2022/08/31 20:08:07 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.8.0 (default, Nov 6 2019, 21:49:08) [GCC 7.3.0] CUDA available: True numpy_random_seed: 1810975628 GPU 0: NVIDIA GeForce GTX 1660 SUPER CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.3, V11.3.109 GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0 PyTorch: 1.9.1+cu111 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb) - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.1 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86 - CuDNN 8.0.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.10.1+cu111 OpenCV: 4.5.4 MMEngine: 0.0.1 Runtime environment: cudnn_benchmark: True mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None Distributed launcher: none Distributed training: False GPU number: 1 ------------------------------------------------------------ 2022/08/31 20:08:07 - mmengine - INFO - Config: default_scope = 'mmocr' env_cfg = dict( cudnn_benchmark=True, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) randomness = dict(seed=None) default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=100), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict(type='CheckpointHook', interval=1), sampler_seed=dict(type='DistSamplerSeedHook'), sync_buffer=dict(type='SyncBuffersHook'), visualization=dict( type='VisualizationHook', interval=1, enable=False, show=False, draw_gt=False, draw_pred=False)) log_level = 'INFO' log_processor = dict(type='LogProcessor', window_size=10, by_epoch=True) load_from = None resume = False visualizer = dict( type='KIELocalVisualizer', name='visualizer', is_openset=True) wildreceipt_openset_data_root = 'data/kie/wildreceipt/' wildreceipt_openset_train = dict( type='WildReceiptDataset', data_root='data/kie/wildreceipt/', metainfo=dict(category=[ dict(id=0, name='bg'), dict(id=1, name='key'), dict(id=2, name='value'), dict(id=3, name='other') ]), ann_file='openset_train.txt', pipeline=[ dict(type='LoadKIEAnnotations'), dict(type='Resize', scale=(1024, 512), keep_ratio=True), dict(type='PackKIEInputs') ]) wildreceipt_openset_test = dict( type='WildReceiptDataset', data_root='data/kie/wildreceipt/', metainfo=dict(category=[ dict(id=0, name='bg'), dict(id=1, name='key'), dict(id=2, name='value'), dict(id=3, name='other') ]), ann_file='openset_test.txt', test_mode=True, pipeline=[ dict(type='LoadKIEAnnotations', key_node_idx=1, value_node_idx=2), dict(type='Resize', scale=(1024, 512), keep_ratio=True), dict(type='PackKIEInputs') ]) optim_wrapper = dict( type='OptimWrapper', optimizer=dict(type='Adam', weight_decay=0.0001)) train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=60, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') param_scheduler = [dict(type='MultiStepLR', milestones=[40, 50], end=60)] num_classes = 26 model = dict( type='SDMGR', kie_head=dict( type='SDMGRHead', visual_dim=16, num_classes=4, module_loss=dict(type='SDMGRModuleLoss'), postprocessor=dict( type='SDMGRPostProcessor', link_type='one-to-many', key_node_idx=1, value_node_idx=2)), dictionary=dict( type='Dictionary', dict_file='data/kie/wildreceipt/dict.txt', with_padding=True, with_unknown=True, unknown_token=None)) train_pipeline = [ dict(type='LoadKIEAnnotations'), dict(type='Resize', scale=(1024, 512), keep_ratio=True), dict(type='PackKIEInputs') ] test_pipeline = [ dict(type='LoadKIEAnnotations', key_node_idx=1, value_node_idx=2), dict(type='Resize', scale=(1024, 512), keep_ratio=True), dict(type='PackKIEInputs') ] val_evaluator = [ dict( type='F1Metric', prefix='node', key='labels', mode=['micro', 'macro'], num_classes=4, cared_classes=[1, 2]), dict( type='F1Metric', prefix='edge', mode='micro', key='edge_labels', cared_classes=[1], num_classes=2) ] test_evaluator = [ dict( type='F1Metric', prefix='node', key='labels', mode=['micro', 'macro'], num_classes=4, cared_classes=[1, 2]), dict( type='F1Metric', prefix='edge', mode='micro', key='edge_labels', cared_classes=[1], num_classes=2) ] node_num_classes = 4 edge_num_classes = 2 key_node_idx = 1 value_node_idx = 2 train_dataloader = dict( batch_size=4, num_workers=1, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='WildReceiptDataset', data_root='data/kie/wildreceipt/', metainfo=dict(category=[ dict(id=0, name='bg'), dict(id=1, name='key'), dict(id=2, name='value'), dict(id=3, name='other') ]), ann_file='openset_train.txt', pipeline=[ dict(type='LoadKIEAnnotations'), dict(type='Resize', scale=(1024, 512), keep_ratio=True), dict(type='PackKIEInputs') ])) val_dataloader = dict( batch_size=1, num_workers=1, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='WildReceiptDataset', data_root='data/kie/wildreceipt/', metainfo=dict(category=[ dict(id=0, name='bg'), dict(id=1, name='key'), dict(id=2, name='value'), dict(id=3, name='other') ]), ann_file='openset_test.txt', test_mode=True, pipeline=[ dict(type='LoadKIEAnnotations', key_node_idx=1, value_node_idx=2), dict(type='Resize', scale=(1024, 512), keep_ratio=True), dict(type='PackKIEInputs') ])) test_dataloader = dict( batch_size=1, num_workers=1, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='WildReceiptDataset', data_root='data/kie/wildreceipt/', metainfo=dict(category=[ dict(id=0, name='bg'), dict(id=1, name='key'), dict(id=2, name='value'), dict(id=3, name='other') ]), ann_file='openset_test.txt', test_mode=True, pipeline=[ dict(type='LoadKIEAnnotations', key_node_idx=1, value_node_idx=2), dict(type='Resize', scale=(1024, 512), keep_ratio=True), dict(type='PackKIEInputs') ])) launcher = 'none' work_dir = './work_dirs/sdmgr_novisual_60e_wildreceipt-openset' 2022/08/31 20:08:10 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used. Name of parameter - Initialization information kie_head.fusion.linear0.weight - torch.Size([1024, 16]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.linear0.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.0.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.0.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.1.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.1.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.2.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.2.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.3.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.3.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.4.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.4.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.5.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.5.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.6.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.6.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.7.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.7.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.8.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.8.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.9.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.9.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.10.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.10.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.11.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.11.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.12.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.12.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.13.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.13.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.14.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.14.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.15.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.15.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.16.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.16.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.17.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.17.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.18.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.18.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.19.weight - torch.Size([540, 36]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears0.19.bias - torch.Size([540]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.0.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.0.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.1.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.1.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.2.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.2.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.3.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.3.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.4.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.4.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.5.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.5.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.6.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.6.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.7.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.7.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.8.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.8.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.9.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.9.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.10.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.10.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.11.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.11.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.12.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.12.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.13.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.13.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.14.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.14.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.15.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.15.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.16.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.16.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.17.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.17.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.18.weight - torch.Size([780, 52]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.18.bias - torch.Size([780]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.19.weight - torch.Size([540, 36]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.merge_linears1.19.bias - torch.Size([540]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.linear_out.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SDMGR kie_head.fusion.linear_out.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SDMGR kie_head.node_embed.weight - torch.Size([92, 32]): The value is the same before and after calling `init_weights` of SDMGR kie_head.rnn.weight_ih_l0 - torch.Size([1024, 32]): The value is the same before and after calling `init_weights` of SDMGR kie_head.rnn.weight_hh_l0 - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SDMGR kie_head.rnn.bias_ih_l0 - torch.Size([1024]): The value is the same before and after calling `init_weights` of SDMGR kie_head.rnn.bias_hh_l0 - torch.Size([1024]): The value is the same before and after calling `init_weights` of SDMGR kie_head.edge_embed.weight - torch.Size([256, 5]): NormalInit: mean=0, std=0.01, bias=0 kie_head.edge_embed.bias - torch.Size([256]): NormalInit: mean=0, std=0.01, bias=0 kie_head.gnn_layers.0.in_fc.weight - torch.Size([256, 768]): The value is the same before and after calling `init_weights` of SDMGR kie_head.gnn_layers.0.in_fc.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SDMGR kie_head.gnn_layers.0.coef_fc.weight - torch.Size([1, 256]): The value is the same before and after calling `init_weights` of SDMGR kie_head.gnn_layers.0.coef_fc.bias - torch.Size([1]): The value is the same before and after calling `init_weights` of SDMGR kie_head.gnn_layers.0.out_fc.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SDMGR kie_head.gnn_layers.0.out_fc.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SDMGR kie_head.gnn_layers.1.in_fc.weight - torch.Size([256, 768]): The value is the same before and after calling `init_weights` of SDMGR kie_head.gnn_layers.1.in_fc.