: 0.5144, decode.d3.loss_mask: 0.7049, decode.d3.loss_dice: 0.9729, decode.d4.loss_cls: 0.4897, decode.d4.loss_mask: 0.7067, decode.d4.loss_dice: 0.9742, decode.d5.loss_cls: 0.4668, decode.d5.loss_mask: 0.7054, decode.d5.loss_dice: 0.9709, decode.d6.loss_cls: 0.4566, decode.d6.loss_mask: 0.7028, decode.d6.loss_dice: 0.9680, decode.d7.loss_cls: 0.4462, decode.d7.loss_mask: 0.7040, decode.d7.loss_dice: 0.9692, decode.d8.loss_cls: 0.4477, decode.d8.loss_mask: 0.7024, decode.d8.loss_dice: 0.9632, loss: 24.9690 2022-05-05 05:04:12,112 - mmseg - INFO - Iter [34750/40000] lr: 1.885e-07, eta: 1:13:20, time: 0.782, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4289, decode.loss_mask: 0.7009, decode.loss_dice: 0.9717, decode.d0.loss_cls: 3.3545, decode.d0.loss_mask: 0.7577, decode.d0.loss_dice: 1.1403, decode.d1.loss_cls: 0.6920, decode.d1.loss_mask: 0.7282, decode.d1.loss_dice: 1.0395, decode.d2.loss_cls: 0.5617, decode.d2.loss_mask: 0.7086, decode.d2.loss_dice: 0.9968, decode.d3.loss_cls: 0.4960, decode.d3.loss_mask: 0.7031, decode.d3.loss_dice: 0.9834, decode.d4.loss_cls: 0.4697, decode.d4.loss_mask: 0.7021, decode.d4.loss_dice: 0.9834, decode.d5.loss_cls: 0.4485, decode.d5.loss_mask: 0.7024, decode.d5.loss_dice: 0.9799, decode.d6.loss_cls: 0.4410, decode.d6.loss_mask: 0.7015, decode.d6.loss_dice: 0.9756, decode.d7.loss_cls: 0.4267, decode.d7.loss_mask: 0.7015, decode.d7.loss_dice: 0.9720, decode.d8.loss_cls: 0.4253, decode.d8.loss_mask: 0.7018, decode.d8.loss_dice: 0.9701, loss: 24.8648 2022-05-05 05:04:51,607 - mmseg - INFO - Iter [34800/40000] lr: 1.867e-07, eta: 1:12:38, time: 0.790, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4349, decode.loss_mask: 0.6850, decode.loss_dice: 0.9640, decode.d0.loss_cls: 3.3822, decode.d0.loss_mask: 0.7447, decode.d0.loss_dice: 1.1314, decode.d1.loss_cls: 0.6799, decode.d1.loss_mask: 0.7241, decode.d1.loss_dice: 1.0410, decode.d2.loss_cls: 0.5573, decode.d2.loss_mask: 0.7060, decode.d2.loss_dice: 0.9987, decode.d3.loss_cls: 0.4961, decode.d3.loss_mask: 0.6992, decode.d3.loss_dice: 0.9816, decode.d4.loss_cls: 0.4795, decode.d4.loss_mask: 0.6930, decode.d4.loss_dice: 0.9733, decode.d5.loss_cls: 0.4563, decode.d5.loss_mask: 0.6909, decode.d5.loss_dice: 0.9674, decode.d6.loss_cls: 0.4464, decode.d6.loss_mask: 0.6890, decode.d6.loss_dice: 0.9627, decode.d7.loss_cls: 0.4335, decode.d7.loss_mask: 0.6869, decode.d7.loss_dice: 0.9691, decode.d8.loss_cls: 0.4268, decode.d8.loss_mask: 0.6878, decode.d8.loss_dice: 0.9710, loss: 24.7595 2022-05-05 05:05:33,111 - mmseg - INFO - Iter [34850/40000] lr: 1.849e-07, eta: 1:11:56, time: 0.830, data_time: 0.058, memory: 51557, decode.loss_cls: 0.4427, decode.loss_mask: 0.6740, decode.loss_dice: 0.9523, decode.d0.loss_cls: 3.3691, decode.d0.loss_mask: 0.7301, decode.d0.loss_dice: 1.1175, decode.d1.loss_cls: 0.6829, decode.d1.loss_mask: 0.7052, decode.d1.loss_dice: 1.0310, decode.d2.loss_cls: 0.5609, decode.d2.loss_mask: 0.6900, decode.d2.loss_dice: 0.9875, decode.d3.loss_cls: 0.5039, decode.d3.loss_mask: 0.6861, decode.d3.loss_dice: 0.9733, decode.d4.loss_cls: 0.4771, decode.d4.loss_mask: 0.6844, decode.d4.loss_dice: 0.9687, decode.d5.loss_cls: 0.4663, decode.d5.loss_mask: 0.6774, decode.d5.loss_dice: 0.9594, decode.d6.loss_cls: 0.4513, decode.d6.loss_mask: 0.6740, decode.d6.loss_dice: 0.9538, decode.d7.loss_cls: 0.4428, decode.d7.loss_mask: 0.6756, decode.d7.loss_dice: 0.9529, decode.d8.loss_cls: 0.4427, decode.d8.loss_mask: 0.6730, decode.d8.loss_dice: 0.9560, loss: 24.5619 2022-05-05 05:06:11,790 - mmseg - INFO - Iter [34900/40000] lr: 1.831e-07, eta: 1:11:13, time: 0.774, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4357, decode.loss_mask: 0.7051, decode.loss_dice: 0.9667, decode.d0.loss_cls: 3.3800, decode.d0.loss_mask: 0.7652, decode.d0.loss_dice: 1.1396, decode.d1.loss_cls: 0.6740, decode.d1.loss_mask: 0.7382, decode.d1.loss_dice: 1.0443, decode.d2.loss_cls: 0.5568, decode.d2.loss_mask: 0.7244, decode.d2.loss_dice: 1.0001, decode.d3.loss_cls: 0.4943, decode.d3.loss_mask: 0.7213, decode.d3.loss_dice: 0.9815, decode.d4.loss_cls: 0.4692, decode.d4.loss_mask: 0.7160, decode.d4.loss_dice: 0.9814, decode.d5.loss_cls: 0.4530, decode.d5.loss_mask: 0.7118, decode.d5.loss_dice: 0.9749, decode.d6.loss_cls: 0.4394, decode.d6.loss_mask: 0.7127, decode.d6.loss_dice: 0.9762, decode.d7.loss_cls: 0.4368, decode.d7.loss_mask: 0.7113, decode.d7.loss_dice: 0.9775, decode.d8.loss_cls: 0.4334, decode.d8.loss_mask: 0.7074, decode.d8.loss_dice: 0.9713, loss: 24.9995 2022-05-05 05:06:50,442 - mmseg - INFO - Iter [34950/40000] lr: 1.813e-07, eta: 1:10:31, time: 0.773, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4596, decode.loss_mask: 0.6711, decode.loss_dice: 0.9675, decode.d0.loss_cls: 3.4183, decode.d0.loss_mask: 0.7332, decode.d0.loss_dice: 1.1394, decode.d1.loss_cls: 0.7146, decode.d1.loss_mask: 0.7038, decode.d1.loss_dice: 1.0415, decode.d2.loss_cls: 0.5840, decode.d2.loss_mask: 0.6849, decode.d2.loss_dice: 0.9977, decode.d3.loss_cls: 0.5262, decode.d3.loss_mask: 0.6790, decode.d3.loss_dice: 0.9772, decode.d4.loss_cls: 0.5044, decode.d4.loss_mask: 0.6754, decode.d4.loss_dice: 0.9802, decode.d5.loss_cls: 0.4828, decode.d5.loss_mask: 0.6721, decode.d5.loss_dice: 0.9712, decode.d6.loss_cls: 0.4697, decode.d6.loss_mask: 0.6748, decode.d6.loss_dice: 0.9748, decode.d7.loss_cls: 0.4678, decode.d7.loss_mask: 0.6711, decode.d7.loss_dice: 0.9703, decode.d8.loss_cls: 0.4627, decode.d8.loss_mask: 0.6711, decode.d8.loss_dice: 0.9695, loss: 24.9159 2022-05-05 05:07:29,991 - mmseg - INFO - Saving checkpoint at 35000 iterations 2022-05-05 05:07:57,465 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 05:07:57,472 - mmseg - INFO - Iter [35000/40000] lr: 1.795e-07, eta: 1:09:53, time: 1.338, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4018, decode.loss_mask: 0.6877, decode.loss_dice: 0.9502, decode.d0.loss_cls: 3.3369, decode.d0.loss_mask: 0.7455, decode.d0.loss_dice: 1.0903, decode.d1.loss_cls: 0.6621, decode.d1.loss_mask: 0.7181, decode.d1.loss_dice: 1.0062, decode.d2.loss_cls: 0.5225, decode.d2.loss_mask: 0.7029, decode.d2.loss_dice: 0.9811, decode.d3.loss_cls: 0.4724, decode.d3.loss_mask: 0.7000, decode.d3.loss_dice: 0.9597, decode.d4.loss_cls: 0.4512, decode.d4.loss_mask: 0.6918, decode.d4.loss_dice: 0.9573, decode.d5.loss_cls: 0.4254, decode.d5.loss_mask: 0.6942, decode.d5.loss_dice: 0.9563, decode.d6.loss_cls: 0.4144, decode.d6.loss_mask: 0.6898, decode.d6.loss_dice: 0.9503, decode.d7.loss_cls: 0.4109, decode.d7.loss_mask: 0.6883, decode.d7.loss_dice: 0.9459, decode.d8.loss_cls: 0.4007, decode.d8.loss_mask: 0.6875, decode.d8.loss_dice: 0.9482, loss: 24.2496 2022-05-05 05:08:36,797 - mmseg - INFO - Iter [35050/40000] lr: 1.777e-07, eta: 1:09:10, time: 0.789, data_time: 0.011, memory: 51557, decode.loss_cls: 0.4444, decode.loss_mask: 0.6774, decode.loss_dice: 0.9932, decode.d0.loss_cls: 3.4120, decode.d0.loss_mask: 0.7306, decode.d0.loss_dice: 1.1549, decode.d1.loss_cls: 0.6877, decode.d1.loss_mask: 0.7120, decode.d1.loss_dice: 1.0647, decode.d2.loss_cls: 0.5723, decode.d2.loss_mask: 0.6919, decode.d2.loss_dice: 1.0242, decode.d3.loss_cls: 0.5154, decode.d3.loss_mask: 0.6837, decode.d3.loss_dice: 1.0028, decode.d4.loss_cls: 0.4913, decode.d4.loss_mask: 0.6814, decode.d4.loss_dice: 1.0068, decode.d5.loss_cls: 0.4678, decode.d5.loss_mask: 0.6804, decode.d5.loss_dice: 0.9960, decode.d6.loss_cls: 0.4549, decode.d6.loss_mask: 0.6794, decode.d6.loss_dice: 0.9933, decode.d7.loss_cls: 0.4479, decode.d7.loss_mask: 0.6780, decode.d7.loss_dice: 0.9907, decode.d8.loss_cls: 0.4438, decode.d8.loss_mask: 0.6777, decode.d8.loss_dice: 0.9899, loss: 25.0465 2022-05-05 05:09:15,378 - mmseg - INFO - Iter [35100/40000] lr: 1.759e-07, eta: 1:08:28, time: 0.772, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4615, decode.loss_mask: 0.6735, decode.loss_dice: 0.9655, decode.d0.loss_cls: 3.3772, decode.d0.loss_mask: 0.7307, decode.d0.loss_dice: 1.1330, decode.d1.loss_cls: 0.7060, decode.d1.loss_mask: 0.7080, decode.d1.loss_dice: 1.0412, decode.d2.loss_cls: 0.5819, decode.d2.loss_mask: 0.6906, decode.d2.loss_dice: 1.0021, decode.d3.loss_cls: 0.5338, decode.d3.loss_mask: 0.6853, decode.d3.loss_dice: 0.9777, decode.d4.loss_cls: 0.5046, decode.d4.loss_mask: 0.6795, decode.d4.loss_dice: 0.9751, decode.d5.loss_cls: 0.4849, decode.d5.loss_mask: 0.6813, decode.d5.loss_dice: 0.9719, decode.d6.loss_cls: 0.4754, decode.d6.loss_mask: 0.6790, decode.d6.loss_dice: 0.9715, decode.d7.loss_cls: 0.4609, decode.d7.loss_mask: 0.6761, decode.d7.loss_dice: 0.9731, decode.d8.loss_cls: 0.4653, decode.d8.loss_mask: 0.6737, decode.d8.loss_dice: 0.9663, loss: 24.9066 2022-05-05 05:09:54,566 - mmseg - INFO - Iter [35150/40000] lr: 1.741e-07, eta: 1:07:46, time: 0.784, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4204, decode.loss_mask: 0.6643, decode.loss_dice: 0.9394, decode.d0.loss_cls: 3.3576, decode.d0.loss_mask: 0.7250, decode.d0.loss_dice: 1.1021, decode.d1.loss_cls: 0.6680, decode.d1.loss_mask: 0.6991, decode.d1.loss_dice: 1.0156, decode.d2.loss_cls: 0.5319, decode.d2.loss_mask: 0.6818, decode.d2.loss_dice: 0.9755, decode.d3.loss_cls: 0.4861, decode.d3.loss_mask: 0.6746, decode.d3.loss_dice: 0.9598, decode.d4.loss_cls: 0.4561, decode.d4.loss_mask: 0.6708, decode.d4.loss_dice: 0.9558, decode.d5.loss_cls: 0.4433, decode.d5.loss_mask: 0.6700, decode.d5.loss_dice: 0.9538, decode.d6.loss_cls: 0.4250, decode.d6.loss_mask: 0.6662, decode.d6.loss_dice: 0.9459, decode.d7.loss_cls: 0.4182, decode.d7.loss_mask: 0.6654, decode.d7.loss_dice: 0.9446, decode.d8.loss_cls: 0.4135, decode.d8.loss_mask: 0.6643, decode.d8.loss_dice: 0.9396, loss: 24.1341 2022-05-05 05:10:33,347 - mmseg - INFO - Iter [35200/40000] lr: 1.723e-07, eta: 1:07:03, time: 0.776, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4512, decode.loss_mask: 0.6821, decode.loss_dice: 0.9925, decode.d0.loss_cls: 3.4552, decode.d0.loss_mask: 0.7467, decode.d0.loss_dice: 1.1647, decode.d1.loss_cls: 0.7054, decode.d1.loss_mask: 0.7134, decode.d1.loss_dice: 1.0542, decode.d2.loss_cls: 0.5687, decode.d2.loss_mask: 0.6977, decode.d2.loss_dice: 1.0203, decode.d3.loss_cls: 0.5109, decode.d3.loss_mask: 0.6899, decode.d3.loss_dice: 0.9980, decode.d4.loss_cls: 0.4923, decode.d4.loss_mask: 0.6894, decode.d4.loss_dice: 0.9985, decode.d5.loss_cls: 0.4718, decode.d5.loss_mask: 0.6881, decode.d5.loss_dice: 0.9949, decode.d6.loss_cls: 0.4545, decode.d6.loss_mask: 0.6840, decode.d6.loss_dice: 0.9899, decode.d7.loss_cls: 0.4442, decode.d7.loss_mask: 0.6833, decode.d7.loss_dice: 0.9940, decode.d8.loss_cls: 0.4488, decode.d8.loss_mask: 0.6843, decode.d8.loss_dice: 0.9925, loss: 25.1613 2022-05-05 05:11:11,861 - mmseg - INFO - Iter [35250/40000] lr: 1.705e-07, eta: 1:06:21, time: 0.770, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4467, decode.loss_mask: 0.6725, decode.loss_dice: 0.9895, decode.d0.loss_cls: 3.4528, decode.d0.loss_mask: 0.7276, decode.d0.loss_dice: 1.1551, decode.d1.loss_cls: 0.7068, decode.d1.loss_mask: 0.7030, decode.d1.loss_dice: 1.0597, decode.d2.loss_cls: 0.5680, decode.d2.loss_mask: 0.6860, decode.d2.loss_dice: 1.0187, decode.d3.loss_cls: 0.5117, decode.d3.loss_mask: 0.6814, decode.d3.loss_dice: 0.9982, decode.d4.loss_cls: 0.4873, decode.d4.loss_mask: 0.6776, decode.d4.loss_dice: 0.9979, decode.d5.loss_cls: 0.4633, decode.d5.loss_mask: 0.6770, decode.d5.loss_dice: 0.9888, decode.d6.loss_cls: 0.4566, decode.d6.loss_mask: 0.6769, decode.d6.loss_dice: 0.9868, decode.d7.loss_cls: 0.4479, decode.d7.loss_mask: 0.6720, decode.d7.loss_dice: 0.9877, decode.d8.loss_cls: 0.4430, decode.d8.loss_mask: 0.6734, decode.d8.loss_dice: 0.9892, loss: 25.0031 2022-05-05 05:11:50,323 - mmseg - INFO - Iter [35300/40000] lr: 1.687e-07, eta: 1:05:39, time: 0.769, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4732, decode.loss_mask: 0.6593, decode.loss_dice: 0.9696, decode.d0.loss_cls: 3.4347, decode.d0.loss_mask: 0.7141, decode.d0.loss_dice: 1.1536, decode.d1.loss_cls: 0.7382, decode.d1.loss_mask: 0.6875, decode.d1.loss_dice: 1.0458, decode.d2.loss_cls: 0.6003, decode.d2.loss_mask: 0.6739, decode.d2.loss_dice: 1.0101, decode.d3.loss_cls: 0.5328, decode.d3.loss_mask: 0.6712, decode.d3.loss_dice: 0.9866, decode.d4.loss_cls: 0.5107, decode.d4.loss_mask: 0.6639, decode.d4.loss_dice: 0.9824, decode.d5.loss_cls: 0.4890, decode.d5.loss_mask: 0.6638, decode.d5.loss_dice: 0.9795, decode.d6.loss_cls: 0.4753, decode.d6.loss_mask: 0.6635, decode.d6.loss_dice: 0.9796, decode.d7.loss_cls: 0.4711, decode.d7.loss_mask: 0.6617, decode.d7.loss_dice: 0.9785, decode.d8.loss_cls: 0.4602, decode.d8.loss_mask: 0.6616, decode.d8.loss_dice: 0.9807, loss: 24.9725 2022-05-05 05:12:29,226 - mmseg - INFO - Iter [35350/40000] lr: 1.669e-07, eta: 1:04:56, time: 0.778, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4853, decode.loss_mask: 0.6937, decode.loss_dice: 1.0062, decode.d0.loss_cls: 3.3930, decode.d0.loss_mask: 0.7595, decode.d0.loss_dice: 1.1749, decode.d1.loss_cls: 0.7456, decode.d1.loss_mask: 0.7304, decode.d1.loss_dice: 1.0838, decode.d2.loss_cls: 0.6156, decode.d2.loss_mask: 0.7144, decode.d2.loss_dice: 1.0402, decode.d3.loss_cls: 0.5621, decode.d3.loss_mask: 0.7057, decode.d3.loss_dice: 1.0161, decode.d4.loss_cls: 0.5374, decode.d4.loss_mask: 0.7009, decode.d4.loss_dice: 1.0162, decode.d5.loss_cls: 0.5119, decode.d5.loss_mask: 0.7005, decode.d5.loss_dice: 1.0172, decode.d6.loss_cls: 0.4958, decode.d6.loss_mask: 0.6981, decode.d6.loss_dice: 1.0076, decode.d7.loss_cls: 0.4836, decode.d7.loss_mask: 0.6947, decode.d7.loss_dice: 1.0114, decode.d8.loss_cls: 0.4858, decode.d8.loss_mask: 0.6947, decode.d8.loss_dice: 1.0070, loss: 25.7892 2022-05-05 05:13:07,444 - mmseg - INFO - Iter [35400/40000] lr: 1.652e-07, eta: 1:04:14, time: 0.764, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4191, decode.loss_mask: 0.6749, decode.loss_dice: 0.9650, decode.d0.loss_cls: 3.3600, decode.d0.loss_mask: 0.7349, decode.d0.loss_dice: 1.1293, decode.d1.loss_cls: 0.6678, decode.d1.loss_mask: 0.7074, decode.d1.loss_dice: 1.0301, decode.d2.loss_cls: 0.5402, decode.d2.loss_mask: 0.6876, decode.d2.loss_dice: 0.9918, decode.d3.loss_cls: 0.4938, decode.d3.loss_mask: 0.6794, decode.d3.loss_dice: 0.9735, decode.d4.loss_cls: 0.4661, decode.d4.loss_mask: 0.6800, decode.d4.loss_dice: 0.9760, decode.d5.loss_cls: 0.4448, decode.d5.loss_mask: 0.6787, decode.d5.loss_dice: 0.9735, decode.d6.loss_cls: 0.4364, decode.d6.loss_mask: 0.6785, decode.d6.loss_dice: 0.9665, decode.d7.loss_cls: 0.4244, decode.d7.loss_mask: 0.6760, decode.d7.loss_dice: 0.9721, decode.d8.loss_cls: 0.4229, decode.d8.loss_mask: 0.6734, decode.d8.loss_dice: 0.9641, loss: 24.4883 2022-05-05 05:13:48,865 - mmseg - INFO - Iter [35450/40000] lr: 1.634e-07, eta: 1:03:32, time: 0.829, data_time: 0.057, memory: 51557, decode.loss_cls: 0.4156, decode.loss_mask: 0.6804, decode.loss_dice: 0.9342, decode.d0.loss_cls: 3.3793, decode.d0.loss_mask: 0.7357, decode.d0.loss_dice: 1.0956, decode.d1.loss_cls: 0.6804, decode.d1.loss_mask: 0.7063, decode.d1.loss_dice: 0.9962, decode.d2.loss_cls: 0.5373, decode.d2.loss_mask: 0.6926, decode.d2.loss_dice: 0.9565, decode.d3.loss_cls: 0.4843, decode.d3.loss_mask: 0.6870, decode.d3.loss_dice: 0.9434, decode.d4.loss_cls: 0.4618, decode.d4.loss_mask: 0.6818, decode.d4.loss_dice: 0.9388, decode.d5.loss_cls: 0.4395, decode.d5.loss_mask: 0.6821, decode.d5.loss_dice: 0.9393, decode.d6.loss_cls: 0.4254, decode.d6.loss_mask: 0.6800, decode.d6.loss_dice: 0.9330, decode.d7.loss_cls: 0.4204, decode.d7.loss_mask: 0.6784, decode.d7.loss_dice: 0.9339, decode.d8.loss_cls: 0.4104, decode.d8.loss_mask: 0.6794, decode.d8.loss_dice: 0.9350, loss: 24.1638 2022-05-05 05:14:27,988 - mmseg - INFO - Iter [35500/40000] lr: 1.616e-07, eta: 1:02:50, time: 0.782, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4531, decode.loss_mask: 0.6747, decode.loss_dice: 0.9890, decode.d0.loss_cls: 3.4794, decode.d0.loss_mask: 0.7328, decode.d0.loss_dice: 1.1716, decode.d1.loss_cls: 0.7292, decode.d1.loss_mask: 0.7014, decode.d1.loss_dice: 1.0642, decode.d2.loss_cls: 0.5841, decode.d2.loss_mask: 0.6870, decode.d2.loss_dice: 1.0239, decode.d3.loss_cls: 0.5220, decode.d3.loss_mask: 0.6841, decode.d3.loss_dice: 1.0060, decode.d4.loss_cls: 0.4937, decode.d4.loss_mask: 0.6826, decode.d4.loss_dice: 0.9998, decode.d5.loss_cls: 0.4727, decode.d5.loss_mask: 0.6783, decode.d5.loss_dice: 0.9995, decode.d6.loss_cls: 0.4770, decode.d6.loss_mask: 0.6744, decode.d6.loss_dice: 0.9937, decode.d7.loss_cls: 0.4624, decode.d7.loss_mask: 0.6739, decode.d7.loss_dice: 