2024/07/07 13:38:36 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.10.14 (main, May 6 2024, 19:42:50) [GCC 11.2.0] CUDA available: True numpy_random_seed: 1796883699 GPU 0,1,2,3: NVIDIA GeForce RTX 4090 CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.8, V11.8.89 GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 PyTorch: 2.1.0+cu118 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201703 - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.8 - NVCC architecture flags: -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;-gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_90,code=sm_90 - CuDNN 8.7 - Magma 2.6.1 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.0, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.16.0+cu118 OpenCV: 4.10.0 MMEngine: 0.8.3 Runtime environment: cudnn_benchmark: True mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: 1796883699 Distributed launcher: pytorch Distributed training: True GPU number: 4 ------------------------------------------------------------ 2024/07/07 13:38:37 - mmengine - INFO - Config: auto_scale_lr = dict(base_batch_size=24) backend_args = None base_img_size = 672 custom_hooks = [ dict( ema_type='ExpMomentumEMA', momentum=0.0002, priority=49, type='EMAHook', update_buffers=True), ] default_hooks = dict( checkpoint=dict( by_epoch=False, interval=1000, max_keep_ckpts=1, type='CheckpointHook'), logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), param_scheduler=dict(type='ParamSchedulerHook'), sampler_seed=dict(type='DistSamplerSeedHook'), timer=dict(type='IterTimerHook'), visualization=dict(draw=True, interval=500, type='SegVisualizationHook')) default_scope = 'mmdet' env_cfg = dict( cudnn_benchmark=True, dist_cfg=dict(backend='nccl'), mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) launcher = 'pytorch' load_from = None log_level = 'INFO' log_processor = dict(by_epoch=False, type='LogProcessor', window_size=4000) max_iters = 120000 model = dict( backbone=dict( arch='base', drop_path_rate=0.1, img_size=672, init_cfg=dict( checkpoint='./sam-base-repeat-10.pth', prefix='backbone.', type='Pretrained'), new_more_layers=[ 'win', 'win', 'win', 'win', 'win', 'win', ], out_channels=0, out_type='featmap', patch_size=16, text_cfg=dict( hidden_size=768, pretrain_path='./blip_embed_extra.pt', type='bert-base', vocab_size=30525), type='ViTGiTPromptMaskMeanV2', use_abs_pos=True, use_checkpoints=True, use_rel_pos=True, window_size=14), data_preprocessor=dict( bgr_to_rgb=True, mean=[ 123.675, 116.28, 103.53, ], pad_seg=True, pad_size_divisor=224, seg_pad_value=255, std=[ 58.395, 57.12, 57.375, ], type='GeneralDataPreprocessor'), head_list=dict( semantic_segmentation_head=dict( cls_loss_weight=5.0, mask_loss_weight=10.0, repeat_times=3, test_cfg=dict(max_per_img=100), train_cfg=dict( assigner=dict( match_costs=[ dict( box_format='xywh', type='PointsL1Cost', weight=5.0), dict(type='MaskRandomCost', weight=100.0), ], type='HungarianAssigner')), type='GiTSemSegHeadMaskIgnoreRepeat')), support_tasks=[ 'detection', 'semantic_segmentation', 'instance_segmentation', 'caption', 'grounding', ], tokenizer=dict(name_or_path='bert-base-uncased', type='BlipTokenizer'), type='GiTPromptMaskSegMeanV2', use_checkpoints=True) optim_wrapper = dict( clip_grad=dict(max_norm=0.1, norm_type=2), dtype='bfloat16', optimizer=dict(lr=0.0002, type='AdamW', weight_decay=0.05), paramwise_cfg=dict( custom_keys=dict({ 'backbone': dict(lr_mult=0.1), 'backbone.embed': dict(lr_mult=1.0), 'backbone.layers.10': dict(lr_mult=0.7429), 'backbone.layers.11': dict(lr_mult=0.8714), 'backbone.layers.12': dict(lr_mult=1.0), 'backbone.layers.13': dict(lr_mult=1.0), 'backbone.layers.14': dict(lr_mult=1.0), 'backbone.layers.15': dict(lr_mult=1.0), 'backbone.layers.16': dict(lr_mult=1.0), 'backbone.layers.17': dict(lr_mult=1.0), 'backbone.layers.6': dict(lr_mult=0.2286), 'backbone.layers.7': dict(lr_mult=0.3571), 'backbone.layers.8': dict(lr_mult=0.4858), 'backbone.layers.9': dict(lr_mult=0.6143), 'reference_points': dict(lr_mult=0.1), 'sampling_offsets': dict(lr_mult=0.1) })), type='AmpOptimWrapper') param_scheduler = [ dict( T_max=120000, begin=0, by_epoch=False, end=120000, eta_min=2e-06, type='CosineAnnealingLR'), ] pretrained = './sam-base-repeat-10.pth' resume = False semseg_cfgs = dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_decoder_length=30, mode='semantic_segmentation', num_vocal=151, samples_grids_eachwin=10) semseg_test_pipeline = [ dict(type='LoadImageFromFile'), dict(keep_ratio=False, scale=( 672, 672, ), type='Resize'), dict(reduce_zero_label=True, type='SegLoadAnnotations'), dict( meta_dict=dict( git_cfg=dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_decoder_length=30, mode='semantic_segmentation', num_vocal=151, samples_grids_eachwin=10), head_cfg=dict( dec_length=30, dec_pixel_resolution=[ 4, 4, ], ignore_index=255, num_classes=150, num_vocal=151), task_name='semantic_segmentation'), type='AddMetaInfo'), dict( meta_keys=( 'img_path', 'seg_map_path', 'ori_shape', 'img_shape', 'pad_shape', 'scale_factor', 'flip', 'flip_direction', 'reduce_zero_label', 'task_name', 'head_cfg', 'git_cfg', ), type='PackSegInputs'), ] semseg_train_pipeline = [ dict(type='LoadImageFromFile'), dict(reduce_zero_label=True, type='SegLoadAnnotations'), dict( meta_dict=dict( git_cfg=dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_decoder_length=30, mode='semantic_segmentation', num_vocal=151, samples_grids_eachwin=10), head_cfg=dict( arg_max_inference=True, dec_length=30, dec_pixel_resolution=[ 4, 4, ], ignore_index=255, num_classes=150, num_vocal=151), task_name='semantic_segmentation'), type='AddMetaInfo'), dict( transforms=[ [ dict( keep_ratio=False, scales=[ ( 672, 672, ), ], type='RandomChoiceResize'), ], [ dict( keep_ratio=False, scales=[ ( 672, 672, ), ( 739, 739, ), ( 806, 806, ), ( 873, 873, ), ( 940, 940, ), ( 1008, 1008, ), ( 1075, 1075, ), ( 1142, 1142, ), ( 1209, 1209, ), ( 1276, 1276, ), ( 1344, 1344, ), ], type='RandomChoiceResize'), dict( cat_max_ratio=0.75, crop_size=( 672, 672, ), type='SegRandomCrop'), ], ], type='RandomChoice'), dict(prob=0.5, type='MMCVRandomFlip'), dict(type='SegPhotoMetricDistortion'), dict( meta_keys=( 'img_path', 'seg_map_path', 'ori_shape', 'img_shape', 'pad_shape', 'scale_factor', 'flip', 'flip_direction', 'reduce_zero_label', 'task_name', 'head_cfg', 'git_cfg', ), type='PackSegInputs'), ] test_cfg = dict(type='TestLoop') test_dataloader = dict( batch_size=1, dataset=dict( data_prefix=dict( img_path='images/validation', seg_map_path='annotations/validation'), data_root='data/ade/ADEChallengeData2016', pipeline=[ dict(type='LoadImageFromFile'), dict(keep_ratio=False, scale=( 672, 672, ), type='Resize'), dict(reduce_zero_label=True, type='SegLoadAnnotations'), dict( meta_dict=dict( git_cfg=dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_decoder_length=30, mode='semantic_segmentation', num_vocal=151, samples_grids_eachwin=10), head_cfg=dict( dec_length=30, dec_pixel_resolution=[ 4, 4, ], ignore_index=255, num_classes=150, num_vocal=151), task_name='semantic_segmentation'), type='AddMetaInfo'), dict( meta_keys=( 'img_path', 'seg_map_path', 'ori_shape', 'img_shape', 'pad_shape', 'scale_factor', 'flip', 'flip_direction', 'reduce_zero_label', 'task_name', 'head_cfg', 'git_cfg', ), type='PackSegInputs'), ], return_classes=True, type='ADE20KDataset'), num_workers=4, persistent_workers=True, sampler=dict(shuffle=False, type='DefaultSampler')) test_evaluator = dict( iou_metrics=[ 'mIoU', ], type='IoUMetric') test_pipeline = [ dict(type='LoadImageFromFile'), dict(keep_ratio=False, scale=( 672, 672, ), type='Resize'), dict(reduce_zero_label=True, type='SegLoadAnnotations'), dict( meta_dict=dict( git_cfg=dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_decoder_length=30, mode='semantic_segmentation', num_vocal=151, samples_grids_eachwin=10), head_cfg=dict( dec_length=30, dec_pixel_resolution=[ 4, 4, ], ignore_index=255, num_classes=150, num_vocal=151), task_name='semantic_segmentation'), type='AddMetaInfo'), dict( meta_keys=( 'img_path', 'seg_map_path', 'ori_shape', 'img_shape', 'pad_shape', 'scale_factor', 'flip', 'flip_direction', 'reduce_zero_label', 'task_name', 'head_cfg', 'git_cfg', ), type='PackSegInputs'), ] train_cfg = dict( max_iters=120000, type='IterBasedTrainLoop', val_interval=5000) train_dataloader = dict( batch_sampler=None, batch_size=6, dataset=dict( datasets=[ dict( data_prefix=dict( img_path='images/training', seg_map_path='annotations/training'), data_root='data/ade/ADEChallengeData2016', pipeline=[ dict(type='LoadImageFromFile'), dict(reduce_zero_label=True, type='SegLoadAnnotations'), dict( meta_dict=dict( git_cfg=dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_decoder_length=30, mode='semantic_segmentation', num_vocal=151, samples_grids_eachwin=10), head_cfg=dict( arg_max_inference=True, dec_length=30, dec_pixel_resolution=[ 4, 4, ], ignore_index=255, num_classes=150, num_vocal=151), task_name='semantic_segmentation'), type='AddMetaInfo'), dict( transforms=[ [ dict( keep_ratio=False, scales=[ ( 672, 672, ), ], type='RandomChoiceResize'), ], [ dict( keep_ratio=False, scales=[ ( 672, 672, ), ( 739, 739, ), ( 806, 806, ), ( 873, 873, ), ( 940, 940, ), ( 1008, 1008, ), ( 1075, 1075, ), ( 1142, 1142, ), ( 1209, 1209, ), ( 1276, 1276, ), ( 1344, 1344, ), ], type='RandomChoiceResize'), dict( cat_max_ratio=0.75, crop_size=( 672, 672, ), type='SegRandomCrop'), ], ], type='RandomChoice'), dict(prob=0.5, type='MMCVRandomFlip'), dict(type='SegPhotoMetricDistortion'), dict( meta_keys=( 'img_path', 'seg_map_path', 'ori_shape', 'img_shape', 'pad_shape', 'scale_factor', 'flip', 'flip_direction', 'reduce_zero_label', 'task_name', 'head_cfg', 'git_cfg', ), type='PackSegInputs'), ], return_classes=True, type='ADE20KDataset'), ], ignore_keys=[ 'reduce_zero_label', 'label_map', 'classes', 'palette', ], type='ConcatDataset'), num_workers=3, persistent_workers=True, sampler=dict( batch_size=6, if_group=[ False, ], shuffle=True, source_ratio=[ 1.0, ], type='GroupMultiSourceNonMixedSampler')) tta_model = dict(type='SegTTAModel') val_cfg = dict(type='ValLoop') val_dataloader = dict( batch_size=1, dataset=dict( data_prefix=dict( img_path='images/validation', seg_map_path='annotations/validation'), data_root='data/ade/ADEChallengeData2016', pipeline=[ dict(type='LoadImageFromFile'), dict(keep_ratio=False, scale=( 672, 672, ), type='Resize'), dict(reduce_zero_label=True, type='SegLoadAnnotations'), dict( meta_dict=dict( git_cfg=dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_decoder_length=30, mode='semantic_segmentation', num_vocal=151, samples_grids_eachwin=10), head_cfg=dict( dec_length=30, dec_pixel_resolution=[ 4, 4, ], ignore_index=255, num_classes=150, num_vocal=151), task_name='semantic_segmentation'), type='AddMetaInfo'), dict( meta_keys=( 'img_path', 'seg_map_path', 'ori_shape', 'img_shape', 'pad_shape', 'scale_factor', 'flip', 'flip_direction', 'reduce_zero_label', 'task_name', 'head_cfg', 'git_cfg', ), type='PackSegInputs'), ], return_classes=True, type='ADE20KDataset'), num_workers=4, persistent_workers=True, sampler=dict(shuffle=False, type='DefaultSampler')) val_evaluator = dict( iou_metrics=[ 'mIoU', ], type='IoUMetric') vis_backends = [ dict(type='LocalVisBackend'), dict(type='TensorboardVisBackend'), ] visualizer = dict( name='visualizer', type='SegLocalVisualizer', vis_backends=[ dict(type='LocalVisBackend'), dict(type='TensorboardVisBackend'), ]) work_dir = './work_dirs/single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2' 2024/07/07 13:39:42 - mmengine - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (BELOW_NORMAL) LoggerHook -------------------- after_load_checkpoint: (49 ) EMAHook -------------------- before_train: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (VERY_LOW ) CheckpointHook -------------------- before_train_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) DistSamplerSeedHook -------------------- before_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook -------------------- after_train_iter: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (NORMAL ) SegVisualizationHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (NORMAL ) IterTimerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_val: (VERY_HIGH ) RuntimeInfoHook -------------------- before_val_epoch: (49 ) EMAHook (NORMAL ) IterTimerHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (NORMAL ) SegVisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_val: (VERY_HIGH ) RuntimeInfoHook -------------------- before_save_checkpoint: (49 ) EMAHook -------------------- after_train: (VERY_HIGH ) RuntimeInfoHook (VERY_LOW ) CheckpointHook -------------------- before_test: (VERY_HIGH ) RuntimeInfoHook -------------------- before_test_epoch: (49 ) EMAHook (NORMAL ) IterTimerHook -------------------- before_test_iter: (NORMAL ) IterTimerHook -------------------- after_test_iter: (NORMAL ) IterTimerHook (NORMAL ) SegVisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_test_epoch: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_test: (VERY_HIGH ) RuntimeInfoHook -------------------- after_run: (BELOW_NORMAL) LoggerHook -------------------- 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.pos_embed:lr=2e-05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.pos_embed:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.pos_embed:lr_mult=0.1 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.patch_embed.projection.weight:lr=2e-05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.patch_embed.projection.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.patch_embed.projection.weight:lr_mult=0.1 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.patch_embed.projection.bias:lr=2e-05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.patch_embed.projection.bias:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.patch_embed.projection.bias:lr_mult=0.1 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.embed.word_embeddings.weight:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.embed.word_embeddings.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.embed.word_embeddings.weight:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.embed.position_embeddings.weight:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.embed.position_embeddings.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.embed.position_embeddings.weight:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.embed.LayerNorm.weight:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.embed.LayerNorm.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.embed.LayerNorm.weight:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.embed.LayerNorm.bias:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.embed.LayerNorm.bias:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.embed.LayerNorm.bias:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.ln1.weight:lr=2e-05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.ln1.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.ln1.weight:lr_mult=0.1 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.ln1.bias:lr=2e-05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.ln1.bias:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.ln1.bias:lr_mult=0.1 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.rel_pos_h:lr=2e-05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.rel_pos_h:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.rel_pos_h:lr_mult=0.1 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.rel_pos_w:lr=2e-05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.rel_pos_w:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.rel_pos_w:lr_mult=0.1 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.qkv.weight:lr=2e-05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.qkv.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.qkv.weight:lr_mult=0.1 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.qkv.bias:lr=2e-05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.qkv.bias:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.qkv.bias:lr_mult=0.1 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.proj.weight:lr=2e-05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.proj.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.proj.weight:lr_mult=0.1 2024/07/07 13:39:49 - mmengine - INFO - 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13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.8.attn.proj.bias:lr=9.716000000000001e-05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.8.attn.proj.bias:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.8.attn.proj.bias:lr_mult=0.4858 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.8.ln2.weight:lr=9.716000000000001e-05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.8.ln2.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.8.ln2.weight:lr_mult=0.4858 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.8.ln2.bias:lr=9.716000000000001e-05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.8.ln2.bias:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.8.ln2.bias:lr_mult=0.4858 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.8.ffn.layers.0.0.weight:lr=9.716000000000001e-05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.8.ffn.layers.0.0.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.8.ffn.layers.0.0.weight:lr_mult=0.4858 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.8.ffn.layers.0.0.bias:lr=9.716000000000001e-05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.8.ffn.layers.0.0.bias:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.8.ffn.layers.0.0.bias:lr_mult=0.4858 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.8.ffn.layers.1.weight:lr=9.716000000000001e-05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.8.ffn.layers.1.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- 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mmengine - INFO - paramwise_options -- backbone.layers.10.attn.rel_pos_h:lr_mult=0.7429 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.rel_pos_w:lr=0.00014858000000000002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.rel_pos_w:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.rel_pos_w:lr_mult=0.7429 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.qkv.weight:lr=0.00014858000000000002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.qkv.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.qkv.weight:lr_mult=0.7429 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.qkv.bias:lr=0.00014858000000000002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- 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mmengine - INFO - paramwise_options -- backbone.layers.10.ln2.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.10.ln2.weight:lr_mult=0.7429 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.10.ln2.bias:lr=0.00014858000000000002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.10.ln2.bias:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.10.ln2.bias:lr_mult=0.7429 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.10.ffn.layers.0.0.weight:lr=0.00014858000000000002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.10.ffn.layers.0.0.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.10.ffn.layers.0.0.weight:lr_mult=0.7429 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- 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backbone.layers.16.attn.qkv.bias:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.qkv.bias:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.qkv.bias:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.proj.weight:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.proj.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.proj.weight:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.proj.bias:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.proj.bias:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.proj.bias:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.ln2.weight:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.ln2.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.ln2.weight:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.ln2.bias:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.ln2.bias:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.ln2.bias:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.0.0.weight:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.0.0.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.0.0.weight:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.0.0.bias:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.0.0.bias:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.0.0.bias:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.1.weight:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.1.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.1.weight:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.1.bias:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.1.bias:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.1.bias:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln1.weight:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln1.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln1.weight:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln1.bias:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln1.bias:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln1.bias:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.qkv.weight:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.qkv.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.qkv.weight:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.qkv.bias:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.qkv.bias:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.qkv.bias:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.proj.weight:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.proj.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.proj.weight:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.proj.bias:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.proj.bias:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.proj.bias:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln2.weight:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln2.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln2.weight:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln2.bias:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln2.bias:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln2.bias:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.0.0.weight:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.0.0.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.0.0.weight:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.0.0.bias:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.0.0.bias:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.0.0.bias:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.1.weight:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.1.weight:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.1.weight:lr_mult=1.0 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.1.bias:lr=0.0002 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.1.bias:weight_decay=0.05 2024/07/07 13:39:49 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.1.bias:lr_mult=1.0 2024/07/07 13:39:50 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. 2024/07/07 13:39:52 - mmengine - INFO - load backbone. in model from: ./sam-base-repeat-10.pth 2024/07/07 13:39:52 - mmengine - INFO - Resize the pos_embed shape from torch.Size([1, 64, 64, 768]) to torch.Size([1, 42, 42, 768]). 2024/07/07 13:39:52 - mmengine - INFO - Resize the layers.2.attn.rel_pos_h from torch.Size([127, 64]) to torch.Size([83, 64]) 2024/07/07 13:39:52 - mmengine - INFO - Resize the layers.2.attn.rel_pos_w from torch.Size([127, 64]) to torch.Size([83, 64]) 2024/07/07 13:39:52 - mmengine - INFO - Resize the layers.5.attn.rel_pos_h from torch.Size([127, 64]) to torch.Size([83, 64]) 2024/07/07 13:39:52 - mmengine - INFO - Resize the layers.5.attn.rel_pos_w from torch.Size([127, 64]) to torch.Size([83, 64]) 2024/07/07 13:39:52 - mmengine - INFO - Resize the layers.8.attn.rel_pos_h from torch.Size([127, 64]) to torch.Size([83, 64]) 2024/07/07 13:39:52 - mmengine - INFO - Resize the layers.8.attn.rel_pos_w from torch.Size([127, 64]) to torch.Size([83, 64]) 2024/07/07 13:39:52 - mmengine - INFO - Resize the layers.11.attn.rel_pos_h from torch.Size([127, 64]) to torch.Size([83, 64]) 2024/07/07 13:39:52 - mmengine - INFO - Resize the layers.11.attn.rel_pos_w from torch.Size([127, 64]) to torch.Size([83, 64]) 2024/07/07 13:39:52 - mmengine - WARNING - The model and loaded state dict do not match exactly unexpected key in source state_dict: channel_reduction.0.weight, channel_reduction.1.weight, channel_reduction.1.bias, channel_reduction.2.weight, channel_reduction.3.weight, channel_reduction.3.bias missing keys in source state_dict: embed.position_ids, embed.word_embeddings.weight, embed.position_embeddings.weight, embed.LayerNorm.weight, embed.LayerNorm.bias Name of parameter - Initialization information backbone.pos_embed - torch.Size([1, 42, 42, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.patch_embed.projection.weight - torch.Size([768, 3, 16, 16]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.patch_embed.projection.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.embed.word_embeddings.weight - torch.Size([30525, 768]): The value is the same before and after calling `init_weights` of GiTPromptMaskSegMeanV2 backbone.embed.position_embeddings.weight - torch.Size([512, 768]): The value is the same before and after calling `init_weights` of GiTPromptMaskSegMeanV2 backbone.embed.LayerNorm.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of GiTPromptMaskSegMeanV2 backbone.embed.LayerNorm.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of GiTPromptMaskSegMeanV2 backbone.layers.0.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.attn.rel_pos_h - torch.Size([83, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.attn.rel_pos_w - torch.Size([83, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.attn.rel_pos_h - torch.Size([83, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.attn.rel_pos_w - torch.Size([83, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.attn.rel_pos_h - torch.Size([83, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.attn.rel_pos_w - torch.Size([83, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.attn.rel_pos_h - torch.Size([83, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.attn.rel_pos_w - torch.Size([83, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth 2024/07/07 13:39:52 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2024/07/07 13:39:52 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2024/07/07 13:39:52 - mmengine - INFO - Checkpoints will be saved to /home/tanghao/mpi/GiT/work_dirs/single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2. 2024/07/07 13:40:47 - mmengine - INFO - Iter(train) [ 50/120000] base_lr: 2.0000e-04 lr: 2.0000e-05 eta: 1 day, 12:45:51 time: 1.1034 data_time: 0.0115 memory: 16318 grad_norm: 1823.8312 loss: 84.9144 semantic_segmentation_loss_cls: 54.1745 semantic_segmentation_loss_mask: 29.8000 semantic_segmentation_loss_dice: 0.9399 2024/07/07 13:41:44 - mmengine - INFO - Iter(train) [ 100/120000] base_lr: 2.0000e-04 lr: 2.0000e-05 eta: 1 day, 13:07:27 time: 1.1147 data_time: 0.0111 memory: 15267 grad_norm: 1157.6344 loss: 48.4583 semantic_segmentation_loss_cls: 31.9189 semantic_segmentation_loss_mask: 15.6284 semantic_segmentation_loss_dice: 0.9111 2024/07/07 13:42:40 - mmengine - INFO - Iter(train) [ 150/120000] base_lr: 2.0000e-04 lr: 2.0000e-05 eta: 1 day, 13:15:21 time: 1.1191 data_time: 0.0110 memory: 15605 grad_norm: 836.9580 loss: 34.0853 semantic_segmentation_loss_cls: 22.2708 semantic_segmentation_loss_mask: 10.9476 semantic_segmentation_loss_dice: 0.8669 2024/07/07 13:43:36 - mmengine - INFO - Iter(train) [ 200/120000] base_lr: 2.0000e-04 lr: 2.0000e-05 eta: 1 day, 13:16:19 time: 1.1200 data_time: 0.0110 memory: 15183 grad_norm: 647.6420 loss: 26.4407 semantic_segmentation_loss_cls: 17.1577 semantic_segmentation_loss_mask: 8.4439 semantic_segmentation_loss_dice: 0.8391 2024/07/07 13:44:33 - mmengine - INFO - Iter(train) [ 250/120000] base_lr: 2.0000e-04 lr: 2.0000e-05 eta: 1 day, 13:25:20 time: 1.1250 data_time: 0.0110 memory: 14523 grad_norm: 526.7860 loss: 21.6206 semantic_segmentation_loss_cls: 13.9753 semantic_segmentation_loss_mask: 6.8457 semantic_segmentation_loss_dice: 0.7997 2024/07/07 13:45:29 - mmengine - INFO - Iter(train) [ 300/120000] base_lr: 2.0000e-04 lr: 2.0000e-05 eta: 1 day, 13:21:26 time: 1.1235 data_time: 0.0111 memory: 15139 grad_norm: 442.0351 loss: 18.3398 semantic_segmentation_loss_cls: 11.8049 semantic_segmentation_loss_mask: 5.7676 semantic_segmentation_loss_dice: 0.7672 2024/07/07 13:46:26 - mmengine - INFO - Iter(train) [ 350/120000] base_lr: 2.0000e-04 lr: 2.0000e-05 eta: 1 day, 13:26:42 time: 1.1266 data_time: 0.0111 memory: 16357 grad_norm: 380.7068 loss: 15.9588 semantic_segmentation_loss_cls: 10.2358 semantic_segmentation_loss_mask: 4.9872 semantic_segmentation_loss_dice: 0.7357 2024/07/07 13:47:24 - mmengine - INFO - Iter(train) [ 400/120000] base_lr: 1.9999e-04 lr: 2.0000e-05 eta: 1 day, 13:29:52 time: 1.1287 data_time: 0.0111 memory: 15653 grad_norm: 335.1466 loss: 14.1770 semantic_segmentation_loss_cls: 9.0593 semantic_segmentation_loss_mask: 4.4032 semantic_segmentation_loss_dice: 0.7144 2024/07/07 13:48:20 - mmengine - INFO - Iter(train) [ 450/120000] base_lr: 1.9999e-04 lr: 1.9999e-05 eta: 1 day, 13:27:26 time: 1.1279 data_time: 0.0111 memory: 15207 grad_norm: 298.5577 loss: 12.7563 semantic_segmentation_loss_cls: 8.1247 semantic_segmentation_loss_mask: 3.9409 semantic_segmentation_loss_dice: 0.6907 2024/07/07 13:49:15 - mmengine - INFO - Iter(train) [ 500/120000] base_lr: 1.9999e-04 lr: 1.9999e-05 eta: 1 day, 13:21:55 time: 1.1257 data_time: 0.0111 memory: 15865 grad_norm: 269.3849 loss: 11.6141 semantic_segmentation_loss_cls: 7.3721 semantic_segmentation_loss_mask: 3.5727 semantic_segmentation_loss_dice: 0.6693 2024/07/07 13:50:10 - mmengine - INFO - Iter(train) [ 550/120000] base_lr: 1.9999e-04 lr: 1.9999e-05 eta: 1 day, 13:15:28 time: 1.1229 data_time: 0.0111 memory: 15667 grad_norm: 245.4943 loss: 10.6767 semantic_segmentation_loss_cls: 6.7578 semantic_segmentation_loss_mask: 3.2679 semantic_segmentation_loss_dice: 0.6510 2024/07/07 13:51:05 - mmengine - INFO - Iter(train) [ 600/120000] base_lr: 1.9999e-04 lr: 1.9999e-05 eta: 1 day, 13:11:22 time: 1.1213 data_time: 0.0111 memory: 14753 grad_norm: 225.3687 loss: 9.8899 semantic_segmentation_loss_cls: 6.2426 semantic_segmentation_loss_mask: 3.0134 semantic_segmentation_loss_dice: 0.6339 2024/07/07 13:52:00 - mmengine - INFO - Iter(train) [ 650/120000] base_lr: 1.9999e-04 lr: 1.9999e-05 eta: 1 day, 13:09:02 time: 1.1206 data_time: 0.0112 memory: 15462 grad_norm: 208.3789 loss: 9.2215 semantic_segmentation_loss_cls: 5.8046 semantic_segmentation_loss_mask: 2.7979 semantic_segmentation_loss_dice: 0.6190 2024/07/07 13:52:56 - mmengine - INFO - Iter(train) [ 700/120000] base_lr: 1.9998e-04 lr: 1.9998e-05 eta: 1 day, 13:06:48 time: 1.1199 data_time: 0.0112 memory: 15005 grad_norm: 193.8161 loss: 8.6407 semantic_segmentation_loss_cls: 5.4260 semantic_segmentation_loss_mask: 2.6113 semantic_segmentation_loss_dice: 0.6035 2024/07/07 13:53:51 - mmengine - INFO - Iter(train) [ 750/120000] base_lr: 1.9998e-04 lr: 1.9998e-05 eta: 1 day, 13:03:37 time: 1.1188 data_time: 0.0112 memory: 14899 grad_norm: 181.1342 loss: 8.1390 semantic_segmentation_loss_cls: 5.0980 semantic_segmentation_loss_mask: 2.4504 semantic_segmentation_loss_dice: 0.5906 2024/07/07 13:54:47 - mmengine - INFO - Iter(train) [ 800/120000] base_lr: 1.9998e-04 lr: 1.9998e-05 eta: 1 day, 13:03:34 time: 1.1192 data_time: 0.0112 memory: 15628 grad_norm: 170.0275 loss: 7.7010 semantic_segmentation_loss_cls: 4.8114 semantic_segmentation_loss_mask: 2.3095 semantic_segmentation_loss_dice: 0.5801 2024/07/07 13:55:44 - mmengine - INFO - Iter(train) [ 850/120000] base_lr: 1.9998e-04 lr: 1.9998e-05 eta: 1 day, 13:03:14 time: 1.1196 data_time: 0.0112 memory: 15025 grad_norm: 160.2100 loss: 7.3148 semantic_segmentation_loss_cls: 4.5589 semantic_segmentation_loss_mask: 2.1847 semantic_segmentation_loss_dice: 0.5712 2024/07/07 13:56:40 - mmengine - INFO - Iter(train) [ 900/120000] base_lr: 1.9997e-04 lr: 1.9998e-05 eta: 1 day, 13:02:35 time: 1.1197 data_time: 0.0113 memory: 14830 grad_norm: 151.4843 loss: 6.9657 semantic_segmentation_loss_cls: 4.3311 semantic_segmentation_loss_mask: 2.0735 semantic_segmentation_loss_dice: 0.5612 2024/07/07 13:57:35 - mmengine - INFO - Iter(train) [ 950/120000] base_lr: 1.9997e-04 lr: 1.9997e-05 eta: 1 day, 13:00:57 time: 1.1193 data_time: 0.0112 memory: 15325 grad_norm: 143.6743 loss: 6.6517 semantic_segmentation_loss_cls: 4.1270 semantic_segmentation_loss_mask: 1.9737 semantic_segmentation_loss_dice: 0.5509 2024/07/07 13:58:32 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 13:58:32 - mmengine - INFO - Iter(train) [ 1000/120000] base_lr: 1.9997e-04 lr: 1.9997e-05 eta: 1 day, 13:00:21 time: 1.1195 data_time: 0.0112 memory: 15221 grad_norm: 136.6330 loss: 6.3702 semantic_segmentation_loss_cls: 3.9438 semantic_segmentation_loss_mask: 1.8839 semantic_segmentation_loss_dice: 0.5425 2024/07/07 13:58:32 - mmengine - INFO - Saving checkpoint at 1000 iterations 2024/07/07 13:59:32 - mmengine - INFO - Iter(train) [ 1050/120000] base_lr: 1.9996e-04 lr: 1.9997e-05 eta: 1 day, 13:07:49 time: 1.1237 data_time: 0.0154 memory: 15844 grad_norm: 130.2704 loss: 6.1132 semantic_segmentation_loss_cls: 3.7766 semantic_segmentation_loss_mask: 1.8022 semantic_segmentation_loss_dice: 0.5344 2024/07/07 14:00:27 - mmengine - INFO - Iter(train) [ 1100/120000] base_lr: 1.9996e-04 lr: 1.9996e-05 eta: 1 day, 13:05:21 time: 1.1230 data_time: 0.0152 memory: 15298 grad_norm: 124.4879 loss: 5.8817 semantic_segmentation_loss_cls: 3.6253 semantic_segmentation_loss_mask: 1.7287 semantic_segmentation_loss_dice: 0.5276 2024/07/07 14:01:23 - mmengine - INFO - Iter(train) [ 1150/120000] base_lr: 1.9996e-04 lr: 1.9996e-05 eta: 1 day, 13:02:39 time: 1.1221 data_time: 0.0150 memory: 15019 grad_norm: 119.1953 loss: 5.6686 semantic_segmentation_loss_cls: 3.4872 semantic_segmentation_loss_mask: 1.6606 semantic_segmentation_loss_dice: 0.5208 2024/07/07 14:02:18 - mmengine - INFO - Iter(train) [ 1200/120000] base_lr: 1.9995e-04 lr: 1.9996e-05 eta: 1 day, 13:00:24 time: 1.1214 data_time: 0.0149 memory: 15384 grad_norm: 114.3522 loss: 5.4746 semantic_segmentation_loss_cls: 3.3609 semantic_segmentation_loss_mask: 1.5988 semantic_segmentation_loss_dice: 0.5149 2024/07/07 14:03:13 - mmengine - INFO - Iter(train) [ 1250/120000] base_lr: 1.9995e-04 lr: 1.9995e-05 eta: 1 day, 12:57:18 time: 1.1203 data_time: 0.0147 memory: 14930 grad_norm: 109.8834 loss: 5.2943 semantic_segmentation_loss_cls: 3.2440 semantic_segmentation_loss_mask: 1.5414 semantic_segmentation_loss_dice: 0.5090 2024/07/07 14:04:08 - mmengine - INFO - Iter(train) [ 1300/120000] base_lr: 1.9994e-04 lr: 1.9995e-05 eta: 1 day, 12:55:40 time: 1.1200 data_time: 0.0146 memory: 15213 grad_norm: 105.7593 loss: 5.1297 semantic_segmentation_loss_cls: 3.1371 semantic_segmentation_loss_mask: 1.4885 semantic_segmentation_loss_dice: 0.5042 2024/07/07 14:05:03 - mmengine - INFO - Iter(train) [ 1350/120000] base_lr: 1.9994e-04 lr: 1.9994e-05 eta: 1 day, 12:53:20 time: 1.1193 data_time: 0.0144 memory: 16185 grad_norm: 101.9536 loss: 4.9765 semantic_segmentation_loss_cls: 3.0375 semantic_segmentation_loss_mask: 1.4395 semantic_segmentation_loss_dice: 0.4994 2024/07/07 14:05:58 - mmengine - INFO - Iter(train) [ 1400/120000] base_lr: 1.9993e-04 lr: 1.9994e-05 eta: 1 day, 12:51:13 time: 1.1187 data_time: 0.0143 memory: 15795 grad_norm: 98.4019 loss: 4.8324 semantic_segmentation_loss_cls: 2.9441 semantic_segmentation_loss_mask: 1.3938 semantic_segmentation_loss_dice: 0.4945 2024/07/07 14:06:53 - mmengine - INFO - Iter(train) [ 1450/120000] base_lr: 1.9993e-04 lr: 1.9994e-05 eta: 1 day, 12:48:19 time: 1.1177 data_time: 0.0142 memory: 15035 grad_norm: 95.0984 loss: 4.6982 semantic_segmentation_loss_cls: 2.8569 semantic_segmentation_loss_mask: 1.3513 semantic_segmentation_loss_dice: 0.4900 2024/07/07 14:07:48 - mmengine - INFO - Iter(train) [ 1500/120000] base_lr: 1.9992e-04 lr: 1.9993e-05 eta: 1 day, 12:46:06 time: 1.1170 data_time: 0.0142 memory: 15456 grad_norm: 92.0143 loss: 4.5733 semantic_segmentation_loss_cls: 2.7758 semantic_segmentation_loss_mask: 1.3115 semantic_segmentation_loss_dice: 0.4860 2024/07/07 14:08:43 - mmengine - INFO - Iter(train) [ 1550/120000] base_lr: 1.9992e-04 lr: 1.9993e-05 eta: 1 day, 12:45:08 time: 1.1170 data_time: 0.0141 memory: 14676 grad_norm: 89.1281 loss: 4.4550 semantic_segmentation_loss_cls: 2.6994 semantic_segmentation_loss_mask: 1.2743 semantic_segmentation_loss_dice: 0.4814 2024/07/07 14:09:39 - mmengine - INFO - Iter(train) [ 1600/120000] base_lr: 1.9991e-04 lr: 1.9992e-05 eta: 1 day, 12:43:18 time: 1.1165 data_time: 0.0140 memory: 15178 grad_norm: 86.4279 loss: 4.3458 semantic_segmentation_loss_cls: 2.6283 semantic_segmentation_loss_mask: 1.2396 semantic_segmentation_loss_dice: 0.4779 2024/07/07 14:10:34 - mmengine - INFO - Iter(train) [ 1650/120000] base_lr: 1.9991e-04 lr: 1.9992e-05 eta: 1 day, 12:41:26 time: 1.1161 data_time: 0.0139 memory: 15753 grad_norm: 83.9232 loss: 4.2421 semantic_segmentation_loss_cls: 2.5611 semantic_segmentation_loss_mask: 1.2069 semantic_segmentation_loss_dice: 0.4741 2024/07/07 14:11:29 - mmengine - INFO - Iter(train) [ 1700/120000] base_lr: 1.9990e-04 lr: 1.9991e-05 eta: 1 day, 12:40:17 time: 1.1160 data_time: 0.0138 memory: 15468 grad_norm: 81.5295 loss: 4.1436 semantic_segmentation_loss_cls: 2.4972 semantic_segmentation_loss_mask: 1.1760 semantic_segmentation_loss_dice: 0.4703 2024/07/07 14:12:25 - mmengine - INFO - Iter(train) [ 1750/120000] base_lr: 1.9990e-04 lr: 1.9991e-05 eta: 1 day, 12:39:17 time: 1.1159 data_time: 0.0138 memory: 15142 grad_norm: 79.2748 loss: 4.0515 semantic_segmentation_loss_cls: 2.4376 semantic_segmentation_loss_mask: 1.1468 semantic_segmentation_loss_dice: 0.4670 2024/07/07 14:13:20 - mmengine - INFO - Iter(train) [ 1800/120000] base_lr: 1.9989e-04 lr: 1.9990e-05 eta: 1 day, 12:37:43 time: 1.1156 data_time: 0.0137 memory: 14890 grad_norm: 77.1415 loss: 3.9635 semantic_segmentation_loss_cls: 2.3806 semantic_segmentation_loss_mask: 1.1193 semantic_segmentation_loss_dice: 0.4636 2024/07/07 14:14:16 - mmengine - INFO - Iter(train) [ 1850/120000] base_lr: 1.9988e-04 lr: 1.9989e-05 eta: 1 day, 12:36:42 time: 1.1156 data_time: 0.0136 memory: 15388 grad_norm: 75.1201 loss: 3.8796 semantic_segmentation_loss_cls: 2.3265 semantic_segmentation_loss_mask: 1.0929 semantic_segmentation_loss_dice: 0.4601 2024/07/07 14:15:12 - mmengine - INFO - Iter(train) [ 1900/120000] base_lr: 1.9988e-04 lr: 1.9989e-05 eta: 1 day, 12:36:22 time: 1.1159 data_time: 0.0135 memory: 15070 grad_norm: 73.2100 loss: 3.7991 semantic_segmentation_loss_cls: 2.2745 semantic_segmentation_loss_mask: 1.0682 semantic_segmentation_loss_dice: 0.4564 2024/07/07 14:16:08 - mmengine - INFO - Iter(train) [ 1950/120000] base_lr: 1.9987e-04 lr: 1.9988e-05 eta: 1 day, 12:35:40 time: 1.1160 data_time: 0.0135 memory: 15251 grad_norm: 71.3947 loss: 3.7235 semantic_segmentation_loss_cls: 2.2259 semantic_segmentation_loss_mask: 1.0445 semantic_segmentation_loss_dice: 0.4531 2024/07/07 14:17:03 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 14:17:03 - mmengine - INFO - Iter(train) [ 2000/120000] base_lr: 1.9986e-04 lr: 1.9988e-05 eta: 1 day, 12:33:55 time: 1.1156 data_time: 0.0135 memory: 15619 grad_norm: 69.6705 loss: 3.6521 semantic_segmentation_loss_cls: 2.1800 semantic_segmentation_loss_mask: 1.0219 semantic_segmentation_loss_dice: 0.4502 2024/07/07 14:17:03 - mmengine - INFO - Saving checkpoint at 2000 iterations 2024/07/07 14:18:03 - mmengine - INFO - Iter(train) [ 2050/120000] base_lr: 1.9986e-04 lr: 1.9987e-05 eta: 1 day, 12:36:43 time: 1.1175 data_time: 0.0156 memory: 15496 grad_norm: 68.0278 loss: 3.5847 semantic_segmentation_loss_cls: 2.1364 semantic_segmentation_loss_mask: 1.0007 semantic_segmentation_loss_dice: 0.4476 2024/07/07 14:18:58 - mmengine - INFO - Iter(train) [ 2100/120000] base_lr: 1.9985e-04 lr: 1.9986e-05 eta: 1 day, 12:35:09 time: 1.1171 data_time: 0.0155 memory: 15252 grad_norm: 66.4697 loss: 3.5209 semantic_segmentation_loss_cls: 2.0952 semantic_segmentation_loss_mask: 0.9808 semantic_segmentation_loss_dice: 0.4450 2024/07/07 14:19:54 - mmengine - INFO - Iter(train) [ 2150/120000] base_lr: 1.9984e-04 lr: 1.9986e-05 eta: 1 day, 12:33:50 time: 1.1169 data_time: 0.0154 memory: 15046 grad_norm: 64.9805 loss: 3.4602 semantic_segmentation_loss_cls: 2.0558 semantic_segmentation_loss_mask: 0.9616 semantic_segmentation_loss_dice: 0.4428 2024/07/07 14:20:49 - mmengine - INFO - Iter(train) [ 2200/120000] base_lr: 1.9984e-04 lr: 1.9985e-05 eta: 1 day, 12:32:13 time: 1.1166 data_time: 0.0153 memory: 14798 grad_norm: 63.5576 loss: 3.4019 semantic_segmentation_loss_cls: 2.0181 semantic_segmentation_loss_mask: 0.9432 semantic_segmentation_loss_dice: 0.4405 2024/07/07 14:21:44 - mmengine - INFO - Iter(train) [ 2250/120000] base_lr: 1.9983e-04 lr: 1.9984e-05 eta: 1 day, 12:30:39 time: 1.1163 data_time: 0.0152 memory: 15253 grad_norm: 62.1985 loss: 3.3450 semantic_segmentation_loss_cls: 1.9815 semantic_segmentation_loss_mask: 0.9256 semantic_segmentation_loss_dice: 0.4380 2024/07/07 14:22:40 - mmengine - INFO - Iter(train) [ 2300/120000] base_lr: 1.9982e-04 lr: 1.9984e-05 eta: 1 day, 12:30:13 time: 1.1165 data_time: 0.0152 memory: 14749 grad_norm: 60.8964 loss: 3.2903 semantic_segmentation_loss_cls: 1.9463 semantic_segmentation_loss_mask: 0.9086 semantic_segmentation_loss_dice: 0.4354 2024/07/07 14:23:37 - mmengine - INFO - Iter(train) [ 2350/120000] base_lr: 1.9981e-04 lr: 1.9983e-05 eta: 1 day, 12:29:42 time: 1.1167 data_time: 0.0152 memory: 14942 grad_norm: 59.6513 loss: 3.2398 semantic_segmentation_loss_cls: 1.9137 semantic_segmentation_loss_mask: 0.8925 semantic_segmentation_loss_dice: 0.4336 2024/07/07 14:24:31 - mmengine - INFO - Iter(train) [ 2400/120000] base_lr: 1.9980e-04 lr: 1.9982e-05 eta: 1 day, 12:27:32 time: 1.1161 data_time: 0.0151 memory: 16139 grad_norm: 58.4560 loss: 3.1904 semantic_segmentation_loss_cls: 1.8820 semantic_segmentation_loss_mask: 0.8770 semantic_segmentation_loss_dice: 0.4314 2024/07/07 14:25:26 - mmengine - INFO - Iter(train) [ 2450/120000] base_lr: 1.9980e-04 lr: 1.9982e-05 eta: 1 day, 12:25:48 time: 1.1157 data_time: 0.0150 memory: 15435 grad_norm: 57.3102 loss: 3.1436 semantic_segmentation_loss_cls: 1.8519 semantic_segmentation_loss_mask: 0.8621 semantic_segmentation_loss_dice: 0.4297 2024/07/07 14:26:21 - mmengine - INFO - Iter(train) [ 2500/120000] base_lr: 1.9979e-04 lr: 1.9981e-05 eta: 1 day, 12:24:52 time: 1.1157 data_time: 0.0149 memory: 15133 grad_norm: 56.2113 loss: 3.0984 semantic_segmentation_loss_cls: 1.8225 semantic_segmentation_loss_mask: 0.8480 semantic_segmentation_loss_dice: 0.4278 2024/07/07 14:27:17 - mmengine - INFO - Iter(train) [ 2550/120000] base_lr: 1.9978e-04 lr: 1.9980e-05 eta: 1 day, 12:23:39 time: 1.1155 data_time: 0.0149 memory: 14874 grad_norm: 55.1539 loss: 3.0538 semantic_segmentation_loss_cls: 1.7939 semantic_segmentation_loss_mask: 0.8342 semantic_segmentation_loss_dice: 0.4257 2024/07/07 14:28:13 - mmengine - INFO - Iter(train) [ 2600/120000] base_lr: 1.9977e-04 lr: 1.9979e-05 eta: 1 day, 12:22:40 time: 1.1155 data_time: 0.0149 memory: 15871 grad_norm: 54.1366 loss: 3.0105 semantic_segmentation_loss_cls: 1.7662 semantic_segmentation_loss_mask: 0.8209 semantic_segmentation_loss_dice: 0.4235 2024/07/07 14:29:08 - mmengine - INFO - Iter(train) [ 2650/120000] base_lr: 1.9976e-04 lr: 1.9978e-05 eta: 1 day, 12:21:27 time: 1.1154 data_time: 0.0148 memory: 15883 grad_norm: 53.1614 loss: 2.9698 semantic_segmentation_loss_cls: 1.7401 semantic_segmentation_loss_mask: 0.8081 semantic_segmentation_loss_dice: 0.4216 2024/07/07 14:30:03 - mmengine - INFO - Iter(train) [ 2700/120000] base_lr: 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12:17:36 time: 1.1153 data_time: 0.0146 memory: 15202 grad_norm: 49.6096 loss: 2.8171 semantic_segmentation_loss_cls: 1.6428 semantic_segmentation_loss_mask: 0.7609 semantic_segmentation_loss_dice: 0.4134 2024/07/07 14:33:47 - mmengine - INFO - Iter(train) [ 2900/120000] base_lr: 1.9972e-04 lr: 1.9974e-05 eta: 1 day, 12:17:01 time: 1.1155 data_time: 0.0146 memory: 15619 grad_norm: 48.7950 loss: 2.7827 semantic_segmentation_loss_cls: 1.6206 semantic_segmentation_loss_mask: 0.7503 semantic_segmentation_loss_dice: 0.4118 2024/07/07 14:34:44 - mmengine - INFO - Iter(train) [ 2950/120000] base_lr: 1.9971e-04 lr: 1.9973e-05 eta: 1 day, 12:16:33 time: 1.1157 data_time: 0.0145 memory: 15083 grad_norm: 48.0077 loss: 2.7475 semantic_segmentation_loss_cls: 1.5982 semantic_segmentation_loss_mask: 0.7398 semantic_segmentation_loss_dice: 0.4095 2024/07/07 14:35:40 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 14:35:40 - mmengine - INFO - Iter(train) [ 3000/120000] base_lr: 1.9970e-04 lr: 1.9972e-05 eta: 1 day, 12:16:17 time: 1.1160 data_time: 0.0145 memory: 14560 grad_norm: 47.2461 loss: 2.7151 semantic_segmentation_loss_cls: 1.5774 semantic_segmentation_loss_mask: 0.7299 semantic_segmentation_loss_dice: 0.4078 2024/07/07 14:35:40 - mmengine - INFO - Saving checkpoint at 3000 iterations 2024/07/07 14:36:41 - mmengine - INFO - Iter(train) [ 3050/120000] base_lr: 1.9968e-04 lr: 1.9971e-05 eta: 1 day, 12:18:23 time: 1.1176 data_time: 0.0159 memory: 14839 grad_norm: 46.5064 loss: 2.6841 semantic_segmentation_loss_cls: 1.5575 semantic_segmentation_loss_mask: 0.7202 semantic_segmentation_loss_dice: 0.4064 2024/07/07 14:37:36 - mmengine - INFO - Iter(train) [ 3100/120000] base_lr: 1.9967e-04 lr: 1.9970e-05 eta: 1 day, 12:17:01 time: 1.1174 data_time: 0.0158 memory: 15609 grad_norm: 45.7922 loss: 2.6540 semantic_segmentation_loss_cls: 1.5382 semantic_segmentation_loss_mask: 0.7108 semantic_segmentation_loss_dice: 0.4050 2024/07/07 14:38:33 - mmengine - INFO - Iter(train) [ 3150/120000] base_lr: 1.9966e-04 lr: 1.9969e-05 eta: 1 day, 12:16:35 time: 1.1176 data_time: 0.0158 memory: 14824 grad_norm: 45.1004 loss: 2.6242 semantic_segmentation_loss_cls: 1.5191 semantic_segmentation_loss_mask: 0.7016 semantic_segmentation_loss_dice: 0.4034 2024/07/07 14:39:30 - mmengine - INFO - Iter(train) [ 3200/120000] base_lr: 1.9965e-04 lr: 1.9968e-05 eta: 1 day, 12:16:12 time: 1.1179 data_time: 0.0157 memory: 14873 grad_norm: 44.4298 loss: 2.5950 semantic_segmentation_loss_cls: 1.5005 semantic_segmentation_loss_mask: 0.6928 semantic_segmentation_loss_dice: 0.4017 2024/07/07 14:40:26 - mmengine - INFO - Iter(train) [ 3250/120000] base_lr: 1.9964e-04 lr: 1.9967e-05 eta: 1 day, 12:15:33 time: 1.1181 data_time: 0.0157 memory: 15235 grad_norm: 43.7808 loss: 2.5668 semantic_segmentation_loss_cls: 1.4826 semantic_segmentation_loss_mask: 0.6842 semantic_segmentation_loss_dice: 0.4000 2024/07/07 14:41:22 - mmengine - INFO - Iter(train) [ 3300/120000] base_lr: 1.9963e-04 lr: 1.9966e-05 eta: 1 day, 12:14:30 time: 1.1180 data_time: 0.0156 memory: 16230 grad_norm: 43.1497 loss: 2.5399 semantic_segmentation_loss_cls: 1.4654 semantic_segmentation_loss_mask: 0.6758 semantic_segmentation_loss_dice: 0.3987 2024/07/07 14:42:18 - mmengine - INFO - Iter(train) [ 3350/120000] base_lr: 1.9962e-04 lr: 1.9965e-05 eta: 1 day, 12:13:48 time: 1.1181 data_time: 0.0156 memory: 15873 grad_norm: 42.5370 loss: 2.5141 semantic_segmentation_loss_cls: 1.4489 semantic_segmentation_loss_mask: 0.6678 semantic_segmentation_loss_dice: 0.3974 2024/07/07 14:42:39 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 14:43:14 - mmengine - INFO - Iter(train) [ 3400/120000] base_lr: 1.9961e-04 lr: 1.9964e-05 eta: 1 day, 12:13:01 time: 1.1182 data_time: 0.0155 memory: 15515 grad_norm: 41.9434 loss: 2.4886 semantic_segmentation_loss_cls: 1.4326 semantic_segmentation_loss_mask: 0.6599 semantic_segmentation_loss_dice: 0.3961 2024/07/07 14:44:10 - mmengine - INFO - Iter(train) [ 3450/120000] base_lr: 1.9960e-04 lr: 1.9963e-05 eta: 1 day, 12:11:47 time: 1.1180 data_time: 0.0155 memory: 14993 grad_norm: 41.3675 loss: 2.4640 semantic_segmentation_loss_cls: 1.4169 semantic_segmentation_loss_mask: 0.6523 semantic_segmentation_loss_dice: 0.3949 2024/07/07 14:45:05 - mmengine - INFO - Iter(train) [ 3500/120000] base_lr: 1.9958e-04 lr: 1.9962e-05 eta: 1 day, 12:10:39 time: 1.1179 data_time: 0.0154 memory: 16013 grad_norm: 40.8086 loss: 2.4395 semantic_segmentation_loss_cls: 1.4013 semantic_segmentation_loss_mask: 0.6448 semantic_segmentation_loss_dice: 0.3934 2024/07/07 14:46:01 - mmengine - INFO - Iter(train) [ 3550/120000] base_lr: 1.9957e-04 lr: 1.9961e-05 eta: 1 day, 12:09:39 time: 1.1179 data_time: 0.0154 memory: 15214 grad_norm: 40.2681 loss: 2.4161 semantic_segmentation_loss_cls: 1.3864 semantic_segmentation_loss_mask: 0.6376 semantic_segmentation_loss_dice: 0.3921 2024/07/07 14:46:57 - mmengine - INFO - Iter(train) [ 3600/120000] base_lr: 1.9956e-04 lr: 1.9960e-05 eta: 1 day, 12:08:38 time: 1.1179 data_time: 0.0153 memory: 14193 grad_norm: 39.7455 loss: 2.3927 semantic_segmentation_loss_cls: 1.3715 semantic_segmentation_loss_mask: 0.6305 semantic_segmentation_loss_dice: 0.3907 2024/07/07 14:47:52 - mmengine - INFO - Iter(train) [ 3650/120000] base_lr: 1.9955e-04 lr: 1.9959e-05 eta: 1 day, 12:07:39 time: 1.1178 data_time: 0.0152 memory: 15812 grad_norm: 39.2298 loss: 2.3699 semantic_segmentation_loss_cls: 1.3570 semantic_segmentation_loss_mask: 0.6237 semantic_segmentation_loss_dice: 0.3892 2024/07/07 14:48:49 - mmengine - INFO - Iter(train) [ 3700/120000] base_lr: 1.9954e-04 lr: 1.9958e-05 eta: 1 day, 12:06:58 time: 1.1180 data_time: 0.0152 memory: 15662 grad_norm: 38.7293 loss: 2.3481 semantic_segmentation_loss_cls: 1.3431 semantic_segmentation_loss_mask: 0.6170 semantic_segmentation_loss_dice: 0.3880 2024/07/07 14:49:45 - mmengine - INFO - Iter(train) [ 3750/120000] base_lr: 1.9952e-04 lr: 1.9957e-05 eta: 1 day, 12:06:03 time: 1.1180 data_time: 0.0152 memory: 15941 grad_norm: 38.2417 loss: 2.3270 semantic_segmentation_loss_cls: 1.3297 semantic_segmentation_loss_mask: 0.6105 semantic_segmentation_loss_dice: 0.3868 2024/07/07 14:50:41 - mmengine - INFO - Iter(train) [ 3800/120000] base_lr: 1.9951e-04 lr: 1.9956e-05 eta: 1 day, 12:05:17 time: 1.1181 data_time: 0.0151 memory: 14754 grad_norm: 37.7665 loss: 2.3056 semantic_segmentation_loss_cls: 1.3161 semantic_segmentation_loss_mask: 0.6042 semantic_segmentation_loss_dice: 0.3853 2024/07/07 14:51:37 - mmengine - INFO - Iter(train) [ 3850/120000] base_lr: 1.9950e-04 lr: 1.9954e-05 eta: 1 day, 12:04:29 time: 1.1181 data_time: 0.0151 memory: 15328 grad_norm: 37.3037 loss: 2.2858 semantic_segmentation_loss_cls: 1.3035 semantic_segmentation_loss_mask: 0.5980 semantic_segmentation_loss_dice: 0.3843 2024/07/07 14:52:32 - mmengine - INFO - Iter(train) [ 3900/120000] base_lr: 1.9948e-04 lr: 1.9953e-05 eta: 1 day, 12:03:11 time: 1.1179 data_time: 0.0151 memory: 15414 grad_norm: 36.8548 loss: 2.2663 semantic_segmentation_loss_cls: 1.2911 semantic_segmentation_loss_mask: 0.5921 semantic_segmentation_loss_dice: 0.3831 2024/07/07 14:53:29 - mmengine - INFO - Iter(train) [ 3950/120000] base_lr: 1.9947e-04 lr: 1.9952e-05 eta: 1 day, 12:02:30 time: 1.1181 data_time: 0.0151 memory: 14924 grad_norm: 36.4161 loss: 2.2471 semantic_segmentation_loss_cls: 1.2787 semantic_segmentation_loss_mask: 0.5864 semantic_segmentation_loss_dice: 0.3820 2024/07/07 14:54:25 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 14:54:25 - mmengine - INFO - Iter(train) [ 4000/120000] base_lr: 1.9946e-04 lr: 1.9951e-05 eta: 1 day, 12:01:55 time: 1.1182 data_time: 0.0150 memory: 15877 grad_norm: 35.9882 loss: 2.2283 semantic_segmentation_loss_cls: 1.2667 semantic_segmentation_loss_mask: 0.5807 semantic_segmentation_loss_dice: 0.3809 2024/07/07 14:54:25 - mmengine - INFO - Saving checkpoint at 4000 iterations 2024/07/07 14:55:25 - mmengine - INFO - Iter(train) [ 4050/120000] base_lr: 1.9944e-04 lr: 1.9949e-05 eta: 1 day, 12:02:40 time: 1.1193 data_time: 0.0162 memory: 14643 grad_norm: 13.2176 loss: 1.1760 semantic_segmentation_loss_cls: 0.5935 semantic_segmentation_loss_mask: 0.2097 semantic_segmentation_loss_dice: 0.3727 2024/07/07 14:56:20 - mmengine - INFO - Iter(train) [ 4100/120000] base_lr: 1.9943e-04 lr: 1.9948e-05 eta: 1 day, 12:01:24 time: 1.1190 data_time: 0.0162 memory: 15187 grad_norm: 7.1022 loss: 1.0354 semantic_segmentation_loss_cls: 0.4768 semantic_segmentation_loss_mask: 0.1932 semantic_segmentation_loss_dice: 0.3653 2024/07/07 14:57:15 - mmengine - INFO - Iter(train) [ 4150/120000] base_lr: 1.9942e-04 lr: 1.9947e-05 eta: 1 day, 12:00:08 time: 1.1188 data_time: 0.0162 memory: 15540 grad_norm: 4.6839 loss: 0.9783 semantic_segmentation_loss_cls: 0.4439 semantic_segmentation_loss_mask: 0.1751 semantic_segmentation_loss_dice: 0.3593 2024/07/07 14:58:11 - mmengine - INFO - Iter(train) [ 4200/120000] base_lr: 1.9940e-04 lr: 1.9946e-05 eta: 1 day, 11:59:01 time: 1.1186 data_time: 0.0163 memory: 14978 grad_norm: 3.7146 loss: 0.9440 semantic_segmentation_loss_cls: 0.4253 semantic_segmentation_loss_mask: 0.1651 semantic_segmentation_loss_dice: 0.3536 2024/07/07 14:59:07 - mmengine - INFO - Iter(train) [ 4250/120000] base_lr: 1.9939e-04 lr: 1.9944e-05 eta: 1 day, 11:58:25 time: 1.1185 data_time: 0.0163 memory: 16120 grad_norm: 3.1982 loss: 0.9235 semantic_segmentation_loss_cls: 0.4134 semantic_segmentation_loss_mask: 0.1610 semantic_segmentation_loss_dice: 0.3491 2024/07/07 15:00:04 - mmengine - INFO - Iter(train) [ 4300/120000] base_lr: 1.9937e-04 lr: 1.9943e-05 eta: 1 day, 11:57:48 time: 1.1187 data_time: 0.0163 memory: 14989 grad_norm: 2.9952 loss: 0.9083 semantic_segmentation_loss_cls: 0.4053 semantic_segmentation_loss_mask: 0.1578 semantic_segmentation_loss_dice: 0.3451 2024/07/07 15:01:00 - mmengine - INFO - Iter(train) [ 4350/120000] base_lr: 1.9936e-04 lr: 1.9942e-05 eta: 1 day, 11:56:52 time: 1.1183 data_time: 0.0163 memory: 15231 grad_norm: 2.8627 loss: 0.8964 semantic_segmentation_loss_cls: 0.3989 semantic_segmentation_loss_mask: 0.1556 semantic_segmentation_loss_dice: 0.3419 2024/07/07 15:01:56 - mmengine - INFO - Iter(train) [ 4400/120000] base_lr: 1.9934e-04 lr: 1.9940e-05 eta: 1 day, 11:55:51 time: 1.1180 data_time: 0.0164 memory: 14875 grad_norm: 2.6869 loss: 0.8841 semantic_segmentation_loss_cls: 0.3924 semantic_segmentation_loss_mask: 0.1532 semantic_segmentation_loss_dice: 0.3384 2024/07/07 15:02:53 - mmengine - INFO - Iter(train) [ 4450/120000] base_lr: 1.9933e-04 lr: 1.9939e-05 eta: 1 day, 11:55:20 time: 1.1182 data_time: 0.0164 memory: 15601 grad_norm: 2.6402 loss: 0.8759 semantic_segmentation_loss_cls: 0.3883 semantic_segmentation_loss_mask: 0.1518 semantic_segmentation_loss_dice: 0.3358 2024/07/07 15:03:49 - mmengine - INFO - Iter(train) [ 4500/120000] base_lr: 1.9931e-04 lr: 1.9938e-05 eta: 1 day, 11:54:21 time: 1.1183 data_time: 0.0164 memory: 15161 grad_norm: 2.5807 loss: 0.8679 semantic_segmentation_loss_cls: 0.3846 semantic_segmentation_loss_mask: 0.1501 semantic_segmentation_loss_dice: 0.3332 2024/07/07 15:04:43 - mmengine - INFO - Iter(train) [ 4550/120000] base_lr: 1.9930e-04 lr: 1.9936e-05 eta: 1 day, 11:52:48 time: 1.1183 data_time: 0.0164 memory: 14861 grad_norm: 2.5256 loss: 0.8609 semantic_segmentation_loss_cls: 0.3809 semantic_segmentation_loss_mask: 0.1489 semantic_segmentation_loss_dice: 0.3310 2024/07/07 15:05:37 - mmengine - INFO - Iter(train) [ 4600/120000] base_lr: 1.9928e-04 lr: 1.9935e-05 eta: 1 day, 11:51:01 time: 1.1179 data_time: 0.0164 memory: 15354 grad_norm: 2.5021 loss: 0.8543 semantic_segmentation_loss_cls: 0.3776 semantic_segmentation_loss_mask: 0.1478 semantic_segmentation_loss_dice: 0.3288 2024/07/07 15:06:31 - mmengine - INFO - Iter(train) [ 4650/120000] base_lr: 1.9927e-04 lr: 1.9933e-05 eta: 1 day, 11:49:15 time: 1.1175 data_time: 0.0164 memory: 14870 grad_norm: 2.4726 loss: 0.8479 semantic_segmentation_loss_cls: 0.3745 semantic_segmentation_loss_mask: 0.1467 semantic_segmentation_loss_dice: 0.3267 2024/07/07 15:07:27 - mmengine - INFO - Iter(train) [ 4700/120000] base_lr: 1.9925e-04 lr: 1.9932e-05 eta: 1 day, 11:48:19 time: 1.1176 data_time: 0.0164 memory: 15637 grad_norm: 2.4433 loss: 0.8437 semantic_segmentation_loss_cls: 0.3724 semantic_segmentation_loss_mask: 0.1460 semantic_segmentation_loss_dice: 0.3253 2024/07/07 15:08:22 - mmengine - INFO - Iter(train) [ 4750/120000] base_lr: 1.9924e-04 lr: 1.9931e-05 eta: 1 day, 11:47:15 time: 1.1177 data_time: 0.0164 memory: 14486 grad_norm: 2.4281 loss: 0.8387 semantic_segmentation_loss_cls: 0.3698 semantic_segmentation_loss_mask: 0.1452 semantic_segmentation_loss_dice: 0.3237 2024/07/07 15:09:17 - mmengine - INFO - Iter(train) [ 4800/120000] base_lr: 1.9922e-04 lr: 1.9929e-05 eta: 1 day, 11:45:59 time: 1.1174 data_time: 0.0164 memory: 14929 grad_norm: 2.4128 loss: 0.8334 semantic_segmentation_loss_cls: 0.3673 semantic_segmentation_loss_mask: 0.1442 semantic_segmentation_loss_dice: 0.3220 2024/07/07 15:10:13 - mmengine - INFO - Iter(train) [ 4850/120000] base_lr: 1.9920e-04 lr: 1.9928e-05 eta: 1 day, 11:44:59 time: 1.1173 data_time: 0.0164 memory: 15081 grad_norm: 2.4012 loss: 0.8286 semantic_segmentation_loss_cls: 0.3649 semantic_segmentation_loss_mask: 0.1435 semantic_segmentation_loss_dice: 0.3202 2024/07/07 15:11:09 - mmengine - INFO - Iter(train) [ 4900/120000] base_lr: 1.9919e-04 lr: 1.9926e-05 eta: 1 day, 11:43:56 time: 1.1171 data_time: 0.0164 memory: 15324 grad_norm: 2.3889 loss: 0.8244 semantic_segmentation_loss_cls: 0.3628 semantic_segmentation_loss_mask: 0.1428 semantic_segmentation_loss_dice: 0.3188 2024/07/07 15:12:05 - mmengine - INFO - Iter(train) [ 4950/120000] base_lr: 1.9917e-04 lr: 1.9925e-05 eta: 1 day, 11:43:10 time: 1.1173 data_time: 0.0164 memory: 15390 grad_norm: 2.3781 loss: 0.8212 semantic_segmentation_loss_cls: 0.3611 semantic_segmentation_loss_mask: 0.1422 semantic_segmentation_loss_dice: 0.3179 2024/07/07 15:13:01 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 15:13:01 - mmengine - INFO - Iter(train) [ 5000/120000] base_lr: 1.9915e-04 lr: 1.9923e-05 eta: 1 day, 11:42:07 time: 1.1172 data_time: 0.0164 memory: 16080 grad_norm: 2.3682 loss: 0.8177 semantic_segmentation_loss_cls: 0.3593 semantic_segmentation_loss_mask: 0.1415 semantic_segmentation_loss_dice: 0.3168 2024/07/07 15:13:01 - mmengine - INFO - Saving checkpoint at 5000 iterations 2024/07/07 15:13:18 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:52 time: 0.2502 data_time: 0.0041 memory: 5013 2024/07/07 15:13:30 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:38 time: 0.2468 data_time: 0.0027 memory: 5190 2024/07/07 15:13:42 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:26 time: 0.2461 data_time: 0.0023 memory: 4460 2024/07/07 15:13:54 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:13 time: 0.2450 data_time: 0.0020 memory: 4543 2024/07/07 15:14:06 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:01 time: 0.2446 data_time: 0.0019 memory: 4645 2024/07/07 15:14:19 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:48 time: 0.2449 data_time: 0.0018 memory: 10983 2024/07/07 15:14:31 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2447 data_time: 0.0018 memory: 4460 2024/07/07 15:14:43 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2448 data_time: 0.0018 memory: 4641 2024/07/07 15:14:55 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2447 data_time: 0.0017 memory: 4473 2024/07/07 15:15:08 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2447 data_time: 0.0017 memory: 4555 2024/07/07 15:15:08 - mmengine - INFO - per class results: 2024/07/07 15:15:08 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 74.66 | 86.24 | | building | 77.7 | 84.98 | | sky | 93.46 | 97.58 | | floor | 79.0 | 91.34 | | tree | 72.34 | 88.54 | | ceiling | 82.49 | 93.06 | | road | 81.77 | 90.79 | | bed | 83.38 | 94.85 | | windowpane | 57.77 | 79.44 | | grass | 71.12 | 92.1 | | cabinet | 56.32 | 70.95 | | sidewalk | 66.66 | 79.88 | | person | 79.84 | 92.84 | | earth | 29.77 | 37.26 | | door | 41.17 | 55.58 | | table | 54.06 | 70.7 | | mountain | 60.66 | 77.9 | | plant | 52.03 | 68.26 | | curtain | 67.58 | 87.69 | | chair | 52.56 | 65.37 | | car | 83.77 | 93.07 | | water | 45.94 | 57.39 | | painting | 66.3 | 87.02 | | sofa | 58.15 | 70.87 | | shelf | 35.5 | 57.46 | | house | 37.55 | 66.69 | | sea | 57.79 | 91.23 | | mirror | 61.33 | 76.17 | | rug | 75.17 | 85.61 | | field | 20.99 | 25.36 | | armchair | 36.51 | 59.89 | | seat | 51.35 | 62.07 | | fence | 25.81 | 36.93 | | desk | 31.87 | 41.08 | | rock | 37.36 | 58.37 | | wardrobe | 45.56 | 56.99 | | lamp | 62.4 | 71.04 | | bathtub | 70.74 | 92.8 | | railing | 30.97 | 43.46 | | cushion | 48.71 | 58.21 | | base | 10.32 | 13.05 | | box | 16.33 | 20.59 | | column | 49.64 | 75.08 | | signboard | 34.98 | 51.77 | | chest of drawers | 33.6 | 67.14 | | counter | 28.31 | 41.14 | | sand | 26.11 | 47.19 | | sink | 68.86 | 75.45 | | skyscraper | 42.57 | 51.58 | | fireplace | 64.07 | 90.06 | | refrigerator | 66.99 | 75.24 | | grandstand | 30.48 | 66.36 | | path | 26.64 | 43.65 | | stairs | 33.08 | 44.08 | | runway | 84.32 | 88.21 | | case | 57.67 | 64.4 | | pool table | 90.24 | 94.31 | | pillow | 47.02 | 61.41 | | screen door | 40.89 | 41.31 | | stairway | 31.4 | 38.94 | | river | 33.84 | 61.16 | | bridge | 22.44 | 25.38 | | bookcase | 21.69 | 46.95 | | blind | 13.65 | 14.62 | | coffee table | 61.53 | 81.48 | | toilet | 79.41 | 86.2 | | flower | 34.11 | 57.7 | | book | 46.73 | 77.12 | | hill | 5.79 | 8.08 | | bench | 31.3 | 33.99 | | countertop | 47.83 | 64.29 | | stove | 71.62 | 81.62 | | palm | 47.32 | 61.69 | | kitchen island | 30.09 | 72.87 | | computer | 49.82 | 54.28 | | swivel chair | 36.51 | 55.34 | | boat | 58.06 | 89.9 | | bar | 15.33 | 16.04 | | arcade machine | 43.83 | 66.66 | | hovel | 18.65 | 21.88 | | bus | 59.45 | 60.64 | | towel | 56.14 | 63.14 | | light | 57.3 | 74.33 | | truck | 21.37 | 43.39 | | tower | 21.19 | 35.4 | | chandelier | 63.72 | 78.67 | | awning | 17.18 | 47.48 | | streetlight | 32.62 | 58.08 | | booth | 33.28 | 33.46 | | television receiver | 62.37 | 80.3 | | airplane | 58.91 | 63.85 | | dirt track | 1.96 | 4.89 | | apparel | 25.15 | 43.91 | | pole | 13.54 | 16.54 | | land | 6.38 | 6.49 | | bannister | 10.2 | 13.02 | | escalator | 0.0 | 0.0 | | ottoman | 37.4 | 57.07 | | bottle | 22.42 | 28.08 | | buffet | 40.01 | 43.46 | | poster | 16.63 | 21.19 | | stage | 8.99 | 11.55 | | van | 10.32 | 14.41 | | ship | 0.31 | 0.31 | | fountain | 1.93 | 2.36 | | conveyer belt | 65.68 | 83.73 | | canopy | 12.39 | 15.04 | | washer | 62.28 | 64.29 | | plaything | 21.96 | 29.04 | | swimming pool | 18.32 | 31.18 | | stool | 41.44 | 46.07 | | barrel | 40.38 | 54.04 | | basket | 27.41 | 35.7 | | waterfall | 35.61 | 37.29 | | tent | 94.59 | 98.45 | | bag | 13.82 | 16.43 | | minibike | 66.2 | 77.4 | | cradle | 59.06 | 75.53 | | oven | 32.6 | 39.64 | | ball | 34.23 | 49.96 | | food | 53.69 | 72.71 | | step | 23.74 | 28.05 | | tank | 34.88 | 40.72 | | trade name | 23.55 | 26.68 | | microwave | 32.06 | 36.59 | | pot | 43.63 | 47.94 | | animal | 55.77 | 60.81 | | bicycle | 55.52 | 75.57 | | lake | 0.0 | 0.0 | | dishwasher | 52.8 | 59.39 | | screen | 74.15 | 85.52 | | blanket | 11.58 | 12.85 | | sculpture | 32.23 | 46.64 | | hood | 42.23 | 71.04 | | sconce | 48.21 | 62.26 | | vase | 33.56 | 51.84 | | traffic light | 27.34 | 30.71 | | tray | 2.48 | 2.52 | | ashcan | 38.29 | 47.36 | | fan | 56.68 | 69.5 | | pier | 42.44 | 45.58 | | crt screen | 4.69 | 11.19 | | plate | 47.76 | 75.94 | | monitor | 0.26 | 0.26 | | bulletin board | 0.0 | 0.0 | | shower | 6.42 | 12.4 | | radiator | 39.5 | 47.74 | | glass | 13.05 | 13.82 | | clock | 25.98 | 28.92 | | flag | 38.24 | 46.21 | +---------------------+-------+-------+ 2024/07/07 15:15:08 - mmengine - INFO - Iter(val) [500/500] aAcc: 80.5300 mIoU: 41.5800 mAcc: 53.2800 data_time: 0.0017 time: 0.2447 2024/07/07 15:16:03 - mmengine - INFO - Iter(train) [ 5050/120000] base_lr: 1.9914e-04 lr: 1.9921e-05 eta: 1 day, 11:41:10 time: 1.1160 data_time: 0.0155 memory: 15407 grad_norm: 2.3565 loss: 0.8147 semantic_segmentation_loss_cls: 0.3580 semantic_segmentation_loss_mask: 0.1410 semantic_segmentation_loss_dice: 0.3158 2024/07/07 15:16:59 - mmengine - INFO - Iter(train) [ 5100/120000] base_lr: 1.9912e-04 lr: 1.9920e-05 eta: 1 day, 11:40:17 time: 1.1162 data_time: 0.0155 memory: 15201 grad_norm: 2.3456 loss: 0.8106 semantic_segmentation_loss_cls: 0.3560 semantic_segmentation_loss_mask: 0.1402 semantic_segmentation_loss_dice: 0.3143 2024/07/07 15:17:55 - mmengine - INFO - Iter(train) [ 5150/120000] base_lr: 1.9910e-04 lr: 1.9918e-05 eta: 1 day, 11:39:22 time: 1.1164 data_time: 0.0155 memory: 14862 grad_norm: 2.3391 loss: 0.8069 semantic_segmentation_loss_cls: 0.3539 semantic_segmentation_loss_mask: 0.1398 semantic_segmentation_loss_dice: 0.3131 2024/07/07 15:18:52 - mmengine - INFO - Iter(train) [ 5200/120000] base_lr: 1.9908e-04 lr: 1.9917e-05 eta: 1 day, 11:38:39 time: 1.1167 data_time: 0.0156 memory: 15277 grad_norm: 2.3280 loss: 0.8028 semantic_segmentation_loss_cls: 0.3520 semantic_segmentation_loss_mask: 0.1390 semantic_segmentation_loss_dice: 0.3118 2024/07/07 15:19:48 - mmengine - INFO - Iter(train) [ 5250/120000] base_lr: 1.9907e-04 lr: 1.9915e-05 eta: 1 day, 11:37:46 time: 1.1170 data_time: 0.0156 memory: 15456 grad_norm: 2.3220 loss: 0.7988 semantic_segmentation_loss_cls: 0.3499 semantic_segmentation_loss_mask: 0.1385 semantic_segmentation_loss_dice: 0.3104 2024/07/07 15:20:43 - mmengine - INFO - Iter(train) [ 5300/120000] base_lr: 1.9905e-04 lr: 1.9914e-05 eta: 1 day, 11:36:43 time: 1.1170 data_time: 0.0156 memory: 14834 grad_norm: 2.3154 loss: 0.7947 semantic_segmentation_loss_cls: 0.3477 semantic_segmentation_loss_mask: 0.1379 semantic_segmentation_loss_dice: 0.3091 2024/07/07 15:21:40 - mmengine - INFO - Iter(train) [ 5350/120000] base_lr: 1.9903e-04 lr: 1.9912e-05 eta: 1 day, 11:35:58 time: 1.1173 data_time: 0.0156 memory: 15735 grad_norm: 2.3038 loss: 0.7908 semantic_segmentation_loss_cls: 0.3457 semantic_segmentation_loss_mask: 0.1373 semantic_segmentation_loss_dice: 0.3077 2024/07/07 15:22:35 - mmengine - INFO - Iter(train) [ 5400/120000] base_lr: 1.9901e-04 lr: 1.9910e-05 eta: 1 day, 11:34:57 time: 1.1175 data_time: 0.0156 memory: 15642 grad_norm: 2.2997 loss: 0.7871 semantic_segmentation_loss_cls: 0.3438 semantic_segmentation_loss_mask: 0.1368 semantic_segmentation_loss_dice: 0.3065 2024/07/07 15:23:32 - mmengine - INFO - Iter(train) [ 5450/120000] base_lr: 1.9899e-04 lr: 1.9909e-05 eta: 1 day, 11:34:12 time: 1.1179 data_time: 0.0157 memory: 15309 grad_norm: 2.2941 loss: 0.7841 semantic_segmentation_loss_cls: 0.3424 semantic_segmentation_loss_mask: 0.1363 semantic_segmentation_loss_dice: 0.3055 2024/07/07 15:24:29 - mmengine - INFO - Iter(train) [ 5500/120000] base_lr: 1.9898e-04 lr: 1.9907e-05 eta: 1 day, 11:33:54 time: 1.1186 data_time: 0.0156 memory: 15017 grad_norm: 2.2889 loss: 0.7803 semantic_segmentation_loss_cls: 0.3405 semantic_segmentation_loss_mask: 0.1358 semantic_segmentation_loss_dice: 0.3040 2024/07/07 15:25:27 - mmengine - INFO - Iter(train) [ 5550/120000] base_lr: 1.9896e-04 lr: 1.9905e-05 eta: 1 day, 11:33:30 time: 1.1191 data_time: 0.0156 memory: 16000 grad_norm: 2.2825 loss: 0.7774 semantic_segmentation_loss_cls: 0.3391 semantic_segmentation_loss_mask: 0.1352 semantic_segmentation_loss_dice: 0.3031 2024/07/07 15:26:22 - mmengine - INFO - Iter(train) [ 5600/120000] base_lr: 1.9894e-04 lr: 1.9903e-05 eta: 1 day, 11:32:22 time: 1.1191 data_time: 0.0156 memory: 14770 grad_norm: 2.2739 loss: 0.7734 semantic_segmentation_loss_cls: 0.3372 semantic_segmentation_loss_mask: 0.1346 semantic_segmentation_loss_dice: 0.3016 2024/07/07 15:27:18 - mmengine - INFO - Iter(train) [ 5650/120000] base_lr: 1.9892e-04 lr: 1.9902e-05 eta: 1 day, 11:31:27 time: 1.1193 data_time: 0.0157 memory: 15983 grad_norm: 2.2534 loss: 0.7710 semantic_segmentation_loss_cls: 0.3360 semantic_segmentation_loss_mask: 0.1341 semantic_segmentation_loss_dice: 0.3009 2024/07/07 15:28:13 - mmengine - INFO - Iter(train) [ 5700/120000] base_lr: 1.9890e-04 lr: 1.9900e-05 eta: 1 day, 11:30:15 time: 1.1192 data_time: 0.0157 memory: 14721 grad_norm: 2.2477 loss: 0.7685 semantic_segmentation_loss_cls: 0.3347 semantic_segmentation_loss_mask: 0.1337 semantic_segmentation_loss_dice: 0.3001 2024/07/07 15:29:09 - mmengine - INFO - Iter(train) [ 5750/120000] base_lr: 1.9888e-04 lr: 1.9898e-05 eta: 1 day, 11:29:17 time: 1.1192 data_time: 0.0157 memory: 14860 grad_norm: 2.2408 loss: 0.7653 semantic_segmentation_loss_cls: 0.3332 semantic_segmentation_loss_mask: 0.1333 semantic_segmentation_loss_dice: 0.2989 2024/07/07 15:30:05 - mmengine - INFO - Iter(train) [ 5800/120000] base_lr: 1.9886e-04 lr: 1.9896e-05 eta: 1 day, 11:28:09 time: 1.1193 data_time: 0.0157 memory: 15310 grad_norm: 2.2352 loss: 0.7628 semantic_segmentation_loss_cls: 0.3320 semantic_segmentation_loss_mask: 0.1329 semantic_segmentation_loss_dice: 0.2980 2024/07/07 15:31:02 - mmengine - INFO - Iter(train) [ 5850/120000] base_lr: 1.9884e-04 lr: 1.9895e-05 eta: 1 day, 11:27:40 time: 1.1197 data_time: 0.0158 memory: 14722 grad_norm: 2.2315 loss: 0.7608 semantic_segmentation_loss_cls: 0.3309 semantic_segmentation_loss_mask: 0.1326 semantic_segmentation_loss_dice: 0.2973 2024/07/07 15:31:58 - mmengine - INFO - Iter(train) [ 5900/120000] base_lr: 1.9882e-04 lr: 1.9893e-05 eta: 1 day, 11:26:54 time: 1.1197 data_time: 0.0159 memory: 15314 grad_norm: 2.2256 loss: 0.7595 semantic_segmentation_loss_cls: 0.3304 semantic_segmentation_loss_mask: 0.1323 semantic_segmentation_loss_dice: 0.2968 2024/07/07 15:32:54 - mmengine - INFO - Iter(train) [ 5950/120000] base_lr: 1.9880e-04 lr: 1.9891e-05 eta: 1 day, 11:25:56 time: 1.1196 data_time: 0.0159 memory: 15057 grad_norm: 2.2206 loss: 0.7569 semantic_segmentation_loss_cls: 0.3290 semantic_segmentation_loss_mask: 0.1318 semantic_segmentation_loss_dice: 0.2960 2024/07/07 15:33:51 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 15:33:51 - mmengine - INFO - Iter(train) [ 6000/120000] base_lr: 1.9878e-04 lr: 1.9889e-05 eta: 1 day, 11:25:12 time: 1.1200 data_time: 0.0159 memory: 14812 grad_norm: 2.2156 loss: 0.7537 semantic_segmentation_loss_cls: 0.3273 semantic_segmentation_loss_mask: 0.1315 semantic_segmentation_loss_dice: 0.2949 2024/07/07 15:33:51 - mmengine - INFO - Saving checkpoint at 6000 iterations 2024/07/07 15:34:52 - mmengine - INFO - Iter(train) [ 6050/120000] base_lr: 1.9876e-04 lr: 1.9887e-05 eta: 1 day, 11:26:04 time: 1.1205 data_time: 0.0160 memory: 14963 grad_norm: 2.2122 loss: 0.7504 semantic_segmentation_loss_cls: 0.3256 semantic_segmentation_loss_mask: 0.1311 semantic_segmentation_loss_dice: 0.2938 2024/07/07 15:35:49 - mmengine - INFO - Iter(train) [ 6100/120000] base_lr: 1.9874e-04 lr: 1.9886e-05 eta: 1 day, 11:25:20 time: 1.1209 data_time: 0.0160 memory: 15468 grad_norm: 2.2056 loss: 0.7475 semantic_segmentation_loss_cls: 0.3242 semantic_segmentation_loss_mask: 0.1304 semantic_segmentation_loss_dice: 0.2929 2024/07/07 15:36:44 - mmengine - INFO - Iter(train) [ 6150/120000] base_lr: 1.9872e-04 lr: 1.9884e-05 eta: 1 day, 11:24:15 time: 1.1209 data_time: 0.0161 memory: 14379 grad_norm: 2.2012 loss: 0.7444 semantic_segmentation_loss_cls: 0.3226 semantic_segmentation_loss_mask: 0.1299 semantic_segmentation_loss_dice: 0.2918 2024/07/07 15:37:40 - mmengine - INFO - Iter(train) [ 6200/120000] base_lr: 1.9870e-04 lr: 1.9882e-05 eta: 1 day, 11:23:06 time: 1.1209 data_time: 0.0161 memory: 15351 grad_norm: 2.1977 loss: 0.7413 semantic_segmentation_loss_cls: 0.3210 semantic_segmentation_loss_mask: 0.1295 semantic_segmentation_loss_dice: 0.2908 2024/07/07 15:38:35 - mmengine - INFO - Iter(train) [ 6250/120000] base_lr: 1.9868e-04 lr: 1.9880e-05 eta: 1 day, 11:22:00 time: 1.1210 data_time: 0.0161 memory: 15841 grad_norm: 2.1932 loss: 0.7387 semantic_segmentation_loss_cls: 0.3198 semantic_segmentation_loss_mask: 0.1291 semantic_segmentation_loss_dice: 0.2899 2024/07/07 15:39:30 - mmengine - INFO - Iter(train) [ 6300/120000] base_lr: 1.9866e-04 lr: 1.9878e-05 eta: 1 day, 11:20:48 time: 1.1207 data_time: 0.0161 memory: 15739 grad_norm: 2.1906 loss: 0.7370 semantic_segmentation_loss_cls: 0.3189 semantic_segmentation_loss_mask: 0.1287 semantic_segmentation_loss_dice: 0.2894 2024/07/07 15:40:26 - mmengine - INFO - Iter(train) [ 6350/120000] base_lr: 1.9864e-04 lr: 1.9876e-05 eta: 1 day, 11:19:45 time: 1.1205 data_time: 0.0160 memory: 15957 grad_norm: 2.1858 loss: 0.7331 semantic_segmentation_loss_cls: 0.3169 semantic_segmentation_loss_mask: 0.1282 semantic_segmentation_loss_dice: 0.2881 2024/07/07 15:41:21 - mmengine - INFO - Iter(train) [ 6400/120000] base_lr: 1.9861e-04 lr: 1.9874e-05 eta: 1 day, 11:18:38 time: 1.1207 data_time: 0.0160 memory: 14554 grad_norm: 2.1880 loss: 0.7302 semantic_segmentation_loss_cls: 0.3153 semantic_segmentation_loss_mask: 0.1278 semantic_segmentation_loss_dice: 0.2872 2024/07/07 15:42:17 - mmengine - INFO - Iter(train) [ 6450/120000] base_lr: 1.9859e-04 lr: 1.9872e-05 eta: 1 day, 11:17:39 time: 1.1210 data_time: 0.0161 memory: 15292 grad_norm: 2.1905 loss: 0.7276 semantic_segmentation_loss_cls: 0.3138 semantic_segmentation_loss_mask: 0.1275 semantic_segmentation_loss_dice: 0.2863 2024/07/07 15:43:12 - mmengine - INFO - Iter(train) [ 6500/120000] base_lr: 1.9857e-04 lr: 1.9870e-05 eta: 1 day, 11:16:33 time: 1.1209 data_time: 0.0161 memory: 15397 grad_norm: 2.1886 loss: 0.7246 semantic_segmentation_loss_cls: 0.3124 semantic_segmentation_loss_mask: 0.1269 semantic_segmentation_loss_dice: 0.2854 2024/07/07 15:44:07 - mmengine - INFO - Iter(train) [ 6550/120000] base_lr: 1.9855e-04 lr: 1.9868e-05 eta: 1 day, 11:15:20 time: 1.1208 data_time: 0.0161 memory: 15197 grad_norm: 2.1884 loss: 0.7231 semantic_segmentation_loss_cls: 0.3115 semantic_segmentation_loss_mask: 0.1268 semantic_segmentation_loss_dice: 0.2849 2024/07/07 15:45:02 - mmengine - INFO - Iter(train) [ 6600/120000] base_lr: 1.9853e-04 lr: 1.9866e-05 eta: 1 day, 11:14:12 time: 1.1207 data_time: 0.0161 memory: 15060 grad_norm: 2.1853 loss: 0.7210 semantic_segmentation_loss_cls: 0.3104 semantic_segmentation_loss_mask: 0.1264 semantic_segmentation_loss_dice: 0.2842 2024/07/07 15:45:57 - mmengine - INFO - Iter(train) [ 6650/120000] base_lr: 1.9850e-04 lr: 1.9864e-05 eta: 1 day, 11:12:59 time: 1.1206 data_time: 0.0161 memory: 15048 grad_norm: 2.1805 loss: 0.7188 semantic_segmentation_loss_cls: 0.3092 semantic_segmentation_loss_mask: 0.1260 semantic_segmentation_loss_dice: 0.2835 2024/07/07 15:46:53 - mmengine - INFO - Iter(train) [ 6700/120000] base_lr: 1.9848e-04 lr: 1.9862e-05 eta: 1 day, 11:12:00 time: 1.1206 data_time: 0.0161 memory: 15238 grad_norm: 2.1651 loss: 0.7164 semantic_segmentation_loss_cls: 0.3080 semantic_segmentation_loss_mask: 0.1257 semantic_segmentation_loss_dice: 0.2827 2024/07/07 15:47:48 - mmengine - INFO - Iter(train) [ 6750/120000] base_lr: 1.9846e-04 lr: 1.9860e-05 eta: 1 day, 11:10:50 time: 1.1204 data_time: 0.0161 memory: 15191 grad_norm: 2.1633 loss: 0.7142 semantic_segmentation_loss_cls: 0.3068 semantic_segmentation_loss_mask: 0.1254 semantic_segmentation_loss_dice: 0.2820 2024/07/07 15:48:43 - mmengine - INFO - Iter(train) [ 6800/120000] base_lr: 1.9844e-04 lr: 1.9858e-05 eta: 1 day, 11:09:42 time: 1.1202 data_time: 0.0161 memory: 15042 grad_norm: 2.1581 loss: 0.7117 semantic_segmentation_loss_cls: 0.3054 semantic_segmentation_loss_mask: 0.1251 semantic_segmentation_loss_dice: 0.2812 2024/07/07 15:49:39 - mmengine - INFO - Iter(train) [ 6850/120000] base_lr: 1.9841e-04 lr: 1.9856e-05 eta: 1 day, 11:08:43 time: 1.1203 data_time: 0.0161 memory: 15174 grad_norm: 2.1556 loss: 0.7104 semantic_segmentation_loss_cls: 0.3046 semantic_segmentation_loss_mask: 0.1248 semantic_segmentation_loss_dice: 0.2810 2024/07/07 15:50:35 - mmengine - INFO - Iter(train) [ 6900/120000] base_lr: 1.9839e-04 lr: 1.9854e-05 eta: 1 day, 11:07:40 time: 1.1201 data_time: 0.0160 memory: 14718 grad_norm: 2.1517 loss: 0.7080 semantic_segmentation_loss_cls: 0.3035 semantic_segmentation_loss_mask: 0.1244 semantic_segmentation_loss_dice: 0.2801 2024/07/07 15:51:31 - mmengine - INFO - Iter(train) [ 6950/120000] base_lr: 1.9837e-04 lr: 1.9851e-05 eta: 1 day, 11:06:48 time: 1.1200 data_time: 0.0160 memory: 14797 grad_norm: 2.1476 loss: 0.7068 semantic_segmentation_loss_cls: 0.3028 semantic_segmentation_loss_mask: 0.1242 semantic_segmentation_loss_dice: 0.2798 2024/07/07 15:52:26 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 15:52:26 - mmengine - INFO - Iter(train) [ 7000/120000] base_lr: 1.9834e-04 lr: 1.9849e-05 eta: 1 day, 11:05:44 time: 1.1196 data_time: 0.0160 memory: 15201 grad_norm: 2.1446 loss: 0.7044 semantic_segmentation_loss_cls: 0.3016 semantic_segmentation_loss_mask: 0.1238 semantic_segmentation_loss_dice: 0.2790 2024/07/07 15:52:26 - mmengine - INFO - Saving checkpoint at 7000 iterations 2024/07/07 15:53:26 - mmengine - INFO - Iter(train) [ 7050/120000] base_lr: 1.9832e-04 lr: 1.9847e-05 eta: 1 day, 11:05:50 time: 1.1194 data_time: 0.0160 memory: 15011 grad_norm: 2.1454 loss: 0.7022 semantic_segmentation_loss_cls: 0.3005 semantic_segmentation_loss_mask: 0.1235 semantic_segmentation_loss_dice: 0.2783 2024/07/07 15:54:21 - mmengine - INFO - Iter(train) [ 7100/120000] base_lr: 1.9830e-04 lr: 1.9845e-05 eta: 1 day, 11:04:46 time: 1.1195 data_time: 0.0160 memory: 15121 grad_norm: 2.1438 loss: 0.6997 semantic_segmentation_loss_cls: 0.2992 semantic_segmentation_loss_mask: 0.1231 semantic_segmentation_loss_dice: 0.2774 2024/07/07 15:55:16 - mmengine - INFO - Iter(train) [ 7150/120000] base_lr: 1.9827e-04 lr: 1.9843e-05 eta: 1 day, 11:03:37 time: 1.1191 data_time: 0.0160 memory: 14988 grad_norm: 2.1458 loss: 0.6981 semantic_segmentation_loss_cls: 0.2983 semantic_segmentation_loss_mask: 0.1230 semantic_segmentation_loss_dice: 0.2768 2024/07/07 15:56:12 - mmengine - INFO - Iter(train) [ 7200/120000] base_lr: 1.9825e-04 lr: 1.9841e-05 eta: 1 day, 11:02:35 time: 1.1188 data_time: 0.0160 memory: 15078 grad_norm: 2.1451 loss: 0.6970 semantic_segmentation_loss_cls: 0.2977 semantic_segmentation_loss_mask: 0.1228 semantic_segmentation_loss_dice: 0.2765 2024/07/07 15:57:08 - mmengine - INFO - Iter(train) [ 7250/120000] base_lr: 1.9822e-04 lr: 1.9838e-05 eta: 1 day, 11:01:36 time: 1.1186 data_time: 0.0160 memory: 15778 grad_norm: 2.1426 loss: 0.6955 semantic_segmentation_loss_cls: 0.2968 semantic_segmentation_loss_mask: 0.1226 semantic_segmentation_loss_dice: 0.2761 2024/07/07 15:58:03 - mmengine - INFO - Iter(train) [ 7300/120000] base_lr: 1.9820e-04 lr: 1.9836e-05 eta: 1 day, 11:00:38 time: 1.1186 data_time: 0.0160 memory: 14602 grad_norm: 2.1421 loss: 0.6932 semantic_segmentation_loss_cls: 0.2955 semantic_segmentation_loss_mask: 0.1224 semantic_segmentation_loss_dice: 0.2753 2024/07/07 15:58:59 - mmengine - INFO - Iter(train) [ 7350/120000] base_lr: 1.9817e-04 lr: 1.9834e-05 eta: 1 day, 10:59:37 time: 1.1185 data_time: 0.0160 memory: 15066 grad_norm: 2.1404 loss: 0.6910 semantic_segmentation_loss_cls: 0.2943 semantic_segmentation_loss_mask: 0.1221 semantic_segmentation_loss_dice: 0.2746 2024/07/07 15:59:54 - mmengine - INFO - Iter(train) [ 7400/120000] base_lr: 1.9815e-04 lr: 1.9832e-05 eta: 1 day, 10:58:31 time: 1.1182 data_time: 0.0160 memory: 14303 grad_norm: 2.1394 loss: 0.6889 semantic_segmentation_loss_cls: 0.2932 semantic_segmentation_loss_mask: 0.1219 semantic_segmentation_loss_dice: 0.2738 2024/07/07 16:00:51 - mmengine - INFO - Iter(train) [ 7450/120000] base_lr: 1.9812e-04 lr: 1.9829e-05 eta: 1 day, 10:57:39 time: 1.1184 data_time: 0.0160 memory: 14641 grad_norm: 2.1368 loss: 0.6868 semantic_segmentation_loss_cls: 0.2920 semantic_segmentation_loss_mask: 0.1216 semantic_segmentation_loss_dice: 0.2731 2024/07/07 16:01:47 - mmengine - INFO - Iter(train) [ 7500/120000] base_lr: 1.9810e-04 lr: 1.9827e-05 eta: 1 day, 10:56:47 time: 1.1186 data_time: 0.0160 memory: 14832 grad_norm: 2.1349 loss: 0.6849 semantic_segmentation_loss_cls: 0.2909 semantic_segmentation_loss_mask: 0.1215 semantic_segmentation_loss_dice: 0.2725 2024/07/07 16:02:42 - mmengine - INFO - Iter(train) [ 7550/120000] base_lr: 1.9807e-04 lr: 1.9825e-05 eta: 1 day, 10:55:43 time: 1.1185 data_time: 0.0160 memory: 15543 grad_norm: 2.1291 loss: 0.6831 semantic_segmentation_loss_cls: 0.2900 semantic_segmentation_loss_mask: 0.1212 semantic_segmentation_loss_dice: 0.2719 2024/07/07 16:03:38 - mmengine - INFO - Iter(train) [ 7600/120000] base_lr: 1.9805e-04 lr: 1.9822e-05 eta: 1 day, 10:54:47 time: 1.1185 data_time: 0.0160 memory: 15151 grad_norm: 2.1227 loss: 0.6817 semantic_segmentation_loss_cls: 0.2893 semantic_segmentation_loss_mask: 0.1209 semantic_segmentation_loss_dice: 0.2714 2024/07/07 16:04:33 - mmengine - INFO - Iter(train) [ 7650/120000] base_lr: 1.9802e-04 lr: 1.9820e-05 eta: 1 day, 10:53:43 time: 1.1184 data_time: 0.0160 memory: 15456 grad_norm: 2.1223 loss: 0.6803 semantic_segmentation_loss_cls: 0.2887 semantic_segmentation_loss_mask: 0.1206 semantic_segmentation_loss_dice: 0.2710 2024/07/07 16:05:29 - mmengine - INFO - Iter(train) [ 7700/120000] base_lr: 1.9800e-04 lr: 1.9818e-05 eta: 1 day, 10:52:44 time: 1.1183 data_time: 0.0160 memory: 15569 grad_norm: 2.1208 loss: 0.6782 semantic_segmentation_loss_cls: 0.2876 semantic_segmentation_loss_mask: 0.1203 semantic_segmentation_loss_dice: 0.2703 2024/07/07 16:06:25 - mmengine - INFO - Iter(train) [ 7750/120000] base_lr: 1.9797e-04 lr: 1.9815e-05 eta: 1 day, 10:51:46 time: 1.1182 data_time: 0.0160 memory: 16302 grad_norm: 2.1182 loss: 0.6766 semantic_segmentation_loss_cls: 0.2867 semantic_segmentation_loss_mask: 0.1200 semantic_segmentation_loss_dice: 0.2699 2024/07/07 16:07:19 - mmengine - INFO - Iter(train) [ 7800/120000] base_lr: 1.9794e-04 lr: 1.9813e-05 eta: 1 day, 10:50:32 time: 1.1178 data_time: 0.0160 memory: 15002 grad_norm: 2.1166 loss: 0.6760 semantic_segmentation_loss_cls: 0.2863 semantic_segmentation_loss_mask: 0.1198 semantic_segmentation_loss_dice: 0.2698 2024/07/07 16:08:15 - mmengine - INFO - Iter(train) [ 7850/120000] base_lr: 1.9792e-04 lr: 1.9811e-05 eta: 1 day, 10:49:32 time: 1.1177 data_time: 0.0159 memory: 16820 grad_norm: 2.1153 loss: 0.6735 semantic_segmentation_loss_cls: 0.2850 semantic_segmentation_loss_mask: 0.1195 semantic_segmentation_loss_dice: 0.2689 2024/07/07 16:09:11 - mmengine - INFO - Iter(train) [ 7900/120000] base_lr: 1.9789e-04 lr: 1.9808e-05 eta: 1 day, 10:48:39 time: 1.1179 data_time: 0.0159 memory: 15356 grad_norm: 2.1115 loss: 0.6718 semantic_segmentation_loss_cls: 0.2841 semantic_segmentation_loss_mask: 0.1193 semantic_segmentation_loss_dice: 0.2684 2024/07/07 16:10:07 - mmengine - INFO - Iter(train) [ 7950/120000] base_lr: 1.9786e-04 lr: 1.9806e-05 eta: 1 day, 10:47:36 time: 1.1177 data_time: 0.0159 memory: 15067 grad_norm: 2.1105 loss: 0.6702 semantic_segmentation_loss_cls: 0.2834 semantic_segmentation_loss_mask: 0.1189 semantic_segmentation_loss_dice: 0.2679 2024/07/07 16:11:03 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 16:11:03 - mmengine - INFO - Iter(train) [ 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mmengine - INFO - Iter(train) [ 8150/120000] base_lr: 1.9776e-04 lr: 1.9796e-05 eta: 1 day, 10:44:30 time: 1.1176 data_time: 0.0159 memory: 14984 grad_norm: 2.1039 loss: 0.6630 semantic_segmentation_loss_cls: 0.2794 semantic_segmentation_loss_mask: 0.1179 semantic_segmentation_loss_dice: 0.2657 2024/07/07 16:14:49 - mmengine - INFO - Iter(train) [ 8200/120000] base_lr: 1.9773e-04 lr: 1.9793e-05 eta: 1 day, 10:43:31 time: 1.1177 data_time: 0.0158 memory: 15033 grad_norm: 2.1023 loss: 0.6612 semantic_segmentation_loss_cls: 0.2785 semantic_segmentation_loss_mask: 0.1176 semantic_segmentation_loss_dice: 0.2651 2024/07/07 16:15:44 - mmengine - INFO - Iter(train) [ 8250/120000] base_lr: 1.9770e-04 lr: 1.9791e-05 eta: 1 day, 10:42:25 time: 1.1173 data_time: 0.0158 memory: 15395 grad_norm: 2.1008 loss: 0.6602 semantic_segmentation_loss_cls: 0.2780 semantic_segmentation_loss_mask: 0.1175 semantic_segmentation_loss_dice: 0.2648 2024/07/07 16:16:39 - mmengine - INFO - Iter(train) [ 8300/120000] base_lr: 1.9767e-04 lr: 1.9788e-05 eta: 1 day, 10:41:14 time: 1.1168 data_time: 0.0158 memory: 14738 grad_norm: 2.1007 loss: 0.6586 semantic_segmentation_loss_cls: 0.2771 semantic_segmentation_loss_mask: 0.1172 semantic_segmentation_loss_dice: 0.2642 2024/07/07 16:17:33 - mmengine - INFO - Iter(train) [ 8350/120000] base_lr: 1.9764e-04 lr: 1.9786e-05 eta: 1 day, 10:40:04 time: 1.1165 data_time: 0.0158 memory: 15086 grad_norm: 2.0999 loss: 0.6577 semantic_segmentation_loss_cls: 0.2767 semantic_segmentation_loss_mask: 0.1170 semantic_segmentation_loss_dice: 0.2639 2024/07/07 16:18:29 - mmengine - INFO - Iter(train) [ 8400/120000] base_lr: 1.9762e-04 lr: 1.9783e-05 eta: 1 day, 10:39:02 time: 1.1164 data_time: 0.0157 memory: 14632 grad_norm: 2.0979 loss: 0.6559 semantic_segmentation_loss_cls: 0.2758 semantic_segmentation_loss_mask: 0.1168 semantic_segmentation_loss_dice: 0.2634 2024/07/07 16:19:25 - mmengine - INFO - Iter(train) [ 8450/120000] base_lr: 1.9759e-04 lr: 1.9781e-05 eta: 1 day, 10:38:16 time: 1.1164 data_time: 0.0157 memory: 15436 grad_norm: 2.0959 loss: 0.6542 semantic_segmentation_loss_cls: 0.2748 semantic_segmentation_loss_mask: 0.1165 semantic_segmentation_loss_dice: 0.2628 2024/07/07 16:20:21 - mmengine - INFO - Iter(train) [ 8500/120000] base_lr: 1.9756e-04 lr: 1.9778e-05 eta: 1 day, 10:37:09 time: 1.1162 data_time: 0.0157 memory: 15391 grad_norm: 2.0946 loss: 0.6530 semantic_segmentation_loss_cls: 0.2741 semantic_segmentation_loss_mask: 0.1163 semantic_segmentation_loss_dice: 0.2625 2024/07/07 16:21:17 - mmengine - INFO - Iter(train) [ 8550/120000] base_lr: 1.9753e-04 lr: 1.9776e-05 eta: 1 day, 10:36:24 time: 1.1167 data_time: 0.0157 memory: 14419 grad_norm: 2.0923 loss: 0.6506 semantic_segmentation_loss_cls: 0.2729 semantic_segmentation_loss_mask: 0.1160 semantic_segmentation_loss_dice: 0.2617 2024/07/07 16:22:12 - mmengine - INFO - Iter(train) [ 8600/120000] base_lr: 1.9750e-04 lr: 1.9773e-05 eta: 1 day, 10:35:18 time: 1.1171 data_time: 0.0157 memory: 15101 grad_norm: 2.0912 loss: 0.6487 semantic_segmentation_loss_cls: 0.2717 semantic_segmentation_loss_mask: 0.1158 semantic_segmentation_loss_dice: 0.2612 2024/07/07 16:23:07 - mmengine - INFO - Iter(train) [ 8650/120000] base_lr: 1.9747e-04 lr: 1.9770e-05 eta: 1 day, 10:34:07 time: 1.1172 data_time: 0.0157 memory: 15467 grad_norm: 2.0897 loss: 0.6475 semantic_segmentation_loss_cls: 0.2709 semantic_segmentation_loss_mask: 0.1156 semantic_segmentation_loss_dice: 0.2609 2024/07/07 16:24:02 - mmengine - INFO - Iter(train) [ 8700/120000] base_lr: 1.9744e-04 lr: 1.9768e-05 eta: 1 day, 10:32:59 time: 1.1170 data_time: 0.0157 memory: 15202 grad_norm: 2.0889 loss: 0.6454 semantic_segmentation_loss_cls: 0.2697 semantic_segmentation_loss_mask: 0.1153 semantic_segmentation_loss_dice: 0.2604 2024/07/07 16:24:57 - mmengine - INFO - Iter(train) [ 8750/120000] base_lr: 1.9741e-04 lr: 1.9765e-05 eta: 1 day, 10:31:51 time: 1.1169 data_time: 0.0157 memory: 15022 grad_norm: 2.0852 loss: 0.6439 semantic_segmentation_loss_cls: 0.2691 semantic_segmentation_loss_mask: 0.1150 semantic_segmentation_loss_dice: 0.2599 2024/07/07 16:25:53 - mmengine - INFO - Iter(train) [ 8800/120000] base_lr: 1.9738e-04 lr: 1.9762e-05 eta: 1 day, 10:30:56 time: 1.1171 data_time: 0.0157 memory: 14874 grad_norm: 2.0819 loss: 0.6425 semantic_segmentation_loss_cls: 0.2683 semantic_segmentation_loss_mask: 0.1148 semantic_segmentation_loss_dice: 0.2594 2024/07/07 16:26:48 - mmengine - INFO - Iter(train) [ 8850/120000] base_lr: 1.9736e-04 lr: 1.9760e-05 eta: 1 day, 10:29:51 time: 1.1169 data_time: 0.0157 memory: 15152 grad_norm: 2.0803 loss: 0.6407 semantic_segmentation_loss_cls: 0.2673 semantic_segmentation_loss_mask: 0.1145 semantic_segmentation_loss_dice: 0.2589 2024/07/07 16:27:44 - mmengine - INFO - Iter(train) [ 8900/120000] base_lr: 1.9733e-04 lr: 1.9757e-05 eta: 1 day, 10:28:56 time: 1.1170 data_time: 0.0157 memory: 15610 grad_norm: 2.0780 loss: 0.6396 semantic_segmentation_loss_cls: 0.2667 semantic_segmentation_loss_mask: 0.1143 semantic_segmentation_loss_dice: 0.2586 2024/07/07 16:28:39 - mmengine - INFO - Iter(train) [ 8950/120000] base_lr: 1.9730e-04 lr: 1.9754e-05 eta: 1 day, 10:27:49 time: 1.1167 data_time: 0.0157 memory: 15087 grad_norm: 2.0759 loss: 0.6379 semantic_segmentation_loss_cls: 0.2659 semantic_segmentation_loss_mask: 0.1140 semantic_segmentation_loss_dice: 0.2580 2024/07/07 16:29:35 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 16:29:35 - mmengine - INFO - Iter(train) [ 9000/120000] base_lr: 1.9727e-04 lr: 1.9751e-05 eta: 1 day, 10:26:55 time: 1.1168 data_time: 0.0157 memory: 15207 grad_norm: 2.0752 loss: 0.6362 semantic_segmentation_loss_cls: 0.2651 semantic_segmentation_loss_mask: 0.1138 semantic_segmentation_loss_dice: 0.2574 2024/07/07 16:29:35 - mmengine - INFO - Saving checkpoint at 9000 iterations 2024/07/07 16:30:36 - mmengine - INFO - Iter(train) [ 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data_time: 0.0166 memory: 14668 grad_norm: 2.0651 loss: 0.6288 semantic_segmentation_loss_cls: 0.2609 semantic_segmentation_loss_mask: 0.1126 semantic_segmentation_loss_dice: 0.2553 2024/07/07 16:37:08 - mmengine - INFO - Iter(train) [ 9400/120000] base_lr: 1.9702e-04 lr: 1.9729e-05 eta: 1 day, 10:20:37 time: 1.1180 data_time: 0.0166 memory: 16095 grad_norm: 2.0623 loss: 0.6278 semantic_segmentation_loss_cls: 0.2603 semantic_segmentation_loss_mask: 0.1125 semantic_segmentation_loss_dice: 0.2550 2024/07/07 16:38:03 - mmengine - INFO - Iter(train) [ 9450/120000] base_lr: 1.9699e-04 lr: 1.9726e-05 eta: 1 day, 10:19:35 time: 1.1178 data_time: 0.0166 memory: 15571 grad_norm: 2.0608 loss: 0.6257 semantic_segmentation_loss_cls: 0.2592 semantic_segmentation_loss_mask: 0.1123 semantic_segmentation_loss_dice: 0.2542 2024/07/07 16:38:58 - mmengine - INFO - Iter(train) [ 9500/120000] base_lr: 1.9695e-04 lr: 1.9723e-05 eta: 1 day, 10:18:32 time: 1.1172 data_time: 0.0166 memory: 15392 grad_norm: 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semantic_segmentation_loss_cls: 0.2565 semantic_segmentation_loss_mask: 0.1115 semantic_segmentation_loss_dice: 0.2527 2024/07/07 16:42:38 - mmengine - INFO - Iter(train) [ 9700/120000] base_lr: 1.9683e-04 lr: 1.9711e-05 eta: 1 day, 10:14:04 time: 1.1161 data_time: 0.0167 memory: 14759 grad_norm: 2.0539 loss: 0.6191 semantic_segmentation_loss_cls: 0.2557 semantic_segmentation_loss_mask: 0.1113 semantic_segmentation_loss_dice: 0.2521 2024/07/07 16:43:34 - mmengine - INFO - Iter(train) [ 9750/120000] base_lr: 1.9679e-04 lr: 1.9708e-05 eta: 1 day, 10:13:06 time: 1.1161 data_time: 0.0166 memory: 14862 grad_norm: 2.0532 loss: 0.6179 semantic_segmentation_loss_cls: 0.2550 semantic_segmentation_loss_mask: 0.1111 semantic_segmentation_loss_dice: 0.2518 2024/07/07 16:44:29 - mmengine - INFO - Iter(train) [ 9800/120000] base_lr: 1.9676e-04 lr: 1.9705e-05 eta: 1 day, 10:12:03 time: 1.1160 data_time: 0.0166 memory: 15212 grad_norm: 2.0532 loss: 0.6164 semantic_segmentation_loss_cls: 0.2541 semantic_segmentation_loss_mask: 0.1110 semantic_segmentation_loss_dice: 0.2514 2024/07/07 16:45:24 - mmengine - INFO - Iter(train) [ 9850/120000] base_lr: 1.9673e-04 lr: 1.9702e-05 eta: 1 day, 10:11:01 time: 1.1155 data_time: 0.0166 memory: 14881 grad_norm: 2.0531 loss: 0.6152 semantic_segmentation_loss_cls: 0.2535 semantic_segmentation_loss_mask: 0.1108 semantic_segmentation_loss_dice: 0.2509 2024/07/07 16:46:20 - mmengine - INFO - Iter(train) [ 9900/120000] base_lr: 1.9669e-04 lr: 1.9699e-05 eta: 1 day, 10:09:59 time: 1.1153 data_time: 0.0165 memory: 15396 grad_norm: 2.0522 loss: 0.6134 semantic_segmentation_loss_cls: 0.2526 semantic_segmentation_loss_mask: 0.1105 semantic_segmentation_loss_dice: 0.2504 2024/07/07 16:47:15 - mmengine - INFO - Iter(train) [ 9950/120000] base_lr: 1.9666e-04 lr: 1.9696e-05 eta: 1 day, 10:08:53 time: 1.1151 data_time: 0.0164 memory: 15395 grad_norm: 2.0513 loss: 0.6129 semantic_segmentation_loss_cls: 0.2523 semantic_segmentation_loss_mask: 0.1104 semantic_segmentation_loss_dice: 0.2502 2024/07/07 16:48:10 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 16:48:10 - mmengine - INFO - Iter(train) [ 10000/120000] base_lr: 1.9663e-04 lr: 1.9693e-05 eta: 1 day, 10:07:51 time: 1.1147 data_time: 0.0164 memory: 15274 grad_norm: 2.0504 loss: 0.6126 semantic_segmentation_loss_cls: 0.2521 semantic_segmentation_loss_mask: 0.1103 semantic_segmentation_loss_dice: 0.2502 2024/07/07 16:48:10 - mmengine - INFO - Saving checkpoint at 10000 iterations 2024/07/07 16:48:27 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:50 time: 0.2448 data_time: 0.0017 memory: 5013 2024/07/07 16:48:39 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:37 time: 0.2445 data_time: 0.0016 memory: 5189 2024/07/07 16:48:51 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:25 time: 0.2448 data_time: 0.0016 memory: 4460 2024/07/07 16:49:03 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:13 time: 0.2448 data_time: 0.0016 memory: 4543 2024/07/07 16:49:16 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:01 time: 0.2449 data_time: 0.0016 memory: 4643 2024/07/07 16:49:28 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:49 time: 0.2451 data_time: 0.0016 memory: 10983 2024/07/07 16:49:41 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2451 data_time: 0.0016 memory: 4460 2024/07/07 16:49:53 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2451 data_time: 0.0016 memory: 4641 2024/07/07 16:50:05 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2449 data_time: 0.0016 memory: 4473 2024/07/07 16:50:17 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2448 data_time: 0.0016 memory: 4555 2024/07/07 16:50:18 - mmengine - INFO - per class results: 2024/07/07 16:50:18 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 77.33 | 86.23 | | building | 80.83 | 88.04 | | sky | 93.79 | 97.66 | | floor | 82.09 | 90.76 | | tree | 73.87 | 88.41 | | ceiling | 84.99 | 93.34 | | road | 82.88 | 91.14 | | bed | 85.24 | 95.46 | | windowpane | 60.95 | 79.68 | | grass | 68.31 | 84.35 | | cabinet | 58.29 | 71.19 | | sidewalk | 65.19 | 81.63 | | person | 81.88 | 92.33 | | earth | 33.26 | 43.22 | | door | 48.81 | 70.94 | | table | 59.29 | 73.61 | | mountain | 57.59 | 66.89 | | plant | 50.46 | 68.32 | | curtain | 73.3 | 89.08 | | chair | 55.96 | 71.76 | | car | 83.66 | 92.59 | | water | 49.14 | 62.8 | | painting | 66.32 | 90.2 | | sofa | 57.8 | 73.23 | | shelf | 45.24 | 67.47 | | house | 45.71 | 74.92 | | sea | 51.65 | 80.51 | | mirror | 67.11 | 78.97 | | rug | 70.8 | 79.27 | | field | 26.56 | 45.23 | | armchair | 38.94 | 60.6 | | seat | 60.78 | 71.26 | | fence | 43.73 | 66.56 | | desk | 42.95 | 61.18 | | rock | 52.79 | 76.87 | | wardrobe | 50.8 | 65.92 | | lamp | 65.26 | 76.52 | | bathtub | 76.94 | 88.76 | | railing | 30.42 | 46.0 | | cushion | 56.04 | 69.56 | | base | 21.24 | 26.9 | | box | 23.93 | 38.85 | | column | 49.72 | 76.54 | | signboard | 37.27 | 56.04 | | chest of drawers | 38.53 | 61.91 | | counter | 32.08 | 42.77 | | sand | 31.02 | 52.39 | | sink | 70.05 | 81.74 | | skyscraper | 46.34 | 58.95 | | fireplace | 68.43 | 92.9 | | refrigerator | 72.73 | 84.89 | | grandstand | 46.49 | 67.65 | | path | 28.58 | 38.61 | | stairs | 38.22 | 49.81 | | runway | 67.69 | 78.43 | | case | 63.35 | 70.42 | | pool table | 80.43 | 96.64 | | pillow | 51.06 | 63.32 | | screen door | 64.63 | 73.21 | | stairway | 33.76 | 43.0 | | river | 16.54 | 33.44 | | bridge | 71.05 | 83.41 | | bookcase | 37.88 | 52.66 | | blind | 42.06 | 45.06 | | coffee table | 69.71 | 85.07 | | toilet | 84.08 | 88.93 | | flower | 35.16 | 52.1 | | book | 51.89 | 75.12 | | hill | 12.97 | 23.65 | | bench | 38.18 | 43.59 | | countertop | 60.81 | 69.67 | | stove | 80.02 | 86.18 | | palm | 50.82 | 68.77 | | kitchen island | 29.55 | 84.49 | | computer | 56.91 | 64.71 | | swivel chair | 38.31 | 55.83 | | boat | 49.05 | 53.98 | | bar | 16.63 | 20.22 | | arcade machine | 51.58 | 59.65 | | hovel | 32.28 | 40.93 | | bus | 77.97 | 83.89 | | towel | 63.54 | 74.46 | | light | 60.18 | 79.97 | | truck | 30.19 | 46.54 | | tower | 32.29 | 51.79 | | chandelier | 66.3 | 81.5 | | awning | 28.3 | 37.46 | | streetlight | 36.13 | 54.31 | | booth | 33.18 | 39.12 | | television receiver | 68.73 | 83.74 | | airplane | 54.63 | 64.51 | | dirt track | 0.0 | 0.0 | | apparel | 29.95 | 49.95 | | pole | 28.24 | 45.54 | | land | 7.55 | 10.05 | | bannister | 12.11 | 19.85 | | escalator | 20.11 | 22.4 | | ottoman | 50.08 | 68.97 | | bottle | 21.09 | 25.2 | | buffet | 42.1 | 42.46 | | poster | 16.9 | 22.85 | | stage | 10.84 | 19.05 | | van | 31.83 | 44.29 | | ship | 60.25 | 84.79 | | fountain | 1.95 | 2.13 | | conveyer belt | 45.83 | 57.56 | | canopy | 31.25 | 41.53 | | washer | 67.29 | 70.53 | | plaything | 26.25 | 34.42 | | swimming pool | 30.12 | 44.58 | | stool | 48.7 | 61.16 | | barrel | 53.07 | 80.24 | | basket | 30.87 | 39.79 | | waterfall | 61.87 | 85.03 | | tent | 92.08 | 98.65 | | bag | 14.55 | 21.62 | | minibike | 72.34 | 79.45 | | cradle | 60.78 | 75.28 | | oven | 39.33 | 52.29 | | ball | 20.24 | 27.19 | | food | 59.57 | 75.5 | | step | 9.21 | 11.97 | | tank | 49.77 | 51.95 | | trade name | 28.04 | 38.01 | | microwave | 36.23 | 39.13 | | pot | 49.78 | 58.42 | | animal | 60.7 | 69.58 | | bicycle | 55.52 | 76.19 | | lake | 0.0 | 0.0 | | dishwasher | 73.89 | 84.75 | | screen | 76.0 | 89.06 | | blanket | 16.7 | 20.36 | | sculpture | 68.14 | 78.12 | | hood | 67.45 | 72.28 | | sconce | 48.34 | 62.71 | | vase | 45.18 | 55.32 | | traffic light | 39.4 | 58.53 | | tray | 7.56 | 11.65 | | ashcan | 43.59 | 57.48 | | fan | 64.25 | 78.72 | | pier | 16.52 | 27.34 | | crt screen | 2.01 | 4.89 | | plate | 47.41 | 61.72 | | monitor | 4.17 | 5.55 | | bulletin board | 18.64 | 20.44 | | shower | 13.23 | 24.39 | | radiator | 41.76 | 49.33 | | glass | 18.13 | 19.98 | | clock | 25.54 | 33.19 | | flag | 43.13 | 54.19 | +---------------------+-------+-------+ 2024/07/07 16:50:18 - mmengine - INFO - Iter(val) [500/500] aAcc: 82.3100 mIoU: 47.0500 mAcc: 59.4000 data_time: 0.0014 time: 0.2448 2024/07/07 16:51:13 - mmengine - INFO - Iter(train) [ 10050/120000] base_lr: 1.9659e-04 lr: 1.9690e-05 eta: 1 day, 10:06:54 time: 1.1133 data_time: 0.0154 memory: 15010 grad_norm: 2.0507 loss: 0.6122 semantic_segmentation_loss_cls: 0.2520 semantic_segmentation_loss_mask: 0.1101 semantic_segmentation_loss_dice: 0.2501 2024/07/07 16:52:09 - mmengine - INFO - Iter(train) [ 10100/120000] base_lr: 1.9656e-04 lr: 1.9687e-05 eta: 1 day, 10:06:03 time: 1.1132 data_time: 0.0154 memory: 15350 grad_norm: 2.0496 loss: 0.6109 semantic_segmentation_loss_cls: 0.2512 semantic_segmentation_loss_mask: 0.1100 semantic_segmentation_loss_dice: 0.2497 2024/07/07 16:53:05 - mmengine - INFO - Iter(train) [ 10150/120000] base_lr: 1.9653e-04 lr: 1.9684e-05 eta: 1 day, 10:05:07 time: 1.1133 data_time: 0.0154 memory: 15367 grad_norm: 2.0478 loss: 0.6100 semantic_segmentation_loss_cls: 0.2506 semantic_segmentation_loss_mask: 0.1099 semantic_segmentation_loss_dice: 0.2495 2024/07/07 16:54:01 - mmengine - INFO - Iter(train) [ 10200/120000] base_lr: 1.9649e-04 lr: 1.9681e-05 eta: 1 day, 10:04:10 time: 1.1134 data_time: 0.0154 memory: 15553 grad_norm: 2.0457 loss: 0.6095 semantic_segmentation_loss_cls: 0.2502 semantic_segmentation_loss_mask: 0.1098 semantic_segmentation_loss_dice: 0.2495 2024/07/07 16:54:56 - mmengine - INFO - Iter(train) [ 10250/120000] base_lr: 1.9646e-04 lr: 1.9678e-05 eta: 1 day, 10:03:08 time: 1.1134 data_time: 0.0154 memory: 15070 grad_norm: 2.0473 loss: 0.6087 semantic_segmentation_loss_cls: 0.2497 semantic_segmentation_loss_mask: 0.1096 semantic_segmentation_loss_dice: 0.2493 2024/07/07 16:55:53 - mmengine - INFO - Iter(train) [ 10300/120000] base_lr: 1.9642e-04 lr: 1.9675e-05 eta: 1 day, 10:02:21 time: 1.1138 data_time: 0.0155 memory: 14953 grad_norm: 2.0464 loss: 0.6072 semantic_segmentation_loss_cls: 0.2489 semantic_segmentation_loss_mask: 0.1095 semantic_segmentation_loss_dice: 0.2488 2024/07/07 16:56:49 - mmengine - INFO - Iter(train) [ 10350/120000] base_lr: 1.9639e-04 lr: 1.9672e-05 eta: 1 day, 10:01:35 time: 1.1141 data_time: 0.0155 memory: 15332 grad_norm: 2.0460 loss: 0.6062 semantic_segmentation_loss_cls: 0.2483 semantic_segmentation_loss_mask: 0.1094 semantic_segmentation_loss_dice: 0.2485 2024/07/07 16:57:45 - mmengine - INFO - Iter(train) [ 10400/120000] base_lr: 1.9635e-04 lr: 1.9669e-05 eta: 1 day, 10:00:38 time: 1.1142 data_time: 0.0155 