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SDMGR kie_head.gnn_layers.1.coef_fc.weight - torch.Size([1, 256]): The value is the same before and after calling `init_weights` of SDMGR kie_head.gnn_layers.1.coef_fc.bias - torch.Size([1]): The value is the same before and after calling `init_weights` of SDMGR kie_head.gnn_layers.1.out_fc.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SDMGR kie_head.gnn_layers.1.out_fc.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SDMGR kie_head.node_cls.weight - torch.Size([4, 256]): The value is the same before and after calling `init_weights` of SDMGR kie_head.node_cls.bias - torch.Size([4]): The value is the same before and after calling `init_weights` of SDMGR kie_head.edge_cls.weight - torch.Size([2, 256]): The value is the same before and after calling `init_weights` of SDMGR kie_head.edge_cls.bias - torch.Size([2]): The value is the same before and after calling `init_weights` of SDMGR 2022/08/31 20:08:18 - mmengine - INFO - Epoch(train) [1][100/317] lr: 1.0000e-03 eta: 0:09:30 time: 0.0297 data_time: 0.0030 memory: 529 loss_node: 0.6293 loss_edge: 0.1365 acc_node: 67.8947 acc_edge: 95.7818 loss: 0.7658 2022/08/31 20:08:21 - mmengine - INFO - Epoch(train) [1][200/317] lr: 1.0000e-03 eta: 0:09:04 time: 0.0251 data_time: 0.0028 memory: 643 loss_node: 0.5171 loss_edge: 0.1040 acc_node: 74.4361 acc_edge: 97.9974 loss: 0.6211 2022/08/31 20:08:23 - mmengine - INFO - Epoch(train) [1][300/317] lr: 1.0000e-03 eta: 0:08:54 time: 0.0248 data_time: 0.0030 memory: 961 loss_node: 0.4813 loss_edge: 0.1163 acc_node: 79.4118 acc_edge: 98.1826 loss: 0.5976 2022/08/31 20:08:24 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:08:24 - mmengine - INFO - Saving checkpoint at 1 epochs 2022/08/31 20:08:25 - mmengine - INFO - Epoch(val) [1][100/472] eta: 0:00:01 time: 0.0046 data_time: 0.0009 memory: 894 2022/08/31 20:08:25 - mmengine - INFO - Epoch(val) [1][200/472] eta: 0:00:01 time: 0.0051 data_time: 0.0008 memory: 138 2022/08/31 20:08:26 - mmengine - INFO - Epoch(val) [1][300/472] eta: 0:00:00 time: 0.0056 data_time: 0.0008 memory: 330 2022/08/31 20:08:26 - mmengine - INFO - Epoch(val) [1][400/472] eta: 0:00:00 time: 0.0049 data_time: 0.0008 memory: 76 2022/08/31 20:08:27 - mmengine - INFO - Epoch(val) [1][472/472] node/macro_f1: 0.8343 node/micro_f1: 0.8580 edge/micro_f1: 0.0713 2022/08/31 20:08:29 - mmengine - INFO - Epoch(train) [2][100/317] lr: 1.0000e-03 eta: 0:08:29 time: 0.0246 data_time: 0.0028 memory: 559 loss_node: 0.3823 loss_edge: 0.0802 acc_node: 85.0299 acc_edge: 95.9267 loss: 0.4625 2022/08/31 20:08:32 - mmengine - INFO - Epoch(train) [2][200/317] lr: 1.0000e-03 eta: 0:08:23 time: 0.0298 data_time: 0.0027 memory: 948 loss_node: 0.4440 loss_edge: 0.0855 acc_node: 65.6250 acc_edge: 95.5491 loss: 0.5295 2022/08/31 20:08:35 - mmengine - INFO - Epoch(train) [2][300/317] lr: 1.0000e-03 eta: 0:08:19 time: 0.0253 data_time: 0.0029 memory: 813 loss_node: 0.3948 loss_edge: 0.0979 acc_node: 81.9149 acc_edge: 98.1485 loss: 0.4928 2022/08/31 20:08:35 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:08:35 - mmengine - INFO - Saving checkpoint at 2 epochs 2022/08/31 20:08:36 - mmengine - INFO - Epoch(val) [2][100/472] eta: 0:00:01 time: 0.0049 data_time: 0.0009 memory: 646 2022/08/31 20:08:36 - mmengine - INFO - Epoch(val) [2][200/472] eta: 0:00:01 time: 0.0055 data_time: 0.0009 memory: 138 2022/08/31 20:08:37 - mmengine - INFO - Epoch(val) [2][300/472] eta: 0:00:01 time: 0.0062 data_time: 0.0010 memory: 330 2022/08/31 20:08:38 - mmengine - INFO - Epoch(val) [2][400/472] eta: 0:00:00 time: 0.0059 data_time: 0.0010 memory: 76 2022/08/31 20:08:38 - mmengine - INFO - Epoch(val) [2][472/472] node/macro_f1: 0.8465 node/micro_f1: 0.8520 edge/micro_f1: 0.0961 2022/08/31 20:08:41 - mmengine - INFO - Epoch(train) [3][100/317] lr: 1.0000e-03 eta: 0:08:17 time: 0.0373 data_time: 0.0033 memory: 942 loss_node: 0.3392 loss_edge: 0.0827 acc_node: 85.4839 acc_edge: 96.6298 loss: 0.4219 2022/08/31 20:08:44 - mmengine - INFO - Epoch(train) [3][200/317] lr: 1.0000e-03 eta: 0:08:14 time: 0.0255 data_time: 0.0027 memory: 1000 loss_node: 0.3097 loss_edge: 0.0752 acc_node: 85.4962 acc_edge: 96.7077 loss: 0.3849 2022/08/31 20:08:46 - mmengine - INFO - Epoch(train) [3][300/317] lr: 1.0000e-03 eta: 0:08:07 time: 0.0255 data_time: 0.0027 memory: 690 loss_node: 0.3011 loss_edge: 0.0555 acc_node: 92.6829 acc_edge: 98.3665 loss: 0.3566 2022/08/31 20:08:46 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:08:46 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/08/31 20:08:47 - mmengine - INFO - Epoch(val) [3][100/472] eta: 0:00:01 time: 0.0046 data_time: 0.0008 memory: 234 2022/08/31 20:08:48 - mmengine - INFO - Epoch(val) [3][200/472] eta: 0:00:01 time: 0.0052 data_time: 0.0008 memory: 138 2022/08/31 20:08:48 - mmengine - INFO - Epoch(val) [3][300/472] eta: 0:00:01 time: 0.0062 data_time: 0.0009 memory: 330 2022/08/31 20:08:49 - mmengine - INFO - Epoch(val) [3][400/472] eta: 0:00:00 time: 0.0051 data_time: 0.0007 memory: 76 2022/08/31 20:08:49 - mmengine - INFO - Epoch(val) [3][472/472] node/macro_f1: 0.8913 node/micro_f1: 0.9070 edge/micro_f1: 0.4900 2022/08/31 20:08:50 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:08:52 - mmengine - INFO - Epoch(train) [4][100/317] lr: 1.0000e-03 eta: 0:07:59 time: 0.0264 data_time: 0.0027 memory: 818 loss_node: 0.2717 loss_edge: 0.0440 acc_node: 92.3077 acc_edge: 98.5853 loss: 0.3157 2022/08/31 20:08:54 - mmengine - INFO - Epoch(train) [4][200/317] lr: 1.0000e-03 eta: 0:07:54 time: 0.0226 data_time: 0.0027 memory: 581 loss_node: 0.2948 loss_edge: 0.0475 acc_node: 86.0963 acc_edge: 98.5221 loss: 0.3422 2022/08/31 20:08:57 - mmengine - INFO - Epoch(train) [4][300/317] lr: 1.0000e-03 eta: 0:07:50 time: 0.0244 data_time: 0.0026 memory: 632 loss_node: 0.2589 loss_edge: 0.0486 acc_node: 96.4467 acc_edge: 98.7916 loss: 0.3074 2022/08/31 20:08:57 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:08:57 - mmengine - INFO - Saving checkpoint at 4 epochs 2022/08/31 20:08:58 - mmengine - INFO - Epoch(val) [4][100/472] eta: 0:00:01 time: 0.0051 data_time: 0.0009 memory: 895 2022/08/31 20:08:59 - mmengine - INFO - Epoch(val) [4][200/472] eta: 0:00:01 time: 0.0057 data_time: 0.0008 memory: 138 2022/08/31 20:08:59 - mmengine - INFO - Epoch(val) [4][300/472] eta: 0:00:00 time: 0.0055 data_time: 0.0008 memory: 330 2022/08/31 20:09:00 - mmengine - INFO - Epoch(val) [4][400/472] eta: 0:00:00 time: 0.0058 data_time: 0.0009 memory: 76 2022/08/31 20:09:00 - mmengine - INFO - Epoch(val) [4][472/472] node/macro_f1: 0.9011 node/micro_f1: 0.9193 edge/micro_f1: 0.6020 2022/08/31 20:09:03 - mmengine - INFO - Epoch(train) [5][100/317] lr: 1.0000e-03 eta: 0:07:45 time: 0.0348 data_time: 0.0031 memory: 930 loss_node: 0.2513 loss_edge: 0.0466 acc_node: 84.4749 acc_edge: 97.6014 loss: 0.2979 2022/08/31 20:09:06 - mmengine - INFO - Epoch(train) [5][200/317] lr: 1.0000e-03 eta: 0:07:42 time: 0.0294 data_time: 0.0027 memory: 1026 loss_node: 0.2124 loss_edge: 0.0472 acc_node: 93.9394 acc_edge: 99.2346 loss: 0.2596 2022/08/31 20:09:08 - mmengine - INFO - Epoch(train) [5][300/317] lr: 1.0000e-03 eta: 0:07:38 time: 0.0273 data_time: 0.0030 memory: 513 loss_node: 0.2501 loss_edge: 0.0467 acc_node: 93.2836 acc_edge: 98.8074 loss: 0.2968 2022/08/31 20:09:09 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:09:09 - mmengine - INFO - Saving checkpoint at 5 epochs 2022/08/31 20:09:09 - mmengine - INFO - Epoch(val) [5][100/472] eta: 0:00:01 time: 0.0046 data_time: 0.0008 memory: 655 2022/08/31 20:09:10 - mmengine - INFO - Epoch(val) [5][200/472] eta: 0:00:01 time: 0.0057 data_time: 0.0008 memory: 138 2022/08/31 20:09:10 - mmengine - INFO - Epoch(val) [5][300/472] eta: 0:00:00 time: 0.0056 data_time: 0.0008 memory: 330 2022/08/31 20:09:11 - mmengine - INFO - Epoch(val) [5][400/472] eta: 0:00:00 time: 0.0053 data_time: 0.0008 memory: 76 2022/08/31 20:09:11 - mmengine - INFO - Epoch(val) [5][472/472] node/macro_f1: 0.9052 node/micro_f1: 0.9149 edge/micro_f1: 0.6232 2022/08/31 20:09:14 - mmengine - INFO - Epoch(train) [6][100/317] lr: 1.0000e-03 eta: 0:07:32 time: 0.0253 data_time: 0.0027 memory: 525 loss_node: 0.1884 loss_edge: 0.0403 acc_node: 87.0370 acc_edge: 98.7213 loss: 0.2287 2022/08/31 20:09:17 - mmengine - INFO - Epoch(train) [6][200/317] lr: 1.0000e-03 eta: 0:07:30 time: 0.0241 data_time: 0.0029 memory: 554 loss_node: 0.2344 loss_edge: 0.0573 acc_node: 87.0000 acc_edge: 98.3700 loss: 0.2917 2022/08/31 20:09:19 - mmengine - INFO - Epoch(train) [6][300/317] lr: 1.0000e-03 eta: 0:07:28 time: 0.0255 data_time: 0.0026 memory: 911 loss_node: 0.2429 loss_edge: 0.0561 acc_node: 89.7959 acc_edge: 98.3999 loss: 0.2990 2022/08/31 20:09:20 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:09:20 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/08/31 20:09:21 - mmengine - INFO - Epoch(val) [6][100/472] eta: 0:00:02 time: 0.0068 data_time: 0.0013 memory: 913 2022/08/31 20:09:21 - mmengine - INFO - Epoch(val) [6][200/472] eta: 0:00:01 time: 0.0060 data_time: 0.0009 memory: 138 2022/08/31 20:09:22 - mmengine - INFO - Epoch(val) [6][300/472] eta: 0:00:00 time: 0.0057 data_time: 0.0009 memory: 330 2022/08/31 20:09:23 - mmengine - INFO - Epoch(val) [6][400/472] eta: 0:00:00 time: 0.0051 data_time: 0.0008 memory: 76 2022/08/31 20:09:23 - mmengine - INFO - Epoch(val) [6][472/472] node/macro_f1: 0.9165 node/micro_f1: 0.9248 edge/micro_f1: 0.7092 2022/08/31 20:09:25 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:09:25 - mmengine - INFO - Epoch(train) [7][100/317] lr: 1.0000e-03 eta: 0:07:23 time: 0.0257 data_time: 0.0028 memory: 898 loss_node: 0.2557 loss_edge: 0.0566 acc_node: 82.2335 acc_edge: 94.4504 loss: 0.3123 2022/08/31 20:09:28 - mmengine - INFO - Epoch(train) [7][200/317] lr: 1.0000e-03 eta: 0:07:21 time: 0.0263 data_time: 0.0031 memory: 936 loss_node: 0.1989 loss_edge: 0.0481 acc_node: 93.1034 acc_edge: 98.1539 loss: 0.2470 2022/08/31 20:09:31 - mmengine - INFO - Epoch(train) [7][300/317] lr: 1.0000e-03 eta: 0:07:19 time: 0.0284 data_time: 0.0027 memory: 662 loss_node: 0.2343 loss_edge: 0.0457 acc_node: 93.0876 acc_edge: 99.0354 loss: 0.2800 2022/08/31 20:09:31 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:09:31 - mmengine - INFO - Saving checkpoint at 7 epochs 2022/08/31 20:09:32 - mmengine - INFO - Epoch(val) [7][100/472] eta: 0:00:01 time: 0.0049 data_time: 0.0008 memory: 338 2022/08/31 20:09:33 - mmengine - INFO - Epoch(val) [7][200/472] eta: 0:00:01 time: 0.0056 data_time: 0.0009 memory: 138 2022/08/31 20:09:33 - mmengine - INFO - Epoch(val) [7][300/472] eta: 0:00:00 time: 0.0056 data_time: 0.0008 memory: 330 2022/08/31 20:09:34 - mmengine - INFO - Epoch(val) [7][400/472] eta: 0:00:00 time: 0.0053 data_time: 0.0007 memory: 76 2022/08/31 20:09:34 - mmengine - INFO - Epoch(val) [7][472/472] node/macro_f1: 0.9136 node/micro_f1: 0.9225 edge/micro_f1: 0.6898 2022/08/31 20:09:37 - mmengine - INFO - Epoch(train) [8][100/317] lr: 1.0000e-03 eta: 0:07:15 time: 0.0255 data_time: 0.0028 memory: 825 loss_node: 0.1690 loss_edge: 0.0389 acc_node: 94.9153 acc_edge: 98.8621 loss: 0.2078 2022/08/31 20:09:39 - mmengine - INFO - Epoch(train) [8][200/317] lr: 1.0000e-03 eta: 0:07:12 time: 0.0255 data_time: 0.0029 memory: 1068 loss_node: 0.2023 loss_edge: 0.0568 acc_node: 86.6995 acc_edge: 94.1049 loss: 0.2592 2022/08/31 20:09:42 - mmengine - INFO - Epoch(train) [8][300/317] lr: 1.0000e-03 eta: 0:07:09 time: 0.0314 data_time: 0.0028 memory: 524 loss_node: 0.2001 loss_edge: 0.0344 acc_node: 94.2675 acc_edge: 98.1499 loss: 0.2346 2022/08/31 20:09:42 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:09:42 - mmengine - INFO - Saving checkpoint at 8 epochs 2022/08/31 20:09:43 - mmengine - INFO - Epoch(val) [8][100/472] eta: 0:00:02 time: 0.0059 data_time: 0.0010 memory: 284 2022/08/31 20:09:44 - mmengine - INFO - Epoch(val) [8][200/472] eta: 0:00:01 time: 0.0069 data_time: 0.0010 memory: 138 2022/08/31 20:09:44 - mmengine - INFO - Epoch(val) [8][300/472] eta: 0:00:01 time: 0.0063 data_time: 0.0009 memory: 330 2022/08/31 20:09:45 - mmengine - INFO - Epoch(val) [8][400/472] eta: 0:00:00 time: 0.0052 data_time: 0.0008 memory: 76 2022/08/31 20:09:45 - mmengine - INFO - Epoch(val) [8][472/472] node/macro_f1: 0.9176 node/micro_f1: 0.9308 edge/micro_f1: 0.7118 2022/08/31 20:09:48 - mmengine - INFO - Epoch(train) [9][100/317] lr: 1.0000e-03 eta: 0:07:06 time: 0.0273 data_time: 0.0030 memory: 509 loss_node: 0.1462 loss_edge: 0.0384 acc_node: 98.7342 acc_edge: 