0.9965, decode.d8.loss_cls: 0.4550, decode.d8.loss_mask: 0.6725, decode.d8.loss_dice: 0.9914, loss: 25.2258 2022-05-05 05:15:06,409 - mmseg - INFO - Iter [35550/40000] lr: 1.598e-07, eta: 1:02:07, time: 0.769, data_time: 0.010, memory: 51557, decode.loss_cls: 0.3963, decode.loss_mask: 0.6797, decode.loss_dice: 0.9457, decode.d0.loss_cls: 3.3495, decode.d0.loss_mask: 0.7377, decode.d0.loss_dice: 1.1179, decode.d1.loss_cls: 0.6711, decode.d1.loss_mask: 0.7087, decode.d1.loss_dice: 1.0188, decode.d2.loss_cls: 0.5270, decode.d2.loss_mask: 0.6919, decode.d2.loss_dice: 0.9767, decode.d3.loss_cls: 0.4689, decode.d3.loss_mask: 0.6849, decode.d3.loss_dice: 0.9607, decode.d4.loss_cls: 0.4480, decode.d4.loss_mask: 0.6843, decode.d4.loss_dice: 0.9532, decode.d5.loss_cls: 0.4245, decode.d5.loss_mask: 0.6804, decode.d5.loss_dice: 0.9529, decode.d6.loss_cls: 0.4145, decode.d6.loss_mask: 0.6804, decode.d6.loss_dice: 0.9551, decode.d7.loss_cls: 0.4081, decode.d7.loss_mask: 0.6756, decode.d7.loss_dice: 0.9533, decode.d8.loss_cls: 0.3977, decode.d8.loss_mask: 0.6773, decode.d8.loss_dice: 0.9494, loss: 24.1904 2022-05-05 05:15:44,861 - mmseg - INFO - Iter [35600/40000] lr: 1.580e-07, eta: 1:01:25, time: 0.769, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4744, decode.loss_mask: 0.6712, decode.loss_dice: 0.9986, decode.d0.loss_cls: 3.4216, decode.d0.loss_mask: 0.7254, decode.d0.loss_dice: 1.1685, decode.d1.loss_cls: 0.7394, decode.d1.loss_mask: 0.7018, decode.d1.loss_dice: 1.0702, decode.d2.loss_cls: 0.6092, decode.d2.loss_mask: 0.6811, decode.d2.loss_dice: 1.0267, decode.d3.loss_cls: 0.5519, decode.d3.loss_mask: 0.6765, decode.d3.loss_dice: 1.0061, decode.d4.loss_cls: 0.5258, decode.d4.loss_mask: 0.6715, decode.d4.loss_dice: 1.0048, decode.d5.loss_cls: 0.5017, decode.d5.loss_mask: 0.6706, decode.d5.loss_dice: 0.9999, decode.d6.loss_cls: 0.4902, decode.d6.loss_mask: 0.6682, decode.d6.loss_dice: 1.0002, decode.d7.loss_cls: 0.4806, decode.d7.loss_mask: 0.6689, decode.d7.loss_dice: 1.0022, decode.d8.loss_cls: 0.4747, decode.d8.loss_mask: 0.6689, decode.d8.loss_dice: 1.0001, loss: 25.3509 2022-05-05 05:16:23,807 - mmseg - INFO - Iter [35650/40000] lr: 1.562e-07, eta: 1:00:43, time: 0.779, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4051, decode.loss_mask: 0.6709, decode.loss_dice: 0.9299, decode.d0.loss_cls: 3.2887, decode.d0.loss_mask: 0.7279, decode.d0.loss_dice: 1.0816, decode.d1.loss_cls: 0.6387, decode.d1.loss_mask: 0.7004, decode.d1.loss_dice: 0.9940, decode.d2.loss_cls: 0.5180, decode.d2.loss_mask: 0.6834, decode.d2.loss_dice: 0.9596, decode.d3.loss_cls: 0.4801, decode.d3.loss_mask: 0.6757, decode.d3.loss_dice: 0.9449, decode.d4.loss_cls: 0.4532, decode.d4.loss_mask: 0.6735, decode.d4.loss_dice: 0.9350, decode.d5.loss_cls: 0.4256, decode.d5.loss_mask: 0.6718, decode.d5.loss_dice: 0.9406, decode.d6.loss_cls: 0.4147, decode.d6.loss_mask: 0.6718, decode.d6.loss_dice: 0.9304, decode.d7.loss_cls: 0.4101, decode.d7.loss_mask: 0.6686, decode.d7.loss_dice: 0.9274, decode.d8.loss_cls: 0.4032, decode.d8.loss_mask: 0.6720, decode.d8.loss_dice: 0.9310, loss: 23.8276 2022-05-05 05:17:02,717 - mmseg - INFO - Iter [35700/40000] lr: 1.544e-07, eta: 1:00:01, time: 0.778, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4290, decode.loss_mask: 0.6721, decode.loss_dice: 0.9675, decode.d0.loss_cls: 3.3414, decode.d0.loss_mask: 0.7252, decode.d0.loss_dice: 1.1361, decode.d1.loss_cls: 0.6859, decode.d1.loss_mask: 0.6979, decode.d1.loss_dice: 1.0372, decode.d2.loss_cls: 0.5512, decode.d2.loss_mask: 0.6836, decode.d2.loss_dice: 0.9994, decode.d3.loss_cls: 0.4915, decode.d3.loss_mask: 0.6731, decode.d3.loss_dice: 0.9825, decode.d4.loss_cls: 0.4701, decode.d4.loss_mask: 0.6737, decode.d4.loss_dice: 0.9794, decode.d5.loss_cls: 0.4515, decode.d5.loss_mask: 0.6740, decode.d5.loss_dice: 0.9766, decode.d6.loss_cls: 0.4300, decode.d6.loss_mask: 0.6734, decode.d6.loss_dice: 0.9742, decode.d7.loss_cls: 0.4185, decode.d7.loss_mask: 0.6736, decode.d7.loss_dice: 0.9740, decode.d8.loss_cls: 0.4228, decode.d8.loss_mask: 0.6725, decode.d8.loss_dice: 0.9687, loss: 24.5065 2022-05-05 05:17:41,985 - mmseg - INFO - Iter [35750/40000] lr: 1.526e-07, eta: 0:59:18, time: 0.785, data_time: 0.008, memory: 51557, decode.loss_cls: 0.4281, decode.loss_mask: 0.6862, decode.loss_dice: 0.9536, decode.d0.loss_cls: 3.3964, decode.d0.loss_mask: 0.7485, decode.d0.loss_dice: 1.1168, decode.d1.loss_cls: 0.6759, decode.d1.loss_mask: 0.7205, decode.d1.loss_dice: 1.0173, decode.d2.loss_cls: 0.5479, decode.d2.loss_mask: 0.7032, decode.d2.loss_dice: 0.9841, decode.d3.loss_cls: 0.4868, decode.d3.loss_mask: 0.6993, decode.d3.loss_dice: 0.9638, decode.d4.loss_cls: 0.4686, decode.d4.loss_mask: 0.6958, decode.d4.loss_dice: 0.9644, decode.d5.loss_cls: 0.4457, decode.d5.loss_mask: 0.6926, decode.d5.loss_dice: 0.9637, decode.d6.loss_cls: 0.4267, decode.d6.loss_mask: 0.6897, decode.d6.loss_dice: 0.9538, decode.d7.loss_cls: 0.4274, decode.d7.loss_mask: 0.6874, decode.d7.loss_dice: 0.9565, decode.d8.loss_cls: 0.4175, decode.d8.loss_mask: 0.6877, decode.d8.loss_dice: 0.9516, loss: 24.5574 2022-05-05 05:18:20,835 - mmseg - INFO - Iter [35800/40000] lr: 1.508e-07, eta: 0:58:36, time: 0.777, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4505, decode.loss_mask: 0.6883, decode.loss_dice: 1.0019, decode.d0.loss_cls: 3.4034, decode.d0.loss_mask: 0.7459, decode.d0.loss_dice: 1.1733, decode.d1.loss_cls: 0.7107, decode.d1.loss_mask: 0.7213, decode.d1.loss_dice: 1.0608, decode.d2.loss_cls: 0.5726, decode.d2.loss_mask: 0.7053, decode.d2.loss_dice: 1.0312, decode.d3.loss_cls: 0.5227, decode.d3.loss_mask: 0.6986, decode.d3.loss_dice: 1.0058, decode.d4.loss_cls: 0.4960, decode.d4.loss_mask: 0.6925, decode.d4.loss_dice: 1.0117, decode.d5.loss_cls: 0.4775, decode.d5.loss_mask: 0.6903, decode.d5.loss_dice: 1.0041, decode.d6.loss_cls: 0.4667, decode.d6.loss_mask: 0.6873, decode.d6.loss_dice: 0.9974, decode.d7.loss_cls: 0.4541, decode.d7.loss_mask: 0.6893, decode.d7.loss_dice: 0.9970, decode.d8.loss_cls: 0.4529, decode.d8.loss_mask: 0.6890, decode.d8.loss_dice: 0.9961, loss: 25.2940 2022-05-05 05:18:59,484 - mmseg - INFO - Iter [35850/40000] lr: 1.490e-07, eta: 0:57:54, time: 0.773, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4201, decode.loss_mask: 0.6766, decode.loss_dice: 0.9497, decode.d0.loss_cls: 3.3720, decode.d0.loss_mask: 0.7306, decode.d0.loss_dice: 1.1188, decode.d1.loss_cls: 0.6863, decode.d1.loss_mask: 0.7062, decode.d1.loss_dice: 1.0230, decode.d2.loss_cls: 0.5439, decode.d2.loss_mask: 0.6906, decode.d2.loss_dice: 0.9798, decode.d3.loss_cls: 0.4873, decode.d3.loss_mask: 0.6854, decode.d3.loss_dice: 0.9633, decode.d4.loss_cls: 0.4626, decode.d4.loss_mask: 0.6808, decode.d4.loss_dice: 0.9585, decode.d5.loss_cls: 0.4447, decode.d5.loss_mask: 0.6776, decode.d5.loss_dice: 0.9582, decode.d6.loss_cls: 0.4294, decode.d6.loss_mask: 0.6788, decode.d6.loss_dice: 0.9550, decode.d7.loss_cls: 0.4211, decode.d7.loss_mask: 0.6760, decode.d7.loss_dice: 0.9543, decode.d8.loss_cls: 0.4206, decode.d8.loss_mask: 0.6754, decode.d8.loss_dice: 0.9494, loss: 24.3761 2022-05-05 05:19:37,907 - mmseg - INFO - Iter [35900/40000] lr: 1.472e-07, eta: 0:57:12, time: 0.768, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4626, decode.loss_mask: 0.6631, decode.loss_dice: 0.9550, decode.d0.loss_cls: 3.3991, decode.d0.loss_mask: 0.7283, decode.d0.loss_dice: 1.1209, decode.d1.loss_cls: 0.7236, decode.d1.loss_mask: 0.6982, decode.d1.loss_dice: 1.0352, decode.d2.loss_cls: 0.5916, decode.d2.loss_mask: 0.6750, decode.d2.loss_dice: 0.9861, decode.d3.loss_cls: 0.5338, decode.d3.loss_mask: 0.6717, decode.d3.loss_dice: 0.9713, decode.d4.loss_cls: 0.5125, decode.d4.loss_mask: 0.6702, decode.d4.loss_dice: 0.9680, decode.d5.loss_cls: 0.4898, decode.d5.loss_mask: 0.6675, decode.d5.loss_dice: 0.9682, decode.d6.loss_cls: 0.4757, decode.d6.loss_mask: 0.6648, decode.d6.loss_dice: 0.9592, decode.d7.loss_cls: 0.4726, decode.d7.loss_mask: 0.6625, decode.d7.loss_dice: 0.9580, decode.d8.loss_cls: 0.4597, decode.d8.loss_mask: 0.6632, decode.d8.loss_dice: 0.9554, loss: 24.7625 2022-05-05 05:20:17,644 - mmseg - INFO - Iter [35950/40000] lr: 1.454e-07, eta: 0:56:30, time: 0.795, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4420, decode.loss_mask: 0.6626, decode.loss_dice: 0.9760, decode.d0.loss_cls: 3.4193, decode.d0.loss_mask: 0.7186, decode.d0.loss_dice: 1.1530, decode.d1.loss_cls: 0.6940, decode.d1.loss_mask: 0.6897, decode.d1.loss_dice: 1.0449, decode.d2.loss_cls: 0.5669, decode.d2.loss_mask: 0.6789, decode.d2.loss_dice: 1.0038, decode.d3.loss_cls: 0.5031, decode.d3.loss_mask: 0.6703, decode.d3.loss_dice: 0.9890, decode.d4.loss_cls: 0.4836, decode.d4.loss_mask: 0.6668, decode.d4.loss_dice: 0.9851, decode.d5.loss_cls: 0.4619, decode.d5.loss_mask: 0.6663, decode.d5.loss_dice: 0.9834, decode.d6.loss_cls: 0.4506, decode.d6.loss_mask: 0.6654, decode.d6.loss_dice: 0.9734, decode.d7.loss_cls: 0.4376, decode.d7.loss_mask: 0.6642, decode.d7.loss_dice: 0.9800, decode.d8.loss_cls: 0.4383, decode.d8.loss_mask: 0.6612, decode.d8.loss_dice: 0.9751, loss: 24.7049 2022-05-05 05:20:59,481 - mmseg - INFO - Saving checkpoint at 36000 iterations 2022-05-05 05:21:24,949 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 05:21:24,952 - mmseg - INFO - Iter [36000/40000] lr: 1.436e-07, eta: 0:55:51, time: 1.343, data_time: 0.058, memory: 51557, decode.loss_cls: 0.4552, decode.loss_mask: 0.6924, decode.loss_dice: 0.9895, decode.d0.loss_cls: 3.3743, decode.d0.loss_mask: 0.7521, decode.d0.loss_dice: 1.1659, decode.d1.loss_cls: 0.7078, decode.d1.loss_mask: 0.7250, decode.d1.loss_dice: 1.0597, decode.d2.loss_cls: 0.5754, decode.d2.loss_mask: 0.7066, decode.d2.loss_dice: 1.0243, decode.d3.loss_cls: 0.5200, decode.d3.loss_mask: 0.7001, decode.d3.loss_dice: 1.0032, decode.d4.loss_cls: 0.4932, decode.d4.loss_mask: 0.6977, decode.d4.loss_dice: 0.9987, decode.d5.loss_cls: 0.4754, decode.d5.loss_mask: 0.6929, decode.d5.loss_dice: 0.9985, decode.d6.loss_cls: 0.4586, decode.d6.loss_mask: 0.6931, decode.d6.loss_dice: 0.9886, decode.d7.loss_cls: 0.4585, decode.d7.loss_mask: 0.6936, decode.d7.loss_dice: 0.9893, decode.d8.loss_cls: 0.4539, decode.d8.loss_mask: 0.6902, decode.d8.loss_dice: 0.9871, loss: 25.2207 2022-05-05 05:21:56,787 - mmseg - INFO - per class results: 2022-05-05 05:21:56,797 - mmseg - INFO - +------------------+-------+-------+ | Class | IoU | Acc | +------------------+-------+-------+ | person | 88.74 | 94.63 | | bicycle | 77.55 | 92.21 | | car | 63.89 | 73.29 | | motorcycle | 90.58 | 95.65 | | airplane | 89.58 | 95.48 | | bus | 87.04 | 91.71 | | train | 83.8 | 97.11 | | truck | 73.36 | 90.32 | | boat | 81.23 | 88.79 | | traffic light | 80.01 | 93.95 | | fire hydrant | 85.49 | 97.36 | | stop sign | 93.64 | 97.87 | | parking meter | 73.64 | 75.87 | | bench | 52.52 | 70.29 | | bird | 80.18 | 86.84 | | cat | 93.09 | 96.08 | | dog | 91.88 | 96.54 | | horse | 91.32 | 96.05 | | sheep | 88.17 | 91.58 | | cow | 95.48 | 98.41 | | elephant | 92.91 | 96.77 | | bear | 92.82 | 94.82 | | zebra | 92.17 | 96.13 | | giraffe | 88.55 | 94.53 | | backpack | 26.76 | 68.77 | | umbrella | 74.91 | 79.26 | | handbag | 23.98 | 37.54 | | tie | 65.62 | 65.98 | | suitcase | 78.36 | 92.71 | | frisbee | 92.29 | 97.03 | | skis | 39.69 | 65.6 | | snowboard | 61.36 | 76.7 | | sports ball | 86.09 | 94.91 | | kite | 66.67 | 82.51 | | baseball bat | 58.97 | 75.7 | | baseball glove | 1.8 | 1.89 | | skateboard | 70.89 | 88.98 | | surfboard | 90.3 | 95.27 | | tennis racket | 55.21 | 56.23 | | bottle | 73.82 | 87.09 | | wine glass | 85.15 | 91.83 | | cup | 71.85 | 82.72 | | fork | 58.21 | 71.29 | | knife | 78.74 | 90.33 | | spoon | 52.34 | 71.47 | | bowl | 56.56 | 65.67 | | banana | 81.54 | 92.42 | | apple | 60.25 | 69.21 | | sandwich | 87.6 | 96.47 | | orange | 68.71 | 88.68 | | broccoli | 92.7 | 97.94 | | carrot | 45.73 | 58.97 | | hot dog | 53.73 | 98.19 | | pizza | 95.49 | 96.89 | | donut | 78.86 | 95.98 | | cake | 84.13 | 89.37 | | chair | 60.55 | 75.36 | | couch | 75.76 | 95.13 | | potted plant | 32.75 | 43.23 | | bed | 67.35 | 78.29 | | dining table | 58.79 | 81.49 | | toilet | 90.08 | 96.43 | | tv | 79.36 | 93.26 | | laptop | 87.86 | 97.29 | | mouse | 84.17 | 91.04 | | remote | 68.15 | 86.56 | | keyboard | 86.71 | 97.96 | | cell phone | 84.89 | 95.91 | | microwave | 62.1 | 74.18 | | oven | 65.12 | 84.93 | | toaster | 84.33 | 87.82 | | sink | 75.87 | 79.75 | | refrigerator | 86.33 | 94.69 | | book | 81.33 | 91.73 | | clock | 76.48 | 79.92 | | vase | 61.68 | 90.1 | | scissors | 80.83 | 92.58 | | teddy bear | 87.48 | 94.89 | | hair drier | 0.0 | 0.0 | | toothbrush | 31.69 | 48.03 | | banner | 32.58 | 65.69 | | blanket | 0.0 | 0.0 | | branch | 34.51 | 36.89 | | bridge | 3.55 | 5.72 | | building-other | 56.94 | 74.07 | | bush | 19.45 | 28.98 | | cabinet | 27.39 | 51.41 | | cage | 14.1 | 81.86 | | cardboard | 24.18 | 30.52 | | carpet | 58.68 | 72.65 | | ceiling-other | 71.03 | 79.69 | | ceiling-tile | 12.6 | 14.16 | | cloth | 3.49 | 4.43 | | clothes | 21.35 | 43.97 | | clouds | 55.08 | 68.89 | | counter | 47.39 | 56.29 | | cupboard | 59.38 | 76.48 | | curtain | 70.32 | 85.74 | | desk-stuff | 33.75 | 38.58 | | dirt | 32.01 | 51.49 | | door-stuff | 46.54 | 59.65 | | fence | 44.27 | 71.16 | | floor-marble | 0.0 | 0.0 | | floor-other | 38.37 | 56.45 | | floor-stone | 24.56 | 25.8 | | floor-tile | 64.04 | 75.47 | | floor-wood | 74.7 | 85.23 | | flower | 20.48 | 55.37 | | fog | 0.0 | 0.0 | | food-other | 47.72 | 58.08 | | fruit | 50.26 | 66.19 | | furniture-other | 18.89 | 25.8 | | grass | 75.7 | 84.83 | | gravel | 30.31 | 38.79 | | ground-other | 9.87 | 22.0 | | hill | 24.71 | 34.96 | | house | 32.29 | 48.65 | | leaves | 11.25 | 18.17 | | light | 41.9 | 58.3 | | mat | 22.57 | 40.72 | | metal | 16.62 | 23.67 | | mirror-stuff | 45.88 | 59.35 | | moss | 0.0 | 0.0 | | mountain | 25.57 | 42.17 | | mud | 1.16 | 2.24 | | napkin | 22.17 | 31.78 | | net | 47.96 | 57.37 | | paper | 36.94 | 47.4 | | pavement | 52.1 | 72.89 | | pillow | 0.0 | 0.0 | | plant-other | 29.21 | 43.93 | | plastic | 27.24 | 35.65 | | platform | 44.61 | 61.65 | | playingfield | 62.08 | 72.21 | | railing | 14.22 | 23.9 | | railroad | 60.27 | 75.42 | | river | 19.03 | 23.19 | | road | 72.21 | 81.58 | | rock | 39.16 | 60.92 | | roof | 3.96 | 5.52 | | rug | 51.69 | 73.35 | | salad | 0.0 | 0.0 | | sand | 70.49 | 87.03 | | sea | 79.8 | 89.64 | | shelf | 28.39 | 54.66 | | sky-other | 63.9 | 79.75 | | skyscraper | 11.1 | 12.71 | | snow | 91.88 | 94.66 | | solid-other | nan | nan | | stairs | 46.51 | 73.73 | | stone | 7.55 | 15.0 | | straw | 11.57 | 36.09 | | structural-other | 19.45 | 30.17 | | table | 17.97 | 27.8 | | tent | 66.88 | 92.75 | | textile-other | 21.38 | 26.96 | | towel | 38.59 | 47.93 | | tree | 77.2 | 87.87 | | vegetable | 38.55 | 58.1 | | wall-brick | 48.49 | 67.63 | | wall-concrete | 23.81 | 28.53 | | wall-other | 63.32 | 82.01 | | wall-panel | 5.82 | 6.61 | | wall-stone | 33.64 | 40.72 | | wall-tile | 56.66 | 84.33 | | wall-wood | 43.32 | 64.62 | | water-other | 39.84 | 58.91 | | waterdrops | 0.0 | nan | | window-blind | 34.87 | 60.6 | | window-other | 51.2 | 65.2 | | wood | 17.52 | 38.06 | +------------------+-------+-------+ 2022-05-05 05:21:56,797 - mmseg - INFO - Summary: 2022-05-05 05:21:56,798 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 76.24 | 52.67 | 64.95 | +-------+-------+-------+ 2022-05-05 05:21:56,802 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 05:21:56,803 - mmseg - INFO - Iter(val) [125] aAcc: 0.7624, mIoU: 0.5267, mAcc: 0.6495, IoU.person: 0.8874, IoU.bicycle: 0.7755, IoU.car: 0.6389, IoU.motorcycle: 0.9058, IoU.airplane: 0.8958, IoU.bus: 0.8704, IoU.train: 0.8380, IoU.truck: 0.7336, IoU.boat: 0.8123, IoU.traffic light: 0.8001, IoU.fire hydrant: 0.8549, IoU.stop sign: 0.9364, IoU.parking meter: 0.7364, IoU.bench: 0.5252, IoU.bird: 0.8018, IoU.cat: 0.9309, IoU.dog: 0.9188, IoU.horse: 0.9132, IoU.sheep: 0.8817, IoU.cow: 0.9548, IoU.elephant: 0.9291, IoU.bear: 0.9282, IoU.zebra: 0.9217, IoU.giraffe: 0.8855, IoU.backpack: 0.2676, IoU.umbrella: 0.7491, IoU.handbag: 0.2398, IoU.tie: 0.6562, IoU.suitcase: 0.7836, IoU.frisbee: 0.9229, IoU.skis: 0.3969, IoU.snowboard: 0.6136, IoU.sports ball: 0.8609, IoU.kite: 0.6667, IoU.baseball bat: 0.5897, IoU.baseball glove: 0.0180, IoU.skateboard: 0.7089, IoU.surfboard: 0.9030, IoU.tennis racket: 0.5521, IoU.bottle: 0.7382, IoU.wine glass: 0.8515, IoU.cup: 0.7185, IoU.fork: 0.5821, IoU.knife: 0.7874, IoU.spoon: 0.5234, IoU.bowl: 0.5656, IoU.banana: 0.8154, IoU.apple: 0.6025, IoU.sandwich: 