memory: 14869 grad_norm: 2.0398 loss: 0.6052 semantic_segmentation_loss_cls: 0.2477 semantic_segmentation_loss_mask: 0.1093 semantic_segmentation_loss_dice: 0.2482 2024/07/07 16:58:40 - mmengine - INFO - Iter(train) [ 10450/120000] base_lr: 1.9632e-04 lr: 1.9665e-05 eta: 1 day, 9:59:35 time: 1.1140 data_time: 0.0154 memory: 15540 grad_norm: 2.0333 loss: 0.6041 semantic_segmentation_loss_cls: 0.2470 semantic_segmentation_loss_mask: 0.1092 semantic_segmentation_loss_dice: 0.2479 2024/07/07 16:59:37 - mmengine - INFO - Iter(train) [ 10500/120000] base_lr: 1.9628e-04 lr: 1.9662e-05 eta: 1 day, 9:58:46 time: 1.1143 data_time: 0.0154 memory: 14396 grad_norm: 2.0310 loss: 0.6027 semantic_segmentation_loss_cls: 0.2463 semantic_segmentation_loss_mask: 0.1090 semantic_segmentation_loss_dice: 0.2474 2024/07/07 17:00:33 - mmengine - INFO - Iter(train) [ 10550/120000] base_lr: 1.9625e-04 lr: 1.9659e-05 eta: 1 day, 9:57:55 time: 1.1146 data_time: 0.0154 memory: 15476 grad_norm: 2.0276 loss: 0.6009 semantic_segmentation_loss_cls: 0.2456 semantic_segmentation_loss_mask: 0.1086 semantic_segmentation_loss_dice: 0.2467 2024/07/07 17:01:28 - mmengine - INFO - Iter(train) [ 10600/120000] base_lr: 1.9621e-04 lr: 1.9656e-05 eta: 1 day, 9:56:51 time: 1.1146 data_time: 0.0154 memory: 14816 grad_norm: 2.0280 loss: 0.5998 semantic_segmentation_loss_cls: 0.2449 semantic_segmentation_loss_mask: 0.1085 semantic_segmentation_loss_dice: 0.2464 2024/07/07 17:02:24 - mmengine - INFO - Iter(train) [ 10650/120000] base_lr: 1.9618e-04 lr: 1.9653e-05 eta: 1 day, 9:55:55 time: 1.1148 data_time: 0.0154 memory: 15213 grad_norm: 2.0272 loss: 0.5985 semantic_segmentation_loss_cls: 0.2442 semantic_segmentation_loss_mask: 0.1083 semantic_segmentation_loss_dice: 0.2460 2024/07/07 17:03:20 - mmengine - INFO - Iter(train) [ 10700/120000] base_lr: 1.9614e-04 lr: 1.9649e-05 eta: 1 day, 9:55:01 time: 1.1149 data_time: 0.0154 memory: 14462 grad_norm: 2.0261 loss: 0.5977 semantic_segmentation_loss_cls: 0.2436 semantic_segmentation_loss_mask: 0.1083 semantic_segmentation_loss_dice: 0.2458 2024/07/07 17:04:17 - mmengine - INFO - Iter(train) [ 10750/120000] base_lr: 1.9611e-04 lr: 1.9646e-05 eta: 1 day, 9:54:12 time: 1.1153 data_time: 0.0154 memory: 14942 grad_norm: 2.0244 loss: 0.5968 semantic_segmentation_loss_cls: 0.2432 semantic_segmentation_loss_mask: 0.1081 semantic_segmentation_loss_dice: 0.2456 2024/07/07 17:05:13 - mmengine - INFO - Iter(train) [ 10800/120000] base_lr: 1.9607e-04 lr: 1.9643e-05 eta: 1 day, 9:53:24 time: 1.1156 data_time: 0.0154 memory: 15223 grad_norm: 2.0231 loss: 0.5966 semantic_segmentation_loss_cls: 0.2429 semantic_segmentation_loss_mask: 0.1081 semantic_segmentation_loss_dice: 0.2455 2024/07/07 17:06:09 - mmengine - INFO - Iter(train) [ 10850/120000] base_lr: 1.9603e-04 lr: 1.9639e-05 eta: 1 day, 9:52:23 time: 1.1155 data_time: 0.0155 memory: 15753 grad_norm: 2.0229 loss: 0.5957 semantic_segmentation_loss_cls: 0.2424 semantic_segmentation_loss_mask: 0.1080 semantic_segmentation_loss_dice: 0.2452 2024/07/07 17:07:05 - mmengine - INFO - Iter(train) [ 10900/120000] base_lr: 1.9600e-04 lr: 1.9636e-05 eta: 1 day, 9:51:28 time: 1.1156 data_time: 0.0155 memory: 14739 grad_norm: 2.0232 loss: 0.5952 semantic_segmentation_loss_cls: 0.2422 semantic_segmentation_loss_mask: 0.1080 semantic_segmentation_loss_dice: 0.2450 2024/07/07 17:08:01 - mmengine - INFO - Iter(train) [ 10950/120000] base_lr: 1.9596e-04 lr: 1.9633e-05 eta: 1 day, 9:50:36 time: 1.1157 data_time: 0.0155 memory: 15144 grad_norm: 2.0221 loss: 0.5949 semantic_segmentation_loss_cls: 0.2421 semantic_segmentation_loss_mask: 0.1079 semantic_segmentation_loss_dice: 0.2449 2024/07/07 17:08:57 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 17:08:57 - mmengine - INFO - Iter(train) [ 11000/120000] base_lr: 1.9592e-04 lr: 1.9629e-05 eta: 1 day, 9:49:47 time: 1.1160 data_time: 0.0155 memory: 14578 grad_norm: 2.0200 loss: 0.5942 semantic_segmentation_loss_cls: 0.2417 semantic_segmentation_loss_mask: 0.1078 semantic_segmentation_loss_dice: 0.2448 2024/07/07 17:08:57 - mmengine - INFO - Saving checkpoint at 11000 iterations 2024/07/07 17:09:59 - mmengine - INFO - Iter(train) [ 11050/120000] base_lr: 1.9589e-04 lr: 1.9626e-05 eta: 1 day, 9:49:46 time: 1.1164 data_time: 0.0155 memory: 15299 grad_norm: 2.0158 loss: 0.5929 semantic_segmentation_loss_cls: 0.2409 semantic_segmentation_loss_mask: 0.1076 semantic_segmentation_loss_dice: 0.2444 2024/07/07 17:10:55 - mmengine - INFO - Iter(train) [ 11100/120000] base_lr: 1.9585e-04 lr: 1.9623e-05 eta: 1 day, 9:48:49 time: 1.1165 data_time: 0.0155 memory: 15233 grad_norm: 2.0134 loss: 0.5913 semantic_segmentation_loss_cls: 0.2400 semantic_segmentation_loss_mask: 0.1074 semantic_segmentation_loss_dice: 0.2439 2024/07/07 17:11:50 - mmengine - INFO - Iter(train) [ 11150/120000] base_lr: 1.9581e-04 lr: 1.9619e-05 eta: 1 day, 9:47:50 time: 1.1166 data_time: 0.0155 memory: 14992 grad_norm: 2.0081 loss: 0.5899 semantic_segmentation_loss_cls: 0.2392 semantic_segmentation_loss_mask: 0.1072 semantic_segmentation_loss_dice: 0.2436 2024/07/07 17:12:45 - mmengine - INFO - Iter(train) [ 11200/120000] base_lr: 1.9578e-04 lr: 1.9616e-05 eta: 1 day, 9:46:48 time: 1.1165 data_time: 0.0155 memory: 16364 grad_norm: 2.0059 loss: 0.5883 semantic_segmentation_loss_cls: 0.2384 semantic_segmentation_loss_mask: 0.1069 semantic_segmentation_loss_dice: 0.2430 2024/07/07 17:13:41 - mmengine - INFO - Iter(train) [ 11250/120000] base_lr: 1.9574e-04 lr: 1.9613e-05 eta: 1 day, 9:45:51 time: 1.1165 data_time: 0.0155 memory: 14963 grad_norm: 2.0044 loss: 0.5875 semantic_segmentation_loss_cls: 0.2379 semantic_segmentation_loss_mask: 0.1068 semantic_segmentation_loss_dice: 0.2428 2024/07/07 17:14:37 - mmengine - INFO - Iter(train) [ 11300/120000] base_lr: 1.9570e-04 lr: 1.9609e-05 eta: 1 day, 9:44:58 time: 1.1166 data_time: 0.0155 memory: 14814 grad_norm: 2.0030 loss: 0.5863 semantic_segmentation_loss_cls: 0.2373 semantic_segmentation_loss_mask: 0.1066 semantic_segmentation_loss_dice: 0.2424 2024/07/07 17:15:33 - mmengine - INFO - Iter(train) [ 11350/120000] base_lr: 1.9566e-04 lr: 1.9606e-05 eta: 1 day, 9:43:56 time: 1.1165 data_time: 0.0155 memory: 15881 grad_norm: 2.0047 loss: 0.5851 semantic_segmentation_loss_cls: 0.2367 semantic_segmentation_loss_mask: 0.1063 semantic_segmentation_loss_dice: 0.2420 2024/07/07 17:16:28 - mmengine - INFO - Iter(train) [ 11400/120000] base_lr: 1.9562e-04 lr: 1.9602e-05 eta: 1 day, 9:42:58 time: 1.1167 data_time: 0.0155 memory: 15553 grad_norm: 2.0026 loss: 0.5840 semantic_segmentation_loss_cls: 0.2360 semantic_segmentation_loss_mask: 0.1062 semantic_segmentation_loss_dice: 0.2418 2024/07/07 17:17:24 - mmengine - INFO - Iter(train) [ 11450/120000] base_lr: 1.9559e-04 lr: 1.9599e-05 eta: 1 day, 9:41:56 time: 1.1164 data_time: 0.0155 memory: 15309 grad_norm: 2.0023 loss: 0.5831 semantic_segmentation_loss_cls: 0.2356 semantic_segmentation_loss_mask: 0.1061 semantic_segmentation_loss_dice: 0.2415 2024/07/07 17:18:19 - mmengine - INFO - Iter(train) [ 11500/120000] base_lr: 1.9555e-04 lr: 1.9595e-05 eta: 1 day, 9:40:55 time: 1.1162 data_time: 0.0155 memory: 16268 grad_norm: 2.0012 loss: 0.5824 semantic_segmentation_loss_cls: 0.2353 semantic_segmentation_loss_mask: 0.1058 semantic_segmentation_loss_dice: 0.2413 2024/07/07 17:19:15 - mmengine - INFO - Iter(train) [ 11550/120000] base_lr: 1.9551e-04 lr: 1.9592e-05 eta: 1 day, 9:40:02 time: 1.1164 data_time: 0.0155 memory: 15310 grad_norm: 2.0013 loss: 0.5815 semantic_segmentation_loss_cls: 0.2347 semantic_segmentation_loss_mask: 0.1058 semantic_segmentation_loss_dice: 0.2410 2024/07/07 17:20:11 - mmengine - INFO - Iter(train) [ 11600/120000] base_lr: 1.9547e-04 lr: 1.9588e-05 eta: 1 day, 9:39:09 time: 1.1165 data_time: 0.0156 memory: 14714 grad_norm: 1.9986 loss: 0.5804 semantic_segmentation_loss_cls: 0.2340 semantic_segmentation_loss_mask: 0.1056 semantic_segmentation_loss_dice: 0.2408 2024/07/07 17:21:07 - mmengine - INFO - Iter(train) [ 11650/120000] base_lr: 1.9543e-04 lr: 1.9585e-05 eta: 1 day, 9:38:12 time: 1.1166 data_time: 0.0156 memory: 15644 grad_norm: 1.9972 loss: 0.5795 semantic_segmentation_loss_cls: 0.2334 semantic_segmentation_loss_mask: 0.1056 semantic_segmentation_loss_dice: 0.2405 2024/07/07 17:22:04 - mmengine - INFO - Iter(train) [ 11700/120000] base_lr: 1.9539e-04 lr: 1.9581e-05 eta: 1 day, 9:37:23 time: 1.1168 data_time: 0.0156 memory: 14880 grad_norm: 1.9965 loss: 0.5795 semantic_segmentation_loss_cls: 0.2334 semantic_segmentation_loss_mask: 0.1056 semantic_segmentation_loss_dice: 0.2405 2024/07/07 17:22:59 - mmengine - INFO - Iter(train) [ 11750/120000] base_lr: 1.9535e-04 lr: 1.9578e-05 eta: 1 day, 9:36:21 time: 1.1167 data_time: 0.0156 memory: 15670 grad_norm: 1.9973 loss: 0.5782 semantic_segmentation_loss_cls: 0.2326 semantic_segmentation_loss_mask: 0.1055 semantic_segmentation_loss_dice: 0.2401 2024/07/07 17:23:54 - mmengine - INFO - Iter(train) [ 11800/120000] base_lr: 1.9531e-04 lr: 1.9574e-05 eta: 1 day, 9:35:22 time: 1.1169 data_time: 0.0156 memory: 15636 grad_norm: 1.9973 loss: 0.5773 semantic_segmentation_loss_cls: 0.2321 semantic_segmentation_loss_mask: 0.1054 semantic_segmentation_loss_dice: 0.2398 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name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 17:27:38 - mmengine - INFO - Iter(train) [ 12000/120000] base_lr: 1.9516e-04 lr: 1.9560e-05 eta: 1 day, 9:31:42 time: 1.1170 data_time: 0.0157 memory: 14560 grad_norm: 1.9924 loss: 0.5726 semantic_segmentation_loss_cls: 0.2296 semantic_segmentation_loss_mask: 0.1047 semantic_segmentation_loss_dice: 0.2383 2024/07/07 17:27:38 - mmengine - INFO - Saving checkpoint at 12000 iterations 2024/07/07 17:28:38 - mmengine - INFO - Iter(train) [ 12050/120000] base_lr: 1.9512e-04 lr: 1.9556e-05 eta: 1 day, 9:31:22 time: 1.1170 data_time: 0.0157 memory: 15992 grad_norm: 1.9925 loss: 0.5713 semantic_segmentation_loss_cls: 0.2287 semantic_segmentation_loss_mask: 0.1045 semantic_segmentation_loss_dice: 0.2380 2024/07/07 17:29:33 - mmengine - INFO - Iter(train) [ 12100/120000] base_lr: 1.9507e-04 lr: 1.9552e-05 eta: 1 day, 9:30:20 time: 1.1170 data_time: 0.0157 memory: 14835 grad_norm: 1.9927 loss: 0.5699 semantic_segmentation_loss_cls: 0.2280 semantic_segmentation_loss_mask: 0.1043 semantic_segmentation_loss_dice: 0.2376 2024/07/07 17:30:29 - mmengine - INFO - Iter(train) [ 12150/120000] base_lr: 1.9503e-04 lr: 1.9549e-05 eta: 1 day, 9:29:20 time: 1.1172 data_time: 0.0157 memory: 15149 grad_norm: 1.9904 loss: 0.5689 semantic_segmentation_loss_cls: 0.2275 semantic_segmentation_loss_mask: 0.1041 semantic_segmentation_loss_dice: 0.2373 2024/07/07 17:31:24 - mmengine - INFO - Iter(train) [ 12200/120000] base_lr: 1.9499e-04 lr: 1.9545e-05 eta: 1 day, 9:28:14 time: 1.1169 data_time: 0.0157 memory: 14981 grad_norm: 1.9897 loss: 0.5679 semantic_segmentation_loss_cls: 0.2269 semantic_segmentation_loss_mask: 0.1040 semantic_segmentation_loss_dice: 0.2370 2024/07/07 17:32:20 - mmengine - INFO - Iter(train) [ 12250/120000] base_lr: 1.9495e-04 lr: 1.9541e-05 eta: 1 day, 9:27:22 time: 1.1172 data_time: 0.0158 memory: 15831 grad_norm: 1.9902 loss: 0.5668 semantic_segmentation_loss_cls: 0.2264 semantic_segmentation_loss_mask: 0.1038 semantic_segmentation_loss_dice: 0.2367 2024/07/07 17:33:15 - mmengine - INFO - Iter(train) [ 12300/120000] base_lr: 1.9491e-04 lr: 1.9537e-05 eta: 1 day, 9:26:21 time: 1.1173 data_time: 0.0158 memory: 14663 grad_norm: 1.9905 loss: 0.5665 semantic_segmentation_loss_cls: 0.2262 semantic_segmentation_loss_mask: 0.1038 semantic_segmentation_loss_dice: 0.2366 2024/07/07 17:34:12 - mmengine - INFO - Iter(train) [ 12350/120000] base_lr: 1.9487e-04 lr: 1.9534e-05 eta: 1 day, 9:25:29 time: 1.1177 data_time: 0.0159 memory: 14445 grad_norm: 1.9887 loss: 0.5652 semantic_segmentation_loss_cls: 0.2253 semantic_segmentation_loss_mask: 0.1037 semantic_segmentation_loss_dice: 0.2362 2024/07/07 17:35:07 - mmengine - INFO - Iter(train) [ 12400/120000] base_lr: 1.9483e-04 lr: 1.9530e-05 eta: 1 day, 9:24:29 time: 1.1177 data_time: 0.0159 memory: 16783 grad_norm: 1.9880 loss: 0.5649 semantic_segmentation_loss_cls: 0.2250 semantic_segmentation_loss_mask: 0.1037 semantic_segmentation_loss_dice: 0.2362 2024/07/07 17:36:01 - mmengine - INFO - Iter(train) [ 12450/120000] base_lr: 1.9479e-04 lr: 1.9526e-05 eta: 1 day, 9:23:20 time: 1.1171 data_time: 0.0158 memory: 15092 grad_norm: 1.9888 loss: 0.5640 semantic_segmentation_loss_cls: 0.2245 semantic_segmentation_loss_mask: 0.1036 semantic_segmentation_loss_dice: 0.2358 2024/07/07 17:36:57 - mmengine - INFO - Iter(train) [ 12500/120000] base_lr: 1.9475e-04 lr: 1.9522e-05 eta: 1 day, 9:22:20 time: 1.1172 data_time: 0.0158 memory: 16175 grad_norm: 1.9877 loss: 0.5633 semantic_segmentation_loss_cls: 0.2241 semantic_segmentation_loss_mask: 0.1036 semantic_segmentation_loss_dice: 0.2356 2024/07/07 17:37:52 - mmengine - INFO - Iter(train) [ 12550/120000] base_lr: 1.9471e-04 lr: 1.9519e-05 eta: 1 day, 9:21:16 time: 1.1168 data_time: 0.0158 memory: 14534 grad_norm: 1.9889 loss: 0.5631 semantic_segmentation_loss_cls: 0.2240 semantic_segmentation_loss_mask: 0.1035 semantic_segmentation_loss_dice: 0.2356 2024/07/07 17:38:48 - mmengine - INFO - Iter(train) [ 12600/120000] base_lr: 1.9466e-04 lr: 1.9515e-05 eta: 1 day, 9:20:25 time: 1.1171 data_time: 0.0158 memory: 15500 grad_norm: 1.9955 loss: 0.5629 semantic_segmentation_loss_cls: 0.2240 semantic_segmentation_loss_mask: 0.1033 semantic_segmentation_loss_dice: 0.2356 2024/07/07 17:39:43 - mmengine - INFO - Iter(train) [ 12650/120000] base_lr: 1.9462e-04 lr: 1.9511e-05 eta: 1 day, 9:19:23 time: 1.1172 data_time: 0.0158 memory: 15090 grad_norm: 1.9947 loss: 0.5619 semantic_segmentation_loss_cls: 0.2234 semantic_segmentation_loss_mask: 0.1032 semantic_segmentation_loss_dice: 0.2353 2024/07/07 17:40:40 - mmengine - INFO - Iter(train) [ 12700/120000] base_lr: 1.9458e-04 lr: 1.9507e-05 eta: 1 day, 9:18:33 time: 1.1176 data_time: 0.0158 memory: 16078 grad_norm: 1.9912 loss: 0.5611 semantic_segmentation_loss_cls: 0.2228 semantic_segmentation_loss_mask: 0.1031 semantic_segmentation_loss_dice: 0.2351 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Iter(train) [ 12900/120000] base_lr: 1.9441e-04 lr: 1.9492e-05 eta: 1 day, 9:14:41 time: 1.1178 data_time: 0.0159 memory: 14513 grad_norm: 1.9932 loss: 0.5565 semantic_segmentation_loss_cls: 0.2201 semantic_segmentation_loss_mask: 0.1026 semantic_segmentation_loss_dice: 0.2338 2024/07/07 17:45:18 - mmengine - INFO - Iter(train) [ 12950/120000] base_lr: 1.9437e-04 lr: 1.9488e-05 eta: 1 day, 9:13:45 time: 1.1180 data_time: 0.0159 memory: 15455 grad_norm: 1.9908 loss: 0.5554 semantic_segmentation_loss_cls: 0.2194 semantic_segmentation_loss_mask: 0.1024 semantic_segmentation_loss_dice: 0.2335 2024/07/07 17:46:14 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 17:46:14 - mmengine - INFO - Iter(train) [ 13000/120000] base_lr: 1.9432e-04 lr: 1.9484e-05 eta: 1 day, 9:12:45 time: 1.1178 data_time: 0.0159 memory: 14970 grad_norm: 1.9903 loss: 0.5538 semantic_segmentation_loss_cls: 0.2185 semantic_segmentation_loss_mask: 0.1023 semantic_segmentation_loss_dice: 0.2329 2024/07/07 17:46:14 - mmengine - INFO - Saving checkpoint at 13000 iterations 2024/07/07 17:47:14 - mmengine - INFO - Iter(train) [ 13050/120000] base_lr: 1.9428e-04 lr: 1.9480e-05 eta: 1 day, 9:12:26 time: 1.1177 data_time: 0.0159 memory: 15283 grad_norm: 1.9893 loss: 0.5527 semantic_segmentation_loss_cls: 0.2180 semantic_segmentation_loss_mask: 0.1021 semantic_segmentation_loss_dice: 0.2326 2024/07/07 17:48:09 - mmengine - INFO - Iter(train) [ 13100/120000] base_lr: 1.9424e-04 lr: 1.9476e-05 eta: 1 day, 9:11:25 time: 1.1175 data_time: 0.0159 memory: 15991 grad_norm: 1.9883 loss: 0.5514 semantic_segmentation_loss_cls: 0.2172 semantic_segmentation_loss_mask: 0.1019 semantic_segmentation_loss_dice: 0.2322 2024/07/07 17:49:05 - mmengine - INFO - Iter(train) [ 13150/120000] base_lr: 1.9419e-04 lr: 1.9472e-05 eta: 1 day, 9:10:26 time: 1.1171 data_time: 0.0159 memory: 14987 grad_norm: 1.9874 loss: 0.5508 semantic_segmentation_loss_cls: 0.2169 semantic_segmentation_loss_mask: 0.1018 semantic_segmentation_loss_dice: 0.2321 2024/07/07 17:50:00 - mmengine - INFO - Iter(train) [ 13200/120000] base_lr: 1.9415e-04 lr: 1.9468e-05 eta: 1 day, 9:09:23 time: 1.1168 data_time: 0.0158 memory: 15660 grad_norm: 1.9860 loss: 0.5497 semantic_segmentation_loss_cls: 0.2162 semantic_segmentation_loss_mask: 0.1017 semantic_segmentation_loss_dice: 0.2318 2024/07/07 17:50:56 - mmengine - INFO - Iter(train) [ 13250/120000] base_lr: 1.9410e-04 lr: 1.9464e-05 eta: 1 day, 9:08:28 time: 1.1168 data_time: 0.0159 memory: 15664 grad_norm: 1.9852 loss: 0.5488 semantic_segmentation_loss_cls: 0.2157 semantic_segmentation_loss_mask: 0.1016 semantic_segmentation_loss_dice: 0.2315 2024/07/07 17:51:52 - mmengine - INFO - Iter(train) [ 13300/120000] base_lr: 1.9406e-04 lr: 1.9460e-05 eta: 1 day, 9:07:31 time: 1.1170 data_time: 0.0159 memory: 15899 grad_norm: 1.9842 loss: 0.5478 semantic_segmentation_loss_cls: 0.2152 semantic_segmentation_loss_mask: 0.1014 semantic_segmentation_loss_dice: 0.2312 2024/07/07 17:52:47 - mmengine - INFO - Iter(train) [ 13350/120000] base_lr: 1.9402e-04 lr: 1.9456e-05 eta: 1 day, 9:06:34 time: 1.1169 data_time: 0.0159 memory: 15002 grad_norm: 1.9850 loss: 0.5476 semantic_segmentation_loss_cls: 0.2151 semantic_segmentation_loss_mask: 0.1015 semantic_segmentation_loss_dice: 0.2311 2024/07/07 17:53:43 - mmengine - INFO - Iter(train) [ 13400/120000] base_lr: 1.9397e-04 lr: 1.9452e-05 eta: 1 day, 9:05:39 time: 1.1170 data_time: 0.0159 memory: 14991 grad_norm: 1.9852 loss: 0.5470 semantic_segmentation_loss_cls: 0.2148 semantic_segmentation_loss_mask: 0.1013 semantic_segmentation_loss_dice: 0.2308 2024/07/07 17:54:39 - mmengine - INFO - Iter(train) [ 13450/120000] base_lr: 1.9393e-04 lr: 1.9448e-05 eta: 1 day, 9:04:42 time: 1.1171 data_time: 0.0159 memory: 14492 grad_norm: 1.9840 loss: 0.5468 semantic_segmentation_loss_cls: 0.2146 semantic_segmentation_loss_mask: 0.1013 semantic_segmentation_loss_dice: 0.2309 2024/07/07 17:55:35 - mmengine - INFO - Iter(train) [ 13500/120000] base_lr: 1.9388e-04 lr: 1.9444e-05 eta: 1 day, 9:03:47 time: 1.1173 data_time: 0.0159 memory: 15432 grad_norm: 1.9827 loss: 0.5462 semantic_segmentation_loss_cls: 0.2144 semantic_segmentation_loss_mask: 0.1011 semantic_segmentation_loss_dice: 0.2307 2024/07/07 17:56:30 - mmengine - INFO - Iter(train) [ 13550/120000] base_lr: 1.9384e-04 lr: 1.9440e-05 eta: 1 day, 9:02:46 time: 1.1173 data_time: 0.0159 memory: 15101 grad_norm: 1.9807 loss: 0.5454 semantic_segmentation_loss_cls: 0.2139 semantic_segmentation_loss_mask: 0.1011 semantic_segmentation_loss_dice: 0.2305 2024/07/07 17:57:26 - mmengine - INFO - Iter(train) [ 13600/120000] base_lr: 1.9379e-04 lr: 1.9436e-05 eta: 1 day, 9:01:51 time: 1.1174 data_time: 0.0158 memory: 15131 grad_norm: 1.9815 loss: 0.5449 semantic_segmentation_loss_cls: 0.2134 semantic_segmentation_loss_mask: 0.1010 semantic_segmentation_loss_dice: 0.2304 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semantic_segmentation_loss_cls: 0.2081 semantic_segmentation_loss_mask: 0.1003 semantic_segmentation_loss_dice: 0.2278 2024/07/07 18:06:47 - mmengine - INFO - Iter(train) [ 14100/120000] base_lr: 1.9333e-04 lr: 1.9394e-05 eta: 1 day, 8:52:46 time: 1.1194 data_time: 0.0168 memory: 14818 grad_norm: 1.9755 loss: 0.5357 semantic_segmentation_loss_cls: 0.2078 semantic_segmentation_loss_mask: 0.1003 semantic_segmentation_loss_dice: 0.2276 2024/07/07 18:07:42 - mmengine - INFO - Iter(train) [ 14150/120000] base_lr: 1.9329e-04 lr: 1.9390e-05 eta: 1 day, 8:51:45 time: 1.1193 data_time: 0.0168 memory: 14785 grad_norm: 1.9758 loss: 0.5345 semantic_segmentation_loss_cls: 0.2072 semantic_segmentation_loss_mask: 0.1001 semantic_segmentation_loss_dice: 0.2272 2024/07/07 18:08:38 - mmengine - INFO - Iter(train) [ 14200/120000] base_lr: 1.9324e-04 lr: 1.9385e-05 eta: 1 day, 8:50:51 time: 1.1194 data_time: 0.0168 memory: 15777 grad_norm: 1.9760 loss: 0.5333 semantic_segmentation_loss_cls: 0.2066 semantic_segmentation_loss_mask: 0.1000 semantic_segmentation_loss_dice: 0.2268 2024/07/07 18:09:33 - mmengine - INFO - Iter(train) [ 14250/120000] base_lr: 1.9319e-04 lr: 1.9381e-05 eta: 1 day, 8:49:49 time: 1.1193 data_time: 0.0168 memory: 14779 grad_norm: 1.9726 loss: 0.5324 semantic_segmentation_loss_cls: 0.2062 semantic_segmentation_loss_mask: 0.0998 semantic_segmentation_loss_dice: 0.2264 2024/07/07 18:10:29 - mmengine - INFO - Iter(train) [ 14300/120000] base_lr: 1.9314e-04 lr: 1.9377e-05 eta: 1 day, 8:48:54 time: 1.1191 data_time: 0.0168 memory: 16367 grad_norm: 1.9719 loss: 0.5316 semantic_segmentation_loss_cls: 0.2057 semantic_segmentation_loss_mask: 0.0997 semantic_segmentation_loss_dice: 0.2262 2024/07/07 18:11:25 - mmengine - INFO - Iter(train) [ 14350/120000] base_lr: 1.9310e-04 lr: 1.9372e-05 eta: 1 day, 8:47:59 time: 1.1189 data_time: 0.0168 memory: 15727 grad_norm: 1.9709 loss: 0.5311 semantic_segmentation_loss_cls: 0.2054 semantic_segmentation_loss_mask: 0.0995 semantic_segmentation_loss_dice: 0.2261 2024/07/07 18:12:22 - mmengine - INFO - Iter(train) [ 14400/120000] base_lr: 1.9305e-04 lr: 1.9368e-05 eta: 1 day, 8:47:06 time: 1.1191 data_time: 0.0168 memory: 15182 grad_norm: 1.9700 loss: 0.5300 semantic_segmentation_loss_cls: 0.2049 semantic_segmentation_loss_mask: 0.0994 semantic_segmentation_loss_dice: 0.2258 2024/07/07 18:13:18 - mmengine - INFO - Iter(train) [ 14450/120000] base_lr: 1.9300e-04 lr: 1.9364e-05 eta: 1 day, 8:46:11 time: 1.1193 data_time: 0.0168 memory: 15097 grad_norm: 1.9686 loss: 0.5284 semantic_segmentation_loss_cls: 0.2041 semantic_segmentation_loss_mask: 0.0991 semantic_segmentation_loss_dice: 0.2252 2024/07/07 18:14:13 - mmengine - INFO - Iter(train) [ 14500/120000] base_lr: 1.9295e-04 lr: 1.9359e-05 eta: 1 day, 8:45:07 time: 1.1188 data_time: 0.0168 memory: 14832 grad_norm: 1.9673 loss: 0.5278 semantic_segmentation_loss_cls: 0.2037 semantic_segmentation_loss_mask: 0.0990 semantic_segmentation_loss_dice: 0.2251 2024/07/07 18:15:08 - mmengine - INFO - Iter(train) [ 14550/120000] base_lr: 1.9291e-04 lr: 1.9355e-05 eta: 1 day, 8:44:05 time: 1.1185 data_time: 0.0168 memory: 15802 grad_norm: 1.9673 loss: 0.5272 semantic_segmentation_loss_cls: 0.2032 semantic_segmentation_loss_mask: 0.0990 semantic_segmentation_loss_dice: 0.2250 2024/07/07 18:16:03 - mmengine - INFO - Iter(train) [ 14600/120000] base_lr: 1.9286e-04 lr: 1.9351e-05 eta: 1 day, 8:43:09 time: 1.1187 data_time: 0.0168 memory: 14994 grad_norm: 1.9647 loss: 0.5269 semantic_segmentation_loss_cls: 0.2030 semantic_segmentation_loss_mask: 0.0989 semantic_segmentation_loss_dice: 0.2250 2024/07/07 18:16:58 - mmengine - INFO - Iter(train) [ 14650/120000] base_lr: 1.9281e-04 lr: 1.9346e-05 eta: 1 day, 8:42:02 time: 1.1184 data_time: 0.0168 memory: 16012 grad_norm: 1.9639 loss: 0.5262 semantic_segmentation_loss_cls: 0.2026 semantic_segmentation_loss_mask: 0.0988 semantic_segmentation_loss_dice: 0.2249 2024/07/07 18:17:52 - mmengine - INFO - Iter(train) [ 14700/120000] base_lr: 1.9276e-04 lr: 1.9342e-05 eta: 1 day, 8:40:53 time: 1.1179 data_time: 0.0168 memory: 14234 grad_norm: 1.9643 loss: 0.5250 semantic_segmentation_loss_cls: 0.2019 semantic_segmentation_loss_mask: 0.0986 semantic_segmentation_loss_dice: 0.2245 2024/07/07 18:18:46 - mmengine - INFO - Iter(train) [ 14750/120000] base_lr: 1.9271e-04 lr: 1.9337e-05 eta: 1 day, 8:39:45 time: 1.1173 data_time: 0.0168 memory: 15057 grad_norm: 1.9644 loss: 0.5244 semantic_segmentation_loss_cls: 0.2015 semantic_segmentation_loss_mask: 0.0986 semantic_segmentation_loss_dice: 0.2243 2024/07/07 18:19:42 - mmengine - INFO - Iter(train) [ 14800/120000] base_lr: 1.9266e-04 lr: 1.9333e-05 eta: 1 day, 8:38:50 time: 1.1171 data_time: 0.0168 memory: 14840 grad_norm: 1.9640 loss: 0.5237 semantic_segmentation_loss_cls: 0.2012 semantic_segmentation_loss_mask: 0.0984 semantic_segmentation_loss_dice: 0.2241 2024/07/07 18:20:38 - mmengine - INFO - Iter(train) [ 14850/120000] base_lr: 1.9261e-04 lr: 1.9328e-05 eta: 1 day, 8:37:53 time: 1.1172 data_time: 0.0168 memory: 14815 grad_norm: 1.9632 loss: 0.5227 semantic_segmentation_loss_cls: 0.2007 semantic_segmentation_loss_mask: 0.0983 semantic_segmentation_loss_dice: 0.2237 2024/07/07 18:21:34 - mmengine - INFO - Iter(train) [ 14900/120000] base_lr: 1.9256e-04 lr: 1.9324e-05 eta: 1 day, 8:37:03 time: 1.1174 data_time: 0.0168 memory: 15018 grad_norm: 1.9615 loss: 0.5217 semantic_segmentation_loss_cls: 0.2001 semantic_segmentation_loss_mask: 0.0981 semantic_segmentation_loss_dice: 0.2234 2024/07/07 18:22:29 - mmengine - INFO - Iter(train) [ 14950/120000] base_lr: 1.9251e-04 lr: 1.9320e-05 eta: 1 day, 8:35:59 time: 1.1170 data_time: 0.0168 memory: 15199 grad_norm: 1.9612 loss: 0.5202 semantic_segmentation_loss_cls: 0.1992 semantic_segmentation_loss_mask: 0.0980 semantic_segmentation_loss_dice: 0.2231 2024/07/07 18:23:25 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 18:23:25 - mmengine - INFO - Iter(train) [ 15000/120000] base_lr: 1.9247e-04 lr: 1.9315e-05 eta: 1 day, 8:35:04 time: 1.1168 data_time: 0.0167 memory: 15153 grad_norm: 1.9616 loss: 0.5197 semantic_segmentation_loss_cls: 0.1989 semantic_segmentation_loss_mask: 0.0979 semantic_segmentation_loss_dice: 0.2229 2024/07/07 18:23:25 - mmengine - INFO - Saving checkpoint at 15000 iterations 2024/07/07 18:23:42 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:50 time: 0.2448 data_time: 0.0016 memory: 5013 2024/07/07 18:23:54 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:37 time: 0.2447 data_time: 0.0015 memory: 5189 2024/07/07 18:24:06 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:25 time: 0.2448 data_time: 0.0015 memory: 4460 2024/07/07 18:24:18 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:13 time: 0.2447 data_time: 0.0015 memory: 4543 2024/07/07 18:24:31 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:01 time: 0.2448 data_time: 0.0015 memory: 4643 2024/07/07 18:24:43 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:49 time: 0.2448 data_time: 0.0015 memory: 10983 2024/07/07 18:24:55 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2448 data_time: 0.0015 memory: 4460 2024/07/07 18:25:08 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2448 data_time: 0.0015 memory: 4641 2024/07/07 18:25:20 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2448 data_time: 0.0015 memory: 4473 2024/07/07 18:25:32 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2448 data_time: 0.0015 memory: 4555 2024/07/07 18:25:33 - mmengine - INFO - per class results: 2024/07/07 18:25:33 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 77.65 | 86.23 | | building | 81.25 | 87.92 | | sky | 94.08 | 97.66 | | floor | 82.9 | 90.85 | | tree | 74.02 | 88.12 | | ceiling | 85.56 | 93.4 | | road | 84.39 | 91.85 | | bed | 87.22 | 95.94 | | windowpane | 62.1 | 80.79 | | grass | 73.63 | 88.99 | | cabinet | 58.82 | 70.95 | | sidewalk | 67.28 | 83.73 | | person | 82.1 | 92.13 | | earth | 33.21 | 43.83 | | door | 51.47 | 71.0 | | table | 59.99 | 74.27 | | mountain | 55.92 | 70.81 | | plant | 51.8 | 68.24 | | curtain | 75.05 | 88.15 | | chair | 56.77 | 71.12 | | car | 84.81 | 90.53 | | water | 51.09 | 65.46 | | painting | 67.48 | 91.29 | | sofa | 63.19 | 76.07 | | shelf | 46.31 | 66.89 | | house | 45.8 | 74.01 | | sea | 53.79 | 81.77 | | mirror | 68.07 | 78.86 | | rug | 74.42 | 83.43 | | field | 37.71 | 53.89 | | armchair | 43.01 | 67.09 | | seat | 57.67 | 73.22 | | fence | 43.01 | 67.14 | | desk | 45.21 | 68.61 | | rock | 39.08 | 58.26 | | wardrobe | 55.56 | 70.68 | | lamp | 67.56 | 78.46 | | bathtub | 84.73 | 90.47 | | railing | 30.71 | 45.86 | | cushion | 58.75 | 70.11 | | base | 28.31 | 40.1 | | box | 28.26 | 39.52 | | column | 47.47 | 75.07 | | signboard | 38.8 | 56.28 | | chest of drawers | 34.42 | 66.48 | | counter | 37.6 | 55.76 | | sand | 32.17 | 50.3 | | sink | 67.86 | 81.27 | | skyscraper | 47.59 | 63.94 | | fireplace | 67.52 | 90.73 | | refrigerator | 67.42 | 82.16 | | grandstand | 39.53 | 66.23 | | path | 29.01 | 39.11 | | stairs | 31.32 | 43.38 | | runway | 75.28 | 87.24 | | case | 54.27 | 60.57 | | pool table | 90.09 | 96.31 | | pillow | 57.83 | 72.21 | | screen door | 68.64 | 79.94 | | stairway | 35.2 | 46.07 | | river | 18.37 | 34.77 | | bridge | 68.21 | 83.08 | | bookcase | 33.56 | 51.45 | | blind | 41.7 | 48.28 | | coffee table | 71.17 | 83.59 | | toilet | 83.91 | 88.63 | | flower | 41.14 | 61.74 | | book | 50.54 | 75.29 | | hill | 14.97 | 24.72 | | bench | 41.06 | 47.69 | | countertop | 51.33 | 64.36 | | stove | 82.73 | 86.21 | | palm | 50.77 | 68.66 | | kitchen island | 29.48 | 71.19 | | computer | 71.7 | 81.93 | | swivel chair | 38.63 | 54.21 | | boat | 46.83 | 52.86 | | bar | 27.28 | 30.9 | | arcade machine | 49.71 | 68.34 | | hovel | 29.46 | 45.95 | | bus | 66.52 | 83.96 | | towel | 67.34 | 75.75 | | light | 60.77 | 80.62 | | truck | 33.09 | 46.03 | | tower | 31.77 | 53.31 | | chandelier | 71.6 | 82.84 | | awning | 29.81 | 44.55 | | streetlight | 37.09 | 55.59 | | booth | 39.39 | 39.79 | | television receiver | 70.41 | 85.64 | | airplane | 56.5 | 65.48 | | dirt track | 12.22 | 19.08 | | apparel | 30.79 | 53.4 | | pole | 29.56 | 48.9 | | land | 5.41 | 7.25 | | bannister | 12.78 | 23.86 | | escalator | 54.33 | 73.11 | | ottoman | 43.97 | 68.06 | | bottle | 23.65 | 29.2 | | buffet | 38.71 | 40.26 | | poster | 22.36 | 36.57 | | stage | 14.92 | 24.17 | | van | 40.3 | 69.06 | | ship | 58.46 | 83.39 | | fountain | 2.17 | 2.19 | | conveyer belt | 85.14 | 90.51 | | canopy | 29.52 | 42.25 | | washer | 70.82 | 73.13 | | plaything | 25.09 | 34.02 | | swimming pool | 34.35 | 50.96 | | stool | 42.47 | 66.47 | | barrel | 30.14 | 53.35 | | basket | 32.56 | 41.43 | | waterfall | 62.29 | 85.97 | | tent | 84.85 | 98.35 | | bag | 17.79 | 24.53 | | minibike | 64.49 | 79.61 | | cradle | 63.81 | 77.78 | | oven | 47.9 | 64.6 | | ball | 25.17 | 33.24 | | food | 59.1 | 77.77 | | step | 25.79 | 34.18 | | tank | 43.29 | 45.39 | | trade name | 32.96 | 43.46 | | microwave | 34.17 | 36.93 | | pot | 55.26 | 65.79 | | animal | 63.09 | 68.83 | | bicycle | 55.29 | 78.64 | | lake | 0.0 | 0.0 | | dishwasher | 72.98 | 84.16 | | screen | 75.59 | 89.14 | | blanket | 17.91 | 22.45 | | sculpture | 64.37 | 82.88 | | hood | 64.34 | 73.88 | | sconce | 49.88 | 64.96 | | vase | 48.01 | 62.59 | | traffic light | 41.69 | 58.5 | | tray | 10.45 | 20.15 | | ashcan | 40.54 | 59.38 | | fan | 65.09 | 80.47 | | pier | 20.97 | 37.82 | | crt screen | 0.61 | 1.39 | | plate | 59.45 | 74.71 | | monitor | 3.85 | 5.76 | | bulletin board | 42.04 | 49.17 | | shower | 12.72 | 23.62 | | radiator | 45.41 | 56.04 | | glass | 18.1 | 19.89 | | clock | 29.42 | 37.29 | | flag | 50.74 | 57.56 | +---------------------+-------+-------+ 2024/07/07 18:25:33 - mmengine - INFO - Iter(val) [500/500] aAcc: 82.9400 mIoU: 48.7200 mAcc: 62.1700 data_time: 0.0015 time: 0.2448 2024/07/07 18:26:29 - mmengine - INFO - Iter(train) [ 15050/120000] base_lr: 1.9242e-04 lr: 1.9310e-05 eta: 1 day, 8:34:14 time: 1.1156 data_time: 0.0158 memory: 14647 grad_norm: 1.9627 loss: 0.5191 semantic_segmentation_loss_cls: 0.1986 semantic_segmentation_loss_mask: 0.0978 semantic_segmentation_loss_dice: 0.2226 2024/07/07 18:27:24 - mmengine - INFO - Iter(train) [ 15100/120000] base_lr: 1.9237e-04 lr: 1.9306e-05 eta: 1 day, 8:33:16 time: 1.1156 data_time: 0.0158 memory: 16130 grad_norm: 1.9638 loss: 0.5195 semantic_segmentation_loss_cls: 0.1988 semantic_segmentation_loss_mask: 0.0979 semantic_segmentation_loss_dice: 0.2228 2024/07/07 18:28:19 - mmengine - INFO - Iter(train) [ 15150/120000] base_lr: 1.9232e-04 lr: 1.9301e-05 eta: 1 day, 8:32:13 time: 1.1154 data_time: 0.0158 memory: 15604 grad_norm: 1.9637 loss: 0.5189 semantic_segmentation_loss_cls: 0.1986 semantic_segmentation_loss_mask: 0.0977 semantic_segmentation_loss_dice: 0.2226 2024/07/07 18:29:14 - mmengine - INFO - Iter(train) [ 15200/120000] base_lr: 1.9227e-04 lr: 1.9297e-05 eta: 1 day, 8:31:13 time: 1.1154 data_time: 0.0158 memory: 15325 grad_norm: 1.9637 loss: 0.5182 semantic_segmentation_loss_cls: 0.1982 semantic_segmentation_loss_mask: 0.0976 semantic_segmentation_loss_dice: 0.2223 2024/07/07 18:30:11 - mmengine - INFO - Iter(train) [ 15250/120000] base_lr: 1.9222e-04 lr: 1.9292e-05 eta: 1 day, 8:30:22 time: 1.1156 data_time: 0.0158 memory: 15448 grad_norm: 1.9633 loss: 0.5166 semantic_segmentation_loss_cls: 0.1975 semantic_segmentation_loss_mask: 0.0973 semantic_segmentation_loss_dice: 0.2218 2024/07/07 18:31:07 - mmengine - INFO - Iter(train) [ 15300/120000] base_lr: 1.9216e-04 lr: 1.9288e-05 eta: 1 day, 8:29:24 time: 1.1154 data_time: 0.0158 memory: 14889 grad_norm: 1.9623 loss: 0.5161 semantic_segmentation_loss_cls: 0.1973 semantic_segmentation_loss_mask: 0.0972 semantic_segmentation_loss_dice: 0.2217 2024/07/07 18:32:02 - mmengine - INFO - Iter(train) [ 15350/120000] base_lr: 1.9211e-04 lr: 1.9283e-05 eta: 1 day, 8:28:25 time: 1.1155 data_time: 0.0158 memory: 14752 grad_norm: 1.9600 loss: 0.5155 semantic_segmentation_loss_cls: 0.1968 semantic_segmentation_loss_mask: 0.0972 semantic_segmentation_loss_dice: 0.2216 2024/07/07 18:32:57 - mmengine - INFO - Iter(train) [ 15400/120000] base_lr: 1.9206e-04 lr: 1.9278e-05 eta: 1 day, 8:27:26 time: 1.1154 data_time: 0.0158 memory: 14796 grad_norm: 1.9596 loss: 0.5148 semantic_segmentation_loss_cls: 0.1964 semantic_segmentation_loss_mask: 0.0971 semantic_segmentation_loss_dice: 0.2214 2024/07/07 18:33:53 - mmengine - INFO - Iter(train) [ 15450/120000] base_lr: 1.9201e-04 lr: 1.9274e-05 eta: 1 day, 8:26:29 time: 1.1155 data_time: 0.0158 memory: 14523 grad_norm: 1.9592 loss: 0.5139 semantic_segmentation_loss_cls: 0.1958 semantic_segmentation_loss_mask: 0.0970 semantic_segmentation_loss_dice: 0.2211 2024/07/07 18:34:48 - mmengine - INFO - Iter(train) [ 15500/120000] base_lr: 1.9196e-04 lr: 1.9269e-05 eta: 1 day, 8:25:29 time: 1.1155 data_time: 0.0158 memory: 15724 grad_norm: 1.9576 loss: 0.5133 semantic_segmentation_loss_cls: 0.1954 semantic_segmentation_loss_mask: 0.0969 semantic_segmentation_loss_dice: 0.2209 2024/07/07 18:35:44 - mmengine - INFO - Iter(train) [ 15550/120000] base_lr: 1.9191e-04 lr: 1.9265e-05 eta: 1 day, 8:24:32 time: 1.1154 data_time: 0.0157 memory: 15351 grad_norm: 1.9578 loss: 0.5124 semantic_segmentation_loss_cls: 0.1950 semantic_segmentation_loss_mask: 0.0968 semantic_segmentation_loss_dice: 0.2207 2024/07/07 18:36:41 - mmengine - INFO - Iter(train) [ 15600/120000] base_lr: 1.9186e-04 lr: 1.9260e-05 eta: 1 day, 8:23:41 time: 1.1155 data_time: 0.0157 memory: 16156 grad_norm: 1.9579 loss: 0.5120 semantic_segmentation_loss_cls: 0.1948 semantic_segmentation_loss_mask: 0.0967 semantic_segmentation_loss_dice: 0.2205 2024/07/07 18:37:36 - mmengine - INFO - Iter(train) [ 15650/120000] base_lr: 1.9181e-04 lr: 1.9255e-05 eta: 1 day, 8:22:41 time: 1.1153 data_time: 0.0157 memory: 15827 grad_norm: 1.9579 loss: 0.5112 semantic_segmentation_loss_cls: 0.1944 semantic_segmentation_loss_mask: 0.0966 semantic_segmentation_loss_dice: 0.2202 2024/07/07 18:38:31 - mmengine - INFO - Iter(train) [ 15700/120000] base_lr: 1.9176e-04 lr: 1.9250e-05 eta: 1 day, 8:21:39 time: 1.1149 data_time: 0.0157 memory: 15224 grad_norm: 1.9568 loss: 0.5100 semantic_segmentation_loss_cls: 0.1937 semantic_segmentation_loss_mask: 0.0964 semantic_segmentation_loss_dice: 0.2199 2024/07/07 18:39:27 - mmengine - INFO - Iter(train) [ 15750/120000] base_lr: 1.9170e-04 lr: 1.9246e-05 eta: 1 day, 8:20:48 time: 1.1152 data_time: 0.0157 memory: 15151 grad_norm: 1.9544 loss: 0.5097 semantic_segmentation_loss_cls: 0.1935 semantic_segmentation_loss_mask: 0.0963 semantic_segmentation_loss_dice: 0.2198 2024/07/07 18:40:24 - mmengine - INFO - Iter(train) [ 15800/120000] base_lr: 1.9165e-04 lr: 1.9241e-05 eta: 1 day, 8:19:55 time: 1.1154 data_time: 0.0158 memory: 14738 grad_norm: 1.9547 loss: 0.5086 semantic_segmentation_loss_cls: 0.1929 semantic_segmentation_loss_mask: 0.0963 semantic_segmentation_loss_dice: 0.2195 2024/07/07 18:41:19 - mmengine - INFO - Iter(train) [ 15850/120000] base_lr: 1.9160e-04 lr: 1.9236e-05 eta: 1 day, 8:18:54 time: 1.1151 data_time: 0.0158 memory: 15588 grad_norm: 1.9542 loss: 0.5086 semantic_segmentation_loss_cls: 0.1927 semantic_segmentation_loss_mask: 0.0963 semantic_segmentation_loss_dice: 0.2196 2024/07/07 18:42:14 - mmengine - INFO - Iter(train) [ 15900/120000] base_lr: 1.9155e-04 lr: 1.9232e-05 eta: 1 day, 8:17:52 time: 1.1147 data_time: 0.0158 memory: 15117 grad_norm: 1.9538 loss: 0.5084 semantic_segmentation_loss_cls: 0.1926 semantic_segmentation_loss_mask: 0.0962 semantic_segmentation_loss_dice: 0.2196 2024/07/07 18:43:09 - mmengine - INFO - Iter(train) [ 15950/120000] base_lr: 1.9149e-04 lr: 1.9227e-05 eta: 1 day, 8:16:54 time: 1.1148 data_time: 0.0158 memory: 15276 grad_norm: 1.9550 loss: 0.5084 semantic_segmentation_loss_cls: 0.1925 semantic_segmentation_loss_mask: 0.0963 semantic_segmentation_loss_dice: 0.2197 2024/07/07 18:44:04 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 18:44:04 - mmengine - INFO - Iter(train) [ 16000/120000] base_lr: 1.9144e-04 lr: 1.9222e-05 eta: 1 day, 8:15:55 time: 1.1147 data_time: 0.0158 memory: 14622 grad_norm: 1.9544 loss: 0.5081 semantic_segmentation_loss_cls: 0.1925 semantic_segmentation_loss_mask: 0.0961 semantic_segmentation_loss_dice: 0.2195 2024/07/07 18:44:04 - mmengine - INFO - Saving checkpoint at 16000 iterations 2024/07/07 18:45:05 - mmengine - INFO - Iter(train) [ 16050/120000] base_lr: 1.9139e-04 lr: 1.9217e-05 eta: 1 day, 8:15:31 time: 1.1149 data_time: 0.0158 memory: 14460 grad_norm: 1.9567 loss: 0.5074 semantic_segmentation_loss_cls: 0.1921 semantic_segmentation_loss_mask: 0.0961 semantic_segmentation_loss_dice: 0.2192 2024/07/07 18:46:01 - mmengine - INFO - Iter(train) [ 16100/120000] base_lr: 1.9134e-04 lr: 1.9212e-05 eta: 1 day, 8:14:37 time: 1.1151 data_time: 0.0158 memory: 15171 grad_norm: 1.9584 loss: 0.5069 semantic_segmentation_loss_cls: 0.1919 semantic_segmentation_loss_mask: 0.0959 semantic_segmentation_loss_dice: 0.2190 2024/07/07 18:46:58 - mmengine - INFO - Iter(train) [ 16150/120000] base_lr: 1.9128e-04 lr: 1.9208e-05 eta: 1 day, 8:13:47 time: 1.1154 data_time: 0.0157 memory: 15262 grad_norm: 1.9605 loss: 0.5067 semantic_segmentation_loss_cls: 0.1918 semantic_segmentation_loss_mask: 0.0959 semantic_segmentation_loss_dice: 0.2190 2024/07/07 18:47:54 - mmengine - INFO - Iter(train) [ 16200/120000] base_lr: 1.9123e-04 lr: 1.9203e-05 eta: 1 day, 8:12:51 time: 1.1157 data_time: 0.0157 memory: 15292 grad_norm: 1.9593 loss: 0.5059 semantic_segmentation_loss_cls: 0.1914 semantic_segmentation_loss_mask: 0.0959 semantic_segmentation_loss_dice: 0.2187 2024/07/07 18:48:49 - mmengine - INFO - Iter(train) [ 16250/120000] base_lr: 1.9118e-04 lr: 1.9198e-05 eta: 1 day, 8:11:52 time: 1.1155 data_time: 0.0157 memory: 14570 grad_norm: 1.9579 loss: 0.5051 semantic_segmentation_loss_cls: 0.1909 semantic_segmentation_loss_mask: 0.0958 semantic_segmentation_loss_dice: 0.2184 2024/07/07 18:49:45 - mmengine - INFO - Iter(train) [ 16300/120000] base_lr: 1.9112e-04 lr: 1.9193e-05 eta: 1 day, 8:10:53 time: 1.1155 data_time: 0.0157 memory: 15045 grad_norm: 1.9559 loss: 0.5040 semantic_segmentation_loss_cls: 0.1904 semantic_segmentation_loss_mask: 0.0956 semantic_segmentation_loss_dice: 0.2181 2024/07/07 18:50:40 - mmengine - INFO - Iter(train) [ 16350/120000] base_lr: 1.9107e-04 lr: 1.9188e-05 eta: 1 day, 8:09:51 time: 1.1152 data_time: 0.0157 memory: 15071 grad_norm: 1.9556 loss: 0.5035 semantic_segmentation_loss_cls: 0.1900 semantic_segmentation_loss_mask: 0.0955 semantic_segmentation_loss_dice: 0.2179 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time: 1.1152 data_time: 0.0159 memory: 16129 grad_norm: 1.9386 loss: 0.4985 semantic_segmentation_loss_cls: 0.1872 semantic_segmentation_loss_mask: 0.0948 semantic_segmentation_loss_dice: 0.2165 2024/07/07 19:00:50 - mmengine - INFO - Iter(train) [ 16900/120000] base_lr: 1.9047e-04 lr: 1.9133e-05 eta: 1 day, 7:59:13 time: 1.1151 data_time: 0.0159 memory: 15075 grad_norm: 1.9381 loss: 0.4982 semantic_segmentation_loss_cls: 0.1870 semantic_segmentation_loss_mask: 0.0947 semantic_segmentation_loss_dice: 0.2164 2024/07/07 19:01:46 - mmengine - INFO - Iter(train) [ 16950/120000] base_lr: 1.9041e-04 lr: 1.9128e-05 eta: 1 day, 7:58:19 time: 1.1151 data_time: 0.0159 memory: 16547 grad_norm: 1.9380 loss: 0.4977 semantic_segmentation_loss_cls: 0.1867 semantic_segmentation_loss_mask: 0.0947 semantic_segmentation_loss_dice: 0.2163 2024/07/07 19:02:42 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 19:02:42 - mmengine - INFO - Iter(train) [ 17000/120000] base_lr: 1.9036e-04 lr: 1.9123e-05 eta: 1 day, 7:57:24 time: 1.1153 data_time: 0.0159 memory: 15272 grad_norm: 1.9381 loss: 0.4973 semantic_segmentation_loss_cls: 0.1864 semantic_segmentation_loss_mask: 0.0946 semantic_segmentation_loss_dice: 0.2162 2024/07/07 19:02:42 - mmengine - INFO - Saving checkpoint at 17000 iterations 2024/07/07 19:03:43 - mmengine - INFO - Iter(train) [ 17050/120000] base_lr: 1.9030e-04 lr: 1.9118e-05 eta: 1 day, 7:56:57 time: 1.1154 data_time: 0.0159 memory: 15682 grad_norm: 1.9371 loss: 0.4963 semantic_segmentation_loss_cls: 0.1859 semantic_segmentation_loss_mask: 0.0946 semantic_segmentation_loss_dice: 0.2159 2024/07/07 19:04:38 - mmengine - INFO - Iter(train) [ 17100/120000] base_lr: 1.9025e-04 lr: 1.9113e-05 eta: 1 day, 7:55:56 time: 1.1153 data_time: 0.0159 memory: 15267 grad_norm: 1.9361 loss: 0.4951 semantic_segmentation_loss_cls: 0.1853 semantic_segmentation_loss_mask: 0.0944 semantic_segmentation_loss_dice: 0.2155 2024/07/07 19:05:34 - mmengine - INFO - Iter(train) [ 17150/120000] base_lr: 1.9019e-04 lr: 1.9108e-05 eta: 1 day, 7:54:58 time: 1.1153 data_time: 0.0159 memory: 15074 grad_norm: 1.9354 loss: 0.4944 semantic_segmentation_loss_cls: 0.1848 semantic_segmentation_loss_mask: 0.0943 semantic_segmentation_loss_dice: 0.2153 2024/07/07 19:06:29 - mmengine - INFO - Iter(train) [ 17200/120000] base_lr: 1.9013e-04 lr: 1.9103e-05 eta: 1 day, 7:53:59 time: 1.1154 data_time: 0.0159 memory: 15240 grad_norm: 1.9358 loss: 0.4936 semantic_segmentation_loss_cls: 0.1844 semantic_segmentation_loss_mask: 0.0942 semantic_segmentation_loss_dice: 0.2150 2024/07/07 19:07:24 - mmengine - INFO - Iter(train) [ 17250/120000] base_lr: 1.9008e-04 lr: 1.9098e-05 eta: 1 day, 7:52:56 time: 1.1151 data_time: 0.0159 memory: 15261 grad_norm: 1.9347 loss: 0.4930 semantic_segmentation_loss_cls: 0.1842 semantic_segmentation_loss_mask: 0.0940 semantic_segmentation_loss_dice: 0.2148 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time: 1.1146 data_time: 0.0159 memory: 15958 grad_norm: 1.9322 loss: 0.4865 semantic_segmentation_loss_cls: 0.1802 semantic_segmentation_loss_mask: 0.0932 semantic_segmentation_loss_dice: 0.2131 2024/07/07 19:17:35 - mmengine - INFO - Iter(train) [ 17800/120000] base_lr: 1.8944e-04 lr: 1.9040e-05 eta: 1 day, 7:42:26 time: 1.1148 data_time: 0.0159 memory: 14997 grad_norm: 1.9314 loss: 0.4861 semantic_segmentation_loss_cls: 0.1798 semantic_segmentation_loss_mask: 0.0932 semantic_segmentation_loss_dice: 0.2131 2024/07/07 19:18:32 - mmengine - INFO - Iter(train) [ 17850/120000] base_lr: 1.8939e-04 lr: 1.9035e-05 eta: 1 day, 7:41:36 time: 1.1153 data_time: 0.0159 memory: 16190 grad_norm: 1.9315 loss: 0.4860 semantic_segmentation_loss_cls: 0.1797 semantic_segmentation_loss_mask: 0.0932 semantic_segmentation_loss_dice: 0.2130 2024/07/07 19:19:28 - mmengine - INFO - Iter(train) [ 17900/120000] base_lr: 1.8933e-04 lr: 1.9030e-05 eta: 1 day, 7:40:41 time: 1.1154 data_time: 0.0159 memory: 15293 grad_norm: 1.9300 loss: 0.4853 semantic_segmentation_loss_cls: 0.1794 semantic_segmentation_loss_mask: 0.0931 semantic_segmentation_loss_dice: 0.2128 2024/07/07 19:20:23 - mmengine - INFO - Iter(train) [ 17950/120000] base_lr: 1.8927e-04 lr: 1.9025e-05 eta: 1 day, 7:39:40 time: 1.1151 data_time: 0.0159 memory: 14440 grad_norm: 1.9291 loss: 0.4848 semantic_segmentation_loss_cls: 0.1790 semantic_segmentation_loss_mask: 0.0930 semantic_segmentation_loss_dice: 0.2127 2024/07/07 19:21:18 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 19:21:18 - mmengine - INFO - Iter(train) [ 18000/120000] base_lr: 1.8921e-04 lr: 1.9019e-05 eta: 1 day, 7:38:41 time: 1.1150 data_time: 0.0159 memory: 15342 grad_norm: 1.9283 loss: 0.4847 semantic_segmentation_loss_cls: 0.1789 semantic_segmentation_loss_mask: 0.0930 semantic_segmentation_loss_dice: 0.2129 2024/07/07 19:21:18 - mmengine - INFO - Saving checkpoint at 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base_lr: 1.8880e-04 lr: 1.8981e-05 eta: 1 day, 7:32:33 time: 1.1152 data_time: 0.0160 memory: 15008 grad_norm: 1.9241 loss: 0.4809 semantic_segmentation_loss_cls: 0.1767 semantic_segmentation_loss_mask: 0.0924 semantic_segmentation_loss_dice: 0.2118 2024/07/07 19:28:48 - mmengine - INFO - Iter(train) [ 18400/120000] base_lr: 1.8874e-04 lr: 1.8976e-05 eta: 1 day, 7:31:32 time: 1.1148 data_time: 0.0160 memory: 15344 grad_norm: 1.9239 loss: 0.4810 semantic_segmentation_loss_cls: 0.1767 semantic_segmentation_loss_mask: 0.0924 semantic_segmentation_loss_dice: 0.2119 2024/07/07 19:29:43 - mmengine - INFO - Iter(train) [ 18450/120000] base_lr: 1.8868e-04 lr: 1.8970e-05 eta: 1 day, 7:30:30 time: 1.1145 data_time: 0.0160 memory: 15796 grad_norm: 1.9239 loss: 0.4810 semantic_segmentation_loss_cls: 0.1767 semantic_segmentation_loss_mask: 0.0924 semantic_segmentation_loss_dice: 0.2120 2024/07/07 19:30:38 - mmengine - INFO - Iter(train) [ 18500/120000] base_lr: 1.8861e-04 lr: 1.8965e-05 eta: 1 day, 7:29:30 time: 1.1146 data_time: 0.0160 memory: 15254 grad_norm: 1.9243 loss: 0.4808 semantic_segmentation_loss_cls: 0.1766 semantic_segmentation_loss_mask: 0.0924 semantic_segmentation_loss_dice: 0.2118 2024/07/07 19:31:33 - mmengine - INFO - Iter(train) [ 18550/120000] base_lr: 1.8855e-04 lr: 1.8960e-05 eta: 1 day, 7:28:30 time: 1.1146 data_time: 0.0160 memory: 15483 grad_norm: 1.9230 loss: 0.4803 semantic_segmentation_loss_cls: 0.1763 semantic_segmentation_loss_mask: 0.0923 semantic_segmentation_loss_dice: 0.2117 2024/07/07 19:32:29 - mmengine - INFO - Iter(train) [ 18600/120000] base_lr: 1.8849e-04 lr: 1.8954e-05 eta: 1 day, 7:27:36 time: 1.1147 data_time: 0.0160 memory: 15468 grad_norm: 1.9236 loss: 0.4794 semantic_segmentation_loss_cls: 0.1757 semantic_segmentation_loss_mask: 0.0923 semantic_segmentation_loss_dice: 0.2114 2024/07/07 19:33:26 - mmengine - INFO - Iter(train) [ 18650/120000] base_lr: 1.8843e-04 lr: 1.8948e-05 eta: 1 day, 7:26:42 time: 1.1151 data_time: 0.0160 memory: 15745 grad_norm: 1.9221 loss: 0.4786 semantic_segmentation_loss_cls: 0.1754 semantic_segmentation_loss_mask: 0.0921 semantic_segmentation_loss_dice: 0.2112 2024/07/07 19:34:21 - mmengine - INFO - Iter(train) [ 18700/120000] base_lr: 1.8837e-04 lr: 1.8943e-05 eta: 1 day, 7:25:45 time: 1.1155 data_time: 0.0160 memory: 14765 grad_norm: 1.9196 loss: 0.4784 semantic_segmentation_loss_cls: 0.1752 semantic_segmentation_loss_mask: 0.0920 semantic_segmentation_loss_dice: 0.2112 2024/07/07 19:35:17 - mmengine - INFO - Iter(train) [ 18750/120000] base_lr: 1.8831e-04 lr: 1.8937e-05 eta: 1 day, 7:24:50 time: 1.1160 data_time: 0.0160 memory: 16232 grad_norm: 1.9177 loss: 0.4778 semantic_segmentation_loss_cls: 0.1749 semantic_segmentation_loss_mask: 0.0919 semantic_segmentation_loss_dice: 0.2110 2024/07/07 19:36:13 - mmengine - INFO - Iter(train) [ 18800/120000] base_lr: 1.8825e-04 lr: 1.8932e-05 eta: 1 day, 7:23:51 time: 1.1158 data_time: 0.0160 memory: 14740 grad_norm: 1.9172 loss: 0.4768 semantic_segmentation_loss_cls: 0.1743 semantic_segmentation_loss_mask: 0.0917 semantic_segmentation_loss_dice: 0.2107 2024/07/07 19:37:09 - mmengine - INFO - Iter(train) [ 18850/120000] base_lr: 1.8819e-04 lr: 1.8926e-05 eta: 1 day, 7:22:56 time: 1.1159 data_time: 0.0160 memory: 15522 grad_norm: 1.9166 loss: 0.4761 semantic_segmentation_loss_cls: 0.1739 semantic_segmentation_loss_mask: 0.0917 semantic_segmentation_loss_dice: 0.2105 2024/07/07 19:38:04 - mmengine - INFO - Iter(train) [ 18900/120000] base_lr: 1.8813e-04 lr: 1.8921e-05 eta: 1 day, 7:21:57 time: 1.1156 data_time: 0.0160 memory: 15166 grad_norm: 1.9148 loss: 0.4752 semantic_segmentation_loss_cls: 0.1734 semantic_segmentation_loss_mask: 0.0915 semantic_segmentation_loss_dice: 0.2102 2024/07/07 19:38:59 - mmengine - INFO - Iter(train) [ 18950/120000] base_lr: 1.8807e-04 lr: 1.8915e-05 eta: 1 day, 7:20:59 time: 1.1157 data_time: 0.0161 memory: 15072 grad_norm: 1.9147 loss: 0.4752 semantic_segmentation_loss_cls: 0.1734 semantic_segmentation_loss_mask: 0.0915 semantic_segmentation_loss_dice: 0.2103 2024/07/07 19:39:56 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 19:39:56 - mmengine - INFO - Iter(train) [ 19000/120000] base_lr: 1.8800e-04 lr: 1.8909e-05 eta: 1 day, 7:20:08 time: 1.1159 data_time: 0.0161 memory: 14686 grad_norm: 1.9149 loss: 0.4748 semantic_segmentation_loss_cls: 0.1732 semantic_segmentation_loss_mask: 0.0914 semantic_segmentation_loss_dice: 0.2102 2024/07/07 19:39:56 - mmengine - INFO - Saving checkpoint at 19000 iterations 2024/07/07 19:40:56 - mmengine - INFO - Iter(train) [ 19050/120000] base_lr: 1.8794e-04 lr: 1.8904e-05 eta: 1 day, 7:19:34 time: 1.1167 data_time: 0.0171 memory: 15178 grad_norm: 1.9132 loss: 0.4743 semantic_segmentation_loss_cls: 0.1728 semantic_segmentation_loss_mask: 0.0913 semantic_segmentation_loss_dice: 0.2101 2024/07/07 19:41:51 - mmengine - INFO - Iter(train) [ 19100/120000] base_lr: 1.8788e-04 lr: 1.8898e-05 eta: 1 day, 7:18:34 time: 1.1166 data_time: 0.0171 memory: 14951 grad_norm: 1.9101 loss: 0.4735 semantic_segmentation_loss_cls: 0.1724 semantic_segmentation_loss_mask: 0.0912 semantic_segmentation_loss_dice: 0.2099 2024/07/07 19:42:47 - mmengine - INFO - Iter(train) [ 19150/120000] base_lr: 1.8782e-04 lr: 1.8893e-05 eta: 1 day, 7:17:37 time: 1.1168 data_time: 0.0171 memory: 15552 grad_norm: 1.9099 loss: 0.4731 semantic_segmentation_loss_cls: 0.1721 semantic_segmentation_loss_mask: 0.0912 semantic_segmentation_loss_dice: 0.2098 2024/07/07 19:43:41 - mmengine - INFO - Iter(train) [ 19200/120000] base_lr: 1.8776e-04 lr: 1.8887e-05 eta: 1 day, 7:16:33 time: 1.1166 data_time: 0.0171 memory: 15052 grad_norm: 1.9091 loss: 0.4729 semantic_segmentation_loss_cls: 0.1720 semantic_segmentation_loss_mask: 0.0912 semantic_segmentation_loss_dice: 0.2098 2024/07/07 19:44:37 - mmengine - INFO - Iter(train) [ 19250/120000] base_lr: 1.8769e-04 lr: 1.8881e-05 eta: 1 day, 7:15:36 time: 1.1163 data_time: 0.0171 memory: 14717 grad_norm: 1.9098 loss: 0.4732 semantic_segmentation_loss_cls: 0.1720 semantic_segmentation_loss_mask: 0.0913 semantic_segmentation_loss_dice: 0.2099 2024/07/07 19:45:33 - mmengine - INFO - Iter(train) [ 19300/120000] base_lr: 1.8763e-04 lr: 1.8875e-05 eta: 1 day, 7:14:41 time: 1.1164 data_time: 0.0171 memory: 15597 grad_norm: 1.9101 loss: 0.4727 semantic_segmentation_loss_cls: 0.1717 semantic_segmentation_loss_mask: 0.0912 semantic_segmentation_loss_dice: 0.2098 2024/07/07 19:46:28 - mmengine - INFO - Iter(train) [ 19350/120000] base_lr: 1.8757e-04 lr: 1.8870e-05 eta: 1 day, 7:13:42 time: 1.1164 data_time: 0.0171 memory: 14855 grad_norm: 1.9094 loss: 0.4721 semantic_segmentation_loss_cls: 0.1715 semantic_segmentation_loss_mask: 0.0911 semantic_segmentation_loss_dice: 0.2095 2024/07/07 19:47:24 - mmengine - INFO - Iter(train) [ 19400/120000] base_lr: 1.8750e-04 lr: 1.8864e-05 eta: 1 day, 7:12:46 time: 1.1165 data_time: 0.0171 memory: 16066 grad_norm: 1.9083 loss: 0.4718 semantic_segmentation_loss_cls: 0.1713 semantic_segmentation_loss_mask: 0.0911 semantic_segmentation_loss_dice: 0.2095 2024/07/07 19:48:19 - mmengine - INFO - Iter(train) [ 19450/120000] base_lr: 1.8744e-04 lr: 1.8858e-05 eta: 1 day, 7:11:48 time: 1.1165 data_time: 0.0171 memory: 14834 grad_norm: 1.9078 loss: 0.4710 semantic_segmentation_loss_cls: 0.1710 semantic_segmentation_loss_mask: 0.0910 semantic_segmentation_loss_dice: 0.2091 2024/07/07 19:49:14 - mmengine - INFO - Iter(train) [ 19500/120000] base_lr: 1.8738e-04 lr: 1.8853e-05 eta: 1 day, 7:10:47 time: 1.1164 data_time: 0.0171 memory: 15266 grad_norm: 1.9076 loss: 0.4705 semantic_segmentation_loss_cls: 0.1705 semantic_segmentation_loss_mask: 0.0909 semantic_segmentation_loss_dice: 0.2091 2024/07/07 19:50:10 - mmengine - INFO - Iter(train) [ 19550/120000] base_lr: 1.8731e-04 lr: 1.8847e-05 eta: 1 day, 7:09:51 time: 1.1164 data_time: 0.0170 memory: 15776 grad_norm: 1.9064 loss: 0.4699 semantic_segmentation_loss_cls: 0.1702 semantic_segmentation_loss_mask: 0.0908 semantic_segmentation_loss_dice: 0.2088 2024/07/07 19:51:06 - mmengine - INFO - Iter(train) [ 19600/120000] base_lr: 1.8725e-04 lr: 1.8841e-05 eta: 1 day, 7:08:58 time: 1.1163 data_time: 0.0170 memory: 14893 grad_norm: 1.9058 loss: 0.4690 semantic_segmentation_loss_cls: 0.1698 semantic_segmentation_loss_mask: 0.0907 semantic_segmentation_loss_dice: 0.2085 2024/07/07 19:52:03 - mmengine - INFO - Iter(train) [ 19650/120000] base_lr: 1.8719e-04 lr: 1.8835e-05 eta: 1 day, 7:08:04 time: 1.1166 data_time: 0.0171 memory: 15229 grad_norm: 1.9043 loss: 0.4681 semantic_segmentation_loss_cls: 0.1693 semantic_segmentation_loss_mask: 0.0906 semantic_segmentation_loss_dice: 0.2082 2024/07/07 19:52:58 - mmengine - INFO - Iter(train) [ 19700/120000] base_lr: 1.8712e-04 lr: 1.8829e-05 eta: 1 day, 7:07:07 time: 1.1168 data_time: 0.0171 memory: 15954 grad_norm: 1.9039 loss: 0.4675 semantic_segmentation_loss_cls: 0.1689 semantic_segmentation_loss_mask: 0.0906 semantic_segmentation_loss_dice: 0.2080 2024/07/07 19:53:52 - mmengine - INFO - Iter(train) [ 19750/120000] base_lr: 1.8706e-04 lr: 1.8824e-05 eta: 1 day, 7:06:03 time: 1.1162 data_time: 0.0170 memory: 15355 grad_norm: 1.9043 loss: 0.4669 semantic_segmentation_loss_cls: 0.1686 semantic_segmentation_loss_mask: 0.0904 semantic_segmentation_loss_dice: 0.2079 2024/07/07 19:54:46 - mmengine - INFO - Iter(train) [ 19800/120000] base_lr: 1.8700e-04 lr: 1.8818e-05 eta: 1 day, 7:04:58 time: 1.1156 data_time: 0.0169 memory: 14925 grad_norm: 1.9027 loss: 0.4665 semantic_segmentation_loss_cls: 0.1684 semantic_segmentation_loss_mask: 0.0903 semantic_segmentation_loss_dice: 0.2079 2024/07/07 19:55:42 - mmengine - INFO - Iter(train) [ 19850/120000] base_lr: 1.8693e-04 lr: 1.8812e-05 eta: 1 day, 7:04:03 time: 1.1159 data_time: 0.0169 memory: 15297 grad_norm: 1.9008 loss: 0.4657 semantic_segmentation_loss_cls: 0.1679 semantic_segmentation_loss_mask: 0.0902 semantic_segmentation_loss_dice: 0.2076 2024/07/07 19:56:39 - mmengine - INFO - Iter(train) [ 19900/120000] base_lr: 1.8687e-04 lr: 1.8806e-05 eta: 1 day, 7:03:12 time: 1.1163 data_time: 0.0169 memory: 14874 grad_norm: 1.9013 loss: 0.4648 semantic_segmentation_loss_cls: 0.1675 semantic_segmentation_loss_mask: 0.0900 semantic_segmentation_loss_dice: 0.2073 2024/07/07 19:57:35 - mmengine - INFO - Iter(train) [ 19950/120000] base_lr: 1.8680e-04 lr: 1.8800e-05 eta: 1 day, 7:02:13 time: 1.1163 data_time: 0.0169 memory: 14840 grad_norm: 1.9004 loss: 0.4634 semantic_segmentation_loss_cls: 0.1668 semantic_segmentation_loss_mask: 0.0899 semantic_segmentation_loss_dice: 0.2067 2024/07/07 19:58:29 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 19:58:29 - mmengine - INFO - Iter(train) [ 20000/120000] base_lr: 1.8674e-04 lr: 1.8794e-05 eta: 1 day, 7:01:11 time: 1.1161 data_time: 0.0169 memory: 15006 grad_norm: 1.9007 loss: 0.4629 semantic_segmentation_loss_cls: 0.1665 semantic_segmentation_loss_mask: 0.0898 semantic_segmentation_loss_dice: 0.2066 2024/07/07 19:58:29 - mmengine - INFO - Saving checkpoint at 20000 iterations 2024/07/07 19:58:46 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:51 time: 0.2449 data_time: 0.0015 memory: 5013 2024/07/07 19:58:58 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:38 time: 0.2449 data_time: 0.0015 memory: 5187 2024/07/07 19:59:11 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:26 time: 0.2449 data_time: 0.0015 memory: 4460 2024/07/07 19:59:23 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:13 time: 0.2449 data_time: 0.0015 memory: 4543 2024/07/07 19:59:35 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:01 time: 0.2449 data_time: 0.0015 memory: 4643 2024/07/07 19:59:48 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:49 time: 0.2450 data_time: 0.0015 memory: 10983 2024/07/07 20:00:00 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2450 data_time: 0.0015 memory: 4460 2024/07/07 20:00:12 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2450 data_time: 0.0015 memory: 4641 2024/07/07 20:00:24 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2450 data_time: 0.0015 memory: 4473 2024/07/07 20:00:37 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2450 data_time: 0.0015 memory: 4555 2024/07/07 20:00:37 - mmengine - INFO - per class results: 2024/07/07 20:00:37 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 77.75 | 86.0 | | building | 80.98 | 87.39 | | sky | 94.15 | 97.64 | | floor | 83.13 | 90.83 | | tree | 74.21 | 88.35 | | ceiling | 85.54 | 93.53 | | road | 84.06 | 91.86 | | bed | 87.15 | 95.89 | | windowpane | 61.37 | 80.57 | | grass | 72.07 | 88.12 | | cabinet | 59.66 | 70.32 | | sidewalk | 66.45 | 81.7 | | person | 81.77 | 91.45 | | earth | 33.05 | 45.08 | | door | 50.59 | 69.78 | | table | 62.04 | 76.58 | | mountain | 55.46 | 70.28 | | plant | 51.72 | 67.96 | | curtain | 74.88 | 88.44 | | chair | 57.26 | 71.69 | | car | 85.35 | 90.99 | | water | 50.96 | 63.35 | | painting | 68.74 | 90.85 | | sofa | 62.75 | 74.42 | | shelf | 46.1 | 66.91 | | house | 44.64 | 77.04 | | sea | 51.74 | 81.67 | | mirror | 68.22 | 79.04 | | rug | 72.29 | 82.85 | | field | 36.4 | 50.82 | | armchair | 39.65 | 64.14 | | seat | 63.91 | 79.88 | | fence | 45.39 | 68.04 | | desk | 50.25 | 72.14 | | rock | 35.84 | 53.95 | | wardrobe | 52.42 | 68.56 | | lamp | 67.19 | 78.44 | | bathtub | 85.09 | 88.98 | | railing | 31.8 | 50.76 | | cushion | 57.99 | 70.69 | | base | 23.79 | 39.93 | | box | 28.19 | 40.22 | | column | 49.47 | 77.44 | | signboard | 38.57 | 57.76 | | chest of drawers | 35.42 | 68.86 | | counter | 41.23 | 55.91 | | sand | 35.23 | 50.94 | | sink | 64.2 | 82.28 | | skyscraper | 48.13 | 61.81 | | fireplace | 69.95 | 92.89 | | refrigerator | 71.71 | 89.34 | | grandstand | 39.2 | 68.22 | | path | 28.74 | 40.28 | | stairs | 30.36 | 42.11 | | runway | 76.71 | 89.34 | | case | 48.43 | 56.23 | | pool table | 91.15 | 95.51 | | pillow | 54.72 | 69.66 | | screen door | 69.25 | 77.75 | | stairway | 34.16 | 43.57 | | river | 16.14 | 37.13 | | bridge | 70.68 | 83.36 | | bookcase | 34.97 | 54.51 | | blind | 40.94 | 47.33 | | coffee table | 73.58 | 85.58 | | toilet | 85.39 | 88.9 | | flower | 39.25 | 61.89 | | book | 51.01 | 77.28 | | hill | 14.55 | 26.41 | | bench | 41.4 | 48.48 | | countertop | 54.19 | 65.7 | | stove | 82.58 | 86.11 | | palm | 51.74 | 69.74 | | kitchen island | 33.64 | 82.32 | | computer | 57.83 | 65.4 | | swivel chair | 39.43 | 54.8 | | boat | 46.6 | 50.91 | | bar | 33.82 | 38.42 | | arcade machine | 55.57 | 67.86 | | hovel | 28.89 | 46.49 | | bus | 75.88 | 84.55 | | towel | 67.95 | 76.73 | | light | 61.26 | 80.71 | | truck | 32.73 | 46.13 | | tower | 30.23 | 53.55 | | chandelier | 69.14 | 82.65 | | awning | 31.86 | 46.66 | | streetlight | 38.07 | 56.73 | | booth | 37.01 | 42.18 | | television receiver | 68.28 | 85.18 | | airplane | 54.17 | 65.23 | | dirt track | 6.71 | 17.63 | | apparel | 33.72 | 53.59 | | pole | 32.25 | 55.43 | | land | 0.99 | 1.43 | | bannister | 11.46 | 21.76 | | escalator | 44.04 | 56.97 | | ottoman | 43.03 | 70.01 | | bottle | 22.73 | 27.86 | | buffet | 65.25 | 71.02 | | poster | 30.06 | 39.95 | | stage | 13.8 | 22.0 | | van | 42.78 | 69.88 | | ship | 49.56 | 83.2 | | fountain | 2.21 | 2.22 | | conveyer belt | 85.14 | 90.02 | | canopy | 24.01 | 36.18 | | washer | 71.08 | 73.77 | | plaything | 24.04 | 33.31 | | swimming pool | 19.15 | 27.43 | | stool | 43.69 | 68.65 | | barrel | 33.54 | 80.73 | | basket | 34.14 | 44.42 | | waterfall | 64.78 | 89.14 | | tent | 92.61 | 98.25 | | bag | 17.71 | 26.14 | | minibike | 65.71 | 80.49 | | cradle | 62.85 | 77.57 | | oven | 35.48 | 65.15 | | ball | 22.45 | 26.23 | | food | 61.45 | 78.37 | | step | 24.89 | 33.01 | | tank | 34.37 | 35.96 | | trade name | 30.09 | 39.72 | | microwave | 34.6 | 37.31 | | pot | 56.05 | 65.79 | | animal | 62.98 | 70.39 | | bicycle | 54.59 | 79.91 | | lake | 0.0 | 0.0 | | dishwasher | 72.16 | 85.04 | | screen | 80.33 | 89.34 | | blanket | 16.64 | 20.58 | | sculpture | 69.77 | 84.45 | | hood | 74.18 | 85.33 | | sconce | 49.43 | 65.94 | | vase | 44.68 | 60.82 | | traffic light | 42.96 | 59.56 | | tray | 13.24 | 18.82 | | ashcan | 43.19 | 59.55 | | fan | 64.55 | 78.72 | | pier | 37.69 | 75.24 | | crt screen | 0.4 | 1.21 | | plate | 57.93 | 71.97 | | monitor | 3.7 | 5.2 | | bulletin board | 41.99 | 50.53 | | shower | 11.35 | 22.94 | | radiator | 49.33 | 59.37 | | glass | 18.7 | 20.61 | | clock | 32.64 | 39.62 | | flag | 45.62 | 57.79 | +---------------------+-------+-------+ 2024/07/07 20:00:37 - mmengine - INFO - Iter(val) [500/500] aAcc: 82.8200 mIoU: 48.8800 mAcc: 62.7300 data_time: 0.0015 time: 0.2456 2024/07/07 20:01:32 - mmengine - INFO - Iter(train) [ 20050/120000] base_lr: 1.8667e-04 lr: 1.8788e-05 eta: 1 day, 7:00:15 time: 1.1148 data_time: 0.0159 memory: 15378 grad_norm: 1.8975 loss: 0.4628 semantic_segmentation_loss_cls: 0.1665 semantic_segmentation_loss_mask: 0.0897 semantic_segmentation_loss_dice: 0.2066 2024/07/07 20:02:27 - mmengine - INFO - Iter(train) [ 20100/120000] base_lr: 1.8661e-04 lr: 1.8783e-05 eta: 1 day, 6:59:15 time: 1.1145 data_time: 0.0159 memory: 14711 grad_norm: 1.8940 loss: 0.4627 semantic_segmentation_loss_cls: 0.1662 semantic_segmentation_loss_mask: 0.0898 semantic_segmentation_loss_dice: 0.2067 2024/07/07 20:03:23 - mmengine - INFO - Iter(train) [ 20150/120000] base_lr: 1.8654e-04 lr: 1.8777e-05 eta: 1 day, 6:58:17 time: 1.1142 data_time: 0.0159 memory: 14618 grad_norm: 1.8908 loss: 0.4620 semantic_segmentation_loss_cls: 0.1658 semantic_segmentation_loss_mask: 0.0897 semantic_segmentation_loss_dice: 0.2065 2024/07/07 20:04:18 - mmengine - INFO - Iter(train) [ 20200/120000] base_lr: 1.8648e-04 lr: 1.8771e-05 eta: 1 day, 6:57:17 time: 1.1140 data_time: 0.0159 memory: 14935 grad_norm: 1.8905 loss: 0.4620 semantic_segmentation_loss_cls: 0.1657 semantic_segmentation_loss_mask: 0.0897 semantic_segmentation_loss_dice: 0.2065 2024/07/07 20:05:13 - mmengine - INFO - Iter(train) [ 20250/120000] base_lr: 1.8641e-04 lr: 1.8765e-05 eta: 1 day, 6:56:19 time: 1.1140 data_time: 0.0159 memory: 14863 grad_norm: 1.8905 loss: 0.4616 semantic_segmentation_loss_cls: 0.1654 semantic_segmentation_loss_mask: 0.0896 semantic_segmentation_loss_dice: 0.2066 2024/07/07 20:06:09 - mmengine - INFO - Iter(train) [ 20300/120000] base_lr: 1.8635e-04 lr: 1.8759e-05 eta: 1 day, 6:55:25 time: 1.1142 data_time: 0.0159 memory: 14816 grad_norm: 1.8893 loss: 0.4609 semantic_segmentation_loss_cls: 0.1649 semantic_segmentation_loss_mask: 0.0896 semantic_segmentation_loss_dice: 0.2064 2024/07/07 20:07:05 - mmengine - INFO - Iter(train) [ 20350/120000] base_lr: 1.8628e-04 lr: 1.8753e-05 eta: 1 day, 6:54:27 time: 1.1143 data_time: 0.0158 memory: 14939 grad_norm: 1.8878 loss: 0.4600 semantic_segmentation_loss_cls: 0.1645 semantic_segmentation_loss_mask: 0.0893 semantic_segmentation_loss_dice: 0.2061 2024/07/07 20:08:01 - mmengine - INFO - Iter(train) [ 20400/120000] base_lr: 1.8621e-04 lr: 1.8747e-05 eta: 1 day, 6:53:34 time: 1.1148 data_time: 0.0159 memory: 15996 grad_norm: 1.8874 loss: 0.4596 semantic_segmentation_loss_cls: 0.1644 semantic_segmentation_loss_mask: 0.0892 semantic_segmentation_loss_dice: 0.2060 2024/07/07 20:08:57 - mmengine - INFO - Iter(train) [ 20450/120000] base_lr: 1.8615e-04 lr: 1.8741e-05 eta: 1 day, 6:52:37 time: 1.1148 data_time: 0.0159 memory: 15439 grad_norm: 1.8853 loss: 0.4597 semantic_segmentation_loss_cls: 0.1644 semantic_segmentation_loss_mask: 0.0892 semantic_segmentation_loss_dice: 0.2061 2024/07/07 20:09:52 - mmengine - INFO - Iter(train) [ 20500/120000] base_lr: 1.8608e-04 lr: 1.8735e-05 eta: 1 day, 6:51:40 time: 1.1147 data_time: 0.0158 memory: 15120 grad_norm: 1.8855 loss: 0.4589 semantic_segmentation_loss_cls: 0.1640 semantic_segmentation_loss_mask: 0.0891 semantic_segmentation_loss_dice: 0.2059 2024/07/07 20:10:47 - mmengine - INFO - Iter(train) [ 20550/120000] base_lr: 1.8602e-04 lr: 1.8729e-05 eta: 1 day, 6:50:40 time: 1.1148 data_time: 0.0158 memory: 14875 grad_norm: 1.8853 loss: 0.4583 semantic_segmentation_loss_cls: 0.1636 semantic_segmentation_loss_mask: 0.0891 semantic_segmentation_loss_dice: 0.2057 2024/07/07 20:11:43 - mmengine - INFO - Iter(train) [ 20600/120000] base_lr: 1.8595e-04 lr: 1.8723e-05 eta: 1 day, 6:49:43 time: 1.1147 data_time: 0.0158 memory: 15676 grad_norm: 1.8856 loss: 0.4576 semantic_segmentation_loss_cls: 0.1632 semantic_segmentation_loss_mask: 0.0889 semantic_segmentation_loss_dice: 0.2054 2024/07/07 20:12:38 - mmengine - INFO - Iter(train) [ 20650/120000] base_lr: 1.8588e-04 lr: 1.8717e-05 eta: 1 day, 6:48:46 time: 1.1149 data_time: 0.0158 memory: 14699 grad_norm: 1.8850 loss: 0.4569 semantic_segmentation_loss_cls: 0.1628 semantic_segmentation_loss_mask: 0.0889 semantic_segmentation_loss_dice: 0.2052 2024/07/07 20:13:35 - mmengine - INFO - Iter(train) [ 20700/120000] base_lr: 1.8582e-04 lr: 1.8711e-05 eta: 1 day, 6:47:51 time: 1.1152 data_time: 0.0158 memory: 15108 grad_norm: 1.8846 loss: 0.4563 semantic_segmentation_loss_cls: 0.1625 semantic_segmentation_loss_mask: 0.0888 semantic_segmentation_loss_dice: 0.2050 2024/07/07 20:14:30 - mmengine - INFO - Iter(train) [ 20750/120000] base_lr: 1.8575e-04 lr: 1.8704e-05 eta: 1 day, 6:46:53 time: 1.1150 data_time: 0.0158 memory: 14915 grad_norm: 1.8840 loss: 0.4556 semantic_segmentation_loss_cls: 0.1622 semantic_segmentation_loss_mask: 0.0887 semantic_segmentation_loss_dice: 0.2047 2024/07/07 20:15:25 - mmengine - INFO - Iter(train) [ 20800/120000] base_lr: 1.8568e-04 lr: 1.8698e-05 eta: 1 day, 6:45:53 time: 1.1145 data_time: 0.0158 memory: 16536 grad_norm: 1.8820 loss: 0.4546 semantic_segmentation_loss_cls: 0.1616 semantic_segmentation_loss_mask: 0.0886 semantic_segmentation_loss_dice: 0.2044 2024/07/07 20:16:21 - mmengine - INFO - Iter(train) [ 20850/120000] base_lr: 1.8562e-04 lr: 1.8692e-05 eta: 1 day, 6:44:58 time: 1.1145 data_time: 0.0158 memory: 16935 grad_norm: 1.8820 loss: 0.4546 semantic_segmentation_loss_cls: 0.1616 semantic_segmentation_loss_mask: 0.0886 semantic_segmentation_loss_dice: 0.2044 2024/07/07 20:17:17 - mmengine - INFO - Iter(train) [ 20900/120000] base_lr: 1.8555e-04 lr: 1.8686e-05 eta: 1 day, 6:44:01 time: 1.1146 data_time: 0.0157 memory: 15083 grad_norm: 1.8814 loss: 0.4542 semantic_segmentation_loss_cls: 0.1614 semantic_segmentation_loss_mask: 0.0885 semantic_segmentation_loss_dice: 0.2043 2024/07/07 20:18:12 - mmengine - INFO - Iter(train) [ 20950/120000] base_lr: 1.8548e-04 lr: 1.8680e-05 eta: 1 day, 6:43:04 time: 1.1145 data_time: 0.0157 memory: 15188 grad_norm: 1.8807 loss: 0.4536 semantic_segmentation_loss_cls: 0.1612 semantic_segmentation_loss_mask: 0.0884 semantic_segmentation_loss_dice: 0.2040 2024/07/07 20:19:08 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 20:19:08 - mmengine - INFO - Iter(train) [ 21000/120000] base_lr: 1.8541e-04 lr: 1.8674e-05 eta: 1 day, 6:42:10 time: 1.1145 data_time: 0.0157 memory: 14612 grad_norm: 1.8789 loss: 0.4535 semantic_segmentation_loss_cls: 0.1611 semantic_segmentation_loss_mask: 0.0884 semantic_segmentation_loss_dice: 0.2040 2024/07/07 20:19:08 - mmengine - INFO - Saving checkpoint at 21000 iterations 2024/07/07 20:20:08 - mmengine - INFO - Iter(train) [ 21050/120000] base_lr: 1.8534e-04 lr: 1.8668e-05 eta: 1 day, 6:41:33 time: 1.1143 data_time: 0.0158 memory: 15059 grad_norm: 1.8803 loss: 0.4531 semantic_segmentation_loss_cls: 0.1610 semantic_segmentation_loss_mask: 0.0883 semantic_segmentation_loss_dice: 0.2038 2024/07/07 20:21:04 - mmengine - INFO - Iter(train) [ 21100/120000] base_lr: 1.8528e-04 lr: 1.8662e-05 eta: 1 day, 6:40:39 time: 1.1146 data_time: 0.0158 memory: 14926 grad_norm: 1.8792 loss: 0.4529 semantic_segmentation_loss_cls: 0.1609 semantic_segmentation_loss_mask: 0.0882 semantic_segmentation_loss_dice: 0.2038 2024/07/07 20:22:00 - mmengine - INFO - Iter(train) [ 21150/120000] base_lr: 1.8521e-04 lr: 1.8655e-05 eta: 1 day, 6:39:43 time: 1.1147 data_time: 0.0158 memory: 15102 grad_norm: 1.8777 loss: 0.4525 semantic_segmentation_loss_cls: 0.1607 semantic_segmentation_loss_mask: 0.0881 semantic_segmentation_loss_dice: 0.2037 2024/07/07 20:22:56 - mmengine - INFO - Iter(train) [ 21200/120000] base_lr: 1.8514e-04 lr: 1.8649e-05 eta: 1 day, 6:38:49 time: 1.1149 data_time: 0.0158 memory: 15007 grad_norm: 1.8774 loss: 0.4521 semantic_segmentation_loss_cls: 0.1604 semantic_segmentation_loss_mask: 0.0881 semantic_segmentation_loss_dice: 0.2035 2024/07/07 20:23:53 - mmengine - INFO - Iter(train) [ 21250/120000] base_lr: 1.8507e-04 lr: 1.8643e-05 eta: 1 day, 6:37:58 time: 1.1154 data_time: 0.0158 memory: 15130 grad_norm: 1.8825 loss: 0.4518 semantic_segmentation_loss_cls: 0.1602 semantic_segmentation_loss_mask: 0.0881 semantic_segmentation_loss_dice: 0.2035 2024/07/07 20:24:49 - mmengine - INFO - Iter(train) [ 21300/120000] base_lr: 1.8500e-04 lr: 1.8637e-05 eta: 1 day, 6:37:02 time: 1.1154 data_time: 0.0157 memory: 16251 grad_norm: 1.8872 loss: 0.4515 semantic_segmentation_loss_cls: 0.1601 semantic_segmentation_loss_mask: 0.0880 semantic_segmentation_loss_dice: 0.2034 2024/07/07 20:25:44 - mmengine - INFO - Iter(train) [ 21350/120000] base_lr: 1.8494e-04 lr: 1.8630e-05 eta: 1 day, 6:36:03 time: 1.1153 data_time: 0.0158 memory: 14818 grad_norm: 1.8869 loss: 0.4505 semantic_segmentation_loss_cls: 0.1597 semantic_segmentation_loss_mask: 0.0878 semantic_segmentation_loss_dice: 0.2030 2024/07/07 20:26:39 - mmengine - INFO - Iter(train) [ 21400/120000] base_lr: 1.8487e-04 lr: 1.8624e-05 eta: 1 day, 6:35:03 time: 1.1152 data_time: 0.0158 memory: 15394 grad_norm: 1.8857 loss: 0.4500 semantic_segmentation_loss_cls: 0.1595 semantic_segmentation_loss_mask: 0.0877 semantic_segmentation_loss_dice: 0.2028 2024/07/07 20:27:35 - mmengine - INFO - Iter(train) [ 21450/120000] base_lr: 1.8480e-04 lr: 1.8618e-05 eta: 1 day, 6:34:06 time: 1.1153 data_time: 0.0159 memory: 15716 grad_norm: 1.8843 loss: 0.4496 semantic_segmentation_loss_cls: 0.1592 semantic_segmentation_loss_mask: 0.0876 semantic_segmentation_loss_dice: 0.2027 2024/07/07 20:28:30 - mmengine - INFO - Iter(train) [ 21500/120000] base_lr: 1.8473e-04 lr: 1.8612e-05 eta: 1 day, 6:33:08 time: 1.1153 data_time: 0.0159 memory: 16026 grad_norm: 1.8852 loss: 0.4496 semantic_segmentation_loss_cls: 0.1592 semantic_segmentation_loss_mask: 0.0876 semantic_segmentation_loss_dice: 0.2027 2024/07/07 20:29:25 - mmengine - INFO - Iter(train) [ 21550/120000] base_lr: 1.8466e-04 lr: 1.8605e-05 eta: 1 day, 6:32:09 time: 1.1151 data_time: 0.0159 memory: 16354 grad_norm: 1.8830 loss: 0.4488 semantic_segmentation_loss_cls: 0.1588 semantic_segmentation_loss_mask: 0.0875 semantic_segmentation_loss_dice: 0.2025 2024/07/07 20:30:20 - mmengine - INFO - Iter(train) [ 21600/120000] base_lr: 1.8459e-04 lr: 1.8599e-05 eta: 1 day, 6:31:08 time: 1.1149 data_time: 0.0159 memory: 15097 grad_norm: 1.8806 loss: 0.4484 semantic_segmentation_loss_cls: 0.1588 semantic_segmentation_loss_mask: 0.0874 semantic_segmentation_loss_dice: 0.2022 2024/07/07 20:31:15 - mmengine - INFO - Iter(train) [ 21650/120000] base_lr: 1.8452e-04 lr: 1.8593e-05 eta: 1 day, 6:30:08 time: 1.1145 data_time: 0.0159 memory: 16749 grad_norm: 1.8788 loss: 0.4483 semantic_segmentation_loss_cls: 0.1587 semantic_segmentation_loss_mask: 0.0874 semantic_segmentation_loss_dice: 0.2022 2024/07/07 20:32:10 - mmengine - INFO - Iter(train) [ 21700/120000] base_lr: 1.8445e-04 lr: 1.8586e-05 eta: 1 day, 6:29:10 time: 1.1145 data_time: 0.0159 memory: 15856 grad_norm: 1.8783 loss: 0.4474 semantic_segmentation_loss_cls: 0.1584 semantic_segmentation_loss_mask: 0.0873 semantic_segmentation_loss_dice: 0.2017 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Iter(train) [ 21900/120000] base_lr: 1.8417e-04 lr: 1.8561e-05 eta: 1 day, 6:25:15 time: 1.1137 data_time: 0.0158 memory: 14779 grad_norm: 1.8785 loss: 0.4453 semantic_segmentation_loss_cls: 0.1574 semantic_segmentation_loss_mask: 0.0868 semantic_segmentation_loss_dice: 0.2011 2024/07/07 20:36:47 - mmengine - INFO - Iter(train) [ 21950/120000] base_lr: 1.8410e-04 lr: 1.8555e-05 eta: 1 day, 6:24:20 time: 1.1140 data_time: 0.0159 memory: 15096 grad_norm: 1.8786 loss: 0.4450 semantic_segmentation_loss_cls: 0.1572 semantic_segmentation_loss_mask: 0.0868 semantic_segmentation_loss_dice: 0.2010 2024/07/07 20:37:42 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 20:37:42 - mmengine - INFO - Iter(train) [ 22000/120000] base_lr: 1.8403e-04 lr: 1.8548e-05 eta: 1 day, 6:23:21 time: 1.1140 data_time: 0.0159 memory: 15435 grad_norm: 1.8779 loss: 0.4447 semantic_segmentation_loss_cls: 0.1570 semantic_segmentation_loss_mask: 0.0867 semantic_segmentation_loss_dice: 0.2009 2024/07/07 20:37:42 - mmengine - INFO - Saving checkpoint at 22000 iterations 2024/07/07 20:38:44 - mmengine - INFO - Iter(train) [ 22050/120000] base_lr: 1.8396e-04 lr: 1.8542e-05 eta: 1 day, 6:22:51 time: 1.1145 data_time: 0.0160 memory: 15046 grad_norm: 1.8763 loss: 0.4440 semantic_segmentation_loss_cls: 0.1567 semantic_segmentation_loss_mask: 0.0866 semantic_segmentation_loss_dice: 0.2007 2024/07/07 20:39:39 - mmengine - INFO - Iter(train) [ 22100/120000] base_lr: 1.8389e-04 lr: 1.8535e-05 eta: 1 day, 6:21:54 time: 1.1142 data_time: 0.0160 memory: 14618 grad_norm: 1.8759 loss: 0.4433 semantic_segmentation_loss_cls: 0.1564 semantic_segmentation_loss_mask: 0.0865 semantic_segmentation_loss_dice: 0.2004 2024/07/07 20:40:34 - mmengine - INFO - Iter(train) [ 22150/120000] base_lr: 1.8382e-04 lr: 1.8529e-05 eta: 1 day, 6:20:54 time: 1.1140 data_time: 0.0160 memory: 14776 grad_norm: 1.8748 loss: 0.4426 semantic_segmentation_loss_cls: 0.1562 semantic_segmentation_loss_mask: 0.0864 semantic_segmentation_loss_dice: 0.2001 2024/07/07 20:41:30 - mmengine - INFO - Iter(train) [ 22200/120000] base_lr: 1.8375e-04 lr: 1.8522e-05 eta: 1 day, 6:19:58 time: 1.1140 data_time: 0.0159 memory: 14773 grad_norm: 1.8744 loss: 0.4425 semantic_segmentation_loss_cls: 0.1561 semantic_segmentation_loss_mask: 0.0863 semantic_segmentation_loss_dice: 0.2001 2024/07/07 20:42:26 - mmengine - INFO - Iter(train) [ 22250/120000] base_lr: 1.8368e-04 lr: 1.8516e-05 eta: 1 day, 6:19:03 time: 1.1140 data_time: 0.0159 memory: 14961 grad_norm: 1.8732 loss: 0.4416 semantic_segmentation_loss_cls: 0.1556 semantic_segmentation_loss_mask: 0.0862 semantic_segmentation_loss_dice: 0.1998 2024/07/07 20:43:22 - mmengine - INFO - Iter(train) [ 22300/120000] base_lr: 1.8360e-04 lr: 1.8509e-05 eta: 1 day, 6:18:09 time: 1.1140 data_time: 0.0159 memory: 14521 grad_norm: 1.8728 loss: 0.4408 semantic_segmentation_loss_cls: 0.1553 semantic_segmentation_loss_mask: 0.0861 semantic_segmentation_loss_dice: 0.1994 2024/07/07 20:44:18 - mmengine - INFO - Iter(train) [ 22350/120000] base_lr: 1.8353e-04 lr: 1.8503e-05 eta: 1 day, 6:17:13 time: 1.1141 data_time: 0.0159 memory: 15501 grad_norm: 1.8715 loss: 0.4405 semantic_segmentation_loss_cls: 0.1552 semantic_segmentation_loss_mask: 0.0860 semantic_segmentation_loss_dice: 0.1993 2024/07/07 20:45:13 - mmengine - INFO - Iter(train) [ 22400/120000] base_lr: 1.8346e-04 lr: 1.8496e-05 eta: 1 day, 6:16:13 time: 1.1141 data_time: 0.0159 memory: 15860 grad_norm: 1.8705 loss: 0.4396 semantic_segmentation_loss_cls: 0.1548 semantic_segmentation_loss_mask: 0.0858 semantic_segmentation_loss_dice: 0.1990 2024/07/07 20:46:08 - mmengine - INFO - Iter(train) [ 22450/120000] base_lr: 1.8339e-04 lr: 1.8490e-05 eta: 1 day, 6:15:16 time: 1.1143 data_time: 0.0159 memory: 15680 grad_norm: 1.8701 loss: 0.4391 semantic_segmentation_loss_cls: 0.1546 semantic_segmentation_loss_mask: 0.0858 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2024/07/07 20:49:50 - mmengine - INFO - Iter(train) [ 22650/120000] base_lr: 1.8310e-04 lr: 1.8464e-05 eta: 1 day, 6:11:26 time: 1.1141 data_time: 0.0159 memory: 15275 grad_norm: 1.8683 loss: 0.4373 semantic_segmentation_loss_cls: 0.1537 semantic_segmentation_loss_mask: 0.0855 semantic_segmentation_loss_dice: 0.1981 2024/07/07 20:50:46 - mmengine - INFO - Iter(train) [ 22700/120000] base_lr: 1.8303e-04 lr: 1.8457e-05 eta: 1 day, 6:10:30 time: 1.1142 data_time: 0.0159 memory: 14766 grad_norm: 1.8693 loss: 0.4368 semantic_segmentation_loss_cls: 0.1534 semantic_segmentation_loss_mask: 0.0854 semantic_segmentation_loss_dice: 0.1979 2024/07/07 20:51:40 - mmengine - INFO - Iter(train) [ 22750/120000] base_lr: 1.8296e-04 lr: 1.8450e-05 eta: 1 day, 6:09:28 time: 1.1138 data_time: 0.0159 memory: 14918 grad_norm: 1.8692 loss: 0.4359 semantic_segmentation_loss_cls: 0.1530 semantic_segmentation_loss_mask: 0.0853 semantic_segmentation_loss_dice: 0.1976 2024/07/07 20:52:36 - mmengine - INFO - 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1.8266e-04 lr: 1.8424e-05 eta: 1 day, 6:05:34 time: 1.1134 data_time: 0.0159 memory: 15185 grad_norm: 1.8662 loss: 0.4342 semantic_segmentation_loss_cls: 0.1520 semantic_segmentation_loss_mask: 0.0850 semantic_segmentation_loss_dice: 0.1971 2024/07/07 20:56:17 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 20:56:17 - mmengine - INFO - Iter(train) [ 23000/120000] base_lr: 1.8259e-04 lr: 1.8417e-05 eta: 1 day, 6:04:37 time: 1.1132 data_time: 0.0159 memory: 15767 grad_norm: 1.8656 loss: 0.4335 semantic_segmentation_loss_cls: 0.1517 semantic_segmentation_loss_mask: 0.0849 semantic_segmentation_loss_dice: 0.1969 2024/07/07 20:56:17 - mmengine - INFO - Saving checkpoint at 23000 iterations 2024/07/07 20:57:17 - mmengine - INFO - Iter(train) [ 23050/120000] base_lr: 1.8252e-04 lr: 1.8411e-05 eta: 1 day, 6:03:59 time: 1.1132 data_time: 0.0160 memory: 16890 grad_norm: 1.8662 loss: 0.4330 semantic_segmentation_loss_cls: 0.1514 semantic_segmentation_loss_mask: 0.0847 semantic_segmentation_loss_dice: 0.1968 2024/07/07 20:58:13 - mmengine - INFO - Iter(train) [ 23100/120000] base_lr: 1.8244e-04 lr: 1.8404e-05 eta: 1 day, 6:03:03 time: 1.1133 data_time: 0.0160 memory: 16495 grad_norm: 1.8668 loss: 0.4326 semantic_segmentation_loss_cls: 0.1511 semantic_segmentation_loss_mask: 0.0847 semantic_segmentation_loss_dice: 0.1967 2024/07/07 20:59:07 - mmengine - INFO - Iter(train) [ 23150/120000] base_lr: 1.8237e-04 lr: 1.8397e-05 eta: 1 day, 6:02:02 time: 1.1131 data_time: 0.0160 memory: 14906 grad_norm: 1.8648 loss: 0.4318 semantic_segmentation_loss_cls: 0.1508 semantic_segmentation_loss_mask: 0.0845 semantic_segmentation_loss_dice: 0.1964 2024/07/07 21:00:03 - mmengine - INFO - Iter(train) [ 23200/120000] base_lr: 1.8230e-04 lr: 1.8390e-05 eta: 1 day, 6:01:05 time: 1.1134 data_time: 0.0160 memory: 16529 grad_norm: 1.8642 loss: 0.4308 semantic_segmentation_loss_cls: 0.1503 semantic_segmentation_loss_mask: 0.0845 semantic_segmentation_loss_dice: 0.1961 2024/07/07 21:00:58 - mmengine - INFO - Iter(train) [ 23250/120000] base_lr: 1.8222e-04 lr: 1.8384e-05 eta: 1 day, 6:00:09 time: 1.1135 data_time: 0.0160 memory: 15366 grad_norm: 1.8633 loss: 0.4305 semantic_segmentation_loss_cls: 0.1501 semantic_segmentation_loss_mask: 0.0844 semantic_segmentation_loss_dice: 0.1960 2024/07/07 21:01:55 - mmengine - INFO - Iter(train) [ 23300/120000] base_lr: 1.8215e-04 lr: 1.8377e-05 eta: 1 day, 5:59:16 time: 1.1136 data_time: 0.0160 memory: 14992 grad_norm: 1.8629 loss: 0.4302 semantic_segmentation_loss_cls: 0.1500 semantic_segmentation_loss_mask: 0.0843 semantic_segmentation_loss_dice: 0.1960 2024/07/07 21:02:51 - mmengine - INFO - Iter(train) [ 23350/120000] base_lr: 1.8207e-04 lr: 1.8370e-05 eta: 1 day, 5:58:22 time: 1.1138 data_time: 0.0160 memory: 15487 grad_norm: 1.8622 loss: 0.4299 semantic_segmentation_loss_cls: 0.1497 semantic_segmentation_loss_mask: 0.0843 semantic_segmentation_loss_dice: 0.1959 2024/07/07 21:03:47 - mmengine - INFO - Iter(train) [ 23400/120000] base_lr: 1.8200e-04 lr: 1.8363e-05 eta: 1 day, 5:57:24 time: 1.1137 data_time: 0.0160 memory: 14472 grad_norm: 1.8627 loss: 0.4294 semantic_segmentation_loss_cls: 0.1494 semantic_segmentation_loss_mask: 0.0842 semantic_segmentation_loss_dice: 0.1958 2024/07/07 21:04:41 - mmengine - INFO - Iter(train) [ 23450/120000] base_lr: 1.8192e-04 lr: 1.8357e-05 eta: 1 day, 5:56:24 time: 1.1135 data_time: 0.0160 memory: 15421 grad_norm: 1.8630 loss: 0.4292 semantic_segmentation_loss_cls: 0.1493 semantic_segmentation_loss_mask: 0.0842 semantic_segmentation_loss_dice: 0.1957 2024/07/07 21:05:37 - mmengine - INFO - Iter(train) [ 23500/120000] base_lr: 1.8185e-04 lr: 1.8350e-05 eta: 1 day, 5:55:29 time: 1.1138 data_time: 0.0160 memory: 14827 grad_norm: 1.8629 loss: 0.4288 semantic_segmentation_loss_cls: 0.1492 semantic_segmentation_loss_mask: 0.0841 semantic_segmentation_loss_dice: 0.1955 2024/07/07 21:06:32 - mmengine - INFO - Iter(train) [ 23550/120000] base_lr: 1.8177e-04 lr: 1.8343e-05 eta: 1 day, 5:54:28 time: 1.1135 data_time: 0.0160 memory: 15510 grad_norm: 1.8626 loss: 0.4286 semantic_segmentation_loss_cls: 0.1490 semantic_segmentation_loss_mask: 0.0841 semantic_segmentation_loss_dice: 0.1956 2024/07/07 21:07:27 - mmengine - INFO - Iter(train) [ 23600/120000] base_lr: 1.8170e-04 lr: 1.8336e-05 eta: 1 day, 5:53:31 time: 1.1133 data_time: 0.0160 memory: 16300 grad_norm: 1.8616 loss: 0.4285 semantic_segmentation_loss_cls: 0.1489 semantic_segmentation_loss_mask: 0.0841 semantic_segmentation_loss_dice: 0.1956 2024/07/07 21:08:23 - mmengine - INFO - Iter(train) [ 23650/120000] base_lr: 1.8162e-04 lr: 1.8329e-05 eta: 1 day, 5:52:33 time: 1.1130 data_time: 0.0160 memory: 15856 grad_norm: 1.8607 loss: 0.4284 semantic_segmentation_loss_cls: 0.1487 semantic_segmentation_loss_mask: 0.0841 semantic_segmentation_loss_dice: 0.1956 2024/07/07 21:09:19 - mmengine - INFO - Iter(train) [ 23700/120000] base_lr: 1.8155e-04 lr: 1.8323e-05 eta: 1 day, 5:51:40 time: 1.1133 data_time: 0.0161 memory: 15071 grad_norm: 1.8582 loss: 0.4280 semantic_segmentation_loss_cls: 0.1485 semantic_segmentation_loss_mask: 0.0840 semantic_segmentation_loss_dice: 0.1955 2024/07/07 21:10:15 - mmengine - INFO - Iter(train) [ 23750/120000] base_lr: 1.8147e-04 lr: 1.8316e-05 eta: 1 day, 5:50:43 time: 1.1136 data_time: 0.0162 memory: 15142 grad_norm: 1.8573 loss: 0.4269 semantic_segmentation_loss_cls: 0.1480 semantic_segmentation_loss_mask: 0.0838 semantic_segmentation_loss_dice: 0.1951 2024/07/07 21:11:10 - mmengine - INFO - Iter(train) [ 23800/120000] base_lr: 1.8140e-04 lr: 1.8309e-05 eta: 1 day, 5:49:45 time: 1.1139 data_time: 0.0162 memory: 14751 grad_norm: 1.8567 loss: 0.4263 semantic_segmentation_loss_cls: 0.1477 semantic_segmentation_loss_mask: 0.0838 semantic_segmentation_loss_dice: 0.1948 2024/07/07 21:12:05 - mmengine - INFO - Iter(train) [ 23850/120000] base_lr: 1.8132e-04 lr: 1.8302e-05 eta: 1 day, 5:48:47 time: 1.1137 data_time: 0.0162 memory: 14469 grad_norm: 1.8560 loss: 0.4256 semantic_segmentation_loss_cls: 0.1474 semantic_segmentation_loss_mask: 0.0837 semantic_segmentation_loss_dice: 0.1945 2024/07/07 21:13:01 - mmengine - INFO - Iter(train) [ 23900/120000] base_lr: 1.8125e-04 lr: 1.8295e-05 eta: 1 day, 5:47:51 time: 1.1135 data_time: 0.0162 memory: 16139 grad_norm: 1.8546 loss: 0.4258 semantic_segmentation_loss_cls: 0.1475 semantic_segmentation_loss_mask: 0.0837 semantic_segmentation_loss_dice: 0.1946 2024/07/07 21:13:56 - mmengine - INFO - Iter(train) [ 23950/120000] base_lr: 1.8117e-04 lr: 1.8288e-05 eta: 1 day, 5:46:51 time: 1.1133 data_time: 0.0162 memory: 14630 grad_norm: 1.8523 loss: 0.4258 semantic_segmentation_loss_cls: 0.1474 semantic_segmentation_loss_mask: 0.0837 semantic_segmentation_loss_dice: 0.1947 2024/07/07 21:14:51 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 21:14:51 - mmengine - INFO - Iter(train) [ 24000/120000] base_lr: 1.8109e-04 lr: 1.8281e-05 eta: 1 day, 5:45:51 time: 1.1134 data_time: 0.0163 memory: 15342 grad_norm: 1.8523 loss: 0.4253 semantic_segmentation_loss_cls: 0.1472 semantic_segmentation_loss_mask: 0.0837 semantic_segmentation_loss_dice: 0.1945 2024/07/07 21:14:51 - mmengine - INFO - Saving checkpoint at 24000 iterations 2024/07/07 21:15:51 - mmengine - INFO - Iter(train) [ 24050/120000] base_lr: 1.8102e-04 lr: 1.8274e-05 eta: 1 day, 5:45:15 time: 1.1147 data_time: 0.0173 memory: 15686 grad_norm: 1.8500 loss: 0.4245 semantic_segmentation_loss_cls: 0.1467 semantic_segmentation_loss_mask: 0.0835 semantic_segmentation_loss_dice: 0.1943 2024/07/07 21:16:48 - mmengine - INFO - Iter(train) [ 24100/120000] base_lr: 1.8094e-04 lr: 1.8267e-05 eta: 1 day, 5:44:21 time: 1.1149 data_time: 0.0174 memory: 14932 grad_norm: 1.8483 loss: 0.4236 semantic_segmentation_loss_cls: 0.1463 semantic_segmentation_loss_mask: 0.0833 semantic_segmentation_loss_dice: 0.1940 2024/07/07 21:17:43 - mmengine - INFO - Iter(train) [ 24150/120000] base_lr: 1.8087e-04 lr: 1.8260e-05 eta: 1 day, 5:43:25 time: 1.1151 data_time: 0.0174 memory: 15215 grad_norm: 1.8482 loss: 0.4232 semantic_segmentation_loss_cls: 0.1462 semantic_segmentation_loss_mask: 0.0832 semantic_segmentation_loss_dice: 0.1938 2024/07/07 21:18:38 - mmengine - INFO - Iter(train) [ 24200/120000] base_lr: 1.8079e-04 lr: 1.8253e-05 eta: 1 day, 5:42:25 time: 1.1150 data_time: 0.0174 memory: 15551 grad_norm: 1.8481 loss: 0.4228 semantic_segmentation_loss_cls: 0.1459 semantic_segmentation_loss_mask: 0.0832 semantic_segmentation_loss_dice: 0.1937 2024/07/07 21:19:33 - mmengine - INFO - Iter(train) [ 24250/120000] base_lr: 1.8071e-04 lr: 1.8247e-05 eta: 1 day, 5:41:25 time: 1.1149 data_time: 0.0174 memory: 15132 grad_norm: 1.8473 loss: 0.4224 semantic_segmentation_loss_cls: 0.1458 semantic_segmentation_loss_mask: 0.0832 semantic_segmentation_loss_dice: 0.1934 2024/07/07 21:20:28 - mmengine - INFO - Iter(train) [ 24300/120000] base_lr: 1.8063e-04 lr: 1.8240e-05 eta: 1 day, 5:40:27 time: 1.1146 data_time: 0.0174 memory: 15642 grad_norm: 1.8469 loss: 0.4226 semantic_segmentation_loss_cls: 0.1460 semantic_segmentation_loss_mask: 0.0832 semantic_segmentation_loss_dice: 0.1935 2024/07/07 21:21:25 - mmengine - INFO - Iter(train) [ 24350/120000] base_lr: 1.8056e-04 lr: 1.8233e-05 eta: 1 day, 5:39:34 time: 1.1149 data_time: 0.0174 memory: 15139 grad_norm: 1.8476 loss: 0.4228 semantic_segmentation_loss_cls: 0.1460 semantic_segmentation_loss_mask: 0.0832 semantic_segmentation_loss_dice: 0.1935 2024/07/07 21:22:20 - mmengine - INFO - Iter(train) [ 24400/120000] base_lr: 1.8048e-04 lr: 1.8225e-05 eta: 1 day, 5:38:36 time: 1.1146 data_time: 0.0174 memory: 15902 grad_norm: 1.8466 loss: 0.4224 semantic_segmentation_loss_cls: 0.1458 semantic_segmentation_loss_mask: 0.0832 semantic_segmentation_loss_dice: 0.1934 2024/07/07 21:23:16 - mmengine - INFO - Iter(train) [ 24450/120000] base_lr: 1.8040e-04 lr: 1.8218e-05 eta: 1 day, 5:37:39 time: 1.1146 data_time: 0.0173 memory: 15725 grad_norm: 1.8450 loss: 0.4220 semantic_segmentation_loss_cls: 0.1456 semantic_segmentation_loss_mask: 0.0831 semantic_segmentation_loss_dice: 0.1933 2024/07/07 21:24:11 - mmengine - INFO - Iter(train) [ 24500/120000] base_lr: 1.8033e-04 lr: 1.8211e-05 eta: 1 day, 5:36:41 time: 1.1145 data_time: 0.0174 memory: 16187 grad_norm: 1.8444 loss: 0.4216 semantic_segmentation_loss_cls: 0.1454 semantic_segmentation_loss_mask: 0.0831 semantic_segmentation_loss_dice: 0.1931 2024/07/07 21:25:06 - mmengine - INFO - Iter(train) [ 24550/120000] base_lr: 1.8025e-04 lr: 1.8204e-05 eta: 1 day, 5:35:44 time: 1.1146 data_time: 0.0174 memory: 15851 grad_norm: 1.8530 loss: 0.4212 semantic_segmentation_loss_cls: 0.1453 semantic_segmentation_loss_mask: 0.0830 semantic_segmentation_loss_dice: 0.1930 2024/07/07 21:26:02 - mmengine - INFO - Iter(train) [ 24600/120000] base_lr: 1.8017e-04 lr: 1.8197e-05 eta: 1 day, 5:34:48 time: 1.1147 data_time: 0.0173 memory: 15978 grad_norm: 1.8563 loss: 0.4205 semantic_segmentation_loss_cls: 0.1449 semantic_segmentation_loss_mask: 0.0828 semantic_segmentation_loss_dice: 0.1927 2024/07/07 21:26:57 - mmengine - INFO - Iter(train) [ 24650/120000] base_lr: 1.8009e-04 lr: 1.8190e-05 eta: 1 day, 5:33:48 time: 1.1145 data_time: 0.0174 memory: 15526 grad_norm: 1.8562 loss: 0.4203 semantic_segmentation_loss_cls: 0.1448 semantic_segmentation_loss_mask: 0.0828 semantic_segmentation_loss_dice: 0.1927 2024/07/07 21:27:51 - mmengine - INFO - Iter(train) [ 24700/120000] base_lr: 1.8001e-04 lr: 1.8183e-05 eta: 1 day, 5:32:45 time: 1.1140 data_time: 0.0173 memory: 14954 grad_norm: 1.8560 loss: 0.4204 semantic_segmentation_loss_cls: 0.1447 semantic_segmentation_loss_mask: 0.0828 semantic_segmentation_loss_dice: 0.1929 2024/07/07 21:28:45 - mmengine - INFO - Iter(train) [ 24750/120000] base_lr: 1.7994e-04 lr: 1.8176e-05 eta: 1 day, 5:31:43 time: 1.1137 data_time: 0.0173 memory: 15298 grad_norm: 1.8560 loss: 0.4199 semantic_segmentation_loss_cls: 0.1445 semantic_segmentation_loss_mask: 0.0827 semantic_segmentation_loss_dice: 0.1928 2024/07/07 21:29:39 - mmengine - INFO - Iter(train) [ 24800/120000] base_lr: 1.7986e-04 lr: 1.8169e-05 eta: 1 day, 5:30:39 time: 1.1134 data_time: 0.0173 memory: 15473 grad_norm: 1.8562 loss: 0.4194 semantic_segmentation_loss_cls: 0.1442 semantic_segmentation_loss_mask: 0.0826 semantic_segmentation_loss_dice: 0.1925 2024/07/07 21:30:32 - mmengine - INFO - Iter(train) [ 24850/120000] base_lr: 1.7978e-04 lr: 1.8162e-05 eta: 1 day, 5:29:35 time: 1.1128 data_time: 0.0173 memory: 14925 grad_norm: 1.8545 loss: 0.4187 semantic_segmentation_loss_cls: 0.1440 semantic_segmentation_loss_mask: 0.0825 semantic_segmentation_loss_dice: 0.1923 2024/07/07 21:31:29 - mmengine - INFO - Iter(train) [ 24900/120000] base_lr: 1.7970e-04 lr: 1.8155e-05 eta: 1 day, 5:28:42 time: 1.1129 data_time: 0.0172 memory: 14435 grad_norm: 1.8561 loss: 0.4181 semantic_segmentation_loss_cls: 0.1436 semantic_segmentation_loss_mask: 0.0824 semantic_segmentation_loss_dice: 0.1921 2024/07/07 21:32:25 - mmengine - INFO - Iter(train) [ 24950/120000] base_lr: 1.7962e-04 lr: 1.8147e-05 eta: 1 day, 5:27:49 time: 1.1132 data_time: 0.0173 memory: 14675 grad_norm: 1.8550 loss: 0.4179 semantic_segmentation_loss_cls: 0.1434 semantic_segmentation_loss_mask: 0.0824 semantic_segmentation_loss_dice: 0.1921 2024/07/07 21:33:21 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 21:33:21 - mmengine - INFO - Iter(train) [ 25000/120000] base_lr: 1.7954e-04 lr: 1.8140e-05 eta: 1 day, 5:26:51 time: 1.1130 data_time: 0.0173 memory: 14552 grad_norm: 1.8545 loss: 0.4173 semantic_segmentation_loss_cls: 0.1431 semantic_segmentation_loss_mask: 0.0823 semantic_segmentation_loss_dice: 0.1919 2024/07/07 21:33:21 - mmengine - INFO - Saving checkpoint at 25000 iterations 2024/07/07 21:33:37 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:50 time: 0.2450 data_time: 0.0015 memory: 5013 2024/07/07 21:33:50 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:38 time: 0.2450 data_time: 0.0015 memory: 5187 2024/07/07 21:34:02 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:25 time: 0.2450 data_time: 0.0015 memory: 4460 2024/07/07 21:34:14 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:13 time: 0.2450 data_time: 0.0015 memory: 4543 2024/07/07 21:34:26 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:01 time: 0.2450 data_time: 0.0015 memory: 4645 2024/07/07 21:34:39 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:49 time: 0.2451 data_time: 0.0015 memory: 10983 2024/07/07 21:34:51 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2452 data_time: 0.0015 memory: 4460 2024/07/07 21:35:04 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2452 data_time: 0.0015 memory: 4641 2024/07/07 21:35:16 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2452 data_time: 0.0015 memory: 4473 2024/07/07 21:35:28 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2452 data_time: 0.0015 memory: 4555 2024/07/07 21:35:29 - mmengine - INFO - per class results: 2024/07/07 21:35:29 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.01 | 86.4 | | building | 81.0 | 87.52 | | sky | 94.08 | 97.66 | | floor | 83.47 | 91.05 | | tree | 74.8 | 88.05 | | ceiling | 85.39 | 93.46 | | road | 84.02 | 91.66 | | bed | 87.74 | 95.78 | | windowpane | 61.42 | 80.72 | | grass | 69.36 | 85.11 | | cabinet | 60.18 | 70.63 | | sidewalk | 67.96 | 83.15 | | person | 82.07 | 91.82 | | earth | 33.29 | 45.18 | | door | 50.4 | 70.87 | | table | 62.74 | 77.27 | | mountain | 57.07 | 70.13 | | plant | 52.6 | 69.26 | | curtain | 76.0 | 88.37 | | chair | 58.27 | 71.81 | | car | 85.5 | 91.26 | | water | 50.73 | 62.59 | | painting | 68.58 | 90.73 | | sofa | 63.49 | 74.26 | | shelf | 45.7 | 65.13 | | house | 44.65 | 76.59 | | sea | 50.54 | 81.93 | | mirror | 71.02 | 82.33 | | rug | 72.33 | 83.79 | | field | 39.9 | 60.1 | | armchair | 42.88 | 69.2 | | seat | 63.45 | 80.32 | | fence | 47.7 | 67.41 | | desk | 48.84 | 69.0 | | rock | 36.01 | 53.87 | | wardrobe | 52.02 | 67.05 | | lamp | 66.25 | 79.05 | | bathtub | 83.35 | 89.78 | | railing | 33.69 | 55.5 | | cushion | 55.47 | 67.55 | | base | 24.33 | 41.61 | | box | 26.75 | 39.34 | | column | 49.72 | 74.91 | | signboard | 39.18 | 58.39 | | chest of drawers | 35.02 | 70.36 | | counter | 37.68 | 58.02 | | sand | 34.93 | 50.86 | | sink | 63.98 | 82.34 | | skyscraper | 47.7 | 62.2 | | fireplace | 72.98 | 92.33 | | refrigerator | 73.35 | 89.86 | | grandstand | 41.05 | 69.41 | | path | 31.1 | 41.98 | | stairs | 30.94 | 43.0 | | runway | 76.56 | 89.09 | | case | 48.93 | 55.21 | | pool table | 91.0 | 95.35 | | pillow | 55.1 | 69.44 | | screen door | 73.53 | 76.17 | | stairway | 38.33 | 44.11 | | river | 19.57 | 43.73 | | bridge | 70.0 | 83.26 | | bookcase | 34.54 | 55.25 | | blind | 38.81 | 45.5 | | coffee table | 73.2 | 85.93 | | toilet | 86.33 | 89.16 | | flower | 36.68 | 57.77 | | book | 49.72 | 77.53 | | hill | 11.3 | 21.64 | | bench | 39.15 | 46.97 | | countertop | 54.68 | 66.27 | | stove | 82.22 | 86.36 | | palm | 54.0 | 72.81 | | kitchen island | 34.33 | 83.22 | | computer | 57.96 | 64.73 | | swivel chair | 44.79 | 62.66 | | boat | 47.17 | 50.41 | | bar | 34.22 | 37.46 | | arcade machine | 58.37 | 68.87 | | hovel | 20.45 | 31.07 | | bus | 86.6 | 89.68 | | towel | 67.65 | 75.99 | | light | 61.53 | 81.15 | | truck | 33.67 | 46.38 | | tower | 27.29 | 54.27 | | chandelier | 65.68 | 78.45 | | awning | 31.69 | 47.59 | | streetlight | 37.85 | 55.94 | | booth | 46.09 | 46.9 | | television receiver | 70.47 | 88.18 | | airplane | 59.99 | 65.16 | | dirt track | 17.19 | 26.87 | | apparel | 35.77 | 56.13 | | pole | 32.87 | 55.09 | | land | 0.75 | 1.06 | | bannister | 15.96 | 27.59 | | escalator | 22.12 | 25.77 | | ottoman | 41.95 | 69.26 | | bottle | 22.51 | 27.7 | | buffet | 68.93 | 71.95 | | poster | 26.56 | 33.81 | | stage | 22.95 | 38.28 | | van | 45.15 | 68.17 | | ship | 59.03 | 83.37 | | fountain | 2.24 | 2.25 | | conveyer belt | 70.11 | 90.01 | | canopy | 21.93 | 37.01 | | washer | 71.98 | 74.38 | | plaything | 27.02 | 36.66 | | swimming pool | 33.9 | 50.23 | | stool | 44.52 | 69.99 | | barrel | 27.45 | 82.94 | | basket | 34.79 | 44.66 | | waterfall | 57.49 | 76.65 | | tent | 80.01 | 98.24 | | bag | 19.45 | 26.87 | | minibike | 69.72 | 82.91 | | cradle | 64.04 | 78.1 | | oven | 29.46 | 64.9 | | ball | 7.25 | 8.31 | | food | 61.3 | 79.0 | | step | 29.74 | 41.12 | | tank | 40.88 | 42.66 | | trade name | 29.66 | 39.1 | | microwave | 39.06 | 41.9 | | pot | 55.23 | 65.19 | | animal | 61.92 | 69.7 | | bicycle | 55.69 | 80.36 | | lake | 0.03 | 0.03 | | dishwasher | 76.6 | 85.08 | | screen | 76.51 | 88.97 | | blanket | 16.04 | 20.32 | | sculpture | 73.41 | 85.34 | | hood | 72.62 | 84.62 | | sconce | 48.86 | 65.16 | | vase | 46.54 | 61.63 | | traffic light | 43.54 | 60.73 | | tray | 14.96 | 21.03 | | ashcan | 43.53 | 60.98 | | fan | 64.28 | 78.41 | | pier | 36.48 | 71.85 | | crt screen | 0.69 | 1.01 | | plate | 60.91 | 75.97 | | monitor | 31.59 | 43.71 | | bulletin board | 43.93 | 53.78 | | shower | 8.68 | 18.57 | | radiator | 53.18 | 63.49 | | glass | 18.99 | 20.7 | | clock | 33.66 | 40.05 | | flag | 43.92 | 55.68 | +---------------------+-------+-------+ 2024/07/07 21:35:29 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.0000 mIoU: 49.3700 mAcc: 63.2900 data_time: 0.0016 time: 0.2459 2024/07/07 21:36:24 - mmengine - INFO - Iter(train) [ 25050/120000] base_lr: 1.7946e-04 lr: 1.8133e-05 eta: 1 day, 5:25:57 time: 1.1121 data_time: 0.0163 memory: 14993 grad_norm: 1.8517 loss: 0.4167 semantic_segmentation_loss_cls: 0.1427 semantic_segmentation_loss_mask: 0.0823 semantic_segmentation_loss_dice: 0.1917 2024/07/07 21:37:19 - mmengine - INFO - Iter(train) [ 25100/120000] base_lr: 1.7939e-04 lr: 1.8126e-05 eta: 1 day, 5:24:58 time: 1.1118 data_time: 0.0163 memory: 15583 grad_norm: 1.8508 loss: 0.4166 semantic_segmentation_loss_cls: 0.1427 semantic_segmentation_loss_mask: 0.0823 semantic_segmentation_loss_dice: 0.1916 2024/07/07 21:38:14 - mmengine - INFO - Iter(train) [ 25150/120000] base_lr: 1.7931e-04 lr: 1.8119e-05 eta: 1 day, 5:23:59 time: 1.1115 data_time: 0.0163 memory: 14833 grad_norm: 1.8517 loss: 0.4164 semantic_segmentation_loss_cls: 0.1426 semantic_segmentation_loss_mask: 0.0823 semantic_segmentation_loss_dice: 0.1914 2024/07/07 21:39:10 - mmengine - INFO - Iter(train) [ 25200/120000] base_lr: 1.7923e-04 lr: 1.8112e-05 eta: 1 day, 5:23:02 time: 1.1114 data_time: 0.0163 memory: 14683 grad_norm: 1.8498 loss: 0.4159 semantic_segmentation_loss_cls: 0.1424 semantic_segmentation_loss_mask: 0.0822 semantic_segmentation_loss_dice: 0.1912 2024/07/07 21:40:05 - mmengine - INFO - Iter(train) [ 25250/120000] base_lr: 1.7915e-04 lr: 1.8104e-05 eta: 1 day, 5:22:06 time: 1.1111 data_time: 0.0163 memory: 15722 grad_norm: 1.8437 loss: 0.4152 semantic_segmentation_loss_cls: 0.1420 semantic_segmentation_loss_mask: 0.0822 semantic_segmentation_loss_dice: 0.1910 2024/07/07 21:41:00 - mmengine - INFO - Iter(train) [ 25300/120000] base_lr: 1.7907e-04 lr: 1.8097e-05 eta: 1 day, 5:21:06 time: 1.1108 data_time: 0.0162 memory: 15540 grad_norm: 1.8395 loss: 0.4150 semantic_segmentation_loss_cls: 0.1418 semantic_segmentation_loss_mask: 0.0823 semantic_segmentation_loss_dice: 0.1909 2024/07/07 21:41:56 - mmengine - INFO - Iter(train) [ 25350/120000] base_lr: 1.7899e-04 lr: 1.8090e-05 eta: 1 day, 5:20:12 time: 1.1111 data_time: 0.0162 memory: 14974 grad_norm: 1.8381 loss: 0.4150 semantic_segmentation_loss_cls: 0.1418 semantic_segmentation_loss_mask: 0.0823 semantic_segmentation_loss_dice: 0.1909 2024/07/07 21:42:51 - mmengine - INFO - Iter(train) [ 25400/120000] base_lr: 1.7891e-04 lr: 1.8083e-05 eta: 1 day, 5:19:13 time: 1.1111 data_time: 0.0162 memory: 14875 grad_norm: 1.8376 loss: 0.4145 semantic_segmentation_loss_cls: 0.1414 semantic_segmentation_loss_mask: 0.0823 semantic_segmentation_loss_dice: 0.1908 2024/07/07 21:43:46 - mmengine - INFO - Iter(train) [ 25450/120000] base_lr: 1.7883e-04 lr: 1.8075e-05 eta: 1 day, 5:18:14 time: 1.1109 data_time: 0.0161 memory: 14994 grad_norm: 1.8371 loss: 0.4135 semantic_segmentation_loss_cls: 0.1410 semantic_segmentation_loss_mask: 0.0822 semantic_segmentation_loss_dice: 0.1903 2024/07/07 21:44:42 - mmengine - INFO - Iter(train) [ 25500/120000] base_lr: 1.7875e-04 lr: 1.8068e-05 eta: 1 day, 5:17:16 time: 1.1109 data_time: 0.0161 memory: 15159 grad_norm: 1.8343 loss: 0.4125 semantic_segmentation_loss_cls: 0.1404 semantic_segmentation_loss_mask: 0.0820 semantic_segmentation_loss_dice: 0.1900 2024/07/07 21:45:39 - mmengine - INFO - Iter(train) [ 25550/120000] base_lr: 1.7867e-04 lr: 1.8061e-05 eta: 1 day, 5:16:25 time: 1.1114 data_time: 0.0161 memory: 15694 grad_norm: 1.8342 loss: 0.4121 semantic_segmentation_loss_cls: 0.1402 semantic_segmentation_loss_mask: 0.0820 semantic_segmentation_loss_dice: 0.1899 2024/07/07 21:46:34 - mmengine - INFO - Iter(train) [ 25600/120000] base_lr: 1.7859e-04 lr: 1.8053e-05 eta: 1 day, 5:15:27 time: 1.1115 data_time: 0.0161 memory: 14905 grad_norm: 1.8326 loss: 0.4114 semantic_segmentation_loss_cls: 0.1398 semantic_segmentation_loss_mask: 0.0819 semantic_segmentation_loss_dice: 0.1898 2024/07/07 21:47:30 - mmengine - INFO - Iter(train) [ 25650/120000] base_lr: 1.7851e-04 lr: 1.8046e-05 eta: 1 day, 5:14:32 time: 1.1118 data_time: 0.0161 memory: 14781 grad_norm: 1.8333 loss: 0.4104 semantic_segmentation_loss_cls: 0.1393 semantic_segmentation_loss_mask: 0.0817 semantic_segmentation_loss_dice: 0.1893 2024/07/07 21:48:25 - mmengine - INFO - Iter(train) [ 25700/120000] base_lr: 1.7843e-04 lr: 1.8039e-05 eta: 1 day, 5:13:35 time: 1.1118 data_time: 0.0162 memory: 14860 grad_norm: 1.8332 loss: 0.4099 semantic_segmentation_loss_cls: 0.1390 semantic_segmentation_loss_mask: 0.0817 semantic_segmentation_loss_dice: 0.1892 2024/07/07 21:49:21 - mmengine - INFO - Iter(train) [ 25750/120000] base_lr: 1.7835e-04 lr: 1.8031e-05 eta: 1 day, 5:12:39 time: 1.1119 data_time: 0.0162 memory: 