98.7057 loss: 0.1846 2022/08/31 20:09:51 - mmengine - INFO - Epoch(train) [9][200/317] lr: 1.0000e-03 eta: 0:07:06 time: 0.0259 data_time: 0.0029 memory: 1027 loss_node: 0.1772 loss_edge: 0.0378 acc_node: 91.5663 acc_edge: 99.2108 loss: 0.2149 2022/08/31 20:09:54 - mmengine - INFO - Epoch(train) [9][300/317] lr: 1.0000e-03 eta: 0:07:03 time: 0.0268 data_time: 0.0028 memory: 589 loss_node: 0.1485 loss_edge: 0.0372 acc_node: 96.8036 acc_edge: 98.8239 loss: 0.1857 2022/08/31 20:09:54 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:09:54 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/08/31 20:09:55 - mmengine - INFO - Epoch(val) [9][100/472] eta: 0:00:01 time: 0.0048 data_time: 0.0008 memory: 548 2022/08/31 20:09:56 - mmengine - INFO - Epoch(val) [9][200/472] eta: 0:00:01 time: 0.0068 data_time: 0.0011 memory: 138 2022/08/31 20:09:56 - mmengine - INFO - Epoch(val) [9][300/472] eta: 0:00:01 time: 0.0070 data_time: 0.0011 memory: 330 2022/08/31 20:09:57 - mmengine - INFO - Epoch(val) [9][400/472] eta: 0:00:00 time: 0.0053 data_time: 0.0008 memory: 76 2022/08/31 20:09:57 - mmengine - INFO - Epoch(val) [9][472/472] node/macro_f1: 0.9215 node/micro_f1: 0.9337 edge/micro_f1: 0.6937 2022/08/31 20:10:00 - mmengine - INFO - Epoch(train) [10][100/317] lr: 1.0000e-03 eta: 0:07:00 time: 0.0269 data_time: 0.0031 memory: 858 loss_node: 0.1559 loss_edge: 0.0396 acc_node: 93.2961 acc_edge: 98.0692 loss: 0.1956 2022/08/31 20:10:02 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:10:03 - mmengine - INFO - Epoch(train) [10][200/317] lr: 1.0000e-03 eta: 0:06:59 time: 0.0224 data_time: 0.0029 memory: 716 loss_node: 0.1718 loss_edge: 0.0333 acc_node: 89.7436 acc_edge: 98.9349 loss: 0.2051 2022/08/31 20:10:06 - mmengine - INFO - Epoch(train) [10][300/317] lr: 1.0000e-03 eta: 0:06:56 time: 0.0265 data_time: 0.0028 memory: 531 loss_node: 0.1791 loss_edge: 0.0349 acc_node: 94.5055 acc_edge: 98.7818 loss: 0.2140 2022/08/31 20:10:06 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:10:06 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/08/31 20:10:07 - mmengine - INFO - Epoch(val) [10][100/472] eta: 0:00:01 time: 0.0049 data_time: 0.0008 memory: 900 2022/08/31 20:10:08 - mmengine - INFO - Epoch(val) [10][200/472] eta: 0:00:01 time: 0.0058 data_time: 0.0009 memory: 138 2022/08/31 20:10:08 - mmengine - INFO - Epoch(val) [10][300/472] eta: 0:00:00 time: 0.0058 data_time: 0.0008 memory: 330 2022/08/31 20:10:09 - mmengine - INFO - Epoch(val) [10][400/472] eta: 0:00:00 time: 0.0058 data_time: 0.0008 memory: 76 2022/08/31 20:10:09 - mmengine - INFO - Epoch(val) [10][472/472] node/macro_f1: 0.9190 node/micro_f1: 0.9301 edge/micro_f1: 0.7383 2022/08/31 20:10:12 - mmengine - INFO - Epoch(train) [11][100/317] lr: 1.0000e-03 eta: 0:06:53 time: 0.0317 data_time: 0.0029 memory: 626 loss_node: 0.1564 loss_edge: 0.0244 acc_node: 95.5752 acc_edge: 99.3895 loss: 0.1808 2022/08/31 20:10:15 - mmengine - INFO - Epoch(train) [11][200/317] lr: 1.0000e-03 eta: 0:06:51 time: 0.0256 data_time: 0.0033 memory: 834 loss_node: 0.1819 loss_edge: 0.0395 acc_node: 96.4072 acc_edge: 98.3755 loss: 0.2214 2022/08/31 20:10:18 - mmengine - INFO - Epoch(train) [11][300/317] lr: 1.0000e-03 eta: 0:06:50 time: 0.0264 data_time: 0.0030 memory: 943 loss_node: 0.1566 loss_edge: 0.0328 acc_node: 96.5909 acc_edge: 98.1154 loss: 0.1894 2022/08/31 20:10:18 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:10:18 - mmengine - INFO - Saving checkpoint at 11 epochs 2022/08/31 20:10:19 - mmengine - INFO - Epoch(val) [11][100/472] eta: 0:00:01 time: 0.0048 data_time: 0.0009 memory: 212 2022/08/31 20:10:20 - mmengine - INFO - Epoch(val) [11][200/472] eta: 0:00:01 time: 0.0067 data_time: 0.0011 memory: 138 2022/08/31 20:10:20 - mmengine - INFO - Epoch(val) [11][300/472] eta: 0:00:01 time: 0.0071 data_time: 0.0011 memory: 330 2022/08/31 20:10:21 - mmengine - INFO - Epoch(val) [11][400/472] eta: 0:00:00 time: 0.0067 data_time: 0.0012 memory: 76 2022/08/31 20:10:22 - mmengine - INFO - Epoch(val) [11][472/472] node/macro_f1: 0.9255 node/micro_f1: 0.9360 edge/micro_f1: 0.7486 2022/08/31 20:10:24 - mmengine - INFO - Epoch(train) [12][100/317] lr: 1.0000e-03 eta: 0:06:46 time: 0.0217 data_time: 0.0028 memory: 484 loss_node: 0.1200 loss_edge: 0.0286 acc_node: 93.0555 acc_edge: 98.4524 loss: 0.1486 2022/08/31 20:10:27 - mmengine - INFO - Epoch(train) [12][200/317] lr: 1.0000e-03 eta: 0:06:44 time: 0.0249 data_time: 0.0027 memory: 670 loss_node: 0.1802 loss_edge: 0.0353 acc_node: 97.0370 acc_edge: 99.1402 loss: 0.2155 2022/08/31 20:10:30 - mmengine - INFO - Epoch(train) [12][300/317] lr: 1.0000e-03 eta: 0:06:41 time: 0.0346 data_time: 0.0030 memory: 960 loss_node: 0.1387 loss_edge: 0.0248 acc_node: 92.7152 acc_edge: 99.3881 loss: 0.1635 2022/08/31 20:10:30 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:10:30 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/08/31 20:10:31 - mmengine - INFO - Epoch(val) [12][100/472] eta: 0:00:01 time: 0.0048 data_time: 0.0009 memory: 358 2022/08/31 20:10:32 - mmengine - INFO - Epoch(val) [12][200/472] eta: 0:00:01 time: 0.0058 data_time: 0.0010 memory: 138 2022/08/31 20:10:32 - mmengine - INFO - Epoch(val) [12][300/472] eta: 0:00:01 time: 0.0061 data_time: 0.0009 memory: 330 2022/08/31 20:10:33 - mmengine - INFO - Epoch(val) [12][400/472] eta: 0:00:00 time: 0.0067 data_time: 0.0011 memory: 76 2022/08/31 20:10:33 - mmengine - INFO - Epoch(val) [12][472/472] node/macro_f1: 0.9268 node/micro_f1: 0.9360 edge/micro_f1: 0.7498 2022/08/31 20:10:36 - mmengine - INFO - Epoch(train) [13][100/317] lr: 1.0000e-03 eta: 0:06:38 time: 0.0227 data_time: 0.0027 memory: 926 loss_node: 0.1538 loss_edge: 0.0358 acc_node: 92.1053 acc_edge: 98.0807 loss: 0.1896 2022/08/31 20:10:39 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:10:39 - mmengine - INFO - Epoch(train) [13][200/317] lr: 1.0000e-03 eta: 0:06:35 time: 0.0234 data_time: 0.0027 memory: 512 loss_node: 0.1198 loss_edge: 0.0413 acc_node: 92.5676 acc_edge: 98.3401 loss: 0.1611 2022/08/31 20:10:42 - mmengine - INFO - Epoch(train) [13][300/317] lr: 1.0000e-03 eta: 0:06:34 time: 0.0358 data_time: 0.0032 memory: 873 loss_node: 0.1588 loss_edge: 0.0291 acc_node: 96.0674 acc_edge: 99.4632 loss: 0.1879 2022/08/31 20:10:42 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:10:42 - mmengine - INFO - Saving checkpoint at 13 epochs 2022/08/31 20:10:43 - mmengine - INFO - Epoch(val) [13][100/472] eta: 0:00:02 time: 0.0058 data_time: 0.0010 memory: 369 2022/08/31 20:10:44 - mmengine - INFO - Epoch(val) [13][200/472] eta: 0:00:01 time: 0.0061 data_time: 0.0009 memory: 138 2022/08/31 20:10:44 - mmengine - INFO - Epoch(val) [13][300/472] eta: 0:00:01 time: 0.0068 data_time: 0.0011 memory: 330 2022/08/31 20:10:45 - mmengine - INFO - Epoch(val) [13][400/472] eta: 0:00:00 time: 0.0067 data_time: 0.0011 memory: 76 2022/08/31 20:10:45 - mmengine - INFO - Epoch(val) [13][472/472] node/macro_f1: 0.9268 node/micro_f1: 0.9354 edge/micro_f1: 0.7497 2022/08/31 20:10:48 - mmengine - INFO - Epoch(train) [14][100/317] lr: 1.0000e-03 eta: 0:06:30 time: 0.0279 data_time: 0.0030 memory: 422 loss_node: 0.0992 loss_edge: 0.0334 acc_node: 95.1049 acc_edge: 99.4931 loss: 0.1326 2022/08/31 20:10:51 - mmengine - INFO - Epoch(train) [14][200/317] lr: 1.0000e-03 eta: 0:06:29 time: 0.0263 data_time: 0.0029 memory: 944 loss_node: 0.1283 loss_edge: 0.0410 acc_node: 99.4737 acc_edge: 99.5694 loss: 0.1693 2022/08/31 20:10:54 - mmengine - INFO - Epoch(train) [14][300/317] lr: 1.0000e-03 eta: 0:06:28 time: 0.0252 data_time: 0.0029 memory: 896 loss_node: 0.1126 loss_edge: 0.0354 acc_node: 95.2756 acc_edge: 98.6001 loss: 0.1480 2022/08/31 20:10:55 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:10:55 - mmengine - INFO - Saving checkpoint at 14 epochs 2022/08/31 20:10:56 - mmengine - INFO - Epoch(val) [14][100/472] eta: 0:00:02 time: 0.0054 data_time: 0.0009 memory: 485 2022/08/31 20:10:56 - mmengine - INFO - Epoch(val) [14][200/472] eta: 0:00:01 time: 0.0066 data_time: 0.0010 memory: 138 2022/08/31 20:10:57 - mmengine - INFO - Epoch(val) [14][300/472] eta: 0:00:01 time: 0.0068 data_time: 0.0010 memory: 330 2022/08/31 20:10:57 - mmengine - INFO - Epoch(val) [14][400/472] eta: 0:00:00 time: 0.0062 data_time: 0.0010 memory: 76 2022/08/31 20:10:58 - mmengine - INFO - Epoch(val) [14][472/472] node/macro_f1: 0.9314 node/micro_f1: 0.9383 edge/micro_f1: 0.7673 2022/08/31 20:11:01 - mmengine - INFO - Epoch(train) [15][100/317] lr: 1.0000e-03 eta: 0:06:25 time: 0.0293 data_time: 0.0032 memory: 675 loss_node: 0.1496 loss_edge: 0.0324 acc_node: 89.6104 acc_edge: 98.7577 loss: 0.1819 2022/08/31 20:11:03 - mmengine - INFO - Epoch(train) [15][200/317] lr: 1.0000e-03 eta: 0:06:22 time: 0.0252 data_time: 0.0027 memory: 932 loss_node: 0.1121 loss_edge: 0.0291 acc_node: 94.5205 acc_edge: 99.1727 loss: 0.1412 2022/08/31 20:11:06 - mmengine - INFO - Epoch(train) [15][300/317] lr: 1.0000e-03 eta: 0:06:20 time: 0.0358 data_time: 0.0030 memory: 870 loss_node: 0.1212 loss_edge: 0.0288 acc_node: 98.1818 acc_edge: 99.5108 loss: 0.1499 2022/08/31 20:11:07 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:11:07 - mmengine - INFO - Saving checkpoint at 15 epochs 2022/08/31 20:11:08 - mmengine - INFO - Epoch(val) [15][100/472] eta: 0:00:01 time: 0.0054 data_time: 0.0009 memory: 262 2022/08/31 20:11:08 - mmengine - INFO - Epoch(val) [15][200/472] eta: 0:00:01 time: 0.0064 data_time: 0.0011 memory: 138 2022/08/31 20:11:09 - mmengine - INFO - Epoch(val) [15][300/472] eta: 0:00:00 time: 0.0057 data_time: 0.0008 memory: 330 2022/08/31 20:11:09 - mmengine - INFO - Epoch(val) [15][400/472] eta: 0:00:00 time: 0.0051 data_time: 0.0008 memory: 76 2022/08/31 20:11:10 - mmengine - INFO - Epoch(val) [15][472/472] node/macro_f1: 0.9251 node/micro_f1: 0.9341 edge/micro_f1: 0.7351 2022/08/31 20:11:13 - mmengine - INFO - Epoch(train) [16][100/317] lr: 1.0000e-03 eta: 0:06:17 time: 0.0366 data_time: 0.0031 memory: 972 loss_node: 0.1085 loss_edge: 0.0366 acc_node: 96.6667 acc_edge: 99.1162 loss: 0.1451 2022/08/31 20:11:16 - mmengine - INFO - Epoch(train) [16][200/317] lr: 1.0000e-03 eta: 0:06:15 time: 0.0229 data_time: 0.0027 memory: 719 loss_node: 0.1559 loss_edge: 0.0313 acc_node: 94.9721 acc_edge: 99.0803 loss: 0.1873 2022/08/31 20:11:17 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:11:18 - mmengine - INFO - Epoch(train) [16][300/317] lr: 1.0000e-03 eta: 0:06:12 time: 0.0245 data_time: 0.0027 memory: 927 loss_node: 0.1491 loss_edge: 0.0350 acc_node: 90.8163 acc_edge: 98.6959 loss: 0.1841 2022/08/31 20:11:19 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:11:19 - mmengine - INFO - Saving checkpoint at 16 epochs 2022/08/31 20:11:20 - mmengine - INFO - Epoch(val) [16][100/472] eta: 0:00:02 time: 0.0059 data_time: 0.0010 memory: 286 2022/08/31 20:11:20 - mmengine - INFO - Epoch(val) [16][200/472] eta: 0:00:01 time: 0.0061 data_time: 0.0010 memory: 138 2022/08/31 20:11:21 - mmengine - INFO - Epoch(val) [16][300/472] eta: 0:00:01 time: 0.0064 data_time: 0.0009 memory: 330 2022/08/31 20:11:21 - mmengine - INFO - Epoch(val) [16][400/472] eta: 0:00:00 time: 0.0068 data_time: 0.0010 memory: 76 2022/08/31 20:11:22 - mmengine - INFO - Epoch(val) [16][472/472] node/macro_f1: 0.9256 node/micro_f1: 0.9381 edge/micro_f1: 0.7804 2022/08/31 20:11:25 - mmengine - INFO - Epoch(train) [17][100/317] lr: 1.0000e-03 eta: 0:06:09 time: 0.0246 data_time: 0.0028 memory: 685 loss_node: 0.1087 loss_edge: 0.0324 acc_node: 95.7983 acc_edge: 98.9293 loss: 0.1411 2022/08/31 20:11:27 - mmengine - INFO - Epoch(train) [17][200/317] lr: 1.0000e-03 eta: 0:06:06 time: 0.0278 data_time: 0.0026 memory: 957 loss_node: 0.0889 loss_edge: 0.0220 acc_node: 98.0263 acc_edge: 99.3639 loss: 0.1109 2022/08/31 20:11:30 - mmengine - INFO - Epoch(train) [17][300/317] lr: 1.0000e-03 eta: 0:06:04 time: 0.0243 data_time: 0.0029 memory: 857 loss_node: 0.0873 loss_edge: 0.0275 acc_node: 97.6190 acc_edge: 99.1479 loss: 0.1148 2022/08/31 20:11:30 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:11:30 - mmengine - INFO - Saving checkpoint at 17 epochs 2022/08/31 20:11:31 - mmengine - INFO - Epoch(val) [17][100/472] eta: 0:00:01 time: 0.0053 data_time: 0.0009 memory: 363 2022/08/31 20:11:32 - mmengine - INFO - Epoch(val) [17][200/472] eta: 0:00:01 time: 0.0055 data_time: 0.0009 memory: 138 2022/08/31 20:11:33 - mmengine - INFO - Epoch(val) [17][300/472] eta: 0:00:00 time: 