0.8760, IoU.orange: 0.6871, IoU.broccoli: 0.9270, IoU.carrot: 0.4573, IoU.hot dog: 0.5373, IoU.pizza: 0.9549, IoU.donut: 0.7886, IoU.cake: 0.8413, IoU.chair: 0.6055, IoU.couch: 0.7576, IoU.potted plant: 0.3275, IoU.bed: 0.6735, IoU.dining table: 0.5879, IoU.toilet: 0.9008, IoU.tv: 0.7936, IoU.laptop: 0.8786, IoU.mouse: 0.8417, IoU.remote: 0.6815, IoU.keyboard: 0.8671, IoU.cell phone: 0.8489, IoU.microwave: 0.6210, IoU.oven: 0.6512, IoU.toaster: 0.8433, IoU.sink: 0.7587, IoU.refrigerator: 0.8633, IoU.book: 0.8133, IoU.clock: 0.7648, IoU.vase: 0.6168, IoU.scissors: 0.8083, IoU.teddy bear: 0.8748, IoU.hair drier: 0.0000, IoU.toothbrush: 0.3169, IoU.banner: 0.3258, IoU.blanket: 0.0000, IoU.branch: 0.3451, IoU.bridge: 0.0355, IoU.building-other: 0.5694, IoU.bush: 0.1945, IoU.cabinet: 0.2739, IoU.cage: 0.1410, IoU.cardboard: 0.2418, IoU.carpet: 0.5868, IoU.ceiling-other: 0.7103, IoU.ceiling-tile: 0.1260, IoU.cloth: 0.0349, IoU.clothes: 0.2135, IoU.clouds: 0.5508, IoU.counter: 0.4739, IoU.cupboard: 0.5938, IoU.curtain: 0.7032, IoU.desk-stuff: 0.3375, IoU.dirt: 0.3201, IoU.door-stuff: 0.4654, IoU.fence: 0.4427, IoU.floor-marble: 0.0000, IoU.floor-other: 0.3837, IoU.floor-stone: 0.2456, IoU.floor-tile: 0.6404, IoU.floor-wood: 0.7470, IoU.flower: 0.2048, IoU.fog: 0.0000, IoU.food-other: 0.4772, IoU.fruit: 0.5026, IoU.furniture-other: 0.1889, IoU.grass: 0.7570, IoU.gravel: 0.3031, IoU.ground-other: 0.0987, IoU.hill: 0.2471, IoU.house: 0.3229, IoU.leaves: 0.1125, IoU.light: 0.4190, IoU.mat: 0.2257, IoU.metal: 0.1662, IoU.mirror-stuff: 0.4588, IoU.moss: 0.0000, IoU.mountain: 0.2557, IoU.mud: 0.0116, IoU.napkin: 0.2217, IoU.net: 0.4796, IoU.paper: 0.3694, IoU.pavement: 0.5210, IoU.pillow: 0.0000, IoU.plant-other: 0.2921, IoU.plastic: 0.2724, IoU.platform: 0.4461, IoU.playingfield: 0.6208, IoU.railing: 0.1422, IoU.railroad: 0.6027, IoU.river: 0.1903, IoU.road: 0.7221, IoU.rock: 0.3916, IoU.roof: 0.0396, IoU.rug: 0.5169, IoU.salad: 0.0000, IoU.sand: 0.7049, IoU.sea: 0.7980, IoU.shelf: 0.2839, IoU.sky-other: 0.6390, IoU.skyscraper: 0.1110, IoU.snow: 0.9188, IoU.solid-other: nan, IoU.stairs: 0.4651, IoU.stone: 0.0755, IoU.straw: 0.1157, IoU.structural-other: 0.1945, IoU.table: 0.1797, IoU.tent: 0.6688, IoU.textile-other: 0.2138, IoU.towel: 0.3859, IoU.tree: 0.7720, IoU.vegetable: 0.3855, IoU.wall-brick: 0.4849, IoU.wall-concrete: 0.2381, IoU.wall-other: 0.6332, IoU.wall-panel: 0.0582, IoU.wall-stone: 0.3364, IoU.wall-tile: 0.5666, IoU.wall-wood: 0.4332, IoU.water-other: 0.3984, IoU.waterdrops: 0.0000, IoU.window-blind: 0.3487, IoU.window-other: 0.5120, IoU.wood: 0.1752, Acc.person: 0.9463, Acc.bicycle: 0.9221, Acc.car: 0.7329, Acc.motorcycle: 0.9565, Acc.airplane: 0.9548, Acc.bus: 0.9171, Acc.train: 0.9711, Acc.truck: 0.9032, Acc.boat: 0.8879, Acc.traffic light: 0.9395, Acc.fire hydrant: 0.9736, Acc.stop sign: 0.9787, Acc.parking meter: 0.7587, Acc.bench: 0.7029, Acc.bird: 0.8684, Acc.cat: 0.9608, Acc.dog: 0.9654, Acc.horse: 0.9605, Acc.sheep: 0.9158, Acc.cow: 0.9841, Acc.elephant: 0.9677, Acc.bear: 0.9482, Acc.zebra: 0.9613, Acc.giraffe: 0.9453, Acc.backpack: 0.6877, Acc.umbrella: 0.7926, Acc.handbag: 0.3754, Acc.tie: 0.6598, Acc.suitcase: 0.9271, Acc.frisbee: 0.9703, Acc.skis: 0.6560, Acc.snowboard: 0.7670, Acc.sports ball: 0.9491, Acc.kite: 0.8251, Acc.baseball bat: 0.7570, Acc.baseball glove: 0.0189, Acc.skateboard: 0.8898, Acc.surfboard: 0.9527, Acc.tennis racket: 0.5623, Acc.bottle: 0.8709, Acc.wine glass: 0.9183, Acc.cup: 0.8272, Acc.fork: 0.7129, Acc.knife: 0.9033, Acc.spoon: 0.7147, Acc.bowl: 0.6567, Acc.banana: 0.9242, Acc.apple: 0.6921, Acc.sandwich: 0.9647, Acc.orange: 0.8868, Acc.broccoli: 0.9794, Acc.carrot: 0.5897, Acc.hot dog: 0.9819, Acc.pizza: 0.9689, Acc.donut: 0.9598, Acc.cake: 0.8937, Acc.chair: 0.7536, Acc.couch: 0.9513, Acc.potted plant: 0.4323, Acc.bed: 0.7829, Acc.dining table: 0.8149, Acc.toilet: 0.9643, Acc.tv: 0.9326, Acc.laptop: 0.9729, Acc.mouse: 0.9104, Acc.remote: 0.8656, Acc.keyboard: 0.9796, Acc.cell phone: 0.9591, Acc.microwave: 0.7418, Acc.oven: 0.8493, Acc.toaster: 0.8782, Acc.sink: 0.7975, Acc.refrigerator: 0.9469, Acc.book: 0.9173, Acc.clock: 0.7992, Acc.vase: 0.9010, Acc.scissors: 0.9258, Acc.teddy bear: 0.9489, Acc.hair drier: 0.0000, Acc.toothbrush: 0.4803, Acc.banner: 0.6569, Acc.blanket: 0.0000, Acc.branch: 0.3689, Acc.bridge: 0.0572, Acc.building-other: 0.7407, Acc.bush: 0.2898, Acc.cabinet: 0.5141, Acc.cage: 0.8186, Acc.cardboard: 0.3052, Acc.carpet: 0.7265, Acc.ceiling-other: 0.7969, Acc.ceiling-tile: 0.1416, Acc.cloth: 0.0443, Acc.clothes: 0.4397, Acc.clouds: 0.6889, Acc.counter: 0.5629, Acc.cupboard: 0.7648, Acc.curtain: 0.8574, Acc.desk-stuff: 0.3858, Acc.dirt: 0.5149, Acc.door-stuff: 0.5965, Acc.fence: 0.7116, Acc.floor-marble: 0.0000, Acc.floor-other: 0.5645, Acc.floor-stone: 0.2580, Acc.floor-tile: 0.7547, Acc.floor-wood: 0.8523, Acc.flower: 0.5537, Acc.fog: 0.0000, Acc.food-other: 0.5808, Acc.fruit: 0.6619, Acc.furniture-other: 0.2580, Acc.grass: 0.8483, Acc.gravel: 0.3879, Acc.ground-other: 0.2200, Acc.hill: 0.3496, Acc.house: 0.4865, Acc.leaves: 0.1817, Acc.light: 0.5830, Acc.mat: 0.4072, Acc.metal: 0.2367, Acc.mirror-stuff: 0.5935, Acc.moss: 0.0000, Acc.mountain: 0.4217, Acc.mud: 0.0224, Acc.napkin: 0.3178, Acc.net: 0.5737, Acc.paper: 0.4740, Acc.pavement: 0.7289, Acc.pillow: 0.0000, Acc.plant-other: 0.4393, Acc.plastic: 0.3565, Acc.platform: 0.6165, Acc.playingfield: 0.7221, Acc.railing: 0.2390, Acc.railroad: 0.7542, Acc.river: 0.2319, Acc.road: 0.8158, Acc.rock: 0.6092, Acc.roof: 0.0552, Acc.rug: 0.7335, Acc.salad: 0.0000, Acc.sand: 0.8703, Acc.sea: 0.8964, Acc.shelf: 0.5466, Acc.sky-other: 0.7975, Acc.skyscraper: 0.1271, Acc.snow: 0.9466, Acc.solid-other: nan, Acc.stairs: 0.7373, Acc.stone: 0.1500, Acc.straw: 0.3609, Acc.structural-other: 0.3017, Acc.table: 0.2780, Acc.tent: 0.9275, Acc.textile-other: 0.2696, Acc.towel: 0.4793, Acc.tree: 0.8787, Acc.vegetable: 0.5810, Acc.wall-brick: 0.6763, Acc.wall-concrete: 0.2853, Acc.wall-other: 0.8201, Acc.wall-panel: 0.0661, Acc.wall-stone: 0.4072, Acc.wall-tile: 0.8433, Acc.wall-wood: 0.6462, Acc.water-other: 0.5891, Acc.waterdrops: nan, Acc.window-blind: 0.6060, Acc.window-other: 0.6520, Acc.wood: 0.3806 2022-05-05 05:22:35,533 - mmseg - INFO - Iter [36050/40000] lr: 1.418e-07, eta: 0:55:12, time: 1.415, data_time: 0.649, memory: 51557, decode.loss_cls: 0.4185, decode.loss_mask: 0.6686, decode.loss_dice: 0.9576, decode.d0.loss_cls: 3.3084, decode.d0.loss_mask: 0.7253, decode.d0.loss_dice: 1.1162, decode.d1.loss_cls: 0.6697, decode.d1.loss_mask: 0.7037, decode.d1.loss_dice: 1.0263, decode.d2.loss_cls: 0.5343, decode.d2.loss_mask: 0.6884, decode.d2.loss_dice: 0.9878, decode.d3.loss_cls: 0.4785, decode.d3.loss_mask: 0.6790, decode.d3.loss_dice: 0.9664, decode.d4.loss_cls: 0.4561, decode.d4.loss_mask: 0.6772, decode.d4.loss_dice: 0.9658, decode.d5.loss_cls: 0.4434, decode.d5.loss_mask: 0.6739, decode.d5.loss_dice: 0.9610, decode.d6.loss_cls: 0.4301, decode.d6.loss_mask: 0.6722, decode.d6.loss_dice: 0.9559, decode.d7.loss_cls: 0.4251, decode.d7.loss_mask: 0.6716, decode.d7.loss_dice: 0.9580, decode.d8.loss_cls: 0.4146, decode.d8.loss_mask: 0.6691, decode.d8.loss_dice: 0.9569, loss: 24.2598 2022-05-05 05:23:14,068 - mmseg - INFO - Iter [36100/40000] lr: 1.400e-07, eta: 0:54:30, time: 0.771, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4353, decode.loss_mask: 0.6625, decode.loss_dice: 0.9623, decode.d0.loss_cls: 3.4054, decode.d0.loss_mask: 0.7241, decode.d0.loss_dice: 1.1336, decode.d1.loss_cls: 0.6859, decode.d1.loss_mask: 0.6955, decode.d1.loss_dice: 1.0335, decode.d2.loss_cls: 0.5632, decode.d2.loss_mask: 0.6778, decode.d2.loss_dice: 0.9882, decode.d3.loss_cls: 0.5070, decode.d3.loss_mask: 0.6758, decode.d3.loss_dice: 0.9707, decode.d4.loss_cls: 0.4703, decode.d4.loss_mask: 0.6698, decode.d4.loss_dice: 0.9659, decode.d5.loss_cls: 0.4571, decode.d5.loss_mask: 0.6675, decode.d5.loss_dice: 0.9655, decode.d6.loss_cls: 0.4411, decode.d6.loss_mask: 0.6630, decode.d6.loss_dice: 0.9595, decode.d7.loss_cls: 0.4301, decode.d7.loss_mask: 0.6661, decode.d7.loss_dice: 0.9631, decode.d8.loss_cls: 0.4266, decode.d8.loss_mask: 0.6643, decode.d8.loss_dice: 0.9619, loss: 24.4924 2022-05-05 05:23:53,463 - mmseg - INFO - Iter [36150/40000] lr: 1.382e-07, eta: 0:53:47, time: 0.788, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4307, decode.loss_mask: 0.6648, decode.loss_dice: 0.9761, decode.d0.loss_cls: 3.3833, decode.d0.loss_mask: 0.7245, decode.d0.loss_dice: 1.1613, decode.d1.loss_cls: 0.6684, decode.d1.loss_mask: 0.6995, decode.d1.loss_dice: 1.0530, decode.d2.loss_cls: 0.5525, decode.d2.loss_mask: 0.6791, decode.d2.loss_dice: 1.0086, decode.d3.loss_cls: 0.4939, decode.d3.loss_mask: 0.6714, decode.d3.loss_dice: 0.9927, decode.d4.loss_cls: 0.4736, decode.d4.loss_mask: 0.6687, decode.d4.loss_dice: 0.9870, decode.d5.loss_cls: 0.4527, decode.d5.loss_mask: 0.6680, decode.d5.loss_dice: 0.9872, decode.d6.loss_cls: 0.4340, decode.d6.loss_mask: 0.6643, decode.d6.loss_dice: 0.9816, decode.d7.loss_cls: 0.4374, decode.d7.loss_mask: 0.6632, decode.d7.loss_dice: 0.9808, decode.d8.loss_cls: 0.4299, decode.d8.loss_mask: 0.6644, decode.d8.loss_dice: 0.9801, loss: 24.6326 2022-05-05 05:24:31,988 - mmseg - INFO - Iter [36200/40000] lr: 1.364e-07, eta: 0:53:05, time: 0.770, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4262, decode.loss_mask: 0.7139, decode.loss_dice: 0.9840, decode.d0.loss_cls: 3.3441, decode.d0.loss_mask: 0.7728, decode.d0.loss_dice: 1.1502, decode.d1.loss_cls: 0.6667, decode.d1.loss_mask: 0.7487, decode.d1.loss_dice: 1.0557, decode.d2.loss_cls: 0.5429, decode.d2.loss_mask: 0.7309, decode.d2.loss_dice: 1.0230, decode.d3.loss_cls: 0.4934, decode.d3.loss_mask: 0.7226, decode.d3.loss_dice: 0.9929, decode.d4.loss_cls: 0.4633, decode.d4.loss_mask: 0.7224, decode.d4.loss_dice: 0.9928, decode.d5.loss_cls: 0.4371, decode.d5.loss_mask: 0.7208, decode.d5.loss_dice: 0.9914, decode.d6.loss_cls: 0.4285, decode.d6.loss_mask: 0.7204, decode.d6.loss_dice: 0.9860, decode.d7.loss_cls: 0.4192, decode.d7.loss_mask: 0.7184, decode.d7.loss_dice: 0.9886, decode.d8.loss_cls: 0.4171, decode.d8.loss_mask: 0.7154, decode.d8.loss_dice: 0.9902, loss: 25.0796 2022-05-05 05:25:10,787 - mmseg - INFO - Iter [36250/40000] lr: 1.346e-07, eta: 0:52:23, time: 0.776, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4069, decode.loss_mask: 0.6779, decode.loss_dice: 0.9307, decode.d0.loss_cls: 3.3131, decode.d0.loss_mask: 0.7414, decode.d0.loss_dice: 1.0901, decode.d1.loss_cls: 0.6466, decode.d1.loss_mask: 0.7090, decode.d1.loss_dice: 1.0002, decode.d2.loss_cls: 0.5245, decode.d2.loss_mask: 0.6894, decode.d2.loss_dice: 0.9607, decode.d3.loss_cls: 0.4796, decode.d3.loss_mask: 0.6794, decode.d3.loss_dice: 0.9447, decode.d4.loss_cls: 0.4601, decode.d4.loss_mask: 0.6824, decode.d4.loss_dice: 0.9400, decode.d5.loss_cls: 0.4407, decode.d5.loss_mask: 0.6782, decode.d5.loss_dice: 0.9351, decode.d6.loss_cls: 0.4185, decode.d6.loss_mask: 0.6806, decode.d6.loss_dice: 0.9361, decode.d7.loss_cls: 0.4146, decode.d7.loss_mask: 0.6784, decode.d7.loss_dice: 0.9352, decode.d8.loss_cls: 0.4092, decode.d8.loss_mask: 0.6791, decode.d8.loss_dice: 0.9308, loss: 24.0135 2022-05-05 05:25:49,694 - mmseg - INFO - Iter [36300/40000] lr: 1.328e-07, eta: 0:51:41, time: 0.778, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4227, decode.loss_mask: 0.6820, decode.loss_dice: 0.9703, decode.d0.loss_cls: 3.3710, decode.d0.loss_mask: 0.7454, decode.d0.loss_dice: 1.1520, decode.d1.loss_cls: 0.6767, decode.d1.loss_mask: 0.7164, decode.d1.loss_dice: 1.0497, decode.d2.loss_cls: 0.5454, decode.d2.loss_mask: 0.6943, decode.d2.loss_dice: 1.0069, decode.d3.loss_cls: 0.4859, decode.d3.loss_mask: 0.6892, decode.d3.loss_dice: 0.9858, decode.d4.loss_cls: 0.4666, decode.d4.loss_mask: 0.6881, decode.d4.loss_dice: 0.9829, decode.d5.loss_cls: 0.4465, decode.d5.loss_mask: 0.6918, decode.d5.loss_dice: 0.9798, decode.d6.loss_cls: 0.4335, decode.d6.loss_mask: 0.6855, decode.d6.loss_dice: 0.9755, decode.d7.loss_cls: 0.4174, decode.d7.loss_mask: 0.6846, decode.d7.loss_dice: 0.9807, decode.d8.loss_cls: 0.4144, decode.d8.loss_mask: 0.6830, decode.d8.loss_dice: 0.9735, loss: 24.6975 2022-05-05 05:26:29,309 - mmseg - INFO - Iter [36350/40000] lr: 1.311e-07, eta: 0:50:58, time: 0.790, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4086, decode.loss_mask: 0.6885, decode.loss_dice: 0.9520, decode.d0.loss_cls: 3.4217, decode.d0.loss_mask: 0.7514, decode.d0.loss_dice: 1.1207, decode.d1.loss_cls: 0.6750, decode.d1.loss_mask: 0.7219, decode.d1.loss_dice: 1.0193, decode.d2.loss_cls: 0.5519, decode.d2.loss_mask: 0.7046, decode.d2.loss_dice: 0.9793, decode.d3.loss_cls: 0.4903, decode.d3.loss_mask: 0.6991, decode.d3.loss_dice: 0.9670, decode.d4.loss_cls: 0.4595, decode.d4.loss_mask: 0.6951, decode.d4.loss_dice: 0.9575, decode.d5.loss_cls: 0.4455, decode.d5.loss_mask: 0.6932, decode.d5.loss_dice: 0.9559, decode.d6.loss_cls: 0.4233, decode.d6.loss_mask: 0.6911, decode.d6.loss_dice: 0.9527, decode.d7.loss_cls: 0.4139, decode.d7.loss_mask: 0.6898, decode.d7.loss_dice: 0.9551, decode.d8.loss_cls: 0.4112, decode.d8.loss_mask: 0.6857, decode.d8.loss_dice: 0.9492, loss: 24.5301 2022-05-05 05:27:07,517 - mmseg - INFO - Iter [36400/40000] lr: 1.293e-07, eta: 0:50:16, time: 0.766, data_time: 0.011, memory: 51557, decode.loss_cls: 0.4081, decode.loss_mask: 0.6694, decode.loss_dice: 0.9184, decode.d0.loss_cls: 3.2862, decode.d0.loss_mask: 0.7435, decode.d0.loss_dice: 1.0876, decode.d1.loss_cls: 0.6589, decode.d1.loss_mask: 0.7020, decode.d1.loss_dice: 0.9962, decode.d2.loss_cls: 0.5331, decode.d2.loss_mask: 0.6873, decode.d2.loss_dice: 0.9512, decode.d3.loss_cls: 0.4788, decode.d3.loss_mask: 0.6806, decode.d3.loss_dice: 0.9365, decode.d4.loss_cls: 0.4530, decode.d4.loss_mask: 0.6798, decode.d4.loss_dice: 0.9323, decode.d5.loss_cls: 0.4321, decode.d5.loss_mask: 0.6744, decode.d5.loss_dice: 0.9268, decode.d6.loss_cls: 0.4184, decode.d6.loss_mask: 0.6726, decode.d6.loss_dice: 0.9241, decode.d7.loss_cls: 0.4103, decode.d7.loss_mask: 0.6682, decode.d7.loss_dice: 0.9236, decode.d8.loss_cls: 0.4126, decode.d8.loss_mask: 0.6707, decode.d8.loss_dice: 0.9245, loss: 23.8612 2022-05-05 05:27:46,152 - mmseg - INFO - Iter [36450/40000] lr: 1.275e-07, eta: 0:49:34, time: 0.773, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4581, decode.loss_mask: 0.6595, decode.loss_dice: 0.9687, decode.d0.loss_cls: 3.4418, decode.d0.loss_mask: 0.7179, decode.d0.loss_dice: 1.1562, decode.d1.loss_cls: 0.7161, decode.d1.loss_mask: 0.6953, decode.d1.loss_dice: 1.0498, decode.d2.loss_cls: 0.5816, decode.d2.loss_mask: 0.6764, decode.d2.loss_dice: 1.0054, decode.d3.loss_cls: 0.5317, decode.d3.loss_mask: 0.6719, decode.d3.loss_dice: 0.9826, decode.d4.loss_cls: 0.5079, decode.d4.loss_mask: 0.6643, decode.d4.loss_dice: 0.9826, decode.d5.loss_cls: 0.4934, decode.d5.loss_mask: 0.6591, decode.d5.loss_dice: 0.9733, decode.d6.loss_cls: 0.4817, decode.d6.loss_mask: 0.6620, decode.d6.loss_dice: 0.9715, decode.d7.loss_cls: 0.4630, decode.d7.loss_mask: 0.6600, decode.d7.loss_dice: 0.9681, decode.d8.loss_cls: 0.4638, decode.d8.loss_mask: 0.6595, decode.d8.loss_dice: 0.9690, loss: 24.8922 2022-05-05 05:28:24,512 - mmseg - INFO - Iter [36500/40000] lr: 1.257e-07, eta: 0:48:52, time: 0.767, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4355, decode.loss_mask: 0.7000, decode.loss_dice: 0.9700, decode.d0.loss_cls: 3.4388, decode.d0.loss_mask: 0.7566, decode.d0.loss_dice: 1.1334, decode.d1.loss_cls: 0.6886, decode.d1.loss_mask: 0.7351, decode.d1.loss_dice: 1.0472, decode.d2.loss_cls: 0.5635, decode.d2.loss_mask: 0.7169, decode.d2.loss_dice: 1.0078, decode.d3.loss_cls: 0.4988, decode.d3.loss_mask: 0.7108, decode.d3.loss_dice: 0.9897, decode.d4.loss_cls: 0.4802, decode.d4.loss_mask: 0.7070, decode.d4.loss_dice: 0.9887, decode.d5.loss_cls: 0.4574, decode.d5.loss_mask: 0.7045, decode.d5.loss_dice: 0.9855, decode.d6.loss_cls: 0.4344, decode.d6.loss_mask: 0.7062, decode.d6.loss_dice: 0.9798, decode.d7.loss_cls: 0.4302, decode.d7.loss_mask: 0.7031, decode.d7.loss_dice: 0.9750, decode.d8.loss_cls: 0.4356, decode.d8.loss_mask: 0.7002, decode.d8.loss_dice: 0.9739, loss: 25.0543 2022-05-05 05:29:05,338 - mmseg - INFO - Iter [36550/40000] lr: 1.239e-07, eta: 0:48:10, time: 0.817, data_time: 0.059, memory: 51557, decode.loss_cls: 0.4478, decode.loss_mask: 0.6841, decode.loss_dice: 0.9839, decode.d0.loss_cls: 3.3936, decode.d0.loss_mask: 0.7478, decode.d0.loss_dice: 1.1488, decode.d1.loss_cls: 0.7079, decode.d1.loss_mask: 