15061 grad_norm: 1.8326 loss: 0.4090 semantic_segmentation_loss_cls: 0.1387 semantic_segmentation_loss_mask: 0.0816 semantic_segmentation_loss_dice: 0.1888 2024/07/07 21:50:16 - mmengine - INFO - Iter(train) [ 25800/120000] base_lr: 1.7826e-04 lr: 1.8024e-05 eta: 1 day, 5:11:40 time: 1.1119 data_time: 0.0162 memory: 15175 grad_norm: 1.8323 loss: 0.4086 semantic_segmentation_loss_cls: 0.1384 semantic_segmentation_loss_mask: 0.0816 semantic_segmentation_loss_dice: 0.1885 2024/07/07 21:51:12 - mmengine - INFO - Iter(train) [ 25850/120000] base_lr: 1.7818e-04 lr: 1.8017e-05 eta: 1 day, 5:10:44 time: 1.1119 data_time: 0.0162 memory: 15236 grad_norm: 1.8310 loss: 0.4083 semantic_segmentation_loss_cls: 0.1383 semantic_segmentation_loss_mask: 0.0816 semantic_segmentation_loss_dice: 0.1885 2024/07/07 21:52:07 - mmengine - INFO - Iter(train) [ 25900/120000] base_lr: 1.7810e-04 lr: 1.8009e-05 eta: 1 day, 5:09:47 time: 1.1121 data_time: 0.0162 memory: 16267 grad_norm: 1.8292 loss: 0.4087 semantic_segmentation_loss_cls: 0.1384 semantic_segmentation_loss_mask: 0.0816 semantic_segmentation_loss_dice: 0.1887 2024/07/07 21:53:03 - mmengine - INFO - Iter(train) [ 25950/120000] base_lr: 1.7802e-04 lr: 1.8002e-05 eta: 1 day, 5:08:53 time: 1.1121 data_time: 0.0162 memory: 14990 grad_norm: 1.8273 loss: 0.4084 semantic_segmentation_loss_cls: 0.1382 semantic_segmentation_loss_mask: 0.0815 semantic_segmentation_loss_dice: 0.1886 2024/07/07 21:53:59 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 21:53:59 - mmengine - INFO - Iter(train) [ 26000/120000] base_lr: 1.7794e-04 lr: 1.7994e-05 eta: 1 day, 5:07:57 time: 1.1123 data_time: 0.0162 memory: 15239 grad_norm: 1.8277 loss: 0.4077 semantic_segmentation_loss_cls: 0.1380 semantic_segmentation_loss_mask: 0.0815 semantic_segmentation_loss_dice: 0.1883 2024/07/07 21:53:59 - mmengine - INFO - Saving checkpoint at 26000 iterations 2024/07/07 21:54:59 - mmengine - INFO - Iter(train) [ 26050/120000] base_lr: 1.7786e-04 lr: 1.7987e-05 eta: 1 day, 5:07:16 time: 1.1119 data_time: 0.0160 memory: 15062 grad_norm: 1.8273 loss: 0.4077 semantic_segmentation_loss_cls: 0.1380 semantic_segmentation_loss_mask: 0.0814 semantic_segmentation_loss_dice: 0.1883 2024/07/07 21:55:54 - mmengine - INFO - Iter(train) [ 26100/120000] base_lr: 1.7778e-04 lr: 1.7980e-05 eta: 1 day, 5:06:19 time: 1.1118 data_time: 0.0160 memory: 14812 grad_norm: 1.8274 loss: 0.4076 semantic_segmentation_loss_cls: 0.1379 semantic_segmentation_loss_mask: 0.0814 semantic_segmentation_loss_dice: 0.1883 2024/07/07 21:56:50 - mmengine - INFO - Iter(train) [ 26150/120000] base_lr: 1.7769e-04 lr: 1.7972e-05 eta: 1 day, 5:05:22 time: 1.1120 data_time: 0.0160 memory: 15120 grad_norm: 1.8270 loss: 0.4076 semantic_segmentation_loss_cls: 0.1378 semantic_segmentation_loss_mask: 0.0814 semantic_segmentation_loss_dice: 0.1884 2024/07/07 21:57:45 - mmengine - INFO - Iter(train) [ 26200/120000] base_lr: 1.7761e-04 lr: 1.7965e-05 eta: 1 day, 5:04:22 time: 1.1117 data_time: 0.0161 memory: 14937 grad_norm: 1.8257 loss: 0.4073 semantic_segmentation_loss_cls: 0.1376 semantic_segmentation_loss_mask: 0.0814 semantic_segmentation_loss_dice: 0.1883 2024/07/07 21:58:40 - mmengine - INFO - Iter(train) [ 26250/120000] base_lr: 1.7753e-04 lr: 1.7957e-05 eta: 1 day, 5:03:26 time: 1.1115 data_time: 0.0160 memory: 14419 grad_norm: 1.8245 loss: 0.4071 semantic_segmentation_loss_cls: 0.1374 semantic_segmentation_loss_mask: 0.0814 semantic_segmentation_loss_dice: 0.1882 2024/07/07 21:59:35 - mmengine - INFO - Iter(train) [ 26300/120000] base_lr: 1.7745e-04 lr: 1.7950e-05 eta: 1 day, 5:02:28 time: 1.1113 data_time: 0.0160 memory: 15537 grad_norm: 1.8223 loss: 0.4064 semantic_segmentation_loss_cls: 0.1371 semantic_segmentation_loss_mask: 0.0813 semantic_segmentation_loss_dice: 0.1881 2024/07/07 22:00:31 - mmengine - INFO - Iter(train) [ 26350/120000] base_lr: 1.7736e-04 lr: 1.7942e-05 eta: 1 day, 5:01:33 time: 1.1114 data_time: 0.0160 memory: 14604 grad_norm: 1.8209 loss: 0.4056 semantic_segmentation_loss_cls: 0.1366 semantic_segmentation_loss_mask: 0.0812 semantic_segmentation_loss_dice: 0.1877 2024/07/07 22:01:28 - mmengine - INFO - Iter(train) [ 26400/120000] base_lr: 1.7728e-04 lr: 1.7935e-05 eta: 1 day, 5:00:40 time: 1.1118 data_time: 0.0160 memory: 14358 grad_norm: 1.8212 loss: 0.4049 semantic_segmentation_loss_cls: 0.1362 semantic_segmentation_loss_mask: 0.0812 semantic_segmentation_loss_dice: 0.1875 2024/07/07 22:02:24 - mmengine - INFO - Iter(train) [ 26450/120000] base_lr: 1.7720e-04 lr: 1.7927e-05 eta: 1 day, 4:59:44 time: 1.1118 data_time: 0.0160 memory: 15022 grad_norm: 1.8214 loss: 0.4046 semantic_segmentation_loss_cls: 0.1361 semantic_segmentation_loss_mask: 0.0811 semantic_segmentation_loss_dice: 0.1875 2024/07/07 22:03:19 - mmengine - INFO - Iter(train) [ 26500/120000] base_lr: 1.7712e-04 lr: 1.7920e-05 eta: 1 day, 4:58:46 time: 1.1119 data_time: 0.0160 memory: 14595 grad_norm: 1.8203 loss: 0.4048 semantic_segmentation_loss_cls: 0.1361 semantic_segmentation_loss_mask: 0.0811 semantic_segmentation_loss_dice: 0.1875 2024/07/07 22:04:14 - mmengine - INFO - Iter(train) [ 26550/120000] base_lr: 1.7703e-04 lr: 1.7912e-05 eta: 1 day, 4:57:47 time: 1.1116 data_time: 0.0160 memory: 15047 grad_norm: 1.8193 loss: 0.4047 semantic_segmentation_loss_cls: 0.1360 semantic_segmentation_loss_mask: 0.0811 semantic_segmentation_loss_dice: 0.1876 2024/07/07 22:05:08 - mmengine - INFO - Iter(train) [ 26600/120000] base_lr: 1.7695e-04 lr: 1.7905e-05 eta: 1 day, 4:56:46 time: 1.1112 data_time: 0.0160 memory: 15566 grad_norm: 1.8176 loss: 0.4045 semantic_segmentation_loss_cls: 0.1360 semantic_segmentation_loss_mask: 0.0810 semantic_segmentation_loss_dice: 0.1875 2024/07/07 22:06:03 - mmengine - INFO - Iter(train) [ 26650/120000] base_lr: 1.7687e-04 lr: 1.7897e-05 eta: 1 day, 4:55:45 time: 1.1111 data_time: 0.0160 memory: 16274 grad_norm: 1.8177 loss: 0.4041 semantic_segmentation_loss_cls: 0.1358 semantic_segmentation_loss_mask: 0.0809 semantic_segmentation_loss_dice: 0.1874 2024/07/07 22:06:57 - mmengine - INFO - Iter(train) [ 26700/120000] base_lr: 1.7678e-04 lr: 1.7890e-05 eta: 1 day, 4:54:47 time: 1.1109 data_time: 0.0160 memory: 15095 grad_norm: 1.8162 loss: 0.4040 semantic_segmentation_loss_cls: 0.1358 semantic_segmentation_loss_mask: 0.0809 semantic_segmentation_loss_dice: 0.1873 2024/07/07 22:07:52 - mmengine - INFO - Iter(train) [ 26750/120000] base_lr: 1.7670e-04 lr: 1.7882e-05 eta: 1 day, 4:53:46 time: 1.1109 data_time: 0.0160 memory: 15048 grad_norm: 1.8152 loss: 0.4039 semantic_segmentation_loss_cls: 0.1358 semantic_segmentation_loss_mask: 0.0808 semantic_segmentation_loss_dice: 0.1873 2024/07/07 22:08:47 - mmengine - INFO - Iter(train) [ 26800/120000] base_lr: 1.7662e-04 lr: 1.7874e-05 eta: 1 day, 4:52:48 time: 1.1108 data_time: 0.0160 memory: 16134 grad_norm: 1.8163 loss: 0.4040 semantic_segmentation_loss_cls: 0.1358 semantic_segmentation_loss_mask: 0.0809 semantic_segmentation_loss_dice: 0.1874 2024/07/07 22:09:43 - mmengine - INFO - Iter(train) [ 26850/120000] base_lr: 1.7653e-04 lr: 1.7867e-05 eta: 1 day, 4:51:52 time: 1.1108 data_time: 0.0160 memory: 14824 grad_norm: 1.8159 loss: 0.4040 semantic_segmentation_loss_cls: 0.1358 semantic_segmentation_loss_mask: 0.0809 semantic_segmentation_loss_dice: 0.1873 2024/07/07 22:10:39 - mmengine - INFO - Iter(train) [ 26900/120000] base_lr: 1.7645e-04 lr: 1.7859e-05 eta: 1 day, 4:50:57 time: 1.1111 data_time: 0.0160 memory: 15310 grad_norm: 1.8161 loss: 0.4038 semantic_segmentation_loss_cls: 0.1356 semantic_segmentation_loss_mask: 0.0809 semantic_segmentation_loss_dice: 0.1873 2024/07/07 22:11:34 - mmengine - INFO - Iter(train) [ 26950/120000] base_lr: 1.7637e-04 lr: 1.7851e-05 eta: 1 day, 4:50:00 time: 1.1113 data_time: 0.0160 memory: 14743 grad_norm: 1.8162 loss: 0.4036 semantic_segmentation_loss_cls: 0.1356 semantic_segmentation_loss_mask: 0.0809 semantic_segmentation_loss_dice: 0.1872 2024/07/07 22:12:31 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 22:12:31 - mmengine - INFO - Iter(train) [ 27000/120000] base_lr: 1.7628e-04 lr: 1.7844e-05 eta: 1 day, 4:49:07 time: 1.1115 data_time: 0.0160 memory: 14556 grad_norm: 1.8147 loss: 0.4036 semantic_segmentation_loss_cls: 0.1355 semantic_segmentation_loss_mask: 0.0810 semantic_segmentation_loss_dice: 0.1871 2024/07/07 22:12:31 - mmengine - INFO - Saving checkpoint at 27000 iterations 2024/07/07 22:13:31 - mmengine - INFO - Iter(train) [ 27050/120000] base_lr: 1.7620e-04 lr: 1.7836e-05 eta: 1 day, 4:48:28 time: 1.1117 data_time: 0.0159 memory: 15597 grad_norm: 1.8137 loss: 0.4033 semantic_segmentation_loss_cls: 0.1353 semantic_segmentation_loss_mask: 0.0811 semantic_segmentation_loss_dice: 0.1869 2024/07/07 22:14:28 - mmengine - INFO - Iter(train) [ 27100/120000] base_lr: 1.7611e-04 lr: 1.7828e-05 eta: 1 day, 4:47:33 time: 1.1118 data_time: 0.0160 memory: 16070 grad_norm: 1.8123 loss: 0.4032 semantic_segmentation_loss_cls: 0.1354 semantic_segmentation_loss_mask: 0.0810 semantic_segmentation_loss_dice: 0.1869 2024/07/07 22:15:24 - mmengine - INFO - Iter(train) [ 27150/120000] base_lr: 1.7603e-04 lr: 1.7821e-05 eta: 1 day, 4:46:39 time: 1.1122 data_time: 0.0160 memory: 14707 grad_norm: 1.8129 loss: 0.4032 semantic_segmentation_loss_cls: 0.1353 semantic_segmentation_loss_mask: 0.0809 semantic_segmentation_loss_dice: 0.1870 2024/07/07 22:16:20 - mmengine - INFO - Iter(train) [ 27200/120000] base_lr: 1.7594e-04 lr: 1.7813e-05 eta: 1 day, 4:45:43 time: 1.1122 data_time: 0.0160 memory: 14834 grad_norm: 1.8123 loss: 0.4035 semantic_segmentation_loss_cls: 0.1354 semantic_segmentation_loss_mask: 0.0810 semantic_segmentation_loss_dice: 0.1872 2024/07/07 22:17:15 - mmengine - INFO - Iter(train) [ 27250/120000] base_lr: 1.7586e-04 lr: 1.7805e-05 eta: 1 day, 4:44:46 time: 1.1122 data_time: 0.0160 memory: 15159 grad_norm: 1.8108 loss: 0.4025 semantic_segmentation_loss_cls: 0.1348 semantic_segmentation_loss_mask: 0.0809 semantic_segmentation_loss_dice: 0.1868 2024/07/07 22:18:10 - mmengine - INFO - Iter(train) [ 27300/120000] base_lr: 1.7577e-04 lr: 1.7798e-05 eta: 1 day, 4:43:48 time: 1.1118 data_time: 0.0160 memory: 15157 grad_norm: 1.8080 loss: 0.4020 semantic_segmentation_loss_cls: 0.1346 semantic_segmentation_loss_mask: 0.0809 semantic_segmentation_loss_dice: 0.1865 2024/07/07 22:19:06 - mmengine - INFO - Iter(train) [ 27350/120000] base_lr: 1.7569e-04 lr: 1.7790e-05 eta: 1 day, 4:42:51 time: 1.1116 data_time: 0.0160 memory: 14893 grad_norm: 1.8064 loss: 0.4014 semantic_segmentation_loss_cls: 0.1344 semantic_segmentation_loss_mask: 0.0807 semantic_segmentation_loss_dice: 0.1863 2024/07/07 22:20:01 - mmengine - INFO - Iter(train) [ 27400/120000] base_lr: 1.7560e-04 lr: 1.7782e-05 eta: 1 day, 4:41:56 time: 1.1117 data_time: 0.0160 memory: 14836 grad_norm: 1.8059 loss: 0.4010 semantic_segmentation_loss_cls: 0.1343 semantic_segmentation_loss_mask: 0.0806 semantic_segmentation_loss_dice: 0.1861 2024/07/07 22:20:57 - mmengine - INFO - Iter(train) [ 27450/120000] base_lr: 1.7552e-04 lr: 1.7774e-05 eta: 1 day, 4:40:59 time: 1.1119 data_time: 0.0160 memory: 15193 grad_norm: 1.8045 loss: 0.4012 semantic_segmentation_loss_cls: 0.1343 semantic_segmentation_loss_mask: 0.0806 semantic_segmentation_loss_dice: 0.1863 2024/07/07 22:21:52 - mmengine - INFO - Iter(train) [ 27500/120000] base_lr: 1.7543e-04 lr: 1.7767e-05 eta: 1 day, 4:39:59 time: 1.1116 data_time: 0.0160 memory: 14978 grad_norm: 1.8035 loss: 0.4009 semantic_segmentation_loss_cls: 0.1341 semantic_segmentation_loss_mask: 0.0805 semantic_segmentation_loss_dice: 0.1862 2024/07/07 22:22:48 - mmengine - INFO - Iter(train) [ 27550/120000] base_lr: 1.7535e-04 lr: 1.7759e-05 eta: 1 day, 4:39:04 time: 1.1120 data_time: 0.0160 memory: 14865 grad_norm: 1.8041 loss: 0.4002 semantic_segmentation_loss_cls: 0.1338 semantic_segmentation_loss_mask: 0.0805 semantic_segmentation_loss_dice: 0.1859 2024/07/07 22:23:43 - mmengine - INFO - Iter(train) [ 27600/120000] base_lr: 1.7526e-04 lr: 1.7751e-05 eta: 1 day, 4:38:07 time: 1.1119 data_time: 0.0160 memory: 15797 grad_norm: 1.8059 loss: 0.3997 semantic_segmentation_loss_cls: 0.1336 semantic_segmentation_loss_mask: 0.0804 semantic_segmentation_loss_dice: 0.1857 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semantic_segmentation_loss_cls: 0.1330 semantic_segmentation_loss_mask: 0.0800 semantic_segmentation_loss_dice: 0.1849 2024/07/07 22:33:04 - mmengine - INFO - Iter(train) [ 28100/120000] base_lr: 1.7440e-04 lr: 1.7673e-05 eta: 1 day, 4:29:01 time: 1.1122 data_time: 0.0158 memory: 14951 grad_norm: 1.8014 loss: 0.3979 semantic_segmentation_loss_cls: 0.1330 semantic_segmentation_loss_mask: 0.0800 semantic_segmentation_loss_dice: 0.1850 2024/07/07 22:34:00 - mmengine - INFO - Iter(train) [ 28150/120000] base_lr: 1.7431e-04 lr: 1.7665e-05 eta: 1 day, 4:28:03 time: 1.1121 data_time: 0.0157 memory: 14687 grad_norm: 1.7998 loss: 0.3974 semantic_segmentation_loss_cls: 0.1326 semantic_segmentation_loss_mask: 0.0799 semantic_segmentation_loss_dice: 0.1848 2024/07/07 22:34:55 - mmengine - INFO - Iter(train) [ 28200/120000] base_lr: 1.7423e-04 lr: 1.7657e-05 eta: 1 day, 4:27:06 time: 1.1122 data_time: 0.0157 memory: 14980 grad_norm: 1.7975 loss: 0.3964 semantic_segmentation_loss_cls: 0.1322 semantic_segmentation_loss_mask: 0.0797 semantic_segmentation_loss_dice: 0.1844 2024/07/07 22:35:50 - mmengine - INFO - Iter(train) [ 28250/120000] base_lr: 1.7414e-04 lr: 1.7649e-05 eta: 1 day, 4:26:07 time: 1.1122 data_time: 0.0157 memory: 15403 grad_norm: 1.7971 loss: 0.3957 semantic_segmentation_loss_cls: 0.1318 semantic_segmentation_loss_mask: 0.0796 semantic_segmentation_loss_dice: 0.1843 2024/07/07 22:36:45 - mmengine - INFO - Iter(train) [ 28300/120000] base_lr: 1.7405e-04 lr: 1.7641e-05 eta: 1 day, 4:25:08 time: 1.1121 data_time: 0.0157 memory: 14884 grad_norm: 1.7962 loss: 0.3951 semantic_segmentation_loss_cls: 0.1315 semantic_segmentation_loss_mask: 0.0795 semantic_segmentation_loss_dice: 0.1840 2024/07/07 22:37:40 - mmengine - INFO - Iter(train) [ 28350/120000] base_lr: 1.7396e-04 lr: 1.7633e-05 eta: 1 day, 4:24:10 time: 1.1118 data_time: 0.0157 memory: 15087 grad_norm: 1.7950 loss: 0.3942 semantic_segmentation_loss_cls: 0.1312 semantic_segmentation_loss_mask: 0.0794 semantic_segmentation_loss_dice: 0.1837 2024/07/07 22:38:34 - mmengine - INFO - Iter(train) [ 28400/120000] base_lr: 1.7388e-04 lr: 1.7625e-05 eta: 1 day, 4:23:11 time: 1.1116 data_time: 0.0157 memory: 15354 grad_norm: 1.7954 loss: 0.3935 semantic_segmentation_loss_cls: 0.1308 semantic_segmentation_loss_mask: 0.0792 semantic_segmentation_loss_dice: 0.1834 2024/07/07 22:39:29 - mmengine - INFO - Iter(train) [ 28450/120000] base_lr: 1.7379e-04 lr: 1.7617e-05 eta: 1 day, 4:22:12 time: 1.1114 data_time: 0.0158 memory: 15459 grad_norm: 1.7967 loss: 0.3927 semantic_segmentation_loss_cls: 0.1304 semantic_segmentation_loss_mask: 0.0791 semantic_segmentation_loss_dice: 0.1831 2024/07/07 22:40:24 - mmengine - INFO - Iter(train) [ 28500/120000] base_lr: 1.7370e-04 lr: 1.7609e-05 eta: 1 day, 4:21:12 time: 1.1113 data_time: 0.0157 memory: 14764 grad_norm: 1.7957 loss: 0.3921 semantic_segmentation_loss_cls: 0.1302 semantic_segmentation_loss_mask: 0.0791 semantic_segmentation_loss_dice: 0.1829 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single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 22:49:39 - mmengine - INFO - Iter(train) [ 29000/120000] base_lr: 1.7281e-04 lr: 1.7529e-05 eta: 1 day, 4:11:46 time: 1.1126 data_time: 0.0161 memory: 14673 grad_norm: 1.7704 loss: 0.3893 semantic_segmentation_loss_cls: 0.1289 semantic_segmentation_loss_mask: 0.0787 semantic_segmentation_loss_dice: 0.1817 2024/07/07 22:49:39 - mmengine - INFO - Saving checkpoint at 29000 iterations 2024/07/07 22:50:39 - mmengine - INFO - Iter(train) [ 29050/120000] base_lr: 1.7272e-04 lr: 1.7520e-05 eta: 1 day, 4:11:03 time: 1.1135 data_time: 0.0172 memory: 16167 grad_norm: 1.7713 loss: 0.3892 semantic_segmentation_loss_cls: 0.1288 semantic_segmentation_loss_mask: 0.0787 semantic_segmentation_loss_dice: 0.1817 2024/07/07 22:51:34 - mmengine - INFO - Iter(train) [ 29100/120000] base_lr: 1.7264e-04 lr: 1.7512e-05 eta: 1 day, 4:10:06 time: 1.1136 data_time: 0.0172 memory: 15847 grad_norm: 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semantic_segmentation_loss_mask: 0.0783 semantic_segmentation_loss_dice: 0.1814 2024/07/07 22:58:02 - mmengine - INFO - Iter(train) [ 29450/120000] base_lr: 1.7201e-04 lr: 1.7455e-05 eta: 1 day, 4:03:28 time: 1.1139 data_time: 0.0172 memory: 14779 grad_norm: 1.7693 loss: 0.3877 semantic_segmentation_loss_cls: 0.1280 semantic_segmentation_loss_mask: 0.0782 semantic_segmentation_loss_dice: 0.1815 2024/07/07 22:58:58 - mmengine - INFO - Iter(train) [ 29500/120000] base_lr: 1.7192e-04 lr: 1.7447e-05 eta: 1 day, 4:02:33 time: 1.1141 data_time: 0.0172 memory: 14652 grad_norm: 1.7693 loss: 0.3875 semantic_segmentation_loss_cls: 0.1279 semantic_segmentation_loss_mask: 0.0782 semantic_segmentation_loss_dice: 0.1814 2024/07/07 22:59:53 - mmengine - INFO - Iter(train) [ 29550/120000] base_lr: 1.7183e-04 lr: 1.7439e-05 eta: 1 day, 4:01:34 time: 1.1135 data_time: 0.0172 memory: 15428 grad_norm: 1.7690 loss: 0.3873 semantic_segmentation_loss_cls: 0.1279 semantic_segmentation_loss_mask: 0.0781 semantic_segmentation_loss_dice: 0.1813 2024/07/07 23:00:49 - mmengine - INFO - Iter(train) [ 29600/120000] base_lr: 1.7173e-04 lr: 1.7430e-05 eta: 1 day, 4:00:39 time: 1.1137 data_time: 0.0172 memory: 15337 grad_norm: 1.7680 loss: 0.3869 semantic_segmentation_loss_cls: 0.1277 semantic_segmentation_loss_mask: 0.0781 semantic_segmentation_loss_dice: 0.1811 2024/07/07 23:01:45 - mmengine - INFO - Iter(train) [ 29650/120000] base_lr: 1.7164e-04 lr: 1.7422e-05 eta: 1 day, 3:59:44 time: 1.1137 data_time: 0.0173 memory: 15715 grad_norm: 1.7647 loss: 0.3870 semantic_segmentation_loss_cls: 0.1277 semantic_segmentation_loss_mask: 0.0781 semantic_segmentation_loss_dice: 0.1812 2024/07/07 23:02:41 - mmengine - INFO - Iter(train) [ 29700/120000] base_lr: 1.7155e-04 lr: 1.7414e-05 eta: 1 day, 3:58:50 time: 1.1139 data_time: 0.0173 memory: 15279 grad_norm: 1.7626 loss: 0.3873 semantic_segmentation_loss_cls: 0.1278 semantic_segmentation_loss_mask: 0.0781 semantic_segmentation_loss_dice: 0.1814 2024/07/07 23:03:38 - mmengine - INFO - Iter(train) [ 29750/120000] base_lr: 1.7146e-04 lr: 1.7406e-05 eta: 1 day, 3:57:56 time: 1.1141 data_time: 0.0173 memory: 15949 grad_norm: 1.7616 loss: 0.3871 semantic_segmentation_loss_cls: 0.1277 semantic_segmentation_loss_mask: 0.0781 semantic_segmentation_loss_dice: 0.1813 2024/07/07 23:04:33 - mmengine - INFO - Iter(train) [ 29800/120000] base_lr: 1.7137e-04 lr: 1.7397e-05 eta: 1 day, 3:56:58 time: 1.1141 data_time: 0.0173 memory: 14967 grad_norm: 1.7597 loss: 0.3864 semantic_segmentation_loss_cls: 0.1274 semantic_segmentation_loss_mask: 0.0779 semantic_segmentation_loss_dice: 0.1811 2024/07/07 23:05:27 - mmengine - INFO - Iter(train) [ 29850/120000] base_lr: 1.7128e-04 lr: 1.7389e-05 eta: 1 day, 3:55:57 time: 1.1137 data_time: 0.0173 memory: 14936 grad_norm: 1.7587 loss: 0.3857 semantic_segmentation_loss_cls: 0.1270 semantic_segmentation_loss_mask: 0.0779 semantic_segmentation_loss_dice: 0.1808 2024/07/07 23:06:21 - mmengine - INFO - Iter(train) [ 29900/120000] base_lr: 1.7119e-04 lr: 1.7381e-05 eta: 1 day, 3:54:56 time: 1.1134 data_time: 0.0173 memory: 14981 grad_norm: 1.7582 loss: 0.3853 semantic_segmentation_loss_cls: 0.1268 semantic_segmentation_loss_mask: 0.0779 semantic_segmentation_loss_dice: 0.1807 2024/07/07 23:07:17 - mmengine - INFO - Iter(train) [ 29950/120000] base_lr: 1.7110e-04 lr: 1.7372e-05 eta: 1 day, 3:54:01 time: 1.1133 data_time: 0.0173 memory: 15064 grad_norm: 1.7577 loss: 0.3849 semantic_segmentation_loss_cls: 0.1266 semantic_segmentation_loss_mask: 0.0778 semantic_segmentation_loss_dice: 0.1805 2024/07/07 23:08:12 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 23:08:12 - mmengine - INFO - Iter(train) [ 30000/120000] base_lr: 1.7101e-04 lr: 1.7364e-05 eta: 1 day, 3:53:04 time: 1.1131 data_time: 0.0172 memory: 15677 grad_norm: 1.7572 loss: 0.3847 semantic_segmentation_loss_cls: 0.1265 semantic_segmentation_loss_mask: 0.0777 semantic_segmentation_loss_dice: 0.1805 2024/07/07 23:08:12 - mmengine - INFO - Saving checkpoint at 30000 iterations 2024/07/07 23:08:29 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:51 time: 0.2453 data_time: 0.0015 memory: 5013 2024/07/07 23:08:42 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:39 time: 0.2453 data_time: 0.0015 memory: 5187 2024/07/07 23:08:54 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:26 time: 0.2454 data_time: 0.0015 memory: 4460 2024/07/07 23:09:06 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:14 time: 0.2454 data_time: 0.0015 memory: 4543 2024/07/07 23:09:19 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:01 time: 0.2454 data_time: 0.0015 memory: 4645 2024/07/07 23:09:31 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:49 time: 0.2454 data_time: 0.0015 memory: 10983 2024/07/07 23:09:43 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:37 time: 0.2454 data_time: 0.0015 memory: 4460 2024/07/07 23:09:56 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2454 data_time: 0.0015 memory: 4641 2024/07/07 23:10:08 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2454 data_time: 0.0015 memory: 4473 2024/07/07 23:10:20 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2454 data_time: 0.0015 memory: 4555 2024/07/07 23:10:21 - mmengine - INFO - per class results: 2024/07/07 23:10:21 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.14 | 86.44 | | building | 81.24 | 87.97 | | sky | 94.11 | 97.71 | | floor | 83.4 | 90.95 | | tree | 74.49 | 87.84 | | ceiling | 85.44 | 93.36 | | road | 84.69 | 92.27 | | bed | 87.5 | 95.5 | | windowpane | 62.13 | 80.98 | | grass | 70.63 | 85.49 | | cabinet | 59.66 | 70.63 | | sidewalk | 68.66 | 82.6 | | person | 81.92 | 91.73 | | earth | 35.62 | 48.29 | | door | 49.53 | 69.4 | | table | 62.56 | 77.91 | | mountain | 57.35 | 69.95 | | plant | 53.17 | 68.6 | | curtain | 75.1 | 87.89 | | chair | 59.39 | 71.67 | | car | 85.54 | 91.74 | | water | 52.39 | 67.35 | | painting | 70.44 | 90.62 | | sofa | 67.38 | 77.59 | | shelf | 45.72 | 65.04 | | house | 47.22 | 80.49 | | sea | 46.4 | 69.9 | | mirror | 71.58 | 82.2 | | rug | 73.1 | 82.31 | | field | 41.55 | 61.2 | | armchair | 46.26 | 74.17 | | seat | 61.4 | 80.64 | | fence | 46.66 | 67.13 | | desk | 52.57 | 71.04 | | rock | 35.9 | 52.74 | | wardrobe | 54.45 | 70.97 | | lamp | 67.39 | 80.86 | | bathtub | 86.54 | 91.35 | | railing | 32.11 | 49.61 | | cushion | 57.37 | 68.96 | | base | 26.12 | 45.98 | | box | 28.17 | 40.14 | | column | 50.53 | 73.56 | | signboard | 38.72 | 57.74 | | chest of drawers | 35.4 | 70.44 | | counter | 32.04 | 47.03 | | sand | 32.03 | 49.8 | | sink | 65.29 | 82.69 | | skyscraper | 49.51 | 63.07 | | fireplace | 71.17 | 92.04 | | refrigerator | 78.91 | 90.19 | | grandstand | 41.41 | 73.25 | | path | 31.17 | 43.19 | | stairs | 30.76 | 43.64 | | runway | 75.99 | 89.3 | | case | 60.5 | 67.66 | | pool table | 92.0 | 96.36 | | pillow | 55.25 | 70.51 | | screen door | 71.96 | 74.05 | | stairway | 39.09 | 44.23 | | river | 23.59 | 43.73 | | bridge | 71.15 | 87.99 | | bookcase | 34.0 | 55.78 | | blind | 37.33 | 43.18 | | coffee table | 72.97 | 87.86 | | toilet | 84.89 | 89.19 | | flower | 34.94 | 55.22 | | book | 50.3 | 77.71 | | hill | 13.55 | 27.74 | | bench | 36.65 | 43.44 | | countertop | 56.01 | 66.3 | | stove | 80.26 | 86.04 | | palm | 53.58 | 72.35 | | kitchen island | 36.05 | 88.18 | | computer | 59.86 | 66.16 | | swivel chair | 45.96 | 63.95 | | boat | 46.59 | 50.05 | | bar | 31.68 | 39.64 | | arcade machine | 62.79 | 68.89 | | hovel | 17.58 | 22.0 | | bus | 87.63 | 91.04 | | towel | 69.35 | 77.46 | | light | 62.28 | 81.1 | | truck | 33.76 | 45.45 | | tower | 30.69 | 54.31 | | chandelier | 69.59 | 80.22 | | awning | 32.54 | 47.72 | | streetlight | 39.3 | 57.74 | | booth | 59.18 | 60.49 | | television receiver | 69.83 | 89.15 | | airplane | 59.87 | 65.18 | | dirt track | 10.21 | 27.59 | | apparel | 37.16 | 55.68 | | pole | 33.42 | 57.25 | | land | 0.09 | 0.14 | | bannister | 15.4 | 27.96 | | escalator | 48.68 | 62.82 | | ottoman | 40.36 | 67.26 | | bottle | 24.0 | 29.49 | | buffet | 48.0 | 53.18 | | poster | 29.7 | 42.85 | | stage | 23.56 | 39.61 | | van | 44.29 | 65.67 | | ship | 54.66 | 85.43 | | fountain | 4.11 | 4.15 | | conveyer belt | 77.91 | 90.64 | | canopy | 15.52 | 28.05 | | washer | 71.87 | 74.68 | | plaything | 25.75 | 34.49 | | swimming pool | 29.63 | 43.91 | | stool | 46.77 | 71.96 | | barrel | 25.32 | 89.5 | | basket | 36.36 | 45.73 | | waterfall | 64.48 | 88.06 | | tent | 79.5 | 98.15 | | bag | 19.4 | 27.53 | | minibike | 69.45 | 85.25 | | cradle | 64.45 | 78.29 | | oven | 23.87 | 55.57 | | ball | 10.94 | 12.64 | | food | 61.43 | 77.89 | | step | 29.25 | 40.55 | | tank | 40.27 | 41.97 | | trade name | 31.0 | 41.18 | | microwave | 39.09 | 41.88 | | pot | 55.38 | 65.26 | | animal | 60.99 | 68.76 | | bicycle | 57.27 | 79.87 | | lake | 63.36 | 63.56 | | dishwasher | 78.75 | 85.42 | | screen | 62.66 | 72.99 | | blanket | 16.55 | 20.47 | | sculpture | 75.2 | 86.12 | | hood | 72.85 | 86.63 | | sconce | 50.29 | 66.38 | | vase | 48.88 | 64.62 | | traffic light | 44.94 | 61.69 | | tray | 15.41 | 20.85 | | ashcan | 42.9 | 59.73 | | fan | 64.85 | 79.59 | | pier | 37.02 | 68.95 | | crt screen | 0.05 | 0.13 | | plate | 61.33 | 76.05 | | monitor | 3.34 | 4.59 | | bulletin board | 48.81 | 57.71 | | shower | 7.28 | 18.96 | | radiator | 55.15 | 65.69 | | glass | 19.12 | 20.89 | | clock | 33.52 | 40.42 | | flag | 43.87 | 55.73 | +---------------------+-------+-------+ 2024/07/07 23:10:21 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.2300 mIoU: 50.0800 mAcc: 63.9400 data_time: 0.0015 time: 0.2464 2024/07/07 23:11:16 - mmengine - INFO - Iter(train) [ 30050/120000] base_lr: 1.7091e-04 lr: 1.7356e-05 eta: 1 day, 3:52:07 time: 1.1120 data_time: 0.0162 memory: 15011 grad_norm: 1.7561 loss: 0.3841 semantic_segmentation_loss_cls: 0.1262 semantic_segmentation_loss_mask: 0.0776 semantic_segmentation_loss_dice: 0.1803 2024/07/07 23:12:11 - mmengine - INFO - Iter(train) [ 30100/120000] base_lr: 1.7082e-04 lr: 1.7347e-05 eta: 1 day, 3:51:09 time: 1.1120 data_time: 0.0162 memory: 14874 grad_norm: 1.7547 loss: 0.3837 semantic_segmentation_loss_cls: 0.1260 semantic_segmentation_loss_mask: 0.0776 semantic_segmentation_loss_dice: 0.1801 2024/07/07 23:13:06 - mmengine - INFO - Iter(train) [ 30150/120000] base_lr: 1.7073e-04 lr: 1.7339e-05 eta: 1 day, 3:50:12 time: 1.1120 data_time: 0.0162 memory: 15384 grad_norm: 1.7535 loss: 0.3834 semantic_segmentation_loss_cls: 0.1258 semantic_segmentation_loss_mask: 0.0776 semantic_segmentation_loss_dice: 0.1801 2024/07/07 23:14:01 - mmengine - INFO - Iter(train) [ 30200/120000] base_lr: 1.7064e-04 lr: 1.7331e-05 eta: 1 day, 3:49:13 time: 1.1119 data_time: 0.0162 memory: 14639 grad_norm: 1.7538 loss: 0.3829 semantic_segmentation_loss_cls: 0.1256 semantic_segmentation_loss_mask: 0.0774 semantic_segmentation_loss_dice: 0.1799 2024/07/07 23:14:57 - mmengine - INFO - Iter(train) [ 30250/120000] base_lr: 1.7055e-04 lr: 1.7322e-05 eta: 1 day, 3:48:20 time: 1.1122 data_time: 0.0162 memory: 15035 grad_norm: 1.7525 loss: 0.3829 semantic_segmentation_loss_cls: 0.1256 semantic_segmentation_loss_mask: 0.0774 semantic_segmentation_loss_dice: 0.1799 2024/07/07 23:15:53 - mmengine - INFO - Iter(train) [ 30300/120000] base_lr: 1.7045e-04 lr: 1.7314e-05 eta: 1 day, 3:47:23 time: 1.1122 data_time: 0.0163 memory: 15780 grad_norm: 1.7534 loss: 0.3837 semantic_segmentation_loss_cls: 0.1260 semantic_segmentation_loss_mask: 0.0775 semantic_segmentation_loss_dice: 0.1803 2024/07/07 23:16:49 - mmengine - INFO - Iter(train) [ 30350/120000] base_lr: 1.7036e-04 lr: 1.7306e-05 eta: 1 day, 3:46:29 time: 1.1124 data_time: 0.0163 memory: 15862 grad_norm: 1.7525 loss: 0.3837 semantic_segmentation_loss_cls: 0.1260 semantic_segmentation_loss_mask: 0.0774 semantic_segmentation_loss_dice: 0.1803 2024/07/07 23:17:45 - mmengine - INFO - Iter(train) [ 30400/120000] base_lr: 1.7027e-04 lr: 1.7297e-05 eta: 1 day, 3:45:34 time: 1.1122 data_time: 0.0163 memory: 15984 grad_norm: 1.7507 loss: 0.3840 semantic_segmentation_loss_cls: 0.1261 semantic_segmentation_loss_mask: 0.0774 semantic_segmentation_loss_dice: 0.1805 2024/07/07 23:18:40 - mmengine - INFO - Iter(train) [ 30450/120000] base_lr: 1.7018e-04 lr: 1.7289e-05 eta: 1 day, 3:44:36 time: 1.1120 data_time: 0.0163 memory: 14840 grad_norm: 1.7489 loss: 0.3838 semantic_segmentation_loss_cls: 0.1259 semantic_segmentation_loss_mask: 0.0775 semantic_segmentation_loss_dice: 0.1804 2024/07/07 23:19:34 - mmengine - INFO - Iter(train) [ 30500/120000] base_lr: 1.7008e-04 lr: 1.7280e-05 eta: 1 day, 3:43:36 time: 1.1118 data_time: 0.0163 memory: 15018 grad_norm: 1.7493 loss: 0.3832 semantic_segmentation_loss_cls: 0.1256 semantic_segmentation_loss_mask: 0.0774 semantic_segmentation_loss_dice: 0.1802 2024/07/07 23:20:29 - mmengine - INFO - Iter(train) [ 30550/120000] base_lr: 1.6999e-04 lr: 1.7272e-05 eta: 1 day, 3:42:37 time: 1.1117 data_time: 0.0163 memory: 15190 grad_norm: 1.7482 loss: 0.3828 semantic_segmentation_loss_cls: 0.1253 semantic_segmentation_loss_mask: 0.0774 semantic_segmentation_loss_dice: 0.1801 2024/07/07 23:21:24 - mmengine - INFO - Iter(train) [ 30600/120000] base_lr: 1.6990e-04 lr: 1.7263e-05 eta: 1 day, 3:41:39 time: 1.1119 data_time: 0.0163 memory: 14956 grad_norm: 1.7483 loss: 0.3822 semantic_segmentation_loss_cls: 0.1249 semantic_segmentation_loss_mask: 0.0773 semantic_segmentation_loss_dice: 0.1799 2024/07/07 23:22:19 - mmengine - INFO - Iter(train) [ 30650/120000] base_lr: 1.6980e-04 lr: 1.7255e-05 eta: 1 day, 3:40:41 time: 1.1120 data_time: 0.0163 memory: 15229 grad_norm: 1.7488 loss: 0.3818 semantic_segmentation_loss_cls: 0.1248 semantic_segmentation_loss_mask: 0.0773 semantic_segmentation_loss_dice: 0.1797 2024/07/07 23:23:15 - mmengine - INFO - Iter(train) [ 30700/120000] base_lr: 1.6971e-04 lr: 1.7246e-05 eta: 1 day, 3:39:45 time: 1.1122 data_time: 0.0163 memory: 15075 grad_norm: 1.7484 loss: 0.3812 semantic_segmentation_loss_cls: 0.1245 semantic_segmentation_loss_mask: 0.0772 semantic_segmentation_loss_dice: 0.1795 2024/07/07 23:24:10 - mmengine - INFO - Iter(train) [ 30750/120000] base_lr: 1.6962e-04 lr: 1.7238e-05 eta: 1 day, 3:38:48 time: 1.1124 data_time: 0.0163 memory: 15544 grad_norm: 1.7479 loss: 0.3808 semantic_segmentation_loss_cls: 0.1243 semantic_segmentation_loss_mask: 0.0771 semantic_segmentation_loss_dice: 0.1794 2024/07/07 23:25:05 - mmengine - INFO - Iter(train) [ 30800/120000] base_lr: 1.6952e-04 lr: 1.7229e-05 eta: 1 day, 3:37:50 time: 1.1125 data_time: 0.0163 memory: 15177 grad_norm: 1.7456 loss: 0.3798 semantic_segmentation_loss_cls: 0.1239 semantic_segmentation_loss_mask: 0.0769 semantic_segmentation_loss_dice: 0.1789 2024/07/07 23:26:01 - mmengine - INFO - Iter(train) [ 30850/120000] base_lr: 1.6943e-04 lr: 1.7221e-05 eta: 1 day, 3:36:55 time: 1.1125 data_time: 0.0163 memory: 15211 grad_norm: 1.7441 loss: 0.3793 semantic_segmentation_loss_cls: 0.1237 semantic_segmentation_loss_mask: 0.0769 semantic_segmentation_loss_dice: 0.1788 2024/07/07 23:26:56 - mmengine - INFO - Iter(train) [ 30900/120000] base_lr: 1.6934e-04 lr: 1.7212e-05 eta: 1 day, 3:35:56 time: 1.1122 data_time: 0.0163 memory: 15014 grad_norm: 1.7416 loss: 0.3788 semantic_segmentation_loss_cls: 0.1234 semantic_segmentation_loss_mask: 0.0768 semantic_segmentation_loss_dice: 0.1785 2024/07/07 23:27:51 - mmengine - INFO - Iter(train) [ 30950/120000] base_lr: 1.6924e-04 lr: 1.7204e-05 eta: 1 day, 3:34:59 time: 1.1121 data_time: 0.0163 memory: 15424 grad_norm: 1.7414 loss: 0.3789 semantic_segmentation_loss_cls: 0.1234 semantic_segmentation_loss_mask: 0.0768 semantic_segmentation_loss_dice: 0.1786 2024/07/07 23:28:46 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 23:28:46 - mmengine - INFO - Iter(train) [ 31000/120000] base_lr: 1.6915e-04 lr: 1.7195e-05 eta: 1 day, 3:34:02 time: 1.1119 data_time: 0.0163 memory: 14930 grad_norm: 1.7418 loss: 0.3780 semantic_segmentation_loss_cls: 0.1232 semantic_segmentation_loss_mask: 0.0765 semantic_segmentation_loss_dice: 0.1783 2024/07/07 23:28:46 - mmengine - INFO - Saving checkpoint at 31000 iterations 2024/07/07 23:29:47 - mmengine - INFO - Iter(train) [ 31050/120000] base_lr: 1.6905e-04 lr: 1.7187e-05 eta: 1 day, 3:33:19 time: 1.1117 data_time: 0.0162 memory: 15660 grad_norm: 1.7410 loss: 0.3776 semantic_segmentation_loss_cls: 0.1231 semantic_segmentation_loss_mask: 0.0764 semantic_segmentation_loss_dice: 0.1781 2024/07/07 23:30:42 - mmengine - INFO - Iter(train) [ 31100/120000] base_lr: 1.6896e-04 lr: 1.7178e-05 eta: 1 day, 3:32:22 time: 1.1116 data_time: 0.0161 memory: 15852 grad_norm: 1.7421 loss: 0.3773 semantic_segmentation_loss_cls: 0.1228 semantic_segmentation_loss_mask: 0.0764 semantic_segmentation_loss_dice: 0.1781 2024/07/07 23:31:38 - mmengine - INFO - Iter(train) [ 31150/120000] base_lr: 1.6887e-04 lr: 1.7170e-05 eta: 1 day, 3:31:28 time: 1.1116 data_time: 0.0161 memory: 15575 grad_norm: 1.7416 loss: 0.3772 semantic_segmentation_loss_cls: 0.1228 semantic_segmentation_loss_mask: 0.0764 semantic_segmentation_loss_dice: 0.1780 2024/07/07 23:32:34 - mmengine - INFO - Iter(train) [ 31200/120000] base_lr: 1.6877e-04 lr: 1.7161e-05 eta: 1 day, 3:30:32 time: 1.1116 data_time: 0.0161 memory: 14468 grad_norm: 1.7402 loss: 0.3763 semantic_segmentation_loss_cls: 0.1225 semantic_segmentation_loss_mask: 0.0762 semantic_segmentation_loss_dice: 0.1776 2024/07/07 23:33:29 - mmengine - INFO - Iter(train) [ 31250/120000] base_lr: 1.6868e-04 lr: 1.7153e-05 eta: 1 day, 3:29:35 time: 1.1115 data_time: 0.0161 memory: 15386 grad_norm: 1.7393 loss: 0.3762 semantic_segmentation_loss_cls: 0.1225 semantic_segmentation_loss_mask: 0.0761 semantic_segmentation_loss_dice: 0.1775 2024/07/07 23:34:25 - mmengine - INFO - Iter(train) [ 31300/120000] base_lr: 1.6858e-04 lr: 1.7144e-05 eta: 1 day, 3:28:38 time: 1.1115 data_time: 0.0161 memory: 15317 grad_norm: 1.7400 loss: 0.3764 semantic_segmentation_loss_cls: 0.1225 semantic_segmentation_loss_mask: 0.0762 semantic_segmentation_loss_dice: 0.1777 2024/07/07 23:35:20 - mmengine - INFO - Iter(train) [ 31350/120000] base_lr: 1.6849e-04 lr: 1.7135e-05 eta: 1 day, 3:27:40 time: 1.1114 data_time: 0.0161 memory: 15322 grad_norm: 1.7394 loss: 0.3765 semantic_segmentation_loss_cls: 0.1225 semantic_segmentation_loss_mask: 0.0762 semantic_segmentation_loss_dice: 0.1778 2024/07/07 23:36:15 - mmengine - INFO - Iter(train) [ 31400/120000] base_lr: 1.6839e-04 lr: 1.7127e-05 eta: 1 day, 3:26:42 time: 1.1113 data_time: 0.0161 memory: 14768 grad_norm: 1.7374 loss: 0.3763 semantic_segmentation_loss_cls: 0.1224 semantic_segmentation_loss_mask: 0.0762 semantic_segmentation_loss_dice: 0.1777 2024/07/07 23:37:11 - mmengine - INFO - Iter(train) [ 31450/120000] base_lr: 1.6830e-04 lr: 1.7118e-05 eta: 1 day, 3:25:49 time: 1.1115 data_time: 0.0162 memory: 15160 grad_norm: 1.7364 loss: 0.3756 semantic_segmentation_loss_cls: 0.1222 semantic_segmentation_loss_mask: 0.0761 semantic_segmentation_loss_dice: 0.1774 2024/07/07 23:38:08 - mmengine - INFO - Iter(train) [ 31500/120000] base_lr: 1.6820e-04 lr: 1.7109e-05 eta: 1 day, 3:24:54 time: 1.1119 data_time: 0.0162 memory: 15230 grad_norm: 1.7352 loss: 0.3752 semantic_segmentation_loss_cls: 0.1221 semantic_segmentation_loss_mask: 0.0759 semantic_segmentation_loss_dice: 0.1772 2024/07/07 23:39:03 - mmengine - INFO - Iter(train) [ 31550/120000] base_lr: 1.6811e-04 lr: 1.7101e-05 eta: 1 day, 3:23:57 time: 1.1118 data_time: 0.0162 memory: 14696 grad_norm: 1.7332 loss: 0.3748 semantic_segmentation_loss_cls: 0.1218 semantic_segmentation_loss_mask: 0.0759 semantic_segmentation_loss_dice: 0.1771 2024/07/07 23:39:58 - mmengine - INFO - Iter(train) [ 31600/120000] base_lr: 1.6801e-04 lr: 1.7092e-05 eta: 1 day, 3:23:01 time: 1.1117 data_time: 0.0162 memory: 14847 grad_norm: 1.7317 loss: 0.3747 semantic_segmentation_loss_cls: 0.1218 semantic_segmentation_loss_mask: 0.0759 semantic_segmentation_loss_dice: 0.1771 2024/07/07 23:40:54 - mmengine - INFO - Iter(train) [ 31650/120000] base_lr: 1.6792e-04 lr: 1.7083e-05 eta: 1 day, 3:22:06 time: 1.1116 data_time: 0.0162 memory: 15984 grad_norm: 1.7311 loss: 0.3743 semantic_segmentation_loss_cls: 0.1215 semantic_segmentation_loss_mask: 0.0759 semantic_segmentation_loss_dice: 0.1770 2024/07/07 23:41:49 - mmengine - INFO - Iter(train) [ 31700/120000] base_lr: 1.6782e-04 lr: 1.7075e-05 eta: 1 day, 3:21:08 time: 1.1115 data_time: 0.0162 memory: 14573 grad_norm: 1.7311 loss: 0.3735 semantic_segmentation_loss_cls: 0.1211 semantic_segmentation_loss_mask: 0.0758 semantic_segmentation_loss_dice: 0.1767 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Iter(train) [ 31900/120000] base_lr: 1.6744e-04 lr: 1.7040e-05 eta: 1 day, 3:17:15 time: 1.1108 data_time: 0.0162 memory: 15166 grad_norm: 1.7282 loss: 0.3723 semantic_segmentation_loss_cls: 0.1205 semantic_segmentation_loss_mask: 0.0755 semantic_segmentation_loss_dice: 0.1763 2024/07/07 23:46:23 - mmengine - INFO - Iter(train) [ 31950/120000] base_lr: 1.6734e-04 lr: 1.7031e-05 eta: 1 day, 3:16:15 time: 1.1104 data_time: 0.0162 memory: 15624 grad_norm: 1.7290 loss: 0.3722 semantic_segmentation_loss_cls: 0.1204 semantic_segmentation_loss_mask: 0.0755 semantic_segmentation_loss_dice: 0.1763 2024/07/07 23:47:18 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/07 23:47:18 - mmengine - INFO - Iter(train) [ 32000/120000] base_lr: 1.6725e-04 lr: 1.7022e-05 eta: 1 day, 3:15:18 time: 1.1103 data_time: 0.0162 memory: 15971 grad_norm: 1.7298 loss: 0.3722 semantic_segmentation_loss_cls: 0.1205 semantic_segmentation_loss_mask: 0.0755 semantic_segmentation_loss_dice: 0.1763 2024/07/07 23:47:18 - mmengine - INFO - Saving checkpoint at 32000 iterations 2024/07/07 23:48:19 - mmengine - INFO - Iter(train) [ 32050/120000] base_lr: 1.6715e-04 lr: 1.7014e-05 eta: 1 day, 3:14:34 time: 1.1103 data_time: 0.0162 memory: 15708 grad_norm: 1.7290 loss: 0.3723 semantic_segmentation_loss_cls: 0.1204 semantic_segmentation_loss_mask: 0.0755 semantic_segmentation_loss_dice: 0.1764 2024/07/07 23:49:13 - mmengine - INFO - Iter(train) [ 32100/120000] base_lr: 1.6705e-04 lr: 1.7005e-05 eta: 1 day, 3:13:36 time: 1.1102 data_time: 0.0162 memory: 15369 grad_norm: 1.7289 loss: 0.3720 semantic_segmentation_loss_cls: 0.1203 semantic_segmentation_loss_mask: 0.0755 semantic_segmentation_loss_dice: 0.1763 2024/07/07 23:50:09 - mmengine - INFO - Iter(train) [ 32150/120000] base_lr: 1.6696e-04 lr: 1.6996e-05 eta: 1 day, 3:12:40 time: 1.1103 data_time: 0.0162 memory: 14813 grad_norm: 1.7282 loss: 0.3716 semantic_segmentation_loss_cls: 0.1201 semantic_segmentation_loss_mask: 0.0754 semantic_segmentation_loss_dice: 0.1761 2024/07/07 23:51:04 - mmengine - INFO - Iter(train) [ 32200/120000] base_lr: 1.6686e-04 lr: 1.6987e-05 eta: 1 day, 3:11:41 time: 1.1100 data_time: 0.0162 memory: 14540 grad_norm: 1.7280 loss: 0.3716 semantic_segmentation_loss_cls: 0.1201 semantic_segmentation_loss_mask: 0.0754 semantic_segmentation_loss_dice: 0.1761 2024/07/07 23:51:59 - mmengine - INFO - Iter(train) [ 32250/120000] base_lr: 1.6676e-04 lr: 1.6978e-05 eta: 1 day, 3:10:45 time: 1.1103 data_time: 0.0162 memory: 15203 grad_norm: 1.7277 loss: 0.3717 semantic_segmentation_loss_cls: 0.1202 semantic_segmentation_loss_mask: 0.0754 semantic_segmentation_loss_dice: 0.1761 2024/07/07 23:52:54 - mmengine - INFO - Iter(train) [ 32300/120000] base_lr: 1.6667e-04 lr: 1.6970e-05 eta: 1 day, 3:09:47 time: 1.1103 data_time: 0.0162 memory: 15330 grad_norm: 1.7270 loss: 0.3712 semantic_segmentation_loss_cls: 0.1200 semantic_segmentation_loss_mask: 0.0753 semantic_segmentation_loss_dice: 0.1759 2024/07/07 23:53:50 - mmengine - INFO - Iter(train) [ 32350/120000] base_lr: 1.6657e-04 lr: 1.6961e-05 eta: 1 day, 3:08:52 time: 1.1105 data_time: 0.0162 memory: 15284 grad_norm: 1.7267 loss: 0.3715 semantic_segmentation_loss_cls: 0.1201 semantic_segmentation_loss_mask: 0.0753 semantic_segmentation_loss_dice: 0.1760 2024/07/07 23:54:45 - mmengine - INFO - Iter(train) [ 32400/120000] base_lr: 1.6647e-04 lr: 1.6952e-05 eta: 1 day, 3:07:54 time: 1.1106 data_time: 0.0162 memory: 15023 grad_norm: 1.7249 loss: 0.3709 semantic_segmentation_loss_cls: 0.1199 semantic_segmentation_loss_mask: 0.0752 semantic_segmentation_loss_dice: 0.1758 2024/07/07 23:55:41 - mmengine - INFO - Iter(train) [ 32450/120000] base_lr: 1.6637e-04 lr: 1.6943e-05 eta: 1 day, 3:06:59 time: 1.1109 data_time: 0.0162 memory: 15645 grad_norm: 1.7233 loss: 0.3708 semantic_segmentation_loss_cls: 0.1199 semantic_segmentation_loss_mask: 0.0752 semantic_segmentation_loss_dice: 0.1758 2024/07/07 23:56:36 - mmengine - INFO - Iter(train) [ 32500/120000] base_lr: 1.6628e-04 lr: 1.6934e-05 eta: 1 day, 3:06:01 time: 1.1110 data_time: 0.0162 memory: 14555 grad_norm: 1.7217 loss: 0.3706 semantic_segmentation_loss_cls: 0.1197 semantic_segmentation_loss_mask: 0.0752 semantic_segmentation_loss_dice: 0.1757 2024/07/07 23:57:32 - mmengine - INFO - Iter(train) [ 32550/120000] base_lr: 1.6618e-04 lr: 1.6925e-05 eta: 1 day, 3:05:05 time: 1.1112 data_time: 0.0162 memory: 14945 grad_norm: 1.7211 loss: 0.3697 semantic_segmentation_loss_cls: 0.1193 semantic_segmentation_loss_mask: 0.0751 semantic_segmentation_loss_dice: 0.1753 2024/07/07 23:58:27 - mmengine - INFO - Iter(train) [ 32600/120000] base_lr: 1.6608e-04 lr: 1.6917e-05 eta: 1 day, 3:04:06 time: 1.1109 data_time: 0.0162 memory: 14622 grad_norm: 1.7203 loss: 0.3696 semantic_segmentation_loss_cls: 0.1193 semantic_segmentation_loss_mask: 0.0750 semantic_segmentation_loss_dice: 0.1753 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semantic_segmentation_loss_cls: 0.1179 semantic_segmentation_loss_mask: 0.0746 semantic_segmentation_loss_dice: 0.1744 2024/07/08 00:07:48 - mmengine - INFO - Iter(train) [ 33100/120000] base_lr: 1.6510e-04 lr: 1.6827e-05 eta: 1 day, 2:54:58 time: 1.1113 data_time: 0.0160 memory: 15548 grad_norm: 1.7115 loss: 0.3668 semantic_segmentation_loss_cls: 0.1178 semantic_segmentation_loss_mask: 0.0745 semantic_segmentation_loss_dice: 0.1744 2024/07/08 00:08:43 - mmengine - INFO - Iter(train) [ 33150/120000] base_lr: 1.6500e-04 lr: 1.6818e-05 eta: 1 day, 2:54:03 time: 1.1114 data_time: 0.0161 memory: 14698 grad_norm: 1.7107 loss: 0.3661 semantic_segmentation_loss_cls: 0.1175 semantic_segmentation_loss_mask: 0.0744 semantic_segmentation_loss_dice: 0.1741 2024/07/08 00:09:39 - mmengine - INFO - Iter(train) [ 33200/120000] base_lr: 1.6490e-04 lr: 1.6809e-05 eta: 1 day, 2:53:07 time: 1.1116 data_time: 0.0161 memory: 15683 grad_norm: 1.7098 loss: 0.3658 semantic_segmentation_loss_cls: 0.1174 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Iter(train) [ 33700/120000] base_lr: 1.6391e-04 lr: 1.6719e-05 eta: 1 day, 2:43:58 time: 1.1126 data_time: 0.0161 memory: 14808 grad_norm: 1.7070 loss: 0.3642 semantic_segmentation_loss_cls: 0.1170 semantic_segmentation_loss_mask: 0.0738 semantic_segmentation_loss_dice: 0.1734 2024/07/08 00:19:55 - mmengine - INFO - Iter(train) [ 33750/120000] base_lr: 1.6381e-04 lr: 1.6710e-05 eta: 1 day, 2:43:01 time: 1.1123 data_time: 0.0161 memory: 14913 grad_norm: 1.7060 loss: 0.3638 semantic_segmentation_loss_cls: 0.1169 semantic_segmentation_loss_mask: 0.0737 semantic_segmentation_loss_dice: 0.1732 2024/07/08 00:20:51 - mmengine - INFO - Iter(train) [ 33800/120000] base_lr: 1.6371e-04 lr: 1.6701e-05 eta: 1 day, 2:42:05 time: 1.1124 data_time: 0.0161 memory: 14886 grad_norm: 1.7066 loss: 0.3636 semantic_segmentation_loss_cls: 0.1167 semantic_segmentation_loss_mask: 0.0737 semantic_segmentation_loss_dice: 0.1732 2024/07/08 00:21:47 - mmengine - INFO - Iter(train) [ 33850/120000] base_lr: 1.6361e-04 lr: 1.6691e-05 eta: 1 day, 2:41:09 time: 1.1129 data_time: 0.0161 memory: 15573 grad_norm: 1.7053 loss: 0.3636 semantic_segmentation_loss_cls: 0.1168 semantic_segmentation_loss_mask: 0.0736 semantic_segmentation_loss_dice: 0.1731 2024/07/08 00:22:42 - mmengine - INFO - Iter(train) [ 33900/120000] base_lr: 1.6351e-04 lr: 1.6682e-05 eta: 1 day, 2:40:11 time: 1.1131 data_time: 0.0161 memory: 15932 grad_norm: 1.7036 loss: 0.3630 semantic_segmentation_loss_cls: 0.1165 semantic_segmentation_loss_mask: 0.0736 semantic_segmentation_loss_dice: 0.1729 2024/07/08 00:23:37 - mmengine - INFO - Iter(train) [ 33950/120000] base_lr: 1.6341e-04 lr: 1.6673e-05 eta: 1 day, 2:39:14 time: 1.1129 data_time: 0.0161 memory: 14546 grad_norm: 1.7027 loss: 0.3628 semantic_segmentation_loss_cls: 0.1164 semantic_segmentation_loss_mask: 0.0736 semantic_segmentation_loss_dice: 0.1729 2024/07/08 00:24:33 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 00:24:33 - mmengine - INFO - Iter(train) [ 34000/120000] base_lr: 1.6330e-04 lr: 1.6664e-05 eta: 1 day, 2:38:19 time: 1.1131 data_time: 0.0162 memory: 14951 grad_norm: 1.7017 loss: 0.3626 semantic_segmentation_loss_cls: 0.1163 semantic_segmentation_loss_mask: 0.0735 semantic_segmentation_loss_dice: 0.1728 2024/07/08 00:24:33 - mmengine - INFO - Saving checkpoint at 34000 iterations 2024/07/08 00:25:33 - mmengine - INFO - Iter(train) [ 34050/120000] base_lr: 1.6320e-04 lr: 1.6655e-05 eta: 1 day, 2:37:34 time: 1.1143 data_time: 0.0173 memory: 14256 grad_norm: 1.7018 loss: 0.3625 semantic_segmentation_loss_cls: 0.1163 semantic_segmentation_loss_mask: 0.0735 semantic_segmentation_loss_dice: 0.1728 2024/07/08 00:26:29 - mmengine - INFO - Iter(train) [ 34100/120000] base_lr: 1.6310e-04 lr: 1.6646e-05 eta: 1 day, 2:36:40 time: 1.1145 data_time: 0.0173 memory: 15097 grad_norm: 1.7014 loss: 0.3623 semantic_segmentation_loss_cls: 0.1161 semantic_segmentation_loss_mask: 0.0735 semantic_segmentation_loss_dice: 0.1726 2024/07/08 00:27:26 - mmengine - INFO - Iter(train) [ 34150/120000] base_lr: 1.6300e-04 lr: 1.6637e-05 eta: 1 day, 2:35:47 time: 1.1149 data_time: 0.0174 memory: 15550 grad_norm: 1.7020 loss: 0.3618 semantic_segmentation_loss_cls: 0.1160 semantic_segmentation_loss_mask: 0.0734 semantic_segmentation_loss_dice: 0.1724 2024/07/08 00:28:21 - mmengine - INFO - Iter(train) [ 34200/120000] base_lr: 1.6290e-04 lr: 1.6627e-05 eta: 1 day, 2:34:49 time: 1.1150 data_time: 0.0174 memory: 14489 grad_norm: 1.7010 loss: 0.3617 semantic_segmentation_loss_cls: 0.1160 semantic_segmentation_loss_mask: 0.0734 semantic_segmentation_loss_dice: 0.1723 2024/07/08 00:29:17 - mmengine - INFO - Iter(train) [ 34250/120000] base_lr: 1.6280e-04 lr: 1.6618e-05 eta: 1 day, 2:33:54 time: 1.1148 data_time: 0.0173 memory: 16365 grad_norm: 1.7002 loss: 0.3613 semantic_segmentation_loss_cls: 0.1158 semantic_segmentation_loss_mask: 0.0733 semantic_segmentation_loss_dice: 0.1722 2024/07/08 00:30:12 - mmengine - INFO - Iter(train) [ 34300/120000] base_lr: 1.6270e-04 lr: 1.6609e-05 eta: 1 day, 2:32:55 time: 1.1147 data_time: 0.0173 memory: 15399 grad_norm: 1.7007 loss: 0.3607 semantic_segmentation_loss_cls: 0.1155 semantic_segmentation_loss_mask: 0.0732 semantic_segmentation_loss_dice: 0.1720 2024/07/08 00:31:08 - mmengine - INFO - Iter(train) [ 34350/120000] base_lr: 1.6260e-04 lr: 1.6600e-05 eta: 1 day, 2:32:00 time: 1.1145 data_time: 0.0173 memory: 16678 grad_norm: 1.7000 loss: 0.3604 semantic_segmentation_loss_cls: 0.1153 semantic_segmentation_loss_mask: 0.0732 semantic_segmentation_loss_dice: 0.1719 2024/07/08 00:32:03 - mmengine - INFO - Iter(train) [ 34400/120000] base_lr: 1.6250e-04 lr: 1.6591e-05 eta: 1 day, 2:31:03 time: 1.1144 data_time: 0.0173 memory: 14910 grad_norm: 1.6992 loss: 0.3596 semantic_segmentation_loss_cls: 0.1151 semantic_segmentation_loss_mask: 0.0731 semantic_segmentation_loss_dice: 0.1715 2024/07/08 00:32:58 - mmengine - INFO - Iter(train) [ 34450/120000] base_lr: 1.6239e-04 lr: 1.6581e-05 eta: 1 day, 2:30:05 time: 1.1144 data_time: 0.0173 memory: 14762 grad_norm: 1.6988 loss: 0.3593 semantic_segmentation_loss_cls: 0.1151 semantic_segmentation_loss_mask: 0.0729 semantic_segmentation_loss_dice: 0.1714 2024/07/08 00:33:53 - mmengine - INFO - Iter(train) [ 34500/120000] base_lr: 1.6229e-04 lr: 1.6572e-05 eta: 1 day, 2:29:08 time: 1.1146 data_time: 0.0173 memory: 15005 grad_norm: 1.6995 loss: 0.3593 semantic_segmentation_loss_cls: 0.1151 semantic_segmentation_loss_mask: 0.0729 semantic_segmentation_loss_dice: 0.1714 2024/07/08 00:34:48 - mmengine - INFO - Iter(train) [ 34550/120000] base_lr: 1.6219e-04 lr: 1.6563e-05 eta: 1 day, 2:28:11 time: 1.1147 data_time: 0.0173 memory: 15239 grad_norm: 1.6990 loss: 0.3589 semantic_segmentation_loss_cls: 0.1150 semantic_segmentation_loss_mask: 0.0728 semantic_segmentation_loss_dice: 0.1711 2024/07/08 00:35:43 - mmengine - INFO - Iter(train) [ 34600/120000] base_lr: 1.6209e-04 lr: 1.6554e-05 eta: 1 day, 2:27:13 time: 1.1147 data_time: 0.0173 memory: 15523 grad_norm: 1.6966 loss: 0.3585 semantic_segmentation_loss_cls: 0.1148 semantic_segmentation_loss_mask: 0.0727 semantic_segmentation_loss_dice: 0.1710 2024/07/08 00:36:39 - mmengine - INFO - Iter(train) [ 34650/120000] base_lr: 1.6199e-04 lr: 1.6544e-05 eta: 1 day, 2:26:16 time: 1.1148 data_time: 0.0173 memory: 14739 grad_norm: 1.6936 loss: 0.3583 semantic_segmentation_loss_cls: 0.1146 semantic_segmentation_loss_mask: 0.0727 semantic_segmentation_loss_dice: 0.1709 2024/07/08 00:37:33 - mmengine - INFO - Iter(train) [ 34700/120000] base_lr: 1.6188e-04 lr: 1.6535e-05 eta: 1 day, 2:25:17 time: 1.1145 data_time: 0.0173 memory: 14427 grad_norm: 1.6934 loss: 0.3578 semantic_segmentation_loss_cls: 0.1143 semantic_segmentation_loss_mask: 0.0727 semantic_segmentation_loss_dice: 0.1708 2024/07/08 00:38:28 - mmengine - INFO - Iter(train) [ 34750/120000] base_lr: 1.6178e-04 lr: 1.6526e-05 eta: 1 day, 2:24:20 time: 1.1144 data_time: 0.0173 memory: 15400 grad_norm: 1.6954 loss: 0.3576 semantic_segmentation_loss_cls: 0.1142 semantic_segmentation_loss_mask: 0.0728 semantic_segmentation_loss_dice: 0.1707 2024/07/08 00:39:23 - mmengine - INFO - Iter(train) [ 34800/120000] base_lr: 1.6168e-04 lr: 1.6516e-05 eta: 1 day, 2:23:21 time: 1.1143 data_time: 0.0173 memory: 15610 grad_norm: 1.6953 loss: 0.3576 semantic_segmentation_loss_cls: 0.1141 semantic_segmentation_loss_mask: 0.0727 semantic_segmentation_loss_dice: 0.1707 2024/07/08 00:40:18 - mmengine - INFO - Iter(train) [ 34850/120000] base_lr: 1.6158e-04 lr: 1.6507e-05 eta: 1 day, 2:22:25 time: 1.1142 data_time: 0.0173 memory: 15405 grad_norm: 1.6955 loss: 0.3574 semantic_segmentation_loss_cls: 0.1140 semantic_segmentation_loss_mask: 0.0727 semantic_segmentation_loss_dice: 0.1706 2024/07/08 00:41:13 - mmengine - INFO - Iter(train) [ 34900/120000] base_lr: 1.6147e-04 lr: 1.6498e-05 eta: 1 day, 2:21:26 time: 1.1141 data_time: 0.0173 memory: 16232 grad_norm: 1.6946 loss: 0.3571 semantic_segmentation_loss_cls: 0.1139 semantic_segmentation_loss_mask: 0.0727 semantic_segmentation_loss_dice: 0.1706 2024/07/08 00:42:06 - mmengine - INFO - Iter(train) [ 34950/120000] base_lr: 1.6137e-04 lr: 1.6488e-05 eta: 1 day, 2:20:25 time: 1.1138 data_time: 0.0172 memory: 15179 grad_norm: 1.6923 loss: 0.3567 semantic_segmentation_loss_cls: 0.1137 semantic_segmentation_loss_mask: 0.0726 semantic_segmentation_loss_dice: 0.1704 2024/07/08 00:43:02 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 00:43:02 - mmengine - INFO - Iter(train) [ 35000/120000] base_lr: 1.6127e-04 lr: 1.6479e-05 eta: 1 day, 2:19:29 time: 1.1138 data_time: 0.0173 memory: 14749 grad_norm: 1.6898 loss: 0.3562 semantic_segmentation_loss_cls: 0.1134 semantic_segmentation_loss_mask: 0.0726 semantic_segmentation_loss_dice: 0.1703 2024/07/08 00:43:02 - mmengine - INFO - Saving checkpoint at 35000 iterations 2024/07/08 00:43:19 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:50 time: 0.2454 data_time: 0.0015 memory: 5013 2024/07/08 00:43:31 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:38 time: 0.2454 data_time: 0.0015 memory: 5187 2024/07/08 00:43:43 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:25 time: 0.2454 data_time: 0.0015 memory: 4460 2024/07/08 00:43:56 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:13 time: 0.2454 data_time: 0.0015 memory: 4543 2024/07/08 00:44:08 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:01 time: 0.2454 data_time: 0.0015 memory: 4643 2024/07/08 00:44:20 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:49 time: 0.2454 data_time: 0.0015 memory: 10983 2024/07/08 00:44:33 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2455 data_time: 0.0015 memory: 4460 2024/07/08 00:44:45 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2455 data_time: 0.0015 memory: 4641 2024/07/08 00:44:57 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2455 data_time: 0.0015 memory: 4473 2024/07/08 00:45:09 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2454 data_time: 0.0015 memory: 4555 2024/07/08 00:45:10 - mmengine - INFO - per class results: 2024/07/08 00:45:10 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.31 | 86.55 | | building | 82.25 | 88.99 | | sky | 94.26 | 97.71 | | floor | 83.32 | 90.84 | | tree | 75.21 | 88.29 | | ceiling | 85.52 | 93.57 | | road | 84.22 | 92.7 | | bed | 86.9 | 95.17 | | windowpane | 62.4 | 80.96 | | grass | 70.01 | 84.97 | | cabinet | 60.98 | 71.66 | | sidewalk | 67.24 | 80.79 | | person | 81.78 | 91.5 | | earth | 35.46 | 48.08 | | door | 50.41 | 69.26 | | table | 60.63 | 76.81 | | mountain | 59.45 | 70.55 | | plant | 53.8 | 68.93 | | curtain | 75.7 | 87.82 | | chair | 59.94 | 72.72 | | car | 85.86 | 91.79 | | water | 48.76 | 63.33 | | painting | 72.15 | 89.49 | | sofa | 64.44 | 76.67 | | shelf | 45.44 | 65.14 | | house | 47.96 | 79.34 | | sea | 46.05 | 69.88 | | mirror | 69.85 | 80.82 | | rug | 73.31 | 82.73 | | field | 38.86 | 58.34 | | armchair | 41.87 | 66.23 | | seat | 60.56 | 79.73 | | fence | 47.16 | 67.25 | | desk | 48.74 | 70.07 | | rock | 43.6 | 65.72 | | wardrobe | 52.32 | 68.76 | | lamp | 66.24 | 80.83 | | bathtub | 87.03 | 91.0 | | railing | 34.05 | 51.77 | | cushion | 57.37 | 68.51 | | base | 24.03 | 42.05 | | box | 29.21 | 39.84 | | column | 50.45 | 72.85 | | signboard | 37.53 | 57.59 | | chest of drawers | 39.03 | 70.78 | | counter | 31.97 | 46.79 | | sand | 32.85 | 50.07 | | sink | 66.49 | 82.24 | | skyscraper | 55.72 | 72.66 | | fireplace | 68.13 | 89.92 | | refrigerator | 80.26 | 89.22 | | grandstand | 40.84 | 72.68 | | path | 31.02 | 43.84 | | stairs | 30.62 | 43.89 | | runway | 76.06 | 89.44 | | case | 57.57 | 66.31 | | pool table | 91.96 | 96.35 | | pillow | 57.61 | 72.3 | | screen door | 76.92 | 78.98 | | stairway | 39.66 | 44.83 | | river | 20.61 | 43.9 | | bridge | 69.98 | 89.01 | | bookcase | 34.04 | 56.5 | | blind | 40.91 | 47.41 | | coffee table | 73.24 | 87.2 | | toilet | 84.42 | 89.33 | | flower | 36.09 | 56.3 | | book | 51.54 | 77.09 | | hill | 12.49 | 26.7 | | bench | 39.02 | 47.14 | | countertop | 57.44 | 65.75 | | stove | 80.87 | 85.74 | | palm | 54.14 | 73.88 | | kitchen island | 33.19 | 78.24 | | computer | 61.46 | 68.03 | | swivel chair | 46.03 | 64.26 | | boat | 46.16 | 49.62 | | bar | 29.9 | 38.6 | | arcade machine | 62.25 | 68.19 | | hovel | 12.82 | 16.11 | | bus | 82.98 | 85.78 | | towel | 69.63 | 77.65 | | light | 63.37 | 80.24 | | truck | 33.94 | 46.65 | | tower | 27.43 | 54.21 | | chandelier | 65.47 | 74.96 | | awning | 33.51 | 47.96 | | streetlight | 40.49 | 58.82 | | booth | 47.47 | 47.97 | | television receiver | 72.27 | 88.87 | | airplane | 58.88 | 65.21 | | dirt track | 16.47 | 24.42 | | apparel | 37.05 | 54.93 | | pole | 32.74 | 54.92 | | land | 0.7 | 1.04 | | bannister | 16.47 | 27.75 | | escalator | 36.18 | 45.84 | | ottoman | 40.91 | 67.17 | | bottle | 22.63 | 27.66 | | buffet | 47.84 | 53.18 | | poster | 28.85 | 45.5 | | stage | 19.29 | 33.08 | | van | 42.37 | 67.05 | | ship | 60.72 | 86.03 | | fountain | 5.88 | 5.94 | | conveyer belt | 82.14 | 90.46 | | canopy | 17.27 | 32.5 | | washer | 71.66 | 74.08 | | plaything | 29.03 | 39.9 | | swimming pool | 46.12 | 51.93 | | stool | 44.74 | 68.87 | | barrel | 14.03 | 55.18 | | basket | 41.76 | 53.11 | | waterfall | 62.45 | 85.92 | | tent | 79.49 | 97.75 | | bag | 19.5 | 26.74 | | minibike | 71.18 | 84.55 | | cradle | 64.69 | 78.27 | | oven | 23.25 | 57.51 | | ball | 40.97 | 48.96 | | food | 61.63 | 78.0 | | step | 28.19 | 38.73 | | tank | 41.57 | 43.02 | | trade name | 32.61 | 43.47 | | microwave | 38.68 | 41.49 | | pot | 54.92 | 63.82 | | animal | 62.49 | 69.17 | | bicycle | 57.02 | 79.21 | | lake | 60.85 | 63.69 | | dishwasher | 67.25 | 85.02 | | screen | 70.89 | 84.67 | | blanket | 14.98 | 19.15 | | sculpture | 73.8 | 86.82 | | hood | 77.26 | 81.99 | | sconce | 50.93 | 66.3 | | vase | 49.3 | 64.59 | | traffic light | 44.33 | 61.28 | | tray | 15.72 | 22.09 | | ashcan | 44.51 | 62.63 | | fan | 66.01 | 79.54 | | pier | 40.75 | 71.71 | | crt screen | 0.29 | 0.63 | | plate | 61.76 | 76.41 | | monitor | 13.31 | 20.5 | | bulletin board | 29.14 | 34.02 | | shower | 6.95 | 18.57 | | radiator | 57.61 | 68.55 | | glass | 18.72 | 20.36 | | clock | 34.05 | 39.9 | | flag | 44.9 | 55.05 | +---------------------+-------+-------+ 2024/07/08 00:45:10 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.3300 mIoU: 50.2400 mAcc: 63.7700 data_time: 0.0016 time: 0.2454 2024/07/08 00:46:06 - mmengine - INFO - Iter(train) [ 35050/120000] base_lr: 1.6117e-04 lr: 1.6470e-05 eta: 1 day, 2:18:34 time: 1.1128 data_time: 0.0163 memory: 14856 grad_norm: 1.6886 loss: 0.3559 semantic_segmentation_loss_cls: 0.1132 semantic_segmentation_loss_mask: 0.0726 semantic_segmentation_loss_dice: 0.1702 2024/07/08 00:47:01 - mmengine - INFO - Iter(train) [ 35100/120000] base_lr: 1.6106e-04 lr: 1.6460e-05 eta: 1 day, 2:17:39 time: 1.1129 data_time: 0.0163 memory: 15997 grad_norm: 1.6873 loss: 0.3554 semantic_segmentation_loss_cls: 0.1129 semantic_segmentation_loss_mask: 0.0725 semantic_segmentation_loss_dice: 0.1700 2024/07/08 00:47:57 - mmengine - INFO - Iter(train) [ 35150/120000] base_lr: 1.6096e-04 lr: 1.6451e-05 eta: 1 day, 2:16:43 time: 1.1128 data_time: 0.0163 memory: 15440 grad_norm: 1.6865 loss: 0.3547 semantic_segmentation_loss_cls: 0.1126 semantic_segmentation_loss_mask: 0.0724 semantic_segmentation_loss_dice: 0.1697 2024/07/08 00:48:52 - mmengine - INFO - Iter(train) [ 35200/120000] base_lr: 1.6086e-04 lr: 1.6442e-05 eta: 1 day, 2:15:45 time: 1.1125 data_time: 0.0163 memory: 15266 grad_norm: 1.6849 loss: 0.3552 semantic_segmentation_loss_cls: 0.1127 semantic_segmentation_loss_mask: 0.0725 semantic_segmentation_loss_dice: 0.1700 2024/07/08 00:49:47 - mmengine - INFO - Iter(train) [ 35250/120000] base_lr: 1.6075e-04 lr: 1.6432e-05 eta: 1 day, 2:14:49 time: 1.1126 data_time: 0.0163 memory: 15756 grad_norm: 1.6854 loss: 0.3550 semantic_segmentation_loss_cls: 0.1126 semantic_segmentation_loss_mask: 0.0725 semantic_segmentation_loss_dice: 0.1699 2024/07/08 00:50:42 - mmengine - INFO - Iter(train) [ 35300/120000] base_lr: 1.6065e-04 lr: 1.6423e-05 eta: 1 day, 2:13:51 time: 1.1125 data_time: 0.0163 memory: 15334 grad_norm: 1.6842 loss: 0.3543 semantic_segmentation_loss_cls: 0.1123 semantic_segmentation_loss_mask: 0.0724 semantic_segmentation_loss_dice: 0.1695 2024/07/08 00:51:38 - mmengine - INFO - Iter(train) [ 35350/120000] base_lr: 1.6055e-04 lr: 1.6413e-05 eta: 1 day, 2:12:55 time: 1.1127 data_time: 0.0163 memory: 15006 grad_norm: 1.6853 loss: 0.3540 semantic_segmentation_loss_cls: 0.1122 semantic_segmentation_loss_mask: 0.0724 semantic_segmentation_loss_dice: 0.1694 2024/07/08 00:52:33 - mmengine - INFO - Iter(train) [ 35400/120000] base_lr: 1.6044e-04 lr: 1.6404e-05 eta: 1 day, 2:11:58 time: 1.1126 data_time: 0.0163 memory: 15724 grad_norm: 1.6856 loss: 0.3538 semantic_segmentation_loss_cls: 0.1122 semantic_segmentation_loss_mask: 0.0723 semantic_segmentation_loss_dice: 0.1693 2024/07/08 00:53:29 - mmengine - INFO - Iter(train) [ 35450/120000] base_lr: 1.6034e-04 lr: 1.6395e-05 eta: 1 day, 2:11:02 time: 1.1124 data_time: 0.0162 memory: 14159 grad_norm: 1.6849 loss: 0.3535 semantic_segmentation_loss_cls: 0.1120 semantic_segmentation_loss_mask: 0.0723 semantic_segmentation_loss_dice: 0.1692 2024/07/08 00:54:24 - mmengine - INFO - Iter(train) [ 35500/120000] base_lr: 1.6024e-04 lr: 1.6385e-05 eta: 1 day, 2:10:04 time: 1.1121 data_time: 0.0162 memory: 15374 grad_norm: 1.6835 loss: 0.3534 semantic_segmentation_loss_cls: 0.1119 semantic_segmentation_loss_mask: 0.0723 semantic_segmentation_loss_dice: 0.1693 2024/07/08 00:55:20 - mmengine - INFO - Iter(train) [ 35550/120000] base_lr: 1.6013e-04 lr: 1.6376e-05 eta: 1 day, 2:09:10 time: 1.1123 data_time: 0.0162 memory: 15065 grad_norm: 1.6821 loss: 0.3537 semantic_segmentation_loss_cls: 0.1121 semantic_segmentation_loss_mask: 0.0723 semantic_segmentation_loss_dice: 0.1694 2024/07/08 00:56:16 - mmengine - INFO - Iter(train) [ 35600/120000] base_lr: 1.6003e-04 lr: 1.6366e-05 eta: 1 day, 2:08:15 time: 1.1125 data_time: 0.0162 memory: 15030 grad_norm: 1.6803 loss: 0.3534 semantic_segmentation_loss_cls: 0.1119 semantic_segmentation_loss_mask: 0.0722 semantic_segmentation_loss_dice: 0.1692 2024/07/08 00:57:11 - mmengine - INFO - Iter(train) [ 35650/120000] base_lr: 1.5992e-04 lr: 1.6357e-05 eta: 1 day, 2:07:18 time: 1.1123 data_time: 0.0162 memory: 16439 grad_norm: 1.6793 loss: 0.3534 semantic_segmentation_loss_cls: 0.1119 semantic_segmentation_loss_mask: 0.0722 semantic_segmentation_loss_dice: 0.1693 2024/07/08 00:58:07 - mmengine - INFO - 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single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 01:03:40 - mmengine - INFO - Iter(train) [ 36000/120000] base_lr: 1.5919e-04 lr: 1.6290e-05 eta: 1 day, 2:00:44 time: 1.1135 data_time: 0.0164 memory: 15224 grad_norm: 1.6695 loss: 0.3513 semantic_segmentation_loss_cls: 0.1109 semantic_segmentation_loss_mask: 0.0719 semantic_segmentation_loss_dice: 0.1684 2024/07/08 01:03:40 - mmengine - INFO - Saving checkpoint at 36000 iterations 2024/07/08 01:04:40 - mmengine - INFO - Iter(train) [ 36050/120000] base_lr: 1.5909e-04 lr: 1.6281e-05 eta: 1 day, 1:59:57 time: 1.1134 data_time: 0.0163 memory: 15393 grad_norm: 1.6702 loss: 0.3512 semantic_segmentation_loss_cls: 0.1110 semantic_segmentation_loss_mask: 0.0719 semantic_segmentation_loss_dice: 0.1683 2024/07/08 01:05:36 - mmengine - INFO - Iter(train) [ 36100/120000] base_lr: 1.5898e-04 lr: 1.6271e-05 eta: 1 day, 1:59:03 time: 1.1137 data_time: 0.0163 memory: 14947 grad_norm: 1.6682 loss: 0.3509 semantic_segmentation_loss_cls: 0.1108 semantic_segmentation_loss_mask: 0.0718 semantic_segmentation_loss_dice: 0.1682 2024/07/08 01:06:32 - mmengine - INFO - Iter(train) [ 36150/120000] base_lr: 1.5888e-04 lr: 1.6262e-05 eta: 1 day, 1:58:07 time: 1.1138 data_time: 0.0163 memory: 15363 grad_norm: 1.6685 loss: 0.3510 semantic_segmentation_loss_cls: 0.1109 semantic_segmentation_loss_mask: 0.0718 semantic_segmentation_loss_dice: 0.1683 2024/07/08 01:07:27 - mmengine - INFO - Iter(train) [ 36200/120000] base_lr: 1.5877e-04 lr: 1.6252e-05 eta: 1 day, 1:57:10 time: 1.1139 data_time: 0.0164 memory: 14970 grad_norm: 1.6685 loss: 0.3509 semantic_segmentation_loss_cls: 0.1109 semantic_segmentation_loss_mask: 0.0718 semantic_segmentation_loss_dice: 0.1682 2024/07/08 01:08:23 - mmengine - INFO - Iter(train) [ 36250/120000] base_lr: 1.5867e-04 lr: 1.6242e-05 eta: 1 day, 1:56:15 time: 1.1140 data_time: 0.0164 memory: 14653 grad_norm: 1.6664 loss: 0.3506 semantic_segmentation_loss_cls: 0.1108 semantic_segmentation_loss_mask: 0.0717 semantic_segmentation_loss_dice: 0.1681 2024/07/08 01:09:18 - mmengine - INFO - Iter(train) [ 36300/120000] base_lr: 1.5856e-04 lr: 1.6233e-05 eta: 1 day, 1:55:18 time: 1.1141 data_time: 0.0164 memory: 16225 grad_norm: 1.6658 loss: 0.3501 semantic_segmentation_loss_cls: 0.1105 semantic_segmentation_loss_mask: 0.0716 semantic_segmentation_loss_dice: 0.1680 2024/07/08 01:10:13 - mmengine - INFO - Iter(train) [ 36350/120000] base_lr: 1.5846e-04 lr: 1.6223e-05 eta: 1 day, 1:54:19 time: 1.1136 data_time: 0.0164 memory: 16022 grad_norm: 1.6643 loss: 0.3492 semantic_segmentation_loss_cls: 0.1101 semantic_segmentation_loss_mask: 0.0715 semantic_segmentation_loss_dice: 0.1676 2024/07/08 01:11:08 - mmengine - INFO - Iter(train) [ 36400/120000] base_lr: 1.5835e-04 lr: 1.6214e-05 eta: 1 day, 1:53:23 time: 1.1138 data_time: 0.0164 memory: 16760 grad_norm: 1.6639 loss: 0.3492 semantic_segmentation_loss_cls: 0.1100 semantic_segmentation_loss_mask: 0.0715 semantic_segmentation_loss_dice: 0.1676 2024/07/08 01:12:03 - mmengine - INFO - Iter(train) [ 36450/120000] base_lr: 1.5825e-04 lr: 1.6204e-05 eta: 1 day, 1:52:25 time: 1.1135 data_time: 0.0164 memory: 15970 grad_norm: 1.6627 loss: 0.3493 semantic_segmentation_loss_cls: 0.1099 semantic_segmentation_loss_mask: 0.0715 semantic_segmentation_loss_dice: 0.1678 2024/07/08 01:12:58 - mmengine - INFO - Iter(train) [ 36500/120000] base_lr: 1.5814e-04 lr: 1.6194e-05 eta: 1 day, 1:51:28 time: 1.1135 data_time: 0.0165 memory: 15183 grad_norm: 1.6613 loss: 0.3494 semantic_segmentation_loss_cls: 0.1100 semantic_segmentation_loss_mask: 0.0716 semantic_segmentation_loss_dice: 0.1679 2024/07/08 01:13:53 - mmengine - INFO - Iter(train) [ 36550/120000] base_lr: 1.5803e-04 lr: 1.6185e-05 eta: 1 day, 1:50:30 time: 1.1134 data_time: 0.0165 memory: 15089 grad_norm: 1.6598 loss: 0.3492 semantic_segmentation_loss_cls: 0.1098 semantic_segmentation_loss_mask: 0.0715 semantic_segmentation_loss_dice: 0.1678 2024/07/08 01:14:48 - mmengine - INFO - Iter(train) [ 36600/120000] base_lr: 1.5793e-04 lr: 1.6175e-05 eta: 1 day, 1:49:33 time: 1.1135 data_time: 0.0165 memory: 15625 grad_norm: 1.6584 loss: 0.3489 semantic_segmentation_loss_cls: 0.1097 semantic_segmentation_loss_mask: 0.0715 semantic_segmentation_loss_dice: 0.1677 2024/07/08 01:15:44 - mmengine - INFO - Iter(train) [ 36650/120000] base_lr: 1.5782e-04 lr: 1.6166e-05 eta: 1 day, 1:48:39 time: 1.1138 data_time: 0.0165 memory: 15128 grad_norm: 1.6570 loss: 0.3487 semantic_segmentation_loss_cls: 0.1096 semantic_segmentation_loss_mask: 0.0714 semantic_segmentation_loss_dice: 0.1676 2024/07/08 01:16:40 - mmengine - INFO - Iter(train) [ 36700/120000] base_lr: 1.5772e-04 lr: 1.6156e-05 eta: 1 day, 1:47:41 time: 1.1137 data_time: 0.0165 memory: 16007 grad_norm: 1.6573 loss: 0.3487 semantic_segmentation_loss_cls: 0.1097 semantic_segmentation_loss_mask: 0.0714 semantic_segmentation_loss_dice: 0.1675 2024/07/08 01:17:36 - mmengine - INFO - Iter(train) [ 36750/120000] base_lr: 1.5761e-04 lr: 1.6146e-05 eta: 1 day, 1:46:47 time: 1.1137 data_time: 0.0165 memory: 15086 grad_norm: 1.6584 loss: 0.3482 semantic_segmentation_loss_cls: 0.1096 semantic_segmentation_loss_mask: 0.0713 semantic_segmentation_loss_dice: 0.1673 2024/07/08 01:18:31 - mmengine - INFO - Iter(train) [ 36800/120000] base_lr: 1.5750e-04 lr: 1.6137e-05 eta: 1 day, 1:45:51 time: 1.1139 data_time: 0.0165 memory: 15762 grad_norm: 1.6579 loss: 0.3482 semantic_segmentation_loss_cls: 0.1096 semantic_segmentation_loss_mask: 0.0713 semantic_segmentation_loss_dice: 0.1674 2024/07/08 01:19:27 - mmengine - INFO - Iter(train) [ 36850/120000] base_lr: 1.5740e-04 lr: 1.6127e-05 eta: 1 day, 1:44:54 time: 1.1136 data_time: 0.0165 memory: 15459 grad_norm: 1.6575 loss: 0.3479 semantic_segmentation_loss_cls: 0.1094 semantic_segmentation_loss_mask: 0.0712 semantic_segmentation_loss_dice: 0.1672 2024/07/08 01:20:21 - mmengine - INFO - Iter(train) [ 36900/120000] base_lr: 1.5729e-04 lr: 1.6117e-05 eta: 1 day, 1:43:55 time: 1.1132 data_time: 0.0165 memory: 15755 grad_norm: 1.6582 loss: 0.3479 semantic_segmentation_loss_cls: 0.1094 semantic_segmentation_loss_mask: 0.0712 semantic_segmentation_loss_dice: 0.1672 2024/07/08 01:21:16 - mmengine - INFO - Iter(train) [ 36950/120000] base_lr: 1.5718e-04 lr: 1.6108e-05 eta: 1 day, 1:42:58 time: 1.1130 data_time: 0.0165 memory: 16065 grad_norm: 1.6572 loss: 0.3479 semantic_segmentation_loss_cls: 0.1094 semantic_segmentation_loss_mask: 0.0712 semantic_segmentation_loss_dice: 0.1673 2024/07/08 01:22:10 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 01:22:10 - mmengine - INFO - Iter(train) [ 37000/120000] base_lr: 1.5708e-04 lr: 1.6098e-05 eta: 1 day, 1:41:58 time: 1.1128 data_time: 0.0165 memory: 15373 grad_norm: 1.6580 loss: 0.3482 semantic_segmentation_loss_cls: 0.1095 semantic_segmentation_loss_mask: 0.0713 semantic_segmentation_loss_dice: 0.1674 2024/07/08 01:22:10 - mmengine - INFO - Saving checkpoint at 37000 iterations 2024/07/08 01:23:09 - mmengine - INFO - Iter(train) [ 37050/120000] base_lr: 1.5697e-04 lr: 1.6088e-05 eta: 1 day, 1:41:10 time: 1.1124 data_time: 0.0163 memory: 16026 grad_norm: 1.6587 loss: 0.3481 semantic_segmentation_loss_cls: 0.1094 semantic_segmentation_loss_mask: 0.0713 semantic_segmentation_loss_dice: 0.1674 2024/07/08 01:24:04 - mmengine - INFO - Iter(train) [ 37100/120000] base_lr: 1.5686e-04 lr: 1.6078e-05 eta: 1 day, 1:40:12 time: 1.1122 data_time: 0.0162 memory: 14602 grad_norm: 1.6575 loss: 0.3473 semantic_segmentation_loss_cls: 0.1091 semantic_segmentation_loss_mask: 0.0712 semantic_segmentation_loss_dice: 0.1671 2024/07/08 01:24:59 - mmengine - INFO - Iter(train) [ 37150/120000] base_lr: 1.5676e-04 lr: 1.6069e-05 eta: 1 day, 1:39:15 time: 1.1120 data_time: 0.0162 memory: 15354 grad_norm: 1.6552 loss: 0.3477 semantic_segmentation_loss_cls: 0.1092 semantic_segmentation_loss_mask: 0.0712 semantic_segmentation_loss_dice: 0.1672 2024/07/08 01:25:54 - mmengine - INFO - Iter(train) [ 37200/120000] base_lr: 1.5665e-04 lr: 1.6059e-05 eta: 1 day, 1:38:18 time: 1.1118 data_time: 0.0161 memory: 14897 grad_norm: 1.6542 loss: 0.3470 semantic_segmentation_loss_cls: 0.1089 semantic_segmentation_loss_mask: 0.0711 semantic_segmentation_loss_dice: 0.1670 2024/07/08 01:26:50 - mmengine - INFO - Iter(train) [ 37250/120000] base_lr: 1.5654e-04 lr: 1.6049e-05 eta: 1 day, 1:37:23 time: 1.1119 data_time: 0.0161 memory: 16498 grad_norm: 1.6537 loss: 0.3461 semantic_segmentation_loss_cls: 0.1085 semantic_segmentation_loss_mask: 0.0710 semantic_segmentation_loss_dice: 0.1666 2024/07/08 01:27:45 - mmengine - INFO - Iter(train) [ 37300/120000] base_lr: 1.5643e-04 lr: 1.6039e-05 eta: 1 day, 1:36:25 time: 1.1117 data_time: 0.0161 memory: 15393 grad_norm: 1.6516 loss: 0.3457 semantic_segmentation_loss_cls: 0.1084 semantic_segmentation_loss_mask: 0.0709 semantic_segmentation_loss_dice: 0.1665 2024/07/08 01:28:42 - mmengine - INFO - Iter(train) [ 37350/120000] base_lr: 1.5633e-04 lr: 1.6030e-05 eta: 1 day, 1:35:31 time: 1.1117 data_time: 0.0162 memory: 14985 grad_norm: 1.6507 loss: 0.3456 semantic_segmentation_loss_cls: 0.1082 semantic_segmentation_loss_mask: 0.0709 semantic_segmentation_loss_dice: 0.1665 2024/07/08 01:29:36 - mmengine - INFO - Iter(train) [ 37400/120000] base_lr: 1.5622e-04 lr: 1.6020e-05 eta: 1 day, 1:34:33 time: 1.1114 data_time: 0.0162 memory: 15238 grad_norm: 1.6514 loss: 0.3452 semantic_segmentation_loss_cls: 0.1080 semantic_segmentation_loss_mask: 0.0709 semantic_segmentation_loss_dice: 0.1663 2024/07/08 01:30:31 - mmengine - INFO - Iter(train) [ 37450/120000] base_lr: 1.5611e-04 lr: 1.6010e-05 eta: 1 day, 1:33:35 time: 1.1111 data_time: 0.0162 memory: 15827 grad_norm: 1.6489 loss: 0.3450 semantic_segmentation_loss_cls: 0.1077 semantic_segmentation_loss_mask: 0.0710 semantic_segmentation_loss_dice: 0.1664 2024/07/08 01:31:26 - mmengine - INFO - Iter(train) [ 37500/120000] base_lr: 1.5600e-04 lr: 1.6000e-05 eta: 1 day, 1:32:38 time: 1.1107 data_time: 0.0161 memory: 14683 grad_norm: 1.6485 loss: 0.3447 semantic_segmentation_loss_cls: 0.1075 semantic_segmentation_loss_mask: 0.0709 semantic_segmentation_loss_dice: 0.1663 2024/07/08 01:32:22 - mmengine - INFO - Iter(train) [ 37550/120000] base_lr: 1.5590e-04 lr: 1.5991e-05 eta: 1 day, 1:31:41 time: 1.1102 data_time: 0.0161 memory: 15134 grad_norm: 1.6479 loss: 0.3441 semantic_segmentation_loss_cls: 0.1073 semantic_segmentation_loss_mask: 0.0708 semantic_segmentation_loss_dice: 0.1660 2024/07/08 01:33:17 - mmengine - INFO - Iter(train) [ 37600/120000] base_lr: 1.5579e-04 lr: 1.5981e-05 eta: 1 day, 1:30:44 time: 1.1098 data_time: 0.0161 memory: 15110 grad_norm: 1.6464 loss: 0.3441 semantic_segmentation_loss_cls: 0.1072 semantic_segmentation_loss_mask: 0.0708 semantic_segmentation_loss_dice: 0.1660 2024/07/08 01:34:13 - mmengine - INFO - Iter(train) [ 37650/120000] base_lr: 1.5568e-04 lr: 1.5971e-05 eta: 1 day, 1:29:49 time: 1.1100 data_time: 0.0161 memory: 15307 grad_norm: 1.6441 loss: 0.3439 semantic_segmentation_loss_cls: 0.1071 semantic_segmentation_loss_mask: 0.0708 semantic_segmentation_loss_dice: 0.1659 2024/07/08 01:35:08 - mmengine - INFO - Iter(train) [ 37700/120000] base_lr: 1.5557e-04 lr: 1.5961e-05 eta: 1 day, 1:28:53 time: 1.1101 data_time: 0.0161 memory: 15614 grad_norm: 1.6444 loss: 0.3438 semantic_segmentation_loss_cls: 0.1071 semantic_segmentation_loss_mask: 0.0708 semantic_segmentation_loss_dice: 0.1659 2024/07/08 01:36:03 - mmengine - INFO - Iter(train) [ 37750/120000] base_lr: 1.5546e-04 lr: 1.5951e-05 eta: 1 day, 1:27:55 time: 1.1099 data_time: 0.0161 memory: 15461 grad_norm: 1.6437 loss: 0.3439 semantic_segmentation_loss_cls: 0.1071 semantic_segmentation_loss_mask: 0.0708 semantic_segmentation_loss_dice: 0.1660 2024/07/08 01:36:58 - mmengine - INFO - 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1.5503e-04 lr: 1.5912e-05 eta: 1 day, 1:24:08 time: 1.1097 data_time: 0.0161 memory: 14839 grad_norm: 1.6449 loss: 0.3437 semantic_segmentation_loss_cls: 0.1072 semantic_segmentation_loss_mask: 0.0706 semantic_segmentation_loss_dice: 0.1659 2024/07/08 01:40:39 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 01:40:39 - mmengine - INFO - Iter(train) [ 38000/120000] base_lr: 1.5492e-04 lr: 1.5902e-05 eta: 1 day, 1:23:10 time: 1.1095 data_time: 0.0161 memory: 14803 grad_norm: 1.6425 loss: 0.3433 semantic_segmentation_loss_cls: 0.1070 semantic_segmentation_loss_mask: 0.0705 semantic_segmentation_loss_dice: 0.1658 2024/07/08 01:40:39 - mmengine - INFO - Saving checkpoint at 38000 iterations 2024/07/08 01:41:38 - mmengine - INFO - Iter(train) [ 38050/120000] base_lr: 1.5481e-04 lr: 1.5892e-05 eta: 1 day, 1:22:23 time: 1.1094 data_time: 0.0159 memory: 15143 grad_norm: 1.6415 loss: 0.3431 semantic_segmentation_loss_cls: 0.1068 semantic_segmentation_loss_mask: 0.0705 semantic_segmentation_loss_dice: 0.1658 2024/07/08 01:42:34 - mmengine - INFO - Iter(train) [ 38100/120000] base_lr: 1.5470e-04 lr: 1.5882e-05 eta: 1 day, 1:21:26 time: 1.1091 data_time: 0.0159 memory: 15136 grad_norm: 1.6405 loss: 0.3427 semantic_segmentation_loss_cls: 0.1067 semantic_segmentation_loss_mask: 0.0704 semantic_segmentation_loss_dice: 0.1657 2024/07/08 01:43:29 - mmengine - INFO - Iter(train) [ 38150/120000] base_lr: 1.5459e-04 lr: 1.5872e-05 eta: 1 day, 1:20:29 time: 1.1087 data_time: 0.0159 memory: 14551 grad_norm: 1.6392 loss: 0.3426 semantic_segmentation_loss_cls: 0.1065 semantic_segmentation_loss_mask: 0.0704 semantic_segmentation_loss_dice: 0.1657 2024/07/08 01:44:23 - mmengine - INFO - Iter(train) [ 38200/120000] base_lr: 1.5449e-04 lr: 1.5862e-05 eta: 1 day, 1:19:31 time: 1.1086 data_time: 0.0159 memory: 14884 grad_norm: 1.6370 loss: 0.3423 semantic_segmentation_loss_cls: 0.1064 semantic_segmentation_loss_mask: 0.0703 semantic_segmentation_loss_dice: 0.1656 2024/07/08 01:45:18 - mmengine - INFO - Iter(train) [ 38250/120000] base_lr: 1.5438e-04 lr: 1.5852e-05 eta: 1 day, 1:18:33 time: 1.1083 data_time: 0.0159 memory: 14963 grad_norm: 1.6376 loss: 0.3424 semantic_segmentation_loss_cls: 0.1065 semantic_segmentation_loss_mask: 0.0703 semantic_segmentation_loss_dice: 0.1656 2024/07/08 01:46:14 - mmengine - INFO - Iter(train) [ 38300/120000] base_lr: 1.5427e-04 lr: 1.5843e-05 eta: 1 day, 1:17:38 time: 1.1086 data_time: 0.0159 memory: 16576 grad_norm: 1.6359 loss: 0.3422 semantic_segmentation_loss_cls: 0.1064 semantic_segmentation_loss_mask: 0.0703 semantic_segmentation_loss_dice: 0.1655 2024/07/08 01:47:10 - mmengine - INFO - Iter(train) [ 38350/120000] base_lr: 1.5416e-04 lr: 1.5833e-05 eta: 1 day, 1:16:43 time: 1.1086 data_time: 0.0159 memory: 14986 grad_norm: 1.6360 loss: 0.3418 semantic_segmentation_loss_cls: 0.1062 semantic_segmentation_loss_mask: 0.0702 semantic_segmentation_loss_dice: 0.1654 2024/07/08 01:48:05 - mmengine - INFO - Iter(train) [ 38400/120000] base_lr: 1.5405e-04 lr: 1.5823e-05 eta: 1 day, 1:15:45 time: 1.1085 data_time: 0.0159 memory: 15625 grad_norm: 1.6360 loss: 0.3419 semantic_segmentation_loss_cls: 0.1062 semantic_segmentation_loss_mask: 0.0703 semantic_segmentation_loss_dice: 0.1654 2024/07/08 01:49:01 - mmengine - INFO - Iter(train) [ 38450/120000] base_lr: 1.5394e-04 lr: 1.5813e-05 eta: 1 day, 1:14:49 time: 1.1087 data_time: 0.0159 memory: 15414 grad_norm: 1.6353 loss: 0.3416 semantic_segmentation_loss_cls: 0.1060 semantic_segmentation_loss_mask: 0.0703 semantic_segmentation_loss_dice: 0.1653 2024/07/08 01:49:56 - mmengine - INFO - Iter(train) [ 38500/120000] base_lr: 1.5383e-04 lr: 1.5803e-05 eta: 1 day, 1:13:53 time: 1.1087 data_time: 0.0159 memory: 15041 grad_norm: 1.6328 loss: 0.3413 semantic_segmentation_loss_cls: 0.1059 semantic_segmentation_loss_mask: 0.0702 semantic_segmentation_loss_dice: 0.1652 2024/07/08 01:50:51 - mmengine - INFO - Iter(train) [ 38550/120000] base_lr: 1.5372e-04 lr: 1.5793e-05 eta: 1 day, 1:12:55 time: 1.1087 data_time: 0.0160 memory: 15774 grad_norm: 1.6322 loss: 0.3414 semantic_segmentation_loss_cls: 0.1059 semantic_segmentation_loss_mask: 0.0702 semantic_segmentation_loss_dice: 0.1653 2024/07/08 01:51:46 - mmengine - INFO - Iter(train) [ 38600/120000] base_lr: 1.5361e-04 lr: 1.5783e-05 eta: 1 day, 1:11:59 time: 1.1088 data_time: 0.0160 memory: 15450 grad_norm: 1.6331 loss: 0.3408 semantic_segmentation_loss_cls: 0.1056 semantic_segmentation_loss_mask: 0.0702 semantic_segmentation_loss_dice: 0.1651 2024/07/08 01:52:41 - mmengine - INFO - Iter(train) [ 38650/120000] base_lr: 1.5350e-04 lr: 1.5773e-05 eta: 1 day, 1:11:02 time: 1.1087 data_time: 0.0160 memory: 14875 grad_norm: 1.6331 loss: 0.3401 semantic_segmentation_loss_cls: 0.1053 semantic_segmentation_loss_mask: 0.0701 semantic_segmentation_loss_dice: 0.1647 2024/07/08 01:53:38 - mmengine - INFO - Iter(train) [ 38700/120000] base_lr: 1.5339e-04 lr: 1.5763e-05 eta: 1 day, 1:10:07 time: 1.1092 data_time: 0.0160 memory: 14679 grad_norm: 1.6324 loss: 0.3402 semantic_segmentation_loss_cls: 0.1054 semantic_segmentation_loss_mask: 0.0701 semantic_segmentation_loss_dice: 0.1647 2024/07/08 01:54:34 - mmengine - INFO - Iter(train) [ 38750/120000] base_lr: 1.5328e-04 lr: 1.5753e-05 eta: 1 day, 1:09:13 time: 1.1095 data_time: 0.0161 memory: 16078 grad_norm: 1.6296 loss: 0.3398 semantic_segmentation_loss_cls: 0.1053 semantic_segmentation_loss_mask: 0.0700 semantic_segmentation_loss_dice: 0.1646 2024/07/08 01:55:29 - mmengine - INFO - Iter(train) [ 38800/120000] base_lr: 1.5317e-04 lr: 1.5743e-05 eta: 1 day, 1:08:15 time: 1.1096 data_time: 0.0161 memory: 15780 grad_norm: 1.6278 loss: 0.3395 semantic_segmentation_loss_cls: 0.1050 semantic_segmentation_loss_mask: 0.0700 semantic_segmentation_loss_dice: 0.1645 2024/07/08 01:56:24 - mmengine - INFO - Iter(train) [ 38850/120000] base_lr: 1.5306e-04 lr: 1.5733e-05 eta: 1 day, 1:07:19 time: 1.1096 data_time: 0.0161 memory: 15432 grad_norm: 1.6278 loss: 0.3394 semantic_segmentation_loss_cls: 0.1050 semantic_segmentation_loss_mask: 0.0699 semantic_segmentation_loss_dice: 0.1645 2024/07/08 01:57:19 - mmengine - INFO - Iter(train) [ 38900/120000] base_lr: 1.5295e-04 lr: 1.5723e-05 eta: 1 day, 1:06:22 time: 1.1097 data_time: 0.0161 memory: 14837 grad_norm: 1.6287 loss: 0.3391 semantic_segmentation_loss_cls: 0.1049 semantic_segmentation_loss_mask: 0.0698 semantic_segmentation_loss_dice: 0.1644 2024/07/08 01:58:15 - mmengine - INFO - Iter(train) [ 38950/120000] base_lr: 1.5284e-04 lr: 1.5713e-05 eta: 1 day, 1:05:26 time: 1.1102 data_time: 0.0162 memory: 15656 grad_norm: 1.6278 loss: 0.3389 semantic_segmentation_loss_cls: 0.1048 semantic_segmentation_loss_mask: 0.0698 semantic_segmentation_loss_dice: 0.1643 2024/07/08 01:59:12 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 01:59:12 - mmengine - INFO - Iter(train) [ 39000/120000] base_lr: 1.5273e-04 lr: 1.5703e-05 eta: 1 day, 1:04:32 time: 1.1105 data_time: 0.0162 memory: 14795 grad_norm: 1.6279 loss: 0.3390 semantic_segmentation_loss_cls: 0.1049 semantic_segmentation_loss_mask: 0.0697 semantic_segmentation_loss_dice: 0.1643 2024/07/08 01:59:12 - mmengine - INFO - Saving checkpoint at 39000 iterations 2024/07/08 02:00:14 - mmengine - INFO - Iter(train) [ 39050/120000] base_lr: 1.5262e-04 lr: 1.5693e-05 eta: 1 day, 1:03:50 time: 1.1120 data_time: 0.0172 memory: 15120 grad_norm: 1.6270 loss: 0.3388 semantic_segmentation_loss_cls: 0.1049 semantic_segmentation_loss_mask: 0.0697 semantic_segmentation_loss_dice: 0.1642 2024/07/08 02:01:10 - mmengine - INFO - Iter(train) [ 39100/120000] base_lr: 1.5251e-04 lr: 1.5683e-05 eta: 1 day, 1:02:56 time: 1.1121 data_time: 0.0172 memory: 15256 grad_norm: 1.6262 loss: 0.3386 semantic_segmentation_loss_cls: 0.1048 semantic_segmentation_loss_mask: 0.0695 semantic_segmentation_loss_dice: 0.1643 2024/07/08 02:02:07 - mmengine - INFO - Iter(train) [ 39150/120000] base_lr: 1.5240e-04 lr: 1.5673e-05 eta: 1 day, 1:02:02 time: 1.1124 data_time: 0.0173 memory: 15302 grad_norm: 1.6259 loss: 0.3388 semantic_segmentation_loss_cls: 0.1048 semantic_segmentation_loss_mask: 0.0696 semantic_segmentation_loss_dice: 0.1643 2024/07/08 02:03:05 - mmengine - INFO - Iter(train) [ 39200/120000] base_lr: 1.5229e-04 lr: 1.5662e-05 eta: 1 day, 1:01:10 time: 1.1131 data_time: 0.0173 memory: 14775 grad_norm: 1.6261 loss: 0.3379 semantic_segmentation_loss_cls: 0.1045 semantic_segmentation_loss_mask: 0.0695 semantic_segmentation_loss_dice: 0.1639 2024/07/08 02:04:01 - mmengine - INFO - Iter(train) [ 39250/120000] base_lr: 1.5218e-04 lr: 1.5652e-05 eta: 1 day, 1:00:16 time: 1.1133 data_time: 0.0173 memory: 15524 grad_norm: 1.6243 loss: 0.3382 semantic_segmentation_loss_cls: 0.1046 semantic_segmentation_loss_mask: 0.0695 semantic_segmentation_loss_dice: 0.1641 2024/07/08 02:04:59 - mmengine - INFO - Iter(train) [ 39300/120000] base_lr: 1.5207e-04 lr: 1.5642e-05 eta: 1 day, 0:59:23 time: 1.1139 data_time: 0.0173 memory: 14520 grad_norm: 1.6245 loss: 0.3378 semantic_segmentation_loss_cls: 0.1044 semantic_segmentation_loss_mask: 0.0695 semantic_segmentation_loss_dice: 0.1640 2024/07/08 02:05:57 - mmengine - INFO - Iter(train) [ 39350/120000] base_lr: 1.5195e-04 lr: 1.5632e-05 eta: 1 day, 0:58:32 time: 1.1145 data_time: 0.0173 memory: 15350 grad_norm: 1.6243 loss: 0.3375 semantic_segmentation_loss_cls: 0.1043 semantic_segmentation_loss_mask: 0.0693 semantic_segmentation_loss_dice: 0.1638 2024/07/08 02:06:54 - mmengine - INFO - Iter(train) [ 39400/120000] base_lr: 1.5184e-04 lr: 1.5622e-05 eta: 1 day, 0:57:40 time: 1.1151 data_time: 0.0173 memory: 14560 grad_norm: 1.6254 loss: 0.3373 semantic_segmentation_loss_cls: 0.1043 semantic_segmentation_loss_mask: 0.0693 semantic_segmentation_loss_dice: 0.1637 2024/07/08 02:07:51 - mmengine - INFO - Iter(train) [ 39450/120000] base_lr: 1.5173e-04 lr: 1.5612e-05 eta: 1 day, 0:56:48 time: 1.1156 data_time: 0.0173 memory: 15686 grad_norm: 1.6240 loss: 0.3373 semantic_segmentation_loss_cls: 0.1043 semantic_segmentation_loss_mask: 0.0693 semantic_segmentation_loss_dice: 0.1638 2024/07/08 02:08:48 - mmengine - INFO - Iter(train) [ 39500/120000] base_lr: 1.5162e-04 lr: 1.5602e-05 eta: 1 day, 0:55:54 time: 1.1160 data_time: 0.0173 memory: 15142 grad_norm: 1.6241 loss: 0.3372 semantic_segmentation_loss_cls: 0.1042 semantic_segmentation_loss_mask: 0.0692 semantic_segmentation_loss_dice: 0.1638 2024/07/08 02:09:43 - mmengine - INFO - Iter(train) [ 39550/120000] base_lr: 1.5151e-04 lr: 1.5592e-05 eta: 1 day, 0:54:57 time: 1.1157 data_time: 0.0173 memory: 15267 grad_norm: 1.6234 loss: 0.3368 semantic_segmentation_loss_cls: 0.1041 semantic_segmentation_loss_mask: 0.0691 semantic_segmentation_loss_dice: 0.1636 2024/07/08 02:10:39 - mmengine - INFO - Iter(train) [ 39600/120000] base_lr: 1.5140e-04 lr: 1.5582e-05 eta: 1 day, 0:54:01 time: 1.1155 data_time: 0.0173 memory: 15148 grad_norm: 1.6222 loss: 0.3366 semantic_segmentation_loss_cls: 0.1041 semantic_segmentation_loss_mask: 0.0690 semantic_segmentation_loss_dice: 0.1635 2024/07/08 02:11:35 - mmengine - INFO - Iter(train) [ 39650/120000] base_lr: 1.5129e-04 lr: 1.5571e-05 eta: 1 day, 0:53:05 time: 1.1157 data_time: 0.0173 memory: 15474 grad_norm: 1.6218 loss: 0.3362 semantic_segmentation_loss_cls: 0.1040 semantic_segmentation_loss_mask: 0.0689 semantic_segmentation_loss_dice: 0.1633 2024/07/08 02:12:30 - mmengine - INFO - Iter(train) [ 39700/120000] base_lr: 1.5117e-04 lr: 1.5561e-05 eta: 1 day, 0:52:09 time: 1.1158 data_time: 0.0173 memory: 14859 grad_norm: 1.6210 loss: 0.3364 semantic_segmentation_loss_cls: 0.1041 semantic_segmentation_loss_mask: 0.0689 semantic_segmentation_loss_dice: 0.1633 2024/07/08 02:13:25 - mmengine - INFO - Iter(train) [ 39750/120000] base_lr: 1.5106e-04 lr: 1.5551e-05 eta: 1 day, 0:51:12 time: 1.1158 data_time: 0.0174 memory: 14723 grad_norm: 1.6210 loss: 0.3361 semantic_segmentation_loss_cls: 0.1040 semantic_segmentation_loss_mask: 0.0689 semantic_segmentation_loss_dice: 0.1632 2024/07/08 02:14:21 - mmengine - INFO - Iter(train) [ 39800/120000] base_lr: 1.5095e-04 lr: 1.5541e-05 eta: 1 day, 0:50:16 time: 1.1156 data_time: 0.0174 memory: 15594 grad_norm: 1.6212 loss: 0.3358 semantic_segmentation_loss_cls: 0.1039 semantic_segmentation_loss_mask: 0.0689 semantic_segmentation_loss_dice: 0.1631 2024/07/08 02:15:16 - mmengine - INFO - Iter(train) [ 39850/120000] base_lr: 1.5084e-04 lr: 1.5531e-05 eta: 1 day, 0:49:19 time: 1.1158 data_time: 0.0174 memory: 15717 grad_norm: 1.6215 loss: 0.3359 semantic_segmentation_loss_cls: 0.1039 semantic_segmentation_loss_mask: 0.0688 semantic_segmentation_loss_dice: 0.1632 2024/07/08 02:16:11 - mmengine - INFO - Iter(train) [ 39900/120000] base_lr: 1.5073e-04 lr: 1.5521e-05 eta: 1 day, 0:48:22 time: 1.1155 data_time: 0.0173 memory: 16650 grad_norm: 1.6215 loss: 0.3362 semantic_segmentation_loss_cls: 0.1040 semantic_segmentation_loss_mask: 0.0689 semantic_segmentation_loss_dice: 0.1633 2024/07/08 02:17:06 - mmengine - INFO - Iter(train) [ 39950/120000] base_lr: 1.5061e-04 lr: 1.5510e-05 eta: 1 day, 0:47:24 time: 1.1152 data_time: 0.0173 memory: 16009 grad_norm: 1.6200 loss: 0.3357 semantic_segmentation_loss_cls: 0.1038 semantic_segmentation_loss_mask: 0.0688 semantic_segmentation_loss_dice: 0.1631 2024/07/08 02:18:01 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 02:18:01 - mmengine - INFO - Iter(train) [ 40000/120000] base_lr: 1.5050e-04 lr: 1.5500e-05 eta: 1 day, 0:46:27 time: 1.1152 data_time: 0.0173 memory: 15366 grad_norm: 1.6207 loss: 0.3355 semantic_segmentation_loss_cls: 0.1036 semantic_segmentation_loss_mask: 0.0688 semantic_segmentation_loss_dice: 0.1631 2024/07/08 02:18:01 - mmengine - INFO - Saving checkpoint at 40000 iterations 2024/07/08 02:18:18 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:50 time: 0.2454 data_time: 0.0015 memory: 5013 2024/07/08 02:18:30 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:38 time: 0.2454 data_time: 0.0015 memory: 5189 2024/07/08 02:18:43 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:26 time: 0.2455 data_time: 0.0015 memory: 4460 2024/07/08 02:18:55 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:13 time: 0.2455 data_time: 0.0015 memory: 4543 2024/07/08 02:19:07 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:01 time: 0.2455 data_time: 0.0015 memory: 4643 2024/07/08 02:19:20 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:49 time: 0.2456 data_time: 0.0015 memory: 10983 2024/07/08 02:19:32 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:37 time: 0.2456 data_time: 0.0015 memory: 4460 2024/07/08 02:19:45 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2456 data_time: 0.0015 memory: 4641 2024/07/08 02:19:57 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2456 data_time: 0.0015 memory: 4473 2024/07/08 02:20:09 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2456 data_time: 0.0015 memory: 4555 2024/07/08 02:20:10 - mmengine - INFO - per class results: 2024/07/08 02:20:10 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.32 | 86.74 | | building | 82.07 | 88.94 | | sky | 94.18 | 97.78 | | floor | 83.13 | 91.05 | | tree | 74.87 | 87.81 | | ceiling | 85.43 | 93.47 | | road | 84.27 | 92.67 | | bed | 87.02 | 94.8 | | windowpane | 62.6 | 81.09 | | grass | 70.23 | 84.72 | | cabinet | 60.68 | 71.33 | | sidewalk | 66.63 | 80.47 | | person | 81.76 | 91.26 | | earth | 34.96 | 48.81 | | door | 51.64 | 69.43 | | table | 61.71 | 77.18 | | mountain | 58.48 | 71.68 | | plant | 54.42 | 68.99 | | curtain | 75.0 | 87.54 | | chair | 59.66 | 72.99 | | car | 85.69 | 91.67 | | water | 48.0 | 63.63 | | painting | 72.54 | 89.52 | | sofa | 64.15 | 75.96 | | shelf | 45.28 | 65.14 | | house | 46.57 | 79.41 | | sea | 45.98 | 69.65 | | mirror | 68.95 | 78.89 | | rug | 69.0 | 77.81 | | field | 42.97 | 61.45 | | armchair | 42.52 | 67.75 | | seat | 59.83 | 77.95 | | fence | 47.2 | 68.06 | | desk | 47.08 | 66.94 | | rock | 37.49 | 57.3 | | wardrobe | 51.78 | 70.12 | | lamp | 66.37 | 80.72 | | bathtub | 86.57 | 90.56 | | railing | 33.88 | 50.76 | | cushion | 58.44 | 70.38 | | base | 20.45 | 36.2 | | box | 27.29 | 37.21 | | column | 50.35 | 72.07 | | signboard | 38.6 | 57.24 | | chest of drawers | 38.63 | 70.57 | | counter | 29.65 | 44.36 | | sand | 34.97 | 51.3 | | sink | 74.92 | 82.4 | | skyscraper | 46.55 | 61.18 | | fireplace | 66.43 | 89.2 | | refrigerator | 79.69 | 88.8 | | grandstand | 39.19 | 71.29 | | path | 30.94 | 43.43 | | stairs | 30.95 | 43.41 | | runway | 75.98 | 89.51 | | case | 60.73 | 67.02 | | pool table | 92.31 | 96.58 | | pillow | 57.13 | 71.44 | | screen door | 76.51 | 79.48 | | stairway | 39.44 | 44.65 | | river | 21.02 | 44.14 | | bridge | 69.86 | 89.29 | | bookcase | 35.85 | 57.23 | | blind | 40.78 | 46.71 | | coffee table | 71.99 | 85.47 | | toilet | 77.86 | 89.4 | | flower | 36.36 | 56.74 | | book | 51.03 | 76.82 | | hill | 10.66 | 22.05 | | bench | 37.71 | 45.02 | | countertop | 55.47 | 65.04 | | stove | 82.28 | 86.69 | | palm | 52.23 | 70.52 | | kitchen island | 30.93 | 78.87 | | computer | 61.62 | 68.48 | | swivel chair | 43.86 | 61.3 | | boat | 44.89 | 49.39 | | bar | 27.19 | 36.94 | | arcade machine | 61.69 | 67.7 | | hovel | 17.13 | 20.69 | | bus | 87.71 | 90.66 | | towel | 68.98 | 76.74 | | light | 63.61 | 80.05 | | truck | 34.28 | 47.1 | | tower | 31.44 | 54.19 | | chandelier | 65.46 | 75.1 | | awning | 32.35 | 47.44 | | streetlight | 40.0 | 58.06 | | booth | 55.16 | 56.8 | | television receiver | 73.59 | 89.72 | | airplane | 60.15 | 65.93 | | dirt track | 14.87 | 21.85 | | apparel | 37.11 | 56.15 | | pole | 32.89 | 53.72 | | land | 0.78 | 1.08 | | bannister | 14.05 | 25.49 | | escalator | 46.2 | 59.49 | | ottoman | 39.31 | 64.36 | | bottle | 22.05 | 26.75 | | buffet | 49.03 | 53.23 | | poster | 30.72 | 45.19 | | stage | 15.24 | 25.94 | | van | 46.92 | 67.42 | | ship | 58.92 | 83.06 | | fountain | 6.12 | 6.23 | | conveyer belt | 81.93 | 90.43 | | canopy | 14.81 | 25.74 | | washer | 71.99 | 74.22 | | plaything | 27.23 | 38.56 | | swimming pool | 24.11 | 27.33 | | stool | 46.66 | 68.54 | | barrel | 13.67 | 55.42 | | basket | 40.08 | 51.68 | | waterfall | 63.49 | 87.81 | | tent | 76.66 | 97.67 | | bag | 19.16 | 26.0 | | minibike | 70.45 | 85.15 | | cradle | 76.21 | 96.93 | | oven | 23.2 | 58.83 | | ball | 43.1 | 52.88 | | food | 62.35 | 78.6 | | step | 22.56 | 30.83 | | tank | 47.12 | 50.04 | | trade name | 30.94 | 41.53 | | microwave | 38.34 | 41.08 | | pot | 54.93 | 64.11 | | animal | 62.0 | 69.03 | | bicycle | 57.08 | 78.66 | | lake | 61.13 | 63.68 | | dishwasher | 74.26 | 85.38 | | screen | 63.16 | 82.63 | | blanket | 15.15 | 19.25 | | sculpture | 71.11 | 85.77 | | hood | 68.28 | 74.5 | | sconce | 50.64 | 65.55 | | vase | 48.36 | 63.99 | | traffic light | 42.33 | 61.05 | | tray | 17.14 | 23.09 | | ashcan | 42.01 | 57.98 | | fan | 66.02 | 81.83 | | pier | 47.4 | 83.37 | | crt screen | 0.24 | 0.65 | | plate | 60.42 | 74.9 | | monitor | 3.31 | 4.83 | | bulletin board | 44.19 | 53.03 | | shower | 7.56 | 17.76 | | radiator | 57.09 | 68.42 | | glass | 19.16 | 20.85 | | clock | 34.15 | 39.82 | | flag | 45.47 | 54.8 | +---------------------+-------+-------+ 2024/07/08 02:20:10 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.2200 mIoU: 50.0100 mAcc: 63.4300 data_time: 0.0015 time: 0.2465 2024/07/08 02:21:04 - mmengine - INFO - Iter(train) [ 40050/120000] base_lr: 1.5039e-04 lr: 1.5490e-05 eta: 1 day, 0:45:31 time: 1.1141 data_time: 0.0164 memory: 15350 grad_norm: 1.6198 loss: 0.3351 semantic_segmentation_loss_cls: 0.1034 semantic_segmentation_loss_mask: 0.0688 semantic_segmentation_loss_dice: 0.1630 2024/07/08 02:21:59 - mmengine - INFO - Iter(train) [ 40100/120000] base_lr: 1.5028e-04 lr: 1.5480e-05 eta: 1 day, 0:44:33 time: 1.1136 data_time: 0.0164 memory: 15213 grad_norm: 1.6193 loss: 0.3351 semantic_segmentation_loss_cls: 0.1033 semantic_segmentation_loss_mask: 0.0687 semantic_segmentation_loss_dice: 0.1630 2024/07/08 02:22:55 - mmengine - INFO - Iter(train) [ 40150/120000] base_lr: 1.5017e-04 lr: 1.5470e-05 eta: 1 day, 0:43:38 time: 1.1138 data_time: 0.0163 memory: 15443 grad_norm: 1.6168 loss: 0.3346 semantic_segmentation_loss_cls: 0.1031 semantic_segmentation_loss_mask: 0.0687 semantic_segmentation_loss_dice: 0.1628 2024/07/08 02:23:51 - mmengine - INFO - Iter(train) [ 40200/120000] base_lr: 1.5005e-04 lr: 1.5459e-05 eta: 1 day, 0:42:41 time: 1.1138 data_time: 0.0164 memory: 15228 grad_norm: 1.6162 loss: 0.3345 semantic_segmentation_loss_cls: 0.1030 semantic_segmentation_loss_mask: 0.0687 semantic_segmentation_loss_dice: 0.1628 2024/07/08 02:24:46 - mmengine - INFO - Iter(train) [ 40250/120000] base_lr: 1.4994e-04 lr: 1.5449e-05 eta: 1 day, 0:41:45 time: 1.1136 data_time: 0.0164 memory: 14523 grad_norm: 1.6167 loss: 0.3341 semantic_segmentation_loss_cls: 0.1028 semantic_segmentation_loss_mask: 0.0687 semantic_segmentation_loss_dice: 0.1626 2024/07/08 02:25:41 - mmengine - INFO - Iter(train) [ 40300/120000] base_lr: 1.4983e-04 lr: 1.5439e-05 eta: 1 day, 0:40:48 time: 1.1137 data_time: 0.0164 memory: 15374 grad_norm: 1.6177 loss: 0.3343 semantic_segmentation_loss_cls: 0.1028 semantic_segmentation_loss_mask: 0.0687 semantic_segmentation_loss_dice: 0.1627 2024/07/08 02:26:37 - mmengine - INFO - Iter(train) [ 40350/120000] base_lr: 1.4971e-04 lr: 1.5429e-05 eta: 1 day, 0:39:54 time: 1.1141 data_time: 0.0165 memory: 15128 grad_norm: 1.6175 loss: 0.3344 semantic_segmentation_loss_cls: 0.1029 semantic_segmentation_loss_mask: 0.0687 semantic_segmentation_loss_dice: 0.1628 2024/07/08 02:27:33 - mmengine - INFO - Iter(train) [ 40400/120000] base_lr: 1.4960e-04 lr: 1.5418e-05 eta: 1 day, 0:38:57 time: 1.1140 data_time: 0.0165 memory: 14809 grad_norm: 1.6162 loss: 0.3345 semantic_segmentation_loss_cls: 0.1030 semantic_segmentation_loss_mask: 0.0688 semantic_segmentation_loss_dice: 0.1628 2024/07/08 02:28:27 - mmengine - INFO - Iter(train) [ 40450/120000] base_lr: 1.4949e-04 lr: 1.5408e-05 eta: 1 day, 0:37:57 time: 1.1138 data_time: 0.0165 memory: 14926 grad_norm: 1.6158 loss: 0.3343 semantic_segmentation_loss_cls: 0.1030 semantic_segmentation_loss_mask: 0.0687 semantic_segmentation_loss_dice: 0.1626 2024/07/08 02:29:20 - mmengine - INFO - Iter(train) [ 40500/120000] base_lr: 1.4938e-04 lr: 1.5398e-05 eta: 1 day, 0:36:57 time: 1.1134 data_time: 0.0164 memory: 15965 grad_norm: 1.6155 loss: 0.3339 semantic_segmentation_loss_cls: 0.1029 semantic_segmentation_loss_mask: 0.0686 semantic_segmentation_loss_dice: 0.1625 2024/07/08 02:30:14 - mmengine - INFO - Iter(train) [ 40550/120000] base_lr: 1.4926e-04 lr: 1.5388e-05 eta: 1 day, 0:35:57 time: 1.1130 data_time: 0.0164 memory: 15879 grad_norm: 1.6145 loss: 0.3339 semantic_segmentation_loss_cls: 0.1029 semantic_segmentation_loss_mask: 0.0686 semantic_segmentation_loss_dice: 0.1624 2024/07/08 02:31:09 - mmengine - INFO - Iter(train) [ 40600/120000] base_lr: 1.4915e-04 lr: 1.5377e-05 eta: 1 day, 0:35:00 time: 1.1130 data_time: 0.0164 memory: 15131 grad_norm: 1.6135 loss: 0.3333 semantic_segmentation_loss_cls: 0.1027 semantic_segmentation_loss_mask: 0.0685 semantic_segmentation_loss_dice: 0.1622 2024/07/08 02:32:04 - mmengine - INFO - Iter(train) [ 40650/120000] base_lr: 1.4904e-04 lr: 1.5367e-05 eta: 1 day, 0:34:04 time: 1.1128 data_time: 0.0164 memory: 15331 grad_norm: 1.6125 loss: 0.3333 semantic_segmentation_loss_cls: 0.1026 semantic_segmentation_loss_mask: 0.0685 semantic_segmentation_loss_dice: 0.1622 2024/07/08 02:32:59 - mmengine - INFO - Iter(train) [ 40700/120000] base_lr: 1.4892e-04 lr: 1.5357e-05 eta: 1 day, 0:33:06 time: 1.1127 data_time: 0.0164 memory: 15194 grad_norm: 1.6102 loss: 0.3329 semantic_segmentation_loss_cls: 0.1023 semantic_segmentation_loss_mask: 0.0684 semantic_segmentation_loss_dice: 0.1621 2024/07/08 02:33:54 - mmengine - INFO - Iter(train) [ 40750/120000] base_lr: 1.4881e-04 lr: 1.5346e-05 eta: 1 day, 0:32:09 time: 1.1124 data_time: 0.0164 memory: 14664 grad_norm: 1.6076 loss: 0.3328 semantic_segmentation_loss_cls: 0.1023 semantic_segmentation_loss_mask: 0.0684 semantic_segmentation_loss_dice: 0.1621 2024/07/08 02:34:50 - mmengine - INFO - Iter(train) [ 40800/120000] base_lr: 1.4870e-04 lr: 1.5336e-05 eta: 1 day, 0:31:14 time: 1.1125 data_time: 0.0164 memory: 14694 grad_norm: 1.6063 loss: 0.3324 semantic_segmentation_loss_cls: 0.1021 semantic_segmentation_loss_mask: 0.0683 semantic_segmentation_loss_dice: 0.1620 2024/07/08 02:35:45 - mmengine - INFO - Iter(train) [ 40850/120000] base_lr: 1.4858e-04 lr: 1.5326e-05 eta: 1 day, 0:30:16 time: 1.1124 data_time: 0.0164 memory: 15191 grad_norm: 1.6050 loss: 0.3325 semantic_segmentation_loss_cls: 0.1021 semantic_segmentation_loss_mask: 0.0683 semantic_segmentation_loss_dice: 0.1621 2024/07/08 02:36:40 - mmengine - INFO - Iter(train) [ 40900/120000] base_lr: 1.4847e-04 lr: 1.5315e-05 eta: 1 day, 0:29:19 time: 1.1125 data_time: 0.0164 memory: 14754 grad_norm: 1.6027 loss: 0.3322 semantic_segmentation_loss_cls: 0.1019 semantic_segmentation_loss_mask: 0.0683 semantic_segmentation_loss_dice: 0.1620 2024/07/08 02:37:35 - mmengine - INFO - Iter(train) [ 40950/120000] base_lr: 1.4835e-04 lr: 1.5305e-05 eta: 1 day, 0:28:23 time: 1.1126 data_time: 0.0164 memory: 14905 grad_norm: 1.6028 loss: 0.3323 semantic_segmentation_loss_cls: 0.1020 semantic_segmentation_loss_mask: 0.0683 semantic_segmentation_loss_dice: 0.1620 2024/07/08 02:38:30 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 02:38:30 - mmengine - INFO - Iter(train) [ 41000/120000] base_lr: 1.4824e-04 lr: 1.5295e-05 eta: 1 day, 0:27:25 time: 1.1128 data_time: 0.0164 memory: 15066 grad_norm: 1.6018 loss: 0.3316 semantic_segmentation_loss_cls: 0.1018 semantic_segmentation_loss_mask: 0.0681 semantic_segmentation_loss_dice: 0.1617 2024/07/08 02:38:30 - mmengine - INFO - Saving checkpoint at 41000 iterations 2024/07/08 02:39:30 - mmengine - INFO - Iter(train) [ 41050/120000] base_lr: 1.4813e-04 lr: 1.5284e-05 eta: 1 day, 0:26:39 time: 1.1131 data_time: 0.0164 memory: 14360 grad_norm: 1.5992 loss: 0.3310 semantic_segmentation_loss_cls: 0.1016 semantic_segmentation_loss_mask: 0.0680 semantic_segmentation_loss_dice: 0.1614 2024/07/08 02:40:26 - mmengine - INFO - Iter(train) [ 41100/120000] base_lr: 1.4801e-04 lr: 1.5274e-05 eta: 1 day, 0:25:43 time: 1.1134 data_time: 0.0164 memory: 15410 grad_norm: 1.6000 loss: 0.3313 semantic_segmentation_loss_cls: 0.1017 semantic_segmentation_loss_mask: 0.0681 semantic_segmentation_loss_dice: 0.1615 2024/07/08 02:41:21 - mmengine - INFO - Iter(train) [ 41150/120000] base_lr: 1.4790e-04 lr: 1.5264e-05 eta: 1 day, 0:24:47 time: 1.1134 data_time: 0.0164 memory: 15457 grad_norm: 1.5997 loss: 0.3310 semantic_segmentation_loss_cls: 0.1016 semantic_segmentation_loss_mask: 0.0680 semantic_segmentation_loss_dice: 0.1614 2024/07/08 02:42:16 - mmengine - INFO - Iter(train) [ 41200/120000] base_lr: 1.4778e-04 lr: 1.5253e-05 eta: 1 day, 0:23:49 time: 1.1134 data_time: 0.0164 memory: 14777 grad_norm: 1.5992 loss: 0.3311 semantic_segmentation_loss_cls: 0.1017 semantic_segmentation_loss_mask: 0.0680 semantic_segmentation_loss_dice: 0.1614 2024/07/08 02:43:10 - mmengine - INFO - Iter(train) [ 41250/120000] base_lr: 1.4767e-04 lr: 1.5243e-05 eta: 1 day, 0:22:50 time: 1.1129 data_time: 0.0164 memory: 15359 grad_norm: 1.6002 loss: 0.3314 semantic_segmentation_loss_cls: 0.1018 semantic_segmentation_loss_mask: 0.0681 semantic_segmentation_loss_dice: 0.1615 2024/07/08 02:44:06 - mmengine - INFO - Iter(train) [ 41300/120000] base_lr: 1.4756e-04 lr: 1.5232e-05 eta: 1 day, 0:21:54 time: 1.1131 data_time: 0.0164 memory: 16065 grad_norm: 1.5998 loss: 0.3311 semantic_segmentation_loss_cls: 0.1016 semantic_segmentation_loss_mask: 0.0680 semantic_segmentation_loss_dice: 0.1615 2024/07/08 02:45:01 - mmengine - INFO - Iter(train) [ 41350/120000] base_lr: 1.4744e-04 lr: 1.5222e-05 eta: 1 day, 0:20:57 