0.0057 data_time: 0.0008 memory: 330 2022/08/31 20:11:33 - mmengine - INFO - Epoch(val) [17][400/472] eta: 0:00:00 time: 0.0062 data_time: 0.0009 memory: 76 2022/08/31 20:11:33 - mmengine - INFO - Epoch(val) [17][472/472] node/macro_f1: 0.9252 node/micro_f1: 0.9364 edge/micro_f1: 0.7659 2022/08/31 20:11:36 - mmengine - INFO - Epoch(train) [18][100/317] lr: 1.0000e-03 eta: 0:06:01 time: 0.0319 data_time: 0.0029 memory: 967 loss_node: 0.1089 loss_edge: 0.0336 acc_node: 98.0583 acc_edge: 99.6593 loss: 0.1426 2022/08/31 20:11:39 - mmengine - INFO - Epoch(train) [18][200/317] lr: 1.0000e-03 eta: 0:05:58 time: 0.0235 data_time: 0.0029 memory: 630 loss_node: 0.0933 loss_edge: 0.0286 acc_node: 96.8553 acc_edge: 99.0896 loss: 0.1219 2022/08/31 20:11:42 - mmengine - INFO - Epoch(train) [18][300/317] lr: 1.0000e-03 eta: 0:05:56 time: 0.0294 data_time: 0.0029 memory: 846 loss_node: 0.0998 loss_edge: 0.0314 acc_node: 97.7987 acc_edge: 99.4234 loss: 0.1312 2022/08/31 20:11:42 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:11:42 - mmengine - INFO - Saving checkpoint at 18 epochs 2022/08/31 20:11:43 - mmengine - INFO - Epoch(val) [18][100/472] eta: 0:00:02 time: 0.0056 data_time: 0.0010 memory: 258 2022/08/31 20:11:44 - mmengine - INFO - Epoch(val) [18][200/472] eta: 0:00:01 time: 0.0057 data_time: 0.0008 memory: 138 2022/08/31 20:11:44 - mmengine - INFO - Epoch(val) [18][300/472] eta: 0:00:01 time: 0.0064 data_time: 0.0010 memory: 330 2022/08/31 20:11:45 - mmengine - INFO - Epoch(val) [18][400/472] eta: 0:00:00 time: 0.0066 data_time: 0.0009 memory: 76 2022/08/31 20:11:46 - mmengine - INFO - Epoch(val) [18][472/472] node/macro_f1: 0.9218 node/micro_f1: 0.9316 edge/micro_f1: 0.7544 2022/08/31 20:11:48 - mmengine - INFO - Epoch(train) [19][100/317] lr: 1.0000e-03 eta: 0:05:52 time: 0.0285 data_time: 0.0027 memory: 933 loss_node: 0.0941 loss_edge: 0.0341 acc_node: 98.1132 acc_edge: 99.0634 loss: 0.1283 2022/08/31 20:11:51 - mmengine - INFO - Epoch(train) [19][200/317] lr: 1.0000e-03 eta: 0:05:50 time: 0.0257 data_time: 0.0027 memory: 644 loss_node: 0.0746 loss_edge: 0.0358 acc_node: 98.1132 acc_edge: 98.8052 loss: 0.1104 2022/08/31 20:11:54 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:11:54 - mmengine - INFO - Epoch(train) [19][300/317] lr: 1.0000e-03 eta: 0:05:47 time: 0.0237 data_time: 0.0029 memory: 919 loss_node: 0.1386 loss_edge: 0.0516 acc_node: 92.5170 acc_edge: 99.1376 loss: 0.1902 2022/08/31 20:11:54 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:11:54 - mmengine - INFO - Saving checkpoint at 19 epochs 2022/08/31 20:11:55 - mmengine - INFO - Epoch(val) [19][100/472] eta: 0:00:01 time: 0.0046 data_time: 0.0008 memory: 479 2022/08/31 20:11:56 - mmengine - INFO - Epoch(val) [19][200/472] eta: 0:00:01 time: 0.0057 data_time: 0.0009 memory: 138 2022/08/31 20:11:56 - mmengine - INFO - Epoch(val) [19][300/472] eta: 0:00:01 time: 0.0066 data_time: 0.0010 memory: 330 2022/08/31 20:11:57 - mmengine - INFO - Epoch(val) [19][400/472] eta: 0:00:00 time: 0.0068 data_time: 0.0010 memory: 76 2022/08/31 20:11:57 - mmengine - INFO - Epoch(val) [19][472/472] node/macro_f1: 0.9229 node/micro_f1: 0.9321 edge/micro_f1: 0.7571 2022/08/31 20:12:00 - mmengine - INFO - Epoch(train) [20][100/317] lr: 1.0000e-03 eta: 0:05:44 time: 0.0253 data_time: 0.0028 memory: 1314 loss_node: 0.0797 loss_edge: 0.0250 acc_node: 95.3333 acc_edge: 99.1952 loss: 0.1048 2022/08/31 20:12:03 - mmengine - INFO - Epoch(train) [20][200/317] lr: 1.0000e-03 eta: 0:05:41 time: 0.0298 data_time: 0.0030 memory: 669 loss_node: 0.0899 loss_edge: 0.0372 acc_node: 94.8276 acc_edge: 98.7032 loss: 0.1272 2022/08/31 20:12:06 - mmengine - INFO - Epoch(train) [20][300/317] lr: 1.0000e-03 eta: 0:05:39 time: 0.0284 data_time: 0.0028 memory: 474 loss_node: 0.1093 loss_edge: 0.0254 acc_node: 92.3077 acc_edge: 99.6532 loss: 0.1347 2022/08/31 20:12:06 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:12:06 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/08/31 20:12:07 - mmengine - INFO - Epoch(val) [20][100/472] eta: 0:00:01 time: 0.0046 data_time: 0.0008 memory: 299 2022/08/31 20:12:08 - mmengine - INFO - Epoch(val) [20][200/472] eta: 0:00:01 time: 0.0060 data_time: 0.0010 memory: 138 2022/08/31 20:12:08 - mmengine - INFO - Epoch(val) [20][300/472] eta: 0:00:01 time: 0.0072 data_time: 0.0011 memory: 330 2022/08/31 20:12:09 - mmengine - INFO - Epoch(val) [20][400/472] eta: 0:00:00 time: 0.0062 data_time: 0.0009 memory: 76 2022/08/31 20:12:09 - mmengine - INFO - Epoch(val) [20][472/472] node/macro_f1: 0.9279 node/micro_f1: 0.9371 edge/micro_f1: 0.7678 2022/08/31 20:12:12 - mmengine - INFO - Epoch(train) [21][100/317] lr: 1.0000e-03 eta: 0:05:36 time: 0.0278 data_time: 0.0030 memory: 909 loss_node: 0.0880 loss_edge: 0.0205 acc_node: 99.3750 acc_edge: 99.3987 loss: 0.1084 2022/08/31 20:12:15 - mmengine - INFO - Epoch(train) [21][200/317] lr: 1.0000e-03 eta: 0:05:33 time: 0.0329 data_time: 0.0029 memory: 440 loss_node: 0.0727 loss_edge: 0.0229 acc_node: 97.8070 acc_edge: 99.8390 loss: 0.0956 2022/08/31 20:12:18 - mmengine - INFO - Epoch(train) [21][300/317] lr: 1.0000e-03 eta: 0:05:31 time: 0.0289 data_time: 0.0028 memory: 1059 loss_node: 0.0991 loss_edge: 0.0227 acc_node: 92.0635 acc_edge: 99.2913 loss: 0.1218 2022/08/31 20:12:18 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:12:18 - mmengine - INFO - Saving checkpoint at 21 epochs 2022/08/31 20:12:19 - mmengine - INFO - Epoch(val) [21][100/472] eta: 0:00:01 time: 0.0051 data_time: 0.0008 memory: 335 2022/08/31 20:12:20 - mmengine - INFO - Epoch(val) [21][200/472] eta: 0:00:01 time: 0.0070 data_time: 0.0011 memory: 138 2022/08/31 20:12:20 - mmengine - INFO - Epoch(val) [21][300/472] eta: 0:00:00 time: 0.0057 data_time: 0.0008 memory: 330 2022/08/31 20:12:21 - mmengine - INFO - Epoch(val) [21][400/472] eta: 0:00:00 time: 0.0052 data_time: 0.0007 memory: 76 2022/08/31 20:12:21 - mmengine - INFO - Epoch(val) [21][472/472] node/macro_f1: 0.9198 node/micro_f1: 0.9271 edge/micro_f1: 0.7475 2022/08/31 20:12:24 - mmengine - INFO - Epoch(train) [22][100/317] lr: 1.0000e-03 eta: 0:05:27 time: 0.0257 data_time: 0.0031 memory: 848 loss_node: 0.0805 loss_edge: 0.0264 acc_node: 93.7063 acc_edge: 99.4264 loss: 0.1069 2022/08/31 20:12:27 - mmengine - INFO - Epoch(train) [22][200/317] lr: 1.0000e-03 eta: 0:05:25 time: 0.0294 data_time: 0.0031 memory: 556 loss_node: 0.1012 loss_edge: 0.0288 acc_node: 98.2759 acc_edge: 99.4284 loss: 0.1300 2022/08/31 20:12:29 - mmengine - INFO - Epoch(train) [22][300/317] lr: 1.0000e-03 eta: 0:05:22 time: 0.0245 data_time: 0.0027 memory: 918 loss_node: 0.0738 loss_edge: 0.0280 acc_node: 94.1935 acc_edge: 98.5048 loss: 0.1018 2022/08/31 20:12:30 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:12:30 - mmengine - INFO - Saving checkpoint at 22 epochs 2022/08/31 20:12:31 - mmengine - INFO - Epoch(val) [22][100/472] eta: 0:00:01 time: 0.0047 data_time: 0.0008 memory: 457 2022/08/31 20:12:31 - mmengine - INFO - Epoch(val) [22][200/472] eta: 0:00:01 time: 0.0056 data_time: 0.0008 memory: 138 2022/08/31 20:12:32 - mmengine - INFO - Epoch(val) [22][300/472] eta: 0:00:01 time: 0.0067 data_time: 0.0010 memory: 330 2022/08/31 20:12:33 - mmengine - INFO - Epoch(val) [22][400/472] eta: 0:00:00 time: 0.0058 data_time: 0.0010 memory: 76 2022/08/31 20:12:33 - mmengine - INFO - Epoch(val) [22][472/472] node/macro_f1: 0.9269 node/micro_f1: 0.9349 edge/micro_f1: 0.7702 2022/08/31 20:12:34 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:12:36 - mmengine - INFO - Epoch(train) [23][100/317] lr: 1.0000e-03 eta: 0:05:20 time: 0.0325 data_time: 0.0035 memory: 1124 loss_node: 0.0681 loss_edge: 0.0183 acc_node: 98.7179 acc_edge: 98.9414 loss: 0.0864 2022/08/31 20:12:39 - mmengine - INFO - Epoch(train) [23][200/317] lr: 1.0000e-03 eta: 0:05:17 time: 0.0241 data_time: 0.0029 memory: 652 loss_node: 0.0701 loss_edge: 0.0303 acc_node: 98.9474 acc_edge: 99.6039 loss: 0.1003 2022/08/31 20:12:42 - mmengine - INFO - Epoch(train) [23][300/317] lr: 1.0000e-03 eta: 0:05:14 time: 0.0261 data_time: 0.0027 memory: 695 loss_node: 0.0674 loss_edge: 0.0291 acc_node: 97.6096 acc_edge: 99.2953 loss: 0.0965 2022/08/31 20:12:42 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:12:42 - mmengine - INFO - Saving checkpoint at 23 epochs 2022/08/31 20:12:43 - mmengine - INFO - Epoch(val) [23][100/472] eta: 0:00:02 time: 0.0060 data_time: 0.0010 memory: 308 2022/08/31 20:12:43 - mmengine - INFO - Epoch(val) [23][200/472] eta: 0:00:01 time: 0.0058 data_time: 0.0008 memory: 138 2022/08/31 20:12:44 - mmengine - INFO - Epoch(val) [23][300/472] eta: 0:00:01 time: 0.0063 data_time: 0.0009 memory: 330 2022/08/31 20:12:45 - mmengine - INFO - Epoch(val) [23][400/472] eta: 0:00:00 time: 0.0070 data_time: 0.0012 memory: 76 2022/08/31 20:12:45 - mmengine - INFO - Epoch(val) [23][472/472] node/macro_f1: 0.9270 node/micro_f1: 0.9356 edge/micro_f1: 0.7664 2022/08/31 20:12:48 - mmengine - INFO - Epoch(train) [24][100/317] lr: 1.0000e-03 eta: 0:05:11 time: 0.0282 data_time: 0.0028 memory: 885 loss_node: 0.0587 loss_edge: 0.0296 acc_node: 98.5866 acc_edge: 99.6689 loss: 0.0884 2022/08/31 20:12:51 - mmengine - INFO - Epoch(train) [24][200/317] lr: 1.0000e-03 eta: 0:05:09 time: 0.0243 data_time: 0.0028 memory: 931 loss_node: 0.1010 loss_edge: 0.0311 acc_node: 97.0803 acc_edge: 99.3328 loss: 0.1321 2022/08/31 20:12:54 - mmengine - INFO - Epoch(train) [24][300/317] lr: 1.0000e-03 eta: 0:05:06 time: 0.0272 data_time: 0.0029 memory: 482 loss_node: 0.0859 loss_edge: 0.0279 acc_node: 94.0540 acc_edge: 99.4169 loss: 0.1138 2022/08/31 20:12:54 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:12:54 - mmengine - INFO - Saving checkpoint at 24 epochs 2022/08/31 20:12:55 - mmengine - INFO - Epoch(val) [24][100/472] eta: 0:00:02 time: 0.0062 data_time: 0.0011 memory: 215 2022/08/31 20:12:56 - mmengine - INFO - Epoch(val) [24][200/472] eta: 0:00:01 time: 0.0066 data_time: 0.0011 memory: 138 2022/08/31 20:12:56 - mmengine - INFO - Epoch(val) [24][300/472] eta: 0:00:01 time: 0.0058 data_time: 0.0008 memory: 330 2022/08/31 20:12:57 - mmengine - INFO - Epoch(val) [24][400/472] eta: 0:00:00 time: 0.0054 data_time: 0.0008 memory: 76 2022/08/31 20:12:57 - mmengine - INFO - Epoch(val) [24][472/472] node/macro_f1: 0.9235 node/micro_f1: 0.9319 edge/micro_f1: 0.7705 2022/08/31 20:13:00 - mmengine - INFO - Epoch(train) [25][100/317] lr: 1.0000e-03 eta: 0:05:02 time: 0.0260 data_time: 0.0029 memory: 527 loss_node: 0.0720 loss_edge: 0.0291 acc_node: 96.0784 acc_edge: 99.2977 loss: 0.1010 2022/08/31 20:13:02 - mmengine - INFO - Epoch(train) [25][200/317] lr: 1.0000e-03 eta: 0:04:59 time: 0.0260 data_time: 0.0029 memory: 566 loss_node: 0.0870 loss_edge: 0.0301 acc_node: 97.5247 acc_edge: 98.8199 loss: 0.1171 2022/08/31 20:13:05 - mmengine - INFO - Epoch(train) [25][300/317] lr: 1.0000e-03 eta: 0:04:57 time: 0.0368 data_time: 0.0031 memory: 894 loss_node: 0.0897 loss_edge: 0.0199 acc_node: 97.1751 acc_edge: 99.4191 loss: 0.1096 2022/08/31 20:13:06 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:13:06 - mmengine - INFO - Saving checkpoint at 25 epochs 2022/08/31 20:13:07 - mmengine - INFO - Epoch(val) [25][100/472] eta: 0:00:01 time: 0.0046 data_time: 0.0008 memory: 623 2022/08/31 20:13:07 - mmengine - INFO - Epoch(val) [25][200/472] eta: 0:00:01 time: 0.0058 data_time: 0.0009 memory: 138 2022/08/31 20:13:08 - mmengine - INFO - Epoch(val) [25][300/472] eta: 0:00:01 time: 0.0064 data_time: 0.0009 memory: 330 2022/08/31 20:13:08 - mmengine - INFO - Epoch(val) [25][400/472] eta: 0:00:00 time: 0.0062 data_time: 0.0009 memory: 76 2022/08/31 20:13:09 - mmengine - INFO - Epoch(val) [25][472/472] node/macro_f1: 0.9265 node/micro_f1: 0.9355 edge/micro_f1: 0.7704 2022/08/31 20:13:11 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:13:11 - mmengine - INFO - Epoch(train) [26][100/317] lr: 1.0000e-03 eta: 0:04:54 time: 0.0287 data_time: 0.0030 memory: 667 loss_node: 0.0596 loss_edge: 0.0212 acc_node: 95.0739 acc_edge: 99.5682 loss: 0.0808 2022/08/31 20:13:14 - mmengine - INFO - Epoch(train) [26][200/317] lr: 1.0000e-03 eta: 0:04:51 time: 0.0295 data_time: 0.0031 memory: 931 loss_node: 0.0673 loss_edge: 0.0272 acc_node: 96.8889 acc_edge: 98.7652 loss: 0.0945 2022/08/31 20:13:17 - mmengine - INFO - Epoch(train) [26][300/317] lr: 1.0000e-03 eta: 0:04:48 time: 0.0256 data_time: 0.0028 memory: 857 loss_node: 0.0900 loss_edge: 0.0328 