0.7250, decode.d1.loss_dice: 1.0681, decode.d2.loss_cls: 0.5819, decode.d2.loss_mask: 0.7006, decode.d2.loss_dice: 1.0150, decode.d3.loss_cls: 0.5299, decode.d3.loss_mask: 0.6904, decode.d3.loss_dice: 0.9944, decode.d4.loss_cls: 0.4958, decode.d4.loss_mask: 0.6900, decode.d4.loss_dice: 0.9943, decode.d5.loss_cls: 0.4671, decode.d5.loss_mask: 0.6881, decode.d5.loss_dice: 0.9914, decode.d6.loss_cls: 0.4518, decode.d6.loss_mask: 0.6875, decode.d6.loss_dice: 0.9887, decode.d7.loss_cls: 0.4478, decode.d7.loss_mask: 0.6820, decode.d7.loss_dice: 0.9878, decode.d8.loss_cls: 0.4447, decode.d8.loss_mask: 0.6861, decode.d8.loss_dice: 0.9913, loss: 25.1137 2022-05-05 05:29:44,552 - mmseg - INFO - Iter [36600/40000] lr: 1.221e-07, eta: 0:47:28, time: 0.784, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4505, decode.loss_mask: 0.6893, decode.loss_dice: 0.9891, decode.d0.loss_cls: 3.4313, decode.d0.loss_mask: 0.7471, decode.d0.loss_dice: 1.1559, decode.d1.loss_cls: 0.7268, decode.d1.loss_mask: 0.7242, decode.d1.loss_dice: 1.0596, decode.d2.loss_cls: 0.5859, decode.d2.loss_mask: 0.7039, decode.d2.loss_dice: 1.0130, decode.d3.loss_cls: 0.5273, decode.d3.loss_mask: 0.7008, decode.d3.loss_dice: 0.9980, decode.d4.loss_cls: 0.4999, decode.d4.loss_mask: 0.6953, decode.d4.loss_dice: 0.9946, decode.d5.loss_cls: 0.4828, decode.d5.loss_mask: 0.6930, decode.d5.loss_dice: 0.9947, decode.d6.loss_cls: 0.4695, decode.d6.loss_mask: 0.6906, decode.d6.loss_dice: 0.9901, decode.d7.loss_cls: 0.4631, decode.d7.loss_mask: 0.6896, decode.d7.loss_dice: 0.9871, decode.d8.loss_cls: 0.4469, decode.d8.loss_mask: 0.6894, decode.d8.loss_dice: 0.9883, loss: 25.2774 2022-05-05 05:30:23,245 - mmseg - INFO - Iter [36650/40000] lr: 1.203e-07, eta: 0:46:45, time: 0.774, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4277, decode.loss_mask: 0.6815, decode.loss_dice: 0.9724, decode.d0.loss_cls: 3.4218, decode.d0.loss_mask: 0.7412, decode.d0.loss_dice: 1.1431, decode.d1.loss_cls: 0.6798, decode.d1.loss_mask: 0.7152, decode.d1.loss_dice: 1.0481, decode.d2.loss_cls: 0.5502, decode.d2.loss_mask: 0.6998, decode.d2.loss_dice: 1.0082, decode.d3.loss_cls: 0.4888, decode.d3.loss_mask: 0.6947, decode.d3.loss_dice: 0.9898, decode.d4.loss_cls: 0.4693, decode.d4.loss_mask: 0.6880, decode.d4.loss_dice: 0.9830, decode.d5.loss_cls: 0.4502, decode.d5.loss_mask: 0.6865, decode.d5.loss_dice: 0.9802, decode.d6.loss_cls: 0.4364, decode.d6.loss_mask: 0.6834, decode.d6.loss_dice: 0.9745, decode.d7.loss_cls: 0.4270, decode.d7.loss_mask: 0.6827, decode.d7.loss_dice: 0.9730, decode.d8.loss_cls: 0.4205, decode.d8.loss_mask: 0.6805, decode.d8.loss_dice: 0.9730, loss: 24.7705 2022-05-05 05:31:01,833 - mmseg - INFO - Iter [36700/40000] lr: 1.185e-07, eta: 0:46:03, time: 0.772, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4307, decode.loss_mask: 0.6617, decode.loss_dice: 0.9299, decode.d0.loss_cls: 3.3565, decode.d0.loss_mask: 0.7257, decode.d0.loss_dice: 1.1027, decode.d1.loss_cls: 0.6834, decode.d1.loss_mask: 0.6958, decode.d1.loss_dice: 1.0148, decode.d2.loss_cls: 0.5581, decode.d2.loss_mask: 0.6760, decode.d2.loss_dice: 0.9679, decode.d3.loss_cls: 0.5016, decode.d3.loss_mask: 0.6700, decode.d3.loss_dice: 0.9425, decode.d4.loss_cls: 0.4864, decode.d4.loss_mask: 0.6676, decode.d4.loss_dice: 0.9412, decode.d5.loss_cls: 0.4689, decode.d5.loss_mask: 0.6664, decode.d5.loss_dice: 0.9339, decode.d6.loss_cls: 0.4513, decode.d6.loss_mask: 0.6656, decode.d6.loss_dice: 0.9325, decode.d7.loss_cls: 0.4378, decode.d7.loss_mask: 0.6628, decode.d7.loss_dice: 0.9274, decode.d8.loss_cls: 0.4341, decode.d8.loss_mask: 0.6628, decode.d8.loss_dice: 0.9255, loss: 24.1817 2022-05-05 05:31:40,313 - mmseg - INFO - Iter [36750/40000] lr: 1.167e-07, eta: 0:45:21, time: 0.770, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4531, decode.loss_mask: 0.6825, decode.loss_dice: 0.9518, decode.d0.loss_cls: 3.4217, decode.d0.loss_mask: 0.7412, decode.d0.loss_dice: 1.1199, decode.d1.loss_cls: 0.7191, decode.d1.loss_mask: 0.7123, decode.d1.loss_dice: 1.0194, decode.d2.loss_cls: 0.5805, decode.d2.loss_mask: 0.6961, decode.d2.loss_dice: 0.9773, decode.d3.loss_cls: 0.5222, decode.d3.loss_mask: 0.6864, decode.d3.loss_dice: 0.9589, decode.d4.loss_cls: 0.4983, decode.d4.loss_mask: 0.6846, decode.d4.loss_dice: 0.9607, decode.d5.loss_cls: 0.4777, decode.d5.loss_mask: 0.6860, decode.d5.loss_dice: 0.9584, decode.d6.loss_cls: 0.4649, decode.d6.loss_mask: 0.6845, decode.d6.loss_dice: 0.9511, decode.d7.loss_cls: 0.4539, decode.d7.loss_mask: 0.6834, decode.d7.loss_dice: 0.9500, decode.d8.loss_cls: 0.4549, decode.d8.loss_mask: 0.6806, decode.d8.loss_dice: 0.9487, loss: 24.7804 2022-05-05 05:32:18,872 - mmseg - INFO - Iter [36800/40000] lr: 1.149e-07, eta: 0:44:39, time: 0.771, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4449, decode.loss_mask: 0.6728, decode.loss_dice: 0.9596, decode.d0.loss_cls: 3.3599, decode.d0.loss_mask: 0.7344, decode.d0.loss_dice: 1.1197, decode.d1.loss_cls: 0.6909, decode.d1.loss_mask: 0.7075, decode.d1.loss_dice: 1.0229, decode.d2.loss_cls: 0.5705, decode.d2.loss_mask: 0.6878, decode.d2.loss_dice: 0.9872, decode.d3.loss_cls: 0.5185, decode.d3.loss_mask: 0.6817, decode.d3.loss_dice: 0.9658, decode.d4.loss_cls: 0.4836, decode.d4.loss_mask: 0.6776, decode.d4.loss_dice: 0.9691, decode.d5.loss_cls: 0.4665, decode.d5.loss_mask: 0.6761, decode.d5.loss_dice: 0.9601, decode.d6.loss_cls: 0.4510, decode.d6.loss_mask: 0.6738, decode.d6.loss_dice: 0.9588, decode.d7.loss_cls: 0.4423, decode.d7.loss_mask: 0.6698, decode.d7.loss_dice: 0.9579, decode.d8.loss_cls: 0.4454, decode.d8.loss_mask: 0.6709, decode.d8.loss_dice: 0.9576, loss: 24.5847 2022-05-05 05:32:57,561 - mmseg - INFO - Iter [36850/40000] lr: 1.131e-07, eta: 0:43:57, time: 0.774, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4225, decode.loss_mask: 0.6664, decode.loss_dice: 0.9575, decode.d0.loss_cls: 3.3753, decode.d0.loss_mask: 0.7268, decode.d0.loss_dice: 1.1223, decode.d1.loss_cls: 0.6774, decode.d1.loss_mask: 0.6952, decode.d1.loss_dice: 1.0276, decode.d2.loss_cls: 0.5477, decode.d2.loss_mask: 0.6838, decode.d2.loss_dice: 0.9960, decode.d3.loss_cls: 0.4928, decode.d3.loss_mask: 0.6774, decode.d3.loss_dice: 0.9753, decode.d4.loss_cls: 0.4647, decode.d4.loss_mask: 0.6724, decode.d4.loss_dice: 0.9689, decode.d5.loss_cls: 0.4489, decode.d5.loss_mask: 0.6680, decode.d5.loss_dice: 0.9646, decode.d6.loss_cls: 0.4310, decode.d6.loss_mask: 0.6664, decode.d6.loss_dice: 0.9598, decode.d7.loss_cls: 0.4187, decode.d7.loss_mask: 0.6683, decode.d7.loss_dice: 0.9620, decode.d8.loss_cls: 0.4174, decode.d8.loss_mask: 0.6673, decode.d8.loss_dice: 0.9587, loss: 24.3809 2022-05-05 05:33:35,848 - mmseg - INFO - Iter [36900/40000] lr: 1.113e-07, eta: 0:43:15, time: 0.766, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4284, decode.loss_mask: 0.6740, decode.loss_dice: 0.9551, decode.d0.loss_cls: 3.4261, decode.d0.loss_mask: 0.7358, decode.d0.loss_dice: 1.1320, decode.d1.loss_cls: 0.6829, decode.d1.loss_mask: 0.7108, decode.d1.loss_dice: 1.0267, decode.d2.loss_cls: 0.5529, decode.d2.loss_mask: 0.6921, decode.d2.loss_dice: 0.9925, decode.d3.loss_cls: 0.4988, decode.d3.loss_mask: 0.6845, decode.d3.loss_dice: 0.9689, decode.d4.loss_cls: 0.4705, decode.d4.loss_mask: 0.6833, decode.d4.loss_dice: 0.9694, decode.d5.loss_cls: 0.4530, decode.d5.loss_mask: 0.6777, decode.d5.loss_dice: 0.9607, decode.d6.loss_cls: 0.4408, decode.d6.loss_mask: 0.6786, decode.d6.loss_dice: 0.9594, decode.d7.loss_cls: 0.4286, decode.d7.loss_mask: 0.6766, decode.d7.loss_dice: 0.9593, decode.d8.loss_cls: 0.4288, decode.d8.loss_mask: 0.6748, decode.d8.loss_dice: 0.9568, loss: 24.5796 2022-05-05 05:34:14,785 - mmseg - INFO - Iter [36950/40000] lr: 1.095e-07, eta: 0:42:33, time: 0.779, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4016, decode.loss_mask: 0.6874, decode.loss_dice: 0.9801, decode.d0.loss_cls: 3.3484, decode.d0.loss_mask: 0.7512, decode.d0.loss_dice: 1.1518, decode.d1.loss_cls: 0.6346, decode.d1.loss_mask: 0.7236, decode.d1.loss_dice: 1.0626, decode.d2.loss_cls: 0.5167, decode.d2.loss_mask: 0.7030, decode.d2.loss_dice: 1.0187, decode.d3.loss_cls: 0.4585, decode.d3.loss_mask: 0.6971, decode.d3.loss_dice: 1.0051, decode.d4.loss_cls: 0.4430, decode.d4.loss_mask: 0.6942, decode.d4.loss_dice: 0.9961, decode.d5.loss_cls: 0.4266, decode.d5.loss_mask: 0.6928, decode.d5.loss_dice: 0.9966, decode.d6.loss_cls: 0.4086, decode.d6.loss_mask: 0.6904, decode.d6.loss_dice: 0.9902, decode.d7.loss_cls: 0.3992, decode.d7.loss_mask: 0.6892, decode.d7.loss_dice: 0.9865, decode.d8.loss_cls: 0.3917, decode.d8.loss_mask: 0.6859, decode.d8.loss_dice: 0.9851, loss: 24.6165 2022-05-05 05:34:53,364 - mmseg - INFO - Saving checkpoint at 37000 iterations 2022-05-05 05:35:19,396 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 05:35:19,404 - mmseg - INFO - Iter [37000/40000] lr: 1.077e-07, eta: 0:41:53, time: 1.290, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4295, decode.loss_mask: 0.6643, decode.loss_dice: 0.9563, decode.d0.loss_cls: 3.3601, decode.d0.loss_mask: 0.7193, decode.d0.loss_dice: 1.1324, decode.d1.loss_cls: 0.6838, decode.d1.loss_mask: 0.6946, decode.d1.loss_dice: 1.0363, decode.d2.loss_cls: 0.5489, decode.d2.loss_mask: 0.6805, decode.d2.loss_dice: 1.0016, decode.d3.loss_cls: 0.4880, decode.d3.loss_mask: 0.6724, decode.d3.loss_dice: 0.9747, decode.d4.loss_cls: 0.4704, decode.d4.loss_mask: 0.6681, decode.d4.loss_dice: 0.9703, decode.d5.loss_cls: 0.4582, decode.d5.loss_mask: 0.6687, decode.d5.loss_dice: 0.9671, decode.d6.loss_cls: 0.4376, decode.d6.loss_mask: 0.6670, decode.d6.loss_dice: 0.9605, decode.d7.loss_cls: 0.4298, decode.d7.loss_mask: 0.6662, decode.d7.loss_dice: 0.9603, decode.d8.loss_cls: 0.4254, decode.d8.loss_mask: 0.6648, decode.d8.loss_dice: 0.9616, loss: 24.4187 2022-05-05 05:35:58,792 - mmseg - INFO - Iter [37050/40000] lr: 1.059e-07, eta: 0:41:10, time: 0.790, data_time: 0.012, memory: 51557, decode.loss_cls: 0.4406, decode.loss_mask: 0.6700, decode.loss_dice: 0.9814, decode.d0.loss_cls: 3.4221, decode.d0.loss_mask: 0.7203, decode.d0.loss_dice: 1.1494, decode.d1.loss_cls: 0.7212, decode.d1.loss_mask: 0.7030, decode.d1.loss_dice: 1.0620, decode.d2.loss_cls: 0.5598, decode.d2.loss_mask: 0.6810, decode.d2.loss_dice: 1.0176, decode.d3.loss_cls: 0.5063, decode.d3.loss_mask: 0.6738, decode.d3.loss_dice: 0.9965, decode.d4.loss_cls: 0.4844, decode.d4.loss_mask: 0.6719, decode.d4.loss_dice: 0.9946, decode.d5.loss_cls: 0.4687, decode.d5.loss_mask: 0.6665, decode.d5.loss_dice: 0.9869, decode.d6.loss_cls: 0.4522, decode.d6.loss_mask: 0.6703, decode.d6.loss_dice: 0.9849, decode.d7.loss_cls: 0.4429, decode.d7.loss_mask: 0.6665, decode.d7.loss_dice: 0.9843, decode.d8.loss_cls: 0.4393, decode.d8.loss_mask: 0.6665, decode.d8.loss_dice: 0.9824, loss: 24.8673 2022-05-05 05:36:40,130 - mmseg - INFO - Iter [37100/40000] lr: 1.041e-07, eta: 0:40:29, time: 0.827, data_time: 0.059, memory: 51557, decode.loss_cls: 0.4312, decode.loss_mask: 0.6807, decode.loss_dice: 0.9645, decode.d0.loss_cls: 3.3599, decode.d0.loss_mask: 0.7467, decode.d0.loss_dice: 1.1366, decode.d1.loss_cls: 0.6889, decode.d1.loss_mask: 0.7158, decode.d1.loss_dice: 1.0415, decode.d2.loss_cls: 0.5511, decode.d2.loss_mask: 0.6988, decode.d2.loss_dice: 0.9960, decode.d3.loss_cls: 0.4944, decode.d3.loss_mask: 0.6894, decode.d3.loss_dice: 0.9791, decode.d4.loss_cls: 0.4710, decode.d4.loss_mask: 0.6868, decode.d4.loss_dice: 0.9769, decode.d5.loss_cls: 0.4527, decode.d5.loss_mask: 0.6836, decode.d5.loss_dice: 0.9746, decode.d6.loss_cls: 0.4386, decode.d6.loss_mask: 0.6831, decode.d6.loss_dice: 0.9680, decode.d7.loss_cls: 0.4337, decode.d7.loss_mask: 0.6807, decode.d7.loss_dice: 0.9690, decode.d8.loss_cls: 0.4357, decode.d8.loss_mask: 0.6798, decode.d8.loss_dice: 0.9651, loss: 24.6741 2022-05-05 05:37:18,682 - mmseg - INFO - Iter [37150/40000] lr: 1.023e-07, eta: 0:39:46, time: 0.771, data_time: 0.008, memory: 51557, decode.loss_cls: 0.3771, decode.loss_mask: 0.6747, decode.loss_dice: 0.9462, decode.d0.loss_cls: 3.2582, decode.d0.loss_mask: 0.7279, decode.d0.loss_dice: 1.0846, decode.d1.loss_cls: 0.6227, decode.d1.loss_mask: 0.7013, decode.d1.loss_dice: 1.0083, decode.d2.loss_cls: 0.4946, decode.d2.loss_mask: 0.6902, decode.d2.loss_dice: 0.9760, decode.d3.loss_cls: 0.4407, decode.d3.loss_mask: 0.6858, decode.d3.loss_dice: 0.9555, decode.d4.loss_cls: 0.4148, decode.d4.loss_mask: 0.6824, decode.d4.loss_dice: 0.9546, decode.d5.loss_cls: 0.4017, decode.d5.loss_mask: 0.6802, decode.d5.loss_dice: 0.9519, decode.d6.loss_cls: 0.3842, decode.d6.loss_mask: 0.6780, decode.d6.loss_dice: 0.9454, decode.d7.loss_cls: 0.3782, decode.d7.loss_mask: 0.6770, decode.d7.loss_dice: 0.9519, decode.d8.loss_cls: 0.3699, decode.d8.loss_mask: 0.6741, decode.d8.loss_dice: 0.9457, loss: 23.7341 2022-05-05 05:37:58,023 - mmseg - INFO - Iter [37200/40000] lr: 1.005e-07, eta: 0:39:04, time: 0.786, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4333, decode.loss_mask: 0.6655, decode.loss_dice: 0.9736, decode.d0.loss_cls: 3.4438, decode.d0.loss_mask: 0.7252, decode.d0.loss_dice: 1.1369, decode.d1.loss_cls: 0.7249, decode.d1.loss_mask: 0.6953, decode.d1.loss_dice: 1.0380, decode.d2.loss_cls: 0.5818, decode.d2.loss_mask: 0.6772, decode.d2.loss_dice: 1.0010, decode.d3.loss_cls: 0.5172, decode.d3.loss_mask: 0.6736, decode.d3.loss_dice: 0.9816, decode.d4.loss_cls: 0.4902, decode.d4.loss_mask: 0.6743, decode.d4.loss_dice: 0.9803, decode.d5.loss_cls: 0.4589, decode.d5.loss_mask: 0.6705, decode.d5.loss_dice: 0.9798, decode.d6.loss_cls: 0.4383, decode.d6.loss_mask: 0.6668, decode.d6.loss_dice: 0.9735, decode.d7.loss_cls: 0.4343, decode.d7.loss_mask: 0.6649, decode.d7.loss_dice: 0.9686, decode.d8.loss_cls: 0.4319, decode.d8.loss_mask: 0.6656, decode.d8.loss_dice: 0.9698, loss: 24.7365 2022-05-05 05:38:36,376 - mmseg - INFO - Iter [37250/40000] lr: 9.875e-08, eta: 0:38:22, time: 0.768, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4236, decode.loss_mask: 0.6699, decode.loss_dice: 0.9650, decode.d0.loss_cls: 3.3827, decode.d0.loss_mask: 0.7286, decode.d0.loss_dice: 1.1425, decode.d1.loss_cls: 0.6870, decode.d1.loss_mask: 0.7031, decode.d1.loss_dice: 1.0475, decode.d2.loss_cls: 0.5606, decode.d2.loss_mask: 0.6834, decode.d2.loss_dice: 0.9979, decode.d3.loss_cls: 0.4936, decode.d3.loss_mask: 0.6829, decode.d3.loss_dice: 0.9828, decode.d4.loss_cls: 0.4738, decode.d4.loss_mask: 0.6774, decode.d4.loss_dice: 0.9776, decode.d5.loss_cls: 0.4529, decode.d5.loss_mask: 0.6775, decode.d5.loss_dice: 0.9758, decode.d6.loss_cls: 0.4328, decode.d6.loss_mask: 0.6729, decode.d6.loss_dice: 0.9725, decode.d7.loss_cls: 0.4281, decode.d7.loss_mask: 0.6728, decode.d7.loss_dice: 0.9655, decode.d8.loss_cls: 0.4238, decode.d8.loss_mask: 0.6736, decode.d8.loss_dice: 0.9662, loss: 24.5942 2022-05-05 05:39:14,988 - mmseg - INFO - Iter [37300/40000] lr: 9.695e-08, eta: 0:37:40, time: 0.772, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4153, decode.loss_mask: 0.6763, decode.loss_dice: 0.9335, decode.d0.loss_cls: 3.3390, decode.d0.loss_mask: 0.7439, decode.d0.loss_dice: 1.1121, decode.d1.loss_cls: 0.6766, decode.d1.loss_mask: 0.7116, decode.d1.loss_dice: 1.0125, decode.d2.loss_cls: 0.5350, decode.d2.loss_mask: 0.6953, decode.d2.loss_dice: 0.9735, decode.d3.loss_cls: 0.4817, decode.d3.loss_mask: 0.6883, decode.d3.loss_dice: 0.9514, decode.d4.loss_cls: 0.4660, decode.d4.loss_mask: 0.6822, decode.d4.loss_dice: 0.9439, decode.d5.loss_cls: 0.4433, decode.d5.loss_mask: 0.6796, decode.d5.loss_dice: 0.9443, decode.d6.loss_cls: 0.4238, decode.d6.loss_mask: 0.6784, decode.d6.loss_dice: 0.9397, decode.d7.loss_cls: 0.4158, decode.d7.loss_mask: 0.6751, decode.d7.loss_dice: 0.9316, decode.d8.loss_cls: 0.4108, decode.d8.loss_mask: 0.6724, decode.d8.loss_dice: 0.9324, loss: 24.1853 2022-05-05 05:39:54,089 - mmseg - INFO - Iter [37350/40000] lr: 9.516e-08, eta: 0:36:58, time: 0.782, data_time: 0.009, memory: 51557, decode.loss_cls: 0.3970, decode.loss_mask: 0.6743, decode.loss_dice: 0.9608, decode.d0.loss_cls: 3.4006, decode.d0.loss_mask: 0.7268, decode.d0.loss_dice: 1.1334, decode.d1.loss_cls: 0.6733, decode.d1.loss_mask: 0.7046, decode.d1.loss_dice: 1.0356, decode.d2.loss_cls: 0.5358, decode.d2.loss_mask: 0.6882, decode.d2.loss_dice: 0.9946, decode.d3.loss_cls: 0.4752, decode.d3.loss_mask: 0.6829, decode.d3.loss_dice: 0.9795, decode.d4.loss_cls: 0.4530, decode.d4.loss_mask: 0.6756, decode.d4.loss_dice: 0.9719, decode.d5.loss_cls: 0.4292, decode.d5.loss_mask: 0.6754, decode.d5.loss_dice: 0.9697, decode.d6.loss_cls: 0.4127, decode.d6.loss_mask: 