time: 1.1127 data_time: 0.0163 memory: 14472 grad_norm: 1.5993 loss: 0.3307 semantic_segmentation_loss_cls: 0.1015 semantic_segmentation_loss_mask: 0.0679 semantic_segmentation_loss_dice: 0.1612 2024/07/08 02:45:56 - mmengine - INFO - Iter(train) [ 41400/120000] base_lr: 1.4733e-04 lr: 1.5212e-05 eta: 1 day, 0:20:00 time: 1.1129 data_time: 0.0163 memory: 15472 grad_norm: 1.5987 loss: 0.3303 semantic_segmentation_loss_cls: 0.1014 semantic_segmentation_loss_mask: 0.0679 semantic_segmentation_loss_dice: 0.1611 2024/07/08 02:46:52 - mmengine - INFO - Iter(train) [ 41450/120000] base_lr: 1.4721e-04 lr: 1.5201e-05 eta: 1 day, 0:19:05 time: 1.1131 data_time: 0.0163 memory: 14471 grad_norm: 1.5976 loss: 0.3301 semantic_segmentation_loss_cls: 0.1013 semantic_segmentation_loss_mask: 0.0678 semantic_segmentation_loss_dice: 0.1609 2024/07/08 02:47:47 - mmengine - INFO - Iter(train) [ 41500/120000] base_lr: 1.4710e-04 lr: 1.5191e-05 eta: 1 day, 0:18:07 time: 1.1130 data_time: 0.0163 memory: 15222 grad_norm: 1.5978 loss: 0.3300 semantic_segmentation_loss_cls: 0.1013 semantic_segmentation_loss_mask: 0.0678 semantic_segmentation_loss_dice: 0.1609 2024/07/08 02:48:42 - mmengine - INFO - Iter(train) [ 41550/120000] base_lr: 1.4698e-04 lr: 1.5180e-05 eta: 1 day, 0:17:10 time: 1.1130 data_time: 0.0163 memory: 14803 grad_norm: 1.5975 loss: 0.3297 semantic_segmentation_loss_cls: 0.1012 semantic_segmentation_loss_mask: 0.0677 semantic_segmentation_loss_dice: 0.1608 2024/07/08 02:49:37 - mmengine - INFO - Iter(train) [ 41600/120000] base_lr: 1.4687e-04 lr: 1.5170e-05 eta: 1 day, 0:16:13 time: 1.1130 data_time: 0.0163 memory: 15515 grad_norm: 1.5975 loss: 0.3293 semantic_segmentation_loss_cls: 0.1010 semantic_segmentation_loss_mask: 0.0677 semantic_segmentation_loss_dice: 0.1606 2024/07/08 02:50:32 - mmengine - INFO - Iter(train) [ 41650/120000] base_lr: 1.4675e-04 lr: 1.5159e-05 eta: 1 day, 0:15:16 time: 1.1127 data_time: 0.0163 memory: 14820 grad_norm: 1.5996 loss: 0.3292 semantic_segmentation_loss_cls: 0.1010 semantic_segmentation_loss_mask: 0.0677 semantic_segmentation_loss_dice: 0.1606 2024/07/08 02:51:28 - mmengine - INFO - Iter(train) [ 41700/120000] base_lr: 1.4664e-04 lr: 1.5149e-05 eta: 1 day, 0:14:21 time: 1.1128 data_time: 0.0163 memory: 14798 grad_norm: 1.5978 loss: 0.3289 semantic_segmentation_loss_cls: 0.1009 semantic_segmentation_loss_mask: 0.0676 semantic_segmentation_loss_dice: 0.1604 2024/07/08 02:52:24 - mmengine - INFO - Iter(train) [ 41750/120000] base_lr: 1.4652e-04 lr: 1.5139e-05 eta: 1 day, 0:13:26 time: 1.1131 data_time: 0.0163 memory: 15076 grad_norm: 1.5977 loss: 0.3286 semantic_segmentation_loss_cls: 0.1006 semantic_segmentation_loss_mask: 0.0676 semantic_segmentation_loss_dice: 0.1603 2024/07/08 02:53:19 - mmengine - INFO - Iter(train) [ 41800/120000] base_lr: 1.4641e-04 lr: 1.5128e-05 eta: 1 day, 0:12:29 time: 1.1131 data_time: 0.0163 memory: 14825 grad_norm: 1.5984 loss: 0.3282 semantic_segmentation_loss_cls: 0.1005 semantic_segmentation_loss_mask: 0.0676 semantic_segmentation_loss_dice: 0.1601 2024/07/08 02:54:14 - mmengine - INFO - Iter(train) [ 41850/120000] base_lr: 1.4629e-04 lr: 1.5118e-05 eta: 1 day, 0:11:31 time: 1.1130 data_time: 0.0163 memory: 15288 grad_norm: 1.5945 loss: 0.3280 semantic_segmentation_loss_cls: 0.1003 semantic_segmentation_loss_mask: 0.0676 semantic_segmentation_loss_dice: 0.1601 2024/07/08 02:55:08 - mmengine - INFO - Iter(train) [ 41900/120000] base_lr: 1.4618e-04 lr: 1.5107e-05 eta: 1 day, 0:10:34 time: 1.1129 data_time: 0.0163 memory: 14707 grad_norm: 1.5933 loss: 0.3280 semantic_segmentation_loss_cls: 0.1003 semantic_segmentation_loss_mask: 0.0676 semantic_segmentation_loss_dice: 0.1601 2024/07/08 02:56:03 - mmengine - INFO - Iter(train) [ 41950/120000] base_lr: 1.4606e-04 lr: 1.5097e-05 eta: 1 day, 0:09:36 time: 1.1127 data_time: 0.0162 memory: 15121 grad_norm: 1.5919 loss: 0.3279 semantic_segmentation_loss_cls: 0.1003 semantic_segmentation_loss_mask: 0.0675 semantic_segmentation_loss_dice: 0.1600 2024/07/08 02:56:58 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 02:56:58 - mmengine - INFO - Iter(train) [ 42000/120000] base_lr: 1.4595e-04 lr: 1.5086e-05 eta: 1 day, 0:08:39 time: 1.1128 data_time: 0.0163 memory: 15213 grad_norm: 1.5926 loss: 0.3278 semantic_segmentation_loss_cls: 0.1004 semantic_segmentation_loss_mask: 0.0675 semantic_segmentation_loss_dice: 0.1599 2024/07/08 02:56:58 - mmengine - INFO - Saving checkpoint at 42000 iterations 2024/07/08 02:57:58 - mmengine - INFO - Iter(train) [ 42050/120000] base_lr: 1.4583e-04 lr: 1.5076e-05 eta: 1 day, 0:07:52 time: 1.1129 data_time: 0.0164 memory: 15249 grad_norm: 1.5933 loss: 0.3275 semantic_segmentation_loss_cls: 0.1003 semantic_segmentation_loss_mask: 0.0674 semantic_segmentation_loss_dice: 0.1598 2024/07/08 02:58:53 - mmengine - INFO - Iter(train) [ 42100/120000] base_lr: 1.4572e-04 lr: 1.5065e-05 eta: 1 day, 0:06:54 time: 1.1128 data_time: 0.0164 memory: 15263 grad_norm: 1.5922 loss: 0.3274 semantic_segmentation_loss_cls: 0.1002 semantic_segmentation_loss_mask: 0.0674 semantic_segmentation_loss_dice: 0.1598 2024/07/08 02:59:48 - mmengine - INFO - Iter(train) [ 42150/120000] base_lr: 1.4560e-04 lr: 1.5055e-05 eta: 1 day, 0:05:57 time: 1.1128 data_time: 0.0164 memory: 14660 grad_norm: 1.5910 loss: 0.3273 semantic_segmentation_loss_cls: 0.1002 semantic_segmentation_loss_mask: 0.0673 semantic_segmentation_loss_dice: 0.1597 2024/07/08 03:00:43 - mmengine - INFO - Iter(train) [ 42200/120000] base_lr: 1.4548e-04 lr: 1.5044e-05 eta: 1 day, 0:05:00 time: 1.1129 data_time: 0.0164 memory: 15156 grad_norm: 1.5912 loss: 0.3268 semantic_segmentation_loss_cls: 0.0999 semantic_segmentation_loss_mask: 0.0673 semantic_segmentation_loss_dice: 0.1596 2024/07/08 03:01:39 - mmengine - INFO - Iter(train) [ 42250/120000] base_lr: 1.4537e-04 lr: 1.5034e-05 eta: 1 day, 0:04:04 time: 1.1131 data_time: 0.0164 memory: 15363 grad_norm: 1.5908 loss: 0.3261 semantic_segmentation_loss_cls: 0.0996 semantic_segmentation_loss_mask: 0.0672 semantic_segmentation_loss_dice: 0.1593 2024/07/08 03:02:34 - mmengine - INFO - Iter(train) [ 42300/120000] base_lr: 1.4525e-04 lr: 1.5023e-05 eta: 1 day, 0:03:08 time: 1.1129 data_time: 0.0163 memory: 14907 grad_norm: 1.5896 loss: 0.3261 semantic_segmentation_loss_cls: 0.0996 semantic_segmentation_loss_mask: 0.0672 semantic_segmentation_loss_dice: 0.1593 2024/07/08 03:03:29 - mmengine - INFO - Iter(train) [ 42350/120000] base_lr: 1.4514e-04 lr: 1.5012e-05 eta: 1 day, 0:02:10 time: 1.1125 data_time: 0.0163 memory: 14804 grad_norm: 1.5883 loss: 0.3264 semantic_segmentation_loss_cls: 0.0998 semantic_segmentation_loss_mask: 0.0672 semantic_segmentation_loss_dice: 0.1594 2024/07/08 03:04:24 - mmengine - INFO - Iter(train) [ 42400/120000] base_lr: 1.4502e-04 lr: 1.5002e-05 eta: 1 day, 0:01:13 time: 1.1126 data_time: 0.0163 memory: 16187 grad_norm: 1.5864 loss: 0.3263 semantic_segmentation_loss_cls: 0.0997 semantic_segmentation_loss_mask: 0.0671 semantic_segmentation_loss_dice: 0.1594 2024/07/08 03:05:20 - mmengine - INFO - Iter(train) [ 42450/120000] base_lr: 1.4491e-04 lr: 1.4991e-05 eta: 1 day, 0:00:18 time: 1.1127 data_time: 0.0164 memory: 15755 grad_norm: 1.5853 loss: 0.3260 semantic_segmentation_loss_cls: 0.0997 semantic_segmentation_loss_mask: 0.0671 semantic_segmentation_loss_dice: 0.1593 2024/07/08 03:06:15 - mmengine - INFO - Iter(train) [ 42500/120000] base_lr: 1.4479e-04 lr: 1.4981e-05 eta: 23:59:21 time: 1.1127 data_time: 0.0164 memory: 15686 grad_norm: 1.5833 loss: 0.3258 semantic_segmentation_loss_cls: 0.0996 semantic_segmentation_loss_mask: 0.0670 semantic_segmentation_loss_dice: 0.1592 2024/07/08 03:07:10 - mmengine - INFO - Iter(train) [ 42550/120000] base_lr: 1.4467e-04 lr: 1.4970e-05 eta: 23:58:24 time: 1.1127 data_time: 0.0164 memory: 14486 grad_norm: 1.5846 loss: 0.3252 semantic_segmentation_loss_cls: 0.0993 semantic_segmentation_loss_mask: 0.0669 semantic_segmentation_loss_dice: 0.1589 2024/07/08 03:08:05 - mmengine - INFO - Iter(train) [ 42600/120000] base_lr: 1.4456e-04 lr: 1.4960e-05 eta: 23:57:27 time: 1.1126 data_time: 0.0163 memory: 14791 grad_norm: 1.5844 loss: 0.3254 semantic_segmentation_loss_cls: 0.0995 semantic_segmentation_loss_mask: 0.0670 semantic_segmentation_loss_dice: 0.1590 2024/07/08 03:08:59 - 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0.0163 memory: 14906 grad_norm: 1.5806 loss: 0.3260 semantic_segmentation_loss_cls: 0.0996 semantic_segmentation_loss_mask: 0.0671 semantic_segmentation_loss_dice: 0.1594 2024/07/08 03:15:24 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 03:15:24 - mmengine - INFO - Iter(train) [ 43000/120000] base_lr: 1.4362e-04 lr: 1.4875e-05 eta: 23:49:49 time: 1.1110 data_time: 0.0163 memory: 14463 grad_norm: 1.5802 loss: 0.3257 semantic_segmentation_loss_cls: 0.0995 semantic_segmentation_loss_mask: 0.0670 semantic_segmentation_loss_dice: 0.1592 2024/07/08 03:15:24 - mmengine - INFO - Saving checkpoint at 43000 iterations 2024/07/08 03:16:23 - mmengine - INFO - Iter(train) [ 43050/120000] base_lr: 1.4351e-04 lr: 1.4864e-05 eta: 23:49:00 time: 1.1102 data_time: 0.0163 memory: 15131 grad_norm: 1.5797 loss: 0.3257 semantic_segmentation_loss_cls: 0.0994 semantic_segmentation_loss_mask: 0.0670 semantic_segmentation_loss_dice: 0.1592 2024/07/08 03:17:19 - mmengine - INFO - Iter(train) [ 43100/120000] base_lr: 1.4339e-04 lr: 1.4854e-05 eta: 23:48:03 time: 1.1100 data_time: 0.0162 memory: 14692 grad_norm: 1.5796 loss: 0.3253 semantic_segmentation_loss_cls: 0.0993 semantic_segmentation_loss_mask: 0.0670 semantic_segmentation_loss_dice: 0.1590 2024/07/08 03:18:13 - mmengine - INFO - Iter(train) [ 43150/120000] base_lr: 1.4327e-04 lr: 1.4843e-05 eta: 23:47:06 time: 1.1094 data_time: 0.0162 memory: 15498 grad_norm: 1.5787 loss: 0.3251 semantic_segmentation_loss_cls: 0.0993 semantic_segmentation_loss_mask: 0.0669 semantic_segmentation_loss_dice: 0.1589 2024/07/08 03:19:08 - mmengine - INFO - Iter(train) [ 43200/120000] base_lr: 1.4315e-04 lr: 1.4832e-05 eta: 23:46:08 time: 1.1087 data_time: 0.0162 memory: 15589 grad_norm: 1.5781 loss: 0.3250 semantic_segmentation_loss_cls: 0.0992 semantic_segmentation_loss_mask: 0.0669 semantic_segmentation_loss_dice: 0.1589 2024/07/08 03:20:03 - 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semantic_segmentation_loss_cls: 0.0982 semantic_segmentation_loss_mask: 0.0664 semantic_segmentation_loss_dice: 0.1581 2024/07/08 03:29:12 - mmengine - INFO - Iter(train) [ 43750/120000] base_lr: 1.4186e-04 lr: 1.4715e-05 eta: 23:35:40 time: 1.1046 data_time: 0.0161 memory: 15287 grad_norm: 1.5676 loss: 0.3229 semantic_segmentation_loss_cls: 0.0983 semantic_segmentation_loss_mask: 0.0664 semantic_segmentation_loss_dice: 0.1582 2024/07/08 03:30:07 - mmengine - INFO - Iter(train) [ 43800/120000] base_lr: 1.4174e-04 lr: 1.4704e-05 eta: 23:34:43 time: 1.1043 data_time: 0.0161 memory: 16559 grad_norm: 1.5658 loss: 0.3224 semantic_segmentation_loss_cls: 0.0980 semantic_segmentation_loss_mask: 0.0663 semantic_segmentation_loss_dice: 0.1581 2024/07/08 03:31:01 - mmengine - INFO - Iter(train) [ 43850/120000] base_lr: 1.4162e-04 lr: 1.4693e-05 eta: 23:33:45 time: 1.1041 data_time: 0.0161 memory: 14647 grad_norm: 1.5646 loss: 0.3220 semantic_segmentation_loss_cls: 0.0978 semantic_segmentation_loss_mask: 0.0663 semantic_segmentation_loss_dice: 0.1579 2024/07/08 03:31:56 - mmengine - INFO - Iter(train) [ 43900/120000] base_lr: 1.4151e-04 lr: 1.4682e-05 eta: 23:32:47 time: 1.1041 data_time: 0.0161 memory: 14836 grad_norm: 1.5628 loss: 0.3212 semantic_segmentation_loss_cls: 0.0975 semantic_segmentation_loss_mask: 0.0661 semantic_segmentation_loss_dice: 0.1576 2024/07/08 03:32:50 - mmengine - INFO - Iter(train) [ 43950/120000] base_lr: 1.4139e-04 lr: 1.4672e-05 eta: 23:31:49 time: 1.1040 data_time: 0.0161 memory: 15764 grad_norm: 1.5612 loss: 0.3214 semantic_segmentation_loss_cls: 0.0975 semantic_segmentation_loss_mask: 0.0661 semantic_segmentation_loss_dice: 0.1577 2024/07/08 03:33:44 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 03:33:44 - mmengine - INFO - Iter(train) [ 44000/120000] base_lr: 1.4127e-04 lr: 1.4661e-05 eta: 23:30:51 time: 1.1037 data_time: 0.0161 memory: 14892 grad_norm: 1.5601 loss: 0.3210 semantic_segmentation_loss_cls: 0.0974 semantic_segmentation_loss_mask: 0.0661 semantic_segmentation_loss_dice: 0.1576 2024/07/08 03:33:44 - mmengine - INFO - Saving checkpoint at 44000 iterations 2024/07/08 03:34:44 - mmengine - INFO - Iter(train) [ 44050/120000] base_lr: 1.4115e-04 lr: 1.4650e-05 eta: 23:30:02 time: 1.1048 data_time: 0.0171 memory: 15835 grad_norm: 1.5605 loss: 0.3208 semantic_segmentation_loss_cls: 0.0973 semantic_segmentation_loss_mask: 0.0661 semantic_segmentation_loss_dice: 0.1574 2024/07/08 03:35:39 - mmengine - INFO - Iter(train) [ 44100/120000] base_lr: 1.4103e-04 lr: 1.4639e-05 eta: 23:29:06 time: 1.1049 data_time: 0.0171 memory: 16008 grad_norm: 1.5589 loss: 0.3207 semantic_segmentation_loss_cls: 0.0974 semantic_segmentation_loss_mask: 0.0661 semantic_segmentation_loss_dice: 0.1573 2024/07/08 03:36:34 - mmengine - INFO - Iter(train) [ 44150/120000] base_lr: 1.4091e-04 lr: 1.4629e-05 eta: 23:28:09 time: 1.1046 data_time: 0.0170 memory: 16870 grad_norm: 1.5590 loss: 0.3208 semantic_segmentation_loss_cls: 0.0975 semantic_segmentation_loss_mask: 0.0660 semantic_segmentation_loss_dice: 0.1573 2024/07/08 03:37:29 - mmengine - INFO - Iter(train) [ 44200/120000] base_lr: 1.4080e-04 lr: 1.4618e-05 eta: 23:27:11 time: 1.1044 data_time: 0.0170 memory: 14994 grad_norm: 1.5572 loss: 0.3206 semantic_segmentation_loss_cls: 0.0974 semantic_segmentation_loss_mask: 0.0660 semantic_segmentation_loss_dice: 0.1573 2024/07/08 03:38:24 - mmengine - INFO - Iter(train) [ 44250/120000] base_lr: 1.4068e-04 lr: 1.4607e-05 eta: 23:26:14 time: 1.1043 data_time: 0.0170 memory: 15414 grad_norm: 1.5562 loss: 0.3208 semantic_segmentation_loss_cls: 0.0975 semantic_segmentation_loss_mask: 0.0660 semantic_segmentation_loss_dice: 0.1574 2024/07/08 03:39:18 - mmengine - INFO - Iter(train) [ 44300/120000] base_lr: 1.4056e-04 lr: 1.4596e-05 eta: 23:25:16 time: 1.1041 data_time: 0.0169 memory: 15425 grad_norm: 1.5547 loss: 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mmengine - INFO - Iter(train) [ 44800/120000] base_lr: 1.3937e-04 lr: 1.4488e-05 eta: 23:15:44 time: 1.1040 data_time: 0.0170 memory: 15349 grad_norm: 1.5555 loss: 0.3188 semantic_segmentation_loss_cls: 0.0965 semantic_segmentation_loss_mask: 0.0656 semantic_segmentation_loss_dice: 0.1567 2024/07/08 03:49:21 - mmengine - INFO - Iter(train) [ 44850/120000] base_lr: 1.3925e-04 lr: 1.4477e-05 eta: 23:14:47 time: 1.1040 data_time: 0.0170 memory: 15530 grad_norm: 1.5546 loss: 0.3187 semantic_segmentation_loss_cls: 0.0965 semantic_segmentation_loss_mask: 0.0656 semantic_segmentation_loss_dice: 0.1566 2024/07/08 03:50:17 - mmengine - INFO - Iter(train) [ 44900/120000] base_lr: 1.3913e-04 lr: 1.4466e-05 eta: 23:13:52 time: 1.1042 data_time: 0.0171 memory: 15207 grad_norm: 1.5540 loss: 0.3189 semantic_segmentation_loss_cls: 0.0966 semantic_segmentation_loss_mask: 0.0656 semantic_segmentation_loss_dice: 0.1567 2024/07/08 03:51:12 - mmengine - INFO - Iter(train) [ 44950/120000] base_lr: 1.3901e-04 lr: 1.4455e-05 eta: 23:12:55 time: 1.1041 data_time: 0.0171 memory: 15031 grad_norm: 1.5539 loss: 0.3187 semantic_segmentation_loss_cls: 0.0965 semantic_segmentation_loss_mask: 0.0656 semantic_segmentation_loss_dice: 0.1565 2024/07/08 03:52:07 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 03:52:07 - mmengine - INFO - Iter(train) [ 45000/120000] base_lr: 1.3889e-04 lr: 1.4444e-05 eta: 23:11:58 time: 1.1041 data_time: 0.0171 memory: 15855 grad_norm: 1.5518 loss: 0.3185 semantic_segmentation_loss_cls: 0.0966 semantic_segmentation_loss_mask: 0.0655 semantic_segmentation_loss_dice: 0.1564 2024/07/08 03:52:07 - mmengine - INFO - Saving checkpoint at 45000 iterations 2024/07/08 03:52:24 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:52 time: 0.2456 data_time: 0.0015 memory: 5013 2024/07/08 03:52:36 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:39 time: 0.2456 data_time: 0.0015 memory: 5187 2024/07/08 03:52:49 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:26 time: 0.2456 data_time: 0.0015 memory: 4460 2024/07/08 03:53:01 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:13 time: 0.2456 data_time: 0.0015 memory: 4543 2024/07/08 03:53:13 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:01 time: 0.2456 data_time: 0.0015 memory: 4643 2024/07/08 03:53:25 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:49 time: 0.2457 data_time: 0.0015 memory: 10983 2024/07/08 03:53:38 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2457 data_time: 0.0015 memory: 4460 2024/07/08 03:53:50 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2457 data_time: 0.0015 memory: 4641 2024/07/08 03:54:02 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2457 data_time: 0.0015 memory: 4473 2024/07/08 03:54:15 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2457 data_time: 0.0015 memory: 4555 2024/07/08 03:54:15 - mmengine - INFO - per class results: 2024/07/08 03:54:15 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.28 | 86.98 | | building | 82.86 | 89.8 | | sky | 94.16 | 97.76 | | floor | 83.25 | 91.32 | | tree | 74.82 | 87.81 | | ceiling | 85.27 | 93.32 | | road | 84.6 | 92.64 | | bed | 86.69 | 94.41 | | windowpane | 61.65 | 80.09 | | grass | 71.11 | 86.24 | | cabinet | 62.47 | 72.92 | | sidewalk | 67.34 | 81.49 | | person | 81.89 | 91.28 | | earth | 34.15 | 48.09 | | door | 51.7 | 68.74 | | table | 61.18 | 76.89 | | mountain | 56.37 | 70.33 | | plant | 53.81 | 68.49 | | curtain | 73.4 | 87.28 | | chair | 59.32 | 72.24 | | car | 85.64 | 91.6 | | water | 50.8 | 66.9 | | painting | 72.94 | 89.01 | | sofa | 65.31 | 77.19 | | shelf | 44.81 | 64.15 | | house | 49.19 | 77.18 | | sea | 48.31 | 70.62 | | mirror | 68.29 | 76.96 | | rug | 69.06 | 78.65 | | field | 40.85 | 55.47 | | armchair | 43.92 | 69.3 | | seat | 58.28 | 78.81 | | fence | 47.27 | 66.7 | | desk | 46.5 | 64.94 | | rock | 37.96 | 54.9 | | wardrobe | 54.31 | 70.38 | | lamp | 67.26 | 80.96 | | bathtub | 87.09 | 90.74 | | railing | 34.77 | 51.73 | | cushion | 58.81 | 69.7 | | base | 20.61 | 36.79 | | box | 25.59 | 36.09 | | column | 49.79 | 70.42 | | signboard | 37.1 | 55.78 | | chest of drawers | 41.35 | 70.44 | | counter | 27.31 | 43.65 | | sand | 34.93 | 50.75 | | sink | 73.47 | 82.6 | | skyscraper | 55.39 | 73.27 | | fireplace | 67.46 | 89.46 | | refrigerator | 76.41 | 89.88 | | grandstand | 41.97 | 72.65 | | path | 29.4 | 40.71 | | stairs | 28.75 | 40.63 | | runway | 75.82 | 89.42 | | case | 53.65 | 59.29 | | pool table | 92.02 | 96.47 | | pillow | 55.64 | 69.87 | | screen door | 73.8 | 81.75 | | stairway | 39.32 | 44.64 | | river | 21.19 | 44.35 | | bridge | 72.95 | 89.15 | | bookcase | 36.08 | 57.37 | | blind | 39.61 | 45.5 | | coffee table | 72.85 | 86.03 | | toilet | 84.55 | 89.42 | | flower | 36.89 | 57.53 | | book | 51.32 | 75.9 | | hill | 11.46 | 23.07 | | bench | 38.52 | 45.18 | | countertop | 56.26 | 65.86 | | stove | 81.33 | 85.79 | | palm | 52.82 | 70.95 | | kitchen island | 31.03 | 79.17 | | computer | 61.79 | 68.12 | | swivel chair | 45.41 | 63.3 | | boat | 45.45 | 49.98 | | bar | 26.09 | 35.39 | | arcade machine | 60.81 | 66.91 | | hovel | 31.56 | 41.47 | | bus | 87.38 | 90.91 | | towel | 67.78 | 75.57 | | light | 63.7 | 79.69 | | truck | 34.57 | 47.04 | | tower | 33.26 | 54.11 | | chandelier | 65.56 | 75.19 | | awning | 31.22 | 46.9 | | streetlight | 40.31 | 57.32 | | booth | 51.55 | 52.95 | | television receiver | 70.55 | 89.62 | | airplane | 60.79 | 66.08 | | dirt track | 8.84 | 14.82 | | apparel | 33.57 | 52.96 | | pole | 32.23 | 52.9 | | land | 0.69 | 0.97 | | bannister | 15.69 | 26.04 | | escalator | 31.59 | 38.4 | | ottoman | 39.43 | 62.97 | | bottle | 21.28 | 26.76 | | buffet | 62.29 | 71.99 | | poster | 30.88 | 43.54 | | stage | 19.57 | 33.21 | | van | 46.63 | 66.95 | | ship | 57.64 | 82.2 | | fountain | 5.27 | 5.57 | | conveyer belt | 81.74 | 90.66 | | canopy | 17.69 | 24.34 | | washer | 71.57 | 73.77 | | plaything | 26.57 | 37.81 | | swimming pool | 25.66 | 28.51 | | stool | 50.46 | 69.54 | | barrel | 13.95 | 56.42 | | basket | 37.17 | 47.69 | | waterfall | 57.85 | 80.23 | | tent | 65.06 | 97.65 | | bag | 19.21 | 26.25 | | minibike | 70.76 | 85.26 | | cradle | 76.28 | 96.88 | | oven | 23.56 | 56.18 | | ball | 44.38 | 52.38 | | food | 62.44 | 77.98 | | step | 25.77 | 37.0 | | tank | 36.9 | 45.75 | | trade name | 31.01 | 41.16 | | microwave | 38.25 | 41.01 | | pot | 54.89 | 63.97 | | animal | 61.63 | 69.52 | | bicycle | 57.1 | 78.02 | | lake | 63.37 | 63.63 | | dishwasher | 79.93 | 85.46 | | screen | 65.13 | 82.47 | | blanket | 14.85 | 19.2 | | sculpture | 70.45 | 84.56 | | hood | 68.95 | 74.04 | | sconce | 51.05 | 65.07 | | vase | 48.75 | 64.68 | | traffic light | 42.44 | 62.68 | | tray | 17.97 | 24.26 | | ashcan | 40.36 | 57.4 | | fan | 65.47 | 82.01 | | pier | 46.56 | 84.44 | | crt screen | 0.18 | 0.25 | | plate | 59.29 | 74.18 | | monitor | 43.25 | 67.69 | | bulletin board | 25.33 | 30.94 | | shower | 7.39 | 17.77 | | radiator | 55.8 | 66.33 | | glass | 18.83 | 20.49 | | clock | 33.54 | 39.96 | | flag | 44.33 | 54.79 | +---------------------+-------+-------+ 2024/07/08 03:54:15 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.3600 mIoU: 50.1300 mAcc: 63.5600 data_time: 0.0015 time: 0.2461 2024/07/08 03:55:11 - mmengine - INFO - Iter(train) [ 45050/120000] base_lr: 1.3877e-04 lr: 1.4433e-05 eta: 23:11:03 time: 1.1030 data_time: 0.0161 memory: 16141 grad_norm: 1.5528 loss: 0.3186 semantic_segmentation_loss_cls: 0.0966 semantic_segmentation_loss_mask: 0.0655 semantic_segmentation_loss_dice: 0.1564 2024/07/08 03:56:05 - mmengine - INFO - Iter(train) [ 45100/120000] base_lr: 1.3865e-04 lr: 1.4423e-05 eta: 23:10:05 time: 1.1027 data_time: 0.0161 memory: 15667 grad_norm: 1.5531 loss: 0.3184 semantic_segmentation_loss_cls: 0.0966 semantic_segmentation_loss_mask: 0.0654 semantic_segmentation_loss_dice: 0.1564 2024/07/08 03:57:00 - mmengine - INFO - Iter(train) [ 45150/120000] base_lr: 1.3853e-04 lr: 1.4412e-05 eta: 23:09:07 time: 1.1025 data_time: 0.0161 memory: 14988 grad_norm: 1.5527 loss: 0.3181 semantic_segmentation_loss_cls: 0.0964 semantic_segmentation_loss_mask: 0.0654 semantic_segmentation_loss_dice: 0.1563 2024/07/08 03:57:54 - mmengine - INFO - Iter(train) [ 45200/120000] base_lr: 1.3841e-04 lr: 1.4401e-05 eta: 23:08:10 time: 1.1024 data_time: 0.0161 memory: 16667 grad_norm: 1.5520 loss: 0.3178 semantic_segmentation_loss_cls: 0.0963 semantic_segmentation_loss_mask: 0.0653 semantic_segmentation_loss_dice: 0.1562 2024/07/08 03:58:49 - mmengine - INFO - Iter(train) [ 45250/120000] base_lr: 1.3829e-04 lr: 1.4390e-05 eta: 23:07:14 time: 1.1027 data_time: 0.0161 memory: 14789 grad_norm: 1.5487 loss: 0.3176 semantic_segmentation_loss_cls: 0.0963 semantic_segmentation_loss_mask: 0.0652 semantic_segmentation_loss_dice: 0.1562 2024/07/08 03:59:44 - mmengine - INFO - Iter(train) [ 45300/120000] base_lr: 1.3817e-04 lr: 1.4379e-05 eta: 23:06:16 time: 1.1024 data_time: 0.0161 memory: 15130 grad_norm: 1.5485 loss: 0.3179 semantic_segmentation_loss_cls: 0.0963 semantic_segmentation_loss_mask: 0.0653 semantic_segmentation_loss_dice: 0.1562 2024/07/08 04:00:39 - mmengine - INFO - Iter(train) [ 45350/120000] base_lr: 1.3805e-04 lr: 1.4368e-05 eta: 23:05:19 time: 1.1024 data_time: 0.0161 memory: 15873 grad_norm: 1.5490 loss: 0.3179 semantic_segmentation_loss_cls: 0.0964 semantic_segmentation_loss_mask: 0.0653 semantic_segmentation_loss_dice: 0.1562 2024/07/08 04:01:33 - mmengine - INFO - Iter(train) [ 45400/120000] base_lr: 1.3793e-04 lr: 1.4357e-05 eta: 23:04:22 time: 1.1022 data_time: 0.0161 memory: 14646 grad_norm: 1.5496 loss: 0.3175 semantic_segmentation_loss_cls: 0.0963 semantic_segmentation_loss_mask: 0.0652 semantic_segmentation_loss_dice: 0.1561 2024/07/08 04:02:28 - mmengine - INFO - Iter(train) [ 45450/120000] base_lr: 1.3781e-04 lr: 1.4346e-05 eta: 23:03:25 time: 1.1021 data_time: 0.0162 memory: 14872 grad_norm: 1.5508 loss: 0.3175 semantic_segmentation_loss_cls: 0.0962 semantic_segmentation_loss_mask: 0.0652 semantic_segmentation_loss_dice: 0.1561 2024/07/08 04:03:23 - mmengine - INFO - Iter(train) [ 45500/120000] base_lr: 1.3769e-04 lr: 1.4335e-05 eta: 23:02:28 time: 1.1021 data_time: 0.0162 memory: 14975 grad_norm: 1.5482 loss: 0.3170 semantic_segmentation_loss_cls: 0.0960 semantic_segmentation_loss_mask: 0.0651 semantic_segmentation_loss_dice: 0.1559 2024/07/08 04:04:18 - mmengine - INFO - Iter(train) [ 45550/120000] base_lr: 1.3757e-04 lr: 1.4324e-05 eta: 23:01:30 time: 1.1019 data_time: 0.0162 memory: 15136 grad_norm: 1.5477 loss: 0.3170 semantic_segmentation_loss_cls: 0.0961 semantic_segmentation_loss_mask: 0.0650 semantic_segmentation_loss_dice: 0.1559 2024/07/08 04:05:13 - mmengine - INFO - Iter(train) [ 45600/120000] base_lr: 1.3745e-04 lr: 1.4313e-05 eta: 23:00:34 time: 1.1019 data_time: 0.0162 memory: 15501 grad_norm: 1.5470 loss: 0.3167 semantic_segmentation_loss_cls: 0.0959 semantic_segmentation_loss_mask: 0.0650 semantic_segmentation_loss_dice: 0.1558 2024/07/08 04:06:08 - mmengine - INFO - Iter(train) [ 45650/120000] base_lr: 1.3733e-04 lr: 1.4302e-05 eta: 22:59:36 time: 1.1019 data_time: 0.0162 memory: 15034 grad_norm: 1.5460 loss: 0.3163 semantic_segmentation_loss_cls: 0.0958 semantic_segmentation_loss_mask: 0.0649 semantic_segmentation_loss_dice: 0.1557 2024/07/08 04:07:02 - mmengine - INFO - Iter(train) [ 45700/120000] base_lr: 1.3721e-04 lr: 1.4291e-05 eta: 22:58:39 time: 1.1015 data_time: 0.0162 memory: 15030 grad_norm: 1.5475 loss: 0.3164 semantic_segmentation_loss_cls: 0.0958 semantic_segmentation_loss_mask: 0.0649 semantic_segmentation_loss_dice: 0.1557 2024/07/08 04:07:57 - mmengine - INFO - Iter(train) [ 45750/120000] base_lr: 1.3708e-04 lr: 1.4280e-05 eta: 22:57:43 time: 1.1013 data_time: 0.0162 memory: 14312 grad_norm: 1.5475 loss: 0.3162 semantic_segmentation_loss_cls: 0.0958 semantic_segmentation_loss_mask: 0.0649 semantic_segmentation_loss_dice: 0.1556 2024/07/08 04:08:52 - mmengine - INFO - Iter(train) [ 45800/120000] base_lr: 1.3696e-04 lr: 1.4269e-05 eta: 22:56:46 time: 1.1013 data_time: 0.0162 memory: 15426 grad_norm: 1.5466 loss: 0.3161 semantic_segmentation_loss_cls: 0.0957 semantic_segmentation_loss_mask: 0.0648 semantic_segmentation_loss_dice: 0.1556 2024/07/08 04:09:47 - mmengine - INFO - Iter(train) [ 45850/120000] base_lr: 1.3684e-04 lr: 1.4259e-05 eta: 22:55:48 time: 1.1012 data_time: 0.0162 memory: 15057 grad_norm: 1.5470 loss: 0.3160 semantic_segmentation_loss_cls: 0.0957 semantic_segmentation_loss_mask: 0.0648 semantic_segmentation_loss_dice: 0.1556 2024/07/08 04:10:42 - mmengine - INFO - Iter(train) [ 45900/120000] base_lr: 1.3672e-04 lr: 1.4248e-05 eta: 22:54:52 time: 1.1013 data_time: 0.0162 memory: 14742 grad_norm: 1.5460 loss: 0.3157 semantic_segmentation_loss_cls: 0.0955 semantic_segmentation_loss_mask: 0.0648 semantic_segmentation_loss_dice: 0.1554 2024/07/08 04:11:37 - mmengine - INFO - Iter(train) [ 45950/120000] base_lr: 1.3660e-04 lr: 1.4237e-05 eta: 22:53:56 time: 1.1016 data_time: 0.0162 memory: 14772 grad_norm: 1.5470 loss: 0.3156 semantic_segmentation_loss_cls: 0.0954 semantic_segmentation_loss_mask: 0.0647 semantic_segmentation_loss_dice: 0.1554 2024/07/08 04:12:33 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 04:12:33 - mmengine - INFO - Iter(train) [ 46000/120000] base_lr: 1.3648e-04 lr: 1.4226e-05 eta: 22:52:59 time: 1.1015 data_time: 0.0162 memory: 14643 grad_norm: 1.5459 loss: 0.3154 semantic_segmentation_loss_cls: 0.0954 semantic_segmentation_loss_mask: 0.0647 semantic_segmentation_loss_dice: 0.1553 2024/07/08 04:12:33 - mmengine - INFO - Saving checkpoint at 46000 iterations 2024/07/08 04:13:33 - mmengine - INFO - Iter(train) [ 46050/120000] base_lr: 1.3636e-04 lr: 1.4215e-05 eta: 22:52:10 time: 1.1015 data_time: 0.0161 memory: 14782 grad_norm: 1.5458 loss: 0.3153 semantic_segmentation_loss_cls: 0.0953 semantic_segmentation_loss_mask: 0.0647 semantic_segmentation_loss_dice: 0.1553 2024/07/08 04:14:28 - mmengine - INFO - Iter(train) [ 46100/120000] base_lr: 1.3624e-04 lr: 1.4204e-05 eta: 22:51:14 time: 1.1016 data_time: 0.0162 memory: 14687 grad_norm: 1.5458 loss: 0.3151 semantic_segmentation_loss_cls: 0.0953 semantic_segmentation_loss_mask: 0.0647 semantic_segmentation_loss_dice: 0.1552 2024/07/08 04:15:23 - mmengine - INFO - Iter(train) [ 46150/120000] base_lr: 1.3612e-04 lr: 1.4193e-05 eta: 22:50:18 time: 1.1017 data_time: 0.0162 memory: 15488 grad_norm: 1.5457 loss: 0.3150 semantic_segmentation_loss_cls: 0.0952 semantic_segmentation_loss_mask: 0.0646 semantic_segmentation_loss_dice: 0.1551 2024/07/08 04:16:18 - mmengine - INFO - Iter(train) [ 46200/120000] base_lr: 1.3600e-04 lr: 1.4181e-05 eta: 22:49:21 time: 1.1017 data_time: 0.0163 memory: 15658 grad_norm: 1.5465 loss: 0.3150 semantic_segmentation_loss_cls: 0.0952 semantic_segmentation_loss_mask: 0.0646 semantic_segmentation_loss_dice: 0.1551 2024/07/08 04:17:13 - mmengine - INFO - Iter(train) [ 46250/120000] base_lr: 1.3588e-04 lr: 1.4170e-05 eta: 22:48:24 time: 1.1015 data_time: 0.0163 memory: 15421 grad_norm: 1.5443 loss: 0.3151 semantic_segmentation_loss_cls: 0.0953 semantic_segmentation_loss_mask: 0.0646 semantic_segmentation_loss_dice: 0.1552 2024/07/08 04:18:08 - mmengine - INFO - Iter(train) [ 46300/120000] base_lr: 1.3575e-04 lr: 1.4159e-05 eta: 22:47:27 time: 1.1013 data_time: 0.0162 memory: 14985 grad_norm: 1.5448 loss: 0.3149 semantic_segmentation_loss_cls: 0.0952 semantic_segmentation_loss_mask: 0.0646 semantic_segmentation_loss_dice: 0.1551 2024/07/08 04:19:04 - mmengine - INFO - Iter(train) [ 46350/120000] base_lr: 1.3563e-04 lr: 1.4148e-05 eta: 22:46:32 time: 1.1017 data_time: 0.0163 memory: 14959 grad_norm: 1.5444 loss: 0.3144 semantic_segmentation_loss_cls: 0.0950 semantic_segmentation_loss_mask: 0.0645 semantic_segmentation_loss_dice: 0.1549 2024/07/08 04:19:59 - mmengine - INFO - Iter(train) [ 46400/120000] base_lr: 1.3551e-04 lr: 1.4137e-05 eta: 22:45:36 time: 1.1017 data_time: 0.0163 memory: 15367 grad_norm: 1.5449 loss: 0.3140 semantic_segmentation_loss_cls: 0.0948 semantic_segmentation_loss_mask: 0.0645 semantic_segmentation_loss_dice: 0.1547 2024/07/08 04:20:54 - mmengine - INFO - Iter(train) [ 46450/120000] base_lr: 1.3539e-04 lr: 1.4126e-05 eta: 22:44:39 time: 1.1016 data_time: 0.0162 memory: 15598 grad_norm: 1.5428 loss: 0.3138 semantic_segmentation_loss_cls: 0.0947 semantic_segmentation_loss_mask: 0.0645 semantic_segmentation_loss_dice: 0.1546 2024/07/08 04:21:49 - mmengine - INFO - Iter(train) [ 46500/120000] base_lr: 1.3527e-04 lr: 1.4115e-05 eta: 22:43:42 time: 1.1014 data_time: 0.0162 memory: 15135 grad_norm: 1.5449 loss: 0.3136 semantic_segmentation_loss_cls: 0.0946 semantic_segmentation_loss_mask: 0.0644 semantic_segmentation_loss_dice: 0.1545 2024/07/08 04:22:44 - mmengine - INFO - Iter(train) [ 46550/120000] base_lr: 1.3515e-04 lr: 1.4104e-05 eta: 22:42:45 time: 1.1013 data_time: 0.0162 memory: 15000 grad_norm: 1.5428 loss: 0.3136 semantic_segmentation_loss_cls: 0.0946 semantic_segmentation_loss_mask: 0.0644 semantic_segmentation_loss_dice: 0.1545 2024/07/08 04:23:38 - mmengine - INFO - Iter(train) [ 46600/120000] base_lr: 1.3502e-04 lr: 1.4093e-05 eta: 22:41:48 time: 1.1013 data_time: 0.0162 memory: 15434 grad_norm: 1.5424 loss: 0.3135 semantic_segmentation_loss_cls: 0.0946 semantic_segmentation_loss_mask: 0.0644 semantic_segmentation_loss_dice: 0.1545 2024/07/08 04:24:33 - mmengine - INFO - Iter(train) [ 46650/120000] base_lr: 1.3490e-04 lr: 1.4082e-05 eta: 22:40:51 time: 1.1013 data_time: 0.0162 memory: 14562 grad_norm: 1.5420 loss: 0.3126 semantic_segmentation_loss_cls: 0.0941 semantic_segmentation_loss_mask: 0.0643 semantic_segmentation_loss_dice: 0.1542 2024/07/08 04:25:28 - mmengine - INFO - Iter(train) [ 46700/120000] base_lr: 1.3478e-04 lr: 1.4071e-05 eta: 22:39:53 time: 1.1013 data_time: 0.0162 memory: 15697 grad_norm: 1.5400 loss: 0.3124 semantic_segmentation_loss_cls: 0.0940 semantic_segmentation_loss_mask: 0.0643 semantic_segmentation_loss_dice: 0.1541 2024/07/08 04:26:22 - mmengine - INFO - Iter(train) [ 46750/120000] base_lr: 1.3466e-04 lr: 1.4060e-05 eta: 22:38:56 time: 1.1012 data_time: 0.0162 memory: 15314 grad_norm: 1.5399 loss: 0.3124 semantic_segmentation_loss_cls: 0.0940 semantic_segmentation_loss_mask: 0.0642 semantic_segmentation_loss_dice: 0.1541 2024/07/08 04:27:16 - mmengine - INFO - Iter(train) [ 46800/120000] base_lr: 1.3454e-04 lr: 1.4049e-05 eta: 22:37:58 time: 1.1010 data_time: 0.0162 memory: 15063 grad_norm: 1.5402 loss: 0.3128 semantic_segmentation_loss_cls: 0.0943 semantic_segmentation_loss_mask: 0.0642 semantic_segmentation_loss_dice: 0.1543 2024/07/08 04:28:12 - 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single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 04:30:56 - mmengine - INFO - Iter(train) [ 47000/120000] base_lr: 1.3405e-04 lr: 1.4004e-05 eta: 22:34:11 time: 1.1010 data_time: 0.0161 memory: 15145 grad_norm: 1.5367 loss: 0.3117 semantic_segmentation_loss_cls: 0.0938 semantic_segmentation_loss_mask: 0.0641 semantic_segmentation_loss_dice: 0.1538 2024/07/08 04:30:56 - mmengine - INFO - Saving checkpoint at 47000 iterations 2024/07/08 04:31:56 - mmengine - INFO - Iter(train) [ 47050/120000] base_lr: 1.3393e-04 lr: 1.3993e-05 eta: 22:33:21 time: 1.1011 data_time: 0.0161 memory: 15096 grad_norm: 1.5364 loss: 0.3113 semantic_segmentation_loss_cls: 0.0937 semantic_segmentation_loss_mask: 0.0640 semantic_segmentation_loss_dice: 0.1537 2024/07/08 04:32:51 - mmengine - INFO - Iter(train) [ 47100/120000] base_lr: 1.3380e-04 lr: 1.3982e-05 eta: 22:32:25 time: 1.1010 data_time: 0.0161 memory: 15099 grad_norm: 1.5351 loss: 0.3115 semantic_segmentation_loss_cls: 0.0938 semantic_segmentation_loss_mask: 0.0640 semantic_segmentation_loss_dice: 0.1537 2024/07/08 04:33:46 - mmengine - INFO - Iter(train) [ 47150/120000] base_lr: 1.3368e-04 lr: 1.3971e-05 eta: 22:31:28 time: 1.1011 data_time: 0.0161 memory: 15323 grad_norm: 1.5338 loss: 0.3112 semantic_segmentation_loss_cls: 0.0937 semantic_segmentation_loss_mask: 0.0639 semantic_segmentation_loss_dice: 0.1536 2024/07/08 04:34:41 - mmengine - INFO - Iter(train) [ 47200/120000] base_lr: 1.3356e-04 lr: 1.3960e-05 eta: 22:30:31 time: 1.1012 data_time: 0.0161 memory: 14370 grad_norm: 1.5336 loss: 0.3112 semantic_segmentation_loss_cls: 0.0937 semantic_segmentation_loss_mask: 0.0639 semantic_segmentation_loss_dice: 0.1536 2024/07/08 04:35:36 - mmengine - INFO - Iter(train) [ 47250/120000] base_lr: 1.3344e-04 lr: 1.3949e-05 eta: 22:29:36 time: 1.1014 data_time: 0.0161 memory: 14486 grad_norm: 1.5334 loss: 0.3111 semantic_segmentation_loss_cls: 0.0937 semantic_segmentation_loss_mask: 0.0639 semantic_segmentation_loss_dice: 0.1535 2024/07/08 04:36:32 - mmengine - INFO - Iter(train) [ 47300/120000] base_lr: 1.3332e-04 lr: 1.3938e-05 eta: 22:28:39 time: 1.1015 data_time: 0.0161 memory: 14824 grad_norm: 1.5323 loss: 0.3108 semantic_segmentation_loss_cls: 0.0936 semantic_segmentation_loss_mask: 0.0638 semantic_segmentation_loss_dice: 0.1534 2024/07/08 04:37:26 - mmengine - INFO - Iter(train) [ 47350/120000] base_lr: 1.3319e-04 lr: 1.3927e-05 eta: 22:27:42 time: 1.1015 data_time: 0.0161 memory: 15302 grad_norm: 1.5304 loss: 0.3106 semantic_segmentation_loss_cls: 0.0935 semantic_segmentation_loss_mask: 0.0637 semantic_segmentation_loss_dice: 0.1534 2024/07/08 04:38:20 - mmengine - INFO - Iter(train) [ 47400/120000] base_lr: 1.3307e-04 lr: 1.3915e-05 eta: 22:26:44 time: 1.1014 data_time: 0.0161 memory: 15552 grad_norm: 1.5302 loss: 0.3104 semantic_segmentation_loss_cls: 0.0934 semantic_segmentation_loss_mask: 0.0637 semantic_segmentation_loss_dice: 0.1532 2024/07/08 04:39:15 - mmengine - INFO - Iter(train) [ 47450/120000] base_lr: 1.3295e-04 lr: 1.3904e-05 eta: 22:25:47 time: 1.1013 data_time: 0.0162 memory: 15752 grad_norm: 1.5300 loss: 0.3102 semantic_segmentation_loss_cls: 0.0933 semantic_segmentation_loss_mask: 0.0637 semantic_segmentation_loss_dice: 0.1531 2024/07/08 04:40:09 - mmengine - INFO - Iter(train) [ 47500/120000] base_lr: 1.3282e-04 lr: 1.3893e-05 eta: 22:24:49 time: 1.1014 data_time: 0.0162 memory: 14682 grad_norm: 1.5301 loss: 0.3096 semantic_segmentation_loss_cls: 0.0930 semantic_segmentation_loss_mask: 0.0637 semantic_segmentation_loss_dice: 0.1528 2024/07/08 04:41:05 - mmengine - INFO - Iter(train) [ 47550/120000] base_lr: 1.3270e-04 lr: 1.3882e-05 eta: 22:23:53 time: 1.1014 data_time: 0.0162 memory: 15793 grad_norm: 1.5310 loss: 0.3093 semantic_segmentation_loss_cls: 0.0929 semantic_segmentation_loss_mask: 0.0637 semantic_segmentation_loss_dice: 0.1527 2024/07/08 04:42:00 - 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0.0163 memory: 16060 grad_norm: 1.5338 loss: 0.3096 semantic_segmentation_loss_cls: 0.0928 semantic_segmentation_loss_mask: 0.0638 semantic_segmentation_loss_dice: 0.1530 2024/07/08 04:48:24 - mmengine - INFO - Iter(train) [ 47950/120000] base_lr: 1.3172e-04 lr: 1.3793e-05 eta: 22:16:19 time: 1.1014 data_time: 0.0163 memory: 15147 grad_norm: 1.5338 loss: 0.3091 semantic_segmentation_loss_cls: 0.0927 semantic_segmentation_loss_mask: 0.0637 semantic_segmentation_loss_dice: 0.1527 2024/07/08 04:49:19 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 04:49:19 - mmengine - INFO - Iter(train) [ 48000/120000] base_lr: 1.3160e-04 lr: 1.3781e-05 eta: 22:15:23 time: 1.1017 data_time: 0.0162 memory: 15156 grad_norm: 1.5322 loss: 0.3090 semantic_segmentation_loss_cls: 0.0927 semantic_segmentation_loss_mask: 0.0636 semantic_segmentation_loss_dice: 0.1527 2024/07/08 04:49:19 - mmengine - INFO - Saving checkpoint at 48000 iterations 2024/07/08 04:50:19 - mmengine - INFO - Iter(train) [ 48050/120000] base_lr: 1.3147e-04 lr: 1.3770e-05 eta: 22:14:33 time: 1.1016 data_time: 0.0162 memory: 15048 grad_norm: 1.5307 loss: 0.3086 semantic_segmentation_loss_cls: 0.0925 semantic_segmentation_loss_mask: 0.0635 semantic_segmentation_loss_dice: 0.1526 2024/07/08 04:51:14 - mmengine - INFO - Iter(train) [ 48100/120000] base_lr: 1.3135e-04 lr: 1.3759e-05 eta: 22:13:37 time: 1.1018 data_time: 0.0162 memory: 14695 grad_norm: 1.5315 loss: 0.3081 semantic_segmentation_loss_cls: 0.0922 semantic_segmentation_loss_mask: 0.0634 semantic_segmentation_loss_dice: 0.1524 2024/07/08 04:52:10 - mmengine - INFO - Iter(train) [ 48150/120000] base_lr: 1.3123e-04 lr: 1.3748e-05 eta: 22:12:41 time: 1.1017 data_time: 0.0162 memory: 14286 grad_norm: 1.5314 loss: 0.3075 semantic_segmentation_loss_cls: 0.0919 semantic_segmentation_loss_mask: 0.0634 semantic_segmentation_loss_dice: 0.1522 2024/07/08 04:53:05 - mmengine - INFO - Iter(train) [ 48200/120000] base_lr: 1.3110e-04 lr: 1.3737e-05 eta: 22:11:44 time: 1.1019 data_time: 0.0162 memory: 15195 grad_norm: 1.5319 loss: 0.3071 semantic_segmentation_loss_cls: 0.0917 semantic_segmentation_loss_mask: 0.0633 semantic_segmentation_loss_dice: 0.1521 2024/07/08 04:53:59 - mmengine - INFO - Iter(train) [ 48250/120000] base_lr: 1.3098e-04 lr: 1.3725e-05 eta: 22:10:47 time: 1.1018 data_time: 0.0162 memory: 15943 grad_norm: 1.5309 loss: 0.3067 semantic_segmentation_loss_cls: 0.0916 semantic_segmentation_loss_mask: 0.0632 semantic_segmentation_loss_dice: 0.1519 2024/07/08 04:54:54 - mmengine - INFO - Iter(train) [ 48300/120000] base_lr: 1.3085e-04 lr: 1.3714e-05 eta: 22:09:50 time: 1.1019 data_time: 0.0162 memory: 14944 grad_norm: 1.5296 loss: 0.3064 semantic_segmentation_loss_cls: 0.0916 semantic_segmentation_loss_mask: 0.0631 semantic_segmentation_loss_dice: 0.1517 2024/07/08 04:55:49 - mmengine - INFO - Iter(train) [ 48350/120000] base_lr: 1.3073e-04 lr: 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semantic_segmentation_loss_dice: 0.1509 2024/07/08 05:07:48 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 05:07:48 - mmengine - INFO - Iter(train) [ 49000/120000] base_lr: 1.2912e-04 lr: 1.3556e-05 eta: 21:56:43 time: 1.1031 data_time: 0.0161 memory: 15016 grad_norm: 1.5241 loss: 0.3036 semantic_segmentation_loss_cls: 0.0902 semantic_segmentation_loss_mask: 0.0626 semantic_segmentation_loss_dice: 0.1509 2024/07/08 05:07:48 - mmengine - INFO - Saving checkpoint at 49000 iterations 2024/07/08 05:08:47 - mmengine - INFO - Iter(train) [ 49050/120000] base_lr: 1.2900e-04 lr: 1.3545e-05 eta: 21:55:53 time: 1.1041 data_time: 0.0171 memory: 15560 grad_norm: 1.5224 loss: 0.3032 semantic_segmentation_loss_cls: 0.0900 semantic_segmentation_loss_mask: 0.0625 semantic_segmentation_loss_dice: 0.1507 2024/07/08 05:09:43 - mmengine - INFO - Iter(train) [ 49100/120000] base_lr: 1.2887e-04 lr: 1.3534e-05 eta: 21:54:58 time: 1.1044 data_time: 0.0171 memory: 15805 grad_norm: 1.5204 loss: 0.3031 semantic_segmentation_loss_cls: 0.0899 semantic_segmentation_loss_mask: 0.0625 semantic_segmentation_loss_dice: 0.1507 2024/07/08 05:10:39 - mmengine - INFO - Iter(train) [ 49150/120000] base_lr: 1.2875e-04 lr: 1.3522e-05 eta: 21:54:02 time: 1.1047 data_time: 0.0171 memory: 15354 grad_norm: 1.5211 loss: 0.3026 semantic_segmentation_loss_cls: 0.0897 semantic_segmentation_loss_mask: 0.0624 semantic_segmentation_loss_dice: 0.1504 2024/07/08 05:11:34 - mmengine - INFO - Iter(train) [ 49200/120000] base_lr: 1.2862e-04 lr: 1.3511e-05 eta: 21:53:06 time: 1.1049 data_time: 0.0171 memory: 15406 grad_norm: 1.5208 loss: 0.3023 semantic_segmentation_loss_cls: 0.0895 semantic_segmentation_loss_mask: 0.0625 semantic_segmentation_loss_dice: 0.1503 2024/07/08 05:12:30 - mmengine - INFO - Iter(train) [ 49250/120000] base_lr: 1.2850e-04 lr: 1.3500e-05 eta: 21:52:11 time: 1.1051 data_time: 0.0171 memory: 15110 grad_norm: 1.5211 loss: 0.3020 semantic_segmentation_loss_cls: 0.0894 semantic_segmentation_loss_mask: 0.0625 semantic_segmentation_loss_dice: 0.1502 2024/07/08 05:13:25 - mmengine - INFO - Iter(train) [ 49300/120000] base_lr: 1.2837e-04 lr: 1.3489e-05 eta: 21:51:15 time: 1.1052 data_time: 0.0171 memory: 15443 grad_norm: 1.5198 loss: 0.3013 semantic_segmentation_loss_cls: 0.0891 semantic_segmentation_loss_mask: 0.0623 semantic_segmentation_loss_dice: 0.1499 2024/07/08 05:14:20 - mmengine - INFO - Iter(train) [ 49350/120000] base_lr: 1.2825e-04 lr: 1.3477e-05 eta: 21:50:18 time: 1.1053 data_time: 0.0171 memory: 15319 grad_norm: 1.5183 loss: 0.3007 semantic_segmentation_loss_cls: 0.0889 semantic_segmentation_loss_mask: 0.0622 semantic_segmentation_loss_dice: 0.1497 2024/07/08 05:15:15 - mmengine - INFO - Iter(train) [ 49400/120000] base_lr: 1.2812e-04 lr: 1.3466e-05 eta: 21:49:21 time: 1.1052 data_time: 0.0171 memory: 14879 grad_norm: 1.5167 loss: 0.3011 semantic_segmentation_loss_cls: 0.0891 semantic_segmentation_loss_mask: 0.0622 semantic_segmentation_loss_dice: 0.1498 2024/07/08 05:16:10 - mmengine - INFO - Iter(train) [ 49450/120000] base_lr: 1.2800e-04 lr: 1.3455e-05 eta: 21:48:24 time: 1.1052 data_time: 0.0171 memory: 16007 grad_norm: 1.5149 loss: 0.3009 semantic_segmentation_loss_cls: 0.0890 semantic_segmentation_loss_mask: 0.0622 semantic_segmentation_loss_dice: 0.1498 2024/07/08 05:17:05 - mmengine - INFO - Iter(train) [ 49500/120000] base_lr: 1.2788e-04 lr: 1.3443e-05 eta: 21:47:27 time: 1.1051 data_time: 0.0171 memory: 15074 grad_norm: 1.5152 loss: 0.3009 semantic_segmentation_loss_cls: 0.0889 semantic_segmentation_loss_mask: 0.0622 semantic_segmentation_loss_dice: 0.1497 2024/07/08 05:18:00 - mmengine - INFO - Iter(train) [ 49550/120000] base_lr: 1.2775e-04 lr: 1.3432e-05 eta: 21:46:32 time: 1.1055 data_time: 0.0171 memory: 15472 grad_norm: 1.5142 loss: 0.3005 semantic_segmentation_loss_cls: 0.0888 semantic_segmentation_loss_mask: 0.0622 semantic_segmentation_loss_dice: 0.1496 2024/07/08 05:18:56 - mmengine - INFO - Iter(train) [ 49600/120000] base_lr: 1.2763e-04 lr: 1.3421e-05 eta: 21:45:36 time: 1.1056 data_time: 0.0172 memory: 14783 grad_norm: 1.5133 loss: 0.3007 semantic_segmentation_loss_cls: 0.0889 semantic_segmentation_loss_mask: 0.0622 semantic_segmentation_loss_dice: 0.1497 2024/07/08 05:19:51 - mmengine - INFO - Iter(train) [ 49650/120000] base_lr: 1.2750e-04 lr: 1.3409e-05 eta: 21:44:39 time: 1.1056 data_time: 0.0172 memory: 15527 grad_norm: 1.5124 loss: 0.3007 semantic_segmentation_loss_cls: 0.0888 semantic_segmentation_loss_mask: 0.0622 semantic_segmentation_loss_dice: 0.1497 2024/07/08 05:20:45 - mmengine - INFO - Iter(train) [ 49700/120000] base_lr: 1.2738e-04 lr: 1.3398e-05 eta: 21:43:41 time: 1.1055 data_time: 0.0172 memory: 15605 grad_norm: 1.5109 loss: 0.3005 semantic_segmentation_loss_cls: 0.0887 semantic_segmentation_loss_mask: 0.0621 semantic_segmentation_loss_dice: 0.1497 2024/07/08 05:21:38 - mmengine - INFO - Iter(train) [ 49750/120000] base_lr: 1.2725e-04 lr: 1.3386e-05 eta: 21:42:43 time: 1.1051 data_time: 0.0172 memory: 15040 grad_norm: 1.5093 loss: 0.3005 semantic_segmentation_loss_cls: 0.0888 semantic_segmentation_loss_mask: 0.0621 semantic_segmentation_loss_dice: 0.1497 2024/07/08 05:22:33 - mmengine - INFO - Iter(train) [ 49800/120000] base_lr: 1.2713e-04 lr: 1.3375e-05 eta: 21:41:46 time: 1.1050 data_time: 0.0172 memory: 15731 grad_norm: 1.5080 loss: 0.3005 semantic_segmentation_loss_cls: 0.0888 semantic_segmentation_loss_mask: 0.0621 semantic_segmentation_loss_dice: 0.1497 2024/07/08 05:23:28 - mmengine - INFO - Iter(train) [ 49850/120000] base_lr: 1.2700e-04 lr: 1.3364e-05 eta: 21:40:49 time: 1.1050 data_time: 0.0172 memory: 15786 grad_norm: 1.5082 loss: 0.3002 semantic_segmentation_loss_cls: 0.0886 semantic_segmentation_loss_mask: 0.0620 semantic_segmentation_loss_dice: 0.1496 2024/07/08 05:24:22 - mmengine - INFO - Iter(train) [ 49900/120000] base_lr: 1.2688e-04 lr: 1.3352e-05 eta: 21:39:52 time: 1.1049 data_time: 0.0172 memory: 14851 grad_norm: 1.5071 loss: 0.3002 semantic_segmentation_loss_cls: 0.0886 semantic_segmentation_loss_mask: 0.0620 semantic_segmentation_loss_dice: 0.1496 2024/07/08 05:25:18 - mmengine - INFO - Iter(train) [ 49950/120000] base_lr: 1.2675e-04 lr: 1.3341e-05 eta: 21:38:56 time: 1.1049 data_time: 0.0172 memory: 14559 grad_norm: 1.5061 loss: 0.2999 semantic_segmentation_loss_cls: 0.0885 semantic_segmentation_loss_mask: 0.0620 semantic_segmentation_loss_dice: 0.1494 2024/07/08 05:26:13 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 05:26:13 - mmengine - INFO - Iter(train) [ 50000/120000] base_lr: 1.2663e-04 lr: 1.3330e-05 eta: 21:38:00 time: 1.1050 data_time: 0.0171 memory: 15513 grad_norm: 1.5055 loss: 0.2997 semantic_segmentation_loss_cls: 0.0884 semantic_segmentation_loss_mask: 0.0620 semantic_segmentation_loss_dice: 0.1493 2024/07/08 05:26:13 - mmengine - INFO - Saving checkpoint at 50000 iterations 2024/07/08 05:26:30 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:52 time: 0.2458 data_time: 0.0015 memory: 5013 2024/07/08 05:26:42 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:38 time: 0.2458 data_time: 0.0015 memory: 5187 2024/07/08 05:26:55 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:26 time: 0.2458 data_time: 0.0015 memory: 4460 2024/07/08 05:27:07 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:13 time: 0.2458 data_time: 0.0015 memory: 4543 2024/07/08 05:27:19 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:01 time: 0.2458 data_time: 0.0015 memory: 4643 2024/07/08 05:27:32 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:49 time: 0.2458 data_time: 0.0015 memory: 10983 2024/07/08 05:27:44 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2458 data_time: 0.0015 memory: 4460 2024/07/08 05:27:56 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2458 data_time: 0.0015 memory: 4641 2024/07/08 05:28:08 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2458 data_time: 0.0015 memory: 4473 2024/07/08 05:28:20 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2458 data_time: 0.0015 memory: 4555 2024/07/08 05:28:21 - mmengine - INFO - per class results: 2024/07/08 05:28:21 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.09 | 86.83 | | building | 83.1 | 89.84 | | sky | 94.24 | 97.73 | | floor | 82.92 | 91.24 | | tree | 74.94 | 87.83 | | ceiling | 85.07 | 93.31 | | road | 84.82 | 92.5 | | bed | 87.99 | 95.13 | | windowpane | 61.61 | 79.76 | | grass | 71.64 | 87.13 | | cabinet | 62.88 | 73.74 | | sidewalk | 67.99 | 81.83 | | person | 82.12 | 91.29 | | earth | 34.29 | 47.62 | | door | 51.25 | 68.07 | | table | 62.69 | 78.89 | | mountain | 56.6 | 70.47 | | plant | 54.18 | 68.43 | | curtain | 73.46 | 88.02 | | chair | 59.14 | 72.49 | | car | 85.49 | 91.73 | | water | 50.38 | 66.78 | | painting | 72.26 | 89.14 | | sofa | 63.2 | 75.63 | | shelf | 45.08 | 64.37 | | house | 51.04 | 78.4 | | sea | 48.04 | 70.19 | | mirror | 67.08 | 77.54 | | rug | 67.77 | 78.78 | | field | 36.53 | 50.41 | | armchair | 42.88 | 68.4 | | seat | 56.92 | 77.06 | | fence | 48.06 | 68.35 | | desk | 46.7 | 66.29 | | rock | 35.73 | 54.18 | | wardrobe | 54.56 | 70.37 | | lamp | 68.09 | 81.05 | | bathtub | 88.39 | 91.29 | | railing | 34.18 | 50.95 | | cushion | 57.27 | 68.17 | | base | 19.51 | 32.45 | | box | 26.22 | 37.91 | | column | 47.39 | 69.95 | | signboard | 37.01 | 55.86 | | chest of drawers | 42.54 | 70.8 | | counter | 27.07 | 43.75 | | sand | 34.51 | 50.44 | | sink | 73.91 | 82.77 | | skyscraper | 59.08 | 77.86 | | fireplace | 67.43 | 89.73 | | refrigerator | 78.55 | 89.65 | | grandstand | 43.67 | 74.65 | | path | 30.56 | 42.35 | | stairs | 27.6 | 40.45 | | runway | 75.58 | 89.19 | | case | 55.86 | 62.11 | | pool table | 91.75 | 96.02 | | pillow | 55.61 | 68.71 | | screen door | 77.69 | 81.32 | | stairway | 39.57 | 44.81 | | river | 20.88 | 44.44 | | bridge | 72.05 | 88.27 | | bookcase | 36.1 | 55.97 | | blind | 40.02 | 46.08 | | coffee table | 72.04 | 85.43 | | toilet | 77.51 | 89.49 | | flower | 37.53 | 57.59 | | book | 51.46 | 76.78 | | hill | 12.12 | 25.34 | | bench | 44.08 | 52.26 | | countertop | 56.56 | 66.31 | | stove | 80.54 | 84.8 | | palm | 52.72 | 70.85 | | kitchen island | 33.59 | 78.73 | | computer | 61.68 | 68.3 | | swivel chair | 42.92 | 60.8 | | boat | 65.48 | 72.13 | | bar | 27.2 | 36.53 | | arcade machine | 58.76 | 64.09 | | hovel | 18.14 | 24.52 | | bus | 85.28 | 88.45 | | towel | 68.62 | 75.21 | | light | 64.0 | 79.72 | | truck | 33.13 | 47.0 | | tower | 33.28 | 54.17 | | chandelier | 67.25 | 78.67 | | awning | 29.49 | 46.85 | | streetlight | 40.4 | 56.83 | | booth | 51.55 | 52.61 | | television receiver | 48.95 | 88.45 | | airplane | 62.88 | 68.53 | | dirt track | 13.79 | 19.45 | | apparel | 35.61 | 54.81 | | pole | 32.18 | 52.26 | | land | 0.67 | 0.97 | | bannister | 15.2 | 25.02 | | escalator | 40.91 | 50.6 | | ottoman | 36.26 | 58.19 | | bottle | 20.81 | 26.3 | | buffet | 66.51 | 71.67 | | poster | 32.44 | 42.85 | | stage | 18.96 | 35.14 | | van | 45.42 | 65.87 | | ship | 69.88 | 81.61 | | fountain | 6.49 | 6.95 | | conveyer belt | 82.03 | 90.72 | | canopy | 23.65 | 33.98 | | washer | 71.2 | 73.5 | | plaything | 27.29 | 37.9 | | swimming pool | 28.86 | 30.59 | | stool | 50.71 | 69.46 | | barrel | 20.51 | 56.33 | | basket | 37.44 | 45.88 | | waterfall | 56.89 | 79.18 | | tent | 64.8 | 97.67 | | bag | 18.42 | 24.93 | | minibike | 70.74 | 85.35 | | cradle | 76.25 | 97.1 | | oven | 45.84 | 55.45 | | ball | 36.63 | 42.08 | | food | 62.63 | 78.43 | | step | 24.41 | 36.45 | | tank | 48.39 | 51.37 | | trade name | 30.59 | 39.24 | | microwave | 38.35 | 41.11 | | pot | 55.95 | 64.54 | | animal | 62.38 | 69.71 | | bicycle | 57.19 | 77.89 | | lake | 63.54 | 63.63 | | dishwasher | 80.23 | 86.04 | | screen | 69.13 | 82.68 | | blanket | 14.45 | 19.18 | | sculpture | 69.74 | 84.49 | | hood | 69.16 | 73.81 | | sconce | 51.4 | 64.78 | | vase | 49.48 | 66.19 | | traffic light | 41.58 | 60.55 | | tray | 17.52 | 23.95 | | ashcan | 44.09 | 58.8 | | fan | 65.2 | 78.61 | | pier | 45.51 | 87.07 | | crt screen | 0.0 | 0.01 | | plate | 60.05 | 74.45 | | monitor | 4.79 | 7.03 | | bulletin board | 27.94 | 35.46 | | shower | 6.15 | 17.62 | | radiator | 56.12 | 67.19 | | glass | 18.65 | 20.23 | | clock | 33.82 | 39.46 | | flag | 40.22 | 50.27 | +---------------------+-------+-------+ 2024/07/08 05:28:21 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.3700 mIoU: 50.2700 mAcc: 63.3300 data_time: 0.0015 time: 0.2454 2024/07/08 05:29:16 - mmengine - INFO - Iter(train) [ 50050/120000] base_lr: 1.2650e-04 lr: 1.3318e-05 eta: 21:37:05 time: 1.1039 data_time: 0.0161 memory: 15720 grad_norm: 1.5029 loss: 0.2996 semantic_segmentation_loss_cls: 0.0883 semantic_segmentation_loss_mask: 0.0620 semantic_segmentation_loss_dice: 0.1493 2024/07/08 05:30:11 - mmengine - INFO - Iter(train) [ 50100/120000] base_lr: 1.2638e-04 lr: 1.3307e-05 eta: 21:36:08 time: 1.1039 data_time: 0.0161 memory: 15271 grad_norm: 1.5010 loss: 0.2993 semantic_segmentation_loss_cls: 0.0882 semantic_segmentation_loss_mask: 0.0619 semantic_segmentation_loss_dice: 0.1492 2024/07/08 05:31:07 - mmengine - INFO - Iter(train) [ 50150/120000] base_lr: 1.2625e-04 lr: 1.3295e-05 eta: 21:35:12 time: 1.1039 data_time: 0.0161 memory: 15139 grad_norm: 1.5005 loss: 0.2993 semantic_segmentation_loss_cls: 0.0882 semantic_segmentation_loss_mask: 0.0619 semantic_segmentation_loss_dice: 0.1492 2024/07/08 05:32:01 - mmengine - INFO - Iter(train) [ 50200/120000] base_lr: 1.2612e-04 lr: 1.3284e-05 eta: 21:34:15 time: 1.1037 data_time: 0.0161 memory: 14420 grad_norm: 1.4994 loss: 0.2991 semantic_segmentation_loss_cls: 0.0881 semantic_segmentation_loss_mask: 0.0619 semantic_segmentation_loss_dice: 0.1491 2024/07/08 05:32:56 - mmengine - INFO - Iter(train) [ 50250/120000] base_lr: 1.2600e-04 lr: 1.3273e-05 eta: 21:33:18 time: 1.1037 data_time: 0.0161 memory: 15182 grad_norm: 1.5002 loss: 0.2990 semantic_segmentation_loss_cls: 0.0881 semantic_segmentation_loss_mask: 0.0618 semantic_segmentation_loss_dice: 0.1490 2024/07/08 05:33:51 - mmengine - INFO - Iter(train) [ 50300/120000] base_lr: 1.2587e-04 lr: 1.3261e-05 eta: 21:32:21 time: 1.1037 data_time: 0.0161 memory: 15269 grad_norm: 1.4992 loss: 0.2990 semantic_segmentation_loss_cls: 0.0880 semantic_segmentation_loss_mask: 0.0619 semantic_segmentation_loss_dice: 0.1491 2024/07/08 05:34:45 - mmengine - INFO - Iter(train) [ 50350/120000] base_lr: 1.2575e-04 lr: 1.3250e-05 eta: 21:31:24 time: 1.1034 data_time: 0.0161 memory: 14617 grad_norm: 1.4990 loss: 0.2988 semantic_segmentation_loss_cls: 0.0879 semantic_segmentation_loss_mask: 0.0618 semantic_segmentation_loss_dice: 0.1490 2024/07/08 05:35:41 - mmengine - INFO - Iter(train) [ 50400/120000] base_lr: 1.2562e-04 lr: 1.3238e-05 eta: 21:30:29 time: 1.1035 data_time: 0.0161 memory: 14814 grad_norm: 1.4986 loss: 0.2991 semantic_segmentation_loss_cls: 0.0881 semantic_segmentation_loss_mask: 0.0618 semantic_segmentation_loss_dice: 0.1491 2024/07/08 05:36:36 - mmengine - INFO - Iter(train) [ 50450/120000] base_lr: 1.2550e-04 lr: 1.3227e-05 eta: 21:29:32 time: 1.1033 data_time: 0.0161 memory: 15028 grad_norm: 1.5003 loss: 0.2991 semantic_segmentation_loss_cls: 0.0881 semantic_segmentation_loss_mask: 0.0618 semantic_segmentation_loss_dice: 0.1491 2024/07/08 05:37:30 - mmengine - INFO - Iter(train) [ 50500/120000] base_lr: 1.2537e-04 lr: 1.3216e-05 eta: 21:28:34 time: 1.1033 data_time: 0.0161 memory: 15951 grad_norm: 1.4991 loss: 0.2991 semantic_segmentation_loss_cls: 0.0881 semantic_segmentation_loss_mask: 0.0618 semantic_segmentation_loss_dice: 0.1492 2024/07/08 05:38:25 - mmengine - INFO - Iter(train) [ 50550/120000] base_lr: 1.2525e-04 lr: 1.3204e-05 eta: 21:27:38 time: 1.1034 data_time: 0.0162 memory: 14622 grad_norm: 1.4993 loss: 0.2989 semantic_segmentation_loss_cls: 0.0880 semantic_segmentation_loss_mask: 0.0618 semantic_segmentation_loss_dice: 0.1491 2024/07/08 05:39:19 - mmengine - INFO - Iter(train) [ 50600/120000] base_lr: 1.2512e-04 lr: 1.3193e-05 eta: 21:26:41 time: 1.1033 data_time: 0.0162 memory: 15343 grad_norm: 1.4975 loss: 0.2985 semantic_segmentation_loss_cls: 0.0878 semantic_segmentation_loss_mask: 0.0618 semantic_segmentation_loss_dice: 0.1489 2024/07/08 05:40:15 - mmengine - INFO - Iter(train) [ 50650/120000] base_lr: 1.2499e-04 lr: 1.3181e-05 eta: 21:25:45 time: 1.1035 data_time: 0.0162 memory: 14813 grad_norm: 1.4980 loss: 0.2989 semantic_segmentation_loss_cls: 0.0880 semantic_segmentation_loss_mask: 0.0618 semantic_segmentation_loss_dice: 0.1491 2024/07/08 05:41:10 - mmengine - INFO - Iter(train) [ 50700/120000] base_lr: 1.2487e-04 lr: 1.3170e-05 eta: 21:24:49 time: 1.1037 data_time: 0.0162 memory: 15235 grad_norm: 1.4994 loss: 0.2989 semantic_segmentation_loss_cls: 0.0880 semantic_segmentation_loss_mask: 0.0618 semantic_segmentation_loss_dice: 0.1491 2024/07/08 05:42:07 - mmengine - INFO - Iter(train) [ 50750/120000] base_lr: 1.2474e-04 lr: 1.3158e-05 eta: 21:23:55 time: 1.1042 data_time: 0.0163 memory: 15856 grad_norm: 1.4988 loss: 0.2987 semantic_segmentation_loss_cls: 0.0879 semantic_segmentation_loss_mask: 0.0618 semantic_segmentation_loss_dice: 0.1490 2024/07/08 05:43:02 - mmengine - INFO - Iter(train) [ 50800/120000] base_lr: 1.2462e-04 lr: 1.3147e-05 eta: 21:22:58 time: 1.1043 data_time: 0.0163 memory: 14718 grad_norm: 1.4979 loss: 0.2981 semantic_segmentation_loss_cls: 0.0877 semantic_segmentation_loss_mask: 0.0616 semantic_segmentation_loss_dice: 0.1488 2024/07/08 05:43:56 - mmengine - INFO - Iter(train) [ 50850/120000] base_lr: 1.2449e-04 lr: 1.3136e-05 eta: 21:22:01 time: 1.1043 data_time: 0.0163 memory: 15629 grad_norm: 1.4967 loss: 0.2979 semantic_segmentation_loss_cls: 0.0876 semantic_segmentation_loss_mask: 0.0616 semantic_segmentation_loss_dice: 0.1487 2024/07/08 05:44:51 - mmengine - INFO - Iter(train) [ 50900/120000] base_lr: 1.2437e-04 lr: 1.3124e-05 eta: 21:21:04 time: 1.1042 data_time: 0.0163 memory: 14961 grad_norm: 1.4961 loss: 0.2978 semantic_segmentation_loss_cls: 0.0876 semantic_segmentation_loss_mask: 0.0616 semantic_segmentation_loss_dice: 0.1487 2024/07/08 05:45:46 - mmengine - INFO - Iter(train) [ 50950/120000] base_lr: 1.2424e-04 lr: 1.3113e-05 eta: 21:20:07 time: 1.1040 data_time: 0.0163 memory: 16099 grad_norm: 1.4942 loss: 0.2978 semantic_segmentation_loss_cls: 0.0876 semantic_segmentation_loss_mask: 0.0616 semantic_segmentation_loss_dice: 0.1487 2024/07/08 05:46:40 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 05:46:40 - mmengine - INFO - Iter(train) [ 51000/120000] base_lr: 1.2411e-04 lr: 1.3101e-05 eta: 21:19:10 time: 1.1040 data_time: 0.0163 memory: 15375 grad_norm: 1.4948 loss: 0.2978 semantic_segmentation_loss_cls: 0.0876 semantic_segmentation_loss_mask: 0.0615 semantic_segmentation_loss_dice: 0.1487 2024/07/08 05:46:40 - mmengine - INFO - Saving checkpoint at 51000 iterations 2024/07/08 05:47:40 - mmengine - INFO - Iter(train) [ 51050/120000] base_lr: 1.2399e-04 lr: 1.3090e-05 eta: 21:18:20 time: 1.1041 data_time: 0.0163 memory: 15712 grad_norm: 1.4958 loss: 0.2976 semantic_segmentation_loss_cls: 0.0875 semantic_segmentation_loss_mask: 0.0615 semantic_segmentation_loss_dice: 0.1486 2024/07/08 05:48:35 - mmengine - INFO - Iter(train) [ 51100/120000] base_lr: 1.2386e-04 lr: 1.3078e-05 eta: 21:17:23 time: 1.1040 data_time: 0.0163 memory: 15300 grad_norm: 1.4962 loss: 0.2973 semantic_segmentation_loss_cls: 0.0873 semantic_segmentation_loss_mask: 0.0615 semantic_segmentation_loss_dice: 0.1485 2024/07/08 05:49:29 - mmengine - INFO - Iter(train) [ 51150/120000] base_lr: 1.2374e-04 lr: 1.3067e-05 eta: 21:16:26 time: 1.1038 data_time: 0.0164 memory: 15528 grad_norm: 1.4952 loss: 0.2974 semantic_segmentation_loss_cls: 0.0872 semantic_segmentation_loss_mask: 0.0616 semantic_segmentation_loss_dice: 0.1486 2024/07/08 05:50:24 - mmengine - INFO - Iter(train) [ 51200/120000] base_lr: 1.2361e-04 lr: 1.3055e-05 eta: 21:15:29 time: 1.1038 data_time: 0.0164 memory: 15734 grad_norm: 1.4950 loss: 0.2974 semantic_segmentation_loss_cls: 0.0872 semantic_segmentation_loss_mask: 0.0616 semantic_segmentation_loss_dice: 0.1486 2024/07/08 05:51:18 - mmengine - INFO - Iter(train) [ 51250/120000] base_lr: 1.2348e-04 lr: 1.3044e-05 eta: 21:14:32 time: 1.1035 data_time: 0.0164 memory: 14769 grad_norm: 1.4965 loss: 0.2973 semantic_segmentation_loss_cls: 0.0872 semantic_segmentation_loss_mask: 0.0615 semantic_segmentation_loss_dice: 0.1486 2024/07/08 05:52:13 - mmengine - INFO - Iter(train) [ 51300/120000] base_lr: 1.2336e-04 lr: 1.3032e-05 eta: 21:13:35 time: 1.1034 data_time: 0.0164 memory: 15461 grad_norm: 1.4960 loss: 0.2970 semantic_segmentation_loss_cls: 0.0870 semantic_segmentation_loss_mask: 0.0615 semantic_segmentation_loss_dice: 0.1485 2024/07/08 05:53:08 - mmengine - INFO - Iter(train) [ 51350/120000] base_lr: 1.2323e-04 lr: 1.3021e-05 eta: 21:12:38 time: 1.1035 data_time: 0.0164 memory: 15941 grad_norm: 1.4960 loss: 0.2969 semantic_segmentation_loss_cls: 0.0870 semantic_segmentation_loss_mask: 0.0614 semantic_segmentation_loss_dice: 0.1485 2024/07/08 05:54:02 - mmengine - INFO - Iter(train) [ 51400/120000] base_lr: 1.2310e-04 lr: 1.3009e-05 eta: 21:11:41 time: 1.1035 data_time: 0.0164 memory: 15094 grad_norm: 1.4953 loss: 0.2967 semantic_segmentation_loss_cls: 0.0869 semantic_segmentation_loss_mask: 0.0613 semantic_segmentation_loss_dice: 0.1484 2024/07/08 05:54:57 - mmengine - INFO - Iter(train) [ 51450/120000] base_lr: 1.2298e-04 lr: 1.2998e-05 eta: 21:10:44 time: 1.1035 data_time: 0.0164 memory: 14255 grad_norm: 1.4949 loss: 0.2965 semantic_segmentation_loss_cls: 0.0868 semantic_segmentation_loss_mask: 0.0613 semantic_segmentation_loss_dice: 0.1483 2024/07/08 05:55:52 - mmengine - INFO - Iter(train) [ 51500/120000] base_lr: 1.2285e-04 lr: 1.2987e-05 eta: 21:09:48 time: 1.1037 data_time: 0.0164 memory: 14856 grad_norm: 1.4935 loss: 0.2969 semantic_segmentation_loss_cls: 0.0870 semantic_segmentation_loss_mask: 0.0614 semantic_segmentation_loss_dice: 0.1486 2024/07/08 05:56:47 - mmengine - INFO - Iter(train) [ 51550/120000] base_lr: 1.2273e-04 lr: 1.2975e-05 eta: 21:08:52 time: 1.1037 data_time: 0.0164 memory: 15658 grad_norm: 1.4926 loss: 0.2967 semantic_segmentation_loss_cls: 0.0869 semantic_segmentation_loss_mask: 0.0613 semantic_segmentation_loss_dice: 0.1484 2024/07/08 05:57:42 - mmengine - INFO - Iter(train) [ 51600/120000] base_lr: 1.2260e-04 lr: 1.2964e-05 eta: 21:07:56 time: 1.1037 data_time: 0.0164 memory: 14641 grad_norm: 1.4919 loss: 0.2966 semantic_segmentation_loss_cls: 0.0869 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semantic_segmentation_loss_dice: 0.1481 2024/07/08 06:01:22 - mmengine - INFO - Iter(train) [ 51800/120000] base_lr: 1.2209e-04 lr: 1.2918e-05 eta: 21:04:10 time: 1.1037 data_time: 0.0163 memory: 15449 grad_norm: 1.4870 loss: 0.2959 semantic_segmentation_loss_cls: 0.0866 semantic_segmentation_loss_mask: 0.0612 semantic_segmentation_loss_dice: 0.1481 2024/07/08 06:02:17 - mmengine - INFO - Iter(train) [ 51850/120000] base_lr: 1.2197e-04 lr: 1.2906e-05 eta: 21:03:13 time: 1.1037 data_time: 0.0163 memory: 14778 grad_norm: 1.4861 loss: 0.2958 semantic_segmentation_loss_cls: 0.0866 semantic_segmentation_loss_mask: 0.0611 semantic_segmentation_loss_dice: 0.1480 2024/07/08 06:03:12 - mmengine - INFO - Iter(train) [ 51900/120000] base_lr: 1.2184e-04 lr: 1.2894e-05 eta: 21:02:16 time: 1.1037 data_time: 0.0163 memory: 15356 grad_norm: 1.4864 loss: 0.2955 semantic_segmentation_loss_cls: 0.0865 semantic_segmentation_loss_mask: 0.0611 semantic_segmentation_loss_dice: 0.1479 2024/07/08 06:04:06 - 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grad_norm: 1.4877 loss: 0.2957 semantic_segmentation_loss_cls: 0.0866 semantic_segmentation_loss_mask: 0.0611 semantic_segmentation_loss_dice: 0.1480 2024/07/08 06:06:54 - mmengine - INFO - Iter(train) [ 52100/120000] base_lr: 1.2133e-04 lr: 1.2848e-05 eta: 20:58:34 time: 1.1029 data_time: 0.0163 memory: 14773 grad_norm: 1.4876 loss: 0.2956 semantic_segmentation_loss_cls: 0.0866 semantic_segmentation_loss_mask: 0.0611 semantic_segmentation_loss_dice: 0.1479 2024/07/08 06:07:49 - mmengine - INFO - Iter(train) [ 52150/120000] base_lr: 1.2121e-04 lr: 1.2837e-05 eta: 20:57:37 time: 1.1028 data_time: 0.0162 memory: 15299 grad_norm: 1.4870 loss: 0.2957 semantic_segmentation_loss_cls: 0.0867 semantic_segmentation_loss_mask: 0.0611 semantic_segmentation_loss_dice: 0.1479 2024/07/08 06:08:44 - mmengine - INFO - Iter(train) [ 52200/120000] base_lr: 1.2108e-04 lr: 1.2825e-05 eta: 20:56:40 time: 1.1028 data_time: 0.0162 memory: 15473 grad_norm: 1.4857 loss: 0.2959 semantic_segmentation_loss_cls: 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grad_norm: 1.4726 loss: 0.2925 semantic_segmentation_loss_cls: 0.0856 semantic_segmentation_loss_mask: 0.0605 semantic_segmentation_loss_dice: 0.1465 2024/07/08 06:32:39 - mmengine - INFO - Iter(train) [ 53500/120000] base_lr: 1.1777e-04 lr: 1.2524e-05 eta: 20:32:21 time: 1.1017 data_time: 0.0162 memory: 14979 grad_norm: 1.4716 loss: 0.2923 semantic_segmentation_loss_cls: 0.0855 semantic_segmentation_loss_mask: 0.0604 semantic_segmentation_loss_dice: 0.1464 2024/07/08 06:33:34 - mmengine - INFO - Iter(train) [ 53550/120000] base_lr: 1.1764e-04 lr: 1.2513e-05 eta: 20:31:25 time: 1.1015 data_time: 0.0162 memory: 14967 grad_norm: 1.4724 loss: 0.2921 semantic_segmentation_loss_cls: 0.0853 semantic_segmentation_loss_mask: 0.0604 semantic_segmentation_loss_dice: 0.1464 2024/07/08 06:34:30 - mmengine - INFO - Iter(train) [ 53600/120000] base_lr: 1.1751e-04 lr: 1.2501e-05 eta: 20:30:29 time: 1.1015 data_time: 0.0162 memory: 15706 grad_norm: 1.4720 loss: 0.2918 semantic_segmentation_loss_cls: 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semantic_segmentation_loss_dice: 0.1457 2024/07/08 06:38:10 - mmengine - INFO - Iter(train) [ 53800/120000] base_lr: 1.1700e-04 lr: 1.2455e-05 eta: 20:26:45 time: 1.1023 data_time: 0.0162 memory: 16429 grad_norm: 1.4733 loss: 0.2906 semantic_segmentation_loss_cls: 0.0848 semantic_segmentation_loss_mask: 0.0602 semantic_segmentation_loss_dice: 0.1456 2024/07/08 06:39:04 - mmengine - INFO - Iter(train) [ 53850/120000] base_lr: 1.1687e-04 lr: 1.2443e-05 eta: 20:25:47 time: 1.1021 data_time: 0.0162 memory: 14485 grad_norm: 1.4733 loss: 0.2903 semantic_segmentation_loss_cls: 0.0847 semantic_segmentation_loss_mask: 0.0601 semantic_segmentation_loss_dice: 0.1455 2024/07/08 06:39:58 - mmengine - INFO - Iter(train) [ 53900/120000] base_lr: 1.1675e-04 lr: 1.2431e-05 eta: 20:24:50 time: 1.1020 data_time: 0.0162 memory: 14471 grad_norm: 1.4734 loss: 0.2899 semantic_segmentation_loss_cls: 0.0845 semantic_segmentation_loss_mask: 0.0601 semantic_segmentation_loss_dice: 0.1453 2024/07/08 06:40:53 - mmengine - INFO - Iter(train) [ 53950/120000] base_lr: 1.1662e-04 lr: 1.2420e-05 eta: 20:23:52 time: 1.1017 data_time: 0.0162 memory: 16181 grad_norm: 1.4736 loss: 0.2896 semantic_segmentation_loss_cls: 0.0844 semantic_segmentation_loss_mask: 0.0600 semantic_segmentation_loss_dice: 0.1452 2024/07/08 06:41:47 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 06:41:47 - mmengine - INFO - Iter(train) [ 54000/120000] base_lr: 1.1649e-04 lr: 1.2408e-05 eta: 20:22:56 time: 1.1015 data_time: 0.0162 memory: 15899 grad_norm: 1.4737 loss: 0.2894 semantic_segmentation_loss_cls: 0.0843 semantic_segmentation_loss_mask: 0.0599 semantic_segmentation_loss_dice: 0.1452 2024/07/08 06:41:47 - mmengine - INFO - Saving checkpoint at 54000 iterations 2024/07/08 06:42:47 - mmengine - INFO - Iter(train) [ 54050/120000] base_lr: 1.1636e-04 lr: 1.2397e-05 eta: 20:22:05 time: 1.1024 data_time: 0.0171 memory: 15439 grad_norm: 1.4731 loss: 0.2891 semantic_segmentation_loss_cls: 0.0843 semantic_segmentation_loss_mask: 0.0599 semantic_segmentation_loss_dice: 0.1449 2024/07/08 06:43:41 - mmengine - INFO - Iter(train) [ 54100/120000] base_lr: 1.1623e-04 lr: 1.2385e-05 eta: 20:21:08 time: 1.1022 data_time: 0.0171 memory: 15620 grad_norm: 1.4732 loss: 0.2892 semantic_segmentation_loss_cls: 0.0843 semantic_segmentation_loss_mask: 0.0598 semantic_segmentation_loss_dice: 0.1450 2024/07/08 06:44:35 - mmengine - INFO - Iter(train) [ 54150/120000] base_lr: 1.1611e-04 lr: 1.2373e-05 eta: 20:20:11 time: 1.1020 data_time: 0.0171 memory: 15218 grad_norm: 1.4735 loss: 0.2889 semantic_segmentation_loss_cls: 0.0842 semantic_segmentation_loss_mask: 0.0599 semantic_segmentation_loss_dice: 0.1449 2024/07/08 06:45:29 - mmengine - INFO - Iter(train) [ 54200/120000] base_lr: 1.1598e-04 lr: 1.2362e-05 eta: 20:19:13 time: 1.1019 data_time: 0.0171 memory: 15157 grad_norm: 1.4734 loss: 0.2889 semantic_segmentation_loss_cls: 0.0842 semantic_segmentation_loss_mask: 0.0599 semantic_segmentation_loss_dice: 0.1449 2024/07/08 06:46:24 - mmengine - INFO - Iter(train) [ 54250/120000] base_lr: 1.1585e-04 lr: 1.2350e-05 eta: 20:18:17 time: 1.1020 data_time: 0.0171 memory: 15018 grad_norm: 1.4716 loss: 0.2887 semantic_segmentation_loss_cls: 0.0841 semantic_segmentation_loss_mask: 0.0598 semantic_segmentation_loss_dice: 0.1448 2024/07/08 06:47:19 - mmengine - INFO - Iter(train) [ 54300/120000] base_lr: 1.1572e-04 lr: 1.2338e-05 eta: 20:17:20 time: 1.1019 data_time: 0.0171 memory: 15868 grad_norm: 1.4706 loss: 0.2884 semantic_segmentation_loss_cls: 0.0839 semantic_segmentation_loss_mask: 0.0598 semantic_segmentation_loss_dice: 0.1447 2024/07/08 06:48:14 - mmengine - INFO - Iter(train) [ 54350/120000] base_lr: 1.1559e-04 lr: 1.2327e-05 eta: 20:16:24 time: 1.1020 data_time: 0.0171 memory: 14729 grad_norm: 1.4695 loss: 0.2887 semantic_segmentation_loss_cls: 0.0840 semantic_segmentation_loss_mask: 0.0598 semantic_segmentation_loss_dice: 0.1448 2024/07/08 06:49:09 - mmengine - INFO - Iter(train) [ 54400/120000] base_lr: 1.1546e-04 lr: 1.2315e-05 eta: 20:15:27 time: 1.1018 data_time: 0.0171 memory: 15241 grad_norm: 1.4684 loss: 0.2881 semantic_segmentation_loss_cls: 0.0837 semantic_segmentation_loss_mask: 0.0598 semantic_segmentation_loss_dice: 0.1446 2024/07/08 06:50:04 - mmengine - INFO - Iter(train) [ 54450/120000] base_lr: 1.1534e-04 lr: 1.2303e-05 eta: 20:14:31 time: 1.1020 data_time: 0.0171 memory: 15069 grad_norm: 1.4684 loss: 0.2880 semantic_segmentation_loss_cls: 0.0837 semantic_segmentation_loss_mask: 0.0598 semantic_segmentation_loss_dice: 0.1445 2024/07/08 06:50:59 - mmengine - INFO - Iter(train) [ 54500/120000] base_lr: 1.1521e-04 lr: 1.2292e-05 eta: 20:13:34 time: 1.1020 data_time: 0.0171 memory: 15187 grad_norm: 1.4681 loss: 0.2878 semantic_segmentation_loss_cls: 0.0836 semantic_segmentation_loss_mask: 0.0597 semantic_segmentation_loss_dice: 0.1444 2024/07/08 06:51:54 - mmengine - INFO - Iter(train) [ 54550/120000] base_lr: 1.1508e-04 lr: 1.2280e-05 eta: 20:12:39 time: 1.1022 data_time: 0.0170 memory: 15302 grad_norm: 1.4666 loss: 0.2877 semantic_segmentation_loss_cls: 0.0836 semantic_segmentation_loss_mask: 0.0597 semantic_segmentation_loss_dice: 0.1444 2024/07/08 06:52:49 - mmengine - INFO - Iter(train) [ 54600/120000] base_lr: 1.1495e-04 lr: 1.2268e-05 eta: 20:11:42 time: 1.1022 data_time: 0.0170 memory: 15895 grad_norm: 1.4688 loss: 0.2880 semantic_segmentation_loss_cls: 0.0837 semantic_segmentation_loss_mask: 0.0597 semantic_segmentation_loss_dice: 0.1446 2024/07/08 06:53:43 - mmengine - INFO - Iter(train) [ 54650/120000] base_lr: 1.1482e-04 lr: 1.2257e-05 eta: 20:10:45 time: 1.1020 data_time: 0.0170 memory: 16563 grad_norm: 1.4680 loss: 0.2879 semantic_segmentation_loss_cls: 0.0837 semantic_segmentation_loss_mask: 0.0598 semantic_segmentation_loss_dice: 0.1445 2024/07/08 06:54:38 - mmengine - INFO - Iter(train) [ 54700/120000] base_lr: 1.1470e-04 lr: 1.2245e-05 eta: 20:09:48 time: 1.1017 data_time: 0.0169 memory: 15444 grad_norm: 1.4667 loss: 0.2877 semantic_segmentation_loss_cls: 0.0836 semantic_segmentation_loss_mask: 0.0597 semantic_segmentation_loss_dice: 0.1444 2024/07/08 06:55:31 - mmengine - INFO - Iter(train) [ 54750/120000] base_lr: 1.1457e-04 lr: 1.2233e-05 eta: 20:08:50 time: 1.1009 data_time: 0.0169 memory: 15328 grad_norm: 1.4670 loss: 0.2878 semantic_segmentation_loss_cls: 0.0837 semantic_segmentation_loss_mask: 0.0597 semantic_segmentation_loss_dice: 0.1444 2024/07/08 06:56:25 - mmengine - INFO - Iter(train) [ 54800/120000] base_lr: 1.1444e-04 lr: 1.2222e-05 eta: 20:07:52 time: 1.1006 data_time: 0.0169 memory: 15365 grad_norm: 1.4644 loss: 0.2874 semantic_segmentation_loss_cls: 0.0835 semantic_segmentation_loss_mask: 0.0597 semantic_segmentation_loss_dice: 0.1442 2024/07/08 06:57:19 - mmengine - INFO - Iter(train) [ 54850/120000] base_lr: 1.1431e-04 lr: 1.2210e-05 eta: 20:06:55 time: 1.1005 data_time: 0.0169 memory: 14966 grad_norm: 1.4649 loss: 0.2870 semantic_segmentation_loss_cls: 0.0833 semantic_segmentation_loss_mask: 0.0596 semantic_segmentation_loss_dice: 0.1441 2024/07/08 06:58:14 - mmengine - INFO - Iter(train) [ 54900/120000] base_lr: 1.1418e-04 lr: 1.2198e-05 eta: 20:05:59 time: 1.1007 data_time: 0.0169 memory: 15787 grad_norm: 1.4642 loss: 0.2867 semantic_segmentation_loss_cls: 0.0832 semantic_segmentation_loss_mask: 0.0595 semantic_segmentation_loss_dice: 0.1440 2024/07/08 06:59:09 - mmengine - INFO - Iter(train) [ 54950/120000] base_lr: 1.1405e-04 lr: 1.2187e-05 eta: 20:05:03 time: 1.1008 data_time: 0.0169 memory: 15647 grad_norm: 1.4639 loss: 0.2864 semantic_segmentation_loss_cls: 0.0830 semantic_segmentation_loss_mask: 0.0595 semantic_segmentation_loss_dice: 0.1439 2024/07/08 07:00:04 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 07:00:04 - mmengine - INFO - Iter(train) [ 55000/120000] base_lr: 1.1392e-04 lr: 1.2175e-05 eta: 20:04:06 time: 1.1008 data_time: 0.0169 memory: 15937 grad_norm: 1.4626 loss: 0.2863 semantic_segmentation_loss_cls: 0.0830 semantic_segmentation_loss_mask: 0.0594 semantic_segmentation_loss_dice: 0.1438 2024/07/08 07:00:04 - mmengine - INFO - Saving checkpoint at 55000 iterations 2024/07/08 07:00:21 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:50 time: 0.2458 data_time: 0.0015 memory: 5013 2024/07/08 07:00:33 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:38 time: 0.2458 data_time: 0.0015 memory: 5187 2024/07/08 07:00:46 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:25 time: 0.2458 data_time: 0.0015 memory: 4460 2024/07/08 07:00:58 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:13 time: 0.2458 data_time: 0.0015 memory: 4543 2024/07/08 07:01:10 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:01 time: 0.2458 data_time: 0.0015 memory: 4643 2024/07/08 07:01:22 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:48 time: 0.2458 data_time: 0.0015 memory: 10983 2024/07/08 07:01:34 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2458 data_time: 0.0015 memory: 4460 2024/07/08 07:01:47 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2458 data_time: 0.0015 memory: 4641 2024/07/08 07:01:59 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2458 data_time: 0.0015 memory: 4473 2024/07/08 07:02:11 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2458 data_time: 0.0015 memory: 4555 2024/07/08 07:02:12 - mmengine - INFO - per class results: 2024/07/08 07:02:12 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.26 | 86.94 | | building | 82.6 | 89.67 | | sky | 94.25 | 97.73 | | floor | 82.85 | 91.27 | | tree | 74.93 | 87.95 | | ceiling | 85.0 | 93.29 | | road | 84.15 | 92.5 | | bed | 88.4 | 95.28 | | windowpane | 61.7 | 80.36 | | grass | 69.59 | 84.93 | | cabinet | 62.64 | 74.03 | | sidewalk | 67.59 | 81.41 | | person | 82.17 | 91.44 | | earth | 35.3 | 48.05 | | door | 52.49 | 69.43 | | table | 63.88 | 79.31 | | mountain | 56.77 | 70.48 | | plant | 52.69 | 67.51 | | curtain | 73.96 | 87.98 | | chair | 59.09 | 72.49 | | car | 85.46 | 91.96 | | water | 50.6 | 67.17 | | painting | 73.53 | 89.16 | | sofa | 65.39 | 75.69 | | shelf | 44.34 | 64.82 | | house | 52.22 | 81.22 | | sea | 47.94 | 70.39 | | mirror | 67.05 | 77.94 | | rug | 65.12 | 77.64 | | field | 32.36 | 47.17 | | armchair | 47.23 | 73.66 | | seat | 57.23 | 78.32 | | fence | 47.1 | 66.76 | | desk | 47.68 | 67.15 | | rock | 37.69 | 56.88 | | wardrobe | 54.1 | 71.63 | | lamp | 67.66 | 80.51 | | bathtub | 88.2 | 90.89 | | railing | 35.71 | 52.83 | | cushion | 57.74 | 68.94 | | base | 15.23 | 23.53 | | box | 26.42 | 38.55 | | column | 48.15 | 70.92 | | signboard | 40.07 | 57.39 | | chest of drawers | 39.98 | 63.1 | | counter | 30.57 | 51.2 | | sand | 32.01 | 46.18 | | sink | 68.56 | 82.56 | | skyscraper | 46.73 | 60.07 | | fireplace | 68.32 | 89.05 | | refrigerator | 81.88 | 89.46 | | grandstand | 44.25 | 76.81 | | path | 30.54 | 42.82 | | stairs | 28.98 | 41.03 | | runway | 75.65 | 89.31 | | case | 56.99 | 63.88 | | pool table | 91.93 | 96.16 | | pillow | 54.74 | 67.97 | | screen door | 79.03 | 82.59 | | stairway | 44.15 | 49.96 | | river | 21.11 | 44.31 | | bridge | 52.92 | 66.04 | | bookcase | 37.06 | 56.7 | | blind | 39.85 | 45.45 | | coffee table | 73.42 | 87.0 | | toilet | 83.95 | 89.41 | | flower | 39.52 | 58.02 | | book | 52.27 | 77.51 | | hill | 13.08 | 24.56 | | bench | 45.59 | 54.17 | | countertop | 57.69 | 69.88 | | stove | 79.13 | 84.26 | | palm | 53.66 | 69.24 | | kitchen island | 34.42 | 78.58 | | computer | 61.18 | 68.1 | | swivel chair | 40.47 | 56.6 | | boat | 74.88 | 81.99 | | bar | 28.57 | 36.0 | | arcade machine | 50.27 | 54.57 | | hovel | 30.72 | 45.01 | | bus | 85.77 | 88.56 | | towel | 67.88 | 75.05 | | light | 64.39 | 79.4 | | truck | 33.69 | 48.09 | | tower | 29.58 | 54.06 | | chandelier | 64.67 | 76.65 | | awning | 33.11 | 47.15 | | streetlight | 39.36 | 55.56 | | booth | 48.14 | 48.95 | | television receiver | 45.89 | 89.26 | | airplane | 56.2 | 67.97 | | dirt track | 14.27 | 19.58 | | apparel | 35.39 | 54.08 | | pole | 32.42 | 51.57 | | land | 2.33 | 3.41 | | bannister | 12.65 | 22.02 | | escalator | 49.41 | 64.85 | | ottoman | 37.58 | 59.81 | | bottle | 20.57 | 25.98 | | buffet | 62.28 | 66.29 | | poster | 31.75 | 43.14 | | stage | 16.58 | 30.22 | | van | 44.66 | 64.45 | | ship | 79.31 | 82.87 | | fountain | 7.83 | 7.94 | | conveyer belt | 66.81 | 90.7 | | canopy | 22.23 | 34.58 | | washer | 70.72 | 72.78 | | plaything | 26.54 | 34.36 | | swimming pool | 32.36 | 35.58 | | stool | 53.42 | 69.32 | | barrel | 20.76 | 55.27 | | basket | 37.7 | 46.41 | | waterfall | 52.28 | 72.99 | | tent | 74.11 | 97.69 | | bag | 16.99 | 25.18 | | minibike | 70.31 | 84.73 | | cradle | 76.34 | 96.92 | | oven | 51.9 | 62.61 | | ball | 38.09 | 44.44 | | food | 64.73 | 80.84 | | step | 26.6 | 37.07 | | tank | 41.07 | 44.01 | | trade name | 31.43 | 40.06 | | microwave | 38.53 | 41.42 | | pot | 55.09 | 63.25 | | animal | 62.21 | 69.39 | | bicycle | 55.53 | 77.36 | | lake | 63.55 | 63.67 | | dishwasher | 79.81 | 85.2 | | screen | 74.21 | 88.73 | | blanket | 10.88 | 14.45 | | sculpture | 68.37 | 84.38 | | hood | 69.21 | 73.22 | | sconce | 51.9 | 65.43 | | vase | 49.82 | 66.28 | | traffic light | 45.17 | 61.1 | | tray | 18.93 | 24.71 | | ashcan | 41.26 | 56.27 | | fan | 65.25 | 78.8 | | pier | 37.99 | 73.19 | | crt screen | 0.01 | 0.01 | | plate | 60.86 | 74.48 | | monitor | 18.75 | 25.88 | | bulletin board | 41.97 | 50.86 | | shower | 7.57 | 17.74 | | radiator | 55.84 | 66.61 | | glass | 18.33 | 19.91 | | clock | 34.74 | 39.37 | | flag | 39.95 | 49.42 | +---------------------+-------+-------+ 2024/07/08 07:02:12 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.3700 mIoU: 50.4200 mAcc: 63.3800 data_time: 0.0014 time: 0.2446 2024/07/08 07:03:07 - mmengine - INFO - Iter(train) [ 55050/120000] base_lr: 1.1380e-04 lr: 1.2163e-05 eta: 20:03:11 time: 1.0997 data_time: 0.0159 memory: 15903 grad_norm: 1.4611 loss: 0.2862 semantic_segmentation_loss_cls: 0.0831 semantic_segmentation_loss_mask: 0.0593 semantic_segmentation_loss_dice: 0.1438 2024/07/08 07:04:01 - mmengine - INFO - Iter(train) [ 55100/120000] base_lr: 1.1367e-04 lr: 1.2152e-05 eta: 20:02:14 time: 1.0998 data_time: 0.0159 memory: 14856 grad_norm: 1.4596 loss: 0.2861 semantic_segmentation_loss_cls: 0.0830 semantic_segmentation_loss_mask: 0.0593 semantic_segmentation_loss_dice: 0.1438 2024/07/08 07:04:56 - mmengine - INFO - Iter(train) [ 55150/120000] base_lr: 1.1354e-04 lr: 1.2140e-05 eta: 20:01:18 time: 1.0999 data_time: 0.0159 memory: 15561 grad_norm: 1.4585 loss: 0.2857 semantic_segmentation_loss_cls: 0.0829 semantic_segmentation_loss_mask: 0.0592 semantic_segmentation_loss_dice: 0.1436 2024/07/08 07:05:51 - mmengine - INFO - Iter(train) [ 55200/120000] base_lr: 1.1341e-04 lr: 1.2128e-05 eta: 20:00:21 time: 1.0998 data_time: 0.0159 memory: 14991 grad_norm: 1.4570 loss: 0.2855 semantic_segmentation_loss_cls: 0.0828 semantic_segmentation_loss_mask: 0.0592 semantic_segmentation_loss_dice: 0.1435 2024/07/08 07:06:45 - mmengine - INFO - Iter(train) [ 55250/120000] base_lr: 1.1328e-04 lr: 1.2117e-05 eta: 19:59:24 time: 1.0999 data_time: 0.0159 memory: 15782 grad_norm: 1.4560 loss: 0.2855 semantic_segmentation_loss_cls: 0.0828 semantic_segmentation_loss_mask: 0.0592 semantic_segmentation_loss_dice: 0.1435 2024/07/08 07:07:40 - mmengine - INFO - Iter(train) [ 55300/120000] base_lr: 1.1315e-04 lr: 1.2105e-05 eta: 19:58:28 time: 1.0999 data_time: 0.0159 memory: 15752 grad_norm: 1.4561 loss: 0.2856 semantic_segmentation_loss_cls: 0.0829 semantic_segmentation_loss_mask: 0.0592 semantic_segmentation_loss_dice: 0.1435 2024/07/08 07:08:35 - mmengine - INFO - Iter(train) [ 55350/120000] base_lr: 1.1302e-04 lr: 1.2093e-05 eta: 19:57:31 time: 1.0999 data_time: 0.0159 memory: 14641 grad_norm: 1.4563 loss: 0.2855 semantic_segmentation_loss_cls: 0.0828 semantic_segmentation_loss_mask: 0.0592 semantic_segmentation_loss_dice: 0.1435 2024/07/08 07:09:30 - mmengine - INFO - Iter(train) [ 55400/120000] base_lr: 1.1290e-04 lr: 1.2081e-05 eta: 19:56:34 time: 1.0999 data_time: 0.0159 memory: 14791 grad_norm: 1.4558 loss: 0.2854 semantic_segmentation_loss_cls: 0.0827 semantic_segmentation_loss_mask: 0.0592 semantic_segmentation_loss_dice: 0.1435 2024/07/08 07:10:24 - mmengine - INFO - Iter(train) [ 55450/120000] base_lr: 1.1277e-04 lr: 1.2070e-05 eta: 19:55:38 time: 1.1000 data_time: 0.0159 memory: 16065 grad_norm: 1.4547 loss: 0.2856 semantic_segmentation_loss_cls: 0.0827 semantic_segmentation_loss_mask: 0.0593 semantic_segmentation_loss_dice: 0.1436 2024/07/08 07:11:19 - mmengine - INFO - Iter(train) [ 55500/120000] base_lr: 1.1264e-04 lr: 1.2058e-05 eta: 19:54:41 time: 1.0998 data_time: 0.0159 memory: 14669 grad_norm: 1.4554 loss: 0.2852 semantic_segmentation_loss_cls: 0.0826 semantic_segmentation_loss_mask: 0.0592 semantic_segmentation_loss_dice: 0.1434 2024/07/08 07:12:14 - mmengine - INFO - Iter(train) [ 55550/120000] base_lr: 1.1251e-04 lr: 1.2046e-05 eta: 19:53:44 time: 1.0997 data_time: 0.0159 memory: 14278 grad_norm: 1.4559 loss: 0.2852 semantic_segmentation_loss_cls: 0.0826 semantic_segmentation_loss_mask: 0.0592 semantic_segmentation_loss_dice: 0.1434 2024/07/08 07:13:08 - mmengine - INFO - Iter(train) [ 55600/120000] base_lr: 1.1238e-04 lr: 1.2035e-05 eta: 19:52:48 time: 1.0995 data_time: 0.0159 memory: 14453 grad_norm: 1.4562 loss: 0.2852 semantic_segmentation_loss_cls: 0.0826 semantic_segmentation_loss_mask: 0.0592 semantic_segmentation_loss_dice: 0.1434 2024/07/08 07:14:03 - mmengine - INFO - Iter(train) [ 55650/120000] base_lr: 1.1225e-04 lr: 1.2023e-05 eta: 19:51:52 time: 1.0996 data_time: 0.0160 memory: 15716 grad_norm: 1.4554 loss: 0.2849 semantic_segmentation_loss_cls: 0.0825 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semantic_segmentation_loss_dice: 0.1428 2024/07/08 07:17:45 - mmengine - INFO - Iter(train) [ 55850/120000] base_lr: 1.1174e-04 lr: 1.1976e-05 eta: 19:48:08 time: 1.0999 data_time: 0.0160 memory: 14986 grad_norm: 1.4555 loss: 0.2839 semantic_segmentation_loss_cls: 0.0822 semantic_segmentation_loss_mask: 0.0590 semantic_segmentation_loss_dice: 0.1427 2024/07/08 07:18:40 - mmengine - INFO - Iter(train) [ 55900/120000] base_lr: 1.1161e-04 lr: 1.1964e-05 eta: 19:47:13 time: 1.1002 data_time: 0.0160 memory: 15330 grad_norm: 1.4533 loss: 0.2840 semantic_segmentation_loss_cls: 0.0823 semantic_segmentation_loss_mask: 0.0590 semantic_segmentation_loss_dice: 0.1427 2024/07/08 07:19:36 - mmengine - INFO - Iter(train) [ 55950/120000] base_lr: 1.1148e-04 lr: 1.1953e-05 eta: 19:46:17 time: 1.1005 data_time: 0.0160 memory: 15184 grad_norm: 1.4518 loss: 0.2836 semantic_segmentation_loss_cls: 0.0821 semantic_segmentation_loss_mask: 0.0589 semantic_segmentation_loss_dice: 0.1426 2024/07/08 07:20:31 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 07:20:31 - mmengine - INFO - Iter(train) [ 56000/120000] base_lr: 1.1135e-04 lr: 1.1941e-05 eta: 19:45:22 time: 1.1007 data_time: 0.0160 memory: 15139 grad_norm: 1.4487 loss: 0.2834 semantic_segmentation_loss_cls: 0.0821 semantic_segmentation_loss_mask: 0.0588 semantic_segmentation_loss_dice: 0.1425 2024/07/08 07:20:31 - mmengine - INFO - Saving checkpoint at 56000 iterations 2024/07/08 07:21:31 - mmengine - INFO - Iter(train) [ 56050/120000] base_lr: 1.1122e-04 lr: 1.1929e-05 eta: 19:44:31 time: 1.1009 data_time: 0.0162 memory: 15318 grad_norm: 1.4469 loss: 0.2834 semantic_segmentation_loss_cls: 0.0822 semantic_segmentation_loss_mask: 0.0588 semantic_segmentation_loss_dice: 0.1424 2024/07/08 07:22:25 - mmengine - INFO - Iter(train) [ 56100/120000] base_lr: 1.1109e-04 lr: 1.1918e-05 eta: 19:43:34 time: 1.1009 data_time: 0.0162 memory: 15869 grad_norm: 1.4450 loss: 0.2835 semantic_segmentation_loss_cls: 0.0822 semantic_segmentation_loss_mask: 0.0588 semantic_segmentation_loss_dice: 0.1426 2024/07/08 07:23:20 - mmengine - INFO - Iter(train) [ 56150/120000] base_lr: 1.1096e-04 lr: 1.1906e-05 eta: 19:42:37 time: 1.1010 data_time: 0.0162 memory: 14573 grad_norm: 1.4440 loss: 0.2833 semantic_segmentation_loss_cls: 0.0821 semantic_segmentation_loss_mask: 0.0587 semantic_segmentation_loss_dice: 0.1425 2024/07/08 07:24:15 - mmengine - INFO - Iter(train) [ 56200/120000] base_lr: 1.1084e-04 lr: 1.1894e-05 eta: 19:41:41 time: 1.1009 data_time: 0.0162 memory: 15382 grad_norm: 1.4449 loss: 0.2832 semantic_segmentation_loss_cls: 0.0821 semantic_segmentation_loss_mask: 0.0587 semantic_segmentation_loss_dice: 0.1424 2024/07/08 07:25:10 - mmengine - INFO - Iter(train) [ 56250/120000] base_lr: 1.1071e-04 lr: 1.1882e-05 eta: 19:40:45 time: 1.1011 data_time: 0.0162 memory: 15624 grad_norm: 1.4459 loss: 0.2831 semantic_segmentation_loss_cls: 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checkpoint at 57000 iterations 2024/07/08 07:39:59 - mmengine - INFO - Iter(train) [ 57050/120000] base_lr: 1.0864e-04 lr: 1.1695e-05 eta: 19:25:55 time: 1.1016 data_time: 0.0164 memory: 15210 grad_norm: 1.4429 loss: 0.2823 semantic_segmentation_loss_cls: 0.0818 semantic_segmentation_loss_mask: 0.0584 semantic_segmentation_loss_dice: 0.1421 2024/07/08 07:40:54 - mmengine - INFO - Iter(train) [ 57100/120000] base_lr: 1.0851e-04 lr: 1.1683e-05 eta: 19:24:59 time: 1.1014 data_time: 0.0163 memory: 15740 grad_norm: 1.4414 loss: 0.2820 semantic_segmentation_loss_cls: 0.0816 semantic_segmentation_loss_mask: 0.0584 semantic_segmentation_loss_dice: 0.1420 2024/07/08 07:41:49 - mmengine - INFO - Iter(train) [ 57150/120000] base_lr: 1.0838e-04 lr: 1.1671e-05 eta: 19:24:04 time: 1.1016 data_time: 0.0163 memory: 15535 grad_norm: 1.4405 loss: 0.2818 semantic_segmentation_loss_cls: 0.0816 semantic_segmentation_loss_mask: 0.0583 semantic_segmentation_loss_dice: 0.1419 2024/07/08 07:42:45 - mmengine - INFO - Iter(train) [ 57200/120000] base_lr: 1.0825e-04 lr: 1.1659e-05 eta: 19:23:08 time: 1.1019 data_time: 0.0163 memory: 15122 grad_norm: 1.4399 loss: 0.2819 semantic_segmentation_loss_cls: 0.0816 semantic_segmentation_loss_mask: 0.0584 semantic_segmentation_loss_dice: 0.1419 2024/07/08 07:43:41 - mmengine - INFO - Iter(train) [ 57250/120000] base_lr: 1.0812e-04 lr: 1.1648e-05 eta: 19:22:12 time: 1.1020 data_time: 0.0163 memory: 15385 grad_norm: 1.4394 loss: 0.2817 semantic_segmentation_loss_cls: 0.0815 semantic_segmentation_loss_mask: 0.0583 semantic_segmentation_loss_dice: 0.1419 2024/07/08 07:44:36 - mmengine - INFO - Iter(train) [ 57300/120000] base_lr: 1.0799e-04 lr: 1.1636e-05 eta: 19:21:17 time: 1.1024 data_time: 0.0164 memory: 15379 grad_norm: 1.4385 loss: 0.2814 semantic_segmentation_loss_cls: 0.0814 semantic_segmentation_loss_mask: 0.0582 semantic_segmentation_loss_dice: 0.1418 2024/07/08 07:45:31 - mmengine - INFO - Iter(train) [ 57350/120000] base_lr: 1.0787e-04 lr: 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semantic_segmentation_loss_dice: 0.1415 2024/07/08 07:57:27 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 07:57:27 - mmengine - INFO - Iter(train) [ 58000/120000] base_lr: 1.0618e-04 lr: 1.1471e-05 eta: 19:08:11 time: 1.1031 data_time: 0.0164 memory: 14455 grad_norm: 1.4229 loss: 0.2800 semantic_segmentation_loss_cls: 0.0807 semantic_segmentation_loss_mask: 0.0580 semantic_segmentation_loss_dice: 0.1413 2024/07/08 07:57:27 - mmengine - INFO - Saving checkpoint at 58000 iterations 2024/07/08 07:58:26 - mmengine - INFO - Iter(train) [ 58050/120000] base_lr: 1.0605e-04 lr: 1.1459e-05 eta: 19:07:20 time: 1.1030 data_time: 0.0163 memory: 14567 grad_norm: 1.4233 loss: 0.2801 semantic_segmentation_loss_cls: 0.0808 semantic_segmentation_loss_mask: 0.0580 semantic_segmentation_loss_dice: 0.1413 2024/07/08 07:59:21 - mmengine - INFO - Iter(train) [ 58100/120000] base_lr: 1.0593e-04 lr: 1.1448e-05 eta: 19:06:23 time: 1.1030 data_time: 0.0163 memory: 16771 grad_norm: 1.4233 loss: 0.2801 semantic_segmentation_loss_cls: 0.0807 semantic_segmentation_loss_mask: 0.0581 semantic_segmentation_loss_dice: 0.1413 2024/07/08 08:00:16 - mmengine - INFO - Iter(train) [ 58150/120000] base_lr: 1.0580e-04 lr: 1.1436e-05 eta: 19:05:27 time: 1.1032 data_time: 0.0163 memory: 15714 grad_norm: 1.4207 loss: 0.2796 semantic_segmentation_loss_cls: 0.0806 semantic_segmentation_loss_mask: 0.0579 semantic_segmentation_loss_dice: 0.1411 2024/07/08 08:01:10 - mmengine - INFO - Iter(train) [ 58200/120000] base_lr: 1.0567e-04 lr: 1.1424e-05 eta: 19:04:30 time: 1.1034 data_time: 0.0163 memory: 15001 grad_norm: 1.4200 loss: 0.2793 semantic_segmentation_loss_cls: 0.0805 semantic_segmentation_loss_mask: 0.0578 semantic_segmentation_loss_dice: 0.1410 2024/07/08 08:02:05 - mmengine - INFO - Iter(train) [ 58250/120000] base_lr: 1.0554e-04 lr: 1.1412e-05 eta: 19:03:34 time: 1.1034 data_time: 0.0163 memory: 14225 grad_norm: 1.4206 loss: 0.2789 semantic_segmentation_loss_cls: 0.0803 semantic_segmentation_loss_mask: 0.0578 semantic_segmentation_loss_dice: 0.1409 2024/07/08 08:03:00 - mmengine - INFO - Iter(train) [ 58300/120000] base_lr: 1.0541e-04 lr: 1.1401e-05 eta: 19:02:38 time: 1.1034 data_time: 0.0163 memory: 14811 grad_norm: 1.4204 loss: 0.2787 semantic_segmentation_loss_cls: 0.0802 semantic_segmentation_loss_mask: 0.0578 semantic_segmentation_loss_dice: 0.1407 2024/07/08 08:03:55 - mmengine - INFO - Iter(train) [ 58350/120000] base_lr: 1.0528e-04 lr: 1.1389e-05 eta: 19:01:42 time: 1.1034 data_time: 0.0163 memory: 15200 grad_norm: 1.4216 loss: 0.2782 semantic_segmentation_loss_cls: 0.0800 semantic_segmentation_loss_mask: 0.0577 semantic_segmentation_loss_dice: 0.1405 2024/07/08 08:04:50 - mmengine - INFO - Iter(train) [ 58400/120000] base_lr: 1.0515e-04 lr: 1.1377e-05 eta: 19:00:46 time: 1.1034 data_time: 0.0163 memory: 14978 grad_norm: 1.4213 loss: 0.2781 semantic_segmentation_loss_cls: 0.0800 semantic_segmentation_loss_mask: 0.0577 semantic_segmentation_loss_dice: 0.1405 2024/07/08 08:05:45 - mmengine - INFO - Iter(train) [ 58450/120000] base_lr: 1.0502e-04 lr: 1.1365e-05 eta: 18:59:49 time: 1.1032 data_time: 0.0163 memory: 15332 grad_norm: 1.4197 loss: 0.2780 semantic_segmentation_loss_cls: 0.0799 semantic_segmentation_loss_mask: 0.0576 semantic_segmentation_loss_dice: 0.1404 2024/07/08 08:06:39 - mmengine - INFO - Iter(train) [ 58500/120000] base_lr: 1.0489e-04 lr: 1.1354e-05 eta: 18:58:52 time: 1.1033 data_time: 0.0163 memory: 14343 grad_norm: 1.4177 loss: 0.2776 semantic_segmentation_loss_cls: 0.0798 semantic_segmentation_loss_mask: 0.0575 semantic_segmentation_loss_dice: 0.1402 2024/07/08 08:07:35 - mmengine - INFO - Iter(train) [ 58550/120000] base_lr: 1.0476e-04 lr: 1.1342e-05 eta: 18:57:56 time: 1.1032 data_time: 0.0163 memory: 16038 grad_norm: 1.4189 loss: 0.2774 semantic_segmentation_loss_cls: 0.0798 semantic_segmentation_loss_mask: 0.0575 semantic_segmentation_loss_dice: 0.1402 2024/07/08 08:08:29 - mmengine - INFO - Iter(train) [ 58600/120000] base_lr: 1.0463e-04 lr: 1.1330e-05 eta: 18:57:00 time: 1.1031 data_time: 0.0163 memory: 14794 grad_norm: 1.4148 loss: 0.2770 semantic_segmentation_loss_cls: 0.0796 semantic_segmentation_loss_mask: 0.0574 semantic_segmentation_loss_dice: 0.1400 2024/07/08 08:09:24 - mmengine - INFO - Iter(train) [ 58650/120000] base_lr: 1.0450e-04 lr: 1.1318e-05 eta: 18:56:03 time: 1.1032 data_time: 0.0163 memory: 14468 grad_norm: 1.4132 loss: 0.2766 semantic_segmentation_loss_cls: 0.0795 semantic_segmentation_loss_mask: 0.0573 semantic_segmentation_loss_dice: 0.1399 2024/07/08 08:10:19 - mmengine - INFO - Iter(train) [ 58700/120000] base_lr: 1.0437e-04 lr: 1.1306e-05 eta: 18:55:07 time: 1.1033 data_time: 0.0163 memory: 15428 grad_norm: 1.4125 loss: 0.2762 semantic_segmentation_loss_cls: 0.0793 semantic_segmentation_loss_mask: 0.0572 semantic_segmentation_loss_dice: 0.1398 2024/07/08 08:11:14 - mmengine - INFO - Iter(train) [ 58750/120000] base_lr: 1.0424e-04 lr: 1.1295e-05 eta: 18:54:11 time: 1.1037 data_time: 0.0163 memory: 16071 grad_norm: 1.4114 loss: 0.2756 semantic_segmentation_loss_cls: 0.0790 semantic_segmentation_loss_mask: 0.0571 semantic_segmentation_loss_dice: 0.1395 2024/07/08 08:12:10 - mmengine - INFO - Iter(train) [ 58800/120000] base_lr: 1.0411e-04 lr: 1.1283e-05 eta: 18:53:16 time: 1.1043 data_time: 0.0163 memory: 14964 grad_norm: 1.4122 loss: 0.2754 semantic_segmentation_loss_cls: 0.0789 semantic_segmentation_loss_mask: 0.0570 semantic_segmentation_loss_dice: 0.1395 2024/07/08 08:13:05 - mmengine - INFO - Iter(train) [ 58850/120000] base_lr: 1.0398e-04 lr: 1.1271e-05 eta: 18:52:20 time: 1.1044 data_time: 0.0163 memory: 15376 grad_norm: 1.4113 loss: 0.2756 semantic_segmentation_loss_cls: 0.0790 semantic_segmentation_loss_mask: 0.0571 semantic_segmentation_loss_dice: 0.1395 2024/07/08 08:14:00 - mmengine - INFO - Iter(train) [ 58900/120000] base_lr: 1.0385e-04 lr: 1.1259e-05 eta: 18:51:24 time: 1.1045 data_time: 0.0163 memory: 15906 grad_norm: 1.4110 loss: 0.2753 semantic_segmentation_loss_cls: 0.0789 semantic_segmentation_loss_mask: 0.0570 semantic_segmentation_loss_dice: 0.1394 2024/07/08 08:14:56 - mmengine - INFO - Iter(train) [ 58950/120000] base_lr: 1.0372e-04 lr: 1.1248e-05 eta: 18:50:29 time: 1.1048 data_time: 0.0163 memory: 16613 grad_norm: 1.4133 loss: 0.2751 semantic_segmentation_loss_cls: 0.0788 semantic_segmentation_loss_mask: 0.0570 semantic_segmentation_loss_dice: 0.1393 2024/07/08 08:15:53 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 08:15:53 - mmengine - INFO - Iter(train) [ 59000/120000] base_lr: 1.0359e-04 lr: 1.1236e-05 eta: 18:49:34 time: 1.1052 data_time: 0.0163 memory: 14999 grad_norm: 1.4132 loss: 0.2749 semantic_segmentation_loss_cls: 0.0787 semantic_segmentation_loss_mask: 0.0570 semantic_segmentation_loss_dice: 0.1392 2024/07/08 08:15:53 - mmengine - INFO - Saving checkpoint at 59000 iterations 2024/07/08 08:16:52 - mmengine - INFO - Iter(train) [ 59050/120000] base_lr: 1.0346e-04 lr: 1.1224e-05 eta: 18:48:42 time: 1.1061 data_time: 0.0173 memory: 16012 grad_norm: 1.4137 loss: 0.2751 semantic_segmentation_loss_cls: 0.0787 semantic_segmentation_loss_mask: 0.0571 semantic_segmentation_loss_dice: 0.1393 2024/07/08 08:17:47 - mmengine - INFO - Iter(train) [ 59100/120000] base_lr: 1.0334e-04 lr: 1.1212e-05 eta: 18:47:47 time: 1.1062 data_time: 0.0173 memory: 14928 grad_norm: 1.4128 loss: 0.2750 semantic_segmentation_loss_cls: 0.0788 semantic_segmentation_loss_mask: 0.0570 semantic_segmentation_loss_dice: 0.1392 2024/07/08 08:18:42 - mmengine - INFO - Iter(train) [ 59150/120000] base_lr: 1.0321e-04 lr: 1.1200e-05 eta: 18:46:50 time: 1.1061 data_time: 0.0172 memory: 14711 grad_norm: 1.4136 loss: 0.2749 semantic_segmentation_loss_cls: 0.0787 semantic_segmentation_loss_mask: 0.0570 semantic_segmentation_loss_dice: 0.1392 2024/07/08 08:19:36 - mmengine - INFO - Iter(train) [ 59200/120000] base_lr: 1.0308e-04 lr: 1.1189e-05 eta: 18:45:54 time: 1.1062 data_time: 0.0172 memory: 15609 grad_norm: 1.4135 loss: 0.2745 semantic_segmentation_loss_cls: 0.0786 semantic_segmentation_loss_mask: 0.0569 semantic_segmentation_loss_dice: 0.1390 2024/07/08 08:20:32 - mmengine - INFO - Iter(train) [ 59250/120000] base_lr: 1.0295e-04 lr: 1.1177e-05 eta: 18:44:58 time: 1.1064 data_time: 0.0173 memory: 15561 grad_norm: 1.4127 loss: 0.2741 semantic_segmentation_loss_cls: 0.0784 semantic_segmentation_loss_mask: 0.0568 semantic_segmentation_loss_dice: 0.1388 2024/07/08 08:21:27 - mmengine - INFO - Iter(train) [ 59300/120000] base_lr: 1.0282e-04 lr: 1.1165e-05 eta: 18:44:02 time: 1.1064 data_time: 0.0173 memory: 15841 grad_norm: 1.4127 loss: 0.2739 semantic_segmentation_loss_cls: 0.0784 semantic_segmentation_loss_mask: 0.0568 semantic_segmentation_loss_dice: 0.1387 2024/07/08 08:22:22 - mmengine - INFO - Iter(train) [ 59350/120000] base_lr: 1.0269e-04 lr: 1.1153e-05 eta: 18:43:06 time: 1.1065 data_time: 0.0173 memory: 15019 grad_norm: 1.4131 loss: 0.2736 semantic_segmentation_loss_cls: 0.0782 semantic_segmentation_loss_mask: 0.0568 semantic_segmentation_loss_dice: 0.1386 2024/07/08 08:23:17 - mmengine - INFO - Iter(train) [ 59400/120000] base_lr: 1.0256e-04 lr: 1.1142e-05 eta: 18:42:10 time: 1.1066 data_time: 0.0173 memory: 14792 grad_norm: 1.4122 loss: 0.2735 semantic_segmentation_loss_cls: 0.0782 semantic_segmentation_loss_mask: 0.0568 semantic_segmentation_loss_dice: 0.1385 2024/07/08 08:24:12 - mmengine - INFO - Iter(train) [ 59450/120000] base_lr: 1.0243e-04 lr: 1.1130e-05 eta: 18:41:13 time: 1.1067 data_time: 0.0173 memory: 15421 grad_norm: 1.4137 loss: 0.2733 semantic_segmentation_loss_cls: 0.0781 semantic_segmentation_loss_mask: 0.0567 semantic_segmentation_loss_dice: 0.1384 2024/07/08 08:25:06 - mmengine - INFO - Iter(train) [ 59500/120000] base_lr: 1.0230e-04 lr: 1.1118e-05 eta: 18:40:17 time: 1.1067 data_time: 0.0173 memory: 14625 grad_norm: 1.4120 loss: 0.2730 semantic_segmentation_loss_cls: 0.0780 semantic_segmentation_loss_mask: 0.0567 semantic_segmentation_loss_dice: 0.1383 2024/07/08 08:26:01 - mmengine - INFO - Iter(train) [ 59550/120000] base_lr: 1.0217e-04 lr: 1.1106e-05 eta: 18:39:21 time: 1.1068 data_time: 0.0173 memory: 14775 grad_norm: 1.4099 loss: 0.2727 semantic_segmentation_loss_cls: 0.0779 semantic_segmentation_loss_mask: 0.0566 semantic_segmentation_loss_dice: 0.1382 2024/07/08 08:26:57 - mmengine - INFO - Iter(train) [ 59600/120000] base_lr: 1.0204e-04 lr: 1.1094e-05 eta: 18:38:25 time: 1.1071 data_time: 0.0173 memory: 15175 grad_norm: 1.4098 loss: 0.2726 semantic_segmentation_loss_cls: 0.0778 semantic_segmentation_loss_mask: 0.0567 semantic_segmentation_loss_dice: 0.1381 2024/07/08 08:27:53 - mmengine - INFO - Iter(train) [ 59650/120000] base_lr: 1.0191e-04 lr: 1.1083e-05 eta: 18:37:30 time: 1.1072 data_time: 0.0173 memory: 16022 grad_norm: 1.4098 loss: 0.2726 semantic_segmentation_loss_cls: 0.0777 semantic_segmentation_loss_mask: 0.0567 semantic_segmentation_loss_dice: 0.1382 2024/07/08 08:28:48 - mmengine - INFO - Iter(train) [ 59700/120000] base_lr: 1.0178e-04 lr: 1.1071e-05 eta: 18:36:34 time: 1.1074 data_time: 0.0173 memory: 15744 grad_norm: 1.4096 loss: 0.2724 semantic_segmentation_loss_cls: 0.0776 semantic_segmentation_loss_mask: 0.0567 semantic_segmentation_loss_dice: 0.1381 2024/07/08 08:29:42 - mmengine - INFO - Iter(train) [ 59750/120000] base_lr: 1.0165e-04 lr: 1.1059e-05 eta: 18:35:37 time: 1.1072 data_time: 0.0172 memory: 15101 grad_norm: 1.4086 loss: 0.2725 semantic_segmentation_loss_cls: 0.0777 semantic_segmentation_loss_mask: 0.0567 semantic_segmentation_loss_dice: 0.1381 2024/07/08 08:30:37 - mmengine - INFO - Iter(train) [ 59800/120000] base_lr: 1.0152e-04 lr: 1.1047e-05 eta: 18:34:40 time: 1.1069 data_time: 0.0172 memory: 15088 grad_norm: 1.4084 loss: 0.2725 semantic_segmentation_loss_cls: 0.0777 semantic_segmentation_loss_mask: 0.0567 semantic_segmentation_loss_dice: 0.1381 2024/07/08 08:31:31 - mmengine - INFO - Iter(train) [ 59850/120000] base_lr: 1.0139e-04 lr: 1.1036e-05 eta: 18:33:44 time: 1.1065 data_time: 0.0172 memory: 14688 grad_norm: 1.4076 loss: 0.2720 semantic_segmentation_loss_cls: 0.0775 semantic_segmentation_loss_mask: 0.0566 semantic_segmentation_loss_dice: 0.1380 2024/07/08 08:32:26 - mmengine - INFO - Iter(train) [ 59900/120000] base_lr: 1.0126e-04 lr: 1.1024e-05 eta: 18:32:48 time: 1.1064 data_time: 0.0172 memory: 15201 grad_norm: 1.4074 loss: 0.2717 semantic_segmentation_loss_cls: 0.0773 semantic_segmentation_loss_mask: 0.0566 semantic_segmentation_loss_dice: 0.1378 2024/07/08 08:33:21 - mmengine - INFO - Iter(train) [ 59950/120000] base_lr: 1.0113e-04 lr: 1.1012e-05 eta: 18:31:51 time: 1.1061 data_time: 0.0172 memory: 15588 grad_norm: 1.4067 loss: 0.2716 semantic_segmentation_loss_cls: 0.0773 semantic_segmentation_loss_mask: 0.0566 semantic_segmentation_loss_dice: 0.1377 2024/07/08 08:34:15 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 08:34:15 - mmengine - INFO - Iter(train) [ 60000/120000] base_lr: 1.0100e-04 lr: 1.1000e-05 eta: 18:30:54 time: 1.1058 data_time: 0.0173 memory: 15396 grad_norm: 1.4068 loss: 0.2715 semantic_segmentation_loss_cls: 0.0772 semantic_segmentation_loss_mask: 0.0566 semantic_segmentation_loss_dice: 0.1377 2024/07/08 08:34:15 - mmengine - INFO - Saving checkpoint at 60000 iterations 2024/07/08 08:34:32 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:52 time: 0.2458 data_time: 0.0015 memory: 5013 2024/07/08 08:34:45 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:38 time: 0.2458 data_time: 0.0015 memory: 5189 2024/07/08 08:34:57 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:26 time: 0.2458 data_time: 0.0015 memory: 4460 2024/07/08 08:35:09 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:13 time: 0.2458 data_time: 0.0015 memory: 4543 2024/07/08 08:35:21 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:01 time: 0.2458 data_time: 0.0015 memory: 4643 2024/07/08 08:35:34 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:49 time: 0.2458 data_time: 0.0015 memory: 10983 2024/07/08 08:35:46 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2458 data_time: 0.0015 memory: 4460 2024/07/08 08:35:58 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2458 data_time: 0.0015 memory: 4641 2024/07/08 08:36:10 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2458 data_time: 0.0015 memory: 4473 2024/07/08 08:36:23 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2458 data_time: 0.0015 memory: 4555 2024/07/08 08:36:23 - mmengine - INFO - per class results: 2024/07/08 08:36:23 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.44 | 87.02 | | building | 83.12 | 90.47 | | sky | 94.25 | 97.74 | | floor | 83.21 | 91.38 | | tree | 74.95 | 87.81 | | ceiling | 85.15 | 93.36 | | road | 83.63 | 91.62 | | bed | 87.84 | 95.4 | | windowpane | 61.63 | 80.12 | | grass | 70.54 | 84.76 | | cabinet | 62.0 | 73.12 | | sidewalk | 67.39 | 82.75 | | person | 82.09 | 91.46 | | earth | 36.36 | 49.12 | | door | 52.78 | 69.36 | | table | 62.63 | 78.41 | | mountain | 57.01 | 70.72 | | plant | 52.75 | 67.8 | | curtain | 73.45 | 87.38 | | chair | 59.57 | 72.81 | | car | 85.43 | 91.91 | | water | 49.97 | 66.86 | | painting | 72.64 | 88.68 | | sofa | 63.99 | 74.9 | | shelf | 44.81 | 66.35 | | house | 52.62 | 81.4 | | sea | 46.51 | 68.43 | | mirror | 67.3 | 76.9 | | rug | 66.08 | 77.73 | | field | 35.88 | 52.81 | | armchair | 45.45 | 68.91 | | seat | 56.97 | 79.03 | | fence | 46.31 | 65.96 | | desk | 49.55 | 69.16 | | rock | 36.76 | 57.16 | | wardrobe | 53.07 | 70.22 | | lamp | 67.41 | 80.28 | | bathtub | 88.29 | 91.51 | | railing | 36.03 | 51.57 | | cushion | 57.28 | 68.34 | | base | 19.77 | 29.62 | | box | 25.9 | 37.84 | | column | 48.98 | 70.49 | | signboard | 40.61 | 56.9 | | chest of drawers | 40.89 | 63.26 | | counter | 32.68 | 52.1 | | sand | 32.41 | 47.62 | | sink | 66.89 | 82.66 | | skyscraper | 45.92 | 59.42 | | fireplace | 69.06 | 89.19 | | refrigerator | 81.49 | 89.46 | | grandstand | 41.84 | 74.88 | | path | 30.9 | 42.69 | | stairs | 30.17 | 41.44 | | runway | 75.89 | 89.39 | | case | 57.64 | 64.39 | | pool table | 91.99 | 96.29 | | pillow | 54.59 | 67.24 | | screen door | 81.47 | 83.92 | | stairway | 42.88 | 48.24 | | river | 20.59 | 44.62 | | bridge | 69.79 | 88.79 | | bookcase | 37.78 | 56.07 | | blind | 40.54 | 46.26 | | coffee table | 73.49 | 86.52 | | toilet | 85.5 | 89.55 | | flower | 40.93 | 58.75 | | book | 52.61 | 77.27 | | hill | 12.27 | 22.04 | | bench | 41.96 | 48.65 | | countertop | 58.36 | 69.2 | | stove | 79.13 | 83.66 | | palm | 54.37 | 69.6 | | kitchen island | 33.47 | 77.65 | | computer | 61.05 | 67.91 | | swivel chair | 40.53 | 56.16 | | boat | 74.67 | 81.72 | | bar | 36.85 | 46.19 | | arcade machine | 57.42 | 62.52 | | hovel | 15.73 | 22.64 | | bus | 85.24 | 88.23 | | towel | 68.34 | 75.24 | | light | 64.3 | 79.12 | | truck | 34.61 | 48.61 | | tower | 31.92 | 52.28 | | chandelier | 63.18 | 75.22 | | awning | 34.21 | 46.83 | | streetlight | 38.95 | 54.75 | | booth | 59.26 | 61.02 | | television receiver | 46.21 | 90.82 | | airplane | 53.91 | 67.06 | | dirt track | 0.26 | 0.34 | | apparel | 38.78 | 53.8 | | pole | 32.43 | 52.07 | | land | 1.77 | 2.63 | | bannister | 12.26 | 21.79 | | escalator | 47.12 | 60.41 | | ottoman | 39.06 | 63.22 | | bottle | 20.77 | 26.11 | | buffet | 61.15 | 64.86 | | poster | 29.99 | 42.2 | | stage | 16.43 | 29.48 | | van | 44.86 | 64.47 | | ship | 81.12 | 84.48 | | fountain | 7.74 | 7.97 | | conveyer belt | 63.68 | 91.07 | | canopy | 34.36 | 52.16 | | washer | 70.8 | 72.83 | | plaything | 28.57 | 37.26 | | swimming pool | 29.25 | 32.13 | | stool | 52.1 | 69.54 | | barrel | 15.59 | 54.66 | | basket | 35.82 | 44.02 | | waterfall | 41.98 | 58.72 | | tent | 90.78 | 97.7 | | bag | 17.13 | 24.95 | | minibike | 71.11 | 85.18 | | cradle | 72.22 | 97.07 | | oven | 53.81 | 64.96 | | ball | 36.34 | 44.23 | | food | 65.09 | 82.19 | | step | 27.5 | 37.84 | | tank | 32.39 | 42.8 | | trade name | 32.33 | 39.89 | | microwave | 38.79 | 41.67 | | pot | 53.1 | 60.45 | | animal | 61.0 | 69.17 | | bicycle | 56.43 | 77.88 | | lake | 63.57 | 63.7 | | dishwasher | 80.22 | 84.81 | | screen | 69.72 | 88.83 | | blanket | 11.62 | 15.07 | | sculpture | 64.61 | 83.75 | | hood | 68.65 | 72.35 | | sconce | 52.87 | 65.27 | | vase | 47.61 | 65.77 | | traffic light | 44.04 | 60.17 | | tray | 18.29 | 22.81 | | ashcan | 49.18 | 65.74 | | fan | 67.71 | 81.06 | | pier | 36.97 | 72.02 | | crt screen | 0.04 | 0.05 | | plate | 60.54 | 74.16 | | monitor | 48.27 | 68.18 | | bulletin board | 37.02 | 46.86 | | shower | 7.18 | 18.5 | | radiator | 57.3 | 68.33 | | glass | 18.25 | 19.73 | | clock | 34.11 | 38.78 | | flag | 44.49 | 54.36 | +---------------------+-------+-------+ 2024/07/08 08:36:23 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.5000 mIoU: 50.7100 mAcc: 63.7000 data_time: 0.0016 time: 0.2456 2024/07/08 08:37:18 - mmengine - INFO - Iter(train) [ 60050/120000] base_lr: 1.0087e-04 lr: 1.0988e-05 eta: 18:29:59 time: 1.1047 data_time: 0.0162 memory: 14849 grad_norm: 1.4071 loss: 0.2710 semantic_segmentation_loss_cls: 0.0769 semantic_segmentation_loss_mask: 0.0566 semantic_segmentation_loss_dice: 0.1375 2024/07/08 08:38:13 - mmengine - INFO - Iter(train) [ 60100/120000] base_lr: 1.0074e-04 lr: 1.0977e-05 eta: 18:29:03 time: 1.1049 data_time: 0.0162 memory: 15386 grad_norm: 1.4060 loss: 0.2707 semantic_segmentation_loss_cls: 0.0769 semantic_segmentation_loss_mask: 0.0565 semantic_segmentation_loss_dice: 0.1373 2024/07/08 08:39:08 - mmengine - INFO - Iter(train) [ 60150/120000] base_lr: 1.0061e-04 lr: 1.0965e-05 eta: 18:28:06 time: 1.1049 data_time: 0.0162 memory: 14477 grad_norm: 1.4059 loss: 0.2706 semantic_segmentation_loss_cls: 0.0768 semantic_segmentation_loss_mask: 0.0565 semantic_segmentation_loss_dice: 0.1373 2024/07/08 08:40:04 - mmengine - INFO - Iter(train) [ 60200/120000] base_lr: 1.0048e-04 lr: 1.0953e-05 eta: 18:27:11 time: 1.1051 data_time: 0.0163 memory: 15207 grad_norm: 1.4052 loss: 0.2702 semantic_segmentation_loss_cls: 0.0766 semantic_segmentation_loss_mask: 0.0565 semantic_segmentation_loss_dice: 0.1371 2024/07/08 08:40:59 - mmengine - INFO - Iter(train) [ 60250/120000] base_lr: 1.0035e-04 lr: 1.0941e-05 eta: 18:26:15 time: 1.1051 data_time: 0.0163 memory: 15785 grad_norm: 1.4037 loss: 0.2698 semantic_segmentation_loss_cls: 0.0765 semantic_segmentation_loss_mask: 0.0564 semantic_segmentation_loss_dice: 0.1369 2024/07/08 08:41:55 - mmengine - INFO - Iter(train) [ 60300/120000] base_lr: 1.0023e-04 lr: 1.0930e-05 eta: 18:25:20 time: 1.1052 data_time: 0.0163 memory: 16190 grad_norm: 1.4017 loss: 0.2700 semantic_segmentation_loss_cls: 0.0767 semantic_segmentation_loss_mask: 0.0564 semantic_segmentation_loss_dice: 0.1369 2024/07/08 08:42:49 - mmengine - INFO - Iter(train) [ 60350/120000] base_lr: 1.0010e-04 lr: 1.0918e-05 eta: 18:24:23 time: 1.1049 data_time: 0.0163 memory: 14737 grad_norm: 1.4017 loss: 0.2694 semantic_segmentation_loss_cls: 0.0764 semantic_segmentation_loss_mask: 0.0563 semantic_segmentation_loss_dice: 0.1367 2024/07/08 08:43:44 - mmengine - INFO - Iter(train) [ 60400/120000] base_lr: 9.9966e-05 lr: 1.0906e-05 eta: 18:23:27 time: 1.1047 data_time: 0.0162 memory: 15183 grad_norm: 1.4011 loss: 0.2689 semantic_segmentation_loss_cls: 0.0760 semantic_segmentation_loss_mask: 0.0563 semantic_segmentation_loss_dice: 0.1365 2024/07/08 08:44:38 - mmengine - INFO - Iter(train) [ 60450/120000] base_lr: 9.9836e-05 lr: 1.0894e-05 eta: 18:22:30 time: 1.1046 data_time: 0.0162 memory: 15366 grad_norm: 1.4007 loss: 0.2688 semantic_segmentation_loss_cls: 0.0760 semantic_segmentation_loss_mask: 0.0563 semantic_segmentation_loss_dice: 0.1365 2024/07/08 08:45:34 - mmengine - INFO - Iter(train) [ 60500/120000] base_lr: 9.9707e-05 lr: 1.0882e-05 eta: 18:21:35 time: 1.1048 data_time: 0.0163 memory: 14754 grad_norm: 1.4009 loss: 0.2686 semantic_segmentation_loss_cls: 0.0759 semantic_segmentation_loss_mask: 0.0563 semantic_segmentation_loss_dice: 0.1363 2024/07/08 08:46:29 - mmengine - INFO - Iter(train) [ 60550/120000] base_lr: 9.9577e-05 lr: 1.0871e-05 eta: 18:20:39 time: 1.1047 data_time: 0.0162 memory: 15871 grad_norm: 1.3971 loss: 0.2684 semantic_segmentation_loss_cls: 0.0757 semantic_segmentation_loss_mask: 0.0564 semantic_segmentation_loss_dice: 0.1363 2024/07/08 08:47:25 - mmengine - INFO - Iter(train) [ 60600/120000] base_lr: 9.9448e-05 lr: 1.0859e-05 eta: 18:19:44 time: 1.1048 data_time: 0.0162 memory: 14787 grad_norm: 1.3974 loss: 0.2683 semantic_segmentation_loss_cls: 0.0756 semantic_segmentation_loss_mask: 0.0564 semantic_segmentation_loss_dice: 0.1363 2024/07/08 08:48:20 - mmengine - INFO - Iter(train) [ 60650/120000] base_lr: 9.9318e-05 lr: 1.0847e-05 eta: 18:18:48 time: 1.1049 data_time: 0.0162 memory: 14960 grad_norm: 1.3975 loss: 0.2683 semantic_segmentation_loss_cls: 0.0756 semantic_segmentation_loss_mask: 0.0564 semantic_segmentation_loss_dice: 0.1363 2024/07/08 08:49:16 - mmengine - INFO - Iter(train) [ 60700/120000] base_lr: 9.9188e-05 lr: 1.0835e-05 eta: 18:17:53 time: 1.1049 data_time: 0.0162 memory: 14728 grad_norm: 1.3967 loss: 0.2680 semantic_segmentation_loss_cls: 0.0754 semantic_segmentation_loss_mask: 0.0563 semantic_segmentation_loss_dice: 0.1362 2024/07/08 08:50:11 - mmengine - INFO - Iter(train) [ 60750/120000] base_lr: 9.9059e-05 lr: 1.0824e-05 eta: 18:16:56 time: 1.1048 data_time: 0.0162 memory: 14450 grad_norm: 1.3959 loss: 0.2674 semantic_segmentation_loss_cls: 0.0752 semantic_segmentation_loss_mask: 0.0563 semantic_segmentation_loss_dice: 0.1359 2024/07/08 08:51:07 - mmengine - INFO - Iter(train) [ 60800/120000] base_lr: 9.8929e-05 lr: 1.0812e-05 eta: 18:16:01 time: 1.1049 data_time: 0.0162 memory: 14889 grad_norm: 1.3967 loss: 0.2672 semantic_segmentation_loss_cls: 0.0752 semantic_segmentation_loss_mask: 0.0562 semantic_segmentation_loss_dice: 0.1358 2024/07/08 08:52:01 - mmengine - INFO - Iter(train) [ 60850/120000] base_lr: 9.8800e-05 lr: 1.0800e-05 eta: 18:15:04 time: 1.1047 data_time: 0.0162 memory: 15635 grad_norm: 1.3976 loss: 0.2671 semantic_segmentation_loss_cls: 0.0751 semantic_segmentation_loss_mask: 0.0562 semantic_segmentation_loss_dice: 0.1358 2024/07/08 08:52:56 - mmengine - INFO - Iter(train) [ 60900/120000] base_lr: 9.8670e-05 lr: 1.0788e-05 eta: 18:14:08 time: 1.1045 data_time: 0.0162 memory: 15507 grad_norm: 1.3973 loss: 0.2669 semantic_segmentation_loss_cls: 0.0751 semantic_segmentation_loss_mask: 0.0561 semantic_segmentation_loss_dice: 0.1356 2024/07/08 08:53:50 - mmengine - INFO - Iter(train) [ 60950/120000] base_lr: 9.8541e-05 lr: 1.0776e-05 eta: 18:13:11 time: 1.1046 data_time: 0.0162 memory: 15196 grad_norm: 1.3961 loss: 0.2666 semantic_segmentation_loss_cls: 0.0751 semantic_segmentation_loss_mask: 0.0560 semantic_segmentation_loss_dice: 0.1355 2024/07/08 08:54:45 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 08:54:45 - mmengine - INFO - Iter(train) [ 61000/120000] base_lr: 9.8411e-05 lr: 1.0765e-05 eta: 18:12:15 time: 1.1044 data_time: 0.0161 memory: 15119 grad_norm: 1.3953 loss: 0.2666 semantic_segmentation_loss_cls: 0.0751 semantic_segmentation_loss_mask: 0.0560 semantic_segmentation_loss_dice: 0.1355 2024/07/08 08:54:45 - mmengine - INFO - Saving checkpoint at 61000 iterations 2024/07/08 08:55:44 - mmengine - INFO - Iter(train) [ 61050/120000] base_lr: 9.8282e-05 lr: 1.0753e-05 eta: 18:11:23 time: 1.1042 data_time: 0.0161 memory: 14832 grad_norm: 1.3974 loss: 0.2665 semantic_segmentation_loss_cls: 0.0751 semantic_segmentation_loss_mask: 0.0560 semantic_segmentation_loss_dice: 0.1355 2024/07/08 08:56:39 - mmengine - INFO - Iter(train) [ 61100/120000] base_lr: 9.8152e-05 lr: 1.0741e-05 eta: 18:10:26 time: 1.1041 data_time: 0.0161 memory: 16335 grad_norm: 1.3968 loss: 0.2666 semantic_segmentation_loss_cls: 0.0751 semantic_segmentation_loss_mask: 0.0560 semantic_segmentation_loss_dice: 0.1355 2024/07/08 08:57:34 - mmengine - INFO - Iter(train) [ 61150/120000] base_lr: 9.8022e-05 lr: 1.0729e-05 eta: 18:09:31 time: 1.1041 data_time: 0.0161 memory: 15965 grad_norm: 1.3955 loss: 0.2664 semantic_segmentation_loss_cls: 0.0750 semantic_segmentation_loss_mask: 0.0559 semantic_segmentation_loss_dice: 0.1354 2024/07/08 08:58:29 - mmengine - INFO - Iter(train) [ 61200/120000] base_lr: 9.7893e-05 lr: 1.0718e-05 eta: 18:08:35 time: 1.1038 data_time: 0.0161 memory: 14915 grad_norm: 1.3956 loss: 0.2662 semantic_segmentation_loss_cls: 0.0750 semantic_segmentation_loss_mask: 0.0559 semantic_segmentation_loss_dice: 0.1353 2024/07/08 08:59:24 - mmengine - INFO - Iter(train) [ 61250/120000] base_lr: 9.7763e-05 lr: 1.0706e-05 eta: 18:07:38 time: 1.1037 data_time: 0.0161 memory: 14953 grad_norm: 1.3953 loss: 0.2661 semantic_segmentation_loss_cls: 0.0750 semantic_segmentation_loss_mask: 0.0559 semantic_segmentation_loss_dice: 0.1353 2024/07/08 09:00:20 - mmengine - INFO - Iter(train) [ 61300/120000] base_lr: 9.7634e-05 lr: 1.0694e-05 eta: 18:06:44 time: 1.1039 data_time: 0.0161 memory: 15754 grad_norm: 1.3938 loss: 0.2659 semantic_segmentation_loss_cls: 0.0749 semantic_segmentation_loss_mask: 0.0559 semantic_segmentation_loss_dice: 0.1352 2024/07/08 09:01:15 - mmengine - INFO - Iter(train) [ 61350/120000] base_lr: 9.7504e-05 lr: 1.0682e-05 eta: 18:05:48 time: 1.1039 data_time: 0.0161 memory: 14848 grad_norm: 1.3936 loss: 0.2659 semantic_segmentation_loss_cls: 0.0749 semantic_segmentation_loss_mask: 0.0559 semantic_segmentation_loss_dice: 0.1351 2024/07/08 09:02:10 - mmengine - INFO - Iter(train) [ 61400/120000] base_lr: 9.7375e-05 lr: 1.0670e-05 eta: 18:04:51 time: 1.1038 data_time: 0.0161 memory: 14630 grad_norm: 1.3935 loss: 0.2657 semantic_segmentation_loss_cls: 0.0748 semantic_segmentation_loss_mask: 0.0558 semantic_segmentation_loss_dice: 0.1351 2024/07/08 09:03:04 - mmengine - INFO - Iter(train) [ 61450/120000] base_lr: 9.7245e-05 lr: 1.0659e-05 eta: 18:03:55 time: 1.1038 data_time: 0.0161 memory: 14775 grad_norm: 1.3914 loss: 0.2657 semantic_segmentation_loss_cls: 0.0748 semantic_segmentation_loss_mask: 0.0558 semantic_segmentation_loss_dice: 0.1350 2024/07/08 09:03:59 - mmengine - INFO - Iter(train) [ 61500/120000] base_lr: 9.7116e-05 lr: 1.0647e-05 eta: 18:02:59 time: 1.1037 data_time: 0.0161 memory: 15460 grad_norm: 1.3914 loss: 0.2657 semantic_segmentation_loss_cls: 0.0748 semantic_segmentation_loss_mask: 0.0558 semantic_segmentation_loss_dice: 0.1351 2024/07/08 09:04:54 - mmengine - INFO - Iter(train) [ 61550/120000] base_lr: 9.6986e-05 lr: 1.0635e-05 eta: 18:02:02 time: 1.1036 data_time: 0.0161 memory: 15023 grad_norm: 1.3920 loss: 0.2654 semantic_segmentation_loss_cls: 0.0747 semantic_segmentation_loss_mask: 0.0558 semantic_segmentation_loss_dice: 0.1349 2024/07/08 09:05:48 - mmengine - INFO - Iter(train) [ 61600/120000] base_lr: 9.6857e-05 lr: 1.0623e-05 eta: 18:01:05 time: 1.1031 data_time: 0.0161 memory: 14797 grad_norm: 1.3911 loss: 0.2652 semantic_segmentation_loss_cls: 0.0746 semantic_segmentation_loss_mask: 0.0557 semantic_segmentation_loss_dice: 0.1349 2024/07/08 09:06:43 - mmengine - INFO - Iter(train) [ 61650/120000] base_lr: 9.6727e-05 lr: 1.0612e-05 eta: 18:00:09 time: 1.1030 data_time: 0.0161 memory: 14987 grad_norm: 1.3906 loss: 0.2650 semantic_segmentation_loss_cls: 0.0746 semantic_segmentation_loss_mask: 0.0557 semantic_segmentation_loss_dice: 0.1347 2024/07/08 09:07:37 - mmengine - INFO - Iter(train) [ 61700/120000] base_lr: 9.6598e-05 lr: 1.0600e-05 eta: 17:59:12 time: 1.1029 data_time: 0.0161 memory: 15247 grad_norm: 1.3910 loss: 0.2649 semantic_segmentation_loss_cls: 0.0745 semantic_segmentation_loss_mask: 0.0556 semantic_segmentation_loss_dice: 0.1347 2024/07/08 09:08:32 - mmengine - INFO - Iter(train) [ 61750/120000] base_lr: 9.6469e-05 lr: 1.0588e-05 eta: 17:58:16 time: 1.1028 data_time: 0.0162 memory: 15583 grad_norm: 1.3906 loss: 0.2647 semantic_segmentation_loss_cls: 0.0744 semantic_segmentation_loss_mask: 0.0556 semantic_segmentation_loss_dice: 0.1347 2024/07/08 09:09:26 - mmengine - INFO - Iter(train) [ 61800/120000] base_lr: 9.6339e-05 lr: 1.0576e-05 eta: 17:57:19 time: 1.1027 data_time: 0.0162 memory: 14599 grad_norm: 1.3926 loss: 0.2645 semantic_segmentation_loss_cls: 0.0743 semantic_segmentation_loss_mask: 0.0556 semantic_segmentation_loss_dice: 0.1345 2024/07/08 09:10:21 - mmengine - INFO - Iter(train) [ 61850/120000] base_lr: 9.6210e-05 lr: 1.0565e-05 eta: 17:56:23 time: 1.1025 data_time: 0.0162 memory: 15429 grad_norm: 1.3918 loss: 0.2642 semantic_segmentation_loss_cls: 0.0742 semantic_segmentation_loss_mask: 0.0556 semantic_segmentation_loss_dice: 0.1344 2024/07/08 09:11:17 - mmengine - INFO - Iter(train) [ 61900/120000] base_lr: 9.6080e-05 lr: 1.0553e-05 eta: 17:55:28 time: 1.1027 data_time: 0.0162 memory: 14860 grad_norm: 1.3908 loss: 0.2638 semantic_segmentation_loss_cls: 0.0742 semantic_segmentation_loss_mask: 0.0555 semantic_segmentation_loss_dice: 0.1342 2024/07/08 09:12:11 - mmengine - INFO - Iter(train) [ 61950/120000] base_lr: 9.5951e-05 lr: 1.0541e-05 eta: 17:54:31 time: 1.1026 data_time: 0.0163 memory: 15567 grad_norm: 1.3906 loss: 0.2637 semantic_segmentation_loss_cls: 0.0741 semantic_segmentation_loss_mask: 0.0554 semantic_segmentation_loss_dice: 0.1342 2024/07/08 09:13:06 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 09:13:06 - mmengine - INFO - Iter(train) [ 62000/120000] base_lr: 9.5821e-05 lr: 1.0529e-05 eta: 17:53:35 time: 1.1027 data_time: 0.0163 memory: 14582 grad_norm: 1.3916 loss: 0.2642 semantic_segmentation_loss_cls: 0.0743 semantic_segmentation_loss_mask: 0.0555 semantic_segmentation_loss_dice: 0.1344 2024/07/08 09:13:06 - mmengine - INFO - Saving checkpoint at 62000 iterations 2024/07/08 09:14:05 - mmengine - INFO - Iter(train) [ 62050/120000] base_lr: 9.5692e-05 lr: 1.0517e-05 eta: 17:52:43 time: 1.1027 data_time: 0.0164 memory: 15974 grad_norm: 1.3911 loss: 0.2640 semantic_segmentation_loss_cls: 0.0742 semantic_segmentation_loss_mask: 0.0554 semantic_segmentation_loss_dice: 0.1343 2024/07/08 09:15:00 - mmengine - INFO - Iter(train) [ 62100/120000] base_lr: 9.5563e-05 lr: 1.0506e-05 eta: 17:51:47 time: 1.1027 data_time: 0.0164 memory: 15763 grad_norm: 1.3913 loss: 0.2641 semantic_segmentation_loss_cls: 0.0743 semantic_segmentation_loss_mask: 0.0554 semantic_segmentation_loss_dice: 0.1344 2024/07/08 09:15:55 - mmengine - INFO - Iter(train) [ 62150/120000] base_lr: 9.5433e-05 lr: 1.0494e-05 eta: 17:50:51 time: 1.1027 data_time: 0.0164 memory: 15045 grad_norm: 1.3927 loss: 0.2643 semantic_segmentation_loss_cls: 0.0744 semantic_segmentation_loss_mask: 0.0555 semantic_segmentation_loss_dice: 0.1345 2024/07/08 09:16:50 - mmengine - INFO - Iter(train) [ 62200/120000] base_lr: 9.5304e-05 lr: 1.0482e-05 eta: 17:49:55 time: 1.1028 data_time: 0.0164 memory: 14875 grad_norm: 1.3919 loss: 0.2642 semantic_segmentation_loss_cls: 0.0743 semantic_segmentation_loss_mask: 0.0554 semantic_segmentation_loss_dice: 0.1344 2024/07/08 09:17:45 - mmengine - INFO - Iter(train) [ 62250/120000] base_lr: 9.5174e-05 lr: 1.0470e-05 eta: 17:48:58 time: 1.1028 data_time: 0.0164 memory: 15055 grad_norm: 1.3901 loss: 0.2642 semantic_segmentation_loss_cls: 0.0744 semantic_segmentation_loss_mask: 0.0554 semantic_segmentation_loss_dice: 0.1345 2024/07/08 09:18:39 - mmengine - INFO - Iter(train) [ 62300/120000] base_lr: 9.5045e-05 lr: 1.0459e-05 eta: 17:48:02 time: 1.1028 data_time: 0.0164 memory: 15197 grad_norm: 1.3887 loss: 0.2641 semantic_segmentation_loss_cls: 0.0744 semantic_segmentation_loss_mask: 0.0554 semantic_segmentation_loss_dice: 0.1344 2024/07/08 09:19:34 - mmengine - INFO - Iter(train) [ 62350/120000] base_lr: 9.4916e-05 lr: 1.0447e-05 eta: 17:47:06 time: 1.1027 data_time: 0.0165 memory: 15181 grad_norm: 1.3875 loss: 0.2643 semantic_segmentation_loss_cls: 0.0745 semantic_segmentation_loss_mask: 0.0554 semantic_segmentation_loss_dice: 0.1345 2024/07/08 09:20:29 - mmengine - INFO - Iter(train) [ 62400/120000] base_lr: 9.4786e-05 lr: 1.0435e-05 eta: 17:46:10 time: 1.1027 data_time: 0.0165 memory: 15032 grad_norm: 1.3876 loss: 0.2644 semantic_segmentation_loss_cls: 0.0745 semantic_segmentation_loss_mask: 0.0554 semantic_segmentation_loss_dice: 0.1345 2024/07/08 09:21:24 - mmengine - INFO - Iter(train) [ 62450/120000] base_lr: 9.4657e-05 lr: 1.0423e-05 eta: 17:45:14 time: 1.1029 data_time: 0.0165 memory: 15052 grad_norm: 1.3881 loss: 0.2641 semantic_segmentation_loss_cls: 0.0745 semantic_segmentation_loss_mask: 0.0553 semantic_segmentation_loss_dice: 0.1343 2024/07/08 09:22:20 - mmengine - INFO - Iter(train) [ 62500/120000] base_lr: 9.4528e-05 lr: 1.0412e-05 eta: 17:44:18 time: 1.1031 data_time: 0.0166 memory: 15375 grad_norm: 1.3878 loss: 0.2643 semantic_segmentation_loss_cls: 0.0745 semantic_segmentation_loss_mask: 0.0554 semantic_segmentation_loss_dice: 0.1344 2024/07/08 09:23:15 - mmengine - INFO - Iter(train) [ 62550/120000] base_lr: 9.4398e-05 lr: 1.0400e-05 eta: 17:43:22 time: 1.1030 data_time: 0.0166 memory: 14987 grad_norm: 1.3856 loss: 0.2642 semantic_segmentation_loss_cls: 0.0744 semantic_segmentation_loss_mask: 0.0554 semantic_segmentation_loss_dice: 0.1343 2024/07/08 09:24:10 - mmengine - INFO - Iter(train) [ 62600/120000] base_lr: 9.4269e-05 lr: 1.0388e-05 eta: 17:42:26 time: 1.1030 data_time: 0.0166 memory: 14922 grad_norm: 1.3860 loss: 0.2640 semantic_segmentation_loss_cls: 0.0743 semantic_segmentation_loss_mask: 0.0554 semantic_segmentation_loss_dice: 0.1343 2024/07/08 09:25:05 - mmengine - INFO - Iter(train) [ 62650/120000] base_lr: 9.4140e-05 lr: 1.0376e-05 eta: 17:41:30 time: 1.1032 data_time: 0.0166 memory: 14890 grad_norm: 1.3857 loss: 0.2638 semantic_segmentation_loss_cls: 0.0743 semantic_segmentation_loss_mask: 0.0553 semantic_segmentation_loss_dice: 0.1341 2024/07/08 09:25:59 - mmengine - INFO - Iter(train) [ 62700/120000] base_lr: 9.4011e-05 lr: 1.0365e-05 eta: 17:40:34 time: 1.1031 data_time: 0.0166 memory: 14511 grad_norm: 1.3851 loss: 0.2635 semantic_segmentation_loss_cls: 0.0742 semantic_segmentation_loss_mask: 0.0553 semantic_segmentation_loss_dice: 0.1340 2024/07/08 09:26:54 - mmengine - INFO - Iter(train) [ 62750/120000] base_lr: 9.3881e-05 lr: 1.0353e-05 eta: 17:39:37 time: 1.1029 data_time: 0.0165 memory: 15622 grad_norm: 1.3851 loss: 0.2639 semantic_segmentation_loss_cls: 0.0744 semantic_segmentation_loss_mask: 0.0554 semantic_segmentation_loss_dice: 0.1341 2024/07/08 09:27:50 - mmengine - INFO - Iter(train) [ 62800/120000] base_lr: 9.3752e-05 lr: 1.0341e-05 eta: 17:38:42 time: 1.1029 data_time: 0.0165 memory: 15216 grad_norm: 1.3840 loss: 0.2637 semantic_segmentation_loss_cls: 0.0743 semantic_segmentation_loss_mask: 0.0554 semantic_segmentation_loss_dice: 0.1340 2024/07/08 09:28:44 - mmengine - INFO - Iter(train) [ 62850/120000] base_lr: 9.3623e-05 lr: 1.0329e-05 eta: 17:37:45 time: 1.1028 data_time: 0.0165 memory: 14961 grad_norm: 1.3830 loss: 0.2634 semantic_segmentation_loss_cls: 0.0743 semantic_segmentation_loss_mask: 0.0553 semantic_segmentation_loss_dice: 0.1339 2024/07/08 09:29:39 - mmengine - INFO - Iter(train) [ 62900/120000] base_lr: 9.3494e-05 lr: 1.0318e-05 eta: 17:36:49 time: 1.1027 data_time: 0.0165 memory: 15780 grad_norm: 1.3826 loss: 0.2634 semantic_segmentation_loss_cls: 0.0742 semantic_segmentation_loss_mask: 0.0553 semantic_segmentation_loss_dice: 0.1339 2024/07/08 09:30:35 - mmengine - INFO - Iter(train) [ 62950/120000] base_lr: 9.3364e-05 lr: 1.0306e-05 eta: 17:35:54 time: 1.1025 data_time: 0.0165 memory: 15802 grad_norm: 1.3808 loss: 0.2630 semantic_segmentation_loss_cls: 0.0740 semantic_segmentation_loss_mask: 0.0552 semantic_segmentation_loss_dice: 0.1337 2024/07/08 09:31:30 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 09:31:30 - mmengine - INFO - Iter(train) [ 63000/120000] base_lr: 9.3235e-05 lr: 1.0294e-05 eta: 17:34:58 time: 1.1023 data_time: 0.0166 memory: 14701 grad_norm: 1.3789 loss: 0.2632 semantic_segmentation_loss_cls: 0.0742 semantic_segmentation_loss_mask: 0.0552 semantic_segmentation_loss_dice: 0.1338 2024/07/08 09:31:30 - mmengine - INFO - Saving checkpoint at 63000 iterations 2024/07/08 09:32:30 - mmengine - INFO - Iter(train) [ 63050/120000] base_lr: 9.3106e-05 lr: 1.0282e-05 eta: 17:34:07 time: 1.1024 data_time: 0.0166 memory: 14909 grad_norm: 1.3781 loss: 0.2630 semantic_segmentation_loss_cls: 0.0741 semantic_segmentation_loss_mask: 0.0552 semantic_segmentation_loss_dice: 0.1338 2024/07/08 09:33:24 - mmengine - INFO - Iter(train) [ 63100/120000] base_lr: 9.2977e-05 lr: 1.0271e-05 eta: 17:33:10 time: 1.1022 data_time: 0.0166 memory: 15431 grad_norm: 1.3784 loss: 0.2629 semantic_segmentation_loss_cls: 0.0739 semantic_segmentation_loss_mask: 0.0552 semantic_segmentation_loss_dice: 0.1337 2024/07/08 09:34:19 - mmengine - INFO - Iter(train) [ 63150/120000] base_lr: 9.2848e-05 lr: 1.0259e-05 eta: 17:32:14 time: 1.1022 data_time: 0.0166 memory: 15382 grad_norm: 1.3786 loss: 0.2625 semantic_segmentation_loss_cls: 0.0738 semantic_segmentation_loss_mask: 0.0551 semantic_segmentation_loss_dice: 0.1336 2024/07/08 09:35:14 - mmengine - INFO - Iter(train) [ 63200/120000] base_lr: 9.2718e-05 lr: 1.0247e-05 eta: 17:31:18 time: 1.1023 data_time: 0.0166 memory: 15066 grad_norm: 1.3780 loss: 0.2625 semantic_segmentation_loss_cls: 0.0738 semantic_segmentation_loss_mask: 0.0551 semantic_segmentation_loss_dice: 0.1336 2024/07/08 09:36:09 - mmengine - INFO - Iter(train) [ 63250/120000] base_lr: 9.2589e-05 lr: 1.0235e-05 eta: 17:30:22 time: 1.1023 data_time: 0.0166 memory: 15259 grad_norm: 1.3771 loss: 0.2625 semantic_segmentation_loss_cls: 0.0738 semantic_segmentation_loss_mask: 0.0551 semantic_segmentation_loss_dice: 0.1336 2024/07/08 09:37:04 - mmengine - INFO - Iter(train) [ 63300/120000] base_lr: 9.2460e-05 lr: 1.0224e-05 eta: 17:29:26 time: 1.1023 data_time: 0.0166 memory: 14639 grad_norm: 1.3780 loss: 0.2625 semantic_segmentation_loss_cls: 0.0738 semantic_segmentation_loss_mask: 0.0551 semantic_segmentation_loss_dice: 0.1337 2024/07/08 09:37:59 - mmengine - INFO - Iter(train) [ 63350/120000] base_lr: 9.2331e-05 lr: 1.0212e-05 eta: 17:28:30 time: 1.1023 data_time: 0.0166 memory: 15198 grad_norm: 1.3756 loss: 0.2626 semantic_segmentation_loss_cls: 0.0739 semantic_segmentation_loss_mask: 0.0550 semantic_segmentation_loss_dice: 0.1337 2024/07/08 09:38:55 - mmengine - INFO - Iter(train) [ 63400/120000] base_lr: 9.2202e-05 lr: 1.0200e-05 eta: 17:27:34 time: 1.1024 data_time: 0.0167 memory: 15957 grad_norm: 1.3752 loss: 0.2625 semantic_segmentation_loss_cls: 0.0739 semantic_segmentation_loss_mask: 0.0550 semantic_segmentation_loss_dice: 0.1337 2024/07/08 09:39:49 - mmengine - INFO - Iter(train) [ 63450/120000] base_lr: 9.2073e-05 lr: 1.0188e-05 eta: 17:26:38 time: 1.1024 data_time: 0.0166 memory: 16195 grad_norm: 1.3720 loss: 0.2623 semantic_segmentation_loss_cls: 0.0738 semantic_segmentation_loss_mask: 0.0550 semantic_segmentation_loss_dice: 0.1335 2024/07/08 09:40:44 - mmengine - INFO - Iter(train) [ 63500/120000] base_lr: 9.1944e-05 lr: 1.0177e-05 eta: 17:25:42 time: 1.1025 data_time: 0.0166 memory: 15504 grad_norm: 1.3715 loss: 0.2624 semantic_segmentation_loss_cls: 0.0738 semantic_segmentation_loss_mask: 0.0550 semantic_segmentation_loss_dice: 0.1336 2024/07/08 09:41:39 - mmengine - INFO - Iter(train) [ 63550/120000] base_lr: 9.1815e-05 lr: 1.0165e-05 eta: 17:24:46 time: 1.1024 data_time: 0.0166 memory: 15480 grad_norm: 1.3703 loss: 0.2625 semantic_segmentation_loss_cls: 0.0739 semantic_segmentation_loss_mask: 0.0550 semantic_segmentation_loss_dice: 0.1336 2024/07/08 09:42:35 - mmengine - INFO - Iter(train) [ 63600/120000] base_lr: 9.1686e-05 lr: 1.0153e-05 eta: 17:23:51 time: 1.1024 data_time: 0.0166 memory: 15005 grad_norm: 1.3690 loss: 0.2621 semantic_segmentation_loss_cls: 0.0738 semantic_segmentation_loss_mask: 0.0549 semantic_segmentation_loss_dice: 0.1334 2024/07/08 09:43:30 - mmengine - INFO - Iter(train) [ 63650/120000] base_lr: 9.1557e-05 lr: 1.0142e-05 eta: 17:22:54 time: 1.1022 data_time: 0.0167 memory: 15192 grad_norm: 1.3666 loss: 0.2619 semantic_segmentation_loss_cls: 0.0737 semantic_segmentation_loss_mask: 0.0548 semantic_segmentation_loss_dice: 0.1333 2024/07/08 09:44:25 - mmengine - INFO - Iter(train) [ 63700/120000] base_lr: 9.1428e-05 lr: 1.0130e-05 eta: 17:21:58 time: 1.1020 data_time: 0.0167 memory: 15099 grad_norm: 1.3650 loss: 0.2620 semantic_segmentation_loss_cls: 0.0738 semantic_segmentation_loss_mask: 0.0548 semantic_segmentation_loss_dice: 0.1334 2024/07/08 09:45:20 - mmengine - INFO - Iter(train) [ 63750/120000] base_lr: 9.1299e-05 lr: 1.0118e-05 eta: 17:21:03 time: 1.1024 data_time: 0.0167 memory: 14658 grad_norm: 1.3658 loss: 0.2615 semantic_segmentation_loss_cls: 0.0735 semantic_segmentation_loss_mask: 0.0548 semantic_segmentation_loss_dice: 0.1332 2024/07/08 09:46:15 - mmengine - INFO - Iter(train) [ 63800/120000] base_lr: 9.1170e-05 lr: 1.0106e-05 eta: 17:20:07 time: 1.1025 data_time: 0.0168 memory: 15308 grad_norm: 1.3655 loss: 0.2611 semantic_segmentation_loss_cls: 0.0733 semantic_segmentation_loss_mask: 0.0548 semantic_segmentation_loss_dice: 0.1331 2024/07/08 09:47:09 - mmengine - INFO - Iter(train) [ 63850/120000] base_lr: 9.1041e-05 lr: 1.0095e-05 eta: 17:19:10 time: 1.1025 data_time: 0.0168 memory: 14496 grad_norm: 1.3653 loss: 0.2611 semantic_segmentation_loss_cls: 0.0733 semantic_segmentation_loss_mask: 0.0547 semantic_segmentation_loss_dice: 0.1330 2024/07/08 09:48:04 - mmengine - INFO - Iter(train) [ 63900/120000] base_lr: 9.0912e-05 lr: 1.0083e-05 eta: 17:18:14 time: 1.1024 data_time: 0.0168 memory: 14999 grad_norm: 1.3654 loss: 0.2612 semantic_segmentation_loss_cls: 0.0733 semantic_segmentation_loss_mask: 0.0547 semantic_segmentation_loss_dice: 0.1331 2024/07/08 09:48:59 - mmengine - INFO - Iter(train) [ 63950/120000] base_lr: 9.0783e-05 lr: 1.0071e-05 eta: 17:17:18 time: 1.1025 data_time: 0.0168 memory: 16044 grad_norm: 1.3651 loss: 0.2613 semantic_segmentation_loss_cls: 0.0733 semantic_segmentation_loss_mask: 0.0548 semantic_segmentation_loss_dice: 0.1333 2024/07/08 09:49:55 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 09:49:55 - mmengine - INFO - Iter(train) [ 64000/120000] base_lr: 9.0654e-05 lr: 1.0059e-05 eta: 17:16:23 time: 1.1029 data_time: 0.0168 memory: 15195 grad_norm: 1.3643 loss: 0.2611 semantic_segmentation_loss_cls: 0.0732 semantic_segmentation_loss_mask: 0.0547 semantic_segmentation_loss_dice: 0.1332 2024/07/08 09:49:55 - mmengine - INFO - Saving checkpoint at 64000 iterations 2024/07/08 09:50:54 - mmengine - INFO - Iter(train) [ 64050/120000] base_lr: 9.0525e-05 lr: 1.0048e-05 eta: 17:15:31 time: 1.1039 data_time: 0.0177 memory: 15034 grad_norm: 1.3638 loss: 0.2612 semantic_segmentation_loss_cls: 0.0732 semantic_segmentation_loss_mask: 0.0547 semantic_segmentation_loss_dice: 0.1333 2024/07/08 09:51:50 - mmengine - INFO - Iter(train) [ 64100/120000] base_lr: 9.0397e-05 lr: 1.0036e-05 eta: 17:14:35 time: 1.1039 data_time: 0.0178 memory: 16415 grad_norm: 1.3647 loss: 0.2611 semantic_segmentation_loss_cls: 0.0732 semantic_segmentation_loss_mask: 0.0547 semantic_segmentation_loss_dice: 0.1332 2024/07/08 09:52:44 - mmengine - INFO - Iter(train) [ 64150/120000] base_lr: 9.0268e-05 lr: 1.0024e-05 eta: 17:13:39 time: 1.1039 data_time: 0.0178 memory: 14834 grad_norm: 1.3650 loss: 0.2612 semantic_segmentation_loss_cls: 0.0732 semantic_segmentation_loss_mask: 0.0547 semantic_segmentation_loss_dice: 0.1332 2024/07/08 09:53:39 - mmengine - INFO - Iter(train) [ 64200/120000] base_lr: 9.0139e-05 lr: 1.0013e-05 eta: 17:12:42 time: 1.1036 data_time: 0.0178 memory: 14660 grad_norm: 1.3658 loss: 0.2610 semantic_segmentation_loss_cls: 0.0731 semantic_segmentation_loss_mask: 0.0547 semantic_segmentation_loss_dice: 0.1331 2024/07/08 09:54:32 - mmengine - INFO - Iter(train) [ 64250/120000] base_lr: 9.0010e-05 lr: 1.0001e-05 eta: 17:11:45 time: 1.1030 data_time: 0.0177 memory: 15082 grad_norm: 1.3664 loss: 0.2610 semantic_segmentation_loss_cls: 0.0732 semantic_segmentation_loss_mask: 0.0547 semantic_segmentation_loss_dice: 0.1331 2024/07/08 09:55:26 - mmengine - INFO - Iter(train) [ 64300/120000] base_lr: 8.9881e-05 lr: 9.9892e-06 eta: 17:10:48 time: 1.1026 data_time: 0.0177 memory: 15614 grad_norm: 1.3649 loss: 0.2609 semantic_segmentation_loss_cls: 0.0730 semantic_segmentation_loss_mask: 0.0547 semantic_segmentation_loss_dice: 0.1332 2024/07/08 09:56:21 - mmengine - INFO - Iter(train) [ 64350/120000] base_lr: 8.9753e-05 lr: 9.9775e-06 eta: 17:09:52 time: 1.1028 data_time: 0.0177 memory: 14849 grad_norm: 1.3632 loss: 0.2609 semantic_segmentation_loss_cls: 0.0731 semantic_segmentation_loss_mask: 0.0547 semantic_segmentation_loss_dice: 0.1332 2024/07/08 09:57:16 - mmengine - INFO - Iter(train) [ 64400/120000] base_lr: 8.9624e-05 lr: 9.9658e-06 eta: 17:08:56 time: 1.1029 data_time: 0.0178 memory: 15025 grad_norm: 1.3611 loss: 0.2611 semantic_segmentation_loss_cls: 0.0732 semantic_segmentation_loss_mask: 0.0547 semantic_segmentation_loss_dice: 0.1332 2024/07/08 09:58:11 - mmengine - INFO - Iter(train) [ 64450/120000] base_lr: 8.9495e-05 lr: 9.9541e-06 eta: 17:08:00 time: 1.1031 data_time: 0.0178 memory: 16140 grad_norm: 1.3593 loss: 0.2611 semantic_segmentation_loss_cls: 0.0732 semantic_segmentation_loss_mask: 0.0547 semantic_segmentation_loss_dice: 0.1332 2024/07/08 09:59:06 - mmengine - INFO - Iter(train) [ 64500/120000] base_lr: 8.9366e-05 lr: 9.9424e-06 eta: 17:07:04 time: 1.1029 data_time: 0.0178 memory: 14952 grad_norm: 1.3570 loss: 0.2609 semantic_segmentation_loss_cls: 0.0731 semantic_segmentation_loss_mask: 0.0546 semantic_segmentation_loss_dice: 0.1332 2024/07/08 10:00:01 - mmengine - INFO - Iter(train) [ 64550/120000] base_lr: 8.9238e-05 lr: 9.9307e-06 eta: 17:06:08 time: 1.1028 data_time: 0.0178 memory: 14742 grad_norm: 1.3562 loss: 0.2608 semantic_segmentation_loss_cls: 0.0731 semantic_segmentation_loss_mask: 0.0546 semantic_segmentation_loss_dice: 0.1331 2024/07/08 10:00:56 - mmengine - INFO - Iter(train) [ 64600/120000] base_lr: 8.9109e-05 lr: 9.9190e-06 eta: 17:05:12 time: 1.1026 data_time: 0.0178 memory: 15581 grad_norm: 1.3556 loss: 0.2607 semantic_segmentation_loss_cls: 0.0731 semantic_segmentation_loss_mask: 0.0546 semantic_segmentation_loss_dice: 0.1330 2024/07/08 10:01:52 - mmengine - INFO - Iter(train) [ 64650/120000] base_lr: 8.8980e-05 lr: 9.9073e-06 eta: 17:04:16 time: 1.1026 data_time: 0.0178 memory: 14524 grad_norm: 1.3554 loss: 0.2604 semantic_segmentation_loss_cls: 0.0729 semantic_segmentation_loss_mask: 0.0545 semantic_segmentation_loss_dice: 0.1329 2024/07/08 10:02:46 - mmengine - INFO - Iter(train) [ 64700/120000] base_lr: 8.8852e-05 lr: 9.8956e-06 eta: 17:03:20 time: 1.1024 data_time: 0.0178 memory: 16319 grad_norm: 1.3570 loss: 0.2602 semantic_segmentation_loss_cls: 0.0729 semantic_segmentation_loss_mask: 0.0545 semantic_segmentation_loss_dice: 0.1328 2024/07/08 10:03:42 - mmengine - INFO - Iter(train) [ 64750/120000] base_lr: 8.8723e-05 lr: 9.8839e-06 eta: 17:02:25 time: 1.1025 data_time: 0.0179 memory: 14774 grad_norm: 1.3567 loss: 0.2602 semantic_segmentation_loss_cls: 0.0728 semantic_segmentation_loss_mask: 0.0545 semantic_segmentation_loss_dice: 0.1329 2024/07/08 10:04:37 - mmengine - INFO - Iter(train) [ 64800/120000] base_lr: 8.8595e-05 lr: 9.8722e-06 eta: 17:01:29 time: 1.1025 data_time: 0.0179 memory: 15054 grad_norm: 1.3549 loss: 0.2600 semantic_segmentation_loss_cls: 0.0728 semantic_segmentation_loss_mask: 0.0545 semantic_segmentation_loss_dice: 0.1328 2024/07/08 10:05:33 - mmengine - INFO - Iter(train) [ 64850/120000] base_lr: 8.8466e-05 lr: 9.8605e-06 eta: 17:00:34 time: 1.1029 data_time: 0.0179 memory: 15629 grad_norm: 1.3544 loss: 0.2603 semantic_segmentation_loss_cls: 0.0729 semantic_segmentation_loss_mask: 0.0545 semantic_segmentation_loss_dice: 0.1329 2024/07/08 10:06:28 - mmengine - INFO - Iter(train) [ 64900/120000] base_lr: 8.8337e-05 lr: 9.8489e-06 eta: 16:59:38 time: 1.1029 data_time: 0.0179 memory: 15127 grad_norm: 1.3535 loss: 0.2605 semantic_segmentation_loss_cls: 0.0729 semantic_segmentation_loss_mask: 0.0546 semantic_segmentation_loss_dice: 0.1330 2024/07/08 10:07:23 - mmengine - INFO - Iter(train) [ 64950/120000] base_lr: 8.8209e-05 lr: 9.8372e-06 eta: 16:58:42 time: 1.1030 data_time: 0.0180 memory: 14863 grad_norm: 1.3525 loss: 0.2605 semantic_segmentation_loss_cls: 0.0730 semantic_segmentation_loss_mask: 0.0545 semantic_segmentation_loss_dice: 0.1330 2024/07/08 10:08:18 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 10:08:18 - mmengine - INFO - Iter(train) [ 65000/120000] base_lr: 8.8080e-05 lr: 9.8255e-06 eta: 16:57:46 time: 1.1032 data_time: 0.0180 memory: 15463 grad_norm: 1.3535 loss: 0.2600 semantic_segmentation_loss_cls: 0.0727 semantic_segmentation_loss_mask: 0.0545 semantic_segmentation_loss_dice: 0.1328 2024/07/08 10:08:18 - mmengine - INFO - Saving checkpoint at 65000 iterations 2024/07/08 10:08:35 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:50 time: 0.2458 data_time: 0.0015 memory: 5013 2024/07/08 10:08:47 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:36 time: 0.2457 data_time: 0.0015 memory: 5189 2024/07/08 10:08:59 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:24 time: 0.2456 data_time: 0.0015 memory: 4460 2024/07/08 10:09:11 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:12 time: 0.2456 data_time: 0.0015 memory: 4543 2024/07/08 10:09:23 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:00 time: 0.2455 data_time: 0.0015 memory: 4645 2024/07/08 10:09:35 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:48 time: 0.2454 data_time: 0.0015 memory: 10983 2024/07/08 10:09:47 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2453 data_time: 0.0015 memory: 4460 2024/07/08 10:09:59 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2452 data_time: 0.0015 memory: 4641 2024/07/08 10:10:11 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2452 data_time: 0.0015 memory: 4473 2024/07/08 10:10:24 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2451 data_time: 0.0015 memory: 4555 2024/07/08 10:10:24 - mmengine - INFO - per class results: 2024/07/08 10:10:24 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.37 | 87.16 | | building | 83.22 | 90.66 | | sky | 94.27 | 97.72 | | floor | 83.01 | 91.36 | | tree | 74.86 | 87.9 | | ceiling | 85.0 | 92.85 | | road | 84.26 | 91.83 | | bed | 86.32 | 95.28 | | windowpane | 62.0 | 80.88 | | grass | 71.79 | 86.53 | | cabinet | 62.49 | 73.87 | | sidewalk | 67.94 | 82.91 | | person | 82.31 | 91.68 | | earth | 36.02 | 48.88 | | door | 52.24 | 68.68 | | table | 63.25 | 77.72 | | mountain | 59.15 | 73.12 | | plant | 53.92 | 67.95 | | curtain | 72.17 | 87.23 | | chair | 59.3 | 72.3 | | car | 85.47 | 91.92 | | water | 47.69 | 64.02 | | painting | 72.78 | 88.47 | | sofa | 63.34 | 74.72 | | shelf | 44.08 | 65.69 | | house | 53.4 | 80.08 | | sea | 44.04 | 67.91 | | mirror | 66.8 | 76.27 | | rug | 70.75 | 77.42 | | field | 36.41 | 53.2 | | armchair | 45.01 | 69.23 | | seat | 59.38 | 79.59 | | fence | 46.55 | 65.18 | | desk | 51.95 | 71.76 | | rock | 38.79 | 58.24 | | wardrobe | 53.71 | 68.65 | | lamp | 67.47 | 79.49 | | bathtub | 87.74 | 91.78 | | railing | 36.56 | 51.26 | | cushion | 56.87 | 69.01 | | base | 19.52 | 32.73 | | box | 26.14 | 37.73 | | column | 49.86 | 70.04 | | signboard | 39.44 | 54.31 | | chest of drawers | 43.75 | 66.92 | | counter | 30.9 | 49.88 | | sand | 35.28 | 51.09 | | sink | 72.78 | 82.19 | | skyscraper | 47.21 | 60.0 | | fireplace | 70.73 | 89.88 | | refrigerator | 81.77 | 89.97 | | grandstand | 41.31 | 75.51 | | path | 30.71 | 42.79 | | stairs | 30.56 | 39.34 | | runway | 76.12 | 89.75 | | case | 50.0 | 55.75 | | pool table | 92.01 | 96.35 | | pillow | 54.94 | 66.6 | | screen door | 81.39 | 84.37 | | stairway | 38.34 | 43.33 | | river | 21.2 | 44.57 | | bridge | 70.53 | 89.25 | | bookcase | 39.85 | 57.04 | | blind | 38.52 | 43.9 | | coffee table | 72.45 | 85.86 | | toilet | 78.66 | 89.56 | | flower | 41.15 | 58.96 | | book | 52.51 | 76.29 | | hill | 11.4 | 20.3 | | bench | 39.86 | 48.96 | | countertop | 57.06 | 69.05 | | stove | 79.49 | 84.25 | | palm | 53.3 | 68.69 | | kitchen island | 34.51 | 77.43 | | computer | 61.1 | 68.08 | | swivel chair | 39.83 | 55.21 | | boat | 74.19 | 82.13 | | bar | 47.71 | 59.77 | | arcade machine | 62.46 | 68.23 | | hovel | 17.23 | 24.8 | | bus | 87.38 | 90.69 | | towel | 69.35 | 75.5 | | light | 63.09 | 78.6 | | truck | 35.54 | 48.39 | | tower | 32.96 | 54.26 | | chandelier | 66.0 | 78.54 | | awning | 32.4 | 45.84 | | streetlight | 39.14 | 54.77 | | booth | 62.02 | 64.59 | | television receiver | 47.7 | 90.31 | | airplane | 55.01 | 66.96 | | dirt track | 0.31 | 0.38 | | apparel | 40.03 | 56.24 | | pole | 33.09 | 52.1 | | land | 2.43 | 3.35 | | bannister | 12.72 | 25.18 | | escalator | 46.89 | 60.16 | | ottoman | 35.19 | 64.89 | | bottle | 21.35 | 26.93 | | buffet | 50.49 | 53.37 | | poster | 28.55 | 42.35 | | stage | 17.95 | 30.03 | | van | 47.27 | 66.17 | | ship | 82.37 | 86.14 | | fountain | 7.49 | 7.79 | | conveyer belt | 61.48 | 91.35 | | canopy | 16.35 | 25.34 | | washer | 69.81 | 72.38 | | plaything | 29.82 | 40.43 | | swimming pool | 29.88 | 32.94 | | stool | 50.2 | 69.29 | | barrel | 14.24 | 55.08 | | basket | 34.37 | 42.92 | | waterfall | 40.74 | 56.91 | | tent | 92.97 | 97.76 | | bag | 16.71 | 24.17 | | minibike | 71.85 | 85.14 | | cradle | 75.65 | 96.98 | | oven | 54.21 | 65.58 | | ball | 36.61 | 44.99 | | food | 64.38 | 81.17 | | step | 28.16 | 37.96 | | tank | 36.85 | 46.92 | | trade name | 32.07 | 39.17 | | microwave | 38.85 | 41.76 | | pot | 53.44 | 60.48 | | animal | 61.59 | 69.26 | | bicycle | 56.05 | 77.81 | | lake | 63.56 | 63.72 | | dishwasher | 80.53 | 85.04 | | screen | 69.89 | 89.29 | | blanket | 11.34 | 14.59 | | sculpture | 63.35 | 82.91 | | hood | 74.19 | 77.7 | | sconce | 53.05 | 65.06 | | vase | 47.69 | 65.84 | | traffic light | 41.16 | 60.76 | | tray | 18.45 | 23.04 | | ashcan | 45.01 | 60.61 | | fan | 68.54 | 81.87 | | pier | 33.27 | 72.62 | | crt screen | 0.04 | 0.05 | | plate | 61.77 | 74.89 | | monitor | 47.81 | 68.11 | | bulletin board | 37.84 | 46.77 | | shower | 7.0 | 17.59 | | radiator | 58.95 | 70.1 | | glass | 18.44 | 19.93 | | clock | 32.44 | 37.08 | | flag | 44.85 | 54.36 | +---------------------+-------+-------+ 2024/07/08 10:10:24 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.5500 mIoU: 50.7200 mAcc: 63.6800 data_time: 0.0016 time: 0.2409 2024/07/08 10:11:20 - mmengine - INFO - Iter(train) [ 65050/120000] base_lr: 8.7952e-05 lr: 9.8138e-06 eta: 16:56:51 time: 1.1025 data_time: 0.0171 memory: 14923 grad_norm: 1.3506 loss: 0.2602 semantic_segmentation_loss_cls: 0.0728 semantic_segmentation_loss_mask: 0.0545 semantic_segmentation_loss_dice: 0.1329 2024/07/08 10:12:15 - mmengine - INFO - Iter(train) [ 65100/120000] base_lr: 8.7824e-05 lr: 9.8021e-06 eta: 16:55:56 time: 1.1027 data_time: 0.0171 memory: 15286 grad_norm: 1.3498 loss: 0.2600 semantic_segmentation_loss_cls: 0.0727 semantic_segmentation_loss_mask: 0.0545 semantic_segmentation_loss_dice: 0.1329 2024/07/08 10:13:10 - mmengine - INFO - Iter(train) [ 65150/120000] base_lr: 8.7695e-05 lr: 9.7905e-06 eta: 16:55:00 time: 1.1025 data_time: 0.0171 memory: 14953 grad_norm: 1.3504 loss: 0.2598 semantic_segmentation_loss_cls: 0.0727 semantic_segmentation_loss_mask: 0.0544 semantic_segmentation_loss_dice: 0.1327 2024/07/08 10:14:06 - mmengine - INFO - Iter(train) [ 65200/120000] base_lr: 8.7567e-05 lr: 9.7788e-06 eta: 16:54:04 time: 1.1027 data_time: 0.0171 memory: 14638 grad_norm: 1.3491 loss: 0.2594 semantic_segmentation_loss_cls: 0.0725 semantic_segmentation_loss_mask: 0.0544 semantic_segmentation_loss_dice: 0.1326 2024/07/08 10:15:01 - mmengine - INFO - Iter(train) [ 65250/120000] base_lr: 8.7438e-05 lr: 9.7671e-06 eta: 16:53:09 time: 1.1029 data_time: 0.0172 memory: 15085 grad_norm: 1.3486 loss: 0.2593 semantic_segmentation_loss_cls: 0.0724 semantic_segmentation_loss_mask: 0.0544 semantic_segmentation_loss_dice: 0.1325 2024/07/08 10:15:56 - mmengine - INFO - Iter(train) [ 65300/120000] base_lr: 8.7310e-05 lr: 9.7555e-06 eta: 16:52:12 time: 1.1024 data_time: 0.0172 memory: 14920 grad_norm: 1.3492 loss: 0.2591 semantic_segmentation_loss_cls: 0.0723 semantic_segmentation_loss_mask: 0.0544 semantic_segmentation_loss_dice: 0.1324 2024/07/08 10:16:52 - mmengine - INFO - Iter(train) [ 65350/120000] base_lr: 8.7182e-05 lr: 9.7438e-06 eta: 16:51:17 time: 1.1026 data_time: 0.0172 memory: 15067 grad_norm: 1.3495 loss: 0.2592 semantic_segmentation_loss_cls: 0.0723 semantic_segmentation_loss_mask: 0.0544 semantic_segmentation_loss_dice: 0.1325 2024/07/08 10:17:47 - mmengine - INFO - Iter(train) [ 65400/120000] base_lr: 8.7053e-05 lr: 9.7321e-06 eta: 16:50:21 time: 1.1027 data_time: 0.0173 memory: 14767 grad_norm: 1.3480 loss: 0.2591 semantic_segmentation_loss_cls: 0.0723 semantic_segmentation_loss_mask: 0.0544 semantic_segmentation_loss_dice: 0.1324 2024/07/08 10:18:42 - mmengine - INFO - Iter(train) [ 65450/120000] base_lr: 8.6925e-05 lr: 9.7205e-06 eta: 16:49:26 time: 1.1029 data_time: 0.0173 memory: 14783 grad_norm: 1.3494 loss: 0.2593 semantic_segmentation_loss_cls: 0.0725 semantic_segmentation_loss_mask: 0.0544 semantic_segmentation_loss_dice: 0.1325 2024/07/08 10:19:37 - mmengine - INFO - Iter(train) [ 65500/120000] base_lr: 8.6797e-05 lr: 9.7088e-06 eta: 16:48:30 time: 1.1029 data_time: 0.0173 memory: 16343 grad_norm: 1.3479 loss: 0.2592 semantic_segmentation_loss_cls: 0.0725 semantic_segmentation_loss_mask: 0.0543 semantic_segmentation_loss_dice: 0.1324 2024/07/08 10:20:32 - mmengine - INFO - Iter(train) [ 65550/120000] base_lr: 8.6669e-05 lr: 9.6971e-06 eta: 16:47:34 time: 1.1031 data_time: 0.0173 memory: 15212 grad_norm: 1.3490 loss: 0.2591 semantic_segmentation_loss_cls: 0.0725 semantic_segmentation_loss_mask: 0.0543 semantic_segmentation_loss_dice: 0.1323 2024/07/08 10:21:28 - mmengine - INFO - Iter(train) [ 65600/120000] base_lr: 8.6540e-05 lr: 9.6855e-06 eta: 16:46:38 time: 1.1034 data_time: 0.0173 memory: 14806 grad_norm: 1.3490 loss: 0.2588 semantic_segmentation_loss_cls: 0.0724 semantic_segmentation_loss_mask: 0.0543 semantic_segmentation_loss_dice: 0.1322 2024/07/08 10:22:23 - mmengine - INFO - Iter(train) [ 65650/120000] base_lr: 8.6412e-05 lr: 9.6738e-06 eta: 16:45:42 time: 1.1035 data_time: 0.0173 memory: 15456 grad_norm: 1.3491 loss: 0.2589 semantic_segmentation_loss_cls: 0.0724 semantic_segmentation_loss_mask: 0.0543 semantic_segmentation_loss_dice: 0.1322 2024/07/08 10:23:18 - mmengine - INFO - Iter(train) [ 65700/120000] base_lr: 8.6284e-05 lr: 9.6622e-06 eta: 16:44:47 time: 1.1037 data_time: 0.0173 memory: 14861 grad_norm: 1.3480 loss: 0.2590 semantic_segmentation_loss_cls: 0.0724 semantic_segmentation_loss_mask: 0.0543 semantic_segmentation_loss_dice: 0.1323 2024/07/08 10:24:14 - mmengine - INFO - Iter(train) [ 65750/120000] base_lr: 8.6156e-05 lr: 9.6505e-06 eta: 16:43:51 time: 1.1040 data_time: 0.0173 memory: 15145 grad_norm: 1.3472 loss: 0.2588 semantic_segmentation_loss_cls: 0.0723 semantic_segmentation_loss_mask: 0.0542 semantic_segmentation_loss_dice: 0.1322 2024/07/08 10:25:10 - mmengine - INFO - Iter(train) [ 65800/120000] base_lr: 8.6028e-05 lr: 9.6389e-06 eta: 16:42:56 time: 1.1045 data_time: 0.0173 memory: 16315 grad_norm: 1.3443 loss: 0.2587 semantic_segmentation_loss_cls: 0.0723 semantic_segmentation_loss_mask: 0.0542 semantic_segmentation_loss_dice: 0.1322 2024/07/08 10:26:05 - mmengine - INFO - Iter(train) [ 65850/120000] base_lr: 8.5900e-05 lr: 9.6272e-06 eta: 16:42:00 time: 1.1045 data_time: 0.0173 memory: 14839 grad_norm: 1.3450 loss: 0.2586 semantic_segmentation_loss_cls: 0.0722 semantic_segmentation_loss_mask: 0.0542 semantic_segmentation_loss_dice: 0.1322 2024/07/08 10:27:00 - mmengine - INFO - Iter(train) [ 65900/120000] base_lr: 8.5772e-05 lr: 9.6156e-06 eta: 16:41:04 time: 1.1043 data_time: 0.0173 memory: 14519 grad_norm: 1.3437 loss: 0.2587 semantic_segmentation_loss_cls: 0.0723 semantic_segmentation_loss_mask: 0.0542 semantic_segmentation_loss_dice: 0.1323 2024/07/08 10:27:55 - mmengine - INFO - Iter(train) [ 65950/120000] base_lr: 8.5644e-05 lr: 9.6040e-06 eta: 16:40:09 time: 1.1045 data_time: 0.0172 memory: 14689 grad_norm: 1.3446 loss: 0.2588 semantic_segmentation_loss_cls: 0.0724 semantic_segmentation_loss_mask: 0.0542 semantic_segmentation_loss_dice: 0.1322 2024/07/08 10:28:50 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 10:28:50 - mmengine - INFO - Iter(train) [ 66000/120000] base_lr: 8.5516e-05 lr: 9.5923e-06 eta: 16:39:13 time: 1.1046 data_time: 0.0172 memory: 14729 grad_norm: 1.3433 loss: 0.2583 semantic_segmentation_loss_cls: 0.0721 semantic_segmentation_loss_mask: 0.0542 semantic_segmentation_loss_dice: 0.1320 2024/07/08 10:28:50 - mmengine - INFO - Saving checkpoint at 66000 iterations 2024/07/08 10:29:50 - mmengine - INFO - Iter(train) [ 66050/120000] base_lr: 8.5388e-05 lr: 9.5807e-06 eta: 16:38:21 time: 1.1047 data_time: 0.0172 memory: 15239 grad_norm: 1.3430 loss: 0.2581 semantic_segmentation_loss_cls: 0.0719 semantic_segmentation_loss_mask: 0.0542 semantic_segmentation_loss_dice: 0.1320 2024/07/08 10:30:46 - mmengine - INFO - Iter(train) [ 66100/120000] base_lr: 8.5260e-05 lr: 9.5691e-06 eta: 16:37:25 time: 1.1050 data_time: 0.0173 memory: 15489 grad_norm: 1.3411 loss: 0.2577 semantic_segmentation_loss_cls: 0.0718 semantic_segmentation_loss_mask: 0.0541 semantic_segmentation_loss_dice: 0.1318 2024/07/08 10:31:42 - mmengine - INFO - Iter(train) [ 66150/120000] base_lr: 8.5132e-05 lr: 9.5574e-06 eta: 16:36:30 time: 1.1052 data_time: 0.0173 memory: 16464 grad_norm: 1.3407 loss: 0.2576 semantic_segmentation_loss_cls: 0.0717 semantic_segmentation_loss_mask: 0.0541 semantic_segmentation_loss_dice: 0.1318 2024/07/08 10:32:37 - mmengine - INFO - Iter(train) [ 66200/120000] base_lr: 8.5004e-05 lr: 9.5458e-06 eta: 16:35:35 time: 1.1053 data_time: 0.0173 memory: 15991 grad_norm: 1.3401 loss: 0.2575 semantic_segmentation_loss_cls: 0.0716 semantic_segmentation_loss_mask: 0.0542 semantic_segmentation_loss_dice: 0.1318 2024/07/08 10:33:33 - mmengine - INFO - Iter(train) [ 66250/120000] base_lr: 8.4876e-05 lr: 9.5342e-06 eta: 16:34:39 time: 1.1055 data_time: 0.0174 memory: 14719 grad_norm: 1.3410 loss: 0.2573 semantic_segmentation_loss_cls: 0.0716 semantic_segmentation_loss_mask: 0.0541 semantic_segmentation_loss_dice: 0.1317 2024/07/08 10:34:28 - mmengine - INFO - Iter(train) [ 66300/120000] base_lr: 8.4748e-05 lr: 9.5226e-06 eta: 16:33:43 time: 1.1055 data_time: 0.0174 memory: 15393 grad_norm: 1.3400 loss: 0.2570 semantic_segmentation_loss_cls: 0.0714 semantic_segmentation_loss_mask: 0.0541 semantic_segmentation_loss_dice: 0.1315 2024/07/08 10:35:23 - mmengine - INFO - Iter(train) [ 66350/120000] base_lr: 8.4620e-05 lr: 9.5109e-06 eta: 16:32:47 time: 1.1056 data_time: 0.0174 memory: 15046 grad_norm: 1.3387 loss: 0.2568 semantic_segmentation_loss_cls: 0.0714 semantic_segmentation_loss_mask: 0.0540 semantic_segmentation_loss_dice: 0.1315 2024/07/08 10:36:19 - mmengine - INFO - Iter(train) [ 66400/120000] base_lr: 8.4492e-05 lr: 9.4993e-06 eta: 16:31:52 time: 1.1058 data_time: 0.0174 memory: 15424 grad_norm: 1.3384 loss: 0.2566 semantic_segmentation_loss_cls: 0.0713 semantic_segmentation_loss_mask: 0.0539 semantic_segmentation_loss_dice: 0.1314 2024/07/08 10:37:15 - mmengine - INFO - Iter(train) [ 66450/120000] base_lr: 8.4365e-05 lr: 9.4877e-06 eta: 16:30:57 time: 1.1061 data_time: 0.0174 memory: 15256 grad_norm: 1.3371 loss: 0.2567 semantic_segmentation_loss_cls: 0.0713 semantic_segmentation_loss_mask: 0.0539 semantic_segmentation_loss_dice: 0.1315 2024/07/08 10:38:10 - mmengine - INFO - Iter(train) [ 66500/120000] base_lr: 8.4237e-05 lr: 9.4761e-06 eta: 16:30:01 time: 1.1060 data_time: 0.0174 memory: 15734 grad_norm: 1.3374 loss: 0.2565 semantic_segmentation_loss_cls: 0.0712 semantic_segmentation_loss_mask: 0.0539 semantic_segmentation_loss_dice: 0.1314 2024/07/08 10:39:05 - mmengine - INFO - Iter(train) [ 66550/120000] base_lr: 8.4109e-05 lr: 9.4645e-06 eta: 16:29:05 time: 1.1059 data_time: 0.0174 memory: 15114 grad_norm: 1.3374 loss: 0.2563 semantic_segmentation_loss_cls: 0.0712 semantic_segmentation_loss_mask: 0.0538 semantic_segmentation_loss_dice: 0.1313 2024/07/08 10:40:00 - mmengine - INFO - Iter(train) [ 66600/120000] base_lr: 8.3982e-05 lr: 9.4529e-06 eta: 16:28:10 time: 1.1061 data_time: 0.0174 memory: 15832 grad_norm: 1.3374 loss: 0.2561 semantic_segmentation_loss_cls: 0.0712 semantic_segmentation_loss_mask: 0.0537 semantic_segmentation_loss_dice: 0.1312 2024/07/08 10:40:56 - mmengine - INFO - Iter(train) [ 66650/120000] base_lr: 8.3854e-05 lr: 9.4413e-06 eta: 16:27:14 time: 1.1064 data_time: 0.0175 memory: 15354 grad_norm: 1.3376 loss: 0.2563 semantic_segmentation_loss_cls: 0.0713 semantic_segmentation_loss_mask: 0.0538 semantic_segmentation_loss_dice: 0.1313 2024/07/08 10:41:51 - mmengine - INFO - Iter(train) [ 66700/120000] base_lr: 8.3726e-05 lr: 9.4297e-06 eta: 16:26:18 time: 1.1064 data_time: 0.0175 memory: 14664 grad_norm: 1.3382 loss: 0.2564 semantic_segmentation_loss_cls: 0.0713 semantic_segmentation_loss_mask: 0.0538 semantic_segmentation_loss_dice: 0.1314 2024/07/08 10:42:46 - mmengine - INFO - Iter(train) [ 66750/120000] base_lr: 8.3599e-05 lr: 9.4181e-06 eta: 16:25:22 time: 1.1065 data_time: 0.0175 memory: 15557 grad_norm: 1.3384 loss: 0.2565 semantic_segmentation_loss_cls: 0.0714 semantic_segmentation_loss_mask: 0.0537 semantic_segmentation_loss_dice: 0.1314 2024/07/08 10:43:42 - mmengine - INFO - Iter(train) [ 66800/120000] base_lr: 8.3471e-05 lr: 9.4065e-06 eta: 16:24:27 time: 1.1065 data_time: 0.0175 memory: 15348 grad_norm: 1.3382 loss: 0.2566 semantic_segmentation_loss_cls: 0.0715 semantic_segmentation_loss_mask: 0.0537 semantic_segmentation_loss_dice: 0.1314 2024/07/08 10:44:37 - mmengine - INFO - Iter(train) [ 66850/120000] base_lr: 8.3344e-05 lr: 9.3949e-06 eta: 16:23:31 time: 1.1067 data_time: 0.0175 memory: 14984 grad_norm: 1.3387 loss: 0.2565 semantic_segmentation_loss_cls: 0.0715 semantic_segmentation_loss_mask: 0.0536 semantic_segmentation_loss_dice: 0.1314 2024/07/08 10:45:32 - mmengine - INFO - Iter(train) [ 66900/120000] base_lr: 8.3216e-05 lr: 9.3833e-06 eta: 16:22:35 time: 1.1067 data_time: 0.0175 memory: 14937 grad_norm: 1.3382 loss: 0.2563 semantic_segmentation_loss_cls: 0.0714 semantic_segmentation_loss_mask: 0.0536 semantic_segmentation_loss_dice: 0.1313 2024/07/08 10:46:28 - mmengine - INFO - Iter(train) [ 66950/120000] base_lr: 8.3089e-05 lr: 9.3717e-06 eta: 16:21:40 time: 1.1069 data_time: 0.0176 memory: 15070 grad_norm: 1.3376 loss: 0.2566 semantic_segmentation_loss_cls: 0.0716 semantic_segmentation_loss_mask: 0.0537 semantic_segmentation_loss_dice: 0.1314 2024/07/08 10:47:24 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 10:47:24 - mmengine - INFO - Iter(train) [ 67000/120000] base_lr: 8.2961e-05 lr: 9.3601e-06 eta: 16:20:45 time: 1.1070 data_time: 0.0175 memory: 14903 grad_norm: 1.3380 loss: 0.2562 semantic_segmentation_loss_cls: 0.0714 semantic_segmentation_loss_mask: 0.0536 semantic_segmentation_loss_dice: 0.1312 2024/07/08 10:47:24 - mmengine - INFO - Saving checkpoint at 67000 iterations 2024/07/08 10:48:24 - mmengine - INFO - Iter(train) [ 67050/120000] base_lr: 8.2834e-05 lr: 9.3485e-06 eta: 16:19:53 time: 1.1071 data_time: 0.0176 memory: 14915 grad_norm: 1.3367 loss: 0.2561 semantic_segmentation_loss_cls: 0.0714 semantic_segmentation_loss_mask: 0.0536 semantic_segmentation_loss_dice: 0.1311 2024/07/08 10:49:20 - mmengine - INFO - Iter(train) [ 67100/120000] base_lr: 8.2706e-05 lr: 9.3370e-06 eta: 16:18:58 time: 1.1074 data_time: 0.0176 memory: 15323 grad_norm: 1.3372 loss: 0.2561 semantic_segmentation_loss_cls: 0.0714 semantic_segmentation_loss_mask: 0.0535 semantic_segmentation_loss_dice: 0.1312 2024/07/08 10:50:15 - mmengine - INFO - Iter(train) [ 67150/120000] base_lr: 8.2579e-05 lr: 9.3254e-06 eta: 16:18:02 time: 1.1076 data_time: 0.0176 memory: 14925 grad_norm: 1.3367 loss: 0.2563 semantic_segmentation_loss_cls: 0.0716 semantic_segmentation_loss_mask: 0.0536 semantic_segmentation_loss_dice: 0.1312 2024/07/08 10:51:11 - mmengine - INFO - Iter(train) [ 67200/120000] base_lr: 8.2452e-05 lr: 9.3138e-06 eta: 16:17:07 time: 1.1078 data_time: 0.0176 memory: 16825 grad_norm: 1.3373 loss: 0.2562 semantic_segmentation_loss_cls: 0.0715 semantic_segmentation_loss_mask: 0.0536 semantic_segmentation_loss_dice: 0.1311 2024/07/08 10:52:06 - mmengine - INFO - Iter(train) [ 67250/120000] base_lr: 8.2325e-05 lr: 9.3022e-06 eta: 16:16:11 time: 1.1078 data_time: 0.0176 memory: 15459 grad_norm: 1.3370 loss: 0.2561 semantic_segmentation_loss_cls: 0.0715 semantic_segmentation_loss_mask: 0.0535 semantic_segmentation_loss_dice: 0.1311 2024/07/08 10:53:02 - mmengine - INFO - Iter(train) [ 67300/120000] base_lr: 8.2197e-05 lr: 9.2907e-06 eta: 16:15:16 time: 1.1079 data_time: 0.0176 memory: 15086 grad_norm: 1.3363 loss: 0.2559 semantic_segmentation_loss_cls: 0.0713 semantic_segmentation_loss_mask: 0.0535 semantic_segmentation_loss_dice: 0.1310 2024/07/08 10:53:56 - mmengine - INFO - Iter(train) [ 67350/120000] base_lr: 8.2070e-05 lr: 9.2791e-06 eta: 16:14:19 time: 1.1077 data_time: 0.0176 memory: 15161 grad_norm: 1.3366 loss: 0.2556 semantic_segmentation_loss_cls: 0.0711 semantic_segmentation_loss_mask: 0.0536 semantic_segmentation_loss_dice: 0.1309 2024/07/08 10:54:52 - mmengine - INFO - Iter(train) [ 67400/120000] base_lr: 8.1943e-05 lr: 9.2675e-06 eta: 16:13:24 time: 1.1077 data_time: 0.0176 memory: 15185 grad_norm: 1.3372 loss: 0.2557 semantic_segmentation_loss_cls: 0.0712 semantic_segmentation_loss_mask: 0.0535 semantic_segmentation_loss_dice: 0.1309 2024/07/08 10:55:47 - mmengine - INFO - Iter(train) [ 67450/120000] base_lr: 8.1816e-05 lr: 9.2560e-06 eta: 16:12:28 time: 1.1079 data_time: 0.0176 memory: 16129 grad_norm: 1.3388 loss: 0.2556 semantic_segmentation_loss_cls: 0.0712 semantic_segmentation_loss_mask: 0.0535 semantic_segmentation_loss_dice: 0.1309 2024/07/08 10:56:42 - mmengine - INFO - Iter(train) [ 67500/120000] base_lr: 8.1689e-05 lr: 9.2444e-06 eta: 16:11:32 time: 1.1080 data_time: 0.0176 memory: 15354 grad_norm: 1.3378 loss: 0.2552 semantic_segmentation_loss_cls: 0.0710 semantic_segmentation_loss_mask: 0.0535 semantic_segmentation_loss_dice: 0.1308 2024/07/08 10:57:37 - mmengine - INFO - Iter(train) [ 67550/120000] base_lr: 8.1562e-05 lr: 9.2329e-06 eta: 16:10:36 time: 1.1079 data_time: 0.0176 memory: 14638 grad_norm: 1.3381 loss: 0.2550 semantic_segmentation_loss_cls: 0.0710 semantic_segmentation_loss_mask: 0.0533 semantic_segmentation_loss_dice: 0.1307 2024/07/08 10:58:32 - mmengine - INFO - Iter(train) [ 67600/120000] base_lr: 8.1434e-05 lr: 9.2213e-06 eta: 16:09:40 time: 1.1077 data_time: 0.0176 memory: 15316 grad_norm: 1.3401 loss: 0.2550 semantic_segmentation_loss_cls: 0.0709 semantic_segmentation_loss_mask: 0.0534 semantic_segmentation_loss_dice: 0.1307 2024/07/08 10:59:27 - mmengine - INFO - Iter(train) [ 67650/120000] base_lr: 8.1307e-05 lr: 9.2098e-06 eta: 16:08:44 time: 1.1078 data_time: 0.0176 memory: 15076 grad_norm: 1.3394 loss: 0.2549 semantic_segmentation_loss_cls: 0.0709 semantic_segmentation_loss_mask: 0.0533 semantic_segmentation_loss_dice: 0.1307 2024/07/08 11:00:22 - mmengine - INFO - Iter(train) [ 67700/120000] base_lr: 8.1180e-05 lr: 9.1982e-06 eta: 16:07:48 time: 1.1078 data_time: 0.0175 memory: 14939 grad_norm: 1.3398 loss: 0.2545 semantic_segmentation_loss_cls: 0.0706 semantic_segmentation_loss_mask: 0.0533 semantic_segmentation_loss_dice: 0.1306 2024/07/08 11:01:18 - mmengine - INFO - Iter(train) [ 67750/120000] base_lr: 8.1054e-05 lr: 9.1867e-06 eta: 16:06:53 time: 1.1079 data_time: 0.0175 memory: 15529 grad_norm: 1.3374 loss: 0.2546 semantic_segmentation_loss_cls: 0.0708 semantic_segmentation_loss_mask: 0.0532 semantic_segmentation_loss_dice: 0.1306 2024/07/08 11:02:13 - mmengine - INFO - Iter(train) [ 67800/120000] base_lr: 8.0927e-05 lr: 9.1751e-06 eta: 16:05:58 time: 1.1081 data_time: 0.0175 memory: 15083 grad_norm: 1.3384 loss: 0.2548 semantic_segmentation_loss_cls: 0.0708 semantic_segmentation_loss_mask: 0.0532 semantic_segmentation_loss_dice: 0.1307 2024/07/08 11:03:09 - mmengine - INFO - Iter(train) [ 67850/120000] base_lr: 8.0800e-05 lr: 9.1636e-06 eta: 16:05:02 time: 1.1084 data_time: 0.0175 memory: 15246 grad_norm: 1.3401 loss: 0.2551 semantic_segmentation_loss_cls: 0.0709 semantic_segmentation_loss_mask: 0.0533 semantic_segmentation_loss_dice: 0.1309 2024/07/08 11:04:04 - mmengine - INFO - Iter(train) [ 67900/120000] base_lr: 8.0673e-05 lr: 9.1521e-06 eta: 16:04:06 time: 1.1084 data_time: 0.0175 memory: 15552 grad_norm: 1.3385 loss: 0.2548 semantic_segmentation_loss_cls: 0.0708 semantic_segmentation_loss_mask: 0.0532 semantic_segmentation_loss_dice: 0.1307 2024/07/08 11:04:59 - mmengine - INFO - Iter(train) [ 67950/120000] base_lr: 8.0546e-05 lr: 9.1406e-06 eta: 16:03:10 time: 1.1084 data_time: 0.0175 memory: 14884 grad_norm: 1.3375 loss: 0.2546 semantic_segmentation_loss_cls: 0.0708 semantic_segmentation_loss_mask: 0.0532 semantic_segmentation_loss_dice: 0.1306 2024/07/08 11:05:54 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 11:05:54 - mmengine - INFO - Iter(train) [ 68000/120000] base_lr: 8.0419e-05 lr: 9.1290e-06 eta: 16:02:14 time: 1.1082 data_time: 0.0175 memory: 15196 grad_norm: 1.3368 loss: 0.2546 semantic_segmentation_loss_cls: 0.0707 semantic_segmentation_loss_mask: 0.0532 semantic_segmentation_loss_dice: 0.1307 2024/07/08 11:05:54 - mmengine - INFO - Saving checkpoint at 68000 iterations 2024/07/08 11:06:54 - mmengine - INFO - Iter(train) [ 68050/120000] base_lr: 8.0293e-05 lr: 9.1175e-06 eta: 16:01:22 time: 1.1083 data_time: 0.0175 memory: 15565 grad_norm: 1.3365 loss: 0.2545 semantic_segmentation_loss_cls: 0.0707 semantic_segmentation_loss_mask: 0.0532 semantic_segmentation_loss_dice: 0.1307 2024/07/08 11:07:48 - mmengine - INFO - Iter(train) [ 68100/120000] base_lr: 8.0166e-05 lr: 9.1060e-06 eta: 16:00:26 time: 1.1081 data_time: 0.0175 memory: 15615 grad_norm: 1.3367 loss: 0.2543 semantic_segmentation_loss_cls: 0.0706 semantic_segmentation_loss_mask: 0.0531 semantic_segmentation_loss_dice: 0.1306 2024/07/08 11:08:43 - mmengine - INFO - Iter(train) [ 68150/120000] base_lr: 8.0039e-05 lr: 9.0945e-06 eta: 15:59:30 time: 1.1081 data_time: 0.0175 memory: 14956 grad_norm: 1.3360 loss: 0.2542 semantic_segmentation_loss_cls: 0.0706 semantic_segmentation_loss_mask: 0.0531 semantic_segmentation_loss_dice: 0.1305 2024/07/08 11:09:37 - mmengine - INFO - Iter(train) [ 68200/120000] base_lr: 7.9913e-05 lr: 9.0830e-06 eta: 15:58:33 time: 1.1081 data_time: 0.0175 memory: 14958 grad_norm: 1.3352 loss: 0.2541 semantic_segmentation_loss_cls: 0.0705 semantic_segmentation_loss_mask: 0.0530 semantic_segmentation_loss_dice: 0.1305 2024/07/08 11:10:32 - mmengine - INFO - Iter(train) [ 68250/120000] base_lr: 7.9786e-05 lr: 9.0714e-06 eta: 15:57:37 time: 1.1085 data_time: 0.0174 memory: 15972 grad_norm: 1.3354 loss: 0.2541 semantic_segmentation_loss_cls: 0.0705 semantic_segmentation_loss_mask: 0.0530 semantic_segmentation_loss_dice: 0.1305 2024/07/08 11:11:27 - mmengine - INFO - Iter(train) [ 68300/120000] base_lr: 7.9659e-05 lr: 9.0599e-06 eta: 15:56:41 time: 1.1088 data_time: 0.0174 memory: 15343 grad_norm: 1.3339 loss: 0.2541 semantic_segmentation_loss_cls: 0.0706 semantic_segmentation_loss_mask: 0.0530 semantic_segmentation_loss_dice: 0.1305 2024/07/08 11:12:22 - mmengine - INFO - Iter(train) [ 68350/120000] base_lr: 7.9533e-05 lr: 9.0484e-06 eta: 15:55:45 time: 1.1087 data_time: 0.0175 memory: 14797 grad_norm: 1.3341 loss: 0.2541 semantic_segmentation_loss_cls: 0.0706 semantic_segmentation_loss_mask: 0.0530 semantic_segmentation_loss_dice: 0.1305 2024/07/08 11:13:18 - mmengine - INFO - Iter(train) [ 68400/120000] base_lr: 7.9406e-05 lr: 9.0369e-06 eta: 15:54:50 time: 1.1088 data_time: 0.0175 memory: 15277 grad_norm: 1.3344 loss: 0.2537 semantic_segmentation_loss_cls: 0.0704 semantic_segmentation_loss_mask: 0.0529 semantic_segmentation_loss_dice: 0.1303 2024/07/08 11:14:13 - mmengine - INFO - Iter(train) [ 68450/120000] base_lr: 7.9280e-05 lr: 9.0254e-06 eta: 15:53:54 time: 1.1089 data_time: 0.0175 memory: 15292 grad_norm: 1.3343 loss: 0.2533 semantic_segmentation_loss_cls: 0.0703 semantic_segmentation_loss_mask: 0.0529 semantic_segmentation_loss_dice: 0.1302 2024/07/08 11:15:08 - mmengine - INFO - Iter(train) [ 68500/120000] base_lr: 7.9153e-05 lr: 9.0140e-06 eta: 15:52:58 time: 1.1090 data_time: 0.0176 memory: 15906 grad_norm: 1.3348 loss: 0.2533 semantic_segmentation_loss_cls: 0.0703 semantic_segmentation_loss_mask: 0.0528 semantic_segmentation_loss_dice: 0.1302 2024/07/08 11:16:04 - mmengine - INFO - Iter(train) [ 68550/120000] base_lr: 7.9027e-05 lr: 9.0025e-06 eta: 15:52:03 time: 1.1091 data_time: 0.0175 memory: 14873 grad_norm: 1.3344 loss: 0.2532 semantic_segmentation_loss_cls: 0.0703 semantic_segmentation_loss_mask: 0.0528 semantic_segmentation_loss_dice: 0.1302 2024/07/08 11:16:59 - mmengine - INFO - Iter(train) [ 68600/120000] base_lr: 7.8901e-05 lr: 8.9910e-06 eta: 15:51:07 time: 1.1092 data_time: 0.0175 memory: 14967 grad_norm: 1.3334 loss: 0.2532 semantic_segmentation_loss_cls: 0.0703 semantic_segmentation_loss_mask: 0.0527 semantic_segmentation_loss_dice: 0.1302 2024/07/08 11:17:54 - mmengine - INFO - Iter(train) [ 68650/120000] base_lr: 7.8774e-05 lr: 8.9795e-06 eta: 15:50:12 time: 1.1092 data_time: 0.0175 memory: 14748 grad_norm: 1.3334 loss: 0.2535 semantic_segmentation_loss_cls: 0.0705 semantic_segmentation_loss_mask: 0.0527 semantic_segmentation_loss_dice: 0.1303 2024/07/08 11:18:49 - mmengine - INFO - Iter(train) [ 68700/120000] base_lr: 7.8648e-05 lr: 8.9680e-06 eta: 15:49:16 time: 1.1092 data_time: 0.0175 memory: 14715 grad_norm: 1.3314 loss: 0.2535 semantic_segmentation_loss_cls: 0.0705 semantic_segmentation_loss_mask: 0.0527 semantic_segmentation_loss_dice: 0.1303 2024/07/08 11:19:45 - mmengine - INFO - Iter(train) [ 68750/120000] base_lr: 7.8522e-05 lr: 8.9565e-06 eta: 15:48:20 time: 1.1092 data_time: 0.0175 memory: 15902 grad_norm: 1.3309 loss: 0.2533 semantic_segmentation_loss_cls: 0.0704 semantic_segmentation_loss_mask: 0.0527 semantic_segmentation_loss_dice: 0.1302 2024/07/08 11:20:40 - mmengine - INFO - Iter(train) [ 68800/120000] base_lr: 7.8396e-05 lr: 8.9451e-06 eta: 15:47:25 time: 1.1092 data_time: 0.0175 memory: 14761 grad_norm: 1.3309 loss: 0.2531 semantic_segmentation_loss_cls: 0.0703 semantic_segmentation_loss_mask: 0.0527 semantic_segmentation_loss_dice: 0.1301 2024/07/08 11:21:35 - mmengine - INFO - Iter(train) [ 68850/120000] base_lr: 7.8270e-05 lr: 8.9336e-06 eta: 15:46:29 time: 1.1090 data_time: 0.0175 memory: 16680 grad_norm: 1.3296 loss: 0.2528 semantic_segmentation_loss_cls: 0.0702 semantic_segmentation_loss_mask: 0.0526 semantic_segmentation_loss_dice: 0.1300 2024/07/08 11:22:31 - mmengine - INFO - Iter(train) [ 68900/120000] base_lr: 7.8144e-05 lr: 8.9221e-06 eta: 15:45:33 time: 1.1091 data_time: 0.0175 memory: 16024 grad_norm: 1.3296 loss: 0.2523 semantic_segmentation_loss_cls: 0.0700 semantic_segmentation_loss_mask: 0.0525 semantic_segmentation_loss_dice: 0.1298 2024/07/08 11:23:25 - mmengine - INFO - Iter(train) [ 68950/120000] base_lr: 7.8017e-05 lr: 8.9107e-06 eta: 15:44:37 time: 1.1090 data_time: 0.0175 memory: 16260 grad_norm: 1.3299 loss: 0.2521 semantic_segmentation_loss_cls: 0.0699 semantic_segmentation_loss_mask: 0.0525 semantic_segmentation_loss_dice: 0.1296 2024/07/08 11:24:21 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 11:24:21 - mmengine - INFO - Iter(train) [ 69000/120000] base_lr: 7.7891e-05 lr: 8.8992e-06 eta: 15:43:41 time: 1.1091 data_time: 0.0175 memory: 15646 grad_norm: 1.3287 loss: 0.2520 semantic_segmentation_loss_cls: 0.0699 semantic_segmentation_loss_mask: 0.0525 semantic_segmentation_loss_dice: 0.1296 2024/07/08 11:24:21 - mmengine - INFO - Saving checkpoint at 69000 iterations 2024/07/08 11:25:20 - mmengine - INFO - Iter(train) [ 69050/120000] base_lr: 7.7765e-05 lr: 8.8878e-06 eta: 15:42:49 time: 1.1100 data_time: 0.0184 memory: 15130 grad_norm: 1.3286 loss: 0.2514 semantic_segmentation_loss_cls: 0.0697 semantic_segmentation_loss_mask: 0.0524 semantic_segmentation_loss_dice: 0.1294 2024/07/08 11:26:16 - mmengine - INFO - Iter(train) [ 69100/120000] base_lr: 7.7639e-05 lr: 8.8763e-06 eta: 15:41:54 time: 1.1100 data_time: 0.0184 memory: 15020 grad_norm: 1.3288 loss: 0.2512 semantic_segmentation_loss_cls: 0.0695 semantic_segmentation_loss_mask: 0.0524 semantic_segmentation_loss_dice: 0.1293 2024/07/08 11:27:11 - mmengine - INFO - Iter(train) [ 69150/120000] base_lr: 7.7514e-05 lr: 8.8649e-06 eta: 15:40:58 time: 1.1100 data_time: 0.0184 memory: 15397 grad_norm: 1.3271 loss: 0.2513 semantic_segmentation_loss_cls: 0.0695 semantic_segmentation_loss_mask: 0.0524 semantic_segmentation_loss_dice: 0.1293 2024/07/08 11:28:06 - mmengine - INFO - Iter(train) [ 69200/120000] base_lr: 7.7388e-05 lr: 8.8534e-06 eta: 15:40:02 time: 1.1099 data_time: 0.0184 memory: 15640 grad_norm: 1.3268 loss: 0.2514 semantic_segmentation_loss_cls: 0.0697 semantic_segmentation_loss_mask: 0.0524 semantic_segmentation_loss_dice: 0.1294 2024/07/08 11:29:01 - mmengine - INFO - Iter(train) [ 69250/120000] base_lr: 7.7262e-05 lr: 8.8420e-06 eta: 15:39:06 time: 1.1097 data_time: 0.0184 memory: 14770 grad_norm: 1.3259 loss: 0.2512 semantic_segmentation_loss_cls: 0.0696 semantic_segmentation_loss_mask: 0.0523 semantic_segmentation_loss_dice: 0.1293 2024/07/08 11:29:55 - mmengine - INFO - Iter(train) [ 69300/120000] base_lr: 7.7136e-05 lr: 8.8306e-06 eta: 15:38:09 time: 1.1095 data_time: 0.0183 memory: 15327 grad_norm: 1.3260 loss: 0.2514 semantic_segmentation_loss_cls: 0.0697 semantic_segmentation_loss_mask: 0.0523 semantic_segmentation_loss_dice: 0.1294 2024/07/08 11:30:49 - mmengine - INFO - Iter(train) [ 69350/120000] base_lr: 7.7010e-05 lr: 8.8191e-06 eta: 15:37:12 time: 1.1092 data_time: 0.0183 memory: 15725 grad_norm: 1.3271 loss: 0.2516 semantic_segmentation_loss_cls: 0.0698 semantic_segmentation_loss_mask: 0.0523 semantic_segmentation_loss_dice: 0.1295 2024/07/08 11:31:44 - mmengine - INFO - Iter(train) [ 69400/120000] base_lr: 7.6885e-05 lr: 8.8077e-06 eta: 15:36:17 time: 1.1091 data_time: 0.0182 memory: 15671 grad_norm: 1.3264 loss: 0.2517 semantic_segmentation_loss_cls: 0.0699 semantic_segmentation_loss_mask: 0.0523 semantic_segmentation_loss_dice: 0.1295 2024/07/08 11:32:39 - mmengine - INFO - Iter(train) [ 69450/120000] base_lr: 7.6759e-05 lr: 8.7963e-06 eta: 15:35:21 time: 1.1089 data_time: 0.0182 memory: 15470 grad_norm: 1.3260 loss: 0.2513 semantic_segmentation_loss_cls: 0.0696 semantic_segmentation_loss_mask: 0.0522 semantic_segmentation_loss_dice: 0.1294 2024/07/08 11:33:35 - mmengine - INFO - Iter(train) [ 69500/120000] base_lr: 7.6633e-05 lr: 8.7848e-06 eta: 15:34:26 time: 1.1093 data_time: 0.0183 memory: 14974 grad_norm: 1.3256 loss: 0.2507 semantic_segmentation_loss_cls: 0.0694 semantic_segmentation_loss_mask: 0.0521 semantic_segmentation_loss_dice: 0.1292 2024/07/08 11:34:31 - mmengine - INFO - Iter(train) [ 69550/120000] base_lr: 7.6508e-05 lr: 8.7734e-06 eta: 15:33:30 time: 1.1094 data_time: 0.0182 memory: 15569 grad_norm: 1.3242 loss: 0.2507 semantic_segmentation_loss_cls: 0.0694 semantic_segmentation_loss_mask: 0.0521 semantic_segmentation_loss_dice: 0.1292 2024/07/08 11:35:25 - mmengine - INFO - Iter(train) [ 69600/120000] base_lr: 7.6382e-05 lr: 8.7620e-06 eta: 15:32:34 time: 1.1093 data_time: 0.0182 memory: 15429 grad_norm: 1.3236 loss: 0.2506 semantic_segmentation_loss_cls: 0.0694 semantic_segmentation_loss_mask: 0.0521 semantic_segmentation_loss_dice: 0.1291 2024/07/08 11:36:21 - mmengine - INFO - Iter(train) [ 69650/120000] base_lr: 7.6257e-05 lr: 8.7506e-06 eta: 15:31:38 time: 1.1092 data_time: 0.0182 memory: 14990 grad_norm: 1.3221 loss: 0.2506 semantic_segmentation_loss_cls: 0.0694 semantic_segmentation_loss_mask: 0.0521 semantic_segmentation_loss_dice: 0.1291 2024/07/08 11:37:15 - mmengine - INFO - Iter(train) [ 69700/120000] base_lr: 7.6131e-05 lr: 8.7392e-06 eta: 15:30:42 time: 1.1091 data_time: 0.0182 memory: 15671 grad_norm: 1.3220 loss: 0.2502 semantic_segmentation_loss_cls: 0.0693 semantic_segmentation_loss_mask: 0.0519 semantic_segmentation_loss_dice: 0.1289 2024/07/08 11:38:10 - mmengine - INFO - Iter(train) [ 69750/120000] base_lr: 7.6006e-05 lr: 8.7278e-06 eta: 15:29:46 time: 1.1089 data_time: 0.0182 memory: 15282 grad_norm: 1.3225 loss: 0.2503 semantic_segmentation_loss_cls: 0.0694 semantic_segmentation_loss_mask: 0.0519 semantic_segmentation_loss_dice: 0.1290 2024/07/08 11:39:05 - mmengine - INFO - Iter(train) [ 69800/120000] base_lr: 7.5880e-05 lr: 8.7164e-06 eta: 15:28:51 time: 1.1087 data_time: 0.0182 memory: 14236 grad_norm: 1.3234 loss: 0.2502 semantic_segmentation_loss_cls: 0.0693 semantic_segmentation_loss_mask: 0.0519 semantic_segmentation_loss_dice: 0.1289 2024/07/08 11:40:01 - mmengine - INFO - Iter(train) [ 69850/120000] base_lr: 7.5755e-05 lr: 8.7050e-06 eta: 15:27:55 time: 1.1088 data_time: 0.0182 memory: 15410 grad_norm: 1.3219 loss: 0.2502 semantic_segmentation_loss_cls: 0.0694 semantic_segmentation_loss_mask: 0.0519 semantic_segmentation_loss_dice: 0.1290 2024/07/08 11:40:56 - mmengine - INFO - Iter(train) [ 69900/120000] base_lr: 7.5630e-05 lr: 8.6936e-06 eta: 15:26:59 time: 1.1087 data_time: 0.0182 memory: 15489 grad_norm: 1.3218 loss: 0.2502 semantic_segmentation_loss_cls: 0.0694 semantic_segmentation_loss_mask: 0.0519 semantic_segmentation_loss_dice: 0.1290 2024/07/08 11:41:50 - mmengine - INFO - Iter(train) [ 69950/120000] base_lr: 7.5505e-05 lr: 8.6822e-06 eta: 15:26:03 time: 1.1086 data_time: 0.0182 memory: 14511 grad_norm: 1.3215 loss: 0.2498 semantic_segmentation_loss_cls: 0.0692 semantic_segmentation_loss_mask: 0.0518 semantic_segmentation_loss_dice: 0.1288 2024/07/08 11:42:46 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 11:42:46 - mmengine - INFO - Iter(train) [ 70000/120000] base_lr: 7.5379e-05 lr: 8.6709e-06 eta: 15:25:07 time: 1.1086 data_time: 0.0182 memory: 15023 grad_norm: 1.3219 loss: 0.2499 semantic_segmentation_loss_cls: 0.0693 semantic_segmentation_loss_mask: 0.0518 semantic_segmentation_loss_dice: 0.1288 2024/07/08 11:42:46 - mmengine - INFO - Saving checkpoint at 70000 iterations 2024/07/08 11:43:03 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:52 time: 0.2452 data_time: 0.0015 memory: 5013 2024/07/08 11:43:15 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:39 time: 0.2452 data_time: 0.0015 memory: 5189 2024/07/08 11:43:28 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:26 time: 0.2451 data_time: 0.0015 memory: 4460 2024/07/08 11:43:40 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:14 time: 0.2452 data_time: 0.0015 memory: 4543 2024/07/08 11:43:52 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:02 time: 0.2452 data_time: 0.0015 memory: 4645 2024/07/08 11:44:05 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:49 time: 0.2452 data_time: 0.0015 memory: 10983 2024/07/08 11:44:17 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:37 time: 0.2452 data_time: 0.0015 memory: 4460 2024/07/08 11:44:30 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2453 data_time: 0.0015 memory: 4641 2024/07/08 11:44:42 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2453 data_time: 0.0015 memory: 4473 2024/07/08 11:44:55 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2454 data_time: 0.0015 memory: 4555 2024/07/08 11:44:55 - mmengine - INFO - per class results: 2024/07/08 11:44:55 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.4 | 87.3 | | building | 83.17 | 90.78 | | sky | 94.28 | 97.68 | | floor | 83.26 | 91.34 | | tree | 74.83 | 87.83 | | ceiling | 85.44 | 93.21 | | road | 83.56 | 91.74 | | bed | 86.6 | 95.09 | | windowpane | 62.18 | 80.59 | | grass | 71.57 | 87.52 | | cabinet | 61.8 | 73.45 | | sidewalk | 67.2 | 81.8 | | person | 82.43 | 91.71 | | earth | 35.19 | 47.53 | | door | 52.59 | 68.38 | | table | 61.2 | 75.28 | | mountain | 59.34 | 72.92 | | plant | 53.82 | 67.93 | | curtain | 72.72 | 87.15 | | chair | 59.29 | 72.45 | | car | 84.55 | 91.61 | | water | 48.02 | 64.12 | | painting | 72.28 | 88.92 | | sofa | 62.83 | 74.41 | | shelf | 44.28 | 65.69 | | house | 50.55 | 76.67 | | sea | 45.88 | 70.76 | | mirror | 67.02 | 76.57 | | rug | 69.6 | 76.68 | | field | 34.51 | 50.12 | | armchair | 44.79 | 69.15 | | seat | 57.68 | 80.74 | | fence | 47.95 | 66.17 | | desk | 47.6 | 69.5 | | rock | 39.11 | 58.03 | | wardrobe | 53.07 | 68.62 | | lamp | 68.45 | 80.22 | | bathtub | 86.4 | 91.56 | | railing | 37.83 | 54.27 | | cushion | 57.81 | 70.42 | | base | 24.09 | 40.31 | | box | 25.73 | 37.19 | | column | 49.55 | 68.7 | | signboard | 37.47 | 53.74 | | chest of drawers | 43.28 | 67.64 | | counter | 33.95 | 48.65 | | sand | 35.11 | 50.63 | | sink | 75.6 | 82.29 | | skyscraper | 48.62 | 61.55 | | fireplace | 68.91 | 88.18 | | refrigerator | 80.42 | 87.91 | | grandstand | 41.25 | 74.8 | | path | 30.74 | 42.69 | | stairs | 30.61 | 41.34 | | runway | 75.93 | 89.61 | | case | 58.72 | 67.91 | | pool table | 92.09 | 96.42 | | pillow | 55.17 | 67.14 | | screen door | 81.6 | 84.98 | | stairway | 39.31 | 44.48 | | river | 21.51 | 44.47 | | bridge | 70.29 | 89.54 | | bookcase | 41.62 | 57.3 | | blind | 38.11 | 43.59 | | coffee table | 73.82 | 87.18 | | toilet | 77.46 | 89.3 | | flower | 40.42 | 58.67 | | book | 52.68 | 75.51 | | hill | 10.93 | 19.6 | | bench | 41.13 | 48.25 | | countertop | 55.65 | 68.69 | | stove | 79.74 | 84.85 | | palm | 53.45 | 69.5 | | kitchen island | 33.67 | 78.09 | | computer | 61.07 | 67.69 | | swivel chair | 39.23 | 54.66 | | boat | 76.1 | 86.37 | | bar | 46.55 | 59.05 | | arcade machine | 58.8 | 65.32 | | hovel | 14.21 | 19.94 | | bus | 89.49 | 93.08 | | towel | 69.32 | 75.72 | | light | 64.33 | 78.05 | | truck | 36.79 | 47.24 | | tower | 32.74 | 54.31 | | chandelier | 66.77 | 79.71 | | awning | 31.45 | 44.96 | | streetlight | 39.03 | 54.2 | | booth | 62.74 | 65.09 | | television receiver | 50.91 | 89.57 | | airplane | 61.89 | 69.02 | | dirt track | 1.01 | 1.23 | | apparel | 38.58 | 54.56 | | pole | 32.16 | 49.03 | | land | 2.44 | 3.07 | | bannister | 13.62 | 25.08 | | escalator | 52.23 | 67.95 | | ottoman | 37.47 | 66.62 | | bottle | 20.9 | 26.17 | | buffet | 41.14 | 45.21 | | poster | 29.76 | 42.43 | | stage | 17.7 | 29.75 | | van | 45.16 | 65.98 | | ship | 84.66 | 87.72 | | fountain | 7.61 | 8.22 | | conveyer belt | 58.57 | 91.62 | | canopy | 15.52 | 24.88 | | washer | 69.31 | 73.23 | | plaything | 29.72 | 39.81 | | swimming pool | 30.7 | 34.12 | | stool | 52.85 | 69.54 | | barrel | 13.97 | 55.64 | | basket | 33.92 | 41.98 | | waterfall | 36.48 | 51.74 | | tent | 93.29 | 97.79 | | bag | 15.91 | 22.98 | | minibike | 53.24 | 63.23 | | cradle | 75.81 | 96.74 | | oven | 54.75 | 66.24 | | ball | 30.02 | 35.45 | | food | 64.14 | 79.52 | | step | 28.82 | 37.85 | | tank | 37.73 | 50.26 | | trade name | 29.39 | 36.12 | | microwave | 38.34 | 41.22 | | pot | 53.46 | 60.73 | | animal | 60.99 | 68.98 | | bicycle | 57.15 | 78.83 | | lake | 63.55 | 63.69 | | dishwasher | 81.12 | 85.16 | | screen | 69.4 | 83.24 | | blanket | 12.08 | 15.51 | | sculpture | 66.32 | 82.48 | | hood | 77.08 | 81.35 | | sconce | 53.02 | 64.71 | | vase | 49.17 | 65.38 | | traffic light | 40.15 | 59.14 | | tray | 17.43 | 22.52 | | ashcan | 44.55 | 59.69 | | fan | 64.84 | 81.46 | | pier | 31.81 | 64.4 | | crt screen | 0.0 | 0.01 | | plate | 61.71 | 74.16 | | monitor | 5.23 | 7.11 | | bulletin board | 23.9 | 29.05 | | shower | 9.59 | 18.64 | | radiator | 60.72 | 72.87 | | glass | 17.93 | 19.31 | | clock | 33.4 | 37.25 | | flag | 45.56 | 54.49 | +---------------------+-------+-------+ 2024/07/08 11:44:55 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.4800 mIoU: 50.2400 mAcc: 62.8900 data_time: 0.0016 time: 0.2483 2024/07/08 11:45:50 - mmengine - INFO - Iter(train) [ 70050/120000] base_lr: 7.5254e-05 lr: 8.6595e-06 eta: 15:24:12 time: 1.1076 data_time: 0.0172 memory: 15653 grad_norm: 1.3218 loss: 0.2496 semantic_segmentation_loss_cls: 0.0692 semantic_segmentation_loss_mask: 0.0517 semantic_segmentation_loss_dice: 0.1287 2024/07/08 11:46:45 - mmengine - INFO - Iter(train) [ 70100/120000] base_lr: 7.5129e-05 lr: 8.6481e-06 eta: 15:23:16 time: 1.1074 data_time: 0.0171 memory: 14462 grad_norm: 1.3225 loss: 0.2496 semantic_segmentation_loss_cls: 0.0691 semantic_segmentation_loss_mask: 0.0517 semantic_segmentation_loss_dice: 0.1287 2024/07/08 11:47:40 - mmengine - INFO - Iter(train) [ 70150/120000] base_lr: 7.5004e-05 lr: 8.6367e-06 eta: 15:22:20 time: 1.1071 data_time: 0.0171 memory: 14800 grad_norm: 1.3226 loss: 0.2494 semantic_segmentation_loss_cls: 0.0690 semantic_segmentation_loss_mask: 0.0517 semantic_segmentation_loss_dice: 0.1287 2024/07/08 11:48:35 - mmengine - INFO - Iter(train) [ 70200/120000] base_lr: 7.4879e-05 lr: 8.6254e-06 eta: 15:21:24 time: 1.1070 data_time: 0.0171 memory: 15348 grad_norm: 1.3207 loss: 0.2492 semantic_segmentation_loss_cls: 0.0690 semantic_segmentation_loss_mask: 0.0516 semantic_segmentation_loss_dice: 0.1286 2024/07/08 11:49:31 - mmengine - INFO - Iter(train) [ 70250/120000] base_lr: 7.4754e-05 lr: 8.6140e-06 eta: 15:20:29 time: 1.1071 data_time: 0.0171 memory: 14936 grad_norm: 1.3207 loss: 0.2492 semantic_segmentation_loss_cls: 0.0690 semantic_segmentation_loss_mask: 0.0516 semantic_segmentation_loss_dice: 0.1286 2024/07/08 11:50:26 - mmengine - INFO - Iter(train) [ 70300/120000] base_lr: 7.4629e-05 lr: 8.6027e-06 eta: 15:19:33 time: 1.1071 data_time: 0.0171 memory: 15267 grad_norm: 1.3208 loss: 0.2490 semantic_segmentation_loss_cls: 0.0689 semantic_segmentation_loss_mask: 0.0516 semantic_segmentation_loss_dice: 0.1285 2024/07/08 11:51:21 - mmengine - INFO - Iter(train) [ 70350/120000] base_lr: 7.4504e-05 lr: 8.5913e-06 eta: 15:18:37 time: 1.1071 data_time: 0.0171 memory: 15576 grad_norm: 1.3213 loss: 0.2488 semantic_segmentation_loss_cls: 0.0688 semantic_segmentation_loss_mask: 0.0516 semantic_segmentation_loss_dice: 0.1284 2024/07/08 11:52:17 - mmengine - INFO - Iter(train) [ 70400/120000] base_lr: 7.4379e-05 lr: 8.5799e-06 eta: 15:17:42 time: 1.1071 data_time: 0.0171 memory: 14947 grad_norm: 1.3217 loss: 0.2488 semantic_segmentation_loss_cls: 0.0688 semantic_segmentation_loss_mask: 0.0516 semantic_segmentation_loss_dice: 0.1284 2024/07/08 11:53:12 - mmengine - INFO - Iter(train) [ 70450/120000] base_lr: 7.4255e-05 lr: 8.5686e-06 eta: 15:16:46 time: 1.1068 data_time: 0.0171 memory: 15117 grad_norm: 1.3210 loss: 0.2486 semantic_segmentation_loss_cls: 0.0687 semantic_segmentation_loss_mask: 0.0516 semantic_segmentation_loss_dice: 0.1283 2024/07/08 11:54:07 - mmengine - INFO - Iter(train) [ 70500/120000] base_lr: 7.4130e-05 lr: 8.5573e-06 eta: 15:15:50 time: 1.1067 data_time: 0.0171 memory: 14975 grad_norm: 1.3206 loss: 0.2488 semantic_segmentation_loss_cls: 0.0687 semantic_segmentation_loss_mask: 0.0516 semantic_segmentation_loss_dice: 0.1284 2024/07/08 11:55:01 - mmengine - INFO - Iter(train) [ 70550/120000] base_lr: 7.4005e-05 lr: 8.5459e-06 eta: 15:14:54 time: 1.1068 data_time: 0.0171 memory: 15095 grad_norm: 1.3211 loss: 0.2487 semantic_segmentation_loss_cls: 0.0687 semantic_segmentation_loss_mask: 0.0516 semantic_segmentation_loss_dice: 0.1284 2024/07/08 11:55:57 - mmengine - INFO - Iter(train) [ 70600/120000] base_lr: 7.3881e-05 lr: 8.5346e-06 eta: 15:13:58 time: 1.1068 data_time: 0.0171 memory: 15102 grad_norm: 1.3215 loss: 0.2487 semantic_segmentation_loss_cls: 0.0687 semantic_segmentation_loss_mask: 0.0516 semantic_segmentation_loss_dice: 0.1284 2024/07/08 11:56:52 - mmengine - INFO - Iter(train) [ 70650/120000] base_lr: 7.3756e-05 lr: 8.5233e-06 eta: 15:13:02 time: 1.1065 data_time: 0.0171 memory: 16301 grad_norm: 1.3197 loss: 0.2485 semantic_segmentation_loss_cls: 0.0686 semantic_segmentation_loss_mask: 0.0516 semantic_segmentation_loss_dice: 0.1282 2024/07/08 11:57:46 - mmengine - INFO - Iter(train) [ 70700/120000] base_lr: 7.3631e-05 lr: 8.5119e-06 eta: 15:12:06 time: 1.1064 data_time: 0.0171 memory: 15172 grad_norm: 1.3194 loss: 0.2484 semantic_segmentation_loss_cls: 0.0686 semantic_segmentation_loss_mask: 0.0516 semantic_segmentation_loss_dice: 0.1282 2024/07/08 11:58:41 - mmengine - INFO - Iter(train) [ 70750/120000] base_lr: 7.3507e-05 lr: 8.5006e-06 eta: 15:11:10 time: 1.1063 data_time: 0.0171 memory: 15585 grad_norm: 1.3184 loss: 0.2478 semantic_segmentation_loss_cls: 0.0683 semantic_segmentation_loss_mask: 0.0515 semantic_segmentation_loss_dice: 0.1279 2024/07/08 11:59:36 - mmengine - INFO - Iter(train) [ 70800/120000] base_lr: 7.3382e-05 lr: 8.4893e-06 eta: 15:10:14 time: 1.1062 data_time: 0.0171 memory: 15279 grad_norm: 1.3184 loss: 0.2476 semantic_segmentation_loss_cls: 0.0682 semantic_segmentation_loss_mask: 0.0516 semantic_segmentation_loss_dice: 0.1279 2024/07/08 12:00:32 - mmengine - INFO - Iter(train) [ 70850/120000] base_lr: 7.3258e-05 lr: 8.4780e-06 eta: 15:09:19 time: 1.1064 data_time: 0.0171 memory: 14869 grad_norm: 1.3168 loss: 0.2476 semantic_segmentation_loss_cls: 0.0680 semantic_segmentation_loss_mask: 0.0516 semantic_segmentation_loss_dice: 0.1279 2024/07/08 12:01:27 - mmengine - INFO - Iter(train) [ 70900/120000] base_lr: 7.3134e-05 lr: 8.4667e-06 eta: 15:08:23 time: 1.1063 data_time: 0.0170 memory: 14812 grad_norm: 1.3169 loss: 0.2475 semantic_segmentation_loss_cls: 0.0681 semantic_segmentation_loss_mask: 0.0516 semantic_segmentation_loss_dice: 0.1278 2024/07/08 12:02:22 - mmengine - INFO - Iter(train) [ 70950/120000] base_lr: 7.3009e-05 lr: 8.4554e-06 eta: 15:07:27 time: 1.1060 data_time: 0.0170 memory: 14506 grad_norm: 1.3148 loss: 0.2472 semantic_segmentation_loss_cls: 0.0679 semantic_segmentation_loss_mask: 0.0516 semantic_segmentation_loss_dice: 0.1277 2024/07/08 12:03:16 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 12:03:16 - mmengine - INFO - Iter(train) [ 71000/120000] base_lr: 7.2885e-05 lr: 8.4441e-06 eta: 15:06:31 time: 1.1056 data_time: 0.0169 memory: 14971 grad_norm: 1.3147 loss: 0.2473 semantic_segmentation_loss_cls: 0.0680 semantic_segmentation_loss_mask: 0.0516 semantic_segmentation_loss_dice: 0.1277 2024/07/08 12:03:16 - mmengine - INFO - Saving checkpoint at 71000 iterations 2024/07/08 12:04:16 - mmengine - INFO - Iter(train) [ 71050/120000] base_lr: 7.2761e-05 lr: 8.4328e-06 eta: 15:05:38 time: 1.1054 data_time: 0.0169 memory: 15387 grad_norm: 1.3139 loss: 0.2467 semantic_segmentation_loss_cls: 0.0678 semantic_segmentation_loss_mask: 0.0515 semantic_segmentation_loss_dice: 0.1274 2024/07/08 12:05:10 - mmengine - INFO - Iter(train) [ 71100/120000] base_lr: 7.2637e-05 lr: 8.4215e-06 eta: 15:04:42 time: 1.1052 data_time: 0.0169 memory: 15516 grad_norm: 1.3121 loss: 0.2466 semantic_segmentation_loss_cls: 0.0677 semantic_segmentation_loss_mask: 0.0515 semantic_segmentation_loss_dice: 0.1274 2024/07/08 12:06:05 - mmengine - INFO - Iter(train) [ 71150/120000] base_lr: 7.2512e-05 lr: 8.4102e-06 eta: 15:03:46 time: 1.1050 data_time: 0.0169 memory: 16147 grad_norm: 1.3111 loss: 0.2464 semantic_segmentation_loss_cls: 0.0675 semantic_segmentation_loss_mask: 0.0515 semantic_segmentation_loss_dice: 0.1274 2024/07/08 12:07:00 - mmengine - INFO - Iter(train) [ 71200/120000] base_lr: 7.2388e-05 lr: 8.3989e-06 eta: 15:02:50 time: 1.1049 data_time: 0.0169 memory: 15273 grad_norm: 1.3092 loss: 0.2462 semantic_segmentation_loss_cls: 0.0676 semantic_segmentation_loss_mask: 0.0514 semantic_segmentation_loss_dice: 0.1272 2024/07/08 12:07:56 - mmengine - INFO - Iter(train) [ 71250/120000] base_lr: 7.2264e-05 lr: 8.3877e-06 eta: 15:01:55 time: 1.1050 data_time: 0.0169 memory: 15221 grad_norm: 1.3088 loss: 0.2461 semantic_segmentation_loss_cls: 0.0675 semantic_segmentation_loss_mask: 0.0514 semantic_segmentation_loss_dice: 0.1272 2024/07/08 12:08:52 - mmengine - INFO - Iter(train) [ 71300/120000] base_lr: 7.2140e-05 lr: 8.3764e-06 eta: 15:01:00 time: 1.1050 data_time: 0.0169 memory: 15721 grad_norm: 1.3062 loss: 0.2459 semantic_segmentation_loss_cls: 0.0675 semantic_segmentation_loss_mask: 0.0513 semantic_segmentation_loss_dice: 0.1271 2024/07/08 12:09:47 - mmengine - INFO - Iter(train) [ 71350/120000] base_lr: 7.2016e-05 lr: 8.3651e-06 eta: 15:00:04 time: 1.1054 data_time: 0.0169 memory: 16219 grad_norm: 1.3055 loss: 0.2458 semantic_segmentation_loss_cls: 0.0676 semantic_segmentation_loss_mask: 0.0512 semantic_segmentation_loss_dice: 0.1270 2024/07/08 12:10:43 - mmengine - INFO - Iter(train) [ 71400/120000] base_lr: 7.1892e-05 lr: 8.3539e-06 eta: 14:59:09 time: 1.1054 data_time: 0.0169 memory: 15764 grad_norm: 1.3056 loss: 0.2458 semantic_segmentation_loss_cls: 0.0676 semantic_segmentation_loss_mask: 0.0512 semantic_segmentation_loss_dice: 0.1270 2024/07/08 12:11:37 - mmengine - INFO - Iter(train) [ 71450/120000] base_lr: 7.1769e-05 lr: 8.3426e-06 eta: 14:58:13 time: 1.1052 data_time: 0.0169 memory: 15854 grad_norm: 1.3035 loss: 0.2457 semantic_segmentation_loss_cls: 0.0675 semantic_segmentation_loss_mask: 0.0512 semantic_segmentation_loss_dice: 0.1270 2024/07/08 12:12:33 - mmengine - INFO - Iter(train) [ 71500/120000] base_lr: 7.1645e-05 lr: 8.3314e-06 eta: 14:57:17 time: 1.1052 data_time: 0.0169 memory: 16269 grad_norm: 1.3038 loss: 0.2458 semantic_segmentation_loss_cls: 0.0676 semantic_segmentation_loss_mask: 0.0512 semantic_segmentation_loss_dice: 0.1270 2024/07/08 12:13:28 - mmengine - INFO - Iter(train) [ 71550/120000] base_lr: 7.1521e-05 lr: 8.3201e-06 eta: 14:56:21 time: 1.1052 data_time: 0.0169 memory: 14809 grad_norm: 1.3038 loss: 0.2454 semantic_segmentation_loss_cls: 0.0673 semantic_segmentation_loss_mask: 0.0512 semantic_segmentation_loss_dice: 0.1268 2024/07/08 12:14:24 - mmengine - INFO - Iter(train) [ 71600/120000] base_lr: 7.1397e-05 lr: 8.3089e-06 eta: 14:55:26 time: 1.1055 data_time: 0.0169 memory: 14685 grad_norm: 1.3020 loss: 0.2454 semantic_segmentation_loss_cls: 0.0674 semantic_segmentation_loss_mask: 0.0512 semantic_segmentation_loss_dice: 0.1268 2024/07/08 12:15:19 - mmengine - INFO - Iter(train) [ 71650/120000] base_lr: 7.1274e-05 lr: 8.2976e-06 eta: 14:54:30 time: 1.1054 data_time: 0.0169 memory: 14911 grad_norm: 1.3036 loss: 0.2453 semantic_segmentation_loss_cls: 0.0673 semantic_segmentation_loss_mask: 0.0512 semantic_segmentation_loss_dice: 0.1268 2024/07/08 12:16:13 - mmengine - INFO - Iter(train) [ 71700/120000] base_lr: 7.1150e-05 lr: 8.2864e-06 eta: 14:53:34 time: 1.1054 data_time: 0.0169 memory: 15415 grad_norm: 1.3024 loss: 0.2452 semantic_segmentation_loss_cls: 0.0673 semantic_segmentation_loss_mask: 0.0512 semantic_segmentation_loss_dice: 0.1267 2024/07/08 12:17:08 - mmengine - INFO - Iter(train) [ 71750/120000] base_lr: 7.1027e-05 lr: 8.2752e-06 eta: 14:52:38 time: 1.1053 data_time: 0.0169 memory: 14881 grad_norm: 1.3025 loss: 0.2451 semantic_segmentation_loss_cls: 0.0672 semantic_segmentation_loss_mask: 0.0512 semantic_segmentation_loss_dice: 0.1267 2024/07/08 12:18:04 - mmengine - INFO - Iter(train) [ 71800/120000] base_lr: 7.0903e-05 lr: 8.2639e-06 eta: 14:51:43 time: 1.1052 data_time: 0.0169 memory: 15395 grad_norm: 1.3001 loss: 0.2450 semantic_segmentation_loss_cls: 0.0673 semantic_segmentation_loss_mask: 0.0512 semantic_segmentation_loss_dice: 0.1265 2024/07/08 12:18:59 - mmengine - INFO - Iter(train) [ 71850/120000] base_lr: 7.0780e-05 lr: 8.2527e-06 eta: 14:50:47 time: 1.1051 data_time: 0.0169 memory: 15402 grad_norm: 1.2959 loss: 0.2447 semantic_segmentation_loss_cls: 0.0672 semantic_segmentation_loss_mask: 0.0511 semantic_segmentation_loss_dice: 0.1264 2024/07/08 12:19:54 - mmengine - INFO - Iter(train) [ 71900/120000] base_lr: 7.0656e-05 lr: 8.2415e-06 eta: 14:49:51 time: 1.1052 data_time: 0.0169 memory: 15120 grad_norm: 1.2966 loss: 0.2448 semantic_segmentation_loss_cls: 0.0673 semantic_segmentation_loss_mask: 0.0511 semantic_segmentation_loss_dice: 0.1264 2024/07/08 12:20:50 - 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grad_norm: 1.2968 loss: 0.2442 semantic_segmentation_loss_cls: 0.0671 semantic_segmentation_loss_mask: 0.0510 semantic_segmentation_loss_dice: 0.1261 2024/07/08 12:23:39 - mmengine - INFO - Iter(train) [ 72100/120000] base_lr: 7.0163e-05 lr: 8.1967e-06 eta: 14:46:11 time: 1.1052 data_time: 0.0170 memory: 15292 grad_norm: 1.2969 loss: 0.2443 semantic_segmentation_loss_cls: 0.0671 semantic_segmentation_loss_mask: 0.0510 semantic_segmentation_loss_dice: 0.1262 2024/07/08 12:24:34 - mmengine - INFO - Iter(train) [ 72150/120000] base_lr: 7.0040e-05 lr: 8.1855e-06 eta: 14:45:15 time: 1.1054 data_time: 0.0170 memory: 15055 grad_norm: 1.2963 loss: 0.2442 semantic_segmentation_loss_cls: 0.0671 semantic_segmentation_loss_mask: 0.0510 semantic_segmentation_loss_dice: 0.1261 2024/07/08 12:25:29 - mmengine - INFO - Iter(train) [ 72200/120000] base_lr: 6.9917e-05 lr: 8.1743e-06 eta: 14:44:20 time: 1.1056 data_time: 0.0170 memory: 15137 grad_norm: 1.2948 loss: 0.2440 semantic_segmentation_loss_cls: 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semantic_segmentation_loss_dice: 0.1257 2024/07/08 12:29:10 - mmengine - INFO - Iter(train) [ 72400/120000] base_lr: 6.9425e-05 lr: 8.1296e-06 eta: 14:40:37 time: 1.1056 data_time: 0.0169 memory: 14513 grad_norm: 1.2936 loss: 0.2431 semantic_segmentation_loss_cls: 0.0666 semantic_segmentation_loss_mask: 0.0509 semantic_segmentation_loss_dice: 0.1257 2024/07/08 12:30:05 - mmengine - INFO - Iter(train) [ 72450/120000] base_lr: 6.9303e-05 lr: 8.1184e-06 eta: 14:39:41 time: 1.1055 data_time: 0.0169 memory: 15091 grad_norm: 1.2939 loss: 0.2432 semantic_segmentation_loss_cls: 0.0666 semantic_segmentation_loss_mask: 0.0509 semantic_segmentation_loss_dice: 0.1256 2024/07/08 12:31:00 - mmengine - INFO - Iter(train) [ 72500/120000] base_lr: 6.9180e-05 lr: 8.1073e-06 eta: 14:38:45 time: 1.1055 data_time: 0.0169 memory: 15131 grad_norm: 1.2935 loss: 0.2432 semantic_segmentation_loss_cls: 0.0666 semantic_segmentation_loss_mask: 0.0509 semantic_segmentation_loss_dice: 0.1256 2024/07/08 12:31:55 - 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[ 73000/120000] base_lr: 6.7956e-05 lr: 7.9960e-06 eta: 14:29:28 time: 1.1052 data_time: 0.0169 memory: 15079 grad_norm: 1.2922 loss: 0.2420 semantic_segmentation_loss_cls: 0.0662 semantic_segmentation_loss_mask: 0.0507 semantic_segmentation_loss_dice: 0.1251 2024/07/08 12:40:12 - mmengine - INFO - Saving checkpoint at 73000 iterations 2024/07/08 12:41:12 - mmengine - INFO - Iter(train) [ 73050/120000] base_lr: 6.7833e-05 lr: 7.9849e-06 eta: 14:28:35 time: 1.1054 data_time: 0.0170 memory: 14848 grad_norm: 1.2917 loss: 0.2419 semantic_segmentation_loss_cls: 0.0661 semantic_segmentation_loss_mask: 0.0507 semantic_segmentation_loss_dice: 0.1251 2024/07/08 12:42:06 - mmengine - INFO - Iter(train) [ 73100/120000] base_lr: 6.7711e-05 lr: 7.9738e-06 eta: 14:27:39 time: 1.1051 data_time: 0.0170 memory: 15678 grad_norm: 1.2902 loss: 0.2420 semantic_segmentation_loss_cls: 0.0662 semantic_segmentation_loss_mask: 0.0507 semantic_segmentation_loss_dice: 0.1251 2024/07/08 12:43:02 - mmengine - INFO 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eta: 14:23:56 time: 1.1057 data_time: 0.0170 memory: 14325 grad_norm: 1.2906 loss: 0.2411 semantic_segmentation_loss_cls: 0.0658 semantic_segmentation_loss_mask: 0.0506 semantic_segmentation_loss_dice: 0.1247 2024/07/08 12:46:42 - mmengine - INFO - Iter(train) [ 73350/120000] base_lr: 6.7102e-05 lr: 7.9184e-06 eta: 14:23:00 time: 1.1058 data_time: 0.0170 memory: 14704 grad_norm: 1.2882 loss: 0.2408 semantic_segmentation_loss_cls: 0.0657 semantic_segmentation_loss_mask: 0.0505 semantic_segmentation_loss_dice: 0.1246 2024/07/08 12:47:38 - mmengine - INFO - Iter(train) [ 73400/120000] base_lr: 6.6980e-05 lr: 7.9073e-06 eta: 14:22:05 time: 1.1061 data_time: 0.0171 memory: 15448 grad_norm: 1.2898 loss: 0.2405 semantic_segmentation_loss_cls: 0.0655 semantic_segmentation_loss_mask: 0.0505 semantic_segmentation_loss_dice: 0.1245 2024/07/08 12:48:33 - mmengine - INFO - Iter(train) [ 73450/120000] base_lr: 6.6858e-05 lr: 7.8962e-06 eta: 14:21:09 time: 1.1060 data_time: 0.0171 memory: 15451 grad_norm: 1.2877 loss: 0.2404 semantic_segmentation_loss_cls: 0.0655 semantic_segmentation_loss_mask: 0.0505 semantic_segmentation_loss_dice: 0.1244 2024/07/08 12:49:28 - mmengine - INFO - Iter(train) [ 73500/120000] base_lr: 6.6737e-05 lr: 7.8852e-06 eta: 14:20:13 time: 1.1057 data_time: 0.0171 memory: 15097 grad_norm: 1.2889 loss: 0.2405 semantic_segmentation_loss_cls: 0.0655 semantic_segmentation_loss_mask: 0.0505 semantic_segmentation_loss_dice: 0.1245 2024/07/08 12:50:23 - mmengine - INFO - Iter(train) [ 73550/120000] base_lr: 6.6615e-05 lr: 7.8741e-06 eta: 14:19:17 time: 1.1055 data_time: 0.0171 memory: 15728 grad_norm: 1.2868 loss: 0.2403 semantic_segmentation_loss_cls: 0.0654 semantic_segmentation_loss_mask: 0.0505 semantic_segmentation_loss_dice: 0.1244 2024/07/08 12:51:17 - mmengine - INFO - Iter(train) [ 73600/120000] base_lr: 6.6494e-05 lr: 7.8631e-06 eta: 14:18:22 time: 1.1055 data_time: 0.0171 memory: 15432 grad_norm: 1.2856 loss: 0.2401 semantic_segmentation_loss_cls: 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semantic_segmentation_loss_dice: 0.1242 2024/07/08 12:54:58 - mmengine - INFO - Iter(train) [ 73800/120000] base_lr: 6.6008e-05 lr: 7.8189e-06 eta: 14:14:38 time: 1.1056 data_time: 0.0171 memory: 15328 grad_norm: 1.2834 loss: 0.2398 semantic_segmentation_loss_cls: 0.0652 semantic_segmentation_loss_mask: 0.0504 semantic_segmentation_loss_dice: 0.1242 2024/07/08 12:55:52 - mmengine - INFO - Iter(train) [ 73850/120000] base_lr: 6.5887e-05 lr: 7.8079e-06 eta: 14:13:42 time: 1.1054 data_time: 0.0172 memory: 15079 grad_norm: 1.2837 loss: 0.2396 semantic_segmentation_loss_cls: 0.0651 semantic_segmentation_loss_mask: 0.0504 semantic_segmentation_loss_dice: 0.1241 2024/07/08 12:56:47 - mmengine - INFO - Iter(train) [ 73900/120000] base_lr: 6.5766e-05 lr: 7.7969e-06 eta: 14:12:46 time: 1.1053 data_time: 0.0172 memory: 15298 grad_norm: 1.2845 loss: 0.2395 semantic_segmentation_loss_cls: 0.0650 semantic_segmentation_loss_mask: 0.0504 semantic_segmentation_loss_dice: 0.1241 2024/07/08 12:57:42 - mmengine - INFO - Iter(train) [ 73950/120000] base_lr: 6.5645e-05 lr: 7.7859e-06 eta: 14:11:50 time: 1.1054 data_time: 0.0172 memory: 16530 grad_norm: 1.2835 loss: 0.2400 semantic_segmentation_loss_cls: 0.0652 semantic_segmentation_loss_mask: 0.0504 semantic_segmentation_loss_dice: 0.1244 2024/07/08 12:58:37 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 12:58:37 - mmengine - INFO - Iter(train) [ 74000/120000] base_lr: 6.5524e-05 lr: 7.7749e-06 eta: 14:10:55 time: 1.1053 data_time: 0.0172 memory: 15167 grad_norm: 1.2826 loss: 0.2400 semantic_segmentation_loss_cls: 0.0652 semantic_segmentation_loss_mask: 0.0505 semantic_segmentation_loss_dice: 0.1244 2024/07/08 12:58:37 - mmengine - INFO - Saving checkpoint at 74000 iterations 2024/07/08 12:59:37 - mmengine - INFO - Iter(train) [ 74050/120000] base_lr: 6.5403e-05 lr: 7.7639e-06 eta: 14:10:02 time: 1.1064 data_time: 0.0183 memory: 14812 grad_norm: 1.2828 loss: 0.2403 semantic_segmentation_loss_cls: 0.0653 semantic_segmentation_loss_mask: 0.0505 semantic_segmentation_loss_dice: 0.1245 2024/07/08 13:00:31 - mmengine - INFO - Iter(train) [ 74100/120000] base_lr: 6.5282e-05 lr: 7.7529e-06 eta: 14:09:06 time: 1.1063 data_time: 0.0184 memory: 14831 grad_norm: 1.2819 loss: 0.2400 semantic_segmentation_loss_cls: 0.0652 semantic_segmentation_loss_mask: 0.0504 semantic_segmentation_loss_dice: 0.1243 2024/07/08 13:01:26 - mmengine - INFO - Iter(train) [ 74150/120000] base_lr: 6.5161e-05 lr: 7.7419e-06 eta: 14:08:10 time: 1.1065 data_time: 0.0184 memory: 15672 grad_norm: 1.2789 loss: 0.2400 semantic_segmentation_loss_cls: 0.0653 semantic_segmentation_loss_mask: 0.0504 semantic_segmentation_loss_dice: 0.1244 2024/07/08 13:02:22 - mmengine - INFO - Iter(train) [ 74200/120000] base_lr: 6.5041e-05 lr: 7.7310e-06 eta: 14:07:15 time: 1.1067 data_time: 0.0184 memory: 14972 grad_norm: 1.2803 loss: 0.2398 semantic_segmentation_loss_cls: 0.0652 semantic_segmentation_loss_mask: 0.0504 semantic_segmentation_loss_dice: 