acc_node: 99.1803 acc_edge: 98.9247 loss: 0.1229 2022/08/31 20:13:17 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:13:17 - mmengine - INFO - Saving checkpoint at 26 epochs 2022/08/31 20:13:18 - mmengine - INFO - Epoch(val) [26][100/472] eta: 0:00:01 time: 0.0052 data_time: 0.0009 memory: 340 2022/08/31 20:13:19 - mmengine - INFO - Epoch(val) [26][200/472] eta: 0:00:01 time: 0.0054 data_time: 0.0008 memory: 138 2022/08/31 20:13:20 - mmengine - INFO - Epoch(val) [26][300/472] eta: 0:00:01 time: 0.0069 data_time: 0.0010 memory: 330 2022/08/31 20:13:20 - mmengine - INFO - Epoch(val) [26][400/472] eta: 0:00:00 time: 0.0052 data_time: 0.0008 memory: 76 2022/08/31 20:13:21 - mmengine - INFO - Epoch(val) [26][472/472] node/macro_f1: 0.9250 node/micro_f1: 0.9352 edge/micro_f1: 0.7513 2022/08/31 20:13:23 - mmengine - INFO - Epoch(train) [27][100/317] lr: 1.0000e-03 eta: 0:04:45 time: 0.0313 data_time: 0.0031 memory: 944 loss_node: 0.0573 loss_edge: 0.0223 acc_node: 95.8333 acc_edge: 99.0058 loss: 0.0795 2022/08/31 20:13:26 - mmengine - INFO - Epoch(train) [27][200/317] lr: 1.0000e-03 eta: 0:04:43 time: 0.0277 data_time: 0.0028 memory: 818 loss_node: 0.0592 loss_edge: 0.0259 acc_node: 98.2857 acc_edge: 97.9744 loss: 0.0850 2022/08/31 20:13:29 - mmengine - INFO - Epoch(train) [27][300/317] lr: 1.0000e-03 eta: 0:04:40 time: 0.0263 data_time: 0.0028 memory: 518 loss_node: 0.0773 loss_edge: 0.0267 acc_node: 92.3077 acc_edge: 99.3932 loss: 0.1040 2022/08/31 20:13:29 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:13:29 - mmengine - INFO - Saving checkpoint at 27 epochs 2022/08/31 20:13:30 - mmengine - INFO - Epoch(val) [27][100/472] eta: 0:00:01 time: 0.0047 data_time: 0.0008 memory: 292 2022/08/31 20:13:31 - mmengine - INFO - Epoch(val) [27][200/472] eta: 0:00:01 time: 0.0055 data_time: 0.0009 memory: 138 2022/08/31 20:13:31 - mmengine - INFO - Epoch(val) [27][300/472] eta: 0:00:01 time: 0.0058 data_time: 0.0008 memory: 330 2022/08/31 20:13:32 - mmengine - INFO - Epoch(val) [27][400/472] eta: 0:00:00 time: 0.0055 data_time: 0.0008 memory: 76 2022/08/31 20:13:32 - mmengine - INFO - Epoch(val) [27][472/472] node/macro_f1: 0.9211 node/micro_f1: 0.9333 edge/micro_f1: 0.7399 2022/08/31 20:13:35 - mmengine - INFO - Epoch(train) [28][100/317] lr: 1.0000e-03 eta: 0:04:37 time: 0.0234 data_time: 0.0027 memory: 959 loss_node: 0.0509 loss_edge: 0.0281 acc_node: 98.0198 acc_edge: 98.6358 loss: 0.0791 2022/08/31 20:13:38 - mmengine - INFO - Epoch(train) [28][200/317] lr: 1.0000e-03 eta: 0:04:34 time: 0.0237 data_time: 0.0026 memory: 539 loss_node: 0.0644 loss_edge: 0.0195 acc_node: 96.8000 acc_edge: 99.0114 loss: 0.0839 2022/08/31 20:13:40 - mmengine - INFO - Epoch(train) [28][300/317] lr: 1.0000e-03 eta: 0:04:31 time: 0.0316 data_time: 0.0030 memory: 693 loss_node: 0.0847 loss_edge: 0.0213 acc_node: 94.3038 acc_edge: 98.7900 loss: 0.1060 2022/08/31 20:13:41 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:13:41 - mmengine - INFO - Saving checkpoint at 28 epochs 2022/08/31 20:13:42 - mmengine - INFO - Epoch(val) [28][100/472] eta: 0:00:01 time: 0.0048 data_time: 0.0009 memory: 308 2022/08/31 20:13:42 - mmengine - INFO - Epoch(val) [28][200/472] eta: 0:00:02 time: 0.0086 data_time: 0.0028 memory: 138 2022/08/31 20:13:43 - mmengine - INFO - Epoch(val) [28][300/472] eta: 0:00:01 time: 0.0073 data_time: 0.0011 memory: 330 2022/08/31 20:13:44 - mmengine - INFO - Epoch(val) [28][400/472] eta: 0:00:00 time: 0.0068 data_time: 0.0012 memory: 76 2022/08/31 20:13:44 - mmengine - INFO - Epoch(val) [28][472/472] node/macro_f1: 0.9259 node/micro_f1: 0.9353 edge/micro_f1: 0.7679 2022/08/31 20:13:47 - mmengine - INFO - Epoch(train) [29][100/317] lr: 1.0000e-03 eta: 0:04:28 time: 0.0259 data_time: 0.0028 memory: 873 loss_node: 0.0624 loss_edge: 0.0279 acc_node: 99.3333 acc_edge: 99.2430 loss: 0.0903 2022/08/31 20:13:47 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:13:49 - mmengine - INFO - Epoch(train) [29][200/317] lr: 1.0000e-03 eta: 0:04:25 time: 0.0270 data_time: 0.0030 memory: 963 loss_node: 0.0730 loss_edge: 0.0193 acc_node: 94.2857 acc_edge: 99.2402 loss: 0.0923 2022/08/31 20:13:52 - mmengine - INFO - Epoch(train) [29][300/317] lr: 1.0000e-03 eta: 0:04:23 time: 0.0296 data_time: 0.0029 memory: 694 loss_node: 0.0758 loss_edge: 0.0190 acc_node: 98.0392 acc_edge: 99.2643 loss: 0.0948 2022/08/31 20:13:52 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:13:52 - mmengine - INFO - Saving checkpoint at 29 epochs 2022/08/31 20:13:53 - mmengine - INFO - Epoch(val) [29][100/472] eta: 0:00:01 time: 0.0047 data_time: 0.0008 memory: 485 2022/08/31 20:13:54 - mmengine - INFO - Epoch(val) [29][200/472] eta: 0:00:01 time: 0.0060 data_time: 0.0009 memory: 138 2022/08/31 20:13:55 - mmengine - INFO - Epoch(val) [29][300/472] eta: 0:00:01 time: 0.0065 data_time: 0.0010 memory: 330 2022/08/31 20:13:55 - mmengine - INFO - Epoch(val) [29][400/472] eta: 0:00:00 time: 0.0056 data_time: 0.0008 memory: 76 2022/08/31 20:13:56 - mmengine - INFO - Epoch(val) [29][472/472] node/macro_f1: 0.9256 node/micro_f1: 0.9330 edge/micro_f1: 0.7223 2022/08/31 20:13:59 - mmengine - INFO - Epoch(train) [30][100/317] lr: 1.0000e-03 eta: 0:04:20 time: 0.0259 data_time: 0.0028 memory: 854 loss_node: 0.0486 loss_edge: 0.0263 acc_node: 98.8701 acc_edge: 99.6495 loss: 0.0749 2022/08/31 20:14:01 - mmengine - INFO - Epoch(train) [30][200/317] lr: 1.0000e-03 eta: 0:04:17 time: 0.0286 data_time: 0.0028 memory: 611 loss_node: 0.0698 loss_edge: 0.0223 acc_node: 98.9011 acc_edge: 99.6085 loss: 0.0921 2022/08/31 20:14:04 - mmengine - INFO - Epoch(train) [30][300/317] lr: 1.0000e-03 eta: 0:04:14 time: 0.0262 data_time: 0.0029 memory: 890 loss_node: 0.0778 loss_edge: 0.0182 acc_node: 97.4843 acc_edge: 99.4533 loss: 0.0960 2022/08/31 20:14:04 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:14:04 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/08/31 20:14:05 - mmengine - INFO - Epoch(val) [30][100/472] eta: 0:00:02 time: 0.0060 data_time: 0.0011 memory: 460 2022/08/31 20:14:06 - mmengine - INFO - Epoch(val) [30][200/472] eta: 0:00:01 time: 0.0060 data_time: 0.0009 memory: 138 2022/08/31 20:14:07 - mmengine - INFO - Epoch(val) [30][300/472] eta: 0:00:01 time: 0.0060 data_time: 0.0009 memory: 330 2022/08/31 20:14:07 - mmengine - INFO - Epoch(val) [30][400/472] eta: 0:00:00 time: 0.0055 data_time: 0.0008 memory: 76 2022/08/31 20:14:08 - mmengine - INFO - Epoch(val) [30][472/472] node/macro_f1: 0.9284 node/micro_f1: 0.9374 edge/micro_f1: 0.7677 2022/08/31 20:14:11 - mmengine - INFO - Epoch(train) [31][100/317] lr: 1.0000e-03 eta: 0:04:11 time: 0.0268 data_time: 0.0028 memory: 965 loss_node: 0.0802 loss_edge: 0.0390 acc_node: 98.2456 acc_edge: 99.3655 loss: 0.1192 2022/08/31 20:14:13 - mmengine - INFO - Epoch(train) [31][200/317] lr: 1.0000e-03 eta: 0:04:08 time: 0.0262 data_time: 0.0028 memory: 434 loss_node: 0.0646 loss_edge: 0.0205 acc_node: 96.8254 acc_edge: 99.3578 loss: 0.0851 2022/08/31 20:14:16 - mmengine - INFO - Epoch(train) [31][300/317] lr: 1.0000e-03 eta: 0:04:06 time: 0.0230 data_time: 0.0028 memory: 828 loss_node: 0.0766 loss_edge: 0.0185 acc_node: 99.5050 acc_edge: 99.7078 loss: 0.0951 2022/08/31 20:14:16 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:14:16 - mmengine - INFO - Saving checkpoint at 31 epochs 2022/08/31 20:14:17 - mmengine - INFO - Epoch(val) [31][100/472] eta: 0:00:01 time: 0.0048 data_time: 0.0008 memory: 216 2022/08/31 20:14:18 - mmengine - INFO - Epoch(val) [31][200/472] eta: 0:00:01 time: 0.0058 data_time: 0.0008 memory: 138 2022/08/31 20:14:18 - mmengine - INFO - Epoch(val) [31][300/472] eta: 0:00:01 time: 0.0058 data_time: 0.0008 memory: 330 2022/08/31 20:14:19 - mmengine - INFO - Epoch(val) [31][400/472] eta: 0:00:00 time: 0.0052 data_time: 0.0008 memory: 76 2022/08/31 20:14:19 - mmengine - INFO - Epoch(val) [31][472/472] node/macro_f1: 0.9279 node/micro_f1: 0.9380 edge/micro_f1: 0.7685 2022/08/31 20:14:22 - mmengine - INFO - Epoch(train) [32][100/317] lr: 1.0000e-03 eta: 0:04:02 time: 0.0343 data_time: 0.0028 memory: 906 loss_node: 0.0461 loss_edge: 0.0222 acc_node: 95.6790 acc_edge: 99.0284 loss: 0.0684 2022/08/31 20:14:24 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:14:24 - mmengine - INFO - Epoch(train) [32][200/317] lr: 1.0000e-03 eta: 0:04:00 time: 0.0327 data_time: 0.0029 memory: 978 loss_node: 0.0793 loss_edge: 0.0256 acc_node: 97.7387 acc_edge: 99.8466 loss: 0.1048 2022/08/31 20:14:27 - mmengine - INFO - Epoch(train) [32][300/317] lr: 1.0000e-03 eta: 0:03:57 time: 0.0247 data_time: 0.0029 memory: 627 loss_node: 0.0476 loss_edge: 0.0259 acc_node: 100.0000 acc_edge: 99.1145 loss: 0.0735 2022/08/31 20:14:28 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:14:28 - mmengine - INFO - Saving checkpoint at 32 epochs 2022/08/31 20:14:29 - mmengine - INFO - Epoch(val) [32][100/472] eta: 0:00:01 time: 0.0054 data_time: 0.0009 memory: 337 2022/08/31 20:14:29 - mmengine - INFO - Epoch(val) [32][200/472] eta: 0:00:01 time: 0.0072 data_time: 0.0012 memory: 138 2022/08/31 20:14:30 - mmengine - INFO - Epoch(val) [32][300/472] eta: 0:00:01 time: 0.0066 data_time: 0.0010 memory: 330 2022/08/31 20:14:31 - mmengine - INFO - Epoch(val) [32][400/472] eta: 0:00:00 time: 0.0063 data_time: 0.0010 memory: 76 2022/08/31 20:14:31 - mmengine - INFO - Epoch(val) [32][472/472] node/macro_f1: 0.9259 node/micro_f1: 0.9361 edge/micro_f1: 0.7531 2022/08/31 20:14:34 - mmengine - INFO - Epoch(train) [33][100/317] lr: 1.0000e-03 eta: 0:03:54 time: 0.0276 data_time: 0.0032 memory: 713 loss_node: 0.0626 loss_edge: 0.0221 acc_node: 96.0000 acc_edge: 98.6128 loss: 0.0847 2022/08/31 20:14:37 - mmengine - INFO - Epoch(train) [33][200/317] lr: 1.0000e-03 eta: 0:03:51 time: 0.0299 data_time: 0.0033 memory: 915 loss_node: 0.0441 loss_edge: 0.0217 acc_node: 97.3154 acc_edge: 99.5432 loss: 0.0658 2022/08/31 20:14:40 - mmengine - INFO - Epoch(train) [33][300/317] lr: 1.0000e-03 eta: 0:03:49 time: 0.0285 data_time: 0.0029 memory: 524 loss_node: 0.0754 loss_edge: 0.0244 acc_node: 98.0645 acc_edge: 99.2277 loss: 0.0998 2022/08/31 20:14:40 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:14:40 - mmengine - INFO - Saving checkpoint at 33 epochs 2022/08/31 20:14:41 - mmengine - INFO - Epoch(val) [33][100/472] eta: 0:00:01 time: 0.0052 data_time: 0.0008 memory: 430 2022/08/31 20:14:42 - mmengine - INFO - Epoch(val) [33][200/472] eta: 0:00:01 time: 0.0071 data_time: 0.0012 memory: 138 2022/08/31 20:14:42 - mmengine - INFO - Epoch(val) [33][300/472] eta: 0:00:01 time: 0.0073 data_time: 0.0011 memory: 330 2022/08/31 20:14:43 - mmengine - INFO - Epoch(val) [33][400/472] eta: 0:00:00 time: 0.0055 data_time: 0.0008 memory: 76 2022/08/31 20:14:43 - mmengine - INFO - Epoch(val) [33][472/472] node/macro_f1: 0.9253 node/micro_f1: 0.9341 edge/micro_f1: 0.7670 2022/08/31 20:14:46 - mmengine - INFO - Epoch(train) [34][100/317] lr: 1.0000e-03 eta: 0:03:46 time: 0.0329 data_time: 0.0030 memory: 938 loss_node: 0.0556 loss_edge: 0.0185 acc_node: 98.5000 acc_edge: 99.7690 loss: 0.0741 2022/08/31 20:14:49 - mmengine - INFO - Epoch(train) [34][200/317] lr: 1.0000e-03 eta: 0:03:43 time: 0.0265 data_time: 0.0030 memory: 514 loss_node: 0.1321 loss_edge: 0.0296 acc_node: 95.0530 acc_edge: 99.3847 loss: 0.1616 2022/08/31 20:14:52 - mmengine - INFO - Epoch(train) [34][300/317] lr: 1.0000e-03 eta: 0:03:40 time: 0.0288 data_time: 0.0032 memory: 935 loss_node: 0.0914 loss_edge: 0.0253 acc_node: 96.5986 acc_edge: 99.5732 loss: 0.1168 2022/08/31 20:14:52 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:14:52 - mmengine - INFO - Saving checkpoint at 34 epochs 2022/08/31 20:14:53 - mmengine - INFO - Epoch(val) [34][100/472] eta: 0:00:02 time: 0.0054 data_time: 0.0009 memory: 221 2022/08/31 20:14:54 - mmengine - INFO - Epoch(val) [34][200/472] eta: 0:00:01 time: 0.0066 data_time: 0.0010 memory: 138 2022/08/31 20:14:55 - mmengine - INFO - Epoch(val) [34][300/472] eta: 0:00:01 time: 0.0068 data_time: 0.0010 memory: 330 2022/08/31 20:14:55 - mmengine - INFO - Epoch(val) [34][400/472] eta: 0:00:00 time: 0.0064 data_time: 0.0011 memory: 76 2022/08/31 20:14:56 - mmengine - INFO - Epoch(val) [34][472/472] node/macro_f1: 0.9239 node/micro_f1: 0.9329 edge/micro_f1: 0.7707 2022/08/31 20:14:59 - mmengine - INFO - Epoch(train) [35][100/317] lr: 1.0000e-03 eta: 0:03:37 time: 0.0284 data_time: 0.0030 memory: 902 loss_node: 0.0696 loss_edge: 0.0261 