0.6742, decode.d6.loss_dice: 0.9612, decode.d7.loss_cls: 0.4018, decode.d7.loss_mask: 0.6721, decode.d7.loss_dice: 0.9627, decode.d8.loss_cls: 0.3999, decode.d8.loss_mask: 0.6741, decode.d8.loss_dice: 0.9633, loss: 24.3590 2022-05-05 05:40:32,691 - mmseg - INFO - Iter [37400/40000] lr: 9.336e-08, eta: 0:36:16, time: 0.772, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4104, decode.loss_mask: 0.6553, decode.loss_dice: 0.9369, decode.d0.loss_cls: 3.3871, decode.d0.loss_mask: 0.7198, decode.d0.loss_dice: 1.1145, decode.d1.loss_cls: 0.6674, decode.d1.loss_mask: 0.6890, decode.d1.loss_dice: 1.0048, decode.d2.loss_cls: 0.5266, decode.d2.loss_mask: 0.6717, decode.d2.loss_dice: 0.9635, decode.d3.loss_cls: 0.4860, decode.d3.loss_mask: 0.6640, decode.d3.loss_dice: 0.9432, decode.d4.loss_cls: 0.4550, decode.d4.loss_mask: 0.6626, decode.d4.loss_dice: 0.9448, decode.d5.loss_cls: 0.4375, decode.d5.loss_mask: 0.6593, decode.d5.loss_dice: 0.9402, decode.d6.loss_cls: 0.4161, decode.d6.loss_mask: 0.6566, decode.d6.loss_dice: 0.9384, decode.d7.loss_cls: 0.4130, decode.d7.loss_mask: 0.6560, decode.d7.loss_dice: 0.9385, decode.d8.loss_cls: 0.4115, decode.d8.loss_mask: 0.6561, decode.d8.loss_dice: 0.9375, loss: 23.9633 2022-05-05 05:41:11,820 - mmseg - INFO - Iter [37450/40000] lr: 9.157e-08, eta: 0:35:34, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4541, decode.loss_mask: 0.6864, decode.loss_dice: 0.9674, decode.d0.loss_cls: 3.3811, decode.d0.loss_mask: 0.7506, decode.d0.loss_dice: 1.1423, decode.d1.loss_cls: 0.7148, decode.d1.loss_mask: 0.7190, decode.d1.loss_dice: 1.0427, decode.d2.loss_cls: 0.5774, decode.d2.loss_mask: 0.7051, decode.d2.loss_dice: 1.0041, decode.d3.loss_cls: 0.5148, decode.d3.loss_mask: 0.6974, decode.d3.loss_dice: 0.9876, decode.d4.loss_cls: 0.4935, decode.d4.loss_mask: 0.6898, decode.d4.loss_dice: 0.9802, decode.d5.loss_cls: 0.4707, decode.d5.loss_mask: 0.6909, decode.d5.loss_dice: 0.9751, decode.d6.loss_cls: 0.4570, decode.d6.loss_mask: 0.6904, decode.d6.loss_dice: 0.9751, decode.d7.loss_cls: 0.4435, decode.d7.loss_mask: 0.6904, decode.d7.loss_dice: 0.9780, decode.d8.loss_cls: 0.4372, decode.d8.loss_mask: 0.6882, decode.d8.loss_dice: 0.9773, loss: 24.9822 2022-05-05 05:41:50,163 - mmseg - INFO - Iter [37500/40000] lr: 8.977e-08, eta: 0:34:52, time: 0.767, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4233, decode.loss_mask: 0.6805, decode.loss_dice: 0.9757, decode.d0.loss_cls: 3.4130, decode.d0.loss_mask: 0.7379, decode.d0.loss_dice: 1.1481, decode.d1.loss_cls: 0.7179, decode.d1.loss_mask: 0.7103, decode.d1.loss_dice: 1.0516, decode.d2.loss_cls: 0.5521, decode.d2.loss_mask: 0.6972, decode.d2.loss_dice: 1.0094, decode.d3.loss_cls: 0.5004, decode.d3.loss_mask: 0.6903, decode.d3.loss_dice: 0.9933, decode.d4.loss_cls: 0.4636, decode.d4.loss_mask: 0.6878, decode.d4.loss_dice: 0.9920, decode.d5.loss_cls: 0.4529, decode.d5.loss_mask: 0.6855, decode.d5.loss_dice: 0.9808, decode.d6.loss_cls: 0.4321, decode.d6.loss_mask: 0.6848, decode.d6.loss_dice: 0.9834, decode.d7.loss_cls: 0.4247, decode.d7.loss_mask: 0.6827, decode.d7.loss_dice: 0.9797, decode.d8.loss_cls: 0.4194, decode.d8.loss_mask: 0.6809, decode.d8.loss_dice: 0.9774, loss: 24.8287 2022-05-05 05:42:29,298 - mmseg - INFO - Iter [37550/40000] lr: 8.798e-08, eta: 0:34:10, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4537, decode.loss_mask: 0.6798, decode.loss_dice: 0.9730, decode.d0.loss_cls: 3.4060, decode.d0.loss_mask: 0.7323, decode.d0.loss_dice: 1.1329, decode.d1.loss_cls: 0.7033, decode.d1.loss_mask: 0.7131, decode.d1.loss_dice: 1.0444, decode.d2.loss_cls: 0.5908, decode.d2.loss_mask: 0.6943, decode.d2.loss_dice: 0.9999, decode.d3.loss_cls: 0.5287, decode.d3.loss_mask: 0.6886, decode.d3.loss_dice: 0.9795, decode.d4.loss_cls: 0.5031, decode.d4.loss_mask: 0.6878, decode.d4.loss_dice: 0.9841, decode.d5.loss_cls: 0.4843, decode.d5.loss_mask: 0.6858, decode.d5.loss_dice: 0.9783, decode.d6.loss_cls: 0.4685, decode.d6.loss_mask: 0.6818, decode.d6.loss_dice: 0.9721, decode.d7.loss_cls: 0.4575, decode.d7.loss_mask: 0.6812, decode.d7.loss_dice: 0.9709, decode.d8.loss_cls: 0.4528, decode.d8.loss_mask: 0.6806, decode.d8.loss_dice: 0.9708, loss: 24.9798 2022-05-05 05:43:08,277 - mmseg - INFO - Iter [37600/40000] lr: 8.618e-08, eta: 0:33:28, time: 0.780, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4310, decode.loss_mask: 0.6709, decode.loss_dice: 0.9281, decode.d0.loss_cls: 3.3794, decode.d0.loss_mask: 0.7296, decode.d0.loss_dice: 1.1003, decode.d1.loss_cls: 0.6965, decode.d1.loss_mask: 0.7012, decode.d1.loss_dice: 0.9992, decode.d2.loss_cls: 0.5598, decode.d2.loss_mask: 0.6823, decode.d2.loss_dice: 0.9584, decode.d3.loss_cls: 0.4955, decode.d3.loss_mask: 0.6751, decode.d3.loss_dice: 0.9409, decode.d4.loss_cls: 0.4665, decode.d4.loss_mask: 0.6753, decode.d4.loss_dice: 0.9366, decode.d5.loss_cls: 0.4522, decode.d5.loss_mask: 0.6729, decode.d5.loss_dice: 0.9420, decode.d6.loss_cls: 0.4314, decode.d6.loss_mask: 0.6731, decode.d6.loss_dice: 0.9339, decode.d7.loss_cls: 0.4260, decode.d7.loss_mask: 0.6709, decode.d7.loss_dice: 0.9295, decode.d8.loss_cls: 0.4260, decode.d8.loss_mask: 0.6717, decode.d8.loss_dice: 0.9329, loss: 24.1889 2022-05-05 05:43:46,593 - mmseg - INFO - Iter [37650/40000] lr: 8.439e-08, eta: 0:32:46, time: 0.766, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4280, decode.loss_mask: 0.6863, decode.loss_dice: 0.9580, decode.d0.loss_cls: 3.3358, decode.d0.loss_mask: 0.7476, decode.d0.loss_dice: 1.1248, decode.d1.loss_cls: 0.6691, decode.d1.loss_mask: 0.7259, decode.d1.loss_dice: 1.0324, decode.d2.loss_cls: 0.5431, decode.d2.loss_mask: 0.7070, decode.d2.loss_dice: 0.9952, decode.d3.loss_cls: 0.4944, decode.d3.loss_mask: 0.6969, decode.d3.loss_dice: 0.9733, decode.d4.loss_cls: 0.4695, decode.d4.loss_mask: 0.6932, decode.d4.loss_dice: 0.9697, decode.d5.loss_cls: 0.4573, decode.d5.loss_mask: 0.6871, decode.d5.loss_dice: 0.9648, decode.d6.loss_cls: 0.4371, decode.d6.loss_mask: 0.6862, decode.d6.loss_dice: 0.9615, decode.d7.loss_cls: 0.4347, decode.d7.loss_mask: 0.6855, decode.d7.loss_dice: 0.9595, decode.d8.loss_cls: 0.4241, decode.d8.loss_mask: 0.6833, decode.d8.loss_dice: 0.9574, loss: 24.5887 2022-05-05 05:44:28,118 - mmseg - INFO - Iter [37700/40000] lr: 8.259e-08, eta: 0:32:04, time: 0.830, data_time: 0.060, memory: 51557, decode.loss_cls: 0.4489, decode.loss_mask: 0.6750, decode.loss_dice: 0.9678, decode.d0.loss_cls: 3.3769, decode.d0.loss_mask: 0.7335, decode.d0.loss_dice: 1.1390, decode.d1.loss_cls: 0.7041, decode.d1.loss_mask: 0.7081, decode.d1.loss_dice: 1.0406, decode.d2.loss_cls: 0.5782, decode.d2.loss_mask: 0.6881, decode.d2.loss_dice: 1.0016, decode.d3.loss_cls: 0.5148, decode.d3.loss_mask: 0.6828, decode.d3.loss_dice: 0.9846, decode.d4.loss_cls: 0.4888, decode.d4.loss_mask: 0.6817, decode.d4.loss_dice: 0.9841, decode.d5.loss_cls: 0.4567, decode.d5.loss_mask: 0.6827, decode.d5.loss_dice: 0.9854, decode.d6.loss_cls: 0.4525, decode.d6.loss_mask: 0.6765, decode.d6.loss_dice: 0.9787, decode.d7.loss_cls: 0.4426, decode.d7.loss_mask: 0.6746, decode.d7.loss_dice: 0.9708, decode.d8.loss_cls: 0.4468, decode.d8.loss_mask: 0.6748, decode.d8.loss_dice: 0.9718, loss: 24.8123 2022-05-05 05:45:06,530 - mmseg - INFO - Iter [37750/40000] lr: 8.080e-08, eta: 0:31:22, time: 0.768, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4365, decode.loss_mask: 0.6545, decode.loss_dice: 0.9430, decode.d0.loss_cls: 3.3966, decode.d0.loss_mask: 0.7271, decode.d0.loss_dice: 1.1161, decode.d1.loss_cls: 0.6947, decode.d1.loss_mask: 0.6927, decode.d1.loss_dice: 1.0205, decode.d2.loss_cls: 0.5557, decode.d2.loss_mask: 0.6740, decode.d2.loss_dice: 0.9787, decode.d3.loss_cls: 0.5045, decode.d3.loss_mask: 0.6685, decode.d3.loss_dice: 0.9635, decode.d4.loss_cls: 0.4764, decode.d4.loss_mask: 0.6654, decode.d4.loss_dice: 0.9559, decode.d5.loss_cls: 0.4568, decode.d5.loss_mask: 0.6612, decode.d5.loss_dice: 0.9485, decode.d6.loss_cls: 0.4482, decode.d6.loss_mask: 0.6603, decode.d6.loss_dice: 0.9483, decode.d7.loss_cls: 0.4294, decode.d7.loss_mask: 0.6580, decode.d7.loss_dice: 0.9471, decode.d8.loss_cls: 0.4304, decode.d8.loss_mask: 0.6573, decode.d8.loss_dice: 0.9457, loss: 24.3157 2022-05-05 05:45:45,449 - mmseg - INFO - Iter [37800/40000] lr: 7.900e-08, eta: 0:30:40, time: 0.779, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4134, decode.loss_mask: 0.6608, decode.loss_dice: 0.9425, decode.d0.loss_cls: 3.4246, decode.d0.loss_mask: 0.7178, decode.d0.loss_dice: 1.1067, decode.d1.loss_cls: 0.6792, decode.d1.loss_mask: 0.6947, decode.d1.loss_dice: 1.0205, decode.d2.loss_cls: 0.5519, decode.d2.loss_mask: 0.6752, decode.d2.loss_dice: 0.9773, decode.d3.loss_cls: 0.4884, decode.d3.loss_mask: 0.6702, decode.d3.loss_dice: 0.9570, decode.d4.loss_cls: 0.4472, decode.d4.loss_mask: 0.6675, decode.d4.loss_dice: 0.9585, decode.d5.loss_cls: 0.4382, decode.d5.loss_mask: 0.6655, decode.d5.loss_dice: 0.9530, decode.d6.loss_cls: 0.4232, decode.d6.loss_mask: 0.6669, decode.d6.loss_dice: 0.9498, decode.d7.loss_cls: 0.4146, decode.d7.loss_mask: 0.6603, decode.d7.loss_dice: 0.9477, decode.d8.loss_cls: 0.4132, decode.d8.loss_mask: 0.6631, decode.d8.loss_dice: 0.9480, loss: 24.1968 2022-05-05 05:46:24,620 - mmseg - INFO - Iter [37850/40000] lr: 7.721e-08, eta: 0:29:58, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4634, decode.loss_mask: 0.6704, decode.loss_dice: 0.9969, decode.d0.loss_cls: 3.4561, decode.d0.loss_mask: 0.7298, decode.d0.loss_dice: 1.1648, decode.d1.loss_cls: 0.7303, decode.d1.loss_mask: 0.7050, decode.d1.loss_dice: 1.0716, decode.d2.loss_cls: 0.5898, decode.d2.loss_mask: 0.6848, decode.d2.loss_dice: 1.0258, decode.d3.loss_cls: 0.5338, decode.d3.loss_mask: 0.6797, decode.d3.loss_dice: 1.0086, decode.d4.loss_cls: 0.5063, decode.d4.loss_mask: 0.6749, decode.d4.loss_dice: 1.0166, decode.d5.loss_cls: 0.4840, decode.d5.loss_mask: 0.6765, decode.d5.loss_dice: 1.0033, decode.d6.loss_cls: 0.4753, decode.d6.loss_mask: 0.6734, decode.d6.loss_dice: 1.0040, decode.d7.loss_cls: 0.4552, decode.d7.loss_mask: 0.6740, decode.d7.loss_dice: 1.0013, decode.d8.loss_cls: 0.4565, decode.d8.loss_mask: 0.6710, decode.d8.loss_dice: 1.0016, loss: 25.2845 2022-05-05 05:47:03,790 - mmseg - INFO - Iter [37900/40000] lr: 7.542e-08, eta: 0:29:16, time: 0.784, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4248, decode.loss_mask: 0.6703, decode.loss_dice: 0.9647, decode.d0.loss_cls: 3.3903, decode.d0.loss_mask: 0.7338, decode.d0.loss_dice: 1.1301, decode.d1.loss_cls: 0.7096, decode.d1.loss_mask: 0.7044, decode.d1.loss_dice: 1.0226, decode.d2.loss_cls: 0.5607, decode.d2.loss_mask: 0.6881, decode.d2.loss_dice: 0.9891, decode.d3.loss_cls: 0.5045, decode.d3.loss_mask: 0.6810, decode.d3.loss_dice: 0.9722, decode.d4.loss_cls: 0.4709, decode.d4.loss_mask: 0.6766, decode.d4.loss_dice: 0.9729, decode.d5.loss_cls: 0.4507, decode.d5.loss_mask: 0.6732, decode.d5.loss_dice: 0.9669, decode.d6.loss_cls: 0.4386, decode.d6.loss_mask: 0.6724, decode.d6.loss_dice: 0.9615, decode.d7.loss_cls: 0.4238, decode.d7.loss_mask: 0.6711, decode.d7.loss_dice: 0.9618, decode.d8.loss_cls: 0.4228, decode.d8.loss_mask: 0.6723, decode.d8.loss_dice: 0.9625, loss: 24.5443 2022-05-05 05:47:42,617 - mmseg - INFO - Iter [37950/40000] lr: 7.362e-08, eta: 0:28:34, time: 0.776, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4564, decode.loss_mask: 0.6964, decode.loss_dice: 0.9768, decode.d0.loss_cls: 3.4506, decode.d0.loss_mask: 0.7500, decode.d0.loss_dice: 1.1419, decode.d1.loss_cls: 0.7192, decode.d1.loss_mask: 0.7255, decode.d1.loss_dice: 1.0469, decode.d2.loss_cls: 0.5850, decode.d2.loss_mask: 0.7108, decode.d2.loss_dice: 1.0090, decode.d3.loss_cls: 0.5233, decode.d3.loss_mask: 0.7025, decode.d3.loss_dice: 0.9860, decode.d4.loss_cls: 0.4865, decode.d4.loss_mask: 0.7021, decode.d4.loss_dice: 0.9879, decode.d5.loss_cls: 0.4668, decode.d5.loss_mask: 0.6988, decode.d5.loss_dice: 0.9838, decode.d6.loss_cls: 0.4637, decode.d6.loss_mask: 0.6962, decode.d6.loss_dice: 0.9770, decode.d7.loss_cls: 0.4498, decode.d7.loss_mask: 0.6999, decode.d7.loss_dice: 0.9812, decode.d8.loss_cls: 0.4492, decode.d8.loss_mask: 0.6944, decode.d8.loss_dice: 0.9753, loss: 25.1931 2022-05-05 05:48:21,445 - mmseg - INFO - Saving checkpoint at 38000 iterations 2022-05-05 05:48:47,212 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 05:48:47,215 - mmseg - INFO - Iter [38000/40000] lr: 7.183e-08, eta: 0:27:53, time: 1.290, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4168, decode.loss_mask: 0.6664, decode.loss_dice: 0.9696, decode.d0.loss_cls: 3.3738, decode.d0.loss_mask: 0.7382, decode.d0.loss_dice: 1.1342, decode.d1.loss_cls: 0.6847, decode.d1.loss_mask: 0.7056, decode.d1.loss_dice: 1.0432, decode.d2.loss_cls: 0.5526, decode.d2.loss_mask: 0.6855, decode.d2.loss_dice: 0.9987, decode.d3.loss_cls: 0.4947, decode.d3.loss_mask: 0.6759, decode.d3.loss_dice: 0.9786, decode.d4.loss_cls: 0.4690, decode.d4.loss_mask: 0.6758, decode.d4.loss_dice: 0.9781, decode.d5.loss_cls: 0.4394, decode.d5.loss_mask: 0.6735, decode.d5.loss_dice: 0.9778, decode.d6.loss_cls: 0.4312, decode.d6.loss_mask: 0.6682, decode.d6.loss_dice: 0.9716, decode.d7.loss_cls: 0.4304, decode.d7.loss_mask: 0.6663, decode.d7.loss_dice: 0.9725, decode.d8.loss_cls: 0.4248, decode.d8.loss_mask: 0.6670, decode.d8.loss_dice: 0.9699, loss: 24.5339 2022-05-05 05:49:26,416 - mmseg - INFO - Iter [38050/40000] lr: 7.003e-08, eta: 0:27:11, time: 0.786, data_time: 0.011, memory: 51557, decode.loss_cls: 0.4466, decode.loss_mask: 0.6697, decode.loss_dice: 0.9472, decode.d0.loss_cls: 3.3914, decode.d0.loss_mask: 0.7440, decode.d0.loss_dice: 1.1343, decode.d1.loss_cls: 0.7146, decode.d1.loss_mask: 0.7040, decode.d1.loss_dice: 1.0338, decode.d2.loss_cls: 0.5828, decode.d2.loss_mask: 0.6871, decode.d2.loss_dice: 0.9844, decode.d3.loss_cls: 0.5276, decode.d3.loss_mask: 0.6794, decode.d3.loss_dice: 0.9618, decode.d4.loss_cls: 0.4980, decode.d4.loss_mask: 0.6761, decode.d4.loss_dice: 0.9637, decode.d5.loss_cls: 0.4763, decode.d5.loss_mask: 0.6743, decode.d5.loss_dice: 0.9578, decode.d6.loss_cls: 0.4540, decode.d6.loss_mask: 0.6732, decode.d6.loss_dice: 0.9504, decode.d7.loss_cls: 0.4456, decode.d7.loss_mask: 0.6715, decode.d7.loss_dice: 0.9459, decode.d8.loss_cls: 0.4458, decode.d8.loss_mask: 0.6706, decode.d8.loss_dice: 0.9452, loss: 24.6569 2022-05-05 05:50:05,716 - mmseg - INFO - Iter [38100/40000] lr: 6.824e-08, eta: 0:26:29, time: 0.786, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4638, decode.loss_mask: 0.6802, decode.loss_dice: 0.9838, decode.d0.loss_cls: 3.3634, decode.d0.loss_mask: 0.7385, decode.d0.loss_dice: 1.1621, decode.d1.loss_cls: 0.7072, decode.d1.loss_mask: 0.7134, decode.d1.loss_dice: 1.0554, decode.d2.loss_cls: 0.5734, decode.d2.loss_mask: 0.6966, decode.d2.loss_dice: 1.0148, decode.d3.loss_cls: 0.5150, decode.d3.loss_mask: 0.6909, decode.d3.loss_dice: 1.0005, decode.d4.loss_cls: 0.4883, decode.d4.loss_mask: 0.6901, decode.d4.loss_dice: 1.0009, decode.d5.loss_cls: 0.4680, decode.d5.loss_mask: 0.6886, decode.d5.loss_dice: 0.9978, decode.d6.loss_cls: 0.4572, decode.d6.loss_mask: 0.6863, decode.d6.loss_dice: 0.9903, decode.d7.loss_cls: 0.4604, decode.d7.loss_mask: 0.6837, decode.d7.loss_dice: 0.9887, decode.d8.loss_cls: 0.4594, decode.d8.loss_mask: 0.6819, decode.d8.loss_dice: 0.9889, loss: 25.0899 2022-05-05 05:50:44,522 - mmseg - INFO - Iter [38150/40000] lr: 6.644e-08, eta: 0:25:47, time: 0.776, data_time: 0.009, memory: 51557, decode.loss_cls: 0.3943, decode.loss_mask: 0.6815, decode.loss_dice: 0.9500, decode.d0.loss_cls: 3.2738, decode.d0.loss_mask: 0.7453, decode.d0.loss_dice: 1.1050, decode.d1.loss_cls: 0.6370, decode.d1.loss_mask: 0.7112, decode.d1.loss_dice: 1.0220, decode.d2.loss_cls: 0.5157, decode.d2.loss_mask: 0.6965, decode.d2.loss_dice: 0.9758, decode.d3.loss_cls: 0.4489, decode.d3.loss_mask: 0.6940, decode.d3.loss_dice: 0.9611, decode.d4.loss_cls: 0.4269, decode.d4.loss_mask: 0.6921, decode.d4.loss_dice: 0.9612, decode.d5.loss_cls: 0.4116, decode.d5.loss_mask: 0.6869, decode.d5.loss_dice: 0.9529, decode.d6.loss_cls: 0.4041, decode.d6.loss_mask: 0.6848, decode.d6.loss_dice: 0.9465, decode.d7.loss_cls: 0.3967, decode.d7.loss_mask: 0.6829, decode.d7.loss_dice: 0.9521, decode.d8.loss_cls: 0.3989, decode.d8.loss_mask: 0.6826, decode.d8.loss_dice: 0.9479, loss: 24.0401 2022-05-05 05:51:23,576 - mmseg - INFO - Iter [38200/40000] lr: 6.465e-08, eta: 0:25:05, time: 0.781, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4080, decode.loss_mask: 0.6487, decode.loss_dice: 0.9335, decode.d0.loss_cls: 