0.1243 2024/07/08 13:03:19 - mmengine - INFO - Iter(train) [ 74250/120000] base_lr: 6.4920e-05 lr: 7.7200e-06 eta: 14:06:20 time: 1.1068 data_time: 0.0184 memory: 15571 grad_norm: 1.2789 loss: 0.2397 semantic_segmentation_loss_cls: 0.0651 semantic_segmentation_loss_mask: 0.0504 semantic_segmentation_loss_dice: 0.1242 2024/07/08 13:04:13 - mmengine - INFO - Iter(train) [ 74300/120000] base_lr: 6.4799e-05 lr: 7.7090e-06 eta: 14:05:24 time: 1.1067 data_time: 0.0184 memory: 15097 grad_norm: 1.2791 loss: 0.2395 semantic_segmentation_loss_cls: 0.0650 semantic_segmentation_loss_mask: 0.0503 semantic_segmentation_loss_dice: 0.1241 2024/07/08 13:05:08 - mmengine - INFO - Iter(train) [ 74350/120000] base_lr: 6.4679e-05 lr: 7.6981e-06 eta: 14:04:28 time: 1.1064 data_time: 0.0185 memory: 15441 grad_norm: 1.2791 loss: 0.2396 semantic_segmentation_loss_cls: 0.0651 semantic_segmentation_loss_mask: 0.0503 semantic_segmentation_loss_dice: 0.1242 2024/07/08 13:06:03 - mmengine - INFO - Iter(train) [ 74400/120000] base_lr: 6.4558e-05 lr: 7.6871e-06 eta: 14:03:32 time: 1.1064 data_time: 0.0185 memory: 15340 grad_norm: 1.2776 loss: 0.2396 semantic_segmentation_loss_cls: 0.0652 semantic_segmentation_loss_mask: 0.0503 semantic_segmentation_loss_dice: 0.1242 2024/07/08 13:06:58 - mmengine - INFO - Iter(train) [ 74450/120000] base_lr: 6.4438e-05 lr: 7.6761e-06 eta: 14:02:36 time: 1.1064 data_time: 0.0185 memory: 14835 grad_norm: 1.2771 loss: 0.2393 semantic_segmentation_loss_cls: 0.0650 semantic_segmentation_loss_mask: 0.0502 semantic_segmentation_loss_dice: 0.1240 2024/07/08 13:07:54 - mmengine - INFO - Iter(train) [ 74500/120000] base_lr: 6.4317e-05 lr: 7.6652e-06 eta: 14:01:41 time: 1.1066 data_time: 0.0186 memory: 16162 grad_norm: 1.2770 loss: 0.2392 semantic_segmentation_loss_cls: 0.0651 semantic_segmentation_loss_mask: 0.0502 semantic_segmentation_loss_dice: 0.1240 2024/07/08 13:08:50 - mmengine - INFO - Iter(train) [ 74550/120000] base_lr: 6.4197e-05 lr: 7.6543e-06 eta: 14:00:46 time: 1.1069 data_time: 0.0186 memory: 15483 grad_norm: 1.2760 loss: 0.2390 semantic_segmentation_loss_cls: 0.0650 semantic_segmentation_loss_mask: 0.0502 semantic_segmentation_loss_dice: 0.1239 2024/07/08 13:09:46 - mmengine - INFO - Iter(train) [ 74600/120000] base_lr: 6.4077e-05 lr: 7.6433e-06 eta: 13:59:51 time: 1.1071 data_time: 0.0186 memory: 15202 grad_norm: 1.2749 loss: 0.2388 semantic_segmentation_loss_cls: 0.0649 semantic_segmentation_loss_mask: 0.0501 semantic_segmentation_loss_dice: 0.1238 2024/07/08 13:10:41 - mmengine - INFO - Iter(train) [ 74650/120000] base_lr: 6.3956e-05 lr: 7.6324e-06 eta: 13:58:55 time: 1.1071 data_time: 0.0187 memory: 15650 grad_norm: 1.2755 loss: 0.2389 semantic_segmentation_loss_cls: 0.0648 semantic_segmentation_loss_mask: 0.0501 semantic_segmentation_loss_dice: 0.1239 2024/07/08 13:11:36 - mmengine - INFO - Iter(train) [ 74700/120000] base_lr: 6.3836e-05 lr: 7.6215e-06 eta: 13:57:59 time: 1.1072 data_time: 0.0187 memory: 15789 grad_norm: 1.2748 loss: 0.2389 semantic_segmentation_loss_cls: 0.0649 semantic_segmentation_loss_mask: 0.0501 semantic_segmentation_loss_dice: 0.1240 2024/07/08 13:12:31 - mmengine - INFO - Iter(train) [ 74750/120000] base_lr: 6.3716e-05 lr: 7.6106e-06 eta: 13:57:03 time: 1.1072 data_time: 0.0187 memory: 16113 grad_norm: 1.2736 loss: 0.2389 semantic_segmentation_loss_cls: 0.0649 semantic_segmentation_loss_mask: 0.0500 semantic_segmentation_loss_dice: 0.1239 2024/07/08 13:13:26 - mmengine - INFO - Iter(train) [ 74800/120000] base_lr: 6.3596e-05 lr: 7.5997e-06 eta: 13:56:07 time: 1.1073 data_time: 0.0188 memory: 15235 grad_norm: 1.2734 loss: 0.2391 semantic_segmentation_loss_cls: 0.0650 semantic_segmentation_loss_mask: 0.0501 semantic_segmentation_loss_dice: 0.1240 2024/07/08 13:14:21 - mmengine - INFO - Iter(train) [ 74850/120000] base_lr: 6.3476e-05 lr: 7.5887e-06 eta: 13:55:12 time: 1.1071 data_time: 0.0188 memory: 15484 grad_norm: 1.2735 loss: 0.2387 semantic_segmentation_loss_cls: 0.0648 semantic_segmentation_loss_mask: 0.0500 semantic_segmentation_loss_dice: 0.1239 2024/07/08 13:15:16 - mmengine - INFO - Iter(train) [ 74900/120000] base_lr: 6.3356e-05 lr: 7.5778e-06 eta: 13:54:16 time: 1.1071 data_time: 0.0188 memory: 17403 grad_norm: 1.2736 loss: 0.2387 semantic_segmentation_loss_cls: 0.0648 semantic_segmentation_loss_mask: 0.0500 semantic_segmentation_loss_dice: 0.1239 2024/07/08 13:16:12 - mmengine - INFO - Iter(train) [ 74950/120000] base_lr: 6.3236e-05 lr: 7.5670e-06 eta: 13:53:20 time: 1.1072 data_time: 0.0189 memory: 15820 grad_norm: 1.2734 loss: 0.2388 semantic_segmentation_loss_cls: 0.0648 semantic_segmentation_loss_mask: 0.0500 semantic_segmentation_loss_dice: 0.1239 2024/07/08 13:17:08 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 13:17:08 - mmengine - INFO - Iter(train) [ 75000/120000] base_lr: 6.3117e-05 lr: 7.5561e-06 eta: 13:52:25 time: 1.1077 data_time: 0.0190 memory: 15195 grad_norm: 1.2722 loss: 0.2382 semantic_segmentation_loss_cls: 0.0646 semantic_segmentation_loss_mask: 0.0500 semantic_segmentation_loss_dice: 0.1237 2024/07/08 13:17:08 - mmengine - INFO - Saving checkpoint at 75000 iterations 2024/07/08 13:17:26 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:51 time: 0.2454 data_time: 0.0015 memory: 5013 2024/07/08 13:17:38 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:37 time: 0.2453 data_time: 0.0015 memory: 5189 2024/07/08 13:17:50 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:25 time: 0.2453 data_time: 0.0015 memory: 4460 2024/07/08 13:18:02 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:12 time: 0.2452 data_time: 0.0015 memory: 4543 2024/07/08 13:18:14 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:00 time: 0.2452 data_time: 0.0015 memory: 4645 2024/07/08 13:18:26 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:48 time: 0.2451 data_time: 0.0015 memory: 10983 2024/07/08 13:18:38 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2450 data_time: 0.0015 memory: 4460 2024/07/08 13:18:50 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2450 data_time: 0.0015 memory: 4641 2024/07/08 13:19:02 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2449 data_time: 0.0015 memory: 4473 2024/07/08 13:19:14 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2448 data_time: 0.0015 memory: 4555 2024/07/08 13:19:15 - mmengine - INFO - per class results: 2024/07/08 13:19:15 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.47 | 87.63 | | building | 82.84 | 90.64 | | sky | 94.28 | 97.71 | | floor | 83.57 | 91.62 | | tree | 74.87 | 87.78 | | ceiling | 85.25 | 93.18 | | road | 84.06 | 92.47 | | bed | 86.74 | 95.19 | | windowpane | 61.75 | 79.54 | | grass | 71.31 | 86.58 | | cabinet | 61.9 | 73.22 | | sidewalk | 67.75 | 81.97 | | person | 82.49 | 91.69 | | earth | 33.3 | 45.42 | | door | 52.94 | 68.96 | | table | 59.83 | 74.2 | | mountain | 59.96 | 74.62 | | plant | 53.62 | 68.39 | | curtain | 72.57 | 87.03 | | chair | 58.72 | 71.23 | | car | 85.76 | 91.9 | | water | 51.12 | 67.56 | | painting | 73.0 | 89.05 | | sofa | 65.0 | 76.7 | | shelf | 44.84 | 65.44 | | house | 49.98 | 76.47 | | sea | 51.56 | 75.98 | | mirror | 66.19 | 75.62 | | rug | 70.48 | 76.76 | | field | 34.48 | 49.26 | | armchair | 44.82 | 69.68 | | seat | 57.11 | 82.63 | | fence | 38.68 | 53.61 | | desk | 47.85 | 69.82 | | rock | 39.29 | 58.26 | | wardrobe | 55.21 | 69.58 | | lamp | 67.72 | 79.73 | | bathtub | 85.8 | 90.14 | | railing | 37.98 | 54.43 | | cushion | 56.03 | 69.55 | | base | 20.11 | 32.98 | | box | 24.8 | 37.12 | | column | 50.16 | 68.28 | | signboard | 37.78 | 53.55 | | chest of drawers | 41.88 | 68.08 | | counter | 32.77 | 48.44 | | sand | 35.16 | 50.24 | | sink | 74.5 | 82.08 | | skyscraper | 38.19 | 47.46 | | fireplace | 66.69 | 86.2 | | refrigerator | 76.21 | 90.01 | | grandstand | 43.74 | 73.59 | | path | 30.29 | 41.87 | | stairs | 33.76 | 42.77 | | runway | 76.0 | 89.57 | | case | 64.52 | 73.44 | | pool table | 92.14 | 96.38 | | pillow | 55.83 | 67.31 | | screen door | 81.4 | 84.76 | | stairway | 38.49 | 43.75 | | river | 20.94 | 44.55 | | bridge | 66.12 | 89.01 | | bookcase | 41.3 | 56.94 | | blind | 38.93 | 43.72 | | coffee table | 73.63 | 87.54 | | toilet | 76.51 | 89.33 | | flower | 40.56 | 58.62 | | book | 52.83 | 75.75 | | hill | 10.46 | 16.69 | | bench | 44.67 | 53.63 | | countertop | 55.4 | 67.93 | | stove | 80.14 | 84.95 | | palm | 53.34 | 68.76 | | kitchen island | 31.93 | 77.95 | | computer | 61.26 | 67.23 | | swivel chair | 39.09 | 55.09 | | boat | 73.43 | 82.8 | | bar | 33.58 | 41.95 | | arcade machine | 57.41 | 63.47 | | hovel | 8.38 | 15.72 | | bus | 88.95 | 92.31 | | towel | 67.37 | 75.3 | | light | 63.92 | 77.6 | | truck | 36.93 | 46.9 | | tower | 32.65 | 54.39 | | chandelier | 64.93 | 77.48 | | awning | 33.72 | 44.51 | | streetlight | 40.2 | 55.38 | | booth | 53.02 | 63.44 | | television receiver | 48.47 | 89.79 | | airplane | 58.45 | 67.93 | | dirt track | 1.87 | 2.23 | | apparel | 35.85 | 50.99 | | pole | 32.38 | 48.3 | | land | 2.45 | 2.89 | | bannister | 14.76 | 24.87 | | escalator | 47.5 | 61.84 | | ottoman | 41.59 | 66.16 | | bottle | 21.5 | 26.1 | | buffet | 42.04 | 46.32 | | poster | 29.72 | 39.54 | | stage | 13.89 | 23.16 | | van | 47.06 | 65.62 | | ship | 85.37 | 88.39 | | fountain | 7.14 | 8.35 | | conveyer belt | 59.24 | 91.72 | | canopy | 20.62 | 33.36 | | washer | 71.08 | 73.49 | | plaything | 29.15 | 39.91 | | swimming pool | 29.79 | 33.52 | | stool | 52.3 | 69.56 | | barrel | 13.94 | 55.83 | | basket | 34.58 | 42.42 | | waterfall | 30.7 | 42.4 | | tent | 92.86 | 97.66 | | bag | 14.67 | 20.35 | | minibike | 68.92 | 84.79 | | cradle | 76.46 | 96.57 | | oven | 54.84 | 66.09 | | ball | 28.57 | 33.95 | | food | 64.39 | 79.99 | | step | 29.21 | 36.33 | | tank | 43.71 | 55.37 | | trade name | 31.15 | 38.13 | | microwave | 38.15 | 41.01 | | pot | 53.93 | 60.82 | | animal | 61.4 | 68.92 | | bicycle | 57.95 | 79.41 | | lake | 63.53 | 63.66 | | dishwasher | 80.18 | 85.17 | | screen | 66.8 | 82.82 | | blanket | 8.91 | 11.44 | | sculpture | 63.37 | 82.36 | | hood | 73.78 | 77.71 | | sconce | 52.14 | 64.45 | | vase | 47.39 | 64.34 | | traffic light | 40.73 | 59.46 | | tray | 18.09 | 23.4 | | ashcan | 44.2 | 57.87 | | fan | 62.58 | 78.91 | | pier | 34.6 | 70.96 | | crt screen | 0.0 | 0.0 | | plate | 60.75 | 73.68 | | monitor | 3.55 | 4.97 | | bulletin board | 22.03 | 25.8 | | shower | 10.75 | 20.39 | | radiator | 55.78 | 67.46 | | glass | 18.12 | 19.59 | | clock | 32.71 | 36.9 | | flag | 48.27 | 56.31 | +---------------------+-------+-------+ 2024/07/08 13:19:15 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.4500 mIoU: 49.8700 mAcc: 62.4700 data_time: 0.0017 time: 0.2411 2024/07/08 13:20:10 - mmengine - INFO - Iter(train) [ 75050/120000] base_lr: 6.2997e-05 lr: 7.5452e-06 eta: 13:51:30 time: 1.1069 data_time: 0.0180 memory: 15362 grad_norm: 1.2720 loss: 0.2383 semantic_segmentation_loss_cls: 0.0646 semantic_segmentation_loss_mask: 0.0499 semantic_segmentation_loss_dice: 0.1238 2024/07/08 13:21:06 - mmengine - INFO - Iter(train) [ 75100/120000] base_lr: 6.2877e-05 lr: 7.5343e-06 eta: 13:50:35 time: 1.1073 data_time: 0.0181 memory: 15026 grad_norm: 1.2728 loss: 0.2383 semantic_segmentation_loss_cls: 0.0646 semantic_segmentation_loss_mask: 0.0499 semantic_segmentation_loss_dice: 0.1238 2024/07/08 13:22:01 - mmengine - INFO - Iter(train) [ 75150/120000] base_lr: 6.2758e-05 lr: 7.5234e-06 eta: 13:49:39 time: 1.1073 data_time: 0.0181 memory: 14296 grad_norm: 1.2723 loss: 0.2381 semantic_segmentation_loss_cls: 0.0645 semantic_segmentation_loss_mask: 0.0499 semantic_segmentation_loss_dice: 0.1236 2024/07/08 13:22:56 - mmengine - INFO - Iter(train) [ 75200/120000] base_lr: 6.2638e-05 lr: 7.5126e-06 eta: 13:48:44 time: 1.1073 data_time: 0.0181 memory: 14899 grad_norm: 1.2729 loss: 0.2381 semantic_segmentation_loss_cls: 0.0645 semantic_segmentation_loss_mask: 0.0499 semantic_segmentation_loss_dice: 0.1237 2024/07/08 13:23:52 - mmengine - INFO - Iter(train) [ 75250/120000] base_lr: 6.2519e-05 lr: 7.5017e-06 eta: 13:47:48 time: 1.1072 data_time: 0.0181 memory: 15199 grad_norm: 1.2730 loss: 0.2381 semantic_segmentation_loss_cls: 0.0645 semantic_segmentation_loss_mask: 0.0499 semantic_segmentation_loss_dice: 0.1237 2024/07/08 13:24:47 - mmengine - INFO - Iter(train) [ 75300/120000] base_lr: 6.2400e-05 lr: 7.4909e-06 eta: 13:46:52 time: 1.1071 data_time: 0.0182 memory: 15798 grad_norm: 1.2721 loss: 0.2381 semantic_segmentation_loss_cls: 0.0645 semantic_segmentation_loss_mask: 0.0499 semantic_segmentation_loss_dice: 0.1237 2024/07/08 13:25:43 - mmengine - INFO - Iter(train) [ 75350/120000] base_lr: 6.2280e-05 lr: 7.4800e-06 eta: 13:45:57 time: 1.1071 data_time: 0.0182 memory: 15717 grad_norm: 1.2713 loss: 0.2380 semantic_segmentation_loss_cls: 0.0644 semantic_segmentation_loss_mask: 0.0500 semantic_segmentation_loss_dice: 0.1237 2024/07/08 13:26:38 - mmengine - INFO - Iter(train) [ 75400/120000] base_lr: 6.2161e-05 lr: 7.4692e-06 eta: 13:45:02 time: 1.1071 data_time: 0.0183 memory: 15780 grad_norm: 1.2697 loss: 0.2375 semantic_segmentation_loss_cls: 0.0641 semantic_segmentation_loss_mask: 0.0499 semantic_segmentation_loss_dice: 0.1235 2024/07/08 13:27:34 - mmengine - INFO - Iter(train) [ 75450/120000] base_lr: 6.2042e-05 lr: 7.4583e-06 eta: 13:44:06 time: 1.1075 data_time: 0.0183 memory: 14807 grad_norm: 1.2703 loss: 0.2376 semantic_segmentation_loss_cls: 0.0642 semantic_segmentation_loss_mask: 0.0499 semantic_segmentation_loss_dice: 0.1235 2024/07/08 13:28:30 - mmengine - INFO - Iter(train) [ 75500/120000] base_lr: 6.1923e-05 lr: 7.4475e-06 eta: 13:43:11 time: 1.1076 data_time: 0.0184 memory: 16168 grad_norm: 1.2708 loss: 0.2377 semantic_segmentation_loss_cls: 0.0642 semantic_segmentation_loss_mask: 0.0500 semantic_segmentation_loss_dice: 0.1235 2024/07/08 13:29:26 - mmengine - INFO - Iter(train) [ 75550/120000] base_lr: 6.1804e-05 lr: 7.4367e-06 eta: 13:42:16 time: 1.1078 data_time: 0.0184 memory: 16018 grad_norm: 1.2693 loss: 0.2379 semantic_segmentation_loss_cls: 0.0643 semantic_segmentation_loss_mask: 0.0500 semantic_segmentation_loss_dice: 0.1236 2024/07/08 13:30:22 - mmengine - INFO - Iter(train) [ 75600/120000] base_lr: 6.1685e-05 lr: 7.4259e-06 eta: 13:41:20 time: 1.1078 data_time: 0.0184 memory: 14940 grad_norm: 1.2679 loss: 0.2374 semantic_segmentation_loss_cls: 0.0641 semantic_segmentation_loss_mask: 0.0499 semantic_segmentation_loss_dice: 0.1234 2024/07/08 13:31:17 - mmengine - INFO - Iter(train) [ 75650/120000] base_lr: 6.1566e-05 lr: 7.4151e-06 eta: 13:40:25 time: 1.1080 data_time: 0.0184 memory: 14763 grad_norm: 1.2668 loss: 0.2373 semantic_segmentation_loss_cls: 0.0640 semantic_segmentation_loss_mask: 0.0498 semantic_segmentation_loss_dice: 0.1234 2024/07/08 13:32:13 - mmengine - INFO - Iter(train) [ 75700/120000] base_lr: 6.1447e-05 lr: 7.4043e-06 eta: 13:39:30 time: 1.1084 data_time: 0.0185 memory: 15170 grad_norm: 1.2672 loss: 0.2372 semantic_segmentation_loss_cls: 0.0641 semantic_segmentation_loss_mask: 0.0498 semantic_segmentation_loss_dice: 0.1234 2024/07/08 13:33:10 - mmengine - INFO - Iter(train) [ 75750/120000] base_lr: 6.1328e-05 lr: 7.3935e-06 eta: 13:38:35 time: 1.1087 data_time: 0.0185 memory: 15821 grad_norm: 1.2671 loss: 0.2372 semantic_segmentation_loss_cls: 0.0641 semantic_segmentation_loss_mask: 0.0497 semantic_segmentation_loss_dice: 0.1233 2024/07/08 13:34:05 - mmengine - INFO - Iter(train) [ 75800/120000] base_lr: 6.1210e-05 lr: 7.3827e-06 eta: 13:37:39 time: 1.1086 data_time: 0.0185 memory: 15294 grad_norm: 1.2677 loss: 0.2372 semantic_segmentation_loss_cls: 0.0641 semantic_segmentation_loss_mask: 0.0497 semantic_segmentation_loss_dice: 0.1234 2024/07/08 13:35:00 - mmengine - INFO - Iter(train) [ 75850/120000] base_lr: 6.1091e-05 lr: 7.3719e-06 eta: 13:36:44 time: 1.1086 data_time: 0.0186 memory: 15061 grad_norm: 1.2683 loss: 0.2371 semantic_segmentation_loss_cls: 0.0641 semantic_segmentation_loss_mask: 0.0497 semantic_segmentation_loss_dice: 0.1233 2024/07/08 13:35:56 - mmengine - INFO - Iter(train) [ 75900/120000] base_lr: 6.0972e-05 lr: 7.3611e-06 eta: 13:35:48 time: 1.1088 data_time: 0.0186 memory: 14569 grad_norm: 1.2669 loss: 0.2364 semantic_segmentation_loss_cls: 0.0638 semantic_segmentation_loss_mask: 0.0497 semantic_segmentation_loss_dice: 0.1230 2024/07/08 13:36:52 - mmengine - INFO - Iter(train) [ 75950/120000] base_lr: 6.0854e-05 lr: 7.3504e-06 eta: 13:34:53 time: 1.1089 data_time: 0.0186 memory: 15165 grad_norm: 1.2663 loss: 0.2363 semantic_segmentation_loss_cls: 0.0637 semantic_segmentation_loss_mask: 0.0496 semantic_segmentation_loss_dice: 0.1230 2024/07/08 13:37:47 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 13:37:47 - mmengine - INFO - Iter(train) [ 76000/120000] base_lr: 6.0735e-05 lr: 7.3396e-06 eta: 13:33:57 time: 1.1091 data_time: 0.0186 memory: 15089 grad_norm: 1.2671 loss: 0.2362 semantic_segmentation_loss_cls: 0.0637 semantic_segmentation_loss_mask: 0.0496 semantic_segmentation_loss_dice: 0.1230 2024/07/08 13:37:47 - mmengine - INFO - Saving checkpoint at 76000 iterations 2024/07/08 13:38:47 - mmengine - INFO - Iter(train) [ 76050/120000] base_lr: 6.0617e-05 lr: 7.3288e-06 eta: 13:33:04 time: 1.1092 data_time: 0.0187 memory: 15377 grad_norm: 1.2670 loss: 0.2364 semantic_segmentation_loss_cls: 0.0637 semantic_segmentation_loss_mask: 0.0497 semantic_segmentation_loss_dice: 0.1230 2024/07/08 13:39:43 - mmengine - INFO - Iter(train) [ 76100/120000] base_lr: 6.0499e-05 lr: 7.3181e-06 eta: 13:32:09 time: 1.1095 data_time: 0.0187 memory: 14908 grad_norm: 1.2656 loss: 0.2361 semantic_segmentation_loss_cls: 0.0636 semantic_segmentation_loss_mask: 0.0497 semantic_segmentation_loss_dice: 0.1229 2024/07/08 13:40:38 - mmengine - INFO - Iter(train) [ 76150/120000] base_lr: 6.0381e-05 lr: 7.3073e-06 eta: 13:31:13 time: 1.1093 data_time: 0.0187 memory: 14750 grad_norm: 1.2653 loss: 0.2360 semantic_segmentation_loss_cls: 0.0635 semantic_segmentation_loss_mask: 0.0496 semantic_segmentation_loss_dice: 0.1229 2024/07/08 13:41:33 - mmengine - INFO - Iter(train) [ 76200/120000] base_lr: 6.0262e-05 lr: 7.2966e-06 eta: 13:30:17 time: 1.1091 data_time: 0.0188 memory: 15032 grad_norm: 1.2644 loss: 0.2359 semantic_segmentation_loss_cls: 0.0635 semantic_segmentation_loss_mask: 0.0496 semantic_segmentation_loss_dice: 0.1228 2024/07/08 13:42:28 - mmengine - INFO - Iter(train) [ 76250/120000] base_lr: 6.0144e-05 lr: 7.2859e-06 eta: 13:29:22 time: 1.1092 data_time: 0.0188 memory: 15430 grad_norm: 1.2657 loss: 0.2363 semantic_segmentation_loss_cls: 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semantic_segmentation_loss_dice: 0.1229 2024/07/08 13:46:12 - mmengine - INFO - Iter(train) [ 76450/120000] base_lr: 5.9673e-05 lr: 7.2430e-06 eta: 13:25:41 time: 1.1101 data_time: 0.0190 memory: 15651 grad_norm: 1.2613 loss: 0.2364 semantic_segmentation_loss_cls: 0.0638 semantic_segmentation_loss_mask: 0.0496 semantic_segmentation_loss_dice: 0.1230 2024/07/08 13:47:08 - mmengine - INFO - Iter(train) [ 76500/120000] base_lr: 5.9555e-05 lr: 7.2323e-06 eta: 13:24:45 time: 1.1103 data_time: 0.0190 memory: 15694 grad_norm: 1.2607 loss: 0.2361 semantic_segmentation_loss_cls: 0.0636 semantic_segmentation_loss_mask: 0.0496 semantic_segmentation_loss_dice: 0.1229 2024/07/08 13:48:03 - mmengine - INFO - Iter(train) [ 76550/120000] base_lr: 5.9437e-05 lr: 7.2216e-06 eta: 13:23:50 time: 1.1104 data_time: 0.0191 memory: 15518 grad_norm: 1.2606 loss: 0.2361 semantic_segmentation_loss_cls: 0.0636 semantic_segmentation_loss_mask: 0.0496 semantic_segmentation_loss_dice: 0.1229 2024/07/08 13:48:59 - 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0.0193 memory: 16169 grad_norm: 1.2537 loss: 0.2358 semantic_segmentation_loss_cls: 0.0635 semantic_segmentation_loss_mask: 0.0494 semantic_segmentation_loss_dice: 0.1228 2024/07/08 13:55:27 - mmengine - INFO - Iter(train) [ 76950/120000] base_lr: 5.8499e-05 lr: 7.1362e-06 eta: 13:16:26 time: 1.1110 data_time: 0.0193 memory: 15015 grad_norm: 1.2546 loss: 0.2355 semantic_segmentation_loss_cls: 0.0634 semantic_segmentation_loss_mask: 0.0494 semantic_segmentation_loss_dice: 0.1227 2024/07/08 13:56:22 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 13:56:22 - mmengine - INFO - Iter(train) [ 77000/120000] base_lr: 5.8382e-05 lr: 7.1256e-06 eta: 13:15:30 time: 1.1110 data_time: 0.0193 memory: 15263 grad_norm: 1.2530 loss: 0.2355 semantic_segmentation_loss_cls: 0.0634 semantic_segmentation_loss_mask: 0.0494 semantic_segmentation_loss_dice: 0.1227 2024/07/08 13:56:22 - mmengine - INFO - Saving checkpoint at 77000 iterations 2024/07/08 13:57:23 - mmengine - INFO - Iter(train) [ 77050/120000] base_lr: 5.8265e-05 lr: 7.1150e-06 eta: 13:14:37 time: 1.1111 data_time: 0.0193 memory: 14617 grad_norm: 1.2523 loss: 0.2356 semantic_segmentation_loss_cls: 0.0635 semantic_segmentation_loss_mask: 0.0494 semantic_segmentation_loss_dice: 0.1227 2024/07/08 13:58:19 - mmengine - INFO - Iter(train) [ 77100/120000] base_lr: 5.8148e-05 lr: 7.1044e-06 eta: 13:13:42 time: 1.1114 data_time: 0.0193 memory: 14440 grad_norm: 1.2529 loss: 0.2359 semantic_segmentation_loss_cls: 0.0636 semantic_segmentation_loss_mask: 0.0494 semantic_segmentation_loss_dice: 0.1229 2024/07/08 13:59:14 - mmengine - INFO - Iter(train) [ 77150/120000] base_lr: 5.8031e-05 lr: 7.0937e-06 eta: 13:12:47 time: 1.1113 data_time: 0.0193 memory: 15163 grad_norm: 1.2510 loss: 0.2357 semantic_segmentation_loss_cls: 0.0636 semantic_segmentation_loss_mask: 0.0494 semantic_segmentation_loss_dice: 0.1228 2024/07/08 14:00:09 - mmengine - INFO - Iter(train) [ 77200/120000] base_lr: 5.7914e-05 lr: 7.0831e-06 eta: 13:11:51 time: 1.1111 data_time: 0.0193 memory: 15601 grad_norm: 1.2521 loss: 0.2360 semantic_segmentation_loss_cls: 0.0637 semantic_segmentation_loss_mask: 0.0494 semantic_segmentation_loss_dice: 0.1229 2024/07/08 14:01:04 - mmengine - INFO - Iter(train) [ 77250/120000] base_lr: 5.7798e-05 lr: 7.0725e-06 eta: 13:10:55 time: 1.1112 data_time: 0.0194 memory: 14863 grad_norm: 1.2530 loss: 0.2359 semantic_segmentation_loss_cls: 0.0637 semantic_segmentation_loss_mask: 0.0494 semantic_segmentation_loss_dice: 0.1228 2024/07/08 14:02:00 - mmengine - INFO - Iter(train) [ 77300/120000] base_lr: 5.7681e-05 lr: 7.0619e-06 eta: 13:10:00 time: 1.1116 data_time: 0.0194 memory: 14991 grad_norm: 1.2516 loss: 0.2359 semantic_segmentation_loss_cls: 0.0636 semantic_segmentation_loss_mask: 0.0494 semantic_segmentation_loss_dice: 0.1228 2024/07/08 14:02:56 - mmengine - INFO - Iter(train) [ 77350/120000] base_lr: 5.7565e-05 lr: 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semantic_segmentation_loss_mask: 0.0494 semantic_segmentation_loss_dice: 0.1227 2024/07/08 14:12:10 - mmengine - INFO - Iter(train) [ 77850/120000] base_lr: 5.6404e-05 lr: 6.9458e-06 eta: 12:59:49 time: 1.1129 data_time: 0.0195 memory: 15299 grad_norm: 1.2445 loss: 0.2353 semantic_segmentation_loss_cls: 0.0633 semantic_segmentation_loss_mask: 0.0493 semantic_segmentation_loss_dice: 0.1226 2024/07/08 14:13:05 - mmengine - INFO - Iter(train) [ 77900/120000] base_lr: 5.6288e-05 lr: 6.9353e-06 eta: 12:58:53 time: 1.1129 data_time: 0.0195 memory: 15015 grad_norm: 1.2436 loss: 0.2351 semantic_segmentation_loss_cls: 0.0633 semantic_segmentation_loss_mask: 0.0493 semantic_segmentation_loss_dice: 0.1225 2024/07/08 14:14:00 - mmengine - INFO - Iter(train) [ 77950/120000] base_lr: 5.6173e-05 lr: 6.9248e-06 eta: 12:57:57 time: 1.1130 data_time: 0.0195 memory: 14956 grad_norm: 1.2433 loss: 0.2348 semantic_segmentation_loss_cls: 0.0632 semantic_segmentation_loss_mask: 0.0493 semantic_segmentation_loss_dice: 0.1223 2024/07/08 14:14:55 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 14:14:55 - mmengine - INFO - Iter(train) [ 78000/120000] base_lr: 5.6057e-05 lr: 6.9143e-06 eta: 12:57:02 time: 1.1129 data_time: 0.0195 memory: 15093 grad_norm: 1.2435 loss: 0.2345 semantic_segmentation_loss_cls: 0.0631 semantic_segmentation_loss_mask: 0.0492 semantic_segmentation_loss_dice: 0.1222 2024/07/08 14:14:55 - mmengine - INFO - Saving checkpoint at 78000 iterations 2024/07/08 14:15:55 - mmengine - INFO - Iter(train) [ 78050/120000] base_lr: 5.5942e-05 lr: 6.9038e-06 eta: 12:56:08 time: 1.1130 data_time: 0.0194 memory: 14818 grad_norm: 1.2412 loss: 0.2342 semantic_segmentation_loss_cls: 0.0629 semantic_segmentation_loss_mask: 0.0492 semantic_segmentation_loss_dice: 0.1220 2024/07/08 14:16:50 - mmengine - INFO - Iter(train) [ 78100/120000] base_lr: 5.5826e-05 lr: 6.8933e-06 eta: 12:55:12 time: 1.1130 data_time: 0.0194 memory: 15700 grad_norm: 1.2412 loss: 0.2342 semantic_segmentation_loss_cls: 0.0630 semantic_segmentation_loss_mask: 0.0492 semantic_segmentation_loss_dice: 0.1221 2024/07/08 14:17:46 - mmengine - INFO - Iter(train) [ 78150/120000] base_lr: 5.5711e-05 lr: 6.8828e-06 eta: 12:54:17 time: 1.1131 data_time: 0.0194 memory: 14941 grad_norm: 1.2418 loss: 0.2341 semantic_segmentation_loss_cls: 0.0629 semantic_segmentation_loss_mask: 0.0492 semantic_segmentation_loss_dice: 0.1220 2024/07/08 14:18:41 - mmengine - INFO - Iter(train) [ 78200/120000] base_lr: 5.5596e-05 lr: 6.8724e-06 eta: 12:53:22 time: 1.1129 data_time: 0.0194 memory: 16179 grad_norm: 1.2412 loss: 0.2344 semantic_segmentation_loss_cls: 0.0631 semantic_segmentation_loss_mask: 0.0492 semantic_segmentation_loss_dice: 0.1221 2024/07/08 14:19:36 - mmengine - INFO - Iter(train) [ 78250/120000] base_lr: 5.5481e-05 lr: 6.8619e-06 eta: 12:52:26 time: 1.1126 data_time: 0.0193 memory: 15449 grad_norm: 1.2418 loss: 0.2346 semantic_segmentation_loss_cls: 0.0631 semantic_segmentation_loss_mask: 0.0492 semantic_segmentation_loss_dice: 0.1222 2024/07/08 14:20:31 - mmengine - INFO - Iter(train) [ 78300/120000] base_lr: 5.5366e-05 lr: 6.8514e-06 eta: 12:51:30 time: 1.1128 data_time: 0.0193 memory: 17116 grad_norm: 1.2410 loss: 0.2348 semantic_segmentation_loss_cls: 0.0632 semantic_segmentation_loss_mask: 0.0492 semantic_segmentation_loss_dice: 0.1223 2024/07/08 14:21:27 - mmengine - INFO - Iter(train) [ 78350/120000] base_lr: 5.5251e-05 lr: 6.8410e-06 eta: 12:50:35 time: 1.1131 data_time: 0.0193 memory: 15212 grad_norm: 1.2391 loss: 0.2344 semantic_segmentation_loss_cls: 0.0631 semantic_segmentation_loss_mask: 0.0492 semantic_segmentation_loss_dice: 0.1221 2024/07/08 14:22:23 - mmengine - INFO - Iter(train) [ 78400/120000] base_lr: 5.5136e-05 lr: 6.8305e-06 eta: 12:49:39 time: 1.1133 data_time: 0.0193 memory: 15170 grad_norm: 1.2390 loss: 0.2342 semantic_segmentation_loss_cls: 0.0630 semantic_segmentation_loss_mask: 0.0492 semantic_segmentation_loss_dice: 0.1220 2024/07/08 14:23:18 - mmengine - INFO - Iter(train) [ 78450/120000] base_lr: 5.5021e-05 lr: 6.8201e-06 eta: 12:48:44 time: 1.1132 data_time: 0.0193 memory: 15595 grad_norm: 1.2393 loss: 0.2341 semantic_segmentation_loss_cls: 0.0629 semantic_segmentation_loss_mask: 0.0492 semantic_segmentation_loss_dice: 0.1220 2024/07/08 14:24:14 - mmengine - INFO - Iter(train) [ 78500/120000] base_lr: 5.4906e-05 lr: 6.8097e-06 eta: 12:47:48 time: 1.1133 data_time: 0.0193 memory: 15316 grad_norm: 1.2378 loss: 0.2337 semantic_segmentation_loss_cls: 0.0627 semantic_segmentation_loss_mask: 0.0492 semantic_segmentation_loss_dice: 0.1218 2024/07/08 14:25:10 - mmengine - INFO - Iter(train) [ 78550/120000] base_lr: 5.4792e-05 lr: 6.7993e-06 eta: 12:46:53 time: 1.1134 data_time: 0.0192 memory: 15862 grad_norm: 1.2381 loss: 0.2336 semantic_segmentation_loss_cls: 0.0627 semantic_segmentation_loss_mask: 0.0491 semantic_segmentation_loss_dice: 0.1218 2024/07/08 14:26:06 - mmengine - INFO - Iter(train) [ 78600/120000] base_lr: 5.4677e-05 lr: 6.7888e-06 eta: 12:45:58 time: 1.1131 data_time: 0.0192 memory: 15960 grad_norm: 1.2371 loss: 0.2339 semantic_segmentation_loss_cls: 0.0628 semantic_segmentation_loss_mask: 0.0492 semantic_segmentation_loss_dice: 0.1219 2024/07/08 14:27:01 - mmengine - INFO - Iter(train) [ 78650/120000] base_lr: 5.4563e-05 lr: 6.7784e-06 eta: 12:45:02 time: 1.1132 data_time: 0.0192 memory: 15686 grad_norm: 1.2375 loss: 0.2336 semantic_segmentation_loss_cls: 0.0627 semantic_segmentation_loss_mask: 0.0491 semantic_segmentation_loss_dice: 0.1218 2024/07/08 14:27:56 - mmengine - INFO - Iter(train) [ 78700/120000] base_lr: 5.4448e-05 lr: 6.7680e-06 eta: 12:44:06 time: 1.1133 data_time: 0.0192 memory: 15076 grad_norm: 1.2359 loss: 0.2335 semantic_segmentation_loss_cls: 0.0627 semantic_segmentation_loss_mask: 0.0491 semantic_segmentation_loss_dice: 0.1218 2024/07/08 14:28:52 - mmengine - INFO - Iter(train) [ 78750/120000] base_lr: 5.4334e-05 lr: 6.7576e-06 eta: 12:43:11 time: 1.1136 data_time: 0.0192 memory: 15770 grad_norm: 1.2358 loss: 0.2334 semantic_segmentation_loss_cls: 0.0626 semantic_segmentation_loss_mask: 0.0491 semantic_segmentation_loss_dice: 0.1217 2024/07/08 14:29:47 - mmengine - INFO - Iter(train) [ 78800/120000] base_lr: 5.4220e-05 lr: 6.7473e-06 eta: 12:42:16 time: 1.1136 data_time: 0.0191 memory: 16369 grad_norm: 1.2347 loss: 0.2331 semantic_segmentation_loss_cls: 0.0625 semantic_segmentation_loss_mask: 0.0490 semantic_segmentation_loss_dice: 0.1216 2024/07/08 14:30:43 - mmengine - INFO - Iter(train) [ 78850/120000] base_lr: 5.4106e-05 lr: 6.7369e-06 eta: 12:41:20 time: 1.1138 data_time: 0.0191 memory: 15253 grad_norm: 1.2359 loss: 0.2334 semantic_segmentation_loss_cls: 0.0626 semantic_segmentation_loss_mask: 0.0491 semantic_segmentation_loss_dice: 0.1217 2024/07/08 14:31:39 - mmengine - INFO - Iter(train) [ 78900/120000] base_lr: 5.3991e-05 lr: 6.7265e-06 eta: 12:40:25 time: 1.1141 data_time: 0.0191 memory: 15991 grad_norm: 1.2349 loss: 0.2332 semantic_segmentation_loss_cls: 0.0625 semantic_segmentation_loss_mask: 0.0491 semantic_segmentation_loss_dice: 0.1216 2024/07/08 14:32:34 - mmengine - INFO - Iter(train) [ 78950/120000] base_lr: 5.3877e-05 lr: 6.7161e-06 eta: 12:39:29 time: 1.1139 data_time: 0.0191 memory: 14694 grad_norm: 1.2352 loss: 0.2329 semantic_segmentation_loss_cls: 0.0624 semantic_segmentation_loss_mask: 0.0490 semantic_segmentation_loss_dice: 0.1215 2024/07/08 14:33:30 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 14:33:30 - mmengine - INFO - Iter(train) [ 79000/120000] base_lr: 5.3764e-05 lr: 6.7058e-06 eta: 12:38:34 time: 1.1139 data_time: 0.0191 memory: 14352 grad_norm: 1.2362 loss: 0.2333 semantic_segmentation_loss_cls: 0.0626 semantic_segmentation_loss_mask: 0.0490 semantic_segmentation_loss_dice: 0.1216 2024/07/08 14:33:30 - mmengine - INFO - Saving checkpoint at 79000 iterations 2024/07/08 14:34:30 - mmengine - INFO - Iter(train) [ 79050/120000] base_lr: 5.3650e-05 lr: 6.6954e-06 eta: 12:37:41 time: 1.1147 data_time: 0.0200 memory: 15198 grad_norm: 1.2369 loss: 0.2334 semantic_segmentation_loss_cls: 0.0627 semantic_segmentation_loss_mask: 0.0491 semantic_segmentation_loss_dice: 0.1217 2024/07/08 14:35:25 - mmengine - INFO - Iter(train) [ 79100/120000] base_lr: 5.3536e-05 lr: 6.6851e-06 eta: 12:36:45 time: 1.1146 data_time: 0.0200 memory: 16063 grad_norm: 1.2366 loss: 0.2335 semantic_segmentation_loss_cls: 0.0628 semantic_segmentation_loss_mask: 0.0491 semantic_segmentation_loss_dice: 0.1217 2024/07/08 14:36:21 - mmengine - INFO - Iter(train) [ 79150/120000] base_lr: 5.3422e-05 lr: 6.6748e-06 eta: 12:35:50 time: 1.1148 data_time: 0.0200 memory: 14891 grad_norm: 1.2360 loss: 0.2335 semantic_segmentation_loss_cls: 0.0628 semantic_segmentation_loss_mask: 0.0491 semantic_segmentation_loss_dice: 0.1217 2024/07/08 14:37:17 - mmengine - INFO - Iter(train) [ 79200/120000] base_lr: 5.3309e-05 lr: 6.6644e-06 eta: 12:34:54 time: 1.1149 data_time: 0.0200 memory: 15821 grad_norm: 1.2346 loss: 0.2335 semantic_segmentation_loss_cls: 0.0628 semantic_segmentation_loss_mask: 0.0490 semantic_segmentation_loss_dice: 0.1217 2024/07/08 14:38:12 - mmengine - INFO - Iter(train) [ 79250/120000] base_lr: 5.3195e-05 lr: 6.6541e-06 eta: 12:33:59 time: 1.1149 data_time: 0.0199 memory: 15076 grad_norm: 1.2359 loss: 0.2331 semantic_segmentation_loss_cls: 0.0626 semantic_segmentation_loss_mask: 0.0490 semantic_segmentation_loss_dice: 0.1215 2024/07/08 14:39:07 - mmengine - INFO - Iter(train) [ 79300/120000] base_lr: 5.3082e-05 lr: 6.6438e-06 eta: 12:33:03 time: 1.1148 data_time: 0.0199 memory: 14626 grad_norm: 1.2356 loss: 0.2328 semantic_segmentation_loss_cls: 0.0624 semantic_segmentation_loss_mask: 0.0490 semantic_segmentation_loss_dice: 0.1214 2024/07/08 14:40:01 - mmengine - INFO - Iter(train) [ 79350/120000] base_lr: 5.2968e-05 lr: 6.6335e-06 eta: 12:32:07 time: 1.1145 data_time: 0.0199 memory: 15475 grad_norm: 1.2366 loss: 0.2326 semantic_segmentation_loss_cls: 0.0624 semantic_segmentation_loss_mask: 0.0489 semantic_segmentation_loss_dice: 0.1213 2024/07/08 14:40:57 - mmengine - INFO - Iter(train) [ 79400/120000] base_lr: 5.2855e-05 lr: 6.6232e-06 eta: 12:31:11 time: 1.1144 data_time: 0.0199 memory: 15075 grad_norm: 1.2397 loss: 0.2328 semantic_segmentation_loss_cls: 0.0625 semantic_segmentation_loss_mask: 0.0490 semantic_segmentation_loss_dice: 0.1213 2024/07/08 14:41:53 - mmengine - INFO - Iter(train) [ 79450/120000] base_lr: 5.2742e-05 lr: 6.6129e-06 eta: 12:30:16 time: 1.1144 data_time: 0.0199 memory: 16079 grad_norm: 1.2385 loss: 0.2326 semantic_segmentation_loss_cls: 0.0624 semantic_segmentation_loss_mask: 0.0489 semantic_segmentation_loss_dice: 0.1213 2024/07/08 14:42:49 - mmengine - INFO - Iter(train) [ 79500/120000] base_lr: 5.2629e-05 lr: 6.6026e-06 eta: 12:29:21 time: 1.1147 data_time: 0.0199 memory: 14508 grad_norm: 1.2379 loss: 0.2322 semantic_segmentation_loss_cls: 0.0623 semantic_segmentation_loss_mask: 0.0488 semantic_segmentation_loss_dice: 0.1211 2024/07/08 14:43:45 - mmengine - INFO - Iter(train) [ 79550/120000] base_lr: 5.2516e-05 lr: 6.5923e-06 eta: 12:28:25 time: 1.1146 data_time: 0.0198 memory: 15965 grad_norm: 1.2373 loss: 0.2322 semantic_segmentation_loss_cls: 0.0623 semantic_segmentation_loss_mask: 0.0488 semantic_segmentation_loss_dice: 0.1211 2024/07/08 14:44:40 - mmengine - INFO - Iter(train) [ 79600/120000] base_lr: 5.2403e-05 lr: 6.5821e-06 eta: 12:27:30 time: 1.1144 data_time: 0.0198 memory: 15701 grad_norm: 1.2376 loss: 0.2327 semantic_segmentation_loss_cls: 0.0625 semantic_segmentation_loss_mask: 0.0489 semantic_segmentation_loss_dice: 0.1213 2024/07/08 14:45:35 - mmengine - INFO - Iter(train) [ 79650/120000] base_lr: 5.2290e-05 lr: 6.5718e-06 eta: 12:26:34 time: 1.1142 data_time: 0.0198 memory: 14390 grad_norm: 1.2378 loss: 0.2327 semantic_segmentation_loss_cls: 0.0627 semantic_segmentation_loss_mask: 0.0488 semantic_segmentation_loss_dice: 0.1212 2024/07/08 14:46:30 - mmengine - INFO - Iter(train) [ 79700/120000] base_lr: 5.2177e-05 lr: 6.5616e-06 eta: 12:25:38 time: 1.1138 data_time: 0.0198 memory: 15187 grad_norm: 1.2361 loss: 0.2325 semantic_segmentation_loss_cls: 0.0626 semantic_segmentation_loss_mask: 0.0488 semantic_segmentation_loss_dice: 0.1211 2024/07/08 14:47:24 - mmengine - INFO - Iter(train) [ 79750/120000] base_lr: 5.2064e-05 lr: 6.5513e-06 eta: 12:24:42 time: 1.1134 data_time: 0.0197 memory: 15690 grad_norm: 1.2364 loss: 0.2323 semantic_segmentation_loss_cls: 0.0624 semantic_segmentation_loss_mask: 0.0488 semantic_segmentation_loss_dice: 0.1211 2024/07/08 14:48:20 - mmengine - INFO - Iter(train) [ 79800/120000] base_lr: 5.1952e-05 lr: 6.5411e-06 eta: 12:23:47 time: 1.1136 data_time: 0.0197 memory: 15950 grad_norm: 1.2344 loss: 0.2319 semantic_segmentation_loss_cls: 0.0622 semantic_segmentation_loss_mask: 0.0488 semantic_segmentation_loss_dice: 0.1209 2024/07/08 14:49:17 - mmengine - INFO - Iter(train) [ 79850/120000] base_lr: 5.1839e-05 lr: 6.5308e-06 eta: 12:22:52 time: 1.1138 data_time: 0.0197 memory: 15537 grad_norm: 1.2335 loss: 0.2319 semantic_segmentation_loss_cls: 0.0622 semantic_segmentation_loss_mask: 0.0488 semantic_segmentation_loss_dice: 0.1210 2024/07/08 14:50:12 - mmengine - INFO - Iter(train) [ 79900/120000] base_lr: 5.1727e-05 lr: 6.5206e-06 eta: 12:21:56 time: 1.1137 data_time: 0.0197 memory: 15843 grad_norm: 1.2346 loss: 0.2327 semantic_segmentation_loss_cls: 0.0625 semantic_segmentation_loss_mask: 0.0489 semantic_segmentation_loss_dice: 0.1214 2024/07/08 14:51:07 - mmengine - INFO - Iter(train) [ 79950/120000] base_lr: 5.1615e-05 lr: 6.5104e-06 eta: 12:21:00 time: 1.1135 data_time: 0.0196 memory: 15385 grad_norm: 1.2368 loss: 0.2326 semantic_segmentation_loss_cls: 0.0624 semantic_segmentation_loss_mask: 0.0489 semantic_segmentation_loss_dice: 0.1213 2024/07/08 14:52:02 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 14:52:02 - mmengine - INFO - Iter(train) [ 80000/120000] base_lr: 5.1502e-05 lr: 6.5002e-06 eta: 12:20:05 time: 1.1135 data_time: 0.0196 memory: 15725 grad_norm: 1.2355 loss: 0.2323 semantic_segmentation_loss_cls: 0.0624 semantic_segmentation_loss_mask: 0.0488 semantic_segmentation_loss_dice: 0.1212 2024/07/08 14:52:02 - mmengine - INFO - Saving checkpoint at 80000 iterations 2024/07/08 14:52:19 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:50 time: 0.2448 data_time: 0.0015 memory: 5013 2024/07/08 14:52:31 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:37 time: 0.2448 data_time: 0.0016 memory: 5189 2024/07/08 14:52:43 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:25 time: 0.2447 data_time: 0.0016 memory: 4460 2024/07/08 14:52:55 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:12 time: 0.2447 data_time: 0.0016 memory: 4543 2024/07/08 14:53:08 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:00 time: 0.2446 data_time: 0.0016 memory: 4645 2024/07/08 14:53:20 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:48 time: 0.2445 data_time: 0.0016 memory: 10983 2024/07/08 14:53:32 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2444 data_time: 0.0016 memory: 4460 2024/07/08 14:53:44 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2443 data_time: 0.0016 memory: 4641 2024/07/08 14:53:56 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2443 data_time: 0.0016 memory: 4473 2024/07/08 14:54:08 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2442 data_time: 0.0016 memory: 4555 2024/07/08 14:54:08 - mmengine - INFO - per class results: 2024/07/08 14:54:08 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.54 | 87.88 | | building | 82.88 | 90.53 | | sky | 94.44 | 97.71 | | floor | 83.63 | 91.65 | | tree | 75.52 | 88.09 | | ceiling | 85.2 | 93.16 | | road | 84.2 | 92.52 | | bed | 86.53 | 94.86 | | windowpane | 61.53 | 79.4 | | grass | 71.39 | 86.45 | | cabinet | 60.84 | 71.75 | | sidewalk | 67.26 | 81.29 | | person | 82.47 | 91.65 | | earth | 34.32 | 46.24 | | door | 53.57 | 69.6 | | table | 62.13 | 75.69 | | mountain | 57.41 | 72.25 | | plant | 54.62 | 68.84 | | curtain | 73.2 | 86.7 | | chair | 58.95 | 71.83 | | car | 85.43 | 91.3 | | water | 51.65 | 67.63 | | painting | 72.62 | 89.0 | | sofa | 65.75 | 77.65 | | shelf | 44.86 | 65.61 | | house | 51.81 | 77.61 | | sea | 51.57 | 76.09 | | mirror | 67.01 | 75.97 | | rug | 70.08 | 76.5 | | field | 37.35 | 53.2 | | armchair | 45.39 | 69.46 | | seat | 55.07 | 81.09 | | fence | 36.83 | 51.08 | | desk | 49.06 | 70.04 | | rock | 35.23 | 53.11 | | wardrobe | 49.25 | 67.33 | | lamp | 67.86 | 79.64 | | bathtub | 85.52 | 90.61 | | railing | 37.21 | 54.21 | | cushion | 57.84 | 70.34 | | base | 19.06 | 33.04 | | box | 25.69 | 37.34 | | column | 49.47 | 68.25 | | signboard | 39.03 | 54.74 | | chest of drawers | 41.44 | 67.59 | | counter | 30.98 | 48.31 | | sand | 35.02 | 50.17 | | sink | 74.17 | 81.97 | | skyscraper | 36.45 | 45.3 | | fireplace | 66.67 | 86.27 | | refrigerator | 79.86 | 89.9 | | grandstand | 43.51 | 73.43 | | path | 30.07 | 42.48 | | stairs | 33.01 | 41.97 | | runway | 76.05 | 89.67 | | case | 61.63 | 70.17 | | pool table | 92.54 | 96.59 | | pillow | 56.62 | 67.99 | | screen door | 81.2 | 84.79 | | stairway | 38.18 | 44.12 | | river | 20.18 | 44.77 | | bridge | 61.51 | 88.6 | | bookcase | 40.6 | 55.4 | | blind | 39.06 | 43.5 | | coffee table | 72.84 | 86.49 | | toilet | 76.67 | 89.64 | | flower | 40.56 | 58.45 | | book | 52.68 | 75.71 | | hill | 13.25 | 23.51 | | bench | 45.5 | 52.72 | | countertop | 55.43 | 66.39 | | stove | 79.4 | 84.52 | | palm | 53.65 | 68.01 | | kitchen island | 31.48 | 77.49 | | computer | 61.01 | 66.76 | | swivel chair | 38.56 | 54.51 | | boat | 73.59 | 81.93 | | bar | 45.96 | 57.92 | | arcade machine | 50.41 | 57.11 | | hovel | 8.19 | 15.67 | | bus | 91.52 | 94.74 | | towel | 68.41 | 75.11 | | light | 63.12 | 77.03 | | truck | 37.38 | 47.5 | | tower | 27.68 | 54.11 | | chandelier | 65.15 | 77.65 | | awning | 33.94 | 44.4 | | streetlight | 40.54 | 55.96 | | booth | 53.71 | 63.96 | | television receiver | 48.31 | 90.13 | | airplane | 56.71 | 67.0 | | dirt track | 2.25 | 2.64 | | apparel | 35.4 | 50.61 | | pole | 30.63 | 46.51 | | land | 2.53 | 3.02 | | bannister | 15.83 | 26.74 | | escalator | 42.44 | 56.58 | | ottoman | 36.63 | 66.26 | | bottle | 22.02 | 26.66 | | buffet | 42.48 | 47.24 | | poster | 25.23 | 33.09 | | stage | 14.74 | 24.42 | | van | 49.66 | 66.76 | | ship | 84.74 | 88.52 | | fountain | 6.68 | 7.82 | | conveyer belt | 60.63 | 91.91 | | canopy | 25.29 | 40.59 | | washer | 71.18 | 73.23 | | plaything | 27.88 | 40.64 | | swimming pool | 30.25 | 33.61 | | stool | 53.75 | 69.7 | | barrel | 14.45 | 56.15 | | basket | 35.05 | 42.5 | | waterfall | 40.62 | 45.01 | | tent | 77.54 | 97.62 | | bag | 16.57 | 22.35 | | minibike | 64.05 | 84.7 | | cradle | 76.53 | 96.63 | | oven | 45.77 | 55.58 | | ball | 31.83 | 38.98 | | food | 63.31 | 77.39 | | step | 27.3 | 35.03 | | tank | 38.98 | 54.82 | | trade name | 30.79 | 37.43 | | microwave | 38.25 | 41.06 | | pot | 51.79 | 58.01 | | animal | 61.34 | 69.02 | | bicycle | 58.13 | 78.36 | | lake | 63.53 | 63.65 | | dishwasher | 81.29 | 84.91 | | screen | 71.53 | 88.57 | | blanket | 10.27 | 13.25 | | sculpture | 65.79 | 82.18 | | hood | 70.62 | 74.37 | | sconce | 52.66 | 64.51 | | vase | 46.02 | 65.71 | | traffic light | 43.63 | 60.42 | | tray | 18.49 | 23.21 | | ashcan | 45.41 | 58.17 | | fan | 62.45 | 78.61 | | pier | 32.44 | 71.12 | | crt screen | 0.0 | 0.0 | | plate | 61.39 | 73.06 | | monitor | 6.32 | 8.98 | | bulletin board | 22.47 | 27.56 | | shower | 10.01 | 17.69 | | radiator | 60.28 | 72.03 | | glass | 17.92 | 19.16 | | clock | 33.74 | 38.05 | | flag | 47.55 | 55.69 | +---------------------+-------+-------+ 2024/07/08 14:54:09 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.4600 mIoU: 49.7300 mAcc: 62.5000 data_time: 0.0018 time: 0.2419 2024/07/08 14:55:05 - mmengine - INFO - Iter(train) [ 80050/120000] base_lr: 5.1390e-05 lr: 6.4900e-06 eta: 12:19:10 time: 1.1127 data_time: 0.0186 memory: 15464 grad_norm: 1.2349 loss: 0.2320 semantic_segmentation_loss_cls: 0.0622 semantic_segmentation_loss_mask: 0.0487 semantic_segmentation_loss_dice: 0.1210 2024/07/08 14:56:00 - mmengine - INFO - Iter(train) [ 80100/120000] base_lr: 5.1278e-05 lr: 6.4798e-06 eta: 12:18:14 time: 1.1126 data_time: 0.0185 memory: 16020 grad_norm: 1.2336 loss: 0.2321 semantic_segmentation_loss_cls: 0.0623 semantic_segmentation_loss_mask: 0.0487 semantic_segmentation_loss_dice: 0.1211 2024/07/08 14:56:56 - mmengine - INFO - Iter(train) [ 80150/120000] base_lr: 5.1166e-05 lr: 6.4696e-06 eta: 12:17:19 time: 1.1128 data_time: 0.0185 memory: 15102 grad_norm: 1.2325 loss: 0.2320 semantic_segmentation_loss_cls: 0.0623 semantic_segmentation_loss_mask: 0.0486 semantic_segmentation_loss_dice: 0.1210 2024/07/08 14:57:51 - mmengine - INFO - Iter(train) [ 80200/120000] base_lr: 5.1054e-05 lr: 6.4595e-06 eta: 12:16:23 time: 1.1131 data_time: 0.0185 memory: 15054 grad_norm: 1.2324 loss: 0.2320 semantic_segmentation_loss_cls: 0.0623 semantic_segmentation_loss_mask: 0.0486 semantic_segmentation_loss_dice: 0.1210 2024/07/08 14:58:47 - mmengine - INFO - Iter(train) [ 80250/120000] base_lr: 5.0942e-05 lr: 6.4493e-06 eta: 12:15:28 time: 1.1131 data_time: 0.0185 memory: 15204 grad_norm: 1.2315 loss: 0.2315 semantic_segmentation_loss_cls: 0.0620 semantic_segmentation_loss_mask: 0.0486 semantic_segmentation_loss_dice: 0.1209 2024/07/08 14:59:43 - mmengine - INFO - Iter(train) [ 80300/120000] base_lr: 5.0830e-05 lr: 6.4391e-06 eta: 12:14:33 time: 1.1135 data_time: 0.0184 memory: 14894 grad_norm: 1.2319 loss: 0.2309 semantic_segmentation_loss_cls: 0.0619 semantic_segmentation_loss_mask: 0.0485 semantic_segmentation_loss_dice: 0.1206 2024/07/08 15:00:39 - mmengine - INFO - Iter(train) [ 80350/120000] base_lr: 5.0719e-05 lr: 6.4290e-06 eta: 12:13:38 time: 1.1134 data_time: 0.0184 memory: 14977 grad_norm: 1.2316 loss: 0.2308 semantic_segmentation_loss_cls: 0.0618 semantic_segmentation_loss_mask: 0.0485 semantic_segmentation_loss_dice: 0.1206 2024/07/08 15:01:35 - mmengine - INFO - Iter(train) [ 80400/120000] base_lr: 5.0607e-05 lr: 6.4188e-06 eta: 12:12:42 time: 1.1131 data_time: 0.0184 memory: 15563 grad_norm: 1.2322 loss: 0.2307 semantic_segmentation_loss_cls: 0.0617 semantic_segmentation_loss_mask: 0.0484 semantic_segmentation_loss_dice: 0.1205 2024/07/08 15:02:30 - mmengine - INFO - Iter(train) [ 80450/120000] base_lr: 5.0496e-05 lr: 6.4087e-06 eta: 12:11:47 time: 1.1130 data_time: 0.0184 memory: 14755 grad_norm: 1.2297 loss: 0.2303 semantic_segmentation_loss_cls: 0.0616 semantic_segmentation_loss_mask: 0.0484 semantic_segmentation_loss_dice: 0.1203 2024/07/08 15:03:25 - mmengine - INFO - Iter(train) [ 80500/120000] base_lr: 5.0384e-05 lr: 6.3986e-06 eta: 12:10:51 time: 1.1128 data_time: 0.0183 memory: 15479 grad_norm: 1.2302 loss: 0.2304 semantic_segmentation_loss_cls: 0.0616 semantic_segmentation_loss_mask: 0.0484 semantic_segmentation_loss_dice: 0.1204 2024/07/08 15:04:20 - mmengine - INFO - Iter(train) [ 80550/120000] base_lr: 5.0273e-05 lr: 6.3884e-06 eta: 12:09:55 time: 1.1124 data_time: 0.0183 memory: 16780 grad_norm: 1.2303 loss: 0.2302 semantic_segmentation_loss_cls: 0.0616 semantic_segmentation_loss_mask: 0.0483 semantic_segmentation_loss_dice: 0.1203 2024/07/08 15:05:15 - mmengine - INFO - Iter(train) [ 80600/120000] base_lr: 5.0162e-05 lr: 6.3783e-06 eta: 12:08:59 time: 1.1122 data_time: 0.0183 memory: 15695 grad_norm: 1.2296 loss: 0.2301 semantic_segmentation_loss_cls: 0.0616 semantic_segmentation_loss_mask: 0.0482 semantic_segmentation_loss_dice: 0.1202 2024/07/08 15:06:10 - mmengine - INFO - Iter(train) [ 80650/120000] base_lr: 5.0050e-05 lr: 6.3682e-06 eta: 12:08:03 time: 1.1119 data_time: 0.0182 memory: 15068 grad_norm: 1.2300 loss: 0.2299 semantic_segmentation_loss_cls: 0.0615 semantic_segmentation_loss_mask: 0.0482 semantic_segmentation_loss_dice: 0.1202 2024/07/08 15:07:05 - mmengine - INFO - Iter(train) [ 80700/120000] base_lr: 4.9939e-05 lr: 6.3581e-06 eta: 12:07:08 time: 1.1119 data_time: 0.0182 memory: 14631 grad_norm: 1.2279 loss: 0.2296 semantic_segmentation_loss_cls: 0.0614 semantic_segmentation_loss_mask: 0.0482 semantic_segmentation_loss_dice: 0.1200 2024/07/08 15:08:00 - mmengine - INFO - Iter(train) [ 80750/120000] base_lr: 4.9828e-05 lr: 6.3480e-06 eta: 12:06:12 time: 1.1117 data_time: 0.0182 memory: 15141 grad_norm: 1.2275 loss: 0.2295 semantic_segmentation_loss_cls: 0.0613 semantic_segmentation_loss_mask: 0.0482 semantic_segmentation_loss_dice: 0.1200 2024/07/08 15:08:56 - mmengine - INFO - Iter(train) [ 80800/120000] base_lr: 4.9718e-05 lr: 6.3380e-06 eta: 12:05:16 time: 1.1119 data_time: 0.0182 memory: 15029 grad_norm: 1.2287 loss: 0.2295 semantic_segmentation_loss_cls: 0.0613 semantic_segmentation_loss_mask: 0.0482 semantic_segmentation_loss_dice: 0.1200 2024/07/08 15:09:51 - mmengine - INFO - Iter(train) [ 80850/120000] base_lr: 4.9607e-05 lr: 6.3279e-06 eta: 12:04:21 time: 1.1119 data_time: 0.0182 memory: 14914 grad_norm: 1.2289 loss: 0.2296 semantic_segmentation_loss_cls: 0.0614 semantic_segmentation_loss_mask: 0.0482 semantic_segmentation_loss_dice: 0.1201 2024/07/08 15:10:46 - mmengine - INFO - Iter(train) [ 80900/120000] base_lr: 4.9496e-05 lr: 6.3178e-06 eta: 12:03:25 time: 1.1121 data_time: 0.0182 memory: 16022 grad_norm: 1.2287 loss: 0.2300 semantic_segmentation_loss_cls: 0.0615 semantic_segmentation_loss_mask: 0.0482 semantic_segmentation_loss_dice: 0.1203 2024/07/08 15:11:41 - mmengine - INFO - Iter(train) [ 80950/120000] base_lr: 4.9385e-05 lr: 6.3078e-06 eta: 12:02:29 time: 1.1121 data_time: 0.0181 memory: 14715 grad_norm: 1.2294 loss: 0.2299 semantic_segmentation_loss_cls: 0.0614 semantic_segmentation_loss_mask: 0.0482 semantic_segmentation_loss_dice: 0.1203 2024/07/08 15:12:36 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 15:12:36 - mmengine - INFO - Iter(train) [ 81000/120000] base_lr: 4.9275e-05 lr: 6.2977e-06 eta: 12:01:34 time: 1.1119 data_time: 0.0181 memory: 15524 grad_norm: 1.2288 loss: 0.2300 semantic_segmentation_loss_cls: 0.0615 semantic_segmentation_loss_mask: 0.0482 semantic_segmentation_loss_dice: 0.1203 2024/07/08 15:12:36 - mmengine - INFO - Saving checkpoint at 81000 iterations 2024/07/08 15:13:37 - mmengine - INFO - Iter(train) [ 81050/120000] base_lr: 4.9164e-05 lr: 6.2877e-06 eta: 12:00:41 time: 1.1119 data_time: 0.0181 memory: 15117 grad_norm: 1.2274 loss: 0.2296 semantic_segmentation_loss_cls: 0.0613 semantic_segmentation_loss_mask: 0.0482 semantic_segmentation_loss_dice: 0.1201 2024/07/08 15:14:32 - mmengine - INFO - Iter(train) [ 81100/120000] base_lr: 4.9054e-05 lr: 6.2776e-06 eta: 11:59:45 time: 1.1118 data_time: 0.0180 memory: 16230 grad_norm: 1.2270 loss: 0.2293 semantic_segmentation_loss_cls: 0.0612 semantic_segmentation_loss_mask: 0.0481 semantic_segmentation_loss_dice: 0.1200 2024/07/08 15:15:28 - mmengine - INFO - Iter(train) [ 81150/120000] base_lr: 4.8944e-05 lr: 6.2676e-06 eta: 11:58:50 time: 1.1119 data_time: 0.0181 memory: 15267 grad_norm: 1.2286 loss: 0.2294 semantic_segmentation_loss_cls: 0.0612 semantic_segmentation_loss_mask: 0.0482 semantic_segmentation_loss_dice: 0.1201 2024/07/08 15:16:24 - mmengine - INFO - Iter(train) [ 81200/120000] base_lr: 4.8834e-05 lr: 6.2576e-06 eta: 11:57:54 time: 1.1121 data_time: 0.0181 memory: 15333 grad_norm: 1.2274 loss: 0.2293 semantic_segmentation_loss_cls: 0.0611 semantic_segmentation_loss_mask: 0.0482 semantic_segmentation_loss_dice: 0.1200 2024/07/08 15:17:20 - mmengine - INFO - Iter(train) [ 81250/120000] base_lr: 4.8723e-05 lr: 6.2476e-06 eta: 11:56:59 time: 1.1122 data_time: 0.0181 memory: 14987 grad_norm: 1.2268 loss: 0.2293 semantic_segmentation_loss_cls: 0.0611 semantic_segmentation_loss_mask: 0.0481 semantic_segmentation_loss_dice: 0.1200 2024/07/08 15:18:16 - mmengine - INFO - Iter(train) [ 81300/120000] base_lr: 4.8613e-05 lr: 6.2376e-06 eta: 11:56:04 time: 1.1122 data_time: 0.0181 memory: 16341 grad_norm: 1.2248 loss: 0.2290 semantic_segmentation_loss_cls: 0.0610 semantic_segmentation_loss_mask: 0.0481 semantic_segmentation_loss_dice: 0.1199 2024/07/08 15:19:11 - mmengine - INFO - Iter(train) [ 81350/120000] base_lr: 4.8504e-05 lr: 6.2276e-06 eta: 11:55:08 time: 1.1122 data_time: 0.0181 memory: 15481 grad_norm: 1.2250 loss: 0.2291 semantic_segmentation_loss_cls: 0.0610 semantic_segmentation_loss_mask: 0.0481 semantic_segmentation_loss_dice: 0.1200 2024/07/08 15:20:07 - mmengine - INFO - Iter(train) [ 81400/120000] base_lr: 4.8394e-05 lr: 6.2176e-06 eta: 11:54:13 time: 1.1122 data_time: 0.0181 memory: 16208 grad_norm: 1.2245 loss: 0.2291 semantic_segmentation_loss_cls: 0.0610 semantic_segmentation_loss_mask: 0.0481 semantic_segmentation_loss_dice: 0.1200 2024/07/08 15:21:03 - mmengine - INFO - Iter(train) [ 81450/120000] base_lr: 4.8284e-05 lr: 6.2076e-06 eta: 11:53:18 time: 1.1122 data_time: 0.0181 memory: 15771 grad_norm: 1.2243 loss: 0.2289 semantic_segmentation_loss_cls: 0.0610 semantic_segmentation_loss_mask: 0.0480 semantic_segmentation_loss_dice: 0.1199 2024/07/08 15:21:58 - mmengine - INFO - Iter(train) [ 81500/120000] base_lr: 4.8174e-05 lr: 6.1977e-06 eta: 11:52:22 time: 1.1120 data_time: 0.0180 memory: 15169 grad_norm: 1.2246 loss: 0.2291 semantic_segmentation_loss_cls: 0.0610 semantic_segmentation_loss_mask: 0.0480 semantic_segmentation_loss_dice: 0.1200 2024/07/08 15:22:54 - mmengine - INFO - Iter(train) [ 81550/120000] base_lr: 4.8065e-05 lr: 6.1877e-06 eta: 11:51:27 time: 1.1121 data_time: 0.0180 memory: 15020 grad_norm: 1.2259 loss: 0.2290 semantic_segmentation_loss_cls: 0.0610 semantic_segmentation_loss_mask: 0.0481 semantic_segmentation_loss_dice: 0.1199 2024/07/08 15:23:51 - mmengine - INFO - Iter(train) [ 81600/120000] base_lr: 4.7955e-05 lr: 6.1778e-06 eta: 11:50:31 time: 1.1124 data_time: 0.0180 memory: 14745 grad_norm: 1.2249 loss: 0.2289 semantic_segmentation_loss_cls: 0.0610 semantic_segmentation_loss_mask: 0.0481 semantic_segmentation_loss_dice: 0.1199 2024/07/08 15:24:46 - mmengine - INFO - Iter(train) [ 81650/120000] base_lr: 4.7846e-05 lr: 6.1678e-06 eta: 11:49:36 time: 1.1126 data_time: 0.0180 memory: 15410 grad_norm: 1.2262 loss: 0.2288 semantic_segmentation_loss_cls: 0.0610 semantic_segmentation_loss_mask: 0.0480 semantic_segmentation_loss_dice: 0.1198 2024/07/08 15:25:43 - mmengine - INFO - Iter(train) [ 81700/120000] base_lr: 4.7737e-05 lr: 6.1579e-06 eta: 11:48:41 time: 1.1129 data_time: 0.0180 memory: 15698 grad_norm: 1.2295 loss: 0.2290 semantic_segmentation_loss_cls: 0.0611 semantic_segmentation_loss_mask: 0.0480 semantic_segmentation_loss_dice: 0.1199 2024/07/08 15:26:38 - mmengine - INFO - Iter(train) [ 81750/120000] base_lr: 4.7627e-05 lr: 6.1480e-06 eta: 11:47:45 time: 1.1129 data_time: 0.0180 memory: 15331 grad_norm: 1.2285 loss: 0.2287 semantic_segmentation_loss_cls: 0.0610 semantic_segmentation_loss_mask: 0.0480 semantic_segmentation_loss_dice: 0.1198 2024/07/08 15:27:33 - mmengine - INFO - Iter(train) [ 81800/120000] base_lr: 4.7518e-05 lr: 6.1380e-06 eta: 11:46:50 time: 1.1129 data_time: 0.0180 memory: 15926 grad_norm: 1.2275 loss: 0.2287 semantic_segmentation_loss_cls: 0.0610 semantic_segmentation_loss_mask: 0.0479 semantic_segmentation_loss_dice: 0.1197 2024/07/08 15:28:28 - mmengine - INFO - Iter(train) [ 81850/120000] base_lr: 4.7409e-05 lr: 6.1281e-06 eta: 11:45:54 time: 1.1128 data_time: 0.0180 memory: 15113 grad_norm: 1.2267 loss: 0.2287 semantic_segmentation_loss_cls: 0.0610 semantic_segmentation_loss_mask: 0.0479 semantic_segmentation_loss_dice: 0.1198 2024/07/08 15:29:23 - mmengine - INFO - Iter(train) [ 81900/120000] base_lr: 4.7300e-05 lr: 6.1182e-06 eta: 11:44:58 time: 1.1129 data_time: 0.0180 memory: 14708 grad_norm: 1.2269 loss: 0.2285 semantic_segmentation_loss_cls: 0.0609 semantic_segmentation_loss_mask: 0.0479 semantic_segmentation_loss_dice: 0.1197 2024/07/08 15:30:18 - mmengine - INFO - Iter(train) [ 81950/120000] base_lr: 4.7192e-05 lr: 6.1083e-06 eta: 11:44:02 time: 1.1129 data_time: 0.0180 memory: 15237 grad_norm: 1.2279 loss: 0.2286 semantic_segmentation_loss_cls: 0.0610 semantic_segmentation_loss_mask: 0.0479 semantic_segmentation_loss_dice: 0.1197 2024/07/08 15:31:13 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 15:31:13 - mmengine - INFO - Iter(train) [ 82000/120000] base_lr: 4.7083e-05 lr: 6.0984e-06 eta: 11:43:07 time: 1.1129 data_time: 0.0180 memory: 15680 grad_norm: 1.2276 loss: 0.2288 semantic_segmentation_loss_cls: 0.0611 semantic_segmentation_loss_mask: 0.0479 semantic_segmentation_loss_dice: 0.1198 2024/07/08 15:31:13 - mmengine - INFO - Saving checkpoint at 82000 iterations 2024/07/08 15:32:13 - mmengine - INFO - Iter(train) [ 82050/120000] base_lr: 4.6974e-05 lr: 6.0886e-06 eta: 11:42:13 time: 1.1128 data_time: 0.0180 memory: 15461 grad_norm: 1.2283 loss: 0.2288 semantic_segmentation_loss_cls: 0.0612 semantic_segmentation_loss_mask: 0.0479 semantic_segmentation_loss_dice: 0.1198 2024/07/08 15:33:08 - mmengine - INFO - Iter(train) [ 82100/120000] base_lr: 4.6866e-05 lr: 6.0787e-06 eta: 11:41:17 time: 1.1129 data_time: 0.0180 memory: 15431 grad_norm: 1.2275 loss: 0.2287 semantic_segmentation_loss_cls: 0.0611 semantic_segmentation_loss_mask: 0.0479 semantic_segmentation_loss_dice: 0.1197 2024/07/08 15:34:04 - mmengine - INFO - Iter(train) [ 82150/120000] base_lr: 4.6757e-05 lr: 6.0688e-06 eta: 11:40:22 time: 1.1130 data_time: 0.0180 memory: 15287 grad_norm: 1.2279 loss: 0.2286 semantic_segmentation_loss_cls: 0.0611 semantic_segmentation_loss_mask: 0.0478 semantic_segmentation_loss_dice: 0.1197 2024/07/08 15:35:00 - mmengine - INFO - Iter(train) [ 82200/120000] base_lr: 4.6649e-05 lr: 6.0590e-06 eta: 11:39:27 time: 1.1130 data_time: 0.0180 memory: 15578 grad_norm: 1.2266 loss: 0.2284 semantic_segmentation_loss_cls: 0.0610 semantic_segmentation_loss_mask: 0.0478 semantic_segmentation_loss_dice: 0.1196 2024/07/08 15:35:55 - mmengine - INFO - Iter(train) [ 82250/120000] base_lr: 4.6541e-05 lr: 6.0491e-06 eta: 11:38:31 time: 1.1133 data_time: 0.0180 memory: 16237 grad_norm: 1.2267 loss: 0.2282 semantic_segmentation_loss_cls: 0.0610 semantic_segmentation_loss_mask: 0.0477 semantic_segmentation_loss_dice: 0.1195 2024/07/08 15:36:50 - mmengine - INFO - Iter(train) [ 82300/120000] base_lr: 4.6432e-05 lr: 6.0393e-06 eta: 11:37:35 time: 1.1131 data_time: 0.0180 memory: 15612 grad_norm: 1.2272 loss: 0.2282 semantic_segmentation_loss_cls: 0.0610 semantic_segmentation_loss_mask: 0.0477 semantic_segmentation_loss_dice: 0.1195 2024/07/08 15:37:46 - mmengine - INFO - Iter(train) [ 82350/120000] base_lr: 4.6324e-05 lr: 6.0295e-06 eta: 11:36:40 time: 1.1131 data_time: 0.0180 memory: 14810 grad_norm: 1.2275 loss: 0.2279 semantic_segmentation_loss_cls: 0.0609 semantic_segmentation_loss_mask: 0.0477 semantic_segmentation_loss_dice: 0.1193 2024/07/08 15:38:41 - mmengine - INFO - Iter(train) [ 82400/120000] base_lr: 4.6216e-05 lr: 6.0197e-06 eta: 11:35:44 time: 1.1130 data_time: 0.0179 memory: 14471 grad_norm: 1.2270 loss: 0.2279 semantic_segmentation_loss_cls: 0.0609 semantic_segmentation_loss_mask: 0.0477 semantic_segmentation_loss_dice: 0.1193 2024/07/08 15:39:37 - mmengine - INFO - Iter(train) [ 82450/120000] base_lr: 4.6109e-05 lr: 6.0099e-06 eta: 11:34:49 time: 1.1131 data_time: 0.0179 memory: 15304 grad_norm: 1.2276 loss: 0.2280 semantic_segmentation_loss_cls: 0.0609 semantic_segmentation_loss_mask: 0.0477 semantic_segmentation_loss_dice: 0.1194 2024/07/08 15:40:32 - mmengine - INFO - Iter(train) [ 82500/120000] base_lr: 4.6001e-05 lr: 6.0001e-06 eta: 11:33:53 time: 1.1129 data_time: 0.0179 memory: 15361 grad_norm: 1.2264 loss: 0.2280 semantic_segmentation_loss_cls: 0.0610 semantic_segmentation_loss_mask: 0.0477 semantic_segmentation_loss_dice: 0.1194 2024/07/08 15:41:28 - mmengine - INFO - Iter(train) [ 82550/120000] base_lr: 4.5893e-05 lr: 5.9903e-06 eta: 11:32:58 time: 1.1127 data_time: 0.0179 memory: 14770 grad_norm: 1.2248 loss: 0.2278 semantic_segmentation_loss_cls: 0.0608 semantic_segmentation_loss_mask: 0.0476 semantic_segmentation_loss_dice: 0.1193 2024/07/08 15:42:22 - mmengine - INFO - Iter(train) [ 82600/120000] base_lr: 4.5785e-05 lr: 5.9805e-06 eta: 11:32:02 time: 1.1126 data_time: 0.0179 memory: 14831 grad_norm: 1.2256 loss: 0.2274 semantic_segmentation_loss_cls: 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semantic_segmentation_loss_dice: 0.1190 2024/07/08 15:46:03 - mmengine - INFO - Iter(train) [ 82800/120000] base_lr: 4.5356e-05 lr: 5.9414e-06 eta: 11:28:19 time: 1.1123 data_time: 0.0179 memory: 15878 grad_norm: 1.2254 loss: 0.2272 semantic_segmentation_loss_cls: 0.0606 semantic_segmentation_loss_mask: 0.0476 semantic_segmentation_loss_dice: 0.1190 2024/07/08 15:46:59 - mmengine - INFO - Iter(train) [ 82850/120000] base_lr: 4.5249e-05 lr: 5.9317e-06 eta: 11:27:24 time: 1.1123 data_time: 0.0178 memory: 14982 grad_norm: 1.2243 loss: 0.2268 semantic_segmentation_loss_cls: 0.0604 semantic_segmentation_loss_mask: 0.0475 semantic_segmentation_loss_dice: 0.1188 2024/07/08 15:47:53 - mmengine - INFO - Iter(train) [ 82900/120000] base_lr: 4.5142e-05 lr: 5.9220e-06 eta: 11:26:28 time: 1.1120 data_time: 0.0178 memory: 15307 grad_norm: 1.2237 loss: 0.2269 semantic_segmentation_loss_cls: 0.0605 semantic_segmentation_loss_mask: 0.0475 semantic_segmentation_loss_dice: 0.1189 2024/07/08 15:48:48 - mmengine - INFO - Iter(train) [ 82950/120000] base_lr: 4.5035e-05 lr: 5.9123e-06 eta: 11:25:32 time: 1.1120 data_time: 0.0178 memory: 15514 grad_norm: 1.2224 loss: 0.2271 semantic_segmentation_loss_cls: 0.0606 semantic_segmentation_loss_mask: 0.0475 semantic_segmentation_loss_dice: 0.1190 2024/07/08 15:49:44 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 15:49:44 - mmengine - INFO - Iter(train) [ 83000/120000] base_lr: 4.4928e-05 lr: 5.9025e-06 eta: 11:24:37 time: 1.1118 data_time: 0.0178 memory: 16188 grad_norm: 1.2212 loss: 0.2268 semantic_segmentation_loss_cls: 0.0604 semantic_segmentation_loss_mask: 0.0475 semantic_segmentation_loss_dice: 0.1189 2024/07/08 15:49:44 - mmengine - INFO - Saving checkpoint at 83000 iterations 2024/07/08 15:50:44 - mmengine - INFO - Iter(train) [ 83050/120000] base_lr: 4.4821e-05 lr: 5.8928e-06 eta: 11:23:43 time: 1.1119 data_time: 0.0178 memory: 14940 grad_norm: 1.2198 loss: 0.2265 semantic_segmentation_loss_cls: 0.0603 semantic_segmentation_loss_mask: 0.0474 semantic_segmentation_loss_dice: 0.1188 2024/07/08 15:51:40 - mmengine - INFO - Iter(train) [ 83100/120000] base_lr: 4.4715e-05 lr: 5.8831e-06 eta: 11:22:48 time: 1.1120 data_time: 0.0178 memory: 14920 grad_norm: 1.2199 loss: 0.2262 semantic_segmentation_loss_cls: 0.0601 semantic_segmentation_loss_mask: 0.0474 semantic_segmentation_loss_dice: 0.1187 2024/07/08 15:52:36 - mmengine - INFO - Iter(train) [ 83150/120000] base_lr: 4.4608e-05 lr: 5.8734e-06 eta: 11:21:52 time: 1.1120 data_time: 0.0178 memory: 15004 grad_norm: 1.2196 loss: 0.2262 semantic_segmentation_loss_cls: 0.0601 semantic_segmentation_loss_mask: 0.0474 semantic_segmentation_loss_dice: 0.1187 2024/07/08 15:53:31 - mmengine - INFO - Iter(train) [ 83200/120000] base_lr: 4.4501e-05 lr: 5.8638e-06 eta: 11:20:57 time: 1.1120 data_time: 0.0178 memory: 14862 grad_norm: 1.2193 loss: 0.2261 semantic_segmentation_loss_cls: 0.0601 semantic_segmentation_loss_mask: 0.0474 semantic_segmentation_loss_dice: 0.1187 2024/07/08 15:54:27 - mmengine - INFO - Iter(train) [ 83250/120000] base_lr: 4.4395e-05 lr: 5.8541e-06 eta: 11:20:01 time: 1.1120 data_time: 0.0178 memory: 15634 grad_norm: 1.2174 loss: 0.2264 semantic_segmentation_loss_cls: 0.0602 semantic_segmentation_loss_mask: 0.0474 semantic_segmentation_loss_dice: 0.1188 2024/07/08 15:55:23 - mmengine - INFO - Iter(train) [ 83300/120000] base_lr: 4.4289e-05 lr: 5.8444e-06 eta: 11:19:06 time: 1.1122 data_time: 0.0178 memory: 15329 grad_norm: 1.2173 loss: 0.2265 semantic_segmentation_loss_cls: 0.0602 semantic_segmentation_loss_mask: 0.0474 semantic_segmentation_loss_dice: 0.1189 2024/07/08 15:56:19 - mmengine - INFO - Iter(train) [ 83350/120000] base_lr: 4.4183e-05 lr: 5.8348e-06 eta: 11:18:11 time: 1.1127 data_time: 0.0178 memory: 15247 grad_norm: 1.2164 loss: 0.2262 semantic_segmentation_loss_cls: 0.0601 semantic_segmentation_loss_mask: 0.0474 semantic_segmentation_loss_dice: 0.1187 2024/07/08 15:57:14 - mmengine - INFO - Iter(train) [ 83400/120000] base_lr: 4.4077e-05 lr: 5.8251e-06 eta: 11:17:15 time: 1.1126 data_time: 0.0178 memory: 14927 grad_norm: 1.2123 loss: 0.2260 semantic_segmentation_loss_cls: 0.0600 semantic_segmentation_loss_mask: 0.0473 semantic_segmentation_loss_dice: 0.1187 2024/07/08 15:58:10 - mmengine - INFO - Iter(train) [ 83450/120000] base_lr: 4.3971e-05 lr: 5.8155e-06 eta: 11:16:20 time: 1.1126 data_time: 0.0178 memory: 15849 grad_norm: 1.2116 loss: 0.2259 semantic_segmentation_loss_cls: 0.0599 semantic_segmentation_loss_mask: 0.0473 semantic_segmentation_loss_dice: 0.1186 2024/07/08 15:59:05 - mmengine - INFO - Iter(train) [ 83500/120000] base_lr: 4.3865e-05 lr: 5.8059e-06 eta: 11:15:24 time: 1.1123 data_time: 0.0178 memory: 15264 grad_norm: 1.2101 loss: 0.2256 semantic_segmentation_loss_cls: 0.0598 semantic_segmentation_loss_mask: 0.0472 semantic_segmentation_loss_dice: 0.1185 2024/07/08 16:00:00 - mmengine - INFO - Iter(train) [ 83550/120000] base_lr: 4.3759e-05 lr: 5.7963e-06 eta: 11:14:28 time: 1.1122 data_time: 0.0178 memory: 15279 grad_norm: 1.2110 loss: 0.2253 semantic_segmentation_loss_cls: 0.0597 semantic_segmentation_loss_mask: 0.0472 semantic_segmentation_loss_dice: 0.1184 2024/07/08 16:00:56 - mmengine - INFO - Iter(train) [ 83600/120000] base_lr: 4.3653e-05 lr: 5.7867e-06 eta: 11:13:33 time: 1.1123 data_time: 0.0178 memory: 15632 grad_norm: 1.2110 loss: 0.2252 semantic_segmentation_loss_cls: 0.0596 semantic_segmentation_loss_mask: 0.0472 semantic_segmentation_loss_dice: 0.1183 2024/07/08 16:01:51 - mmengine - INFO - Iter(train) [ 83650/120000] base_lr: 4.3548e-05 lr: 5.7771e-06 eta: 11:12:37 time: 1.1125 data_time: 0.0178 memory: 14516 grad_norm: 1.2110 loss: 0.2250 semantic_segmentation_loss_cls: 0.0594 semantic_segmentation_loss_mask: 0.0472 semantic_segmentation_loss_dice: 0.1183 2024/07/08 16:02:46 - mmengine - INFO - Iter(train) [ 83700/120000] base_lr: 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0.0177 memory: 15210 grad_norm: 1.2118 loss: 0.2249 semantic_segmentation_loss_cls: 0.0595 semantic_segmentation_loss_mask: 0.0472 semantic_segmentation_loss_dice: 0.1182 2024/07/08 16:06:29 - mmengine - INFO - Iter(train) [ 83900/120000] base_lr: 4.3021e-05 lr: 5.7292e-06 eta: 11:08:00 time: 1.1128 data_time: 0.0177 memory: 15879 grad_norm: 1.2098 loss: 0.2243 semantic_segmentation_loss_cls: 0.0594 semantic_segmentation_loss_mask: 0.0470 semantic_segmentation_loss_dice: 0.1179 2024/07/08 16:07:25 - mmengine - INFO - Iter(train) [ 83950/120000] base_lr: 4.2916e-05 lr: 5.7197e-06 eta: 11:07:04 time: 1.1129 data_time: 0.0177 memory: 15504 grad_norm: 1.2083 loss: 0.2245 semantic_segmentation_loss_cls: 0.0595 semantic_segmentation_loss_mask: 0.0470 semantic_segmentation_loss_dice: 0.1180 2024/07/08 16:08:20 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 16:08:20 - mmengine - INFO - Iter(train) [ 84000/120000] base_lr: 4.2811e-05 lr: 5.7101e-06 eta: 11:06:09 time: 1.1129 data_time: 0.0177 memory: 14941 grad_norm: 1.2072 loss: 0.2244 semantic_segmentation_loss_cls: 0.0595 semantic_segmentation_loss_mask: 0.0470 semantic_segmentation_loss_dice: 0.1178 2024/07/08 16:08:20 - mmengine - INFO - Saving checkpoint at 84000 iterations 2024/07/08 16:09:20 - mmengine - INFO - Iter(train) [ 84050/120000] base_lr: 4.2707e-05 lr: 5.7006e-06 eta: 11:05:15 time: 1.1137 data_time: 0.0187 memory: 14881 grad_norm: 1.2079 loss: 0.2244 semantic_segmentation_loss_cls: 0.0595 semantic_segmentation_loss_mask: 0.0470 semantic_segmentation_loss_dice: 0.1178 2024/07/08 16:10:15 - mmengine - INFO - Iter(train) [ 84100/120000] base_lr: 4.2602e-05 lr: 5.6911e-06 eta: 11:04:20 time: 1.1136 data_time: 0.0187 memory: 15072 grad_norm: 1.2084 loss: 0.2243 semantic_segmentation_loss_cls: 0.0595 semantic_segmentation_loss_mask: 0.0470 semantic_segmentation_loss_dice: 0.1178 2024/07/08 16:11:11 - mmengine - INFO - Iter(train) [ 84150/120000] base_lr: 4.2497e-05 lr: 5.6816e-06 eta: 11:03:24 time: 1.1135 data_time: 0.0187 memory: 15322 grad_norm: 1.2105 loss: 0.2241 semantic_segmentation_loss_cls: 0.0594 semantic_segmentation_loss_mask: 0.0470 semantic_segmentation_loss_dice: 0.1177 2024/07/08 16:12:06 - mmengine - INFO - Iter(train) [ 84200/120000] base_lr: 4.2393e-05 lr: 5.6721e-06 eta: 11:02:28 time: 1.1133 data_time: 0.0187 memory: 15175 grad_norm: 1.2097 loss: 0.2242 semantic_segmentation_loss_cls: 0.0594 semantic_segmentation_loss_mask: 0.0470 semantic_segmentation_loss_dice: 0.1178 2024/07/08 16:13:01 - mmengine - INFO - Iter(train) [ 84250/120000] base_lr: 4.2288e-05 lr: 5.6626e-06 eta: 11:01:33 time: 1.1133 data_time: 0.0187 memory: 15304 grad_norm: 1.2098 loss: 0.2245 semantic_segmentation_loss_cls: 0.0596 semantic_segmentation_loss_mask: 0.0470 semantic_segmentation_loss_dice: 0.1179 2024/07/08 16:13:56 - mmengine - INFO - Iter(train) [ 84300/120000] base_lr: 4.2184e-05 lr: 5.6531e-06 eta: 11:00:37 time: 1.1130 data_time: 0.0187 memory: 14943 grad_norm: 1.2087 loss: 0.2246 semantic_segmentation_loss_cls: 0.0595 semantic_segmentation_loss_mask: 0.0470 semantic_segmentation_loss_dice: 0.1181 2024/07/08 16:14:51 - mmengine - INFO - Iter(train) [ 84350/120000] base_lr: 4.2080e-05 lr: 5.6436e-06 eta: 10:59:41 time: 1.1127 data_time: 0.0187 memory: 16308 grad_norm: 1.2095 loss: 0.2247 semantic_segmentation_loss_cls: 0.0596 semantic_segmentation_loss_mask: 0.0470 semantic_segmentation_loss_dice: 0.1181 2024/07/08 16:15:45 - mmengine - INFO - Iter(train) [ 84400/120000] base_lr: 4.1976e-05 lr: 5.6342e-06 eta: 10:58:45 time: 1.1125 data_time: 0.0187 memory: 15033 grad_norm: 1.2097 loss: 0.2249 semantic_segmentation_loss_cls: 0.0597 semantic_segmentation_loss_mask: 0.0470 semantic_segmentation_loss_dice: 0.1182 2024/07/08 16:16:41 - mmengine - INFO - Iter(train) [ 84450/120000] base_lr: 4.1872e-05 lr: 5.6247e-06 eta: 10:57:50 time: 1.1124 data_time: 0.0187 memory: 14918 grad_norm: 1.2105 loss: 0.2249 semantic_segmentation_loss_cls: 0.0597 semantic_segmentation_loss_mask: 0.0471 semantic_segmentation_loss_dice: 0.1182 2024/07/08 16:17:37 - mmengine - INFO - Iter(train) [ 84500/120000] base_lr: 4.1768e-05 lr: 5.6153e-06 eta: 10:56:54 time: 1.1127 data_time: 0.0187 memory: 14899 grad_norm: 1.2092 loss: 0.2246 semantic_segmentation_loss_cls: 0.0595 semantic_segmentation_loss_mask: 0.0470 semantic_segmentation_loss_dice: 0.1180 2024/07/08 16:18:32 - mmengine - INFO - Iter(train) [ 84550/120000] base_lr: 4.1664e-05 lr: 5.6058e-06 eta: 10:55:59 time: 1.1129 data_time: 0.0187 memory: 14567 grad_norm: 1.2102 loss: 0.2247 semantic_segmentation_loss_cls: 0.0596 semantic_segmentation_loss_mask: 0.0471 semantic_segmentation_loss_dice: 0.1181 2024/07/08 16:19:27 - mmengine - INFO - Iter(train) [ 84600/120000] base_lr: 4.1560e-05 lr: 5.5964e-06 eta: 10:55:03 time: 1.1130 data_time: 0.0187 memory: 15753 grad_norm: 1.2099 loss: 0.2245 semantic_segmentation_loss_cls: 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semantic_segmentation_loss_dice: 0.1179 2024/07/08 16:23:11 - mmengine - INFO - Iter(train) [ 84800/120000] base_lr: 4.1147e-05 lr: 5.5588e-06 eta: 10:51:22 time: 1.1136 data_time: 0.0187 memory: 14667 grad_norm: 1.2085 loss: 0.2243 semantic_segmentation_loss_cls: 0.0595 semantic_segmentation_loss_mask: 0.0470 semantic_segmentation_loss_dice: 0.1178 2024/07/08 16:24:07 - mmengine - INFO - Iter(train) [ 84850/120000] base_lr: 4.1044e-05 lr: 5.5494e-06 eta: 10:50:26 time: 1.1138 data_time: 0.0187 memory: 16480 grad_norm: 1.2076 loss: 0.2241 semantic_segmentation_loss_cls: 0.0594 semantic_segmentation_loss_mask: 0.0470 semantic_segmentation_loss_dice: 0.1177 2024/07/08 16:25:02 - mmengine - INFO - Iter(train) [ 84900/120000] base_lr: 4.0941e-05 lr: 5.5400e-06 eta: 10:49:31 time: 1.1138 data_time: 0.0187 memory: 15988 grad_norm: 1.2092 loss: 0.2242 semantic_segmentation_loss_cls: 0.0594 semantic_segmentation_loss_mask: 0.0470 semantic_segmentation_loss_dice: 0.1178 2024/07/08 16:25:58 - mmengine - INFO - Iter(train) [ 84950/120000] base_lr: 4.0838e-05 lr: 5.5307e-06 eta: 10:48:35 time: 1.1140 data_time: 0.0188 memory: 15637 grad_norm: 1.2069 loss: 0.2241 semantic_segmentation_loss_cls: 0.0595 semantic_segmentation_loss_mask: 0.0469 semantic_segmentation_loss_dice: 0.1177 2024/07/08 16:26:53 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 16:26:53 - mmengine - INFO - Iter(train) [ 85000/120000] base_lr: 4.0735e-05 lr: 5.5213e-06 eta: 10:47:40 time: 1.1140 data_time: 0.0188 memory: 15491 grad_norm: 1.2071 loss: 0.2241 semantic_segmentation_loss_cls: 0.0594 semantic_segmentation_loss_mask: 0.0470 semantic_segmentation_loss_dice: 0.1178 2024/07/08 16:26:53 - mmengine - INFO - Saving checkpoint at 85000 iterations 2024/07/08 16:27:10 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:50 time: 0.2442 data_time: 0.0016 memory: 5013 2024/07/08 16:27:22 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:37 time: 0.2441 data_time: 0.0016 memory: 5189 2024/07/08 16:27:34 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:24 time: 0.2441 data_time: 0.0016 memory: 4460 2024/07/08 16:27:46 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:12 time: 0.2440 data_time: 0.0016 memory: 4543 2024/07/08 16:27:58 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:00 time: 0.2440 data_time: 0.0016 memory: 4645 2024/07/08 16:28:11 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:48 time: 0.2439 data_time: 0.0016 memory: 10983 2024/07/08 16:28:23 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2438 data_time: 0.0016 memory: 4460 2024/07/08 16:28:35 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2437 data_time: 0.0016 memory: 4641 2024/07/08 16:28:47 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2437 data_time: 0.0016 memory: 4473 2024/07/08 16:28:59 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2436 data_time: 0.0016 memory: 4555 2024/07/08 16:28:59 - mmengine - INFO - per class results: 2024/07/08 16:28:59 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.53 | 87.86 | | building | 82.78 | 90.55 | | sky | 94.48 | 97.73 | | floor | 83.53 | 91.6 | | tree | 75.71 | 88.17 | | ceiling | 85.06 | 92.97 | | road | 84.19 | 92.41 | | bed | 87.29 | 95.1 | | windowpane | 60.9 | 79.17 | | grass | 71.42 | 86.52 | | cabinet | 60.22 | 71.26 | | sidewalk | 66.72 | 81.56 | | person | 82.39 | 91.79 | | earth | 33.42 | 45.23 | | door | 53.43 | 69.12 | | table | 62.43 | 75.73 | | mountain | 57.88 | 72.51 | | plant | 54.54 | 68.79 | | curtain | 72.87 | 87.05 | | chair | 59.32 | 72.25 | | car | 83.87 | 91.46 | | water | 51.33 | 67.6 | | painting | 72.63 | 89.05 | | sofa | 63.73 | 74.48 | | shelf | 45.29 | 64.78 | | house | 50.39 | 76.58 | | sea | 49.63 | 73.61 | | mirror | 67.62 | 76.09 | | rug | 67.08 | 76.12 | | field | 38.28 | 55.45 | | armchair | 45.36 | 69.9 | | seat | 55.79 | 83.43 | | fence | 37.28 | 50.69 | | desk | 47.5 | 68.2 | | rock | 35.24 | 53.36 | | wardrobe | 48.74 | 67.43 | | lamp | 67.14 | 79.27 | | bathtub | 85.16 | 90.69 | | railing | 36.85 | 54.2 | | cushion | 57.43 | 69.89 | | base | 20.33 | 31.77 | | box | 25.32 | 36.89 | | column | 49.84 | 67.98 | | signboard | 39.87 | 55.56 | | chest of drawers | 40.77 | 66.88 | | counter | 30.84 | 47.78 | | sand | 35.11 | 50.07 | | sink | 75.05 | 82.22 | | skyscraper | 35.73 | 44.53 | | fireplace | 65.33 | 86.52 | | refrigerator | 79.47 | 89.99 | | grandstand | 42.55 | 74.81 | | path | 29.15 | 41.41 | | stairs | 32.95 | 41.67 | | runway | 76.04 | 89.77 | | case | 63.33 | 76.67 | | pool table | 92.43 | 96.38 | | pillow | 55.87 | 67.41 | | screen door | 81.55 | 85.15 | | stairway | 37.33 | 44.04 | | river | 20.45 | 44.65 | | bridge | 60.75 | 88.19 | | bookcase | 41.63 | 56.68 | | blind | 39.02 | 42.99 | | coffee table | 72.3 | 86.36 | | toilet | 87.05 | 89.74 | | flower | 42.03 | 58.22 | | book | 52.42 | 75.42 | | hill | 14.2 | 24.11 | | bench | 43.32 | 53.02 | | countertop | 55.52 | 65.31 | | stove | 79.65 | 84.55 | | palm | 52.48 | 67.96 | | kitchen island | 30.76 | 74.12 | | computer | 61.01 | 66.66 | | swivel chair | 40.56 | 57.26 | | boat | 74.32 | 81.27 | | bar | 46.88 | 58.55 | | arcade machine | 21.32 | 24.52 | | hovel | 17.26 | 30.52 | | bus | 91.67 | 94.92 | | towel | 67.7 | 74.72 | | light | 63.07 | 77.38 | | truck | 35.35 | 48.2 | | tower | 27.29 | 54.04 | | chandelier | 62.72 | 74.64 | | awning | 34.09 | 44.41 | | streetlight | 40.81 | 56.16 | | booth | 48.5 | 57.8 | | television receiver | 48.62 | 90.07 | | airplane | 59.07 | 67.03 | | dirt track | 1.66 | 2.07 | | apparel | 33.83 | 50.24 | | pole | 31.03 | 47.28 | | land | 4.04 | 4.78 | | bannister | 17.33 | 27.27 | | escalator | 42.4 | 58.8 | | ottoman | 38.82 | 63.85 | | bottle | 22.31 | 26.95 | | buffet | 42.53 | 47.34 | | poster | 27.26 | 35.03 | | stage | 15.21 | 25.01 | | van | 48.71 | 65.88 | | ship | 83.99 | 88.41 | | fountain | 5.74 | 6.66 | | conveyer belt | 58.38 | 91.89 | | canopy | 19.83 | 33.67 | | washer | 71.01 | 73.18 | | plaything | 25.51 | 40.22 | | swimming pool | 31.15 | 33.99 | | stool | 54.13 | 69.6 | | barrel | 16.28 | 56.07 | | basket | 41.1 | 50.54 | | waterfall | 46.25 | 52.45 | | tent | 81.64 | 97.53 | | bag | 17.73 | 23.77 | | minibike | 65.1 | 84.22 | | cradle | 76.31 | 96.38 | | oven | 48.49 | 59.07 | | ball | 31.67 | 38.74 | | food | 62.35 | 76.26 | | step | 28.75 | 34.19 | | tank | 28.31 | 46.8 | | trade name | 28.27 | 34.04 | | microwave | 37.85 | 40.96 | | pot | 44.03 | 48.87 | | animal | 61.87 | 69.06 | | bicycle | 57.84 | 77.45 | | lake | 63.51 | 63.64 | | dishwasher | 82.12 | 85.46 | | screen | 69.92 | 88.21 | | blanket | 10.22 | 13.12 | | sculpture | 65.7 | 82.18 | | hood | 64.63 | 67.71 | | sconce | 52.96 | 64.35 | | vase | 39.38 | 65.43 | | traffic light | 44.65 | 60.48 | | tray | 19.19 | 24.16 | | ashcan | 50.44 | 65.25 | | fan | 62.55 | 78.82 | | pier | 31.68 | 71.56 | | crt screen | 0.04 | 0.05 | | plate | 62.03 | 73.31 | | monitor | 48.41 | 70.77 | | bulletin board | 38.3 | 46.15 | | shower | 9.22 | 16.35 | | radiator | 58.88 | 68.72 | | glass | 18.41 | 19.61 | | clock | 32.4 | 37.08 | | flag | 47.55 | 55.58 | +---------------------+-------+-------+ 2024/07/08 16:29:00 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.4200 mIoU: 49.8300 mAcc: 62.7500 data_time: 0.0017 time: 0.2411 2024/07/08 16:29:55 - mmengine - INFO - Iter(train) [ 85050/120000] base_lr: 4.0632e-05 lr: 5.5120e-06 eta: 10:46:45 time: 1.1130 data_time: 0.0178 memory: 14934 grad_norm: 1.2080 loss: 0.2242 semantic_segmentation_loss_cls: 0.0594 semantic_segmentation_loss_mask: 0.0470 semantic_segmentation_loss_dice: 0.1178 2024/07/08 16:30:50 - mmengine - INFO - Iter(train) [ 85100/120000] base_lr: 4.0529e-05 lr: 5.5027e-06 eta: 10:45:49 time: 1.1129 data_time: 0.0178 memory: 15473 grad_norm: 1.2059 loss: 0.2237 semantic_segmentation_loss_cls: 0.0592 semantic_segmentation_loss_mask: 0.0469 semantic_segmentation_loss_dice: 0.1176 2024/07/08 16:31:45 - mmengine - INFO - Iter(train) [ 85150/120000] base_lr: 4.0427e-05 lr: 5.4933e-06 eta: 10:44:53 time: 1.1128 data_time: 0.0177 memory: 15380 grad_norm: 1.2049 loss: 0.2235 semantic_segmentation_loss_cls: 0.0591 semantic_segmentation_loss_mask: 0.0469 semantic_segmentation_loss_dice: 0.1175 2024/07/08 16:32:40 - mmengine - INFO - Iter(train) [ 85200/120000] base_lr: 4.0324e-05 lr: 5.4840e-06 eta: 10:43:57 time: 1.1126 data_time: 0.0177 memory: 15775 grad_norm: 1.2054 loss: 0.2235 semantic_segmentation_loss_cls: 0.0592 semantic_segmentation_loss_mask: 0.0469 semantic_segmentation_loss_dice: 0.1175 2024/07/08 16:33:35 - mmengine - INFO - Iter(train) [ 85250/120000] base_lr: 4.0222e-05 lr: 5.4747e-06 eta: 10:43:02 time: 1.1123 data_time: 0.0177 memory: 15272 grad_norm: 1.2037 loss: 0.2232 semantic_segmentation_loss_cls: 0.0590 semantic_segmentation_loss_mask: 0.0468 semantic_segmentation_loss_dice: 0.1174 2024/07/08 16:34:30 - mmengine - INFO - Iter(train) [ 85300/120000] base_lr: 4.0120e-05 lr: 5.4654e-06 eta: 10:42:06 time: 1.1122 data_time: 0.0177 memory: 16812 grad_norm: 1.2047 loss: 0.2233 semantic_segmentation_loss_cls: 0.0590 semantic_segmentation_loss_mask: 0.0468 semantic_segmentation_loss_dice: 0.1174 2024/07/08 16:35:26 - mmengine - INFO - Iter(train) [ 85350/120000] base_lr: 4.0018e-05 lr: 5.4561e-06 eta: 10:41:10 time: 1.1121 data_time: 0.0177 memory: 15176 grad_norm: 1.2050 loss: 0.2229 semantic_segmentation_loss_cls: 0.0589 semantic_segmentation_loss_mask: 0.0467 semantic_segmentation_loss_dice: 0.1172 2024/07/08 16:36:22 - mmengine - INFO - Iter(train) [ 85400/120000] base_lr: 3.9915e-05 lr: 5.4469e-06 eta: 10:40:15 time: 1.1121 data_time: 0.0177 memory: 15805 grad_norm: 1.2060 loss: 0.2230 semantic_segmentation_loss_cls: 0.0589 semantic_segmentation_loss_mask: 0.0468 semantic_segmentation_loss_dice: 0.1173 2024/07/08 16:37:17 - mmengine - INFO - Iter(train) [ 85450/120000] base_lr: 3.9814e-05 lr: 5.4376e-06 eta: 10:39:19 time: 1.1118 data_time: 0.0177 memory: 14967 grad_norm: 1.2038 loss: 0.2228 semantic_segmentation_loss_cls: 0.0588 semantic_segmentation_loss_mask: 0.0468 semantic_segmentation_loss_dice: 0.1172 2024/07/08 16:38:12 - mmengine - INFO - Iter(train) [ 85500/120000] base_lr: 3.9712e-05 lr: 5.4283e-06 eta: 10:38:24 time: 1.1119 data_time: 0.0177 memory: 15563 grad_norm: 1.2020 loss: 0.2227 semantic_segmentation_loss_cls: 0.0587 semantic_segmentation_loss_mask: 0.0468 semantic_segmentation_loss_dice: 0.1171 2024/07/08 16:39:07 - mmengine - INFO - Iter(train) [ 85550/120000] base_lr: 3.9610e-05 lr: 5.4191e-06 eta: 10:37:28 time: 1.1115 data_time: 0.0177 memory: 16836 grad_norm: 1.2002 loss: 0.2226 semantic_segmentation_loss_cls: 0.0588 semantic_segmentation_loss_mask: 0.0467 semantic_segmentation_loss_dice: 0.1171 2024/07/08 16:40:02 - mmengine - INFO - Iter(train) [ 85600/120000] base_lr: 3.9508e-05 lr: 5.4099e-06 eta: 10:36:32 time: 1.1111 data_time: 0.0177 memory: 15084 grad_norm: 1.1987 loss: 0.2224 semantic_segmentation_loss_cls: 0.0587 semantic_segmentation_loss_mask: 0.0467 semantic_segmentation_loss_dice: 0.1171 2024/07/08 16:40:56 - mmengine - INFO - Iter(train) [ 85650/120000] base_lr: 3.9407e-05 lr: 5.4006e-06 eta: 10:35:36 time: 1.1109 data_time: 0.0177 memory: 16049 grad_norm: 1.1988 loss: 0.2225 semantic_segmentation_loss_cls: 0.0587 semantic_segmentation_loss_mask: 0.0467 semantic_segmentation_loss_dice: 0.1172 2024/07/08 16:41:51 - mmengine - INFO - Iter(train) [ 85700/120000] base_lr: 3.9305e-05 lr: 5.3914e-06 eta: 10:34:41 time: 1.1106 data_time: 0.0177 memory: 15530 grad_norm: 1.1955 loss: 0.2223 semantic_segmentation_loss_cls: 0.0585 semantic_segmentation_loss_mask: 0.0467 semantic_segmentation_loss_dice: 0.1171 2024/07/08 16:42:47 - mmengine - INFO - Iter(train) [ 85750/120000] base_lr: 3.9204e-05 lr: 5.3822e-06 eta: 10:33:45 time: 1.1106 data_time: 0.0177 memory: 14586 grad_norm: 1.1961 loss: 0.2223 semantic_segmentation_loss_cls: 0.0585 semantic_segmentation_loss_mask: 0.0467 semantic_segmentation_loss_dice: 0.1172 2024/07/08 16:43:43 - mmengine - INFO - Iter(train) [ 85800/120000] base_lr: 3.9103e-05 lr: 5.3730e-06 eta: 10:32:50 time: 1.1109 data_time: 0.0178 memory: 14894 grad_norm: 1.1967 loss: 0.2222 semantic_segmentation_loss_cls: 0.0584 semantic_segmentation_loss_mask: 0.0467 semantic_segmentation_loss_dice: 0.1171 2024/07/08 16:44:39 - mmengine - INFO - Iter(train) [ 85850/120000] base_lr: 3.9002e-05 lr: 5.3638e-06 eta: 10:31:55 time: 1.1111 data_time: 0.0178 memory: 16546 grad_norm: 1.1964 loss: 0.2219 semantic_segmentation_loss_cls: 0.0583 semantic_segmentation_loss_mask: 0.0466 semantic_segmentation_loss_dice: 0.1169 2024/07/08 16:45:35 - mmengine - INFO - Iter(train) [ 85900/120000] base_lr: 3.8901e-05 lr: 5.3546e-06 eta: 10:30:59 time: 1.1114 data_time: 0.0178 memory: 15326 grad_norm: 1.1998 loss: 0.2219 semantic_segmentation_loss_cls: 0.0583 semantic_segmentation_loss_mask: 0.0467 semantic_segmentation_loss_dice: 0.1169 2024/07/08 16:46:30 - mmengine - INFO - Iter(train) [ 85950/120000] base_lr: 3.8800e-05 lr: 5.3455e-06 eta: 10:30:04 time: 1.1114 data_time: 0.0178 memory: 15160 grad_norm: 1.1981 loss: 0.2215 semantic_segmentation_loss_cls: 0.0582 semantic_segmentation_loss_mask: 0.0466 semantic_segmentation_loss_dice: 0.1167 2024/07/08 16:47:26 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 16:47:26 - mmengine - INFO - Iter(train) [ 86000/120000] base_lr: 3.8699e-05 lr: 5.3363e-06 eta: 10:29:08 time: 1.1116 data_time: 0.0178 memory: 15216 grad_norm: 1.1960 loss: 0.2212 semantic_segmentation_loss_cls: 0.0581 semantic_segmentation_loss_mask: 0.0466 semantic_segmentation_loss_dice: 0.1166 2024/07/08 16:47:26 - mmengine - INFO - Saving checkpoint at 86000 iterations 2024/07/08 16:48:26 - mmengine - INFO - Iter(train) [ 86050/120000] base_lr: 3.8599e-05 lr: 5.3271e-06 eta: 10:28:14 time: 1.1117 data_time: 0.0178 memory: 14696 grad_norm: 1.1956 loss: 0.2209 semantic_segmentation_loss_cls: 0.0579 semantic_segmentation_loss_mask: 0.0465 semantic_segmentation_loss_dice: 0.1165 2024/07/08 16:49:21 - mmengine - INFO - Iter(train) [ 86100/120000] base_lr: 3.8498e-05 lr: 5.3180e-06 eta: 10:27:19 time: 1.1118 data_time: 0.0178 memory: 15504 grad_norm: 1.1959 loss: 0.2207 semantic_segmentation_loss_cls: 0.0578 semantic_segmentation_loss_mask: 0.0465 semantic_segmentation_loss_dice: 0.1164 2024/07/08 16:50:17 - mmengine - INFO - Iter(train) [ 86150/120000] base_lr: 3.8398e-05 lr: 5.3089e-06 eta: 10:26:23 time: 1.1115 data_time: 0.0178 memory: 15413 grad_norm: 1.1949 loss: 0.2206 semantic_segmentation_loss_cls: 0.0577 semantic_segmentation_loss_mask: 0.0465 semantic_segmentation_loss_dice: 0.1164 2024/07/08 16:51:12 - mmengine - INFO - Iter(train) [ 86200/120000] base_lr: 3.8297e-05 lr: 5.2998e-06 eta: 10:25:27 time: 1.1114 data_time: 0.0178 memory: 15778 grad_norm: 1.1951 loss: 0.2206 semantic_segmentation_loss_cls: 0.0577 semantic_segmentation_loss_mask: 0.0465 semantic_segmentation_loss_dice: 0.1164 2024/07/08 16:52:07 - mmengine - INFO - Iter(train) [ 86250/120000] base_lr: 3.8197e-05 lr: 5.2906e-06 eta: 10:24:32 time: 1.1113 data_time: 0.0178 memory: 14705 grad_norm: 1.1941 loss: 0.2207 semantic_segmentation_loss_cls: 0.0577 semantic_segmentation_loss_mask: 0.0465 semantic_segmentation_loss_dice: 0.1164 2024/07/08 16:53:02 - mmengine - INFO - Iter(train) [ 86300/120000] base_lr: 3.8097e-05 lr: 5.2815e-06 eta: 10:23:36 time: 1.1114 data_time: 0.0178 memory: 15200 grad_norm: 1.1934 loss: 0.2203 semantic_segmentation_loss_cls: 0.0576 semantic_segmentation_loss_mask: 0.0464 semantic_segmentation_loss_dice: 0.1163 2024/07/08 16:53:58 - mmengine - INFO - Iter(train) [ 86350/120000] base_lr: 3.7997e-05 lr: 5.2724e-06 eta: 10:22:41 time: 1.1115 data_time: 0.0178 memory: 14912 grad_norm: 1.1926 loss: 0.2205 semantic_segmentation_loss_cls: 0.0576 semantic_segmentation_loss_mask: 0.0464 semantic_segmentation_loss_dice: 0.1165 2024/07/08 16:54:53 - mmengine - INFO - Iter(train) [ 86400/120000] base_lr: 3.7897e-05 lr: 5.2634e-06 eta: 10:21:45 time: 1.1113 data_time: 0.0178 memory: 15287 grad_norm: 1.1932 loss: 0.2206 semantic_segmentation_loss_cls: 0.0576 semantic_segmentation_loss_mask: 0.0465 semantic_segmentation_loss_dice: 0.1165 2024/07/08 16:55:48 - mmengine - INFO - Iter(train) [ 86450/120000] base_lr: 3.7797e-05 lr: 5.2543e-06 eta: 10:20:49 time: 1.1113 data_time: 0.0178 memory: 15073 grad_norm: 1.1916 loss: 0.2207 semantic_segmentation_loss_cls: 0.0577 semantic_segmentation_loss_mask: 0.0464 semantic_segmentation_loss_dice: 0.1165 2024/07/08 16:56:44 - mmengine - INFO - Iter(train) [ 86500/120000] base_lr: 3.7698e-05 lr: 5.2452e-06 eta: 10:19:54 time: 1.1115 data_time: 0.0178 memory: 15209 grad_norm: 1.1914 loss: 0.2207 semantic_segmentation_loss_cls: 0.0577 semantic_segmentation_loss_mask: 0.0464 semantic_segmentation_loss_dice: 0.1165 2024/07/08 16:57:40 - mmengine - INFO - Iter(train) [ 86550/120000] base_lr: 3.7598e-05 lr: 5.2362e-06 eta: 10:18:59 time: 1.1116 data_time: 0.0178 memory: 15299 grad_norm: 1.1918 loss: 0.2209 semantic_segmentation_loss_cls: 0.0578 semantic_segmentation_loss_mask: 0.0465 semantic_segmentation_loss_dice: 0.1166 2024/07/08 16:58:35 - mmengine - INFO - Iter(train) [ 86600/120000] base_lr: 3.7498e-05 lr: 5.2271e-06 eta: 10:18:03 time: 1.1116 data_time: 0.0178 memory: 15003 grad_norm: 1.1925 loss: 0.2210 semantic_segmentation_loss_cls: 0.0579 semantic_segmentation_loss_mask: 0.0465 semantic_segmentation_loss_dice: 0.1166 2024/07/08 16:59:30 - mmengine - INFO - Iter(train) [ 86650/120000] base_lr: 3.7399e-05 lr: 5.2181e-06 eta: 10:17:07 time: 1.1115 data_time: 0.0179 memory: 15354 grad_norm: 1.1917 loss: 0.2212 semantic_segmentation_loss_cls: 0.0579 semantic_segmentation_loss_mask: 0.0465 semantic_segmentation_loss_dice: 0.1168 2024/07/08 17:00:26 - mmengine - INFO - Iter(train) [ 86700/120000] base_lr: 3.7300e-05 lr: 5.2091e-06 eta: 10:16:12 time: 1.1117 data_time: 0.0179 memory: 15358 grad_norm: 1.1921 loss: 0.2213 semantic_segmentation_loss_cls: 0.0580 semantic_segmentation_loss_mask: 0.0465 semantic_segmentation_loss_dice: 0.1168 2024/07/08 17:01:21 - mmengine - INFO - Iter(train) [ 86750/120000] base_lr: 3.7201e-05 lr: 5.2001e-06 eta: 10:15:16 time: 1.1120 data_time: 0.0179 memory: 15472 grad_norm: 1.1933 loss: 0.2212 semantic_segmentation_loss_cls: 0.0580 semantic_segmentation_loss_mask: 0.0465 semantic_segmentation_loss_dice: 0.1168 2024/07/08 17:02:18 - mmengine - INFO - Iter(train) [ 86800/120000] base_lr: 3.7102e-05 lr: 5.1911e-06 eta: 10:14:21 time: 1.1121 data_time: 0.0179 memory: 14715 grad_norm: 1.1909 loss: 0.2210 semantic_segmentation_loss_cls: 0.0579 semantic_segmentation_loss_mask: 0.0464 semantic_segmentation_loss_dice: 0.1167 2024/07/08 17:03:13 - mmengine - INFO - Iter(train) [ 86850/120000] base_lr: 3.7003e-05 lr: 5.1821e-06 eta: 10:13:26 time: 1.1121 data_time: 0.0179 memory: 15079 grad_norm: 1.1898 loss: 0.2212 semantic_segmentation_loss_cls: 0.0580 semantic_segmentation_loss_mask: 0.0464 semantic_segmentation_loss_dice: 0.1168 2024/07/08 17:04:08 - mmengine - INFO - Iter(train) [ 86900/120000] base_lr: 3.6904e-05 lr: 5.1731e-06 eta: 10:12:30 time: 1.1122 data_time: 0.0179 memory: 15518 grad_norm: 1.1897 loss: 0.2211 semantic_segmentation_loss_cls: 0.0580 semantic_segmentation_loss_mask: 0.0464 semantic_segmentation_loss_dice: 0.1168 2024/07/08 17:05:04 - mmengine - INFO - Iter(train) [ 86950/120000] base_lr: 3.6805e-05 lr: 5.1641e-06 eta: 10:11:34 time: 1.1122 data_time: 0.0179 memory: 16650 grad_norm: 1.1907 loss: 0.2209 semantic_segmentation_loss_cls: 0.0578 semantic_segmentation_loss_mask: 0.0464 semantic_segmentation_loss_dice: 0.1167 2024/07/08 17:06:00 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 17:06:00 - mmengine - INFO - Iter(train) [ 87000/120000] base_lr: 3.6707e-05 lr: 5.1551e-06 eta: 10:10:39 time: 1.1123 data_time: 0.0179 memory: 15539 grad_norm: 1.1900 loss: 0.2209 semantic_segmentation_loss_cls: 0.0579 semantic_segmentation_loss_mask: 0.0464 semantic_segmentation_loss_dice: 0.1167 2024/07/08 17:06:00 - mmengine - INFO - Saving checkpoint at 87000 iterations 2024/07/08 17:07:00 - mmengine - INFO - Iter(train) [ 87050/120000] base_lr: 3.6608e-05 lr: 5.1462e-06 eta: 10:09:45 time: 1.1124 data_time: 0.0179 memory: 15151 grad_norm: 1.1908 loss: 0.2212 semantic_segmentation_loss_cls: 0.0580 semantic_segmentation_loss_mask: 0.0464 semantic_segmentation_loss_dice: 0.1168 2024/07/08 17:07:55 - mmengine - INFO - Iter(train) [ 87100/120000] base_lr: 3.6510e-05 lr: 5.1372e-06 eta: 10:08:50 time: 1.1122 data_time: 0.0179 memory: 15722 grad_norm: 1.1895 loss: 0.2211 semantic_segmentation_loss_cls: 0.0580 semantic_segmentation_loss_mask: 0.0464 semantic_segmentation_loss_dice: 0.1168 2024/07/08 17:08:50 - mmengine - INFO - Iter(train) [ 87150/120000] base_lr: 3.6411e-05 lr: 5.1283e-06 eta: 10:07:54 time: 1.1121 data_time: 0.0179 memory: 15229 grad_norm: 1.1878 loss: 0.2208 semantic_segmentation_loss_cls: 0.0579 semantic_segmentation_loss_mask: 0.0463 semantic_segmentation_loss_dice: 0.1166 2024/07/08 17:09:46 - mmengine - INFO - Iter(train) [ 87200/120000] base_lr: 3.6313e-05 lr: 5.1194e-06 eta: 10:06:59 time: 1.1121 data_time: 0.0178 memory: 15421 grad_norm: 1.1883 loss: 0.2207 semantic_segmentation_loss_cls: 0.0579 semantic_segmentation_loss_mask: 0.0463 semantic_segmentation_loss_dice: 0.1166 2024/07/08 17:10:42 - mmengine - INFO - Iter(train) [ 87250/120000] base_lr: 3.6215e-05 lr: 5.1105e-06 eta: 10:06:03 time: 1.1123 data_time: 0.0178 memory: 15943 grad_norm: 1.1880 loss: 0.2203 semantic_segmentation_loss_cls: 0.0577 semantic_segmentation_loss_mask: 0.0462 semantic_segmentation_loss_dice: 0.1164 2024/07/08 17:11:37 - mmengine - INFO - Iter(train) [ 87300/120000] base_lr: 3.6117e-05 lr: 5.1016e-06 eta: 10:05:08 time: 1.1121 data_time: 0.0178 memory: 14735 grad_norm: 1.1887 loss: 0.2205 semantic_segmentation_loss_cls: 0.0578 semantic_segmentation_loss_mask: 0.0462 semantic_segmentation_loss_dice: 0.1165 2024/07/08 17:12:32 - mmengine - INFO - Iter(train) [ 87350/120000] base_lr: 3.6020e-05 lr: 5.0927e-06 eta: 10:04:12 time: 1.1118 data_time: 0.0178 memory: 14787 grad_norm: 1.1878 loss: 0.2207 semantic_segmentation_loss_cls: 0.0579 semantic_segmentation_loss_mask: 0.0462 semantic_segmentation_loss_dice: 0.1165 2024/07/08 17:13:28 - mmengine - INFO - Iter(train) [ 87400/120000] base_lr: 3.5922e-05 lr: 5.0838e-06 eta: 10:03:16 time: 1.1119 data_time: 0.0178 memory: 15109 grad_norm: 1.1866 loss: 0.2208 semantic_segmentation_loss_cls: 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semantic_segmentation_loss_dice: 0.1169 2024/07/08 17:17:09 - mmengine - INFO - Iter(train) [ 87600/120000] base_lr: 3.5532e-05 lr: 5.0484e-06 eta: 9:59:34 time: 1.1118 data_time: 0.0178 memory: 16501 grad_norm: 1.1858 loss: 0.2211 semantic_segmentation_loss_cls: 0.0581 semantic_segmentation_loss_mask: 0.0462 semantic_segmentation_loss_dice: 0.1168 2024/07/08 17:18:05 - mmengine - INFO - Iter(train) [ 87650/120000] base_lr: 3.5435e-05 lr: 5.0395e-06 eta: 9:58:39 time: 1.1117 data_time: 0.0178 memory: 14880 grad_norm: 1.1855 loss: 0.2209 semantic_segmentation_loss_cls: 0.0581 semantic_segmentation_loss_mask: 0.0462 semantic_segmentation_loss_dice: 0.1167 2024/07/08 17:19:01 - mmengine - INFO - Iter(train) [ 87700/120000] base_lr: 3.5338e-05 lr: 5.0307e-06 eta: 9:57:43 time: 1.1120 data_time: 0.0178 memory: 15547 grad_norm: 1.1851 loss: 0.2210 semantic_segmentation_loss_cls: 0.0581 semantic_segmentation_loss_mask: 0.0462 semantic_segmentation_loss_dice: 0.1167 2024/07/08 17:19:56 - 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lr: 4.9955e-06 eta: 9:54:01 time: 1.1117 data_time: 0.0178 memory: 15362 grad_norm: 1.1826 loss: 0.2203 semantic_segmentation_loss_cls: 0.0578 semantic_segmentation_loss_mask: 0.0461 semantic_segmentation_loss_dice: 0.1164 2024/07/08 17:23:37 - mmengine - INFO - Iter(train) [ 87950/120000] base_lr: 3.4854e-05 lr: 4.9868e-06 eta: 9:53:05 time: 1.1115 data_time: 0.0178 memory: 14921 grad_norm: 1.1818 loss: 0.2203 semantic_segmentation_loss_cls: 0.0577 semantic_segmentation_loss_mask: 0.0461 semantic_segmentation_loss_dice: 0.1164 2024/07/08 17:24:33 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 17:24:33 - mmengine - INFO - Iter(train) [ 88000/120000] base_lr: 3.4758e-05 lr: 4.9780e-06 eta: 9:52:10 time: 1.1115 data_time: 0.0179 memory: 15194 grad_norm: 1.1816 loss: 0.2204 semantic_segmentation_loss_cls: 0.0577 semantic_segmentation_loss_mask: 0.0461 semantic_segmentation_loss_dice: 0.1165 2024/07/08 17:24:33 - mmengine - INFO - Saving checkpoint at 88000 iterations 2024/07/08 17:25:33 - mmengine - INFO - Iter(train) [ 88050/120000] base_lr: 3.4662e-05 lr: 4.9692e-06 eta: 9:51:16 time: 1.1116 data_time: 0.0180 memory: 14807 grad_norm: 1.1815 loss: 0.2204 semantic_segmentation_loss_cls: 0.0577 semantic_segmentation_loss_mask: 0.0461 semantic_segmentation_loss_dice: 0.1165 2024/07/08 17:26:29 - mmengine - INFO - Iter(train) [ 88100/120000] base_lr: 3.4566e-05 lr: 4.9605e-06 eta: 9:50:20 time: 1.1118 data_time: 0.0181 memory: 15019 grad_norm: 1.1822 loss: 0.2200 semantic_segmentation_loss_cls: 0.0576 semantic_segmentation_loss_mask: 0.0461 semantic_segmentation_loss_dice: 0.1163 2024/07/08 17:27:24 - mmengine - INFO - Iter(train) [ 88150/120000] base_lr: 3.4470e-05 lr: 4.9518e-06 eta: 9:49:25 time: 1.1117 data_time: 0.0181 memory: 14958 grad_norm: 1.1809 loss: 0.2200 semantic_segmentation_loss_cls: 0.0575 semantic_segmentation_loss_mask: 0.0461 semantic_segmentation_loss_dice: 0.1164 2024/07/08 17:28:19 - mmengine - INFO - Iter(train) [ 88200/120000] base_lr: 3.4374e-05 lr: 4.9431e-06 eta: 9:48:29 time: 1.1118 data_time: 0.0181 memory: 15131 grad_norm: 1.1831 loss: 0.2195 semantic_segmentation_loss_cls: 0.0574 semantic_segmentation_loss_mask: 0.0460 semantic_segmentation_loss_dice: 0.1161 2024/07/08 17:29:15 - mmengine - INFO - Iter(train) [ 88250/120000] base_lr: 3.4278e-05 lr: 4.9344e-06 eta: 9:47:34 time: 1.1118 data_time: 0.0181 memory: 14833 grad_norm: 1.1832 loss: 0.2191 semantic_segmentation_loss_cls: 0.0572 semantic_segmentation_loss_mask: 0.0460 semantic_segmentation_loss_dice: 0.1159 2024/07/08 17:30:11 - mmengine - INFO - Iter(train) [ 88300/120000] base_lr: 3.4182e-05 lr: 4.9257e-06 eta: 9:46:38 time: 1.1120 data_time: 0.0181 memory: 15768 grad_norm: 1.1826 loss: 0.2191 semantic_segmentation_loss_cls: 0.0572 semantic_segmentation_loss_mask: 0.0460 semantic_segmentation_loss_dice: 0.1159 2024/07/08 17:31:06 - 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lr: 4.8910e-06 eta: 9:42:55 time: 1.1118 data_time: 0.0181 memory: 15687 grad_norm: 1.1823 loss: 0.2190 semantic_segmentation_loss_cls: 0.0570 semantic_segmentation_loss_mask: 0.0460 semantic_segmentation_loss_dice: 0.1159 2024/07/08 17:34:46 - mmengine - INFO - Iter(train) [ 88550/120000] base_lr: 3.3706e-05 lr: 4.8823e-06 eta: 9:42:00 time: 1.1120 data_time: 0.0181 memory: 15286 grad_norm: 1.1802 loss: 0.2186 semantic_segmentation_loss_cls: 0.0570 semantic_segmentation_loss_mask: 0.0459 semantic_segmentation_loss_dice: 0.1158 2024/07/08 17:35:42 - mmengine - INFO - Iter(train) [ 88600/120000] base_lr: 3.3611e-05 lr: 4.8737e-06 eta: 9:41:05 time: 1.1121 data_time: 0.0180 memory: 14913 grad_norm: 1.1792 loss: 0.2187 semantic_segmentation_loss_cls: 0.0570 semantic_segmentation_loss_mask: 0.0459 semantic_segmentation_loss_dice: 0.1158 2024/07/08 17:36:38 - mmengine - INFO - Iter(train) [ 88650/120000] base_lr: 3.3516e-05 lr: 4.8651e-06 eta: 9:40:09 time: 1.1122 data_time: 0.0180 memory: 15133 grad_norm: 1.1804 loss: 0.2184 semantic_segmentation_loss_cls: 0.0567 semantic_segmentation_loss_mask: 0.0459 semantic_segmentation_loss_dice: 0.1157 2024/07/08 17:37:34 - mmengine - INFO - Iter(train) [ 88700/120000] base_lr: 3.3421e-05 lr: 4.8564e-06 eta: 9:39:14 time: 1.1122 data_time: 0.0180 memory: 14966 grad_norm: 1.1791 loss: 0.2183 semantic_segmentation_loss_cls: 0.0567 semantic_segmentation_loss_mask: 0.0459 semantic_segmentation_loss_dice: 0.1157 2024/07/08 17:38:28 - mmengine - INFO - Iter(train) [ 88750/120000] base_lr: 3.3326e-05 lr: 4.8478e-06 eta: 9:38:18 time: 1.1118 data_time: 0.0180 memory: 15952 grad_norm: 1.1785 loss: 0.2183 semantic_segmentation_loss_cls: 0.0567 semantic_segmentation_loss_mask: 0.0459 semantic_segmentation_loss_dice: 0.1157 2024/07/08 17:39:24 - mmengine - INFO - Iter(train) [ 88800/120000] base_lr: 3.3232e-05 lr: 4.8392e-06 eta: 9:37:22 time: 1.1116 data_time: 0.0180 memory: 15267 grad_norm: 1.1799 loss: 0.2185 semantic_segmentation_loss_cls: 0.0567 semantic_segmentation_loss_mask: 0.0459 semantic_segmentation_loss_dice: 0.1158 2024/07/08 17:40:19 - mmengine - INFO - Iter(train) [ 88850/120000] base_lr: 3.3137e-05 lr: 4.8307e-06 eta: 9:36:27 time: 1.1115 data_time: 0.0180 memory: 15028 grad_norm: 1.1790 loss: 0.2183 semantic_segmentation_loss_cls: 0.0567 semantic_segmentation_loss_mask: 0.0459 semantic_segmentation_loss_dice: 0.1157 2024/07/08 17:41:14 - mmengine - INFO - Iter(train) [ 88900/120000] base_lr: 3.3043e-05 lr: 4.8221e-06 eta: 9:35:31 time: 1.1114 data_time: 0.0180 memory: 14910 grad_norm: 1.1776 loss: 0.2178 semantic_segmentation_loss_cls: 0.0565 semantic_segmentation_loss_mask: 0.0459 semantic_segmentation_loss_dice: 0.1155 2024/07/08 17:42:09 - mmengine - INFO - Iter(train) [ 88950/120000] base_lr: 3.2949e-05 lr: 4.8135e-06 eta: 9:34:35 time: 1.1111 data_time: 0.0180 memory: 15049 grad_norm: 1.1788 loss: 0.2178 semantic_segmentation_loss_cls: 0.0565 semantic_segmentation_loss_mask: 0.0458 semantic_segmentation_loss_dice: 0.1155 2024/07/08 17:43:04 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 17:43:04 - mmengine - INFO - Iter(train) [ 89000/120000] base_lr: 3.2855e-05 lr: 4.8050e-06 eta: 9:33:40 time: 1.1111 data_time: 0.0180 memory: 14772 grad_norm: 1.1780 loss: 0.2177 semantic_segmentation_loss_cls: 0.0565 semantic_segmentation_loss_mask: 0.0458 semantic_segmentation_loss_dice: 0.1154 2024/07/08 17:43:04 - mmengine - INFO - Saving checkpoint at 89000 iterations 2024/07/08 17:44:05 - mmengine - INFO - Iter(train) [ 89050/120000] base_lr: 3.2761e-05 lr: 4.7964e-06 eta: 9:32:46 time: 1.1122 data_time: 0.0190 memory: 15512 grad_norm: 1.1790 loss: 0.2179 semantic_segmentation_loss_cls: 0.0567 semantic_segmentation_loss_mask: 0.0458 semantic_segmentation_loss_dice: 0.1155 2024/07/08 17:45:01 - mmengine - INFO - Iter(train) [ 89100/120000] base_lr: 3.2667e-05 lr: 4.7879e-06 eta: 9:31:51 time: 1.1125 data_time: 0.0190 memory: 14726 grad_norm: 1.1809 loss: 0.2178 semantic_segmentation_loss_cls: 0.0567 semantic_segmentation_loss_mask: 0.0457 semantic_segmentation_loss_dice: 0.1155 2024/07/08 17:45:57 - mmengine - INFO - Iter(train) [ 89150/120000] base_lr: 3.2573e-05 lr: 4.7794e-06 eta: 9:30:55 time: 1.1127 data_time: 0.0191 memory: 15783 grad_norm: 1.1818 loss: 0.2175 semantic_segmentation_loss_cls: 0.0565 semantic_segmentation_loss_mask: 0.0457 semantic_segmentation_loss_dice: 0.1153 2024/07/08 17:46:53 - mmengine - INFO - Iter(train) [ 89200/120000] base_lr: 3.2480e-05 lr: 4.7709e-06 eta: 9:30:00 time: 1.1128 data_time: 0.0191 memory: 15194 grad_norm: 1.1798 loss: 0.2174 semantic_segmentation_loss_cls: 0.0564 semantic_segmentation_loss_mask: 0.0457 semantic_segmentation_loss_dice: 0.1153 2024/07/08 17:47:48 - mmengine - INFO - Iter(train) [ 89250/120000] base_lr: 3.2386e-05 lr: 4.7624e-06 eta: 9:29:04 