acc_node: 99.1936 acc_edge: 98.8278 loss: 0.0957 2022/08/31 20:15:02 - mmengine - INFO - Epoch(train) [35][200/317] lr: 1.0000e-03 eta: 0:03:35 time: 0.0300 data_time: 0.0029 memory: 547 loss_node: 0.0425 loss_edge: 0.0201 acc_node: 99.5516 acc_edge: 99.7213 loss: 0.0626 2022/08/31 20:15:02 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:15:05 - mmengine - INFO - Epoch(train) [35][300/317] lr: 1.0000e-03 eta: 0:03:32 time: 0.0301 data_time: 0.0032 memory: 917 loss_node: 0.0650 loss_edge: 0.0255 acc_node: 97.7612 acc_edge: 98.1228 loss: 0.0904 2022/08/31 20:15:05 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:15:05 - mmengine - INFO - Saving checkpoint at 35 epochs 2022/08/31 20:15:06 - mmengine - INFO - Epoch(val) [35][100/472] eta: 0:00:02 time: 0.0060 data_time: 0.0011 memory: 485 2022/08/31 20:15:07 - mmengine - INFO - Epoch(val) [35][200/472] eta: 0:00:01 time: 0.0069 data_time: 0.0011 memory: 138 2022/08/31 20:15:07 - mmengine - INFO - Epoch(val) [35][300/472] eta: 0:00:01 time: 0.0071 data_time: 0.0010 memory: 330 2022/08/31 20:15:08 - mmengine - INFO - Epoch(val) [35][400/472] eta: 0:00:00 time: 0.0069 data_time: 0.0012 memory: 76 2022/08/31 20:15:09 - mmengine - INFO - Epoch(val) [35][472/472] node/macro_f1: 0.9241 node/micro_f1: 0.9337 edge/micro_f1: 0.7618 2022/08/31 20:15:12 - mmengine - INFO - Epoch(train) [36][100/317] lr: 1.0000e-03 eta: 0:03:29 time: 0.0347 data_time: 0.0035 memory: 881 loss_node: 0.0384 loss_edge: 0.0192 acc_node: 98.9744 acc_edge: 99.4869 loss: 0.0577 2022/08/31 20:15:14 - mmengine - INFO - Epoch(train) [36][200/317] lr: 1.0000e-03 eta: 0:03:27 time: 0.0307 data_time: 0.0028 memory: 913 loss_node: 0.0377 loss_edge: 0.0223 acc_node: 99.4083 acc_edge: 99.6641 loss: 0.0600 2022/08/31 20:15:17 - mmengine - INFO - Epoch(train) [36][300/317] lr: 1.0000e-03 eta: 0:03:24 time: 0.0257 data_time: 0.0030 memory: 521 loss_node: 0.0721 loss_edge: 0.0265 acc_node: 95.0276 acc_edge: 99.4511 loss: 0.0986 2022/08/31 20:15:17 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:15:17 - mmengine - INFO - Saving checkpoint at 36 epochs 2022/08/31 20:15:18 - mmengine - INFO - Epoch(val) [36][100/472] eta: 0:00:01 time: 0.0047 data_time: 0.0008 memory: 290 2022/08/31 20:15:19 - mmengine - INFO - Epoch(val) [36][200/472] eta: 0:00:01 time: 0.0054 data_time: 0.0008 memory: 138 2022/08/31 20:15:20 - mmengine - INFO - Epoch(val) [36][300/472] eta: 0:00:00 time: 0.0058 data_time: 0.0008 memory: 330 2022/08/31 20:15:20 - mmengine - INFO - Epoch(val) [36][400/472] eta: 0:00:00 time: 0.0055 data_time: 0.0008 memory: 76 2022/08/31 20:15:21 - mmengine - INFO - Epoch(val) [36][472/472] node/macro_f1: 0.9226 node/micro_f1: 0.9344 edge/micro_f1: 0.7293 2022/08/31 20:15:24 - mmengine - INFO - Epoch(train) [37][100/317] lr: 1.0000e-03 eta: 0:03:21 time: 0.0271 data_time: 0.0029 memory: 900 loss_node: 0.0512 loss_edge: 0.0164 acc_node: 99.2806 acc_edge: 99.4600 loss: 0.0676 2022/08/31 20:15:26 - mmengine - INFO - Epoch(train) [37][200/317] lr: 1.0000e-03 eta: 0:03:18 time: 0.0302 data_time: 0.0029 memory: 869 loss_node: 0.0727 loss_edge: 0.0259 acc_node: 95.3271 acc_edge: 99.1136 loss: 0.0987 2022/08/31 20:15:29 - mmengine - INFO - Epoch(train) [37][300/317] lr: 1.0000e-03 eta: 0:03:16 time: 0.0282 data_time: 0.0031 memory: 589 loss_node: 0.0672 loss_edge: 0.0177 acc_node: 98.3146 acc_edge: 99.8354 loss: 0.0850 2022/08/31 20:15:29 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:15:29 - mmengine - INFO - Saving checkpoint at 37 epochs 2022/08/31 20:15:30 - mmengine - INFO - Epoch(val) [37][100/472] eta: 0:00:02 time: 0.0057 data_time: 0.0010 memory: 217 2022/08/31 20:15:31 - mmengine - INFO - Epoch(val) [37][200/472] eta: 0:00:01 time: 0.0073 data_time: 0.0013 memory: 138 2022/08/31 20:15:32 - mmengine - INFO - Epoch(val) [37][300/472] eta: 0:00:01 time: 0.0070 data_time: 0.0011 memory: 330 2022/08/31 20:15:32 - mmengine - INFO - Epoch(val) [37][400/472] eta: 0:00:00 time: 0.0059 data_time: 0.0009 memory: 76 2022/08/31 20:15:33 - mmengine - INFO - Epoch(val) [37][472/472] node/macro_f1: 0.9206 node/micro_f1: 0.9313 edge/micro_f1: 0.7552 2022/08/31 20:15:35 - mmengine - INFO - Epoch(train) [38][100/317] lr: 1.0000e-03 eta: 0:03:12 time: 0.0253 data_time: 0.0030 memory: 742 loss_node: 0.0523 loss_edge: 0.0151 acc_node: 99.3939 acc_edge: 99.7774 loss: 0.0674 2022/08/31 20:15:38 - mmengine - INFO - Epoch(train) [38][200/317] lr: 1.0000e-03 eta: 0:03:10 time: 0.0340 data_time: 0.0042 memory: 860 loss_node: 0.0404 loss_edge: 0.0191 acc_node: 97.2414 acc_edge: 99.4709 loss: 0.0595 2022/08/31 20:15:40 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:15:41 - mmengine - INFO - Epoch(train) [38][300/317] lr: 1.0000e-03 eta: 0:03:07 time: 0.0246 data_time: 0.0027 memory: 903 loss_node: 0.0374 loss_edge: 0.0195 acc_node: 98.6487 acc_edge: 99.3128 loss: 0.0569 2022/08/31 20:15:42 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:15:42 - mmengine - INFO - Saving checkpoint at 38 epochs 2022/08/31 20:15:42 - mmengine - INFO - Epoch(val) [38][100/472] eta: 0:00:01 time: 0.0046 data_time: 0.0007 memory: 283 2022/08/31 20:15:43 - mmengine - INFO - Epoch(val) [38][200/472] eta: 0:00:01 time: 0.0062 data_time: 0.0009 memory: 138 2022/08/31 20:15:44 - mmengine - INFO - Epoch(val) [38][300/472] eta: 0:00:01 time: 0.0059 data_time: 0.0008 memory: 330 2022/08/31 20:15:44 - mmengine - INFO - Epoch(val) [38][400/472] eta: 0:00:00 time: 0.0053 data_time: 0.0008 memory: 76 2022/08/31 20:15:45 - mmengine - INFO - Epoch(val) [38][472/472] node/macro_f1: 0.9269 node/micro_f1: 0.9357 edge/micro_f1: 0.7635 2022/08/31 20:15:47 - mmengine - INFO - Epoch(train) [39][100/317] lr: 1.0000e-03 eta: 0:03:04 time: 0.0277 data_time: 0.0028 memory: 1003 loss_node: 0.0267 loss_edge: 0.0334 acc_node: 98.7805 acc_edge: 99.5215 loss: 0.0601 2022/08/31 20:15:50 - mmengine - INFO - Epoch(train) [39][200/317] lr: 1.0000e-03 eta: 0:03:01 time: 0.0253 data_time: 0.0028 memory: 511 loss_node: 0.0294 loss_edge: 0.0255 acc_node: 98.9583 acc_edge: 99.2750 loss: 0.0549 2022/08/31 20:15:52 - mmengine - INFO - Epoch(train) [39][300/317] lr: 1.0000e-03 eta: 0:02:58 time: 0.0266 data_time: 0.0031 memory: 991 loss_node: 0.0566 loss_edge: 0.0232 acc_node: 98.9637 acc_edge: 98.5966 loss: 0.0798 2022/08/31 20:15:53 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:15:53 - mmengine - INFO - Saving checkpoint at 39 epochs 2022/08/31 20:15:54 - mmengine - INFO - Epoch(val) [39][100/472] eta: 0:00:01 time: 0.0052 data_time: 0.0009 memory: 642 2022/08/31 20:15:54 - mmengine - INFO - Epoch(val) [39][200/472] eta: 0:00:01 time: 0.0057 data_time: 0.0008 memory: 138 2022/08/31 20:15:55 - mmengine - INFO - Epoch(val) [39][300/472] eta: 0:00:01 time: 0.0058 data_time: 0.0008 memory: 330 2022/08/31 20:15:55 - mmengine - INFO - Epoch(val) [39][400/472] eta: 0:00:00 time: 0.0053 data_time: 0.0008 memory: 76 2022/08/31 20:15:56 - mmengine - INFO - Epoch(val) [39][472/472] node/macro_f1: 0.9222 node/micro_f1: 0.9321 edge/micro_f1: 0.7466 2022/08/31 20:15:58 - mmengine - INFO - Epoch(train) [40][100/317] lr: 1.0000e-03 eta: 0:02:55 time: 0.0238 data_time: 0.0028 memory: 417 loss_node: 0.0440 loss_edge: 0.0268 acc_node: 97.8947 acc_edge: 97.9240 loss: 0.0708 2022/08/31 20:16:01 - mmengine - INFO - Epoch(train) [40][200/317] lr: 1.0000e-03 eta: 0:02:52 time: 0.0225 data_time: 0.0027 memory: 630 loss_node: 0.0701 loss_edge: 0.0289 acc_node: 98.9899 acc_edge: 99.3106 loss: 0.0990 2022/08/31 20:16:04 - mmengine - INFO - Epoch(train) [40][300/317] lr: 1.0000e-03 eta: 0:02:50 time: 0.0271 data_time: 0.0028 memory: 1063 loss_node: 0.0501 loss_edge: 0.0206 acc_node: 98.0892 acc_edge: 98.3215 loss: 0.0708 2022/08/31 20:16:04 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:16:04 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/08/31 20:16:05 - mmengine - INFO - Epoch(val) [40][100/472] eta: 0:00:01 time: 0.0047 data_time: 0.0009 memory: 491 2022/08/31 20:16:05 - mmengine - INFO - Epoch(val) [40][200/472] eta: 0:00:01 time: 0.0055 data_time: 0.0008 memory: 138 2022/08/31 20:16:06 - mmengine - INFO - Epoch(val) [40][300/472] eta: 0:00:00 time: 0.0057 data_time: 0.0008 memory: 330 2022/08/31 20:16:07 - mmengine - INFO - Epoch(val) [40][400/472] eta: 0:00:00 time: 0.0053 data_time: 0.0008 memory: 76 2022/08/31 20:16:07 - mmengine - INFO - Epoch(val) [40][472/472] node/macro_f1: 0.9249 node/micro_f1: 0.9327 edge/micro_f1: 0.7541 2022/08/31 20:16:10 - mmengine - INFO - Epoch(train) [41][100/317] lr: 1.0000e-04 eta: 0:02:47 time: 0.0261 data_time: 0.0027 memory: 841 loss_node: 0.0313 loss_edge: 0.0148 acc_node: 98.3471 acc_edge: 99.4218 loss: 0.0461 2022/08/31 20:16:12 - mmengine - INFO - Epoch(train) [41][200/317] lr: 1.0000e-04 eta: 0:02:44 time: 0.0224 data_time: 0.0028 memory: 902 loss_node: 0.0233 loss_edge: 0.0183 acc_node: 99.3711 acc_edge: 99.4625 loss: 0.0416 2022/08/31 20:16:15 - mmengine - INFO - Epoch(train) [41][300/317] lr: 1.0000e-04 eta: 0:02:41 time: 0.0265 data_time: 0.0028 memory: 753 loss_node: 0.0405 loss_edge: 0.0170 acc_node: 95.1087 acc_edge: 99.0739 loss: 0.0575 2022/08/31 20:16:15 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:16:15 - mmengine - INFO - Saving checkpoint at 41 epochs 2022/08/31 20:16:16 - mmengine - INFO - Epoch(val) [41][100/472] eta: 0:00:01 time: 0.0046 data_time: 0.0008 memory: 470 2022/08/31 20:16:17 - mmengine - INFO - Epoch(val) [41][200/472] eta: 0:00:01 time: 0.0058 data_time: 0.0009 memory: 138 2022/08/31 20:16:17 - mmengine - INFO - Epoch(val) [41][300/472] eta: 0:00:01 time: 0.0061 data_time: 0.0009 memory: 330 2022/08/31 20:16:18 - mmengine - INFO - Epoch(val) [41][400/472] eta: 0:00:00 time: 0.0060 data_time: 0.0009 memory: 76 2022/08/31 20:16:18 - mmengine - INFO - Epoch(val) [41][472/472] node/macro_f1: 0.9309 node/micro_f1: 0.9394 edge/micro_f1: 0.7776 2022/08/31 20:16:18 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:16:21 - mmengine - INFO - Epoch(train) [42][100/317] lr: 1.0000e-04 eta: 0:02:38 time: 0.0251 data_time: 0.0027 memory: 394 loss_node: 0.0188 loss_edge: 0.0221 acc_node: 97.0149 acc_edge: 98.3583 loss: 0.0409 2022/08/31 20:16:23 - mmengine - INFO - Epoch(train) [42][200/317] lr: 1.0000e-04 eta: 0:02:35 time: 0.0256 data_time: 0.0028 memory: 656 loss_node: 0.0168 loss_edge: 0.0227 acc_node: 100.0000 acc_edge: 99.6406 loss: 0.0394 2022/08/31 20:16:26 - mmengine - INFO - Epoch(train) [42][300/317] lr: 1.0000e-04 eta: 0:02:32 time: 0.0241 data_time: 0.0027 memory: 921 loss_node: 0.0175 loss_edge: 0.0170 acc_node: 99.2701 acc_edge: 99.3450 loss: 0.0345 2022/08/31 20:16:26 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:16:26 - mmengine - INFO - Saving checkpoint at 42 epochs 2022/08/31 20:16:27 - mmengine - INFO - Epoch(val) [42][100/472] eta: 0:00:01 time: 0.0048 data_time: 0.0009 memory: 362 2022/08/31 20:16:28 - mmengine - INFO - Epoch(val) [42][200/472] eta: 0:00:01 time: 0.0055 data_time: 0.0008 memory: 138 2022/08/31 20:16:28 - mmengine - INFO - Epoch(val) [42][300/472] eta: 0:00:01 time: 0.0060 data_time: 0.0008 memory: 330 2022/08/31 20:16:29 - mmengine - INFO - Epoch(val) [42][400/472] eta: 0:00:00 time: 0.0056 data_time: 0.0008 memory: 76 2022/08/31 20:16:29 - mmengine - INFO - Epoch(val) [42][472/472] node/macro_f1: 0.9311 node/micro_f1: 0.9393 edge/micro_f1: 0.7823 2022/08/31 20:16:32 - mmengine - INFO - Epoch(train) [43][100/317] lr: 1.0000e-04 eta: 0:02:29 time: 0.0267 data_time: 0.0027 memory: 908 loss_node: 0.0122 loss_edge: 0.0181 acc_node: 99.4118 acc_edge: 99.7903 loss: 0.0303 2022/08/31 20:16:35 - mmengine - INFO - Epoch(train) [43][200/317] lr: 1.0000e-04 eta: 0:02:27 time: 0.0340 data_time: 0.0028 memory: 870 loss_node: 0.0164 loss_edge: 0.0134 acc_node: 99.3055 acc_edge: 99.5397 loss: 0.0298 2022/08/31 20:16:37 - mmengine - INFO - Epoch(train) [43][300/317] lr: 1.0000e-04 eta: 0:02:24 time: 0.0219 data_time: 0.0028 memory: 528 loss_node: 0.0137 loss_edge: 0.0162 acc_node: 98.8764 acc_edge: 99.5245 loss: 0.0299 2022/08/31 20:16:38 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:16:38 - mmengine - INFO - Saving checkpoint at 43 epochs 2022/08/31 20:16:38 - mmengine - INFO - Epoch(val) [43][100/472] eta: 0:00:01 time: 0.0047 data_time: 0.0009 memory: 288 2022/08/31 20:16:39 - mmengine - INFO - Epoch(val) [43][200/472] eta: 0:00:01 time: 0.0057 data_time: 0.0009 memory: 138 2022/08/31 20:16:40 - mmengine - INFO - Epoch(val) [43][300/472] eta: 0:00:01 time: 0.0060 data_time: 0.0009 memory: 330 2022/08/31 20:16:40 - mmengine - INFO - Epoch(val) [43][400/472] eta: 0:00:00 time: 0.0056 data_time: 0.0009 memory: 76 2022/08/31 20:16:41 - mmengine - INFO - Epoch(val) [43][472/472] node/macro_f1: 0.9304 node/micro_f1: 0.9392 edge/micro_f1: 0.7867 2022/08/31 20:16:43 - mmengine - INFO - Epoch(train) [44][100/317] lr: 1.0000e-04 eta: 0:02:21 time: 0.0222 data_time: 0.0027 memory: 929 loss_node: 0.0194 loss_edge: 0.0215 acc_node: 98.0132 acc_edge: 99.2986 loss: 0.0410 2022/08/31 20:16:46 - mmengine - INFO - Epoch(train) [44][200/317] lr: 1.0000e-04 eta: 0:02:18 time: 0.0325 data_time: 0.0029 memory: 888 loss_node: 0.0133 loss_edge: 0.0157 acc_node: 100.0000 acc_edge: 99.6525 loss: 0.0289 2022/08/31 20:16:48 - mmengine - INFO - Epoch(train) [44][300/317] lr: 1.0000e-04 eta: 0:02:15 time: 0.0263 data_time: 0.0027 memory: 659 loss_node: 0.0130 loss_edge: 0.0132 acc_node: 100.0000 acc_edge: 99.7385 loss: 0.0262 2022/08/31 20:16:49 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:16:49 - mmengine - INFO - Saving checkpoint at 44 epochs 2022/08/31 20:16:50 - mmengine - INFO - Epoch(val) [44][100/472] eta: 0:00:01 time: 0.0047 data_time: 0.0008 memory: 384 2022/08/31 20:16:50 - mmengine - INFO - Epoch(val) [44][200/472] eta: 0:00:01 time: 0.0058 data_time: 0.0008 memory: 138 2022/08/31 20:16:51 - mmengine - INFO - Epoch(val) [44][300/472] eta: 0:00:00 time: 0.0057 data_time: 0.0008 memory: 330 2022/08/31 20:16:51 - mmengine - INFO - Epoch(val) [44][400/472] eta: 0:00:00 time: 0.0057 data_time: 0.0009 memory: 76 2022/08/31 20:16:52 - mmengine - INFO - Epoch(val) [44][472/472] node/macro_f1: 0.9304 node/micro_f1: 0.9394 edge/micro_f1: 0.7885 2022/08/31 20:16:53 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:16:54 - mmengine - INFO - Epoch(train) [45][100/317] lr: 1.0000e-04 eta: 0:02:12 time: 0.0293 data_time: 0.0028 memory: 999 loss_node: 0.0168 loss_edge: 0.0166 acc_node: 99.4048 acc_edge: 99.4341 loss: 0.0334 2022/08/31 20:16:57 - mmengine - INFO - Epoch(train) [45][200/317] lr: 1.0000e-04 eta: 0:02:09 time: 0.0271 data_time: 0.0029 memory: 713 loss_node: 0.0140 loss_edge: 0.0183 acc_node: 100.0000 acc_edge: 99.6382 loss: 0.0323 2022/08/31 20:17:00 - mmengine - INFO - Epoch(train) [45][300/317] lr: 1.0000e-04 eta: 0:02:07 time: 0.0248 data_time: 0.0028 memory: 862 loss_node: 0.0142 loss_edge: 0.0169 acc_node: 100.0000 acc_edge: 99.2168 loss: 0.0311 2022/08/31 20:17:00 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:17:00 - mmengine - INFO - Saving checkpoint at 45 epochs 2022/08/31 20:17:01 - mmengine - INFO - Epoch(val) [45][100/472] eta: 0:00:02 time: 0.0056 data_time: 0.0010 memory: 409 2022/08/31 20:17:02 - mmengine - INFO - Epoch(val) [45][200/472] eta: 0:00:01 time: 0.0057 data_time: 0.0009 memory: 138 2022/08/31 20:17:02 - mmengine - INFO - Epoch(val) [45][300/472] eta: 0:00:01 time: 0.0064 data_time: 0.0009 memory: 330 2022/08/31 20:17:03 - mmengine - INFO - Epoch(val) [45][400/472] eta: 0:00:00 time: 0.0054 data_time: 0.0008 memory: 76 2022/08/31 20:17:03 - mmengine - INFO - Epoch(val) [45][472/472] node/macro_f1: 0.9303 node/micro_f1: 0.9394 edge/micro_f1: 0.7872 2022/08/31 20:17:06 - mmengine - INFO - Epoch(train) [46][100/317] lr: 1.0000e-04 eta: 0:02:04 time: 0.0289 data_time: 0.0029 memory: 474 loss_node: 0.0134 loss_edge: 0.0149 acc_node: 100.0000 acc_edge: 99.4715 loss: 0.0283 2022/08/31 20:17:09 - mmengine - INFO - Epoch(train) [46][200/317] lr: 1.0000e-04 eta: 0:02:01 time: 0.0242 data_time: 0.0030 memory: 587 loss_node: 0.0101 loss_edge: 0.0150 acc_node: 100.0000 acc_edge: 99.7128 loss: 0.0251 2022/08/31 20:17:12 - mmengine - INFO - Epoch(train) [46][300/317] lr: 1.0000e-04 eta: 0:01:58 time: 0.0265 data_time: 0.0028 memory: 1136 loss_node: 0.0077 loss_edge: 0.0186 acc_node: 100.0000 acc_edge: 99.7737 loss: 0.0263 2022/08/31 20:17:12 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:17:12 - mmengine - INFO - Saving checkpoint at 46 epochs 2022/08/31 20:17:13 - mmengine - INFO - Epoch(val) [46][100/472] eta: 0:00:01 time: 0.0049 data_time: 0.0009 memory: 699 2022/08/31 20:17:14 - mmengine - INFO - Epoch(val) [46][200/472] eta: 0:00:01 time: 0.0060 data_time: 0.0009 memory: 138 2022/08/31 20:17:14 - mmengine - INFO - Epoch(val) [46][300/472] eta: 0:00:01 time: 0.0061 data_time: 0.0008 memory: 330 2022/08/31 20:17:15 - mmengine - INFO - Epoch(val) [46][400/472] eta: 0:00:00 time: 0.0066 data_time: 0.0010 memory: 76 2022/08/31 20:17:15 - mmengine - INFO - Epoch(val) [46][472/472] node/macro_f1: 0.9297 node/micro_f1: 0.9383 edge/micro_f1: 0.7865 2022/08/31 20:17:18 - mmengine - INFO - Epoch(train) [47][100/317] lr: 1.0000e-04 eta: 0:01:55 time: 0.0265 data_time: 0.0027 memory: 882 loss_node: 0.0058 loss_edge: 0.0118 acc_node: 99.5868 acc_edge: 99.6159 loss: 0.0176 2022/08/31 20:17:21 - mmengine - INFO - Epoch(train) [47][200/317] lr: 1.0000e-04 eta: 0:01:53 time: 0.0229 data_time: 0.0027 memory: 613 loss_node: 0.0082 loss_edge: 0.0162 acc_node: 100.0000 acc_edge: 99.4967 loss: 0.0244 2022/08/31 20:17:23 - mmengine - INFO - Epoch(train) [47][300/317] lr: 1.0000e-04 eta: 0:01:50 time: 0.0239 data_time: 0.0028 memory: 810 loss_node: 0.0173 loss_edge: 0.0249 acc_node: 99.3548 acc_edge: 99.4413 loss: 0.0422 2022/08/31 20:17:24 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:17:24 - mmengine - INFO - Saving checkpoint at 47 epochs 2022/08/31 20:17:25 - mmengine - INFO - Epoch(val) [47][100/472] eta: 0:00:01 time: 0.0050 data_time: 0.0009 memory: 249 2022/08/31 20:17:25 - mmengine - INFO - Epoch(val) [47][200/472] eta: 0:00:01 time: 0.0058 data_time: 0.0009 memory: 138 2022/08/31 20:17:26 - mmengine - INFO - Epoch(val) [47][300/472] eta: 0:00:01 time: 0.0059 data_time: 0.0008 memory: 330 2022/08/31 20:17:26 - mmengine - INFO - Epoch(val) [47][400/472] eta: 0:00:00 time: 0.0055 data_time: 0.0008 memory: 76 2022/08/31 20:17:27 - mmengine - INFO - Epoch(val) [47][472/472] node/macro_f1: 0.9306 node/micro_f1: 0.9392 edge/micro_f1: 0.7843 2022/08/31 20:17:29 - mmengine - INFO - Epoch(train) [48][100/317] lr: 1.0000e-04 eta: 0:01:47 time: 0.0255 data_time: 0.0027 memory: 907 loss_node: 0.0062 loss_edge: 0.0199 acc_node: 100.0000 acc_edge: 99.8490 loss: 0.0262 2022/08/31 20:17:30 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:17:32 - mmengine - INFO - Epoch(train) [48][200/317] lr: 1.0000e-04 eta: 0:01:44 time: 0.0238 data_time: 0.0027 memory: 471 loss_node: 0.0102 loss_edge: 0.0178 acc_node: 100.0000 acc_edge: 99.4295 loss: 0.0280 2022/08/31 20:17:35 - mmengine - INFO - Epoch(train) [48][300/317] lr: 1.0000e-04 eta: 0:01:41 time: 0.0250 data_time: 0.0029 memory: 490 loss_node: 0.0045 loss_edge: 0.0134 acc_node: 100.0000 acc_edge: 99.9046 loss: 0.0180 2022/08/31 20:17:35 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:17:35 - mmengine - INFO - Saving checkpoint at 48 epochs 2022/08/31 20:17:36 - mmengine - INFO - Epoch(val) [48][100/472] eta: 0:00:01 time: 0.0047 data_time: 0.0008 memory: 256 2022/08/31 20:17:36 - mmengine - INFO - Epoch(val) [48][200/472] eta: 0:00:01 time: 0.0057 data_time: 0.0008 memory: 138 2022/08/31 20:17:37 - mmengine - INFO - Epoch(val) [48][300/472] eta: 0:00:01 time: 0.0065 data_time: 0.0010 memory: 330 2022/08/31 20:17:38 - mmengine - INFO - Epoch(val) [48][400/472] eta: 0:00:00 time: 0.0055 data_time: 0.0008 memory: 76 2022/08/31 20:17:38 - mmengine - INFO - Epoch(val) [48][472/472] node/macro_f1: 0.9303 node/micro_f1: 0.9392 edge/micro_f1: 0.7883 2022/08/31 20:17:41 - mmengine - INFO - Epoch(train) [49][100/317] lr: 1.0000e-04 eta: 0:01:38 time: 0.0227 data_time: 0.0026 memory: 1318 loss_node: 0.0149 loss_edge: 0.0194 acc_node: 100.0000 acc_edge: 99.6408 loss: 0.0343 2022/08/31 20:17:43 - mmengine - INFO - Epoch(train) [49][200/317] lr: 1.0000e-04 eta: 0:01:35 time: 0.0258 data_time: 0.0027 memory: 729 loss_node: 0.0071 loss_edge: 0.0164 acc_node: 100.0000 acc_edge: 99.7741 loss: 0.0235 2022/08/31 20:17:46 - mmengine - INFO - Epoch(train) [49][300/317] lr: 1.0000e-04 eta: 0:01:33 time: 0.0259 data_time: 0.0027 memory: 886 loss_node: 0.0179 loss_edge: 0.0160 acc_node: 99.5098 acc_edge: 99.8338 loss: 0.0339 2022/08/31 20:17:46 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:17:46 - mmengine - INFO - Saving checkpoint at 49 epochs 2022/08/31 20:17:47 - mmengine - INFO - Epoch(val) [49][100/472] eta: 0:00:01 time: 0.0046 data_time: 0.0008 memory: 218 2022/08/31 20:17:48 - mmengine - INFO - Epoch(val) [49][200/472] eta: 0:00:01 time: 0.0059 data_time: 0.0009 memory: 138 2022/08/31 20:17:48 - mmengine - INFO - Epoch(val) [49][300/472] eta: 0:00:01 time: 0.0058 data_time: 0.0008 memory: 330 2022/08/31 20:17:49 - mmengine - INFO - Epoch(val) [49][400/472] eta: 0:00:00 time: 0.0059 data_time: 0.0008 memory: 76 2022/08/31 20:17:49 - mmengine - INFO - Epoch(val) [49][472/472] node/macro_f1: 0.9306 node/micro_f1: 0.9396 edge/micro_f1: 0.7835 2022/08/31 20:17:52 - mmengine - INFO - Epoch(train) [50][100/317] lr: 1.0000e-04 eta: 0:01:30 time: 0.0231 data_time: 0.0028 memory: 679 loss_node: 0.0101 loss_edge: 0.0165 acc_node: 99.6255 acc_edge: 99.5318 loss: 0.0266 2022/08/31 20:17:54 - mmengine - INFO - Epoch(train) [50][200/317] lr: 1.0000e-04 eta: 0:01:27 time: 0.0228 data_time: 0.0027 memory: 481 loss_node: 0.0076 loss_edge: 0.0169 acc_node: 99.3055 acc_edge: 99.2512 loss: 0.0246 2022/08/31 20:17:57 - mmengine - INFO - Epoch(train) [50][300/317] lr: 1.0000e-04 eta: 0:01:24 time: 0.0234 data_time: 0.0027 memory: 1075 loss_node: 0.0190 loss_edge: 0.0174 acc_node: 95.6204 acc_edge: 97.4840 loss: 0.0364 2022/08/31 20:17:57 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:17:57 - mmengine - INFO - Saving checkpoint at 50 epochs 2022/08/31 20:17:58 - mmengine - INFO - Epoch(val) [50][100/472] eta: 0:00:01 time: 0.0050 data_time: 0.0008 memory: 228 2022/08/31 20:17:59 - mmengine - INFO - Epoch(val) [50][200/472] eta: 0:00:01 time: 0.0059 data_time: 0.0009 memory: 138 2022/08/31 20:17:59 - mmengine - INFO - Epoch(val) [50][300/472] eta: 0:00:01 time: 0.0063 data_time: 0.0008 memory: 330 2022/08/31 20:18:00 - mmengine - INFO - Epoch(val) [50][400/472] eta: 0:00:00 time: 0.0055 data_time: 0.0008 memory: 76 2022/08/31 20:18:00 - mmengine - INFO - Epoch(val) [50][472/472] node/macro_f1: 0.9310 node/micro_f1: 0.9402 edge/micro_f1: 0.7921 2022/08/31 20:18:03 - mmengine - INFO - Epoch(train) [51][100/317] lr: 1.0000e-05 eta: 0:01:21 time: 0.0243 data_time: 0.0027 memory: 904 loss_node: 0.0039 loss_edge: 0.0122 acc_node: 100.0000 acc_edge: 99.5935 loss: 0.0161 2022/08/31 20:18:04 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:18:06 - mmengine - INFO - Epoch(train) [51][200/317] lr: 1.0000e-05 eta: 0:01:18 time: 0.0242 data_time: 0.0028 memory: 624 loss_node: 0.0196 loss_edge: 0.0187 acc_node: 100.0000 acc_edge: 99.4203 loss: 0.0383 2022/08/31 20:18:08 - mmengine - INFO - Epoch(train) [51][300/317] lr: 1.0000e-05 eta: 0:01:16 time: 0.0269 data_time: 0.0028 memory: 557 loss_node: 0.0051 loss_edge: 0.0105 acc_node: 100.0000 acc_edge: 99.6160 loss: 0.0157 2022/08/31 20:18:09 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:18:09 - mmengine - INFO - Saving checkpoint at 51 epochs 2022/08/31 20:18:09 - mmengine - INFO - Epoch(val) [51][100/472] eta: 0:00:01 time: 0.0046 data_time: 0.0008 memory: 808 2022/08/31 20:18:10 - mmengine - INFO - Epoch(val) [51][200/472] eta: 0:00:01 time: 0.0067 data_time: 0.0010 memory: 138 2022/08/31 20:18:11 - mmengine - INFO - Epoch(val) [51][300/472] eta: 0:00:01 time: 0.0059 data_time: 0.0008 memory: 330 2022/08/31 20:18:11 - mmengine - INFO - Epoch(val) [51][400/472] eta: 0:00:00 time: 0.0054 data_time: 0.0008 memory: 76 2022/08/31 20:18:12 - mmengine - INFO - Epoch(val) [51][472/472] node/macro_f1: 0.9304 node/micro_f1: 0.9394 edge/micro_f1: 0.7899 2022/08/31 20:18:14 - mmengine - INFO - Epoch(train) [52][100/317] lr: 1.0000e-05 eta: 0:01:13 time: 0.0229 data_time: 0.0027 memory: 880 loss_node: 0.0046 loss_edge: 0.0135 acc_node: 99.4118 acc_edge: 99.6224 loss: 0.0182 2022/08/31 20:18:17 - mmengine - INFO - Epoch(train) [52][200/317] lr: 1.0000e-05 eta: 0:01:10 time: 0.0237 data_time: 0.0027 memory: 583 loss_node: 0.0098 loss_edge: 0.0161 acc_node: 100.0000 acc_edge: 99.8129 loss: 0.0259 2022/08/31 20:18:19 - mmengine - INFO - Epoch(train) [52][300/317] lr: 1.0000e-05 eta: 0:01:07 time: 0.0307 data_time: 