3.3138, decode.d0.loss_mask: 0.7122, decode.d0.loss_dice: 1.0969, decode.d1.loss_cls: 0.6613, decode.d1.loss_mask: 0.6826, decode.d1.loss_dice: 1.0139, decode.d2.loss_cls: 0.5175, decode.d2.loss_mask: 0.6634, decode.d2.loss_dice: 0.9708, decode.d3.loss_cls: 0.4801, decode.d3.loss_mask: 0.6577, decode.d3.loss_dice: 0.9496, decode.d4.loss_cls: 0.4422, decode.d4.loss_mask: 0.6552, decode.d4.loss_dice: 0.9456, decode.d5.loss_cls: 0.4270, decode.d5.loss_mask: 0.6532, decode.d5.loss_dice: 0.9389, decode.d6.loss_cls: 0.4191, decode.d6.loss_mask: 0.6488, decode.d6.loss_dice: 0.9323, decode.d7.loss_cls: 0.4070, decode.d7.loss_mask: 0.6499, decode.d7.loss_dice: 0.9385, decode.d8.loss_cls: 0.4082, decode.d8.loss_mask: 0.6480, decode.d8.loss_dice: 0.9320, loss: 23.7558 2022-05-05 05:52:04,477 - mmseg - INFO - Iter [38250/40000] lr: 6.285e-08, eta: 0:24:24, time: 0.818, data_time: 0.059, memory: 51557, decode.loss_cls: 0.3935, decode.loss_mask: 0.6884, decode.loss_dice: 0.9584, decode.d0.loss_cls: 3.3792, decode.d0.loss_mask: 0.7511, decode.d0.loss_dice: 1.1269, decode.d1.loss_cls: 0.6620, decode.d1.loss_mask: 0.7248, decode.d1.loss_dice: 1.0397, decode.d2.loss_cls: 0.5166, decode.d2.loss_mask: 0.7083, decode.d2.loss_dice: 0.9994, decode.d3.loss_cls: 0.4617, decode.d3.loss_mask: 0.7015, decode.d3.loss_dice: 0.9816, decode.d4.loss_cls: 0.4436, decode.d4.loss_mask: 0.6976, decode.d4.loss_dice: 0.9809, decode.d5.loss_cls: 0.4247, decode.d5.loss_mask: 0.6951, decode.d5.loss_dice: 0.9751, decode.d6.loss_cls: 0.4115, decode.d6.loss_mask: 0.6932, decode.d6.loss_dice: 0.9673, decode.d7.loss_cls: 0.3976, decode.d7.loss_mask: 0.6946, decode.d7.loss_dice: 0.9652, decode.d8.loss_cls: 0.3956, decode.d8.loss_mask: 0.6899, decode.d8.loss_dice: 0.9587, loss: 24.4838 2022-05-05 05:52:43,704 - mmseg - INFO - Iter [38300/40000] lr: 6.106e-08, eta: 0:23:42, time: 0.785, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4019, decode.loss_mask: 0.6830, decode.loss_dice: 0.9285, decode.d0.loss_cls: 3.2987, decode.d0.loss_mask: 0.7403, decode.d0.loss_dice: 1.0918, decode.d1.loss_cls: 0.6449, decode.d1.loss_mask: 0.7108, decode.d1.loss_dice: 0.9951, decode.d2.loss_cls: 0.5301, decode.d2.loss_mask: 0.6885, decode.d2.loss_dice: 0.9560, decode.d3.loss_cls: 0.4719, decode.d3.loss_mask: 0.6904, decode.d3.loss_dice: 0.9430, decode.d4.loss_cls: 0.4462, decode.d4.loss_mask: 0.6861, decode.d4.loss_dice: 0.9401, decode.d5.loss_cls: 0.4273, decode.d5.loss_mask: 0.6823, decode.d5.loss_dice: 0.9331, decode.d6.loss_cls: 0.4081, decode.d6.loss_mask: 0.6857, decode.d6.loss_dice: 0.9344, decode.d7.loss_cls: 0.4006, decode.d7.loss_mask: 0.6830, decode.d7.loss_dice: 0.9334, decode.d8.loss_cls: 0.4011, decode.d8.loss_mask: 0.6806, decode.d8.loss_dice: 0.9252, loss: 23.9422 2022-05-05 05:53:22,679 - mmseg - INFO - Iter [38350/40000] lr: 5.926e-08, eta: 0:23:00, time: 0.779, data_time: 0.011, memory: 51557, decode.loss_cls: 0.4233, decode.loss_mask: 0.6757, decode.loss_dice: 0.9807, decode.d0.loss_cls: 3.4262, decode.d0.loss_mask: 0.7346, decode.d0.loss_dice: 1.1491, decode.d1.loss_cls: 0.6996, decode.d1.loss_mask: 0.7064, decode.d1.loss_dice: 1.0544, decode.d2.loss_cls: 0.5595, decode.d2.loss_mask: 0.6873, decode.d2.loss_dice: 1.0075, decode.d3.loss_cls: 0.5060, decode.d3.loss_mask: 0.6832, decode.d3.loss_dice: 0.9970, decode.d4.loss_cls: 0.4712, decode.d4.loss_mask: 0.6814, decode.d4.loss_dice: 0.9963, decode.d5.loss_cls: 0.4531, decode.d5.loss_mask: 0.6780, decode.d5.loss_dice: 0.9875, decode.d6.loss_cls: 0.4390, decode.d6.loss_mask: 0.6773, decode.d6.loss_dice: 0.9816, decode.d7.loss_cls: 0.4314, decode.d7.loss_mask: 0.6754, decode.d7.loss_dice: 0.9781, decode.d8.loss_cls: 0.4268, decode.d8.loss_mask: 0.6758, decode.d8.loss_dice: 0.9772, loss: 24.8205 2022-05-05 05:54:01,349 - mmseg - INFO - Iter [38400/40000] lr: 5.747e-08, eta: 0:22:18, time: 0.773, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4312, decode.loss_mask: 0.6441, decode.loss_dice: 0.9666, decode.d0.loss_cls: 3.4344, decode.d0.loss_mask: 0.7075, decode.d0.loss_dice: 1.1451, decode.d1.loss_cls: 0.7120, decode.d1.loss_mask: 0.6770, decode.d1.loss_dice: 1.0497, decode.d2.loss_cls: 0.5708, decode.d2.loss_mask: 0.6586, decode.d2.loss_dice: 1.0028, decode.d3.loss_cls: 0.5083, decode.d3.loss_mask: 0.6520, decode.d3.loss_dice: 0.9854, decode.d4.loss_cls: 0.4854, decode.d4.loss_mask: 0.6477, decode.d4.loss_dice: 0.9789, decode.d5.loss_cls: 0.4575, decode.d5.loss_mask: 0.6461, decode.d5.loss_dice: 0.9738, decode.d6.loss_cls: 0.4474, decode.d6.loss_mask: 0.6466, decode.d6.loss_dice: 0.9750, decode.d7.loss_cls: 0.4371, decode.d7.loss_mask: 0.6416, decode.d7.loss_dice: 0.9649, decode.d8.loss_cls: 0.4339, decode.d8.loss_mask: 0.6428, decode.d8.loss_dice: 0.9643, loss: 24.4883 2022-05-05 05:54:40,439 - mmseg - INFO - Iter [38450/40000] lr: 5.567e-08, eta: 0:21:36, time: 0.781, data_time: 0.009, memory: 51557, decode.loss_cls: 0.3983, decode.loss_mask: 0.6938, decode.loss_dice: 0.9435, decode.d0.loss_cls: 3.3486, decode.d0.loss_mask: 0.7644, decode.d0.loss_dice: 1.1166, decode.d1.loss_cls: 0.6616, decode.d1.loss_mask: 0.7314, decode.d1.loss_dice: 1.0241, decode.d2.loss_cls: 0.5233, decode.d2.loss_mask: 0.7104, decode.d2.loss_dice: 0.9776, decode.d3.loss_cls: 0.4605, decode.d3.loss_mask: 0.7047, decode.d3.loss_dice: 0.9617, decode.d4.loss_cls: 0.4434, decode.d4.loss_mask: 0.7013, decode.d4.loss_dice: 0.9613, decode.d5.loss_cls: 0.4197, decode.d5.loss_mask: 0.6960, decode.d5.loss_dice: 0.9570, decode.d6.loss_cls: 0.4113, decode.d6.loss_mask: 0.6984, decode.d6.loss_dice: 0.9563, decode.d7.loss_cls: 0.4045, decode.d7.loss_mask: 0.6956, decode.d7.loss_dice: 0.9486, decode.d8.loss_cls: 0.3991, decode.d8.loss_mask: 0.6979, decode.d8.loss_dice: 0.9480, loss: 24.3588 2022-05-05 05:55:19,455 - mmseg - INFO - Iter [38500/40000] lr: 5.388e-08, eta: 0:20:54, time: 0.781, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4062, decode.loss_mask: 0.6901, decode.loss_dice: 0.9624, decode.d0.loss_cls: 3.3736, decode.d0.loss_mask: 0.7590, decode.d0.loss_dice: 1.1324, decode.d1.loss_cls: 0.6636, decode.d1.loss_mask: 0.7281, decode.d1.loss_dice: 1.0381, decode.d2.loss_cls: 0.5347, decode.d2.loss_mask: 0.7111, decode.d2.loss_dice: 0.9970, decode.d3.loss_cls: 0.4845, decode.d3.loss_mask: 0.7047, decode.d3.loss_dice: 0.9787, decode.d4.loss_cls: 0.4517, decode.d4.loss_mask: 0.7016, decode.d4.loss_dice: 0.9778, decode.d5.loss_cls: 0.4350, decode.d5.loss_mask: 0.6973, decode.d5.loss_dice: 0.9714, decode.d6.loss_cls: 0.4301, decode.d6.loss_mask: 0.6922, decode.d6.loss_dice: 0.9635, decode.d7.loss_cls: 0.4218, decode.d7.loss_mask: 0.6927, decode.d7.loss_dice: 0.9677, decode.d8.loss_cls: 0.4096, decode.d8.loss_mask: 0.6897, decode.d8.loss_dice: 0.9651, loss: 24.6317 2022-05-05 05:55:58,092 - mmseg - INFO - Iter [38550/40000] lr: 5.208e-08, eta: 0:20:12, time: 0.773, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4385, decode.loss_mask: 0.6634, decode.loss_dice: 0.9687, decode.d0.loss_cls: 3.4476, decode.d0.loss_mask: 0.7263, decode.d0.loss_dice: 1.1333, decode.d1.loss_cls: 0.7230, decode.d1.loss_mask: 0.6956, decode.d1.loss_dice: 1.0380, decode.d2.loss_cls: 0.5872, decode.d2.loss_mask: 0.6798, decode.d2.loss_dice: 0.9958, decode.d3.loss_cls: 0.5276, decode.d3.loss_mask: 0.6699, decode.d3.loss_dice: 0.9813, decode.d4.loss_cls: 0.4892, decode.d4.loss_mask: 0.6678, decode.d4.loss_dice: 0.9762, decode.d5.loss_cls: 0.4765, decode.d5.loss_mask: 0.6680, decode.d5.loss_dice: 0.9750, decode.d6.loss_cls: 0.4580, decode.d6.loss_mask: 0.6640, decode.d6.loss_dice: 0.9663, decode.d7.loss_cls: 0.4422, decode.d7.loss_mask: 0.6639, decode.d7.loss_dice: 0.9634, decode.d8.loss_cls: 0.4415, decode.d8.loss_mask: 0.6653, decode.d8.loss_dice: 0.9700, loss: 24.7634 2022-05-05 05:56:36,560 - mmseg - INFO - Iter [38600/40000] lr: 5.029e-08, eta: 0:19:30, time: 0.769, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4274, decode.loss_mask: 0.6766, decode.loss_dice: 0.9574, decode.d0.loss_cls: 3.3357, decode.d0.loss_mask: 0.7353, decode.d0.loss_dice: 1.1090, decode.d1.loss_cls: 0.6764, decode.d1.loss_mask: 0.7065, decode.d1.loss_dice: 1.0190, decode.d2.loss_cls: 0.5327, decode.d2.loss_mask: 0.6930, decode.d2.loss_dice: 0.9853, decode.d3.loss_cls: 0.4894, decode.d3.loss_mask: 0.6840, decode.d3.loss_dice: 0.9656, decode.d4.loss_cls: 0.4566, decode.d4.loss_mask: 0.6896, decode.d4.loss_dice: 0.9677, decode.d5.loss_cls: 0.4442, decode.d5.loss_mask: 0.6852, decode.d5.loss_dice: 0.9593, decode.d6.loss_cls: 0.4381, decode.d6.loss_mask: 0.6801, decode.d6.loss_dice: 0.9532, decode.d7.loss_cls: 0.4289, decode.d7.loss_mask: 0.6769, decode.d7.loss_dice: 0.9523, decode.d8.loss_cls: 0.4310, decode.d8.loss_mask: 0.6752, decode.d8.loss_dice: 0.9531, loss: 24.3847 2022-05-05 05:57:15,475 - mmseg - INFO - Iter [38650/40000] lr: 4.849e-08, eta: 0:18:48, time: 0.778, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4067, decode.loss_mask: 0.6685, decode.loss_dice: 0.9579, decode.d0.loss_cls: 3.3903, decode.d0.loss_mask: 0.7305, decode.d0.loss_dice: 1.1319, decode.d1.loss_cls: 0.6713, decode.d1.loss_mask: 0.7014, decode.d1.loss_dice: 1.0307, decode.d2.loss_cls: 0.5382, decode.d2.loss_mask: 0.6864, decode.d2.loss_dice: 0.9880, decode.d3.loss_cls: 0.4704, decode.d3.loss_mask: 0.6762, decode.d3.loss_dice: 0.9728, decode.d4.loss_cls: 0.4460, decode.d4.loss_mask: 0.6737, decode.d4.loss_dice: 0.9725, decode.d5.loss_cls: 0.4294, decode.d5.loss_mask: 0.6728, decode.d5.loss_dice: 0.9681, decode.d6.loss_cls: 0.4201, decode.d6.loss_mask: 0.6706, decode.d6.loss_dice: 0.9577, decode.d7.loss_cls: 0.4108, decode.d7.loss_mask: 0.6689, decode.d7.loss_dice: 0.9606, decode.d8.loss_cls: 0.4045, decode.d8.loss_mask: 0.6694, decode.d8.loss_dice: 0.9628, loss: 24.3089 2022-05-05 05:57:54,249 - mmseg - INFO - Iter [38700/40000] lr: 4.670e-08, eta: 0:18:06, time: 0.775, data_time: 0.011, memory: 51557, decode.loss_cls: 0.4472, decode.loss_mask: 0.6706, decode.loss_dice: 0.9337, decode.d0.loss_cls: 3.3637, decode.d0.loss_mask: 0.7380, decode.d0.loss_dice: 1.1060, decode.d1.loss_cls: 0.6969, decode.d1.loss_mask: 0.7037, decode.d1.loss_dice: 1.0074, decode.d2.loss_cls: 0.5749, decode.d2.loss_mask: 0.6848, decode.d2.loss_dice: 0.9674, decode.d3.loss_cls: 0.5141, decode.d3.loss_mask: 0.6762, decode.d3.loss_dice: 0.9431, decode.d4.loss_cls: 0.4890, decode.d4.loss_mask: 0.6763, decode.d4.loss_dice: 0.9437, decode.d5.loss_cls: 0.4562, decode.d5.loss_mask: 0.6753, decode.d5.loss_dice: 0.9490, decode.d6.loss_cls: 0.4473, decode.d6.loss_mask: 0.6718, decode.d6.loss_dice: 0.9374, decode.d7.loss_cls: 0.4386, decode.d7.loss_mask: 0.6711, decode.d7.loss_dice: 0.9413, decode.d8.loss_cls: 0.4455, decode.d8.loss_mask: 0.6702, decode.d8.loss_dice: 0.9346, loss: 24.3751 2022-05-05 05:58:32,694 - mmseg - INFO - Iter [38750/40000] lr: 4.490e-08, eta: 0:17:24, time: 0.769, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4102, decode.loss_mask: 0.6928, decode.loss_dice: 0.9677, decode.d0.loss_cls: 3.3008, decode.d0.loss_mask: 0.7595, decode.d0.loss_dice: 1.1265, decode.d1.loss_cls: 0.6380, decode.d1.loss_mask: 0.7247, decode.d1.loss_dice: 1.0295, decode.d2.loss_cls: 0.5335, decode.d2.loss_mask: 0.7062, decode.d2.loss_dice: 0.9916, decode.d3.loss_cls: 0.4744, decode.d3.loss_mask: 0.6994, decode.d3.loss_dice: 0.9767, decode.d4.loss_cls: 0.4480, decode.d4.loss_mask: 0.7006, decode.d4.loss_dice: 0.9806, decode.d5.loss_cls: 0.4302, decode.d5.loss_mask: 0.6992, decode.d5.loss_dice: 0.9703, decode.d6.loss_cls: 0.4153, decode.d6.loss_mask: 0.6987, decode.d6.loss_dice: 0.9671, decode.d7.loss_cls: 0.4113, decode.d7.loss_mask: 0.6974, decode.d7.loss_dice: 0.9659, decode.d8.loss_cls: 0.4083, decode.d8.loss_mask: 0.6949, decode.d8.loss_dice: 0.9662, loss: 24.4853 2022-05-05 05:59:14,286 - mmseg - INFO - Iter [38800/40000] lr: 4.311e-08, eta: 0:16:42, time: 0.832, data_time: 0.056, memory: 51557, decode.loss_cls: 0.4293, decode.loss_mask: 0.6800, decode.loss_dice: 0.9814, decode.d0.loss_cls: 3.4203, decode.d0.loss_mask: 0.7419, decode.d0.loss_dice: 1.1492, decode.d1.loss_cls: 0.6749, decode.d1.loss_mask: 0.7173, decode.d1.loss_dice: 1.0562, decode.d2.loss_cls: 0.5495, decode.d2.loss_mask: 0.6976, decode.d2.loss_dice: 1.0167, decode.d3.loss_cls: 0.4948, decode.d3.loss_mask: 0.6911, decode.d3.loss_dice: 0.9944, decode.d4.loss_cls: 0.4678, decode.d4.loss_mask: 0.6905, decode.d4.loss_dice: 0.9943, decode.d5.loss_cls: 0.4553, decode.d5.loss_mask: 0.6865, decode.d5.loss_dice: 0.9916, decode.d6.loss_cls: 0.4401, decode.d6.loss_mask: 0.6838, decode.d6.loss_dice: 0.9874, decode.d7.loss_cls: 0.4411, decode.d7.loss_mask: 0.6804, decode.d7.loss_dice: 0.9864, decode.d8.loss_cls: 0.4235, decode.d8.loss_mask: 0.6829, decode.d8.loss_dice: 0.9909, loss: 24.8969 2022-05-05 05:59:52,771 - mmseg - INFO - Iter [38850/40000] lr: 4.132e-08, eta: 0:16:01, time: 0.769, data_time: 0.008, memory: 51557, decode.loss_cls: 0.4186, decode.loss_mask: 0.6744, decode.loss_dice: 0.9311, decode.d0.loss_cls: 3.3411, decode.d0.loss_mask: 0.7321, decode.d0.loss_dice: 1.1005, decode.d1.loss_cls: 0.6667, decode.d1.loss_mask: 0.7009, decode.d1.loss_dice: 1.0005, decode.d2.loss_cls: 0.5438, decode.d2.loss_mask: 0.6846, decode.d2.loss_dice: 0.9645, decode.d3.loss_cls: 0.4883, decode.d3.loss_mask: 0.6833, decode.d3.loss_dice: 0.9495, decode.d4.loss_cls: 0.4688, decode.d4.loss_mask: 0.6778, decode.d4.loss_dice: 0.9446, decode.d5.loss_cls: 0.4430, decode.d5.loss_mask: 0.6801, decode.d5.loss_dice: 0.9437, decode.d6.loss_cls: 0.4291, decode.d6.loss_mask: 0.6743, decode.d6.loss_dice: 0.9369, decode.d7.loss_cls: 0.4214, decode.d7.loss_mask: 0.6736, decode.d7.loss_dice: 0.9371, decode.d8.loss_cls: 0.4212, decode.d8.loss_mask: 0.6731, decode.d8.loss_dice: 0.9339, loss: 24.1386 2022-05-05 06:00:34,066 - mmseg - INFO - Iter [38900/40000] lr: 3.952e-08, eta: 0:15:19, time: 0.827, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4093, decode.loss_mask: 0.6928, decode.loss_dice: 0.9617, decode.d0.loss_cls: 3.3552, decode.d0.loss_mask: 0.7595, decode.d0.loss_dice: 1.1239, decode.d1.loss_cls: 0.6750, decode.d1.loss_mask: 0.7235, decode.d1.loss_dice: 1.0261, decode.d2.loss_cls: 0.5335, decode.d2.loss_mask: 0.7098, decode.d2.loss_dice: 0.9971, decode.d3.loss_cls: 0.4768, decode.d3.loss_mask: 0.7049, decode.d3.loss_dice: 0.9741, decode.d4.loss_cls: 0.4607, decode.d4.loss_mask: 0.7003, decode.d4.loss_dice: 0.9654, decode.d5.loss_cls: 0.4411, decode.d5.loss_mask: 0.6949, decode.d5.loss_dice: 0.9657, decode.d6.loss_cls: 0.4262, decode.d6.loss_mask: 0.6940, decode.d6.loss_dice: 0.9561, decode.d7.loss_cls: 0.4176, decode.d7.loss_mask: 0.6944, decode.d7.loss_dice: 0.9621, decode.d8.loss_cls: 0.4088, decode.d8.loss_mask: 0.6944, decode.d8.loss_dice: 0.9610, loss: 24.5661 2022-05-05 06:01:13,317 - mmseg - INFO - Iter [38950/40000] lr: 3.773e-08, eta: 0:14:37, time: 0.785, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4466, decode.loss_mask: 0.6758, decode.loss_dice: 0.9726, decode.d0.loss_cls: 3.3894, decode.d0.loss_mask: 0.7385, decode.d0.loss_dice: 1.1383, decode.d1.loss_cls: 0.6962, decode.d1.loss_mask: 0.7114, decode.d1.loss_dice: 1.0438, decode.d2.loss_cls: 0.5755, decode.d2.loss_mask: 0.6896, decode.d2.loss_dice: 0.9973, decode.d3.loss_cls: 0.5170, decode.d3.loss_mask: 0.6850, decode.d3.loss_dice: 0.9838, decode.d4.loss_cls: 0.4852, decode.d4.loss_mask: 0.6830, decode.d4.loss_dice: 0.9841, decode.d5.loss_cls: 0.4676, decode.d5.loss_mask: 0.6788, decode.d5.loss_dice: 0.9809, decode.d6.loss_cls: 0.4566, decode.d6.loss_mask: 0.6781, decode.d6.loss_dice: 0.9733, decode.d7.loss_cls: 0.4433, decode.d7.loss_mask: 0.6760, decode.d7.loss_dice: 0.9806, decode.d8.loss_cls: 0.4435, decode.d8.loss_mask: 0.6747, decode.d8.loss_dice: 0.9693, loss: 24.8355 2022-05-05 06:01:51,884 - mmseg - INFO - Saving checkpoint at 39000 iterations 2022-05-05 06:02:19,362 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 06:02:19,364 - mmseg - INFO - Iter [39000/40000] lr: 3.593e-08, eta: 0:13:56, time: 1.319, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4535, decode.loss_mask: 0.6766, decode.loss_dice: 0.9682, decode.d0.loss_cls: 3.4438, decode.d0.loss_mask: 0.7379, decode.d0.loss_dice: 1.1343, decode.d1.loss_cls: 0.7085, decode.d1.loss_mask: 0.7125, decode.d1.loss_dice: 1.0403, decode.d2.loss_cls: 0.5821, decode.d2.loss_mask: 0.6940, decode.d2.loss_dice: 0.9986, decode.d3.loss_cls: 0.5304, decode.d3.loss_mask: 0.6852, decode.d3.loss_dice: 0.9756, decode.d4.loss_cls: 0.4964, decode.d4.loss_mask: 0.6832, decode.d4.loss_dice: 