time: 1.1130 data_time: 0.0191 memory: 15510 grad_norm: 1.1806 loss: 0.2175 semantic_segmentation_loss_cls: 0.0565 semantic_segmentation_loss_mask: 0.0457 semantic_segmentation_loss_dice: 0.1153 2024/07/08 17:48:42 - mmengine - INFO - Iter(train) [ 89300/120000] base_lr: 3.2293e-05 lr: 4.7539e-06 eta: 9:28:08 time: 1.1127 data_time: 0.0191 memory: 14713 grad_norm: 1.1807 loss: 0.2173 semantic_segmentation_loss_cls: 0.0564 semantic_segmentation_loss_mask: 0.0457 semantic_segmentation_loss_dice: 0.1153 2024/07/08 17:49:36 - mmengine - INFO - Iter(train) [ 89350/120000] base_lr: 3.2200e-05 lr: 4.7454e-06 eta: 9:27:12 time: 1.1123 data_time: 0.0190 memory: 15379 grad_norm: 1.1788 loss: 0.2176 semantic_segmentation_loss_cls: 0.0565 semantic_segmentation_loss_mask: 0.0457 semantic_segmentation_loss_dice: 0.1154 2024/07/08 17:50:31 - mmengine - INFO - Iter(train) [ 89400/120000] base_lr: 3.2106e-05 lr: 4.7370e-06 eta: 9:26:16 time: 1.1121 data_time: 0.0190 memory: 15284 grad_norm: 1.1769 loss: 0.2174 semantic_segmentation_loss_cls: 0.0565 semantic_segmentation_loss_mask: 0.0456 semantic_segmentation_loss_dice: 0.1152 2024/07/08 17:51:26 - mmengine - INFO - Iter(train) [ 89450/120000] base_lr: 3.2013e-05 lr: 4.7285e-06 eta: 9:25:21 time: 1.1120 data_time: 0.0190 memory: 15027 grad_norm: 1.1774 loss: 0.2173 semantic_segmentation_loss_cls: 0.0565 semantic_segmentation_loss_mask: 0.0456 semantic_segmentation_loss_dice: 0.1152 2024/07/08 17:52:21 - mmengine - INFO - Iter(train) [ 89500/120000] base_lr: 3.1921e-05 lr: 4.7201e-06 eta: 9:24:25 time: 1.1120 data_time: 0.0190 memory: 15051 grad_norm: 1.1767 loss: 0.2174 semantic_segmentation_loss_cls: 0.0566 semantic_segmentation_loss_mask: 0.0456 semantic_segmentation_loss_dice: 0.1152 2024/07/08 17:53:16 - mmengine - INFO - Iter(train) [ 89550/120000] base_lr: 3.1828e-05 lr: 4.7116e-06 eta: 9:23:29 time: 1.1120 data_time: 0.0190 memory: 14746 grad_norm: 1.1767 loss: 0.2172 semantic_segmentation_loss_cls: 0.0565 semantic_segmentation_loss_mask: 0.0456 semantic_segmentation_loss_dice: 0.1151 2024/07/08 17:54:11 - mmengine - INFO - Iter(train) [ 89600/120000] base_lr: 3.1735e-05 lr: 4.7032e-06 eta: 9:22:34 time: 1.1122 data_time: 0.0190 memory: 15569 grad_norm: 1.1782 loss: 0.2172 semantic_segmentation_loss_cls: 0.0565 semantic_segmentation_loss_mask: 0.0456 semantic_segmentation_loss_dice: 0.1151 2024/07/08 17:55:08 - mmengine - INFO - Iter(train) [ 89650/120000] base_lr: 3.1643e-05 lr: 4.6948e-06 eta: 9:21:39 time: 1.1127 data_time: 0.0190 memory: 14523 grad_norm: 1.1767 loss: 0.2170 semantic_segmentation_loss_cls: 0.0564 semantic_segmentation_loss_mask: 0.0456 semantic_segmentation_loss_dice: 0.1150 2024/07/08 17:56:04 - mmengine - INFO - Iter(train) [ 89700/120000] base_lr: 3.1550e-05 lr: 4.6864e-06 eta: 9:20:43 time: 1.1130 data_time: 0.0190 memory: 15473 grad_norm: 1.1754 loss: 0.2167 semantic_segmentation_loss_cls: 0.0563 semantic_segmentation_loss_mask: 0.0455 semantic_segmentation_loss_dice: 0.1149 2024/07/08 17:57:00 - mmengine - INFO - Iter(train) [ 89750/120000] base_lr: 3.1458e-05 lr: 4.6780e-06 eta: 9:19:48 time: 1.1130 data_time: 0.0190 memory: 15326 grad_norm: 1.1744 loss: 0.2164 semantic_segmentation_loss_cls: 0.0562 semantic_segmentation_loss_mask: 0.0455 semantic_segmentation_loss_dice: 0.1147 2024/07/08 17:57:55 - mmengine - INFO - Iter(train) [ 89800/120000] base_lr: 3.1366e-05 lr: 4.6696e-06 eta: 9:18:52 time: 1.1129 data_time: 0.0190 memory: 15708 grad_norm: 1.1730 loss: 0.2164 semantic_segmentation_loss_cls: 0.0563 semantic_segmentation_loss_mask: 0.0455 semantic_segmentation_loss_dice: 0.1146 2024/07/08 17:58:51 - mmengine - INFO - Iter(train) [ 89850/120000] base_lr: 3.1274e-05 lr: 4.6612e-06 eta: 9:17:57 time: 1.1128 data_time: 0.0190 memory: 15041 grad_norm: 1.1751 loss: 0.2163 semantic_segmentation_loss_cls: 0.0563 semantic_segmentation_loss_mask: 0.0454 semantic_segmentation_loss_dice: 0.1147 2024/07/08 17:59:47 - mmengine - INFO - Iter(train) [ 89900/120000] base_lr: 3.1182e-05 lr: 4.6529e-06 eta: 9:17:02 time: 1.1128 data_time: 0.0190 memory: 14704 grad_norm: 1.1711 loss: 0.2164 semantic_segmentation_loss_cls: 0.0563 semantic_segmentation_loss_mask: 0.0454 semantic_segmentation_loss_dice: 0.1147 2024/07/08 18:00:43 - mmengine - INFO - Iter(train) [ 89950/120000] base_lr: 3.1090e-05 lr: 4.6445e-06 eta: 9:16:06 time: 1.1130 data_time: 0.0190 memory: 15938 grad_norm: 1.1712 loss: 0.2166 semantic_segmentation_loss_cls: 0.0564 semantic_segmentation_loss_mask: 0.0454 semantic_segmentation_loss_dice: 0.1148 2024/07/08 18:01:38 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 18:01:38 - mmengine - INFO - Iter(train) [ 90000/120000] base_lr: 3.0998e-05 lr: 4.6362e-06 eta: 9:15:11 time: 1.1129 data_time: 0.0190 memory: 14830 grad_norm: 1.1719 loss: 0.2165 semantic_segmentation_loss_cls: 0.0563 semantic_segmentation_loss_mask: 0.0454 semantic_segmentation_loss_dice: 0.1148 2024/07/08 18:01:38 - mmengine - INFO - Saving checkpoint at 90000 iterations 2024/07/08 18:01:55 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:49 time: 0.2435 data_time: 0.0016 memory: 5013 2024/07/08 18:02:07 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:36 time: 0.2435 data_time: 0.0016 memory: 5189 2024/07/08 18:02:19 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:24 time: 0.2434 data_time: 0.0016 memory: 4460 2024/07/08 18:02:31 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:12 time: 0.2434 data_time: 0.0016 memory: 4543 2024/07/08 18:02:43 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:00 time: 0.2434 data_time: 0.0016 memory: 4645 2024/07/08 18:02:56 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:48 time: 0.2434 data_time: 0.0016 memory: 10983 2024/07/08 18:03:08 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2434 data_time: 0.0016 memory: 4460 2024/07/08 18:03:20 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2434 data_time: 0.0016 memory: 4641 2024/07/08 18:03:32 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2433 data_time: 0.0016 memory: 4473 2024/07/08 18:03:44 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2433 data_time: 0.0016 memory: 4555 2024/07/08 18:03:45 - mmengine - INFO - per class results: 2024/07/08 18:03:45 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.47 | 88.01 | | building | 82.82 | 90.49 | | sky | 94.38 | 97.78 | | floor | 83.13 | 91.45 | | tree | 75.78 | 88.0 | | ceiling | 85.12 | 92.91 | | road | 83.86 | 92.53 | | bed | 88.13 | 95.05 | | windowpane | 61.39 | 79.1 | | grass | 71.33 | 86.56 | | cabinet | 60.19 | 71.08 | | sidewalk | 67.03 | 81.56 | | person | 82.29 | 91.78 | | earth | 32.92 | 44.73 | | door | 53.76 | 68.91 | | table | 62.9 | 75.99 | | mountain | 57.04 | 72.18 | | plant | 54.44 | 68.78 | | curtain | 73.0 | 87.1 | | chair | 59.98 | 72.75 | | car | 83.66 | 91.2 | | water | 50.89 | 67.21 | | painting | 71.89 | 88.32 | | sofa | 64.98 | 76.43 | | shelf | 45.17 | 64.05 | | house | 53.56 | 81.68 | | sea | 49.89 | 73.44 | | mirror | 67.8 | 76.15 | | rug | 65.96 | 76.48 | | field | 36.08 | 51.64 | | armchair | 46.07 | 69.19 | | seat | 56.43 | 82.61 | | fence | 45.06 | 60.63 | | desk | 48.82 | 68.98 | | rock | 37.29 | 58.02 | | wardrobe | 50.64 | 72.13 | | lamp | 67.61 | 79.35 | | bathtub | 84.22 | 90.61 | | railing | 36.83 | 53.11 | | cushion | 58.18 | 69.54 | | base | 22.2 | 31.01 | | box | 25.17 | 37.43 | | column | 49.41 | 67.2 | | signboard | 39.5 | 55.09 | | chest of drawers | 40.57 | 68.7 | | counter | 28.73 | 44.26 | | sand | 33.4 | 47.74 | | sink | 76.11 | 82.43 | | skyscraper | 35.57 | 44.36 | | fireplace | 66.15 | 86.23 | | refrigerator | 80.05 | 89.96 | | grandstand | 44.47 | 74.74 | | path | 28.93 | 41.44 | | stairs | 32.7 | 41.52 | | runway | 75.98 | 89.81 | | case | 62.43 | 76.81 | | pool table | 92.25 | 96.19 | | pillow | 56.6 | 67.82 | | screen door | 76.85 | 80.89 | | stairway | 37.64 | 43.98 | | river | 20.65 | 44.61 | | bridge | 60.51 | 88.68 | | bookcase | 40.81 | 57.44 | | blind | 37.68 | 43.04 | | coffee table | 72.15 | 86.92 | | toilet | 87.06 | 89.67 | | flower | 41.81 | 58.19 | | book | 52.11 | 74.2 | | hill | 6.77 | 11.29 | | bench | 40.99 | 52.94 | | countertop | 54.96 | 64.74 | | stove | 80.67 | 85.85 | | palm | 52.75 | 67.82 | | kitchen island | 31.05 | 74.48 | | computer | 60.94 | 66.66 | | swivel chair | 38.73 | 54.51 | | boat | 71.86 | 78.23 | | bar | 48.04 | 59.94 | | arcade machine | 21.71 | 25.17 | | hovel | 20.17 | 30.0 | | bus | 92.33 | 95.45 | | towel | 67.96 | 74.78 | | light | 62.88 | 76.72 | | truck | 36.43 | 48.21 | | tower | 27.84 | 53.94 | | chandelier | 64.59 | 76.95 | | awning | 32.86 | 44.34 | | streetlight | 40.64 | 55.61 | | booth | 46.59 | 55.33 | | television receiver | 71.63 | 89.93 | | airplane | 56.28 | 66.92 | | dirt track | 3.09 | 3.65 | | apparel | 36.61 | 50.72 | | pole | 31.88 | 46.5 | | land | 2.04 | 2.45 | | bannister | 15.34 | 25.0 | | escalator | 28.7 | 38.51 | | ottoman | 36.59 | 65.9 | | bottle | 22.16 | 26.81 | | buffet | 43.03 | 47.6 | | poster | 29.42 | 36.64 | | stage | 17.26 | 27.64 | | van | 50.02 | 67.44 | | ship | 80.99 | 88.13 | | fountain | 5.98 | 6.6 | | conveyer belt | 59.62 | 91.92 | | canopy | 20.34 | 36.67 | | washer | 71.58 | 73.14 | | plaything | 27.96 | 38.96 | | swimming pool | 31.41 | 34.62 | | stool | 53.28 | 68.75 | | barrel | 19.91 | 67.85 | | basket | 35.16 | 43.8 | | waterfall | 46.14 | 51.25 | | tent | 73.05 | 97.6 | | bag | 18.35 | 24.19 | | minibike | 65.14 | 84.52 | | cradle | 76.57 | 96.79 | | oven | 24.34 | 62.1 | | ball | 25.23 | 30.2 | | food | 65.38 | 79.92 | | step | 28.18 | 32.02 | | tank | 28.65 | 46.45 | | trade name | 29.63 | 35.59 | | microwave | 37.98 | 41.27 | | pot | 43.77 | 48.49 | | animal | 62.23 | 68.88 | | bicycle | 58.24 | 77.55 | | lake | 63.47 | 63.63 | | dishwasher | 81.88 | 85.04 | | screen | 68.07 | 88.16 | | blanket | 9.32 | 11.91 | | sculpture | 57.92 | 81.97 | | hood | 65.97 | 68.35 | | sconce | 53.46 | 64.63 | | vase | 39.12 | 65.71 | | traffic light | 43.99 | 60.76 | | tray | 19.39 | 24.38 | | ashcan | 49.71 | 62.52 | | fan | 62.52 | 78.87 | | pier | 31.75 | 71.77 | | crt screen | 0.1 | 0.12 | | plate | 62.47 | 73.68 | | monitor | 47.7 | 68.53 | | bulletin board | 37.98 | 45.03 | | shower | 4.88 | 16.71 | | radiator | 59.66 | 68.58 | | glass | 18.49 | 19.7 | | clock | 33.25 | 37.85 | | flag | 47.4 | 54.35 | +---------------------+-------+-------+ 2024/07/08 18:03:45 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.4400 mIoU: 49.5900 mAcc: 62.5700 data_time: 0.0019 time: 0.2430 2024/07/08 18:04:40 - mmengine - INFO - Iter(train) [ 90050/120000] base_lr: 3.0907e-05 lr: 4.6279e-06 eta: 9:14:15 time: 1.1119 data_time: 0.0179 memory: 15423 grad_norm: 1.1706 loss: 0.2166 semantic_segmentation_loss_cls: 0.0564 semantic_segmentation_loss_mask: 0.0454 semantic_segmentation_loss_dice: 0.1148 2024/07/08 18:05:36 - mmengine - INFO - Iter(train) [ 90100/120000] base_lr: 3.0815e-05 lr: 4.6196e-06 eta: 9:13:20 time: 1.1119 data_time: 0.0180 memory: 14826 grad_norm: 1.1696 loss: 0.2166 semantic_segmentation_loss_cls: 0.0563 semantic_segmentation_loss_mask: 0.0455 semantic_segmentation_loss_dice: 0.1148 2024/07/08 18:06:32 - mmengine - INFO - Iter(train) [ 90150/120000] base_lr: 3.0724e-05 lr: 4.6113e-06 eta: 9:12:24 time: 1.1121 data_time: 0.0180 memory: 15600 grad_norm: 1.1693 loss: 0.2167 semantic_segmentation_loss_cls: 0.0564 semantic_segmentation_loss_mask: 0.0454 semantic_segmentation_loss_dice: 0.1149 2024/07/08 18:07:28 - mmengine - INFO - Iter(train) [ 90200/120000] base_lr: 3.0633e-05 lr: 4.6030e-06 eta: 9:11:29 time: 1.1123 data_time: 0.0180 memory: 15201 grad_norm: 1.1684 loss: 0.2163 semantic_segmentation_loss_cls: 0.0562 semantic_segmentation_loss_mask: 0.0454 semantic_segmentation_loss_dice: 0.1147 2024/07/08 18:08:23 - mmengine - INFO - Iter(train) [ 90250/120000] base_lr: 3.0542e-05 lr: 4.5947e-06 eta: 9:10:33 time: 1.1124 data_time: 0.0180 memory: 15509 grad_norm: 1.1683 loss: 0.2162 semantic_segmentation_loss_cls: 0.0562 semantic_segmentation_loss_mask: 0.0454 semantic_segmentation_loss_dice: 0.1146 2024/07/08 18:09:19 - mmengine - INFO - Iter(train) [ 90300/120000] base_lr: 3.0451e-05 lr: 4.5864e-06 eta: 9:09:38 time: 1.1126 data_time: 0.0180 memory: 14995 grad_norm: 1.1671 loss: 0.2162 semantic_segmentation_loss_cls: 0.0562 semantic_segmentation_loss_mask: 0.0454 semantic_segmentation_loss_dice: 0.1146 2024/07/08 18:10:15 - mmengine - INFO - Iter(train) [ 90350/120000] base_lr: 3.0360e-05 lr: 4.5782e-06 eta: 9:08:43 time: 1.1127 data_time: 0.0181 memory: 16665 grad_norm: 1.1667 loss: 0.2159 semantic_segmentation_loss_cls: 0.0561 semantic_segmentation_loss_mask: 0.0453 semantic_segmentation_loss_dice: 0.1144 2024/07/08 18:11:11 - mmengine - INFO - Iter(train) [ 90400/120000] base_lr: 3.0269e-05 lr: 4.5699e-06 eta: 9:07:47 time: 1.1128 data_time: 0.0181 memory: 16292 grad_norm: 1.1645 loss: 0.2157 semantic_segmentation_loss_cls: 0.0562 semantic_segmentation_loss_mask: 0.0452 semantic_segmentation_loss_dice: 0.1143 2024/07/08 18:12:07 - mmengine - INFO - Iter(train) [ 90450/120000] base_lr: 3.0178e-05 lr: 4.5617e-06 eta: 9:06:52 time: 1.1130 data_time: 0.0181 memory: 14885 grad_norm: 1.1645 loss: 0.2155 semantic_segmentation_loss_cls: 0.0561 semantic_segmentation_loss_mask: 0.0452 semantic_segmentation_loss_dice: 0.1142 2024/07/08 18:13:04 - mmengine - INFO - Iter(train) [ 90500/120000] base_lr: 3.0088e-05 lr: 4.5534e-06 eta: 9:05:57 time: 1.1131 data_time: 0.0181 memory: 15115 grad_norm: 1.1662 loss: 0.2154 semantic_segmentation_loss_cls: 0.0560 semantic_segmentation_loss_mask: 0.0452 semantic_segmentation_loss_dice: 0.1141 2024/07/08 18:14:00 - mmengine - INFO - Iter(train) [ 90550/120000] base_lr: 2.9998e-05 lr: 4.5452e-06 eta: 9:05:01 time: 1.1132 data_time: 0.0181 memory: 15007 grad_norm: 1.1655 loss: 0.2153 semantic_segmentation_loss_cls: 0.0561 semantic_segmentation_loss_mask: 0.0451 semantic_segmentation_loss_dice: 0.1141 2024/07/08 18:14:56 - mmengine - INFO - Iter(train) [ 90600/120000] base_lr: 2.9907e-05 lr: 4.5370e-06 eta: 9:04:06 time: 1.1135 data_time: 0.0181 memory: 15253 grad_norm: 1.1633 loss: 0.2152 semantic_segmentation_loss_cls: 0.0560 semantic_segmentation_loss_mask: 0.0451 semantic_segmentation_loss_dice: 0.1141 2024/07/08 18:15:51 - mmengine - INFO - Iter(train) [ 90650/120000] base_lr: 2.9817e-05 lr: 4.5288e-06 eta: 9:03:10 time: 1.1136 data_time: 0.0181 memory: 14307 grad_norm: 1.1639 loss: 0.2151 semantic_segmentation_loss_cls: 0.0560 semantic_segmentation_loss_mask: 0.0451 semantic_segmentation_loss_dice: 0.1140 2024/07/08 18:16:48 - mmengine - INFO - Iter(train) [ 90700/120000] base_lr: 2.9727e-05 lr: 4.5207e-06 eta: 9:02:15 time: 1.1138 data_time: 0.0181 memory: 14633 grad_norm: 1.1629 loss: 0.2149 semantic_segmentation_loss_cls: 0.0559 semantic_segmentation_loss_mask: 0.0451 semantic_segmentation_loss_dice: 0.1139 2024/07/08 18:17:43 - mmengine - INFO - Iter(train) [ 90750/120000] base_lr: 2.9637e-05 lr: 4.5125e-06 eta: 9:01:20 time: 1.1137 data_time: 0.0181 memory: 14815 grad_norm: 1.1623 loss: 0.2147 semantic_segmentation_loss_cls: 0.0558 semantic_segmentation_loss_mask: 0.0450 semantic_segmentation_loss_dice: 0.1139 2024/07/08 18:18:39 - mmengine - INFO - Iter(train) [ 90800/120000] base_lr: 2.9548e-05 lr: 4.5043e-06 eta: 9:00:24 time: 1.1135 data_time: 0.0181 memory: 15298 grad_norm: 1.1641 loss: 0.2148 semantic_segmentation_loss_cls: 0.0558 semantic_segmentation_loss_mask: 0.0451 semantic_segmentation_loss_dice: 0.1139 2024/07/08 18:19:34 - mmengine - INFO - Iter(train) [ 90850/120000] base_lr: 2.9458e-05 lr: 4.4962e-06 eta: 8:59:29 time: 1.1136 data_time: 0.0181 memory: 14870 grad_norm: 1.1666 loss: 0.2146 semantic_segmentation_loss_cls: 0.0557 semantic_segmentation_loss_mask: 0.0451 semantic_segmentation_loss_dice: 0.1138 2024/07/08 18:20:29 - mmengine - INFO - Iter(train) [ 90900/120000] base_lr: 2.9368e-05 lr: 4.4880e-06 eta: 8:58:33 time: 1.1135 data_time: 0.0181 memory: 14456 grad_norm: 1.1659 loss: 0.2143 semantic_segmentation_loss_cls: 0.0556 semantic_segmentation_loss_mask: 0.0450 semantic_segmentation_loss_dice: 0.1137 2024/07/08 18:21:24 - mmengine - INFO - Iter(train) [ 90950/120000] base_lr: 2.9279e-05 lr: 4.4799e-06 eta: 8:57:37 time: 1.1134 data_time: 0.0181 memory: 14659 grad_norm: 1.1652 loss: 0.2143 semantic_segmentation_loss_cls: 0.0556 semantic_segmentation_loss_mask: 0.0450 semantic_segmentation_loss_dice: 0.1137 2024/07/08 18:22:20 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 18:22:20 - mmengine - INFO - Iter(train) [ 91000/120000] base_lr: 2.9190e-05 lr: 4.4718e-06 eta: 8:56:42 time: 1.1134 data_time: 0.0181 memory: 15649 grad_norm: 1.1644 loss: 0.2142 semantic_segmentation_loss_cls: 0.0556 semantic_segmentation_loss_mask: 0.0450 semantic_segmentation_loss_dice: 0.1137 2024/07/08 18:22:20 - mmengine - INFO - Saving checkpoint at 91000 iterations 2024/07/08 18:23:20 - mmengine - INFO - Iter(train) [ 91050/120000] base_lr: 2.9101e-05 lr: 4.4637e-06 eta: 8:55:48 time: 1.1133 data_time: 0.0182 memory: 15742 grad_norm: 1.1644 loss: 0.2137 semantic_segmentation_loss_cls: 0.0553 semantic_segmentation_loss_mask: 0.0449 semantic_segmentation_loss_dice: 0.1134 2024/07/08 18:24:16 - mmengine - INFO - Iter(train) [ 91100/120000] base_lr: 2.9012e-05 lr: 4.4556e-06 eta: 8:54:52 time: 1.1135 data_time: 0.0182 memory: 15522 grad_norm: 1.1635 loss: 0.2138 semantic_segmentation_loss_cls: 0.0554 semantic_segmentation_loss_mask: 0.0450 semantic_segmentation_loss_dice: 0.1134 2024/07/08 18:25:12 - mmengine - INFO - Iter(train) [ 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1.1134 data_time: 0.0182 memory: 15206 grad_norm: 1.1635 loss: 0.2136 semantic_segmentation_loss_cls: 0.0554 semantic_segmentation_loss_mask: 0.0450 semantic_segmentation_loss_dice: 0.1133 2024/07/08 18:28:53 - mmengine - INFO - Iter(train) [ 91350/120000] base_lr: 2.8568e-05 lr: 4.4153e-06 eta: 8:50:15 time: 1.1135 data_time: 0.0183 memory: 15097 grad_norm: 1.1633 loss: 0.2139 semantic_segmentation_loss_cls: 0.0555 semantic_segmentation_loss_mask: 0.0450 semantic_segmentation_loss_dice: 0.1135 2024/07/08 18:29:49 - mmengine - INFO - Iter(train) [ 91400/120000] base_lr: 2.8480e-05 lr: 4.4073e-06 eta: 8:49:19 time: 1.1137 data_time: 0.0183 memory: 14848 grad_norm: 1.1654 loss: 0.2138 semantic_segmentation_loss_cls: 0.0554 semantic_segmentation_loss_mask: 0.0450 semantic_segmentation_loss_dice: 0.1134 2024/07/08 18:30:46 - mmengine - INFO - Iter(train) [ 91450/120000] base_lr: 2.8392e-05 lr: 4.3993e-06 eta: 8:48:24 time: 1.1140 data_time: 0.0183 memory: 15442 grad_norm: 1.1657 loss: 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semantic_segmentation_loss_dice: 0.1133 2024/07/08 18:37:17 - mmengine - INFO - Iter(train) [ 91800/120000] base_lr: 2.7778e-05 lr: 4.3435e-06 eta: 8:41:56 time: 1.1148 data_time: 0.0184 memory: 15257 grad_norm: 1.1617 loss: 0.2135 semantic_segmentation_loss_cls: 0.0554 semantic_segmentation_loss_mask: 0.0449 semantic_segmentation_loss_dice: 0.1133 2024/07/08 18:38:13 - mmengine - INFO - Iter(train) [ 91850/120000] base_lr: 2.7691e-05 lr: 4.3356e-06 eta: 8:41:01 time: 1.1148 data_time: 0.0184 memory: 15837 grad_norm: 1.1618 loss: 0.2135 semantic_segmentation_loss_cls: 0.0553 semantic_segmentation_loss_mask: 0.0449 semantic_segmentation_loss_dice: 0.1133 2024/07/08 18:39:10 - mmengine - INFO - Iter(train) [ 91900/120000] base_lr: 2.7604e-05 lr: 4.3276e-06 eta: 8:40:05 time: 1.1150 data_time: 0.0184 memory: 14930 grad_norm: 1.1626 loss: 0.2133 semantic_segmentation_loss_cls: 0.0552 semantic_segmentation_loss_mask: 0.0449 semantic_segmentation_loss_dice: 0.1132 2024/07/08 18:40:05 - 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grad_norm: 1.1590 loss: 0.2129 semantic_segmentation_loss_cls: 0.0550 semantic_segmentation_loss_mask: 0.0448 semantic_segmentation_loss_dice: 0.1131 2024/07/08 18:42:56 - mmengine - INFO - Iter(train) [ 92100/120000] base_lr: 2.7257e-05 lr: 4.2961e-06 eta: 8:36:25 time: 1.1151 data_time: 0.0183 memory: 15525 grad_norm: 1.1572 loss: 0.2131 semantic_segmentation_loss_cls: 0.0552 semantic_segmentation_loss_mask: 0.0448 semantic_segmentation_loss_dice: 0.1131 2024/07/08 18:43:52 - mmengine - INFO - Iter(train) [ 92150/120000] base_lr: 2.7171e-05 lr: 4.2883e-06 eta: 8:35:29 time: 1.1152 data_time: 0.0183 memory: 14938 grad_norm: 1.1556 loss: 0.2130 semantic_segmentation_loss_cls: 0.0552 semantic_segmentation_loss_mask: 0.0447 semantic_segmentation_loss_dice: 0.1131 2024/07/08 18:44:47 - mmengine - INFO - Iter(train) [ 92200/120000] base_lr: 2.7085e-05 lr: 4.2804e-06 eta: 8:34:33 time: 1.1151 data_time: 0.0183 memory: 14920 grad_norm: 1.1538 loss: 0.2130 semantic_segmentation_loss_cls: 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semantic_segmentation_loss_dice: 0.1131 2024/07/08 18:48:28 - mmengine - INFO - Iter(train) [ 92400/120000] base_lr: 2.6741e-05 lr: 4.2492e-06 eta: 8:30:51 time: 1.1150 data_time: 0.0182 memory: 15640 grad_norm: 1.1529 loss: 0.2129 semantic_segmentation_loss_cls: 0.0552 semantic_segmentation_loss_mask: 0.0447 semantic_segmentation_loss_dice: 0.1131 2024/07/08 18:49:23 - mmengine - INFO - Iter(train) [ 92450/120000] base_lr: 2.6655e-05 lr: 4.2414e-06 eta: 8:29:56 time: 1.1152 data_time: 0.0182 memory: 16553 grad_norm: 1.1538 loss: 0.2125 semantic_segmentation_loss_cls: 0.0551 semantic_segmentation_loss_mask: 0.0446 semantic_segmentation_loss_dice: 0.1128 2024/07/08 18:50:18 - mmengine - INFO - Iter(train) [ 92500/120000] base_lr: 2.6570e-05 lr: 4.2336e-06 eta: 8:29:00 time: 1.1153 data_time: 0.0182 memory: 14988 grad_norm: 1.1511 loss: 0.2125 semantic_segmentation_loss_cls: 0.0551 semantic_segmentation_loss_mask: 0.0445 semantic_segmentation_loss_dice: 0.1128 2024/07/08 18:51:15 - 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lr: 4.2026e-06 eta: 8:25:18 time: 1.1151 data_time: 0.0183 memory: 15198 grad_norm: 1.1513 loss: 0.2121 semantic_segmentation_loss_cls: 0.0550 semantic_segmentation_loss_mask: 0.0445 semantic_segmentation_loss_dice: 0.1126 2024/07/08 18:54:56 - mmengine - INFO - Iter(train) [ 92750/120000] base_lr: 2.6144e-05 lr: 4.1949e-06 eta: 8:24:22 time: 1.1152 data_time: 0.0183 memory: 15316 grad_norm: 1.1517 loss: 0.2122 semantic_segmentation_loss_cls: 0.0550 semantic_segmentation_loss_mask: 0.0445 semantic_segmentation_loss_dice: 0.1126 2024/07/08 18:55:52 - mmengine - INFO - Iter(train) [ 92800/120000] base_lr: 2.6059e-05 lr: 4.1872e-06 eta: 8:23:27 time: 1.1154 data_time: 0.0183 memory: 15346 grad_norm: 1.1505 loss: 0.2118 semantic_segmentation_loss_cls: 0.0549 semantic_segmentation_loss_mask: 0.0444 semantic_segmentation_loss_dice: 0.1124 2024/07/08 18:56:48 - mmengine - INFO - Iter(train) [ 92850/120000] base_lr: 2.5975e-05 lr: 4.1795e-06 eta: 8:22:31 time: 1.1153 data_time: 0.0183 memory: 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base_lr: 2.5721e-05 lr: 4.1565e-06 eta: 8:19:45 time: 1.1157 data_time: 0.0183 memory: 15315 grad_norm: 1.1484 loss: 0.2117 semantic_segmentation_loss_cls: 0.0549 semantic_segmentation_loss_mask: 0.0444 semantic_segmentation_loss_dice: 0.1124 2024/07/08 18:59:34 - mmengine - INFO - Saving checkpoint at 93000 iterations 2024/07/08 19:00:33 - mmengine - INFO - Iter(train) [ 93050/120000] base_lr: 2.5637e-05 lr: 4.1489e-06 eta: 8:18:50 time: 1.1154 data_time: 0.0183 memory: 14642 grad_norm: 1.1484 loss: 0.2115 semantic_segmentation_loss_cls: 0.0548 semantic_segmentation_loss_mask: 0.0444 semantic_segmentation_loss_dice: 0.1123 2024/07/08 19:01:28 - mmengine - INFO - Iter(train) [ 93100/120000] base_lr: 2.5553e-05 lr: 4.1412e-06 eta: 8:17:55 time: 1.1151 data_time: 0.0183 memory: 15104 grad_norm: 1.1460 loss: 0.2115 semantic_segmentation_loss_cls: 0.0548 semantic_segmentation_loss_mask: 0.0444 semantic_segmentation_loss_dice: 0.1123 2024/07/08 19:02:24 - mmengine - INFO - Iter(train) [ 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semantic_segmentation_loss_dice: 0.1122 2024/07/08 19:14:27 - mmengine - INFO - Iter(train) [ 93800/120000] base_lr: 2.4391e-05 lr: 4.0356e-06 eta: 8:04:57 time: 1.1163 data_time: 0.0183 memory: 14500 grad_norm: 1.1464 loss: 0.2106 semantic_segmentation_loss_cls: 0.0544 semantic_segmentation_loss_mask: 0.0442 semantic_segmentation_loss_dice: 0.1121 2024/07/08 19:15:24 - mmengine - INFO - Iter(train) [ 93850/120000] base_lr: 2.4309e-05 lr: 4.0281e-06 eta: 8:04:02 time: 1.1164 data_time: 0.0183 memory: 15523 grad_norm: 1.1447 loss: 0.2107 semantic_segmentation_loss_cls: 0.0544 semantic_segmentation_loss_mask: 0.0442 semantic_segmentation_loss_dice: 0.1121 2024/07/08 19:16:20 - mmengine - INFO - Iter(train) [ 93900/120000] base_lr: 2.4228e-05 lr: 4.0207e-06 eta: 8:03:07 time: 1.1164 data_time: 0.0182 memory: 14707 grad_norm: 1.1431 loss: 0.2105 semantic_segmentation_loss_cls: 0.0543 semantic_segmentation_loss_mask: 0.0442 semantic_segmentation_loss_dice: 0.1120 2024/07/08 19:17:15 - mmengine - INFO - Iter(train) [ 93950/120000] base_lr: 2.4146e-05 lr: 4.0133e-06 eta: 8:02:11 time: 1.1162 data_time: 0.0182 memory: 15424 grad_norm: 1.1428 loss: 0.2105 semantic_segmentation_loss_cls: 0.0542 semantic_segmentation_loss_mask: 0.0442 semantic_segmentation_loss_dice: 0.1121 2024/07/08 19:18:10 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 19:18:10 - mmengine - INFO - Iter(train) [ 94000/120000] base_lr: 2.4064e-05 lr: 4.0058e-06 eta: 8:01:15 time: 1.1162 data_time: 0.0183 memory: 15382 grad_norm: 1.1448 loss: 0.2102 semantic_segmentation_loss_cls: 0.0542 semantic_segmentation_loss_mask: 0.0441 semantic_segmentation_loss_dice: 0.1119 2024/07/08 19:18:10 - mmengine - INFO - Saving checkpoint at 94000 iterations 2024/07/08 19:19:10 - mmengine - INFO - Iter(train) [ 94050/120000] base_lr: 2.3983e-05 lr: 3.9984e-06 eta: 8:00:21 time: 1.1170 data_time: 0.0192 memory: 15193 grad_norm: 1.1457 loss: 0.2099 semantic_segmentation_loss_cls: 0.0540 semantic_segmentation_loss_mask: 0.0441 semantic_segmentation_loss_dice: 0.1118 2024/07/08 19:20:05 - mmengine - INFO - Iter(train) [ 94100/120000] base_lr: 2.3901e-05 lr: 3.9910e-06 eta: 7:59:26 time: 1.1170 data_time: 0.0191 memory: 16348 grad_norm: 1.1452 loss: 0.2098 semantic_segmentation_loss_cls: 0.0540 semantic_segmentation_loss_mask: 0.0441 semantic_segmentation_loss_dice: 0.1117 2024/07/08 19:21:00 - mmengine - INFO - Iter(train) [ 94150/120000] base_lr: 2.3820e-05 lr: 3.9836e-06 eta: 7:58:30 time: 1.1169 data_time: 0.0191 memory: 15695 grad_norm: 1.1452 loss: 0.2099 semantic_segmentation_loss_cls: 0.0541 semantic_segmentation_loss_mask: 0.0441 semantic_segmentation_loss_dice: 0.1117 2024/07/08 19:21:56 - mmengine - INFO - Iter(train) [ 94200/120000] base_lr: 2.3739e-05 lr: 3.9763e-06 eta: 7:57:34 time: 1.1168 data_time: 0.0191 memory: 15060 grad_norm: 1.1457 loss: 0.2101 semantic_segmentation_loss_cls: 0.0542 semantic_segmentation_loss_mask: 0.0441 semantic_segmentation_loss_dice: 0.1118 2024/07/08 19:22:51 - mmengine - INFO - Iter(train) [ 94250/120000] base_lr: 2.3658e-05 lr: 3.9689e-06 eta: 7:56:39 time: 1.1166 data_time: 0.0191 memory: 15689 grad_norm: 1.1442 loss: 0.2098 semantic_segmentation_loss_cls: 0.0541 semantic_segmentation_loss_mask: 0.0440 semantic_segmentation_loss_dice: 0.1117 2024/07/08 19:23:45 - mmengine - INFO - Iter(train) [ 94300/120000] base_lr: 2.3577e-05 lr: 3.9616e-06 eta: 7:55:43 time: 1.1163 data_time: 0.0191 memory: 15620 grad_norm: 1.1437 loss: 0.2098 semantic_segmentation_loss_cls: 0.0541 semantic_segmentation_loss_mask: 0.0440 semantic_segmentation_loss_dice: 0.1117 2024/07/08 19:24:39 - mmengine - INFO - Iter(train) [ 94350/120000] base_lr: 2.3497e-05 lr: 3.9542e-06 eta: 7:54:47 time: 1.1157 data_time: 0.0191 memory: 15553 grad_norm: 1.1442 loss: 0.2100 semantic_segmentation_loss_cls: 0.0542 semantic_segmentation_loss_mask: 0.0441 semantic_segmentation_loss_dice: 0.1118 2024/07/08 19:25:34 - mmengine - INFO - Iter(train) [ 94400/120000] base_lr: 2.3416e-05 lr: 3.9469e-06 eta: 7:53:51 time: 1.1155 data_time: 0.0191 memory: 14880 grad_norm: 1.1454 loss: 0.2099 semantic_segmentation_loss_cls: 0.0541 semantic_segmentation_loss_mask: 0.0441 semantic_segmentation_loss_dice: 0.1118 2024/07/08 19:26:30 - mmengine - INFO - Iter(train) [ 94450/120000] base_lr: 2.3336e-05 lr: 3.9396e-06 eta: 7:52:56 time: 1.1154 data_time: 0.0191 memory: 14697 grad_norm: 1.1443 loss: 0.2098 semantic_segmentation_loss_cls: 0.0539 semantic_segmentation_loss_mask: 0.0441 semantic_segmentation_loss_dice: 0.1118 2024/07/08 19:27:25 - mmengine - INFO - Iter(train) [ 94500/120000] base_lr: 2.3255e-05 lr: 3.9323e-06 eta: 7:52:00 time: 1.1151 data_time: 0.0190 memory: 15735 grad_norm: 1.1417 loss: 0.2095 semantic_segmentation_loss_cls: 0.0538 semantic_segmentation_loss_mask: 0.0441 semantic_segmentation_loss_dice: 0.1116 2024/07/08 19:28:20 - mmengine - INFO - Iter(train) [ 94550/120000] base_lr: 2.3175e-05 lr: 3.9250e-06 eta: 7:51:05 time: 1.1149 data_time: 0.0190 memory: 15048 grad_norm: 1.1420 loss: 0.2095 semantic_segmentation_loss_cls: 0.0538 semantic_segmentation_loss_mask: 0.0441 semantic_segmentation_loss_dice: 0.1117 2024/07/08 19:29:16 - mmengine - INFO - Iter(train) [ 94600/120000] base_lr: 2.3095e-05 lr: 3.9177e-06 eta: 7:50:09 time: 1.1148 data_time: 0.0190 memory: 15303 grad_norm: 1.1430 loss: 0.2094 semantic_segmentation_loss_cls: 0.0538 semantic_segmentation_loss_mask: 0.0440 semantic_segmentation_loss_dice: 0.1116 2024/07/08 19:30:11 - mmengine - INFO - Iter(train) [ 94650/120000] base_lr: 2.3015e-05 lr: 3.9105e-06 eta: 7:49:13 time: 1.1148 data_time: 0.0190 memory: 14922 grad_norm: 1.1419 loss: 0.2095 semantic_segmentation_loss_cls: 0.0539 semantic_segmentation_loss_mask: 0.0440 semantic_segmentation_loss_dice: 0.1116 2024/07/08 19:31:06 - mmengine - INFO - Iter(train) [ 94700/120000] base_lr: 2.2935e-05 lr: 3.9032e-06 eta: 7:48:18 time: 1.1144 data_time: 0.0190 memory: 15203 grad_norm: 1.1421 loss: 0.2096 semantic_segmentation_loss_cls: 0.0539 semantic_segmentation_loss_mask: 0.0440 semantic_segmentation_loss_dice: 0.1116 2024/07/08 19:32:01 - mmengine - INFO - Iter(train) [ 94750/120000] base_lr: 2.2856e-05 lr: 3.8960e-06 eta: 7:47:22 time: 1.1143 data_time: 0.0190 memory: 15397 grad_norm: 1.1406 loss: 0.2096 semantic_segmentation_loss_cls: 0.0539 semantic_segmentation_loss_mask: 0.0441 semantic_segmentation_loss_dice: 0.1116 2024/07/08 19:32:57 - mmengine - INFO - Iter(train) [ 94800/120000] base_lr: 2.2776e-05 lr: 3.8887e-06 eta: 7:46:27 time: 1.1143 data_time: 0.0190 memory: 14772 grad_norm: 1.1394 loss: 0.2094 semantic_segmentation_loss_cls: 0.0539 semantic_segmentation_loss_mask: 0.0440 semantic_segmentation_loss_dice: 0.1115 2024/07/08 19:33:53 - mmengine - INFO - Iter(train) [ 94850/120000] base_lr: 2.2697e-05 lr: 3.8815e-06 eta: 7:45:31 time: 1.1144 data_time: 0.0190 memory: 15284 grad_norm: 1.1360 loss: 0.2095 semantic_segmentation_loss_cls: 0.0540 semantic_segmentation_loss_mask: 0.0440 semantic_segmentation_loss_dice: 0.1115 2024/07/08 19:34:48 - mmengine - INFO - Iter(train) [ 94900/120000] base_lr: 2.2618e-05 lr: 3.8743e-06 eta: 7:44:36 time: 1.1145 data_time: 0.0190 memory: 15144 grad_norm: 1.1379 loss: 0.2097 semantic_segmentation_loss_cls: 0.0541 semantic_segmentation_loss_mask: 0.0440 semantic_segmentation_loss_dice: 0.1116 2024/07/08 19:35:44 - mmengine - INFO - Iter(train) [ 94950/120000] base_lr: 2.2539e-05 lr: 3.8671e-06 eta: 7:43:40 time: 1.1147 data_time: 0.0190 memory: 14737 grad_norm: 1.1366 loss: 0.2093 semantic_segmentation_loss_cls: 0.0539 semantic_segmentation_loss_mask: 0.0440 semantic_segmentation_loss_dice: 0.1114 2024/07/08 19:36:39 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 19:36:39 - mmengine - INFO - Iter(train) [ 95000/120000] base_lr: 2.2460e-05 lr: 3.8600e-06 eta: 7:42:44 time: 1.1145 data_time: 0.0190 memory: 14866 grad_norm: 1.1371 loss: 0.2094 semantic_segmentation_loss_cls: 0.0539 semantic_segmentation_loss_mask: 0.0440 semantic_segmentation_loss_dice: 0.1115 2024/07/08 19:36:39 - mmengine - INFO - Saving checkpoint at 95000 iterations 2024/07/08 19:36:56 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:49 time: 0.2433 data_time: 0.0016 memory: 5013 2024/07/08 19:37:08 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:36 time: 0.2432 data_time: 0.0016 memory: 5189 2024/07/08 19:37:20 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:24 time: 0.2432 data_time: 0.0017 memory: 4460 2024/07/08 19:37:32 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:12 time: 0.2432 data_time: 0.0017 memory: 4543 2024/07/08 19:37:44 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:00 time: 0.2432 data_time: 0.0017 memory: 4645 2024/07/08 19:37:57 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:48 time: 0.2431 data_time: 0.0017 memory: 10983 2024/07/08 19:38:09 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2431 data_time: 0.0017 memory: 4460 2024/07/08 19:38:21 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2430 data_time: 0.0017 memory: 4641 2024/07/08 19:38:33 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2430 data_time: 0.0017 memory: 4473 2024/07/08 19:38:45 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2430 data_time: 0.0017 memory: 4555 2024/07/08 19:38:46 - mmengine - INFO - per class results: 2024/07/08 19:38:46 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.53 | 88.02 | | building | 82.98 | 90.75 | | sky | 94.41 | 97.8 | | floor | 83.01 | 91.57 | | tree | 75.73 | 87.98 | | ceiling | 85.07 | 92.95 | | road | 84.21 | 92.98 | | bed | 88.28 | 95.07 | | windowpane | 61.89 | 79.15 | | grass | 71.42 | 86.39 | | cabinet | 60.68 | 72.02 | | sidewalk | 67.56 | 81.06 | | person | 82.18 | 91.72 | | earth | 33.19 | 45.35 | | door | 55.08 | 69.83 | | table | 62.74 | 75.85 | | mountain | 57.87 | 72.76 | | plant | 54.07 | 68.91 | | curtain | 73.23 | 87.2 | | chair | 59.62 | 72.65 | | car | 83.51 | 91.31 | | water | 50.43 | 67.01 | | painting | 72.12 | 89.23 | | sofa | 65.42 | 76.9 | | shelf | 45.52 | 64.66 | | house | 52.23 | 80.76 | | sea | 50.25 | 73.97 | | mirror | 67.92 | 75.44 | | rug | 65.58 | 76.5 | | field | 36.94 | 51.69 | | armchair | 45.78 | 68.84 | | seat | 56.25 | 81.97 | | fence | 46.17 | 62.85 | | desk | 47.5 | 68.87 | | rock | 38.24 | 60.21 | | wardrobe | 53.11 | 71.05 | | lamp | 67.59 | 79.24 | | bathtub | 86.07 | 90.68 | | railing | 37.01 | 53.3 | | cushion | 56.97 | 68.92 | | base | 23.22 | 32.24 | | box | 25.38 | 37.62 | | column | 48.8 | 67.21 | | signboard | 39.55 | 55.0 | | chest of drawers | 38.6 | 68.63 | | counter | 28.97 | 42.47 | | sand | 35.42 | 50.66 | | sink | 76.18 | 82.54 | | skyscraper | 39.77 | 50.09 | | fireplace | 67.49 | 87.41 | | refrigerator | 82.16 | 89.63 | | grandstand | 44.26 | 74.78 | | path | 28.83 | 41.32 | | stairs | 32.29 | 42.02 | | runway | 76.15 | 89.94 | | case | 64.03 | 78.6 | | pool table | 92.88 | 96.34 | | pillow | 56.72 | 68.54 | | screen door | 80.16 | 85.17 | | stairway | 38.5 | 43.55 | | river | 23.01 | 47.21 | | bridge | 64.82 | 88.55 | | bookcase | 39.8 | 56.25 | | blind | 37.87 | 43.27 | | coffee table | 70.87 | 85.61 | | toilet | 86.24 | 89.64 | | flower | 40.39 | 57.81 | | book | 52.64 | 74.87 | | hill | 9.42 | 15.24 | | bench | 42.39 | 53.78 | | countertop | 54.88 | 65.06 | | stove | 80.24 | 85.36 | | palm | 52.56 | 67.75 | | kitchen island | 31.47 | 73.96 | | computer | 61.2 | 66.88 | | swivel chair | 39.11 | 54.56 | | boat | 70.77 | 77.18 | | bar | 46.93 | 58.38 | | arcade machine | 22.13 | 24.83 | | hovel | 16.67 | 22.28 | | bus | 92.66 | 95.68 | | towel | 67.9 | 74.63 | | light | 62.47 | 77.33 | | truck | 37.78 | 48.55 | | tower | 30.01 | 54.04 | | chandelier | 64.9 | 75.37 | | awning | 31.62 | 44.29 | | streetlight | 38.95 | 52.98 | | booth | 46.34 | 54.91 | | television receiver | 73.43 | 89.68 | | airplane | 57.47 | 67.04 | | dirt track | 4.72 | 5.51 | | apparel | 37.36 | 51.69 | | pole | 31.4 | 47.05 | | land | 1.19 | 1.44 | | bannister | 15.45 | 24.91 | | escalator | 28.12 | 36.28 | | ottoman | 45.66 | 65.98 | | bottle | 22.07 | 26.54 | | buffet | 42.34 | 46.91 | | poster | 25.5 | 33.9 | | stage | 19.47 | 31.3 | | van | 49.37 | 66.5 | | ship | 79.64 | 87.95 | | fountain | 5.83 | 6.69 | | conveyer belt | 58.4 | 91.73 | | canopy | 15.98 | 29.86 | | washer | 71.3 | 72.76 | | plaything | 27.96 | 38.51 | | swimming pool | 30.96 | 33.97 | | stool | 47.14 | 68.71 | | barrel | 16.86 | 55.74 | | basket | 39.69 | 49.66 | | waterfall | 46.3 | 51.46 | | tent | 68.83 | 97.67 | | bag | 18.64 | 23.99 | | minibike | 63.65 | 85.14 | | cradle | 76.42 | 96.45 | | oven | 22.21 | 59.69 | | ball | 28.57 | 34.73 | | food | 65.57 | 80.84 | | step | 24.91 | 27.68 | | tank | 33.68 | 42.31 | | trade name | 29.07 | 35.03 | | microwave | 37.97 | 41.28 | | pot | 44.27 | 49.22 | | animal | 62.72 | 68.72 | | bicycle | 57.87 | 77.04 | | lake | 63.46 | 63.66 | | dishwasher | 81.29 | 84.2 | | screen | 61.41 | 83.1 | | blanket | 9.36 | 12.04 | | sculpture | 63.8 | 82.02 | | hood | 66.23 | 68.61 | | sconce | 52.55 | 64.18 | | vase | 39.88 | 65.5 | | traffic light | 41.58 | 56.69 | | tray | 18.3 | 22.74 | | ashcan | 45.16 | 59.46 | | fan | 62.57 | 78.72 | | pier | 32.75 | 71.48 | | crt screen | 0.03 | 0.07 | | plate | 62.55 | 73.79 | | monitor | 3.42 | 4.66 | | bulletin board | 23.99 | 28.66 | | shower | 10.86 | 16.42 | | radiator | 60.83 | 69.78 | | glass | 18.91 | 20.24 | | clock | 33.5 | 37.72 | | flag | 47.8 | 55.08 | +---------------------+-------+-------+ 2024/07/08 19:38:46 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.5200 mIoU: 49.3300 mAcc: 61.8900 data_time: 0.0017 time: 0.2419 2024/07/08 19:39:41 - mmengine - INFO - Iter(train) [ 95050/120000] base_lr: 2.2381e-05 lr: 3.8528e-06 eta: 7:41:49 time: 1.1137 data_time: 0.0180 memory: 15668 grad_norm: 1.1358 loss: 0.2094 semantic_segmentation_loss_cls: 0.0539 semantic_segmentation_loss_mask: 0.0440 semantic_segmentation_loss_dice: 0.1115 2024/07/08 19:40:37 - mmengine - INFO - Iter(train) [ 95100/120000] base_lr: 2.2302e-05 lr: 3.8456e-06 eta: 7:40:54 time: 1.1137 data_time: 0.0180 memory: 16482 grad_norm: 1.1357 loss: 0.2089 semantic_segmentation_loss_cls: 0.0536 semantic_segmentation_loss_mask: 0.0439 semantic_segmentation_loss_dice: 0.1114 2024/07/08 19:41:34 - mmengine - INFO - Iter(train) [ 95150/120000] base_lr: 2.2224e-05 lr: 3.8385e-06 eta: 7:39:58 time: 1.1138 data_time: 0.0180 memory: 15075 grad_norm: 1.1391 loss: 0.2092 semantic_segmentation_loss_cls: 0.0538 semantic_segmentation_loss_mask: 0.0440 semantic_segmentation_loss_dice: 0.1114 2024/07/08 19:42:29 - mmengine - INFO - Iter(train) [ 95200/120000] base_lr: 2.2145e-05 lr: 3.8314e-06 eta: 7:39:03 time: 1.1138 data_time: 0.0181 memory: 14959 grad_norm: 1.1376 loss: 0.2091 semantic_segmentation_loss_cls: 0.0537 semantic_segmentation_loss_mask: 0.0440 semantic_segmentation_loss_dice: 0.1114 2024/07/08 19:43:25 - mmengine - INFO - Iter(train) [ 95250/120000] base_lr: 2.2067e-05 lr: 3.8243e-06 eta: 7:38:07 time: 1.1138 data_time: 0.0180 memory: 15666 grad_norm: 1.1382 loss: 0.2089 semantic_segmentation_loss_cls: 0.0536 semantic_segmentation_loss_mask: 0.0440 semantic_segmentation_loss_dice: 0.1113 2024/07/08 19:44:21 - mmengine - INFO - Iter(train) [ 95300/120000] base_lr: 2.1989e-05 lr: 3.8172e-06 eta: 7:37:12 time: 1.1141 data_time: 0.0180 memory: 15773 grad_norm: 1.1367 loss: 0.2087 semantic_segmentation_loss_cls: 0.0535 semantic_segmentation_loss_mask: 0.0439 semantic_segmentation_loss_dice: 0.1112 2024/07/08 19:45:16 - mmengine - INFO - Iter(train) [ 95350/120000] base_lr: 2.1911e-05 lr: 3.8101e-06 eta: 7:36:16 time: 1.1141 data_time: 0.0180 memory: 15102 grad_norm: 1.1368 loss: 0.2083 semantic_segmentation_loss_cls: 0.0534 semantic_segmentation_loss_mask: 0.0439 semantic_segmentation_loss_dice: 0.1110 2024/07/08 19:46:11 - mmengine - INFO - Iter(train) [ 95400/120000] base_lr: 2.1833e-05 lr: 3.8030e-06 eta: 7:35:21 time: 1.1139 data_time: 0.0180 memory: 14937 grad_norm: 1.1348 loss: 0.2079 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0438 semantic_segmentation_loss_dice: 0.1108 2024/07/08 19:47:07 - mmengine - INFO - Iter(train) [ 95450/120000] base_lr: 2.1755e-05 lr: 3.7959e-06 eta: 7:34:25 time: 1.1137 data_time: 0.0180 memory: 15033 grad_norm: 1.1346 loss: 0.2075 semantic_segmentation_loss_cls: 0.0531 semantic_segmentation_loss_mask: 0.0437 semantic_segmentation_loss_dice: 0.1106 2024/07/08 19:48:03 - mmengine - INFO - Iter(train) [ 95500/120000] base_lr: 2.1677e-05 lr: 3.7889e-06 eta: 7:33:30 time: 1.1138 data_time: 0.0180 memory: 14861 grad_norm: 1.1351 loss: 0.2073 semantic_segmentation_loss_cls: 0.0531 semantic_segmentation_loss_mask: 0.0437 semantic_segmentation_loss_dice: 0.1105 2024/07/08 19:48:58 - mmengine - INFO - Iter(train) [ 95550/120000] base_lr: 2.1600e-05 lr: 3.7818e-06 eta: 7:32:34 time: 1.1136 data_time: 0.0180 memory: 14941 grad_norm: 1.1358 loss: 0.2069 semantic_segmentation_loss_cls: 0.0529 semantic_segmentation_loss_mask: 0.0436 semantic_segmentation_loss_dice: 0.1104 2024/07/08 19:49:53 - mmengine - INFO - Iter(train) [ 95600/120000] base_lr: 2.1523e-05 lr: 3.7748e-06 eta: 7:31:39 time: 1.1132 data_time: 0.0179 memory: 15546 grad_norm: 1.1342 loss: 0.2067 semantic_segmentation_loss_cls: 0.0529 semantic_segmentation_loss_mask: 0.0436 semantic_segmentation_loss_dice: 0.1103 2024/07/08 19:50:49 - mmengine - INFO - Iter(train) [ 95650/120000] base_lr: 2.1445e-05 lr: 3.7678e-06 eta: 7:30:43 time: 1.1132 data_time: 0.0179 memory: 14922 grad_norm: 1.1342 loss: 0.2068 semantic_segmentation_loss_cls: 0.0529 semantic_segmentation_loss_mask: 0.0436 semantic_segmentation_loss_dice: 0.1103 2024/07/08 19:51:45 - mmengine - INFO - Iter(train) [ 95700/120000] base_lr: 2.1368e-05 lr: 3.7608e-06 eta: 7:29:48 time: 1.1133 data_time: 0.0179 memory: 15008 grad_norm: 1.1345 loss: 0.2068 semantic_segmentation_loss_cls: 0.0529 semantic_segmentation_loss_mask: 0.0436 semantic_segmentation_loss_dice: 0.1103 2024/07/08 19:52:40 - mmengine - INFO - Iter(train) [ 95750/120000] base_lr: 2.1291e-05 lr: 3.7538e-06 eta: 7:28:52 time: 1.1130 data_time: 0.0179 memory: 16556 grad_norm: 1.1353 loss: 0.2068 semantic_segmentation_loss_cls: 0.0529 semantic_segmentation_loss_mask: 0.0436 semantic_segmentation_loss_dice: 0.1103 2024/07/08 19:53:35 - mmengine - INFO - Iter(train) [ 95800/120000] base_lr: 2.1215e-05 lr: 3.7468e-06 eta: 7:27:56 time: 1.1127 data_time: 0.0179 memory: 14821 grad_norm: 1.1346 loss: 0.2067 semantic_segmentation_loss_cls: 0.0528 semantic_segmentation_loss_mask: 0.0436 semantic_segmentation_loss_dice: 0.1102 2024/07/08 19:54:30 - 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single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 19:57:18 - mmengine - INFO - Iter(train) [ 96000/120000] base_lr: 2.0909e-05 lr: 3.7190e-06 eta: 7:24:14 time: 1.1128 data_time: 0.0179 memory: 15492 grad_norm: 1.1353 loss: 0.2065 semantic_segmentation_loss_cls: 0.0528 semantic_segmentation_loss_mask: 0.0436 semantic_segmentation_loss_dice: 0.1102 2024/07/08 19:57:18 - mmengine - INFO - Saving checkpoint at 96000 iterations 2024/07/08 19:58:17 - mmengine - INFO - Iter(train) [ 96050/120000] base_lr: 2.0833e-05 lr: 3.7121e-06 eta: 7:23:20 time: 1.1127 data_time: 0.0179 memory: 15163 grad_norm: 1.1363 loss: 0.2066 semantic_segmentation_loss_cls: 0.0529 semantic_segmentation_loss_mask: 0.0436 semantic_segmentation_loss_dice: 0.1101 2024/07/08 19:59:13 - mmengine - INFO - Iter(train) [ 96100/120000] base_lr: 2.0757e-05 lr: 3.7052e-06 eta: 7:22:24 time: 1.1125 data_time: 0.0179 memory: 15446 grad_norm: 1.1371 loss: 0.2064 semantic_segmentation_loss_cls: 0.0528 semantic_segmentation_loss_mask: 0.0435 semantic_segmentation_loss_dice: 0.1100 2024/07/08 20:00:08 - mmengine - INFO - Iter(train) [ 96150/120000] base_lr: 2.0681e-05 lr: 3.6983e-06 eta: 7:21:29 time: 1.1125 data_time: 0.0179 memory: 15322 grad_norm: 1.1367 loss: 0.2064 semantic_segmentation_loss_cls: 0.0528 semantic_segmentation_loss_mask: 0.0435 semantic_segmentation_loss_dice: 0.1100 2024/07/08 20:01:03 - mmengine - INFO - Iter(train) [ 96200/120000] base_lr: 2.0605e-05 lr: 3.6914e-06 eta: 7:20:33 time: 1.1124 data_time: 0.0179 memory: 15070 grad_norm: 1.1365 loss: 0.2062 semantic_segmentation_loss_cls: 0.0527 semantic_segmentation_loss_mask: 0.0435 semantic_segmentation_loss_dice: 0.1100 2024/07/08 20:01:58 - mmengine - INFO - Iter(train) [ 96250/120000] base_lr: 2.0530e-05 lr: 3.6845e-06 eta: 7:19:37 time: 1.1125 data_time: 0.0179 memory: 15085 grad_norm: 1.1349 loss: 0.2063 semantic_segmentation_loss_cls: 0.0528 semantic_segmentation_loss_mask: 0.0435 semantic_segmentation_loss_dice: 0.1100 2024/07/08 20:02:54 - mmengine - INFO - Iter(train) [ 96300/120000] base_lr: 2.0454e-05 lr: 3.6777e-06 eta: 7:18:42 time: 1.1127 data_time: 0.0179 memory: 15566 grad_norm: 1.1346 loss: 0.2063 semantic_segmentation_loss_cls: 0.0528 semantic_segmentation_loss_mask: 0.0435 semantic_segmentation_loss_dice: 0.1100 2024/07/08 20:03:50 - mmengine - INFO - Iter(train) [ 96350/120000] base_lr: 2.0379e-05 lr: 3.6708e-06 eta: 7:17:46 time: 1.1127 data_time: 0.0179 memory: 14987 grad_norm: 1.1343 loss: 0.2063 semantic_segmentation_loss_cls: 0.0528 semantic_segmentation_loss_mask: 0.0435 semantic_segmentation_loss_dice: 0.1100 2024/07/08 20:04:44 - mmengine - INFO - Iter(train) [ 96400/120000] base_lr: 2.0304e-05 lr: 3.6640e-06 eta: 7:16:51 time: 1.1125 data_time: 0.0178 memory: 14699 grad_norm: 1.1355 loss: 0.2063 semantic_segmentation_loss_cls: 0.0528 semantic_segmentation_loss_mask: 0.0435 semantic_segmentation_loss_dice: 0.1100 2024/07/08 20:05:40 - mmengine - INFO - Iter(train) [ 96450/120000] base_lr: 2.0229e-05 lr: 3.6572e-06 eta: 7:15:55 time: 1.1125 data_time: 0.0178 memory: 14670 grad_norm: 1.1350 loss: 0.2063 semantic_segmentation_loss_cls: 0.0529 semantic_segmentation_loss_mask: 0.0435 semantic_segmentation_loss_dice: 0.1100 2024/07/08 20:06:36 - mmengine - INFO - Iter(train) [ 96500/120000] base_lr: 2.0154e-05 lr: 3.6504e-06 eta: 7:15:00 time: 1.1126 data_time: 0.0178 memory: 14948 grad_norm: 1.1346 loss: 0.2065 semantic_segmentation_loss_cls: 0.0530 semantic_segmentation_loss_mask: 0.0435 semantic_segmentation_loss_dice: 0.1101 2024/07/08 20:07:31 - mmengine - INFO - Iter(train) [ 96550/120000] base_lr: 2.0079e-05 lr: 3.6436e-06 eta: 7:14:04 time: 1.1124 data_time: 0.0178 memory: 16342 grad_norm: 1.1339 loss: 0.2065 semantic_segmentation_loss_cls: 0.0530 semantic_segmentation_loss_mask: 0.0435 semantic_segmentation_loss_dice: 0.1100 2024/07/08 20:08:26 - 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lr: 3.6165e-06 eta: 7:10:21 time: 1.1120 data_time: 0.0178 memory: 14506 grad_norm: 1.1328 loss: 0.2068 semantic_segmentation_loss_cls: 0.0532 semantic_segmentation_loss_mask: 0.0435 semantic_segmentation_loss_dice: 0.1101 2024/07/08 20:12:05 - mmengine - INFO - Iter(train) [ 96800/120000] base_lr: 1.9708e-05 lr: 3.6098e-06 eta: 7:09:26 time: 1.1117 data_time: 0.0178 memory: 15032 grad_norm: 1.1326 loss: 0.2071 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0435 semantic_segmentation_loss_dice: 0.1103 2024/07/08 20:13:00 - mmengine - INFO - Iter(train) [ 96850/120000] base_lr: 1.9634e-05 lr: 3.6031e-06 eta: 7:08:30 time: 1.1115 data_time: 0.0177 memory: 15243 grad_norm: 1.1332 loss: 0.2072 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0436 semantic_segmentation_loss_dice: 0.1103 2024/07/08 20:13:55 - mmengine - INFO - Iter(train) [ 96900/120000] base_lr: 1.9560e-05 lr: 3.5964e-06 eta: 7:07:34 time: 1.1115 data_time: 0.0177 memory: 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iterations 2024/07/08 20:16:46 - mmengine - INFO - Iter(train) [ 97050/120000] base_lr: 1.9340e-05 lr: 3.5763e-06 eta: 7:04:49 time: 1.1116 data_time: 0.0177 memory: 15448 grad_norm: 1.1295 loss: 0.2071 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0436 semantic_segmentation_loss_dice: 0.1103 2024/07/08 20:17:42 - mmengine - INFO - Iter(train) [ 97100/120000] base_lr: 1.9266e-05 lr: 3.5697e-06 eta: 7:03:53 time: 1.1118 data_time: 0.0177 memory: 16614 grad_norm: 1.1296 loss: 0.2071 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0436 semantic_segmentation_loss_dice: 0.1102 2024/07/08 20:18:38 - mmengine - INFO - Iter(train) [ 97150/120000] base_lr: 1.9193e-05 lr: 3.5630e-06 eta: 7:02:58 time: 1.1118 data_time: 0.0177 memory: 16068 grad_norm: 1.1301 loss: 0.2075 semantic_segmentation_loss_cls: 0.0535 semantic_segmentation_loss_mask: 0.0436 semantic_segmentation_loss_dice: 0.1104 2024/07/08 20:19:34 - mmengine - INFO - Iter(train) [ 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mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 20:34:23 - mmengine - INFO - Iter(train) [ 98000/120000] base_lr: 1.7973e-05 lr: 3.4521e-06 eta: 6:47:14 time: 1.1117 data_time: 0.0178 memory: 15613 grad_norm: 1.1243 loss: 0.2074 semantic_segmentation_loss_cls: 0.0536 semantic_segmentation_loss_mask: 0.0436 semantic_segmentation_loss_dice: 0.1102 2024/07/08 20:34:23 - mmengine - INFO - Saving checkpoint at 98000 iterations 2024/07/08 20:35:23 - mmengine - INFO - Iter(train) [ 98050/120000] base_lr: 1.7903e-05 lr: 3.4457e-06 eta: 6:46:20 time: 1.1118 data_time: 0.0179 memory: 15653 grad_norm: 1.1251 loss: 0.2077 semantic_segmentation_loss_cls: 0.0537 semantic_segmentation_loss_mask: 0.0436 semantic_segmentation_loss_dice: 0.1104 2024/07/08 20:36:19 - mmengine - INFO - Iter(train) [ 98100/120000] base_lr: 1.7832e-05 lr: 3.4393e-06 eta: 6:45:24 time: 1.1118 data_time: 0.0179 memory: 15223 grad_norm: 1.1255 loss: 0.2079 semantic_segmentation_loss_cls: 0.0538 semantic_segmentation_loss_mask: 0.0436 semantic_segmentation_loss_dice: 0.1105 2024/07/08 20:37:13 - mmengine - INFO - Iter(train) [ 98150/120000] base_lr: 1.7762e-05 lr: 3.4329e-06 eta: 6:44:28 time: 1.1116 data_time: 0.0179 memory: 14815 grad_norm: 1.1249 loss: 0.2075 semantic_segmentation_loss_cls: 0.0537 semantic_segmentation_loss_mask: 0.0435 semantic_segmentation_loss_dice: 0.1103 2024/07/08 20:38:09 - mmengine - INFO - Iter(train) [ 98200/120000] base_lr: 1.7692e-05 lr: 3.4265e-06 eta: 6:43:33 time: 1.1116 data_time: 0.0179 memory: 14890 grad_norm: 1.1264 loss: 0.2073 semantic_segmentation_loss_cls: 0.0536 semantic_segmentation_loss_mask: 0.0435 semantic_segmentation_loss_dice: 0.1101 2024/07/08 20:39:04 - mmengine - INFO - Iter(train) [ 98250/120000] base_lr: 1.7622e-05 lr: 3.4202e-06 eta: 6:42:37 time: 1.1117 data_time: 0.0179 memory: 14956 grad_norm: 1.1283 loss: 0.2073 semantic_segmentation_loss_cls: 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semantic_segmentation_loss_dice: 0.1100 2024/07/08 20:42:46 - mmengine - INFO - Iter(train) [ 98450/120000] base_lr: 1.7344e-05 lr: 3.3949e-06 eta: 6:38:55 time: 1.1124 data_time: 0.0179 memory: 16243 grad_norm: 1.1289 loss: 0.2070 semantic_segmentation_loss_cls: 0.0535 semantic_segmentation_loss_mask: 0.0435 semantic_segmentation_loss_dice: 0.1100 2024/07/08 20:43:41 - mmengine - INFO - Iter(train) [ 98500/120000] base_lr: 1.7274e-05 lr: 3.3886e-06 eta: 6:37:59 time: 1.1123 data_time: 0.0179 memory: 15144 grad_norm: 1.1300 loss: 0.2072 semantic_segmentation_loss_cls: 0.0536 semantic_segmentation_loss_mask: 0.0435 semantic_segmentation_loss_dice: 0.1101 2024/07/08 20:44:36 - mmengine - INFO - Iter(train) [ 98550/120000] base_lr: 1.7205e-05 lr: 3.3823e-06 eta: 6:37:04 time: 1.1123 data_time: 0.0179 memory: 15333 grad_norm: 1.1290 loss: 0.2070 semantic_segmentation_loss_cls: 0.0535 semantic_segmentation_loss_mask: 0.0435 semantic_segmentation_loss_dice: 0.1100 2024/07/08 20:45:31 - mmengine - INFO - Iter(train) [ 98600/120000] base_lr: 1.7136e-05 lr: 3.3760e-06 eta: 6:36:08 time: 1.1120 data_time: 0.0179 memory: 15502 grad_norm: 1.1286 loss: 0.2071 semantic_segmentation_loss_cls: 0.0535 semantic_segmentation_loss_mask: 0.0435 semantic_segmentation_loss_dice: 0.1100 2024/07/08 20:46:25 - mmengine - INFO - Iter(train) [ 98650/120000] base_lr: 1.7068e-05 lr: 3.3698e-06 eta: 6:35:12 time: 1.1119 data_time: 0.0179 memory: 15621 grad_norm: 1.1285 loss: 0.2067 semantic_segmentation_loss_cls: 0.0534 semantic_segmentation_loss_mask: 0.0435 semantic_segmentation_loss_dice: 0.1099 2024/07/08 20:47:20 - mmengine - INFO - Iter(train) [ 98700/120000] base_lr: 1.6999e-05 lr: 3.3635e-06 eta: 6:34:16 time: 1.1118 data_time: 0.0179 memory: 15742 grad_norm: 1.1281 loss: 0.2063 semantic_segmentation_loss_cls: 0.0532 semantic_segmentation_loss_mask: 0.0434 semantic_segmentation_loss_dice: 0.1097 2024/07/08 20:48:15 - mmengine - INFO - Iter(train) [ 98750/120000] base_lr: 1.6930e-05 lr: 3.3573e-06 eta: 6:33:21 time: 1.1117 data_time: 0.0179 memory: 15284 grad_norm: 1.1278 loss: 0.2062 semantic_segmentation_loss_cls: 0.0532 semantic_segmentation_loss_mask: 0.0433 semantic_segmentation_loss_dice: 0.1097 2024/07/08 20:49:10 - mmengine - INFO - Iter(train) [ 98800/120000] base_lr: 1.6862e-05 lr: 3.3511e-06 eta: 6:32:25 time: 1.1116 data_time: 0.0179 memory: 15210 grad_norm: 1.1268 loss: 0.2061 semantic_segmentation_loss_cls: 0.0531 semantic_segmentation_loss_mask: 0.0433 semantic_segmentation_loss_dice: 0.1096 2024/07/08 20:50:05 - mmengine - INFO - Iter(train) [ 98850/120000] base_lr: 1.6794e-05 lr: 3.3449e-06 eta: 6:31:29 time: 1.1114 data_time: 0.0179 memory: 15616 grad_norm: 1.1286 loss: 0.2059 semantic_segmentation_loss_cls: 0.0531 semantic_segmentation_loss_mask: 0.0433 semantic_segmentation_loss_dice: 0.1095 2024/07/08 20:50:59 - mmengine - INFO - Iter(train) [ 98900/120000] base_lr: 1.6726e-05 lr: 3.3387e-06 eta: 6:30:34 time: 1.1111 data_time: 0.0179 memory: 15080 grad_norm: 1.1263 loss: 0.2057 semantic_segmentation_loss_cls: 0.0530 semantic_segmentation_loss_mask: 0.0433 semantic_segmentation_loss_dice: 0.1094 2024/07/08 20:51:56 - mmengine - INFO - Iter(train) [ 98950/120000] base_lr: 1.6658e-05 lr: 3.3325e-06 eta: 6:29:38 time: 1.1112 data_time: 0.0179 memory: 15161 grad_norm: 1.1261 loss: 0.2059 semantic_segmentation_loss_cls: 0.0532 semantic_segmentation_loss_mask: 0.0433 semantic_segmentation_loss_dice: 0.1095 2024/07/08 20:52:51 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 20:52:51 - mmengine - INFO - Iter(train) [ 99000/120000] base_lr: 1.6590e-05 lr: 3.3264e-06 eta: 6:28:43 time: 1.1113 data_time: 0.0179 memory: 14688 grad_norm: 1.1262 loss: 0.2060 semantic_segmentation_loss_cls: 0.0532 semantic_segmentation_loss_mask: 0.0432 semantic_segmentation_loss_dice: 0.1095 2024/07/08 20:52:51 - mmengine - INFO - Saving checkpoint at 99000 iterations 2024/07/08 20:53:51 - mmengine - INFO - Iter(train) [ 99050/120000] base_lr: 1.6522e-05 lr: 3.3202e-06 eta: 6:27:48 time: 1.1121 data_time: 0.0188 memory: 15076 grad_norm: 1.1258 loss: 0.2061 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0432 semantic_segmentation_loss_dice: 0.1095 2024/07/08 20:54:45 - mmengine - INFO - Iter(train) [ 99100/120000] base_lr: 1.6455e-05 lr: 3.3141e-06 eta: 6:26:52 time: 1.1118 data_time: 0.0188 memory: 15256 grad_norm: 1.1258 loss: 0.2064 semantic_segmentation_loss_cls: 0.0534 semantic_segmentation_loss_mask: 0.0433 semantic_segmentation_loss_dice: 0.1096 2024/07/08 20:55:40 - mmengine - INFO - Iter(train) [ 99150/120000] base_lr: 1.6387e-05 lr: 3.3080e-06 eta: 6:25:57 time: 1.1115 data_time: 0.0188 memory: 15620 grad_norm: 1.1227 loss: 0.2062 semantic_segmentation_loss_cls: 0.0534 semantic_segmentation_loss_mask: 0.0433 semantic_segmentation_loss_dice: 0.1096 2024/07/08 20:56:35 - mmengine - INFO - Iter(train) [ 99200/120000] base_lr: 1.6320e-05 lr: 3.3018e-06 eta: 6:25:01 time: 1.1113 data_time: 0.0188 memory: 15111 grad_norm: 1.1234 loss: 0.2061 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0432 semantic_segmentation_loss_dice: 0.1095 2024/07/08 20:57:30 - mmengine - INFO - Iter(train) [ 99250/120000] base_lr: 1.6253e-05 lr: 3.2957e-06 eta: 6:24:05 time: 1.1111 data_time: 0.0188 memory: 15415 grad_norm: 1.1236 loss: 0.2061 semantic_segmentation_loss_cls: 0.0534 semantic_segmentation_loss_mask: 0.0432 semantic_segmentation_loss_dice: 0.1094 2024/07/08 20:58:25 - mmengine - INFO - Iter(train) [ 99300/120000] base_lr: 1.6186e-05 lr: 3.2897e-06 eta: 6:23:10 time: 1.1108 data_time: 0.0188 memory: 14895 grad_norm: 1.1249 loss: 0.2061 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0433 semantic_segmentation_loss_dice: 0.1095 2024/07/08 20:59:19 - mmengine - INFO - Iter(train) [ 99350/120000] base_lr: 1.6120e-05 lr: 3.2836e-06 eta: 6:22:14 time: 1.1106 data_time: 0.0188 memory: 15504 grad_norm: 1.1267 loss: 0.2060 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0433 semantic_segmentation_loss_dice: 0.1094 2024/07/08 21:00:15 - mmengine - INFO - Iter(train) [ 99400/120000] base_lr: 1.6053e-05 lr: 3.2775e-06 eta: 6:21:18 time: 1.1106 data_time: 0.0189 memory: 15577 grad_norm: 1.1270 loss: 0.2061 semantic_segmentation_loss_cls: 0.0534 semantic_segmentation_loss_mask: 0.0433 semantic_segmentation_loss_dice: 0.1095 2024/07/08 21:01:10 - mmengine - INFO - Iter(train) [ 99450/120000] base_lr: 1.5986e-05 lr: 3.2715e-06 eta: 6:20:23 time: 1.1104 data_time: 0.0188 memory: 15740 grad_norm: 1.1271 loss: 0.2061 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0433 semantic_segmentation_loss_dice: 0.1095 2024/07/08 21:02:05 - mmengine - INFO - Iter(train) [ 99500/120000] base_lr: 1.5920e-05 lr: 3.2655e-06 eta: 6:19:27 time: 1.1102 data_time: 0.0188 memory: 15032 grad_norm: 1.1266 loss: 0.2062 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0433 semantic_segmentation_loss_dice: 0.1096 2024/07/08 21:03:00 - mmengine - INFO - Iter(train) [ 99550/120000] base_lr: 1.5854e-05 lr: 3.2594e-06 eta: 6:18:31 time: 1.1102 data_time: 0.0188 memory: 15744 grad_norm: 1.1256 loss: 0.2062 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0433 semantic_segmentation_loss_dice: 0.1095 2024/07/08 21:03:55 - mmengine - INFO - Iter(train) [ 99600/120000] base_lr: 1.5788e-05 lr: 3.2534e-06 eta: 6:17:36 time: 1.1102 data_time: 0.0188 memory: 15383 grad_norm: 1.1246 loss: 0.2062 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0433 semantic_segmentation_loss_dice: 0.1096 2024/07/08 21:04:50 - mmengine - INFO - Iter(train) [ 99650/120000] base_lr: 1.5722e-05 lr: 3.2475e-06 eta: 6:16:40 time: 1.1100 data_time: 0.0188 memory: 15301 grad_norm: 1.1243 loss: 0.2062 semantic_segmentation_loss_cls: 0.0534 semantic_segmentation_loss_mask: 0.0433 semantic_segmentation_loss_dice: 0.1095 2024/07/08 21:05:45 - mmengine - INFO - Iter(train) [ 99700/120000] base_lr: 1.5656e-05 lr: 3.2415e-06 eta: 6:15:44 time: 1.1097 data_time: 0.0188 memory: 15881 grad_norm: 1.1236 loss: 0.2059 semantic_segmentation_loss_cls: 0.0532 semantic_segmentation_loss_mask: 0.0432 semantic_segmentation_loss_dice: 0.1094 2024/07/08 21:06:40 - mmengine - INFO - Iter(train) [ 99750/120000] base_lr: 1.5591e-05 lr: 3.2355e-06 eta: 6:14:49 time: 1.1098 data_time: 0.0189 memory: 15178 grad_norm: 1.1242 loss: 0.2058 semantic_segmentation_loss_cls: 0.0532 semantic_segmentation_loss_mask: 0.0432 semantic_segmentation_loss_dice: 0.1094 2024/07/08 21:07:35 - mmengine - INFO - Iter(train) [ 99800/120000] base_lr: 1.5525e-05 lr: 3.2296e-06 eta: 6:13:53 time: 1.1099 data_time: 0.0188 memory: 15416 grad_norm: 1.1244 loss: 0.2059 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0432 semantic_segmentation_loss_dice: 0.1094 2024/07/08 21:08:31 - mmengine - INFO - Iter(train) [ 99850/120000] base_lr: 1.5460e-05 lr: 3.2236e-06 eta: 6:12:58 time: 1.1100 data_time: 0.0189 memory: 15878 grad_norm: 1.1268 loss: 0.2060 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0432 semantic_segmentation_loss_dice: 0.1095 2024/07/08 21:09:27 - mmengine - INFO - Iter(train) [ 99900/120000] base_lr: 1.5395e-05 lr: 3.2177e-06 eta: 6:12:02 time: 1.1101 data_time: 0.0189 memory: 15176 grad_norm: 1.1251 loss: 0.2061 semantic_segmentation_loss_cls: 0.0534 semantic_segmentation_loss_mask: 0.0432 semantic_segmentation_loss_dice: 0.1095 2024/07/08 21:10:22 - mmengine - INFO - Iter(train) [ 99950/120000] base_lr: 1.5330e-05 lr: 3.2118e-06 eta: 6:11:07 time: 1.1098 data_time: 0.0188 memory: 15501 grad_norm: 1.1250 loss: 0.2062 semantic_segmentation_loss_cls: 0.0534 semantic_segmentation_loss_mask: 0.0432 semantic_segmentation_loss_dice: 0.1095 2024/07/08 21:11:18 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 21:11:18 - mmengine - INFO - Iter(train) [100000/120000] base_lr: 1.5265e-05 lr: 3.2059e-06 eta: 6:10:11 time: 1.1099 data_time: 0.0188 memory: 15085 grad_norm: 1.1239 loss: 0.2058 semantic_segmentation_loss_cls: 0.0532 semantic_segmentation_loss_mask: 0.0432 semantic_segmentation_loss_dice: 0.1094 2024/07/08 21:11:18 - mmengine - INFO - Saving checkpoint at 100000 iterations 2024/07/08 21:11:35 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:49 time: 0.2429 data_time: 0.0017 memory: 5013 2024/07/08 21:11:47 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:36 time: 0.2428 data_time: 0.0017 memory: 5189 2024/07/08 21:12:00 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:24 time: 0.2428 data_time: 0.0017 memory: 4460 2024/07/08 21:12:12 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:12 time: 0.2428 data_time: 0.0017 memory: 4543 2024/07/08 21:12:24 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:00 time: 0.2427 data_time: 0.0017 memory: 4645 2024/07/08 21:12:36 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:48 time: 0.2427 data_time: 0.0017 memory: 10983 2024/07/08 21:12:48 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2427 data_time: 0.0017 memory: 4460 2024/07/08 21:13:00 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2426 data_time: 0.0017 memory: 4641 2024/07/08 21:13:12 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2425 data_time: 0.0017 memory: 4473 2024/07/08 21:13:24 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2425 data_time: 0.0017 memory: 4555 2024/07/08 21:13:25 - mmengine - INFO - per class results: 2024/07/08 21:13:25 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.55 | 87.79 | | building | 83.33 | 91.34 | | sky | 94.43 | 97.76 | | floor | 82.88 | 91.56 | | tree | 75.93 | 88.3 | | ceiling | 85.04 | 93.08 | | road | 84.06 | 92.75 | | bed | 87.98 | 95.16 | | windowpane | 61.75 | 79.14 | | grass | 71.01 | 85.82 | | cabinet | 60.99 | 72.31 | | sidewalk | 67.33 | 81.25 | | person | 82.29 | 91.77 | | earth | 33.15 | 45.41 | | door | 55.38 | 69.91 | | table | 62.71 | 76.46 | | mountain | 57.8 | 72.93 | | plant | 54.43 | 68.75 | | curtain | 73.08 | 87.23 | | chair | 59.38 | 72.92 | | car | 83.06 | 91.71 | | water | 49.57 | 66.55 | | painting | 71.78 | 88.82 | | sofa | 65.68 | 77.27 | | shelf | 45.24 | 64.42 | | house | 52.15 | 80.16 | | sea | 48.16 | 70.99 | | mirror | 67.35 | 75.03 | | rug | 64.38 | 75.62 | | field | 37.63 | 52.55 | | armchair | 45.38 | 67.77 | | seat | 55.78 | 82.02 | | fence | 38.2 | 52.04 | | desk | 47.75 | 69.0 | | rock | 37.86 | 60.47 | | wardrobe | 53.41 | 71.18 | | lamp | 67.69 | 79.39 | | bathtub | 86.23 | 90.69 | | railing | 38.03 | 53.28 | | cushion | 57.57 | 69.14 | | base | 24.32 | 34.29 | | box | 24.98 | 37.44 | | column | 48.24 | 65.85 | | signboard | 40.63 | 55.31 | | chest of drawers | 38.34 | 68.3 | | counter | 33.33 | 42.75 | | sand | 35.13 | 50.28 | | sink | 76.19 | 82.14 | | skyscraper | 46.56 | 58.66 | | fireplace | 69.98 | 89.15 | | refrigerator | 81.9 | 89.58 | | grandstand | 44.03 | 74.35 | | path | 29.26 | 41.51 | | stairs | 31.64 | 41.04 | | runway | 76.28 | 90.03 | | case | 65.02 | 79.73 | | pool table | 92.69 | 96.56 | | pillow | 55.97 | 67.35 | | screen door | 80.15 | 84.93 | | stairway | 38.36 | 43.24 | | river | 22.65 | 47.38 | | bridge | 65.23 | 89.38 | | bookcase | 40.47 | 57.2 | | blind | 37.78 | 42.77 | | coffee table | 69.36 | 83.24 | | toilet | 87.28 | 89.72 | | flower | 39.35 | 56.55 | | book | 52.26 | 74.49 | | hill | 12.58 | 20.83 | | bench | 42.69 | 53.25 | | countertop | 54.93 | 65.97 | | stove | 80.88 | 85.55 | | palm | 51.72 | 67.27 | | kitchen island | 33.61 | 74.09 | | computer | 61.4 | 67.26 | | swivel chair | 39.1 | 54.29 | | boat | 67.19 | 75.52 | | bar | 46.74 | 58.24 | | arcade machine | 21.48 | 24.46 | | hovel | 22.94 | 41.91 | | bus | 92.95 | 95.82 | | towel | 68.4 | 75.09 | | light | 62.47 | 76.76 | | truck | 37.82 | 48.43 | | tower | 25.31 | 41.78 | | chandelier | 65.16 | 75.04 | | awning | 32.02 | 44.21 | | streetlight | 39.01 | 52.64 | | booth | 47.57 | 56.31 | | television receiver | 47.91 | 89.71 | | airplane | 57.96 | 67.53 | | dirt track | 4.75 | 5.72 | | apparel | 36.86 | 51.03 | | pole | 30.69 | 46.66 | | land | 0.24 | 0.3 | | bannister | 16.76 | 27.11 | | escalator | 26.79 | 38.8 | | ottoman | 41.63 | 66.05 | | bottle | 21.92 | 26.35 | | buffet | 42.9 | 47.94 | | poster | 24.77 | 33.53 | | stage | 18.67 | 30.07 | | van | 48.9 | 65.9 | | ship | 77.96 | 87.58 | | fountain | 6.05 | 6.95 | | conveyer belt | 59.77 | 91.68 | | canopy | 11.12 | 17.5 | | washer | 71.17 | 72.96 | | plaything | 28.47 | 37.7 | | swimming pool | 30.05 | 32.94 | | stool | 54.03 | 68.67 | | barrel | 16.22 | 55.85 | | basket | 34.39 | 42.8 | | waterfall | 46.39 | 51.64 | | tent | 68.44 | 97.74 | | bag | 19.2 | 23.88 | | minibike | 52.68 | 64.38 | | cradle | 63.37 | 77.87 | | oven | 48.89 | 59.32 | | ball | 26.99 | 32.69 | | food | 65.59 | 80.2 | | step | 23.95 | 25.79 | | tank | 33.33 | 43.73 | | trade name | 29.02 | 35.17 | | microwave | 37.98 | 41.23 | | pot | 44.26 | 49.15 | | animal | 62.9 | 68.73 | | bicycle | 58.07 | 76.49 | | lake | 63.39 | 63.65 | | dishwasher | 81.08 | 84.56 | | screen | 63.77 | 88.91 | | blanket | 9.45 | 12.63 | | sculpture | 64.91 | 81.53 | | hood | 71.57 | 74.99 | | sconce | 52.42 | 64.25 | | vase | 39.65 | 65.18 | | traffic light | 42.68 | 56.25 | | tray | 18.07 | 22.36 | | ashcan | 46.71 | 61.9 | | fan | 62.55 | 78.58 | | pier | 38.58 | 71.21 | | crt screen | 0.0 | 0.0 | | plate | 63.02 | 73.72 | | monitor | 3.99 | 5.46 | | bulletin board | 42.63 | 50.94 | | shower | 11.47 | 17.24 | | radiator | 58.01 | 66.23 | | glass | 18.46 | 19.76 | | clock | 32.98 | 37.13 | | flag | 47.52 | 55.1 | +---------------------+-------+-------+ 2024/07/08 21:13:25 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.5200 mIoU: 49.3400 mAcc: 61.7200 data_time: 0.0017 time: 0.2415 2024/07/08 21:14:21 - mmengine - INFO - Iter(train) [100050/120000] base_lr: 1.5200e-05 lr: 3.2000e-06 eta: 6:09:16 time: 1.1091 data_time: 0.0179 memory: 16854 grad_norm: 1.1234 loss: 0.2059 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0432 semantic_segmentation_loss_dice: 0.1095 2024/07/08 21:15:16 - mmengine - INFO - Iter(train) [100100/120000] base_lr: 1.5135e-05 lr: 3.1941e-06 eta: 6:08:21 time: 1.1093 data_time: 0.0179 memory: 15327 grad_norm: 1.1223 loss: 0.2060 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0432 semantic_segmentation_loss_dice: 0.1095 2024/07/08 21:16:12 - mmengine - INFO - Iter(train) [100150/120000] base_lr: 1.5071e-05 lr: 3.1883e-06 eta: 6:07:25 time: 1.1094 data_time: 0.0179 memory: 14746 grad_norm: 1.1231 loss: 0.2061 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0433 semantic_segmentation_loss_dice: 0.1096 2024/07/08 21:17:08 - mmengine - INFO - Iter(train) [100200/120000] base_lr: 1.5007e-05 lr: 3.1824e-06 eta: 6:06:30 time: 1.1097 data_time: 0.0179 memory: 16617 grad_norm: 1.1210 loss: 0.2064 semantic_segmentation_loss_cls: 0.0535 semantic_segmentation_loss_mask: 0.0432 semantic_segmentation_loss_dice: 0.1096 2024/07/08 21:18:04 - mmengine - INFO - Iter(train) [100250/120000] base_lr: 1.4943e-05 lr: 3.1766e-06 eta: 6:05:34 time: 1.1097 data_time: 0.0179 memory: 15214 grad_norm: 1.1193 loss: 0.2062 semantic_segmentation_loss_cls: 0.0534 semantic_segmentation_loss_mask: 0.0432 semantic_segmentation_loss_dice: 0.1096 2024/07/08 21:18:59 - mmengine - INFO - Iter(train) [100300/120000] base_lr: 1.4879e-05 lr: 3.1708e-06 eta: 6:04:38 time: 1.1096 data_time: 0.0179 memory: 15598 grad_norm: 1.1190 loss: 0.2060 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0432 semantic_segmentation_loss_dice: 0.1095 2024/07/08 21:19:55 - mmengine - INFO - Iter(train) [100350/120000] base_lr: 1.4815e-05 lr: 3.1650e-06 eta: 6:03:43 time: 1.1096 data_time: 0.0179 memory: 15411 grad_norm: 1.1185 loss: 0.2060 semantic_segmentation_loss_cls: 0.0533 semantic_segmentation_loss_mask: 0.0432 semantic_segmentation_loss_dice: 0.1095 2024/07/08 21:20:50 - mmengine - INFO - Iter(train) [100400/120000] base_lr: 1.4751e-05 lr: 3.1592e-06 eta: 6:02:47 time: 1.1096 data_time: 0.0179 memory: 16139 grad_norm: 1.1170 loss: 0.2060 semantic_segmentation_loss_cls: 0.0534 semantic_segmentation_loss_mask: 0.0432 semantic_segmentation_loss_dice: 0.1095 2024/07/08 21:21:45 - mmengine - INFO - Iter(train) [100450/120000] base_lr: 1.4688e-05 lr: 3.1534e-06 eta: 6:01:52 time: 1.1095 data_time: 0.0179 memory: 14631 grad_norm: 1.1178 loss: 0.2059 semantic_segmentation_loss_cls: 0.0532 semantic_segmentation_loss_mask: 0.0432 semantic_segmentation_loss_dice: 0.1095 2024/07/08 21:22:41 - mmengine - INFO - Iter(train) [100500/120000] base_lr: 1.4624e-05 lr: 3.1477e-06 eta: 6:00:56 time: 1.1096 data_time: 0.0180 memory: 15124 grad_norm: 1.1180 loss: 0.2056 semantic_segmentation_loss_cls: 0.0531 semantic_segmentation_loss_mask: 0.0431 semantic_segmentation_loss_dice: 0.1094 2024/07/08 21:23:36 - mmengine - INFO - Iter(train) [100550/120000] base_lr: 1.4561e-05 lr: 3.1419e-06 eta: 6:00:01 time: 1.1096 data_time: 0.0180 memory: 15009 grad_norm: 1.1174 loss: 0.2057 semantic_segmentation_loss_cls: 0.0531 semantic_segmentation_loss_mask: 0.0431 semantic_segmentation_loss_dice: 0.1095 2024/07/08 21:24:30 - mmengine - INFO - Iter(train) [100600/120000] base_lr: 1.4498e-05 lr: 3.1362e-06 eta: 5:59:05 time: 1.1094 data_time: 0.0179 memory: 14956 grad_norm: 1.1166 loss: 0.2056 semantic_segmentation_loss_cls: 0.0530 semantic_segmentation_loss_mask: 0.0431 semantic_segmentation_loss_dice: 0.1095 2024/07/08 21:25:26 - mmengine - INFO - Iter(train) [100650/120000] base_lr: 1.4435e-05 lr: 3.1304e-06 eta: 5:58:09 time: 1.1095 data_time: 0.0180 memory: 14895 grad_norm: 1.1173 loss: 0.2053 semantic_segmentation_loss_cls: 0.0529 semantic_segmentation_loss_mask: 0.0431 semantic_segmentation_loss_dice: 0.1094 2024/07/08 21:26:20 - mmengine - INFO - Iter(train) [100700/120000] base_lr: 1.4372e-05 lr: 3.1247e-06 eta: 5:57:14 time: 1.1094 data_time: 0.0180 memory: 15127 grad_norm: 1.1159 loss: 0.2053 semantic_segmentation_loss_cls: 0.0529 semantic_segmentation_loss_mask: 0.0431 semantic_segmentation_loss_dice: 0.1094 2024/07/08 21:27:15 - mmengine - INFO - Iter(train) [100750/120000] base_lr: 1.4309e-05 lr: 3.1190e-06 eta: 5:56:18 time: 1.1095 data_time: 0.0180 memory: 15806 grad_norm: 1.1178 loss: 0.2051 semantic_segmentation_loss_cls: 0.0527 semantic_segmentation_loss_mask: 0.0430 semantic_segmentation_loss_dice: 0.1093 2024/07/08 21:28:10 - mmengine - INFO - Iter(train) [100800/120000] base_lr: 1.4247e-05 lr: 3.1134e-06 eta: 5:55:22 time: 1.1096 data_time: 0.0179 memory: 15404 grad_norm: 1.1193 loss: 0.2050 semantic_segmentation_loss_cls: 0.0528 semantic_segmentation_loss_mask: 0.0431 semantic_segmentation_loss_dice: 0.1092 2024/07/08 21:29:07 - mmengine - INFO - Iter(train) [100850/120000] base_lr: 1.4185e-05 lr: 3.1077e-06 eta: 5:54:27 time: 1.1100 data_time: 0.0180 memory: 15899 grad_norm: 1.1194 loss: 0.2048 semantic_segmentation_loss_cls: 0.0527 semantic_segmentation_loss_mask: 0.0430 semantic_segmentation_loss_dice: 0.1091 2024/07/08 21:30:03 - mmengine - INFO - Iter(train) [100900/120000] base_lr: 1.4122e-05 lr: 3.1020e-06 eta: 5:53:32 time: 1.1102 data_time: 0.0180 memory: 15250 grad_norm: 1.1189 loss: 0.2047 semantic_segmentation_loss_cls: 0.0526 semantic_segmentation_loss_mask: 0.0430 semantic_segmentation_loss_dice: 0.1091 2024/07/08 21:30:58 - mmengine - INFO - Iter(train) [100950/120000] base_lr: 1.4060e-05 lr: 3.0964e-06 eta: 5:52:36 time: 1.1102 data_time: 0.0180 memory: 15173 grad_norm: 1.1202 loss: 0.2048 semantic_segmentation_loss_cls: 0.0527 semantic_segmentation_loss_mask: 0.0430 semantic_segmentation_loss_dice: 0.1091 2024/07/08 21:31:54 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 21:31:54 - mmengine - INFO - Iter(train) [101000/120000] base_lr: 1.3998e-05 lr: 3.0908e-06 eta: 5:51:41 time: 1.1102 data_time: 0.0180 memory: 14963 grad_norm: 1.1210 loss: 0.2049 semantic_segmentation_loss_cls: 0.0528 semantic_segmentation_loss_mask: 0.0430 semantic_segmentation_loss_dice: 0.1092 2024/07/08 21:31:54 - mmengine - INFO - Saving checkpoint at 101000 iterations 2024/07/08 21:32:55 - mmengine - INFO - Iter(train) [101050/120000] base_lr: 1.3937e-05 lr: 3.0851e-06 eta: 5:50:46 time: 1.1104 data_time: 0.0180 memory: 14942 grad_norm: 1.1201 loss: 0.2046 semantic_segmentation_loss_cls: 0.0527 semantic_segmentation_loss_mask: 0.0429 semantic_segmentation_loss_dice: 0.1090 2024/07/08 21:33:50 - mmengine - INFO - Iter(train) [101100/120000] base_lr: 1.3875e-05 lr: 3.0795e-06 eta: 5:49:50 time: 1.1102 data_time: 0.0180 memory: 14818 grad_norm: 1.1202 loss: 0.2045 semantic_segmentation_loss_cls: 0.0525 semantic_segmentation_loss_mask: 0.0429 semantic_segmentation_loss_dice: 0.1091 2024/07/08 21:34:45 - mmengine - INFO - Iter(train) [101150/120000] base_lr: 1.3813e-05 lr: 3.0740e-06 eta: 5:48:55 time: 1.1101 data_time: 0.0180 memory: 15055 grad_norm: 1.1202 loss: 0.2042 semantic_segmentation_loss_cls: 0.0524 semantic_segmentation_loss_mask: 0.0429 semantic_segmentation_loss_dice: 0.1089 2024/07/08 21:35:41 - mmengine - INFO - Iter(train) [101200/120000] base_lr: 1.3752e-05 lr: 3.0684e-06 eta: 5:47:59 time: 1.1099 data_time: 0.0180 memory: 15256 grad_norm: 1.1176 loss: 0.2042 semantic_segmentation_loss_cls: 0.0524 semantic_segmentation_loss_mask: 0.0428 semantic_segmentation_loss_dice: 0.1089 2024/07/08 21:36:35 - mmengine - INFO - Iter(train) [101250/120000] base_lr: 1.3691e-05 lr: 3.0628e-06 eta: 5:47:04 time: 1.1096 data_time: 0.0180 memory: 15506 grad_norm: 1.1161 loss: 0.2042 semantic_segmentation_loss_cls: 0.0525 semantic_segmentation_loss_mask: 0.0428 semantic_segmentation_loss_dice: 0.1089 2024/07/08 21:37:30 - mmengine - INFO - Iter(train) [101300/120000] base_lr: 1.3630e-05 lr: 3.0573e-06 eta: 5:46:08 time: 1.1094 data_time: 0.0179 memory: 15551 grad_norm: 1.1164 loss: 0.2041 semantic_segmentation_loss_cls: 0.0524 semantic_segmentation_loss_mask: 0.0428 semantic_segmentation_loss_dice: 0.1089 2024/07/08 21:38:25 - mmengine - INFO - Iter(train) [101350/120000] base_lr: 1.3569e-05 lr: 3.0517e-06 eta: 5:45:12 time: 1.1094 data_time: 0.0179 memory: 15832 grad_norm: 1.1159 loss: 0.2039 semantic_segmentation_loss_cls: 0.0524 semantic_segmentation_loss_mask: 0.0428 semantic_segmentation_loss_dice: 0.1088 2024/07/08 21:39:20 - mmengine - INFO - Iter(train) [101400/120000] base_lr: 1.3508e-05 lr: 3.0462e-06 eta: 5:44:17 time: 1.1092 data_time: 0.0179 memory: 15067 grad_norm: 1.1169 loss: 0.2040 semantic_segmentation_loss_cls: 0.0524 semantic_segmentation_loss_mask: 0.0427 semantic_segmentation_loss_dice: 0.1088 2024/07/08 21:40:15 - mmengine - INFO - Iter(train) [101450/120000] base_lr: 1.3448e-05 lr: 3.0407e-06 eta: 5:43:21 time: 1.1089 data_time: 0.0179 memory: 15148 grad_norm: 1.1151 loss: 0.2038 semantic_segmentation_loss_cls: 0.0523 semantic_segmentation_loss_mask: 0.0427 semantic_segmentation_loss_dice: 0.1087 2024/07/08 21:41:10 - mmengine - INFO - Iter(train) [101500/120000] base_lr: 1.3387e-05 lr: 3.0352e-06 eta: 5:42:25 time: 1.1088 data_time: 0.0179 memory: 15042 grad_norm: 1.1147 loss: 0.2038 semantic_segmentation_loss_cls: 0.0523 semantic_segmentation_loss_mask: 0.0427 semantic_segmentation_loss_dice: 0.1087 2024/07/08 21:42:06 - mmengine - INFO - Iter(train) [101550/120000] base_lr: 1.3327e-05 lr: 3.0297e-06 eta: 5:41:30 time: 1.1091 data_time: 0.0179 memory: 14599 grad_norm: 1.1159 loss: 0.2036 semantic_segmentation_loss_cls: 0.0523 semantic_segmentation_loss_mask: 0.0427 semantic_segmentation_loss_dice: 0.1087 2024/07/08 21:43:01 - mmengine - INFO - Iter(train) [101600/120000] base_lr: 1.3267e-05 lr: 3.0243e-06 eta: 5:40:34 time: 1.1090 data_time: 0.0179 memory: 14627 grad_norm: 1.1149 loss: 0.2034 semantic_segmentation_loss_cls: 0.0521 semantic_segmentation_loss_mask: 0.0427 semantic_segmentation_loss_dice: 0.1086 2024/07/08 21:43:57 - mmengine - INFO - Iter(train) [101650/120000] base_lr: 1.3207e-05 lr: 3.0188e-06 eta: 5:39:39 time: 1.1091 data_time: 0.0179 memory: 15416 grad_norm: 1.1142 loss: 0.2032 semantic_segmentation_loss_cls: 0.0521 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1085 2024/07/08 21:44:53 - mmengine - INFO - Iter(train) [101700/120000] base_lr: 1.3147e-05 lr: 3.0134e-06 eta: 5:38:43 time: 1.1093 data_time: 0.0179 memory: 15743 grad_norm: 1.1144 loss: 0.2035 semantic_segmentation_loss_cls: 0.0522 semantic_segmentation_loss_mask: 0.0427 semantic_segmentation_loss_dice: 0.1086 2024/07/08 21:45:49 - mmengine - INFO - Iter(train) [101750/120000] base_lr: 1.3088e-05 lr: 3.0080e-06 eta: 5:37:48 time: 1.1094 data_time: 0.0179 memory: 15308 grad_norm: 1.1151 loss: 0.2031 semantic_segmentation_loss_cls: 0.0521 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1084 2024/07/08 21:46:44 - mmengine - INFO - Iter(train) [101800/120000] base_lr: 1.3028e-05 lr: 3.0026e-06 eta: 5:36:52 time: 1.1094 data_time: 0.0179 memory: 14838 grad_norm: 1.1142 loss: 0.2029 semantic_segmentation_loss_cls: 0.0520 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1083 2024/07/08 21:47:39 - mmengine - INFO - Iter(train) [101850/120000] base_lr: 1.2969e-05 lr: 2.9972e-06 eta: 5:35:57 time: 1.1092 data_time: 0.0179 memory: 15281 grad_norm: 1.1146 loss: 0.2029 semantic_segmentation_loss_cls: 0.0520 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1083 2024/07/08 21:48:33 - mmengine - INFO - Iter(train) [101900/120000] base_lr: 1.2910e-05 lr: 2.9918e-06 eta: 5:35:01 time: 1.1089 data_time: 0.0179 memory: 15411 grad_norm: 1.1140 loss: 0.2027 semantic_segmentation_loss_cls: 0.0519 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1083 2024/07/08 21:49:28 - mmengine - INFO - Iter(train) [101950/120000] base_lr: 1.2850e-05 lr: 2.9864e-06 eta: 5:34:05 time: 1.1087 data_time: 0.0179 memory: 15031 grad_norm: 1.1143 loss: 0.2028 semantic_segmentation_loss_cls: 0.0520 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1083 2024/07/08 21:50:23 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 21:50:23 - mmengine - INFO - Iter(train) [102000/120000] base_lr: 1.2792e-05 lr: 2.9810e-06 eta: 5:33:10 time: 1.1084 data_time: 0.0179 memory: 14915 grad_norm: 1.1164 loss: 0.2030 semantic_segmentation_loss_cls: 0.0521 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1083 2024/07/08 21:50:23 - mmengine - INFO - Saving checkpoint at 102000 iterations 2024/07/08 21:51:24 - mmengine - INFO - Iter(train) [102050/120000] base_lr: 1.2733e-05 lr: 2.9757e-06 eta: 5:32:15 time: 1.1084 data_time: 0.0177 memory: 14830 grad_norm: 1.1144 loss: 0.2027 semantic_segmentation_loss_cls: 0.0520 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1081 2024/07/08 21:52:19 - mmengine - INFO - Iter(train) [102100/120000] base_lr: 1.2674e-05 lr: 2.9704e-06 eta: 5:31:19 time: 1.1084 data_time: 0.0178 memory: 14894 grad_norm: 1.1142 loss: 0.2025 semantic_segmentation_loss_cls: 0.0519 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1081 2024/07/08 21:53:15 - mmengine - INFO - Iter(train) [102150/120000] base_lr: 1.2616e-05 lr: 2.9651e-06 eta: 5:30:24 time: 1.1088 data_time: 0.0178 memory: 14468 grad_norm: 1.1141 loss: 0.2027 semantic_segmentation_loss_cls: 0.0520 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1082 2024/07/08 21:54:11 - mmengine - INFO - Iter(train) [102200/120000] base_lr: 1.2557e-05 lr: 2.9598e-06 eta: 5:29:29 time: 1.1088 data_time: 0.0177 memory: 15150 grad_norm: 1.1113 loss: 0.2027 semantic_segmentation_loss_cls: 0.0519 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1082 2024/07/08 21:55:07 - mmengine - INFO - Iter(train) [102250/120000] base_lr: 1.2499e-05 lr: 2.9545e-06 eta: 5:28:33 time: 1.1091 data_time: 0.0177 memory: 15456 grad_norm: 1.1097 loss: 0.2025 semantic_segmentation_loss_cls: 0.0518 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1081 2024/07/08 21:56:03 - mmengine - INFO - Iter(train) [102300/120000] base_lr: 1.2441e-05 lr: 2.9492e-06 eta: 5:27:38 time: 1.1093 data_time: 0.0178 memory: 15028 grad_norm: 1.1119 loss: 0.2023 semantic_segmentation_loss_cls: 0.0517 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1080 2024/07/08 21:56:58 - mmengine - INFO - Iter(train) [102350/120000] base_lr: 1.2383e-05 lr: 2.9439e-06 eta: 5:26:42 time: 1.1091 data_time: 0.0178 memory: 15440 grad_norm: 1.1124 loss: 0.2023 semantic_segmentation_loss_cls: 0.0517 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1080 2024/07/08 21:57:53 - mmengine - INFO - Iter(train) [102400/120000] base_lr: 1.2326e-05 lr: 2.9387e-06 eta: 5:25:46 time: 1.1091 data_time: 0.0178 memory: 14769 grad_norm: 1.1131 loss: 0.2021 semantic_segmentation_loss_cls: 0.0516 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1079 2024/07/08 21:58:49 - mmengine - INFO - Iter(train) [102450/120000] base_lr: 1.2268e-05 lr: 2.9335e-06 eta: 5:24:51 time: 1.1092 data_time: 0.0178 memory: 15056 grad_norm: 1.1134 loss: 0.2019 semantic_segmentation_loss_cls: 0.0515 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1078 2024/07/08 21:59:44 - mmengine - INFO - Iter(train) [102500/120000] base_lr: 1.2211e-05 lr: 2.9282e-06 eta: 5:23:55 time: 1.1092 data_time: 0.0178 memory: 16288 grad_norm: 1.1144 loss: 0.2018 semantic_segmentation_loss_cls: 0.0515 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1078 2024/07/08 22:00:41 - mmengine - INFO - Iter(train) [102550/120000] base_lr: 1.2154e-05 lr: 2.9230e-06 eta: 5:23:00 time: 1.1094 data_time: 0.0178 memory: 14644 grad_norm: 1.1145 loss: 0.2020 semantic_segmentation_loss_cls: 0.0516 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1078 2024/07/08 22:01:37 - mmengine - INFO - Iter(train) [102600/120000] base_lr: 1.2096e-05 lr: 2.9179e-06 eta: 5:22:05 time: 1.1098 data_time: 0.0178 memory: 15240 grad_norm: 1.1139 loss: 0.2020 semantic_segmentation_loss_cls: 0.0517 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1078 2024/07/08 22:02:32 - mmengine - INFO - Iter(train) [102650/120000] base_lr: 1.2039e-05 lr: 2.9127e-06 eta: 5:21:09 time: 1.1100 data_time: 0.0178 memory: 15099 grad_norm: 1.1145 loss: 0.2023 semantic_segmentation_loss_cls: 0.0518 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1079 2024/07/08 22:03:27 - mmengine - INFO - Iter(train) [102700/120000] base_lr: 1.1983e-05 lr: 2.9075e-06 eta: 5:20:13 time: 1.1102 data_time: 0.0178 memory: 15237 grad_norm: 1.1159 loss: 0.2023 semantic_segmentation_loss_cls: 0.0518 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1079 2024/07/08 22:04:23 - mmengine - INFO - Iter(train) [102750/120000] base_lr: 1.1926e-05 lr: 2.9024e-06 eta: 5:19:18 time: 1.1103 data_time: 0.0178 memory: 15553 grad_norm: 1.1148 loss: 0.2024 semantic_segmentation_loss_cls: 0.0519 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1080 2024/07/08 22:05:17 - mmengine - INFO - Iter(train) [102800/120000] base_lr: 1.1870e-05 lr: 2.8972e-06 eta: 5:18:22 time: 1.1102 data_time: 0.0178 memory: 15539 grad_norm: 1.1152 loss: 0.2025 semantic_segmentation_loss_cls: 0.0518 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1080 2024/07/08 22:06:12 - mmengine - INFO - Iter(train) [102850/120000] base_lr: 1.1813e-05 lr: 2.8921e-06 eta: 5:17:26 time: 1.1101 data_time: 0.0178 memory: 14747 grad_norm: 1.1147 loss: 0.2024 semantic_segmentation_loss_cls: 0.0517 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1080 2024/07/08 22:07:07 - mmengine - INFO - Iter(train) [102900/120000] base_lr: 1.1757e-05 lr: 2.8870e-06 eta: 5:16:31 time: 1.1102 data_time: 0.0178 memory: 15521 grad_norm: 1.1149 loss: 0.2024 semantic_segmentation_loss_cls: 0.0518 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1080 2024/07/08 22:08:02 - mmengine - INFO - Iter(train) [102950/120000] base_lr: 1.1701e-05 lr: 2.8819e-06 eta: 5:15:35 time: 1.1099 data_time: 0.0177 memory: 14908 grad_norm: 1.1150 loss: 0.2022 semantic_segmentation_loss_cls: 0.0517 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1079 2024/07/08 22:08:57 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 22:08:57 - mmengine - INFO - Iter(train) [103000/120000] base_lr: 1.1645e-05 lr: 2.8768e-06 eta: 5:14:40 time: 1.1098 data_time: 0.0177 memory: 15088 grad_norm: 1.1143 loss: 0.2020 semantic_segmentation_loss_cls: 0.0516 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1078 2024/07/08 22:08:57 - mmengine - INFO - Saving checkpoint at 103000 iterations 2024/07/08 22:09:57 - mmengine - INFO - Iter(train) [103050/120000] base_lr: 1.1589e-05 lr: 2.8718e-06 eta: 5:13:45 time: 1.1099 data_time: 0.0177 memory: 14929 grad_norm: 1.1137 loss: 0.2019 semantic_segmentation_loss_cls: 0.0515 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1078 2024/07/08 22:10:53 - mmengine - INFO - Iter(train) [103100/120000] base_lr: 1.1534e-05 lr: 2.8667e-06 eta: 5:12:49 time: 1.1102 data_time: 0.0177 memory: 15289 grad_norm: 1.1133 loss: 0.2018 semantic_segmentation_loss_cls: 0.0515 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1078 2024/07/08 22:11:49 - mmengine - INFO - Iter(train) [103150/120000] base_lr: 1.1478e-05 lr: 2.8617e-06 eta: 5:11:54 time: 1.1104 data_time: 0.0177 memory: 15393 grad_norm: 1.1133 loss: 0.2017 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1077 2024/07/08 22:12:44 - mmengine - INFO - Iter(train) [103200/120000] base_lr: 1.1423e-05 lr: 2.8567e-06 eta: 5:10:58 time: 1.1105 data_time: 0.0177 memory: 15342 grad_norm: 1.1146 loss: 0.2015 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1077 2024/07/08 22:13:39 - mmengine - INFO - Iter(train) [103250/120000] base_lr: 1.1368e-05 lr: 2.8516e-06 eta: 5:10:03 time: 1.1105 data_time: 0.0177 memory: 15318 grad_norm: 1.1118 loss: 0.2015 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1077 2024/07/08 22:14:34 - mmengine - INFO - Iter(train) [103300/120000] base_lr: 1.1313e-05 lr: 2.8467e-06 eta: 5:09:07 time: 1.1106 data_time: 0.0177 memory: 14993 grad_norm: 1.1102 loss: 0.2016 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1077 2024/07/08 22:15:29 - mmengine - INFO - Iter(train) [103350/120000] base_lr: 1.1258e-05 lr: 2.8417e-06 eta: 5:08:11 time: 1.1108 data_time: 0.0177 memory: 15035 grad_norm: 1.1081 loss: 0.2016 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1078 2024/07/08 22:16:24 - mmengine - INFO - Iter(train) [103400/120000] base_lr: 1.1204e-05 lr: 2.8367e-06 eta: 5:07:16 time: 1.1106 data_time: 0.0176 memory: 15321 grad_norm: 1.1085 loss: 0.2016 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1077 2024/07/08 22:17:20 - mmengine - INFO - Iter(train) [103450/120000] base_lr: 1.1149e-05 lr: 2.8317e-06 eta: 5:06:20 time: 1.1107 data_time: 0.0176 memory: 15257 grad_norm: 1.1086 loss: 0.2013 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0424 semantic_segmentation_loss_dice: 0.1076 2024/07/08 22:18:15 - mmengine - INFO - Iter(train) [103500/120000] base_lr: 1.1095e-05 lr: 2.8268e-06 eta: 5:05:25 time: 1.1107 data_time: 0.0176 memory: 15589 grad_norm: 1.1080 loss: 0.2013 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0424 semantic_segmentation_loss_dice: 0.1076 2024/07/08 22:19:11 - mmengine - INFO - Iter(train) [103550/120000] base_lr: 1.1041e-05 lr: 2.8219e-06 eta: 5:04:29 time: 1.1109 data_time: 0.0177 memory: 15031 grad_norm: 1.1081 loss: 0.2016 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1077 2024/07/08 22:20:06 - mmengine - INFO - Iter(train) [103600/120000] base_lr: 1.0987e-05 lr: 2.8170e-06 eta: 5:03:34 time: 1.1111 data_time: 0.0176 memory: 14789 grad_norm: 1.1095 loss: 0.2016 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1077 2024/07/08 22:21:01 - mmengine - INFO - Iter(train) [103650/120000] base_lr: 1.0933e-05 lr: 2.8121e-06 eta: 5:02:38 time: 1.1112 data_time: 0.0177 memory: 14630 grad_norm: 1.1091 loss: 0.2016 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1077 2024/07/08 22:21:57 - mmengine - INFO - Iter(train) [103700/120000] base_lr: 1.0879e-05 lr: 2.8072e-06 eta: 5:01:43 time: 1.1115 data_time: 0.0177 memory: 15544 grad_norm: 1.1110 loss: 0.2020 semantic_segmentation_loss_cls: 0.0516 semantic_segmentation_loss_mask: 0.0426 semantic_segmentation_loss_dice: 0.1079 2024/07/08 22:22:53 - mmengine - INFO - Iter(train) [103750/120000] base_lr: 1.0826e-05 lr: 2.8023e-06 eta: 5:00:47 time: 1.1116 data_time: 0.0177 memory: 15888 grad_norm: 1.1101 loss: 0.2017 semantic_segmentation_loss_cls: 0.0515 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1078 2024/07/08 22:23:49 - mmengine - INFO - Iter(train) [103800/120000] base_lr: 1.0772e-05 lr: 2.7975e-06 eta: 4:59:52 time: 1.1118 data_time: 0.0177 memory: 15495 grad_norm: 1.1091 loss: 0.2018 semantic_segmentation_loss_cls: 0.0515 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1078 2024/07/08 22:24:45 - mmengine - INFO - Iter(train) [103850/120000] base_lr: 1.0719e-05 lr: 2.7926e-06 eta: 4:58:56 time: 1.1119 data_time: 0.0177 memory: 14632 grad_norm: 1.1071 loss: 0.2017 semantic_segmentation_loss_cls: 0.0516 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1077 2024/07/08 22:25:40 - mmengine - INFO - Iter(train) [103900/120000] base_lr: 1.0666e-05 lr: 2.7878e-06 eta: 4:58:01 time: 1.1116 data_time: 0.0177 memory: 14702 grad_norm: 1.1079 loss: 0.2016 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1076 2024/07/08 22:26:36 - mmengine - INFO - Iter(train) [103950/120000] base_lr: 1.0613e-05 lr: 2.7830e-06 eta: 4:57:05 time: 1.1118 data_time: 0.0177 memory: 15486 grad_norm: 1.1091 loss: 0.2016 semantic_segmentation_loss_cls: 0.0515 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1077 2024/07/08 22:27:32 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 22:27:32 - mmengine - INFO - Iter(train) [104000/120000] base_lr: 1.0560e-05 lr: 2.7782e-06 eta: 4:56:10 time: 1.1117 data_time: 0.0176 memory: 15395 grad_norm: 1.1122 loss: 0.2020 semantic_segmentation_loss_cls: 0.0516 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1078 2024/07/08 22:27:32 - mmengine - INFO - Saving checkpoint at 104000 iterations 2024/07/08 22:28:32 - mmengine - INFO - Iter(train) [104050/120000] base_lr: 1.0507e-05 lr: 2.7734e-06 eta: 4:55:15 time: 1.1125 data_time: 0.0186 memory: 15411 grad_norm: 1.1123 loss: 0.2016 semantic_segmentation_loss_cls: 0.0515 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1077 2024/07/08 22:29:26 - mmengine - INFO - Iter(train) [104100/120000] base_lr: 1.0455e-05 lr: 2.7686e-06 eta: 4:54:19 time: 1.1122 data_time: 0.0186 memory: 15077 grad_norm: 1.1118 loss: 0.2016 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1077 2024/07/08 22:30:21 - mmengine - INFO - Iter(train) [104150/120000] base_lr: 1.0403e-05 lr: 2.7639e-06 eta: 4:53:23 time: 1.1120 data_time: 0.0186 memory: 14815 grad_norm: 1.1111 loss: 0.2015 semantic_segmentation_loss_cls: 0.0515 semantic_segmentation_loss_mask: 0.0424 semantic_segmentation_loss_dice: 0.1076 2024/07/08 22:31:17 - mmengine - INFO - Iter(train) [104200/120000] base_lr: 1.0350e-05 lr: 2.7591e-06 eta: 4:52:28 time: 1.1119 data_time: 0.0185 memory: 15427 grad_norm: 1.1134 loss: 0.2015 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1077 2024/07/08 22:32:13 - mmengine - INFO - Iter(train) [104250/120000] base_lr: 1.0298e-05 lr: 2.7544e-06 eta: 4:51:32 time: 1.1120 data_time: 0.0185 memory: 15238 grad_norm: 1.1158 loss: 0.2016 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1077 2024/07/08 22:33:07 - mmengine - INFO - Iter(train) [104300/120000] base_lr: 1.0247e-05 lr: 2.7497e-06 eta: 4:50:37 time: 1.1118 data_time: 0.0185 memory: 15207 grad_norm: 1.1143 loss: 0.2017 semantic_segmentation_loss_cls: 0.0515 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1077 2024/07/08 22:34:01 - mmengine - INFO - Iter(train) [104350/120000] base_lr: 1.0195e-05 lr: 2.7450e-06 eta: 4:49:41 time: 1.1114 data_time: 0.0185 memory: 14587 grad_norm: 1.1150 loss: 0.2017 semantic_segmentation_loss_cls: 0.0516 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1076 2024/07/08 22:34:56 - mmengine - INFO - Iter(train) [104400/120000] base_lr: 1.0143e-05 lr: 2.7403e-06 eta: 4:48:45 time: 1.1113 data_time: 0.0186 memory: 15837 grad_norm: 1.1156 loss: 0.2014 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1075 2024/07/08 22:35:51 - mmengine - INFO - Iter(train) [104450/120000] base_lr: 1.0092e-05 lr: 2.7356e-06 eta: 4:47:50 time: 1.1114 data_time: 0.0186 memory: 14971 grad_norm: 1.1151 loss: 0.2014 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1076 2024/07/08 22:36:48 - mmengine - INFO - Iter(train) [104500/120000] base_lr: 1.0041e-05 lr: 2.7310e-06 eta: 4:46:54 time: 1.1115 data_time: 0.0186 memory: 15492 grad_norm: 1.1148 loss: 0.2015 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0425 semantic_segmentation_loss_dice: 0.1076 2024/07/08 22:37:43 - mmengine - INFO - Iter(train) [104550/120000] base_lr: 9.9896e-06 lr: 2.7263e-06 eta: 4:45:59 time: 1.1116 data_time: 0.0186 memory: 15116 grad_norm: 1.1156 loss: 0.2011 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0424 semantic_segmentation_loss_dice: 0.1074 2024/07/08 22:38:39 - mmengine - INFO - Iter(train) [104600/120000] base_lr: 9.9387e-06 lr: 2.7217e-06 eta: 4:45:03 time: 1.1118 data_time: 0.0186 memory: 16086 grad_norm: 1.1169 loss: 0.2009 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0424 semantic_segmentation_loss_dice: 0.1073 2024/07/08 22:39:34 - mmengine - INFO - Iter(train) [104650/120000] base_lr: 9.8879e-06 lr: 2.7171e-06 eta: 4:44:08 time: 1.1119 data_time: 0.0186 memory: 15619 grad_norm: 1.1159 loss: 0.2011 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0424 semantic_segmentation_loss_dice: 0.1074 2024/07/08 22:40:30 - mmengine - INFO - Iter(train) [104700/120000] base_lr: 9.8373e-06 lr: 2.7125e-06 eta: 4:43:12 time: 1.1121 data_time: 0.0186 memory: 14799 grad_norm: 1.1163 loss: 0.2008 semantic_segmentation_loss_cls: 0.0512 semantic_segmentation_loss_mask: 0.0424 semantic_segmentation_loss_dice: 0.1072 2024/07/08 22:41:26 - mmengine - INFO - Iter(train) [104750/120000] base_lr: 9.7869e-06 lr: 2.7079e-06 eta: 4:42:17 time: 1.1124 data_time: 0.0186 memory: 15305 grad_norm: 1.1125 loss: 0.2009 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0423 semantic_segmentation_loss_dice: 0.1072 2024/07/08 22:42:22 - mmengine - INFO - Iter(train) [104800/120000] base_lr: 9.7366e-06 lr: 2.7033e-06 eta: 4:41:21 time: 1.1126 data_time: 0.0186 memory: 14868 grad_norm: 1.1102 loss: 0.2008 semantic_segmentation_loss_cls: 0.0512 semantic_segmentation_loss_mask: 0.0424 semantic_segmentation_loss_dice: 0.1072 2024/07/08 22:43:18 - mmengine - INFO - Iter(train) [104850/120000] base_lr: 9.6864e-06 lr: 2.6988e-06 eta: 4:40:26 time: 1.1124 data_time: 0.0186 memory: 14710 grad_norm: 1.1088 loss: 0.2007 semantic_segmentation_loss_cls: 0.0512 semantic_segmentation_loss_mask: 0.0423 semantic_segmentation_loss_dice: 0.1072 2024/07/08 22:44:13 - mmengine - INFO - Iter(train) [104900/120000] base_lr: 9.6364e-06 lr: 2.6942e-06 eta: 4:39:30 time: 1.1124 data_time: 0.0186 memory: 15965 grad_norm: 1.1082 loss: 0.2006 semantic_segmentation_loss_cls: 0.0512 semantic_segmentation_loss_mask: 0.0423 semantic_segmentation_loss_dice: 0.1071 2024/07/08 22:45:09 - mmengine - INFO - Iter(train) [104950/120000] base_lr: 9.5866e-06 lr: 2.6897e-06 eta: 4:38:35 time: 1.1124 data_time: 0.0186 memory: 15471 grad_norm: 1.1083 loss: 0.2005 semantic_segmentation_loss_cls: 0.0511 semantic_segmentation_loss_mask: 0.0423 semantic_segmentation_loss_dice: 0.1071 2024/07/08 22:46:04 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 22:46:04 - mmengine - INFO - Iter(train) [105000/120000] base_lr: 9.5369e-06 lr: 2.6852e-06 eta: 4:37:39 time: 1.1124 data_time: 0.0186 memory: 16719 grad_norm: 1.1076 loss: 0.2001 semantic_segmentation_loss_cls: 0.0510 semantic_segmentation_loss_mask: 0.0422 semantic_segmentation_loss_dice: 0.1069 2024/07/08 22:46:04 - mmengine - INFO - Saving checkpoint at 105000 iterations 2024/07/08 22:46:21 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:48 time: 0.2424 data_time: 0.0017 memory: 5013 2024/07/08 22:46:33 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:36 time: 0.2424 data_time: 0.0017 memory: 5189 2024/07/08 22:46:45 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:24 time: 0.2424 data_time: 0.0017 memory: 4460 2024/07/08 22:46:57 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:12 time: 0.2425 data_time: 0.0017 memory: 4543 2024/07/08 22:47:09 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:00 time: 0.2425 data_time: 0.0017 memory: 4645 2024/07/08 22:47:21 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:48 time: 0.2425 data_time: 0.0017 memory: 10983 2024/07/08 22:47:33 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2425 data_time: 0.0017 memory: 4460 2024/07/08 22:47:45 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2425 data_time: 0.0017 memory: 4641 2024/07/08 22:47:57 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2424 data_time: 0.0017 memory: 4473 2024/07/08 22:48:09 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2424 data_time: 0.0017 memory: 4555 2024/07/08 22:48:10 - mmengine - INFO - per class results: 2024/07/08 22:48:10 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.7 | 87.82 | | building | 83.65 | 91.66 | | sky | 94.43 | 97.78 | | floor | 82.71 | 91.37 | | tree | 75.97 | 88.34 | | ceiling | 85.1 | 93.23 | | road | 84.16 | 92.95 | | bed | 88.23 | 95.19 | | windowpane | 61.76 | 78.79 | | grass | 70.97 | 85.75 | | cabinet | 60.8 | 71.58 | | sidewalk | 67.28 | 80.79 | | person | 82.36 | 91.8 | | earth | 33.9 | 46.32 | | door | 55.18 | 69.23 | | table | 62.75 | 76.51 | | mountain | 57.75 | 72.92 | | plant | 54.6 | 68.87 | | curtain | 73.07 | 87.45 | | chair | 59.56 | 72.73 | | car | 85.26 | 91.69 | | water | 49.91 | 67.44 | | painting | 71.53 | 88.29 | | sofa | 65.14 | 76.68 | | shelf | 45.05 | 64.55 | | house | 52.26 | 80.47 | | sea | 46.67 | 68.81 | | mirror | 67.39 | 75.35 | | rug | 63.22 | 74.6 | | field | 38.07 | 52.5 | | armchair | 45.59 | 68.27 | | seat | 55.47 | 82.21 | | fence | 37.62 | 50.6 | | desk | 47.83 | 69.62 | | rock | 37.97 | 61.3 | | wardrobe | 53.2 | 71.67 | | lamp | 67.58 | 79.1 | | bathtub | 85.91 | 90.54 | | railing | 39.01 | 54.29 | | cushion | 57.85 | 69.26 | | base | 25.14 | 35.23 | | box | 25.29 | 37.45 | | column | 48.55 | 65.71 | | signboard | 40.81 | 54.8 | | chest of drawers | 38.14 | 68.52 | | counter | 34.27 | 44.73 | | sand | 35.1 | 50.21 | | sink | 76.2 | 82.1 | | skyscraper | 46.69 | 58.62 | | fireplace | 68.55 | 88.82 | | refrigerator | 81.53 | 89.57 | | grandstand | 43.68 | 74.6 | | path | 29.06 | 41.39 | | stairs | 32.37 | 41.19 | | runway | 76.28 | 90.11 | | case | 66.3 | 79.41 | | pool table | 93.71 | 96.54 | | pillow | 55.14 | 66.29 | | screen door | 80.13 | 84.86 | | stairway | 38.07 | 42.6 | | river | 21.78 | 44.47 | | bridge | 69.46 | 89.35 | | bookcase | 41.3 | 58.14 | | blind | 36.72 | 42.72 | | coffee table | 69.68 | 84.76 | | toilet | 87.38 | 89.7 | | flower | 40.18 | 57.16 | | book | 52.17 | 73.85 | | hill | 11.73 | 19.37 | | bench | 40.39 | 53.26 | | countertop | 55.18 | 66.88 | | stove | 81.11 | 85.34 | | palm | 51.57 | 67.05 | | kitchen island | 31.52 | 74.3 | | computer | 60.87 | 66.62 | | swivel chair | 39.36 | 54.56 | | boat | 69.02 | 78.66 | | bar | 46.54 | 57.87 | | arcade machine | 46.29 | 53.73 | | hovel | 21.92 | 42.22 | | bus | 92.73 | 95.54 | | towel | 68.05 | 74.5 | | light | 62.38 | 76.78 | | truck | 38.23 | 49.5 | | tower | 31.94 | 54.3 | | chandelier | 64.54 | 74.48 | | awning | 32.59 | 44.15 | | streetlight | 39.03 | 52.65 | | booth | 48.85 | 58.05 | | television receiver | 47.54 | 89.87 | | airplane | 58.92 | 67.13 | | dirt track | 4.34 | 5.5 | | apparel | 36.63 | 50.42 | | pole | 30.69 | 46.47 | | land | 1.34 | 1.61 | | bannister | 16.57 | 27.04 | | escalator | 27.35 | 38.27 | | ottoman | 37.27 | 65.88 | | bottle | 22.04 | 26.46 | | buffet | 41.31 | 46.48 | | poster | 25.33 | 34.45 | | stage | 15.77 | 33.32 | | van | 49.24 | 65.82 | | ship | 80.97 | 87.8 | | fountain | 6.38 | 7.32 | | conveyer belt | 62.2 | 91.57 | | canopy | 12.13 | 19.35 | | washer | 71.45 | 73.22 | | plaything | 28.7 | 38.29 | | swimming pool | 29.74 | 32.71 | | stool | 54.04 | 68.33 | | barrel | 15.56 | 55.88 | | basket | 34.47 | 42.74 | | waterfall | 47.01 | 52.19 | | tent | 69.69 | 97.75 | | bag | 18.3 | 23.52 | | minibike | 66.36 | 85.3 | | cradle | 76.51 | 96.29 | | oven | 50.64 | 61.24 | | ball | 29.08 | 35.36 | | food | 65.49 | 79.88 | | step | 25.08 | 26.92 | | tank | 36.73 | 45.11 | | trade name | 29.09 | 35.09 | | microwave | 38.11 | 41.22 | | pot | 44.5 | 49.5 | | animal | 62.36 | 68.75 | | bicycle | 57.57 | 76.01 | | lake | 63.42 | 63.64 | | dishwasher | 81.16 | 84.6 | | screen | 52.95 | 72.71 | | blanket | 9.96 | 12.73 | | sculpture | 64.41 | 82.13 | | hood | 71.96 | 75.95 | | sconce | 52.1 | 64.67 | | vase | 40.54 | 65.58 | | traffic light | 43.18 | 56.76 | | tray | 18.96 | 24.09 | | ashcan | 47.58 | 62.14 | | fan | 62.93 | 79.19 | | pier | 37.07 | 71.59 | | crt screen | 0.01 | 0.02 | | plate | 62.62 | 73.25 | | monitor | 4.11 | 5.63 | | bulletin board | 42.89 | 50.14 | | shower | 11.54 | 17.3 | | radiator | 58.64 | 68.09 | | glass | 18.59 | 19.87 | | clock | 32.07 | 35.75 | | flag | 46.47 | 53.89 | +---------------------+-------+-------+ 2024/07/08 22:48:10 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.5800 mIoU: 49.7500 mAcc: 62.2700 data_time: 0.0017 time: 0.2405 2024/07/08 22:49:05 - mmengine - INFO - Iter(train) [105050/120000] base_lr: 9.4874e-06 lr: 2.6807e-06 eta: 4:36:44 time: 1.1113 data_time: 0.0176 memory: 15523 grad_norm: 1.1066 loss: 0.2003 semantic_segmentation_loss_cls: 0.0510 semantic_segmentation_loss_mask: 0.0422 semantic_segmentation_loss_dice: 0.1070 2024/07/08 22:50:00 - mmengine - INFO - Iter(train) [105100/120000] base_lr: 9.4380e-06 lr: 2.6762e-06 eta: 4:35:48 time: 1.1112 data_time: 0.0176 memory: 15162 grad_norm: 1.1075 loss: 0.2007 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0423 semantic_segmentation_loss_dice: 0.1072 2024/07/08 22:50:55 - mmengine - INFO - Iter(train) [105150/120000] base_lr: 9.3888e-06 lr: 2.6717e-06 eta: 4:34:53 time: 1.1111 data_time: 0.0176 memory: 15404 grad_norm: 1.1066 loss: 0.2004 semantic_segmentation_loss_cls: 0.0512 semantic_segmentation_loss_mask: 0.0422 semantic_segmentation_loss_dice: 0.1070 2024/07/08 22:51:50 - mmengine - INFO - Iter(train) [105200/120000] base_lr: 9.3398e-06 lr: 2.6673e-06 eta: 4:33:57 time: 1.1110 data_time: 0.0176 memory: 14796 grad_norm: 1.1069 loss: 0.2004 semantic_segmentation_loss_cls: 0.0512 semantic_segmentation_loss_mask: 0.0422 semantic_segmentation_loss_dice: 0.1070 2024/07/08 22:52:46 - mmengine - INFO - Iter(train) [105250/120000] base_lr: 9.2909e-06 lr: 2.6628e-06 eta: 4:33:01 time: 1.1112 data_time: 0.0176 memory: 15803 grad_norm: 1.1093 loss: 0.2007 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0423 semantic_segmentation_loss_dice: 0.1072 2024/07/08 22:53:41 - mmengine - INFO - Iter(train) [105300/120000] base_lr: 9.2422e-06 lr: 2.6584e-06 eta: 4:32:06 time: 1.1114 data_time: 0.0176 memory: 15311 grad_norm: 1.1079 loss: 0.2011 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0423 semantic_segmentation_loss_dice: 0.1073 2024/07/08 22:54:36 - mmengine - INFO - Iter(train) [105350/120000] base_lr: 9.1936e-06 lr: 2.6540e-06 eta: 4:31:10 time: 1.1113 data_time: 0.0176 memory: 15525 grad_norm: 1.1095 loss: 0.2012 semantic_segmentation_loss_cls: 0.0515 semantic_segmentation_loss_mask: 0.0424 semantic_segmentation_loss_dice: 0.1074 2024/07/08 22:55:33 - mmengine - INFO - Iter(train) [105400/120000] base_lr: 9.1452e-06 lr: 2.6496e-06 eta: 4:30:15 time: 1.1116 data_time: 0.0176 memory: 16125 grad_norm: 1.1088 loss: 0.2012 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0424 semantic_segmentation_loss_dice: 0.1074 2024/07/08 22:56:29 - mmengine - INFO - Iter(train) [105450/120000] base_lr: 9.0969e-06 lr: 2.6452e-06 eta: 4:29:19 time: 1.1120 data_time: 0.0176 memory: 14914 grad_norm: 1.1092 loss: 0.2009 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0423 semantic_segmentation_loss_dice: 0.1073 2024/07/08 22:57:25 - mmengine - INFO - Iter(train) [105500/120000] base_lr: 9.0488e-06 lr: 2.6408e-06 eta: 4:28:24 time: 1.1122 data_time: 0.0176 memory: 14954 grad_norm: 1.1132 loss: 0.2010 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0423 semantic_segmentation_loss_dice: 0.1073 2024/07/08 22:58:21 - mmengine - INFO - Iter(train) [105550/120000] base_lr: 9.0009e-06 lr: 2.6364e-06 eta: 4:27:28 time: 1.1123 data_time: 0.0176 memory: 15575 grad_norm: 1.1114 loss: 0.2011 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0424 semantic_segmentation_loss_dice: 0.1073 2024/07/08 22:59:16 - mmengine - INFO - Iter(train) [105600/120000] base_lr: 8.9531e-06 lr: 2.6321e-06 eta: 4:26:33 time: 1.1123 data_time: 0.0176 memory: 15269 grad_norm: 1.1111 loss: 0.2011 semantic_segmentation_loss_cls: 0.0515 semantic_segmentation_loss_mask: 0.0423 semantic_segmentation_loss_dice: 0.1074 2024/07/08 23:00:12 - mmengine - INFO - Iter(train) [105650/120000] base_lr: 8.9055e-06 lr: 2.6278e-06 eta: 4:25:37 time: 1.1122 data_time: 0.0176 memory: 14780 grad_norm: 1.1101 loss: 0.2013 semantic_segmentation_loss_cls: 0.0515 semantic_segmentation_loss_mask: 0.0423 semantic_segmentation_loss_dice: 0.1074 2024/07/08 23:01:07 - mmengine - INFO - Iter(train) [105700/120000] base_lr: 8.8580e-06 lr: 2.6235e-06 eta: 4:24:42 time: 1.1122 data_time: 0.0176 memory: 14830 grad_norm: 1.1075 loss: 0.2008 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0423 semantic_segmentation_loss_dice: 0.1072 2024/07/08 23:02:02 - mmengine - INFO - Iter(train) [105750/120000] base_lr: 8.8107e-06 lr: 2.6192e-06 eta: 4:23:46 time: 1.1119 data_time: 0.0176 memory: 14808 grad_norm: 1.1082 loss: 0.2006 semantic_segmentation_loss_cls: 0.0512 semantic_segmentation_loss_mask: 0.0423 semantic_segmentation_loss_dice: 0.1072 2024/07/08 23:02:59 - mmengine - INFO - Iter(train) [105800/120000] base_lr: 8.7635e-06 lr: 2.6149e-06 eta: 4:22:51 time: 1.1123 data_time: 0.0177 memory: 16877 grad_norm: 1.1082 loss: 0.2005 semantic_segmentation_loss_cls: 0.0512 semantic_segmentation_loss_mask: 0.0422 semantic_segmentation_loss_dice: 0.1071 2024/07/08 23:03:54 - mmengine - INFO - Iter(train) [105850/120000] base_lr: 8.7165e-06 lr: 2.6106e-06 eta: 4:21:55 time: 1.1124 data_time: 0.0177 memory: 14821 grad_norm: 1.1066 loss: 0.2003 semantic_segmentation_loss_cls: 0.0511 semantic_segmentation_loss_mask: 0.0422 semantic_segmentation_loss_dice: 0.1070 2024/07/08 23:04:50 - mmengine - INFO - Iter(train) [105900/120000] base_lr: 8.6697e-06 lr: 2.6063e-06 eta: 4:21:00 time: 1.1126 data_time: 0.0177 memory: 14748 grad_norm: 1.1071 loss: 0.2003 semantic_segmentation_loss_cls: 0.0511 semantic_segmentation_loss_mask: 0.0422 semantic_segmentation_loss_dice: 0.1070 2024/07/08 23:05:45 - mmengine - INFO - Iter(train) [105950/120000] base_lr: 8.6230e-06 lr: 2.6021e-06 eta: 4:20:04 time: 1.1128 data_time: 0.0176 memory: 15799 grad_norm: 1.1059 loss: 0.2000 semantic_segmentation_loss_cls: 0.0509 semantic_segmentation_loss_mask: 0.0422 semantic_segmentation_loss_dice: 0.1069 2024/07/08 23:06:40 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 23:06:40 - mmengine - INFO - Iter(train) [106000/120000] base_lr: 8.5765e-06 lr: 2.5979e-06 eta: 4:19:09 time: 1.1127 data_time: 0.0176 memory: 15014 grad_norm: 1.1045 loss: 0.1999 semantic_segmentation_loss_cls: 0.0508 semantic_segmentation_loss_mask: 0.0422 semantic_segmentation_loss_dice: 0.1069 2024/07/08 23:06:40 - mmengine - INFO - Saving checkpoint at 106000 iterations 2024/07/08 23:07:40 - mmengine - INFO - Iter(train) [106050/120000] base_lr: 8.5301e-06 lr: 2.5936e-06 eta: 4:18:14 time: 1.1125 data_time: 0.0176 memory: 15099 grad_norm: 1.1052 loss: 0.2000 semantic_segmentation_loss_cls: 0.0508 semantic_segmentation_loss_mask: 0.0422 semantic_segmentation_loss_dice: 0.1070 2024/07/08 23:08:35 - mmengine - INFO - Iter(train) [106100/120000] base_lr: 8.4839e-06 lr: 2.5894e-06 eta: 4:17:18 time: 1.1125 data_time: 0.0176 memory: 15128 grad_norm: 1.1054 loss: 0.2001 semantic_segmentation_loss_cls: 0.0509 semantic_segmentation_loss_mask: 0.0422 semantic_segmentation_loss_dice: 0.1069 2024/07/08 23:09:30 - mmengine - INFO - Iter(train) [106150/120000] base_lr: 8.4378e-06 lr: 2.5853e-06 eta: 4:16:22 time: 1.1122 data_time: 0.0176 memory: 14125 grad_norm: 1.1046 loss: 0.1999 semantic_segmentation_loss_cls: 0.0508 semantic_segmentation_loss_mask: 0.0422 semantic_segmentation_loss_dice: 0.1069 2024/07/08 23:10:24 - mmengine - INFO - Iter(train) [106200/120000] base_lr: 8.3920e-06 lr: 2.5811e-06 eta: 4:15:27 time: 1.1119 data_time: 0.0176 memory: 15355 grad_norm: 1.1062 loss: 0.2000 semantic_segmentation_loss_cls: 0.0509 semantic_segmentation_loss_mask: 0.0422 semantic_segmentation_loss_dice: 0.1069 2024/07/08 23:11:19 - mmengine - INFO - Iter(train) [106250/120000] base_lr: 8.3462e-06 lr: 2.5769e-06 eta: 4:14:31 time: 1.1116 data_time: 0.0176 memory: 15147 grad_norm: 1.1073 loss: 0.2003 semantic_segmentation_loss_cls: 0.0511 semantic_segmentation_loss_mask: 0.0422 semantic_segmentation_loss_dice: 0.1070 2024/07/08 23:12:14 - mmengine - INFO - Iter(train) [106300/120000] base_lr: 8.3007e-06 lr: 2.5728e-06 eta: 4:13:36 time: 1.1115 data_time: 0.0176 memory: 14943 grad_norm: 1.1051 loss: 0.2004 semantic_segmentation_loss_cls: 0.0511 semantic_segmentation_loss_mask: 0.0422 semantic_segmentation_loss_dice: 0.1071 2024/07/08 23:13:10 - mmengine - INFO - Iter(train) [106350/120000] base_lr: 8.2552e-06 lr: 2.5687e-06 eta: 4:12:40 time: 1.1116 data_time: 0.0176 memory: 14866 grad_norm: 1.1051 loss: 0.2004 semantic_segmentation_loss_cls: 0.0512 semantic_segmentation_loss_mask: 0.0421 semantic_segmentation_loss_dice: 0.1070 2024/07/08 23:14:06 - mmengine - INFO - Iter(train) [106400/120000] base_lr: 8.2100e-06 lr: 2.5645e-06 eta: 4:11:45 time: 1.1117 data_time: 0.0176 memory: 14581 grad_norm: 1.1038 loss: 0.2003 semantic_segmentation_loss_cls: 0.0511 semantic_segmentation_loss_mask: 0.0421 semantic_segmentation_loss_dice: 0.1070 2024/07/08 23:15:02 - mmengine - INFO - Iter(train) [106450/120000] base_lr: 8.1649e-06 lr: 2.5604e-06 eta: 4:10:49 time: 1.1117 data_time: 0.0176 memory: 15580 grad_norm: 1.1035 loss: 0.2005 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0421 semantic_segmentation_loss_dice: 0.1071 2024/07/08 23:15:57 - mmengine - INFO - Iter(train) [106500/120000] base_lr: 8.1200e-06 lr: 2.5564e-06 eta: 4:09:53 time: 1.1118 data_time: 0.0176 memory: 15255 grad_norm: 1.1023 loss: 0.2005 semantic_segmentation_loss_cls: 0.0512 semantic_segmentation_loss_mask: 0.0421 semantic_segmentation_loss_dice: 0.1071 2024/07/08 23:16:53 - mmengine - INFO - Iter(train) [106550/120000] base_lr: 8.0752e-06 lr: 2.5523e-06 eta: 4:08:58 time: 1.1115 data_time: 0.0176 memory: 15255 grad_norm: 1.1033 loss: 0.2005 semantic_segmentation_loss_cls: 0.0512 semantic_segmentation_loss_mask: 0.0421 semantic_segmentation_loss_dice: 0.1071 2024/07/08 23:17:48 - mmengine - INFO - Iter(train) [106600/120000] base_lr: 8.0306e-06 lr: 2.5482e-06 eta: 4:08:02 time: 1.1114 data_time: 0.0176 memory: 15508 grad_norm: 1.1020 loss: 0.2002 semantic_segmentation_loss_cls: 0.0511 semantic_segmentation_loss_mask: 0.0421 semantic_segmentation_loss_dice: 0.1070 2024/07/08 23:18:44 - mmengine - INFO - Iter(train) [106650/120000] base_lr: 7.9861e-06 lr: 2.5442e-06 eta: 4:07:07 time: 1.1114 data_time: 0.0176 memory: 15129 grad_norm: 1.0999 loss: 0.1998 semantic_segmentation_loss_cls: 0.0509 semantic_segmentation_loss_mask: 0.0421 semantic_segmentation_loss_dice: 0.1068 2024/07/08 23:19:38 - mmengine - INFO - Iter(train) [106700/120000] base_lr: 7.9418e-06 lr: 2.5402e-06 eta: 4:06:11 time: 1.1113 data_time: 0.0176 memory: 15300 grad_norm: 1.0973 loss: 0.1998 semantic_segmentation_loss_cls: 0.0510 semantic_segmentation_loss_mask: 0.0421 semantic_segmentation_loss_dice: 0.1068 2024/07/08 23:20:34 - mmengine - INFO - Iter(train) [106750/120000] base_lr: 7.8977e-06 lr: 2.5362e-06 eta: 4:05:16 time: 1.1113 data_time: 0.0176 memory: 15859 grad_norm: 1.0973 loss: 0.1998 semantic_segmentation_loss_cls: 0.0509 semantic_segmentation_loss_mask: 0.0420 semantic_segmentation_loss_dice: 0.1068 2024/07/08 23:21:29 - mmengine - INFO - Iter(train) [106800/120000] base_lr: 7.8537e-06 lr: 2.5322e-06 eta: 4:04:20 time: 1.1114 data_time: 0.0176 memory: 15270 grad_norm: 1.0972 loss: 0.2000 semantic_segmentation_loss_cls: 0.0511 semantic_segmentation_loss_mask: 0.0420 semantic_segmentation_loss_dice: 0.1069 2024/07/08 23:22:25 - mmengine - INFO - Iter(train) [106850/120000] base_lr: 7.8099e-06 lr: 2.5282e-06 eta: 4:03:25 time: 1.1119 data_time: 0.0176 memory: 14372 grad_norm: 1.0967 loss: 0.2000 semantic_segmentation_loss_cls: 0.0512 semantic_segmentation_loss_mask: 0.0420 semantic_segmentation_loss_dice: 0.1069 2024/07/08 23:23:21 - mmengine - INFO - Iter(train) [106900/120000] base_lr: 7.7662e-06 lr: 2.5242e-06 eta: 4:02:29 time: 1.1121 data_time: 0.0176 memory: 14664 grad_norm: 1.0964 loss: 0.1999 semantic_segmentation_loss_cls: 0.0511 semantic_segmentation_loss_mask: 0.0420 semantic_segmentation_loss_dice: 0.1068 2024/07/08 23:24:17 - mmengine - INFO - Iter(train) [106950/120000] base_lr: 7.7227e-06 lr: 2.5202e-06 eta: 4:01:34 time: 1.1123 data_time: 0.0176 memory: 15138 grad_norm: 1.0971 loss: 0.1998 semantic_segmentation_loss_cls: 0.0511 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1068 2024/07/08 23:25:12 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 23:25:12 - mmengine - INFO - Iter(train) [107000/120000] base_lr: 7.6794e-06 lr: 2.5163e-06 eta: 4:00:38 time: 1.1122 data_time: 0.0176 memory: 15382 grad_norm: 1.0963 loss: 0.2001 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1068 2024/07/08 23:25:12 - mmengine - INFO - Saving checkpoint at 107000 iterations 2024/07/08 23:26:12 - mmengine - INFO - Iter(train) [107050/120000] base_lr: 7.6362e-06 lr: 2.5124e-06 eta: 3:59:43 time: 1.1123 data_time: 0.0177 memory: 15915 grad_norm: 1.0958 loss: 0.2001 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0420 semantic_segmentation_loss_dice: 0.1068 2024/07/08 23:27:07 - mmengine - INFO - Iter(train) [107100/120000] base_lr: 7.5932e-06 lr: 2.5085e-06 eta: 3:58:48 time: 1.1121 data_time: 0.0177 memory: 15128 grad_norm: 1.0960 loss: 0.2000 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0420 semantic_segmentation_loss_dice: 0.1068 2024/07/08 23:28:02 - mmengine - INFO - Iter(train) [107150/120000] base_lr: 7.5503e-06 lr: 2.5046e-06 eta: 3:57:52 time: 1.1120 data_time: 0.0177 memory: 14929 grad_norm: 1.0955 loss: 0.2001 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0420 semantic_segmentation_loss_dice: 0.1068 2024/07/08 23:28:58 - mmengine - INFO - Iter(train) [107200/120000] base_lr: 7.5076e-06 lr: 2.5007e-06 eta: 3:56:56 time: 1.1120 data_time: 0.0177 memory: 15547 grad_norm: 1.0932 loss: 0.2002 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0420 semantic_segmentation_loss_dice: 0.1069 2024/07/08 23:29:54 - mmengine - INFO - Iter(train) [107250/120000] base_lr: 7.4651e-06 lr: 2.4968e-06 eta: 3:56:01 time: 1.1122 data_time: 0.0177 memory: 15445 grad_norm: 1.0951 loss: 0.2003 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0420 semantic_segmentation_loss_dice: 0.1069 2024/07/08 23:30:49 - mmengine - INFO - Iter(train) [107300/120000] base_lr: 7.4227e-06 lr: 2.4930e-06 eta: 3:55:05 time: 1.1124 data_time: 0.0177 memory: 14827 grad_norm: 1.0957 loss: 0.2006 semantic_segmentation_loss_cls: 0.0515 semantic_segmentation_loss_mask: 0.0420 semantic_segmentation_loss_dice: 0.1071 2024/07/08 23:31:46 - mmengine - INFO - Iter(train) [107350/120000] base_lr: 7.3804e-06 lr: 2.4891e-06 eta: 3:54:10 time: 1.1126 data_time: 0.0177 memory: 14511 grad_norm: 1.0956 loss: 0.2003 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0420 semantic_segmentation_loss_dice: 0.1070 2024/07/08 23:32:41 - mmengine - INFO - Iter(train) [107400/120000] base_lr: 7.3384e-06 lr: 2.4853e-06 eta: 3:53:14 time: 1.1127 data_time: 0.0177 memory: 14622 grad_norm: 1.0943 loss: 0.2003 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0420 semantic_segmentation_loss_dice: 0.1070 2024/07/08 23:33:36 - mmengine - INFO - Iter(train) [107450/120000] base_lr: 7.2965e-06 lr: 2.4815e-06 eta: 3:52:19 time: 1.1127 data_time: 0.0177 memory: 15580 grad_norm: 1.0937 loss: 0.2004 semantic_segmentation_loss_cls: 0.0514 semantic_segmentation_loss_mask: 0.0420 semantic_segmentation_loss_dice: 0.1070 2024/07/08 23:34:32 - mmengine - INFO - Iter(train) [107500/120000] base_lr: 7.2548e-06 lr: 2.4777e-06 eta: 3:51:23 time: 1.1128 data_time: 0.0177 memory: 14935 grad_norm: 1.0934 loss: 0.2003 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0420 semantic_segmentation_loss_dice: 0.1069 2024/07/08 23:35:27 - mmengine - INFO - Iter(train) [107550/120000] base_lr: 7.2132e-06 lr: 2.4739e-06 eta: 3:50:28 time: 1.1127 data_time: 0.0177 memory: 14794 grad_norm: 1.0926 loss: 0.2000 semantic_segmentation_loss_cls: 0.0512 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1069 2024/07/08 23:36:24 - mmengine - INFO - Iter(train) [107600/120000] base_lr: 7.1718e-06 lr: 2.4702e-06 eta: 3:49:32 time: 1.1129 data_time: 0.0177 memory: 15668 grad_norm: 1.0915 loss: 0.2001 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1069 2024/07/08 23:37:19 - mmengine - INFO - Iter(train) [107650/120000] base_lr: 7.1305e-06 lr: 2.4664e-06 eta: 3:48:37 time: 1.1129 data_time: 0.0177 memory: 15123 grad_norm: 1.0905 loss: 0.2001 semantic_segmentation_loss_cls: 0.0513 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1068 2024/07/08 23:38:14 - mmengine - INFO - Iter(train) [107700/120000] base_lr: 7.0894e-06 lr: 2.4627e-06 eta: 3:47:41 time: 1.1126 data_time: 0.0177 memory: 15554 grad_norm: 1.0887 loss: 0.1995 semantic_segmentation_loss_cls: 0.0511 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1066 2024/07/08 23:39:09 - mmengine - INFO - Iter(train) [107750/120000] base_lr: 7.0485e-06 lr: 2.4590e-06 eta: 3:46:45 time: 1.1124 data_time: 0.0177 memory: 15195 grad_norm: 1.0901 loss: 0.1996 semantic_segmentation_loss_cls: 0.0511 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1067 2024/07/08 23:40:04 - mmengine - INFO - Iter(train) [107800/120000] base_lr: 7.0077e-06 lr: 2.4552e-06 eta: 3:45:50 time: 1.1122 data_time: 0.0177 memory: 14694 grad_norm: 1.0911 loss: 0.1994 semantic_segmentation_loss_cls: 0.0510 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1066 2024/07/08 23:40:59 - mmengine - INFO - Iter(train) [107850/120000] base_lr: 6.9671e-06 lr: 2.4516e-06 eta: 3:44:54 time: 1.1119 data_time: 0.0177 memory: 15269 grad_norm: 1.0911 loss: 0.1993 semantic_segmentation_loss_cls: 0.0509 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1066 2024/07/08 23:41:55 - mmengine - INFO - Iter(train) [107900/120000] base_lr: 6.9266e-06 lr: 2.4479e-06 eta: 3:43:59 time: 1.1121 data_time: 0.0177 memory: 15958 grad_norm: 1.0891 loss: 0.1995 semantic_segmentation_loss_cls: 0.0510 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1067 2024/07/08 23:42:51 - mmengine - INFO - Iter(train) [107950/120000] base_lr: 6.8863e-06 lr: 2.4442e-06 eta: 3:43:03 time: 1.1122 data_time: 0.0177 memory: 15311 grad_norm: 1.0877 loss: 0.1994 semantic_segmentation_loss_cls: 0.0510 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1066 2024/07/08 23:43:46 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/08 23:43:46 - mmengine - INFO - Iter(train) [108000/120000] base_lr: 6.8462e-06 lr: 2.4406e-06 eta: 3:42:08 time: 1.1121 data_time: 0.0177 memory: 14999 grad_norm: 1.0855 loss: 0.1992 semantic_segmentation_loss_cls: 0.0509 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1065 2024/07/08 23:43:46 - mmengine - INFO - Saving checkpoint at 108000 iterations 2024/07/08 23:44:46 - mmengine - INFO - Iter(train) [108050/120000] base_lr: 6.8062e-06 lr: 2.4369e-06 eta: 3:41:13 time: 1.1121 data_time: 0.0178 memory: 15150 grad_norm: 1.0856 loss: 0.1992 semantic_segmentation_loss_cls: 0.0509 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1065 2024/07/08 23:45:41 - mmengine - INFO - Iter(train) [108100/120000] base_lr: 6.7664e-06 lr: 2.4333e-06 eta: 3:40:17 time: 1.1123 data_time: 0.0178 memory: 15120 grad_norm: 1.0859 loss: 0.1994 semantic_segmentation_loss_cls: 0.0510 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1065 2024/07/08 23:46:37 - mmengine - INFO - Iter(train) [108150/120000] base_lr: 6.7268e-06 lr: 2.4297e-06 eta: 3:39:22 time: 1.1125 data_time: 0.0178 memory: 15655 grad_norm: 1.0858 loss: 0.1994 semantic_segmentation_loss_cls: 0.0510 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1066 2024/07/08 23:47:33 - mmengine - INFO - Iter(train) 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time: 1.1130 data_time: 0.0178 memory: 15021 grad_norm: 1.0843 loss: 0.1986 semantic_segmentation_loss_cls: 0.0505 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1063 2024/07/08 23:51:14 - mmengine - INFO - Iter(train) [108400/120000] base_lr: 6.5310e-06 lr: 2.4119e-06 eta: 3:34:44 time: 1.1131 data_time: 0.0178 memory: 14953 grad_norm: 1.0845 loss: 0.1987 semantic_segmentation_loss_cls: 0.0506 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1063 2024/07/08 23:52:09 - mmengine - INFO - Iter(train) [108450/120000] base_lr: 6.4923e-06 lr: 2.4084e-06 eta: 3:33:48 time: 1.1129 data_time: 0.0178 memory: 15089 grad_norm: 1.0845 loss: 0.1985 semantic_segmentation_loss_cls: 0.0506 semantic_segmentation_loss_mask: 0.0417 semantic_segmentation_loss_dice: 0.1062 2024/07/08 23:53:05 - mmengine - INFO - Iter(train) [108500/120000] base_lr: 6.4538e-06 lr: 2.4049e-06 eta: 3:32:53 time: 1.1129 data_time: 0.0178 memory: 15513 grad_norm: 1.0840 loss: 0.1984 semantic_segmentation_loss_cls: 0.0505 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1061 2024/07/08 23:54:01 - mmengine - INFO - Iter(train) [108550/120000] base_lr: 6.4154e-06 lr: 2.4014e-06 eta: 3:31:57 time: 1.1129 data_time: 0.0178 memory: 15367 grad_norm: 1.0823 loss: 0.1984 semantic_segmentation_loss_cls: 0.0505 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1061 2024/07/08 23:54:56 - mmengine - INFO - Iter(train) [108600/120000] base_lr: 6.3773e-06 lr: 2.3979e-06 eta: 3:31:02 time: 1.1130 data_time: 0.0178 memory: 14688 grad_norm: 1.0828 loss: 0.1986 semantic_segmentation_loss_cls: 0.0506 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1062 2024/07/08 23:55:52 - mmengine - INFO - Iter(train) [108650/120000] base_lr: 6.3392e-06 lr: 2.3945e-06 eta: 3:30:06 time: 1.1129 data_time: 0.0177 memory: 15802 grad_norm: 1.0830 loss: 0.1986 semantic_segmentation_loss_cls: 0.0506 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1062 2024/07/08 23:56:46 - mmengine - INFO - Iter(train) [108700/120000] base_lr: 6.3014e-06 lr: 2.3910e-06 eta: 3:29:11 time: 1.1127 data_time: 0.0177 memory: 15422 grad_norm: 1.0813 loss: 0.1985 semantic_segmentation_loss_cls: 0.0505 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1062 2024/07/08 23:57:42 - mmengine - INFO - Iter(train) [108750/120000] base_lr: 6.2637e-06 lr: 2.3876e-06 eta: 3:28:15 time: 1.1125 data_time: 0.0177 memory: 15376 grad_norm: 1.0835 loss: 0.1985 semantic_segmentation_loss_cls: 0.0505 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1062 2024/07/08 23:58:37 - mmengine - INFO - Iter(train) [108800/120000] base_lr: 6.2261e-06 lr: 2.3842e-06 eta: 3:27:19 time: 1.1123 data_time: 0.0177 memory: 14895 grad_norm: 1.0832 loss: 0.1983 semantic_segmentation_loss_cls: 0.0504 semantic_segmentation_loss_mask: 0.0417 semantic_segmentation_loss_dice: 0.1061 2024/07/08 23:59:33 - mmengine - INFO - Iter(train) [108850/120000] base_lr: 6.1887e-06 lr: 2.3808e-06 eta: 3:26:24 time: 1.1124 data_time: 0.0178 memory: 15230 grad_norm: 1.0832 loss: 0.1982 semantic_segmentation_loss_cls: 0.0503 semantic_segmentation_loss_mask: 0.0417 semantic_segmentation_loss_dice: 0.1061 2024/07/09 00:00:28 - mmengine - INFO - Iter(train) [108900/120000] base_lr: 6.1515e-06 lr: 2.3774e-06 eta: 3:25:28 time: 1.1123 data_time: 0.0178 memory: 15704 grad_norm: 1.0841 loss: 0.1981 semantic_segmentation_loss_cls: 0.0503 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1061 2024/07/09 00:01:24 - mmengine - INFO - Iter(train) [108950/120000] base_lr: 6.1145e-06 lr: 2.3740e-06 eta: 3:24:33 time: 1.1123 data_time: 0.0178 memory: 15628 grad_norm: 1.0839 loss: 0.1981 semantic_segmentation_loss_cls: 0.0502 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1061 2024/07/09 00:02:19 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/09 00:02:19 - mmengine - INFO - Iter(train) [109000/120000] base_lr: 6.0776e-06 lr: 2.3707e-06 eta: 3:23:37 time: 1.1121 data_time: 0.0178 memory: 15191 grad_norm: 1.0836 loss: 0.1982 semantic_segmentation_loss_cls: 0.0502 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1062 2024/07/09 00:02:19 - mmengine - INFO - Saving checkpoint at 109000 iterations 2024/07/09 00:03:19 - mmengine - INFO - Iter(train) [109050/120000] base_lr: 6.0409e-06 lr: 2.3674e-06 eta: 3:22:42 time: 1.1130 data_time: 0.0188 memory: 15418 grad_norm: 1.0857 loss: 0.1985 semantic_segmentation_loss_cls: 0.0503 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1063 2024/07/09 00:04:14 - mmengine - INFO - Iter(train) [109100/120000] base_lr: 6.0043e-06 lr: 2.3640e-06 eta: 3:21:47 time: 1.1132 data_time: 0.0188 memory: 15099 grad_norm: 1.0854 loss: 0.1980 semantic_segmentation_loss_cls: 0.0501 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1061 2024/07/09 00:05:10 - mmengine - INFO - Iter(train) [109150/120000] base_lr: 5.9679e-06 lr: 2.3607e-06 eta: 3:20:51 time: 1.1134 data_time: 0.0188 memory: 14843 grad_norm: 1.0866 loss: 0.1981 semantic_segmentation_loss_cls: 0.0501 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1061 2024/07/09 00:06:05 - mmengine - INFO - Iter(train) [109200/120000] base_lr: 5.9316e-06 lr: 2.3574e-06 eta: 3:19:56 time: 1.1135 data_time: 0.0188 memory: 14375 grad_norm: 1.0870 loss: 0.1978 semantic_segmentation_loss_cls: 0.0500 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1060 2024/07/09 00:07:00 - mmengine - INFO - Iter(train) [109250/120000] base_lr: 5.8956e-06 lr: 2.3541e-06 eta: 3:19:00 time: 1.1133 data_time: 0.0188 memory: 15055 grad_norm: 1.0863 loss: 0.1975 semantic_segmentation_loss_cls: 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semantic_segmentation_loss_dice: 0.1056 2024/07/09 00:10:40 - mmengine - INFO - Iter(train) [109450/120000] base_lr: 5.7529e-06 lr: 2.3412e-06 eta: 3:15:18 time: 1.1125 data_time: 0.0189 memory: 15006 grad_norm: 1.0841 loss: 0.1969 semantic_segmentation_loss_cls: 0.0496 semantic_segmentation_loss_mask: 0.0417 semantic_segmentation_loss_dice: 0.1055 2024/07/09 00:11:35 - mmengine - INFO - Iter(train) [109500/120000] base_lr: 5.7176e-06 lr: 2.3380e-06 eta: 3:14:22 time: 1.1124 data_time: 0.0189 memory: 15586 grad_norm: 1.0810 loss: 0.1969 semantic_segmentation_loss_cls: 0.0496 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1056 2024/07/09 00:12:31 - mmengine - INFO - Iter(train) [109550/120000] base_lr: 5.6825e-06 lr: 2.3348e-06 eta: 3:13:26 time: 1.1123 data_time: 0.0189 memory: 15283 grad_norm: 1.0825 loss: 0.1971 semantic_segmentation_loss_cls: 0.0497 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1057 2024/07/09 00:13:27 - mmengine - INFO - Iter(train) [109600/120000] base_lr: 5.6476e-06 lr: 2.3316e-06 eta: 3:12:31 time: 1.1124 data_time: 0.0189 memory: 15834 grad_norm: 1.0823 loss: 0.1971 semantic_segmentation_loss_cls: 0.0497 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1056 2024/07/09 00:14:23 - mmengine - INFO - Iter(train) [109650/120000] base_lr: 5.6128e-06 lr: 2.3284e-06 eta: 3:11:35 time: 1.1124 data_time: 0.0189 memory: 15247 grad_norm: 1.0833 loss: 0.1970 semantic_segmentation_loss_cls: 0.0497 semantic_segmentation_loss_mask: 0.0417 semantic_segmentation_loss_dice: 0.1056 2024/07/09 00:15:18 - mmengine - INFO - Iter(train) [109700/120000] base_lr: 5.5782e-06 lr: 2.3253e-06 eta: 3:10:40 time: 1.1124 data_time: 0.0189 memory: 16315 grad_norm: 1.0843 loss: 0.1971 semantic_segmentation_loss_cls: 0.0498 semantic_segmentation_loss_mask: 0.0417 semantic_segmentation_loss_dice: 0.1056 2024/07/09 00:16:13 - mmengine - INFO - Iter(train) [109750/120000] base_lr: 5.5438e-06 lr: 2.3222e-06 eta: 3:09:44 time: 1.1124 data_time: 0.0189 memory: 15326 grad_norm: 1.0824 loss: 0.1974 semantic_segmentation_loss_cls: 0.0499 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1058 2024/07/09 00:17:08 - mmengine - INFO - Iter(train) [109800/120000] base_lr: 5.5095e-06 lr: 2.3190e-06 eta: 3:08:49 time: 1.1121 data_time: 0.0188 memory: 15504 grad_norm: 1.0840 loss: 0.1976 semantic_segmentation_loss_cls: 0.0500 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1059 2024/07/09 00:18:04 - mmengine - INFO - Iter(train) [109850/120000] base_lr: 5.4754e-06 lr: 2.3159e-06 eta: 3:07:53 time: 1.1122 data_time: 0.0188 memory: 14972 grad_norm: 1.0821 loss: 0.1977 semantic_segmentation_loss_cls: 0.0500 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1059 2024/07/09 00:18:59 - mmengine - INFO - Iter(train) [109900/120000] base_lr: 5.4414e-06 lr: 2.3129e-06 eta: 3:06:58 time: 1.1119 data_time: 0.0188 memory: 15585 grad_norm: 1.0820 loss: 0.1976 semantic_segmentation_loss_cls: 0.0499 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1059 2024/07/09 00:19:54 - mmengine - INFO - Iter(train) [109950/120000] base_lr: 5.4076e-06 lr: 2.3098e-06 eta: 3:06:02 time: 1.1119 data_time: 0.0188 memory: 14821 grad_norm: 1.0829 loss: 0.1977 semantic_segmentation_loss_cls: 0.0500 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1059 2024/07/09 00:20:49 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/09 00:20:49 - mmengine - INFO - Iter(train) [110000/120000] base_lr: 5.3740e-06 lr: 2.3067e-06 eta: 3:05:06 time: 1.1119 data_time: 0.0188 memory: 14480 grad_norm: 1.0827 loss: 0.1976 semantic_segmentation_loss_cls: 0.0500 semantic_segmentation_loss_mask: 0.0417 semantic_segmentation_loss_dice: 0.1059 2024/07/09 00:20:49 - mmengine - INFO - Saving checkpoint at 110000 iterations 2024/07/09 00:21:06 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:48 time: 0.2423 data_time: 0.0017 memory: 5013 2024/07/09 00:21:18 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:35 time: 0.2422 data_time: 0.0017 memory: 5189 2024/07/09 00:21:30 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:24 time: 0.2421 data_time: 0.0017 memory: 4460 2024/07/09 00:21:42 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:12 time: 0.2421 data_time: 0.0017 memory: 4543 2024/07/09 00:21:54 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:00 time: 0.2420 data_time: 0.0017 memory: 4645 2024/07/09 00:22:06 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:48 time: 0.2419 data_time: 0.0017 memory: 10983 2024/07/09 00:22:18 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2418 data_time: 0.0017 memory: 4460 2024/07/09 00:22:30 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2417 data_time: 0.0017 memory: 4641 2024/07/09 00:22:42 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2416 data_time: 0.0017 memory: 4473 2024/07/09 00:22:54 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2415 data_time: 0.0017 memory: 4555 2024/07/09 00:22:55 - mmengine - INFO - per class results: 2024/07/09 00:22:55 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.73 | 87.88 | | building | 83.7 | 91.73 | | sky | 94.41 | 97.77 | | floor | 82.86 | 91.42 | | tree | 75.9 | 88.17 | | ceiling | 84.95 | 93.08 | | road | 84.06 | 92.95 | | bed | 88.18 | 95.23 | | windowpane | 61.9 | 78.98 | | grass | 71.0 | 85.69 | | cabinet | 60.79 | 71.83 | | sidewalk | 67.47 | 80.86 | | person | 82.33 | 91.85 | | earth | 33.79 | 45.94 | | door | 55.41 | 69.29 | | table | 63.55 | 76.41 | | mountain | 57.66 | 72.69 | | plant | 54.64 | 68.67 | | curtain | 72.8 | 87.19 | | chair | 59.44 | 72.79 | | car | 85.54 | 91.63 | | water | 50.8 | 67.43 | | painting | 71.63 | 88.57 | | sofa | 65.99 | 77.47 | | shelf | 45.5 | 64.97 | | house | 53.29 | 81.55 | | sea | 47.61 | 71.34 | | mirror | 66.64 | 75.13 | | rug | 64.49 | 75.48 | | field | 37.32 | 52.89 | | armchair | 44.77 | 68.24 | | seat | 56.01 | 82.1 | | fence | 47.37 | 63.91 | | desk | 47.65 | 69.38 | | rock | 37.99 | 60.68 | | wardrobe | 52.91 | 72.09 | | lamp | 67.46 | 78.91 | | bathtub | 86.03 | 90.36 | | railing | 38.81 | 53.66 | | cushion | 57.58 | 68.95 | | base | 24.35 | 33.72 | | box | 25.4 | 37.54 | | column | 48.94 | 66.18 | | signboard | 40.32 | 54.67 | | chest of drawers | 38.15 | 67.88 | | counter | 31.26 | 44.02 | | sand | 32.96 | 47.26 | | sink | 75.8 | 82.24 | | skyscraper | 46.63 | 58.53 | | fireplace | 67.11 | 86.92 | | refrigerator | 81.09 | 89.7 | | grandstand | 42.81 | 74.41 | | path | 29.18 | 41.42 | | stairs | 32.24 | 41.05 | | runway | 76.17 | 90.09 | | case | 65.47 | 79.89 | | pool table | 93.56 | 96.41 | | pillow | 54.89 | 65.98 | | screen door | 79.66 | 84.5 | | stairway | 37.78 | 42.37 | | river | 21.8 | 44.56 | | bridge | 67.53 | 89.46 | | bookcase | 41.61 | 58.37 | | blind | 37.15 | 42.52 | | coffee table | 69.6 | 84.96 | | toilet | 87.25 | 89.77 | | flower | 40.74 | 57.59 | | book | 52.48 | 73.69 | | hill | 14.2 | 23.44 | | bench | 38.85 | 52.74 | | countertop | 53.99 | 67.1 | | stove | 80.69 | 84.68 | | palm | 50.77 | 67.0 | | kitchen island | 31.19 | 74.38 | | computer | 60.58 | 66.43 | | swivel chair | 39.42 | 54.69 | | boat | 66.81 | 76.76 | | bar | 45.84 | 57.51 | | arcade machine | 55.38 | 61.44 | | hovel | 30.36 | 42.6 | | bus | 92.53 | 95.38 | | towel | 68.72 | 74.94 | | light | 62.01 | 76.32 | | truck | 37.93 | 49.52 | | tower | 32.46 | 54.36 | | chandelier | 64.53 | 74.46 | | awning | 32.81 | 43.98 | | streetlight | 39.8 | 53.35 | | booth | 48.37 | 57.72 | | television receiver | 49.31 | 89.57 | | airplane | 59.01 | 67.36 | | dirt track | 2.74 | 3.44 | | apparel | 35.33 | 49.85 | | pole | 30.69 | 45.68 | | land | 1.57 | 1.88 | | bannister | 16.98 | 26.97 | | escalator | 27.71 | 38.63 | | ottoman | 39.29 | 64.87 | | bottle | 22.37 | 26.68 | | buffet | 41.03 | 46.28 | | poster | 25.44 | 33.91 | | stage | 14.62 | 33.83 | | van | 48.42 | 65.31 | | ship | 79.53 | 88.01 | | fountain | 7.39 | 8.51 | | conveyer belt | 62.95 | 91.34 | | canopy | 15.14 | 24.53 | | washer | 71.59 | 73.34 | | plaything | 28.71 | 38.46 | | swimming pool | 30.11 | 33.15 | | stool | 55.43 | 67.72 | | barrel | 15.38 | 55.63 | | basket | 34.37 | 42.88 | | waterfall | 46.63 | 51.94 | | tent | 71.66 | 97.76 | | bag | 17.74 | 23.46 | | minibike | 67.19 | 85.43 | | cradle | 76.86 | 96.45 | | oven | 47.47 | 58.06 | | ball | 28.93 | 34.99 | | food | 64.07 | 77.63 | | step | 25.1 | 26.76 | | tank | 36.84 | 45.17 | | trade name | 29.16 | 35.17 | | microwave | 38.1 | 41.26 | | pot | 44.34 | 49.3 | | animal | 61.78 | 68.64 | | bicycle | 57.77 | 75.7 | | lake | 63.47 | 63.65 | | dishwasher | 80.9 | 84.43 | | screen | 56.98 | 71.22 | | blanket | 10.34 | 13.2 | | sculpture | 67.33 | 81.8 | | hood | 71.92 | 76.33 | | sconce | 51.59 | 64.39 | | vase | 40.32 | 65.68 | | traffic light | 42.37 | 56.46 | | tray | 19.3 | 24.74 | | ashcan | 47.65 | 62.02 | | fan | 62.79 | 78.83 | | pier | 36.98 | 73.2 | | crt screen | 0.0 | 0.0 | | plate | 62.33 | 72.97 | | monitor | 21.29 | 30.0 | | bulletin board | 43.19 | 50.85 | | shower | 11.71 | 17.62 | | radiator | 59.28 | 68.97 | | glass | 18.48 | 19.69 | | clock | 32.08 | 35.43 | | flag | 46.78 | 53.77 | +---------------------+-------+-------+ 2024/07/09 00:22:55 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.6600 mIoU: 50.0300 mAcc: 62.5400 data_time: 0.0017 time: 0.2409 2024/07/09 00:23:51 - mmengine - INFO - Iter(train) [110050/120000] base_lr: 5.3406e-06 lr: 2.3037e-06 eta: 3:04:11 time: 1.1113 data_time: 0.0179 memory: 15262 grad_norm: 1.0820 loss: 0.1977 semantic_segmentation_loss_cls: 0.0500 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1059 2024/07/09 00:24:46 - mmengine - INFO - Iter(train) [110100/120000] base_lr: 5.3073e-06 lr: 2.3007e-06 eta: 3:03:16 time: 1.1114 data_time: 0.0179 memory: 14521 grad_norm: 1.0831 loss: 0.1976 semantic_segmentation_loss_cls: 0.0500 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1058 2024/07/09 00:25:41 - mmengine - INFO - Iter(train) [110150/120000] base_lr: 5.2741e-06 lr: 2.2976e-06 eta: 3:02:20 time: 1.1114 data_time: 0.0179 memory: 15398 grad_norm: 1.0837 loss: 0.1977 semantic_segmentation_loss_cls: 0.0500 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1059 2024/07/09 00:26:36 - mmengine - INFO - Iter(train) [110200/120000] base_lr: 5.2412e-06 lr: 2.2947e-06 eta: 3:01:24 time: 1.1115 data_time: 0.0179 memory: 14800 grad_norm: 1.0839 loss: 0.1977 semantic_segmentation_loss_cls: 0.0500 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1059 2024/07/09 00:27:32 - mmengine - INFO - Iter(train) [110250/120000] base_lr: 5.2083e-06 lr: 2.2917e-06 eta: 3:00:29 time: 1.1116 data_time: 0.0179 memory: 15083 grad_norm: 1.0832 loss: 0.1978 semantic_segmentation_loss_cls: 0.0500 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1059 2024/07/09 00:28:28 - mmengine - INFO - Iter(train) [110300/120000] base_lr: 5.1757e-06 lr: 2.2887e-06 eta: 2:59:33 time: 1.1119 data_time: 0.0179 memory: 14988 grad_norm: 1.0825 loss: 0.1979 semantic_segmentation_loss_cls: 0.0501 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1060 2024/07/09 00:29:24 - mmengine - INFO - Iter(train) [110350/120000] base_lr: 5.1432e-06 lr: 2.2857e-06 eta: 2:58:38 time: 1.1119 data_time: 0.0179 memory: 16595 grad_norm: 1.0801 loss: 0.1979 semantic_segmentation_loss_cls: 0.0500 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1060 2024/07/09 00:30:20 - mmengine - INFO - Iter(train) [110400/120000] base_lr: 5.1109e-06 lr: 2.2828e-06 eta: 2:57:42 time: 1.1119 data_time: 0.0179 memory: 14652 grad_norm: 1.0781 loss: 0.1982 semantic_segmentation_loss_cls: 0.0501 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1062 2024/07/09 00:31:15 - mmengine - INFO - Iter(train) [110450/120000] base_lr: 5.0788e-06 lr: 2.2799e-06 eta: 2:56:47 time: 1.1117 data_time: 0.0179 memory: 14898 grad_norm: 1.0770 loss: 0.1979 semantic_segmentation_loss_cls: 0.0500 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1061 2024/07/09 00:32:10 - mmengine - INFO - Iter(train) [110500/120000] base_lr: 5.0468e-06 lr: 2.2770e-06 eta: 2:55:51 time: 1.1117 data_time: 0.0179 memory: 15238 grad_norm: 1.0776 loss: 0.1977 semantic_segmentation_loss_cls: 0.0498 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1060 2024/07/09 00:33:07 - mmengine - INFO - Iter(train) [110550/120000] base_lr: 5.0150e-06 lr: 2.2741e-06 eta: 2:54:56 time: 1.1120 data_time: 0.0179 memory: 15957 grad_norm: 1.0759 loss: 0.1976 semantic_segmentation_loss_cls: 0.0498 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1060 2024/07/09 00:34:03 - mmengine - INFO - Iter(train) [110600/120000] base_lr: 4.9833e-06 lr: 2.2712e-06 eta: 2:54:00 time: 1.1121 data_time: 0.0180 memory: 14802 grad_norm: 1.0762 loss: 0.1976 semantic_segmentation_loss_cls: 0.0498 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1060 2024/07/09 00:34:58 - mmengine - INFO - Iter(train) [110650/120000] base_lr: 4.9518e-06 lr: 2.2683e-06 eta: 2:53:05 time: 1.1120 data_time: 0.0180 memory: 14831 grad_norm: 1.0771 loss: 0.1976 semantic_segmentation_loss_cls: 0.0497 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1060 2024/07/09 00:35:53 - mmengine - INFO - Iter(train) [110700/120000] base_lr: 4.9205e-06 lr: 2.2655e-06 eta: 2:52:09 time: 1.1120 data_time: 0.0180 memory: 14395 grad_norm: 1.0791 loss: 0.1978 semantic_segmentation_loss_cls: 0.0498 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1061 2024/07/09 00:36:48 - mmengine - INFO - Iter(train) [110750/120000] base_lr: 4.8893e-06 lr: 2.2627e-06 eta: 2:51:13 time: 1.1119 data_time: 0.0180 memory: 15501 grad_norm: 1.0800 loss: 0.1976 semantic_segmentation_loss_cls: 0.0497 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1061 2024/07/09 00:37:42 - mmengine - INFO - Iter(train) [110800/120000] base_lr: 4.8583e-06 lr: 2.2598e-06 eta: 2:50:18 time: 1.1117 data_time: 0.0180 memory: 15596 grad_norm: 1.0800 loss: 0.1974 semantic_segmentation_loss_cls: 0.0496 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1060 2024/07/09 00:38:38 - mmengine - INFO - Iter(train) [110850/120000] base_lr: 4.8275e-06 lr: 2.2570e-06 eta: 2:49:22 time: 1.1115 data_time: 0.0180 memory: 14993 grad_norm: 1.0809 loss: 0.1972 semantic_segmentation_loss_cls: 0.0494 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1059 2024/07/09 00:39:33 - mmengine - INFO - Iter(train) [110900/120000] base_lr: 4.7968e-06 lr: 2.2543e-06 eta: 2:48:27 time: 1.1115 data_time: 0.0180 memory: 14857 grad_norm: 1.0808 loss: 0.1973 semantic_segmentation_loss_cls: 0.0494 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1060 2024/07/09 00:40:29 - mmengine - INFO - Iter(train) [110950/120000] base_lr: 4.7663e-06 lr: 2.2515e-06 eta: 2:47:31 time: 1.1114 data_time: 0.0180 memory: 15078 grad_norm: 1.0802 loss: 0.1974 semantic_segmentation_loss_cls: 0.0495 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1060 2024/07/09 00:41:24 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/09 00:41:24 - mmengine - INFO - Iter(train) [111000/120000] base_lr: 4.7360e-06 lr: 2.2487e-06 eta: 2:46:36 time: 1.1115 data_time: 0.0180 memory: 14648 grad_norm: 1.0811 loss: 0.1971 semantic_segmentation_loss_cls: 0.0493 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1059 2024/07/09 00:41:24 - mmengine - INFO - Saving checkpoint at 111000 iterations 2024/07/09 00:42:24 - mmengine - INFO - Iter(train) [111050/120000] base_lr: 4.7058e-06 lr: 2.2460e-06 eta: 2:45:41 time: 1.1114 data_time: 0.0179 memory: 14655 grad_norm: 1.0808 loss: 0.1970 semantic_segmentation_loss_cls: 0.0492 semantic_segmentation_loss_mask: 0.0419 semantic_segmentation_loss_dice: 0.1059 2024/07/09 00:43:19 - mmengine - INFO - Iter(train) [111100/120000] base_lr: 4.6758e-06 lr: 2.2433e-06 eta: 2:44:45 time: 1.1114 data_time: 0.0179 memory: 15033 grad_norm: 1.0817 loss: 0.1969 semantic_segmentation_loss_cls: 0.0493 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1059 2024/07/09 00:44:14 - mmengine - INFO - Iter(train) [111150/120000] base_lr: 4.6460e-06 lr: 2.2405e-06 eta: 2:43:49 time: 1.1114 data_time: 0.0179 memory: 16293 grad_norm: 1.0814 loss: 0.1967 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0418 semantic_segmentation_loss_dice: 0.1058 2024/07/09 00:45:10 - mmengine - INFO - Iter(train) [111200/120000] base_lr: 4.6163e-06 lr: 2.2378e-06 eta: 2:42:54 time: 1.1115 data_time: 0.0179 memory: 15756 grad_norm: 1.0816 loss: 0.1964 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0417 semantic_segmentation_loss_dice: 0.1057 2024/07/09 00:46:05 - mmengine - INFO - Iter(train) [111250/120000] base_lr: 4.5868e-06 lr: 2.2352e-06 eta: 2:41:58 time: 1.1113 data_time: 0.0179 memory: 14998 grad_norm: 1.0794 loss: 0.1961 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0417 semantic_segmentation_loss_dice: 0.1055 2024/07/09 00:47:00 - mmengine - INFO - Iter(train) [111300/120000] base_lr: 4.5574e-06 lr: 2.2325e-06 eta: 2:41:03 time: 1.1112 data_time: 0.0179 memory: 15147 grad_norm: 1.0795 loss: 0.1959 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1054 2024/07/09 00:47:55 - mmengine - INFO - Iter(train) [111350/120000] base_lr: 4.5282e-06 lr: 2.2298e-06 eta: 2:40:07 time: 1.1109 data_time: 0.0179 memory: 15152 grad_norm: 1.0794 loss: 0.1961 semantic_segmentation_loss_cls: 0.0490 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1054 2024/07/09 00:48:50 - mmengine - INFO - Iter(train) [111400/120000] base_lr: 4.4992e-06 lr: 2.2272e-06 eta: 2:39:12 time: 1.1109 data_time: 0.0179 memory: 15117 grad_norm: 1.0800 loss: 0.1961 semantic_segmentation_loss_cls: 0.0490 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1054 2024/07/09 00:49:46 - mmengine - INFO - Iter(train) [111450/120000] base_lr: 4.4704e-06 lr: 2.2246e-06 eta: 2:38:16 time: 1.1108 data_time: 0.0179 memory: 14773 grad_norm: 1.0801 loss: 0.1960 semantic_segmentation_loss_cls: 0.0490 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1055 2024/07/09 00:50:41 - mmengine - INFO - Iter(train) [111500/120000] base_lr: 4.4417e-06 lr: 2.2220e-06 eta: 2:37:20 time: 1.1108 data_time: 0.0179 memory: 15080 grad_norm: 1.0808 loss: 0.1960 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1055 2024/07/09 00:51:37 - mmengine - INFO - Iter(train) [111550/120000] base_lr: 4.4132e-06 lr: 2.2194e-06 eta: 2:36:25 time: 1.1109 data_time: 0.0179 memory: 14420 grad_norm: 1.0810 loss: 0.1959 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1054 2024/07/09 00:52:32 - mmengine - INFO - Iter(train) [111600/120000] base_lr: 4.3848e-06 lr: 2.2168e-06 eta: 2:35:29 time: 1.1106 data_time: 0.0179 memory: 15136 grad_norm: 1.0811 loss: 0.1958 semantic_segmentation_loss_cls: 0.0488 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1054 2024/07/09 00:53:28 - mmengine - INFO - Iter(train) [111650/120000] base_lr: 4.3566e-06 lr: 2.2142e-06 eta: 2:34:34 time: 1.1106 data_time: 0.0179 memory: 15432 grad_norm: 1.0823 loss: 0.1958 semantic_segmentation_loss_cls: 0.0488 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1054 2024/07/09 00:54:23 - mmengine - INFO - Iter(train) [111700/120000] base_lr: 4.3286e-06 lr: 2.2117e-06 eta: 2:33:38 time: 1.1108 data_time: 0.0179 memory: 15442 grad_norm: 1.0836 loss: 0.1963 semantic_segmentation_loss_cls: 0.0490 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1056 2024/07/09 00:55:18 - mmengine - INFO - Iter(train) [111750/120000] base_lr: 4.3007e-06 lr: 2.2092e-06 eta: 2:32:43 time: 1.1107 data_time: 0.0179 memory: 14757 grad_norm: 1.0817 loss: 0.1962 semantic_segmentation_loss_cls: 0.0490 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1056 2024/07/09 00:56:13 - mmengine - INFO - Iter(train) [111800/120000] base_lr: 4.2730e-06 lr: 2.2066e-06 eta: 2:31:47 time: 1.1109 data_time: 0.0179 memory: 16002 grad_norm: 1.0813 loss: 0.1964 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0417 semantic_segmentation_loss_dice: 0.1056 2024/07/09 00:57:09 - mmengine - INFO - Iter(train) [111850/120000] base_lr: 4.2455e-06 lr: 2.2041e-06 eta: 2:30:52 time: 1.1110 data_time: 0.0179 memory: 14563 grad_norm: 1.0807 loss: 0.1961 semantic_segmentation_loss_cls: 0.0490 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1055 2024/07/09 00:58:04 - mmengine - INFO - Iter(train) [111900/120000] base_lr: 4.2182e-06 lr: 2.2017e-06 eta: 2:29:56 time: 1.1107 data_time: 0.0179 memory: 16710 grad_norm: 1.0838 loss: 0.1961 semantic_segmentation_loss_cls: 0.0490 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1055 2024/07/09 00:58:59 - mmengine - INFO - Iter(train) [111950/120000] base_lr: 4.1910e-06 lr: 2.1992e-06 eta: 2:29:00 time: 1.1104 data_time: 0.0179 memory: 14949 grad_norm: 1.0846 loss: 0.1962 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1055 2024/07/09 00:59:55 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/09 00:59:55 - mmengine - INFO - Iter(train) [112000/120000] base_lr: 4.1639e-06 lr: 2.1967e-06 eta: 2:28:05 time: 1.1106 data_time: 0.0179 memory: 15074 grad_norm: 1.0857 loss: 0.1961 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1054 2024/07/09 00:59:55 - mmengine - INFO - Saving checkpoint at 112000 iterations 2024/07/09 01:00:55 - mmengine - INFO - Iter(train) [112050/120000] base_lr: 4.1371e-06 lr: 2.1943e-06 eta: 2:27:10 time: 1.1107 data_time: 0.0179 memory: 16175 grad_norm: 1.0837 loss: 0.1958 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1053 2024/07/09 01:01:50 - mmengine - INFO - Iter(train) [112100/120000] base_lr: 4.1104e-06 lr: 2.1919e-06 eta: 2:26:14 time: 1.1107 data_time: 0.0179 memory: 14957 grad_norm: 1.0840 loss: 0.1953 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1051 2024/07/09 01:02:45 - mmengine - INFO - Iter(train) [112150/120000] base_lr: 4.0838e-06 lr: 2.1894e-06 eta: 2:25:19 time: 1.1105 data_time: 0.0179 memory: 15642 grad_norm: 1.0835 loss: 0.1953 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1051 2024/07/09 01:03:41 - mmengine - INFO - Iter(train) [112200/120000] base_lr: 4.0575e-06 lr: 2.1870e-06 eta: 2:24:23 time: 1.1105 data_time: 0.0179 memory: 14876 grad_norm: 1.0836 loss: 0.1950 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1050 2024/07/09 01:04:38 - mmengine - INFO - Iter(train) [112250/120000] base_lr: 4.0313e-06 lr: 2.1847e-06 eta: 2:23:28 time: 1.1108 data_time: 0.0179 memory: 15348 grad_norm: 1.0838 loss: 0.1950 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1049 2024/07/09 01:05:34 - mmengine - INFO - Iter(train) [112300/120000] base_lr: 4.0052e-06 lr: 2.1823e-06 eta: 2:22:32 time: 1.1108 data_time: 0.0179 memory: 16303 grad_norm: 1.0823 loss: 0.1954 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1051 2024/07/09 01:06:30 - mmengine - INFO - Iter(train) [112350/120000] base_lr: 3.9794e-06 lr: 2.1799e-06 eta: 2:21:37 time: 1.1111 data_time: 0.0179 memory: 14928 grad_norm: 1.0827 loss: 0.1958 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1053 2024/07/09 01:07:26 - mmengine - INFO - Iter(train) [112400/120000] base_lr: 3.9537e-06 lr: 2.1776e-06 eta: 2:20:41 time: 1.1113 data_time: 0.0179 memory: 14370 grad_norm: 1.0820 loss: 0.1958 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1053 2024/07/09 01:08:21 - mmengine - INFO - Iter(train) [112450/120000] base_lr: 3.9281e-06 lr: 2.1753e-06 eta: 2:19:46 time: 1.1115 data_time: 0.0179 memory: 16059 grad_norm: 1.0815 loss: 0.1959 semantic_segmentation_loss_cls: 0.0490 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1054 2024/07/09 01:09:17 - mmengine - INFO - Iter(train) [112500/120000] base_lr: 3.9028e-06 lr: 2.1730e-06 eta: 2:18:50 time: 1.1114 data_time: 0.0179 memory: 15263 grad_norm: 1.0828 loss: 0.1959 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1054 2024/07/09 01:10:12 - mmengine - INFO - Iter(train) [112550/120000] base_lr: 3.8776e-06 lr: 2.1707e-06 eta: 2:17:55 time: 1.1112 data_time: 0.0179 memory: 14491 grad_norm: 1.0832 loss: 0.1958 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1053 2024/07/09 01:11:07 - mmengine - INFO - Iter(train) [112600/120000] base_lr: 3.8525e-06 lr: 2.1684e-06 eta: 2:16:59 time: 1.1110 data_time: 0.0179 memory: 15582 grad_norm: 1.0824 loss: 0.1957 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1052 2024/07/09 01:12:03 - mmengine - INFO - Iter(train) [112650/120000] base_lr: 3.8277e-06 lr: 2.1662e-06 eta: 2:16:03 time: 1.1112 data_time: 0.0179 memory: 14841 grad_norm: 1.0820 loss: 0.1955 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1052 2024/07/09 01:12:58 - mmengine - INFO - Iter(train) [112700/120000] base_lr: 3.8030e-06 lr: 2.1639e-06 eta: 2:15:08 time: 1.1114 data_time: 0.0179 memory: 15896 grad_norm: 1.0830 loss: 0.1955 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1052 2024/07/09 01:13:54 - mmengine - INFO - Iter(train) [112750/120000] base_lr: 3.7784e-06 lr: 2.1617e-06 eta: 2:14:12 time: 1.1116 data_time: 0.0179 memory: 15448 grad_norm: 1.0814 loss: 0.1952 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1050 2024/07/09 01:14:50 - mmengine - INFO - Iter(train) [112800/120000] base_lr: 3.7540e-06 lr: 2.1595e-06 eta: 2:13:17 time: 1.1118 data_time: 0.0179 memory: 14404 grad_norm: 1.0831 loss: 0.1955 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1051 2024/07/09 01:15:46 - mmengine - INFO - Iter(train) [112850/120000] base_lr: 3.7298e-06 lr: 2.1573e-06 eta: 2:12:21 time: 1.1117 data_time: 0.0179 memory: 15698 grad_norm: 1.0839 loss: 0.1957 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1052 2024/07/09 01:16:42 - mmengine - INFO - Iter(train) [112900/120000] base_lr: 3.7058e-06 lr: 2.1551e-06 eta: 2:11:26 time: 1.1117 data_time: 0.0179 memory: 15295 grad_norm: 1.0845 loss: 0.1957 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1052 2024/07/09 01:17:37 - mmengine - INFO - Iter(train) [112950/120000] base_lr: 3.6819e-06 lr: 2.1529e-06 eta: 2:10:30 time: 1.1119 data_time: 0.0179 memory: 15122 grad_norm: 1.0852 loss: 0.1957 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1052 2024/07/09 01:18:33 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/09 01:18:33 - mmengine - INFO - Iter(train) [113000/120000] base_lr: 3.6582e-06 lr: 2.1507e-06 eta: 2:09:35 time: 1.1121 data_time: 0.0179 memory: 15063 grad_norm: 1.0848 loss: 0.1956 semantic_segmentation_loss_cls: 0.0490 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1051 2024/07/09 01:18:33 - mmengine - INFO - Saving checkpoint at 113000 iterations 2024/07/09 01:19:33 - mmengine - INFO - Iter(train) [113050/120000] base_lr: 3.6347e-06 lr: 2.1486e-06 eta: 2:08:39 time: 1.1120 data_time: 0.0178 memory: 15050 grad_norm: 1.0825 loss: 0.1955 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1050 2024/07/09 01:20:28 - mmengine - INFO - Iter(train) [113100/120000] base_lr: 3.6113e-06 lr: 2.1465e-06 eta: 2:07:44 time: 1.1120 data_time: 0.0178 memory: 14461 grad_norm: 1.0841 loss: 0.1955 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1051 2024/07/09 01:21:24 - mmengine - INFO - Iter(train) [113150/120000] base_lr: 3.5881e-06 lr: 2.1444e-06 eta: 2:06:48 time: 1.1120 data_time: 0.0178 memory: 14816 grad_norm: 1.0831 loss: 0.1955 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1051 2024/07/09 01:22:19 - mmengine - INFO - Iter(train) [113200/120000] base_lr: 3.5651e-06 lr: 2.1423e-06 eta: 2:05:53 time: 1.1118 data_time: 0.0178 memory: 16013 grad_norm: 1.0840 loss: 0.1959 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1053 2024/07/09 01:23:14 - mmengine - INFO - Iter(train) [113250/120000] base_lr: 3.5422e-06 lr: 2.1402e-06 eta: 2:04:57 time: 1.1120 data_time: 0.0178 memory: 15215 grad_norm: 1.0820 loss: 0.1958 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1052 2024/07/09 01:24:09 - mmengine - INFO - Iter(train) [113300/120000] base_lr: 3.5195e-06 lr: 2.1381e-06 eta: 2:04:02 time: 1.1123 data_time: 0.0178 memory: 15714 grad_norm: 1.0827 loss: 0.1960 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1053 2024/07/09 01:25:04 - mmengine - INFO - Iter(train) [113350/120000] base_lr: 3.4970e-06 lr: 2.1361e-06 eta: 2:03:06 time: 1.1123 data_time: 0.0178 memory: 14909 grad_norm: 1.0833 loss: 0.1958 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1052 2024/07/09 01:25:58 - mmengine - INFO - Iter(train) [113400/120000] base_lr: 3.4746e-06 lr: 2.1341e-06 eta: 2:02:11 time: 1.1120 data_time: 0.0177 memory: 14787 grad_norm: 1.0837 loss: 0.1959 semantic_segmentation_loss_cls: 0.0492 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1053 2024/07/09 01:26:54 - mmengine - INFO - Iter(train) [113450/120000] base_lr: 3.4524e-06 lr: 2.1320e-06 eta: 2:01:15 time: 1.1120 data_time: 0.0177 memory: 15213 grad_norm: 1.0842 loss: 0.1962 semantic_segmentation_loss_cls: 0.0492 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1054 2024/07/09 01:27:49 - mmengine - INFO - Iter(train) [113500/120000] base_lr: 3.4304e-06 lr: 2.1300e-06 eta: 2:00:19 time: 1.1119 data_time: 0.0177 memory: 15178 grad_norm: 1.0842 loss: 0.1963 semantic_segmentation_loss_cls: 0.0493 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1055 2024/07/09 01:28:45 - mmengine - INFO - Iter(train) [113550/120000] base_lr: 3.4085e-06 lr: 2.1280e-06 eta: 1:59:24 time: 1.1119 data_time: 0.0177 memory: 16425 grad_norm: 1.0834 loss: 0.1961 semantic_segmentation_loss_cls: 0.0492 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1054 2024/07/09 01:29:40 - mmengine - INFO - Iter(train) [113600/120000] base_lr: 3.3868e-06 lr: 2.1261e-06 eta: 1:58:28 time: 1.1117 data_time: 0.0177 memory: 16043 grad_norm: 1.0847 loss: 0.1962 semantic_segmentation_loss_cls: 0.0492 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1054 2024/07/09 01:30:36 - mmengine - INFO - Iter(train) [113650/120000] base_lr: 3.3653e-06 lr: 2.1241e-06 eta: 1:57:33 time: 1.1119 data_time: 0.0177 memory: 15274 grad_norm: 1.0853 loss: 0.1961 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1054 2024/07/09 01:31:32 - mmengine - INFO - Iter(train) [113700/120000] base_lr: 3.3439e-06 lr: 2.1222e-06 eta: 1:56:37 time: 1.1121 data_time: 0.0177 memory: 15562 grad_norm: 1.0852 loss: 0.1962 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0417 semantic_segmentation_loss_dice: 0.1054 2024/07/09 01:32:28 - mmengine - INFO - Iter(train) [113750/120000] base_lr: 3.3227e-06 lr: 2.1202e-06 eta: 1:55:42 time: 1.1123 data_time: 0.0177 memory: 15191 grad_norm: 1.0862 loss: 0.1961 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1054 2024/07/09 01:33:24 - mmengine - INFO - Iter(train) [113800/120000] base_lr: 3.3017e-06 lr: 2.1183e-06 eta: 1:54:46 time: 1.1125 data_time: 0.0177 memory: 14722 grad_norm: 1.0870 loss: 0.1959 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1053 2024/07/09 01:34:19 - mmengine - INFO - Iter(train) [113850/120000] base_lr: 3.2808e-06 lr: 2.1164e-06 eta: 1:53:51 time: 1.1123 data_time: 0.0177 memory: 15076 grad_norm: 1.0902 loss: 0.1959 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1053 2024/07/09 01:35:15 - mmengine - INFO - Iter(train) [113900/120000] base_lr: 3.2601e-06 lr: 2.1146e-06 eta: 1:52:55 time: 1.1126 data_time: 0.0177 memory: 14933 grad_norm: 1.0902 loss: 0.1961 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1053 2024/07/09 01:36:11 - mmengine - INFO - Iter(train) [113950/120000] base_lr: 3.2396e-06 lr: 2.1127e-06 eta: 1:52:00 time: 1.1128 data_time: 0.0178 memory: 15621 grad_norm: 1.0913 loss: 0.1961 semantic_segmentation_loss_cls: 0.0492 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1054 2024/07/09 01:37:06 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/09 01:37:06 - mmengine - INFO - Iter(train) [114000/120000] base_lr: 3.2193e-06 lr: 2.1108e-06 eta: 1:51:04 time: 1.1126 data_time: 0.0178 memory: 15207 grad_norm: 1.0900 loss: 0.1960 semantic_segmentation_loss_cls: 0.0492 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1053 2024/07/09 01:37:06 - mmengine - INFO - Saving checkpoint at 114000 iterations 2024/07/09 01:38:05 - mmengine - INFO - Iter(train) [114050/120000] base_lr: 3.1991e-06 lr: 2.1090e-06 eta: 1:50:09 time: 1.1133 data_time: 0.0187 memory: 15388 grad_norm: 1.0900 loss: 0.1959 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1053 2024/07/09 01:39:00 - mmengine - INFO - Iter(train) [114100/120000] base_lr: 3.1790e-06 lr: 2.1072e-06 eta: 1:49:13 time: 1.1132 data_time: 0.0187 memory: 16057 grad_norm: 1.0882 loss: 0.1960 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0416 semantic_segmentation_loss_dice: 0.1054 2024/07/09 01:39:56 - mmengine - INFO - Iter(train) [114150/120000] base_lr: 3.1592e-06 lr: 2.1054e-06 eta: 1:48:18 time: 1.1133 data_time: 0.0187 memory: 14656 grad_norm: 1.0899 loss: 0.1957 semantic_segmentation_loss_cls: 0.0490 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1052 2024/07/09 01:40:51 - mmengine - INFO - Iter(train) [114200/120000] base_lr: 3.1395e-06 lr: 2.1036e-06 eta: 1:47:22 time: 1.1134 data_time: 0.0187 memory: 14936 grad_norm: 1.0897 loss: 0.1956 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1052 2024/07/09 01:41:46 - mmengine - INFO - Iter(train) [114250/120000] base_lr: 3.1200e-06 lr: 2.1018e-06 eta: 1:46:27 time: 1.1132 data_time: 0.0187 memory: 14935 grad_norm: 1.0898 loss: 0.1954 semantic_segmentation_loss_cls: 0.0488 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1051 2024/07/09 01:42:40 - mmengine - INFO - Iter(train) [114300/120000] base_lr: 3.1006e-06 lr: 2.1001e-06 eta: 1:45:31 time: 1.1127 data_time: 0.0187 memory: 15213 grad_norm: 1.0902 loss: 0.1951 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0414 semantic_segmentation_loss_dice: 0.1050 2024/07/09 01:43:34 - mmengine - INFO - Iter(train) [114350/120000] base_lr: 3.0814e-06 lr: 2.0983e-06 eta: 1:44:35 time: 1.1124 data_time: 0.0187 memory: 15758 grad_norm: 1.0942 loss: 0.1951 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0414 semantic_segmentation_loss_dice: 0.1050 2024/07/09 01:44:30 - mmengine - INFO - Iter(train) [114400/120000] base_lr: 3.0624e-06 lr: 2.0966e-06 eta: 1:43:40 time: 1.1123 data_time: 0.0187 memory: 15344 grad_norm: 1.0963 loss: 0.1950 semantic_segmentation_loss_cls: 0.0488 semantic_segmentation_loss_mask: 0.0414 semantic_segmentation_loss_dice: 0.1049 2024/07/09 01:45:25 - mmengine - INFO - Iter(train) [114450/120000] base_lr: 3.0436e-06 lr: 2.0949e-06 eta: 1:42:44 time: 1.1123 data_time: 0.0187 memory: 16221 grad_norm: 1.0981 loss: 0.1951 semantic_segmentation_loss_cls: 0.0488 semantic_segmentation_loss_mask: 0.0414 semantic_segmentation_loss_dice: 0.1049 2024/07/09 01:46:21 - mmengine - INFO - Iter(train) [114500/120000] base_lr: 3.0249e-06 lr: 2.0932e-06 eta: 1:41:49 time: 1.1123 data_time: 0.0187 memory: 14945 grad_norm: 1.0975 loss: 0.1951 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1049 2024/07/09 01:47:17 - mmengine - INFO - Iter(train) [114550/120000] base_lr: 3.0064e-06 lr: 2.0915e-06 eta: 1:40:53 time: 1.1122 data_time: 0.0187 memory: 15996 grad_norm: 1.0977 loss: 0.1952 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0414 semantic_segmentation_loss_dice: 0.1049 2024/07/09 01:48:13 - mmengine - INFO - Iter(train) [114600/120000] base_lr: 2.9880e-06 lr: 2.0898e-06 eta: 1:39:58 time: 1.1123 data_time: 0.0186 memory: 15156 grad_norm: 1.0973 loss: 0.1952 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0414 semantic_segmentation_loss_dice: 0.1049 2024/07/09 01:49:08 - mmengine - INFO - Iter(train) [114650/120000] base_lr: 2.9698e-06 lr: 2.0882e-06 eta: 1:39:02 time: 1.1123 data_time: 0.0186 memory: 15465 grad_norm: 1.0974 loss: 0.1951 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1049 2024/07/09 01:50:03 - mmengine - INFO - Iter(train) [114700/120000] base_lr: 2.9518e-06 lr: 2.0865e-06 eta: 1:38:07 time: 1.1123 data_time: 0.0186 memory: 15297 grad_norm: 1.0946 loss: 0.1949 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1048 2024/07/09 01:50:58 - mmengine - INFO - Iter(train) [114750/120000] base_lr: 2.9340e-06 lr: 2.0849e-06 eta: 1:37:11 time: 1.1125 data_time: 0.0187 memory: 16157 grad_norm: 1.0942 loss: 0.1948 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0414 semantic_segmentation_loss_dice: 0.1048 2024/07/09 01:51:53 - mmengine - INFO - Iter(train) [114800/120000] base_lr: 2.9163e-06 lr: 2.0833e-06 eta: 1:36:15 time: 1.1126 data_time: 0.0187 memory: 14595 grad_norm: 1.0952 loss: 0.1948 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1048 2024/07/09 01:52:49 - mmengine - INFO - Iter(train) [114850/120000] base_lr: 2.8988e-06 lr: 2.0817e-06 eta: 1:35:20 time: 1.1126 data_time: 0.0186 memory: 15170 grad_norm: 1.0967 loss: 0.1948 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1048 2024/07/09 01:53:46 - mmengine - INFO - Iter(train) [114900/120000] base_lr: 2.8815e-06 lr: 2.0801e-06 eta: 1:34:24 time: 1.1129 data_time: 0.0186 memory: 15622 grad_norm: 1.0966 loss: 0.1949 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0414 semantic_segmentation_loss_dice: 0.1048 2024/07/09 01:54:41 - mmengine - INFO - Iter(train) [114950/120000] base_lr: 2.8643e-06 lr: 2.0786e-06 eta: 1:33:29 time: 1.1129 data_time: 0.0187 memory: 14976 grad_norm: 1.0972 loss: 0.1952 semantic_segmentation_loss_cls: 0.0488 semantic_segmentation_loss_mask: 0.0414 semantic_segmentation_loss_dice: 0.1049 2024/07/09 01:55:37 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/09 01:55:37 - mmengine - INFO - Iter(train) [115000/120000] base_lr: 2.8473e-06 lr: 2.0770e-06 eta: 1:32:33 time: 1.1129 data_time: 0.0187 memory: 14546 grad_norm: 1.0973 loss: 0.1952 semantic_segmentation_loss_cls: 0.0488 semantic_segmentation_loss_mask: 0.0414 semantic_segmentation_loss_dice: 0.1050 2024/07/09 01:55:37 - mmengine - INFO - Saving checkpoint at 115000 iterations 2024/07/09 01:55:53 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:48 time: 0.2414 data_time: 0.0017 memory: 5013 2024/07/09 01:56:05 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:36 time: 0.2414 data_time: 0.0017 memory: 5189 2024/07/09 01:56:17 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:24 time: 0.2414 data_time: 0.0017 memory: 4460 2024/07/09 01:56:29 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:12 time: 0.2414 data_time: 0.0017 memory: 4543 2024/07/09 01:56:42 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:00 time: 0.2414 data_time: 0.0017 memory: 4645 2024/07/09 01:56:54 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:48 time: 0.2415 data_time: 0.0017 memory: 10983 2024/07/09 01:57:06 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2415 data_time: 0.0017 memory: 4460 2024/07/09 01:57:18 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2416 data_time: 0.0017 memory: 4641 2024/07/09 01:57:30 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2416 data_time: 0.0017 memory: 4473 2024/07/09 01:57:43 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2416 data_time: 0.0017 memory: 4555 2024/07/09 01:57:43 - mmengine - INFO - per class results: 2024/07/09 01:57:43 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.68 | 87.8 | | building | 83.38 | 91.86 | | sky | 94.51 | 97.76 | | floor | 82.79 | 91.37 | | tree | 76.12 | 88.35 | | ceiling | 84.78 | 93.01 | | road | 84.28 | 93.1 | | bed | 87.85 | 95.18 | | windowpane | 62.13 | 79.1 | | grass | 70.78 | 85.68 | | cabinet | 60.74 | 71.54 | | sidewalk | 67.63 | 80.83 | | person | 82.28 | 91.88 | | earth | 34.85 | 46.77 | | door | 55.57 | 69.55 | | table | 63.46 | 76.6 | | mountain | 57.29 | 72.83 | | plant | 54.86 | 68.76 | | curtain | 73.22 | 87.54 | | chair | 59.64 | 72.71 | | car | 85.45 | 91.54 | | water | 52.78 | 67.64 | | painting | 71.53 | 88.64 | | sofa | 66.06 | 77.42 | | shelf | 45.29 | 64.72 | | house | 50.83 | 77.37 | | sea | 51.37 | 80.81 | | mirror | 66.87 | 74.84 | | rug | 65.26 | 75.51 | | field | 41.82 | 57.32 | | armchair | 46.04 | 70.49 | | seat | 55.69 | 81.2 | | fence | 46.59 | 62.35 | | desk | 47.41 | 69.01 | | rock | 36.44 | 58.08 | | wardrobe | 52.83 | 71.81 | | lamp | 67.38 | 78.77 | | bathtub | 85.93 | 90.36 | | railing | 38.54 | 53.88 | | cushion | 57.67 | 68.72 | | base | 22.78 | 33.22 | | box | 25.21 | 37.38 | | column | 48.92 | 66.28 | | signboard | 40.17 | 54.73 | | chest of drawers | 38.75 | 68.61 | | counter | 30.03 | 42.84 | | sand | 35.01 | 50.2 | | sink | 75.78 | 82.24 | | skyscraper | 36.88 | 46.27 | | fireplace | 66.85 | 86.45 | | refrigerator | 80.75 | 89.79 | | grandstand | 42.63 | 73.96 | | path | 29.19 | 41.38 | | stairs | 32.65 | 41.2 | | runway | 75.95 | 90.1 | | case | 64.83 | 80.0 | | pool table | 93.42 | 96.24 | | pillow | 54.95 | 66.03 | | screen door | 79.34 | 83.99 | | stairway | 37.82 | 42.61 | | river | 25.5 | 44.57 | | bridge | 66.41 | 88.88 | | bookcase | 40.22 | 57.33 | | blind | 38.08 | 42.47 | | coffee table | 70.64 | 84.73 | | toilet | 87.22 | 89.78 | | flower | 41.26 | 57.51 | | book | 52.23 | 73.34 | | hill | 14.02 | 23.09 | | bench | 39.36 | 52.37 | | countertop | 53.82 | 68.02 | | stove | 80.75 | 84.95 | | palm | 51.16 | 67.24 | | kitchen island | 30.79 | 74.35 | | computer | 60.7 | 66.63 | | swivel chair | 39.34 | 54.48 | | boat | 69.5 | 80.28 | | bar | 46.79 | 58.32 | | arcade machine | 54.85 | 60.45 | | hovel | 30.19 | 42.66 | | bus | 92.74 | 95.58 | | towel | 68.58 | 74.69 | | light | 62.32 | 76.18 | | truck | 36.26 | 48.26 | | tower | 32.91 | 54.34 | | chandelier | 64.33 | 74.6 | | awning | 34.0 | 43.93 | | streetlight | 39.59 | 53.08 | | booth | 45.24 | 54.35 | | television receiver | 48.28 | 89.63 | | airplane | 59.2 | 67.48 | | dirt track | 4.15 | 5.26 | | apparel | 40.34 | 57.36 | | pole | 30.75 | 45.66 | | land | 1.59 | 1.9 | | bannister | 16.92 | 26.77 | | escalator | 27.76 | 39.16 | | ottoman | 39.01 | 64.06 | | bottle | 22.38 | 26.7 | | buffet | 41.81 | 47.28 | | poster | 25.24 | 33.07 | | stage | 13.03 | 34.75 | | van | 48.92 | 65.16 | | ship | 82.58 | 88.02 | | fountain | 7.12 | 8.18 | | conveyer belt | 61.56 | 91.51 | | canopy | 13.64 | 22.02 | | washer | 71.66 | 73.28 | | plaything | 25.17 | 33.75 | | swimming pool | 29.65 | 33.51 | | stool | 56.47 | 67.93 | | barrel | 15.15 | 55.52 | | basket | 35.48 | 44.44 | | waterfall | 46.45 | 51.71 | | tent | 74.8 | 97.68 | | bag | 17.81 | 23.62 | | minibike | 65.48 | 85.26 | | cradle | 76.6 | 96.55 | | oven | 47.97 | 57.86 | | ball | 30.23 | 36.78 | | food | 64.23 | 77.56 | | step | 26.16 | 28.22 | | tank | 36.25 | 45.38 | | trade name | 28.93 | 34.89 | | microwave | 38.09 | 41.22 | | pot | 43.34 | 48.21 | | animal | 61.71 | 68.79 | | bicycle | 58.09 | 75.95 | | lake | 63.44 | 63.66 | | dishwasher | 80.86 | 84.58 | | screen | 64.13 | 88.54 | | blanket | 10.33 | 12.54 | | sculpture | 67.5 | 81.43 | | hood | 74.45 | 79.13 | | sconce | 51.42 | 64.64 | | vase | 39.43 | 65.53 | | traffic light | 42.64 | 56.65 | | tray | 19.31 | 25.05 | | ashcan | 47.73 | 61.85 | | fan | 62.87 | 78.6 | | pier | 36.31 | 73.08 | | crt screen | 0.0 | 0.0 | | plate | 61.99 | 72.91 | | monitor | 24.28 | 34.31 | | bulletin board | 43.06 | 51.23 | | shower | 11.25 | 17.36 | | radiator | 59.22 | 68.93 | | glass | 18.44 | 19.64 | | clock | 31.98 | 35.34 | | flag | 46.74 | 53.38 | +---------------------+-------+-------+ 2024/07/09 01:57:43 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.7000 mIoU: 50.1200 mAcc: 62.6900 data_time: 0.0017 time: 0.2423 2024/07/09 01:58:39 - mmengine - INFO - Iter(train) [115050/120000] base_lr: 2.8305e-06 lr: 2.0755e-06 eta: 1:31:38 time: 1.1121 data_time: 0.0177 memory: 15279 grad_norm: 1.0973 loss: 0.1953 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0414 semantic_segmentation_loss_dice: 0.1050 2024/07/09 01:59:35 - mmengine - INFO - Iter(train) [115100/120000] base_lr: 2.8138e-06 lr: 2.0740e-06 eta: 1:30:42 time: 1.1122 data_time: 0.0177 memory: 15088 grad_norm: 1.0969 loss: 0.1955 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:00:30 - mmengine - INFO - Iter(train) [115150/120000] base_lr: 2.7973e-06 lr: 2.0725e-06 eta: 1:29:47 time: 1.1122 data_time: 0.0176 memory: 15443 grad_norm: 1.0973 loss: 0.1955 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:01:25 - mmengine - INFO - Iter(train) [115200/120000] base_lr: 2.7810e-06 lr: 2.0710e-06 eta: 1:28:51 time: 1.1122 data_time: 0.0177 memory: 15508 grad_norm: 1.0968 loss: 0.1954 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:02:21 - mmengine - INFO - Iter(train) [115250/120000] base_lr: 2.7648e-06 lr: 2.0695e-06 eta: 1:27:56 time: 1.1122 data_time: 0.0177 memory: 14772 grad_norm: 1.0989 loss: 0.1957 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:03:16 - mmengine - INFO - Iter(train) [115300/120000] base_lr: 2.7488e-06 lr: 2.0681e-06 eta: 1:27:00 time: 1.1124 data_time: 0.0177 memory: 14701 grad_norm: 1.0984 loss: 0.1955 semantic_segmentation_loss_cls: 0.0490 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1050 2024/07/09 02:04:12 - mmengine - INFO - Iter(train) [115350/120000] base_lr: 2.7330e-06 lr: 2.0666e-06 eta: 1:26:05 time: 1.1125 data_time: 0.0177 memory: 15295 grad_norm: 1.0987 loss: 0.1956 semantic_segmentation_loss_cls: 0.0490 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:05:07 - mmengine - INFO - Iter(train) [115400/120000] base_lr: 2.7173e-06 lr: 2.0652e-06 eta: 1:25:09 time: 1.1126 data_time: 0.0177 memory: 16441 grad_norm: 1.0985 loss: 0.1956 semantic_segmentation_loss_cls: 0.0490 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:06:03 - mmengine - INFO - Iter(train) [115450/120000] base_lr: 2.7018e-06 lr: 2.0638e-06 eta: 1:24:13 time: 1.1126 data_time: 0.0177 memory: 15354 grad_norm: 1.0990 loss: 0.1956 semantic_segmentation_loss_cls: 0.0490 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:06:58 - mmengine - INFO - Iter(train) [115500/120000] base_lr: 2.6865e-06 lr: 2.0624e-06 eta: 1:23:18 time: 1.1126 data_time: 0.0177 memory: 14580 grad_norm: 1.0973 loss: 0.1956 semantic_segmentation_loss_cls: 0.0490 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:07:54 - mmengine - INFO - Iter(train) [115550/120000] base_lr: 2.6714e-06 lr: 2.0610e-06 eta: 1:22:22 time: 1.1126 data_time: 0.0177 memory: 15151 grad_norm: 1.0959 loss: 0.1957 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0415 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:08:50 - mmengine - INFO - Iter(train) [115600/120000] base_lr: 2.6564e-06 lr: 2.0597e-06 eta: 1:21:27 time: 1.1129 data_time: 0.0177 memory: 15694 grad_norm: 1.0945 loss: 0.1955 semantic_segmentation_loss_cls: 0.0491 semantic_segmentation_loss_mask: 0.0414 semantic_segmentation_loss_dice: 0.1050 2024/07/09 02:09:45 - mmengine - INFO - Iter(train) [115650/120000] base_lr: 2.6416e-06 lr: 2.0583e-06 eta: 1:20:31 time: 1.1128 data_time: 0.0177 memory: 14896 grad_norm: 1.0928 loss: 0.1953 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0414 semantic_segmentation_loss_dice: 0.1050 2024/07/09 02:10:41 - mmengine - INFO - Iter(train) [115700/120000] base_lr: 2.6269e-06 lr: 2.0570e-06 eta: 1:19:36 time: 1.1128 data_time: 0.0177 memory: 15914 grad_norm: 1.0917 loss: 0.1950 semantic_segmentation_loss_cls: 0.0488 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1049 2024/07/09 02:11:36 - mmengine - INFO - Iter(train) [115750/120000] base_lr: 2.6125e-06 lr: 2.0557e-06 eta: 1:18:40 time: 1.1130 data_time: 0.0177 memory: 16004 grad_norm: 1.0920 loss: 0.1950 semantic_segmentation_loss_cls: 0.0488 semantic_segmentation_loss_mask: 0.0414 semantic_segmentation_loss_dice: 0.1048 2024/07/09 02:12:32 - mmengine - INFO - Iter(train) [115800/120000] base_lr: 2.5981e-06 lr: 2.0544e-06 eta: 1:17:45 time: 1.1130 data_time: 0.0177 memory: 15329 grad_norm: 1.0912 loss: 0.1948 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1048 2024/07/09 02:13:27 - mmengine - INFO - Iter(train) [115850/120000] base_lr: 2.5840e-06 lr: 2.0531e-06 eta: 1:16:49 time: 1.1128 data_time: 0.0177 memory: 14743 grad_norm: 1.0912 loss: 0.1951 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1049 2024/07/09 02:14:21 - mmengine - INFO - Iter(train) [115900/120000] base_lr: 2.5700e-06 lr: 2.0518e-06 eta: 1:15:54 time: 1.1127 data_time: 0.0177 memory: 15479 grad_norm: 1.0891 loss: 0.1948 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1048 2024/07/09 02:15:16 - mmengine - INFO - Iter(train) [115950/120000] base_lr: 2.5562e-06 lr: 2.0506e-06 eta: 1:14:58 time: 1.1128 data_time: 0.0177 memory: 15526 grad_norm: 1.0898 loss: 0.1947 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1048 2024/07/09 02:16:11 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/09 02:16:11 - mmengine - INFO - Iter(train) [116000/120000] base_lr: 2.5426e-06 lr: 2.0493e-06 eta: 1:14:02 time: 1.1124 data_time: 0.0177 memory: 15715 grad_norm: 1.0883 loss: 0.1947 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1049 2024/07/09 02:16:11 - mmengine - INFO - Saving checkpoint at 116000 iterations 2024/07/09 02:17:11 - mmengine - INFO - Iter(train) [116050/120000] base_lr: 2.5291e-06 lr: 2.0481e-06 eta: 1:13:07 time: 1.1123 data_time: 0.0177 memory: 15373 grad_norm: 1.0895 loss: 0.1947 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1048 2024/07/09 02:18:06 - mmengine - INFO - Iter(train) [116100/120000] base_lr: 2.5158e-06 lr: 2.0469e-06 eta: 1:12:12 time: 1.1122 data_time: 0.0177 memory: 15362 grad_norm: 1.0885 loss: 0.1951 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1050 2024/07/09 02:19:01 - mmengine - INFO - Iter(train) [116150/120000] base_lr: 2.5027e-06 lr: 2.0457e-06 eta: 1:11:16 time: 1.1124 data_time: 0.0177 memory: 15776 grad_norm: 1.0897 loss: 0.1949 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1049 2024/07/09 02:19:58 - mmengine - INFO - Iter(train) [116200/120000] base_lr: 2.4898e-06 lr: 2.0445e-06 eta: 1:10:20 time: 1.1125 data_time: 0.0177 memory: 15108 grad_norm: 1.0899 loss: 0.1950 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1050 2024/07/09 02:20:53 - mmengine - INFO - Iter(train) [116250/120000] base_lr: 2.4770e-06 lr: 2.0434e-06 eta: 1:09:25 time: 1.1122 data_time: 0.0177 memory: 15488 grad_norm: 1.0911 loss: 0.1950 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1050 2024/07/09 02:21:49 - mmengine - INFO - Iter(train) [116300/120000] base_lr: 2.4643e-06 lr: 2.0422e-06 eta: 1:08:29 time: 1.1122 data_time: 0.0177 memory: 15087 grad_norm: 1.0905 loss: 0.1946 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1048 2024/07/09 02:22:44 - mmengine - INFO - Iter(train) [116350/120000] base_lr: 2.4519e-06 lr: 2.0411e-06 eta: 1:07:34 time: 1.1119 data_time: 0.0177 memory: 15102 grad_norm: 1.0918 loss: 0.1943 semantic_segmentation_loss_cls: 0.0484 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1047 2024/07/09 02:23:39 - mmengine - INFO - Iter(train) [116400/120000] base_lr: 2.4396e-06 lr: 2.0400e-06 eta: 1:06:38 time: 1.1116 data_time: 0.0177 memory: 15437 grad_norm: 1.0913 loss: 0.1941 semantic_segmentation_loss_cls: 0.0484 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1046 2024/07/09 02:24:34 - mmengine - INFO - Iter(train) [116450/120000] base_lr: 2.4275e-06 lr: 2.0389e-06 eta: 1:05:43 time: 1.1116 data_time: 0.0177 memory: 15622 grad_norm: 1.0910 loss: 0.1941 semantic_segmentation_loss_cls: 0.0483 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1046 2024/07/09 02:25:29 - mmengine - INFO - Iter(train) [116500/120000] base_lr: 2.4155e-06 lr: 2.0378e-06 eta: 1:04:47 time: 1.1115 data_time: 0.0177 memory: 14678 grad_norm: 1.0898 loss: 0.1944 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1047 2024/07/09 02:26:25 - mmengine - INFO - Iter(train) [116550/120000] base_lr: 2.4038e-06 lr: 2.0367e-06 eta: 1:03:52 time: 1.1116 data_time: 0.0177 memory: 14552 grad_norm: 1.0898 loss: 0.1944 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1047 2024/07/09 02:27:21 - mmengine - INFO - Iter(train) [116600/120000] base_lr: 2.3922e-06 lr: 2.0357e-06 eta: 1:02:56 time: 1.1120 data_time: 0.0177 memory: 14535 grad_norm: 1.0909 loss: 0.1946 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1048 2024/07/09 02:28:17 - mmengine - INFO - Iter(train) [116650/120000] base_lr: 2.3807e-06 lr: 2.0346e-06 eta: 1:02:01 time: 1.1119 data_time: 0.0177 memory: 14828 grad_norm: 1.0913 loss: 0.1949 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1049 2024/07/09 02:29:14 - mmengine - INFO - Iter(train) [116700/120000] base_lr: 2.3695e-06 lr: 2.0336e-06 eta: 1:01:05 time: 1.1121 data_time: 0.0177 memory: 15742 grad_norm: 1.0917 loss: 0.1950 semantic_segmentation_loss_cls: 0.0488 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1050 2024/07/09 02:30:09 - mmengine - INFO - Iter(train) [116750/120000] base_lr: 2.3584e-06 lr: 2.0326e-06 eta: 1:00:10 time: 1.1121 data_time: 0.0177 memory: 15613 grad_norm: 1.0923 loss: 0.1953 semantic_segmentation_loss_cls: 0.0489 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:31:05 - mmengine - INFO - Iter(train) [116800/120000] base_lr: 2.3474e-06 lr: 2.0316e-06 eta: 0:59:14 time: 1.1120 data_time: 0.0177 memory: 15145 grad_norm: 1.0922 loss: 0.1950 semantic_segmentation_loss_cls: 0.0488 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1050 2024/07/09 02:31:59 - mmengine - INFO - Iter(train) [116850/120000] base_lr: 2.3367e-06 lr: 2.0306e-06 eta: 0:58:18 time: 1.1117 data_time: 0.0177 memory: 14517 grad_norm: 1.0907 loss: 0.1949 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1050 2024/07/09 02:32:54 - mmengine - INFO - Iter(train) [116900/120000] base_lr: 2.3261e-06 lr: 2.0296e-06 eta: 0:57:23 time: 1.1115 data_time: 0.0177 memory: 14941 grad_norm: 1.0899 loss: 0.1948 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1049 2024/07/09 02:33:49 - mmengine - INFO - Iter(train) [116950/120000] base_lr: 2.3156e-06 lr: 2.0287e-06 eta: 0:56:27 time: 1.1113 data_time: 0.0177 memory: 15629 grad_norm: 1.0892 loss: 0.1949 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1050 2024/07/09 02:34:45 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/09 02:34:45 - mmengine - INFO - Iter(train) [117000/120000] base_lr: 2.3054e-06 lr: 2.0278e-06 eta: 0:55:32 time: 1.1112 data_time: 0.0177 memory: 15249 grad_norm: 1.0891 loss: 0.1951 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:34:45 - mmengine - INFO - Saving checkpoint at 117000 iterations 2024/07/09 02:35:45 - mmengine - INFO - Iter(train) [117050/120000] base_lr: 2.2953e-06 lr: 2.0268e-06 eta: 0:54:36 time: 1.1113 data_time: 0.0177 memory: 15478 grad_norm: 1.0895 loss: 0.1950 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:36:41 - mmengine - INFO - Iter(train) [117100/120000] base_lr: 2.2854e-06 lr: 2.0259e-06 eta: 0:53:41 time: 1.1116 data_time: 0.0177 memory: 14675 grad_norm: 1.0878 loss: 0.1951 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:37:37 - mmengine - INFO - Iter(train) [117150/120000] base_lr: 2.2756e-06 lr: 2.0251e-06 eta: 0:52:45 time: 1.1115 data_time: 0.0177 memory: 14895 grad_norm: 1.0878 loss: 0.1952 semantic_segmentation_loss_cls: 0.0488 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:38:32 - mmengine - INFO - Iter(train) [117200/120000] base_lr: 2.2661e-06 lr: 2.0242e-06 eta: 0:51:50 time: 1.1115 data_time: 0.0177 memory: 15843 grad_norm: 1.0858 loss: 0.1949 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1050 2024/07/09 02:39:27 - mmengine - INFO - Iter(train) [117250/120000] base_lr: 2.2566e-06 lr: 2.0233e-06 eta: 0:50:54 time: 1.1116 data_time: 0.0177 memory: 14856 grad_norm: 1.0870 loss: 0.1951 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:40:23 - mmengine - INFO - Iter(train) [117300/120000] base_lr: 2.2474e-06 lr: 2.0225e-06 eta: 0:49:59 time: 1.1117 data_time: 0.0177 memory: 15037 grad_norm: 1.0869 loss: 0.1950 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:41:18 - mmengine - INFO - Iter(train) [117350/120000] base_lr: 2.2383e-06 lr: 2.0217e-06 eta: 0:49:03 time: 1.1119 data_time: 0.0177 memory: 15230 grad_norm: 1.0860 loss: 0.1954 semantic_segmentation_loss_cls: 0.0488 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1053 2024/07/09 02:42:14 - mmengine - INFO - Iter(train) [117400/120000] base_lr: 2.2294e-06 lr: 2.0209e-06 eta: 0:48:08 time: 1.1121 data_time: 0.0177 memory: 14949 grad_norm: 1.0841 loss: 0.1952 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1052 2024/07/09 02:43:09 - mmengine - INFO - Iter(train) [117450/120000] base_lr: 2.2207e-06 lr: 2.0201e-06 eta: 0:47:12 time: 1.1122 data_time: 0.0178 memory: 14010 grad_norm: 1.0854 loss: 0.1949 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1050 2024/07/09 02:44:05 - mmengine - INFO - Iter(train) [117500/120000] base_lr: 2.2121e-06 lr: 2.0193e-06 eta: 0:46:17 time: 1.1123 data_time: 0.0178 memory: 15388 grad_norm: 1.0850 loss: 0.1947 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1049 2024/07/09 02:45:00 - mmengine - INFO - Iter(train) [117550/120000] base_lr: 2.2037e-06 lr: 2.0185e-06 eta: 0:45:21 time: 1.1120 data_time: 0.0178 memory: 15788 grad_norm: 1.0857 loss: 0.1948 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1050 2024/07/09 02:45:56 - mmengine - INFO - Iter(train) [117600/120000] base_lr: 2.1955e-06 lr: 2.0178e-06 eta: 0:44:25 time: 1.1121 data_time: 0.0178 memory: 15549 grad_norm: 1.0863 loss: 0.1949 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:46:51 - mmengine - INFO - Iter(train) [117650/120000] base_lr: 2.1875e-06 lr: 2.0170e-06 eta: 0:43:30 time: 1.1119 data_time: 0.0178 memory: 14844 grad_norm: 1.0845 loss: 0.1950 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1052 2024/07/09 02:47:46 - mmengine - INFO - Iter(train) [117700/120000] base_lr: 2.1796e-06 lr: 2.0163e-06 eta: 0:42:34 time: 1.1117 data_time: 0.0178 memory: 15151 grad_norm: 1.0839 loss: 0.1949 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:48:41 - mmengine - INFO - Iter(train) [117750/120000] base_lr: 2.1719e-06 lr: 2.0156e-06 eta: 0:41:39 time: 1.1115 data_time: 0.0178 memory: 15323 grad_norm: 1.0842 loss: 0.1949 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:49:37 - mmengine - INFO - Iter(train) [117800/120000] base_lr: 2.1643e-06 lr: 2.0149e-06 eta: 0:40:43 time: 1.1114 data_time: 0.0178 memory: 14849 grad_norm: 1.0844 loss: 0.1948 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1050 2024/07/09 02:50:33 - mmengine - INFO - Iter(train) [117850/120000] base_lr: 2.1569e-06 lr: 2.0143e-06 eta: 0:39:48 time: 1.1116 data_time: 0.0178 memory: 15994 grad_norm: 1.0824 loss: 0.1949 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:51:28 - mmengine - INFO - Iter(train) [117900/120000] base_lr: 2.1497e-06 lr: 2.0136e-06 eta: 0:38:52 time: 1.1115 data_time: 0.0178 memory: 15495 grad_norm: 1.0827 loss: 0.1949 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:52:23 - mmengine - INFO - Iter(train) [117950/120000] base_lr: 2.1427e-06 lr: 2.0130e-06 eta: 0:37:57 time: 1.1114 data_time: 0.0178 memory: 15512 grad_norm: 1.0812 loss: 0.1949 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1051 2024/07/09 02:53:18 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/09 02:53:18 - mmengine - INFO - Iter(train) [118000/120000] base_lr: 2.1358e-06 lr: 2.0123e-06 eta: 0:37:01 time: 1.1114 data_time: 0.0178 memory: 15008 grad_norm: 1.0823 loss: 0.1948 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1050 2024/07/09 02:53:18 - mmengine - INFO - Saving checkpoint at 118000 iterations 2024/07/09 02:54:18 - mmengine - INFO - Iter(train) [118050/120000] base_lr: 2.1291e-06 lr: 2.0117e-06 eta: 0:36:06 time: 1.1115 data_time: 0.0178 memory: 16622 grad_norm: 1.0843 loss: 0.1949 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1050 2024/07/09 02:55:13 - mmengine - INFO - Iter(train) [118100/120000] base_lr: 2.1226e-06 lr: 2.0111e-06 eta: 0:35:10 time: 1.1114 data_time: 0.0178 memory: 15143 grad_norm: 1.0838 loss: 0.1946 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1049 2024/07/09 02:56:07 - mmengine - INFO - Iter(train) [118150/120000] base_lr: 2.1162e-06 lr: 2.0106e-06 eta: 0:34:14 time: 1.1112 data_time: 0.0178 memory: 14661 grad_norm: 1.0815 loss: 0.1947 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1049 2024/07/09 02:57:03 - mmengine - INFO - Iter(train) [118200/120000] base_lr: 2.1100e-06 lr: 2.0100e-06 eta: 0:33:19 time: 1.1112 data_time: 0.0178 memory: 16391 grad_norm: 1.0809 loss: 0.1946 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1049 2024/07/09 02:57:58 - mmengine - INFO - Iter(train) [118250/120000] base_lr: 2.1040e-06 lr: 2.0095e-06 eta: 0:32:23 time: 1.1114 data_time: 0.0178 memory: 14458 grad_norm: 1.0793 loss: 0.1946 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0411 semantic_segmentation_loss_dice: 0.1048 2024/07/09 02:58:53 - mmengine - INFO - Iter(train) [118300/120000] base_lr: 2.0981e-06 lr: 2.0089e-06 eta: 0:31:28 time: 1.1115 data_time: 0.0178 memory: 14746 grad_norm: 1.0811 loss: 0.1947 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0411 semantic_segmentation_loss_dice: 0.1049 2024/07/09 02:59:49 - mmengine - INFO - Iter(train) [118350/120000] base_lr: 2.0925e-06 lr: 2.0084e-06 eta: 0:30:32 time: 1.1119 data_time: 0.0178 memory: 15151 grad_norm: 1.0788 loss: 0.1949 semantic_segmentation_loss_cls: 0.0488 semantic_segmentation_loss_mask: 0.0411 semantic_segmentation_loss_dice: 0.1050 2024/07/09 03:00:44 - mmengine - INFO - Iter(train) [118400/120000] base_lr: 2.0869e-06 lr: 2.0079e-06 eta: 0:29:37 time: 1.1118 data_time: 0.0178 memory: 14626 grad_norm: 1.0780 loss: 0.1948 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0411 semantic_segmentation_loss_dice: 0.1050 2024/07/09 03:01:39 - mmengine - INFO - Iter(train) [118450/120000] base_lr: 2.0816e-06 lr: 2.0074e-06 eta: 0:28:41 time: 1.1117 data_time: 0.0178 memory: 15217 grad_norm: 1.0766 loss: 0.1945 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0411 semantic_segmentation_loss_dice: 0.1049 2024/07/09 03:02:34 - mmengine - INFO - Iter(train) [118500/120000] base_lr: 2.0764e-06 lr: 2.0069e-06 eta: 0:27:46 time: 1.1117 data_time: 0.0178 memory: 14995 grad_norm: 1.0772 loss: 0.1946 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1049 2024/07/09 03:03:29 - mmengine - INFO - Iter(train) [118550/120000] base_lr: 2.0714e-06 lr: 2.0065e-06 eta: 0:26:50 time: 1.1115 data_time: 0.0179 memory: 15952 grad_norm: 1.0764 loss: 0.1944 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1048 2024/07/09 03:04:25 - mmengine - INFO - Iter(train) [118600/120000] base_lr: 2.0666e-06 lr: 2.0061e-06 eta: 0:25:55 time: 1.1114 data_time: 0.0179 memory: 14663 grad_norm: 1.0755 loss: 0.1944 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1048 2024/07/09 03:05:20 - mmengine - INFO - Iter(train) [118650/120000] base_lr: 2.0619e-06 lr: 2.0056e-06 eta: 0:24:59 time: 1.1114 data_time: 0.0179 memory: 14990 grad_norm: 1.0743 loss: 0.1945 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1048 2024/07/09 03:06:15 - mmengine - INFO - Iter(train) [118700/120000] base_lr: 2.0574e-06 lr: 2.0052e-06 eta: 0:24:04 time: 1.1114 data_time: 0.0179 memory: 15787 grad_norm: 1.0769 loss: 0.1946 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1049 2024/07/09 03:07:10 - mmengine - INFO - Iter(train) [118750/120000] base_lr: 2.0531e-06 lr: 2.0048e-06 eta: 0:23:08 time: 1.1113 data_time: 0.0179 memory: 15209 grad_norm: 1.0773 loss: 0.1949 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1050 2024/07/09 03:08:06 - mmengine - INFO - Iter(train) [118800/120000] base_lr: 2.0489e-06 lr: 2.0044e-06 eta: 0:22:12 time: 1.1114 data_time: 0.0179 memory: 15584 grad_norm: 1.0766 loss: 0.1951 semantic_segmentation_loss_cls: 0.0488 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1051 2024/07/09 03:09:01 - mmengine - INFO - Iter(train) [118850/120000] base_lr: 2.0449e-06 lr: 2.0041e-06 eta: 0:21:17 time: 1.1113 data_time: 0.0179 memory: 15622 grad_norm: 1.0753 loss: 0.1950 semantic_segmentation_loss_cls: 0.0488 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1051 2024/07/09 03:09:57 - mmengine - INFO - Iter(train) [118900/120000] base_lr: 2.0411e-06 lr: 2.0037e-06 eta: 0:20:21 time: 1.1111 data_time: 0.0179 memory: 14843 grad_norm: 1.0752 loss: 0.1948 semantic_segmentation_loss_cls: 0.0487 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1049 2024/07/09 03:10:53 - mmengine - INFO - Iter(train) [118950/120000] base_lr: 2.0375e-06 lr: 2.0034e-06 eta: 0:19:26 time: 1.1112 data_time: 0.0179 memory: 14949 grad_norm: 1.0740 loss: 0.1943 semantic_segmentation_loss_cls: 0.0484 semantic_segmentation_loss_mask: 0.0411 semantic_segmentation_loss_dice: 0.1048 2024/07/09 03:11:48 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/09 03:11:48 - mmengine - INFO - Iter(train) [119000/120000] base_lr: 2.0340e-06 lr: 2.0031e-06 eta: 0:18:30 time: 1.1111 data_time: 0.0179 memory: 15257 grad_norm: 1.0739 loss: 0.1944 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0411 semantic_segmentation_loss_dice: 0.1048 2024/07/09 03:11:48 - mmengine - INFO - Saving checkpoint at 119000 iterations 2024/07/09 03:12:48 - mmengine - INFO - Iter(train) [119050/120000] base_lr: 2.0307e-06 lr: 2.0028e-06 eta: 0:17:35 time: 1.1120 data_time: 0.0189 memory: 15532 grad_norm: 1.0754 loss: 0.1943 semantic_segmentation_loss_cls: 0.0484 semantic_segmentation_loss_mask: 0.0411 semantic_segmentation_loss_dice: 0.1048 2024/07/09 03:13:43 - mmengine - INFO - Iter(train) [119100/120000] base_lr: 2.0275e-06 lr: 2.0025e-06 eta: 0:16:39 time: 1.1118 data_time: 0.0189 memory: 15823 grad_norm: 1.0755 loss: 0.1943 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0411 semantic_segmentation_loss_dice: 0.1047 2024/07/09 03:14:38 - mmengine - INFO - Iter(train) [119150/120000] base_lr: 2.0246e-06 lr: 2.0022e-06 eta: 0:15:44 time: 1.1118 data_time: 0.0189 memory: 14868 grad_norm: 1.0753 loss: 0.1944 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0411 semantic_segmentation_loss_dice: 0.1048 2024/07/09 03:15:33 - mmengine - INFO - Iter(train) [119200/120000] base_lr: 2.0218e-06 lr: 2.0020e-06 eta: 0:14:48 time: 1.1116 data_time: 0.0189 memory: 14942 grad_norm: 1.0758 loss: 0.1946 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0411 semantic_segmentation_loss_dice: 0.1049 2024/07/09 03:16:27 - mmengine - INFO - Iter(train) [119250/120000] base_lr: 2.0191e-06 lr: 2.0017e-06 eta: 0:13:53 time: 1.1113 data_time: 0.0189 memory: 14823 grad_norm: 1.0754 loss: 0.1945 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0411 semantic_segmentation_loss_dice: 0.1049 2024/07/09 03:17:21 - mmengine - INFO - Iter(train) [119300/120000] base_lr: 2.0167e-06 lr: 2.0015e-06 eta: 0:12:57 time: 1.1110 data_time: 0.0189 memory: 15079 grad_norm: 1.0753 loss: 0.1947 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0411 semantic_segmentation_loss_dice: 0.1050 2024/07/09 03:18:17 - mmengine - INFO - Iter(train) [119350/120000] base_lr: 2.0144e-06 lr: 2.0013e-06 eta: 0:12:02 time: 1.1108 data_time: 0.0189 memory: 15415 grad_norm: 1.0760 loss: 0.1945 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0411 semantic_segmentation_loss_dice: 0.1048 2024/07/09 03:19:12 - mmengine - INFO - Iter(train) [119400/120000] base_lr: 2.0123e-06 lr: 2.0011e-06 eta: 0:11:06 time: 1.1110 data_time: 0.0189 memory: 15254 grad_norm: 1.0763 loss: 0.1944 semantic_segmentation_loss_cls: 0.0484 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1049 2024/07/09 03:20:09 - mmengine - INFO - Iter(train) [119450/120000] base_lr: 2.0103e-06 lr: 2.0009e-06 eta: 0:10:10 time: 1.1112 data_time: 0.0189 memory: 14908 grad_norm: 1.0765 loss: 0.1947 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1050 2024/07/09 03:21:04 - mmengine - INFO - Iter(train) [119500/120000] base_lr: 2.0085e-06 lr: 2.0008e-06 eta: 0:09:15 time: 1.1111 data_time: 0.0189 memory: 14781 grad_norm: 1.0779 loss: 0.1948 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1051 2024/07/09 03:22:00 - mmengine - INFO - Iter(train) [119550/120000] base_lr: 2.0069e-06 lr: 2.0006e-06 eta: 0:08:19 time: 1.1112 data_time: 0.0189 memory: 15641 grad_norm: 1.0794 loss: 0.1949 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1051 2024/07/09 03:22:55 - mmengine - INFO - Iter(train) [119600/120000] base_lr: 2.0055e-06 lr: 2.0005e-06 eta: 0:07:24 time: 1.1110 data_time: 0.0189 memory: 15871 grad_norm: 1.0800 loss: 0.1949 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1051 2024/07/09 03:23:51 - mmengine - INFO - Iter(train) [119650/120000] base_lr: 2.0042e-06 lr: 2.0004e-06 eta: 0:06:28 time: 1.1110 data_time: 0.0189 memory: 15470 grad_norm: 1.0805 loss: 0.1948 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1050 2024/07/09 03:24:45 - mmengine - INFO - Iter(train) [119700/120000] base_lr: 2.0031e-06 lr: 2.0003e-06 eta: 0:05:33 time: 1.1109 data_time: 0.0189 memory: 14784 grad_norm: 1.0804 loss: 0.1949 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1051 2024/07/09 03:25:41 - mmengine - INFO - Iter(train) [119750/120000] base_lr: 2.0021e-06 lr: 2.0002e-06 eta: 0:04:37 time: 1.1109 data_time: 0.0189 memory: 15665 grad_norm: 1.0799 loss: 0.1950 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1051 2024/07/09 03:26:37 - mmengine - INFO - Iter(train) [119800/120000] base_lr: 2.0014e-06 lr: 2.0001e-06 eta: 0:03:42 time: 1.1109 data_time: 0.0189 memory: 15531 grad_norm: 1.0810 loss: 0.1951 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1052 2024/07/09 03:27:32 - mmengine - INFO - Iter(train) [119850/120000] base_lr: 2.0008e-06 lr: 2.0001e-06 eta: 0:02:46 time: 1.1110 data_time: 0.0189 memory: 15415 grad_norm: 1.0810 loss: 0.1951 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1052 2024/07/09 03:28:27 - mmengine - INFO - Iter(train) [119900/120000] base_lr: 2.0003e-06 lr: 2.0000e-06 eta: 0:01:51 time: 1.1111 data_time: 0.0189 memory: 14634 grad_norm: 1.0807 loss: 0.1948 semantic_segmentation_loss_cls: 0.0485 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1051 2024/07/09 03:29:23 - mmengine - INFO - Iter(train) [119950/120000] base_lr: 2.0001e-06 lr: 2.0000e-06 eta: 0:00:55 time: 1.1113 data_time: 0.0189 memory: 15410 grad_norm: 1.0793 loss: 0.1949 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0412 semantic_segmentation_loss_dice: 0.1051 2024/07/09 03:30:19 - mmengine - INFO - Exp name: single_semanticseg_base_672_prompt_mask_weight_ignore_repeat_maskcost_random_trick3_v2_20240707_133834 2024/07/09 03:30:19 - mmengine - INFO - Iter(train) [120000/120000] base_lr: 2.0000e-06 lr: 2.0000e-06 eta: 0:00:00 time: 1.1116 data_time: 0.0189 memory: 15658 grad_norm: 1.0811 loss: 0.1949 semantic_segmentation_loss_cls: 0.0486 semantic_segmentation_loss_mask: 0.0413 semantic_segmentation_loss_dice: 0.1051 2024/07/09 03:30:19 - mmengine - INFO - Saving checkpoint at 120000 iterations 2024/07/09 03:30:36 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:01:50 time: 0.2416 data_time: 0.0017 memory: 5013 2024/07/09 03:30:48 - mmengine - INFO - Iter(val) [100/500] eta: 0:01:36 time: 0.2416 data_time: 0.0017 memory: 5189 2024/07/09 03:31:00 - mmengine - INFO - Iter(val) [150/500] eta: 0:01:24 time: 0.2416 data_time: 0.0017 memory: 4460 2024/07/09 03:31:12 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:12 time: 0.2416 data_time: 0.0017 memory: 4543 2024/07/09 03:31:24 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:00 time: 0.2416 data_time: 0.0017 memory: 4645 2024/07/09 03:31:36 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:48 time: 0.2416 data_time: 0.0017 memory: 10983 2024/07/09 03:31:49 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.2417 data_time: 0.0017 memory: 4460 2024/07/09 03:32:01 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:24 time: 0.2417 data_time: 0.0017 memory: 4641 2024/07/09 03:32:13 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:12 time: 0.2417 data_time: 0.0017 memory: 4473 2024/07/09 03:32:25 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2417 data_time: 0.0017 memory: 4555 2024/07/09 03:32:25 - mmengine - INFO - per class results: 2024/07/09 03:32:25 - mmengine - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.7 | 87.89 | | building | 83.29 | 91.95 | | sky | 94.54 | 97.75 | | floor | 82.82 | 91.39 | | tree | 76.14 | 88.34 | | ceiling | 84.8 | 92.99 | | road | 84.31 | 93.11 | | bed | 87.87 | 95.21 | | windowpane | 62.15 | 79.12 | | grass | 70.73 | 85.69 | | cabinet | 60.79 | 71.75 | | sidewalk | 67.62 | 80.83 | | person | 82.4 | 91.86 | | earth | 34.99 | 46.93 | | door | 55.48 | 69.59 | | table | 63.83 | 76.62 | | mountain | 57.2 | 72.88 | | plant | 54.65 | 68.66 | | curtain | 72.75 | 87.16 | | chair | 59.46 | 72.58 | | car | 85.51 | 91.63 | | water | 53.13 | 67.91 | | painting | 71.71 | 88.54 | | sofa | 66.55 | 77.61 | | shelf | 45.13 | 64.16 | | house | 49.64 | 75.32 | | sea | 51.58 | 81.3 | | mirror | 66.14 | 74.71 | | rug | 65.47 | 75.67 | | field | 42.94 | 57.95 | | armchair | 45.95 | 70.71 | | seat | 56.1 | 81.57 | | fence | 47.08 | 62.37 | | desk | 47.48 | 68.86 | | rock | 36.28 | 57.76 | | wardrobe | 52.48 | 71.03 | | lamp | 67.41 | 78.75 | | bathtub | 85.59 | 90.39 | | railing | 38.76 | 54.21 | | cushion | 57.67 | 68.88 | | base | 23.77 | 34.69 | | box | 25.24 | 37.36 | | column | 48.99 | 66.25 | | signboard | 39.62 | 55.02 | | chest of drawers | 38.83 | 68.73 | | counter | 33.02 | 45.07 | | sand | 35.17 | 50.44 | | sink | 75.78 | 82.2 | | skyscraper | 35.6 | 44.59 | | fireplace | 66.99 | 86.27 | | refrigerator | 80.78 | 89.68 | | grandstand | 42.0 | 72.25 | | path | 29.1 | 41.21 | | stairs | 32.64 | 41.14 | | runway | 75.92 | 90.09 | | case | 64.76 | 80.06 | | pool table | 93.47 | 96.25 | | pillow | 54.8 | 65.72 | | screen door | 79.64 | 83.55 | | stairway | 37.79 | 42.69 | | river | 25.65 | 44.56 | | bridge | 67.08 | 88.78 | | bookcase | 40.2 | 57.42 | | blind | 38.42 | 42.48 | | coffee table | 69.4 | 84.44 | | toilet | 87.11 | 89.79 | | flower | 41.64 | 57.48 | | book | 52.29 | 73.52 | | hill | 14.27 | 23.73 | | bench | 39.52 | 52.42 | | countertop | 53.93 | 68.31 | | stove | 80.81 | 85.18 | | palm | 51.28 | 67.23 | | kitchen island | 30.9 | 74.24 | | computer | 60.87 | 66.62 | | swivel chair | 39.32 | 54.45 | | boat | 69.62 | 80.14 | | bar | 47.61 | 59.08 | | arcade machine | 53.37 | 58.62 | | hovel | 29.47 | 42.28 | | bus | 92.57 | 95.41 | | towel | 67.9 | 74.27 | | light | 62.18 | 76.11 | | truck | 36.49 | 48.16 | | tower | 33.23 | 54.01 | | chandelier | 63.27 | 74.84 | | awning | 34.07 | 43.86 | | streetlight | 39.89 | 53.45 | | booth | 46.32 | 55.25 | | television receiver | 48.62 | 89.69 | | airplane | 59.3 | 67.58 | | dirt track | 4.22 | 5.35 | | apparel | 38.94 | 55.33 | | pole | 30.96 | 45.57 | | land | 1.82 | 2.18 | | bannister | 16.96 | 26.68 | | escalator | 27.61 | 39.53 | | ottoman | 39.16 | 64.23 | | bottle | 22.1 | 26.27 | | buffet | 41.36 | 47.53 | | poster | 25.82 | 33.14 | | stage | 13.17 | 35.07 | | van | 49.04 | 65.64 | | ship | 82.46 | 87.97 | | fountain | 6.97 | 8.01 | | conveyer belt | 63.56 | 91.53 | | canopy | 14.77 | 23.25 | | washer | 71.79 | 73.39 | | plaything | 25.86 | 34.69 | | swimming pool | 30.14 | 33.12 | | stool | 56.08 | 68.02 | | barrel | 15.17 | 55.75 | | basket | 35.58 | 44.31 | | waterfall | 45.86 | 51.22 | | tent | 79.91 | 97.7 | | bag | 17.41 | 23.5 | | minibike | 66.79 | 85.06 | | cradle | 76.66 | 96.61 | | oven | 46.35 | 57.49 | | ball | 31.62 | 38.54 | | food | 64.32 | 77.33 | | step | 25.9 | 27.6 | | tank | 36.4 | 45.46 | | trade name | 29.16 | 34.92 | | microwave | 38.1 | 41.22 | | pot | 43.92 | 48.69 | | animal | 62.06 | 68.93 | | bicycle | 57.72 | 75.69 | | lake | 63.45 | 63.66 | | dishwasher | 80.78 | 84.57 | | screen | 68.04 | 88.55 | | blanket | 10.53 | 12.72 | | sculpture | 67.44 | 81.22 | | hood | 73.37 | 78.92 | | sconce | 51.76 | 64.33 | | vase | 39.54 | 65.46 | | traffic light | 42.58 | 56.96 | | tray | 19.31 | 25.23 | | ashcan | 47.38 | 61.72 | | fan | 62.11 | 78.25 | | pier | 36.31 | 73.25 | | crt screen | 0.0 | 0.0 | | plate | 62.48 | 73.15 | | monitor | 12.25 | 17.04 | | bulletin board | 31.06 | 36.74 | | shower | 10.75 | 16.97 | | radiator | 58.14 | 68.91 | | glass | 18.66 | 19.86 | | clock | 32.76 | 36.1 | | flag | 45.88 | 52.67 | +---------------------+-------+-------+ 2024/07/09 03:32:26 - mmengine - INFO - Iter(val) [500/500] aAcc: 83.7100 mIoU: 50.0500 mAcc: 62.4700 data_time: 0.0018 time: 0.2423