0.0028 memory: 1007 loss_node: 0.0082 loss_edge: 0.0131 acc_node: 99.3506 acc_edge: 99.3425 loss: 0.0214 2022/08/31 20:18:20 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:18:20 - mmengine - INFO - Saving checkpoint at 52 epochs 2022/08/31 20:18:21 - mmengine - INFO - Epoch(val) [52][100/472] eta: 0:00:01 time: 0.0048 data_time: 0.0008 memory: 701 2022/08/31 20:18:21 - mmengine - INFO - Epoch(val) [52][200/472] eta: 0:00:01 time: 0.0057 data_time: 0.0008 memory: 138 2022/08/31 20:18:22 - mmengine - INFO - Epoch(val) [52][300/472] eta: 0:00:01 time: 0.0059 data_time: 0.0008 memory: 330 2022/08/31 20:18:22 - mmengine - INFO - Epoch(val) [52][400/472] eta: 0:00:00 time: 0.0054 data_time: 0.0008 memory: 76 2022/08/31 20:18:23 - mmengine - INFO - Epoch(val) [52][472/472] node/macro_f1: 0.9308 node/micro_f1: 0.9396 edge/micro_f1: 0.7904 2022/08/31 20:18:25 - mmengine - INFO - Epoch(train) [53][100/317] lr: 1.0000e-05 eta: 0:01:04 time: 0.0242 data_time: 0.0027 memory: 627 loss_node: 0.0050 loss_edge: 0.0147 acc_node: 100.0000 acc_edge: 99.0811 loss: 0.0197 2022/08/31 20:18:28 - mmengine - INFO - Epoch(train) [53][200/317] lr: 1.0000e-05 eta: 0:01:01 time: 0.0323 data_time: 0.0028 memory: 1003 loss_node: 0.0046 loss_edge: 0.0120 acc_node: 99.0950 acc_edge: 99.9333 loss: 0.0165 2022/08/31 20:18:31 - mmengine - INFO - Epoch(train) [53][300/317] lr: 1.0000e-05 eta: 0:00:59 time: 0.0357 data_time: 0.0031 memory: 809 loss_node: 0.0058 loss_edge: 0.0158 acc_node: 100.0000 acc_edge: 99.7637 loss: 0.0216 2022/08/31 20:18:31 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:18:31 - mmengine - INFO - Saving checkpoint at 53 epochs 2022/08/31 20:18:32 - mmengine - INFO - Epoch(val) [53][100/472] eta: 0:00:01 time: 0.0050 data_time: 0.0009 memory: 406 2022/08/31 20:18:32 - mmengine - INFO - Epoch(val) [53][200/472] eta: 0:00:01 time: 0.0065 data_time: 0.0009 memory: 138 2022/08/31 20:18:33 - mmengine - INFO - Epoch(val) [53][300/472] eta: 0:00:01 time: 0.0061 data_time: 0.0008 memory: 330 2022/08/31 20:18:34 - mmengine - INFO - Epoch(val) [53][400/472] eta: 0:00:00 time: 0.0055 data_time: 0.0008 memory: 76 2022/08/31 20:18:34 - mmengine - INFO - Epoch(val) [53][472/472] node/macro_f1: 0.9307 node/micro_f1: 0.9396 edge/micro_f1: 0.7899 2022/08/31 20:18:37 - mmengine - INFO - Epoch(train) [54][100/317] lr: 1.0000e-05 eta: 0:00:56 time: 0.0228 data_time: 0.0027 memory: 515 loss_node: 0.0068 loss_edge: 0.0113 acc_node: 100.0000 acc_edge: 99.7475 loss: 0.0181 2022/08/31 20:18:39 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:18:39 - mmengine - INFO - Epoch(train) [54][200/317] lr: 1.0000e-05 eta: 0:00:53 time: 0.0264 data_time: 0.0028 memory: 1016 loss_node: 0.0085 loss_edge: 0.0138 acc_node: 100.0000 acc_edge: 99.8930 loss: 0.0223 2022/08/31 20:18:42 - mmengine - INFO - Epoch(train) [54][300/317] lr: 1.0000e-05 eta: 0:00:50 time: 0.0244 data_time: 0.0028 memory: 905 loss_node: 0.0063 loss_edge: 0.0176 acc_node: 100.0000 acc_edge: 99.4764 loss: 0.0240 2022/08/31 20:18:42 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:18:42 - mmengine - INFO - Saving checkpoint at 54 epochs 2022/08/31 20:18:43 - mmengine - INFO - Epoch(val) [54][100/472] eta: 0:00:01 time: 0.0048 data_time: 0.0008 memory: 376 2022/08/31 20:18:44 - mmengine - INFO - Epoch(val) [54][200/472] eta: 0:00:01 time: 0.0058 data_time: 0.0008 memory: 138 2022/08/31 20:18:44 - mmengine - INFO - Epoch(val) [54][300/472] eta: 0:00:01 time: 0.0061 data_time: 0.0008 memory: 330 2022/08/31 20:18:45 - mmengine - INFO - Epoch(val) [54][400/472] eta: 0:00:00 time: 0.0057 data_time: 0.0009 memory: 76 2022/08/31 20:18:45 - mmengine - INFO - Epoch(val) [54][472/472] node/macro_f1: 0.9306 node/micro_f1: 0.9395 edge/micro_f1: 0.7892 2022/08/31 20:18:48 - mmengine - INFO - Epoch(train) [55][100/317] lr: 1.0000e-05 eta: 0:00:47 time: 0.0234 data_time: 0.0027 memory: 868 loss_node: 0.0108 loss_edge: 0.0106 acc_node: 100.0000 acc_edge: 99.5066 loss: 0.0213 2022/08/31 20:18:50 - mmengine - INFO - Epoch(train) [55][200/317] lr: 1.0000e-05 eta: 0:00:45 time: 0.0226 data_time: 0.0027 memory: 410 loss_node: 0.0106 loss_edge: 0.0180 acc_node: 100.0000 acc_edge: 99.9415 loss: 0.0286 2022/08/31 20:18:53 - mmengine - INFO - Epoch(train) [55][300/317] lr: 1.0000e-05 eta: 0:00:42 time: 0.0301 data_time: 0.0028 memory: 714 loss_node: 0.0056 loss_edge: 0.0124 acc_node: 100.0000 acc_edge: 99.7972 loss: 0.0180 2022/08/31 20:18:53 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:18:53 - mmengine - INFO - Saving checkpoint at 55 epochs 2022/08/31 20:18:54 - mmengine - INFO - Epoch(val) [55][100/472] eta: 0:00:01 time: 0.0049 data_time: 0.0009 memory: 921 2022/08/31 20:18:55 - mmengine - INFO - Epoch(val) [55][200/472] eta: 0:00:01 time: 0.0056 data_time: 0.0008 memory: 138 2022/08/31 20:18:55 - mmengine - INFO - Epoch(val) [55][300/472] eta: 0:00:01 time: 0.0059 data_time: 0.0008 memory: 330 2022/08/31 20:18:56 - mmengine - INFO - Epoch(val) [55][400/472] eta: 0:00:00 time: 0.0055 data_time: 0.0008 memory: 76 2022/08/31 20:18:56 - mmengine - INFO - Epoch(val) [55][472/472] node/macro_f1: 0.9303 node/micro_f1: 0.9393 edge/micro_f1: 0.7872 2022/08/31 20:18:59 - mmengine - INFO - Epoch(train) [56][100/317] lr: 1.0000e-05 eta: 0:00:39 time: 0.0241 data_time: 0.0028 memory: 873 loss_node: 0.0027 loss_edge: 0.0132 acc_node: 100.0000 acc_edge: 99.0718 loss: 0.0159 2022/08/31 20:19:02 - mmengine - INFO - Epoch(train) [56][200/317] lr: 1.0000e-05 eta: 0:00:36 time: 0.0237 data_time: 0.0027 memory: 886 loss_node: 0.0091 loss_edge: 0.0209 acc_node: 100.0000 acc_edge: 99.4575 loss: 0.0300 2022/08/31 20:19:04 - mmengine - INFO - Epoch(train) [56][300/317] lr: 1.0000e-05 eta: 0:00:34 time: 0.0238 data_time: 0.0027 memory: 627 loss_node: 0.0118 loss_edge: 0.0129 acc_node: 100.0000 acc_edge: 99.4769 loss: 0.0247 2022/08/31 20:19:05 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:19:05 - mmengine - INFO - Saving checkpoint at 56 epochs 2022/08/31 20:19:06 - mmengine - INFO - Epoch(val) [56][100/472] eta: 0:00:01 time: 0.0048 data_time: 0.0007 memory: 275 2022/08/31 20:19:06 - mmengine - INFO - Epoch(val) [56][200/472] eta: 0:00:01 time: 0.0058 data_time: 0.0008 memory: 138 2022/08/31 20:19:07 - mmengine - INFO - Epoch(val) [56][300/472] eta: 0:00:01 time: 0.0060 data_time: 0.0008 memory: 330 2022/08/31 20:19:07 - mmengine - INFO - Epoch(val) [56][400/472] eta: 0:00:00 time: 0.0058 data_time: 0.0009 memory: 76 2022/08/31 20:19:08 - mmengine - INFO - Epoch(val) [56][472/472] node/macro_f1: 0.9306 node/micro_f1: 0.9393 edge/micro_f1: 0.7876 2022/08/31 20:19:10 - mmengine - INFO - Epoch(train) [57][100/317] lr: 1.0000e-05 eta: 0:00:30 time: 0.0225 data_time: 0.0028 memory: 489 loss_node: 0.0097 loss_edge: 0.0159 acc_node: 99.3289 acc_edge: 99.3136 loss: 0.0257 2022/08/31 20:19:13 - mmengine - INFO - Epoch(train) [57][200/317] lr: 1.0000e-05 eta: 0:00:28 time: 0.0241 data_time: 0.0029 memory: 620 loss_node: 0.0083 loss_edge: 0.0181 acc_node: 100.0000 acc_edge: 99.4516 loss: 0.0263 2022/08/31 20:19:14 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:19:15 - mmengine - INFO - Epoch(train) [57][300/317] lr: 1.0000e-05 eta: 0:00:25 time: 0.0251 data_time: 0.0028 memory: 952 loss_node: 0.0032 loss_edge: 0.0113 acc_node: 99.3055 acc_edge: 99.6223 loss: 0.0145 2022/08/31 20:19:16 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:19:16 - mmengine - INFO - Saving checkpoint at 57 epochs 2022/08/31 20:19:17 - mmengine - INFO - Epoch(val) [57][100/472] eta: 0:00:01 time: 0.0049 data_time: 0.0009 memory: 231 2022/08/31 20:19:17 - mmengine - INFO - Epoch(val) [57][200/472] eta: 0:00:01 time: 0.0059 data_time: 0.0009 memory: 138 2022/08/31 20:19:18 - mmengine - INFO - Epoch(val) [57][300/472] eta: 0:00:00 time: 0.0058 data_time: 0.0008 memory: 330 2022/08/31 20:19:18 - mmengine - INFO - Epoch(val) [57][400/472] eta: 0:00:00 time: 0.0053 data_time: 0.0008 memory: 76 2022/08/31 20:19:19 - mmengine - INFO - Epoch(val) [57][472/472] node/macro_f1: 0.9309 node/micro_f1: 0.9394 edge/micro_f1: 0.7871 2022/08/31 20:19:21 - mmengine - INFO - Epoch(train) [58][100/317] lr: 1.0000e-05 eta: 0:00:22 time: 0.0246 data_time: 0.0027 memory: 933 loss_node: 0.0057 loss_edge: 0.0168 acc_node: 100.0000 acc_edge: 99.3205 loss: 0.0226 2022/08/31 20:19:24 - mmengine - INFO - Epoch(train) [58][200/317] lr: 1.0000e-05 eta: 0:00:19 time: 0.0234 data_time: 0.0028 memory: 922 loss_node: 0.0050 loss_edge: 0.0154 acc_node: 99.4118 acc_edge: 99.1831 loss: 0.0204 2022/08/31 20:19:27 - mmengine - INFO - Epoch(train) [58][300/317] lr: 1.0000e-05 eta: 0:00:17 time: 0.0217 data_time: 0.0028 memory: 497 loss_node: 0.0068 loss_edge: 0.0135 acc_node: 100.0000 acc_edge: 99.6114 loss: 0.0202 2022/08/31 20:19:27 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:19:27 - mmengine - INFO - Saving checkpoint at 58 epochs 2022/08/31 20:19:28 - mmengine - INFO - Epoch(val) [58][100/472] eta: 0:00:01 time: 0.0049 data_time: 0.0008 memory: 292 2022/08/31 20:19:29 - mmengine - INFO - Epoch(val) [58][200/472] eta: 0:00:01 time: 0.0061 data_time: 0.0008 memory: 138 2022/08/31 20:19:29 - mmengine - INFO - Epoch(val) [58][300/472] eta: 0:00:01 time: 0.0063 data_time: 0.0009 memory: 330 2022/08/31 20:19:30 - mmengine - INFO - Epoch(val) [58][400/472] eta: 0:00:00 time: 0.0058 data_time: 0.0009 memory: 76 2022/08/31 20:19:30 - mmengine - INFO - Epoch(val) [58][472/472] node/macro_f1: 0.9307 node/micro_f1: 0.9394 edge/micro_f1: 0.7879 2022/08/31 20:19:33 - mmengine - INFO - Epoch(train) [59][100/317] lr: 1.0000e-05 eta: 0:00:14 time: 0.0300 data_time: 0.0029 memory: 620 loss_node: 0.0045 loss_edge: 0.0131 acc_node: 100.0000 acc_edge: 99.6973 loss: 0.0176 2022/08/31 20:19:35 - mmengine - INFO - Epoch(train) [59][200/317] lr: 1.0000e-05 eta: 0:00:11 time: 0.0244 data_time: 0.0027 memory: 650 loss_node: 0.0064 loss_edge: 0.0122 acc_node: 100.0000 acc_edge: 99.4532 loss: 0.0186 2022/08/31 20:19:38 - mmengine - INFO - Epoch(train) [59][300/317] lr: 1.0000e-05 eta: 0:00:08 time: 0.0216 data_time: 0.0028 memory: 954 loss_node: 0.0071 loss_edge: 0.0162 acc_node: 100.0000 acc_edge: 99.6603 loss: 0.0232 2022/08/31 20:19:39 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:19:39 - mmengine - INFO - Saving checkpoint at 59 epochs 2022/08/31 20:19:40 - mmengine - INFO - Epoch(val) [59][100/472] eta: 0:00:01 time: 0.0048 data_time: 0.0009 memory: 659 2022/08/31 20:19:40 - mmengine - INFO - Epoch(val) [59][200/472] eta: 0:00:01 time: 0.0056 data_time: 0.0008 memory: 138 2022/08/31 20:19:41 - mmengine - INFO - Epoch(val) [59][300/472] eta: 0:00:01 time: 0.0058 data_time: 0.0008 memory: 330 2022/08/31 20:19:41 - mmengine - INFO - Epoch(val) [59][400/472] eta: 0:00:00 time: 0.0054 data_time: 0.0008 memory: 76 2022/08/31 20:19:42 - mmengine - INFO - Epoch(val) [59][472/472] node/macro_f1: 0.9305 node/micro_f1: 0.9394 edge/micro_f1: 0.7884 2022/08/31 20:19:44 - mmengine - INFO - Epoch(train) [60][100/317] lr: 1.0000e-05 eta: 0:00:05 time: 0.0268 data_time: 0.0028 memory: 875 loss_node: 0.0081 loss_edge: 0.0130 acc_node: 100.0000 acc_edge: 99.0087 loss: 0.0210 2022/08/31 20:19:47 - mmengine - INFO - Epoch(train) [60][200/317] lr: 1.0000e-05 eta: 0:00:03 time: 0.0242 data_time: 0.0029 memory: 340 loss_node: 0.0032 loss_edge: 0.0135 acc_node: 100.0000 acc_edge: 99.7719 loss: 0.0168 2022/08/31 20:19:50 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:19:50 - mmengine - INFO - Epoch(train) [60][300/317] lr: 1.0000e-05 eta: 0:00:00 time: 0.0239 data_time: 0.0029 memory: 878 loss_node: 0.0104 loss_edge: 0.0114 acc_node: 100.0000 acc_edge: 99.7906 loss: 0.0219 2022/08/31 20:19:50 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt-openset_20220831_200807 2022/08/31 20:19:50 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/08/31 20:19:51 - mmengine - INFO - Epoch(val) [60][100/472] eta: 0:00:01 time: 0.0046 data_time: 0.0009 memory: 246 2022/08/31 20:19:51 - mmengine - INFO - Epoch(val) [60][200/472] eta: 0:00:01 time: 0.0056 data_time: 0.0008 memory: 138 2022/08/31 20:19:52 - mmengine - INFO - Epoch(val) [60][300/472] eta: 0:00:01 time: 0.0059 data_time: 0.0009 memory: 330 2022/08/31 20:19:53 - mmengine - INFO - Epoch(val) [60][400/472] eta: 0:00:00 time: 0.0055 data_time: 0.0009 memory: 76 2022/08/31 20:19:53 - mmengine - INFO - Epoch(val) [60][472/472] node/macro_f1: 0.9308 node/micro_f1: 0.9394 edge/micro_f1: 0.7872