0.9731, decode.d5.loss_cls: 0.4703, decode.d5.loss_mask: 0.6815, decode.d5.loss_dice: 0.9756, decode.d6.loss_cls: 0.4699, decode.d6.loss_mask: 0.6785, decode.d6.loss_dice: 0.9626, decode.d7.loss_cls: 0.4561, decode.d7.loss_mask: 0.6765, decode.d7.loss_dice: 0.9690, decode.d8.loss_cls: 0.4451, decode.d8.loss_mask: 0.6776, decode.d8.loss_dice: 0.9690, loss: 24.9259 2022-05-05 06:02:58,521 - mmseg - INFO - Iter [39050/40000] lr: 3.414e-08, eta: 0:13:14, time: 0.786, data_time: 0.011, memory: 51557, decode.loss_cls: 0.4135, decode.loss_mask: 0.6728, decode.loss_dice: 0.9699, decode.d0.loss_cls: 3.3181, decode.d0.loss_mask: 0.7354, decode.d0.loss_dice: 1.1296, decode.d1.loss_cls: 0.6631, decode.d1.loss_mask: 0.7076, decode.d1.loss_dice: 1.0457, decode.d2.loss_cls: 0.5384, decode.d2.loss_mask: 0.6889, decode.d2.loss_dice: 1.0015, decode.d3.loss_cls: 0.4793, decode.d3.loss_mask: 0.6813, decode.d3.loss_dice: 0.9833, decode.d4.loss_cls: 0.4536, decode.d4.loss_mask: 0.6772, decode.d4.loss_dice: 0.9774, decode.d5.loss_cls: 0.4457, decode.d5.loss_mask: 0.6747, decode.d5.loss_dice: 0.9708, decode.d6.loss_cls: 0.4345, decode.d6.loss_mask: 0.6729, decode.d6.loss_dice: 0.9668, decode.d7.loss_cls: 0.4208, decode.d7.loss_mask: 0.6751, decode.d7.loss_dice: 0.9688, decode.d8.loss_cls: 0.4155, decode.d8.loss_mask: 0.6731, decode.d8.loss_dice: 0.9664, loss: 24.4217 2022-05-05 06:03:37,685 - mmseg - INFO - Iter [39100/40000] lr: 3.234e-08, eta: 0:12:32, time: 0.783, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4124, decode.loss_mask: 0.6862, decode.loss_dice: 0.9586, decode.d0.loss_cls: 3.3650, decode.d0.loss_mask: 0.7361, decode.d0.loss_dice: 1.1124, decode.d1.loss_cls: 0.6779, decode.d1.loss_mask: 0.7145, decode.d1.loss_dice: 1.0220, decode.d2.loss_cls: 0.5476, decode.d2.loss_mask: 0.6996, decode.d2.loss_dice: 0.9829, decode.d3.loss_cls: 0.4934, decode.d3.loss_mask: 0.6927, decode.d3.loss_dice: 0.9645, decode.d4.loss_cls: 0.4593, decode.d4.loss_mask: 0.6917, decode.d4.loss_dice: 0.9677, decode.d5.loss_cls: 0.4417, decode.d5.loss_mask: 0.6863, decode.d5.loss_dice: 0.9675, decode.d6.loss_cls: 0.4283, decode.d6.loss_mask: 0.6813, decode.d6.loss_dice: 0.9571, decode.d7.loss_cls: 0.4276, decode.d7.loss_mask: 0.6834, decode.d7.loss_dice: 0.9591, decode.d8.loss_cls: 0.4140, decode.d8.loss_mask: 0.6850, decode.d8.loss_dice: 0.9594, loss: 24.4753 2022-05-05 06:04:16,591 - mmseg - INFO - Iter [39150/40000] lr: 3.055e-08, eta: 0:11:50, time: 0.778, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4019, decode.loss_mask: 0.6685, decode.loss_dice: 0.9431, decode.d0.loss_cls: 3.3920, decode.d0.loss_mask: 0.7298, decode.d0.loss_dice: 1.1128, decode.d1.loss_cls: 0.6610, decode.d1.loss_mask: 0.6977, decode.d1.loss_dice: 1.0094, decode.d2.loss_cls: 0.5256, decode.d2.loss_mask: 0.6835, decode.d2.loss_dice: 0.9756, decode.d3.loss_cls: 0.4753, decode.d3.loss_mask: 0.6767, decode.d3.loss_dice: 0.9599, decode.d4.loss_cls: 0.4558, decode.d4.loss_mask: 0.6755, decode.d4.loss_dice: 0.9546, decode.d5.loss_cls: 0.4299, decode.d5.loss_mask: 0.6744, decode.d5.loss_dice: 0.9550, decode.d6.loss_cls: 0.4131, decode.d6.loss_mask: 0.6723, decode.d6.loss_dice: 0.9454, decode.d7.loss_cls: 0.4060, decode.d7.loss_mask: 0.6704, decode.d7.loss_dice: 0.9422, decode.d8.loss_cls: 0.4020, decode.d8.loss_mask: 0.6718, decode.d8.loss_dice: 0.9439, loss: 24.1251 2022-05-05 06:04:55,334 - mmseg - INFO - Iter [39200/40000] lr: 2.875e-08, eta: 0:11:08, time: 0.775, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4537, decode.loss_mask: 0.6635, decode.loss_dice: 0.9842, decode.d0.loss_cls: 3.4449, decode.d0.loss_mask: 0.7256, decode.d0.loss_dice: 1.1610, decode.d1.loss_cls: 0.7261, decode.d1.loss_mask: 0.6965, decode.d1.loss_dice: 1.0638, decode.d2.loss_cls: 0.5998, decode.d2.loss_mask: 0.6793, decode.d2.loss_dice: 1.0225, decode.d3.loss_cls: 0.5344, decode.d3.loss_mask: 0.6711, decode.d3.loss_dice: 1.0047, decode.d4.loss_cls: 0.4973, decode.d4.loss_mask: 0.6706, decode.d4.loss_dice: 1.0029, decode.d5.loss_cls: 0.4778, decode.d5.loss_mask: 0.6683, decode.d5.loss_dice: 0.9929, decode.d6.loss_cls: 0.4619, decode.d6.loss_mask: 0.6664, decode.d6.loss_dice: 0.9876, decode.d7.loss_cls: 0.4576, decode.d7.loss_mask: 0.6644, decode.d7.loss_dice: 0.9851, decode.d8.loss_cls: 0.4543, decode.d8.loss_mask: 0.6610, decode.d8.loss_dice: 0.9856, loss: 25.0650 2022-05-05 06:05:33,755 - mmseg - INFO - Iter [39250/40000] lr: 2.696e-08, eta: 0:10:26, time: 0.768, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4120, decode.loss_mask: 0.6712, decode.loss_dice: 0.9701, decode.d0.loss_cls: 3.3063, decode.d0.loss_mask: 0.7314, decode.d0.loss_dice: 1.1287, decode.d1.loss_cls: 0.6667, decode.d1.loss_mask: 0.7035, decode.d1.loss_dice: 1.0338, decode.d2.loss_cls: 0.5267, decode.d2.loss_mask: 0.6843, decode.d2.loss_dice: 0.9920, decode.d3.loss_cls: 0.4751, decode.d3.loss_mask: 0.6762, decode.d3.loss_dice: 0.9733, decode.d4.loss_cls: 0.4457, decode.d4.loss_mask: 0.6768, decode.d4.loss_dice: 0.9773, decode.d5.loss_cls: 0.4378, decode.d5.loss_mask: 0.6749, decode.d5.loss_dice: 0.9733, decode.d6.loss_cls: 0.4216, decode.d6.loss_mask: 0.6731, decode.d6.loss_dice: 0.9681, decode.d7.loss_cls: 0.4156, decode.d7.loss_mask: 0.6732, decode.d7.loss_dice: 0.9726, decode.d8.loss_cls: 0.4031, decode.d8.loss_mask: 0.6705, decode.d8.loss_dice: 0.9647, loss: 24.2998 2022-05-05 06:06:12,210 - mmseg - INFO - Iter [39300/40000] lr: 2.516e-08, eta: 0:09:45, time: 0.769, data_time: 0.009, memory: 51557, decode.loss_cls: 0.3884, decode.loss_mask: 0.6647, decode.loss_dice: 0.9342, decode.d0.loss_cls: 3.3283, decode.d0.loss_mask: 0.7299, decode.d0.loss_dice: 1.0949, decode.d1.loss_cls: 0.6278, decode.d1.loss_mask: 0.6950, decode.d1.loss_dice: 0.9973, decode.d2.loss_cls: 0.5097, decode.d2.loss_mask: 0.6842, decode.d2.loss_dice: 0.9695, decode.d3.loss_cls: 0.4551, decode.d3.loss_mask: 0.6792, decode.d3.loss_dice: 0.9539, decode.d4.loss_cls: 0.4246, decode.d4.loss_mask: 0.6765, decode.d4.loss_dice: 0.9532, decode.d5.loss_cls: 0.4061, decode.d5.loss_mask: 0.6696, decode.d5.loss_dice: 0.9429, decode.d6.loss_cls: 0.3960, decode.d6.loss_mask: 0.6700, decode.d6.loss_dice: 0.9354, decode.d7.loss_cls: 0.3888, decode.d7.loss_mask: 0.6661, decode.d7.loss_dice: 0.9400, decode.d8.loss_cls: 0.3857, decode.d8.loss_mask: 0.6662, decode.d8.loss_dice: 0.9371, loss: 23.7700 2022-05-05 06:06:53,375 - mmseg - INFO - Iter [39350/40000] lr: 2.337e-08, eta: 0:09:03, time: 0.823, data_time: 0.057, memory: 51557, decode.loss_cls: 0.4148, decode.loss_mask: 0.6656, decode.loss_dice: 0.9687, decode.d0.loss_cls: 3.4193, decode.d0.loss_mask: 0.7300, decode.d0.loss_dice: 1.1400, decode.d1.loss_cls: 0.6838, decode.d1.loss_mask: 0.7030, decode.d1.loss_dice: 1.0384, decode.d2.loss_cls: 0.5634, decode.d2.loss_mask: 0.6852, decode.d2.loss_dice: 0.9972, decode.d3.loss_cls: 0.4939, decode.d3.loss_mask: 0.6751, decode.d3.loss_dice: 0.9803, decode.d4.loss_cls: 0.4600, decode.d4.loss_mask: 0.6758, decode.d4.loss_dice: 0.9766, decode.d5.loss_cls: 0.4403, decode.d5.loss_mask: 0.6739, decode.d5.loss_dice: 0.9737, decode.d6.loss_cls: 0.4283, decode.d6.loss_mask: 0.6726, decode.d6.loss_dice: 0.9651, decode.d7.loss_cls: 0.4198, decode.d7.loss_mask: 0.6703, decode.d7.loss_dice: 0.9703, decode.d8.loss_cls: 0.4208, decode.d8.loss_mask: 0.6645, decode.d8.loss_dice: 0.9658, loss: 24.5365 2022-05-05 06:07:31,966 - mmseg - INFO - Iter [39400/40000] lr: 2.157e-08, eta: 0:08:21, time: 0.772, data_time: 0.008, memory: 51557, decode.loss_cls: 0.4297, decode.loss_mask: 0.6703, decode.loss_dice: 0.9713, decode.d0.loss_cls: 3.3792, decode.d0.loss_mask: 0.7320, decode.d0.loss_dice: 1.1269, decode.d1.loss_cls: 0.6890, decode.d1.loss_mask: 0.7052, decode.d1.loss_dice: 1.0433, decode.d2.loss_cls: 0.5638, decode.d2.loss_mask: 0.6831, decode.d2.loss_dice: 1.0011, decode.d3.loss_cls: 0.5003, decode.d3.loss_mask: 0.6766, decode.d3.loss_dice: 0.9792, decode.d4.loss_cls: 0.4774, decode.d4.loss_mask: 0.6772, decode.d4.loss_dice: 0.9784, decode.d5.loss_cls: 0.4589, decode.d5.loss_mask: 0.6733, decode.d5.loss_dice: 0.9753, decode.d6.loss_cls: 0.4459, decode.d6.loss_mask: 0.6740, decode.d6.loss_dice: 0.9694, decode.d7.loss_cls: 0.4375, decode.d7.loss_mask: 0.6701, decode.d7.loss_dice: 0.9683, decode.d8.loss_cls: 0.4325, decode.d8.loss_mask: 0.6711, decode.d8.loss_dice: 0.9680, loss: 24.6283 2022-05-05 06:08:10,448 - mmseg - INFO - Iter [39450/40000] lr: 1.978e-08, eta: 0:07:39, time: 0.770, data_time: 0.010, memory: 51557, decode.loss_cls: 0.3963, decode.loss_mask: 0.6563, decode.loss_dice: 0.9530, decode.d0.loss_cls: 3.3614, decode.d0.loss_mask: 0.7188, decode.d0.loss_dice: 1.1269, decode.d1.loss_cls: 0.6640, decode.d1.loss_mask: 0.6884, decode.d1.loss_dice: 1.0175, decode.d2.loss_cls: 0.5278, decode.d2.loss_mask: 0.6722, decode.d2.loss_dice: 0.9757, decode.d3.loss_cls: 0.4628, decode.d3.loss_mask: 0.6673, decode.d3.loss_dice: 0.9657, decode.d4.loss_cls: 0.4402, decode.d4.loss_mask: 0.6634, decode.d4.loss_dice: 0.9623, decode.d5.loss_cls: 0.4240, decode.d5.loss_mask: 0.6600, decode.d5.loss_dice: 0.9596, decode.d6.loss_cls: 0.4107, decode.d6.loss_mask: 0.6579, decode.d6.loss_dice: 0.9528, decode.d7.loss_cls: 0.4025, decode.d7.loss_mask: 0.6582, decode.d7.loss_dice: 0.9513, decode.d8.loss_cls: 0.3964, decode.d8.loss_mask: 0.6577, decode.d8.loss_dice: 0.9521, loss: 24.0031 2022-05-05 06:08:49,781 - mmseg - INFO - Iter [39500/40000] lr: 1.798e-08, eta: 0:06:57, time: 0.787, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4223, decode.loss_mask: 0.6519, decode.loss_dice: 0.9561, decode.d0.loss_cls: 3.3872, decode.d0.loss_mask: 0.7144, decode.d0.loss_dice: 1.1230, decode.d1.loss_cls: 0.6702, decode.d1.loss_mask: 0.6777, decode.d1.loss_dice: 1.0298, decode.d2.loss_cls: 0.5508, decode.d2.loss_mask: 0.6676, decode.d2.loss_dice: 0.9902, decode.d3.loss_cls: 0.5018, decode.d3.loss_mask: 0.6572, decode.d3.loss_dice: 0.9655, decode.d4.loss_cls: 0.4720, decode.d4.loss_mask: 0.6553, decode.d4.loss_dice: 0.9611, decode.d5.loss_cls: 0.4567, decode.d5.loss_mask: 0.6525, decode.d5.loss_dice: 0.9557, decode.d6.loss_cls: 0.4383, decode.d6.loss_mask: 0.6513, decode.d6.loss_dice: 0.9508, decode.d7.loss_cls: 0.4223, decode.d7.loss_mask: 0.6510, decode.d7.loss_dice: 0.9542, decode.d8.loss_cls: 0.4174, decode.d8.loss_mask: 0.6506, decode.d8.loss_dice: 0.9544, loss: 24.2092 2022-05-05 06:09:28,460 - mmseg - INFO - Iter [39550/40000] lr: 1.619e-08, eta: 0:06:15, time: 0.774, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4406, decode.loss_mask: 0.6529, decode.loss_dice: 0.9459, decode.d0.loss_cls: 3.4176, decode.d0.loss_mask: 0.7178, decode.d0.loss_dice: 1.1216, decode.d1.loss_cls: 0.7014, decode.d1.loss_mask: 0.6865, decode.d1.loss_dice: 1.0178, decode.d2.loss_cls: 0.5666, decode.d2.loss_mask: 0.6677, decode.d2.loss_dice: 0.9748, decode.d3.loss_cls: 0.4988, decode.d3.loss_mask: 0.6611, decode.d3.loss_dice: 0.9533, decode.d4.loss_cls: 0.4698, decode.d4.loss_mask: 0.6602, decode.d4.loss_dice: 0.9580, decode.d5.loss_cls: 0.4596, decode.d5.loss_mask: 0.6571, decode.d5.loss_dice: 0.9568, decode.d6.loss_cls: 0.4575, decode.d6.loss_mask: 0.6548, decode.d6.loss_dice: 0.9477, decode.d7.loss_cls: 0.4372, decode.d7.loss_mask: 0.6550, decode.d7.loss_dice: 0.9489, decode.d8.loss_cls: 0.4387, decode.d8.loss_mask: 0.6545, decode.d8.loss_dice: 0.9493, loss: 24.3296 2022-05-05 06:10:06,764 - mmseg - INFO - Iter [39600/40000] lr: 1.439e-08, eta: 0:05:34, time: 0.766, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4130, decode.loss_mask: 0.6796, decode.loss_dice: 0.9216, decode.d0.loss_cls: 3.3456, decode.d0.loss_mask: 0.7460, decode.d0.loss_dice: 1.0878, decode.d1.loss_cls: 0.6666, decode.d1.loss_mask: 0.7196, decode.d1.loss_dice: 0.9942, decode.d2.loss_cls: 0.5469, decode.d2.loss_mask: 0.6993, decode.d2.loss_dice: 0.9505, decode.d3.loss_cls: 0.4813, decode.d3.loss_mask: 0.6905, decode.d3.loss_dice: 0.9375, decode.d4.loss_cls: 0.4543, decode.d4.loss_mask: 0.6892, decode.d4.loss_dice: 0.9360, decode.d5.loss_cls: 0.4358, decode.d5.loss_mask: 0.6837, decode.d5.loss_dice: 0.9288, decode.d6.loss_cls: 0.4229, decode.d6.loss_mask: 0.6812, decode.d6.loss_dice: 0.9247, decode.d7.loss_cls: 0.4184, decode.d7.loss_mask: 0.6795, decode.d7.loss_dice: 0.9263, decode.d8.loss_cls: 0.4148, decode.d8.loss_mask: 0.6791, decode.d8.loss_dice: 0.9260, loss: 24.0808 2022-05-05 06:10:45,063 - mmseg - INFO - Iter [39650/40000] lr: 1.260e-08, eta: 0:04:52, time: 0.766, data_time: 0.010, memory: 51557, decode.loss_cls: 0.3930, decode.loss_mask: 0.6632, decode.loss_dice: 0.9294, decode.d0.loss_cls: 3.2685, decode.d0.loss_mask: 0.7240, decode.d0.loss_dice: 1.0820, decode.d1.loss_cls: 0.6248, decode.d1.loss_mask: 0.7001, decode.d1.loss_dice: 0.9980, decode.d2.loss_cls: 0.5051, decode.d2.loss_mask: 0.6826, decode.d2.loss_dice: 0.9613, decode.d3.loss_cls: 0.4518, decode.d3.loss_mask: 0.6749, decode.d3.loss_dice: 0.9443, decode.d4.loss_cls: 0.4315, decode.d4.loss_mask: 0.6751, decode.d4.loss_dice: 0.9420, decode.d5.loss_cls: 0.4098, decode.d5.loss_mask: 0.6693, decode.d5.loss_dice: 0.9387, decode.d6.loss_cls: 0.3993, decode.d6.loss_mask: 0.6688, decode.d6.loss_dice: 0.9306, decode.d7.loss_cls: 0.3890, decode.d7.loss_mask: 0.6683, decode.d7.loss_dice: 0.9331, decode.d8.loss_cls: 0.3935, decode.d8.loss_mask: 0.6663, decode.d8.loss_dice: 0.9342, loss: 23.6524 2022-05-05 06:11:23,796 - mmseg - INFO - Iter [39700/40000] lr: 1.080e-08, eta: 0:04:10, time: 0.775, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4330, decode.loss_mask: 0.6723, decode.loss_dice: 0.9362, decode.d0.loss_cls: 3.3106, decode.d0.loss_mask: 0.7431, decode.d0.loss_dice: 1.1085, decode.d1.loss_cls: 0.6786, decode.d1.loss_mask: 0.7058, decode.d1.loss_dice: 1.0157, decode.d2.loss_cls: 0.5453, decode.d2.loss_mask: 0.6898, decode.d2.loss_dice: 0.9786, decode.d3.loss_cls: 0.4937, decode.d3.loss_mask: 0.6816, decode.d3.loss_dice: 0.9525, decode.d4.loss_cls: 0.4601, decode.d4.loss_mask: 0.6816, decode.d4.loss_dice: 0.9514, decode.d5.loss_cls: 0.4515, decode.d5.loss_mask: 0.6778, decode.d5.loss_dice: 0.9443, decode.d6.loss_cls: 0.4360, decode.d6.loss_mask: 0.6733, decode.d6.loss_dice: 0.9376, decode.d7.loss_cls: 0.4313, decode.d7.loss_mask: 0.6747, decode.d7.loss_dice: 0.9348, decode.d8.loss_cls: 0.4320, decode.d8.loss_mask: 0.6727, decode.d8.loss_dice: 0.9391, loss: 24.2437 2022-05-05 06:12:02,896 - mmseg - INFO - Iter [39750/40000] lr: 9.010e-09, eta: 0:03:28, time: 0.782, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4412, decode.loss_mask: 0.6536, decode.loss_dice: 0.9569, decode.d0.loss_cls: 3.3856, decode.d0.loss_mask: 0.7120, decode.d0.loss_dice: 1.1335, decode.d1.loss_cls: 0.7066, decode.d1.loss_mask: 0.6847, decode.d1.loss_dice: 1.0307, decode.d2.loss_cls: 0.5703, decode.d2.loss_mask: 0.6669, decode.d2.loss_dice: 0.9860, decode.d3.loss_cls: 0.5106, decode.d3.loss_mask: 0.6643, decode.d3.loss_dice: 0.9713, decode.d4.loss_cls: 0.4895, decode.d4.loss_mask: 0.6606, decode.d4.loss_dice: 0.9698, decode.d5.loss_cls: 0.4739, decode.d5.loss_mask: 0.6556, decode.d5.loss_dice: 0.9594, decode.d6.loss_cls: 0.4582, decode.d6.loss_mask: 0.6568, decode.d6.loss_dice: 0.9575, decode.d7.loss_cls: 0.4430, decode.d7.loss_mask: 0.6533, decode.d7.loss_dice: 0.9588, decode.d8.loss_cls: 0.4439, decode.d8.loss_mask: 0.6531, decode.d8.loss_dice: 0.9571, loss: 24.4648 2022-05-05 06:12:41,420 - mmseg - INFO - Iter [39800/40000] lr: 7.215e-09, eta: 0:02:47, time: 0.771, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4142, decode.loss_mask: 0.6929, decode.loss_dice: 0.9660, decode.d0.loss_cls: 3.3431, decode.d0.loss_mask: 0.7526, decode.d0.loss_dice: 1.1239, decode.d1.loss_cls: 0.6723, decode.d1.loss_mask: 0.7290, decode.d1.loss_dice: 1.0362, decode.d2.loss_cls: 0.5492, decode.d2.loss_mask: 0.7115, decode.d2.loss_dice: 0.9957, decode.d3.loss_cls: 0.4850, decode.d3.loss_mask: 0.7038, decode.d3.loss_dice: 0.9818, decode.d4.loss_cls: 0.4612, decode.d4.loss_mask: 0.6997, decode.d4.loss_dice: 0.9763, decode.d5.loss_cls: 0.4432, decode.d5.loss_mask: 0.6968, decode.d5.loss_dice: 0.9708, decode.d6.loss_cls: 0.4351, decode.d6.loss_mask: 0.6976, decode.d6.loss_dice: 0.9705, decode.d7.loss_cls: 0.4264, decode.d7.loss_mask: 0.6947, decode.d7.loss_dice: 0.9683, decode.d8.loss_cls: 0.4215, decode.d8.loss_mask: 0.6929, decode.d8.loss_dice: 0.9648, loss: 24.6769 2022-05-05 06:13:20,274 - mmseg - INFO - Iter [39850/40000] lr: 5.420e-09, eta: 0:02:05, time: 0.777, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4258, decode.loss_mask: 0.6552, decode.loss_dice: 0.9883, decode.d0.loss_cls: 3.4004, decode.d0.loss_mask: 0.7082, decode.d0.loss_dice: 1.1588, decode.d1.loss_cls: 0.7052, decode.d1.loss_mask: 0.6816, decode.d1.loss_dice: 1.0708, decode.d2.loss_cls: 0.5629, decode.d2.loss_mask: 0.6675, decode.d2.loss_dice: 1.0247, decode.d3.loss_cls: 0.5026, decode.d3.loss_mask: 0.6642, decode.d3.loss_dice: 1.0102, decode.d4.loss_cls: 0.4764, decode.d4.loss_mask: 0.6613, decode.d4.loss_dice: 1.0053, decode.d5.loss_cls: 0.4596, decode.d5.loss_mask: 0.6561, decode.d5.loss_dice: 0.9980, decode.d6.loss_cls: 0.4409, decode.d6.loss_mask: 0.6533, decode.d6.loss_dice: 0.9930, decode.d7.loss_cls: 0.4270, decode.d7.loss_mask: 0.6533, decode.d7.loss_dice: 0.9900, decode.d8.loss_cls: 0.4265, decode.d8.loss_mask: 0.6547, decode.d8.loss_dice: 0.9969, loss: 24.7185 2022-05-05 06:13:58,686 - mmseg - INFO - Iter [39900/40000] lr: 3.625e-09, eta: 0:01:23, time: 0.768, data_time: 0.009, memory: 51557, decode.loss_cls: 0.4355, decode.loss_mask: 0.6869, decode.loss_dice: 0.9626, decode.d0.loss_cls: 3.3894, decode.d0.loss_mask: 0.7525, decode.d0.loss_dice: 1.1394, decode.d1.loss_cls: 0.6808, decode.d1.loss_mask: 0.7174, decode.d1.loss_dice: 1.0399, decode.d2.loss_cls: 0.5519, decode.d2.loss_mask: 0.7027, decode.d2.loss_dice: 1.0019, decode.d3.loss_cls: 0.5005, decode.d3.loss_mask: 0.6952, decode.d3.loss_dice: 0.9818, decode.d4.loss_cls: 0.4855, decode.d4.loss_mask: 0.6901, decode.d4.loss_dice: 0.9801, decode.d5.loss_cls: 0.4543, decode.d5.loss_mask: 0.6890, decode.d5.loss_dice: 0.9769, decode.d6.loss_cls: 0.4403, decode.d6.loss_mask: 0.6884, decode.d6.loss_dice: 0.9726, decode.d7.loss_cls: 0.4334, decode.d7.loss_mask: 0.6909, decode.d7.loss_dice: 0.9709, decode.d8.loss_cls: 0.4326, decode.d8.loss_mask: 0.6877, decode.d8.loss_dice: 0.9669, loss: 24.7983 2022-05-05 06:14:39,976 - mmseg - INFO - Iter [39950/40000] lr: 1.831e-09, eta: 0:00:41, time: 0.825, data_time: 0.056, memory: 51557, decode.loss_cls: 0.3973, decode.loss_mask: 0.6677, decode.loss_dice: 0.9387, decode.d0.loss_cls: 3.3701, decode.d0.loss_mask: 0.7372, decode.d0.loss_dice: 1.1117, decode.d1.loss_cls: 0.6550, decode.d1.loss_mask: 0.7005, decode.d1.loss_dice: 1.0042, decode.d2.loss_cls: 0.5325, decode.d2.loss_mask: 0.6816, decode.d2.loss_dice: 0.9685, decode.d3.loss_cls: 0.4665, decode.d3.loss_mask: 0.6736, decode.d3.loss_dice: 0.9505, decode.d4.loss_cls: 0.4409, decode.d4.loss_mask: 0.6717, decode.d4.loss_dice: 0.9507, decode.d5.loss_cls: 0.4274, decode.d5.loss_mask: 0.6708, decode.d5.loss_dice: 0.9446, decode.d6.loss_cls: 0.4099, decode.d6.loss_mask: 0.6697, decode.d6.loss_dice: 0.9371, decode.d7.loss_cls: 0.3999, decode.d7.loss_mask: 0.6659, decode.d7.loss_dice: 0.9328, decode.d8.loss_cls: 0.4034, decode.d8.loss_mask: 0.6689, decode.d8.loss_dice: 0.9351, loss: 23.9844 2022-05-05 06:15:19,074 - mmseg - INFO - Saving checkpoint at 40000 iterations 2022-05-05 06:15:45,786 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 06:15:45,795 - mmseg - INFO - Iter [40000/40000] lr: 3.589e-11, eta: 0:00:00, time: 1.314, data_time: 0.010, memory: 51557, decode.loss_cls: 0.4362, decode.loss_mask: 0.6632, decode.loss_dice: 0.9435, decode.d0.loss_cls: 3.3757, decode.d0.loss_mask: 0.7366, decode.d0.loss_dice: 1.1280, decode.d1.loss_cls: 0.6975, decode.d1.loss_mask: 0.7052, decode.d1.loss_dice: 1.0321, decode.d2.loss_cls: 0.5602, decode.d2.loss_mask: 0.6826, decode.d2.loss_dice: 0.9833, decode.d3.loss_cls: 0.5013, decode.d3.loss_mask: 0.6775, decode.d3.loss_dice: 0.9671, decode.d4.loss_cls: 0.4789, decode.d4.loss_mask: 0.6736, decode.d4.loss_dice: 0.9645, decode.d5.loss_cls: 0.4604, decode.d5.loss_mask: 0.6698, decode.d5.loss_dice: 0.9556, decode.d6.loss_cls: 0.4533, decode.d6.loss_mask: 0.6674, decode.d6.loss_dice: 0.9484, decode.d7.loss_cls: 0.4361, decode.d7.loss_mask: 0.6670, decode.d7.loss_dice: 0.9460, decode.d8.loss_cls: 0.4332, decode.d8.loss_mask: 0.6655, decode.d8.loss_dice: 0.9479, loss: 24.4575 2022-05-05 06:16:16,341 - mmseg - INFO - per class results: 2022-05-05 06:16:16,350 - mmseg - INFO - +------------------+-------+-------+ | Class | IoU | Acc | +------------------+-------+-------+ | person | 88.77 | 94.66 | | bicycle | 77.55 | 92.18 | | car | 52.75 | 60.23 | | motorcycle | 90.6 | 95.58 | | airplane | 89.69 | 95.26 | | bus | 86.93 | 91.73 | | train | 84.42 | 97.11 | | truck | 65.69 | 90.9 | | boat | 81.68 | 89.29 | | traffic light | 82.51 | 91.96 | | fire hydrant | 85.43 | 97.28 | | stop sign | 93.85 | 97.89 | | parking meter | 73.75 | 75.94 | | bench | 52.95 | 68.53 | | bird | 80.02 | 85.74 | | cat | 93.17 | 96.25 | | dog | 91.82 | 96.48 | | horse | 91.36 | 96.01 | | sheep | 87.95 | 91.5 | | cow | 95.42 | 98.35 | | elephant | 93.13 | 97.04 | | bear | 92.79 | 95.09 | | zebra | 92.07 | 95.88 | | giraffe | 88.64 | 94.59 | | backpack | 23.8 | 55.66 | | umbrella | 75.56 | 79.68 | | handbag | 24.39 | 37.29 | | tie | 65.25 | 65.54 | | suitcase | 76.45 | 92.08 | | frisbee | 95.01 | 97.69 | | skis | 37.1 | 60.32 | | snowboard | 59.15 | 76.85 | | sports ball | 86.61 | 94.63 | | kite | 67.14 | 83.27 | | baseball bat | 57.81 | 74.29 | | baseball glove | 1.79 | 1.88 | | skateboard | 70.92 | 89.22 | | surfboard | 90.51 | 95.23 | | tennis racket | 30.46 | 31.04 | | bottle | 74.11 | 86.83 | | wine glass | 85.27 | 91.84 | | cup | 73.86 | 85.47 | | fork | 58.33 | 70.61 | | knife | 78.62 | 90.39 | | spoon | 53.59 | 70.8 | | bowl | 56.32 | 63.8 | | banana | 80.87 | 91.25 | | apple | 70.33 | 81.01 | | sandwich | 87.31 | 96.36 | | orange | 67.96 | 89.74 | | broccoli | 91.53 | 98.06 | | carrot | 43.69 | 55.87 | | hot dog | 53.33 | 98.2 | | pizza | 95.45 | 97.2 | | donut | 78.66 | 95.9 | | cake | 83.44 | 88.74 | | chair | 60.91 | 75.58 | | couch | 73.58 | 95.67 | | potted plant | 33.03 | 41.76 | | bed | 68.19 | 79.96 | | dining table | 60.8 | 79.6 | | toilet | 90.11 | 96.51 | | tv | 80.48 | 92.63 | | laptop | 87.96 | 97.24 | | mouse | 85.27 | 90.74 | | remote | 69.36 | 87.65 | | keyboard | 84.99 | 98.02 | | cell phone | 85.71 | 96.13 | | microwave | 62.16 | 74.37 | | oven | 65.9 | 84.37 | | toaster | 79.94 | 83.25 | | sink | 75.02 | 79.01 | | refrigerator | 86.28 | 94.94 | | book | 79.65 | 92.14 | | clock | 76.41 | 79.33 | | vase | 59.52 | 89.56 | | scissors | 82.0 | 93.34 | | teddy bear | 86.81 | 94.36 | | hair drier | 0.0 | 0.0 | | toothbrush | 31.92 | 46.72 | | banner | 35.84 | 65.31 | | blanket | 0.0 | 0.0 | | branch | 20.63 | 21.97 | | bridge | 3.57 | 6.39 | | building-other | 56.84 | 75.53 | | bush | 19.35 | 26.94 | | cabinet | 25.17 | 48.89 | | cage | 14.62 | 79.58 | | cardboard | 23.66 | 29.83 | | carpet | 58.46 | 73.92 | | ceiling-other | 71.82 | 81.78 | | ceiling-tile | 12.63 | 14.21 | | cloth | 1.54 | 2.26 | | clothes | 25.12 | 35.01 | | clouds | 58.34 | 74.72 | | counter | 43.7 | 54.36 | | cupboard | 58.32 | 73.07 | | curtain | 68.98 | 85.7 | | desk-stuff | 32.22 | 38.95 | | dirt | 31.41 | 51.71 | | door-stuff | 44.46 | 57.73 | | fence | 46.52 | 72.06 | | floor-marble | 0.0 | 0.0 | | floor-other | 38.68 | 55.63 | | floor-stone | 29.49 | 33.6 | | floor-tile | 65.53 | 78.33 | | floor-wood | 74.84 | 85.4 | | flower | 21.65 | 53.6 | | fog | 0.0 | 0.0 | | food-other | 46.97 | 57.87 | | fruit | 51.41 | 66.95 | | furniture-other | 19.06 | 27.57 | | grass | 75.63 | 84.73 | | gravel | 26.41 | 33.99 | | ground-other | 9.53 | 20.52 | | hill | 24.27 | 34.08 | | house | 31.25 | 47.89 | | leaves | 11.17 | 18.17 | | light | 42.02 | 56.92 | | mat | 15.85 | 25.64 | | metal | 18.15 | 26.29 | | mirror-stuff | 45.99 | 59.71 | | moss | 0.0 | 0.0 | | mountain | 23.97 | 42.01 | | mud | 0.7 | 1.27 | | napkin | 88.95 | 91.52 | | net | 43.59 | 56.04 | | paper | 48.15 | 67.01 | | pavement | 50.55 | 71.57 | | pillow | 0.0 | 0.0 | | plant-other | 28.99 | 44.7 | | plastic | 27.02 | 36.87 | | platform | 45.46 | 61.31 | | playingfield | 64.29 | 76.18 | | railing | 14.22 | 23.33 | | railroad | 60.75 | 77.72 | | river | 24.45 | 36.95 | | road | 71.66 | 81.58 | | rock | 41.83 | 59.82 | | roof | 3.89 | 5.49 | | rug | 49.64 | 73.35 | | salad | 0.0 | 0.0 | | sand | 70.51 | 87.16 | | sea | 80.87 | 90.78 | | shelf | 24.66 | 49.56 | | sky-other | 64.64 | 77.83 | | skyscraper | 10.93 | 12.54 | | snow | 91.74 | 94.53 | | solid-other | nan | nan | | stairs | 46.79 | 73.31 | | stone | 7.24 | 15.09 | | straw | 12.49 | 36.43 | | structural-other | 15.96 | 24.57 | | table | 23.81 | 39.22 | | tent | 67.82 | 92.38 | | textile-other | 21.87 | 26.8 | | towel | 39.72 | 48.06 | | tree | 77.26 | 87.58 | | vegetable | 45.64 | 68.21 | | wall-brick | 49.12 | 66.52 | | wall-concrete | 33.3 | 39.97 | | wall-other | 63.59 | 83.34 | | wall-panel | 6.12 | 6.54 | | wall-stone | 33.0 | 37.67 | | wall-tile | 57.48 | 82.29 | | wall-wood | 42.88 | 62.68 | | water-other | 35.68 | 48.93 | | waterdrops | 0.0 | nan | | window-blind | 36.86 | 60.73 | | window-other | 52.68 | 67.09 | | wood | 18.05 | 38.01 | +------------------+-------+-------+ 2022-05-05 06:16:16,350 - mmseg - INFO - Summary: 2022-05-05 06:16:16,351 - mmseg - INFO - +------+------+-------+ | aAcc | mIoU | mAcc | +------+------+-------+ | 76.4 | 52.9 | 64.95 | +------+------+-------+ 2022-05-05 06:16:16,354 - mmseg - INFO - Exp name: mask2former_beit_large_24_adapter_512_slide_40k_cocostuff10k_ss.py 2022-05-05 06:16:16,354 - mmseg - INFO - Iter(val) [125] aAcc: 0.7640, mIoU: 0.5290, mAcc: 0.6495, IoU.person: 0.8877, IoU.bicycle: 0.7755, IoU.car: 0.5275, IoU.motorcycle: 0.9060, IoU.airplane: 0.8969, IoU.bus: 0.8693, IoU.train: 0.8442, IoU.truck: 0.6569, IoU.boat: 0.8168, IoU.traffic light: 0.8251, IoU.fire hydrant: 0.8543, IoU.stop sign: 0.9385, IoU.parking meter: 0.7375, IoU.bench: 0.5295, IoU.bird: 0.8002, IoU.cat: 0.9317, IoU.dog: 0.9182, IoU.horse: 0.9136, IoU.sheep: 0.8795, IoU.cow: 0.9542, IoU.elephant: 0.9313, IoU.bear: 0.9279, IoU.zebra: 0.9207, IoU.giraffe: 0.8864, IoU.backpack: 0.2380, IoU.umbrella: 0.7556, IoU.handbag: 0.2439, IoU.tie: 0.6525, IoU.suitcase: 0.7645, IoU.frisbee: 0.9501, IoU.skis: 0.3710, IoU.snowboard: 0.5915, IoU.sports ball: 0.8661, IoU.kite: 0.6714, IoU.baseball bat: 0.5781, IoU.baseball glove: 0.0179, IoU.skateboard: 0.7092, IoU.surfboard: 0.9051, IoU.tennis racket: 0.3046, IoU.bottle: 0.7411, IoU.wine glass: 0.8527, IoU.cup: 0.7386, IoU.fork: 0.5833, IoU.knife: 0.7862, IoU.spoon: 0.5359, IoU.bowl: 0.5632, IoU.banana: 0.8087, IoU.apple: 0.7033, IoU.sandwich: 0.8731, IoU.orange: 0.6796, IoU.broccoli: 0.9153, IoU.carrot: 0.4369, IoU.hot dog: 0.5333, IoU.pizza: 0.9545, IoU.donut: 0.7866, IoU.cake: 0.8344, IoU.chair: 0.6091, IoU.couch: 0.7358, IoU.potted plant: 0.3303, IoU.bed: 0.6819, IoU.dining table: 0.6080, IoU.toilet: 0.9011, IoU.tv: 0.8048, IoU.laptop: 0.8796, IoU.mouse: 0.8527, IoU.remote: 0.6936, IoU.keyboard: 0.8499, IoU.cell phone: 0.8571, IoU.microwave: 0.6216, IoU.oven: 0.6590, IoU.toaster: 0.7994, IoU.sink: 0.7502, IoU.refrigerator: 0.8628, IoU.book: 0.7965, IoU.clock: 0.7641, IoU.vase: 0.5952, IoU.scissors: 0.8200, IoU.teddy bear: 0.8681, IoU.hair drier: 0.0000, IoU.toothbrush: 0.3192, IoU.banner: 0.3584, IoU.blanket: 0.0000, IoU.branch: 0.2063, IoU.bridge: 0.0357, IoU.building-other: 0.5684, IoU.bush: 0.1935, IoU.cabinet: 0.2517, IoU.cage: 0.1462, IoU.cardboard: 0.2366, IoU.carpet: 0.5846, IoU.ceiling-other: 0.7182, IoU.ceiling-tile: 0.1263, IoU.cloth: 0.0154, IoU.clothes: 0.2512, IoU.clouds: 0.5834, IoU.counter: 0.4370, IoU.cupboard: 0.5832, IoU.curtain: 0.6898, IoU.desk-stuff: 0.3222, IoU.dirt: 0.3141, IoU.door-stuff: 0.4446, IoU.fence: 0.4652, IoU.floor-marble: 0.0000, IoU.floor-other: 0.3868, IoU.floor-stone: 0.2949, IoU.floor-tile: 0.6553, IoU.floor-wood: 0.7484, IoU.flower: 0.2165, IoU.fog: 0.0000, IoU.food-other: 0.4697, IoU.fruit: 0.5141, IoU.furniture-other: 0.1906, IoU.grass: 0.7563, IoU.gravel: 0.2641, IoU.ground-other: 0.0953, IoU.hill: 0.2427, IoU.house: 0.3125, IoU.leaves: 0.1117, IoU.light: 0.4202, IoU.mat: 0.1585, IoU.metal: 0.1815, IoU.mirror-stuff: 0.4599, IoU.moss: 0.0000, IoU.mountain: 0.2397, IoU.mud: 0.0070, IoU.napkin: 0.8895, IoU.net: 0.4359, IoU.paper: 0.4815, IoU.pavement: 0.5055, IoU.pillow: 0.0000, IoU.plant-other: 0.2899, IoU.plastic: 0.2702, IoU.platform: 0.4546, IoU.playingfield: 0.6429, IoU.railing: 0.1422, IoU.railroad: 0.6075, IoU.river: 0.2445, IoU.road: 0.7166, IoU.rock: 0.4183, IoU.roof: 0.0389, IoU.rug: 0.4964, IoU.salad: 0.0000, IoU.sand: 0.7051, IoU.sea: 0.8087, IoU.shelf: 0.2466, IoU.sky-other: 0.6464, IoU.skyscraper: 0.1093, IoU.snow: 0.9174, IoU.solid-other: nan, IoU.stairs: 0.4679, IoU.stone: 0.0724, IoU.straw: 0.1249, IoU.structural-other: 0.1596, IoU.table: 0.2381, IoU.tent: 0.6782, IoU.textile-other: 0.2187, IoU.towel: 0.3972, IoU.tree: 0.7726, IoU.vegetable: 0.4564, IoU.wall-brick: 0.4912, IoU.wall-concrete: 0.3330, IoU.wall-other: 0.6359, IoU.wall-panel: 0.0612, IoU.wall-stone: 0.3300, IoU.wall-tile: 0.5748, IoU.wall-wood: 0.4288, IoU.water-other: 0.3568, IoU.waterdrops: 0.0000, IoU.window-blind: 0.3686, IoU.window-other: 0.5268, IoU.wood: 0.1805, Acc.person: 0.9466, Acc.bicycle: 0.9218, Acc.car: 0.6023, Acc.motorcycle: 0.9558, Acc.airplane: 0.9526, Acc.bus: 0.9173, Acc.train: 0.9711, Acc.truck: 0.9090, Acc.boat: 0.8929, Acc.traffic light: 0.9196, Acc.fire hydrant: 0.9728, Acc.stop sign: 0.9789, Acc.parking meter: 0.7594, Acc.bench: 0.6853, Acc.bird: 0.8574, Acc.cat: 0.9625, Acc.dog: 0.9648, Acc.horse: 0.9601, Acc.sheep: 0.9150, Acc.cow: 0.9835, Acc.elephant: 0.9704, Acc.bear: 0.9509, Acc.zebra: 0.9588, Acc.giraffe: 0.9459, Acc.backpack: 0.5566, Acc.umbrella: 0.7968, Acc.handbag: 0.3729, Acc.tie: 0.6554, Acc.suitcase: 0.9208, Acc.frisbee: 0.9769, Acc.skis: 0.6032, Acc.snowboard: 0.7685, Acc.sports ball: 0.9463, Acc.kite: 0.8327, Acc.baseball bat: 0.7429, Acc.baseball glove: 0.0188, Acc.skateboard: 0.8922, Acc.surfboard: 0.9523, Acc.tennis racket: 0.3104, Acc.bottle: 0.8683, Acc.wine glass: 0.9184, Acc.cup: 0.8547, Acc.fork: 0.7061, Acc.knife: 0.9039, Acc.spoon: 0.7080, Acc.bowl: 0.6380, Acc.banana: 0.9125, Acc.apple: 0.8101, Acc.sandwich: 0.9636, Acc.orange: 0.8974, Acc.broccoli: 0.9806, Acc.carrot: 0.5587, Acc.hot dog: 0.9820, Acc.pizza: 0.9720, Acc.donut: 0.9590, Acc.cake: 0.8874, Acc.chair: 0.7558, Acc.couch: 0.9567, Acc.potted plant: 0.4176, Acc.bed: 0.7996, Acc.dining table: 0.7960, Acc.toilet: 0.9651, Acc.tv: 0.9263, Acc.laptop: 0.9724, Acc.mouse: 0.9074, Acc.remote: 0.8765, Acc.keyboard: 0.9802, Acc.cell phone: 0.9613, Acc.microwave: 0.7437, Acc.oven: 0.8437, Acc.toaster: 0.8325, Acc.sink: 0.7901, Acc.refrigerator: 0.9494, Acc.book: 0.9214, Acc.clock: 0.7933, Acc.vase: 0.8956, Acc.scissors: 0.9334, Acc.teddy bear: 0.9436, Acc.hair drier: 0.0000, Acc.toothbrush: 0.4672, Acc.banner: 0.6531, Acc.blanket: 0.0000, Acc.branch: 0.2197, Acc.bridge: 0.0639, Acc.building-other: 0.7553, Acc.bush: 0.2694, Acc.cabinet: 0.4889, Acc.cage: 0.7958, Acc.cardboard: 0.2983, Acc.carpet: 0.7392, Acc.ceiling-other: 0.8178, Acc.ceiling-tile: 0.1421, Acc.cloth: 0.0226, Acc.clothes: 0.3501, Acc.clouds: 0.7472, Acc.counter: 0.5436, Acc.cupboard: 0.7307, Acc.curtain: 0.8570, Acc.desk-stuff: 0.3895, Acc.dirt: 0.5171, Acc.door-stuff: 0.5773, Acc.fence: 0.7206, Acc.floor-marble: 0.0000, Acc.floor-other: 0.5563, Acc.floor-stone: 0.3360, Acc.floor-tile: 0.7833, Acc.floor-wood: 0.8540, Acc.flower: 0.5360, Acc.fog: 0.0000, Acc.food-other: 0.5787, Acc.fruit: 0.6695, Acc.furniture-other: 0.2757, Acc.grass: 0.8473, Acc.gravel: 0.3399, Acc.ground-other: 0.2052, Acc.hill: 0.3408, Acc.house: 0.4789, Acc.leaves: 0.1817, Acc.light: 0.5692, Acc.mat: 0.2564, Acc.metal: 0.2629, Acc.mirror-stuff: 0.5971, Acc.moss: 0.0000, Acc.mountain: 0.4201, Acc.mud: 0.0127, Acc.napkin: 0.9152, Acc.net: 0.5604, Acc.paper: 0.6701, Acc.pavement: 0.7157, Acc.pillow: 0.0000, Acc.plant-other: 0.4470, Acc.plastic: 0.3687, Acc.platform: 0.6131, Acc.playingfield: 0.7618, Acc.railing: 0.2333, Acc.railroad: 0.7772, Acc.river: 0.3695, Acc.road: 0.8158, Acc.rock: 0.5982, Acc.roof: 0.0549, Acc.rug: 0.7335, Acc.salad: 0.0000, Acc.sand: 0.8716, Acc.sea: 0.9078, Acc.shelf: 0.4956, Acc.sky-other: 0.7783, Acc.skyscraper: 0.1254, Acc.snow: 0.9453, Acc.solid-other: nan, Acc.stairs: 0.7331, Acc.stone: 0.1509, Acc.straw: 0.3643, Acc.structural-other: 0.2457, Acc.table: 0.3922, Acc.tent: 0.9238, Acc.textile-other: 0.2680, Acc.towel: 0.4806, Acc.tree: 0.8758, Acc.vegetable: 0.6821, Acc.wall-brick: 0.6652, Acc.wall-concrete: 0.3997, Acc.wall-other: 0.8334, Acc.wall-panel: 0.0654, Acc.wall-stone: 0.3767, Acc.wall-tile: 0.8229, Acc.wall-wood: 0.6268, Acc.water-other: 0.4893, Acc.waterdrops: nan, Acc.window-blind: 0.6073, Acc.window-other: 0.6709, Acc.wood: 0.3801